The avian lung mycobiome: phylogenetic and ecological drivers of lung-fungal communities and their potential pathogens

doi:10.21203/rs.3.rs-5214380/v1

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The avian lung mycobiome: phylogenetic and ecological drivers of lung-fungal communities and their potential pathogens

https://doi.org/10.21203/rs.3.rs-5214380/v1

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Vertebrate lungs contain diverse microbial communities, but little is known the causes of community composition or its consequences for health. Lung microbiome assembly by processes such as dispersal, coevolution, and host-switching can be elucidated with comparative surveys. However, few comparative surveys exist for lung microbiomes, particularly for the fungal component, the mycobiome. Distinguishing fungal taxa that are generalist or specialist symbionts, potential pathogens, or incidentally inhaled spores is urgent because of high potential for emerging disease. Here, we provide the first characterization of the avian lung mycobiome and we test the relative influences of environment, phylogeny, and functional traits. We used metabarcoding and culturing from 195 lung samples representing 32 bird species across 20 families. We identified 532 fungal taxa (zOTUs) including many that are opportunistic pathogens. These were composed predominantly of the phylum Ascomycota (79%) followed by Basidiomycota (16%) and Mucoromycota (5%). Yeast and yeast-like taxa (Malassezia, Filobasidium, Saccharomyces, Meyerozyma, and Aureobasidium) and filamentous fungi (Cladosporium, Alternaria, Neurospora, Fusarium, and Aspergillus) were abundant. Lung mycobiomes were strongly shaped by environmental exposure, and further modulated by host identity, traits, and phylogenetic affinities. Our results implicate migratory bird species as potential vectors for long-distance dispersal of opportunistically pathogenic fungi.

Biological sciences/Ecology/Microbial ecology

Biological sciences/Microbiology/Fungi

Aspergillus

birds

Candida

yeasts

lungs

microbiome

migration

phylosymbiosis

sandhill crane

Vertebrate lung microbiomes and their fungal constituents (mycobiome) are emerging frontiers for ecology and public health. The emergence of antifungal-resistant pathogens^1–3 and climate-driven shifts affecting host reservoirs⁴ underpin the urgency to describe the lung mycobiomes of diverse animal species. An essential task in understanding the public health implications of animal lung mycobiomes is parsing fungal taxa incidentally inhaled from exposure to airborne propagules⁵ from taxa that colonize lung tissues and/or act as opportunistic pathogens; this has been challenging to assess due to the paucity of data on lung-fungal microbe associations⁶. One common approach to assess the relative contributions of incidental and specialized fungi to the lung mycobiome is by comparing fungal taxa common in environmental samples (soil and air, for example) with taxa frequently encountered in lung mycobiome studies^7–9. However, the lack of data on how fungal and host geographic distributions align complicates the identification of host-specialized taxa. A formerly prevailing view in fungal biogeography—that most fungi are not subject to the same dispersal barriers and biogeographic processes as plants and animal taxa—is now recognized as an oversimplification^10,11. Although some fungal taxa have cosmopolitan ranges, many others conform to biogeographic barriers¹², which can result in false inference of host-specialization.

Lungs, like other organs, have imposing immune defenses that act as a selective filter on microbial symbionts. The pulmonary mucosal immune response quickly clears most inhaled fungi; however, colonization can occur when innate immune defenses are diminished¹³ or evaded by coevolved taxa¹⁴. Among many animals, apparent fungal associations and limited evidence for severe disease suggest deep shared evolutionary histories between lung fungi and their hosts¹⁵. Signs of coevolution, or phylosymbiosis, can elucidate the relative contributions of transient versus host-adapted taxa to the microbiome^16,17. A few notable members of the microbiome have the capability to transcend host barriers and transmit from vertebrate animals to humans (zoonoses)¹⁸. Notably rodents and bats have received attention because these spillover events have had devastating consequences for humans (e.g., coronaviruses¹⁹, hantaviruses²⁰, and filoviruses²¹). However, a recent study of host-virus relationships found that host breadth explained the frequency of zoonoses that threaten humans more strongly than ecological or immunological traits²². Thus, expanding research to include a diverse array of vertebrate hosts is important to identify those likely to cause spillover events.

Among vertebrates, migratory birds require special consideration as a group with unique spillover potential because they are exposed to fungi in both feeding and nesting areas and at varying altitudes of flight^23,24. Previous work on fungal opportunistic pathogens have highlighted a number of taxa linked to birds. For example, aspergillosis, caused by globally distributed species of Aspergillus, is perhaps the most widely recognized fungal infection (mycosis) of birds^25,26. Documented on all continents except Antarctica²⁷, Aspergillus spp. (mainly A. fumigatus) typically cause disease when dosage thresholds are surpassed or exposed individuals are immunocompromised²⁸. Species of Candida have also been identified as common commensals of birds and occasionally implicated in bird mycoses^29,30. Bird guano is considered a natural reservoir for Cryptococcus neoformans causing cryptococcosis, which can have severe presentations in humans such as fungal meningitis^31–33. Several other species of Cryptococcus (e.g., C. gattii, C. uniguttulatus, and C. laurentii) have been isolated from multiple bird species, and are occasionally implicated in human and bird mycoses^34–36. Considering that concern about emerging fungal pathogens is increasing, identifying potential pathways of exposure is paramount.

Of the over 11,000 bird species currently recognized³⁷, ~ 20% are migratory; yet, there has been no comprehensive survey of the avian lung mycobiome or their potential as reservoirs for undescribed, host-generalist and specialist pathogens. Many migratory birds travel thousands of kilometers during migration, suggesting that they may be important vectors capable of transporting and dispersing fungi over long distances, even spanning hemispheres³⁸. Specifically, birds have been insinuated in the transport of Candida auris, a multidrug resistant pathogenic yeast³⁹. The connection between life history traits like migration, and the transport of fungal pathogens highlights the importance of characterizing avian mycobiomes and testing potential drivers of mycobiome diversity and composition. It is critical to account for host phylogeny because the susceptibility of birds to diverse pathogens has been shown to be phylogenetically conserved⁴⁰. Microbial diversity may additionally be driven, in part, by host traits^41–44 like body size, tissue pH, and diet, as well as aspects of the environment such as seasonality and precipitation⁴⁵. Long-distance migration and flight might obscure ecologically or phylogenetically driven variation in the mycobiomes of host species, which are exposed to locally distinct fungal communities at different times of the year^46,47. Yet, the presence of fungal lineages specialized on particular host clades could remain discernible despite the homogenization resulting from host dispersal.

