Search

Your search keyword '"Azra Krek"' showing total 22 results
Start Over Author "Azra Krek"
22 results on '"Azra Krek"'

1. Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer’s disease: review, recommendation, implementation and application

Author

Minghui Wang, Won-min Song, Chen Ming, Qian Wang, Xianxiao Zhou, Peng Xu, Azra Krek, Yonejung Yoon, Lap Ho, Miranda E. Orr, Guo-Cheng Yuan, and Bin Zhang

Subjects
Alzheimer’s disease, Single cell sequencing, Single cell RNA-sequencing, Single cell ATAC-sequencing, Spatial transcriptomics, Clustering analysis, Neurology. Diseases of the nervous system, RC346-429, Geriatrics, RC952-954.6
Abstract

Abstract Alzheimer’s disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

Published
2022
Full Text
View/download PDF

2. Genome-wide mapping of the distribution of CarD, RNAP σA, and RNAP β on the Mycobacterium smegmatis chromosome using chromatin immunoprecipitation sequencing

Author

Robert Landick, Azra Krek, Michael S. Glickman, Nicholas D. Socci, and Christina L. Stallings

Subjects
Mycobacteria, RNA polymerase, Transcription, Tuberculosis, CarD, Genetics, QH426-470
Abstract

CarD is an essential mycobacterial protein that binds the RNA polymerase (RNAP) and affects the transcriptional profile of Mycobacterium smegmatis and Mycobacterium tuberculosis [6]. We predicted that CarD was directly regulating RNAP function but our prior experiments had not determined at what stage of transcription CarD was functioning and at which genes CarD interacted with the RNAP. To begin to address these open questions, we performed chromatin immunoprecipitation sequencing (ChIP-seq) to survey the distribution of CarD throughout the M. smegmatis chromosome. The distribution of RNAP subunits β and σA were also profiled. We expected that RNAP β would be present throughout transcribed regions and RNAP σA would be predominantly enriched at promoters based on work in Escherichia coli [3], however this had yet to be determined in mycobacteria. The ChIP-seq analyses revealed that CarD was never present on the genome in the absence of RNAP, was primarily associated with promoter regions, and was highly correlated with the distribution of RNAP σA. The colocalization of σA and CarD led us to propose that in vivo, CarD associates with RNAP initiation complexes at most promoters and is therefore a global regulator of transcription initiation. Here we describe in detail the data from the ChIP-seq experiments associated with the study published by Srivastava and colleagues in the Proceedings of the National Academy of Science in 2013 [5] as well as discuss the findings from this dataset in relation to both CarD and mycobacterial transcription as a whole. The ChIP-seq data have been deposited in the Gene Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (accession no. GSE48164).

Published
2014
Full Text
View/download PDF

3. microRNAs regulate cell-to-cell variability of endogenous target gene expression in developing mouse thymocytes.

Author

Rory Blevins, Ludovica Bruno, Thomas Carroll, James Elliott, Antoine Marcais, Christina Loh, Arnulf Hertweck, Azra Krek, Nikolaus Rajewsky, Chang-Zheng Chen, Amanda G Fisher, and Matthias Merkenschlager

Subjects
Genetics, QH426-470
Abstract

The development and homeostasis of multicellular organisms relies on gene regulation within individual constituent cells. Gene regulatory circuits that increase the robustness of gene expression frequently incorporate microRNAs as post-transcriptional regulators. Computational approaches, synthetic gene circuits and observations in model organisms predict that the co-regulation of microRNAs and their target mRNAs can reduce cell-to-cell variability in the expression of target genes. However, whether microRNAs directly regulate variability of endogenous gene expression remains to be tested in mammalian cells. Here we use quantitative flow cytometry to show that microRNAs impact on cell-to-cell variability of protein expression in developing mouse thymocytes. We find two distinct mechanisms that control variation in the activation-induced expression of the microRNA target CD69. First, the expression of miR-17 and miR-20a, two members of the miR-17-92 cluster, is co-regulated with the target mRNA Cd69 to form an activation-induced incoherent feed-forward loop. Another microRNA, miR-181a, acts at least in part upstream of the target mRNA Cd69 to modulate cellular responses to activation. The ability of microRNAs to render gene expression more uniform across mammalian cell populations may be important for normal development and for disease.

Published
2015
Full Text
View/download PDF

4. Gut-associated lymphoid tissue attrition associates with response to anti-α4β7 therapy in ulcerative colitis

Author

Pablo Canales-Herrerias, Mathieu Uzzan, Akihiro Seki, Rafael S. Czepielewski, Bram Verstockt, Alexandra Livanos, Fiona Raso, Alexandra Dunn, Daniel Dai, Andrew Wang, Zainab Al-taie, Jerome Martin, Huaibin M. Ko, Minami Tokuyama, Michael Tankelevich, Hadar Meringer, Francesca Cossarini, Divya Jha, Azra Krek, John D. Paulsen, M. Zuber Nakadar, Joshua Wong, Emma C. Erlich, Emily J. Onufer, Beth A. Helmink, Keshav Sharma, Adam Rosenstein, Grace Chung, Travis Dawson, Julius Juarez, Vijay Yajnik, Andrea Cerutti, Jeremiah Faith, Mayte Suarez-Farinas, Carmen Argmann, Francesca Petralia, Gwendalyn J. Randolph, Alexandros D. Polydorides, Andrea Reboldi, Jean Frederic Colombel, and Saurabh Mehandru

Abstract

Targeting the α4β7-MAdCAM-1 axis with vedolizumab (VDZ) is a front-line therapeutic paradigm in ulcerative colitis (UC). However, mechanism(s) of action (MOA) of VDZ remain relatively undefined. Here, we examined three distinct cohorts of patients with UC (n=83, n=60, and n=21), to determine the effect of VDZ on the mucosal and peripheral immune system. Transcriptomic studies with protein level validation were used to study drug MOA using conventional and transgenic murine models. We found a significant decrease in colonic and ileal naïve B and T cells and circulating gut-homing plasmablasts (β7+) in VDZ-treated patients, pointing to gut-associated lymphoid tissue (GALT) targeting by VDZ. Murine Peyer’s patches (PP) demonstrated a significant loss cellularity associated with reduction in follicular B cells, including a unique population of epithelium-associated B cells, following anti-α4β7 antibody (mAb) administration. Photoconvertible (KikGR) mice unequivocally demonstrated impaired cellular entry into PPs in anti-α4β7 mAb treated mice. In VDZ-treated, but not anti-tumor necrosis factor-treated UC patients, lymphoid aggregate size was significantly reduced in treatment responders compared to non-responders, with an independent validation cohort further confirming these data. GALT targeting represents a novel MOA of α4β7-targeted therapies, with major implications for this therapeutic paradigm in UC, and for the development of new therapeutic strategies.

Published
2023

5. BayesDeBulk: A Flexible Bayesian Algorithm for the Deconvolution of Bulk Tumor Data

Author

Francesca Petralia, Azra Krek, Anna P. Calinawan, Daniel Charytonowicz, Robert Sebra, Song Feng, Sara Gosline, Pietro Pugliese, Amanda G. Paulovich, Jacob J. Kennedy, Michele Ceccarelli, and Pei Wang

Subjects
Profiling (computer programming), Set (abstract data type), Computer science, Bayesian probability, A priori and a posteriori, Bayesian algorithm, Deconvolution, Computational biology, Data type
Abstract

Characterizing the tumor microenvironment is crucial in order to improve responsiveness to immunotherapy and develop new therapeutic strategies. The fraction of different cell-types in the tumor microenvironment can be estimated based on transcriptomic profiling of bulk tumor data via deconvolution algorithms. One class of such algorithms, known as reference-based, rely on a reference signature containing gene expression data for various cell-types. The limitation of these methods is that such a signature is derived from the gene expression of pure cell-types, which might not be consistent with the transcriptomic profiling in solid tumors. On the other hand, reference-free methods usually require only a set of cell-specific markers to perform deconvolution; however, once the different components have been estimated from the data, their labeling can be problematic. To overcome these limitations, we propose BayesDeBulk - a new reference-free Bayesian method for bulk deconvolution based on gene expression data. Given a list of markers expressed in each cell-type (cell-specific markers), a repulsive prior is placed on the mean of gene expression in different cell-types to ensure that cell-specific markers are upregulated in a particular component. Contrary to existing reference-free methods, the labeling of different components is decided a priori through a repulsive prior. Furthermore, the advantage over reference-based algorithms is that the cell fractions as well as the gene expression of different cells are estimated from the data, simultaneously. Given its flexibility, BayesDeBulk can be utilized to perform bulk deconvolution beyond transcriptomic data, based on other data types such as proteomic profiles or the integration of both transcriptomic and proteomic profiles.

