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.

Fungal pathogens (i.e. fungal organisms able to cause disease in a given host) are of high relevance for human health, affecting annually a billion people and causing 1.5 million deaths worldwide [1]. In addition, they are a threat to food crops [2] and other ecosystems, where fungal outbreaks have brought several species to the brink of extinction [3]. Fungal pathogens belong to diverse lineages of the fungal tree of life, including microsporidia, ascomycotina, basidiomycotina and mucoromycotina [4–7]. In addition, the set of species that are pathogenic to a given organism can be evolutionarily distant and have close non-pathogenic relatives, indicating that virulence has emerged multiple times independently [8,9]. Most fungal pathogens are opportunistic, meaning that they can have a commensal existence within the host or do not even require a host. This ‘plasticity’ of the pathogenic trait is also illustrated by the common emergence of new pathogenic species [10–12]. The diversity of fungal pathogens makes their identification challenging [13] and is also reflected in a variety of virulence mechanisms that are generally poorly understood. To complicate things further, antifungal drug resistance is on the rise [14–17]. As virulence mechanisms, resistance mechanisms can vary across and within species, and how resistance is acquired through evolution is poorly understood [15]. In summary, the molecular and evolutionary mechanisms behind the acquisition of virulence or drug resistance remain obscure, which constrains our ability to fight fungal pathogens.

Phenotypes like virulence or drug resistance are ultimately dependent on genomic information. Hence, comparing genome sequences across species or isolates with different virulence or resistance profiles is a promising avenue to identify the genetic bases of these traits. Fungal pathogens have highly dynamic genomes, showing large differences across species [18–20], within species [21–24], and even within clonal populations in a host [25,26]. Such genomic changes have been linked to rapid adaptation in changing environments, likely underlying the emergence of antifungal resistance [14,27,28] and virulence [28–30].

Compared with traditional molecular methods, current high throughput genome sequencing approaches provide a more comprehensive picture of genetic changes and do not require prior knowledge on potentially relevant loci. This has revolutionized the way in which fungal pathogens can be studied. In this review, we survey the major genomic approaches that have been instrumental in studying virulence and drug resistance in fungi, and illustrate them with specific hallmark studies. Given length limits and the large amount of relevant studies, we cannot be comprehensive. Similarly, we use a narrow definition of genomics, restricted to the study of genome sequences, and not the broader meaning including transcriptomics or epigenomics. Each of the following sections is focused on a particular genomic approach (see Figure 1).

Several techniques have been used to understand the genomic drivers of drug resistance or virulence.

Figure 1.
Several techniques have been used to understand the genomic drivers of drug resistance or virulence.

(A) Comparative genomics refers to the comparison of gene content and genomic sequences across species differing in a given trait of interest (e.g. virulence towards a given host). By identifying genomic changes that correlate with the trait, hypotheses can be made on their possible relationships. This example shows a gene family with extra copies in some species, which may underlie virulence or drug resistance. (B) Population genomics is the comparative study of genomic variation within and across populations of a given species. This technique has been used to correlate genomic and phenotypic variation across strains of fungal pathogen species. This example shows a gene with several variants (in red) that underlie the emergence of virulence or drug resistance in some strains. Note that, since there is some divergence between strains, it is not trivial to distinguish causal (red) from passenger (black) variants. (C) Directed evolution experiments consist in using selective regimes (either in vivo (left) or in vitro (right)) to ‘force’ the appearance of the phenotypes under study. The selected strains are sequenced to identify variants underlying the phenotypes. This approach simplifies the detection of causal mutations as compared with population genomics studies because the evolutionary conditions are more controlled. This example represents a gene that acquired a single causal variant (in red) driving virulence or drug resistance during artificial evolution. Note that identifying causal variants here is easier (as compared with population genomics techniques (B)), because the evolutionary period is shorter and more controlled.

Figure 1.
Several techniques have been used to understand the genomic drivers of drug resistance or virulence.

(A) Comparative genomics refers to the comparison of gene content and genomic sequences across species differing in a given trait of interest (e.g. virulence towards a given host). By identifying genomic changes that correlate with the trait, hypotheses can be made on their possible relationships. This example shows a gene family with extra copies in some species, which may underlie virulence or drug resistance. (B) Population genomics is the comparative study of genomic variation within and across populations of a given species. This technique has been used to correlate genomic and phenotypic variation across strains of fungal pathogen species. This example shows a gene with several variants (in red) that underlie the emergence of virulence or drug resistance in some strains. Note that, since there is some divergence between strains, it is not trivial to distinguish causal (red) from passenger (black) variants. (C) Directed evolution experiments consist in using selective regimes (either in vivo (left) or in vitro (right)) to ‘force’ the appearance of the phenotypes under study. The selected strains are sequenced to identify variants underlying the phenotypes. This approach simplifies the detection of causal mutations as compared with population genomics studies because the evolutionary conditions are more controlled. This example represents a gene that acquired a single causal variant (in red) driving virulence or drug resistance during artificial evolution. Note that identifying causal variants here is easier (as compared with population genomics techniques (B)), because the evolutionary period is shorter and more controlled.

Close modal

Comparative genomics refers to the evolutionary comparison of gene content and genomic sequences across species differing in a given trait of interest (e.g. virulence towards a given host). By identifying genomic changes (e.g. acceleration, duplication, loss, or acquisition of certain genes) that correlate with the presence or the strength of the trait, hypotheses can be made on their possible relationships. Such studies are increasingly powerful given the growing availability of complete genome sequences of fungal pathogens and their close non-pathogenic relatives.

Several studies have successfully exploited comparative genomics to understand how virulence and intrinsic drug resistance have emerged in different lineages of fungal pathogens (Figure 1). For example, an analysis of Nakaseomyces species identified expansions and diversification of cell adhesion proteins as drivers of the emergence of virulence in different Candida species. This suggests that increased adherence favored host adaptation [12]. Similarly, the expansion of drug efflux pumps and point mutations on the azole target ERG11 in the genome of Candida auris may underlie its intrinsic drug resistance as compared with closely related species [18,31]. Another example can be found in Fusarium oxysporum, which may have become a broad plant pathogen thanks to the horizontal acquisition of virulence genes [32]. In addition, some studies suggested that the pathogenic behavior of F. oxysporum sub-species is driven by meiotically unstable ‘accessory chromosomes’, which encode genes related to host specificity [33,34].

