Project description:Mycorrhizae, symbiotic interactions between soil fungi and tree roots, are ubiquitous in terrestrial ecosystems. The fungi contribute phosphorous, nitrogen and mobilized nutrients from organic matter in the soil and in return the fungus receives photosynthetically-derived carbohydrates. This union of plant and fungal metabolisms is the mycorrhizal metabolome. Understanding this symbiotic relationship at a molecular level provides important contributions to the understanding of forest ecosystems and global carbon cycling. We generated next generation short-read transcriptomic sequencing data from fully-formed ectomycorrhizae between Laccaria bicolor and aspen (Populus tremuloides) roots. The transcriptomic data was used to identify statistically significantly expressed gene models using a bootstrap-style approach, and these expressed genes were mapped to specific metabolic pathways. Integration of expressed genes that code for metabolic enzymes and the set of expressed membrane transporters generates a predictive model of the ectomycorrhizal metabolome. Results indicate the specific compounds glycine, glutamate, and allantoin are synthesized by L. bicolor and that these compounds or their metabolites may be used for the benefit of aspen in exchange for the photosynthetically-derived sugars fructose and glucose.The analysis illustrates an approach to generate testable biological hypotheses to investigate the complex molecular interactions that drive ectomycorrhizal symbiosis. These models are consistent with experimental environmental data and provide insight into the molecular exchange processes for organisms in this complex ecosystem. The method used here for predicting metabolomic models of mycorrhizal systems from deep RNA sequencing data can be generalized and is broadly applicable to transcriptomic data derived from complex systems. Fully formed L.bicolor::P.trichocapra mycorrhizae in duplicate
Project description:We developed an accurate taxonomic annotation strategy from metagenomic data for deep metaproteomic coverage, and also compared the performance of the state-of-the-art LC-MS/MS techniques using a simulated microbial community with 12 species. In addition, we also achieved deep proteome coverage of human gut microbiome from stool samples.
Project description:With the emergence of zebrafish as an important model organism, a concerted effort has been made to study its transcriptome. This effort is limited by gaps in zebrafish annotation, which is especially pronounced concerning transcripts dynamically expressed during zygotic genome activation (ZGA). To date, short read sequencing has been the principal technology for zebrafish transcriptome annotation. In part because these sequence reads are too short for assembly methods to resolve the full complexity of the transcriptome, the current annotation is rudimentary. By providing direct observation of full-length transcripts, recently refined long-read sequencing platforms can dramatically improve annotation coverage and accuracy. Here, we leveraged the SMRT platform to study the early ZGA-stage zebrafish transcriptome. Our analysis revealed additional novelty and complexity in the zebrafish transcriptome, identifying 2748 high confidence novel transcripts that originated from previously unannotated loci and 1835 new isoforms in previously annotated genes.
Project description:Mycorrhizae, symbiotic interactions between soil fungi and tree roots, are ubiquitous in terrestrial ecosystems. The fungi contribute phosphorous, nitrogen and mobilized nutrients from organic matter in the soil and in return the fungus receives photosynthetically-derived carbohydrates. This union of plant and fungal metabolisms is the mycorrhizal metabolome. Understanding this symbiotic relationship at a molecular level provides important contributions to the understanding of forest ecosystems and global carbon cycling. We generated next generation short-read transcriptomic sequencing data from fully-formed ectomycorrhizae between Laccaria bicolor and aspen (Populus tremuloides) roots. The transcriptomic data was used to identify statistically significantly expressed gene models using a bootstrap-style approach, and these expressed genes were mapped to specific metabolic pathways. Integration of expressed genes that code for metabolic enzymes and the set of expressed membrane transporters generates a predictive model of the ectomycorrhizal metabolome. Results indicate the specific compounds glycine, glutamate, and allantoin are synthesized by L. bicolor and that these compounds or their metabolites may be used for the benefit of aspen in exchange for the photosynthetically-derived sugars fructose and glucose.The analysis illustrates an approach to generate testable biological hypotheses to investigate the complex molecular interactions that drive ectomycorrhizal symbiosis. These models are consistent with experimental environmental data and provide insight into the molecular exchange processes for organisms in this complex ecosystem. The method used here for predicting metabolomic models of mycorrhizal systems from deep RNA sequencing data can be generalized and is broadly applicable to transcriptomic data derived from complex systems.
Project description:The mapping and functional analysis of quantitative traits in Brassica rapa can be greatly improved with the availability of physically positioned, gene-based genetic markers and accurate genome annotation. In this study, deep transcriptome RNA sequencing (RNA-Seq) of Brassica rapa was undertaken with two objectives: SNP detection and improved transcriptome annotation. We performed SNP detection on two varieties that are parents of a mapping population to aid in development of a marker system for this population and subsequent development of high-resolution genetic map. An improved Brassica rapa transcriptome was constructed to detect novel transcripts and to improve the current genome annotation. Deep RNA-Seq of two Brassica rapa genotypesâR500 (var. trilocularis, Yellow Sarson) and IMB211 (a rapid cycling variety)âusing eight different tissues (root, internode, leaf, petiole, apical meristem, floral meristem, silique, and seedling) grown across three different environments (growth chamber, greenhouse and field) and under two different treatments (simulated sun and simulated shade) generated 2.3 billion high-quality Illumina reads. In this experiment, two pools were made, with one pool consisting of 66 samples collected from growth chamber and another pool consisting of 60 samples collected from greenhouse and field. Each pool was sequenced on eight lanes (total 16 lanes) of an Illumina Genome Analyzer (GAIIx) as 100-bp paired end reads.
Project description:Background: The purpose of the study was to find out the molecular and metabolic characteristics of human superficial (SSAT) and deep subcutaneous adipose tissue (DSAT) by performing transcriptomics and metabolomics analysis Methods: We conducted RNA-sequencing and metabolome analysis of SSAT and DSAT in a 48-year-old female patient with a BMI of 27.6 kg/m2. Six samples for each group were collected during a deep inferior epigastric perforator flap breast reconstruction. The calculated transcripts per million values were processed for unsupervised hierarchically clustered heat map generation, and Gene set enrichment analysis. For metabolomics analysis, the samples were analyzed in two modes for cationic and anionic metabolites by Capillary Electrophoresis Time-of-Flight Mass Spectrometry (CE-TOFMS). The metabolome was processed for principal component analysis (PCA) and heat map generation. For indirect metabolic flux analyses, primary adipocytes obtained from SSAT and DSAT were analyzed with Seahorse® extracellular flux analyzer. Results: PCA and heat map data revealed global differences in the transcriptome and metabolome of SSAT and DSAT. The top up-regulated gene sets in the SSAT were related to adipogenesis (FDR q < 0.0001), oxidative phosphorylation (FDR q < 0.0001), fatty acid metabolism (FDR q < 0.0001) and glycolysis (FDR q = 0.001), while inflammatory response (FDR q < 0.05) was the top up-regulated gene set in DSAT. Consequently, the metabolites related to glycolysis were abundant in SSAT, while the metabolites related to fatty acid metabolism and oxidative phosphorylation were abundant in DSAT. In cellular flux analysis, SSAT showed higher level of glycolysis and spare oxidative phosphorylation capacities. Conclusion: The global transcriptome and metabolome difference suggest that human superficial and deep subcutaneous adipose tissue are metabolically distinguishable subcompartments.