HIV Genetic Sequences Research Articles

Immunocapture of cell surface proteins embedded in HIV envelopes uncovers considerable virion genetic diversity associated with different source cell types.

HIV particles in the blood largely originate from activated lymphocytes and can overshadow variants which may be expressed from other cell types. Investigations of virus persistence must be able to distinguish cells refractory to viral clearance that serve as reservoirs. To investigate additional cell types that may be associated with in vivo HIV expression we developed a virus particle immunomagnetic capture method targeting several markers of cellular origin that become embedded within virion envelopes during budding. We evaluated the ability of markers to better distinguish cell lineage source subpopulations by assessing combinations of different antibodies with cell-sorted in vitro culture and clinical specimens. Various deductive algorithms were designed to discriminate source cell lineages and subsets. From the particle capture algorithms, we identified distinct variants expressed within individuals that were associated with disparate cellular markers. Among the variants uncovered were minority-level viruses with drug resistance mutations undetected by sequencing and often were associated with markers indicative of myeloid lineage (CD3-/CD10-/CD16+ or /CD14+, and CD3-/CD16-/CD14-/CD11c+ or /HLA-DR+) cell sources. The diverse HIV genetic sequences expressed from different cell types within individuals, further supported by the appearance of distinct drug-resistant variants, highlights the complexity of HIV reservoirs in vivo which must be considered for HIV cure strategies. This approach could also be helpful in examining in vivo host cell origins and genetic diversity in infections involving other families of budding viruses.

Toward Consent in Molecular HIV Surveillance?: Perspectives of Critical Stakeholders

Background The emergence of molecular HIV surveillance (MHS) and cluster detection and response (CDR) programs as key features of the United States (US) HIV strategy since 2018 has caused major controversies. HIV surveillance programs that re-use individuals’ routinely collected clinical HIV data do not require consent on the basis that the public benefit of these programs outweighs individuals’ rights to opt out. However, criticisms of MHS/CDR have questioned whether expanded uses of HIV genetic sequence data for prevention reach beyond traditional public health ethics frameworks. This study aimed to explore views on consent within MHS/CDR among critical stakeholders. Methods In 2021 we interviewed 26 US HIV stakeholders who identified as being critical or concerned about the rollout of MHS/CDR. Stakeholders included participants belonging to networks of people living with HIV, other advocates, academics, and public health professionals. This analysis focused on identifying the range of positions among critical and concerned stakeholders on consent affordances, opt-outs, how to best inform people living with HIV about how data about them are used in public health programs, and related ethical issues. Results Participants were broadly supportive of introducing some forms of consent into MHS/CDR. However, they differed on the specifics of implementing consent. While some participants did not support introducing consent affordances, all supported the idea that people living with HIV should be informed about how HIV surveillance and prevention is conducted and how individuals’ data are used. Conclusions MHS/CDR has caused sustained controversy. Among critical stakeholders, consent is generally desirable but contested, although the right for people living with HIV to be informed was centrally supported. In an era of big data-driven public health interventions and routine uses of HIV genetic sequence data in surveillance and prevention, CDC and other agencies should revisit public health ethics frameworks and consider the possibility of consent processes.

Identifying HIV sequences that escape antibody neutralization using random forests and collaborative targeted learning

