Endotoxin Tolerance Acquisition and Altered Hepatic Fatty Acid Profile in Aged Mice

(1) Background: Aging is linked to an altered immune response and metabolism. Inflammatory conditions, such as sepsis, COVID-19, and steatohepatitis are more prevalent in the elderly and steatosis is linked both to severe COVID-19 and sepsis. We hypothesized that aging is linked to a loss of endotoxin tolerance, which normally protects the host from excessive inflammation, and that this is accompanied by elevated levels of hepatic lipids. (2) Methods: An in vivo lipopolysaccharide (LPS) tolerance model in young and old mice was used and the cytokine serum levels were measured by ELISA. Cytokine and toll-like receptor gene expression was determined by qPCR in the lungs and the liver; hepatic fatty acid composition was assessed by GC–MS. (3) Results: The old mice showed a distinct potential for endotoxin tolerance as suggested by the serum cytokine levels and gene expression in the lung tissue. Endotoxin tolerance was less pronounced in the livers of the aged mice. However, the fatty acid composition strongly differed in the liver tissues of the young and old mice with a distinct change in the ratio of C18 to C16 fatty acids. (4) Conclusions: Endotoxin tolerance is maintained in advanced age, but changes in the metabolic tissue homeostasis may lead to an altered immune response in old individuals

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

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

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

Module walking using an SH3-like cell-wall-binding domain leads to a new GH184 family of muramidases

Muramidases (also known as lysozymes) hydrolyse the peptidoglycan component of the bacterial cell wall and are found in many glycoside hydrolase (GH) families. Similar to other glycoside hydrolases, muramidases sometimes have noncatalytic domains that facilitate their interaction with the substrate. Here, the identification, characterization and X-ray structure of a novel fungal GH24 muramidase from Trichophaea saccata is first described, in which an SH3-like cell-wall-binding domain (CWBD) was identified by structure comparison in addition to its catalytic domain. Further, a complex between a triglycine peptide and the CWBD from T. saccata is presented that shows a possible anchor point of the peptidoglycan on the CWBD. A `domain-walking' approach, searching for other sequences with a domain of unknown function appended to the CWBD, was then used to identify a group of fungal muramidases that also contain homologous SH3-like cell-wall-binding modules, the catalytic domains of which define a new GH family. The properties of some representative members of this family are described as well as X-ray structures of the independent catalytic and SH3-like domains of the Kionochaeta sp., Thermothielavioides terrestris and Penicillium virgatum enzymes. This work confirms the power of the module-walking approach, extends the library of known GH families and adds a new noncatalytic module to the muramidase arsenal

A Novel Framework for the Comparative Analysis of Biological Networks

Genome sequencing projects provide nearly complete lists of the individual components present in an organism, but reveal little about how they work together. Follow-up initiatives have deciphered thousands of dynamic and context-dependent interrelationships between gene products that need to be analyzed with novel bioinformatics approaches able to capture their complex emerging properties. Here, we present a novel framework for the alignment and comparative analysis of biological networks of arbitrary topology. Our strategy includes the prediction of likely conserved interactions, based on evolutionary distances, to counter the high number of missing interactions in the current interactome networks, and a fast assessment of the statistical significance of individual alignment solutions, which vastly increases its performance with respect to existing tools. Finally, we illustrate the biological significance of the results through the identification of novel complex components and potential cases of cross-talk between pathways and alternative signaling routes

Organization of Physical Interactomes as Uncovered by Network Schemas

Large-scale protein-protein interaction networks provide new opportunities for understanding cellular organization and functioning. We introduce network schemas to elucidate shared mechanisms within interactomes. Network schemas specify descriptions of proteins and the topology of interactions among them. We develop algorithms for systematically uncovering recurring, over-represented schemas in physical interaction networks. We apply our methods to the S. cerevisiae interactome, focusing on schemas consisting of proteins described via sequence motifs and molecular function annotations and interacting with one another in one of four basic network topologies. We identify hundreds of recurring and over-represented network schemas of various complexity, and demonstrate via graph-theoretic representations how more complex schemas are organized in terms of their lower-order constituents. The uncovered schemas span a wide range of cellular activities, with many signaling and transport related higher-order schemas. We establish the functional importance of the schemas by showing that they correspond to functionally cohesive sets of proteins, are enriched in the frequency with which they have instances in the H. sapiens interactome, and are useful for predicting protein function. Our findings suggest that network schemas are a powerful paradigm for organizing, interrogating, and annotating cellular networks

Network Compression as a Quality Measure for Protein Interaction Networks

With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients

The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery

The International Human Epigenome Consortium (IHEC) coordinates the generation of a catalog of high-resolution reference epigenomes of major primary human cell types. The studies now presented (see the Cell Press IHEC web portal at http://www.cell.com/consortium/IHEC) highlight the coordinated achievements of IHEC teams to gather and interpret comprehensive epigenomic datasets to gain insights in the epigenetic control of cell states relevant for human health and disease