2002; Gow et al. 2003; Jacobsen et al. 2011; Tobin, May and Wheeler 2012; Brunke et al. 2015).

In vitro cell culture systems also provide useful models of *C. albicans* infection. These are less expensive, provide higher throughput and present fewer ethical concerns compared to *in vivo* models. For example, static cell culture models that mimic *C. albicans* interactions with intestinal epithelial cells have been used to dissect processes involved in translocation through intestinal barriers (Allert et al. 2018). Also, *in vitro* circulatory *C. albicans*-endothelium interaction models have been used to study endothelial adhesion events under conditions of physiological blood pressure (Wilson and Hube 2010). Reconstituted Human Epithelium (RHE) uses Transwell® technology to form polarised epithelia and allows easy access to the apical and basolateral compartments for infection studies (Schaller et al. 2006). Such models closely recapitulate the histology of normal vaginal and oral mucosae and relevant aspects of innate immune responses (Schaller et al. 2005; Yadev et al. 2011) and can mimic epithelial interactions with phagocytes (Weindl et al. 2007). However, RHE models do have limitations, such as the lack of supporting cell types, the absence of mucins, non-constant desquamation, and the overgrowth of microbes due to static conditions (Tabatabaei, Moharamzadeh and Tayebi 2020). These limitations need to be addressed to gain accurate views of fungal infection.

Recently, human cell lines were incorporated into an oral-mucosa-on-a-chip model to study host-microbiota interactions (Rahimi et al. 2018). Also, oral mucosa organoids, which recapitulate the original tissue genetically, histologically and functionally, have been established (Driehuis et al. 2019). In principle, these organoids could be developed to integrate, for example, supporting cells and saliva, to further enhance their relevance to the natural oral mucosa. Organ-on-a-chip models of vaginal infection are under development (https://gtr.ukri.org/projects?ref=studentship-1818626; https://ncats.nih.gov/tissuechip/chip/female). Ideally, these models would include iron restriction and hypoxia as these conditions are known to influence the behaviour of *C. albicans* (Moosa et al. 2004; Sosinska et al. 2008; Rastogi et al. 2016; Pradhan et al. 2018, 2019). In the future, organ-on-a-chip models of oral and vaginal infection will exploit microfluidic platforms to combine patient-derived primary cells and microbes to represent donor variability and permit the development of predictive and potentially personalized infection models.

## Gut simulating models

Models are also critical for the experimental dissection of host-microbiota interactions in the GI tract. These include organoid models as well as specialised fermentation systems (Fehlbaum et al. 2015; Park et al. 2017; Bein et al. 2018; Pearce et al. 2018; Pham et al. 2019), but the co-culturing of human and microbial cells remains a technical challenge. *L. rhamnosus* has been shown recently to modulate *C. albicans* pathogenicity in a commensal-like co-culture model (Graf et al. 2019). A similar model, involving co-culture of intestinal epithelial cells and M-cells, revealed that *C. albicans* translocate preferentially through the M-cell (Albac et al. 2016). These models are high-throughput, cost-efficient and able to recapitulate epithelial cell diversity by co-culturing different epithelial cell types. Nevertheless, such models do not provide the complex tissue architecture of the intestinal epithelium *in vivo* and, due to the static conditions, they only offer a short assay window before rapid microbial overgrowth occurs

(Albac et al. 2016; Park et al. 2017; Pearce et al. 2018; Graf et al. 2019).

On-chip technologies permit the culture of human cells under perfusion, enabling their differentiation into a polarised columnar epithelium (Hyun Jung Kim et al. 2016; Trietsch et al. 2017). This has been extended to develop an immuno-competent intestine-on-a-chip model using caco-2 epithelial cells, endothelial cells and peripheral blood mononuclear cells (PBMCs) to study the interaction between *C. albicans* and probiotic *L. rhamnosus* (Maurer et al. 2019). Although this model already provides three-dimensional structures that resemble organotypic microanatomical structures and mimic microphysiological niches of the human intestine, further improvements to increase mucosa cellular diversity and mucus production are possible (Pan et al. 2015; Pearce et al. 2018). In the future, long-term cultures of patient-derived intestinal organoids may be feasible, which opens new avenues for the development of gut models that are even more physiologically relevant (Sato et al. 2009; Mottawea et al. 2019). Patient-derived ileal organoids and faecal samples have been used to culture a complex microbiota in an anaerobic gut-on-a-chip model for up to 5 days (Jalili-Firoozinezhad et al. 2019). This type of model is important because it permits the analysis of donor variability and potentially allows the development of personalised therapies.

