Feces metagenome
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA491335 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR7989810_1.fastq.gz | Fastqsanger.gz | |||
| SRR7989810_2.fastq.gz | Fastqsanger.gz | |||
| SRR7989820_1.fastq.gz | Fastqsanger.gz | |||
| SRR7989820_2.fastq.gz | Fastqsanger.gz | |||
| SRR7989830_1.fastq.gz | Fastqsanger.gz |
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Project description:Here is reported the first study of transcriptome analyses using the Illumina HiSeq 4000 platform for three kinds of wheat (G represents Strong gluten wheat, Z represents middle gluten wheat,R represents weak gluten wheat). The variation of wheat varieties with different gluten content is mainly shown in the content of gluten, flour is divided into high gluten powder ( > 30%), medium gluten powder (26%-30%) and low gluten powder ( < 20%), according to the wet gluten content. In total, over 102.6 Gb clean reads were produced and 114, 621 unigenes were assembled; more than 59,085 unigenes had at least one significant match to an existing gene model. Differentially expressed gene analysis identified 2339 and 2600 unigenes which were expressed higher or lower among strong gluten, middle gluten and weak gluten wheat. After functional annotation and classification, three dominant pathways including protein isomerase, antioxidase activity and energy metabolism, and 410 unigenes related to gluten strength polymerization of wheat were discovered. In strong-gluten wheat, low molecular weight subunit content is higher than weak-gluten wheat, and the activity of cysteine synthase and isomerase is increased, which may promote the cross-linking of low molecular weight protein to high molecular weight protein. Meanwhile, POD enzyme strengthens gluten network and CAT enzyme affects gluten polymerization, along with higher ATPase activity, which will provides energy for protein polymerization reaction in comparison of strong-gluten wheat and weak-gluten wheat. The accuracy of these RNA-seq data was validated by qRT-PCR analysis. These data will extend our knowledge of quality characteristics of wheat and provide a theoretical foundation for molecular mechanism research of wheat.
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Project description:A Metaproteomic Workflow for Sample Preparation and Data Analysis Applied to Mouse Faeces: 1 MTD project_description Many diseases have been associated with gut microbiome abnormalities. The root cause of such diseases is not only due to bacterial dysbiosis, but also to change in bacterial functions, which are best studied by proteomic approaches. Although bacterial proteomics is well established, metaproteomics is hindered by challenges associated with the sample physical structure, contaminating proteins, the simultaneous analysis of hundreds of species and the subsequent data analysis. Here, we present a systematic assessment of sample preparation and data analysis methodologies applied to LC-MS/MS metaproteomics experiment. We could show that low speed centrifugation (LSC) has a significant impact on both peptide identifications and reproducibility. LSC led to increase in peptide and proteins identifications compare to no LSC. Notably, the dominant bacterial phyla, i.e. Firmicutes and Bacteroidetes, showed divergent representation between LSC and no-LSC. In terms of data processing, protein sequence databases derived from mouse faeces metagenome provided at least four times more MS/MS identification compared to databases of concatenated single organisms. We also demonstrated that two-steps database search strategy comes at the expense of a dramatic rise in number of false positives compared to single-step strategy. Overall, we found a positive correlation between matching metaproteome and metagenome abundance, which could be linked to core microbial functions, such as glycolysis-gluconeogenesis, citrate cycle and carbon metabolism. We observed significant overlap and correlation at the phylum, class, order and family taxonomic levels between taxonomy-derived from metagenome and metaproteome. Notably, nearly all functional categories (e.g., membrane transport, translation, transcription) were differentially abundant in the metaproteome (activity) compared to what would be expected from the metagenome (potential). In conclusion, these results highlight the need to perform metaproteomics when studying complex microbiome samples.
