PLoS neglected tropical diseasesGenomicsPeer-reviewed journal article
Population genomics reveals multi-scale mechanisms sustaining schistosomiasis re-emergence in a near-elimination setting
Hannah D. Guss, Yannick Z. FrancioliiD, Elise N. GroveriD, Andrew Hill, Wei Zou, Kristen J. WadeiD, Hamish PikeiD, Siddharth S. GopalaniD, Liu Yang, Zhong Bo, David D. PollockiD, Elizabeth J. CarltoniD, Todd A. CastoeiD
Abstract
In China, sustained snail control, environmental management, and mass drug administration with praziquantel reduced schistosomiasis to near-elimination levels, yet re-emergence in Sichuan Province during the early 2000s exposed vulnerabilities in late-stage control. We conducted a novel investigation of Schistosoma japonicum re-emergence in Sichuan in order to identify the processes underlying re-emergence and to showcase how genomic data can be used in such investigations. We sequenced whole genomes from 270 miracidia collected from 53 human hosts across 17 villages in 2007 - the year after re-emergence was documented. Population genomic analyses revealed a broadly cohesive regional parasite population with weak geographic structuring. Genome-wide diversity was substantial, and demographic reconstructions revealed no recent decline in effective population size, demonstrating that parasite populations were not demographically fragmented at the onset of re-emergence, hinting at maintenance in reservoir populations. At finer spatial scales, several villages exhibited low diversity and elevated inbreeding, consistent with localized transmission maintained by small founding populations. Estimates of pairwise genetic relatedness revealed dense within-village sibling clusters alongside second- and third-degree relationships spanning villages, and rare first- and second-degree cross-village links. These findings are consistent with highly focal transmission with episodic parasite dispersal across villages, leading to a regional transmission network. Inferred minimum worm burden across hosts varied from 1 to 11 adult worm pairs, indicating heterogeneity in within-host parasite diversity, although uneven sampling limited inference. Together, these findings indicate that schistosomiasis re-emergence in this near-elimination setting was likely facilitated by a diverse parasite population maintained in reservoir populations, and that transmission, while predominantly local was occurring across a network of connected villages. This work illustrates how population genomics can reveal mechanisms driving re-emergence in late-stage elimination in complex, multi-host transmission systems. Author SummarySchistosomiasis is a parasitic disease that affects millions of people worldwide. Understanding the factors that contribute to persistence and transmission, even in the face of control programs, is central to reducing and ultimately eliminating the disease. In China, decades of intensive control efforts have dramatically reduced infections, yet the disease continued to persist in some regions. In this study, we analyzed the genomes of 270 parasite larvae collected from infected humans across 17 villages in Sichuan Province, China, shortly after schistosomiasis re-emerged in the region. By examining genetic relationships among parasites, we reconstructed patterns of transmission across villages and within individual infections. Our findings suggest that, despite major reductions in human infection prevalence, parasite populations sizes do not show signatures of recent population decline, indicating that non-human hosts may serve as key reservoirs promoting persistence. We also find that local transmission pathways, including clonal infections infecting multiple humans derived from a relatively small number of snail hosts, maintain parasite transmission pathways. Collectively, these findings suggest non-human hosts, intact local transmission pathways, and regional connectivity together enabled re-emergence, highlighting important challenges for elimination programs
- organism: human
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- 19th percentile
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