Abstract Recent studies have indicated that it is possible to protect individuals from HIV infection using passive infusion of monoclonal antibodies. However, in order for monoclonal antibodies to confer robust protection, the antibodies must be capable of neutralizing many possible strains of the virus. This is particularly challenging in the context of a highly diverse pathogen like HIV. It is therefore of great interest to leverage existing observational data sources to discover antibodies that are able to neutralize HIV viruses via residues where existing antibodies show modest protection. Such information feeds directly into the clinical trial pipeline for monoclonal antibody therapies by providing information on (i) whether and to what extent combinations of antibodies can generate superior protection and (ii) strategies for analyzing past clinical trials to identify in vivo evidence of antibody resistance. These observational data include genetic features of many diverse HIV genetic sequences, as well as in vitro measures of antibody resistance. The statistical learning problem we are interested in is developing statistical methodology that can be used to analyze these data to identify important genetic features that are significantly associated with antibody resistance. This is a challenging problem owing to the high-dimensional and strongly correlated nature of the genetic sequence data. To overcome these challenges, we propose an outcome-adaptive, collaborative targeted minimum loss-based estimation approach using random forests. We demonstrate via simulation that the approach enjoys important statistical benefits over existing approaches in terms of bias, mean squared error, and type I error. We apply the approach to the Compile, Analyze, and Tally Nab Panels database to identify AA positions that are potentially causally related to resistance to neutralization by several different antibodies.

Detection and Response to an HIV Cluster: People Living Homeless and Using Drugs in Seattle, Washington

The HIV epidemic in King County, Washington has traditionally been highly concentrated among men who have sex with men, and incidence has gradually declined over 2 decades. In 2018, King County experienced a geographically concentrated outbreak of HIV among heterosexual people who inject drugs. Data sources to describe the 2018 outbreak and King County's response were partner services interview data, HIV case reports, syringe service program client surveys, hospital data, and data from a rapid needs assessment of homeless individuals and people who inject drugs. In 2020, the authors examined the impact of delays in molecular sequence analyses and cluster member size thresholds, for identifying genetically similar clusters, on the timing of outbreak identification. In 2018, the health department identified a North Seattle cluster, growing to 30 people with related HIV infections diagnosed in 2008-2019. In total, 70% of cluster members were female, 77% were people who inject drugs, 87% were homeless, and 27% reported exchanging sex. Intervention activities included a rapid needs assessment, 2,485 HIV screening tests in a jail and other outreach settings, provision of 87,488 clean syringes in the outbreak area, and public communications. A lower cluster size threshold and more rapid receipt and analyses of data would have identified this outbreak 4-16 months earlier. This outbreak shows the vulnerability of people who inject drugs to HIV infection, even in areas with robust syringe service programs and declining HIV epidemics. Although molecular HIV surveillance did not identify this outbreak, it may have done so with a lower threshold for defining clusters and more rapid receipt and analyses of HIV genetic sequences.

Reassessing the Ethics of Molecular HIV Surveillance in the Era of Cluster Detection and Response: Toward HIV Data Justice

In the United States, clinical HIV data reported to surveillance systems operated by jurisdictional departments of public health are re-used for epidemiology and prevention. In 2018, all jurisdictions began using HIV genetic sequence data from clinical drug resistance tests to identify people living with HIV in “clusters” of others with genetically similar strains. This is called “molecular HIV surveillance” (MHS). In 2019, “cluster detection and response” (CDR) programs that re-use MHS data became the “fourth pillar” of the national HIV strategy. Public health re-uses of HIV data are done without consent and are a source of concern among stakeholders. This article presents three cases that illuminate bioethical challenges associated with re-uses of clinical HIV data for public health. We focus on evidence-base, risk-benefit ratio, determining directionality of HIV transmission, consent, and ethical re-use. The conclusion offers strategies for “HIV data justice.” The essay contributes to a “bioethics of the oppressed.”

Ethical issues associated with HIV molecular epidemiology: a qualitative exploratory study using inductive analytic approaches

