Valentina Di Francesco

A 2.91-billion base pair (bp) consensus sequence of the euchromatic portion of the human genome was generated by the whole-genome shotgun sequencing method. The 14.8-billion bp DNA sequence was generated over 9 months from 27,271,853 high-quality sequence reads (5.11-fold coverage of the genome) from both ends of plasmid clones made from the DNA of five individuals. Two assembly strategies-a whole-genome assembly and a regional chromosome assembly-were used, each combining sequence data from Celera and the publicly funded genome effort. The public data were shredded into 550-bp segments to create a 2.9-fold coverage of those genome regions that had been sequenced, without including biases inherent in the cloning and assembly procedure used by the publicly funded group. This brought the effective coverage in the assemblies to eightfold, reducing the number and size of gaps in the final assembly over what would be obtained with 5.11-fold coverage. The two assembly strategies yielded very similar results that largely agree with independent mapping data. The assemblies effectively cover the euchromatic regions of the human chromosomes. More than 90% of the genome is in scaffold assemblies of 100,000 bp or more, and 25% of the genome is in scaffolds of 10 million bp or larger. Analysis of the genome sequence revealed 26,588 protein-encoding transcripts for which there was strong corroborating evidence and an additional approximately 12,000 computationally derived genes with mouse matches or other weak supporting evidence. Although gene-dense clusters are obvious, almost half the genes are dispersed in low G+C sequence separated by large tracts of apparently noncoding sequence. Only 1.1% of the genome is spanned by exons, whereas 24% is in introns, with 75% of the genome being intergenic DNA. Duplications of segmental blocks, ranging in size up to chromosomal lengths, are abundant throughout the genome and reveal a complex evolutionary history. Comparative genomic analysis indicates vertebrate expansions of genes associated with neuronal function, with tissue-specific developmental regulation, and with the hemostasis and immune systems. DNA sequence comparisons between the consensus sequence and publicly funded genome data provided locations of 2.1 million single-nucleotide polymorphisms (SNPs). A random pair of human haploid genomes differed at a rate of 1 bp per 1250 on average, but there was marked heterogeneity in the level of polymorphism across the genome. Less than 1% of all SNPs resulted in variation in proteins, but the task of determining which SNPs have functional consequences remains an open challenge.

Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome. The Human Microbiome Project Consortium reports the first results of their analysis of microbial communities from distinct, clinically relevant body habitats in a human cohort; the insights into the microbial communities of a healthy population lay foundations for future exploration of the epidemiology, ecology and translational applications of the human microbiome. The Human Microbiome Project (HMP), supported by the National Institutes of Health Common Fund, has the goal of characterizing the microbial communities that inhabit and interact with the human body in sickness and in health. In two Articles in this issue of Nature, the HMP Consortium presents the first population-scale details of the organismal and functional composition of the microbiota across five areas of the body. An associated News & Views discusses the initial results — which, along with those of a series of co-publications, already constitute the most extensive catalogue of organisms and genes related to the human microbiome yet published — and highlights some of the major questions that the project will tackle in the next few years.

A variety of microbial communities and their genes (the microbiome) exist throughout the human body, with fundamental roles in human health and disease. The National Institutes of Health (NIH)-funded Human Microbiome Project Consortium has established a population-scale framework to develop metagenomic protocols, resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far. In parallel, approximately 800 reference strains isolated from the human body have been sequenced. Collectively, these data represent the largest resource describing the abundance and variety of the human microbiome, while providing a framework for current and future studies.

The Human Microbiome Project (HMP), funded as an initiative of the NIH Roadmap for Biomedical Research ( http://nihroadmap.nih.gov ), is a multi-component community resource. The goals of the HMP are: (1) to take advantage of new, high-throughput technologies to characterize the human microbiome more fully by studying samples from multiple body sites from each of at least 250 “normal” volunteers; (2) to determine whether there are associations between changes in the microbiome and health/disease by studying several different medical conditions; and (3) to provide both a standardized data resource and new technological approaches to enable such studies to be undertaken broadly in the scientific community. The ethical, legal, and social implications of such research are being systematically studied as well. The ultimate objective of the HMP is to demonstrate that there are opportunities to improve human health through monitoring or manipulation of the human microbiome. The history and implementation of this new program are described here.

Starting with the launch of the Human Genome Project three decades ago, and continuing after its completion in 2003, genomics has progressively come to have a central and catalytic role in basic and translational research. In addition, studies increasingly demonstrate how genomic information can be effectively used in clinical care. In the future, the anticipated advances in technology development, biological insights, and clinical applications (among others) will lead to more widespread integration of genomics into almost all areas of biomedical research, the adoption of genomics into mainstream medical and public-health practices, and an increasing relevance of genomics for everyday life. On behalf of the research community, the National Human Genome Research Institute recently completed a multi-year process of strategic engagement to identify future research priorities and opportunities in human genomics, with an emphasis on health applications. Here we describe the highest-priority elements envisioned for the cutting-edge of human genomics going forward—that is, at ‘The Forefront of Genomics’. In this Perspective, authors from the National Human Genome Research Institute (NHGRI) present a vision for human genomics research for the coming decade.

The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) was developed to address a widespread community need for a unified computing environment for genomics data storage, management, and analysis. In this perspective, we present AnVIL, describe its ecosystem and interoperability with other platforms, and highlight how this platform and associated initiatives contribute to improved genomic data sharing efforts. The AnVIL is a federated cloud platform designed to manage and store genomics and related data, enable population-scale analysis, and facilitate collaboration through the sharing of data, code, and analysis results. By inverting the traditional model of data sharing, the AnVIL eliminates the need for data movement while also adding security measures for active threat detection and monitoring and provides scalable, shared computing resources for any researcher. We describe the core data management and analysis components of the AnVIL, which currently consists of Terra, Gen3, Galaxy, RStudio/Bioconductor, Dockstore, and Jupyter, and describe several flagship genomics datasets available within the AnVIL. We continue to extend and innovate the AnVIL ecosystem by implementing new capabilities, including mechanisms for interoperability and responsible data sharing, while streamlining access management. The AnVIL opens many new opportunities for analysis, collaboration, and data sharing that are needed to drive research and to make discoveries through the joint analysis of hundreds of thousands to millions of genomes along with associated clinical and molecular data types.

