Molecular epidemiologic characteristics and transmission clusters of HIV-1 CRF01_AE in Zhejiang, China

doi:10.21203/rs.3.rs-4334165/v1

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Molecular epidemiologic characteristics and transmission clusters of HIV-1 CRF01_AE in Zhejiang, China

https://doi.org/10.21203/rs.3.rs-4334165/v1

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Background

HIV-1 CRF01_AE is becoming the predominant HIV-1 subtype among patients in China.The distribution and molecular epidemiologic characteristics of transmission clusters of HIV-1 CRF01_AE in Zhejiang,China remains unclear.This study analyzed the molecular epidemiologic characteristics and transmission clusters of HIV-1 CRF01_AE in Zhejiang.

Methods

Plasma samples obtained from 152 patients with HIV-1 CRF01_AE infection not undergoing ART were used to amplify HIV-1 pol and env. CRF01_AE drug resistance mutations (DRM) prevalence was analysed using Stanford University’s HIV Drug Resistance Database. A phylogenetic tree was constructed using FastTree (version 2.1.11) based on the GTR nucleotide substitution model and visualized using Figtree (version 1.4.4) and The Interactive Tree of Life; the Chinese HIV Gene Sequence Data Platform was used to construct genetic transmission networks.

Result

Most samples could be grouped into CRF01_AE transmission Clusters 1 (11.18%), 4 (64.5%), and 5 (7.24%). The CD4 + T-cell counts associated with Clusters 1, 4a, 4b, and 5 were 15, 38, 30, and 248 cells/mm³, respectively ( P < 0.05). DRM rates in Clusters 4a and 4b were 25.45% and 17.65%, respectively (P < 0.05), whereas they were 17.65% and 18.18% in Clusters 1 and 5, respectively. The high X4 tropism rates were 13.2%, 11.8%, 20%, and 0% in Clusters 1, 4a, 4b, and 5, respectively. In total, 24 transmission genetic networks, comprising 72 sequences and 61 links, were discovered; of them, 61.2%, 11.7%, and 18.2% were from Clusters 4, 1, and 5, respectively (P < 0.05).

Conclusions

In Zhejiang, different CRF01_AE clusters exhibited disease progression and outcome differences. The surveillance of molecular epidemiology of HIV-1 should be enhanced to improve prognosis of the disease and minimize its transmission.

Since the discovery of AIDS in 1981, HIV strains have evolved and spread widely.¹ Similarly, the distribution of HIV-1 subtypes has considerably evolved over the years.^2–4 In China, numerous HIV-1 subtypes and circulating recombinant forms (CRFs) have been detected, each with varying effects on disease progression.⁵

HIV-1 CRF01_AE, which originated in Africa⁶, has spread worldwide, primarily through sexual contact, particularly within the men who have sex with men (MSM) community.^7,8 Moreover, CRF01_AE infection may reduce CD4 levels with an accelerated prognosis.^9,10 CRF01_AE is becoming the predominant HIV-1 subtype among patients in China.^7,11 In eastern China, the predominant HIV-1 subtypes include CRF01_AE (46.05%) and CRF07_BC (24.93%).¹² Furthermore, in 2015, CRF01_AE was the predominant subtype (41.69%) among patients with HIV infection in Fujian.¹³ CRF01_AE in China can be categorized into seven genetic subgroups, each with unique geographical characteristics and associated risk factors.¹⁴ Studies on the frequency of drug resistance in HIV patients within Zhejiang have revealed that CRF_01AE is the most common HIV-1 subtype in the area.¹⁵ However, the characteristics of the CRF01_AE clusters in Zhejiang remain unclear.

The objective of the current study, therefore, was to assess the characteristics of the prevalent transmission clusters of CRF01_AE in Zhejiang, China. In this study, we enrolled patients who were confirmed to have HIV-1 CRF01_AE subtype infection but were not under ART in Zhejiang and analysed the prevalence of drug resistance mutations (DRMs) related to CRF01_AE among them. Next, we constructed a phylogenetic tree to identify the major CRF01_AE clusters with different characteristics and performed a genetic transmission network analysis.

