A bacterial pan-genome makes gene essentiality strain-dependent and evolvable.

Many bacterial species are represented by a pan-genome, whose genetic repertoire far outstrips that of any single bacterial genome. Here we investigate how a bacterial pan-genome might influence gene essentiality and whether essential genes that are initially critical for the survival of an organism can evolve to become non-essential. By using Transposon insertion sequencing (Tn-seq), whole-genome sequencing and RNA-seq on a set of 36 clinical Streptococcus pneumoniae strains representative of >68% of the species' pan-genome, we identify a species-wide 'essentialome' that can be subdivided into universal, core strain-specific and accessory essential genes. By employing 'forced-evolution experiments', we show that specific genetic changes allow bacteria to bypass essentiality. Moreover, by untangling several genetic mechanisms, we show that gene essentiality can be highly influenced by and/or be dependent on: (1) the composition of the accessory genome, (2) the accumulation of toxic intermediates, (3) functional redundancy, (4) efficient recycling of critical metabolites and (5) pathway rewiring. While this functional characterization underscores the evolvability potential of many essential genes, we also show that genes with differential essentiality remain important antimicrobial drug target candidates, as their inactivation almost always has a severe fitness cost in vivo.

Entropy of a bacterial stress response is a generalizable predictor for fitness and antibiotic sensitivity.

Current approaches explore bacterial genes that change transcriptionally upon stress exposure as diagnostics to predict antibiotic sensitivity. However, transcriptional changes are often specific to a species or antibiotic, limiting implementation to known settings only. While a generalizable approach, predicting bacterial fitness independent of strain, species or type of stress, would eliminate such limitations, it is unclear whether a stress-response can be universally captured. By generating a multi-stress and species RNA-Seq and experimental evolution dataset, we highlight the strengths and limitations of existing gene-panel based methods. Subsequently, we build a generalizable method around the observation that global transcriptional disorder seems to be a common, low-fitness, stress response. We quantify this disorder using entropy, which is a specific measure of randomness, and find that in low fitness cases increasing entropy and transcriptional disorder results from a loss of regulatory gene-dependencies. Using entropy as a single feature, we show that fitness and quantitative antibiotic sensitivity predictions can be made that generalize well beyond training data. Furthermore, we validate entropy-based predictions in 7 species under antibiotic and non-antibiotic conditions. By demonstrating the feasibility of universal predictions of bacterial fitness, this work establishes the fundamentals for potentially new approaches in infectious disease diagnostics.

ShinyOmics: collaborative exploration of omics-data.

BACKGROUND: Omics-profiling is a collection of increasingly prominent approaches that result in large-scale biological datasets, for instance capturing an organism's behavior and response in an environment. It can be daunting to manually analyze and interpret such large datasets without some programming experience. Additionally, with increasing amounts of data; management, storage and sharing challenges arise. RESULTS: Here, we present ShinyOmics, a web-based application that allows rapid collaborative exploration of omics-data. By using Tn-Seq, RNA-Seq, microarray and proteomics datasets from two human pathogens, we exemplify several conclusions that can be drawn from a rich dataset. We identify a protease and several chaperone proteins upregulated under aminoglycoside stress, show that antibiotics with the same mechanism of action trigger similar transcriptomic responses, point out the dissimilarity in different omics-profiles, and overlay the transcriptional response on a metabolic network. CONCLUSIONS: ShinyOmics is easy to set up and customize, and can utilize user supplied metadata. It offers several visualization and comparison options that are designed to assist in novel hypothesis generation, as well as data management, online sharing and exploration. Moreover, ShinyOmics can be used as an interactive supplement accompanying research articles or presentations.

Small-World Networks and RNA Secondary Structures.

