Increase in HIV-1 transmitted drug resistance among untreated 16~25-y-old youths in the China-Myanmar border areas during 2009~2017

Background Transmitted drug resistance (TDR) can affect antiretroviral therapy (ART) ecacy. Surveillance drug resistance mutations in untreated youths newly reported with HIV-1 are highly representative of local TDR. We investigated HIV-1 TDR, TDR transmission based on molecular networks, and the effect of TDR mutations (TDRMs) on the CD4 count among youths in the China-Myanmar border area near the "Golden Triangle" to better understand TDR and guide ART. Methods From 2009 to 2017, 573 ART-naïve youths (16~25 y) newly reported with HIV-1 were enrolled. CD4 counts were obtained from whole blood. HIV pol gene sequences were amplied from RNA extracted from plasma. The Stanford REGA program and phylogenetic trees were used to determine genotypes. TDRMs were analyzed using the Stanford Calibrated Population Resistance tool. TDR transmission was evaluated from molecular networks of HIV-1 pol genes. Results The average prevalence of TDR was 6.3%, and the resistance to NNRTIs, NRTIs, and PIs was 3.49%, 2.62%, and 0.52%, respectively. TDR prevalence increased signicantly during the period 2009~2017 (3.92%~9.48%, p<0.05). The mean CD4 count was signicantly lower among individuals with TDRMs (373/mm 3 vs. 496/mm 3 , p=0.013). The rate of network entry of youths harboring TDRMs (63.89%) was signicantly higher than that of youths without TDRMs (44.9%). Conclusions The HIV-1 TDR increase and low CD4 count of patients with TDRMs in Dehong at the China-Myanmar border suggest the need for early ART and completion of resistance testing before initiating ART in HIV hotspots. Youths with TDRMs are likely to have links to others, necessitating intervention in onward transmission.


Introduction
Worldwide, 37.9 million people live with HIV, and 24.5 million people were receiving antiretroviral therapy (ART) at the end of 2018. [1] However, with the increasing use of ART, the problem of drug resistance (DR) has also been a focus. DR, transmitted HIV drug resistance (TDR) and acquired drug resistance (ADR) are caused by one or more mutations in the viral genetic structure that affect the e cacy of ART. ADR develops during viral replication in the presence of ART drugs. TDR is found in ART-naive populations and occurs when uninfected individuals are infected with virus that carries DR mutations. [2] TDR surveys can effectively guide future rst-and second-line ART regimens, help prevent mother-to-child transmission and aid pre-/postexposure prophylactic therapy. [3] TDR has been detected in ART-naïve populations in the United States, Switzerland, Peru, Argentina, Brazil, and Colombia, and the estimate of the number of people infected with virus carrying TDR mutations has increased with time. [3][4][5] In these populations, demographic and clinical characteristics usually cannot predict TDR. Interestingly, some studies reported higher CD4 counts in people with TDR mutations (TDRMs) than in individuals without TDRMs, [6,7] but others came to the opposite conclusion. [8,9] Additional evidence is needed to verify an association between TDR and decreased CD4 count. In addition, molecular networks analysis can identify genetically similar sequences; viruses with close genetic similarity indicate the spread between individuals. These network-forming clusters increase the e ciency of HIV spread in a population. [10] Molecular network analysis can play a crucial role in revealing potential transmission relationships and in evaluating the relationship of TDR within the context of a cohort. [9,11] Free ART has been widely provided in China since 2006. At the end of 2019, 0.958 million people in China were living with HIV, and 0.83 million people (86.6%) were accessing ART. [12] Yunnan Province is in the southwestern part of China, bordering Myanmar, Vietnam, and Laos. By the end of 2016, the number of people living with HIV/AIDS in Yunnan (91,986) was the second highest of all provinces in China; of these, 70,577 (76.7%) were receiving ART. Dehong city, as a hotspot of HIV recombination and transmission, [13] is a major city for trading in the Yunnan-Myanmar border area. Dehong shares an international border with Kachin and with Shan state, Myanmar, two of the major states of the "Golden Triangle" (Fig 1). The "Golden Triangle" is one of the world's largest drug production centers, [14] and there is a severe HIV transmission problem around this area. In China, the rst HIV spread in people who inject drugs (PWID) was found in Dehong. [15] According to the WHO HIV drug resistance (HIVDR) threshold survey method, untreated youths (<25 y) are more likely to have recent and incident infections. [2,16,17] To better understand TDR and guide ART therapy, we aimed to investigate TDRMs in youths over a 9-year period. We examined the effect of TDRMs on CD4 counts and the relationships of HIV youths in molecular networks in Dehong city in the China-Myanmar border area near the "Golden Triangle".

