Phenotype-Based Threat Assessment

A systematic approach to microbial forensics

The coronavirus disease 2019 pandemic accelerated developments in biotechnology that underpin infection science. These advances present an opportunity to refresh the microbial forensic toolkit. Integration of novel analytical techniques with established forensic methods will speed up acquisition of evidence and better support lines of enquiry. A critical part of any such investigation is demonstration of a robust causal relationship and attribution of responsibility for an incident. In the wider context of a formal investigation into agency, motivation and intent, the quick and efficient assembly of microbiological evidence sets the tone and tempo of the entire investigation. Integration of established and novel analytical techniques from infection science into a systematic approach to microbial forensics will therefore ensure that major perspectives are correctly used to frame and shape the evidence into a clear narrative, while recognizing that forensic hypothesis generation, testing and refinement comprise an iterative process. Development of multidisciplinary training exercises that use this approach will enable translation into practice and efficient implementation when the need arises.

High-throughput functional trait testing for bacterial pathogens

Functional traits are characteristics that affect the fitness and metabolic function of a microorganism. There is growing interest in using high-throughput methods to characterize bacterial pathogens based on functional virulence traits. Traditional methods that phenotype a single organism for a single virulence trait can be time consuming and labor intensive. Alternatively, machine learning of whole-genome sequences (WGS) has shown some success in predicting virulence. However, relying solely on WGS can miss functional traits, particularly for organisms lacking classical virulence factors. We propose that high-throughput assays for functional virulence trait identification should become a prominent method of characterizing bacterial pathogens on a population scale. This work is critical as we move from compiling lists of bacterial species associated with disease to pathogen-agnostic approaches capable of detecting novel microbes. We discuss six key areas of functional trait testing and how advancing high-throughput methods could provide a greater understanding of pathogens.

Neural network based integration of assays to assess pathogenic potential

Limited data significantly hinders our capability of biothreat assessment of novel bacterial strains. Integration of data from additional sources that can provide context about the strain can address this challenge. Datasets from different sources, however, are generated with a specific objective and which makes integration challenging. Here, we developed a deep learning-based approach called the neural network embedding model (NNEM) that integrates data from conventional assays designed to classify species with new assays that interrogate hallmarks of pathogenicity for biothreat assessment. We used a dataset of metabolic characteristics from a de-identified set of known bacterial strains that the Special Bacteriology Reference Laboratory (SBRL) of the Centers for Disease Control and Prevention (CDC) has curated for use in species identification. The NNEM transformed results from SBRL assays into vectors to supplement unrelated pathogenicity assays from de-identified microbes. The enrichment resulted in a significant improvement in accuracy of 9% for biothreat. Importantly, the dataset used in our analysis is large, but noisy. Therefore, the performance of our system is expected to improve as additional types of pathogenicity assays are developed and deployed. The proposed NNEM strategy thus provides a generalizable framework for enrichment of datasets with previously collected assays indicative of species.