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12 pages, 11116 KiB

Open AccessArticle

Engineered Cell Line Imaging Assay Differentiates Pathogenic from Non-Pathogenic Bacteria

by Shelby M. B. Phillips, Carson Bergstrom, Brian Walker, George Wang, Trinidad Alfaro, Zachary R. Stromberg and Becky M. Hess

Pathogens 2022, 11(2), 209; https://doi.org/10.3390/pathogens11020209 - 4 Feb 2022

Cited by 3 | Viewed by 2310

Abstract

Cell culture systems have greatly expanded our understanding of how bacterial pathogens target signaling pathways to manipulate the host and cause infection. Advances in genetic engineering have allowed for the creation of fluorescent protein readouts within signaling pathways, but these techniques have been [...] Read more.

Cell culture systems have greatly expanded our understanding of how bacterial pathogens target signaling pathways to manipulate the host and cause infection. Advances in genetic engineering have allowed for the creation of fluorescent protein readouts within signaling pathways, but these techniques have been underutilized in pathogen biology. Here, we genetically engineered a lung cell line with fluorescent reporters for extracellular signal-related kinase (ERK) and the downstream transcription factor FOS-related antigen 1 (Fra1) and evaluated signaling after inoculation with pathogenic and non-pathogenic bacteria. Cells were inoculated with 100 colony-forming units of Acinetobacter baylyi, Klebsiella pneumoniae, Pseudomonas aeruginosa, Streptococcus agalactiae, or Staphylococcus epidermidis and imaged in a multi-mode reader. The alamarBlue cell viability assay was used as a reference test and showed that pathogenic P. aeruginosa induced significant (p < 0.05) cell death after 8 h in both wild-type and engineered cell lines compared to non-pathogenic S. epidermidis. In engineered cells, we found that Fra1 signaling was disrupted in as little as 4 h after inoculation with bacterial pathogens compared to delayed disruption in signaling by non-pathogenic S. epidermidis. Overall, we demonstrate that low levels of pathogenic versus non-pathogenic bacteria can be rapidly and sensitively screened based on ERK-Fra1 signaling. Full article

(This article belongs to the Special Issue Advances in Biosurveillance for Human, Animal, and Plant Health)

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18 pages, 4714 KiB

Open AccessArticle

A Comparison of Infectious Disease Forecasting Methods across Locations, Diseases, and Time

by Samuel Dixon, Ravikiran Keshavamurthy, Daniel H. Farber, Andrew Stevens, Karl T. Pazdernik and Lauren E. Charles

Pathogens 2022, 11(2), 185; https://doi.org/10.3390/pathogens11020185 - 29 Jan 2022

Cited by 9 | Viewed by 4811

Abstract

Accurate infectious disease forecasting can inform efforts to prevent outbreaks and mitigate adverse impacts. This study compares the performance of statistical, machine learning (ML), and deep learning (DL) approaches in forecasting infectious disease incidences across different countries and time intervals. We forecasted three [...] Read more.

Accurate infectious disease forecasting can inform efforts to prevent outbreaks and mitigate adverse impacts. This study compares the performance of statistical, machine learning (ML), and deep learning (DL) approaches in forecasting infectious disease incidences across different countries and time intervals. We forecasted three diverse diseases: campylobacteriosis, typhoid, and Q-fever, using a wide variety of features (n = 46) from public datasets, e.g., landscape, climate, and socioeconomic factors. We compared autoregressive statistical models to two tree-based ML models (extreme gradient boosted trees [XGB] and random forest [RF]) and two DL models (multi-layer perceptron and encoder–decoder model). The disease models were trained on data from seven different countries at the region-level between 2009–2017. Forecasting performance of all models was assessed using mean absolute error, root mean square error, and Poisson deviance across Australia, Israel, and the United States for the months of January through August of 2018. The overall model results were compared across diseases as well as various data splits, including country, regions with highest and lowest cases, and the forecasted months out (i.e., nowcasting, short-term, and long-term forecasting). Overall, the XGB models performed the best for all diseases and, in general, tree-based ML models performed the best when looking at data splits. There were a few instances where the statistical or DL models had minutely smaller error metrics for specific subsets of typhoid, which is a disease with very low case counts. Feature importance per disease was measured by using four tree-based ML models (i.e., XGB and RF with and without region name as a feature). The most important feature groups included previous case counts, region name, population counts and density, mortality causes of neonatal to under 5 years of age, sanitation factors, and elevation. This study demonstrates the power of ML approaches to incorporate a wide range of factors to forecast various diseases, regardless of location, more accurately than traditional statistical approaches. Full article

