Project description:Escherichia coli, one of the main causative agents of bovine mastitis, is responsible for significant losses on dairy farms. In order to better understand the pathogenicity of E. coli mastitis, an accurate characterization of E. coli strains isolated from mastitis cases is required. By using phylogenetic analyses and whole genome comparison of 5 currently available mastitis E. coli genome sequences, we searched for genotypic traits specific for mastitis isolates. Our data confirm that there is a bias in the distribution of mastitis isolates in the different phylogenetic groups of the E. coli species, with the majority of strains belonging to phylogenetic groups A and B1. An interesting feature is that clustering of strains based on their accessory genome is very similar to that obtained using the core genome. This finding illustrates the fact that phenotypic properties of strains from different phylogroups are likely to be different. As a consequence, it is possible that different strategies could be used by mastitis isolates of different phylogroups to trigger mastitis. Our results indicate that mastitis E. coli isolates analyzed in this study carry very few of the virulence genes described in other pathogenic E. coli strains. A more detailed analysis of the presence/absence of genes involved in LPS synthesis, iron acquisition and type 6 secretion systems did not uncover specific properties of mastitis isolates. Altogether, these results indicate that mastitis E. coli isolates are rather characterized by a lack of bona fide currently described virulence genes.
Project description:We describe a multi-omic approach to understanding the effects that the anti-malarial drug pyrimethamine has on immune physiology in rhesus macaques (Macaca mulatta). Whole blood and bone marrow (BM) RNA-Seq and plasma metabolome profiles (each with over 15,000 features) have been generated for five naïve individuals at up to seven timepoints before, during and after three rounds of drug administration. Linear modeling and Bayesian network analyses are both considered, alongside investigations of the impact of statistical modeling strategies on biological inference. Individual macaques were found to be a major source of variance for both omic data types, and factoring individuals into subsequent modeling increases power to detect temporal effects. A major component of the whole blood transcriptome follows the BM with a time-delay, while other components of variation are unique to each compartment. We demonstrate that pyrimethamine administration does impact both compartments throughout the experiment, but very limited perturbation of transcript or metabolite abundance was observed following each round of drug exposure. New insights into the mode of action of the drug are presented in the context of pyrimethamine's predicted effect on suppression of cell division and metabolism in the immune system.
Project description:MicroRNAs are important negative regulators of protein coding gene expression, and have been studied intensively over the last few years. To this purpose, different measurement platforms to determine their RNA abundance levels in biological samples have been developed. In this study, we have systematically compared 12 commercially available microRNA expression platforms by measuring an identical set of 20 standardized positive and negative control samples, including human universal reference RNA, human brain RNA and titrations thereof, human serum samples, and synthetic spikes from homologous microRNA family members. We developed novel quality metrics in order to objectively assess platform performance of very different technologies such as small RNA sequencing, RT-qPCR and (microarray) hybridization. We assessed reproducibility, sensitivity, quantitative performance, and specificity. The results indicate that each method has its strengths and weaknesses, which helps guiding informed selection of a quantitative microRNA gene expression platform in function of particular study goals.
Project description:Chaenomeles speciosa (Sweet) Nakai (C. speciosa) is not only a Chinese herbal medicine but also a functional food widely planted in China. Its fruits are used to treat many diseases or can be processed into food products. This study aims to find key metabolic components, distinguish the differences between geographical regions and find more medicinal and edible values of C. speciosa fruits. We used ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) and widely targeted metabolomics analysis to reveal key and differential metabolites. We identified 974 metabolites and screened 548 differential metabolites from 8 regions. We selected significantly high-content differential metabolites to visualize a regional biomarker map. Comparative analysis showed Yunnan had the highest content of total flavonoids, the highest amounts of compounds related to disease resistance and drug targets and the most significant difference from the other regions according to the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database, a unique platform for studying the systematic pharmacology of Chinese herbal medicine and capturing the relationship between drugs, targets and diseases. We used oral bioavailability (OB) ≥ 30% and drug likeness (DL) ≥ 0.18 as the selection criteria and found 101 key active metabolites, which suggests that C. speciosa fruits were rich in healthy metabolites. These results provide valuable information for the development of C. speciosa.
