Protein Coding Gene Nucleotide Substitution Pattern in the Apicomplexan Protozoa Cryptosporidium parvum and Cryptosporidium hominis

Cryptosporidium parvum and C. hominis are related protozoan pathogens which infect the intestinal epithelium of humans and other vertebrates. To explore the evolution of these parasites, and identify genes under positive selection, we performed a pairwise whole-genome comparison between all orthologous protein coding genes in C. parvum and C. hominis. Genome-wide calculation of the ratio of nonsynonymous versus synonymous nucleotide substitutions (dN/dS) was performed to detect the impact of positive and purifying selection. Of 2465 pairs of orthologous genes, a total of 27 (1.1%) showed a high ratio of nonsynonymous substitutions, consistent with positive selection. A majority of these genes were annotated as hypothetical proteins. In addition, proteins with transmembrane and signal peptide domains are significantly more frequent in the high dN/dS group

Biology and biotechnology of Trichoderma

Fungi of the genus Trichoderma are soilborne, green-spored ascomycetes that can be found all over the world. They have been studied with respect to various characteristics and applications and are known as successful colonizers of their habitats, efficiently fighting their competitors. Once established, they launch their potent degradative machinery for decomposition of the often heterogeneous substrate at hand. Therefore, distribution and phylogeny, defense mechanisms, beneficial as well as deleterious interaction with hosts, enzyme production and secretion, sexual development, and response to environmental conditions such as nutrients and light have been studied in great detail with many species of this genus, thus rendering Trichoderma one of the best studied fungi with the genome of three species currently available. Efficient biocontrol strains of the genus are being developed as promising biological fungicides, and their weaponry for this function also includes secondary metabolites with potential applications as novel antibiotics. The cellulases produced by Trichoderma reesei, the biotechnological workhorse of the genus, are important industrial products, especially with respect to production of second generation biofuels from cellulosic waste. Genetic engineering not only led to significant improvements in industrial processes but also to intriguing insights into the biology of these fungi and is now complemented by the availability of a sexual cycle in T. reesei/Hypocrea jecorina, which significantly facilitates both industrial and basic research. This review aims to give a broad overview on the qualities and versatility of the best studied Trichoderma species and to highlight intriguing findings as well as promising applications

Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles

Abstract Background Pancreatic cancer is the fourth leading cause of cancer death in the United States. Consequently, identification of clinically relevant biomarkers for the early detection of this cancer type is urgently needed. In recent years, proteomics profiling techniques combined with various data analysis methods have been successfully used to gain critical insights into processes and mechanisms underlying pathologic conditions, particularly as they relate to cancer. However, the high dimensionality of proteomics data combined with their relatively small sample sizes poses a significant challenge to current data mining methodology where many of the standard methods cannot be applied directly. Here, we propose a novel methodological framework using machine learning method, in which decision tree based classifier ensembles coupled with feature selection methods, is applied to proteomics data generated from premalignant pancreatic cancer. Results This study explores the utility of three different feature selection schemas (Student t test, Wilcoxon rank sum test and genetic algorithm) to reduce the high dimensionality of a pancreatic cancer proteomic dataset. Using the top features selected from each method, we compared the prediction performances of a single decision tree algorithm C4.5 with six different decision-tree based classifier ensembles (Random forest, Stacked generalization, Bagging, Adaboost, Logitboost and Multiboost). We show that ensemble classifiers always outperform single decision tree classifier in having greater accuracies and smaller prediction errors when applied to a pancreatic cancer proteomics dataset. Conclusion In our cross validation framework, classifier ensembles generally have better classification accuracies compared to that of a single decision tree when applied to a pancreatic cancer proteomic dataset, thus suggesting its utility in future proteomics data analysis. Additionally, the use of feature selection method allows us to select biomarkers with potentially important roles in cancer development, therefore highlighting the validity of this method.</p

Design and performance research of single rubber cylinder for high pressure gas injection packer

In order to solve the problem of &quot;gas explosion&quot; at the end of common rubber cylinder in the process of high temperature, high pressure and gas drive operation, the rubber cylinder with new structure suitable for 51/2 in casing pipe is developed. The &quot;M&quot; type single rubber cylinder structure is adopted in the new structure rubber cylinder, and the &quot;gas explosion&quot; problem of the end gas in the low-pressure side is solved by setting the double-layer staggered slotted steel cover to prevent outburst. The finite element method is used to simulate the setting of the rubber cylinder, and the structural parameters of the new rubber cylinder are obtained by single factor analysis and orthogonal optimization, simulation test and seal test were carried out to verify the sealing performance of the rubber cylinder. According to the actual working condition, the simulation test results and seal test results show that the sealing capacity of the packer reaches 50 MPa under the temperature resistance of 120℃, and the end steel cover is fully opened to wrap the rubber cylinder, which meets the operation requirements of high temperature and high pressure gas injection packer

Identification and characterization of CTRP9, a novel secreted glycoprotein, from adipose tissue that reduces serum glucose in mice and forms heterotrimers with adiponectin

