Host-mycobiome metabolic interactions in health and disease

Figures & data

Table 1. The effect of fungal species in human health. The table indicates the fungal species causing diseases with references.

Species	Disease	Reference
Candida spp.; Cryptococcus spp., S. cerevisiae	Liver disease	Bajaj et al., 2013; Hwang et al., 2014; Yang et al., 2017; Fernandez et al., 2017
S. cerevisiae; Candida spp.; Malassezia spp.	Irritable Bowel Disease	Ott et al., 2008; Sokol et al., 2017; Nishida et al., 2018;
Candida spp.; Malassezia spp.	Crohn’s Disease	Barclay et al., 1992; Poulain et al., 2009; Standaert-Vitse et al., 2009; Hoarau et al. 2016; Liguori et al., 2016; Limon et al., 2019
Aspergillus sp.	Cystic Fibrosis	Knutsen et al., 2003; Horre et al., 2004; Bakare et al., 2003; Delhaes et al., 2012; Willger et al., 2014;
Aspergillus spp.; Epicoccum nigrum; Wallemia sebi	Lung Diseases	Denning et al., 2011; Knutsen et al., 2012; Fairs et al., 2010; Yan et al 2009; McCarthy and Walsh, 2017
Aspergillus spp.	Chronic Obstructive Pulmonary Disease (COPD)	Garcia-Vidal et al., 2008; Guinea et al., 2010; Huerta et al., 2014; Molinos-Castro et al., 2020
Candida spp.; Aspergillus spp.	Nosocomial Blood Infections	Wisplinghoff et al., 2004; Morgan et al., 2005; Pfaller et al., 2007; Wang et al., 2020; Sfeir et al., 2020
Candida spp.	Vaginal Infection	Taylor et al., 2005; Barousse et al., 2007; Sobel, 2007; llkit and Guzel, 2011
Cryptococcus spp.	Meningitis	Gottfredsson and Perfect, 2000; Lui e tal., 2012; Blatzer et al., 2020; Spencer et al., 2020
Candida spp.	Oral infection	Barclay et al., 1992; Poulain et al., 2009; Standaert-Vitse et al., 2009; Hoarau et al. 2016; Liguori et al., 2016; Limon et al., 2019
Candida spp.; Aspergillus spp.; Coccidioides posadasii;	Neurological Infection	Sharma et al., 1997; Chopra et al., 2006; Thurtell et al., 2013; Suresh, 2015; Pisa et a;., 2015; Benito-Leon and Laurence, 2017; Forbes et al., 2019
Candida spp.	Autism spectrum	Strati et al., 2017; Forbes et al., 2019
S.cerevisiae; Candida spp.	Schizoprenia	Severance et al., 2016; Severance et al., 2017; Cihakova et al., 2019; Zhang et al, 2020

Table 2. Mycobiome tabulated data types. The table indicates references with fungal data types for discerning fungal genera.

