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2. NEAT1 modulates the TIRR/53BP1 complex to maintain genome integrity

Author

Susan Kilgas, Aleem Syed, Patrick Toolan-Kerr, Michelle L. Swift, Shrabasti Roychoudhury, Aniruddha Sarkar, Sarah Wilkins, Mikayla Quigley, Anna R. Poetsch, Maria Victoria Botuyan, Gaofeng Cui, Georges Mer, Jernej Ule, Pascal Drané, and Dipanjan Chowdhury

Subjects
Science
Abstract

Abstract Tudor Interacting Repair Regulator (TIRR) is an RNA-binding protein (RBP) that interacts directly with 53BP1, restricting its access to DNA double-strand breaks (DSBs) and its association with p53. We utilized iCLIP to identify RNAs that directly bind to TIRR within cells, identifying the long non-coding RNA NEAT1 as the primary RNA partner. The high affinity of TIRR for NEAT1 is due to prevalent G-rich motifs in the short isoform (NEAT1_1) region of NEAT1. This interaction destabilizes the TIRR/53BP1 complex, promoting 53BP1’s function. NEAT1_1 is enriched during the G1 phase of the cell cycle, thereby ensuring that TIRR-dependent inhibition of 53BP1’s function is cell cycle-dependent. TDP-43, an RBP that is implicated in neurodegenerative diseases, modulates the TIRR/53BP1 complex by promoting the production of the NEAT1 short isoform, NEAT1_1. Together, we infer that NEAT1_1, and factors regulating NEAT1_1, may impact 53BP1-dependent DNA repair processes, with implications for a spectrum of diseases.

Published
2024
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3. RNA modifications detection by comparative Nanopore direct RNA sequencing

Author

Adrien Leger, Paulo P. Amaral, Luca Pandolfini, Charlotte Capitanchik, Federica Capraro, Valentina Miano, Valentina Migliori, Patrick Toolan-Kerr, Theodora Sideri, Anton J. Enright, Konstantinos Tzelepis, Folkert J. van Werven, Nicholas M. Luscombe, Isaia Barbieri, Jernej Ule, Tomas Fitzgerald, Ewan Birney, Tommaso Leonardi, and Tony Kouzarides

Subjects
Science
Abstract

Nanopore direct RNA Sequencing data contain information about the presence of RNA modifications, but their detection poses substantial challenges. Here the authors introduce Nanocompore, a new methodology for modification detection from Nanopore data.

Published
2021
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4. RNA modifications detection by comparative Nanopore direct RNA sequencing

Author

Leger, Adrien, Amaral, Paulo P., Pandolfini, Luca, Capitanchik, Charlotte, Capraro, Federica, Miano, Valentina, Migliori, Valentina, Toolan-Kerr, Patrick, Sideri, Theodora, Enright, Anton J., Tzelepis, Konstantinos, van Werven, Folkert J., Luscombe, Nicholas M., Barbieri, Isaia, Ule, Jernej, Fitzgerald, Tomas, Birney, Ewan, Leonardi, Tommaso, and Kouzarides, Tony

Published
2021
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5. How Do You Identify m6 A Methylation in Transcriptomes at High Resolution? A Comparison of Recent Datasets

Author

Charlotte Capitanchik, Patrick Toolan-Kerr, Nicholas M. Luscombe, and Jernej Ule

Subjects
RNA, N6-methyladenosine, m6A, epitranscriptomics, bioinformatics, Genetics, QH426-470
Abstract

A flurry of methods has been developed in recent years to identify N6-methyladenosine (m6A) sites across transcriptomes at high resolution. This raises the need to understand both the common features and those that are unique to each method. Here, we complement the analyses presented in the original papers by reviewing their various technical aspects and comparing the overlap between m6A-methylated messenger RNAs (mRNAs) identified by each. Specifically, we examine eight different methods that identify m6A sites in human cells with high resolution: two antibody-based crosslinking and immunoprecipitation (CLIP) approaches, two using endoribonuclease MazF, one based on deamination, two using Nanopore direct RNA sequencing, and finally, one based on computational predictions. We contrast the respective datasets and discuss the challenges in interpreting the overlap between them, including a prominent expression bias in detected genes. This overview will help guide researchers in making informed choices about using the available data and assist with the design of future experiments to expand our understanding of m6A and its regulation.

