Development of Anti-Yersinia pestis Human Antibodies with Features Required for Diagnostic and Therapeutic Applications.

BACKGROUND: Yersinia pestis is a category A infective agent that causes bubonic, septicemic, and pneumonic plague. Notably, the acquisition of antimicrobial or multidrug resistance through natural or purposed means qualifies Y. pestis as a potential biothreat agent. Therefore, high-quality antibodies designed for accurate and sensitive Y. pestis diagnostics, and therapeutics potentiating or replacing traditional antibiotics are of utmost need for national security and public health preparedness. METHODS: Here, we describe a set of human monoclonal immunoglobulins (IgG1s) targeting Y. pestis fraction 1 (F1) antigen, previously derived from in vitro evolution of a phage-display library of single-chain antibodies (scFv). We extensively characterized these antibodies and their effect on bacterial and mammalian cells via: ELISA, flow cytometry, mass spectrometry, spectroscopy, and various metabolic assays. RESULTS: Two of our anti-F1 IgG (αF1Ig 2 and αF1Ig 8) stood out for high production yield, specificity, and stability. These two antibodies were additionally attractive in that they displayed picomolar affinity, did not compete when binding Y. pestis, and retained immunoreactivity upon chemical derivatization. Most importantly, these antibodies detected <1,000 Y. pestis cells in sandwich ELISA, did not harm respiratory epithelial cells, induced Y. pestis agglutination at low concentration (350 nM), and caused apparent reduction in cell growth when radiolabeled at a nonagglutinating concentration (34 nM). CONCLUSION: These antibodies are amenable to the development of accurate and sensitive diagnostics and immuno/radioimmunotherapeutics.

Development of a Supervised Learning Algorithm for Detection of Potential Disease Reemergence: A Proof of Concept.

Infectious disease reemergence is an important yet ambiguous concept that lacks a quantitative definition. Currently, reemergence is identified without specific criteria describing what constitutes a reemergent event. This practice affects reproducible assessments of high-consequence public health events and disease response prioritization. This in turn can lead to misallocation of resources. More important, early recognition of reemergence facilitates effective mitigation. We used a supervised machine learning approach to detect potential disease reemergence. We demonstrate the feasibility of applying a machine learning classifier to identify reemergence events in a systematic way for 4 different infectious diseases. The algorithm is applicable to temporal trends of disease incidence and includes disease-specific features to identify potential reemergence. Through this study, we offer a structured means of identifying potential reemergence using a data-driven approach.

Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks.

BACKGROUND: Information from historical infectious disease outbreaks provides real-world data about outbreaks and their impacts on affected populations. These data can be used to develop a picture of an unfolding outbreak in its early stages, when incoming information is sparse and isolated, to identify effective control measures and guide their implementation. OBJECTIVE: This study aimed to develop a publicly accessible Web-based visual analytic called Analytics for the Investigation of Disease Outbreaks (AIDO) that uses historical disease outbreak information for decision support and situational awareness of an unfolding outbreak. METHODS: We developed an algorithm to allow the matching of unfolding outbreak data to a representative library of historical outbreaks. This process provides epidemiological clues that facilitate a user's understanding of an unfolding outbreak and facilitates informed decisions about mitigation actions. Disease-specific properties to build a complete picture of the unfolding event were identified through a data-driven approach. A method of analogs approach was used to develop a short-term forecasting feature in the analytic. The 4 major steps involved in developing this tool were (1) collection of historic outbreak data and preparation of the representative library, (2) development of AIDO algorithms, (3) development of user interface and associated visuals, and (4) verification and validation. RESULTS: The tool currently includes representative historical outbreaks for 39 infectious diseases with over 600 diverse outbreaks. We identified 27 different properties categorized into 3 broad domains (population, location, and disease) that were used to evaluate outbreaks across all diseases for their effect on case count and duration of an outbreak. Statistical analyses revealed disease-specific properties from this set that were included in the disease-specific similarity algorithm. Although there were some similarities across diseases, we found that statistically important properties tend to vary, even between similar diseases. This may be because of our emphasis on including diverse representative outbreak presentations in our libraries. AIDO algorithm evaluations (similarity algorithm and short-term forecasting) were conducted using 4 case studies and we have shown details for the Q fever outbreak in Bilbao, Spain (2014), using data from the early stages of the outbreak. Using data from only the initial 2 weeks, AIDO identified historical outbreaks that were very similar in terms of their epidemiological picture (case count, duration, source of exposure, and urban setting). The short-term forecasting algorithm accurately predicted case count and duration for the unfolding outbreak. CONCLUSIONS: AIDO is a decision support tool that facilitates increased situational awareness during an unfolding outbreak and enables informed decisions on mitigation strategies. AIDO analytics are available to epidemiologists across the globe with access to internet, at no cost. In this study, we presented a new approach to applying historical outbreak data to provide actionable information during the early stages of an unfolding infectious disease outbreak.

Complete Genome Sequences of Historic Clostridioides difficile Food-Dwelling Ribotype 078 Strains in Canada Identical to That of the Historic Human Clinical Strain M120 in the United Kingdom.

Clostridioides (Clostridium) difficile is a spore-forming anaerobic bacterium that causes severe intestinal diseases in humans. Here, we report the complete genome sequence of the first C. difficile foodborne type strain (PCR ribotype 078) isolated from food animals in Canada in 2004, which has 100% similarity to the genome sequence of the historic human clinical strain M120.