1
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Wang D, Yang D, Yang L, Diao L, Zhang Y, Li Y, Wang H, Ren J, Cheng L, Tan Q, Zhang R, Han X, Zhang X, Wang B, Li D, Chen M, Hermjakob H, Li Y, LaBaer J, Zhou Z, Yu X. Human Autoantigen Atlas: Searching for the Hallmarks of Autoantigens. J Proteome Res 2023. [PMID: 37183442 DOI: 10.1021/acs.jproteome.2c00799] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Understanding autoimmunity to endogenous proteins is crucial in diagnosing and treating autoimmune diseases. In this work, we developed a user-friendly AAgAtlas portal (http://biokb.ncpsb.org.cn/aagatlas_portal/index.php#), which can be used to search for 8045 non-redundant autoantigens (AAgs) and 47 post-translationally modified AAgs against 1073 human diseases that are prioritized by a credential score developed by multisource evidence. Using AAgAtlas, the immunogenic properties of human AAgs was systematically elucidated according to their genetic, biophysical, cytological, expression profile, and evolutionary characteristics. The results indicated that human AAgs are evolutionally conserved in protein sequence and enriched in three hydrophilic and polar amino acid residues (K, D, and E) that are located at the protein surface. AAgs are enriched in proteins that are involved in nucleic acid binding, transferase, and the cytoskeleton. Genome, transcriptome, and proteome analyses further indicated that AAb production is associated with gene variance and abnormal protein expression related to the pathological activities of different tumors. Collectively, our data outlines the hallmarks of human AAgs that facilitate the understanding of humoral autoimmunity and the identification of biomarkers of human diseases.
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Affiliation(s)
- Dan Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Dong Yang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Liuhui Yang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Lihong Diao
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Yuqi Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Yang Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Hongye Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Jing Ren
- Department of Clinical Laboratory, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Linlin Cheng
- Department of Clinical Laboratory, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Qiaoyun Tan
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Ran Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Xiaohong Han
- Clinical Pharmacology Research Center, Peking Union Medical College Hospital, State Key Laboratory of Complex Severe and Rare Diseases, NMPA Key Laboratory for Clinical Research and Evaluation of Drug, Beijing Key Laboratory of Clinical PK & PD Investigation for Innovative Drugs, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Xiaohan Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing 102206, China
- College of Medicine and Integrated Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Bingwei Wang
- College of Medicine and Integrated Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Dong Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Meng Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Yongzhe Li
- Department of Clinical Laboratory, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Joshua LaBaer
- The Virginia G. Piper Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, Arizona 85287, United States
| | - Zhou Zhou
- Department of Laboratory Medicine, National Center for Cardiovascular Diseases and Fuwai Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Xiaobo Yu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing 102206, China
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2
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Wang H, Yang Z, Yang D. Approaches for the Identification of Intrinsically Disordered Protein Domains. Methods Mol Biol 2023; 2581:403-412. [PMID: 36413333 DOI: 10.1007/978-1-0716-2784-6_28] [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] [Indexed: 06/16/2023]
Abstract
Intrinsically disordered protein domains are those with high disorder proportion or a consecutive disordered region. They have no stable spatial structure but play an important role in the regulation of complex cellular functions and contribute to the increasing organism complexity during evolution. Here, we describe the approaches to predict intrinsic disorder values of residues in proteins and methods to identify the intrinsically disordered domains.
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Affiliation(s)
- Huqiang Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Zhixiang Yang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Dong Yang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China.
