1
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Ho WM, Chen CY, Chiang TW, Chuang TJ. A longer time to relapse is associated with a larger increase in differences between paired primary and recurrent IDH wild-type glioblastomas at both the transcriptomic and genomic levels. Acta Neuropathol Commun 2024; 12:77. [PMID: 38762464 PMCID: PMC11102269 DOI: 10.1186/s40478-024-01790-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 05/05/2024] [Indexed: 05/20/2024] Open
Abstract
Glioblastoma (GBM) is the most common malignant brain tumor in adults, which remains incurable and often recurs rapidly after initial therapy. While large efforts have been dedicated to uncover genomic/transcriptomic alternations associated with the recurrence of GBMs, the evolutionary trajectories of matched pairs of primary and recurrent (P-R) GBMs remain largely elusive. It remains challenging to identify genes associated with time to relapse (TTR) and construct a stable and effective prognostic model for predicting TTR of primary GBM patients. By integrating RNA-sequencing and genomic data from multiple datasets of patient-matched longitudinal GBMs of isocitrate dehydrogenase wild-type (IDH-wt), here we examined the associations of TTR with heterogeneities between paired P-R GBMs in gene expression profiles, tumor mutation burden (TMB), and microenvironment. Our results revealed a positive correlation between TTR and transcriptomic/genomic differences between paired P-R GBMs, higher percentages of non-mesenchymal-to-mesenchymal transition and mesenchymal subtype for patients with a short TTR than for those with a long TTR, a high correlation between paired P-R GBMs in gene expression profiles and TMB, and a negative correlation between the fitting level of such a paired P-R GBM correlation and TTR. According to these observations, we identified 55 TTR-associated genes and thereby constructed a seven-gene (ZSCAN10, SIGLEC14, GHRHR, TBX15, TAS2R1, CDKL1, and CD101) prognostic model for predicting TTR of primary IDH-wt GBM patients using univariate/multivariate Cox regression analyses. The risk scores estimated by the model were significantly negatively correlated with TTR in the training set and two independent testing sets. The model also segregated IDH-wt GBM patients into two groups with significantly divergent progression-free survival outcomes and showed promising performance for predicting 1-, 2-, and 3-year progression-free survival rates in all training and testing sets. Our findings provide new insights into the molecular understanding of GBM progression at recurrence and potential targets for therapeutic treatments.
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Affiliation(s)
- Wei-Min Ho
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
- Ph.D. Program in Translational Medicine, National Taiwan University and Academia Sinica, Taipei, Taiwan
- Department of Neurology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
- School of Medicine, National Tsing Hua University, Hsinchu, Taiwan
| | - Chia-Ying Chen
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - Tai-Wei Chiang
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - Trees-Juen Chuang
- Genomics Research Center, Academia Sinica, Taipei, Taiwan.
- Ph.D. Program in Translational Medicine, National Taiwan University and Academia Sinica, Taipei, Taiwan.
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2
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Han CZ, Li RZ, Hansen E, Trescott S, Fixsen BR, Nguyen CT, Mora CM, Spann NJ, Bennett HR, Poirion O, Buchanan J, Warden AS, Xia B, Schlachetzki JCM, Pasillas MP, Preissl S, Wang A, O'Connor C, Shriram S, Kim R, Schafer D, Ramirez G, Challacombe J, Anavim SA, Johnson A, Gupta M, Glass IA, Levy ML, Haim SB, Gonda DD, Laurent L, Hughes JF, Page DC, Blurton-Jones M, Glass CK, Coufal NG. Human microglia maturation is underpinned by specific gene regulatory networks. Immunity 2023; 56:2152-2171.e13. [PMID: 37582369 PMCID: PMC10529991 DOI: 10.1016/j.immuni.2023.07.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 04/11/2023] [Accepted: 07/21/2023] [Indexed: 08/17/2023]
Abstract
Microglia phenotypes are highly regulated by the brain environment, but the transcriptional networks that specify the maturation of human microglia are poorly understood. Here, we characterized stage-specific transcriptomes and epigenetic landscapes of fetal and postnatal human microglia and acquired corresponding data in induced pluripotent stem cell (iPSC)-derived microglia, in cerebral organoids, and following engraftment into humanized mice. Parallel development of computational approaches that considered transcription factor (TF) co-occurrence and enhancer activity allowed prediction of shared and state-specific gene regulatory networks associated with fetal and postnatal microglia. Additionally, many features of the human fetal-to-postnatal transition were recapitulated in a time-dependent manner following the engraftment of iPSC cells into humanized mice. These data and accompanying computational approaches will facilitate further efforts to elucidate mechanisms by which human microglia acquire stage- and disease-specific phenotypes.
