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217 results on '"Fillmore, Thomas L."'

1. Pumping iron: A multi-omics analysis of two extremophilic algae reveals iron economy management

Author

Davidi, Lital, Gallaher, Sean D, Ben-David, Eyal, Purvine, Samuel O, Fillmore, Thomas L, Nicora, Carrie D, Craig, Rory J, Schmollinger, Stefan, Roje, Sanja, Blaby-Haas, Crysten E, Auber, Robert P, Wisecaver, Jennifer H, and Merchant, Sabeeha S

Subjects
Plant Biology, Biological Sciences, Life Below Water, Iron, Extremophiles, Multiomics, Proteomics, Photosynthesis, Proteins, Chlorophyceae, iron homeostasis, phytoplankton, Chlamydomonas reinhardtii, iron starvation-induced protein, flavin biosynthesis, iron starvation–induced protein
Abstract

Marine algae are responsible for half of the world's primary productivity, but this critical carbon sink is often constrained by insufficient iron. One species of marine algae, Dunaliella tertiolecta, is remarkable for its ability to maintain photosynthesis and thrive in low-iron environments. A related species, Dunaliella salina Bardawil, shares this attribute but is an extremophile found in hypersaline environments. To elucidate how algae manage their iron requirements, we produced high-quality genome assemblies and transcriptomes for both species to serve as a foundation for a comparative multiomics analysis. We identified a host of iron-uptake proteins in both species, including a massive expansion of transferrins and a unique family of siderophore-iron-uptake proteins. Complementing these multiple iron-uptake routes, ferredoxin functions as a large iron reservoir that can be released by induction of flavodoxin. Proteomic analysis revealed reduced investment in the photosynthetic apparatus coupled with remodeling of antenna proteins by dramatic iron-deficiency induction of TIDI1, which is closely related but identifiably distinct from the chlorophyll binding protein, LHCA3. These combinatorial iron scavenging and sparing strategies make Dunaliella unique among photosynthetic organisms.

Published
2023

2. Multi-scale signaling and tumor evolution in high-grade gliomas

Author

Liu, Jingxian, Cao, Song, Imbach, Kathleen J., Gritsenko, Marina A., Lih, Tung-Shing M., Kyle, Jennifer E., Yaron-Barir, Tomer M., Binder, Zev A., Li, Yize, Strunilin, Ilya, Wang, Yi-Ting, Tsai, Chia-Feng, Ma, Weiping, Chen, Lijun, Clark, Natalie M., Shinkle, Andrew, Naser Al Deen, Nataly, Caravan, Wagma, Houston, Andrew, Simin, Faria Anjum, Wyczalkowski, Matthew A., Wang, Liang-Bo, Storrs, Erik, Chen, Siqi, Illindala, Ritvik, Li, Yuping D., Jayasinghe, Reyka G., Rykunov, Dmitry, Cottingham, Sandra L., Chu, Rosalie K., Weitz, Karl K., Moore, Ronald J., Sagendorf, Tyler, Petyuk, Vladislav A., Nestor, Michael, Bramer, Lisa M., Stratton, Kelly G., Schepmoes, Athena A., Couvillion, Sneha P., Eder, Josie, Kim, Young-Mo, Gao, Yuqian, Fillmore, Thomas L., Zhao, Rui, Monroe, Matthew E., Southard-Smith, Austin N., Li, Yang E., Jui-Hsien Lu, Rita, Johnson, Jared L., Wiznerowicz, Maciej, Hostetter, Galen, Newton, Chelsea J., Ketchum, Karen A., Thangudu, Ratna R., Barnholtz-Sloan, Jill S., Wang, Pei, Fenyö, David, An, Eunkyung, Thiagarajan, Mathangi, Robles, Ana I., Mani, D.R., Smith, Richard D., Porta-Pardo, Eduard, Cantley, Lewis C., Iavarone, Antonio, Chen, Feng, Mesri, Mehdi, Nasrallah, MacLean P., Zhang, Hui, Resnick, Adam C., Chheda, Milan G., Rodland, Karin D., Liu, Tao, and Ding, Li

Published
2024
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4. Proteogenomic insights suggest druggable pathways in endometrial carcinoma

Author

Antczak, Andrzej, Anurag, Meenakshi, Bauer, Thomas, Birger, Chet, Birrer, Michael J., Borucki, Melissa, Cai, Shuang, Calinawan, Anna, Carr, Steven A., Castro, Patricia, Cerda, Sandra, Chan, Daniel W., Chesla, David, Cieslik, Marcin P., Cottingham, Sandra, Dhir, Rajiv, Domagalski, Marcin J., Druker, Brian J., Duffy, Elizabeth, Edwards, Nathan J., Edwards, Robert, Ellis, Matthew J., Eschbacher, Jennifer, Fam, Mina, Fevrier-Sullivan, Brenda, Francis, Jesse, Freymann, John, Gabriel, Stacey, Getz, Gad, Gillette, Michael A., Godwin, Andrew K., Goldthwaite, Charles A., Grady, Pamela, Hafron, Jason, Hariharan, Pushpa, Hindenach, Barbara, Hoadley, Katherine A., Huang, Jasmine, Ittmann, Michael M., Johnson, Ashlie, Jones, Corbin D., Ketchum, Karen A., Kirby, Justin, Le, Toan, Ma'ayan, Avi, Madan, Rashna, Mareedu, Sailaja, McGarvey, Peter B., Modugno, Francesmary, Montgomery, Rebecca, Nyce, Kristen, Paulovich, Amanda G., Pruetz, Barbara L., Qi, Liqun, Richey, Shannon, Schadt, Eric E., Shutack, Yvonne, Singh, Shilpi, Smith, Michael, Tansil, Darlene, Thangudu, Ratna R., Tobin, Matt, Um, Ki Sung, Vatanian, Negin, Webster, Alex, Wilson, George D., Wright, Jason, Zaalishvili, Kakhaber, Zhang, Zhen, Zhao, Grace, Dou, Yongchao, Katsnelson, Lizabeth, Gritsenko, Marina A., Hu, Yingwei, Reva, Boris, Hong, Runyu, Wang, Yi-Ting, Kolodziejczak, Iga, Lu, Rita Jui-Hsien, Tsai, Chia-Feng, Bu, Wen, Liu, Wenke, Guo, Xiaofang, An, Eunkyung, Arend, Rebecca C., Bavarva, Jasmin, Chen, Lijun, Chu, Rosalie K., Czekański, Andrzej, Davoli, Teresa, Demicco, Elizabeth G., DeLair, Deborah, Devereaux, Kelly, Dhanasekaran, Saravana M., Dottino, Peter, Dover, Bailee, Fillmore, Thomas L., Foxall, McKenzie, Hermann, Catherine E., Hiltke, Tara, Hostetter, Galen, Jędryka, Marcin, Jewell, Scott D., Johnson, Isabelle, Kahn, Andrea G., Ku, Amy T., Kumar-Sinha, Chandan, Kurzawa, Paweł, Lazar, Alexander J., Lazcano, Rossana, Lei, Jonathan T., Li, Yi, Liao, Yuxing, Lih, Tung-Shing M., Lin, Tai-Tu, Martignetti, John A., Masand, Ramya P., Matkowski, Rafał, McKerrow, Wilson, Mesri, Mehdi, Monroe, Matthew E., Moon, Jamie, Moore, Ronald J., Nestor, Michael D., Newton, Chelsea, Omelchenko, Tatiana, Omenn, Gilbert S., Payne, Samuel H., Petyuk, Vladislav A., Robles, Ana I., Rodriguez, Henry, Ruggles, Kelly V., Rykunov, Dmitry, Savage, Sara R., Schepmoes, Athena A., Shi, Tujin, Shi, Zhiao, Tan, Jimin, Taylor, Mason, Thiagarajan, Mathangi, Wang, Joshua M., Weitz, Karl K., Wen, Bo, Williams, C.M., Wu, Yige, Wyczalkowski, Matthew A., Yi, Xinpei, Zhang, Xu, Zhao, Rui, Mutch, David, Chinnaiyan, Arul M., Smith, Richard D., Nesvizhskii, Alexey I., Wang, Pei, Wiznerowicz, Maciej, Ding, Li, Mani, D.R., Zhang, Hui, Anderson, Matthew L., Rodland, Karin D., Zhang, Bing, Liu, Tao, and Fenyö, David