On another line, some studies have focused on understanding how genomic changes can drive a switch in the ecological niche of fungal pathogens, which improves our understanding about the virulence or drug resistance mechanisms. For example, a comparative study revealed that fungal pathogens had more gene-disrupting transposable element insertions than non-pathogenic species [19]. Similarly, the comparison of pathogenic and environmental Sporothrix species revealed that gene loss (i.e. of plant degrading enzymes and virulence inhibitors) is more important than gene gain in the evolution of pathogenesis [35]. Furthermore, it has been proposed that horizontal gene transfer of virulence genes has shaped the adaptability of Verticillium dahliae subspecies towards different hosts (i.e. tomato or cotton) [36]. In addition, a recent phylogenomics study proposed that the loss of mitochondrial complex I in some fungi (i.e. Candida glabrata [37]) may have generated increased tolerance to oxidative stress, predisposing them towards intrinsic drug resistance [38,39].

Population genomics refers to the comparative study of genomic variation within and across populations of a given species. Once a reference genome of a species is available, population genomics can be performed by sequencing additional individuals to identify genetic variants. The frequency and distribution of such variants can inform about the genetic structure of a species, identify sub-clades, reconstruct the history of populations, and identify genomic regions under selection. Fungal pathogens can display large genetic and phenotypic variation across different strains of the same organism [21–23,25,26]. Population genomics techniques have been used to correlate genomic and phenotypic variation across isolates of fungal pathogen species (Figure 1).

Most population genomics studies reconstruct a phylogeny showing the relationships between the isolates, which helps to understand the evolutionary process underlying the emergence of a given phenotype of interest. The populations of most fungal pathogens include clearly separated clades, and some of these clades have particular drug resistance or virulence properties [21,23,40]. For example, some clades of Cryptococcus neoformans are particularly virulent, possibly through resistance to oxidative stress [21]. Similarly, there are clades in Candida auris and Candida albicans that lost the ancestral azole drug resistance [22] and virulence capabilities [40], respectively. Conversely, there is not a clear association between clade identity and antifungal resistance patterns in C. glabrata [23,41], or Aspergillus fumigatus [42].

Beyond describing when and where phenotypes appeared, population genomics techniques have been used to infer underlying mechanisms. The most simple approach involves testing specific hypotheses about mutations contributing to a given phenotype, which are formulated from known mechanisms of drug resistance or virulence. For example, a recent study in Candida auris found that ERG11 (the target of azoles) mutations and copy-number variants are associated with azole resistance, while FKS1 (echinocandin target) mutations are related to echinocandin resistance [22]. Another study in Candida glabrata found that FKS1 mutations are related to echinocandin resistance, while PDR1 and CDR1 mutations are associated with azole resistance [41].

However, such approaches mostly investigate a few genes, and Genome-Wide Association Studies (GWAS) are a promising, more comprehensive, alternative. GWAS was used to find variants associated with azole resistance in Candida glabrata (two regulatory SNPs in CST6 [43]) and Aspergillus fumigatus (two missense mutations in a sterol-modulator gene [42] and missense mutations in ABC transporters and mitochondrial proteins [44]). In addition, GWAS comparing environmental and clinical isolates of Cryptococcus neoformans revealed major differences in virulence and oxidative stress-related genes, potentially involved in pathogenesis [21]. Similarly, a GWAS study on Aspergillus fumigatus isolates found that variants in genes related to growth in hypoxia, iron homeostasis and regulation of gluconeogenesis are linked to clinical phenotypes [45]. Finally, GWAS comparing virulent and avirulent strains of the wheat pathogen Zymoseptoria tritici revealed tens of small variants and rearrangements underlying pathogenesis [46]. Importantly, some of these processes affect isolates of human pathogens from the same patient [25,26], suggesting that real-time monitoring could be key for an optimal treatment.

While population genomics can be powerful, it faces two main limitations for understanding the mechanisms of drug resistance or virulence. First, the study of natural variation is mostly useful for phenotypes that emerged independently several times in a population. For example, population genomics may be underpowered to study drug resistance of a recently approved compound (i.e. beauvericin [47]), the virulence mechanisms of an emergent fungal pathogen (i.e. Candida blankii [48]) or the genomic drivers of a phenotype that emerged only once in the population (i.e. the virulence loss at the common ancestor of the isolates in clade 13 from Candida albicans [40]). Second, large divergence between isolates with different phenotypes complicates the (key) distinction between causal and passenger mutations (as reviewed in [49]).

Directed (artificial) evolution (either in vitro [50,51] or in vivo [52]) of drug resistance or virulence followed by whole-genome sequencing (WGS) of the adapted strains offer a promising solution to overcome some of these problems. In directed evolution experiments the conditions are controlled, and the phenotypes under study are ‘forced' to appear in otherwise wild type strains by using selective regimes. This means that phenotype-causing mutations are expected to be fixed in the selected populations and, if the process is repeated, they are expected to appear recurrently. This usually simplifies the detection of such mutations as compared with population genomics studies (Figure 1). Such approaches have been used to understand the in vitro evolution of azole and echinocandin resistance in Candida glabrata, which revealed genes related to resistance and cross-resistance [53]. In addition, similar experiments were performed in Candida auris [54,55], Candida parapsilosis [56] and Candida albicans [57].

The mechanisms of virulence have also been partially studied through directed in vivo evolution experiments. A study of Candida albicans evolved avirulent strains (starting from a virulent parental) in murine models and found that changes in EFG1 and FLO8, related to hyphal growth, were related to the loss of virulence [52]. This exemplifies how evolution in the host can yield strains with lower virulence [58]. Another study found that passing C. albicans through murine models (in vivo) results in highly diverse populations, which revealed genomic changes underlying host adaptation [59].

A similar approach is the use of random mutagenesis coupled to sequencing of mutants with a particular phenotype to ascertain the underlying genomic changes. For example, chemical mutagenesis (with ethyl methanesulfonate) followed by whole-genome sequencing was used in Puccinia species to understand their virulence mechanisms towards wheat [60,61]. In addition, genome-wide transposon-mediated mutagenesis revealed that the lncRNA DINOR is a regulator of stress responses (including drug tolerance) in Candida auris [62].

