Wenzhong Xiao

A cornerstone of modern biomedical research is the use of mouse models to explore basic pathophysiological mechanisms, evaluate new therapeutic approaches, and make go or no-go decisions to carry new drug candidates forward into clinical trials. Systematic studies evaluating how well murine models mimic human inflammatory diseases are nonexistent. Here, we show that, although acute inflammatory stresses from different etiologies result in highly similar genomic responses in humans, the responses in corresponding mouse models correlate poorly with the human conditions and also, one another. Among genes changed significantly in humans, the murine orthologs are close to random in matching their human counterparts (e.g., R(2) between 0.0 and 0.1). In addition to improvements in the current animal model systems, our study supports higher priority for translational medical research to focus on the more complex human conditions rather than relying on mouse models to study human inflammatory diseases.

Denaturing high-performance liquid chromatography (DHPLC) compares two or more chromosomes as a mixture of denatured and reannealed PCR amplicons, revealing the presence of a mutation by the differential retention of homo- and heteroduplex DNA on reversed-phase chromatography supports under partial denaturation. Temperature determines sensitivity, and its optimum can be predicted by computation. Single-nucleotide substitutions, deletions, and insertions have been detected successfully by on-line UV or fluorescence monitoring within 2-3 minutes in unpurified amplicons as large as 1.5 Kb. Sensitivity and specificity of DHPLC consistently exceed 96%. These features and its low cost make DHPLC one of the most powerful tools for the re-sequencing of the human and other genomes. Aside from its application to the mutational analysis of candidate genes, DHPLC has proven instrumental in elucidating human evolution and in the mapping of genes. Employing completely denaturing conditions, the utility of DHPLC has been extended to the genotyping of known polymorphisms by utilizing the ability of poly(styrene-divinylbenzene) to resolve single-stranded DNA molecules of identical size that differ in a single base. Under completely denaturing conditions, it is thus possible to resolve all possible base substitutions with the single exception of C-->G transversions. Improvements in throughput became feasible with the recent introduction of monolithic poly(styrene-divinylbenzene) capillaries that lend themselves to the fabrication of arrays connected to a multi-color laser induced fluorescence scanner or a mass spectrometer.

Characterizing the genome-wide dynamic regulation of gene expression is important and will be of much interest in the future. However, there is currently no established method for identifying differentially expressed genes in a time course study. Here we propose a significance method for analyzing time course microarray studies that can be applied to the typical types of comparisons and sampling schemes. This method is applied to two studies on humans. In one study, genes are identified that show differential expression over time in response to in vivo endotoxin administration. By using our method, 7,409 genes are called significant at a 1% false-discovery rate level, whereas several existing approaches fail to identify any genes. In another study, 417 genes are identified at a 10% false-discovery rate level that show expression changing with age in the kidney cortex. Here it is also shown that as many as 47% of the genes change with age in a manner more complex than simple exponential growth or decay. The methodology proposed here has been implemented in the freely distributed and open-source edge software package.

The enumeration of rare circulating epithelial cells (CEpCs) in the peripheral blood of metastatic cancer patients has shown promise for improved cancer prognosis. Moving beyond enumeration, molecular analysis of CEpCs may provide candidate surrogate endpoints to diagnose, treat, and monitor malignancy directly from the blood samples. Thorough molecular analysis of CEpCs requires the development of new sample preparation methods that yield easily accessible and purified CEpCs for downstream biochemical assays. Here, we describe a new immunomagnetic cell separator, the MagSweeper, which gently enriches target cells and eliminates cells that are not bound to magnetic particles. The isolated cells are easily accessible and can be extracted individually based on their physical characteristics to deplete any cells nonspecifically bound to beads. We have shown that our device can process 9 mL of blood per hour and captures >50% of CEpCs as measured in spiking experiments. We have shown that the separation process does not perturb the gene expression of rare cells. To determine the efficiency of our platform in isolating CEpCs from patients, we have isolated CEpCs from all 47 tubes of 9-mL blood samples collected from 17 women with metastatic breast cancer. In contrast, we could not find any circulating epithelial cells in samples from 5 healthy donors. The isolated CEpCs are all stored individually for further molecular analysis.

Pathologic extraskeletal bone formation, or heterotopic ossification (HO), occurs following mechanical trauma, burns, orthopedic operations, and in patients with hyperactivating mutations of the type I bone morphogenetic protein receptor ACVR1 (Activin type 1 receptor). Extraskeletal bone forms through an endochondral process with a cartilage intermediary prompting the hypothesis that hypoxic signaling present during cartilage formation drives HO development and that HO precursor cells derive from a mesenchymal lineage as defined by Paired related homeobox 1 (Prx). Here we demonstrate that Hypoxia inducible factor-1α (Hif1α), a key mediator of cellular adaptation to hypoxia, is highly expressed and active in three separate mouse models: trauma-induced, genetic, and a hybrid model of genetic and trauma-induced HO. In each of these models, Hif1α expression coincides with the expression of master transcription factor of cartilage, Sox9 [(sex determining region Y)-box 9]. Pharmacologic inhibition of Hif1α using PX-478 or rapamycin significantly decreased or inhibited extraskeletal bone formation. Importantly, de novo soft-tissue HO was eliminated or significantly diminished in treated mice. Lineage-tracing mice demonstrate that cells forming HO belong to the Prx lineage. Burn/tenotomy performed in lineage-specific Hif1α knockout mice (Prx-Cre/Hif1α(fl:fl)) resulted in substantially decreased HO, and again lack of de novo soft-tissue HO. Genetic loss of Hif1α in mesenchymal cells marked by Prx-cre prevents the formation of the mesenchymal condensations as shown by routine histology and immunostaining for Sox9 and PDGFRα. Pharmacologic inhibition of Hif1α had a similar effect on mesenchymal condensation development. Our findings indicate that Hif1α represents a promising target to prevent and treat pathologic extraskeletal bone.

Circulating tumor DNA (ctDNA) sequencing is being rapidly adopted in precision oncology, but the accuracy, sensitivity and reproducibility of ctDNA assays is poorly understood. Here we report the findings of a multi-site, cross-platform evaluation of the analytical performance of five industry-leading ctDNA assays. We evaluated each stage of the ctDNA sequencing workflow with simulations, synthetic DNA spike-in experiments and proficiency testing on standardized, cell-line-derived reference samples. Above 0.5% variant allele frequency, ctDNA mutations were detected with high sensitivity, precision and reproducibility by all five assays, whereas, below this limit, detection became unreliable and varied widely between assays, especially when input material was limited. Missed mutations (false negatives) were more common than erroneous candidates (false positives), indicating that the reliable sampling of rare ctDNA fragments is the key challenge for ctDNA assays. This comprehensive evaluation of the analytical performance of ctDNA assays serves to inform best practice guidelines and provides a resource for precision oncology. Reliable detection of mutations below 0.5% variant allele frequency remains a key challenge for circulating tumor DNA sequencing assays.

In this study, we found 985 genes that change expression in the cortex and the medulla of the kidney with age. Some of the genes whose transcripts increase in abundance with age are known to be specifically expressed in immune cells, suggesting that immune surveillance or inflammation increases with age. The age-regulated genes show a similar aging profile in the cortex and the medulla, suggesting a common underlying mechanism for aging. Expression profiles of these age-regulated genes mark not only age, but also the relative health and physiology of the kidney in older individuals. Finally, the set of aging-regulated kidney genes suggests specific mechanisms and pathways that may play a role in kidney degeneration with age.

High-efficiency nanoscale reversed-phase liquid chromatography (chromatographic peak capacities of ∼1000: Shen, Y.; Zhao, R.; Berger, S. J.; Anderson, G. A.; Rodriguez, N.; Smith, R. D. Anal. Chem. 2002, 74, 4235. Shen, Y.; Moore, R. J.; Zhao, R.; Blonder, J.; Auberry, D. L.; Masselon, C.; Pasa-Tolic, L.; Hixson, K. K.; Auberry, K. J.; Smith, R. D. Anal. Chem. 2003, 75, 3596.) and strong cation exchange LC was used to obtain ultra-high-efficiency separations (combined chromatographic peak capacities of >104) in conjunction with tandem mass spectrometry (MS/MS) for characterization of the human plasma proteome. Using conservative SEQUEST peptide identification criteria (i.e., without considering chymotryptic or elastic peptides) and peptide LC normalized elution time constraints, the separation quality enabled the identification of proteins over a dynamic range of greater than 8 orders of magnitude in relative abundance using ion trap MS/MS instrumentation. Between 800 and 1682 human proteins were identified, depending on the criteria used for identification, from a total of 365 μg of human plasma. The analyses identified relatively low-level (∼pg/mL) proteins (e.g., cytokines) coexisting with high-abundance proteins (e.g., mg/mL-level serum albumin).

To characterize the mechanism by which receptors propagate conformational changes across membranes, nitroxide spin labels were attached at strategic positions in the bacterial aspartate receptor. By collecting the electron paramagnetic resonance spectra of these labeled receptors in the presence and absence of the ligand aspartate, ligand binding was shown to generate an approximately 1 angstrom intrasubunit piston-type movement of one transmembrane helix downward relative to the other transmembrane helix. The receptor-associated phosphorylation cascade proteins CheA and CheW did not alter the ligand-induced movement. Because the piston movement is very small, the ability of receptors to produce large outcomes in response to stimuli is caused by the ability of the receptor-coupled enzymes to detect small changes in the conformation of the receptor.

The application of genome-wide expression analysis to a large-scale, multicentered program in critically ill patients poses a number of theoretical and technical challenges. We describe here an analytical and organizational approach to a systematic evaluation of the variance associated with genome-wide expression analysis specifically tailored to study human disease. We analyzed sources of variance in genome-wide expression analyses performed with commercial oligonucleotide arrays. In addition, variance in gene expression in human blood leukocytes caused by repeated sampling in the same subject, among different healthy subjects, among different leukocyte subpopulations, and the effect of traumatic injury, were also explored. We report that analytical variance caused by sample processing was acceptably small. Blood leukocyte gene expression in the same individual over a 24-h period was remarkably constant. In contrast, genome-wide expression varied significantly among different subjects and leukocyte subpopulations. Expectedly, traumatic injury induced dramatic changes in apparent gene expression that were greater in magnitude than the analytical noise and interindividual variance. We demonstrate that the development of a nation-wide program for gene expression analysis with careful attention to analytical details can reduce the variance in the clinical setting to a level where patterns of gene expression are informative among different healthy human subjects, and can be studied with confidence in human disease.

In this study yeast mitochondria were used as a model system to apply, evaluate, and integrate different genomic approaches to define the proteins of an organelle. Liquid chromatography mass spectrometry applied to purified mitochondria identified 546 proteins. By expression analysis and comparison to other proteome studies, we demonstrate that the proteomic approach identifies primarily highly abundant proteins. By expanding our evaluation to other types of genomic approaches, including systematic deletion phenotype screening, expression profiling, subcellular localization studies, protein interaction analyses, and computational predictions, we show that an integration of approaches moves beyond the limitations of any single approach. We report the success of each approach by benchmarking it against a reference set of known mitochondrial proteins, and predict approximately 700 proteins associated with the mitochondrial organelle from the integration of 22 datasets. We show that a combination of complementary approaches like deletion phenotype screening and mass spectrometry can identify over 75% of the known mitochondrial proteome. These findings have implications for choosing optimal genome-wide approaches for the study of other cellular systems, including organelles and pathways in various species. Furthermore, our systematic identification of genes involved in mitochondrial function and biogenesis in yeast expands the candidate genes available for mapping Mendelian and complex mitochondrial disorders in humans.

The analysis of gene expression data in clinical medicine has been plagued by the lack of a critical evaluation of accepted methodologies for the collection, processing, and labeling of RNA. In the present report, the reliability of two commonly used techniques to isolate RNA from whole blood or its leukocyte compartment was compared by examining their reproducibility, variance, and signal-to-noise ratios. Whole blood was obtained from healthy subjects and was either untreated or stimulated ex vivo with Staphylococcus enterotoxin B (SEB). Blood samples were also obtained from trauma patients but were not stimulated with SEB ex vivo. Total RNA was isolated from whole blood with the PAXgene proprietary blood collection system or from isolated leukocytes. Biotin-labeled cRNA was hybridized to Affymetrix GeneChips. The Pearson correlation coefficient for gene expression measurements in replicates from healthy subjects with both techniques was excellent, exceeding 0.985. Unsupervised analyses, including hierarchical cluster analysis, however, revealed that the RNA isolation method resulted in greater differences in gene expression than stimulation with SEB or among different trauma patients. The intraclass correlation, a measure of signal-to-noise ratio, of the difference between SEB-stimulated and unstimulated blood from healthy subjects was significantly higher in leukocyte-derived samples than in whole blood: 0.75 vs. 0.46 (P = 0.002). At the P < 0.001 level of significance, twice as many probe sets discriminated between SEB-stimulated and unstimulated blood with leukocyte isolation than with PAXgene. The findings suggest that the method of RNA isolation from whole blood is a critical variable in the design of clinical studies using microarray analyses.