Published
2023
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5. Plasma protein biomarkers predict the development of persistent autoantibodies and type 1 diabetes 6 months prior to the onset of autoimmunity

Author

Nakayasu, Ernesto S., Bramer, Lisa M., Ansong, Charles, Schepmoes, Athena A., Fillmore, Thomas L., Gritsenko, Marina A., Clauss, Therese R., Gao, Yuqian, Piehowski, Paul D., Stanfill, Bryan A., Engel, Dave W., Orton, Daniel J., Moore, Ronald J., Qian, Wei-Jun, Sechi, Salvatore, Frohnert, Brigitte I., Toppari, Jorma, Ziegler, Anette-G., Lernmark, Åke, Hagopian, William, Akolkar, Beena, Smith, Richard D., Rewers, Marian J., Webb-Robertson, Bobbie-Jo M., and Metz, Thomas O.

Published
2023
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6. Interlaboratory Comparison of Antibody-Free LC-MS/MS Measurements of C-peptide and Insulin

Author

Moradian, Annie, primary, Goonatilleke, Elisha, additional, Lin, Tai-Tu, additional, Hatten-Beck, Maya, additional, Emrick, Michelle, additional, Schepmoes, Athena A, additional, Fillmore, Thomas L, additional, MacCoss, Michael J, additional, Sechi, Salvatore, additional, Sobhani, Kimia, additional, Little, Randie, additional, Kabytaev, Kuanysh, additional, van Eyk, Jennifer E, additional, Qian, Wei-Jun, additional, and Hoofnagle, Andrew N, additional

Published
2024
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8. Coupling Microdroplet-Based Sample Preparation, Multiplexed Isobaric Labeling, and Nanoflow Peptide Fractionation for Deep Proteome Profiling of the Tissue Microenvironment

Author

Veličković, Marija, Fillmore, Thomas L., Attah, Isaac Kwame, Posso, Camilo, Pino, James C., Zhao, Rui, Williams, Sarah M., Veličković, Dušan, Jacobs, Jon M., Burnum-Johnson, Kristin E., Zhu, Ying, and Piehowski, Paul D.

Abstract

There is increasing interest in developing in-depth proteomic approaches for mapping tissue heterogeneity in a cell-type-specific manner to better understand and predict the function of complex biological systems such as human organs. Existing spatially resolved proteomics technologies cannot provide deep proteome coverage due to limited sensitivity and poor sample recovery. Herein, we seamlessly combined laser capture microdissection with a low-volume sample processing technology that includes a microfluidic device named microPOTS (microdroplet processing in one pot for trace samples), multiplexed isobaric labeling, and a nanoflow peptide fractionation approach. The integrated workflow allowed us to maximize proteome coverage of laser-isolated tissue samples containing nanogram levels of proteins. We demonstrated that the deep spatial proteomics platform can quantify more than 5000 unique proteins from a small-sized human pancreatic tissue pixel (∼60,000 μm2) and differentiate unique protein abundance patterns in pancreas. Furthermore, the use of the microPOTS chip eliminated the requirement for advanced microfabrication capabilities and specialized nanoliter liquid handling equipment, making it more accessible to proteomic laboratories.

Published
2024
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9. Mining the human urine proteome for monitoring renal transplant injury

Author

Sigdel, Tara K, Gao, Yuqian, He, Jintang, Wang, Anyou, Nicora, Carrie D, Fillmore, Thomas L, Shi, Tujin, Webb-Robertson, Bobbie-Jo, Smith, Richard D, Qian, Wei-Jun, Salvatierra, Oscar, Camp, David G, and Sarwal, Minnie M

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Urologic Diseases, Clinical Research, Transplantation, Biotechnology, Kidney Disease, Organ Transplantation, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, Renal and urogenital, Adolescent, Adult, Allografts, BK Virus, Biomarkers, Biopsy, Child, Chromatography, Liquid, Female, Graft Rejection, Humans, Kidney, Kidney Transplantation, Male, Mass Spectrometry, Nephritis, Proteomics, Urinalysis, Young Adult, acute rejection, kidney transplantation injury, noninvasive biomarkers, protein biomarkers, urine proteomics, Urology & Nephrology, Clinical sciences
Abstract

The human urinary proteome provides an assessment of kidney injury with specific biomarkers for different kidney injury phenotypes. In an effort to fully map and decipher changes in the urine proteome and peptidome after kidney transplantation, renal allograft biopsy matched urine samples were collected from 396 kidney transplant recipients. Centralized and blinded histology data from paired graft biopsies was used to classify urine samples into diagnostic categories of acute rejection, chronic allograft nephropathy, BK virus nephritis, and stable graft. A total of 245 urine samples were analyzed by liquid chromatography-mass spectrometry using isobaric Tags for Relative and Absolute Quantitation (iTRAQ) reagents. From a group of over 900 proteins identified in transplant injury, a set of 131 peptides were assessed by selected reaction monitoring for their significance in accurately segregating organ injury causation and pathology in an independent cohort of 151 urine samples. Ultimately, a minimal set of 35 proteins were identified for their ability to segregate the 3 major transplant injury clinical groups, comprising the final panel of 11 urinary peptides for acute rejection (93% area under the curve [AUC]), 12 urinary peptides for chronic allograft nephropathy (99% AUC), and 12 urinary peptides for BK virus nephritis (83% AUC). Thus, urinary proteome discovery and targeted validation can identify urine protein panels for rapid and noninvasive differentiation of different causes of kidney transplant injury, without the requirement of an invasive biopsy.

