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Chen S, Tyagi IS, Mat WK, Khan MA, Fan W, Wu Z, Hu T, Yang C, Xue H. Forward-reverse mutation cycles in cancer cell lines under chemical treatments. Hum Genomics 2024; 18:106. [PMID: 39334413 PMCID: PMC11437743 DOI: 10.1186/s40246-024-00661-1] [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: 04/08/2024] [Accepted: 08/19/2024] [Indexed: 09/30/2024] Open
Abstract
Spontaneous forward-reverse mutations were reported by us earlier in clinical samples from various types of cancers and in HeLa cells under normal culture conditions. To investigate the effects of chemical stimulations on such mutation cycles, the present study examined single nucleotide variations (SNVs) and copy number variations (CNVs) in HeLa and A549 cells exposed to wogonin-containing or acidic medium. In wogonin, both cell lines showed a mutation cycle during days 16-18. In acidic medium, both cell lines displayed multiple mutation cycles of different magnitudes. Genomic feature colocalization analysis suggests that CNVs tend to occur in expanded and unstable regions, and near promoters, histones, and non-coding transcription sites. Moreover, phenotypic variations in cell morphology occurred during the forward-reverse mutation cycles under both types of chemical treatments. In conclusion, chemical stresses imposed by wogonin or acidity promoted cyclic forward-reverse mutations in both HeLa and A549 cells to different extents.
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Affiliation(s)
- Si Chen
- Guangzhou HKUST Fok Ying Tung Research Institute, Science and Technology Building, Nansha Information Technology Park, Nansha, Guangzhou, 511458, China
- Division of Life Science and Center of Applied Genomics, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Iram S Tyagi
- Division of Life Science and Center of Applied Genomics, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Wai Kin Mat
- Division of Life Science and Center of Applied Genomics, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Muhammad A Khan
- Division of Life Science and Center of Applied Genomics, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Weijian Fan
- Division of Life Science and Center of Applied Genomics, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Zhenggang Wu
- Guangzhou HKUST Fok Ying Tung Research Institute, Science and Technology Building, Nansha Information Technology Park, Nansha, Guangzhou, 511458, China
- Division of Life Science and Center of Applied Genomics, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
- Center for Cancer Genomics, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Taobo Hu
- Division of Life Science and Center of Applied Genomics, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Can Yang
- Guangzhou HKUST Fok Ying Tung Research Institute, Science and Technology Building, Nansha Information Technology Park, Nansha, Guangzhou, 511458, China.
- Department of Mathematics, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
| | - Hong Xue
- Guangzhou HKUST Fok Ying Tung Research Institute, Science and Technology Building, Nansha Information Technology Park, Nansha, Guangzhou, 511458, China.
- Division of Life Science and Center of Applied Genomics, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
- Center for Cancer Genomics, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.
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2
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Xue H, Wu Z, Long X, Ullah A, Chen S, Mat WK, Sun P, Gao MZ, Wang JQ, Wang HJ, Li X, Sun WJ, Qiao MQ. Copy number variation profile-based genomic typing of premenstrual dysphoric disorder in Chinese. J Genet Genomics 2021; 48:1070-1080. [PMID: 34530168 DOI: 10.1016/j.jgg.2021.08.012] [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: 03/10/2021] [Revised: 08/13/2021] [Accepted: 08/13/2021] [Indexed: 11/16/2022]
Abstract
Premenstrual dysphoric disorder (PMDD) affects nearly 5% women of reproductive age. Symptomatic heterogeneity, together with largely unknown genetics, have greatly hindered its effective treatment. In the present study, analysis of genomic sequencing-based copy-number-variations (CNVs) called from 100-kb white blood cell DNA sequence windows by means of semi-supervised clustering led to the segregation of patient genomes into the D and V groups, which correlated with the depression and invasion clinical types respectively with 89.0% consistency. Application of diagnostic CNV features selected using the correlation-based machine-learning method enabled the classification of the CNVs obtained into the D group, V group, total-patient group and control group with an average accuracy of 83.0%. The power of the diagnostic CNV features was 0.98 on average, suggesting that these CNV features could be employed for the molecular diagnosis of the major clinical types of PMDD. This demonstrated concordnce between the CNV profiles and clinical types of PMDD supported the validity of symptom-based diagnosis of PMDD for differentiating between its two major clinical types, as well as the predominanly genetic nature of PMDD with a host of overlaps between multiple susceptibility genes/pathways and the diagnostic CNV features as indicators of involvement in PMDD etiology.
