Genomic Characterization of a Relative of Mumps Virus in Lesser Dawn Bats of Southeast Asia.

The importance of genomic surveillance on emerging diseases continues to be highlighted with the ongoing SARS-CoV-2 pandemic. Here, we present an analysis of a new bat-borne mumps virus (MuV) in a captive colony of lesser dawn bats (Eonycteris spelaea). This report describes an investigation of MuV-specific data originally collected as part of a longitudinal virome study of apparently healthy, captive lesser dawn bats in Southeast Asia (BioProject ID PRJNA561193) which was the first report of a MuV-like virus, named dawn bat paramyxovirus (DbPV), in bats outside of Africa. More in-depth analysis of these original RNA sequences in the current report reveals that the new DbPV genome shares only 86% amino acid identity with the RNA-dependent RNA polymerase of its closest relative, the African bat-borne mumps virus (AbMuV). While there is no obvious immediate cause for concern, it is important to continue investigating and monitoring bat-borne MuVs to determine the risk of human infection.

Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study.

Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease. We selected 73 of these 704 participants with reliable confirmation of COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset of COVID-19 using machine learning classification. The algorithm identified COVID-19 an average of 2.75 days before participants sought diagnostic testing with a sensitivity of 82% and specificity of 63%. The receiving operating characteristic (ROC) area under the curve (AUC) was 0.819 (95% CI [0.809, 0.830]). Including continuous temperature yielded an AUC 4.9% higher than without this feature. For further validation, we obtained SARS CoV-2 antibody in a subset of participants and identified 10 additional participants who self-reported COVID-19 disease with antibody confirmation. The algorithm had an overall ROC AUC of 0.819 (95% CI [0.809, 0.830]), with a sensitivity of 90% and specificity of 80% in these additional participants. Finally, we observed substantial variation in accuracy based on age and biological sex. Findings highlight the importance of including temperature assessment, using continuous physiological features for alignment, and including diverse populations in algorithm development to optimize accuracy in COVID-19 detection from wearables.

An Observational Study of Sepsis in Takeo Province Cambodia: An in-depth examination of pathogens causing severe infections.

The world's most consequential pathogens occur in regions with the fewest diagnostic resources, leaving the true burden of these diseases largely under-represented. During a prospective observational study of sepsis in Takeo Province Cambodia, we enrolled 200 patients over an 18-month period. By coupling traditional diagnostic methods such as culture, serology, and PCR to Next Generation Sequencing (NGS) and advanced statistical analyses, we successfully identified a pathogenic cause in 46.5% of our cohort. In all, we detected 25 infectious agents in 93 patients, including severe threat pathogens such as Burkholderia pseudomallei and viral pathogens such as Dengue virus. Approximately half of our cohort remained undiagnosed; however, an independent panel of clinical adjudicators determined that 81% of those patients had infectious causes of their hospitalization, further underscoring the difficulty of diagnosing severe infections in resource-limited settings. We garnered greater insight as to the clinical features of severe infection in Cambodia through analysis of a robust set of clinical data.

Identification of new regulatory genes through expression pattern analysis of a global RNA-seq dataset from a Helicobacter pylori co-culture system.

Helicobacter pylori is a gram-negative bacterium that persistently colonizes the human stomach by inducing immunoregulatory responses. We have used a novel platform that integrates a bone marrow-derived macrophage and live H. pylori co-culture with global time-course transcriptomics analysis to identify new regulatory genes based on expression patterns resembling those of genes with known regulatory function. We have used filtering criteria based on cellular location and novelty parameters to select 5 top lead candidate targets. Of these, Plexin domain containing 2 (Plxdc2) was selected as the top lead immunoregulatory target. Loss of function studies with in vivo models of H. pylori infection as well as a chemically-induced model of colitis, confirmed its predicted regulatory function and significant impact on modulation of the host immune response. Our integrated bioinformatics analyses and experimental validation platform has enabled the discovery of new immunoregulatory genes. This pipeline can be used for the identification of genes with therapeutic applications for treating infectious, inflammatory, and autoimmune diseases.

Detection of Recombinant Rousettus Bat Coronavirus GCCDC1 in Lesser Dawn Bats (Eonycteris spelaea) in Singapore.

