Characterization of In Vivo Keratin 19 Phosphorylation on Tyrosine-391

Keratin polypeptide 19 (K19) is a type I intermediate filament protein that is expressed in stratified and simple-type epithelia. Although K19 is known to be phosphorylated on tyrosine residue(s), conclusive site-specific characterization of these residue(s) and identification potential kinases that may be involved has not been reported.In this study, biochemical, molecular and immunological approaches were undertaken in order to identify and characterize K19 tyrosine phosphorylation. Upon treatment with pervanadate, a tyrosine phosphatase inhibitor, human K19 (hK19) was phosphorylated on tyrosine 391, located in the 'tail' domain of the protein. K19 Y391 phosphorylation was confirmed using site-directed mutagenesis and cell transfection coupled with the generation of a K19 phospho (p)-Y391-specific rabbit antibody. The antibody also recognized mouse phospho-K19 (K19 pY394). This tyrosine residue is not phosphorylated under basal conditions, but becomes phosphorylated in the presence of Src kinase in vitro and in cells expressing constitutively-active Src. Pervanadate treatment in vivo resulted in phosphorylation of K19 Y394 and Y391 in colonic epithelial cells of non-transgenic mice and hK19-overexpressing mice, respectively.Human K19 tyrosine 391 is phosphorylated, potentially by Src kinase, and is the first well-defined tyrosine phosphorylation site of any keratin protein. The lack of detection of K19 pY391 in the absence of tyrosine phosphatase inhibition suggests that its phosphorylation is highly dynamic

Structure-based statistical analysis of transmembrane helices

Recent advances in determination of the high-resolution structure of membrane proteins now enable analysis of the main features of amino acids in transmembrane (TM) segments in comparison with amino acids in water-soluble helices. In this work, we conducted a large-scale analysis of the prevalent locations of amino acids by using a data set of 170 structures of integral membrane proteins obtained from the MPtopo database and 930 structures of water-soluble helical proteins obtained from the protein data bank. Large hydrophobic amino acids (Leu, Val, Ile, and Phe) plus Gly were clearly prevalent in TM helices whereas polar amino acids (Glu, Lys, Asp, Arg, and Gln) were less frequent in this type of helix. The distribution of amino acids along TM helices was also examined. As expected, hydrophobic and slightly polar amino acids are commonly found in the hydrophobic core of the membrane whereas aromatic (Trp and Tyr), Pro, and the hydrophilic amino acids (Asn, His, and Gln) occur more frequently in the interface regions. Charged amino acids are also statistically prevalent outside the hydrophobic core of the membrane, and whereas acidic amino acids are frequently found at both cytoplasmic and extra-cytoplasmic interfaces, basic amino acids cluster at the cytoplasmic interface. These results strongly support the experimentally demonstrated biased distribution of positively charged amino acids (that is, the so-called the positive-inside rule) with structural data

Conceptualising production, productivity and technology in pharmacy practice: a novel framework for policy, education and research.

CONTEXT AND BACKGROUND: People and health systems worldwide face serious challenges due to shifting disease demographics, rising population demands and weaknesses in healthcare provision, including capacity shortages and lack of impact of healthcare services. These multiple challenges, linked with the global push to achieve universal health coverage, have made apparent the importance of investing in workforce development to improve population health and economic well-being. In relation to medicines, health systems face challenges in terms of access to needed medicines, optimising medicines use and reducing risk. In 2017, the International Pharmaceutical Federation (FIP) published global policy on workforce development ('the Nanjing Statements') that describe an envisioned future for professional education and training. The documents make clear that expanding the pharmacy workforce benefits patients, and continually improving education and training produces better clinical outcomes. AIMS AND PURPOSE: The opportunities for harnessing new technologies in pharmacy practice have been relatively ignored. This paper presents a conceptual framework for analysing production methods, productivity and technology in pharmacy practice that differentiates between dispensing and pharmaceutical care services. We outline a framework that may be employed to study the relationship between pharmacy practice and productivity, shaped by educational and technological inputs. METHOD AND RESULTS: The analysis is performed from the point of view of health systems economics. In relation to pharmaceutical care (patient-oriented practice), pharmacists are service providers; however, their primary purpose is not to deliver consultations, but to maximise the quantum of health gain they secure. Our analysis demonstrates that 'technology shock' is clearly beneficial compared with orthodox notions of productivity or incremental gain implementations. Additionally, the whole process of providing professional services using 'pharmaceutical care technologies' is governed by local institutional frames, suggesting that activities may be structured differently in different places and countries. DISCUSSION AND CONCLUSION: Addressing problems with medication use with the development of a pharmaceutical workforce that is sufficient in quantity and competence is a long-term issue. As a result of this analysis, there emerges a challenge about the profession's relationship with existing and emerging technical innovations. Our novel framework is designed to facilitate policy, education and research by providing an analytical approach to service delivery. By using this approach, the profession could develop examples of good practice in both developed and developing countries worldwide

