A molecular transmission network of recent hepatitis C infection in people with and without HIV: Implications for targeted treatment strategies

Transmission network of Hepatitis C virus subtype 2a in Huazhou County, Shaanxi Province, China

BACKGROUND

Huazhou County has one of the highest rates of hepatitis C virus (HCV) infection incidence and prevalence in Shaanxi Province, northwest China. Understanding the characteristics of HCV transmission patterns in this area could help guide targeted prevention strategies. This study employed phylogenetic analysis and the construction of a molecular transmission network of HCV-infected people in Huazhou County to describe the predominant strains of HCV and identify factors associated with onward transmission.

METHODS

Whole blood samples were obtained from HCV RNA-positive individuals for sequencing of the non-structural protein 5B region. A maximum-likelihood (ML) phylogenetic tree was constructed to determine HCV subgenotypes, and Bayesian phylogenetic analysis was employed to estimate the evolutionary history. The transmission network was constructed using the ML phylogenetic tree and pairwise distances. Logistic regression was used to identify factors associated with clustering in the transmission network.

RESULTS

ML phylogenetic analysis confirmed that the 61 sequences analyzed in the study belonged to subtype 2a. Bayesian phylogenetic analysis showed that the majority of subtype 2a sequences originated in the northwest of China and had descended approximately 8 to 20 years before sampling. Overall, 26.2% of participant sequences were grouped into phylogenetic network clusters. Multivariate logistic regression showed that individuals who had a history of blood transfusions and were living in Shi Village, Huazhou County, were more likely to form clusters within the transmission network.

CONCLUSION

HCV transmission in Huazhou County was predominantly associated with subtype 2a. Having a history of blood transfusions and living in residential Shi Village, Huazhou County, were factors associated with a high risk of HCV infection transmission. Prioritizing targeted interventions for these patient groups may help to prevent further infections.

AUTO-TUNE: selecting the distance threshold for inferring HIV transmission clusters

Molecular surveillance of viral pathogens and inference of transmission networks from genomic data play an increasingly important role in public health efforts, especially for HIV-1. For many methods, the genetic distance threshold used to connect sequences in the transmission network is a key parameter informing the properties of inferred networks. Using a distance threshold that is too high can result in a network with many spurious links, making it difficult to interpret. Conversely, a distance threshold that is too low can result in a network with too few links, which may not capture key insights into clusters of public health concern. Published research using the HIV-TRACE software package frequently uses the default threshold of 0.015 substitutions/site for HIV pol gene sequences, but in many cases, investigators heuristically select other threshold parameters to better capture the underlying dynamics of the epidemic they are studying. Here, we present a general heuristic scoring approach for tuning a distance threshold adaptively, which seeks to prevent the formation of giant clusters. We prioritize the ratio of the sizes of the largest and the second largest cluster, maximizing the number of clusters present in the network. We apply our scoring heuristic to outbreaks with different characteristics, such as regional or temporal variability, and demonstrate the utility of using the scoring mechanism's suggested distance threshold to identify clusters exhibiting risk factors that would have otherwise been more difficult to identify. For example, while we found that a 0.015 substitutions/site distance threshold is typical for US-like epidemics, recent outbreaks like the CRF07_BC subtype among men who have sex with men (MSM) in China have been found to have a lower optimal threshold of 0.005 to better capture the transition from injected drug use (IDU) to MSM as the primary risk factor. Alternatively, in communities surrounding Lake Victoria in Uganda, where there has been sustained heterosexual transmission for many years, we found that a larger distance threshold is necessary to capture a more risk factor-diverse population with sparse sampling over a longer period of time. Such identification may allow for more informed intervention action by respective public health officials.

Hepatitis C virus subtype diversity and transmission clusters characteristics among drug users in Zhuhai, South China

BACKGROUND

Hepatitis C virus (HCV) infection poses a major public health challenge globally, especially among injecting drug users. China has the world's largest burden of HCV infections. However, little is known about the characteristics of transmission networks among drug user populations. This study aims to investigate the molecular epidemiology and transmission characteristics of HCV infections among drug users in Zhuhai, a bustling port city connecting Mainland China and its Special Administrative Regions.

METHODS

Participants enrolled in this study were drug users incarcerated at Zhuhai's drug rehabilitation center in 2015. Their sociodemographic and behavioral information, including gender, promiscuity, drug use method, and so forth, was collected using a standardized questionnaire. Plasmas separated from venous blood were analyzed for HCV infection through ELISA and RT-PCR methods to detect anti-HCV antibodies and HCV RNA. The 5'UTR fragment of the HCV genome was amplified and further sequenced for subtype identifications and phylogenetic analysis. The phylogenetic tree was inferred using the Maximum Likelihood method based on the Tamura-Nei model, and the transmission cluster network was constructed using Cytoscape3.8.0 software with a threshold of 0.015. Binary logistic regression models were employed to assess the factors associated with HCV infection.