Published
2021

6. Proteogenomic and metabolomic characterization of human glioblastoma

Author

Cristina E. Tognon, Larisa Polonskaya, Tara Skelly, Shuang Cai, Francesmary Modugno, Larissa Rossell, Nancy Roche, Chen Huang, Jessika Baral, Fulvio D'Angelo, Wen-Wei Liang, Chia-Feng Tsai, Sneha P. Couvillion, Karin D. Rodland, Jun Zhu, Liang-Bo Wang, Paul D. Piehowski, Antonio Colaprico, Anupriya Agarwal, Matthew A. Wyczalkowski, Umut Ozbek, Francesca Petralia, Alexis Demopoulos, William W. Maggio, Lin Chen, Katherine A. Hoadley, Richard D. Smith, Sandra Cottingham, John McGee, Marcin J. Domagalski, Houxiang Zhu, Emek Demir, Rebecca I. Montgomery, Jamie Moon, Rashna Madan, George D. Wilson, Luciano Garofano, Ewa P. Malc, Chelsea J. Newton, Steven A. Carr, Chandan Kumar-Sinha, Donghui Tan, Christopher R. Kinsinger, Oxana Paklina, Weiqing Wan, Stephanie De Young, Sandra Cerda, Shankha Satpathy, Wojciech Kaspera, Linda Hannick, Gad Getz, Runyu Hong, Shuangjia Lu, Ziad Hanhan, Daniel C. Rohrer, Annette Marrero-Oliveras, Wojciech Szopa, Yuxing Liao, Amanda G. Paulovich, Jiayi Ji, Denis A. Golbin, Tara Hiltke, Weiva Sieh, Piotr A. Mieczkowski, Matthew E. Monroe, Gilbert S. Omenn, Jill S. Barnholtz-Sloan, Azra Krek, Bing Zhang, Brittany Henderson, Peter B. McGarvey, Ratna R. Thangudu, Maciej Wiznerowicz, Saravana M. Dhanasekaran, Alex Webster, Kai Li, Karna Robinson, Nan Ji, Karl K. Weitz, Simina M. Boca, Xiaoyu Song, Anna Calinawan, Adam C. Resnick, Brian J. Druker, Dana R. Valley, David J. Clark, Tao Liu, Eric J. Jaehnig, Alicia Francis, Michele Ceccarelli, Rui Zhao, Dmitry Rykunov, Boris Reva, Elizabeth R. Duffy, Antonio Iavarone, Dave Tabor, Joshua F. McMichael, Daniel Cui Zhou, Maureen Dyer, Kimberly Elburn, Scott D. Jewell, Negin Vatanian, Shirley Tsang, Seungyeul Yoo, Alexander R. Pico, Grace Zhao, Kent J. Bloodsworth, Chet Birger, Jena Lilly, Eunkyung An, Jeffrey R. Whiteaker, Albert H. Kim, Yige Wu, Karen A. Ketchum, Felipe D. Leprevost, Alcida Karz, Uma Borate, Nathan Edwards, Uma Velvulou, Melissa Borucki, Vasileios Stathias, Sanford P. Markey, Corbin D. Jones, Ronald J. Moore, MacIntosh Cornwell, Karsten Krug, Michael J. Birrer, James Suh, Tomasz Czernicki, Jason E. McDermott, Emily S. Boja, Pei Wang, Nina Martinez, Wenke Liu, Yan Shi, Lili Blumenberg, Emily Kawaler, Jeffrey W. Tyner, Feng Chen, Jakub Stawicki, Ki Sung Um, Arul M. Chinnaiyan, Robert Zelt, Jacob J. Day, Zhen Zhang, Caleb M. Lindgren, Li Ding, Nikolay Gabrovski, Hongwei Liu, Jonathan T. Lei, Alla Karpova, Ramani B. Kothadia, Sailaja Mareedu, Mitual Amin, Hannah Boekweg, Jennifer E. Kyle, Sara R. Savage, Brian R. Rood, Yuriy Zakhartsev, Matthew L. Anderson, Alyssa Charamut, Wagma Caravan, Shakti Ramkissoon, Junmei Wang, Song Cao, Samuel H. Payne, Rosalie K. Chu, Rajiv Dhir, David W. Andrews, Galen Hostetter, Liqun Qi, Zhiao Shi, Milan G. Chheda, Robert Edwards, Hui Zhang, Weiping Ma, Jennifer M. Eschbacher, Stacey Gabriel, Jan Lubinski, Lijun Yao, Erika M. Zink, Kelly L. Stratton, William Bocik, Mathangi Thiagarajan, Shilpi Singh, Michael A. Gillette, Lisa M. Bramer, Thomas L. Bauer, Michael Vernon, Henry Rodriguez, Dimitris G. Placantonakis, Eric E. Schadt, Alexey I. Nesvizhskii, Vladislav A. Petyuk, Ana I. Robles, Yvonne Shutack, Anna Malovannaya, Stephen E. Stein, Xi Chen, Lyndon Kim, Yize Li, Shannon Richey, Stephan C. Schürer, Barbara Hindenach, Matthew J. Ellis, Yongchao Dou, David Fenyö, Amy M. Perou, Olga Potapova, Shrabanti Chowdhury, Andrew K. Godwin, Marcin Cieślik, Michael C. Wendl, Marina A. Gritsenko, Pietro Pugliese, Elie Traer, Simona Migliozzi, D. R. Mani, Houston Culpepper, Gregory J. Riggins, Xiaolu Yang, Mehdi Mesri, David Chesla, Lindsey K. Olsen, Lori J. Sokoll, Suhas Vasaikar, Liwei Zhang, Meghan C. Burke, Kelly V. Ruggles, Qing Kay Li, Daniel W. Chan, Bo Wen, Nicollette Maunganidze, Darlene Tansil, Joseph H. Rothstein, Barbara Pruetz, Pushpa Hariharan, Wang, L. -B., Karpova, A., Gritsenko, M. A., Kyle, J. E., Cao, S., Li, Y., Rykunov, D., Colaprico, A., Rothstein, J. H., Hong, R., Stathias, V., Cornwell, M., Petralia, F., Wu, Y., Reva, B., Krug, K., Pugliese, P., Kawaler, E., Olsen, L. K., Liang, W. -W., Song, X., Dou, Y., Wendl, M. C., Caravan, W., Liu, W., Cui Zhou, D., Ji, J., Tsai, C. -F., Petyuk, V. A., Moon, J., Ma, W., Chu, R. K., Weitz, K. K., Moore, R. J., Monroe, M. E., Zhao, R., Yang, X., Yoo, S., Krek, A., Demopoulos, A., Zhu, H., Wyczalkowski, M. A., Mcmichael, J. F., Henderson, B. L., Lindgren, C. M., Boekweg, H., Lu, S., Baral, J., Yao, L., Stratton, K. G., Bramer, L. M., Zink, E., Couvillion, S. P., Bloodsworth, K. J., Satpathy, S., Sieh, W., Boca, S. M., Schurer, S., Chen, F., Wiznerowicz, M., Ketchum, K. A., Boja, E. S., Kinsinger, C. R., Robles, A. I., Hiltke, T., Thiagarajan, M., Nesvizhskii, A. I., Zhang, B., Mani, D. R., Ceccarelli, M., Chen, X. S., Cottingham, S. L., Li, Q. K., Kim, A. H., Fenyo, D., Ruggles, K. V., Rodriguez, H., Mesri, M., Payne, S. H., Resnick, A. C., Wang, P., Smith, R. D., Iavarone, A., Chheda, M. G., Barnholtz-Sloan, J. S., Rodland, K. D., Liu, T., Ding, L., Agarwal, A., Amin, M., An, E., Anderson, M. L., Andrews, D. W., Bauer, T., Birger, C., Birrer, M. J., Blumenberg, L., Bocik, W. E., Borate, U., Borucki, M., Burke, M. C., Cai, S., Calinawan, A. P., Carr, S. A., Cerda, S., Chan, D. W., Charamut, A., Chen, L. S., Chesla, D., Chinnaiyan, A. M., Chowdhury, S., Cieslik, M. P., Clark, D. J., Culpepper, H., Czernicki, T., D'Angelo, F., Day, J., De Young, S., Demir, E., Dhanasekaran, S. M., Dhir, R., Domagalski, M. J., Druker, B., Duffy, E., Dyer, M., Edwards, N. J., Edwards, R., Elburn, K., Ellis, M. J., Eschbacher, J., Francis, A., Gabriel, S., Gabrovski, N., Garofano, L., Getz, G., Gillette, M. A., Godwin, A. K., Golbin, D., Hanhan, Z., Hannick, L. I., Hariharan, P., Hindenach, B., Hoadley, K. A., Hostetter, G., Huang, C., Jaehnig, E., Jewell, S. D., Ji, N., Jones, C. D., Karz, A., Kaspera, W., Kim, L., Kothadia, R. B., Kumar-Sinha, C., Lei, J., Leprevost, F. D., Li, K., Liao, Y., Lilly, J., Liu, H., Lubinski, J., Madan, R., Maggio, W., Malc, E., Malovannaya, A., Mareedu, S., Markey, S. P., Marrero-Oliveras, A., Martinez, N., Maunganidze, N., Mcdermott, J. E., Mcgarvey, P. B., Mcgee, J., Mieczkowski, P., Migliozzi, S., Modugno, F., Montgomery, R., Newton, C. J., Omenn, G. S., Ozbek, U., Paklina, O. V., Paulovich, A. G., Perou, A. M., Pico, A. R., Piehowski, P. D., Placantonakis, D. G., Polonskaya, L., Potapova, O., Pruetz, B., Qi, L., Ramkissoon, S., Resnick, A., Richey, S., Riggins, G., Robinson, K., Roche, N., Rohrer, D. C., Rood, B. R., Rossell, L., Savage, S. R., Schadt, E. E., Shi, Y., Shi, Z., Shutack, Y., Singh, S., Skelly, T., Sokoll, L. J., Stawicki, J., Stein, S. E., Suh, J., Szopa, W., Tabor, D., Tan, D., Tansil, D., Thangudu, R. R., Tognon, C., Traer, E., Tsang, S., Tyner, J., Um, K. S., Valley, D. R., Vasaikar, S., Vatanian, N., Velvulou, U., Vernon, M., Wan, W., Wang, J., Webster, A., Wen, B., Whiteaker, J. R., Wilson, G. D., Zakhartsev, Y., Zelt, R., Zhang, H., Zhang, L., Zhang, Z., Zhao, G., and Zhu, J.

Subjects
Proteomics, 0301 basic medicine, Cancer Research, CPTAC, Histone H2B acetylation, Protein Tyrosine Phosphatase, Non-Receptor Type 11, Computational biology, Biology, Article, 03 medical and health sciences, lipidome, 0302 clinical medicine, Metabolomics, proteogenomic, Humans, Phosphorylation, EP300, proteomic, Proteogenomics, acetylome, single nuclei RNA-seq, Brain Neoplasms, Phospholipase C gamma, glioblastoma, Computational Biology, Lipidome, 030104 developmental biology, Histone, Oncology, Acetylation, 030220 oncology & carcinogenesis, Mutation, biology.protein, metabolome, signaling
Abstract

Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology. We identify key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation, as well as potential targets for EGFR-, TP53-, and RB1-altered tumors. Immune subtypes with distinct immune cell types are discovered using bulk omics methodologies, validated by snRNA-seq, and correlated with specific expression and histone acetylation patterns. Histone H2B acetylation in classical-like and immune-low GBM is driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identify specific lipid distributions across subtypes and distinct global metabolic changes in IDH-mutated tumors. This work highlights biological relationships that could contribute to stratification of GBM patients for more effective treatment. Wang et al. perform integrated proteogenomic analysis of adult glioblastoma (GBM), including metabolomics, lipidomics, and single nuclei RNA-Seq, revealing insights into the immune landscape of GBM, cell-specific nature of EMT signatures, histone acetylation in classical GBM, and the existence of signaling hubs which could provide therapeutic vulnerabilities.

