Sashka Krumova1, Svetla Todinova1, Deyan Mavrov2, Pencho Marinov3, Vassia Atanassova1, Krassimir Atanassov1, Stefka G Taneva4. 1. Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. "G. Bonchev" Str. Bl. 21 and Bl. 105, 1113 Sofia, Bulgaria. 2. Computer Systems and Technologies Department, "Prof. Dr. Asen Zlatarov" University, 1 "Prof. Yakimov" Blvd., 8010 Burgas, Bulgaria. 3. Institute of Information and Communication Technologies, Acad. G. Bonchev Str., Bl. 25A, 1113 Sofia, Bulgaria. 4. Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. "G. Bonchev" Str. Bl. 21 and Bl. 105, 1113 Sofia, Bulgaria. Electronic address: sgtaneva@gmail.com.
Abstract
BACKGROUND: Biological microcalorimetry has entered into a phase where its potential for disease diagnostics is readily recognized. A wide variety of oncological and immunological disorders have been characterized by differential scanning calorimetry (DSC) and characteristic thermodynamic profiles were reported. Now the challenge before DSC is not the experimental data collection but the development of analysis protocols for reliable data stratification/classification and discrimination of disease specific features (calorimetric markers). METHODS: In this work we apply InterCriteria Analysis (ICA) approach combined with Pearson's and Spearman's correlation analysis to a large dataset of calorimetric and biochemical parameters derived for the serum proteome of patients diagnosed with multiple myeloma (MM). RESULTS: We have identified intercriteria dependences that are general for the various types of MM and thus can be regarded as a characteristic of this largely heterogeneous disease: strong contribution of the monoclonal (M) protein concentration to the excess heat capacity of the immunoglobulins-assigned thermal transition; shift of the albumin assigned calorimetric transition to allocation where it overlaps with the globulins assigned transition and strong shift of the globulins assigned transition temperature attributable to M proteins conformational changes. CONCLUSIONS: Our data justify the applicability of ICA for deciphering of the complex thermodynamic behavior of the MM blood serum proteome. GENERAL SIGNIFICANCE: The applied approach is suitable for more general application in the analysis of biocalorimetric data since it can help identify the biological relevance of the distinguished thermodynamic features observed for variety of diseases.
BACKGROUND: Biological microcalorimetry has entered into a phase where its potential for disease diagnostics is readily recognized. A wide variety of oncological and immunological disorders have been characterized by differential scanning calorimetry (DSC) and characteristic thermodynamic profiles were reported. Now the challenge before DSC is not the experimental data collection but the development of analysis protocols for reliable data stratification/classification and discrimination of disease specific features (calorimetric markers). METHODS: In this work we apply InterCriteria Analysis (ICA) approach combined with Pearson's and Spearman's correlation analysis to a large dataset of calorimetric and biochemical parameters derived for the serum proteome of patients diagnosed with multiple myeloma (MM). RESULTS: We have identified intercriteria dependences that are general for the various types of MM and thus can be regarded as a characteristic of this largely heterogeneous disease: strong contribution of the monoclonal (M) protein concentration to the excess heat capacity of the immunoglobulins-assigned thermal transition; shift of the albumin assigned calorimetric transition to allocation where it overlaps with the globulins assigned transition and strong shift of the globulins assigned transition temperature attributable to M proteins conformational changes. CONCLUSIONS: Our data justify the applicability of ICA for deciphering of the complex thermodynamic behavior of the MM blood serum proteome. GENERAL SIGNIFICANCE: The applied approach is suitable for more general application in the analysis of biocalorimetric data since it can help identify the biological relevance of the distinguished thermodynamic features observed for variety of diseases.