RESULTS

The overall prevalence of HCV infection among drug users was 44.37%, with approximately 19.69% appearing to clear the HCV virus successfully. Binary logistic regression analysis revealed that those aged over 40, engaging in injecting drug use, and being native residents were at heightened risk for HCV infection among drug user cohorts. The predominant HCV subtypes circulating among those drug users were 6a (60.26%), followed by 3b (16.7%), 3a (12.8%), 1b (6.41%) and 1a (3.85%), respectively. Molecular transmission network analysis unveiled the presence of six transmission clusters, with the largest propagation cluster consisting of 41 individuals infected with HCV subtype 6a. Furthermore, distinct transmission clusters involved eight individuals infected with subtype 3b and seven with subtype 3a were also observed.

CONCLUSION

The genetic transmission networks revealed a complex transmission pattern among drug users in Zhuhai, emphasizing the imperative for a targeted and effective intervention strategy to mitigate HCV dissemination. These insights are pivotal for shaping future national policies on HCV screening, treatment, and prevention in port cities.

AUTO-TUNE: SELECTING THE DISTANCE THRESHOLD FOR INFERRING HIV TRANSMISSION CLUSTERS

Molecular surveillance of viral pathogens and inference of transmission networks from genomic data play an increasingly important role in public health efforts, especially for HIV-1. For many methods, the genetic distance threshold used to connect sequences in the transmission network is a key parameter informing the properties of inferred networks. Using a distance threshold that is too high can result in a network with many spurious links, making it difficult to interpret. Conversely, a distance threshold that is too low can result in a network with too few links, which may not capture key insights into clusters of public health concern. Published research using the HIV-TRACE software package frequently uses the default threshold of 0.015 substitutions/site for HIV pol gene sequences, but in many cases, investigators heuristically select other threshold parameters to better capture the underlying dynamics of the epidemic they are studying. Here, we present a general heuristic scoring approach for tuning a distance threshold adaptively, which seeks to prevent the formation of giant clusters. We prioritize the ratio of the sizes of the largest and the second largest cluster, maximizing the number of clusters present in the network. We apply our scoring heuristic to outbreaks with different characteristics, such as regional or temporal variability, and demonstrate the utility of using the scoring mechanism's suggested distance threshold to identify clusters exhibiting risk factors that would have otherwise been more difficult to identify. For example, while we found that a 0.015 substitutions/site distance threshold is typical for US-like epidemics, recent outbreaks like the CRF07_BC subtype among men who have sex with men (MSM) in China have been found to have a lower optimal threshold of 0.005 to better capture the transition from injected drug use (IDU) to MSM as the primary risk factor. Alternatively, in communities surrounding Lake Victoria in Uganda, where there has been sustained hetero-sexual transmission for many years, we found that a larger distance threshold is necessary to capture a more risk factor-diverse population with sparse sampling over a longer period of time. Such identification may allow for more informed intervention action by respective public health officials.

Distribution pattern, molecular transmission networks, and phylodynamic of hepatitis C virus in China

In China, few molecular epidemiological data on hepatitis C virus (HCV) are available and all previous studies were limited by small sample sizes or specific population characteristics. Here, we report characterization of the epidemic history and transmission dynamics of HCV strains in China. We included HCV sequences of individuals belonging to three HCV surveillance programs: 1) patients diagnosed with HIV infection at the Beijing HIV laboratory network, most of whom were people who inject drugs and former paid blood donors, 2) men who have sex with men, and 3) the general population. We also used publicly available HCV sequences sampled in China in our study. In total, we obtained 1,603 Ns5b and 865 C/E2 sequences from 1,811 individuals. The most common HCV strains were subtypes 1b (29.1%), 3b (25.5%) and 3a (15.1%). In transmission network analysis, factors independently associated with clustering included the region (OR: 0.37, 95% CI: 0.19-0.71), infection subtype (OR: 0.23, 95% CI: 0.1-0.52), and sampling period (OR: 0.43, 95% CI: 0.27-0.68). The history of the major HCV subtypes was complex, which coincided with some important sociomedical events in China. Of note, five of eight HCV subtype (1a, 1b, 2a, 3a, and 3b), which constituted 81.8% HCV strains genotyped in our study, showed a tendency towards decline in the effective population size during the past decade until present, which is a good omen for the goal of eliminating HCV by 2030 in China.

