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Iida M, Kuniki Y, Yagi K, Goda M, Namba S, Takeshita JI, Sawada R, Iwata M, Zamami Y, Ishizawa K, Yamanishi Y. A network-based trans-omics approach for predicting synergistic drug combinations. COMMUNICATIONS MEDICINE 2024; 4:154. [PMID: 39075184 PMCID: PMC11286857 DOI: 10.1038/s43856-024-00571-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 07/04/2024] [Indexed: 07/31/2024] Open
Abstract
BACKGROUND Combination therapy can offer greater efficacy on medical treatments. However, the discovery of synergistic drug combinations is challenging. We propose a novel computational method, SyndrumNET, to predict synergistic drug combinations by network propagation with trans-omics analyses. METHODS The prediction is based on the topological relationship, network-based proximity, and transcriptional correlation between diseases and drugs. SyndrumNET was applied to analyzing six diseases including asthma, diabetes, hypertension, colorectal cancer, acute myeloid leukemia (AML), and chronic myeloid leukemia (CML). RESULTS Here we show that SyndrumNET outperforms the previous methods in terms of high accuracy. We perform in vitro cell survival assays to validate our prediction for CML. Of the top 17 predicted drug pairs, 14 drug pairs successfully exhibits synergistic anticancer effects. Our mode-of-action analysis also reveals that the drug synergy of the top predicted combination of capsaicin and mitoxantrone is due to the complementary regulation of 12 pathways, including the Rap1 signaling pathway. CONCLUSIONS The proposed method is expected to be useful for discovering synergistic drug combinations for various complex diseases.
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Affiliation(s)
- Midori Iida
- Department of Physics and Information Technology, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan
| | - Yurika Kuniki
- Department of Clinical Pharmacology and Therapeutics, Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan
| | - Kenta Yagi
- Department of Clinical Pharmacology and Therapeutics, Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan
- Clinical Research Center for Developmental Therapeutics, Tokushima University Hospital, Tokushima, Japan
| | - Mitsuhiro Goda
- Department of Clinical Pharmacology and Therapeutics, Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan
- Department of Pharmacy, Tokushima University Hospital, Tokushima, Japan
| | - Satoko Namba
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan
- Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi, Japan
| | - Jun-Ichi Takeshita
- Research Institute of Science for Safety and Sustainability, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan
| | - Ryusuke Sawada
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan
- Department of Pharmacology, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama, Okayama, Japan
| | - Michio Iwata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan
| | - Yoshito Zamami
- Department of Clinical Pharmacology and Therapeutics, Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan
- Department of Pharmacy, Okayama University Hospital, Kita-ku, Okayama, Japan
| | - Keisuke Ishizawa
- Department of Clinical Pharmacology and Therapeutics, Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan
- Clinical Research Center for Developmental Therapeutics, Tokushima University Hospital, Tokushima, Japan
- Department of Pharmacy, Tokushima University Hospital, Tokushima, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan.
- Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi, Japan.
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2
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Su C, Hou Y, Xu J, Xu Z, Zhou M, Ke A, Li H, Xu J, Brendel M, Maasch JRMA, Bai Z, Zhang H, Zhu Y, Cincotta MC, Shi X, Henchcliffe C, Leverenz JB, Cummings J, Okun MS, Bian J, Cheng F, Wang F. Identification of Parkinson's disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data. NPJ Digit Med 2024; 7:184. [PMID: 38982243 PMCID: PMC11233682 DOI: 10.1038/s41746-024-01175-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Accepted: 06/21/2024] [Indexed: 07/11/2024] Open
Abstract
Parkinson's disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals' phenotypic progression trajectories for PD subtyping. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We found cerebrospinal fluid P-tau/α-synuclein ratio and atrophy in certain brain regions as potential markers of these subtypes. Analyses of genetic and transcriptomic profiles with network-based approaches identified molecular modules associated with each subtype. For instance, the PD-R-specific module suggested STAT3, FYN, BECN1, APOA1, NEDD4, and GATA2 as potential driver genes of PD-R. It also suggested neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis pathways as potential drivers for rapid PD progression (i.e., PD-R). Moreover, we identified repurposable drug candidates by targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data. We further estimated their treatment effects using two large-scale real-world patient databases; the real-world evidence we gained highlighted the potential of metformin in ameliorating PD progression. In conclusion, this work helps better understand clinical and pathophysiological complexity of PD progression and accelerate precision medicine.
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Grants
- R21 AG083003 NIA NIH HHS
- R01 AG082118 NIA NIH HHS
- R56 AG074001 NIA NIH HHS
- R01AG076448 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1AG072449 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- MJFF-023081 Michael J. Fox Foundation for Parkinson's Research (Michael J. Fox Foundation)
- R01AG080991 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P30 AG072959 NIA NIH HHS
- 3R01AG066707-01S1 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R21AG083003 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01AG066707 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R35 AG071476 NIA NIH HHS
- RF1 AG082211 NIA NIH HHS
- R56AG074001 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01AG082118 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R25 AG083721 NIA NIH HHS
- RF1AG082211 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- U01 NS093334 NINDS NIH HHS
- AG083721-01 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1NS133812 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P20GM109025 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1 NS133812 NINDS NIH HHS
- R35AG71476 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- U01 AG073323 NIA NIH HHS
- R01 AG066707 NIA NIH HHS
- R01AG053798 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01AG076234 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01 AG076448 NIA NIH HHS
- R01 AG080991 NIA NIH HHS
- R01 AG076234 NIA NIH HHS
- U01NS093334 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P20 GM109025 NIGMS NIH HHS
- P30AG072959 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1 AG072449 NIA NIH HHS
- R01 AG053798 NIA NIH HHS
- 3R01AG066707-02S1 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- U01AG073323 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- ALZDISCOVERY-1051936 Alzheimer's Association
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Affiliation(s)
- Chang Su
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Yu Hou
- Department of Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Jielin Xu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Zhenxing Xu
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Manqi Zhou
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Alison Ke
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Haoyang Li
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Jie Xu
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Matthew Brendel
- Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Jacqueline R M A Maasch
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Department of Computer Science, Cornell Tech, Cornell University, New York, NY, USA
| | - Zilong Bai
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Haotan Zhang
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Yingying Zhu
- Department of Computer Science, University of Texas at Arlington, Arlington, TX, USA
| | - Molly C Cincotta
- Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
| | - Xinghua Shi
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
| | - Claire Henchcliffe
- Department of Neurology, University of California Irvine, Irvine, CA, USA
| | - James B Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Pam Quirk Brain Health and Biomarker Laboratory, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA
| | - Michael S Okun
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA.
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA.
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3
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Baghdassarian HM, Dimitrov D, Armingol E, Saez-Rodriguez J, Lewis NE. Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples. CELL REPORTS METHODS 2024; 4:100758. [PMID: 38631346 PMCID: PMC11046036 DOI: 10.1016/j.crmeth.2024.100758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 12/22/2023] [Accepted: 03/22/2024] [Indexed: 04/19/2024]
Abstract
In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. Here, we integrate two tools, LIANA and Tensor-cell2cell, which, when combined, can deploy multiple existing methods and resources to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this work, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step by step in both Python and R and provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This workflow typically takes ∼1.5 h to complete from installation to downstream visualizations on a graphics processing unit-enabled computer for a dataset of ∼63,000 cells, 10 cell types, and 12 samples.
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Affiliation(s)
- Hratch M Baghdassarian
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Daniel Dimitrov
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, 69120 Heidelberg, Germany
| | - Erick Armingol
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, 69120 Heidelberg, Germany.
