Search

Cardiovascular disorders are influenced by genetic and environmental factors. The TIGR rodent expression web-based resource (TREX) contains over 2,200 microarray hybridizations, involving over 800 animals from 18 different rat strains. These strains comprise genetically diverse parental animals and a panel of chromosomal substitution strains derived by introgressing individual chromosomes from normotensive Brown Norway (BN/NHsdMcwi) rats into the background of Dahl salt sensitive (SS/JrHsdMcwi) rats. The profiles document gene-expression changes in both genders, four tissues (heart, lung, liver, kidney) and two environmental conditions (normoxia, hypoxia). This translates into almost 400 high-quality direct comparisons (not including replicates) and over 100,000 pairwise comparisons. As each individual chromosomal substitution strain represents on average less than a 5% change from the parental genome, consomic strains provide a useful mechanism to dissect complex traits and identify causative genes. We performed a variety of data-mining manipulations on the profiles and used complementary physiological data from the PhysGen resource to demonstrate how TREX can be used by the cardiovascular community for hypothesis generation.

Background: The identification of differentially expressed tumor-associated proteins and genomic alterations driving neoplasia is critical in the development of clinical assays to detect cancers and forms the foundation for understanding cancer biology. One of the challenges in the analysis of pancreatic ductal adenocarcinoma (PDAC) is the low neoplastic cellularity and heterogeneous composition of bulk tumors. To enrich neoplastic cells from bulk tumor tissue, coring, and laser microdissection (LMD) sampling techniques have been employed. In this study, we assessed the protein and KRAS mutation changes associated with samples obtained by these enrichment techniques and evaluated the fraction of neoplastic cells in PDAC for proteomic and genomic analyses. Methods: Three fresh frozen PDAC tumors and their tumor-matched normal adjacent tissues (NATs) were obtained from three sampling techniques using bulk, coring, and LMD; and analyzed by TMT-based quantitative proteomics. The protein profiles and characterizations of differentially expressed proteins in three sampling groups were determined. These three PDACs and samples of five additional PDACs obtained by the same three sampling techniques were also subjected to genomic analysis to characterize KRAS mutations. Results: The neoplastic cellularity of eight PDACs ranged from less than 10% to over 80% based on morphological review. Distinctive proteomic patterns and abundances of certain tumor-associated proteins were revealed when comparing the tumors and NATs by different sampling techniques. Coring and bulk tissues had comparable proteome profiles, while LMD samples had the most distinct proteome composition compared to bulk tissues. Further genomic analysis of bulk, cored, or LMD samples demonstrated that KRAS mutations were significantly enriched in LMD samples while coring was less effective in enriching for KRAS mutations when bulk tissues contained a relatively low neoplastic cellularity. Conclusions: In addition to bulk tissues, samples from LMD and coring techniques can be used for proteogenomic studies. The greatest enrichment of neoplastic cellularity is obtained with the LMD technique. [ABSTRACT FROM AUTHOR]

Bacterial community composition and functional potential change subtly across gradients in the surface ocean. In contrast, while there are significant phylogenetic divergences between communities from freshwater and marine habitats, the underlying mechanisms to this phylogenetic structuring yet remain unknown. We hypothesized that the functional potential of natural bacterial communities is linked to this striking divide between microbiomes. To test this hypothesis, metagenomic sequencing of microbial communities along a 1,800 km transect in the Baltic Sea area, encompassing a continuous natural salinity gradient from limnic to fully marine conditions, was explored. Multivariate statistical analyses showed that salinity is the main determinant of dramatic changes in microbial community composition, but also of large scale changes in core metabolic functions of bacteria. Strikingly, genetically and metabolically different pathways for key metabolic processes, such as respiration, biosynthesis of quinones and isoprenoids, glycolysis and osmolyte transport, were differentially abundant at high and low salinities. These shifts in functional capacities were observed at multiple taxonomic levels and within dominant bacterial phyla, while bacteria, such as SAR11, were able to adapt to the entire salinity gradient. We propose that the large differences in central metabolism required at high and low salinities dictate the striking divide between freshwater and marine microbiomes, and that the ability to inhabit different salinity regimes evolved early during bacterial phylogenetic differentiation. These findings significantly advance our understanding of microbial distributions and stress the need to incorporate salinity in future climate change models that predict increased levels of precipitation and a reduction in salinity. [ABSTRACT FROM AUTHOR]

Microbial communities carry out the majority of the biochemical activity on the planet, and they play integral roles in processes including metabolism and immune homeostasis in the human microbiome. Shotgun sequencing of such communities' metagenomes provides information complementary to organismal abundances from taxonomic markers, but the resulting data typically comprise short reads from hundreds of different organisms and are at best challenging to assemble comparably to single-organism genomes. Here, we describe an alternative approach to infer the functional and metabolic potential of a microbial community metagenome. We determined the gene families and pathways present or absent within a community, as well as their relative abundances, directly from short sequence reads. We validated this methodology using a collection of synthetic metagenomes, recovering the presence and abundance both of large pathways and of small functional modules with high accuracy. We subsequently applied this method, HUMAnN, to the microbial communities of 649 metagenomes drawn from seven primary body sites on 102 individuals as part of the Human Microbiome Project (HMP). This provided a means to compare functional diversity and organismal ecology in the human microbiome, and we determined a core of 24 ubiquitously present modules. Core pathways were often implemented by different enzyme families within different body sites, and 168 functional modules and 196 metabolic pathways varied in metagenomic abundance specifically to one or more niches within the microbiome. These included glycosaminoglycan degradation in the gut, as well as phosphate and amino acid transport linked to host phenotype (vaginal pH) in the posterior fornix. An implementation of our methodology is available at http://huttenhower.sph.harvard.edu/humann. This provides a means to accurately and efficiently characterize microbial metabolic pathways and functional modules directly from high-throughput sequencing reads, enabling the determination of community roles in the HMP cohort and in future metagenomic studies.

As metagenomic studies continue to increase in their number, sequence volume and complexity, the scalability of biological analysis frameworks has become a rate-limiting factor to meaningful data interpretation. To address this issue, we have developed JCVI Metagenomics Reports (METAREP) as an open source tool to query, browse, and compare extremely large volumes of metagenomic annotations. Here we present improvements to this software including the implementation of a dynamic weighting of taxonomic and functional annotation, support for distributed searches, advanced clustering routines, and integration of additional annotation input formats. The utility of these improvements to data interpretation are demonstrated through the application of multiple comparative analysis strategies to shotgun metagenomic data produced by the National Institutes of Health Roadmap for Biomedical Research Human Microbiome Project (HMP) (http://nihroadmap.nih.gov). Specifically, the scalability of the dynamic weighting feature is evaluated and established by its application to the analysis of over 400 million weighted gene annotations derived from 14 billion short reads as predicted by the HMP Unified Metabolic Analysis Network (HUMAnN) pipeline. Further, the capacity of METAREP to facilitate the identification and simultaneous comparison of taxonomic and functional annotations including biological pathway and individual enzyme abundances from hundreds of community samples is demonstrated by providing scenarios that describe how these data can be mined to answer biological questions related to the human microbiome. These strategies provide users with a reference of how to conduct similar large-scale metagenomic analyses using METAREP with their own sequence data, while in this study they reveal insights into the nature and extent of variation in taxonomic and functional profiles across body habitats and individuals. Over one thousand HMP WGS datasets and the latest open source code are available at http://www.jcvi.org/hmp-metarep.