Phylogenetic and Phylodynamic Analyses of HCV Strains Circulating among Patients Using Injectable Drugs in Central Italy

Abstract

Approximately 71 million people worldwide are infected with the hepatitis C virus (HCV). Injectable drug use represents the most common route of transmission in Europe and other developed countries. We studied the molecular characteristics of the HCV infection among mono-infected people who used drugs (PWUD) in Italy. Among 208 PWUD with anti-HCV antibodies, 101 (48.6%) were HCV RNA-positive, the majority (47%) were infected with the HCV genotype (Gt)1a, followed by Gt3a (34.9%), Gt4 (9.1%), Gt1b (4.5%), and Gt2 (4.5%). Bayesian phylogenetic analyses of clustered HCV NS5B sequences from 66 HCV-positive PWUDs with available plasma samples indicated age and neighborhood proximity as the most common characteristics between closely related HCV strains. Population dynamics, as measured by a coalescent Bayesian skyline analysis, revealed an increase in HCV Gt1a infections from the mid-1980s to mid-1990s. While HCV Gt3a infections were first detected in the 1980s, patient numbers with this genotype subtype remained relatively constant. For both Gt1a and Gt3a, Birth–Death Bayesian Skyline analyses produced higher reproduction numbers post 2014. For earlier time intervals, slow growths were observed for both Gt1a and Gt3a with reproduction numbers (Re) of approximately 1. The evolutionary rates for Gt1a and Gt3a were estimated as 2.23 × 10−4 and 3.85 × 10−4, respectively.

Molecular Epidemiology and Baseline Resistance of Hepatitis C Virus to Direct Acting Antivirals in Croatia

“…However, median ages of all patients included in this study were overall at least 10 years higher which suggests aging of the patient population and could be attributed to long persistence of HCV infection and the relatively recent availability of the effective direct antiviral therapy. The results of previous European and national studies are concordant with our finding that GT1a and GT3a were more frequently associated with IDU [ 3 , 11 , 17 , 32 ].…”

“…Sequence analysis of fast evolving viruses such as HCV was shown to be efficient method for viral transmission tracing [ 16 , 17 , 49 , 51 ]. Phylogenetic analysis demonstrated that 27 (9.0%) of Croatian HCV sequences had a presumed epidemiological link with another sequence and classified into 13 transmission pairs or clusters which were the most common in GT3a, while IDU was shown to be a risk factor statistically associated with clustering.…”

“…In Croatia, blood transfusion and haemodialysis before 1992 were strongly associated with an infection with HCV GT1, especially subtype 1b (GT1b) [ 11 , 14 ]. Today, intravenous drug use represents the major route of HCV transmission in developed countries, with a prevalence of 2–80% among intravenous drug users (IDU) [ 15 , 16 , 17 ]. Estimated population size of IDU in Croatia is around 15,000, and the most common HCV subtypes in this population are subtype 3a (GT3a) (61%) and subtype 1a (GT1a) (24%) [ 11 , 18 ].…”

Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models

“…One powerful variant, the episodic birth-death-sampling (EBDS) model [ 9 , 16 – 18 ] permits birth, death, and sampling rates to change in discrete epochs throughout time to capture more complicated population dynamics. Recent inference based on EBDS models has found its way already into many applications, especially on the understanding of the spread of infectious disease [ 4 , 19 , 20 ].…”

Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models

“…One powerful variant, the episodic birth-death-sampling (EBDS) model (Lambert & Stadler 2013, Stadler et al 2013, Gavryushkina et al 2014, Du Plessis 2016) permits birth, death, and sampling rates to change in discrete epochs throughout time to capture more complicated population dynamics. Recent inference based on EBDS models has found its way already into many applications, especially on the understanding of the spread of infectious disease (Novitsky et al 2015, Vasylyeva et al 2020, Minosse et al 2021).…”