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. 2023 Jan 19;18(1):e0278134. doi: 10.1371/journal.pone.0278134

Transcriptome analysis reveals increased abundance and diversity of opportunistic fungal pathogens in nasopharyngeal tract of COVID-19 patients

M Nazmul Hoque 1,#, M Shaminur Rahman 2,#, Md Murshed Hasan Sarkar 3,#, Md Ahashan Habib 3, Shahina Akter 3, Tanjina Akhtar Banu 3, Barna Goswami 3, Iffat Jahan 3, M Anwar Hossain 4, M Salim Khan 3,*, Tofazzal Islam 5,*
Editor: David M Ojcius6
PMCID: PMC9851516  PMID: 36656835

Abstract

We previously reported that SARS-CoV-2 infection reduces human nasopharyngeal commensal microbiomes (bacteria, archaea and commensal respiratory viruses) with inclusion of pathobionts. This study aimed to assess the possible changes in the abundance and diversity of resident mycobiome in the nasopharyngeal tract (NT) of humans due to SARS-CoV-2 infections. Twenty-two (n = 22) nasopharyngeal swab samples (including COVID-19 = 8, Recovered = 7, and Healthy = 7) were collected for RNA-sequencing followed by taxonomic profiling of mycobiome. Our analyses indicate that SARS-CoV-2 infection significantly increased (p < 0.05, Wilcoxon test) the population and diversity of fungi in the NT with inclusion of a high proportion of opportunistic pathogens. We detected 863 fungal species including 533, 445, and 188 species in COVID-19, Recovered, and Healthy individuals, respectively that indicate a distinct mycobiome dysbiosis due to the SARS-CoV-2 infection. Remarkably, 37% of the fungal species were exclusively associated with SARS-CoV-2 infection, where S. cerevisiae (88.62%) and Phaffia rhodozyma (10.30%) were two top abundant species. Likewise, Recovered humans NT samples were predominated by Aspergillus penicillioides (36.64%), A. keveii (23.36%), A. oryzae (10.05%) and A. pseudoglaucus (4.42%). Conversely, Nannochloropsis oceanica (47.93%), Saccharomyces pastorianus (34.42%), and S. cerevisiae (2.80%) were the top abundant fungal species in Healthy controls nasal swabs. Importantly, 16% commensal fungal species found in the Healthy controls were not detected in either COVID-19 patients or when they were cured from COVID-19 (Recovered). We also detected several altered metabolic pathways correlated with the dysbiosis of fungal mycobiota in COVID-19 patients. Our results suggest that SARS-CoV-2 infection causes significant dysbiosis of mycobiome and related metabolic functions possibly play a determining role in the progression of SARS-CoV-2 pathogenesis. These findings might be helpful for developing mycobiome-based diagnostics, and also devising appropriate therapeutic regimens including antifungal drugs for prevention and control of concurrent fungal coinfections in COVID-19 patients.

Introduction

Coronavirus disease (COVID-19), emerged as one of the deadliest human diseases, is considered as the fast expanding pandemics since the 1918 Spanish flu with serious consequences for global health and economy [1–3]. Since SARS-CoV-2 emerged in the human population, the global scientific community is working round the clock to find good strategies for the containment and treatment of this pandemic virus, SARS-CoV-2 [4]. Upon inhalation, SARS-CoV-2 primarily enters the nasal epithelial cells of the human NT through the ACE2 and TMPRSS2 receptors [5], and then gradually move towards the lung to initiate infection followed by onset of acute respiratory distress syndrome (ARDS) [6]. The capability of the SARS-CoV-2 for swiftly adapting to diverse environments of the host body could be linked with coinfecting pathogens [7, 8]. Viral replication in the nasopharyngeal epithelial cells elicits direct adverse effects on resilient microbiomes [4, 9], and induces local immune cells to quickly and abundantly secrete cytokines and chemokines [10]. Subsequently, severe lung damage and immunologic derangement resulting from SARS-CoV-2 infection or its treatment predispose to coinfections with multiple pathogens, including bacteria, other viruses and fungi [11–13]. Clinical trials and high throughput sequencing (metagenomic and RNA-seq)-based investigations on SARS-CoV-2 revealed that severely and non-severely ill COVID-19 patients had coinfections with respiratory viral pathogens [14], and bacteria and/or fungi [11, 13, 15, 16]. A retrospective study found that the coinfection rate of SARS-CoV-2 and influenza virus was as high as 57.3% in COVID-19 patients during the outbreak period in Wuhan [17]. The coinfection in COVID-19 patients may be a predisposing factor of increased morbidity and mortality rates throughout the globe [11, 14, 18]. Previously, the SARS outbreak was characterised by an high rate of nosocomial transmission of drug-resistant microorganisms [13, 19].

Fungal infections are known to be among the infectious complications related to the damage caused by viral pulmonary infections, particularly in patients admitted to intensive care units with ARDS [11]. Patients with severe COVID-19 have also emerged as a population with a high risk of fungal infections [11, 20]. There are reports that immunocompromised COVID-19 patients were at a higher risk of development of mysterious fungal infection known as mucormycosis or black fungus [21, 22]. Recently other non-Aspergillus fungal coinfections, including mucormycosis in India, have been reported in those with severe COVID-19 pulmonary disease [23]. Meanwhile, a descriptive study held by Chen et al. (2020) showed that the coinfected fungi includes Aspergillus spp., Candida albicans, and Candida glabrata [24]. Fungal coinfection was the main cause of death for SARS patients, accounting for 25–73.7% in all causes of death [25]. Besides, in the past decade, increasing reports of severe influenza pneumonia resulting in ARDS complicated by fungal infection were published [26]. With the aggravating pathogenesis of SARS-CoV-2, most of the COVID-19 patients usually undergone to the in-time use of broad spectrum antibiotics, dexamethasone, and immunosuppressive therapies with corticosteroids or immunomodulators for bacterial coinfections [20, 27], while the diagnosis of fungal coinfection is always delayed or neglected. Based on the experience of SARS in 2003 and the cases of invasive aspergillosis combined with severe influenza, it is critically important to pay attention to the probability of COVID-19 accompanied by fungal infections. However, as for fungal coinfection in COVID-19 patients, only few studies have reported it, which may have been neglected. Clinically, many COVID-19 patients did not undergo fungal assessment at the beginning, moreover, it is difficult to detect fungus with a single sputum fungal culture [25].

Human microbiota plays a critical role in immunity and health of individual hosts, and thus, microbiome dysbiosis in the respiratory tract by the pathogenic virus like SARS-CoV-2 can increase the mortality rate in patients [13, 28, 29]. Coinfection can also change the upper airway microbiome homeostasis and thus, triggers the infection and stimulates immune cells to produce more severe inflammation [11, 13, 30]. Therefore, we hypothesize that during this migration, propagation and immune response, the inhabitant fungal microbiomes in the respiratory airways are altered, and inclusion of some of the fungal pathobionts might aggravate the progression and lethality in COVID-19 patients. Therefore, timely diagnosis of coinfecting fungal pathogen(s) in COVID-19 patients is important to initiate appropriate therapy and limit the overuse of antimicrobial agents. To shed light on the effects and consequences of SARS-CoV-2 infections on changes in the resident mycobiome in the NT, we conducted a high throughput RNA-seq analysis of the nasopharyngeal swabs randomly collected from Healthy controls, COVID-19 patients and COVID-19 Recovered individuals. Our results demonstrate that SARS-CoV-2 infection is critical for inclusion of opportunistic mycobiota with loss of salutary fungi in the NT. Besides, we conducted a comparative metabolic functional analysis to identify the potential biological mechanisms linking the shift of fungal population in NT, and their correlation with differentially abundant fungal taxa in the COVID-19 patients, Recovered humans and Healthy controls. Taken together, our data suggest a critical association of altered mycobiome in the pathophysiology of SARS-CoV-2 infections in the nasal cavity of COVID-19 patients.

Materials and methods

Subject recruitment and sample collection

We collected twenty-two (n = 22) nasopharyngeal samples (including COVID-19 = 8, Recovered = 7, and Healthy = 7) from Dhaka city of Bangladesh during May to July, 2020. The suspected patients were diagnosed positive for SARS-CoV-2 infections (COVID-19) through RT-qPCR. The confirmed patients were admitted into the dedicated COVID-19 isolation wards, and received medication. These patients were tested negative for COVID-19 after 17.5 (ranged from11 to 32) days of SARS-CoV-2 infection, and categorized as Recovered humans (S1 Table). The nasopharyngeal swab samples from COVID-19 and Recovered subjects were collected on the test day (COVID-19 positive and COVID-19 negative confirmation day). The Healthy control subjects were randomly selected and these people did not show any signs and symptoms of respiratory illness. Nasopharyngeal swabs from these Healthy people were collected following the same protocol for COVID-19 and Recovered humans. The collected samples were placed in sample collection vial containing PBS (phosphate buffered saline). The RT-qPCR was performed for ORF1ab and N genes of SARS-CoV-2 using novel Coronavirus (2019-nCoV) Nucleic Acid Diagnostic Kit (PCR-Fluorescence Probing, Sansure Biotech Inc.) following the manufacturer’s instructions. The collected samples were immediately sent for RNA extraction, library preparation and sequencing [13].