Study population

Plasma samples were collected from 152 HIV patients admitted to the First Affiliated Hospital of Zhejiang University in Zhejiang, China, over 2013–2017. All the included patients were aged 18–80 years, were diagnosed or newly diagnosed as having HIV-1 CRF01_AE infection, and had not yet initiated ART. We also collected relevant demographic characteristics, such as age, sex, marital status, and CD4 + T-cell count.

RNA extraction, amplification, and sequencing

HIV-1 RNA was extracted from the plasma samples by using the QIAamp Viral RNA Mini Kit (Tiangen, China). The gene fragments of HIV-1 pol (HXB2 2425–3332) and env (HBX2 7022–7644) were amplified using nested polymerase chain reaction (PCR). The PCR products were sent to the company for Sanger sequencing.

DRM analysis

We identified DRMs by submitting the gene sequences to Stanford University’s HIV Drug Resistance Database (https://HIVdb.Stanford.edu/HIVdb/by-sequences/).

Phylogenetic tree analysis

The gene sequences were aligned and manually adjusted using Bioedit. Next, a phylogenetic tree based on the GTR nucleotide substitution model was constructed in FastTree (version 2.1.11). Transmission chains were defined as clades with a branch probability of ≥ 0.80. Finally, the results were visualized using Figtree (version 1.4.4) and The Interactive Tree Of Life (https://itol.embl.de/).

Viral tropism prediction

Viral tropism genotypes were predicted using the Geno2pheno clone algorithm.¹⁶ Sequencing replicates for the position-specific scoring matrices with scores < − 6.96 were labelled as r5, whereas those with scores ≥ − 6.96 were labelled as x4.¹⁷ A high level of accuracy was noted for X4 tropism in CRF01_AE Clusters 4 and 5, specifically in China¹⁸.

Genetic transmission network analysis

All 152 gene sequences were submitted to the Chinese HIV Gene Sequence Data Platform (https://nmdc.cn/hiv/) for analysing transmission clusters. Transmission clusters were defined as pairs of sequences with a genetic distance of ≤ 1.5%.

Statistical analysis

Depending on their distribution, continuous variables are presented as mean ± standard deviation or median (25th and 75th percentiles), whereas categorical variables are reported as numbers (percentages). We used SPSS (version 29.0) to perform the chi-square, independent-samples t, and Mann–Whitney U tests. Two-tailed P < 0.05 was considered to indicate statistical significance.

Sequence Data

Data is deposited in National Microbiology Data Center (NMDC) with accession numbers NMDC10018822(https://nmdc.cn/resource/genomics/project/detail/NMDC10018822).

Demographic characteristics of our patients

We obtained partial pol and env sequences for all 152 patients. Of them, most were male (90.13%, 137/152), single or divorced (57.24%, 87/152), middle-aged, and less educated (Table 1).

Table 1

Demographic characteristics of our patients living with HIV-1 CRF01_AE infection
Characteristic	Value
Sex, n (%)
Male	137 (90.13)
Female	15 (9.87)
Age, n (%)
≤ 40 years	108 (71.05)
> 40 years	44 (28.95)
Education level, n (%)
University and higher	48 (31.58)
Middle school	92 (60.53)
Primary school and lower	12 (7.89)
Marital status n (%)
Married	65 (42.76)
Single or divorced	87 (57.24)
CD4 + T-cell count (cells/mm³), median, (25th, 75th percentiles)	27 (1, 1106)

DRMs

Of all 152 patients, 24 (15.79%) were found to have at least one DRM. Specifically, the rates of DRMs in the nucleoside reverse transcriptase inhibitors (NRTIs), non-NRTIs (NNRTIs), and protease inhibitors (PIs) were 8.55%, 9.21%, and 1.32%, respectively (n = 13, 14, and 2, respectively).

