Jamison McCorrison

Legumes (Fabaceae or Leguminosae) are unique among cultivated plants for their ability to carry out endosymbiotic nitrogen fixation with rhizobial bacteria, a process that takes place in a specialized structure known as the nodule. Legumes belong to one of the two main groups of eurosids, the Fabidae, which includes most species capable of endosymbiotic nitrogen fixation. Legumes comprise several evolutionary lineages derived from a common ancestor 60 million years ago (Myr ago). Papilionoids are the largest clade, dating nearly to the origin of legumes and containing most cultivated species. Medicago truncatula is a long-established model for the study of legume biology. Here we describe the draft sequence of the M. truncatula euchromatin based on a recently completed BAC assembly supplemented with Illumina shotgun sequence, together capturing ∼94% of all M. truncatula genes. A whole-genome duplication (WGD) approximately 58 Myr ago had a major role in shaping the M. truncatula genome and thereby contributed to the evolution of endosymbiotic nitrogen fixation. Subsequent to the WGD, the M. truncatula genome experienced higher levels of rearrangement than two other sequenced legumes, Glycine max and Lotus japonicus. M. truncatula is a close relative of alfalfa (Medicago sativa), a widely cultivated crop with limited genomics tools and complex autotetraploid genetics. As such, the M. truncatula genome sequence provides significant opportunities to expand alfalfa's genomic toolbox.

News from the Inner Tube of Life A major initiative by the U.S. National Institutes of Health to sequence 900 genomes of microorganisms that live on the surfaces and orifices of the human body has established standardized protocols and methods for such large-scale reference sequencing. By combining previously accumulated data with new data, Nelson et al. (p. 994 ) present an initial analysis of 178 bacterial genomes. The sampling so far barely scratches the surface of the microbial diversity found on humans, but the work provides an important baseline for future analyses.

A protocol is described for sequencing the transcriptome of a cell nucleus. Nuclei are isolated from specimens and sorted by FACS, cDNA libraries are constructed and RNA-seq is performed, followed by data analysis. Some steps follow published methods (Smart-seq2 for cDNA synthesis and Nextera XT barcoded library preparation) and are not described in detail here. Previous single-cell approaches for RNA-seq from tissues include cell dissociation using protease treatment at 30 °C, which is known to alter the transcriptome. We isolate nuclei at 4 °C from tissue homogenates, which cause minimal damage. Nuclear transcriptomes can be obtained from postmortem human brain tissue stored at -80 °C, making brain archives accessible for RNA-seq from individual neurons. The method also allows investigation of biological features unique to nuclei, such as enrichment of certain transcripts and precursors of some noncoding RNAs. By following this procedure, it takes about 4 d to construct cDNA libraries that are ready for sequencing.

We describe convergent evidence from transcriptomics, morphology, and physiology for a specialized GABAergic neuron subtype in human cortex. Using unbiased single-nucleus RNA sequencing, we identify ten GABAergic interneuron subtypes with combinatorial gene signatures in human cortical layer 1 and characterize a group of human interneurons with anatomical features never described in rodents, having large ‘rosehip’-like axonal boutons and compact arborization. These rosehip cells show an immunohistochemical profile (GAD1+CCK+, CNR1–SST–CALB2–PVALB–) matching a single transcriptomically defined cell type whose specific molecular marker signature is not seen in mouse cortex. Rosehip cells in layer 1 make homotypic gap junctions, predominantly target apical dendritic shafts of layer 3 pyramidal neurons, and inhibit backpropagating pyramidal action potentials in microdomains of the dendritic tuft. These cells are therefore positioned for potent local control of distal dendritic computation in cortical pyramidal neurons. The authors use single-nucleus RNA-seq to identify 10 GABAergic interneuron subtypes in human cortex layer 1. Molecular, morphological, and physiological evidence points to an emerging human cell type, the rosehip cell, not found in other species.

von Economo neurons (VENs) are bipolar, spindle-shaped neurons restricted to layer 5 of human frontoinsula and anterior cingulate cortex that appear to be selectively vulnerable to neuropsychiatric and neurodegenerative diseases, although little is known about other VEN cellular phenotypes. Single nucleus RNA-sequencing of frontoinsula layer 5 identifies a transcriptomically-defined cell cluster that contained VENs, but also fork cells and a subset of pyramidal neurons. Cross-species alignment of this cell cluster with a well-annotated mouse classification shows strong homology to extratelencephalic (ET) excitatory neurons that project to subcerebral targets. This cluster also shows strong homology to a putative ET cluster in human temporal cortex, but with a strikingly specific regional signature. Together these results suggest that VENs are a regionally distinctive type of ET neuron. Additionally, we describe the first patch clamp recordings of VENs from neurosurgically-resected tissue that show distinctive intrinsic membrane properties relative to neighboring pyramidal neurons.

