Ethics Statement

The study conformed to the ethical guidelines of the 1975 Declaration of Helsinki; ethical approval was obtained from the Victorian Department of Health Human Research Ethics Committee (project 02/05). Participation was voluntary, written informed consent was obtained from each participant, and all were offered pre- and post-test counselling for HCV, HBV and HIV.

Recruitment

Networks 2 is a cohort study of PWID recruited from three major illicit drug markets located across metropolitan Melbourne, Australia. Recruitment utilized a social networks approach: at specified interviews, participants were asked to describe their relationships with up to five injecting partners and to introduce them to our field researchers [13]. Participants were bled and interviewed about their risk behaviour and injecting partners at approximately three-month intervals. Most participants were recruited into the study between July 2005 and January 2006, although recruitment of existing participants' injecting partners continued. This paper reports on data collected between July 2005 and August 2008.

Laboratory methods

Blood samples were screened for antibodies to HCV (anti-HCV) by a third-generation enzyme immunoassay (Abbott Laboratories, Chicago, IL, USA) and anti-HCV positive specimens were tested again by Murex anti-HCV version 4.0 (Murex Biotech, Kyalami, South Africa) for confirmation. Irrespective of anti-HCV status, all samples were tested for HCV RNA by the COBAS AMPLICOR HCV test version 2.0 (Roche Molecular Systems, Branchburg, NJ, USA). HIV and hepatitis B virus (HBV) status were determined by measuring serological markers as described previously [14].

HCV RNA positive blood samples were genotyped by a reverse-phase hybridisation line probe assay (LiPA, Versant HCV Genotype Assay, Siemens Healthcare Diagnostics, Tarrytown, NY, USA) [19]. For molecular studies, amplification was performed using a nested PCR with primers specific to the core region (331 nucleotides; positions 373–703 relative to the H77 reference sequence [20]) as previously described [21]. Direct sequencing was then performed on the PCR product using ABI PRISM™ Dye Terminator Cycle Sequencing Ready Reaction Kit (PE Applied Biosystems, Foster City, CA, USA) according to the manufacturers' instructions. Sequences with ambiguities (35 of 563) and sequences that were shorter than 331 nucleotides (6 of 563) were removed prior to analysis.

Phylogenetic analyses

HCV core sequences were aligned using Clustal W through MEGA version 4.0 and MUSCLE [22]–[24]. The alignments were compared using the AltAVisT web tool and edited by hand where appropriate [25]. An initial evolutionary history was inferred using the neighbour-joining method, with sequence distances calculated using the maximum composite likelihood method [26]. The percentage of replicate trees in which the associated taxa clustered together was calculated using a bootstrap test (1000 replicates, 70% cut-off for defining clusters) [27]. All positions containing gaps and missing data were eliminated from the dataset. Neighbour-Joining trees and bootstrapping analyses were conducted in MEGA version 4.0 [23]. A second evolutionary history was inferred using the maximum likelihood approach, assuming a general time reversible model of evolution with unequal rates among sites (four gamma distributed rates), and a proportion of invariable sites; branch support was determined using bootstrapping (1000 replicates, conducted in MEGA version 5.0 [28], [29]) . The results reported in the main manuscript are based on clusters identified using the neighbour-joining phylogeny (branch support determined using a bootstrap test with a cut-off of 70%); maximum likelihood phylogeny was conducted as part of the sensitivity analysis described below (statistical analysis section) and results are presented in Table S1.

For minor genotypes (<20 infections), clusters were verified by phlyogenetic analysis with reference sequences from the Los Alamos HCV Sequence Database. Reference sequences were identified from the database as follows: all HCV sequences of the relevant HCV genotypes that contained the core region were downloaded and checked for sequencing ambiguities. Sequences with ambiguities were removed. If more than 50 sequences were available, random samples of 50 sequences were chosen using the MS Excel random number generator. Only 11 sequences were available for genotype 6l and 50 for genotype 6e, all of which were included in the analysis.