Fermentation-based models provide powerful *in vitro* tools that permit the dissection of microbial processes in the human GI tract. Static batch fermentations with faecal inocula are the simplest and most frequently used models (Walker et al. 2005). These have provided a powerful first approach to study bacterial-fungal interactions and to screen novel therapeutics, but they do not recapitulate the richness of GI compartments (Hillman et al. 2017). Therefore, multicompartmental models have been developed (Guerra et al. 2012; Venema and van den Abbeele 2013). These often contain three-stage culture reactors (Gibson, Cummings and Macfarlane 1988) that can reproduce differences between proximal (acidic, carbohydrate-rich) and distal colonic regions (neutral, carbohydrate-depleted). The multicompartmental M-SHIME system is a powerful tool that permits the analysis of complex, rich and relatively stable microbial communities within GI compartments from the stomach to descending colon (Van de Wiele et al. 2015; Molly, Vande Woestyne and Verstraete 1993). This model has been used to study bacterial-bacterial interactions and the impact of diet and drugs on these interactions (Sivieri et al. 2014; Van den Abbeele et al. 2016; Marzorati et al. 2017; Rivière et al. 2018; Lambrecht et al. 2019).

Despite their power, these fermentation systems have rarely been used to examine fungal-bacterial interactions. In two studies, *C. albicans* colonisation and outgrowth was shown to be strongly correlated with antibiotic treatment, but mitigated by *L. plantarum* (Payne et al. 2003; Wynne et al. 2004). However, more recent work has shown that it is important to include the mucus layer to properly simulate the human gut environment *in vitro* (Van den Abbeele, Van de Wiele et al. 2011; Van den Abbeele, Gérard et al. 2011; Van den Abbeele et al. 2013). Indeed, the presence of mucus influenced interactions between the yeast *Saccharomyces boulardii* and *L. rhamnosus* GG and their ability to limit the outgrowth of toxigenic *E. coli* (Moens et al. 2019). Therefore, a M-SHIME-based system that includes a mucus-rich environment (Van den Abbeele et al. 2012) would seem most appropriate for the dissection of *C. albicans*-microbiota interactions in the GI tract.
## SUMMARY AND OUTLOOK

To summarise, it is clear that the interactions between C. albicans, the human host, and the local microbiota have a major impact upon the likelihood of mucosal and systemic infections and the severity of these infections. It is also apparent that these fungus-host-microbiota interactions are dynamic, iterative and enormously complex (Fig. 8). This immense complexity is increased further by the genetic and phenotypic variation within the species of C. albicans, and by numerous factors that contribute to the variability of individuals and their microbiotas. Yet this complexity must be addressed and defined if the research community is to develop: (i) sensitive and accurate diagnostics capable of distinguishing C. albicans infection from commensalism, and at an early stage when the infection is more amenable to therapy; (ii) novel and efficacious anti-fungal therapies that complement the limited antifungal drugs that are currently available, and that address the problematic emergence of drug resistance and drug resistant species; (iii) tests that quickly establish whether a particular patient is at risk of developing severe candidaemia or recurrent candidiasis and (iv) personalised therapeutic strategies that address the specific make-up and needs of the individual patient.

Despite these challenges, our increased understanding of antifungal immunity and responses is offering potential immunotherapeutic opportunities (De Luca et al. 2013; Davidson, Netea and Kullberg 2018). Effective anti-Candida vaccines, which have proven elusive for so long, are now in sight (Cassone 2015; De Bernardis et al. 2018; Edwards et al. 2018). Our deeper comprehension of fungal immune evasion strategies affords the potential to block these phenotypes and thereby enhance the efficacy of natural antifungal immunity mechanisms (Childers et al. 2020). Significantly, the dramatic expansion in genomic and phenotypic datasets for clinical isolates of C. albicans is providing a much clearer picture of the nature of fungal variability and in-patient evolution (Selmecki, Forche and Berman 2006; Ford et al. 2014; Hirakawa et al. 2015; Ropars et al. 2018; Sitterlé et al. 2019). This information is vital because it will reveal ways in which this microevolution might be inhibited or exploited therapeutically. This information will also highlight essential fungal processes that are less prone to variability and hence present better therapeutic targets. Rapid advances in metagenomics and culturomics are highlighting fungal-bacterial associations between the gut microbiota that are likely to yield useful prognostic tools for patients at risk of systemic candidiasis (Yachida et al. 2019). Our increased knowledge of local fungus-microbiota interactions is facilitating the development of probiotic therapies to address VVC, OPC and C. albicans colonisation of the GI tract (Romeo et al. 2011; Hu et al. 2013; Morais et al. 2017; Vladareanu et al. 2018). In the future, the availability of effective probiotics should help to reduce our dependence on antifungal drugs while, at the same time, enhancing antifungal immunity (Ubeda and Pamer 2012).