In summary, using directed evolution or random mutagenesis coupled with genome sequencing has allowed the exploration of drug resistance and virulence in controlled settings. However, such methods may not recapitulate entirely the natural evolutionary process. For example, natural evolution of drug resistance or virulence involves synergistic action of multiple selective forces that may be absent from some artificial settings settings. We thus consider that complementary approaches (such as population genomics) are also key to obtain the complete picture. The following sections describe some of the genetic alterations linked to these phenotypes through directed evolution experiments and population genomics techniques.

Most research linking genomic variants to drug resistance or virulence is focused on finding Single Nucleotide Polymorphisms (SNPs) and small insertions/deletions (INDELs) between isolates of a given fungal pathogen species. These strains may be different isolates from a population [21–23], pairs of parent-daughter lineages from a directed evolution experiment [52,54] or serial isolates from a given patient [25,26]. To identify such ‘small’ variants, most studies use custom pipelines that include mapping of sequencing reads to a reference genome and a variant calling step, which is performed with algorithms like GATK [63] or freebayes [64]. This is usually followed by variant annotation (using tools like VEP [65] or SNPeff [66]) to prioritize candidate mutations. Some automatic pipelines that call and interpret small variants directly from the reads have been developed to simplify this task, such as YMAP [67] and perSVade [68], which have been specifically developed for fungi. Below we describe some examples of how small variants can generate drug resistance and virulence mechanisms (see Figure 2).

Several types of genomic changes can drive the acquisition of virulence or drug resistance.

Figure 2.
Several types of genomic changes can drive the acquisition of virulence or drug resistance.

We show illustrative examples of how small variants (top), duplications (middle) and deletions (bottom) can alter virulence (left) or drug resistance (right) across species or within isolates of the same species. This figure is a graphical support to the sections ‘Small variants bringing large change’ and ‘Do not neglect structural variants’ of the main text.

Figure 2.
Several types of genomic changes can drive the acquisition of virulence or drug resistance.

We show illustrative examples of how small variants (top), duplications (middle) and deletions (bottom) can alter virulence (left) or drug resistance (right) across species or within isolates of the same species. This figure is a graphical support to the sections ‘Small variants bringing large change’ and ‘Do not neglect structural variants’ of the main text.

Close modal

Missense mutations in the target enzymes may impair the drug binding and cause resistance. For example, echinocandin resistance is caused by missense changes or codon deletions in the FKS genes of Candida glabrata [53,69], Candida auris [22,55], Candida lusitaniae [70] and Candida albicans [71]. Similarly, mutations in ERG11 gene (involved in ergosterol biosynthesis) have been associated with azole resistance in Candida glabrata [53], Candida albicans [71] and Candida auris [22]. In addition, small variants affecting other proteins of the ergosterol biosynthesis have been linked to multidrug resistance through unknown mechanisms. Loss-of-Function (LoF) variants in ERG3 have been linked to both azole and echinocandin resistance in Candida glabrata [53], Candida parapsilosis [56,72], and Candida albicans [71], and polyene resistance in Candida lusitaniae [70]. In addition, missense and regulatory SNPs in ergosterol biosynthesis genes (i.e. atg26) have been linked to azole resistance in Aspergillus fumigatus [42].

Another resistance mechanism involves the hyperactivation of drug tolerance pathways through Gain-of-Function (GoF) mutations. For example, azole resistance in Candida glabrata and Candida auris are often caused by GoF mutations in PDR1 (C. glabrata) or TAC1b (C. auris) transcription factors (TF), driving the overexpression of the CDR1 drug efflux pump [26,53–55,71,73]. Similarly, (potentially GoF) CDR1 missense mutations have also been linked to azole resistance [53,71].

Small variants have also been linked to changes in virulence. For example, GoF changes in PDR1 drive EPA1 (adhesin) overexpression and higher host adherence [26]. This mutation also caused azole resistance, which exemplifies how a single variant can drive both phenotypes. Similarly, LoF mutations in the TF BZP4 of Cryptococcus neoformans reduced melanization and virulence [21]. In addition, LoF small variants in the TF FLO8 were associated with reduced hyphal growth in Candida albicans avirulent strains [52]. Although the recurrent presence of mutations associated with a phenotype is a strong mechanistic hint, the causative relationship can be experimentally validated through subsequent re-introduction of the mutations in a naive background or replacement of the wild type alleles in the mutant. This underscores the power of combining genomic and molecular biology techniques.

Beyond small variants, complex Structural Variants (SVs) (i.e. chromosomal aneuploidies, duplications, deletions, inversions and other rearrangements) have been shown to modulate differences in drug resistance and virulence across strains of fungal pathogens. Most current studies analyzed the role of a subset of SVs, copy-number variants (CNVs), identified from changes in genomic read depth.

On the one hand, whole-chromosome aneuploidies (losses or gains) have been linked to azole resistance in Candida glabrata [53], Candida auris [54] and Candida albicans [74], likely due to overexpression of genes encoding drug target enzymes (which lower the impact of the drug) and/or efflux pumps (reducing the intracellular drug concentration). Interestingly, such aneuploidies may generate multidrug resistance for compounds with different mechanisms of action. For example, a study in Candida albicans found that chromosome 2 trisomy promotes resistance to hydroxyurea and caspofungin, while chromosome 5 monosomy generates resistance to azoles and echinocandins [74]. Similarly, most aneuploidies in Candida albicans yielded condition-specific fitness benefits [75], suggesting they are major drivers of adaptive evolution. This has also implications for the emergence of virulence. For example, it has been proposed that aneuploidies in the forest pathogen Dothistroma septosporum drive increased levels of dothistromin (a mycotoxin), resulting in strains with higher virulence [76]. In addition, plant-associated (virulent) isolates of Phytophthora ramorum (oak pathogen) had more aneuploidies than in vitro-grown strains, suggesting that they underlie virulent phenotypes [77].

On the other hand, smaller CNVs (i.e. duplications generating overexpression of ERG11 or CDR1 genes) have been linked to azole resistance in Candida glabrata [73], Cryptococcus neoformans [21], Candida albicans [78] and Candida auris [22]. Such CNVs have also been linked to changes in virulence, including CNVs in adhesins [43] or secreted aspartyl proteases [79] in Candida glabrata, or CNVs in cell wall and stress-response genes in Candida auris [80]. Another example was found in the plant pathogen Zymoseptoria tritici, where virulence was associated with a small deletion breaking the Zt_8_609 gene, potentially involved in a specific interaction with a plant resistance gene. This interaction protects the plant from infection, so that the deletion is key for virulence [46]. This further illustrates how gene loss can drive important phenotypes in fungi.