Identification of novel diagnostic or therapeutic biomarkers from human blood plasma would benefit significantly from quantitative measurements of the proteome constituents over a range of physiological conditions. Herein we describe an initial demonstration of proteome-wide quantitative analysis of human plasma. The approach utilizes postdigestion trypsin-catalyzed 16O/18O peptide labeling, two-dimensional LC-FTICR mass spectrometry, and the accurate mass and time (AMT) tag strategy to identify and quantify peptides/proteins from complex samples. A peptide accurate mass and LC elution time AMT tag data base was initially generated using MS/MS following extensive multidimensional LC separations to provide the basis for subsequent peptide identifications. The AMT tag data base contains >8,000 putative identified peptides, providing 938 confident plasma protein identifications. The quantitative approach was applied without depletion of high abundance proteins for comparative analyses of plasma samples from an individual prior to and 9 h after lipopolysaccharide (LPS) administration. Accurate quantification of changes in protein abundance was demonstrated by both 1:1 labeling of control plasma and the comparison between the plasma samples following LPS administration. A total of 429 distinct plasma proteins were quantified from the comparative analyses, and the protein abundances for 25 proteins, including several known inflammatory response mediators, were observed to change significantly following LPS administration. Identification of novel diagnostic or therapeutic biomarkers from human blood plasma would benefit significantly from quantitative measurements of the proteome constituents over a range of physiological conditions. Herein we describe an initial demonstration of proteome-wide quantitative analysis of human plasma. The approach utilizes postdigestion trypsin-catalyzed 16O/18O peptide labeling, two-dimensional LC-FTICR mass spectrometry, and the accurate mass and time (AMT) tag strategy to identify and quantify peptides/proteins from complex samples. A peptide accurate mass and LC elution time AMT tag data base was initially generated using MS/MS following extensive multidimensional LC separations to provide the basis for subsequent peptide identifications. The AMT tag data base contains >8,000 putative identified peptides, providing 938 confident plasma protein identifications. The quantitative approach was applied without depletion of high abundance proteins for comparative analyses of plasma samples from an individual prior to and 9 h after lipopolysaccharide (LPS) administration. Accurate quantification of changes in protein abundance was demonstrated by both 1:1 labeling of control plasma and the comparison between the plasma samples following LPS administration. A total of 429 distinct plasma proteins were quantified from the comparative analyses, and the protein abundances for 25 proteins, including several known inflammatory response mediators, were observed to change significantly following LPS administration. The human plasma proteome has been widely recognized for its significant potential in providing diagnostic or therapeutic biomarkers for various diseases as well as its potential contribution to personalized medicine (1Anderson N.L. Anderson N.G. The human plasma proteome: history, character, and diagnostic prospects.Mol. Cell. Proteomics. 2002; 1: 845-867Google Scholar). As a result, there has been increased interest in comprehensively characterizing the human plasma proteome for the purpose of establishing an extensive data base of proteins that could be used for future identification of protein biomarkers indicative of diseases (2Pieper R. Gatlin C.L. Makusky A.J. Russo P.S. Schatz C.R. Miller S.S. Su Q. McGrath A.M. Estock M.A. Parmar P.P. Zhao M. Huang S.T. Zhou J. Wang F. Esquer-Blasco R. Anderson N.L. Taylor J. Steiner S. The human serum proteome: display of nearly 3700 chromatographically separated protein spots on two-dimensional electrophoresis gels and identification of 325 distinct proteins.Proteomics. 2003; 3: 1345-1364Google Scholar, 3Adkins J.N. Varnum S.M. Auberry K.J. Moore R.J. Angell N.H. Smith R.D. Springer D.L. Pounds J.G. Toward a human blood serum proteome: analysis by multidimensional separation coupled with mass spectrometry.Mol. Cell Proteomics. 2002; 1: 947-955Google Scholar, 4Shen Y. Jacobs J.M. Camp D.G. Fang R. Moore R.J. Smith R.D. Xiao W. Davis R.W. Tompkins R.G. High efficiency SCXLC/RPLC/MS/MS for high dynamic range characterization of the human plasma proteome.Anal. Chem. 2004; 76: 1134-1144Google Scholar, 5Anderson N.L. Polanski M. Pieper R. Gatlin T. Tirumalai R.S. Conrads T.P. Veenstra T.D. Adkins J.N. Pounds J.G. Fagan R. Lobley A. The human plasma proteome: a nonredundant list developed by combination of four separate sources.Mol. Cell. Proteomics. 2004; 3: 311-316Google Scholar, 6Tirumalai R.S. Chan K.C. Prieto D.A. Issaq H.J. Conrads T.P. Veenstra T.D. Characterization of the low molecular weight human serum proteome.Mol. Cell. Proteomics. 2003; 2: 1096-1103Google Scholar). Adkins et al. (3Adkins J.N. Varnum S.M. Auberry K.J. Moore R.J. Angell N.H. Smith R.D. Springer D.L. Pounds J.G. Toward a human blood serum proteome: analysis by multidimensional separation coupled with mass spectrometry.Mol. Cell Proteomics. 2002; 1: 947-955Google Scholar) reported the application of two-dimensional (2D) 1The abbreviations used are: 2D, two-dimensional; LPS, lipopolysaccharide; SCX, strong cation exchange; NET, normalized elution time; AMT, accurate mass and time. 1The abbreviations used are: 2D, two-dimensional; LPS, lipopolysaccharide; SCX, strong cation exchange; NET, normalized elution time; AMT, accurate mass and time. LC-MS/MS for the analysis of an immunoglobulin-depleted human serum sample that resulted in the identification of 490 proteins. Pieper et al. (2Pieper R. Gatlin C.L. Makusky A.J. Russo P.S. Schatz C.R. Miller S.S. Su Q. McGrath A.M. Estock M.A. Parmar P.P. Zhao M. Huang S.T. Zhou J. Wang F. Esquer-Blasco R. Anderson N.L. Taylor J. Steiner S. The human serum proteome: display of nearly 3700 chromatographically separated protein spots on two-dimensional electrophoresis gels and identification of 325 distinct proteins.Proteomics. 2003; 3: 1345-1364Google Scholar) reported a comprehensive analysis of human serum by using a three-dimensional whole protein separation process (immunosubtraction/ion exchange/size exclusion) followed by 2D electrophoresis and MS identifications of gel spots. MS analysis of 1800 gel spots resulted in identification of 325 proteins (2Pieper R. Gatlin C.L. Makusky A.J. Russo P.S. Schatz C.R. Miller S.S. Su Q. McGrath A.M. Estock M.A. Parmar P.P. Zhao M. Huang S.T. Zhou J. Wang F. Esquer-Blasco R. Anderson N.L. Taylor J. Steiner S. The human serum proteome: display of nearly 3700 chromatographically separated protein spots on two-dimensional electrophoresis gels and identification of 325 distinct proteins.Proteomics. 2003; 3: 1345-1364Google Scholar). Recently we reported on the comprehensive analysis of human plasma and on the qualitative comparison between two different plasma samples using a high resolution 2D LC-MS/MS approach; both studies resulted in ∼800 plasma protein identifications (4Shen Y. Jacobs J.M. Camp D.G. Fang R. Moore R.J. Smith R.D. Xiao W. Davis R.W. Tompkins R.G. High efficiency SCXLC/RPLC/MS/MS for high dynamic range characterization of the human plasma proteome.Anal. Chem. 2004; 76: 1134-1144Google Scholar, 7Qian W.J. Jacobs J.M. Camp II, D.G. Monroe M.E. Moore R.J. Gritsenko M.A. Calvano S.E. Lowry S.F. Xiao W. Moldawer L.L. Davis R.W. Tompkins R.G. Smith R.D. Comparative proteome analyses of human plasma following lipopolysaccharide treatment using mass spectrometry.Proteomics. 2005; 5: 572-584Google Scholar). In addition, the Plasma Proteome Project initiative formed within the Human Proteome Organization (HUPO) is working to obtain a comprehensive analysis of the protein constituents of human plasma and to identify biological sources of variations within individuals over time and across populations (8Omenn G.S. The Human Proteome Organization Plasma Proteome Project pilot phase: reference specimens, technology platform comparisons, and standardized data submissions and analyses.Proteomics. 2004; 4: 1235-1240Google Scholar).Although the majority of plasma proteome characterization efforts to date have been qualitative or semiquantitative, the discovery of novel biomarkers or signature proteins would benefit significantly from quantitative measurements of the differences in plasma protein concentration from different states (e.g. normal versus diseased states). Recently several laboratories have reported the applicability of using postdigestion 16O/18O labeling as a quantitative proteomic approach for analysis of complex samples (9Yao X. Afonso C. Fenselau C. Dissection of proteolytic 18O labeling: endoprotease-catalyzed 16O-to-18O exchange of truncated peptide substrates.J. Proteome Res. 2003; 2: 147-152Google Scholar, 10Heller M. Mattou H. Menzel C. Yao X. Trypsin catalyzed 16O-to-18O exchange for comparative proteomics: tandem mass spectrometry comparison using MALDI-TOF, ESI-QTOF, and ESI-ion trap mass spectrometers.J. Am. Soc. Mass Spectrom. 2003; 14: 704-718Google Scholar, 11Liu T. Qian W.J. Strittmatter E.F. Camp D.G. Anderson G.A. Thrall B.D. Smith R.D. High throughput comparative proteome analysis using a quantitative cysteinyl-peptide enrichment technology.Anal. Chem. 2004; 76: 5345-5353Google Scholar, 12Brown K.J. Fenselau C. Investigation of doxorubicin resistance in MCF-7 breast cancer cells using shot-gun comparative proteomics with proteolytic 18O labeling.J. Proteome Res. 2004; 3: 455-462Google Scholar, 13Staes A. Demol H. Van Damme J. Martens L. Vandekerckhove J. Gevaert K. Global differential non-gel proteomics by quantitative and stable labeling of tryptic peptides with oxygen-18.J. Proteome Res. 2004; 3: 786-791Google Scholar). In the work reported herein, we describe a global quantitative proteomic approach and its application for comparative analyses of two human plasma samples obtained from a healthy individual prior to (control) and after lipopolysaccharide (LPS) administration (LPS-treated). A 9-h time point was used in this work only for the initial demonstration of the approach. LPS is a purified bacterial endotoxin known to induce a broad range of inflammatory reactions, including cytokine production, cell migration, and production of acute phase proteins (14Van Amersfoort E.S. Van Berkel T.J. Kuiper J. Receptors, mediators, and mechanisms involved in bacterial sepsis and septic shock.Clin. Microbiol. Rev. 2003; 16: 379-414Google Scholar, 15Lakhani S.A. Bogue C.W. Toll-like receptor signaling in sepsis.Curr. Opin. Pediatr. 2003; 15: 278-282Google Scholar, 16Paludan S.R. Synergistic action of pro-inflammatory agents: cellular and molecular aspects.J. Leukoc. Biol. 2000; 67: 18-25Google Scholar). One of our objectives was to identify acute phase plasma proteome changes in response to a prototypical inflammatory challenge at different time points (0–24 h) following the LPS administration. Our quantitative proteomic approach combines postdigestion trypsin-catalyzed 16O/18O labeling, strong cation exchange fractionation after labeling, and LC-FTICR analyses with the accurate mass and time (AMT) tag strategy (11Liu T. Qian W.J. Strittmatter E.F. Camp D.G. Anderson G.A. Thrall B.D. Smith R.D. High throughput comparative proteome analysis using a quantitative cysteinyl-peptide enrichment technology.Anal. Chem. 2004; 76: 5345-5353Google Scholar, 17Qian W.J. Camp D.G. Smith R.D. High throughput proteomics using Fourier transform ion cyclotron resonance (FTICR) mass spectrometry.Expert Rev. Proteomics. 2004; 1: 89-97Google Scholar, 18Smith R.D. Anderson G.A. Lipton M.S. Pasa-Tolic L. Shen Y. Conrads T.P. Veenstra T.D. Udseth H.R. An accurate mass tag strategy for quantitative and high throughput proteome measurements.Proteomics. 2002; 2: 513-523Google Scholar, 19Lipton M.S. Pasa-Tolic L. Anderson G.A. Anderson D.J. Auberry D.L. Battista J.R. Daly M.J. Fredrickson J. Hixson K.K. Kostandarithes H. Masselon C. Markillie L.M. Moore R. Romine M.F. Shen Y. Strittmatter E. Tolic N. Udseth H.R. Venkateswaran A. Wong K.K. Zhao R. Smith R.D. Global analysis of the Deinococcus radiodurans R1 proteome by using accurate mass tags.Proc. Natl. Acad. Sci. U. S. A. 2002; 99: 11049-11054Google Scholar) for peptide identification and quantification. This 16O/18O labeling-AMT tag approach was demonstrated to be amenable for high throughput quantitative proteome analyses such as studying the proteomic changes in human plasma following the LPS administration. In a previous initial study, we reported on a qualitative comparison of the two plasma samples following LPS administration based on the number of peptide identifications from LC-MS/MS analyses. Here we demonstrate more accurate detection of proteomic changes following LPS treatment by using a quantitative approach. Several known inflammatory response or acute phase mediators were accurately quantified following the administration of LPS.EXPERIMENTAL PROCEDURESHuman Plasma Sample Preparation—Approval for the conduct of this study was obtained from the Institutional Review Boards of the University of Florida College of Medicine, the Robert Wood Johnson Medical School, the Stanford University School of Medicine, and the Pacific Northwest National Laboratory in accordance with federal regulations.The human plasma samples were supplied by the Department of Surgery at the University of Florida College of Medicine, which serves as the Sample Collection and Coordination Site for a multicentered clinical study (Inflammation and the Host Response to Injury). The original sample was generated from a healthy adult subject at the Department of Surgery at the Robert Wood Johnson Medical School who, after signed informed consent, received an intravenous injection of Clinical Center Reference Endotoxin (Lot 2) LPS (2 ng/kg of body weight administered over 5 min). Arterial or venous blood was collected at various time points between 0 and 24 h following endotoxin administration. White blood cell counts and various vital signs including body temperature, blood pressure, and heart rate were monitored for the subject throughout the 24-h study period. This subject manifested signs and symptoms consistent with those observed after intravenous endotoxin administration to humans (20Van der Poll T. Lowry S.F. Biological response to endotoxin in humans.in: Tellado J.M. Forse R.A. Solomkin J.S. Modulation of the Inflammatory Response in Severe Sepsis. Vol. 20. Karger, Basel1995: 18-32Google Scholar). The plasma samples were prepared from whole blood by centrifugation; samples at T = 0 h (control, base line immediately prior to endotoxin administration) and T = 9 h (LPS-treated, 9 h following LPS administration) were used for this study. Another set of reference plasma samples obtained from the Stanford University School of Medicine was also used to generate an initial data base of peptide identifications.Aliquots of 200 μl each of the control and LPS-treated plasma samples were diluted and denatured using 8 m urea, 50 mm NH4HCO3, pH 8.2 for 1 h at 37 °C and reduced with 10 mm DTT for 30 min at 37 °C. Protein cysteinyl residues were alkylated with 40 mm iodoacetamide for 90 min at room temperature, and samples were desalted using a prepacked PD-10 column containing Sephadex G-25 (Amersham Biosciences). The protein concentrations for the desalted samples were measured using a BCA protein assay (Pierce) that gave total protein amounts of 15.0 and 13.9 mg for the control and LPS-treated plasma samples, respectively. The samples were then digested into peptides using sequencing grade trypsin (Promega, Madison, WI) overnight at 37 °C with a 1:50 (w/w) trypsin-to-protein ratio. Tryptic activity of residual trypsin was quenched by boiling the samples for 10 min and immediately placing the samples on ice.Trypsin-catalyzed 16O/18O Labeling—Trypsin-catalyzed 16O/18O labeling was carried out as described previously (11Liu T. Qian W.J. Strittmatter E.F. Camp D.G. Anderson G.A. Thrall B.D. Smith R.D. High throughput comparative proteome analysis using a quantitative cysteinyl-peptide enrichment technology.Anal. Chem. 2004; 76: 5345-5353Google Scholar). After residual trypsin activity was quenched via the boiling and quick cooling steps, an aliquot of peptides (1 mg each) was removed from the control and LPS-treated samples, and each aliquot was lyophilized. To dissolve the dried peptides, 40 μl of acetonitrile were first added to the dried digest followed by the addition of 200 μl of 50 mm NH4HCO3 in either 18O-enriched water (95%, Isotec, Miamisburg, OH) or regular 16O water. Then 2 μl of 1 m CaCl2 and 10 μl of immobilized trypsin (Applied Biosystems, Foster City, CA) were added to the digests, and the samples were mixed continuously for 24 h at 30 °C. Peptides from the control sample were labeled with 16O, and peptides from the LPS-treated samples were labeled with 18O. After labeling, supernatant was collected from each sample after centrifuging the samples for 5 min at 15,000 × g. The corresponding 16O- and 18O-labeled samples were pooled, combined, and then lyophilized.Strong Cation Exchange (SCX) Fractionation—The 16O/18O-labeled peptide samples from the control and LPS-treated plasma samples were fractionated by SCX similar to that described previously (7Qian W.J. Jacobs J.M. Camp II, D.G. Monroe M.E. Moore R.J. Gritsenko M.A. Calvano S.E. Lowry S.F. Xiao W. Moldawer L.L. Davis R.W. Tompkins R.G. Smith R.D. Comparative proteome analyses of human plasma following lipopolysaccharide treatment using mass spectrometry.Proteomics. 2005; 5: 572-584Google Scholar, 11Liu T. Qian W.J. Strittmatter E.F. Camp D.G. Anderson G.A. Thrall B.D. Smith R.D. High throughput comparative proteome analysis using a quantitative cysteinyl-peptide enrichment technology.Anal. Chem. 2004; 76: 5345-5353Google Scholar). The lyophilized sample was resuspended in 1.5 ml of 10 mm ammonium formate, 25% acetonitrile, pH 3.0 and injected onto a 10 × 4.6-mm guard column attached to a polysulfoethyl A 200 × 4.6-mm (5-μm, 300-Å) column (Poly LC, Columbia, MD). The mobile phases consisted of solvent A (10 mm ammonium formate, 25% acetonitrile, pH 3.0) and solvent B (500 mm ammonium formate, 25% acetonitrile, pH 6.8). After sample loading, the separation was isocratic for 10 min with 100% solvent A with a flow rate of 1 ml/min. Peptides were eluted using sequential linear gradients from 100% solvent A to 50% solvent B over 40 min and from 50% solvent B to 100% solvent B over another 10 min. The mobile phase was held at 100% solvent B for another 15 min. 1-ml fractions (1 min/fraction) were collected after the start of the gradient using a Shimadzu FRC-10A fraction collector (Kyoto, Japan) and combined into 30 fractions. Each fraction was lyophilized and analyzed by reversed-phase LC-FTICR.Reversed-phase Capillary LC-FTICR Analyses—Peptide samples were analyzed using a fully automated custom built capillary LC system (21Shen Y. Zhao R. Belov M.E. Conrads T.P. Anderson G.A. Tang K. Pasa-Tolic L. Veenstra T.D. Lipton M.S. Smith R.D. Packed capillary reversed-phase liquid chromatography with high-performance electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry for proteomics.Anal. Chem. 2001; 73: 1766-1775Google Scholar) coupled on line using an in-house manufactured ESI interface to an Apex III 9.4-tesla FTICR mass spectrometer (Bruker Daltonics, Billerica, MA). The capillary column was made by slurry packing 3-μm Jupiter C18 bonded particles (Phenomenex, Torrence, CA) into a 65-cm-long, 150-μm-inner diameter fused silica capillary column (Polymicro Technologies, Phoenix, AZ). The mobile phase consisted of 0.2% acetic acid and 0.05% TFA in water (A) and 0.1% TFA in 90% acetonitrile, 10% water (B). Mobile phases were degassed on line using a vacuum degasser (Jones Chromatography Inc., Lakewood, CO). The SCX fractions were dissolved in 50 μl of 25 mm NH4HCO3, pH 8.0. 10-μl aliquots from each peptide sample were injected onto the reversed-phase capillary column for either LC-MS/MS or LC-FTICR analysis. The mobile phase was held at 100% A for 20 min followed by a non-linear exponential gradient elution generated by increasing the mobile phase composition to ∼70% B over 150 min using a stainless steel mixing chamber. The LC-FTICR mass spectrometer was configured and operated as described elsewhere (22Belov M.E. Anderson G.A. Wingerd M.A. Udseth H.R. Tang K. Prior D.C. Swanson K.R. Buschbach M.A. Strittmatter E.F. Moore R.J. Smith R.D. An automated high performance capillary liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometer for high-throughput proteomics.J. Am. Soc. Mass Spectrom. 2004; 15: 212-232Google Scholar).Generation of a Peptide AMT Tag Data Base—A data base of identified peptides was created based on the results of extensive LC-MS/MS analyses from multiple sample sources. The application of multidimensional LC-MS/MS analyses for profiling two different sets of plasma samples without depletion of any abundant plasma proteins was the same as described previously (4Shen Y. Jacobs J.M. Camp D.G. Fang R. Moore R.J. Smith R.D. Xiao W. Davis R.W. Tompkins R.G. High efficiency SCXLC/RPLC/MS/MS for high dynamic range characterization of the human plasma proteome.Anal. Chem. 2004; 76: 1134-1144Google Scholar, 7Qian W.J. Jacobs J.M. Camp II, D.G. Monroe M.E. Moore R.J. Gritsenko M.A. Calvano S.E. Lowry S.F. Xiao W. Moldawer L.L. Davis R.W. Tompkins R.G. Smith R.D. Comparative proteome analyses of human plasma following lipopolysaccharide treatment using mass spectrometry.Proteomics. 2005; 5: 572-584Google Scholar). Human serum albumin and immunoglobulins were removed from the reference plasma sample obtained from Stanford by using a commercial anti-human serum albumin cartridge followed by a Protein G cartridge (Applied Biosystems, Framingham, MA) according to the manufacturer’s instructions. Both the flow-through following depletion and the eluent were subjected to trypsin digestion, further SCX peptide fractionation, and LC-MS/MS analyses. All LC-MS/MS data sets were analyzed using the SEQUEST algorithm (23Eng J.K. McCormack A.L. Yates J.R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database.J. Am. Soc. Mass Spectrom. 1994; 5: 976-989Google Scholar) (ThermoElectron, San Jose, CA) for peptide and protein identification by searching the MS/MS spectra against the human International Protein Index data base (consisting of 41,216 protein entries, Version 2.29, April, 2004; available on line at www.ebi.ac.uk/IPI). A static mass modification on cysteinyl residues that corresponded to alkylation with iodoacetamide (57.0215 Da) was applied during the SEQUEST analysis. The results from all data sets were combined and further filtered for the generation of the AMT tag data base and the list of confidently identified peptides.A set of recently developed filtering criteria (24Qian W.J. Liu T. Monroe M.E. Strittmatter E.F. Jacobs J.M. Kangas L.J. Petritis K. Camp D.G. Smith R.D. Probability-based evaluation of peptide and protein identifications from tandem mass spectrometry and SEQUEST analysis: the human proteome.J. Proteome Res. 2005; 4: 53-62Google Scholar) was applied to filter the data following SEQUEST analyses to generate a list of confidently identified peptides. These criteria are: for the 1+ charge state, Xcorr ≥ 2.0 for fully tryptic peptides and Xcorr ≥ 3.0 for partially tryptic peptides; for the 2+ charge state, Xcorr ≥ 2.4 for fully tryptic peptides and Xcorr ≥ 3.5 for partially tryptic peptides; and for the 3+ charge state, Xcorr ≥ 3.7 for fully tryptic peptides and Xcorr ≥ 4.5 for partially tryptic peptides; ΔCn value of ≥0.1 for all charge states. Two additional ΔCn cutoff values of 0.05 and 0.15 were applied to reduce false negatives while maintaining the same level of confidence for peptide assignments (24Qian W.J. Liu T. Monroe M.E. Strittmatter E.F. Jacobs J.M. Kangas L.J. Petritis K. Camp D.G. Smith R.D. Probability-based evaluation of peptide and protein identifications from tandem mass spectrometry and SEQUEST analysis: the human proteome.J. Proteome Res. 2005; 4: 53-62Google Scholar). With the ΔCn value ≥0.05, the minimum acceptable Xcorr value was raised to achieve a comparable false positive rate, and similarly, for ΔCn value ≥0.15, the minimum acceptable Xcorr value was reduced. In an attempt to remove redundant protein entries in the reported results, the software program ProteinProphet was used as a clustering tool to group similar or related protein entries into a “Protein Group” (25Nesvizhskii A.I. Keller A. Kolker E. Aebersold R. A statistical model for identifying proteins by tandem mass spectrometry.Anal. Chem. 2003; 75: 4646-4658Google Scholar). All peptides that passed the filtering criteria were assigned an identical probability score of 1 and entered into the ProteinProphet program solely for clustering analysis to generate the final non-redundant list of proteins or protein groups.Peptides that met the same criteria with the exception that ΔCn ≥ 0 were included in the AMT tag data base. The peptide retention times from each LC-MS/MS analysis were normalized to a range of 0–1 using a predictive peptide LC-normalized elution time (NET) model and linear regression as previously reported (26Petritis K. Kangas L.J. Ferguson P.L. Anderson G.A. Pasa-Tolic L. Lipton M.S. Auberry K.J. Strittmatter E. Shen Y. Zhao R. Smith R.D. Use of artificial neural networks for the prediction of peptide liquid chromatography elution times in proteome analyses.Anal. Chem. 2003; 75: 1039-1048Google Scholar). An average NET value and NET standard deviation were assigned to each identified peptide if the same peptide was observed in multiple runs. Both the calculated accurate monoisotopic mass and NET of the identified peptides were included in the AMT tag data base.LC-FTICR Data Analysis—The LC-FTICR data sets were automatically analyzed using in-house software tools that included ICR2LS. The initial analysis of raw LC-FTICR data involved a mass transformation or deisotoping step using ICR2LS, which is based on the THRASH algorithm (27Horn D.M. Zubarev R.A. McLafferty F.W. Automated reduction and interpretation of high resolution electrospray mass spectra of large molecules.J. Am. Soc. Mass Spectrom. 2000; 11: 320-332Google Scholar). The ICR2LS analysis generates a text file report for each LC-FTICR data set, and the report includes both the monoisotopic masses and the corresponding intensities for all detected species for each spectrum. Following ICR2LS analysis, data were processed automatically to yield a two-dimensional mass and LC elution time data set. Data processing steps included filtering data, finding features (i.e. a peak with unique mass and elution time) and pairs of features, computing abundance ratios for pairs of features, normalizing LC elution times, and matching the accurate measured masses and NET values of each feature to the corresponding AMT tag in the data base to identify peptide sequences. The peptide sequences of a given feature or pair of features were assigned when the measured mass and NET for each given feature matched the calculated mass and NET of a peptide in the AMT tag data base within a 5-ppm mass error and a 5% NET error.The abundance ratios (18O/16O) for labeled peptide pairs were accurately computed using an equation (Equation 1) similar to that previously reported (28Yao X. Freas A. Ramirez J. Demirev P.A. Fenselau C. Proteolytic 18O labeling for comparative proteomics: model studies with two serotypes of adenovirus.Anal. Chem. 2001; 73: 2836-2842Google Scholar), R(18O16O)= I4−M4M0 I0+I2 (1−M2M0)−(1−M2M0)M2M0 I0I0(Eq. 1) where I0, I2, and I4 are the measured intensities for the monoisotopic peak for a peptide without 18O label, the peak with a mass 2 Da higher than the monoisotopic peak, and the peak with a mass 4 Da higher mass than the monoisotopic peak, respectively. M0, M2, and M4 are the predicted relative abundances for the monoisotopic peak for a peptide, the peak with mass 2 Da higher than the monoisotopic peak, and the peak with mass 4 Da higher than the monoisotopic peak, respectively. The M2/M0 and M4/M0 ratios are estimated using the following two equations (Equations 2 and 3) according to a recent report (29Johnson K.L. Muddiman D.C. A method for calculating 16O/18O peptide ion ratios for the relative quantification of proteomes.J. Am. Soc. Mass Spectrom. 2004; 15: 437-445Google Scholar); Mr represents the peptide molecular weight. M2M0=3×10−7 Mr1.9241(Eq. 2) M4M0=2×10−12 Mr3.2684(Eq. 3) Ratios from multiple observations of the same peptide across different analyses were averaged to give one ratio per peptide. All quantified peptides were rolled up to non-redundant protein groups using ProteinProphet, and the abundance ratio for each protein group was calculated by averaging the ratio of multiple unique peptides stemming from the same protein group.RESULTSThe Quantitative Proteomic Strategy—Postdigestion trypsin-catalyzed 18O labeling, SCX fractionation, and the AMT tag approach (11Liu T. Qian W.J. Strittmatter E.F. Camp D.G. Anderson G.A. Thrall B.D. Smith R.D. High throughput comparative proteome analysis using a quantitative cysteinyl-peptide enrichment technology.Anal. Chem. 2004; 76: 5345-5353Google Scholar, 17Qian W.J. Camp D.G. Smith R.D. High throughput proteomics using Fourier transform ion cyclotron resonance (FTICR) mass spectrometry.Expert Rev. Proteomics. 2004; 1: 89-97Google Scholar, 18Smith R.D. Anderson

ABSTRACT Pseudomonas aeruginosa is a gram-negative pathogen that infects immunocompromised and cystic fibrosis patients. The molecular basis of the host- P. aeruginosa interaction and the effect of specific P. aeruginosa virulence factors on various components of the innate immunity pathways are largely unknown. We examine interactions between P. aeruginosa virulence factors and components of innate immunity response in the Drosophila melanogaster model system to reveal the importance of the Toll signaling pathway in resistance to infection by the P. aeruginosa human isolate PA14. Using the two PA14-isogenic mutants plcS and dsbA , we show that Drosophila loss-of-function mutants of Spatzle, the extracellular ligand of Toll, and Dorsal and Dif, two NF-κB-like transcription factors, allow increased P. aeruginosa infectivity within fly tissues. In contrast, a constitutively active Toll mutant and a loss-of-function mutant of Cactus, an IκB-like factor that inhibits the Toll signaling, reduce infectivity. Our finding that Dorsal activity is required to restrict P. aeruginosa infectivity in Drosophila provides direct in vivo evidence for Dorsal function in adult fly immunity. Additionally, our results provide the basis for future studies into interactions between P. aeruginosa virulence factors and components of the Toll signaling pathway, which is functionally conserved between flies and humans.

The primary cause of poor outcome following allogeneic hematopoietic cell transplantation (HCT) for chronic lymphocytic leukemia (CLL) is disease recurrence. Detection of increasing minimal residual disease (MRD) following HCT may permit early intervention to prevent clinical relapse; however, MRD quantification remains an uncommon diagnostic test because of logistical and financial barriers to widespread use. Here we describe a method for quantifying CLL MRD using widely available consensus primers for amplification of all Ig heavy chain (IGH) genes in a mixture of peripheral blood mononuclear cells, followed by high-throughput sequencing (HTS) for disease-specific IGH sequence quantification. To achieve accurate MRD quantification, we developed a systematic bioinformatic methodology to aggregate cancer clone sequence variants arising from systematic and random artifacts occurring during IGH-HTS. We then compared the sensitivity of IGH-HTS, flow cytometry, and allele-specific oligonucleotide PCR for MRD quantification in 28 samples collected from 6 CLL patients following allogeneic HCT. Using amplimer libraries generated with consensus primers from patient blood samples, we demonstrate the sensitivity of IGH-HTS with 454 pyrosequencing to be 10 −5 , with a high correlation between quantification by allele-specific oligonucleotide PCR and IGH-HTS ( r = 0.85). From the same dataset used to quantify MRD, IGH-HTS also allowed us to profile IGH repertoire reconstitution after HCT—information not provided by the other MRD methods. IGH-HTS using consensus primers will broaden the availability of MRD quantification in CLL and other B cell malignancies, and this approach has potential for quantitative evaluation of immune diversification following transplant and nontransplant therapies.

Insights into the host factors and mechanisms mediating the primary host responses after pathogen presentation remain limited, due in part to the complexity and genetic intractability of host systems. Here, we employ the model Drosophila melanogaster to dissect and identify early host responses that function in the initiation and progression of Pseudomonas aeruginosa pathogenesis. First, we use immune potentiation and genetic studies to demonstrate that flies mount a heightened defense against the highly virulent P. aeruginosa strain PA14 when first inoculated with strain CF5, which is avirulent in flies; this effect is mediated via the Imd and Toll signaling pathways. Second, we use whole-genome expression profiling to assess and compare the Drosophila early defense responses triggered by the PA14 vs. CF5 strains to identify genes whose expression patterns are different in susceptible vs. resistant host-pathogen interactions, respectively. Our results identify pathogenesis- and defense-specific genes and uncover a previously undescribed mechanism used by P. aeruginosa in the initial stages of its host interaction: suppression of Drosophila defense responses by limiting antimicrobial peptide gene expression. These results provide insights into the genetic factors that mediate or restrict pathogenesis during the early stages of the bacterial-host interaction to advance our understanding of P. aeruginosa-human infections.

Standard methods of neutrophil isolation require skilled personnel, are time consuming and use large blood volumes. Kotz and his colleagues have developed a rapid microfluidic chip-based approach for rapidly isolating neutrophils directly from whole blood with 'on-chip' processing for mRNA and protein isolation. The device, which yields sufficient quantities and purities for downstream genomic or proteomic analysis, was validated in a multicenter clinical study of the immune response to severe trauma and burn injury. Neutrophils have key roles in modulating the immune response. We present a robust methodology for rapidly isolating neutrophils directly from whole blood with 'on-chip' processing for mRNA and protein isolation for genomics and proteomics. We validate this device with an ex vivo stimulation experiment and by comparison with standard bulk isolation methodologies. Last, we implement this tool as part of a near-patient blood processing system within a multi-center clinical study of the immune response to severe trauma and burn injury. The preliminary results from a small cohort of subjects in our study and healthy controls show a unique time-dependent gene expression pattern clearly demonstrating the ability of this tool to discriminate temporal transcriptional events of neutrophils within a clinical setting.