Here, we report the first comprehensive characterization of the avian lung mycobiome, derived from migratory and resident birds from the southwestern USA. Using both fungal metabarcoding and culture data, we compared lung mycobiomes among 32 bird species, and identified host-associated fungal species and potential pathogens. We then tested fungal-host associations at multiple host-phylogenetic levels, from avian subspecies to order. To evaluate the relative contributions of environment versus hosts, we tested for host phylogenetic signal of lung fungal alpha diversity, compared fungal community turnover among hosts with disparate breeding ranges, migration strategies, foraging modes, and sampling years. We additionally modeled fungal community differences as a function of geographic distance, dispersal-related traits, and host phylogeny and evaluated co-occurrence patterns among fungal lung taxa. Our results revealed moderately diverse lung-fungal communities that were subtly structured by aspects of host life history and phylogeny. Many of the identified fungal lineages are potential opportunistic pathogens that were non-randomly associated with migratory sandhill cranes. This multi-pronged characterization of avian lung mycobiome reveals factors driving mycobiome community assembly at evolutionary timescales and the potential role of birds as agents of fungal dispersal.

Sample acquisition

We sampled a total of lung tissues (n = 195) from birds salvaged throughout New Mexico, USA representing 32 species within 20 families (Supplementary Table 1). The majority were hunter-donated tissues from two subspecies of sandhill cranes (Antigone canadensis, n = 146). The remaining (n = 49) were salvaged after death by private individuals and wildlife rescue organizations and subsequently donated to the Museum of Southwestern Biology (MSB). Lung tissues were frozen on dry ice and stored in -80°C freezers before being archived in vapor phase nitrogen freezers (-196°C) in the MSB Division of Genomic Resources at the University of New Mexico (UNM). We identified cranes to subspecies (A.c. canadensis and A.c. tabida) in the field using several diagnostic morphological parameters including mass, wing chord length, culmen length, and tarsus length. All birds were sexed internally by inspecting gonads during dissection and aged by the presence and size of the bursa of Fabricious (present in young birds); cranes were additionally aged by plumage and iris coloration. Precise GPS coordinates of sampling locations were not available for many salvaged avian samples. To estimate sample location, we calculated the latitude and longitude centroids of the New Mexico county where each bird was recovered using the R package geosphere⁴⁸. We additionally recorded the date of salvage to assess temporal variation in fungal communities.

Ecological and behavioral variables of avian hosts

Using published data (AVONET⁴⁹ database and reported estimates⁵⁰) and expert knowledge, for each species in the dataset we assigned feeding guild, range size in km², migration distance in km, and mean hand-wing index (a measure of avian flight ability)⁵¹ to each species in the dataset. When available, we included the recorded mass in grams of individual birds; when mass was not recorded, we included the mean species mass as reported in the AVONET dataset.

lllumina ITS2 amplicon sequencing and processing

We lyophilized approximately 25 mg of lung tissue for 24 hours, and then performed a CTAB DNA extraction followed by an AMPure bead (Agencourt Bioscience Corporation, Beverly, MA, USA) purification. The ITS2 rRNA region was amplified with the ITS3F and ITS4R primers^52,53 in a 30-cycle PCR using the HotStarTaq Plus Master Mix Kit (Qiagen Inc, Cambridge, MA, USA) under the following conditions: 95°C for 5 min, followed by 30 cycles of 95°C for 30 sec, 53°C for 40 sec and 72°C for 1 min, after which a final elongation step at 72°C for 10 min was performed. PCR products were checked on a 2% agarose gel. Based on DNA quality and quantity, 183 samples were multiplexed and pooled in equal proportions based on molecular weight and DNA concentrations. Pooled products were purified using Ampure XP beads (Beckman Coulter Inc, Brea, CA, USA) following manufacturer instructions and were used to create DNA libraries following the Illumina MiSeq DNA library preparation protocol for paired-end reads. Samples were sequenced by MR DNA on an Illumina MiSeq Shallowater, Texas (http://www.mrdnalab.com). The raw reads were submitted to the NCBI Short Read Archive (SRA) under BioProject PRJNA1136563.

We used USEARCH⁵⁴ v.11.0.667 to merge paired-end reads, remove adapters and primers, and perform quality filtering using a maximum expected error threshold of 1.0. With UNOISE3⁵⁵, we performed error correction (denoised) on unique sequences and clustered them into zero-radius operational taxonomic units (zOTUs), preferred to distinguish between species and strains that would otherwise be grouped together. Initial taxonomic assignment was performed with the SINTAX⁵⁶ algorithm against the UNITE v.7 database and compared with BLASTn. We removed all non-fungal (Metazoa and Viridiplantae) sequences. We evaluated sufficient sequencing depth using the rarecurve function in vegan⁵⁷ v.2.6-4.

Assignment of trophic mode

We used FUNGuild⁵⁸ v.1.1 to assign trophic modes (i.e., pathotroph, saprotroph, or symbiotroph) and functional guilds to fungal zOTUs. Animal-fungal symbionts from each sample were pooled by determining the percent of reads in each sample that belonged to zOTUs whose guild assignments included animal pathogen, animal parasite, and animal symbiotroph.

Identification of core mycobiome

The core mycobiome refers to the common set of fungal zOTUs in a host taxon. To identify the core lung mycobiome of the migratory birds in our dataset, we first transformed the full dataset to proportional abundances and filtered the zOTUs, keeping only those zOTUs that were detected in > 50% of bird samples and with > 0.01% relative abundance. We then ran the function core from the package microbiome⁵⁹, to obtain a subset of core taxa and used it for downstream co-occurrence analyses.