Phylogenetic Analysis of Hepatitis C Virus Infections in a Large Belgian Cohort Using Next-Generation Sequencing of Full-Length Genomes

The hepatitis C virus (HCV) epidemic in Western countries is primarily perpetuated by the sub-populations of men who have sex with men (MSM) and people who inject drugs (PWID). Understanding the dynamics of transmission in these communities is crucial for removing the remaining hurdles towards HCV elimination. We sequenced 269 annotated HCV plasma samples using probe enrichment and next-generation sequencing, obtaining 224 open reading frames of HCV (OR497849-OR498072). Maximum likelihood phylogenies were generated on the four most prevalent subtypes in this study (HCV1a, 1b, 3a, 4d) with a subsequent transmission cluster analysis. The highest rate of clustering was observed for HCV4d samples (13/17 (76.47%)). The second highest rate of clustering was observed in HCV1a samples (42/78 (53.85%)) with significant association with HIV-positive MSM. HCV1b and HCV3a had very low rates of clustering (2/83 (2.41%) and (0/29)). The spread of the prevalent subtype HCV1b appears to have been largely curtailed, and we demonstrate the onwards transmission of HCV1a and HCV4d in the HIV-positive MSM population across municipal borders. More systematic data collection and sequencing is needed to allow a better understanding of the HCV transmission among the community of PWID and overcome the remaining barriers for HCV elimination in Belgium.

The distribution of hepatitis C viral genotypes shifted among chronic hepatitis C patients in Yunnan, China, between 2008-2018

OBJECT

The hepatitis C virus (HCV) is prevalent across China, with a distinctive genotypic distribution that varies by geographical region and mode of transmission. Yunnan is one such geographical region wherein the local population continues to experience a high level of HCV infection, severely straining public health resources. This high prevalence is likely due to the increased incidence of intravenous drug use in that region, as Yunnan is a major point of entry for illegal heroin into China.

METHODS

We investigated 510 individuals with chronic HCV infections in Yunnan Province from 2008 through 2018. Using reverse transcription PCR and Sanger sequencing to amplify and sequence samples. Bayesian analyses was performed to estimate the common ancestors and Bayesian skyline plot to estimate the effective viral population size. Molecular network was conducted to explore the characteristics of HCV transmission.

RESULTS

We successfully amplified and sequenced a total of 503 viral samples and genotyped each as either 3b (37.6%), 3a (21.9%), 1b (19.3%), 2a (10.5%), HCV-6 (10.1%), or 1a (0.6%). Over this 11-year period, we observed that the proportion of 3a and 3b subtypes markedly increased and, concomitantly, that the proportion of 1b and 2a subtypes decreased. We also performed Bayesian analyses to estimate the common ancestors of the four major subtypes, 1b, 2a, 3a, and 3b. Finally, we determined that our Bayesian skyline plot and transmission network data correlated well with the changes we observed in the proportions of HCV subtypes over time.

CONCLUSIONS

Taken together, our results indicate that the prevalence of HCV 3a and 3b subtypes is rapidly increasing in Yunnan, thus demonstrating a steadily growing public health requirement to implement more stringent preventative and therapeutic measures to curb the spread of the virus.

Patterns and correlates of hepatitis C virus phylogenetic clustering among people living with HIV in Australia in the direct-acting antiviral era: A molecular epidemiology study among participants in the CEASE cohort

Background and Aims

In moving towards the elimination of hepatitis C virus (HCV) infection among people living with HIV, understanding HCV transmission patterns may provide insights to guide and evaluate interventions. In this study, we evaluated patterns of, and factors associated with HCV phylogenetic clustering among people living with HIV/HCV co-infection in Australia in the direct-acting antiviral era.

Methods

HCV RNA was extracted from dried blood spot (DBS) samples collected between 2014 and 2018 in the CEASE cohort study. The HCV Core-E2 region was amplified by a polymerase chain reaction and Sanger sequenced. Maximum likelihood phylogenetic trees (1000 bootstrap replicates) were used to identify patterns of clustering (3% genetic distance threshold). Mixed-effects logistic regression was used to determine correlates of phylogenetic clustering. Factors assessed were sexual risk behavior, education, injecting drug use, housing, employment, HIV viral load, age, sex, and sexuality.

Results

Phylogenetic trees were reconstructed for HCV subtype 1a (n = 139) and 3a (n = 63) sequences, with 29% (58/202) in a pair or cluster. Overall (n = 202), phylogenetic clustering was positively associated with younger age (under 40; adjusted odds ratio [aOR] 2.52, 95% confidence interval [CI] 1.20-5.29), and among gay and bisexual men (n = 168), was positively associated with younger age (aOR 2.61, 95% CI 1.10-6.19), higher education (aOR 2.58, 95% CI 1.09-6.13), and reporting high-risk sexual behavior (aOR 3.94, 95% CI 1.31-11.84). During follow-up, five reinfections were observed, but none were in phylogenetic clusters.