BackgroundHIV molecular epidemiology is increasingly recognized as a vital source of information for understanding HIV transmission dynamics. Despite extensive use of these data-intensive techniques in both research and public health settings, the ethical issues associated with this science have received minimal attention. As the discipline evolves, there is reasonable concern that existing ethical and legal frameworks and standards might lag behind the rapid methodological developments in this field. This is a follow-up on our earlier work that applied a predetermined analytical framework to examine the perspectives of a sample of scientists from the fields of epidemiology, public health, virology and bioethics on key ethical issues associated with HIV molecular epidemiology in HIV network research.MethodsFourteen in-depth interviews were conducted with scientists from the fields of molecular epidemiology, public health, virology and bioethics. Inductive analytical approaches were applied to identify key themes that emerged from the data.ResultsOur interviewees acknowledged the potential positive impact of molecular epidemiology in the fight against HIV. However, they were concerned that HIV phylogenetics research messages may be incorrectly interpreted if not presented at the appropriate level. There was consensus that HIV phylogenetics research presents a potential risk to privacy, but the probability and magnitude of this risk was less obvious. Although participants acknowledged the social value that could be realized from the analysis of HIV genetic sequences, there was a perceived fear that the boundaries for use of HIV sequence data were not clearly defined.Conclusions Our findings highlight distinct ethical issues arising from HIV molecular epidemiology. As the discipline evolves and HIV sequence data become increasingly available, it is critical to ensure that ethical standards keep pace with biomedical advancements. We argue that the ethical issues raised in this study, whether real or perceived, require further conceptual and empirical examination.

Understanding disclosed and cryptic HIV transmission risk via genetic analysis: what are we missing and when does it matter?

To discuss the recent HIV phylogenetic analyses examining HIV transmission patterns among and within risk groups. Phylodynamic analysis has recently been applied to multiple HIV outbreaks among people who inject drugs to determine whether HIV transmission is ongoing. Large-scale analyses of datasets of HIV sequences collected for drug-resistance testing provide population-level insights into transmission patterns. One focus across world regions has been to investigate whether age-disparity is a driver of HIV transmission. In sub-Saharan Africa, researchers have examined transmission between heterosexuals and MSM and between high prevalence fishing communities and inland communities. In the US and the UK, cryptic risk groups such as nondisclosed MSM and the partners of transgender women are increasingly being uncovered based on their position in densely sampled molecular transmission networks. Analysis of HIV genetic sequence can resolve viral transmission patterns between risk groups at unprecedented scales and levels of detail. Future research should focus on understanding the effect of missing data on inferences and the biases of different methods. Uncovering groups and patterns obscured from traditional epidemiolocal analyses is exciting but should not compromise the privacy of the groups in question.

Identifying Clusters of Recent and Rapid HIV Transmission Through Analysis of Molecular Surveillance Data.

Detecting recent and rapid spread of HIV can help prioritize prevention and early treatment for those at highest risk of transmission. HIV genetic sequence data can identify transmission clusters, but previous approaches have not distinguished clusters of recent, rapid transmission. We assessed an analytic approach to identify such clusters in the United States. We analyzed 156,553 partial HIV-1 polymerase sequences reported to the National HIV Surveillance System and inferred transmission clusters using 2 genetic distance thresholds (0.5% and 1.5%) and 2 periods for diagnoses (all years and 2013-2015, ie, recent diagnoses). For rapidly growing clusters (with ≥5 diagnoses during 2015), molecular clock phylogenetic analysis estimated the time to most recent common ancestor for all divergence events within the cluster. Cluster transmission rates were estimated using these phylogenies. A distance threshold of 1.5% identified 103 rapidly growing clusters using all diagnoses and 73 using recent diagnoses; at 0.5%, 15 clusters were identified using all diagnoses and 13 using recent diagnoses. Molecular clock analysis estimated that the 13 clusters identified at 0.5% using recent diagnoses had been diversifying for a median of 4.7 years, compared with 6.5-13.2 years using other approaches. The 13 clusters at 0.5% had a transmission rate of 33/100 person-years, compared with previous national estimates of 4/100 person-years. Our approach identified clusters with transmission rates 8 times those of previous national estimates. This method can identify groups involved in rapid transmission and help programs effectively direct and prioritize limited public health resources.

What Do Clusters of Similar HIV Genetic Sequences Tell Us About HIV Risks in Africa?