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Affiliation(s)
- Claudia Z Han
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Rick Z Li
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Emily Hansen
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Sanford Consortium for Regenerative Medicine, La Jolla, CA 92037, USA
| | - Samantha Trescott
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Sanford Consortium for Regenerative Medicine, La Jolla, CA 92037, USA
| | - Bethany R Fixsen
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Celina T Nguyen
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Sanford Consortium for Regenerative Medicine, La Jolla, CA 92037, USA
| | - Cristina M Mora
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Sanford Consortium for Regenerative Medicine, La Jolla, CA 92037, USA
| | - Nathanael J Spann
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Hunter R Bennett
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Olivier Poirion
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Center for Epigenomics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Justin Buchanan
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Center for Epigenomics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Anna S Warden
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Sanford Consortium for Regenerative Medicine, La Jolla, CA 92037, USA
| | - Bing Xia
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Sanford Consortium for Regenerative Medicine, La Jolla, CA 92037, USA
| | - Johannes C M Schlachetzki
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Martina P Pasillas
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Sebastian Preissl
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Center for Epigenomics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Allen Wang
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Center for Epigenomics, University of California, San Diego, La Jolla, CA 92093, USA
| | | | - Shreya Shriram
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Sanford Consortium for Regenerative Medicine, La Jolla, CA 92037, USA
| | - Roy Kim
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Sanford Consortium for Regenerative Medicine, La Jolla, CA 92037, USA
| | - Danielle Schafer
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Sanford Consortium for Regenerative Medicine, La Jolla, CA 92037, USA
| | - Gabriela Ramirez
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Sanford Consortium for Regenerative Medicine, La Jolla, CA 92037, USA
| | - Jean Challacombe
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Samuel A Anavim
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Sanford Consortium for Regenerative Medicine, La Jolla, CA 92037, USA
| | - Avalon Johnson
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Sanford Consortium for Regenerative Medicine, La Jolla, CA 92037, USA
| | - Mihir Gupta
- Department of Neurosurgery, University of California, San Diego, La Jolla, CA 92037, USA
| | - Ian A Glass
- Department of Pediatrics, University of Washington and Seattle Children's Research Institute, Seattle, WA, USA
| | - Michael L Levy
- Department of Neurosurgery, University of California, San Diego-Rady Children's Hospital, San Diego, CA 92123, USA
| | - Sharona Ben Haim
- Department of Neurosurgery, University of California, San Diego, La Jolla, CA 92037, USA
| | - David D Gonda
- Department of Neurosurgery, University of California, San Diego-Rady Children's Hospital, San Diego, CA 92123, USA
| | - Louise Laurent
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | | | - David C Page
- Whitehead Institute, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Howard Hughes Medical Institute, Whitehead Institute, Cambridge, MA 02142, USA
| | - Mathew Blurton-Jones
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA 92696, USA
| | - Christopher K Glass
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Nicole G Coufal
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Sanford Consortium for Regenerative Medicine, La Jolla, CA 92037, USA; Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
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3
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Pierson Smela MD, Kramme CC, Fortuna PRJ, Adams JL, Su R, Dong E, Kobayashi M, Brixi G, Kavirayuni VS, Tysinger E, Kohman RE, Shioda T, Chatterjee P, Church GM. Directed differentiation of human iPSCs to functional ovarian granulosa-like cells via transcription factor overexpression. eLife 2023; 12:e83291. [PMID: 36803359 PMCID: PMC9943069 DOI: 10.7554/elife.83291] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 01/18/2023] [Indexed: 02/22/2023] Open
Abstract
