Subtype-specific differences in transmission cluster dynamics of HIV-1 B and CRF01_AE in New South Wales, Australia

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.

Molecular epidemiology to aid virtual elimination of HIV transmission in Australia

The Global UNAIDS 95/95/95 targets aim to increase the percentage of persons who know their HIV status, receive antiretroviral therapy, and have achieved viral suppression. Achieving these targets requires efforts to improve the public health response to increase access to care for those living with HIV, identify those yet undiagnosed with HIV early, and increase access to prevention for those most at risk of HIV acquisition. HIV infections in Australia are among the lowest globally having recorded significant declines in new diagnoses in the last decade. However, the HIV epidemic has changed with an increasing proportion of newly diagnosed infections among those born outside Australia observed in the last five years. Thus, the current prevention efforts are not enough to achieve the UNAIDS targets and virtual elimination across all population groups. We believe both are possible by including molecular epidemiology in the public health response. Molecular epidemiology methods have been crucial in the field of HIV prevention, particularly in demonstrating the efficacy of treatment as prevention. Cluster detection using molecular epidemiology can provide opportunities for the real-time detection of new outbreaks before they grow, and cluster detection programs are now part of the public health response in the USA and Canada. Here, we review what molecular epidemiology has taught us about HIV evolution and spread. We summarize how we can use this knowledge to improve public health measures by presenting case studies from the USA and Canada. We discuss the successes and challenges of current public health programs in Australia, and how we could use cluster detection as an add-on to identify gaps in current prevention measures easier and respond quicker to growing clusters. Lastly, we raise important ethical and legal challenges that need to be addressed when HIV genotypic data is used in combination with personal data.

Revealing the drug resistance mechanism of saquinavir due to G48V and V82F mutations in subtype CRF01_AE HIV-1 protease: molecular dynamics simulation and binding free energy calculations

Human immunodeficiency virus-1 (HIV-1) protease is one of the important targets in AIDS therapy. The majority of HIV infections are caused due to non-B subtypes in developing countries. The co-occurrence of mutations along with naturally occurring polymorphisms in HIV-1 protease cause resistance to the FDA approved drugs, thereby posing a major challenge in the treatment of antiretroviral therapy. In this work, the resistance mechanism against SQV due to active site mutations G48V and V82F in CRF01_AE (AE) protease was explored. The binding free energy calculations showed that the direct substitution of valine at position 48 introduces a bulkier side chain, directly impairing the interaction with SQV in the binding pocket. Also, the intramolecular hydrogen bonding network of the neighboring residues is altered, indirectly affecting the binding of SQV. Interestingly, the substitution of phenylalanine at position 82 induces conformational changes in the 80's loop and the flap region, thereby favoring the binding of SQV. The V82F mutant structure also maintains similar intramolecular hydrogen bond interactions as observed in AE-WT.Communicated by Ramaswamy H. Sarma.

Phylodynamic analysis reveals changing transmission dynamics of HIV-1 CRF01_AE in Japan from heterosexuals to men who have sex with men

BACKGROUND

HIV-1 circulating recombinant form (CRF) 01_AE is the second major subtype in Japan. Our previous study indicated that CRF01_AE was predominantly circulating in heterosexuals/injecting drug users (IDUs). With implications of increased CRF01_AE infections among men who have sex with men (MSM), this study sought to investigate whether the transmission dynamics of CRF01_AE infections in Japan have changed.

METHODS

Sequences from 8032 newly diagnosed HIV-1-infected individuals were analysed. For 614 (7.6%) of CRF01_AE cases, clusters were identified and categorised by transmission risks. Median times to the most recent common ancestors (tMRCA) were estimated.

RESULTS

The individuals were predominantly Japanese (64%) and male (72%). MSM became the predominant transmission risk from 2014. Thirty transmission clusters (TCs) and 48 pairs, including 40% of individuals, were identified. MSM were approximately five times more likely to be in a TC compared to heterosexuals, and were the major contributors to TCs. tMRCA data suggest that MSM TCs emerged from 1996 and became predominant around 2000.

CONCLUSIONS

CRF01_AE has spread among MSM, with frequent and continuous cluster formations, and MSM has become the predominant transmission risk. Our study suggested that CRF01_AE transmission has shifted from heterosexuals/IDUs to MSM. Prevention measures targeting key populations should be considered for controlling CRF01_AE spread.

SARS-CoV-2 Molecular Transmission Clusters and Containment Measures in Ten European Regions during the First Pandemic Wave

Background: The spatiotemporal profiling of molecular transmission clusters (MTCs) using viral genomic data can effectively identify transmission networks in order to inform public health actions targeting SARS-CoV-2 spread. Methods: We used whole genome SARS-CoV-2 sequences derived from ten European regions belonging to eight countries to perform phylogenetic and phylodynamic analysis. We developed dedicated bioinformatics pipelines to identify regional MTCs and to assess demographic factors potentially associated with their formation. Results: The total number and the scale of MTCs varied from small household clusters identified in all regions, to a super-spreading event found in Uusimaa-FI. Specific age groups were more likely to belong to MTCs in different regions. The clustered sequences referring to the age groups 50–100 years old (y.o.) were increased in all regions two weeks after the establishment of the lockdown, while those referring to the age group 0–19 y.o. decreased only in those regions where schools’ closure was combined with a lockdown. Conclusions: The spatiotemporal profiling of the SARS-CoV-2 MTCs can be a useful tool to monitor the effectiveness of the interventions and to reveal cryptic transmissions that have not been identified through contact tracing.