1) Know the enemy: Accurate and rapid diagnosis of fungal pathogens

Accurate diagnosis is a key first step to mitigating the burden of fungal pathogens. Traditional diagnosis methods use microscopy, histopathology, culturing, and MALDI-TOF [8]. Although helpful, these methods often lack specificity, especially for cryptic pathogens. Molecular typing—such as phylogenetic analysis of one or a few loci—has proven more accurate and brought cryptic pathogens to light [3,6]. However, few loci can, at times, lack insufficient information for accurate species determination. For example, Aspergillus latus—a cryptic pathogen and allodiploid hybrid that originated from a fusion event between A. spinulosporus and a close relative of A. quadrilineatus, two species that are closely related to A. nidulans—has gone largely undetected in the clinical setting due to the shortcomings of a single-locus typing in a hybrid genome [4]. Population genome sequencing and extensive phenotyping has revealed diagnostics for robust identification of A. latus, such as a large genome size, which can be detected using genome sequencing, fluorescence-activated cell sorting, or measuring conidia sizes [9].

Other genome-scale approaches can overcome the insufficient information in molecular typing of one or a few loci. For example, data-rich phylogenomics has revealed instances of misidentified human and plant pathogens in the genus Aspergillus [10]. The greater specificity of phylogenomics comes with the drawbacks of requiring more expertise (i.e., knowledge of processing and analysis of genomic data), resources (e.g., sequencing and computational resources), and time. In the clinical setting, rapid and cost-effective diagnosis is critical for lowering mortality rates [8]. An unexplored genome-scale alternative approach ripe for application to fungi is average nucleotide identity. This and similar alternative approaches could be coupled to (semi)automated genome sequencing, assembly, and analysis infrastructure, powered by robotics and cloud computing. Comparative analysis of diverse methods for diagnosing fungal infections will elucidate the most efficacious method, or combination thereof, for faithful diagnostics.

Beyond identification, examining phenotypic profiles among pathogens will elucidate other key aspects of pathogen biology relevant to combatting and preventing disease. This is particularly true for cryptic fungal pathogens whose phenotypic profiles remain largely unknown, and numerous examples have revealed cryptic fungal pathogens have distinct drug resistance profiles compared to close relatives [4–6]. For example, the cryptic pathogen A. latus is substantially more drug resistant to caspofungin than reference isolates of A. nidulans [9]. Considering strain heterogeneity—phenotypic variation within a species, including variation in virulence and resistance to antifungal drugs—has emerged as an important consideration when studying microbial pathogenicity [4,9].

2) Know yourself: Understanding the human influence and patient populations

Although most efforts aim to prevent and treat fungal diseases, humans can sometimes unwittingly contribute to their spread. For example, hospital-associated outbreaks of invasive fungal infections have been linked to factors such as hospital construction, indoor water damage, inadequate air filtration, and transmission via contaminated surfaces [11]. Other factors may impact fungal diseases, such as human-to-human transmission, the influence of diet on modulating the abundance of certain fungi in the mycobiome, and the rise of antifungal resistance mainly through the abundant usage of fungicides with a similar mechanism of action in agriculture, confounded by the ability of some fungi to evolve drug resistance rapidly [12,13]. For example, genome sequencing efforts have revealed that drug-resistant environmental isolates of A. fumigatus—wherein resistance was likely acquired from agricultural use of first-line clinical azoles—can cause human disease [13].

Consideration of the patient population and genetic backgrounds is also crucial for understanding the role of host biology in disease susceptibility [3,5]. For example, patients with chronic granulomatous disease are at greater risk of A. nidulans infections [4,9]. Of note, the association between cryptic pathogens and certain patient populations remains largely unknown due to the absence of highly specific detection methods in the clinical setting, as discussed in the previous section.

3) Know the enemy and know yourself: Elucidating the immune response to pathogens will inform disease management strategies

Investigating the interaction between fungal pathogens and the host immune system offers valuable insights into mechanisms of pathogen clearance or persistence. Studies tackling this issue have revealed key immunological checkpoints necessary for pathogen recognition, such as a sufficient number of properly functioning myeloid phagocytes, conidial engulfment by alveolar macrophages, and proper activation of pro-inflammatory cascades via detection of β-1,3-glucan, a sugar found on the germinating conidia, through a dectin 1–dependent recognition process [14]. Considering the site of infection, as immune defenses differ between infections occurring at the skin or mucous membranes versus deeper anatomical sites, will also be important [14].

Immune cells can also launch direct assaults on microbes. Examination of NETosis, wherein neutrophils release extracellular traps to kill microbes, revealed substantial heterogeneity among species and strains of Aspergillus species from the section Nidulantes; for example, among the strains tested, NETosis of the cryptic pathogen A. latus resulted in more pathogen lysis than NETosis of A. spinulosporus [4]. Certain inborn errors in immune system function render some populations more susceptible than others and should be considered in these studies. For example, those with chronic granulomatous disease are susceptible to aspergillosis because immune cells have a reduced capacity to generate reactive oxygen species, a key component of the immune defense system [14]. Elucidating the interaction between host and pathogen will help pave the way for developing more targeted treatment and prevention regimes.

