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:BackgroundDespite enormous achievements in the production of high-throughput datasets, constructing comprehensive maps of interactions remains a major challenge. Lack of sufficient experimental evidence on interactions is more significant for heterogeneous molecular types. Hence, developing strategies to predict inter-omics connections is essential to construct holistic maps of disease.ResultsHere, as a novel nonlinear deep learning method, Data Integration with Deep Learning (DIDL) was proposed to predict inter-omics interactions. It consisted of an encoder that performs automatic feature extraction for biomolecules according to existing interactions coupled with a predictor that predicts unforeseen interactions. Applicability of DIDL was assessed on different networks, namely drug-target protein, transcription factor-DNA element, and miRNA-mRNA. Also, validity of the novel predictions was evaluated by literature surveys. According to the results, the DIDL outperformed state-of-the-art methods. For all three networks, the areas under the curve and the precision-recall curve exceeded 0.85 and 0.83, respectively.ConclusionsDIDL offers several advantages like automatic feature extraction from raw data, end-to-end training, and robustness to network sparsity. In addition, reliance solely on existing inter-layer interactions and independence of biochemical features of interacting molecules make this algorithm applicable for a wide variety of networks. DIDL paves the way to understand the underlying mechanisms of complex disorders through constructing integrative networks.
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Project description:MotivationSingle-cell multi-omics assays simultaneously measure different molecular features from the same cell. A key question is how to benefit from the complementary data available and perform cross-modal clustering of cells.ResultsWe propose Single-Cell Multi-omics Clustering (scMoC), an approach to identify cell clusters from data with comeasurements of scRNA-seq and scATAC-seq from the same cell. We overcome the high sparsity of the scATAC-seq data by using an imputation strategy that exploits the less-sparse scRNA-seq data available from the same cell. Subsequently, scMoC identifies clusters of cells by merging clusterings derived from both data domains individually. We tested scMoC on datasets generated using different protocols with variable data sparsity levels. We show that scMoC (i) is able to generate informative scATAC-seq data due to its RNA-guided imputation strategy and (ii) results in integrated clusters based on both RNA and ATAC information that are biologically meaningful either from the RNA or from the ATAC perspective.Availability and implementationThe data used in this manuscript is publicly available, and we refer to the original manuscript for their description and availability. For convience sci-CAR data is available at NCBI GEO under the accession number of GSE117089. SNARE-seq data is available at NCBI GEO under the accession number of GSE126074. The 10X multiome data is available at the following link https://www.10xgenomics.com/resources/datasets/pbmc-from-a-healthy-donor-no-cell-sorting-3-k-1-standard-2-0-0.Supplementary informationSupplementary data are available at Bioinformatics Advances online.
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