Identification of system-level features in HIV migration within a host
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. Using HIV DNA deep sequencing data generated across multiple tissues from 8 people with HIV, we represent the complex dependencies o...
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| Veröffentlicht in: | PloS one 2023-09, Vol.18 (9), p.e0291367-e0291367 |
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| creator | Goyal, Ravi De Gruttola, Victor Gianella, Sara Caballero, Gemma Porrachia, Magali Ignacio, Caroline Woodworth, Brendon Smith, Davey M Chaillon, Antoine |
| description | 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.
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.
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.
Identification of common features in viral migration may increase the efficiency of HIV cure efforts as it enables targeting specific processes. |
| doi_str_mv | 10.1371/journal.pone.0291367 |
| format | Article |
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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.
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.
Identification of common features in viral migration may increase the efficiency of HIV cure efforts as it enables targeting specific processes.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0291367</identifier><identifier>PMID: 37751407</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Antiretroviral drugs ; Biology and Life Sciences ; Care and treatment ; Complications and side effects ; Computer and Information Sciences ; Confidence intervals ; Disease transmission ; DNA sequencing ; Earth Sciences ; Estimates ; Heterogeneity ; Highly active antiretroviral therapy ; HIV ; HIV (Viruses) ; HIV Infections ; Human immunodeficiency virus ; Humans ; Lewis Blood Group Antigens ; Medicine and Health Sciences ; Missing data ; Missing persons ; Nervous system ; Patient outcomes ; Social Sciences ; Statistical analysis ; Tissues</subject><ispartof>PloS one, 2023-09, Vol.18 (9), p.e0291367-e0291367</ispartof><rights>Copyright: © 2023 Goyal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Goyal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 Goyal et al 2023 Goyal et al</rights><rights>2023 Goyal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c642t-283a9653352ce18113c812f47d94511ce62432aace9840e995c6a4ac77b9f5fd3</cites><orcidid>0000-0002-0358-2435</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521982/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521982/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37751407$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Ahemad, Nafees</contributor><creatorcontrib>Goyal, Ravi</creatorcontrib><creatorcontrib>De Gruttola, Victor</creatorcontrib><creatorcontrib>Gianella, Sara</creatorcontrib><creatorcontrib>Caballero, Gemma</creatorcontrib><creatorcontrib>Porrachia, Magali</creatorcontrib><creatorcontrib>Ignacio, Caroline</creatorcontrib><creatorcontrib>Woodworth, Brendon</creatorcontrib><creatorcontrib>Smith, Davey M</creatorcontrib><creatorcontrib>Chaillon, Antoine</creatorcontrib><title>Identification of system-level features in HIV migration within a host</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>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.
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.
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.
Identification of common features in viral migration may increase the efficiency of HIV cure efforts as it enables targeting specific processes.</description><subject>Analysis</subject><subject>Antiretroviral drugs</subject><subject>Biology and Life Sciences</subject><subject>Care and treatment</subject><subject>Complications and side effects</subject><subject>Computer and Information Sciences</subject><subject>Confidence intervals</subject><subject>Disease transmission</subject><subject>DNA sequencing</subject><subject>Earth Sciences</subject><subject>Estimates</subject><subject>Heterogeneity</subject><subject>Highly active antiretroviral therapy</subject><subject>HIV</subject><subject>HIV (Viruses)</subject><subject>HIV Infections</subject><subject>Human immunodeficiency virus</subject><subject>Humans</subject><subject>Lewis Blood Group Antigens</subject><subject>Medicine and Health Sciences</subject><subject>Missing data</subject><subject>Missing persons</subject><subject>Nervous 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one</jtitle><addtitle>PLoS One</addtitle><date>2023-09-26</date><risdate>2023</risdate><volume>18</volume><issue>9</issue><spage>e0291367</spage><epage>e0291367</epage><pages>e0291367-e0291367</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>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.
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.
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.
Identification of common features in viral migration may increase the efficiency of HIV cure efforts as it enables targeting specific processes.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>37751407</pmid><doi>10.1371/journal.pone.0291367</doi><tpages>e0291367</tpages><orcidid>https://orcid.org/0000-0002-0358-2435</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Analysis Antiretroviral drugs Biology and Life Sciences Care and treatment Complications and side effects Computer and Information Sciences Confidence intervals Disease transmission DNA sequencing Earth Sciences Estimates Heterogeneity Highly active antiretroviral therapy HIV HIV (Viruses) HIV Infections Human immunodeficiency virus Humans Lewis Blood Group Antigens Medicine and Health Sciences Missing data Missing persons Nervous system Patient outcomes Social Sciences Statistical analysis Tissues |
| title | Identification of system-level features in HIV migration within a host |
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