RNA extraction and sequencing

Total RNA was extracted using a PureLink viral RNA/DNA minikit (Thermo Fisher Scientific, USA). RNA was extracted from a 20 μL swab sample through lysis with sample release reagent provided by the kit and then directly used for RT-qPCR. A thermal cycling of 50°C for 30 min was performed for reverse transcription, followed by 95°C for 1 min, and then 45 cycles of 95°C for 15 s, 60°C for 30 s on an Analytik-Jena qTOWER instrument (Analytik Jena, Germany). RNA-seq libraries were prepared from total RNA using the Nextera DNA Flex library preparation kit (Illumina, Inc., San Diego, CA) according to the manufacturer’s instructions where the first-strand cDNA was synthesized using SuperScript II Reverse Transcriptase (Thermo Fisher) and random primers. Paired-end (2 × 150 bp reads) sequencing of the prepared RNA library pools was performed using a NextSeq high throughput kit under Illumina platform with an Illumina NextSeq 550 sequencer at the Genomic Research Laboratory, Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, Bangladesh [13].

Data processing and taxonomic identification of fungal communities

The raw sequencing reads generated from Illumina platform were adapter and quality trimmed through BBDuk (with options k = 21, mink = 6, ktrim = r, ftm = 5, qtrim = rl, trimq = 20, minlen = 30, overwrite = true) [31]. Any sequence below these thresholds or reads containing more than one ‘N’ were discarded [31]. The good quality reads from COVID-19, Recovered and Healthy samples (n = 22) were analyzed using two different bioinformatics tools: the IDSeq (an open-source cloud-based pipeline to assign taxonomy) [32] and the MG-RAST (release version 4.1) (MR) and both use mapping and assembly-based hybrid method [33]. IDseq- an open-source cloud-based pipeline has been used to assign taxonomy with NT L (nucleotide alignment length in bp) > = 50 and NT %id > = 97 [13]. This pipeline used quality control, host filtering, assembly-based alignment and taxonomic reporting aligning to NCBI nucleotide database. In MR analysis, the uploaded reads were subjected to optional quality filtering with dereplication and host DNA removal, and finally functional assignment. For this pipeline, we employed the ‘‘Best Hit Classification” option to determine taxonomic abundance using the NCBI database as a reference with the following set parameters: maximum e-value of 1x10-30; minimum identity of 90% using a minimum alignment length of 20 as the set parameters. Microbial taxa that were detected in one group of sample but not detected in rest of the two groups are denoted as solely (unique) associated microbiomes [31]. We simultaneously uploaded the filtered sequence data in both pipelines with proper embedded metadata.

Functional profiling of the nasopharyngeal mycobiome

We performed the fungal metabolic functional classification through mapping the reads onto the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database [34], and SEED subsystem identifiers [33] of the MR server using the partially modified set parameters (e-value cut off: 1×10−30, min. % identity cut off: 90%, and min. alignment length cut off: 20) [35]. The association between metabolic functions and dominant fungal species were measured using the Spearman’s correlation coefficient and significance tests [36]. The R packages, Hmisc (https://cran.r-project.org/web/packages/Hmisc/index.html) and corrplot (https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html) were used respectively to analyse and visualize the data.

Statistical analysis

Read normalization in each sample was performed using median sequencing depth through Phyloseq (version 4.1) package in R [37]. We calculated the alpha diversity (diversity within samples) using the observed species, Chao1, ACE, Shannon, Simpson and InvSimpson diversity indices, and performed the non-parametric Wilcoxon rank-sum test to evaluate diversity differences in different samples. A principal coordinate analysis (PCoA) based on the Bray-Curtis distance method was performed to visualize differences in fungal diversity across three metagenomes. To calculate the significance of variability patterns of the mycobiome (generated between sample categories), we performed PERMANOVA (permutational multivariate analysis of variance) using 999 permutations on all three sample types at the same time and compared them pairwise. For these statistical analyses, pairwise non-parametric Wilcoxon test was performed using the Phyloseq and Vegan (package 2.5.1 of R 3.4.2) programs [38]. Dominant fungal community were determined with ≥ 1% median relative abundance by groups. After filtering, 11 taxa remained for which Spearman’s correlation analysis between KEGG pathways and SEED functions pathways was done in Hmisc’s rcorr function [39] and the corrplot function [40] of the corrplot R package as mentioned in the previous section. In addition, Kruskal-Wallis test was also applied at different KEGG and SEED subsystems levels through IBM SPSS (SPSS, Version 23.0, IBM Corp., NY, USA) [31].

Results

In order to detect the dysbiosis of the inhabitant mycobiome in the NT after SARS-CoV-2 infection, we analysed 22 nasal swab samples from Healthy individuals, COVID-19 patients and Recovered humans. The demographics, health-related characteristics, and symptomatology of the study subjects are described in S1 Table. Overall, 15 people were male (68.18%) and seven were female (31.82%) with a median age of 40.87 years. In this study, patients were diagnosed positive for SARS-CoV-2 infections (COVID-19) on an average 5.6 days after the onset of pneumonia like clinical symptoms, and these patients tested negative for SARS-CoV-2 (Recovered) on an average 15.12 days after the initial COVID-19 confirmatory diagnosis. The confirmed COVID-19 patients received medication for an average period of 15.71 days. The Healthy control subjects however did not show any signs and symptoms of respiratory illness (S1 Table). We were able to characterize both common and differentially abundant fungal taxa in each sample groups along with concurrent metabolic functional perturbations.

SARS-CoV-2 infection alters mycobiome diversity in the nasopharyngeal tract

To understand whether SARS-CoV-2 infection alters the composition and diversity of the NT mycobiome, we examined both within sample (alpha) and across the samples (beta) diversities of the detected fungal community in Healthy human, COVID-19 patients and Recovered humans (Fig 1). The alpha diversity measured using Observed, Chao1, ACE, Shannon, Simpson and InvSimpson indices showed significant differences in fungal community richness, keeping substantially higher diversity in the microbial niche of COVID-19 (p = 0.01; Wilcoxon test) followed by Recovered (p = 0.05; Wilcoxon test) and Healthy (p > 0.05; Wilcoxon test) samples (Fig 1A). The Bray–Curtis dissimilarity distance estimated principal coordinate analysis (PCoA) plot showed that fungal composition in the NT nasal cavity differed according to sample categories of the study people (Fig 1B). The beta diversity of microbiomes however did not vary significantly (p > 0.05, PERMANOVA test) according to the sex (male or female) of the study people (Fig 1B).

Fig 1. Mycobiome diversity.

Fig 1

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(A) Within subject (Alpha) diversity measure. Observed species, Chao1, ACE, Shannon, Simpson and InvSimpson indices estimated within sample fungal diversity of Healthy, COVID-19 and Recovered cases are plotted on boxplots and comparisons are made with pairwise Wilcoxon rank sum tests. Significance level (p-value) 0.0001, 0.001, 0.01, and 0.05 are represented by the symbols "****", "***", "**", and "*", respectively. (B) Between subject (Beta) diversity measure. Fungal beta diversity is measured with Bray-Curtis dissimilarity distances and visualized on principal coordinate analysis (PCoA) plot. The samples are colored according to subject groups (e.g., red: Healthy, green: COVID-19 and blue: Recovered) and joined with the respective ellipses. The shapes represent the gender of the assigned subjects: circular for female (F) and triangular for male (M). Pairwise comparisons on a distance matrix using PERMANOVA test under reduced model shows significant fungal community differences among the groups (p < 0.01, PERMANOVA test).

The unique and shared distribution of fungal taxa found in the three metagenome groups is presented by comprehensive Venn diagrams (Fig 2). A total of 28 fungal phyla were detected in three metagenomes including 16, 24 and 18 phyla in Healthy, COVID-19 and Recovered samples, respectively. Among these phyla, six phyla had sole association with SARS-CoV-2 infections, and 10 were found to be shared across three sample groups (Fig 2A, S1 Data). We detected 190 orders of fungi including 78, 131 and 104 in Healthy, COVID-19, and Recovered nasopharyngeal samples, respectively, and of them, 52 orders had sole association with SARS-CoV-2 infection, and only 35 were common in three sample groups (Fig 2B, S1 Data). Likewise, 532 fungal genera were identified, of which 57, 213 and 128 genera had sole association with Healthy, COVID-19, and Recovered subjects, respectively, and only 34 genera were found to be shared across three metagenomes (Fig 2C, S1 Data). One of the noteworthy findings of the present study was the detection of 862 fungal species and of them, 188, 533 and 445 species were found in Healthy, COVID-19, and Recovered samples, respectively. However, among the detected fungal species, only 65 (7.54%) were found to be shared across the given conditions (Fig 2D). Remarkably, compared to Healthy controls and Recovered cases, COVID-19 patients had sole association of higher number of fungal species (n = 315, 36.54%) which probably due to the opportunistic inclusion during the pathogenesis of SARS-CoV-2 infection. Similarly, Recovered humans swab samples had sole association of 227 (26.33%) fungal species revealing the re-establishment beneficial commensal flora after the recovery of SARS-CoV-2 infections. Conversely, 81 fungal species had sole association with healthy states (Healthy control) of the humans (Fig 2D), and none of these species was detected in the COVID-19 patients swab samples demonstrating that these commensal microbes underwent to dysbiosis through the effect of SARS-CoV-2 infection (Fig 2D, S1 Data).

Fig 2. Taxonomic composition of mycobiome.