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Affiliation(s)
- Hong Xue
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250355, China; Division of Life Science and Applied Genomics Center, Hong Kong University of Science and Technology, Hong Kong, China; School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, Jiangsu, 210009, China; HKUST Shenzhen Research Institute, 9 Yuexing First Road, Nanshan, Shenzhen, China; Guangzhou HKUST Fok Ying Tung Research Institute, Science and Technology Building, Nansha Information Technology Park, Nansha, Guangzhou, 511458, China.
| | - Zhenggang Wu
- Division of Life Science and Applied Genomics Center, Hong Kong University of Science and Technology, Hong Kong, China; School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, Jiangsu, 210009, China; HKUST Shenzhen Research Institute, 9 Yuexing First Road, Nanshan, Shenzhen, China; Guangzhou HKUST Fok Ying Tung Research Institute, Science and Technology Building, Nansha Information Technology Park, Nansha, Guangzhou, 511458, China
| | - Xi Long
- Division of Life Science and Applied Genomics Center, Hong Kong University of Science and Technology, Hong Kong, China; School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, Jiangsu, 210009, China; HKUST Shenzhen Research Institute, 9 Yuexing First Road, Nanshan, Shenzhen, China; Guangzhou HKUST Fok Ying Tung Research Institute, Science and Technology Building, Nansha Information Technology Park, Nansha, Guangzhou, 511458, China
| | - Ata Ullah
- Division of Life Science and Applied Genomics Center, Hong Kong University of Science and Technology, Hong Kong, China
| | - Si Chen
- Division of Life Science and Applied Genomics Center, Hong Kong University of Science and Technology, Hong Kong, China
| | - Wai-Kin Mat
- Division of Life Science and Applied Genomics Center, Hong Kong University of Science and Technology, Hong Kong, China
| | - Peng Sun
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250355, China
| | - Ming-Zhou Gao
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250355, China
| | - Jie-Qiong Wang
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250355, China
| | - Hai-Jun Wang
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250355, China
| | - Xia Li
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250355, China
| | - Wen-Jun Sun
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250355, China
| | - Ming-Qi Qiao
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250355, China.
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3
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Long X, Xue H. Genetic-variant hotspots and hotspot clusters in the human genome facilitating adaptation while increasing instability. Hum Genomics 2021; 15:19. [PMID: 33741065 PMCID: PMC7976700 DOI: 10.1186/s40246-021-00318-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Accepted: 03/04/2021] [Indexed: 12/25/2022] Open
Abstract
Background Genetic variants, underlining phenotypic diversity, are known to distribute unevenly in the human genome. A comprehensive understanding of the distributions of different genetic variants is important for insights into genetic functions and disorders. Methods Herein, a sliding-window scan of regional densities of eight kinds of germline genetic variants, including single-nucleotide-polymorphisms (SNPs) and four size-classes of copy-number-variations (CNVs) in the human genome has been performed. Results The study has identified 44,379 hotspots with high genetic-variant densities, and 1135 hotspot clusters comprising more than one type of hotspots, accounting for 3.1% and 0.2% of the genome respectively. The hotspots and clusters are found to co-localize with different functional genomic features, as exemplified by the associations of hotspots of middle-size CNVs with histone-modification sites, work with balancing and positive selections to meet the need for diversity in immune proteins, and facilitate the development of sensory-perception and neuroactive ligand-receptor interaction pathways in the function-sparse late-replicating genomic sequences. Genetic variants of different lengths co-localize with retrotransposons of different ages on a “long-with-young” and “short-with-all” basis. Hotspots and clusters are highly associated with tumor suppressor genes and oncogenes (p < 10−10), and enriched with somatic tumor CNVs and the trait- and disease-associated SNPs identified by genome-wise association studies, exceeding tenfold enrichment in clusters comprising SNPs and extra-long CNVs. Conclusions In conclusion, the genetic-variant hotspots and clusters represent two-edged swords that spearhead both positive and negative genomic changes. Their strong associations with complex traits and diseases also open up a potential “Common Disease-Hotspot Variant” approach to the missing heritability problem. Supplementary Information The online version contains supplementary material available at 10.1186/s40246-021-00318-3.
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Affiliation(s)
- Xi Long
- Division of Life Science and Applied Genomics Centre, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.,HKUST Shenzhen Research Institute, 9 Yuexing First Road, Nanshan, Shenzhen, China
| | - Hong Xue
- Division of Life Science and Applied Genomics Centre, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China. .,HKUST Shenzhen Research Institute, 9 Yuexing First Road, Nanshan, Shenzhen, China. .,Centre for Cancer Genomics, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, Jiangsu, China.