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.
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4
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Yu M, Xu J, Dutta R, Trapp B, Pieper AA, Cheng F. Network medicine informed multi-omics integration identifies drug targets and repurposable medicines for Amyotrophic Lateral Sclerosis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.27.586949. [PMID: 38585774 PMCID: PMC10996626 DOI: 10.1101/2024.03.27.586949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Amyotrophic Lateral Sclerosis (ALS) is a devastating, immensely complex neurodegenerative disease by lack of effective treatments. To date, the challenge to establishing effective treatment for ALS remains formidable, partly due to inadequate translation of existing human genetic findings into actionable ALS-specific pathobiology for subsequent therapeutic development. This study evaluates the feasibility of network medicine methodology via integrating human brain-specific multi-omics data to prioritize drug targets and repurposable treatments for ALS. Using human brain-specific genome-wide quantitative trait loci (x-QTLs) under a network-based deep learning framework, we identified 105 putative ALS-associated genes enriched in various known ALS pathobiological pathways, including regulation of T cell activation, monocyte differentiation, and lymphocyte proliferation. Specifically, we leveraged non-coding ALS loci effects from genome-wide associated studies (GWAS) on brain-specific expression quantitative trait loci (QTL) (eQTL), protein QTLs (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL). Applying network proximity analysis of predicted ALS-associated gene-coding targets and existing drug-target networks under the human protein-protein interactome (PPI) model, we identified a set of potential repurposable drugs (including Diazoxide, Gefitinib, Paliperidone, and Dimethyltryptamine) for ALS. Subsequent validation established preclinical and clinical evidence for top-prioritized repurposable drugs. In summary, we presented a network-based multi-omics framework to identify potential drug targets and repurposable treatments for ALS and other neurodegenerative disease if broadly applied.
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Affiliation(s)
- Mucen Yu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- College of Arts and Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Jielin Xu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Ranjan Dutta
- Department of Neuroscience, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
| | - Bruce Trapp
- Department of Neuroscience, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
| | - Andrew A. Pieper
- Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
- Department of Psychiatry, Case Western Reserve University, Cleveland, OH 44106, USA
- Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center; Cleveland, OH 44106, USA
- Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland 44106, OH, USA
- Department of Neuroscience, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
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5
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Someya W, Akutsu T, Schwartz JM, Nacher JC. Measuring criticality in control of complex biological networks. NPJ Syst Biol Appl 2024; 10:9. [PMID: 38245555 PMCID: PMC10799883 DOI: 10.1038/s41540-024-00333-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 01/04/2024] [Indexed: 01/22/2024] Open
Abstract
Recent controllability analyses have demonstrated that driver nodes tend to be associated to genes related to important biological functions as well as human diseases. While researchers have focused on identifying critical nodes, intermittent nodes have received much less attention. Here, we propose a new efficient algorithm based on the Hamming distance for computing the importance of intermittent nodes using a Minimum Dominating Set (MDS)-based control model. We refer to this metric as criticality. The application of the proposed algorithm to compute criticality under the MDS control framework allows us to unveil the biological importance and roles of the intermittent nodes in different network systems, from cellular level such as signaling pathways and cell-cell interactions such as cytokine networks, to the complete nervous system of the nematode worm C. elegans. Taken together, the developed computational tools may open new avenues for investigating the role of intermittent nodes in many biological systems of interest in the context of network control.
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Affiliation(s)
- Wataru Someya
- Department of Information Science, Faculty of Science, Toho University, Funabashi, Chiba, 274-8510, Japan
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Uji, 611-0011, Japan
| | - Jean-Marc Schwartz
- School of Biological Sciences, University of Manchester, Manchester, M13 9PT, UK
| | - Jose C Nacher
- Department of Information Science, Faculty of Science, Toho University, Funabashi, Chiba, 274-8510, Japan.
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6
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Martini L, Baek SH, Lo I, Raby BA, Silverman E, Weiss S, Glass K, Halu A. Detecting and dissecting signaling crosstalk via the multilayer network integration of signaling and regulatory interactions. Nucleic Acids Res 2024; 52:e5. [PMID: 37953325 PMCID: PMC10783515 DOI: 10.1093/nar/gkad1035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 06/27/2023] [Accepted: 10/23/2023] [Indexed: 11/14/2023] Open
Abstract
The versatility of cellular response arises from the communication, or crosstalk, of signaling pathways in a complex network of signaling and transcriptional regulatory interactions. Understanding the various mechanisms underlying crosstalk on a global scale requires untargeted computational approaches. We present a network-based statistical approach, MuXTalk, that uses high-dimensional edges called multilinks to model the unique ways in which signaling and regulatory interactions can interface. We demonstrate that the signaling-regulatory interface is located primarily in the intermediary region between signaling pathways where crosstalk occurs, and that multilinks can differentiate between distinct signaling-transcriptional mechanisms. Using statistically over-represented multilinks as proxies of crosstalk, we infer crosstalk among 60 signaling pathways, expanding currently available crosstalk databases by more than five-fold. MuXTalk surpasses existing methods in terms of model performance metrics, identifies additions to manual curation efforts, and pinpoints potential mediators of crosstalk. Moreover, it accommodates the inherent context-dependence of crosstalk, allowing future applications to cell type- and disease-specific crosstalk.
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Affiliation(s)
- Leonardo Martini
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, 00185, Italy
| | - Seung Han Baek
- Division of Pulmonary Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Ian Lo
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Benjamin A Raby
- Division of Pulmonary Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Kimberly Glass
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Arda Halu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
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7
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Lee JH, Kim J, Kim HS, Kang YJ. Unraveling Connective Tissue Growth Factor as a Therapeutic Target and Assessing Kahweol as a Potential Drug Candidate in Triple-Negative Breast Cancer Treatment. Int J Mol Sci 2023; 24:16307. [PMID: 38003505 PMCID: PMC10671558 DOI: 10.3390/ijms242216307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 11/02/2023] [Accepted: 11/12/2023] [Indexed: 11/26/2023] Open
Abstract
Triple-negative breast cancer (TNBC) is characterized by aggressive behavior and limited treatment options, necessitating the identification of novel therapeutic targets. In this study, we investigated the clinical significance of connective tissue growth factor (CTGF) as a prognostic marker and explored the potential therapeutic effects of kahweol, a coffee diterpene molecule, in TNBC treatment. Initially, through a survival analysis on breast cancer patients from The Cancer Genome Atlas (TCGA) database, we found that CTGF exhibited significant prognostic effects exclusively in TNBC patients. To gain mechanistic insights, we performed the functional annotation and gene set enrichment analyses, revealing the involvement of CTGF in migratory pathways relevant to TNBC treatment. Subsequently, in vitro experiments using MDA-MB 231 cells, a representative TNBC cell line, demonstrated that recombinant CTGF (rCTGF) administration enhanced cell motility, whereas CTGF knockdown using CTGF siRNA resulted in reduced motility. Notably, rCTGF restored kahweol-reduced cell motility, providing compelling evidence for the role of CTGF in mediating kahweol's effects. At the molecular level, kahweol downregulated the protein expression of CTGF as well as critical signaling molecules, such as p-ERK, p-P38, p-PI3K/AKT, and p-FAK, associated with cell motility. In summary, our findings propose CTGF as a potential prognostic marker for guiding TNBC treatment and suggest kahweol as a promising antitumor compound capable of regulating CTGF expression to suppress cell motility in TNBC. These insights hold promise for the development of targeted therapies and improved clinical outcomes for TNBC patients.
