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Record W2945897619 · doi:10.3389/fimmu.2019.01062

Immune Correlates of Disease Progression in Linked HIV-1 Infection

2019· article· en· W2945897619 on OpenAlexafffund
Michael Tuen, Jude Bimela, Andrew N. Banin, Shilei Ding, Gordon W. Harkins, Svenja Weiß, Vincenza Itri, Allison R. Durham, Stephen F. Porcella, Sonal Soni, Luzia Mayr, Josephine Meli, Judith Torimiro, Marcel Tongo, Xiaohong Wang, Xiang‐Peng Kong, Arthur Nádas, Daniel E. Kaufmann, Zabrina L. Brumme, Aubin Nanfack, Thomas C. Quinn, Susan Zolla‐Pazner, Andrew D. Redd, Andrés Finzi, Miroslaw K. Górny, Phillipe N. Nyambi, Ralf Duerr

Bibliographic record

VenueFrontiers in Immunology · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsSt. Paul's HospitalSimon Fraser UniversityUniversité de MontréalMcGill UniversityCentre Hospitalier de l’Université de Montréal
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchMichael Smith Health Research BCFogarty International CenterCanada Excellence Research Chairs, Government of CanadaNHLBI Division of Intramural ResearchYork UniversityNational Institutes of HealthDelta Research and Educational FoundationNational Institute of Allergy and Infectious DiseasesDivision of Intramural Research, National Institute of Allergy and Infectious Diseases
KeywordsAntibody-dependent cell-mediated cytotoxicityVaccine trialImmunologyAntibodyImmune systemBiologyVirologyEpitopeHIV vaccineMonoclonal antibody

Abstract

fetched live from OpenAlex

Genetic and immunologic analyses of epidemiologically-linked HIV transmission enable insights into the impact of immune responses on clinical outcomes. Human vaccine trials and animal studies of HIV-1 infection have suggested immune correlates of protection, however, their role in natural infection in terms of protection from disease progression is mostly unknown. Four HIV-1+ Cameroonian individuals, three of them epidemiologically-linked in a polygamous heterosexual relationship and one incidence-matched case, were studied over 15 years for heterologous and cross-neutralizing antibody responses, antibody binding, IgA/IgG levels, antibody-dependent cellular cytotoxicity (ADCC), viral evolution, Env epitopes, and host factors including HLA-I alleles. Despite viral infection with related strains, the members of the transmission cluster experienced contrasting clinical outcomes including cases of rapid progression and long-term non-progression in the absence of strongly protective HLA-I or CCR5Δ32 alleles. Slower progression and higher CD4/CD8 ratios were associated with enhanced IgG antibody binding to native Env and stronger V1V2 antibody binding responses in the presence of viruses with residue K169 in V2. ADCC against cells expressing native CD4-exposed Env in combination with low Env-specific IgA/IgG ratios correlated with better clinical outcome. This data set highlights for the first time that V1V2-directed antibody responses and ADCC in the presence of low plasma IgA/IgG ratios can correlate with clinical outcome in natural infection. These parameters are comparable to the major correlates of protection, identified post-hoc in the RV144 vaccine trial; thus they may also modulate the rate of clinical progression once infected. The observation that ADCC against cells expressing CD4-exposed Env more strongly correlated with delayed disease progression compared with ADCC against cells expressing wild-type Env suggests a vital role for antibodies targeting open Env conformations in protective antibody-mediated immune responses in natural infection. The findings illustrate the potential of immune correlate analysis in natural infection to guide vaccine development.

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)0.002
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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.244
Teacher spread0.238 · 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

Citations19
Published2019
Admission routes2
Has abstractyes

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