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Ullah A, Long X, Mat WK, Hu T, Khan MI, Hui L, Zhang X, Sun P, Gao M, Wang J, Wang H, Li X, Sun W, Qiao M, Xue H. Highly Recurrent Copy Number Variations in GABRB2 Associated With Schizophrenia and Premenstrual Dysphoric Disorder. Front Psychiatry 2020; 11:572. [PMID: 32695026 PMCID: PMC7338560 DOI: 10.3389/fpsyt.2020.00572] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 06/03/2020] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVE Although single-nucleotide polymorphisms in GABRB2, the gene encoding for GABAA receptors β2 subunit, have been associated with schizophrenia (SCZ), it is unknown whether there is any association of copy number variations (CNVs) in this gene with either SCZ or premenstrual dysphoric disorder (PMDD). METHODS In this study, the occurrences of the recurrent CNVs esv2730987 in Intron 6 and nsv1177513 in Exon 11 of GABRB2 in Chinese and German SCZ, and Chinese PMDD patients were compared to controls of same ethnicity and gender by quantitative PCR (qPCR). RESULTS The results demonstrated that copy-number-gains were enriched in both SCZ and PMDD patients with significant odds ratios (OR). For combined-gender SCZ patients versus controls, about two-fold increases were observed in both ethnic groups at both esv2730987 (OR = 2.15, p = 5.32E-4 in Chinese group; OR = 2.79, p = 8.84E-3 in German group) and nsv1177513 (OR = 3.29, p = 1.28E-11 in Chinese group; OR = 2.44, p = 6.17E-5 in German group). The most significant copy-number-gains were observed in Chinese females at nsv1177513 (OR = 3.41), and German females at esv2730987 (OR=3.96). Copy-number-gains were also enriched in Chinese PMDD patients versus controls at esv2730987 (OR = 10.53, p = 4.34E-26) and nsv1177513 (OR = 2.39, p = 3.19E-5). CONCLUSION These findings established for the first time the association of highly recurrent CNVs with SCZ and PMDD, suggesting the presence of an overlapping genetic basis with shared biomarkers for these two common psychiatric disorders.
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Affiliation(s)
- Ata Ullah
- Applied Genomics Center and State Key Laboratory of Molecular Neuroscience, Hong Kong University of Science and Technology, Division of Life Science, Hong Kong, Hong Kong
| | - Xi Long
- Applied Genomics Center and State Key Laboratory of Molecular Neuroscience, Hong Kong University of Science and Technology, Division of Life Science, Hong Kong, Hong Kong
| | - Wai-Kin Mat
- Applied Genomics Center and State Key Laboratory of Molecular Neuroscience, Hong Kong University of Science and Technology, Division of Life Science, Hong Kong, Hong Kong
| | - Taobo Hu
- Applied Genomics Center and State Key Laboratory of Molecular Neuroscience, Hong Kong University of Science and Technology, Division of Life Science, Hong Kong, Hong Kong
| | - Muhammad Ismail Khan
- Applied Genomics Center and State Key Laboratory of Molecular Neuroscience, Hong Kong University of Science and Technology, Division of Life Science, Hong Kong, Hong Kong
| | - Li Hui
- Suzhou Guangji Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Xiangyang Zhang
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Peng Sun
- School of Basic Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Mingzhou Gao
- School of Basic Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Jieqiong Wang
- School of Basic Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Haijun Wang
- School of Basic Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xia Li
- School of Basic Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Wenjun Sun
- School of Basic Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Mingqi Qiao
- School of Basic Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Hong Xue
- Applied Genomics Center and State Key Laboratory of Molecular Neuroscience, Hong Kong University of Science and Technology, Division of Life Science, Hong Kong, Hong Kong
- School of Basic Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