Although human plasma represents an attractive sample for disease biomarker discovery, the extreme complexity and large dynamic range in protein concentrations present significant challenges for characterization, candidate biomarker discovery, and validation. Herein we describe a strategy that combines immunoaffinity subtraction and subsequent chemical fractionation based on cysteinyl peptide and N-glycopeptide captures with two-dimensional LC-MS/MS to increase the dynamic range of analysis for plasma. Application of this “divide-and-conquer” strategy to trauma patient plasma significantly improved the overall dynamic range of detection and resulted in confident identification of 22,267 unique peptides from four different peptide populations (cysteinyl peptides, non-cysteinyl peptides, N-glycopeptides, and non-glycopeptides) that covered 3654 different proteins with 1494 proteins identified by multiple peptides. Numerous low abundance proteins were identified, exemplified by 78 “classic” cytokines and cytokine receptors and by 136 human cell differentiation molecules. Additionally a total of 2910 different N-glycopeptides that correspond to 662 N-glycoproteins and 1553 N-glycosylation sites were identified. A panel of the proteins identified in this study is known to be involved in inflammation and immune responses. This study established an extensive reference protein database for trauma patients that provides a foundation for future high throughput quantitative plasma proteomic studies designed to elucidate the mechanisms that underlie systemic inflammatory responses.

Monitoring of renal graft status through peripheral blood (PB) rather than invasive biopsy is important as it will lessen the risk of infection and other stresses, while reducing the costs of rejection diagnosis. Blood gene biomarker panels were discovered by microarrays at a single center and subsequently validated and cross-validated by QPCR in the NIH SNSO1 randomized study from 12 US pediatric transplant programs. A total of 367 unique human PB samples, each paired with a graft biopsy for centralized, blinded phenotype classification, were analyzed (115 acute rejection (AR), 180 stable and 72 other causes of graft injury). Of the differentially expressed genes by microarray, Q-PCR analysis of a five gene-set (DUSP1, PBEF1, PSEN1, MAPK9 and NKTR) classified AR with high accuracy. A logistic regression model was built on independent training-set (n = 47) and validated on independent test-set (n = 198)samples, discriminating AR from STA with 91% sensitivity and 94% specificity and AR from all other non-AR phenotypes with 91% sensitivity and 90% specificity. The 5-gene set can diagnose AR potentially avoiding the need for invasive renal biopsy. These data support the conduct of a prospective study to validate the clinical predictive utility of this diagnostic tool. Monitoring of renal graft status through peripheral blood (PB) rather than invasive biopsy is important as it will lessen the risk of infection and other stresses, while reducing the costs of rejection diagnosis. Blood gene biomarker panels were discovered by microarrays at a single center and subsequently validated and cross-validated by QPCR in the NIH SNSO1 randomized study from 12 US pediatric transplant programs. A total of 367 unique human PB samples, each paired with a graft biopsy for centralized, blinded phenotype classification, were analyzed (115 acute rejection (AR), 180 stable and 72 other causes of graft injury). Of the differentially expressed genes by microarray, Q-PCR analysis of a five gene-set (DUSP1, PBEF1, PSEN1, MAPK9 and NKTR) classified AR with high accuracy. A logistic regression model was built on independent training-set (n = 47) and validated on independent test-set (n = 198)samples, discriminating AR from STA with 91% sensitivity and 94% specificity and AR from all other non-AR phenotypes with 91% sensitivity and 90% specificity. The 5-gene set can diagnose AR potentially avoiding the need for invasive renal biopsy. These data support the conduct of a prospective study to validate the clinical predictive utility of this diagnostic tool. The accurate and timely diagnosis of acute renal allograft rejection (AR) is necessary to optimize immunosuppressive drug management and preserve renal function in kidney transplant recipients. Unfortunately, the methods of diagnosis remain imperfect. Since many conditions other than AR lead to renal allograft dysfunction, the diagnosis of AR cannot be made on functional grounds alone and requires confirmation using a kidney biopsy. Although, the diagnostic biopsy criteria for AR have been codified over time (1Racusen LC The Banff schema and differential diagnosis of allograft dysfunction.Transpl Proc. 2004; 36: 753-754Crossref PubMed Scopus (20) Google Scholar), the diagnosis using biopsy process remains limited by sampling error, assessment variability, procedural morbidity and cost. Additionally, renal allograft dysfunction is a relatively insensitive means of detecting early AR; approximately 10% of patients with clinically normal renal function are found to have evidence of AR on surveillance biopsy (2Thierry A Thervet E Vuiblet V et al.Long-term impact of subclinical inflammation diagnosed by protocol biopsy one year after renal transplantation.Am J Transplant. 2011; 11: 2153-2161Abstract Full Text Full Text PDF PubMed Scopus (49) Google Scholar). Ideally, a less-invasive means for diagnosing AR, could be used for surveillance of transplant recipients, thereby reducing the need for biopsy and providing a more efficient means of immune management of graft injury. Transcriptional profiling studies on renal allograft biopsy specimens have demonstrated substantial, coordinated expression changes in many genes that uniquely identify patients with established AR, as well as other conditions in the differential diagnosis for allograft dysfunction (3Sarwal M Chua MS Kambham N et al.Molecular heterogeneity in acute renal allograft rejection identified by DNA microarray profiling.N Engl J Med. 2003; 349: 125-138Crossref PubMed Scopus (629) Google Scholar, 4Park W Griffin M Grande JP Cosio F Stegall MD Molecular evidence of injury and inflammation in normal and fibrotic renal allografts one year posttransplant.Transplantation. 2007; 83: 1466-1476Crossref PubMed Scopus (35) Google Scholar, 5Hoffmann SC Hale DA Kleiner DE et al.Functionally significant renal allograft rejection is defined by transcriptional criteria.Am J Transpl. 2005; 5: 573-581Crossref PubMed Scopus (117) Google Scholar, 6Mannon RB Kirk AD Beyond histology: Novel tools to diagnose allograft dysfunction.Clin J Am Soc Nephrol. 2006; 1: 358-366Crossref PubMed Scopus (29) Google Scholar). In general, these changes are related to the inflammatory infiltrate resident cells within the kidney, and associated transcriptional changes in renal tissue. However, when these studies have been applied to peripheral blood (PB) (7Deng MC Eisen HJ Mehra MR et al.Noninvasive discrimination of rejection in cardiac allograft recipients using gene expression profiling.Am J Transpl. 2006; 6: 150-160Crossref PubMed Scopus (422) Google Scholar,8Flechner SM Kurian SM Head SR et al.Kidney transplant rejection and tissue injury by gene profiling of biopsies and peripheral blood lymphocytes.Am J Transpl. 2004; 4: 1475-1489Crossref PubMed Scopus (245) Google Scholar), the diagnostic changes related to AR have been less evident, presumably due to a reduced signal to noise ratio inherent in a site remote from the allograft (9Li L Ying L Naesens M et al.Interference of globin genes with biomarker discovery for allograft rejection in peripheral blood samples.Physiol Genom. 2008; 32: 190-197Crossref PubMed Scopus (37) Google Scholar). In order to increase the sensitivity and specificity of detection for relatively rare biomarkers within molecularly heterogeneous samples such as PB, we employed a carefully designed methodological approach to integrate the transcriptional profiles of PB samples from patients with and without biopsy-proven AR from three different microarray platforms. Changes in PB transcriptional profiles were correlated with biopsy-proven AR, and used to distinguish AR from other common conditions arising in kidney transplant patients. The examination of changes across a highly regulated set of genes was used to assess their utility for the noninvasive diagnosis of AR and a diagnostic alternative to the invasive renal biopsy. And 367 PB samples from 236 unique pediatric and young adult kidney transplant recipients were enrolled (as shown in Figure 1). Within this cohort, 137 patients were enrolled from Stanford University for discovery and validation, and 99 patients from the NIH/NIAID prospective study from 12 US transplant centers, “Suppressing the Immune System With or Without Steroids in Children Who Have Received Kidney Transplants”(SNS01; NCT00141037; ClinicalTrials.gov) were enrolled for independent external validation (complete clinical data from the SNS study is discussed elsewhere in Sarwal et al. (10Sarwal M Ettenger R Dharnidharka V et al.Complete steroid avoidance is effective and safe in children with renal transplants: A prospective multicenter randomized controlled trial with 3 year follow up.Am J Transpl. 2012; (June 13, PMID: 22694755 [Epub ahead of print].)Abstract Full Text Full Text PDF Scopus (108) Google Scholar). The study was governed by IRB approval and informed consent. Each PB sample in this study was paired with a contemporary renal allograft biopsy (within 48 hours) from the same patient. Surveillance biopsies were obtained from all patients at engraftment, 3, 6, 12 and 24 months posttransplantation and additionally at the times of suspected graft dysfunction (for SNS clinical study details see Sarwal et al. [10Sarwal M Ettenger R Dharnidharka V et al.Complete steroid avoidance is effective and safe in children with renal transplants: A prospective multicenter randomized controlled trial with 3 year follow up.Am J Transpl. 2012; (June 13, PMID: 22694755 [Epub ahead of print].)Abstract Full Text Full Text PDF Scopus (108) Google Scholar]; for SNS histology study details see Naesens et al. [11Naesens M Salvatierra Jr, O Benfield M et al.Subclinical inflammation and chronic renal allograft injury in a randomized trial on steroid avoidance in pediatric kidney transplantation.Am J Transpl. 2012; 12: 2730-2743Abstract Full Text Full Text PDF PubMed Scopus (36) Google Scholar]). Multiple PB-biopsy pairs from the same patient were utilized as long as each biopsy had a conclusive phenotypic diagnosis. Each biopsy was scored by the center pathologist for each enrolling clinical site; but given the possibility of discordance in biopsy reads across centers, all biopsies were blindly rescored by a single central pathologist using to the Banff (12Mengel M Sis B Haas M et al.Banff 2011 Meeting Report: New concepts in antibody-mediated rejection.Am J Transpl. 2012; 12: 563-570Abstract Full Text Full Text PDF PubMed Scopus (337) Google Scholar) classification (complete SNS histology data in Naesens et al. [11Naesens M Salvatierra Jr, O Benfield M et al.Subclinical inflammation and chronic renal allograft injury in a randomized trial on steroid avoidance in pediatric kidney transplantation.Am J Transpl. 2012; 12: 2730-2743Abstract Full Text Full Text PDF PubMed Scopus (36) Google Scholar]). The PB-biopsy pairs were categorized as “acute rejection” (AR; n = 115), or as “stable” (STA, n = 180), if there was absence of AR and any other substantial pathology. A third category of PB-biopsy pairs were characterized as “non-AR/non-STA” (n = 72) if they exhibited no evidence of Banff graded AR, but either met the Banff criteria for “borderline” classification (n = 12), had a diagnosis of chronic allograft nephropathy (CAN; samples had IFTA grade ≥ 1; n = 37), or chronic calcineurin inhibitor toxicity (CNIT; n = 16), or bacterial/viral infection or other undefined chronic graft injury (n = 7). Blood was collected in 2.5 mL PAXgene™ Blood RNA Tubes (PreAnalytiX, Qiagen) or in Ficoll tubes for peripheral blood (PBL) isolation (the latter samples were only used for microarray discovery on Affymetrix). Total RNA was extracted using a previously published protocol9. Our goal was to maximize the power of discovering a robust gene-set for AR, and to minimize platform specific artifacts (e.g., issues of cross-hybridization (13Naef F Lim DA Patil N Magnasco M DNA hybridization to mismatched templates: A chip study.Phys Rev E Stat Nonlin Soft Matter Phys. 2002; 65: 040902Crossref PubMed Scopus (1) Google Scholar), specificity of hybridization (14Alizadeh AA Eisen MB Davis RE et al.Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling.Nature. 2000; 403: 503-511Crossref PubMed Scopus (8032) Google Scholar), globin gene effect9 of whole blood on the Affymetrix platform, differential stability of Cy dyes (15Randolph JB Waggoner AS Stability, specificity and fluorescence brightness of multiply-labeled fluorescent DNA probes.Nucleic Acids Res. 1997; 25: 2923-2929Crossref PubMed Scopus (143) Google Scholar), platform specific bias). Furthermore, because each array platform uses different sets of genes that are represented by different probe set IDs, we used AILUN (http://ailun.stanford.edu) (16Chen R Li L Butte AJ AILUN: Reannotating gene expression data automatically.Nat Methods. 2007; 4: 879Crossref PubMed Scopus (86) Google Scholar) to re-annotate the probe set IDs with the current Entrez Gene IDs. All Gene expression values were transformed to log2 for further analysis. We applied significance analysis of microarrays (SAM) (17Tusher VG Tibshirani R Chu G Significance analysis of microarrays applied to the ionizing radiation response.Proc Natl Acad Sci USA. 2001; 98: 5116-5121Crossref PubMed Scopus (9775) Google Scholar) to identify differentially expressed genes for AR on all 3 platforms, with a threshold false discovery rate (FDR) < 5%. Standard protocols were used for Q-PCR reactions on the ABI 7900 Sequence Detection System (Applied Biosystems, Foster City, CA) under standard cycle conditions (10 min at 95°C, 40 cycles of 15s 95°C, 30 s at 60°C), using gene expression assays (Applied Biosystems, Foster City, CA). The relative amount of RNA expression was calculated using a comparative CT method. Expression values were normalized to 18S using ribosomal RNA endogenous reference and universal RNA (Agilent Inc., Santa Clara; Cat #740000). We used Ingenuity Pathway Analysis (IPA) to identify significant signaling pathways. We chose -log10P > 1.3 as a threshold for identifying significant pathways in IPA. We used BioGPS (18Wu C Orozco C Boyer J et al.BioGPS: An extensible and customizable portal for querying and organizing gene annotation resources..Genome Biol. 2009; 10: R130Crossref PubMed Scopus (1097) Google Scholar,19Su AI Wiltshire T Batalov S et al.A gene atlas of the mouse and human protein-encoding transcriptomes.Proc Natl Acad Sci USA. 2004; 101: 6062-6067Crossref PubMed Scopus (2852) Google Scholar) to identify the blood cell types in which the differentially expressed genes were highly expressed. A gene was highly expressed in a blood cell type if its expression in a given blood cell type was greater than 10 times its median expression over all tissues. We used hypergeometric test to determine whether the proportion of the highly expressed genes in each cell type was statistically significant or not. The p-values from hypergeometric test were corrected for multiple hypotheses using Benjamini–Hochberg correction. A schematic outline of the study is presented in Figure 1 and shows the number of samples used for discovery by microarrays (122 PB), verification by QPCR (34 PB), building an AR logistic regression model by penalized maximum likelihood method, in an independent sample set by QPCR (47 PB) and testing the performance of the model in the SNS clinical study (198 PB). Summary statistics for patient demographic and clinical variables are provided in Table 1.Table 1:Demographic information of PB samples for microarray experiments (n = 122) and PCR validation (n = 106)*Clinical characteristicsMicroarray discoveryPCR validationTraining setTest setAR (n = 60)STA (n = 62)p-ValueAR (n = 23)STA (n = 24)p-ValueAR (n = 32)Non-AR (n = 166)p-ValueRecipientsGender, % females31.91%44.64%0.1920.00%47.37%0.0952.00%36.49%0.41Mean age, year12.49 ± 5.0410.94 ± 6.010.1710.31 ± 5.2414.24 ± 5.870.0511.91 ± 5.5711.85 ± 5.690.78Immunosuppression, %SF51.06%57.14%0.5466.67%73.68%0.6744%44.6%1.00HLA match2.45 ± 1.362.41 ± 1.410.903.13 ± 1.412.59 ± 1.430.271.22 ± 1.191.39 ± 1.230.09DonorsDonor source %LRD63.64%75.51%0.2180.00%84.21%0.7624.00%41.89%0.36Gender, % females53.49%48.00%0.6046.67%63.16%0.3540.00%41.89%1.00Mean age, year33.26 ± 13.1032.72 ± 11.030.8332.41 ± 13.8739.17 ± 10.310.1327.79 ± 9.6928.50 ± 10.080.59Values are means ± SD. AR = acute rejection; STA = stable; SF = steroid free; txp = transplant; LRD = living related donor. The sample numbers for each dataset are shown. Discovery, and Training Set samples came from 137 unique Stanford patients; Test Set samples came from 99 unique SNSO1 patients. The Verification set samples came from a subset of samples used for Microarray Discovery. Open table in a new tab Values are means ± SD. AR = acute rejection; STA = stable; SF = steroid free; txp = transplant; LRD = living related donor. The sample numbers for each dataset are shown. Discovery, and Training Set samples came from 137 unique Stanford patients; Test Set samples came from 99 unique SNSO1 patients. The Verification set samples came from a subset of samples used for Microarray Discovery. The 5-gene model was validated in a second independent cohort of 198 samples from SNS01 (Test Set). The Test set consisted of blood samples collected at the time of biopsy confirmed AR (n = 32; [20Racusen LC Solez K Colvin RB et al.The Banff 97 working classification of renal allograft pathology.Kidney Int. 1999; 55: 713-723Abstract Full Text Full Text PDF PubMed Scopus (2773) Google Scholar]) with clinical graft dysfunction (greater than 10% increase from baseline serum creatinine values), and blood samples collected at the time of protocol biopsies with stable graft function (STA; n = 94). There was an additional phenotype of samples within the SNSO1 sample set that was not used in the earlier process of single–center discovery and validation. These were PB collected at the time of biopsies where the diagnosis was not one of either Banff graded AR or one of normal renal histology; these samples were codified nonAR/nonSTA, and consisted of a collection of samples with different pathologies; n = 72). In this latter category, many samples had clinical graft dysfunction and the different pathological categories were based on the centralized biopsy read-outs (12 borderline AR, 37 CAN, 16 CNIT and 7 other pathology). To examine if any demographic, clinical or immunosuppression confounders at baseline or at the time of sampling could have driven the segregation of the 5-gene set prediction score for AR, 18 different clinical confounders on the single-center samples were correlated with Q-PCR expression of each of the 5 genes in the single center data on 81 samples (34 Verification + 47 Training Set) using Pearson correlation. Additionally, we also performed univariate logistic regression for each clinical confounder with the risk of AR as well as a multivariate logistic regression model for a combination of all 18 clinical confounders and 5 genes’ expression values. The confounders were posttransplant time, recipient age, recipient gender, donor gender, donor source, donor age, steroid-free vs. steroid-based immunosuppression, total white blood cell count, hematocrit, CMV status, EBV status, BK virus infection, bacterial Infection, presence of donor-specific antibodies (DSA), panel reactive antibodies, use of induction therapy (either Daclizumab or T cell depleting antibodies), use of calcineurin inhibitors (tacrolimus or cyclosporine), and use of anti-metabolites (mycophenolate mofetil or azathioprine). From 122 PB, we identified 2382 differentially expressed genes (false discovery rate; FDR < 5%). All of the samples have been deposited at GSE14067 to NCBI Gene Expression Omnibus (GEO) database. These genes play a role in leukocyte extravasation, and chemokine, T cell and B cell receptor signaling (-log10P>1.3; IPA®; http://www.ingenuity.com). They are enriched (10x median intensity across all tissues; http://www.BioGPS.org) (19Su AI Wiltshire T Batalov S et al.A gene atlas of the mouse and human protein-encoding transcriptomes.Proc Natl Acad Sci USA. 2004; 101: 6062-6067Crossref PubMed Scopus (2852) Google Scholar) in different blood cells, namely CD8+ T cells (126, p = 3.80e-16), CD4+ T cells (118, p = 5e-13), CD56+ NK cells (149, p = 1.3e-9), CD33+ Myeloid cells (150, p = 1.7e-8), Dendritic Cells (130, p = 8.8e-8), CD14+ Monocytes (111, p = 1.1e-4), CD34+ cells (119, p = 4.8e-6) and CD19+ B cells (91, p = 1.6e-3). We chose 32 genes for QPCR verification (Figure 1A) that were differentially expressed in all microarray data sets, and were biologically relevant with enrichment of cell–specific immune responses in AR. These genes were DUSP1, IL1RAP, MCM7, NKTR, MAPK9, PSEN1, PTPRC, SLPI, STAT1, STAT3, CFLAR, IL32, PBEF1, PHLDA1, IFNGR1, IL8RA, ITGAX, PLCG1, PTPN11, TNFAIP6, ZAP70, GOLGA8A, RYBP, TLR8, RNF130, F2RL1, GRZYB, PFN1, FCGR1A, NFATC3 and IL6R. Given the recent research on the dual role of FOXP3 in rejection (21Brown K Moxham V Karegli J Phillips R Sacks SH Wong W Ultra-localization of Foxp3+ T cells within renal allografts shows infiltration of tubules mimicking rejection.Am J Pathol. 2007; 171: 1915-1922Abstract Full Text Full Text PDF PubMed Scopus (33) Google Scholar,22Fan Z Spencer JA Lu Y et al.In vivo tracking of ’color-coded’ effector, natural and induced regulatory T cells in the allograft response.Nat Med. 2010; 16: 718-722Crossref PubMed Scopus (130) Google Scholar) and tolerance (23Bestard O Cruzado JM Mestre M et al.Achieving donor-specific hyporesponsiveness is associated with FOXP3+ regulatory T cell recruitment in human renal allograft infiltrates.J Immunol. 2007; 179: 4901-4909Crossref PubMed Scopus (141) Google Scholar,24Graca L Cobbold SP Waldmann H Identification of regulatory T cells in tolerated allografts.J Exp Med. 2002; 195: 1641-1646Crossref PubMed Scopus (487) Google Scholar), it was also selected for verification. 15 genes were significantly differentially expressed between AR and STA (p-value < 0.05). Out of these 15 genes, five genes (F2RL1, STAT1, FOXP3, PTPRC and IL6R; p < 0.05) have previously been shown to be involved in AR. Out of the remaining 10 genes, 8 genes were over-expressed in AR (CFLAR, p = 0.0016; DUSP1,p = 0.0013; IFNGR1, p = 0.0062; ITGAX, p = 0.0011; PBEF1, p = 0.00008; PSEN1, p = 0.00007; RNF130, p = 0.0459; and RYBP, p = 0.0012), and 2 genes were under-expressed in AR (MAPK9, p = 0.0006; NKTR, p = 0.0016). We applied logistic regression with best subset selection to the Verification Set in order to find the minimum number of genes necessary for the proper classification of biopsy-confirmed AR(25Derksen S Keselman HJ Backward, forward and stepwise automated subset selection algorithms: Frequency of obtaining authentic and noise variables.Br J Math Stat Psychol. 1992; 45: 265-282Crossref Scopus (503) Google Scholar). Chi-square score for logistic regression models built using the 10 genes showed that in the data-set used, using five genes would have the same performance as a model using six or more genes. Additional selection criteria were used such as biological relevance and model performance (high statistical significance 10 p-value < 0.005 and low standard error of mean [SEM]), resulting in DUSP1, MAPK9, NKTR, PBEF1, and PSEN1. Expression of each of the five genes in an independent Training set of 47 Stanford samples (23 AR, 24 STA) was also significantly different (p-value < 0.05) (Figure 2A). This data was used to develop a logistic regression model with a penalized maximum likelihood method, which was a more robust estimation procedure than the usual maximum likelihood methods (26Heinze G Schemper M A solution to the problem of separation in logistic regression.Stat Med. 2002; 21: 2409-2419Crossref PubMed Scopus (1228) Google Scholar,27Heinze G. A comparative investigation of methods for logistic regression with separated or nearly separated data.Stat Med. 2006; 25: 4216-4226Crossref PubMed Scopus (264) Google Scholar). In the 5 gene-set model, each of the regression coefficients describes the size of the contribution of that gene as a risk factor for diagnosing AR, where the larger the coefficient, the greater the influence of that gene in AR (Table S1). To examine if any demographic, clinical or immunosuppression confounders at baseline or at the time of sampling could have driven the segregation of the 5-gene set prediction score for AR, 18 different clinical confounders on the single-center samples were correlated with Q-PCR expression of each of the 5 genes in the Training set of 47 samples (23 AR, 24 STA) using Pearson correlation. Univariate logistic regression was also done for each clinical confounder with the risk of AR as well as a multivariate logistic regression model for a combination of all 18 clinical confounders and 5 genes’ expression values. By t-test, all 5 genes had significant change in expression only with the presence of donor specific antibody (DSA; p < 0.05). By univariate logistic regression model, all 5 genes were significantly associated with AR (p < 0.0001; AUC from 0.829-0.938) and DSA positivity (p < 0.0001; AUC = 0.828) while there was no association with the histology grade or C4d positivity (p = 0.80 for Banff score; p = 0.79 for C4d positivity). These data thus underscore that the coordinated expression of the 5-gene set in PB can diagnose AR with high confidence, irrespective of the differences in patient characteristics, immunosuppression and rejection timing. The 5-gene model was validated in a second independent cohort of 198 samples (Test Set) collected in 12 different centers as part of the SNSO1 study (Figure 2B). The test set consisted of PB-biopsy pairs with AR, STA, and an additional phenotype of samples within the SNSO1 sample set that was not used in the earlier process of single–center discovery and validation. These PB samples were collected at the time of biopsies where the diagnosis was not one of either Banff graded AR or one of normal renal histology; these samples were codified nonAR/nonSTA, and consisted of a collection of samples with different pathologies; n = 72; 12 borderline AR, 37 CAN, 16 CNIT and 7 other pathology. The accuracy of the 5-gene model was assessed by evaluating the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) on the AR and STA samples, as well as the AR and non-AR in the Test Set (Figure 3A). The 5-gene model has 91% sensitivity, 94% specificity, 83% PPV, 97% NPV and 92% accuracy, to separate AR from STA samples (AUC 0.955; Figure 3B); and 91% sensitivity, 90% specificity, and 90% accuracy to separate AR samples from all other phenotypes (STA and non-AR/non-STA; Figure 3C; AUC 0.937, Figure 3D). It is important to note that 8/12 of samples from patients classified as borderline rejection on biopsy were classified as AR by the 5-gene model (these have been classified as true negatives in the non-AR/non-STA cohort, but it can be argued that these samples could also be true positives). The high prediction of an AR phenotype in the borderline AR samples suggests that preclinical injury in AR may also be identified by Q-PCR analysis of a PB sample and suggest earlier treatment for the patient. In the current study, we used a cross-platform, high-throughput, transcript profiling approach to identify a highly specific, biologically meaningful, concise gene set in PB whose expression correlates well with the AR/no AR status of contemporaneous biopsies collected from the same patients. A logistic regression model built on a set of 5 genes in PB and extensively validated by Q-PCR, accurately diagnosed rejection, with 91% sensitivity and 90% specificity, substantially improving on any current available method for specifically diagnosing AR. Importantly, though the 5-gene test was developed for a binary comparison of AR and STA samples, it was validated in an independent cohort that comprised of samples obtained at 12 different transplant centers, in patients with varying demographics and across multiple clinical phenotypes, such as CAN, CNIT, infection, and acute tubular necrosis, sub-clinical AR, clinical AR and STA. As the model was built using samples from a single center, and was validated in an independent multicenter cohort, general applicability of this test in real-world appears feasible where the patient population will contain heterogeneous graft conditions along the continuum from stable to AR. The SNSO1 trial arbitrarily assigned borderline AR in the nonAR/nonSTA category, but in retrospect, this might not have been biologically accurate as most of the “misclassifications” were actually borderline AR, and their inclusion in the AR group would further enhance the PPV of the test. This suggests a longer biologic process than previously thought in immune changes leading to rejection. It would be important to evaluate serial samples from patients developing clinical AR episodes to examine if the 5-gene model can detect subclinical acute rejection, that is, acute rejection prior to its becoming clinically evident. Some of the Banff graded ARs were borderline (n = 12) but were scored as AR biopsies in the SNSO1 validation sample to help compensate for the small number of AR events (n = 32) that met the conventional AR criteria. Thus the 5-gene model was used to predict the set of AR and subclinical borderline AR biopsies in the SNS01 subjects. Early minimally invasive diagnosis of AR would be a significant advance over current practice standards that depend on biopsy for diagnosis and initiation of treatment. At present, by the time a clinical trigger is available to warrant doing a biopsy for rejection diagnosis, the rejection has evolved with its full humoral or cellular mechanisms. Having a clinical indication for the rejection episode, based on the high score on the 5-gene test, that is earlier than a rise in the serum creatinine, would be a significant advance for the management of patients, as it would result in the earlier diagnosis of rejection and provide an early trigger for performing an indicated biopsy, if warranted. Work is underway in our group to refine the performance of the larger gene-set for discriminating cellular from humoral rejection, clinically important for discriminating treatment for AR. The excellent positive and negative predictive values of the 5-gene model suggest that a PB test based on these genes could be useful for screening patients for absence of AR. Given the excellent discrimination of this test, there is strong justification for a larger, more definitive follow-up study with a larger number of AR patients for study, to evaluate if a higher AR gene score translates into risk of more aggressive AR or humoral versus cellular AR. The strong negative predictive value of the model for diagnosing absence of AR opens the door for personalized therapy, where patients can be potentially screened serially by the 5-gene test, and in the absence of AR risk, have reduced follow-up, be candidates to avoid unnecessary protocol biopsies and, in the presence of graft dysfunction, be evaluated for alternative etiologies, such as infection, obstruction or toxicity. The 5-gene blood test may also provide a new means to monitor for resolution of AR after treatment intensification. Additional samples will have to be evaluated from patients undergoing treatment of AR to examine if immunosuppression intensification causes a decrement of the 5-gene test prediction score, commensurate with histological resolution of the AR episode, perhaps guiding assessment of a patient’s response to therapy. The PB genes most strongly associated with graft rejection, do not correlate with multiple demographic, clinical, treatment modality and bacterial/viral infection parameters. Although there is significant correlation with DSA positivity, our model predicts AR, irrespective of cellular or humoral AR. We are further analyzing our data to develop a blood gene-based model that can further distinguish humoral from cellular rejection. Even though this is a minimal set of 5 genes for AR classification, expanding out to other populations may require the inclusion of the 10 gene-set. The 5 genes are central to leukocyte trafficking and T/B cell activation, and are mostly expressed in by activated monocytes in the peripheral circulation, reflecting injury mechanisms relating to oxidative cellular stress responses (DUSP1), apoptosis (MAPK9), IL2 dependant activation of cytolytic genes (NKTR), increased cell adhesion via the e-cadherin/ catenin complex (PSEN1), and vascular smooth muscle injury (PBEF1). It is likely that these genes play a pivotal role in the mechanism of cytolysis and graft microvasculature injury from activated monocytes in graft rejection (28Steiniger B Stehling O Scriba A Grau V Monocytes in the rat: Phenotype and function during acute allograft rejection.Immunol Rev. 2001; 184: 38-44Crossref PubMed Scopus (35) Google Scholar,29Stehling O Grau V Steiniger B Monocyte cytotoxicity during acute kidney graft rejection in rats.Int Immunol. 2004; 16: 101-110Crossref PubMed Scopus (15) Google Scholar) The association of the gene profile of the selected genes in blood with DSA and peripheral trafficking of monocytes supports the growing recognition of DSA as a culprit in graft injury (30Zhu L Lee PC Everly MJ Terasaki PI Detailed examination of HLA antibody development on renal allograft failure and function.Clin Transpl. 2008; : 171-187PubMed Google Scholar,31Terasaki P Mizutani K Antibody mediated rejection: Update 2006.Clin J Am Soc Nephrol. 2006; 1: 400-403Crossref PubMed Scopus (40) Google Scholar) and monocytes as primary culprits in graft dysfunction (32Zecher D van Rooijen N Rothstein DM Shlomchik WD Lakkis FG An innate response to allogeneic nonself mediated by monocytes.J Immunol. 2009; 183: 7810-7816Crossref PubMed Scopus (80) Google Scholar,33Fahim T Bohmig GA Exner M et al.The cellular lesion of humoral rejection: Predominant recruitment of monocytes to peritubular and glomerular capillaries.Am J Transpl. 2007; 7: 385-393Crossref PubMed Scopus (84) Google Scholar). Serial performance of the 5-gene test proposed in the current study suggests a means to stratify patients as high or low risk for rejections, even in the presence of other histological injuries in the graft. It may be anticipated that the more frequent assessment of risk afforded by the minimally invasive nature of this assay will facilitate more prompt therapeutic management which may alter the course of rejection, providing a critical, and as yet unavailable, new dimension of immunosuppression customization for a transplant patient. However, a couple of caveats should be noted. The sample numbers in the discovery set are limited, but are offset by the power of validating the discovery in the SNSO1 multicenter study. As this study was performed in children and young adults, the nature of the rejection may be more aggressive due to either the size mismatch of adult-sized organ and infant recipient, or the higher rate of treatment nonadherence adolescent recipient, both of which could result in stronger immune response signal. Additionally, none of the pediatric patients in this study received induction with anti-CD52 depletion therapy or with co-stimulatory blockade. Therefore, the performance of the 5-genes model should be further studied for its potential to diagnose rejection in patients of all ages, in larger sample cohorts and in different immunosuppressive regimens. The empirical results of the diagnostic potential of the selected 5-gene panel in this study suggest potential clinical utility and support the future development of a prospective clinical trial in children and extension of this work in adult renal transplant recipients to confirm clinical application. We deeply appreciate the participation of patients at Stanford University and the NIH funded multicenter randomized study of steroid-avoidance versus steroid-based immunosuppression (SNSO1). We also are indebted to the support with patient recruitment and sample collections from transplant patients in the SNSO1 multicenter study centers. We thank Nancy Bridges and Daniel Rotrosen from NIH/NIAID for their continuous support and advice on the SNSO1 study. We thank Nikki Williams for the support throughout the manuscript preparation. The authors are grateful to Dr. Neeraja Kambham from Stanford University for her centralized, blinded reads of graft pathology and to Dr. Allan Kirk from Emory University for his support and suggestions to make the manuscript more meaningful. We are thankful to David Ikle, Michael Riggs, and Katie Poole in the validation phase of this project. Support from for this project was funded by NIH grants UO1AI055795 (OS awarded within the Cooperative Clinical Trials in Pediatric Transplantation Consortium), RO1AI061739 (MS), and ARRA funding 3UO1 AI077821-0351 (AK) awarded within the Cooperative Clinical Trials in Pediatric Transplantation Consortium. Dr. Sarwal receives consulting and lecture fees from Bristol Meyers Squibb, Genentech and Astellas and has equity/ownership stock in Organ-I; Dr. Butte receives consulting fees from Johnson & Johnson, Genstruct, Lilly and Tercica, lecture fees from Siemens and Lilly and equity ownership/stock from Genstruct and NuMedii; Dr. Davis has equity ownership/stock in Affymetrix and Organ-I. VRD has received consulting fees from Bristol–Myers–Squibb and honoraria from Genzyme and Alexion. Additional Supporting Information may be found in the online version of this article: Download .xls (2.73 MB) Help with xls files Table S1. Patient Demographics for all PB samples included in the microarray and Q-PCR studies. P values for age and posttransplant time were calculated using the T test with unequal variance. Probabilities of steroid usage, gender and donor source were calculated using Chi-Square analysis. Please note: Wiley-Blackwell is not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.

