Accelerating pathogen detection with nanopore sequencing in Kenya: a One health approach

Résumé

Background Infectious diseases of livestock, particularly those with zoonotic potential, pose significant threats to animal and human health globally. Conventional diagnostic methods often fail to detect novel pathogens or co-infections, delaying outbreak response. Nanopore sequencing offers a rapid, field-deployable alternative for comprehensive pathogen detection. Objective To evaluate the utility of Oxford Nanopore Technology (ONT) MinION sequencing for rapid and accurate pathogen identification from ruminant blood samples in Kenya. Methods A total of 301 livestock blood samples preserved in RNAlater were processed. Bacterial RNA was extracted using an automated system (TANBead ® Nucleic Acid Extractor), reverse-transcribed to cDNA (LunaScript ® RT SuperMix Kit), and sequenced on the MinION device using the Rapid Barcoding Kit 96 (SQK-RBK110.96). Real-time bioinformatics analysis was performed using MinKNOW software and the What’s In My Pot (WIMP) workflow on the EPI2ME platform for taxonomic classification. Results Sequencing generated 205,584 bacterial reads, enabling species-level identification of 22 potential bacterial pathogens (52,967 reads). The most abundant species was Clostridium botulinum (38,919 reads, 18.9% of total bacterial reads), followed by Escherichia marmotae (5,881 reads, 2.9%), Salmonella enterica (4,698 reads, 2.3%), and Acinetobacter seifertii (1,485 reads, 0.7%). Clinically significant zoonotic pathogens detected included Anaplasma phagocytophilum (372 reads), Rickettsia felis (22 reads), Bacillus anthracis (15 reads), and Yersinia pseudotuberculosis (4 reads). Co-detections were identified in 35.4% of samples, demonstrating the capacity of nanopore sequencing to detect polymicrobial infections. Conclusion Nanopore sequencing enables rapid, comprehensive pathogen identification at species-level resolution directly from livestock blood, including detection of zoonotic and novel pathogens. This technology is particularly valuable for outbreak investigations in resource-limited settings and should be integrated into One Health surveillance programs in Africa.

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