Neurokinin-1 receptor antagonists CP96,345 and L-733,060 protect mice from cytokine-mediated liver injury

ABSTRACT Previously, we have shown that primary afferent sensory neurons are necessary for disease activity in T cell-mediated immune hepatitis in mice. In the present study, we analyzed the possible role of substance P (SP), an important proinflammatory neuropeptide of these nerve fibers, in an in vivo mouse model of liver inflammation

Metabolic implication of tigecycline as an efficacious second-line treatment for sorafenib-resistant hepatocellular carcinoma

Sorafenib represents the current standard of care for patients with advanced-stage hepatocellular carcinoma (HCC). However, acquired drug resistance occurs frequently during therapy and is accompanied by rapid tumor regrowth after sorafenib therapy termination. To identify the mechanism of this therapy-limiting growth resumption, we established robust sorafenib resistance HCC cell models that exhibited mitochondrial dysfunction and chemotherapeutic crossresistance. We found a rapid relapse of tumor cell proliferation after sorafenib withdrawal, which was caused by renewal of mitochondrial structures alongside a metabolic switch toward high electron transport system (ETS) activity. The translation-inhibiting antibiotic tigecycline impaired the biogenesis of mitochondrial DNA-encoded ETS subunits and limited the electron acceptor turnover required for glutamine oxidation. Thereby, tigecycline prevented the tumor relapse in vitro and in murine xenografts in vivo. These results offer a promising second-line therapeutic approach for advanced-stage HCC patients with progressive disease undergoing sorafenib therapy or treatment interruption due to severe adverse events

The long non-coding {RNA} {H19} suppresses carcinogenesis and chemoresistance in hepatocellular carcinoma

The long non-coding RNA (lncRNA) H19 represents a maternally expressed and epigenetically regulated imprinted gene product and is discussed to have either tumor-promoting or tumor-suppressive actions. Recently, H19 was shown to be regulated under inflammatory conditions. Therefore, aim of this study was to determine the function of H19 in hepatocellular carcinoma (HCC), an inflammation-associated type of tumor. In four different human HCC patient cohorts H19 was distinctly downregulated in tumor tissue compared to normal or non-tumorous adjacent tissue. We therefore determined the action of H19 in three different human hepatoma cell lines (HepG2, Plc/Prf5, and Huh7). Clonogenicity and proliferation assays showed that H19 overexpression could suppress tumor cell survival and proliferation after treatment with either sorafenib or doxorubicin, suggesting chemosensitizing actions of H19. Since HCC displays a highly chemoresistant tumor entity, cell lines resistant to doxorubicin or sorafenib were established. In all six chemoresistant cell lines H19 expression was significantly downregulated. The promoter methylation of the H19 gene was significantly different in chemoresistant cell lines compared to their sensitive counterparts. Chemoresistant cells were sensitized after H19 overexpression by either increasing the cytotoxic action of doxorubicin or decreasing cell proliferation upon sorafenib treatment. An H19 knockout mouse model (H19Δ3) showed increased tumor development and tumor cell proliferation after treatment with the carcinogen diethylnitrosamine (DEN) independent of the reciprocally imprinted insulin-like growth factor 2 (IGF2). In conclusion, H19 suppresses hepatocarcinogenesis, hepatoma cell growth, and HCC chemoresistance. Thus, mimicking H19 action might be a potential target to overcome chemoresistance in future HCC therapy

Differential cell reaction upon Toll-like receptor 4 and 9 activation in human alveolar and lung interstitial macrophages