Fluorescence-Based Methods for Detecting Caries Lesions: Systematic Review, Meta-Analysis and Sources of Heterogeneity

Background Fluorescence-based methods have been proposed to aid caries lesion detection. Summarizing and analysing findings of studies about fluorescence-based methods could clarify their real benefits. Objective We aimed to perform a comprehensive systematic review and meta-analysis to evaluate the accuracy of fluorescence-based methods in detecting caries lesions. Data Source Two independent reviewers searched PubMed, Embase and Scopus through June 2012 to identify papers/articles published. Other sources were checked to identify non-published literature. Study Eligibility Criteria, Participants and Diagnostic Methods The eligibility criteria were studies that: (1) have assessed the accuracy of fluorescence-based methods of detecting caries lesions on occlusal, approximal or smooth surfaces, in both primary or permanent human teeth, in the laboratory or clinical setting; (2) have used a reference standard; and (3) have reported sufficient data relating to the sample size and the accuracy of methods. Study Appraisal and Synthesis Methods A diagnostic 2×2 table was extracted from included studies to calculate the pooled sensitivity, specificity and overall accuracy parameters (Diagnostic Odds Ratio and Summary Receiver-Operating curve). The analyses were performed separately for each method and different characteristics of the studies. The quality of the studies and heterogeneity were also evaluated. Results Seventy five studies met the inclusion criteria from the 434 articles initially identified. The search of the grey or non-published literature did not identify any further studies. In general, the analysis demonstrated that the fluorescence-based method tend to have similar accuracy for all types of teeth, dental surfaces or settings. There was a trend of better performance of fluorescence methods in detecting more advanced caries lesions. We also observed moderate to high heterogeneity and evidenced publication bias. Conclusions Fluorescence-based devices have similar overall performance; however, better accuracy in detecting more advanced caries lesions has been observed

Mining and state-space modeling and verification of sub-networks from large-scale biomolecular networks

<p>Abstract</p> <p>Background</p> <p>Biomolecular networks dynamically respond to stimuli and implement cellular function. Understanding these dynamic changes is the key challenge for cell biologists. As biomolecular networks grow in size and complexity, the model of a biomolecular network must become more rigorous to keep track of all the components and their interactions. In general this presents the need for computer simulation to manipulate and understand the biomolecular network model.</p> <p>Results</p> <p>In this paper, we present a novel method to model the regulatory system which executes a cellular function and can be represented as a biomolecular network. Our method consists of two steps. First, a novel scale-free network clustering approach is applied to the large-scale biomolecular network to obtain various sub-networks. Second, a state-space model is generated for the sub-networks and simulated to predict their behavior in the cellular context. The modeling results represent <it>hypotheses </it>that are tested against high-throughput data sets (microarrays and/or genetic screens) for both the natural system and perturbations. Notably, the dynamic modeling component of this method depends on the automated network structure generation of the first component and the sub-network clustering, which are both essential to make the solution tractable.</p> <p>Conclusion</p> <p>Experimental results on time series gene expression data for the human cell cycle indicate our approach is promising for sub-network mining and simulation from large-scale biomolecular network.</p

Genome wide prediction of protein function via a generic knowledge discovery approach based on evidence integration

BACKGROUND: The automation of many common molecular biology techniques has resulted in the accumulation of vast quantities of experimental data. One of the major challenges now facing researchers is how to process this data to yield useful information about a biological system (e.g. knowledge of genes and their products, and the biological roles of proteins, their molecular functions, localizations and interaction networks). We present a technique called Global Mapping of Unknown Proteins (GMUP) which uses the Gene Ontology Index to relate diverse sources of experimental data by creation of an abstraction layer of evidence data. This abstraction layer is used as input to a neural network which, once trained, can be used to predict function from the evidence data of unannotated proteins. The method allows us to include almost any experimental data set related to protein function, which incorporates the Gene Ontology, to our evidence data in order to seek relationships between the different sets. RESULTS: We have demonstrated the capabilities of this method in two ways. We first collected various experimental datasets associated with yeast (Saccharomyces cerevisiae) and applied the technique to a set of previously annotated open reading frames (ORFs). These ORFs were divided into training and test sets and were used to examine the accuracy of the predictions made by our method. Then we applied GMUP to previously un-annotated ORFs and made 1980, 836 and 1969 predictions corresponding to the GO Biological Process, Molecular Function and Cellular Component sub-categories respectively. We found that GMUP was particularly successful at predicting ORFs with functions associated with the ribonucleoprotein complex, protein metabolism and transportation. CONCLUSION: This study presents a global and generic gene knowledge discovery approach based on evidence integration of various genome-scale data. It can be used to provide insight as to how certain biological processes are implemented by interaction and coordination of proteins, which may serve as a guide for future analysis. New data can be readily incorporated as it becomes available to provide more reliable predictions or further insights into processes and interactions

