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14 results on '"McNally, Dylan R."'

1. ChromaFold predicts the 3D contact map from single-cell chromatin accessibility

Author

Gao, Vianne R., Yang, Rui, Das, Arnav, Luo, Renhe, Luo, Hanzhi, McNally, Dylan R., Karagiannidis, Ioannis, Rivas, Martin A., Wang, Zhong-Min, Barisic, Darko, Karbalayghareh, Alireza, Wong, Wilfred, Zhan, Yingqian A., Chin, Christopher R., Noble, William S., Bilmes, Jeff A., Apostolou, Effie, Kharas, Michael G., Béguelin, Wendy, Viny, Aaron D., Huangfu, Danwei, Rudensky, Alexander Y., Melnick, Ari M., and Leslie, Christina S.

Published
2024
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2. Smc3 dosage regulates B cell transit through germinal centers and restricts their malignant transformation

Author

Rivas, Martín A., Meydan, Cem, Chin, Christopher R., Challman, Matt F., Kim, Daleum, Bhinder, Bhavneet, Kloetgen, Andreas, Viny, Aaron D., Teater, Matt R., McNally, Dylan R., Doane, Ashley S., Béguelin, Wendy, Fernández, María Teresa Calvo, Shen, Hao, Wang, Xiang, Levine, Ross L., Chen, Zhengming, Tsirigos, Aristotelis, Elemento, Olivier, Mason, Christopher E., and Melnick, Ari M.

Published
2021
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3. ChromaFold predicts the 3D contact map from single-cell chromatin accessibility

Author

Gao, Vianne R., primary, Yang, Rui, additional, Das, Arnav, additional, Luo, Renhe, additional, Luo, Hanzhi, additional, McNally, Dylan R., additional, Karagiannidis, Ioannis, additional, Rivas, Martin A., additional, Wang, Zhong-Min, additional, Barisic, Darko, additional, Karbalayghareh, Alireza, additional, Wong, Wilfred, additional, Zhan, Yingqian A., additional, Chin, Christopher R., additional, Noble, William, additional, Bilmes, Jeff A., additional, Apostolou, Effie, additional, Kharas, Michael G., additional, Béguelin, Wendy, additional, Viny, Aaron D., additional, Huangfu, Danwei, additional, Rudensky, Alexander Y., additional, Melnick, Ari M., additional, and Leslie, Christina S., additional

Published
2023
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4. Data from An Aged/Autoimmune B-cell Program Defines the Early Transformation of Extranodal Lymphomas

Author

Venturutti, Leandro, primary, Rivas, Martin A., primary, Pelzer, Benedikt W., primary, Flümann, Ruth, primary, Hansen, Julia, primary, Karagiannidis, Ioannis, primary, Xia, Min, primary, McNally, Dylan R., primary, Isshiki, Yusuke, primary, Lytle, Andrew, primary, Teater, Matt, primary, Chin, Christopher R., primary, Meydan, Cem, primary, Knittel, Gero, primary, Ricker, Edd, primary, Mason, Christopher E., primary, Ye, Xiaofei, primary, Pan-Hammarström, Qiang, primary, Steidl, Christian, primary, Scott, David W., primary, Reinhardt, Hans Christian, primary, Pernis, Alessandra B., primary, Béguelin, Wendy, primary, and Melnick, Ari M., primary

Published
2023
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6. Table S1 from An Aged/Autoimmune B-cell Program Defines the Early Transformation of Extranodal Lymphomas

Author

Venturutti, Leandro, primary, Rivas, Martin A., primary, Pelzer, Benedikt W., primary, Flümann, Ruth, primary, Hansen, Julia, primary, Karagiannidis, Ioannis, primary, Xia, Min, primary, McNally, Dylan R., primary, Isshiki, Yusuke, primary, Lytle, Andrew, primary, Teater, Matt, primary, Chin, Christopher R., primary, Meydan, Cem, primary, Knittel, Gero, primary, Ricker, Edd, primary, Mason, Christopher E., primary, Ye, Xiaofei, primary, Pan-Hammarström, Qiang, primary, Steidl, Christian, primary, Scott, David W., primary, Reinhardt, Hans Christian, primary, Pernis, Alessandra B., primary, Béguelin, Wendy, primary, and Melnick, Ari M., primary

Published
2023
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7. Figures S1-S7 from An Aged/Autoimmune B-cell Program Defines the Early Transformation of Extranodal Lymphomas