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Wu Z, Long X, Tsang SY, Hu T, Yang JF, Mat WK, Wang H, Xue H. Genomic subtyping of liver cancers with prognostic application. BMC Cancer 2020; 20:84. [PMID: 32005109 PMCID: PMC6995214 DOI: 10.1186/s12885-020-6546-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Accepted: 01/15/2020] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Cancer subtyping has mainly relied on pathological and molecular means. Massively parallel sequencing-enabled subtyping requires genomic markers to be developed based on global features rather than individual mutations for effective implementation. METHODS In the present study, the whole genome sequences (WGS) of 110 liver cancers of Japanese patients published with different pathologies were analyzed with respect to their single nucleotide variations (SNVs) comprising both gain-of-heterozygosity (GOH) and loss-of-heterozygosity (LOH) mutations, the signatures of combined GOH and LOH mutations, along with recurrent copy number variations (CNVs). RESULTS The results, obtained based on the WGS sequences as well as the Exome subset within the WGSs that covered ~ 2.0% of the WGS and the AluScan-subset within the WGSs that were amplifiable by Alu element-consensus primers and covered ~ 2.1% of the WGS, indicated that the WGS samples could be employed with the mutational parameters of SNV load, LOH%, the Signature α%, and survival-associated recurrent CNVs (srCNVs) as genomic markers for subtyping to stratify liver cancer patients prognostically into the long and short survival subgroups. The usage of the AluScan-subset data, which could be implemented with sub-micrograms of DNA samples and vastly reduced sequencing analysis task, outperformed the usage of WGS data when LOH% was employed as stratifying criterion. CONCLUSIONS Thus genomic subtyping performed with novel genomic markers identified in this study was effective in predicting patient-survival duration, with cohorts of hepatocellular carcinomas alone and those including intrahepatic cholangiocarcinomas. Such relatively heterogeneity-insensitive genomic subtyping merits further studies with a broader spectrum of cancers.
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Affiliation(s)
- Zhenggang Wu
- HKUST Shenzhen Research Institute, 9 Yuexing First Road, Nanshan, Shenzhen, China.,Division of Life Science and Applied Genomics Center, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Xi Long
- Division of Life Science and Applied Genomics Center, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Shui Ying Tsang
- Division of Life Science and Applied Genomics Center, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Taobo Hu
- Division of Life Science and Applied Genomics Center, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Jian-Feng Yang
- Division of Life Science and Applied Genomics Center, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Wai Kin Mat
- Division of Life Science and Applied Genomics Center, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Hongyang Wang
- Eastern Hepatobiliary Surgery Institute, Second Military Medical University, Shanghai, China.
| | - Hong Xue
- HKUST Shenzhen Research Institute, 9 Yuexing First Road, Nanshan, Shenzhen, China. .,Division of Life Science and Applied Genomics Center, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China. .,Center for Cancer Genomics, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China. .,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China. .,Division of Life Science, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
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Wang C, Deng S, Sun L, Li L, Hu YQ. A nonparametric test for association with multiple loci in the retrospective case-control study. Stat Methods Med Res 2019; 29:589-602. [PMID: 30987531 DOI: 10.1177/0962280219842892] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The genome-wide association studies aim at identifying common or rare variants associated with common diseases and explaining more heritability. It is well known that common diseases are influenced by multiple single nucleotide polymorphisms (SNPs) that are usually correlated in location or function. In order to powerfully detect association signals, it is highly desirable to take account of correlations or linkage disequilibrium (LD) information among multiple SNPs in testing for association. In this article, we propose a test SLIDE that depicts the difference of the average multi-locus genotypes between cases and controls and derive its variance-covariance matrix in the retrospective design. This matrix is composed of the pairwise LD between SNPs. Thus SLIDE can borrow the strength from an external database in the population of interest with a few thousands to hundreds of thousands individuals to improve the power for detecting association. Extensive simulations show that SLIDE has apparent superiority over the existing methods, especially in the situation involving both common and rare variants, both protective and deleterious variants. Furthermore, the efficiency of the proposed method is demonstrated in the application to the data from the Wellcome Trust Case Control Consortium.
