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Wang XW, Madeddu L, Spirohn K, Martini L, Fazzone A, Becchetti L, Wytock TP, Kovács IA, Balogh OM, Benczik B, Pétervári M, Ágg B, Ferdinandy P, Vulliard L, Menche J, Colonnese S, Petti M, Scarano G, Cuomo F, Hao T, Laval F, Willems L, Twizere JC, Vidal M, Calderwood MA, Petrillo E, Barabási AL, Silverman EK, Loscalzo J, Velardi P, Liu YY. Assessment of community efforts to advance network-based prediction of protein-protein interactions. Nat Commun 2023; 14:1582. [PMID: 36949045 PMCID: PMC10033937 DOI: 10.1038/s41467-023-37079-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 03/02/2023] [Indexed: 03/24/2023] Open
Abstract
Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.
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Affiliation(s)
- Xu-Wen Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Lorenzo Madeddu
- Translational and Precision Medicine Department Sapienza University of Rome, Rome, Italy
| | - Kerstin Spirohn
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Leonardo Martini
- Department of Computer, Control, and Management Engineering "Antonio Rubert", Sapienza University of Rome, Rome, Italy
| | | | - Luca Becchetti
- Department of Computer, Control, and Management Engineering "Antonio Rubert", Sapienza University of Rome, Rome, Italy
| | - Thomas P Wytock
- Department of Physics and Astronomy, Northwestern University, Evanston, IL, 60208, USA
| | - István A Kovács
- Department of Physics and Astronomy, Northwestern University, Evanston, IL, 60208, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, 60208, USA
| | - Olivér M Balogh
- Cardiometabolic and MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary
| | - Bettina Benczik
- Cardiometabolic and MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary
- Pharmahungary Group, 6722, Szeged, Hungary
| | - Mátyás Pétervári
- Cardiometabolic and MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary
| | - Bence Ágg
- Cardiometabolic and MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary
- Pharmahungary Group, 6722, Szeged, Hungary
| | - Péter Ferdinandy
- Cardiometabolic and MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary
- Pharmahungary Group, 6722, Szeged, Hungary
| | - Loan Vulliard
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Department of Structural and Computational Biology, Max Perutz Labs, University of Vienna, Vienna, Austria
| | - Jörg Menche
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Department of Structural and Computational Biology, Max Perutz Labs, University of Vienna, Vienna, Austria
- Faculty of Mathematics, University of Vienna, Vienna, Austria
| | - Stefania Colonnese
- Department of Information Engineering, Electronics, and Telecommunications (DIET), University of Rome "Sapienza", Rome, Italy
| | - Manuela Petti
- Department of Computer, Control, and Management Engineering "Antonio Rubert", Sapienza University of Rome, Rome, Italy
| | - Gaetano Scarano
- Department of Information Engineering, Electronics, and Telecommunications (DIET), University of Rome "Sapienza", Rome, Italy
| | - Francesca Cuomo
- Department of Information Engineering, Electronics, and Telecommunications (DIET), University of Rome "Sapienza", Rome, Italy
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Florent Laval
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Laboratory of Molecular and Cellular Epigenetic, GIGA Institute, University of Liège, Liège, Belgium
- Laboratory of Viral Interactomes, GIGA Institute, University of Liège, Liège, Belgium
- TERRA Teaching and Research Centre, University of Liège, Gembloux, Belgium
| | - Luc Willems
- Laboratory of Molecular and Cellular Epigenetic, GIGA Institute, University of Liège, Liège, Belgium
- TERRA Teaching and Research Centre, University of Liège, Gembloux, Belgium
| | - Jean-Claude Twizere
- Laboratory of Viral Interactomes, GIGA Institute, University of Liège, Liège, Belgium
- TERRA Teaching and Research Centre, University of Liège, Gembloux, Belgium
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Michael A Calderwood
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Enrico Petrillo
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
- Department of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Albert-László Barabási
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA, 02115, USA
- Department of Network and Data Science, Central European University, Budapest, H-1051, Hungary
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Paola Velardi
- Translational and Precision Medicine Department Sapienza University of Rome, Rome, Italy.
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA.
- Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, 61801, USA.
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2
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Przanowska RK, Weidmann CA, Saha S, Cichewicz MA, Jensen KN, Przanowski P, Irving PS, Janes KA, Guertin MJ, Weeks KM, Dutta A. Distinct MUNC lncRNA structural domains regulate transcription of different promyogenic factors. Cell Rep 2022; 38:110361. [PMID: 35172143 PMCID: PMC8937029 DOI: 10.1016/j.celrep.2022.110361] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 11/03/2021] [Accepted: 01/19/2022] [Indexed: 12/27/2022] Open
Abstract
Many lncRNAs have been discovered using transcriptomic data; however, it is unclear what fraction of lncRNAs is functional and what structural properties affect their phenotype. MUNC lncRNA (also known as DRReRNA) acts as an enhancer RNA for the Myod1 gene in cis and stimulates the expression of other promyogenic genes in trans by recruiting the cohesin complex. Here, experimental probing of the RNA structure revealed that MUNC contains multiple structural domains not detected by prediction algorithms in the absence of experimental information. We show that these specific and structurally distinct domains are required for induction of promyogenic genes, for binding genomic sites and gene expression regulation, and for binding the cohesin complex. Myod1 induction and cohesin interaction comprise only a subset of MUNC phenotype. Our study reveals unexpectedly complex, structure-driven functions for the MUNC lncRNA and emphasizes the importance of experimentally determined structures for understanding structure-function relationships in lncRNAs.
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Affiliation(s)
- Roza K Przanowska
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA; Department of Biomedical Engineering, University of Virginia School of Engineering, Charlottesville, VA 22908, USA
| | - Chase A Weidmann
- Department of Chemistry, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Biological Chemistry and Center for RNA Biomedicine, University of Michigan Medical School, Ann Arbor, MI 48103, USA
| | - Shekhar Saha
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Magdalena A Cichewicz
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Kate N Jensen
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Piotr Przanowski
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA; Department of Biomedical Engineering, University of Virginia School of Engineering, Charlottesville, VA 22908, USA
| | - Patrick S Irving
- Department of Chemistry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Kevin A Janes
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA; Department of Biomedical Engineering, University of Virginia School of Engineering, Charlottesville, VA 22908, USA
| | - Michael J Guertin
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA; R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut, Farmington, CT 06030, USA
| | - Kevin M Weeks
- Department of Chemistry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Anindya Dutta
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA; Department of Genetics, University of Alabama, Birmingham, AL 35233, USA.