Published
2021

7. A proteogenomic portrait of lung squamous cell carcinoma

Author

Shuang Cai, Elizabeth R. Duffy, Felipe da Veiga Leprevost, D. R. Mani, Antonio Colaprico, Jiayi Ji, Mehdi Mesri, Alicia Francis, Peter B. McGarvey, Myvizhi Esai Selvan, Corbin D. Jones, Michael J. Birrer, Robert J. Welsh, Lori Bernard, Shankha Satpathy, Li Ding, Sara R. Savage, Eugene S. Fedorov, Fernanda Martins Rodrigues, Marcin J. Domagalski, Jennifer M. Eschbacher, Shayan C. Avanessian, Boris Reva, Harsh Batra, Suhas Vasaikar, Nathan Edwards, Michael A. Gillette, Chet Birger, Scott D. Jewell, Kei Suzuki, William Bocik, Shilpi Singh, Meenakshi Anurag, Karen E. Christianson, Namrata D. Udeshi, Vasileios Stathias, Warren G. Tourtellotte, Karl R. Clauser, Shutack, Andrii Karnuta, Dana R. Valley, Kelly V. Ruggles, Qing Kay Li, Amanda G. Paulovich, MacIntosh Cornwell, Shankara Anand, Bartosz Kubisa, Pierre M. Jean Beltran, James Suh, Gilbert S. Omenn, Azra Krek, Wohaib Hasan, Yongchao Dou, David Fenyö, Henry Rodriguez, Samuel H. Payne, Małgorzata Wojtyś, Daniel W. Chan, Bo Wen, Nicollette Maunganidze, Özgün Babur, Renganayaki Pandurengan, Karen A. Ketchum, Nikolay Gabrovski, Pankaj Vats, Saravana M. Dhanasekaran, Richard D. Smith, Gad Getz, Sailaja Mareedu, Yuxing Liao, Mikhail Krotevich, Hui Zhang, Eric J. Jaehnig, Charles A. Goldthwaite, Alexey I. Nesvizhskii, Katherine A. Hoadley, Alexander A. Green, Francesca Petralia, Chandan Kumar-Sinha, Karsten Krug, Eunkyung An, Elena V. Ponomareva, Ximing Tang, Nancy Roche, Daniel C. Rohrer, David I. Heiman, Arul M. Chinnaiyan, Pamela Grady, Rebecca I. Montgomery, Galen Hostetter, Liqun Qi, Stephan C. Schürer, George D. Wilson, Pushpa Hariharan, Zhen Zhang, Yvonne, David Chesla, Chia-Kuei Mo, Maria Gabriela Raso, Negin Vatanian, Paul K. Paik, Fei Ding, Thomas L. Bauer, Barbara Hindenach, Matthew J. Ellis, Chen Huang, Karin D. Rodland, Oluwole Fadare, Ramaswamy Govindan, Eric E. Schadt, Sandra Cottingham, Barbara Pruetz, Sendurai A. Mani, Shirley Tsang, Carissa Huynh, Weiping Ma, Jennifer E. Maas, Tobias Schraink, Stacey Gabriel, Bing Zhang, Tara Hiltke, Rama Soundararajan, Tatiana Omelchenko, Brian J. Druker, Matthew A. Wyczalkowski, Neil R. Mucci, Ziad Hanhan, Donna E. Hansel, Yifat Geffen, Mathangi Thiagarajan, Xiaojun Jing, Pei Wang, Alfredo Molinolo, Tanmayi Vashist, Ratna R. Thangudu, Maciej Wiznerowicz, Edwin R. Parra, Tanvi H. Visal, Maureen Dyer, Melissa Borucki, Ki Sung Um, Jonathan T. Lei, Marcin Cieslik, Christopher R. Kinsinger, M. Harry Kane, Houxiang Zhu, Chelsea J. Newton, Steven A. Carr, Tao Liu, Wenke Liu, Volodymyr Sovenko, Olga Potapova, Eric J. Burks, Song Cao, Ana I. Robles, Yuping Zhang, Yize Li, Midie Xu, Erik J. Bergstrom, Zeynep H. Gümüş, Kai Li, and Xiaoyu Song

Subjects
Adult, Male, Epithelial-Mesenchymal Transition, Lung Neoplasms, Biology, Proteomics, Receptor Tyrosine Kinase-like Orphan Receptors, General Biochemistry, Genetics and Molecular Biology, Article, SOX2, CDKN2A, Survivin, medicine, Cluster Analysis, Humans, Receptors, Platelet-Derived Growth Factor, Phosphorylation, Lung cancer, Aged, Proteogenomics, Aged, 80 and over, EZH2, Ubiquitination, Cyclin-Dependent Kinase 4, Acetylation, Cyclin-Dependent Kinase 6, Middle Aged, medicine.disease, Chromatin, Neoplasm Proteins, Gene Expression Regulation, Neoplastic, Mutation, Cancer research, Carcinoma, Squamous Cell, Female, Protein Binding, Signal Transduction
Abstract

Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape of LSCC, providing a deeper exposition of LSCC biology with potential therapeutic implications. We identify NSD3 as an alternative driver in FGFR1-amplified tumors and low-p63 tumors overexpressing the therapeutic target survivin. SOX2 is considered undruggable, but our analyses provide rationale for exploring chromatin modifiers such as LSD1 and EZH2 to target SOX2-overexpressing tumors. Our data support complex regulation of metabolic pathways by crosstalk between post-translational modifications including ubiquitylation. Numerous immune-related proteogenomic observations suggest directions for further investigation. Proteogenomic dissection of CDKN2A mutations argue for more nuanced assessment of RB1 protein expression and phosphorylation before declaring CDK4/6 inhibition unsuccessful. Finally, triangulation between LSCC, LUAD, and HNSCC identified both unique and common therapeutic vulnerabilities. These observations and proteogenomics data resources may guide research into the biology and treatment of LSCC.

Published
2020

8. Proteogenomic insights into the biology and treatment of HPV-negative head and neck squamous cell carcinoma

Author

Tara Skelly, Wen Jiang, Zhen Zhang, Anupriya Agarwal, Amy M. Perou, Olga Potapova, Christopher R. Kinsinger, Matthew A. Wyczalkowski, David J. Clark, Shuang Cai, Felipe da Veiga Leprevost, Linda Hannick, Chen Huang, Paul D. Piehowski, John McGee, Marcin J. Domagalski, Dmitris Placantonakis, Jianbo Pan, Dana R. Valley, Zhiao Shi, Hui Yin Chang, Karen A. Ketchum, Charles A. Goldthwaite, Małgorzata Wierzbicka, Karsten Krug, Yvonne Shutack, Sara R. Savage, Matthew L. Anderson, Alyssa Charamut, Chandan Kumar-Sinha, Sanford P. Markey, Ratna R. Thangudu, Weiping Ma, Oliver F. Bathe, Antonio Colaprico, Yuxing Liao, Eric E. Schadt, Tomasz Czernicki, Seungyeul Yoo, Xi Chen, Stacey Gabriel, Karl R. Clauser, Daniel C. Rohrer, Uma Borate, Uma Velvulou, Larisa Polonskaya, M. Harry Kane, Dmitry M. Avtonomov, Boris Reva, Jacob J. Day, Barbara Hindenach, Matthew J. Ellis, Katherine A. Hoadley, Emek Demir, Rebecca I. Montgomery, Ewa P. Malc, Fengchao Yu, Lijun Yao, Maciej Wiznerowicz, Annette Marrero-Oliveras, Wojciech Szopa, Sailaja Mareedu, Galen Hostetter, Liqun Qi, Hui Zhang, Yige Wu, David N. Hayes, Shankha Satpathy, Corbin D. Jones, Michael J. Birrer, Xinpei Yi, Nathan Edwards, Fei Ding, Jiang Qian, Ning Qu, Alicia Francis, Daniel Cui Zhou, Jakub Stawicki, Bing Zhang, Rodrigo Vargas Eguez, Tao Liu, Dave Tabor, Maureen Dyer, Brian J. Druker, Gilbert S. Omenn, Azra Krek, Meenakshi Anurag, Melissa Borucki, Mathangi Thiagarajan, Shirley Tsang, Shakti Ramkissoon, Alexey I. Nesvizhskii, Li Ding, Lyubomir Valkov Vasilev, Yifat Geffen, James Suh, Tatiana S. Ermakova, Kakhaber Zaalishvili, Adel K. El-Naggar, Ki Sung Um, Ana I. Robles, Wen-Wei Liang, Richard D. Smith, Pei Wang, Emily S. Boja, Anna Calinawan, Yingwei Hu, Jiayi Ji, Renata Ferrarotto, Hongwei Liu, Jonathan T. Lei, Ramani B. Kothadia, Yize Li, Chelsea J. Newton, Anna Malovannaya, Steven A. Carr, Sandra Cerda, Yuriy Zakhartsev, Stephanie De Young, Eric J. Jaehnig, Peter B. McGarvey, Yan Shi, David I. Heiman, Joseph C. Dort, Karin D. Rodland, Lili Blumenberg, Michael A. Gillette, Piotr A. Mieczkowski, Pankaj Vats, Chet Birger, Yongchao Dou, David Fenyö, Saravana M. Dhanasekaran, Pushpa Hariharan, Eunkyung An, Jeffrey R. Whiteaker, George Miles, Jan Lubinski, Shayan C. Avanessian, Samuel H. Payne, Amanda G. Paulovich, Dmitry Rykunov, Lyudmila Petrenko, Martin Hyrcza, Guo Ci Teo, Alissa M. Weaver, D. R. Mani, Houston Culpepper, Meghan C. Burke, Daniel W. Chan, Bo Wen, Nicollette Maunganidze, Elie Traer, Darlene Tansil, Simona Migliozzi, Luciano Garofano, Qing Kay Li, Donghui Tan, Lori J. Sokoll, Mehdi Mesri, Karna Robinson, Fulvio D'Angelo, Kimberly Elburn, Alexander R. Pico, Umut Ozbek, Michael Schnaubelt, Gad Getz, Francesca Petralia, Andrew G. Sikora, Kai Li, Elena V. Ponomareva, Arul M. Chinnaiyan, Robert Zelt, Jun Zhu, Midie Xu, Dimitar Dimitrov Pazardzhikliev, Negin Vatanian, Grace Zhao, Thomas F. Westbrook, Kyung-Cho Cho, Yuefan Wang, Jason E. McDermott, Jeffrey W. Tyner, William Bocik, Shilpi Singh, Stephen E. Stein, Nancy Roche, Alicia Karz, Shannon Richey, Tara Hiltke, Michael Vernon, Lijun Chen, Henry Rodriguez, Xiaoyu Song, Elizabeth R. Duffy, Lin S. Chen, Liwei Cao, Shrabanti Chowdhury, Marcin Cieślik, Michael C. Wendl, Scott D. Jewell, and Cristina E. Tognon

