Biodefense Oriented Genomic-Based Pathogen Classification Systems: Challenges and Opportunities

Known and Unknown Transboundary Infectious Diseases as Hybrid Threats

The pathogenicity, transmissibility, environmental stability, and potential for genetic manipulation make microbes hybrid threats that could blur the distinction between peace and war. These agents can fall below the detection, attribution, and response capabilities of a nation and seriously affect their health, trade, and security. A framework that could enhance horizon scanning regarding the potential risk of microbes used as hybrid threats requires not only accurately discriminating known and unknown pathogens but building novel scenarios to deploy mitigation strategies. This demands the transition of analyst-based biosurveillance tracking a narrow set of pathogens toward an autonomous biosurveillance enterprise capable of processing vast data streams beyond human cognitive capabilities. Autonomous surveillance systems must gather, integrate, analyze, and visualize billions of data points from different and unrelated sources. Machine learning and artificial intelligence algorithms can contextualize capability information for different stakeholders at different levels of resolution: strategic and tactical. This document provides a discussion of the use of microorganisms as hybrid threats and considerations to quantitatively estimate their risk to ensure societal awareness, preparedness, mitigation, and resilience.

Clustering of Vibrio parahaemolyticus Isolates Using MLST and Whole-Genome Phylogenetics and Protein Motif Fingerprinting

Vibrio parahaemolyticus is a ubiquitous and abundant member of native microbial assemblages in coastal waters and shellfish. Though V. parahaemolyticus is predominantly environmental, some strains have infected human hosts and caused outbreaks of seafood-related gastroenteritis. In order to understand differences among clinical and environmental V. parahaemolyticus strains, we used high quality DNA sequencing data to compare the genomes of V. parahaemolyticus isolates (n = 43) from a variety of geographic locations and clinical and environmental sample matrices. We used phylogenetic trees inferred from multilocus sequence typing (MLST) and whole-genome (WG) alignments, as well as a novel classification and genome clustering approach that relies on protein motif fingerprints (MFs), to assess relationships between V. parahaemolyticus strains and identify novel molecular targets associated with virulence. Differences in strain clustering at more than one position were observed between the MLST and WG phylogenetic trees. The WG phylogeny had higher support values and strain resolution since isolates of the same sequence type could be differentiated. The MF analysis revealed groups of protein motifs that were associated with the pathogenic MLST type ST36 and a large group of clinical strains isolated from human stool. A subset of the stool and ST36-associated protein motifs were selected for further analysis and the motif sequences were found in genes with a variety of functions, including transposases, secretion system components and effectors, and hypothetical proteins. DNA sequences associated with these protein motifs are candidate targets for future molecular assays in order to improve surveys of pathogenic V. parahaemolyticus in the environment and seafood.

Global Spread of Hemorrhagic Fever Viruses: Predicting Pandemics

As successive epidemics have swept the world, the scientific community has quickly learned from them about the emergence and transmission of communicable diseases. Epidemics usually occur when health systems are unprepared. During an unexpected epidemic, health authorities engage in damage control, fear drives action, and the desire to understand the threat is greatest. As humanity recovers, policy-makers seek scientific expertise to improve their "preparedness" to face future events.Global spread of disease is exemplified by the spread of yellow fever from Africa to the Americas, by the spread of dengue fever through transcontinental migration of mosquitos, by the relentless influenza virus pandemics, and, most recently, by the unexpected emergence of Ebola virus, spread by motorbike and long haul carriers. Other pathogens that are remarkable for their epidemic expansions include the arenavirus hemorrhagic fevers and hantavirus diseases carried by rodents over great geographic distances and the arthropod-borne viruses (West Nile, chikungunya and Zika) enabled by ecology and vector adaptations. Did we learn from the past epidemics? Are we prepared for the worst?The ultimate goal is to develop a resilient global health infrastructure. Besides acquiring treatments, vaccines, and other preventive medicine, bio-surveillance is critical to preventing disease emergence and to counteracting its spread. So far, only the western hemisphere has a large and established monitoring system; however, diseases continue to emerge sporadically, in particular in Southeast Asia and South America, illuminating the imperfections of our surveillance. Epidemics destabilize fragile governments, ravage the most vulnerable populations, and threaten the global community.Pandemic risk calculations employ new technologies like computerized maintenance of geographical and historical datasets, Geographic Information Systems (GIS), Next Generation sequencing, and Metagenomics to trace the molecular changes in pathogens during their emergence, and mathematical models to assess risk. Predictions help to pinpoint the hot spots of emergence, the populations at risk, and the pathogens under genetic evolution. Preparedness anticipates the risks, the needs of the population, the capacities of infrastructure, the sources of emergency funding, and finally, the international partnerships needed to manage a disaster before it occurs. At present, the world is in an intermediate phase of trying to reduce health disparities despite exponential population growth, political conflicts, migration, global trade, urbanization, and major environmental changes due to global warming. For the sake of humanity, we must focus on developing the necessary capacities for health surveillance, epidemic preparedness, and pandemic response.

