Abundant dsRNA picobirnaviruses show little geographic or host association in terrestrial systems.

Picobirnaviruses are double-stranded RNA viruses known from a wide range of host species and locations but with unknown pathogenicity and host relationships. Here, we examined the diversity of picobirnaviruses from cattle and gorillas within and around Bwindi Impenetrable Forest National Park (BIFNP), Uganda, where wild and domesticated animals and humans live in relatively close contact. We use metagenomic sequencing with bioinformatic analyses to examine genetic diversity. We compared our findings to global Picobirnavirus diversity using clustering-based analyses. Picobirnavirus diversity at Bwindi was high, with 14 near-complete RdRp and 15 capsid protein sequences, and 497 new partial viral sequences recovered from 44 gorilla samples and 664 from 16 cattle samples. Sequences were distributed throughout a phylogenetic tree of globally derived picobirnaviruses. The relationship with Picobirnavirus diversity and host taxonomy follows a similar pattern to the global dataset, generally lacking pattern with either host or geography.

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.

What We Need to Consider During and After the SARS-CoV-2 Pandemic.

Even though extreme containment and mitigation strategies were implemented by numerous governments around the world to slow down the spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the number of critically ill patients and fatalities keeps rising. This crisis has highlighted the socioeconomic disparities of health care systems within and among countries. As new CoVID policies and responses are implemented to lessen the impact of the virus, it is imperative (1) to consider additional mitigation strategies critical for the development of effective countermeasures, (2) to promote long-term policies and strict regulations of the trade of wildlife and live animal markets, and (3) to advocate for necessary funding and investments in global health, specifically for the prevention of and response to natural and manmade pandemics. This document considers some of these challenges.

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.

Crowdsourced direct-to-consumer genomic analysis of a family quartet.

BACKGROUND: We describe the pioneering experience of a Spanish family pursuing the goal of understanding their own personal genetic data to the fullest possible extent using Direct to Consumer (DTC) tests. With full informed consent from the Corpas family, all genotype, exome and metagenome data from members of this family, are publicly available under a public domain Creative Commons 0 (CC0) license waiver. All scientists or companies analysing these data ("the Corpasome") were invited to return results to the family. METHODS: We released 5 genotypes, 4 exomes, 1 metagenome from the Corpas family via a blog and figshare under a public domain license, inviting scientists to join the crowdsourcing efforts to analyse the genomes in return for coauthorship or acknowldgement in derived papers. Resulting analysis data were compiled via social media and direct email. RESULTS: Here we present the results of our investigations, combining the crowdsourced contributions and our own efforts. Four companies offering annotations for genomic variants were applied to four family exomes: BIOBASE, Ingenuity, Diploid, and GeneTalk. Starting from a common VCF file and after selecting for significant results from company reports, we find no overlap among described annotations. We additionally report on a gut microbiome analysis of a member of the Corpas family. CONCLUSIONS: This study presents an analysis of a diverse set of tools and methods offered by four DTC companies. The striking discordance of the results mirrors previous findings with respect to DTC analysis of SNP chip data, and highlights the difficulties of using DTC data for preventive medical care. To our knowledge, the data and analysis results from our crowdsourced study represent the most comprehensive exome and analysis for a family quartet using solely DTC data generation to date.

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.

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

Countermeasures that will effectively prevent or diminish the impact of a biological attack will depend on the rapid and accurate generation and analysis of genomic information. Because of their increasing level of sensitivity, rapidly decreasing cost, and their ability to effectively interrogate the genomes of previously unknown organisms, Next Generation Sequencing (NGS) technologies are revolutionizing the biological sciences. However, the exponential accumulation microbial data is equally outpacing the computational performance of existing analytical tools in their ability to translate DNA information into reliable detection, prophylactic and therapeutic countermeasures. It is now evident that the bottleneck for next-generation sequence data analysis will not be solved simply by scaling up our computational resources, but rather accomplished by implementing novel biodefense-oriented algorithms that overcome exiting vulnerabilities of speed, sensitivity and accuracy. Considering these circumstances, this document highlights the challenges and opportunities that biodefense stakeholders must consider in order to exploit more efficiently genomic information and translate this data into integrated countermeasures. The document overviews different genome analysis methods and explains concepts of DNA fingerprints, motif fingerprints, genomic barcodes and genomic signatures. A series of recommendations to promote genomics and bioinformatics as an effective form of deterrence and a valuable scientific platform for rapid technological insertion of detection, prophylactic, therapeutic countermeasures are discussed.

Bioinformatics for biodefense: challenges and opportunities.