Alpha and beta diversity

We rarefied the data set using the rarefy_even_depth function in phyloseq⁶⁰ v.1.42.0 using the sample with the fewest reads as the target sample depth. Rarefaction removed 20 zOTUs leaving 511 zOTUs for downstream diversity analyses. Using the rarefied community data we calculated zOTU richness, Chao1 index, Shannon diversity index, and inverse Simpson index using the function estimate_richness from the phyloseq⁶⁰ package. We used non-metric multidimensional scaling (NMDS) to explore drivers of diversity and visualize groupings of fungal lung communities into host phylogenetic, demographic and ecological categories. To visually compare lung mycobiome beta diversity and host phylogenetic diversity we first merged fungal reads by host-taxa, repeatedly rarefied at 1,000 reads, calculated the Bray-Curtis dissimilarity index between each sample, and averaged the matrices. We then used the averaged matrix to generate a heatmap and corresponding dendrogram using the function heatmap2 from the package gplots⁶¹ v.3.1.3.1. We generated a phylogenetic distance matrix from the maximum clade credibility (MCC) tree (see phylogenetic analyses below) with the cophentic_phylo function from the R package ape⁶². We plotted the phylogenetic distance matrix below the beta diversity matrix and created a tanglegram to indicate discordance between the phylogenetic tree and the Bray-Curtis dissimilarities. To test for differences in bird fungal assemblages among multiple life history traits and phylogenetic scales, we used Bray–Curtis and PERMANOVA (permutational multivariate analysis of variance) models (Bray-Curtis ~ group, 10,000 permutations) using the adonis2 function in vegan⁵⁷. If there were differences in multivariate dispersion assessed with the betadispr function from vegan⁵⁷,an ANOSIM (analysis of similarities) test was used in lieu of a PERMANOVA. We repeated PERMANOVA and ANOSIM tests of community dissimilarity with a subset of fungal taxa containing known animal symbionts and opportunistic pathogens. We investigated the correlation between geographic distance and fungal community similarity using a Mantel correlogram and Mantel test in vegan⁵⁷. Because similarity is expected to decrease with geographical distance, we plotted distance decay by computing Bray-Curtis between each pair of samples along a spatial gradient.

Co-occurrence analyses

To evaluate microbial associations within the avian mycobiome, we ran co-occurrence analyses on the core fungal zOTUs transformed to proportional abundance and the rarefied subset of animal-symbionts. We calculated Spearman rank abundance correlations and tested for significant correlations using the co_occurrence function from the phylosmith⁶³ package v.1.0.6. We used the co_occurrence_network function from phylosmith⁶³ to plot co-occurrence patterns.

Generalized dissimilarity modeling

We used generalized dissimilarity modeling (GDM) in the R package gdm⁶⁴ v.1.5.0-9.1 to evaluate fungal-host phylogenetic associations and test the effects of geographic and morphological correlates of dispersal and migration on host fungal community dissimilarity. GDM predicts changes in beta diversity over space, time, and environmental gradients and allows the inclusion of several types of distance matrices as response and predictor variables (e.g. Jaccard, phylogenetic distance). GDMs use linear combinations of I-splines to model changes in predictors across ecological distance with the relative heights of the I-splines^64,65. Using rarefied abundance-weighted fungal zOTU tables, we calculated Bray-Curtis dissimilarity matrices as response variables in our GDMs. To address uneven sampling, we ran 1,000 GDMs on the repeatedly rarefied datasets of abundances summed within host species. To examine the effect of host phylogenetic distance on fungal community dissimilarity, we additionally generated a phylogenetic distance matrix from the MCC tree using the cophentic_phylo function from the R package ape⁶². We tested the effects of five predictor variables: host mass, species hand-wing index, range size (km²), migratory distance (km), and host phylogenetic distance on Bray-Curtis matrices of fungal community dissimilarity. We scaled or log₁₀ transformed each predictor. We extracted and plotted all I-splines regardless of their significance from each of the 1,000 fitted models. We used the gdm.varImp function from the gdm⁶⁴ package to evaluate model fit and variable importance among all 1,000 models.

Cultured lung fungi

We cultured a portion (25–50 mg) of each lung sample on yeast glucose media (1% yeast extract, 2% glucose, 1.5% agar) with the addition of tetracycline (10 mg/L) and chloramphenicol (50 mg/L). Each plate received 3–5 fragments (approximately 0.25 cm each) of tissue from a single lung. Plates were incubated at room temperature for two days to three months depending on fungal growth rate. Individual yeast colonies or hyphal tips were transferred to fresh media to obtain single-isolate cultures. We sorted isolates based on morphology and chose representative isolates for molecular barcoding of the internal transcribed spacer (ITS) region. We extracted DNA following a standard CTAB procedure with an isoamyl alcohol-chloroform extraction. Subsequently, we amplified the DNA using polymerase chain reaction (PCR) with ITS1-F and ITS4^52,66. The following PCR cycle was implemented: initial denaturation at 95°C for 10 min, followed by 34 cycles of 95°C for 15 sec, annealing at 52°C for 30 sec, and extension at 72°C for 1 min, with a final extension at 72°C for 5 min. We confirmed products with gel electrophoresis and purified them with ExoSAP-IT (Affymetrix, Santa Clara, CA, USA) following manufacturer recommendations. The entire ITS region was targeted for Sanger sequencing using BigDye Terminator v3.1 (Applied Biosystems, Foster City, CA, USA), and the sequences were identified using BLAST⁶⁷. If samples within one morphotype varied in taxonomic classification, we sequenced additional fungal cultures for more accurate identification. We identified Fusarium sequences to species complexes by employing FUSARIUM-ID⁶⁸ v.3.0. We deposited sequences in GenBank under accession numbers PP804255-PP804425 (Supplementary Table 2).

