101
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Dong B, Wang G, Yao J, Yuan P, Kang W, Zhi L, He X. Predicting novel genes and pathways associated with osteosarcoma by using bioinformatics analysis. Gene 2017; 628:32-37. [PMID: 28687333 DOI: 10.1016/j.gene.2017.06.058] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2017] [Revised: 04/27/2017] [Accepted: 06/30/2017] [Indexed: 12/24/2022]
Abstract
This aim of this study was to explore novel biomarkers related to osteosarcoma. The mRNA expression profile GSE41293 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which included seven osteosarcoma and six control samples. After preprocessing, the FASTQ format reads of 13 samples were mapped to the reference sequences to screen for unique mapping reads. Differentially expressed genes (DEGs) were selected, which were then used for pathway and protein-protein interaction (PPI) network analyses. Moreover, the microarray data GSE63631 were downloaded from GEO database to verify our results. The percentages of unique mapping reads for osteosarcomas and control samples were both >85%. A total of 6157 DEGs were identified between the two groups. DEGs that were upregulated were significantly enriched in 19 pathways, and those that were downregulated were enriched in 14 pathways. In the PPI network, DEGs such as SRC, ERBB2, and CAV3 in cluster 1 were enriched in the pathway responsible for focal adhesions. The DEGs in cluster 2, such as CDK4 and CDK6, were enriched in the cell cycle pathway. In GSE63631, DEGs were significantly enriched in focal adhesion pathway, which was in accordance with the result in GSE41293. Thus, the focal adhesion and cell cycle pathways may play important roles in osteosarcoma progression, and SRC, ERBB2, CAV3, CDK4, and CDK6 may be used as critical biomarkers of osteosarcoma.
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Affiliation(s)
- Bo Dong
- Department of Orthopedics, Second Affiliated Hospital of Xi'an Jiao Tong University, Xi'an 710004, Shaanxi, China; Department of Qrthopedics, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712000, Shaanxi, China
| | - Guozhu Wang
- Department of Orthopedics, The Second Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712083, Shaanxi, China
| | - Jie Yao
- Nursing School, Shaanxi University of Chinese Medicine, Xianyang 712000, Shaanxi, China
| | - Puwei Yuan
- Department of Qrthopedics, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712000, Shaanxi, China
| | - Wulin Kang
- Department of Qrthopedics, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712000, Shaanxi, China
| | - Liqiang Zhi
- Department of Orthopedics, The First Affiliated Hospital of Xi'an Jiao Tong University, Xi'an 710061, Shaanxi, China
| | - Xijing He
- Department of Orthopedics, Second Affiliated Hospital of Xi'an Jiao Tong University, Xi'an 710004, Shaanxi, China.
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102
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He X, Deng Y, Yue W. Investigating critical genes and gene interaction networks that mediate cyclophosphamide sensitivity in chronic myelogenous leukemia. Mol Med Rep 2017; 16:523-532. [PMID: 28560425 PMCID: PMC5482156 DOI: 10.3892/mmr.2017.6636] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Accepted: 02/15/2017] [Indexed: 01/06/2023] Open
Abstract
Drug resistance is an obstacle in the treatment of chronic myelogenous leukemia (CML), and is a common reason for treatment failure or disease progression. However, the underlying mechanisms of cyclophosphamide resistance remain poorly defined. In the present study, microarray data concerning cyclophosphamide‑sensitive and ‑resistant chronic myelogenous leukemia cell lines were analyzed. A total of 258 differentially‑expressed genes (DEGs) were identified between these two groups, from which 139 DEGs were upregulated and 119 were downregulated. Several candidate genes that were associated with cyclophosphamide resistance were also identified. These DEGs were subsequently classified using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analysis. A total of 487 biological processes and 17 KEGG pathways were revealed to be enriched. Furthermore, an interaction network was established to identify the core genes that regulated cyclophosphamide resistance. Signal transducer and activator of transcription 5A (STAT5A), FYN proto‑oncogene, Src family tyrosine kinase and spleen associated tyrosine kinase were revealed to be the hub genes in multiple enriched biological processes and signaling pathways, indicating that these were involved in mediating cyclophosphamide sensitivity in CML cells. The expression levels of 5 DEGs were also confirmed in two human CML cell lines (K‑562 and KU812) by reverse transcription‑quantitative polymerase chain reaction. Furthermore, selective knockdown of STAT5A and S100 calcium binding protein A4 (S100A4) recovered cyclophosphamide sensitivity in K‑562 cells, suggesting their involvement in drug resistance. The present study identified several potential genes and pathways contributing to cyclophosphamide resistance, and confirmed the involvement of STAT5A and S100A4 in drug resistance. These results enable improved understanding of the mechanisms underlying drug resistance in CML cells.
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MESH Headings
- Antineoplastic Agents/pharmacology
- Antineoplastic Agents/therapeutic use
- Cell Line, Tumor
- Computational Biology/methods
- Cyclophosphamide/pharmacology
- Cyclophosphamide/therapeutic use
- Databases, Nucleic Acid
- Drug Resistance, Neoplasm/genetics
- Epistasis, Genetic
- Gene Expression Profiling
- Gene Expression Regulation, Leukemic/drug effects
- Gene Ontology
- Gene Regulatory Networks
- Humans
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/drug therapy
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/genetics
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/metabolism
- Reproducibility of Results
- S100 Calcium-Binding Protein A4/genetics
- S100 Calcium-Binding Protein A4/metabolism
- STAT5 Transcription Factor/genetics
- STAT5 Transcription Factor/metabolism
- Transcriptome
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Affiliation(s)
- Xiao He
- Blood Transfusion Department, The First People's Hospital of Yancheng City, Yancheng, Jiangsu 224006, P.R. China
| | - Yuying Deng
- Blood Transfusion Department, The First People's Hospital of Yancheng City, Yancheng, Jiangsu 224006, P.R. China
| | - Wei Yue
- Blood Transfusion Department, The First People's Hospital of Yancheng City, Yancheng, Jiangsu 224006, P.R. China
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103
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Ma CY, Chen YPP, Berger B, Liao CS. Identification of protein complexes by integrating multiple alignment of protein interaction networks. Bioinformatics 2017; 33:1681-1688. [PMID: 28130237 PMCID: PMC5860626 DOI: 10.1093/bioinformatics/btx043] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Revised: 11/22/2016] [Accepted: 01/20/2017] [Indexed: 01/04/2023] Open
Abstract
MOTIVATION Protein complexes are one of the keys to studying the behavior of a cell system. Many biological functions are carried out by protein complexes. During the past decade, the main strategy used to identify protein complexes from high-throughput network data has been to extract near-cliques or highly dense subgraphs from a single protein-protein interaction (PPI) network. Although experimental PPI data have increased significantly over recent years, most PPI networks still have many false positive interactions and false negative edge loss due to the limitations of high-throughput experiments. In particular, the false negative errors restrict the search space of such conventional protein complex identification approaches. Thus, it has become one of the most challenging tasks in systems biology to automatically identify protein complexes. RESULTS In this study, we propose a new algorithm, NEOComplex ( NE CC- and O rtholog-based Complex identification by multiple network alignment), which integrates functional orthology information that can be obtained from different types of multiple network alignment (MNA) approaches to expand the search space of protein complex detection. As part of our approach, we also define a new edge clustering coefficient (NECC) to assign weights to interaction edges in PPI networks so that protein complexes can be identified more accurately. The NECC is based on the intuition that there is functional information captured in the common neighbors of the common neighbors as well. Our results show that our algorithm outperforms well-known protein complex identification tools in a balance between precision and recall on three eukaryotic species: human, yeast, and fly. As a result of MNAs of the species, the proposed approach can tolerate edge loss in PPI networks and even discover sparse protein complexes which have traditionally been a challenge to predict. AVAILABILITY AND IMPLEMENTATION http://acolab.ie.nthu.edu.tw/bionetwork/NEOComplex. CONTACT bab@csail.mit.edu or csliao@ie.nthu.edu.tw. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Cheng-Yu Ma
- Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan
- Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Vic, Australia
| | - Yi-Ping Phoebe Chen
- Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Vic, Australia
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Mathematics and Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Chung-Shou Liao
- Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan
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104
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Niu G, Wang D, Pei Y, Sun L. Systematic identification of key genes and pathways in the development of invasive cervical cancer. Gene 2017; 618:28-41. [DOI: 10.1016/j.gene.2017.03.018] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Revised: 02/13/2017] [Accepted: 03/16/2017] [Indexed: 11/30/2022]
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105
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Huttlin EL, Bruckner RJ, Paulo JA, Cannon JR, Ting L, Baltier K, Colby G, Gebreab F, Gygi MP, Parzen H, Szpyt J, Tam S, Zarraga G, Pontano-Vaites L, Swarup S, White AE, Schweppe DK, Rad R, Erickson BK, Obar RA, Guruharsha KG, Li K, Artavanis-Tsakonas S, Gygi SP, Harper JW. Architecture of the human interactome defines protein communities and disease networks. Nature 2017; 545:505-509. [PMID: 28514442 PMCID: PMC5531611 DOI: 10.1038/nature22366] [Citation(s) in RCA: 977] [Impact Index Per Article: 139.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2016] [Accepted: 04/11/2017] [Indexed: 02/07/2023]
Abstract
The physiology of a cell can be viewed as the product of thousands of proteins acting in concert to shape the cellular response. Coordination is achieved in part through networks of protein-protein interactions that assemble functionally related proteins into complexes, organelles, and signal transduction pathways. Understanding the architecture of the human proteome has the potential to inform cellular, structural, and evolutionary mechanisms and is critical to elucidation of how genome variation contributes to disease1–3. Here, we present BioPlex 2.0 (Biophysical Interactions of ORFEOME-derived complexes), which employs robust affinity purification-mass spectrometry (AP-MS) methodology4 to elucidate protein interaction networks and co-complexes nucleated by more than 25% of protein coding genes from the human genome, and constitutes the largest such network to date. With >56,000 candidate interactions, BioPlex 2.0 contains >29,000 previously unknown co-associations and provides functional insights into hundreds of poorly characterized proteins while enhancing network-based analyses of domain associations, subcellular localization, and co-complex formation. Unsupervised Markov clustering (MCL)5 of interacting proteins identified more than 1300 protein communities representing diverse cellular activities. Genes essential for cell fitness6,7 are enriched within 53 communities representing central cellular functions. Moreover, we identified 442 communities associated with more than 2000 disease annotations, placing numerous candidate disease genes into a cellular framework. BioPlex 2.0 exceeds previous experimentally derived interaction networks in depth and breadth, and will be a valuable resource for exploring the biology of incompletely characterized proteins and for elucidating larger-scale patterns of proteome organization.
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Affiliation(s)
- Edward L Huttlin
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Raphael J Bruckner
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Joao A Paulo
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Joe R Cannon
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Lily Ting
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Kurt Baltier
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Greg Colby
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Fana Gebreab
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Melanie P Gygi
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Hannah Parzen
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - John Szpyt
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Stanley Tam
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Gabriela Zarraga
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Laura Pontano-Vaites
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Sharan Swarup
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Anne E White
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Devin K Schweppe
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Ramin Rad
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Brian K Erickson
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Robert A Obar
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA.,Biogen Inc., 250 Binney Street, Cambridge, Massachusetts 02142, USA
| | - K G Guruharsha
- Biogen Inc., 250 Binney Street, Cambridge, Massachusetts 02142, USA
| | - Kejie Li
- Biogen Inc., 250 Binney Street, Cambridge, Massachusetts 02142, USA
| | - Spyros Artavanis-Tsakonas
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA.,Biogen Inc., 250 Binney Street, Cambridge, Massachusetts 02142, USA
| | - Steven P Gygi
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - J Wade Harper
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
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106
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He F, Zhu G, Wang YY, Zhao XM, Huang DS. PCID: A Novel Approach for Predicting Disease Comorbidity by Integrating Multi-Scale Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:678-686. [PMID: 27076462 DOI: 10.1109/tcbb.2016.2550443] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Disease comorbidity is the presence of one or more diseases along with a primary disorder, which causes additional pain to patients and leads to the failure of standard treatments compared with single diseases. Therefore, the identification of potential comorbidity can help prevent those comorbid diseases when treating a primary disease. Unfortunately, most of current known disease comorbidities are discovered occasionally in clinic, and our knowledge about comorbidity is far from complete. Despite the fact that many efforts have been made to predict disease comorbidity, the prediction accuracy of existing computational approaches needs to be improved. By investigating the factors underlying disease comorbidity, e.g., mutated genes and rewired protein-protein interactions (PPIs), we here present a novel algorithm to predict disease comorbidity by integrating multi-scale data ranging from genes to phenotypes. Benchmark results on real data show that our approach outperforms existing algorithms, and some of our novel predictions are validated with those reported in literature, indicating the effectiveness and predictive power of our approach. In addition, we identify some pathway and PPI patterns that underlie the co-occurrence between a primary disease and certain disease classes, which can help explain how the comorbidity is initiated from molecular perspectives.