In order to estimate the false discovery rate for identifying possible transmission clusters using the viral region analysed, 300 previously published sequences from the same region of the virus were randomly selected from the Los Alamos HCV Sequence Database and analysed as a control experiment. The random sample was stratified by genotype and included 70 genotype 1a and 3a, 50 genotype 1b, 6a, and 6e, and ten 6l sequences. Among those sequences for which the participant identification code was specified in the sequence database, duplicate sequences from the same participant were removed prior to selecting the sample. Sequence alignment and phylogenetic analysis were undertaken using the methods described above. Sequences that clustered in the phylogenetic analyses were investigated using PubMed. Those that were epidemiologically related (from studies of multiple sequences isolated from the same individual) were discarded.

Social network construction

In general, social networks encompass a set of nodes (points) and edges (connections). In this case (similar to our previous publication [30]), the nodes were study participants and edges were injecting relationships, defined as participants injecting in the same place and time in the three months before interview. For this analysis two social networks were constructed: in the first, henceforth called the baseline injecting network, nodes were participants recruited in the main waves early in the study and the injecting partners they reported in their baseline interviews (July 2005–January 2006), and edges were the injecting relationships reported in those participants' first interviews. In the second network, henceforth called the flattened injecting network, nodes were participants recruited up to August 2008, and injecting relationships reported throughout the period of interest were included as edges. Both networks were undirected, meaning that the direction of nomination (whether participant A nominated B or vice versa, or whether both participants nominated each other) was discounted. Images of the social networks were constructed using Ucinet 6 and Netdraw 2 [31], [32].

HCV infection definitions

1. Participants with HCV infection at enrolment were defined by a positive HCV RNA test (HCV RNA limit of detection: 50 IU/mL) at the first study visit (irrespective of anti-HCV status).
2. Among participants without HCV infection at enrolment, those with past HCV infection were defined by a positive anti-HCV test at the first study visit.
3. Participants with newly acquired primary HCV infection were defined by either:
   1. A positive HCV RNA test and negative anti-HCV test at the first study visit (indicating very early infection); or
   2. A negative HCV RNA test and negative anti-HCV test at their first study visit and a subsequent positive anti-HCV/HCV RNA test during follow-up (HCV seroconversion).
4. Some participants had multiple infections during the study. Participants were defined as having a newly acquired reinfection if they:
   1. tested HCV RNA negative on two occasions (at least 28 days apart) and subsequently tested HCV RNA positive; or
   2. tested HCV RNA positive and subsequently tested HCV RNA negative on one occasion, then tested HCV RNA positive; and
      1. at least 28 days had elapsed between the HCV RNA negative test and the subsequent positive test; and
      2. the sequence distance between the two HCV RNA positive tests was at least 4% in the core region (331 nucleotides). The methodology used to determine the 4% cut-off is described below (definition 6).
5. Participants were defined as having a new viral strain if they did not satisfy the definition of reinfection because they had no intervening negative test but had two consecutive HCV RNA positive tests with sequence distance at least 4% in the core region (331 nucleotides). The methodology used to determine the 4% cut-off is described below (definition 6).
6. All blood tests positive for HCV RNA underwent viral sequencing (HCV core region, 331 nucleotides). Viral sequences were compared pairwise, and the maximum composite likelihood distances were calculated. The mean (SD) distance between viral sequences taken from different participants with the same genotype and subtype was 3.5% (1.3%). The cut-off for defining a new viral strain was set at 4%, equal to approximately three standard deviations (3×1.3%) of the distribution of pairwise differences from viral sequences from different participants with the same genotype and subtype. This method for defining a cut-off was based on the method used by Pham and colleagues [33]. When consecutive sequences from the same participant were compared (including sets with intervening blood tests with no viral sequence – for example, if the blood test was HCV RNA negative), 70% of consecutive sequences were identical (distance = 0%).