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3
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Schagdarsurengin U, Luo C, Slanina H, Sheridan D, Füssel S, Böğürcü-Seidel N, Gattenloehner S, Baretton GB, Hofbauer LC, Wagenlehner F, Dansranjav T. Tracing TET1 expression in prostate cancer: discovery of malignant cells with a distinct oncogenic signature. Clin Epigenetics 2021; 13:211. [PMID: 34844636 PMCID: PMC8630881 DOI: 10.1186/s13148-021-01201-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 11/22/2021] [Indexed: 11/10/2022] Open
Abstract
Background Ten–eleven translocation methylcytosine dioxygenase 1 (TET1) is involved in DNA demethylation and transcriptional regulation, plays a key role in the maintenance of stem cell pluripotency, and is dysregulated in malignant cells. The identification of cancer stem cells (CSCs) driving tumor growth and metastasis is the primary objective of biomarker discovery in aggressive prostate cancer (PCa). In this context, we analyzed TET1 expression in PCa.
Methods A large-scale immunohistochemical analysis of TET1 was performed in normal prostate (NOR) and PCa using conventional slides (50 PCa specimens) and tissue microarrays (669 NOR and 1371 PCa tissue cores from 371 PCa specimens). Western blotting, RT-qPCR, and 450 K methylation array analyses were performed on PCa cell lines. Genome-wide correlation, gene regulatory network, and functional genomics studies were performed using publicly available data sources and bioinformatics tools. Results In NOR, TET1 was exclusively expressed in normal cytokeratin 903 (CK903)–positive basal cells. In PCa, TET1 was frequently detected in alpha-methylacyl-CoA racemase (AMACR)–positive tumor cell clusters and was detectable at all tumor stages and Gleason scores. Pearson’s correlation analyses of PCa revealed 626 TET1-coactivated genes (r > 0.5) primarily encoding chromatin remodeling and mitotic factors. Moreover, signaling pathways regulating antiviral processes (62 zinc finger, ZNF, antiviral proteins) and the pluripotency of stem cells were activated. A significant proportion of detected genes exhibited TET1-correlated promoter hypomethylation. There were 161 genes encoding transcription factors (TFs), of which 133 were ZNF-TFs with promoter binding sites in TET1 and in the vast majority of TET1-coactivated genes. Conclusions TET1-expressing cells are an integral part of PCa and may represent CSCs with oncogenic potential. Supplementary Information The online version contains supplementary material available at 10.1186/s13148-021-01201-7.
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Affiliation(s)
- U Schagdarsurengin
- Clinic of Urology, Pediatric Urology and Andrology, Justus-Liebig-University Giessen, Giessen, Germany.,Working Group Epigenetics of Urogenital System, Clinic of Urology, Pediatric Urology and Andrology, Justus-Liebig-University Giessen, Giessen, Germany
| | - C Luo
- Clinic of Urology, Pediatric Urology and Andrology, Justus-Liebig-University Giessen, Giessen, Germany
| | - H Slanina
- Institute of Medical Virology, Justus-Liebig-University Giessen, Giessen, Germany
| | - D Sheridan
- Institute of Pathology, Justus-Liebig-University Giessen, Giessen, Germany
| | - S Füssel
- Department of Urology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - N Böğürcü-Seidel
- Institute of Neuropathology, Justus-Liebig-University Giessen, Giessen, Germany
| | - S Gattenloehner
- Institute of Pathology, Justus-Liebig-University Giessen, Giessen, Germany
| | - G B Baretton
- Institute of Pathology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - L C Hofbauer
- Division of Endocrinology, Diabetes, and Bone Diseases, Department of Medicine III and University Center for Healthy Aging, Technische Universität Dresden, Dresden, Germany
| | - F Wagenlehner
- Clinic of Urology, Pediatric Urology and Andrology, Justus-Liebig-University Giessen, Giessen, Germany
| | - T Dansranjav
- Clinic of Urology, Pediatric Urology and Andrology, Justus-Liebig-University Giessen, Giessen, Germany.
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Dhusia K, Wu Y. Classification of protein-protein association rates based on biophysical informatics. BMC Bioinformatics 2021; 22:408. [PMID: 34404340 PMCID: PMC8371850 DOI: 10.1186/s12859-021-04323-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 08/10/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Proteins form various complexes to carry out their versatile functions in cells. The dynamic properties of protein complex formation are mainly characterized by the association rates which measures how fast these complexes can be formed. It was experimentally observed that the association rates span an extremely wide range with over ten orders of magnitudes. Identification of association rates within this spectrum for specific protein complexes is therefore essential for us to understand their functional roles. RESULTS To tackle this problem, we integrate physics-based coarse-grained simulations into a neural-network-based classification model to estimate the range of association rates for protein complexes in a large-scale benchmark set. The cross-validation results show that, when an optimal threshold was selected, we can reach the best performance with specificity, precision, sensitivity and overall accuracy all higher than 70%. The quality of our cross-validation data has also been testified by further statistical analysis. Additionally, given an independent testing set, we can successfully predict the group of association rates for eight protein complexes out of ten. Finally, the analysis of failed cases suggests the future implementation of conformational dynamics into simulation can further improve model. CONCLUSIONS In summary, this study demonstrated that a new modeling framework that combines biophysical simulations with bioinformatics approaches is able to identify protein-protein interactions with low association rates from those with higher association rates. This method thereby can serve as a useful addition to a collection of existing experimental approaches that measure biomolecular recognition.
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Affiliation(s)
- Kalyani Dhusia
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
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5
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Ahmed M, Min DS, Kim DR. Integrating binding and expression data to predict transcription factors combined function. BMC Genomics 2020; 21:610. [PMID: 32894066 PMCID: PMC7487729 DOI: 10.1186/s12864-020-06977-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 08/11/2020] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Transcription factor binding to the regulatory region of a gene induces or represses its gene expression. Transcription factors share their binding sites with other factors, co-factors and/or DNA-binding proteins. These proteins form complexes which bind to the DNA as one-units. The binding of two factors to a shared site does not always lead to a functional interaction. RESULTS We propose a method to predict the combined functions of two factors using comparable binding and expression data (target). We based this method on binding and expression target analysis (BETA), which we re-implemented in R and extended for this purpose. target ranks the factor's targets by importance and predicts the dominant type of interaction between two transcription factors. We applied the method to simulated and real datasets of transcription factor-binding sites and gene expression under perturbation of factors. We found that Yin Yang 1 transcription factor (YY1) and YY2 have antagonistic and independent regulatory targets in HeLa cells, but they may cooperate on a few shared targets. CONCLUSION We developed an R package and a web application to integrate binding (ChIP-seq) and expression (microarrays or RNA-seq) data to determine the cooperative or competitive combined function of two transcription factors.
