Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts.

Univariate analyses of metabolomics data currently follow a frequentist approach, using p-values to reject a null hypothesis. We here propose the use of Bayesian statistics to quantify evidence supporting different hypotheses and discriminate between the null hypothesis versus the lack of statistical power. We used metabolomics data from three independent human cohorts that studied the plasma signatures of subjects with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The data are publicly available, covering 84-197 subjects in each study with 562-888 identified metabolites of which 777 were common between the two studies and 93 were compounds reported in all three studies. We show how Bayesian statistics incorporates results from one study as "prior information" into the next study, thereby improving the overall assessment of the likelihood of finding specific differences between plasma metabolite levels. Using classic statistics and Benjamini-Hochberg FDR-corrections, Study 1 detected 18 metabolic differences and Study 2 detected no differences. Using Bayesian statistics on the same data, we found a high likelihood that 97 compounds were altered in concentration in Study 2, after using the results of Study 1 as the prior distributions. These findings included lower levels of peroxisome-produced ether-lipids, higher levels of long-chain unsaturated triacylglycerides, and the presence of exposome compounds that are explained by the difference in diet and medication between healthy subjects and ME/CFS patients. Although Study 3 reported only 92 compounds in common with the other two studies, these major differences were confirmed. We also found that prostaglandin F2alpha, a lipid mediator of physiological relevance, was reduced in ME/CFS patients across all three studies. The use of Bayesian statistics led to biological conclusions from metabolomic data that were not found through frequentist approaches. We propose that Bayesian statistics is highly useful for studies with similar research designs if similar metabolomic assays are used.

Natural history of nonhuman primates after conjunctival exposure to Ebola virus.

Transmission of Ebola virus (EBOV) primarily occurs via contact exposure of mucosal surfaces with infected body fluids. Historically, nonhuman primate (NHP) challenge studies have employed intramuscular (i.m.) or small particle aerosol exposure, which are largely lethal routes of infection, but mimic worst-case scenarios such as a needlestick or intentional release, respectively. When exposed by more likely routes of natural infection, limited NHP studies have shown delayed onset of disease and reduced mortality. Here, we performed a series of systematic natural history studies in cynomolgus macaques with a range of conjunctival exposure doses. Challenge with 10,000 plaque forming units (PFU) of EBOV was uniformly lethal, whereas 5/6 subjects survived lower dose challenges (100 or 500 PFU). Conjunctival challenge resulted in a protracted time-to death compared to i.m. Asymptomatic infection was observed in survivors with limited detection of EBOV replication. Inconsistent seropositivity in survivors may suggest physical or natural immunological barriers are sufficient to prevent widespread viral dissemination.

Virome of Ixodes ricinus, Dermacentor reticulatus, and Haemaphysalis concinna Ticks from Croatia.

Tick-borne diseases are a serious threat to both public and veterinary health. In this study, we used high-throughput sequencing to characterize the virome of three tick species implicated in the spread of vector-borne disease throughout Croatia. Ten viruses were identified, including seven potential novel species within the viral families Flaviviridae, Nyamiviridae, Rhabdoviridae, Peribunyaviridae, Phenuiviridae, and Nairoviridae.

Detection of anti-NS1 antibodies after pandemic influenza exposure: Evaluation of a serological method for distinguishing H1N1pdm09 infected from vaccinated cases.

BACKGROUND: Reliable exposure information is crucial for assessing health outcomes of influenza infection and vaccination. Current serological methods are unable to distinguish between anti-hemagglutinin (HA) antibodies induced by infection or vaccination. OBJECTIVES: We aimed to explore an alternative method for differentiating influenza infection and vaccination. METHODS: Sera from animals inoculated with influenza viruses or purified H1N1pdm09 HA were obtained. Human samples were selected from a pregnancy cohort established during the 2009 H1N1 pandemic. Unvaccinated, laboratory-confirmed cases (N = 18), vaccinated cases without influenza-like-illness (N = 18) and uninfected, unvaccinated controls (N = 18) were identified based on exposure data from questionnaires, national registries and maternal hemagglutination inhibition (HI) titres at delivery. Animal and human samples were tested for antibodies against the non-structural protein 1 (NS1) and HA from H1N1pdm09, using a Luciferase Immunoprecipitation System (LIPS). RESULTS: Anti-NS1 H1N1pdm09 antibodies were detected in sera from experimentally infected, but not from vaccinated, animals. Anti-HA H1N1pdm09 antibodies were detectable after either of these exposures. In human samples, 28% of individuals with laboratory-confirmed influenza were seropositive for H1N1pdm09 NS1, whereas vaccinated cases and controls were seronegative. There was a trend for H1N1pdm09 NS1 seropositive cases reporting more severe and longer duration of symptomatic illness than seronegative cases. Anti-HA H1N1pdm09 antibodies were detected in all cases and in 61% of controls. CONCLUSIONS: The LIPS method could differentiate between sera from experimentally infected and vaccinated animals. However, in human samples obtained more than 6 months after the pandemic, LIPS was specific, but not sufficiently sensitive for ascertaining cases by exposure.