Subjects
Adult, Male, Proteomics, 0301 basic medicine, Cancer Research, medicine.medical_treatment, Cell, Biology, Article, Young Adult, 03 medical and health sciences, Antineoplastic Agents, Immunological, 0302 clinical medicine, Cyclin-dependent kinase, medicine, Humans, Aged, Proteogenomics, Aged, 80 and over, Squamous Cell Carcinoma of Head and Neck, Papillomavirus Infections, Phosphoproteomics, Immunotherapy, Middle Aged, medicine.disease, Head and neck squamous-cell carcinoma, Immune checkpoint, ErbB Receptors, 030104 developmental biology, medicine.anatomical_structure, Oncology, 030220 oncology & carcinogenesis, Cancer research, biology.protein, Female
Abstract

We present a proteogenomic study of 108 human papilloma virus (HPV)-negative head and neck squamous cell carcinomas (HNSCCs). Proteomic analysis systematically catalogs HNSCC-associated proteins and phosphosites, prioritizes copy number drivers, and highlights an oncogenic role for RNA processing genes. Proteomic investigation of mutual exclusivity between FAT1 truncating mutations and 11q13.3 amplifications reveals dysregulated actin dynamics as a common functional consequence. Phosphoproteomics characterizes two modes of EGFR activation, suggesting a new strategy to stratify HNSCCs based on EGFR ligand abundance for effective treatment with inhibitory EGFR monoclonal antibodies. Widespread deletion of immune modulatory genes accounts for low immune infiltration in immune-cold tumors, whereas concordant upregulation of multiple immune checkpoint proteins may underlie resistance to anti-programmed cell death protein 1 monotherapy in immune-hot tumors. Multi-omic analysis identifies three molecular subtypes with high potential for treatment with CDK inhibitors, anti-EGFR antibody therapy, and immunotherapy, respectively. Altogether, proteogenomics provides a systematic framework to inform HNSCC biology and treatment.

Published
2021

9. Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer

Author

Matthew E. Monroe, Saravana M. Dhanasekaran, Brian R. Rood, Zeynep H. Gümüş, Jena Lilly, Samuel G. Winebrake, Richard G. Ivey, William Bocik, Mahdi Sarmady, Alicia Francis, Lamiya Tauhid, Nathan Edwards, Lizabeth Katsnelson, Rui Zhao, Matilda Broberg, Jo Lynne Rokita, Mateusz Koptyra, Henry Rodriguez, Cassie Kline, Shrabanti Chowdhury, Nicole Tignor, Ying Wang, Christopher R. Kinsinger, Antonio Colaprico, Amanda G. Paulovich, Weiping Ma, Emily S. Boja, Tara Hiltke, Sabine Mueller, Liang-Bo Wang, Javad Nazarian, Marcin J. Domagalski, Karl K. Weitz, Jessica B. Foster, Robert Lober, Carina A. Leonard, Bo Zhang, Gerald A. Grant, Anna Calinawan, Gonzalo Lopez, Shuang Cai, Joanna J. Phillips, Guo Ci Teo, July E. Palma, Felipe da Veiga Leprevost, Yiran Guo, Angela Waanders, Xiaoyu Song, Li Ding, Allison Heath, Steven P. Gygi, Rosalie K. Chu, Vasileios Stathias, Bailey Farrow, Oren J. Becher, Dmitry Rykunov, Nithin D. Adappa, Ron Firestein, Adam C. Resnick, Marcin Cieślik, Jennifer Mason, D. R. Mani, Selim Kalayci, Boris Reva, Antonio Iavarone, MacIntosh Cornwell, Uliana J. Voytovich, Gabrielle S. Stone, Miguel A. Brown, Jacob J. Kennedy, Tao Liu, Ronald J. Moore, Emily Kawaler, Eric H. Raabe, Marina A. Gritsenko, Valerie Baubet, Francesca Petralia, Maciej Wiznerowicz, Olena Morozova Vaske, Eric E. Schadt, Ian F. Pollack, Arul M. Chinnaiyan, Meghan Connors, Jason E. Cain, Lei Zhao, Matthew A. Wyczalkowski, Nalin Gupta, Bing Zhang, Jiayi Ji, Marilyn M. Li, Samuel Rivero-Hinojosa, Mariarita Santi, Wenke Liu, John Szpyt, Brian Ennis, Alexey I. Nesvizhskii, Joshua M. Wang, Jeffrey P. Greenfield, Sanjukta Guha Thakurta, Hui Yin Chang, Peter B. McGarvey, Xi Chen, Karen A. Ketchum, Stephan C. Schürer, Sarah Leary, Lili Blumenberg, Matthew J. Ellis, Pei Wang, Anna Maria Buccoliero, Karsten Krug, Chiara Caporalini, Gad Getz, David E. Kram, Pichai Raman, Eric M. Jackson, James N. Palmer, Mehdi Mesri, Kelly V. Ruggles, Chunde Li, Jun Zhu, Sonia Partap, Jeffrey R. Whiteaker, Mirko Scagnet, Krutika S. Gaonkar, Azra Krek, Allison M. Morgan, Tatiana Omelchenko, Richard D. Smith, Elizabeth Appert, Karin D. Rodland, Derek Hanson, Phillip B. Storm, Jamie Moon, Vladislav A. Petyuk, Nathan Young, Travis D. Lorentzen, David Fenyö, Angela N. Viaene, Seungyeul Yoo, Yuankun Zhu, Nicholas A Vitanza, Toan Le, Tatiana Patton, and Ana I. Robles

Subjects
DNA Copy Number Variations, Computational biology, Biology, Proteomics, Article, General Biochemistry, Genetics and Molecular Biology, Ganglioglioma, 03 medical and health sciences, Lymphocytes, Tumor-Infiltrating, 0302 clinical medicine, Glioma, medicine, Humans, Gene Regulatory Networks, RNA, Messenger, Copy-number variation, Phosphorylation, Child, Proteogenomics, 030304 developmental biology, Medulloblastoma, 0303 health sciences, Brain Neoplasms, Genome, Human, Phosphoproteomics, Phosphoproteins, medicine.disease, Gene Expression Regulation, Neoplastic, Mutation, Atypical teratoid rhabdoid tumor, Neoplasm Grading, Neoplasm Recurrence, Local, Transcriptome, 030217 neurology & neurosurgery
Abstract

We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses. Proteomics data further reveal functional effects of somatic mutations and copy number variations (CNVs) not evident in transcriptomics data. Kinase-substrate association and co-expression network analysis identify important biological mechanisms of tumorigenesis. This is the first large-scale proteogenomics analysis across traditional histological boundaries to uncover foundational pediatric brain tumor biology and inform rational treatment selection.

Published
2020

10. TBIO-19. INTEGRATED GENOMIC, PROTEOMIC AND PHOSPHOPROTEOMIC ANALYSIS OF SEVEN TYPES OF PEDIATRIC BRAIN CANCER

Author

Pichae Raman, Antonio Colaprico, Pei Wang, Jeffrey R. Whiteaker, Jiayi Ji, Richard G. Ivey, Shrabanti Chowdhury, Nicole Tignor, David Fenyö, Weiping Ma, Brian R. Rood, Boris Revas, Francesca Petralia, Steven P. Gygi, Antonio Iavarone, Yuankun Zhu, Dmitri Rykunov, Henry Rodriguez, Adam C. Resnick, Tara Hiltke, Xiaoyu Song, Azra Krek, Amanda G. Paulovich, and Alexey I. Nesvizhskii

Subjects
Oncology, Cancer Research, medicine.medical_specialty, business.industry, Cancer, medicine.disease, Pediatric brain, Internal medicine, medicine, AcademicSubjects/MED00300, AcademicSubjects/MED00310, Neurology (clinical), business, Tumor Biology (not fitting a specific disease category)
Abstract

We performed a comprehensive proteogenomic analysis across seven childhood brain tumors for a deeper understanding of their functional biology. Whole genome sequencing, RNAseq, quantitative proteomic profiling and phosphoproteomics were performed on 219 fresh frozen tumor samples representing the histologic diagnoses of: low grade astrocytoma (93), ependymoma (32), high grade astrocytoma (26), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16) and atypical teratoid rhabdoid tumor (12). Unsupervised clustering analysis based on proteomics data reveals eight clusters with distinct protein profiles and pathway activities. While some clusters coincide with histologic diagnoses, a couple of clusters appear to be a mixture of different diagnoses, including one cluster consisting of “aggressive” tumors characterized by poor survival and high stemness scores. By integrating proteomic data with RNAseq and WGS data, we characterize the impact of mutations (H3K27M, BRAFV600E, BRAF fusion) and CNVs upon the proteome across various diagnoses. Multiomics based kinase-substrate association analysis and co-expression network analysis reveal targetable active kinase networks within these tumors. Proteomic data reveals unique biology associated with H3K27M mutation status in HGG and BRAF aberrations in LGG. Characterization of the tumor microenvironment through deconvolution analyses based on multi-omics data reveals 5 distinct tumor clusters associated with different populations of infiltrating immune cells and the relative activity of the immune system based upon the expression of pro-inflammation or immunosuppressive markers. This study reports the first large-scale deep comprehensive proteogenomic analysis crossing traditional histologic boundaries to uncover foundational pediatric brain tumor biology including functional insight that helps drive translational efforts.

