Project description:The shift from a hunter-gatherer (HG) to an agricultural (AG) mode of subsistence is believed to have been associated with profound changes in the burden and diversity of pathogens across human populations. Yet, the extent to which the advent of agriculture impacted the evolution of the human immune system remains unknown. Here we present a comparative study of variation in the transcriptional responses of peripheral blood mononuclear cells (PBMCs) to bacterial and viral stimuli between the Batwa, a rainforest hunter-gatherer, and the Bakiga, an agriculturalist population from Central Africa. We observed increased divergence between hunter-gatherers and farmers in the transcriptional response to viruses compared to that for bacterial stimuli. We demonstrate that a significant fraction of these transcriptional differences are under genetic control, and we show that positive natural selection has helped to shape population differences in immune regulation. Unexpectedly, we found stronger signatures of recent natural selection in the rainforest hunter-gatherers, which argues against the popularized notion that shifts in pathogen exposure due to the advent of agriculture imposed radically heightened selective pressures in agriculturalist populations. Overall design: Gene expression profiles of peripheral blood mononuclear cells (PBMCs) that were simulated with the viral ligand Gardiquimod (GARD), the bacterial ligand lippopolysacharide (LPS), or a match un-stimulated control (CTL)
| GSE120502 | GEO
Project description:Genomic and epigenomic signatures of climate-mediated selection in cattle
Project description:A population and admixture analysis of Mesoamerican Totonacs and South American Bolivians. A panel of highly informative ancestry informative markers (AIMs) for New World populations is identified. Regions coinciding with AIMs are have moderate signatures of selection. Population structure and differentiation were assessed with a genome-wide panel of 815,377 autosomal markers, Y-chromosome STR and SNPs, and mtDNA sequence data.
Project description:We explored genomics, transcriptomics (mRNA and sRNA) and metabolomics of maize parent lines as predictors for agronomic performance of single-cross hybrids. Our results indicate that the merit of any individual predictor is trait dependent and that combining predictors has advantages for application across traits. We conclude that downstream “omics” can complement genomics for hybrid prediction and thereby contribute to more efficient selection of hybrid candidates. Overall design: This dataset reports on the analysis of sRNAs in 64 Dent and 41 Flint inbred lines from a maize breeding population with 10 replicated samples
Project description:The mechanisms by which vaccines interact with human APCs remain elusive. We applied systems biology to define the transcriptional programs induced in human DCs by pathogens, innate receptor ligands and vaccines. Upon exposing DCs to influenza, Salmonella enterica and Staphylococcus aureus, we built a modular framework containing 204 pathogen-induced transcript clusters. Module fingerprints were then analyzed in DCs activated with 16 innate receptor ligands. This framework was then used to characterize human monocytes, IL-4 DC and blood DC subsets responses to 13 vaccines. Different vaccines induced distinct signatures based on pathogen type, adjuvant formulation and APC targeted. Fluzone broadly activated IL-4 DC whereas pneumovax only activated monocytes and gardasil (HPV) only activated CD1c+ mDC. This highlights that different antigen-presenting cells respond to different vaccines. Finally, the blood signatures from individuals vaccinated with fluzone or infected with influenza were interpreted using these modules. We identified a signature of adaptive immunity activation following vaccination and symptomatic infections, but not asymptomatic infections. These data, offered with a web interface, might guide the development of improved vaccines. 5 donors; 88 samples; duplicate technical replicates for the medium control for each donor for the BDCA1+ mDC population; single medium control for each donor for the BDCA3+ mDC population (15 total medium controls).
Project description:This is a model of the genome scale reconstruction of the Vibrio vulnificus metabolic network, VvuMBEL943, described in the article:
Integrative genome-scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery
Hyun Uk Kim, Soo Young Kim, Haeyoung Jeong, Tae Yong Kim, Jae Jong Kim, Hyon E Choy, Kyu Yang Yi, Joon Haeng Rhee, and Sang Yup Lee. Molecular Systems Biology
7:460 Jan 2011 doi: 10.1038/msb.2010.115
Although the genomes of many microbial pathogens have been studied to help identify effective drug targets and novel drugs, such efforts have not yet reached full fruition. In this study, we report a systems biological approach that efficiently utilizes genomic information for drug targeting and discovery, and apply this approach to the opportunistic pathogen Vibrio vulnificus CMCP6. First, we partially re-sequenced and fully re-annotated the V. vulnificus CMCP6 genome, and accordingly reconstructed its genome-scale metabolic network, VvuMBEL943. The validated network model was employed to systematically predict drug targets using the concept of metabolite essentiality, along with additional filtering criteria. Target genes encoding enzymes that interact with the five essential metabolites finally selected were experimentally validated. These five essential metabolites are critical to the survival of the cell, and hence were used to guide the cost-effective selection of chemical analogs, which were then screened for antimicrobial activity in a whole-cell assay. This approach is expected to help fill the existing gap between genomics and drug discovery.
This metabolic network model has been thoroughly validated by the authors. VvuMBEL943 is a stoichiometric model that contains the metabolic information of the microbial pathogen, Vibrio vulnificus CMCP6, at genome-scale. The SBML version was generated by Hyun Uk Kim using MetaFluxNet.
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