Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine

Metabolites, Journal Year: 2023, Volume and Issue: 13(7), P. 855 - 855

Published: July 18, 2023

Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic (GEMs) provide robust framework for studying complex systems. GEMs significantly contributed our understanding human metabolism, including the intrinsic relationship between gut microbiome and host metabolism. In this review, we highlight contributions discuss critical challenges that must be overcome ensure reproducibility enhance prediction accuracy, particularly context precision medicine. We also explore role machine learning addressing GEMs. The integration with has potential lead new insights, advance molecular mechanisms health disease.

Language: Английский

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LIANA+: an all-in-one cell-cell communication framework

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Aug. 21, 2023

Abstract The growing availability of single-cell and spatially-resolved transcriptomics has led to the rapidly popularity methods infer cell-cell communication. Many approaches have emerged, each capturing only a partial view complex landscape Here, we present LIANA+, scalable framework decode coordinated inter- intracellular signalling events from single- multi-condition datasets in both data. Beyond integrating extending established methodologies rich knowledge base, LIANA+ enables novel analyses using diverse molecular mediators, including those measured multi-omics Accessible as an open-source Python package at https://github.com/saezlab/liana-py , provides comprehensive set synergistic components study Figure

Language: Английский

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Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics

Biology, Journal Year: 2024, Volume and Issue: 13(11), P. 848 - 848

Published: Oct. 22, 2024

With the advent of high-throughput technologies, field omics has made significant strides in characterizing biological systems at various levels complexity. Transcriptomics, proteomics, and metabolomics are three most widely used each providing unique insights into different layers a system. However, analyzing data set separately may not provide comprehensive understanding subject under study. Therefore, integrating multi-omics become increasingly important bioinformatics research. In this article, we review strategies for transcriptomics, data, including co-expression analysis, metabolite-gene networks, constraint-based models, pathway enrichment interactome analysis. We discuss combined integration approaches, correlation-based strategies, machine learning techniques that utilize one or more types data. By presenting these methods, aim to researchers with better how integrate gain view system, facilitating identification complex patterns interactions might be missed by single-omics analyses.

Language: Английский

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The 2023 Report on the Proteome from the HUPO Human Proteome Project

Journal of Proteome Research, Journal Year: 2024, Volume and Issue: 23(2), P. 532 - 549

Published: Jan. 17, 2024

Since 2010, the Human Proteome Project (HPP), flagship initiative of Organization (HUPO), has pursued two goals: (1) to credibly identify protein parts list and (2) make proteomics an integral part multiomics studies human health disease. The HPP relies on international collaboration, data sharing, standardized reanalysis MS sets by PeptideAtlas MassIVE-KB using Guidelines for quality assurance, integration curation non-MS neXtProt, plus extensive use antibody profiling carried out Protein Atlas. According neXtProt release 2023-04-18, expression now been detected (PE1) 18,397 19,778 predicted proteins coded in genome (93%). Of these PE1 proteins, 17,453 were with mass spectrometry (MS) accordance 944 a variety methods. number PE2, PE3, PE4 missing stands at 1381. Achieving unambiguous identification 93% encoded from across all chromosomes represents remarkable experimental progress list. Meanwhile, there are several categories that have proved resistant detection regardless protein-based methods used. Additionally some PE1–4 probably should be reclassified PE5, specifically 21 LINC entries ∼30 HERV entries; being addressed present year. Applying wide array biological clinical ensures other omics platforms as reported Biology Disease-driven teams pathology resource pillars. Current positioned transition its Grand Challenge focused determining primary function(s) every itself networks pathways within context

Language: Английский

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Transcriptomics-driven metabolic pathway analysis reveals similar alterations in lipid metabolism in mouse MASH model and human

Communications Medicine, Journal Year: 2024, Volume and Issue: 4(1)

Published: March 5, 2024

Abstract Background Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent chronic worldwide, and can rapidly progress to metabolic steatohepatitis (MASH). Accurate preclinical models methodologies are needed understand underlying mechanisms develop treatment strategies. Through meta-analysis of currently proposed mouse models, we hypothesized that diet- chemical-induced MASH model closely resembles the observed lipid metabolism alterations in humans. Methods We developed transcriptomics-driven pathway analysis (TDMPA), method aid evaluation resemblance. TDMPA uses genome-scale calculate enzymatic reaction perturbations from gene expression data. performed score compare human signatures. used an already-established WD+CCl4-induced functional assays lipidomics confirm findings. Results Both exhibit numerous altered pathways, including triglyceride biosynthesis, fatty acid beta-oxidation, bile cholesterol metabolism, oxidative phosphorylation. significant reduction mitochondrial functions bioenergetics, as well acylcarnitines for model. identify wide range species within most perturbed pathways predicted by TDMPA. Triglycerides, phospholipids, acids increased significantly liver, confirming our initial observations. Conclusions introduce TDMPA, methodology evaluating disorders. By comparing signatures typify MASH, show good resemblance WD+CCl4 Our presented approach provides valuable tool defining space experimental design assessing metabolism.

Language: Английский

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