Background: The freshwater snail Oncomelania hupensis is the obligate intermediate host for Schistosoma japonicum in China. Transcriptomic examination of snail-schistosome interactions can provide valuable information of host response at physiological and immune levels. Methods: To investigate S. japonicum-induced changes in O. hupensis gene expression, we utilized high-throughput sequencing to identify transcripts that were differentially expressed between infected snails and their uninfected controls at two key time-point, Day 7 and Day 30 after challenge. Time-series transcriptomic profiles were analyzed using R package DESeq 2, followed by GO, KEGG and (weighted gene correlation network analysis) WGCNA analysis to elucidate and identify important molecular mechanism, and subsequently understand host-parasite relationship. The identified unigenes was verified by bioinformatics and real-time PCR. Possible adaptation molecular mechanisms of O. hupensis to S. japonicum challenge were proposed. Results: Transcriptomic analyses of O. hupensis by S. japonicum invasion yielded billion reads including 92,144 annotated transcripts. Over 5000 differentially expressed genes (DEGs) were identified by pairwise comparisons of infected libraries from two time points to uninfected libraries in O. hupensis. In total, 6032 gene ontology terms and 149 KEGG pathways were enriched. After the snails were infected with S. japonicum on Day 7 and Day 30, DEGs were shown to be involved in many key processes associated with biological regulation and innate immunity pathways. Gene expression patterns differed after exposure to S. japonicum. Using WGCNA, 16 modules were identified. Module-trait analysis identified that a module involved in RNA binding, ribosome, translation, mRNA processing, and structural constituent of ribosome were strongly associated with S. japonicum invasion. Many of the genes from enriched KEGG pathways were involved in lysosome, spliceosome and ribosome, indicating that S. japonicum invasion may activate the regulation of ribosomes and immune response to infection in O. hupensis. Conclusions: Our analysis provided a temporally dynamic gene expression pattern of O. hupensis by S. japonicum invasion. The identification of gene candidates serves as a foundation for future investigations of S. japonicum infection. Additionally, major DEGs expression patterns and putative key regulatory pathways would provide useful information to construct gene regulatory networks between host-parasite crosstalk.

Background Schistosomiasis control is striving forward to transmission interruption and even elimination, evidence-lead control is of vital importance to eliminate the hidden dangers of schistosomiasis. This study attempts to identify high risk areas of schistosomiasis in China by using information value and machine learning. Methods The local case distribution from schistosomiasis surveillance data in China between 2005 and 2019 was assessed based on 19 variables including climate, geography, and social economy. Seven models were built in three categories including information value (IV), three machine learning models [logistic regression (LR), random forest (RF), generalized boosted model (GBM)], and three coupled models (IV + LR, IV + RF, IV + GBM). Accuracy, area under the curve (AUC), and F1-score were used to evaluate the prediction performance of the models. The optimal model was selected to predict the risk distribution for schistosomiasis. Results There is a more prone to schistosomiasis epidemic provided that paddy fields, grasslands, less than 2.5 km from the waterway, annual average temperature of 11.5–19.0 °C, annual average rainfall of 1000–1550 mm. IV + GBM had the highest prediction effect (accuracy = 0.878, AUC = 0.902, F1 = 0.920) compared with the other six models. The results of IV + GBM showed that the risk areas are mainly distributed in the coastal regions of the middle and lower reaches of the Yangtze River, the Poyang Lake region, and the Dongting Lake region. High-risk areas are primarily distributed in eastern Changde, western Yueyang, northeastern Yiyang, middle Changsha of Hunan province; southern Jiujiang, northern Nanchang, northeastern Shangrao, eastern Yichun in Jiangxi province; southern Jingzhou, southern Xiantao, middle Wuhan in Hubei province; southern Anqing, northwestern Guichi, eastern Wuhu in Anhui province; middle Meishan, northern Leshan, and the middle of Liangshan in Sichuan province. Conclusions The risk of schistosomiasis transmission in China still exists, with high-risk areas relatively concentrated in the coastal regions of the middle and lower reaches of the Yangtze River. Coupled models of IV and machine learning provide for effective analysis and prediction, forming a scientific basis for evidence-lead surveillance and control. Graphic Abstract