The most common NRTI DRM was S68G (3.95%, n = 6), followed by S68SG (1.32%, n = 2). Moreover, the NNRTI DRM with the highest frequency was V179D (3.95%, n = 6), followed by V106I and V179VD (both 1.32%, n = 2). Finally, only one PI DRM was observed at M46I (0.66%, n = 1) and an accessory DRM at T74PS (0.66%, n = 1).

Four patients demonstrated DRMs in both NRTIs and NNRTIs, and one demonstrated DRMs in both PIs and NRTIs; however, no patient demonstrated DRMs in all three genes. Finally, one and two patients demonstrated two and three NNRTI DRMs, respectively, and two patients demonstrated two NRTI DRMs.

Distribution of CRF01_AE transmission clusters in Zhejiang

To ascertain CRF01_AE cluster distribution in Zhejiang, we next compared 152 sequences from this study (in Zhejiang) with 88 sequences reported in a study¹⁴ that examined the distribution of CRF01_AE clusters in China (Fig. 2).

In total, 17 (11.18%), 2 (1.32%), 1 (0.66%), 93 (61.18%), and 11 (7.24%) patients were grouped in Clusters 1, 2, 3, 4, and 5, respectively. Within Cluster 4, 38 (25%) and 55 (36.18%) were grouped in Clusters 4a and 4b, respectively. Considering their predominance, we analysed only Clusters 1, 4, and 5 further (Table 2).

Table 2

Demographic characteristics and DRMs in the HIV-1 CRF01_AE clusters
Characteristic	Cluster 4a	Cluster 4b	Cluster 1	Cluster 5	P
Sex, n (%)					0.001
Male	38 (100.0)	51 (92.7)	11 (64.7)	9 (81.8)
Female	0 (0.0)	4 (7.3)	6 (35.3)	2 (18.2)
Age, n (%)					0.003
< 41 years	32 (84.2)	43 (78.2)	6 (35.3)	8 (72.7)
> 40 years	6 (15.8)	12 (21.8)	11 (64.7)	3 (27.3)
Marital status, n (%)					0.013
Married	13 (34.2)	17 (30.9)	12 (70.6)	2 (18.2)
Single or divorced	25 (65.8)	38 (69.1)	5 (29.4)	9 (81.8)
Education level, n (%)					0.256
University or higher	13 (34.5)	19 (34.5)	2 (11.8)	5 (45.5)
Middle school	22 (57.9)	33 (60.0)	11 (67.7)	5 (45.5)
Primary school or lower	3 (7.9)	3 (5.5)	4 (23.5)	1 (9.1)
CD4 + T-cell count (cells/mm³), median, (25th, 75th percentiles)	38 (24, 111)	30 (8, 90)	15 (8, 26)	248 (22.5, 508)	0.031
DRMs, n (%)
NRTIs
A62V	0 (0.0)	1 (1.82)	0 (0.0)	0 (0.0)
D67DN	0 (0.0)	1 (1.82)	0 (0.0)	0 (0.0)
M184V	0 (0.0)	1 (1.82)	0 (0.0)	0 (0.0)
M184MI	0 (0.0)	1 (1.82)	0 (0.0)	0 (0.0)
M184MV	0 (0.0)	1 (1.82)	0 (0.0)	0 (0.0)
M41ML	0 (0.0)	1 (1.82)	0 (0.0)	0 (0.0)
S68G	1 (2.63)	3 (5.45)	1 (5.88)	1 (9.09)
S68SG	0 (0.0)	1 (1.82)	0 (0.0)	0 (0.0)
T215TA	0 (0.0)	1 (1.82)	0 (0.0)	0 (0.0)
NNRTIs
K101KE	0 (0.0)	1 (1.82)	0 (0.0)	0 (0.0)
E138EK	0 (0.0)	1 (1.82)	0 (0.0)	0 (0.0)
G190GA	0 (0.0)	1 (1.82)	0 (0.0)	0 (0.0)
Y188YFHL	0 (0.0)	1 (1.82)	0 (0.0)	0 (0.0)
V106I	0 (0.0)	1 (1.82)	1 (5.88)	0 (0.0)
V106M	0 (0.0)	1 (1.82)	0 (0.0)	0 (0.0)
V106VM	0 (0.0)	1 (1.82)	0 (0.0)	0 (0.0)
V179D	2 (5.26)	3 (5.45)	0 (0.0)	1 (9.09)
V179VD	0 (0.0)	0 (0.0)	1 (5.88)	1 (9.09)
V179E	0 (0.0)	1 (1.82)	0 (0.0)	0 (0.0)
PIs
M46I	0 (0.0)	1 (1.82)	0 (0.0)	0 (0.0)
T74PS	1 (2.63)	0 (0.0)	0 (0.0)	0 (0.0)