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Affiliation(s)
- Mahmoud Ahmed
- Department of Biochemistry and Convergence Medical Sciences and Institute of Health Sciences, Gyeongsang National University School of Medicine, Jinju, 52727, Republic of Korea
| | - Do Sik Min
- College of Pharmacy, Yonsei University, Incheon, 21983, Republic of Korea
| | - Deok Ryong Kim
- Department of Biochemistry and Convergence Medical Sciences and Institute of Health Sciences, Gyeongsang National University School of Medicine, Jinju, 52727, Republic of Korea.
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Using Coarse-Grained Simulations to Characterize the Mechanisms of Protein-Protein Association. Biomolecules 2020; 10:biom10071056. [PMID: 32679892 PMCID: PMC7407674 DOI: 10.3390/biom10071056] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 07/10/2020] [Accepted: 07/13/2020] [Indexed: 12/22/2022] Open
Abstract
The formation of functionally versatile protein complexes underlies almost every biological process. The estimation of how fast these complexes can be formed has broad implications for unravelling the mechanism of biomolecular recognition. This kinetic property is traditionally quantified by association rates, which can be measured through various experimental techniques. To complement these time-consuming and labor-intensive approaches, we developed a coarse-grained simulation approach to study the physical processes of protein–protein association. We systematically calibrated our simulation method against a large-scale benchmark set. By combining a physics-based force field with a statistically-derived potential in the simulation, we found that the association rates of more than 80% of protein complexes can be correctly predicted within one order of magnitude relative to their experimental measurements. We further showed that a mixture of force fields derived from complementary sources was able to describe the process of protein–protein association with mechanistic details. For instance, we show that association of a protein complex contains multiple steps in which proteins continuously search their local binding orientations and form non-native-like intermediates through repeated dissociation and re-association. Moreover, with an ensemble of loosely bound encounter complexes observed around their native conformation, we suggest that the transition states of protein–protein association could be highly diverse on the structural level. Our study also supports the idea in which the association of a protein complex is driven by a “funnel-like” energy landscape. In summary, these results shed light on our understanding of how protein–protein recognition is kinetically modulated, and our coarse-grained simulation approach can serve as a useful addition to the existing experimental approaches that measure protein–protein association rates.
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7
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Di Cosimo S, Appierto V, Silvestri M, Pruneri G, Vingiani A, Perrone F, Busico A, Folli S, Scaperrotta G, de Braud FG, Bianchi GV, Cavalieri S, Daidone MG, Dugo M. Targeted-Gene Sequencing to Catch Triple Negative Breast Cancer Heterogeneity before and after Neoadjuvant Chemotherapy. Cancers (Basel) 2019; 11:E1753. [PMID: 31717320 PMCID: PMC6895966 DOI: 10.3390/cancers11111753] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 10/23/2019] [Accepted: 11/05/2019] [Indexed: 01/04/2023] Open
Abstract
Triple negative breast cancer (TNBC) patients not attaining pathological Complete Response (pCR) after neo-adjuvant chemotherapy (NAC) have poor prognosis. We characterized 19 patients for somatic mutations in primary tumor biopsy and residual disease (RD) at surgery by 409 cancer-related gene sequencing (IonAmpliSeqTM Comprehensive Cancer Panel). A median of four (range 1-66) genes was mutated in each primary tumor biopsy, and the most common mutated gene was TP53 followed by a long tail of low frequency mutations. There were no recurrent mutations significantly associated with pCR. However, half of patients with RD had primary tumor biopsy with mutations in genes related to the immune system compared with none of those achieving pCR. Overall, the number of mutations showed a downward trend in post- as compared to pre-NAC samples. PIK3CA was the most common altered gene after NAC. The mutational profile of TNBC during treatment as inferred from patterns of mutant allele frequencies in matched pre-and post-NAC samples showed that RD harbored alterations of cell cycle progression, PI3K/Akt/mTOR, and EGFR tyrosine kinase inhibitor-resistance pathways. Our findings support the use of targeted-gene sequencing for TNBC therapeutic development, as patients without pCR may present mutations of immune-related pathways in their primary tumor biopsy, or actionable targets in the RD.
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Affiliation(s)
- Serena Di Cosimo
- Biomarker Unit, Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Giovanni Antonio Amadeo 42, 20133 Milano, Italy; (V.A.); (M.S.); (G.P.); (A.V.); (F.P.); (A.B.); (S.F.); ) (G.S.); (F.G.d.B.); (G.V.B.); (S.C.); (M.G.D.)
| | - Valentina Appierto
- Biomarker Unit, Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Giovanni Antonio Amadeo 42, 20133 Milano, Italy; (V.A.); (M.S.); (G.P.); (A.V.); (F.P.); (A.B.); (S.F.); ) (G.S.); (F.G.d.B.); (G.V.B.); (S.C.); (M.G.D.)
| | - Marco Silvestri
- Biomarker Unit, Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Giovanni Antonio Amadeo 42, 20133 Milano, Italy; (V.A.); (M.S.); (G.P.); (A.V.); (F.P.); (A.B.); (S.F.); ) (G.S.); (F.G.d.B.); (G.V.B.); (S.C.); (M.G.D.)
| | - Giancarlo Pruneri
- Biomarker Unit, Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Giovanni Antonio Amadeo 42, 20133 Milano, Italy; (V.A.); (M.S.); (G.P.); (A.V.); (F.P.); (A.B.); (S.F.); ) (G.S.); (F.G.d.B.); (G.V.B.); (S.C.); (M.G.D.)
- Oncology and Hemato-Oncology Department, University of Milan, Via Festa del Perdono 7, 20122 Milano, Italy
| | - Andrea Vingiani
- Biomarker Unit, Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Giovanni Antonio Amadeo 42, 20133 Milano, Italy; (V.A.); (M.S.); (G.P.); (A.V.); (F.P.); (A.B.); (S.F.); ) (G.S.); (F.G.d.B.); (G.V.B.); (S.C.); (M.G.D.)