Reference	Data type
Han, S. H. et al. Analysis of the skin mycobiome in adult patients with atopic dermatitis. Exp. Dermatol. 27, 366–373 (2018).	Human data
Mukherjee, P. K. et al. Oral Mycobiome Analysis of HIV-Infected Patients: Identification of Pichia as an Antagonist of Opportunistic Fungi. PLoS Pathog. 10, (2014).	Human data (saliva)
Hoffmann, C. et al. Archaea and Fungi of the Human Gut Microbiome: Correlations with Diet and Bacterial Residents. PLoS ONE 8, (2013).	Human data (stool)
Nash, A. K. et al. The gut mycobiome of the Human Microbiome Project healthy cohort. Microbiome 5, 153–153 (2017).	Human data (HMPS stool)
Ghannoum, M. A. et al. Characterization of the oral fungal microbiome (mycobiome) in healthy individuals. PLoS Pathog. 6, (2010).	Human data (oral rinse)
Drell, T. et al. Characterization of the Vaginal Micro- and Mycobiome in Asymptomatic Reproductive-Age Estonian Women. PLoS ONE 8, (2013).	Human data
David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).	Human data (stool)
Mar Rodríguez, M. et al. Obesity changes the human gut mycobiome. Sci. Rep. 5, 14,600–14,600 (2015).	Human data (stool)
Sokol, H. et al. Fungal microbiota dysbiosis in IBD. Gut 66, 1039–1048 (2017)	Human data (stool)
Trojanowska, D. et al. The role of Candida in inflammatory bowel disease. Estimation of transmission of C. albicans fungi in gastrointestinal tract based on genetic affinity between strains. Med. Sci. Monit. Int. Med. J. Exp. Clin. Res. 16, CR451-457 (2010).	Human data (biopsy)
Ott, S. J. et al. Fungi and inflammatory bowel diseases: Alterations of composition and diversity. Scand. J. Gastroenterol. 43, 831–841 (2008	Human data (biopsy)
Alonso, R. et al. Fungal infection in patients with Alzheimer’s disease. J. Alzheimers Dis. JAD 41, 301–311 (2014)	Human data (biopsy)
Alonso, R., Pisa, D., Aguado, B. & Carrasco, L. Identification of Fungal Species in Brain Tissue from Alzheimer’s Disease by Next-Generation Sequencing. J. Alzheimers Dis. JAD 58, 55–67 (2017)	Human data (biopsy)
Severance, E. G. et al. Gastrointestinal inflammation and associated immune activation in schizophrenia. Schizophr. Res. 138, 48–53 (2012)	Human data (blood)
Severance, E. G. et al. Candida albicans exposures, sex specificity and cognitive deficits in schizophrenia and bipolar disorder. Npj Schizophr. 2, 1–7 (2016)	Human data (blood)
Severance, E. G. et al. Probiotic normalization of Candida albicans in schizophrenia: A randomized, placebo-controlled, longitudinal pilot study. Brain. Behav. Immun. 62, 41–45 (2017).	Human data (blood)
Hoarau, G. et al. Bacteriome and mycobiome interactions underscore microbial dysbiosis in familial Crohn’s disease. mBio 7, (2016).	Human data (stool)
Aykut, B. et al. The fungal mycobiome promotes pancreatic oncogenesis via activation of MBL. Nature 1–4 (2019) doi:10.1038/s41586-019-1608-2.	in vivo (mice)
Harriott, M. M. & Noverr, M. C. Candida albicans and Staphylococcus aureus Form Polymicrobial Biofilms: Effects on Antimicrobial Resistance. Antimicrob. Agents Chemother. 53, 3914–3922 (2009)	in vitro
Guinan, J., Wang, S., Hazbun, T. R., Yadav, H. & Thangamani, S. Antibiotic-induced decreases in the levels of microbial-derived short-chain fatty acids correlate with increased gastrointestinal colonization of Candida albicans. Sci. Rep. 9, 8872–8872 (2019).	in vivo (mice)
Nguyen, L. N., Lopes, L. C. L., Cordero, R. J. B. & Nosanchuk, J. D. Sodium butyrate inhibits pathogenic yeast growth and enhances the functions of macrophages. J. Antimicrob. Chemother. 66, 2573–2580 (2011).	in vitro
Noverr, M. C. & Huffnagle, G. B. Regulation of Candida albicans Morphogenesis by Fatty Acid Metabolites. Infect. Immun. 72, 6206–6210 (2004).	in vitro
García, C. et al. The Human Gut Microbial Metabolome Modulates Fungal Growth via the TOR Signaling Pathway. mSphere 2, e00555-17 (2017)	in vitro
Baltierra-Trejo, E., Sánchez-Yáñez, J. M., Buenrostro-Delgado, O. & Márquez-Benavides, L. Production of short-chain fatty acids from the biodegradation of wheat straw lignin by Aspergillus fumigatus. Bioresour. Technol. 196, 418–425 (2015)	in vitro
Borges, F. M. et al. Fungal Diversity of Human Gut Microbiota Among Eutrophic, Overweight, and Obese Individuals Based on Aerobic Culture-Dependent Approach. Curr. Microbiol. 75, 726–735 (2018).	Human data (stool)
Auchtung, T. A. et al. Investigating Colonization of the Healthy Adult Gastrointestinal Tract by Fungi. mSphere 3, e00092-18 (2018).	Human data (saliva, stool HMP)
Strati, F. et al. Age and Gender Affect the Composition of Fungal Population of the Human Gastrointestinal Tract. Front. Microbiol. 7, (2016)	Human data (stool)
Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010)	Human data (stool)
Raimondi, S. et al. Longitudinal Survey of Fungi in the Human Gut: ITS Profiling, Phenotyping, and Colonization. Front. Microbiol. 10, (2019)	Human data (stool)
Botschuijver, S. et al. Intestinal Fungal Dysbiosis Is Associated With Visceral Hypersensitivity in Patients With Irritable Bowel Syndrome and Rats. Gastroenterology 153, 1026–1039 (2017).	Human data (stool)
Iliev, I. D. et al. Interactions between commensal fungi and the C-type lectin receptor Dectin-1 influence colitis. Science 336, 1314–1317 (2012)	in vivo (mice)
Kaur, J. et al. “Pseudomonas aeruginosa inhibits the growth of Scedosporium aurantiacum, an opportunistic fungal pathogen isolated from the lungs of cystic fibrosis patients.” Frontiers in microbiology, 866. (2015).	in vitro (human sputum)

