Strain heterogeneity in a non-pathogenic Aspergillus fungus highlights factors associated with virulence

Abstract

Fungal pathogens exhibit extensive strain heterogeneity, including variation in virulence. Whether closely related non-pathogenic species also exhibit strain heterogeneity remains unknown. Here, we comprehensively characterized the pathogenic potentials (i.e., the ability to cause morbidity and mortality) of 16 diverse strains of Aspergillus fischeri, a non-pathogenic close relative of the major pathogen Aspergillus fumigatus. In vitro immune response assays and in vivo virulence assays using a mouse model of pulmonary aspergillosis showed that A. fischeri strains varied widely in their pathogenic potential. Furthermore, pangenome analyses suggest that A. fischeri genomic and phenotypic diversity is even greater. Genomic, transcriptomic, and metabolic profiling identified several pathways and secondary metabolites associated with variation in virulence. Notably, strain virulence was associated with the simultaneous presence of the secondary metabolites hexadehydroastechrome and gliotoxin. We submit that examining the pathogenic potentials of non-pathogenic close relatives is key for understanding the origins of fungal pathogenicity.

Fig. 1. Macrophage response to conidia is highly variable across A. fischeri strains.

A Survival of conidia (asexual spores) from 16 A. fischeri strains (DTO1 - DTO16) as estimated by the number of colony-forming units (CFU) recovered after 24 h (h) of exposure to bone marrow-derived macrophages (BMDMs) at 37 °C. The clinically derived and highly virulent A1163 strain of the major pathogen A. fumigatus was used as a reference. Boxplots show the mean and interquartile range; whiskers show max/min values of data that are within ±1.5× the interquartile range, respectively. B Pro-inflammatory cytokine levels produced by BMDMs after 48 h incubation with conidia at 37 °C. Lipopolysaccharide (LPS) and cell medium were used as positive and negative controls, respectively. Bars represent the mean of eight separate measurements carried out across two replicates. Error bars show standard deviation. Full statistics are presented in Fig. S7 and supplementary data file S7. Images are a combination of original artwork and open-source images (under a Creative Commons license CC BY 4.0) retrieved from PhyloPic (https://www.phylopic.org).

Fig. 2. Murine model of pulmonary aspergillosis reveals wide range of virulence across A. fischeri strains.

Shown are the 15-day survival rates of mice exposed to 16 strains of A. fischeri. The clinically derived and highly virulent A1163 strain of the major pathogen A. fumigatus was used as a reference. Left: Scatter plot comparing the mean number of days until the first recorded death (x-axis) and the cumulative survival rate (y-axis) for each of the strains. Right: Kaplan-Meier curves of the survival rates (y-axis: percent survival, and x-axis: days) for two weakly virulent (DTO2 and DTO6) and two strongly virulent (DTO7 and DTO14) strains of A. fischeri. The two positive controls for DTO7 are overlapping. Images are a combination of original artwork and open-source images (under a Creative Commons license CC BY 4.0) retrieved from PhyloPic (https://www.phylopic.org).

Fig. 3. A. fischeri comprises at least two populations and has an open pangenome.

A Phylogeny of the 16 strains of A. fischeri inferred from 3354 single-copy BUSCO orthologs present in all strains; strains CEA10 and Af293 of A. fumigatus were used as outgroups. Bootstrap values were 100 for all branches and are not shown. B Results of ADMIXTURE population inference analyses of 8763 linkage disequilibrium-pruned single nucleotide variants (SNVs). The optimal number of populations K is either 2 or 3 depending on the parameters chosen. C Relatedness of A. fischeri strains as determined by SNV-based discriminant analysis of principal components (DAPC). Colors represent ADMIXTURE groups when the number of populations K = 4. D Heatmap showing the distribution (presence/absence) of the 4669 accessory genes (genes present in two to 15 strains) and 2426 singleton genes (genes present in only strain) of the pangenome. Bar plot shows the totals per strain. The 9053 core genes (present in every strain) are not shown; only the longest isoforms are being counted. E Random subsampling (without replacement) of the genomes of 16 A. fischeri strains is used to evaluate each subsample for shared gene content. Subsample size was incrementally increased from one to 16 genomes, with 10,000 replicates performed at each step. Results illustrate that the A. fischeri pangenome is open (Heaps’ law regression, γ = 0.858). Dashed line indicates the 9053 core genes of the pangenome of all 16 strains.