An in vitro model of human ovarian follicles would greatly benefit the study of female reproduction. Ovarian development requires the combination of germ cells and several types of somatic cells. Among these, granulosa cells play a key role in follicle formation and support for oogenesis. Whereas efficient protocols exist for generating human primordial germ cell-like cells (hPGCLCs) from human induced pluripotent stem cells (hiPSCs), a method of generating granulosa cells has been elusive. Here, we report that simultaneous overexpression of two transcription factors (TFs) can direct the differentiation of hiPSCs to granulosa-like cells. We elucidate the regulatory effects of several granulosa-related TFs and establish that overexpression of NR5A1 and either RUNX1 or RUNX2 is sufficient to generate granulosa-like cells. Our granulosa-like cells have transcriptomes similar to human fetal ovarian cells and recapitulate key ovarian phenotypes including follicle formation and steroidogenesis. When aggregated with hPGCLCs, our cells form ovary-like organoids (ovaroids) and support hPGCLC development from the premigratory to the gonadal stage as measured by induction of DAZL expression. This model system will provide unique opportunities for studying human ovarian biology and may enable the development of therapies for female reproductive health.
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Affiliation(s)
- Merrick D Pierson Smela
- Wyss Institute, Harvard UniversityBostonUnited States
- Department of Genetics, Harvard Medical SchoolBostonUnited States
| | - Christian C Kramme
- Wyss Institute, Harvard UniversityBostonUnited States
- Department of Genetics, Harvard Medical SchoolBostonUnited States
| | - Patrick RJ Fortuna
- Wyss Institute, Harvard UniversityBostonUnited States
- Department of Genetics, Harvard Medical SchoolBostonUnited States
| | - Jessica L Adams
- Wyss Institute, Harvard UniversityBostonUnited States
- Department of Genetics, Harvard Medical SchoolBostonUnited States
| | - Rui Su
- Wyss Institute, Harvard UniversityBostonUnited States
- Department of Genetics, Harvard Medical SchoolBostonUnited States
| | - Edward Dong
- Wyss Institute, Harvard UniversityBostonUnited States
- Department of Genetics, Harvard Medical SchoolBostonUnited States
| | - Mutsumi Kobayashi
- Massachusetts General Hospital Center for Cancer Research, Harvard Medical SchoolCharlestownUnited States
| | - Garyk Brixi
- Wyss Institute, Harvard UniversityBostonUnited States
- Department of Genetics, Harvard Medical SchoolBostonUnited States
- Department of Biomedical Engineering, Duke UniversityDurhamUnited States
- Department of Computer Science, Duke UniversityDurhamUnited States
| | - Venkata Srikar Kavirayuni
- Department of Biomedical Engineering, Duke UniversityDurhamUnited States
- Department of Computer Science, Duke UniversityDurhamUnited States
| | - Emma Tysinger
- Department of Biomedical Engineering, Duke UniversityDurhamUnited States
- Department of Computer Science, Duke UniversityDurhamUnited States
| | - Richie E Kohman
- Wyss Institute, Harvard UniversityBostonUnited States
- Department of Genetics, Harvard Medical SchoolBostonUnited States
| | - Toshi Shioda
- Massachusetts General Hospital Center for Cancer Research, Harvard Medical SchoolCharlestownUnited States
| | - Pranam Chatterjee
- Wyss Institute, Harvard UniversityBostonUnited States
- Department of Genetics, Harvard Medical SchoolBostonUnited States
- Department of Biomedical Engineering, Duke UniversityDurhamUnited States
- Department of Computer Science, Duke UniversityDurhamUnited States
| | - George M Church
- Wyss Institute, Harvard UniversityBostonUnited States
- Department of Genetics, Harvard Medical SchoolBostonUnited States
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4
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Julca I, Tan QW, Mutwil M. Toward kingdom-wide analyses of gene expression. TRENDS IN PLANT SCIENCE 2023; 28:235-249. [PMID: 36344371 DOI: 10.1016/j.tplants.2022.09.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 09/22/2022] [Accepted: 09/30/2022] [Indexed: 06/16/2023]
Abstract
Gene expression data for Archaeplastida are accumulating exponentially, with more than 300 000 RNA-sequencing (RNA-seq) experiments available for hundreds of species. The gene expression data stem from thousands of experiments that capture gene expression in various organs, tissues, cell types, (a)biotic perturbations, and genotypes. Advances in software tools make it possible to process all these data in a matter of weeks on modern office computers, giving us the possibility to study gene expression in a kingdom-wide manner for the first time. We discuss how the expression data can be accessed and processed and outline analyses that take advantage of cross-species analyses, allowing us to generate powerful and robust hypotheses about gene function and evolution.
