Recent gene selection and drug resistance underscore clinical adaptation across Candida species.

Understanding how microbial pathogens adapt to treatments, humans and clinical environments is key to infer mechanisms of virulence, transmission and drug resistance. This may help improve therapies and diagnostics for infections with a poor prognosis, such as those caused by fungal pathogens, including Candida. Here we analysed genomic variants across approximately 2,000 isolates from six Candida species (C. glabrata, C. auris, C. albicans, C. tropicalis, C. parapsilosis and C. orthopsilosis) and identified genes under recent selection, suggesting a highly complex clinical adaptation. These involve species-specific and convergently affected adaptive mechanisms, such as adhesion. Using convergence-based genome-wide association studies we identified known drivers of drug resistance alongside potentially novel players. Finally, our analyses reveal an important role of structural variants and suggest an unexpected involvement of (para)sexual recombination in the spread of resistance. Our results provide insights on how opportunistic pathogens adapt to human-related environments and unearth candidate genes that deserve future attention.

PerSVade: personalized structural variant detection in any species of interest.

Structural variants (SVs) underlie genomic variation but are often overlooked due to difficult detection from short reads. Most algorithms have been tested on humans, and it remains unclear how applicable they are in other organisms. To solve this, we develop perSVade (personalized structural variation detection), a sample-tailored pipeline that provides optimally called SVs and their inferred accuracy, as well as small and copy number variants. PerSVade increases SV calling accuracy on a benchmark of six eukaryotes. We find no universal set of optimal parameters, underscoring the need for sample-specific parameter optimization. PerSVade will facilitate SV detection and study across diverse organisms.

Using genomics to understand the mechanisms of virulence and drug resistance in fungal pathogens.

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.

Narrow mutational signatures drive acquisition of multidrug resistance in the fungal pathogen Candida glabrata.

Fungal infections are a growing medical concern, in part due to increased resistance to one or multiple antifungal drugs. However, the evolutionary processes underpinning the acquisition of antifungal drug resistance are poorly understood. Here, we used experimental microevolution to study the adaptation of the yeast pathogen Candida glabrata to fluconazole and anidulafungin, two widely used antifungal drugs with different modes of action. Our results show widespread ability of rapid adaptation to one or both drugs. Resistance, including multidrug resistance, is often acquired at moderate fitness costs and mediated by mutations in a limited set of genes that are recurrently and specifically mutated in strains adapted to each of the drugs. Importantly, we uncover a dual role of ERG3 mutations in resistance to anidulafungin and cross-resistance to fluconazole in a subset of anidulafungin-adapted strains. Our results shed light on the mutational paths leading to resistance and cross-resistance to antifungal drugs.

Shared evolutionary footprints suggest mitochondrial oxidative damage underlies multiple complex I losses in fungi.

Oxidative phosphorylation is among the most conserved mitochondrial pathways. However, one of the cornerstones of this pathway, the multi-protein complex NADH : ubiquinone oxidoreductase (complex I) has been lost multiple independent times in diverse eukaryotic lineages. The causes and consequences of these convergent losses remain poorly understood. Here, we used a comparative genomics approach to reconstruct evolutionary paths leading to complex I loss and infer possible evolutionary scenarios. By mining available mitochondrial and nuclear genomes, we identified eight independent events of mitochondrial complex I loss across eukaryotes, of which six occurred in fungal lineages. We focused on three recent loss events that affect closely related fungal species, and inferred genomic changes convergently associated with complex I loss. Based on these results, we predict novel complex I functional partners and relate the loss of complex I with the presence of increased mitochondrial antioxidants, higher fermentative capabilities, duplications of alternative dehydrogenases, loss of alternative oxidases and adaptation to antifungal compounds. To explain these findings, we hypothesize that a combination of previously acquired compensatory mechanisms and exposure to environmental triggers of oxidative stress (such as hypoxia and/or toxic chemicals) induced complex I loss in fungi.

Poor codon optimality as a signal to degrade transcripts with frameshifts.

Frameshifting errors are common and mRNA quality control pathways, such as nonsense-mediated decay (NMD), exist to degrade these aberrant transcripts. Recent work has shown the existence of a genetic link between NMD and codon-usage mediated mRNA decay. Here we present computational evidence that these pathways are synergic for removing frameshifts.

Promoter Activity Buffering Reduces the Fitness Cost of Misregulation.

Organisms regulate gene expression through changes in the activity of transcription factors (TFs). In yeast, the response of genes to changes in TF activity is generally assumed to be encoded in the promoter. To directly test this assumption, we chose 42 genes and, for each, replaced the promoter with a synthetic inducible promoter and measured how protein expression changes as a function of TF activity. Most genes exhibited gene-specific TF dose-response curves not due to differences in mRNA stability, translation, or protein stability. Instead, most genes have an intrinsic ability to buffer the effects of promoter activity. This can be encoded in the open reading frame and the 3' end of genes and can be implemented by both autoregulatory feedback and by titration of limiting trans regulators. We show experimentally and computationally that, when misexpression of a gene is deleterious, this buffering insulates cells from fitness defects due to misregulation.

Promoter architecture determines cotranslational regulation of mRNA.

Information that regulates gene expression is encoded throughout each gene but if different regulatory regions can be understood in isolation, or if they interact, is unknown. Here we measure mRNA levels for 10,000 open reading frames (ORFs) transcribed from either an inducible or constitutive promoter. We find that the strength of cotranslational regulation on mRNA levels is determined by promoter architecture. By using a novel computational genetic screen of 6402 RNA-seq experiments, we identify the RNA helicase Dbp2 as the mechanism by which cotranslational regulation is reduced specifically for inducible promoters. Finally, we find that for constitutive genes, but not inducible genes, most of the information encoding regulation of mRNA levels in response to changes in growth rate is encoded in the ORF and not in the promoter. Thus, the ORF sequence is a major regulator of gene expression, and a nonlinear interaction between promoters and ORFs determines mRNA levels.

A synthetic gene circuit for measuring autoregulatory feedback control.

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.