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Tengölics R, Szappanos B, Mülleder M, Kalapis D, Grézal G, Sajben C, Agostini F, Mokochinski JB, Bálint B, Nagy LG, Ralser M, Papp B. The metabolic domestication syndrome of budding yeast. Proc Natl Acad Sci U S A 2024; 121:e2313354121. [PMID: 38457520 PMCID: PMC10945815 DOI: 10.1073/pnas.2313354121] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 12/11/2023] [Indexed: 03/10/2024] Open
Abstract
Cellular metabolism evolves through changes in the structure and quantitative states of metabolic networks. Here, we explore the evolutionary dynamics of metabolic states by focusing on the collection of metabolite levels, the metabolome, which captures key aspects of cellular physiology. Using a phylogenetic framework, we profiled metabolites in 27 populations of nine budding yeast species, providing a graduated view of metabolic variation across multiple evolutionary time scales. Metabolite levels evolve more rapidly and independently of changes in the metabolic network's structure, providing complementary information to enzyme repertoire. Although metabolome variation accumulates mainly gradually over time, it is profoundly affected by domestication. We found pervasive signatures of convergent evolution in the metabolomes of independently domesticated clades of Saccharomyces cerevisiae. Such recurring metabolite differences between wild and domesticated populations affect a substantial part of the metabolome, including rewiring of the TCA cycle and several amino acids that influence aroma production, likely reflecting adaptation to human niches. Overall, our work reveals previously unrecognized diversity in central metabolism and the pervasive influence of human-driven selection on metabolite levels in yeasts.
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Affiliation(s)
- Roland Tengölics
- Hungarian Centre of Excellence for Molecular Medicine - Biological Research Centre Metabolic Systems Biology Lab, Szeged6726, Hungary
- Synthetic and System Biology Unit, National Laboratory of Biotechnology, Institute of Biochemistry, Biological Research Centre, Hungarian Research Network, Szeged6726, Hungary
- Metabolomics Lab, Core facilities, Biological Research Centre, Hungarian Research Network, Szeged6726, Hungary
| | - Balázs Szappanos
- Hungarian Centre of Excellence for Molecular Medicine - Biological Research Centre Metabolic Systems Biology Lab, Szeged6726, Hungary
- Synthetic and System Biology Unit, National Laboratory of Biotechnology, Institute of Biochemistry, Biological Research Centre, Hungarian Research Network, Szeged6726, Hungary
- Department of Biotechnology, University of Szeged, Szeged6726, Hungary
| | - Michael Mülleder
- Charité Universitätsmedizin, Core Facility High-Throughput Mass Spectrometry, Berlin10117, Germany
| | - Dorottya Kalapis
- Hungarian Centre of Excellence for Molecular Medicine - Biological Research Centre Metabolic Systems Biology Lab, Szeged6726, Hungary
- Synthetic and System Biology Unit, National Laboratory of Biotechnology, Institute of Biochemistry, Biological Research Centre, Hungarian Research Network, Szeged6726, Hungary
| | - Gábor Grézal
- Hungarian Centre of Excellence for Molecular Medicine - Biological Research Centre Metabolic Systems Biology Lab, Szeged6726, Hungary
- Synthetic and System Biology Unit, National Laboratory of Biotechnology, Institute of Biochemistry, Biological Research Centre, Hungarian Research Network, Szeged6726, Hungary
| | - Csilla Sajben
- Metabolomics Lab, Core facilities, Biological Research Centre, Hungarian Research Network, Szeged6726, Hungary
| | - Federica Agostini
- Department of Biochemistry, Charité Universitätsmedizin, Berlin10117, Germany
| | - João Benhur Mokochinski
- Synthetic and System Biology Unit, National Laboratory of Biotechnology, Institute of Biochemistry, Biological Research Centre, Hungarian Research Network, Szeged6726, Hungary
| | - Balázs Bálint
- Institute of Biochemistry, Biological Research Centre, Hungarian Research Network, Szeged6726, Hungary
| | - László G. Nagy
- Institute of Biochemistry, Biological Research Centre, Hungarian Research Network, Szeged6726, Hungary
| | - Markus Ralser
- Department of Biochemistry, Charité Universitätsmedizin, Berlin10117, Germany
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, LondonNW11AT, United Kingdom
| | - Balázs Papp
- Hungarian Centre of Excellence for Molecular Medicine - Biological Research Centre Metabolic Systems Biology Lab, Szeged6726, Hungary
- Synthetic and System Biology Unit, National Laboratory of Biotechnology, Institute of Biochemistry, Biological Research Centre, Hungarian Research Network, Szeged6726, Hungary
- National Laboratory for Health Security, Biological Research Centre, Hungarian Research Network, Szeged6726, Hungary
