Kristen Naegle

Associate Professor, Biomedical Engineering

Primary Location

409 Lane Road
MR4 1116
Charlottesville, MO 22903

Bio & Overview

Tyrosine phosphorylation is a protein modification that can occur during or after translation of a protein.The phosphate addition to a tyrosine residue, regulated by tyrosine kinases and phosphatases, can result in changes in protein function, regulation and localization. It is key to important cell signaling processes, which are the processes that convert extracellular cues, like growth factors and insulin, into biochemical networks that result in a change to the cell. Tyrosine phosphorylation is specifically utilized in the early events of receptor tyrosine kinase (RTK) networks, which are fundamental to many processes in the development and homeostasis of complex organisms. Improvements in measurement technologies have enabled the ability to detect and monitor tyrosine phosphorylation and now we know that tyrosine phosphorylation is extensive — occurring on thousands of tyrosines in the human proteome. Given the sheer size of the challenge, we use both computational and molecular technologies to predict and test the role of tyrosine phosphorylation on proteins and in cellular networks. Although we incorporate new mathematical and computational methods as needed to tackle the fundamental problems of our research, those techniques always have a foundation in statistical robustness. Hypotheses are tested in molecular and cellular systems, closing the loop between computation and experimentation. The questions that drive us include:

How do we increase the capabilities of research to gain new understanding of tyrosine phosphorylation rapidly, i.e. in a high-throughput manner that matches the rate of discovery of these modifications?
How do we develop new capabilities to understand how these networks act in specific contexts? Cell context refers to the differences we see between tissue types and the states of the network components that lead to differential responses of tissues to the same cue. As a philosophy, we approach network dysregulation that occurs in disease as an alteration in cell context.

Academic Information

Department: Biomedical Engineering

Academic Title: Associate Professor

Primary Education

BS, Electrical Engineering, University of Washington, 2001
MS, Electrical Engineering, University of Washington, 2004
MS, Biological Engineering, Massachusetts Institute of Technology, 2006
PhD, Biological Engineering, Massachusetts Institute of Technology, 2010

Other Appointments

Associate Professor, Computer Science

Other Affiliations

ENG
Center for Public Health Genomics
MSTP
Associate Professor, BIMS
Genomic Sciences

Research Interests

Regulation and function of tyrosine phosphorylation in complex networks

Research Disciplines

Biochemistry
Bioinformatics and Genomics
Biotechnology
Cancer Biology
Cell and Developmental Biology
Computational Biology
Experimental Pathology
Molecular Biology
Structural Biology

Research Opportunities

Biochemistry
Bioinformatics & Genomics
Biotechnology
Cancer Biology
Cell Biology
Computational Biology
Experimental Pathology
Molecular Biology

PhD Programs

Experimental Pathology

Selected Publications

2024

Kandoor, A., Martinez, G., Hitchcock, J. M., Angel, S., Campbell, L., Rizvi, S., & Naegle, K. M. (2024). CoDIAC: A comprehensive approach for interaction analysis reveals novel insights into SH2 domain function and regulation.. bioRxiv. doi:10.1101/2024.07.18.604100

Crowl, S., Coleman, M. B., Chaphiv, A., & Naegle, K. M. (2024). A systematic analysis of the effects of splicing on the diversity of post-translational modifications in protein isoforms.. bioRxiv. doi:10.1101/2024.01.10.575062

2023

Nelson, A. R., Christiansen, S. L., Naegle, K. M., & Saucerman, J. J. (2023). Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts.. bioRxiv. doi:10.1101/2023.03.01.530599

2022

Bose, S., Barroso, M., Chheda, M. G., Clevers, H., Elez, E., Kaochar, S., . . . Shen, X. (2022). A path to translation: How 3D patient tumor avatars enable next generation precision oncology. CANCER CELL, 40(12), 1448-1453. doi:10.1016/j.ccell.2022.09.017

Crowl, S., Jordan, B. T., Ahmed, H., Ma, C. X., & Naegle, K. M. (2022). KSTAR: An algorithm to predict patientspecific kinase activities from phosphoproteomic data. NATURE COMMUNICATIONS, 13(1). doi:10.1038/s41467-022-32017-5

