More than half of intravenous drug users (IDUs) in China suffer from the Hepatitis C virus (HCV). The virus is also more prevalent in non-injection drug users (NIDUs) than in the general population. However, not much is known about HCV subtype distribution in these populations.
Our research team conducted a cross-sectional study in four provinces in China. We sampled 825 IDUs and 244 NIDUs (1162 total), genotyped each DU’s virus, and performed a phylogenetic analysis to differentiate HCV subtypes.
Nucleic acid testing (NAT) determined that 82% percent (952/1162) of samples were HCV positive; we subtyped 90% (859/952) of these. We found multiple HCV subtypes: 3b (249, 29.0%), 3a (225, 26.2%), 6a (156, 18.2%), 1b (137, 15.9%), 6n (50, 5.9%), 1a (27, 3.1%), and 2a (15, 1.7%). An analysis of subtype distributions adjusted for province found statistically significant differences between HCV subtypes in IDUs and NIDUs.
HCV subtypes 3b, 3a, 6a, and 1b were the most common in our study, together accounting for 89% of infections. The subtype distribution differences we found between IDUs and NIDUs suggested that sharing syringes was not the most likely pathway for HCV transmission in NIDUs. However, further studies are needed to elucidate how NIDUs were infected.
Citation: Tao J, Liang J, Zhang H, Pei L, Qian H-z, Chambers MC, et al. (2015) The Molecular Epidemiological Study of HCV Subtypes among Intravenous Drug Users and Non-Injection Drug Users in China. PLoS ONE 10(10): e0140263. https://doi.org/10.1371/journal.pone.0140263
Editor: Jason Blackard, University of Cincinnati College of Medicine, UNITED STATES
Received: May 26, 2015; Accepted: September 22, 2015; Published: October 14, 2015
Copyright: © 2015 Tao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Data Availability: Data are ethically and legally restricted from public sharing by the National Center for HIV/AIDS Control and Prevention, Chinese Center for Disease Control and Prevention. Interested researchers may contact author Xiao Yao at email@example.com for data requests.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
A meta-analysis of studies from 25 countries estimated that 60–80% of intravenous drug users (IDUs) were infected with the Hepatitis C virus (HCV).  In China, HCV prevalence ranged from 50% to 95% among IDUs; [2–4] the estimated incidence rate was from 11.6 to 29 per 100 person-years. [5–7] About 1.6 million IDUs tested positive for HCV-antibodies in 2011.  The HCV epidemic in non-injection drug users (NIDUs) is not well studied, despite a meta-analysis that reported an HCV prevalence of 2.3–35.3%.  Chronic HCV infection was strongly associated with liver cirrhosis and hepatocellular carcinoma (HCC); 5–20% of HCV chronic patients progress to liver cirrhosis in 20 to 25 years, and around 4% will suffer from HCC. [9,10] Sharing needles, syringes, and drug-preparation equipment has been demonstrated as the most common HCV transmission pathway in IDUs.  Sexual HCV transmission was previously thought to be unlikely, but recent research has reported a higher likelihood. [12–14]
HCV treatment can be effective in reducing risk for HCV-related liver diseases and HCV secondary transmission.  However, patients infected by different HCV genotypes do not respond to treatment in the same way. Compared to HCV genotypes 2, 3, 5 and 6, types 1 and 4 are treated with a higher dose of medication taken for a longer period but are still less likely to achieve sustained virologic response (SVR). [16–19] SVR is non-detection of HCV RNA at the end of the treatment and for the following six months. Recent research has suggested treatment efficacy also differs at subtype level. [20–22] However, few studies have focused on these treatment efficacy differences in the Chinese population. Furthermore, little is known about HCV subtype distribution among DUs, especially NIDUs. Hence, studies are needed to fill in this knowledge gap and further explore the association between HCV subtype and treatment efficacy.
To describe HCV subtype distribution among DUs in China, we conducted a cross-sectional study in four provinces. Between 2008 and 2011, we sampled 1,162 HCV-infected DUs from the national networks of methadone clinics and the HIV sentinel surveillance clinics in the Chinese provinces of Guizhou, Hebei, Jiangsu, and Shanxi. Two rounds of HCV-antibody enzyme-linked immunsorbent assays (ELISA) were used to determine HCV serostatus. All our study participants tested positive in both rounds. The national clinics de-identified participants’ demographic data and sent both the data and their blood samples to the Center for Disease Control (CDC) in China. The de-identified dataset still included demographic and behavioral information. Our study was approved by the Vanderbilt University School of Medicine IRB (number: 131253).