Study population and ethical review
The ethical review of this study was approved by the Medical Ethics Certi cation Committee of the Chinese Center for Disease Control and Prevention (approval no. X190111549).
From 2009 to 2017, a total of 10832 people were newly reported with HIV-1 infection at the Dehong border of China. Among these individuals, 2210 are youths (<25 y). We attempted to amplify the HIV-1 pol gene in virus isolated from 666 of these youths who were chosen according to the following criteria: 1) 16~25 y, newly diagnosed with HIV within 3-6 months, and not infected as a result of mother-to-infant transmission; 2) never received ART; 3) agreed to provide written informed consent and to allow plasma samples to be collected and stored for follow-up studies; 4) agreed to provide epidemiological information. Finally, 573 (86%, 573/666) youths from whom the viral pol gene was successfully ampli ed were used in the analysis. The sampling percentage for this study, by year, was 13.58% in 2009~2011, 22.65% in 2012~2013, 26.90% in 2014~2015, and 52.25% in 2016~2017 (Table 1). Identi cation of HIV-1 genotypes was conducted using the Stanford REGA HIV-1 Subtyping Tool 3.0. [20] If the result of the REGA tool contained 'Recombination', 'potential-Recombination' or 'check the report', the jpHMM recombination prediction tool [21] was used for con rmation. Discordant subtyping results between the two tools were analyzed by using the phylogenetic tree. reverse transcriptase inhibitors (NRTIs) and nonnucleoside reverse transcriptase inhibitors (NNRTIs). [23] Sequences were determined to be either susceptible (<15, including potential resistance) or resistant (15≤low<30, 30≤medium<60, or high>60) based on their scores.
HIV molecular network analysis of TDR Pairwise genetic distances were calculated by HyPhy 2.2.4 under the TN93 model. Cytoscape_v3.7.1 was used to visualize and analyze the molecular transmission network. We tested distance thresholds from 0.25% to 2% and inferred 1.25% as the genetic distance threshold, since 1.25% identi ed the maximum number of clusters. [24] We de ned a node as one youth and an edge as a single link between two nodes.
Edges linked nodes to each other when their genetic distance was =<1.25%. Nodes were classi ed as belonging to a network if they were linked to at least one other node.

Statistical analysis
Statistical analyses were conducted using R 3.6.2 and R-Studio 1.1.463. The Chi-square test or Fisher's exact test was used to verify differences in the distribution of demographic and clinical characteristics and genotypes. The Cochran-Armitage trend test was used to test trends such as TDR trend and the proportion of genotypes changed. Univariate logistic analysis was used to report proportions of TDRMs or network inclusion by participant characteristics (for example, nationality, educational status, etc.).
Multivariate logistic analysis was performed on variables that showed statistically signi cant differences in the univariate logistic analysis. When appropriate, p<0.05 was de ned as statistically signi cant.

Demographic and clinical characteristics
Overall, the pol gene was ampli ed successfully from 573 untreated youths with HIV-1; of these, 351 (61%) were male, and 222 (39%) were female. The most common infection route was sex among heterosexuals (70.51%), followed by PWID (19.20%) and men who have sex with men (MSM) (8.9%). The proportion of the MSM population is increasing annually (p<0.001). More than half of the youths were single (58.81%), and the rest were married/cohabiting (37.35%) or divorced (3.84%). The subjects were Chinese (55.67%) or Burmese (44.33%). The proportion of PWID was higher among Burmese than among Chinese subjects (29.13% vs. 11.29%, p<0.001). The educational status of the subjects was mainly primary education (28.27%) and junior high school education (27.23%) ( Table 2). The Chinese subjects had a higher rate of junior high school education (34.84%) than the others, while the Burmese subjects had a higher illiteracy rate (38.58%). High genotype diversity among ART-naïve HIV-infected youths The Stanford REGA online tool [20] was used for classi cation of genotypes, and the classi cation was then rechecked in the phylogenetic tree and using the jpHMM recombination prediction tool.  (Fig 1b). The prevalence of genotypes changed over time; the prevalence of B and C decreased from 2009~2017, while that of CRF01AE, CRF07BC and URFs continued to increase (Fig 1c). According to the route of infection, the proportion of URF and C in PWID was higher than that observed via sexual transmission (heterosexuals and MSM), but the proportions of CRF01_AE and CRF_08BC in cases of sexual transmission were high (p<0.001) (Fig 1d). The distribution of infection routes differed between China and Myanmar; the distribution of genotypes also differed. URF was higher in Burmese subjects than in Chinese subjects (31.89% vs. 21.63%, p<0.05), and CRF07_BC was higher in Chinese subjects than in Burmese subjects (8.46% vs. 1.57%, p<0.01) (Fig 1e).  (Fig 2a).