Rousettus bat coronavirus GCCDC1 (RoBat-CoV GCCDC1) is a cross-family recombinant coronavirus that has previously only been reported in wild-caught bats in Yúnnan, China. We report the persistence of a related strain in a captive colony of lesser dawn bats captured in Singapore. Genomic evidence of the virus was detected using targeted enrichment sequencing, and further investigated using deeper, unbiased high throughput sequencing. RoBat-CoV GCCDC1 Singapore shared 96.52% similarity with RoBat-CoV GCCDC1 356 (NC_030886) at the nucleotide level, and had a high prevalence in the captive bat colony. It was detected at five out of six sampling time points across the course of 18 months. A partial segment 1 from an ancestral Pteropine orthoreovirus, p10, makes up the recombinant portion of the virus, which shares high similarity with previously reported RoBat-CoV GCCDC1 strains that were detected in Yúnnan, China. RoBat-CoV GCCDC1 is an intriguing, cross-family recombinant virus, with a geographical range that expands farther than was previously known. The discovery of RoBat-CoV GCCDC1 in Singapore indicates that this recombinant coronavirus exists in a broad geographical range, and can persist in bat colonies long-term.

The temporal RNA virome patterns of a lesser dawn bat (Eonycteris spelaea) colony revealed by deep sequencing.

The virosphere is largely unexplored and the majority of viruses are yet to be represented in public sequence databases. Bats are rich reservoirs of viruses, including several zoonoses. In this study, high throughput sequencing (HTS) of viral RNA extracted from swabs of four body sites per bat per timepoint is used to characterize the virome through a longitudinal study of a captive colony of fruit nectar bats, species Eonycteris spelaea in Singapore. Through unbiased shotgun and target enrichment sequencing, we identify both known and previously unknown viruses of zoonotic relevance and define the population persistence and temporal patterns of viruses from families that have the capacity to jump the species barrier. To our knowledge, this is the first study that combines probe-based viral enrichment with HTS to create a viral profile from multiple swab sites on individual bats and their cohort. This work demonstrates temporal patterns of the lesser dawn bat virome, including several novel viruses. Given the known risk for bat-human zoonoses, a more complete understanding of the viral dynamics in South-eastern Asian bats has significant implications for disease prevention and control. The findings of this study will be of interest to U.S. Department of Defense personnel stationed in the Asia-Pacific region and regional public health laboratories engaged in emerging infectious disease surveillance efforts.

Direct-from-blood RNA sequencing identifies the cause of post-bronchoscopy fever.

BACKGROUND: Antibiotic resistance is rising at disturbing rates and contributes to the deaths of millions of people yearly. Antibiotic resistant infections disproportionately affect those with immunocompromising conditions, chronic colonization, and frequent antibiotic use such as transplant patients or those with cystic fibrosis. However, clinicians lack the diagnostic tools to confidently diagnose and treat infections, leading to widespread use of empiric broad spectrum antimicrobials, often for prolonged duration. CASE PRESENTATION: A 22 year-old Caucasian female with cystic fibrosis received a bilateral orthotopic lung transplantation 5 months prior to the index hospitalization. She underwent routine surveillance bronchoscopy and was admitted for post-procedure fever. A clear cause of infection was not identified by routine methods. Imaging and bronchoscopic lung biopsy did not identify an infectious agent or rejection. She was treated with a prolonged course of antimicrobials targeting known colonizing organisms from prior bronchoalveolar lavage cultures (Pseudomonas, Staphylococcus aureus, and Aspergillus). However, we identified Stenotrophomonas maltophilia in two independent whole blood samples using direct-pathogen sequencing, which was not identified by other methods. CONCLUSIONS: This case represents a common clinical conundrum: identification of infection in a high-risk, complex patient. Here, direct-pathogen sequencing identified a pathogen that would not otherwise have been identified by common techniques. Had results been clinically available, treatment could have been customized, avoiding a prolonged course of broad spectrum antimicrobials that would only exacerbate resistance. Direct-pathogen sequencing is poised to fill a diagnostic gap for pathogen identification, allowing early identification and customization of treatment in a culture-independent, pathogen-agnostic manner.

multiPhATE: bioinformatics pipeline for functional annotation of phage isolates.

