Transcriptomic Analysis of Peritoneal Cells in a Mouse Model of Sepsis: Confirmatory and Novel Results in Early and Late Sepsis.

Background The events leading to sepsis start with an invasive infection of a primary organ of the body followed by an overwhelming systemic response. Intra-abdominal infections are the second most common cause of sepsis. Peritoneal fluid is the primary site of infection in these cases. A microarray-based approach was used to study the temporal changes in cells from the peritoneal cavity of septic mice and to identify potential biomarkers and therapeutic targets for this subset of sepsis patients. Results We conducted microarray analysis of the peritoneal cells of mice infected with a non-pathogenic strain of Escherichia coli. Differentially expressed genes were identified at two early (1 h, 2 h) and one late time point (18 h). A multiplexed bead array analysis was used to confirm protein expression for several cytokines which showed differential expression at different time points based on the microarray data. Gene Ontology based hypothesis testing identified a positive bias of differentially expressed genes associated with cellular development and cell death at 2 h and 18 h respectively. Most differentially expressed genes common to all 3 time points had an immune response related function, consistent with the observation that a few bacteria are still present at 18 h. Conclusions Transcriptional regulators like PLAGL2, EBF1, TCF7, KLF10 and SBNO2, previously not described in sepsis, are differentially expressed at early and late time points. Expression pattern for key biomarkers in this study is similar to that reported in human sepsis, indicating the suitability of this model for future studies of sepsis, and the observed differences in gene expression suggest species differences or differences in the response of blood leukocytes and peritoneal leukocytes

Deficiency of Pkc1 activity affects glycerol metabolism in Saccharomyces cerevisiae

In pressProtein kinase C is apparently involved in the control of many cellular systems: the cell wall integrity pathway, the synthesis of ribosomes, the appropriated reallocation of transcription factors under specific stress conditions and also the regulation of N-glycosylation activity. All these observations suggest the existence of additional targets not yet identified. In the context of the control of carbon metabolism, previous data demonstrated that Pkc1 p might play a central role in the control of cellular growth and metabolism in yeast. In particular, it has been suggested that it might be involved in the derepression of genes under glucose-repression by driving an appropriated subcellular localization of transcriptional factors, such as Mig1 p. In this work, we show that pkc1∆ mutant is unable to grow on glycerol because it cannot perform the derepression of GUT1 gene that encodes for glycerol kinase. Additionally, active transport is also partially affected. Using this phenotype, we were able to isolate a new pkc1∆ revertant. We also isolated two transformants identified as the nuclear exportin Msn5 and the histone deacetylase Hos2 extragenic suppressors of this mutation. Based on these results, we postulate that Pkc1 p may be involved in the control of the cellular localization and/or regulation of the activity of nuclear proteins implicated in gene expression.Fundação Universidade Federal de Ouro Preto (FUFOP). Fundação de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG) - CBS-1875/95. Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) - 300998/89-9 to R.L.B., 301255/01-6 to L.G.F

Cell-selective labeling using amino acid precursors for proteomic studies of multicellular environments.

We report a technique to selectively and continuously label the proteomes of individual cell types in coculture, named cell type-specific labeling using amino acid precursors (CTAP). Through transgenic expression of exogenous amino acid biosynthesis enzymes, vertebrate cells overcome their dependence on supplemented essential amino acids and can be selectively labeled through metabolic incorporation of amino acids produced from heavy isotope-labeled precursors. When testing CTAP in several human and mouse cell lines, we could differentially label the proteomes of distinct cell populations in coculture and determine the relative expression of proteins by quantitative mass spectrometry. In addition, using CTAP we identified the cell of origin of extracellular proteins secreted from cells in coculture. We believe that this method, which allows linking of proteins to their cell source, will be useful in studies of cell-cell communication and potentially for discovery of biomarkers

25-hydroxyvitamin D3 and 1,25-dihydroxyvitamin D3 exert distinct effects on human skeletal muscle function and gene expression