- Oncology and Hemato-Oncology Department, University of Milan, Via Festa del Perdono 7, 20122 Milano, Italy
| | - Federica Perrone
- Biomarker Unit, Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Giovanni Antonio Amadeo 42, 20133 Milano, Italy; (V.A.); (M.S.); (G.P.); (A.V.); (F.P.); (A.B.); (S.F.); ) (G.S.); (F.G.d.B.); (G.V.B.); (S.C.); (M.G.D.)
| | - Adele Busico
- Biomarker Unit, Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Giovanni Antonio Amadeo 42, 20133 Milano, Italy; (V.A.); (M.S.); (G.P.); (A.V.); (F.P.); (A.B.); (S.F.); ) (G.S.); (F.G.d.B.); (G.V.B.); (S.C.); (M.G.D.)
| | - Secondo Folli
- Biomarker Unit, Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Giovanni Antonio Amadeo 42, 20133 Milano, Italy; (V.A.); (M.S.); (G.P.); (A.V.); (F.P.); (A.B.); (S.F.); ) (G.S.); (F.G.d.B.); (G.V.B.); (S.C.); (M.G.D.)
| | - Gianfranco Scaperrotta
- Biomarker Unit, Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Giovanni Antonio Amadeo 42, 20133 Milano, Italy; (V.A.); (M.S.); (G.P.); (A.V.); (F.P.); (A.B.); (S.F.); ) (G.S.); (F.G.d.B.); (G.V.B.); (S.C.); (M.G.D.)
| | - Filippo Guglielmo de Braud
- Biomarker Unit, Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Giovanni Antonio Amadeo 42, 20133 Milano, Italy; (V.A.); (M.S.); (G.P.); (A.V.); (F.P.); (A.B.); (S.F.); ) (G.S.); (F.G.d.B.); (G.V.B.); (S.C.); (M.G.D.)
- Oncology and Hemato-Oncology Department, University of Milan, Via Festa del Perdono 7, 20122 Milano, Italy
| | - Giulia Valeria Bianchi
- Biomarker Unit, Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Giovanni Antonio Amadeo 42, 20133 Milano, Italy; (V.A.); (M.S.); (G.P.); (A.V.); (F.P.); (A.B.); (S.F.); ) (G.S.); (F.G.d.B.); (G.V.B.); (S.C.); (M.G.D.)
| | - Stefano Cavalieri
- Biomarker Unit, Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Giovanni Antonio Amadeo 42, 20133 Milano, Italy; (V.A.); (M.S.); (G.P.); (A.V.); (F.P.); (A.B.); (S.F.); ) (G.S.); (F.G.d.B.); (G.V.B.); (S.C.); (M.G.D.)
| | - Maria Grazia Daidone
- Biomarker Unit, Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Giovanni Antonio Amadeo 42, 20133 Milano, Italy; (V.A.); (M.S.); (G.P.); (A.V.); (F.P.); (A.B.); (S.F.); ) (G.S.); (F.G.d.B.); (G.V.B.); (S.C.); (M.G.D.)
| | - Matteo Dugo
- Biomarker Unit, Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Giovanni Antonio Amadeo 42, 20133 Milano, Italy; (V.A.); (M.S.); (G.P.); (A.V.); (F.P.); (A.B.); (S.F.); ) (G.S.); (F.G.d.B.); (G.V.B.); (S.C.); (M.G.D.)
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8
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Ding D, Shu C, Sun X. Transcriptional regulatory module analysis reveals that bridge proteins reconcile multiple signals in extracellular electron transfer pathways. Proteins 2019; 88:196-205. [PMID: 31344265 DOI: 10.1002/prot.25789] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 05/01/2019] [Accepted: 07/06/2019] [Indexed: 01/17/2023]
Abstract
Shewanella oneidensis MR-1 shows remarkable respiratory versatility with a large variety of extracellular electron acceptors (termed extracellular electron transfer, EET). To utilize the various electron acceptors, the bacterium must employ complex regulatory mechanisms to elicit the relevant EET pathways. To investigate the relevant mechanisms, we integrated EET genes and related transcriptional factors (TFs) into transcriptional regulatory modules (TRMs) and showed that many bridge proteins in these modules were signal proteins, which generally contained one or more signal processing domains (eg, GGDEF, EAL, PAS, etc.). Since Shewanella has to respond to diverse environmental conditions despite encoding few EET-relevant TFs, the overabundant signal proteins involved in the TRMs can help decipher the mechanism by which these microbes elicit a wide range of condition-specific responses. By combining proteomic data and protein bioinformatic analysis, we demonstrated that diverse signal proteins reconciled the different EET pathways, and we discussed the functional roles of signal proteins involved in the well-known MtrCAB pathway. Additionally, we showed that the signal proteins SO_2145 and SO_1417 played central roles in triggering EET pathways in anaerobic environments. Taken together, our results suggest that signal proteins have a profound impact on the transcriptional regulation of EET genes and thus have potential applications in microbial fuel cells.
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Affiliation(s)
- Dewu Ding
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, PR China.,School of Mathematics and Computer Science, Yichun University, Yichun, PR China
| | - Chuanjun Shu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, PR China.,Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, PR China
| | - Xiao Sun
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, PR China
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9
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A Central Edge Selection Based Overlapping Community Detection Algorithm for the Detection of Overlapping Structures in Protein⁻Protein Interaction Networks. Molecules 2018; 23:molecules23102633. [PMID: 30322177 PMCID: PMC6222769 DOI: 10.3390/molecules23102633] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2018] [Revised: 10/08/2018] [Accepted: 10/09/2018] [Indexed: 02/06/2023] Open
Abstract
Overlapping structures of protein⁻protein interaction networks are very prevalent in different biological processes, which reflect the sharing mechanism to common functional components. The overlapping community detection (OCD) algorithm based on central node selection (CNS) is a traditional and acceptable algorithm for OCD in networks. The main content of CNS is the central node selection and the clustering procedure. However, the original CNS does not consider the influence among the nodes and the importance of the division of the edges in networks. In this paper, an OCD algorithm based on a central edge selection (CES) algorithm for detection of overlapping communities of protein⁻protein interaction (PPI) networks is proposed. Different from the traditional CNS algorithms for OCD, the proposed algorithm uses community magnetic interference (CMI) to obtain more reasonable central edges in the process of CES, and employs a new distance between the non-central edge and the set of the central edges to divide the non-central edge into the correct cluster during the clustering procedure. In addition, the proposed CES improves the strategy of overlapping nodes pruning (ONP) to make the division more precisely. The experimental results on three benchmark networks and three biological PPI networks of Mus. musculus, Escherichia coli, and Cerevisiae show that the CES algorithm performs well.