SUMMARY: To address the need for improved phage annotation tools that scale, we created an automated throughput annotation pipeline: multiple-genome Phage Annotation Toolkit and Evaluator (multiPhATE). multiPhATE is a throughput pipeline driver that invokes an annotation pipeline (PhATE) across a user-specified set of phage genomes. This tool incorporates a de novo phage gene calling algorithm and assigns putative functions to gene calls using protein-, virus- and phage-centric databases. multiPhATE's modular construction allows the user to implement all or any portion of the analyses by acquiring local instances of the desired databases and specifying the desired analyses in a configuration file. We demonstrate multiPhATE by annotating two newly sequenced Yersinia pestis phage genomes. Within multiPhATE, the PhATE processing pipeline can be readily implemented across multiple processors, making it adaptable for throughput sequencing projects. Software documentation assists the user in configuring the system. AVAILABILITY AND IMPLEMENTATION: multiPhATE was implemented in Python 3.7, and runs as a command-line code under Linux or Unix. multiPhATE is freely available under an open-source BSD3 license from https://github.com/carolzhou/multiPhATE. Instructions for acquiring the databases and third-party codes used by multiPhATE are included in the distribution README file. Users may report bugs by submitting to the github issues page associated with the multiPhATE distribution. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Characterizing Phage Genomes for Therapeutic Applications.

Multi-drug resistance is increasing at alarming rates. The efficacy of phage therapy, treating bacterial infections with bacteriophages alone or in combination with traditional antibiotics, has been demonstrated in emergency cases in the United States and in other countries, however remains to be approved for wide-spread use in the US. One limiting factor is a lack of guidelines for assessing the genomic safety of phage candidates. We present the phage characterization workflow used by our team to generate data for submitting phages to the Federal Drug Administration (FDA) for authorized use. Essential analysis checkpoints and warnings are detailed for obtaining high-quality genomes, excluding undesirable candidates, rigorously assessing a phage genome for safety and evaluating sequencing contamination. This workflow has been developed in accordance with community standards for high-throughput sequencing of viral genomes as well as principles for ideal phages used for therapy. The feasibility and utility of the pipeline is demonstrated on two new phage genomes that meet all safety criteria. We propose these guidelines as a minimum standard for phages being submitted to the FDA for review as investigational new drug candidates.

Cooperation of Gastric Mononuclear Phagocytes with Helicobacter pylori during Colonization.

Helicobacter pylori, the dominant member of the human gastric microbiota, elicits immunoregulatory responses implicated in protective versus pathological outcomes. To evaluate the role of macrophages during infection, we employed a system with a shifted proinflammatory macrophage phenotype by deleting PPARγ in myeloid cells and found a 5- to 10-fold decrease in gastric bacterial loads. Higher levels of colonization in wild-type mice were associated with increased presence of mononuclear phagocytes and in particular with the accumulation of CD11b+F4/80hiCD64+CX3CR1+ macrophages in the gastric lamina propria. Depletion of phagocytic cells by clodronate liposomes in wild-type mice resulted in a reduction of gastric H. pylori colonization compared with nontreated mice. PPARγ-deficient and macrophage-depleted mice presented decreased IL-10-mediated myeloid and T cell regulatory responses soon after infection. IL-10 neutralization during H. pylori infection led to increased IL-17-mediated responses and increased neutrophil accumulation at the gastric mucosa. In conclusion, we report the induction of IL-10-driven regulatory responses mediated by CD11b+F4/80hiCD64+CX3CR1+ mononuclear phagocytes that contribute to maintaining high levels of H. pylori loads in the stomach by modulating effector T cell responses at the gastric mucosa.

Brief Protocol for EDGE Bioinformatics: Analyzing Microbial and Metagenomic NGS Data.

Next-generation sequencing (NGS) offers unparalleled resolution for untargeted organism detection and characterization. However, the majority of NGS analysis programs require users to be proficient in programming and command-line interfaces. EDGE bioinformatics was developed to offer scientists with little to no bioinformatics expertise a point-and-click platform for analyzing sequencing data in a rapid and reproducible manner. EDGE (Empowering the Development of Genomics Expertise) v1.0 released in January 2017, is an intuitive web-based bioinformatics platform engineered for the analysis of microbial and metagenomic NGS-based data ( Li et al., 2017 ). The EDGE bioinformatics suite combines vetted publicly available tools, and tracks settings to ensure reliable and reproducible analysis workflows. To execute the EDGE workflow, only raw sequencing reads and a project ID are necessary. Users can access in-house data, or run analyses on samples deposited in Sequence Read Archive. Default settings offer a robust first-glance and are often sufficient for novice users. All analyses are modular; users can easily turn workflows on/off, and modify parameters to cater to project needs. Results are compiled and available for download in a PDF-formatted report containing publication quality figures. We caution that interpreting results still requires in-depth scientific understanding, however report visuals are often informative, even to novice users.