A fundamental characteristic of multicellular organisms is the specialization of functional cell types through the process of differentiation. These specialized cell types not only characterize the normal functioning of different organs and tissues, they can also be used as cellular biomarkers of a variety of different disease states and therapeutic/vaccine responses. In order to serve as a reference for cell type representation, the Cell Ontology has been developed to provide a standard nomenclature of defined cell types for comparative analysis and biomarker discovery. Historically, these cell types have been defined based on unique cellular shapes and structures, anatomic locations, and marker protein expression. However, we are now experiencing a revolution in cellular characterization resulting from the application of new high-throughput, high-content cytometry and sequencing technologies. The resulting explosion in the number of distinct cell types being identified is challenging the current paradigm for cell type definition in the Cell Ontology.In this paper, we provide examples of state-of-the-art cellular biomarker characterization using high-content cytometry and single cell RNA sequencing, and present strategies for standardized cell type representations based on the data outputs from these cutting-edge technologies, including "context annotations" in the form of standardized experiment metadata about the specimen source analyzed and marker genes that serve as the most useful features in machine learning-based cell type classification models. We also propose a statistical strategy for comparing new experiment data to these standardized cell type representations.The advent of high-throughput/high-content single cell technologies is leading to an explosion in the number of distinct cell types being identified. It will be critical for the bioinformatics community to develop and adopt data standard conventions that will be compatible with these new technologies and support the data representation needs of the research community. The proposals enumerated here will serve as a useful starting point to address these challenges.

While the microbiota resident in the human gut is now known to provide a range of functions relevant to host health, many of the microbial members of the community have not yet been cultured or are represented by a limited number of isolates. We describe here the draft genome sequence of Turicibacter sanguinis PC909, isolated from a pooled healthy human fecal sample as part of the Australian Human Gut Microbiome Project.

The use of smartphone apps to monitor and deliver health care guidance and interventions has received considerable attention recently, particularly with regard to behavioral disorders, stress relief, negative emotional state, and poor mood in general. Unfortunately, there is little research investigating the long-term and repeated effects of apps meant to impact mood and emotional state.We aimed to investigate the effects of both immediate point-of-intervention and long-term use (ie, at least 10 engagements) of a guided meditation and mindfulness smartphone app on users' emotional states. Data were collected from users of a mobile phone app developed by the company Stop, Breathe & Think (SBT) for achieving emotional wellness. To explore the long-term effects, we assessed changes in the users' basal emotional state before they completed an activity (eg, a guided meditation). We also assessed the immediate effects of the app on users' emotional states from preactivity to postactivity.The SBT app collects information on the emotional state of the user before and after engagement in one or several mediation and mindfulness activities. These activities are recommended and provided by the app based on user input. We considered data on over 120,000 users of the app who collectively engaged in over 5.5 million sessions with the app during an approximate 2-year period. We focused our analysis on users who had at least 10 engagements with the app over an average of 6 months. We explored the changes in the emotional well-being of individuals with different emotional states at the time of their initial engagement with the app using mixed-effects models. In the process, we compared 2 different methods of classifying emotional states: (1) an expert-defined a priori mood classification and (2) an empirically driven cluster-based classification.We found that among long-term users of the app, there was an association between the length of use and a positive change in basal emotional state (4% positive mood increase on a 2-point scale every 10 sessions). We also found that individuals who were anxious or depressed tended to have a favorable long-term emotional transition (eg, from a sad emotional state to a happier emotional state) after using the app for an extended period (the odds ratio for achieving a positive emotional state was 3.2 and 6.2 for anxious and depressed individuals, respectively, compared with users with fewer sessions).Our analyses provide evidence for an association between both immediate and long-term use of an app providing guided meditations and improvements in the emotional state.

