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Fernandez ME, Martinez-Romero J, Aon MA, Bernier M, Price NL, de Cabo R. How is Big Data reshaping preclinical aging research? Lab Anim (NY) 2023; 52:289-314. [PMID: 38017182 DOI: 10.1038/s41684-023-01286-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 10/10/2023] [Indexed: 11/30/2023]
Abstract
The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.
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Affiliation(s)
- Maria Emilia Fernandez
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Jorge Martinez-Romero
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
- Laboratory of Epidemiology and Population Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Miguel A Aon
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
- Laboratory of Cardiovascular Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Michel Bernier
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Nathan L Price
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Rafael de Cabo
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
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Swart PC, Du Plessis M, Rust C, Womersley JS, van den Heuvel LL, Seedat S, Hemmings SMJ. Identifying genetic loci that are associated with changes in gene expression in PTSD in a South African cohort. J Neurochem 2023; 166:705-719. [PMID: 37522158 PMCID: PMC10953375 DOI: 10.1111/jnc.15919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 06/30/2023] [Accepted: 07/05/2023] [Indexed: 08/01/2023]
Abstract
The molecular mechanisms underlying posttraumatic stress disorder (PTSD) are yet to be fully elucidated, especially in underrepresented population groups. Expression quantitative trait loci (eQTLs) are DNA sequence variants that influence gene expression, in a local (cis-) or distal (trans-) manner, and subsequently impact cellular, tissue, and system physiology. This study aims to identify genetic loci associated with gene expression changes in a South African PTSD cohort. Genome-wide genotype and RNA-sequencing data were obtained from 32 trauma-exposed controls and 35 PTSD cases of mixed-ancestry, as part of the SHARED ROOTS project. The first approach utilised 108 937 single-nucleotide polymorphisms (SNPs) (MAF > 10%) and 11 312 genes with Matrix eQTL to map potential eQTLs, while controlling for covariates as appropriate. The second analysis was focused on 5638 SNPs related to a previously calculated PTSD polygenic risk score for this cohort. SNP-gene pairs were considered eQTLs if they surpassed Bonferroni correction and had a false discovery rate <0.05. We did not identify eQTLs that significantly influenced gene expression in a PTSD-dependent manner. However, several known cis-eQTLs, independent of PTSD diagnosis, were observed. rs8521 (C > T) was associated with TAGLN and SIDT2 expression, and rs11085906 (C > T) was associated with ZNF333 expression. This exploratory study provides insight into the molecular mechanisms associated with PTSD in a non-European, admixed sample population. This study was limited by the cross-sectional design and insufficient statistical power. Overall, this study should encourage further multi-omics approaches towards investigating PTSD in diverse populations.
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Affiliation(s)
- Patricia C. Swart
- Department of Psychiatry, Faculty of Medicine and Health SciencesStellenbosch UniversityCape TownSouth Africa
- South African Medical Research Council/Stellenbosch University Genomics of Brain Disorders UnitCape TownSouth Africa
| | - Morne Du Plessis
- Department of Psychiatry, Faculty of Medicine and Health SciencesStellenbosch UniversityCape TownSouth Africa
- South African Medical Research Council/Stellenbosch University Genomics of Brain Disorders UnitCape TownSouth Africa
| | - Carlien Rust
- Department of Psychiatry, Faculty of Medicine and Health SciencesStellenbosch UniversityCape TownSouth Africa
- South African Medical Research Council/Stellenbosch University Genomics of Brain Disorders UnitCape TownSouth Africa
| | - Jacqueline S. Womersley
- Department of Psychiatry, Faculty of Medicine and Health SciencesStellenbosch UniversityCape TownSouth Africa
- South African Medical Research Council/Stellenbosch University Genomics of Brain Disorders UnitCape TownSouth Africa
| | - Leigh L. van den Heuvel
- Department of Psychiatry, Faculty of Medicine and Health SciencesStellenbosch UniversityCape TownSouth Africa
- South African Medical Research Council/Stellenbosch University Genomics of Brain Disorders UnitCape TownSouth Africa
| | - Soraya Seedat
- Department of Psychiatry, Faculty of Medicine and Health SciencesStellenbosch UniversityCape TownSouth Africa
- South African Medical Research Council/Stellenbosch University Genomics of Brain Disorders UnitCape TownSouth Africa
| | - Sian M. J. Hemmings
- Department of Psychiatry, Faculty of Medicine and Health SciencesStellenbosch UniversityCape TownSouth Africa
- South African Medical Research Council/Stellenbosch University Genomics of Brain Disorders UnitCape TownSouth Africa
