Human Immunodeficiency Virus (HIV) Genetic Diversity Informs Stage of HIV-1 Infection Among Patients Receiving Antiretroviral Therapy in Botswana
Abstract Background Human immunodeficiency virus (HIV)-1 genetic diversity increases during infection and can help infer the time elapsed since infection. However, the effect of antiretroviral treatment (ART) on the inference remains unknown. Methods Participants with estimated duration of HIV-1 inf...
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| Veröffentlicht in: | The Journal of infectious diseases 2022-04, Vol.225 (8), p.1330-1338 |
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| container_title | The Journal of infectious diseases |
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| creator | Ragonnet-Cronin, Manon Golubchik, Tanya Moyo, Sikhulile Fraser, Christophe Essex, Max Novitsky, Vlad Volz, Erik |
| description | Abstract
Background
Human immunodeficiency virus (HIV)-1 genetic diversity increases during infection and can help infer the time elapsed since infection. However, the effect of antiretroviral treatment (ART) on the inference remains unknown.
Methods
Participants with estimated duration of HIV-1 infection based on repeated testing were sourced from cohorts in Botswana (n = 1944). Full-length HIV genome sequencing was performed from proviral deoxyribonucleic acid. We optimized a machine learning model to classify infections as < or >1 year based on viral genetic diversity, demographic, and clinical data.
Results
The best predictive model included variables for genetic diversity of HIV-1 gag, pol, and env, viral load, age, sex, and ART status. Most participants were on ART. Balanced accuracy was 90.6% (95% confidence interval, 86.7%–94.1%). We tested the algorithm among newly diagnosed participants with or without documented negative HIV tests. Among those without records, those who self-reported a negative HIV test within 1 year previously. There was no difference in classification between those self-reporting a negative HIV test |
| doi_str_mv | 10.1093/infdis/jiab293 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9016439</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><oup_id>10.1093/infdis/jiab293</oup_id><sourcerecordid>2659008820</sourcerecordid><originalsourceid>FETCH-LOGICAL-c452t-9c0194461ef4d829b609182542c539a50ddc5d86436858b796338215cda38ea13</originalsourceid><addsrcrecordid>eNqFkc1uEzEURi0EoqGwZYkssWkX0_pnPGNvkEIpTaRKICjdWo7Hk94oY6e2JyiP0TfGVUIFbFhZuj4-_q4-hN5SckaJ4ufg-w7S-QrMgin-DE2o4G3VNJQ_RxNCGKuoVOoIvUppRQipedO-REe8Jm0raDtBD7NxMB7Ph2H0oXM9WHDe7vAtxDHhk9n89hRfOe8yWPwJti4myDs8932IQ8Lfs1k6HHpcuIo-jp3NEDyeDsEv8VeTiy0n_M1ZB1soo6nPEF2OYQvRrPHNnYtms8Pg8ceQ00_jzWv0ojfr5N4czmP04_PlzcWsuv5yNb-YXle2FixXyhKq6rqhrq87ydSiIYpKJmpmBVdGkK6zopNN2VgKuWhVw7lkVNjOcOkM5cfow967GReD62wJWhLpTYTBxJ0OBvTfNx7u9DJstSK0WFURnBwEMdyPLmU9QLJuvTbehTFpJsrXhJZEBX3_D7oKY_RlPc0aoQiRkpFCne0pG0NK0fVPYSjRj23rfdv60HZ58O7PFZ7w3_UW4HQPhHHzP9kvfWu3Fg</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2659008820</pqid></control><display><type>article</type><title>Human Immunodeficiency Virus (HIV) Genetic Diversity Informs Stage of HIV-1 Infection Among Patients Receiving Antiretroviral Therapy in Botswana</title><source>MEDLINE</source><source>Oxford University Press Journals All Titles (1996-Current)</source><source>Alma/SFX Local Collection</source><creator>Ragonnet-Cronin, Manon ; Golubchik, Tanya ; Moyo, Sikhulile ; Fraser, Christophe ; Essex, Max ; Novitsky, Vlad ; Volz, Erik</creator><creatorcontrib>Ragonnet-Cronin, Manon ; Golubchik, Tanya ; Moyo, Sikhulile ; Fraser, Christophe ; Essex, Max ; Novitsky, Vlad ; Volz, Erik</creatorcontrib><description>Abstract
Background
Human immunodeficiency virus (HIV)-1 genetic diversity increases during infection and can help infer the time elapsed since infection. However, the effect of antiretroviral treatment (ART) on the inference remains unknown.