BACKGROUND: Investigations on pulmonary macrophages (MΦ) mostly focus on alveolar MΦ (AM) as a well-defined cell population. Characteristics of MΦ in the interstitium, referred to as lung interstitial MΦ (IM), are rather ill-defined. In this study we therefore aimed to elucidate differences between AM and IM obtained from human lung tissue. METHODS: Human AM and IM were isolated from human non-tumor lung tissue from patients undergoing lung resection. Cell morphology was visualized using either light, electron or confocal microscopy. Phagocytic activity was analyzed by flow cytometry as well as confocal microscopy. Surface marker expression was measured by flow cytometry. Toll-like receptor (TLR) expression patterns as well as cytokine expression upon TLR4 or TLR9 stimulation were assessed by real time RT-PCR and cytokine protein production was measured using a fluorescent bead-based immunoassay. RESULTS: IM were found to be smaller and morphologically more heterogeneous than AM, whereas phagocytic activity was similar in both cell types. HLA-DR expression was markedly higher in IM compared to AM. Although analysis of TLR expression profiles revealed no differences between the two cell populations, AM and IM clearly varied in cell reaction upon activation. Both MΦ populations were markedly activated by LPS as well as DNA isolated from attenuated mycobacterial strains (M. bovis H37Ra and BCG). Whereas AM expressed higher amounts of inflammatory cytokines upon activation, IM were more efficient in producing immunoregulatory cytokines, such as IL10, IL1ra, and IL6.CONCLUSION: AM appear to be more effective as a non-specific first line of defence against inhaled pathogens, whereas IM show a more pronounced regulatory function. These dissimilarities should be taken into consideration in future studies on the role of human lung MΦ in the inflammatory response

AMS 3.0: prediction of post-translational modifications

<p>Abstract</p> <p>Background</p> <p>We present here the recent update of AMS algorithm for identification of post-translational modification (PTM) sites in proteins based only on sequence information, using artificial neural network (ANN) method. The query protein sequence is dissected into overlapping short sequence segments. Ten different physicochemical features describe each amino acid; therefore nine residues long segment is represented as a point in a 90 dimensional space. The database of sequence segments with confirmed by experiments post-translational modification sites are used for training a set of ANNs.</p> <p>Results</p> <p>The efficiency of the classification for each type of modification and the prediction power of the method is estimated here using recall (sensitivity), precision values, the area under receiver operating characteristic (ROC) curves and leave-one-out tests (LOOCV). The significant differences in the performance for differently optimized neural networks are observed, yet the AMS 3.0 tool integrates those heterogeneous classification schemes into the single consensus scheme, and it is able to boost the precision and recall values independent of a PTM type in comparison with the currently available state-of-the art methods.</p> <p>Conclusions</p> <p>The standalone version of AMS 3.0 presents an efficient way to indentify post-translational modifications for whole proteomes. The training datasets, precompiled binaries for AMS 3.0 tool and the source code are available at <url>http://code.google.com/p/automotifserver</url> under the Apache 2.0 license scheme.</p

Discovery and Expansion of Gene Modules by Seeking Isolated Groups in a Random Graph Process

BACKGROUND: A central problem in systems biology research is the identification and extension of biological modules-groups of genes or proteins participating in a common cellular process or physical complex. As a result, there is a persistent need for practical, principled methods to infer the modular organization of genes from genome-scale data. RESULTS: We introduce a novel approach for the identification of modules based on the persistence of isolated gene groups within an evolving graph process. First, the underlying genomic data is summarized in the form of ranked gene-gene relationships, thereby accommodating studies that quantify the relevant biological relationship directly or indirectly. Then, the observed gene-gene relationship ranks are viewed as the outcome of a random graph process and candidate modules are given by the identifiable subgraphs that arise during this process. An isolation index is computed for each module, which quantifies the statistical significance of its survival time. CONCLUSIONS: The Miso (module isolation) method predicts gene modules from genomic data and the associated isolation index provides a module-specific measure of confidence. Improving on existing alternative, such as graph clustering and the global pruning of dendrograms, this index offers two intuitively appealing features: (1) the score is module-specific; and (2) different choices of threshold correlate logically with the resulting performance, i.e. a stringent cutoff yields high quality predictions, but low sensitivity. Through the analysis of yeast phenotype data, the Miso method is shown to outperform existing alternatives, in terms of the specificity and sensitivity of its predictions