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3
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Wang H, Zhong H, Gao C, Zang J, Yang D. The Distinct Properties of the Consecutive Disordered Regions Inside or Outside Protein Domains and Their Functional Significance. Int J Mol Sci 2021; 22:ijms221910677. [PMID: 34639018 PMCID: PMC8508753 DOI: 10.3390/ijms221910677] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 09/26/2021] [Accepted: 09/29/2021] [Indexed: 11/17/2022] Open
Abstract
The consecutive disordered regions (CDRs) are the basis for the formation of intrinsically disordered proteins, which contribute to various biological functions and increasing organism complexity. Previous studies have revealed that CDRs may be present inside or outside protein domains, but a comprehensive analysis of the property differences between these two types of CDRs and the proteins containing them is lacking. In this study, we investigated this issue from three viewpoints. Firstly, we found that in-domain CDRs are more hydrophilic and stable but have less stickiness and fewer post-translational modification sites compared with out-domain CDRs. Secondly, at the protein level, we found that proteins with only in-domain CDRs originated late, evolved rapidly, and had weak functional constraints, compared with the other two types of CDR-containing proteins. Proteins with only in-domain CDRs tend to be expressed spatiotemporal specifically, but they tend to have higher abundance and are more stable. Thirdly, we screened the CDR-containing protein domains that have a strong correlation with organism complexity. The CDR-containing domains tend to be evolutionarily young, or they changed from a domain without CDR to a CDR-containing domain during evolution. These results provide valuable new insights about the evolution and function of CDRs and protein domains.
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Affiliation(s)
| | | | | | | | - Dong Yang
- Correspondence: ; Tel.: +86-10-61777051
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4
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Gao C, Ma C, Wang H, Zhong H, Zang J, Zhong R, He F, Yang D. Intrinsic disorder in protein domains contributes to both organism complexity and clade-specific functions. Sci Rep 2021; 11:2985. [PMID: 33542394 PMCID: PMC7862400 DOI: 10.1038/s41598-021-82656-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 01/22/2021] [Indexed: 11/09/2022] Open
Abstract
Interestingly, some protein domains are intrinsically disordered (abbreviated as IDD), and the disorder degree of same domains may differ in different contexts. However, the evolutionary causes and biological significance of these phenomena are unclear. Here, we address these issues by genome-wide analyses of the evolutionary and functional features of IDDs in 1,870 species across the three superkingdoms. As the result, there is a significant positive correlation between the proportion of IDDs and organism complexity with some interesting exceptions. These phenomena may be due to the high disorder of clade-specific domains and the different disorder degrees of the domains shared in different clades. The functions of IDDs are clade-specific and the higher proportion of post-translational modification sites may contribute to their complex functions. Compared with metazoans, fungi have more IDDs with a consecutive disorder region but a low disorder ratio, which reflects their different functional requirements. As for disorder variation, it’s greater for domains among different proteins than those within the same proteins. Some clade-specific ‘no-variation’ or ‘high-variation’ domains are involved in clade-specific functions. In sum, intrinsic domain disorder is related to both the organism complexity and clade-specific functions. These results deepen the understanding of the evolution and function of IDDs.
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Affiliation(s)
- Chao Gao
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing Proteome Research Center, Beijing Institute of Lifeomics, 38 Science Park Road, Changping District, Beijing, 102206, China
| | - Chong Ma
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing Proteome Research Center, Beijing Institute of Lifeomics, 38 Science Park Road, Changping District, Beijing, 102206, China.,Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China
| | - Huqiang Wang
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing Proteome Research Center, Beijing Institute of Lifeomics, 38 Science Park Road, Changping District, Beijing, 102206, China
| | - Haolin Zhong
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing Proteome Research Center, Beijing Institute of Lifeomics, 38 Science Park Road, Changping District, Beijing, 102206, China
| | - Jiayin Zang
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing Proteome Research Center, Beijing Institute of Lifeomics, 38 Science Park Road, Changping District, Beijing, 102206, China
| | - Rugang Zhong
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China
| | - Fuchu He
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing Proteome Research Center, Beijing Institute of Lifeomics, 38 Science Park Road, Changping District, Beijing, 102206, China.
| | - Dong Yang
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing Proteome Research Center, Beijing Institute of Lifeomics, 38 Science Park Road, Changping District, Beijing, 102206, China.