Better knowledge about how HIV transmits in sub-Saharan Africa can guide more effective HIV prevention efforts. Studies that look for similarities among HIV genetic sequences can provide insights into patterns of HIV transmission: similarities point to recent transmission links. This review found seven studies that sampled HIV from communities in Africa, systematically identified at least some sexual partners, sequenced HIV, and looked for similarities pointing to transmission links. Across these seven studies, 0-13% (median 9.3%) of sequences were similar to (clustered with) sequences from known sexual partners. Because the HIV source for the first person infected in a sex-linked cluster is unknown, these studies provide a sexual explanation for 0-7.5% (median 4.6%) of HIV infections. More sequences were in clusters with no known sexual link. Although most sex partners are in or close to home, genetic diversity showed little or no geographic structure in the three studies that looked at the issue. Evidence from these studies does not support the common view that sex accounts for most HIV infections in Africa. Studies did not do what they could to elucidate risks: no study traced and tested sexual partners not already in the sample or reported blood-borne risks.

Determinants of HIV Phylogenetic Clustering in Chicago Among Young Black Men Who Have Sex With Men From the uConnect Cohort.

Phylogenetic analysis determines similarities among HIV genetic sequences from persons infected with HIV, identifying clusters of transmission. We determined characteristics associated with both membership in an HIV transmission cluster and the number of clustered sequences among a cohort of young black men who have sex with men (YBMSM) in Chicago. Pairwise genetic distances of HIV-1 pol sequences were collected during 2013-2016. Potential transmission ties were identified among HIV-infected persons whose sequences were ≤1.5% genetically distant. Putative transmission pairs were defined as ≥1 tie to another sequence. We then determined demographic and risk attributes associated with both membership in an HIV transmission cluster and the number of ties to the sequences from other persons in the cluster. Of 86 available sequences, 31 (36.0%) were tied to ≥1 other sequence. Through multivariable analyses, we determined that those who reported symptoms of depression and those who had a higher number of confidants in their network had significantly decreased odds of membership in transmission clusters. We found that those who had unstable housing and who reported heavy marijuana use had significantly more ties to other individuals within transmission clusters, whereas those identifying as bisexual, those participating in group sex, and those with higher numbers of sexual partners had significantly fewer ties. This study demonstrates the potential for combining phylogenetic and individual and network attributes to target HIV control efforts to persons with potentially higher transmission risk, as well as suggesting some unappreciated specific predictors of transmission risk among YBMSM in Chicago for future study.

HIV-1 Sequence Data Coverage in Central East Africa from 1959 to 2013.

Central and Eastern African HIV sequence data have been most critical in understanding the establishment and evolution of the global HIV pandemic. Here we report on the extent of publicly available HIV genetic sequence data in the Los Alamos National Laboratory Sequence Database sampled from 1959 to 2013 from six African countries: Uganda, Kenya, Tanzania, Burundi, the Democratic Republic of Congo, and Rwanda. We have summarized these data, including HIV subtypes, the years sampled, and the genomic regions sequenced. We also provide curated alignments for this important geographic area in five HIV genomic regions with substantial coverage.

HIV-1 Transmission during Early Infection in Men Who Have Sex with Men: A Phylodynamic Analysis