Published
2020

11. Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma

Author

Yuxing Liao, Meghan C. Burke, Donghui Tan, Xiaoyu Song, Lauren C. Tang, Elizabeth R. Duffy, Shayan C. Avanessian, Daniel Cui Zhou, Maureen Dyer, Sara R. Savage, Jennifer M. Eschbacher, Shaleigh Smith, Alex Webster, Alicia Francis, Kelly V. Ruggles, Christopher R. Kinsinger, Shirley Tsang, Melissa Borucki, Nancy Roche, Pei Wang, Qing Kay Li, David Chesla, Ronald Matteotti, Zeynep H. Gümüş, Kai Li, Kei Suzuki, Thomas L. Bauer, Lori J. Sokoll, John McGee, Marcin J. Domagalski, Ki Sung Um, Tara Hiltke, Hai-Quan Chen, Hongwei Liu, Eric E. Schadt, Antonio Colaprico, Alyssa Charamut, Lijun Chen, Emily Kawaler, Ramani B. Kothadia, Mehdi Mesri, Jiayi Ji, Simina M. Boca, Myvizhi Esai Selvan, Emily S. Boja, Xi Chen, Shuang Cai, Kim Elburn, Samuel H. Payne, George D. Wilson, Peter B. McGarvey, Chelsea J. Newton, Felipe da Veiga Leprevost, Uma Velvulou, Tara Skelly, Corbin D. Jones, Michael J. Birrer, Steven A. Carr, Erik J. Bergstrom, Jeffrey R. Whiteaker, Michael H.A. Roehrl, Gad Getz, Tanya Krubit, Zhen Zhang, Yan Shi, Lili Blumenberg, Melanie A. MacMullan, Song Cao, Zhiao Shi, Weiping Ma, Chet Birger, Karl R. Clauser, Runyu Hong, Shankha Satpathy, Andrii Karnuta, Boris Reva, Barbara Hindenach, Matthew J. Ellis, Amanda G. Paulovich, Michael C. Wendl, Bing Zhang, Marina A. Gritsenko, Matthew A. Wyczalkowski, Stacey Gabriel, Michael A. Gillette, Jacob J. Day, Maciej Wiznerowicz, Stephen E. Stein, Ewa P. Malc, Robert J. Welsh, Sunita Shankar, Brian J. Druker, Li Ding, Lijun Yao, David J. Clark, Małgorzata Wojtyś, Dmitry Rykunov, Eugene S. Fedorov, Linda Hannick, Andrew K. Godwin, Sailaja Mareedu, Pushpa Hariharan, Mary Beth Beasley, Stephanie De Young, Arul M. Chinnaiyan, Robert Zelt, Mathangi Thiagarajan, Munziba Khan, Suhas Vasaikar, Tao Liu, Karin D. Rodland, Katherine A. Hoadley, Wen-Wei Liang, Ana I. Robles, Dana R. Valley, Sanford P. Markey, Mikhail Krotevich, David I. Heiman, Piotr A. Mieczkowski, Galen Hostetter, Liqun Qi, Yige Wu, Hui Zhang, Liang-Bo Wang, Nathan Edwards, Ramaswamy Govindan, Yifat Geffen, Seema Chugh, Alexey I. Nesvizhskii, Karen A. Ketchum, Pankaj Vats, Matthew E. Monroe, Marcin Cieslik, Yingwei Hu, Karsten Krug, Yosef E. Maruvka, Yize Li, Ratna R. Thangudu, William W. Maggio, Sandra Cottingham, Saravana M. Dhanasekaran, Wenke Liu, Hua Sun, Sonya Carter, Volodymyr Sovenko, M. Harry Kane, Annette Marrero-Oliveras, Barbara Pruetz, Amy M. Perou, Gilbert S. Omenn, Azra Krek, Olga Potapova, Michael S. Noble, Daniel W. Chan, Seungyeul Yoo, Eric J. Burks, Bo Wen, William Bocik, Michael Vernon, Henry Rodriguez, James Suh, Scott D. Jewell, MacIntosh Cornwell, Richard D. Smith, Chandan Kumar-Sinha, Halina M. Krzystek, Daniel C. Rohrer, Tatiana Omelchenko, D. R. Mani, Houston Culpepper, Vladislav A. Petyuk, Meng-Hong Sun, Michelle Chaikin, David Fenyö, Rahul Mannan, Bartosz Kubisa, Rohit Mehra, Rajwanth R. Veluswamy, Umut Ozbek, Michael Schnaubelt, Francesca Petralia, Elena V. Ponomareva, Rashna Madan, Pamela Grady, Karna Robinson, and Negin Vatanian

Subjects
0303 health sciences, Phosphoproteomics, Genomics, Environmental exposure, Computational biology, Biology, Proteomics, Proteogenomics, medicine.disease_cause, Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, 0302 clinical medicine, Neutrophil degranulation, medicine, KRAS, 030217 neurology & neurosurgery, 030304 developmental biology, Epigenomics
Abstract

To explore the biology of lung adenocarcinoma (LUAD) and identify new therapeutic opportunities, we performed comprehensive proteogenomic characterization of 110 tumors and 101 matched normal adjacent tissues (NATs) incorporating genomics, epigenomics, deep-scale proteomics, phosphoproteomics, and acetylproteomics. Multi-omics clustering revealed four subgroups defined by key driver mutations, country, and gender. Proteomic and phosphoproteomic data illuminated biology downstream of copy number aberrations, somatic mutations, and fusions and identified therapeutic vulnerabilities associated with driver events involving KRAS, EGFR, and ALK. Immune subtyping revealed a complex landscape, reinforced the association of STK11 with immune-cold behavior, and underscored a potential immunosuppressive role of neutrophil degranulation. Smoking-associated LUADs showed correlation with other environmental exposure signatures and a field effect in NATs. Matched NATs allowed identification of differentially expressed proteins with potential diagnostic and therapeutic utility. This proteogenomics dataset represents a unique public resource for researchers and clinicians seeking to better understand and treat lung adenocarcinomas.

Published
2020

12. Proteogenomic Characterization of Endometrial Carcinoma

Author

David J. Clark, Jiayi Ji, Beom-Jun Kim, Doug W. Chan, Zhen Zhang, Kim Elburn, Munziba Khan, Katherine Fuh, Katherine A. Hoadley, Jeffrey R. Whiteaker, Peter B. McGarvey, Dana R. Valley, Douglas A. Levine, Sanford P. Markey, Hui Zhang, Tanya Krubit, Christopher R. Kinsinger, Hui Yin Chang, Yuxing Liao, Karen A. Ketchum, Piotr A. Mieczkowski, Pankaj Vats, Matthew E. Monroe, Donghui Tan, Alex Webster, Chet Birger, Kai Li, Ramani Kothadia, Saravana M. Dhanasekaran, Sara R. Savage, Karsten Krug, Marcin Jędryka, Matthew L. Anderson, Alyssa Charamut, Alexander R. Pico, Amanda G. Paulovich, Karna Robinson, Hua Zhou, John McGee, Sean J.I. Beecroft, Amanda E. Oliphant, Annette Marrero-Oliveras, Dmitry Rykunov, Shuang Cai, Francesmary Modugno, Marcin J. Domagalski, Emily Kawaler, Mehdi Mesri, Dmitry M. Avtonomov, Milan G. Chheda, Sue Hilsenbeck, Gilbert S. Omenn, Emek Demir, Rebecca I. Montgomery, Qingsong Gao, Azra Krek, Eric E. Schadt, Li Ding, George D. Wilson, Stephen E. Stein, David Chesla, Gad Getz, Negin Vatanian, Thomas F. Westbrook, Deborah DeLair, David I. Heiman, Karl R. Clauser, Rafal Matkowski, Lori J. Sokoll, Uma Borate, Antonio Colaprico, Jin Chen, Eric J. Jaehnig, Karin D. Rodland, Richard D. Smith, Linda Hannick, Uma Velvulou, Shannon Richey, Andrzej Czekański, Bing Zhang, Arul M. Chinnaiyan, Robert Zelt, Daniel J. Geiszler, Emily L. Hoskins, Ronald J. Moore, Pushpa Hariharan, Suhas Vasaikar, Jason E. McDermott, Yige Wu, Anna Malovannaya, Brian J. Druker, Jeffrey W. Tyner, Yan Shi, Lili Blumenberg, Jamie Moon, Cristina E. Tognon, Chandan Kumar-Sinha, Nathan Edwards, Yifat Geffen, Barbara Hindenach, Matthew J. Ellis, Zhi Li, Michael Schnaubelt, David C. Wheeler, Tara Skelly, Ewa P. Malc, Shrabanti Chowdhury, Andrew K. Godwin, Zhiao Shi, Francesca Petralia, Lin Chen, Scott D. Jewell, Daniel C. Rohrer, Elie Traer, Michael Ittmann, Shankha Satpathy, Marcin Cieslik, Weiping Ma, Daniel Cui Zhou, Maureen Dyer, Boris Reva, Rashna Madan, William Bocik, Stacey Gabriel, Stephanie De Young, Yosef E. Maruvka, Sandra Cottingham, Pamela Grady, D. R. Mani, Houston Culpepper, Meenakshi Anurag, Michael T. Lewis, Anupriya Agarwal, Felipe D. Leprevost, Jonathan C. Jarman, Michael Vernon, Henry Rodriguez, Matthew A. Wyczalkowski, Rui Zhao, Vladislav A. Petyuk, Michelle Chaikin, James Suh, Daniel W. Chan, Bo Wen, Patricia Castro, Alexey I. Nesvizhskii, Chia-Feng Tsai, Grace Zhao, Alicia Francis, Feng Chen, Mathangi Thiagarajan, Pei Wang, Marina A. Gritsenko, Anna Calinawan, David G. Mutch, Melissa Borucki, Xi Steven Chen, Guo Ci Teo, Peter Dottino, Corbin D. Jones, Michael J. Birrer, Ying Wang, Meghan C. Burke, MacIntosh Cornwell, Song Cao, Rosalie K. Chu, Larisa Polonskaya, Samuel H. Payne, Darlene Tansil, Yongchao Dou, David Fenyö, Kelly V. Ruggles, Qing Kay Li, Yuping Zhang, James J. Hsieh, Andy T. Kong, Ana I. Robles, Emily S. Boja, Chelsea J. Newton, Steven A. Carr, Sandra Cerda, Runyu Hong, Jacob J. Day, Sailaja Mareedu, Jan Lubinski, Galen Hostetter, Liqun Qi, Yize Li, Chen Huang, Liang-Bo Wang, Tao Liu, Renee Karabon, Paul D. Piehowski, Samuel L. Pugh, Maciej Wiznerowicz, Ratna R. Thangudu, Wenke Liu, Jayson B. Field, Sonya Carter, Ki Sung Um, Hongwei Liu, Alla Karpova, Yuriy Zakhartsev, Amy M. Perou, Michael S. Noble, Rajiv Dhir, Nancy Roche, Sunantha Sethuraman, Tara Hiltke, Lijun Chen, Xiaoyu Song, Elizabeth R. Duffy, Robert Edwards, Yingwei Hu, John A. Martignetti, Simina M. Boca, Michael A. Gillette, and David W Adams

