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Your search keyword '"Azeloglu, Evren U."' showing total 23 results
Start Over Author "Azeloglu, Evren U." Publication Year Range Last 3 years Publication Type Academic Journals
23 results on '"Azeloglu, Evren U."'

5. Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection

Author

Paranjpe, Ishan, Jayaraman, Pushkala, Su, Chen-Yang, Zhou, Sirui, Chen, Steven, Thompson, Ryan, Del Valle, Diane Marie, Kenigsberg, Ephraim, Zhao, Shan, Jaladanki, Suraj, Chaudhary, Kumardeep, Ascolillo, Steven, Vaid, Akhil, Gonzalez-Kozlova, Edgar, Kauffman, Justin, Kumar, Arvind, Paranjpe, Manish, Hagan, Ross O., Kamat, Samir, Gulamali, Faris F., Xie, Hui, Harris, Joceyln, Patel, Manishkumar, Argueta, Kimberly, Batchelor, Craig, Nie, Kai, Dellepiane, Sergio, Scott, Leisha, Levin, Matthew A., He, John Cijiang, Suarez-Farinas, Mayte, Coca, Steven G., Chan, Lili, Azeloglu, Evren U., Schadt, Eric, Beckmann, Noam, Gnjatic, Sacha, Merad, Miram, Kim-Schulze, Seunghee, Richards, Brent, Glicksberg, Benjamin S., Charney, Alexander W., and Nadkarni, Girish N.

Published
2023
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6. A reference tissue atlas for the human kidney

Author

Hansen, Jens, Sealfon, Rachel, Menon, Rajasree, Eadon, Michael T, Lake, Blue B, Steck, Becky, Anjani, Kavya, Parikh, Samir, Sigdel, Tara K, Zhang, Guanshi, Velickovic, Dusan, Barwinska, Daria, Alexandrov, Theodore, Dobi, Dejan, Rashmi, Priyanka, Otto, Edgar A, Rivera, Miguel, Rose, Michael P, Anderton, Christopher R, Shapiro, John P, Pamreddy, Annapurna, Winfree, Seth, Xiong, Yuguang, He, Yongqun, de Boer, Ian H, Hodgin, Jeffrey B, Barisoni, Laura, Naik, Abhijit S, Sharma, Kumar, Sarwal, Minnie M, Zhang, Kun, Himmelfarb, Jonathan, Rovin, Brad, El-Achkar, Tarek M, Laszik, Zoltan, He, John Cijiang, Dagher, Pierre C, Valerius, M Todd, Jain, Sanjay, Satlin, Lisa M, Troyanskaya, Olga G, Kretzler, Matthias, Iyengar, Ravi, Azeloglu, Evren U, and Project, Kidney Precision Medicine

Subjects
Biotechnology, Genetics, Kidney Disease, Aetiology, 2.1 Biological and endogenous factors, Underpinning research, 1.1 Normal biological development and functioning, Generic health relevance, Renal and urogenital, Good Health and Well Being, Humans, Kidney, Kidney Diseases, Metabolomics, Proteomics, Transcriptome, Kidney Precision Medicine Project
Abstract

Kidney Precision Medicine Project (KPMP) is building a spatially specified human kidney tissue atlas in health and disease with single-cell resolution. Here, we describe the construction of an integrated reference map of cells, pathways, and genes using unaffected regions of nephrectomy tissues and undiseased human biopsies from 56 adult subjects. We use single-cell/nucleus transcriptomics, subsegmental laser microdissection transcriptomics and proteomics, near-single-cell proteomics, 3D and CODEX imaging, and spatial metabolomics to hierarchically identify genes, pathways, and cells. Integrated data from these different technologies coherently identify cell types/subtypes within different nephron segments and the interstitium. These profiles describe cell-level functional organization of the kidney following its physiological functions and link cell subtypes to genes, proteins, metabolites, and pathways. They further show that messenger RNA levels along the nephron are congruent with the subsegmental physiological activity. This reference atlas provides a framework for the classification of kidney disease when multiple molecular mechanisms underlie convergent clinical phenotypes.