Unbiased Strain-Typing of Arbovirus Directly from Mosquitoes Using Nanopore Sequencing: A Field-forward Biosurveillance Protocol

The future of infectious disease surveillance and outbreak response is trending towards smaller hand-held solutions for point-of-need pathogen detection. Here, samples of Culex cedecei mosquitoes collected in Southern Florida, USA were tested for Venezuelan Equine Encephalitis Virus (VEEV), a previously-weaponized arthropod-borne RNA-virus capable of causing acute and fatal encephalitis in animal and human hosts. A single 20-mosquito pool tested positive for VEEV by quantitative reverse transcription polymerase chain reaction (RT-qPCR) on the Biomeme two3. The virus-positive sample was subjected to unbiased metatranscriptome sequencing on the Oxford Nanopore MinION and shown to contain Everglades Virus (EVEV), an alphavirus in the VEEV serocomplex. Our results demonstrate, for the first time, the use of unbiased sequence-based detection and subtyping of a high-consequence biothreat pathogen directly from an environmental sample using field-forward protocols. The development and validation of methods designed for field-based diagnostic metagenomics and pathogen discovery, such as those suitable for use in mobile “pocket laboratories”, will address a growing demand for public health teams to carry out their mission where it is most urgent: at the point-of-need.

Biosurveillance enterprise for operational awareness, a genomic-based approach for tracking pathogen virulence

To protect our civilians and warfighters against both known and unknown pathogens, biodefense stakeholders must be able to foresee possible technological trends that could affect their threat risk assessment. However, significant flaws in how we prioritize our countermeasure-needs continue to limit their development. As recombinant biotechnology becomes increasingly simplified and inexpensive, small groups, and even individuals, can now achieve the design, synthesis, and production of pathogenic organisms for offensive purposes. Under these daunting circumstances, a reliable biosurveillance approach that supports a diversity of users could better provide early warnings about the emergence of new pathogens (both natural and manmade), reverse engineer pathogens carrying traits to avoid available countermeasures, and suggest the most appropriate detection, prophylactic, and therapeutic solutions. While impressive in data mining capabilities, real-time content analysis of social media data misses much of the complexity in the factual reality. Quality issues within freeform user-provided hashtags and biased referencing can significantly undermine our confidence in the information obtained to make critical decisions about the natural vs. intentional emergence of a pathogen. At the same time, errors in pathogen genomic records, the narrow scope of most databases, and the lack of standards and interoperability across different detection and diagnostic devices, continue to restrict the multidimensional biothreat assessment. The fragmentation of our biosurveillance efforts into different approaches has stultified attempts to implement any new foundational enterprise that is more reliable, more realistic and that avoids the scenario of the warning that comes too late. This discussion focus on the development of genomic-based decentralized medical intelligence and laboratory system to track emerging and novel microbial health threats in both military and civilian settings and the use of virulence factors for risk assessment. Examples of the use of motif fingerprints for pathogen discrimination are provided.