Desbuquois dysplasia type 1 (DBQD1) is a chondrodysplasia caused by mutations in CANT1 gene encoding an ER/Golgi calcium activated nucleotidase 1 that hydrolyses UDP. Here, using Cant1 knock-in and knock-out mice recapitulating DBQD1 phenotype, we report that CANT1 plays a crucial role in cartilage proteoglycan synthesis and in endochondral ossification. Specifically, the glycosaminoglycan synthesis was decreased in chondrocytes from Cant1 knock-out mice and their hydrodynamic size was reduced, whilst the sulfation was increased and the overall proteoglycan secretion was delayed. Interestingly, knock-out chondrocytes had dilated ER cisternae suggesting delayed protein secretion and cellular stress; however, no canonical ER stress response was detected using microarray analysis, Xbp1 splicing and protein levels of BiP and ATF4. The observed proteoglycan defects caused deregulated chondrocyte proliferation and maturation in the growth plate resulting in the reduced skeletal growth. In conclusion, the pathogenic mechanism of DBQD1 comprises deregulated chondrocyte performance due to defective intracellular proteoglycan synthesis and altered proteoglycan properties in the extracellular matrix.

Mutations in the p53 tumor suppressor are found in over 50% of cancers. p53 function is controlled through posttranslational modifications and cofactor interactions. In this study, we investigated the posttranslationally modified p53, including p53 acetylated at lysine 382 (K382), p53 phosphorylated at serine 46 (S46), and the p53 cofactor TTC5/STRAP (Tetratricopeptide repeat domain 5/ Stress-responsive activator of p300-TTC5) proteins in lung cancer. Immunohistochemical (IHC) analysis of lung cancer tissues from 250 patients was carried out and the results were correlated with clinicopathological features. Significant associations between total or modified p53 with a higher grade of the tumour and shorter overall survival (OS) probability were detected, suggesting that mutant and/or modified p53 acts as an oncoprotein in these patients. Acetylated at K382 p53 was predominantly nuclear in some samples and cytoplasmic in others. The localization of the K382 acetylated p53 was significantly associated with the gender and grade of the disease. The TTC5 protein levels were significantly associated with the grade, tumor size, and node involvement in a complex manner. SIRT1 expression was evaluated in 50 lung cancer patients and significant positive correlation was found with p53 S46 intensity, whereas negative TTC5 staining was associated with SIRT1 expression. Furthermore, p53 protein levels showed positive association with poor OS, whereas TTC5 protein levels showed positive association with better OS outcome. Overall, our results indicate that an analysis of p53 modified versions together with TTC5 expression, upon testing on a larger sample size of patients, could serve as useful prognostic factors or drug targets for lung cancer treatment.

The tumor suppressor gene TP53 is implicated in the majority of all human cancers, thus pivotal to genomic integrity. There are more than 72,000 scientific publications describing p53’s function, yet, due to the complexity of its interactions we are still far from fully elucidating p53’s role in tumorigenesis. Computational methodologies are novel tools to depict and dissect complex disease networks. The Boolean PKT206 p53 – DNA damage model has previously demonstrated good predictive capability for p53 wild-type and null tumors in various in silico knockouts. Here, we have expanded PKT206 to generate a more clinically robust representation of p53 dynamics. The new PMH260 model incorporates 260 nodes representing genes, with 980 interactions between them representing inhibitions and activations. Additional biological outputs, including angiogenesis, cell cycle arrest and DNA repair were also amalgamated into the model. Three in silico knockouts of highly connected nodes (p53, MDM2 and FGF2) were generated and logical steady state analysis and dependency relationships applied. 71 % of predictions were considered true from superimposition of human osteosarcoma and HCT116 microarray profiles. In silico knockout analysis revealed 98 potential novel predictions, of which 13 were validated by literature; 83 % of them were overlapping with PKT206. Thus the expanded Boolean PMH260 model offers a promising platform for clinical potential in targeted cancer therapeutics.

Glucocorticoid hormones are used in the treatment of variety of diseases, due to their diverse range of effects. Acute lymphoblastic leukaemia (ALL), the most common form of childhood cancer, is one such disease treated by glucocorticoids (GCs). Although there has been much success in the treatment of ALL with GCs, drug resistance remains a problem, despite the detailed knowledge of the signalling networks underlying the GC actions through the glucocorticoid receptor (GR). Systems biology offers the opportunity to gain a holistic view of protein interaction networks, whilst also allowing for the generation of predictions through in silico knockouts which can be validated through conventional laboratory approaches. Systems biology has shown great success in identifying the functions of seemingly-redundant proteins. Here we present the beginnings of a model of the GR interactome, constructed through MATLAB using the add-on CellNetAnalyzer, and visualised through Cytoscape. The model currently consists of proteins that interact with the GR and interactions between these proteins. Protein interactions were extracted automatically from the STRING database using a Java program, and then manually curated by literature mining to remove false positives. The curated interactions were imported into CellNetAnalyzer. Future work will be directed towards expanding and connecting this model to drugs or processes like apoptosis, to detecting potential therapeutic targets, whilst also validating it through analysis of microarray data from ALL patients, paving the way for personalized treatment.