Author

Venturutti, Leandro, primary, Rivas, Martin A., primary, Pelzer, Benedikt W., primary, Flümann, Ruth, primary, Hansen, Julia, primary, Karagiannidis, Ioannis, primary, Xia, Min, primary, McNally, Dylan R., primary, Isshiki, Yusuke, primary, Lytle, Andrew, primary, Teater, Matt, primary, Chin, Christopher R., primary, Meydan, Cem, primary, Knittel, Gero, primary, Ricker, Edd, primary, Mason, Christopher E., primary, Ye, Xiaofei, primary, Pan-Hammarström, Qiang, primary, Steidl, Christian, primary, Scott, David W., primary, Reinhardt, Hans Christian, primary, Pernis, Alessandra B., primary, Béguelin, Wendy, primary, and Melnick, Ari M., primary

Published
2023
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8. An Aged/Autoimmune B-cell Program Defines the Early Transformation of Extranodal Lymphomas

Author

Venturutti, Leandro, primary, Rivas, Martin A., additional, Pelzer, Benedikt W., additional, Flümann, Ruth, additional, Hansen, Julia, additional, Karagiannidis, Ioannis, additional, Xia, Min, additional, McNally, Dylan R., additional, Isshiki, Yusuke, additional, Lytle, Andrew, additional, Teater, Matt, additional, Chin, Christopher R., additional, Meydan, Cem, additional, Knittel, Gero, additional, Ricker, Edd, additional, Mason, Christopher E., additional, Ye, Xiaofei, additional, Pan-Hammarström, Qiang, additional, Steidl, Christian, additional, Scott, David W., additional, Reinhardt, Hans Christian, additional, Pernis, Alessandra B., additional, Béguelin, Wendy, additional, and Melnick, Ari M., additional

Published
2022
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10. Reshaping of the androgen-driven chromatin landscape in normal prostate cells by early cancer drivers and effect on therapeutic sensitivity

Author

Grbesa, Ivana, primary, Augello, Michael A., additional, Liu, Deli, additional, McNally, Dylan R., additional, Gaffney, Christopher D., additional, Huang, Dennis, additional, Lin, Kevin, additional, Ivenitsky, Daria, additional, Goueli, Ramy, additional, Robinson, Brian D., additional, Khani, Francesca, additional, Deonarine, Lesa D., additional, Blattner, Mirjam, additional, Elemento, Olivier, additional, Davicioni, Elai, additional, Sboner, Andrea, additional, and Barbieri, Christopher E., additional

Published
2021
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11. SPOP mutation confers sensitivity to AR-targeted therapy in prostate cancer by reshaping the androgen-driven chromatin landscape

Author

Grbesa, Ivana, primary, Augello, Michael A., additional, Liu, Deli, additional, McNally, Dylan R., additional, Gaffney, Christopher D., additional, Huang, Dennis, additional, Lin, Kevin, additional, Goueli, Ramy, additional, Robinson, Brian D., additional, Khani, Francesca, additional, Deonarine, Lesa D., additional, Blattner, Mirjam, additional, Elemento, Olivier, additional, Davicioni, Elai, additional, Sboner, Andrea, additional, and Barbieri, Christopher E., additional

Published
2021
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12. Dynamic Incorporation of Histone H3 Variants into Chromatin Is Essential for Acquisition of Aggressive Traits and Metastatic Colonization

Author

Gomes, Ana P., primary, Ilter, Didem, additional, Low, Vivien, additional, Rosenzweig, Adam, additional, Shen, Zih-Jie, additional, Schild, Tanya, additional, Rivas, Martin A., additional, Er, Ekrem E., additional, McNally, Dylan R., additional, Mutvei, Anders P., additional, Han, Julie, additional, Ou, Yi-Hung, additional, Cavaliere, Paola, additional, Mullarky, Edouard, additional, Nagiec, Michal, additional, Shin, Sejeong, additional, Yoon, Sang-Oh, additional, Dephoure, Noah, additional, Massagué, Joan, additional, Melnick, Ari M., additional, Cantley, Lewis C., additional, Tyler, Jessica K., additional, and Blenis, John, additional

Published
2019
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13. ChromaFold predicts the 3D contact map from single-cell chromatin accessibility.