Published
2022

8. Participant Experience with Protocol Research Kidney Biopsies in the Kidney Precision Medicine Project

Author

Victoria-Castro, Angela M., Corona-Villalobos, Celia P., Xu, Alan Y., Onul, Ingrid, Huynh, Courtney, Chen, Sarah W., Ugwuowo, Ugochukwu, Sarkisova, Natalya, Dighe, Ashveena L., Blank, Kristina N., Blanc, Victoria M., Rose, Michael P., Himmelfarb, Jonathan, de Boer, Ian H., Tuttle, Katherine R., Roberts, Glenda V., Alexandrov, Theodore, Alloway, Rita R., Alpers, Charles E., Amodu, Afolarin A., Anderton, Christopher R., Anjani, Kavya, Appelbaum, Paul, Ardayfio, Joseph, Arora, Tanima, Ascani, Heather, El-Achkar, Tarek M., Aulisio, Mark, Azeloglu, Evren U., Balderes, Olivia, Balis, Ulysses G.J., Bansal, Shweta, Barasch, Jonathan M., Bansal, Shweta, Barkell, Alex, Barwinska, Daria, Basit, Mujeeb, Basta, Jeanine, Bebiak, Jack, Beck, Laurence H., Bender, Filitsa, Berglund, Ashley, Bernard, Lauren, Berrouet, Cecilia, Berry, Brooke, Bjornstad, Petter M., Blanc, Victoria M., Blank, Kristina N., Bledsoe, Sharon, Boada, Patrick, Bogen, Steve, Bomback, Andrew S., Bonevich, Nikole, Borner, Katy, Brown, Keith, Bueckle, Andreas, Burg, Ashley R., Burgess, Adam, Bush, Lakeshia, Bush, William S., Campbell, Catherine E., Campbell, Taneisha, Canetta, Pietro A., Cantley, Lloyd G., Caprioli, Richard M., Carson, Jonas, Chen, Sarah, Chen, Yijiang M., Cheng, Yinghua, Cimino, Jim, Colona, Mia R., Conser, Ninive C., Cooperman, Leslie, Crawford, Dana C., DʼAgati, Vivette D., Dagher, Pierre C., Daniel, Stephen, Daratha, Kenn, de Boer, Ian H., Diettman, Sabine M., Dighe, Ashveena L., Donohoe, Isabel, Dowd, Frederick, Dunn, Kenneth W., Eadon, Michael T., Eddy, Sean, Elder, Michele M., Ferkowicz, Michael J., Frey, Renee, Gadegbeku, Crystal A., Gaut, Joseph P., Gilliam, Matthew, Ginley, Brandon, Gisch, Debora, Goltsev, Yury, Gonzalez-Vicente, Agustin, Greka, Anna, Grewenow, Stephanie M., Hacohen, Nir, Hall, Daniel E., Hansen, Jens, Hayashi, Lynda, He, Cijang, He, Yougqun, Hedayati, S. Susan, Henderson, Joel M., Hendricks, Allen H., Herlitz, Leal, Herr, Bruce W., Himmelfarb, Jonathan, Hodgin, Jeffrey B., Hoofnagle, Andrew N., Hoover, Paul J., Ilori, Titlayo, Iyengar, Ravi, Jain, Sanjay, Jain, Yashvardhan, Janowczyk, Andrew, Jefferson, Nichole, Johansen, Camille, Jolly, Stacey, Kakade, Vijaykumar R., Kellum, John A., Kelly, Katherine J., Kermani, Asra, Kiryluk, Krzysztof, Knight, Richard, Koewler, Robert, Kretzler, Matthias, Kudose, Satoru, Lake, Blue B., Larson, Brandon, Laszik, Zoltan G., Lecker, Stewart H., Lee, Paul J., Lee, Simon C., Lienczewski, Chrysta, Limonte, Christine, Lu, Christopher Y., Lucarelli, Nicholas, Lukowski, Jessica, Luo, Jinghui, Lutnick, Brendon, Ma, Shihong, Madabhushi, Anant, Madhavan, Sethu M., Maikhor, Shana, Mariani, Laura H., Marshall, Jamie L., McClelland, Robyn L., McMahon, Gearoid M., Mehl, Karla, Ferreira, Ricardo Melo, Menez, Steven, Menon, Rajasree, Miller, R. Tyler, Moe, Orson W., Moledina, Dennis, Montellano, Richard, Mooney, Sean D., Morales, Martha Catalina, Mukatash, Tariq, Murugan, Raghavan, Nam, Yunbi, Nguyen, Jane, Nolan, Garry, Oʼtoole, John, Oliver, George (Holt), Onul, Ingrid, Otto, Edgar, Palevsky, Paul M., Palmer, Ellen, Pamreddy, Annapurna, Parikh, Chirag R., Parikh, Samir, Park, Christopher, Park, Harold, Pasa-Tolic, Ljiljana, Patel, Jiten, Patterson, Nathan, Phuong, Jim, Pillai, Anil, Pinkeney, Roy, Poggio, Emilio, Pollack, Ari, Prasad, Pottumarthi, Pyle, Laura, Quardokus, Ellen M., Randhawa, Parmjeet, Rauchman, Michael I., Record, Elizabeth, Rennke, Helmut, Rezaei, Kasra, Rike, Adele, Rivera, Marcelino, Roberts, Glenda V., Rosas, Sylvia E., Rosenberg, Avi, Rosengart, Matthew, Rovin, Brad, Roy, Neil, Sabatello, Maya, Sambandam, Kamalanathan, Sarder, Pinaki, Sarkisova, Natalya, Sarwal, Minnie, Saul, John, Schaub, Jennifer, Schmidt, Insa, Sealfon, Rachel, Sedor, John, Sendrey, Dianna, Shang, Ning, Shankland, Stuart, Shapiro, John P., Sharma, Kumar, Sharman, Kavya, Shaw, Melissa M., Shi, Tiffany, Shpigel, Anna, Sigdel, Tara, Slade, Austen, Snyder, Jamie, Spates-Harden, Kassandra, Spraggins, Jeffrey M., Srivastava, Anand, Steck, Becky, Stillman, Isaac, Stutzke, Christy, Su, Jing, Sun, Jennifer, Sutton, Timothy A., Taliercio, Jonathan, Tan, Roderick, Torrealba, Jose, Toto, Robert D., Troyanskaya, Olga, Tublin, Mitchell, Tuttle, Katherine R., Ugwuowo, Ugochukwu, Valerius, M. Todd, Van de Plas, Raf, Varela, German, Vazquez, Miguel, Velickovic, Dusan, Venkatachalam, Manjeri, Verma, Ashish, Victoria-Castro, Angela M., Vijayan, Anitha, Corona-Villalobos, Celia P., Vinovskis, Carissa, Viswanathan, Vidya S., Vita, Tina, Waikar, Sushrut, Wang, Ashley, Wang, Ruikang, Wang, Nancy, Weins, Astrid, Wen, Natasha, Wen, Yumeng, Wilcox, Adam, Williams, James C., Jr., Kayleen Williams, Williams, Mark, Wilson, Francis P., Winfree, Seth, Winters, James, Wofford, Stephanie, Wong, Aaron, Woodle, E. Steve, Xiong, Yuguang, Xu, Alan, Yadati, Pranav, Ye, Hongping, Yu, Guanghao, Zhang, Dianbo, Zhang, Guanshi, and Zhang, Kun