The data-intensive fields of genomics and machine learning (ML) are in an early stage of convergence. Genomics researchers increasingly seek to harness the power of ML methods to extract knowledge from their data; conversely, ML scientists recognize that genomics offers a wealth of large, complex, and well-annotated datasets that can be used as a substrate for developing biologically relevant algorithms and applications. The National Human Genome Research Institute (NHGRI) inquired with researchers working in these two fields to identify common challenges and receive recommendations to better support genomic research efforts using ML approaches. Those included increasing the amount and variety of training datasets by integrating genomic with multiomics, context-specific (e.g., by cell type), and social determinants of health datasets; reducing the inherent biases of training datasets; prioritizing transparency and interpretability of ML methods; and developing privacy-preserving technologies for research participants’ data.

Post-traumatic osteoarthritis (PTOA) associated with joint injury triggers a degenerative cycle of matrix destruction and inflammatory signaling, leading to pain and loss of function. Here, prolonged RNA interference (RNAi) of matrix metalloproteinase 13 (MMP13) is tested as a PTOA disease modifying therapy. MMP13 is upregulated in PTOA and degrades the key cartilage structural protein type II collagen. Short interfering RNA (siRNA) loaded nanoparticles (siNPs) were encapsulated in shape-defined poly(lactic-co-glycolic acid) (PLGA) based microPlates (μPLs) to formulate siNP-μPLs that maintained siNPs in the joint significantly longer than delivery of free siNPs. Treatment with siNP-μPLs against MMP13 (siMMP13-μPLs) in a mechanical load-induced mouse model of PTOA maintained potent (65-75%) MMP13 gene expression knockdown and reduced MMP13 protein production in joint tissues throughout a 28-day study. MMP13 silencing reduced PTOA articular cartilage degradation/fibrillation, meniscal deterioration, synovial hyperplasia, osteophytes, and pro-inflammatory gene expression, supporting the therapeutic potential of long-lasting siMMP13-μPL therapy for PTOA.

Neuroblastoma is a biologically heterogeneous extracranial tumor, derived from the sympathetic nervous system, that affects most often the pediatric population. Therapeutic strategies relying on aggressive chemotherapy, surgery, radiotherapy, and immunotherapy have a negative outcome in advanced or recurrent disease. Here, spherical polymeric nanomedicines (SPN) are engineered to co-deliver a potent combination therapy, including the cytotoxic docetaxel (DTXL) and the natural wide-spectrum anti-inflammatory curcumin (CURC). Using an oil-in-water emulsion/solvent evaporation technique, four SPN configurations were engineered depending on the therapeutic payload and characterized for their physico-chemical and pharmacological properties. All SPN configurations presented a hydrodynamic diameter of ∼ 185 nm with a narrow size distribution. A biphasic release profile was observed for all the configurations, with almost 90 % of the total drug mass released within the first 24 h. SPN cytotoxic potential was assessed on a panel of human neuroblastoma cells, returning IC50 values in the order of 1 nM at 72 h and documenting a strong synergism between CURC and DTXL. Therapeutic efficacy was tested in a clinically relevant orthotopic model of neuroblastoma, following the injection of SH-SY5Y-Luc+ cells in the left adrenal gland of athymic mice. Although ∼ 2 % of the injected SPN per mass tissue reached the tumor, the overall survival of mice treated with CURC/DTXL-SPN was extended by 50 % and 25 % as compared to the untreated control and the monotherapies, respectively. In conclusion, these results demonstrate that the therapeutic potential of the DTXL/CURC combination can be fully exploited only by reformulating these two compounds into systemically injectable nanoparticles.

Designing effective and accurate tools for identifying the functional and structural elements in a genome remains at the frontier of genome annotation owing to incompleteness and inaccuracy of the data, limitations in the computational models, and shifting paradigms in genomics, such as alternative splicing. We present a methodology for the automated annotation of genes and their alternatively spliced mRNA transcripts based on existing cDNA and protein sequence evidence from the same species or projected from a related species using syntenic mapping information. At the core of the method is the splice graph, a compact representation of a gene, its exons, introns, and alternatively spliced isoforms. The putative transcripts are enumerated from the graph and assigned confidence scores based on the strength of sequence evidence, and a subset of the high-scoring candidates are selected and promoted into the annotation. The method is highly selective, eliminating the unlikely candidates while retaining 98% of the high-quality mRNA evidence in well-formed transcripts, and produces annotation that is measurably more accurate than some evidence-based gene sets. The process is fast, accurate, and fully automated, and combines the traditionally distinct gene annotation and alternative splicing detection processes in a comprehensive and systematic way, thus considerably aiding in the ensuing manual curation efforts.