Statistical analysis

To address the study aim (to determine the relationship between social networks and molecular phylogenetics in the context of incident HCV infection), the association between reported injecting partnerships (injecting partner - yes/no) and relationships defined by clustering in the phylogenetic analysis (in the same phylogenetic cluster – yes/no), and the association between social geodesic distance (the smallest number of injecting partnerships connecting two nodes) and HCV core sequence distance (maximum composite likelihood) were measured. An adjusted Jaccard similarity coefficient was used to measure the association between the reported injecting partnerships and relationships defined by clustering in the phylogenetic analysis in the baseline and flattened injecting networks. Tau c rank correlation was used to measure the association between social distance and HCV core sequence distance in the baseline and flattened social networks. To control for confounding between HCV genotype and socio-behavioural characteristics and mixing between participants infected with different HCV genotypes at baseline, this analysis was stratified by genotype and undertaken only amongst participants infected with the major genotypes in the study population, 1a and 3a (more information is provided in Appendix S1). The HCV core sequence from each participant's most recent HCV RNA positive test was analysed. The statistical significance of observed Jaccard similarity coefficients and tau c rank correlation coefficients was assessed using the quadratic assignment procedure (QAP) [34]. Adjusted Jaccard similarity coefficients and related QAP analyses were undertaken in Ucinet 6 (12500 permutations) [31]. Tau c rank correlations and related QAP analyses were undertaken in Stata 11 (Lakeway Drive, Texas; 5000 permutations). These methods are discussed in detail in Appendix S2.

In order to assess whether the relationship between the phylogenetic clusters and the social networks was dependent on the methodology used to define the phylogenetic clusters or the social network, the following variations were implemented:

• Defining ties by reporting ever having used a needle/syringe before or after the other participant without sterilisation, rather than having used in the same room as the other participant in the past three months;
• Defining phylogenetic clusters using cut-off for branch support of 80% rather than 70%; and
• Defining phylogenetic clusters based on the maximum likelihood phylogeny rather than the neighbour-joining phylogeny.

For each of these variations, an adjusted Jaccard similarity coefficient was used to measure the association between the reported injecting partnerships and relationships defined by clustering in the phylogenetic analysis in the baseline and flattened injecting networks. The statistical significance of observed Jaccard similarity coefficients was assessed using the quadratic assignment procedure (QAP, 12500 permutations). Adjusted Jaccard similarity coefficients were similar across these variations; results from these sensitivity analyses are reported in Table S1.

To explore the effect of conducting this investigation in the context of a longitudinal rather than cross-sectional study, using logistic regression in Stata 11 we investigated whether incident infections and new viral strains increased the likelihood of being in a phylogenetic cluster compared to other infections. Age, gender, ethnicity, neighbourhood of recruitment, and HCV genotype (1, 3 or 6) were considered as potential confounders. Univariable logistic regression was used to identify candidate predictors for inclusion in the multivariable model. Stepwise backward multiple logistic regression was used to select the final model and its goodness-of-fit was assessed using the Hosmer-Lemeshow test.

Finally, we examined whether newly-acquired infection or new viral strain might be associated with reporting greater numbers of non-recruited injecting partners, thereby negatively confounding the association between the social network and the HCV phylogeny. The Wilcoxon rank-sum test was used to compare the median number of reported injecting partners, and the median number of reported injecting partners not included in the injecting networks, amongst participants with and without newly-acquired infection or new viral strain. The Wilcoxon rank-sum test was used because these variables were not normally distributed. Wilcoxon rank-sum tests were undertaken in Stata 11.

Participants

Between July 2005 and August 2008, 398 participants were recruited into the study. Four more participants were included in this analysis to maximize the completeness of the social network information because they were nominated as injecting partners before August 2008 but recruited at a later date. Of the 402 participants included in the flattened injecting network, 307 were recruited prior to February 2006. Another 19 participants recruited later were nominated as injecting partners before February 2006. Therefore, the baseline injecting network consisted of 326 participants. Participant characteristics were very similar in the baseline and flattened injecting networks (Table 1).