Published
2024
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9. A large-scale retrospective study enabled deep-learning based pathological assessment of frozen procurement kidney biopsies to predict graft loss and guide organ utilization

Author

Yi, Zhengzi, Xi, Caixia, Menon, Madhav C., Cravedi, Paolo, Tedla, Fasika, Soto, Alan, Sun, Zeguo, Liu, Keyu, Zhang, Jason, Wei, Chengguo, Chen, Man, Wang, Wenlin, Veremis, Brandon, Garcia-barros, Monica, Kumar, Abhishek, Haakinson, Danielle, Brody, Rachel, Azeloglu, Evren U., Gallon, Lorenzo, O’Connell, Philip, Naesens, Maarten, Shapiro, Ron, Colvin, Robert B., Ward, Stephen, Salem, Fadi, and Zhang, Weijia

Published
2024
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10. Patient-Specific Pharmacokinetics and Dasatinib Nephrotoxicity

Author

Adegbite, Benjamin O., Abramson, Matthew H., Gutgarts, Victoria, Musteata, Florin M., Chauhan, Kinsuk, Muwonge, Alecia N., Meliambro, Kristin A., Salvatore, Steven P., El Ghaity-Beckley, Sebastian, Kremyanskaya, Marina, Marcellino, Bridget, Mascarenhas, John O., Campbell, Kirk N., Chan, Lili, Coca, Steven G., Berman, Ellin M., Jaimes, Edgar A., and Azeloglu, Evren U.

Published
2023
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11. Evaluation of Plasma Biomarkers to Predict Major Adverse Kidney Events in Hospitalized Patients With COVID-19

Author

Deng, Jie, Atta, Mo, Bagnasco, Serena M., Ko, Albert, Iwasaki, Akiko, Farhadian, Shelli, Nelson, Allison, Casanovas-Massana, Arnau, White, Elizabeth B., Schulz, Wade, Coppi, Andreas, Young, Patrick, Nunez, Angela, Shepard, Denise, Matos, Irene, Strong, Yvette, Anastasio, Kelly, Brower, Kristina, Kuang, Maxine, Chiorazzi, Michael, Bermejo, Santos, Vijayakumar, Pavithra, Geng, Bertie, Fournier, John, Minasyan, Maksym, Muenker, M. Catherine, Moore, Adam J., Nadkarni, Girish, Menez, Steven, Coca, Steven G., Moledina, Dennis G., Wen, Yumeng, Chan, Lili, Thiessen-Philbrook, Heather, Obeid, Wassim, Garibaldi, Brian T., Azeloglu, Evren U., Ugwuowo, Ugochukwu, Sperati, C. John, Arend, Lois J., Rosenberg, Avi Z., Kaushal, Madhurima, Jain, Sanjay, Wilson, F. Perry, and Parikh, Chirag R.

Published
2023
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13. Cadherin-11, Sparc-related modular calcium binding protein-2, and Pigment epithelium-derived factor are promising non-invasive biomarkers of kidney fibrosis