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10
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Santpere G, Alcaráz-Sanabria A, Corrales-Sánchez V, Pandiella A, Győrffy B, Ocaña A. Transcriptome evolution from breast epithelial cells to basal-like tumors. Oncotarget 2017; 9:453-463. [PMID: 29416627 PMCID: PMC5787480 DOI: 10.18632/oncotarget.23065] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Accepted: 11/14/2017] [Indexed: 02/06/2023] Open
Abstract
In breast cancer, it is unclear the functional modifications at a transcriptomic level that are associated with the evolution from epithelial cells and ductal carcinoma in situ (DCIS) to basal-like tumors. By applying weighted gene co-expression network analysis (WGCNA), we identified 17 gene co-expression modules in normal, DCIS and basal-like tumor samples. We then correlated the expression pattern of these gene modules with disease progression from normal to basal-like tumours and found eight modules exhibiting a high and statistically significant correlation. M4 included genes mainly related to cell cycle/division and DNA replication like CCNA2 or CDK1. The M7 module included genes linked with the immune response showing top hub genes such as CD86 or PTPRC. M10 was found specifically correlated to DCIS, but not to basal-like tumor samples, and showed enrichment in ubiquitination or ubiquitin-like processes. We observed that genes in some of these modules were associated with clinical outcome and/or represented druggable opportunities, including AURKA, AURKB, PLK1, MCM2, CDK1, YWHAE, HSP90AB1, LCK, or those targeting ubiquitination. In conclusion, we describe relevant gene modules related to biological functions that can influence survival and be targeted pharmacologically.
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Affiliation(s)
- Gabriel Santpere
- Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | - Ana Alcaráz-Sanabria
- Translational Research Unit and CIBERONC, Albacete University Hospital, Albacete, Spain
| | | | - Atanasio Pandiella
- Cancer Research Center and CIBERONC, CSIC-University of Salamanca, Salamanca, Spain
| | - Balázs Győrffy
- MTA TTK Lendület Cancer Biomarker Research Group, Budapest, Hungary.,Semmelweis University 2nd Department of Pediatrics, Budapest, Hungary
| | - Alberto Ocaña
- Translational Research Unit and CIBERONC, Albacete University Hospital, Albacete, Spain.,Regional Biomedical Research Center (CRIB), Castilla La Mancha University, Albacete, Spain
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11
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Xie ZR, Chen J, Wu Y. Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning. Sci Rep 2017; 7:46622. [PMID: 28418043 PMCID: PMC5394550 DOI: 10.1038/srep46622] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Accepted: 03/21/2017] [Indexed: 12/20/2022] Open
Abstract
Protein–protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved our final prediction by using a cross-validation test. This method was then applied to a new independent test set and resulted in a similar prediction accuracy to that obtained using the training set. It has been thought that diffusion-limited protein association is dominated by long-range interactions. Our results provide strong evidence that the conformational flexibility also plays an important role in regulating protein association. Our studies provide new insights into the mechanism of protein association and offer a computationally efficient tool for predicting its rate.
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Affiliation(s)
- Zhong-Ru Xie
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Yeshiva University, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Jiawen Chen
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Yeshiva University, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Yeshiva University, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
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12
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Santpere G, Garcia-Esparcia P, Andres-Benito P, Lorente-Galdos B, Navarro A, Ferrer I. Transcriptional network analysis in frontal cortex in Lewy body diseases with focus on dementia with Lewy bodies. Brain Pathol 2017; 28:315-333. [PMID: 28321951 DOI: 10.1111/bpa.12511] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Accepted: 03/15/2017] [Indexed: 12/13/2022] Open
Abstract
The present study investigates global transcriptional changes in frontal cortex area 8 in incidental Lewy Body disease (iLBD), Parkinson disease (PD) and Dementia with Lewy bodies (DLB). We identified different coexpressed gene sets associated with disease stages, and gene ontology categories enriched in gene modules and differentially expressed genes including modules or gene clusters correlated to iLBD comprising upregulated dynein genes and taste receptors, and downregulated innate inflammation. Focusing on DLB, we found modules with genes significantly enriched in functions related to RNA and DNA production, mitochondria and energy metabolism, purine metabolism, chaperone and protein folding system and synapses and neurotransmission (particularly the GABAergic system). The expression of more than fifty selected genes was assessed with real time quantitative polymerase chain reaction. Our findings provide, for the first time, evidence of molecular cortical alterations in iLBD and involvement of several key metabolic pathways and gene hubs in DLB which may underlie cognitive impairment and dementia.