HCV RNA extraction and nucleic acid testing (NAT)
At the CDC, we used participants’ blood samples to determine their HCV subtypes. We extracted HCV RNA using the MagNA Pure LC automated platform (Roche Ltd, Basel, Switzerland). The nucleic acid test (NAT) to detect the virus used a nested reverse transcription polymerase chain reaction (RT-PCR) process. First, we amplified a 170bp fragment in the 5’-untranslated region (5’-UTR). The primers we used for this process have been previously published (see S1 Text).  Secondly, we ran a one-step RT-PCR using TransScriptTM One-Step RT-PCR Supermix (Beijing TransGen Biotech CO. LTD, China) as the reagent mixture. It contained 5μL RNA extraction, 17μL DEPC water, 25μL Mix (dNTP, MgCl2 and buffer), 1μL mixture of enzymes (MMLV reverse transcriptase, Taq DNA polymerase and RNase Inhibitor), 1μL sense primer (5’-UTR-IS), and 1μL antisense primer (5’-UTR-IAS). We ran this first RT-PCR round for 30 min. at 50°C, 2 min. at 94°C, followed by 30 cycles of 30 sec. at 94°C, 30 sec. at 55°C, and 1 min. at 72°C. We allowed 7 min. at 72°C for elongation, and then stored the PCR product at 4°C. Third, we ran another nested-PCR step using 2×Taq Master Mix (Tiangen Biotech CO. LTD, Beijing, China) as the reaction mixture. It contained 2μL PCR products from RT-PCR, 21μL DEPC water, 25μL Mix (Taq DNA polymerase, dNTP, loading buffer, MgCl2, and buffer), 1μL sense primer (5’-UTR-NS), and 1μL antisense primer (5’-UTR-NAS). We ran this round for 2 min. at 94°C, followed by 34 cycles of 30 sec. at 94°C, 30 sec. at 55°C, and 1 min. at 72°C. We used the same elongation and storage protocol as we did in the first step.
We amplified genes using a GeneAmp PCR 9700 thermal cycler (Applied Biosystems, NY, USA), and then used gel electrophoresis to detect the target DNA fragments.
Amplification of the sequencing fragment in HCV NS5B region
For the RNA samples that tested positive for HCV, we amplified a 350bp fragment in the NS5B region. We then applied a nested RT-PCR procedure similar to the NAT procedure, except using different primers (S1 Table). We used gel electrophoresis to detect the target fragment. Amplified products were sent to Biomed Laboratory (Beijing, China) for sequencing.
Subtyping and statistical analysis
We used ChromasPro 1.5 to trim and assemble sequences. Then we used BioEdit 4.0 to perform multiple alignments against the NS5B reference sequences from the HCV Sequence Database (https://hcv.lanl.gov/content/sequence/HCV/ToolsOutline.html). Finally, we ran Mega 4.0 to conduct the phylogenetic analyses. Subtype information was read from the resulting phylogenetic trees (using the neighbor-joining method with the Kimura-2-parameter).
We compared HCV subtype distributions among demographic variables using a chi-squared test. To control for confounding effects by region, we analyzed the subgroups on a per-province basis. We conducted all analyses using Statistical Analysis System 9.3 (SAS Institute Inc, Cary, NC, USA).
The median age in our study population was 38 years (interquartile range [IQR]: 32, 43). Most participants were of Han ethnicity, male, single, and had less than an 8th-grade education. Our sample population was not spread equally across each province: 26.6% were from Guizhou, 27.1% from Shanxi, 30.2% from Jiangsu, and 16.1% from Hebei. Nearly equal numbers of participants were sampled from methadone clinics and HIV sentinels (560 vs. 602). Most participants in our study population (77.2%) were IDUs.
Results of NAT and subtyping
In this study, 952 (81.9%) DUs tested positive for HCV RNA, and we successfully subtyped 859 of those 952 (90.2%). The proportion of NAT positive and subtyping rate differed by province and sample source (Table 1). For example, participants from Hebei were less likely to be NAT positive and to be subtyped. Participants from methadone clinics also had a low rate of RNA detection and subtyping. The result of our phylogenetic analysis on the HCV subtypes can be seen in Fig 1.
