Sequence Motifs in MADS Transcription Factors Responsible for Specificity and Diversification of Protein-Protein Interaction

Protein sequences encompass tertiary structures and contain information about specific molecular interactions, which in turn determine biological functions of proteins. Knowledge about how protein sequences define interaction specificity is largely missing, in particular for paralogous protein families with high sequence similarity, such as the plant MADS domain transcription factor family. In comparison to the situation in mammalian species, this important family of transcription regulators has expanded enormously in plant species and contains over 100 members in the model plant species Arabidopsis thaliana. Here, we provide insight into the mechanisms that determine protein-protein interaction specificity for the Arabidopsis MADS domain transcription factor family, using an integrated computational and experimental approach. Plant MADS proteins have highly similar amino acid sequences, but their dimerization patterns vary substantially. Our computational analysis uncovered small sequence regions that explain observed differences in dimerization patterns with reasonable accuracy. Furthermore, we show the usefulness of the method for prediction of MADS domain transcription factor interaction networks in other plant species. Introduction of mutations in the predicted interaction motifs demonstrated that single amino acid mutations can have a large effect and lead to loss or gain of specific interactions. In addition, various performed bioinformatics analyses shed light on the way evolution has shaped MADS domain transcription factor interaction specificity. Identified protein-protein interaction motifs appeared to be strongly conserved among orthologs, indicating their evolutionary importance. We also provide evidence that mutations in these motifs can be a source for sub- or neo-functionalization. The analyses presented here take us a step forward in understanding protein-protein interactions and the interplay between protein sequences and network evolution

Genes controlling skeletal muscle glucose uptake and their regulation by endurance and resistance exercise.

Exercise improves the insulin sensitivity of glucose uptake in skeletal muscle. Due to that, exercise has become a cornerstone treatment for type 2 diabetes mellitus (T2DM). The mechanisms by which exercise improves skeletal muscle insulin sensitivity are, however, incompletely understood. We conducted a systematic review to identify all genes whose gain or loss of function alters skeletal muscle glucose uptake. We subsequently cross-referenced these genes with recently generated data sets on exercise-induced gene expression and signaling. Our search revealed 176 muscle glucose-uptake genes, meaning that their genetic manipulation altered glucose uptake in skeletal muscle. Notably, exercise regulates the expression or phosphorylation of more than 50% of the glucose-uptake genes or their protein products. This included many genes that previously have not been associated with exercise-induced insulin sensitivity. Interestingly, endurance and resistance exercise triggered some common but mostly unique changes in expression and phosphorylation of glucose-uptake genes or their protein products. Collectively, our work provides a resource of potentially new molecular effectors that play a role in the incompletely understood regulation of muscle insulin sensitivity by exercise