*in vitro (lab experiments), in vivo (animal experiments), in silico (computational analysis) and human data (clinical sample collection)

Figure 1. Fundamental metabolic interactions between host-mycobiome in health and disease. A. indicates Candida contribution to the production of γ-amino butyric acid (GABA) through the tricarboxylic acid cycle to produce succinate may perceptively occur during systemic infection. GABA has a role in dampening the effect of the nervous system, and generally, deficiency involves epilepsy.¹⁰⁹ Indirectly targeting mycobiome in specific regions such as Candida species can have a better effect than providing analogues of GABA that has been identified to lead to anxiety, stress and seizures. Exploring this metabolic pathway of the mycobiome, especially targeting the succinate pathway, the production of semi-aldehydrase could lead to alteration of disease with better regulation from the microbiota, as alternatively, high levels of GABA have been linked to Alzheimer’s disease.¹¹⁰ B. Penicillium species’ ability to produce the mycotoxin called citrinin is toxic to cause renal nephrosis.^111–113 Drugs have been developed to inhibit the effect of citrinin in hepatoma, and several animal studies have shown the benefit.¹¹⁴ Ostry et al., 2013 have highlighted the dangerous effect of citrinin in dietary sources.¹¹⁵ There has been no established link of mycobiome being the contributor of toxin in humans and whether diverting the polyketide pathway can reduce renal cell toxicity. C Aflatoxin is a common secondary metabolite of the Aspergillus family. It has been associated with cancer morbidity and inducing tumour suppressor gene p53.^116–119 The potential effect of the presence of Aspergillus needs to be investigated and whether manipulating the metabolic pathway can give a better indicator of being able to reverse the effects of aflatoxin by reducing the levels of the malonyl-coA pathway. All these potential pathways give us a clear indication of how the study of the metabolism of the mycobiome is essential in understanding pathogenesis across a wide breadth of diseases.

Table 3. Highlight the effect of metabolites on the host. Identification of a wide variety of secondary metabolites from various fungal species, the class grouping attributed to the metabolite, based upon the synthesis, fungal species responsible for chemically engineering the metabolite and the effect on the human host. The properties include anti-bacterial activity to cancerous effect. The extended table includes the effect on the environment, marine biology and produce. *IL = interleukin ** blue highlight indicates human effect.

