Deciphering the distance to antibiotic resistance for the pneumococcus using genome sequencing data

Advances in genome sequencing technologies and genome-wide association studies (GWAS) have provided unprecedented insights into the molecular basis of microbial phenotypes and enabled the identification of the underlying genetic variants in real populations. However, utilization of genome sequencing in clinical phenotyping of bacteria is challenging due to the lack of reliable and accurate approaches. Here, we report a method for predicting microbial resistance patterns using genome sequencing data. We analyzed whole genome sequences of 1,680 Streptococcus pneumoniae isolates from four independent populations using GWAS and identified probable hotspots of genetic variation which correlate with phenotypes of resistance to essential classes of antibiotics. With the premise that accumulation of putative resistance-conferring SNPs, potentially in combination with specific resistance genes, precedes full resistance, we retrogressively surveyed the hotspot loci and quantified the number of SNPs and/or genes, which if accumulated would confer full resistance to an otherwise susceptible strain. We name this approach the € distance to resistance'. It can be used to identify the creep towards complete antibiotics resistance in bacteria using genome sequencing. This approach serves as a basis for the development of future sequencing-based methods for predicting resistance profiles of bacterial strains in hospital microbiology and public health settings

Draft Genome Sequence of Streptococcus thermophilus C106, a Dairy Isolate from an Artisanal Cheese Produced in the Countryside of Ireland

Item does not contain fulltextThe lactic acid bacterium Streptococcus thermophilus is widely used for the fermentation of dairy products. Here, we present the draft genome sequence of S. thermophilus C106 isolated from an artisanal cheese produced in the countryside of Ireland

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From microbial gene essentiality to novel antimicrobial drug targets

Background: Bacterial respiratory tract infections, mainly caused by Streptococcus pneumoniae, Haemophilus influenzae and Moraxella catarrhalis are among the leading causes of global mortality and morbidity. Increased resistance of these pathogens to existing antibiotics necessitates the search for novel targets to develop potent antimicrobials. Result: Here, we report a proof of concept study for the reliable identification of potential drug targets in these human respiratory pathogens by combining high-density transposon mutagenesis, high-throughput sequencing, and integrative genomics. Approximately 20% of all genes in these three species were essential for growth and viability, including 128 essential and conserved genes, part of 47 metabolic pathways. By comparing these essential genes to the human genome, and a database of genes from commensal human gut microbiota, we identified and excluded potential drug targets in respiratory tract pathogens that will have off-target effects in the host, or disrupt the natural host microbiota. We propose 249 potential drug targets, 67 of which are targets for 75 FDA-approved antimicrobials and 35 other researched small molecule inhibitors. Two out of four selected novel targets were experimentally validated, proofing the concept. Conclusion: Here we have pioneered an attempt in systematically combining the power of high-density transposon mutagenesis, high-throughput sequencing, and integrative genomics to discover potential drug targets at genome-scale. By circumventing the time-consuming and expensive laboratory screens traditionally used to select potential drug targets, our approach provides an attractive alternative that could accelerate the much needed discovery of novel antimicrobials

Microbiome dynamics of human epidermis following skin barrier disruption

Background - Recent advances in sequencing technologies have enabled metagenomic analyses of many human body sites. Several studies have catalogued the composition of bacterial communities of the surface of human skin, mostly under static conditions in healthy volunteers. Skin injury will disturb the cutaneous homeostasis of the host tissue and its commensal microbiota, but the dynamics of this process have not been studied before. Here we analyzed the microbiota of the surface layer and the deeper layers of the stratum corneum of normal skin, and we investigated the dynamics of recolonization of skin microbiota following skin barrier disruption by tape stripping as a model of superficial injury. Results - We observed gender differences in microbiota composition and showed that bacteria are not uniformly distributed in the stratum corneum. Phylogenetic distance analysis was employed to follow microbiota development during recolonization of injured skin. Surprisingly, the developing neo-microbiome at day 14 was more similar to that of the deeper stratum corneum layers than to the initial surface microbiome. In addition, we also observed variation in the host response towards superficial injury as assessed by the induction of antimicrobial protein expression in epidermal keratinocytes. Conclusions - We suggest that the microbiome of the deeper layers, rather than that of the superficial skin layer, may be regarded as the host indigenous microbiome. Characterization of the skin microbiome under dynamic conditions, and the ensuing response of the microbial community and host tissue, will shed further light on the complex interaction between resident bacteria and epidermi

Application of state-of-art sequencing technologies to indigenous food fermentations

Item does not contain fulltextFermented foods and beverages are an integral part of the human diet globally. Understanding the microbial interactions within these fermenting ecosystems is required to deliver safe products with desirable consumer properties, and moreover, maintenance of these traditions. Effective tools are required for documentation of cultures in traditional and artisanal fermented products, for sensory quality and safety improvements, in some cases for starter culture design for commercialization and potentially for supporting sustainable food systems. Here we trace the developments of sequence-based molecular technologies for investigating the diversity and functionality of microbiota in traditional or indigenous fermented foods and beverages. The opportunities of phylobiomics, metagenomics and metatranscriptomics to enrich our knowledge of fermenting microbial ecosystems are presented