Chronic inflammatory diseases, including diabetes and cardiovascular disease, are heterogeneous and often co-morbid, with increasing global prevalence. Uncontrolled type 2 diabetes (T2D) can result in severe inflammatory complications. As neutrophils are essential to normal and aberrant inflammation, we conducted RNA-seq transcriptomic analyses to investigate the association between neutrophil gene expression and T2D phenotype. As specialized pro-resolving lipid mediators (SPM) act to resolve inflammation, we further surveyed the impact of neutrophil receptor binding SPM resolvin E1 (RvE1) on isolated diabetic and healthy neutrophils.Cell isolation and RNA-seq analysis of neutrophils from N = 11 T2D and N = 7 healthy individuals with available clinical data was conducted. Additionally, cultured neutrophils (N = 3 T2D, N = 3 healthy) were perturbed with increasing RvE1 doses (0 nM, 1 nM, 10 nM, or 100 nM) prior to RNA-seq. Data was evaluated through a bioinformatics pipeline including pathway analysis and post hoc false discovery rate (FDR)-correction.We observed significant differential expression of 50 genes between T2D and healthy neutrophils (p < 0.05), including decreased T2D gene expression in inflammatory- and lipid-related genes SLC9A4, NECTIN2, and PLPP3 (p < 0.003). RvE1 treatment induced dose-dependent differential gene expression (uncorrected p < 0.05) across groups, including 59 healthy and 216 T2D neutrophil genes. Comparing T2D to healthy neutrophils, 1097 genes were differentially expressed across RvE1 doses, including two significant genes, LILRB5 and AKR1C1, involved in inflammation (p < 0.05).The neutrophil transcriptomic database revealed novel chronic inflammatory- and lipid-related genes that were differentially expressed between T2D cells when compared to controls, and cells responded to RvE1 dose-dependently by gene expression changes. Unraveling the mechanisms regulating abnormalities in diabetic neutrophil responses could lead to better diagnostics and therapeutics targeting inflammation and inflammation resolution.

ABSTRACT The Burkholderia cepacia complex (BCC) is a group of closely related bacteria that are responsible for respiratory infections in immunocompromised humans, most notably those with cystic fibrosis (CF). We report the genome sequences for Burkholderia cenocepacia ET12 lineage CF isolates K56-2 and BC7.

Here we report a summary classification and the features of five anaerobic oral bacteria from the family Peptostreptococcaceae. Bacterial strains were isolated from human subgingival plaque. Strains ACC19a, CM2, CM5, and OBRC8 represent the first known cultivable members of "yet uncultured" human oral taxon 081; strain AS15 belongs to "cultivable" human oral taxon 377. Based on 16S rRNA gene sequence comparisons, strains ACC19a, CM2, CM5, and OBRC8 are distantly related to Eubacterium yurii subs. yurii and Filifactor alocis, with 93.2 - 94.4 % and 85.5 % of sequence identity, respectively. The genomes of strains ACC19a, CM2, CM5, OBRC8 and AS15 are 2,541,543; 2,312,592; 2,594,242; 2,553,276; and 2,654,638 bp long. The genomes are comprised of 2277, 1973, 2325, 2277, and 2308 protein-coding genes and 54, 57, 54, 36, and 28 RNA genes, respectively. Based on the distinct characteristics presented here, we suggest that strains ACC19a, CM2, CM5, and OBRC8 represent a novel genus and species within the family Peptostreptococcaceae, for which we propose the name Peptoanaerobacter stomatis gen. nov., sp. nov. The type strain is strain ACC19a(T) (=HM-483(T); =DSM 28705(T); =ATCC BAA-2665(T)).

Deep shotgun sequencing on next generation sequencing (NGS) platforms has contributed significant amounts of data to enrich our understanding of genomes, transcriptomes, amplified single-cell genomes, and metagenomes. However, deep coverage variations in short-read data sets and high sequencing error rates of modern sequencers present new computational challenges in data interpretation, including mapping and de novo assembly. New lab techniques such as multiple displacement amplification (MDA) of single cells and sequence independent single primer amplification (SISPA) allow for sequencing of organisms that cannot be cultured, but generate highly variable coverage due to amplification biases.Here we introduce NeatFreq, a software tool that reduces a data set to more uniform coverage by clustering and selecting from reads binned by their median kmer frequency (RMKF) and uniqueness. Previous algorithms normalize read coverage based on RMKF, but do not include methods for the preferred selection of (1) extremely low coverage regions produced by extremely variable sequencing of random-primed products and (2) 2-sided paired-end sequences. The algorithm increases the incorporation of the most unique, lowest coverage, segments of a genome using an error-corrected data set. NeatFreq was applied to bacterial, viral plaque, and single-cell sequencing data. The algorithm showed an increase in the rate at which the most unique reads in a genome were included in the assembled consensus while also reducing the count of duplicative and erroneous contigs (strings of high confidence overlaps) in the deliverable consensus. The results obtained from conventional Overlap-Layout-Consensus (OLC) were compared to simulated multi-de Bruijn graph assembly alternatives trained for variable coverage input using sequence before and after normalization of coverage. Coverage reduction was shown to increase processing speed and reduce memory requirements when using conventional bacterial assembly algorithms.The normalization of deep coverage spikes, which would otherwise inhibit consensus resolution, enables High Throughput Sequencing (HTS) assembly projects to consistently run to completion with existing assembly software. The NeatFreq software package is free, open source and available at https://github.com/bioh4x/NeatFreq .

