Microorganisms produce a wide range of natural products (NPs) with clinically and agriculturally relevant biological activities. In bacteria and fungi, genes encoding successive steps in a biosynthetic pathway tend to be clustered on the chromosome as biosynthetic gene clusters (BGCs). Historically, “activity-guided” approaches to NP discovery have focused on bioactivity screening of NPs produced by culturable microbes. In contrast, recent “genome mining” approaches first identify candidate BGCs, express these biosynthetic genes using synthetic biology methods, and finally test for the production of NPs. Fungal genome mining efforts and the exploration of novel sequence and NP space are limited, however, by the lack of a comprehensive catalog of BGCs encoding experimentally-validated products. In this study, we generated a comprehensive reference set of fungal NPs whose biosynthetic gene clusters are described in the published literature. To generate this dataset, we first identified NCBI records that included both a peer-reviewed article and an associated nucleotide record. We filtered these records by text and homology criteria to identify putative NP-related articles and BGCs. Next, we manually curated the resulting articles, chemical structures, and protein sequences. The resulting catalog contains 197 unique NP compounds covering several major classes of fungal NPs, including polyketides, non-ribosomal peptides, terpenoids, and alkaloids. The distribution of articles published per compound shows a bias towards the study of certain popular compounds, such as the aflatoxins. Phylogenetic analysis of biosynthetic genes suggests that much chemical and enzymatic diversity remains to be discovered in fungi. Our catalog was incorporated into the recently launched Minimum Information about Biosynthetic Gene cluster (MIBiG) repository to create the largest known set of fungal BGCs and associated NPs, a resource that we anticipate will guide future genome mining and synthetic biology efforts toward discovering novel fungal enzymes and metabolites.

BackgroundHuman infections with highly pathogenic avian influenza (HPAI) A (H5N1) viruses have occurred in 15 countries, with high mortality to date. Determining risk factors for morbidity and mortality from HPAI H5N1 can inform preventive and therapeutic interventions.MethodsWe included all cases of human HPAI H5N1 reported in World Health Organization Global Alert and Response updates and those identified through a systematic search of multiple databases (PubMed, Scopus, and Google Scholar), including articles in all languages. We abstracted predefined clinical and demographic predictors and mortality and used bivariate logistic regression analyses to examine the relationship of each candidate predictor with mortality. We developed and pruned a decision tree using nonparametric Classification and Regression Tree methods to create risk strata for mortality.FindingsWe identified 617 human cases of HPAI H5N1 occurring between December 1997 and April 2013. The median age of subjects was 18 years (interquartile range 6–29 years) and 54% were female. HPAI H5N1 case-fatality proportion was 59%. The final decision tree for mortality included age, country, per capita government health expenditure, and delay from symptom onset to hospitalization, with an area under the receiver operator characteristic (ROC) curve of 0.81 (95% CI: 0.76–0.86).InterpretationA model defined by four clinical and demographic predictors successfully estimated the probability of mortality from HPAI H5N1 illness. These parameters highlight the importance of early diagnosis and treatment and may enable early, targeted pharmaceutical therapy and supportive care for symptomatic patients with HPAI H5N1 virus infection.

BackgroundHuman cases of highly pathogenic avian influenza (HPAI) A (H5N1) have high mortality. Despite abundant data on seasonal patterns in influenza epidemics, it is unknown whether similar patterns exist for human HPAI H5N1 cases worldwide. Such knowledge could help decrease avian-to-human transmission through increased prevention and control activities during peak periods.MethodsWe performed a systematic search of published human HPAI H5N1 cases to date, collecting month, year, country, season, hemisphere, and climate data. We used negative binomial regression to predict changes in case incidence as a function of season. To investigate hemisphere as a potential moderator, we used AIC and the likelihood-ratio test to compare the season-only model to nested models including a main effect or interaction with hemisphere. Finally, we visually assessed replication of seasonal patterns across climate groups based on the Köppen-Geiger climate classification.FindingsWe identified 617 human cases (611 with complete seasonal data) occurring in 15 countries in Southeast Asia, Africa, and the Middle East. Case occurrence was much higher in winter (n = 285, p = 0.03) than summer (n = 64), and the winter peak occurred across diverse climate groups. There was no significant interaction between hemisphere and season.InterpretationAcross diverse climates, HPAI H5N1 virus infection in humans increases significantly in winter. This is consistent with increased poultry outbreaks and HPAI H5N1 virus transmission during cold and dry conditions. Prioritizing prevention and control activities among poultry and focusing public health messaging to reduce poultry exposures during winter months may help to reduce zoonotic transmission of HPAI H5N1 virus in resource-limited settings.

Background: Direct-to-consumer (DTC) prescription drug advertisements are thought to induce “boomerang effects,” meaning they reduce the perceived effectiveness of a potential alternative option: non-pharmaceutical treatment via lifestyle change. Past research has observed such effects using artificially created, text-only advertisements that may not adequate capture the complex, conflicting portrayal of lifestyle change in real television advertisements. In other risk domains, individual “problem status” often moderates boomerang effects, such that subjects who currently engage in the risky behavior exhibit the strongest boomerang effects.Objectives: We aimed to assess whether priming with real DTC television advertisements elicited boomerang effects on perceptions of lifestyle change and whether these effects, if present, were moderated by individual problem status.Methods: We assembled a sample of real, previously aired DTC television advertisements in order to naturalistically capture the portrayal of lifestyle change in real advertisements. We randomized 819 adults in the United States recruited via Amazon Mechanical Turk to view or not view an advertisement for a prescription drug. We further randomized subjects to judge either lifestyle change or drugs on three measures: general effectiveness, disease severity for a hypothetical patient, and personal intention to use the intervention if diagnosed with the target health condition.Results: Advertisement exposure induced a statistically significant, but weak, boomerang effect on general effectiveness (p = 0.01, partial R2 = 0.007) and did not affect disease severity score (p = 0.32, partial R2 = 0.0009). Advertisement exposure elicited a reverse boomerang effect of similar effect size on personal intentions, such that advertisement-exposed subjects reported comparatively higher intentions to use lifestyle change relative to drugs (p = 0.006, partial R2 = 0.008). Individual problem status did not significantly moderate these effects.Conclusion: In contrast to previous literature finding large boomerang effects using artificial advertisement stimuli, real television advertisements elicited only a weak boomerang effect on perceived effectiveness and elicited an unexpected reverse boomerang effect on personal intentions to use lifestyle change versus drugs. These findings may reflect real advertisements’ induction of descriptive norms and self-efficacy; future research could address such possibilities by systematically manipulating advertisement content.

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