Age-associated decline in muscle function represents a significant public health burden. Vitamin D-deficiency is also prevalent in aging subjects, and has been linked to loss of muscle mass and strength (sarcopenia), but the precise role of specific vitamin D metabolites in determining muscle phenotype and function is still unclear. To address this we quantified serum concentrations of multiple vitamin D metabolites, and assessed the impact of these metabolites on body composition/muscle function parameters, and muscle biopsy gene expression in a retrospective study of a cohort of healthy volunteers. Active serum 1,25-dihydroxyvitamin D3 (1α,25(OH)2D3), but not inactive 25-hydroxyvitamin D3 (25OHD3), correlated positively with measures of lower limb strength including power (rho = 0.42, p = 0.02), velocity (Vmax, rho = 0.40, p = 0.02) and jump height (rho = 0.36, p = 0.04). Lean mass correlated positively with 1α,25(OH)2D3 (rho = 0.47, p = 0.02), in women. Serum 25OHD3 and inactive 24,25-dihydroxyvitamin D3 (24,25(OH)2D3) had an inverse relationship with body fat (rho = -0.30, p = 0.02 and rho = -0.33, p = 0.01, respectively). Serum 25OHD3 and 24,25(OH)2D3 were also correlated with urinary steroid metabolites, suggesting a link with glucocorticoid metabolism. PCR array analysis of 92 muscle genes identified vitamin D receptor (VDR) mRNA in all muscle biopsies, with this expression being negatively correlated with serum 25OHD3, and Vmax, and positively correlated with fat mass. Of the other 91 muscle genes analysed by PCR array, 24 were positively correlated with 25OHD3, but only 4 were correlated with active 1α,25(OH)2D3. These data show that although 25OHD3 has potent actions on muscle gene expression, the circulating concentrations of this metabolite are more closely linked to body fat mass, suggesting that 25OHD3 can influence muscle function via indirect effects on adipose tissue. By contrast, serum 1α,25(OH)2D3 has limited effects on muscle gene expression, but is associated with increased muscle strength and lean mass in women. These pleiotropic effects of the vitamin D ‘metabolome’ on muscle function indicate that future supplementation studies should not be restricted to conventional analysis of the major circulating form of vitamin D, 25OHD3

ProtQuant: a tool for the label-free quantification of MudPIT proteomics data

<p>Abstract</p> <p>Background</p> <p>Effective and economical methods for quantitative analysis of high throughput mass spectrometry data are essential to meet the goals of directly identifying, characterizing, and quantifying proteins from a particular cell state. Multidimensional Protein Identification Technology (MudPIT) is a common approach used in protein identification. Two types of methods are used to detect differential protein expression in MudPIT experiments: those involving stable isotope labelling and the so-called label-free methods. Label-free methods are based on the relationship between protein abundance and sampling statistics such as peptide count, spectral count, probabilistic peptide identification scores, and sum of peptide Sequest XCorr scores (ΣXCorr). Although a number of label-free methods for protein quantification have been described in the literature, there are few publicly available tools that implement these methods. We describe ProtQuant, a Java-based tool for label-free protein quantification that uses the previously published ΣXCorr method for quantification and includes an improved method for handling missing data.</p> <p>Results</p> <p><it>ProtQuant </it>was designed for ease of use and portability for the bench scientist. It implements the ΣXCorr method for label free protein quantification from MudPIT datasets. <it>ProtQuant </it>has a graphical user interface, accepts multiple file formats, is not limited by the size of the input files, and can process any number of replicates and any number of treatments. In addition,<it>ProtQuant </it>implements a new method for dealing with missing values for peptide scores used for quantification. The new algorithm, called ΣXCorr*, uses "below threshold" peptide scores to provide meaningful non-zero values for missing data points. We demonstrate that ΣXCorr* produces an average reduction in false positive identifications of differential expression of 25% compared to ΣXCorr.</p> <p>Conclusion</p> <p><it>ProtQuant </it>is a tool for protein quantification built for multi-platform use with an intuitive user interface. <it>ProtQuant </it>efficiently and uniquely performs label-free quantification of protein datasets produced with Sequest and provides the user with facilities for data management and analysis. Importantly, <it>ProtQuant </it>is available as a self-installing executable for the Windows environment used by many bench scientists.</p

A ONE DIMENSIONAL MATHEMATICAL MODEL FOR URODYNAMICS

ABSTRACT Millions of people in the world suffer from urinary incontinence and overactive bladder with the major causes for the symptoms being stress, urge, overflow and functional incontinence. For a more effective treatment of these ailments, a detailed understanding of the urinary flow dynamics is required. This challenging task is not easy to achieve due to the complexity of the problem and the lack of tools to study the underlying mechanisms of the urination process. Theoretical models can help find a better solution for the various disorders of the lower urinary tract, including urinary incontinence, through simulating the interaction between various components involved in the continence mechanism. Using a lumped parameter analysis, a one-dimensional, transient mathematical model was built to simulate a complete cycle of filling and voiding of the bladder. Both the voluntary and involuntary contraction of the bladder walls is modeled along with the transient response of both the internal and external sphincters which dynamically control the urination process. The model also includes the effects signals from the bladder outlet (urethral sphincter, pelvic floor muscles and fascia), the muscles involved in evacuation of the urinary bladder (detrusor muscle) as well as the abdominal wall musculature. The necessary geometrical parameters of the urodynamics model were obtained from the 3D visualization data based on the visible human project. Preliminary results show good agreement with the experimental results found in the literature. The current model could be used as a diagnostic tool for detecting incontinence and simulating possible scenarios for the circumstances leading to incontinence. INTRODUCTION Urinary incontinence has been reported to affect 35% of American women over 50 years of age an almost 15% who have leakage on a daily basi