Heterotopic ossification induced by injuries and burns is mediated by signaling through the SMAD pathway and can be targeted with topical apyrase.

A 6.9 million-feature oligonucleotide array of the human transcriptome [Glue Grant human transcriptome (GG-H array)] has been developed for high-throughput and cost-effective analyses in clinical studies. This array allows comprehensive examination of gene expression and genome-wide identification of alternative splicing as well as detection of coding SNPs and noncoding transcripts. The performance of the array was examined and compared with mRNA sequencing (RNA-Seq) results over multiple independent replicates of liver and muscle samples. Compared with RNA-Seq of 46 million uniquely mappable reads per replicate, the GG-H array is highly reproducible in estimating gene and exon abundance. Although both platforms detect similar expression changes at the gene level, the GG-H array is more sensitive at the exon level. Deeper sequencing is required to adequately cover low-abundance transcripts. The array has been implemented in a multicenter clinical program and has generated high-quality, reproducible data. Considering the clinical trial requirements of cost, sample availability, and throughput, the GG-H array has a wide range of applications. An emerging approach for large-scale clinical genomic studies is to first use RNA-Seq to the sufficient depth for the discovery of transcriptome elements relevant to the disease process followed by high-throughput and reliable screening of these elements on thousands of patient samples using custom-designed arrays.

A vibrant discussion of the merits and limitations of animal models is long overdue. The limitation of space precludes addressing many of the questionable approaches and statements by Takao and Miyakawa (1).

We present a simple molecular indexing method for quantitative targeted RNA sequencing, in which mRNAs of interest are selectively captured from complex cDNA libraries and sequenced to determine their absolute concentrations. cDNA fragments are individually labeled so that each molecule can be tracked from the original sample through the library preparation and sequencing process. Multiple copies of cDNA fragments of identical sequence become distinct through labeling, and replicate clones created during PCR amplification steps can be identified and assigned to their distinct parent molecules. Selective capture enables efficient use of sequencing for deep sampling and for the absolute quantitation of rare or transient transcripts that would otherwise escape detection by standard sequencing methods. We have also constructed a set of synthetic barcoded RNA molecules, which can be introduced as controls into the sample preparation mix and used to monitor the efficiency of library construction. The quantitative targeted sequencing revealed extremely low efficiency in standard library preparations, which were further confirmed by using synthetic barcoded RNA molecules. This finding shows that standard library preparation methods result in the loss of rare transcripts and highlights the need for monitoring library efficiency and for developing more efficient sample preparation methods.

Objective: Many patients have complicated recoveries following severe trauma due to the development of organ injury. Physiological and anatomical prognosticators have had limited success in predicting clinical trajectories. We report on the development and retrospective validation of a simple genomic composite score that can be rapidly used to predict clinical outcomes. Design: Retrospective cohort study. Setting: Multi-institutional level 1 trauma centers. Patients: Data were collected from 167 severely traumatized (injury severity score >15) adult (18–55 yr) patients. Methods: Microarray-derived genomic data obtained from 167 severely traumatized patients over 28 days were assessed for differences in messenger RNA abundance among individuals with different clinical trajectories. Once a set of genes was identified based on differences in expression over the entire study period, messenger RNA abundance from these subjects obtained in the first 24 hours was analyzed in a blinded fashion using a rapid multiplex platform, and genomic data reduced to a single metric. Results: From the existing genomic dataset, we identified 63 genes whose leukocyte expression differed between an uncomplicated and complicated clinical outcome over 28 days. Using a multiplex approach that can quantitate messenger RNA abundance in less than 12 hours, we reassessed total messenger RNA abundance from the first 24 hours after trauma and reduced the genomic data to a single composite score using the difference from reference. This composite score showed good discriminatory capacity to distinguish patients with a complicated outcome (area under a receiver–operator curve, 0.811; p <0.001). This was significantly better than the predictive power of either Acute Physiology and Chronic Health Evaluation II or new injury severity score scoring systems. Conclusions: A rapid genomic composite score obtained in the first 24 hours after trauma can retrospectively identify trauma patients who are likely to develop complicated clinical trajectories. A novel platform is described in which this genomic score can be obtained within 12 hours of blood collection, making it available for clinical decision making.

In Brief Objective: To determine and compare outcomes with accepted benchmarks in burn care at 6 academic burn centers. Background: Since the 1960s, US morbidity and mortality rates have declined tremendously for burn patients, likely related to improvements in surgical and critical care treatment. We describe the baseline patient characteristics and well-defined outcomes for major burn injuries. Methods: We followed 300 adults and 241 children from 2003 to 2009 through hospitalization, using standard operating procedures developed at study onset. We created an extensive database on patient and injury characteristics, anatomic and physiological derangement, clinical treatment, and outcomes. These data were compared with existing benchmarks in burn care. Results: Study patients were critically injured, as demonstrated by mean % total body surface area (TBSA) (41.2 ± 18.3 for adults and 57.8 ± 18.2 for children) and presence of inhalation injury in 38% of the adults and 54.8% of the children. Mortality in adults was 14.1% for those younger than 55 years and 38.5% for those aged 55 years and older. Mortality in patients younger than 17 years was 7.9%. Overall, the multiple organ failure rate was 27%. When controlling for age and % TBSA, presence of inhalation injury continues to be significant. Conclusions: This study provides the current benchmark for major burn patients. Mortality rates, notwithstanding significant % TBSA and presence of inhalation injury, have significantly declined compared with previous benchmarks. Modern day surgical and medically intensive management has markedly improved to the point where we can expect patients younger than 55 years with severe burn injuries and inhalation injury to survive these devastating conditions. In this study, we describe the baseline patient characteristics and well-defined outcomes of persons hospitalized in the United States for major burn injury. This study provides a current benchmark for patients with major burn injuries.

Active translocation of ions across membranes requires alternating access of the ion binding site inside the pump to the two membrane surfaces. Proton translocation by bacteriorhodopsin (bR), the light-driven proton pump in Halobacterium salinarium, involves this kind of a change in the accessibility of the centrally located retinal Schiff base. This key event in bR’s photocycle ensures that proton release occurs to the extracellular side and proton uptake from the cytoplasmic side. To study the role of protein conformational changes in this reprotonation switch, spin labels were attached to pairs of engineered cysteine residues in the cytoplasmic interhelical loops of bR. Light-induced changes in the distance between a spin label on the EF interhelical loop and a label on either the AB or the CD interhelical loop were observed, and the changes were monitored following photoactivation with time-resolved electron paramagnetic resonance (EPR) spectroscopy. Both distances increase transiently by about 5 Å during the photocycle. This opening occurs between proton release and uptake, and may be the conformational switch that changes the accessibility of the retinal Schiff base to the cytoplasmic surface after proton release to the extracellular side.

PROTEOMICSVolume 5, Issue 2 p. 572-584 Regular Article Comparative proteome analyses of human plasma following in vivo lipopolysaccharide administration using multidimensional separations coupled with tandem mass spectrometry Wei-Jun Qian, Wei-Jun Qian Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, RichlandSearch for more papers by this authorJon M. Jacobs, Jon M. Jacobs Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, RichlandSearch for more papers by this authorDavid G. Camp II, David G. Camp II Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, RichlandSearch for more papers by this authorMatthew E. Monroe, Matthew E. Monroe Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, RichlandSearch for more papers by this authorRonald J. Moore, Ronald J. Moore Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, RichlandSearch for more papers by this authorMarina A. Gritsenko, Marina A. Gritsenko Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, RichlandSearch for more papers by this authorSteve E. Calvano, Steve E. Calvano Department of Surgery, UMDNJ-Robert Wood Johnson Medical School, New Brunswick, NJSearch for more papers by this authorStephen F. Lowry, Stephen F. Lowry Department of Surgery, UMDNJ-Robert Wood Johnson Medical School, New Brunswick, NJSearch for more papers by this authorWenzhong Xiao, Wenzhong Xiao Stanford Genome Technology Center, Stanford University School of Medicine, Palo Alto, CASearch for more papers by this authorLyle L. Moldawer, Lyle L. Moldawer Laboratory of Inflammation Biology and Surgical Science, Department of Surgery, University of Florida College of Medicine, Gainesville, FLSearch for more papers by this authorRonald W. Davis, Ronald W. Davis Stanford Genome Technology Center, Stanford University School of Medicine, Palo Alto, CASearch for more papers by this authorRonald G. Tompkins, Ronald G. Tompkins Department of Surgery, Shriners Burn Center and Massachusetts General Hospital, Harvard Medical School, Boston, MASearch for more papers by this authorRichard D. Smith, Corresponding Author Richard D. Smith rds@pnl.gov Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, RichlandEnvironmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, P.O. Box 999, MSIN: K8-98, Richland WA, 99352, USA===Search for more papers by this author Wei-Jun Qian, Wei-Jun Qian Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, RichlandSearch for more papers by this authorJon M. Jacobs, Jon M. Jacobs Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, RichlandSearch for more papers by this authorDavid G. Camp II, David G. Camp II Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, RichlandSearch for more papers by this authorMatthew E. Monroe, Matthew E. Monroe Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, RichlandSearch for more papers by this authorRonald J. Moore, Ronald J. Moore Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, RichlandSearch for more papers by this authorMarina A. Gritsenko, Marina A. Gritsenko Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, RichlandSearch for more papers by this authorSteve E. Calvano, Steve E. Calvano Department of Surgery, UMDNJ-Robert Wood Johnson Medical School, New Brunswick, NJSearch for more papers by this authorStephen F. Lowry, Stephen F. Lowry Department of Surgery, UMDNJ-Robert Wood Johnson Medical School, New Brunswick, NJSearch for more papers by this authorWenzhong Xiao, Wenzhong Xiao Stanford Genome Technology Center, Stanford University School of Medicine, Palo Alto, CASearch for more papers by this authorLyle L. Moldawer, Lyle L. Moldawer Laboratory of Inflammation Biology and Surgical Science, Department of Surgery, University of Florida College of Medicine, Gainesville, FLSearch for more papers by this authorRonald W. Davis, Ronald W. Davis Stanford Genome Technology Center, Stanford University School of Medicine, Palo Alto, CASearch for more papers by this authorRonald G. Tompkins, Ronald G. Tompkins Department of Surgery, Shriners Burn Center and Massachusetts General Hospital, Harvard Medical School, Boston, MASearch for more papers by this authorRichard D. Smith, Corresponding Author Richard D. Smith rds@pnl.gov Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, RichlandEnvironmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, P.O. Box 999, MSIN: K8-98, Richland WA, 99352, USA===Search for more papers by this author First published: 08 February 2005 https://doi.org/10.1002/pmic.200400942Citations: 100AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract There is significant interest in characterization of the human plasma proteome due to its potential for providing biomarkers applicable to clinical diagnosis and treatment and for gaining a better understanding of human diseases. We describe here a strategy for comparative proteome analyses of human plasma, which is applicable to biomarker identifications for various disease states. Multidimensional liquid chromatography-mass spectrometry (LC-MS/MS) has been applied to make comparative proteome analyses of plasma samples from an individual prior to and 9 h after lipopolysaccharide (LPS) administration. Peptide peak areas and the number of peptide identifications for each protein were used to evaluate the reproducibility of LC-MS/MS and to compare relative changes in protein concentration between the samples following LPS treatment. A total of 804 distinct plasma proteins (not including immunoglobulins) were confidently identified with 32 proteins observed to be significantly increased in concentration following LPS administration, including several known inflammatory response or acute-phase mediators such as C-reactive protein, serum amyloid A and A2, LPS-binding protein, LPS-responsive and beige-like anchor protein, hepatocyte growth factor activator, and von Willebrand factor, and thus, constituting potential biomarkers for inflammatory response. Citing Literature Supporting Information Supporting information for this article is available on the WWW under http://www.wiley-vch.de/contents/jc_2120/2005/pro0942_s.pdf or from the author. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. Volume5, Issue2No. 2 February 2005Pages 572-584 RelatedInformation

Abstract Breast cancer outcome is highly variable. Whether inadvertent exposure to environmental xenobiotics evokes a biological response promoting cancer aggressiveness and a higher probability of tumor recurrence remains unknown. To determine specific molecular alterations which arise in high-risk breast tissue in the presence of the ubiquitous xenoestrogen, bisphenol A (BPA), we used nonmalignant random periareolar fine-needle aspirates in a novel functional assay. Early events induced by BPA in epithelial-stromal cocultures derived from the contralateral tissue of patients with breast cancer included gene expression patterns which facilitate apoptosis evasion, endurance of microenvironmental stress, and cell cycle deregulation without a detectable increase in cell numbers. This BPA response profile was significantly associated with breast tumors characterized by high histologic grade (P &amp;lt; 0.001) and large tumor size (P = 0.002), resulting in decreased recurrence-free patient survival (P &amp;lt; 0.001). Our assays show a biological “fingerprint” of probable prior exposure to endocrine-disrupting agents, and suggest a scenario in which their presence in the microenvironmental milieu of high-risk breast tissue could play a deterministic role in establishing and maintaining tumor aggressiveness and poor patient outcome. [Cancer Res 2008;68(7):2076–80]

Abstract Purpose: Acute rejection (AR) remains the primary risk factor for renal transplant outcome; development of non‐invasive diagnostic biomarkers for AR is an unmet need. Experimental design: We used shotgun proteomics applying LC‐MS/MS and ELISA to analyze a set of 92 urine samples, from patients with AR, stable grafts (STA), proteinuria (NS), and healthy controls. Results: A total of 1446 urinary proteins (UP) were identified along with a number of non‐specific proteinuria‐specific, renal transplantation specific and AR‐specific proteins. Relative abundance of identified UP was measured by protein‐level spectral counts adopting a weighted fold‐change statistic, assigning increased weight for more frequently observed proteins. We have identified alterations in a number of specific UP in AR, primarily relating to MHC antigens, the complement cascade and extra‐cellular matrix proteins. A subset of proteins (uromodulin, SERPINF1 and CD44), have been further cross‐validated by ELISA in an independent set of urine samples, for significant differences in the abundance of these UP in AR. Conclusions and clinical relevance: This label‐free, semi‐quantitative approach for sampling the urinary proteome in normal and disease states provides a robust and sensitive method for detection of UP for serial, non‐invasive clinical monitoring for graft rejection after kidney transplantation.