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Affiliation(s)
- Gabriel Santpere
- Department of Neurobiology, Yale School of Medicine, New Haven, CT.,Department of Experimental and Health Sciences, IBE, Institute of Evolutionary Biology, Universitat Pompeu Fabra-CSIC, Barcelona, Spain
| | - Paula Garcia-Esparcia
- Department of Pathology and Experimental Therapeutics, University of Barcelona, L'Hospitalet de Llobregat, Spain
| | - Pol Andres-Benito
- Department of Pathology and Experimental Therapeutics, University of Barcelona, L'Hospitalet de Llobregat, Spain
| | - Belen Lorente-Galdos
- Department of Neurobiology, Yale School of Medicine, New Haven, CT.,Department of Experimental and Health Sciences, IBE, Institute of Evolutionary Biology, Universitat Pompeu Fabra-CSIC, Barcelona, Spain
| | - Arcadi Navarro
- Department of Experimental and Health Sciences, IBE, Institute of Evolutionary Biology, Universitat Pompeu Fabra-CSIC, Barcelona, Spain.,Institute of Science and Technology, Centre for Genomic Regulation (CRG), Barcelona, Spain.,National Institute for Bioinformatics (INB), Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Isidro Ferrer
- Department of Pathology and Experimental Therapeutics, University of Barcelona, L'Hospitalet de Llobregat, Spain.,Institute of Neuropathology, Service of Pathologic Anatomy, IDIBELL-Hospital Universitari de Bellvitge, Hospitalet de Llobregat, Spain.,Institute of Neurosciences, University of Barcelona, Hospitalet de Llobregat, Spain.,CIBERNED, Network Centre for Biomedical Research of Neurodegenerative Diseases, Institute Carlos III, Spain
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13
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Yang CC, Chen MH, Lin SY, Andrews EH, Cheng C, Liu CC, Chen JJW. Inferring condition-specific targets of human TF-TF complexes using ChIP-seq data. BMC Genomics 2017; 18:61. [PMID: 28068916 PMCID: PMC5223348 DOI: 10.1186/s12864-016-3450-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Accepted: 12/21/2016] [Indexed: 01/18/2023] Open
Abstract
Background Transcription factors (TFs) often interact with one another to form TF complexes that bind DNA and regulate gene expression. Many databases are created to describe known TF complexes identified by either mammalian two-hybrid experiments or data mining. Lately, a wealth of ChIP-seq data on human TFs under different experiment conditions are available, making it possible to investigate condition-specific (cell type and/or physiologic state) TF complexes and their target genes. Results Here, we developed a systematic pipeline to infer Condition-Specific Targets of human TF-TF complexes (called the CST pipeline) by integrating ChIP-seq data and TF motifs. In total, we predicted 2,392 TF complexes and 13,504 high-confidence or 127,994 low-confidence regulatory interactions amongst TF complexes and their target genes. We validated our predictions by (i) comparing predicted TF complexes to external TF complex databases, (ii) validating selected target genes of TF complexes using ChIP-qPCR and RT-PCR experiments, and (iii) analysing target genes of select TF complexes using gene ontology enrichment to demonstrate the accuracy of our work. Finally, the predicted results above were integrated and employed to construct a CST database. Conclusions We built up a methodology to construct the CST database, which contributes to the analysis of transcriptional regulation and the identification of novel TF-TF complex formation in a certain condition. This database also allows users to visualize condition-specific TF regulatory networks through a user-friendly web interface. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3450-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Chia-Chun Yang
- Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan.,Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, Taiwan.,Institute of Biomedical Sciences, National Chung Hsing University, No. 250, Kuo-Kuang Rd., 40227, Taichung, Taiwan
| | - Min-Hsuan Chen
- Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, Taiwan
| | - Sheng-Yi Lin
- Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan.,Institute of Biomedical Sciences, National Chung Hsing University, No. 250, Kuo-Kuang Rd., 40227, Taichung, Taiwan
| | - Erik H Andrews
- Department of Genetics, Geisel School of Medicine at Dartmouth, 03755, Hanover, NH, USA
| | - Chao Cheng
- Department of Genetics, Geisel School of Medicine at Dartmouth, 03755, Hanover, NH, USA. .,Institute for Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, 03766, Lebanon, NH, USA.
| | - Chun-Chi Liu
- Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, Taiwan. .,Institute of Biomedical Sciences, National Chung Hsing University, No. 250, Kuo-Kuang Rd., 40227, Taichung, Taiwan. .,Agricultural Biotechnology Centre, National Chung Hsing University, Taichung, Taiwan.
| | - Jeremy J W Chen
- Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan. .,Institute of Biomedical Sciences, National Chung Hsing University, No. 250, Kuo-Kuang Rd., 40227, Taichung, Taiwan. .,Agricultural Biotechnology Centre, National Chung Hsing University, Taichung, Taiwan.
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14
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Waks Z, Weissbrod O, Carmeli B, Norel R, Utro F, Goldschmidt Y. Driver gene classification reveals a substantial overrepresentation of tumor suppressors among very large chromatin-regulating proteins. Sci Rep 2016; 6:38988. [PMID: 28008934 PMCID: PMC5180091 DOI: 10.1038/srep38988] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Accepted: 11/07/2016] [Indexed: 12/18/2022] Open
Abstract
Compiling a comprehensive list of cancer driver genes is imperative for oncology diagnostics and drug development. While driver genes are typically discovered by analysis of tumor genomes, infrequently mutated driver genes often evade detection due to limited sample sizes. Here, we address sample size limitations by integrating tumor genomics data with a wide spectrum of gene-specific properties to search for rare drivers, functionally classify them, and detect features characteristic of driver genes. We show that our approach, CAnceR geNe similarity-based Annotator and Finder (CARNAF), enables detection of potentially novel drivers that eluded over a dozen pan-cancer/multi-tumor type studies. In particular, feature analysis reveals a highly concentrated pool of known and putative tumor suppressors among the <1% of genes that encode very large, chromatin-regulating proteins. Thus, our study highlights the need for deeper characterization of very large, epigenetic regulators in the context of cancer causality.
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Affiliation(s)
- Zeev Waks
- Machine Learning for Healthcare and Life Sciences, IBM Research - Haifa, Mount Carmel Campus, Israel
| | - Omer Weissbrod
- Machine Learning for Healthcare and Life Sciences, IBM Research - Haifa, Mount Carmel Campus, Israel
| | - Boaz Carmeli
- Machine Learning for Healthcare and Life Sciences, IBM Research - Haifa, Mount Carmel Campus, Israel
| | - Raquel Norel
- Computational Biology Center, IBM T. J. Watson Research, Yorktown Heights, NY 10598, USA
| | - Filippo Utro
- Computational Biology Center, IBM T. J. Watson Research, Yorktown Heights, NY 10598, USA
| | - Yaara Goldschmidt
- Machine Learning for Healthcare and Life Sciences, IBM Research - Haifa, Mount Carmel Campus, Israel
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15
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Design principles for clinical network-based proteomics. Drug Discov Today 2016; 21:1130-8. [DOI: 10.1016/j.drudis.2016.05.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2015] [Revised: 04/18/2016] [Accepted: 05/20/2016] [Indexed: 01/10/2023]
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16
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Xie ZR, Chen J, Wu Y. Multiscale Model for the Assembly Kinetics of Protein Complexes. J Phys Chem B 2016; 120:621-32. [DOI: 10.1021/acs.jpcb.5b08962] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Zhong-Ru Xie
- Department of Systems and
Computational Biology, Albert Einstein College of Medicine, 1300 Morris
Park Avenue, Bronx, New York 10461, United States
| | - Jiawen Chen
- Department of Systems and
Computational Biology, Albert Einstein College of Medicine, 1300 Morris
Park Avenue, Bronx, New York 10461, United States
| | - Yinghao Wu
- Department of Systems and
Computational Biology, Albert Einstein College of Medicine, 1300 Morris
Park Avenue, Bronx, New York 10461, United States
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17
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Schmid MW, Schmidt A, Grossniklaus U. The female gametophyte: an emerging model for cell type-specific systems biology in plant development. FRONTIERS IN PLANT SCIENCE 2015; 6:907. [PMID: 26579157 PMCID: PMC4630298 DOI: 10.3389/fpls.2015.00907] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Accepted: 10/10/2015] [Indexed: 05/03/2023]
Abstract
Systems biology, a holistic approach describing a system emerging from the interactions of its molecular components, critically depends on accurate qualitative determination and quantitative measurements of these components. Development and improvement of large-scale profiling methods ("omics") now facilitates comprehensive measurements of many relevant molecules. For multicellular organisms, such as animals, fungi, algae, and plants, the complexity of the system is augmented by the presence of specialized cell types and organs, and a complex interplay within and between them. Cell type-specific analyses are therefore crucial for the understanding of developmental processes and environmental responses. This review first gives an overview of current methods used for large-scale profiling of specific cell types exemplified by recent advances in plant biology. The focus then lies on suitable model systems to study plant development and cell type specification. We introduce the female gametophyte of flowering plants as an ideal model to study fundamental developmental processes. Moreover, the female reproductive lineage is of importance for the emergence of evolutionary novelties such as an unequal parental contribution to the tissue nurturing the embryo or the clonal production of seeds by asexual reproduction (apomixis). Understanding these processes is not only interesting from a developmental or evolutionary perspective, but bears great potential for further crop improvement and the simplification of breeding efforts. We finally highlight novel methods, which are already available or which will likely soon facilitate large-scale profiling of the specific cell types of the female gametophyte in both model and non-model species. We conclude that it may take only few years until an evolutionary systems biology approach toward female gametogenesis may decipher some of its biologically most interesting and economically most valuable processes.