The National Institute of Allergy and Infectious Diseases (NIAID) began a new bioinformatic venture in July 2004 intended to integrate the vast amount of genomic and other biological data that are both available and being produced by the rapid increase in biodefense research. Eight Bioinformatics

High throughput sequencing has accelerated the determination of genome sequences for thousands of human infectious disease pathogens and dozens of their vectors. The scale and scope of these data are enabling genotype-phenotype association studies to identify genetic determinants of pathogen virulence and drug/insecticide resistance, and phylogenetic studies to track the origin and spread of disease outbreaks. To maximize the utility of genomic sequences for these purposes, it is essential that metadata about the pathogen/vector isolate characteristics be collected and made available in organized, clear, and consistent formats. Here we report the development of the GSCID/BRC Project and Sample Application Standard, developed by representatives of the Genome Sequencing Centers for Infectious Diseases (GSCIDs), the Bioinformatics Resource Centers (BRCs) for Infectious Diseases, and the U.S. National Institute of Allergy and Infectious Diseases (NIAID), part of the National Institutes of Health (NIH), informed by interactions with numerous collaborating scientists. It includes mapping to terms from other data standards initiatives, including the Genomic Standards Consortium's minimal information (MIxS) and NCBI's BioSample/BioProjects checklists and the Ontology for Biomedical Investigations (OBI). The standard includes data fields about characteristics of the organism or environmental source of the specimen, spatial-temporal information about the specimen isolation event, phenotypic characteristics of the pathogen/vector isolated, and project leadership and support. By modeling metadata fields into an ontology-based semantic framework and reusing existing ontologies and minimum information checklists, the application standard can be extended to support additional project-specific data fields and integrated with other data represented with comparable standards. The use of this metadata standard by all ongoing and future GSCID sequencing projects will provide a consistent representation of these data in the BRC resources and other repositories that leverage these data, allowing investigators to identify relevant genomic sequences and perform comparative genomics analyses that are both statistically meaningful and biologically relevant.

Over the years, nanoparticles, microparticles, implants of poly(D,l-lactide-co-glycolide) (PLGA) have been demonstrated for diverse biomedical applications. Yet, initial burst release and optimal modulation of the release profiles limit their clinical use. Here, shape-defined PLGA microPlates (μPLs) were realized for the sustained release of two anti-inflammatory molecules, the natural polyphenol curcumin (CURC) and the corticosteroid dexamethasone (DEX). Under the electron microscope, μPLs appeared as square prisms with an edge length of 20 μm. The top-down fabrication process allowed the authors to vary, readily and systematically, the μPL height from 5 to 10 μm and the PLGA mass from 1 to 5, 10 and 20 mg. ‘Taller’ particles realized with higher PLGA concentrations encapsulated more drug reaching on average values of about 150 pg/μPL, for both CURC and DEX. The μPL height and PLGA concentration had major effects on drug release, too. Under sink conditions, DEX release from tall μPLs at 1 h reduced from 50% to 10% and 2% for the 5, 10 and 20 mg PLGA configurations, respectively. Also, DEX was released more slowly from taller as compared to short μPLs. The opposite trend was observed for CURC, possibly for its lower hydrophobicity and molecular weight as compared to DEX. This was also confirmed by quantifying the free energy of translocation for the two drugs via molecular dynamics simulations. Finally, the anti-inflammatory activity of μPLs was tested in vitro on LPS-stimulated rat monocytes and in vivo on a murine model of UVB-induced skin burns. Both in vitro and in vivo, the expression of pro-inflammatory cytokines (IL-6, IL-1β, and TNF-α) was significantly reduced by the application of μPLs as compared to the free compounds. In vivo, one single topical deposition of CURC-μPLs outperformed multiple, free CURC applications. This work demonstrates that geometry and polymer density can be effectively used to modulate the pharmacological performance of microparticles and mitigate the initial burst release.

Osteoarthritis (OA) is treated with the intra-articular injection of steroids such as dexamethasone (DEX) to provide short-term pain management. However, DEX treatment suffers from rapid joint clearance. Here, 20 × 10 μm, shape-defined poly(d,l-lactide-co-glycolide)acid microPlates (μPLs) are created and intra-articularly deposited for the sustained release of DEX. Under confined conditions, DEX release is projected to persist for several months, with only ∼20% released in the first month. In a highly rigorous murine knee overload injury model (post-traumatic osteoarthritis), a single intra-articular injection of Cy5-μPLs is detected in the cartilage surface, infrapatellar fat pad/synovium, joint capsule, and posterior joint space up to 30 days. One intra-articular injection of DEX-μPL (1 mg kg–1) decreased the expression of interleukin (IL)-1β, tumor necrosis factor (TNF)-α, IL-6, and matrix metalloproteinase (MMP)-13 by approximately half compared to free DEX at 4 weeks post-treatment. DEX-μPL also reduced load-induced histological changes in the articular cartilage and synovial tissues relative to saline or free DEX. In sum, the μPLs provide sustained drug release along with the capability to precisely control particle geometry and mechanical properties, yielding long-lasting benefits in overload-induced OA. This work motivates further study and development of particles that provide combined pharmacological and mechanical benefits.

Abstract Most recent protein secondary structure prediction methods use sequence alignments to improve the prediction quality. We investigate the relationship between the location of secondary structural elements, gaps, and variable residue positions in multiple sequence alignments. We further investigate how these relationships compare with those found in structurally aligned protein families. We show how such associations may be used to improve the quality of prediction of the secondary structure elements, using the Quadratic‐Logistic method with profiles. Furthermore, we analyze the extent to which the number of homologous sequences influences the quality of prediction. The analysis of variable residue positions shows that surprisingly, helical regions exhibit greater variability than do coil regions, which are generally thought to be the most common secondary structure elements in loops. However, the correlation between variability and the presence of helices does not significantly improve prediction quality. Gaps are a distinct signal for coil regions. Increasing the coil propensity for those residues occurring in gap regions enhances the overall prediction quality. Prediction accuracy increases initially with the number of homologues, but changes negligibly as the number of homologues exceeds about 14. The alignment quality affects the prediction more than other factors, hence a careful selection and alignment of even a small number of homologues can lead to significant improvements in prediction accuracy.

