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Collinson RJ, Wilson L, Boey D, Ng ZY, Mirzai B, Chuah HS, Howman R, Grove CS, Malherbe JAJ, Leahy MF, Linden MD, Fuller KA, Erber WN, Guo BB. Transcription factor 3 is dysregulated in megakaryocytes in myelofibrosis. Platelets 2024; 35:2304173. [PMID: 38303515 DOI: 10.1080/09537104.2024.2304173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 01/02/2024] [Indexed: 02/03/2024]
Abstract
Transcription factor 3 (TCF3) is a DNA transcription factor that modulates megakaryocyte development. Although abnormal TCF3 expression has been identified in a range of hematological malignancies, to date, it has not been investigated in myelofibrosis (MF). MF is a Philadelphia-negative myeloproliferative neoplasm (MPN) that can arise de novo or progress from essential thrombocythemia [ET] and polycythemia vera [PV] and where dysfunctional megakaryocytes have a role in driving the fibrotic progression. We aimed to examine whether TCF3 is dysregulated in megakaryocytes in MPN, and specifically in MF. We first assessed TCF3 protein expression in megakaryocytes using an immunohistochemical approach analyses and showed that TCF3 was reduced in MF compared with ET and PV. Further, the TCF3-negative megakaryocytes were primarily located near trabecular bone and had the typical "MF-like" morphology as described by the WHO. Genomic analysis of isolated megakaryocytes showed three mutations, all predicted to result in a loss of function, in patients with MF; none were seen in megakaryocytes isolated from ET or PV marrow samples. We then progressed to transcriptomic sequencing of platelets which showed loss of TCF3 in MF. These proteomic, genomic and transcriptomic analyses appear to indicate that TCF3 is downregulated in megakaryocytes in MF. This infers aberrations in megakaryopoiesis occur in this progressive phase of MPN. Further exploration of this pathway could provide insights into TCF3 and the evolution of fibrosis and potentially lead to new preventative therapeutic targets.
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Affiliation(s)
- Ryan J Collinson
- School of Biomedical Sciences, The University of Western Australia, Crawley, WA, Australia
| | - Lynne Wilson
- School of Biomedical Sciences, The University of Western Australia, Crawley, WA, Australia
| | - Darren Boey
- School of Biomedical Sciences, The University of Western Australia, Crawley, WA, Australia
| | - Zi Yun Ng
- School of Biomedical Sciences, The University of Western Australia, Crawley, WA, Australia
- Department of Haematology, Royal Perth Hospital, Perth, WA, Australia
| | - Bob Mirzai
- PathWest Laboratory Medicine, Nedlands, WA, Australia
| | - Hun S Chuah
- Department of Haematology, Royal Perth Hospital, Perth, WA, Australia
- PathWest Laboratory Medicine, Nedlands, WA, Australia
- Department of Haematology, Rockingham General Hospital, Rockingham, WA, Australia
| | - Rebecca Howman
- Department of Haematology, Sir Charles Gairdner Hospital Nedlands Australia
| | - Carolyn S Grove
- School of Biomedical Sciences, The University of Western Australia, Crawley, WA, Australia
- Department of Haematology, Royal Perth Hospital, Perth, WA, Australia
- Department of Haematology, Sir Charles Gairdner Hospital Nedlands Australia
| | | | - Michael F Leahy
- Department of Haematology, Royal Perth Hospital, Perth, WA, Australia
- PathWest Laboratory Medicine, Nedlands, WA, Australia
| | - Matthew D Linden
- School of Biomedical Sciences, The University of Western Australia, Crawley, WA, Australia
| | - Kathryn A Fuller
- School of Biomedical Sciences, The University of Western Australia, Crawley, WA, Australia
| | - Wendy N Erber
- School of Biomedical Sciences, The University of Western Australia, Crawley, WA, Australia
- PathWest Laboratory Medicine, Nedlands, WA, Australia
| | - Belinda B Guo
- School of Biomedical Sciences, The University of Western Australia, Crawley, WA, Australia
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Gao L, Xing X, Guo R, Li Q, Xu Y, Pan H, Ji P, Wang P, Yu C, Li J, An Q. Effect of Different Dietary Iron Contents on Liver Transcriptome Characteristics in Wujin Pigs. Animals (Basel) 2024; 14:2399. [PMID: 39199933 PMCID: PMC11350824 DOI: 10.3390/ani14162399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2024] [Revised: 07/27/2024] [Accepted: 08/13/2024] [Indexed: 09/01/2024] Open
Abstract
Iron is an important trace element that affects the growth and development of animals and regulates oxygen transport, hematopoiesis, and hypoxia adaptations. Wujin pig has unique hypoxic adaptability and iron homeostasis; however, the specific regulatory mechanisms have rarely been reported. This study randomly divided 18 healthy Wujin piglets into three groups: the control group, supplemented with 100 mg/kg iron (as iron glycinate); the low-iron group, no iron supplementation; and the high-iron group, supplemented with 200 mg/kg iron (as iron glycinate). The pre-feeding period was 5 days, and the formal period was 30 days. Serum was collected from empty stomachs before slaughter and at slaughter to detect changes in the serum iron metabolism parameters. Gene expression in the liver was analyzed via transcriptome analysis to determine the effects of low- and high-iron diets on transcriptome levels. Correlation analysis was performed for apparent serum parameters, and transcriptome sequencing was performed using weighted gene co-expression network analysis to reveal the key pathways underlying hypoxia regulation and iron metabolism. The main results are as follows. (1) Except for the hypoxia-inducible factor 1 (HIF-1) content (between the low- and high-iron groups), significant differences were not observed among the serum iron metabolic parameters. The serum HIF-1 content of the low-iron group was significantly higher than that of the high-iron group (p < 0.05). (2) Sequencing analysis of the liver transcriptome revealed 155 differentially expressed genes (DEGs) between the low-iron and control groups, 229 DEGs between the high-iron and control groups, and 279 DEGs between the low- and high-iron groups. Bioinformatics analysis showed that the HIF-1 and transforming growth factor-beta (TGF-β) signaling pathways were the key pathways for hypoxia regulation and iron metabolism. Four genes were selected for qPCR validation, and the results were consistent with the transcriptome sequencing data. In summary, the serum iron metabolism parameter results showed that under the influence of low- and high-iron diets, Wujin piglets maintain a steady state of physiological and biochemical indices via complex metabolic regulation of the body, which reflects their stress resistance and adaptability. The transcriptome results revealed the effects of low-iron and high-iron diets on the gene expression level in the liver and showed that the HIF-1 and TGF-β signaling pathways were key for regulating hypoxia adaptability and iron metabolism homeostasis under low-iron and high-iron diets. Moreover, HIF-1α and HEPC were the key genes. The findings provide a theoretical foundation for exploring the regulatory pathways and characteristics of iron metabolism in Wujin pigs.
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Affiliation(s)
- Lin Gao
- Yunnan Provincial Key Laboratory of Tropical and Subtropical Animal Viral Diseases, Yunnan Academy of Animal Husbandry and Veterinary Sciences, Kunming 650201, China;
| | - Xiaokun Xing
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed Science, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (X.X.); (R.G.); (Q.L.); (H.P.); (P.J.); (P.W.); (C.Y.); (J.L.)
| | - Rongfu Guo
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed Science, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (X.X.); (R.G.); (Q.L.); (H.P.); (P.J.); (P.W.); (C.Y.); (J.L.)
| | - Qihua Li
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed Science, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (X.X.); (R.G.); (Q.L.); (H.P.); (P.J.); (P.W.); (C.Y.); (J.L.)
| | - Yan Xu
- Yunnan East Hunter Agriculture and Forestry Development Co., Ltd., Shuifu 657803, China;
| | - Hongbin Pan
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed Science, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (X.X.); (R.G.); (Q.L.); (H.P.); (P.J.); (P.W.); (C.Y.); (J.L.)
| | - Peng Ji
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed Science, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (X.X.); (R.G.); (Q.L.); (H.P.); (P.J.); (P.W.); (C.Y.); (J.L.)
| | - Ping Wang
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed Science, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (X.X.); (R.G.); (Q.L.); (H.P.); (P.J.); (P.W.); (C.Y.); (J.L.)
| | - Chuntang Yu
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed Science, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (X.X.); (R.G.); (Q.L.); (H.P.); (P.J.); (P.W.); (C.Y.); (J.L.)
| | - Jintao Li
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed Science, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (X.X.); (R.G.); (Q.L.); (H.P.); (P.J.); (P.W.); (C.Y.); (J.L.)
| | - Qingcong An
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed Science, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (X.X.); (R.G.); (Q.L.); (H.P.); (P.J.); (P.W.); (C.Y.); (J.L.)
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3
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Wang D, Liu Y, Zhang Y, Chen Q, Han Y, Hou W, Liu C, Yu Y, Li Z, Li Z, Zhao J, Shi L, Zheng Y, Li J, Zhang R. A real-world multi-center RNA-seq benchmarking study using the Quartet and MAQC reference materials. Nat Commun 2024; 15:6167. [PMID: 39039053 PMCID: PMC11263697 DOI: 10.1038/s41467-024-50420-y] [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: 12/12/2023] [Accepted: 07/02/2024] [Indexed: 07/24/2024] Open
Abstract
Translating RNA-seq into clinical diagnostics requires ensuring the reliability and cross-laboratory consistency of detecting clinically relevant subtle differential expressions, such as those between different disease subtypes or stages. As part of the Quartet project, we present an RNA-seq benchmarking study across 45 laboratories using the Quartet and MAQC reference samples spiked with ERCC controls. Based on multiple types of 'ground truth', we systematically assess the real-world RNA-seq performance and investigate the influencing factors involved in 26 experimental processes and 140 bioinformatics pipelines. Here we show greater inter-laboratory variations in detecting subtle differential expressions among the Quartet samples. Experimental factors including mRNA enrichment and strandedness, and each bioinformatics step, emerge as primary sources of variations in gene expression. We underscore the profound influence of experimental execution, and provide best practice recommendations for experimental designs, strategies for filtering low-expression genes, and the optimal gene annotation and analysis pipelines. In summary, this study lays the foundation for developing and quality control of RNA-seq for clinical diagnostic purposes.
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Affiliation(s)
- Duo Wang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, PR China
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China
| | - Yaqing Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuanfeng Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, PR China
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China
| | - Qingwang Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yanxi Han
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, PR China
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China
| | - Wanwan Hou
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Cong Liu
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, PR China
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ziyang Li
- Department of Laboratory Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, PR China
| | - Ziqiang Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, PR China
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China
| | - Jiaxin Zhao
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, PR China
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, and Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes, Shanghai, China.
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, and Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes, Shanghai, China.
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, PR China.
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China.
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China.
| | - Rui Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, PR China.
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China.
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China.
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4
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Yu Y, Hou W, Liu Y, Wang H, Dong L, Mai Y, Chen Q, Li Z, Sun S, Yang J, Cao Z, Zhang P, Zi Y, Liu R, Gao J, Zhang N, Li J, Ren L, Jiang H, Shang J, Zhu S, Wang X, Qing T, Bao D, Li B, Li B, Suo C, Pi Y, Wang X, Dai F, Scherer A, Mattila P, Han J, Zhang L, Jiang H, Thierry-Mieg D, Thierry-Mieg J, Xiao W, Hong H, Tong W, Wang J, Li J, Fang X, Jin L, Xu J, Qian F, Zhang R, Shi L, Zheng Y. Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling. Nat Biotechnol 2024; 42:1118-1132. [PMID: 37679545 PMCID: PMC11251996 DOI: 10.1038/s41587-023-01867-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 06/15/2023] [Indexed: 09/09/2023]
Abstract
Certified RNA reference materials are indispensable for assessing the reliability of RNA sequencing to detect intrinsically small biological differences in clinical settings, such as molecular subtyping of diseases. As part of the Quartet Project for quality control and data integration of multi-omics profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets between two samples, providing cross-platform and cross-laboratory 'ground truth'. Investigation of the intrinsically subtle biological differences among the Quartet samples enables sensitive assessment of cross-batch integration of transcriptomic measurements at the ratio level. The Quartet RNA reference materials, combined with the ratio-based reference datasets, can serve as unique resources for assessing and improving the quality of transcriptomic data in clinical and biological settings.
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Affiliation(s)
- Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Wanwan Hou
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yaqing Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Haiyan Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | | | - Yuanbang Mai
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qingwang Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zhihui Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Shanyue Sun
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingcheng Yang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
- Greater Bay Area Institute of Precision Medicine, Guangzhou, China
| | - Zehui Cao
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Peipei Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yi Zi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ruimei Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jian Gao
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Naixin Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingjing Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
- Nextomics Biosciences Institute, Wuhan, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - He Jiang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jun Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Sibo Zhu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xiaolin Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Tao Qing
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ding Bao
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bingying Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bin Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Chen Suo
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yan Pi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xia Wang
- National Institute of Metrology, Beijing, China
| | | | - Andreas Scherer
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- EATRIS ERIC-European Infrastructure for Translational Medicine, Amsterdam, The Netherlands
| | - Pirkko Mattila
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- EATRIS ERIC-European Infrastructure for Translational Medicine, Amsterdam, The Netherlands
| | | | - Lijun Zhang
- Nanjing Vazyme Biotech Co. Ltd., Nanjing, China
| | | | - Danielle Thierry-Mieg
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Jean Thierry-Mieg
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Wenming Xiao
- Office of Oncologic Diseases, Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Jing Wang
- National Institute of Metrology, Beijing, China
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China
- National Center of Gerontology, Beijing, China
| | - Xiang Fang
- National Institute of Metrology, Beijing, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA.
| | - Feng Qian
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
- Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.
| | - Rui Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China.
- National Center of Gerontology, Beijing, China.
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes, Shanghai, China.
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
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5
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Dong L, Zhang Y, Fu B, Swart C, Jiang H, Liu Y, Huggett J, Wielgosz R, Niu C, Li Q, Zhang Y, Park SR, Sui Z, Yu L, Liu Y, Xie Q, Zhang H, Yang Y, Dai X, Shi L, Yin Y, Fang X. Reliable biological and multi-omics research through biometrology. Anal Bioanal Chem 2024; 416:3645-3663. [PMID: 38507042 DOI: 10.1007/s00216-024-05239-3] [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: 01/12/2024] [Revised: 02/28/2024] [Accepted: 03/04/2024] [Indexed: 03/22/2024]
Abstract
Metrology is the science of measurement and its applications, whereas biometrology is the science of biological measurement and its applications. Biometrology aims to achieve accuracy and consistency of biological measurements by focusing on the development of metrological traceability, biological reference measurement procedures, and reference materials. Irreproducibility of biological and multi-omics research results from different laboratories, platforms, and analysis methods is hampering the translation of research into clinical uses and can often be attributed to the lack of biologists' attention to the general principles of metrology. In this paper, the progresses of biometrology including metrology on nucleic acid, protein, and cell measurements and its impacts on the improvement of reliability and comparability in biological research are reviewed. Challenges in obtaining more reliable biological and multi-omics measurements due to the lack of primary reference measurement procedures and new standards for biological reference materials faced by biometrology are discussed. In the future, in addition to establishing reliable reference measurement procedures, developing reference materials from single or multiple parameters to multi-omics scale should be emphasized. Thinking in way of biometrology is warranted for facilitating the translation of high-throughput omics research into clinical practices.