4) Beyond the bipartite of host and pathogen: The influence of the microbiome on health and disease

Investigating the interaction between fungal pathogens and the biology of the human host offer valuable insights into the clearance or persistence of pathogens within the human body [4]. However, fungal pathogens are just one of the many organisms in the human microbial ecosystem known as the microbiome. Exploring the microbiome, including the mycobiome, holds immense potential in elucidating the impact of diverse species involved in complex species–species interactions that can contribute to or prevent disease. Perturbations in the mycobiome, induced by antifungal treatments, can exacerbate allergic airway disease, indicating mycobiome imbalances contribute to disease severity [15]. Pathogens can also influence the mycobiome; for example, A. fumigatus colonization of the lung shifts microbiome composition to be more beneficial for fungal growth [16]. This and other findings suggest that targeting the mycobiome may have therapeutic potential.

Although most mycobiome studies have provided insights at the genus level, delving into species- and strain-level variations will offer a detailed understanding of how specific species and individual isolates thereof influence the mycobiome and human health. This issue is also prevalent in some traditional methodologies. For example, cryptic Aspergillus pathogens often go undetected because only genus-level identification is possible (Table 1). Accurate species and strain-specific identification is particularly important due to genomic and phenotypic strain heterogeneity among clinically relevant traits of pathogenic microbes [4,9,16]. As a result, some dimensions of the microbiome go undetected, limiting our understanding of the relationship between the microbiome and the host. These investigations will also shed light on the prevalence of cryptic fungal pathogens among healthy individuals, for which little is known. The occurrence of superinfections—coinfections of multiple pathogens, such as Aspergillus and SARS-CoV-2, which causes COVID-19—is becoming better appreciated and is associated with devastating mortality [17], underscoring the importance of considering complex species-species interactions.

5) The evolution of pathogenesis and identifying potential future threats

While the present article has predominantly addressed pathogens, especially cryptic ones, within the clinical setting, investigating nonpathogenic species remains essential [18]. This is due to the potential emergence of previously unrecognized species as disease-causing agents in clinical contexts. An illustrative example is A. lentulus, an opportunistic human pathogen with high mortality rates first described in 2005 [19]. Therefore, studying pathogenic species and closely related non-pathogens can provide valuable insights into the evolutionary dynamics underlying the emergence of pathogenicity [18].

Moreover, a broader understanding of the fungal lifestyle may provide insight into ecologies and life histories that tend to give rise to pathogens. For example, growth in warming climates may predispose certain fungi to better grow at the human body temperature. Thus, providing a geographic context to studies of microbial pathogens and their close relatives will not only elucidate epidemiologic patterns among known pathogens, but also potentially help identify species that pose a future threat to human health. Ecologies are largely unknown among cryptic pathogens, further obfuscating our understanding of their habitat and our capacity to predict outbreaks. Furthermore, comprehensive exploration of diverse fungi can aid in identifying species that may pose a future threat to human welfare due to possessing pathogen-like characteristics, such as growth at human body temperature and rapid evolution of drug resistance.

Concluding remarks: Embracing the complexity of the fungal pathogen problem

Addressing the aforementioned challenges relies on overcoming the major challenge of establishing the necessary infrastructure for accurate diagnostic testing and dissemination of findings. Such infrastructure will facilitate monitoring disease burden, elucidating pathogen epidemiology, identifying susceptible patient populations, illuminating geographic locales with increased prevalence of certain pathogens, and facilitating the identification of early outbreaks and emerging pathogens [12]. In the context of cryptic pathogens, basic knowledge gaps, such as clinical frequency, will come to light and help inform fungal pathogen priority lists like the one established by the World Health Organization. More broadly, accurately identifying microbial pathogens is the key first step in treating infection.

Insights from other surveillance systems can guide tackling this Herculean objective. For example, a platform like Nextstrain (https://nextstrain.org) or Microreact (https://microreact.org/)—web-based applications with geographic, temporal, and genomic information for comparative and phylogenetic analysis of influenza, Dengue, SARS-CoV-2, and other pathogens—can serve as inspiration for a fungal-focused platform. More broadly, integrating other dimensions of data, such as host genetics and electronic health records as well as microbiome and pathogen data, will offer a comprehensive data set for researchers teams across the globe to address the fungal pathogen problem.

Acknowledgments

We thank Drs. Matt Mead and Nick Oberlies for helpful discussions throughout the years. JLS thanks Dr. Jos Houbraken for kindly providing images of exemplary cryptic pathogens. More broadly, JLS thanks the fungal research community for cultivating a rich environment for scientific discovery.