Methods
Participants with estimated duration of HIV-1 infection based on repeated testing were sourced from cohorts in Botswana (n = 1944). Full-length HIV genome sequencing was performed from proviral deoxyribonucleic acid. We optimized a machine learning model to classify infections as < or >1 year based on viral genetic diversity, demographic, and clinical data.
Results
The best predictive model included variables for genetic diversity of HIV-1 gag, pol, and env, viral load, age, sex, and ART status. Most participants were on ART. Balanced accuracy was 90.6% (95% confidence interval, 86.7%–94.1%). We tested the algorithm among newly diagnosed participants with or without documented negative HIV tests. Among those without records, those who self-reported a negative HIV test within <1 year were more frequently classified as recent than those who reported a test >1 year previously. There was no difference in classification between those self-reporting a negative HIV test <1 year, whether or not they had a record.
Conclusions
These results indicate that recency of HIV-1 infection can be inferred from viral sequence diversity even among patients on suppressive ART.
A single HIV virus is usually transmitted. HIV then replicates, making errors, and over time genetic diversity increases. We found that time since HIV infection can be estimated from within-patient HIV genetic diversity, even when patients are on treatment.</description><identifier>ISSN: 0022-1899</identifier><identifier>EISSN: 1537-6613</identifier><identifier>DOI: 10.1093/infdis/jiab293</identifier><identifier>PMID: 34077517</identifier><language>eng</language><publisher>US: Oxford University Press</publisher><subject>Anti-Retroviral Agents - pharmacology ; Anti-Retroviral Agents - therapeutic use ; Antiretroviral drugs ; Antiretroviral therapy ; Botswana - epidemiology ; Gag protein ; Genetic diversity ; Genetic Variation ; Genomes ; HIV ; HIV Infections - drug therapy ; HIV-1 - genetics ; Human immunodeficiency virus ; Humans ; Immune system ; Infections ; Major and Brief Reports ; Medical tests ; Patients ; Prediction models ; Viral Load</subject><ispartof>The Journal of infectious diseases, 2022-04, Vol.225 (8), p.1330-1338</ispartof><rights>The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. 2021</rights><rights>The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c452t-9c0194461ef4d829b609182542c539a50ddc5d86436858b796338215cda38ea13</citedby><cites>FETCH-LOGICAL-c452t-9c0194461ef4d829b609182542c539a50ddc5d86436858b796338215cda38ea13</cites><orcidid>0000-0002-4879-2209 ; 0000-0001-6268-8937</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,1584,27923,27924</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34077517$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ragonnet-Cronin, Manon</creatorcontrib><creatorcontrib>Golubchik, Tanya</creatorcontrib><creatorcontrib>Moyo, Sikhulile</creatorcontrib><creatorcontrib>Fraser, Christophe</creatorcontrib><creatorcontrib>Essex, Max</creatorcontrib><creatorcontrib>Novitsky, Vlad</creatorcontrib><creatorcontrib>Volz, Erik</creatorcontrib><title>Human Immunodeficiency Virus (HIV) Genetic Diversity Informs Stage of HIV-1 Infection Among Patients Receiving Antiretroviral Therapy in Botswana</title><title>The Journal of infectious diseases</title><addtitle>J Infect Dis</addtitle><description>Abstract
Background
Human immunodeficiency virus (HIV)-1 genetic diversity increases during infection and can help infer the time elapsed since infection. However, the effect of antiretroviral treatment (ART) on the inference remains unknown.
Methods
Participants with estimated duration of HIV-1 infection based on repeated testing were sourced from cohorts in Botswana (n = 1944). Full-length HIV genome sequencing was performed from proviral deoxyribonucleic acid. We optimized a machine learning model to classify infections as < or >1 year based on viral genetic diversity, demographic, and clinical data.
