Fitness Landscape of the Fission Yeast Genome

Abstract

The relationship between DNA sequence, biochemical function, and molecular evolution is relatively well-described for protein-coding regions of genomes, but far less clear in noncoding regions, particularly, in eukaryote genomes. In part, this is because we lack a complete description of the essential noncoding elements in a eukaryote genome. To contribute to this challenge, we used saturating transposon mutagenesis to interrogate the Schizosaccharomyces pombe genome. We generated 31 million transposon insertions, a theoretical coverage of 2.4 insertions per genomic site. We applied a five-state hidden Markov model (HMM) to distinguish insertion-depleted regions from insertion biases. Both raw insertion-density and HMM-defined fitness estimates showed significant quantitative relationships to gene knockout fitness, genetic diversity, divergence, and expected functional regions based on transcription and gene annotations. Through several analyses, we conclude that transposon insertions produced fitness effects in 66–90% of the genome, including substantial portions of the noncoding regions. Based on the HMM, we estimate that 10% of the insertion depleted sites in the genome showed no signal of conservation between species and were weakly transcribed, demonstrating limitations of comparative genomics and transcriptomics to detect functional units. In this species, 3′- and 5′-untranslated regions were the most prominent insertion-depleted regions that were not represented in measures of constraint from comparative genomics. We conclude that the combination of transposon mutagenesis, evolutionary, and biochemical data can provide new insights into the relationship between genome function and molecular evolution.

Network centrality is the most important variable determining gene sequence evolution rates inSchizosaccharomyces pombe

“…To determine which factors are the most influential in determining the rate of molecular evolution, various functional genomics data were retrieved from Grech and colleagues (21), Pom-Base (22), and STRING 11 (23). These included general gene and protein data (location, size, length, charge, amino acid composition), expression data, gene ontology data (cellular process, biochemical function, and cellular component), interactors in the protein-protein network, essentiality and knockout fitness data, and data on constraint, measured as phyloP score (24).…”

“…The gene list (n = 5609) of most protein coding genes in the fission yeast genome, with data on constraint, gene and protein expression expression, gene length, chromosome, essentiality and solid media fitness, was retrieved from Grech and colleagues (21), downloaded manually from their article's Figshare repository. Interactome data were retrieved from STRING using FTP download.…”

“…Additionally, a host of variables influencing the rate of sequence evolution in a range of organisms has been reported before, including but not limited to protein length (17), pleiotropy (16), intron presence (18), cellular location (19), and codon bias (20); Zhang and Yang (4) provide a comprehensive review of the various variables implicated in sequence evolution. The present study uses publicly available sequence evolution and functional genomics data mainly from Grech and colleagues (21), with additional data from the PomBase (22) and STRING (23) databases, to investigate some of the variables that may influence gene sequence evolution. At time of writing, there appears to be no readily available data on protein sequence evolution in S. pombe ; this article does nonetheless discuss sequence evolution in both protein and DNA sequences.…”

“…The present study uses publicly available sequence evolution and functional genomics data from Grech and colleagues (Grech et al 2019), with additional data from the PomBase (Lock et al 2019) and STRING (Szklarczyk et al 2019) databases, to construct a PLS model of the rate of sequence evolution in the fission yeast genome. The VIP scores are then extracted and used as a metric of how important each variable is as a predictor of sequence evolution constraint.…”

Genome-wide identification of sexual-reproduction genes in fission yeast via transposon-insertion sequencing

“…While only used lightly in eukaryotes, it has been pioneered in fungal genetics in the model fission yeast Schizosaccharomyces pombe [28]. Subsequent studies in fission yeast have used TN-seq to identify factors involved in heterochromatin formation [29] and to study the fitness landscape of the genome [30]. More recently, TN-seq systems have also been developed in two other model yeasts, Saccharomyces cerevisiae [31,32] and Candida albicans [33], as well as a few less commonly studied fungi [34,35].…”