Let \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $${{ \cal S}_n}$$ \end{document} denote the network of all RNA secondary structures of length n, in which undirected edges exist between structures s, t such that t is obtained from s by the addition, removal, or shift of a single base pair. Using context-free grammars, generating functions, and complex analysis, we show that the asymptotic average degree is \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$O ( n )$$ \end{document} , and that the asymptotic clustering coefficient is \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$O ( 1 / n )$$ \end{document} , from which it follows that the family \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $${{ \cal S}_n}$$ \end{document} , \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$n = 1 , 2 , 3 , \ldots$$ \end{document} of secondary structure networks is not small world.

A role for long-chain acyl-CoA synthetase-4 (ACSL4) in diet-induced phospholipid remodeling and obesity-associated adipocyte dysfunction.

OBJECTIVE: Regulation of fatty acid (FA) metabolism is central to adipocyte dysfunction during diet-induced obesity (DIO). Long-chain acyl-CoA synthetase-4 (ACSL4) has been hypothesized to modulate the metabolic fates of polyunsaturated FA (PUFA), including arachidonic acid (AA), but the in vivo actions of ACSL4 are unknown. The purpose of our studies was to determine the in vivo role of adipocyte ACSL4 in regulating obesity-associated adipocyte dysfunction. METHODS: We developed a novel mouse model with adipocyte-specific ablation of ACSL4 (Ad-KO) using loxP Cre recombinase technology. Metabolic phenotyping of Ad-KO mice relative to their floxed littermates (ACSL4floxed) was performed, including body weight and body composition over time; insulin and glucose tolerance tests; and energy expenditure, activity, and food intake in metabolic cages. Adipocytes were isolated for ex vivo adipocyte oxygen consumption by Clark electrode and lipidomics analysis. In vitro adipocyte analysis including oxygen consumption by Seahorse and real-time PCR analysis were performed to confirm our in vivo findings. RESULTS: Ad-KO mice were protected against DIO, adipocyte death, and metabolic dysfunction. Adipocytes from Ad-KO mice fed high-fat diet (HFD) had reduced incorporation of AA into phospholipids (PL), free AA, and levels of the AA lipid peroxidation product 4-hydroxynonenal (4-HNE). Additionally, adipocytes from Ad-KO mice fed HFD had reduced p53 activation and increased adipocyte oxygen consumption (OCR), which we demonstrated are direct effects of 4-HNE on adipocytes in vitro. CONCLUSION: These studies are the first to elucidate ACSL4's in vivo actions to regulate the incorporation of AA into PL and downstream effects on DIO-associated adipocyte dysfunction. By reducing the incorporation of AA into PL and free fatty acid pools in adipocytes, Ad-KO mice were significantly protected against HFD-induced increases in adipose and liver fat accumulation, adipocyte death, gonadal white adipose tissue (gWAT) inflammation, and insulin resistance (IR). Additionally, deficiency of adipocyte ACSL4 expression in mice fed a HFD resulted in increased gWAT adipocyte OCR and whole body energy expenditure (EE).

Use of a Probabilistic Motif Search to Identify Histidine Phosphotransfer Domain-Containing Proteins.

The wealth of newly obtained proteomic information affords researchers the possibility of searching for proteins of a given structure or function. Here we describe a general method for the detection of a protein domain of interest in any species for which a complete proteome exists. In particular, we apply this approach to identify histidine phosphotransfer (HPt) domain-containing proteins across a range of eukaryotic species. From the sequences of known HPt domains, we created an amino acid occurrence matrix which we then used to define a conserved, probabilistic motif. Examination of various organisms either known to contain (plant and fungal species) or believed to lack (mammals) HPt domains established criteria by which new HPt candidates were identified and ranked. Search results using a probabilistic motif matrix compare favorably with data to be found in several commonly used protein structure/function databases: our method identified all known HPt proteins in the Arabidopsis thaliana proteome, confirmed the absence of such motifs in mice and humans, and suggests new candidate HPts in several organisms. Moreover, probabilistic motif searching can be applied more generally, in a manner both readily customized and computationally compact, to other protein domains; this utility is demonstrated by our identification of histones in a range of eukaryotic organisms.