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Sethi A, Melamud E. Joint inference of physiological network and survival analysis identifies factors associated with aging rate. CELL REPORTS METHODS 2022; 2:100356. [PMID: 36590696 PMCID: PMC9795372 DOI: 10.1016/j.crmeth.2022.100356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 07/11/2022] [Accepted: 11/10/2022] [Indexed: 12/04/2022]
Abstract
We describe methodology for joint reconstruction of physiological-survival networks from observational data capable of identifying key survival-associated variables, inferring a minimal physiological network structure, and bridging this network to the Gompertzian survival layer. Using synthetic network structures, we show that the method is capable of identifying aging variables in cohorts as small as 5,000 participants. Applying the methodology to the observational human cohort, we find that interleukin-6, vascular calcification, and red-blood distribution width are strong predictors of baseline fitness. More important, we find that red blood cell counts, kidney function, and phosphate level are directly linked to the Gompertzian aging rate. Our model therefore enables discovery of processes directly linked to the aging rate of our species. We further show that this epidemiological framework can be applied as a causal inference engine to simulate the effects of interventions on physiology and longevity.
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Affiliation(s)
- Anurag Sethi
- Calico Life Sciences LLC, 1170 Veterans Blvd., South San Francisco, CA 94080, USA
| | - Eugene Melamud
- Calico Life Sciences LLC, 1170 Veterans Blvd., South San Francisco, CA 94080, USA
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Williams EG, Pfister N, Roy S, Statzer C, Haverty J, Ingels J, Bohl C, Hasan M, Čuklina J, Bühlmann P, Zamboni N, Lu L, Ewald CY, Williams RW, Aebersold R. Multiomic profiling of the liver across diets and age in a diverse mouse population. Cell Syst 2022; 13:43-57.e6. [PMID: 34666007 PMCID: PMC8776606 DOI: 10.1016/j.cels.2021.09.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 07/12/2021] [Accepted: 09/14/2021] [Indexed: 01/21/2023]
Abstract
We profiled the liver transcriptome, proteome, and metabolome in 347 individuals from 58 isogenic strains of the BXD mouse population across age (7 to 24 months) and diet (low or high fat) to link molecular variations to metabolic traits. Several hundred genes are affected by diet and/or age at the transcript and protein levels. Orthologs of two aging-associated genes, St7 and Ctsd, were knocked down in C. elegans, reducing longevity in wild-type and mutant long-lived strains. The multiomics data were analyzed as segregating gene networks according to each independent variable, providing causal insight into dietary and aging effects. Candidates were cross-examined in an independent diversity outbred mouse liver dataset segregating for similar diets, with ∼80%-90% of diet-related candidate genes found in common across datasets. Together, we have developed a large multiomics resource for multivariate analysis of complex traits and demonstrate a methodology for moving from observational associations to causal connections.
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Affiliation(s)
- Evan G Williams
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
| | - Niklas Pfister
- Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Suheeta Roy
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Cyril Statzer
- Department of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland
| | - Jack Haverty
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Jesse Ingels
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Casey Bohl
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Moaraj Hasan
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland
| | - Jelena Čuklina
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland
| | - Peter Bühlmann
- Department of Mathematics, Seminar for Statistics, ETH Zürich, Zurich, Switzerland
| | - Nicola Zamboni
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland
| | - Lu Lu
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Collin Y Ewald
- Department of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland; Faculty of Science, University of Zürich, Zurich, Switzerland
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