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1. Single-Cell Transcriptome Analysis Reveals Dynamic Changes in lncRNA Expression during Reprogramming

Author

Kim, Daniel H, Marinov, Georgi K, Pepke, Shirley, Singer, Zakary S, He, Peng, Williams, Brian, Schroth, Gary P, Elowitz, Michael B, and Wold, Barbara J

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Human Genome, 1.1 Normal biological development and functioning, Underpinning research, Generic health relevance, Animals, Cell Lineage, Cellular Reprogramming, Gene Expression Regulation, Developmental, Genes, Developmental, Hematopoiesis, Induced Pluripotent Stem Cells, Mice, Pluripotent Stem Cells, RNA, Long Noncoding, Signal Transduction, Single-Cell Analysis, Transcriptome, ras Proteins, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

Cellular reprogramming highlights the epigenetic plasticity of the somatic cell state. Long noncoding RNAs (lncRNAs) have emerging roles in epigenetic regulation, but their potential functions in reprogramming cell fate have been largely unexplored. We used single-cell RNA sequencing to characterize the expression patterns of over 16,000 genes, including 437 lncRNAs, during defined stages of reprogramming to pluripotency. Self-organizing maps (SOMs) were used as an intuitive way to structure and interrogate transcriptome data at the single-cell level. Early molecular events during reprogramming involved the activation of Ras signaling pathways, along with hundreds of lncRNAs. Loss-of-function studies showed that activated lncRNAs can repress lineage-specific genes, while lncRNAs activated in multiple reprogramming cell types can regulate metabolic gene expression. Our findings demonstrate that reprogramming cells activate defined sets of functionally relevant lncRNAs and provide a resource to further investigate how dynamic changes in the transcriptome reprogram cell state.

Published
2015

2. Integrating and mining the chromatin landscape of cell-type specificity using self-organizing maps.

Author

Mortazavi, Ali, Pepke, Shirley, Jansen, Camden, Marinov, Georgi K, Ernst, Jason, Kellis, Manolis, Hardison, Ross C, Myers, Richard M, and Wold, Barbara J

Subjects
Cell Line, K562 Cells, Chromatin, Humans, Histones, Transcription Factors, Chromosome Mapping, Computational Biology, Algorithms, User-Computer Interface, Promoter Regions, Genetic, Data Mining, Human Umbilical Vein Endothelial Cells, Gene Ontology, Vaccine Related, Biotechnology, Human Genome, HIV/AIDS, Genetics, Underpinning research, 1.5 Resources and infrastructure (underpinning), Generic health relevance, Biological Sciences, Medical and Health Sciences, Bioinformatics
Abstract

We tested whether self-organizing maps (SOMs) could be used to effectively integrate, visualize, and mine diverse genomics data types, including complex chromatin signatures. A fine-grained SOM was trained on 72 ChIP-seq histone modifications and DNase-seq data sets from six biologically diverse cell lines studied by The ENCODE Project Consortium. We mined the resulting SOM to identify chromatin signatures related to sequence-specific transcription factor occupancy, sequence motif enrichment, and biological functions. To highlight clusters enriched for specific functions such as transcriptional promoters or enhancers, we overlaid onto the map additional data sets not used during training, such as ChIP-seq, RNA-seq, CAGE, and information on cis-acting regulatory modules from the literature. We used the SOM to parse known transcriptional enhancers according to the cell-type-specific chromatin signature, and we further corroborated this pattern on the map by EP300 (also known as p300) occupancy. New candidate cell-type-specific enhancers were identified for multiple ENCODE cell types in this way, along with new candidates for ubiquitous enhancer activity. An interactive web interface was developed to allow users to visualize and custom-mine the ENCODE SOM. We conclude that large SOMs trained on chromatin data from multiple cell types provide a powerful way to identify complex relationships in genomic data at user-selected levels of granularity.

