Genomic epidemiology of Histoplasma in Africa

Abstract

Histoplasmosis, the disease caused by thermally dimorphic fungi in the genus Histoplasma, is usually associated with pulmonary involvement in healthy individuals and a disseminated syndrome in immunocompromised patients. Among African patients, lung disease is less commonly reported than skin, lymph node, or bone disease. Because different species or strains may be associated with different disease presentations and outcomes, understanding genetic and phenotypic variation in the genus Histoplasma is important for accurate diagnosis and treatment. We sequenced the genomes of 36 Histoplasma isolates from Africa and used population genomics to measure the genetic variation of the genus on the African continent and to compare the genetic diversity of these isolates to the previously described Indian and American phylogenetic species. We found that strains from Africa belong to genetic lineages that are differentiated enough to be considered a phylogenetic species. The first, the Africa lineage, is consistent with a previously described species (Histoplasma capsulatum duboisii) which includes clinical cases more frequently associated with extrapulmonary manifestations than cases caused by other lineages. While there is some evidence of gene flow between Histoplasma lineages, it has not precluded divergence. A second lineage corresponding to Histoplasma capsulatum farciminosum (Hcf) includes all the isolates from equine samples. We identified loci under selection in these two better-sampled lineages and found loci that have undergone parallel positive selection. A single African isolate resembles a South American lineage. Finally, we measured the potential range expansion of the disease using climatic projections, highlighting the need to implement surveillance to monitor phylogenetic species of Histoplasma across Africa.IMPORTANCEHistoplasma fungi, which cause histoplasmosis, are widespread and considered high-priority pathogens. While researchers have identified multiple genetically distinct lineages worldwide, little is known about Histoplasma diversity in Africa due to minimal sampling and inadequate diagnostics. Our study addresses this gap using population genomics to analyze stored African isolates. We identified three distinct groups: one of them is endemic to Africa and aligns with Histoplasma capsulatum duboisii, a lineage linked to skin-involved infections, while another lineage (Hcf) matches Histoplasma capsulatum farciminosum, associated with equine lymphangitis. Additionally, one African isolate closely resembles a South American lineage (mz5-like). These three lineages are genetically unique enough to be considered separate species. By integrating phylogenetics, clinical data, and environmental modeling, we provide the most comprehensive genetic assessment of African Histoplasma to date. This work not only enhances our understanding of an overlooked pathogen but also offers a model for studying other neglected fungi with global health implications.

Keywords: Africa; Histoplasma; introgression; selection; speciation.

Fig 1

The partition of genetic diversity in Histoplasma shows that the Africa and Hcf lineages are differentiated monophyletic groups. (A) BUSCO tree showing the genealogical relationships between the different lineages of Histoplasma. Isolates collected in Africa belong to two groups, Africa and Hcf, both marked with black bars. T-3-1, a North American isolate, is marked with a blue circle. All the samples in the Africa lineage are newly sequenced with the exception of the two tips labeled with a red pentagon (Hc_duboisii-A and Hc_duboisii-B). The only isolate from Africa that clustered outside of the Africa and Hcf lineages clustered with mz5-like (a species described in reference 34) and is labeled with a yellow triangle. Numbers in the nodes show the bootstrap and concordance factors for each node. (B) The first two components (PCs) of a principal component analysis show differentiation of the African lineages with the rest of genetic clusters in Histoplasma and explain over 40% of the genetic variance. (C) PC3 and PC4 explain 29% of the variance. (D) Scree plot showing the contributions of each PC. Light tan bars show the value of the eigenvalues. Brown bars show the expected eigenvalues in a broken stick model, and the red dashed line shows the expected contributions of the eigenvalues in a uniform distribution model.

Fig 2

Genetic variation and differentiation between Histoplasma spp. (A) Nucleotide diversity analysis of seven Histoplasma spp. In all cases, Dxy is larger than pairwise π. (B) π along the genome in Hcd. (C) π along the genome in H. capsulatum ss. (D) Dxy between Africa and H. capsulatum sensu stricto shows extensive genome-wide differentiation between the two clades. Figure S4 shows π along the genome in Hcf, and Fig. S5 shows Dxy between Africa and Hcf.