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Affiliation(s)
- Chan Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Institute of Biostatistics, Fudan University, Shanghai, China.,Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, NY, USA
| | - Shufang Deng
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Institute of Biostatistics, Fudan University, Shanghai, China
| | - Leiming Sun
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Institute of Biostatistics, Fudan University, Shanghai, China
| | - Liming Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Institute of Biostatistics, Fudan University, Shanghai, China
| | - Yue-Qing Hu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Institute of Biostatistics, Fudan University, Shanghai, China.,Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
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Hu T, Kumar Y, Shazia I, Duan SJ, Li Y, Chen L, Chen JF, Yin R, Kwong A, Leung GKK, Mat WK, Wu Z, Long X, Chan CH, Chen S, Lee P, Ng SK, Ho TYC, Yang J, Ding X, Tsang SY, Zhou X, Zhang DH, Zhou EX, Xu L, Poon WS, Wang HY, Xue H. Forward and reverse mutations in stages of cancer development. Hum Genomics 2018; 12:40. [PMID: 30134973 PMCID: PMC6104001 DOI: 10.1186/s40246-018-0170-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Accepted: 07/26/2018] [Indexed: 11/15/2022] Open
Abstract
Background Massive occurrences of interstitial loss of heterozygosity (LOH) likely resulting from gene conversions were found by us in different cancers as a type of single-nucleotide variations (SNVs), comparable in abundance to the commonly investigated gain of heterozygosity (GOH) type of SNVs, raising the question of the relationships between these two opposing types of cancer mutations. Methods In the present study, SNVs in 12 tetra sample and 17 trio sample sets from four cancer types along with copy number variations (CNVs) were analyzed by AluScan sequencing, comparing tumor with white blood cells as well as tissues vicinal to the tumor. Four published “nontumor”-tumor metastasis trios and 246 pan-cancer pairs analyzed by whole-genome sequencing (WGS) and 67 trios by whole-exome sequencing (WES) were also examined. Results Widespread GOHs enriched with CG-to-TG changes and associated with nearby CNVs and LOHs enriched with TG-to-CG changes were observed. Occurrences of GOH were 1.9-fold higher than LOH in “nontumor” tissues more than 2 cm away from the tumors, and a majority of these GOHs and LOHs were reversed in “paratumor” tissues within 2 cm of the tumors, forming forward-reverse mutation cycles where the revertant LOHs displayed strong lineage effects that pointed to a sequential instead of parallel development from “nontumor” to “paratumor” and onto tumor cells, which was also supported by the relative frequencies of 26 distinct classes of CNVs between these three types of cell populations. Conclusions These findings suggest that developing cancer cells undergo sequential changes that enable the “nontumor” cells to acquire a wide range of forward mutations including ones that are essential for oncogenicity, followed by revertant mutations in the “paratumor” cells to avoid growth retardation by excessive mutation load. Such utilization of forward-reverse mutation cycles as an adaptive mechanism was also observed in cultured HeLa cells upon successive replatings. An understanding of forward-reverse mutation cycles in cancer development could provide a genomic basis for improved early diagnosis, staging, and treatment of cancers. Electronic supplementary material The online version of this article (10.1186/s40246-018-0170-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Taobo Hu
- Division of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Yogesh Kumar
- Division of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Iram Shazia
- Division of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Shen-Jia Duan
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yi Li
- Department of Surgery, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Lei Chen
- Eastern Hepatobiliary Surgery Institute, Second Military Medical University, Shanghai, China
| | - Jin-Fei Chen
- Department of Oncology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Rong Yin
- Jiangsu Key Laboratory of Cancer Molecular Biology and Translational Medicine, Jiangsu Cancer Hospital, Nanjing, China
| | - Ava Kwong
- Division of Neurosurgery, Department of Surgery, Li Ka Shing Faculty of Medicine, Queen Mary Hospital, The University of Hong Kong, 102 Pokfulam Road, Pokfulam, Hong Kong, China
| | - Gilberto Ka-Kit Leung
- Division of Neurosurgery, Department of Surgery, Li Ka Shing Faculty of Medicine, Queen Mary Hospital, The University of Hong Kong, 102 Pokfulam Road, Pokfulam, Hong Kong, China
| | - Wai-Kin Mat
- Division of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Zhenggang Wu
- Division of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Xi Long
- Division of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Cheuk-Hin Chan
- Division of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Si Chen
- Division of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Peggy Lee
- Division of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Siu-Kin Ng
- Division of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Timothy Y C Ho
- Division of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Jianfeng Yang
- Division of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Xiaofan Ding
- Division of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Shui-Ying Tsang
- Division of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Xuqing Zhou
- Division of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Dan-Hua Zhang
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | | | - En-Xiang Zhou
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Lin Xu
- Jiangsu Key Laboratory of Cancer Molecular Biology and Translational Medicine, Jiangsu Cancer Hospital, Nanjing, China
| | - Wai-Sang Poon
- Department of Surgery, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Hong-Yang Wang
- Eastern Hepatobiliary Surgery Institute, Second Military Medical University, Shanghai, China
| | - Hong Xue
- Division of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China. .,School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.