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Table 1. Participant characteristics at study entry in the baseline and flattened injecting networks.

https://doi.org/10.1371/journal.pone.0047335.t001

HCV infection

Anti-HCV and HCV RNA results were available for 376 participants at study entry. The remaining 26 participants could not be bled or had indeterminate anti-HCV or HCV RNA results at study entry. Initial HCV status and HCV infection events during the study are summarized in Figure 1. In total, 21 newly acquired primary HCV infections and 20 reinfections were observed. Furthermore, in 15 instances participants did not satisfy the definition of reinfection and were defined as having a new viral strain due to substantial change in HCV core sequence (defined in detail above) over two consecutive study visits. The most common genotypes detected were 1 and 3; amongst participants who were anti-HCV positive at study entry, 45% of infections were genotype 1 and 42% were genotype 3. Of the newly acquired infections and changes in viral sequence, 39% were genotype 1 and 44% were genotype 3. Twelve participants were infected with genotype 6 at study entry (6%) and four newly acquired infections were genotype 6 (10%). The distribution of HCV infection and HCV viral genotype in the baseline social network is illustrated in Figure 2.

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Figure 1. HCV primary infections, reinfections and changes in viral sequence by HCV status at study entry.

HCV test results: anti-HCV and qualitative HCV RNA results. Those with missing results could not be bled or had indeterminate results; HCV infection at enrolment: anti-HCV positive and positive HCV RNA test (HCV RNA limit of detection: 50 IU/mL); past HCV infection at enrolment: positive anti-HCV test and negative HCV RNA test at the first study visit; seroconverting at enrolment: anti-HCV negative and positive HCV RNA test at the first study visit (indicating very early infection); never HCV infected at enrolment: negative anti-HCV test and negative HCV RNA test at first study visit. Participants with newly acquired primary HCV infection were defined by either: (a) a positive HCV RNA test and negative anti-HCV test at the first study visit (that is, seroconverting at enrolment); or (b) a negative HCV RNA test and negative anti- HCV test at their first study visit (that is, never infected at enrolment) and a subsequent positive anti-HCV/HCV RNA test during follow-up. Participants were defined as having a newly acquired reinfection if they tested HCV RNA negative on two occasions (at least 28 days apart) and subsequently tested HCV RNA positive; or tested HCV RNA positive and subsequently tested HCV RNA negative on one occasion, then tested HCV RNA positive, and the sequence distance between the two HCV RNA positive tests was at least 4% in the core region (331 nucleotides). Participants were defined as having a new viral strain if they did not satisfy the definition of reinfection because they had no intervening negative test but had two consecutive HCV RNA positive tests with sequence distance at least 4% in the core region (331 nucleotides). The methodology used to determine the 4% cut-off is described in the Materials and Methods section (definition 6).

https://doi.org/10.1371/journal.pone.0047335.g001

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Figure 2. HCV infection status and genotype in the baseline injecting network.

Note: The infection status of each participant in the network is denoted by the shape of the node. Amongst participants who were infected at enrolment – that is, infection status is “current infection” or “seroconverting” – genotype is indicated by colour. Nodes that are not coloured represent participants who were not infected at baseline (white upward triangle or diamond) or participants who were infected at baseline but for whom the genotype could not be determined due to insufficient serum and/or low viral load (white square or downward triangle).

https://doi.org/10.1371/journal.pone.0047335.g002

HIV and HBV infection

HIV and HBV infection were rare in this cohort. Of the 376 participants with anti-HCV and HCV RNA results at study entry, two (0.5%) were HIV infected – both of these were HCV coinfected – and no additional HIV seroconversions were observed. At study entry, 135 (36%) participants had evidence of prior but not current HBV infection. A further 13 (3%) participants were infected with HBV at study entry (hepatitis B surface antigen positive); eight of these were HCV coinfected, including one who was seroconverting to HCV. Two participants became HBV infected during the study but did not have evidence of HCV coinfection. One of these participants spontaneously cleared their HBV infection; the other became infected with HBV within the last three months of follow-up so it was not possible to determine whether they cleared their infection.