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Affiliation(s)
- Lianhua Dong
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China.
| | - Yu Zhang
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China
| | - Boqiang Fu
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China
| | - Claudia Swart
- Physikalisch-Technische Bundesanstalt, 38116, Braunschweig, Germany
| | | | - Yahui Liu
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China
| | - Jim Huggett
- National Measurement Laboratory at LGC (NML), Teddington, Middlesex, UK
| | - Robert Wielgosz
- Bureau International Des Poids Et Mesures (BIPM), Pavillon de Breteuil, 92312, Sèvres Cedex, France
| | - Chunyan Niu
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China
| | - Qianyi Li
- BGI, BGI-Shenzhen, Shenzhen, 518083, China
| | - Yongzhuo Zhang
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China
| | - Sang-Ryoul Park
- Korea Research Institute of Standards and Science, Daejeon, Republic of Korea
| | - Zhiwei Sui
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China
| | - Lianchao Yu
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China
| | | | - Qing Xie
- BGI, BGI-Shenzhen, Shenzhen, 518083, China
| | - Hongfu Zhang
- BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China
| | | | - Xinhua Dai
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China.
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | - Ye Yin
- BGI, BGI-Shenzhen, Shenzhen, 518083, China.
| | - Xiang Fang
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China.
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6
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Dai R, Zhang M, Chu T, Kopp R, Zhang C, Liu K, Wang Y, Wang X, Chen C, Liu C. Precision and Accuracy of Single-Cell/Nuclei RNA Sequencing Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.12.589216. [PMID: 38659857 PMCID: PMC11042208 DOI: 10.1101/2024.04.12.589216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Single-cell/nuclei RNA sequencing (sc/snRNA-Seq) is widely used for profiling cell-type gene expressions in biomedical research. An important but underappreciated issue is the quality of sc/snRNA-Seq data that would impact the reliability of downstream analyses. Here we evaluated the precision and accuracy in 18 sc/snRNA-Seq datasets. The precision was assessed on data from human brain studies with a total of 3,483,905 cells from 297 individuals, by utilizing technical replicates. The accuracy was evaluated with sample-matched scRNA-Seq and pooled-cell RNA-Seq data of cultured mononuclear phagocytes from four species. The results revealed low precision and accuracy at the single-cell level across all evaluated data. Cell number and RNA quality were highlighted as two key factors determining the expression precision, accuracy, and reproducibility of differential expression analysis in sc/snRNA-Seq. This study underscores the necessity of sequencing enough high-quality cells per cell type per individual, preferably in the hundreds, to mitigate noise in expression quantification.
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Affiliation(s)
- Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Ming Zhang
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Tianyao Chu
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Richard Kopp
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Chunling Zhang
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Kefu Liu
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, VA, USA
| | - Xusheng Wang
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
- Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Chao Chen
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Furong Laboratory, Changsha, Hunan, China
- Hunan Key Laboratory of Animal Models for Human Diseases, Central South University, Changsha, China
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
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7
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Chen Q, Guo X, Wang H, Sun S, Jiang H, Zhang P, Shang E, Zhang R, Cao Z, Niu Q, Zhang C, Liu Y, Shi L, Yu Y, Hou W, Zheng Y. Plasma-Free Blood as a Potential Alternative to Whole Blood for Transcriptomic Analysis. PHENOMICS (CHAM, SWITZERLAND) 2024; 4:109-124. [PMID: 38884056 PMCID: PMC11169349 DOI: 10.1007/s43657-023-00121-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 06/29/2023] [Accepted: 07/13/2023] [Indexed: 06/18/2024]
Abstract
RNA sequencing (RNAseq) technology has become increasingly important in precision medicine and clinical diagnostics, and emerged as a powerful tool for identifying protein-coding genes, performing differential gene analysis, and inferring immune cell composition. Human peripheral blood samples are widely used for RNAseq, providing valuable insights into individual biomolecular information. Blood samples can be classified as whole blood (WB), plasma, serum, and remaining sediment samples, including plasma-free blood (PFB) and serum-free blood (SFB) samples that are generally considered less useful byproducts during the processes of plasma and serum separation, respectively. However, the feasibility of using PFB and SFB samples for transcriptome analysis remains unclear. In this study, we aimed to assess the suitability of employing PFB or SFB samples as an alternative RNA source in transcriptomic analysis. We performed a comparative analysis of WB, PFB, and SFB samples for different applications. Our results revealed that PFB samples exhibit greater similarity to WB samples than SFB samples in terms of protein-coding gene expression patterns, detection of differentially expressed genes, and immunological characterizations, suggesting that PFB can serve as a viable alternative to WB for transcriptomic analysis. Our study contributes to the optimization of blood sample utilization and the advancement of precision medicine research. Supplementary Information The online version contains supplementary material available at 10.1007/s43657-023-00121-1.
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Affiliation(s)
- Qingwang Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, 200438 China
| | - Xiaorou Guo
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, 200438 China
| | - Haiyan Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, 200438 China
| | - Shanyue Sun
- Shandong Provincial Hospital, Shandong First Medical University, Jinan, 250021 China
| | - He Jiang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, 200438 China
| | - Peipei Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, 200438 China
| | - Erfei Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, 200438 China
| | - Ruolan Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, 200438 China
| | - Zehui Cao
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, 200438 China
| | - Quanne Niu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, 200438 China
| | - Chao Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, 200438 China
| | - Yaqing Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, 200438 China
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, 200438 China
- The International Human Phenome Institutes, Shanghai, 200438 China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, 200438 China
| | - Wanwan Hou
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, 200438 China
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, 200438 China
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8
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Jogi HR, Smaraki N, Nayak SS, Rajawat D, Kamothi DJ, Panigrahi M. Single cell RNA-seq: a novel tool to unravel virus-host interplay. Virusdisease 2024; 35:41-54. [PMID: 38817399 PMCID: PMC11133279 DOI: 10.1007/s13337-024-00859-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 02/12/2024] [Indexed: 06/01/2024] Open
Abstract
Over the last decade, single cell RNA sequencing (scRNA-seq) technology has caught the momentum of being a vital revolutionary tool to unfold cellular heterogeneity by high resolution assessment. It evades the inadequacies of conventional sequencing technology which was able to detect only average expression level among cell populations. In the era of twenty-first century, several epidemic and pandemic viruses have emerged. Being an intracellular entity, viruses totally rely on host. Complex virus-host dynamics result when the virus tend to obtain factors from host cell required for its replication and establishment of infection. As a prevailing tool, scRNA-seq is able to understand virus-host interplay by comprehensive transcriptome profiling. Because of technological and methodological advancement, this technology is capable to recognize viral genome and host cell response heterogeneity. Further development in analytical methods with multiomics approach and increased availability of accessible scRNA-seq datasets will improve the understanding of viral pathogenesis that can be helpful for development of novel antiviral therapeutic strategies.
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Affiliation(s)
- Harsh Rajeshbhai Jogi
- Division of Veterinary Microbiology, Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
| | - Nabaneeta Smaraki
- Division of Veterinary Microbiology, Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
| | - Sonali Sonejita Nayak
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
| | - Divya Rajawat
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
| | - Dhaval J. Kamothi
- Division of Pharmacology and Toxicology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
| | - Manjit Panigrahi
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP 243122 India
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9
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Brancato V, Esposito G, Coppola L, Cavaliere C, Mirabelli P, Scapicchio C, Borgheresi R, Neri E, Salvatore M, Aiello M. Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine. J Transl Med 2024; 22:136. [PMID: 38317237 PMCID: PMC10845786 DOI: 10.1186/s12967-024-04891-8] [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: 11/28/2023] [Accepted: 01/14/2024] [Indexed: 02/07/2024] Open
Abstract
Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine.
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Affiliation(s)
| | - Giuseppina Esposito
- Bio Check Up S.R.L, 80121, Naples, Italy
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131, Naples, Italy
| | | | | | - Peppino Mirabelli
- UOS Laboratori di Ricerca e Biobanca, AORN Santobono-Pausilipon, Via Teresa Ravaschieri, 8, 80122, Naples, Italy
| | - Camilla Scapicchio
- Academic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy
| | - Rita Borgheresi
- Academic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy
| | - Emanuele Neri
- Academic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy
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10
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Chen M, Li L, Wang S, Wang P, Li Y. Transcriptome sequencing and screening of genes related to the MADS-box gene family in Clematis courtoisii. PLoS One 2024; 19:e0294426. [PMID: 38315679 PMCID: PMC10843124 DOI: 10.1371/journal.pone.0294426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 10/31/2023] [Indexed: 02/07/2024] Open
Abstract
The MADS-box gene family controls plant flowering and floral organ development; therefore, it is particularly important in ornamental plants. To investigate the genes associated with the MADS-box family in Clematis courtoisii, we performed full-length transcriptome sequencing on C. courtoisii using the PacBio Sequel third-generation sequencing platform, as no reference genome data was available. A total of 12.38 Gb of data, containing 9,476,585 subreads and 50,439 Unigenes were obtained. According to functional annotation, a total of 37,923 Unigenes (75.18% of the total) were assigned with functional annotations, and 50 Unigenes were identified as MADS-box related genes. Subsequently, we employed hmmerscan to perform protein sequence similarity search for the translated Unigene sequences and successfully identified 19 Unigenes associated with the MADS-box gene family, including MIKC*(1) and MIKCC (18) genes. Furthermore, within the MIKCC group, six subclasses can be further distinguished.
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Affiliation(s)
- Mingjian Chen
- Department of Ornamental Plant Research Center, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing, China
| | - Linfang Li
- Department of Ornamental Plant Research Center, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing, China
| | - Shu’an Wang
- Department of Ornamental Plant Research Center, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing, China
| | - Peng Wang
- Department of Ornamental Plant Research Center, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing, China
| | - Ya Li
- Department of Ornamental Plant Research Center, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing, China
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11
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Gharibi H, Ashkarran AA, Jafari M, Voke E, Landry MP, Saei AA, Mahmoudi M. A uniform data processing pipeline enables harmonized nanoparticle protein corona analysis across proteomics core facilities. Nat Commun 2024; 15:342. [PMID: 38184668 PMCID: PMC10771434 DOI: 10.1038/s41467-023-44678-x] [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: 09/06/2023] [Accepted: 12/20/2023] [Indexed: 01/08/2024] Open
Abstract
Protein corona, a layer of biomolecules primarily comprising proteins, forms dynamically on nanoparticles in biological fluids and is crucial for predicting nanomedicine safety and efficacy. The protein composition of the corona layer is typically analyzed using liquid chromatography-mass spectrometry (LC-MS/MS). Our recent study, involving identical samples analyzed by 17 proteomics facilities, highlighted significant data variability, with only 1.8% of proteins consistently identified across these centers. Here, we implement an aggregated database search unifying parameters such as variable modifications, enzyme specificity, number of allowed missed cleavages and a stringent 1% false discovery rate at the protein and peptide levels. Such uniform search dramatically harmonizes the proteomics data, increasing the reproducibility and the percentage of consistency-identified unique proteins across distinct cores. Specifically, out of the 717 quantified proteins, 253 (35.3%) are shared among the top 5 facilities (and 16.2% among top 11 facilities). Furthermore, we note that reduction and alkylation are important steps in protein corona sample processing and as expected, omitting these steps reduces the number of total quantified peptides by around 20%. These findings underscore the need for standardized procedures in protein corona analysis, which is vital for advancing clinical applications of nanoscale biotechnologies.
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Affiliation(s)
- Hassan Gharibi
- Division of Chemistry I, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Ali Akbar Ashkarran
- Department of Radiology and Precision Health Program, Michigan State University, East Lansing, MI, USA
| | - Maryam Jafari
- Division of ENT Diseases, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
| | - Elizabeth Voke
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA, USA
| | - Markita P Landry
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA, USA
- Innovative Genomics Institute, Berkeley, CA, USA
- California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Amir Ata Saei
- Centre for Translational Microbiome Research, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 17165, Sweden.
- Biozentrum, University of Basel, 4056, Basel, Switzerland.
| | - Morteza Mahmoudi
- Department of Radiology and Precision Health Program, Michigan State University, East Lansing, MI, USA.
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12
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Janosevic D, De Luca T, Ferreira RM, Gisch DL, Hato T, Luo J, Yang Y, Hodgin JB, Dagher PC, Eadon MT. miRNA and mRNA Signatures in Human Acute Kidney Injury Tissue. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.11.557054. [PMID: 37745313 PMCID: PMC10515816 DOI: 10.1101/2023.09.11.557054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Acute kidney injury (AKI) is an important contributor to the development of chronic kidney disease (CKD). There is a need to understand molecular mediators that drive either recovery or progression to CKD. In particular, the role of miRNA and its regulatory role in AKI is poorly understood. We performed miRNA and mRNA sequencing on biobanked human kidney tissues obtained in the routine clinical care of patients with the diagnoses of AKI and minimal change disease (MCD), in addition to nephrectomized (Ref) tissue from individuals without known kidney disease. Transcriptomic analysis of mRNA revealed that Ref tissues exhibited a similar injury signature to AKI, not identified in MCD samples. The transcriptomic signature of human AKI was enriched with genes in pathways involved in cell adhesion and epithelial-to-mesenchymal transition (e.g., CDH6, ITGB6, CDKN1A ). miRNA DE analysis revealed upregulation of miRNA associated with immune cell recruitment and inflammation (e.g., miR-146a, miR-155, miR-142, miR-122). These miRNA (i.e., miR-122, miR-146) are also associated with downregulation of mRNA such as DDR2 and IGFBP6 , respectively. These findings suggest integrated interactions between miRNAs and target mRNAs in AKI-related processes such as inflammation, immune cell activation and epithelial-to-mesenchymal transition. These data contribute several novel findings when describing the epigenetic regulation of AKI by miRNA, and also underscores the importance of utilizing an appropriate reference control tissue to understand canonical pathway alterations in AKI.
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13
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Stokes T, Cen HH, Kapranov P, Gallagher IJ, Pitsillides AA, Volmar C, Kraus WE, Johnson JD, Phillips SM, Wahlestedt C, Timmons JA. Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq. ADVANCED GENETICS (HOBOKEN, N.J.) 2023; 4:2200024. [PMID: 37288167 PMCID: PMC10242409 DOI: 10.1002/ggn2.202200024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Indexed: 06/09/2023]
Abstract
Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.
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Affiliation(s)
- Tanner Stokes
- Faculty of ScienceMcMaster UniversityHamiltonL8S 4L8Canada
| | - Haoning Howard Cen
- Life Sciences InstituteUniversity of British ColumbiaVancouverV6T 1Z3Canada
| | | | - Iain J Gallagher
- School of Applied SciencesEdinburgh Napier UniversityEdinburghEH11 4BNUK
| | | | | | | | - James D. Johnson
- Life Sciences InstituteUniversity of British ColumbiaVancouverV6T 1Z3Canada
| | | | | | - James A. Timmons
- Miller School of MedicineUniversity of MiamiMiamiFL33136USA
- William Harvey Research InstituteQueen Mary University LondonLondonEC1M 6BQUK
- Augur Precision Medicine LTDStirlingFK9 5NFUK
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14
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Li Z, Hu Y, Ma X, Da L, She J, Liu Y, Yi X, Cao Y, Xu W, Jiao Y, Su Z. WheatCENet: A Database for Comparative Co-expression Networks Analysis of Allohexaploid Wheat and Its Progenitors. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:324-336. [PMID: 35660007 PMCID: PMC10626052 DOI: 10.1016/j.gpb.2022.04.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 03/16/2022] [Accepted: 05/08/2022] [Indexed: 06/15/2023]
Abstract
Genetic and epigenetic changes after polyploidization events could result in variable gene expression and modified regulatory networks. Here, using large-scale transcriptome data, we constructed co-expression networks for diploid, tetraploid, and hexaploid wheat species, and built a platform for comparing co-expression networks of allohexaploid wheat and its progenitors, named WheatCENet. WheatCENet is a platform for searching and comparing specific functional co-expression networks, as well as identifying the related functions of the genes clustered therein. Functional annotations like pathways, gene families, protein-protein interactions, microRNAs (miRNAs), and several lines of epigenome data are integrated into this platform, and Gene Ontology (GO) annotation, gene set enrichment analysis (GSEA), motif identification, and other useful tools are also included. Using WheatCENet, we found that the network of WHEAT ABERRANT PANICLE ORGANIZATION 1 (WAPO1) has more co-expressed genes related to spike development in hexaploid wheat than its progenitors. We also found a novel motif of CCWWWWWWGG (CArG) specifically in the promoter region of WAPO-A1, suggesting that neofunctionalization of the WAPO-A1 gene affects spikelet development in hexaploid wheat. WheatCENet is useful for investigating co-expression networks and conducting other analyses, and thus facilitates comparative and functional genomic studies in wheat. WheatCENet is freely available at http://bioinformatics.cpolar.cn/WheatCENet and http://bioinformatics.cau.edu.cn/WheatCENet.