Phylogenetic analyses

We constructed phylogenies for select fungal groups with human health consequences, including Aspergillacaeae, Cryptococcus-like yeasts, Onygenales, and Saccharomycetales. We aligned sequences with mafft^69,70 v.7.487 using automatically determined settings and trimmed the resulting alignment with trimal⁷¹ v.1.4.1 in automated1 mode. The final alignments were 246 bp for Aspergillaceae, 251 bp for Cryptococcus-like yeasts, 241 bp for Onygenales, and 252 bp for Saccharomycetales. We constructed maximum likelihood trees in IQ-Tree⁷² v.1.6.12 using the GTR + GAMMA substitution model with 10,000 ultrafast bootstraps. The resulting trees were visualized in ggtree⁷³. Sequences were checked against reference sequences from GenBank as well as compared to those from our previous study of the lung communities of small mammals in the southwestern U.S.⁸ (Supplementary Table 3).

We tested the level of host phylogenetic signal in alpha diversity indices: zOTU richness, Shannon diversity, inverse Simpson, and Chao1. For each alpha diversity index, we calculated a mean value at the host taxon level from the rarefied dataset as trait inputs for tests of phylogenetic signal. We downloaded a set of 10,000 phylogenetic trees from https://birdtree.org/ with the Hackett backbone^74,75 and generated a maximum clade credibility tree using the function maxCladeCred from the R package phangorn⁷⁶ v.2.5.5. We then pruned the tree to the taxa in our dataset using the keep.tip function in ape⁶². We tested for phylogenetic signal using Moran’s I, Abouheif’s Cmean, and Pagel’s Lamda tests using the R packages adephylo⁷⁷ v.1.1–13 and phytools⁷⁸ v.2.0. We evaluated adjusted P values against a 0.05 significance level from permutation tests (n = 10,000) to identify phylogenetic signal in each alpha diversity metric.

Sample acquisition

Of the 195 avian lung tissues, 120 were from greater sandhill cranes (A.c. tabida) and 26 lesser sandhill cranes (A.c. canadensis) from Valencia and Socorro counties, New Mexico collected between November and December 2019 and January 2020. The remaining 49 bird lungs were obtained from specimens salvaged across ten New Mexican counties (Bernalillo, Chaves, Curry, Eddy, Los Alamos, Rio Arriba, Sandoval, Santa Fe, San Juan, and Taos) between 2006 and 2018. In total, we examined the mycobiome of 195 avian lung samples representing 33 taxa across 20 families within 12 orders.

Lung mycobiome community analysis

After removing samples that did not pass DNA quality and quantity standards, we performed Illumina ITS2 metabarcoding on 183 avian lung tissues. After filtering non-fungal reads, we removed 16 samples with less than 2,000 reads each. The final avian lung mycobiome community dataset was comprised of 167 samples: 100 greater sandhill cranes, 22 lesser sandhill cranes, and 45 salvaged birds (Supplementary Table 1). Our amplicon sequencing efforts produced a total of 6,656,903 fungal reads clustered into 532 zOTUs. On average, sequencing yielded 39,862 reads per sample although this varied substantially (σ = 35,984). The greatest number of fungal reads were captured from a common loon (Gavia immer; MSB:Bird:60464; n = 240,528), a barn owl (Tyto alba; MSB:Bird:60463; n = 145,669), and a marsh wren (Cistothorus palustris; MSB:Bird:60445; n = 141,996).

Rarefaction curves indicated substantial coverage of fungal diversity (Supplementary Fig. 1). Taxa were predominantly within the phylum Ascomycota (79%) followed by Basidiomycota (16%) and Mucoromycota (5%). There was only one zOTU within Chytridiomycota and three of the zOTUs could not be identified to phylum using either NCBI BLAST or UNITE databases. The most abundant and frequent zOTUs were from the Malasseziomycetes (Malassezia) and Tremellomycetes (Cryptococcus) within Basidiomycota, and the Dothideomycetes (Cladosporium, Alternaria, and Aureobasidium), Eurotiomycetes (Thermomyces and Aspergillus), Sordariomycetes (Neurospora and Fusarium), and Saccharomycetes (Meyerozyma, Saccharomyces, and Clavispora) within Ascomycota (Fig. 1). We identified 11 Malasseziomycetes, which included the two most prevalent zOTUs in the community dataset: zOTU2 detected in all 167 samples and zOTU21 found in 158 (94.6% of the samples). Additional diversity of basidiomycetous yeasts fell within the Tremellomycetes (n = 36) and included 17 Cryptococcus-like yeasts (Supplementary Fig. 2). The most frequent and abundant was Filobasidium (Cryptococcus) magnum (zOTU20) detected in 112 samples (67.1%). Among the Ascomycota the greatest representation was from the Eurotiomycetes (120 of 418 zOTUs, 28.7%). Of note, 35 of these zOTUs were classified within the family Aspergillaceae of which 8 (zOTU19, zOTU467, zOTU849, zOTU990, zOTU1294, zOTU2655, zOTU3349, zOTU3362), were classified as Aspergillus fumigatus, the primary causative agent of aspergillosis (Supplementary Fig. 3). Due to the use of zOTUs it is possible that some taxa, like A. fumigatus, may be over split into several zOTUs due to intra-species or intra-individual variation. The dominant taxon from this group, zOTU19, was detected in 96 samples (57.5%) and represented greater than a third of the sequences from two greater sandhill cranes (Antigone canadensis tabida, MSB:Bird:56461, 90% and MSB:Bird:56426, 36%) and a common loon (Gavia immer, MSB:Bird:60464, 36%), although it was otherwise generally in low abundance (0–5% of reads). Dothideomycetes were the second most prevalent Ascomycota (104 zOTUs) and included two species of Cladosporium (zOTU 6 in 92.2% and zOTU8 in 86.8% of samples), Alternaria alternata (zOTU3 in 89.2% of samples), and to a lesser extent Aureobasidium pullulans (zOTU 14 in 54.5% of samples; Fig. 1). We also noted a great diversity (60 zOTUs) within the ascomycetous yeast order Saccharomycetales. This order contained several etiological agents of human candidemia including Clavispora (Candida) lusitaniae (zOTU179 in 53.9% of samples), Meyerozyma (Candida) guilliermondii complex (zOTU16 in 52.1% of samples), Candida tropicalis (zOTU 233 in 39.5% of samples), and Candida parapsilosis (zOTU 47 in 6.8% of samples; Fig. 1, Supplementary Fig. 4).