Whole genome sequencing of viruses and bacteriophages is often hindered because of the need for large quantities of genomic material. A method is described that combines single plaque sequencing with an optimization of Sequence Independent Single Primer Amplification (SISPA). This method can be used for de novo whole genome next-generation sequencing of any cultivable virus without the need for large-scale production of viral stocks or viral purification using centrifugal techniques.A single viral plaque of a variant of the 2009 pandemic H1N1 human Influenza A virus was isolated and amplified using the optimized SISPA protocol. The sensitivity of the SISPA protocol presented here was tested with bacteriophage F_HA0480sp/Pa1651 DNA. The amplified products were sequenced with 454 and Illumina HiSeq platforms. Mapping and de novo assemblies were performed to analyze the quality of data produced from this optimized method.Analysis of the sequence data demonstrated that from a single viral plaque of Influenza A, a mapping assembly with 3590-fold average coverage representing 100% of the genome could be produced. The de novo assembled data produced contigs with 30-fold average sequence coverage, representing 96.5% of the genome. Using only 10 pg of starting DNA from bacteriophage F_HA0480sp/Pa1651 in the SISPA protocol resulted in sequencing data that gave a mapping assembly with 3488-fold average sequence coverage, representing 99.9% of the reference and a de novo assembly with 45-fold average sequence coverage, representing 98.1% of the genome.The optimized SISPA protocol presented here produces amplified product that when sequenced will give high quality data that can be used for de novo assembly. The protocol requires only a single viral plaque or as little as 10 pg of DNA template, which will facilitate rapid identification of viruses during an outbreak and viruses that are difficult to propagate.

Although Bacteroides vulgatus is one of the most prevalent microorganisms in the human gastrointestinal tract, little is known about the genetic potential of this species. Here, we describe the annotated draft genome sequence of B. vulgatus PC510 isolated from human feces.

We report the genome sequences of four isolates of a human gut symbiont, Bifidobacterium longum. Strains 44B and 35B were isolated from two 1-year-old infants, while 1-6B and 2-2B were isolated from the same children 5 years later. The sequences permit investigations of factors enabling long-term colonization of bifidobacteria.

Pneumococcal pneumonia has decreased significantly since the implementation of the pneumococcal conjugate vaccine (PCV), nevertheless, in many developing countries pneumonia mortality in infants remains high. We have undertaken a study of the nasopharyngeal (NP) microbiome during the first year of life in infants from The Philippines and South Africa. The study entailed the determination of the Streptococcus sp. carriage using a lytA qPCR assay, whole metagenomic sequencing, and in silico serotyping of Streptococcus pneumoniae, as well as 16S rRNA amplicon based community profiling. The lytA carriage in both populations increased with infant age and lytA+ samples ranged from 24 to 85% of the samples at each sampling time point. We next developed informatic tools for determining Streptococcus community composition and pneumococcal serotype from metagenomic sequences derived from a subset of longitudinal lytA-positive Streptococcus enrichment cultures from The Philippines (n = 26 infants, 50% vaccinated) and South African (n = 7 infants, 100% vaccinated). NP samples from infants were passaged in enrichment media, and metagenomic DNA was purified and sequenced. In silico capsular serotyping of these 51 metagenomic assemblies assigned known serotypes in 28 samples, and the co-occurrence of serotypes in 5 samples. Eighteen samples were not typeable using known serotypes but did encode for capsule biosynthetic cluster genes similar to non-encapsulated reference sequences. In addition, we performed metagenomic assembly and 16S rRNA amplicon profiling to understand co-colonization dynamics of Streptococcus sp. and other NP genera, revealing the presence of multiple Streptococcus species as well as potential respiratory pathogens in healthy infants. A range of virulence and drug resistant elements were identified as circulating in the NP microbiomes of these infants. This study revealed the frequent co-occurrence of multiple S. pneumoniae strains along with Streptococcus sp. and other potential pathogens such as S. aureus in the NP microbiome of these infants. In addition, the in silico serotype analysis proved powerful in determining the serotypes in S. pneumoniae carriage, and may lead to developing better targeted vaccines to prevent invasive pneumococcal disease (IPD) in these countries. These findings suggest that NP colonization by S. pneumoniae during the first years of life is a dynamic process involving multiple serotypes and species.