Published
2013

4. An integrated encyclopedia of DNA elements in the human genome

Author

Dunham, Ian, Kundaje, Anshul, Aldred, Shelley F., Collins, Patrick J., Davis, Carrie A., Doyle, Francis, Epstein, Charles B., Frietze, Seth, Harrow, Jennifer, Kaul, Rajinder, Khatun, Jainab, Lajoie, Bryan R., Landt, Stephen G., Lee, Bum-Kyu, Pauli, Florencia, Rosenbloom, Kate R., Sabo, Peter, Safi, Alexias, Sanyal, Amartya, Shoresh, Noam, Simon, Jeremy M., Song, Lingyun, Trinklein, Nathan D., Altshuler, Robert C., Birney, Ewan, Brown, James B., Cheng, Chao, Djebali, Sarah, Dong, Xianjun, Ernst, Jason, Furey, Terrence S., Gerstein, Mark, Giardine, Belinda, Greven, Melissa, Hardison, Ross C., Harris, Robert S., Herrero, Javier, Hoffman, Michael M., Iyer, Sowmya, Kellis, Manolis, Kheradpour, Pouya, Lassmann, Timo, Li, Qunhua, Lin, Xinying, Marinov, Georgi K., Merkel, Angelika, Mortazavi, Ali, Parker, Stephen C. J., Reddy, Timothy E., Rozowsky, Joel, Schlesinger, Felix, Thurman, Robert E., Wang, Jie, Ward, Lucas D., Whitfield, Troy W., Wilder, Steven P., Wu, Weisheng, Xi, Hualin S., Yip, Kevin Y., Zhuang, Jiali, Bernstein, Bradley E., Green, Eric D., Gunter, Chris, Snyder, Michael, Pazin, Michael J., Lowdon, Rebecca F., Dillon, Laura A. L., Adams, Leslie B., Kelly, Caroline J., Zhang, Julia, Wexler, Judith R., Good, Peter J., Feingold, Elise A., Crawford, Gregory E., Dekker, Job, Elnitski, Laura, Farnham, Peggy J., Giddings, Morgan C., Gingeras, Thomas R., Guigo, Roderic, Hubbard, Timothy J., Kent, W. James, Lieb, Jason D., Margulies, Elliott H., Myers, Richard M., Stamatoyannopoulos, John A., Tenenbaum, Scott A., Weng, Zhiping, White, Kevin P., Wold, Barbara, Yu, Yanbao, Wrobel, John, Risk, Brian A., Gunawardena, Harsha P., Kuiper, Heather C., Maier, Christopher W., Xie, Ling, Chen, Xian, Mikkelsen, Tarjei S., Gillespie, Shawn, Goren, Alon, Ram, Oren, Zhang, Xiaolan, Wang, Li, Issner, Robbyn, Coyne, Michael J., Durham, Timothy, Ku, Manching, Truong, Thanh, Eaton, Matthew L., Dobin, Alex, Tanzer, Andrea, Lagarde, Julien, Lin, Wei, Xue, Chenghai, Williams, Brian A., Zaleski, Chris, Roder, Maik, Kokocinski, Felix, Abdelhamid, Rehab F., Alioto, Tyler, Antoshechkin, Igor, Baer, Michael T., Batut, Philippe, Bell, Ian, Bell, Kimberly, Chakrabortty, Sudipto, Chrast, Jacqueline, Curado, Joao, Derrien, Thomas, Drenkow, Jorg, Dumais, Erica, Dumais, Jackie, Duttagupta, Radha, Fastuca, Megan, Fejes-Toth, Kata, Ferreira, Pedro, Foissac, Sylvain, Fullwood, Melissa J., Gao, Hui, Gonzalez, David, Gordon, Assaf, Howald, Cedric, Jha, Sonali, Johnson, Rory, Kapranov, Philipp, King, Brandon, Kingswood, Colin, Li, Guoliang, Luo, Oscar J., Park, Eddie, Preall, Jonathan B., Presaud, Kimberly, Ribeca, Paolo, Robyr, Daniel, Ruan, Xiaoan, Sammeth, Michael, Sandhu, Kuljeet Singh, Schaeffer, Lorain, See, Lei-Hoon, Shahab, Atif, Skancke, Jorgen, Suzuki, Ana Maria, Takahashi, Hazuki, Tilgner, Hagen, Trout, Diane, Walters, Nathalie, Wang, Huaien, Hayashizaki, Yoshihide, Reymond, Alexandre, Antonarakis, Stylianos E., Hannon, Gregory J., Ruan, Yijun, Carninci, Piero, Sloan, Cricket A., Learned, Katrina, Malladi, Venkat S., Wong, Matthew C., Barber, Galt P., Cline, Melissa S., Dreszer, Timothy R., Heitner, Steven G., Karolchik, Donna, Kirkup, Vanessa M., Meyer, Laurence R., Long, Jeffrey C., Maddren, Morgan, Raney, Brian J., Grasfeder, Linda L., Giresi, Paul G., Battenhouse, Anna, Sheffield, Nathan C., Showers, Kimberly A., London, Darin, Bhinge, Akshay A., Shestak, Christopher, Schaner, Matthew R., Ki Kim, Seul, Zhang, Zhuzhu Z., Mieczkowski, Piotr A., Mieczkowska, Joanna O., Liu, Zheng, McDaniell, Ryan M., Ni, Yunyun, Rashid, Naim U., Kim, Min Jae, Adar, Sheera, Zhang, Zhancheng, Wang, Tianyuan, Winter, Deborah, Keefe, Damian, Iyer, Vishwanath R., Zheng, Meizhen, Wang, Ping, Gertz, Jason, Vielmetter, Jost, Partridge, E., Varley, Katherine