Transcriptomic evidence that von Economo neurons are regionally specialized extratelencephalic-projecting excitatory neurons.

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 framework for human microbiome research

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

Structure, function and diversity of the healthy human microbiome

Author Posting. © The Authors, 2012. This article is posted here by permission of Nature Publishing Group. The definitive version was published in Nature 486 (2012): 207-214, doi:10.1038/nature11234.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.This research was supported in part by National Institutes of Health grants U54HG004969 to B.W.B.; U54HG003273 to R.A.G.; U54HG004973 to R.A.G., S.K.H. and J.F.P.; U54HG003067 to E.S.Lander; U54AI084844 to K.E.N.; N01AI30071 to R.L.Strausberg; U54HG004968 to G.M.W.; U01HG004866 to O.R.W.; U54HG003079 to R.K.W.; R01HG005969 to C.H.; R01HG004872 to R.K.; R01HG004885 to M.P.; R01HG005975 to P.D.S.; R01HG004908 to Y.Y.; R01HG004900 to M.K.Cho and P. Sankar; R01HG005171 to D.E.H.; R01HG004853 to A.L.M.; R01HG004856 to R.R.; R01HG004877 to R.R.S. and R.F.; R01HG005172 to P. Spicer.; R01HG004857 to M.P.; R01HG004906 to T.M.S.; R21HG005811 to E.A.V.; M.J.B. was supported by UH2AR057506; G.A.B. was supported by UH2AI083263 and UH3AI083263 (G.A.B., C. N. Cornelissen, L. K. Eaves and J. F. Strauss); S.M.H. was supported by UH3DK083993 (V. B. Young, E. B. Chang, F. Meyer, T. M. S., M. L. Sogin, J. M. Tiedje); K.P.R. was supported by UH2DK083990 (J. V.); J.A.S. and H.H.K. were supported by UH2AR057504 and UH3AR057504 (J.A.S.); DP2OD001500 to K.M.A.; N01HG62088 to the Coriell Institute for Medical Research; U01DE016937 to F.E.D.; S.K.H. was supported by RC1DE0202098 and R01DE021574 (S.K.H. and H. Li); J.I. was supported by R21CA139193 (J.I. and D. S. Michaud); K.P.L. was supported by P30DE020751 (D. J. Smith); Army Research Office grant W911NF-11-1-0473 to C.H.; National Science Foundation grants NSF DBI-1053486 to C.H. and NSF IIS-0812111 to M.P.; The Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231 for P.S. C.; LANL Laboratory-Directed Research and Development grant 20100034DR and the US Defense Threat Reduction Agency grants B104153I and B084531I to P.S.C.; Research Foundation - Flanders (FWO) grant to K.F. and J.Raes; R.K. is an HHMI Early Career Scientist; Gordon&BettyMoore Foundation funding and institutional funding fromthe J. David Gladstone Institutes to K.S.P.; A.M.S. was supported by fellowships provided by the Rackham Graduate School and the NIH Molecular Mechanisms in Microbial Pathogenesis Training Grant T32AI007528; a Crohn’s and Colitis Foundation of Canada Grant in Aid of Research to E.A.V.; 2010 IBM Faculty Award to K.C.W.; analysis of the HMPdata was performed using National Energy Research Scientific Computing resources, the BluBioU Computational Resource at Rice University

Exploitation of Metadata in Molecular Genomics Studies

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

Exploitation of Metadata in Molecular Genomics Studies

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

Genetic Support for Longevity-Enhancing Drug Targets: Issues, Preliminary Data, and Future Directions.

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

Transcriptomics of type 2 diabetic and healthy human neutrophils

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 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. 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 (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. Results 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). Conclusions 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