In summary, read-depth based CNV calling revealed that SVs play a fundamental role in fungal pathogens. However, the technique itself has several limitations. First, such CNV calling has limited resolution (often ignoring small CNVs) and lacks precision in defining breakpoints positions [53,54]. Second, read depth can be noisy and biased by factors like GC content, read mapping errors [67,81] or the distance to the telomeres [67], which limit accuracy. Third, CNVs are only a subset of all SVs, which means the role of more complex SVs (like inversions or translocations) has been overlooked. This is likely due to difficult SV detection from short reads, which may be solved by using long reads [82] or recent methods for accurate short read-based SV calling [68,83,84]. For example, a recent study in Candida glabrata based on short reads found that translocations around FKS and ERG11 genes may play a role in echinocandin and azole resistance, respectively [53]. These results indicate that further research should consider the role of complex SVs in drug resistance and virulence of fungal pathogens. Figure 2 provides a graphical representation of how SVs generate such phenotypes.

Genomic techniques face several limitations, and we enumerate here the most important ones, together with future directions that may solve them.

On one hand, the functional interpretation of genomic changes relies on annotations which are often suboptimal, mostly based on homology-based predictions. Further experimental work and better annotation tools may solve this. Similarly, the impact of missense mutations on protein function is difficult to predict, which hampers such functional interpretation. This could be addressed with variant annotators that rely on structure information (i.e. FoldX [85]) based on sequence-based structure predictions (i.e. AlphaFold2 [86]). In addition, using CRISPR-Cas9 can be key (as done in [87] or [53]), since it allows to reproduce mutations in isolation to study their phenotypic impact, even in non-model species.

On another hand, the typical approaches could be missing important genomic variation. Many studies may be underpowered because of their small sample sizes. Combined analyses of genomic and phenotypic information from different studies may increase this power and reveal novel mechanisms of drug resistance and virulence. In addition, the contribution of SVs to these phenotypes has been often ignored, likely due to the technical difficulties in calling them from short-read data. This could be solved by using recent short read-based SV callers (i.e. perSVade [68]) or long read sequencing. Similarly, the epistatic interactions shaping the genotype-to-phenotype relations are poorly understood, although they could be very important [88]. Increasing sample sizes and considering strains of genetically distinct clades may be essential to fully characterize how genomic changes drive virulence and drug resistance. Finally, most GWAS studies only evaluated the correlation between specific variants and a given phenotype. This approach can be underpowered to detect genes related to resistance or virulence, since different variants in the same gene may have similar phenotypic effects [53]. The use of collapsing GWAS methods [89], which group variants into functional categories (i.e. genes or pathways) to test groups instead of variants, may be a useful solution.

Another setback is that the generated knowledge does not always have a direct clinical impact for human pathogens. Further efforts are required to apply this knowledge and approaches to monitor the phenotypes in the clinics or design better treatment guidelines. We consider that addressing these limitations will be necessary to understand these phenotypes and fight fungal pathogens.

  • Fungal pathogens pose a growing threat to human health, food security and ecosystem diversity. We have shown how genomic studies have been instrumental to understand the mechanisms of virulence and drug resistance in order to fight these pathogens.

  • Comparative and population genomics studies can reveal associations between naturally occurring phenotypes and underlying mutations. However, their power can be limited if the compared strains are too divergent, and directed evolution coupled with genome sequencing can overcome this limitation.

  • These approaches have revealed that small variants, mostly affecting protein sequences, often underlie drug resistance and virulence phenotypes. In addition, structural variants can also contribute to these phenotypes, although their role is still poorly understood.

The authors declare that there are no competing interests associated with the manuscript.

TG group acknowledges support from the Spanish Ministry of Science and Innovation for grant PGC2018-099921-B-I00, cofounded by European Regional Development Fund (ERDF); from the Catalan Research Agency (AGAUR) SGR423; from the European Union's Horizon 2020 research and innovation programme (ERC-2016-724173); from the Gordon and Betty Moore Foundation (Grant GBMF9742); from the ‘La Caixa’ foundation (Grant LCF/PR/HR21/00737), and from the Instituto de Salud Carlos III (INB Grant PT17/0009/0023 and CIBERINFEC CB21/13/00061- ISCIII-SGEFI/ERDF). MAST received a predoctoral fellowship from the ‘Caixa' Foundation (LCF/BQ/DR19/11740023).

Both authors conceived and wrote the review.

The authors want to thank other members of the Gabaldón group and collaborators for insightful discussions on this matter.

