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Weaver S, Dávila Conn VM, Ji D, Verdonk H, Ávila-Ríos S, Leigh Brown AJ, Wertheim JO, Kosakovsky Pond SL. AUTO-TUNE: selecting the distance threshold for inferring HIV transmission clusters. FRONTIERS IN BIOINFORMATICS 2024; 4:1400003. [PMID: 39086842 PMCID: PMC11289888 DOI: 10.3389/fbinf.2024.1400003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 05/17/2024] [Indexed: 08/02/2024] Open
Abstract
Molecular surveillance of viral pathogens and inference of transmission networks from genomic data play an increasingly important role in public health efforts, especially for HIV-1. For many methods, the genetic distance threshold used to connect sequences in the transmission network is a key parameter informing the properties of inferred networks. Using a distance threshold that is too high can result in a network with many spurious links, making it difficult to interpret. Conversely, a distance threshold that is too low can result in a network with too few links, which may not capture key insights into clusters of public health concern. Published research using the HIV-TRACE software package frequently uses the default threshold of 0.015 substitutions/site for HIV pol gene sequences, but in many cases, investigators heuristically select other threshold parameters to better capture the underlying dynamics of the epidemic they are studying. Here, we present a general heuristic scoring approach for tuning a distance threshold adaptively, which seeks to prevent the formation of giant clusters. We prioritize the ratio of the sizes of the largest and the second largest cluster, maximizing the number of clusters present in the network. We apply our scoring heuristic to outbreaks with different characteristics, such as regional or temporal variability, and demonstrate the utility of using the scoring mechanism's suggested distance threshold to identify clusters exhibiting risk factors that would have otherwise been more difficult to identify. For example, while we found that a 0.015 substitutions/site distance threshold is typical for US-like epidemics, recent outbreaks like the CRF07_BC subtype among men who have sex with men (MSM) in China have been found to have a lower optimal threshold of 0.005 to better capture the transition from injected drug use (IDU) to MSM as the primary risk factor. Alternatively, in communities surrounding Lake Victoria in Uganda, where there has been sustained heterosexual transmission for many years, we found that a larger distance threshold is necessary to capture a more risk factor-diverse population with sparse sampling over a longer period of time. Such identification may allow for more informed intervention action by respective public health officials.
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Affiliation(s)
- Steven Weaver
- Center for Viral Evolution, Temple University, Philadelphia, PA, United States
| | - Vanessa M. Dávila Conn
- Center for Research in Infectious Diseases, National Institute of Respiratory Diseases, Mexico City, Mexico
| | - Daniel Ji
- Department of Medicine, University of California San Diego, La Jolla, CA, United States
| | - Hannah Verdonk
- Center for Viral Evolution, Temple University, Philadelphia, PA, United States
| | | | - Andrew J. Leigh Brown
- Department of Medicine, University of California San Diego, La Jolla, CA, United States
| | - Joel O. Wertheim
- Department of Medicine, University of California San Diego, La Jolla, CA, United States
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Weaver S, Dávila-Conn V, Ji D, Verdonk H, Ávila-Ríos S, Leigh Brown AJ, Wertheim JO, Kosakovsky Pond SL. AUTO-TUNE: SELECTING THE DISTANCE THRESHOLD FOR INFERRING HIV TRANSMISSION CLUSTERS. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.11.584522. [PMID: 38559140 PMCID: PMC10979987 DOI: 10.1101/2024.03.11.584522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Molecular surveillance of viral pathogens and inference of transmission networks from genomic data play an increasingly important role in public health efforts, especially for HIV-1. For many methods, the genetic distance threshold used to connect sequences in the transmission network is a key parameter informing the properties of inferred networks. Using a distance threshold that is too high can result in a network with many spurious links, making it difficult to interpret. Conversely, a distance threshold that is too low can result in a network with too few links, which may not capture key insights into clusters of public health concern. Published research using the HIV-TRACE software package frequently uses the default threshold of 0.015 substitutions/site for HIV pol gene sequences, but in many cases, investigators heuristically select other threshold parameters to better capture the underlying dynamics of the epidemic they are studying. Here, we present a general heuristic scoring approach for tuning a distance threshold adaptively, which seeks to prevent the formation of giant clusters. We prioritize the ratio of the sizes of the largest and the second largest cluster, maximizing the number of clusters present in the network. We apply our scoring heuristic to outbreaks with different characteristics, such as regional or temporal variability, and demonstrate the utility of using the scoring mechanism's suggested distance threshold to identify clusters exhibiting risk factors that would have otherwise been more difficult to identify. For example, while we found that a 0.015 substitutions/site distance threshold is typical for US-like epidemics, recent outbreaks like the CRF07_BC subtype among men who have sex with men (MSM) in China have been found to have a lower optimal threshold of 0.005 to better capture the transition from injected drug use (IDU) to MSM as the primary risk factor. Alternatively, in communities surrounding Lake Victoria in Uganda, where there has been sustained hetero-sexual transmission for many years, we found that a larger distance threshold is necessary to capture a more risk factor-diverse population with sparse sampling over a longer period of time. Such identification may allow for more informed intervention action by respective public health officials.
