A Nationwide Study about the Dispersal Patterns of the Predominant HIV-1 Subtypes A1 and B in Greece: Inference of the Molecular Transmission Clusters

Limnaios

Roussos

et al. 2020

Preprint

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Improving HIV diagnosis, access to care and effective antiretroviral treatment provides our global strategy to reduce HIV incidence. To reach this goal we need to increase our knowledge about local epidemics. HIV infection dates would be an important information towards this goal, but they are largely unknown. To date, methods to estimate the dates of HIV infection are based mainly on laboratory or molecular methods. Our aim was to validate molecular clock inferred infection dates that were estimated by analysing sequences from 145 people living with HIV (PLHIV) with known transmission dates (clinically estimated infection dates). All HIV sequences were obtained by Sanger sequencing and were previously found to belong to well-established molecular transmission clusters (MTCs). Our analysis showed that the molecular clock inferred infection dates were correlated with the clinically estimated ones (Spearman’s Correlation coefficient = 0.93, p<0.001) and that there was an agreement between them (Lin’s concordance correlation coefficient = 0.92, p<0.001). For most cases (61.4%), the molecular clock inferred preceded the clinically estimated infection dates. The median difference between clinically and molecularly estimated dates of infection was of 0.18 (IQR: -0.21, 0.89) years. The lowest differences were identified in people who inject drugs of our study population. Our study shows that the estimated time to more recent common ancestor (tMRCA) of nodes within clusters provides a reliable approximation of HIV infections for PLHIV infected within MTCs. Next-generation sequencing data and molecular clock estimates based on heterochronous sequences provide, probably, more reliable methods for inferring infection dates. However, since these data are not available in most of the HIV clinical laboratories, our approach, under specific conditions, can provide a reliable estimation of HIV infection dates and can be used for HIV public health interventions.

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Validation of molecular clock inferred HIV infection ages: evidence for accurate estimation of infection dates

Limnaios

Roussos

et al. 2020

Preprint

Self Cite

“…Although HIV-1 cases declined after 2012, the proportion of diagnoses among PWID remained higher (i.e., between 16.0% and 17.9%) than before the outbreak (around 5%) (https://eody.gov.gr/, accessed on 15 September 2021). Our previous analysis, using a dense sample of sequences from HIV-1 individuals sampled during 1999-2015, showed that the subtypes A1 and B were the most prevalent clades in Greece [22]. Regarding the patterns of HIV-1 transmission, 93.8% and 77.0% of sequences for subtype A1 and B, respectively, were found to belong within local clusters [22].…”

Section: Introductionmentioning

confidence: 98%

“…Traditionally, subtyping has been used to monitor viral genetic diversity and to map the distribution of HIV-1 clades across different geographic areas. Sophisticated phylogenetic and phylodynamic analyses have been implemented to investigate the characteristics of HIV dispersal (identification of clusters) and to estimate the clusters' transmission dynamics over time [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Dating the Origin and Estimating the Transmission Rates of the Major HIV-1 Clusters in Greece: Evidence about the Earliest Subtype A1 Epidemic in Europe

Limnaios

Adamis

et al. 2022

Viruses

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Our aim was to estimate the date of the origin and the transmission rates of the major local clusters of subtypes A1 and B in Greece. Phylodynamic analyses were conducted in 14 subtype A1 and 31 subtype B clusters. The earliest dates of origin for subtypes A1 and B were in 1982.6 and in 1985.5, respectively. The transmission rate for the subtype A1 clusters ranged between 7.54 and 39.61 infections/100 person years (IQR: 9.39, 15.88), and for subtype B clusters between 4.42 and 36.44 infections/100 person years (IQR: 7.38, 15.04). Statistical analysis revealed that the average difference in the transmission rate between the PWID and the MSM clusters was 6.73 (95% CI: 0.86 to 12.60; p = 0.026). Our study provides evidence that the date of introduction of subtype A1 in Greece was the earliest in Europe. Transmission rates were significantly higher for PWID than MSM clusters due to the conditions that gave rise to an extensive PWID HIV-1 outbreak ten years ago in Athens, Greece. Transmission rate can be considered as a valuable measure for public health since it provides a proxy of the rate of epidemic growth within a cluster and, therefore, it can be useful for targeted HIV prevention programs.

“…A time-space cluster occurs by focusing on the identification of MTCs in a particular geographic area while using time-reversible phylodynamics approaches. MTCs have been used to examine the likely impact of genetic (mutations and subtypes of viruses) [ 27 , 28 , 29 ], demographic and clinical [ 22 , 25 , 26 ] factors on regional phylogenetic clustering. Identification of MTCs for HIV has resulted in the characterization of the risk factors that are associated with the spread of the virus in different countries [ 22 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

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

Bousali

Dimadi

et al. 2021

Life

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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.