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Affiliation(s)
- Jeong Hee Lee
- Department of Biological Sciences, Sungkyunkwan University, Suwon 16419, Republic of Korea; (J.H.L.); (J.K.)
| | - Jongsu Kim
- Department of Biological Sciences, Sungkyunkwan University, Suwon 16419, Republic of Korea; (J.H.L.); (J.K.)
| | - Hong Sook Kim
- Department of Biological Sciences, Sungkyunkwan University, Suwon 16419, Republic of Korea; (J.H.L.); (J.K.)
| | - Young Jin Kang
- Department of Pharmacology, College of Medicine, Yeungnam University, Daegu 42415, Republic of Korea
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8
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Zhao A, Varady S, O'Kelley-Bangsberg M, Deng V, Platenkamp A, Wijngaard P, Bern M, Gormley W, Kushkowski E, Thompson K, Tibbetts L, Conner AT, Noeckel D, Teran A, Ritz A, Applewhite DA. From network analysis to experimental validation: identification of regulators of non-muscle myosin II contractility using the folded-gastrulation signaling pathway. BMC Mol Cell Biol 2023; 24:32. [PMID: 37821823 PMCID: PMC10568788 DOI: 10.1186/s12860-023-00492-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 09/29/2023] [Indexed: 10/13/2023] Open
Abstract
The morphogenetic process of apical constriction, which relies on non-muscle myosin II (NMII) generated constriction of apical domains of epithelial cells, is key to the development of complex cellular patterns. Apical constriction occurs in almost all multicellular organisms, but one of the most well-characterized systems is the Folded-gastrulation (Fog)-induced apical constriction that occurs in Drosophila. The binding of Fog to its cognizant receptors Mist/Smog results in a signaling cascade that leads to the activation of NMII-generated contractility. Despite our knowledge of key molecular players involved in Fog signaling, we sought to explore whether other proteins have an undiscovered role in its regulation. We developed a computational method to predict unidentified candidate NMII regulators using a network of pairwise protein-protein interactions called an interactome. We first constructed a Drosophila interactome of over 500,000 protein-protein interactions from several databases that curate high-throughput experiments. Next, we implemented several graph-based algorithms that predicted 14 proteins potentially involved in Fog signaling. To test these candidates, we used RNAi depletion in combination with a cellular contractility assay in Drosophila S2R + cells, which respond to Fog by contracting in a stereotypical manner. Of the candidates we screened using this assay, two proteins, the serine/threonine phosphatase Flapwing and the putative guanylate kinase CG11811 were demonstrated to inhibit cellular contractility when depleted, suggestive of their roles as novel regulators of the Fog pathway.
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Affiliation(s)
- Andy Zhao
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - Sophia Varady
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | | | - Vicki Deng
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - Amy Platenkamp
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - Petra Wijngaard
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - Miriam Bern
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - Wyatt Gormley
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - Elaine Kushkowski
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - Kat Thompson
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - Logan Tibbetts
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - A Tamar Conner
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - David Noeckel
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - Aidan Teran
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA
| | - Anna Ritz
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA.
| | - Derek A Applewhite
- Reed College Department of Biology, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA.
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9
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Ohnmacht AJ, Stahler A, Stintzing S, Modest DP, Holch JW, Westphalen CB, Hölzel L, Schübel MK, Galhoz A, Farnoud A, Ud-Dean M, Vehling-Kaiser U, Decker T, Moehler M, Heinig M, Heinemann V, Menden MP. The Oncology Biomarker Discovery framework reveals cetuximab and bevacizumab response patterns in metastatic colorectal cancer. Nat Commun 2023; 14:5391. [PMID: 37666855 PMCID: PMC10477267 DOI: 10.1038/s41467-023-41011-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 08/17/2023] [Indexed: 09/06/2023] Open
Abstract
Precision medicine has revolutionised cancer treatments; however, actionable biomarkers remain scarce. To address this, we develop the Oncology Biomarker Discovery (OncoBird) framework for analysing the molecular and biomarker landscape of randomised controlled clinical trials. OncoBird identifies biomarkers based on single genes or mutually exclusive genetic alterations in isolation or in the context of tumour subtypes, and finally, assesses predictive components by their treatment interactions. Here, we utilise the open-label, randomised phase III trial (FIRE-3, AIO KRK-0306) in metastatic colorectal carcinoma patients, who received either cetuximab or bevacizumab in combination with 5-fluorouracil, folinic acid and irinotecan (FOLFIRI). We systematically identify five biomarkers with predictive components, e.g., patients with tumours that carry chr20q amplifications or lack mutually exclusive ERK signalling mutations benefited from cetuximab compared to bevacizumab. In summary, OncoBird characterises the molecular landscape and outlines actionable biomarkers, which generalises to any molecularly characterised randomised controlled trial.
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Affiliation(s)
- Alexander J Ohnmacht
- Computational Health Center, Helmholtz Munich, 85764, Neuherberg, Germany
- Department of Biology, Ludwig-Maximilians University Munich, 82152, Martinsried, Germany
| | - Arndt Stahler
- Charité Universitätsmedizin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Hematology, Oncology, and Cancer Immunology, Charitéplatz 1, 10117, Berlin, Germany
| | - Sebastian Stintzing
- Charité Universitätsmedizin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Hematology, Oncology, and Cancer Immunology, Charitéplatz 1, 10117, Berlin, Germany
- German Cancer Consortium (DKTK), partner sites Berlin and Munich, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Dominik P Modest
- Charité Universitätsmedizin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Hematology, Oncology, and Cancer Immunology, Charitéplatz 1, 10117, Berlin, Germany
| | - Julian W Holch
- German Cancer Consortium (DKTK), partner sites Berlin and Munich, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
- Department of Medicine III and Comprehensive Cancer Center Munich, University Hospital, Ludwig-Maximilians University Munich, 81377, Munich, Germany
| | - C Benedikt Westphalen
- Department of Medicine III and Comprehensive Cancer Center Munich, University Hospital, Ludwig-Maximilians University Munich, 81377, Munich, Germany
| | - Linus Hölzel
- Computational Health Center, Helmholtz Munich, 85764, Neuherberg, Germany
| | - Marisa K Schübel
- Computational Health Center, Helmholtz Munich, 85764, Neuherberg, Germany
- Department of Biology, Ludwig-Maximilians University Munich, 82152, Martinsried, Germany
| | - Ana Galhoz
- Computational Health Center, Helmholtz Munich, 85764, Neuherberg, Germany
- Department of Biology, Ludwig-Maximilians University Munich, 82152, Martinsried, Germany
| | - Ali Farnoud
- Computational Health Center, Helmholtz Munich, 85764, Neuherberg, Germany
| | - Minhaz Ud-Dean
- Computational Health Center, Helmholtz Munich, 85764, Neuherberg, Germany
| | | | | | - Markus Moehler
- Department of Medicine I and Research Center for Immunotherapy (FZI), Johannes Gutenberg-University Clinic, 55131, Mainz, Germany
| | - Matthias Heinig
- Computational Health Center, Helmholtz Munich, 85764, Neuherberg, Germany
| | - Volker Heinemann
- Department of Medicine III and Comprehensive Cancer Center Munich, University Hospital, Ludwig-Maximilians University Munich, 81377, Munich, Germany.
| | - Michael P Menden
- Computational Health Center, Helmholtz Munich, 85764, Neuherberg, Germany.
