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BackgroundPoor treatment outcomes among people with drug-resistant TB (DR-TB) are a major concern. Heteroresistance (presence of susceptible and resistant Mycobacterium tuberculosis in the same sample) has been identified in some people with TB, but its impact on treatment outcomes is unknown.MethodsWe used targeted deep sequencing to identify mutations associated with DR-TB and heteroresistance in culture samples of 624 people with DR-TB. We evaluated the association between heteroresistance and time to unfavorable treatment outcome using Cox proportional hazards regression.ResultsThe proportion of drug-resistant isolates with a known mutation conferring resistance was lower for streptomycin (45.2%) and second-line injectables (79.1%) than for fluoroquinolones (86.7%), isoniazid (93.2%) and rifampin (96.5%). Fifty-two (8.3%) had heteroresistance, and it was more common for fluoroquinolones (4.6%) than rifampin (2.2%), second-line injectables (1.4%), streptomycin (1.7%), or isoniazid (1.3%). There was no association between heteroresistance and time to unfavorable outcome among people with multidrug-resistant TB (adjusted hazard ratio [aHR] 1.74, 95% CI 0.39-7.72) or pre-extensively DR-TB (aHR 0.65, 95% CI 0.24-1.72).ConclusionsHeteroresistance was relatively common (8.3%) among people with DR-TB in the Philippines. However, we found insufficient evidence to demonstrate an impact on unfavorable treatment outcomes.

Modern microbial and ecosystem sciences require diverse interdisciplinary teams that are often challenged in “speaking” to one another due to different languages and data product types. Here we introduce the IsoGenie Database (IsoGenieDB; https://isogenie-db.asc.Ohio-state.edu/), a de novo developed data management and exploration platform, as a solution to this challenge of accurately representing and integrating heterogenous environmental and microbial data across ecosystem scales. The IsoGenieDB is a public and private data infrastructure designed to store and query data generated by the IsoGenie Project, a ~10 year DOE-funded project focused on discovering ecosystem climate feedbacks in a thawing permafrost landscape. The IsoGenieDB provides (i) a platform for IsoGenie Project members to explore the project's interdisciplinary datasets across scales through the inherent relationships among data entities, (ii) a framework to consolidate and harmonize the datasets needed by the team's modelers, and (iii) a public venue that leverages the same spatially explicit, disciplinarily integrated data structure to share published datasets. The IsoGenieDB is also being expanded to cover the NASA-funded Archaea to Atmosphere (A2A) project, which scales the findings of IsoGenie to a broader suite of Arctic peatlands, via the umbrella A2A Database (A2A-DB). The IsoGenieDB's expandability and flexible architecture allow it to serve as an example ecosystems database.

Biogenesis of hydrogen sulfide (H2S) (microbial souring) has detrimental impacts on oil production operations and can cause health and safety problems. Understanding the processes that control the rates and patterns of sulfate reduction is crucial in developing a predictive understanding of reservoir souring and associated mitigation processes. This work demonstrates an approach to utilize genomic information to constrain the biological parameters needed for modeling souring, providing a pathway for using microbial data derived from oil reservoir studies. Minimum generation times were calculated based on codon usage bias and optimal growth temperatures based on the frequency of amino acids. We show how these derived parameters can be used in a simplified multiphase reactive transport model by simulating the injection of cold (30 °C) seawater into a 70 °C reservoir, modeling the shift in sulfate reducing microorganisms (SRM) community composition, sulfate and sulfide concentrations through time and space. Finally, we explore the question of necessary model complexity by comparing results using different numbers of SRM. Simulations showed that the kinetics of a SRM community consisting of twenty-five SRM could be adequately represented by a reduced community consisting of nine SRM with parameter values derived from the mean and standard deviations of the original SRM.

© 2015 PLOS ONE.According to World Health Organization statistics of 2011, infectious diseases remain in the top five causes of mortality worldwide. However, despite sophisticated research tools for microbial detection, rapid and accurate molecular diagnos