     
  • CNVs

    copy-number variants

  •  
  • GWAS

    genome-wide association studies

  •  
  • SNPs

    single nucleotide polymorphisms

  •  
  • SVs

    structural variants

  •  
  • TF

    transcription factors

1
Bongomin
,
F.
,
Gago
,
S.
,
Oladele
,
R.O.
and
Denning
,
D.W.
(
2017
)
Global and multi-national prevalence of fungal diseases-estimate precision
.
J. Fungi (Basel)
3
,
57
2
Fisher
,
M.C.
,
Henk
,
D.A.
,
Briggs
,
C.J.
,
Brownstein
,
J.S.
,
Madoff
,
L.C.
,
McCraw
,
S.L.
et al (
2012
)
Emerging fungal threats to animal, plant and ecosystem health
.
Nature
484
,
186
194
3
Seyedmousavi
,
S.
,
Bosco
,
S.M.G.
,
de Hoog
,
S.
,
Ebel
,
F.
,
Elad
,
D.
,
Gomes
,
R.R.
et al (
2018
)
Fungal infections in animals: a patchwork of different situations
.
Med. Mycol.
56
,
165
187
4
Kaczmarek
,
A.
and
Boguś
,
M.I.
(
2021
)
Fungi of entomopathogenic potential in Chytridiomycota and Blastocladiomycota, and in fungal allies of the Oomycota and Microsporidia
.
IMA Fungus
12
,
29
5
Rajasingham
,
R.
,
Smith
,
R.M.
,
Park
,
B.J.
,
Jarvis
,
J.N.
,
Govender
,
N.P.
,
Chiller
,
T.M.
et al (
2017
)
Global burden of disease of HIV-associated cryptococcal meningitis: an updated analysis
.
Lancet Infect. Dis.
17
,
873
881
6
Denning
,
D.W.
,
Pleuvry
,
A.
and
Cole
,
D.C.
(
2013
)
Global burden of chronic pulmonary aspergillosis complicating sarcoidosis
.
Eur. Respir. J.
41
,
621
626
7
Walther
,
G.
,
Wagner
,
L.
and
Kurzai
,
O.
(
2020
)
Outbreaks of mucorales and the species involved
.
Mycopathologia
185
,
765
781
8
Köhler
,
J.R.
,
Casadevall
,
A.
and
Perfect
,
J.
(
2014
)
The spectrum of fungi that infects humans
.
Cold Spring Harb. Perspect. Med.
5
,
a019273
9
Naranjo-Ortiz
,
M.A.
and
Gabaldón
,
T.
(
2019
)
Fungal evolution: major ecological adaptations and evolutionary transitions
.
Biol. Rev. Camb. Philos. Soc.
94
,
1443
1476
10
Casadevall
,
A.
(
2018
)
Fungal diseases in the 21st century: the near and far horizons
.
Pathog. Immun.
3
,
183
196
11
Rhodes
,
J.
(
2019
)
Rapid worldwide emergence of pathogenic fungi
.
Cell Host Microbe
26
,
12
14
12
Gabaldón
,
T.
and
Carreté
,
L.
(
2016
)
The birth of a deadly yeast: tracing the evolutionary emergence of virulence traits in Candida glabrata
.
FEMS Yeast Res.
16
,
fov110
13
Consortium OPATHY
. and
Gabaldón
,
T.
(
2019
)
Recent trends in molecular diagnostics of yeast infections: from PCR to NGS
.
FEMS Microbiol. Rev.
43
,
517
547
14
Pristov
,
K.E.
and
Ghannoum
,
M.A.
(
2019
)
Resistance of Candida to azoles and echinocandins worldwide
.
Clin. Microbiol. Infect.
25
,
792
798
15
Ksiezopolska
,
E.
and
Gabaldón
,
T.
(
2018
)
Evolutionary emergence of drug resistance in candida opportunistic pathogens
.
Genes
9
,
461
16
Fisher
,
M.C.
,
Hawkins
,
N.J.
,
Sanglard
,
D.
and
Gurr
,
S.J.
(
2018
)
Worldwide emergence of resistance to antifungal drugs challenges human health and food security
.
Science
360
,
739
742
17
Arastehfar
,
A.
,
Gabaldón
,
T.
,
Garcia-Rubio
,
R.
,
Jenks
,
J.D.
,
Hoenigl
,
M.
,
Salzer
,
H.J.F.
et al (
2020
)
Drug-resistant fungi: an emerging challenge threatening our limited antifungal armamentarium
.
Antibiotics (Basel)
9
,
877
18
Chatterjee
,
S.
,
Alampalli
,
S.V.
,
Nageshan
,
R.K.
,
Chettiar
,
S.T.
,
Joshi
,
S.
and
Tatu
,
U.S.
(
2015
)
Draft genome of a commonly misdiagnosed multidrug resistant pathogen Candida auris
.
BMC Genomics
16
,
686
19
Muszewska
,
A.
,
Steczkiewicz
,
K.
,
Stepniewska-Dziubinska
,
M.
and
Ginalski
,
K.
(
2019
)
Transposable elements contribute to fungal genes and impact fungal lifestyle
.
Sci. Rep.
9
,
4307
20
Bing
,
J.
,
You
,
Z.
,
Zheng
,
Q.
,
Tang
,
J.
,
Ran
,
Y.
and
Huang
,
G.
(
2020
)
Biological and genomic analyses of a clinical isolate of Yarrowia galli from China
.
Curr. Genet.
66
,
549
559
21
Desjardins
,
C.A.
,
Giamberardino
,
C.
,
Sykes
,
S.M.
,
Yu
,
C.H.
,
Tenor
,
J.L.
,
Chen
,
Y.
et al (
2017
)
Population genomics and the evolution of virulence in the fungal pathogen Cryptococcus neoformans
.
Genome Res.
27
,
1207
1219
22
Chow
,
N.A.
,
Muñoz
,
J.F.
,
Gade
,
L.
,
Berkow
,
E.L.
,
Li
,
X.
,
Welsh
,
R.M.
et al (
2020
)
Tracing the evolutionary history and global expansion of Candida auris using population genomic analyses
.
MBio
11
,
e03364-19
23
Carreté
,
L.
,
Ksiezopolska
,
E.
,
Pegueroles
,
C.
,
Gómez-Molero
,
E.
,
Saus
,
E.
,
Iraola-Guzmán
,
S.
et al (
2018
)
Patterns of genomic variation in the opportunistic pathogen Candida glabrata suggest the existence of mating and a secondary association with humans
.
Curr. Biol.
28
,
15
27.e7
24
Mixão
,
V.
and
Gabaldón
,
T.
(
2020
)
Genomic evidence for a hybrid origin of the yeast opportunistic pathogen Candida albicans