Macrophage inflammation and maturation into foam cells, following the engulfment of oxidized low-density lipoproteins (oxLDL), are major hallmarks in the onset and progression of atherosclerosis. Yet, chronic treatments with anti-inflammatory agents, such as methotrexate (MTX), failed to modulate disease progression, possibly for the limited drug bioavailability and plaque deposition. Here, MTX–lipid conjugates, based on 1,2-distearoyl-sn-glycero-3-phosphoethanolamine (DSPE), were integrated in the structure of spherical polymeric nanoparticles (MTX-SPNs) or intercalated in the lipid bilayer of liposomes (MTX-LIP). Although, both nanoparticles were colloidally stable with an average diameter of ∼200 nm, MTX-LIP exhibited a higher encapsulation efficiency (>70%) and slower release rate (∼50% at 10 h) compared to MTX-SPN. In primary bone marrow derived macrophages (BMDMs), MTX-LIP modulated the transcellular transport of oxLDL more efficiently than free MTX mostly by inducing a 2-fold overexpression of ABCA1 (regulating oxLDL efflux), while the effect on CD36 and SRA-1 (regulating oxLDL influx) was minimal. Furthermore, in BMDMs, MTX-LIP showed a stronger anti-inflammatory activity than free MTX, reducing the expression of IL-1β by 3-fold, IL-6 by 2-fold, and also moderately of TNF-α. In 28 days high-fat-diet-fed apoE–/– mice, MTX-LIP reduced the mean plaque area by 2-fold and the hematic amounts of RANTES by half as compared to free MTX. These results would suggest that the nanoenhanced delivery to vascular plaques of the anti-inflammatory DSPE-MTX conjugate could effectively modulate the disease progression by halting monocytes' maturation and recruitment already at the onset of atherosclerosis.

In the tumor microenvironment, mesenchymal stromal cells (MSCs) are key modulators of cancer cell behavior in response to several stimuli. Intratumoral acidosis is a metabolic trait of fast-growing tumors that can induce a pro-tumorigenic phenotype in MSCs through the activation of the NF-κB-mediated inflammatory pathway, driving tumor clonogenicity, invasion, and chemoresistance. Recent studies have indicated that curcumin, a natural ingredient extracted from Curcuma longa, acts as an NF-κB inhibitor with anti-inflammatory properties. In this work, highly proliferating osteosarcoma cells were used to study the ability of curcumin to reduce the supportive effect of MSCs when stimulated by acidosis. Due to the poor solubility of curcumin in biological fluids, we used spherical polymeric nanoparticles as carriers (SPN-curc) to optimize its uptake by MSCs. We showed that SPN-curc inhibited the release of inflammatory cytokines (IL6 and IL8) by acidity-stimulated MSCs at a higher extent than by free curcumin. SPN-curc treatment was also successful in blocking tumor stemness, migration, and invasion that were driven by the secretome of acid-stressed MSCs. Overall, these data encourage the use of lipid–polymeric nanoparticles encapsulating NF-κB inhibitors such as curcumin to treat cancers whose progression is stimulated by an activated mesenchymal stroma.

The three-dimensional fold of a protein is described by the organization of its secondary structure elements in 3D space, i.e. its “topology”. We find that the protein topology can be recognized from the 1D sequence of secondary structure states of the residues alone. Automated recognition is facilitated by use of hidden Markov models (HMMs) to represent topology families of proteins. Such models can be trained on the experimentally observed secondary structure sequences of family members using well established algorithms. Here, we model various topology groups in the alpha class of proteins and identify, from a large database, those proteins having the topology described by each model. The correct topology family for protein secondary structure sequences could be recognized 12 out of 14 times. When the observed secondary structure sequences are replaced with predicted sequences recognitiion is still achievable 8 out of 14 times. The success rate for observed sequences indicates that our approach will become increasingly useful as the accuracy of secondary prediction algorithms is improved. Our study indicates that the HMMs are useful for protein topology recognition even when no detectable primary amino acid sequence similarity is present. To illustrate the potential utility of our method, protein topology recognition is attempted on leptin, the obese gene product, and the human interleukin-6 sequence, for which fold predictions have been previously published.

Fine-tuning loading and release of therapeutic and imaging agents associated with polymeric matrices is a fundamental step in the preclinical development of novel nanomedicines. Here, 1,000  400 nm Discoidal Polymeric Nanoconstructs (DPNs) were realized via a top-down, template-based fabrication approach, mixing together poly(lactic-co-glycolic acid) (PLGA) and poly(ethylene glycol)-diacrylate (PEG-DA) chains in a single polymer paste. Two different loading strategies were tested, namely the “direct loading” and the “absorption loading”. In the first case, the agent was directly mixed with the polymeric paste to realize DPNs whereas, in the second case, DPNs were first lyophilized and then rehydrated upon exposure to a concentrated aqueous solution of the agent. Under these two loading conditions, the encapsulation efficiencies and release profiles of different agents were systematically assessed. Specifically, six agents were realized by conjugating lipid chains (DSPE) or polymeric chains (PEG) to the near-infrared imaging molecule Cy5.5 (DSPE-Cy5 A and DSPE-Cy5 B); the chemotherapeutic molecules methotrexate (DSPE-MTX and PEG-MTX) and doxorubicin (LA-DOX and DSPE-DOX). Moderately hydrophobic compounds with low molecular weights (MW) returned encapsulation efficiencies as high as 80% for the absorption loading. In general, direct loading was associated with encapsulation efficiencies lower than 1%. The agent hydrophobicity and MW were shown to be critical also in tailoring the release profiles from DPNs. On triple-negative breast cancer cells (MDA-MB-231), absorption loaded DOX–DPNs showed cytotoxic activities comparable to free DOX but slightly delayed in time. Preliminary in vivo studies demonstrated the high stability of Cy5-DPNs. Collectively, these results demonstrate that the pharmacological properties of DPNs can be finely optimized by changing the loading strategies (direct vs absorption) and compound attributes (hydrophobicity and molecular weight).

Theranostics, literally derived from the combination of the words diagnostics and therapy, is an emerging field of clinical and preclinical research, where contrast agents, drugs and diagnostic techniques are combined to simultaneously diagnose and treat pathologies. Nanoparticles are extensively employed in theranostics due to their potential to target specific organs and their multifunctional capacity. In this review, we will discuss the current state of theranostic nanomedicine, providing key examples of its application in the imaging and treatment of cardiovascular inflammation.

