Near Real-Time Identification of Recent Human Immunodeficiency Virus Transmissions, Transmitted Drug Resistance Mutations, and Transmission Networks by Multiplexed Primer ID-Next-Generation Sequencing in North Carolina

Use of Next-Generation Sequencing in a State-Wide Strategy of HIV-1 Surveillance: Impact of the SARS-COV-2 Pandemic on HIV-1 Diagnosis and Transmission

BACKGROUND

The ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic posed an unpreceded threat to the management of other pandemics such as human immunodeficiency virus-1 (HIV-1) in the United States. The full impact of the SARS-CoV-2 pandemic on the HIV-1 pandemic needs to be evaluated.

METHODS

All individuals with newly reported HIV-1 diagnoses from NC State Laboratory of Public Health were enrolled in this prospective observational study, 2018-2021. We used a sequencing-based recency assay to identify recent HIV-1 infections and to determine the days postinfection (DPI) for each person at the time of diagnosis.

RESULTS

Sequencing used diagnostic serum samples from 814 individuals with new HIV-1 diagnoses spanning this 4-year period. Characteristics of individuals diagnosed in 2020 differed from those in other years. People of color diagnosed in 2021 were on average 6 months delayed in their diagnosis compared to those diagnosed in 2020. There was a trend that genetic networks were more known for individuals diagnosed in 2021. We observed no major integrase resistance mutations over the course of the study.

CONCLUSIONS

SARS-CoV-2 pandemic may contribute to the spread of HIV-1. Public health resources need to focus on restoring HIV-1 testing and interrupting active, ongoing, transmission.

High Genetic Diversity of HIV-1 and Active Transmission Clusters among Male-to-Male Sexual Contacts (MMSCs) in Zhuhai, China

Monitoring genetic diversity and recent HIV infections (RHIs) is critical for understanding HIV epidemiology. Here, we report HIV-1 genetic diversity and RHIs in blood samples from 190 HIV-positive MMSCs in Zhuhai, China. MMSCs with newly reported HIV were enrolled from January 2020 to June 2022. A nested PCR was performed to amplify the HIV polymerase gene fragments at HXB2 positions 2604-3606. We constructed genetic transmission network at both 0.5% and 1.5% distance thresholds using the Tamura-Nei93 model. RHIs were identified using a recent infection testing algorithm (RITA) combining limiting antigen avidity enzyme immunoassay (LAg-EIA) assay with clinical data. The results revealed that 19.5% (37/190) were RHIs and 48.4% (92/190) were CRF07_BC. Two clusters were identified at a 0.5% distance threshold. Among them, one was infected with CRF07_BC for the long term, and the other was infected with CRF55_01B recently. We identified a total of 15 clusters at a 1.5% distance threshold. Among them, nine were infected with CRF07_BC subtype, and RHIs were found in 38.8% (19/49) distributed in eight genetic clusters. We identified a large active transmission cluster (n = 10) infected with a genetic variant, CRF79_0107. The multivariable logistic regression model showed that clusters were more likely to be RHIs (adjusted OR: 3.64, 95% CI: 1.51~9.01). The RHI algorithm can help to identify recent or ongoing transmission clusters where the prevention tools are mostly needed. Prompt public health measures are needed to contain the further spread of active transmission clusters.

How Can Progress Toward Ending the Human Immunodeficiency Virus Epidemic in the United States Be Monitored?

The plan for Ending the HIV (human immunodeficiency virus) Epidemic (EHE) in the United States aims to reduce new infections by 75% by 2025 and by 90% by 2030. For EHE to be successful, it is important to accurately measure changes in numbers of new HIV infections after 5 and 10 years (to determine whether the EHE goals have been achieved) but also over shorter timescales (to monitor progress and intensify prevention efforts if required). In this viewpoint, we aim to demonstrate why the method used to monitor progress toward the EHE goals must be carefully considered. We briefly describe and discuss different methods to estimate numbers of new HIV infections based on longitudinal cohort studies, cross-sectional incidence surveys, and routine surveillance data. We particularly focus on identifying conditions under which unadjusted and adjusted estimates based on routine surveillance data can be used to estimate changes in new HIV infections.

Incorporating Within-Host Diversity in Phylogenetic Analyses for Detecting Clusters of New HIV Diagnoses

Background

Phylogenetic analyses of HIV sequences are used to detect clusters and inform public health interventions. Conventional approaches summarize within-host HIV diversity with a single consensus sequence per host of the pol gene, obtained from Sanger or next-generation sequencing (NGS). There is growing recognition that this approach discards potentially important information about within-host sequence variation, which can impact phylogenetic inference. However, whether alternative summary methods that incorporate intra-host variation impact phylogenetic inference of transmission network features is unknown.