Conclusion

This study found a high proportion of phylogenetic relatedness, predominantly among younger people and gay and bisexual men reporting high-risk sexual behavior. Despite this, few reinfections were observed, and reinfections demonstrated little relationship with known clusters. These findings highlight the importance of rapid HCV treatment initiation, together with monitoring of the phylogeny.

Transmission networks of hepatitis C virus among HIV/HCV-coinfected patients in Guangdong, China

Background

Coinfection with hepatitis C virus (HCV) is common in human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) patients due to shared routes of transmission. We aimed to investigate the characteristics of HCV subgenotypes among HIV/HCV-coinfected patients in Guangdong and explore the molecular transmission networks and related risk factors for HCV strains.

Methods

Plasma samples were obtained from 356 HIV/HCV-coinfected patients for HCV NS5B region sequencing. A neighbor-joining phylogenetic tree was constructed to affirm HCV subgenotypes. The transmission networks based on maximum likelihood phylogenetic tree were determined by Cluster Picker, and visualized using Cytoscape 3.2.1.

Results

A total of 302 HCV NS5B sequences were successfully amplified and sequenced from the 356 plasma samples. A neighbor-joining phylogenetic tree based on the 302 NS5B sequences revealed the profile of HCV subgenotypes circulating among HIV/HCV coinfection patients in Guangdong. Two predominant strains were found to be 6a (58.28%, 176/302) and 1b (18.54%, 56/302), followed by 3a (10.93%, 33/302), 3b (6.95%, 21/302), 1a (3.64%, 11/302), 2a (0.99%, 3/302) and 6n (0.66%, 2/302). A molecular transmission network of five major HCV genotypes was constructed, with a clustering rate of 44.04%. The clustering rates of subgenotypes 1a, 3a, 3b, 1b, and 6a were 18.18% (2/11), 42.42%, 52.38%, 48.21%, and 44.89%, respectively. Multivariate logistic regression analysis showed no significant effects from sex, age, transmission route, geographical region, baseline CD4 + T cell count or subgenotype (P > 0.05), except marital status. Married or cohabiting people (compared with unmarried people) had more difficulty forming transmission networks.

Conclusions

In summary, this study, based on HCV NS5B subgenotypes, revealed the HCV subtype diversity and distribution among HIV/HCV-coinfected patients in Guangdong. Marital status inclined to be the factor influencing HCV transmission networks formation.

Evolutionary characteristics and immune mutation of hepatitis C virus genotype 1b among intravenous drug users in mainland, China

China is one of the countries with the heaviest burden of hepatitis C virus (HCV) worldwide, especially subtype 1b. To better control hepatitis C, insights into the characteristics of dynamic spread and genomic mutations are urgently needed. We retrieved sequences of HCV-1b NS5B among intravenous drug users (IDUs) and general people (Non-IDUs) in China from 2000 to 2011 in NCBI. Bayesian phylogenetic and phylogeographic analyses were used to evaluate the transmission dynamics of HCV-1b. Non-synonymous substitutions were detected to illustrate immune adaptation. Evolutionary history demonstrated that HCV-1b effective population size experienced a sharp increase in 1990. HCV-1b sequences among IDUs had a higher estimated evolutionary rate (5.7185 × 10^-3  substitutions/site/year) than overall (7.7332 × 10^-4 ). 105/136 (77.2%) of HCV-1b sequences clustered into 38 networks. The average non-synonymous HCV-1b immune epitopes among IDUs were 0.211, higher than non-IDUs, especially in the HLA-A*02 molecular recognition region. All of these posed significant challenges for the prevention and treatment of HCV. Heterogeneity and genetic linkages of HCV-1b suggest that evolutionary surveillance of HCV in cities in east-central China and among IDUs could not be neglected.

Interventions to reduce acute hepatitis C virus in HIV-positive MSM

PURPOSE OF REVIEW

The WHO has set ambitious targets for hepatitis C virus (HCV) elimination by 2030. In this review, we explore the possibility of HCV micro-elimination in HIV-positive (+) MSM, discussing strategies for reducing acute HCV incidence and the likely interventions required to meet these targets.

RECENT FINDINGS

With wider availability of directly acting antivirals (DAAs) in recent years, reductions in acute HCV incidence have been reported in some cohorts of HIV+ MSM. Recent evidence demonstrates that treatment in early infection is well tolerated, cost effective and may reduce the risk of onward transmission. Modelling studies suggest that to reduce incidence, a combination approach including behavioural interventions and access to early treatment, targeting both HIV+ and negative high-risk groups, will be required. HCV vaccine trials have not yet demonstrated efficacy in human studies, however phase one and two studies are ongoing.

SUMMARY

Some progress towards the WHO HCV elimination targets has been reported. Achieving sustained HCV elimination is likely to require a combination approach including early access to DAAs in acute infection and reinfection, validated and reproducible behavioural interventions and an efficacious HCV vaccine.

