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Record W4412972942 · doi:10.1128/mbio.00564-25

Genomic epidemiology of <i>Histoplasma</i> in Africa

2025· article· en· W4412972942 on OpenAlexaff
Rutendo E. Mapengo, Tsidiso G. Maphanga, Gaston I. Jofre, Jonathan A. Rader, David A. Turissini, Monica Birkhead, Sarah Ama Kwabia, Victoria E. Sepúlveda, María José Buitrago, Marcus de Melo Teixeira, Bridget M. Barker, Alexandre Alanio, Aude Sturny-Leclère, Dea Garcia‐Hermoso, Nelesh P. Govender, Daniel R. Matute

Bibliographic record

VenuemBio · 2025
Typearticle
Languageen
FieldMedicine
TopicFungal Infections and Studies
Canadian institutionsInstitute of Infection and Immunity
FundersNational Institute of Environmental Health SciencesNational Institute of Allergy and Infectious DiseasesNational Institute for Health and Care Research
KeywordsBiologyLineage (genetic)Phylogenetic treeHistoplasmaEvolutionary biologyHistoplasma capsulatumPhylogeneticsPopulationGeneticsHistoplasmosisGeneMedicine

Abstract

fetched live from OpenAlex

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. IMPORTANCE Histoplasma 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.121

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.307
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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