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Affiliation(s)
- Zhongqiu Li
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Yiheng Hu
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xuelian Ma
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Lingling Da
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Jiajie She
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Yue Liu
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Xin Yi
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Yaxin Cao
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Wenying Xu
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Yuannian Jiao
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Zhen Su
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China.
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15
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A comprehensive genomic and transcriptomic dataset of triple-negative breast cancers. Sci Data 2022; 9:587. [PMID: 36153392 PMCID: PMC9509351 DOI: 10.1038/s41597-022-01681-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 08/09/2022] [Indexed: 11/28/2022] Open
Abstract
Molecular subtyping of triple-negative breast cancer (TNBC) is essential for understanding the mechanisms and discovering actionable targets of this highly heterogeneous type of breast cancer. We previously performed a large single-center and multiomics study consisting of genomics, transcriptomics, and clinical information from 465 patients with primary TNBC. To facilitate reusing this unique dataset, we provided a detailed description of the dataset with special attention to data quality in this study. The multiomics data were generally of high quality, but a few sequencing data had quality issues and should be noted in subsequent data reuse. Furthermore, we reconduct data analyses with updated pipelines and the updated version of the human reference genome from hg19 to hg38. The updated profiles were in good concordance with those previously published in terms of gene quantification, variant calling, and copy number alteration. Additionally, we developed a user-friendly web-based database for convenient access and interactive exploration of the dataset. Our work will facilitate reusing the dataset, maximize the values of data and further accelerate cancer research. Measurement(s) | RNA expression profiling • whole-exome sequencing (WES) • somatic mutations • copy number alterations (CNAs) | Technology Type(s) | RNA sequencing • DNA sequencing • OncoScan CNV assay | Factor Type(s) | Intervention or procedure | Sample Characteristic - Organism | Homo sapiens | Sample Characteristic - Location | China |
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16
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Garcia Alvarez HM, Koşaloğlu-Yalçın Z, Peters B, Nielsen M. The role of antigen expression in shaping the repertoire of HLA presented ligands. iScience 2022; 25:104975. [PMID: 36060059 PMCID: PMC9437844 DOI: 10.1016/j.isci.2022.104975] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 07/21/2022] [Accepted: 08/14/2022] [Indexed: 11/26/2022] Open
Abstract
Human leukocyte antigen (HLA) presentation of peptides is a prerequisite of T cell immune activation. The understanding of the rules defining this event has large implications for our knowledge of basic immunology and for the rational design of immuno-therapeutics and vaccines. Historically, most of the available prediction methods have been solely focused on the information related to antigen processing and presentation. Recent work has, however, demonstrated that method performance can be boosted by integrating information related to antigen abundance. Here we expand on these later findings and develop an extended version of NetMHCpan, called NetMHCpanExp, integrating information on antigen abundance from RNA-Seq experiments. In line with earlier works, the model demonstrates improved performance for both HLA ligand and cancer neoantigen epitope prediction. Optimal results are obtained by use of sample-specific abundance information but also reference datasets can be applied with a limited performance drop. The developed tool is available at https://services.healthtech.dtu.dk/service.php?NetMHCpanExp-1.0.
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Affiliation(s)
- Heli M Garcia Alvarez
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, CP 1650 San Martín, Argentina
| | - Zeynep Koşaloğlu-Yalçın
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, 92037 CA, USA
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, 92037 CA, USA.,Department of Medicine, University of California, San Diego, La Jolla, 92093 CA, USA
| | - Morten Nielsen
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, CP 1650 San Martín, Argentina.,Department of Health Technology, Technical University of Denmark, DK-2800 Lyngby, Denmark
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17
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Baratta AM, Brandner AJ, Plasil SL, Rice RC, Farris SP. Advancements in Genomic and Behavioral Neuroscience Analysis for the Study of Normal and Pathological Brain Function. Front Mol Neurosci 2022; 15:905328. [PMID: 35813067 PMCID: PMC9259865 DOI: 10.3389/fnmol.2022.905328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 06/06/2022] [Indexed: 11/16/2022] Open
Abstract
Psychiatric and neurological disorders are influenced by an undetermined number of genes and molecular pathways that may differ among afflicted individuals. Functionally testing and characterizing biological systems is essential to discovering the interrelationship among candidate genes and understanding the neurobiology of behavior. Recent advancements in genetic, genomic, and behavioral approaches are revolutionizing modern neuroscience. Although these tools are often used separately for independent experiments, combining these areas of research will provide a viable avenue for multidimensional studies on the brain. Herein we will briefly review some of the available tools that have been developed for characterizing novel cellular and animal models of human disease. A major challenge will be openly sharing resources and datasets to effectively integrate seemingly disparate types of information and how these systems impact human disorders. However, as these emerging technologies continue to be developed and adopted by the scientific community, they will bring about unprecedented opportunities in our understanding of molecular neuroscience and behavior.
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Affiliation(s)
- Annalisa M. Baratta
- Center for Neuroscience, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Adam J. Brandner
- Center for Neuroscience, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Sonja L. Plasil
- Department of Pharmacology & Chemical Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Rachel C. Rice
- Center for Neuroscience, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Sean P. Farris
- Center for Neuroscience, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Anesthesiology and Perioperative Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- *Correspondence: Sean P. Farris,
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18
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Wei Y, Yi K, Shen C, Chen X, Iqbal T, Cao M, Chen T, Luo Y, Li J, Zhou X, Li C, Chen L. Whole Transcriptome Profiling of the Effects of Cadmium on the Liver of the Xiangxi Yellow Heifer. Front Vet Sci 2022; 9:846662. [PMID: 35498726 PMCID: PMC9047687 DOI: 10.3389/fvets.2022.846662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 03/07/2022] [Indexed: 11/21/2022] Open
Abstract
Cadmium (Cd) is a major heavy metal toxicant found in industrial zones. Humans and animals are exposed to it through their diet, which results in various physiological problems. In the current study, the toxic effects of Cd on the liver were investigated by whole-transcriptome sequencing (RNA-seq) of the livers of Xiangxi heifers fed a diet with excess Cd. We randomly divided six healthy heifers into two groups. The first group received a control diet, whereas the second group received Cd-exceeding diets for 100 days. After 100 days, the livers were collected. A total of 551 differentially expressed mRNAs, 24 differentially expressed miRNAs, and 169 differentially expressed lncRNAs were identified (p < 0.05, |log2FC| >1). Differentially expressed genes (DEGs) were analyzed by gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. We found that under Cd exposure, DEGs were enriched in the adenosine 5'-monophosphate-activated protein kinase pathway, which is involved in autophagy regulation, and the peroxisome proliferator-activated receptor pathway, which is involved in lipid metabolism. In addition, the apolipoprotein A4 gene, which has anti-inflammatory and antioxidant effects, the anti-apoptotic gene ATPase H+/K+ transporting the nongastric alpha2 subunit, and the cholesterol metabolism-associated gene endothelial lipase gene were significantly downregulated. C-X-C motif chemokine ligand 3, cholesterol 7α-hydroxylase, and stearoyl-CoA desaturase, which are involved in the development of fatty liver, were significantly upregulated. These genes revealed the main effects of Cd on the liver of Xiangxi yellow heifers. The current study provides insightful information regarding the DEGs involved in autophagy regulation, apoptosis, lipid metabolism, anti-inflammation, and antioxidant enzyme activity. These may serve as useful biomarkers for predicting and treating Cd-related diseases in the future.
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Affiliation(s)
- Yameng Wei
- College of Animal Sciences, Jilin University, Changchun, China
| | - Kangle Yi
- Grassland and Herbivore Research Laboratory, Hunan Animal Husbandry and Veterinary Research Institute, Changsha, China
| | - Caomeihui Shen
- College of Animal Sciences, Jilin University, Changchun, China
| | - Xue Chen
- College of Animal Sciences, Jilin University, Changchun, China
| | - Tariq Iqbal
- College of Animal Sciences, Jilin University, Changchun, China
| | - Maosheng Cao
- College of Animal Sciences, Jilin University, Changchun, China
| | - Tong Chen
- College of Animal Sciences, Jilin University, Changchun, China
| | - Yang Luo
- Grassland and Herbivore Research Laboratory, Hunan Animal Husbandry and Veterinary Research Institute, Changsha, China
| | - Jianbo Li
- Grassland and Herbivore Research Laboratory, Hunan Animal Husbandry and Veterinary Research Institute, Changsha, China
| | - Xu Zhou
- College of Animal Sciences, Jilin University, Changchun, China
| | - Chunjin Li
- College of Animal Sciences, Jilin University, Changchun, China
| | - Lu Chen
- College of Animal Sciences, Jilin University, Changchun, China
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19
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Case study: Targeted RNA-sequencing of aged formalin-fixed paraffin-embedded samples for understanding chemical mode of action. Toxicol Rep 2022; 9:883-894. [DOI: 10.1016/j.toxrep.2022.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 04/12/2022] [Accepted: 04/13/2022] [Indexed: 11/19/2022] Open
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20
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Mikheenko A, Prjibelski AD, Joglekar A, Tilgner HU. Sequencing of individual barcoded cDNAs using Pacific Biosciences and Oxford Nanopore Technologies reveals platform-specific error patterns. Genome Res 2022; 32:726-737. [PMID: 35301264 PMCID: PMC8997348 DOI: 10.1101/gr.276405.121] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 03/05/2022] [Indexed: 12/04/2022]
Abstract
Long-read transcriptomics require understanding error sources inherent to technologies. Current approaches cannot compare methods for an individual RNA molecule. Here, we present a novel platform-comparison method that combines barcoding strategies and long-read sequencing to sequence cDNA copies representing an individual RNA molecule on both Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT). We compare these long-read pairs in terms of sequence content and isoform patterns. Although individual read pairs show high similarity, we find differences in (1) aligned length, (2) transcription start site (TSS), (3) polyadenylation site (poly(A)-site) assignment, and (4) exon-intron structures. Overall, 25% of read pairs disagree on either TSS, poly(A)-site, or splice site. Intron-chain disagreement typically arises from alignment errors of microexons and complicated splice sites. Our single-molecule technology comparison reveals that inconsistencies are often caused by sequencing error-induced inaccurate ONT alignments, especially to downstream GUNNGU donor motifs. However, annotation-disagreeing upstream shifts in NAGNAG acceptors in ONT are often confirmed by PacBio and are thus likely real. In both barcoded and nonbarcoded ONT reads, we find that intron number and proximity of GU/AGs better predict inconsistencies with the annotation than read quality alone. We summarize these findings in an annotation-based algorithm for spliced alignment correction that improves subsequent transcript construction with ONT reads.
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Affiliation(s)
- Alla Mikheenko
- Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034, Russia
| | - Andrey D Prjibelski
- Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034, Russia
| | - Anoushka Joglekar
- Brain and Mind Research Institute and Center for Neurogenetics, Weill Cornell Medicine, New York, New York 10065, USA
| | - Hagen U Tilgner
- Brain and Mind Research Institute and Center for Neurogenetics, Weill Cornell Medicine, New York, New York 10065, USA
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21
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Piya S, Hawk T, Patel B, Baldwin L, Rice JH, Stewart CN, Hewezi T. Kinase-dead mutation: A novel strategy for improving soybean resistance to soybean cyst nematode Heterodera glycines. MOLECULAR PLANT PATHOLOGY 2022; 23:417-430. [PMID: 34851539 PMCID: PMC8828698 DOI: 10.1111/mpp.13168] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 11/12/2021] [Accepted: 11/12/2021] [Indexed: 05/29/2023]
Abstract
Protein kinases phosphorylate proteins for functional changes and are involved in nearly all cellular processes, thereby regulating almost all aspects of plant growth and development, and responses to biotic and abiotic stresses. We generated two independent co-expression networks of soybean genes using control and stress response gene expression data and identified 392 differentially highly interconnected kinase hub genes among the two networks. Of these 392 kinases, 90 genes were identified as "syncytium highly connected hubs", potentially essential for activating kinase signalling pathways in the nematode feeding site. Overexpression of wild-type coding sequences of five syncytium highly connected kinase hub genes using transgenic soybean hairy roots enhanced plant susceptibility to soybean cyst nematode (SCN; Heterodera glycines) Hg Type 0 (race 3). In contrast, overexpression of kinase-dead variants of these five syncytium kinase hub genes significantly enhanced soybean resistance to SCN. Additionally, three of the five tested kinase hub genes enhanced soybean resistance to SCN Hg Type 1.2.5.7 (race 2), highlighting the potential of the kinase-dead approach to generate effective and durable resistance against a wide range of SCN Hg types. Subcellular localization analysis revealed that kinase-dead mutations do not alter protein cellular localization, confirming the structure-function of the kinase-inactive variants in producing loss-of-function phenotypes causing significant decrease in nematode susceptibility. Because many protein kinases are highly conserved and are involved in plant responses to various biotic and abiotic stresses, our approach of identifying kinase hub genes and their inactivation using kinase-dead mutation could be translated for biotic and abiotic stress tolerance.
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Affiliation(s)
- Sarbottam Piya
- Department of Plant SciencesUniversity of TennesseeKnoxvilleTennesseeUSA
| | - Tracy Hawk
- Department of Plant SciencesUniversity of TennesseeKnoxvilleTennesseeUSA
| | - Bhoomi Patel
- Department of Plant SciencesUniversity of TennesseeKnoxvilleTennesseeUSA
| | - Logan Baldwin
- Department of Plant SciencesUniversity of TennesseeKnoxvilleTennesseeUSA
| | - John H. Rice
- Department of Plant SciencesUniversity of TennesseeKnoxvilleTennesseeUSA
| | - C. Neal Stewart
- Department of Plant SciencesUniversity of TennesseeKnoxvilleTennesseeUSA
| | - Tarek Hewezi
- Department of Plant SciencesUniversity of TennesseeKnoxvilleTennesseeUSA
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22
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Ergin S, Kherad N, Alagoz M. RNA sequencing and its applications in cancer and rare diseases. Mol Biol Rep 2022; 49:2325-2333. [PMID: 34988891 PMCID: PMC8731134 DOI: 10.1007/s11033-021-06963-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 11/16/2021] [Indexed: 12/19/2022]
Abstract
With the invention of RNA sequencing over a decade ago, diagnosis and identification of the gene-related diseases entered a new phase that enabled more accurate analysis of the diseases that are difficult to approach and analyze. RNA sequencing has availed in-depth study of transcriptomes in different species and provided better understanding of rare diseases and taxonomical classifications of various eukaryotic organisms. Development of single-cell, short-read, long-read and direct RNA sequencing using both blood and biopsy specimens of the organism together with recent advancement in computational analysis programs has made the medical professional's ability in identifying the origin and cause of genetic disorders indispensable. Altogether, such advantages have evolved the treatment design since RNA sequencing can detect the resistant genes against the existing therapies and help medical professions to take a further step in improving methods of treatments towards higher effectiveness and less side effects. Therefore, it is of essence to all researchers and scientists to have deeper insight in all available methods of RNA sequencing while taking a step-in therapy design.