The intentional release of traditional or combinatorial bioweapons remains one of the most important challenges that will continue to shape homeland security. The misuse of dual-use and how-to methods and techniques in the fields of molecular, synthetic, and computational biology can lessen the technical barriers for launching attacks, even for small groups or individuals. Bioinformatics is guiding the implementation of several biodefense countermeasures. However, existing algorithms have not effectively translated available pathogen genomic data into standardized diagnostics, rational vaccine development, or broad spectrum therapeutics. Despite its potential, bioinformatics has a limited impact on forensic and intelligence operations. More than 12 biodefense databases and information exchange architectures lack interoperability and a common layer that restricts scalability and the development of biodefense enterprises. Therefore, in order to use next-generation genome sequencing for medical intelligence, forensic operations, biothreat awareness, and mitigation, the attention has to be redirected toward the development of computational biology applications. This article debates some of the challenges that the bioinformatics field confronts in terms of biodefense problems and proposes potential opportunities to use pathogen genomic data. Issues related to the analysis of pathogen genomes and emerging methods including genomic barcoding, active curation, and knowledge management and their impact on intelligence, forensics, and policymaking are discussed.

ORION-VIRCAT: a tool for mapping ICTV and NCBI taxonomies.

Viruses, viroids and prions are the smallest infectious biological entities that depend on their host for replication. The number of pathogenic viruses is considerably large and their impact in human global health is well documented. Currently, the International Committee on the Taxonomy of Viruses (ICTV) has classified approximately 4379 virus species while the National Center for Biotechnology Information Viral Genomes Resource (NCBI-VGR) database has mapped 617 705 proteins to eight large taxonomic groups. Despite these efforts, an automated approach for mapping the ICTV master list and its officially accepted virus naming to the NCBI-VGR's taxonomical classification is not available. Due to metagenomic sequencing, it is likely that the discovery and naming of new viral species will increase by at least ten fold. Unfortunately, existing viral databases are not adequately prepared to scale, maintain and annotate automatically ultra-high throughput sequences and place this information into specific taxonomic categories. ORION-VIRCAT is a scalable and interoperable object-relational database designed to serve as a resource for the integration and verification of taxonomical classifications generated by the ICTV and NCBI-VGR. The current release (v1.0) of ORION-VIRCAT is implemented in PostgreSQL and it has been extended to ORACLE, MySQL and SyBase. ORION-VIRCAT automatically mapped and joined 617 705 entries from the NCBI-VGR to the viral naming of the ICTV. This detailed analysis revealed that 399 095 entries from the NCBI-VGR can be mapped to the ICTV classification and that one Order, 10 families, 35 genera and 503 species listed in the ICTV disagree with the the NCBI-VGR classification schema. Nevertheless, we were eable to correct several discrepancies mapping 234 000 additional entries.Database URL:http://www.orionbiosciences.com/research/orion-vircat.html.

The next meta-challenge for Bioinformatics.

The direct sequencing of uncultivable organisms present in complex biological and environmental samples has opportunities to discover new life forms and metabolic processes. This transformational field, known as metagenomics, is generating massive amounts of molecular information that can overwhelm the performance of conventional analysis and visualization algorithms. Here, I briefly highlight some of the emerging challenges this new discipline presents to the computational biology community and point some of the opportunities to develop applications that can translate metagenomic information into biomedical, agricultural, environmental, and industrial applications.

Transcriptional interactions during smallpox infection and identification of early infection biomarkers.

Smallpox is a deadly disease that can be intentionally reintroduced into the human population as a bioweapon. While host gene expression microarray profiling can be used to detect infection, the analysis of this information using unsupervised and supervised classification techniques can produce contradictory results. Here, we present a novel computational approach to incorporate molecular genome annotation features that are key for identifying early infection biomarkers (EIB). Our analysis identified 58 EIBs expressed in peripheral blood mononuclear cells (PBMCs) collected from 21 cynomolgus macaques (Macaca fascicularis) infected with two variola strains via aerosol and intravenous exposure. The level of expression of these EIBs was correlated with disease progression and severity. No overlap between the EIBs co-expression and protein interaction data reported in public databases was found. This suggests that a pathogen-specific re-organization of the gene expression and protein interaction networks occurs during infection. To identify potential genome-wide protein interactions between variola and humans, we performed a protein domain analysis of all smallpox and human proteins. We found that only 55 of the 161 protein domains in smallpox are also present in the human genome. These co-occurring domains are mostly represented in proteins involved in blood coagulation, complement activation, angiogenesis, inflammation, and hormone transport. Several of these proteins are within the EIBs category and suggest potential new targets for the development of therapeutic countermeasures.