Fig 2

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Venn diagrams representing the unique and shared fungal taxa in Healthy, COVID-19, and Recovered nasopharyngeal sample groups. (A) Venn diagram showing unique and shared fungal phyla. Out of 28 detected phyla, only 10 phyla (highlighted in blue circle) were found to be shared in the metagenomes. (B) Venn diagram comparison of 190 orders of fungi detected across the sample groups, of which only 35 (highlighted in blue circle) orders were found to be shared among the conditions. (C) Venn diagrams representing unique and shared fungal genera identified in three metagenomes. Of the detected fungal genera (n = 532), 57, 213 and 128 genera had sole association with Healthy, COVID-19, and Recovered subjects, respectively, and only 34 genera (highlighted in blue circle) were found to be shared across three metagenomes. (D) Venn diagrams representing unique and shared fungal species identified in three metagenomes. Of the detected fungal species (n = 862), the Healthy, COVID-19 and Recovered cases had sole association of 81, 227 and 315 species, respectively, and 65 species (highlighted in blue circle) were found to be shared across the study sample groups. More information on the taxonomic result is also available in S1 Data.

SARS-CoV-2 infection induces nasopharyngeal mycobiome dysbiosis

To determine whether SARS-CoV-2 infection induces dysbiosis of the NT mycobiome, we characterized fungal taxa at different taxonomic ranks (phylum to species level) across three metagenomes. In this study, fungal communities in COVID-19 patients, Recovered humans and Healthy control samples were predominated by Ascomycota (> 87.0%) phylum, however, other abundant phyla were Basidiomycota (2.26 to 3.83%), Streptophyta (1.41 to 2.20%), and Mucoromycota (2.06%) (S1 Fig). Rest of the fungal phyla detected in all of the metagenomes had relatively lower abundances (< 1.0%) (S1 Data). Moreover, the average phyla distribution in SARS-CoV-2 infection associated metagenomes (COVID-19 and Recovered) was different compared to that in Healthy controls. Notably, the distribution phyla in COVID-19 and Recovered cases demonstrated greater similarities than those detected in Healthy controls (S1 Fig).

We then pairwise compared the NT fungal phyla between the subjects with SARS-CoV-2 infection (COVID-19) and without SARS-CoV-2 infection (Healthy and Recovered) on a distance matrix using PERMANOVA test under reduced model which showed significant differences (p < 0.01, PERMANOVA test) in microbial community across the study groups. Pairwise Wilcoxon tests identified that five phyla (Ascomycota, Streptophyta, Tubulinea, Bacillariophyta and Evosea) were significantly different (p < 0.05, Wilcoxon test) in the Healthy, COVID-19 and Recovered metagenomes (Fig 3). Healthy and Recovered samples had significantly higher (p = 0.01, Wilcoxon test) relative abundance of Streptophyta than COVID-19 samples (p = 0.05, Wilcoxon test). In addition to Ascomycota (96.70%), the COVID-19 samples had significantly higher (p < 0.05, Wilcoxon test) relative abundance of Tubulinea and Bacillariophyta (Fig 3). The Recovered metagenome had significantly higher relative abundance of Mucoromycota (2.06%) and Apicomplexa (0.32%) compared to Healthy controls and COVID-19 patients (≤ 0.01% in both). Conversely, the Healthy humans NT swab samples had higher relative abundances of Cnidaria, Chlorophyta, Discosea, Evosea and Rhodophyta compared to COVID-19 and Recovered samples (Fig 3, S1 Data).

Fig 3. Top twelve fungal phyla detected.

Fig 3

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The phylum-level taxonomic abundance of fungal microbiomes in COVID-19, Recovered and Healthy nasopharyngeal samples. The diversity for each phylum is plotted on boxplots and comparisons are made with pairwise Wilcoxon test rank sum tests. Significance level (p-value) 0.0001, 0.001, 0.01, 0.05, and 0.1 are represented by the symbols "****", "***", "**", "*", and "n.s", respectively.

We also demonstrated notable differences in both composition and the relative abundances of fungal taxa at genus-level among COVID-19 patients, Recovered humans and Healthy controls. The relative abundances of the top 15 fungal genera were compared among the Healthy, COVID-19 and Recovered cohorts (Fig 4). Among these predominating genera, Nannochloropsis (81.58%) was the top abundant genus in Healthy controls while Saccharomyces (96.49%) and Aspergillus (81.90%) were the predominating genera in COVID-19 patients and Recovered humans NT swabs, respectively (Fig 4, S1 Data). The other predominant fungal genera in Healthy controls were Aspergillus (3.19%), Saccharomyces (2.60%), Triticum (2.17%), Auricularia (1.30%) and Eremothecium (1.10%). Conversely, Phaffia (2.88%) in COVID-19 patients, and Saccharomyces (4.71%), Malassezia (3.49%), Triticum (1.69%), and Eremothecium (1.20%) in Recovered humans were other abundant fungal genera. Though rest of the genera had relatively lower abundances (<1.0%), but their relative abundances differed across three sample groups (Fig 4, S1 Data). Fungal genera identified in the Healthy humans NT swabs resemble more similarity to those detected in Recovered humans NT swab when compared with those detected in COVID-19 patients NT swabs (S1 Data).

Fig 4. The genus-level taxonomic profile of mycobiome.

Fig 4

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The bar plots representing the relative abundance of 15 top abundant fungal genera in Healthy (H1-H7), COVID-19 (C1-C8), and Recovered (R1-R7) human nasopharyngeal samples. Fourteen genera are sorted from bottom to top by their deceasing proportion of the mean relative abundances, with the remaining genera keeping as ‘Other genera’. Each stacked bar plot represents the abundance of fungal genera in each sample of the corresponding category. Notable differences in fungal populations are those where the taxon is abundant in COVID-19 and Recovered samples, and effectively not detected in the Healthy controls. The distribution and relative abundance of the fungal genera in the study metagenomes are also available in S1 Data.

Differentially abundant and altered fungal species are correlated with COVID-19 pathophysiology

To examine whether species level composition and relative abundance of the fungal taxa statistically differ across the sample groups, we examined pairwise Spearman correlation of abundance of all taxa identified. This differential analysis revealed that genus level mycobiome composition and diversity discrepancy was more evident at species level. However, presence of few predominating fungal species in each sample category suggested that the crucial differences might be found at the strain level. The Healthy controls nasal swab samples were dominated by Nannochloropsis oceanica (47.93%), Saccharomyces pastorianus (34.42%), Saccharomyces cerevisiae (2.80%), Aspergillus pseudoglaucus (1.84%), Aspergillus penicillioides (1.25%), Paecilomyces variotii (1.24%), and Eremothecium gossypii (1.06%) (Fig 5, S1 Data). Despite, 36.54% of the fungal species were exclusively associated with SARS-CoV-2 infection, only S. cerevisiae (88.62%) was detected as the most predominating species in COVID-19 patients. However, other abundant species identified in this metagenome included Phaffia rhodozyma (10.30%), S. pastorianus (0.43%), Paecilomyces variotii (0.37%), and A. pseudoglaucus (0.17%) (Fig 5, S1 Data). Rest of the species had relatively lower abundances (< 0.1%) in COVID-19 metagenome and possibly played an opportunistic role in the SARS-CoV-2 pathogenesis (S1 Data). In addition, Recovered humans NT swab samples were mostly dominated by different species of Aspergillus genus (> 80.0%) such as A. penicillioides (36.64%), A. keveii (23.36%), A. oryzae (10.05%), A. pseudoglaucus (4.42%), A. flavus (1.44%), A. fumigatus (1.34%), A. glaucus (1.16%) and A. lentulus (1.10%). However, other dominating fungal species in this metagenome were S. cerevisiae (4.66%), Malassezia restricta (2.63%), E. gossypii (1.20%), P. variotii (1.08%), and rest of the genera had relatively lower abundances (< 1.0%) (Fig 5, S1 Data).

Fig 5. Major fungal species detected.

Fig 5

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The species-level taxonomic profile of mycobiome in COVID-19 (C1-C8), Recovered (R1-R7) and Healthy (H1-H7) nasopharyngeal samples. Species with > 1% mean relative abundance are represented by different color codes against respective sample groups. Other species (< 1%) indicate the rare less abundant taxa in each group, with a mean relative abundance of < 1%. Each stacked bar plot represents the abundance of fungal species in each sample of the corresponding category. Notable differences in fungal populations are those where the species is abundant in COVID-19 and Recovered samples, and effectively not detected in the Healthy controls. The distribution and relative abundance of the fungal genera in the study metagenomes are also available in S1 Data.

Our primary microbiome compositional analysis identified 11 species as differentially abundant across Healthy, COVID-19 and Recovered metagenomes. The pairwise statistical relationship analysis of the relative abundances of these 11 taxa and health biomarkers (Healthy, SARS-CoV-2 infection and SARS-CoV-2 recovery) of the study participants showed significant variations (p ≤ 0.05, Wilcoxon test) in 11 differentially abundant fungal species (Fig 6). For instance, N. oceanica (p = 0.01, Wilcoxon test) and S. pastorianus (p = 0.05, Wilcoxon test) were the two significant and differentially abundant fungal species in Healthy controls compared to either COVID-19 patients or Recovered humans (Fig 6). The COVID-19 samples however harboured only one significantly abundant fungal species, S. cerevisiae (p = 0.01, Wilcoxon test) over the Healthy controls or Recovered humans. The Recovered humans however had significantly higher relative abundances of A. penicillioides (p = 0.01), A. pseudoglaucus (p = 0.01), A. flavus (p = 0.01), A. fumigatus (p = 0.05) and E. gossypii (p = 0.05) compared to either COVID-19 patients or Healthy controls (Fig 6). Similarly, for those species that were less abundant in three metagenomes, we also observed a stronger correlation among the microbiomes of COVID-19 patients and Recovered humans (Fig 6, S1 Data).

Fig 6. Top twelve fungal species detected.