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Affiliation(s)
- Selvi Ergin
- Department of Molecular Biology and Genetics, Biruni University, Istanbul, Turkey
| | - Nasim Kherad
- Department of Molecular Biology and Genetics, Biruni University, Istanbul, Turkey
| | - Meryem Alagoz
- Department of Molecular Biology and Genetics, Biruni University, Istanbul, Turkey.
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23
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Islam MT, Coutin JF, Shukla M, Dhaliwal AK, Nigg M, Bernier L, Sherif SM, Saxena PK. Deciphering the Genome-Wide Transcriptomic Changes during Interactions of Resistant and Susceptible Genotypes of American Elm with Ophiostoma novo-ulmi. J Fungi (Basel) 2022; 8:120. [PMID: 35205874 PMCID: PMC8874831 DOI: 10.3390/jof8020120] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/12/2022] [Accepted: 01/22/2022] [Indexed: 12/10/2022] Open
Abstract
Dutch elm disease (DED), caused by Ophiostoma novo-ulmi (Onu), is a destructive disease of American elm (Ulmus americana L.). The molecular mechanisms of resistance and susceptibility against DED in American elm are still largely uncharacterized. In the present study, we performed a de novo transcriptome (RNA-sequencing; RNA-Seq) assembly of U. americana and compared the gene expression in a resistant genotype, 'Valley Forge', and a susceptible (S) elm genotype at 0 and 96 h post-inoculation of Onu. A total of 85,863 non-redundant unigenes were identified. Compared to the previously characterized U. minor transcriptome, U. americana has 35,290 similar and 55,499 unique genes. The transcriptomic variations between 'Valley Forge' and 'S' were found primarily in the photosynthesis and primary metabolism, which were highly upregulated in the susceptible genotype irrespective of the Onu inoculation. The resistance to DED was associated with the activation of RPM1-mediated effector-triggered immunity that was demonstrated by the upregulation of genes involved in the phenylpropanoids biosynthesis and PR genes. The most significantly enriched gene ontology (GO) terms in response to Onu were response to stimulus (GO:0006950), response to stress (GO:0050896), and secondary metabolic process (GO:0008152) in both genotypes. However, only in the resistant genotype, the defense response (GO:0006952) was among the topmost significantly enriched GO terms. Our findings revealed the molecular regulations of DED resistance and susceptibility and provide a platform for marker-assisted breeding of resistant American elm genotypes.
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Affiliation(s)
- Md Tabibul Islam
- Alson H. Smith Jr. Agricultural Research and Extension Center, School of Plant and Environmental Sciences, Virginia Tech, Winchester, VA 22602, USA;
| | - Jose Freixas Coutin
- Department of Plant Agriculture, Gosling Research Institute for Plant Preservation (GRIPP), University of Guelph, Guelph, ON N1G 2W1, Canada; (J.F.C.); (M.S.); (A.K.D.)
| | - Mukund Shukla
- Department of Plant Agriculture, Gosling Research Institute for Plant Preservation (GRIPP), University of Guelph, Guelph, ON N1G 2W1, Canada; (J.F.C.); (M.S.); (A.K.D.)
| | - Amandeep Kaur Dhaliwal
- Department of Plant Agriculture, Gosling Research Institute for Plant Preservation (GRIPP), University of Guelph, Guelph, ON N1G 2W1, Canada; (J.F.C.); (M.S.); (A.K.D.)
| | - Martha Nigg
- Centre d’Étude de la Forêt, Université Laval, Québec, QC G1V 0A6, Canada; (M.N.); (L.B.)
| | - Louis Bernier
- Centre d’Étude de la Forêt, Université Laval, Québec, QC G1V 0A6, Canada; (M.N.); (L.B.)
| | - Sherif M. Sherif
- Alson H. Smith Jr. Agricultural Research and Extension Center, School of Plant and Environmental Sciences, Virginia Tech, Winchester, VA 22602, USA;
| | - Praveen K. Saxena
- Department of Plant Agriculture, Gosling Research Institute for Plant Preservation (GRIPP), University of Guelph, Guelph, ON N1G 2W1, Canada; (J.F.C.); (M.S.); (A.K.D.)
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24
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Potemkin N, Cawood SMF, Treece J, Guévremont D, Rand CJ, McLean C, Stanton JAL, Williams JM. A method for simultaneous detection of small and long RNA biotypes by ribodepleted RNA-Seq. Sci Rep 2022; 12:621. [PMID: 35022475 PMCID: PMC8755727 DOI: 10.1038/s41598-021-04209-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 11/24/2021] [Indexed: 11/09/2022] Open
Abstract
RNA sequencing offers unprecedented access to the transcriptome. Key to this is the identification and quantification of many different species of RNA from the same sample at the same time. In this study we describe a novel protocol for simultaneous detection of coding and non-coding transcripts using modifications to the Ion Total RNA-Seq kit v2 protocol, with integration of QIASeq FastSelect rRNA removal kit. We report highly consistent sequencing libraries can be produced from both frozen high integrity mouse hippocampal tissue and the more challenging post-mortem human tissue. Removal of rRNA using FastSelect was extremely efficient, resulting in less than 1.5% rRNA content in the final library. We identified > 30,000 unique transcripts from all samples, including protein-coding genes and many species of non-coding RNA, in biologically-relevant proportions. Furthermore, the normalized sequencing read count for select genes significantly negatively correlated with Ct values from qRT-PCR analysis from the same samples. These results indicate that this protocol accurately and consistently identifies and quantifies a wide variety of transcripts simultaneously. The highly efficient rRNA depletion, coupled with minimized sample handling and without complicated and high-loss size selection protocols, makes this protocol useful to researchers wishing to investigate whole transcriptomes.
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Affiliation(s)
- Nikita Potemkin
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, New Zealand
- Brain Health Research Centre, Brain Research New Zealand-Rangahau Roro Aotearoa, University of Otago, Dunedin, New Zealand
| | - Sophie M F Cawood
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, New Zealand
- Brain Health Research Centre, Brain Research New Zealand-Rangahau Roro Aotearoa, University of Otago, Dunedin, New Zealand
| | - Jackson Treece
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, New Zealand
| | - Diane Guévremont
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, New Zealand
- Brain Health Research Centre, Brain Research New Zealand-Rangahau Roro Aotearoa, University of Otago, Dunedin, New Zealand
| | - Christy J Rand
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, New Zealand
| | - Catriona McLean
- Victorian Brain Bank, The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
- Anatomical Pathology, The Alfred Hospital, Melbourne, VIC, Australia
| | - Jo-Ann L Stanton
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, New Zealand
| | - Joanna M Williams
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, New Zealand.
- Brain Health Research Centre, Brain Research New Zealand-Rangahau Roro Aotearoa, University of Otago, Dunedin, New Zealand.
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25
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Goll JB, Bosinger SE, Jensen TL, Walum H, Grimes T, Tharp GK, Natrajan MS, Blazevic A, Head RD, Gelber CE, Steenbergen KJ, Patel NB, Sanz P, Rouphael NG, Anderson EJ, Mulligan MJ, Hoft DF. The Vacc-SeqQC project: Benchmarking RNA-Seq for clinical vaccine studies. Front Immunol 2022; 13:1093242. [PMID: 36741404 PMCID: PMC9893923 DOI: 10.3389/fimmu.2022.1093242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 12/30/2022] [Indexed: 01/20/2023] Open
Abstract
Introduction Over the last decade, the field of systems vaccinology has emerged, in which high throughput transcriptomics and other omics assays are used to probe changes of the innate and adaptive immune system in response to vaccination. The goal of this study was to benchmark key technical and analytical parameters of RNA sequencing (RNA-seq) in the context of a multi-site, double-blind randomized vaccine clinical trial. Methods We collected longitudinal peripheral blood mononuclear cell (PBMC) samples from 10 subjects before and after vaccination with a live attenuated Francisella tularensis vaccine and performed RNA-Seq at two different sites using aliquots from the same sample to generate two replicate datasets (5 time points for 50 samples each). We evaluated the impact of (i) filtering lowly-expressed genes, (ii) using external RNA controls, (iii) fold change and false discovery rate (FDR) filtering, (iv) read length, and (v) sequencing depth on differential expressed genes (DEGs) concordance between replicate datasets. Using synthetic mRNA spike-ins, we developed a method for empirically establishing minimal read-count thresholds for maintaining fold change accuracy on a per-experiment basis. We defined a reference PBMC transcriptome by pooling sequence data and established the impact of sequencing depth and gene filtering on transcriptome representation. Lastly, we modeled statistical power to detect DEGs for a range of sample sizes, effect sizes, and sequencing depths. Results and Discussion Our results showed that (i) filtering lowly-expressed genes is recommended to improve fold-change accuracy and inter-site agreement, if possible guided by mRNA spike-ins (ii) read length did not have a major impact on DEG detection, (iii) applying fold-change cutoffs for DEG detection reduced inter-set agreement and should be used with caution, if at all, (iv) reduction in sequencing depth had a minimal impact on statistical power but reduced the identifiable fraction of the PBMC transcriptome, (v) after sample size, effect size (i.e. the magnitude of fold change) was the most important driver of statistical power to detect DEG. The results from this study provide RNA sequencing benchmarks and guidelines for planning future similar vaccine studies.
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Affiliation(s)
- Johannes B Goll
- Department of Biomedical Data Science and Bioinformatics, The Emmes Company, LLC, Rockville, MD, United States
| | - Steven E Bosinger
- Division of Microbiology & Immunology, Emory National Primate Research Center, Emory University, Atlanta, GA, United States.,Department of Pathology & Laboratory Medicine, School of Medicine, Emory University, Atlanta, GA, United States.,Emory NPRC Genomics Core, Emory National Primate Research Center, Emory University, Atlanta, GA, United States.,Emory Vaccine Center, Emory University School of Medicine, Atlanta, GA, United States
| | - Travis L Jensen
- Department of Biomedical Data Science and Bioinformatics, The Emmes Company, LLC, Rockville, MD, United States
| | - Hasse Walum
- Division of Microbiology & Immunology, Emory National Primate Research Center, Emory University, Atlanta, GA, United States
| | - Tyler Grimes
- Department of Biomedical Data Science and Bioinformatics, The Emmes Company, LLC, Rockville, MD, United States
| | - Gregory K Tharp
- Emory NPRC Genomics Core, Emory National Primate Research Center, Emory University, Atlanta, GA, United States
| | - Muktha S Natrajan
- Emory Vaccine Center, Emory University School of Medicine, Atlanta, GA, United States.,Hope Clinic of the Emory Vaccine Center, Emory University, Atlanta, GA, United States
| | - Azra Blazevic
- Division of Infectious Diseases, Allergy, and Immunology, Department of Internal Medicine, Saint Louis University School of Medicine, St. Louis, MO, United States
| | - Richard D Head
- McDonnell Genome Institute, Washington University, St. Louis, MO, United States
| | - Casey E Gelber
- Department of Biomedical Data Science and Bioinformatics, The Emmes Company, LLC, Rockville, MD, United States
| | - Kristen J Steenbergen
- Department of Biomedical Data Science and Bioinformatics, The Emmes Company, LLC, Rockville, MD, United States
| | - Nirav B Patel
- Emory NPRC Genomics Core, Emory National Primate Research Center, Emory University, Atlanta, GA, United States
| | - Patrick Sanz
- Office of Biodefense, Research Resources and Translational Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, United States
| | - Nadine G Rouphael
- Emory Vaccine Center, Emory University School of Medicine, Atlanta, GA, United States.,Hope Clinic of the Emory Vaccine Center, Emory University, Atlanta, GA, United States.,Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Emory University, Atlanta, GA, United States
| | - Evan J Anderson
- Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Emory University, Atlanta, GA, United States.,Center for Childhood Infections and Vaccines (CCIV) of Children's Healthcare of Atlanta and Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, United States
| | - Mark J Mulligan
- Emory Vaccine Center, Emory University School of Medicine, Atlanta, GA, United States.,Hope Clinic of the Emory Vaccine Center, Emory University, Atlanta, GA, United States.,Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Emory University, Atlanta, GA, United States.,New York University Vaccine Center, New York, NY, United States
| | - Daniel F Hoft
- Division of Infectious Diseases, Allergy, and Immunology, Department of Internal Medicine, Saint Louis University School of Medicine, St. Louis, MO, United States.,Department of Molecular Microbiology & Immunology, Saint Louis University, St. Louis, MO, United States
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26
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Zhang X, McFarland TJ, Vartanian K, Zhu Y, Harrington CA, Chu CQ. RNA isolation from micro-quantity of articular cartilage for quantitative gene expression by microarray analysis. Int J Med Sci 2022; 19:98-104. [PMID: 34975303 PMCID: PMC8692110 DOI: 10.7150/ijms.65343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 10/19/2021] [Indexed: 11/16/2022] Open
Abstract
Isolation of quality RNA from articular cartilage has been challenging due to low cellularity and the high abundance of extracellular matrix and proteoglycan proteins. Recently developed methods for isolation of high quality RNA from cartilage are more applicable to larger cartilage specimens typically weighing at least 25 mg. While these methods generate RNA suitable for analysis, they are less successful with smaller tissue inputs. For the study of small focal defect cartilage specimens an improved RNA extraction method is needed. Here we report a protocol for direct RNA isolation from less than 3 mg of wet weight rabbit articular cartilage for quantitative microarray gene profiling. This protocol is useful for identifying differentially expressed genes in chondrocytes following focal cartilage repair and can potentially be adopted for gene expression analysis of cartilage biopsy specimens from human joints.
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Affiliation(s)
- Xiaowei Zhang
- Division of Arthritis and Rheumatic Diseases, Oregon Health & Science University, Portland, Oregon 97239
- Section of Rheumatology, VA Portland Health Care System, Portland, Oregon 97239
| | - Trevor J. McFarland
- Gene Profiling Shared Resource, Oregon Health & Science University; Portland, Oregon 97239
| | - Kristina Vartanian
- Gene Profiling Shared Resource, Oregon Health & Science University; Portland, Oregon 97239
| | - Yong Zhu
- Vivoscript, Inc, P. O. Box 63025, Irvine, CA 92602
| | - Christina A. Harrington
- Gene Profiling Shared Resource, Oregon Health & Science University; Portland, Oregon 97239
- Department of Molecular and Medical Genetics, Oregon Health & Science University; Portland, Oregon 97239
| | - Cong-Qiu Chu
- Division of Arthritis and Rheumatic Diseases, Oregon Health & Science University, Portland, Oregon 97239
- Section of Rheumatology, VA Portland Health Care System, Portland, Oregon 97239
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27
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Zolotareva O, Nasirigerdeh R, Matschinske J, Torkzadehmahani R, Bakhtiari M, Frisch T, Späth J, Blumenthal DB, Abbasinejad A, Tieri P, Kaissis G, Rückert D, Wenke NK, List M, Baumbach J. Flimma: a federated and privacy-aware tool for differential gene expression analysis. Genome Biol 2021; 22:338. [PMID: 34906207 PMCID: PMC8670124 DOI: 10.1186/s13059-021-02553-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 11/22/2021] [Indexed: 12/13/2022] Open
Abstract
Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma ( https://exbio.wzw.tum.de/flimma/ ) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches fail.