Monitoring genome-wide, cell-specific responses to human disease, although challenging, holds great promise for the future of medicine. Patients with injuries severe enough to develop multiple organ dysfunction syndrome have multiple immune derangements, including T cell apoptosis and anergy combined with depressed monocyte antigen presentation. Genome-wide expression analysis of highly enriched circulating leukocyte subpopulations, combined with cell-specific pathway analyses, offers an opportunity to discover leukocyte regulatory networks in critically injured patients. Severe injury induced significant changes in T cell (5,693 genes), monocyte (2,801 genes), and total leukocyte (3,437 genes) transcriptomes, with only 911 of these genes common to all three cell populations (12%). T cell-specific pathway analyses identified increased gene expression of several inhibitory receptors (PD-1, CD152, NRP-1, and Lag3) and concomitant decreases in stimulatory receptors (CD28, CD4, and IL-2Rα). Functional analysis of T cells and monocytes confirmed reduced T cell proliferation and increased cell surface expression of negative signaling receptors paired with decreased monocyte costimulation ligands. Thus, genome-wide expression from highly enriched cell populations combined with knowledge-based pathway analyses leads to the identification of regulatory networks differentially expressed in injured patients. Importantly, application of cell separation, genome-wide expression, and cell-specific pathway analyses can be used to discover pathway alterations in human disease.

In Brief Objective: To determine and compare outcomes with accepted benchmarks in trauma care at 7 academic level I trauma centers in which patients were treated on the basis of a series of standard operating procedures (SOPs). Background: Injury remains the leading cause of death for those younger than 45 years. This study describes the baseline patient characteristics and well-defined outcomes of persons hospitalized in the United States for severe blunt trauma. Methods: We followed 1637 trauma patients from 2003 to 2009 up to 28 hospital days using SOPs developed at the onset of the study. An extensive database on patient and injury characteristics, clinical treatment, and outcomes was created. These data were compared with existing trauma benchmarks. Results: The study patients were critically injured and were in shock. SOP compliance improved 10% to 40% during the study period. Multiple organ failure and mortality rates were 34.8% and 16.7%, respectively. Time to recovery, defined as the time until the patient was free of organ failure for at least 2 consecutive days, was developed as a new outcome measure. There was a reduction in mortality rate in the cohort during the study that cannot be explained by changes in the patient population. Conclusions: This study provides the current benchmark and the overall positive effect of implementing SOPs for severely injured patients. Over the course of the study, there were improvements in morbidity and mortality rates and increasing compliance with SOPs. Mortality was surprisingly low, given the degree of injury, and improved over the duration of the study, which correlated with improved SOP compliance. This study describes the baseline patient characteristics and outcomes of persons hospitalized in the United States for severe blunt trauma. It provides a current benchmark for severely injured patients treated, and the positive effect of a series of standard operating procedures.

Early transplant dysfunction and failure because of immunological and nonimmunological factors still presents a significant clinical problem for transplant recipients. A critical unmet need is the noninvasive detection and prediction of immune injury such that acute injury can be reversed by proactive immunosuppression titration. In this study, we used iTRAQ -based proteomic discovery and targeted ELISA validation to discover and validate candidate urine protein biomarkers from 262 renal allograft recipients with biopsy-confirmed allograft injury. Urine samples were randomly split into a training set of 108 patients and an independent validation set of 154 patients, which comprised the clinical biopsy-confirmed phenotypes of acute rejection (AR) (n = 74), stable graft (STA) (n = 74), chronic allograft injury (CAI) (n = 58), BK virus nephritis (BKVN) (n = 38), nephrotic syndrome (NS) (n = 8), and healthy, normal control (HC) (n = 10). A total of 389 proteins were measured that displayed differential abundances across urine specimens of the injury types (p < 0.05) with a significant finding that SUMO2 (small ubiquitin-related modifier 2) was identified as a "hub" protein for graft injury irrespective of causation. Sixty-nine urine proteins had differences in abundance (p < 0.01) in AR compared with stable graft, of which 12 proteins were up-regulated in AR with a mean fold increase of 2.8. Nine urine proteins were highly specific for AR because of their significant differences (p < 0.01; fold increase >1.5) from all other transplant categories (HLA class II protein HLA-DRB1, KRT14, HIST1H4B, FGG, ACTB, FGB, FGA, KRT7, DPP4). Increased levels of three of these proteins, fibrinogen beta (FGB; p = 0.04), fibrinogen gamma (FGG; p = 0.03), and HLA DRB1 (p = 0.003) were validated by ELISA in AR using an independent sample set. The fibrinogen proteins further segregated AR from BK virus nephritis (FGB p = 0.03, FGG p = 0.02), a finding that supports the utility of monitoring these urinary proteins for the specific and sensitive noninvasive diagnosis of acute renal allograft rejection.

Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance.In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5-100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels.These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

Time-course microarray experiments are capable of capturing dynamic gene expression profiles. It is important to study how these dynamic profiles depend on the multiple factors that characterize the experimental condition under which the time course is observed. Analytic methods are needed to simultaneously handle the time course and factorial structure in the data. We developed a method to evaluate factor effects by pooling information across the time course while accounting for multiple testing and nonnormality of the microarray data. The method effectively extracts gene-specific response features and models their dependency on the experimental factors. Both longitudinal and cross-sectional time-course data can be handled by our approach. The method was used to analyze the impact of age on the temporal gene response to burn injury in a large-scale clinical study. Our analysis reveals that 21% of the genes responsive to burn are age-specific, among which expressions of mitochondria and immunoglobulin genes are differentially perturbed in pediatric and adult patients by burn injury. These new findings in the body's response to burn injury between children and adults support further investigations of therapeutic options targeting specific age groups. The methodology proposed here has been implemented in R package "TANOVA" and submitted to the Comprehensive R Archive Network at http://www.r-project.org/. It is also available for download at http://gluegrant1.stanford.edu/TANOVA/.

Emerging proteomics techniques can be used to establish proteomic outcome signatures and to identify candidate biomarkers for survival following traumatic injury. We applied high-resolution liquid chromatography-mass spectrometry and multiplex cytokine analysis to profile the plasma proteome of survivors and nonsurvivors of massive burn injury to determine the proteomic survival signature following a major burn injury.Proteomic discovery study.Five burn hospitals across the United States.Thirty-two burn patients (16 nonsurvivors and 16 survivors), 19-89 years old, were admitted within 96 hours of injury to the participating hospitals with burns covering more than 20% of the total body surface area and required at least one surgical intervention.None.We found differences in circulating levels of 43 proteins involved in the acute-phase response, hepatic signaling, the complement cascade, inflammation, and insulin resistance. Thirty-two of the proteins identified were not previously known to play a role in the response to burn. Interleukin-4, interleukin-8, granulocyte macrophage colony-stimulating factor, monocyte chemotactic protein-1, and β2-microglobulin correlated well with survival and may serve as clinical biomarkers.These results demonstrate the utility of these techniques for establishing proteomic survival signatures and for use as a discovery tool to identify candidate biomarkers for survival. This is the first clinical application of a high-throughput, large-scale liquid chromatography-mass spectrometry-based quantitative plasma proteomic approach for biomarker discovery for the prediction of patient outcome following burn, trauma, or critical illness.

Trauma-induced heterotopic ossification (tHO) is a condition of pathologic wound healing, defined by the progressive formation of ectopic bone in soft tissue following severe burns or trauma. Because previous studies have shown that genetic variants of HO, such as fibrodysplasia ossificans progressiva (FOP), are caused by hyperactivating mutations of the type I bone morphogenetic protein receptor (T1-BMPR) ACVR1/ALK2, studies evaluating therapies for HO have been directed primarily toward drugs for this specific receptor. However, patients with tHO do not carry known T1-BMPR mutations. Here we show that, although BMP signaling is required for tHO, no single T1-BMPR (ACVR1/ALK2, BMPR1a/ALK3, or BMPR1b/ALK6) alone is necessary for this disease, suggesting that these receptors have functional redundancy in the setting of tHO. By utilizing two different classes of BMP signaling inhibitors, we developed a translational approach to treatment, integrating treatment choice with existing diagnostic options. Our treatment paradigm balances either immediate therapy with reduced risk for adverse effects (Alk3-Fc) or delayed therapy with improved patient selection but greater risk for adverse effects (LDN-212854).

The quantitative comparison of protein abundances across a large number of biological or patient samples represents an important proteomics challenge that needs to be addressed for proteomics discovery applications. Herein, we describe a strategy that incorporates a stable isotope 18O-labeled "universal" reference sample as a comprehensive set of internal standards for analyzing large sample sets quantitatively. As a pooled sample, the 18O-labeled "universal" reference sample is spiked into each individually processed unlabeled biological sample and the peptide/protein abundances are quantified based on 16O/18O isotopic peptide pair abundance ratios that compare each unlabeled sample to the identical reference sample. This approach also allows for the direct application of label-free quantitation across the sample set simultaneously along with the labeling-approach (i.e., dual-quantitation) since each biological sample is unlabeled except for the labeled reference sample that is used as internal standards. The effectiveness of this approach for large-scale quantitative proteomics is demonstrated by its application to a set of 18 plasma samples from severe burn patients. When immunoaffinity depletion and cysteinyl-peptide enrichment-based fractionation with high resolution LC-MS measurements were combined, a total of 312 plasma proteins were confidently identified and quantified with a minimum of two unique peptides per protein. The isotope labeling data was directly compared with the label-free 16O-MS intensity data extracted from the same data sets. The results showed that the 18O reference-based labeling approach had significantly better quantitative precision compared to the label-free approach. The relative abundance differences determined by the two approaches also displayed strong correlation, illustrating the complementary nature of the two quantitative methods. The simplicity of including the 18O-reference for accurate quantitation makes this strategy especially attractive when a large number of biological samples are involved in a study where label-free quantitation may be problematic, for example, due to issues associated with instrument platform robustness. The approach will also be useful for more effectively discovering subtle abundance changes in broad systems biology studies.

A burn injury represents one of the most severe forms of human trauma and is responsible for significant mortality worldwide. Here, we present the first quantitative proteomics investigation of the blood plasma proteome response to severe burn injury by comparing the plasma protein concentrations of 10 healthy control subjects with those of 15 severe burn patients at two time-points following the injury. The overall analytical strategy for this work integrated immunoaffinity depletion of the 12 most abundant plasma proteins with cysteinyl-peptide enrichment-based fractionation prior to LC−MS analyses of individual patient samples. Incorporation of an 18O-labeled “universal” reference among the sample sets enabled precise relative quantification across samples. In total, 313 plasma proteins confidently identified with two or more unique peptides were quantified. Following statistical analysis, 110 proteins exhibited significant abundance changes in response to the burn injury. The observed changes in protein concentrations suggest significant inflammatory and hypermetabolic response to the injury, which is supported by the fact that many of the identified proteins are associated with acute phase response signaling, the complement system, and coagulation system pathways. The regulation of ∼35 proteins observed in this study is in agreement with previous results reported for inflammatory or burn response, but approximately 50 potentially novel proteins previously not known to be associated with burn response or inflammation are also found. Elucidating proteins involved in the response to severe burn injury may reveal novel targets for therapeutic interventions as well as potential predictive biomarkers for patient outcomes such as multiple organ failure.

In Brief Objective: To prospectively validate a previously discovered transcriptomic biomarker consisting of 63 blood leukocyte gene expression (S63) values to discriminate between trauma patients who rapidly recover and those with prolonged hospital stays who would benefit from early biological interventions. Background: Many severe trauma patients are successfully resuscitated but have complicated clinical trajectories leading to long-term functional, physical, and cognitive deficiencies. Identifying those trauma patients early would improve treatment plans and resource allocation. Unfortunately, current clinical scores and biomarkers used in trauma clinical trials have typically lacked adequate predictive ability. Methods: An independent, prospective, observational cohort study was performed involving 127 trauma subjects. The prospective cohort included patients admitted between October 2013 and August 2016 at 2 United States Level-1 trauma centers. An additional secondary analysis was performed using the Activation of Coagulation and Inflammation in Trauma (ACIT2) database of 26 trauma patients. Results: The S63 transcriptomic metric (AUC 0.80) outperformed clinical markers and plasma interleukin-6 for prospectively predicting trauma patients who require intensive care unit stays longer than 5 days with ongoing organ dysfunction. The same metric applied to an existing dataset (ACIT2) was similarly effective (AUC 0.85) at predicting multiorgan failure. Conclusions: A single transcriptomic metric of blood leukocyte gene expression can be used in blunt trauma cohorts at 24 hours to distinguish patients who rapidly recover from those with complicated clinical trajectories. The transcriptomic metric has been operationalized on an Food and Drug Administration 510(k)-cleared platform otherwise used for cancer diagnostics. This metric is only modestly improved when combined with clinical markers.

Spin labeling EPR spectroscopy has been used to characterize light-induced conformational changes of bacteriorhodopsin (bR). Pairs of nitroxide spin labels were attached to engineered cysteine residues at strategic positions near the cytoplasmic ends of transmembrane alpha-helices B, F, and G in order to monitor distance changes upon light activation. The EPR analysis of six doubly labeled bR mutants indicates that the cytoplasmic end of helix F not only tilts outwards, but also rotates counter-clockwise during the photocycle. The direction of the rotation of helix F is the opposite of the clockwise rotation previously reported for bovine rhodopsin. The opposite chirality of the F helix rotation in the two systems is perhaps related to the differences in the cis-trans photoisomerization of the retinal in the two proteins. Using time-resolved EPR, we monitored the rotation of helix F also in real time, and found that the signal from the rotation arises concurrently with the reprotonation of the retinal Schiff base.

Abstract Thoracoabdominal aortic aneurysm repair, with its requisite intraoperative mesenteric ischemia-reperfusion, often results in the development of systemic inflammatory response syndrome, multiorgan dysfunction syndrome (MODS), and death. In the present study, an adverse clinical outcome following thoracoabdominal aortic aneurysm repair was identified by blood leukocyte genomic and plasma proteomic responses. Time-dependent changes in the expression of 146 genes from blood leukocytes were observed (p &amp;lt; 0.001). Expression of 138 genes (p &amp;lt; 0.001) and the concentration of seven plasma proteins discriminated between patients who developed MODS and those who did not, and many of these differences were evident even before surgery. These findings suggest that changes in blood leukocyte gene expression and plasma protein concentrations can illuminate pathophysiological processes that are subsequently associated with the clinical sequelae of systemic inflammatory response syndrome and MODS. These changes in gene expression and plasma protein concentrations are often observed before surgery, consistent with either a genetic predisposition or pre-existing inflammatory state.

Microarray technology is a powerful tool in the discovery of new biomarkers for disease. After solid organ transplantation, where the detection of rejection is usually made on invasive biopsies, it could be hypothesized that noninvasive transcriptional profiling of peripheral blood will reveal rejection-specific expression patterns from circulating immune cells. However, in kidney transplant rejection, the analysis of gene expression data in whole blood has proven difficult for detecting significant genes specific for acute graft rejection. Previous studies have demonstrated that the abundance of globin genes in whole blood may mask the underlying biological differences between whole blood samples. In the present study, we compared the gene expression profiles of peripheral blood of nine stable renal allograft recipients with seven matched patients having an ongoing acute renal transplant rejection, using four different protocols of preparation, amplification, and synthesis of cRNA or cDNA and hybridization on the Affymetrix platform. We demonstrated that the globin reduction method is not sufficient to unmask clinically relevant rejection-specific transcriptome profiles in whole blood. Applying an additional mathematical depletion of the globin genes improves the efficacy of globin reduction but cannot remove the confounding influence of globin gene hybridization. Sampling of peripheral blood leukocytes alone, without the confounding influence of globin mRNA, provides sensitive and specific peripheral signatures for graft rejection, with many of these signals overlapping with rejection-driven tissue (kidney)-specific signatures from matched biopsies. Similar applications may exist for array-based biomarker discovery for other diseases associated with changes in leukocyte trafficking, activation, or function.