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Islam M, Behura SK. Role of paralogs in the sex-bias transcriptional and metabolic regulation of the brain-placental axis in mice. Placenta 2024; 145:143-150. [PMID: 38134547 DOI: 10.1016/j.placenta.2023.12.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/12/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023]
Abstract
INTRODUCTION Duplicated genes or paralogs play important roles in the adaptive function of eukaryotic genomes. Animal studies have shown evidence for the functional role of paralogs in pregnancy, but our knowledge about the role of paralogs in the fetoplacental regulation remains limited. In particular, if fetoplacental metabolic regulation is modulated by differential expression of paralogs remains unexamined. METHODS In this study, gene expression profiles of day-15 placenta and fetal brain were compared to identify families or groups of paralogous genes expressed in the placenta and brain of male versus female fetuses in mice. A Bayesian modeling was applied to infer directional relationship of transcriptional variation of the paralogs relative to the phylogenetic variation of the genes in each family. Gas chromatography-mass spectrometry (GC-MS) was used to perform untargeted metabolomics analysis of day-15 placenta and fetal brain of both sexes. RESULTS We identified paralog groups that were expressed in a sex and/or tissue biased manner between the placenta and fetal brain. Bayesian modeling showed evidence for directional relationship between expression and phylogeny of specific paralogs. These relationships were sex specific. GC-MS analysis identified metabolites that were expressed in a sex-bias manner between the placenta and fetal brain. By performing integrative analysis of the metabolomics and gene expression data, we showed that specific groups of metabolites and paralogous genes were expressed in a coordinated manner between the placenta and fetal brain. DISCUSSION The findings of this study collectively suggest that paralogs play an influential role in the regulation of the brain-placental axis in mice.
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Affiliation(s)
- Maliha Islam
- Division of Animal Sciences, University of Missouri, 920 East Campus Drive, Columbia, Missouri, 65211, USA
| | - Susanta K Behura
- Division of Animal Sciences, University of Missouri, 920 East Campus Drive, Columbia, Missouri, 65211, USA; MU Institute for Data Science and Informatics, University of Missouri, USA; Interdisciplinary Reproduction and Health Group, University of Missouri, USA; Interdisciplinary Neuroscience Program, University of Missouri, USA.
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Chen SAA, Kern AF, Ang RML, Xie Y, Fraser HB. Gene-by-environment interactions are pervasive among natural genetic variants. CELL GENOMICS 2023; 3:100273. [PMID: 37082145 PMCID: PMC10112290 DOI: 10.1016/j.xgen.2023.100273] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 10/09/2022] [Accepted: 01/31/2023] [Indexed: 04/22/2023]
Abstract
Gene-by-environment (GxE) interactions, in which a genetic variant's phenotypic effect is condition specific, are fundamental for understanding fitness landscapes and evolution but have been difficult to identify at the single-nucleotide level. Although many condition-specific quantitative trait loci (QTLs) have been mapped, these typically contain numerous inconsequential variants in linkage, precluding understanding of the causal GxE variants. Here, we introduce BARcoded Cas9 retron precise parallel editing via homology (CRISPEY-BAR), a high-throughput precision genome editing strategy, and use it to map GxE interactions of naturally occurring genetic polymorphisms impacting yeast growth. We identified hundreds of GxE variants within condition-specific QTLs, revealing unexpected genetic complexity. Moreover, we found that 93.7% of non-neutral natural variants within ergosterol biosynthesis pathway genes showed GxE interactions, including many impacting antifungal drug resistance through diverse molecular mechanisms. In sum, our results suggest an extremely complex, context-dependent fitness landscape characterized by pervasive GxE interactions while also demonstrating massively parallel genome editing as an effective means for investigating this complexity.
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Affiliation(s)
- Shi-An A. Chen
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Alexander F. Kern
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Roy Moh Lik Ang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Yihua Xie
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Hunter B. Fraser
- Department of Biology, Stanford University, Stanford, CA 94305, USA
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