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Affiliation(s)
- Olga Zolotareva
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany. .,Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany.
| | - Reza Nasirigerdeh
- AI in Medicine and Healthcare, Technical University of Munich, Munich, Germany.,Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Julian Matschinske
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | | | - Mohammad Bakhtiari
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Tobias Frisch
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Julian Späth
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - David B Blumenthal
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Amir Abbasinejad
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.,Sapienza University of Rome, Rome, Italy
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy.,Sapienza University of Rome, Rome, Italy
| | - Georgios Kaissis
- AI in Medicine and Healthcare, Technical University of Munich, Munich, Germany.,Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Biomedical Image Analysis Group, Imperial College London, London, UK.,OpenMined, Oxford, UK
| | - Daniel Rückert
- AI in Medicine and Healthcare, Technical University of Munich, Munich, Germany.,Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Biomedical Image Analysis Group, Imperial College London, London, UK
| | - Nina K Wenke
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany.,Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
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Mercer TR, Xu J, Mason CE, Tong W. The Sequencing Quality Control 2 study: establishing community standards for sequencing in precision medicine. Genome Biol 2021; 22:306. [PMID: 34749795 PMCID: PMC8574019 DOI: 10.1186/s13059-021-02528-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Tim R Mercer
- Australian Institute of Bioengineering and Nanotechnology, University of Queensland, Brisbane, Australia
- Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA.
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29
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Johnson KL, Qi Z, Yan Z, Wen X, Nguyen TC, Zaleta-Rivera K, Chen CJ, Fan X, Sriram K, Wan X, Chen ZB, Zhong S. Revealing protein-protein interactions at the transcriptome scale by sequencing. Mol Cell 2021; 81:4091-4103.e9. [PMID: 34348091 PMCID: PMC8500946 DOI: 10.1016/j.molcel.2021.07.006] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 06/12/2021] [Accepted: 07/08/2021] [Indexed: 02/07/2023]
Abstract
We describe PROPER-seq (protein-protein interaction sequencing) to map protein-protein interactions (PPIs) en masse. PROPER-seq first converts transcriptomes of input cells into RNA-barcoded protein libraries, in which all interacting protein pairs are captured through nucleotide barcode ligation, recorded as chimeric DNA sequences, and decoded at once by sequencing and mapping. We applied PROPER-seq to human embryonic kidney cells, T lymphocytes, and endothelial cells and identified 210,518 human PPIs (collected in the PROPER v.1.0 database). Among these, 1,365 and 2,480 PPIs are supported by published co-immunoprecipitation (coIP) and affinity purification-mass spectrometry (AP-MS) data, 17,638 PPIs are predicted by the prePPI algorithm without previous experimental validation, and 100 PPIs overlap human synthetic lethal gene pairs. In addition, four previously uncharacterized interaction partners with poly(ADP-ribose) polymerase 1 (PARP1) (a critical protein in DNA repair) known as XPO1, MATR3, IPO5, and LEO1 are validated in vivo. PROPER-seq presents a time-effective technology to map PPIs at the transcriptome scale, and PROPER v.1.0 provides a rich resource for studying PPIs.
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Affiliation(s)
- Kara L Johnson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zhijie Qi
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zhangming Yan
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Xingzhao Wen
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Tri C Nguyen
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Kathia Zaleta-Rivera
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Chien-Ju Chen
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Xiaochen Fan
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Kiran Sriram
- Department of Diabetes Complications and Metabolism, Beckman Research Institute, City of Hope, Duarte, CA 91010, USA
| | - Xueyi Wan
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zhen Bouman Chen
- Department of Diabetes Complications and Metabolism, Beckman Research Institute, City of Hope, Duarte, CA 91010, USA
| | - Sheng Zhong
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.
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30
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Foox J, Tighe SW, Nicolet CM, Zook JM, Byrska-Bishop M, Clarke WE, Khayat MM, Mahmoud M, Laaguiby PK, Herbert ZT, Warner D, Grills GS, Jen J, Levy S, Xiang J, Alonso A, Zhao X, Zhang W, Teng F, Zhao Y, Lu H, Schroth GP, Narzisi G, Farmerie W, Sedlazeck FJ, Baldwin DA, Mason CE. Performance assessment of DNA sequencing platforms in the ABRF Next-Generation Sequencing Study. Nat Biotechnol 2021; 39:1129-1140. [PMID: 34504351 PMCID: PMC8985210 DOI: 10.1038/s41587-021-01049-5] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 08/05/2021] [Indexed: 02/08/2023]
Abstract
Assessing the reproducibility, accuracy and utility of massively parallel DNA sequencing platforms remains an ongoing challenge. Here the Association of Biomolecular Resource Facilities (ABRF) Next-Generation Sequencing Study benchmarks the performance of a set of sequencing instruments (HiSeq/NovaSeq/paired-end 2 × 250-bp chemistry, Ion S5/Proton, PacBio circular consensus sequencing (CCS), Oxford Nanopore Technologies PromethION/MinION, BGISEQ-500/MGISEQ-2000 and GS111) on human and bacterial reference DNA samples. Among short-read instruments, HiSeq 4000 and X10 provided the most consistent, highest genome coverage, while BGI/MGISEQ provided the lowest sequencing error rates. The long-read instrument PacBio CCS had the highest reference-based mapping rate and lowest non-mapping rate. The two long-read platforms PacBio CCS and PromethION/MinION showed the best sequence mapping in repeat-rich areas and across homopolymers. NovaSeq 6000 using 2 × 250-bp read chemistry was the most robust instrument for capturing known insertion/deletion events. This study serves as a benchmark for current genomics technologies, as well as a resource to inform experimental design and next-generation sequencing variant calling.
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Affiliation(s)
- Jonathan Foox
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Scott W Tighe
- University of Vermont Cancer Center, Vermont Integrative Genomics Resource, University of Vermont, Burlington, VT, USA
| | - Charles M Nicolet
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Justin M Zook
- Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | | | | | - Michael M Khayat
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Medhat Mahmoud
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Phoebe K Laaguiby
- University of Vermont Cancer Center, Vermont Integrative Genomics Resource, University of Vermont, Burlington, VT, USA
| | - Zachary T Herbert
- Molecular Biology Core Facilities, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Derek Warner
- DNA Sequencing Core, University of Utah, Salt Lake City, UT, USA
| | - George S Grills
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, USA
| | - Jin Jen
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Shawn Levy
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Jenny Xiang
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Alicia Alonso
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Xia Zhao
- BGI-Shenzhen, Shenzhen, China
- MGI, BGI-Shenzhen, Shenzhen, China
| | | | | | - Yonggang Zhao
- BGI-Shenzhen, Shenzhen, China
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| | - Haorong Lu
- BGI-Shenzhen, Shenzhen, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen, China
| | | | | | - William Farmerie
- Interdisciplinary Center for Biotechnology Research, University of Florida, Gainesville, FL, USA
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
| | - Don A Baldwin
- Department of Pathology, Fox Chase Cancer Center, Philadelphia, PA, USA.
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.
- The Feil Family Brain and Mind Research Institute, New York, NY, USA.
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA.
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31
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Moore KJM, Cahill J, Aidelberg G, Aronoff R, Bektaş A, Bezdan D, Butler DJ, Chittur SV, Codyre M, Federici F, Tanner NA, Tighe SW, True R, Ware SB, Wyllie AL, Afshin EE, Bendesky A, Chang CB, Dela Rosa R, Elhaik E, Erickson D, Goldsborough AS, Grills G, Hadasch K, Hayden A, Her SY, Karl JA, Kim CH, Kriegel AJ, Kunstman T, Landau Z, Land K, Langhorst BW, Lindner AB, Mayer BE, McLaughlin LA, McLaughlin MT, Molloy J, Mozsary C, Nadler JL, D'Silva M, Ng D, O'Connor DH, Ongerth JE, Osuolale O, Pinharanda A, Plenker D, Ranjan R, Rosbash M, Rotem A, Segarra J, Schürer S, Sherrill-Mix S, Solo-Gabriele H, To S, Vogt MC, Yu AD, Mason CE. Loop-Mediated Isothermal Amplification Detection of SARS-CoV-2 and Myriad Other Applications. J Biomol Tech 2021; 32:228-275. [PMID: 35136384 PMCID: PMC8802757 DOI: 10.7171/jbt.21-3203-017] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
As the second year of the COVID-19 pandemic begins, it remains clear that a massive increase in the ability to test for SARS-CoV-2 infections in a myriad of settings is critical to controlling the pandemic and to preparing for future outbreaks. The current gold standard for molecular diagnostics is the polymerase chain reaction (PCR), but the extraordinary and unmet demand for testing in a variety of environments means that both complementary and supplementary testing solutions are still needed. This review highlights the role that loop-mediated isothermal amplification (LAMP) has had in filling this global testing need, providing a faster and easier means of testing, and what it can do for future applications, pathogens, and the preparation for future outbreaks. This review describes the current state of the art for research of LAMP-based SARS-CoV-2 testing, as well as its implications for other pathogens and testing. The authors represent the global LAMP (gLAMP) Consortium, an international research collective, which has regularly met to share their experiences on LAMP deployment and best practices; sections are devoted to all aspects of LAMP testing, including preanalytic sample processing, target amplification, and amplicon detection, then the hardware and software required for deployment are discussed, and finally, a summary of the current regulatory landscape is provided. Included as well are a series of first-person accounts of LAMP method development and deployment. The final discussion section provides the reader with a distillation of the most validated testing methods and their paths to implementation. This review also aims to provide practical information and insight for a range of audiences: for a research audience, to help accelerate research through sharing of best practices; for an implementation audience, to help get testing up and running quickly; and for a public health, clinical, and policy audience, to help convey the breadth of the effect that LAMP methods have to offer.
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Affiliation(s)
- Keith J M Moore
- School of Science and Engineering, Ateneo de Manila University, Quezon City 1108, Philippines
| | | | - Guy Aidelberg
- Université de Paris, INSERM U1284, Center for Research and Interdisciplinarity (CRI), 75006 Paris, France
- Just One Giant Lab, Centre de Recherches Interdisciplinaires (CRI), 75004 Paris, France
| | - Rachel Aronoff
- Just One Giant Lab, Centre de Recherches Interdisciplinaires (CRI), 75004 Paris, France
- Action for Genomic Integrity Through Research! (AGiR!), Lausanne, Switzerland
- Association Hackuarium, Lausanne, Switzerland
| | - Ali Bektaş
- Oakland Genomics Center, Oakland, CA 94609, USA
| | - Daniela Bezdan
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, 72076 Tübingen, Germany
- NGS Competence Center Tübingen (NCCT), University of Tübingen, 72076 Tübingen, Germany
- Poppy Health, Inc, San Francisco, CA 94158, USA
- Institute of Medical Virology and Epidemiology of Viral Diseases, University Hospital, 72076 Tübingen, Germany
| | - Daniel J Butler
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Sridar V Chittur
- Center for Functional Genomics, Department of Biomedical Sciences, School of Public Health, University at Albany, State University of New York, Rensselaer, 12222, USA
| | - Martin Codyre
- GiantLeap Biotechnology Ltd, Wicklow A63 Kv91, Ireland
| | - Fernan Federici
- ANID, Millennium Science Initiative Program, Millennium Institute for Integrative Biology (iBio), Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
| | | | | | - Randy True
- FloodLAMP Biotechnologies, San Carlos, CA 94070, USA
| | - Sarah B Ware
- Just One Giant Lab, Centre de Recherches Interdisciplinaires (CRI), 75004 Paris, France
- BioBlaze Community Bio Lab, 1800 W Hawthorne Ln, Ste J-1, West Chicago, IL 60185, USA
- Blossom Bio Lab, 1800 W Hawthorne Ln, Ste K-2, West Chicago, IL 60185, USA
| | - Anne L Wyllie
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Evan E Afshin
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10065, USA
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY 10065, USA
| | - Andres Bendesky
- Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY 10027, USA
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Connie B Chang
- Department of Chemical and Biological Engineering, Montana State University, Bozeman, 59717, USA
- Center for Biofilm Engineering, Montana State University, Bozeman, 59717, USA
| | - Richard Dela Rosa
- School of Science and Engineering, Ateneo de Manila University, Quezon City 1108, Philippines
| | - Eran Elhaik
- Department of Biology, Lund University, Sölvegatan 35, Lund, Sweden
| | - David Erickson
- Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14850, USA
| | | | - George Grills
- Department of Microbiology, University of Pennsylvania, Philadelphia, 19104, USA
| | - Kathrin Hadasch
- Université de Paris, INSERM U1284, Center for Research and Interdisciplinarity (CRI), 75006 Paris, France
- Department of Biology, Membrane Biophysics, Technische Universität Darmstadt, 64289 Darmstadt, Germany
- Lab3 eV, Labspace Darmstadt, 64295 Darmstadt, Germany
- IANUS Verein für Friedensorientierte Technikgestaltung eV, 64289 Darmstadt, Germany
| | - Andrew Hayden
- Center for Functional Genomics, Department of Biomedical Sciences, School of Public Health, University at Albany, State University of New York, Rensselaer, 12222, USA
| | | | - Julie A Karl
- Department of Pathology and Laboratory Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Madison 53705, USA
| | | | | | | | - Zeph Landau
- Department of Computer Science, University of California, Berkeley, Berkeley, 94720, USA
| | - Kevin Land
- Mologic, Centre for Advanced Rapid Diagnostics, (CARD), Bedford Technology Park, Thurleigh MK44 2YA, England
- Department of Electrical, Electronic and Computer Engineering, University of Pretoria, 0028 Pretoria, South Africa
| | | | - Ariel B Lindner
- Université de Paris, INSERM U1284, Center for Research and Interdisciplinarity (CRI), 75006 Paris, France
| | - Benjamin E Mayer
- Department of Biology, Membrane Biophysics, Technische Universität Darmstadt, 64289 Darmstadt, Germany
- Lab3 eV, Labspace Darmstadt, 64295 Darmstadt, Germany
| | | | - Matthew T McLaughlin
- Department of Pathology and Laboratory Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Madison 53705, USA
| | - Jenny Molloy
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, England
| | - Christopher Mozsary
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Jerry L Nadler
- Department of Pharmacology, New York Medical College, Valhalla, 10595, USA
| | - Melinee D'Silva
- Department of Pharmacology, New York Medical College, Valhalla, 10595, USA
| | - David Ng
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - David H O'Connor
- Department of Pathology and Laboratory Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Madison 53705, USA
| | - Jerry E Ongerth
- University of Wollongong, Environmental Engineering, Wollongong NSW 2522, Australia
| | - Olayinka Osuolale
- Applied Environmental Metagenomics and Infectious Diseases Research (AEMIDR), Department of Biological Sciences, Elizade University, Ilara Mokin, Nigeria
| | - Ana Pinharanda
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA
| | - Dennis Plenker
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Ravi Ranjan
- Genomics Resource Laboratory, Institute for Applied Life Sciences, University of Massachusetts, Amherst, 01003, USA
| | - Michael Rosbash
- Howard Hughes Medical Institute and Department of Biology, Brandeis University, Waltham, MA 02453, USA
| | | | | | | | - Scott Sherrill-Mix
- Department of Microbiology, University of Pennsylvania, Philadelphia, 19104, USA
| | | | - Shaina To
- School of Science and Engineering, Ateneo de Manila University, Quezon City 1108, Philippines
| | - Merly C Vogt
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA
| | - Albert D Yu
- Howard Hughes Medical Institute and Department of Biology, Brandeis University, Waltham, MA 02453, USA
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10065, USA
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY 10065, USA
- The Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
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32
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Novel Facets of the Liver Transcriptome Are Associated with the Susceptibility and Resistance to Lipid-Related Metabolic Disorders in Periparturient Holstein Cows. Animals (Basel) 2021; 11:ani11092558. [PMID: 34573524 PMCID: PMC8470208 DOI: 10.3390/ani11092558] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 08/23/2021] [Accepted: 08/25/2021] [Indexed: 02/06/2023] Open
Abstract
Simple Summary Energy and nutrient demands of the early lactation period can result in the development of metabolic disorders, such as ketosis and fatty liver, in dairy cows. Variability in the incidence of these disorders suggests that some cows have an ability to adapt. The objective of this study was to discover differences in liver gene expression that are associated with a cow’s susceptibility (disposition to disorder during typical conditions) or resistance (disposition to disorder onset and severity when presented a challenge) to metabolic disorders. Cows in a control treatment and a ketosis induction protocol treatment were retrospectively grouped into susceptibility and resistance groups, respectively, by a machine learning algorithm using lipid biomarker concentrations. Whole-transcriptome RNA sequencing was performed on liver samples from these cows. More susceptible cows had lower expression of glutathione metabolism genes, while less resistant cows had greater expression of eicosanoid-metabolism-related genes. Additionally, differentially expressed genes suggested a role for immune-response-related genes in conferring susceptibility and resistance to metabolic disorders. The overall inferred metabolism suggests that responses to oxidative stress may determine susceptibility and resistance to metabolic disorders, with novel implications for immunometabolism. Abstract Lipid-related metabolic disorders (LRMD) are prevalent in early lactation dairy cows, and have detrimental effects on productivity and health. Our objectives were to identify cows resistant or susceptible to LRMD using a ketosis induction protocol (KIP) to discover differentially expressed liver genes and metabolic pathways associated with disposition. Clustering cows based on postpartum lipid metabolite concentrations within dietary treatments identified cows more or less susceptible (MS vs. LS) to LRMD within the control treatment, and more or less resistant (MR vs. LR) within the KIP treatment. Whole-transcriptome RNA sequencing was performed on liver samples (−28, +1, and +14 days relative to calving) to assess differential gene and pathway expression (LS vs. MS; MR vs. LR; n = 3 cows per cluster). Cows within the MS and LR clusters had evidence of greater blood serum β-hydroxybutyrate concentration and liver triglyceride content than the LS and MR clusters, respectively. The inferred metabolism of differentially expressed genes suggested a role of immune response (i.e., interferon-inducible proteins and major histocompatibility complex molecules). Additionally, unique roles for glutathione metabolism and eicosanoid metabolism in modulating susceptibility and resistance, respectively, were implicated. Overall, this research provides novel insight into the role of immunometabolism in LRMD pathology, and suggests the potential for unique control points for LRMD progression and severity.