Conventional epidemiological surveillance of infectious diseases is focused on characterization of incident infections and estimation of the number of prevalent infections. Advances in methods for the analysis of the population-level genetic variation of viruses can potentially provide information about donors, not just recipients, of infection. Genetic sequences from many viruses are increasingly abundant, especially HIV, which is routinely sequenced for surveillance of drug resistance mutations. We conducted a phylodynamic analysis of HIV genetic sequence data and surveillance data from a US population of men who have sex with men (MSM) and estimated incidence and transmission rates by stage of infection. We analyzed 662 HIV-1 subtype B sequences collected between October 14, 2004, and February 24, 2012, from MSM in the Detroit metropolitan area, Michigan. These sequences were cross-referenced with a database of 30,200 patients diagnosed with HIV infection in the state of Michigan, which includes clinical information that is informative about the recency of infection at the time of diagnosis. These data were analyzed using recently developed population genetic methods that have enabled the estimation of transmission rates from the population-level genetic diversity of the virus. We found that genetic data are highly informative about HIV donors in ways that standard surveillance data are not. Genetic data are especially informative about the stage of infection of donors at the point of transmission. We estimate that 44.7% (95% CI, 42.2%-46.4%) of transmissions occur during the first year of infection. In this study, almost half of transmissions occurred within the first year of HIV infection in MSM. Our conclusions may be sensitive to un-modeled intra-host evolutionary dynamics, un-modeled sexual risk behavior, and uncertainty in the stage of infected hosts at the time of sampling. The intensity of transmission during early infection may have significance for public health interventions based on early treatment of newly diagnosed individuals.

The 2-sample problem for failure rates depending on a continuous mark: an application to vaccine efficacy

The efficacy of an HIV vaccine to prevent infection is likely to depend on the genetic variation of the exposing virus. This paper addresses the problem of using data on the HIV sequences that infect vaccine efficacy trial participants to (1) test for vaccine efficacy more powerfully than procedures that ignore the sequence data and (2) evaluate the dependence of vaccine efficacy on the divergence of infecting HIV strains from the HIV strain that is contained in the vaccine. Because hundreds of amino acid sites in each HIV genome are sequenced, it is natural to treat the genetic divergence as a continuous mark variable that accompanies each failure (infection) time. Problems (1) and (2) can then be approached by testing whether the ratio of the mark-specific hazard functions for the vaccine and placebo groups is unity or independent of the mark. We develop nonparametric and semiparametric tests for these null hypotheses and nonparametric techniques for estimating the mark-specific relative risks. The asymptotic properties of the procedures are established. In addition, the methods are studied in simulations and are applied to HIV genetic sequence data collected in the first HIV vaccine efficacy trial.

A Grid-Based Hiv Expert System

This paper addresses Grid-based integration and access of distributed data from infectious disease patient databases, literature on in-vitro and in-vivo pharmaceutical data, mutation databases, clinical trials, simulations and medical expert knowledge. Multivariate analyses combined with rule-based fuzzy logic are applied to the integrated data to provide ranking of patient-specific drugs. In addition, cellular automata-based simulations are used to predict the drug behaviour over time. Access to and integration of data is done through existing Internet servers and emerging Grid-based frameworks like Globus. Data presentation is done by standalone PC based software, Web-access and PDA roaming WAP access. The experiments were carried out on the DAS2, a Dutch Grid testbed. The output of the problem-solving environment (PSE) consists of a prediction of the drug sensitivity of the virus, generated by comparing the viral genotype to a relational database which contains a large number of phenotype-genotype pairs. Artificial Intelligence and Grid technology are effectively used to abstract knowledge from the data and provide the physicians with adaptive interactive advice on treatment applied to drug resistant HIV. An important aspect of our research is to use a variety of statistical and numerical methods to identify relationships between HIV genetic sequences and antiviral resistance to investigate consistency of results.

Goodness-of-fit tests for semiparametric biased sampling models

Consider an s-sample biased sampling model in which the distribution function for each of the first s−1 samples is related to the unknown distribution function G of the sth sample by a known parametric selection bias weight function. Gilbert et al. (Biometrika 86 (1999) 27) gave a procedure for semiparametric maximum likelihood estimation of the parameters in this model. In many applications, information are scarce for basing the choice of the parametric weight function(s), motivating the need for goodness-of-fit tests of the hypothesis that the weight functions are correctly specified. Cramér–von Mises-type, Anderson–Darling-type, and Kolmogorov–Smirnov-type test statistics are studied which compare discrepancies between the empirical distribution of G and the semiparametric maximum likelihood estimator of G. Finite-sample properties of the tests are evaluated with simulations and with a real example of HIV genetic sequence data.