Methods

We introduce profile sampling, a method to incorporate within-host NGS sequence diversity into phylogenetic HIV cluster inference. We compare this approach to Sanger- and NGS-derived pol and near-whole-genome consensus sequences and evaluate its potential benefits in identifying molecular clusters among all newly-HIV-diagnosed individuals over six months at the largest HIV center in Rhode Island.

Results

Profile sampling cluster inference demonstrated that within-host viral diversity impacts phylogenetic inference across individuals, and that consensus sequence approaches can obscure both magnitude and effect of these impacts. Clustering differed between Sanger- and NGS-derived consensus and profile sampling sequences, and across gene regions.

Discussion

Profile sampling can incorporate within-host HIV diversity captured by NGS into phylogenetic analyses. This additional information can improve robustness of cluster detection.

HIV-1 drug resistance and genetic diversity in a cohort of people with HIV-1 in Nigeria

OBJECTIVE

This study was designed to provide information on the genetic diversity of HIV-1 and drug resistance mutations in Nigeria, as there is limited understanding of variants circulating in the country.

METHODS

We used an advanced next-generation sequencing platform, Primer ID, to: investigate the presence of high and low abundance drug resistance mutations; characterize preexisting Integrase Strand Transfer Inhibitor (INSTI) mutations in antiretroviral therapy (ART)-experienced but dolutegravir-naive individuals; detect recent HIV-1 infections and characterize subtype diversity from a cohort of people with HIV-1 (PWH).

RESULTS

HIV-1 subtype analysis revealed the predominance of CRF02_AG and subtype G in our study population. At detection sensitivity of 30% abundance, drug resistance mutations (DRMs) were identified in 3% of samples. At a sensitivity level of 10%, DRMs were identified in 27.3% of samples. We did not detect any major INSTI mutation associated with dolutegravir-resistance. Only one recent infection was detected in our study population.

CONCLUSION

Our study suggests that dolutegravir-containing antiretroviral regimens will be effective in Nigeria. Our study also further emphasizes the high genetic diversity of HIV-1 in Nigeria and that CRF02_AG and subtype G are the dominant circulating forms of HIV-1 in Nigeria. These two circulating forms of the virus are largely driving the epidemic in the country.

Methodological approaches for the prediction of opioid use-related epidemics in the United States: a narrative review and cross-disciplinary call to action

The opioid crisis in the United States has been defined by waves of drug- and locality-specific Opioid use-Related Epidemics (OREs) of overdose and bloodborne infections, among a range of health harms. The ability to identify localities at risk of such OREs, and better yet, to predict which ones will experience them, holds the potential to mitigate further morbidity and mortality. This narrative review was conducted to identify and describe quantitative approaches aimed at the "risk assessment," "detection" or "prediction" of OREs in the United States. We implemented a PubMed search composed of the: (1) objective (eg, prediction), (2) epidemiologic outcome (eg, outbreak), (3) underlying cause (ie, opioid use), (4) health outcome (eg, overdose, HIV), (5) location (ie, US). In total, 46 studies were included, and the following information extracted: discipline, objective, health outcome, drug/substance type, geographic region/unit of analysis, and data sources. Studies identified relied on clinical, epidemiological, behavioral and drug markets surveillance and applied a range of methods including statistical regression, geospatial analyses, dynamic modeling, phylogenetic analyses and machine learning. Studies for the prediction of overdose mortality at national/state/county and zip code level are rapidly emerging. Geospatial methods are increasingly used to identify hotspots of opioid use and overdose. In the context of infectious disease OREs, routine genetic sequencing of patient samples to identify growing transmission clusters via phylogenetic methods could increase early detection capacity. A coordinated implementation of multiple, complementary approaches would increase our ability to successfully anticipate outbreak risk and respond preemptively. We present a multi-disciplinary framework for the prediction of OREs in the US and reflect on challenges research teams will face in implementing such strategies along with good practices.

Fact and Fiction about 1%: Next Generation Sequencing and the Detection of Minor Drug Resistant Variants in HIV-1 Populations with and without Unique Molecular Identifiers

Next generation sequencing (NGS) platforms have the ability to generate almost limitless numbers of sequence reads starting with a PCR product. This gives the illusion that it is possible to analyze minor variants in a viral population. However, including a PCR step obscures the sampling depth of the viral population, the key parameter needed to understand the utility of the data set for finding minor variants. Also, these high throughput sequencing platforms are error prone at the level where minor variants are of interest, confounding the interpretation of detected minor variants. A simple strategy has been applied in multiple applications of NGS to solve these problems. Prior to PCR, individual molecules are “tagged” with a unique molecular identifier (UMI) that can be used to establish the actual sample size of viral genomes sequenced after PCR and sequencing. In addition, since PCR generates many copies of each sequence tagged to a specific UMI, a template consensus sequence (TCS) can be created from the many reads of each template, removing virtually all of the method error. From this perspective we examine our own use of a UMI, called Primer ID, in the detection of minor drug resistant variants in HIV-1 populations.