- Department of Biology, Ludwig-Maximilians University Munich, 82152, Martinsried, Germany.
- Department of Biochemistry and Pharmacology, University of Melbourne, Victoria, 3010, Australia.
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10
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Yun R, Hong E, Kim J, Park B, Kim SJ, Lee B, Song YS, Kim SJ, Park S, Kang JM. N-linked glycosylation is essential for anti-tumor activities of KIAA1324 in gastric cancer. Cell Death Dis 2023; 14:546. [PMID: 37612293 PMCID: PMC10447535 DOI: 10.1038/s41419-023-06083-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 08/04/2023] [Accepted: 08/16/2023] [Indexed: 08/25/2023]
Abstract
KIAA1324 is a transmembrane protein largely reported as a tumor suppressor and favorable prognosis marker in various cancers, including gastric cancer. In this study, we report the role of N-linked glycosylation in KIAA1324 as a functional post-translational modification (PTM). Loss of N-linked glycosylation eliminated the potential of KIAA1324 to suppress cancer cell proliferation and migration. Furthermore, we demonstrated that KIAA1324 undergoes fucosylation, a modification of the N-glycan mediated by fucosyltransferase, and inhibition of fucosylation also significantly suppressed KIAA1324-induced cell growth inhibition and apoptosis of gastric cancer cells. In addition, KIAA1324-mediated apoptosis and tumor regression were inhibited by the loss of N-linked glycosylation. RNA sequencing (RNAseq) analysis revealed that genes most relevant to the apoptosis and cell cycle arrest pathways were modulated by KIAA1324 with the N-linked glycosylation, and Gene Regulatory Network (GRN) analysis suggested novel targets of KIAA1324 for anti-tumor effects in the transcription level. The N-linked glycosylation blockade decreased protein stability through rapid proteasomal degradation. The non-glycosylated mutant also showed altered localization and lost apoptotic activity that inhibits the interaction between GRP78 and caspase 7. These data demonstrate that N-linked glycosylation of KIAA1324 is essential for the suppressive role of KIAA1324 protein in gastric cancer progression and indicates that KIAA1324 may have anti-tumor effects by targeting cancer-related genes with N-linked glycosylation. In conclusion, our study suggests the PTM of KIAA1324 including N-linked glycosylation and fucosylation is a necessary factor to consider for cancer prognosis and therapy improvement.
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Affiliation(s)
- Rebecca Yun
- GILO Institute, GILO Foundation, Seoul, 06668, Republic of Korea
- Interdisciplinary Program in Cancer Biology, Seoul National University, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Eunji Hong
- GILO Institute, GILO Foundation, Seoul, 06668, Republic of Korea
- Department of Biomedical Science, College of Life Science, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Junil Kim
- School of Systems Biomedical Science, Soongsil University, Seoul, 06978, Republic of Korea
| | - Bora Park
- WellSpan York Hospital Family Medicine Residency Program, York, PA, USA
| | - Staci Jakyong Kim
- International Institute for Integrative Sleep Medicine, University of Tsukuba, Tsukuba, Japan
| | - Bona Lee
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Yong Sang Song
- Interdisciplinary Program in Cancer Biology, Seoul National University, Gwanak-gu, Seoul, 08826, Republic of Korea
- Department of Obstetrics and Gynecology, College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - Seong-Jin Kim
- GILO Institute, GILO Foundation, Seoul, 06668, Republic of Korea
- Medpacto Inc., Seoul, 06668, Republic of Korea
| | - Sujin Park
- GILO Institute, GILO Foundation, Seoul, 06668, Republic of Korea.
| | - Jin Muk Kang
- Department of Pediatric Hematology & Oncology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
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11
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Athanasiadis P, Ravikumar B, Elliott RJ, Dawson JC, Carragher NO, Clemons PA, Johanssen T, Ebner D, Aittokallio T. Chemogenomic library design strategies for precision oncology, applied to phenotypic profiling of glioblastoma patient cells. iScience 2023; 26:107209. [PMID: 37485377 PMCID: PMC10359939 DOI: 10.1016/j.isci.2023.107209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 02/21/2023] [Accepted: 06/21/2023] [Indexed: 07/25/2023] Open
Abstract
Designing a targeted screening library of bioactive small molecules is a challenging task since most compounds modulate their effects through multiple protein targets with varying degrees of potency and selectivity. We implemented analytic procedures for designing anticancer compound libraries adjusted for library size, cellular activity, chemical diversity and availability, and target selectivity. The resulting compound collections cover a wide range of protein targets and biological pathways implicated in various cancers, making them widely applicable to precision oncology. We characterized the compound and target spaces of the virtual libraries, in comparison with a minimal screening library of 1,211 compounds for targeting 1,386 anticancer proteins. In a pilot screening study, we identified patient-specific vulnerabilities by imaging glioma stem cells from patients with glioblastoma (GBM), using a physical library of 789 compounds that cover 1,320 of the anticancer targets. The cell survival profiling revealed highly heterogeneous phenotypic responses across the patients and GBM subtypes.
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Affiliation(s)
- Paschalis Athanasiadis
- Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, 0310 Oslo, Norway
- Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, 0317 Oslo, Norway
| | - Balaguru Ravikumar
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, 20520 00290 Helsinki, Finland
| | - Richard J.R. Elliott
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - John C. Dawson
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Neil O. Carragher
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Paul A. Clemons
- Chemical Biology and Therapeutics Science Program, Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, United States
| | - Timothy Johanssen
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
| | - Daniel Ebner
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
| | - Tero Aittokallio
- Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, 0310 Oslo, Norway
- Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, 0317 Oslo, Norway
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, 20520 00290 Helsinki, Finland
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12
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Baghdassarian H, Dimitrov D, Armingol E, Saez-Rodriguez J, Lewis NE. Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.28.538731. [PMID: 37162916 PMCID: PMC10168343 DOI: 10.1101/2023.04.28.538731] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. While multiple cell-cell communication tools exist, results are specific to the tool of choice, due to the diverse assumptions made across computational frameworks. Moreover, tools are often limited to analyzing single samples or to performing pairwise comparisons. As experimental design complexity and sample numbers continue to increase in single-cell datasets, so does the need for generalizable methods to decipher cell-cell communication in such scenarios. Here, we integrate two tools, LIANA and Tensor-cell2cell, which combined can deploy multiple existing methods and resources, to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this protocol, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step-by-step in both Python and R, and we provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This protocol typically takes ~1.5h to complete from installation to downstream visualizations on a GPU-enabled computer, for a dataset of ~63k cells, 10 cell types, and 12 samples.