Circulating tumor cells (CTCs) have a great potential for noninvasive diagnosis and real-time monitoring of cancer. A comprehensive evaluation of four whole genome amplification (WGA)/next-generation sequencing workflows for genomic analysis of single CTCs, including PCR-based (GenomePlex and Ampli1), multiple displacement amplification (Repli-g), and hybrid PCR- and multiple displacement amplification–based [multiple annealing and loop-based amplification cycling (MALBAC)] is reported herein. To demonstrate clinical utilities, copy number variations (CNVs) in single CTCs isolated from four patients with squamous non–small-cell lung cancer were profiled. Results indicate that MALBAC and Repli-g WGA have significantly broader genomic coverage compared with GenomePlex and Ampli1. Furthermore, MALBAC coupled with low-pass whole genome sequencing has better coverage breadth, uniformity, and reproducibility and is superior to Repli-g for genome-wide CNV profiling and detecting focal oncogenic amplifications. For mutation analysis, none of the WGA methods were found to achieve sufficient sensitivity and specificity by whole exome sequencing. Finally, profiling of single CTCs from patients with non–small-cell lung cancer revealed potentially clinically relevant CNVs. In conclusion, MALBAC WGA coupled with low-pass whole genome sequencing is a robust workflow for genome-wide CNV profiling at single-cell level and has great potential to be applied in clinical investigations. Nevertheless, data suggest that none of the evaluated single-cell sequencing workflows can reach sufficient sensitivity or specificity for mutation detection required for clinical applications. Circulating tumor cells (CTCs) have a great potential for noninvasive diagnosis and real-time monitoring of cancer. A comprehensive evaluation of four whole genome amplification (WGA)/next-generation sequencing workflows for genomic analysis of single CTCs, including PCR-based (GenomePlex and Ampli1), multiple displacement amplification (Repli-g), and hybrid PCR- and multiple displacement amplification–based [multiple annealing and loop-based amplification cycling (MALBAC)] is reported herein. To demonstrate clinical utilities, copy number variations (CNVs) in single CTCs isolated from four patients with squamous non–small-cell lung cancer were profiled. Results indicate that MALBAC and Repli-g WGA have significantly broader genomic coverage compared with GenomePlex and Ampli1. Furthermore, MALBAC coupled with low-pass whole genome sequencing has better coverage breadth, uniformity, and reproducibility and is superior to Repli-g for genome-wide CNV profiling and detecting focal oncogenic amplifications. For mutation analysis, none of the WGA methods were found to achieve sufficient sensitivity and specificity by whole exome sequencing. Finally, profiling of single CTCs from patients with non–small-cell lung cancer revealed potentially clinically relevant CNVs. In conclusion, MALBAC WGA coupled with low-pass whole genome sequencing is a robust workflow for genome-wide CNV profiling at single-cell level and has great potential to be applied in clinical investigations. Nevertheless, data suggest that none of the evaluated single-cell sequencing workflows can reach sufficient sensitivity or specificity for mutation detection required for clinical applications. Circulating tumor cells (CTCs) are a population of cells that are shed from a primary or metastatic tumor into the bloodstream. As a new type of liquid biopsy, CTCs bear tremendous potential for noninvasive diagnosis and real-time monitoring of cancer. Genomic analysis of CTCs using single-cell sequencing may help to reveal the underlying mechanisms of tumor metastasis and intratumor heterogeneity and to identify gene mutations that potentially contribute to disease relapse or drug resistance.1Wang Y. Waters J. Leung M.L. Unruh A. Roh W. Shi X. Chen K. Scheet P. Vattathil S. Liang H. Multani A. Zhang H. Zhao R. Michor F. Meric-Bernstam F. Navin N.E. Clonal evolution in breast cancer revealed by single nucleus genome sequencing.Nature. 2014; 512: 155-160Crossref PubMed Scopus (693) Google Scholar, 2Alderton G.K. Genomics: one cell at a time.Nat Rev Cancer. 2011; 11: 312Crossref PubMed Scopus (2) Google Scholar, 3Navin N. Kendall J. Troge J. Andrews P. Rodgers L. McIndoo J. Cook K. Stepansky A. Levy D. Esposito D. Muthuswamy L. Krasnitz A. McCombie W.R. Hicks J. Wigler M. Tumour evolution inferred by single-cell sequencing.Nature. 2011; 472: 90-94Crossref PubMed Scopus (1768) Google Scholar, 4Maheswaran S. Sequist L.V. Nagrath S. Ulkus L. Brannigan B. Collura C.V. Inserra E. Diederichs S. Iafrate A.J. Bell D.W. Digumarthy S. Muzikansky A. Irimia D. Settleman J. Tompkins R.G. Lynch T.J. Toner M. Haber D.A. Detection of mutations in EGFR in circulating lung-cancer cells.N Engl J Med. 2008; 359: 366-377Crossref PubMed Scopus (1446) Google Scholar, 5Polzer B. Medoro G. Pasch S. Fontana F. Zorzino L. Andergassen U. Meier-stiegen F. Czyz Z.T. Alberter B. Schamberger T. Sergio M. Bregola G. Doffini A. Gianni S. Calanca A. Signorini G. Bolognesi C. Hartmann A. Fasching P.A. Maria T. Molecular profiling of single circulating tumor cells with diagnostic intention.EMBO Mol Med. 2014; 6: 1371-1387Crossref PubMed Scopus (178) Google Scholar To achieve accurate genomic analysis of CTCs at the single-cell level, whole genome amplification (WGA) of genomic DNA from a single cell must be performed with sufficient breadth and precision. Depending on the mode of downstream genomic analysis [ie, single-nucleotide variants (SNVs), insertions/deletions (indels), or copy number variations (CNVs)], specific performance metrics may be required for different applications. For example, a robust CNV analysis requires wide genome coverage (breadths) as well as high coverage uniformity and reproducibility. On the other hand, to achieve the high sensitivity and specificity required for the analysis of SNVs and indels, high genome coverage/low allele dropout (ADO) rate, and low amplification errors would be critical.6de Bourcy CFa De Vlaminck I. Kanbar J.N. Wang J. Gawad C. Quake S.R. A quantitative comparison of single-cell whole genome amplification methods.PLoS One. 2014; 9: e105585Crossref PubMed Scopus (209) Google Scholar,7Hou Y. Song L. Zhu P. Zhang B. Tao Y. Xu X. et al.Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm.Cell. 2012; 148: 873-885Abstract Full Text Full Text PDF PubMed Scopus (402) Google Scholar Currently, the most commonly implemented WGA methods for single-cell genomic analyses are PCR or isothermal multiple displacement amplification (MDA) based.8Gawad C. Koh W. Quake S.R. Single-cell genome sequencing: current state of the science.Nat Rev Genet. 2016; 17: 175-188Crossref PubMed Scopus (734) Google Scholar PCR-based WGA methods typically conduct PCR amplification using degenerate-oligos as primers (ie, PicoPlex)9Deleye L. Tilleman L. Vander Plaetsen A.S. Cornelis S. Deforce D. Van Nieuwerburgh F. Performance of four modern whole genome amplification methods for copy number variant detection in single cells.Sci Rep. 2017; 7: 3422Crossref PubMed Scopus (31) Google Scholar,10Huang L. Ma F. Chapman A. Lu S. Xie X.S. Single-cell whole-genome amplification and sequencing: methodology and applications.Annu Rev Genomics Hum Genet. 2015; 16: 79-102Crossref PubMed Scopus (224) Google Scholar or linker adaptors with universal sequences ligated to the DNA fragments (ie, GenomePlex and Ampli1).10Huang L. Ma F. Chapman A. Lu S. Xie X.S. Single-cell whole-genome amplification and sequencing: methodology and applications.Annu Rev Genomics Hum Genet. 2015; 16: 79-102Crossref PubMed Scopus (224) Google Scholar In general, PCR-based WGA methods are believed to generate higher coverage and uniformity but at the expense of introducing sequence-dependent coverage bias and significantly more single-nucleotide errors than MDA-based methods. On the other hand, the isothermal MDA-based methods (ie, Repli-g)11Deleye L. Gansemans Y. De Coninck D. Van Nieuwerburgh F. Deforce D. Massively parallel sequencing of micro-manipulated cells targeting a comprehensive panel of disease-causing genes: a comparative evaluation of upstream whole-genome amplification methods.PLoS One. 2018; 13: e0196334Crossref PubMed Scopus (7) Google Scholar use high-fidelity ф29 DNA polymerase for linear amplification of larger DNA fragments across the genome with higher fidelity than PCR-based approaches, making it potentially better suited for identification of point mutations. However, MDA-based methods are also known to suffer from amplification biases and nonuniformity that can prevent applications in CNV analysis. WGA methods that hybridize the principles of PCR- and MDA-based approaches have also been reported [ie, multiple annealing and loop-based amplification cycling (MALBAC)].12Zong C. Lu S. Chapman A. Xie X. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell.Science. 2012; : 1622-1627Crossref PubMed Scopus (720) Google Scholar MALBAC generates looped DNA molecules during the initial multiple rounds of displacement preamplification using a specific oligo design, which is intended to reduce the bias often observed with nonlinear amplification. These DNA loops are then further amplified using PCR amplification. Although such a hybrid approach has broader genomic coverage while maintaining uniformity sufficient for CNV analysis, it can still result in >30% base dropout,13Szulwach K.E. Chen P. Wang X. Wang J. Weaver L.S. Gonzales M.L. Sun G. Unger M.A. Ramakrishnan R. Single-cell genetic analysis using automated microfluidics to resolve somatic mosaicism.PLoS One. 2015; 10: e0135007Crossref PubMed Scopus (35) Google Scholar a potential significant sacrifice in sensitivity of detecting single-nucleotide mutations. Despite intensive efforts in method development in recent years, a comprehensive evaluation of methods and workflows for accurate single-cell genomic analysis has been lacking. In this study, the performance of four different WGA methods for single CTC analysis, including two PCR-based methods (GenomePlex and Ampli1), an MDA-based method (Repli-g), and a hybrid approach (MALBAC), were systematically evaluated. Results indicate that MALBAC and Repli-g WGA have significantly higher genome coverage compared with GenomPlex and Ampli1, the two PCR-based WGA methods. Furthermore, MALBAC coupled with low-pass whole genome sequencing (LP-WGS) was found to be superior to Repli-g for genome-wide CNV profiling and detecting focal oncogenic amplifications. When coupled with whole exome sequencing (WES), MALBAC WGA had higher sensitivity but lower specificity in SNV/indel detection compared with Repli-g. Nevertheless, none of the WGA methods can achieve sufficient sensitivity and specificity for genome-wide point mutation analysis at the single-cell level. Finally, the clinical utilities of genetic analysis of single CTCs were found by profiling CNVs in single CTCs isolated from four patients with squamous non–small cell lung cancer (NSCLC) enrolled in a phase 2 trial (NCT01493843) treated with pictilisib in combination with chemotherapy. To mimic CTCs in blood, synthetic CTC samples were created by spiking viable tumor cells from three tumor cell lines, EBC-1, KPL-4, and PC-3 (see cell line information in Supplemental Table S1), into 3.75-mL normal human donor blood samples. The spiked-in CTCs were selected with the CellSearch platform (Menarini Silicon Biosystems Inc., Huntington Valley, PA), a semiautomated system that enriches for cells expressing epithelial cell adhesion molecules (EpCAMs) but lacking the leukocyte-specific molecule CD45. Cells are further immunostained with fluorescent-labeled anti-keratin antibodies identifying, among others, cytokeratin (CK) 8, CK18, and CK19, and individual single cells were then isolated using DEPArray System (Menarini Silicon Biosystems). Four patients with squamous NSCLC treated with the phosphatidylinositol 3-kinase inhibitor pictilisib in combination with carboplatin and paclitaxel in a phase 2 trial (FIGARO, GO27912, NCT01493843) were selected for clinical application of single CTC analysis. All patient blood samples were obtained from the institutional review board or ethics committee at each site. Informed consent was obtained from all patients. Patients' blood (7.5 mL) was collected in Streck tubes (Streck Inc., La Vista, NE) and shipped to Epic Sciences within 48 hours and processed immediately on arrival. Erythrocytes were lysed, and approximately 3 million nucleated blood cells were dispensed onto each of 10 to 16 glass microscope slides and placed at −80°C for long-term storage according to methods previously described.14Werner S.L. Graf R.P. Landers M. Valenta D.T. Schroeder M. Greene S.B. Bales N. Dittamore R. Marrinucci D. Analytical validation and capabilities of the epic CTC platform: enrichment-free circulating tumour cell detection and characterization.J Circ Biomark. 2015; 4: 3Crossref PubMed Scopus (83) Google Scholar,15Beltran H. Jendrisak A. Landers M. Mosquera J.M. Kossai M. Louw J. Krupa R. Graf R.P. Schreiber N.A. Nanus D.M. Tagawa S.T. Marrinucci D. Dittamore R. Scher H.I. The initial detection and partial characterization of circulating tumor cells in neuroendocrine prostate cancer.Clin Cancer Res. 2016; 22: 1510-1519Crossref PubMed Scopus (93) Google Scholar Prepared slides were thawed and subjected to automated immunofluorescent staining for CK, DAPI (DNA marker), and CD45 (blood lineage marker). Automated scanning identified candidate cells of interest among nucleated cell populations based on size and morphologic features of the cells, nuclear features, and CK expression in the absence of blood-lineage CD45 expression. Candidate cells were then reviewed by California-licensed clinical laboratory scientists to confirm immunohistochemical staining profile as well as to assess the cytomorphometric features of the cell (size, shape, nucleus/cytoplasm ratio, and so on as they relate to the features associated with CTCs). Candidate cells were given histologic classification of single cells, clusters (more than one sharing cytoplasmic boundaries), or apoptotic cells (nuclear features consistent with apoptosis). Captured single CTCs were stored in a 0.2-mL PCR tube stored at −80°C. All single cells were washed with phosphate-buffered saline buffer; phosphate-buffered saline volume carried over with the cell sample into the amplification protocol should not exceed 1 μL. To avoid DNA contamination from external sources or from the amplified DNA product, sample preparation steps before amplification were performed in pre-PCR hood and room. Diluted control human genomic DNA in concentrations of 30 pg/μL and 1 μL (30 pg) was used for positive control for single-cell WGA. Four different WGA workflows were performed with commercial available kits (MALBAC Single Cell WGA Kit, catalog number YK001B, Yikon Genomics, Shanghai, China; Repli-g Single Cell Kit, catalog number 150345, Qigen, Venlo, the Netherlands; GenomePlex Single Cell Whole Genome Amplification Kit, product number WGA4, Sigma-Aldrich, St. Louis, MO; and Ampli1 WGA Kit, reference number WG 001 050 R02, Silicon Biosystems) and strictly followed by manufacturer's manual. Supplemental Figure S1 shows the schemes of these four different WGA methods. Targeted sequencing was performed using a matrix metalloproteinase sequencing (MMP-seq) panel (963 amplicons that targeted 88 oncogenic and tumor suppresser genes) and workflow that developed and reported previously.16Bourgon R. Lu S. Yan Y. Lackner M.R. Wang W. Weigman V. Wang D. Guan Y. Ryner L. Koeppen H. Patel R. Hampton G.M. Amler L.C. Wang Y. High-throughput detection of clinically relevant mutations in archived tumor samples by multiplexed PCR and next-generation sequencing.Clin Cancer Res. 2014; 20: 2080-2091Crossref PubMed Scopus (52) Google Scholar The experiments were performed according to the Multiplex Amplicon Tagging Protocol from the manufacturer (Fluidigm, South San Francisco, CA). The resulting sequencing-ready amplicon libraries were sequenced on MiSeq using Illumina MiSeq version 2 chemistry [2 × 108-bp paired-end (PE) reads; Illumina Inc., San Diego, CA]. The mean yield is 18 million per run. TruSeq PCR-free libraries (Illumina) were generated from 2 μg of Repli-g amplified sample DNA. Libraries from 150 ng of MALBAC amplified DNA for WGS were prepared from the adaptor-ligated DNA before the pooling step in exome library preparation. Eight-cycle enrichment PCR was performed on an aliquot of adaptor-ligated DNA to complete the adaptor for Illumina PE sequencing. Both libraries were checked for quality (TapeStation, Agilent Technologies Inc., Santa Clara, CA) and quantity (KAPA Biosciences Library Quantification, Kapa Biosystems Inc., Wilmington, MA) and sequenced to 0.1× using Illumina MiSeq version chemistry (2 × 100 PE reads). Libraries were sequenced to 0.1× using Illumina MiSeq version chemistry (2 × 100 PE reads). SureSelectXT (Agilent Technologies Inc.) next-generation sequencing libraries were prepared using Repli-g and MALBAC amplified samples. Repli-g (500 ng) and MALBAC (150 ng) amplified DNA was sheared to approximately 150-bp fragments using the Covaris E220 Focused ultrasonicator system (Covaris Inc., Woburn, MA). Fragmented DNA was processed according to manufacturer's protocol with slight modifications to generate partial adaptor ligated DNA suitable for target enrichment using the SureSelectXT Exome Target Enrichment System for Illumina Sequencing version 5 DNA baits (Agilent Technologies Inc.). Exome-enriched libraries were PCR amplified to complete the Illumina adaptor and then sequenced to 100× coverage (2 × 100 PE) using Illumina HiSeq 2500 Rapid Run with on-board cluster generation version 1 chemistry (Agilent Technologies Inc.). All sequencing data (including MMP-Seq, LP-WGS, and whole exome sequencing) in this study have been submitted to the Sequence Read Archive (https://trace.ncbi.nlm.nih.gov/Traces/sra; SRA number SRP256948). All the paired FASTQ files were mapped to the GRCh37 human reference genome with BWA-MEM version 0.7.15,17Li H. Aligning Sequence Reads, Clone Sequences and Assembly Contigs with BWA-MEM. [Epub].arXiv. 2013; : 1303.3997Google Scholar and the BAM alignment files were sorted and indexed with SAMtools version 1.3.1.18Li H. Handsaker B. Wysoker A. Fennell T. Ruan J. Homer N. Marth G. Abecasis G. Durbin R. The sequence alignment/map format and SAMtools.Bioinformatics. 2009; 25: 2078-2079Crossref PubMed Scopus (29177) Google Scholar For the exome sequencing and WGS, BAM files (biobambam219Tischler G. Leonard S. biobambam: tools for read pair collation based algorithms on BAM files.Source Code Biol Med. 2014; 9: 13Crossref Scopus (108) Google Scholar) were used to mark duplicates. For each WGA platform, two of the 12 single cells (four replicas for each of the three cell lines) were removed from further analyses because of their low depth of coverage. For MMP-seq, the locations of 963 amplicons were merged to 416 amplified regions according to the overlapping of amplicons using Bedtools.20Quinlan A.R. Hall I.M. BEDTools: a flexible suite of utilities for comparing genomic features.Bioinformatics. 2010; 26: 841-842Crossref PubMed Scopus (10744) Google Scholar The amplified regions cover approximately 100 Kb of the human genome. For WES, the targeted 230,417 exonic regions from the Agilent SureSelect platform (Agilent Technologies Inc.) cover approximately 50 Mb of the genome. The mean depth of coverage of the amplified regions and the exonic regions were calculated for the MMP-seq and WES sequencing data, respectively, using Sambamba21Tarasov A. Vilella A.J. Cuppen E. Nijman I.J. Prins P. Sambamba: fast processing of NGS alignment formats.Bioinformatics. 2015; 31: 2032-2034Crossref PubMed Scopus (606) Google Scholar with the default setting. For WGS, the genome was split into nonoverlapping windows of fixed length. The breadth of coverage is defined as the number of windows covered by at least one read divided by the total number of windows. The number of covering reads is calculated by the program readCounter in HMMCopy software version 1.28.22Lai D. Ha G. HMMcopy: A Package for Bias-Free Copy Number Estimation and Robust CNA Detection in Tumour Samples from WGS HTS Data. R Foundation for Statistical Computing, Vienna, Austria2016: 14Google Scholar R package ineq software version 0.2-1323Zeileis A. ineq: Measuring Inequality, Concentration, and Poverty. R Foundation for Statistical Computing, Vienna, Austria2014Google Scholar was used to generate Lorenz curves to represent inequality of the depths of coverage across windows or targeted regions. To calculate the mean depth of coverage for the three genes ERBB2, MET, and PTEN, Sambamba was used for the WGS and WES data. For WES, the depth of coverage was further normalized according to the total length of the exonic regions in the gene. The same strategy was applied for the WES and MMP-seq alignment data. With the use of the alignment data, the variants of four MALBAC replicates, four Repli-g replicates, and the bulk cell line DNA were jointly called with FreeBayes24Garrison E. Marth G. Haplotype-Based Variant Detection from Short-Read Sequencing.2012Google Scholar for the three cell lines PC-3, EBC-1, and KPL4, respectively. Only the variants in the exonic regions and in the amplified regions were reported for WES and for MMP, respectively. A variant was marked as known if it was in dbSNP Human Build 150 release25Sherry S.T. Ward M.H. Kholodov M. Baker J. Phan L. Smigielski E.M. Sirotkin K. dbSNP: the NCBI database of genetic variation.Nucleic Acids Res. 2001; 29: 308-311Crossref PubMed Scopus (4643) Google Scholar and unknown if otherwise. The joint calling distinguished reference calls from noncovered sites for all the sites called in at least one tested sample. For each cell line, the sites where the depth of coverage is <10 in bulk were filtered, and the genotypes of bulk at these sites were used as the ground truth. The variants were further annotated with SnpEff software version 4.3g.26Cingolani P. Platts A. Wang leL. Coon M. Nguyen T. Wang L. Land S.J. Lu X. Ruden D.M. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3.Fly (Austin). 2012; 6: 80-92Crossref PubMed Scopus (5112) Google Scholar For each cancer cell line, first, the sites that were not genotyped as homologous reference and had depth of coverage ≥10× in the bulk DNA sample were identified as the ground truth. Supplemental Table S2 gives the number of these sites for the three cell lines using MMP-seq and WES. The sensitivity of the variant calls for the single cell is defined as the percentage of these sites having the same genotypes as in bulk DNA sequencing. The false discovery rate (FDR) was calculated as previously reported.13Szulwach K.E. Chen P. Wang X. Wang J. Weaver L.S. Gonzales M.L. Sun G. Unger M.A. Ramakrishnan R. Single-cell genetic analysis using automated microfluidics to resolve somatic mosaicism.PLoS One. 2015; 10: e0135007Crossref PubMed Scopus (35) Google Scholar Briefly, high-confidence homozygous reference sites where the coverage is at least 20× and there was no evidence of a nonreference allele (checked with samtools mpileup) were identified. The numbers of these sites are listed in Supplemental Table S3. Next, the frequency of nonreference alleles detected at these sites in single cells was measured. The ADO rates27Issadore D. Chung J. Shao H. Liong M. Ghazani A.A. Castro C.M. Weissleder R. Lee H. Ultrasensitive clinical enumeration of rare cells ex vivo using a micro-hall detector.Sci Transl Med. 2012; 4: 141ra192Crossref Scopus (175) Google Scholar were accessed using a similar approach as previously described.13Szulwach K.E. Chen P. Wang X. Wang J. Weaver L.S. Gonzales M.L. Sun G. Unger M.A. Ramakrishnan R. Single-cell genetic analysis using automated microfluidics to resolve somatic mosaicism.PLoS One. 2015; 10: e0135007Crossref PubMed Scopus (35) Google Scholar High-confidence heterozygous sites where the coverage is at least 10× and genotypes are heterozygous in bulk were identified. The numbers of these sites are listed in Supplemental Table S4. The frequency of homozygous alleles detected at these sites in single cells was then measured. For WGS data, HMMCopy was used to correct the number of reads across the windows based on the guanine-cytosine content and mapability. The corrected numbers of reads were then used for visualization and to identify the copy numbers with the HMM model where the parameter E20Quinlan A.R. Hall I.M. BEDTools: a flexible suite of utilities for comparing genomic features.Bioinformatics. 2010; 26: 841-842Crossref PubMed Scopus (10744) Google Scholar was set as 0.9999999 and the window size was set as 200 Kb. For WES data, for each 200-Kb window, the number of reads by the total length of the exonic regions in the window was normalized, and windows without mapping reads or with a total length of the exonic regions <1 Kb were removed. HMMCopy was again used to identify the CNV for the windows. First, the performance of four existing WGA methods (Supplemental Figure S1), including PCR-based (GenomePlex and Ampli1), MDA (Repli-g), and hybrid PCR- and MDA-based (MALBAC), was systematically evaluated. Figure 1A summarizes the experimental design and analyzes the workflow. For this evaluation, three synthetic CTC samples were created by spiking viable tumor cells from three tumor cell lines, EBC-1, KPL-4, and PC-3 (Table 1), into normal human donor blood samples. The CTCs were then captured with the CellSearch platform (EpCAM+, CK+, and CD45−), and individual single cells were isolated using the DEPArray system (Materials and Methods). Four single tumor cells and nine white blood cells from each of the three synthetic CTC samples were subjected to different WGA methods followed by targeted sequencing, LP-WGS, and WES. As controls, 50 ng of bulk genomic DNA from each of the three cancer cell lines and a normal peripheral blood mononuclear cell sample were also sequenced without WGA (Figure 1A).Table1Cell Lines Used in This StudyCancer cell lineIndicationCopy number variationNo. of copiesPloidyPC-3Prostate cancerPTEN homozygous deletion02.9EBC-1Lung cancerMET amplification92.8KPL-4Breast cancerERBB2 amplification62.4 Open table in a new tab To have an initial assessment of the performance of the four WGA methods, first, targeted deep sequencing was performed by MMP-seq, which targets 88 clinically relevant oncogenes and tumor suppressor genes.16Bourgon R. Lu S. Yan Y. Lackner M.R. Wang W. Weigman V. Wang D. Guan Y. Ryner L. Koeppen H. Patel R. Hampton G.M. Amler L.C. Wang Y. High-throughput detection of clinically relevant mutations in archived tumor samples by multiplexed PCR and next-generation sequencing.Clin Cancer Res. 2014; 20: 2080-2091Crossref PubMed Scopus (52) Google Scholar The first performance metric evaluated was the coverage breadth of each WGA method, which was assessed as the fraction of the 416 amplified regions with >50× depth of coverage. MALBAC and Repli-g WGA had significantly broader coverage breadth (73.6% ± 9.6% and 69.0% ± 9.3%, respectively) than Ampli1 and GenomePlex (48.1% ± 5.7% and 17.9% ± 11.7%, respectively), whereas the breadth of the bulk was 97.0% ± 1.4%, indicating a vast difference in amplicon dropout rates among these WGA methods (Figure 1B and Supplemental Figure 2A). To further investigate whether the different amplicon dropout rates observed in different WGA methods are random or systematic events, a two-cell strategy was applied that calculates the number of amplicons covered by at least two of the four single cells of the same synthetic CTC sample. Use of the two-cell strategy was found to slightly improve the genomic coverage for each WGA method (Supplemental Table S2 and Supplemental Figure S2B), suggesting that the amplicon dropout is likely a result of systematic amplification bias intrinsic to each of the WGA methods. The second performance metric evaluated was the coverage uniformity, which assesses whether the amplifications are biased toward some regions more than others. Among the four WGA methods evaluated, MALBAC showed the best coverage uniformity with a sharp peak in distribution of amplicon depth, whereas Repli-g and GenomePlex showed a wide range of amplicon coverage distribution, indicating unevenness in coverage across amplicons (Figure 1C). Interestingly, Ampli1 showed a unique binary distribution in amplicon coverage. This finding may be due to the bias introduced by the restriction enzyme digestion step to fragment genomic DNA before PCR amplification in this workflow (ie, genomic DNA regions that were digested into too small or too big fragments by the restriction enzyme may result in no or less amplification products). Last, the reproducibility of each of the WGA methods was evaluated. For this, coverage depth of the same amplicons among different single cells isolated from the same synthetic CTC sample was compared. MALBAC and Ampli1 had higher cell-to-cell reproducibility (mean R = 0.63 and 0.76, respectively), whereas the Repli-g and GenomePlex had poor coverage reproducibility (mean R = 0.39 and 0.16, respectively) (Figure 1D). To further confirm findings from the targeted sequencing evaluation, MALBAC and Repli-g, the two WGA methods with better performance in the targeted sequencing evaluation, were the next focus. LP-WGS (approximately 0.1× mean sequencing depth) and WES (approximately 100× mean sequencing depth) were performed on the t

The high resolving power of the chromatographic separation of single- and double-stranded nucleic acids in 200 microm i.d. monolithic poly(styrene-divinylbenzene) capillary columns was utilized for mutation screening in polymerase chain reaction amplified polymorphic loci. Recognition of mutations is based on the separation of homo- and heteroduplex species by ion-pair reversed-phase high-performance liquid chromatography (IP-RP-HPLC) under partially denaturing conditions, resulting in characteristic peak patterns both for homozygous and heterozygous samples. Six different single nucleotide substitutions and combinations thereof were confidently identified in 413 bp amplicons from six heterozygous individuals each of which yielded a different unique chromatographic profile. Alternatively, mutations were identified in short, 62 bp PCR products upon their complete on-line denaturation at 75 degrees C taking advantage of the ability of IP-RP-HPLC to resolve single-stranded nucleic acids of identical length that differ in a single nucleotide. Separations in monolithic capillary columns can be readily hyphenated to electrospray ionization mass spectrometry and promise increased sample throughput by operating in arrays similar to those already used in capillary electrophoresis.

Pseudomonas aeruginosa is one of the most significant opportunistic bacterial pathogens in humans causing infections and premature death in patients with cystic fibrosis, AIDS, severe burns, organ transplants, or cancer. Liquid chromatography coupled online with tandem mass spectrometry was used for the large-scale proteomic analysis of the P. aeruginosa membrane subproteome. Concomitantly, an affinity labeling technique, using iodoacetyl-PEO biotin to tag cysteinyl-containing proteins, permitted the enrichment and detection of lower abundance membrane proteins. The application of these approaches resulted in the identification of 786 proteins. A total of 333 proteins (42%) had a minimum of one transmembrane domain (ranging from 1 to14) and 195 proteins were classified as hydrophobic based on their positive GRAVY values (ranging from 0.01 to 1.32). Key integral inner and outer membrane proteins involved in adaptation and antibiotic resistance were conclusively identified, including the detection of 53% of all predicted opr-type porins (outer integral membrane proteins) and all the components of the mexA-mexB-oprM transmembrane protein complex. This work represents one of the most comprehensive proteomic analyses of the membrane subproteome of P. aeruginosa and for prokaryotes in general. Keywords: proteome • membrane proteins • low abundance • LC−MS/MS • affinity labeling

Understanding the transcriptional regulatory networks that map out the coordinated dynamic responses of signaling proteins, transcription factors and target genes over time would represent a significant advance in the application of genome wide expression analysis. The primary challenge is monitoring transcription factor activities over time, which is not yet available at the large scale. Instead, there have been several developments to estimate activities computationally. For example, Network Component Analysis (NCA) is an approach that can predict transcription factor activities over time as well as the relative regulatory influence of factors on each target gene.In this study, we analyzed a gene expression data set in blood leukocytes from human subjects administered with lipopolysaccharide (LPS), a prototypical inflammatory challenge, in the context of a reconstructed regulatory network including 10 transcription factors, 99 target genes and 149 regulatory interactions. We found that the computationally estimated activities were well correlated to their coordinated action. Furthermore, we found that clustering the genes in the context of regulatory influences greatly facilitated interpretation of the expression data, as clusters of gene expression corresponded to the activity of specific factors or more interestingly, factor combinations which suggest coordinated regulation of gene expression. The resulting clusters were therefore more biologically meaningful, and also led to identification of additional genes under the same regulation.Using NCA, we were able to build a network that accounted for between 8-11% genes in the known transcriptional response to LPS in humans. The dynamic network illustrated changes of transcription factor activities and gene expressions as well as interactions of signaling proteins, transcription factors and target genes.