Subjects
Epithelial-Mesenchymal Transition, Proteome, Druggability, Biology, Proteomics, Genomic Instability, Article, General Biochemistry, Genetics and Molecular Biology, Mice, 03 medical and health sciences, 0302 clinical medicine, Antigens, Neoplasm, medicine, Animals, Humans, Phosphorylation, 030304 developmental biology, Feedback, Physiological, 0303 health sciences, Endometrial cancer, Carcinoma, Wnt signaling pathway, Cancer, Acetylation, medicine.disease, Proteogenomics, Endometrial Neoplasms, Gene Expression Regulation, Neoplastic, MicroRNAs, Serous fluid, Histone, Cancer research, biology.protein, Female, Transcriptome, Protein Processing, Post-Translational, 030217 neurology & neurosurgery, Microsatellite Repeats, Signal Transduction
Abstract

We undertook a comprehensive proteogenomic characterization of 95 prospectively collected endometrial carcinomas, comprising 83 endometrioid and 12 serous tumors. This analysis revealed possible new consequences of perturbations to the p53 and Wnt/β-catenin pathways, identified a potential role for circRNAs in the epithelial-mesenchymal transition, and provided new information about proteomic markers of clinical and genomic tumor subgroups, including relationships to known druggable pathways. An extensive genome-wide acetylation survey yielded insights into regulatory mechanisms linking Wnt signaling and histone acetylation. We also characterized aspects of the tumor immune landscape, including immunogenic alterations, neoantigens, common cancer/testis antigens, and the immune microenvironment, all of which can inform immunotherapy decisions. Collectively, our multi-omic analyses provide a valuable resource for researchers and clinicians, identify new molecular associations of potential mechanistic significance in the development of endometrial cancers, and suggest novel approaches for identifying potential therapeutic targets.

Published
2020

13. Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma

Author

David J. Clark, Jianbo Pan, Gerald W. Hart, Katherine A. Hoadley, Negin Vatanian, Shuang Cai, Yige Wu, Felipe da Veiga Leprevost, A. Ari Hakimi, Sanford P. Markey, Thomas F. Westbrook, Maciej Wiznerowicz, Nathan Edwards, Alla Y. Karpova, Sohini Sengupta, Marcin Cieslik, Samuel H. Payne, Xi Steven Chen, Guo Ci Teo, Jin Chen, Boris Reva, Corbin D. Jones, Michael J. Birrer, Ying Wang, Kelly V. Ruggles, Doug W. Chan, John McGee, Marcin J. Domagalski, Song Cao, Linda Hannick, Christopher R. Kinsinger, David I. Heiman, Jennifer M. Eschbacher, Munziba Khan, Jason E. McDermott, Dmitry M. Avtonomov, Sue Hilsenbeck, Qing Kay Li, Jiayi Ji, Emek Demir, Rebecca I. Montgomery, Qingsong Gao, Beom-Jun Kim, Xiaoyu Song, Karl R. Clauser, Christian P. Pavlovich, Richard D. Smith, Maureen Dyer, Jeffrey W. Tyner, Amy M. Perou, Yuping Zhang, Dana R. Valley, George D. Wilson, Shiyong Ma, Minghui Ao, Jiang Qian, Umut Ozbek, Melissa Borucki, Zhi Li, Michael Schnaubelt, Chen Huang, Piotr A. Mieczkowski, Francesca Petralia, Abdul Samad Hashimi, Hui Yin Chang, Liang-Bo Wang, Matthew E. Monroe, Peter B. McGarvey, Tao Liu, Karen A. Ketchum, Hui Zhang, Bing Zhang, D. R. Mani, Houston Culpepper, Hua Zhou, Saravana M. Dhanasekaran, Paul D. Piehowski, Zhidong Tu, Brian J. Druker, Ki Sung Um, Zhiao Shi, Uma Borate, Uma Velvulou, Michael Ittmann, Weiping Ma, Steven M. Foltz, Heng Zhu, Stacey Gabriel, Hongwei Liu, Ramani B. Kothadia, Lin Chen, Ewa P. Malc, Marina A. Gritsenko, Jun Zhu, David Chesla, Lori J. Sokoll, Stephen E. Stein, Andrzej Antczak, Matthew L. Anderson, Alyssa Charamut, Pamela Grady, Michael T. Lewis, Shannon Richey, Tanya Krubit, Alexander R. Pico, Kyung-Cho Cho, Daniel C. Rohrer, Francesmary Modugno, Stephanie De Young, Li Ding, Michael Smith, Mathangi Thiagarajan, Alexey I. Nesvizhskii, Shrabanti Chowdhury, Noam D. Beckmann, Kimberly R. Holloway, Ratna R. Thangudu, Sherri R. Davies, Tung-Shing M. Lih, Nicole Tignor, Anna Calinawan, Meghan C. Burke, Karna Robinson, Chet Birger, Shalin Patel, Antonio Colaprico, Sarah Keegan, Daniel J. Geiszler, Scott D. Jewell, William Bocik, Snehal Patil, Pei Wang, MacIntosh Cornwell, Emily Kawaler, Seungyeul Yoo, Jasmine Huang, Vladislav A. Petyuk, Ross Bremner, Donghui Tan, Stefani N. Thomas, Emily S. Boja, Anna Malovannaya, Xi Chen, Wenke Liu, Eric E. Schadt, Shankha Satpathy, Nancy Roche, Rajiv Dhir, Cristina E. Tognon, Michelle Chaikin, Gabriel Bromiński, Daniel C. Zhou, Yifat Geffen, Tara Skelly, Jacob J. Day, Sunantha Sethuraman, Sonya Carter, Zhen Zhang, Selim Kalayci, Michael Vernon, Zeynep H. Gümüş, Kai Li, Barbara Hindenach, Matthew J. Ellis, Meenakshi Anurag, David C. Wheeler, Sailaja Mareedu, Andy T. Kong, Arul M. Chinnaiyan, Robert Zelt, Annette Marrero-Oliveras, Henry Rodriguez, James Suh, Anupriya Agarwal, David Fenyö, Galen Hostetter, Liqun Qi, Matthew A. Wyczalkowski, W. Marston Linehan, Tara Hiltke, Feng Chen, Lijun Chen, Jan Lubinski, Chelsea J. Newton, Steven A. Carr, Tatiana Omelchenko, Gilbert S. Omenn, Karsten Krug, Ana I. Robles, Azra Krek, Runyu Hong, Milan G. Chheda, Yize Li, Yan Shi, Lili Blumenberg, Ruiyang Liu, Karin D. Rodland, Hua Sun, Kim Elburn, Jeffrey R. Whiteaker, Christopher J. Ricketts, Gaddy Getz, Daniel W. Chan, Bo Wen, Robert Edwards, Patricia Castro, Yingwei Hu, Pushpa Hariharan, Simina M. Boca, Darlene Tansil, Phillip M. Pierorazio, Yosef E. Maruvka, Sandra Cottingham, James J. Hsieh, Amanda G. Paulovich, Barbara Pruetz, Michael A. Gillette, Yihao Lu, Dmitry Rykunov, Mehdi Mesri, Marc M. Loriaux, Reyka G Jayasinghe, and Suhas Vasaikar

Subjects
Adult, Male, Cell, Computational biology, Biology, Proteomics, Disease-Free Survival, Oxidative Phosphorylation, Article, General Biochemistry, Genetics and Molecular Biology, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Exome Sequencing, medicine, Biomarkers, Tumor, Tumor Microenvironment, Humans, Exome, Phosphorylation, Carcinoma, Renal Cell, 030304 developmental biology, Epigenomics, Aged, Proteogenomics, Aged, 80 and over, 0303 health sciences, Tumor microenvironment, Genome, Human, Phosphoproteomics, Middle Aged, medicine.disease, Neoplasm Proteins, Gene Expression Regulation, Neoplastic, Clear cell renal cell carcinoma, medicine.anatomical_structure, Female, 030217 neurology & neurosurgery, Signal Transduction
Abstract

SUMMARY To elucidate the deregulated functional modules that drive clear cell renal cell carcinoma (ccRCC), we performed comprehensive genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic characterization of treatment-naive ccRCC and paired normal adjacent tissue samples. Genomic analyses identified a distinct molecular subgroup associated with genomic instability. Integration of proteogenomic measurements uniquely identified protein dysregulation of cellular mechanisms impacted by genomic alterations, including oxidative phosphorylation-related metabolism, protein translation processes, and phospho-signaling modules. To assess the degree of immune infiltration in individual tumors, we identified microenvironment cell signatures that delineated four immune-based ccRCC subtypes characterized by distinct cellular pathways. This study reports a large-scale proteogenomic analysis of ccRCC to discern the functional impact of genomic alterations and provides evidence for rational treatment selection stemming from ccRCC pathobiology., Graphical Abstract, In Brief Comprehensive proteogenomic characterization in 103 treatment-naive clear cell renal cell carcinoma patient samples highlights tumor-specific alterations at the proteomic level that are unrevealed by transcriptomic profiling and proposes a revised subtyping scheme based on integrated omics analysis.