E., Gasper, Clarke, Bansal, Anita, Pepke, Shirley, Jain, Preti, Amrhein, Henry, Bowling, Kevin M., Anaya, Michael, Cross, Marie K., Muratet, Michael A., Newberry, Kimberly M., McCue, Kenneth, Nesmith, Amy S., Fisher-Aylor, Katherine I., Pusey, Barbara, DeSalvo, Gilberto, Parker, Stephanie L., Balasubramanian, Sreeram, Davis, Nicholas S., Meadows, Sarah K., Eggleston, Tracy, Newberry, J. Scott, Levy, Shawn E., Absher, Devin M., Wong, Wing H., Blow, Matthew J., Visel, Axel, Pennachio, Len A., Petrykowska, Hanna M., Abyzov, Alexej, Aken, Bronwen, Barrell, Daniel, Barson, Gemma, Berry, Andrew, Bignell, Alexandra, Boychenko, Veronika, Bussotti, Giovanni, Davidson, Claire, Despacio-Reyes, Gloria, Diekhans, Mark, Ezkurdia, Iakes, Frankish, Adam, Gilbert, James, Gonzalez, Jose Manuel, Griffiths, Ed, Harte, Rachel, Hendrix, David A., Hunt, Toby, Jungreis, Irwin, Kay, Mike, Khurana, Ekta, Leng, Jing, Lin, Michael F., Loveland, Jane, Lu, Zhi, Manthravadi, Deepa, Mariotti, Marco, Mudge, Jonathan, Mukherjee, Gaurab, Notredame, Cedric, Pei, Baikang, Rodriguez, Jose Manuel, Saunders, Gary, Sboner, Andrea, Searle, Stephen, Sisu, Cristina, Snow, Catherine, Steward, Charlie, Tapanari, Electra, Tress, Michael L., van Baren, Marijke J., Washietl, Stefan, Wilming, Laurens, Zadissa, Amonida, Zhang, Zhengdong, Brent, Michael, Haussler, David, Valencia, Alfonso, Addleman, Nick, Alexander, Roger P., Auerbach, Raymond K., Balasubramanian, Suganthi, Bettinger, Keith, Bhardwaj, Nitin, Boyle, Alan P., Cao, Alina R., Cayting, Philip, Charos, Alexandra, Cheng, Yong, Eastman, Catharine, Euskirchen, Ghia, Fleming, Joseph D., Grubert, Fabian, Habegger, Lukas, Hariharan, Manoj, Harmanci, Arif, Iyengar, Sushma, Jin, Victor X., Karczewski, Konrad J., Kasowski, Maya, Lacroute, Phil, Lam, Hugo, Lamarre-Vincent, Nathan, Lian, Jin, Lindahl-Allen, Marianne, Min, Renqiang, Miotto, Benoit, Monahan, Hannah, Moqtaderi, Zarmik, Mu, Xinmeng J., Ouyang, Zhengqing, Patacsil, Dorrelyn, Raha, Debasish, Ramirez, Lucia, Reed, Brian, Shi, Minyi, Slifer, Teri, Witt, Heather, Wu, Linfeng, Xu, Xiaoqin, Yan, Koon-Kiu, Yang, Xinqiong, Struhl, Kevin, Weissman, Sherman M., Penalva, Luiz O., Karmakar, Subhradip, Bhanvadia, Raj R., Choudhury, Alina, Domanus, Marc, Ma, Lijia, Moran, Jennifer, Victorsen, Alec, Auer, Thomas, Centanin, Lazaro, Eichenlaub, Michael, Gruhl, Franziska, Heermann, Stephan, Hoeckendorf, Burkhard, Inoue, Daigo, Kellner, Tanja, Kirchmaier, Stephan, Mueller, Claudia, Reinhardt, Robert, Schertel, Lea, Schneider, Stephanie, Sinn, Rebecca, Wittbrodt, Beate, Wittbrodt, Jochen, Partridge, E. Christopher, Jain, Gaurav, Balasundaram, Gayathri, Bates, Daniel L., Byron, Rachel, Canfield, Theresa K., Diegel, Morgan J., Dunn, Douglas, Ebersol, Abigail K., Frum, Tristan, Garg, Kavita, Gist, Erica, Hansen, R. Scott, Boatman, Lisa, Haugen, Eric, Humbert, Richard, Johnson, Audra K., Johnson, Ericka M., Kutyavin, Tattyana V., Lee, Kristen, Lotakis, Dimitra, Maurano, Matthew T., Neph, Shane J., Neri, Fiedencio V., Nguyen, Eric D., Qu, Hongzhu, Reynolds, Alex P., Roach, Vaughn, Rynes, Eric, Sanchez, Minerva E., Sandstrom, Richard S., Shafer, Anthony O., Stergachis, Andrew B., Thomas, Sean, Vernot, Benjamin, Vierstra, Jeff, Vong, Shinny, Weaver, Molly A., Yan, Yongqi, Zhang, Miaohua, Akey, Joshua M., Bender, Michael, Dorschner, Michael O., Groudine, Mark, MacCoss, Michael J., Navas, Patrick, Stamatoyannopoulos, George, Beal, Kathryn, Brazma, Alvis, Flicek, Paul, Johnson, Nathan, Lukk, Margus, Luscombe, Nicholas M., Sobral, Daniel, Vaquerizas, Juan M., Batzoglou, Serafim, Sidow, Arend, Hussami, Nadine, Kyriazopoulou-Panagiotopoulou, Sofia, Libbrecht, Max W., Schaub, Marc A., Miller, Webb, Bickel, Peter J., Banfai, Balazs, Boley, Nathan P., Huang, Haiyan, Li, Jingyi Jessica, Noble, William Stafford, Bilmes, Jeffrey A., Buske, Orion J., Sahu, Avinash D., Kharchenko, Peter V., Park, Peter J., Baker, Dannon, Taylor, James, and Lochovsky, Lucas

