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Record W4414512471 · doi:10.1128/msystems.00751-25

Global proteomic analysis of <i>Cryptococcus neoformans</i> clinical strains reveals significant differences between latent and lethal infection

2025· article· en· W4414512471 on OpenAlexaff
Jovany J. Betancourt, Jason A. McAlister, Jesenia M. Perez, David B. Meya, Stefani N. Thomas, Jennifer Geddes‐McAlister, Kirsten Nielsen

Bibliographic record

VenuemSystems · 2025
Typearticle
Languageen
FieldMedicine
TopicFungal Infections and Studies
Canadian institutionsUniversity of Guelph
FundersNational Institute of Neurological Disorders and StrokeNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesNational Cancer Institute
KeywordsImmune systemImmunosuppressionPathogenDiseaseProteomicsImmunityInfectious disease (medical specialty)Virulence

Abstract

fetched live from OpenAlex

ABSTRACT To predict the outcomes of disseminated fungal disease, a deeper understanding of host-pathogen interactions at the site of infection is needed to identify targets for clinical intervention and diagnostic development. Cryptococcus neoformans is the causative agent of cryptococcosis, the largest infectious killer of individuals living with HIV. Cryptococcal infection begins in the lungs, and loss of immunological control leads to disseminated central nervous system disease and death. Using advanced mass spectrometry-based proteomic techniques, in vivo infection models, and patient-derived clinical strains, we explored the proteomic profiles of C. neoformans infections related to differences in strain virulence. Our findings reveal that non-lethal latent infection produces a proteomic response that differs significantly from the response caused by lethal infections, and that the proteomic profiles of typical and hypervirulent infections are markedly similar despite differences in time-to-death. Overall, the mouse pulmonary proteomic response in latent infection is defined by enrichment of proteins and pathways involved in extracellular matrix organization, cell adhesion, and structural changes, while the lethal infection is dominated by host defense, translation, and metabolic processes. These results provide clinically relevant information on how infections caused by different Cryptococcus strains may produce significantly different outcomes. We also identified abundant fungal proteins that could be future drug targets in latent and lethal cryptococcal infection. IMPORTANCE Cryptococcus neoformans is a fungal pathogen that causes substantial morbidity and mortality in immunocompromised individuals. The initial infection begins in the lungs after exposure to inhaled spores, after which local immune cells respond by either killing or containing the fungal cells. Immunosuppression weakens the immune system and allows fungal cells in the lungs to escape through the circulatory system and invade the central nervous system and cause fatal disease. However, differences between fungal strains influence the severity of disease manifestation. Our group has previously described genetic differences that contribute to strain-specific disease manifestations. In this study, we expanded our analysis to investigate the proteomic differences between strains of C. neoformans to identify candidate proteins and pathways that contribute to disease manifestation. We found that latent infection differs significantly from lethal disease from both the host and pathogen proteomic perspectives and identified several fungal protein targets for future study.

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)0.001
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 score0.004
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.049
GPT teacher head0.359
Teacher spread0.309 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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