AlignNemo: A Local Network Alignment Method to Integrate Homology and Topology

Local network alignment is an important component of the analysis of protein-protein interaction networks that may lead to the identification of evolutionary related complexes. We present AlignNemo, a new algorithm that, given the networks of two organisms, uncovers subnetworks of proteins that relate in biological function and topology of interactions. The discovered conserved subnetworks have a general topology and need not to correspond to specific interaction patterns, so that they more closely fit the models of functional complexes proposed in the literature. The algorithm is able to handle sparse interaction data with an expansion process that at each step explores the local topology of the networks beyond the proteins directly interacting with the current solution. To assess the performance of AlignNemo, we ran a series of benchmarks using statistical measures as well as biological knowledge. Based on reference datasets of protein complexes, AlignNemo shows better performance than other methods in terms of both precision and recall. We show our solutions to be biologically sound using the concept of semantic similarity applied to Gene Ontology vocabularies. The binaries of AlignNemo and supplementary details about the algorithms and the experiments are available at: sourceforge.net/p/alignnemo

VIGOR, an annotation program for small viral genomes

<p>Abstract</p> <p>Background</p> <p>The decrease in cost for sequencing and improvement in technologies has made it easier and more common for the re-sequencing of large genomes as well as parallel sequencing of small genomes. It is possible to completely sequence a small genome within days and this increases the number of publicly available genomes. Among the types of genomes being rapidly sequenced are those of microbial and viral genomes responsible for infectious diseases. However, accurate gene prediction is a challenge that persists for decoding a newly sequenced genome. Therefore, accurate and efficient gene prediction programs are highly desired for rapid and cost effective surveillance of RNA viruses through full genome sequencing.</p> <p>Results</p> <p>We have developed VIGOR (Viral Genome ORF Reader), a web application tool for gene prediction in influenza virus, rotavirus, rhinovirus and coronavirus subtypes. VIGOR detects protein coding regions based on sequence similarity searches and can accurately detect genome specific features such as frame shifts, overlapping genes, embedded genes, and can predict mature peptides within the context of a single polypeptide open reading frame. Genotyping capability for influenza and rotavirus is built into the program. We compared VIGOR to previously described gene prediction programs, ZCURVE_V, GeneMarkS and FLAN. The specificity and sensitivity of VIGOR are greater than 99% for the RNA viral genomes tested.</p> <p>Conclusions</p> <p>VIGOR is a user friendly web-based genome annotation program for five different viral agents, influenza, rotavirus, rhinovirus, coronavirus and SARS coronavirus. This is the first gene prediction program for rotavirus and rhinovirus for public access. VIGOR is able to accurately predict protein coding genes for the above five viral types and has the capability to assign function to the predicted open reading frames and genotype influenza virus. The prediction software was designed for performing high throughput annotation and closure validation in a post-sequencing production pipeline.</p

Effect of Hydrogen Peroxide on Immersion Challenge of Rainbow Trout Fry with Flavobacterium psychrophilum

An experimental model for immersion challenge of rainbow trout fry (Oncorhynchus mykiss) with Flavobacterium psychrophilum, the causative agent of rainbow trout fry syndrome and bacterial cold water disease was established in the present study. Although injection-based infection models are reliable and produce high levels of mortality attempts to establish a reproducible immersion model have been less successful. Various concentrations of hydrogen peroxide (H₂O₂) were evaluated before being used as a pre-treatment stressor prior to immersion exposure to F. psychrophilum. H₂O₂ accelerated the onset of mortality and increased mortality approximately two-fold; from 9.1% to 19.2% and from 14.7% to 30.3% in two separate experiments. Clinical signs observed in the infected fish corresponded to symptoms characteristically seen during natural outbreaks. These findings indicate that pre-treatment with H₂O₂ can increase the level of mortality in rainbow trout fry after exposure to F. psychrophilum