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107
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ZK DrugResist 2.0: A TextMiner to extract semantic relations of drug resistance from PubMed. J Biomed Inform 2017; 69:93-98. [DOI: 10.1016/j.jbi.2017.04.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Revised: 03/06/2017] [Accepted: 04/02/2017] [Indexed: 11/30/2022]
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108
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Bahrami A, Miraie-Ashtiani SR, Sadeghi M, Najafi A. miRNA-mRNA network involved in folliculogenesis interactome: systems biology approach. Reproduction 2017; 154:51-65. [PMID: 28450315 DOI: 10.1530/rep-17-0049] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Revised: 04/06/2017] [Accepted: 04/24/2017] [Indexed: 01/01/2023]
Abstract
At later phases of folliculogenesis, the mammalian ovarian follicle contains layers of granulosa cells surrounding an antral cavity. To better understand the molecular basis of follicular growth and granulosa cell maturation, we study transcriptome profiling of granulosa cells from small (<5 mm) and large (>10 mm) bovine follicles using simultaneous method of Affymetrix microarrays (24,128 probe sets) and RNA-Seq data sets. This study proposes a computational method to discover the functional miRNA-mRNA regulatory modules, that is, groups of miRNAs and their target mRNAs that are believed to take part cooperatively in post-transcriptional gene regulation under specific conditions. The reconstructed network was named Integrated miRNA-mRNA Bipartite Network. 277 genes and 6 key modules were disclosed through clustering for mRNA master list. The 66 genes are among the genes that belong to at least two modules. All these genes, being involved in at least one of the phenomena, namely cell survival, proliferation, metastasis and apoptosis, have an overexpression pattern (P < 0.01). For miRNA master list, a total of 172 sequences were differentially expressed (P < 0.01) between dominant (large) and each of subordinate (small) follicles. Within the follicle, these miRNAs were predominantly expressed in mural granulosa cells. Finally, predicted and validated targets of these miRNAs enriched in dominant (large) follicles were identified, which are mapped to signaling pathways involved in follicular cell proliferation, steroidogenesis, PI3K/AKT/mTOR and Ras/Raf/MEK/ERK. The identification of miRNAs and their target mRNAs and the construction of their regulatory networks may give new insights into biological procedures.
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Affiliation(s)
- Abolfazl Bahrami
- Department of Animal ScienceUniversity College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Seyed Reza Miraie-Ashtiani
- Department of Animal ScienceUniversity College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Mostafa Sadeghi
- Department of Animal ScienceUniversity College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Ali Najafi
- Molecular Biology Research CenterBaqiyatallah University of Medical Sciences, Tehran, Iran
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109
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Chen X, Gao C, Guo L, Hu G, Luo Q, Liu J, Nielsen J, Chen J, Liu L. DCEO Biotechnology: Tools To Design, Construct, Evaluate, and Optimize the Metabolic Pathway for Biosynthesis of Chemicals. Chem Rev 2017; 118:4-72. [DOI: 10.1021/acs.chemrev.6b00804] [Citation(s) in RCA: 109] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Xiulai Chen
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Cong Gao
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Liang Guo
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Guipeng Hu
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Qiuling Luo
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Jia Liu
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Jens Nielsen
- Department
of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg SE-412 96, Sweden
- Novo
Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800 Lyngby, Denmark
| | - Jian Chen
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Liming Liu
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Department
of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg SE-412 96, Sweden
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
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110
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Abstract
Paralleling the increasing availability of protein-protein interaction (PPI) network data, several network alignment methods have been proposed. Network alignments have been used to uncover functionally conserved network parts and to transfer annotations. However, due to the computational intractability of the network alignment problem, aligners are heuristics providing divergent solutions and no consensus exists on a gold standard, or which scoring scheme should be used to evaluate them. We comprehensively evaluate the alignment scoring schemes and global network aligners on large scale PPI data and observe that three methods, HUBALIGN, L-GRAAL and NATALIE, regularly produce the most topologically and biologically coherent alignments. We study the collective behaviour of network aligners and observe that PPI networks are almost entirely aligned with a handful of aligners that we unify into a new tool, Ulign. Ulign enables complete alignment of two networks, which traditional global and local aligners fail to do. Also, multiple mappings of Ulign define biologically relevant soft clusterings of proteins in PPI networks, which may be used for refining the transfer of annotations across networks. Hence, PPI networks are already well investigated by current aligners, so to gain additional biological insights, a paradigm shift is needed. We propose such a shift come from aligning all available data types collectively rather than any particular data type in isolation from others.
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111
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Han S, Cai H, Che D, Zhang Y, Huang Y, Xie M. Metrical Consistency NMF for Predicting Gene-Phenotype Associations. Interdiscip Sci 2017; 10:189-194. [PMID: 28391494 DOI: 10.1007/s12539-017-0224-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2016] [Revised: 02/13/2017] [Accepted: 03/09/2017] [Indexed: 10/19/2022]
Abstract
Discovering gene-phenotype associations is significant to understand the disease mechanisms. Nonnegative matrix factorization (NMF) has been widely used in computational biology for its good performance and interpretability. In this paper, we proposed a novel metrical consistency NMF (MCNMF) method for candidate gene prioritization. The MCNMF method assume that phenotype similarities, calculated from various independent ways, should be consistent in case that the associations between genes and phenotypes are completely known. Experiment results show that our method can recover the gene-phenotype associations effectively and outperform the comparative methods.
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Affiliation(s)
- Shuai Han
- College of Software, Nankai University, Tianjin, 300350, China
| | - Hong Cai
- College of Software, Nankai University, Tianjin, 300350, China
| | - Dan Che
- College of Software, Nankai University, Tianjin, 300350, China
| | - Yaogong Zhang
- College of Software, Nankai University, Tianjin, 300350, China
| | - Yalou Huang
- College of Software, Nankai University, Tianjin, 300350, China
| | - Maoqiang Xie
- College of Software, Nankai University, Tianjin, 300350, China.
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112
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Rouka E, Vavougios GD, Solenov EI, Gourgoulianis KI, Hatzoglou C, Zarogiannis SG. Transcriptomic Analysis of the Claudin Interactome in Malignant Pleural Mesothelioma: Evaluation of the Effect of Disease Phenotype, Asbestos Exposure, and CDKN2A Deletion Status. Front Physiol 2017; 8:156. [PMID: 28377727 PMCID: PMC5359316 DOI: 10.3389/fphys.2017.00156] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Accepted: 02/28/2017] [Indexed: 01/14/2023] Open
Abstract
Malignant pleural mesothelioma (MPM) is a highly aggressive tumor primarily associated with asbestos exposure. Early detection of MPM is restricted by the long latency period until clinical presentation, the ineffectiveness of imaging techniques in early stage detection and the lack of non-invasive biomarkers with high sensitivity and specificity. In this study we used transcriptome data mining in order to determine which CLAUDIN (CLDN) genes are differentially expressed in MPM as compared to controls. Using the same approach we identified the interactome of the differentially expressed CLDN genes and assessed their expression profile. Subsequently, we evaluated the effect of tumor histology, asbestos exposure, CDKN2A deletion status, and gender on the gene expression level of the claudin interactome. We found that 5 out of 15 studied CLDNs (4, 5, 8, 10, 15) and 4 out of 27 available interactors (S100B, SHBG, CDH5, CXCL8) were differentially expressed in MPM specimens vs. healthy tissues. The genes encoding the CLDN-15 and S100B proteins present differences in their expression profile between the three histological subtypes of MPM. Moreover, CLDN-15 is significantly under-expressed in the cohort of patients with previous history of asbestos exposure. CLDN-15 was also found significantly underexpressed in patients lacking the CDKN2A gene. These results warrant the detailed in vitro investigation of the role of CDLN-15 in the pathobiology of MPM.
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Affiliation(s)
- Erasmia Rouka
- Gradute Program in Primary Health Care, Faculty of Medicine, University of Thessaly Larissa, Greece
| | - Georgios D Vavougios
- Department of Respiratory Medicine, University of Thessaly Medical School Larissa, Greece
| | - Evgeniy I Solenov
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of SciencesNovosibirsk, Russia; Department of Physiology, Novosibirsk State UniversityNovosibirsk, Russia
| | - Konstantinos I Gourgoulianis
- Gradute Program in Primary Health Care, Faculty of Medicine, University of ThessalyLarissa, Greece; Department of Respiratory Medicine, University of Thessaly Medical SchoolLarissa, Greece
| | - Chrissi Hatzoglou
- Gradute Program in Primary Health Care, Faculty of Medicine, University of ThessalyLarissa, Greece; Department of Respiratory Medicine, University of Thessaly Medical SchoolLarissa, Greece; Department of Physiology, Faculty of Medicine, University of ThessalyLarissa, Greece
| | - Sotirios G Zarogiannis
- Gradute Program in Primary Health Care, Faculty of Medicine, University of ThessalyLarissa, Greece; Department of Respiratory Medicine, University of Thessaly Medical SchoolLarissa, Greece; Department of Physiology, Faculty of Medicine, University of ThessalyLarissa, Greece
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113
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Caufield JH, Wimble C, Shary S, Wuchty S, Uetz P. Bacterial protein meta-interactomes predict cross-species interactions and protein function. BMC Bioinformatics 2017; 18:171. [PMID: 28298180 PMCID: PMC5353844 DOI: 10.1186/s12859-017-1585-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Accepted: 03/04/2017] [Indexed: 11/24/2022] Open
Abstract
Background Protein-protein interactions (PPIs) can offer compelling evidence for protein function, especially when viewed in the context of proteome-wide interactomes. Bacteria have been popular subjects of interactome studies: more than six different bacterial species have been the subjects of comprehensive interactome studies while several more have had substantial segments of their proteomes screened for interactions. The protein interactomes of several bacterial species have been completed, including several from prominent human pathogens. The availability of interactome data has brought challenges, as these large data sets are difficult to compare across species, limiting their usefulness for broad studies of microbial genetics and evolution. Results In this study, we use more than 52,000 unique protein-protein interactions (PPIs) across 349 different bacterial species and strains to determine their conservation across data sets and taxonomic groups. When proteins are collapsed into orthologous groups (OGs) the resulting meta-interactome still includes more than 43,000 interactions, about 14,000 of which involve proteins of unknown function. While conserved interactions provide support for protein function in their respective species data, we found only 429 PPIs (~1% of the available data) conserved in two or more species, rendering any cross-species interactome comparison immediately useful. The meta-interactome serves as a model for predicting interactions, protein functions, and even full interactome sizes for species with limited to no experimentally observed PPI, including Bacillus subtilis and Salmonella enterica which are predicted to have up to 18,000 and 31,000 PPIs, respectively. Conclusions In the course of this work, we have assembled cross-species interactome comparisons that will allow interactomics researchers to anticipate the structures of yet-unexplored microbial interactomes and to focus on well-conserved yet uncharacterized interactors for further study. Such conserved interactions should provide evidence for important but yet-uncharacterized aspects of bacterial physiology and may provide targets for anti-microbial therapies. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1585-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- J Harry Caufield
- Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Christopher Wimble
- Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Semarjit Shary
- Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Stefan Wuchty
- Department of Computer Science, University of Miami, Coral Gables, Florida, USA.,Center for Computational Science, University of Miami, Coral Gables, Florida, USA.,Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, Florida, USA
| | - Peter Uetz
- Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, Virginia, USA.
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You ZH, Li X, Chan KCC. An improved sequence-based prediction protocol for protein-protein interactions using amino acids substitution matrix and rotation forest ensemble classifiers. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.10.042] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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115
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Ledgerwood EC, Marshall JW, Weijman JF. The role of peroxiredoxin 1 in redox sensing and transducing. Arch Biochem Biophys 2017; 617:60-67. [DOI: 10.1016/j.abb.2016.10.009] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Revised: 10/06/2016] [Accepted: 10/14/2016] [Indexed: 12/11/2022]
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116
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Arneson D, Shu L, Tsai B, Barrere-Cain R, Sun C, Yang X. Multidimensional Integrative Genomics Approaches to Dissecting Cardiovascular Disease. Front Cardiovasc Med 2017; 4:8. [PMID: 28289683 PMCID: PMC5327355 DOI: 10.3389/fcvm.2017.00008] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Accepted: 02/09/2017] [Indexed: 12/19/2022] Open
Abstract
Elucidating the mechanisms of complex diseases such as cardiovascular disease (CVD) remains a significant challenge due to multidimensional alterations at molecular, cellular, tissue, and organ levels. To better understand CVD and offer insights into the underlying mechanisms and potential therapeutic strategies, data from multiple omics types (genomics, epigenomics, transcriptomics, metabolomics, proteomics, microbiomics) from both humans and model organisms have become available. However, individual omics data types capture only a fraction of the molecular mechanisms. To address this challenge, there have been numerous efforts to develop integrative genomics methods that can leverage multidimensional information from diverse data types to derive comprehensive molecular insights. In this review, we summarize recent methodological advances in multidimensional omics integration, exemplify their applications in cardiovascular research, and pinpoint challenges and future directions in this incipient field.
