 |
|
|
| |
Over One Third of New HIV Diagnoses in
North Carolina Involve Recent Infection
|
| |
| |
HIVR4P Virtual, January 27-28 and February 3-4, 2021
Mark Mascolini
Among people newly diagnosed with HIV in the North Carolina state lab in 2018-2019, 39% had recent HIV infection [1]. University of North Carolina (UNC) Chapel Hill researchers conducted the study with a new all-in-one Phylodynamics Platform that determines recency of HIV infection, drug-resistance mutations, and transmission clusters.
The UNC investigators argued that preventing HIV infection depends largely on identifying recent (incident) HIV infection. Recent HIV infection signals ongoing transmission and can point to active transmission clusters. Real-time intervention with people identified in transmission clusters can prevent further spread of HIV in a cost-effective way.
HIV load and antibody response to the virus change over time in a person with HIV and can help broadly gauge time since HIV infection. Viral diversity may offer a more precise tool to determine recency of infection because infection usually begins with a single founder virus that evolves to an increasingly diverse viral population over time [2],
To measure viral diversity in this analysis, researchers relied on Primer ID Deep Sequencing, a universal molecular identifier (UMI) using a next-generation sequencing (NGS) library prep approach that overcomes certain limitations of the conventional library prep [3]. Primer ID Deep Sequencing has an error rate as low as 0.01%. Moreover, it can determine "true sampling depths" of sequencing, which are critical to accurately calculating viral diversity and to performing phylogenetic analysis. The investigators also developed a multiplexing approach that let them sequence several HIV regions in a single reaction.
This approach determined that viral diversity at the HIV reverse transcriptase (RT) and envelope V1V3 region correlated positively and strongly with duration of HIV infection (r = 0.9) in samples from the acute HIV infection CHAVI cohort [4]. They called this new approach the Phylodynamics Platform because it assesses HIV recency (within 9 months of transmission), drug resistance mutations, and transmission clusters.
Based on these results, the researchers built an HIV recency prediction algorithm and applied it to all new HIV infections diagnosed in the public health lab of North Carolina starting in 2018. The sample represented about one third of all new HIV diagnoses in North Carolina in that period. Weekly or biweekly samples shipped from the state lab can be analyzed in about 8 workdays.
Among 515 people newly diagnosed with HIV in 2018-2019, the platform determined that 202 of them (39%) had been infected within the past 9 months. Another 47% were chronically infected at diagnosis, while the remaining 14% had an indeterminate infection duration at diagnosis. The proportion of newly diagnosed people with recent HIV infection rose from 35% in 2018 to 44% in 2019. People younger than 30 years proved significantly more likely to be recently infected at diagnosis (P < 0.01).
At a 1% sensitivity level in the viral population, over one quarter of viral samples from newly diagnosed people (27.7%) carried a reverse transcriptase mutation, usually conferring resistance to a nonnucleoside reverse transcriptase inhibitor (NNRTI) (22.9%) and less often to a reverse transcriptase inhibitor (7.2%). The most frequently detected resistance mutation was K103N (11.3%), which makes HIV resistant to first-generation NNRTIs. Other clinically relevant reverse transcriptase mutations were rare. Almost one quarter of viral samples, 24.4%, carried mutations making HIV resistant to protease inhibitors (M46I in 16.3%, M46L in 3.5%), while only 3.3% of samples carried mutations conferring resistance to integrase inhibitors (T97A in 3.3%).
In 2018 the Phylodynamics Platform identified 28 HIV transmission clusters ranging in size from 2 to 4 individuals. The analysis also determined with 32% of recent HIV infections appeared in a cluster, compared with 18% of chronic infections, a significant difference (P = 0.03).
The UNC team believes their findings show that "monitoring recency of new HIV diagnoses in real time will greatly benefit HIV prevention efforts."
References
1. Zhou S, Sizemore S, Moeser M, et al. Profiling the HIV epidemic with recency of infection instead of recency of diagnosis: 2 years of experience in North Carolina, USA. HIVR4P (HIV Research for Prevention) Virtual, January 27-28 and February 3-4, 2021. Abstract OA02.01.
2. Moyo S, Wilkinson E, Novitsky V, et al. Identifying recent HIV infections: from serological assays to genomics. Viruses. 2015;7:5508-5524. doi: 10.3390/v7102887.
3. Head SR, Komori HK, LaMere SA, et al. Library construction for next-generation sequencing: Overviews and challenges. Biotechniques. 2018;56(2). https://www.future-science.com/doi/10.2144/000114133
4. Dennis AM, Zhou S, Sellers CJ, et al. Using primer-ID deep sequencing to detect recent human immunodeficiency virus type 1 infection. J Infect Dis. 2018;218:1777-1782. doi: 10.1093/infdis/jiy426.
,
|
| |
|
|
|
|
|