Fig 6

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The species-level taxonomic abundance of mycobiome in COVID-19, Recovered and Healthy nasopharyngeal samples. The diversity for each species is plotted on boxplots and comparisons are made with pairwise Wilcoxon test rank sum tests. Significance level (p-value) 0.0001, 0.001, 0.01, 0.05, and 0.1 are represented by the symbols "****", "***", "**", "*", and "n.s", respectively.

Phylogenetic relatedness of the mycobiome of the nasopharyngeal tract

The unrooted maximum likelihood tree exhibited a species-level (n = 50 top abundant species) topology that was completely congruent with SARS-CoV-2 infection mediated mycobiome dysbiosis (Fig 7). The resulting species tree provided high resolution of the basal relationships among fungal clades, and enabled us to evaluate the established taxonomic hierarchies. Phylogenetic analysis showed that out of 50 species, 13 descended from Eurotiales (26.0%) followed by 10 from Saccharomycetales (20.0%), four from Auriculariales (8.0%), one from Eustigmatales (2.0%) and rest of the species fall into other orders (44.0%) (Fig 7, S2 Table). Despite stronger associations among these 50 species used for phylogenetic tree reconstruction, SARS-CoV-2 infection facilitated the opportunistic inclusion of 36.54% fungal species including S. paradoxus, S. kudriavzevii, K. lactis, K. aquatica, Y. lipolytica, C. gloeosporioides, P. album, C. sphaerospermum, F. flavus, P. hubeiensis, P. rhodozyma, Tarsonemidae sp. AD1063, C. remanei, T. govanianum, and G. rogosa (Fig 7, S2 Table). Among these species, P. rhodozyma was found as the top abundant (10.30%) opportunistic pathogen in COVID-19 samples (S1 Data). None of these species was detected in the Healthy control samples. Simultaneously, SARS-CoV-2 inflammation was also associated with depletion of 16.0% commensal fungal population such as E. tenuicula, P. caudatus, A. ringens, N. nucifera, A. castellanii, M. brevicollis, V. Vermiformis, A.polytricha etc. (Fig 7, S2 Table). These species were solely present in Healthy humans nasal cavity, and not merely detected in COVID-19 patients samples Moreover, changes in mycobiome composition were supported by high bootstrap values (98%–100% for each species) (Fig 7).

Fig 7. Phylogenetic relationships among detected fungal communities.

Fig 7

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Unrooted phylogenetic tree showing the relationship of 50 top abundant fungal species identified in Healthy people, COVID-19 patients and Recovered humans nasopharyngeal samples. Taxonomic groups indicated by the different color ranges show the phylogenetic origin and associations of different species in the respective fungal orders, with green for Saccharomycetales, yellow for Eurotiales, pink for Auriculariales, red for Eustigmatales, purple for Malasseziales, and green for other orders fungal orders. The upward arrow indicates the species those had opportunistic inclusion in COVID-19 patients and not merely detected in Healthy control samples. Conversely, the downward arrow denotes the Healthy people commensal species those are dysbiosed after SARS-CoV-2 infection (absent in COVID-19 patients). Bootstrap values are calculated based on 1000 replications and the tree scale in number of substitutions per site. The fungal species in the phylogenetic tree are also available in S1 Data.

Differentially abundant fungal communities show positive correlation with metabolic functional perturbations

To shed light on whether SARS-CoV-2 infection could influence the metabolic functional potentials of the concurrent fungal mycobiota, we analysed the RNA-seq data through KEGG pathway and SEED subsystems. By examining the correlations between the different gene families (n = 33) of the same KEGG pathway for COVID-19 patients, Recovered humans, and Healthy control’s NT mycobiome, we found significant differences (p = 0.01, Kruskal-Wallis test) in their composition and relative abundances (S2 Fig, S3 Table). Differentially abundant fungal species had significant correlations (i.e., positive or negative) with different KEGG pathways including cytokine-cytokine receptor functions, cellular processes, methane oxidation, malate dehydrogenase (mdh), oxidative phosphorylation, sulfur, protein and carbohydrates metabolism and methionine degradation have positive correlation with the dominant fungal species (Fig 8A). For instance, cytokine-cytokine receptor functions revealed strongest positive correlation with S. cerevisiae (Spearman correlation; r > 0.65, p < 0.001); the top abundant fungal species in COVID-19 metagenome. Likewise, cellular processes showed significant positive associations with A. pseudoglaucus (Spearman correlation; r > 0.6, p < 0.01) and M. globosa (Spearman correlation; r > 0.5, p < 0.05); two dominating fungal species in Recovered samples. In addition, M. globosa displayed significant positive correlations (Spearman correlation; r > 0.4, p ≤ 0.05) with methane oxidation, mdh, oxidative phosphorylation and sulfur metabolism (Fig 8A). Conversely, two predominating fungal species in Healthy controls, N. oceanica (Spearman correlation; r > 0.5, p < 0.01) and S. pastorianus (Spearman correlation; r > 0.5, p < 0.01) had significant negative correlation with cytokine-cytokine receptor functions, sulfur metabolism, and succinyl-CoA synthetase subunits (sucC, sucD). Similarly, A. oryzae, one of the prevalent fungal species in Recovered humans had strong negative correlations (Spearman correlation; r ≥ 0.5, p ≤ 0.05) with cytokine-cytokine receptor functions, sulfur metabolism and phosphotransferase system. In addition, pyruvate carboxylase (pyc), phagosome activity and focal adhesion related metabolic activities also had significant negative correlations (Spearman correlation; r ≥ 0.4, p ≤ 0.05) with the top abundant fungal species of Recovered humans (Fig 8A).

Fig 8. Spearman’s rank correlation matrix of the dominant fungal species and related metabolic functions.

Fig 8

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(A) Correlation between KEGG orthologues (KOs) and predominant fungal species, and (B) correlation between SEED subsystems and top abundant fungal species. The numbers display pairwise Spearman’s correlation coefficient (r). Blue and red colors indicate positive and negative correlation, respectively. The color density, circle size, and numbers reflect the scale of correlation. *Significant level (*p < 0.05; **p < 0.01; ***p < 0.001). The R packages, Hmisc (https://cran.r-project.org/web/packages/Hmisc/index.html) and corrplot (https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html) were used respectively to analyze and visualize the data.

We also sought to gain further insight into the SEED hierarchical protein functions, and found 37 statistically different (p = 0.013, Kruskal-Wallis test) subsystems in COVID (COVID-19 and Recovered) and Healthy control metagenomes. These SEED functions had significant correlations (either positive or negative) with the dominating fungal species in the respective metagenomes. As for example, the predominantly abundant fungal species in COVID-19 patients nasal swabs (S. cerevisiae) showed significant positive correlations with cytokine-cytokine interactions (Spearman correlation; r > 0.7, p <0.001), cell growth and death (Spearman correlation; r > 0.5, p <0.05), ECM-receptor interaction (Spearman correlation; r > 0.4, p <0.05). However, this species had also substantial negative association with BarA-UvrY(SirA) two-component regulatory system (Spearman correlation; r > 0.5, p <0.05) (Fig 8B). In contrast, BarA-UvrY(SirA) two-component regulatory system was found as the strongly correlated metabolic function in S. pastorianus (Spearman correlation; r > 0.7, p < 0.001), E. gossypii (Spearman correlation; r > 0.65, p < 0.001), A. oryzae (Spearman correlation; r > 0.55, p < 0.01) and N. oceanica (Spearman correlation; r > 0.5, p < 0.05). Likewise, M. globosa revealed significant positive correlation (Spearman correlation; r > 0.6, p < 0.01) with cell division and cell cycle related SEED functions (Fig 8B). Conversely, SEED functions including systemic lupus erythematosus, MT1-MMP pericellular network, proteolysis pathways, oxidative stress, ECM-receptor interaction and membrane transport had significant negative correlations (Spearman correlation; r ≥ 0.5, p < 0.05) with most of the top abundant fungal species in all three metagenomes (Fig 8B).

Discussion

Emerging evidence indicated that SARS-CoV-2-infected individuals had an increased risk for coinfections. Therefore, the physicians need to be cognizant about excluding other treatable respiratory pathogens [11, 13, 41]. There are a great number of diverse beneficial commensal microorganisms constitutively colonizing the mucosal lining of the upper airway especially the nasopharyngeal tract (NT). These microbes comprise viruses, phages, bacteria, and fungi [11, 13, 42] that have elegant mutualistic relationships with the human host. In our previous study, we reported that SARS-CoV-2 infection induced dysbiosis of NT commensal bacteria, archaea and viruses with high inclusion of opportunistic pathobionts that elicite metabolic functional potential perturbations in COVID-19 patients [13]. In this study, we postulated that SARS-CoV-2 infection may also alter the NT commensal fungal population and diversity along with perturbations in their metabolic functions. To validate this hypothetical interplay between SARS-CoV-2 and resident commensal mycobiota in the nasal cavity of humans, we compared 22 high-throughput RNA-Seq data obtained from Healthy individuals, COVID-19 patients and Recovered humans.

The rapid development of automated, high-throughput sequencing methods including metagenomics, RNA-Seq, and bioinformatics [43] have made it possible to study the global biodiversity of fungi in various epidemiological niches including in COVI1D-9 patients. In the present study, we found a remarkable shift in the diversity and composition of the NT mycobiome in COVID-19 patients and Recovered humans compared to the Healthy controls. Although, several questions remain about defining the nature of dysbiosis for any particular fungal species, our present findings showed that SARS-CoV-2 infection reduces commensal fungal population with inclusion of pathobionts in the NT of human (Fig 3). Moreover, we detected a number of microbial genomic features, altered metabolic pathways, and functional genes associated with SARS-CoV-2 pathogenesis. Although it is well established that gut microbiota has a critical role in pulmonary immunity and host’s defense against SARS-CoV-2 infection [44–46], this study for the first time determined the increased diversity and relative abundances of mycobiota in the NT of humans due to the interactions of SARS-CoV-2 infection with resident mycobiome in human nasal cavity.