Correlates of TDR
The characteristics of individuals with and without TDR were comparable with respect to nationality, year of diagnosis, infection route, gender, ethnicity, marital status, educational level and CD4 cell count (   (Fig 3). The network entry rate of sequences containing TDRMs was signi cantly higher than that of sequences without TDRMs (63.89% vs. 44.9%, p=0.037, Chi-square test). The result also shows the different frequencies of TDRMs in in-network versus nonnetwork youths (8.7% vs. 4.2%). We determined that people who have a risk factor, i.e., those with TDRMs and those with CRF07_BC or C genotypes, were likely to have a link to others (Table S1). Y181C, D67E, V106M, and K103N were cases of shared DRMs with recent transmission (gene distance <0.5%) (Fig 3).

Discussion
The city of Dehong is located in the China-Myanmar border area near the "Golden Triangle" and is a hotspot of HIV transmission and recombination, having a strong impact on the HIV-1 epidemic in China. [25,26] We determined and statistically analyzed the age distribution of newly reported HIV infections in Dehong city (Table 1). TDR can better guide future ART regimens, prevent mother-to-child transmission and aid pre-/post-exposure prophylactic therapy. Untreated youths (<25 y) are more likely to have recent and incident infections. [2,16,17] Therefore, we analyzed the TDR of untreated youths (16~25 y) newly diagnosed with HIV-1 over a relatively long period (from 2009 to 2017) in Dehong.
The distribution of HIV-1 genotypes in China is mainly CRF01AE, CRF07BC, CRF08BC, and B, while C, URF, and other circulating recombinant forms (CRFs) account for only a small proportion of cases. [27] However, the distribution of genotypes differs in Dehong, which has a high prevalence of URF and C subtypes. [28,29] Similar to previous studies, the distribution of HIV genotypes in this study was diverse and complex. Interestingly, the prevalence of B and C has decreased annually, while that of CRF01AE, CRF07BC and URFs continues to increase. We also found that the proportion of URFs in Burmese and PWID populations was signi cantly higher than that in other populations. The result may indicate that due to the in uence of drug injection in the "Golden Triangle", the presence of HIV-1 recombination networks occurred early among PWID in Dehong. [30][31][32] This has had a long-term impact on the HIV-1 epidemic in this area, making Dehong a hotspot for HIV recombination.
Frequent recombination is more effective than mutation in spreading drug resistance mutations. [33,34] Frequent communication around the China-Myanmar border has increased the frequency of recombination [28,35] and the probability of TDRM transmission. Overall, the average prevalence of TDR was 5.4%, a value that exceeds the 5% moderate prevalence level. It is worth noting that during 2016~2017, the prevalence of TDR was 9.48%, signi cantly higher than the average TDR prevalence in China [36] and Myanmar. [3] The prevalence of TDR in this study does not represent the average resistance level in China and Myanmar but indicates the increase in TDR among youths in hotspots of HIV transmission and recombination. In this study, no signi cant difference was found in TDR prevalence between Burmese and Chinese subjects. Furthermore, whereas the prevalence of TDR in Chinese subjects increased from 2009 to 2017 (from 3.92% to 5.93%), the prevalence of TDR in Burmese migrants increased signi cantly from 2010 to 2017 (from 4.00% to 13.16%). Burmese migrants are a key population for HIV prevention in this region.
Previous studies suggested that DRMs impair viral tness, resulting in increased CD4 count. [6,7] However, some DRMs have a low impact on viral tness and even improve it, which may lead to a more rapid decline in CD4 count. [9,41] In the latest research, [8] no association was found between DRMs and decreased CD4 count. In this study, HIV-1-infected youths with TDRMs had low CD4 counts; this provides some evidence for a relationship between TDRMs and decreased CD4 count.
We analyzed TDR transmission based on the molecular transmission network. The rate of entry into the network (46.1%) of youths in our study was signi cantly higher than that reported in other studies. [42,43] Moreover, youths with TDR (63.89%) were more likely to enter the network. Consent for publication: Not applicable.
Con icts of Interest: The authors report no con icts of interest in this work.
Availability of data and materials: The datasets are available from the corresponding author on reasonable request.   Figure 1 Genotypes analysis of HIV-1 pol genes. Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.

Figure 2
Resistance level and TDR prevalence trend