Carbapenemase-Producing Carbapenem-Resistant Enterobacteriaceae from Bangkok, Thailand, and Their Detection by the Carba NP and Modified Carbapenem Inactivation Method Tests.

AIM: The purpose of the study was to determine the epidemiology of carbapenemase genes among carbapenem-resistant Enterobacteriaceae and evaluate the Carba NP and modified carbapenem inactivation method (mCIM) tests in their detection. MATERIALS AND METHODS: A total of 287 nonduplicated Enterobacteriaceae isolates, which were at least resistant to one of the carbapenems, were identified and detected for carbapenemase genes by multiplex PCR covering blaKPC, blaNDM, blaVIM, blaIMP, and blaOXA-48-like. All positive genes were then sequenced. These isolates were phenotypically tested for the production of carbapenemases by mCIM and Carba NP tests to evaluate the efficacy of these methods. RESULTS: Seven species of carbapenem-resistant isolates mainly Klebsiella pneumoniae, Escherichia coli, and Enterobacter cloacae were detected. Of these isolates, three families of carbapenemase genes, including blaNDM (blaNDM-1, -4, -5, -9), blaOXA (blaOXA-48, -181, -232), and blaIMP-14, were found. Of these, 223 (77.70%) carried at least one of the carbapenemase genes. The blaNDM was detected in 160/223 (71.75%) isolates, of which 153/160 (95.63%) were the blaNDM-1. Three types of the blaOXA-48-like group, blaOXA-48, blaOXA-181, and blaOXA-232, were found, 91/104 (87.5%) harbored the blaOXA-232. In addition, 25.11% (56/223) of the carbapenemase-producing isolates harbored a combination of blaNDM and blaOXA-48-like. Phenotypic detection methods, mCIM and Carba NP, showed 100% sensitivity and specificity to blaNDM, blaIMP-14, and blaOXA-48, while the mCIM was positive in all blaOXA-181 and blaOXA-232 isolates, only 12.5% (1/8) and 28.95% (11/38), respectively, were detected by the Carba NP test. CONCLUSIONS: This study revealed a unique prevalence of carbapenemase genes in Bangkok, Thailand, as well as demonstrated the efficacy and limitation of phenotypic detection methods of carbapenemase in the area where blaNDM-1 and blaOXA-232 were predominant.

Probing the origin of estrogen receptor alpha inhibition via large-scale QSAR study.

Estrogen is an important component for the sustenance of normal physiological functions of the mammary glands, particularly for growth and differentiation. Approximately, two-thirds of breast cancers are positive for estrogen receptor (ERs), which is a predisposing factor for the growth of breast cancer cells. As such, ERα represents a lucrative therapeutic target for breast cancer that has attracted wide interest in the search for inhibitory agents. However, the conventional laboratory processes are cost- and time-consuming. Thus, it is highly desirable to develop alternative methods such as quantitative structure-activity relationship (QSAR) models for predicting ER-mediated endocrine agitation as to simplify their prioritization for future screening. In this study, we compiled and curated a large, non-redundant data set of 1231 compounds with ERα inhibitory activity (pIC50). Using comprehensive validation tests, it was clearly observed that the model utilizing the substructure count as descriptors, performed well considering two objectives: using less descriptors for model development and achieving high predictive performance (R Tr 2 = 0.94, Q CV 2 = 0.73, and Q Ext 2 = 0.73). It is anticipated that our proposed QSAR model may become a useful high-throughput tool for identifying novel inhibitors against ERα.

Validation of Alternative Transcript Splicing in Chicken Lines that Differ in Genetic Resistance to Marek's Disease.

Utilizing RNA-seq data, 1,574 candidate genes with alternative splicing were previously identified between two chicken lines that differ in Marek's disease (MD) genetic resistance under control and Marek's disease virus infection conditions. After filtering out 1,530 genes with splice variants in the first or last exon, 44 genes were screened for possible exon loss or gain using PCR and gel electrophoresis. Consequently, 7 genes exhibited visually detectable differential expression of splice variants between lines 6 (MD resistant) and 7 (MD susceptible), and the resultant PCR products verified by DNA sequencing. Birds from inbred line 6 have transcripts that preferentially retain an exon compared to line 7 chickens for ITGB2, SGPL1, and COMMD5. Birds from inbred line 7 have alleles that preferentially retain an exon compared to line 6 for MOCS2. CCBL2 exon 1a is absent and ATAD1 exon 2 is truncated by 87 nucleotides in transcripts expressed by line 7 compared to those from line 6. For CHTF18, line 6 transcripts have an indel mutation with 7 additional nucleotides in exon 21 compared to line 7. The current study validates 7 genes with alternatively spliced isomers between the two chicken lines, which helps provide potential underlying mechanisms for the phenotypic differences.

osFP: a web server for predicting the oligomeric states of fluorescent proteins.