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Poverennaya EV, Pyatnitskiy MA, Dolgalev GV, Arzumanian VA, Kiseleva OI, Kurbatov IY, Kurbatov LK, Vakhrushev IV, Romashin DD, Kim YS, Ponomarenko EA. Exploiting Multi-Omics Profiling and Systems Biology to Investigate Functions of TOMM34. BIOLOGY 2023; 12:198. [PMID: 36829477 PMCID: PMC9952762 DOI: 10.3390/biology12020198] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 01/17/2023] [Accepted: 01/25/2023] [Indexed: 01/31/2023]
Abstract
Although modern biology is now in the post-genomic era with vastly increased access to high-quality data, the set of human genes with a known function remains far from complete. This is especially true for hundreds of mitochondria-associated genes, which are under-characterized and lack clear functional annotation. However, with the advent of multi-omics profiling methods coupled with systems biology algorithms, the cellular role of many such genes can be elucidated. Here, we report genes and pathways associated with TOMM34, Translocase of Outer Mitochondrial Membrane, which plays role in the mitochondrial protein import as a part of cytosolic complex together with Hsp70/Hsp90 and is upregulated in various cancers. We identified genes, proteins, and metabolites altered in TOMM34-/- HepG2 cells. To our knowledge, this is the first attempt to study the functional capacity of TOMM34 using a multi-omics strategy. We demonstrate that TOMM34 affects various processes including oxidative phosphorylation, citric acid cycle, metabolism of purine, and several amino acids. Besides the analysis of already known pathways, we utilized de novo network enrichment algorithm to extract novel perturbed subnetworks, thus obtaining evidence that TOMM34 potentially plays role in several other cellular processes, including NOTCH-, MAPK-, and STAT3-signaling. Collectively, our findings provide new insights into TOMM34's cellular functions.
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Affiliation(s)
| | - Mikhail A. Pyatnitskiy
- Institute of Biomedical Chemistry, Moscow 119121, Russia
- Faculty Of Computer Science, National Research University Higher School of Economics, Moscow 101000, Russia
| | | | | | | | | | | | | | | | - Yan S. Kim
- Institute of Biomedical Chemistry, Moscow 119121, Russia
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Emelianova M, Gainullina A, Poperechnyi N, Loboda A, Artyomov M, Sergushichev A. Shiny GATOM: omics-based identification of regulated metabolic modules in atom transition networks. Nucleic Acids Res 2022; 50:W690-W696. [PMID: 35639928 PMCID: PMC9252739 DOI: 10.1093/nar/gkac427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/22/2022] [Accepted: 05/10/2022] [Indexed: 11/25/2022] Open
Abstract
Multiple high-throughput omics techniques provide different angles on systematically quantifying and studying metabolic regulation of cellular processes. However, an unbiased analysis of such data and, in particular, integration of multiple types of data remains a challenge. Previously, for this purpose we developed GAM web-service for integrative metabolic network analysis. Here we describe an updated pipeline GATOM and the corresponding web-service Shiny GATOM, which takes as input transcriptional and/or metabolomic data and finds a metabolic subnetwork most regulated between the two conditions of interest. GATOM features a new metabolic network topology based on atom transition, which significantly improves interpretability of the analysis results. To address computational challenges arising with the new network topology, we introduce a new variant of the maximum weight connected subgraph problem and provide a corresponding exact solver. To make the used networks up-to-date we upgraded the KEGG-based network construction pipeline and developed one based on the Rhea database, which allows analysis of lipidomics data. Finally, we simplified local installation, providing R package mwcsr for solving relevant graph optimization problems and R package gatom, which implements the GATOM pipeline. The web-service is available at https://ctlab.itmo.ru/shiny/gatom and https://artyomovlab.wustl.edu/shiny/gatom.
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Affiliation(s)
- Mariia Emelianova
- Computer Technologies Laboratory, ITMO University, Saint Petersburg, Russia 197101, Russia
| | - Anastasiia Gainullina
- Computer Technologies Laboratory, ITMO University, Saint Petersburg, Russia 197101, Russia.,Koltzov Institute of Developmental Biology, Russian Academy of Sciences, Moscow 119334, Russia
| | - Nikolay Poperechnyi
- Computer Technologies Laboratory, ITMO University, Saint Petersburg, Russia 197101, Russia
| | - Alexander Loboda
- Computer Technologies Laboratory, ITMO University, Saint Petersburg, Russia 197101, Russia
| | - Maxim Artyomov
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO 63110, USA.,Computer Technologies Laboratory, ITMO University, Saint Petersburg, Russia 197101, Russia
| | - Alexey Sergushichev
- Computer Technologies Laboratory, ITMO University, Saint Petersburg, Russia 197101, Russia.,Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO 63110, USA
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