Characteristics of DRMs within different clusters

Among Clusters 1, 4, and 5, Cluster 1 was noted mainly in women (35.3%), as well as in middle-aged and older patients, most of whom were married (all P < 0.05). Cluster 4, mainly an MSM cluster, could be further subdivided into Clusters 4a from northern China and Cluster 4b from eastern and southern China. Both Clusters 4a and 4b primarily consisted of patients who were single or divorced [65.8% (25/38) and 69.1% (38/55), respectively], were aged < 41 years [84.2% (32/38) and 78.2% (43/55), respectively], and had middle school education [57.9% (22/38) and 60% (33/55), respectively]. Finally, patients in Cluster 5 demonstrated the highest CD4 + T-cell counts (P < 0.05); however, their other characteristics were comparable to those in Cluster 4.

DRMs and viral tropism prediction within different clusters

Next, we examined DRMs within each cluster and observed revealed that the rate of DRMs in Clusters 4a and 4b were 7.89% (3/38) and 25.45% (14/55), respectively (P < 0.05). Here, S68G and V179D emerged as the most prevalent NRTIs and NNRTIs DRMs.

In contrast, the rates of DRMs in Clusters 1 and 5 were 17.65% (3/17) and 18.18% (2/11), respectively. Notably, Clusters 1, 4a, and 5, but not 4b, harboured S68G, the DRM in NRTIs. Moreover, NNRTI DRMs were predominantly observed in V179D, V179VD, and V106I. A high X4 tropism rate of 20% was observed in Cluster 4b; in contrast, this value was only 13.2% and 11.8% in Clusters 4a and 1, respectively; however, no high X4 tropism was noted in Cluster 5.

Genetic transmission networks

In total, 62 links and 24 genetic transmission networks, including 73 (48.03%) of all 153 patients, were identified (Fig. 3). Of the 73 patients, 58.90% shared transmission events with only one patient, whereas 41.10% shared them with at least two other patients. Ten patients demonstrate at least one DRM, with 70% carrying an NRTI DRM and 30% carrying an NNRTI DRM. The most common DRM was S68G, followed by S68SG. Two patients had multiple DRMs, including S68G + V179D and E138EA + V79FI. Moreover, one patient demonstrated T74PS, a minor PI DRM.

The largest cluster comprised 11 patients, 2 of whom had five links. Two S68G mutations were identified within this cluster, and mutual connections were observed. The network included six drug-resistant HIV patients—with three, two, and one being NNRTI, NRTI, and PI, respectively—each in separate clusters. However, no evidence of transmission was observed between the drug-resistant HIV patients. In this network, the proportion of Cluster 4 (61.2%, 60/98) was significantly higher than that of Cluster 1 (11.7%, 2/17) and Cluster 5 (18.2%, 2/11; P < 0.05). Further analysis revealed that 73.7% (28/38) of the sequences belonged to Cluster 4a, whereas 49.1% (27/55) belonged to Cluster 4b (Fig. 4). Notably, a comparison between patients in Clusters 4a and 4b within the network revealed that Cluster 4b had more DRMs (P < 0.05) and that Cluster 4a had no DRMs. No differences were noted between the two clusters regarding age, sex, marital status, CD4 + T-cell count, and DRM rate.