(This article belongs to the Special Issue Advances in Biosurveillance for Human, Animal, and Plant Health)

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21 pages, 2380 KiB

Open AccessArticle

Advances in the Detection of Emerging Tree Diseases by Measurements of VOCs and HSPs Gene Expression, Application to Ash Dieback Caused by Hymenoscyphus fraxineus

by Piotr Borowik, Tomasz Oszako, Tadeusz Malewski, Zuzanna Zwierzyńska, Leszek Adamowicz, Rafał Tarakowski, Sławomir Ślusarski and Justyna Anna Nowakowska

Pathogens 2021, 10(11), 1359; https://doi.org/10.3390/pathogens10111359 - 21 Oct 2021

Cited by 4 | Viewed by 2337

Abstract

Ash shoot dieback has now spread throughout Europe. It is caused by an interaction between fungi that attack shoots (Hymenoscyphus fraxineus) and roots (Armillaria spp., in our case Armillaria gallica). While detection of the pathogen is relatively easy when [...] Read more.

Ash shoot dieback has now spread throughout Europe. It is caused by an interaction between fungi that attack shoots (Hymenoscyphus fraxineus) and roots (Armillaria spp., in our case Armillaria gallica). While detection of the pathogen is relatively easy when disease symptoms are present, it is virtually impossible when the infestation is latent. Such situations occur in nurseries when seedlings become infected (the spores are carried by the wind several dozen miles). The diseases are masked by pesticides, fertilisers, and adequate irrigation to protect the plants. Root rot that develops in the soil is also difficult to detect. Currently, there is a lack of equipment that can detect root rot pathogens without digging up root systems, which risks damaging trees. For this reason, the use of an electronic nose to detect pathogens in infected tissue of ash trees grown in pots and inoculated with the above fungi was attempted. Disease symptoms were detected in all ash trees exposed to natural infection (via spores) in the forest. The electronic nose was able to detect the pathogens (compared to the control). Detection of the pathogens in seedlings will enable foresters to remove diseased trees and prevent the path from nursery to forest plantations by such selection. Full article

(This article belongs to the Special Issue Advances in Biosurveillance for Human, Animal, and Plant Health)

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16 pages, 1518 KiB

Open AccessArticle

Coronavirus Disease 2019 on the Heels of Ebola Virus Disease in West Africa

by Zygmunt F. Dembek, Kierstyn T. Schwartz-Watjen, Anna L. Swiatecka, Katherine M. Broadway, Steven J. Hadeed, Jerry L. Mothershead, Tesema Chekol, Akeisha N. Owens and Aiguo Wu

Pathogens 2021, 10(10), 1266; https://doi.org/10.3390/pathogens10101266 - 1 Oct 2021

Cited by 3 | Viewed by 3243

Abstract

This study utilized modeling and simulation to examine the effectiveness of current and potential future COVID-19 response interventions in the West African countries of Guinea, Liberia, and Sierra Leone. A comparison between simulations can highlight which interventions could have an effect on the [...] Read more.

This study utilized modeling and simulation to examine the effectiveness of current and potential future COVID-19 response interventions in the West African countries of Guinea, Liberia, and Sierra Leone. A comparison between simulations can highlight which interventions could have an effect on the pandemic in these countries. An extended compartmental model was used to run simulations incorporating multiple vaccination strategies and non-pharmaceutical interventions (NPIs). In addition to the customary categories of susceptible, exposed, infected, and recovered (SEIR) compartments, this COVID-19 model incorporated early and late disease states, isolation, treatment, and death. Lessons learned from the 2014–2016 Ebola virus disease outbreak—especially the optimization of each country’s resource allocation—were incorporated in the presented models. For each country, models were calibrated to an estimated number of infections based on actual reported cases and deaths. Simulations were run to test the potential future effects of vaccination and NPIs. Multiple levels of vaccination were considered, based on announced vaccine allocation plans and notional scenarios. Increased vaccination combined with NPI mitigation strategies resulted in thousands of fewer COVID-19 infections in each country. This study demonstrates the importance of increased vaccinations. The levels of vaccination in this study would require substantial increases in vaccination supplies obtained through national purchases or international aid. While this study does not aim to develop a model that predicts the future, it can provide useful information for decision-makers in low- and middle-income nations. Such information can be used to prioritize and optimize limited available resources for targeted interventions that will have the greatest impact on COVID-19 pandemic response. Full article