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Affiliation(s)
- Douglas Arneson
- Department of Integrative Biology and Physiology, University of California Los Angeles, Los Angeles, CA, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Le Shu
- Department of Integrative Biology and Physiology, University of California Los Angeles, Los Angeles, CA, USA; Molecular, Cellular, and Integrative Physiology Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Brandon Tsai
- Department of Integrative Biology and Physiology, University of California Los Angeles , Los Angeles, CA , USA
| | - Rio Barrere-Cain
- Department of Integrative Biology and Physiology, University of California Los Angeles , Los Angeles, CA , USA
| | - Christine Sun
- Department of Integrative Biology and Physiology, University of California Los Angeles , Los Angeles, CA , USA
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California Los Angeles, Los Angeles, CA, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA; Molecular, Cellular, and Integrative Physiology Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA; Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, CA, USA; Molecular Biology Institute, University of California Los Angeles, Los Angeles, CA, USA
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117
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Chen JY, Pandey R, Nguyen TM. HAPPI-2: a Comprehensive and High-quality Map of Human Annotated and Predicted Protein Interactions. BMC Genomics 2017; 18:182. [PMID: 28212602 PMCID: PMC5314692 DOI: 10.1186/s12864-017-3512-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2015] [Accepted: 01/24/2017] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Human protein-protein interaction (PPI) data is essential to network and systems biology studies. PPI data can help biochemists hypothesize how proteins form complexes by binding to each other, how extracellular signals propagate through post-translational modification of de-activated signaling molecules, and how chemical reactions are coupled by enzymes involved in a complex biological process. Our capability to develop good public database resources for human PPI data has a direct impact on the quality of future research on genome biology and medicine. RESULTS The database of Human Annotated and Predicted Protein Interactions (HAPPI) version 2.0 is a major update to the original HAPPI 1.0 database. It contains 2,922,202 unique protein-protein interactions (PPI) linked by 23,060 human proteins, making it the most comprehensive database covering human PPI data today. These PPIs contain both physical/direct interactions and high-quality functional/indirect interactions. Compared with the HAPPI 1.0 database release, HAPPI database version 2.0 (HAPPI-2) represents a 485% of human PPI data coverage increase and a 73% protein coverage increase. The revamped HAPPI web portal provides users with a friendly search, curation, and data retrieval interface, allowing them to retrieve human PPIs and available annotation information on the interaction type, interaction quality, interacting partner drug targeting data, and disease information. The updated HAPPI-2 can be freely accessed by Academic users at http://discovery.informatics.uab.edu/HAPPI . CONCLUSIONS While the underlying data for HAPPI-2 are integrated from a diverse data sources, the new HAPPI-2 release represents a good balance between data coverage and data quality of human PPIs, making it ideally suited for network biology.
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Affiliation(s)
- Jake Y Chen
- Wenzhou Medical University First Affiliate Hospital, Wenzhou, Zhejiang Province, China. .,Medeolinx, LLC, Indianapolis, IN, 46280, USA. .,The Informatics Institute, University of Alabama at Birmingham School of Medicine, Birmingham, AL, 35294, USA. .,Indiana Center for Systems Biology and Personalized Medicine, Indiana University School of Informatics and Computing, Indianapolis, IN, 46202, USA.
| | | | - Thanh M Nguyen
- Indiana Center for Systems Biology and Personalized Medicine, Indiana University School of Informatics and Computing, Indianapolis, IN, 46202, USA
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118
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Mamano N, Hayes WB. SANA: simulated annealing far outperforms many other search algorithms for biological network alignment. Bioinformatics 2017; 33:2156-2164. [DOI: 10.1093/bioinformatics/btx090] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 02/08/2017] [Indexed: 11/14/2022] Open
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119
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Law PJ, Berndt SI, Speedy HE, Camp NJ, Sava GP, Skibola CF, Holroyd A, Joseph V, Sunter NJ, Nieters A, Bea S, Monnereau A, Martin-Garcia D, Goldin LR, Clot G, Teras LR, Quintela I, Birmann BM, Jayne S, Cozen W, Majid A, Smedby KE, Lan Q, Dearden C, Brooks-Wilson AR, Hall AG, Purdue MP, Mainou-Fowler T, Vajdic CM, Jackson GH, Cocco P, Marr H, Zhang Y, Zheng T, Giles GG, Lawrence C, Call TG, Liebow M, Melbye M, Glimelius B, Mansouri L, Glenn M, Curtin K, Diver WR, Link BK, Conde L, Bracci PM, Holly EA, Jackson RD, Tinker LF, Benavente Y, Boffetta P, Brennan P, Maynadie M, McKay J, Albanes D, Weinstein S, Wang Z, Caporaso NE, Morton LM, Severson RK, Riboli E, Vineis P, Vermeulen RCH, Southey MC, Milne RL, Clavel J, Topka S, Spinelli JJ, Kraft P, Ennas MG, Summerfield G, Ferri GM, Harris RJ, Miligi L, Pettitt AR, North KE, Allsup DJ, Fraumeni JF, Bailey JR, Offit K, Pratt G, Hjalgrim H, Pepper C, Chanock SJ, Fegan C, Rosenquist R, de Sanjose S, Carracedo A, Dyer MJS, Catovsky D, Campo E, Cerhan JR, Allan JM, Rothman N, Houlston R, Slager S. Genome-wide association analysis implicates dysregulation of immunity genes in chronic lymphocytic leukaemia. Nat Commun 2017; 8:14175. [PMID: 28165464 PMCID: PMC5303820 DOI: 10.1038/ncomms14175] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2016] [Accepted: 12/06/2016] [Indexed: 02/07/2023] Open
Abstract
Several chronic lymphocytic leukaemia (CLL) susceptibility loci have been reported; however, much of the heritable risk remains unidentified. Here we perform a meta-analysis of six genome-wide association studies, imputed using a merged reference panel of 1,000 Genomes and UK10K data, totalling 6,200 cases and 17,598 controls after replication. We identify nine risk loci at 1p36.11 (rs34676223, P=5.04 × 10-13), 1q42.13 (rs41271473, P=1.06 × 10-10), 4q24 (rs71597109, P=1.37 × 10-10), 4q35.1 (rs57214277, P=3.69 × 10-8), 6p21.31 (rs3800461, P=1.97 × 10-8), 11q23.2 (rs61904987, P=2.64 × 10-11), 18q21.1 (rs1036935, P=3.27 × 10-8), 19p13.3 (rs7254272, P=4.67 × 10-8) and 22q13.33 (rs140522, P=2.70 × 10-9). These new and established risk loci map to areas of active chromatin and show an over-representation of transcription factor binding for the key determinants of B-cell development and immune response.
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Affiliation(s)
- Philip J. Law
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London SW7 3RP, UK
| | - Sonja I. Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA
| | - Helen E. Speedy
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London SW7 3RP, UK
| | - Nicola J. Camp
- Department of Internal Medicine, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah 84112, USA
| | - Georgina P. Sava
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London SW7 3RP, UK
| | - Christine F. Skibola
- Department of Epidemiology, School of Public Health and Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, Alabama 35233, USA
| | - Amy Holroyd
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London SW7 3RP, UK
| | - Vijai Joseph
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA
| | - Nicola J. Sunter
- Northern Institute for Cancer Research, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Alexandra Nieters
- Center for Chronic Immunodeficiency, University Medical Center Freiburg, Freiburg, Baden-Württemberg 79108, Germany
| | - Silvia Bea
- Institut d'Investigacions Biomèdiques August Pi iSunyer (IDIBAPS), Hospital Clínic, Barcelona 08036, Spain
| | - Alain Monnereau
- Registre des hémopathies malignes de la Gironde, Institut Bergonié, Inserm U1219 EPICENE, 33076 Bordeaux, France
- Epidemiology of Childhood and Adolescent Cancers Group, Inserm, Center of Research in Epidemiology and Statistics Sorbonne Paris Cité, Paris, F-94807, France
- Université Paris Descartes, Paris 75270, France
| | - David Martin-Garcia
- Institut d'Investigacions Biomèdiques August Pi iSunyer (IDIBAPS), Hospital Clínic, Barcelona 08036, Spain
| | - Lynn R. Goldin
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA
| | - Guillem Clot
- Institut d'Investigacions Biomèdiques August Pi iSunyer (IDIBAPS), Hospital Clínic, Barcelona 08036, Spain
| | - Lauren R. Teras
- Epidemiology Research Program, American Cancer Society, Atlanta, Georgia 30303, USA
| | - Inés Quintela
- Grupo de Medicina Xenomica, Universidade de Santiago de Compostela, Centro Nacional de Genotipado (CeGen-PRB2-ISCIII), CIBERER, 15782 Santiago de Compostela, Spain
| | - Brenda M. Birmann
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Sandrine Jayne
- Ernest and Helen Scott Haematological Research Institute, University of Leicester, Leicester LE2 7LX, UK
| | - Wendy Cozen
- Department of Preventive Medicine, USC Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA
- Norris Comprehensive Cancer Center, USC Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA
| | - Aneela Majid
- Ernest and Helen Scott Haematological Research Institute, University of Leicester, Leicester LE2 7LX, UK
| | - Karin E. Smedby
- Unit of Clinical Epidemiology, Department of Medicine Solna, Karolinska Institutet, Hematology Center, Karolinsak University Hospital, Stockholm 17176, Sweden
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA
| | - Claire Dearden
- The Royal Marsden NHS Foundation Trust, London SM2 5PT, UK
| | - Angela R. Brooks-Wilson
- Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, Canada V5Z1L3
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia V5A1S6, Canada
| | - Andrew G. Hall
- Northern Institute for Cancer Research, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Mark P. Purdue
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA
| | - Tryfonia Mainou-Fowler
- Haematological Sciences, Medical School, Newcastle University, Newcastle-upon-Tyne NE2 4HH, UK
| | - Claire M. Vajdic
- Centre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Graham H. Jackson
- Department of Haematology, Royal Victoria Infirmary, Newcastle upon Tyne NE1 4LP, UK
| | - Pierluigi Cocco
- Department of Public Health, Clinical and Molecular Medicine, University of Cagliari, Monserrato, Cagliari 09042, Italy
| | - Helen Marr
- Northern Institute for Cancer Research, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Yawei Zhang
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut 06520, USA
| | - Tongzhang Zheng
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut 06520, USA
| | - Graham G. Giles
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria 3004, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria 3010, Australia
| | | | - Timothy G. Call
- Division of Hematology, Mayo Clinic, Rochester, Minnesota 55905, USA
| | - Mark Liebow
- Department of Medicine, Mayo Clinic, Rochester, Minnesota 55905, USA
| | - Mads Melbye
- Department of Epidemiology Research, Division of Health Surveillance and Research, Statens Serum Institut, 2300 Copenhagen, Denmark
- Department of Medicine, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Bengt Glimelius
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, 75105 Uppsala, Sweden
| | - Larry Mansouri
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, 75105 Uppsala, Sweden
| | - Martha Glenn
- Department of Internal Medicine, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah 84112, USA
| | - Karen Curtin
- Department of Internal Medicine, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah 84112, USA
| | - W Ryan Diver
- Epidemiology Research Program, American Cancer Society, Atlanta, Georgia 30303, USA
| | - Brian K. Link
- Department of Internal Medicine, Carver College of Medicine, The University of Iowa, Iowa City, Iowa 52242, USA
| | - Lucia Conde
- Department of Epidemiology, School of Public Health and Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, Alabama 35233, USA
| | - Paige M. Bracci
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California 94118, USA
| | - Elizabeth A. Holly
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California 94118, USA
| | - Rebecca D. Jackson
- Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, Ohio 43210, USA
| | - Lesley F. Tinker
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98117, USA
| | - Yolanda Benavente
- Cancer Epidemiology Research Programme, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Barcelona 08908, Spain
- CIBER de Epidemiología y Salud Pública (CIBERESP), Barcelona 08036, Spain
| | - Paolo Boffetta
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Paul Brennan
- International Agency for Research on Cancer, Lyon 69372, France
| | - Marc Maynadie
- Registre des Hémopathies Malignes de Côte d'Or, University of Burgundy and Dijon University Hospital, Dijon 21070, France
| | - James McKay
- International Agency for Research on Cancer, Lyon 69372, France
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA
| | - Stephanie Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA
| | - Zhaoming Wang
- Department of Computational Biology, St Jude Children's Research Hospital, Memphis, Tennessee 38105, USA
| | - Neil E. Caporaso
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA
| | - Lindsay M. Morton
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA
| | - Richard K. Severson
- Department of Family Medicine and Public Health Sciences, Wayne State University, Detroit, Michigan 48201, USA
| | - Elio Riboli
- School of Public Health, Imperial College London, London W2 1PG, UK
| | - Paolo Vineis
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W2 1PG, UK
- Human Genetics Foundation, 10126 Turin, Italy
| | - Roel C. H. Vermeulen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht 3508 TD, The Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands
| | - Melissa C. Southey
- Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Roger L. Milne
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria 3004, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Jacqueline Clavel
- Epidemiology of Childhood and Adolescent Cancers Group, Inserm, Center of Research in Epidemiology and Statistics Sorbonne Paris Cité (CRESS), Paris F-94807, France
- Université Paris Descartes, 75270 Paris, France
| | - Sabine Topka
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA
| | - John J. Spinelli
- Cancer Control Research, BC Cancer Agency, Vancouver, British Columbia, Canada V5Z1L3
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada V6T1Z3
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, USA
| | - Maria Grazia Ennas
- Department of Biomedical Science, University of Cagliari, Monserrato, Cagliari 09042, Italy
| | | | - Giovanni M. Ferri
- Interdisciplinary Department of Medicine, University of Bari, Bari 70124, Italy
| | - Robert J. Harris
- Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool L69 3BX, UK
| | - Lucia Miligi
