Epistatic genetic interactions govern morphogenesis during sexual reproduction and infection in a global human fungal pathogen

Discovery of CO2 tolerance genes associated with virulence in the fungal pathogen Cryptococcus neoformans

Cryptococcus neoformans is a ubiquitous soil fungus and airborne pathogen that causes over 180,000 deaths each year. Cryptococcus must adapt to host CO2 levels to cause disease, but the genetic basis for this adaptation is unknown. We utilized quantitative trait loci mapping with 374 progeny from a cross between a CO2-tolerant clinical isolate and a CO2-sensitive environmental isolate to identify genetic regions regulating CO2 tolerance. To identify specific quantitative trait genes, we applied fine mapping through bulk segregant analysis of near-isogenic progeny with distinct tolerance levels to CO2. We found that virulence among near-isogenic strains in a murine model of cryptococcosis correlated with CO2 tolerance. Moreover, we discovered that sensitive strains may adapt in vivo to become more CO2 tolerant and more virulent. These findings highlight the underappreciated role of CO2 tolerance and its importance in the ability of an opportunistic environmental pathogen to cause disease.

Improved quality metrics for association and reproducibility in chromatin accessibility data using mutual information

BACKGROUND

Correlation metrics are widely utilized in genomics analysis and often implemented with little regard to assumptions of normality, homoscedasticity, and independence of values. This is especially true when comparing values between replicated sequencing experiments that probe chromatin accessibility, such as assays for transposase-accessible chromatin via sequencing (ATAC-seq). Such data can possess several regions across the human genome with little to no sequencing depth and are thus non-normal with a large portion of zero values. Despite distributed use in the epigenomics field, few studies have evaluated and benchmarked how correlation and association statistics behave across ATAC-seq experiments with known differences or the effects of removing specific outliers from the data. Here, we developed a computational simulation of ATAC-seq data to elucidate the behavior of correlation statistics and to compare their accuracy under set conditions of reproducibility.

RESULTS

Using these simulations, we monitored the behavior of several correlation statistics, including the Pearson's R and Spearman's [Formula: see text] coefficients as well as Kendall's [Formula: see text] and Top-Down correlation. We also test the behavior of association measures, including the coefficient of determination R[Formula: see text], Kendall's W, and normalized mutual information. Our experiments reveal an insensitivity of most statistics, including Spearman's [Formula: see text], Kendall's [Formula: see text], and Kendall's W, to increasing differences between simulated ATAC-seq replicates. The removal of co-zeros (regions lacking mapped sequenced reads) between simulated experiments greatly improves the estimates of correlation and association. After removing co-zeros, the R[Formula: see text] coefficient and normalized mutual information display the best performance, having a closer one-to-one relationship with the known portion of shared, enhanced loci between simulated replicates. When comparing values between experimental ATAC-seq data using a random forest model, mutual information best predicts ATAC-seq replicate relationships.

CONCLUSIONS

Collectively, this study demonstrates how measures of correlation and association can behave in epigenomics experiments. We provide improved strategies for quantifying relationships in these increasingly prevalent and important chromatin accessibility assays.

Amoeba predation of Cryptococcus: A quantitative and population genomic evaluation of the accidental pathogen hypothesis

The "Amoeboid Predator-Fungal Animal Virulence Hypothesis" posits that interactions with environmental phagocytes shape the evolution of virulence traits in fungal pathogens. In this hypothesis, selection to avoid predation by amoeba inadvertently selects for traits that contribute to fungal escape from phagocytic immune cells. Here, we investigate this hypothesis in the human fungal pathogens Cryptococcus neoformans and Cryptococcus deneoformans. Applying quantitative trait locus (QTL) mapping and comparative genomics, we discovered a cross-species QTL region that is responsible for variation in resistance to amoeba predation. In C. neoformans, this same QTL was found to have pleiotropic effects on melanization, an established virulence factor. Through fine mapping and population genomic comparisons, we identified the gene encoding the transcription factor Bzp4 that underlies this pleiotropic QTL and we show that decreased expression of this gene reduces melanization and increases susceptibility to amoeba predation. Despite the joint effects of BZP4 on amoeba resistance and melanin production, we find no relationship between BZP4 genotype and escape from macrophages or virulence in murine models of disease. Our findings provide new perspectives on how microbial ecology shapes the genetic architecture of fungal virulence, and suggests the need for more nuanced models for the evolution of pathogenesis that account for the complexities of both microbe-microbe and microbe-host interactions.

Lessons learned: from mentored to mentor

This Viewpoint was written in association with the 25th anniversary of the American Society for Clinical Investigation's (ASCI's) Stanley J. Korsmeyer Award, which honors the highest standards of scientific excellence, meritorious research, intellectual integrity, and the mentoring of future life-science researchers. In 2018, the award recognized Joseph Heitman (Figure 1), for his key contributions to our understanding of how eukaryotic microbial pathogens evolve, cause disease, and develop drug resistance and his discovery of TOR and FKBP12 as targets of the immunosuppressive chemotherapeutic drug rapamycin. Dr. Heitman has mentored numerous undergraduates, medical students, graduate students, and postdoctoral and medical fellows, many of whom have developed independent careers in medicine and basic biomedical research.

Regulatory basis for reproductive flexibility in a meningitis-causing fungal pathogen

Pathogenic fungi of the genus Cryptococcus can undergo two sexual cycles, involving either bisexual diploidization (after fusion of haploid cells of different mating type) or unisexual diploidization (by autodiploidization of a single cell). Here, we construct a gene-deletion library for 111 transcription factor genes in Cryptococcus deneoformans, and explore the roles of these regulatory networks in the two reproductive modes. We show that transcription factors crucial for bisexual syngamy induce the expression of known mating determinants as well as other conserved genes of unknown function. Deletion of one of these genes, which we term FMP1, leads to defects in bisexual reproduction in C. deneoformans, its sister species Cryptococcus neoformans, and the ascomycete Neurospora crassa. Furthermore, we show that a recently evolved regulatory cascade mediates pre-meiotic unisexual autodiploidization, supporting that this reproductive process is a recent evolutionary innovation. Our findings indicate that genetic circuits with different evolutionary ages govern hallmark events distinguishing unisexual and bisexual reproduction in Cryptococcus.

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.