JAK/STAT3 pathway inhibition blocks skeletal muscle wasting downstream of IL-6 and in experimental cancer cachexia

Cachexia, the metabolic dysregulation leading to sustained loss of muscle and adipose tissue, is a devastating complication of cancer and other chronic diseases. Interleukin-6 and related cytokines are associated with muscle wasting in clinical and experimental cachexia, although the mechanisms by which they might induce muscle wasting are unknown. One pathway activated strongly by IL-6 family ligands is the JAK/STAT3 pathway, the function of which has not been evaluated in regulation of skeletal muscle mass. Recently, we showed that skeletal muscle STAT3 phosphorylation, nuclear localization, and target gene expression are activated in C26 cancer cachexia, a model with high IL-6 family ligands. Here, we report that STAT3 activation is a common feature of muscle wasting, activated in muscle by IL-6 in vivo and in vitro and by different types of cancer and sterile sepsis. Moreover, STAT3 activation proved both necessary and sufficient for muscle wasting. In C(2)C(12) myotubes and in mouse muscle, mutant constitutively activated STAT3-induced muscle fiber atrophy and exacerbated wasting in cachexia. Conversely, inhibiting STAT3 pharmacologically with JAK or STAT3 inhibitors or genetically with dominant negative STAT3 and short hairpin STAT3 reduced muscle atrophy downstream of IL-6 or cancer. These results indicate that STAT3 is a primary mediator of muscle wasting in cancer cachexia and other conditions of high IL-6 family signaling. Thus STAT3 could represent a novel therapeutic target for the preservation of skeletal muscle in cachexia.     

Detecting and Removing Inconsistencies between Experimental Data and Signaling Network Topologies Using Integer Linear Programming on Interaction Graphs

Cross-referencing experimental data with our current knowledge of signaling network topologies is one central goal of mathematical modeling of cellular signal transduction networks. We present a new methodology for data-driven interrogation and training of signaling networks. While most published methods for signaling network inference operate on Bayesian, Boolean, or ODE models, our approach uses integer linear programming (ILP) on interaction graphs to encode constraints on the qualitative behavior of the nodes. These constraints are posed by the network topology and their formulation as ILP allows us to predict the possible qualitative changes (up, down, no effect) of the activation levels of the nodes for a given stimulus. We provide four basic operations to detect and remove inconsistencies between measurements and predicted behavior: (i) find a topology-consistent explanation for responses of signaling nodes measured in a stimulus-response experiment (if none exists, find the closest explanation); (ii) determine a minimal set of nodes that need to be corrected to make an inconsistent scenario consistent; (iii) determine the optimal subgraph of the given network topology which can best reflect measurements from a set of experimental scenarios; (iv) find possibly missing edges that would improve the consistency of the graph with respect to a set of experimental scenarios the most. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGFR/ErbB signaling against a library of high-throughput phosphoproteomic data measured in primary hepatocytes. Our methods detect interactions that are likely to be inactive in hepatocytes and provide suggestions for new interactions that, if included, would significantly improve the goodness of fit. Our framework is highly flexible and the underlying model requires only easily accessible biological knowledge. All related algorithms were implemented in a freely available toolbox SigNetTrainer making it an appealing approach for various applications.     

Characterization of a novel SHV β-lactamase variant that resembles the SHV-5 enzyme

Abstract An SHV type β-lactamase frequently found in enterobacteria isolated in Greek hospitals was analyzed. The enzyme (SHV-5a) conferred resistance to ceftazidime and aztreonam. The DNA sequence of the structural gene was determined. The deduced amino acid sequence showed that positions 70–73 were occupied by the active site tetrad Ser-Thr-Phe-Lys. As in SHV-5, Ser-238 and Lys-240 were present. However, one deletion (Gly-54) and three substitutions (Arg-140 for Ala, Asn-192 for Lys and Val-193 for Leu) differentiate SHV-5a β-lactamase from SHV-5. Asn-192 and Val-193 have been reported to date only in the R974 plasmid-mediated SHV-1 β-lactamase. Hydrolysis studies with SHV-5a and SHV-5 showed that the enzymes behaved similarly. Additional evidence that they were functionally indistinguishable was provided by the similar MICs of β-lactams when the enzymes were expressed under isogenic conditions. The sequence differences, however, indicate that they are derived from different ancestors.     

Crystallographic Studies on α- and β-D-glucopyranosyl Formamide Analogues,Inhibitors of Glycogen Phosphorylase

Abstract

The catalytic site of glycogen phosphorylase (GP) is currently under investigation as a target for inhibition of hepatic glycogenolysis under high glucose conditions. Three D-glucopyranosyl analogues, C-(1-azido-α-D-glucopyranosyl) formamide, C-(1-acetamido-α-D-glucopyranosyl) formamide, and C-(1-hydroxy-β-D-glucopyranosyl) formamide, were recognised as moderate competitive inhibitors of muscle glycogen phosphorylase b (GPb) [with respect to α-D-glucose 1-phosphate (Glc-1-P)] with K_i values of 1.80 (±0.2) mM, 0.31 (±0.01) mM, and 0.88 (±0.04) mM, respectively. In order to elucidate the structural basis of inhibition, we determined the structure of muscle GPb complexed with the three compounds at 2.1, 2.06 and 2.0 Å resolution, respectively. The complex structures revealed that the inhibitors can be accommodated in the catalytic site of T-state GPb with very little change of the tertiary structure, and provide a rationalisation for understanding potency of the inhibitors. The glucopyranose moiety makes the standard hydrogen bonds and van der Waals contacts as observed in the GPb-glucose complex, while the substituent groups in the α- and β-position of the C1 atom make additional hydrogen bonding and van der Walls interactions to the protein.