One of the most striking findings of the current study was the significant differences in mycobiome diversity between COVID-19 patients and Healthy controls nasal cavity keeping the closest relationship of fungal population in COVID-19 patients with Recovered humans. The COVID-19 patients NT mycobiome exhibited a statistically significant higher diversity (both within and between sample diversities) than those of Recovered humans and Healthy controls (Figs 1 and 2), which supports our hypothesis of dysbiosis and also agree with several recent reports [9, 47]. In contrast, most of the previous studies reported that SARS-CoV-2 infection reduces the bacterial diversity in COVID-19 patients respiratory tract and gut compared to their healthy counterpart [13, 48]. However, SARS-CoV-2 infection did not significantly alter mycobiome diversity in relation to the gender of the study population.

Results from the current analysis showed that Ascomycota was the most predominating fungal phylum in all of three metagenomes with highest relative abundances (~ 96.0%) in COVID-19 samples followed by Recovered humans (90.0%) and Healthy controls (88.0%). Our analysis revealed that COVID-19 patients exhibited a different composition of NT fungal communities than Recovered humans and Healthy controls. Six phyla, 52 orders, 213 genera and 315 species of fungi had sole association with the SARS-CoV-2 infection (Fig 3). We found vast differences in genus-level mycobiome signatures in nasal cavities of Healthy controls, COVID-19 patients and Recovered humans irrespective of the homogeneous genetic backgrounds and living status. For instance, Nannochloropsis had several-folds higher relative abundances in Healthy controls compared to COVID-19 and Recovered samples. Likewise, the COVID-19 patients and Recovered humans had several-folds higher abundance of Saccharomyces and Aspergillus, respectively than the Healthy controls. Moreover, the inter-individual variations in mycobiome signature (fungal genera) of participants was also observed. Remarkably, more than 43.0% fungal species had sole association with Healthy humans nasal microbiomes, which were not detected in COVID-19 patients and Recovered humans indicating the potential dysbiosis of these commensal species during the pathogenic magnitudes of SARS-CoV-2 infection. These results are corroborated with our previous study where we reported that 79% commensal bacterial species found in Healthy controls were not detected in COVID-19 and Recovered humans [13]. There are lines of emerging evidences that the mycobiome communities in different parts of the host body can be altered in relation to pathophysiological changes [31, 49, 50]. Our present findings are also consistent with several earlier studies which reported that interactions between SARS-CoV-2 and oral microbiomes [30], and SARS-CoV-2 and gut microbiomes [51] is associated with the pathophysiology of lung diseases.

Despite hundreds of thousands of fungal species, only a few causes disease in humans. In this study, N. oceanica and S. pastorianus were two predominantly abundant fungal species in Healthy human nasopharyngeal swabs. Different species of Nannochloropsis are eco-sustainable bioactive microalgae which can provide human with nutritional elements including polyunsaturated fatty acids (PUFAs), polyphenols, carotenoids and vitamins [52]. Recent evidences suggested that N. oceanica is a chief source of eicosapentaenoic acid (EPA, 20:5n-3) and docosahexaenoic acid (DHA, 22:6 n-3) recommended for humans use due to their beneficial effects including anti-atherogenic, anti-thrombotic and anti-inflammatory properties [52, 53]. On the other hand, S. pastorianus is a recently evolved interspecies hybrid of Saccharomyces genus commonly used in the brewing industry. Different species of Saccharomyces are recognised as beneficial probiotics that can help humans with IBS, Crohn’s disease, diarrhoea, and a range of gastrointestinal infections [54, 55]. The co-evolution of humans and fungi suggests that complex mechanisms exist to allow the host immune system to respond to fungi [56].

One of the hallmark findings of the present study was the predominant association of S. cerevisiae, P. rhodozyma and P. variotii with SARS-CoV-2 infections in COVID-19 patients. A series of recent study suggested that S. cerevisiae should be considered as a potential opportunistic pathogen especially for patients with immunosuppression, cancer and other critical illnesses [54]. One of the latest studies demonstrated that the abundance S. cerevisiae in the guts of COVID-19 patients with fever were significantly higher than in COVID-19 patients with non-fever supporting our present results [57]. Bloodstream infection by S. cerevisiae in critically ill COVID-19 patients has recently been reported in several studies [55, 58]. P. variotii is considered the most prevalent agents of human infection, can affect various organ systems, primarily in immunocompromised patients or those with indwelling material [59]. Importantly, different species of Aspergillus were predominantly abundant in Recovered humans nasal cavity, and this shift in the fungal community is believed to be associated with re-establishment of eubiosis or a balanced NT microbiome after clearance of SARS-CoV-2. Although, A. penicillioides was detected as one of the top abundant fungal species in Recovered human nasal swab, this species has yet rarely been reported as a human pathogen except for cystic fibrosis in infants [60]. One of pioneering researches reported that A. fumigatus was the most common species causing coinfection in COVID-19 patients, followed by A. flavus and suggested that clinicians should keep alerting the possible occurrence of pulmonary aspergillosis in severe/critical COVID-19 patients [61, 62]. Saprophytic fungal species like A. keveii of the Aspergillus genus are the common contaminant of food and soil, their spores are ubiquitous and responsible for developing invasive aspergillosis in millions of humans each year [63]. A. oryzae is a low pathogenic fungus but may, like many other harmless microorganisms, grow in human tissue under exceptional circumstances [64]. In addition to Aspergillus spp., S. cerevisiae and M. restricta were also found to be dominating in the Recovered humans NT mycobiome. Malassezia spp. are lipid-dependent yeasts, inhabiting the skin and mucosa of humans and animals [65]. In adults, M. restricta and M. globosa are the major component of the healthy human skin mycobiome especially in Asia [66], and have not been linked to in the pathogenesis of any infectious diseases [67]. The interactions between human host and these fungal species can be mediated directly by specific pattern recognition receptors found on host cells and pathogen-associated molecular patterns present on fungal cell walls [66]. However, detailed clinical context is available for a very limited number of these species and data concerning their role in the pathophysiology of COVID-19 are even more scarce.

Remarkably, COVID-19 patients largely had inclusion of > 36.0% opportunistic fungal species, a part of commensal mycobiome that may become pathogenic in the event of host perturbation, such as dysbiosis or immunocompromised host. P. rhodozyma, one of the top abundant opportunistic fungal species found in COVID-19 patients nasal cavity, is an important microorganism for its use in both the pharmaceutical industries and food industry [68]. Although, numerous studies have addressed the molecular regulatory mechanisms of cell growth and astaxanthin synthesis by P. rhodozyma [69, 70], however, association of this species in disease causation has not been reported yet. Different species of Saccharomycetales such as S. paradoxus, S. kudriavzevii, K. lactis, K. aquatica, Y. lipolytica had an opportunistic inclusion in COVID-19 patients. Earlier evidences suggested that different species Saccharomycetales can only perform opportunistic or passive crossings when epithelial barrier integrity of the NT is previously compromised by other infectious agents [70]. For instance, S. kudriavzevii an opportunistic pathogen especially potential hazard to the health of immunocompromised workers in the wine industry, and potentially also to consumers [71]. S. paradoxus, mainly found in the wild environment, is the closest relative of the domesticated yeast S. cerevisiae. At least five different killer toxins are produced by S. paradoxus which can inhibit the growth of other competing fungal species in immunocompromised host [72], and likely to cause opportunistic infection.

Despite the striking discrepancy in the phylogenetic composition and relative abundances of fungal species in three metagenomes, we found significant associations between differentially abundant fungal species and different metabolic functional pathways. Our findings revealed that enrichment of certain metabolic activities related to cytokine-cytokine receptor functions, cellular processes, methane oxidation, malate dehydrogenase, oxidative phosphorylation, sulfur, protein and carbohydrates metabolism, cell growth and death, and methionine degradation had strong positive correlation with 11 dominant fungal species, irrespective of the sample categories. The predominant fungal species of COVID-19 patients nasal cavity, S. cerevisiae was positively associated with cytokine-cytokine interactions, cell growth and death, and ECM-receptor interaction. These metabolic functional changes in COVID-19-associated mycobiome corroborated with previously reported other respiratory viral diseases [13, 73, 74]. Thus, our results provided evidence that enrichment of these metabolic activities are linked to consistent shifts in the structure and composition of the NT mycobiome with the progression of SARS-CoV-2 pathogenesis. Similar association was also found in the dominating fungi (e.g., A. pseudoglaucus and M. globose) of Recovered humans nasal cavity, in which metabolic functions like cellular processes, methane oxidation, malate dehydrogenase, oxidative phosphorylation and sulfur metabolism were found to be positively correlated. Based on this correlative evidence, it is tempting to speculate that SARS-CoV-2 infection associated shifts in mycobiome in the nasal cavity might also alter the metabolic functional potentials of the related microbiomes. Spearman’s correlation analyses also revealed that the pyruvate carboxylase, phagosome activity, focal adhesion, MT1-MMP pericellular network, proteolysis pathways, oxidative stress, and membrane transport have significant negative correlations with the dominating fungal species. The metabolic health of an individual is represented by the proper functioning of organismal metabolic processes coordinated by multiple physiological systems [74]. The differentially abundant functions and pathways identified in this study corroborated with the findings from previous reports [75], and to COVID-19. However, some of the predicted metabolic features differed between COVID-19 and Healthy controls, perhaps representing metabolic changes associated with the progression of SARS-CoV-2 pathogenesis, and typical host-microbiome interactions in SARS-CoV-2 infected patients.