We cultured 300 fungal isolates from 107 avian lungs. At least one fungus was isolated from 55% of the lungs plated. The majority of pure cultures (n = 274, 91.3%) were isolated from sandhill cranes. Cultures were sorted by morphotype followed by preliminary identification by sequencing the ribosomal ITS region. A total of 171 sequences from fungal cultures were obtained representing 48 operational taxonomic units (OTUs) when clustered at 97% similarity (Table 1). Most of the cultures belonged to Ascomycota (n = 239, 79.7%) followed by Mucoromycota (n = 53, 17.7%) and Basidiomycota (n = 8, 2.7%). The most abundant and diverse class was the Eurotiomycetes (n = 121, 16 OTUs). Within the Eurotiomycetes (order Eurotiales) there was great diversity within the genera Penicillium (9 OTUs) and Aspergillus (7 OTUs; Supplementary Fig. 3). One species of Penicillium (Talaromyces) was the second most commonly cultured taxon (n = 45, 15.0%). The second most abundant class was Dothidiomycetes (n = 64) of which half the cultures were from a single species of Cladosporium. The third most commonly cultured taxon was within the Sordariomycetes (n = 50), which is a member of the Fusarium fujikuroi species complex (n = 23). The Mucoromycota fungi all fell within Mucoromycetes (n = 53) and were identified within the genera Lichtheimia, Mucor, and Rhizopus. The most frequently isolated fungus of the entire culture collection was Rhizopus arrhizus (n = 46, 15.3%) obtained from 45 individual cranes.

Alpha and beta diversity

Mean zOTU richness was 41.8 ± 19.7 among all lung samples (range = 9–125; Supplementary Fig. 5, Supplementary Fig. 6). Although most fungi appeared to be host-generalist, several fungal orders were detected only in sandhill crane samples: Botryosphaeriales (9 OTUs, n = 23,969), Onygenales (7 OTUs, n = 17,234), Lichenostigmatales (3 OTUs, n = 9,911), Filobasidiales (zOTU 499, n = 2,771), Polyporales (zOTU 485, n = 1,854), and Melanosporales (zOTU 1013, n = 642). Although ~ 67% of sampled birds in our dataset were cranes, Entylomatales (zOTU 1341, n = 12,470), Atractiellales (zOTU 1329, n = 299), Erysiphales (zOTU 228, n = 283), and Boletales (zOTU 2438, n = 98) were found only in non-crane samples. Among all lung samples, mean Chao1 index was 54.8 ± 28.9 (range = 9–175.6) mean Shannon diversity was 1.50 ± 0.75 (range = 0.21–3.56), and mean inverse Simpson’s index was 3.79 ± 2.98 (range = 1.26–24.5). We found no phylogenetic signal in any of the lung fungal community alpha diversity estimates for both the full rarefied dataset or the animal-symbiont subset of zOTUs (Supplementary Table 4).

Fungal beta diversity differed among avian orders (R = 0.161, P = 0.001), although these differences appeared to be driven by the strong sampling of cranes (without cranes: R = 0.005, P = 0.476). Phylogenetic and Bray-Curtis dissimilarity showed little correspondence but some conspecifics (Antigone) and congenerics (Accipiter) fell adjacent to one another in Bray-Curtis matrices suggesting shallow intra-specific host specificity (Fig. 2). Fungal diversity differed among avian host life history traits, including migration strategy (R = 0.1801, P = 0.002; Supplementary Fig. 7a) and foraging guild (R = 0.121, P = 0.012; Supplementary Fig. 7b). After removing cranes, where the lesser and greater subspecies differ substantially in migration distance (Fig. 3a and 3b), feeding guild remained a significant driver of fungal diversity (R = 0.239, P = 0.001) and migration became marginally significant (DF = 2, F = 1.350, P = 0.043). We found no differences between subspecies (R = 0.009, P = 0.266) when analyzing data for only cranes. Hybridization occurs between A. c. tabida and A. c. canadensis resulting in intermediate forms, and the inclusion of intermediates could obscure differences in fungal diversity. However, we excluded suspected intermediates from the dataset and therefore anticipate few unidentified intermediates remained in our analyses. When testing just the subset of zOTUs that included only animal symbionts as identified with FUNGuild, we observed a moderate signal of avian order (R = 0.080, P = 0.083); however, in this comparison, neither feeding guild (R = 0.071, P = 0.105) nor migratory strategy (R = 0.018, P = 0.375) was significant. Beta diversity among sampling years was highly significant for all bird species (R = 0.1809, P = 9.999e^− 05; Supplementary Fig. 7c) and when subsetting by only sandhill cranes (F = 2.227, P = 9.999e^− 05). The signal held for the animal symbiont subset (R = 0.064, P = 0.022), but not when subsetting by only non-crane birds (R=-0.072, P = 0.916).

A Mantel test showed a non-significant correlation between geographic distance and fungal community dissimilarity (r = 0.0375, P = 0.11); however, positive spatial autocorrelation was seen from 0 km to 58 km (P < 0.05) indicating that samples collected closer together tended to have more similar fungal communities (Supplementary Fig. 8).

FUNGuild assigned coarse functional guilds at the class or genus level for 452 zOTUs (85%). Avian lungs contained substantial numbers of putative animal symbionts. Every sample contained at least one fungal zOTU designated as an animal symbiont. The average abundance of fungal animal symbionts per avian sample was 44.3%; however, this varied greatly between samples (σ = 33.9%, range = 0.05–98.4%). Although not statistically significant (t-test, P = 0.11), the average abundance of animal symbionts among sandhill cranes was higher in greater sandhill cranes (47.9%) than lesser sandhill cranes (34.7%; Fig. 3c). Within both lineages of sandhill crane, we further assessed the prevalence of a subset of fungal animal symbionts with representatives implicated as pathogens. Both subspecies had nearly equivalent prevalence of zOTUs from Cryptococcus-like yeasts (lesser: 4.3%, greater: 4.1%), Onygenales (lesser: 1.6%, greater: 1.7%), Saccharomycetales (lesser: 10.5%, greater: 10%), and Aspergillaceae (lesser: 5.1%, greater: 7.4%; Fig. 3d).