Results
The best predictive model included variables for genetic diversity of HIV-1 gag, pol, and env, viral load, age, sex, and ART status. Most participants were on ART. Balanced accuracy was 90.6% (95% confidence interval, 86.7%–94.1%). We tested the algorithm among newly diagnosed participants with or without documented negative HIV tests. Among those without records, those who self-reported a negative HIV test within <1 year were more frequently classified as recent than those who reported a test >1 year previously. There was no difference in classification between those self-reporting a negative HIV test <1 year, whether or not they had a record.
Conclusions
These results indicate that recency of HIV-1 infection can be inferred from viral sequence diversity even among patients on suppressive ART.
A single HIV virus is usually transmitted. HIV then replicates, making errors, and over time genetic diversity increases. We found that time since HIV infection can be estimated from within-patient HIV genetic diversity, even when patients are on treatment.</description><subject>Anti-Retroviral Agents - pharmacology</subject><subject>Anti-Retroviral Agents - therapeutic use</subject><subject>Antiretroviral drugs</subject><subject>Antiretroviral therapy</subject><subject>Botswana - epidemiology</subject><subject>Gag protein</subject><subject>Genetic diversity</subject><subject>Genetic Variation</subject><subject>Genomes</subject><subject>HIV</subject><subject>HIV Infections - drug therapy</subject><subject>HIV-1 - genetics</subject><subject>Human immunodeficiency virus</subject><subject>Humans</subject><subject>Immune system</subject><subject>Infections</subject><subject>Major and Brief Reports</subject><subject>Medical tests</subject><subject>Patients</subject><subject>Prediction models</subject><subject>Viral Load</subject><issn>0022-1899</issn><issn>1537-6613</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><sourceid>EIF</sourceid><recordid>eNqFkc1uEzEURi0EoqGwZYkssWkX0_pnPGNvkEIpTaRKICjdWo7Hk94oY6e2JyiP0TfGVUIFbFhZuj4-_q4-hN5SckaJ4ufg-w7S-QrMgin-DE2o4G3VNJQ_RxNCGKuoVOoIvUppRQipedO-REe8Jm0raDtBD7NxMB7Ph2H0oXM9WHDe7vAtxDHhk9n89hRfOe8yWPwJti4myDs8932IQ8Lfs1k6HHpcuIo-jp3NEDyeDsEv8VeTiy0n_M1ZB1soo6nPEF2OYQvRrPHNnYtms8Pg8ceQ00_jzWv0ojfr5N4czmP04_PlzcWsuv5yNb-YXle2FixXyhKq6rqhrq87ydSiIYpKJmpmBVdGkK6zopNN2VgKuWhVw7lkVNjOcOkM5cfow967GReD62wJWhLpTYTBxJ0OBvTfNx7u9DJstSK0WFURnBwEMdyPLmU9QLJuvTbehTFpJsrXhJZEBX3_D7oKY_RlPc0aoQiRkpFCne0pG0NK0fVPYSjRj23rfdv60HZ58O7PFZ7w3_UW4HQPhHHzP9kvfWu3Fg</recordid><startdate>20220419</startdate><enddate>20220419</enddate><creator>Ragonnet-Cronin, Manon</creator><creator>Golubchik, Tanya</creator><creator>Moyo, Sikhulile</creator><creator>Fraser, Christophe</creator><creator>Essex, Max</creator><creator>Novitsky, Vlad</creator><creator>Volz, Erik</creator><general>Oxford University Press</general><scope>TOX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-4879-2209</orcidid><orcidid>https://orcid.org/0000-0001-6268-8937</orcidid></search><sort><creationdate>20220419</creationdate><title>Human Immunodeficiency Virus (HIV) Genetic Diversity Informs Stage of HIV-1 Infection Among Patients Receiving Antiretroviral Therapy in Botswana</title><author>Ragonnet-Cronin, Manon ; Golubchik, Tanya ; Moyo, Sikhulile ; Fraser, Christophe ; Essex, Max ; Novitsky, Vlad ; Volz, Erik</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c452t-9c0194461ef4d829b609182542c539a50ddc5d86436858b796338215cda38ea13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Anti-Retroviral Agents - pharmacology</topic><topic>Anti-Retroviral Agents - therapeutic use</topic><topic>Antiretroviral drugs</topic><topic>Antiretroviral therapy</topic><topic>Botswana - epidemiology</topic><topic>Gag protein</topic><topic>Genetic diversity</topic><topic>Genetic Variation</topic><topic>Genomes</topic><topic>HIV</topic><topic>HIV Infections - drug therapy</topic><topic>HIV-1 - genetics</topic><topic>Human immunodeficiency virus</topic><topic>Humans</topic><topic>Immune system</topic><topic>Infections</topic><topic>Major and Brief Reports</topic><topic>Medical