Conclusions

Human nasopharyngeal microbiota plays a crucial role in providing protective responses against pathogens. Any alteration in the NT microbiota or their metabolites can cause immune dysregulation and impair the antiviral activity against respiratory viruses like SARS-CoV-2. Overall, the results of this study suggested that SARS-CoV-2 infection induces remarkable depletion of nasopharyngeal commensal fungal population with inclusion of different opportunistic pathogens in COVID-19 and Recovered samples. Several predicted functional pathways differed between COVID-19 patient and Recovered people nasopharyngeal samples compared to Healthy individuals which reflect the roles microbial metabolic changes with the progression of SARS-CoV-2 pathogenesis. These interactions were further complicated by the common co-existence of dominant mycobiota that interact with both host and SARS-CoV-2. The distinguishable fluctuations in the fungal population and associated genomic features detected in this study may serve as a benchmark for mycobiome-based diagnostic markers and formulating therapeutics for COVID-19 patients. Furthermore, it would be interesting to conduct future studies with a larger sample size to elucidate the modulation of commensal mycobiome, their functional potentials and genomic expression during the pathophysiology of SARS-CoV-2 infection which will help in the development of specific therapeutic regimes against this pandemic disease.

Supporting information

S1 Fig. Major fungal phyla detected.

The phylum-level taxonomic profile of fungal microbiomes in COVID-19 (C1-C8), Recovered (R1-R7) and Healthy (H1-H7) nasopharyngeal samples. Phyla with > 1% mean relative abundance are represented by different color codes against respective sample groups. Others (< 1%) indicates the rare taxa in each group, with mean relative abundance of < 1%.

(PNG)

Click here for additional data file. (172.9KB, png)
S2 Fig. Metabolic functional potentials of the fungal microbiota.

Heatmap showing (A) KEGG orthologues (KOs) and (B) SEED subsystems associated with fungal metabolism in Healthy, COVID-19 and Recovered metagenomes.

(JPG)

Click here for additional data file. (227.2KB, jpg)
S1 Table. Demographic characteristics of the study people: Clinical diagnosis, treatment and recovery history of SARS-CoV-2 infections.

(DOCX)

Click here for additional data file. (20.4KB, docx)
S2 Table. Dysbiosis of mycobiomes after SARS-CoV-2 infections.

(DOCX)

Click here for additional data file. (18.7KB, docx)
S3 Table. Metabolic functional potentials of the fungal microbiome.

(DOCX)

Click here for additional data file. (20.4KB, docx)
S1 Data. Taxonomic information on fungal microbiomes.

(XLSX)

Click here for additional data file. (64.7KB, xlsx)

Acknowledgments

The authors would like to thank the Ministry of Science and Technology, Government of the People’s Republic of Bangladesh for supporting this research. The authors would like to thank those who provided us the samples.

Ethical approval

The protocol for sample collection from COVID-19, Recovered and Healthy humans, sample processing, transport, and RNA extraction was approved by the National Institute of Laboratory Medicine and Referral Center of Bangladesh. The study participants received a written informed consent letter consistent with the experiment.

Data Availability

The sequence data reported in this article has been deposited in the National Center for Biotechnology Information (NCBI) under BioProject accession number PRJNA720904.

Funding Statement

This project is financed by the Ministry of Science and Technology, Government of the People’s Republic of Bangladesh.

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Decision Letter 0

David M Ojcius

16 Aug 2022

PONE-D-22-14092Transcriptome analysis in nasopharyngeal samples reveals increased abundance and diversity of opportunistic fungal pathogens in COVID-19 patientsPLOS ONE

Dear Dr. Islam,

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Reviewers' comments:

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: This is a well-designed study with a good representation of data. However, there are some concerns and issues that need to be addressed by the authors before considering for publication in PLOS One.

Abstract

Rephrase the first sentence of the abstract and remove “we previously reported”.

Line 45: Surprising to see S. cerevisiae (88.62%) and Phaffia rhodozyma (10.30%) comprised 99% total species where authors reported 863 species.

Introduction:

1. Clinical trials and high throughput sequencing ………..respiratory viral pathogens [14], and bacteria and/or fungi [11, 13, 15, 16], make no sense. Rephrase the sentence.

2. Similarly, Fungal infections are known…….patients admitted to intensive care units with ARDS [11]. Revise the sentence to make it meaningful to the readers.

3. The hypothesis is time demanding and carries significant importance. However, the big sentence representing the hypothesis is very difficult to follow and coordinate. Please rephrase it to make a simpler and reader-friendly statement.

Method

1. The samples were collected more than two years ago. In the meantime, the variants of COVID have been changed a couple of times. In that case, what approach should be used by the authors to address concerns like currently circulating COVID strains?

Results

1. Our primary microbiome compositional analysis….. 11 species as differentially abundant across Healthy, COVID-19 and Recovered metagenomes. Make no sense. Rephrase it.

2. What is the necessity of mentioning major fungal species detected (Fig 5) and the top twelve fungal species detected (Fig 6) in two different figures? Isn’t it an exaggeration? Keep a single figure to represent major or top abundant fungal species and the rest may be replaced in the supplementary figures.

3. The Figures 5 and 6 legends for species names should be italic.

Discussion

1. Needs to correlate fungal infection with SARS-CoV2 infection in the discussion section.

2. Is there any report of secondary infection of fungus after COVID-19 infection?

3. Discuss fungal opportunistic pathogenic character in COVID-19 cases.

Reviewer #2: Summary

This study compared the nasopharyngeal fungal microbiome of 15 people: Eight COVID-19 patients, and seven healthy controls. Seven of the COVID-19 patients were subsequently included in the recovered group after tested negative and recovered from COVID-19.

The authors found that the three groups had unique fungal microbiome: 37% of fungal species were exclusively associated with SARS-CoV-2, with Saccharomyces cerevisiae and Phaffia rhodozyma being the two species with the highest abundance. The recovered patients’ fungal microbiome was dominated by several species of the Aspergillus genus, including A. penicillioides, A. keveii, A. oryzae, and A. pseudoglaucus.; Healthy controls had high abundance of Nannochloroopsis oceanica and Saccharomyces pastoriaus. Another main finding of the current study was that there was an increase in the alpha diversity of fungal microbiome in COVID-19 patients. The authors went on to perform metabolic functions analysis and postulated on the possible role of dysbiosis in the role of COVID-19 pathogenesis.

Overall Comment

This is an important study to allow deeper understanding of nasopharyngeal microbiome in COVID-19 patients. Extensive bioinformation analysis work was performed with interesting findings.

The description of patient recruitment could be made clearer. It took me some time to understand the 7 subjects in the recovered group were the sample subjects from the COVID-19 group, after they have recovered. The authors should also explain why only 7 of the 8 COVID-19 patients were included in the recovered group.

There was inconsistency in the terminology used for the three groups: For example: line 129 “Recovered”, line 133 “Recovered humans”, line 134 “Recovered subjects; Another example: line 138 “Healthy people”, line 136 “Healthy control subjects”.

The difference between dysbiosis and clinical infection was not clear throughout the manuscript. For example, in Lines 110-115, the authors stated the importance of understanding fungal microbiome in COVID-19 patients, however, in the next sentence, the authors advocated a timely diagnosis of fungal co-infections to limit the overuse of antimicrobial agents.

The authors included subject information in Table S1 with a column stated whether the subjects received “COVID-19 medicine”. Specific information on antibiotics used would be helpful to the interpretation of the study results: One major finding of the current study was that COVID-19 patients exhibited higher fungal microbiome diversities than those of recovered human and healthy controls, could it be due to effect of antibiotics, killing off most bacteria that allow the blooming of fungal organisms? Another major finding of the current study was that various species in the Aspergillus genus were over-represented in the recovered group. Aspergillus is well known to be present in the healthcare environment [1]. Could the observation be explained by nasopharynx flora being colonized by hospital molds with the aid of antibiotics therapy used?

Specific Comments

Lines 63 - 65: Redundance, can omit the second “SARS-CoV-2” of the sentence

Lines 65 – 68: The sentence seemed to suggest that all COVID-19 infections will result in ARDS. Suggest rephrasing.

Line 71: What is the meaning of “resilient microbiomes”?

Line 88: Mucormycosis is well studied and should not be considered as “mysterious fungal infection”

Lines 95-98: broad spectrum antibiotics are for treatment of bacterial coinfections, dexamethasone and immunosuppressive therapies are for immune modulation for treatment of immune dysregulation such as ARDS.

Lines 111: what is the meaning of “migration, propagation and immune response”?

Lines 250-251: “….which probably due to the opportunistic inclusion during the pathogenesis of SARS-CoV-2 infection” should be moved to discussion section

Line 314: Can consider delete the word “However”

Line 427: Typo: “COVI1D-9”

Line 488: redundance: “…..with SARS-CoV-2 infections in COVID-19 patients”

Line 502-505: Different species of aspergillus has different pathogenic potential. As the authors rightly pointed out A. fumigatus, and A. flavus to a lesser extent, are associated with clinical important aspergillus infections, but not the A. penicilliodes and A. keveii that is found in abundance in recovered patients. Suggest discussing pathogenic and non-pathogenic aspergillus species separately.

Line 570-572: was there any specific impairment of antiviral activities that was mentioned in the text?

