Prevalence of Non-B HIV-1 Subtypes in North Italy and Analysis of Transmission Clusters Based on Sequence Data Analysis

Abstract

HIV-1 diversity is increasing in European countries due to immigration flows, as well as travels and human mobility, leading to the circulation of both new viral subtypes and new recombinant forms, with important implications for public health. We analyzed 710 HIV-1 sequences comprising protease and reverse-transcriptase (PR/RT) coding regions, sampled from 2011 to 2017, from naive patients in Spedali Civili Hospital, Brescia, Italy. Subtyping was performed by using a combination of different tools; the phylogenetic analysis with a structured coalescence model and Makarov Chain Monte Carlo was used on the datasets, to determine clusters and evolution. We detected 304 (43%) patients infected with HIV-1 non-B variants, of which only 293 sequences were available, with four pure subtypes and five recombinant forms; subtype F1 (17%) and CRF02_AG (51.1%) were most common. Twenty-five transmission clusters were identified, three of which included >10 patients, belonging to subtype CRF02_AG and subtype F. Most cases of alleged transmission were between heterosexual couples. Probably due to strong migratory flows, we have identified different subtypes with particular patterns of recombination or, as in the case of the subtype G (18/293, 6.1%), to a complete lack of relationship between the sequenced strains, revealing that they are all singletons. Continued HIV molecular surveillance is most important to analyze the dynamics of the boost of transmission clusters in order to implement public health interventions aimed at controlling the HIV epidemic.

HIV-1-Transmitted Drug Resistance and Transmission Clusters in Newly Diagnosed Patients in Portugal Between 2014 and 2019

“…However, this finding agrees with the results obtained through transmission cluster analyses, which showed that subtype G heterosexuals ( Pineda-Peña et al, 2019 ) were more frequently inside clusters when compared to subtypes A and B, indicating that transmission is more active in this subgroup. This is in contrast to what other European studies showed, where it was concluded that non-B subtypes are associated with the heterosexual population and are less frequently found in the transmission clusters ( Lorenzin et al, 2019 ; Paraskevis et al, 2019 ; Pimentel et al, 2022 ). Since our study is based on newly diagnosed patients, we believe that our results present a more recent view of the epidemic in the country that could already reflect a successful impact of pre-exposure prophylaxis (PrEP) among the MSM patients, with a slowdown of HIV transmission clusters in this risk group.…”

Increasing Frequency and Transmission of HIV-1 Non-B Subtypes Among Men Who Have Sex With Men in the Swiss HIV Cohort Study

“…In this study, we established a subtype classification algorithm to define rules for 300 (2.3%) individuals having multiple pol sequences from different time points with subtype variations. In previous studies, the subtyping tools REGA [24] and COMET [25] demonstrated concordant classification for 96.5% [38] or 85.6% [9], respectively, mitigating this problem. Despite these limitations, the densely sampled SHCS remains highly representative and provides important insights on the ongoing transmission of non-subtype B genetic forms in Switzerland [19] [19].…”

“…Several European countries have reported increased transmission of non-B subtypes in MSM. This increase has been linked to imported infections from countries where these non-B subtypes are predominant, followed by transmission among the local MSM population [9,10,[12][13][14][38][39][40][41]. Overall, the highest temporal increase in non-B forms among MSM was observed for subtype A and CRF02_AG, both of which are commonly found among HETs and are among the most predominant forms (together with subtype C) in Western Europe [7].…”

Dynamics of HIV-1 Molecular Networks Reveal Effective Control of Large Transmission Clusters in an Area Affected by an Epidemic of Multiple HIV Subtypes

“…For example, in our study a large number of new cases were referred from other surveillance sites to the study center for treatment since 2014; therefore, a peak was observed in the cluster growth predictor curve of many clusters in 2014 (orange lines in Figure 4), whereas no corresponding change was observed in the PDR curves (blue lines in Figure 4). R e , the third phylodynamic parameter that is based on sequence diversity, reflects the efficiency with which an infectious agent is transmitted and is frequently used to model infection dynamics (Novitsky et al, 2015;Ragonnet-Cronin et al, 2018;Jovanovic et al, 2019;Lorenzin et al, 2019;Vasylyeva et al, 2019;Vinken et al, 2019;Zai et al, 2020). We found that R e was accurate and reliable.…”

“…Clusters with ≥ 10 cases and between two and nine cases were defined as large and small/medium clusters, respectively. The definition of a large cluster is similar to that used in previous studies (Hughes et al, 2009;Leigh Brown et al, 2011;Lorenzin et al, 2019;Rhee et al, 2019;Dennis et al, 2020). We evaluated demographic features of the study population including the time of diagnosis, sex, risk group, age, ethnic group, resident city, marital status, education, and occupation.…”