Published
2020

14. Genome-wide mapping of the distribution of CarD, RNAP σA, and RNAP β on the Mycobacterium smegmatis chromosome using chromatin immunoprecipitation sequencing

Author

Christina L. Stallings, Michael S. Glickman, Azra Krek, Nicholas D. Socci, and Robert Landick

Subjects
lcsh:QH426-470, genetic processes, medicine.disease_cause, Biochemistry, Genome, Mycobacterium tuberculosis, 03 medical and health sciences, chemistry.chemical_compound, Transcription (biology), RNA polymerase, Data in Brief, Genetics, medicine, Tuberculosis, Gene, Escherichia coli, 030304 developmental biology, 0303 health sciences, biology, 030306 microbiology, Mycobacterium smegmatis, Mycobacteria, CarD, Promoter, biology.organism_classification, 3. Good health, lcsh:Genetics, enzymes and coenzymes (carbohydrates), chemistry, Molecular Medicine, bacteria, Transcription, Biotechnology
Abstract

CarD is an essential mycobacterial protein that binds the RNA polymerase (RNAP) and affects the transcriptional profile of Mycobacterium smegmatis and Mycobacterium tuberculosis [6]. We predicted that CarD was directly regulating RNAP function but our prior experiments had not determined at what stage of transcription CarD was functioning and at which genes CarD interacted with the RNAP. To begin to address these open questions, we performed chromatin immunoprecipitation sequencing (ChIP-seq) to survey the distribution of CarD throughout the M. smegmatis chromosome. The distribution of RNAP subunits β and σA were also profiled. We expected that RNAP β would be present throughout transcribed regions and RNAP σA would be predominantly enriched at promoters based on work in Escherichia coli [3], however this had yet to be determined in mycobacteria. The ChIP-seq analyses revealed that CarD was never present on the genome in the absence of RNAP, was primarily associated with promoter regions, and was highly correlated with the distribution of RNAP σA. The colocalization of σA and CarD led us to propose that in vivo, CarD associates with RNAP initiation complexes at most promoters and is therefore a global regulator of transcription initiation. Here we describe in detail the data from the ChIP-seq experiments associated with the study published by Srivastava and colleagues in the Proceedings of the National Academy of Science in 2013 [5] as well as discuss the findings from this dataset in relation to both CarD and mycobacterial transcription as a whole.The ChIP-seq data have been deposited in the Gene Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (accession no. GSE48164).

Published
2014

15. Dicer Ablation Affects Antibody Diversity and Cell Survival in the B Lymphocyte Lineage

Author

Gunther R. Galler, Nikolaus Rajewsky, Tirtha Chakraborty, Kari Jensen, Sergei B. Koralov, Azra Krek, Matthias Merkenschlager, Stefan A. Muljo, Bradley S. Cobb, Klaus Rajewsky, and Chryssa Kanellopoulou

Subjects
Biochemistry, Genetics and Molecular Biology(all), Lymphocyte, DEVBIO, Gene rearrangement, Biology, Molecular biology, General Biochemistry, Genetics and Molecular Biology, Gene expression profiling, medicine.anatomical_structure, Terminal deoxynucleotidyl transferase, Transcription (biology), medicine, biology.protein, RNA, Antibody, B cell, Dicer
Abstract

To explore the role of Dicer-dependent control mechanisms in B lymphocyte development, we ablated this enzyme in early B cell progenitors. This resulted in a developmental block at the pro- to pre-B cell transition. Gene-expression profiling revealed a miR-17 approximately 92 signature in the 3'UTRs of genes upregulated in Dicer-deficient pro-B cells; a top miR-17 approximately 92 target, the proapoptotic molecule Bim, was highly upregulated. Accordingly, B cell development could be partially rescued by ablation of Bim or transgenic expression of the prosurvival protein Bcl-2. This allowed us to assess the impact of Dicer deficiency on the V(D)J recombination program in developing B cells. We found intact Ig gene rearrangements in immunoglobulin heavy (IgH) and kappa chain loci, but increased sterile transcription and usage of D(H) elements of the DSP family in IgH, and increased N sequence addition in Igkappa due to deregulated transcription of the terminal deoxynucleotidyl transferase gene.

Published
2008

16. Erratum to: Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data

Author

Mono Pirun, Doron Betel, Paul Zumbo, Christopher E. Mason, Raya Khanin, Nicholas D. Socci, Yupu Liang, Franck Rapaport, and Azra Krek

Subjects
Brain Chemistry, Red Hat Enterprise Linux, Normalization (statistics), Source code, Xeon, Sequence Analysis, RNA, Gene Expression Profiling, media_common.quotation_subject, Datasets as Topic, Gene Expression, High-Throughput Nucleotide Sequencing, Nerve Tissue Proteins, Signal-To-Noise Ratio, Biology, Bioinformatics, Differential analysis, Cell Line, Humans, RNA, Erratum, Arithmetic, Software, Analysis method, Oligonucleotide Array Sequence Analysis, media_common
Abstract

We previously published a report on the comprehensive evaluation of RNA-seq differential analysis (DE) methods [1] where we compared a number of popular DE tools using a variety of different criteria. Since publication we received valuable feedback and suggestions from the community including from the authors of the algorithms. Here we report on two errors that came to our attention following publication. 1. Soon after publication we were notified about a discrepancy in table 2. The correction was posted as comment to the main article and now included as erratum. In the last row of table 2 called "Runtime for experiments with 3-5 replicates…" the values for edgeR and limmaVoom should be Seconds not Minutes. For completeness, the following table is the runtime performance (in seconds) of six DE analysis methods, comparing the 5 replicates from groups A and B from the SEQC data, as measured on Red Hat Enterprise Linux Server release 5.4, with 12 dual cores Intel Xeon 3.33GHz, and 100G RAM. 2. Zhou and Robinson performed a follow up analysis to our manuscript where they reanalyzed DE when genes are expressed in one condition (see correspondence to Rapaport et al.). They identified a coding error in the calculation of edgeR signal to noise values due to incorrect normalization of edgeR count values. Library size values were not used to scale gene counts. This coding error was fixed and a corrected version is deposited in the source code repository available at: http://bitbucket.org/soccin/seqc. We redid the analysis for evaluation of genes expressed in only one condition and the main conclusions remain unchanged. The corrected version of Figure 4 in the main manuscript is presented here as Fig. ​Fig.11. Fig. 1 a edgeR correlation between signal-to-noise and –log10(p-values) using the corrected normalization of gene counts. b ROC analysis of curves for detection of DE at signal-to-noise ratio of ≥3. Note that edgeR AUC has improved from 0.788 ... We note that our reanalysis is based on the exact same procedure used in the original publication. However, Zhou and Robinson introduce a different version of this analysis where the ROC analysis is based on a common set of genes. Second, most packages used in our original publication have since been updated and in some cases the algorithms revised substantially. Therefore, it is possible that a similar comparison with the latest versions of the packages may result in different conclusions.

Published
2015

17. Cell-type-specific signatures of microRNAs on target mRNA expression

Author

Pranidhi Sood, Nikolaus Rajewsky, Azra Krek, Mihaela Zavolan, and Giuseppe Macino

Subjects
Genetics, Regulation of gene expression, Untranslated region, Gene knockdown, Multidisciplinary, Three prime untranslated region, Gene Expression Profiling, Computational Biology, Biological Sciences, Biology, Cell biology, Gene expression profiling, MicroRNAs, Gene Expression Regulation, Gene expression, microRNA, Animals, Humans, Gene silencing, Tissue Distribution, RNA, Messenger, 3' Untranslated Regions, Oligonucleotide Array Sequence Analysis
Abstract

Although it is known that the human genome contains hundreds of microRNA (miRNA) genes and that each miRNA can regulate a large number of mRNA targets, the overall effect of miRNAs on mRNA tissue profiles has not been systematically elucidated. Here, we show that predicted human mRNA targets of several highly tissue-specific miRNAs are typically expressed in the same tissue as the miRNA but at significantly lower levels than in tissues where the miRNA is not present. Conversely, highly expressed genes are often enriched in mRNAs that do not have the recognition motifs for the miRNAs expressed in these tissues. Together, our data support the hypothesis that miRNA expression broadly contributes to tissue specificity of mRNA expression in many human tissues. Based on these insights, we apply a computational tool to directly correlate 3′ UTR motifs with changes in mRNA levels upon miRNA overexpression or knockdown. We show that this tool can identify functionally important 3′ UTR motifs without cross-species comparison.

