Strutt, J. P. B. | bioRxiv Science

bioRxiv2024

Rapid Nanopore cloud-based monitoring and analysis of commercial aquaculture microbiomes

Aquaculture stands as a crucial component of global food security and sustainable development, yet it faces challenges in disease management and ecological balance. Here, we present a novel approach using rapid nanopore sequencing, cloud-based monitoring and analysis for aquaculture microbiomes. Our study aimed to perform untargeted, agnostic biological monitoring of a commercial aquaculture facility, emphasizing rapidity, specificity, and sensitivity. We employed Oxford Nanopore Technologies MinION sequencer with an optimised rapid sequencing protocol, enabling on-site operation by facility staff. Three separate sampling efforts resulting in thirteen sequencing runs were conducted, revealing a representative microbiome baseline across aquaculture system components within a 24-hour timeframe. Our results demonstrated the feasibility of rapid monitoring and analysis of nitrogen-associated organisms, essential for same-day water quality management and infection event detection. Notably, Moving Bed Biofilm Reactor (MBBR media) or BioDiscs exhibited the highest diversity and abundance of nitrogen-associated organisms, confirming a pivotal role in nitrification processes. Critically, our approach addressed challenges in metagenomic sample purity and false positives, offering insights for future refinement and application. Our findings underscore the potential of rapid sequencing technologies in enhancing aquaculture management and sustainability. This approach holds promise for mitigating disease outbreaks, optimizing productivity, and advancing ecological balance in aquaculture systems.

microbiology↗

bioRxiv2022

Machine-learning based detection of adventitious microbes in T-cell therapy cultures using long read sequencing

Assuring that cell therapy products are safe before releasing them for use in patients is critical. Currently, compendial sterility testing for bacteria and fungi can take 7-14 days. The goal of this work was to develop a rapid untargeted approach for the sensitive detection of microbial contaminants at low abundance from low volume samples during the manufacturing process of cell therapies. We developed a long-read sequencing methodology using Oxford Nanopore Technologies MinION platform with 16S and 18S amplicon sequencing to detect USP<71> organisms and other microbial species. Reads are classified metagenomically to predict the microbial species. We used an extreme gradient boosting machine learning algorithm (XGBoost) to first assess if a sample is contaminated and second, determine whether the predicted contaminant is correctly classified or misclassified. The model was used to make a final decision on the sterility status of the input sample. An optimised experimental and bioinformatics pipeline starting from spiked species through to sequenced reads allowed for the detection of microbial samples at 10 CFU / mL using metagenomic classification. Machine learning can be coupled with long read sequencing to detect and identify sample sterility status and microbial species present in T-cell cultures, including the USP<71> organisms to 10 CFU / mL. ImportanceThis research presents a novel method for rapidly and accurately detecting microbial contaminants in cell therapy products, which is essential for ensuring patient safety. Traditional testing methods are time-consuming, taking 7-14 days, while our approach can significantly reduce this time. By combining advanced long read Nanopore sequencing techniques and machine learning, we can effectively identify the presence and types of microbial contaminants at low abundance levels. This breakthrough has the potential to improve the safety and efficiency of cell therapy manufacturing, leading to better patient outcomes and a more streamlined production process.

bioinformatics↗