Understanding patterns of rRNA evolution is critical for a number of fields, including structure prediction and phylogeny. The standard model of RNA evolution is that compensatory mutations in stems make up the bulk of the changes between homologous sequences, while unpaired regions are relatively homogeneous. We show that considerable heterogeneity exists in the relative rates of evolution of different secondary structure categories (stems, loops, bulges, etc.) within the rRNA, and that in eukaryotes, loops actually evolve much faster than stems. Both rates of evolution and abundance of different structural categories vary with distance from functionally important parts of the ribosome such as the tRNA path and the peptidyl transferase center. For example, fast-evolving residues are mainly found at the surface; stems are enriched at the subunit interface, and junctions near the peptidyl transferase center. However, different secondary structure categories evolve at different rates even when these effects are accounted for. The results demonstrate that relative rates and patterns of evolution are lineage specific, suggesting that phylogenetically and structurally specific models will improve evolutionary and structural predictions.

Structural elements in RNA molecules have a distinct nucleotide composition, which changes gradually over evolutionary time. We discovered certain features of these compositional patterns that are shared between all RNA families. Based on this information, we developed a structure prediction method that evaluates candidate structures for a set of homologous RNAs on their ability to reproduce the patterns exhibited by biological structures. The method is named SPuNC for 'Structure Prediction using Nucleotide Composition'. In a performance test on a diverse set of RNA families we demonstrate that the SPuNC algorithm succeeds in selecting the most realistic structures in an ensemble. The average accuracy of top-scoring structures is significantly higher than the average accuracy of all ensemble members (improvements of more than 20% observed). In addition, a consensus structure that includes the most reliable base pairs gleaned from a set of top-scoring structures is generally more accurate than a consensus derived from the full structural ensemble. Our method achieves better accuracy than existing methods on several RNA families, including novel riboswitches and ribozymes. The results clearly show that nucleotide composition can be used to reveal the quality of RNA structures and thus the presented technique should be added to the set of prediction tools.

Airborne electromagnetic (AEM) data can be inverted to recover models of the electrical resistivity of the subsurface; these, in turn, can be transformed to obtain models of sediment type. AEM data were acquired in Butte and Glenn Counties, California, USA to improve the understanding of the aquifer system. Around 800 line-kilometers of high-quality data were acquired, imaging to a depth of ∼300 m. We developed a workflow designed to obtain, from the AEM data, information about the large-scale structure and heterogeneity of the aquifer system to better understand the vertical connectivity. Using six different inversions incorporating various forms of available information and posterior sampling of the recovered resistivity models, we produced 6,006 resistivity models. These models were transformed to models of sediment type and estimates of percentage of sand/gravel. Exploring the model space, containing the resistivity models and the derived models, allowed us to delineate the large-scale structure of the aquifer system in a way that captures and communicates the uncertainty in the identified sediment type. The uncertainty increased, as expected, with depth, but also served to indicate, as areas of high uncertainty in sediment type, the location of both large-scale and small-scale interfaces between sediment types. A plan view map of the integrated percentage of sand/gravel, when compared to existing hydrographs, revealed the extent of lateral changes in vertical connectivity within the aquifer system throughout the study area.

Nonalcoholic fatty liver disease (NALFD) is now a leading cause of chronic liver disease worldwide, in part, as a consequence of rapidly rising levels of obesity and metabolic syndrome and is a major risk factor for cirrhosis, hepatocellular carcinoma, and liver-related mortality. From NAFLD stems a myriad of clinical challenges related to both diagnosis and management. A growing body of evidence suggests an intricate linkage between the gut microbiome and the pathogenesis of NAFLD. We highlight how our current knowledge of the gut-liver axis in NAFLD may be leveraged to develop gut microbiome-based personalized approaches for disease management, including its use as a non-invasive biomarker for diagnosis and staging, as a target for therapeutic modulation, and as a marker of drug response. We will also discuss current limitations of these microbiome-based approaches. Ultimately, a better understanding of microbiota-host interactions in NAFLD will inform the development of novel preventative strategies and precise therapeutic targets.

Due to the significantly high levels of comorbid substance use and mental health diagnosis among urban poor populations, examining the intersection of drug policy and place requires a consideration of the role of housing in drug user mental health. In San Francisco, geographic boundedness and progressive health and housing polices have coalesced to make single room occupancy hotels (SROs) a key urban built environment used to house poor populations with co-occurring drug use and mental health issues. Unstably housed women who use illicit drugs have high rates of lifetime and current trauma, which manifests in disproportionately high rates of post-traumatic stress disorder (PTSD), anxiety, and depression when compared to stably housed women. We report data from a qualitative interview study (n=30) and four years of ethnography conducted with housing policy makers and unstably housed women who use drugs and live in SROs. Women in the study lived in a range of SRO built environments, from publicly-funded, newly built SROs to privately-owned, dilapidated buildings, which presented a rich opportunity for ethnographic comparison. Applying Rhodes et al.’s framework of socio-structural vulnerability, we explore how SROs can operate as “mental health risk environments” in which macro-structural factors (housing policies shaping the built environment) interact with meso-level factors (social relations within SROs) and micro-level, behavioral coping strategies to impact women’s mental health. The degree to which SRO built environments were “trauma-sensitive” at the macro level significantly influenced women’s mental health at meso- and micro- levels. Women who were living in SROs which exacerbated fear and anxiety attempted, with limited success, to deploy strategies on the meso- and micro- level to manage their mental health symptoms. Study findings underscore the importance of housing polices which consider substance use in the context of current and cumulative trauma experiences in order to improve quality of life and mental health for unstably housed women.