Author

Knight, Richard, Lecker, Stewart H., Stillman, Isaac, Bogen, Steve, Amodu, Afolarin A., Ilori, Titlayo, Maikhor, Shana, Schmidt, Insa M., Beck, Laurence H., Henderson, Joel M., Onul, Ingrid, Verma, Ashish, McMahon, Gearoid M., Valerius, M. Todd, Waikar, Sushrut, Weins, Astrid, Colona, Mia R., Greka, Anna, Hacohen, Nir, Hoover, Paul J., Marshall, Jamie L., Aulisio, Mark, Chen, Yijiang M., Janowczyk, Andrew, Jayapandian, Catherine, Viswanathan, Vidya S., Bush, William S., Crawford, Dana C., Madabhushi, Anant, Bush, Lakeshia, Cooperman, Leslie, Gonzalez-Vicente, Agustin, Herlitz, Leal, Jolly, Stacey, Nguyen, Jane, O’toole, John, Palmer, Ellen, Poggio, Emilio, Sedor, John, Sendrey, Dianna, Spates-Harden, Kassandra, Taliercio, Jonathan, Bjornstad, Petter M., Pyle, Laura, Vinovskis, Carissa, Appelbaum, Paul, Balderes, Olivia, Barasch, Jonathan M., Bomback, Andrew S., Canetta, Pietro A., D’Agati, Vivette D., Kiryluk, Krzysztof, Kudose, Satoru, Mehl, Karla, Shang, Ning, Bansal, Shweta, Alexandrov, Theodore, Rennke, Helmut, El-Achkar, Tarek M., Barwinska, Daria, Bledso, Sharon, Borner, Katy, Bueckle, Andreas, Cheng, Yinghua, Dagher, Pierre C., Dunn, Kenneth W., Eadon, Michael T., Ferkowicz, Michael J., Herr, Bruce W., Kelly, Katherine J., Ferreira, Ricardo Melo, Quardokus, Ellen M., Record, Elizabeth, Rivera, Marcelino, Su, Jing, Sutton, Timothy A., Williams, James C., Jr., Winfree, Seth, Jain, Yashvardhan, Menez, Steven, Parikh, Chirag R., Rosenberg, Avi, Corona-Villalobos, Celia P., Wen, Yumeng, Johansen, Camille, Rosas, Sylvia E., Roy, Neil, Sun, Jennifer, Williams, Mark, Azeloglu, Evren U., Hansen, Jens, He, Cijang, Iyengar, Ravi, Xiong, Yuguang, Prasad, Pottumarthi, Srivastava, Anand, Madhavan, Sethu M., Parikh, Samir, Rovin, Brad, Shapiro, John P., Anderton, Christopher R., Lukowski, Jessica, Pasa-Tolic, Ljiljana, Velickovic, Dusan, Oliver, George (Holt), Ardayfio, Joseph, Bebiak, Jack, Brown, Keith, Campbell, Taneisha, Campbell, Catherine E., Hayashi, Lynda, Jefferson, Nichole, Roberts, Glenda V., Saul, John, Shpigel, Anna, Stutzke, Christy, Koewler, Robert, Pinkeney, Roy, Sealfon, Rachel, Troyanskaya, Olga, Wong, Aaron, Tuttle, Katherine R., Pollack, Ari, Goltsev, Yury, Ginley, Brandon, Lucarelli, Nicholas, Lutnick, Brendon, Sarder, Pinaki, Lake, Blue B., Zhang, Kun, Boada, Patrick, Laszik, Zoltan G., Nolan, Garry, Anjani, Kavya, Sarwal, Minnie, Mukatash, Tariq, Sigdel, Tara, Alloway, Rita R., Burg, Ashley R., Lee, Paul J., Rike, Adele, Shi, Tiffany, Woodle, E. Steve, Ascani, Heather, Balis, Ulysses G.J., Blanc, Victoria M., Conser, Ninive C., Eddy, Sean, Frey, Renee, He, Yougqun, Hodgin, Jeffrey B., Kretzler, Matthias, Lienczewski, Chrysta, Luo, Jinghui, Mariani, Laura H., Menon, Rajasree, Otto, Edgar, Schaub, Jennifer, Steck, Becky, Elder, Michele M., Gilliam, Matthew, Hall, Daniel E., Murugan, Raghavan, Palevsky, Paul M., Randhawa, Parmjeet, Rosengart, Matthew, Tublin, Mitchell, Vita, Tina, Winters, James, Kellum, John A., Alpers, Charles E., Berglund, Ashley, Berry, Brooke, Blank, Kristina N., Carson, Jonas, Daniel, Stephen, De Boer, Ian H., Dighe, Ashveena L., Dowd, Frederick, Grewenow, Stephanie M., Himmelfarb, Jonathan, Hoofnagle, Andrew N., Limonte, Christine, McClelland, Robyn L., Mooney, Sean D., Rezaei, Kasra, Shankland, Stuart, Snyder, Jamie, Wang, Ruikang, Wilcox, Adam, Williams, Kayleen, Park, Christopher, Montellano, Richard, Pamreddy, Annapurna, Sharma, Kumar, Venkatachalam, Manjeri, Ye, Hongping, Zhang, Guanshi, Basit, Mujeeb, Hedayati, S. Susan, Kermani, Asra, Lee, Simon C., Lu, Christopher Y., Miller, R. Tyler, Moe, Orson W., Patel, Jiten, Pillai, Anil, Sambandam, Kamalanathan, Torrealba, Jose, Toto, Robert D., Vazquez, Miguel, Wang, Nancy, Wen, Natasha, Zhang, Dianbo, Park, Harold, Caprioli, Richard M., Patterson, Nathan, Sharman, Kavya, Spraggins, Jeffrey M., Van de Plas, Raf, Basta, Jeanine, Diettman, Sabine M., Gaut, Joseph P., Jain, Sanjay, Rauchman, Michael I., Vijayan, Anitha, Cantley, Lloyd G., Kakade, Vijaykumar R., Moledina, Dennis, Shaw, Melissa M., Ugwuowo, Ugochukwu, Wilson, Francis P., Arora, Tanima, Kestenbaum, Bryan R., Alexopoulos, Leonidas G., Palsson, Ragnar, Liu, Jing, Stillman, Isaac E., Rennke, Helmut G., Vaidya, Vishal S., Wu, Haojia, Humphreys, Benjamin D., and Waikar, Sushrut S.

Published
2021
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14. Plexin-B2 orchestrates collective stem cell dynamics via actomyosin contractility, cytoskeletal tension and adhesion

Author

Junqueira Alves, Chrystian, Dariolli, Rafael, Haydak, Jonathan, Kang, Sangjo, Hannah, Theodore, Wiener, Robert J., DeFronzo, Stefanie, Tejero, Rut, Gusella, Gabriele L., Ramakrishnan, Aarthi, Alves Dias, Rodrigo, Wojcinski, Alexandre, Kesari, Santosh, Shen, Li, Sobie, Eric A., Rodrigues Furtado de Mendonça, José Paulo, Azeloglu, Evren U., Zou, Hongyan, and Friedel, Roland H.