Introduction Immunotherapy with biologics targeting programmed cell death protein-1 (PD-1) is highly effective in the treatment of various malignancies. Nevertheless, it is frequently responsible for unexpected cutaneous manifestations, including psoriasis-like dermatitis. The pathogenesis of anti-PD-1-induced psoriasis has yet to be clarified, even though it is plausible that some innate and adaptive immunity processes are in common with canonical psoriasis. The genetic predisposition to psoriasis of patients could also be a contributing factor. Here, we investigated the immunological and genetic profiles of two patients with metastatic melanoma and one patient affected by lung cancer, who developed severe psoriasis after receiving anti-PD-1 nivolumab therapy. Methods The immune patterns of the three patients were compared with those detectable in classical, chronic plaque-type psoriasis or paradoxical psoriasis induced by anti-TNF-α therapy, mostly sustained by adaptive and innate immunity processes, respectively. Therefore, immunohistochemistry and mRNA analyses of innate and adaptive immunity molecules were conducted on skin biopsy of patients. Genetic analysis of polymorphisms predisposing to psoriasis was carried out by NGS technology. Results We found that anti-PD-1-induced psoriasis showed immunological features similar to chronic psoriasis, characterized by the presence of cellular players of adaptive immunity, with abundant CD3 + , CD8 + T cells and CD11c + dendritic cells infiltrating skin lesions, and producing IL-23, IL-6, TNF-α, IFN-γ and IL-17. On the contrary, a lower number of innate immunity cells (BDCA2 + plasmacytoid dendritic cells, CD15 + neutrophils, CD117 + mast cells) and reduced IFN-α/β, lymphotoxin (LT)-α/β, were observed in anti-PD-1-induced psoriasis lesions, as compared with anti-TNF-α-induced paradoxical psoriasis. Importantly, the disintegrin and metalloprotease domain containing thrombospondin type 1 motif-like 5 (ADAMTSL5) psoriasis autoantigen was significantly upregulated in psoriasis lesions of anti-PD-1-treated patients, at levels comparable with chronic plaque-type psoriasis. Finally, NGS analysis revealed that all patients carried several allelic variants in psoriasis susceptibility genes, such as HLA-C , ERAP1 and other genes of the major psoriasis susceptibility PSORS1 locus. Discussion Our study showed that adaptive immunity predominates over innate immunity in anti-PD-1-induced psoriasis lesions, consistently with the local ADAMTSL5 overexpression. The presence of numerous SNPs in psoriasis susceptibility genes of the three patients also suggested their strong predisposition to the disease.

A method for recognizing the three-dimensional fold from the protein amino acid sequence based on a combination of hidden Markov models (HMMs) and secondary structure prediction was recently developed for proteins in the Mainly-Alpha structural class. Here, this methodology is extended to Mainly-Beta and Alpha-Beta class proteins. Compared to other fold recognition methods based on HMMs, this approach is novel in that only secondary structure information is used. Each HMM is trained from known secondary structure sequences of proteins having a similar fold. Secondary structure prediction is performed for the amino acid sequence of a query protein. The predicted fold of a query protein is the fold described by the model fitting the predicted sequence the best.After model cross-validation, the success rate on 44 test proteins covering the three structural classes was found to be 59%. On seven fold predictions performed prior to the publication of experimental structure, the success rate was 71%. In conclusion, this approach manages to capture important information about the fold of a protein embedded in the length and arrangement of the predicted helices, strands and coils along the polypeptide chain. When a more extensive library of HMMs representing the universe of known structural families is available (work in progress), the program will allow rapid screening of genomic databases and sequence annotation when fold similarity is not detectable from the amino acid sequence.FORESST web server at http://absalpha.dcrt.nih.gov:8008/ for the library of HMMs of structural families used in this paper. FORESST web server at http://www.tigr.org/ for a more extensive library of HMMs (work in progress).valedf@tigr.org; munson@helix.nih.gov; garnier@helix.nih.gov

The association of machine learning (ML) tools with the synthesis of nanoparticles has the potential to streamline the development of more efficient and effective nanomedicines. The continuous-flow synthesis of nanoparticles via microfluidics represents an ideal playground for ML tools, where multiple engineering parameters - flow rates and mixing configurations, type and concentrations of the reagents - contribute in a non-trivial fashion to determine the resultant morphological and pharmacological attributes of nanomedicines. Here we present the application of ML models towards the microfluidic-based synthesis of liposomes loaded with a model hydrophobic therapeutic agent, curcumin. After generating over 200 different liposome configurations by systematically modulating flow rates, lipid concentrations, organic:water mixing volume ratios, support-vector machine models and feed-forward artificial neural networks were trained to predict, respectively, the liposome dispersity/stability and size. This work presents an initial step towards the application and cultivation of ML models to instruct the microfluidic formulation of nanoparticles.

Neurological disorders are a diverse group of conditions that pose an increasing health burden worldwide. There is a general lack of effective therapies due to multiple reasons, of which a key obstacle is the presence of the blood-brain barrier, which limits drug delivery to the central nervous system, and generally restricts the pool of candidate drugs to small, lipophilic molecules. However, in many cases, these are unable to target key pathways in the pathogenesis of neurological disorders. As a group, RNA therapies have shown tremendous promise in treating various conditions because they offer unique opportunities for specific targeting by leveraging Watson-Crick base pairing systems, opening up possibilities to modulate pathological mechanisms that previously could not be addressed by small molecules or antibody-protein interactions. This potential paradigm shift in disease management has been enabled by recent advances in synthesizing, purifying, and delivering RNA. This review explores the use of RNA-based therapies specifically for central nervous system disorders, where we highlight the inherent limitations of RNA therapy and present strategies to augment the effectiveness of RNA therapeutics, including physical, chemical, and biological methods. We then describe translational challenges to the widespread use of RNA therapies and close with a consideration of future prospects in this field.

