Adrenal Hormone Interactions and Metabolism: A Single Sample Multi-Omics Approach.
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ABSTRACT:
SUBMITTER: Bechmann N
PROVIDER: S-EPMC8105089 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Peptides present in growth media are essential for nitrogen nutrition and optimal growth of lactic acid bacteria. In addition, according to their amino acid composition, they can also directly or indirectly play regulatory roles and influence global metabolism. This is especially relevant during the propagation phase to produce high cell counts of active lactic acid bacteria used as starters in the dairy industry. In the present work, we aimed at investigating how the respective compositions of two different yeast extracts, with a specific focus on peptide content, influenced Streptococcus thermophilus metabolism during growth under pH-controlled conditions. In addition to free amino acid quantification, we used a multi-omics approach (peptidomics, proteomics, and transcriptomics) to identify peptides initially present in the two culture media and to follow S. thermophilus gene expression and bacterial protein production during growth. The free amino acid and peptide compositions of the two yeast extracts differed qualitatively and quantitatively. Nevertheless, the two yeast extracts sustained similar levels of growth of S. thermophilus and led to equivalent final biomasses. However, transcriptomics and proteomics showed differential gene expression and protein production in several S. thermophilus metabolic pathways, especially amino acid, citrate, urease, purine, and pyrimidine metabolisms. The probable role of the regulator CodY is discussed in this context. Moreover, we observed significant differences in the production of regulators and of a quorum sensing regulatory system. The possible roles of yeast extract peptides on the modulation of the quorum sensing system expression are evaluated.IMPORTANCE Improving the performance and industrial robustness of bacteria used in fermentations and food industry remains a challenge. We showed here that two Streptococcus thermophilus fermentations, performed with the same strain in media that differ only by their yeast extract compositions and, more especially, their peptide contents, led to similar growth kinetics and final biomasses, but several genes and proteins were differentially expressed/produced. In other words, subtle variations in peptide composition of the growth medium can finely tune the metabolism status of the starter. Our work, therefore, suggests that acting on growth medium components and especially on their peptide content, we could modulate bacterial metabolism and produce bacteria differently programmed for further purposes. This might have applications for preparing active starter cultures.
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Project description:Developing a single-cell multi-omics technology with mtDNA mutation profiling at its core will enable precise determination of the relationship between mtDNA mutations and cellular phenotypes. To date, the development of permeability-engineered compartmentalization for the isolation and single-cell library preparation of individual cells, which includes Tn5 tagmentation and single-cell barcoding operations in the permeabilized microcapsule format, remains a formidable challenge. Herein, we present a approach for single-cell mitochondrial analysis, harnessing microfluidics to generate heterogeneous hydrogels as artificial membranes. These hydrogels serve to regulate the passage of mitochondrial DNA and nuclear genomes subsequent to the cell lysis step. This approach enables high-throughput mitochondrial DNA genotyping and accessible chromatin profiling at the single-cell level, significantly enhancing the capabilities of permeability-engineered compartmentalization.We conducted a comprehensive evaluation of cell retention and the selective permeability of the capsule reaction system, optimizing conditions for cell lysis, Tn5 tagmentation, and barcode labeling. Validation experiments were performed using Human 293T and Mouse 3T3 cells to assess identification and quantification performance. Our method offers a new strategy for the study of the functions of various mtDNA mutations in biological processes, while also offering a new system for the construction of single-cell multi-omics libraries.