.
BMC Biol.
18
,
48
25
Barber
,
A.E.
,
Weber
,
M.
,
Kaerger
,
K.
,
Linde
,
J.
,
Gölz
,
H.
,
Duerschmied
,
D.
et al (
2019
)
Comparative genomics of serial isolates and the rapid acquisition of echinocandin resistance during therapy
.
Antimicrob. Agents Chemother.
63
,
e01628
18
26
Ni
,
Q.
,
Wang
,
C.
,
Tian
,
Y.
,
Dong
,
D.
,
Jiang
,
C.
,
Mao
,
E.
et al (
2018
)
CgPDR1 gain-of-function mutations lead to azole-resistance and increased adhesion in clinical Candida glabrata strains
.
Mycoses
61
,
430
440
27
Perlin
,
D.S.
(
2015
)
Echinocandin resistance in candida
.
Clin. Infect. Dis.
61
,
S612
S617
28
Dos Santos
,
R.A.C.
,
Steenwyk
,
J.L.
,
Rivero-Menendez
,
O.
,
Mead
,
M.E.
,
Silva
,
L.P.
,
Bastos
,
R.W.
et al (
2020
)
Genomic and phenotypic heterogeneity of clinical isolates of the human pathogens Aspergillus fumigatus, Aspergillus lentulus, and Aspergillus fumigatiaffinis
.
Front. Genet.
11
,
459
29
Ferrari
,
S.
,
Ischer
,
F.
,
Calabrese
,
D.
,
Posteraro
,
B.
,
Sanguinetti
,
M.
,
Fadda
,
G.
et al (
2009
)
Gain of function mutations in CgPDR1 of Candida glabrata not only mediate antifungal resistance but also enhance virulence
.
PLoS Pathog.
5
,
e1000268
30
Bombassaro
,
A.
,
Schneider
,
G.X.
,
Costa
,
F.F.
,
Leão
,
A.C.R.
,
Soley
,
B.S.
,
Medeiros
,
F.
et al (
2020
)
Genomics and virulence of Fonsecaea pugnacius, agent of disseminated chromoblastomycosis
.
Front. Genet.
11
,
822
31
Muñoz
,
J.F.
,
Gade
,
L.
,
Chow
,
N.A.
,
Loparev
,
V.N.
,
Juieng
,
P.
,
Berkow
,
E.L.
et al (
2018
)
Genomic insights into multidrug-resistance, mating and virulence in Candida auris and related emerging species
.
Nat. Commun.
9
,
5346
32
Ma
,
L.J.
,
van der Does
,
H.C.
,
Borkovich
,
K.A.
,
Coleman
,
J.J.
,
Daboussi
,
M.J.
,
Di Pietro
,
A.
et al (
2010
)
Comparative genomics reveals mobile pathogenicity chromosomes in Fusarium
.
Nature
464
,
367
373
33
Bertazzoni
,
S.
,
Williams
,
A.H.
,
Jones
,
D.A.
,
Syme
,
R.A.
,
Tan
,
K.C.
and
Hane
,
J.K.
(
2018
)
Accessories make the outfit: accessory chromosomes and other dispensable DNA regions in plant-pathogenic fungi
.
Mol. Plant Microbe Interact.
31
,
779
788
34
Williams
,
A.H.
,
Sharma
,
M.
,
Thatcher
,
L.F.
,
Azam
,
S.
,
Hane
,
J.K.
,
Sperschneider
,
J.
et al (
2016
)
Comparative genomics and prediction of conditionally dispensable sequences in legume–infecting fusarium oxysporum formae speciales facilitates identification of candidate effectors
.
BMC Genomics
17
,
1
24
35
Huang
,
M.
,
Ma
,
Z.
and
Zhou
,
X.
(
2020
)
Comparative genomic data provide new insight on the evolution of pathogenicity in sporothrix species
.
Front. Microbiol.
11
,
565439
36
Chen
,
J.Y.
,
Liu
,
C.
,
Gui
,
Y.J.
,
Si
,
K.W.
,
Zhang
,
D.D.
,
Wang
,
J.
et al (
2018
)
Comparative genomics reveals cotton-specific virulence factors in flexible genomic regions in Verticillium dahliae and evidence of horizontal gene transfer from Fusarium
.
New Phytol.
217
,
756
770
37
Marcet-Houben
,
M.
,
Marceddu
,
G.
and
Gabaldón
,
T.
(
2009
)
Phylogenomics of the oxidative phosphorylation in fungi reveals extensive gene duplication followed by functional divergence
.
BMC Evol. Biol.
9
,
295
38
Schikora-Tamarit
,
M.À
,
Marcet-Houben
,
M.
,
Nosek
,
J.
and
Gabaldón
,
T.
(
2021
)
Shared evolutionary footprints suggest mitochondrial oxidative damage underlies multiple complex I losses in fungi
.
Open Biol.
11
,
200362
39
Speijer
,
D.
(
2017
)
Evolution of peroxisomes illustrates symbiogenesis
.
Bioessays
39
.
40
Ropars
,
J.
,
Maufrais
,
C.
,
Diogo
,
D.
,
Marcet-Houben
,
M.
,
Perin
,
A.
,
Sertour
,
N.
et al (
2018
)
Gene flow contributes to diversification of the major fungal pathogen Candida albicans
.
Nat. Commun.
9
,
2253
41
Biswas
,
C.
,
Marcelino
,
V.R.
,
Van Hal
,
S.
,
Halliday
,
C.
,
Martinez
,
E.
,
Wang
,
Q.
et al (
2018
)
Whole genome sequencing of Australian Candida glabrata isolates reveals genetic diversity and novel sequence types
.
Front. Microbiol.
9
,
2946
42
Zhao
,
S.
,
Ge
,
W.
,
Watanabe
,
A.
,
Fortwendel
,
J.R.
and
Gibbons
,
J.G.
(
2021
)
Genome-wide association for itraconazole sensitivity in non-resistant clinical isolates of Aspergillus fumigatus
.
Front. Fungal Biol.
1
.
43
Guo
,
X.
,
Zhang
,
R.
,
Li
,
Y.
,
Wang
,
Z.
,
Ishchuk
,
O.P.
,
Ahmad
,
K.M.
et al (
2020
)
Understand the genomic diversity and evolution of fungal pathogen Candida glabrata by genome-wide analysis of genetic variations
.
Methods
176
,
82
90
44
Fan
,
Y.
,
Wang
,
Y.
,
Korfanty
,
G.A.
,
Archer
,
M.
and
Xu
,
J.
(
2021
)
Genome-wide association analysis for triazole resistance in Aspergillus fumigatus
.
Pathogens
10
,
701
45
Barber
,
A.E.
,
Sae-Ong
,
T.
,
Kang
,
K.
,
Seelbinder
,
B.
,
