Characterization of HIV-1 Transmission Clusters Inferred from the Brazilian Nationwide Genotyping Service Database

The study of HIV-1 transmission networks inferred from viral genetic data can be used to clarify important factors about the dynamics of HIV-1 transmission, such as network growth rate and demographic composition. In Brazil, HIV transmission has been stable since the early 2000s and the study of transmission clusters can provide valuable data to understand the drivers of virus spread. In this work, we analyzed a nation-wide database of approximately 53,000 HIV-1 nucleotide pol sequences sampled from genotyped patients from 2008-2017. Phylogenetic trees were reconstructed for the HIV-1 subtypes B, C and F1 in Brazil and transmission clusters were inferred by applying genetic distances thresholds of 1.5%, 3.0% and 4.5%, as well as high (>0.9) cluster statistical support. An odds ratio test revealed that young men (15-24 years) and individuals with more years of education presented higher odds to cluster. The assortativity coefficient revealed that individuals with similar demographic features tended to cluster together, with emphasis on features, such as place of residence and age. We also observed that assortativity weakens as the genetic distance threshold increases. Our results indicate that the phylogenetic clusters identified here are likely representative of the contact networks that shape HIV transmission, and this is a valuable tool even in sites with low sampling density, such as Brazil.

Cluster-specific gene markers enhance Shigella and enteroinvasive Escherichia coli in silico serotyping

Shigella and enteroinvasive Escherichia coli (EIEC) cause human bacillary dysentery with similar invasion mechanisms and share similar physiological, biochemical and genetic characteristics. Differentiation of Shigella from EIEC is important for clinical diagnostic and epidemiological investigations. However, phylogenetically, Shigella and EIEC strains are composed of multiple clusters and are different forms of E. coli, making it difficult to find genetic markers to discriminate between Shigella and EIEC. In this study, we identified 10 Shigella clusters, seven EIEC clusters and 53 sporadic types of EIEC by examining over 17000 publicly available Shigella and EIEC genomes. We compared Shigella and EIEC accessory genomes to identify cluster-specific gene markers for the 17 clusters and 53 sporadic types. The cluster-specific gene markers showed 99.64% accuracy and more than 97.02% specificity. In addition, we developed a freely available in silico serotyping pipeline named Shigella EIEC Cluster Enhanced Serotype Finder (ShigEiFinder) by incorporating the cluster-specific gene markers and established Shigella and EIEC serotype-specific O antigen genes and modification genes into typing. ShigEiFinder can process either paired-end Illumina sequencing reads or assembled genomes and almost perfectly differentiated Shigella from EIEC with 99.70 and 99.74% cluster assignment accuracy for the assembled genomes and read mapping respectively. ShigEiFinder was able to serotype over 59 Shigella serotypes and 22 EIEC serotypes and provided a high specificity of 99.40% for assembled genomes and 99.38% for read mapping for serotyping. The cluster-specific gene markers and our new serotyping tool, ShigEiFinder (installable package: https://github.com/LanLab/ShigEiFinder, online tool: https://mgtdb.unsw.edu.au/ShigEiFinder/), will be useful for epidemiological and diagnostic investigations.

A Phylodynamic Workflow to Rapidly Gain Insights into the Dispersal History and Dynamics of SARS-CoV-2 Lineages

Since the start of the COVID-19 pandemic, an unprecedented number of genomic sequences of SARS-CoV-2 have been generated and shared with the scientific community. The unparalleled volume of available genetic data presents a unique opportunity to gain real-time insights into the virus transmission during the pandemic, but also a daunting computational hurdle if analyzed with gold-standard phylogeographic approaches. To tackle this practical limitation, we here describe and apply a rapid analytical pipeline to analyze the spatiotemporal dispersal history and dynamics of SARS-CoV-2 lineages. As a proof of concept, we focus on the Belgian epidemic, which has had one of the highest spatial densities of available SARS-CoV-2 genomes. Our pipeline has the potential to be quickly applied to other countries or regions, with key benefits in complementing epidemiological analyses in assessing the impact of intervention measures or their progressive easement.

Phylogenetic Clustering among Asylum Seekers with New HIV-1 Diagnoses in Montreal, QC, Canada

Migrants are at an increased risk of HIV acquisition. We aimed to use phylogenetics to characterize transmission clusters among newly-diagnosed asylum seekers and to understand the role of networks in local HIV transmission. Retrospective chart reviews of asylum seekers linked to HIV care between 1 June 2017 and 31 December 2018 at the McGill University Health Centre and the Jewish General Hospital in Montreal were performed. HIV-1 partial pol sequences were analyzed among study participants and individuals in the provincial genotyping database. Trees were reconstructed using MEGA10 neighbor-joining analysis. Clustering of linked viral sequences was based on a strong bootstrap support (>97%) and a short genetic distance (<0.01). Overall, 10,645 provincial sequences and 105 asylum seekers were included. A total of 13/105 participant sequences (12%; n = 7 males) formed part of eight clusters. Four clusters (two to three people) included only study participants (n = 9) and four clusters (two to three people) included four study participants clustered with six individuals from the provincial genotyping database. Six (75%) clusters were HIV subtype B. We identified the presence of HIV-1 phylogenetic clusters among asylum seekers and at a population-level. Our findings highlight the complementary role of cohort data and population-level genotypic surveillance to better characterize transmission clusters in Quebec.