- Environmental and Occupational Epidemiology Unit, Cancer Prevention and Research Institute (ISPO), Florence 50139, Italy
| | - Andrew R. Pettitt
- Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool L69 3BX, UK
| | - Kari E. North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
- Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
| | - David J. Allsup
- Queens Centre for Haematology and Oncology, Castle Hill Hospital, Hull and East Yorkshire NHS Trust, Cottingham HU16 5JQ, UK
| | - Joseph F. Fraumeni
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA
| | - James R. Bailey
- Queens Centre for Haematology and Oncology, Castle Hill Hospital, Hull and East Yorkshire NHS Trust, Cottingham HU16 5JQ, UK
| | - Kenneth Offit
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA
| | - Guy Pratt
- Department of Haematology, Birmingham Heartlands Hospital, Birmingham B9 5SS, UK
| | - Henrik Hjalgrim
- Department of Epidemiology Research, Division of Health Surveillance and Research, Statens Serum Institut, 2300 Copenhagen, Denmark
| | - Chris Pepper
- Division of Cancer and Genetics, School of Medicine, Cardiff University, Cardiff CF14 4XN, UK
| | - Stephen J. Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA
| | - Chris Fegan
- Cardiff and Vale National Health Service Trust, Heath Park, Cardiff CF14 4XW, UK
| | - Richard Rosenquist
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, 75105 Uppsala, Sweden
| | - Silvia de Sanjose
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
- International Agency for Research on Cancer, Lyon 69372, France
| | - Angel Carracedo
- Grupo de Medicina Xenomica, Universidade de Santiago de Compostela, Centro Nacional de Genotipado (CeGen-PRB2-ISCIII), CIBERER, 15782 Santiago de Compostela, Spain
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah 21589, KSA
| | - Martin J. S. Dyer
- Ernest and Helen Scott Haematological Research Institute, University of Leicester, Leicester LE2 7LX, UK
| | - Daniel Catovsky
- Division of Molecular Pathology, The Institute of Cancer Research, London SW7 3RP, UK
| | - Elias Campo
- Institut d'Investigacions Biomèdiques August Pi iSunyer (IDIBAPS), Hospital Clínic, Barcelona 08036, Spain
- Unitat de Hematología, Hospital Clínic, IDIBAPS, Universitat de Barcelona, Barcelona 08036, Spain
| | - James R. Cerhan
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55905, USA
| | - James M. Allan
- Northern Institute for Cancer Research, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Nathanial Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA
| | - Richard Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London SW7 3RP, UK
| | - Susan Slager
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55905, USA
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Du J, Zhang L. Pathway deviation-based biomarker and multi-effect target identification in asbestos-related squamous cell carcinoma of the lung. Int J Mol Med 2017; 39:579-586. [PMID: 28204826 PMCID: PMC5360351 DOI: 10.3892/ijmm.2017.2878] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Accepted: 01/20/2017] [Indexed: 12/19/2022] Open
Abstract
Asbestos-related lung carcinoma is one of the most devastating occupational cancers, and effective techniques for early diagnosis are still lacking. In the present study, a systematic approach was applied to detect a potential biomarker for asbestos-related lung cancer (ARLC); in particular asbestos-related squamous cell carcinoma (ARLC-SCC). Microarray data (GSE23822) were retrieved from the Gene Expression Omnibus database, including 26 ARLC-SCCs and 30 non-asbestos-related squamous cell lung carcinomas (NARLC-SCCs). Differentially expressed genes (DEGs) were identified by the limma package, and then a protein-protein interaction (PPI) network was constructed according to the BioGRID and HPRD databases. A novel scoring approach integrating an expression deviation score and network degree of the gene was then proposed to weight the DEGs. Subsequently, the important genes were uploaded to DAVID for pathway enrichment analysis. Pathway correlation analysis was carried out using Spearman's rank correlation coefficient of the pathscore. In total, 1,333 DEGs, 391 upregulated and 942 downregulated, were obtained between the ARLC-SCCs and NARLC-SCCs. A total of 524 important genes for ARLC-SCC were significantly enriched in 22 KEGG pathways. Correlation analysis of these pathways showed that the pathway of SNARE interactions in vesicular transport was significantly correlated with 12 other pathways. Additionally, obvious correlations were found between multiple pathways by sharing cross-talk genes (EGFR, PRKX, PDGFB, PIK3R3, SLK, IGF1, CDC42 and PRKCA). On the whole, our data demonstrate that 8 cross-talk genes were found to bridge multiple ARLC-SCC-specific pathways, which may be used as candidate biomarkers and potential multi-effect targets. As these genes are involved in multiple pathways, it is possible that drugs targeting these genes may thus be able to influence multiple pathways simultaneously.
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Affiliation(s)
- Jiang Du
- Department of Thoracic Surgery, First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China
| | - Lin Zhang
- Department of Thoracic Surgery, First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China
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Smoly I, Shemesh N, Ziv-Ukelson M, Ben-Zvi A, Yeger-Lotem E. An Asymmetrically Balanced Organization of Kinases versus Phosphatases across Eukaryotes Determines Their Distinct Impacts. PLoS Comput Biol 2017; 13:e1005221. [PMID: 28135269 PMCID: PMC5279721 DOI: 10.1371/journal.pcbi.1005221] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Accepted: 10/24/2016] [Indexed: 12/22/2022] Open
Abstract
Protein phosphorylation underlies cellular response pathways across eukaryotes and is governed by the opposing actions of phosphorylating kinases and de-phosphorylating phosphatases. While kinases and phosphatases have been extensively studied, their organization and the mechanisms by which they balance each other are not well understood. To address these questions we performed quantitative analyses of large-scale 'omics' datasets from yeast, fly, plant, mouse and human. We uncovered an asymmetric balance of a previously-hidden scale: Each organism contained many different kinase genes, and these were balanced by a small set of highly abundant phosphatase proteins. Kinases were much more responsive to perturbations at the gene and protein levels. In addition, kinases had diverse scales of phenotypic impact when manipulated. Phosphatases, in contrast, were stable, highly robust and flatly organized, with rather uniform impact downstream. We validated aspects of this organization experimentally in nematode, and supported additional aspects by theoretic analysis of the dynamics of protein phosphorylation. Our analyses explain the empirical bias in the protein phosphorylation field toward characterization and therapeutic targeting of kinases at the expense of phosphatases. We show quantitatively and broadly that this is not only a historical bias, but stems from wide-ranging differences in their organization and impact. The asymmetric balance between these opposing regulators of protein phosphorylation is also common to opposing regulators of two other post-translational modification systems, suggesting its fundamental value. Protein phosphorylation is a reversible modification that underlies cellular responses to stimuli across organisms. Historically, the study of protein phosphorylation concentrated on the role of kinases, which introduce the phosphate, at the expense of phosphatases, which remove it. Many kinases have been associated with specific phenotypes and considered attractive drug targets, while phosphatases remained far less characterized. It has been unclear whether this discrepancy is due to historical biases or reflects real systemic differences between these enzymes. By analyzing large-scale ‘omics’ datasets across genes, transcripts, proteins, interactions, and organisms, we uncovered an asymmetric architecture of kinases versus phosphatases that balances between them, determines their distinct impact patterns, and affects their therapeutic potential. This architecture is conserved from yeast to human and is partially shared by two other protein modification systems, suggesting it is a general feature of these systems.
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Affiliation(s)
- Ilan Smoly
- Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Netta Shemesh
- National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Department of Life Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Michal Ziv-Ukelson
- Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Anat Ben-Zvi
- National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Department of Life Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Esti Yeger-Lotem
- National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- * E-mail:
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Yuan X, Chen J, Lin Y, Li Y, Xu L, Chen L, Hua H, Shen B. Network Biomarkers Constructed from Gene Expression and Protein-Protein Interaction Data for Accurate Prediction of Leukemia. J Cancer 2017; 8:278-286. [PMID: 28243332 PMCID: PMC5327377 DOI: 10.7150/jca.17302] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 10/29/2016] [Indexed: 12/14/2022] Open
Abstract
Leukemia is a leading cause of cancer deaths in the developed countries. Great efforts have been undertaken in search of diagnostic biomarkers of leukemia. However, leukemia is highly complex and heterogeneous, involving interaction among multiple molecular components. Individual molecules are not necessarily sensitive diagnostic indicators. Network biomarkers are considered to outperform individual molecules in disease characterization. We applied an integrative approach that identifies active network modules as putative biomarkers for leukemia diagnosis. We first reconstructed the leukemia-specific PPI network using protein-protein interactions from the Protein Interaction Network Analysis (PINA) and protein annotations from GeneGo. The network was further integrated with gene expression profiles to identify active modules with leukemia relevance. Finally, the candidate network-based biomarker was evaluated for the diagnosing performance. A network of 97 genes and 400 interactions was identified for accurate diagnosis of leukemia. Functional enrichment analysis revealed that the network biomarkers were enriched in pathways in cancer. The network biomarkers could discriminate leukemia samples from the normal controls more effectively than the known biomarkers. The network biomarkers provide a useful tool to diagnose leukemia and also aids in further understanding the molecular basis of leukemia.
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Affiliation(s)
- Xuye Yuan
- Center for Systems Biology, Soochow University, Suzhou, 215006, China
| | - Jiajia Chen
- School of Chemistry and Biological Engineering, Suzhou University of Science and Technology, Suzhou, 215011, China
| | - Yuxin Lin
- Center for Systems Biology, Soochow University, Suzhou, 215006, China
| | - Yin Li
- Center for Systems Biology, Soochow University, Suzhou, 215006, China
| | - Lihua Xu
- Department of Pediatrics, The First People's Hospital of Lianyungang, Lianyungang, 222002, China
| | - Luonan Chen
- Key Laboratory of Systems Biology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Haiying Hua
- Department of Hematology, The Third Hospital Affiliated to Nantong University, No. 585 North Xingyuan Road, Wuxi, Jiangsu214041, China
| | - Bairong Shen
- Center for Systems Biology, Soochow University, Suzhou, 215006, China
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Greene CS, Himmelstein DS. Genetic Association-Guided Analysis of Gene Networks for the Study of Complex Traits. ACTA ACUST UNITED AC 2017; 9:179-84. [PMID: 27094199 DOI: 10.1161/circgenetics.115.001181] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Accepted: 03/08/2016] [Indexed: 12/29/2022]
Affiliation(s)
- Casey S Greene
- From the Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia (C.S.G.); and Biological and Medical Informatics, University of California, San Francisco (D.S.H.).
| | - Daniel S Himmelstein
- From the Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia (C.S.G.); and Biological and Medical Informatics, University of California, San Francisco (D.S.H.)
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124
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Wang S, Qu M, Peng J. PROSNET: INTEGRATING HOMOLOGY WITH MOLECULAR NETWORKS FOR PROTEIN FUNCTION PREDICTION. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2017; 22:27-38. [PMID: 27896959 PMCID: PMC5319591 DOI: 10.1142/9789813207813_0004] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Automated annotation of protein function has become a critical task in the post-genomic era. Network-based approaches and homology-based approaches have been widely used and recently tested in large-scale community-wide assessment experiments. It is natural to integrate network data with homology information to further improve the predictive performance. However, integrating these two heterogeneous, high-dimensional and noisy datasets is non-trivial. In this work, we introduce a novel protein function prediction algorithm ProSNet. An integrated heterogeneous network is first built to include molecular networks of multiple species and link together homologous proteins across multiple species. Based on this integrated network, a dimensionality reduction algorithm is introduced to obtain compact low-dimensional vectors to encode proteins in the network. Finally, we develop machine learning classification algorithms that take the vectors as input and make predictions by transferring annotations both within each species and across different species. Extensive experiments on five major species demonstrate that our integration of homology with molecular networks substantially improves the predictive performance over existing approaches.
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Affiliation(s)
- Sheng Wang
- Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA
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125
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Bonnici V, Giugno R. On the Variable Ordering in Subgraph Isomorphism Algorithms. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:193-203. [PMID: 26761859 DOI: 10.1109/tcbb.2016.2515595] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Graphs are mathematical structures to model several biological data. Applications to analyze them require to apply solutions for the subgraph isomorphism problem, which is NP-complete. Here, we investigate the existing strategies to reduce the subgraph isomorphism algorithm running time with emphasis on the importance of the order with which the graph vertices are taken into account during the search, called variable ordering, and its incidence on the total running time of the algorithms. We focus on two recent solutions, which are based on an effective variable ordering strategy. We discuss their comparison both with the variable ordering strategies reviewed in the paper and the other algorithms present in the ICPR2014 contest on graph matching algorithms for pattern search in biological databases.
