Back

Adaptive Mate Selection in Saccharomyces cerevisiae Revealed Through a High-Throughput Mating Assay

Struass, S. K.; Liti, G.; Dahan, O.; Pilpel, Y. T.

2024-02-21 evolutionary biology
10.1101/2024.02.18.580925 bioRxiv
Show abstract

In evolutionary biology, sexual mating plays a pivotal role in facilitating the combination of beneficial alleles among individuals. Cross-species data suggest that organisms selectively mate with partners based on factors such as genetic distance and partner fitness. Understanding the determinants of pair-specific mating affinity is crucial for unraveling the impact of sex on evolution. However, despite the significance of this phenomenon, the availability of large and consistent datasets is limited, leading to inconsistent conclusions. To address this gap, we present a comprehensive mating assay enabling the simultaneous quantification of mating affinity among approximately 100 natural Saccharomyces cerevisiae strains. Our study demonstrates that mating in an en masse manner, allowing mating based on affinity, enhances the overall fitness of the hybrids offspring population compared to mating that enforces one specific partner at a time. By employing a DNA barcode recombination system integrated into natural isolates genomes, we sequence recombined barcode pairs and revealed mating frequencies and affinities among all strains in different environments. Our findings unveil strain-specific mating affinity among natural yeast strains, with certain parental pairs exhibiting a heightened affinity for each other over other strains, whereas certain strains combinations are avoided. Notably, among the pairs with the highest affinity, there is a preference for lower genetic distances. Intriguingly, multiple strains show a propensity for mating with partners that yield higher-fitness hybrids on average. Collectively, our results provide compelling evidence that yeast actively engages in adaptive mate affinity.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.