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Affiliation(s)
- Irene Julca
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore
| | - Qiao Wen Tan
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore
| | - Marek Mutwil
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore.
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5
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Tang M, Zhao Y, Zhao J, Wei S, Liu M, Zheng N, Geng D, Han S, Zhang Y, Zhong G, Li S, Zhang X, Wang C, Yan H, Cao X, Li L, Bai X, Ji J, Feng XH, Qin J, Liang T, Zhao B. Liver cancer heterogeneity modeled by in situ genome editing of hepatocytes. SCIENCE ADVANCES 2022; 8:eabn5683. [PMID: 35731873 PMCID: PMC9216519 DOI: 10.1126/sciadv.abn5683] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Mechanistic study and precision treatment of primary liver cancer (PLC) are hindered by marked heterogeneity, which is challenging to recapitulate in any given liver cancer mouse model. Here, we report the generation of 25 mouse models of PLC by in situ genome editing of hepatocytes recapitulating 25 single or combinations of human cancer driver genes. These mouse tumors represent major histopathological types of human PLCs and could be divided into three human-matched molecular subtypes based on transcriptomic and proteomic profiles. Phenotypical characterization identified subtype- or genotype-specific alterations in immune microenvironment, metabolic reprogramming, cell proliferation, and expression of drug targets. Furthermore, single-cell analysis and expression tracing revealed spatial and temporal dynamics in expression of pyruvate kinase M2 (Pkm2). Tumor-specific knockdown of Pkm2 by multiplexed genome editing reversed the Warburg effect and suppressed tumorigenesis in a genotype-specific manner. Our study provides mouse PLC models with defined genetic drivers and characterized phenotypical heterogeneity suitable for mechanistic investigation and preclinical testing.
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Affiliation(s)
- Mei Tang
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
- Cancer Center, Zhejiang University, Hangzhou 310058, China
| | - Yang Zhao
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
- Cancer Center, Zhejiang University, Hangzhou 310058, China
| | - Jianhui Zhao
- Cancer Center, Zhejiang University, Hangzhou 310058, China
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shumei Wei
- Department of Pathology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Mingwei Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Nairen Zheng
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Didi Geng
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
| | - Shixun Han
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
| | - Yuchao Zhang
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
| | - Guoxuan Zhong
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
| | - Shuaifeng Li
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
| | - Xiuming Zhang
- Department of Pathology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Chenliang Wang
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
| | - Huan Yan
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
| | - Xiaolei Cao
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
| | - Li Li
- School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou 311121, China
| | - Xueli Bai
- Cancer Center, Zhejiang University, Hangzhou 310058, China
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Junfang Ji
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
- Cancer Center, Zhejiang University, Hangzhou 310058, China
| | - Xin-Hua Feng
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
- Cancer Center, Zhejiang University, Hangzhou 310058, China
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Tingbo Liang
- Cancer Center, Zhejiang University, Hangzhou 310058, China
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- Corresponding author. (T.L.); (B.Z.)
| | - Bin Zhao
- The MOE Key Laboratory of Biosystems Homeostasis & Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
- Cancer Center, Zhejiang University, Hangzhou 310058, China
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- Shaoxing Institute, Zhejiang University, Shaoxing 321000, China
- Corresponding author. (T.L.); (B.Z.)