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8
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Wang W, Jiang W, Liu J, Li Y, Gai J, Li Y. Genome-wide characterization of the aldehyde dehydrogenase gene superfamily in soybean and its potential role in drought stress response. BMC Genomics 2017; 18:518. [PMID: 28687067 PMCID: PMC5501352 DOI: 10.1186/s12864-017-3908-y] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Accepted: 06/27/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Aldehyde dehydrogenases (ALDHs) represent a group of enzymes that detoxify aldehydes by facilitating their oxidation to carboxylic acids, and have been shown to play roles in plant response to abiotic stresses. However, the comprehensive analysis of ALDH superfamily in soybean (Glycine max) has been limited. RESULTS In present study, a total of 53 GmALDHs were identified in soybean, and grouped into 10 ALDH families according to the ALDH Gene Nomenclature Committee and phylogenetic analysis. These groupings were supported by their gene structures and conserved motifs. Soybean ALDH superfamily expanded mainly by whole genome duplication/segmental duplications. Gene network analysis identified 1146 putative co-functional genes of 51 GmALDHs. Gene Ontology (GO) enrichment analysis suggested the co-functional genes of these 51 GmALDHs were enriched (FDR < 1e-3) in the process of lipid metabolism, photosynthesis, proline catabolism, and small molecule catabolism. In addition, 22 co-functional genes of GmALDHs are related to plant response to water deprivation/water transport. GmALDHs exhibited various expression patterns in different soybean tissues. The expression levels of 13 GmALDHs were significantly up-regulated and 14 down-regulated in response to water deficit. The occurrence frequencies of three drought-responsive cis-elements (ABRE, CRT/DRE, and GTGCnTGC/G) were compared in GmALDH genes that were up-, down-, or non-regulated by water deficit. Higher frequency of these three cis-elements was observed for the group of up-regulated GmALDH genes as compared to the group of down- or non- regulated GmALDHs by drought stress, implying their potential roles in the regulation of soybean response to drought stress. CONCLUSIONS A total of 53 ALDH genes were identified in soybean genome and their phylogenetic relationships and duplication patterns were analyzed. The potential functions of GmALDHs were predicted by analyses of their co-functional gene networks, gene expression profiles, and cis-regulatory elements. Three GmALDH genes, including GmALDH3H2, GmALDH12A2 and GmALDH18B3, were highly induced by drought stress in soybean leaves. Our study provides a foundation for future investigations of GmALDH gene function in soybean.
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Affiliation(s)
- Wei Wang
- National Key Laboratory of Crop Genetics and Germplasm Enhancement / National Center for Soybean Improvement / Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture) / Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu China
| | - Wei Jiang
- National Key Laboratory of Crop Genetics and Germplasm Enhancement / National Center for Soybean Improvement / Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture) / Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu China
| | - Juge Liu
- National Key Laboratory of Crop Genetics and Germplasm Enhancement / National Center for Soybean Improvement / Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture) / Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu China
| | - Yang Li
- National Key Laboratory of Crop Genetics and Germplasm Enhancement / National Center for Soybean Improvement / Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture) / Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu China
| | - Junyi Gai
- National Key Laboratory of Crop Genetics and Germplasm Enhancement / National Center for Soybean Improvement / Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture) / Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu China
| | - Yan Li
- National Key Laboratory of Crop Genetics and Germplasm Enhancement / National Center for Soybean Improvement / Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture) / Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu China
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Long M, Hu T. Tripartite genome of all species. F1000Res 2016; 5:195. [PMID: 27366319 PMCID: PMC4911623 DOI: 10.12688/f1000research.8008.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/19/2016] [Indexed: 11/25/2022] Open
Abstract
Neutral theory has dominated the molecular evolution field for more than half a century, but it has been severely challenged by the recently emerged Maximum Genetic Diversity (MGD) theory. However, based on our recent work of tripartite human genome architecture, we found that MGD theory may have overlooked the regulatory but variable genomic regions that increase with species complexity. Here we propose a new molecular evolution theory named Increasing Functional Variation (IFV) hypothesis. According to the IFV hypothesis, the genome of all species is divided into three regions that are ‘functional and invariable’, ‘functional and variable’ and ‘non-functional and variable’. While the ‘non-functional and variable’ region decreases as species become more complex, the other two regions increase.
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Affiliation(s)
- MengPing Long
- Division of Life Science, Hong Kong University of Science and Technology, Hong Kong SAR, China.,Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - TaoBo Hu
- Division of Life Science, Hong Kong University of Science and Technology, Hong Kong SAR, China.,Applied Genomics Center, Hong Kong University of Science and Technology, Hong Kong SAR, China.,Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, China
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