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Affiliation(s)
| | | | - Ueli Grossniklaus
- Department of Plant & Microbial Biology and Zurich-Basel Plant Science Center, University of ZurichZurich, Switzerland
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18
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Discovering distinct functional modules of specific cancer types using protein-protein interaction networks. BIOMED RESEARCH INTERNATIONAL 2015; 2015:146365. [PMID: 26495282 PMCID: PMC4606133 DOI: 10.1155/2015/146365] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2015] [Revised: 03/12/2015] [Accepted: 03/31/2015] [Indexed: 11/18/2022]
Abstract
Background. The molecular profiles exhibited in different cancer types are very different; hence, discovering distinct functional modules associated with specific cancer types is very important to understand the distinct functions associated with them. Protein-protein interaction networks carry vital information about molecular interactions in cellular systems, and identification of functional modules (subgraphs) in these networks is one of the most important applications of biological network analysis. Results. In this study, we developed a new graph theory based method to identify distinct functional modules from nine different cancer protein-protein interaction networks. The method is composed of three major steps: (i) extracting modules from protein-protein interaction networks using network clustering algorithms; (ii) identifying distinct subgraphs from the derived modules; and (iii) identifying distinct subgraph patterns from distinct subgraphs. The subgraph patterns were evaluated using experimentally determined cancer-specific protein-protein interaction data from the Ingenuity knowledgebase, to identify distinct functional modules that are specific to each cancer type. Conclusion. We identified cancer-type specific subgraph patterns that may represent the functional modules involved in the molecular pathogenesis of different cancer types. Our method can serve as an effective tool to discover cancer-type specific functional modules from large protein-protein interaction networks.
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19
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Will T, Helms V. PPIXpress: construction of condition-specific protein interaction networks based on transcript expression. Bioinformatics 2015; 32:571-8. [PMID: 26508756 DOI: 10.1093/bioinformatics/btv620] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2015] [Accepted: 10/20/2015] [Indexed: 12/13/2022] Open
Abstract
UNLABELLED Protein-protein interaction networks are an important component of modern systems biology. Yet, comparatively few efforts have been made to tailor their topology to the actual cellular condition being studied. Here, we present a network construction method that exploits expression data at the transcript-level and thus reveals alterations in protein connectivity not only caused by differential gene expression but also by alternative splicing. We achieved this by establishing a direct correspondence between individual protein interactions and underlying domain interactions in a complete but condition-unspecific protein interaction network. This knowledge was then used to infer the condition-specific presence of interactions from the dominant protein isoforms. When we compared contextualized interaction networks of matched normal and tumor samples in breast cancer, our transcript-based construction identified more significant alterations that affected proteins associated with cancerogenesis than a method that only uses gene expression data. The approach is provided as the user-friendly tool PPIXpress. AVAILABILITY AND IMPLEMENTATION PPIXpress is available at https://sourceforge.net/projects/ppixpress/.
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Affiliation(s)
- Thorsten Will
- Center for Bioinformatics and Graduate School of Computer Science, Saarland University, Saarbrücken, Germany
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20
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Parfitt DE, Shen MM. From blastocyst to gastrula: gene regulatory networks of embryonic stem cells and early mouse embryogenesis. Philos Trans R Soc Lond B Biol Sci 2015; 369:rstb.2013.0542. [PMID: 25349451 DOI: 10.1098/rstb.2013.0542] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
To date, many regulatory genes and signalling events coordinating mammalian development from blastocyst to gastrulation stages have been identified by mutational analyses and reverse-genetic approaches, typically on a gene-by-gene basis. More recent studies have applied bioinformatic approaches to generate regulatory network models of gene interactions on a genome-wide scale. Such models have provided insights into the gene networks regulating pluripotency in embryonic and epiblast stem cells, as well as cell-lineage determination in vivo. Here, we review how regulatory networks constructed for different stem cell types relate to corresponding networks in vivo and provide insights into understanding the molecular regulation of the blastocyst-gastrula transition.
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Affiliation(s)
- David-Emlyn Parfitt
- Department of Medicine, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA Department of Genetics and Development, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA Department of Urology, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA Department of Systems Biology, Herbert Irving Comprehensive Cancer Center, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA
| | - Michael M Shen
- Department of Medicine, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA Department of Genetics and Development, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA Department of Urology, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA Department of Systems Biology, Herbert Irving Comprehensive Cancer Center, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA
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21
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Genomic analysis of LPS-stimulated myeloid cells identifies a common pro-inflammatory response but divergent IL-10 anti-inflammatory responses. Sci Rep 2015; 5:9100. [PMID: 25765318 PMCID: PMC4650320 DOI: 10.1038/srep09100] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2014] [Accepted: 02/12/2015] [Indexed: 01/02/2023] Open
Abstract
Inflammation is an essential physiological response to infection and injury that must be kept within strict bounds. The IL-10/STAT3 anti-inflammatory response (AIR) is indispensable for controlling the extent of inflammation, although the complete mechanisms downstream of STAT3 have not yet been elucidated. The AIR is widely known to extend to other myeloid cells, but it has best been characterized in macrophages. Here we set out to characterize the LPS-mediated pro-inflammatory response and the AIR across a range of myeloid cells. We found that whereas the LPS-induced pro-inflammatory response is broadly similar among macrophages, dendritic cells, neutrophils, mast cells and eosinophils, the AIR is drastically different across all myeloid cell types that respond to IL-10 (all bar eosinophils). We propose a model whereby the IL-10/STAT3 AIR works by selectively inhibiting specific pathways in distinct cell types: in macrophages the AIR most likely works through the inhibition of NF-κB target genes; in DCs and mast cells through indirect IRF disruption; and in neutrophils through IRF disruption and possibly also indirect NF-κB inhibition. In summary, no conserved IL-10/STAT3 AIR effectors were identified; instead a cell type-specific model of the AIR is proposed.