2021

Naegle, K. M. (2021). Ten simple rules for effective presentation slides. PLOS COMPUTATIONAL BIOLOGY, 17(12). doi:10.1371/journal.pcbi.1009554

Galdieri, L., Jash, A., Malkova, O., Mao, D. D., DeSouza, P. A., Chu, Y. E., . . . Chheda, M. G. (2021). Defining phenotypic and functional heterogeneity of glioblastoma stem cells by mass cytometry. JCI INSIGHT, 6(4). doi:10.1172/jci.insight.128456

Xue, B., Jordan, B., Rizvi, S., & Naegle, K. M. (2021). KinPred: A unified and sustainable approach for harnessing proteome-level human kinase-substrate predictions. PLOS COMPUTATIONAL BIOLOGY, 17(2). doi:10.1371/journal.pcbi.1008681

2020

Ronan, T., Garnett, R., & Naegle, K. M. (2020). New analysis pipeline for high-throughput domain-peptide affinity experiments improves SH2 interaction data. JOURNAL OF BIOLOGICAL CHEMISTRY, 295(32), 11346-11363. doi:10.1074/jbc.RA120.012503

Mooradian, A. D., van der Post, S., Naegle, K. M., & Held, J. M. (2020). ProteoClade: A taxonomic toolkit for multi-species and metaproteomic analysis. PLOS COMPUTATIONAL BIOLOGY, 16(3). doi:10.1371/journal.pcbi.1007741

Ronan, T., Garnett, R., & Naegle, K. (2020). New analysis pipeline for high-throughput domain-peptide affinity experiments improves SH2 interaction data. doi:10.1101/2020.01.02.892901

2019

Sloutsky, R., & Naegle, K. M. (2019). ASPEN, a methodology for reconstructing protein evolution with improved accuracy using ensemble models. ELIFE, 8. doi:10.7554/eLife.47676

Zhu, W., Mazzanti, A., Voelker, T. L., Hou, P., Moreno, J. D., Angsutararux, P., . . . Silva, J. R. (2019). Predicting Patient Response to the Antiarrhythmic Mexiletine Based on Genetic Variation Personalized Medicine for Long QT Syndrome. CIRCULATION RESEARCH, 124(4), 539-552. doi:10.1161/CIRCRESAHA.118.314050

2018

Ronan, T., Anastasio, S., Qi, Z., Sloutsky, R., Naegle, K. M., & Tavares, P. H. S. V. (2018). OpenEnsembles: A Python Resource for Ensemble Clustering. JOURNAL OF MACHINE LEARNING RESEARCH, 19. Retrieved from https://www.webofscience.com/

2017

Sloutsky, R., & Naegle, K. M. (2018). Proteome-Level Analysis Indicates Global Mechanisms for Post-Translational Regulation of RRM Domains. JOURNAL OF MOLECULAR BIOLOGY, 430(1), 41-44. doi:10.1016/j.jmb.2017.11.001

Mooradian, A. D., Held, J. M., & Naegle, K. M. (2017). Using ProteomeScout: A Resource of Post-Translational Modifications, Their Experiments, and the Proteins That They Annotate.. Current protocols in bioinformatics, 59, 13.32.1-13.32.27. doi:10.1002/cpbi.31

Sloutsky, R., & Naegle, K. (2017). ASPEN: A methodology for reconstructing protein evolution with improved accuracy using ensemble models. doi:10.1101/170787

Schaberg, K. E., Shirure, V. S., Worley, E. A., George, S. C., & Naegle, K. M. (2017). Ensemble clustering of phosphoproteomic data identifies differences in protein interactions and cell-cell junction integrity of HER2-overexpressing cells. INTEGRATIVE BIOLOGY, 9(6), 539-547. doi:10.1039/c7ib00054e

2016

Ronan, T., Qi, Z., & Naegle, K. M. (2016). Avoiding common pitfalls when clustering biological data. SCIENCE SIGNALING, 9(432). doi:10.1126/scisignal.aad1932