Subjects
Genetic research, Human genome -- Research, Genetic transcription -- Research, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research., Author(s): The ENCODE Project Consortium; Overall coordination (data analysis coordination); Ian Dunham [2]; Anshul Kundaje [3, 82]; Data production leads (data production); Shelley F. Aldred [4]; Patrick J. Collins [4]; [...]

Published
2012
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7. Automated Diagnosis and Control of Complex Systems

Author

Kurien, James, Plaunt, Christian, Cannon, Howard, Shirley, Mark, Taylor, Will, Nayak, P, Hudson, Benoit, Bachmann, Andrew, Brownston, Lee, Hayden, Sandra, Wragg, Steve, Millar, William, Pepke, Shirley, Christa, Scott, and Garcia, Ray

Subjects
Man/System Technology And Life Support
Abstract

Livingstone2 is a reusable, artificial intelligence (AI) software system designed to assist spacecraft, life support systems, chemical plants, or other complex systems by operating with minimal human supervision, even in the face of hardware failures or unexpected events. The software diagnoses the current state of the spacecraft or other system, and recommends commands or repair actions that will allow the system to continue operation. Livingstone2 is an enhancement of the Livingstone diagnosis system that was flight-tested onboard the Deep Space One spacecraft in 1999. This version tracks multiple diagnostic hypotheses, rather than just a single hypothesis as in the previous version. It is also able to revise diagnostic decisions made in the past when additional observations become available. In such cases, Livingstone might arrive at an incorrect hypothesis. Re-architecting and re-implementing the system in C++ has increased performance. Usability has been improved by creating a set of development tools that is closely integrated with the Livingstone2 engine. In addition to the core diagnosis engine, Livingstone2 includes a compiler that translates diagnostic models written in a Java-like language into Livingstone2's language, and a broad set of graphical tools for model development.

Published
2007

13. Modelling Ca2+ -dependent proteins in the spine - challenges and solutions

Author

Stefan, Melanie I., Pepke, Shirley, Mihalas, Stefan, Bartol, Thomas, Sejnowski, Terrence J., and Kennedy, Mary B.