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Affiliation(s)
- Hratch Baghdassarian
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Daniel Dimitrov
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, 69120, Heidelberg, Germany
| | - Erick Armingol
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, 69120, Heidelberg, Germany
| | - Nathan E. Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
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13
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Szegvari G, Dora D, Lohinai Z. Effective Reversal of Macrophage Polarization by Inhibitory Combinations Predicted by a Boolean Protein–Protein Interaction Model. BIOLOGY 2023; 12:biology12030376. [PMID: 36979068 PMCID: PMC10045914 DOI: 10.3390/biology12030376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/17/2023] [Accepted: 02/22/2023] [Indexed: 03/03/2023]
Abstract
Background: The function and polarization of macrophages has a significant impact on the outcome of many diseases. Targeting tumor-associated macrophages (TAMs) is among the greatest challenges to solve because of the low in vitro reproducibility of the heterogeneous tumor microenvironment (TME). To create a more comprehensive model and to understand the inner workings of the macrophage and its dependence on extracellular signals driving polarization, we propose an in silico approach. Methods: A Boolean control network was built based on systematic manual curation of the scientific literature to model the early response events of macrophages by connecting extracellular signals (input) with gene transcription (output). The network consists of 106 nodes, classified as 9 input, 75 inner and 22 output nodes, that are connected by 217 edges. The direction and polarity of edges were manually verified and only included in the model if the literature plainly supported these parameters. Single or combinatory inhibitions were simulated mimicking therapeutic interventions, and output patterns were analyzed to interpret changes in polarization and cell function. Results: We show that inhibiting a single target is inadequate to modify an established polarization, and that in combination therapy, inhibiting numerous targets with individually small effects is frequently required. Our findings show the importance of JAK1, JAK3 and STAT6, and to a lesser extent STK4, Sp1 and Tyk2, in establishing an M1-like pro-inflammatory polarization, and NFAT5 in creating an anti-inflammatory M2-like phenotype. Conclusions: Here, we demonstrate a protein–protein interaction (PPI) network modeling the intracellular signalization driving macrophage polarization, offering the possibility of therapeutic repolarization and demonstrating evidence for multi-target methods.
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Affiliation(s)
- Gabor Szegvari
- Translational Medicine Institute, Semmelweis University, 1094 Budapest, Hungary
| | - David Dora
- Department of Anatomy, Histology and Embryology, Semmelweis University, 1094 Budapest, Hungary
- Correspondence: (D.D.); (Z.L.); Tel.: +36-1-2156920 (D.D.)
| | - Zoltan Lohinai
- Translational Medicine Institute, Semmelweis University, 1094 Budapest, Hungary
- Pulmonary Hospital Torokbalint, 2045 Torokbalint, Hungary
- Correspondence: (D.D.); (Z.L.); Tel.: +36-1-2156920 (D.D.)
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14
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Xu J, Xu J, Meng Y, Lu C, Cai L, Zeng X, Nussinov R, Cheng F. Graph embedding and Gaussian mixture variational autoencoder network for end-to-end analysis of single-cell RNA sequencing data. CELL REPORTS METHODS 2023; 3:100382. [PMID: 36814845 PMCID: PMC9939381 DOI: 10.1016/j.crmeth.2022.100382] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 10/31/2022] [Accepted: 12/08/2022] [Indexed: 05/25/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a revolutionary technology to determine the precise gene expression of individual cells and identify cell heterogeneity and subpopulations. However, technical limitations of scRNA-seq lead to heterogeneous and sparse data. Here, we present autoCell, a deep-learning approach for scRNA-seq dropout imputation and feature extraction. autoCell is a variational autoencoding network that combines graph embedding and a probabilistic depth Gaussian mixture model to infer the distribution of high-dimensional, sparse scRNA-seq data. We validate autoCell on simulated datasets and biologically relevant scRNA-seq. We show that interpolation of autoCell improves the performance of existing tools in identifying cell developmental trajectories of human preimplantation embryos. We identify disease-associated astrocytes (DAAs) and reconstruct DAA-specific molecular networks and ligand-receptor interactions involved in cell-cell communications using Alzheimer's disease as a prototypical example. autoCell provides a toolbox for end-to-end analysis of scRNA-seq data, including visualization, clustering, imputation, and disease-specific gene network identification.
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Affiliation(s)
- Junlin Xu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Jielin Xu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Yajie Meng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Changcheng Lu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Lijun Cai
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD 21702, USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
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15
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Kumar K, Bhowmik D, Mandloi S, Gautam A, Lahiri A, Biswas N, Paul S, Chakrabarti S. Integrating Multi-Omics Data to Construct Reliable Interconnected Models of Signaling, Gene Regulatory, and Metabolic Pathways. Methods Mol Biol 2023; 2634:139-151. [PMID: 37074577 DOI: 10.1007/978-1-0716-3008-2_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
Alteration of the status of the metabolic enzymes could be a probable way to regulate metabolic reprogramming, which is a critical cellular adaptation mechanism especially for cancer cells. Coordination among biological pathways, such as gene-regulatory, signaling, and metabolic pathways is crucial for regulating metabolic adaptation. Also, incorporation of resident microbial metabolic potential in human body can influence the interplay between the microbiome and the systemic or tissue metabolic environments. Systemic framework for model-based integration of multi-omics data can ultimately improve our understanding of metabolic reprogramming at holistic level. However, the interconnectivity and novel meta-pathway regulatory mechanisms are relatively lesser explored and understood. Hence, we propose a computational protocol that utilizes multi-omics data to identify probable cross-pathway regulatory and protein-protein interaction (PPI) links connecting signaling proteins or transcription factors or miRNAs to metabolic enzymes and their metabolites using network analysis and mathematical modeling. These cross-pathway links were shown to play important roles in metabolic reprogramming in cancer scenarios.
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Affiliation(s)
- Krishna Kumar
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India
| | - Debaleena Bhowmik
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
| | - Sapan Mandloi
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India
| | - Anupam Gautam
- Algorithms in Bioinformatics, Institute for Bioinformatics and Medical Informatics, University of Tübingen,, Tübingen, Germany
- International Max Planck Research School "From Molecules to Organisms," Max Planck Institute for Biology Tübingen, Tübingen, Germany
- Cluster of Excellence: EXC 2124: Controlling Microbes to Fight Infection, University of Tübingen, Tübingen, Germany
| | - Abhishake Lahiri
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
| | - Nupur Biswas
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India
| | - Sandip Paul
- JIS Institute of Advanced Studies and Research, JIS University, Kolkata, India.
| | - Saikat Chakrabarti
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India.
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16
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Xu J, Mao C, Hou Y, Luo Y, Binder JL, Zhou Y, Bekris LM, Shin J, Hu M, Wang F, Eng C, Oprea TI, Flanagan ME, Pieper AA, Cummings J, Leverenz JB, Cheng F. Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer's disease. Cell Rep 2022; 41:111717. [PMID: 36450252 PMCID: PMC9837836 DOI: 10.1016/j.celrep.2022.111717] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 09/01/2022] [Accepted: 11/02/2022] [Indexed: 12/03/2022] Open
Abstract
Translating human genetic findings (genome-wide association studies [GWAS]) to pathobiology and therapeutic discovery remains a major challenge for Alzheimer's disease (AD). We present a network topology-based deep learning framework to identify disease-associated genes (NETTAG). We leverage non-coding GWAS loci effects on quantitative trait loci, enhancers and CpG islands, promoter regions, open chromatin, and promoter flanking regions under the protein-protein interactome. Via NETTAG, we identified 156 AD-risk genes enriched in druggable targets. Combining network-based prediction and retrospective case-control observations with 10 million individuals, we identified that usage of four drugs (ibuprofen, gemfibrozil, cholecalciferol, and ceftriaxone) is associated with reduced likelihood of AD incidence. Gemfibrozil (an approved lipid regulator) is significantly associated with 43% reduced risk of AD compared with simvastatin using an active-comparator design (95% confidence interval 0.51-0.63, p < 0.0001). In summary, NETTAG offers a deep learning methodology that utilizes GWAS and multi-genomic findings to identify pathobiology and drug repurposing in AD.