Metagenomics is increasingly used not just to show patterns of microbial diversity but also as a culture-independent method to detect individual organisms of intense clinical, epidemiological, conservation, forensic, or regulatory interest. A widely reported metagenomic study of the New York subway suggested that the pathogens Yersinia pestis and Bacillus anthracis were part of the "normal subway microbiome." In their article in mSystems, Hsu and collaborators (mSystems 1(3):e00018-16, 2016, http://dx.doi.org/10.1128/mSystems.00018-16) showed that microbial communities on transit surfaces in the Boston subway system are maintained from a metapopulation of human skin commensals and environmental generalists and that reanalysis of the New York subway data with appropriate methods did not detect the pathogens. We note that commonly used software pipelines can produce results that lack prima facie validity (e.g., reporting widespread distribution of notorious endemic species such as the platypus or the presence of pathogens) but that appropriate use of inclusion and exclusion sets can avoid this issue.

Background: Studies of the cystic fibrosis (CF) lung microbiome have consistently shown that lung function decline is associated with decreased microbial diversity due to the dominance of opportunistic pathogens. However, how this phenomenon is reflected in the metabolites and chemical environment of lung secretions remains poorly understood. Methods: Here we investigated the microbial and molecular composition of CF sputum samples using 16S rRNA gene amplicon sequencing and untargeted tandem mass spectrometry to determine their interrelationships and associations with clinical measures of disease severity. Results: The CF metabolome was found to exist in two states: one from patients with more severe disease that had higher molecular diversity and more Pseudomonas aeruginosa and the other from patients with better lung function having lower metabolite diversity and fewer pathogenic bacteria. The two molecular states were differentiated by the abundance and diversity of peptides and amino acids. Patients with severe disease and more pathogenic bacteria had higher levels of peptides. Analysis of the carboxyl terminal residues of these peptides indicated that neutrophil elastase and cathepsin G were responsible for their generation, and accordingly, these patients had higher levels of proteolytic activity from these enzymes in their sputum. The CF pathogen Pseudomonas aeruginosa was correlated with the abundance of amino acids and is known to primarily feed on them in the lung. Conclusions: In cases of severe CF lung disease, proteolysis by host enzymes creates an amino acid-rich environment that P. aeruginosa comes to dominate, which may contribute to the pathogen's persistence by providing its preferred carbon source.

Microbial communities along mucosal surfaces throughout the digestive tract are hypothesized as risk factors for impaired glucose regulation and the development of clinical cardiometabolic disease. We investigated whether baseline measures of subgingival microbiota predicted fasting plasma glucose (FPG) longitudinally. The Oral Infections, Glucose Intolerance and Insulin Resistance Study (ORIGINS) enrolled 230 diabetes-free adults (77% female) aged 20 to 55 y (mean ± SD, 34 ± 10 y) from whom baseline subgingival plaque and longitudinal FPG were measured. DNA was extracted from subgingival plaque, and V3 to V4 regions of the 16S rRNA gene were sequenced. FPG was measured at baseline and again at 2 y; glucose change was defined as follow-up minus baseline. Multivariable linear models regressed 2-y glucose change onto baseline measures of community diversity and abundances of 369 individual taxa. A microbial dysbiosis index (MDI) summarizing top individual taxa associated with glucose change was calculated and used in regression models. Models were adjusted for age, sex, race/ethnicity, education, smoking status, body mass index, and baseline glucose levels. Statistical significance was based on the false discovery rate (FDR; <0.05) or a Bonferroni-corrected P value of 1 × 10^-4, derived from the initial 369 hypothesis tests for specific taxa. Mean 2-y FPG change was 1.5 ± 8 mg/dL. Baseline levels of 9 taxa predicted FPG change (all FDR <0.05), among which Stomatobaculum sp oral taxon 097 and Atopobium spp predicted greater FPG change, while Leptotrichia sp oral taxon 498 predicted lesser FPG change (all 3 P values, Bonferroni significant). The MDI explained 6% of variation in longitudinal glucose change (P < 0.001), and baseline glucose levels explained 10% of variation (P < 0.0001). FPG change values ± SE in the third versus first tertile of the MDI were 4.5 ± 0.9 versus 1.6 ± 0.9 (P < 1 × 10^-4). Subgingival microbiota predict 2-y glucose change among diabetes-free men and women.