Enterococcus faecalis is commonly isolated from the gastrointestinal tract of healthy infants and adults, where it contributes to host health and well-being. We describe here the draft genome sequence of E. faecalis PC1.1, a candidate probiotic strain isolated from human feces.

Next generation sequencing of the RNA content of single cells or single nuclei (sc/nRNA-seq) has become a powerful approach to understand the cellular complexity and diversity of multicellular organisms and environmental ecosystems. However, the fact that the procedure begins with a relatively small amount of starting material, thereby pushing the limits of the laboratory procedures required, dictates that careful approaches for sample quality control (QC) are essential to reduce the impact of technical noise and sample bias in downstream analysis applications. Here we present a preliminary framework for sample level quality control that is based on the collection of a series of quantitative laboratory and data metrics that are used as features for the construction of QC classification models using random forest machine learning approaches. We've applied this initial framework to a dataset comprised of 2272 single nuclei RNA-seq results and determined that ~79% of samples were of high quality. Removal of the poor quality samples from downstream analysis was found to improve the cell type clustering results. In addition, this approach identified quantitative features related to the proportion of unique or duplicate reads and the proportion of reads remaining after quality trimming as useful features for pass/fail classification. The construction and use of classification models for the identification of poor quality samples provides for an objective and scalable approach to sc/nRNA-seq quality control.

Burkholderia pseudomallei is a biothreat and the causative agent of melioidosis. There are at least seven known colony morphotypes of B. pseudomallei that appear to have different virulence properties in animal models. We report the genome sequence of B. pseudomallei strain NCTC 13392 and the genomic variations of its eight morphotype derivatives.

Interventions meant to promote longevity and healthy aging have often been designed or observed to modulate very specific gene or protein targets. If there are naturally occurring genetic variants in such a target that affect longevity as well as the molecular function of that target (eg, the variants influence the expression of the target, acting as "expression quantitative trait loci" or "eQTLs"), this could support a causal relationship between the pharmacologic modulation of the target and longevity and thereby validate the target at some level. We considered the gene targets of many pharmacologic interventions hypothesized to enhance human longevity and explored how many variants there are in those targets that affect gene function (eg, as expression quantitative trait loci). We also determined whether variants in genes associated with longevity-related phenotypes affect gene function or are in linkage disequilibrium with variants that do, and whether pharmacologic studies point to compounds exhibiting activity against those genes. Our results are somewhat ambiguous, suggesting that integrating genetic association study results with functional genomic and pharmacologic studies is necessary to shed light on genetically mediated targets for longevity-enhancing drugs. Such integration will require more sophisticated data sets, phenotypic definitions, and bioinformatics approaches to be useful.

ABSTRACT Objectives Chronic inflammatory diseases, including diabetes and cardiovascular disease, are heterogeneous and often co-morbid, with increasing global prevalence. Uncontrolled type 2 diabetes (T2D) can result in severe inflammatory complications. As neutrophils are essential to inflammation, we conducted RNA-seq transcriptomic analyses to investigate the association between neutrophil gene expression and T2D phenotype. Further, as specialized pro-resolving lipid mediators, including resolvin E1 (RvE1), can actively resolve inflammation, we further surveyed the impact of RvE1 on isolated neutrophils. Methods Cell isolation and RNA-seq analysis of neutrophils from N=11 T2D and N=7 healthy individuals with available clinical data was conducted. Additionally, cultured neutrophils (N=3 T2D, N=3 healthy) were perturbed with increasing RvE1 doses (0nM, 1nM, 10nM, or 100nM) prior to RNA-seq. Data was evaluated through a bioinformatics pipeline including pathway analysis and post hoc false-discovery rate (FDR)-correction. Results We observed significant differential expression of 50 genes between T2D and healthy neutrophils (p&lt;0.05), including decreased T2D gene expression in inflammatory- and lipid-related genes SLC9A4, NECTIN2 and PLPP3 (p&lt;0.003). RvE1 treatment induced dose-dependent differential gene expression (uncorrected p&lt;0.05) across groups, including 59 healthy and 216 T2D neutrophil genes. Comparing T2D to healthy neutrophils, 1097 genes were differentially expressed across RvE1 doses, including two significant genes, LILRB5 and AKR1C1 , involved in inflammation (p&lt;0.05). Conclusions Inflammatory- and lipid-related genes were differentially expressed between T2D and healthy neutrophils, and RvE1 dose-dependently modified gene expression in both groups. Unraveling the mechanisms regulating abnormalities in diabetic neutrophil responses could lead to better diagnostics and therapeutics targeting inflammation and inflammation resolution.

