Use of 16S rRNA Gene for Identification of a Broad Range of Clinically Relevant Bacterial Pathogens

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 diagnostics for identification of infection in humans have not been extensively adopted. Time-consuming culture-based methods remain to the forefront of clinical microbial detection. The 16S rRNA gene, a molecular marker for identification of bacterial species, is ubiquitous to members of this domain and, thanks to ever-expanding databases of sequence information, a useful tool for bacterial identification. In this study, we assembled an extensive repository of clinical isolates (n = 617), representing 30 medically important pathogenic species and originally identified using traditional culture-based or non-16S molecular methods. This strain repository was used to systematically evaluate the ability of 16S rRNA for species level identification. To enable the most accurate species level classification based on the paucity of sequence data accumulated in public databases, we built a Naïve Bayes classifier representing a diverse set of high-quality sequences from medically important bacterial organisms. We show that for species identification, a model-based approach is superior to an alignment based method. Overall, between 16S gene based and clinical identities, our study shows a genus-level concordance rate of 96% and a species-level concordance rate of 87.5%. We point to multiple cases of probable clinical misidentification with traditional culture based identification across a wide range of gram-negative rods and gram-positive cocci as well as common gram-negative cocci.     

Potassium channel blocker crafted by α-hairpinin scaffold engineering

The α-Hairpinins are a family of plant defense peptides with a common fold presenting two short α-helices stabilized by two invariant S–S-bridges. We have shown previously that substitution of just two amino acid residues in a wheat α-hairpinin Tk-AMP-X2 leads to Tk-hefu-2 that features specific affinity to voltage-gated potassium channels K_V1.3. Here, we utilize a combined molecular modeling approach based on molecular dynamics simulations and protein surface topography technique to improve the affinity of Tk-hefu-2 to K_V1.3 while preserving its specificity. An important advance of this work compared with our previous studies is transition from the analysis of various physicochemical properties of an isolated toxin molecule to its consideration in complex with its target, a membrane-bound ion channel. As a result, a panel of computationally designed Tk-hefu-2 derivatives was synthesized and tested against K_V1.3. The most active mutant Tk-hefu-10 showed a half-maximal inhibitory concentration of ～150 nM being >10 times more active than Tk-hefu-2 and >200 times more active than the original Tk-hefu. We conclude that α-hairpinins provide an attractive disulfide-stabilized scaffold for the rational design of ion channel inhibitors. Furthermore, the success rate can be considerably increased by the proposed “target-based” iterative strategy of molecular design.     

Screening drug effects in patient‐derived cancer cells links organoid responses to genome alterations

Cancer drug screening in patient‐derived cells holds great promise for personalized oncology and drug discovery but lacks standardization. Whether cells are cultured as conventional monolayer or advanced, matrix‐dependent organoid cultures influences drug effects and thereby drug selection and clinical success. To precisely compare drug profiles in differently cultured primary cells, we developed DeathPro, an automated microscopy‐based assay to resolve drug‐induced cell death and proliferation inhibition. Using DeathPro, we screened cells from ovarian cancer patients in monolayer or organoid culture with clinically relevant drugs. Drug‐induced growth arrest and efficacy of cytostatic drugs differed between the two culture systems. Interestingly, drug effects in organoids were more diverse and had lower therapeutic potential. Genomic analysis revealed novel links between drug sensitivity and DNA repair deficiency in organoids that were undetectable in monolayers. Thus, our results highlight the dependency of cytostatic drugs and pharmacogenomic associations on culture systems, and guide culture selection for drug tests.     

Weighted averaging partial least squares regression (WA-PLS): an improved method for reconstructing environmental variables from species assemblages

Weighted averaging regression and calibration form a simple, yet powerful method for reconstructing environmental variables from species assemblages. Based on the concepts of niche-space partitioning and ecological optima of species (indicator values), it performs well with noisy, species-rich data that cover a long ecological gradient (>3 SD units). Partial least squares regression is a linear method for multivariate calibration that is popular in chemometrics as a robust alternative to principal component regression. It successively selects linear components so as to maximize predictive power. In this paper the ideas of the two methods are combined. It is shown that the weighted averaging method is a form of partial least squares regression applied to transformed data that uses the first PLS-component only. The new combined method, ast squares, consists of using further components, namely as many as are useful in terms of predictive power. The further components utilize the residual structure in the species data to improve the species parameters (optima) in the final weighted averaging predictor. Simulations show that the new method can give 70% reduction in prediction error in data sets with low noise, but only a small reduction in noisy data sets. In three real data sets of diatom assemblages collected for the reconstruction of acidity and salinity, the reduction in prediction error was zero, 19% and 32%.     

Weighted gene coexpression network analysis strategies applied to mouse weight

Systems-oriented genetic approaches that incorporate gene expression and genotype data are valuable in the quest for genetic regulatory loci underlying complex traits. Gene coexpression network analysis lends itself to identification of entire groups of differentially regulated genes—a highly relevant endeavor in finding the underpinnings of complex traits that are, by definition, polygenic in nature. Here we describe one such approach based on liver gene expression and genotype data from an F₂ mouse intercross utilizing weighted gene coexpression network analysis (WGCNA) of gene expression data to identify physiologically relevant modules. We describe two strategies: single-network analysis and differential network analysis. Single-network analysis reveals the presence of a physiologically interesting module that can be found in two distinct mouse crosses. Module quantitative trait loci (mQTLs) that perturb this module were discovered. In addition, we report a list of genetic drivers for this module. Differential network analysis reveals differences in connectivity and module structure between two networks based on the liver expression data of lean and obese mice. Functional annotation of these genes suggests a biological pathway involving epidermal growth factor (EGF). Our results demonstrate the utility of WGCNA in identifying genetic drivers and in finding genetic pathways represented by gene modules. These examples provide evidence that integration of network properties may well help chart the path across the gene–trait chasm. Electronic supplementary material The online version of this article (doi: ) contains supplementary material, which is available to authorized users. Tova F. Fuller, Anatole Ghazalpour contributed equally to this work.     

HIV Sexual Transmission Is Predominantly Driven by Single Individuals Rather than Discordant Couples: A Model-Based Approach

Understanding the relative contribution to HIV transmission from different social groups is important for public-health policy. Information about the importance of stable serodiscordant couples (when one partner is infected but not the other) relative to contacts outside of stable partnerships in spreading disease can aid in designing and targeting interventions. However, the overall importance of within-couple transmission, and the determinants and correlates of this importance, are not well understood. Here, we explore how mechanistic factors – like partnership dynamics and rates of extra-couple transmission – affect various routes of transmission, using a compartmental model with parameters based on estimates from Sub-Saharan Africa. Under our assumptions, when sampling model parameters within a realistic range, we find that infection of uncoupled individuals is usually the predominant route (median 0.62, 2.5%–97.5% quantiles: 0.26–0.88), while transmission within discordant couples is usually important, but rarely represents the majority of transmissions (median 0.33, 2.5%–97.5% quantiles: 0.10–0.67). We find a strong correlation between long-term HIV prevalence and the contact rate of uncoupled individuals, implying that this rate may be a key driver of HIV prevalence. For a given level of prevalence, we find a negative correlation between the proportion of discordant couples and the within-couple transmission rate, indicating that low discordance in a population may reflect a relatively high rate of within-couple transmission. Transmission within or outside couples and among uncoupled individuals are all likely to be important in sustaining heterosexual HIV transmission in Sub-Saharan Africa. Hence, intervention policies should be broadly targeted when practical.