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Le Priol C, Azencott CA, Gidrol X. Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression. PLoS Comput Biol 2023; 19:e1010342. [PMID: 36893104 PMCID: PMC9997931 DOI: 10.1371/journal.pcbi.1010342] [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: 07/01/2022] [Accepted: 02/09/2023] [Indexed: 03/10/2023] Open
Abstract
The majority of gene expression studies focus on the search for genes whose mean expression is different between two or more populations of samples in the so-called "differential expression analysis" approach. However, a difference in variance in gene expression may also be biologically and physiologically relevant. In the classical statistical model used to analyze RNA-sequencing (RNA-seq) data, the dispersion, which defines the variance, is only considered as a parameter to be estimated prior to identifying a difference in mean expression between conditions of interest. Here, we propose to evaluate four recently published methods, which detect differences in both the mean and dispersion in RNA-seq data. We thoroughly investigated the performance of these methods on simulated datasets and characterized parameter settings to reliably detect genes with a differential expression dispersion. We applied these methods to The Cancer Genome Atlas datasets. Interestingly, among the genes with an increased expression dispersion in tumors and without a change in mean expression, we identified some key cellular functions, most of which were related to catabolism and were overrepresented in most of the analyzed cancers. In particular, our results highlight autophagy, whose role in cancerogenesis is context-dependent, illustrating the potential of the differential dispersion approach to gain new insights into biological processes and to discover new biomarkers.
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Affiliation(s)
- Christophe Le Priol
- Univ. Grenoble Alpes, INSERM, CEA-IRIG, Biomics, Grenoble, France
- * E-mail: (CLP); (XG)
| | - Chloé-Agathe Azencott
- Center for Computational Biology, Mines ParisTech, PSL Research University, Paris, France
- Institut Curie, Paris, France
- INSERM U900, Paris, France
| | - Xavier Gidrol
- Univ. Grenoble Alpes, INSERM, CEA-IRIG, Biomics, Grenoble, France
- * E-mail: (CLP); (XG)
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Expression Profile of Housekeeping Genes and Tissue-Specific Genes in Multiple Tissues of Pigs. Animals (Basel) 2022; 12:ani12243539. [PMID: 36552460 PMCID: PMC9774903 DOI: 10.3390/ani12243539] [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: 11/07/2022] [Revised: 12/09/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
Pigs have become an ideal model system for human disease research and development and an important farm animal that provides a valuable source of nutrition. To profile the all-sided gene expression and their biological functions across multiple tissues, we conducted a comprehensive analysis of gene expression on a large scale around the side of housekeeping genes (HKGs), tissue specific genes (TSGs), and the co-expressed genes in 14 various tissues. In this study, we identified 2351 HKGs and 3018 TSGs across tissues, among which 4 HKGs (COX1, UBB, OAZ1/NPFF) exhibited low variation and high expression levels, and 31 particular TSGs (e.g., PDC, FKBP6, STAT2, and COL1A1) were exclusively expressed in several tissues, including endocrine brain, ovaries, livers, backfat, jejunum, kidneys, lungs, and longissimus dorsi muscles. We also obtained 17 modules with 230 hub genes (HUBGs) by weighted gene co-expression network analysis. On the other hand, HKGs functions were enriched in the signaling pathways of the ribosome, spliceosome, thermogenesis, oxidative phosphorylation, and nucleocytoplasmic transport, which have been highly suggested to involve in the basic biological tissue activities. While TSGs were highly enriched in the signaling pathways that were involved in specific physiological processes, such as the ovarian steroidogenesis pathway in ovaries and the renin-angiotensin system pathway in kidneys. Collectively, these stable, specifical, and co-expressed genes provided useful information for the investigation of the molecular mechanism for an understanding of the genetic and biological processes of complex traits in pigs.
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Ji X, Li P, Fuscoe JC, Chen G, Xiao W, Shi L, Ning B, Liu Z, Hong H, Wu J, Liu J, Guo L, Kreil DP, Łabaj PP, Zhong L, Bao W, Huang Y, He J, Zhao Y, Tong W, Shi T. A comprehensive rat transcriptome built from large scale RNA-seq-based annotation. Nucleic Acids Res 2020; 48:8320-8331. [PMID: 32749457 PMCID: PMC7470976 DOI: 10.1093/nar/gkaa638] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Revised: 07/14/2020] [Accepted: 07/21/2020] [Indexed: 01/01/2023] Open
Abstract
The rat is an important model organism in biomedical research for studying human disease mechanisms and treatments, but its annotated transcriptome is far from complete. We constructed a Rat Transcriptome Re-annotation named RTR using RNA-seq data from 320 samples in 11 different organs generated by the SEQC consortium. Totally, there are 52 807 genes and 114 152 transcripts in RTR. Transcribed regions and exons in RTR account for ∼42% and ∼6.5% of the genome, respectively. Of all 73 074 newly annotated transcripts in RTR, 34 213 were annotated as high confident coding transcripts and 24 728 as high confident long noncoding transcripts. Different tissues rather than different stages have a significant influence on the expression patterns of transcripts. We also found that 11 715 genes and 15 852 transcripts were expressed in all 11 tissues and that 849 house-keeping genes expressed different isoforms among tissues. This comprehensive transcriptome is freely available at http://www.unimd.org/rtr/. Our new rat transcriptome provides essential reference for genetics and gene expression studies in rat disease and toxicity models.
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Affiliation(s)
- Xiangjun Ji
- Center for Bioinformatics and Computational Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China.,School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Peng Li
- Center for Bioinformatics and Computational Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China.,Massachusetts General Hospital, Harvard Medical School, 51 Blossom St, Boston, MA 02114, USA
| | - James C Fuscoe
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Geng Chen
- Center for Bioinformatics and Computational Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Wenzhong Xiao
- Massachusetts General Hospital, Harvard Medical School, 51 Blossom St, Boston, MA 02114, USA
| | - Leming Shi
- Center for Pharmacogenomics, School of Pharmacy, Fudan University, Shanghai, 200438, China
| | - Baitang Ning
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Zhichao Liu
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Huixiao Hong
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Jinghua Liu
- School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Lei Guo
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, 72079, USA
| | - David P Kreil
- Department of Biotechnology, Boku University Vienna, 1190 Muthgasse 18, Austria
| | - Paweł P Łabaj
- Department of Biotechnology, Boku University Vienna, 1190 Muthgasse 18, Austria.,Małopolska Centre of Biotechnology, Jagiellonian University, ul. Gronostajowa 7A, 30-387 Kraków, Poland
| | - Liping Zhong
- Biological Targeting Diagnosis and Therapy Research Center, Guangxi Medical University, Nanning 530021, China
| | - Wenjun Bao
- SAS Institute Inc., Cary, NC, 27513, USA
| | - Yong Huang
- Biological Targeting Diagnosis and Therapy Research Center, Guangxi Medical University, Nanning 530021, China
| | - Jian He
- Biological Targeting Diagnosis and Therapy Research Center, Guangxi Medical University, Nanning 530021, China
| | - Yongxiang Zhao
- Biological Targeting Diagnosis and Therapy Research Center, Guangxi Medical University, Nanning 530021, China
| | - Weida Tong
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Tieliu Shi
- Center for Bioinformatics and Computational Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University & Capital Medical University, Beijing, 100083, China
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