Methyl Palmitate Nanoparticles (MPN) boost the tumor accumulation and chemotherapeutic efficacy of nanomedicines by transiently and reversibly inhibiting the phagocytic properties of tissue-resident macrophages.

On November 18-19, 2016, the Human Frontier Science Program Organization (HFSPO) hosted a meeting of senior managers of key data resources and leaders of several major funding organizations to discuss the challenges associated with sustaining biological and biomedical (i.e., life sciences) data resources and associated infrastructure. A strong consensus emerged from the group that core data resources for the life sciences should be supported through a coordinated international effort(s) that better ensure long-term sustainability and that appropriately align funding with scientific impact. Ideally, funding for such data resources should allow for access at no charge, as is presently the usual (and preferred) mechanism. Below, the rationale for this vision is described, and some important considerations for developing a new international funding model to support core data resources for the life sciences are presented.

We present an analysis of the blind predictions submitted to the fold recognition category for the second meeting on the Critical Assessment of techniques for protein Structure Prediction. Our method achieves fold recognition from predicted secondary structure sequences using hidden Markov models (HMMs) of protein folds. HMMs are trained only with experimentally derived secondary structure sequences of proteins having similar fold, therefore protein structures are described by the models at a remarkably simplified level. We submitted predictions for five target sequences, of which four were later found to be suitable for threading. Our approach correctly predicted the fold for three of them. For a fourth sequence the fold could have been correctly predicted if a better model for its structure was available. We conclude that we have additional evidence that secondary structure information represents an important factor for achieving fold recognition. Proteins, Suppl. 1:123–128, 1997. Published 1998 Wiley-Liss, Inc.1

Polymeric anisotropic soft microparticles show interesting behavior in biological environments and hold promise for drug delivery and biomedical applications. However, self-assembly and substrate-based lithographic techniques are limited by low resolution, batch operation or specific particle geometry and deformability. Two-photon polymerization in microfluidic channels may offer the required resolution to continuously fabricate anisotropic micro-hydrogels in sub-10 µm size-range.Here, a pulsed laser source is used to perform two-photon polymerization under microfluidic flow of a poly(ethylene glycol) diacrylate (PEGDA) solution with the objective of realizing anisotropic micro-hydrogels carrying payloads of various nature, including small molecules and nanoparticles. The fabrication process is described via a reactive-convective-diffusion system of equations, whose solution under proper auxiliary conditions is used to corroborate the experimental observations and sample the configuration space.By tuning the flow velocity, exposure time and pre-polymer composition, anisotropic PEGDA micro-hydrogels are obtained in the 1-10 μm size-range and exhibit an aspect ratio varying from 1 to 5. Furthermore, 200 nm curcumin-loaded poly(lactic-co-glycolic acid) (PLGA) nanoparticles and 100 nm ssRNA-encapsulating lipid nanoparticles were entrapped within square PEGDA micro-hydrogels. The proposed approach could support the fabrication of micro-hydrogels of well-defined morphology, stiffness, and surface properties for the sustained release of therapeutic agents.

A bstract Posttraumatic osteoarthritis (PTOA) is mostly treated via corticosteroid administration, and total joint arthroplasty continues to be the sole effective intervention in severe conditions. To assess the therapeutic potential of CCR2 targeting in PTOA, we used biodegradable microplates (µPLs) to achieve a slow and sustained intraarticular release of the CCR2 inhibitor RS504393 into injured knees and followed joint damage during disease progression. RS504393 - loaded µPLs (RS-µPLs) were fabricated via a template-replica molding technique. A mixture of poly(lactic-co-glycolic acid) (PLGA) and RS504393 was deposited into 20 × 10 μm (length × height) wells in a polyvinyl alcohol (PVA) square-patterned template. After physicochemical and toxicological characterizations, the RS504393 release profile from µPL was assessed in PBS buffer. C57BL/6 J male mice were subjected to destabilization of the medial meniscus (DMM)/sham surgery, and RS-µPLs (1 mg/kg) were administered intraarticularly 1 week postsurgery. Administrations were repeated at 4 and 7 weeks post-DMM. Drug free-µPLs (DF-µPLs) and saline injections were performed as controls. Mice were euthanized at 4 and 10 weeks post-DMM, corresponding to the early and severe PTOA stages, respectively. Knees were evaluated for cartilage structure score (ACS, H&E), matrix loss (safranin O score), osteophyte formation and maturation from cartilage to bone (cartilage quantification), and subchondral plate thickness. The RS-µPL architecture ensured the sustained release of CCR2 inhibitors over several weeks, with ~ 20% of RS504393 still available at 21 days. This prolonged release improved cartilage structure and reduced bone damage and synovial hyperplasia at both PTOA stages. Extracellular matrix loss was also attenuated, although with less efficacy. The results indicate that local sustained delivery is needed to optimize CCR2-targeted therapies. Graphical abstract

Methotrexate (MTX), a compound originally used as an anticancer drug, has also found applications in a broad variety of autoimmune disorders thanks to its anti-inflammation and immunomodulatory functions. The broad application of MTX is anyway limited by its poor solubility in biological fluids, its poor bioavailability and its toxicity. In addition, encapsulating its original form in nanoformulation is very arduous due to its considerable hydrophobicity. In this work, two strategies to efficiently encapsulate MTX into liposomal particles are proposed to overcome the limitations mentioned above and to improve MTX bioavailability. MTX solubility was increased by conjugating the molecule to two different compounds: DSPE and PEG. These two compounds commonly enrich liposome formulations, and their encapsulation efficiency is very high. By using these two prodrugs (DSPE-MTX and PEG-MTX), we were able to generate liposomes comprising one or both of them and characterized their physiochemical features and their toxicity in primary macrophages. These formulations represent an initial step to the development of targeted liposomes or particles, which can be tailored for the specific application MTX is used for (cancer, autoimmune disease or others).