Noren, D. P., Long, B. L., Norel, R., Rrhissorrakrai, K., Hess, K., Hu, C. W., . . . Qutub, A. A. (2016). A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis. PLOS COMPUTATIONAL BIOLOGY, 12(6). doi:10.1371/journal.pcbi.1004890

Ronan, T., Macdonald-Obermann, J. L., Huelsmann, L., Bessman, N. J., Naegle, K. M., & Pike, L. J. (2016). Different Epidermal Growth Factor Receptor (EGFR) Agonists Produce Unique Signatures for the Recruitment of Downstream Signaling Proteins. JOURNAL OF BIOLOGICAL CHEMISTRY, 291(11), 5528-5540. doi:10.1074/jbc.M115.710087

Sloutsky, R., & Naegle, K. M. (2016). High-Resolution Identification of Specificity Determining Positions in the LacI Protein Family Using Ensembles of Sub-Sampled Alignments. PLOS ONE, 11(9). doi:10.1371/journal.pone.0162579

2015

Cho, Y., Sloutsky, R., Naegle, K. M., & Cavalli, V. (2015). Injury-Induced HDAC5 Nuclear Export Is Essential for Axon Regeneration (vol 155, pg 894, 2013). CELL, 161(3), 691. doi:10.1016/j.cell.2015.04.019

Naegle, K., Gough, N. R., & Yaffe, M. B. (2015). Criteria for biological reproducibility: What does "n" mean?. SCIENCE SIGNALING, 8(371). doi:10.1126/scisignal.aab1125

Holehouse, A. S., & Naegle, K. M. (2015). Reproducible Analysis of Post-Translational Modifications in Proteomes-Application to Human Mutations. PLOS ONE, 10(12). doi:10.1371/journal.pone.0144692

2014

Matlock, M. K., Holehouse, A. S., & Naegle, K. M. (2015). ProteomeScout: a repository and analysis resource for post-translational modifications and proteins. NUCLEIC ACIDS RESEARCH, 43(D1), D521-D530. doi:10.1093/nar/gku1154

2013

Cho, Y., Sloutsky, R., Naegle, K. M., & Cavalli, V. (2013). Injury-Induced HDAC5 Nuclear Export Is Essential for Axon Regeneration. CELL, 155(4), 894-908. doi:10.1016/j.cell.2013.10.004

Iwai, L. K., Payne, L. S., Luczynski, M. T., Chang, F., Xu, H., Clinton, R. W., . . . Huang, P. H. (2013). Phosphoproteomics of collagen receptor networks reveals SHP-2 phosphorylation downstream of wild-type DDR2 and its lung cancer mutants. BIOCHEMICAL JOURNAL, 454, 501-513. doi:10.1042/BJ20121750

2012

Sloutsky, R., Jimenez, N., Swamidass, S. J., & Naegle, K. M. (2013). Accounting for noise when clustering biological data. BRIEFINGS IN BIOINFORMATICS, 14(4), 423-436. doi:10.1093/bib/bbs057

Naegle, K. M., White, F. M., Lauffenburger, D. A., & Yaffe, M. B. (2012). Robust co-regulation of tyrosine phosphorylation sites on proteins reveals novel protein interactions. MOLECULAR BIOSYSTEMS, 8(10), 2771-2782. doi:10.1039/c2mb25200g

2011

Naegle, K. M., Welsch, R. E., Yaffe, M. B., White, F. M., & Lauffenburger, D. A. (2011). MCAM: Multiple Clustering Analysis Methodology for Deriving Hypotheses and Insights from High-Throughput Proteomic Datasets. PLOS COMPUTATIONAL BIOLOGY, 7(7). doi:10.1371/journal.pcbi.1002119

2010

Naegle, K. M., Gymrek, M., Joughin, B. A., Wagner, J. P., Welsch, R. E., Yaffe, M. B., . . . White, F. M. (2010). PTMScout, a Web Resource for Analysis of High Throughput Post-translational Proteomics Studies. MOLECULAR & CELLULAR PROTEOMICS, 9(11), 2558-2570. doi:10.1074/mcp.M110.001206