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
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
- Rahman M, Previs SF, Kasumov T, Sadygov RG, Gaussian Process Modeling of Protein Turnover, J Proteome Res. 2016 Jul 1;15(7):2115-22. PMCID: PMC5292319
- 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
- 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
- Sadygov RG, Using SEQUEST with Theoretically Complete Sequence Databases, J Am Soc Mass Spectrom. 2015 Nov;26(11):1858-64 PMCID: PMC4607654
- 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].
- 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
- 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
- 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
- Kalita M, Kasumov T, Brasier AR, Sadygov RG., Use of Theoretical Peptide Distributions in Phosphoproteome Analysis. J Proteome Res. 2013 Jun 3
- 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
- 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)
- 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.
- 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
- Nefedov A. V., Sadygov RG, A Parallel Method for Enumerating Amino Acid Compositions, BMC Bioinformatics, 2011:12:432
- 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.
- 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.
- 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.
- 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,
- 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.