Metabolite	Class	Fungi	Activity	Reference
Averufin	PKS	Aspergillus versicolor	Anti-bacterial activity	Miao et al., 2012; Goyal, 2016; Yu, 2012
Aflatrem	DMATs	Aspergillus flavus	Acute neurotoxic effects	Hoffmeister, 2016; Valdes et al., 1985
Agrocalvin-I	Alkaloids	Pencillium sp.	Induce changes in GABA providing inhibitory effect of CNS, anti-bacterial and anti-tumor activites	Selala et al., 1989; Griffin et al., 2013; Niehaus etl al., 2016; Kumar et al., 2018
Ascomycone A (6-deoxyfusarubin)	PKS	Biatriospora sp.	Cytotoxic	Stodulkova et al., 2015; Goyal, 2016
Asperdiazapinones	Alkaloids	Aspergillus sp. PSU-RSPG185	Anti-fungal, cytotoxic and insecticidal activities	Rukachaisirikul et al., 2013; Yin et al, 2009; Siddiquee, 2018
Asperochrins	PKS	Aspergillus oryzae	Antibacterial acitivity; inhibitory cidal activity against aquatic species	Liu et al., 2015; Siddiquee, 2018
Chanoclavine	Alkaloids	Pencillium spp.	Induce changes in GABA providing inhibitory effect of CNS	Selala et al., 1989; Griffin et al., 2012; Xu et al., 2015; Kumar et al., 2018;
Citrinin	PKS	Pencillium spp.	Anti-bacterial activity; Toxins damaging organs (renal disease)	Hetherington and Raistrick, 1931; Subramani et al., 2013; Zain et al., 2011; Samson et al., 2011a; Bouslimi et al., 2008; Goyal, 2016
Cladosin C	PKS	Cladosporium sphaerospermum	Anti-viral activity	Wu et al., 2014; Goyal, 2016
Deoxynivalenol (vomitoxin)	NRPS	Fusarium graminearum; Fusarium culmorum	Immunosupressive activity; potential suspectible to viral and bacterial infections	Marasas et al, 1984; Bondy and Peska, 2000; Palazzini et al., 2016; Moss, 2011
Elymoclavine	DMATs	Aspergillus spp.	Inhibitory effect of CNS	Robbers, 1979; Hoffmeister, 2016
Festuclavines	Alkaloids	Pencillium spp.	Induce changes in GABA providing inhibitory effect of CNS, anti-bacterial and anti-tumor activites	Selala et al., 1989; Griffin et al., 2014; Kumar et al., 2018
Flaviphenalenones	PKS	Aspergillus flavipes	Anti-malarial cytotoxic and antimicrobial activity	Nazir et al., 2015; Gutierrez et al., 2013; Elsebai et al., 2011; Siddiquee, 2018
Fumagillin	Terpene	Aspergillus fumigatus	aNtibiotic treatment for protozoa, analogues treatment for angiogensis and supress tumour growth	Schenk et al, 1953; hanson and Elbe, 1949; Ingber et al., 1990; Molina et al., 2002; Moss, 2011; Siddiquee, 2018
Isochanoclavine	Alkaloids	Pencillium spp.	Induce changes in GABA providing inhibitory effect of CNS	Selala et al., 1989; Griffin et al., 2016; Kumar et al., 2018; Rabha and Jha, 2018
Isofellutanine	Alkaloids	Pencillium spp.	Anti-bacterial and anti-tumor activites	Jouda et al., 2016; Kumar et al., 2018
Naptho-γ-pyrones	PKS	Aspergillus brasiliensis	Anti-tumoral, anti-bacterial, anti-fungal	Song et al., 2004; Koyama et al., 1988; Samson et al., 2007; Siddiquee, 2018; Se-Kwon Kim, 2013
Neoechinulin A	Alkaloids	Eurotium spp.	Anti-inflammatory effect	Kim et al., 2013; Goyal, 2016; Chen et al., 2015
Pestalotiopsone A	PKS-NRPS	Pestalotiopsis spp.	Anti-bacterial activity	Hemberger et al., 2013; Goyal, 2016; Kumar et al., 2018

Figure 2. System and synthetic biology approach in investigating mycobiome’s important role in the gut-liver axis. This image shows the interaction of the microbiome-mycobiome with the host cells in the gut, translocation of intermediaries through the blood, and prospectively leading to the composition of the liver environment to change. The biological knowledge from the collection of omics data such as proteome, metabolome and transcriptome generates information that can be integrated into functional mathematical models. The structural data is placed into the stoichiometric matrix and after several steps of adding biochemical information the draft of the GSMM can be reconstructed and further validated for new biological interpretation and predictions. Different constraint can be applied on the GSMMs to investigate the models for novel discovery. CRISPR as one of the gene editing approaches in validating predictions based on GSMM; these can be done using the CRISPR-cas9 system to perform genome modification such as gene slicing, gene mutation, secondary metabolite modification, base editing, and gene editing tagging and gene overexpression, down-regulation methods. The application can be used in tackling drug resistance, alteration of the metabolic pathway with the possibility of amending toxic pathways in pathogenesis, rationalising gene essentiality and genome-wide screening. The application of these potential methods in assessing the mycobiome community and particularly within the gut-liver axis.

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