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Affiliation(s)
- Jielin Xu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Chengsheng Mao
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Yuan Hou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Yuan Luo
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Jessica L Binder
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Lynn M Bekris
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
| | - Jiyoung Shin
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44106, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
| | - Tudor I Oprea
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
| | - Margaret E Flanagan
- Department of Pathology and Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Andrew A Pieper
- Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA; Department of Psychiatry, Case Western Reserve University, Cleveland, OH 44106, USA; Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, OH 44106, USA; Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland 44106, OH, USA; Department of Neuroscience, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - James B Leverenz
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA; Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA; Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA.
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17
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Li F, Sun Z, Liu JX, Shang J, Dai L, Liu X, Li Y. NESM: a network embedding method for tumor stratification by integrating multi-omics data. G3 GENES|GENOMES|GENETICS 2022; 12:6705238. [PMID: 36124952 PMCID: PMC9635646 DOI: 10.1093/g3journal/jkac243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/30/2022] [Indexed: 11/23/2022]
Abstract
Tumor stratification plays an important role in cancer diagnosis and individualized treatment. Recent developments in high-throughput sequencing technologies have produced huge amounts of multi-omics data, making it possible to stratify cancer types using multiple molecular datasets. We introduce a Network Embedding method for tumor Stratification by integrating Multi-omics data. Network Embedding method for tumor Stratification by integrating Multi-omics pregroup the samples, integrate the gene features and somatic mutation corresponding to cancer types within each group to construct patient features, and then integrate all groups to obtain comprehensive patient information. The gene features contain network topology information, because it is extracted by integrating deoxyribonucleic acid methylation, messenger ribonucleic acid expression data, and protein–protein interactions through network embedding method. On the one hand, a supervised learning method Light Gradient Boosting Machine is used to classify cancer types based on patient features. When compared with other 3 methods, Network Embedding method for tumor Stratification by integrating Multi-omics has the highest AUC in most cancer types. The average AUC for stratifying cancer types is 0.91, indicating that the patient features extracted by Network Embedding method for tumor Stratification by integrating Multi-omics are effective for tumor stratification. On the other hand, an unsupervised clustering algorithm Density-Based Spatial Clustering of Applications with Noise is utilized to divide single cancer subtypes. The vast majority of the subtypes identified by Network Embedding method for tumor Stratification by integrating Multi-omics are significantly associated with patient survival.
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Affiliation(s)
- Feng Li
- School of Computer Science, Qufu Normal University , Rizhao 276826, China
| | - Zhensheng Sun
- School of Computer Science, Qufu Normal University , Rizhao 276826, China
| | - Jin-Xing Liu
- School of Computer Science, Qufu Normal University , Rizhao 276826, China
| | - Junliang Shang
- School of Computer Science, Qufu Normal University , Rizhao 276826, China
| | - Lingyun Dai
- School of Computer Science, Qufu Normal University , Rizhao 276826, China
| | - Xikui Liu
- Department of Electrical Engineering and Information Technology, Shandong University of Science and Technology , Jinan, Shandong 250031, China
| | - Yan Li
- Department of Electrical Engineering and Information Technology, Shandong University of Science and Technology , Jinan, Shandong 250031, China
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18
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Langraf V, Babosová R, Petrovičová K, Schlarmannová J, Brygadyrenko V. Storing and structuring big data in histological research (vertebrates) using a relational database in SQL. REGULATORY MECHANISMS IN BIOSYSTEMS 2022. [DOI: 10.15421/022226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Database systems store data (big data) for various areas dealing with finance (banking, insurance) and are also an essential part of corporate firms. In the field of biology, however, not much attention has been paid to database systems, with the exception of genetics (RNA, DNA) and human protein. Therefore data storage and subsequent implementation is insufficient for this field. The current situation in the field of data use for the assessment of biological relationships and trends is conditioned by constantly changing requirements, while data stored in simple databases used in the field of biology cannot respond operatively to these changes. In the recent period, developments in technology in the field of histology caused an increase in biological information stored in databases with which database technology did not deal. We proposed a new database for histology with designed data types (data format) in database program Microsoft SQL Server Management Studio. In order that the information to support identification of biological trends and regularities is relevant, the data must be provided in real time and in the required format at the strategic, tactical and operational levels. We set the data type according to the needs of our database, we used numeric (smallint,numbers, float), text string (nvarchar, varchar) and date. To select, insert, modify and delete data, we used Structured Query Language (SQL), which is currently the most widely used language in relational databases. Our results represent a new database for information about histology, focusing on histological structures in systems of animals. The structure and relational relations of the histology database will help in analysis of big data, the objective of which was to find relations between histological structures in species and the diversity of habitats in which species live. In addition to big data, the successful estimation of biological relationships and trends also requires the rapid accuracy of scientists who derive key information from the data. A properly functioning database for meta-analyses, data warehousing, and data mining includes, in addition to technological aspects, planning, design, implementation, management, and implementation.
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19
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Gupta C, Xu J, Jin T, Khullar S, Liu X, Alatkar S, Cheng F, Wang D. Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer’s disease. PLoS Comput Biol 2022; 18:e1010287. [PMID: 35849618 PMCID: PMC9333448 DOI: 10.1371/journal.pcbi.1010287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 07/28/2022] [Accepted: 06/07/2022] [Indexed: 12/03/2022] Open
Abstract
Dysregulation of gene expression in Alzheimer’s disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, AD-induced regulatory changes across brain cell types remains uncharted. To address this, we integrated single-cell multi-omics datasets to predict the gene regulatory networks of four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes, in control and AD brains. Importantly, we analyzed and compared the structural and topological features of networks across cell types and examined changes in AD. Our analysis shows that hub TFs are largely common across cell types and AD-related changes are relatively more prominent in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are also highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes. The further disease-module-drug association analysis suggests cell-type candidate drugs and their potential target genes. Finally, our network-based machine learning analysis systematically prioritized cell type risk genes likely involved in AD. Our strategy is validated using an independent dataset which showed that top ranked genes can predict clinical phenotypes (e.g., cognitive impairment) of AD with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals cell-type gene dysregulation in AD. Alzheimer’s Disease (AD) is the leading cause of dementia. It affects parts of the brain that control language, behavior, and memory. The human brain is comprised of tens of billions of cells, such as neuronal cells that transmit information via electrical and chemical signals, and glial cells that maintain the brain’s immune system. Researchers have found that AD causes changes in the expression of genes within the brain cells. Gene expression is a tightly regulated process involving interconnected networks of multiple genes. Understanding how these gene networks change in AD is critical to identifying genetic biomarkers and potential drug targets. Using genomic data of post-mortem brains diagnosed with AD and healthy individuals, we identified gene networks that play a crucial role in regulating biological processes within neuronal and glial cells. We utilized these gene networks to make predictions on existing FDA approved drugs that could potentially be repurposed for AD. Furthermore, we used a machine learning strategy to identify novel genes that are more likely to be involved in AD pathology. The systems-level approach lends itself to analysis of single-cell genomics data of other human diseases.