Abstract We describe convergent evidence from transcriptomics, morphology and physiology for a specialized GABAergic neuron subtype in human cortex. Using unbiased single nucleus RNA sequencing, we identify ten GABAergic interneuron subtypes with combinatorial gene signatures in human cortical layer 1 and characterize a novel group of human interneurons with anatomical features never described in rodents having large, “rosehip”-like axonal boutons and compact arborization. These rosehip cells show an immunohistochemical profile (GAD1/CCK-positive, CNR1/SST/CALB2/PVALB-negative) matching a single transcriptomically-defined cell type whose molecular signature is not seen in mouse cortex. Rosehip cells make homotypic gap junctions, predominantly target apical dendritic shafts of layer 3 pyramidal neurons and inhibit backpropagating pyramidal action potentials in microdomains of the dendritic tuft. These cells are therefore positioned for potent local control of distal dendritic computation in cortical pyramidal neurons.

Background The increasing demand for mental health care, a lack of mental health care providers, and unequal access to mental health care services have created a need for innovative approaches to mental health care. Digital device apps, including digital therapeutics, that provide recommendations and feedback for dealing with stress, depression, and other mental health issues can be used to adjust mood and ultimately show promise to help meet this demand. In addition, the recommendations delivered through such apps can also be tailored to an individual’s needs (ie, personalized) and thereby potentially provide greater benefits than traditional “one-size-fits-all” recommendations. Objective This study aims to characterize individual transitions from one emotional state to another during the prolonged use of a digital app designed to provide a user with guided meditations based on their initial, potentially negative, emotional state. Understanding the factors that mediate such transitions can lead to improved recommendations for specific mindfulness and meditation interventions or activities (MMAs) provided in mental health apps. Methods We analyzed data collected during the use of the Stop, Breathe &amp; Think (SBT) mindfulness app. The SBT app prompts users to input their emotional state before and immediately after engaging with MMAs recommended by the app. Data were collected from more than 650,000 SBT users engaging in nearly 5 million MMAs. We limited the scope of our analysis to users with 10 or more MMA sessions that included at least 6 basal emotional state evaluations. Using clustering techniques, we grouped emotions recorded by individual users and then applied longitudinal mixed effect models to assess the associations between individual recommended MMAs and transitions from one group of emotions to another. Results We found that basal emotional states have a strong influence on transitions from one emotional state to another after MMA engagement. We also found that different MMAs impact these transitions, and many were effective in eliciting a healthy transition but only under certain conditions. In addition, we observed gender and age effects on these transitions. Conclusions We found that the initial emotional state of an SBT app user determines the type of SBT MMAs that will have a favorable effect on their transition from one emotional state to another. Our results have implications for the design and use of guided mental health recommendations for digital device apps.

Abstract Objectives: Chronic inflammatory diseases, including diabetes and cardiovascular disease, are heterogeneous and often co-morbid, with increasing global prevalence. Uncontrolled type 2 diabetes (T2D) can result in severe inflammatory complications. As neutrophils are essential to inflammation, we conducted RNA-seq transcriptomic analyses to investigate the association between neutrophil gene expression and T2D phenotype. Further, as specialized pro-resolving lipid mediators, including resolvin E1 (RvE1), can actively resolve inflammation, we further surveyed the impact of RvE1 on isolated neutrophils. Methods: Cell isolation and RNA-seq analysis of neutrophils from N=11 T2D and N=7 healthy individuals with available clinical data was conducted. Additionally, cultured neutrophils (N=3 T2D, N=3 healthy) were perturbed with increasing RvE1 doses (0nM, 1nM, 10nM, or 100nM) prior to RNA-seq. Data was evaluated through a bioinformatics pipeline including pathway analysis and post hoc false-discovery rate (FDR)-correction. Results: We observed significant differential expression of 50 genes between T2D and healthy neutrophils (p&lt;0.05), including decreased T2D gene expression in inflammatory- and lipid-related genes SLC9A4, NECTIN2 and PLPP3 (p&lt;0.003). RvE1 treatment induced dose-dependent differential gene expression (uncorrected p&lt;0.05) across groups, including 59 healthy and 216 T2D neutrophil genes. Comparing T2D to healthy neutrophils, 1097 genes were differentially expressed across RvE1 doses, including two significant genes, LILRB5 and AKR1C1 , involved in inflammation (p&lt;0.05). Conclusions: Inflammatory- and lipid-related genes were differentially expressed between T2D and healthy neutrophils, and RvE1 dose-dependently modified gene expression in both groups. Unraveling the mechanisms regulating abnormalities in diabetic neutrophil responses could lead to better diagnostics and therapeutics targeting inflammation and inflammation resolution.