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Project description:Coeliac disease is a small intestinal disorder caused by an abarrent immune response towards dietary gluten due to activation of pro-inflammatory gluten specific CD4+ T cells. Histological evaluation and classification of gluten induced musosal changes is part of the diagnostic work up. of adults. The kinetics of mucosal recovery following commencesment of a gluten free diet (disease remission) and the degree of mucosal changes induced by gluten reintroduction (gluten challenge) varies between patients. Also, patients classified with similar clinical and histological disease remission, can develop different degree of mucosal damage following the same gluten challenge regime. This variation poses a challenge for the interpretation of gluten induced mucosal changes both in a diagnostic and clinical trial settings. In this study, we have analysed material from small intestinal biopsies collected from 19 treated coeliac disease patients before and after completion of a 14-day oral gluten challenge. These patients are part of a previoslpreviouslyu described study where all patients were in clinical and mucosal remission at baseline but only some patients developed histological changes in the mucosa in response to gluten. We have performed shotgun LC-MSMS analysis and label-free quantification of total gut tissue and from laser capture microdissected epithelial cell layer samples. We found that differences in tissue proteome expression could separate patients as responders and non-responders to the gluten challenge. Patients whoich responded strongly to gluten , had signs of gut inflammation already at baseline, supported by presence of low-level blood inflammatory parameters and a slight increase in numbers of gluten-specific CD4+ T cells at baseline. Our proteomics analysis demonstrated baseline differences in gut tissue state between patients that were not evident from routine clinical and histological evaluation. These baseline differences likely explains why some patients respond more strongly to gluten challenge than others.
2021-03-03 | PXD018977 | Pride
Project description:Objective Development of novel treatments for coeliac disease (CeD) is dependent on precise tools to monitor changes in gluten-induced mucosal damage. Current histology measures are subjective and difficult to standardise. Biopsy proteome scoring is an objective alternative to histology which is based on robust changes in biological pathways that directly reflect gluten-induced mucosal damage. In this study we aimed to evaluate biopsy proteome scoring as effect measure in a clinical trial setting by measuring intestinal remodeling in response to oral gluten challenge. Design We analyzed biopsies from a gluten challenge trial of treated CeD patients that consumed 3 g (n=6) or 10 g (n=7) gluten per day for 14 days. Sections from individually embedded biopsies collected before and after challenge were processed for proteome scoring (n = 109) and measurement of Vh:Cd ratio (n = 58). Proteome scores were compared to histology, intraepithelial lymphocyte (IEL) frequency and plasma interleukin-2 (IL-2). Results Change in proteome scores were significant for the group of patients who consumed 10 g gluten, but not for the group who consumed 3 g gluten. Altogether, 8 of 13 patients had changes in delta proteome scores above cut-off. Proteome scores correlated with Vh:Cd ratios both at run-in and at day 15. Proteome scores at day 15 correlated with IEL frequency and with serum IL-2 levels measured 4 hours post gluten intake. Conclusion Biopsy proteome scoring is a simple and reliable measure of gluten-induced mucosal remodeling in response to 14-day oral gluten challenge.
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Project description:Many diseases have been associated with gut microbiome abnormalities. The root cause of such diseases is not only due to bacterial dysbiosis, but also to change in bacterial functions, which are best studied by proteomic approaches. Although bacterial proteomics is well established, metaproteomics is hindered by challenges associated with the sample physical structure, contaminating proteins, the simultaneous analysis of hundreds of species and the subsequent data analysis. Here, we present a systematic assessment of sample preparation and data analysis methodologies applied to LC-MS/MS metaproteomics experiment. We could show that low speed centrifugation (LSC) has a significant impact on both peptide identifications and reproducibility. LSC led to increase in peptide and proteins identifications compare to no LSC. Notably, the dominant bacterial phyla, i.e. Firmicutes and Bacteroidetes, showed divergent representation between LSC and no-LSC. In terms of data processing, protein sequence databases derived from mouse faeces metagenome provided at least four times more MS/MS identification compared to databases of concatenated single organisms. We also demonstrated that two-steps database search strategy comes at the expense of a dramatic rise in number of false positives compared to single-step strategy. Overall, we found a positive correlation between matching metaproteome and metagenome abundance, which could be linked to core microbial functions, such as glycolysis-gluconeogenesis, citrate cycle and carbon metabolism. We observed significant overlap and correlation at the phylum, class, order and family taxonomic levels between taxonomy-derived from metagenome and metaproteome. Notably, nearly all functional categories (e.g., membrane transport, translation, transcription) were differentially abundant in the metaproteome (activity) compared to what would be expected from the metagenome (potential). In conclusion, these results highlight the need to perform metaproteomics when studying complex microbiome samples.