clusterMaker: a multi-algorithm clustering plugin for Cytoscape

<p>Abstract</p> <p>Background</p> <p>In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present <it>clusterMaker</it>, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. <it>clusterMaker </it>is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view), k-means, k-medoid, SCPS, AutoSOME, and native (Java) MCL.</p> <p>Results</p> <p>Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast <it>Saccharomyces cerevisiae</it>; and the cluster analysis of the vicinal oxygen chelate (VOC) enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section.</p> <p>Conclusions</p> <p>The Cytoscape plugin <it>clusterMaker </it>provides a number of clustering algorithms and visualizations that can be used independently or in combination for analysis and visualization of biological data sets, and for confirming or generating hypotheses about biological function. Several of these visualizations and algorithms are only available to Cytoscape users through the <it>clusterMaker </it>plugin. <it>clusterMaker </it>is available via the Cytoscape plugin manager.</p

An Improved, Bias-Reduced Probabilistic Functional Gene Network of Baker's Yeast, Saccharomyces cerevisiae

Background: Probabilistic functional gene networks are powerful theoretical frameworks for integrating heterogeneous functional genomics and proteomics data into objective models of cellular systems. Such networks provide syntheses of millions of discrete experimental observations, spanning DNA microarray experiments, physical protein interactions, genetic interactions, and comparative genomics; the resulting networks can then be easily applied to generate testable hypotheses regarding specific gene functions and associations. Methodology/Principal Findings: We report a significantly improved version (v. 2) of a probabilistic functional gene network [1] of the baker's yeast, Saccharomyces cerevisiae. We describe our optimization methods and illustrate their effects in three major areas: the reduction of functional bias in network training reference sets, the application of a probabilistic model for calculating confidences in pair-wise protein physical or genetic interactions, and the introduction of simple thresholds that eliminate many false positive mRNA co-expression relationships. Using the network, we predict and experimentally verify the function of the yeast RNA binding protein Puf6 in 60S ribosomal subunit biogenesis. Conclusions/Significance: YeastNet v. 2, constructed using these optimizations together with additional data, shows significant reduction in bias and improvements in precision and recall, in total covering 102,803 linkages among 5,483 yeast proteins (95% of the validated proteome). YeastNet is available from http://www.yeastnet.org.This work was supported by grants from the N.S.F. (IIS-0325116, EIA-0219061), N.I.H. (GM06779-01,GM076536-01), Welch (F-1515), and a Packard Fellowship (EMM). These agencies were not involved in the design and conduct of the study, in the collection, analysis, and interpretation of the data, or in the preparation, review, or approval of the manuscript.Cellular and Molecular Biolog

CREB Is Activated by Muscle Injury and Promotes Muscle Regeneration

The cAMP response element binding protein (CREB) plays key roles in differentiation of embryonic skeletal muscle progenitors and survival of adult skeletal muscle. However, little is known about the physiologic signals that activate CREB in normal muscle. Here we show that CREB phosphorylation and target genes are induced after acute muscle injury and during regeneration due to genetic mutation. Activated CREB localizes to both myogenic precursor cells and newly regenerating myofibers within regenerating areas. Moreover, we found that signals from damaged skeletal muscle tissue induce CREB phosphorylation and target gene expression in primary mouse myoblasts. An activated CREB mutant (CREBY134F) potentiates myoblast proliferation as well as expression of early myogenic transcription factors in cultured primary myocytes. Consistently, activated CREB-YF promotes myoblast proliferation after acute muscle injury in vivo and enhances muscle regeneration in dystrophic mdx mice. Our findings reveal a new physiologic function for CREB in contributing to skeletal muscle regeneration

Reduced Neutrophil Apoptosis in Diabetic Mice during Staphylococcal Infection Leads to Prolonged Tnfα Production and Reduced Neutrophil Clearance

Diabetes is a frequent underlying medical condition among individuals with Staphylococcus aureus infections, and diabetic patients often suffer from chronic inflammation and prolonged infections. Neutrophils are the most abundant inflammatory cells during the early stages of bacterial diseases, and previous studies have reported deficiencies in neutrophil function in diabetic hosts. We challenged age-matched hyperglycemic and normoglycemic NOD mice intraperitoneally with S. aureus and evaluated the fate of neutrophils recruited to the peritoneal cavity. Neutrophils were more abundant in the peritoneal fluids of infected diabetic mice by 48 h after bacterial inoculation, and they showed prolonged viability ex vivo compared to neutrophils from infected nondiabetic mice. These differences correlated with reduced apoptosis of neutrophils from diabetic mice and were dependent upon the presence of S. aureus and a functional neutrophil respiratory burst. Decreased apoptosis correlated with impaired clearance of neutrophils by macrophages both in vitro and in vivo and prolonged production of proinflammatory tumor necrosis factor alpha by neutrophils from diabetic mice. Our results suggest that defects in neutrophil apoptosis may contribute to the chronic inflammation and the inability to clear staphylococcal infections observed in diabetic patients