Injecting networks

The baseline injecting network consisted of 326 participants and 259 injecting relationships (Figure 2). The flattened injecting network consisted of 402 participants and 466 injecting relationships. Participants in the baseline injecting network reported injecting with a median of four people (IQR: 2–7). Throughout the study, participants in the flattened injecting network reported injecting with a median of three other people (IQR: 1–5); typically one of those injecting partners was not recruited into the study (IQR: 0–4). Participants were asked to estimate the duration of each of their reported injecting relationships; the median duration was three years (IQR: 2–6 years). Participants with newly acquired infection (primary or reinfection) or new viral strain reported injecting with similar numbers of people to other participants (median: 3, IQR: 2–4, p = 0.849); and similar to other participants, typically one of their reported injecting partners was not recruited into the study (IQR: 0–2; p = 0.229).

HCV phlyogeny

A total of 526 HCV core DNA sequences from 227 study participants (sampled over the course of the study) were obtained from HCV infected participants. Phylogenetic analyses identified 26 clusters containing 69 distinct infections (Figures 3, 4, 5, 6). Within the identified clusters, pairwise nucleotide sequence identity ranged from 98–100%. Phylogenetic clusters incorporated 25% of HCV infections at enrolment that were not classified as newly acquired (48 of 195); 48% of primary infections (10 of 21), 29% of reinfections (5 of 17), and 43% of new viral strains (6 of 14). Newly acquired infections (primary and reinfection) and new viral strains were more than twice as likely to be in phylogenetic clusters than infections present at enrolment that were not classified as newly acquired (OR: 2.07; 95% CI: 1.09–3.94; p = 0.026). This association remained after adjusting for HCV genotype (Table 2; AOR: 2.03; 95% CI: 1.04–3.96; p = 0.037).

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Figure 3. Neighbor-joining analysis of HCV genotype 1a core sequences.

Phylogenetic clusters defined by bootstrap analysis (cut-off 70%) with infections from multiple individuals in the study are highlighted in blue and bootstrap values for these clusters are indicated. Newly acquired infections and changes in viral sequence are denoted using black-filled circles.

https://doi.org/10.1371/journal.pone.0047335.g003

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Figure 4. Neighbour-joining analysis of HCV genotype 1b core sequences.

Comparison sequences were genotype 1b sequences randomly selected from the LANL HCV database. Study participants are denoted by diamonds. The first four letters of the name of the country of origin of LANL sequences is included in the ID. Phylogenetic clusters defined by bootstrap analysis (cut-off 70%) with infections from multiple individuals in the study are highlighted in blue and bootstrap values for these clusters are indicated.

https://doi.org/10.1371/journal.pone.0047335.g004

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Figure 5. Neighbor-joining analysis of HCV genotype 3a core sequences.

Phylogenetic clusters defined by bootstrap analysis (cut-off 70%) with infections from multiple individuals in the study are highlighted in blue and bootstrap values for these clusters are indicated. Newly acquired infections and changes in viral sequence are denoted using black-filled circles.

https://doi.org/10.1371/journal.pone.0047335.g005

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Figure 6. Neighbor-joining analysis of HCV genotype 6a, 6e, and 6l core sequences.

Comparison sequences were randomly selected genotype 6a, 6e, and 6l sequences from the LANL HCV database. Study participants are denoted by diamonds. The first four letters of the name of the country of origin of LANL sequences is included in the ID. Phylogenetic clusters defined by bootstrap analysis (cut-off 70%) with infections from multiple individuals in the study are highlighted in blue and bootstrap values for these clusters are indicated. Newly acquired infections and changes in viral sequence are denoted using black-filled circles.

https://doi.org/10.1371/journal.pone.0047335.g006

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Table 2. Adjusted and unadjusted odds ratios of an infection being in a phylogenetic cluster.

https://doi.org/10.1371/journal.pone.0047335.t002

Estimated false discovery rate

Among 300 previously published HCV core DNA sequences that were randomly selected from the Los Alamos sequence database, three phylogenetic clusters were supported at the 70% bootstrap level and one was supported at the 80% bootstrap level. These results suggest an approximate false discovery rate of one per 100 infections.

