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 inInfection, genetics and evolution Vol. 18; pp. 125 - 131
Main Authors Andersson, Emmi, Shao, Wei, Bontell, Irene, Cham, Fatim, Cuong, Do Duy, Wondwossen, Amogne, Morris, Lynn, Hunt, Gillian, Sönnerborg, Anders, Bertagnolio, Silvia, Maldarelli, Frank, Jordan, Michael R.
Format Journal Article
LanguageEnglish
Published 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.
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
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  surname: Bontell
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  fullname: Cham, Fatim
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  organization: World Health Organization, Harare, Zimbabwe
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  givenname: Do Duy
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  givenname: Amogne
  surname: Wondwossen
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  email: wonamogne@yahoo.com
  organization: Addis Ababa University, Addis Ababa, Ethiopia
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  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
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  surname: Sönnerborg
  fullname: Sönnerborg, Anders
  email: Anders.Sonnerborg@ki.se
  organization: Department of Laboratory Medicine, Karolinska Institutet, 141 86 Huddinge, Sweden
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  email: bertagnolios@who.int
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  surname: Maldarelli
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  organization: National Cancer Institute, Frederick, MD, USA
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  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
Volume 18
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