A review of network simulation models of hepatitis C virus and HIV among people who inject drugs

Network modelling is a valuable tool for simulating hepatitis C virus (HCV) and HIV transmission among people who inject drugs (PWID) and assessing the potential impact of treatment and harm-reduction interventions. In this paper, we review literature on network simulation models, highlighting key structural considerations and questions that network models are well suited to address. We describe five approaches (Erdös-Rényi, Stochastic Block, Watts-Strogatz, Barabási-Albert, and Exponential Random Graph Model) used to model partnership formation with emphasis on the strengths of each approach in simulating different features of real-world PWID networks. We also review two important structural considerations when designing or interpreting results from a network simulation study: (1) dynamic vs. static network and (2) injection only vs. both injection and sexual networks. Dynamic network simulations allow partnerships to evolve and disintegrate over time, capturing corresponding shifts in individual and population-level risk behaviour; however, their high level of complexity and reliance on difficult-to-observe data has driven others to develop static network models. Incorporating both sexual and injection partnerships increases model complexity and data demands, but more accurately represents HIV transmission between PWID and their sexual partners who may not also use drugs. Network models add the greatest value when used to investigate how leveraging network structure can maximize the effectiveness of health interventions and optimize investments. For example, network models have shown that features of a given network and epidemic influence whether the greatest community benefit would be achieved by allocating hepatitis C or HIV treatment randomly, versus to those with the most partners. They have also demonstrated the potential for syringe services and "buddy sharing" programs to reduce disease transmission.

Analysis of heterogeneous genomic samples using image normalization and machine learning

BACKGROUND

Analysis of heterogeneous populations such as viral quasispecies is one of the most challenging bioinformatics problems. Although machine learning models are becoming to be widely employed for analysis of sequence data from such populations, their straightforward application is impeded by multiple challenges associated with technological limitations and biases, difficulty of selection of relevant features and need to compare genomic datasets of different sizes and structures.

RESULTS

We propose a novel preprocessing approach to transform irregular genomic data into normalized image data. Such representation allows to restate the problems of classification and comparison of heterogeneous populations as image classification problems which can be solved using variety of available machine learning tools. We then apply the proposed approach to two important problems in molecular epidemiology: inference of viral infection stage and detection of viral transmission clusters using next-generation sequencing data. The infection staging method has been applied to HCV HVR1 samples collected from 108 recently and 257 chronically infected individuals. The SVM-based image classification approach achieved more than 95% accuracy for both recently and chronically HCV-infected individuals. Clustering has been performed on the data collected from 33 epidemiologically curated outbreaks, yielding more than 97% accuracy.

CONCLUSIONS

Sequence image normalization method allows for a robust conversion of genomic data into numerical data and overcomes several issues associated with employing machine learning methods to viral populations. Image data also help in the visualization of genomic data. Experimental results demonstrate that the proposed method can be successfully applied to different problems in molecular epidemiology and surveillance of viral diseases. Simple binary classifiers and clustering techniques applied to the image data are equally or more accurate than other models.

Large transmission cluster of acute hepatitis C identified among HIV-positive men who have sex with men in Bangkok, Thailand

A rapidly emerging and highly concentrated hepatitis C virus (HCV) outbreak has recently been observed among both acute and chronic HIV-positive men who have sex with men (MSM) in Bangkok, Thailand. NS5B regions of the HCV genome were amplified using nested PCR and sequenced. Phylogenetic inference was constructed by Maximum Likelihood methods and clusters were identified with support and genetic distance thresholds of 85% and of 4.5%. Forty-eight (25 acute HIV and 23 chronic HIV) MSM with incident HCV infection were included in the analysis. HCV genotype (GT) was 85% GT 1a and 15% GT 3a or 3b. Median age at HCV diagnosis was 34 (interquartile range, 28-41) years. 83.3% (40/48) had history of syphilis infection and 36% (16/44) reported crystal methamphetamine use. Only 2 (4%) reported ever injecting drugs, both crystal methamphetamine. In the phylogenetic clustering analysis, 83% belonged to one of two clusters: one large (75%) and one small (8%) cluster. All clusters were GT 1a. MSM with acute HIV infection were more likely to be in a cluster (92%) than those with chronic infection (74%). HCV screening should be regularly performed for MSM in ART clinics, and offering direct-acting antiviral agents to all MSM with HCV infection might contain the HCV epidemic from expanding further.