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5
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Shen P, Xu A, Hou Y, Wang H, Gao C, He F, Yang D. Conserved paradoxical relationships among the evolutionary, structural and expressional features of KRAB zinc-finger proteins reveal their special functional characteristics. BMC Mol Cell Biol 2021; 22:7. [PMID: 33482715 PMCID: PMC7821633 DOI: 10.1186/s12860-021-00346-w] [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: 09/20/2020] [Accepted: 01/13/2021] [Indexed: 12/03/2022] Open
Abstract
Background One striking feature of the large KRAB domain-containing zinc finger protein (KZFP) family is its rapid evolution, leading to hundreds of member genes with various origination time in a certain mammalian genome. However, a comprehensive genome-wide and across-taxa analysis of the structural and expressional features of KZFPs with different origination time is lacking. This type of analysis will provide valuable clues about the functional characteristics of this special family. Results In this study, we found several conserved paradoxical phenomena about this issue. 1) Ordinary young domains/proteins tend to be disordered, but most of KRAB domains are completely structured in 64 representative species across the superclass of Sarcopterygii and most of KZFPs are also highly structured, indicating their rigid and unique structural and functional characteristics; as exceptions, old-zinc-finger-containing KZFPs have relatively disordered KRAB domains and linker regions, contributing to diverse interacting partners and functions. 2) In general, young or highly structured proteins tend to be spatiotemporal specific and have low abundance. However, by integrated analysis of 29 RNA-seq datasets, including 725 samples across early embryonic development, embryonic stem cell differentiation, embryonic and adult organs, tissues in 7 mammals, we found that KZFPs tend to express ubiquitously with medium abundance regardless of evolutionary age and structural disorder degree, indicating the wide functional requirements of KZFPs in various states. 3) Clustering and correlation analysis reveal that there are differential expression patterns across different spatiotemporal states, suggesting the specific-high-expression KZFPs may play important roles in the corresponding states. In particular, part of young-zinc-finger-containing KZFPs are highly expressed in early embryonic development and ESCs differentiation into endoderm or mesoderm. Co-expression analysis revealed that young-zinc-finger-containing KZFPs are significantly enriched in five co-expression modules. Among them, one module, including 13 young-zinc-finger-containing KZFPs, showed an ‘early-high and late-low’ expression pattern. Further functional analysis revealed that they may function in early embryonic development and ESC differentiation via participating in cell cycle related processes. Conclusions This study shows the conserved and special structural, expressional features of KZFPs, providing new clues about their functional characteristics and potential causes of their rapid evolution. Supplementary Information The online version contains supplementary material available at 10.1186/s12860-021-00346-w.
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Affiliation(s)
- Pan Shen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Aishi Xu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.,Animal Sciences College of Jilin University, Changchun, 130062, China
| | - Yushan Hou
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Huqiang Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Chao Gao
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Fuchu He
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.
| | - Dong Yang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.
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6
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Yang D, Xu A, Shen P, Gao C, Zang J, Qiu C, Ouyang H, Jiang Y, He F. A two-level model for the role of complex and young genes in the formation of organism complexity and new insights into the relationship between evolution and development. EvoDevo 2018; 9:22. [PMID: 30455862 PMCID: PMC6231269 DOI: 10.1186/s13227-018-0111-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Accepted: 10/25/2018] [Indexed: 11/14/2022] Open
Abstract
Background How genome complexity affects organismal phenotypic complexity is a fundamental question in evolutionary developmental biology. Previous studies proposed various contributing factors of genome complexity and tried to find the connection between genomic complexity and organism complexity. However, a general model to answer this question is lacking. Here, we introduce a ‘two-level’ model for the realization of genome complexity at phenotypic level. Results Five representative species across Protostomia and Deuterostomia were involved in this study. The intrinsic gene properties contributing to genome complexity were classified into two generalized groups: the complexity and age degree of both protein-coding and noncoding genes. We found that young genes tend to be simpler; however, the mid-age genes, rather than the oldest genes, show the highest proportion of high complexity. Complex genes tend to be utilized preferentially in each stage of embryonic development, with maximum representation during the late stage of organogenesis. This trend is mainly attributed to mid-age complex genes. In contrast, young genes tend to be expressed in specific spatiotemporal states. An obvious correlation between the time point of the change in over- and under-representation and the order of gene age was observed, which supports the funnel-like model of the conservation pattern of development. In