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Identification of system-level features in HIV migration within a host

OBJECTIVE: Identify system-level features in HIV migration within a host across body tissues. Evaluate heterogeneity in the presence and magnitude of these features across hosts. METHOD: Using HIV DNA deep sequencing data generated across multiple tissues from 8 people with HIV, we represent the com...

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Autores principales: Goyal, Ravi, De Gruttola, Victor, Gianella, Sara, Caballero, Gemma, Porrachia, Magali, Ignacio, Caroline, Woodworth, Brendon, Smith, Davey M., Chaillon, Antoine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521982/
https://www.ncbi.nlm.nih.gov/pubmed/37751407
http://dx.doi.org/10.1371/journal.pone.0291367
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author Goyal, Ravi
De Gruttola, Victor
Gianella, Sara
Caballero, Gemma
Porrachia, Magali
Ignacio, Caroline
Woodworth, Brendon
Smith, Davey M.
Chaillon, Antoine
author_facet Goyal, Ravi
De Gruttola, Victor
Gianella, Sara
Caballero, Gemma
Porrachia, Magali
Ignacio, Caroline
Woodworth, Brendon
Smith, Davey M.
Chaillon, Antoine
author_sort Goyal, Ravi
collection PubMed
description OBJECTIVE: Identify system-level features in HIV migration within a host across body tissues. Evaluate heterogeneity in the presence and magnitude of these features across hosts. METHOD: Using HIV DNA deep sequencing data generated across multiple tissues from 8 people with HIV, we represent the complex dependencies of HIV migration among tissues as a network and model these networks using the family of exponential random graph models (ERGMs). ERGMs allow for the statistical assessment of whether network features occur more (or less) frequently in viral migration than might be expected by chance. The analysis investigates five potential features of the viral migration network: (1) bi-directional flow between tissues; (2) preferential migration among tissues in the same biological system; (3) heterogeneity in the level of viral migration related to HIV reservoir size; (4) hierarchical structure of migration; and (5) cyclical migration among several tissues. We calculate the Cohran’s Q statistic to assess heterogeneity in the magnitude of the presence of these features across hosts. The analysis adjusts for missing data on body tissues. RESULTS: We observe strong evidence for bi-directional flow between tissues; migration among tissues in the same biological system; and hierarchical structure of the viral migration network. This analysis shows no evidence for differential level of viral migration with respect to the HIV reservoir size of a tissue. There is evidence that cyclical migration among three tissues occurs less frequent than expected given the amount of viral migration. The analysis also provides evidence for heterogeneity in the magnitude that these features are present across hosts. Adjusting for missing tissue data identifies system-level features within a host as well as heterogeneity in the presence of these features across hosts that are not detected when the analysis only considers the observed data. DISCUSSION: Identification of common features in viral migration may increase the efficiency of HIV cure efforts as it enables targeting specific processes.
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spelling pubmed-105219822023-09-27 Identification of system-level features in HIV migration within a host Goyal, Ravi De Gruttola, Victor Gianella, Sara Caballero, Gemma Porrachia, Magali Ignacio, Caroline Woodworth, Brendon Smith, Davey M. Chaillon, Antoine PLoS One Research Article OBJECTIVE: Identify system-level features in HIV migration within a host across body tissues. Evaluate heterogeneity in the presence and magnitude of these features across hosts. METHOD: Using HIV DNA deep sequencing data generated across multiple tissues from 8 people with HIV, we represent the complex dependencies of HIV migration among tissues as a network and model these networks using the family of exponential random graph models (ERGMs). ERGMs allow for the statistical assessment of whether network features occur more (or less) frequently in viral migration than might be expected by chance. The analysis investigates five potential features of the viral migration network: (1) bi-directional flow between tissues; (2) preferential migration among tissues in the same biological system; (3) heterogeneity in the level of viral migration related to HIV reservoir size; (4) hierarchical structure of migration; and (5) cyclical migration among several tissues. We calculate the Cohran’s Q statistic to assess heterogeneity in the magnitude of the presence of these features across hosts. The analysis adjusts for missing data on body tissues. RESULTS: We observe strong evidence for bi-directional flow between tissues; migration among tissues in the same biological system; and hierarchical structure of the viral migration network. This analysis shows no evidence for differential level of viral migration with respect to the HIV reservoir size of a tissue. There is evidence that cyclical migration among three tissues occurs less frequent than expected given the amount of viral migration. The analysis also provides evidence for heterogeneity in the magnitude that these features are present across hosts. Adjusting for missing tissue data identifies system-level features within a host as well as heterogeneity in the presence of these features across hosts that are not detected when the analysis only considers the observed data. DISCUSSION: Identification of common features in viral migration may increase the efficiency of HIV cure efforts as it enables targeting specific processes. Public Library of Science 2023-09-26 /pmc/articles/PMC10521982/ /pubmed/37751407 http://dx.doi.org/10.1371/journal.pone.0291367 Text en © 2023 Goyal et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Goyal, Ravi
De Gruttola, Victor
Gianella, Sara
Caballero, Gemma
Porrachia, Magali
Ignacio, Caroline
Woodworth, Brendon
Smith, Davey M.
Chaillon, Antoine
Identification of system-level features in HIV migration within a host
title Identification of system-level features in HIV migration within a host
title_full Identification of system-level features in HIV migration within a host
title_fullStr Identification of system-level features in HIV migration within a host
title_full_unstemmed Identification of system-level features in HIV migration within a host
title_short Identification of system-level features in HIV migration within a host
title_sort identification of system-level features in hiv migration within a host
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521982/
https://www.ncbi.nlm.nih.gov/pubmed/37751407
http://dx.doi.org/10.1371/journal.pone.0291367
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