Phylogenetic and Demographic Characterization of Directed HIV-1 Transmission Using Deep Sequences from High-Risk and General Population Cohorts/Groups in Uganda

Across sub-Saharan Africa, key populations with elevated HIV-1 incidence and/or prevalence have been identified, but their contribution to disease spread remains unclear. We performed viral deep-sequence phylogenetic analyses to quantify transmission dynamics between the general population (GP), fisherfolk communities (FF), and women at high risk of infection and their clients (WHR) in central and southwestern Uganda. Between August 2014 and August 2017, 6185 HIV-1 positive individuals were enrolled in 3 GP and 10 FF communities, 3 WHR enrollment sites. A total of 2531 antiretroviral therapy (ART) naïve participants with plasma viral load >1000 copies/mL were deep-sequenced. One hundred and twenty-three transmission networks were reconstructed, including 105 phylogenetically highly supported source–recipient pairs. Only one pair involved a WHR and male participant, suggesting that improved population sampling is needed to assess empirically the role of WHR to the transmission dynamics. More transmissions were observed from the GP communities to FF communities than vice versa, with an estimated flow ratio of 1.56 (95% CrI 0.68–3.72), indicating that fishing communities on Lake Victoria are not a net source of transmission flow to neighboring communities further inland. Men contributed disproportionally to HIV-1 transmission flow regardless of age, suggesting that prevention efforts need to better aid men to engage with and stay in care.

High-Resolution Evolutionary Analysis of Within-Host Hepatitis C Virus Infection

BACKGROUND

Despite recent breakthroughs in treatment of hepatitis C virus (HCV) infection, we have limited understanding of how virus diversity generated within individuals impacts the evolution and spread of HCV variants at the population scale. Addressing this gap is important for identifying the main sources of disease transmission and evaluating the risk of drug-resistance mutations emerging and disseminating in a population.

METHODS

We have undertaken a high-resolution analysis of HCV within-host evolution from 4 individuals coinfected with human immunodeficiency virus 1 (HIV-1). We used long-read, deep-sequenced data of full-length HCV envelope glycoprotein, longitudinally sampled from acute to chronic HCV infection to investigate the underlying viral population and evolutionary dynamics.

RESULTS

We found statistical support for population structure maintaining the within-host HCV genetic diversity in 3 out of 4 individuals. We also report the first population genetic estimate of the within-host recombination rate for HCV (0.28 × 10-7 recombination/site/year), which is considerably lower than that estimated for HIV-1 and the overall nucleotide substitution rate estimated during HCV infection.

CONCLUSIONS

Our findings indicate that population structure and strong genetic linkage shapes within-host HCV evolutionary dynamics. These results will guide the future investigation of potential HCV drug resistance adaptation during infection, and at the population scale.

Incorporation of information diffusion model for enhancing analyses in HIV molecular surveillance

Molecular surveillance of infections is essential in monitoring their transmission in the population. In this study, newly diagnosed HIV patients' phylogenetic, clinical and behavioural data were integrated, and an information diffusion model was incorporated in analysing transmission dynamics. A genetic network was constructed from HIV sequences, from which transmission cascades were extracted. From the transmission cascades, CRF01_AE had higher values of information diffusion metrics, including scale, speed and range, than that of B, signifying the distinct transmission patterns of two circulating subtypes in Hong Kong. Patients connected in the network, were more likely male, younger, of main circulating subtypes, to have acquired HIV infection locally, and a higher CD4 level at diagnosis. Genetic connections varied among men who have sex with men (MSM) who used different channels of sex networking and varied in their engagement in risk behaviours. MSM using recreational drugs for sex held positions of greater importance within the network. Significant differences in network metrics were observed among MSM as differentiated by their mobile apps usage patterns, evidencing the impact of social network on transmission networks. The applied model in the presence of consistently collected longitudinal data could enhance HIV molecular epidemiologic surveillance for informing future intervention planning.

Natural selection favoring more transmissible HIV detected in United States molecular transmission network

HIV molecular epidemiology can identify clusters of individuals with elevated rates of HIV transmission. These variable transmission rates are primarily driven by host risk behavior; however, the effect of viral traits on variable transmission rates is poorly understood. Viral load, the concentration of HIV in blood, is a heritable viral trait that influences HIV infectiousness and disease progression. Here, we reconstruct HIV genetic transmission clusters using data from the United States National HIV Surveillance System and report that viruses in clusters, inferred to be frequently transmitted, have higher viral loads at diagnosis. Further, viral load is higher in people in larger clusters and with increased network connectivity, suggesting that HIV in the United States is experiencing natural selection to be more infectious and virulent. We also observe a concurrent increase in viral load at diagnosis over the last decade. This evolutionary trajectory may be slowed by prevention strategies prioritized toward rapidly growing transmission clusters.