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Project description:Multi-omics methods have greatly advanced our understanding of the biological organism and its microbial associates. However, they are not routinely used in clinical or industrial applications, due to the length of time required to generate and analyze omics data. Here, we applied a novel integrated omics pipeline for the analysis of human and environmental samples in under 48 h. Human subjects that ferment their own foods provided swab samples from skin, feces, oral cavity, fermented foods, and household surfaces to assess the impact of home food fermentation on their microbial and chemical ecology. These samples were analyzed with 16S rRNA gene sequencing, inferred gene function profiles, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolomics through the Qiita, PICRUSt, and GNPS pipelines, respectively. The human sample microbiomes clustered with the corresponding sample types in the American Gut Project (http://www.americangut.org), and the fermented food samples produced a separate cluster. The microbial communities of the household surfaces were primarily sourced from the fermented foods, and their consumption was associated with increased gut microbial diversity. Untargeted metabolomics revealed that human skin and fermented food samples had separate chemical ecologies and that stool was more similar to fermented foods than to other sample types. Metabolites from the fermented foods, including plant products such as procyanidin and pheophytin, were present in the skin and stool samples of the individuals consuming the foods. Some food metabolites were modified during digestion, and others were detected in stool intact. This study represents a first-of-its-kind analysis of multi-omics data that achieved time intervals matching those of classic microbiological culturing. IMPORTANCE Polymicrobial infections are difficult to diagnose due to the challenge in comprehensively cultivating the microbes present. Omics methods, such as 16S rRNA sequencing, metagenomics, and metabolomics, can provide a more complete picture of a microbial community and its metabolite production, without the biases and selectivity of microbial culture. However, these advanced methods have not been applied to clinical or industrial microbiology or other areas where complex microbial dysbioses require immediate intervention. The reason for this is the length of time required to generate and analyze omics data. Here, we describe the development and application of a pipeline for multi-omics data analysis in time frames matching those of the culture-based approaches often used for these applications. This study applied multi-omics methods effectively in clinically relevant time frames and sets a precedent toward their implementation in clinical medicine and industrial microbiology.
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Project description:Organoids were generated from H9 cells. Single cells were sorted from 4-month-old brain organoids differentiated using the telencephalon organoids protocol.
2023-07-10 | E-MTAB-13144 | biostudies-arrayexpress
Project description:Joint profiling of chromatin accessibility and gene expression from the same single cell provides critical information about cell types in a tissue and cell states during a dynamic process. These emerging multi-omics techniques help the investigation of cell-type resolved gene regulatory mechanisms. Here, we developed in situ SHERRY after ATAC-seq (ISSAAC-seq), a highly sensitive and flexible single cell multi-omics method to interrogate chromatin accessibility and gene expression from the same single cell. We demonstrated that ISSAAC-seq is sensitive and provides high quality data with orders of magnitude more features than existing methods. Using the joint profiles from thousands of nuclei from the mouse cerebral cortex, we uncovered major and rare cell types together with their cell-type specific regulatory elements and expression profiles. Finally, we revealed distinct dynamics and relationships of transcription and chromatin accessibility during an oligodendrocyte maturation trajectory.
2022-06-15 | E-MTAB-11264 | biostudies-arrayexpress
Project description:BackgroundGenome-wide association studies (GWAS) that link genotype to phenotype represent an effective means to associate an individual genetic background with a disease or trait. However, single-omics data only provide limited information on biological mechanisms, and it is necessary to improve the accuracy for predicting the biological association between genotype and phenotype by integrating multi-omics data. Typically, gene expression data are integrated to analyze the effect of single nucleotide polymorphisms (SNPs) on phenotype. Such multi-omics data integration mainly follows two approaches: multi-staged analysis and meta-dimensional analysis, which respectively ignore intra-omics and inter-omics associations. Moreover, both approaches require omics data from a single sample set, and the large feature set of SNPs necessitates a large sample size for model establishment, but it is difficult to obtain multi-omics data from a single, large sample set.ResultsTo address this problem, we propose a method of genotype-phenotype association based on multi-omics data from small samples. The workflow of this method includes clustering genes using a protein-protein interaction network and gene expression data, screening gene clusters with group lasso, obtaining SNP clusters corresponding to the selected gene clusters through expression quantitative trait locus data, integrating SNP clusters and corresponding gene clusters and phenotypes into three-layer network blocks, analyzing and predicting based on each block, and obtaining the final prediction by taking the average.ConclusionsWe compare this method to others using two datasets and find that our method shows better results in both cases. Our method can effectively solve the prediction problem in multi-omics data of small sample, and provide valuable resources for further studies on the fusion of more omics data.