Published
2016

10. Validation of a Proteomic Signature of Lung Cancer Risk from Bronchial Specimens of Risk-Stratified Individuals

Author

Rahman, S.M. Jamshedur, primary, Chen, Sheau-Chiann, additional, Wang, Yi-Ting, additional, Gao, Yuqian, additional, Schepmoes, Athena A., additional, Fillmore, Thomas L., additional, Shi, Tujin, additional, Chen, Heidi, additional, Rodland, Karin D., additional, Massion, Pierre P., additional, Grogan, Eric L., additional, and Liu, Tao, additional

Published
2023
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11. Proteogenomic insights suggest druggable pathways in endometrial carcinoma

Author

Dou, Yongchao, primary, Katsnelson, Lizabeth, additional, Gritsenko, Marina A., additional, Hu, Yingwei, additional, Reva, Boris, additional, Hong, Runyu, additional, Wang, Yi-Ting, additional, Kolodziejczak, Iga, additional, Lu, Rita Jui-Hsien, additional, Tsai, Chia-Feng, additional, Bu, Wen, additional, Liu, Wenke, additional, Guo, Xiaofang, additional, An, Eunkyung, additional, Arend, Rebecca C., additional, Bavarva, Jasmin, additional, Chen, Lijun, additional, Chu, Rosalie K., additional, Czekański, Andrzej, additional, Davoli, Teresa, additional, Demicco, Elizabeth G., additional, DeLair, Deborah, additional, Devereaux, Kelly, additional, Dhanasekaran, Saravana M., additional, Dottino, Peter, additional, Dover, Bailee, additional, Fillmore, Thomas L., additional, Foxall, McKenzie, additional, Hermann, Catherine E., additional, Hiltke, Tara, additional, Hostetter, Galen, additional, Jędryka, Marcin, additional, Jewell, Scott D., additional, Johnson, Isabelle, additional, Kahn, Andrea G., additional, Ku, Amy T., additional, Kumar-Sinha, Chandan, additional, Kurzawa, Paweł, additional, Lazar, Alexander J., additional, Lazcano, Rossana, additional, Lei, Jonathan T., additional, Li, Yi, additional, Liao, Yuxing, additional, Lih, Tung-Shing M., additional, Lin, Tai-Tu, additional, Martignetti, John A., additional, Masand, Ramya P., additional, Matkowski, Rafał, additional, McKerrow, Wilson, additional, Mesri, Mehdi, additional, Monroe, Matthew E., additional, Moon, Jamie, additional, Moore, Ronald J., additional, Nestor, Michael D., additional, Newton, Chelsea, additional, Omelchenko, Tatiana, additional, Omenn, Gilbert S., additional, Payne, Samuel H., additional, Petyuk, Vladislav A., additional, Robles, Ana I., additional, Rodriguez, Henry, additional, Ruggles, Kelly V., additional, Rykunov, Dmitry, additional, Savage, Sara R., additional, Schepmoes, Athena A., additional, Shi, Tujin, additional, Shi, Zhiao, additional, Tan, Jimin, additional, Taylor, Mason, additional, Thiagarajan, Mathangi, additional, Wang, Joshua M., additional, Weitz, Karl K., additional, Wen, Bo, additional, Williams, C.M., additional, Wu, Yige, additional, Wyczalkowski, Matthew A., additional, Yi, Xinpei, additional, Zhang, Xu, additional, Zhao, Rui, additional, Mutch, David, additional, Chinnaiyan, Arul M., additional, Smith, Richard D., additional, Nesvizhskii, Alexey I., additional, Wang, Pei, additional, Wiznerowicz, Maciej, additional, Ding, Li, additional, Mani, D.R., additional, Zhang, Hui, additional, Anderson, Matthew L., additional, Rodland, Karin D., additional, Zhang, Bing, additional, Liu, Tao, additional, Fenyö, David, additional, Antczak, Andrzej, additional, Anurag, Meenakshi, additional, Bauer, Thomas, additional, Birger, Chet, additional, Birrer, Michael J., additional, Borucki, Melissa, additional, Cai, Shuang, additional, Calinawan, Anna, additional, Carr, Steven A., additional, Castro, Patricia, additional, Cerda, Sandra, additional, Chan, Daniel W., additional, Chesla, David, additional, Cieslik, Marcin P., additional, Cottingham, Sandra, additional, Dhir, Rajiv, additional, Domagalski, Marcin J., additional, Druker, Brian J., additional, Duffy, Elizabeth, additional, Edwards, Nathan J., additional, Edwards, Robert, additional, Ellis, Matthew J., additional, Eschbacher, Jennifer, additional, Fam, Mina, additional, Fevrier-Sullivan, Brenda, additional, Francis, Jesse, additional, Freymann, John, additional, Gabriel, Stacey, additional, Getz, Gad, additional, Gillette, Michael A., additional, Godwin, Andrew K., additional, Goldthwaite, Charles A., additional, Grady, Pamela, additional, Hafron, Jason, additional, Hariharan, Pushpa, additional, Hindenach, Barbara, additional, Hoadley, Katherine A., additional, Huang, Jasmine, additional, Ittmann, Michael M., additional, Johnson, Ashlie, additional, Jones, Corbin D., additional, Ketchum, Karen A., additional, Kirby, Justin, additional, Le, Toan, additional, Ma'ayan, Avi, additional, Madan, Rashna, additional, Mareedu, Sailaja, additional, McGarvey, Peter B., additional, Modugno, Francesmary, additional, Montgomery, Rebecca, additional, Nyce, Kristen, additional, Paulovich, Amanda G., additional, Pruetz, Barbara L., additional, Qi, Liqun, additional, Richey, Shannon, additional, Schadt, Eric E., additional, Shutack, Yvonne, additional, Singh, Shilpi, additional, Smith, Michael, additional, Tansil, Darlene, additional, Thangudu, Ratna R., additional, Tobin, Matt, additional, Um, Ki Sung, additional, Vatanian, Negin, additional, Webster, Alex, additional, Wilson, George D., additional, Wright, Jason, additional, Zaalishvili, Kakhaber, additional, Zhang, Zhen, additional, and Zhao, Grace, additional

Published
2023
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12. Coupling microdroplet-based sample preparation, multiplexed isobaric labeling, and nanoflow peptide fractionation for deep proteome profiling of tissue microenvironment

Author

Veličković, Marija, primary, Fillmore, Thomas L., additional, Attah, Kwame, additional, Posso, Camilo, additional, Pino, James C., additional, Zhao, Rui, additional, Williams, Sarah M., additional, Veličković, Dušan, additional, Jacobs, Jon M., additional, Burnum-Johnson, Kristin E., additional, Zhu, Ying, additional, and Piehowski, Paul D., additional

Published
2023
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21. The AML microenvironment catalyzes a stepwise evolution to gilteritinib resistance