addition, we found some probable causes for the seemingly contradictory ‘funnel-like’ or ‘hourglass’ model. Conclusions These results indicate that complex and young genes contribute to organismal complexity at two different levels: Complex genes contribute to the complexity of individual proteomes in certain states, whereas young genes contribute to the diversity of proteomes in different spatiotemporal states. This conclusion is valid across the five species investigated, indicating it is a conserved model across Protostomia and Deuterostomia. The results in this study also support ‘funnel-like model’ from a new viewpoint and explain why there are different evo–devo relation models. Electronic supplementary material The online version of this article (10.1186/s13227-018-0111-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Dong Yang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206 The People's Republic of China
| | - Aishi Xu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206 The People's Republic of China
| | - Pan Shen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206 The People's Republic of China
| | - Chao Gao
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206 The People's Republic of China
| | - Jiayin Zang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206 The People's Republic of China
| | - Chen Qiu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206 The People's Republic of China
| | - Hongsheng Ouyang
- 2Animal Sciences College of Jilin University, Changchun, 130062 The People's Republic of China
| | - Ying Jiang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206 The People's Republic of China
| | - Fuchu He
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206 The People's Republic of China
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7
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Zhou Y, Shen P, Lan Q, Deng C, Zhang Y, Li Y, Wei W, Wang Y, Su N, He F, Xie Q, Lyu Z, Yang D, Xu P. High-coverage proteomics reveals methionine auxotrophy in Deinococcus radiodurans. Proteomics 2017; 17. [PMID: 28608649 DOI: 10.1002/pmic.201700072] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2017] [Revised: 05/13/2017] [Accepted: 06/02/2017] [Indexed: 11/09/2022]
Abstract
Deinococcus radiodurans is a robust bacterium best known for its capacity to resist to radiation. In this study, the SDS-PAGE coupled with high-precision LC-MS/MS was used to study the D. radiodurans proteome. A total of 1951 proteins were identified which covers 63.18% protein-coding genes. Comparison of the identified proteins to the key enzymes in amino acid biosyntheses from KEGG database showed the methionine biosynthesis module is incomplete while other amino acid biosynthesis modules are complete, which indicated methionine auxotrophy in D. radiodurans. The subsequent amino acid-auxotrophic screening has verified methionine instead of other amino acids is essential for the growth of D. radiodurans. With molecular evolutionary genetic analysis, we found the divergence in methionine biosynthesis during the evolution of the common ancestor of bacteria. We also found D. radiodurans lost the power of synthesizing methionine because of the missing metA and metX in two types of methionine biosyntheses. For the first time, this study used high-coverage proteome analysis to identify D. radiodurans amino acid auxotrophy, which provides the important reference for the development of quantitative proteomics analysis using stable isotope labeling in metabolomics of D. radiodurans and in-depth analysis of the molecular mechanism of radiation resistance.
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Affiliation(s)
- Yanxia Zhou
- College of Life Sciences, Hebei University and Key Laboratory of Microbial Diversity Research and Application of Hebei Province, Baoding, P. R. China.,State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, P. R. China
| | - Pan Shen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, P. R. China
| | - Qiuyan Lan
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, P. R. China.,School of Basic Medical Science, Key Laboratory of Combinatorial Biosynthesis and Drug Discovery of Ministry of Education, School of Pharmaceutical Sciences, Wuhan University, Wuhan, P. R. China
| | - Chen Deng
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, P. R. China
| | - Yao Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, P. R. China.,State Key Laboratory of Biocontrol and Guangdong Provincial Key Laboratory of Plant Resources, College of Ecology and Evolution, Sun Yat-Sen University, Guangzhou, P. R. China
| | - Yanchang Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, P. R. China
| | - Wei Wei
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, P. R. China
| | - Yihao Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, P. R. China
| | - Na Su
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, P. R. China
| | - Fuchu He
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, P. R. China
| | - Qiong Xie
- China Astronaut Research and Training Center, Beijing, P. R. China
| | - Zhitang Lyu
- College of Life Sciences, Hebei University and Key Laboratory of Microbial Diversity Research and Application of Hebei Province, Baoding, P. R. China
| | - Dong Yang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, P. R. China
| | - Ping Xu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, P. R. China.,School of Basic Medical Science, Key Laboratory of Combinatorial Biosynthesis and Drug Discovery of Ministry of Education, School of Pharmaceutical Sciences, Wuhan University, Wuhan, P. R. China
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8
