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Rovshan Sadygov, PhD

Associate Professor, Department of Biochemistry & Molecular Biology,
Sealy Center for Molecular Medicine

Phone: (409) 772-3287
Rovshan Sadygov, PhD

Research Summary

Our research focuses on the theoretical development of bioinformatics and statistics approaches to address challenges posed in biological inferences from high-throughput proteomics data, and their application to biological problems. For this purpose, we develop algorithms for peak detection and quantification, identification of structures in multivariate data, stochastic time-course modeling to extract dynamical features, construction of protein networks and error control in the resulting inferences. In collaboration with my experimentalist colleagues, we apply these techniques in various systems for systematic studies of post-translational modifications and, proteome dynamics, signal transductions and mass informatics. Our goal is to promote identification of functional dysregulations associated with changes in the state of a biological system. We are involved in three inter-related projects, described below.

We apply stochastic models to infer changes in proteome dynamics as result of a disease. In collaboration with colleagues, we are studying changes in mitochondrial proteins due to the non-alcoholic fatty liver (NAFLD) disease and induced heart failure in rats. The experiments use heavy water labeling and liquid-chromatography mass spectrometry. The animal models are metabolically labeled with deuterium by providing heavy water in their diet. They are sacrificed at certain time points. The organs are harvested and mitochondria are isolated. We approximate the rate of protein turnover with the rate of deuterium incorporation. The time course of the relative isotope fractions are used in Gaussian Process (GP) modeling that we have developed to extract the protein turnover rates. When compared to the traditional exponential curve fitting the GP produces 2-fold increase in the number of proteins that can be measured.

We study changes in signal transduction pathways that accompany the Epithelial-Mesenchymal Transition (EMT) of human small airway cells. While numerous studies have been done on the mechanisms of the transition itself, few studies have investigated the system effects of EMT on signaling networks. We use mixed effects modeling to develop a computational model of phospho-protein signaling data that compares human small airway epithelial cells (hSAECs) with their EMT-transformed counterparts across a series of perturbations with 8 ligands and 5 inhibitors, revealing previously uncharacterized changes in signaling in the EMT state. Construction of network topology maps showed significant changes between the two cellular states, including a linkage between GSK-3a and SMAD2. The model also predicted a loss of p38 mitogen activated protein kinase-independent HSP-27 signaling, which we experimentally validated. We further characterized the relationship between HSP27 and signal STAT3 signaling, and determined that loss of HSP27 following EMT is only partially responsible for the downregulation of STAT3. These rewired connections represent therapeutic targets that could potentially reverse EMT and restore a normal phenotype to the respiratory mucosa. The project is a collaborative work Allan Brasier's lab, and we continue the developments to incorporate models for determining causative effects and time-course experiments.

We develop novel methods for the detection of post-translational modifications in high mass accuracy MS spectra. We use the discreteness of the amino acid masses to probe the whole mass axis in an unbiased approach to identify regions of the mass axis that are highly populated with unmodified peptides. While it has been known for a while that not all mass regions are populated by peptides, the actual mapping of the peptide distributions has been not feasible, due to the fact that the complexity of the peptide space increases as power law with the base 20. We have developed a recursive algorithm that bypasses the sequence generation and directly generate compositions. As a result, we have been able to map the peptide mass axis up to the 3.5 kDa - the upper mass limit often used in proteomics. We have located the peaks and valleys (forbidden/quiet zones) in the mass distributions and have shown that post-translational modifications, such phosphorylation and glycosylation, create distributions separate from the nonmodified peptides. We have used this property to predict the amount of the phosphoproteins in a sample without referring to peptide fragmentation and database search - only based on the masses of the precursor peptides. This advance has provided an alternative approach to evaluate the sample preparation. In another study, we have established that the data-dependent acquisition can be modeled as a sampling from a single well defined peak. To obtain the distribution, we have introduced a new concept and termed it a peak deviation. We have shown that unlike the traditionally used mass defect, peak deviations form a unimodal distribution whose characteristics are related to the properties of the peptides in the sample.