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6
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Manatakis DV, VanDevender A, Manolakos ES. An information-theoretic approach for measuring the distance of organ tissue samples using their transcriptomic signatures. Bioinformatics 2021; 36:5194-5204. [PMID: 32683449 PMCID: PMC7850114 DOI: 10.1093/bioinformatics/btaa654] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 05/26/2020] [Accepted: 07/14/2020] [Indexed: 12/02/2022] Open
Abstract
Motivation Recapitulating aspects of human organ functions using in vitro (e.g.
plates, transwells, etc.), in vivo (e.g. mouse, rat, etc.), or
ex vivo (e.g. organ chips, 3D systems, etc.) organ models is of
paramount importance for drug discovery and precision medicine. It will allow us to
identify potential side effects and test the effectiveness of new therapeutic approaches
early in their design phase, and will inform the development of better disease models.
Developing mathematical methods to reliably compare the ‘distance/similarity’ of organ
models from/to the real human organ they represent is an understudied problem with
important applications in biomedicine and tissue engineering. Results We introduce the Transcriptomic Signature Distance (TSD), an
information-theoretic distance for assessing the transcriptomic similarity of two tissue
samples, or two groups of tissue samples. In developing TSD, we are
leveraging next-generation sequencing data as well as information retrieved from
well-curated databases providing signature gene sets characteristic for human organs. We
present the justification and mathematical development of the new distance and
demonstrate its effectiveness and advantages in different scenarios of practical
importance using several publicly available RNA-seq datasets. Availability and Implementation The computation of both TSD versions (simple and weighted) has been
implemented in R and can be downloaded from
https://github.com/Cod3B3nd3R/Transcriptomic-Signature-Distance. Contact dimitris.manatakis@emulatebio.com Supplementary information Supplementary data are
available at Bioinformatics online.
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Affiliation(s)
| | | | - Elias S Manolakos
- Department of Informatics and Telecommunications, University of Athens, Athens 15784, Greece.,Bouve College of Health Sciences, Northeastern University, Boston, MA 02115, USA
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7
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Kwon SB, Ernst J. Learning a genome-wide score of human-mouse conservation at the functional genomics level. Nat Commun 2021; 12:2495. [PMID: 33941776 PMCID: PMC8093196 DOI: 10.1038/s41467-021-22653-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 03/24/2021] [Indexed: 01/06/2023] Open
Abstract
Identifying genomic regions with functional genomic properties that are conserved between human and mouse is an important challenge in the context of mouse model studies. To address this, we develop a method to learn a score of evidence of conservation at the functional genomics level by integrating information from a compendium of epigenomic, transcription factor binding, and transcriptomic data from human and mouse. The method, Learning Evidence of Conservation from Integrated Functional genomic annotations (LECIF), trains neural networks to generate this score for the human and mouse genomes. The resulting LECIF score highlights human and mouse regions with shared functional genomic properties and captures correspondence of biologically similar human and mouse annotations. Analysis with independent datasets shows the score also highlights loci associated with similar phenotypes in both species. LECIF will be a resource for mouse model studies by identifying loci whose functional genomic properties are likely conserved.
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Affiliation(s)
- Soo Bin Kwon
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA.,Department of Biological Chemistry, University of California, Los Angeles, CA, USA
| | - Jason Ernst
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA. .,Department of Biological Chemistry, University of California, Los Angeles, CA, USA. .,Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research at University of California, Los Angeles, CA, USA. .,Computer Science Department, University of California, Los Angeles, CA, USA. .,Department of Computational Medicine, University of California, Los Angeles, CA, USA. .,Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA. .,Molecular Biology Institute, University of California, Los Angeles, CA, USA.