Fig. 4. Strain heterogeneity in the transcriptional response to the disease-relevant condition of growth at 37°C.

A Temperature-dependent (30 °C vs 37 °C) differential expression of 5715 genes (x-axis) in each of 15 strains of A. fischeri (y-axis). The differential expression profiles of strains are clustered hierarchically (left) and their clustering is compared to the strain phylogeny using a tanglegram (right). Strains showed very diverse transcriptional profiles in response to growth at elevated temperatures. Heatmap intensities reflect the log2 fold changes of each gene over all replicates between the two treatment conditions. B Distribution of 204 virulence-associated genes (x-axis) in each of 15 A. fischeri strains (y-axis); the strain phylogeny is shown at the far left. Virulence associated genes are shown as being present and expressed (blue), present and not expressed (black), or not present (white). Heatmap (red) reflects the number of virulence-associated genes expressed in each strain. Gene content clustering is compared to the strain phylogeny using a tanglegram (right). All 204 genes were expressed in at least one strain, and more than half (108) were expressed in all strains. Among the strains, DTO13 showed the most (186) virulence-associated genes expressed and DTO8 the least (162). Expression presence was assessed by the presence of at least ten reads that uniquely align to the gene in at least one sample. Note: strain DTO7 was excluded from transcriptome profiling because of poor growth at 37 °C.

Fig. 5. Variation in secondary metabolite production among strains is principally driven by strain diversity.

A Chromatograms of the 16 strains of A. fischeri displayed three distinct profiles at both 30 °C and 37 °C temperatures. Shown are representative chromatograms of each profile. The chromatograms report retention times (x-axis) and signal intensities normalized to the highest value (y-axis) of produced metabolites; different peaks correspond to different metabolites. B Principal component analysis based on retention times and individual peak areas for different metabolites from each of the 16 strains following growth at either 30 °C or 37 °C. Approximately 50% of the variation in the chemistry profiles comes from variation between strains (PC1), and 29% of the variation is due to temperature (PC2). C Temperature-dependent differential expression of transcripts for constituent genes in each of four, virulence-associated biosynthetic gene clusters in 15 A. fischeri strains (DTO7 was excluded because of poor growth at 37 °C). Secondary metabolite (SM) presence/absence at 30 °C and 37 °C is indicated to the right of each heat map. White cells on the heatmap indicate lack of transcriptional activity. HAS hexadehydroastechrome, tryp trypacidin, bisGT bisdethiobis(methylthio)gliotoxin, GT gliotoxin, verr verruculogen, fumA fumitremorgin A, and fumB fumitremorgin B.

Fig. 6. Generalized model of strain heterogeneity and its relevance to the pathogenic potential of fungal strains.

Reference strain model: Conventional, reference-based approach whereby all strains of a fungal species are assigned the phenotypic features from a reference strain. This typological or essentialist approach contracts the phenotypic space of any species around that of the reference strain. In rapidly evolving species, this approach may spuriously associate strain-specific characteristics (e.g., genotype or metabolic profile) with a phenotype-of-interest (e.g., pathogenic potential) that is characterized only in the reference strain. This problem can be further exacerbated when reference stains are chosen based up their extreme phenotypes. Under this view, examination of the reference strains from two closely related species, one of which is a pathogen and one of which is non-pathogenic, reveals a large difference in their pathogenic potential. Pangenome/panphenome model: A more comprehensive, population-level characterization of genetic and phenotypic traits across a diversity of strains. This approach stems from “population thinking”. In the context of pathogenicity, a population-thinking approach will increase emphasis of the reality of genetic and phenotypic variation exhibited by individual strains and can more fully capture the potential overlap (or its absence) in the pathogenic potential of closely related species.