06/07/2021
By Susan Pryputniewicz

The Biomedical Engineering and Biotechnology program invites you to attend a doctoral dissertation defense by Manyun Yang on “Novel surveillance platforms for multiplex detection of viable pathogens in food.

Ph.D. Candidate: Manyun Yang
Date: Monday, June 21, 2021
Time: 9:30 to 11:30 a.m.
Location: This will be a virtual defense via Zoom. Those interested in attending should contact the student Manyun_Yang@student.uml.edu and committee advisor Boce_Zhang@uml.edu at least 24 hours prior to the defense to request access to the meeting.

Committee Chair (Advisor): Boce Zhang, Ph.D., Assistant Professor, Department of Biomedical and Nutritional Sciences, University of Massachusetts Lowell

Committee Members:

  • Nancy Goodyear, Ph.D., Associate Professor, Department of Biomedical and Nutritional Sciences, University of Massachusetts Lowell
  • Hengyong Yu, Ph.D., Professor, Department of Electrical and Computer Engineering, University of Massachusetts Lowell
  • Yaguang Luo (Sunny), Ph.D., Agricultural Research Service, U.S. Department of Agriculture
  • Ming-Qun Xu, Ph.D., Research and Development, New England Biolabs

Abstract:
Viable bacterial pathogens are major biohazards that pose a significant threat to food safety and public health. Despite the recent developments in detection platforms, multiplex identification of viable pathogens in food remains a major hurdle. In this dissertation, novel surveillance platforms were developed and validated to address this challenge through direct metatranscriptome RNA-seq on Oxford Nanopore Technologies (ONT) MinION and paper chromogenic array (PCA) enabled by machine learning (ML). For the metatranscriptome RNA-seq method, protocols of ONT MinION were evaluated using a cocktail community standard of E. coli O157:H7, Salmonella Enteritidis, and Listeria monocytogenes in different standard media or food models. Metatranscriptomic data was further analyzed to achieve phylotranscriptomic identification using different bioinformatic pipelines, involving removal of ribosomal RNA (rRNA), assembly, mapping, annotation, and visualization. Performance and accuracy of metatranscriptome RNA-seq were further validated against next-generation sequencing (NGS) and plate count data. For the PCA-ML method, a novel system was developed by using a PCA consisted of a paper substrate impregnated with 23 chromogenic dyes, to undergo color changes upon exposure to volatile organic compounds emitted by pathogens of interest. These color changes are digitized and used to train a multi-layer neural network (NN) with ML, giving it strain-specific pathogen identification and quantification capabilities with 91-95% accuracy. A cocktail culture of bacteria was used to inoculate standard media and then detected by PCA-ML. The PCA-ML was validated against cocktails of pathogens on real food models, including fresh produce and seafood. Specifically, E. coli O157:H7 transformed with plasmid GFP-Amp (FS4200) and Listeria monocytogenes were used on fresh-cut Romaine lettuce. Shewanella putrefaciens, Shewanella putrefaciens transformed with pGFP vector, and Morganella morganii were used on cod and salmon. Via nondestructive sensing, the trained PCA-ML system can respond to, and distinguish between, viable E. coli, E. coli O157:H7, and others, and can simultaneously identify both E. coli O157:H7 and Listeria monocytogenes on fresh-cut Romaine lettuce. Similar results are reported on cod and salmon. These two novel surveillance platforms achieve effective and efficient surveillance and monitoring of multiplex identification of viable pathogens on food. They hold strong promise in the ‘New Era of Smarter Food Safety’ as new platforms for the environment and supply chain surveillance, outbreak investigation, and recall decision-making.

All interested students and faculty members are invited to attend the online defense via remote access.