Published
2021
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15. Predicting individual-specific cardiotoxicity responses induced by tyrosine kinase inhibitors.

Author

Shim, Jaehee V., Yuguang Xiong, Dhanan, Priyanka, Dariolli, Rafael, Azeloglu, Evren U., Bin Hu, Jayaraman, Gomathi, Schaniel, Christoph, Birtwistle, Marc R., Iyengar, Ravi, Dubois, Nicole C., and Sobie, Eric A.

Subjects
PROTEIN-tyrosine kinase inhibitors, CARDIOTOXICITY, INDUCED pluripotent stem cells, ION channels, ARRHYTHMIA, ACTION potentials, PROTEIN-tyrosine kinases, INTRACELLULAR calcium
Abstract

Introduction: Tyrosine kinase inhibitor drugs (TKIs) are highly effective cancer drugs, yet many TKIs are associated with various forms of cardiotoxicity. The mechanisms underlying these drug-induced adverse events remain poorly understood. We studied mechanisms of TKI-induced cardiotoxicity by integrating several complementary approaches, including comprehensive transcriptomics, mechanistic mathematical modeling, and physiological assays in cultured human cardiac myocytes. Methods: Induced pluripotent stem cells (iPSCs) from two healthy donors were differentiated into cardiac myocytes (iPSC-CMs), and cells were treated with a panel of 26 FDA-approved TKIs. Drug-induced changes in gene expression were quantified using mRNA-seq, changes in gene expression were integrated into a mechanistic mathematical model of electrophysiology and contraction, and simulation results were used to predict physiological outcomes. Results: Experimental recordings of action potentials, intracellular calcium, and contraction in iPSC-CMs demonstrated that modeling predictions were accurate, with 81% of modeling predictions across the two cell lines confirmed experimentally. Surprisingly, simulations of how TKI-treated iPSC-CMs would respond to an additional arrhythmogenic insult, namely, hypokalemia, predicted dramatic differences between cell lines in how drugs affected arrhythmia susceptibility, and these predictions were confirmed experimentally. Computational analysis revealed that differences between cell lines in the upregulation or downregulation of particular ion channels could explain how TKI-treated cells responded differently to hypokalemia. Discussion: Overall, the study identifies transcriptional mechanisms underlying cardiotoxicity caused by TKIs, and illustrates a novel approach for integrating transcriptomics with mechanistic mathematical models to generate experimentally testable, individual-specific predictions of adverse event risk. [ABSTRACT FROM AUTHOR]

Published
2023
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16. Integrated single-cell sequencing and histopathological analyses reveal diverse injury and repair responses in a participant with acute kidney injury: a clinical-molecular-pathologic correlation

Author

Knight, Richard, Lecker, Stewart H., Stillman, Isaac, Bogen, Steve, Beck, Laurence H., Waikar, Sushrut, McMahon, Gearoid M., Weins, Astrid, Colona, Mia R., Hacohen, Nir, Hoover, Paul J., Aulisio, Mark, Bush, William S., Crawford, Dana C., O'toole, John, Poggio, Emilio, Sedor, John, Cooperman, Leslie, Jolly, Stacey, Herlitz, Leal, Nguyen, Jane, Gonzalez-Vicente, Agustin, Palmer, Ellen, Sendrey, Dianna, Vinovskis, Carissa, Bjornstad, Petter M., Appelbaum, Paul, Barasch, Jonathan M., Bomback, Andrew S., D'Agati, Vivette D., Kiryluk, Krzysztof, Mehl, Karla, Canetta, Pietro A., Shang, Ning, Balderes, Olivia, Kudose, Satoru, Bansal, Shweta, Alexandrov, Theodore, Rennke, Helmut, El-Achkar, Tarek M., Cheng, Yinghua, Dagher, Pierre C., Eadon, Michael T., Dunn, Kenneth W., Kelly, Katherine J., Sutton, Timothy A., Barwinska, Daria, Ferkowicz, Michael J., Winfree, Seth, Bledsoe, Sharon, Rivera, Marcelino, Williams, James C., Jr., Ferreira, Ricardo Melo, Parikh, Chirag R., Corona-Villalobos, Celia P., Menez, Steven, Rosenberg, Avi, Rosas, Sylvia E., Roy, Neil, Williams, Mark, Azeloglu, Evren U., He, Cijang, Iyengar, Ravi, Hansen, Jens, Xiong, Yuguang, Rovin, Brad, Parikh, Samir, Shapiro, John P., Anderton, Christopher R., Pasa-Tolic, Ljiljana, Velickovic, Dusan, Lukowski, Jessica, Oliver, George, Ardayfio, Joseph, Bebiak, Jack, Brown, Keith, Campbell, Catherine E., Saul, John, Shpigel, Anna, Stutzke, Christy, Koewler, Robert, Campbell, Taneisha, Hayashi, Lynda, Jefferson, Nichole, Roberts, Glenda V., Pinkeney, Roy, Troyanskaya, Olga, Sealfon, Rachel, Tuttle, Katherine R., Goltsev, Yury, Zhang, Kun, Lake, Blue B., Laszik, Zoltan G., Nolan, Garry, Boada, Patrick, Sarwal, Minnie, Sigdel, Tara, Lee, Paul J., Alloway, Rita R., Woodle, E. Steve, Ascani, Heather, Balis, Ulysses G.J., Hodgin, Jeffrey B., Kretzler, Matthias, Lienczewski, Chrysta, Mariani, Laura H., Menon, Rajasree, Steck, Becky, He, Yougqun, Otto, Edgar, Schaub, Jennifer, Blanc, Victoria M., Eddy, Sean, Conser, Ninive C., Luo, Jinghui, Palevsky, Paul M., Rosengart, Matthew, Kellum, John A., Hall, Daniel E., Randhawa, Parmjeet, Tublin, Mitchell, Murugan, Raghavan, Elder, Michele M., Winters, James, Alpers, Charles E., Blank, Kristina N., Carson, Jonas, De Boer, Ian H., Dighe, Ashveena L., Himmelfarb, Jonathan, Mooney, Sean D., Shankland, Stuart, Williams, Kayleen, Park, Christopher, Dowd, Frederick, McClelland, Robyn L., Daniel, Stephen, Hoofnagle, Andrew N., Wilcox, Adam, Grewenow, Stephanie M., Sharma, Kumar, Venkatachalam, Manjeri, Zhang, Guanshi, Pamreddy, Annapurna, Ye, Hongping, Montellano, Richard, Toto, Robert D., Vazquez, Miguel, Lee, Simon C., Miller, R. Tyler, Moe, Orson W., Torrealba, Jose, Wang, Nancy, Kermani, Asra, Sambandam, Kamalanathan, Park, Harold, Hedayati, S. Susan, Lu, Christopher Y., Jain, Sanjay, Vijayan, Anitha, Gaut, Joseph P., Moledina, Dennis, Wilson, Francis P., Ugwuowo, Ugochukwu, Arora, Tanima, and D’Agati, Vivette D.