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Affiliation(s)
- Chirag Gupta
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Jielin Xu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Ting Jin
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Saniya Khullar
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Xiaoyu Liu
- Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Sayali Alatkar
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Daifeng Wang
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- * E-mail:
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20
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Dimitrov D, Türei D, Garrido-Rodriguez M, Burmedi PL, Nagai JS, Boys C, Ramirez Flores RO, Kim H, Szalai B, Costa IG, Valdeolivas A, Dugourd A, Saez-Rodriguez J. Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data. Nat Commun 2022; 13:3224. [PMID: 35680885 PMCID: PMC9184522 DOI: 10.1038/s41467-022-30755-0] [Citation(s) in RCA: 142] [Impact Index Per Article: 71.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 05/17/2022] [Indexed: 12/18/2022] Open
Abstract
The growing availability of single-cell data, especially transcriptomics, has sparked an increased interest in the inference of cell-cell communication. Many computational tools were developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we systematically compare 16 cell-cell communication inference resources and 7 methods, plus the consensus between the methods’ predictions. Among the resources, we find few unique interactions, a varying degree of overlap, and an uneven coverage of specific pathways and tissue-enriched proteins. We then examine all possible combinations of methods and resources and show that both strongly influence the predicted intercellular interactions. Finally, we assess the agreement of cell-cell communication methods with spatial colocalisation, cytokine activities, and receptor protein abundance and find that predictions are generally coherent with those data modalities. To facilitate the use of the methods and resources described in this work, we provide LIANA, a LIgand-receptor ANalysis frAmework as an open-source interface to all the resources and methods. Multiple methods to infer cell-cell communication (CCC) from single cell data are currently available. Here, the authors systematically compare 16 CCC inference resources and 7 methods, and develop the LIANA framework as an interface to use and compare all these approaches.
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Affiliation(s)
- Daniel Dimitrov
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, Heidelberg, Germany
| | - Dénes Türei
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, Heidelberg, Germany
| | - Martin Garrido-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, Heidelberg, Germany
| | - Paul L Burmedi
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, Heidelberg, Germany
| | - James S Nagai
- Institute for Computational Genomics, Faculty of Medicine, RWTH Aachen University, Aachen, 52074, Germany.,Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, Aachen, Germany
| | - Charlotte Boys
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, Heidelberg, Germany
| | - Ricardo O Ramirez Flores
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, Heidelberg, Germany
| | - Hyojin Kim
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, Heidelberg, Germany
| | - Bence Szalai
- Faculty of Medicine, Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Ivan G Costa
- Institute for Computational Genomics, Faculty of Medicine, RWTH Aachen University, Aachen, 52074, Germany.,Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, Aachen, Germany
| | - Alberto Valdeolivas
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Aurélien Dugourd
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, Heidelberg, Germany
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, Heidelberg, Germany.
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21
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Lee WY, Lee CY, Lee JS, Kim CE. Identifying Candidate Flavonoids for Non-Alcoholic Fatty Liver Disease by Network-Based Strategy. Front Pharmacol 2022; 13:892559. [PMID: 35721123 PMCID: PMC9204489 DOI: 10.3389/fphar.2022.892559] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 04/22/2022] [Indexed: 11/16/2022] Open
Abstract
Nonalcoholic fatty liver disease (NAFLD) is the most common type of chronic liver disease and lacks guaranteed pharmacological therapeutic options. In this study, we applied a network-based framework for comprehensively identifying candidate flavonoids for the prevention and/or treatment of NAFLD. Flavonoid-target interaction information was obtained from combining experimentally validated data and results obtained using a recently developed machine-learning model, AI-DTI. Flavonoids were then prioritized by calculating the network proximity between flavonoid targets and NAFLD-associated proteins. The preventive effects of the candidate flavonoids were evaluated using FFA-induced hepatic steatosis in HepG2 and AML12 cells. We reconstructed the flavonoid-target network and found that the number of re-covered compound-target interactions was significantly higher than the chance level. Proximity scores have successfully rediscovered flavonoids and their potential mechanisms that are reported to have therapeutic effects on NAFLD. Finally, we revealed that discovered candidates, particularly glycitin, significantly attenuated lipid accumulation and moderately inhibited intracellular reactive oxygen species production. We further confirmed the affinity of glycitin with the predicted target using molecular docking and found that glycitin targets are closely related to several proteins involved in lipid metabolism, inflammatory responses, and oxidative stress. The predicted network-level effects were validated at the levels of mRNA. In summary, our study offers and validates network-based methods for the identification of candidate flavonoids for NAFLD.
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Affiliation(s)
- Won-Yung Lee
- Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, South Korea
- Department of Herbal Formula, College of Korean Medicine, Dongguk University, Goyang-si, South Korea
| | - Choong-Yeol Lee
- Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, South Korea
| | - Jin-Seok Lee
- Institute of Bioscience and Integrative Medicine, Daejeon Oriental Hospital of Daejeon University, Daejeon, South Korea
- *Correspondence: Jin-Seok Lee, ; Chang-Eop Kim,
| | - Chang-Eop Kim
- Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, South Korea
- *Correspondence: Jin-Seok Lee, ; Chang-Eop Kim,
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22
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Noor F, Tahir ul Qamar M, Ashfaq UA, Albutti A, Alwashmi ASS, Aljasir MA. Network Pharmacology Approach for Medicinal Plants: Review and Assessment. Pharmaceuticals (Basel) 2022; 15:572. [PMID: 35631398 PMCID: PMC9143318 DOI: 10.3390/ph15050572] [Citation(s) in RCA: 107] [Impact Index Per Article: 53.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 04/27/2022] [Accepted: 04/27/2022] [Indexed: 12/13/2022] Open
Abstract
Natural products have played a critical role in medicine due to their ability to bind and modulate cellular targets involved in disease. Medicinal plants hold a variety of bioactive scaffolds for the treatment of multiple disorders. The less adverse effects, affordability, and easy accessibility highlight their potential in traditional remedies. Identifying pharmacological targets from active ingredients of medicinal plants has become a hot topic for biomedical research to generate innovative therapies. By developing an unprecedented opportunity for the systematic investigation of traditional medicines, network pharmacology is evolving as a systematic paradigm and becoming a frontier research field of drug discovery and development. The advancement of network pharmacology has opened up new avenues for understanding the complex bioactive components found in various medicinal plants. This study is attributed to a comprehensive summary of network pharmacology based on current research, highlighting various active ingredients, related techniques/tools/databases, and drug discovery and development applications. Moreover, this study would serve as a protocol for discovering novel compounds to explore the full range of biological potential of traditionally used plants. We have attempted to cover this vast topic in the review form. We hope it will serve as a significant pioneer for researchers working with medicinal plants by employing network pharmacology approaches.
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Affiliation(s)
- Fatima Noor
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad 38000, Pakistan; (F.N.); (M.T.u.Q.)
| | - Muhammad Tahir ul Qamar
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad 38000, Pakistan; (F.N.); (M.T.u.Q.)
| | - Usman Ali Ashfaq
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad 38000, Pakistan; (F.N.); (M.T.u.Q.)
| | - Aqel Albutti
- Department of Medical Biotechnology, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia
| | - Ameen S. S. Alwashmi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia; (A.S.S.A.); (M.A.A.)
| | - Mohammad Abdullah Aljasir
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia; (A.S.S.A.); (M.A.A.)