Li
,
J.
,
Walther
,
G.
et al (
2021
)
Aspergillus fumigatus pan-genome analysis identifies genetic variants associated with human infection
.
Nat. Microbiol.
6
,
1526
1536
46
Hartmann
,
F.E.
,
Sánchez-Vallet
,
A.
,
McDonald
,
B.A.
and
Croll
,
D.
(
2017
)
A fungal wheat pathogen evolved host specialization by extensive chromosomal rearrangements
.
ISME J.
11
,
1189
1204
47
Wu
,
Q.
,
Patocka
,
J.
,
Nepovimova
,
E.
and
Kuca
,
K.
(
2018
)
A review on the synthesis and bioactivity aspects of beauvericin, a mycotoxin
.
Front. Pharmacol.
9
,
1338
48
Nobrega de Almeida
, Jr,
J.
,
Campos
,
S.V.
,
Thomaz
,
D.Y.
,
Thomaz
,
L.
,
de Almeida
,
R.K.G.
,
Del Negro
,
G.M.B.
et al (
2018
)
Candida blankii: an emergent opportunistic yeast with reduced susceptibility to antifungals
.
Emerg. Microbes Infect.
7
,
24
49
Sanglard
,
D.
(
2019
)
Finding the needle in a haystack: mapping antifungal drug resistance in fungal pathogen by genomic approaches
.
PLoS Pathog.
15
,
e1007478
50
Pais
,
P.
,
Galocha
,
M.
,
Viana
,
R.
,
Cavalheiro
,
M.
,
Pereira
,
D.
and
Teixeira
,
M.C.
(
2019
)
Microevolution of the pathogenic yeasts Candida albicans and Candida glabrata during antifungal therapy and host infection
.
Microb. Cell Fact.
6
,
142
159
51
Elena
,
S.F.
and
Lenski
,
R.E.
(
2003
)
Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation
.
Nat. Rev. Genet.
4
,
457
469
52
Tso
,
G.H.W.
,
Reales-Calderon
,
J.A.
,
Tan
,
A.S.M.
,
Sem
,
X.
,
Le
,
G.T.T.
,
Tan
,
T.G.
et al (
2018
)
Experimental evolution of a fungal pathogen into a gut symbiont
.
Science
362
,
589
595
53
Ksiezopolska
,
E.
,
Schikora-Tamarit
,
M.À
,
Beyer
,
R.
,
Nunez-Rodriguez
,
J.C.
,
Schüller
,
C.
and
Gabaldón
,
T.
(
2021
)
Narrow mutational signatures drive acquisition of multidrug resistance in the fungal pathogen Candida glabrata
.
Curr. Biol.
31
,
5314
5326.e10
54
Bing
,
J.
,
Hu
,
T.
,
Zheng
,
Q.
,
Muñoz
,
J.F.
,
Cuomo
,
C.A.
and
Huang
,
G.
(
2020
)
Experimental evolution identifies adaptive aneuploidy as a mechanism of fluconazole resistance in Candida auris
.
Antimicrob. Agents Chemother.
65
,
e01466
20
55
Carolus
,
H.
,
Pierson
,
S.
,
Muñoz
,
J.F.
,
Subotić
,
A.
,
Cruz
,
R.B.
,
Cuomo
,
C.A.
et al (
2020
)
Genome-wide analysis of experimentally evolved Candida auris reveals multiple novel mechanisms of multidrug-resistance
.
bioRxiv
56
Branco
,
J.
,
Ola
,
M.
,
Silva
,
R.M.
,
Fonseca
,
E.
,
Gomes
,
N.C.
,
Martins-Cruz
,
C.
et al (
2017
)
Impact of ERG3 mutations and expression of ergosterol genes controlled by UPC2 and NDT80 in Candida parapsilosis azole resistance
.
Clin. Microbiol. Infect.
23
,
575.e1
575.e8
57
Todd
,
R.T.
,
Wikoff
,
T.D.
,
Forche
,
A.
and
Selmecki
,
A.
(
2019
)
Genome plasticity in Candida albicans is driven by long repeat sequences
.
eLIfe
8
,
e45954
58
Rafaluk
,
C.
,
Jansen
,
G.
,
Schulenburg
,
H.
and
Joop
,
G.
(
2015
)
When experimental selection for virulence leads to loss of virulence
.
Trends Parasitol.
31
,
426
434
59
Forche
,
A.
,
Magee
,
P.T.
,
Selmecki
,
A.
,
Berman
,
J.
and
May
,
G.
(
2009
)
Evolution in Candida albicans populations during a single passage through a mouse host
.
Genetics
182
,
799
811
60
Kangara
,
N.
,
Kurowski
,
T.J.
,
Radhakrishnan
,
G.V.
,
Ghosh
,
S.
,
Cook
,
N.M.
,
Yu
,
G.
et al (
2020
)
Mutagenesis of Puccinia graminis f. sp. tritici and selection of gain-of-virulence mutants
.
Front. Plant Sci.
11
,
570180
61
Li
,
Y.
,
Xia
,
C.
,
Wang
,
M.
,
Yin
,
C.
and
Chen
,
X.
(
2020
)
Whole-genome sequencing of Puccinia striiformis f. sp. tritici mutant isolates identifies avirulence gene candidates
.
BMC Genomics
21
,
1
22
62
Gao
,
J.
,
Chow
,
E.W.L.
,
Wang
,
H.
,
Xu
,
X.
,
Cai
,
C.
,
Song
,
Y.
et al (
2021
)
LncRNA DINOR is a virulence factor and global regulator of stress responses in Candida auris
.
Nat. Microbiol.
6
,
842
851
63
Poplin
,
R.
,
Ruano-Rubio
,
V.
,
DePristo
,
M.A.
,
Fennell
,
T.J.
,
Carneiro
,
M.O.
,
Van der Auwera
,
G.A.
et al (
2017
)
Scaling accurate genetic variant discovery to tens of thousands of samples
.
bioRxiv
64
Freebayes. Available from
: https://github.com/freebayes/freebayes
65
McLaren
,
W.
,
Gil
,
L.
,
Hunt
,
S.E.
,
Riat
,
H.S.
,
Ritchie
,
G.R.S.
,
Thormann
,
A.
et al (
2016
)
The ensembl variant effect predictor
.
Genome Biol.
17
,
122
66
Cingolani
,
P.
,
Platts
,
A.
,
Wang
,
L.L.
,
Coon
,
M.
,
Nguyen
,
T.
,
Wang
,
L.
et al (
2012
)
A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3
.
Fly
6
,
80
92
67
Abbey
,
D.A.
,
Funt
,
J.
,
Lurie-Weinberger
,
M.N.
,
Thompson
,
D.A.
,
Regev
,
A.
,
Myers
,
C.L.
et al (
2014
)
YMAP: a pipeline for visualization of copy number variation and loss of heterozygosity in eukaryotic pathogens
.
Genome Med.