Published
2006

18. Combinatorial microRNA target predictions

Author

Kristin C. Gunsalus, Eric J Epstein, Azra Krek, Philip MacMenamin, Isabelle da Piedade, Lauren Rosenberg, Rachel Wolf, Dominic Grün, Matthew N. Poy, Markus Stoffel, and Nikolaus Rajewsky

Subjects
Genetics, Lin-4 microRNA precursor, MicroRNA Gene, Computational Biology, RNA, PAR-CLIP, Biology, Genome, MicroRNAs, microRNA, Animals, Gene silencing, Gene, Algorithms
Abstract

MicroRNAs are small noncoding RNAs that recognize and bind to partially complementary sites in the 3' untranslated regions of target genes in animals and, by unknown mechanisms, regulate protein production of the target transcript. Different combinations of microRNAs are expressed in different cell types and may coordinately regulate cell-specific target genes. Here, we present PicTar, a computational method for identifying common targets of microRNAs. Statistical tests using genome-wide alignments of eight vertebrate genomes, PicTar's ability to specifically recover published microRNA targets, and experimental validation of seven predicted targets suggest that PicTar has an excellent success rate in predicting targets for single microRNAs and for combinations of microRNAs. We find that vertebrate microRNAs target, on average, roughly 200 transcripts each. Furthermore, our results suggest widespread coordinate control executed by microRNAs. In particular, we experimentally validate common regulation of Mtpn by miR-375, miR-124 and let-7b and thus provide evidence for coordinate microRNA control in mammals.

Published
2005

19. microRNAs regulate cell-to-cell variability of endogenous target gene expression in developing mouse thymocytes

Author

Ludovica Bruno, James I. Elliott, Azra Krek, Arnulf Hertweck, Nikolaus Rajewsky, Matthias Merkenschlager, Christina Loh, Thomas L. Carroll, Amanda G. Fisher, Rory Blevins, Chang-Zheng Chen, and Antoine Marçais

Subjects
SELECTION, Cancer Research, NOISE, ACTIVATION, Mice, ROBUSTNESS, Gene expression, NETWORK, TRANSCRIPTION, Genetics (clinical), Regulator gene, Regulation of gene expression, Genetics, Genetics & Heredity, Thymocytes, biology, Gene Expression Regulation, Developmental, Flow Cytometry, ABSENCE, Cell biology, DIFFERENTIATION, Life Sciences & Biomedicine, Research Article, lcsh:QH426-470, Cell Survival, TH1 RESPONSES, Cell Line, Tumor, microRNA, Animals, RNA, Messenger, Molecular Biology, Gene, Post-transcriptional regulation, Ecology, Evolution, Behavior and Systematics, 0604 Genetics, Science & Technology, Three prime untranslated region, DICER, EVOLUTION, Hematopoiesis, lcsh:Genetics, MicroRNAs, Cardiovascular and Metabolic Diseases, DROSOPHILA-MELANOGASTER, Protein Biosynthesis, biology.protein, Dicer, RESPONSES, Developmental Biology
Abstract

The development and homeostasis of multicellular organisms relies on gene regulation within individual constituent cells. Gene regulatory circuits that increase the robustness of gene expression frequently incorporate microRNAs as post-transcriptional regulators. Computational approaches, synthetic gene circuits and observations in model organisms predict that the co-regulation of microRNAs and their target mRNAs can reduce cell-to-cell variability in the expression of target genes. However, whether microRNAs directly regulate variability of endogenous gene expression remains to be tested in mammalian cells. Here we use quantitative flow cytometry to show that microRNAs impact on cell-to-cell variability of protein expression in developing mouse thymocytes. We find two distinct mechanisms that control variation in the activation-induced expression of the microRNA target CD69. First, the expression of miR-17 and miR-20a, two members of the miR-17-92 cluster, is co-regulated with the target mRNA Cd69 to form an activation-induced incoherent feed-forward loop. Another microRNA, miR-181a, acts at least in part upstream of the target mRNA Cd69 to modulate cellular responses to activation. The ability of microRNAs to render gene expression more uniform across mammalian cell populations may be important for normal development and for disease., Author Summary microRNAs are integral to many developmental processes and may 'canalise' development by reducing cell-to-cell variation in gene expression. This idea is supported by computational studies that have modeled the impact of microRNAs on the expression of their targets and the construction of artificial incoherent feedforward loops using synthetic biology tools. Here we show that this interesting principle of microRNA regulation actually occurs in a mammalian developmental system. We examine cell-to-cell variation of protein expression in developing mouse thymocytes by quantitative flow cytometry and find that the absence of microRNAs results in increased cell-to-cell variation in the expression of the microRNA target Cd69. Mechanistically, T cell receptor signaling induces both Cd69 and miR-17 and miR-20a, two microRNAs that target Cd69. Co-regulation of microRNAs and their target mRNA dampens the expression of Cd69 and forms an incoherent feedforward loop that reduces cell-to-cell variation on CD69 expression. In addition, miR-181, which also targets Cd69 and is a known modulator of T cell receptor signaling, also affects cell-to-cell variation of CD69 expression. The ability of microRNAs to control the uniformity of gene expression across mammalian cell populations may be important for normal development and for disease.

Published
2014

20. A human snoRNA with microRNA-like functions

Author

Sébastien Pfeffer, Christine Ender, Nikolaus Rajewsky, Azra Krek, Lasse Weinmann, Marc R. Friedländer, Wei Chen, Michaela Beitzinger, Gunter Meister, Institut de biologie moléculaire des plantes (IBMP), and Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)

Subjects
Ribonuclease III, Lin-4 microRNA precursor, Trans-acting siRNA, Eukaryotic Initiation Factor-2, [SDV.BC]Life Sciences [q-bio]/Cellular Biology, Biology, Kidney, Models, Biological, Cell Line, DEAD-box RNA Helicases, 03 medical and health sciences, 0302 clinical medicine, Genes, Reporter, Endoribonucleases, RasiRNA, Humans, RNA, Small Nucleolar, RNA-Induced Silencing Complex, Small nucleolar RNA, Eukaryotic Initiation Factors, RNA Processing, Post-Transcriptional, RNA, Small Interfering, Luciferases, Molecular Biology, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, Genetics, 0303 health sciences, Computational Biology, Cell Biology, Argonaute, Non-coding RNA, Long non-coding RNA, Cell biology, RNA silencing, MicroRNAs, 030220 oncology & carcinogenesis, Argonaute Proteins, RNA Interference, Algorithms
Abstract

Small noncoding RNAs function in concert with Argonaute (Ago) proteins to regulate gene expression at the level of transcription, mRNA stability, or translation. Ago proteins bind small RNAs and form the core of silencing complexes. Here, we report the analysis of small RNAs associated with human Ago1 and Ago2 revealed by immunoprecipitation and deep sequencing. Among the reads, we find small RNAs originating from the small nucleolar RNA (snoRNA) ACA45. Moreover, processing of ACA45 requires Dicer activity but is independent of Drosha/DGCR8. Using bioinformatic prediction algorithms and luciferase reporter assays, we uncover the mediator subunit CDC2L6 as one potential mRNA target of ACA45 small RNAs, suggesting a role for ACA45-processing products in posttranscriptional gene silencing. We further identify a number of human snoRNAs with microRNA (miRNA)-like processing signatures. We have, therefore, identified a class of small RNAs in human cells that originate from snoRNAs and can function like miRNAs.

Published
2008

21. A genome-wide map of conserved microRNA targets in C. elegans

Author

Yi Lu Wang, Kevin Chen, Nicolas Bray, Dominic Grün, Azra Krek, Sabbi Lall, Teresa Colombo, Colin N. Dewey, Lior Pachter, Philip MacMenamin, Nikolaus Rajewsky, Fabio Piano, Huey Ling Kao, Pranidhi Sood, and Kristin C. Gunsalus

Subjects
Molecular Sequence Data, Sequence alignment, Context (language use), Biology, Genome, General Biochemistry, Genetics and Molecular Biology, Conserved sequence, 03 medical and health sciences, 0302 clinical medicine, Genes, Reporter, microRNA, Animals, Caenorhabditis elegans, Gene, Conserved Sequence, 030304 developmental biology, Genetics, Regulation of gene expression, 0303 health sciences, Genome, Helminth, Agricultural and Biological Sciences(all), Base Sequence, Biochemistry, Genetics and Molecular Biology(all), Gene Expression Profiling, Chromosome Mapping, Computational Biology, Genomics, MicroRNAs, Gene Expression Regulation, RNA, General Agricultural and Biological Sciences, Sequence Alignment, 030217 neurology & neurosurgery, Function (biology)
Abstract

Background: Metazoan miRNAs regulate protein-coding genes by binding the 3′ UTR of cognate mRNAs. Identifying targets for the 115 known C. elegans miRNAs is essential for understanding their function. Results: By using a new version of PicTar and sequence alignments of three nematodes, we predict that miRNAs regulate at least 10% of C. elegans genes through conserved interactions. We have developed a new experimental pipeline to assay 3′ UTR-mediated posttranscriptional gene regulation via an endogenous reporter expression system amenable to high-throughput cloning, demonstrating the utility of this system using one of the most intensely studied miRNAs, let-7. Our expression analyses uncover several new potential let-7 targets and suggest a new let-7 activity in head muscle and neurons. To explore genome-wide trends in miRNA function, we analyzed functional categories of predicted target genes, finding that one-third of C. elegans miRNAs target gene sets are enriched for specific functional annotations. We have also integrated miRNA target predictions with other functional genomic data from C. elegans. Conclusions: At least 10% of C. elegans genes are predicted miRNA targets, and a number of nematode miRNAs seem to regulate biological processes by targeting functionally related genes. We have also developed and successfully utilized an in vivo system for testing miRNA target predictions in likely endogenous expression domains. The thousands of genome-wide miRNA target predictions for nematodes, humans, and flies are available from the PicTar website and are linked to an accessible graphical network-browsing tool allowing exploration of miRNA target predictions in the context of various functional genomic data resources.

Published
2005

22. Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data

Author

Yupu Liang, Christopher E. Mason, Raya Khanin, Nicholas D. Socci, Mono Pirun, Azra Krek, Doron Betel, Paul Zumbo, and Franck Rapaport

Subjects
Genetics, Normalization (statistics), 0303 health sciences, Sequence analysis, Method, RNA-Seq, Replicate, Computational biology, Biology, ENCODE, Deep sequencing, Gene expression profiling, 03 medical and health sciences, 0302 clinical medicine, Gene expression, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. We find significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq. Our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth.

Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results