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Affiliation(s)
- Steven Weaver
- Center for Viral Evolution, Temple University, Philadelphia, PA, USA
| | - Vanessa Dávila-Conn
- Center for Research in Infectious Diseases, National Institute of Respiratory Diseases, Mexico City, Mexico
| | - Daniel Ji
- Department of Computer Science & Engineering, UC San Diego, La Jolla, CA 92093, USA
| | - Hannah Verdonk
- Center for Viral Evolution, Temple University, Philadelphia, PA, USA
| | - Santiago Ávila-Ríos
- Center for Research in Infectious Diseases, National Institute of Respiratory Diseases, Mexico City, Mexico
| | - Andrew J Leigh Brown
- School of Biological Sciences, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Joel O Wertheim
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
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Liu X, Wang D, Hu J, Song C, Liao L, Feng Y, Li D, Xing H, Ruan Y. Changes in HIV-1 Subtypes/Sub-Subtypes, and Transmitted Drug Resistance Among ART-Naïve HIV-Infected Individuals - China, 2004-2022. China CDC Wkly 2023; 5:664-671. [PMID: 37593123 PMCID: PMC10427497 DOI: 10.46234/ccdcw2023.129] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 06/15/2023] [Indexed: 08/19/2023] Open
Abstract
Introduction The efficacy of treatment and clinical outcomes may be jeopardized by factors such as transmitted drug resistance (TDR) and the genetic diversity of the human immunodeficiency virus type 1 (HIV-1). This comprehensive study aims to examine the alterations in HIV-1 subtypes or sub-subtypes and TDR among Chinese individuals, who have been diagnosed with HIV infection and are previously untreated with antiretroviral therapy (ART), across the span of 2004 to 2022. Methods Sequences of the HIV-1 pol gene region were obtained from ART-naïve HIV-positive individuals across 31 provincial-level administrative divisions between 2004 and 2022. To predict susceptibility to 12 antiretroviral drugs, the research utilized the Stanford HIV Drug Resistance Database. The Cochran-Armitage trend test facilitated the analysis of changes in HIV-1 subtype/sub-subtype prevalence and TDR. This analysis was conducted in alignment with the progression of the National Free Antiretroviral Treatment Program's stages between 2004 and 2022. Results Among the 57,902 ART-naïve individuals infected with HIV, there was a notable decline in the prevalence of CRF01_AE, B, and C from 37.3%, 24.1%, and 1.3% respectively in 2004-2007 to 29.4%, 7.3%, and 0.2% respectively in 2020-2022. Simultaneously, a significant increase was observed in the proportions of CRF07_BC, CRF08_BC, CRF55_01B, other CRFs, and URFs, from 24.1%, 11.5%, 0.1%, 0.4%, and 0.9% respectively in 2004-2007 to 40.8%, 11.5%, 3.8%, 3.7%, and 2.8% respectively in 2020-2022 (all P<0.001 for trend). The prevalence of TDR to overall, non-nucleoside reverse transcriptase inhibitor (NNRTI), efavirenz, and nevirapine also significantly increased from 2.6%, 1.8%, 1.6%, and 1.8% respectively in 2004-2007 to 7.8%, 6.7%, 6.3%, and 6.7% respectively in 2020-2022 (all P<0.001 for trend). However, there were no meaningful changes in the TDR prevalence of nucleoside reverse transcriptase inhibitor and protease inhibitor. Notably, in 2020-2022, the overall TDR prevalence exceeded 15% in Xinjiang. Conclusions The total prevalence of TDR in China has achieved a moderate level (7.8%) from 2020 to 2022, with NNRTI resistance standing prominently at 6.7%. Consequently, measures to curb TDR are urgently required, particularly among ART-naïve HIV-infected individuals in China.
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Affiliation(s)
- Xiu Liu
- State Key Laboratory of Infectious Disease Prevention and Control (SKLID), National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Beijing, China
| | - Dong Wang
- State Key Laboratory of Infectious Disease Prevention and Control (SKLID), National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Beijing, China
| | - Jing Hu
- State Key Laboratory of Infectious Disease Prevention and Control (SKLID), National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Beijing, China
| | - Chang Song
- State Key Laboratory of Infectious Disease Prevention and Control (SKLID), National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Beijing, China
| | - Lingjie Liao
- State Key Laboratory of Infectious Disease Prevention and Control (SKLID), National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Beijing, China
| | - Yi Feng
- State Key Laboratory of Infectious Disease Prevention and Control (SKLID), National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Beijing, China
| | - Dan Li
- State Key Laboratory of Infectious Disease Prevention and Control (SKLID), National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Beijing, China
| | - Hui Xing
- State Key Laboratory of Infectious Disease Prevention and Control (SKLID), National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Beijing, China
| | - Yuhua Ruan
- State Key Laboratory of Infectious Disease Prevention and Control (SKLID), National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Beijing, China
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Molecular Transmission Dynamics of Primary HIV Infections in Lazio Region, Years 2013-2020. Viruses 2021; 13:v13020176. [PMID: 33503987 PMCID: PMC7911907 DOI: 10.3390/v13020176] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 01/19/2021] [Accepted: 01/20/2021] [Indexed: 12/12/2022] Open
Abstract
Molecular investigation of primary HIV infections (PHI) is crucial to describe current dynamics of HIV transmission. Aim of the study was to investigate HIV transmission clusters (TC) in PHI referred during the years 2013–2020 to the National Institute for Infectious Diseases in Rome (INMI), that is the Lazio regional AIDS reference centre, and factors possibly associated with inclusion in TC. These were identified by phylogenetic analysis, based on population sequencing of pol; a more in depth analysis was performed on TC of B subtype, using ultra-deep sequencing (UDS) of env. Of 270 patients diagnosed with PHI during the study period, 229 were enrolled (median follow-up 168 (IQR 96–232) weeks). Median age: 39 (IQR 32–48) years; 94.8% males, 86.5% Italians, 83.4% MSM, 56.8% carrying HIV-1 subtype B. Of them, 92.6% started early treatment within a median of 4 (IQR 2–7) days after diagnosis; median time to sustained suppression was 20 (IQR 8–32) weeks. Twenty TC (median size 3, range 2–9 individuals), including 68 patients, were identified. A diagnosis prior to 2015 was the unique factor associated with inclusion in a TC. Added value of UDS was the identification of shared quasispecies components in transmission pairs within TC.
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