The current results clarified the epidemiological characteristics and transmission cluster features of patients with HIV-1 CRF01_AE infection in Zhejiang, China.

HIV patients from different regions have been noted to display varied DRMs.¹⁹ In Ethiopia, K103N and K219Q were the predominant NNRTI and NRTI DRMs among pretreatment drug–resistant HIV patients over 2003–2018.²⁰ In China, K103N was the most prevalent NNRTI DRM among HIV patients in 2017, followed by V179D and E138A.²¹ In the current study, the most frequent NNRTI DRM was V179D (3.95%, 6/152), followed by V179VD (1.32%, 2/152) and V106I (1.32%, 2/152). The most commonly observed NRTI DRM was S68G (3.95%,6/152). S68G, particularly prevalent among CRF01_AE strains, has been noted to be the most frequent natural polymorphism, typically followed by K65R; it can partially offset the replication impairment linked to K65R, another NRTI DRM.^22,23 Nevertheless, in the current study, patients with S68G did not exhibit K65R. As such, further investigation on the relationship between S68G and K65R is warranted.

A study determined the strain lineage of HIV-1 CRF01_AE in China and noted the unique characteristics of each cluster.¹⁴ In the present study, we noted that most CRF01_AE strains in Zhejiang could be placed in Clusters 1, 4, and 5—with each strain exhibiting unique traits.

Cluster 4, the primary CRF01-AE strain in Zhejiang, may have originated from northern MSM populations and have become transmitted to Zhejiang. Thus, the CRF01_AE strains may be mainly transmitted via the MSM route in Zhejiang. Moreover, the patients in Cluster 4 were young or middle-aged men, most of whom were single and therefore may have had multiple sexual partners. The median CD4 + T-cell counts for Clusters 4a and 4b patients were 38 (24/111) and 30 (8/90), respectively. Moreover, the X4 tropism rates in Clusters 4a and 4b were high (13.2% and 20%, respectively). Studies have indicated that high X4 tropism is correlated with low CD4 + T-cell counts²⁴ and that genetic clusters exhibiting the X4 phenotype reduce ART efficacy.¹⁸ Notably, DRM rates were more variable and higher in Cluster 4b than in Cluster 4a. Moreover, the predominant NNRTI DRM was V179D. However, Cluster 4b DRMs can lead to decreased susceptibility to efavirenz and nevirapine.²⁵ As such, the use of these drugs in ART for patients with Cluster 4b infections should be restricted. In summary, Cluster 4, particularly Cluster 4b, may be a high-risk cluster in Zhejiang, with young men being the most affected population. Patients with Cluster 4 infections may require immune reconstitution over an extended period and may be relatively vulnerable to severe opportunistic infections. Careful consideration of the timing of ART and detection of opportunistic infections may improve the quality of life of these patients.

Cluster 5 was noted to share similarities with Cluster 4 in terms of its characteristics; however, it was associated with considerably higher CD4 + T-cell counts but without any X4 tropism. This result corroborates the findings of previous studies, indicating that Cluster 5 is stable.

A study indicated that Cluster 1 may have been transmitted to Zhejiang via heterosexual individuals and injection drug users in southern China. In the current study, Cluster 1 was mainly present in middle-aged and older married patients, with 35.3% of them being female, and the median CD4 + T-cell count associated with Cluster 1 was 15. A study reported that the likelihood of CD4 + T-cell count recovery and immune reconstruction is relatively low among Cluster 1 patients.²⁶ As such, we propose that middle-aged and older married individuals, particularly women, should be considered the key target group for Cluster 1 infections. Furthermore, equal attention should be paid to marital harmony and well-being among this population.