We have compared a novel sequence-structure matching technique, FORESST, for detecting remote homologs to three existing sequence based methods, including local amino acid sequence similarity by BLASTP, hidden Markov models (HMMs) of sequences of protein families using SAM, HMMs based on sequence motifs identified using meta-MEME. FORESST compares predicted secondary structures to a library of structural families of proteins, using HMMs. Altogether 45 proteins from nine structural families in the database CATH were used in a cross-validated test of the fold assignment accuracy of each method. Local sequence similarity of a query sequence to a protein family is measured by the highest segment pair (HSP) score. Each of the HMM-based approaches (FORESST, MEME, amino acid sequence-based HMM) yielded log-odds score for the query sequence. In order to make a fair comparison among these methods, the scores for each method were converted to Z-scores in a uniform way by comparing the raw scores of a query protein with the corresponding scores for a set of unrelated proteins. Z-Scores were analyzed as a function of the maximum pairwise sequence identity (MPSID) of the query sequence to sequences used in training the model. For MPSID above 20%, the Z-scores increase linearly with MPSID for the sequence-based methods but remain roughly constant for FORESST. Below 15%, average Z-scores are close to zero for the sequence-based methods, whereas the FORESST method yielded average Z-scores of 1.8 and 1.1, using observed and predicted secondary structures, respectively. This demonstrates the advantage of the sequence-structure method for detecting remote homologs.

Inflammation is a key hallmark in atherosclerosis initiation, progression, and thrombotic manifestations. Several studies have used potent anti-inflammatory drugs, such as methotrexate (MTX) and colchicine (COL), for the treatment of atherosclerosis returning only modest improvements. This should be mostly ascribed to the complexity of the disease and the poor bioavailability and tissue specificity of the freely administered anti-inflammatory drugs. Hence, to overcome these limitations, the current work explores the efficacy of a combination therapy resulting from the co-encapsulation of MTX and COL into liposomes. MTX-lipid conjugate of 1,2-distearoyl-sn-glycero-3-phosphoethanolamine (DSPE) were first synthesized and integrated into the lipid bilayer of liposomes containing aqueous cores enriched with COL. These combination liposomes (Combo-LIP) were realized using an automated microfluidic system and exhibited an average size of 100 nm with long colloidal stability. Encapsulation efficiencies as high as ∼30% for MTX and COL combined were documented at a 16:1 drug ratio. In primary bone marrow derived monocytes (BMDM), Combo-LIP significantly ameliorated LPS-induced inflammation with up to a 3-fold reduction in the expression of IL-1β and IL-6. Similar results were also documented for oxidized low-density lipoprotein (oxLDL) – induced inflammation. Furthermore, Combo-LIP supported the overexpression of ABCA1 and downregulation of CD36 and SRA-1, reducing the overall accumulation of oxLDL into macrophages. Collectively these preliminary results vividly suggest that the liposomal reformulation of MTX and COL could mitigate cell inflammation and possibly halt the progression of atherosclerosis.

Abstract The association of machine learning (ML) tools with the synthesis of nanoparticles has the potential to streamline the development of more efficient and effective nanomedicines. The continuous-flow synthesis of nanoparticles via microfluidics represents an ideal playground for ML tools, where multiple engineering parameters – flow rates and mixing configurations, type and concentrations of the reagents – contribute in a non-trivial fashion to determine the resultant morphological and pharmacological attributes of nanomedicines. Here we present the application of ML models towards the microfluidic-based synthesis of liposomes loaded with a model hydrophobic therapeutic agent, curcumin. After generating over 200 different liposome configurations by systematically modulating flow rates, lipid concentrations, organic:water mixing volume ratios, support-vector machine models and feed-forward artificial neural networks were trained to predict, respectively, the liposome dispersity/stability and size. This work presents an initial step towards the application and cultivation of ML models to instruct the microfluidic formulation of nanoparticles.

Chronic rhinosinusitis (CRS) is a chronic inflammatory disease of the paranasal sinuses which represents a significant health burden due to its widespread prevalence and impact on patients’ quality of life. As the molecular pathways driving and sustaining inflammation in CRS become better elucidated, the diversity of treatment options is likely to widen significantly. Nanotechnology offers several tools to enhance the effectiveness of topical therapies, which has been limited by factors such as poor drug retention, mucosal permeation and adhesion, removal by epithelial efflux pumps and the inability to effectively penetrate biofilms. In this review, we highlight the successful application of nanomedicine in the field of CRS therapeutics, discuss current limitations and propose opportunities for future work.

We present an analysis of the blind predictions submitted to the fold recognition category for the second meeting on the Critical Assessment of techniques for protein Structure Prediction. Our method achieves fold recognition from predicted secondary structure sequences using hidden Markov models (HMMs) of protein folds. HMMs are trained only with experimentally derived secondary structure sequences of proteins having similar fold, therefore protein structures are described by the models at a remarkably simplified level. We submitted predictions for five target sequences, of which four were later found to be suitable for threading. Our approach correctly predicted the fold for three of them. For a fourth sequence the fold could have been correctly predicted if a better model for its structure was available. We conclude that we have additional evidence that secondary structure information represents an important factor for achieving fold recognition.

Recent improvements in automated sequencing techniques have produced more than 20 completely sequenced genomes from organisms belonging to every kingdom of life. Sequencing efforts in these organisms provide valuable, abundant data about their functional complexity and genetic organization.

Here we propose an approach to include global structural information in the secondary structure prediction procedure based on hidden Markov models (HMMs) of protein folds. We first identify the correct fold or 'topology' of a protein by means of the HMMs of topology families of proteins. Then the most likely structural model for that protein is used to modify the sequence of secondary structure states previously obtained with a prediction algorithm. Our goal is to investigate the effect on the prediction accuracy of including global structural information in the secondary structure prediction scheme, by means of the HMMs. We find that when the HMM of the predicted topology of a protein is used to adjust the secondary structure sequence predicted originally with the Quadratic-Logistic method, the cross-validated prediction accuracy (Q3) improves by 3%. The topology is correctly predicted in 68% of the cases. We conclude that this HMM based approach is a promising tool for effectively incorporating global structural information in the secondary structure prediction scheme.