MASSXPLORER - To visit the website for software developed by us click here

Selected Publications

  1. Rahman M, Previs SF, Kasumov T, Sadygov RG, Gaussian Process Modeling of Protein Turnover,  J Proteome Res. 2016 Jul 1;15(7):2115-22PMCID: PMC5292319
  2. Zhao Y, Tian B, Sadygov RG, Zhang Y, Brasier AR, Integrative proteomic analysis reveals reprograming tumor necrosis factor signaling in epithelial mesenchymal transition, J Proteomics. 2016 Jul 25;148:126-138   PMCID:PMC5292320
  3. Li L, Bebek G, Previs SF, Smith JD, Sadygov RG, McCullough AJ, Willard B, Kasumov T., Proteome Dynamics Reveals Pro-Inflammatory Remodeling of Plasma Proteome in a Mouse Model of NAFLD,  J Proteome Res. 2016
  4. Sadygov RG, Using SEQUEST with Theoretically Complete Sequence Databases, J Am Soc Mass Spectrom. 2015 Nov;26(11):1858-64 PMCID: PMC4607654
  5. Sadygov RG, Use of Singular Value Decomposition Analysis to Differentiate Phosphorylated Precursors in Strong Cation Exchange Fractions.Electrophoresis. 2014 Jun 9. doi: 10.1002/elps.201400053. [Epub ahead of print].
  6. Shekar KC, Li L, Dabkowski ER, Xu W, Ribeiro RF Jr, Hecker PA, Recchia FA, Sadygov RG, Willard B, Kasumov T, Stanley WC. Cardiac mitochondrial proteome dynamics with heavy water reveals stable rate of mitochondrial protein synthesis in heart failure despite decline in mitochondrial oxidative capacity. J Mol Cell Cardiol. 2014 Jul 1;75C:88-97. doi: 10.1016/j.yjmcc.2014.06.014
  7. Nenov MN, Laezza F, Haidacher SJ, Zhao Y, Sadygov RG, Starkey JM, Spratt H, Luxon BA, Dineley KT, Denner L., Cognitive Enhancing Treatment with a PPARy Agonist Normalizes Dentate Granule Cell Presynaptic Function in Tg2576 APP Mice. J Neurosci. 2014 Jan 15;34(3):1028-36
  8. Guptarak, J.; Wu, Y.; Wiktorowicz, E.J.; Sadygov RG; Zivadinovic, D.; Palucci AA.; Nesic, O., Cancer drug Tamoxifen: A potential therapeutic treatment for spinal cord injury, J Neurotrauma. 2014 Feb 1;31(3):268-83
  9. Kalita M, Kasumov T, Brasier AR, Sadygov RG., Use of Theoretical Peptide Distributions in Phosphoproteome Analysis. J Proteome Res. 2013 Jun 3
  10. Mitra I., Nefedov A. V., Brasier A. R., Sadygov RG, Improved mass defect model for theoretical tryptic peptides ,Anal Chem., 2012;84(6):3026-32
  11. Li L., Willard B., Rachdaoui N., Kirwan JP., Sadygov RG, Stanley WC, Previs S., McCullough A. J., Kasumov T., Plasma proteome dynamics: analysis of lipoproteins and acute phase response proteins with 2H2O metabolic labeling. Mol Cell Proteomics. 2012;11(7)
  12. Denner LA, Rodriguez-Rivera J, Haidacher SJ, Jahrling JB, Carmical JR, Hernandez CM, Zhao Y, Sadygov RG, Starkey JM, Spratt H, Luxon BA, Wood TG, Dineley KT., Cognitive enhancement with rosiglitazone links the hippocampal PPARy and ERK MAPK signaling pathways. Journal of Neuroscience. 2012 Nov 21;32(47):16725-35a.
  13. Leitch M.C., Mitra I., Sadygov RG, Generalized Linear and Mixed Models for Label-Free Shotgun Proteomics, Stat. and Its Interface. 2012;5(1):89-98
  14. Nefedov A. V., Sadygov RG, A Parallel Method for Enumerating Amino Acid Compositions, BMC Bioinformatics, 2011:12:432
  15. Nefedov AV, Mitra I, Brasier AR, Sadygov RG, Examining Troughs in the Mass Distribution of All Theoretically Possible Tryptic Peptides, J. Proteome Res. 2011; 10, 4150.
  16. Nefedov A., Gilski M., Sadygov RG, An SVM Model for Quality Assessment of Medium Resolution Mass Spectra from 18O-water Labeling Experiments, J. of Proteome Research, 2011;10(4):2095-103.
  17. Kasumov T, Ilchenko S, Li L, Rachdaoui N, Sadygov RG, Willard B, McCullough AJ, Previs S., Measuring protein synthesis using metabolic 2H labeling, high-resolution mass spectrometry, and an algorithm, Analytical Biochemistry. 2011;412(1):47-55.
  18. J. M. Starkey, Y. Zhao, R.G. Sadygov, S.J. Haidacher, W. S. LeJeune, N. Dey, B. A. Luxon, M. A. Kane, J.L. Napoli, Larry Denner, R.G. Tilton. Altered Retinoic Acid Metabolism in Diabetic Mouse Kidney Identified by 18O Isotopic Labeling and 2D Mass Spectrometry, PLoS ONE; 2010:5:1-10,
  19. RG Sadygov, Y. Zhao, SJ Headache, J. M. Starkey, R. G. Tilton and L. Denner, Using Power Spectrum Analysis to Evaluate 18O-Water Labeling Data Acquired from Low Resolution Mass Spectrometers J. Proteome Res., 2010: 9:4306-4312.