Fungal co-occurrence

We found evidence for microbial interactions among taxa of the core avian mycobiome. Co-occurrence associations of the core mycobiome families ranged from − 0.262–0.332 (Spearman rank-abundance). There were more positive associations (67%) than negative associations (33%) among the core mycobiome members (Supplementary Fig. 9, Supplementary Table 5). The strongest positive correlation (Spearman rank correlation coefficient; ρ) was found between Cryptococcus-like yeasts and Cladosporiaceae (ρ = 0.332, P = 1.138e^− 05; Supplementary Table 5). The greatest negative correlation was between Trichocomaceae and Coniosporiaceae (ρ=-0.262, P = 6.278e^− 04; Supplementary Table 5). Co-occurrence associations calculated as Spearman rank-abundance of the animal-symbiont subset of the mycobiomes ranged from − 0.376–0.426. There were roughly equivalent positive associations (52%) relative to negative associations (48%) among the animal-symbiont mycobiome members (Supplementary Fig. 10, Supplementary Table 6). The strongest positive correlation was between Ajellomycetaceae and Triticharaceae (ρ = 0.427, P = 9.172e^− 09; Supplementary Table 6). The strongest negative association was between Malasseziaceae and Cryptococcus-like yeasts (ρ=-0.376, P = 5.583e^− 07; Supplementary Table 6).

Generalized Dissimilarity Modeling

Aspects of host biology and evolutionary history best explained dissimilarity among fungal communities. The GDMs explained a modest amount of the dissimilarity among the fungal communities (mean deviance explained 9.26% ± 0.24). The predictors with the highest summed I-spline coefficients were hand-wing index (0.621 ± 0.544), followed by host phylogenetic distance (0.235 ± 0.332), and migratory distance (0.150 ± 0.212); Supplementary Fig. 11, Supplementary Table 7). The predictors with the highest importance were hand-wing index (40.1 ± 1.56% Dev − variable i − Dev), followed by migration distance (22.1 ± 1.39% Dev − variable i − Dev), and host phylogenetic distance (9.92 ± 0.617 Dev − variable i − Dev; Supplementary Fig. 11, Supplementary Table 7).

Mycobiome diversity reflects exposure to a multitude of environmental fungi

This study provides an initial characterization of the lung mycobiome in wild birds. The average of 35.5 ± 16.5 zOTU richness recovered from bird lungs (Supplementary Fig. 5a, Fig. 6) was comparable to the fungal richness found in a recent survey of the rodent lung mycobiome⁸. However, estimates of lung zOTU richness were lower than previously characterized mycobiomes from the avian gut^79,80 and arid soil⁸¹ — though certain individuals, like a barn owl (MSB:Bird:60463) and an orange-crowned warbler (MSB:Bird:60434), showed comparably high zOTU richness. After accounting for fungal abundance and taxonomic evenness using the Shannon diversity index, there was still substantial variation among bird species in our dataset (0.20–3.48; Supplementary Fig. 5b). Differences in microbiome diversity among organ systems are well documented for bacterial communities^82,83 and may reflect local, organ-specific adaptation of microbes.

Fungal richness also varied substantially within host taxa (Supplementary Fig. 6). For example, barn owl (n = 4; MSB:Bird:60459, MSB:Bird:60448, MSB:Bird:60458, MSB:Bird:60463) mycobiome richness ranged from 19–101 zOTUs. Similar levels of intra-individual fungal richness were recovered from mammalian lungs⁸. High variation in gut mycobiome diversity among individuals has been previously documented in wild birds⁷⁹ and is likely driven, in part, by the spatial and temporal heterogeneity of fungal cells in the environment.

We found high levels of lung mycobiome turnover between bird species (x̅ Bray-Curtis dissimilarity = 0.70 ± 0.35; Fig. 2). This pattern could be produced by stochastic assembly due to incidental inhalation from a heterogeneous microbial environment, or it could reflect host specialization or differential filtering of fungal taxa due to host ecology or immunology. Many mycobiomes of closely related hosts had higher Bray-Curtis dissimilarity than those between more distantly related host taxa (Fig. 2). Only the similar mycobiomes of host congeners in the genera Antigone and Accipiter, respectively, provided evidence of decisive host phylogenetic effects on community assembly. Otherwise, the tendency for large differences in microbiome composition between bird hosts at various phylogenetic distances emphasizes the roles of environmental heterogeneity and incidentally inhaled fungi as drivers of variation in the core lung mycobiome of birds.

Phylogeny, diet, year, and geography predict lung mycobiome differences

Although our dataset suggests that the lung mycobiome of birds is assembled largely by heterogeneous environmental exposure, we found evidence of phylosymbiosis at shallow host phylogenetic levels (Fig. 2, Supplementary Fig. 11, Supplementary Table 7). Host specialization was evident within a widespread and morphologically variable species (the sandhill crane) and within the Accipiter genus of hawks (Fig. 2). Although these associations could reflect coevolved symbioses at shallow phylogenetic levels, we cannot rule out that they could be the result of shared ecologies among closely related taxa.

Dietary effects on the differentiation of gut microbial communities have been commonly reported in the literature^42,79, but whether and to what degree dietary and foraging differences shape the lung mycobiome has been less clear. We found that carnivorous taxa, like falcons, showed some of the lowest Shannon diversity values (Supplementary Fig. 5b). Tests of beta diversity confirmed significant fungal community turnover among dietary guilds (Supplementary Fig. 7b). Lung mycobiome composition and dietary habits may be linked in instances where species with specialized foraging strategies and substrates inhale different local fungal cells. For example, species probing in mud and foraging in agricultural fields are exposed to more saprotrophic and soil-associated fungal taxa through feeding than aerial hunters or ‘salliers’ that rarely come into contact with soil fungal communities. Likewise, nesting and roosting behavior may play a role in differentiating lung fungal communities by exposing species to various fungal microbes associated with nesting materials and general nest locations. Future comparative work could evaluate lung mycobiome differences among cavity, cup, and ground-nesting species.