Published
2020
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6. m6A-ELISA, a simple method for quantifying N6-methyladenosine from mRNA populations

Author

Ensinck, Imke, Sideri, Theodora, Modic, Miha, Capitanchik, Charlotte, Vivori, Claudia, Toolan-Kerr, Patrick, and van Werven, Folkert J.

Abstract

N6-methyladenosine (m6A) is a widely studied and abundant RNA modification. The m6A mark regulates the fate of RNAs in various ways, which in turn drives changes in cell physiology, development, and disease pathology. Over the last decade, numerous methods have been developed to map and quantify m6A sites genome-wide through deep sequencing. Alternatively, m6A levels can be quantified from a population of RNAs using techniques such as liquid chromatography-mass spectrometry or thin layer chromatography. However, many methods for quantifying m6A levels involve extensive protocols and specialized data analysis, and often only a few samples can be handled in a single experiment. Here, we developed a simple method for determining relative m6A levels in mRNA populations from various sources based on an enzyme-linked immunosorbent-based assay (m6A-ELISA). We have optimized various steps of m6A-ELISA, such as sample preparation and the background signal resulting from the primary antibody. We validated the method using mRNA populations from budding yeast and mouse embryonic stem cells. The full protocol takes less than a day, requiring only 25 ng of mRNA. The m6A-ELISA protocol is quick, cost-effective, and scalable, making it a valuable tool for determining relative m6A levels in samples from various sources that could be adapted to detect other mRNA modifications.

Published
2023
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7. Pandemic peak SARS-CoV-2 infection and seroconversion rates in London frontline health-care workers

