Evaluation of sequence ambiguities of the HIV-1 pol gene as a method to identify recent HIV-1 infection in transmitted drug resistance surveys
•We analyze 691 recent and chronic HIV pol sequences for proportion of mixed bases.•Significant difference in proportion is seen between recent and chronic infection.•A cutoff of <0.47% mixed bases to identify recent (<1year) infection is established.•We apply it to 36 TDR surveys and 71% of i...
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Published in | Infection, genetics and evolution Vol. 18; pp. 125 - 131 |
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Main Authors | , , , , , , , , , , , |
Format | Journal Article |
Language | English |
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Netherlands
Elsevier B.V
01.08.2013
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Abstract | •We analyze 691 recent and chronic HIV pol sequences for proportion of mixed bases.•Significant difference in proportion is seen between recent and chronic infection.•A cutoff of <0.47% mixed bases to identify recent (<1year) infection is established.•We apply it to 36 TDR surveys and 71% of infections are classified as recent.•The approach may provide additional information on a population level.
Identification of recent HIV infection within populations is a public health priority for accurate estimation of HIV incidence rates and transmitted drug resistance at population level. Determining HIV incidence rates by prospective follow-up of HIV-uninfected individuals is challenging and serological assays have important limitations. HIV diversity within an infected host increases with duration of infection. We explore a simple bioinformatics approach to assess viral diversity by determining the percentage of ambiguous base calls in sequences derived from standard genotyping of HIV-1 protease and reverse transcriptase. Sequences from 691 recently infected (⩽1year) and chronically infected (>1year) individuals from Sweden, Vietnam and Ethiopia were analyzed for ambiguity. A significant difference (p<0.0001) in the proportion of ambiguous bases was observed between sequences from individuals with recent and chronic infection in both HIV-1 subtype B and non-B infection, consistent with previous studies. In our analysis, a cutoff of <0.47% ambiguous base calls identified recent infection with a sensitivity and specificity of 88.8% and 74.6% respectively. 1,728 protease and reverse transcriptase sequences from 36 surveys of transmitted HIV drug resistance performed following World Health Organization guidance were analyzed for ambiguity. The 0.47% ambiguity cutoff was applied and survey sequences were classified as likely derived from recently or chronically infected individuals. 71% of patients were classified as likely to have been infected within one year of genotyping but results varied considerably amongst surveys. This bioinformatics approach may provide supporting population-level information to identify recent infection but its application is limited by infection with more than one viral variant, decreasing viral diversity in advanced disease and technical aspects of population based sequencing. Standardization of sequencing techniques and base calling and the addition of other parameters such as CD4 cell count may address some of the technical limitations and increase the usefulness of the approach. |
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AbstractList | Identification of recent HIV infection within populations is a public health priority for accurate estimation of HIV incidence rates and transmitted drug resistance at population level. Determining HIV incidence rates by prospective follow-up of HIV-uninfected individuals is challenging and serological assays have important limitations. HIV diversity within an infected host increases with duration of infection. We explore a simple bioinformatics approach to assess viral diversity by determining the percentage of ambiguous base calls in sequences derived from standard genotyping of HIV-1 protease and reverse transcriptase. Sequences from 691 recently infected (≤1 year) and chronically infected (>1 year) individuals from Sweden, Vietnam and Ethiopia were analyzed for ambiguity. A significant difference (p<0.0001) in the proportion of ambiguous bases was observed between sequences from individuals with recent and chronic infection in both HIV-1 subtype B and non-B infection, consistent with previous studies. In our analysis, a cutoff of <0.47% ambiguous base calls identified recent infection with a sensitivity and specificity of 88.8% and 74.6% respectively. 1,728 protease and reverse transcriptase sequences from 36 surveys of transmitted HIV drug resistance performed following World Health Organization guidance were analyzed for ambiguity. The 0.47% ambiguity cutoff was applied and survey sequences were classified as likely derived from recently or chronically infected individuals. 