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Record W4404900092 · doi:10.3389/fimmu.2024.1483346

Spatial transcriptomics and in situ immune cell profiling of the host ectocervical landscape of HIV infected Kenyan sex working women

2024· article· en· W4404900092 on OpenAlexaff
Mathias Franzén Boger, Vilde Kaldhusdal, Anna Pascual Reguant, Sandy Kroh, Ralf Uecker, Adam Burgener, Julie Lajoie, Kenneth Omollo, Joshua Kimani, Keith R. Fowke, Anja E. Hauser, Annelie Tjernlund, Kristina Broliden

Bibliographic record

VenueFrontiers in Immunology · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsKenyaProfiling (computer programming)TranscriptomeImmune systemHuman immunodeficiency virus (HIV)Host (biology)In situImmunologyBiologyMedicineComputational biologyEcologyGeographyComputer scienceGeneGene expressionGenetics

Abstract

fetched live from OpenAlex

Introduction Chronic immune activation is a hallmark of human immunodeficiency virus (HIV) infection that significantly impacts disease pathogenesis. However, in-depth studies characterizing the immunological landscape of the ectocervix during chronic HIV infection remain scarce despite the importance of this tissue site for HIV transmission. Methods Ectocervical tissue samples were obtained from antiretroviral-naïve HIV-seropositive and -seronegative Kenyan female sex workers. These samples were assessed by spatial transcriptomics and Gene Set Enrichment Analysis. We further performed multi-epitope ligand cartography (MELC) using an in situ staining panel that included 17 markers of primarily T cell–mediated immune responses. Results Spatial transcriptomics revealed tissue-wide immune activation encompassing immune responses associated with chronic HIV infection. First, both the epithelial and submucosal compartments showed diverse but significant upregulation of humoral immune responses, as indicated by the expression of several antibody-related genes. Second, an antiviral state–associated cellular immunity was also observed in the HIV-seropositive group, characterized by upregulation of genes involved in interferon signaling across the mucosal tissue and a more spatially restricted mucosal expression of genes related to T cell activity and effector functions relative to the HIV-seronegative group. Additionally, HIV associated structural alterations were evident within both compartments. Downregulated genes across the epithelium were mainly linked to epithelial integrity, with the outer layer involved in terminal differentiation and the inner layer associated with epithelial structure. MELC analysis further revealed a significantly increased ectocervical leukocyte population in HIV-seropositive participants, primarily driven by an increase in CD8+ T cells while the CD4+ T cell population remained stable. Consistent with our spatial transcriptomics data, T cells from HIV-seropositive participants showed an increased effector phenotype, defined by elevated expression of various granzymes. Conclusion By combining spatial transcriptomics and MELC, we identified significant HIV-associated cervical immune activity driven by induction of both T and B cell activity, together with a general antiviral state characterized by sustained interferon induction. These findings underscore that chronic HIV infection is associated with an altered ectocervical mucosal immune landscape years after primary infection. This sheds light on HIV pathogenesis at distant local sites and complements current knowledge on HIV-associated systemic immune activation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

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