Exploratory Studies With BT-11: A Proposed Orally Active Therapeutic for Crohn's Disease.

Lanthionine synthetase cyclase-like receptor 2 (LANCL2) is a novel therapeutic target for Crohn's disease (CD). BT-11 is a small molecule that binds LANCL2, is orally active, and has demonstrated therapeutic efficacy in 3 validated mouse models of colitis at doses as low as 8 mg/kg/d. Exploratory experiments evaluated BT-11 in male Harlan Sprague Dawley rats with a single oral dose of 500 mg/kg and 80 mg/kg/d for 14 days (n = 10 rats dosed/group). Treated and control rats were observed for behavioral detriments, and blood and tissues were collected for clinical pathology and histopathological examination. A functional observational battery demonstrated no differences between treated and control groups over multiple times of observation for quantal, categorical, and continuous end points, including posture, in cage activity, approach, response to touch, weight, grip strength, body temperature, and time on a rotarod. Histopathological examination of the brain, kidney, liver, adrenal gland, testes, stomach, small and large intestines, duodenum, pancreas, heart, lungs, spleen, thymus, and rib found no significant differences between the groups. Plasma enzymes associated with liver function were transiently elevated 2 to 4 days after the 500 mg/kg single dose but returned to normal values by 8 days and were not observed at any time in rats given 80 mg/kg/d for 14 days. One hour after oral administration of a single dose of 80 mg/kg, BT-11 had a maximal concentration of 21 ng/mL; the half-life was 3 hours. These experimental results demonstrated that BT-11 is well tolerated in rats, and, with further testing, may hold promise as an orally active therapeutic for CD.

NLRX1 suppresses tumorigenesis and attenuates histiocytic sarcoma through the negative regulation of NF-κB signaling.

Histiocytic sarcoma is an uncommon malignancy in both humans and veterinary species. Research exploring the pathogenesis of this disease is scarce; thus, diagnostic and therapeutic options for patients are limited. Recent publications have suggested a role for the NLR, NLRX1, in acting as a tumor suppressor. Based on these prior findings, we hypothesized that NLRX1 would function to inhibit tumorigenesis and thus the development of histiocytic sarcoma. To test this, we utilized Nlrx1-/- mice and a model of urethane-induced tumorigenesis. Nlrx1-/- mice exposed to urethane developed splenic histiocytic sarcoma that was associated with significant up-regulation of the NF-κB signaling pathway. Additionally, development of these tumors was also significantly associated with the increased regulation of genes associated with AKT signaling, cell death and autophagy. Together, these data show that NLRX1 suppresses tumorigenesis and reveals new genetic pathways involved in the pathobiology of histiocytic sarcoma.

Systems-wide analyses of mucosal immune responses to Helicobacter pylori at the interface between pathogenicity and symbiosis.

Helicobacter pylori is the dominant member of the gastric microbiota in over half of the human population of which 5-15% develop gastritis or gastric malignancies. Immune responses to H. pylori are characterized by mixed T helper cell, cytotoxic T cell and NK cell responses. The presence of Tregs is essential for the control of gastritis and together with regulatory CX3CR1+ mononuclear phagocytes and immune-evasion strategies they enable life-long persistence of H. pylori. This H. pylori-induced regulatory environment might contribute to its cross-protective effect in inflammatory bowel disease and obesity. Here we review host-microbe interactions, the development of pro- and anti-inflammatory immune responses and how the latter contribute to H. pylori's role as beneficial member of the gut microbiota. Furthermore, we present the integration of existing and new data into a computational/mathematical model and its use for the investigation of immunological mechanisms underlying initiation, progression and outcomes of H. pylori infection.

Modeling-Enabled Systems Nutritional Immunology.