Associations between social network and phylogenetic clustering patterns

Figure 7 illustrates the phylogenetic clusters in the context of the baseline and flattened injecting networks. Of the 67 participants who had sequences in phylogenetic clusters, 32 (48%) were directly connected with at least one other participant in their phylogenetic cluster in the baseline injecting network and 36 (54%) were directly connected with at least one other participant in their phylogenetic cluster in the flattened injecting network. Of the 32 participants directly connected with at least one other participant in their phylogenetic cluster in the baseline injecting network, 22 (69%) reported ever sharing a needle or syringe with that injecting partner and 17 (53%) had done so in the three months before baseline interview. Of the 36 participants directly connected to at least one other participant in their phylogenetic cluster in the flattened injecting network, 30 (83.3%) shared a needle or syringe with that injecting partner during the study period. Of the 20 participants with newly acquired primary infections or reinfections or new viral strains that were part of phylogenetic clusters, 50% (n = 10) and 65% (n = 13) reported injecting in the same room as other participants in their cluster in the baseline and flattened networks, respectively; 60% (n = 12) reported sharing needles/syringes with other participants in their cluster during the study.

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Figure 7. Network diagrams with phylogenetic clusters indicated by colour.

Panel A. Baseline injecting network including all injecting ties reported in baseline interviews in main recruitment wave (July 2005–Feb 2006). Phylogenetic clusters identified using data from throughout the study period (July 2005–August 2008). Panel B. Flattened injecting network including all injecting ties reported throughout the study (July 2005–August 2008). Phylogenetic clusters identified using data from throughout the study period (July 2005–August 2008).

https://doi.org/10.1371/journal.pone.0047335.g007

Being in the same phylogenetic cluster was correlated with reported injecting relationships in both the baseline and flattened injecting networks (Table 3). The adjusted Jaccard similarity coefficient was 0.300 for the baseline injecting network and 0.292 for the flattened injecting network. These adjusted Jaccard similarities were highly statistically significant based on the results of the QAP. Indeed, fewer than 0.1% of random permutations in the empirical sampling distribution had adjusted Jaccard similarity coefficients greater than or equal to the observed statistics (p<0.001). These associations were robust to changes in the definition of the social networks (defining networks on the basis of needle/syringe sharing rather than using in the same space), and changes in the definition of the phylogenetic clusters (raising the cut-off for branch support from 70% to 80%, inferring the phylogeny and assessing branch support using a maximum likelihood rather than neighbour-joining approach, or defining clusters on the basis of core sequence identity rather than phylogenetic methods; Table S1).

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Table 3. Correlation between injecting networks and HCV phylogeny.

https://doi.org/10.1371/journal.pone.0047335.t003

In contrast, genetic distance (by maximum composite likelihood) was not well correlated with social distance (geodesic), neither in the baseline social network nor in the flattened social network (Table 3). The Tau c rank correlation coefficients for the baseline and flattened injecting networks respectively were 0.018 and 0.071 for participants with genotype 1a infection and −0.002 and −0.007 for participants with genotype 3a infection. The low correlation between genetic distance and social distance (0.072) for genotype 1a infections in the flattened injecting network was considered statistically significant based on the results of the QAP: only 1.7% of the random permutations had tau c coefficients of greater than 0.072 (Table 3). The other correlations were not considered statistically significant based on the results of the QAP: more than 5% of the random permutations had tau c coefficients of greater magnitude than the observed statistic (Table 3).