BACKGROUND: Currently, monomeric fluorescent proteins (FP) are ideal markers for protein tagging. The prediction of oligomeric states is helpful for enhancing live biomedical imaging. Computational prediction of FP oligomeric states can accelerate the effort of protein engineering efforts of creating monomeric FPs. To the best of our knowledge, this study represents the first computational model for predicting and analyzing FP oligomerization directly from the amino acid sequence. RESULTS: After data curation, an exhaustive data set consisting of 397 non-redundant FP oligomeric states was compiled from the literature. Results from benchmarking of the protein descriptors revealed that the model built with amino acid composition descriptors was the top performing model with accuracy, sensitivity and specificity in excess of 80% and MCC greater than 0.6 for all three data subsets (e.g. training, tenfold cross-validation and external sets). The model provided insights on the important residues governing the oligomerization of FP. To maximize the benefit of the generated predictive model, it was implemented as a web server under the R programming environment. CONCLUSION: osFP affords a user-friendly interface that can be used to predict the oligomeric state of FP using the protein sequence. The advantage of osFP is that it is platform-independent meaning that it can be accessed via a web browser on any operating system and device. osFP is freely accessible at http://codes.bio/osfp/ while the source code and data set is provided on GitHub at https://github.com/chaninn/osFP/.Graphical Abstract.

Muscle transcriptome response to ACTH administration in a free-ranging marine mammal.

While much of our understanding of stress physiology is derived from biomedical studies, little is known about the downstream molecular consequences of adaptive stress responses in free-living animals. We examined molecular effectors of the stress hormones cortisol and aldosterone in the northern elephant seal, a free-ranging study system in which extreme physiological challenges and cortisol fluctuations are a routine part of life history. We stimulated the neuroendocrine stress axis by administering exogenous adrenocorticotropic hormone (ACTH) and examined the resultant effects by measuring corticosteroid hormones, metabolites, and gene expression before, during, and following administration. ACTH induced an elevation in cortisol, aldosterone, glucose, and fatty acids within 2 h, with complete recovery observed within 24 h of administration. The global transcriptional response of elephant seal muscle tissue to ACTH was evaluated by transcriptomics and involved upregulation of a highly coordinated network of conserved glucocorticoid (GC) target genes predicted to promote metabolic substrate availability without causing deleterious effects seen in laboratory animals. Transcriptional recovery from ACTH was characterized by downregulation of GC target genes and restoration of cell proliferation, metabolism, and tissue maintenance pathways within 24 h. Differentially expressed genes included several adipokines not previously described in muscle, reflecting unique metabolic physiology in fasting-adapted animals. This study represents one of the first transcriptome analyses of cellular responses to hypothalamic-pituitary-adrenal axis stimulation in a free-living marine mammal and suggests that compensatory, tissue-sparing mechanisms may enable marine mammals to maintain cortisol and aldosterone sensitivity while avoiding deleterious long-term consequences of stress.

Transcriptome analysis of northern elephant seal (Mirounga angustirostris) muscle tissue provides a novel molecular resource and physiological insights.

BACKGROUND: The northern elephant seal, Mirounga angustirostris, is a valuable animal model of fasting adaptation and hypoxic stress tolerance. However, no reference sequence is currently available for this and many other marine mammal study systems, hindering molecular understanding of marine adaptations and unique physiology. RESULTS: We sequenced a transcriptome of M. angustirostris derived from muscle sampled during an acute stress challenge experiment to identify species-specific markers of stress axis activation and recovery. De novo assembly generated 164,966 contigs and a total of 522,699 transcripts, of which 68.70% were annotated using mouse, human, and domestic dog reference protein sequences. To reduce transcript redundancy, we removed highly similar isoforms in large gene families and produced a filtered assembly containing 336,657 transcripts. We found that a large number of annotated genes are associated with metabolic signaling, immune and stress responses, and muscle function. Preliminary differential expression analysis suggests a limited transcriptional response to acute stress involving alterations in metabolic and immune pathways and muscle tissue maintenance, potentially driven by early response transcription factors such as Cebpd. CONCLUSIONS: We present the first reference sequence for Mirounga angustirostris produced by RNA sequencing of muscle tissue and cloud-based de novo transcriptome assembly. We annotated 395,102 transcripts, some of which may be novel isoforms, and have identified thousands of genes involved in key physiological processes. This resource provides elephant seal-specific gene sequences, complementing existing metabolite and protein expression studies and enabling future work on molecular pathways regulating adaptations such as fasting, hypoxia, and environmental stress responses in marine mammals.

AutoWeka: toward an automated data mining software for QSAR and QSPR studies.

