Ishwor Thapa

KPC-4 Is Encoded within a Truncated Tn4401 in an IncL/M Plasmid, pNE1280, Isolated from Enterobacter cloacae and Serratia marcescens

ABSTRACT We describe the transfer of blaKPC-4 from Enterobacter cloacae to Serratia marcescens in a single patient. DNA sequencing revealed that KPC-4 was encoded on an IncL/M plasmid, pNE1280, closely related to pCTX-M360. Further analysis found that KPC-4 was encoded within a novel Tn4401 element (Tn4401f) containing a truncated tnpA and lacking tnpR, ISKpn7 left, and Tn4401 IRL-1, which are conserved in other Tn4401 transposons. This study highlights the continued evolution of Tn4401 transposons and movement to multiple plasmid backbones that results in acquisition by multiple species of Gram-negative bacilli.

Ibudilast reverses the decrease in the synaptic signaling protein phosphatidylethanolamine-binding protein 1 (PEBP1) produced by chronic methamphetamine intake in rats

BACKGROUND Chronic methamphetamine intake has been shown to induce a neuroinflammatory state leading to significant changes in brain functioning including behavioral changes. These changes can persist for years after drug use is discontinued and likely contribute to the risk of relapse. A better understanding of inflammation responses associated with methamphetamine intake may help in designing novel and more efficacious treatment strategies. METHODS Rats were trained to self-administer methamphetamine or saline on a variable ratio 3 schedule of reinforcement (25 days). This training was followed by 12 days of extinction (i.e., methamphetamine unavailable) during which rats received daily post-session administration of ibudilast (AV411; 2.5 or 7.5mg/kg) or saline. Following extinction, synaptosomes were isolated from the prefrontal cortex (PFC) and the differential pattern of synaptic proteins was assessed using mass spectrometry based proteomics. RESULTS Treatment with ibudilast allowed for deeper extinction of active lever pressing. Quantitative mass spectrometry based proteomics on the PFC identified one potential hit; the synaptic signaling protein phosphatidylethanolamine-binding protein 1 (PEBP1). While methamphetamine intake was associated with reduced PEBP1 protein levels, treatment with ibudilast reversed this effect. Furthermore, decreased PEBP1 expression was correlated with subsequent activation of Raf-1, MEK, and ERK signaling components of the mitogen-activated protein kinase cascade (MAPK). Raf-1, MEK, and ERK expression levels were also attenuated by ibudilast treatment. CONCLUSION PEBP1, given its synaptic localization and its role as a signaling molecule acting via the ERK/MAPK pathway, could be a potential therapeutic target mediating drug-seeking behaviors associated with neuroinflammation.

Bio-logic builder: a non-technical tool for building dynamical, qualitative models.

Computational modeling of biological processes is a promising tool in biomedical research. While a large part of its potential lies in the ability to integrate it with laboratory research, modeling currently generally requires a high degree of training in mathematics and/or computer science. To help address this issue, we have developed a web-based tool, Bio-Logic Builder, that enables laboratory scientists to define mathematical representations (based on a discrete formalism) of biological regulatory mechanisms in a modular and non-technical fashion. As part of the user interface, generalized “bio-logic” modules have been defined to provide users with the building blocks for many biological processes. To build/modify computational models, experimentalists provide purely qualitative information about a particular regulatory mechanisms as is generally found in the laboratory. The Bio-Logic Builder subsequently converts the provided information into a mathematical representation described with Boolean expressions/rules. We used this tool to build a number of dynamical models, including a 130-protein large-scale model of signal transduction with over 800 interactions, influenza A replication cycle with 127 species and 200+ interactions, and mammalian and budding yeast cell cycles. We also show that any and all qualitative regulatory mechanisms can be built using this tool.

Review of Methods for the Identification of Zygomycetes With an Emphasis on Advances in Molecular Diagnostics

Zygomycosis is a rapidly emerging fungal infection caused by the zygomycetes. The identification of specific species in the clinical laboratory using phenotypic methods is difficult. This article provides a generalized review on the current classification and diagnostic aspects of the zygomycetes with an emphasis on the application of molecular techniques for identification. * CBS : Centraalbureau voor Schimmelcultures ITS : internal transcribed spacer NCBI : National Center for Biotechnology Information PCR : polymerase chain reaction RFLP : restriction fragment length polymorphism

Image Analysis of DETECHIP® – A Molecular Sensing Array

Several image analysis techniques were applied to a colorimetric chemical sensor array called DETECHIP®. Analytes such as illegal and over the counter drugs can be detected and identified by digital image analysis. Jpeg images of DETECHIP® arrays with and without analytes were obtained using a camera and a simple flatbed scanner. Color information was obtained by measuring red-green-blue (RGB) values with image software like GIMP, Photoshop, and ImageJ. Several image analysis methods were evaluated for analysis of both photographs and scanned images of DETECHIP®. We determined that when compared to photographs, scanned images of DETECHIP® gave better results through the elimination of parallax and shading that lead to inconsistent results. Furthermore, results using an ImageJ macro technique showed improved consistency versus the previous method when human eyesight was used as a detection method.

