Highlights d The ability for fast acquisition of drug resistance is widespread in Candida glabrata d Resistance-conferring mutations are very diverse but affect a small number of genes d Cross-resistance to fluconazole is common in strains adapted to anidulafungin d ERG3 mutations often drive fluconazole resistance in anidulafungin-adapted strains

Autoregulatory feedback loops occur in the regulation of molecules ranging from ATP to MAP kinases to zinc. Negative feedback loops can increase a system's robustness, while positive feedback loops can mediate transitions between cell states. Recent genome-wide experimental and computational studies predict hundreds of novel feedback loops. However, not all physical interactions are regulatory, and many experimental methods cannot detect self-interactions. Our understanding of regulatory feedback loops is therefore hampered by the lack of high-throughput methods to experimentally quantify the presence, strength and temporal dynamics of autoregulatory feedback loops. Here we present a mathematical and experimental framework for high-throughput quantification of feedback regulation and apply it to RNA binding proteins (RBPs) in yeast. Our method is able to determine the existence of both direct and indirect positive and negative feedback loops, and to quantify the strength of these loops. We experimentally validate our model using two RBPs which lack native feedback loops and by the introduction of synthetic feedback loops. We find that RBP Puf3 does not natively participate in any direct or indirect feedback regulation, but that replacing the native 3'UTR with that of COX17 generates an auto-regulatory negative feedback loop which reduces gene expression noise. Likewise, RBP Pub1 does not natively participate in any feedback loops, but a synthetic positive feedback loop involving Pub1 results in increased expression noise. Our results demonstrate a synthetic experimental system for quantifying the existence and strength of feedback loops using a combination of high-throughput experiments and mathematical modeling. This system will be of great use in measuring auto-regulatory feedback by RNA binding proteins, a regulatory motif that is difficult to quantify using existing high-throughput methods.

Structural variants (SVs) like translocations, deletions, and other rearrangements underlie genetic and phenotypic variation. SVs are often overlooked due to difficult detection from short-read sequencing. Most algorithms yield low recall on humans, but the performance in other organisms is unclear. Similarly, despite remarkable differences across species’ genomes, most approaches use parameters optimized for humans. To overcome this and enable species-tailored approaches, we developed perSVade (personalized Structural Variation Detection), a pipeline that identifies SVs in a way that is optimized for any input sample. Starting from short reads, perSVade uses simulations on the reference genome to choose the best SV calling parameters. The output includes the optimally-called SVs and the accuracy, useful to assess the confidence in the results. In addition, perSVade can call small variants and copy-number variations. In summary, perSVade automatically identifies several types of genomic variation from short reads using sample-optimized parameters. We validated that perSVade increases the SV calling accuracy on simulated variants for six diverse eukaryotes, and on datasets of validated human variants. Importantly, we found no universal set of “optimal” parameters, which underscores the need for species-specific parameter optimization. PerSVade will improve our understanding about the role of SVs in non-human organisms.

Fungal pathogens pose an increasingly worrying threat to human health, food security and ecosystem diversity. To tackle fungal infections and improve current diagnostic and therapeutic tools it is necessary to understand virulence and antifungal drug resistance mechanisms in diverse species. Recent advances in genomics approaches have provided a suitable framework to understand these phenotypes, which ultimately depend on genetically encoded determinants. In this work, we review how the study of genome sequences has been key to ascertain the bases of virulence and drug resistance traits. We focus on the contribution of comparative genomics, population genomics and directed evolution studies. In addition, we discuss how different types of genomic mutations (small or structural variants) contribute to intraspecific differences in virulence or drug resistance. Finally, we review current challenges in the field and anticipate future directions to solve them. In summary, this work provides a short overview of how genomics can be used to understand virulence and drug resistance in fungal pathogens.