HIV co-infection is associated with increased transmission risk in patients with chronic hepatitis C virus

Molecular epidemiological analysis of viral pathogens can identify factors associated with increased transmission risk. We investigated the frequency of genetic clustering in a large data set of NS34A, NS5A, and NS5B viral sequences from patients with chronic hepatitis C virus (HCV). Within a subset of patients with longitudinal samples, Receiver Operator Characteristic (ROC) analysis was applied which identified a threshold of 0.02 substitutions/site as most appropriate for clustering. From the 7457 patients with chronic HCV infection included in this analysis, we inferred 256 clusters comprising 541 patients (7.3%). We found that HCV/HIV co-infection, young age, and high HCV viral load were all associated with increased clustering frequency, an indicator of increased transmission risk. In light of previous work on HCV/HIV co-infection in acute HCV cohorts, our results suggest that patients with HCV/HIV co-infection may disproportionately be the source of new HCV infections and treatment efforts should be geared towards viral elimination in this vulnerable population.

Correlates of hepatitis C viral clustering among people who inject drugs in Baltimore

This study examines correlates of hepatitis C virus (HCV) genetic clustering among community-recruited people who inject drugs enrolled in the AIDS Linked to the IntraVenous Experience cohort in Baltimore between 1988 and 1989. HCV RNA was extracted and the core/envelope-1 region was sequenced. Clusters were identified from maximum likelihood trees with 1000 bootstrap replicates using a 70% aLRT and a 4% genetic-distance threshold in Cluster Picker. Overall, 46% of participants were in a cluster, including 122 genotype-1a and 36 genotype-1b clusters with an average of 2-3 genetically linked HCV infections. The largest cluster consists of 9 participants. In univariable analysis, black race (PR = 1.66 [95% CI: 1.12-2.45]), age <35 years (PR = 1.18 [95% CI: 1.02-1.37]), and injection drug use of cocaine alone (PR = 1.30 [95% CI: 1.02-1.65]) were significantly associated with being in a cluster. Conversely, a history of medication-associated treatment (MAT) was negatively associated with being in a cluster (PR = 0.82 [95% CI: 0.71-0.95]). In multivariable analysis, black race (APR = 1.62 [95% CI: 1.11-2.38]) remained independently associated being in a cluster while MAT (APR = 0.85 [95% CI: 0.74-0.99]) remained negatively associated with clustering. Our findings suggest strong locally-propagated transmission networks during the early epidemic that was driven by younger PWID. In light of the current opioid epidemic in the US, these findings suggest an urgent need for preventive interventions to mitigate the growth of large HCV transmission networks.

HIV-TRACE (TRAnsmission Cluster Engine): a Tool for Large Scale Molecular Epidemiology of HIV-1 and Other Rapidly Evolving Pathogens

In modern applications of molecular epidemiology, genetic sequence data are routinely used to identify clusters of transmission in rapidly evolving pathogens, most notably HIV-1. Traditional 'shoe-leather' epidemiology infers transmission clusters by tracing chains of partners sharing epidemiological connections (e.g., sexual contact). Here, we present a computational tool for identifying a molecular transmission analog of such clusters: HIV-TRACE (TRAnsmission Cluster Engine). HIV-TRACE implements an approach inspired by traditional epidemiology, by identifying chains of partners whose viral genetic relatedness imply direct or indirect epidemiological connections. Molecular transmission clusters are constructed using codon-aware pairwise alignment to a reference sequence followed by pairwise genetic distance estimation among all sequences. This approach is computationally tractable and is capable of identifying HIV-1 transmission clusters in large surveillance databases comprising tens or hundreds of thousands of sequences in near real time, that is, on the order of minutes to hours. HIV-TRACE is available at www.hivtrace.org and from www.github.com/veg/hivtrace, along with the accompanying result visualization module from www.github.com/veg/hivtrace-viz. Importantly, the approach underlying HIV-TRACE is not limited to the study of HIV-1 and can be applied to study outbreaks and epidemics of other rapidly evolving pathogens.

A latent class approach to identify multi-risk profiles associated with phylogenetic clustering of recent hepatitis C virus infection in Australia and New Zealand from 2004 to 2015

INTRODUCTION

Over the last two decades, the incidence of hepatitis C virus (HCV) co-infection among men who have sex with men (MSM) living with HIV began increasing in post-industrialized countries. Little is known about transmission of acute or recent HCV, in particular among MSM living with HIV co-infection, which creates uncertainty about potential for reinfection after HCV treatment. Using phylogenetic methods, clinical, epidemiological and molecular data can be combined to better understand transmission patterns. These insights may help identify strategies to reduce reinfection risk, enhancing effectiveness of HCV treatment as prevention strategies. The aim of this study was to identify multi-risk profiles and factors associated with phylogenetic pairs and clusters among people with recent HCV infection.

METHODS

Data and specimens from five studies of recent HCV in Australia and New Zealand (2004 to 2015) were used. HCV Core-E2 sequences were used to infer maximum likelihood trees. Clusters were identified using 90% bootstrap and 5% genetic distance threshold. Multivariate logistic regression and latent class analyses were performed.