Author

Joshi, Sunil K., primary, Nechiporuk, Tamilla, additional, Bottomly, Daniel, additional, Piehowski, Paul D., additional, Reisz, Julie A., additional, Pittsenbarger, Janét, additional, Kaempf, Andy, additional, Gosline, Sara J.C., additional, Wang, Yi-Ting, additional, Hansen, Joshua R., additional, Gritsenko, Marina A., additional, Hutchinson, Chelsea, additional, Weitz, Karl K., additional, Moon, Jamie, additional, Cendali, Francesca, additional, Fillmore, Thomas L., additional, Tsai, Chia-Feng, additional, Schepmoes, Athena A., additional, Shi, Tujin, additional, Arshad, Osama A., additional, McDermott, Jason E., additional, Babur, Ozgun, additional, Watanabe-Smith, Kevin, additional, Demir, Emek, additional, D'Alessandro, Angelo, additional, Liu, Tao, additional, Tognon, Cristina E., additional, Tyner, Jeffrey W., additional, McWeeney, Shannon K., additional, Rodland, Karin D., additional, Druker, Brian J., additional, and Traer, Elie, additional

Published
2021
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22. Accumulation of succinyl coenzyme a perturbs the methicillin-resistant staphylococcus aureus (Mrsa) succinylome and is associated with increased susceptibility to beta-lactam antibiotics

Author

Campbell, Christopher, Fingleton, Claire, Zeden, Merve S., Bueno, Emilio, Gallagher, Laura A., Shinde, Dhananjay, Ahn, Jongsam, Olson, Heather M., Fillmore, Thomas L., Adkins, Joshua N., Razvi, Fareha, Bayles, Kenneth W., Fey, Paul D., Thomas, Vinai C., Cava, Felipe, Clair, Geremy C., O’gara, James P., Campbell, Christopher, Fingleton, Claire, Zeden, Merve S., Bueno, Emilio, Gallagher, Laura A., Shinde, Dhananjay, Ahn, Jongsam, Olson, Heather M., Fillmore, Thomas L., Adkins, Joshua N., Razvi, Fareha, Bayles, Kenneth W., Fey, Paul D., Thomas, Vinai C., Cava, Felipe, Clair, Geremy C., and O’gara, James P.

Abstract

Penicillin binding protein 2a (PBP2a)-dependent resistance to β-lactam antibiotics in methicillin-resistant Staphylococcus aureus (MRSA) is regulated by the activity of the tricarboxylic acid (TCA) cycle via a poorly understood mechanism. We report that mutations in sucC and sucD, but not other TCA cycle enzymes, negatively impact β-lactam resistance without changing PBP2a expression. Increased intracellular levels of succinyl coenzyme A (succinyl-CoA) in the sucC mutant significantly perturbed lysine succinylation in the MRSA proteome. Suppressor mutations in sucA or sucB, responsible for succinyl-CoA biosynthesis, reversed sucC mutant phenotypes. The major autolysin (Atl) was the most succinylated protein in the proteome, and increased Atl succinylation in the sucC mutant was associated with loss of autolytic activity. Although PBP2a and PBP2 were also among the most succinylated proteins in the MRSA proteome, peptidoglycan architecture and cross-linking were unchanged in the sucC mutant. These data reveal that perturbation of the MRSA succinylome impacts two interconnected cell wall phenotypes, leading to repression of autolytic activity and increased susceptibility to β-lactam antibiotics.

Published
2021
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23. Antibody-independent targeted quantification of TMPRSS2-ERG fusion protein products in prostate cancer

Author

He, Jintang, Sun, Xuefei, Shi, Tujin, Schepmoes, Athena A., Fillmore, Thomas L., Petyuk, Vladislav A., Xie, Fang, Zhao, Rui, Gritsenko, Marina A., Yang, Feng, Kitabayashi, Naoki, Chae, Sung-Suk, Rubin, Mark A., Siddiqui, Javed, Wei, John T., Chinnaiyan, Arul M., Qian, Wei-Jun, Smith, Richard D., Kagan, Jacob, Srivastava, Sudhir, Rodland, Karin D., Liu, Tao, and Camp, David G., II

Published
2014
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24. Accumulation of Succinyl Coenzyme A Perturbs the Methicillin-Resistant Staphylococcus aureus (MRSA) Succinylome and Is Associated with Increased Susceptibility to Beta-Lactam Antibiotics

Author

Campbell, Christopher, primary, Fingleton, Claire, additional, Zeden, Merve S., additional, Bueno, Emilio, additional, Gallagher, Laura A., additional, Shinde, Dhananjay, additional, Ahn, Jongsam, additional, Olson, Heather M., additional, Fillmore, Thomas L., additional, Adkins, Joshua N., additional, Razvi, Fareha, additional, Bayles, Kenneth W., additional, Fey, Paul D., additional, Thomas, Vinai C., additional, Cava, Felipe, additional, Clair, Geremy C., additional, and O’Gara, James P., additional

Published
2021
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26. The AML Microenvironment Catalyzes a Step-Wise Evolution to Gilteritinib Resistance

Author

Joshi, Sunil, primary, Nechiporuk, Tamilla, additional, Bottomly, Daniel, additional, Piehowski, Paul D., additional, Reisz, Julie A., additional, Pittsenbarger, Janét, additional, Kaempf, Andy, additional, Gosline, Sara J. C., additional, Wang, Yi-Ting, additional, Hansen, Joshua R., additional, Gritsenko, Marina A., additional, Hutchinson, Chelsea, additional, Weitz, Karl K., additional, Moon, Jamie, additional, Fillmore, Thomas L., additional, Tsai, Chia-Feng, additional, Schepmoes, Athena A., additional, Shi, Tujin, additional, Arshad, Osama A., additional, McDermott, Jason E., additional, Babur, Ozgun, additional, Watanabe-Smith, Kevin, additional, Demir, Emek, additional, D'Alessandro, Angelo, additional, Liu, Tao, additional, Tognon, Cristina E., additional, Tyner, Jeffrey W., additional, McWeeney, Shannon K., additional, Rodland, Karin D., additional, Druker, Brian J., additional, and Traer, Elie, additional

Published
2021
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27. High-Throughput Large-Scale Targeted Proteomics Assays for Quantifying Pathway Proteins in Pseudomonas putida KT2440

Author

Gao, Yuqian, primary, Fillmore, Thomas L., additional, Munoz, Nathalie, additional, Bentley, Gayle J., additional, Johnson, Christopher W., additional, Kim, Joonhoon, additional, Meadows, Jamie A., additional, Zucker, Jeremy D., additional, Burnet, Meagan C., additional, Lipton, Anna K., additional, Bilbao, Aivett, additional, Orton, Daniel J., additional, Kim, Young-Mo, additional, Moore, Ronald J., additional, Robinson, Errol W., additional, Baker, Scott E., additional, Webb-Robertson, Bobbie-Jo M., additional, Guss, Adam M., additional, Gladden, John M., additional, Beckham, Gregg T., additional, Magnuson, Jon K., additional, and Burnum-Johnson, Kristin E., additional

Published
2020
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28. Detection of Head and Neck Cancer Based on Longitudinal Changes in Serum Protein Abundance