To evaluate the anti-platelet aggregation effects of extracts from 31 Traditional Chinese Medicines (TCM) with the property of activating blood and resolving stasis in terms of TCM theory. The 31 TCMs extracts were prepared using water, 90% ethanol and ethyl acetate., and the effects on anti-platelet aggregation were tested on a platelet aggregation analyzer in vitro with adenosine 5'-diphosphate, bovine thrombin and arachidonic acid (AA) as aggregation inducers, respectively. Aspirin was the positive control. Lots of the tested TCMs had inhibitory effects with concentration-dependent manner on platelet aggregations induced by various agonists. Especially, some of the TCMs such as Chuanxiong (Rhizoma Chuanxiong), Yanhusuo (Rhizoma Corydalis Yanhusuo) and Danshen (Radix Salviae Miltiorrhizae) showed good anti-platelet aggregation effect similar or higher than that in positive control group. The study provided scientific references that several TCMs such as Chuanxiong (Rhizoma Chuanxiong), Yanhusuo (Rhizoma Corydalis Yanhusuo) and Danshen (Radix Salviae Miltiorrhizae), possess the property of anti-platelet aggregation.

A 30-residue polypeptide [H21(30-mer)] with the sequence Ac-K(IEALEGK)2(IEALEHK)(IEALEGK)G-NH2 was synthesized. The circular dichroism (CD) spectrum of the peptide shows minima at 208 and 222 nm and θ222/θ208 = 1.06, which indicates the formation of a self-assembled coiled-coil when dissolved in aqueous solution. The concentration dependence of the CD data can be fit to an expression that describes a two-state monomer−dimer equilibrium for the apopeptide (Kd = 1.5 ± 0.4 μM and θmax = −23 800 ± 130 deg cm2 dmol-1), showing that it has a maximum helicity of 69%. A [MTSL-C21(30-mer)] dimer was also prepared in which MTSL is the thiol-specific nitroxide spin label 1-oxyl-2,2,5,5-tetramethyl-Δ3-pyrroline-3-methyl-methanethiosulfonate attached to C21 of the 30-mer. Fourier deconvolution analysis of the dipolar line broadening of the electron paramagnetic resonance (EPR) spectrum yields a measure of the interchain CαCα distance of 13.5 ± 0.9 Å at position 21 of the coiled-coil, which is nearly identical to those distances observed for the isostructural family of bZip proteins. Two metallohomodimers, [Ru(trpy)(bpy)-H21(30-mer)]2 and [Ru(NH3)5-H21(30-mer)]2, in which the ruthenium complexes were coordinated with the H21 site of the 30-mer, were prepared. Sodium dodecyl sulfate−polyacrylamide gel electrophoresis (SDS−PAGE), chemical cross-linking studies, and analytical ultracentrifugation show that the peptides exist as a dimeric coiled-coil with a molecular weight of ∼7.5 kDa. The electron transfer (ET) heterodimer, [Ru(trpy)(bpy)-H21(30-mer)]/[Ru(NH3)5-H21(30-mer)], was prepared, and molecular modeling shows that the two metal complexes are separated by a metal-to-metal distance of ∼24 Å across the noncovalent peptide interface. Pulse radiolysis was used to measure an ET rate constant of ket = 380 ± 80 s-1 for the intracomplex electron transfer (ΔG° = −1.11 eV) from the RuII(NH3)5-H21 donor to the RuIII(trpy)(bpy)-H21 acceptor. The value for ket falls within the range reported for modified proteins over comparable distances and supersedes the one reported in an earlier communication.

The aim of this study was to compare gene expression profiles of leukocytes from blood (white blood cells; WBCs) and spleen harvested at an early time point after injury or sham injury in mice subjected to trauma/hemorrhage, burn injury, or lipopolysaccharide (LPS) infusion at three experimental sites. Groups of injured or LPS-infused animals and sham controls were killed at 2 h after injury and resuscitation, blood and spleen were harvested, and leukocyte populations were recovered after erythrocyte lysis. RNA was extracted from postlysis leukocyte populations. Complementary RNA was synthesized from each RNA sample and hybridized to microarrays. A large number (500-1,400) of genes were differentially expressed at the 2-h time point in injured or LPS-infused vs. sham animals. Thirteen of the differentially expressed genes in blood, and 46 in the spleen, were upregulated or downregulated in common among all three animal models and may represent a common, early transcriptional response to systemic inflammation from a variety of causes. The majority of these genes could be assigned to pathways involved in the immune response and cell death. The up- or downregulation of a cohort of 23 of these genes was validated by RT-PCR. This large-scale microarray analysis shows that, at the 2-h time point, there is marked alteration in leukocyte gene expression in three animal models of injury and inflammation. Although there is some commonality among the models, the majority of the differentially expressed genes appear to be uniquely associated with the type of injury and/or the inflammatory stimulus.

Despite recent advances in our understanding the pathophysiology of trauma, the basis of the predisposition of trauma patients to infection remains unclear. A Drosophila melanogaster/Pseudomonas aeruginosa injury and infection model was used to identify host genetic components that contribute to the hyper-susceptibility to infection that follows severe trauma. We show that P. aeruginosa compromises skeletal muscle gene (SMG) expression at the injury site to promote infection. We demonstrate that activation of SMG structural components is under the control of cJun-N-terminal Kinase (JNK) Kinase, Hemipterous (Hep), and activation of this pathway promotes local resistance to P. aeruginosa in flies and mice. Our study links SMG expression and function to increased susceptibility to infection, and suggests that P. aeruginosa affects SMG homeostasis locally by restricting SMG expression in injured skeletal muscle tissue. Local potentiation of these host responses, and/or inhibition of their suppression by virulent P. aeruginosa cells, could lead to novel therapies that prevent or treat deleterious and potentially fatal infections in severely injured individuals.

The SOS response to DNA damage in bacteria is a well-known component of the complex transcriptional responses to genotoxic environmental stresses such as exposure to reactive oxygen species, alkylating agents, and many of the antibiotics targeting DNA replication. However, bacteria such as Bacillus subtilis also respond to conditions that perturb DNA replication via a transcriptional response mediated by the replication initiation protein DnaA. In addition to regulating the initiation of DNA replication, DnaA directly regulates the transcription of specific genes. Conditions that perturb DNA replication can trigger the accumulation of active DnaA, activating or repressing the transcription of genes in the DnaA regulon. We report here that simply growing B. subtilis in LB medium altered DnaA-dependent gene expression in a manner consistent with the accumulation of active DnaA and that this was part of a general transcriptional response to manganese limitation. The SOS response to DNA damage was not induced under these conditions. One of the genes positively regulated by DnaA in Bacillus subtilis encodes a protein that inhibits the initiation of sporulation, Sda. Sda expression was induced as cells entered stationary phase in LB medium but not in LB medium supplemented with manganese, and the induction of Sda inhibited sporulation-specific gene expression and the onset of spore morphogenesis. In the absence of Sda, manganese-limited cells initiated spore development but failed to form mature spores. These data highlight that DnaA-dependent gene expression may influence the response of bacteria to a range of environmental conditions, including conditions that are not obviously associated with genotoxic stress.

A simple CZE method with ionic liquid as additive was developed for the simultaneous determination of eight isoflavones in Radix Puerariae. Ionic liquid shows potential applications as additive in CZE analysis of natural products.

Searching for bioactive compounds from natural resources such as plant materials has become a focus for study. Several models, such as animal (biofluid, organ and tissue) and cellular (several kinds of cell lines), have traditionally been used for this purpose. As a fast, economic and effective way to identify or predict bioactive compounds in complex matrices, biochromatography has developed rapidly during the past years. Combing the properties of traditional chromatography and biomaterials, biochromatographic analysis possesses features of simultaneous screening, separation and structural identification for active compounds in a complex matrix. According to the process, biochromatography can be divided into offline and online approaches. For offline bioextraction, the biomaterials are used as the extraction phase and followed by routine chromatographic analysis. For online biochromatography, the biomaterials are directly used as the stationary phase for chromatographic analysis. This paper reviews the applications of offline bioextraction followed by chromatographic analysis and online biochromatography, including molecular, cell membrane and cell, and artificial biomembrane chromatography in the screening or predicting active compounds from natural sources.

A primary objective of the large collaborative project entitled "Inflammation and the Host Response to Injury" was to identify leukocyte genes that are differentially expressed after two different types of injury in mouse models and to test the hypothesis that both forms of injury would induce similar changes in gene expression. We report here the genes that are expressed in white blood cells (WBCs) and in splenocytes at 2 h, 1 day, 3 days, and 7 days after burn and sham injury or trauma-hemorrhage (T-H) and sham T-H. Affymetrix Mouse Genome 430 2.0 GeneChips were used to profile gene expression, and the results were analyzed by dCHIP, BRB Array Tools, and Ingenuity Pathway Analysis (IPA) software. We found that the highest number of genes differentially expressed following burn injury were at day 1 for both WBCs (4,989) and for splenocytes (4,715) and at day 1 for WBCs (1,167) and at day 3 for splenocytes (1,117) following T-H. The maximum overlap of genes that were expressed after both forms of injury were at day 1 in WBCs (136 genes) and at day 7 in splenocytes (433 genes). IPA revealed that the cell-to-cell signaling, cell death, immune response, antiapoptosis, and cell cycle control pathways were affected most significantly. In summary, this report provides a database of genes that are modulated in WBCs and splenocytes at sequential time points after burn or T-H in mice and reveals that relatively few leukocyte genes are expressed in common after these two forms of injury.

Patients with severe burns are highly susceptible to bacterial infection. While immunosuppression facilitates infection, the contribution of soft tissues to infection beyond providing a portal for bacterial entry remains unclear. We showed previously that glutathione S-transferase S1 (gstS1), an enzyme with conjugating activity against the lipid peroxidation byproduct 4-hydroxynonenal (4HNE), is important for resistance against wound infection in Drosophila muscle. The importance of the mammalian functional counterpart of GstS1 in the context of wounds and infection has not been investigated. Here we demonstrate that the presence of a burn wound dramatically affects expression of both human (hGSTA4) and mouse (mGsta4) 4HNE scavengers. hGSTA4 is down-regulated significantly within 1 wk of thermal burn injury in the muscle and fat tissues of patients from the large-scale collaborative Inflammation and the Host Response to Injury multicentered study. Similarly, mGsta4, the murine GST with the highest catalytic efficiency for 4HNE, is down-regulated to approximately half of normal levels in mouse muscle immediately postburn. Consequently, 4HNE protein adducts are increased 4- to 5-fold in mouse muscle postburn. Using an open wound infection model, we show that deletion of mGsta4 renders mice more susceptible to infection with the prevalent wound pathogen Pseudomonas aeruginosa, while muscle hGSTA4 expression negatively correlates with burn wound infection episodes per patient. Our data suggest that hGSTA4 down-regulation and the concomitant increase in 4HNE adducts in human muscle are indicative of susceptibility to infection in individuals with severely thermal injuries.

A microemulsion electrokinetic chromatography (MEEKC) method was developed for the simultaneous determination of three curcuminoids to avoid their alkaline degradation during capillary electrophoresis (CE) analysis with alkaline buffers.

One in four myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) patients are estimated to be severely affected by the disease, and these house-bound or bedbound patients are currently understudied. Here, we report a comprehensive examination of the symptoms and clinical laboratory tests of a cohort of severely ill patients and healthy controls. The greatly reduced quality of life of the patients was negatively correlated with clinical depression. The most troublesome symptoms included fatigue (85%), pain (65%), cognitive impairment (50%), orthostatic intolerance (45%), sleep disturbance (35%), post-exertional malaise (30%), and neurosensory disturbance (30%). Sleep profiles and cognitive tests revealed distinctive impairments. Lower morning cortisol level and alterations in its diurnal rhythm were observed in the patients, and antibody and antigen measurements showed no evidence for acute infections by common viral or bacterial pathogens. These results highlight the urgent need of developing molecular diagnostic tests for ME/CFS. In addition, there was a striking similarity in symptoms between long COVID and ME/CFS, suggesting that studies on the mechanism and treatment of ME/CFS may help prevent and treat long COVID and vice versa.

In this study, the finite-time consensus tracking problem for a class of high-order multiagent systems with the external disturbances and bounded control input is investigated, and a continuous finite-time consensus protocol via a class of distributed observers is proposed. The protocol is designed by adopting a power integrator technique and the super-twisting algorithm with saturation constraint. Despite the presence of disturbances, the proposed protocol enables the states of all agents to track the states of the leader and the tracking errors converge to zero in finite time. Besides, the input signal is bounded and its upper bound can be calculated. Some sufficient conditions on realizing the consensus of multiagent systems in finite time are deduced by employing the inductive analysis and the Lyapunov-based technology. Finally, a numerical example is provided for demonstrating the validity of developed protocols.

An ultrasonic-assisted liquid-liquid microextraction pretreatment method was developed using magnetic nanoparticles and deep eutectic solvent, and employed to separate the Norfloxacin and Enrofloxacin from pure milk samples by ultraviolet-high performance liquid chromatography. A DES-based ferromagnetic fluid with strong extraction power was prepared through the ultrasonic treatment of deep eutectic solvent (HBA: HBD = 1:2) and magnetic nanoparticles. The exceptional repeatability, reproducibility and long-term stability effect was mainly achieved through the strong interactions between the extractant and the analyte, such as hydrophobic interaction, hydrogen bond attraction, and electrostatic interaction. The results showed that the Norfloxacin and Enrofloxacin presented good linear relationship within the concentration range of 0.005 ∼ 500 mg/mL, with detection limits of 0.00035 mg/mL. The method was successfully applied to determine Norfloxacin and Enrofloxacin in milk samples by HPLC with acceptable recovery of 81 ∼ 104% and relative standard deviations (RSD) below 3%. The established method was simple, rapid and efficient, making it applicable for the detection of Norfloxacin and Enrofloxacin residues in milk. The results of this study were compared with reported methods in the literature revealing its advantages.

<h2>Abstract</h2><h3>Ethnopharmacological relevance</h3> Astragalus polysaccharide (APS), the most biologically active ingredient of Astragali Radix, is used to treat diabetes mellitus (DM)-related chronic wounds in traditional Chinese medicine for several decades. This herb possesses an anti-inflammatory effect. Our study proved that APS can reduce excessive inflammation at the late phase of wound-healing in diabetic ulcers. <h3>Aim of the study</h3> To clarify the molecular mechanism of APS in promoting wound-healing via reducing excessive inflammation in diabetic ulcers during the late stages of wound-healing. <h3>Methods and materials</h3> The rat model of the diabetic ulcers was established via intraperitoneal injection of streptozocin (60 mg/kg). We detected the regulation of APS on diabetic ulcers by measuring wound-healing rates. Bioinformatics was used to predict the target genes of APS, and autodocking was used to predict the combination of APS and target genes. Immunohistochemistry, Enzyme-linked immunosorbent assay, Western blot, immunofluorescence staining, flow cytometry, and flow cytometric sorting were investigated. <h3>Results</h3> The results demonstrated that APS promoted wound-healing and inhibited excessive inflammation at the late phase of wound-healing in diabetic rats. Mechanistic findings showed that APS promoted the expression of β-catenin and Rspo3 while inhibiting the expression of NF-KB and GSK-3β, which leads to the transformation of M1-type macrophages into M2-type macrophages and thus reducing excessive inflammation at the late phase of wound-healing in diabetic ulcers. <h3>Conclusion</h3> We found an interesting finding that APS promoted the polarization of macrophages towards M2-type through the β-catenin/NF-κB axis to reduce excessive inflammation at the late phase of wound-healing. Therefore, APS may be a promising drug for treating diabetic ulcers in clinic.

High-throughput genome-wide studies of alternatively spliced mRNA transcripts have become increasingly important in clinical research. Consequently, easy-to-use software tools are required to process data from these studies, for example, using exon and junction arrays. Here, we introduce JETTA, an integrated software package for the calculation of gene expression indices as well as the identification and visualization of alternative splicing events. We demonstrate the software using data of human liver and muscle samples hybridized on an exon-junction array.JETTA and its demonstrations are freely available at http://igenomed.stanford.edu/~junhee/JETTA/index.html

Polymorphonuclear neutrophils (PMNs) play an important role in mediating the innate immune response after severe traumatic injury; however, the cellular proteome response to traumatic condition is still largely unknown.We applied 2D-LC-MS/MS-based shotgun proteomics to perform comparative proteome profiling of human PMNs from severe trauma patients and healthy controls.A total of 197 out of ~2500 proteins (being identified with at least two peptides) were observed with significant abundance changes following the injury. The proteomics data were further compared with transcriptomics data for the same genes obtained from an independent patient cohort. The comparison showed that the protein abundance changes for the majority of proteins were consistent with the mRNA abundance changes in terms of directions of changes. Moreover, increased protein secretion was suggested as one of the mechanisms contributing to the observed discrepancy between protein and mRNA abundance changes. Functional analyses of the altered proteins showed that many of these proteins were involved in immune response, protein biosynthesis, protein transport, NRF2-mediated oxidative stress response, the ubiquitin-proteasome system, and apoptosis pathways.Our data suggest increased neutrophil activation and inhibited neutrophil apoptosis in response to trauma. The study not only reveals an overall picture of functional neutrophil response to trauma at the proteome level, but also provides a rich proteomics data resource of trauma-associated changes in the neutrophil that will be valuable for further studies of the functions of individual proteins in PMNs.

Mass spectrometry (MS)-based high-throughput quantitative proteomics shows great potential in large-scale clinical biomarker studies, identifying and quantifying thousands of proteins in biological samples. However, there are unique challenges in analyzing the quantitative proteomics data. One issue is that the quantification of a given peptide is often missing in a subset of the experiments, especially for less abundant peptides. Another issue is that different MS experiments of the same study have significantly varying numbers of peptides quantified, which can result in more missing peptide abundances in an experiment that has a smaller total number of quantified peptides. To detect as many biomarker proteins as possible, it is necessary to develop bioinformatics methods that appropriately handle these challenges.We propose a Significance Analysis for Large-scale Proteomics Studies (SALPS) that handles missing peptide intensity values caused by the two mechanisms mentioned above. Our model has a robust performance in both simulated data and proteomics data from a large clinical study. Because varying patients' sample qualities and deviating instrument performances are not avoidable for clinical studies performed over the course of several years, we believe that our approach will be useful to analyze large-scale clinical proteomics data.R codes for SALPS are available at http://www.stanford.edu/%7eclairesr/software.html.

For over 50 years, the International HLA and Immunogenetics Workshops (IHIW) have advanced the fields of histocompatibility and immunogenetics (H&I) via community sharing of technology, experience and reagents, and the establishment of ongoing collaborative projects. Held in the fall of 2017, the 17th IHIW focused on the application of next generation sequencing (NGS) technologies for clinical and research goals in the H&I fields. NGS technologies have the potential to allow dramatic insights and advances in these fields, but the scope and sheer quantity of data associated with NGS raise challenges for their analysis, collection, exchange and storage. The 17th IHIW adopted a centralized approach to these issues, and we developed the tools, services and systems to create an effective system for capturing and managing these NGS data. We worked with NGS platform and software developers to define a set of distinct but equivalent NGS typing reports that record NGS data in a uniform fashion. The 17th IHIW database applied our standards, tools and services to collect, validate and store those structured, multi-platform data in an automated fashion. We have created community resources to enable exploration of the vast store of curated sequence and allele-name data in the IPD-IMGT/HLA Database, with the goal of creating a long-term community resource that integrates these curated data with new NGS sequence and polymorphism data, for advanced analyses and applications.

Journal Article Genome-wide transcriptome analysis of 150 cell samples Get access Daniel Irimia, Daniel Irimia BioMEMS Resource Center, Center for Engineering in Medicine and Surgical Services, Massachusetts General Hospital, Shriners Hospital for Children, and Harvard Medical School, Boston, MA 02114, USA E-mail: dirimia@hms.harvard.edu Search for other works by this author on: Oxford Academic Google Scholar Michael Mindrinos, Michael Mindrinos Stanford Genome Technology Center and Department of Biochemistry, Stanford University, Stanford, CA 94305, USA E-mail: mindrinos@stanford.edu Search for other works by this author on: Oxford Academic Google Scholar Aman Russom, Aman Russom BioMEMS Resource Center, Center for Engineering in Medicine and Surgical Services, Massachusetts General Hospital, Shriners Hospital for Children, and Harvard Medical School, Boston, MA 02114, USA Search for other works by this author on: Oxford Academic Google Scholar Wenzhong Xiao, Wenzhong Xiao Stanford Genome Technology Center and Department of Biochemistry, Stanford University, Stanford, CA 94305, USA Search for other works by this author on: Oxford Academic Google Scholar Julie Wilhelmy, Julie Wilhelmy Stanford Genome Technology Center and Department of Biochemistry, Stanford University, Stanford, CA 94305, USA Search for other works by this author on: Oxford Academic Google Scholar Shenglong Wang, Shenglong Wang NuGEN Technologies Inc., San Carlos, CA 94070, USA Search for other works by this author on: Oxford Academic Google Scholar Joe Don Heath, Joe Don Heath NuGEN Technologies Inc., San Carlos, CA 94070, USA Search for other works by this author on: Oxford Academic Google Scholar Nurith Kurn, Nurith Kurn NuGEN Technologies Inc., San Carlos, CA 94070, USA Search for other works by this author on: Oxford Academic Google Scholar Ronald G. Tompkins, Ronald G. Tompkins BioMEMS Resource Center, Center for Engineering in Medicine and Surgical Services, Massachusetts General Hospital, Shriners Hospital for Children, and Harvard Medical School, Boston, MA 02114, USA Search for other works by this author on: Oxford Academic Google Scholar Ronald W. Davis, Ronald W. Davis Stanford Genome Technology Center and Department of Biochemistry, Stanford University, Stanford, CA 94305, USA Search for other works by this author on: Oxford Academic Google Scholar ... Show more Mehmet Toner Mehmet Toner BioMEMS Resource Center, Center for Engineering in Medicine and Surgical Services, Massachusetts General Hospital, Shriners Hospital for Children, and Harvard Medical School, Boston, MA 02114, USA Search for other works by this author on: Oxford Academic Google Scholar Integrative Biology, Volume 1, Issue 1, January 2009, Pages 99–107, https://doi.org/10.1039/b814329c Published: 12 November 2008 Article history Received: 19 August 2008 Accepted: 30 September 2008 Published: 12 November 2008

Whether stromal components facilitate growth, invasion, and dissemination of cancer cells or suppress neoplastic lesions from further malignant progression is a continuing conundrum in tumor biology. Conceptualizing a dynamic picture of tumorigenesis is complicated by inter-individual heterogeneity. In the post genomic era, unraveling such complexity remains a challenge for the cancer biologist. Towards establishing a functional association between cellular crosstalk and differential cancer aggressiveness, we identified a signature of malignant breast epithelial response to stromal signaling. Proximity to fibroblasts resulted in gene transcript alterations of >2-fold for 107 probes, collectively designated as Fibroblast Triggered Gene Expression in Tumor (FTExT). The hazard ratio predicted by the FTExT classifier for distant relapse in patients with intermediate and high grade breast tumors was significant compared to routine clinical variables (dataset 1, n = 258, HR--2.11, 95% CI 1.17-3.80, p-value 0.01; dataset 2, n = 171, HR--3.07, 95% CI 1.21-7.83, p-value 0.01). Biofunctions represented by FTExT included inflammatory signaling, free radical scavenging, cell death, and cell proliferation. Unlike genes of the 'proliferation cluster', which are overexpressed in aggressive primary tumors, FTExT genes were uniquely repressed in such cases. As proof of concept for our correlative findings, which link stromal-epithelial crosstalk and tumor behavior, we show a distinctive differential in stromal impact on prognosis-defining functional endpoints of cell cycle progression, and resistance to therapy-induced growth arrest and apoptosis in low vs. high grade cancer cells. Our experimental data thus reveal aspects of 'paracrine cooperativity' that are exclusively contingent upon the histopathologically defined grade of interacting tumor epithelium, and demonstrate that epithelial responsiveness to the tumor microenvironment is a deterministic factor underlying clinical outcome. In this light, early attenuation of epithelial-stromal crosstalk could improve the management of cases prone to be clinically challenging.