Distinct Gut Virome Profile of Pregnant Women With Type 1 Diabetes in the ENDIA Study.

BACKGROUND: The importance of gut bacteria in human physiology, immune regulation, and disease pathogenesis is well established. In contrast, the composition and dynamics of the gut virome are largely unknown; particularly lacking are studies in pregnancy. We used comprehensive virome capture sequencing to characterize the gut virome of pregnant women with and without type 1 diabetes (T1D), longitudinally followed in the Environmental Determinants of Islet Autoimmunity study. METHODS: In total, 61 pregnant women (35 with T1D and 26 without) from Australia were examined. Nucleic acid was extracted from serial fecal specimens obtained at prenatal visits, and viral genomes were sequenced by virome capture enrichment. The frequency, richness, and abundance of viruses were compared between women with and without T1D. RESULTS: Two viruses were more prevalent in pregnant women with T1D: picobirnaviruses (odds ratio [OR], 4.2; 95% confidence interval [CI], 1.0-17.1; P = .046) and tobamoviruses (OR, 3.2; 95% CI, 1.1-9.3; P = .037). The abundance of 77 viruses significantly differed between the 2 maternal groups (≥2-fold difference; P < .02), including 8 Enterovirus B types present at a higher abundance in women with T1D. CONCLUSIONS: These findings provide novel insight into the composition of the gut virome during pregnancy and demonstrate a distinct profile of viruses in women with T1D.

Diagnostics and Discovery in Viral Central Nervous System Infections.

The range of viruses implicated in central nervous system disease continues to grow with globalization of travel and trade, emergence and reemergence of zoonoses and investments in discovery science. Diagnosis of viral central nervous system infections is challenging in that brain tissue, where the pathogen concentration is likely to be highest, is not readily obtained and sensitive methods for molecular and serological detection of infection are not available in most clinical microbiology laboratories. Here we review these challenges and discuss how they may be addressed using advances in molecular, proteomic and immunological methods.

Identification of a novel nidovirus in an outbreak of fatal respiratory disease in ball pythons (Python regius).

BACKGROUND: Respiratory infections are important causes of morbidity and mortality in reptiles; however, the causative agents are only infrequently identified. FINDINGS: Pneumonia, tracheitis and esophagitis were reported in a collection of ball pythons (Python regius). Eight of 12 snakes had evidence of bacterial pneumonia. High-throughput sequencing of total extracted nucleic acids from lung, esophagus and spleen revealed a novel nidovirus. PCR indicated the presence of viral RNA in lung, trachea, esophagus, liver, and spleen. In situ hybridization confirmed the presence of intracellular, intracytoplasmic viral nucleic acids in the lungs of infected snakes. Phylogenetic analysis based on a 1,136 amino acid segment of the polyprotein suggests that this virus may represent a new species in the subfamily Torovirinae. CONCLUSIONS: This report of a novel nidovirus in ball pythons may provide insight into the pathogenesis of respiratory disease in this species and enhances our knowledge of the diversity of nidoviruses.

Viral surveillance and discovery.

The field of virus discovery has burgeoned with the advent of high throughput sequencing platforms and bioinformatics programs that enable rapid identification and molecular characterization of known and novel agents, investments in global microbial surveillance that include wildlife and domestic animals as well as humans, and recognition that viruses may be implicated in chronic as well as acute diseases. Here we review methods for viral surveillance and discovery, strategies and pitfalls in linking discoveries to disease, and identify opportunities for improvements in sequencing instrumentation and analysis, the use of social media and medical informatics that will further advance clinical medicine and public health.