Aleksandra Nita-Lazar, Ph.D.
Cellular Networks Proteomic Unit
Building 4, Room B209
4 Memorial Drive
Bethesda, MD 20892
Research in the Cellular Networks Proteomics Unit focuses on understanding the changes that occur in the cell proteome in response to exogenous factors such as pathogen-derived molecules, cytokines, and chemokines, which alter the differentiation state of cells in the immune system or whose production characterizes specific disease states. We are especially interested in large-scale absolute quantitative measurements of immune cell signaling cascade components and in the characterization of post-translational modification (PTM) dynamics on a global scale. We use the resulting large datasets to create predictive models of molecular interactions using the Simmune software generated by the Computational Biology Unit. The predictions of these models will in turn be employed to elucidate biological responses to stimuli at multiple scales of biological organization, including the cell, tissue, and, eventually, whole organism.
We employ mass-spectrometry-based technology together with other proteomic and biochemical methods using state-of-the art equipment and technologies available in our laboratory and at NIH.
The following are examples of our projects:
- Protein modifications involved in cell signaling: Because dynamic PTMs such as phosphorylation, ubiquitination, or glycosylation are essential for the regulation of cell signaling, it is crucial to quantitatively map the PTMs of proteins involved in signaling cascades. Examples of our interests include Toll-like receptor (TLR) signaling in macrophages (Sjoelund et al., J Proteome Res., 2014) or chemotaxis of the immune cells.
- Absolute quantification of molecular representation and interaction: Mathematical modeling of biological events is most reliable when the absolute quantities of molecules are known and used to set parameters in the simulations. Therefore, we are interested in absolute quantification of protein expression and protein-protein interactions. We have established the methodology for the lipid-induced signaling pathways involving the S1P1 and S1P2 receptors in monocyte/macrophage cell lineage-derived osteoclast precursors that control cell mobilization at bone surfaces (Manes et al., Mol Cell Proteomics 2015). We are currently working on the absolute quantification of molecules in the macrophages exposed to different TLR ligands.
Special Interest Groups
- Systems Biology
- Mass Spectrometry
Dr. Nita-Lazar received her Ph.D. in biochemistry in 2003 from the University of Basel for studies performed at the Friedrich Miescher Institute for Biomedical Research, where she analyzed protein glycosylation using mass spectrometry methods. After postdoctoral training at Stony Brook University and Massachusetts Institute of Technology, where she continued to investigate post-translational protein modifications and their influence on cell signaling, she joined the Program in Systems Immunology and Infectious Disease Research, now the Laboratory of Systems Biology, in April 2009.
Manes NP, Shulzhenko N, Nuccio AG, Azeem S, Morgun A, Nita-Lazar A. Multi-omics Comparative Analysis Reveals Multiple Layers of Host Signaling Pathway Regulation by the Gut Microbiota. mSystems. 2017;2(5).
Koppenol-Raab M, Sjoelund V, Manes NP, Gottschalk RA, Dutta B, Benet ZL, Fraser ID, Nita-Lazar A. Proteome and Secretome Analysis Reveals Differential Post-transcriptional Regulation of Toll-like Receptor Responses. Mol Cell Proteomics. 2017;16(4 suppl 1):S172-S186.
Sjoelund V, Smelkinson M, Nita-Lazar A. Phosphoproteome profiling of the macrophage response to different toll-like receptor ligands identifies differences in global phosphorylation dynamics. J Proteome Res. 2014;13(11):5185-97.
An E, Narayanan M, Manes NP, Nita-Lazar A. Characterization of functional reprogramming during osteoclast development using quantitative proteomics and mRNA profiling. Mol Cell Proteomics. 2014;13(10):2687-704.
Germain RN, Meier-Schellersheim M, Nita-Lazar A, Fraser ID. Systems biology in immunology: a computational modeling perspective. Annu Rev Immunol. 2011;29:527-85.
Related Scientific Focus Areas
Microbiology and Infectious Diseases
Molecular Biology and Biochemistry
This page was last updated on December 6th, 2018