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Record W2767097191 · doi:10.1128/jvi.01128-17

Weaker HLA Footprints on HIV in the Unique and Highly Genetically Admixed Host Population of Mexico

2017· article· en· W2767097191 on OpenAlexafffundabout
Maribel Soto-Nava, Santiago Ávila‐Ríos, Humberto Valenzuela-Ponce, Claudia García‐Morales, Jonathan M. Carlson, Daniela Tapia‐Trejo, Daniela Garrido-Rodríguez, Selma Alva-Hernández, Thalía García-Téllez, Akio Murakami-Ogasawara, S. Mallal, Mina John, Mark A. Brockman, Chanson J. Brumme, Zabrina L. Brumme, Gustavo Reyes‐Terán

Bibliographic record

VenueJournal of Virology · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsAIDS VancouverSimon Fraser University
FundersCanadian Institutes of Health ResearchConsejo Nacional de Ciencia y Tecnología
KeywordsHuman leukocyte antigenBiologyPopulationAlleleGeneticsVirologyImmunologyDemographyAntigenGene

Abstract

fetched live from OpenAlex

ABSTRACT HIV circumvents HLA class I-restricted CD8 + T-cell responses through selection of escape mutations that leave characteristic mutational “footprints,” also known as HLA-associated polymorphisms (HAPs), on HIV sequences at the population level. While many HLA footprints are universal across HIV subtypes and human populations, others can be region specific as a result of the unique immunogenetic background of each host population. Using a published probabilistic phylogenetically informed model, we compared HAPs in HIV Gag and Pol (PR-RT) in 1,612 subtype B-infected, antiretroviral treatment-naive individuals from Mexico and 1,641 individuals from Canada/United States. A total of 252 HLA class I allele subtypes were represented, including 140 observed in both cohorts, 67 unique to Mexico, and 45 unique to Canada/United States. At the predefined statistical threshold of a q value of <0.2, 358 HAPs (201 in Gag, 157 in PR-RT) were identified in Mexico, while 905 (534 in Gag and 371 in PR-RT) were identified in Canada/United States. HAPs identified in Mexico included both canonical HLA-associated escape pathways and novel associations, in particular with HLA alleles enriched in Amerindian and mestizo populations. Remarkably, HLA footprints on HIV in Mexico were not only fewer but also, on average, significantly weaker than those in Canada/United States, although some exceptions were noted. Moreover, exploratory analyses suggested that the weaker HLA footprint on HIV in Mexico may be due, at least in part, to weaker and/or less reproducible HLA-mediated immune pressures on HIV in this population. The implications of these differences for natural and vaccine-induced anti-HIV immunity merit further investigation. IMPORTANCE HLA footprints on HIV identify viral regions under intense and consistent pressure by HLA-restricted immune responses and the common mutational pathways that HIV uses to evade them. In particular, HLA footprints can identify novel immunogenic regions and/or epitopes targeted by understudied HLA alleles; moreover, comparative analyses across immunogenetically distinct populations can illuminate the extent to which HIV immunogenic regions and escape pathways are shared versus population-specific pathways, information which can in turn inform the design of universal or geographically tailored HIV vaccines. We compared HLA-associated footprints on HIV in two immunogenetically distinct North American populations, those of Mexico and Canada/United States. We identify both shared and population-specific pathways of HIV adaptation but also make the surprising observation that HLA footprints on HIV in Mexico overall are fewer and weaker than those in Canada/United States, raising the possibility that HLA-restricted antiviral immune responses in Mexico are weaker, and/or escape pathways somewhat less consistent, than those in other populations.

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)0.001
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.294
Teacher spread0.275 · 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

Citations10
Published2017
Admission routes3
Has abstractyes

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