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Verstrepen, K.

Publications and source records attributed to Verstrepen, K..

3 recordsLinked to original sources

Domestication and deep lineage divergence define two discrete trajectories in Kluyveromyces marxianus

Although genomics has greatly improved our ability to detect population structure and genetic divergence, it remains difficult to determine when diversity within a recognized species reflects continuous variation and when it marks the emergence of discrete evolutionary lineages. In yeasts, population-genomic studies often reveal overlapping signatures of environmental association, admixture, and domestication, making this distinction especially challenging. By analyzing 178 globally sampled genomes from Kluyveromyces marxianus, an emerging yeast of biotechnological interest, we show that diversity within this species resolves into two contrasting evolutionary trajectories that do not map onto broad geographic origin. Most industrial and clinical isolates belong to a lineage marked by gene loss, aneuploidy, and reduced fertility, consistent with genomic hallmarks of domestication. In contrast, isolates confined to traditional agave fermentations in Mexico and South Africa form a highly distinct lineage, separated from the other lineages by up to 4.8% genome-wide nucleotide divergence, widespread reciprocal monophyly, no detectable recent gene flow, and reduced mating compatibility despite co-occurrence with other lineages. Together, these results show that within a single yeast species, domestication-associated genome remodeling and deep lineage divergence can define distinct trajectories, with implications for strain selection in biotechnology.

evolutionary biology↗

A chromatin-associated regulator of RNA Polymerase III assembly at tRNA genes revealed by locus-specific proteomics

Transcription of tRNA genes by RNA Polymerase III (RNAPIII) is tightly regulated by signaling cascades in response to nutrient availability. The emerging notion of differential tRNA gene regulation implies the existence of additional regulatory mechanisms. However, tRNA gene-specific regulatory factors have not been described. For that reason, we decoded the proteome of a single native tRNA gene locus in yeast. We observed dynamic reprogramming of the core RNAPIII transcription machinery upon nutrient perturbation. In addition, we identified Fpt1, a protein of unknown function. Fpt1 uniquely occupied tRNA genes but its occupancy varied and correlated with the efficiency of RNAPIII eviction upon nutrient perturbation. Decoding the proteome of a tRNA gene in the absence of Fpt1 revealed that Fpt1 promotes eviction of RNAPIII. Cells without Fpt1 also showed impaired shutdown of ribosome biogenesis genes upon nutrient perturbation. Our findings provide support for a chromatin-associated mechanism required for RNAPIII eviction from tRNA genes and for tuning an integrated physiological response to changing metabolic demands. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/534528v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1b2ef53org.highwire.dtl.DTLVardef@7e6234org.highwire.dtl.DTLVardef@1614b5borg.highwire.dtl.DTLVardef@9a4933_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

Harnessing the power of technical and natural variation in 116 yeast datasets to benchmark long read assembly pipelines

With increases in throughput and reductions in cost, long read sequencing has become the standard for most genome assembly projects and has opened up new avenues for large-scale genomic research. While more amenable to assembly than short-read sequence data, long-read datasets tend to have higher error rates. To address this problem numerous tools have been developed to correct reads before assembly and polish assembled contigs. Although, numerous studies have been conducted to assess or benchmark these tools, few capture the real variance in long read sequence data that might affect tool performance much less full pipeline performance. To address these shortcomings, we compiled a dataset containing long-read sequences of 116 different strains of brewers yeast, S. cerevisiae, gathered largely from public databases and evaluated different assembly-related tools as well as their interactions. We found that pre-assembly short-read error correction of long reads combined with post-assembly short-read polishing provided the best assemblies. We also found that correction/polishing steps with uncorrected long reads often lead to degradation of assembly quality. Finally, we show which tools and pipelines work best with different types of input data.

bioinformatics↗