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Computational models using metabolic reconstructions for in silico simulation of metabolic disorders such as type 2 diabetes mellitus (T2DM) can provide a better understanding of disease pathophysiology and avoid high experimentation costs. There is a limited amount of computational work, using metabolic reconstructions, performed in this field for the better understanding of T2DM. In this study, a new algorithm for generating tissue-specific metabolic models is presented, along with the resulting multi-confidence level (MCL) multi-tissue model. The effect of T2DM on liver, muscle, and fat in MKR mice was first studied by microarray analysis and subsequently the changes in gene expression of frank T2DM MKR mice versus healthy mice were applied to the multi-tissue model to test the effect. Using the first multi-tissue genome-scale model of all metabolic pathways in T2DM, we found out that branched-chain amino acids' degradation and fatty acids oxidation pathway is downregulated in T2DM MKR mice. Microarray data showed low expression of genes in MKR mice versus healthy mice in the degradation of branched-chain amino acids and fatty-acid oxidation pathways. In addition, the flux balance analysis using the MCL multi-tissue model showed that the degradation pathways of branched-chain amino acid and fatty acid oxidation were significantly downregulated in MKR mice versus healthy mice. Validation of the model was performed using data derived from the literature regarding T2DM. Microarray data was used in conjunction with the model to predict fluxes of various other metabolic pathways in the T2DM mouse model and alterations in a number of pathways were detected. The Type 2 Diabetes MCL multi-tissue model may explain the high level of branched-chain amino acids and free fatty acids in plasma of Type 2 Diabetic subjects from a metabolic fluxes perspective. [ABSTRACT FROM AUTHOR]

Pasteurella multocida causes acute septicemic and respiratory diseases, including haemorrhagic septicaemia, in cattle and buffalo with case fatality of 100%. In the present study, mice were infected with P. multocida (1.6 × 103 cfu, intraperitoneal) to evaluate host gene expression profile at early and late stages of infection using high throughput microarray transcriptome analyses. Several differentially expressed genes (DEGs) at both the time points were identified in P.multocida infected spleen, liver and lungs. Functional annotation of these DEGs showed enrichment of key pathways such as TLR, NF-κB, MAPK, TNF, JAK-STAT and NOD like receptor signaling pathways. Several DEGs overlapped across different KEGG pathways indicating a crosstalk between them. The predicted protein—protein interaction among these DEGs suggested, that the recognition of P. multocida LPS or outer membrane components by TLR4 and CD14, results in intracellular signaling via MyD88, IRAKs and/or TRAF6 leading to activation of NFκB and MAPK pathways and associated cytokines. [ABSTRACT FROM AUTHOR]