Figures: some figures placed healthy control on the left side of the charts (e.g. Fig 1A, Fig 4), while others were in the opposite order, with COVID-19 on left side (e.g. Fig 3, Fig 5, and 6)

Figure 5: the color used for different species are similar to each other.

Table S1: Inconsistency noted: there were information included for 7 COVID-19 patients, 7 recovered patients, and 7 healthy controls. However, there were 8 COVID patients recruited in the study (line 37).

References

David J. Weber, MD, Amanda Peppercorn, Melissa B. Miller, Emily Sickbert-Benett, William A. Rutala, Preventing healthcare-associated Aspergillus infections: review of recent CDC/HICPAC recommendations, Medical Mycology, Volume 47, Issue Supplement_1, 2009, Pages S199–S209, https://doi.org/10.1080/13693780802709073

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2023 Jan 19;18(1):e0278134. doi: 10.1371/journal.pone.0278134.r002

Author response to Decision Letter 0

15 Oct 2022

Point-by-point responses to the reviewer comments

Reviewer # 1

This is a well-designed study with a good representation of data. However, there are some concerns and issues that need to be addressed by the authors before considering for publication in PLOS One.

Abstract

Reviewer comment: Rephrase the first sentence of the abstract and remove “we previously reported”.

Our Response: We would like to thank the reviewer for complementing our manuscript, and suggestion for further revision. We are happy to revise and edit the Abstract as per reviewer’s suggestion. You may kindly go through the Abstract in the revised manuscript.

Reviewer comment: Line 45: Surprising to see S. cerevisiae (88.62%) and Phaffia rhodozyma (10.30%) comprised 99% total species where authors reported 863 species.

Our Response: Thank you very much for this question. We detected 533 fungal species in COVID-19 patients NT samples (out of 863 species detected in all of three groups). Among these species S. cerevisiae (88.62%) and Phaffia rhodozyma (10.30%) were the two top abundant species contributing about ~99.0% of total species, and rest of the species had <0.5% relative abundances. Higher abundance of Saccharomyces organisms in COVID-19 patients could be due to invasive infection in immunocompromised or critically ill patients. Severe bloodstream infection by Saccharomyces in hospitalized ICU patients, due to severe COVID-19, has been reported previously (Ventoulis et al., 2020; DOI: 10.3390/jof6030098). Furthermore, oral administration of immunomodulatory component derived from S. cerevisiae reduced intestinal inflammation and promoted the reduction of overgrowth of other fungal species such as Candida glabrata, C. albicans etc. in the gut (Jawhara et al., 2020; DOI: 10.1186/s13099-020-00385-2). One of the latest studies demonstrated that the abundance S. cerevisiae in the guts of COVID-19 patients with fever were significantly higher than in COVID-19 patients with non-fever supporting our present results (Zhou et al., 2021; DOI: 10.2147/JIR.S311518). We have well discussed this issue in the Discussion section. You may kindly go through Lines 518-528 in the Discussion section of the revised manuscript.

Introduction

Reviewer comment: Clinical trials and high throughput sequencing ………..respiratory viral pathogens [14], and bacteria and/or fungi [11, 13, 15, 16], make no sense. Rephrase the sentence.

Our Response: We would like to thank the reviewer for this nice suggestion. We have rephrased the sentence You may kindly go through Lines 77-79 in the revised manuscript.

Reviewer comment: Similarly, Fungal infections are known…….patients admitted to intensive care units with ARDS [11]. Revise the sentence to make it meaningful to the readers.

Our Response: We would like to thank the reviewer for this nice suggestion. We have rephrased the sentence You may kindly go through Lines 85-87 in the revised manuscript.

Reviewer comment: The hypothesis is time demanding and carries significant importance. However, the big sentence representing the hypothesis is very difficult to follow and coordinate. Please rephrase it to make a simpler and reader-friendly statement.

Our Response: We would like to thank the reviewer for this valid suggestion. We have revised the hypothesis to making a simpler and reader-friendly statement. You may kindly go through Lines 113-116 in the revised manuscript.

Method

Reviewer comment: The samples were collected more than two years ago. In the meantime, the variants of COVID have been changed a couple of times. In that case, what approach should be used by the authors to address concerns like currently circulating COVID strains?

Our Response: We would like to thank the reviewer for this critical concern. We do agree with the Reviewer’s opinion. Unfortunately, we are too late (due to some unavoidable issues) to publish this article timely. It would be good if we could publish the findings earlier. SARS-CoV-2 is the fast-evolving virus, and altering genetic mutations of SARS-CoV-2 variants has decreased the effectiveness of therapeutics and vaccines. SARS-CoV-2 can mutate in individuals, and these variants can be propagated across populations over time. Although, scattered information on fungal co-infections in SARS-CoV-2 is available, however, no study has elaborated on the association between gut microbiota and fungal microbiomes in COVID-19 patients. Therefore, despite the emergence of different variants of SARS-COV-2 after we collected sample, our data/findings of the present study would increase our understanding on early interaction of this virus (SARS-CoV-2) with co-infecting microbes (e.g., fungi) of the host. The present findings could shed light on developing microbiome-based diagnostics, and also devising appropriate therapeutic regimens including antifungal drugs for prevention and control of concurrent fungal coinfections in COVID-19 patients. We hope that the judicious Reviewer will be considerate to accept our limitation in this regard.

Results

Reviewer comment: Our primary microbiome compositional analysis. 11 species as differentially abundant across Healthy, COVID-19 and Recovered metagenomes. Make no sense. Rephrase it.

Our Response: We would like to thank the reviewer for this valid suggestion. We have rephrased the sentence to making it a reader-friendly statement. You may kindly go through Lines 341-342 in the revised manuscript.

Reviewer comment: What is the necessity of mentioning major fungal species detected (Fig 5) and the top twelve fungal species detected (Fig 6) in two different figures? Isn’t it an exaggeration? Keep a single figure to represent major or top abundant fungal species and the rest may be replaced in the supplementary figures.

Our Response: We would like to thank the reviewer for this concern. Figure 5 represents the species-level taxonomic profile (top abundant 11 species) of fungal microbiomes in COVID-19 (C1-C8), Recovered (R1-R7) and Healthy (H1-H7) nasopharyngeal samples. Species with > 1% relative abundance are represented against respective sample groups, and rest of the species are indicated as less abundant taxa in each group (i.e., Other species; < 1%). Conversely, Figure 6 shows the pairwise statistical relationship of 12 differentially fungal species and health biomarkers (COVID-19 patients, Recovered humans and Healthy controls) of the study participants with significant variations (p ≤ 0.05, Wilcoxon test). Both number and name of fungal species differ in both figures. Therefore, we firmly believe that both figures carry significant information for the readers. We hope that the sensible Reviewer will agree with us to keep both Figure 5 and Figures 6 as main figures, independently.

Reviewer comment: The Figures 5 and 6 legends for species names should be italic.

Our Response: We would like to thank the reviewer for this valuable suggestion. We have revised both figures accordingly. You may kindly see Figure 5 and Figure 6 in the revised manuscript.

Discussion

Reviewer comment: Needs to correlate fungal infection with SARS-CoV2 infection in the discussion section.

Our Response: We would like to thank the reviewer for this valuable suggestion. We have revised and edited the discussion section with correlation between SARS-CoV2 infection and fungal co-infection. You may kindly go through Lines 447-460 in the revised manuscript.

Reviewer comment: Is there any report of secondary infection of fungus after COVID-19 infection?

Our Response: Thank you very much for this important question. Yes, there are reports of secondary infection of fungus after SARS-CoV-2 infection. For instance, severe bloodstream infection by Saccharomyces in hospitalized ICU patients, due to severe COVID-19, has been reported previously (Ventoulis et al., 2020; DOI: 10.3390/jof6030098). Moreover, aspergillosis, invasive candidiasis, and mucormycosis (black fungus) have been reported as the most commonly reported fungal infections in patients with COVID-19 (Hoenigl M, 2021; DOI: 10.1093/cid/ciaa1342, Gangneux et al., 2020; DOI: 10.1016/j.mycmed.2020.100971).

Reviewer comment: Discuss fungal opportunistic pathogenic character in COVID-19 cases.

Our Response: We would like to thank the reviewer for this nice comment. We have discussed the role of opportunistic fungal infections amid COVID-19 in several paragraphs of the discussion section. You may kindly go through Lines 447-460 and 518-528 in the revised manuscript.

Reviewer ## 2

Overall Comment

This is an important study to allow deeper understanding of nasopharyngeal microbiome in COVID-19 patients. Extensive bioinformation analysis work was performed with interesting findings.

Our Response: We would like to express our sincere thanks to the expert reviewer for complementing our manuscript.

Reviewer comment: The description of patient recruitment could be made clearer. It took me some time to understand the 7 subjects in the recovered group were the sample subjects from the COVID-19 group, after they have recovered. The authors should also explain why only 7 of the 8 COVID-19 patients were included in the recovered group.

Our Response: We would like to thank the reviewer for this valid suggestion. We have revised the methodology of subject recruitment. The confirmed COVID-19 patients (n = 8) were admitted into the dedicated COVID-19 isolation wards, and received medication. Unfortunately, one confirmed COVID-19 patient died in the ICU (intensive care unit) during the course of medication (six days after confirmatory diagnosis). These rest of the patients (n = 7) were tested negative for COVID-19 after 17.5 (ranged from11 to 32) days of SARS-CoV-2 infection, and categorized as Recovered humans (S1 Table). You may kindly go through Lines 142-148 in the revised manuscript.