Abstract

Background / Purpose: Modelling post-synaptic proteins poses three technical problems: small absolute molecule numbers, large numbers of possible states, and the complex geometry of the spine, which is not a well-mixed compartment. Computational approaches are needed that solve all three of these problems. Main conclusion: Stochastic simulation methods can be used for systems with small molecule numbers, agent-based methods to represent multi-state molecules, and spatial methods to simulate events in complex geometries. We used the agent-based spatial stochastic simulator MCell to model the Ca2+-dependent activation of calmodulin and Ca2+/calmodulin-dependent kinase II (CaMKII) in the spine. Next steps: Next steps will include the extension of our model to include more interaction partners, and to represent some of the regulation events in more detail.

Published
2012

14. Integrating and mining the chromatin landscape of cell-type specificity using self-organizing maps

Author

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Kellis, Manolis, Mortazavi, Ali, Pepke, Shirley, Jansen, Camden, Marinov, Georgi K., Ernst, Jason, Hardison, Ross C., Myers, Richard M., Wold, Barbara J., Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Kellis, Manolis, Mortazavi, Ali, Pepke, Shirley, Jansen, Camden, Marinov, Georgi K., Ernst, Jason, Hardison, Ross C., Myers, Richard M., and Wold, Barbara J.

Abstract

We tested whether self-organizing maps (SOMs) could be used to effectively integrate, visualize, and mine diverse genomics data types, including complex chromatin signatures. A fine-grained SOM was trained on 72 ChIP-seq histone modifications and DNase-seq data sets from six biologically diverse cell lines studied by The ENCODE Project Consortium. We mined the resulting SOM to identify chromatin signatures related to sequence-specific transcription factor occupancy, sequence motif enrichment, and biological functions. To highlight clusters enriched for specific functions such as transcriptional promoters or enhancers, we overlaid onto the map additional data sets not used during training, such as ChIP-seq, RNA-seq, CAGE, and information on cis-acting regulatory modules from the literature. We used the SOM to parse known transcriptional enhancers according to the cell-type-specific chromatin signature, and we further corroborated this pattern on the map by EP300 (also known as p300) occupancy. New candidate cell-type-specific enhancers were identified for multiple ENCODE cell types in this way, along with new candidates for ubiquitous enhancer activity. An interactive web interface was developed to allow users to visualize and custom-mine the ENCODE SOM. We conclude that large SOMs trained on chromatin data from multiple cell types provide a powerful way to identify complex relationships in genomic data at user-selected levels of granularity.

Published
2014

20. A Dynamic Model of Interactions of Ca2+, Calmodulin, and Catalytic Subunits of Ca2+/Calmodulin-Dependent Protein Kinase II.

Author

Pepke, Shirley, Kinzer-Ursem, Tamara, Mihalas, Stefan, and Kennedy, Mary B.

Subjects
*CALCIUM ions, *CALMODULIN, *PROTEIN kinases, *PHOSPHORYLATION, *SYNAPSES, *NEURAL circuitry
Abstract

During the acquisition of memories, influx of Ca2+ into the postsynaptic spine through the pores of activated N-methyl-Daspartate- type glutamate receptors triggers processes that change the strength of excitatory synapses. The pattern of Ca2+ influx during the first few seconds of activity is interpreted within the Ca2+-dependent signaling network such that synaptic strength is eventually either potentiated or depressed. Many of the critical signaling enzymes that control synaptic plasticity, including Ca2+/calmodulin-dependent protein kinase II (CaMKII), are regulated by calmodulin, a small protein that can bind up to 4 Ca2+ ions. As a first step toward clarifying how the Ca2+-signaling network decides between potentiation or depression, we have created a kinetic model of the interactions of Ca2+, calmodulin, and CaMKII that represents our best understanding of the dynamics of these interactions under conditions that resemble those in a postsynaptic spine. We constrained parameters of the model from data in the literature, or from our own measurements, and then predicted time courses of activation and autophosphorylation of CaMKII under a variety of conditions. Simulations showed that species of calmodulin with fewer than four bound Ca2+ play a significant role in activation of CaMKII in the physiological regime, supporting the notion that processing of Ca2+ signals in a spine involves competition among target enzymes for binding to unsaturated species of CaM in an environment in which the concentration of Ca2+ is fluctuating rapidly. Indeed, we showed that dependence of activation on the frequency of Ca2+ transients arises from the kinetics of interaction of fluctuating Ca2+ with calmodulin/CaMKII complexes. We used parameter sensitivity analysis to identify which parameters will be most beneficial to measure more carefully to improve the accuracy of predictions. This model provides a quantitative base from which to build more complex dynamic models of postsynaptic signal transduction during learning. [ABSTRACT FROM AUTHOR]

Published
2010
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