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33
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Toth GP, Bencic DC, Martinson JW, Flick RW, Lattier DL, Kostich MS, Huang W, Biales AD. Development of omics biomarkers for estrogen exposure using mRNA, miRNA and piRNAs. AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2021; 235:105807. [PMID: 33838496 DOI: 10.1016/j.aquatox.2021.105807] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 03/03/2021] [Accepted: 03/05/2021] [Indexed: 06/12/2023]
Abstract
The number of chemicals requiring risk evaluation exceeds our capacity to provide the underlying data using traditional methodology. This has led to an increased focus on the development of novel approach methodologies. This work aimed to expand the panel of gene expression-based biomarkers to include responses to estrogens, to identify training strategies that maximize the range of applicable concentrations, and to evaluate the potential for two classes of small non-coding RNAs (sncRNAs), microRNA (miRNA) and piwi-interacting RNA (piRNA), as biomarkers. To this end larval Pimephales promelas (96 hpf +/- 1h) were exposed to 5 concentrations of 17α- ethinylestradiol (0.12, 1.25, 2.5, 5.0, 10.0 ng/L) for 48 h. For mRNA-based biomarker development, RNA-seq was conducted across all concentrations. For sncRNA biomarkers, small RNA libraries were prepared only for the control and 10.0 ng/L EE2 treatment. In order to develop an mRNA classifier that remained accurate over the range of exposure concentrations, three different training strategies were employed that focused on 10 ng/L, 2.5 ng/L or a combination of both. Classifiers were tested against an independent test set of individuals exposed to the same concentrations used in training and subsequently against concentrations not included in model training. Both random forest (RF) and logistic regression with elastic net regularizations (glmnet) models trained on 10 ng/L EE2 performed poorly when applied to lower concentrations. RF models trained with either the 2.5 ng/L or combination (2.5 + 10 ng/L) treatments were able to accurately discriminate exposed vs. non-exposed across all but the lowest concentrations. glmnet models were unable to accurately classify below 5 ng/L. With the exception of the 10 ng/L treatment, few mRNA differentially expressed genes (DEG) were observed, however, there was marked overlap of DEGs across treatments. Overlapping DEGs have well established linkages to estrogen and several of the 81 DEGs identified in the 10 ng/L treatment have been previously utilized as estrogenic biomarkers (vitellogenin, estrogen receptor-β). Following multiple test correction, no sncRNAs were found to be differentially expressed, however, both miRNA and piRNA classifiers were able to accurately discriminate control and 10 ng/L exposed organisms with AUCs of 0.83 and 1.0 respectively. We have developed a highly discriminative estrogenic mRNA biomarker that is accurate over a range of concentrations likely to be found in real-world exposures. We have demonstrated that both miRNA and piRNA are responsive to estrogenic exposure, suggesting the need to further investigate their regulatory roles in the estrogenic response.
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Affiliation(s)
- Gregory P Toth
- US Environmental Protection Agency, Office of Research and Development, 26 W. Martin Luther King Dr., Cincinnati, OH 45268, United States
| | - David C Bencic
- US Environmental Protection Agency, Office of Research and Development, 26 W. Martin Luther King Dr., Cincinnati, OH 45268, United States
| | - John W Martinson
- US Environmental Protection Agency, Office of Research and Development, 26 W. Martin Luther King Dr., Cincinnati, OH 45268, United States
| | - Robert W Flick
- US Environmental Protection Agency, Office of Research and Development, 26 W. Martin Luther King Dr., Cincinnati, OH 45268, United States
| | - David L Lattier
- US Environmental Protection Agency, Office of Research and Development, 26 W. Martin Luther King Dr., Cincinnati, OH 45268, United States
| | - Mitchell S Kostich
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Dr, Farmington, CT 06032, United States
| | - Weichun Huang
- US Environmental Protection Agency, Office of Research and Development, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Adam D Biales
- US Environmental Protection Agency, Office of Research and Development, 26 W. Martin Luther King Dr., Cincinnati, OH 45268, United States.
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Van Houtven J, Cuypers B, Meysman P, Hooyberghs J, Laukens K, Valkenborg D. Constrained Standardization of Count Data from Massive Parallel Sequencing. J Mol Biol 2021; 433:166966. [PMID: 33794260 DOI: 10.1016/j.jmb.2021.166966] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 02/26/2021] [Accepted: 03/23/2021] [Indexed: 11/22/2022]
Abstract
In high-throughput omics disciplines like transcriptomics, researchers face a need to assess the quality of an experiment prior to an in-depth statistical analysis. To efficiently analyze such voluminous collections of data, researchers need triage methods that are both quick and easy to use. Such a normalization method for relative quantitation, CONSTANd, was recently introduced for isobarically-labeled mass spectra in proteomics. It transforms the data matrix of abundances through an iterative, convergent process enforcing three constraints: (I) identical column sums; (II) each row sum is fixed (across matrices) and (III) identical to all other row sums. In this study, we investigate whether CONSTANd is suitable for count data from massively parallel sequencing, by qualitatively comparing its results to those of DESeq2. Further, we propose an adjustment of the method so that it may be applied to identically balanced but differently sized experiments for joint analysis. We find that CONSTANd can process large data sets at well over 1 million count records per second whilst mitigating unwanted systematic bias and thus quickly uncovering the underlying biological structure when combined with a PCA plot or hierarchical clustering. Moreover, it allows joint analysis of data sets obtained from different batches, with different protocols and from different labs but without exploiting information from the experimental setup other than the delineation of samples into identically processed sets (IPSs). CONSTANd's simplicity and applicability to proteomics as well as transcriptomics data make it an interesting candidate for integration in multi-omics workflows.
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Affiliation(s)
- Joris Van Houtven
- Flemish Institute for Technological Research (VITO), Boeretang 200, B-2400 Mol, Belgium; Universiteit Hasselt, Data Science Institute (DSI), Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Agoralaan, Diepenbeek BE 3590, Belgium; Universiteit Antwerpen, Centre for Proteomics, Groenenborgerlaan 171, Antwerpen BE 2020, Belgium.
| | - Bart Cuypers
- Universiteit Antwerpen, Biomedical Informatics Network Antwerp (Biomina), Middelheimlaan 1, Antwerpen BE 2020, Belgium; Molecular Parasitology Unit, Institute of Tropical Medicine, Nationalestraat 155, Antwerpen BE 2020, Belgium; Universiteit Antwerpen, Adrem Data Lab, Department of Computer Sciences, Middelheimlaan 1, Antwerpen BE 2020, Belgium
| | - Pieter Meysman
- Universiteit Antwerpen, Biomedical Informatics Network Antwerp (Biomina), Middelheimlaan 1, Antwerpen BE 2020, Belgium; Universiteit Antwerpen, Adrem Data Lab, Department of Computer Sciences, Middelheimlaan 1, Antwerpen BE 2020, Belgium
| | - Jef Hooyberghs
- Flemish Institute for Technological Research (VITO), Boeretang 200, B-2400 Mol, Belgium; Universiteit Hasselt, Data Science Institute (DSI), Theoretical Physics, Agoralaan, Diepenbeek BE 3590, Belgium
| | - Kris Laukens
- Universiteit Antwerpen, Biomedical Informatics Network Antwerp (Biomina), Middelheimlaan 1, Antwerpen BE 2020, Belgium; Universiteit Antwerpen, Adrem Data Lab, Department of Computer Sciences, Middelheimlaan 1, Antwerpen BE 2020, Belgium
| | - Dirk Valkenborg
- Universiteit Hasselt, Data Science Institute (DSI), Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Agoralaan, Diepenbeek BE 3590, Belgium; Universiteit Antwerpen, Centre for Proteomics, Groenenborgerlaan 171, Antwerpen BE 2020, Belgium.
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Gene expression analysis of combined RNA-seq experiments using a receiver operating characteristic calibrated procedure. Comput Biol Chem 2021; 93:107515. [PMID: 34044204 DOI: 10.1016/j.compbiolchem.2021.107515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 05/12/2021] [Indexed: 10/21/2022]
Abstract
Because of rapid advancements in sequencing technology, the experimental platforms of RNA-seq are updated frequently. It is quite common to combine data sets from several experimental platforms for analysis in order to increase the sample size and achieve more powerful tests for detecting the presence of differential gene expression. The data sets combined from different experimental platforms will have a complex data distribution, which causes a major problem in statistical modeling as well as in multiple testing. Although plenty of research have studied this problem by modeling the batch effects, there are no general and robust data-driven procedures for RNA-seq analysis. In this paper we propose a new robust procedure which combines the use of popular methods (packages) with a data-driven simulation (DDS). We construct the average receiver operating characteristic curve through the DDS to provide the calibrated levels of significance for multiple testing. Instead of further modifying the adjusted p-values, we calibrated the levels of significance for each specific method and mean effect model. The procedure was demonstrated with several popular RNA-seq analysis methods (edgeR, DEseq2, limma+voom). The proposed procedure relaxes the stringent assumptions of data distributions for RNA-seq analysis methods and is illustrated using colorectal cancer studies from The Cancer Genome Atlas database.
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Bias in RNA-seq Library Preparation: Current Challenges and Solutions. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6647597. [PMID: 33987443 PMCID: PMC8079181 DOI: 10.1155/2021/6647597] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 04/09/2021] [Indexed: 12/26/2022]
Abstract
Although RNA sequencing (RNA-seq) has become the most advanced technology for transcriptome analysis, it also confronts various challenges. As we all know, the workflow of RNA-seq is extremely complicated and it is easy to produce bias. This may damage the quality of RNA-seq dataset and lead to an incorrect interpretation for sequencing result. Thus, our detailed understanding of the source and nature of these biases is essential for the interpretation of RNA-seq data, finding methods to improve the quality of RNA-seq experimental, or development bioinformatics tools to compensate for these biases. Here, we discuss the sources of experimental bias in RNA-seq. And for each type of bias, we discussed the method for improvement, in order to provide some useful suggestions for researcher in RNA-seq experimental.
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Fridman H, Bormans C, Einhorn M, Au D, Bormans A, Porat Y, Sanchez LF, Manning B, Levy-Lahad E, Behar DM. Performance comparison: exome sequencing as a single test replacing Sanger sequencing. Mol Genet Genomics 2021; 296:653-663. [PMID: 33694043 DOI: 10.1007/s00438-021-01772-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 03/02/2021] [Indexed: 01/23/2023]
Abstract
Next generation sequencing tests are used routinely as first-choice tests in the clinic. However, systematic performance comparing the results of exome sequencing as a single test replacing Sanger sequencing of targeted gene(s) is still lacking. Performance comparison data are critically important for clinical case management. In this study, we compared Sanger-sequencing results of 258 genes to those obtained from next generation sequencing (NGS) using two exome-sequencing enrichment kits: Agilent-SureSelectQXT and Illumina-Nextera. Sequencing was performed on leukocytes and buccal-derived DNA from a single individual, and all 258 genes were sequenced a total of 11 times (using different sequencing methods and DNA sources). Sanger sequencing was completed for all exons, including flanking ± 8 bp regions. For the 258 genes, NGS mean coverage was > 20 × for > 98 and > 91% of the regions targeted by SureSelect and Nextera, respectively. Overall, 449 variants were identified in at least one experiment, and 407/449 (90.6%) were detected by all. Of the 42 discordant variants, 23 were determined as true calls, summing-up to a truth set of 430 variants. Sensitivity of true-variant detection was 99% for Sanger sequencing and 97-100% for the NGS experiments. Mean false-positive rates were 3.7E-6 for Sanger sequencing, 2.5E-6 for SureSelect-NGS and 5.2E-6 for Nextera-NGS. Our findings suggest a high overall concordance between Sanger sequencing and NGS performances. Both methods demonstrated false-positive and false-negative calls. High clinical suspicion for a specific diagnosis should, therefore, override negative results of either Sanger sequencing or NGS.
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Affiliation(s)
- Hila Fridman
- Medical Genetics Institute, Shaare Zedek Medical Center, 91031, Jerusalem, Israel. .,Faculty of Medicine, The Hebrew University of Jerusalem, 91120, Jerusalem, Israel.
| | | | - Moshe Einhorn
- Data Analysis Group, 6688218, Genoox, Tel Aviv, Israel
| | - Daniel Au
- Genomic Research Center, Gene By Gene, Houston, TX, 77008, USA
| | - Arjan Bormans
- Genomic Research Center, Gene By Gene, Houston, TX, 77008, USA
| | - Yuval Porat
- Data Analysis Group, 6688218, Genoox, Tel Aviv, Israel
| | | | - Brent Manning
- Genomic Research Center, Gene By Gene, Houston, TX, 77008, USA
| | - Ephrat Levy-Lahad
- Medical Genetics Institute, Shaare Zedek Medical Center, 91031, Jerusalem, Israel.,Faculty of Medicine, The Hebrew University of Jerusalem, 91120, Jerusalem, Israel
| | - Doron M Behar
- Genomic Research Center, Gene By Gene, Houston, TX, 77008, USA
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Schmeing S, Robinson MD. ReSeq simulates realistic Illumina high-throughput sequencing data. Genome Biol 2021; 22:67. [PMID: 33608040 PMCID: PMC7896392 DOI: 10.1186/s13059-021-02265-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 01/07/2021] [Indexed: 12/18/2022] Open
Abstract
In high-throughput sequencing data, performance comparisons between computational tools are essential for making informed decisions at each step of a project. Simulations are a critical part of method comparisons, but for standard Illumina sequencing of genomic DNA, they are often oversimplified, which leads to optimistic results for most tools. ReSeq improves the authenticity of synthetic data by extracting and reproducing key components from real data. Major advancements are the inclusion of systematic errors, a fragment-based coverage model and sampling-matrix estimates based on two-dimensional margins. These improvements lead to more faithful performance evaluations. ReSeq is available at https://github.com/schmeing/ReSeq.