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8
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Li WV, Li JJ. Modeling and analysis of RNA-seq data: a review from a statistical perspective. QUANTITATIVE BIOLOGY 2018; 6:195-209. [PMID: 31456901 PMCID: PMC6711375 DOI: 10.1007/s40484-018-0144-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Revised: 02/23/2018] [Accepted: 03/29/2018] [Indexed: 12/21/2022]
Abstract
BACKGROUND Since the invention of next-generation RNA sequencing (RNA-seq) technologies, they have become a powerful tool to study the presence and quantity of RNA molecules in biological samples and have revolutionized transcriptomic studies. The analysis of RNA-seq data at four different levels (samples, genes, transcripts, and exons) involve multiple statistical and computational questions, some of which remain challenging up to date. RESULTS We review RNA-seq analysis tools at the sample, gene, transcript, and exon levels from a statistical perspective. We also highlight the biological and statistical questions of most practical considerations. CONCLUSIONS The development of statistical and computational methods for analyzing RNA-seq data has made significant advances in the past decade. However, methods developed to answer the same biological question often rely on diverse statistical models and exhibit different performance under different scenarios. This review discusses and compares multiple commonly used statistical models regarding their assumptions, in the hope of helping users select appropriate methods as needed, as well as assisting developers for future method development.
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Affiliation(s)
- Wei Vivian Li
- Department of Statistics, University of California, Los Angeles, Los Angeles, CA 90095-1554, USA
| | - Jingyi Jessica Li
- Department of Statistics, University of California, Los Angeles, Los Angeles, CA 90095-1554, USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095-088, USA
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Tong X, Feng Y, Li JJ. Neyman-Pearson classification algorithms and NP receiver operating characteristics. SCIENCE ADVANCES 2018; 4:eaao1659. [PMID: 29423442 PMCID: PMC5804623 DOI: 10.1126/sciadv.aao1659] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Accepted: 01/03/2018] [Indexed: 05/24/2023]
Abstract
In many binary classification applications, such as disease diagnosis and spam detection, practitioners commonly face the need to limit type I error (that is, the conditional probability of misclassifying a class 0 observation as class 1) so that it remains below a desired threshold. To address this need, the Neyman-Pearson (NP) classification paradigm is a natural choice; it minimizes type II error (that is, the conditional probability of misclassifying a class 1 observation as class 0) while enforcing an upper bound, α, on the type I error. Despite its century-long history in hypothesis testing, the NP paradigm has not been well recognized and implemented in classification schemes. Common practices that directly limit the empirical type I error to no more than α do not satisfy the type I error control objective because the resulting classifiers are likely to have type I errors much larger than α, and the NP paradigm has not been properly implemented in practice. We develop the first umbrella algorithm that implements the NP paradigm for all scoring-type classification methods, such as logistic regression, support vector machines, and random forests. Powered by this algorithm, we propose a novel graphical tool for NP classification methods: NP receiver operating characteristic (NP-ROC) bands motivated by the popular ROC curves. NP-ROC bands will help choose α in a data-adaptive way and compare different NP classifiers. We demonstrate the use and properties of the NP umbrella algorithm and NP-ROC bands, available in the R package nproc, through simulation and real data studies.