Genotype-phenotype matching analysis of 38 Lactococcus lactis strains using random forest methods

Contains fulltext : 118486.pdf (publisher's version ) (Open Access)BACKGROUND: Lactococcus lactis is used in dairy food fermentation and for the efficient production of industrially relevant enzymes. The genome content and different phenotypes have been determined for multiple L. lactis strains in order to understand intra-species genotype and phenotype diversity and annotate gene functions. In this study, we identified relations between gene presence and a collection of 207 phenotypes across 38 L. lactis strains of dairy and plant origin. Gene occurrence and phenotype data were used in an iterative gene selection procedure, based on the Random Forest algorithm, to identify genotype-phenotype relations. RESULTS: A total of 1388 gene-phenotype relations were found, of which some confirmed known gene-phenotype relations, such as the importance of arabinose utilization genes only for strains of plant origin. We also identified a gene cluster related to growth on melibiose, a plant disaccharide; this cluster is present only in melibiose-positive strains and can be used as a genetic marker in trait improvement. Additionally, several novel gene-phenotype relations were uncovered, for instance, genes related to arsenite resistance or arginine metabolism. CONCLUSIONS: Our results indicate that genotype-phenotype matching by integrating large data sets provides the possibility to identify gene-phenotype relations, possibly improve gene function annotation and identified relations can be used for screening bacterial culture collections for desired phenotypes. In addition to all gene-phenotype relations, we also provide coherent phenotype data for 38 Lactococcus strains assessed in 207 different phenotyping experiments, which to our knowledge is the largest to date for the Lactococcus lactis species

Transcriptomic signatures of peroxisome proliferator-activated receptor a (PPARa) in different mouse liver models identify novel aspects of its biology

Background The peroxisome proliferator-activated receptor alpha (PPARa) is a ligand-activated transcription factor that regulates lipid catabolism and inflammation and is hepatocarcinogenic in rodents. It is presumed that the functions of PPARa in liver depend on cross-talk between parenchymal (hepatocytes) and non-parenchymal (Kupffer and endothelial cells) fractions as well as inter-organ interactions. In order to determine how cellular composition and inter-organ interactions influence gene expression upon pharmacological activation of PPARa, we performed a meta-analysis of transcriptomics data obtained from mouse hepatocytes (containing only the parenchymal fraction), mouse liver slices (containing both fractions), and mouse livers exposed to a PPARa agonist. The aim was to obtain a comprehensive view of common and model-specific PPARa-dependent genes and biological processes to understand the impact of cross-talk between parenchymal and non-parenchymal fractions as well as the effect of inter-organ interactions on the hepatic PPARa transcriptome. To this end we analyzed microarray data of experiments performed in mouse primary hepatocytes treated with the PPARa agonist Wy14643 for 6 or 24 h (in vitro), mouse precision cut liver slices treated with Wy14643 for 24 h (ex vivo), and livers of wild type and Ppara knockout mice treated with Wy14643 for 6 h or 5 days (in vivo). Results In all models, activation of PPARa significantly altered processes related to various aspects of lipid metabolism. In ex vivo and in vivo models, PPARa activation significantly regulated processes involved in inflammation; these processes were unaffected in hepatocytes. Only in vivo models showed significant regulation of genes involved in coagulation, carcinogenesis, as well as vesicular trafficking and extracellular matrix. Conclusions PPARa-dependent regulation of genes/processes involved in lipid metabolism is mostly independent of the presence of non-parenchymal cells or systemic factors, as it was observed in all liver models. PPARa-dependent regulation of inflammatory genes requires the presence of non-parenchymal cells, as it was observed only ex vivo and in vivo. However, the full spectrum of PPARa biology at the level of lipid metabolism, immunity, carcinogenesis, as well as novel aspects of PPARa signaling such as coagulation, vesicular trafficking and the extracellular matrix, seems to require systemic factors, as it was observed exclusively in vivo

Screening metatranscriptomes for toxin genes as functional drivers of human colorectal cancer

Item does not contain fulltextThe colonic mucosa is in constant physical interaction with a dense and complex bacterial community that comprises health-promoting and pathogenic microbes. Here, we highlight important clinical studies and experimental models that have linked the intestinal microbiota to the development of colorectal cancer (CRC). Moreover, we use recently published metatranscriptome sequencing data to test whether potentially carcinogenic toxin genes exhibit higher expression levels in human CRC tissue compared to adjacent non-malignant mucosa. Our analyses show a large variation in expression of toxin(-related) genes from different species. Surprisingly, Enterobacterial toxins were among the highest expressed, while Enterobacteria were not among the most abundant species in these samples. Although we can differentiate on- and off-tumour sites based on toxin reads, the read depth profiles are quite similar and show only limited coverage of the toxin genes. Thus, extended metagenomic studies are needed to obtain a high-resolution picture of host-pathogen interactions during human CRC