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Affiliation(s)
- Stephan Schmeing
- Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland. .,SIB Swiss Institute of Bioinformatics, Winterthurerstrasse 190, Zurich, 8057, Switzerland.
| | - Mark D Robinson
- Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland. .,SIB Swiss Institute of Bioinformatics, Winterthurerstrasse 190, Zurich, 8057, Switzerland.
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39
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Fu C, Marczyk M, Samuels M, Trevarton AJ, Qu J, Lau R, Du L, Pappas T, Sinn BV, Gould RE, Pusztai L, Hatzis C, Symmans WF. Targeted RNAseq assay incorporating unique molecular identifiers for improved quantification of gene expression signatures and transcribed mutation fraction in fixed tumor samples. BMC Cancer 2021; 21:114. [PMID: 33541297 PMCID: PMC7860187 DOI: 10.1186/s12885-021-07814-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 01/17/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Our objective was to assess whether modifications to a customized targeted RNA sequencing (RNAseq) assay to include unique molecular identifiers (UMIs) that collapse read counts to their source mRNA counts would improve quantification of transcripts from formalin-fixed paraffin-embedded (FFPE) tumor tissue samples. The assay (SET4) includes signatures that measure hormone receptor and PI3-kinase related transcriptional activity (SETER/PR and PI3Kges), and measures expression of selected activating point mutations and key breast cancer genes. METHODS Modifications included steps to introduce eight nucleotides-long UMIs during reverse transcription (RT) in bulk solution, followed by polymerase chain reaction (PCR) of labeled cDNA in droplets, with optimization of the polymerase enzyme and reaction conditions. We used Lin's concordance correlation coefficient (CCC) to measure concordance, including precision (Rho) and accuracy (Bias), and nonparametric tests (Wilcoxon, Levene's) to compare the modified (NEW) SET4 assay to the original (OLD) SET4 assay and to whole transcriptome RNAseq using RNA from matched fresh frozen (FF) and FFPE samples from 12 primary breast cancers. RESULTS The modified (NEW) SET4 assay measured single transcripts (p< 0.001) and SETER/PR (p=0.002) more reproducibly in technical replicates from FFPE samples. The modified SET4 assay was more precise for measuring single transcripts (Rho 0.966 vs 0.888, p< 0.01) but not multigene expression signatures SETER/PR (Rho 0.985 vs 0.968) or PI3Kges (Rho 0.985 vs 0.946) in FFPE, compared to FF samples. It was also more precise than wtRNAseq of FFPE for measuring transcripts (Rho 0.986 vs 0.934, p< 0.001) and SETER/PR (Rho 0.993 vs 0.915, p=0.004), but not PI3Kges (Rho 0.988 vs 0.945, p=0.051). Accuracy (Bias) was comparable between protocols. Two samples carried a PIK3CA mutation, and measurements of transcribed mutant allele fraction was similar in FF and FFPE samples and appeared more precise with the modified SET4 assay. Amplification efficiency (reads per UMI) was consistent in FF and FFPE samples, and close to the theoretically expected value, when the library size exceeded 400,000 aligned reads. CONCLUSIONS Modifications to the targeted RNAseq protocol for SET4 assay significantly increased the precision of UMI-based and reads-based measurements of individual transcripts, multi-gene signatures, and mutant transcript fraction, particularly with FFPE samples.
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Affiliation(s)
- Chunxiao Fu
- Departments of Pathology and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Michal Marczyk
- Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
| | | | - Alexander J Trevarton
- Departments of Pathology and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Rosanna Lau
- Departments of Pathology and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lili Du
- Departments of Pathology and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Bruno V Sinn
- Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Rebekah E Gould
- Departments of Pathology and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lajos Pusztai
- Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | - Christos Hatzis
- Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | - W Fraser Symmans
- Departments of Pathology and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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Eadon MT, Lampe S, Baig MM, Collins KS, Melo Ferreira R, Mang H, Cheng YH, Barwinska D, El-Achkar TM, Schwantes-An TH, Winfree S, Temm CJ, Ferkowicz MJ, Dunn KW, Kelly KJ, Sutton TA, Moe SM, Moorthi RN, Phillips CL, Dagher PC. Clinical, histopathologic and molecular features of idiopathic and diabetic nodular mesangial sclerosis in humans. Nephrol Dial Transplant 2021; 37:72-84. [PMID: 33537765 DOI: 10.1093/ndt/gfaa331] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Idiopathic nodular mesangial sclerosis, also called idiopathic nodular glomerulosclerosis (ING), is a rare clinical entity with an unclear pathogenesis. The hallmark of this disease is the presence of nodular mesangial sclerosis on histology without clinical evidence of diabetes mellitus or other predisposing diagnoses. To achieve insights into its pathogenesis, we queried the clinical, histopathologic and transcriptomic features of ING and nodular diabetic nephropathy (DN). METHODS All renal biopsy reports accessioned at Indiana University Health from 2001 to 2016 were reviewed to identify 48 ING cases. Clinical and histopathologic features were compared between individuals with ING and DN (n = 751). Glomeruli of ING (n = 5), DN (n = 18) and reference (REF) nephrectomy (n = 9) samples were isolated by laser microdissection and RNA was sequenced. Immunohistochemistry of proline-rich 36 (PRR36) protein was performed. RESULTS ING subjects were frequently hypertensive (95.8%) with a smoking history (66.7%). ING subjects were older, had lower proteinuria and had less hyaline arteriolosclerosis than DN subjects. Butanoate metabolism was an enriched pathway in ING samples compared with either REF or DN samples. The top differentially expressed gene, PRR36, had increased expression in glomeruli 248-fold [false discovery rate (FDR) P = 5.93 × 10-6] compared with the REF and increased 109-fold (FDR P = 1.85 × 10-6) compared with DN samples. Immunohistochemistry revealed a reduced proportion of cells with perinuclear reaction in ING samples as compared to DN. CONCLUSIONS Despite similar clinical and histopathologic characteristics in ING and DN, the uncovered transcriptomic signature suggests that ING has distinct molecular features from nodular DN. Further study is warranted to understand these relationships.
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Affiliation(s)
- Michael T Eadon
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sam Lampe
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Mirza M Baig
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kimberly S Collins
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Ricardo Melo Ferreira
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Henry Mang
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Ying-Hua Cheng
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Daria Barwinska
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Tarek M El-Achkar
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Tae-Hwi Schwantes-An
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Seth Winfree
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.,Department of Cellular and Integrative Physiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Constance J Temm
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Michael J Ferkowicz
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kenneth W Dunn
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Katherine J Kelly
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Timothy A Sutton
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sharon M Moe
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Ranjani N Moorthi
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Carrie L Phillips
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Pierre C Dagher
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
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Barwinska D, El-Achkar TM, Melo Ferreira R, Syed F, Cheng YH, Winfree S, Ferkowicz MJ, Hato T, Collins KS, Dunn KW, Kelly KJ, Sutton TA, Rovin BH, Parikh SV, Phillips CL, Dagher PC, Eadon MT. Molecular characterization of the human kidney interstitium in health and disease. SCIENCE ADVANCES 2021; 7:7/7/eabd3359. [PMID: 33568476 PMCID: PMC7875540 DOI: 10.1126/sciadv.abd3359] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 12/23/2020] [Indexed: 05/23/2023]
Abstract
The gene expression signature of the human kidney interstitium is incompletely understood. The cortical interstitium (excluding tubules, glomeruli, and vessels) in reference nephrectomies (N = 9) and diabetic kidney biopsy specimens (N = 6) was laser microdissected (LMD) and sequenced. Samples underwent RNA sequencing. Gene signatures were deconvolved using single nuclear RNA sequencing (snRNAseq) data derived from overlapping specimens. Interstitial LMD transcriptomics uncovered previously unidentified markers including KISS1, validated with in situ hybridization. LMD transcriptomics and snRNAseq revealed strong correlation of gene expression within corresponding kidney regions. Relevant enriched interstitial pathways included G-protein coupled receptor. binding and collagen biosynthesis. The diabetic interstitium was enriched for extracellular matrix organization and small-molecule catabolism. Cell type markers with unchanged expression (NOTCH3, EGFR, and HEG1) and those down-regulated in diabetic nephropathy (MYH11, LUM, and CCDC3) were identified. LMD transcriptomics complements snRNAseq; together, they facilitate mapping of interstitial marker genes to aid interpretation of pathophysiology in precision medicine studies.
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Affiliation(s)
- Daria Barwinska
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Tarek M El-Achkar
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
- Roudebush Veteran Affairs Medical Center, Indianapolis, IN 46202, USA
- Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Ricardo Melo Ferreira
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Farooq Syed
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Ying-Hua Cheng
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Seth Winfree
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Michael J Ferkowicz
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Takashi Hato
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Kimberly S Collins
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Kenneth W Dunn
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Katherine J Kelly
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Roudebush Veteran Affairs Medical Center, Indianapolis, IN 46202, USA
| | - Timothy A Sutton
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Brad H Rovin
- Division of Nephrology, Department of Medicine, Ohio State University Wexner Medical Center, OH 433210, USA
| | - Samir V Parikh
- Division of Nephrology, Department of Medicine, Ohio State University Wexner Medical Center, OH 433210, USA
| | - Carrie L Phillips
- Division of Pathology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Pierre C Dagher
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Roudebush Veteran Affairs Medical Center, Indianapolis, IN 46202, USA
| | - Michael T Eadon
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
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Kellman BP, Baghdassarian HM, Pramparo T, Shamie I, Gazestani V, Begzati A, Li S, Nalabolu S, Murray S, Lopez L, Pierce K, Courchesne E, Lewis NE. Multiple freeze-thaw cycles lead to a loss of consistency in poly(A)-enriched RNA sequencing. BMC Genomics 2021; 22:69. [PMID: 33478392 PMCID: PMC7818915 DOI: 10.1186/s12864-021-07381-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 01/08/2021] [Indexed: 11/10/2022] Open
Abstract
Background Both RNA-Seq and sample freeze-thaw are ubiquitous. However, knowledge about the impact of freeze-thaw on downstream analyses is limited. The lack of common quality metrics that are sufficiently sensitive to freeze-thaw and RNA degradation, e.g. the RNA Integrity Score, makes such assessments challenging. Results Here we quantify the impact of repeated freeze-thaw cycles on the reliability of RNA-Seq by examining poly(A)-enriched and ribosomal RNA depleted RNA-seq from frozen leukocytes drawn from a toddler Autism cohort. To do so, we estimate the relative noise, or percentage of random counts, separating technical replicates. Using this approach we measured noise associated with RIN and freeze-thaw cycles. As expected, RIN does not fully capture sample degradation due to freeze-thaw. We further examined differential expression results and found that three freeze-thaws should extinguish the differential expression reproducibility of similar experiments. Freeze-thaw also resulted in a 3′ shift in the read coverage distribution along the gene body of poly(A)-enriched samples compared to ribosomal RNA depleted samples, suggesting that library preparation may exacerbate freeze-thaw-induced sample degradation. Conclusion The use of poly(A)-enrichment for RNA sequencing is pervasive in library preparation of frozen tissue, and thus, it is important during experimental design and data analysis to consider the impact of repeated freeze-thaw cycles on reproducibility. Graphical abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07381-z.
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Affiliation(s)
- Benjamin P Kellman
- Department of Pediatrics, University of California, San Diego, USA.,Bioinformatics and Systems Biology Program, University of California San Diego, San Diego, USA
| | - Hratch M Baghdassarian
- Department of Pediatrics, University of California, San Diego, USA.,Bioinformatics and Systems Biology Program, University of California San Diego, San Diego, USA
| | - Tiziano Pramparo
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, San Diego, USA
| | - Isaac Shamie
- Department of Pediatrics, University of California, San Diego, USA.,Bioinformatics and Systems Biology Program, University of California San Diego, San Diego, USA
| | - Vahid Gazestani
- Department of Pediatrics, University of California, San Diego, USA.,Autism Center of Excellence, Department of Neuroscience, University of California San Diego, San Diego, USA
| | - Arjana Begzati
- Department of Medicine, University of California San Diego, San Diego, USA
| | - Shangzhong Li
- Department of Pediatrics, University of California, San Diego, USA.,Department of Bioengineering, University of California San Diego, San Diego, USA
| | - Srinivasa Nalabolu
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, San Diego, USA
| | - Sarah Murray
- Department of Pathology, University of California San Diego, San Diego, USA
| | - Linda Lopez
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, San Diego, USA
| | - Karen Pierce
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, San Diego, USA
| | - Eric Courchesne
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, San Diego, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, USA. .,Department of Bioengineering, University of California San Diego, San Diego, USA. .,Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego, La Jolla, USA.
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43
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Szymiczek A, Lone A, Akbari MR. Molecular intrinsic versus clinical subtyping in breast cancer: A comprehensive review. Clin Genet 2020; 99:613-637. [PMID: 33340095 DOI: 10.1111/cge.13900] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 12/12/2020] [Accepted: 12/14/2020] [Indexed: 12/15/2022]
Abstract
Breast cancer is a heterogeneous disease manifesting diversity at the molecular, histological and clinical level. The development of breast cancer classification was centered on informing clinical decisions. The current approach to the classification of breast cancer, which categorizes this disease into clinical subtypes based on the detection of estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and proliferation marker Ki67, is not ideal. This is manifested as a heterogeneity of therapeutic responses and outcomes within the clinical subtypes. The newer classification model, based on gene expression profiling (intrinsic subtyping) informs about transcriptional responses downstream from IHC single markers, revealing deeper appreciation for the disease heterogeneity and capturing tumor biology in a more comprehensive way than an expression of a single protein or gene alone. While accumulating evidences suggest that intrinsic subtypes provide clinically relevant information beyond clinical surrogates, it is imperative to establish whether the current conventional immunohistochemistry-based clinical subtyping approach could be improved by gene expression profiling and if this approach has a potential to translate into clinical practice.
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Affiliation(s)
- Agata Szymiczek
- Women's College Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Amna Lone
- Women's College Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Mohammad R Akbari
- Women's College Research Institute, University of Toronto, Toronto, Ontario, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
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44
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Hong M, Tao S, Zhang L, Diao LT, Huang X, Huang S, Xie SJ, Xiao ZD, Zhang H. RNA sequencing: new technologies and applications in cancer research. J Hematol Oncol 2020; 13:166. [PMID: 33276803 PMCID: PMC7716291 DOI: 10.1186/s13045-020-01005-x] [Citation(s) in RCA: 214] [Impact Index Per Article: 53.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 11/22/2020] [Indexed: 02/06/2023] Open
Abstract
Over the past few decades, RNA sequencing has significantly progressed, becoming a paramount approach for transcriptome profiling. The revolution from bulk RNA sequencing to single-molecular, single-cell and spatial transcriptome approaches has enabled increasingly accurate, individual cell resolution incorporated with spatial information. Cancer, a major malignant and heterogeneous lethal disease, remains an enormous challenge in medical research and clinical treatment. As a vital tool, RNA sequencing has been utilized in many aspects of cancer research and therapy, including biomarker discovery and characterization of cancer heterogeneity and evolution, drug resistance, cancer immune microenvironment and immunotherapy, cancer neoantigens and so on. In this review, the latest studies on RNA sequencing technology and their applications in cancer are summarized, and future challenges and opportunities for RNA sequencing technology in cancer applications are discussed.