RESULTS

Among 237 participants with Core-E2 sequences, 47% were in a pair/cluster. Among HIV/HCV co-infected participants, 60% (74/123) were in a pair/cluster, compared to 30% (34/114) with HCV mono-infection (p < 0.001). HIV/HCV co-infection (vs. HCV mono-infection; adjusted odds ratio (AOR), 2.37, 95% confidence interval (CI), 1.45, 5.15) was independently associated with phylogenetic clustering. Latent class analysis identified three distinct risk profiles: (1) people who inject drugs, (2) HIV-positive gay and bisexual men (GBM) with low probability of injecting drug use (IDU) and (3) GBM with IDU & sexual risk behaviour. Class 2 (vs. Class 1, AOR 3.40; 95% CI, 1.52, 7.60), was independently associated with phylogenetic clustering. Many clusters displayed homogeneous characteristics, such as containing individuals exclusively from one city, individuals all with HIV/HCV co-infection or individuals sharing the same route of acquisition of HCV.

CONCLUSIONS

Clusters containing individuals with specific characteristics suggest that HCV transmission occurs through discrete networks, particularly among HIV/HCV co-infected individuals. The greater proportion of clustering found among HIV/HCV co-infected participants highlights the need to provide broad direct-acting antiviral access encouraging rapid uptake in this population and ongoing monitoring of the phylogeny.

QUENTIN: reconstruction of disease transmissions from viral quasispecies genomic data

Motivation

Genomic analysis has become one of the major tools for disease outbreak investigations. However, existing computational frameworks for inference of transmission history from viral genomic data often do not consider intra-host diversity of pathogens and heavily rely on additional epidemiological data, such as sampling times and exposure intervals. This impedes genomic analysis of outbreaks of highly mutable viruses associated with chronic infections, such as human immunodeficiency virus and hepatitis C virus, whose transmissions are often carried out through minor intra-host variants, while the additional epidemiological information often is either unavailable or has a limited use.

Results

The proposed framework QUasispecies Evolution, Network-based Transmission INference (QUENTIN) addresses the above challenges by evolutionary analysis of intra-host viral populations sampled by deep sequencing and Bayesian inference using general properties of social networks relevant to infection dissemination. This method allows inference of transmission direction even without the supporting case-specific epidemiological information, identify transmission clusters and reconstruct transmission history. QUENTIN was validated on experimental and simulated data, and applied to investigate HCV transmission within a community of hosts with high-risk behavior. It is available at https://github.com/skumsp/QUENTIN.

Contact

pskums@gsu.edu or alexz@cs.gsu.edu or rahul@sfsu.edu or yek0@cdc.gov.

Supplementary information

Supplementary data are available at Bioinformatics online.

Intelligent Network DisRuption Analysis (INDRA): A targeted strategy for efficient interruption of hepatitis C transmissions

Hepatitis C virus (HCV) infection is a global public health problem. The implementation of public health interventions (PHI) to control HCV infection could effectively interrupt HCV transmission. PHI targeting high-risk populations, e.g., people who inject drugs (PWID), are most efficient but there is a lack of tools for prioritizing individuals within a high-risk community. Here, we present Intelligent Network DisRuption Analysis (INDRA), a targeted strategy for efficient interruption of hepatitis C transmissions.Using a large HCV transmission network among PWID in Indiana as an example, we compare effectiveness of random and targeted strategies in reducing the rate of HCV transmission in two settings: (1) long-established and (2) rapidly spreading infections (outbreak). Identification of high centrality for the network nodes co-infected with HIV or > 1 HCV subtype indicates that the network structure properly represents the underlying contacts among PWID relevant to the transmission of these infections. Changes in the network's global efficiency (GE) were used as a measure of the PHI effects. In setting 1, simulation experiments showed that a 50% GE reduction can be achieved by removing 11.2 times less nodes using targeted vs random strategies. A greater effect of targeted strategies on GE was consistently observed when networks were simulated: (1) with a varying degree of errors in node sampling and link assignment, and (2) at different levels of transmission reduction at affected nodes. In simulations considering a 10% removal of infected nodes, targeted strategies were ~2.8 times more effective than random in reducing incidence. Peer-education intervention (PEI) was modeled as a probabilistic distribution of actionable knowledge of safe injection practices from the affected node to adjacent nodes in the network. Addition of PEI to the models resulted in a 2-3 times greater reduction in incidence than from direct PHI alone. In setting 2, however, random direct PHI were ~3.2 times more effective in reducing incidence at the simulated conditions. Nevertheless, addition of PEI resulted in a ~1.7-fold greater efficiency of targeted PHI. In conclusion, targeted PHI facilitated by INDRA outperforms random strategies in decreasing circulation of long-established infections. Network-based PEI may amplify effects of PHI on incidence reduction in both settings.