This review highlights the fundamental role of nutrition in the maintenance of health, the immune response, and disease prevention. Emerging global mechanistic insights in the field of nutritional immunology cannot be gained through reductionist methods alone or by analyzing a single nutrient at a time. We propose to investigate nutritional immunology as a massively interacting system of interconnected multistage and multiscale networks that encompass hidden mechanisms by which nutrition, microbiome, metabolism, genetic predisposition, and the immune system interact to delineate health and disease. The review sets an unconventional path to apply complex science methodologies to nutritional immunology research, discovery, and development through "use cases" centered around the impact of nutrition on the gut microbiome and immune responses. Our systems nutritional immunology analyses, which include modeling and informatics methodologies in combination with pre-clinical and clinical studies, have the potential to discover emerging systems-wide properties at the interface of the immune system, nutrition, microbiome, and metabolism.

Modeling-Enabled Characterization of Novel NLRX1 Ligands.

Nucleotide-binding domain and leucine-rich repeat containing (NLR) family are intracellular sentinels of cytosolic homeostasis that orchestrate immune and inflammatory responses in infectious and immune-mediated diseases. NLRX1 is a mitochondrial-associated NOD-like receptor involved in the modulation of immune and metabolic responses. This study utilizes molecular docking approaches to investigate the structure of NLRX1 and experimentally assesses binding to naturally occurring compounds from several natural product and lipid databases. Screening of compound libraries predicts targeting of NLRX1 by conjugated trienes, polyketides, prenol lipids, sterol lipids, and coenzyme A-containing fatty acids for activating the NLRX1 pathway. The ligands of NLRX1 were identified by docking punicic acid (PUA), eleostearic acid (ESA), and docosahexaenoic acid (DHA) to the C-terminal fragment of the human NLRX1 (cNLRX1). Their binding and that of positive control RNA to cNLRX1 were experimentally determined by surface plasmon resonance (SPR) spectroscopy. In addition, the ligand binding sites of cNLRX1 were predicted in silico and validated experimentally. Target mutagenesis studies demonstrate that mutation of 4 critical residues ASP677, PHE680, PHE681, and GLU684 to alanine resulted in diminished affinity of PUA, ESA, and DHA to NLRX1. Consistent with the regulatory actions of NLRX1 on the NF-κB pathway, treatment of bone marrow derived macrophages (BMDM)s with PUA and DHA suppressed NF-κB activity in a NLRX1 dependent mechanism. In addition, a series of pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. Our findings showed that the regulatory function of PUA on colitis is NLRX1 dependent. Thus, we identified novel small molecules that bind to NLRX1 and exert anti-inflammatory actions.

Modeling the Regulatory Mechanisms by Which NLRX1 Modulates Innate Immune Responses to Helicobacter pylori Infection.

Helicobacter pylori colonizes half of the world's population as the dominant member of the gastric microbiota resulting in a lifelong chronic infection. Host responses toward the bacterium can result in asymptomatic, pathogenic or even favorable health outcomes; however, mechanisms underlying the dual role of H. pylori as a commensal versus pathogenic organism are not well characterized. Recent evidence suggests mononuclear phagocytes are largely involved in shaping dominant immunity during infection mediating the balance between host tolerance and succumbing to overt disease. We combined computational modeling, bioinformatics and experimental validation in order to investigate interactions between macrophages and intracellular H. pylori. Global transcriptomic analysis on bone marrow-derived macrophages (BMDM) in a gentamycin protection assay at six time points unveiled the presence of three sequential host response waves: an early transient regulatory gene module followed by sustained and late effector responses. Kinetic behaviors of pattern recognition receptors (PRRs) are linked to differential expression of spatiotemporal response waves and function to induce effector immunity through extracellular and intracellular detection of H. pylori. We report that bacterial interaction with the host intracellular environment caused significant suppression of regulatory NLRC3 and NLRX1 in a pattern inverse to early regulatory responses. To further delineate complex immune responses and pathway crosstalk between effector and regulatory PRRs, we built a computational model calibrated using time-series RNAseq data. Our validated computational hypotheses are that: 1) NLRX1 expression regulates bacterial burden in macrophages; and 2) early host response cytokines down-regulate NLRX1 expression through a negative feedback circuit. This paper applies modeling approaches to characterize the regulatory role of NLRX1 in mechanisms of host tolerance employed by macrophages to respond to and/or to co-exist with intracellular H. pylori.

Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection.

Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close "neighborhood" of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.