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126
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Yao Q, Xu D. Bioinformatics Analysis of Protein Phosphorylation in Plant Systems Biology Using P3DB. Methods Mol Biol 2017; 1558:127-138. [PMID: 28150236 DOI: 10.1007/978-1-4939-6783-4_6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Protein phosphorylation is one of the most pervasive protein post-translational modification events in plant cells. It is involved in many plant biological processes, such as plant growth, organ development, and plant immunology, by regulating or switching signaling and metabolic pathways. High-throughput experimental methods like mass spectrometry can easily characterize hundreds to thousands of phosphorylation events in a single experiment. With the increasing volume of the data sets, Plant Protein Phosphorylation DataBase (P3DB, http://p3db.org ) provides a comprehensive, systematic, and interactive online platform to deposit, query, analyze, and visualize these phosphorylation events in many plant species. It stores the protein phosphorylation sites in the context of identified mass spectra, phosphopeptides, and phosphoproteins contributed from various plant proteome studies. In addition, P3DB associates these plant phosphorylation sites to protein physicochemical information in the protein charts and tertiary structures, while various protein annotations from hierarchical kinase phosphatase families, protein domains, and gene ontology are also added into the database. P3DB not only provides rich information, but also interconnects and provides visualization of the data in networks, in systems biology context. Currently, P3DB includes the KiC (Kinase Client) assay network, the protein-protein interaction network, the kinase-substrate network, the phosphatase-substrate network, and the protein domain co-occurrence network. All of these are available to query for and visualize existing phosphorylation events. Although P3DB only hosts experimentally identified phosphorylation data, it provides a plant phosphorylation prediction model for any unknown queries on the fly. P3DB is an entry point to the plant phosphorylation community to deposit and visualize any customized data sets within this systems biology framework. Nowadays, P3DB has become one of the major bioinformatics platforms of protein phosphorylation in plant biology.
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Affiliation(s)
- Qiuming Yao
- Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, 1201 Rollins St., Columbia, MO, 65211, USA.
| | - Dong Xu
- Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, 1201 Rollins St., Columbia, MO, 65211, USA
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127
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Xia L, Liu Y, Fu Y, Dongye S, Wang D. Integrated analysis reveals candidate mRNA and their potential roles in uterine leiomyomas. J Obstet Gynaecol Res 2016; 43:149-156. [PMID: 27987347 DOI: 10.1111/jog.13172] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2016] [Accepted: 08/15/2016] [Indexed: 01/04/2023]
Abstract
AIM Uterine leiomyomas (UL) are the most common pelvic tumors, and the etiology and pathophysiology are not well understood. We aimed to elucidate the genes responsible for UL development. METHODS Integrated analyses of four datasets of mRNA profiling for UL were performed. Functional annotation of differentially expressed genes (DEG) was used to systematically characterize the global expression profiles. The UL-specific protein-protein interaction network was constructed. RESULTS Integrated analysis led to the discovery of 2167 DEG (1042 upregulated and 1125 downregulated). The aberrant expression of NAV2, KIF5C, DCX, CAPN6, COL4A2, ALDH1A1, and DPT may play important roles in UL tumorigenesis. In addition, the dysregulation of MEST, LGALS3, and TLR3 may be involved in the progression of UL by a common mechanism. Functional annotation showed that extracellular matrix receptor interaction may be more active and cause the extracellular matrix abnormally formed in UL. Moreover, focal adhesion and cell adhesion molecules may play roles in the development of UL. Furthermore, chemokine signaling pathway and cytokine-cytokine receptor interaction were most probably involved in the development of UL. CONCLUSION In conclusion, our study observed that a set of aberrantly expressed genes and the related biochemical pathways may lead to transformation of normal myometrium in pathological focuses.
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Affiliation(s)
- Liping Xia
- Department of Ultrasonography, TaiShan Medical College Affiliated Hospital of Shandong Province, Taian, China
| | - Yan Liu
- Department of Ultrasonography, TaiShan Medical College Affiliated Hospital of Shandong Province, Taian, China
| | - Yan Fu
- Personnel Section, TaiShan Medical College Affiliated Hospital of Shandong Province, Taian, China
| | - Shengyi Dongye
- Department of Pathology, TaiShan Medical College Affiliated Hospital of Shandong Province, Taian, China
| | - Dewei Wang
- Department of Ultrasonography, TaiShan Medical College Affiliated Hospital of Shandong Province, Taian, China
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Kazemi E, Hassani H, Grossglauser M, Pezeshgi Modarres H. PROPER: global protein interaction network alignment through percolation matching. BMC Bioinformatics 2016; 17:527. [PMID: 27955623 PMCID: PMC5153870 DOI: 10.1186/s12859-016-1395-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Accepted: 11/29/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The alignment of protein-protein interaction (PPI) networks enables us to uncover the relationships between different species, which leads to a deeper understanding of biological systems. Network alignment can be used to transfer biological knowledge between species. Although different PPI-network alignment algorithms were introduced during the last decade, developing an accurate and scalable algorithm that can find alignments with high biological and structural similarities among PPI networks is still challenging. RESULTS In this paper, we introduce a new global network alignment algorithm for PPI networks called PROPER. Compared to other global network alignment methods, our algorithm shows higher accuracy and speed over real PPI datasets and synthetic networks. We show that the PROPER algorithm can detect large portions of conserved biological pathways between species. Also, using a simple parsimonious evolutionary model, we explain why PROPER performs well based on several different comparison criteria. CONCLUSIONS We highlight that PROPER has high potential in further applications such as detecting biological pathways, finding protein complexes and PPI prediction. The PROPER algorithm is available at http://proper.epfl.ch .
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Affiliation(s)
- Ehsan Kazemi
- School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland.
| | - Hamed Hassani
- Department of Computer Science, ETHZ, Zurich, Switzerland
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129
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Co-expression network analyses identify functional modules associated with development and stress response in Gossypium arboreum. Sci Rep 2016; 6:38436. [PMID: 27922095 PMCID: PMC5138846 DOI: 10.1038/srep38436] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Accepted: 11/09/2016] [Indexed: 11/22/2022] Open
Abstract
Cotton is an economically important crop, essential for the agriculture and textile industries. Through integrating transcriptomic data, we discovered that multi-dimensional co-expression network analysis was powerful for predicting cotton gene functions and functional modules. Here, the recently available transcriptomic data on Gossypium arboreum, including data on multiple growth stages of tissues and stress treatment samples were applied to construct a co-expression network exploring multi-dimensional expression (development and stress) through multi-layered approaches. Based on differential gene expression and network analysis, a fibre development regulatory module of the gene GaKNL1 was found to regulate the second cell wall through repressing the activity of REVOLUTA, and a tissue-selective module of GaJAZ1a was examined in response to water stress. Moreover, comparative genomics analysis of the JAZ1-related regulatory module revealed high conservation across plant species. In addition, 1155 functional modules were identified through integrating the co-expression network, module classification and function enrichment tools, which cover functions such as metabolism, stress responses, and transcriptional regulation. In the end, an online platform was built for network analysis (http://structuralbiology.cau.edu.cn/arboreum), which could help to refine the annotation of cotton gene function and establish a data mining system to identify functional genes or modules with important agronomic traits.
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130
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Ehsani R, Bahrami S, Drabløs F. Feature-based classification of human transcription factors into hypothetical sub-classes related to regulatory function. BMC Bioinformatics 2016; 17:459. [PMID: 27842491 PMCID: PMC5109715 DOI: 10.1186/s12859-016-1349-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2016] [Accepted: 11/10/2016] [Indexed: 12/15/2022] Open
Abstract
Background Transcription factors are key proteins in the regulation of gene transcription. An important step in this process is the opening of chromatin in order to make genomic regions available for transcription. Data on DNase I hypersensitivity has previously been used to label a subset of transcription factors as Pioneers, Settlers and Migrants to describe their potential role in this process. These labels represent an interesting hypothesis on gene regulation and possibly a useful approach for data analysis, and therefore we wanted to expand the set of labeled transcription factors to include as many known factors as possible. We have used a well-annotated dataset of 1175 transcription factors as input to supervised machine learning methods, using the subset with previously assigned labels as training set. We then used the final classifier to label the additional transcription factors according to their potential role as Pioneers, Settlers and Migrants. The full set of labeled transcription factors was used to investigate associated properties and functions of each class, including an analysis of interaction data for transcription factors based on DNA co-binding and protein-protein interactions. We also used the assigned labels to analyze a previously published set of gene lists associated with a time course experiment on cell differentiation. Results The analysis showed that the classification of transcription factors with respect to their potential role in chromatin opening largely was determined by how they bind to DNA. Each subclass of transcription factors was enriched for properties that seemed to characterize the subclass relative to its role in gene regulation, with very general functions for Pioneers, whereas Migrants to a larger extent were associated with specific processes. Further analysis showed that the expanded classification is a useful resource for analyzing other datasets on transcription factors with respect to their potential role in gene regulation. The analysis of transcription factor interaction data showed complementary differences between the subclasses, where transcription factors labeled as Pioneers often interact with other transcription factors through DNA co-binding, whereas Migrants to a larger extent use protein-protein interactions. The analysis of time course data on cell differentiation indicated a shift in the regulatory program associated with Pioneer-like transcription factors during differentiation. Conclusions The expanded classification is an interesting resource for analyzing data on gene regulation, as illustrated here on transcription factor interaction data and data from a time course experiment. The potential regulatory function of transcription factors seems largely to be determined by how they bind DNA, but is also influenced by how they interact with each other through cooperativity and protein-protein interactions. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1349-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Rezvan Ehsani
- Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, PO Box 8905, NO-7491, Trondheim, Norway.,Department of Mathematics, University of Zabol, Zabol, Iran
| | - Shahram Bahrami
- Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, PO Box 8905, NO-7491, Trondheim, Norway.,St. Olavs Hospital, Trondheim University Hospital, NO-7006, Trondheim, Norway
| | - Finn Drabløs
- Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, PO Box 8905, NO-7491, Trondheim, Norway.
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131
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Kotni MK, Zhao M, Wei DQ. Gene expression profiles and protein-protein interaction networks in amyotrophic lateral sclerosis patients with C9orf72 mutation. Orphanet J Rare Dis 2016; 11:148. [PMID: 27814735 PMCID: PMC5097384 DOI: 10.1186/s13023-016-0531-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Accepted: 10/24/2016] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that involves the death of neurons. ALS is associated with many gene mutations as previously studied. In order to explore the molecular mechanisms underlying ALS with C9orf72 mutation, gene expression profiles of ALS fibroblasts and control fibroblasts were subjected to bioinformatics analysis. Genes with critical functional roles can be detected by a measure of node centrality in biological networks. In gene co-expression networks, highly connected genes called as candidate hubs have been associated with key disease-related pathways. Herein, this method was applied to find the hub genes related to ALS disease. METHODS Illumina HiSeq microarray gene expression dataset GSE51684 was retrieved from Gene Expression Omnibus (GEO) database which included four Sporadic ALS, twelve Familial ALS and eight control samples. Differentially Expressed Genes (DEGs) were identified using the Student's t test statistical method and gene co-expression networking. Gene ontology (GO) function and KEGG pathway enrichment analysis of DEGs were performed using the DAVID online tool. Protein-protein interaction (PPI) networks were constructed by mapping the DEGs onto protein-protein interaction data from publicly available databases to identify the pathways where DEGs are involved in. PPI interaction network was divided into subnetworks using MCODE algorithm and was analyzed using Cytoscape. RESULTS The results revealed that the expression of DEGs was mainly involved in cell adhesion, cell-cell signaling, Extra cellular matrix region GO processes and focal adhesion, neuroactive ligand receptor interaction, Extracellular matrix receptor interaction. Tumor necrosis factor (TNF), Endothelin 1 (EDN1), Angiotensin (AGT) and many cell adhesion molecules (CAM) were detected as hub genes that can be targeted as novel therapeutic targets for ALS disease. CONCLUSION These analyses and findings enhance the understanding of ALS pathogenesis and provide references for ALS therapy.
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Affiliation(s)
- Meena Kumari Kotni
- College of Life Science and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240 China
| | - Mingzhu Zhao
- Instrumental Analysis Center, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240 China
| | - Dong-Qing Wei
- College of Life Science and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240 China
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132
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Wang X, Wang SS, Zhou L, Yu L, Zhang LM. A network-pathway based module identification for predicting the prognosis of ovarian cancer patients. J Ovarian Res 2016; 9:73. [PMID: 27806724 PMCID: PMC5093979 DOI: 10.1186/s13048-016-0285-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2016] [Accepted: 10/25/2016] [Indexed: 12/19/2022] Open
Abstract
Background This study aimed to screen multiple genes biomarkers based on gene expression data for predicting the survival of ovarian cancer patients. Methods Two microarray data of ovarian cancer samples were collected from The Cancer Genome Atlas (TCGA) database. The data in the training set were used to construct Reactome functional interactions network, which then underwent Markov clustering, supervised principal components, Cox proportional hazard model to screen significantly prognosis related modules. The distinguishing ability of each module for survival was further evaluated by the testing set. Gene Ontology (GO) functional and pathway annotations were performed to identify the roles of genes in each module for ovarian cancer. Results The network based approach identified two 7-gene functional interaction modules (31: DCLRE1A, EXO1, KIAA0101, KIN, PCNA, POLD3, POLD2; 35: DKK3, FABP3, IRF1, AIM2, GBP1, GBP2, IRF2) that are associated with prognosis of ovarian cancer patients. These network modules are related to DNA repair, replication, immune and cytokine mediated signaling pathways. Conclusions The two 7-gene expression signatures may be accurate predictors of clinical outcome in patients with ovarian cancer and has the potential to develop new therapeutic strategies for ovarian cancer patients.