How relevant are these points to other fungal pathogens of humans, such as Aspergillus, Cryptococcus, Pneumocystis and other pathogenic Candida species? Pathogenic Aspergillus and Cryptococcus species are environmental fungi that infect humans via the lung. Therefore, while fundamental principles relating to local antifungal immunity, immunotherapy and microbiota-mediated colonisation resistance are clearly of relevance (Armstrong-James et al. 2017; Dumas et al. 2018; Hernández-Santos et al. 2018; Drummond and Lionakis 2019; Maschirow, Suttorp and Opitz 2019; Warris, Bercusson and Armstrong-James

2019), the specific details will differ significantly. Pneumocystis jirovecii also infects the lung, but this fungus is an intracellular parasite that is obligately associated with its human host. In this case, the lung microbiota has not been particularly informative in distinguishing infected from uninfected patients (Kehrmann et al. 2017). Although other Candida pathogens may infect from environmental reservoirs, these species cause similar types of infection to C. albicans, and therefore the points raised in this review will be of general relevance to these species. However, some differences in tissue tropism and patient type exist between species (Sullivan et al. 1995; Silva et al. 2012; Pammi et al. 2013), and some species differ in their immune avoidance strategies (Brunke and Hube 2013; Kasper, Seider and Hube 2015). Nevertheless, the importance of the general principles discussed in this C. albicans-oriented review cannot be understated, most notably the major impact of variability in the fungus, the individual host, and the local microbiota upon disease severity and outcome (Carvalho et al. 2010; Farrer et al. 2015; Hube 2015; Ballard et al. 2018; Stone et al. 2019; Vandeplassche et al. 2019).

## ACKNOWLEDGMENTS

We thank our friends and colleagues in the medical mycology, fungal immunology and microbiota fields for many thought-provoking discussions.

## FUNDING

We received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie action, Innovative Training Network: Fun-HoMic; grant N° 812969. CdE received funding from the French Government 'Investissement d'Avenir' program (Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases, ANR-10-LABX-62-IBEID), the Agence Nationale de la Recherche (ERA-Net Infect-ERA, FUNCOPATH, ANR-14-IFEC-0004), the EU Horizon2020 consortium 'Host-Directed Medicine in invasive FUNgal infections'-HDM-FUN (Grant Agreement 847507). SLL and CdE received funding from the Swiss National Science Foundation (Sinergia program, #CRSIIS_173863). BIOASTER received funding from the French Government 'Investissement d'Avenir' program (Grant No. ANR-10-AIRT-03). MSG was supported by a Humboldt Research Fellowship for Postdoctoral Researchers by the Alexander von Humboldt-Foundation and the Deutsche Forschungsgemeinschaft (DFG) Emmy Noether Program (project no. 434385622/GR 5617/1-1). BH was supported by the Deutsche Forschungsgemeinschaft (DFG) project Hu 532/20-1, project C1 within the Collaborative Research Centre (CRC)/Transregio 124 FungiNet and the Balance of the Microverse Cluster under Germany's Excellence Strategy-EXC 2051-Project-ID 390713860, the EU Horizon2020 consortium 'Host-Directed Medicine in invasive FUNgal infections'-HDM-FUN (Grant Agreement 847507), the Leibniz Association Campus InfectoOptics SAS-2015-HKI-LWC and the Wellcome Trust (215599/Z/19/Z). IDJ was supported by the Deutsche Forschungsgemeinschaft (DFG) project C5 within the Collaborative Research Centre (CRC)/Transregio 124 FungiNet and the Balance of the Microverse Cluster under Germany's Excellence Strategy-EXC 2051-Project-ID 390713860, the Leibniz Association Campus InfectoOptics SAS-2015-HKI-LWC and the Wellcome Trust (Grant 215599/Z/19/Z). CM received funding from the Instituto de Salud Carlos III/FEDER. MGN was supported by an ERC Advanced Grant (#833247) and
a Spinoza grant of the Netherlands Organization for Scientific Research. CAM was supported by EU Horizon2020 consortium 'Host-Directed Medicine in invasive FUNgal infections'—HDM-FUN (Grant Agreement 847507) and the Wellcome Trust Strategic Award for Medical Mycology and Fungal Immunology (097377/Z/11/Z). AWW receives core funding support from the Scottish Government's Rural and Environment Science and Analytical Services (RESAS). AJPB was supported by a programme grant from the UK Medical Research Council (MR/M026663/1) and by the Medical Research Council Centre for Medical Mycology at the University of Exeter (MR/N006364/1).

**Conflicts of Interest.** None declared.

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