Evidence map›Paper›PMID 38117834›Full record

ArticlePLoS pathogens2023

Using viral sequence diversity to estimate time of HIV infection in infants.

Magdalena L Russell, Carolyn S Fish, Sara Drescher, Noah A J Cassidy, Pritha Chanana, Sarah Benki-Nugent, Jennifer Slyker, Dorothy Mbori-Ngacha, Rose Bosire, Barbra Richardson and 6 more

Open access · goldAbstract read
In one paragraph

Article in PLoS pathogens, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 38% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors at 5 institutions in 2 countries.

Magdalena L RussellComputational Biology Program, Fred Hutch Cancer Center, Seattle, Washington, United States of America.ORCID 0000-0002-1068-1968
Carolyn S FishDivision of Human Biology, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America.
Sara DrescherUniversity of Washington Medical Center, Seattle, Washington, United States of America.
Noah A J CassidyDivision of Human Biology, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America.
Pritha ChananaBioinformatics Shared Resource, Fred Hutch Cancer Center, Seattle, Washington, United States of America.
Sarah Benki-NugentDepartment of Global Health, University of Washington, Seattle, Washington, United States of America.
Jennifer SlykerDepartment of Global Health, University of Washington, Seattle, Washington, United States of America.
Dorothy Mbori-NgachaDepartment of Pediatrics and Child Health, University of Nairobi, Nairobi, Kenya.
Rose BosireCentre for Clinical Research, Kenya Medical Research Institute, Nairobi, Kenya.
Barbra RichardsonDepartment of Global Health, University of Washington, Seattle, Washington, United States of America.
Dalton WamalwaDepartment of Global Health, University of Washington, Seattle, Washington, United States of America.
Elizabeth Maleche-ObimboDepartment of Pediatrics and Child Health, University of Nairobi, Nairobi, Kenya.
Julie OverbaughDivision of Human Biology, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America.
Grace John-StewartDepartment of Global Health, University of Washington, Seattle, Washington, United States of America.
Frederick A MatsenComputational Biology Program, Fred Hutch Cancer Center, Seattle, Washington, United States of America.ORCID 0000-0003-0607-6025
Dara A LehmanDivision of Human Biology, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America.ORCID 0000-0002-0372-0631
University of Washington · USFred Hutch Cancer Center · USUniversity of Nairobi · KEHoward Hughes Medical Institute · USKenya Medical Research Institute · KE

Funding

Translational Bioimaging Core Shared ResourceP30CA015704 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Eric Collisson · 1985 to 2026
$296.4M
Urgent Versus Post-Stabilization ART in HIV+ Children with Severe Co-infectionsR01HD023412 · NICHD · UNIVERSITY OF WASHINGTON · PI Grace C. John-Stewart, DALTON CHEKOKO WAMALWA · 1987 to 2026
$12.2M
THE ROLE OF ANTIBODIES IN MOTHER TO CHILD HIV TRANSMISSIONR01AI076105 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI OVERBAUGH, JULIE M. · 2008 to 2023
$8.8M
Infant immune mechanisms of HIV reservoir size and decayR01HD094718 · NICHD · UNIVERSITY OF WASHINGTON · PI JOHN-STEWART, GRACE C., LEHMAN, DARA ANN · 2017 to 2021
$3.6M
Blending deep learning with probabilistic mechanistic models to predict and understand the evolution and function of adaptive immune receptorsR01AI146028 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI MATSEN, FREDERICK ALBERT · 2019 to 2024
$3.4M
Health-Professional Education Partnership Initiative (HEPI) - KenyaR25TW011212 · FIC · UNIVERSITY OF NAIROBI · PI NDUATI, RUTH WANJIRU, WAMALWA, DALTON CHEKOKO · 2018 to 2022
$3.2M
High-Performance Compute Cluster for Comprehensive Cancer and Infectious Diseases ResearchS10OD028685 · OD · FRED HUTCHINSON CANCER RESEARCH CENTER · PI BRADLEY, PHILIP · 2020 to 2020
$2.0M
FIC NIH HHS R25 TW011212NCI NIH HHS P30 CA015704NIAID NIH HHS R01 AI076105NIAID NIH HHS R01 AI146028NICHD NIH HHS R01 HD023412NICHD NIH HHS R01 HD094718NIH HHS S10 OD028685
6 · The paper itself

Abstract

Age at HIV acquisition may influence viral pathogenesis in infants, and yet infection timing (i.e. date of infection) is not always known. Adult studies have estimated infection timing using rates of HIV RNA diversification, however, it is unknown whether adult-trained models can provide accurate predictions when used for infants due to possible differences in viral dynamics. While rates of viral diversification have been well defined for adults, there are limited data characterizing these dynamics for infants. Here, we performed Illumina sequencing of gag and pol using longitudinal plasma samples from 22 Kenyan infants with well-characterized infection timing. We used these data to characterize viral diversity changes over time by designing an infant-trained Bayesian hierarchical regression model that predicts time since infection using viral diversity. We show that diversity accumulates with time for most infants (median rate within pol = 0.00079 diversity/month), and diversity accumulates much faster than in adults (compare previously-reported adult rate within pol = 0.00024 diversity/month [1]). We find that the infant rate of viral diversification varies by individual, gene region, and relative timing of infection, but not by set-point viral load or rate of CD4+ T cell decline. We compare the predictive performance of this infant-trained Bayesian hierarchical regression model with simple linear regression models trained using the same infant data, as well as existing adult-trained models [1]. Using an independent dataset from an additional 15 infants with frequent HIV testing to define infection timing, we demonstrate that infant-trained models more accurately estimate time since infection than existing adult-trained models. This work will be useful for timing HIV acquisition for infants with unknown infection timing and for refining our understanding of how viral diversity accumulates in infants, both of which may have broad implications for the future development of infant-specific therapeutic and preventive interventions.

Indexed as

HIV InfectionsAdultBayes TheoremCD4-Positive T-LymphocytesHumansInfantKenyaViral Load

Identifiers

PMID38117834
PMCPMC10732395
OpenAlexW4389990396

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.