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

Abstract

Introduction: The human immunodeficiency virus 1 (HIV-1) pandemic is characterized by numerous distinct sub-epidemics (clusters) that continually fuel local transmission. The aims of this study were to identify active growing clusters, to understand which factors most influence the transmission dynamics, how these vary between different subtypes and how this information might contribute to effective public health responses.

Methods: We used HIV-1 genomic sequence data linked to demographic factors that accounted for approximately 70% of all new HIV-1 notifications in New South Wales (NSW). We assessed differences in transmission cluster dynamics between subtype B and circulating recombinant form 01_AE (CRF01_AE). Separate phylogenetic trees were estimated using 2919 subtype B and 473 CRF01_AE sequences sampled between 2004 and 2018 in combination with global sequence data and NSW-specific clades were classified as clusters, pairs or singletons. Significant differences in demographics between subtypes were assessed with Chi-Square statistics.

Results: We identified 104 subtype B and 11 CRF01_AE growing clusters containing a maximum of 29 and 11 sequences for subtype B and CRF01_AE respectively. We observed a > 2-fold increase in the number of NSW-specific CRF01_AE clades over time. Subtype B clusters were associated with individuals reporting men who have sex with men (MSM) as their transmission risk factor, being born in Australia, and being diagnosed during the early stage of infection (p < 0.01). CRF01_AE infections clusters were associated with infections among individuals diagnosed during the early stage of infection (p < 0.05) and CRF01_AE singletons were more likely to be from infections among individuals reporting heterosexual transmission (p < 0.05). We found six subtype B clusters with an above-average growth rate (>1.5 sequences / 6-months) and which consisted of a majority of infections among MSM. We also found four active growing CRF01_AE clusters containing only infections among MSM. Finally, we found 47 subtype B and seven CRF01_AE clusters that contained a large gap in time (>1 year) between infections and may be indicative of intermediate transmissions via undiagnosed individuals.

Conclusions: The large number of active and growing clusters among MSM are the driving force of the ongoing epidemic in NSW for subtype B and CRF01_AE.

Keywords: HIV1; demographic differences; early infections; public health; subtype B and CRF01_AE; transmission cluster.

Figure 1

Number of clusters for subtype B and CRF01_AE between 2013 and 2018. Top Panel The total number of sequences (light grey) and new sequences added (dark grey) accumulating over time. Middle Panel: The total number of pairs (yellow) and clusters (light blue) identified across all data subsets. Note the different y‐axis scales for subtype B and CRF01_AE. Lower Panel: Proportion of sequences from newly notified infections belonging to a cluster (light blue), a sequence pair (yellow), or being a singleton (green) across the different data subsets. D, interval ending in December; J, interval ending in June.

Figure 2

Correlations between sequence demographics and cluster association. Correlation plots shows the Chi‐square statistics for demographics. For each association (cell) the Pearson residual value is shown. A positive association is indicated in blue, a negative association is shown in red. More intense colours and larger squares equal stronger contribution to the overall Chi‐square score, and which are indicated below each correlation plot. As there were less than five sequences with the PWID transmission risk factor, this risk factor category was combined with “Other”. (A) Demographics compared to all subtype B and CRF01_AE, (B) Demographics compared to infections associated with clusters, pairs and singletons for subtype B (top panel) and CRF01_AE (bottom panel). MSM, men who have sex with men; PWID, person who inject drugs.

Figure 3

Time intervals between sampling of infections in growing clusters. (A) (left and middle panels) Changes in cluster size shown as the difference between the minimum number of sequences when the cluster was first identified in the phylogeny to the maximum number of sequences when the most recent sequence associated with that cluster was sampled. (right panel) Cluster growth rate was normalised to the number of sequences added per 6‐months interval. (B) Cluster size at baseline (including sequence data from 2004 to 2012). (middle) Each line represents a cluster and the circles represent a sequence sampled during that data subset coloured according to the stage of infection at diagnosis. Blue = early, green = CD4 T‐cell count < 500, light blue CD4 < 350, red = advanced (see Material and Methods), white = no data. Grey circles represent the starting point of clusters that appeared between 2013 and 2018. Red lines indicated clusters that only contained sequences sampled during the advanced stage of infection for sampling dates 2013 ‐ 2018 and thus are regarded as potentially extinct. (right) Final cluster size at the end of the time period investigated. Top panels show data for subtype B, bottom panels show data for CRF01_AE. D, December; J, June.