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Affiliation(s)
- Mingye Hong
- Institute of Laboratory Medicine, Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, School of Medical Technology, Guangdong Medical University, Dongguan, 523808, China
| | - Shuang Tao
- Biotherapy Center, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, China
| | - Ling Zhang
- Health Science Center, The University of Texas, Houston, 77030, USA
| | - Li-Ting Diao
- Biotherapy Center, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, China
| | - Xuanmei Huang
- Institute of Laboratory Medicine, Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, School of Medical Technology, Guangdong Medical University, Dongguan, 523808, China
| | - Shaohui Huang
- Institute of Laboratory Medicine, Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, School of Medical Technology, Guangdong Medical University, Dongguan, 523808, China
| | - Shu-Juan Xie
- Biotherapy Center, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, China
| | - Zhen-Dong Xiao
- Biotherapy Center, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, China.
| | - Hua Zhang
- Institute of Laboratory Medicine, Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, School of Medical Technology, Guangdong Medical University, Dongguan, 523808, China.
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45
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Lai Polo SH, Saravia-Butler AM, Boyko V, Dinh MT, Chen YC, Fogle H, Reinsch SS, Ray S, Chakravarty K, Marcu O, Chen RB, Costes SV, Galazka JM. RNAseq Analysis of Rodent Spaceflight Experiments Is Confounded by Sample Collection Techniques. iScience 2020; 23:101733. [PMID: 33376967 PMCID: PMC7756143 DOI: 10.1016/j.isci.2020.101733] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 10/04/2020] [Accepted: 10/22/2020] [Indexed: 02/07/2023] Open
Abstract
To understand the physiological changes that occur in response to spaceflight, mice are transported to the International Space Station (ISS) and housed for variable periods of time before euthanasia on-orbit or return to Earth. Sample collection under such difficult conditions introduces confounding factors that need to be identified and addressed. We found large changes in the transcriptome of mouse tissues dissected and preserved on-orbit compared with tissues from mice euthanized on-orbit, preserved, and dissected after return to Earth. Changes due to preservation method eclipsed those between flight and ground samples, making it difficult to identify spaceflight-specific changes. Follow-on experiments to interrogate the roles of euthanasia methods, tissue and carcass preservation protocols, and library preparation methods suggested that differences due to preservation protocols are exacerbated when coupled with polyA selection. This has important implications for the interpretation of existing datasets and the design of future experiments. Experimentation is necessary to understand how organisms respond to space Specialized protocols are used for preserving biological samples on the ISS RNAseq datasets are impacted by preservation protocols used on the ISS Impacts can be alleviated with improved carcass preservation protocols
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Affiliation(s)
- San-Huei Lai Polo
- KBR, NASA Ames Research Center, Moffett Field, CA 94035, USA.,NASA Ames Research Center, Moffett Field, CA 94035, USA
| | - Amanda M Saravia-Butler
- NASA Ames Research Center, Moffett Field, CA 94035, USA.,Logyx, LLC, Mountain View, CA 94043, USA
| | - Valery Boyko
- NASA Ames Research Center, Moffett Field, CA 94035, USA.,The Bionetics Corporation, NASA Ames Research Center, Moffett Field, CA 94035, USA
| | - Marie T Dinh
- NASA Ames Research Center, Moffett Field, CA 94035, USA.,Logyx, LLC, Mountain View, CA 94043, USA
| | - Yi-Chun Chen
- KBR, NASA Ames Research Center, Moffett Field, CA 94035, USA.,NASA Ames Research Center, Moffett Field, CA 94035, USA
| | - Homer Fogle
- NASA Ames Research Center, Moffett Field, CA 94035, USA.,The Bionetics Corporation, NASA Ames Research Center, Moffett Field, CA 94035, USA
| | | | - Shayoni Ray
- NGM Biopharmaceuticals, South San Francisco, CA 94080, USA
| | | | - Oana Marcu
- Carl Sagan Center, SETI Institute, Mountain View, CA 94043, USA
| | - Rick B Chen
- KBR, NASA Ames Research Center, Moffett Field, CA 94035, USA.,NASA Ames Research Center, Moffett Field, CA 94035, USA
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46
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Mav D, Phadke DP, Balik-Meisner MR, Merrick BA, Auerbach S, Niemeijer M, Huppelschoten S, Baze A, Parmentier C, Richert L, van de Water B, Shah RR, Paules RS. Utility of Extrapolating Human S1500+ Genes to the Whole Transcriptome: Tunicamycin Case Study. Bioinform Biol Insights 2020; 14:1177932220952742. [PMID: 33088175 PMCID: PMC7545517 DOI: 10.1177/1177932220952742] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 08/04/2020] [Indexed: 01/07/2023] Open
Abstract
The TempO-Seq S1500+ platform(s), now available for human, mouse, rat, and zebrafish, measures a discrete number of genes that are representative of biological and pathway co-regulation across the entire genome in a given species. While measurement of these genes alone provides a direct assessment of gene expression activity, extrapolating expression values to the whole transcriptome (~26 000 genes in humans) can estimate measurements of non-measured genes of interest and increases the power of pathway analysis algorithms by using a larger background gene expression space. Here, we use data from primary hepatocytes of 54 donors that were treated with the endoplasmic reticulum (ER) stress inducer tunicamycin and then measured on the human S1500+ platform containing ~3000 representative genes. Measurements for the S1500+ genes were then used to extrapolate expression values for the remaining human transcriptome. As a case study of the improved downstream analysis achieved by extrapolation, the “measured only” and “whole transcriptome” (measured + extrapolated) gene sets were compared. Extrapolation increased the number of significant genes by 49%, bringing to the forefront many that are known to be associated with tunicamycin exposure. The extrapolation procedure also correctly identified established tunicamycin-related functional pathways reflected by coordinated changes in interrelated genes while maintaining the sample variability observed from the “measured only” genes. Extrapolation improved the gene- and pathway-level biological interpretations for a variety of downstream applications, including differential expression analysis, gene set enrichment pathway analysis, DAVID keyword analysis, Ingenuity Pathway Analysis, and NextBio correlated compound analysis. The extrapolated data highlight the role of metabolism/metabolic pathways, the ER, immune response, and the unfolded protein response, each of which are key activities associated with tunicamycin exposure that were unrepresented or underrepresented in one or more of the analyses of the original “measured only” dataset. Furthermore, the inclusion of the extrapolated genes raised “tunicamycin” from third to first upstream regulator in Ingenuity Pathway Analysis and from sixth to second most correlated compound in NextBio analysis. Therefore, our case study suggests an approach to extend and enhance data from the S1500+ platform for improved insight into biological mechanisms and functional outcomes of diseases, drugs, and other perturbations.
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Affiliation(s)
- Deepak Mav
- Sciome LLC, Research Triangle Park, NC, USA
| | | | | | - B Alex Merrick
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Scott Auerbach
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Marije Niemeijer
- Division of Toxicology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Suzanna Huppelschoten
- Division of Toxicology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | | | | | | | - Bob van de Water
- Division of Toxicology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | | | - Richard S Paules
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
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47
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Xu Z, Asakawa S. Physiological RNA dynamics in RNA-Seq analysis. Brief Bioinform 2020; 20:1725-1733. [PMID: 30010714 DOI: 10.1093/bib/bby045] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Revised: 04/24/2018] [Indexed: 02/06/2023] Open
Abstract
Physiological RNA dynamics cause problems in transcriptome analysis. Physiological RNA accumulation affects the analysis of RNA quantification, and physiological RNA degradation affects the analysis of the RNA sequence length, feature site and quantification. In the present article, we review the effects of physiological degradation and accumulation of RNA on analysing RNA sequencing data. Physiological RNA accumulation and degradation probably led to such phenomena as incorrect estimations of transcription quantification, differential expressions, co-expressions, RNA decay rates, alternative splicing, boundaries of transcription, novel genes, new single-nucleotide polymorphisms, small RNAs and gene fusion. Thus, the transcriptomic data obtained up to date warrant further scrutiny. New and improved techniques and bioinformatics software are needed to produce accurate data in transcriptome research.
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Affiliation(s)
- Zhongneng Xu
- Department of Ecology, Jinan University, Guangzhou 510632, China
| | - Shuichi Asakawa
- Department of Aquatic Bioscience, Graduate School of Agricultural and Life Science, The University of Tokyo, Tokyo 113-8657, Japan
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48
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Abstract
Cholera is a devastating illness that kills tens of thousands of people annually. Vibrio cholerae, the causative agent of cholera, is an important model organism to investigate both bacterial pathogenesis and the impact of horizontal gene transfer on the emergence and dissemination of new virulent strains. Despite the importance of this pathogen, roughly one-third of V. cholerae genes are functionally unannotated, leaving large gaps in our understanding of this microbe. Through coexpression network analysis of existing RNA sequencing data, this work develops an approach to uncover novel gene-gene relationships and contextualize genes with no known function, which will advance our understanding of V. cholerae virulence and evolution. Research into the evolution and pathogenesis of Vibrio cholerae has benefited greatly from the generation of high-throughput sequencing data to drive molecular analyses. The steady accumulation of these data sets now provides a unique opportunity for in silico hypothesis generation via coexpression analysis. Here, we leverage all published V. cholerae RNA sequencing data, in combination with select data from other platforms, to generate a gene coexpression network that validates known gene interactions and identifies novel genetic partners across the entire V. cholerae genome. This network provides direct insights into genes influencing pathogenicity, metabolism, and transcriptional regulation, further clarifies results from previous sequencing experiments in V. cholerae (e.g., transposon insertion sequencing [Tn-seq] and chromatin immunoprecipitation sequencing [ChIP-seq]), and expands upon microarray-based findings in related Gram-negative bacteria. IMPORTANCE Cholera is a devastating illness that kills tens of thousands of people annually. Vibrio cholerae, the causative agent of cholera, is an important model organism to investigate both bacterial pathogenesis and the impact of horizontal gene transfer on the emergence and dissemination of new virulent strains. Despite the importance of this pathogen, roughly one-third of V. cholerae genes are functionally unannotated, leaving large gaps in our understanding of this microbe. Through coexpression network analysis of existing RNA sequencing data, this work develops an approach to uncover novel gene-gene relationships and contextualize genes with no known function, which will advance our understanding of V. cholerae virulence and evolution.
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49
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Schmidt MA, Meydan C, Schmidt CM, Afshinnekoo E, Mason CE. The NASA Twins Study: The Effect of One Year in Space on Long-Chain Fatty Acid Desaturases and Elongases. Lifestyle Genom 2020; 13:107-121. [PMID: 32375154 DOI: 10.1159/000506769] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 02/18/2020] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND At present, there is no clear understanding of the effect of long-duration spaceflight on the major enzymes that govern the metabolism of omega-6 and omega-3 fatty acids. To address this gap in knowledge, we used data from the NASA Twins Study, which includes a multiscale omics investigation of the changes that occurred during a year-long (340 days) human spaceflight. Embedded within the NASA Twins data are specific analytes associated with fatty acid metabolism. OBJECTIVES To examine the long-chain fatty acid desaturases and elongases in a single human during 1 year in space. METHOD One male twin was on board the International Space Station (ISS) for 1 year, while his monozygotic twin served as a genetically matched ground control. Longitudinal assessments included the genome, epige-nome, transcriptome, proteome, metabolome, microbiome, and immunome during the mission, as well as 6 months before and after. The gene-specific fatty acid desaturase and elongase transcriptome data (FADS1, FADS2, ELOVL2, and ELOVL5) were extracted from untargeted RNA-seq measurements derived from white blood cell fractions. RESULTS Most data from the elongases and desaturases exhibited relatively similar expression profiles (R2 >0.6) over time for the CD8, CD19, and lymphocyte-depleted (LD) cell fractions, indicating overall conservation of function within and between the subjects. Both cell-type and temporal specificity was observed in some cases, and some differences were also apparent between the polyadenylated (polyA) fraction of processed RNAs versus the ribodepleted (ribo-) fraction. The flight subject showed a stronger enrichment of the fatty acid metabolic process pathway across almost all cell types (columns, CD4, CD8, CPT, and LD), most especially in the ribodepleted fraction of RNA, but also with the polyA+ fraction of RNA. Gene set enrichment analysis (GSEA) measures across three related fatty acid metabolism pathways showed a differential between the ground and the flight subject. CONCLUSIONS There appears to be no persistent alteration of desaturase and elongase gene expression associated with 1 year in space. However, these data provide evidence that cellular lipid metabolism can be responsive and dynamic to spaceflight, even though it appears cell-type and context specific, most notably in terms of the fraction of RNA measured and the collection protocols. These results also provide new evidence of mid-flight spikes in expression of selected genes, which may indicate transient responses to specific insults during spaceflight.
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Affiliation(s)
- Michael A Schmidt
- Advanced Pattern Analysis & Countermeasures Group, Boulder, Colorado, USA,
- Sovaris Aerospace, Boulder, Colorado, USA,
| | - Cem Meydan
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York, USA
| | - Caleb M Schmidt
- Sovaris Aerospace, Boulder, Colorado, USA
- Department of Systems Engineering, Colorado State University, Fort Collins, Colorado, USA
| | - Ebrahim Afshinnekoo
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York, USA
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, New York, USA
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York, USA
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, New York, USA
- The Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA
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50
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Mische SM, Fisher NC, Meyn SM, Sol-Church K, Hegstad-Davies RL, Weis-Garcia F, Adams M, Ashton JM, Delventhal KM, Dragon JA, Holmes L, Jagtap P, Kubow KE, Mason CE, Palmblad M, Searle BC, Turck CW, Knudtson KL. A Review of the Scientific Rigor, Reproducibility, and Transparency Studies Conducted by the ABRF Research Groups. J Biomol Tech 2020; 31:11-26. [PMID: 31969795 PMCID: PMC6959150 DOI: 10.7171/jbt.20-3101-003] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Shared research resource facilities, also known as core laboratories (Cores), are responsible for generating a significant and growing portion of the research data in academic biomedical research institutions. Cores represent a central repository for institutional knowledge management, with deep expertise in the strengths and limitations of technology and its applications. They inherently support transparency and scientific reproducibility by protecting against cognitive bias in research design and data analysis, and they have institutional responsibility for the conduct of research (research ethics, regulatory compliance, and financial accountability) performed in their Cores. The Association of Biomolecular Resource Facilities (ABRF) is a FASEB-member scientific society whose members are scientists and administrators that manage or support Cores. The ABRF Research Groups (RGs), representing expertise for an array of cutting-edge and established technology platforms, perform multicenter research studies to determine and communicate best practices and community-based standards. This review provides a summary of the contributions of the ABRF RGs to promote scientific rigor and reproducibility in Cores from the published literature, ABRF meetings, and ABRF RGs communications.
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Affiliation(s)
- Sheenah M. Mische
- New York University (NYU) Langone Medical Center, New
York, New York 10016, USA
| | - Nancy C. Fisher
- University of North Carolina at Chapel Hill, Chapel
Hill, North Carolina 27599, USA
| | - Susan M. Meyn
- Vanderbilt University Medical Center, Nashville,
Tennessee 37212, USA
| | - Katia Sol-Church
- University of Virginia School of Medicine,
Charlottesville, Virginia 22908, USA
| | | | | | - Marie Adams
- Van Andel Institute, Grand Rapids, Michigan 49503,
USA
| | - John M. Ashton
- University of Rochester Medical Center, West
Henrietta, New York 14642, USA
| | - Kym M. Delventhal
- Stowers Institute for Medical Research, Kansas City,
Missouri 64110, USA
| | | | - Laura Holmes
- Stowers Institute for Medical Research, Kansas City,
Missouri 64110, USA
| | - Pratik Jagtap
- University of Minnesota, Minneapolis, Minnesota
55455, USA
| | | | | | - Magnus Palmblad
- Leiden University Medical Center, Leiden 2333, The
Netherlands
| | - Brian C. Searle
- Institute for Systems Biology, Seattle, Washington
98109, USA
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