(This article belongs to the Special Issue Advances in Biosurveillance for Human, Animal, and Plant Health)

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16 pages, 5832 KiB

Open AccessArticle

Nanopore-Based Surveillance of Zoonotic Bacterial Pathogens in Farm-Dwelling Peridomestic Rodents

by Nusrat A. Jahan, Laramie L. Lindsey, Evan J. Kipp, Adam Reinschmidt, Bradley J. Heins, Amy M. Runck and Peter A. Larsen

Pathogens 2021, 10(9), 1183; https://doi.org/10.3390/pathogens10091183 - 13 Sep 2021

Cited by 8 | Viewed by 5018

Abstract

The effective control of rodent populations on farms is crucial for food safety, as rodents are reservoirs and vectors for several zoonotic pathogens. Clear links have been identified between rodents and farm-level outbreaks of pathogens throughout Europe and Asia; however, comparatively little research [...] Read more.

The effective control of rodent populations on farms is crucial for food safety, as rodents are reservoirs and vectors for several zoonotic pathogens. Clear links have been identified between rodents and farm-level outbreaks of pathogens throughout Europe and Asia; however, comparatively little research has been devoted to studying the rodent–agricultural interface in the USA. Here, we address this knowledge gap by metabarcoding bacterial communities of rodent pests collected from Minnesota and Wisconsin food animal farms. We leveraged the Oxford Nanopore MinION sequencer to provide a rapid real-time survey of putative zoonotic foodborne pathogens, among others. Rodents were live trapped (n = 90) from three dairy and mixed animal farms. DNA extraction was performed on 63 rodent colons along with 2 shrew colons included as outgroups in the study. Full-length 16S amplicon sequencing was performed. Our farm-level rodent-metabarcoding data indicate the presence of multiple foodborne pathogens, including Salmonella spp., Campylobacter spp., Staphylococcus aureus, and Clostridium spp., along with many mastitis pathogens circulating within five rodent species (Microtus pennsylvanicus, Mus musculus, Peromyscus leucopus, Peromyscus maniculatus, and Rattus norvegicus) and a shrew (Blarina brevicauda). Interestingly, we observed a higher abundance of enteric pathogens (e.g., Salmonella) in shrew feces compared to the rodents analyzed in our study. Knowledge gained from our research efforts will directly inform and improve farm-level biosecurity efforts and public health interventions to reduce future outbreaks of foodborne and zoonotic disease. Full article

(This article belongs to the Special Issue Advances in Biosurveillance for Human, Animal, and Plant Health)

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11 pages, 785 KiB

Open AccessArticle

Digital Biosurveillance for Zoonotic Disease Detection in Kenya

by Ravikiran Keshavamurthy, Samuel M. Thumbi and Lauren E. Charles

Pathogens 2021, 10(7), 783; https://doi.org/10.3390/pathogens10070783 - 22 Jun 2021

Cited by 4 | Viewed by 3494

Abstract

Infectious disease surveillance is crucial for early detection and situational awareness of disease outbreaks. Digital biosurveillance monitors large volumes of open-source data to flag potential health threats. This study investigates the potential of digital surveillance in the detection of the top five priority [...] Read more.

Infectious disease surveillance is crucial for early detection and situational awareness of disease outbreaks. Digital biosurveillance monitors large volumes of open-source data to flag potential health threats. This study investigates the potential of digital surveillance in the detection of the top five priority zoonotic diseases in Kenya: Rift Valley fever (RVF), anthrax, rabies, brucellosis, and trypanosomiasis. Open-source disease events reported between August 2016 and October 2020 were collected and key event-specific information was extracted using a newly developed disease event taxonomy. A total of 424 disease reports encompassing 55 unique events belonging to anthrax (43.6%), RVF (34.6%), and rabies (21.8%) were identified. Most events were first reported by news media (78.2%) followed by international health organizations (16.4%). News media reported the events 4.1 (±4.7) days faster than the official reports. There was a positive association between official reporting and RVF events (odds ratio (OR) 195.5, 95% confidence interval (CI); 24.01–4756.43, p < 0.001) and a negative association between official reporting and local media coverage of events (OR 0.03, 95% CI; 0.00–0.17, p = 0.030). This study highlights the usefulness of local news in the detection of potentially neglected zoonotic disease events and the importance of digital biosurveillance in resource-limited settings. Full article