Published
2022
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17. Integrated multiomics implicates dysregulation of ECM and cell adhesion pathways as drivers of severe COVID-associated kidney injury.

Author

Anandakrishnan N, Yi Z, Sun Z, Liu T, Haydak J, Eddy S, Jayaraman P, DeFronzo S, Saha A, Sun Q, Yang D, Mendoza A, Mosoyan G, Wen HH, Schaub JA, Fu J, Kehrer T, Menon R, Otto EA, Godfrey B, Suarez-Farinas M, Leffters S, Twumasi A, Meliambro K, Charney AW, García-Sastre A, Campbell KN, Gusella GL, He JC, Miorin L, Nadkarni GN, Wisnivesky J, Li H, Kretzler M, Coca SG, Chan L, Zhang W, and Azeloglu EU

Abstract

COVID-19 has been a significant public health concern for the last four years; however, little is known about the mechanisms that lead to severe COVID-associated kidney injury. In this multicenter study, we combined quantitative deep urinary proteomics and machine learning to predict severe acute outcomes in hospitalized COVID-19 patients. Using a 10-fold cross-validated random forest algorithm, we identified a set of urinary proteins that demonstrated predictive power for both discovery and validation set with 87% and 79% accuracy, respectively. These predictive urinary biomarkers were recapitulated in non-COVID acute kidney injury revealing overlapping injury mechanisms. We further combined orthogonal multiomics datasets to understand the mechanisms that drive severe COVID-associated kidney injury. Functional overlap and network analysis of urinary proteomics, plasma proteomics and urine sediment single-cell RNA sequencing showed that extracellular matrix and autophagy-associated pathways were uniquely impacted in severe COVID-19. Differentially abundant proteins associated with these pathways exhibited high expression in cells in the juxtamedullary nephron, endothelial cells, and podocytes, indicating that these kidney cell types could be potential targets. Further, single-cell transcriptomic analysis of kidney organoids infected with SARS-CoV-2 revealed dysregulation of extracellular matrix organization in multiple nephron segments, recapitulating the clinically observed fibrotic response across multiomics datasets. Ligand-receptor interaction analysis of the podocyte and tubule organoid clusters showed significant reduction and loss of interaction between integrins and basement membrane receptors in the infected kidney organoids. Collectively, these data suggest that extracellular matrix degradation and adhesion-associated mechanisms could be a main driver of COVID-associated kidney injury and severe outcomes.

Published
2024
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18. Deep learning on electronic medical records identifies distinct subphenotypes of diabetic kidney disease driven by genetic variations in the Rho pathway.

Author

Paranjpe I, Wang X, Anandakrishnan N, Haydak JC, Van Vleck T, DeFronzo S, Li Z, Mendoza A, Liu R, Fu J, Forrest I, Zhou W, Lee K, O'Hagan R, Dellepiane S, Menon KM, Gulamali F, Kamat S, Gusella GL, Charney AW, Hofer I, Cho JH, Do R, Glicksberg BS, He JC, Nadkarni GN, and Azeloglu EU

Abstract

Kidney disease affects 50% of all diabetic patients; however, prediction of disease progression has been challenging due to inherent disease heterogeneity. We use deep learning to identify novel genetic signatures prognostically associated with outcomes. Using autoencoders and unsupervised clustering of electronic health record data on 1,372 diabetic kidney disease patients, we establish two clusters with differential prevalence of end-stage kidney disease. Exome-wide associations identify a novel variant in ARHGEF18, a Rho guanine exchange factor specifically expressed in glomeruli. Overexpression of ARHGEF18 in human podocytes leads to impairments in focal adhesion architecture, cytoskeletal dynamics, cellular motility, and RhoA/Rac1 activation. Mutant GEF18 is resistant to ubiquitin mediated degradation leading to pathologically increased protein levels. Our findings uncover the first known disease-causing genetic variant that affects protein stability of a cytoskeletal regulator through impaired degradation, a potentially novel class of expression quantitative trait loci that can be therapeutically targeted.