Author

Houlihan, Catherine F, Vora, Nina, Byrne, Thomas, Lewer, Dan, Kelly, Gavin, Heaney, Judith, Gandhi, Sonia, Spyer, Moira J, Beale, Rupert, Cherepanov, Peter, Moore, David, Gilson, Richard, Gamblin, Steve, Kassiotis, George, McCoy, Laura E, Swanton, Charles, Hayward, Andrew, Nastouli, Eleni, Aitken, Jim, Allen, Zoe, Ambler, Rachel, Ambrose, Karen, Ashton, Emma, Avola, Alida, Balakrishnan, Samutheswari, Barns-Jenkins, Caitlin, Barr, Genevieve, Barrell, Sam, Basu, Souradeep, Beale, Rupert, Beesley, Clare, Bhardwaj, Nisha, Bibi, Shahnaz, Bineva-Todd, Ganka, Biswas, Dhruva, Blackman, Michael J, Bonnet, Dominique, Bowker, Faye, Broncel, Malgorzata, Brooks, Claire, Buck, Michael D, Buckton, Andrew, Budd, Timothy, Burrell, Alana, Busby, Louise, Bussi, Claudio, Butterworth, Simon, Byrne, Fiona, Byrne, Richard, Caidan, Simon, Campbell, Joanna, Canton, Johnathan, Cardoso, Ana, Carter, Nick, Carvalho, Luiz, Carzaniga, Raffaella, Chandler, Natalie, Chen, Qu, Cherepanov, Peter, Churchward, Laura, Clark, Graham, Clayton, Bobbi, Cobolli Gigli, Clementina, Collins, Zena, Cottrell, Sally, Crawford, Margaret, Cubitt, Laura, Cullup, Tom, Davies, Heledd, Davis, Patrick, Davison, Dara, D'Avola, Annalisa, Dearing, Vicky, Debaisieux, Solene, Diaz-Romero, Monica, Dibbs, Alison, Diring, Jessica, Driscoll, Paul C, Earl, Christopher, Edwards, Amelia, Ekin, Chris, Evangelopoulos, Dimitrios, Faraway, Rupert, Fearns, Antony, Ferron, Aaron, Fidanis, Efthymios, Fitz, Dan, Fleming, James, Frederico, Bruno, Gaiba, Alessandra, Gait, Anthony, Gamblin, Steve, Gandhi, Sonia, Gaul, Liam, Golding, Helen M, Goldman, Jacki, Goldstone, Robert, Gomez Dominguez, Belen, Gong, Hui, Grant, Paul R, Greco, Maria, Grobler, Mariana, Guedan, Anabel, Gutierrez, Maximiliano G, Hackett, Fiona, Hall, Ross, Halldorsson, Steinar, Harris, Suzanne, Hashim, Sugera, Healy, Lyn, Heaney, Judith, Herbst, Susanne, Hewitt, Graeme, Higgins, Theresa, Hindmarsh, Steve, Hirani, Rajnika, Hope, Joshua, Horton, Elizabeth, Hoskins, Beth, Houlihan, Catherine F, Howell, Michael, Howitt, Louise, Hoyle, Jacqueline, Htun, Mint R, Hubank, Michael, Huerga Encabo, Hector, Hughes, Deborah, Hughes, Jane, Huseynova, Almaz, Hwang, Ming-Shih, Instrell, Rachael, Jackson, Deborah, Jamal-Hanjani, Mariam, Jenkins, Lucy, Jiang, Ming, Johnson, Mark, Jones, Leigh, Kanu, Nnennaya, Kassiotis, George, Kiely, Louise, King Spert Teixeira, Anastacio, Kirk, Stuart, Kjaer, Svend, Knuepfer, Ellen, Komarov, Nikita, Kotzampaltiris, Paul, Kousis, Konstantinos, Krylova, Tammy, Kucharska, Ania, Labrum, Robyn, Lambe, Catherine, Lappin, Michelle, Lee, Stacey-Ann, Levett, Andrew, Levett, Lisa, Levi, Marcel, Liu, Hon-Wing, Loughlin, Sam, Lu, Wei-Ting, MacRae, James I, Madoo, Akshay, Marczak, Julie A, Martensson, Mimmi, Martinez, Thomas, Marzook, Bishara, Matthews, John, Matz, Joachim M, McCall, Samuel, McCoy, Laura E, McKay, Fiona, McNamara, Edel C, Minutti, Carlos M, Mistry, Gita, Molina-Arcas, Miriam, Montaner, Beatriz, Montgomery, Kylie, Moore, Catherine, Moore, David, Moraiti, Anastasia, Moreira-Teixeira, Lucia, Mukherjee, Joyita, Naceur-Lombardelli, Cristina, Nastouli, Eleni, Nelson, Aileen, Nicod, Jerome, Nightingale, Luke, Nofal, Stephanie, Nurse, Paul, Nutan, Savita, Oedekoven, Caroline, O'Garra, Anne, O'Leary, Jean D, Olsen, Jessica, O'Neill, Olga, Ordonez Suarez, Paula, O'Reilly, Nicola, Osborne, Neil, Pabari, Amar, Pajak, Aleksandra, Papayannopoulos, Venizelos, Patel, Namita, Patel, Yogen, Paun, Oana, Peat, Nigel, Peces-Barba Castano, Laura, Perez Caballero, Ana, Perez-Lloret, Jimena, Perrault, Magali S, Perrin, Abigail, Poh, Roy, Poirier, Enzo Z, Polke, James M, Pollitt, Marc, Prieto-Godino, Lucia, Proust, Alize, Shah