A cornerstone of modern biomedical research is the use of animal models to study disease mechanisms and to develop new therapeutic approaches. In order to help the research community to better explore the similarities and differences of genomic response between human inflammatory diseases and murine models, we developed KERIS: kaleidoscope of gene responses to inflammation between species (available at http://www.igenomed.org/keris/). As of June 2016, KERIS includes comparisons of the genomic response of six human inflammatory diseases (burns, trauma, infection, sepsis, endotoxin and acute respiratory distress syndrome) and matched mouse models, using 2257 curated samples from the Inflammation and the Host Response to Injury Glue Grant studies and other representative studies in Gene Expression Omnibus. A researcher can browse, query, visualize and compare the response patterns of genes, pathways and functional modules across different diseases and corresponding murine models. The database is expected to help biologists choosing models when studying the mechanisms of particular genes and pathways in a disease and prioritizing the translation of findings from disease models into clinical studies.

Using novel monolithic poly(styrene-divinylbenzene) capillary columns with an internal diameter of 0.2 mm, we demonstrate for the first time the feasibility of constructing high-performance liquid chromatography arrays for the detection of mutations by heteroduplex analysis under partially denaturing conditions. In one embodiment, such an array can be used to analyze one sample simultaneously at different temperatures to maximize the detection of mutations in DNA fragments containing multiple discrete melting domains. Alternatively, one may inject different samples onto columns kept at the same effective temperature. Further improvements in throughput can be obtained by means of laser-induced fluorescence detection and the differential labeling of samples with up to four different fluorophores. Major advantages of monolithic capillary high-performance liquid chromatographic arrays over their capillary electrophoretic analogs are the chemical inertness of the poly(styrene-divinylbenzene) stationary phase, the physical robustness of the column bed due to its covalent linkage to the inner surface of the fused silica capillary, and the feasibility to modify the stationary phase thereby allowing the separation of compounds not only on the principle of size exclusion, but also adsorption, distribution, and ion exchange. Analyses times are on the order of a few minutes and turnaround time is extremely short as there is no need for the replenishment of the separation matrix between runs.

Human transcriptome arrays (HTA) have recently been developed for high-throughput alternative splicing analysis by measuring signals not only from exons but also from exon-exon junctions. Effective use of these rich signals requires the development of computational methods for better gene and alternative splicing analyses. In this work, we introduce a computational method, Robust Alternative Splicing Analysis (RASA), for the analysis of the new transcriptome arrays by effective integration of the exon and junction signals. To increase robustness, RASA calculates the expression of each gene by selecting exons classified as not alternatively spliced. It then identifies alternatively spliced exons that are supported by both exon and junction signals to reduce the false positives. Finally, it detects additional alternative splicing candidates that are supported by only exon signals because the signals from the corresponding junctions are not well detected. RASA was demonstrated with Affymetrix HTAs and its performance was evaluated with mRNA-Seq and RT-PCR. The validation rate is 52.4%, which is a 60% increase when compared with previous methods that do not use selected exons for gene expression calculation and junction signals for splicing detection. These results suggest that RASA significantly improves alternative splicing analyses on HTA platforms.

Denaturing high-performance liquid chromatography (DHPLC) is a sensitive, robust, and operationally inexpensive method for the detection of single-base substitutions and small deletions and insertions. To increase sample throughout, we have developed a multiplexing strategy using fluorophores to distinguish different PCR products. The system combines recent advances in the synthesis of monolithic poly(styrene-divinylbenzene) capillary columns with four-color confocal argon ion laser-induced fluorescence detection. Depending on the change in retention caused by the fluorophores, adjustments in the analysis temperature may be required to ensure the maximum mutation detection sensitivity.

The characterization of global gene expression patterns of microscale samples is important in many areas of biological and clinical research. The choice of carrier is critical for the efficient isolation and successful amplification of RNA at the nanogram level. Here we show that recovery of nanograms of RNA is significantly higher when carrier linear polyacrylamide is supplemented with carrier tRNA. Reverse transcription and in vitro transcription reactions remain efficient and specific in the presence of carrier tRNA. Finally, comparison of GeneChip TM array hybridization patterns demonstrates that the presence of carrier tRNA does not cause detectable distortion in global amplification. Taken together, tRNA is a superior carrier for the isolation and global amplification of microscale RNA.

The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions.In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification.High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data.

We appreciate the comments of Cauwels et al. (1) regarding our recent publication (2). Cauwels et al. cite the use of anti-TNF treatment for rheumatoid arthritis and inflammatory bowel disease and the failure of nitric oxide (NO) synthases in sepsis as examples of the successful use of mouse models. Ironically, these examples could in fact be used to illustrate the differences between mice and humans. Anti-TNF treatment was developed for the treatment of sepsis because it protected mice in models of sepsis (3). However, anti-TNF failed in human trials of sepsis (4). It was only later that anti-TNF was shown to be helpful for rheumatoid arthritis and inflammatory bowel disease. In the case of NO, it should be noted that although mouse macrophages readily produce NO in vitro, human macrophages do not (5). The many trials for sepsis in which drugs protected in mice but failed in humans suggest that ability of mouse efficacy models to predict human inflammatory diseases is close to random, and therefore it should not be surprising that occasionally there is a correlation. However, for such a model to be helpful, it needs to prospectively predict the human condition. In this case, mouse models appear to perform very poorly indeed.

In this study, three different oil phases include 1-butyl-1-methylpyrrolidinium bis(trifluoromethanesulfonyl)imide (BMPy[NTf2]), 1-butyl-3-methylimidazolium hexafluorophosphate (BMImPF6) and n-octane were compared for the MEEKC analysis (SDS as surfactant and n-butanol as co-surfactant) of eight isoflavones from pueraria. The investigated isoflavones can be well separated by all of those three microemulsion systems after careful optimization, and the MEEKC with n-octane as the oil phase was the best choice (good symmetry and high resolutions of peaks with short analysis time) for the analysis. The optimum conditions of MEEKC method were as follows: 70 mM SDS, 0.7 M n-butanol and 0.5% (w/v) n-octane in 10 mM sodium tetraborate (STB) at pH 8.5, applied voltage was 23 kV and cassette temperature was set at 30°C. And then the developed method was fully validated (limit of detection, limit of quantification, intraday precision, interday precision and recovery) and successfully applied to determine the eight analytes in three Radix Puerariae samples. In addition, although the MEEKC with classic oil phase (n-octane) showed better results for isoflavones analysis in this study, the MEEKC with ionic liquids (BMPy[NTf2] and BMImPF6) also showed great separation potential for analytes, which may be further applied in the analysis of other natural products.

Abstract Background Constraint-based metabolic modeling has been applied to understand metabolism related disease mechanisms, to predict potential new drug targets and anti-metabolites, and to identify biomarkers of complex diseases. Although the state-of-art modeling toolbox, COBRA 3.0, is powerful, it requires substantial computing time conducting flux balance analysis, knockout analysis, and Markov Chain Monte Carlo (MCMC) sampling, which may limit its application in large scale genome-wide analysis. Results Here, we rewrote the underlying code of COBRA 3.0 using C/C++, and developed a toolbox, termed FastMM, to effectively conduct constraint-based metabolic modeling. The results showed that FastMM is 2~400 times faster than COBRA 3.0 in performing flux balance analysis and knockout analysis and returns consistent outputs. When applied to MCMC sampling, FastMM is 8 times faster than COBRA 3.0. FastMM is also faster than some efficient metabolic modeling applications, such as Cobrapy and Fast-SL. In addition, we developed a Matlab/Octave interface for fast metabolic modeling. This interface was fully compatible with COBRA 3.0, enabling users to easily perform complex applications for metabolic modeling. For example, users who do not have deep constraint-based metabolic model knowledge can just type one command in Matlab/Octave to perform personalized metabolic modeling. Users can also use the advance and multiple threading parameters for complex metabolic modeling. Thus, we provided an efficient and user-friendly solution to perform large scale genome-wide metabolic modeling. For example, FastMM can be applied to the modeling of individual cancer metabolic profiles of hundreds to thousands of samples in the Cancer Genome Atlas (TCGA). Conclusion FastMM is an efficient and user-friendly toolbox for large-scale personalized constraint-based metabolic modeling. It can serve as a complementary and invaluable improvement to the existing functionalities in COBRA 3.0. FastMM is under GPL license and can be freely available at GitHub site: https://github.com/GonghuaLi/FastMM .

While most transcriptome analyses in high-throughput clinical studies focus on gene level expression, the existence of alternative isoforms of gene transcripts is a major source of the diversity in the biological functionalities of the human genome. It is, therefore, essential to annotate isoforms of gene transcripts for genome-wide transcriptome studies. Recently developed mRNA sequencing technology presents an unprecedented opportunity to discover new forms of transcripts, and at the same time brings bioinformatic challenges due to its short read length and incomplete coverage for the transcripts. In this work, we proposed a computational approach to reconstruct new mRNA transcripts from short sequencing reads with reference information of known transcripts in existing databases. The prior knowledge helped to define exon boundaries and fill in the transcript regions not covered by sequencing data. This approach was demonstrated using a deep sequencing data set of human muscle tissue with transcript annotations in RefSeq as prior knowledge. We identified 2,973 junctions, 7,471 exons, and 7,571 transcripts not previously annotated in RefSeq. 73% of these new transcripts found supports from UCSC Known Genes, Ensembl or EST transcript annotations. In addition, the reconstructed transcripts were much longer than those from de novo approaches that assume no prior knowledge. These previously un-annotated transcripts can be integrated with known transcript annotations to improve both the design of microarrays and the follow-up analyses of isoform expression. The overall results demonstrated that incorporating transcript annotations from genomic databases significantly helps the reconstruction of novel transcripts from short sequencing reads for transcriptome research.

Osterburg et al. (1) raise questions from our recent publication (2). First, in our program, a single mouse strain, C57B/6, was selected because it has been the most commonly used in the field for decades. Furthermore, all strains of mice are remarkably resistant to LPS relative to humans. If anything, C57B/6 mice are less resistant and therefore potentially closer to the human response than many other mouse strains (3). Last, figure 4 and table 1 of ref. 2 show that our results are consistent with those of other independent mouse studies and not specific to strain, model, or investigator.

Nature 437, 1032–1037 (2005) doi:10.1038/nature03985 In this Letter, the affiliations of authors participating in the Inflammation and Host Response to Injury Large Scale Collaborative Research Program are incorrectly listed. The renumbered and amended footnote listing is given here.

Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn’t been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge.

Carbonylation of methanol to acetic acid is an aqueous homogeneous catalytic process widely used in industry. In the existing methanol carbonylation industry, a large amount of water (14–15 wt.%) is required in the Monsanto process to inhibit catalyst deactivation, which also consumes a lot of energy to separate the water from the acetic acid product. Here, non-aqueous liquid phase synthesis of acetic acid was carried out by ionic liquid promoted homogeneous carbonylation of methanol over Ir(III) catalysts. It was found that 1-butyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide ([Bmim]Tf2N) and N-butyl pyridinium bis(trifluoromethanesulfonyl)imide ([BPy]Tf2N) could promote the acetic acid selectivity (>98%) and methanol conversion (>99%) under a relatively mild reaction condition of 160 °C and 3.0 MPa. In the reaction, Tf2N− formed an Ir*[Tf2N] complex with Ir to promote the stability of Ir and enhance the activation of CO. From DFT calculation results, the CO insertion was a rate controlling step on the Ir-base catalyst in the reaction cycle, determining the conversion and selectivity of the reaction. The oxygen and nitrogen groups from Tf2N− interacted with Ir to form an Ir*[Tf2N] complex, which could effectively reduce the energy barrier of the CO insertion step, enhancing the selectivity of HAc. Moreover, the catalyst system could be easily recycled and reused with the methanol conversion of 89.66% after five cycles. The methanol carbonylation based on the ionic liquid promoted catalyst is a promising non-aqueous liquid-phase sustainable process. Here, non-aqueous liquid phase synthesis of acetic acid was carried out by ionic liquid promoted homogeneous carbonylation of methanol over Ir(III) catalysts. It was found that 1-butyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide ([Bmim]Tf2N) and N-butyl pyridinium bis(trifluoromethanesulfonyl)imide ([BPy]Tf2N) could promoted the acetic acid selectivity (>98%) and methanol conversion (>99%) under a relatively mild reaction condition of 160 °C and 3.0 MPa.

For the analysis of longitudinal data with nonignorable and nonmonotone missing responses, a full likelihood method often requires intensive computation, especially when there are many follow-up times. The authors propose and explore a Monte Carlo method, based on importance sampling, for approximating the maximum likelihood estimators. The finite-sample properties of the proposed estimators are studied using simulations. An application of the proposed method is also provided using longitudinal data on peptide intensities obtained from a proteomics experiment of trauma patients.

EDITORIAL article Front. Genet., 16 February 2023Sec. Computational Genomics Volume 14 - 2023 | https://doi.org/10.3389/fgene.2023.1154398

To study the damage to the target plate under the combined action of shock wave and fragment, using the complete restart technique to extract the damage of the target plate after the action of the shock wave, and then a single fragment penetrating target plate is simulated. The Arbitrary Lagrange-Euler (ALE) algorithm and Particle Blast Method (PBM) algorithm are used to analyze the damage to the target plate by prefabricated fragments after the explosion and compare with the test to verify the feasibility of the PBM algorithm. Completely restart the target plate under the action of the shock wave generated by PBM algorithm. The Ls-dyna software is used to simulate the fragment penetration into the target plate, so as to obtain the damage of the target plate under different incidence angles. The simulation results show that the combined action of shock wave and fragment has a strong penetration ability; the end penetration capability of cylindrical fragment is greater than that of lateral penetration; the fragment penetration ability is the strongest at vertical incidence.

In order to study the injury characteristics of the passenger-side occupant in the process of near-ground side ramp rollover, a finite element model of an off-road vehicle was established, and the acceleration of the center of mass of the vehicle rollover was obtained through simulation, and the accuracy of the model was verified through real vehicle rollover test. Hybrid III 50% male dummy was placed in the three rows of seats on the passenger side, and the restraint system was the same as that of the original vehicle, and the damage to various parts of the dummy was obtained for comparative analysis. The study shows that: the peak injury moments of different positions are similar, only the peak injury moments of the head and chest of the rear dummy appear earlier; the rear occupant's body dives and the neck is compressed, resulting in excessive axial force on the neck, and the subsequent improvement should focus on the restraint system.

<b>Background</b>: ME/CFS is a complex disease with unclear etiology. Current diagnostic criteria lack objective laboratory measures. <b>Aims</b>: This study aimed to investigate the plasma proteomic profile of ME/CFS patients and determine any differentially expressed proteins compared to controls. <b>Methods</b>: Plasma samples obtained from 19 ME/CFS patients and 9 controls underwent analysis (Somalogic, Inc, CO). The ME/CFS patients met the National Academy of Medicine criteria for the disease. Samples were collected from a mixed venous compartment. Statistical analysis and a Mixed Graphical Model were used to identify candidate biomarker. <b>Results</b>: Among ~7000 proteins detected, ~400 were differentially expressed between patients and controls (False Discovery Rate&lt;0.05 and Absolute Fold Change ≥1.5). Selectin E (SELE), ATP Synthase Subunit F6 (ATP5PF), and Transcobalamin 2 (TCN2) were identified as top candidates. A classifier of these proteins in pulmonary artery blood of patients were distinguishable from controls (AUC =0.99). <b>Conclusion:</b> The study highlighted potential biomarkers for ME/CFS, the top candidates of which are involved in inflammation, cellular energy metabolism, and Vitamin B12 transport. The plasma proteomic signature identifies ME/CFS from normals and suggests that the disease’s pathophysiology is driven by abnormalities of aerobic metabolism, vascular dysregulation, and Vitamin B12 metabolism.

Denaturing high-performance liquid chromatography (DHPLC) compares two or more chromosomes as a mixture of denatured and reannealed PCR amplicons, revealing the presence of a mutation by the differential retention of homo- and heteroduplex DNA on reversed-phase chromatography supports under partial denaturation. Temperature determines sensitivity, and its optimum can be predicted by computation. Single-nucleotide substitutions, deletions, and insertions have been detected successfully by on-line UV or fluorescence monitoring within 2–3 minutes in unpurified amplicons as large as 1.5 Kb. Sensitivity and specificity of DHPLC consistently exceed 96%. These features and its low cost make DHPLC one of the most powerful tools for the re-sequencing of the human and other genomes. Aside from its application to the mutational analysis of candidate genes, DHPLC has proven instrumental in elucidating human evolution and in the mapping of genes. Employing completely denaturing conditions, the utility of DHPLC has been extended to the genotyping of known polymorphisms by utilizing the ability of poly(styrene-divinylbenzene) to resolve single-stranded DNA molecules of identical size that differ in a single base. Under completely denaturing conditions, it is thus possible to resolve all possible base substitutions with the single exception of C→G transversions. Improvements in throughput became feasible with the recent introduction of monolithic poly(styrene-divinylbenzene) capillaries that lend themselves to the fabrication of arrays connected to a multi-color laser induced fluorescence scanner or a mass spectrometer. Hum Mutat 17:439–474, 2001. © 2001 Wiley-Liss, Inc.

Large-scale transcriptome profiling in clinical studies often involves assaying multiple samples of a patient to monitor disease progression, treatment effect, and host response in multiple tissues. Such profiling is prone to human error, which often results in mislabeled samples. Here, we present a method to detect mislabeled sample outliers using coding single nucleotide polymorphisms (cSNPs) specifically designed on the microarray and demonstrate that the mislabeled samples can be efficiently identified by either simple clustering of allele-specific expression scores or Mahalanobis distance-based outlier detection method. Based on our results, we recommend the incorporation of cSNPs into future transcriptome array designs as intrinsic markers for sample tracking.

An amendment to this paper has been published and can be accessed via the original article.

Abstract Contact: wenzhong.xiao@mgh.harvard.edu

Introduction: Inability to effectively monitor graft injuries leads to graft dysfunction. A non-invasive tool to monitor such injuries is an unmet need. Method: Through a robust and comprehensive proteomics analysis, rigorous bioinformatics, and ELISA on a set of 480 urine samples with matched biopsies, we identified and validated potential biomarkers for acute rejection (AR), chronic allograft injury (CAI), and BK virus nephropathy (BKVN). Result: A panel of 100 statistically significant proteins as potential biomarkers for different transplant injury is identified. Four proteins, FGB, FGG, SUMO2, and HLA-DRB1 were selected based on their relevance in AR. Urinary FBB in AR was significantly higher vs STA (p=0.04), vs CAI (p=0.05), and vs BKV (p=0.03). The increased urine protein level of FBG in AR (p=0.04), vs CAI (p=0.05), and vs BKV (p=0.02). The urine protein level of HLA-DRB1 was significantly higher in AR vs STA (p=0.001), vs CAI (p=0.003), and vs BKV (p=0.04). Urine SUMO2 level was significantly higher in AR vs STA urine (p=0.005), and vs BKVN urine (p=0.04). An ROC analysis to identify AR from the rest of the phenotypes (CAI, BK, and STA) on the data from FBB, FGG, and HLA-DRB1 yielded an AUC of 0.8.Table: No Caption available.Figure: No Caption available.Conclusion: A panel of 3 urinary proteins, mined by high throughput proteomics, and validated by customized ELISA, allows for non-invasive diagnosis of AR, not confounded by BKVN. This urinary assay requires no local sample processing, is robust and can be used as a sensitive assay for out-patient renal transplant injury surveillance.

Many patients following severe trauma have complicated recoveries due to the development organ injury and sepsis. To date, physiological, anatomical, and immunological prognosticators of organ injury have had limited success in the clinical setting predicting clinical trajectories, especially when combined with therapeutic interventions. Application of multiplex genomic platforms in critically ill patients has been hampered by the time and expense required, and the need to reduce complex data to a single interpretable composite metric. We report here on the development and retrospective validation of a simple genomic composite score that can be rapidly used to predict clinical outcomes.

A method was established for the determination of α-solanine in potato by high performance liquid chromatography.The α-solanine was extracted from potato by refluxing extraction.The effects of different mobile phase composition,concentration,concentration ratio,flow speed,column temperature and other factors on α-solanine separation were studied.When the mobile phase was a mixture of acetonitrile and 0.4% phosphoric acid aqueous solution,flow speed was 0.8 mL/min,temperature was 30 ℃,detection wavelength was set at 210 nm,α-solanine could be separated well.The calibration of α-solanine was in good linearity in the range of 0.16-0.80 g/L.The average recovery was 96.3% with relative standard deviation of 1.3%(n=6).

Context: The response to a major burn injury is characterized by systemic inflammation, hypermetabolism, and immune suppression involving a reprioritization of energy metabolism and protein synthesis, which persists for up to 60 days post-injury. Objective: Determine changes in the leukocyte transcriptome to obtain unique insights into the initiation and resolution of severe burn injury; to facilitate this, an interactive web site was designed that allows investigators to examine gene expression patterns and the associated signaling pathways. Setting and Patients: Burn patients and control subjects were enrolled as part of the multi-center Inflammation and the Host Response to Injury glue grant collaborative research program. Blood samples from one hundred forty-four burn patients were harvested from the time of admission until sixty days post injury and were compared to samples from 99 control subjects. Total blood leukocyte gene expression was analyzed using the Affymetrix U133 plus 2.0 GeneChip™. Clinical data were collected prospectively and entered into the trial database. Genomic data was then organized by age and survival status. Results: Four thousand four hundred sixteen probe sets were identified as burn responsive based on having a coefficient of variation greater than 0.5 within the dataset. The patients were then grouped according to age and outcome (survival), and k-means clustering was then used to group probe sets according to expression pattern. Significant perturbations in leukocyte gene expression correlated with pathways central to the immuno-inflammatory response and leukocyte function, including T-cell receptor signaling, antigen presentation, and leukocyte extravasation. Determination of the genomic changes induced by a severe burn injury and persisting throughout the acute hospitalization period will allow identification of age- and sex-specific responses to burn, interpretation of pathways impacted by a burn injury in addition to development of outcome trajectories for people at risk for death. Conclusion: Changes in leukocyte gene expression indicate increased inflammation and reduced antigen presentation and lymphocyte function. Expression of these immuno-inflammatory genes is also associated with age-specific and survival-related clinical trajectories, giving unique insights that may result in the development of novel clinical and therapeutic strategies. Furthermore, we provide a tool that investigators can use to identify genes that change in response to a burn and to demonstrate the utility of the web-based tool.