Fig 3

Morphological characteristics of the Histoplasma Africa samples. We show the morphology of isolate SA1704, but all other isolates show similar characteristics. (A) Morphology of a 3-week-old Africa isolate growing on a plate of Sabouraud dextrose agar at 25°C. (B) Light microscope slide stained with lactophenol blue showing the mycelial stage of Histoplasma Africa. (C) A similarly prepared slide for T-3-1 which belongs to H. ohiense. (D) Culture of a 2-week-old Africa growing on brain heart infusion agar at 35°C. (E) Slide of electron microscopy image of a 2-week-old Africa (SA0297) growing on Sabouraud dextrose agar at 25°C showing the microconidia of mold form (magnification = ×2,000, extra high tension = 5.00 kV, working distance = 9.2 mm, Signal A = SE2, bar 10 µm). (F) Electron microscopy image of a 2-week-old Hcf (SA20VMK) growing on Sabouraud dextrose agar at 25°C showing a tuberculate macroconidium (magnification = x2,000, EHT = 5.00 kV, WD = 9.2 mm, Signal A = SE2, bar 10 µm).

Fig 4

Comparative demography between Africa and H. capsulatum sensu stricto. (A) PCA shows the existence of genetic clusters within Africa consistent with population structure not consistent with geographical isolation (see text). Each color represents a cluster. (B) Two-species allele frequency spectrum in the data of the two species and in simulated data. Each bin shows the abundance of a combination of allele frequencies in the two species of Histoplasma. Abundance is marked by the color scheme in the legend. The two rightmost panels show the residuals between the observed data and the best-fitting simulated data in GADMA as the difference between the two data sets in the allele frequencies per bin and as a histogram of the deviation between models. (C) Both Africa and H. capsulatum sensu stricto have experienced an effective population size expansion in the last ~4 to 5 million generations. The population expansion is more noticeable in H. capsulatum sensu stricto.

Fig 5

Parallel instances of selection in Africa and Hcf. (A) Population branch excess (PBE) along the genome in Africa lineage. We calculated PBE for non-overlapping windows containing 100 SNPs. Windows were 15 kb long on average. (B) Gene Ontology analysis for the upper fifth percentile most differentiated genes in Africa. (C) PBE along the genome in Hcf. (D) Gene Ontology analysis for the upper fifth percentile most differentiated genes in Hcf.

Fig 6

Evidence of introgression in the African species of Histoplasma. Fbranch (f_b), based on Patterson’s D-statistic, shows excess sharing of derived alleles between the taxa on the x-axis and the branches on the y-axis. f_b represents the strength of the signal for introgression but is not a percentage. The color of a tile indicates the support for an introgression event between each given pair using f_b as a metric. White tiles show no evidence for introgression (f_b = 0); gray tiles show pairs for which the test could not be performed. Dotted branches symbolize internal branches.

Fig 7

Bioclimatic niche characterization of African histoplasmosis. (A) The national caseload among African countries is weakly predicted (P = 0.067) by the climatic breadth of the country. National climatic niche distributions were modeled by randomly sampling 1,000 geographical points within each country’s borders and extracting data for the points from the WorldClim database (2.5 arcminute resolution). We modeled climatic breadth in each country using multivariate hypervolumes, including temperature and precipitation, along with their diel and seasonal fluctuations. (B) There was broad overlap among the countries with the largest and smallest caseloads on all climatic niche axes. Gray distributions depict the pooled climatic niche across all countries for which we have case data. The brown distributions show the climate niche of the country with the lowest caseload, contrasting with the blue distributions that depict the climatic breadth of the country with the highest caseload.

Fig 8

Modeling current and future climatic suitability of African histoplasmosis cases. (A) Bioclimatic niche modeling suggests that climatic conditions are suitable for the presence of histoplasmosis across most of the African continent, with some regions (in yellow) of high climatic suitability (>0.6 in the MaxENT model, 6.5% of land area). The majority of land area in Africa is moderately suitable to support Histoplasma (between 0.4 and 0.6, 83.1%), with less land area being of low suitability (<0.4, 15%). (B) Projections of climatic suitability across three models of climatic warming show that climate will remain broadly suitable across Africa, but that the geographical distribution of climatic suitability will shift across the continent. Increases in climatic suitability are shown in red; decreases are shown in blue. Climate suitability models are shown in Fig. S8.