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Record W3094190624 · doi:10.1101/2020.10.20.347948

Epistatic interactions shape the interplay between beneficial alleles and gain or loss of pathways in the evolution of novel metabolism

2020· preprint· en· W3094190624 on OpenAlexaff
Eric L. Bruger, Lon M. Chubiz, José Rojas-Echenique, Caleb J Renshaw, Nora Victoria Espericueta, Jeremy A. Draghi, Christopher J. Marx

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsUniversity of Toronto
FundersNational Institutes of HealthNational Science Foundation
KeywordsEpistasisBiologyAlleleGeneticsContext (archaeology)Metabolic pathwayGenePhenotypeFitness landscapeComputational biologyEvolutionary biologyPopulation

Abstract

fetched live from OpenAlex

Abstract Fitness landscapes are often invoked to interpret the effects of allele substitutions and their interactions; however, evolution also includes larger changes like gene loss and acquisition. Previous work with the methylotrophic bacterium Methylorubrum extorquens AM1 identified strongly beneficial mutations in a strain evolved to utilize a novel, Foreign pathway in place of its native central metabolic pathway for growth on methanol. These mutations were consistently beneficial, regardless of the order in which they arose. Here we extend this analysis to consider loss or acquisition of metabolic pathways by examining strains relying upon either the Native pathway, or both (‘ Dual ’) pathways present. Unlike in the Foreign pathway context in which they evolved, these alleles were often deleterious in these alternative genetic backgrounds, following patterns that were strongly contingent on the specific pathways and other evolved alleles present. Landscapes for these alternative pathway backgrounds altered which genotypes correspond to local fitness peaks and would restrict the set of accessible evolutionary trajectories. These epistatic interactions negatively impact the probability of maintaining multiple degenerate pathways, making it more difficult for these pathways to coevolve. Together, our results highlight the uncertainty of retaining novel functions acquired via horizontal gene transfer (HGT), and that the potential for cells to either adopt novel functions or to maintain degenerate pathways together in a genome is heavily dependent upon the underlying epistatic interactions between them. Author Summary The evolution of physiology in microbes has important impacts ranging from global cycling of elements to the emergence and spread of pathogens and their resistance to antibiotics. While genetic interactions between mutations in evolving lineages of microbes have been investigated, these have not included the acquisition of novel genes on elements like plasmids, and thus how these elements interact with existing alleles. The dynamics of novel gene retention are of interest from both positive (e.g., biotechnology) and negative (e.g., antimicrobial resistance) practical impacts. We find that the patterns of interactions between evolved alleles appear substantially different, and generally much less positive, when moved into novel genetic backgrounds. Additionally, these preexisting alleles were found to have strong impacts on the ability of genotypes to maintain – and in rare cases coevolve with – novel genes and pathways. These results show that even though they evolved separately, the particular alleles in a genetic background, and importantly the physiological impacts they confer, weigh heavily on whether genes for novel metabolic processes are maintained.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.259
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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