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Project description:An increasingly common method for predicting gene activity is genome-wide chromatin immunoprecipitation of M-bM-^@M-^XactiveM-bM-^@M-^Y chromatin modifications followed by massively parallel sequencing (ChIP-seq). Using a novel ChIP-seq quantification method (cRPKM), we tested the power of such ChIP-seq strategies to predict relative protein and RNA levels at the pre-pro-B and pro-B differentiation stages in early B cell lymphopoiesis. Using a multi-omics approach that compares promoter chromatin status (ChIP-seq; published in GSE:21978) with ongoing active transcription (GRO-seq; published in GSE:40173), steady state mRNA (RNA-seq), inferred mRNA stability, and relative proteome abundance measurements (iTRAQ), we demonstrate that active chromatin modifications at promoters are a good indicator of transcription and steady state mRNA levels. Moreover, we found that promoters with active chromatin modifications exclusively in one of these cell states frequently predicted differentially expressed proteins. However, we found that many genes whose promoters have non-differential but active chromatin modifications also displayed changes in expression of their cognate proteins. This large class of developmentally and differentially regulated proteins that was uncoupled from chromatin status used mostly post-transcriptional mechanisms. Interestingly, the most differentially expressed protein in our B-cell development system, 2410004B18Rik, was regulated by a post-transcriptional mechanism, which further analyses indicated was mediated by an identified miRNA. These data provide a striking example of how our integrated multi-omics data set can be useful in uncovering regulatory mechanisms. Total RNA from mouse pre-pro-B and pro-B cells, depleted of rRNA and small RNAs, was sequenced using a strand specific, single end sequencing strategy.
2013-11-19 | E-GEOD-52450 | biostudies-arrayexpress
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2022-08-15 | GSE210285 | GEO
Project description:Background Lactococcus lactis is recognised as a safe (GRAS) microorganism and has hence gained interest in numerous biotechnological approaches. As it is fastidious for several amino acids, optimization of processes which involve this organism requires a thorough understanding of its metabolic regulations during multisubstrate growth. Results Using glucose limited continuous cultivations, specific growth rate dependent metabolism of L. Lactis including utilization of amino acids was studied based on extracellular metabolome, global transcriptome and proteome analysis. A new growth medium was designed with reduced amino acid concentrations to increase precision of measurements of consumption of amino acids. Consumption patterns were calculated for all 20 amino acids and measured carbon balance showed good fit of the data at all growth rates studied. It was observed that metabolism of L. lactis became more efficient with rising specific growth rate in the range 0.10 – 0.60 h-1, indicated by 30% increase in biomass yield based on glucose consumption, 50% increase in efficiency of nitrogen use for biomass synthesis, and 40% reduction in energy spilling. The latter was realized by decrease in the overall product formation and higher efficiency of incorporation of amino acids into biomass. L. lactis global transcriptome and proteome profiles showed good correlation supporting the general idea of transcription level control of bacterial metabolism, but the data indicated that substrate transport systems together with lower part of glycolysis in L. lactis were presumably under allosteric control. Conclusions The current study demonstrates advantages of the usage of strictly controlled continuous cultivation methods combined with multi-omics approach for quantitative understanding of amino acid and energy metabolism of Lactococcus lactis which is a valuable new knowledge for development of balanced growth media, gene manipulations for desired product formation etc. Moreover, collected dataset is an excellent input for developing metabolic models. For microarray analysis, steady state chemostat culture of L. lactis IL1403 was used as reference (? = 0.10 1/h). Subsequent quasi steady state points from A-stat experiment at specific growth rates 0.52 ± 0.03; 0.42 ± 0.02; 0.29 ± 0.01 1/h in biological duplicates and 0.17 1/h were compared to the reference sample.
2011-01-11 | E-GEOD-26536 | biostudies-arrayexpress