The distribution of HCV subtypes
Table 2 describes the distribution of subtypes among HCV-infected DUs. HCV 3b (249, 29.0%), 3a (225, 26.2%), and 6a (156, 18.2%) were the most common strains among drug users. Bivariate analysis suggested that frequencies of HCV subtype differed between IDUs and NIDUs (Table 2). They also differed by province (Fig 2). When we stratified the data by province, the difference in frequencies of HCV subtypes between IDUs and NIDUs was statistically significant.
Our study population was on average older than those in other studies, but the ratio of males to females was similar (3.5:1). [24,25] Previous studies have reported that young DUs are more likely to inject drugs while older DUs may switch to non-injection drugs.  Since our study population included both IDUs and NIDUs, it was not surprising that our participants were older than the general IDU populations in the literature. Older DUs were more likely to be married and sexually active. Thus, they should have a higher chance of transmitting HCV to their sexual partners than younger DUs would.
The main HCV subtypes in our DUs were 3b, 3a, 6a, and 1b, which was concordant with other studies. [24,26] By contrast, in Korea, the main strains among DUs were 1b and 2a/2c , while in Vietnam, 1a, 1b, and 6a were the most common.  Because we sampled HCV-infected DUs from 4 provinces, our study population better represented Chinese DUs than previously published studies, which focused on small samples of IDUs in a single geographic region. We replicated the geographic distribution of HCV subtype found by a previous study : 1b and 3b were the main strains in the northern part, 3a and 3b in the central part, and 3b and 6a in the southern part. However, HCV subtypes, which used to be seen only in a certain geographic region, were also found in different regions. Migration may have broken the geographic limitation of HCV subtypes.
We found different HCV subtype distributions between IDUs and NIDUs, which contradicted research conducted in the Netherlands.  Van den Berg et al. found frequencies of HCV subtypes to be similar between IDUs and NIDUs. In our study, HCV subtype 1b, 3b, and 6a were the main strains in NIDUs, whereas 3a and 3b were the most common in IDUs. Our finding was biologically plausible because certain HCV subtypes are mainly transmitted by IDUs through sharing of needles, while true NIDUs must be infected via a different pathway, such as sexual transmission. We say “true NIDUs” because we observed that a small proportion of HCV sequences from IDUs and NIDUs clustered into the same group in our phylogenetic analyses (Fig 1). This indicated that some NIDUs injected drugs but reported as non-injection DUs. However, response bias alone cannot explain the HCV epidemic among NIDUs, and more research is needed to understand the mechanism of HCV transmission in this population.
This study had several limitations. The quality of plasma is critical for RNA extraction and genotyping. Variable plasma quality from clinics in different provinces can be observed, and the HCV genotyping failures might be largely dependent on that variable. Thus, we consider HCV subtype to be missing at random (MAR). Drug use method (IDU vs. NIDU) may also be MAR, as the majority of missing responses were from participants from Guizhou. As a result, when conducting analyses between provinces, our findings could be either inflated or attenuated. As mentioned before, response bias from DUs seems to have caused some of them to be misclassified. However, we partially offset this by studying a large sample covering four geographic areas in China. We successfully replicated the geographic distribution of HCV subtype from previous studies, and discovered that the HCV subtype distribution was different between IDUs and NIDUs. Our study provided information for future HCV transmission research among NIDUs, and was potentially useful for guiding HCV treatment in China.
We appreciate the staff in the Centers for Disease Control and Prevention in the Guizhou, Hebei, Jiangsu, and Shanxi provinces. They made a great effort to collect samples and extract demographic data for this study. The authors have no conflicts of interest in this paper.
Conceived and designed the experiments: JT YX YJ. Performed the experiments: JT JL. Analyzed the data: JT HQ. Contributed reagents/materials/analysis tools: HZ LP. Wrote the paper: JT HQ YX MC.
- 1. Nelson PK, Mathers BM, Cowie B, Hagan H, Des Jarlais D, Horyniak D, et al. Global epidemiology of hepatitis B and hepatitis C in people who inject drugs: results of systematic reviews. The Lancet. 2011;378 (9791):571–83.