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Project description:Many diseases have been associated with gut microbiome abnormalities. The root cause of such diseases is not only due to bacterial dysbiosis, but also to change in bacterial functions, which are best studied by proteomic approaches. Although bacterial proteomics is well established, metaproteomics is hindered by challenges associated with the sample physical structure, contaminating proteins, the simultaneous analysis of hundreds of species and the subsequent data analysis. Here, we present a systematic assessment of sample preparation and data analysis methodologies applied to LC-MS/MS metaproteomics experiment. We could show that low speed centrifugation (LSC) has a significant impact on both peptide identifications and reproducibility. LSC led to increase in peptide and proteins identifications compare to no LSC. Notably, the dominant bacterial phyla, i.e. Firmicutes and Bacteroidetes, showed divergent representation between LSC and no-LSC. In terms of data processing, protein sequence databases derived from mouse faeces metagenome provided at least four times more MS/MS identification compared to databases of concatenated single organisms. We also demonstrated that two-steps database search strategy comes at the expense of a dramatic rise in number of false positives compared to single-step strategy. Overall, we found a positive correlation between matching metaproteome and metagenome abundance, which could be linked to core microbial functions, such as glycolysis-gluconeogenesis, citrate cycle and carbon metabolism. We observed significant overlap and correlation at the phylum, class, order and family taxonomic levels between taxonomy-derived from metagenome and metaproteome. Notably, nearly all functional categories (e.g., membrane transport, translation, transcription) were differentially abundant in the metaproteome (activity) compared to what would be expected from the metagenome (potential). In conclusion, these results highlight the need to perform metaproteomics when studying complex microbiome samples.
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Project description:Many diseases have been associated with gut microbiome abnormalities. The root cause of such diseases is not only due to bacterial dysbiosis, but also to change in bacterial functions, which are best studied by proteomic approaches. Although bacterial proteomics is well established, metaproteomics is hindered by challenges associated with the sample physical structure, contaminating proteins, the simultaneous analysis of hundreds of species and the subsequent data analysis. Here, we present a systematic assessment of sample preparation and data analysis methodologies applied to LC-MS/MS metaproteomics experiment. We could show that low speed centrifugation (LSC) has a significant impact on both peptide identifications and reproducibility. LSC led to increase in peptide and proteins identifications compare to no LSC. Notably, the dominant bacterial phyla, i.e. Firmicutes and Bacteroidetes, showed divergent representation between LSC and no-LSC. In terms of data processing, protein sequence databases derived from mouse faeces metagenome provided at least four times more MS/MS identification compared to databases of concatenated single organisms. We also demonstrated that two-steps database search strategy comes at the expense of a dramatic rise in number of false positives compared to single-step strategy. Overall, we found a positive correlation between matching metaproteome and metagenome abundance, which could be linked to core microbial functions, such as glycolysis-gluconeogenesis, citrate cycle and carbon metabolism. We observed significant overlap and correlation at the phylum, class, order and family taxonomic levels between taxonomy-derived from metagenome and metaproteome. Notably, nearly all functional categories (e.g., membrane transport, translation, transcription) were differentially abundant in the metaproteome (activity) compared to what would be expected from the metagenome (potential). In conclusion, these results highlight the need to perform metaproteomics when studying complex microbiome samples.
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