ArticlePLoS computational biology2011
Loss of genetic redundancy in reductive genome evolution.
Article in PLoS computational biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed, 85 citations in OpenAlex.
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- Glyceraldehyde-3-phosphate dehydrogenase homologs as bifunctional gatekeepers of metabolic segregation inProceedings of the National Academy of Sciences of the United States of America · 2025Article
- Molecular mechanisms of UCP1-independent thermogenesis: the role of futile cycles in energy dissipation.Journal of physiology and biochemistry · 2025Review
- Unique trajectory of gene family evolution from genomic analysis of nearly all known species in an ancient yeast lineage.Molecular systems biology · 2025Article
- Transcriptional regulation of two redundant 3-bromo-4-hydroxybenzoate catabolic operons via two different regulatory modes inApplied and environmental microbiology · 2025Article
- Stochasticity, determinism, and contingency shape genome evolution of endosymbiotic bacteria.Nature communications · 2024Article
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- Shining the spotlight on the neglected: new high-quality genome assemblies as a gateway to understanding the evolution of Trypanosomatidae.BMC genomics · 2023Article
- Environmental stress leads to genome streamlining in a widely distributed species of soil bacteria.The ISME journal · 2022Article
- Unraveling Fe(II)-Oxidizing Mechanisms in a Facultative Fe(II) Oxidizer, Sideroxydans lithotrophicus Strain ES-1, via Culturing, Transcriptomics, and Reverse Transcription-Quantitative PCR.Applied and environmental microbiology · 2022Article
- Paving the Way: Contributions of Big Data to Apicomplexan and Kinetoplastid Research.Frontiers in cellular and infection microbiology · 2022Review
- Peptidoglycan enzymes ofFrontiers in microbiology · 2022Review
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- Genomic analysis finds no evidence of canonical eukaryotic DNA processing complexes in a free-living protist.Nature communications · 2021Article
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- Symbiosis and the Anthropocene.Symbiosis (Philadelphia, Pa.) · 2021Article
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- Phylogenetic Relationships and Potential Functional Attributes of the GenusFrontiers in microbiology · 2020Article
- Domain-based Comparative Analysis of Bacterial Proteomes: Uniqueness, Interactions, and the Dark Matter.Current genomics · 2019Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biological systems evolved to be functionally robust in uncertain environments, but also highly adaptable. Such robustness is partly achieved by genetic redundancy, where the failure of a specific component through mutation or environmental challenge can be compensated by duplicate components capable of performing, to a limited extent, the same function. Highly variable environments require very robust systems. Conversely, predictable environments should not place a high selective value on robustness. Here we test this hypothesis by investigating the evolutionary dynamics of genetic redundancy in extremely reduced genomes, found mostly in intracellular parasites and endosymbionts. By combining data analysis with simulations of genome evolution we show that in the extensive gene loss suffered by reduced genomes there is a selective drive to keep the diversity of protein families while sacrificing paralogy. We show that this is not a by-product of the known drivers of genome reduction and that there is very limited convergence to a common core of families, indicating that the repertoire of protein families in reduced genomes is the result of historical contingency and niche-specific adaptations. We propose that our observations reflect a loss of genetic redundancy due to a decreased selection for robustness in a predictable environment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.