Author

Gao VR, Yang R, Das A, Luo R, Luo H, McNally DR, Karagiannidis I, Rivas MA, Wang ZM, Barisic D, Karbalayghareh A, Wong W, Zhan YA, Chin CR, Noble W, Bilmes JA, Apostolou E, Kharas MG, Béguelin W, Viny AD, Huangfu D, Rudensky AY, Melnick AM, and Leslie CS

Abstract

The identification of cell-type-specific 3D chromatin interactions between regulatory elements can help to decipher gene regulation and to interpret the function of disease-associated non-coding variants. However, current chromosome conformation capture (3C) technologies are unable to resolve interactions at this resolution when only small numbers of cells are available as input. We therefore present ChromaFold, a deep learning model that predicts 3D contact maps and regulatory interactions from single-cell ATAC sequencing (scATAC-seq) data alone. ChromaFold uses pseudobulk chromatin accessibility, co-accessibility profiles across metacells, and predicted CTCF motif tracks as input features and employs a lightweight architecture to enable training on standard GPUs. Once trained on paired scATAC-seq and Hi-C data in human cell lines and tissues, ChromaFold can accurately predict both the 3D contact map and peak-level interactions across diverse human and mouse test cell types. In benchmarking against a recent deep learning method that uses bulk ATAC-seq, DNA sequence, and CTCF ChIP-seq to make cell-type-specific predictions, ChromaFold yields superior prediction performance when including CTCF ChIP-seq data as an input and comparable performance without. Finally, fine-tuning ChromaFold on paired scATAC-seq and Hi-C in a complex tissue enables deconvolution of chromatin interactions across cell subpopulations. ChromaFold thus achieves state-of-the-art prediction of 3D contact maps and regulatory interactions using scATAC-seq alone as input data, enabling accurate inference of cell-type-specific interactions in settings where 3C-based assays are infeasible., Competing Interests: C.S.L. is an SAB member and co-inventor of IP with Episteme Prognostics, unrelated to the current study. M.G.K. is a SAB member of 858 Therapeutics and received honorarium from Kumquat, AstraZeneca and Consultancy with Transition Bio. A.D.V is an SAB member of Arima Genomics. A.Y.R. is an SAB member and has equity in Sonoma Biotherapeutics, Santa Ana Bio, RAPT Therapeutics and Vedanta Biosciences. He is an SEB member of Amgen and BioInvent and is a co-inventor or has IP licensed to Takeda that is unrelated to the content of the present study. A.M. has research funding from Janssen, Epizyme and Daiichi Sankyo. A.M. has consulted for Exo Therapeutics, Treeline Biosciences, Astra Zeneca. The remaining authors declare no competing interests.

Published
2023
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14. An Aged/Autoimmune B-cell Program Defines the Early Transformation of Extranodal Lymphomas.

Author

Venturutti L, Rivas MA, Pelzer BW, Flümann R, Hansen J, Karagiannidis I, Xia M, McNally DR, Isshiki Y, Lytle A, Teater M, Chin CR, Meydan C, Knittel G, Ricker E, Mason CE, Ye X, Pan-Hammarström Q, Steidl C, Scott DW, Reinhardt HC, Pernis AB, Béguelin W, and Melnick AM

Subjects
Humans, Animals, Mice, Aged, Prospective Studies, Mutation, Prognosis, B-Lymphocytes, Lymphoma, Large B-Cell, Diffuse pathology
Abstract

A third of patients with diffuse large B-cell lymphoma (DLBCL) present with extranodal dissemination, which is associated with inferior clinical outcomes. MYD88L265P is a hallmark extranodal DLBCL mutation that supports lymphoma proliferation. Yet extranodal lymphomagenesis and the role of MYD88L265P in transformation remain mostly unknown. Here, we show that B cells expressing Myd88L252P (MYD88L265P murine equivalent) activate, proliferate, and differentiate with minimal T-cell costimulation. Additionally, Myd88L252P skewed B cells toward memory fate. Unexpectedly, the transcriptional and phenotypic profiles of B cells expressing Myd88L252P, or other extranodal lymphoma founder mutations, resembled those of CD11c+T-BET+ aged/autoimmune memory B cells (AiBC). AiBC-like cells progressively accumulated in animals prone to develop lymphomas, and ablation of T-BET, the AiBC master regulator, stripped mouse and human mutant B cells of their competitive fitness. By identifying a phenotypically defined prospective lymphoma precursor population and its dependencies, our findings pave the way for the early detection of premalignant states and targeted prophylactic interventions in high-risk patients., Significance: Extranodal lymphomas feature a very poor prognosis. The identification of phenotypically distinguishable prospective precursor cells represents a milestone in the pursuit of earlier diagnosis, patient stratification, and prophylactic interventions. Conceptually, we found that extranodal lymphomas and autoimmune disorders harness overlapping pathogenic trajectories, suggesting these B-cell disorders develop and evolve within a spectrum. See related commentary by Leveille et al. (Blood Cancer Discov 2023;4:8-11). This article is highlighted in the In This Issue feature, p. 1., (©2022 American Association for Cancer Research.)

Published
2023
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