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22
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Xie ZR, Chen J, Zhao Y, Wu Y. Decomposing the space of protein quaternary structures with the interface fragment pair library. BMC Bioinformatics 2015; 16:14. [PMID: 25592649 PMCID: PMC4384354 DOI: 10.1186/s12859-014-0437-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2014] [Accepted: 12/18/2014] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The physical interactions between proteins constitute the basis of protein quaternary structures. They dominate many biological processes in living cells. Deciphering the structural features of interacting proteins is essential to understand their cellular functions. Similar to the space of protein tertiary structures in which discrete patterns are clearly observed on fold or sub-fold motif levels, it has been found that the space of protein quaternary structures is highly degenerate due to the packing of compact secondary structure elements at interfaces. Therefore, it is necessary to further decompose the protein quaternary structural space into a more local representation. RESULTS Here we constructed an interface fragment pair library from the current structure database of protein complexes. After structural-based clustering, we found that more than 90% of these interface fragment pairs can be represented by a limited number of highly abundant motifs. These motifs were further used to guide complex assembly. A large-scale benchmark test shows that the native-like binding is highly likely in the structural ensemble of modeled protein complexes that were built through the library. CONCLUSIONS Our study therefore presents supportive evidences that the space of protein quaternary structures can be represented by the combination of a small set of secondary-structure-based packing at binding interfaces. Finally, after future improvements such as adding sequence profiles, we expect this new library will be useful to predict structures of unknown protein-protein interactions.
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Affiliation(s)
- Zhong-Ru Xie
- Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
| | - Jiawen Chen
- Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
| | - Yilin Zhao
- Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
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Abstract
Motivation: Eukaryotic gene expression is controlled through molecular logic circuits that combine regulatory signals of many different factors. In particular, complexation of transcription factors (TFs) and other regulatory proteins is a prevailing and highly conserved mechanism of signal integration within critical regulatory pathways and enables us to infer controlled genes as well as the exerted regulatory mechanism. Common approaches for protein complex prediction that only use protein interaction networks, however, are designed to detect self-contained functional complexes and have difficulties to reveal dynamic combinatorial assemblies of physically interacting proteins. Results: We developed the novel algorithm DACO that combines protein–protein interaction networks and domain–domain interaction networks with the cluster-quality metric cohesiveness. The metric is locally maximized on the holistic level of protein interactions, and connectivity constraints on the domain level are used to account for the exclusive and thus inherently combinatorial nature of the interactions within such assemblies. When applied to predicting TF complexes in the yeast Saccharomyces cerevisiae, the proposed approach outperformed popular complex prediction methods by far. Furthermore, we were able to assign many of the predictions to target genes, as well as to a potential regulatory effect in agreement with literature evidence. Availability and implementation: A prototype implementation is freely available at https://sourceforge.net/projects/dacoalgorithm/. Contact:volkhard.helms@bioinformatik.uni-saarland.de Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Thorsten Will
- Center for Bioinformatics, Campus Building E2.1, Saarland University, D-66123 Saarbrücken, Germany
| | - Volkhard Helms
- Center for Bioinformatics, Campus Building E2.1, Saarland University, D-66123 Saarbrücken, Germany
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Diez D, Agustí A, Wheelock CE. Network Analysis in the Investigation of Chronic Respiratory Diseases. From Basics to Application. Am J Respir Crit Care Med 2014; 190:981-8. [DOI: 10.1164/rccm.201403-0421pp] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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25
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Liu F, Miranda-Saavedra D. rTRM-web: a web tool for predicting transcriptional regulatory modules for ChIP-seq-ed transcription factors. Gene 2014; 546:417-20. [PMID: 24927962 DOI: 10.1016/j.gene.2014.06.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2014] [Revised: 06/04/2014] [Accepted: 06/09/2014] [Indexed: 10/25/2022]
Abstract
Transcription factors (TFs) bind to specific DNA regions, although their binding specificities cannot account for their cell type-specific functions. It has been shown in well-studied systems that TFs combine with co-factors into transcriptional regulatory modules (TRMs), which endow them with cell type-specific functions and additional modes of regulation. Therefore, the prediction of TRMs can provide fundamental mechanistic insights, especially when experimental data are limiting or when no regulatory proteins have been identified. Our method rTRM predicts TRMs by integrating genomic information from TF ChIP-seq data, cell type-specific gene expression and protein-protein interaction data. Here we present a freely available web interface to rTRM (http://www.rTRM.org/) supporting all the options originally described for rTRM while featuring flexible display and network calculation parameters, publication-quality figures as well as annotated information on the list of genes constituting the TRM.
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Affiliation(s)
- Fengkai Liu
- School of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics (BUAA), Building 16, Min'an Street, Dong Cheng District, Beijing 100007, China
| | - Diego Miranda-Saavedra
- Fibrosis Laboratories, Institute of Cellular Medicine, Newcastle University Medical School, Framlington Place, Newcastle upon Tyne NE2 4HH, United Kingdom.
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Chen J, Xie ZR, Wu Y. A multiscale model for simulating binding kinetics of proteins with flexible linkers. Proteins 2014; 82:2512-22. [DOI: 10.1002/prot.24614] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2014] [Revised: 05/16/2014] [Accepted: 05/22/2014] [Indexed: 12/17/2022]
Affiliation(s)
- Jiawen Chen
- Department of Systems and Computational Biology; Albert Einstein College of Medicine of Yeshiva University; Bronx New York 10461
| | - Zhong-Ru Xie
- Department of Systems and Computational Biology; Albert Einstein College of Medicine of Yeshiva University; Bronx New York 10461
| | - Yinghao Wu
- Department of Systems and Computational Biology; Albert Einstein College of Medicine of Yeshiva University; Bronx New York 10461
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