Cells have sophisticated methods of sensing stimuli and communicating with nearby cells in response to these stimuli. For example, macrophages can sense environmental stimuli—tissue damage, foreign microbes, allergens, etc.—and secrete proteins called cytokines and chemokines to activate neighboring cells’ functions to promote tissue repair or microbe/allergen clearance. Because the signaling pathways that drive protein secretion often trigger an array of cytokine or chemokine secretions, it can be challenging to dissect these overlapping mechanisms. However, finding the connections in the chaos of intracellular signaling is the specialty of the Gujral lab at Fred Hutchinson Cancer Center. Dr. Taran Gujral, an Associate Professor in the Human Biology Division, sought to use his lab’s expertise in de-convoluting kinase-dependent signaling pathways to interrogate the regulation of cytokine and chemokine secretion. To do this his interdisciplinary team developed a new platform, kinase to cytokine explorer or KinCytE, and incorporated phenotypic data from modeled lipopolysaccharide (LPS)-dependent activation of cytokine and chemokine secretion by macrophages. The KinCytE platform design and characterization was published recently in eLife and provides specific tabs to ‘identify drugs’, ‘identify kinases’, and ‘explore cytokines’ for those interested in studying the regulatory signaling of chemokines and cytokines.
Since a single stimulus can activate the secretion of many proteins, dissecting the mechanisms that drive cytokine secretion involves complex network deconvolution. Kinases are known for their essential roles in regulating signal transduction pathways within cells and make their mark on most if not all cellular processes including protein secretion. “In this manuscript, we emphasize the important role of protein kinases in mediating cellular communication through cytokines and chemokines,” commented Dr. Gujral. He continued by highlighting the utility of studying these signaling networks linking kinases and cytokines as a crucial aspect of understanding processes like inflammation, immune responses, and cell growth. Furthermore, “dysregulation in these kinase-cytokine signaling pathways can lead to diseases such as autoimmune disorders, allergies, chronic inflammation, and cancer.”
“We introduce KinCytE, an innovative platform designed to identify molecular regulators and potential drugs targeting cytokine functions,” stated Dr. Gujral. “KinCytE uses a kinase-focused protein interaction network from the Kyoto Encyclopedia of Genes and Genomes Pathway database to pinpoint protein kinases regulating specific cytokines or chemokines. It also provides a list of FDA-approved kinase inhibitors that could modulate cytokine/chemokine activity, facilitating the study of cytokine signaling networks.” To generate this innovative platform, the Gujral lab used LPS-mediated stimulation of human macrophages—which triggers protein secretion—to model cytokine release from cells. 24-hour treatment of human macrophage with LPS was followed by quantification of 191 secreted factors. Of these, 37 secreted factors were reliably detected and 34 were significantly changed between control and LPS-treated conditions including several cytokines and chemokines known to be upregulated by LPS treatment (IL-1, IL-6, CCL7, etc.). With this system of cytokine/chemokine secretion validated, the researchers applied their kinase inhibitor-based screen called KiR to the system in order to identify kinase-cytokine signaling connections. Their screen predicted three kinases (PTK5, TIE2, and GSK3A) as positive secretion regulators of > 15 cytokines/chemokines and several others with > 10. The researchers knocked down a few kinases and were able to confirm their connection to enhanced cytokine and chemokine secretion. Additionally, several kinase inhibitors were predicted from the screen to reduce protein secretion. A few of these were tested and demonstrated a high prediction accuracy of 80% between predicted and experimentally validated levels of secreted factors. Lastly, the researchers integrated pathway analysis tools via their previously developed computational approach called KiRNet to complement the KiR screen data and inform on pathways connecting kinases and cytokine/chemokine secretion. Together, the KinCytE platform demonstrates the successful application of previously generated methods for studying kinase-dependent signaling (KiR and KiRNet) to the study of cytokine/chemokine secretion. The user-friendly template provided at https://atlas.fredhutch.org/kincyte/ enables easy search-based investigation of kinases, kinase inhibitors, and kinase-cytokine signaling connections related to LPS-mediated activation of cytokine secretion by human macrophage.
“This work also incorporated Nomic Bio's innovative nElisa technology to measure 191 secreted factors in the conditioned media, enhancing the precision and depth of the study's experimental approach,” commented Dr. Gujral. “KinCytE aims to deepen our understanding of the complexities within kinase-cytokine/chemokine signaling networks, offering significant insights for translational research in immunology, cancer biology, virology, and related fields. Overall, this project showcases a successful collaboration between experimental biologists, computational biologists, programmers, and data visualization experts, highlighting the strength of a multidisciplinary team at Fred Hutch.” The researchers intend to add more datasets to the KinCytE platform to continue building upon this initial resource and inform our understanding of kinase-dependent cytokine secretion pathways and the potential of re-purposing kinase inhibitors to treat an array of cytokine-related human diseases.
The spotlighted research was funded by the National Science Foundation.
Fred Hutch/University of Washington/Seattle Children's Cancer Consortium member Taran Gujral contributed to this work.
Chan M, Kang Y, Osborne S, Zager M, Gujral TS. 2024. A kinase to cytokine explorer to identify molecular regulators and potential therapeutic opportunities. Elife. 12:RP91472.