6
,
100
68
Schikora-Tamarit
,
M.À
and
Gabaldón
,
T.
(
2021
)
PerSVade: personalized structural variation detection in your species of interest
.
bioRxiv
69
Singh-Babak
,
S.D.
,
Babak
,
T.
,
Diezmann
,
S.
,
Hill
,
J.A.
,
Xie
,
J.L.
,
Chen
,
Y.L.
et al (
2012
)
Global analysis of the evolution and mechanism of echinocandin resistance in Candida glabrata
.
PLoS Pathog.
8
,
e1002718
70
Kannan
,
A.
,
Asner
,
S.A.
,
Trachsel
,
E.
,
Kelly
,
S.
,
Parker
,
J.
and
Sanglard
,
D.
(
2019
)
Comparative genomics for the elucidation of multidrug resistance in Candida lusitaniae
.
MBio
10
,
e02512-19
71
Spettel
,
K.
,
Barousch
,
W.
,
Makristathis
,
A.
,
Zeller
,
I.
,
Nehr
,
M.
,
Selitsch
,
B.
et al (
2019
)
Analysis of antifungal resistance genes in Candida albicans and Candida glabrata using next generation sequencing
.
PLoS ONE
14
,
e0210397
72
Rybak
,
J.M.
,
Dickens
,
C.M.
,
Parker
,
J.E.
,
Caudle
,
K.E.
,
Manigaba
,
K.
,
Whaley
,
S.G.
et al (
2017
)
Loss of C-5 sterol desaturase activity results in increased resistance to azole and echinocandin antifungals in a clinical isolate of Candida parapsilosis
.
Antimicrob. Agents Chemother.
61
,
e00651-17
73
Carreté
,
L.
,
Ksiezopolska
,
E.
,
Gómez-Molero
,
E.
,
Angoulvant
,
A.
,
Bader
,
O.
,
Fairhead
,
C.
et al (
2019
)
Genome comparisons of serial clinical isolates reveal patterns of genetic variation in infecting clonal populations
.
Front. Microbiol.
10
,
112
74
Yang
,
F.
,
Teoh
,
F.
,
Tan
,
A.S.M.
,
Cao
,
Y.
,
Pavelka
,
N.
and
Berman
,
J.
(
2019
)
Aneuploidy enables cross-adaptation to unrelated drugs
.
Mol. Biol. Evol.
36
,
1768
1782
75
Yang
,
F.
,
Todd
,
R.T.
,
Selmecki
,
A.
,
Jiang
,
Y.Y.
,
Cao
,
Y.B.
and
Berman
,
J.
(
2021
)
The fitness costs and benefits of trisomy of each Candida albicans chromosome
.
Genetics
218
,
iyab056
76
Covo
,
S.
(
2020
)
Genomic instability in fungal plant pathogens
.
Genes
11
,
421
77
Kasuga
,
T.
,
Bui
,
M.
,
Bernhardt
,
E.
,
Swiecki
,
T.
,
Aram
,
K.
,
Cano
,
L.M.
et al (
2016
)
Host-induced aneuploidy and phenotypic diversification in the sudden oak death pathogen Phytophthora ramorum
.
BMC Genomics
17
,
385
78
Todd
,
R.T.
and
Selmecki
,
A.
(
2020
)
Expandable and reversible copy number amplification drives rapid adaptation to antifungal drugs
.
eLife
9
,
e58349
79
Poláková
,
S.
,
Blume
,
C.
,
Zárate
,
J.A.
,
Mentel
,
M.
,
Jørck-Ramberg
,
D.
,
Stenderup
,
J.
et al (
2009
)
Formation of new chromosomes as a virulence mechanism in yeast Candida glabrata
.
Proc. Natl Acad. Sci. U.S.A.
106
,
2688
2693
80
Sekizuka
,
T.
,
Iguchi
,
S.
,
Umeyama
,
T.
,
Inamine
,
Y.
,
Makimura
,
K.
,
Kuroda
,
M.
et al (
2019
)
Clade II Candida auris possess genomic structural variations related to an ancestral strain
.
PLoS ONE
14
,
e0223433
81
Kebschull
,
J.M.
and
Zador
,
A.M.
(
2015
)
Sources of PCR-induced distortions in high-throughput sequencing data sets
.
Nucleic Acids Res.
43
,
e143
82
Mahmoud
,
M.
,
Gobet
,
N.
,
Cruz-Dávalos
,
D.I.
,
Mounier
,
N.
,
Dessimoz
,
C.
and
Sedlazeck
,
F.J.
(
2019
)
Structural variant calling: the long and the short of it
.
Genome Biol.
20
,
246
83
Cameron
,
D.L.
,
Baber
,
J.
,
Shale
,
C.
,
Valle-Inclan
,
J.E.
,
Besselink
,
N.
,
van Hoeck
,
A.
et al (
2021
)
GRIDSS2: comprehensive characterisation of somatic structural variation using single breakend variants and structural variant phasing
.
Genome Biol.
22
,
202
84
Schröder
,
J.
,
Wirawan
,
A.
,
Schmidt
,
B.
and
Papenfuss
,
A.T.
(
2017
)
CLOVE: classification of genomic fusions into structural variation events
.
BMC Bioinformatics
18
,
346
85
Schymkowitz
,
J.
,
Borg
,
J.
,
Stricher
,
F.
,
Nys
,
R.
,
Rousseau
,
F.
and
Serrano
,
L.
(
2005
)
The FoldX web server: an online force field
.
Nucleic Acids Res.
33
,
W382
W388
86
Jumper
,
J.
,
Evans
,
R.
,
Pritzel
,
A.
,
Green
,
T.
,
Figurnov
,
M.
,
Ronneberger
,
O.
et al (
2021
)
Highly accurate protein structure prediction with AlphaFold
.
Nature
596
,
583
589
87
Al Abdallah
,
Q.
,
Ge
,
W.
and
Fortwendel
,
J.R.
(
2017
)
A simple and universal system for gene manipulation in Aspergillus fumigatus: in vitro assembled Cas9-guide RNA ribonucleoproteins coupled with microhomology repair templates
.
mSphere
2
,
e00446-17
88
Sun
,
S.
,
Roth
,
C.
,
Floyd Averette
,
A.
,
Magwene
,
P.M.
and
Heitman
,
J.
(
2022
)
Epistatic genetic interactions govern morphogenesis during sexual reproduction and infection in a global human fungal pathogen
.
Proc. Natl Acad. Sci. U.S.A.
119
,
e2122293119
89
Saund
,
K.
and
Snitkin
,
E.S.
(
2020
)
Hogwash: three methods for genome-wide association studies in bacteria
.
Microb. Genom.
6
,
mgen000469
This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).