71% of patients were classified as likely to have been infected within one year of genotyping but results varied considerably amongst surveys. This bioinformatics approach may provide supporting population-level information to identify recent infection but its application is limited by infection with more than one viral variant, decreasing viral diversity in advanced disease and technical aspects of population based sequencing. Standardization of sequencing techniques and base calling and the addition of other parameters such as CD4 cell count may address some of the technical limitations and increase the usefulness of the approach. Identification of recent HIV infection within populations is a public health priority for accurate estimation of HIV incidence rates and transmitted drug resistance. Determining HIV incidence rates by prospective follow-up of HIV-uninfected individuals is challenging and serological assays have important limitations. HIV diversity within an infected host increases with duration of infection. In this analysis, we explore a simple bioinformatics approach to assess viral diversity by determining the percentage of ambiguous base calls in sequences derived from standard genotyping of HIV-1 protease and reverse transcriptase. Sequences from 691 recently infected (≤1 year) and chronically infected (>1 year) individuals from Sweden, Vietnam and Ethiopia were analyzed for ambiguity. A significant difference (p <0.0001) in the proportion of ambiguous bases was observed between sequences from individuals with recent and chronic infection in both HIV-1 subtype B and non-B infection, consistent with previous studies. In our analysis, a cutoff of <0.47% ambiguous base calls identified recent infection with a sensitivity and specificity of 88.8% and 74.6% respectively. 1,728 protease and reverse transcriptase sequences from 36 surveys of transmitted HIV drug resistance performed following World Health Organization guidance were analyzed for ambiguity. The 0.47% ambiguity cutoff was applied and survey sequences were classified as likely derived from recently or chronically infected individuals. 71% of patients were classified as likely to have been infected within one year of genotyping but results varied considerably amongst surveys. This bioinformatics approach may provide supporting population-level information to identify recent infection but its application is limited by infection with more than one viral variant, decreasing viral diversity in advanced disease and technical aspects of population based sequencing. Standardization of sequencing techniques and base calling and the addition of other parameters such as CD4 cell count may address some of the technical limitations and increase the usefulness of the approach. •We analyze 691 recent and chronic HIV pol sequences for proportion of mixed bases.•Significant difference in proportion is seen between recent and chronic infection.•A cutoff of <0.47% mixed bases to identify recent (<1year) infection is established.•We apply it to 36 TDR surveys and 71% of infections are classified as recent.•The approach may provide additional information on a population level. Identification of recent HIV infection within populations is a public health priority for accurate estimation of HIV incidence rates and transmitted drug resistance at population level. Determining HIV incidence rates by prospective follow-up of HIV-uninfected individuals is challenging and serological assays have important limitations. HIV diversity within an infected host increases with duration of infection. We explore a simple bioinformatics approach to assess viral diversity by determining the percentage of ambiguous base calls in sequences derived from standard genotyping of HIV-1 protease and reverse transcriptase. Sequences from 691 recently infected (⩽1year) and chronically infected (>1year) individuals from Sweden, Vietnam and Ethiopia were analyzed for ambiguity. A significant difference (p<0.0001) in the proportion of ambiguous bases was observed between sequences from individuals with recent and chronic infection in both HIV-1 subtype B and non-B infection, consistent with previous studies. In our analysis, a cutoff of <0.47% ambiguous base calls identified recent infection with a sensitivity and specificity of 88.8% and 74.6% respectively. 1,728 protease and reverse transcriptase sequences from 36 surveys of transmitted HIV drug resistance performed following World Health Organization guidance were analyzed for ambiguity. The 0.47% ambiguity cutoff was applied and survey sequences were classified as likely derived from recently or chronically infected individuals. 71% of patients were classified as likely to have been infected within one year of genotyping but results varied considerably amongst surveys. This bioinformatics approach may provide supporting population-level information to identify recent infection but its application is limited by infection with more than one viral variant, decreasing viral diversity in advanced disease and technical aspects of population based sequencing. Standardization of sequencing techniques and base calling and the addition of other parameters such as CD4 cell count may address some of the technical limitations and increase the usefulness of the approach. |