Evidence of post translational modification bias extracted from the tRNA and corresponding amino acid interplay across a set of diverse organisms

A post-translational modification (PTM) describes a form of biosynthesis for the task of initializing proteins for specific functions. PTMs are complexes which are involved in developing or customizing proteins to increase their functional diversity. In times of protein stress, PTMs may be involved in altering protein structures to allow for better chances of survival. Once the stress-condition has elapsed, PTMs are able to transform the proteins structure back to its original form for the continued survival of the protein. PTMs are not applied uniformly across organismal proteins and differing PTM preferences and usages may often exist between proteins of the same organism. Here, we study the frequency of factors (PTM predominance and their associated active sites, tRNAs and amino acids) which likely influence a PTM bias. We extract and study these factor frequencies across both mitochondrial (Mt) and non-Mt proteins of nine diverse organisms (closely following two, Arabidopsis thaliana and Caenorhabditis elegans, due to space limitations) to illustrate their remarkable differences which may strongly influence natural PTM selection. By this work, we offer evidence to argue that this PTM bias may be the result of these factors which combine in a poorly understood system to affect and control PTM interactions. Our analysis is made up of an application of frequency information concerning PTMs, active sites, tRNA and amino acids and is used to create network models for the clear visualization of its mechanisms for this PTM natural selection.

On Mining Biological Signals Using Correlation Networks

Correlation networks have been used in biological networks to analyze and model high-throughput biological data, such as gene expression from micro array or RNA-seq assays. Typically in biological network modeling, structures can be mined from these networks that represent biological functions, for example, a cluster of proteins in an interactome can represent a protein complex. In correlation networks built from high-throughput gene expression data, it has often been speculated or even assumed that clusters represent sets of genes that are co-regulated. This research aims to validate this concept using network systems biology and data mining by identification of correlation network clusters via multiple clustering approaches and cross-validation of regulatory elements in these clusters via motif finding software. The results show that the majority (81-100%) of genes in any given cluster will share at least one predicted transcription factor binding site. With this in mind, new regulatory relationships can be proposed using known transcription factors and their binding sites by integrating regulatory information and the network model itself.

Assessing the impact of refactoring activities on the JHotDraw project

Refactoring is a well-known technique for improving the maintainability of software products. However, it is not easy to justify the time and effort needed to refactor code as the benefits are difficult to quantify, especially the perception of improved maintainability. In this paper, we highlight some results of a retrospective case study undertaken to shed light on how refactoring affects maintainability of a software product. There are several findings. First of all, refactoring affects the amount of subsequent changes. Furthermore, refactoring has a positive impact on the coupling relationships with dependent software applications.

A study of bias and increasing organismal complexity from their post-translational modifications and reaction site interplays

Abstract Post‐translational modifications (PTMs) are important steps in the biosynthesis of proteins. Aside from their integral contributions to protein development, i.e. perform specialized proteolytic cleavage of regulatory subunits, the covalent addition of functional groups of proteins or the degradation of entire proteins, PTMs are also involved in enabling proteins to withstand and recover from temporary environmental stresses (heat shock, microgravity and many others). The literature supports evidence of thousands of recently discovered PTMs, many of which may likely contribute similarly (perhaps, even, interchangeably) to protein stress response. Although there are many PTM actors on the biological stage, our study determines that these PTMs are generally cast into organism‐specific, preferential roles. In this work, we study the PTM compositions across the mitochondrial (Mt) and non‐Mt proteomes of 11 diverse organisms to illustrate that each organism appears to have a unique list of PTMs, and an equally unique list of PTM‐associated residue reaction sites (RSs), where PTMs interact with protein. Despite the present limitation of available PTM data across different species, we apply existing and current protein data to illustrate particular organismal biases. We explore the relative frequencies of observed PTMs, the RSs and general amino‐acid compositions of Mt and non‐Mt proteomes. We apply these data to create networks and heatmaps to illustrate the evidence of bias. We show that the number of PTMs and RSs appears to grow along with organismal complexity, which may imply that environmental stress could play a role in this bias.

Identifying enriched drug fragments as possible candidates for metabolic engineering

BackgroundFragment-based approaches have now become an important component of the drug discovery process. At the same time, pharmaceutical chemists are more often turning to the natural world and its extremely large and diverse collection of natural compounds to discover new leads that can potentially be turned into drugs. In this study we introduce and discuss a computational pipeline to automatically extract statistically overrepresented chemical fragments in therapeutic classes, and search for similar fragments in a large database of natural products. By systematically identifying enriched fragments in therapeutic groups, we are able to extract and focus on few fragments that are likely to be active or structurally important.ResultsWe show that several therapeutic classes (including antibacterial, antineoplastic, and drugs active on the cardiovascular system, among others) have enriched fragments that are also found in many natural compounds. Further, our method is able to detect fragments shared by a drug and a natural product even when the global similarity between the two molecules is generally low.ConclusionsA further development of this computational pipeline is to help predict putative therapeutic activities of natural compounds, and to help identify novel leads for drug discovery.