In the present study, 48.03% (73/152) of individuals constituted the genetic transmission networks. HIV CRF_01AE transmission risk appears to be higher in Zhejiang than in the southwest border region of China (32.2%) or in Guangdong, China (25.8%).^27,28

Our results also indicated that the prevalence of NNRTI DRMs was higher than that of NRTI DRMs. In the largest cluster, two patients shared a DRM (S68G), indicating the potential for transmission between them. Because a larger number of individuals from Cluster 4 were present in the genetic transmission networks, Cluster 4 may be a high-risk transmission cluster. Cluster 4b demonstrated more DRMs than Cluster 4a, suggesting that the mutual DRM transmission risk between Clusters 4a and 4b is high. Thus, to minimize the spread of Cluster 4 HIV-1 CRF01_AE strains and potentially prevent their evolution, drug resistance testing and dynamic transmission monitoring of these strains, particularly Cluster 4b strains, should be prioritized.

This study has a few limitations. First, our HIV patient sample was small and enrolled from a single centre; this may have led to sampling errors and limited the generalizability of our findings size. Consequently, further comprehensive analysis of the characteristics of patients with HIV-1 CRF01_AE infections in Zhejiang as a whole is warranted. Second, all the included HIV patients were hospitalized; thus, the higher proportion of DRMs among them could have been due to impairments in their immune function and infection status. Furthermore, before 2017, integrase strand transfer inhibitors (INSTIs) were rarely used as first-line treatment for HIV infection, and our study did not include sequencing for INSTIs. Nevertheless, INSTI resistance may be more prevalent in HIV-experienced patients than in HIV-naïve patients.²⁹

In conclusion, our study revealed the presence of HIV-1 CRF01_AE Clusters 1, 4, and 5 in Zhejiang, China, with each cluster demonstrating distinct characteristics. Cluster 4 was noted to be the most significant in terms of both DRMs and transmission risks. To minimize the spread of HIV strains, effective targeted drug resistance testing and ongoing transmission dynamics surveillance are essential.

AIDS Acquired immunodeficiency syndrome

HIV Human immunodeficiency virus

CRFs Circulating recombinant forms

MSM Men who have sex with men

DRM Drug resistance mutations

NNRTI Non-nucleoside reverse-transcriptase inhibitor

NRTI Nucleoside reverse-transcriptase inhibitor

PI Protease inhibitor

INSTI Integrase strand transfer inhibitor

Ethics approval

The study protocol was approved by the ethical committee of the First Affiliated Hospital, Zhejiang University School of Medicine (No. IIT2022052B-R1) and was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

Consent for publication

All the authors declare their consent for publication.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

All the authors declare that they have no competing interests.

Funding

This work was supported by the National Key R and D Program of China [grant number 2022YFC2305202].

Author contributions

BD, XP carried out most of the experiments and wrote the manuscript. JS,XZ,XL,YX,ZW,DX,JH,CY,HL helped with the experiments. BZ designed the experiments and revised the manuscript. All of the authors read and approved the final version of this manuscript.

*Corresponding author. E-mail: [email protected]

†Bohao Dai and Xiaorong Peng contributed equally to this work.

Author details

¹The Department of Infectious Diseases, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, 79 Qing Chun Road, Hangzhou 310006, China.

Acknowledgements

We would like to thank the native English-speaking experts of Elixigen CO Company for English language review.

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No competing interests reported.

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Molecular epidemiologic characteristics and transmission clusters of HIV-1 CRF01_AE in Zhejiang, China

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Abstract

Background

Methods

Result

Conclusions

Figures

Introduction

Materials and methods

Study population

RNA extraction, amplification, and sequencing

DRM analysis

Phylogenetic tree analysis

Viral tropism prediction

Genetic transmission network analysis

Statistical analysis

Sequence Data

Results

Demographic characteristics of our patients

DRMs

Distribution of CRF01_AE transmission clusters in Zhejiang

Characteristics of DRMs within different clusters

DRMs and viral tropism prediction within different clusters

Genetic transmission networks

Discussion

Abbreviations

Declarations

References

Additional Declarations

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