tests</topic><topic>Patients</topic><topic>Prediction models</topic><topic>Viral Load</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ragonnet-Cronin, Manon</creatorcontrib><creatorcontrib>Golubchik, Tanya</creatorcontrib><creatorcontrib>Moyo, Sikhulile</creatorcontrib><creatorcontrib>Fraser, Christophe</creatorcontrib><creatorcontrib>Essex, Max</creatorcontrib><creatorcontrib>Novitsky, Vlad</creatorcontrib><creatorcontrib>Volz, Erik</creatorcontrib><collection>Oxford Journals Open Access Collection</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>The Journal of infectious diseases</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ragonnet-Cronin, Manon</au><au>Golubchik, Tanya</au><au>Moyo, Sikhulile</au><au>Fraser, Christophe</au><au>Essex, Max</au><au>Novitsky, Vlad</au><au>Volz, Erik</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Human Immunodeficiency Virus (HIV) Genetic Diversity Informs Stage of HIV-1 Infection Among Patients Receiving Antiretroviral Therapy in Botswana</atitle><jtitle>The Journal of infectious diseases</jtitle><addtitle>J Infect Dis</addtitle><date>2022-04-19</date><risdate>2022</risdate><volume>225</volume><issue>8</issue><spage>1330</spage><epage>1338</epage><pages>1330-1338</pages><issn>0022-1899</issn><eissn>1537-6613</eissn><abstract>Abstract
Background
Human immunodeficiency virus (HIV)-1 genetic diversity increases during infection and can help infer the time elapsed since infection. However, the effect of antiretroviral treatment (ART) on the inference remains unknown.
Methods
Participants with estimated duration of HIV-1 infection based on repeated testing were sourced from cohorts in Botswana (n = 1944). Full-length HIV genome sequencing was performed from proviral deoxyribonucleic acid. We optimized a machine learning model to classify infections as < or >1 year based on viral genetic diversity, demographic, and clinical data.
Results
The best predictive model included variables for genetic diversity of HIV-1 gag, pol, and env, viral load, age, sex, and ART status. Most participants were on ART. Balanced accuracy was 90.6% (95% confidence interval, 86.7%–94.1%). We tested the algorithm among newly diagnosed participants with or without documented negative HIV tests. Among those without records, those who self-reported a negative HIV test within <1 year were more frequently classified as recent than those who reported a test >1 year previously. There was no difference in classification between those self-reporting a negative HIV test <1 year, whether or not they had a record.
Conclusions
These results indicate that recency of HIV-1 infection can be inferred from viral sequence diversity even among patients on suppressive ART.
A single HIV virus is usually transmitted. HIV then replicates, making errors, and over time genetic diversity increases. We found that time since HIV infection can be estimated from within-patient HIV genetic diversity, even when patients are on treatment.</abstract><cop>US</cop><pub>Oxford University Press</pub><pmid>34077517</pmid><doi>10.1093/infdis/jiab293</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-4879-2209</orcidid><orcidid>https://orcid.org/0000-0001-6268-8937</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | The Journal of infectious diseases, 2022-04, Vol.225 (8), p.1330-1338 |
| issn | 0022-1899 1537-6613 |
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| source | MEDLINE; Oxford University Press Journals All Titles (1996-Current); Alma/SFX Local Collection |
| subjects | Anti-Retroviral Agents - pharmacology Anti-Retroviral Agents - therapeutic use Antiretroviral drugs Antiretroviral therapy Botswana - epidemiology Gag protein Genetic diversity Genetic Variation Genomes HIV HIV Infections - drug therapy HIV-1 - genetics Human immunodeficiency virus Humans Immune system Infections Major and Brief Reports Medical tests Patients Prediction models Viral Load |
| title | Human Immunodeficiency Virus (HIV) Genetic Diversity Informs Stage of HIV-1 Infection Among Patients Receiving Antiretroviral Therapy in Botswana |
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