ABSTRACT Enterococcus faecium is commonly isolated from the human gastrointestinal tract; however, important intraspecies variations exist with relevance for host health and well-being. Here, we describe the draft genome sequence of E. faecium PC4.1, a clade B strain isolated from human feces.

Abstract von Economo neurons (VENs) are bipolar, spindle-shaped neurons restricted to layer 5 of human frontoinsula and anterior cingulate cortex that appear to be selectively vulnerable to neuropsychiatric and neurodegenerative diseases, although little is known about other VEN cellular phenotypes. Single nucleus RNA-sequencing of frontoinsula layer 5 identified a transcriptomically-defined cell cluster that contained VENs, but also fork cells and a subset of pyramidal neurons. Cross-species alignment of this cell cluster with a well-annotated mouse classification shows strong homology to extratelencephalic (ET) excitatory neurons that project to subcerebral targets. This cluster also shows strong homology to a putative ET cluster in human temporal cortex, but with a strikingly specific regional signature. Together these results predict VENs are a regionally distinctive type of ET neuron, and we additionally describe the first patch clamp recordings of VENs from neurosurgically-resected tissue that show distinctive intrinsic membrane properties relative to neighboring pyramidal neurons.

<sec> <title>BACKGROUND</title> The increasing demand for mental health care, a lack of mental health care providers, and unequal access to mental health care services have created a need for innovative approaches to mental health care. Digital device apps, including digital therapeutics, that provide recommendations and feedback for dealing with stress, depression, and other mental health issues can be used to adjust mood and ultimately show promise to help meet this demand. In addition, the recommendations delivered through such apps can also be tailored to an individual’s needs (ie, personalized) and thereby potentially provide greater benefits than traditional “one-size-fits-all” recommendations. </sec> <sec> <title>OBJECTIVE</title> This study aims to characterize individual transitions from one emotional state to another during the prolonged use of a digital app designed to provide a user with guided meditations based on their initial, potentially negative, emotional state. Understanding the factors that mediate such transitions can lead to improved recommendations for specific mindfulness and meditation interventions or activities (MMAs) provided in mental health apps. </sec> <sec> <title>METHODS</title> We analyzed data collected during the use of the Stop, Breathe &amp;amp; Think (SBT) mindfulness app. The SBT app prompts users to input their emotional state before and immediately after engaging with MMAs recommended by the app. Data were collected from more than 650,000 SBT users engaging in nearly 5 million MMAs. We limited the scope of our analysis to users with 10 or more MMA sessions that included at least 6 basal emotional state evaluations. Using clustering techniques, we grouped emotions recorded by individual users and then applied longitudinal mixed effect models to assess the associations between individual recommended MMAs and transitions from one group of emotions to another. </sec> <sec> <title>RESULTS</title> We found that basal emotional states have a strong influence on transitions from one emotional state to another after MMA engagement. We also found that different MMAs impact these transitions, and many were effective in eliciting a healthy transition but only under certain conditions. In addition, we observed gender and age effects on these transitions. </sec> <sec> <title>CONCLUSIONS</title> We found that the initial emotional state of an SBT app user determines the type of SBT MMAs that will have a favorable effect on their transition from one emotional state to another. Our results have implications for the design and use of guided mental health recommendations for digital device apps. </sec>

<sec> <title>BACKGROUND</title> The use of smartphone apps to monitor and deliver health care guidance and interventions has received considerable attention recently, particularly with regard to behavioral disorders, stress relief, negative emotional state, and poor mood in general. Unfortunately, there is little research investigating the long-term and repeated effects of apps meant to impact mood and emotional state. </sec> <sec> <title>OBJECTIVE</title> We aimed to investigate the effects of both immediate point-of-intervention and long-term use (ie, at least 10 engagements) of a guided meditation and mindfulness smartphone app on users’ emotional states. Data were collected from users of a mobile phone app developed by the company Stop, Breathe &amp; Think (SBT) for achieving emotional wellness. To explore the long-term effects, we assessed changes in the users’ basal emotional state before they completed an activity (eg, a guided meditation). We also assessed the immediate effects of the app on users’ emotional states from preactivity to postactivity. </sec> <sec> <title>METHODS</title> The SBT app collects information on the emotional state of the user before and after engagement in one or several mediation and mindfulness activities. These activities are recommended and provided by the app based on user input. We considered data on over 120,000 users of the app who collectively engaged in over 5.5 million sessions with the app during an approximate 2-year period. We focused our analysis on users who had at least 10 engagements with the app over an average of 6 months. We explored the changes in the emotional well-being of individuals with different emotional states at the time of their initial engagement with the app using mixed-effects models. In the process, we compared 2 different methods of classifying emotional states: (1) an expert-defined a priori mood classification and (2) an empirically driven cluster-based classification. </sec> <sec> <title>RESULTS</title> We found that among long-term users of the app, there was an association between the length of use and a positive change in basal emotional state (4% positive mood increase on a 2-point scale every 10 sessions). We also found that individuals who were anxious or depressed tended to have a favorable long-term emotional transition (eg, from a sad emotional state to a happier emotional state) after using the app for an extended period (the odds ratio for achieving a positive emotional state was 3.2 and 6.2 for anxious and depressed individuals, respectively, compared with users with fewer sessions). </sec> <sec> <title>CONCLUSIONS</title> Our analyses provide evidence for an association between both immediate and long-term use of an app providing guided meditations and improvements in the emotional state. </sec>