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Affiliation(s)
- Xin Wang
- Department of Gynaecology and Obstetrics, The 306 Hospital of PLA, Beijing, 100037, China
| | - Shan-Shan Wang
- Outpatient Pharmacy, Outpatient Department, NO.16 Chengzhuang Fengtai Distinct, Beijing, 100071, China
| | - Lin Zhou
- Department of Gynaecology and Obstetrics, The 306 Hospital of PLA, Beijing, 100037, China
| | - Li Yu
- Department of Gynaecology and Obstetrics, The 306 Hospital of PLA, Beijing, 100037, China
| | - Lan-Mei Zhang
- Department of Gynaecology and Obstetrics, The 306 Hospital of PLA, Beijing, 100037, China.
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Hou L, Chen M, Wang M, Cui X, Gao Y, Xing T, Li J, Deng S, Hu J, Yang H, Jiang J. Systematic analyses of key genes and pathways in the development of invasive breast cancer. Gene 2016; 593:1-12. [DOI: 10.1016/j.gene.2016.08.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2016] [Revised: 07/15/2016] [Accepted: 08/04/2016] [Indexed: 11/29/2022]
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Sutphin GL, Mahoney JM, Sheppard K, Walton DO, Korstanje R. WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning. PLoS Comput Biol 2016; 12:e1005182. [PMID: 27812085 PMCID: PMC5094675 DOI: 10.1371/journal.pcbi.1005182] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Accepted: 10/05/2016] [Indexed: 01/01/2023] Open
Abstract
The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs) between 6 eukaryotic species-humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/.
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Affiliation(s)
| | - J. Matthew Mahoney
- Department of Neurological Sciences, University of Vermont College of Medicine, Burlington, VT, United States of America
| | - Keith Sheppard
- The Jackson Laboratory, Bar Harbor, ME, United States of America
| | - David O. Walton
- The Jackson Laboratory, Bar Harbor, ME, United States of America
| | - Ron Korstanje
- The Jackson Laboratory, Bar Harbor, ME, United States of America
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135
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Multi-OMIC profiling of survival and metabolic signaling networks in cells subjected to photodynamic therapy. Cell Mol Life Sci 2016; 74:1133-1151. [PMID: 27803950 PMCID: PMC5309296 DOI: 10.1007/s00018-016-2401-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Revised: 09/30/2016] [Accepted: 10/18/2016] [Indexed: 02/06/2023]
Abstract
Photodynamic therapy (PDT) is an established palliative treatment for perihilar cholangiocarcinoma that is clinically promising. However, tumors tend to regrow after PDT, which may result from the PDT-induced activation of survival pathways in sublethally afflicted tumor cells. In this study, tumor-comprising cells (i.e., vascular endothelial cells, macrophages, perihilar cholangiocarcinoma cells, and EGFR-overexpressing epidermoid cancer cells) were treated with the photosensitizer zinc phthalocyanine that was encapsulated in cationic liposomes (ZPCLs). The post-PDT survival pathways and metabolism were studied following sublethal (LC50) and supralethal (LC90) PDT. Sublethal PDT induced survival signaling in perihilar cholangiocarcinoma (SK-ChA-1) cells via mainly HIF-1-, NF-кB-, AP-1-, and heat shock factor (HSF)-mediated pathways. In contrast, supralethal PDT damage was associated with a dampened survival response. PDT-subjected SK-ChA-1 cells downregulated proteins associated with EGFR signaling, particularly at LC90. PDT also affected various components of glycolysis and the tricarboxylic acid cycle as well as metabolites involved in redox signaling. In conclusion, sublethal PDT activates multiple pathways in tumor-associated cell types that transcriptionally regulate cell survival, proliferation, energy metabolism, detoxification, inflammation/angiogenesis, and metastasis. Accordingly, tumor cells sublethally afflicted by PDT are a major therapeutic culprit. Our multi-omic analysis further unveiled multiple druggable targets for pharmacological co-intervention.
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136
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Wu CH, Hsu CL, Lu PC, Lin WC, Juan HF, Huang HC. Identification of lncRNA functions in lung cancer based on associated protein-protein interaction modules. Sci Rep 2016; 6:35939. [PMID: 27786280 PMCID: PMC5081511 DOI: 10.1038/srep35939] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Accepted: 10/07/2016] [Indexed: 02/01/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) have been found to play important roles in various biological processes; however, many of their functions remain unclear. In this study, we present a novel approach to identify the lncRNA-associated protein-protein interaction (PPI) modules and ascertain their functions in human lung squamous cell carcinoma. We collected lncRNA and mRNA expression profiles of lung squamous cell carcinoma from The Cancer Genome Atlas. To identify the lncRNA-associated PPI modules, lncRNA-mRNA co-expression networks were first constructed based on the mutual ranks of expression correlations. Next, we examined whether the co-expressed mRNAs of a specific lncRNA were closely connected by PPIs. For this, a significantly connected mRNA set was considered to be the lncRNA-associated PPI module. Finally, the prospective functions of a lncRNA was inferred using Gene Ontology enrichment analysis on the associated module. We found that lncRNA-associated PPI modules were subtype-dependent and each subtype had unique molecular mechanisms. In addition, antisense lncRNAs and sense genes tended to be functionally associated. Our results might provide new directions for understanding lncRNA regulations in lung cancer. The analysis pipeline was implemented in a web tool, available at http://lncin.ym.edu.tw/.
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Affiliation(s)
- Chih-Hsun Wu
- Institute of Biotechnology in Medicine, National Yang-Ming University, Taipei 112, Taiwan.,Institute of Biomedical Informatics, Center for Systems and Synthetic Biology, National Yang-Ming University, Taipei 112, Taiwan
| | - Chia-Lang Hsu
- Department of Life Science, National Taiwan University, Taipei 106, Taiwan
| | - Pei-Chun Lu
- Institute of Biomedical Informatics, Center for Systems and Synthetic Biology, National Yang-Ming University, Taipei 112, Taiwan
| | - Wen-Chang Lin
- Institute of Biotechnology in Medicine, National Yang-Ming University, Taipei 112, Taiwan.,Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
| | - Hsueh-Fen Juan
- Department of Life Science, National Taiwan University, Taipei 106, Taiwan.,Graduate Institute of Biomedical Electronics and Bioinformatics, Institute of Molecular and Cellular Biology, National Taiwan University, Taipei 106, Taiwan
| | - Hsuan-Cheng Huang
- Institute of Biomedical Informatics, Center for Systems and Synthetic Biology, National Yang-Ming University, Taipei 112, Taiwan
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137
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Abstract
Developing improved approaches for diagnosis, treatment, and prevention of diseases is a major goal of biomedical research. Therefore, the discovery of biomarker signatures from high-throughput "omics" data is an active research topic in the field of bioinformatics and systems medicine. A major issue is the low reproducibility and the limited biological interpretability of candidate biomarker signatures identified from high-throughput data. This impedes the use of discovered biomarker signatures into clinical applications. Currently, much focus is placed on developing strategies to improve reproducibility and interpretability. Researchers have fruitfully started to incorporate prior knowledge derived from pathways and molecular networks into the process of biomarker identification. In this chapter, after giving a general introduction to the problem of disease classification and biomarker discovery, we will review two types of network-assisted approaches: (1) approaches inferring activity scores for specific pathways which are subsequently used for classification and (2) approaches identifying subnetworks or modules of molecular networks by differential network analysis which can serve as biomarker signatures.
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138
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Lu S, Cai C, Yan G, Zhou Z, Wan Y, Chen V, Chen L, Cooper GF, Obeid LM, Hannun YA, Lee AV, Lu X. Signal-Oriented Pathway Analyses Reveal a Signaling Complex as a Synthetic Lethal Target for p53 Mutations. Cancer Res 2016; 76:6785-6794. [PMID: 27758891 DOI: 10.1158/0008-5472.can-16-1740] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Revised: 08/31/2016] [Accepted: 09/18/2016] [Indexed: 11/16/2022]
Abstract
Defining processes that are synthetic lethal with p53 mutations in cancer cells may reveal possible therapeutic strategies. In this study, we report the development of a signal-oriented computational framework for cancer pathway discovery in this context. We applied our bipartite graph-based functional module discovery algorithm to identify transcriptomic modules abnormally expressed in multiple tumors, such that the genes in a module were likely regulated by a common, perturbed signal. For each transcriptomic module, we applied our weighted k-path merge algorithm to search for a set of somatic genome alterations (SGA) that likely perturbed the signal, that is, the candidate members of the pathway that regulate the transcriptomic module. Computational evaluations indicated that our methods-identified pathways were perturbed by SGA. In particular, our analyses revealed that SGA affecting TP53, PTK2, YWHAZ, and MED1 perturbed a set of signals that promote cell proliferation, anchor-free colony formation, and epithelial-mesenchymal transition (EMT). These proteins formed a signaling complex that mediates these oncogenic processes in a coordinated fashion. Disruption of this signaling complex by knocking down PTK2, YWHAZ, or MED1 attenuated and reversed oncogenic phenotypes caused by mutant p53 in a synthetic lethal manner. This signal-oriented framework for searching pathways and therapeutic targets is applicable to all cancer types, thus potentially impacting precision medicine in cancer. Cancer Res; 76(23); 6785-94. ©2016 AACR.
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Affiliation(s)
- Songjian Lu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania.,Center for Causal Discovery, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Chunhui Cai
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania.,Center for Causal Discovery, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Gonghong Yan
- University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania.,Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania.,Magee-Womens Research Institute, Pittsburgh, Pennsylvania
| | - Zhuan Zhou
- University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania.,Department of Cell Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Yong Wan
- University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania.,Department of Cell Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Vicky Chen
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania.,Center for Causal Discovery, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Lujia Chen
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania.,Center for Causal Discovery, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Gregory F Cooper
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania.,Center for Causal Discovery, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Lina M Obeid
- Department of Medicine, the State University of New York at Stony Brook, Stony Brook, New York
| | - Yusuf A Hannun
- Department of Medicine, the State University of New York at Stony Brook, Stony Brook, New York
| | - Adrian V Lee
- Center for Causal Discovery, University of Pittsburgh, Pittsburgh, Pennsylvania. .,University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania.,Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania.,Magee-Womens Research Institute, Pittsburgh, Pennsylvania
| | - Xinghua Lu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania. .,Center for Causal Discovery, University of Pittsburgh, Pittsburgh, Pennsylvania
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139
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Xu J, E C, Yao Y, Ren S, Wang G, Jin H. Matrix metalloproteinase expression and molecular interaction network analysis in gastric cancer. Oncol Lett 2016; 12:2403-2408. [PMID: 27698806 PMCID: PMC5038516 DOI: 10.3892/ol.2016.5013] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2015] [Accepted: 06/27/2016] [Indexed: 12/21/2022] Open
Abstract
Gastric cancer (GC) is one of the most common types of cancer of the digestive tract. Invasion of tumor cells into surrounding tissue and metastasis are among the most significant checkpoints in tumor progression. It is known that matrix metalloproteinases (MMPs) are involved in these processes; however, knowledge of their molecular interaction networks is still limited. Investigation of these networks could provide a more comprehensive picture of the function of MMPs in tumorigenesis. Furthermore, it could be used to develop new approaches to targeted anticancer therapy. In this study, we performed microarray analysis, and 1666 genes that were aberrantly expressed in GC tissues were identified (fold change >2, P<0.05). In addition, quantitative polymerase chain reaction analysis has confirmed that MMP1, MMP3, MMP7, MMP10, MMP11 and MMP12 expression is upregulated in GC. In addition, the MMP3 expression level was negatively correlated with GC differentiation (P<0.05). By integrating the microarray information and BioGRID and STRING databases, we constructed an MMP-related molecular interaction network and observed that 18 genes (including MMPs) were highly expressed in GC tissues. The most enriched of these 18 genes in the Gene Oncology (GO) and pathway analysis were in extracellular matrix disassembly (GO biological process) and extracellular matrix-receptor interaction (KEGG pathway), which are closely correlated with cancer invasion and metastasis. Collectively, our results suggest that the MMP-related interaction network has a role in GC progression, and therefore further studies are required in order to investigate these network interactions in tumorigenesis.