Published
2023
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19. Evaluation of Plasma Biomarkers to Predict Major Adverse Kidney Events in Hospitalized Patients With COVID-19.

Author

Menez S, Coca SG, Moledina DG, Wen Y, Chan L, Thiessen-Philbrook H, Obeid W, Garibaldi BT, Azeloglu EU, Ugwuowo U, Sperati CJ, Arend LJ, Rosenberg AZ, Kaushal M, Jain S, Wilson FP, and Parikh CR

Subjects
Humans, Prospective Studies, Kidney, Biomarkers, Risk Factors, COVID-19 complications, Acute Kidney Injury epidemiology
Abstract

Rationale & Objective: Patients hospitalized with COVID-19 are at increased risk for major adverse kidney events (MAKE). We sought to identify plasma biomarkers predictive of MAKE in patients hospitalized with COVID-19., Study Design: Prospective cohort study., Setting & Participants: A total of 576 patients hospitalized with COVID-19 between March 2020 and January 2021 across 3 academic medical centers., Exposure: Twenty-six plasma biomarkers of injury, inflammation, and repair from first available blood samples collected during hospitalization., Outcome: MAKE, defined as KDIGO stage 3 acute kidney injury (AKI), dialysis-requiring AKI, or mortality up to 60 days., Analytical Approach: Cox proportional hazards regression to associate biomarker level with MAKE. We additionally applied the least absolute shrinkage and selection operator (LASSO) and random forest regression for prediction modeling and estimated model discrimination with time-varying C index., Results: The median length of stay for COVID-19 hospitalization was 9 (IQR, 5-16) days. In total, 95 patients (16%) experienced MAKE. Each 1 SD increase in soluble tumor necrosis factor receptor 1 (sTNFR1) and sTNFR2 was significantly associated with an increased risk of MAKE (adjusted HR [AHR], 2.30 [95% CI, 1.86-2.85], and AHR, 2.26 [95% CI, 1.73-2.95], respectively). The C index of sTNFR1 alone was 0.80 (95% CI, 0.78-0.84), and the C index of sTNFR2 was 0.81 (95% CI, 0.77-0.84). LASSO and random forest regression modeling using all biomarkers yielded C indexes of 0.86 (95% CI, 0.83-0.89) and 0.84 (95% CI, 0.78-0.91), respectively., Limitations: No control group of hospitalized patients without COVID-19., Conclusions: We found that sTNFR1 and sTNFR2 are independently associated with MAKE in patients hospitalized with COVID-19 and can both also serve as predictors for adverse kidney outcomes., Plain-Language Summary: Patients hospitalized with COVID-19 are at increased risk for long-term adverse health outcomes, but not all patients suffer long-term kidney dysfunction. Identification of patients with COVID-19 who are at high risk for adverse kidney events may have important implications in terms of nephrology follow-up and patient counseling. In this study, we found that the plasma biomarkers soluble tumor necrosis factor receptor 1 (sTNFR1) and sTNFR2 measured in hospitalized patients with COVID-19 were associated with a greater risk of adverse kidney outcomes. Along with clinical variables previously shown to predict adverse kidney events in patients with COVID-19, both sTNFR1 and sTNFR2 are also strong predictors of adverse kidney outcomes., (Copyright © 2023 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.)

Published
2023
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20. Dasatinib nephrotoxicity correlates with patient-specific pharmacokinetics.

Author

Adegbite BO, Abramson MH, Gutgarts V, Musteata MF, Chauhan K, Muwonge AN, Meliambro KA, Salvatore SP, Ghaity-Beckley SE, Kremyanskaya M, Marcellino B, Mascarenhas JO, Campbell KN, Chan L, Coca SG, Berman EM, Jaimes EA, and Azeloglu EU

Abstract

Introduction: Dasatinib has been associated with nephrotoxicity. We sought to examine the incidence of proteinuria on dasatinib and determine potential risk factors that may increase dasatinib-associated glomerular injury., Methods: We examine glomerular injury via urine albumin-to-creatinine ratio (UACR) in 101 chronic myelogenous leukemia patients who were on tyrosine-kinase inhibitor (TKI) therapy for at least 90 days. We assay plasma dasatinib pharmacokinetics using tandem mass spectroscopy, and further describe a case study of a patient who experienced nephrotic-range proteinuria while on dasatinib., Results: Patients treated with dasatinib (n= 32) had significantly higher UACR levels (median 28.0 mg/g, IQR 11.5 - 119.5) than patients treated with other TKIs (n=50; median 15.0 mg/g, IQR 8.0 - 35.0; p < 0.001). In total, 10% of dasatinib users exhibited severely increased albuminuria (UACR > 300 mg/g) versus zero in other TKIs. Average steady state concentrations of dasatinib were positively correlated with UACR (ρ = 0.54, p = 0.03) as well as duration of treatment ( p =0.003). There were no associations with elevated blood pressure or other confounding factors. In the case study, kidney biopsy revealed global glomerular damage with diffuse foot process effacement that recovered upon termination of dasatinib treatment., Conclusions: Exposure to dasatinib is associated a significant chance of developing proteinuria compared to other similar TKIs. Dasatinib plasma concentration significantly correlates with increased risk of developing proteinuria while receiving dasatinib. Screening for renal dysfunction and proteinuria is strongly advised for all dasatinib patients.