Author | Jordan, Michael R. Cuong, Do Duy Maldarelli, Frank Sönnerborg, Anders Wondwossen, Amogne Hunt, Gillian Morris, Lynn Andersson, Emmi Shao, Wei Bertagnolio, Silvia Cham, Fatim Bontell, Irene |
AuthorAffiliation | i National Cancer Institute, Frederick, MD, USA c Department of Medicine, Karolinska Institutet, 141 86 Huddinge, Sweden d World Health Organization, Harare, Zimbabwe a Department of Laboratory Medicine, Karolinska Institutet, 141 86 Huddinge, Sweden j Tufts University School of Medicine, Boston, MA, USA g Center for HIV and STI, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa h World Health Organization, Geneva, Switzerland f Addis Ababa University, Addis Ababa, Ethiopia b Advanced Biomedical Computing Center, SAIC-Frederick, Inc, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA e Department of Infectious Diseases, Bach Mai Hospital, Hanoi, Vietnam |
AuthorAffiliation_xml | – name: f Addis Ababa University, Addis Ababa, Ethiopia – name: b Advanced Biomedical Computing Center, SAIC-Frederick, Inc, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA – name: j Tufts University School of Medicine, Boston, MA, USA – name: e Department of Infectious Diseases, Bach Mai Hospital, Hanoi, Vietnam – name: d World Health Organization, Harare, Zimbabwe – name: i National Cancer Institute, Frederick, MD, USA – name: c Department of Medicine, Karolinska Institutet, 141 86 Huddinge, Sweden – name: a Department of Laboratory Medicine, Karolinska Institutet, 141 86 Huddinge, Sweden – name: g Center for HIV and STI, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa – name: h World Health Organization, Geneva, Switzerland |
Author_xml | – sequence: 1 givenname: Emmi surname: Andersson fullname: Andersson, Emmi email: emmi.andersson@gmail.com organization: Department of Laboratory Medicine, Karolinska Institutet, 141 86 Huddinge, Sweden – sequence: 2 givenname: Wei surname: Shao fullname: Shao, Wei email: shaow@mail.nih.gov organization: Advanced Biomedical Computing Center, SAIC-Frederick, Inc, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA – sequence: 3 givenname: Irene surname: Bontell fullname: Bontell, Irene email: irene.bontell@ki.se organization: Department of Medicine, Karolinska Institutet, 141 86 Huddinge, Sweden – sequence: 4 givenname: Fatim surname: Cham fullname: Cham, Fatim email: qualabs@gmail.com organization: World Health Organization, Harare, Zimbabwe – sequence: 5 givenname: Do Duy surname: Cuong fullname: Cuong, Do Duy email: doduy.cuong@gmail.com organization: Department of Infectious Diseases, Bach Mai Hospital, Hanoi, Vietnam – sequence: 6 givenname: Amogne surname: Wondwossen fullname: Wondwossen, Amogne email: wonamogne@yahoo.com organization: Addis Ababa University, Addis Ababa, Ethiopia – sequence: 7 givenname: Lynn surname: Morris fullname: Morris, Lynn email: lynnm@nicd.ac.za organization: Center for HIV and STI, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa – sequence: 8 givenname: Gillian surname: Hunt fullname: Hunt, Gillian email: GillianH@nicd.ac.za organization: Center for HIV and STI, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa – sequence: 9 givenname: Anders surname: Sönnerborg fullname: Sönnerborg, Anders email: Anders.Sonnerborg@ki.se organization: Department of Laboratory Medicine, Karolinska Institutet, 141 86 Huddinge, Sweden – sequence: 10 givenname: Silvia surname: Bertagnolio fullname: Bertagnolio, Silvia email: bertagnolios@who.int organization: World Health Organization, Geneva, Switzerland – sequence: 11 givenname: Frank surname: Maldarelli fullname: Maldarelli, Frank email: fmalli@mail.nih.gov organization: National Cancer Institute, Frederick, MD, USA – sequence: 12 givenname: Michael R. surname: Jordan fullname: Jordan, Michael R. email: mjordan@tuftsmedicalcenter.org organization: Tufts University School of Medicine, Boston, MA, USA |
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Keywords | Resistance Viral diversity HIV Bioinformatics Ambiguity Incidence |
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Snippet | •We analyze 691 recent and chronic HIV pol sequences for proportion of mixed bases.•Significant difference in proportion is seen between recent and chronic... Identification of recent HIV infection within populations is a public health priority for accurate estimation of HIV incidence rates and transmitted drug... |
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SubjectTerms | Ambiguity Bioinformatics Chronic Disease Databases, Genetic Drug Resistance, Viral Female Genes, pol HIV HIV Infections - classification HIV Infections - epidemiology HIV Infections - transmission HIV Infections - virology HIV-1 - genetics Humans Incidence Male Medicin och hälsovetenskap Resistance Sequence Alignment Sequence Analysis, RNA Viral diversity |
Title | Evaluation of sequence ambiguities of the HIV-1 pol gene as a method to identify recent HIV-1 infection in transmitted drug resistance surveys |
URI | https://dx.doi.org/10.1016/j.meegid.2013.03.050 https://www.ncbi.nlm.nih.gov/pubmed/23583545 https://search.proquest.com/docview/1415605410 https://pubmed.ncbi.nlm.nih.gov/PMC4066879 http://kipublications.ki.se/Default.aspx?queryparsed=id:127427746 |
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