Species breeding or wintering in geographically isolated areas are likely exposed to different fungal communities, which may result in distinct lung mycobiomes. Our data provided limited support for this hypothesis. We found significant fungal community turnover among bird species with different migratory strategies (Supplementary Fig. 7a), and—although subtle—migration distance and hand-wing index were the top predictors of fungal community dissimilarity (Supplementary Fig. 11). A stronger test was provided by the comparison between sandhill crane subspecies that differ strikingly in migratory distance, but that test showed no significant community turnover (Fig. 3). This lack of fungal community structure within a widespread bird species, despite highly divergent breeding ranges and migratory distances corroborates the hypothesis that the process of lung mycobiome assembly may be largely passive, dictated by temporally and spatially variable distributions of fungal microbes.

Skeen et al. found significant year effects on bacterial beta diversity among four species of Catharus thrush⁴⁷. Indeed, we found a strong effect of sampling year on fungal beta diversity, highlighting how temporal change in exposure to fungal cells may be an important driver of fungal lung diversity (Supplementary Fig. 7c) even among animal-symbiont taxa. All crane individuals in our dataset were collected in the same region during the non-breeding season, when they shared the same riparian and agricultural habitats. Lung fungal communities that may be differentiated on the breeding grounds could equilibrate, and therefore become less distinguishable on shared wintering areas. Seasonal equilibration could occur rapidly after arrival in shared wintering areas; a study on broiler chickens showed that it took only three days for their initial mycobiota to be replaced with environmental fungi from dietary sources⁸⁰. Another study found rapid temporal shifts in the bacterial gut microbiome of blackpoll warblers during migration which they attributed to changes in host physiological and energetic demands⁸⁴. Further work comparing samples between species collected at their breeding and non-breeding sites, across different life stages, and at various time points during the annual cycle could offer more insights into how shared and isolated ranges impact lung mycobiomes.

Birds as hosts and vehicles for opportunistic fungal pathogens

One of the goals of the current study was to evaluate the frequency with which fungi that are known to be opportunistic pathogens may occur in the lungs of healthy wild birds. Substantial attention has been given to the potential for birds to serve as vectors for disease^85–87. Although direct transmission of bacterial, fungal, and viral diseases from wild birds to humans may be rare⁸⁸, the role of birds in pathogen transmission is also important for both domestic and wild animals. This is particularly relevant for opportunistic fungal pathogens because many of these organisms typically act as saprotrophs in non-host environments. A few studies have addressed the potential for migratory birds to harbor and disperse pathogenic and nonpathogenic fungi^89,90. The present study has found a wide variety of known opportunistic lineages alongside many putative animal symbionts.

In a previous study of the lung mycobiome, Salazar-Hamm et al. identified sequences from multiple opportunistic fungal pathogens (e.g., Aspergillus fumigatus, as well as species of Candida, Pneumocystis, Blastomyces, and Coccidioides) that were common in small mammals of New Mexico, Arizona, and California⁸. Bird lungs differed from mammalian lungs in that Pneumocystis and Coccidioides were absent. However, as in mammalian lungs, birds shared multiple other opportunistic fungal pathogens including: Aspergillus fumigatus, Blastomyces parvus, Clavispora (Candida) lusitaniae, members of the Meyerozyma (Candida) guilliermondii complex, Filobasidium (Cryptococcus) magnum, and members of Mucorales (Fig. 1, Supplementary Table 3). This commonality may largely reflect the commensal nature of many fungi generally considered to be pathogens^6,91.

Not only did our extensive host sampling using frozen tissues from the MSB facilitate comparisons among the lung fungal communities of diverse bird species, but also allowed us to make detailed intraspecific comparisons between two subspecies of sandhill crane. The smaller-sized lesser sandhill crane can migrate over 3,000 km to breed at high latitudes in northern Canada, Alaska (USA), and Eastern Russia, whereas the overwintering greater sandhill cranes are thought to migrate less than 1,500 km between New Mexico and the northern Rocky Mountains⁵⁰ (Fig. 3b). On their New Mexico wintering grounds, state-managed hunting and foraging at agricultural sites place these large-bodied migratory birds in close contact with human populations. Accordingly, migration could relocate pathogens with subsequent opportunities for transmission to other hosts. The number of fungal zOTUs representing potential animal symbionts, including fungal pathogens, was highly variable among individual cranes (Fig. 3c). These zOTUs represent several fungal groups that are important to human and animal health (Onygenales, Saccharomycetales, Aspergillacaeae, and Cryptococcus-like yeasts) (Fig. 3d).

Dimorphic fungi within Onygenales can evade immune responses of healthy hosts and cause disease. The mammalian lung mycobiome exhibits significant diversity and abundance of members within the order Onygenales (e.g., Blastomyces parvus and Emmonsia-like species)^8,92; however, they appear to be less common in birds, and in this study they were only recovered from cranes (Supplementary Fig. 12). Coccidioidomycosis is endemic to the southwestern U.S. including New Mexico and is caused by the Onygenalean fungus, Coccidioides posadasii⁹³. Although this disease has been identified in birds^94,95, Coccidioides was absent from our dataset. Conversely, sequences from species of Coccidioides were common in mammals from this region⁸.

We sought to evaluate whether migratory birds act as reservoirs for emergent Candida species, as hypothesized for Candida auris³⁹. Although C. auris was not detected here, we did capture many other opportunistic pathogens within Saccharomycetales (e.g., species of Candida, Meyerozyma, Pichia, Clavispora, and Debaryomyces)⁹⁶ (Supplementary Fig. 4). The closest relative to C. auris identified in our dataset was Clavispora (Candida) lusitaniae, an infrequent cause of human candidemia, but a species for which several emerging antifungal-resistant strains have been documented⁹⁷. Candida parapsilosis and Candida tropicalis, the third and fourth most common agents causing candidemia worldwide⁹⁸, were also detected in avian lungs.