Cytokine preconditioning of engineered cartilage provides protection against interleukin-1 insult

Research reported in this publication was supported in part by the National Institute of Arthritis and Musculoskeletal and Skin Diseases and National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under Award Number R01AR60361, R01AR061988, P41EB002520). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. ART was supported by a National Science Foundation Graduate Fellowship

Aberrantly Expressed Genes in HaCaT Keratinocytes Chronically Exposed to Arsenic Trioxide

Inorganic arsenic is a known environmental toxicant and carcinogen of global public health concern. Arsenic is genotoxic and cytotoxic to human keratinocytes. However, the biological pathways perturbed in keratinocytes by low chronic dose inorganic arsenic are not completely understood. The objective of the investigation was to discover the mechanism of arsenic carcinogenicity in human epidermal keratinocytes. We hypothesize that a combined strategy of DNA microarray, qRT-PCR and gene function annotation will identify aberrantly expressed genes in HaCaT keratinocyte cell line after chronic treatment with arsenic trioxide. Microarray data analysis identified 14 up-regulated genes and 21 down-regulated genes in response to arsenic trioxide. The expression of 4 up-regulated genes and 1 down-regulated gene were confirmed by qRT-PCR. The up-regulated genes were AKR1C3 (Aldo-Keto Reductase family 1, member C3), IGFL1 (Insulin Growth Factor-Like family member 1), IL1R2 (Interleukin 1 Receptor, type 2), and TNFSF18 (Tumor Necrosis Factor [ligand] SuperFamily, member 18) and down-regulated gene was RGS2 (Regulator of G-protein Signaling 2). The observed over expression of TNFSF18 (167 fold) coupled with moderate expression of IGFL1 (3.1 fold), IL1R2 (5.9 fold) and AKR1C3 (9.2 fold) with a decreased RGS2 (2.0 fold) suggests that chronic arsenic exposure could produce sustained levels of TNF with modulation by an IL-1 analogue resulting in chronic immunologic insult. A concomitant decrease in growth inhibiting gene (RGS2) and increase in AKR1C3 may contribute to chronic inflammation leading to metaplasia, which may eventually lead to carcinogenicity in the skin keratinocytes. Also, increased expression of IGFL1 may trigger cancer development and progression in HaCaT keratinocytes

AgBase: a functional genomics resource for agriculture

BACKGROUND: Many agricultural species and their pathogens have sequenced genomes and more are in progress. Agricultural species provide food, fiber, xenotransplant tissues, biopharmaceuticals and biomedical models. Moreover, many agricultural microorganisms are human zoonoses. However, systems biology from functional genomics data is hindered in agricultural species because agricultural genome sequences have relatively poor structural and functional annotation and agricultural research communities are smaller with limited funding compared to many model organism communities. DESCRIPTION: To facilitate systems biology in these traditionally agricultural species we have established "AgBase", a curated, web-accessible, public resource for structural and functional annotation of agricultural genomes. The AgBase database includes a suite of computational tools to use GO annotations. We use standardized nomenclature following the Human Genome Organization Gene Nomenclature guidelines and are currently functionally annotating chicken, cow and sheep gene products using the Gene Ontology (GO). The computational tools we have developed accept and batch process data derived from different public databases (with different accession codes), return all existing GO annotations, provide a list of products without GO annotation, identify potential orthologs, model functional genomics data using GO and assist proteomics analysis of ESTs and EST assemblies. Our journal database helps prevent redundant manual GO curation. We encourage and publicly acknowledge GO annotations from researchers and provide a service for researchers interested in GO and analysis of functional genomics data. CONCLUSION: The AgBase database is the first database dedicated to functional genomics and systems biology analysis for agriculturally important species and their pathogens. We use experimental data to improve structural annotation of genomes and to functionally characterize gene products. AgBase is also directly relevant for researchers in fields as diverse as agricultural production, cancer biology, biopharmaceuticals, human health and evolutionary biology. Moreover, the experimental methods and bioinformatics tools we provide are widely applicable to many other species including model organisms