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Xu A, Li G, Yang D, Wu S, Ouyang H, Xu P, He F. Evolutionary Characteristics of Missing Proteins: Insights into the Evolution of Human Chromosomes Related to Missing-Protein-Encoding Genes. J Proteome Res 2015; 14:4985-94. [DOI: 10.1021/acs.jproteome.5b00450] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Aishi Xu
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, Beijing 102206, P. R. China
- Animal Sciences College of Jilin University, Changchun 130062, P. R. China
| | - Guang Li
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, Beijing 102206, P. R. China
| | - Dong Yang
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, Beijing 102206, P. R. China
| | - Songfeng Wu
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, Beijing 102206, P. R. China
| | - Hongsheng Ouyang
- Animal Sciences College of Jilin University, Changchun 130062, P. R. China
| | - Ping Xu
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, Beijing 102206, P. R. China
| | - Fuchu He
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, Beijing 102206, P. R. China
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9
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Noble LM, Andrianopoulos A. Fungal genes in context: genome architecture reflects regulatory complexity and function. Genome Biol Evol 2013; 5:1336-52. [PMID: 23699226 PMCID: PMC3730340 DOI: 10.1093/gbe/evt077] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Gene context determines gene expression, with local chromosomal environment most influential. Comparative genomic analysis is often limited in scope to conserved or divergent gene and protein families, and fungi are well suited to this approach with low functional redundancy and relatively streamlined genomes. We show here that one aspect of gene context, the amount of potential upstream regulatory sequence maintained through evolution, is highly predictive of both molecular function and biological process in diverse fungi. Orthologs with large upstream intergenic regions (UIRs) are strongly enriched in information processing functions, such as signal transduction and sequence-specific DNA binding, and, in the genus Aspergillus, include the majority of experimentally studied, high-level developmental and metabolic transcriptional regulators. Many uncharacterized genes are also present in this class and, by implication, may be of similar importance. Large intergenic regions also share two novel sequence characteristics, currently of unknown significance: they are enriched for plus-strand polypyrimidine tracts and an information-rich, putative regulatory motif that was present in the last common ancestor of the Pezizomycotina. Systematic consideration of gene UIR in comparative genomics, particularly for poorly characterized species, could help reveal organisms’ regulatory priorities.
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Affiliation(s)
- Luke M Noble
- Department of Genetics, University of Melbourne, Victoria, Australia
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10
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Liu Z, Guo F, Zhang J, Wang J, Lu L, Li D, He F. Proteome-wide prediction of self-interacting proteins based on multiple properties. Mol Cell Proteomics 2013; 12:1689-700. [PMID: 23422585 DOI: 10.1074/mcp.m112.021790] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Self-interacting proteins, whose two or more copies can interact with each other, play important roles in cellular functions and the evolution of protein interaction networks (PINs). Knowing whether a protein can self-interact can contribute to and sometimes is crucial for the elucidation of its functions. Previous related research has mainly focused on the structures and functions of specific self-interacting proteins, whereas knowledge on their overall properties is limited. Meanwhile, the two current most common high throughput protein interaction assays have limited ability to detect self-interactions because of biological artifacts and design limitations, whereas the bioinformatic prediction method of self-interacting proteins is lacking. This study aims to systematically study and predict self-interacting proteins from an overall perspective. We find that compared with other proteins the self-interacting proteins in the structural aspect contain more domains; in the evolutionary aspect they tend to be conserved and ancient; in the functional aspect they are significantly enriched with enzyme genes, housekeeping genes, and drug targets, and in the topological aspect tend to occupy important positions in PINs. Furthermore, based on these features, after feature selection, we use logistic regression to integrate six representative features, including Gene Ontology term, domain, paralogous interactor, enzyme, model organism self-interacting protein, and betweenness centrality in the PIN, to develop a proteome-wide prediction model of self-interacting proteins. Using 5-fold cross-validation and an independent test, this model shows good performance. Finally, the prediction model is developed into a user-friendly web service SLIPPER (SeLf-Interacting Protein PrEdictoR). Users may submit a list of proteins, and then SLIPPER will return the probability_scores measuring their possibility to be self-interacting proteins and various related annotation information. This work helps us understand the role self-interacting proteins play in cellular functions from an overall perspective, and the constructed prediction model may contribute to the high throughput finding of self-interacting proteins and provide clues for elucidating their functions.
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Affiliation(s)
- Zhongyang Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100850, China
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