Author(s): McCorrison, Jamison | Advisor(s): Schork, Nicholas J; Bansal, Vikas | Abstract: There is a great deal of interest in analyzing very large data sets in the biomedical sciences. This is due to the availability of high-throughput assays, such as DNA sequencing technologies and high-resolution imaging devices, advances in data storage and high-performance computing, and analytic techniques rooted in artificial intelligence and machine learning. However, many modern data sets are constructed from individual component data sets which create issues for data harmonization and scientific integration. ‘Metadata,’ i.e., data about the data within component data sets, can be used to facilitate integration and drawing inferences from the combined data sets, but requires care and is sensitive to how those data can be used. Metadata also arises in many situations in which the combination of data sets has more subtle and nuanced aspects to it, such as in analyzing species differences in evolutionary studies, where the species data are often collected independently with different techniques, making it important to know what specific protocols and techniques were used in order to organize and enable relevant comparisons and avoid batch effects, false positives, and other phenomena associated with heterogeneous data sets. I describe the application of statistical methods in four different contexts in which metadata are available. First, I describe an analysis involving the classification of emotions recorded as part of a digital therapeutic implemented in smart phone app designed to reduce stress. Meta data arise when considering the sources and settings of individual data collections. Second, I consider an analysis relating fibroblast transcriptomes to longevity across 49 avian species, where each species has a unique genome, but only a subset of species actually have available reference genomes. Third, I describe studies exploring variation in single cell gene expression patterns from studies of the human brain using expression profiles generated with different protocols and which have different quality control profiles. Fourth, I consider the analysis of genetically-mediated drug targets for longevity in which information from different sources is used to make more compelling and comprehensive statements of the candidacy of any one gene for drug development. I also consider general themes about the use of metadata in contemporary biomedical sciences and discuss areas for future research.

Abstract Objectives: Chronic inflammatory diseases, including diabetes and cardiovascular disease, are heterogeneous and often co-morbid, with increasing global prevalence. Uncontrolled type 2 diabetes (T2D) can result in severe inflammatory complications. As neutrophils are essential to inflammation, we conducted RNA-seq transcriptomic analyses to investigate the association between neutrophil gene expression and T2D phenotype. Further, as specialized pro-resolving lipid mediators, including resolvin E1 (RvE1), can actively resolve inflammation, we further surveyed the impact of RvE1 on isolated neutrophils. Methods: Cell isolation and RNA-seq analysis of neutrophils from N=11 T2D and N=7 healthy individuals with available clinical data was conducted. Additionally, cultured neutrophils (N=3 T2D, N=3 healthy) were perturbed with increasing RvE1 doses (0nM, 1nM, 10nM, or 100nM) prior to RNA-seq. Data was evaluated through a bioinformatics pipeline including pathway analysis and post hoc false-discovery rate (FDR)-correction. Results: We observed significant differential expression of 50 genes between T2D and healthy neutrophils (p&lt;0.05), including decreased T2D gene expression in inflammatory- and lipid-related genes SLC9A4, NECTIN2 and PLPP3 (p&lt;0.003). RvE1 treatment induced dose-dependent differential gene expression (uncorrected p&lt;0.05) across groups, including 59 healthy and 216 T2D neutrophil genes. Comparing T2D to healthy neutrophils, 1097 genes were differentially expressed across RvE1 doses, including two significant genes, LILRB5 and AKR1C1 , involved in inflammation (p&lt;0.05). Conclusions: Inflammatory- and lipid-related genes were differentially expressed between T2D and healthy neutrophils, and RvE1 dose-dependently modified gene expression in both groups. Unraveling the mechanisms regulating abnormalities in diabetic neutrophil responses could lead to better diagnostics and therapeutics targeting inflammation and inflammation resolution.

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