(This article belongs to the Special Issue Advances in Biosurveillance for Human, Animal, and Plant Health)

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11 pages, 6620 KiB

Open AccessCommentary

Beyond the List: Bioagent-Agnostic Signatures Could Enable a More Flexible and Resilient Biodefense Posture Than an Approach Based on Priority Agent Lists Alone

by Owen P. Leiser, Errett C. Hobbs, Amy C. Sims, George W. Korch and Karen L. Taylor

Pathogens 2021, 10(11), 1497; https://doi.org/10.3390/pathogens10111497 - 17 Nov 2021

Cited by 12 | Viewed by 5546

Abstract

As of 2021, the biothreat policy and research communities organize their efforts around lists of priority agents, which elides consideration of novel pathogens and biotoxins. For example, the Select Agents and Toxins list is composed of agents that historic biological warfare programs had [...] Read more.

As of 2021, the biothreat policy and research communities organize their efforts around lists of priority agents, which elides consideration of novel pathogens and biotoxins. For example, the Select Agents and Toxins list is composed of agents that historic biological warfare programs had weaponized or that have previously caused great harm during natural outbreaks. Similarly, lists of priority agents promulgated by the World Health Organization and the National Institute of Allergy and Infectious Diseases are composed of previously known pathogens and biotoxins. To fill this gap, we argue that the research/scientific and biodefense/biosecurity communities should categorize agents based on how they impact their hosts to augment current list-based paradigms. Specifically, we propose integrating the results of multi-omics studies to identify bioagent-agnostic signatures (BASs) of disease—namely, patterns of biomarkers that accurately and reproducibly predict the impacts of infection or intoxication without prior knowledge of the causative agent. Here, we highlight three pathways that investigators might exploit as sources of signals to construct BASs and their applicability to this framework. The research community will need to forge robust interdisciplinary teams to surmount substantial experimental, technical, and data analytic challenges that stand in the way of our long-term vision. However, if successful, our functionality-based BAS model could present a means to more effectively surveil for and treat known and novel agents alike. Full article

(This article belongs to the Special Issue Advances in Biosurveillance for Human, Animal, and Plant Health)

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6 pages, 197 KiB

Open AccessPerspective

Operational Considerations in Global Health Modeling

by Katherine M. Broadway, Kierstyn T. Schwartz-Watjen, Anna L. Swiatecka, Steven J. Hadeed, Akeisha N. Owens, Sweta R. Batni and Aiguo Wu

Pathogens 2021, 10(10), 1348; https://doi.org/10.3390/pathogens10101348 - 19 Oct 2021

Cited by 2 | Viewed by 2206

Abstract

Epidemiological modeling and simulation can contribute cooperatively across multifaceted areas of biosurveillance systems. These efforts can be used to support real-time decision-making during public health emergencies and response operations. Robust epidemiological modeling and simulation tools are crucial to informing risk assessment, risk management, [...] Read more.

Epidemiological modeling and simulation can contribute cooperatively across multifaceted areas of biosurveillance systems. These efforts can be used to support real-time decision-making during public health emergencies and response operations. Robust epidemiological modeling and simulation tools are crucial to informing risk assessment, risk management, and other biosurveillance processes. The Defense Threat Reduction Agency (DTRA) has sponsored the development of numerous modeling and decision support tools to address questions of operational relevance in response to emerging epidemics and pandemics. These tools were used during the ongoing COVID-19 pandemic and the Ebola outbreaks in West Africa and the Democratic Republic of the Congo. This perspective discusses examples of the considerations DTRA has made when employing epidemiological modeling to inform on public health crises and highlights some of the key lessons learned. Future considerations for researchers developing epidemiological modeling tools to support biosurveillance and public health operations are recommended. Full article

(This article belongs to the Special Issue Advances in Biosurveillance for Human, Animal, and Plant Health)

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