Author

Lee, Ju Yeon, primary, Shi, Tujin, additional, Petyuk, Vladislav A., additional, Schepmoes, Athena A., additional, Fillmore, Thomas L., additional, Wang, Yi-Ting, additional, Cardoni, Wayne, additional, Coppit, George, additional, Srivastava, Shiv, additional, Goodman, Joseph F., additional, Shriver, Craig D., additional, Liu, Tao, additional, and Rodland, Karin D., additional

Published
2020
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29. Proteomic Tissue-Based Classifier for Early Prediction of Prostate Cancer Progression

Author

Gao, Yuqian, primary, Wang, Yi-Ting, additional, Chen, Yongmei, additional, Wang, Hui, additional, Young, Denise, additional, Shi, Tujin, additional, Song, Yingjie, additional, Schepmoes, Athena A., additional, Kuo, Claire, additional, Fillmore, Thomas L., additional, Qian, Wei-Jun, additional, Smith, Richard D., additional, Srivastava, Sudhir, additional, Kagan, Jacob, additional, Dobi, Albert, additional, Sesterhenn, Isabell A., additional, Rosner, Inger L., additional, Petrovics, Gyorgy, additional, Rodland, Karin D., additional, Srivastava, Shiv, additional, Cullen, Jennifer, additional, and Liu, Tao, additional

Published
2020
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30. Proteomic Insights into Phycobilisome Degradation, A Selective and Tightly Controlled Process in The Fast-Growing Cyanobacterium Synechococcus elongatus UTEX 2973

Author

Nagarajan, Aparna, primary, Zhou, Mowei, additional, Nguyen, Amelia Y., additional, Liberton, Michelle, additional, Kedia, Komal, additional, Shi, Tujin, additional, Piehowski, Paul, additional, Shukla, Anil, additional, Fillmore, Thomas L., additional, Nicora, Carrie, additional, Smith, Richard D., additional, Koppenaal, David W., additional, Jacobs, Jon M., additional, and Pakrasi, Himadri B., additional

Published
2019
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31. Automated Nanoflow Two-Dimensional Reversed-Phase Liquid Chromatography System Enables In-Depth Proteome and Phosphoproteome Profiling of Nanoscale Samples

Author

Dou, Maowei, primary, Tsai, Chia-Feng, additional, Piehowski, Paul D., additional, Wang, Yang, additional, Fillmore, Thomas L., additional, Zhao, Rui, additional, Moore, Ronald J., additional, Zhang, Pengfei, additional, Qian, Wei-Jun, additional, Smith, Richard D., additional, Liu, Tao, additional, Kelly, Ryan T., additional, Shi, Tujin, additional, and Zhu, Ying, additional

Published
2019
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32. Carrier-Assisted Single-Tube Processing Approach for Targeted Proteomics Analysis of Low Numbers of Mammalian Cells

Author

Zhang, Pengfei, primary, Gaffrey, Matthew J., additional, Zhu, Ying, additional, Chrisler, William B., additional, Fillmore, Thomas L., additional, Yi, Lian, additional, Nicora, Carrie D., additional, Zhang, Tong, additional, Wu, Huanming, additional, Jacobs, Jon, additional, Tang, Keqi, additional, Kagan, Jacob, additional, Srivastava, Sudhir, additional, Rodland, Karin D., additional, Qian, Wei-Jun, additional, Smith, Richard D., additional, Liu, Tao, additional, Wiley, H. Steven, additional, and Shi, Tujin, additional

Published
2018
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34. Addressing the challenge of soil metaproteome complexity by improving metaproteome depth of coverage through two-dimensional liquid chromatography

Author

Callister, Stephen J., primary, Fillmore, Thomas L., additional, Nicora, Carrie D., additional, Shaw, Jared B., additional, Purvine, Samuel O., additional, Orton, Daniel J., additional, White, Richard Allen, additional, Moore, Ronald J., additional, Burnet, Meagan C., additional, Nakayasu, Ernesto S., additional, Payne, Samuel H., additional, Jansson, Janet K., additional, and Paša-Tolić, Ljiljana, additional

Published
2018
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35. Effective coupling of CE with nanoESI MS via a true sheathless metal-coated emitter interface for robust and high sensitivity sample quantification (ASMS 2016)

Author

PNNL Omics, Keqi Tang, Xuejiang Guo, Fillmore, Thomas L., and Yuqian Gao

Abstract

Capillary electrophoresis (CE) coupled with mass spectrometry (MS) is a promising alternative to conventional liquid chromatography (LC) MS in chemical and biological sample analysis due to its high resolving power and fast separation speed. Reproducibility and ruggedness problems, suffered to a certain degree by almost all the CE-MS interfaces, limit its broad applications. We present the development of a new sheathless CE-MS interface aiming at overcoming these problems and pushing CE-MS suitable to routine sample analysis with high sensitivity. A systematic evaluation of the new interface was performed using a hybrid capillary isotachophoresis (CITP) and capillary zone electrophoresis (CZE) separation coupled with electrospray ionization (ESI) MS for its achievable sensitivity and reproducibility in sample quantification.

Published
2017
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36. Evaluation of SRM-, PRM- and DIA-based Targeted Quantification (ASMS 2017)

Author

PNNL Omics, Ehwang Song, Tujin Shi, Wang, Hui, Shukla, Anil K., Fillmore, Thomas L., Yuqian Gao, Nie, Song, Moore, Ronald J., Gaffrey, Matthew J., Schepmoes, Athena A., Rodland, Karin D., Smith, Richard D., and Liu, Tao

Abstract

Mass spectrometry (MS) based proteomics has increasingly been considered as a pivotal technology for biological and biomedical research. Verification studies using targeted proteomics are often required to provide high selectivity, accuracy, and sample throughput while maintaining high sensitivity. Selected reaction monitoring (SRM) has widely been used as a targeted MS technique with high sensitivity and selectivity. With recent advances in MS instrumentation especially the Orbitrap based hybrid mass spectrometers, two novel approaches, namely parallel reaction monitoring (PRM) and data independent acquisition (DIA), were also introduced for targeted proteomics analysis. Herein, we systematically assessed the analytical characteristics of SRM-, PRM-, and DIA-based targeted quantification and demonstrated the complementarity of these techniques for fit-for-purpose applications. Twenty peptides were selected for the initial evaluation of the 3 techniques. The heavy stable isotope-labeled peptides were synthesized and spiked into the samples as the internal standard. SRM was performed on the Thermo TSQ Vantage triple quadrupole instrument while PRM and DIA were carried out on the Thermo Q Exactive HF instrument with the same LC system and setup. Each transition and its corresponding collision energy were optimized for SRM while HCD of target precursor ions was acquired with an isolation window of 2 and 5 m/z for PRM and DIA, respectively. The performance of the 3 techniques was further evaluated using 133 and 110 peptides associated with EGFR pathway and prostate cancer, respectively. Data were analyzed using Skyline. The reverse response curve was created for the 20 peptides in HMEC cell digest to characterize the linear dynamic range and lower limit of quantification (LLOQ) of the 3 techniques. Overall, all 20 target peptides were quantified by SRM while 18 and 15 peptides were quantified by PRM and DIA, respectively; 6 peptides showed better LLOQ values by SRM over PRM and DIA while 3 peptides showed better LLOQ values by PRM over SRM and DIA. SRM also exhibited slightly better reproducibility. An averaged CV values were 8.3, 10.0, and 12.2 by SRM, PRM, and DIA, respectively. One of the challenges in SRM is potential interferences in complex samples due to the low mass resolution and accuracy achievable on the triple quadrupole instrument. PRM and DIA data acquired on high-resolution Q Exactive instrument are more flexible for minimizing interferences by requiring higher mass accuracy and post-acquisition transition refinement and peak integration, contributing to better selectivity and quantification accuracy for 8 of the target peptides. To further test the potential impact of multiplexing levels on the analytical performance, SRM and PRM were also performed for 110 peptides associated with prostate cancer. Two injection times (60 and 150 ms) were used for PRM. SRM and PRM quantified 44 and 48 peptides, respectively. Both ion injection time settings in PRM showed the same level of peptide detection and high correlation with SRM (R2>0.98); however the use of longer injection time resulted in better MS response and reproducibility. We are currently applying the 3 techniques to quantify 133 EGFR pathway peptides. Front-end enrichment techniques will also be tested for their impact on the sensitivity and selectivity of the 3 targeted quantification methods.