Adiponectin is a major insulin-sensitizing, multimeric hormone derived from adipose tissue that acts on muscle and liver to regulate whole-body glucose and lipid metabolism. Here, we describe a novel and highly conserved paralog of adiponectin designated as C1q/TNF-related protein (CTRP) 9. Of all the CTRP paralogs, CTRP9 shows the highest degree of amino acid identity to adiponectin in its globular C1q domain. CTRP9 is expressed predominantly in adipose tissue and females expresses higher levels of the transcript than males. Moreover, its expression levels in ob/ob mice changed in an age-dependent manner, with significant up-regulation in younger mice. CTRP9 is a secreted glycoprotein with multiple post-translational modifications in its collagen domain that include hydroxylated prolines and hydroxylated and glycosylated lysines. It is secreted as multimers (predominantly trimers) from transfected cells and circulates in the mouse serum with levels varying according to sex and metabolic state of mice. Furthermore, CTRP9 and adiponectin can be secreted as heterooligomers when cotransfected into mammalian cells, and in vivo, adiponectin/CTRP9 complexes can be reciprocally coimmunoprecipitated from the serum of adiponectin and CTRP9 transgenic mice. Biochemical analysis demonstrates that adiponectin and CTRP9 associate via their globular C1q domain, and this interaction does not require their conserved N-terminal cysteines or their collagen domains. Furthermore, we show that adiponectin and CTRP9 form heterotrimers. In cultured myotubes, CTRP9 specifically activates AMPK, Akt, and p44/42 MAPK signaling pathways. Adenovirus-mediated overexpression of CTRP9 in obese (ob/ob) mice significantly lowered serum glucose levels. Collectively, these results suggest that CTRP9 is a novel adipokine, and further study of CTRP9 will yield novel mechanistic insights into its physiological and metabolic function.—Wong, G. W., Krawczyk, S. A., Kitidis-Mitrokostas, C., Ge, G., Spooner, E., Hug, C., Gimeno, R., Lodish, H. F. Identification and characterization of CTRP9, a novel secreted glycoprotein from adipose tissue that reduces serum glucose in mice and forms heterotrimers with adiponectin

Table_3_Evaluation of multiplex nanopore sequencing for Salmonella serotype prediction and antimicrobial resistance gene and virulence gene detection.xlsx

In a previous study, Multiplex-nanopore-sequencing based whole genome sequencing (WGS) allowed for accurate in silico serotype prediction of Salmonella within one day for five multiplexed isolates, using both SISTR and SeqSero2. Since only ten serotypes were tested in our previous study, the conclusions above were yet to be evaluated in a larger scale test. In the current study we evaluated this workflow with 69 Salmonella serotypes and also explored the feasibility of using multiplex-nanopore-sequencing based WGS for antimicrobial resistance gene (AMR) and virulence gene detection. We found that accurate in silico serotype prediction with nanopore-WGS data was achieved within about five hours of sequencing at a minimum of 30× Salmonella genome coverage, with SeqSero2 as the serotype prediction tool. For each tested isolate, small variations were observed between the AMR/virulence gene profiles from the Illumina and Nanopore sequencing platforms. Taking results generated using Illumina data as the benchmark, the average precision value per isolate was 0.99 for both AMR and virulence gene detection. We found that the resistance gene identifier – RGI identified AMR genes with nanopore data at a much lower accuracy compared to Abricate, possibly due to RGI’s less stringent minimum similarity and coverage by default for database matching. This study is an evaluation of multiplex-nanopore-sequencing based WGS as a cost-efficient and rapid Salmonella classification method, and a starting point for future validation and verification of using it as a AMR/virulence gene profiling tool for the food industry. This study paves the way for the application of nanopore sequencing in surveillance, tracking, and risk assessment of Salmonella across the food supply chain.</p

Data_Sheet_1_Evaluation of multiplex nanopore sequencing for Salmonella serotype prediction and antimicrobial resistance gene and virulence gene detection.docx

In a previous study, Multiplex-nanopore-sequencing based whole genome sequencing (WGS) allowed for accurate in silico serotype prediction of Salmonella within one day for five multiplexed isolates, using both SISTR and SeqSero2. Since only ten serotypes were tested in our previous study, the conclusions above were yet to be evaluated in a larger scale test. In the current study we evaluated this workflow with 69 Salmonella serotypes and also explored the feasibility of using multiplex-nanopore-sequencing based WGS for antimicrobial resistance gene (AMR) and virulence gene detection. We found that accurate in silico serotype prediction with nanopore-WGS data was achieved within about five hours of sequencing at a minimum of 30× Salmonella genome coverage, with SeqSero2 as the serotype prediction tool. For each tested isolate, small variations were observed between the AMR/virulence gene profiles from the Illumina and Nanopore sequencing platforms. Taking results generated using Illumina data as the benchmark, the average precision value per isolate was 0.99 for both AMR and virulence gene detection. We found that the resistance gene identifier – RGI identified AMR genes with nanopore data at a much lower accuracy compared to Abricate, possibly due to RGI’s less stringent minimum similarity and coverage by default for database matching. This study is an evaluation of multiplex-nanopore-sequencing based WGS as a cost-efficient and rapid Salmonella classification method, and a starting point for future validation and verification of using it as a AMR/virulence gene profiling tool for the food industry. This study paves the way for the application of nanopore sequencing in surveillance, tracking, and risk assessment of Salmonella across the food supply chain.</p