Punatar, Rajvee, Puvirajasinghe, Clinda, Queval, Christophe, Ramachandran, Vijaya, Ramaprasad, Abhinay, Ratcliffe, Peter, Reed, Laura, Reis e Sousa, Caetano, Richardson, Kayleigh, Ridewood, Sophie, Roberts, Rowenna, Rodgers, Angela, Romero Clavijo, Pablo, Rosa, Annachiara, Rossi, Alice, Roustan, Chloe, Rowan, Andrew, Sahai, Erik, Sait, Aaron, Sala, Katarzyna, Sanderson, Theo, Santucci, Pierre, Sardar, Fatima, Sateriale, Adam, Saunders, Jill A, Sawyer, Chelsea, Schlott, Anja, Schweighoffer, Edina, Segura-Bayona, Sandra, Shaw, Joe, Shin, Gee Yen, Silva Dos Santos, Mariana, Silvestre, Margaux, Singer, Matthew, Snell, Daniel M, Song, Ok-Ryul, Spyer, Moira J, Steel, Louisa, Strange, Amy, Sullivan, Adrienne E, Swanton, Charles, Tan, Michele SY, Tautz-Davis, Zoe H, Taylor, Effie, Taylor, Gunes, Taylor, Harriet B, Taylor-Beadling, Alison, Teixeira Subtil, Fernanda, Terré Torras, Berta, Toolan-Kerr, Patrick, Torelli, Francesca, Toteva, Tea, Treeck, Moritz, Trojer, Hadija, Tsai, Ming-Han C, Turner, James MA, Turner, Melanie, Ule, Jernej, Ulferts, Rachel, Vanloo, Sharon P, Veeriah, Selvaraju, Venkatesan, Subramanian, Vousden, Karen, Wack, Andreas, Walder, Claire, Walker, Philip A, Wang, Yiran, Ward, Sophia, Wenman, Catharina, Wiliams, Luke, Williams, Matthew J, Wong, Wai Keong, Wright, Joshua, Wu, Mary, Wynne, Lauren, Xiang, Zheng, Yap, Melvyn, Zagalak, Julian A, Zecchin, Davide, Zillwood, Rachel, Matthews, Rebecca, Severn, Abigail, Adam, Sajida, Enfield, Louise, McBride, Angela, Gärtner, Kathleen, Edwards, Sarah, Lorencatto, Fabiana, Michie, Susan, Manley, Ed, Shahmanesh, Maryam, Lukha, Hinal, Prymas, Paulina, McBain, Hazel, Shortman, Robert, Wood, Leigh, Davies, Claudia, Williams, Bethany, Ng, Kevin W, Cornish, Georgina H, Faulkner, Nikhil, Riddell, Andrew, Hobson, Philip, Agua-Doce, Ana, Bartolovic, Kerol, Russell, Emma, Carr, Lotte, Sanchez, Emilie, Frampton, Daniel, Byott, Matthew, Paraskevopoulou, Stavroula M, Crayton, Elise, Meyer, Carly, Vora, Nina, Gkouleli, Triantafylia, Stoltenberg, Andrea, Ranieri, Veronica, Byrne, Tom, Lewer, Dan, Hayward, Andrew, Gilson, Richard, Kelly, Gavin, Roberts, Fiona, and Hatipoglu, Emine

Published
2020
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8. How Do You Identify m 6 A Methylation in Transcriptomes at High Resolution? A Comparison of Recent Datasets.

Author

Capitanchik C, Toolan-Kerr P, Luscombe NM, and Ule J

Abstract

A flurry of methods has been developed in recent years to identify N6-methyladenosine (m 6 A) sites across transcriptomes at high resolution. This raises the need to understand both the common features and those that are unique to each method. Here, we complement the analyses presented in the original papers by reviewing their various technical aspects and comparing the overlap between m 6 A-methylated messenger RNAs (mRNAs) identified by each. Specifically, we examine eight different methods that identify m 6 A sites in human cells with high resolution: two antibody-based crosslinking and immunoprecipitation (CLIP) approaches, two using endoribonuclease MazF, one based on deamination, two using Nanopore direct RNA sequencing, and finally, one based on computational predictions. We contrast the respective datasets and discuss the challenges in interpreting the overlap between them, including a prominent expression bias in detected genes. This overview will help guide researchers in making informed choices about using the available data and assist with the design of future experiments to expand our understanding of m 6 A and its regulation., (Copyright © 2020 Capitanchik, Toolan-Kerr, Luscombe and Ule.)

Published
2020
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