Published
2017
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37. Evaluation of SRM-, PRM- and DIA-based Targeted Quantification

Author

Ehwang Song, Tujin Shi, Wang, Hui, Shukla, Anil K., Fillmore, Thomas L., Yuqian Gao, Nie, Song, Moore, Ronald J., Gaffrey, Matthew J., Schepmoes, Athena A., Rodland, Karin D., Smith, Richard D., Liu, Tao, and PNNL Omics

Abstract

Mass spectrometry (MS) based proteomics has increasingly been considered as a pivotal technology for biological and biomedical research. Verification studies using targeted proteomics are often required to provide high selectivity, accuracy, and sample throughput while maintaining high sensitivity. Selected reaction monitoring (SRM) has widely been used as a targeted MS technique with high sensitivity and selectivity. With recent advances in MS instrumentation especially the Orbitrap based hybrid mass spectrometers, two novel approaches, namely parallel reaction monitoring (PRM) and data independent acquisition (DIA), were also introduced for targeted proteomics analysis. Herein, we systematically assessed the analytical characteristics of SRM-, PRM-, and DIA-based targeted quantification and demonstrated the complementarity of these techniques for fit-for-purpose applications. Twenty peptides were selected for the initial evaluation of the 3 techniques. The heavy stable isotope-labeled peptides were synthesized and spiked into the samples as the internal standard. SRM was performed on the Thermo TSQ Vantage triple quadrupole instrument while PRM and DIA were carried out on the Thermo Q Exactive HF instrument with the same LC system and setup. Each transition and its corresponding collision energy were optimized for SRM while HCD of target precursor ions was acquired with an isolation window of 2 and 5 m/z for PRM and DIA, respectively. The performance of the 3 techniques was further evaluated using 133 and 110 peptides associated with EGFR pathway and prostate cancer, respectively. Data were analyzed using Skyline. The reverse response curve was created for the 20 peptides in HMEC cell digest to characterize the linear dynamic range and lower limit of quantification (LLOQ) of the 3 techniques. Overall, all 20 target peptides were quantified by SRM while 18 and 15 peptides were quantified by PRM and DIA, respectively; 6 peptides showed better LLOQ values by SRM over PRM and DIA while 3 peptides showed better LLOQ values by PRM over SRM and DIA. SRM also exhibited slightly better reproducibility. An averaged CV values were 8.3, 10.0, and 12.2 by SRM, PRM, and DIA, respectively. One of the challenges in SRM is potential interferences in complex samples due to the low mass resolution and accuracy achievable on the triple quadrupole instrument. PRM and DIA data acquired on high-resolution Q Exactive instrument are more flexible for minimizing interferences by requiring higher mass accuracy and post-acquisition transition refinement and peak integration, contributing to better selectivity and quantification accuracy for 8 of the target peptides. To further test the potential impact of multiplexing levels on the analytical performance, SRM and PRM were also performed for 110 peptides associated with prostate cancer. Two injection times (60 and 150 ms) were used for PRM. SRM and PRM quantified 44 and 48 peptides, respectively. Both ion injection time settings in PRM showed the same level of peptide detection and high correlation with SRM (R2>0.98); however the use of longer injection time resulted in better MS response and reproducibility. We are currently applying the 3 techniques to quantify 133 EGFR pathway peptides. Front-end enrichment techniques will also be tested for their impact on the sensitivity and selectivity of the 3 targeted quantification methods.

Published
2017
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38. Verification of prostate cancer genomics biomarker candidates at protein level using SRM-MS (ASMS 2017)

Author

PNNL Omics, Wang, Hui, Yuqian Gao, Schepmoes, Athena A., Gyorgy Petrovics, Cullen, Jennifer, Fillmore, Thomas L., Tujin Shi, Qian, Wei-Jun, Smith, Richard D., Weaver, Brandi, Leach, Robin, Thompson, Ian M., Srivastava, Sudhir, Dobi, Albert, Rodland, Karin D., Kagan, Jacob, Srivastava, Shiv, and Liu, Tao

Abstract

Targeted MS-based proteomics such as selected reaction monitoring (SRM) provides an attractive alternatives to affinity reagent based methods (e.g., ELISA) for rapid, sensitive, specific, and multiplexed verification of genomics biomarker candidates at the protein level. In this study, 52 biomarker candidates for prostate cancer were selected from existing genomics data sets and validated cancer drivers, and analyzed in tumor and control tissue samples using the highly sensitive PRISM (high-pressure, high-resolution separations coupled with intelligent selection and multiplexing)-SRM approach to identify a panel of proteins with the potential to discriminate between aggressive and indolent forms of prostate cancer and predict prostate cancer progression. PRISM-SRM assays have been developed for the 52 prostate cancer biomarker candidates. Two sets of tissue samples were analyzed using PRISM-SRM: 1) 10 high Gleason-score (7-9) primary prostate tumors and 10 benign prostatic hyperplasia (BPH) tissues (OCT-embedded specimens); and 2) 10 primary tumors from patients showing metastatic progression, 10 primary tumors from patients who showed biochemical recurrence (BCR), and 10 primary tumors from patients with no BCR or metastatic progression after more than ten years of follow-up after radical prostatectomy (FFPE whole mount prostate specimens). After protein extraction, reduction, alkylation and tryptic digestion, peptide mixtures with spike-in heavy standards were analyzed. All SRM data were generated on ThermoScientific TSQ Vantage QQQ mass spectrometer and analyzed by Xcalibur and Skyline. PRISM-SRM analyses of all the patient tissue samples enabled the detection of 48 out of 52 biomarker candidates, suggesting extremely low level of expression of the remaining 4 genes in the prostate tissues (HXC6, OSTP, TWST1, and ERG8); in comparison regular LC-SRM can only detect 21 of these candidates at protein level. In the 10 high Gleason-scores tumors and 10 BPH controls, a total of 42 proteins were quantified and compared, among which 13 proteins were found differentially abundant in tumors versus controls, with P 0.7. The finding that these kinases and proteases were overexpressed in prostate cancer was consistent with previous genetic studies. Although PSA and ERG had p values slightly greater than 0.05, they also showed up-regulated protein expression levels in tumors (2.2- and 4-fold, respectively). PRISM-SRM analysis of the 30 FFPE tissue samples quantified a total of 42 proteins. The ERG status of the index tumor (which was dissected for protein extraction), determined by immunohistochemistry on the whole mounted prostate sections, correlated very well with the protein concentrations measured by PRISM-SRM, demonstrating the sensitivity and reliability of these PRISM-SRM assays. WE found 3 proteins discriminating between “metastatic progression” and “no progression” tumors, 1 protein discriminated between BCR and “no progression” tumors, and 4 proteins discriminated between metastatic progression and BCR tumors (P