In addition to A. fumigatus, other species of Aspergillus (e.g., A. terreus, A. flavus, and A. niger) that can cause allergic asthma to invasive pulmonary aspergillosis²⁵ in humans and animals were detected in avian fungal communities (Supplementary Fig. 3). Because pathogenic or lethal doses must be considerably higher for immunocompetent individuals (~ 10⁶ spores per individual^99–101), the low abundance of these sequences across the majority of bird samples suggests these are present as a result of transient inhalation of spores from the environment and/or low load commensals rather than causing chronic or acute infections^9,102.

Species of the Cryptococcus have been frequently isolated from bird cloacae and feces^29,103,104, suggesting birds may be important reservoirs and dispersers of opportunistic yeast pathogens. While Cryptococcus spp. were not detected in the avian lungs in our study, and the basidiomycete yeasts that were detected are rarely pathogenic^105,106 (Supplementary Fig. 2), evidence of direct transmission³¹ suggests that domestic and wild birds in close contact with human populations have spillover potential.

Climate change is expected to alter host-fungal pathogen dynamics in multiple ways. Casadevall et al. used phylogenetic analyses of thermal tolerance to argue that C. auris evolved as a competitive animal symbiont as a result of selection imposed by a warming climate³⁹. The geographic ranges of several fungal pathogens are shifting or expected to shift in response to climate change^4,107. As the distributions of pathogenic fungi expand, novel overlaps between fungal and avian ranges are expected to occur, thereby increasing opportunities for migratory birds to transport fungi over long distances.

The lung mycobiomes of wild birds are diverse, with substantial variation among individuals and species. Diversity was comparable to that found in small mammals previously sampled in the same region. Host phylogenetic effects on lung-fungal community assembly were evident at shallow phylogenetic scales in both cranes (Antigone) and hawks (Accipiter). Within cranes, the two subspecies sampled at their shared wintering ground exhibited largely shared lung mycobiomes. We found no direct effects of higher-level host phylogeny on fungal communities. These patterns generally support environmental exposure as the main driver of fungal lung communities, with substantial stochastic variation. We identified numerous putative animal symbionts and potential pathogens among the migratory bird lungs we surveyed. Although the targeted known emerging (Candida auris) and endemic (Coccidioides spp.) fungal pathogens were absent among the bird lungs that we surveyed; we did detect close fungal relatives (e.g., Blastomyces parvus, Spiromastix spp., and Candida spp.) that should be monitored for pathogenesis. Our study suggests that host pathogen dynamics among bird species, and the potential for long-distance pathogen transport, is tied to the fungi of local environments to which birds are exposed. Predicted changes in host and fungal species distributions in response to climate warming are expected to result in novel host lung-fungi combinations and altered host-fungi dynamics.

Acknowledgements

This project was made possible utilizing the frozen collections preserved at the Genomic Resources Division in the Museum of Southwestern Biology at the University of New Mexico (UNM) and the support of New Mexico Department of Game and Fish personnel from the Bernardo hunter check station. We thank Matthew Baumann, Selina Bauernfeind, Trinity Casaus, Marialejandra Castro Farías, Sarah Shrum Davis, Cory Griego, Tina Guo, Alesia Hallmark, Paige Handley, Andy Johnson, Ryan Laughlin, Ethan Linck, Diana Macias, Xena Mapel, Aaron Matins, Kristen Oliver, David Tan, and Brenda Villanueva for assistance with sample collection, specimen preparation, and curation. We additionally would like to thank the UNM Department of Biology’s Molecular Biology Facility, supported by the UNM Center for Evolutionary & Theoretical Immunology (CETI) under National Institutes of Health grant P30GM110907 and the UNM Center for Advanced Research Computing (CARC) for analytical support.

Funding

Funding was acquired through student research awards from the New Mexico Ornithological Society (MAM), the American Ornithological Society (CRG), and the UNM Graduate and Professional Student Association (PSS-H). This work was also partially supported by a National Science Foundation PIPP Phase I grant (NSF 2155222, Joseph Cook PI), the UNM Sevilleta Field Station Endowment fund, and the UNM Museum of Southwestern Biology.

Author contributions

Conceptualization: PSS-H, CRG, CCW, and DON; investigation: PSS-H, CRG, MS, EFG, SSB, JLW, OMT, KNM, MB, CCW and DON; formal analysis: PSS-H, CRG, and MAM; data curation: PSS-H and CRG; supervision: CCW and DON; writing—original draft preparation: PSS-H and CRG; writing—review and editing, MAM, MS, KNM, MB, CCW, EFG, SSB, OMT, JLW, and DON; funding acquisition: PSS-H, CRG, MAM, CCW, and DON. All authors have read and agreed to the published version of the manuscript.

Data availability

Raw sequences generated for this project were deposited in GenBank Short Read Archives (BioProject PRJNA1136563). The ribosomal ITS sequences generated for the fungal cultures were deposited in GenBank’s nucleotide database under accessions: PP804255-PP804425 (Supplementary Table 2). All sequence data generated in this study is linked to host voucher specimens from the Museum of Southwestern Biology archived in the Arctos database (https://arctos.database.museum).

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Table 1 is available in the Supplementary Files section.

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The avian lung mycobiome: phylogenetic and ecological drivers of lung-fungal communities and their potential pathogens

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Abstract

Figures

Introduction

Materials and Methods

Sample acquisition

Ecological and behavioral variables of avian hosts

lllumina ITS2 amplicon sequencing and processing

Assignment of trophic mode

Identification of core mycobiome

Alpha and beta diversity

Co-occurrence analyses

Generalized dissimilarity modeling

Cultured lung fungi

Phylogenetic analyses

Results

Sample acquisition

Lung mycobiome community analysis

Alpha and beta diversity

Fungal co-occurrence

Generalized Dissimilarity Modeling

Discussion

Mycobiome diversity reflects exposure to a multitude of environmental fungi

Phylogeny, diet, year, and geography predict lung mycobiome differences

Birds as hosts and vehicles for opportunistic fungal pathogens

Conclusions

Declarations

References

Tables

Additional Declarations

Supplementary Files

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