An integrated proteomics analysis of bone tissues in response to mechanical stimulation

Bone cells can sense physical forces and convert mechanical stimulation conditions into biochemical signals that lead to expression of mechanically sensitive genes and proteins. However, it is still poorly understood how genes and proteins in bone cells are orchestrated to respond to mechanical stimulations. In this research, we applied integrated proteomics, statistical, and network biology techniques to study proteome-level changes to bone tissue cells in response to two different conditions, normal loading and fatigue loading. We harvested ulna midshafts and isolated proteins from the control, loaded, and fatigue loaded Rats. Using a label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) experimental proteomics technique, we derived a comprehensive list of 1,058 proteins that are differentially expressed among normal loading, fatigue loading, and controls. By carefully developing protein selection filters and statistical models, we were able to identify 42 proteins representing 21 Rat genes that were significantly associated with bone cells' response to quantitative changes between normal loading and fatigue loading conditions. We further applied network biology techniques by building a fatigue loading activated protein-protein interaction subnetwork involving 9 of the human-homolog counterpart of the 21 rat genes in a large connected network component. Our study shows that the combination of decreased anti-apoptotic factor, Raf1, and increased pro-apoptotic factor, PDCD8, results in significant increase in the number of apoptotic osteocytes following fatigue loading. We believe controlling osteoblast differentiation/proliferation and osteocyte apoptosis could be promising directions for developing future therapeutic solutions for related bone diseases

Ontology-Driven and Network–Enabled Systems Biology Case Studies

With the progress in high-throughput technologies and bioinformatics in recent years, it is possible to determine to what extent genetic or environmental manipulation of a biological system affects the expression of thousands of genes and proteins. This study requires a shift from the conventional pure hypothesis-driven approach to an integrated approach--systems biology method. Systems biology studies the relationships and interactions between various parts of a biological system. It allows individual genes or proteins to be placed in a global context of cellular functions. This analysis can answer the question of how networks of genes/proteins, differentially regulated respond to genetic or environmental modification, are placed in the global context of the protein interaction map. In this project, we establish a protein interaction network-based systems biology approach, and use the method for two case studies. In particular, our systems biology studies consist of the following parts: (1) Analysis of mass-spectrometry derived proteomics experimental data to identify differentially expressed proteins in different genetic or environmental conditions; (2) Integration of genomics and proteomics data with experimental results, the molecular context of protein-protein interaction networks and gene functional categories; (3) Visual interpretation of molecular networks. Our approach has been validated in two case studies by comparing our discoveries with existing findings. We also obtained new insights. In the first case study, the proteomes of cisplatin-sensitive and cisplatin-resistant ovarian cancer cells were compared and we observed that cellular physiological process is significantly activated in cisplatin-resistant cell lines, and this response arises from endogenous, abiotic, and stress-related signals. We found that cisplatin-resistant cell lines demonstrated unusually high level of protein-binding activities, and a broad spectrum of across-the-board drug-binding and nucleotide-binding mechanisms are all activated. In the second case study, we found that the significantly enriched GO categories included genes that are related to Grr1 perturbation induced morphological phenotype change are highly connected in the GO sub-network, which implies that Grr1 could be affecting this process by affecting a small core group of proteins. These biological discoveries support the significance of developing a common framework of evaluating functional genomics and proteomics data, using networks and systems approaches