Reviewer comment: There was inconsistency in the terminology used for the three groups: For example: line 129 “Recovered”, line 133 “Recovered humans”, line 134 “Recovered subjects; Another example: line 138 “Healthy people”, line 136 “Healthy control subjects”.

Our Response: We would like to thank the reviewer for pointing out this inconsistency. We revised the manuscript using unique terminology for three groups i.e., COVID-19 patients, Recovered humans and Healthy controls. We hope that the judicious reviewer will find no more inconsistency regarding group names throughout the manuscript. You may kindly go through the revised manuscript

Reviewer comment: The difference between dysbiosis and clinical infection was not clear throughout the manuscript. For example, in Lines 110-115, the authors stated the importance of understanding fungal microbiome in COVID-19 patients, however, in the next sentence, the authors advocated a timely diagnosis of fungal co-infections to limit the overuse of antimicrobial agents.

Our Response: We would like to thank the reviewer for this valid comment. We have deleted the conflicting sentence from the manuscript, and added few statements on fungal dysbiosis and clinical infection of SARS-CoV-2. You may kindly go through Lines 97-101 in the revised manuscript.

Reviewer comment: The authors included subject information in Table S1 with a column stated whether the subjects received “COVID-19 medicine”. Specific information on antibiotics used would be helpful to the interpretation of the study results: One major finding of the current study was that COVID-19 patients exhibited higher fungal microbiome diversities than those of recovered human and healthy controls, could it be due to effect of antibiotics, killing off most bacteria that allow the blooming of fungal organisms? Another major finding of the current study was that various species in the Aspergillus genus were over-represented in the recovered group. Aspergillus is well known to be present in the healthcare environment [1]. Could the observation be explained by nasopharynx flora being colonized by hospital molds with the aid of antibiotics therapy used?

Our Response: We would like to thank the reviewer for this valuable information and suggestion. We have added the suggested information (i.e., medication) in the method, results and discussion sections of the manuscript. The confirmed COVID-19 patients received medication with broad spectrum antibiotics (e.g., azithromycin and cefuroxime) and ivermectin for an average period of 15.71 days. One of the major findings of the current study was that COVID-19 patients exhibited higher fungal microbiome diversities than those of Recovered humans and Healthy controls, which could be due to the effect of medication with broad-spectrum antibiotics (e.g., azithromycin and cefuroxime), and thereby, killing off most bacteria that allow the blooming of fungal organisms. Another important finding of the present study was the detection of various species in the Aspergillus genus with higher relative abundances in the Recovered humans group. Aspergillus is well known to be present in the healthcare environment [44], and thus, could colonize in the nasopharyngeal cavity of the patients by invasive molds commonly found within a healthcare facility. You may kindly go through Lines 97-121, 230-232, and 447-460 in the revised manuscript.

Specific comments

Lines 63 - 65: Redundance, can omit the second “SARS-CoV-2” of the sentence

Our Response: We would like to thank the reviewer for this valid suggestion. We have deleted the sentence/lines. You may kindly go through the revised manuscript.

Lines 65 – 68: The sentence seemed to suggest that all COVID-19 infections will result in ARDS. Suggest rephrasing.

Our Response: We would like to thank the reviewer for this important comment. We have rephrased the mentioned lines. You may kindly go through Lines 64-71 in the revised manuscript.

Line 71: What is the meaning of “resilient microbiomes”?

Our Response: Thank you for this nice question. Resilient microbiomes mean healthy commensal microbiota. However, we have replaced the words with healthy commensal microbiomes. You may kindly go through Line 73 in the revised manuscript.

Line 88: Mucormycosis is well studied and should not be considered as “mysterious fungal infection”

Our Response: We would like to thank the reviewer for this valid suggestion. We have rephrased the Line. You may kindly go through Line 89-90 in the revised manuscript.

Lines 95-98: broad spectrum antibiotics are for treatment of bacterial coinfections, dexamethasone and immunosuppressive therapies are for immune modulation for treatment of immune dysregulation such as ARDS.

Our Response: We would like to thank the reviewer for this concern. We have paraphrased the mentioned lines. You may kindly go through Lines 97-101 in the revised manuscript.

Lines 111: what is the meaning of “migration, propagation and immune response”?

Our Response: We would like to thank the reviewer for this valid suggestion. We have revised the Line. You may kindly go through Lines 113-116 in the revised manuscript.

Lines 250-251: “….which probably due to the opportunistic inclusion during the pathogenesis of SARS-CoV-2 infection” should be moved to discussion section

Our Response: We would like to thank the reviewer for this valid suggestion. We have shifted this statement in the Discussion section. You may kindly go through Lines 261-264 and 447-460 in the revised manuscript.

Line 314: Can consider delete the word “However”

Our Response: We would like to thank the reviewer for this valid suggestion. The word has been deleted. You may kindly see Line 330 in the revised manuscript.

Line 427: Typo: “COVI1D-9”

Our Response: Thank you very much. We’ve corrected the typo-mistakes in the mentioned line. You may kindly go through Line 444 in the revised manuscript.

Line 488: redundance: “…..with SARS-CoV-2 infections in COVID-19 patients”

Our Response: We would like to thank the reviewer for this noticing this redundancy. We have deleted the redundant words from the mentioned lines. You may kindly go through Lines 518-519 in the revised manuscript.

Line 502-505: Different species of aspergillus has different pathogenic potential. As the authors rightly pointed out A. fumigatus, and A. flavus to a lesser extent, are associated with clinical important aspergillus infections, but not the A. penicilliodes and A. keveii that is found in abundance in recovered patients. Suggest discussing pathogenic and non-pathogenic aspergillus species separately.

Our Response: We would like to thank the reviewer for this nice suggestion. We have discussed the issue of pathogenic and non-pathogenic aspergillus species separately. You may kindly go through Lines 518-528 in the revised manuscript.

Line 570-572: was there any specific impairment of antiviral activities that was mentioned in the text?

Our Response: We would like to thank the reviewer for this nice query. We have deleted the confusing statement. You may kindly go through the revised manuscript.

Figures: some figures placed healthy control on the left side of the charts (e.g. Fig 1A, 1B), while others were in the opposite order, with COVID-19 on left side (e.g. Fig 3, Fig 5, and 6)

Our Response: We would like to thank the reviewer for this inconsistency. We are happy to revise the mentioned Figures keeping uniformity. You may kindly see the revised Figures in the revised manuscript.

Figure 5: the color used for different species are similar to each other.

Our Response: Thank you for this concern. We have revised the Figure. You may kindly see the Figure 5 in the revised manuscript.

Table S1: Inconsistency noted: there were information included for 7 COVID-19 patients, 7 recovered patients, and 7 healthy controls. However, there were 8 COVID patients recruited in the study (line 37).

Our Response: We would like to thank the reviewer for noticing inconsistency. We have revised the Table S1 including the information of COVID-8 (ICU death). You may kindly see Table S1, and the revised manuscript.

Attachment

Submitted filename: Response to Reviewers-R1.docx

Click here for additional data file. (33.2KB, docx)

Decision Letter 1

David M Ojcius

10 Nov 2022

Transcriptome analysis in nasopharyngeal samples reveals increased abundance and diversity of opportunistic fungal pathogens in COVID-19 patients

PONE-D-22-14092R1

Dear Dr. Islam,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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PLOS ONE

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Reviewers' comments:

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Authors addressed my comments and concerns with sufficient details. Though there are some limitations including sampling time (two-years back), however the findings are interesting and significant in terms of fungal pathogens in COVID19 infection.

Reviewer #2: The authors had adequately addressed all the comments and concerns I raised previously. The revised manuscript has improved significantly and is good for publication.

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Acceptance letter

David M Ojcius

5 Jan 2023

PONE-D-22-14092R1

Transcriptome analysis reveals increased abundance and diversity of opportunistic fungal pathogens in nasopharyngeal tract of COVID-19 patients

Dear Dr. Islam:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Major fungal phyla detected.

    The phylum-level taxonomic profile of fungal microbiomes in COVID-19 (C1-C8), Recovered (R1-R7) and Healthy (H1-H7) nasopharyngeal samples. Phyla with > 1% mean relative abundance are represented by different color codes against respective sample groups. Others (< 1%) indicates the rare taxa in each group, with mean relative abundance of < 1%.

    (PNG)

    Click here for additional data file. (172.9KB, png)
    S2 Fig. Metabolic functional potentials of the fungal microbiota.

    Heatmap showing (A) KEGG orthologues (KOs) and (B) SEED subsystems associated with fungal metabolism in Healthy, COVID-19 and Recovered metagenomes.

    (JPG)

    Click here for additional data file. (227.2KB, jpg)
    S1 Table. Demographic characteristics of the study people: Clinical diagnosis, treatment and recovery history of SARS-CoV-2 infections.

    (DOCX)

    Click here for additional data file. (20.4KB, docx)
    S2 Table. Dysbiosis of mycobiomes after SARS-CoV-2 infections.

    (DOCX)

    Click here for additional data file. (18.7KB, docx)
    S3 Table. Metabolic functional potentials of the fungal microbiome.

    (DOCX)

    Click here for additional data file. (20.4KB, docx)
    S1 Data. Taxonomic information on fungal microbiomes.

    (XLSX)

    Click here for additional data file. (64.7KB, xlsx)
    Attachment

    Submitted filename: Response to Reviewers-R1.docx

    Click here for additional data file. (33.2KB, docx)

    Data Availability Statement

    The sequence data reported in this article has been deposited in the National Center for Biotechnology Information (NCBI) under BioProject accession number PRJNA720904.

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