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Affiliation(s)
- Jianting Xu
- Cancer Centre, First Hospital of Jilin University, Changchun, Jilin 130021, P.R. China
| | - Changyong E
- Department of Hepatobiliary and Pancreatic Surgery, China-Japan Union Hospital of Jilin University, Changchun, Jilin 130033, P.R. China
| | - Yongfang Yao
- Department of Hepatobiliary and Pancreatic Surgery, China-Japan Union Hospital of Jilin University, Changchun, Jilin 130033, P.R. China
| | - Shuangchun Ren
- Department of Pathogenobiology, Key Laboratory of Pathobiology, Ministry of Education, College of Basic Medical Sciences, Jilin University, Changchun, Jilin 130021, P.R. China
| | - Guoqing Wang
- Department of Pathogenobiology, Key Laboratory of Pathobiology, Ministry of Education, College of Basic Medical Sciences, Jilin University, Changchun, Jilin 130021, P.R. China
| | - Haofan Jin
- Cancer Centre, First Hospital of Jilin University, Changchun, Jilin 130021, P.R. China
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140
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Memišević V, Kumar K, Zavaljevski N, DeShazer D, Wallqvist A, Reifman J. DBSecSys 2.0: a database of Burkholderia mallei and Burkholderia pseudomallei secretion systems. BMC Bioinformatics 2016; 17:387. [PMID: 27650316 PMCID: PMC5029111 DOI: 10.1186/s12859-016-1242-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 09/08/2016] [Indexed: 01/08/2023] Open
Abstract
Background Burkholderia mallei and B. pseudomallei are the causative agents of glanders and melioidosis, respectively, diseases with high morbidity and mortality rates. B. mallei and B. pseudomallei are closely related genetically; B. mallei evolved from an ancestral strain of B. pseudomallei by genome reduction and adaptation to an obligate intracellular lifestyle. Although these two bacteria cause different diseases, they share multiple virulence factors, including bacterial secretion systems, which represent key components of bacterial pathogenicity. Despite recent progress, the secretion system proteins for B. mallei and B. pseudomallei, their pathogenic mechanisms of action, and host factors are not well characterized. Results We previously developed a manually curated database, DBSecSys, of bacterial secretion system proteins for B. mallei. Here, we report an expansion of the database with corresponding information about B. pseudomallei. DBSecSys 2.0 contains comprehensive literature-based and computationally derived information about B. mallei ATCC 23344 and literature-based and computationally derived information about B. pseudomallei K96243. The database contains updated information for 163 B. mallei proteins from the previous database and 61 additional B. mallei proteins, and new information for 281 B. pseudomallei proteins associated with 5 secretion systems, their 1,633 human- and murine-interacting targets, and 2,400 host-B. mallei interactions and 2,286 host-B. pseudomallei interactions. The database also includes information about 13 pathogenic mechanisms of action for B. mallei and B. pseudomallei secretion system proteins inferred from the available literature or computationally. Additionally, DBSecSys 2.0 provides details about 82 virulence attenuation experiments for 52 B. mallei secretion system proteins and 98 virulence attenuation experiments for 61 B. pseudomallei secretion system proteins. We updated the Web interface and data access layer to speed-up users’ search of detailed information for orthologous proteins related to secretion systems of the two pathogens. Conclusions The updates of DBSecSys 2.0 provide unique capabilities to access comprehensive information about secretion systems of B. mallei and B. pseudomallei. They enable studies and comparisons of corresponding proteins of these two closely related pathogens and their host-interacting partners. The database is available at http://dbsecsys.bhsai.org.
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Affiliation(s)
- Vesna Memišević
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA
| | - Kamal Kumar
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA
| | - Nela Zavaljevski
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA
| | - David DeShazer
- Bacteriology Division, U.S. Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD 21702, USA
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA
| | - Jaques Reifman
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.
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141
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Huang XT, Zhu Y, Chan LLH, Zhao Z, Yan H. An integrative C. elegans protein-protein interaction network with reliability assessment based on a probabilistic graphical model. MOLECULAR BIOSYSTEMS 2016; 12:85-92. [PMID: 26555698 DOI: 10.1039/c5mb00417a] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
In Caenorhabditis elegans, a large number of protein-protein interactions (PPIs) are identified by different experiments. However, a comprehensive weighted PPI network, which is essential for signaling pathway inference, is not yet available in this model organism. Therefore, we firstly construct an integrative PPI network in C. elegans with 12,951 interactions involving 5039 proteins from seven molecular interaction databases. Then, a reliability score based on a probabilistic graphical model (RSPGM) is proposed to assess PPIs. It assumes that the random number of interactions between two proteins comes from the Bernoulli distribution to avoid multi-links. The main parameter of the RSPGM score contains a few latent variables which can be considered as several common properties between two proteins. Validations on high-confidence yeast datasets show that RSPGM provides more accurate evaluation than other approaches, and the PPIs in the reconstructed PPI network have higher biological relevance than that in the original network in terms of gene ontology, gene expression, essentiality and the prediction of known protein complexes. Furthermore, this weighted integrative PPI network in C. elegans is employed on inferring interaction path of the canonical Wnt/β-catenin pathway as well. Most genes on the inferred interaction path have been validated to be Wnt pathway components. Therefore, RSPGM is essential and effective for evaluating PPIs and inferring interaction path. Finally, the PPI network with RSPGM scores can be queried and visualized on a user interactive website, which is freely available at .
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Affiliation(s)
- Xiao-Tai Huang
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China
| | - Yuan Zhu
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China and School of Automation, China University of Geosciences, Wuhan, China.
| | - Leanne Lai Hang Chan
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China
| | - Zhongying Zhao
- Department of Biology, Faculty of Science, Hong Kong Baptist University, Hong Kong, China
| | - Hong Yan
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China
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142
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Pathway Analysis Incorporating Protein-Protein Interaction Networks Identified Candidate Pathways for the Seven Common Diseases. PLoS One 2016; 11:e0162910. [PMID: 27622767 PMCID: PMC5021324 DOI: 10.1371/journal.pone.0162910] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Accepted: 08/30/2016] [Indexed: 01/08/2023] Open
Abstract
Pathway analysis has become popular as a secondary analysis strategy for genome-wide association studies (GWAS). Most of the current pathway analysis methods aggregate signals from the main effects of single nucleotide polymorphisms (SNPs) in genes within a pathway without considering the effects of gene-gene interactions. However, gene-gene interactions can also have critical effects on complex diseases. Protein-protein interaction (PPI) networks have been used to define gene pairs for the gene-gene interaction tests. Incorporating the PPI information to define gene pairs for interaction tests within pathways can increase the power for pathway-based association tests. We propose a pathway association test, which aggregates the interaction signals in PPI networks within a pathway, for GWAS with case-control samples. Gene size is properly considered in the test so that genes do not contribute more to the test statistic simply due to their size. Simulation studies were performed to verify that the method is a valid test and can have more power than other pathway association tests in the presence of gene-gene interactions within a pathway under different scenarios. We applied the test to the Wellcome Trust Case Control Consortium GWAS datasets for seven common diseases. The most significant pathway is the chaperones modulate interferon signaling pathway for Crohn’s disease (p-value = 0.0003). The pathway modulates interferon gamma, which induces the JAK/STAT pathway that is involved in Crohn’s disease. Several other pathways that have functional implications for the seven diseases were also identified. The proposed test based on gene-gene interaction signals in PPI networks can be used as a complementary tool to the current existing pathway analysis methods focusing on main effects of genes. An efficient software implementing the method is freely available at http://puppi.sourceforge.net.
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143
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Machado JP, Philip S, Maldonado E, O'Brien SJ, Johnson WE, Antunes A. Positive Selection Linked with Generation of Novel Mammalian Dentition Patterns. Genome Biol Evol 2016; 8:2748-59. [PMID: 27613398 PMCID: PMC5630915 DOI: 10.1093/gbe/evw200] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
A diverse group of genes are involved in the tooth development of mammals. Several studies, focused mainly on mice and rats, have provided a detailed depiction of the processes coordinating tooth formation and shape. Here we surveyed 236 tooth-associated genes in 39 mammalian genomes and tested for signatures of selection to assess patterns of molecular adaptation in genes regulating mammalian dentition. Of the 236 genes, 31 (∼13.1%) showed strong signatures of positive selection that may be responsible for the phenotypic diversity observed in mammalian dentition. Mammalian-specific tooth-associated genes had accelerated mutation rates compared with older genes found across all vertebrates. More recently evolved genes had fewer interactions (either genetic or physical), were associated with fewer Gene Ontology terms and had faster evolutionary rates compared with older genes. The introns of these positively selected genes also exhibited accelerated evolutionary rates, which may reflect additional adaptive pressure in the intronic regions that are associated with regulatory processes that influence tooth-gene networks. The positively selected genes were mainly involved in processes like mineralization and structural organization of tooth specific tissues such as enamel and dentin. Of the 236 analyzed genes, 12 mammalian-specific genes (younger genes) provided insights on diversification of mammalian teeth as they have higher evolutionary rates and exhibit different expression profiles compared with older genes. Our results suggest that the evolution and development of mammalian dentition occurred in part through positive selection acting on genes that previously had other functions.
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Affiliation(s)
- João Paulo Machado
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Porto, Portugal Abel Salazar Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal
| | - Siby Philip
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Porto, Portugal Department of Biology, Faculty of Sciences, University of Porto, Porto, Portugal
| | - Emanuel Maldonado
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Porto, Portugal
| | - Stephen J O'Brien
- Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia Oceanographic Center, Nova Southeastern University, Ft Lauderdale
| | - Warren E Johnson
- Smithsonian Conservation Biology Institute, National Zoological Park, Front Royal, Virginia, USA
| | - Agostinho Antunes
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Porto, Portugal Abel Salazar Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal Department of Biology, Faculty of Sciences, University of Porto, Porto, Portugal
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144
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Arneson D, Bhattacharya A, Shu L, Mäkinen VP, Yang X. Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration. BMC Genomics 2016; 17:722. [PMID: 27612452 PMCID: PMC5016927 DOI: 10.1186/s12864-016-3057-8] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Accepted: 08/30/2016] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Human diseases are commonly the result of multidimensional changes at molecular, cellular, and systemic levels. Recent advances in genomic technologies have enabled an outpour of omics datasets that capture these changes. However, separate analyses of these various data only provide fragmented understanding and do not capture the holistic view of disease mechanisms. To meet the urgent needs for tools that effectively integrate multiple types of omics data to derive biological insights, we have developed Mergeomics, a computational pipeline that integrates multidimensional disease association data with functional genomics and molecular networks to retrieve biological pathways, gene networks, and central regulators critical for disease development. RESULTS To make the Mergeomics pipeline available to a wider research community, we have implemented an online, user-friendly web server ( http://mergeomics. RESEARCH idre.ucla.edu/ ). The web server features a modular implementation of the Mergeomics pipeline with detailed tutorials. Additionally, it provides curated genomic resources including tissue-specific expression quantitative trait loci, ENCODE functional annotations, biological pathways, and molecular networks, and offers interactive visualization of analytical results. Multiple computational tools including Marker Dependency Filtering (MDF), Marker Set Enrichment Analysis (MSEA), Meta-MSEA, and Weighted Key Driver Analysis (wKDA) can be used separately or in flexible combinations. User-defined summary-level genomic association datasets (e.g., genetic, transcriptomic, epigenomic) related to a particular disease or phenotype can be uploaded and computed real-time to yield biologically interpretable results, which can be viewed online and downloaded for later use. CONCLUSIONS Our Mergeomics web server offers researchers flexible and user-friendly tools to facilitate integration of multidimensional data into holistic views of disease mechanisms in the form of tissue-specific key regulators, biological pathways, and gene networks.
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Affiliation(s)
- Douglas Arneson
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, 90095, USA
| | - Anindya Bhattacharya
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, 90095, USA
| | - Le Shu
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, 90095, USA
| | - Ville-Petteri Mäkinen
- South Australian Health and Medical Research Institute, Adelaide, Australia.,School of Biological Sciences, University of Adelaide, Adelaide, Australia.,Institute of Health Sciences, University of Oulu, Oulu, Finland
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, 90095, USA.
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145
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Li H, Yang S, Wang C, Zhou Y, Zhang Z. AraPPISite: a database of fine-grained protein-protein interaction site annotations for Arabidopsis thaliana. PLANT MOLECULAR BIOLOGY 2016; 92:105-16. [PMID: 27338257 DOI: 10.1007/s11103-016-0498-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2016] [Accepted: 05/26/2016] [Indexed: 05/18/2023]
Abstract
Knowledge about protein interaction sites provides detailed information of protein-protein interactions (PPIs). To date, nearly 20,000 of PPIs from Arabidopsis thaliana have been identified. Nevertheless, the interaction site information has been largely missed by previously published PPI databases. Here, AraPPISite, a database that presents fine-grained interaction details for A. thaliana PPIs is established. First, the experimentally determined 3D structures of 27 A. thaliana PPIs are collected from the Protein Data Bank database and the predicted 3D structures of 3023 A. thaliana PPIs are modeled by using two well-established template-based docking methods. For each experimental/predicted complex structure, AraPPISite not only provides an interactive user interface for browsing interaction sites, but also lists detailed evolutionary and physicochemical properties of these sites. Second, AraPPISite assigns domain-domain interactions or domain-motif interactions to 4286 PPIs whose 3D structures cannot be modeled. In this case, users can easily query protein interaction regions at the sequence level. AraPPISite is a free and user-friendly database, which does not require user registration or any configuration on local machines. We anticipate AraPPISite can serve as a helpful database resource for the users with less experience in structural biology or protein bioinformatics to probe the details of PPIs, and thus accelerate the studies of plant genetics and functional genomics. AraPPISite is available at http://systbio.cau.edu.cn/arappisite/index.html .
