The Role of Structural Variation in Adaptation and Evolution of Yeast and Other Fungi

Gorkovskiy, Anton; Verstrepen, Kevin J.

doi:10.3390/genes12050699

Cited by 21 publications

(15 citation statements)

References 298 publications

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“…In addition to loss or gain of entire chromosomes, we also detect large (>40 kb) CNVs in our evolved clones, including eight unique amplifications and two deletions (Table 2 and Supplementary Figure 5). Although CNVs are a common adaptive mechanism (Gorkovskiy and Verstrepen 2021), we do not find recurrent CNVs associated with galactose metabolism (Table 2 and Supplementary Figure 5). Although we found several CNVs in the right arm of Chromosome II, in only population A4 does the amplification include the GAL1 - GAL10 - GAL7 gene cluster (chrII: 268984-275164).…”

Section: Resultsmentioning

confidence: 67%

Long-term adaptation to galactose as a sole carbon source selects for mutations in nutrient signaling pathways

Martínez

Conboy

Buskirk

et al. 2022

Preprint

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Galactose is a secondary fermentable sugar that requires specific regulatory and structural genes for its assimilation, which are under catabolite repression by glucose. When glucose is absent, the catabolic repression is attenuated, and the structural GAL genes are fully activated. In Saccharomyces cerevisiae, the GAL pathway is under selection in environments where galactose is present. However, it is unclear the adaptive strategies in response to long-term propagation in galactose as a sole carbon source in laboratory evolution experiments. Here, we performed a 4,000-generation evolution experiment using 48 diploid Saccharomyces cerevisiae populations to study adaptation in galactose. We show that fitness gains were greater in the galactose-evolved population than in identically evolved populations with glucose as a sole carbon source. Whole-genome sequencing of 96 evolved clones revealed recurrent de novo single nucleotide mutations in candidate targets of selection, copy number variations, and ploidy changes. We find that most mutations that improve fitness in galactose lie outside of the canonical GAL pathway and are involved in nutrient signaling. Reconstruction of specific evolved alleles in candidate target of selection, SEC23 and IRA1, showed a significant increase in fitness in galactose compared to glucose. In addition, most of our evolved populations (28/46; 61%) fixed aneuploidies on Chromosome VIII, suggesting a parallel adaptive amplification. Finally, we show greater loss of extrachromosomal elements in our glucose-evolved lineages compared with previous glucose evolution. Broadly, these data further our understanding of the evolutionary pressures that drive adaptation to less-preferred carbon sources.

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Section: Resultsmentioning

confidence: 67%

Long-term adaptation to galactose as a sole carbon source selects for mutations in nutrient signaling pathways

Martínez

Conboy

Buskirk

et al. 2022

Preprint

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“…Maintenance of genome organization and chromosomal variations are interdependent and go hand-in-hand in the course of genome evolution. On one hand, structural variations play a major role in the adaptation and evolution of the organism ( Straus and Hoffmann 1975 ; Sonti and Roth 1989 ; Rocha et al 1999 ; Rocha 2002 ; Srinivasan et al 2015 ; Veetil et al 2020 ; Gorkovskiy and Verstrepen 2021 ), and on the other hand, they can disrupt the genomic organization and thus affect fitness ( Hill and Harnish 1981 ; Hill and Gray 1988 ; Rocha 2004 ; Adler et al 2014 ). Taken together, by analyzing ∼6,000 genomes, our study finds an association between intrachromosomal long DNA repeats and replication-dependent bacterial genome organization.…”

Section: Discussionmentioning

confidence: 99%

Replication-Dependent Organization Constrains Positioning of Long DNA Repeats in Bacterial Genomes

Malhotra

Seshasayee

2022

Genome Biology and Evolution

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Bacterial genome organisation is primarily driven by chromosomal replication from a single origin of replication. However chromosomal rearrangements, which can disrupt such organisation, are inevitable in nature. Long DNA repeats are major players mediating rearrangements, large and small, via homologous recombination. Since changes to genome organisation affect bacterial fitness - and more so in fast-growing than slow-growing bacteria - and are under selection, it is reasonable to expect that genomic positioning of long DNA repeats is also under selection. To test this, we identified identical DNA repeats of at least 100 base pairs across ∼6,000 bacterial genomes and compared their distribution in fast and slow-growing bacteria. We found that long identical DNA repeats are distributed in a non-random manner across bacterial genomes. Their distribution differs in their overall number, orientation and proximity to the origin of replication, between fast and slow-growing bacteria. We show that their positioning - which might arise from a combination of the processes that produce repeats and selection on rearrangements that recombination between repeat elements might cause - permits less disruption to the replication-dependent genome organisation of bacteria compared to random suggesting it as a major constraint to positioning of long DNA repeats

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“…The availability of long read sequencing technologies represented a big step forward towards an accurate identification and genotyping of structural variants (Fujimoto et.al., 2021; Thibodeau et.al., 2020). Achieving this goal is becoming a requirement for current genomics, given the documented role of SVs as drivers of phenotyping variability and evolution (Alonge et al, 2020; Gorkovskiy et.al., 2021; Qiao et al, 2019; Wang et al, 2009). In this work we present the results of our efforts to develop novel algorithmic techniques, aiming to increase accuracy of both discovery and genotyping of SVs.…”

Section: Discussionmentioning

confidence: 99%

A graph clustering algorithm for detection and genotyping of structural variants from long reads

Gaitan

Duitama

2022

Preprint

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Structural variants (SV) are polymorphisms defined by their length (>50 bp). The usual types of SVs are deletions, insertions, translocations, inversions, and copy number variants. SV detection and genotyping is fundamental given the role of SVs in phenomena such as phenotypic variation and evolutionary events. Thus, methods to identify SVs using long read sequencing data have been recently developed. We present an accurate and efficient algorithm to predict SVs from long-read sequencing data. The algorithm starts collecting evidence (Signatures) of SVs from read alignments. Then, signatures are clustered based on a Euclidean graph with coordinates calculated from lengths and genomic positions. Clustering is performed by the DBSCAN algorithm, which provides the advantage of delimiting clusters with high resolution. Clusters are transformed into SVs and a Bayesian model allows to precisely genotype SVs based on their supporting evidence. This algorithm is integrated in the single sample variants detector of the Next Generation Sequencing Experience Platform (NGSEP), which facilitates the integration with other functionalities for genomics analysis. For benchmarking, our algorithm is compared against different tools using VISOR for simulation and the GIAB SV dataset for real data. For indel calls in a 20x depth Nanopore simulated dataset, the DBSCAN algorithm performed better, achieving an F-score of 98%, compared to 97.8 for Dysgu, 97.8 for SVIM, 97.7 for CuteSV, and 96.8 for Sniffles. We believe that this work makes a significant contribution to the development of bioinformatic strategies to maximize the use of long read sequencing technologies.

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The Role of Structural Variation in Adaptation and Evolution of Yeast and Other Fungi

Cited by 21 publications

References 298 publications

Long-term adaptation to galactose as a sole carbon source selects for mutations in nutrient signaling pathways

Long-term adaptation to galactose as a sole carbon source selects for mutations in nutrient signaling pathways

Replication-Dependent Organization Constrains Positioning of Long DNA Repeats in Bacterial Genomes

A graph clustering algorithm for detection and genotyping of structural variants from long reads

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