Published
2023
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21. KIBRA upregulation increases susceptibility to podocyte injury and glomerular disease progression.

Author

Meliambro K, Yang Y, de Cos M, Rodriguez Ballestas E, Malkin C, Haydak J, Lee JR, Salem F, Mariani LH, Gordon RE, Basgen JM, Wen HH, Fu J, Azeloglu EU, He JC, Wong JS, and Campbell KN

Subjects
Humans, Mice, Animals, Up-Regulation, Kidney Glomerulus metabolism, Signal Transduction, Disease Progression, Intracellular Signaling Peptides and Proteins genetics, Intracellular Signaling Peptides and Proteins metabolism, Podocytes metabolism, Kidney Diseases genetics, Kidney Diseases metabolism
Abstract

Despite recent progress in the identification of mediators of podocyte injury, mechanisms underlying podocyte loss remain poorly understood, and cell-specific therapy is lacking. We previously reported that kidney and brain expressed protein (KIBRA), encoded by WWC1, promotes podocyte injury in vitro through activation of the Hippo signaling pathway. KIBRA expression is increased in the glomeruli of patients with focal segmental glomerulosclerosis, and KIBRA depletion in vivo is protective against acute podocyte injury. Here, we tested the consequences of transgenic podocyte-specific WWC1 expression in immortalized human podocytes and in mice, and we explored the association between glomerular WWC1 expression and glomerular disease progression. We found that KIBRA overexpression in immortalized human podocytes promoted cytoplasmic localization of Yes-associated protein (YAP), induced actin cytoskeletal reorganization, and altered focal adhesion expression and morphology. WWC1-transgenic (KIBRA-overexpressing) mice were more susceptible to acute and chronic glomerular injury, with evidence of YAP inhibition in vivo. Of clinical relevance, glomerular WWC1 expression negatively correlated with renal survival among patients with primary glomerular diseases. These findings highlight the importance of KIBRA/YAP signaling to the regulation of podocyte structural integrity and identify KIBRA-mediated injury as a potential target for podocyte-specific therapy in glomerular disease.

Published
2023
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22. Proteomic Characterization of Acute Kidney Injury in Patients Hospitalized with SARS-CoV2 Infection.

Author

Paranjpe I, Jayaraman P, Su CY, Zhou S, Chen S, Thompson R, Del Valle DM, Kenigsberg E, Zhao S, Jaladanki S, Chaudhary K, Ascolillo S, Vaid A, Kumar A, Kozlova E, Paranjpe M, O'Hagan R, Kamat S, Gulamali FF, Kauffman J, Xie H, Harris J, Patel M, Argueta K, Batchelor C, Nie K, Dellepiane S, Scott L, Levin MA, He JC, Suarez-Farinas M, Coca SG, Chan L, Azeloglu EU, Schadt E, Beckmann N, Gnjatic S, Merad M, Kim-Schulze S, Richards B, Glicksberg BS, Charney AW, and Nadkarni GN

Abstract

Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. Using measurements of ∼4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N= 437), we identified 413 higher plasma abundances of protein targets and 40 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p <0.05). Of these, 62 proteins were validated in an external cohort (p <0.05, N =261). We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p <0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury. Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

Published
2022
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23. A library of induced pluripotent stem cells from clinically well-characterized, diverse healthy human individuals.

Author

Schaniel C, Dhanan P, Hu B, Xiong Y, Raghunandan T, Gonzalez DM, Dariolli R, D'Souza SL, Yadaw AS, Hansen J, Jayaraman G, Mathew B, Machado M, Berger SI, Tripodi J, Najfeld V, Garg J, Miller M, Surlyn CS, Michelis KC, Tangirala NC, Weerahandi H, Thomas DC, Beaumont KG, Sebra R, Mahajan M, Schadt E, Vidovic D, Schürer SC, Goldfarb J, Azeloglu EU, Birtwistle MR, Sobie EA, Kovacic JC, Dubois NC, and Iyengar R

Subjects
Adult, Calcium Signaling, Cell Differentiation, Cell Line, Clone Cells, Ethnicity, Female, Gene Expression Profiling, Gene Expression Regulation, Genetic Predisposition to Disease, Genetic Variation, Heart Atria cytology, Heart Ventricles cytology, Humans, Male, Middle Aged, Myocytes, Cardiac cytology, Myocytes, Cardiac metabolism, Risk Factors, Young Adult, Health, Induced Pluripotent Stem Cells cytology
Abstract

A library of well-characterized human induced pluripotent stem cell (hiPSC) lines from clinically healthy human subjects could serve as a useful resource of normal controls for in vitro human development, disease modeling, genotype-phenotype association studies, and drug response evaluation. We report generation and extensive characterization of a gender-balanced, racially/ethnically diverse library of hiPSC lines from 40 clinically healthy human individuals who range in age from 22 to 61 years. The hiPSCs match the karyotype and short tandem repeat identities of their parental fibroblasts, and have a transcription profile characteristic of pluripotent stem cells. We provide whole-genome sequencing data for one hiPSC clone from each individual, genomic ancestry determination, and analysis of mendelian disease genes and risks. We document similar transcriptomic profiles, single-cell RNA-sequencing-derived cell clusters, and physiology of cardiomyocytes differentiated from multiple independent hiPSC lines. This extensive characterization makes this hiPSC library a valuable resource for many studies on human biology., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Published
2021
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