- 2. Hahn JA, Page-Shafer K, Lum PJ, Bourgois P, Stein E, Evans JL, et al. Hepatitis C virus seroconversion among young injection drug users: relationships and risks. J Infect Dis. 2002;186(11):1558–64. Epub 2002/11/26. JID020503 [pii] pmid:12447730.
- 3. Alter MJ. Epidemiology of hepatitis C virus infection. World J Gastroenterol. 2007;13(17):2436–41. Epub 2007/06/07. pmid:17552026.
- 4. Zhuang X, Liang Y, Chow EP, Wang Y, Wilson DP, Zhang L. HIV and HCV prevalence among entrants to methadone maintenance treatment clinics in China: a systematic review and meta-analysis. BMC infectious diseases. 2012;12:130. Epub 2012/06/12. pmid:22682091; PubMed Central PMCID: PMCPMC3434111.
- 5. Rezza G, Sagliocca L, Zaccarelli M, Nespoli M, Siconolfi M, Baldassarre C. Incidence rate and risk factors for HCV seroconversion among injecting drug users in an area with low HIV seroprevalence. Scand J Infect Dis. 1996;28(1):27–9. Epub 1996/01/01. pmid:9122628.
- 6. Hagan H, Thiede H, Weiss NS, Hopkins SG, Duchin JS, Alexander ER. Sharing of drug preparation equipment as a risk factor for hepatitis C. Am J Public Health. 2001;91(1):42–6. Epub 2001/02/24. pmid:11189822; PubMed Central PMCID: PMC1446500.
- 7. Hagan H, Thiede H, Des Jarlais DC. Hepatitis C virus infection among injection drug users: survival analysis of time to seroconversion. Epidemiology. 2004;15(5):543–9. Epub 2004/08/17. 00001648-200409000-00011 [pii]. pmid:15308953.
- 8. Scheinmann R, Hagan H, Lelutiu-Weinberger C, Stern R, Des Jarlais DC, Flom PL, et al. Non-injection drug use and hepatitis C virus: a systematic review. Drug and alcohol dependence. 2007;89(1):1–12. pmid:17174481
- 9. Bruno S, Silini E, Crosignani A, Borzio F, Leandro G, Bono F, et al. Hepatitis C virus genotypes and risk of hepatocellular carcinoma in cirrhosis: a prospective study. Hepatology. 1997;25(3):754–8. Epub 1997/03/01. S027091399700150X [pii] pmid:9049231.
- 10. Freeman AJ, Dore GJ, Law MG, Thorpe M, Von Overbeck J, Lloyd AR, et al. Estimating progression to cirrhosis in chronic hepatitis C virus infection. Hepatology. 2001;34(4 Pt 1):809–16. Epub 2001/10/05. S0270-9139(01)33319-0 [pii] pmid:11584380.
- 11. Fuller CM, Vlahov D, Latkin CA, Ompad DC, Celentano DD, Strathdee SA. Social circumstances of initiation of injection drug use and early shooting gallery attendance: implications for HIV intervention among adolescent and young adult injection drug users. J Acquir Immune Defic Syndr. 2003;32(1):86–93. Epub 2003/01/07. pmid:12514419.
- 12. Wejstal R. Sexual transmission of hepatitis C virus. J Hepatol. 1999;31 Suppl 1:92–5. pmid:10622568.
- 13. CDC. Survelliance for acute viral hepatitis. MMWR; 2008. p. 1–24.
- 14. Terrault NA. Sexual activity as a risk factor for hepatitis C. Hepatology. 2002;36(5 Suppl 1):S99–105. Epub 2002/10/31. S0270913902001775 [pii] pmid:12407582.
- 15. Strader DB, Wright T, Thomas DL, Seeff LB. Diagnosis, management, and treatment of hepatitis C. Hepatology. 2004;39(4):1147–71. pmid:15057920
- 16. Di Bisceglle AM, Hoofnagie JH. Optimal therapy of Hepatitis C. Hepatology. 2002;36:s121–s7. pmid:12407585
- 17. Derbala M, Amer A, Bener A, Lopez AC, Omar M, El Ghannam M. Pegylated interferon-alpha 2b-ribavirin combination in Egyptian patients with genotype 4 chronic hepatitis. J Viral Hepat. 2005;12(4):380–5. Epub 2005/06/30. JVH604 [pii] pmid:15985008.