Published
2017
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39. A carrier-assisted targeted mass spectrometry approach for proteomics analysis of single cells (ASMS 2017)

Author

PNNL Omics, Tujin Shi, Gaffrey, Matthew J., Fillmore, Thomas L., Nie, Song, Nicora, Carrie D., H Steven Wiley, Rodland, Karin D., Liu, Tao, Smith, Richard D., and Qian, Wei-Jun

Abstract

Antibody-based flow cytometry and mass cytometry are predominant technologies for targeted proteomics analysis of single cells. However, they share common shortcomings with other antibody-based methods (e.g., low-multiplex, the need of high-quality antibodies, and unavailability of antibodies for new proteins). Furthermore, they lack quantitation accuracy to provide accurate protein concentrations. Mass spectrometry (MS)-based targeted proteomics has emerged as a promising alternative for antibody-free precise high-multiplex quantification of target proteins. However, it does not meet the merit for analysis of single cells due to insufficient sensitivity. To tackle this issue, we recently developed a new targeted MS approach that couples carrier-assisted sample preparation to a highly sensitive targeted MS platform for enabling proteomics analysis of single cells. In complex biological samples low abundant proteins can be reliably detected without loss because highly abundant proteins severe as an effective carrier to prevent their loss. With this observation, we recently developed a simple preparation method with using exogenous BSA protein as a carrier for lossless processing of single cells. A highly sensitive targeted MS platform PRISM-SRM/PRM (high-pressure, high-resolution separations with intelligent selection and multiplexing coupled to selected/parallel reaction monitoring) was then used for absolute quantification of key EGFR pathway proteins in 1-100 HMEC cells. The Waters nanoACQUITY UPLC system and the Thermo Scientific TSQ Vantage or Q Exactive MS instrument were used for LC separation and targeted quantification, respectively. The Skyline software was employed for analysis of SRM/PRM data.Recently PRISM-SRM was demonstrated for enabling highly sensitive quantification of target proteins at ≤100 copies per human cell when starting with only ~25 µg of cell lysate digests, which is equal to ~250,000 human cells. However, in the real clinical applications the available sample amount could be much smaller (e.g., 1-10 circulating tumor cells) and sample loss is almost inevitable for current targeted MS analysis. With the introduction of exogenous proteins as a carrier to prevent sample loss we refined our PRISM termed carrier-assisted PRISM (i.e., cPRISM) for enabling targeted quantification of proteins in single cells by coupling with SRM/PRM. In our proof-of-concept experiment BSA protein was selected as a carrier for assisting processing of a small number of cells. 1-100 isolated HMEC cells were collected into 50 µg of BSA carrier-containing tubes to prevent cell adhesion to the tube wall. When mixed with BSA carrier, the small number of cells were processed as easily as bulk cells with minimal sample loss. The constant ‘unbiased’ peptide recovery was observed across all the samples with confident detection of multiple endogenous peptides with different hydrophobicity at 1-5 HMEC cells as well as the linear response curves of target proteins from 1 to 100 cells. This result has shown the carrier-assisted method can be used to effectively process single cells for highly sensitive PRISM-SRM quantification. Furthermore, detection of SFADINLYR derived from NRAS in small numbers of HMEC cells (~80,000 NRAS molecules per HMEC cell from bulk cells) suggested that our current targeted MS platforms can provided ~100 zmol level of sensitivity. Currently we are working on further improving cPRISM-SRM performance in sensitivity, throughput and robustness. We envision that this new targeted MS approach will have broad utilities in biomedical research (e.g., single cells and clinical applications with extremely small sample amounts).

Published
2017
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40. A Customizable Flow Injection System for Automated, High Throughput, and Time Sensitive Ion Mobility Spectrometry and Mass Spectrometry Measurements

Author

Orton, Daniel J., Tfaily, Malak M., Moore, Ronald J., LaMarche, Brian L., Zheng, Xueyun, Fillmore, Thomas L., Chu, Rosalie K., Weitz, Karl K., Monroe, Matthew E., Kelly, Ryan T., Smith, Richard D., and Baker, Erin S.

Abstract

To better understand disease conditions and environmental perturbations, multiomic studies combining proteomic, lipidomic, and metabolomic analyses are vastly increasing in popularity. In a multiomic study, a single sample is typically extracted in multiple ways, and various analyses are performed using different instruments, most often based upon mass spectrometry (MS). Thus, one sample becomes many measurements, making high throughput and reproducible evaluations a necessity. One way to address the numerous samples and varying instrumental conditions is to utilize a flow injection analysis (FIA) system for rapid sample injections. While some FIA systems have been created to address these challenges, many have limitations such as costly consumables, low pressure capabilities, limited pressure monitoring, and fixed flow rates. To address these limitations, we created an automated, customizable FIA system capable of operating at a range of flow rates (∼50 nL/min to 500 μL/min) to accommodate both low- and high-flow MS ionization sources. This system also functions at varying analytical throughputs from 24 to 1200 samples per day to enable different MS analysis approaches. Applications ranging from native protein analyses to molecular library construction were performed using the FIA system, and results showed a highly robust and reproducible platform capable of providing consistent performance over many days without carryover, as long as washing buffers specific to each molecular analysis were utilized.

Published
2024
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41. Detection of Head and Neck Cancer Based on Longitudinal Changes in Serum Protein Abundance.

Author

Ju Yeon Lee, Tujin Shi, Petyuk, Vladislav A., Schepmoes, Athena A., Fillmore, Thomas L., Yi-Ting Wang, Cardoni, Wayne, Coppit, George, Srivastava, Shiv, Goodman, Joseph F., Shriver, Craig D., Tao Liu, and Rodland, Karin D.