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Affiliation(s)
- Xin Tong
- Department of Data Sciences and Operations, Marshall School of Business, University of Southern California, Los Angeles, CA 90089, USA
| | - Yang Feng
- Department of Statistics, Columbia University, New York, NY 10027–5927, USA
| | - Jingyi Jessica Li
- Department of Statistics, University of California, Los Angeles, CA 90095–1554, USA
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Yang Y, Yang YCT, Yuan J, Lu ZJ, Li JJ. Large-scale mapping of mammalian transcriptomes identifies conserved genes associated with different cell states. Nucleic Acids Res 2017; 45:1657-1672. [PMID: 27980097 PMCID: PMC5389511 DOI: 10.1093/nar/gkw1256] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2016] [Accepted: 12/01/2016] [Indexed: 12/30/2022] Open
Abstract
Distinguishing cell states based only on gene expression data remains a challenging task. This is true even for analyses within a species. In cross-species comparisons, the results obtained by different groups have varied widely. Here, we integrate RNA-seq data from more than 40 cell and tissue types of four mammalian species to identify sets of associated genes as indicators for specific cell states in each species. We employ a statistical method, TROM, to identify both protein-coding and non-coding indicators. Next, we map the cell states within each species and also between species using these indicator genes. We recapitulate known phenotypic similarity between related cell and tissue types and reveal molecular basis for their similarity. We also report novel associations between several tissues and cell types with functional support. Moreover, our identified conserved associated genes are found to be a good resource for studying cell differentiation and reprogramming. Lastly, long non-coding RNAs can serve well as associated genes to indicate cell states. We further infer the biological functions of those non-coding associated genes based on their co-expressed protein-coding genes. This study demonstrates that combining statistical modeling with public RNA-seq data can be powerful for improving our understanding of cell identity control.
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Affiliation(s)
- Yang Yang
- PKU-Tsinghua-NIBS Graduate Program, School of Life Sciences, Peking University, Beijing 100871, China.,MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Center for Plant Biology and Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Yu-Cheng T Yang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Center for Plant Biology and Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Jiapei Yuan
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Center for Plant Biology and Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Zhi John Lu
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Center for Plant Biology and Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Jingyi Jessica Li
- Department of Statistics, University of California, Los Angeles, CA 90095-1554, USA.,Department of Human Genetics, University of California, Los Angeles, CA 90095-7088, USA
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Gao R, Li JJ. Correspondence of D. melanogaster and C. elegans developmental stages revealed by alternative splicing characteristics of conserved exons. BMC Genomics 2017; 18:234. [PMID: 28302059 PMCID: PMC5353869 DOI: 10.1186/s12864-017-3600-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2016] [Accepted: 02/22/2017] [Indexed: 11/21/2022] Open
Abstract
Background We report a statistical study to find correspondence of D. melanogaster and C. elegans developmental stages based on alternative splicing (AS) characteristics of conserved cassette exons using modENCODE RNA-seq data. We identify “stage-associated exons” to capture the AS characteristics of each stage and use these exons to map pairwise stages within and between the two species by an overlap test. Results Within fly and worm, adjacent developmental stages are mapped to each other, i.e., a strong diagonal pattern is observed as expected, supporting the validity of our approach. Between fly and worm, two parallel mapping patterns are observed between fly early embryos to early larvae and worm life cycle, and between fly late larvae to adults and worm late embryos to adults. We also apply this approach to compare tissues and cells from fly and worm. Findings include the high similarity between fly/worm adults and fly/worm embryos, groupings of fly cell lines, and strong mappings of fly head tissues to worm late embryos and male adults. Gene ontology and KEGG enrichment analyses provide a detailed functional annotation of the identified stage-associated exons, as well as a functional explanation of the observed correspondence map between fly and worm developmental stages. Conclusions Our results suggest that AS dynamics of the exon pairs that share similar DNA sequences are informative for finding transcriptomic similarity of biological samples. Our study is innovative in two aspects. First, to our knowledge, our study is the first comprehensive study of AS events in fly and worm developmental stages, tissues, and cells. AS events provide an alternative perspective of transcriptome dynamics, compared to gene expression events. Second, our results do not entirely rely on the information of orthologous genes. Interesting results are also observed for fly and worm cassette exon pairs with DNA sequence similarity but not in orthologous gene pairs. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3600-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ruiqi Gao
- Department of Statistics, University of California, Los Angeles, USA
| | - Jingyi Jessica Li
- Department of Statistics, University of California, Los Angeles, USA. .,Department of Human Genetics, University of California, Los Angeles, USA.
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