- 18. Hadziyannis SJ, Sette H Jr., Morgan TR, Balan V, Diago M, Marcellin P, et al. Peginterferon-alpha2a and ribavirin combination therapy in chronic hepatitis C: a randomized study of treatment duration and ribavirin dose. Ann Intern Med. 2004;140(5):346–55. Epub 2004/03/05. 140/5/346 [pii]. pmid:14996676.
- 19. Nguyen MH, Keeffe EB. Prevalence and treatment of hepatitis C virus genotypes 4, 5, and 6. Clin Gastroenterol Hepatol. 2005;3(10 Suppl 2):S97–S101. Epub 2005/10/20. pmid:16234071.
- 20. Legrand-Abravanel F, Colson P, Leguillou-Guillemette H, Alric L, Ravaux I, Lunel-Fabiani F, et al. Influence of the HCV subtype on the virological response to pegylated interferon and ribavirin therapy. J Med Virol. 2009;81(12):2029–35. Epub 2009/10/27. pmid:19856464.
- 21. Roulot D, Bourcier V, Grando V, Deny P, Baazia Y, Fontaine H, et al. Epidemiological characteristics and response to peginterferon plus ribavirin treatment of hepatitis C virus genotype 4 infection. J Viral Hepat. 2007;14(7):460–7. Epub 2007/06/20. pmid:17576387.
- 22. Pellicelli AM, Romano M, Stroffolini T, Mazzoni E, Mecenate F, Monarca R, et al. HCV genotype 1a shows a better virological response to antiviral therapy than HCV genotype 1b. BMC gastroenterology. 2012;12:162. Epub 2012/11/20. pmid:23157720; PubMed Central PMCID: PMCPMC3526494.
- 23. Tao J, Liu Y, Tong Y, Yu S, Zhang H, Jiang Y, et al. Hepatitis C virus genotyping analysis among drug users in Guizhou, China. Chin J Viral Dis. 2012 2(2):117–22.
- 24. Xia X, Lu L, Kok KT, Zhao W, Wu J, Yu J, et al. The unique HCV genotype distribution and the discovery of a novel subtype 6u among IDUs co-infected with HIV-1 in Yunnan, China. Journal of Medical Virology. 2008;80(7):1142–52. pmid:2008246138
- 25. Zhang L, Chen Z, Cao Y, Yu J, Li G, Yu W, et al. Molecular characterization of human immunodeficiency virus type 1 and hepatitis C virus in paid blood donors and injection drug users in china. Journal of virology. 2004;78(24):13591–9. pmid:15564470; PubMed Central PMCID: PMC533913.
- 26. Garten RJ, Zhang J, Lai S, Liu W, Chen J, Yu XF. Coinfection with HIV and hepatitis C virus among injection drug users in southern China. Clin Infect Dis. 2005;41 Suppl 1:S18–24. Epub 2005/11/03. pmid:16265609.
- 27. Min JA, Yoon Y, Lee HJ, Choi J, Kwon M, Kim K, et al. Prevalence and associated clinical characteristics of hepatitis B, C, and HIV infections among injecting drug users in Korea. J Med Virol. 2013;85(4):575–82. Epub 2013/02/01. pmid:23364858.
- 28. Tanimoto T, Nguyen HC, Ishizaki A, Chung PT, Hoang TT, Nguyen VT, et al. Multiple routes of hepatitis C virus transmission among injection drug users in Hai Phong, Northern Vietnam. J Med Virol. 2010;82(8):1355–63. Epub 2010/06/24. pmid:20572071.
- 29. Bowden DS, Berzsenyi MD. Chronic hepatitis C virus infection: genotyping and its clinical role. Future Microbiology. 2006;1(1):103–12. Epub 2007/07/31. pmid:17661689.
- 30. van den Berg CH, van de Laar TJ, Kok A, Zuure FR, Coutinho RA, Prins M. Never injected, but hepatitis C virus-infected: a study among self- declared never-injecting drug users from the Amsterdam Cohort Studies. Journal of Viral Hepatitis. 2009;16(8):568–77. Epub 2009/02/27. JVH1105 [pii] pmid:19243497; PubMed Central PMCID: PMC2759985.