Abstract

Background: Approximately 85% of the U.S. military active duty population is male and less than 50 years of age, with elevated levels of known risk factors for oropharyngeal squamous cell carcinoma (OPSCC), including smoking, excessive use of alcohol, and greater numbers of sexual partners, and elevated prevalence of human papilloma virus (HPV). Given the recent rise in incidence of OPSCC related to the HPV, the Department of Defense Serum Repository provides an unparalleled resource for longitudinal studies of OPSCC in the military for the identification of early detection biomarkers. Methods: We identified 175 patients diagnosed with OPSCC with 175 matched healthy controls and retrieved a total of 978 serum samples drawn at the time of diagnosis, 2 and 4 years prior to diagnosis, and 2 years after diagnosis. Following immunoaffinity depletion, serum samples were analyzed by targeted proteomics assays for multiplexed quantification of a panel of 146 candidate protein biomarkers from the curated literature. Results: Using a Random Forest machine learning approach, we derived a 13-protein signature that distinguishes cases versus controls based on longitudinal changes in serum protein concentration. The abundances of each of the 13 proteins remain constant over time in control subjects. The AUC for the derived Random Forest classifier was 0.90. Conclusions: This 13-protein classifier is highly promising for detection of OPSCC prior to overt symptoms. Impact: Use of longitudinal samples has significant potential to identify biomarkers for detection and risk stratification. [ABSTRACT FROM AUTHOR]

Published
2020
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43. Targeted brain proteomics uncover multiple pathways to Alzheimer's dementia

Author

Yu, Lei, primary, Petyuk, Vladislav A., additional, Gaiteri, Chris, additional, Mostafavi, Sara, additional, Young‐Pearse, Tracy, additional, Shah, Raj C., additional, Buchman, Aron S., additional, Schneider, Julie A., additional, Piehowski, Paul D., additional, Sontag, Ryan L., additional, Fillmore, Thomas L., additional, Shi, Tujin, additional, Smith, Richard D., additional, Jager, Philip L., additional, and Bennett, David A., additional

Published
2018
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45. A Customizable Flow Injection System for Automated, High Throughput, and Time Sensitive Ion Mobility Spectrometry and Mass Spectrometry Measurements

Author

Orton, Daniel J., primary, Tfaily, Malak M., additional, Moore, Ronald J., additional, LaMarche, Brian L., additional, Zheng, Xueyun, additional, Fillmore, Thomas L., additional, Chu, Rosalie K., additional, Weitz, Karl K., additional, Monroe, Matthew E., additional, Kelly, Ryan T., additional, Smith, Richard D., additional, and Baker, Erin S., additional

Published
2017
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46. Multiplexed targeted mass spectrometry assays for prostate cancer-associated urinary proteins

Author

Shi, Tujin, primary, Quek, Sue-Ing, additional, Gao, Yuqian, additional, Nicora, Carrie D., additional, Nie, Song, additional, Fillmore, Thomas L., additional, Liu, Tao, additional, Rodland, Karin D., additional, Smith, Richard D., additional, Leach, Robin J., additional, Thompson, Ian M., additional, Vitello, Elizabeth A., additional, Ellis, William J., additional, Liu, Alvin Y., additional, and Qian, Wei-Jun, additional

Published
2017
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47. Leucine Biosynthesis Is Involved in Regulating High Lipid Accumulation in Yarrowia lipolytica

Author

Lee, Sang Yup, Kerkhoven, Eduard J., Kim, Young-Mo, Wei, Siwei, Nicora, Carrie D., Fillmore, Thomas L., Purvine, Samuel O., Webb-Robertson, Bobbie-Jo, Smith, Richard D., Baker, Scott E., Metz, Thomas O., Nielsen, Jens, Lee, Sang Yup, Kerkhoven, Eduard J., Kim, Young-Mo, Wei, Siwei, Nicora, Carrie D., Fillmore, Thomas L., Purvine, Samuel O., Webb-Robertson, Bobbie-Jo, Smith, Richard D., Baker, Scott E., Metz, Thomas O., and Nielsen, Jens

Abstract

The yeast Yarrowia lipolytica is a potent accumulator of lipids, and lipogenesis in this organism can be influenced by a variety of factors, such as genetics and environmental conditions. Using a multifactorial study, we elucidated the effects of both genetic and environmental factors on regulation of lipogenesis in Y. lipolytica and identified how two opposite regulatory states both result in lipid accumulation. This study involved comparison of a strain overexpressing diacylglycerol acyltransferase (DGA1) with a control strain grown under either nitrogen or carbon limitation conditions. A strong correlation was observed between the responses on the transcript and protein levels. Combination of DGA1 overexpression with nitrogen limitation resulted in a high level of lipid accumulation accompanied by downregulation of several amino acid biosynthetic pathways, including that of leucine in particular, and these changes were further correlated with a decrease in metabolic fluxes. This downregulation was supported by the measured decrease in the level of 2-isopropylmalate, an intermediate of leucine biosynthesis. Combining the multi-omics data with putative transcription factor binding motifs uncovered a contradictory role for TORC1 in controlling lipid accumulation, likely mediated through 2-isopropylmalate and a Leu3-like transcription factor.IMPORTANCE The ubiquitous metabolism of lipids involves refined regulation, and an enriched understanding of this regulation would have wide implications. Various factors can influence lipid metabolism, including the environment and genetics. We demonstrated, using a multi-omics and multifactorial experimental setup, that multiple factors affect lipid accumulation in the yeast Yarrowia lipolytica. Using integrative analysis, we identified novel interactions between nutrient restriction and genetic factors involving regulators that are highly conserved among eukaryotes. Given that lipid metabolism is involved in many diseases but

Published
2017

48. Quantification of mutant SPOP proteins in prostate cancer using mass spectrometry-based targeted proteomics

Author

Wang, Hui, primary, Barbieri, Christopher E., additional, He, Jintang, additional, Gao, Yuqian, additional, Shi, Tujin, additional, Wu, Chaochao, additional, Schepmoes, Athena A., additional, Fillmore, Thomas L., additional, Chae, Sung-Suk, additional, Huang, Dennis, additional, Mosquera, Juan Miguel, additional, Qian, Wei-Jun, additional, Smith, Richard D., additional, Srivastava, Sudhir, additional, Kagan, Jacob, additional, Camp, David G., additional, Rodland, Karin D., additional, Rubin, Mark A., additional, and Liu, Tao, additional

Published
2017
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49. Deep-Dive Targeted Quantification for Ultrasensitive Analysis of Proteins in Nondepleted Human Blood Plasma/Serum and Tissues

Author

Nie, Song, primary, Shi, Tujin, additional, Fillmore, Thomas L., additional, Schepmoes, Athena A., additional, Brewer, Heather, additional, Gao, Yuqian, additional, Song, Ehwang, additional, Wang, Hui, additional, Rodland, Karin D., additional, Qian, Wei-Jun, additional, Smith, Richard D., additional, and Liu, Tao, additional

Published
2017
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50. Targeted proteomic assays for quantitation of proteins identified by proteogenomic analysis of ovarian cancer

Author

Song, Ehwang, primary, Gao, Yuqian, additional, Wu, Chaochao, additional, Shi, Tujin, additional, Nie, Song, additional, Fillmore, Thomas L., additional, Schepmoes, Athena A., additional, Gritsenko, Marina A., additional, Qian, Wei-Jun, additional, Smith, Richard D., additional, Rodland, Karin D., additional, and Liu, Tao, additional

Published
2017
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