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Affiliation(s)
- Hong Li
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Shiping Yang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Chuan Wang
- Department of Plant Biology, Carnegie Institution for Science, Stanford, CA, 94305, USA
| | - Yuan Zhou
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.
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146
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Luo C, Lim JH, Lee Y, Granter SR, Thomas A, Vazquez F, Widlund HR, Puigserver P. A PGC1α-mediated transcriptional axis suppresses melanoma metastasis. Nature 2016; 537:422-426. [PMID: 27580028 DOI: 10.1038/nature19347] [Citation(s) in RCA: 145] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2014] [Accepted: 08/08/2016] [Indexed: 12/17/2022]
Abstract
Melanoma is the deadliest form of commonly encountered skin cancer because of its rapid progression towards metastasis. Although metabolic reprogramming is tightly associated with tumour progression, the effect of metabolic regulatory circuits on metastatic processes is poorly understood. PGC1α is a transcriptional coactivator that promotes mitochondrial biogenesis, protects against oxidative stress and reprograms melanoma metabolism to influence drug sensitivity and survival. Here, we provide data indicating that PGC1α suppresses melanoma metastasis, acting through a pathway distinct from that of its bioenergetic functions. Elevated PGC1α expression inversely correlates with vertical growth in human melanoma specimens. PGC1α silencing makes poorly metastatic melanoma cells highly invasive and, conversely, PGC1α reconstitution suppresses metastasis. Within populations of melanoma cells, there is a marked heterogeneity in PGC1α levels, which predicts their inherent high or low metastatic capacity. Mechanistically, PGC1α directly increases transcription of ID2, which in turn binds to and inactivates the transcription factor TCF4. Inactive TCF4 causes downregulation of metastasis-related genes, including integrins that are known to influence invasion and metastasis. Inhibition of BRAFV600E using vemurafenib, independently of its cytostatic effects, suppresses metastasis by acting on the PGC1α-ID2-TCF4-integrin axis. Together, our findings reveal that PGC1α maintains mitochondrial energetic metabolism and suppresses metastasis through direct regulation of parallel acting transcriptional programs. Consequently, components of these circuits define new therapeutic opportunities that may help to curb melanoma metastasis.
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Affiliation(s)
- Chi Luo
- Department of Cancer Biology, Dana-Farber Cancer Institute and Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Ji-Hong Lim
- Department of Cancer Biology, Dana-Farber Cancer Institute and Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Yoonjin Lee
- Department of Cancer Biology, Dana-Farber Cancer Institute and Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA.,Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, USA
| | - Scott R Granter
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Ajith Thomas
- Department of Cancer Biology, Dana-Farber Cancer Institute and Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Francisca Vazquez
- Department of Cancer Biology, Dana-Farber Cancer Institute and Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA.,Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Hans R Widlund
- Department of Dermatology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Pere Puigserver
- Department of Cancer Biology, Dana-Farber Cancer Institute and Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
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147
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Li P, Nie Y, Yu J. Fusing literature and full network data improves disease similarity computation. BMC Bioinformatics 2016; 17:326. [PMID: 27578323 PMCID: PMC5006367 DOI: 10.1186/s12859-016-1205-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2016] [Accepted: 08/24/2016] [Indexed: 01/01/2023] Open
Abstract
Background Identifying relatedness among diseases could help deepen understanding for the underlying pathogenic mechanisms of diseases, and facilitate drug repositioning projects. A number of methods for computing disease similarity had been developed; however, none of them were designed to utilize information of the entire protein interaction network, using instead only those interactions involving disease causing genes. Most of previously published methods required gene-disease association data, unfortunately, many diseases still have very few or no associated genes, which impeded broad adoption of those methods. In this study, we propose a new method (MedNetSim) for computing disease similarity by integrating medical literature and protein interaction network. MedNetSim consists of a network-based method (NetSim), which employs the entire protein interaction network, and a MEDLINE-based method (MedSim), which computes disease similarity by mining the biomedical literature. Results Among function-based methods, NetSim achieved the best performance. Its average AUC (area under the receiver operating characteristic curve) reached 95.2 %. MedSim, whose performance was even comparable to some function-based methods, acquired the highest average AUC in all semantic-based methods. Integration of MedSim and NetSim (MedNetSim) further improved the average AUC to 96.4 %. We further studied the effectiveness of different data sources. It was found that quality of protein interaction data was more important than its volume. On the contrary, higher volume of gene-disease association data was more beneficial, even with a lower reliability. Utilizing higher volume of disease-related gene data further improved the average AUC of MedNetSim and NetSim to 97.5 % and 96.7 %, respectively. Conclusions Integrating biomedical literature and protein interaction network can be an effective way to compute disease similarity. Lacking sufficient disease-related gene data, literature-based methods such as MedSim can be a great addition to function-based algorithms. It may be beneficial to steer more resources torward studying gene-disease associations and improving the quality of protein interaction data. Disease similarities can be computed using the proposed methods at http://www.digintelli.com:8000/. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1205-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ping Li
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yaling Nie
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jingkai Yu
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, 100190, China.
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148
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Sajesh BV, McManus KJ. Targeting SOD1 induces synthetic lethal killing in BLM- and CHEK2-deficient colorectal cancer cells. Oncotarget 2016; 6:27907-22. [PMID: 26318585 PMCID: PMC4695034 DOI: 10.18632/oncotarget.4875] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Accepted: 07/21/2015] [Indexed: 12/22/2022] Open
Abstract
Cancer is a major cause of death throughout the world, and there is a large need for better and more personalized approaches to combat the disease. Over the past decade, synthetic lethal approaches have been developed that are designed to exploit the aberrant molecular origins (i.e. defective genes) that underlie tumorigenesis. BLM and CHEK2 are two evolutionarily conserved genes that are somatically altered in a number of tumor types. Both proteins normally function in preserving genome stability through facilitating the accurate repair of DNA double strand breaks. Thus, uncovering synthetic lethal interactors of BLM and CHEK2 will identify novel candidate drug targets and lead chemical compounds. Here we identify an evolutionarily conserved synthetic lethal interaction between SOD1 and both BLM and CHEK2 in two distinct cell models. Using quantitative imaging microscopy, real-time cellular analyses, colony formation and tumor spheroid models we show that SOD1 silencing and inhibition (ATTM and LCS-1 treatments), or the induction of reactive oxygen species (2ME2 treatment) induces selective killing within BLM- and CHEK2-deficient cells relative to controls. We further show that increases in reactive oxygen species follow SOD1 silencing and inhibition that are associated with the persistence of DNA double strand breaks, and increases in apoptosis. Collectively, these data identify SOD1 as a novel candidate drug target in BLM and CHEK2 cancer contexts, and further suggest that 2ME2, ATTM and LCS-1 are lead therapeutic compounds warranting further pre-clinical study.
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Affiliation(s)
- Babu V Sajesh
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada.,Research Institute of Oncology and Hematology, Winnipeg, Manitoba, Canada
| | - Kirk J McManus
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada.,Research Institute of Oncology and Hematology, Winnipeg, Manitoba, Canada
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149
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Rahimi M, Vinciguerra M, Daghighi M, Özcan B, Akbarkhanzadeh V, Sheedfar F, Amini M, Mazza T, Pazienza V, Motazacker MM, Mahmoudi M, De Rooij FWM, Sijbrands E, Peppelenbosch MP, Rezaee F. Age-related obesity and type 2 diabetes dysregulate neuronal associated genes and proteins in humans. Oncotarget 2016; 6:29818-32. [PMID: 26337083 PMCID: PMC4745765 DOI: 10.18632/oncotarget.4904] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2015] [Accepted: 08/07/2015] [Indexed: 12/29/2022] Open
Abstract
Despite numerous developed drugs based on glucose metabolism interventions for treatment of age-related diseases such as diabetes neuropathies (DNs), DNs are still increasing in patients with type 1 or type 2 diabetes (T1D, T2D). We aimed to identify novel candidates in adipose tissue (AT) and pancreas with T2D for targeting to develop new drugs for DNs therapy. AT-T2D displayed 15 (e.g. SYT4 up-regulated and VGF down-regulated) and pancreas-T2D showed 10 (e.g. BAG3 up-regulated, VAV3 and APOA1 down-regulated) highly differentially expressed genes with neuronal functions as compared to control tissues. ELISA was blindly performed to measure proteins of 5 most differentially expressed genes in 41 human subjects. SYT4 protein was upregulated, VAV3 and APOA1 were down-regulated, and BAG3 remained unchanged in 1- Obese and 2- Obese-T2D without insulin, VGF protein was higher in these two groups as well as in group 3- Obese-T2D receiving insulin than 4-lean subjects. Interaction networks analysis of these 5 genes showed several metabolic pathways (e.g. lipid metabolism and insulin signaling). Pancreas is a novel site for APOA1 synthesis. VGF is synthesized in AT and could be considered as good diagnostic, and even prognostic, marker for age-induced diseases obesity and T2D. This study provides new targets for rational drugs development for the therapy of age-related DNs.
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Affiliation(s)
- Mehran Rahimi
- Faculty of Medical Science, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Manlio Vinciguerra
- Institute for Liver and Digestive Health, Division of Medicine, University College London (UCL), London, UK.,Gastroenterology Unit, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Mojtaba Daghighi
- Department of Biomedical Engineering, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Behiye Özcan
- Department of Endocrinology, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | - Fareeba Sheedfar
- Department of Physiology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marzyeh Amini
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Tommaso Mazza
- Bioinformatics Unit, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Valerio Pazienza
- Gastroenterology Unit, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Mahdi M Motazacker
- Department of Clinical Genetics, Academic Medical Center, Amsterdam, The Netherlands
| | - Morteza Mahmoudi
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States.,Department of Nanotechnology and Nanotechnology Research Center, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
| | - Felix W M De Rooij
- Department of Cardiovascular Genetics, Metabolism, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Eric Sijbrands
- Department of Cardiovascular Genetics, Metabolism, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Maikel P Peppelenbosch
- Department of Gastroenterology and Hepatology, Erasmus Medical Center, University of Rotterdam, Rotterdam, The Netherlands
| | - Farhad Rezaee
- Department of Gastroenterology and Hepatology, Erasmus Medical Center, University of Rotterdam, Rotterdam, The Netherlands.,Department of Cell Biology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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150
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Patrick R, Horin C, Kobe B, Cao KAL, Bodén M. Prediction of kinase-specific phosphorylation sites through an integrative model of protein context and sequence. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2016; 1864:1599-608. [PMID: 27507704 DOI: 10.1016/j.bbapap.2016.08.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Revised: 07/08/2016] [Accepted: 08/03/2016] [Indexed: 01/17/2023]
Abstract
Identifying kinase substrates and the specific phosphorylation sites they regulate is an important factor in understanding protein function regulation and signalling pathways. Computational prediction of kinase targets - assigning kinases to putative substrates, and selecting from protein sequence the sites that kinases can phosphorylate - requires the consideration of both the cellular context that kinases operate in, as well as their binding affinity. This consideration enables investigation of how phosphorylation influences a range of biological processes. We report here a novel probabilistic model for classifying kinase-specific phosphorylation sites from sequence across three model organisms: human, mouse and yeast. The model incorporates position-specific amino acid frequencies, and counts of co-occurring amino acids from kinase binding sites. We show how this model can be seamlessly integrated with protein interactions and cell-cycle abundance profiles. When evaluating the prediction accuracy of our method, PhosphoPICK, on an independent hold-out set of kinase-specific phosphorylation sites, it achieved an average specificity of 97%, with 32% sensitivity. We compared PhosphoPICK's ability, through cross-validation, to predict kinase-specific phosphorylation sites with alternative methods, and show that at high levels of specificity PhosphoPICK obtains greater sensitivity for most comparisons made. We investigated the relationship between kinase-specific phosphorylation sites and nuclear localisation signals. We show that kinases PKA, Akt1 and AurB have an over-representation of predicted binding sites at particular positions downstream from predicted nuclear localisation signals, demonstrating an important role for these kinases in regulating the nuclear import of proteins. PhosphoPICK is freely available as a web-service at http://bioinf.scmb.uq.edu.au/phosphopick.
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Affiliation(s)
- Ralph Patrick
- School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia 4072, Australia.
| | - Coralie Horin
- Polytech Nice-Sophia, Université Nice Sophia-Antipolis, Nice 06103, France
| | - Bostjan Kobe
- School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia 4072, Australia; Institute for Molecular Bioscience, The University of Queensland, St Lucia 4072, Australia; Australian Infectious Diseases Research Centre, The University of Queensland, St Lucia 4072, Australia
| | - Kim-Anh Lê Cao
- The University of Queensland Diamantina Institute, Translational Research Institute, Woolloongabba, QLD 4102, Australia
| | - Mikael Bodén
- School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia 4072, Australia; Institute for Molecular Bioscience, The University of Queensland, St Lucia 4072, Australia
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