UNLABELLED: In biology and chemistry, a key goal is to discover novel compounds affording potent biological activity or chemical properties. This could be achieved through a chemical intuition-driven trial-and-error process or via data-driven predictive modeling. The latter is based on the concept of quantitative structure-activity/property relationship (QSAR/QSPR) when applied in modeling the biological activity and chemical properties, respectively, of compounds. Data mining is a powerful technology underlying QSAR/QSPR as it harnesses knowledge from large volumes of high-dimensional data via multivariate analysis. Although extremely useful, the technicalities of data mining may overwhelm potential users, especially those in the life sciences. Herein, we aim to lower the barriers to access and utilization of data mining software for QSAR/QSPR studies. AutoWeka is an automated data mining software tool that is powered by the widely used machine learning package Weka. The software provides a user-friendly graphical interface along with an automated parameter search capability. It employs two robust and popular machine learning methods: artificial neural networks and support vector machines. This chapter describes the practical usage of AutoWeka and relevant tools in the development of predictive QSAR/QSPR models. AVAILABILITY: The software is freely available at http://www.mt.mahidol.ac.th/autoweka.

An MHC class I immune evasion gene of Marek׳s disease virus.

Marek׳s disease virus (MDV) is a widespread α-herpesvirus of chickens that causes T cell tumors. Acute, but not latent, MDV infection has previously been shown to lead to downregulation of cell-surface MHC class I (Virology 282:198-205 (2001)), but the gene(s) involved have not been identified. Here we demonstrate that an MDV gene, MDV012, is capable of reducing surface expression of MHC class I on chicken cells. Co-expression of an MHC class I-binding peptide targeted to the endoplasmic reticulum (bypassing the requirement for the TAP peptide transporter) partially rescued MHC class I expression in the presence of MDV012, suggesting that MDV012 is a TAP-blocking MHC class I immune evasion protein. This is the first unique non-mammalian MHC class I immune evasion gene identified, and suggests that α-herpesviruses have conserved this function for at least 100 million years.

Transcriptional profiling of mEq-dependent genes in Marek's disease resistant and susceptible inbred chicken lines.

Marek's disease (MD) is an economically significant disease in chickens caused by the highly oncogenic Marek's disease virus (MDV). Understanding the genes and biological pathways that confer MD genetic resistance should lead towards the development of more disease resistant commercial poultry flocks or improved MD vaccines. MDV mEq, a bZIP transcription factor, is largely attributed to viral oncogenicity though only a few host target genes have been described, which has impeded our understanding of MDV-induced tumorigenesis. Given the importance of mEq in MDV-induced pathogenesis, we explored the role of mEq in genetic resistance to MDV. Using global transcriptome analysis and cells from MD resistant or susceptible birds, we compared the response to infection with either wild type MDV or a nononcogenic recombinant lacking mEq. As a result, we identified a number of specific genes and pathways associated with either MD resistance or susceptibility. Additionally, integrating prior information from ChIP-seq, microarray analysis, and SNPs exhibiting allele-specific expression (ASE) in response to MDV infection, we were able to provide evidence for 24 genes that are polymorphic within mEq binding sites are likely to account for gene expression in an allele-specific manner and potentially for the underlying genetic differences in MD incidence.

Integrated analyses of genome-wide DNA occupancy and expression profiling identify key genes and pathways involved in cellular transformation by a Marek's disease virus oncoprotein, Meq.

Marek's disease (MD) is an economically significant disease in chickens that is caused by the highly oncogenic Marek's disease virus (MDV). A major unanswered question is the mechanism of MDV-induced tumor formation. Meq, a bZIP transcription factor discovered in the 1990s, is critically involved in viral oncogenicity, but only a few of its host target genes have been described, impeding our understanding of MDV-induced tumorigenesis. Using chromatin immunoprecipitation-sequencing (ChIP-seq) and microarray analysis, a high-confidence list of Meq binding sites in the chicken genome and a global transcriptome of Meq-responsive genes were generated. Meq binding sites were found to be enriched in the promoter regions of upregulated genes but not in those of downregulated genes. ChIP-seq was also performed for c-Jun, a known heterodimeric partner of Meq. The close location of binding sites of Meq and c-Jun was noted, suggesting cooperativity between these two factors in modulating transcription. Pathway analysis indicated that Meq transcriptionally regulates many genes that are part of several signaling pathways including the extracellular signal-regulated kinase /mitogen-activated protein kinase (ERK/MAPK), Jak-STAT, and ErbB pathways, which are critical for oncogenesis and/or include signaling mediators involved in apoptosis. Meq activates oncogenic signaling cascades by transcriptionally activating major kinases in the ERK/MAPK pathway and simultaneously repressing phosphatases, as verified using inhibitors of MEK and ERK1/2 in a cell proliferation assay. This study provides significant insights into the mechanistic basis of Meq-dependent cell transformation.

PyBact: an algorithm for bacterial identification.

PyBact is a software written in Python for bacterial identification. The code simulates the predefined behavior of bacterial species by generating a simulated data set based on the frequency table of biochemical tests from diagnostic microbiology textbook. The generated data was used for predictive model construction by machine learning approaches and results indicated that the classifiers could accurately predict its respective bacterial class with accuracy in excess of 99 %.