In Action – An Early Warning System for the Detection of Unexpected or Novel Pathogens

Pro-active approaches in preventing future epidemics include pathogen discovery prior to their emergence in human and/or animal populations. Playing an important role in pathogen discovery, high-throughput sequencing (HTS) enables the characterization of microbial and viral genetic diversity within a given sample. In particular, metagenomic HTS allows the unbiased taxonomic profiling of sequences; hence, it can identify novel and highly divergent pathogens such as viruses. Newly discovered viral sequences must be further investigated using genomic characterization, molecular and serological screening, and/or in-vitro and in-vivo characterization. Several outbreak and surveillance studies apply unbiased generic HTS to characterize whole genome sequences of suspected pathogens. In contrast, this study aimed to screen for novel and unexpected pathogens in previously generated HTS datasets and use this information as a starting point for the establishment of an early warning system (EWS). As a proof of concept, the EWS was applied to HTS datasets and archived samples from the 2018-19 West Nile virus (WNV) epidemic in Germany. A metagenomics read classifier detected sequences related to genome sequences of various members of Riboviria. We focused the further EWS investigation on viruses belonging to the families Peribunyaviridae and Reoviridae, under suspicion of causing co-infections in WNV-infected birds. Phylogenetic analyses revealed that the reovirus genome sequences clustered with sequences assigned to the species Umatilla virus, whereas a new peribunyavirid, tentatively named “Hedwig virus” belonged to a putative novel genus of the family Peribunyaviridae. In follow up studies, newly developed molecular diagnostic assays detected fifteen Umatilla virus-positive wild birds from different German cities and eight Hedwig virus-positive captive birds from two zoological gardens. Umatilla virus was successfully cultivated in mosquito C6/36 cells inoculated with a blackbird liver. In conclusion, this study demonstrates the power of the applied EWS for the discovery and characterization of unexpected viruses in repurposed sequence datasets, followed by virus screening and cultivation using archived sample material. The EWS enhances the strategies for pathogen recognition before causing sporadic cases and massive outbreaks and proves to be a reliable tool for modern outbreak preparedness.

Next Generation Sequencing and Bioinformatics Methodologies for Infectious Disease Research and Public Health: Approaches, Applications, and Considerations for Development of Laboratory Capacity

Next generation sequencing (NGS) combined with bioinformatics has successfully been used in a vast array of analyses for infectious disease research of public health relevance. For instance, NGS and bioinformatics approaches have been used to identify outbreak origins, track transmissions, investigate epidemic dynamics, determine etiological agents of a disease, and discover novel human pathogens. However, implementation of high-quality NGS and bioinformatics in research and public health laboratories can be challenging. These challenges mainly include the choice of the sequencing platform and the sequencing approach, the choice of bioinformatics methodologies, access to the appropriate computation and information technology infrastructure, and recruiting and retaining personnel with the specialized skills and experience in this field. In this review, we summarize the most common NGS and bioinformatics workflows in the context of infectious disease genomic surveillance and pathogen discovery, and highlight the main challenges and considerations for setting up an NGS and bioinformatics-focused infectious disease research public health laboratory. We describe the most commonly used sequencing platforms and review their strengths and weaknesses. We review sequencing approaches that have been used for various pathogens and study questions, as well as the most common difficulties associated with these approaches that should be considered when implementing in a public health or research setting. In addition, we provide a review of some common bioinformatics tools and procedures used for pathogen discovery and genome assembly, along with the most common challenges and solutions. Finally, we summarize the bioinformatics of advanced viral, bacterial, and parasite pathogen characterization, including types of study questions that can be answered when utilizing NGS and bioinformatics.

Leaf Disease Analysis using Smart System

The recognizable proof of infection on the plant is a vital key to keep a substantial loss of yield and the amount of horticultural item. The indications can be seen on the pieces of the plants, for example, leaf, stems, sores and natural products. The leaf demonstrates the indications by evolving shading, demonstrating the marks on it. This recognizable proof of the malady is finished by manual perception and pathogen discovery which can devour additional time and may demonstrate exorbitant. The point of the venture is to distinguish and group the infection precisely from the leaf pictures. The means required in the process are Preprocessing, Practicing and Identification. The sickness considered are Powdery Mildew, Downey Mildew which can make substantial misfortune paddy crop. For distinguishing proof of illness highlights of leaf, for example, real hub, minor pivot and so forth are separated from leaf and given to classifier for characterization.

From the Pipeline to the Bedside: Advances and Challenges in Clinical Metagenomics

Next-generation sequencing (NGS) technologies have revolutionized multiple areas in the field of infectious diseases, from pathogen discovery to characterization of genes mediating drug resistance. Consequently, there is much anticipation that NGS technologies may be harnessed in the realm of diagnostic methods to complement or replace current culture-based and molecular microbiologic techniques. In this context, much consideration has been given to hypothesis-free, culture-independent tests that can be performed directly on primary clinical samples. The closest realizations of such universal diagnostic methods achieved to date are based on targeted amplicon and unbiased metagenomic shotgun NGS approaches. Depending on the exact details of implementation and analysis, these approaches have the potential to detect viruses, bacteria, fungi, parasites, and archaea, including organisms that were previously undiscovered and those that are uncultivatable. Shotgun metagenomics approaches additionally can provide information on the presence of virulence and resistance genetic elements. While many limitations to the use of NGS in clinical microbiology laboratories are being overcome with decreasing technology costs, expanding curated pathogen sequence databases, and better data analysis tools, there remain many challenges to the routine use and implementation of these methods. This review summarizes recent advances in applications of targeted amplicon and shotgun-based metagenomics approaches to infectious disease diagnostic methods. Technical and conceptual challenges are considered, along with expectations for future applications of these techniques.

The problem of scale in the prediction and management of pathogen spillover

Disease emergence events, epidemics and pandemics all underscore the need to predict zoonotic pathogen spillover. Because cross-species transmission is inherently hierarchical, involving processes that occur at varying levels of biological organization, such predictive efforts can be complicated by the many scales and vastness of data potentially required for forecasting. A wide range of approaches are currently used to forecast spillover risk (e.g. macroecology, pathogen discovery, surveillance of human populations, among others), each of which is bound within particular phylogenetic, spatial and temporal scales of prediction. Here, we contextualize these diverse approaches within their forecasting goals and resulting scales of prediction to illustrate critical areas of conceptual and pragmatic overlap. Specifically, we focus on an ecological perspective to envision a research pipeline that connects these different scales of data and predictions from the aims of discovery to intervention. Pathogen discovery and predictions focused at the phylogenetic scale can first provide coarse and pattern-based guidance for which reservoirs, vectors and pathogens are likely to be involved in spillover, thereby narrowing surveillance targets and where such efforts should be conducted. Next, these predictions can be followed with ecologically driven spatio-temporal studies of reservoirs and vectors to quantify spatio-temporal fluctuations in infection and to mechanistically understand how pathogens circulate and are transmitted to humans. This approach can also help identify general regions and periods for which spillover is most likely. We illustrate this point by highlighting several case studies where long-term, ecologically focused studies (e.g. Lyme disease in the northeast USA, Hendra virus in eastern Australia, Plasmodium knowlesi in Southeast Asia) have facilitated predicting spillover in space and time and facilitated the design of possible intervention strategies. Such studies can in turn help narrow human surveillance efforts and help refine and improve future large-scale, phylogenetic predictions. We conclude by discussing how greater integration and exchange between data and predictions generated across these varying scales could ultimately help generate more actionable forecasts and interventions. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.