Using a new database of 20 proteins not included in any of the previously used training datasets, we have incorporated multiple alignment information from homologous proteins into two well-characterized prediction methods: COMBINE (a jury method) and the Q-L (or quadratic-logistic) method. It is found that the increase in accuracy from the use of related proteins is similar for both methods (5.8% and 6.3%, respectively) yielding a per residue prediction accuracy (Q3) of 68.7% and 69.0%, respectively, for a three state prediction. Most of the improvement came from consideration of averaging, profiling or consensus predictions. Of this improvement, a small amount (0.5%) came from recognition that "gap-permissive" positions in the alignment are most frequently in the coil state. Our finding is consistent with the hypothesis of a common secondary structure for the aligned family, and that improved accuracy is due to reduced noise in the prediction.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Abstract Post traumatic osteoarthritis (PTOA) is mostly treated via corticosteroids administration and total joint arthroplasty continues to be the sole effective intervention in severe conditions. To assess the therapeutic potential of CCR2 targeting in PTOA, we used biodegradable microPlates (µPL) to achieve a slow and sustained intra-articular release of the CCR2 inhibitor RS504393 into injured knees and followed joint damage during disease progression. RS504393 - loaded µPL (RS-µPL) were fabricated via a template-replica molding technique. A mixture of poly(lactic-co-glycolic acid) (PLGA) and RS504393 was deposited into 20´10 μm (length´height) wells realized in a polyvinyl alcohol (PVA) square-patterned template. After physicochemical and toxicological characterizations, the RS504393 release profile from µPL was assessed in PBS buffer. C57BL/6J male mice were subjected to destabilization of the medial meniscus (DMM)/sham surgery and RS-µPL (1 mg/kg) were administered intra-articularly 1-week post-surgery. Administrations were repeated at 4- and 7-weeks post-DMM. Drug free-µPL (DF-µPL) and saline injections were performed as controls. Mice were euthanized at 4- and 10-weeks post-DMM (early and severe PTOA, respectively. Knees were evaluated for cartilage structure score (ACS, H&amp;E), matrix loss (Safranin-O score), osteophyte formation and maturation from cartilage to bone, (cartilage quantification) and subchondral plate thickness. The RS-µPL architecture ensured the sustained release of CCR2 inhibitors over several weeks, with ~20% of RS504393 being still available at 21-days. This prolonged release improved cartilage structure, reduced bone damage and synovial hyperplasia at both PTOA stages. Extracellular matrix loss was also attenuated, although with less efficacy. Results indicate that local delivery is needed to optimize CCR2-targeting therapies.

Abstract Introduction: Neuroblastoma (NB) is a form of extracranial tumor derived from the sympathetic nervous system that affects most often infants and young children. It is a very heterogeneous tumor with different levels of aggressiveness. Despite the multiple therapeutic strategies (i.e. aggressive chemotherapy, surgery, radiotherapy, immunotherapy), the outcome in advanced stages or recurrent diseases is negative. New strategies are needed to improve the therapeutic efficacy of existing drugs and reduce their toxicity. Nanotechnology represents a good tool for reaching this goal. Taken this in mind, the focus of this experimental work was to engineer polymeric biodegradable nanomedicines for co-delivering anti-inflammatory and chemotherapeutic molecules to NB malignant masses. More specifically, the work focused on the synthesis, physico-chemical and biopharmaceutical characterization, in vitro testing and in vivo validation of nanomedicines loaded with the cytotoxic drug Docetaxel (DTXL) and the natural anti-inflammatory compound, Curcumin (CURC). Methods: Four configurations of Spherical Polymeric Nanoparticles (SPNs) - loaded with CURC (CURC-SPNs), loaded with DTXL (DTXL-SPNs), loaded with the combination thereof (CURC/DTXL-SPNs), and empty (SPNs) - were synthesized using an oil-in water emulsion/solvent evaporation technique. SPNs size, zeta potential, and polydispersity index (PDI) were measured by dynamic light scattering. The toxicity of SPNs was determined by an MTT assay on the human NB cell line SH-SY5Y. For in vivo efficacy and biodistribution experiments, homozygous CD1 nu/nu athymic female mice (4 to 6-weeks old) were orthotopically injected with SH-SY5Y cells in the left adrenal gland. Results: Empty, DTXL-SPNs, CURC-SPNs, and CURC/DTXL-SPNs were characterized by a narrow size distribution (PdI &amp;lt; 0.15) with an average hydrodynamic diameter of about 185 nm. All the formulations showed a negative surface ζ-potential, associated with the carboxylate groups in the DSPE-PEG coating. A biphasic release profile was observed for all the 3 formulations, with almost 90% of the total drug mass released within the first 24 hours. In vivo results indicated that mice treated with CURC/DTXL -SPNs had a significant increase in life span as compared to untreated mice (control) (p=0.0002), mice treated with CURC-SPNs (p=0.0205), DTXL-SPNs (p=0.0391), and free DTXL (p=0.0054). Biodistribution experiments showed a 2% ID/g accumulation of the injected dose per tumor mass, regardless of the tumor development stage. This behavior is in agreement with results from a longitudinal Magnetic Resonance Imaging analysis of the malignant masses. Conclusion: The obtained results would suggest that nanomedicines could effectively delivery two therapeutic molecules within the malignant mass and modulate its progression leading to a significant increase in overall survival. Citation Format: Agnese Fragassi, Martina Di Francesco, Fabio Pastorino, Miguel Ferreira, Valentina Di Francesco, Anna Lisa Palange, Christian Celia, Luisa Di Marzio, Michele Cilli, Veronica Bensa, Mirco Ponzoni, Paolo Decuzzi. Delivering docetaxel and curcumin via a nano-combination-therapy for modulating the progression of neuroblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5069.