Project description:Protein biosynthesis on the ribosome requires accurate reading of the genetic code in mRNA. Two conformational rearrangements in the small ribosomal subunit, a closing of the head and body around the incoming tRNA and an RNA helical switch near the mRNA decoding site, have been proposed to select for complementary base-pairing between mRNA codons and tRNA anticodons. We determined x-ray crystal structures of the WT and a hyper-accurate variant of the Escherichia coli ribosome at resolutions of 10 and 9 A, respectively, revealing that formation of the intact 70S ribosome from its two subunits closes the conformation of the head of the small subunit independent of mRNA decoding. Moreover, no change in the conformation of the switch helix is observed in two steps of tRNA discrimination. These 70S ribosome structures indicate that mRNA decoding is coupled primarily to movement of the small subunit body, consistent with previous proposals, whereas closing of the head and the helical switch may function in other steps of protein synthesis.
Project description:Mass spectrometry remains an important method for analysis of modified nucleosides ubiquitously present in cellular RNAs, in particular for ribosomal and transfer RNAs that play crucial roles in mRNA translation and decoding. Furthermore, modifications have effect on the lifetimes of nucleic acids in plasma and cells and are consequently incorporated into RNA therapeutics. To provide an analytical tool for sequence characterization of modified RNAs, we developed Pytheas, an open-source software package for automated analysis of tandem MS data for RNA. This dataset contains the analysis of 14N and 15N-labeled 16S RNA from E. coli, including all the known RNA modifications (excluding pseudouridines). The analysis has been performed using three different protocols and instruments: Agilent Q-TOF, Waters Synapt G2-S, and Thermo Scientific Orbitrap Fusion Lumos.
Project description:Many citrus varieties are hybridized to improve their quality and to overcome the effects of climate change. However, there is limited information on the effect of the chemical profiles of hybrid varieties on their quality. In this study, we analyzed 10 citrus varieties and evaluated the correlation with their general characteristics and antioxidant activities. Chemical profiles, including the contents of sugars, organic acid compounds, flavonoids, limonoids, and carotenoids, which are related to taste, color, and health benefits, were significantly different depending on the citrus varieties, leading to different antioxidant capacities and general quality parameters. Based on these data, the correlations were investigated, and 10 citrus varieties were clustered into four groups-Changshou kumquat and Jeramon (cluster I); Setoka (cluster II-1); Natsumi, Satsuma mandarin, and Navel orange (cluster II-2); Kanpei, Tamnaneunbong, Saybyeolbong, and Shiranui (cluster II-3). Moreover, a metabolomic pathway was proposed. Although citrus peels were not analyzed and the sensory and functional qualities of the citrus varieties were not investigated in this study, our results are useful to better understand the relationship between citrus quality and metabolite profiles, which can provide basic information for the development and improvement of new citrus varieties.
Project description:Detergents and salts are widely used in lysis buffer to enhance protein extraction from biological samples, facilitating in-depth proteome analysis, however, to efficiently remove these detergent and salt additives from the digested peptides is essential for generating high quality mass spectra in LC-MS/MS analysis. The sample preparation methods, such as Filter-Aided Sample Preparation (FASP), acetone precipitation followed by in-solution digestion (AP), strong cation exchange-based centrifugal proteomic reactor (CPR), are commonly used for proteomic sample process, but the additives removal efficiency and the application scope of these methods need to be further investigated. In this study, we demonstrated an integrative work flow for systematical small molecular additives quantification as well as peptide/protein identification to provide a comprehensive evaluation for additive cleanup effect of proteomic sample preparation pipelines. The multiple reaction monitoring (MRM)-based LC-MS approach was developed for six additives (i.e., Tris, Urea, CHAPS, SDS, SDC and Triton X-100) quantification. For the peptide and protein identification, although FASP outperformed the other two methods, these three methods were complementary to each other, in terms of peptide/ protein identification, as well as GRAVY index and amino acids distributions. By integrating analysis of small molecular quantification and protein identification datasets, we first bridged the quantitative influence of additive concentration to the peptide analysis performance. Our results provide a valuable dataset for appropriate sample preparation method selection and a guideline for evaluation of small molecular additives cleanup efficiency in the sample preparation procedures.