Network hubs affect evolvability

Helsen, Jana; Frickel, Jens; Jelier, Rob; Verstrepen, Kevin J.

doi:10.1371/journal.pbio.3000111

Cited by 21 publications

(18 citation statements)

References 23 publications

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“…It has been hypothesized that losing genes with many connections (so-called hubs) would lead to a more diverse evolutionary outcome ( Koubkova-Yu et al. 2018 ; Helsen et al. 2019 ).…”

Section: Resultsmentioning

confidence: 99%

Gene Loss Predictably Drives Evolutionary Adaptation

Helsen

Voordeckers

Vanderwaeren

et al. 2020

Molecular Biology and Evolution

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Abstract Loss of gene function is common throughout evolution, even though it often leads to reduced fitness. In this study, we systematically evaluated how an organism adapts after deleting genes that are important for growth under oxidative stress. By evolving, sequencing, and phenotyping over 200 yeast lineages, we found that gene loss can enhance an organism’s capacity to evolve and adapt. Whereas gene loss often led to an immediate decrease in fitness, many mutants rapidly acquired suppressor mutations that restored fitness. Depending on the strain’s genotype, some ultimately even attained higher fitness levels than similarly adapted wild-type cells. Further, cells with deletions in different modules of the genetic network followed distinct and predictable mutational trajectories. Finally, losing highly connected genes increased evolvability by facilitating the emergence of a more diverse array of phenotypes after adaptation. Together, our findings show that loss of specific parts of a genetic network can facilitate adaptation by opening alternative evolutionary paths.

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“…It has been hypothesized that losing genes with many connections (so-called hubs) would lead to a more diverse evolutionary outcome ( Koubkova-Yu et al. 2018 ; Helsen et al. 2019 ).…”

Section: Resultsmentioning

confidence: 99%

Gene Loss Predictably Drives Evolutionary Adaptation

Helsen

Voordeckers

Vanderwaeren

et al. 2020

Molecular Biology and Evolution

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“…Rapid evolution of the diapause transcriptome could be facilitated by the evolution of genes that serve as hubs in a transcriptional network, wherein changes in hub transcription are correlated with changes in the transcriptional pattern of many genes connected to the hub, for example protein–protein or transcription factor–target interactions ( 39 ). We tested whether transcripts DE between the host races tended to exhibit high centrality (i.e., act as hubs) in a network with transcripts as nodes and edges assigned based on the strength of the correlation in the expression time series between nodes ( SI Appendix , Supplementary Methods ).…”

Section: Resultsmentioning

confidence: 99%

“…Second, we tested whether expression trajectories during diapause diverged between the host races, including an analysis of the contribution of hub-like genes. Although a polygenic model might predict expression divergence of many genes, it does not preclude a disproportionate role for well-connected, hub-like genes that may facilitate rapid adaptation (39). Third, we used genomic surveys of natural populations and a pooled sequencing variant of genome-wide association (GWA) analysis of diapause duration to 1) determine the genomic distribution of associated variants, 2) test for signatures of selective sweeps, 3) test for functional enrichment of genes associated with morphogenic development, and 4) test for signals of cis regulatory variation associated with differentially expressed transcripts.…”

Section: Significancementioning

confidence: 99%

“…Thus, although evolved differences are polygenic, with no particular gene having a major effect, the rapid evolution of phenology in R. pomonella appears to have favored changes in transcription at loci with higher network conductivity than expected by chance. Recent work indeed predicts that the evolution of hub-like genes can facilitate rapid evolution by facilitating larger "jumps" in phenotypic space (39,68). However, the generality of this result may depend on the strength of natural selection.…”

Section: Cns Transcriptome Trajectories Show Widespread Evolutionarymentioning

confidence: 99%

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Genome-wide variation and transcriptional changes in diverse developmental processes underlie the rapid evolution of seasonal adaptation

Dowle

Powell

Doellman

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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Many organisms enter a dormant state in their life cycle to deal with predictable changes in environments over the course of a year. The timing of dormancy is therefore a key seasonal adaptation, and it evolves rapidly with changing environments. We tested the hypothesis that differences in the timing of seasonal activity are driven by differences in the rate of development during diapause in Rhagoletis pomonella, a fly specialized to feed on fruits of seasonally limited host plants. Transcriptomes from the central nervous system across a time series during diapause show consistent and progressive changes in transcripts participating in diverse developmental processes, despite a lack of gross morphological change. Moreover, population genomic analyses suggested that many genes of small effect enriched in developmental functional categories underlie variation in dormancy timing and overlap with gene sets associated with development rate in Drosophila melanogaster. Our transcriptional data also suggested that a recent evolutionary shift from a seasonally late to a seasonally early host plant drove more rapid development during diapause in the early fly population. Moreover, genetic variants that diverged during the evolutionary shift were also enriched in putative cis regulatory regions of genes differentially expressed during diapause development. Overall, our data suggest polygenic variation in the rate of developmental progression during diapause contributes to the evolution of seasonality in R. pomonella. We further discuss patterns that suggest hourglass-like developmental divergence early and late in diapause development and an important role for hub genes in the evolution of transcriptional divergence.

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“…Even more broadly, it is notable that differences and dysfunctions of basic learning mechanisms accompany a wide range of mental disorders including substance abuse, depression and schizophrenia [198]. Future work could seek to explain neuro-typical and neuro-atypical individual differences in network learning by assessing the trajectories of adaptation that are possible from the underlying neural network architecture [114].…”

Section: Pedagogical Principles Conducive To Curious Thoughtmentioning

confidence: 99%

Network architectures supporting learnability

Zurn

Bassett

2020

Phil. Trans. R. Soc. B

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Human learners acquire complex interconnected networks of relational knowledge. The capacity for such learning naturally depends on two factors: the architecture (or informational structure) of the knowledge network itself and the architecture of the computational unit—the brain—that encodes and processes the information. That is, learning is reliant on integrated network architectures at two levels: the epistemic and the computational, or the conceptual and the neural. Motivated by a wish to understand conventional human knowledge, here, we discuss emerging work assessing network constraints on the learnability of relational knowledge, and theories from statistical physics that instantiate the principles of thermodynamics and information theory to offer an explanatory model for such constraints. We then highlight similarities between those constraints on the learnability of relational networks, at one level, and the physical constraints on the development of interconnected patterns in neural systems, at another level, both leading to hierarchically modular networks. To support our discussion of these similarities, we employ an operational distinction between the modeller (e.g. the human brain), the model (e.g. a single human’s knowledge) and the modelled (e.g. the information present in our experiences). We then turn to a philosophical discussion of whether and how we can extend our observations to a claim regarding explanation and mechanism for knowledge acquisition. What relation between hierarchical networks, at the conceptual and neural levels, best facilitate learning? Are the architectures of optimally learnable networks a topological reflection of the architectures of comparably developed neural networks? Finally, we contribute to a unified approach to hierarchies and levels in biological networks by proposing several epistemological norms for analysing the computational brain and social epistemes, and for developing pedagogical principles conducive to curious thought. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.

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Network hubs affect evolvability

Cited by 21 publications

References 23 publications

Gene Loss Predictably Drives Evolutionary Adaptation

Gene Loss Predictably Drives Evolutionary Adaptation

Genome-wide variation and transcriptional changes in diverse developmental processes underlie the rapid evolution of seasonal adaptation

Network architectures supporting learnability

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