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23
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Catozzi S, Ternet C, Gourrege A, Wynne K, Oliviero G, Kiel C. Reconstruction and analysis of a large-scale binary Ras-effector signaling network. Cell Commun Signal 2022; 20:24. [PMID: 35246154 PMCID: PMC8896392 DOI: 10.1186/s12964-022-00823-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 12/18/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Ras is a key cellular signaling hub that controls numerous cell fates via multiple downstream effector pathways. While pathways downstream of effectors such as Raf, PI3K and RalGDS are extensively described in the literature, how other effectors signal downstream of Ras is often still enigmatic. METHODS A comprehensive and unbiased Ras-effector network was reconstructed downstream of 43 effector proteins (converging onto 12 effector classes) using public pathway and protein-protein interaction (PPI) databases. The output is an oriented graph of pairwise interactions defining a 3-layer signaling network downstream of Ras. The 2290 proteins comprising the network were studied for their implication in signaling crosstalk and feedbacks, their subcellular localizations, and their cellular functions. RESULTS The final Ras-effector network consists of 2290 proteins that are connected via 19,080 binary PPIs, increasingly distributed across the downstream layers, with 441 PPIs in layer 1, 1660 in layer 2, and 16,979 in layer 3. We identified a high level of crosstalk among proteins of the 12 effector classes. A class-specific Ras sub-network was generated in CellDesigner (.xml file) and a functional enrichment analysis thereof shows that 58% of the processes have previously been associated to a respective effector pathway, with the remaining providing insights into novel and unexplored functions of specific effector pathways. CONCLUSIONS Our large-scale and cell general Ras-effector network is a crucial steppingstone towards defining the network boundaries. It constitutes a 'reference interactome' and can be contextualized for specific conditions, e.g. different cell types or biopsy material obtained from cancer patients. Further, it can serve as a basis for elucidating systems properties, such as input-output relationships, crosstalk, and pathway redundancy. Video Abstract.
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Affiliation(s)
- Simona Catozzi
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.,UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Camille Ternet
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.,UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Alize Gourrege
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.,UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Kieran Wynne
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.,Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland
| | - Giorgio Oliviero
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.,UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Christina Kiel
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland. .,UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland. .,Department of Molecular Medicine, University of Pavia, 27100, Pavia, Italy.
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24
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Moon Y, Korcsmáros T, Nagappan A, Ray N. MicroRNA target-based network predicts androgen receptor-linked mycotoxin stress. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 230:113130. [PMID: 34968797 DOI: 10.1016/j.ecoenv.2021.113130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 11/15/2021] [Accepted: 12/25/2021] [Indexed: 06/14/2023]
Abstract
Stress-responsive microRNAs (miRNAs) contribute to the regulation of cellular homeostasis or pathological processes, including carcinogenesis, by reprogramming target gene expression following human exposure to environmental or dietary xenobiotics. Herein, we predicted the targets of carcinogenic mycotoxin-responsive miRNAs and analyzed their association with disease and functionality. miRNA target-derived prediction indicated potent associations of oncogenic mycotoxin exposure with metabolism- or hormone-related diseases, including sex hormone-linked cancers. Mechanistically, the signaling network evaluation suggested androgen receptor (AR)-linked signaling as a common pivotal cluster associated with metabolism- or hormone-related tumorigenesis in response to aflatoxin B1 and ochratoxin A co-exposure. Particularly, high levels of AR and AR-linked genes for the retinol and xenobiotic metabolic enzymes were positively associated with attenuated disease biomarkers and good prognosis in patients with liver or kidney cancers. Moreover, AR-linked signaling was protective against OTA-induced genetic insults in human hepatocytes whereas it was positively involved in AFB1-induced genotoxic actions. Collectively, miRNA target network-based predictions provide novel clinical insights into the progression or intervention against malignant adverse outcomes of human exposure to environmental oncogenic insults.
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Affiliation(s)
- Yuseok Moon
- Laboratory of Mucosal Exposome and Biomodulation, Department of Integrative Biomedical Sciences and Biomedical Research Institute, Pusan National University, Yangsan 50612, Republic of Korea; Graduate Program of Genomic Data Sciences, Pusan National University, Yangsan 50612, Republic of Korea.
| | - Tamás Korcsmáros
- Earlham Institute, Norwich NR4 7UZ, UK; Quadram Institute Bioscience, Norwich NR4 7UZ, UK
| | - Arulkumar Nagappan
- Laboratory of Mucosal Exposome and Biomodulation, Department of Integrative Biomedical Sciences and Biomedical Research Institute, Pusan National University, Yangsan 50612, Republic of Korea
| | - Navin Ray
- Laboratory of Mucosal Exposome and Biomodulation, Department of Integrative Biomedical Sciences and Biomedical Research Institute, Pusan National University, Yangsan 50612, Republic of Korea
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25
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Pastrello C, Niu Y, Jurisica I. Pathway Enrichment Analysis of Microarray Data. Methods Mol Biol 2022; 2401:147-159. [PMID: 34902127 DOI: 10.1007/978-1-0716-1839-4_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Microarray analyses usually result in a list of differential genes that need to be annotated to link them the phenotype being studied, help planning validation experiments and interpretation of the results. Pathway enrichment analyses are frequently used for such purpose, where pathways are human created models of molecular activities and processes. While different types of pathway enrichment are available, we focus this protocol on the most frequent type-overrepresentation analysis. Many databases collect different sets of pathways and curate different sets of genes for the same pathways, so it is important to carefully choose the most suitable pathway source to perform enrichment analysis. To provide a comprehensive pathway analysis, in this protocol we will use pathDIP, which supports comprehensive enrichment analysis by integrating 22 main pathway databases. We will also describe the steps needed to visualize the enriched pathways using GSOAP.
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Affiliation(s)
- Chiara Pastrello
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada
- Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Yun Niu
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada
- Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Igor Jurisica
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada.
- Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada.
- Departments of Medical Biophysics and Computer Science, University of Toronto, Toronto, ON, Canada.
- Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia.
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26
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Three topological features of regulatory networks control life-essential and specialized subsystems. Sci Rep 2021; 11:24209. [PMID: 34930908 PMCID: PMC8688434 DOI: 10.1038/s41598-021-03625-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 12/07/2021] [Indexed: 11/08/2022] Open
Abstract
Gene regulatory networks (GRNs) play key roles in development, phenotype plasticity, and evolution. Although graph theory has been used to explore GRNs, associations amongst topological features, transcription factors (TFs), and systems essentiality are poorly understood. Here we sought the relationship amongst the main GRN topological features that influence the control of essential and specific subsystems. We found that the Knn, page rank, and degree are the most relevant GRN features: the ones are conserved along the evolution and are also relevant in pluripotent cells. Interestingly, life-essential subsystems are governed mainly by TFs with intermediary Knn and high page rank or degree, whereas specialized subsystems are mainly regulated by TFs with low Knn. Hence, we suggest that the high probability of TFs be toured by a random signal, and the high probability of the signal propagation to target genes ensures the life-essential subsystems' robustness. Gene/genome duplication is the main evolutionary process to rise Knn as the most relevant feature. Herein, we shed light on unexplored topological GRN features to assess how they are related to subsystems and how the duplications shaped the regulatory systems along the evolution. The classification model generated can be found here: https://github.com/ivanrwolf/NoC/ .
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27
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Kamburov A, Herwig R. ConsensusPathDB 2022: molecular interactions update as a resource for network biology. Nucleic Acids Res 2021; 50:D587-D595. [PMID: 34850110 PMCID: PMC8728246 DOI: 10.1093/nar/gkab1128] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/21/2021] [Accepted: 11/04/2021] [Indexed: 01/01/2023] Open
Abstract
Molecular interactions are key drivers of biological function. Providing interaction resources to the research community is important since they allow functional interpretation and network-based analysis of molecular data. ConsensusPathDB (http://consensuspathdb.org) is a meta-database combining interactions of diverse types from 31 public resources for humans, 16 for mice and 14 for yeasts. Using ConsensusPathDB, researchers commonly evaluate lists of genes, proteins and metabolites against sets of molecular interactions defined by pathways, Gene Ontology and network neighborhoods and retrieve complex molecular neighborhoods formed by heterogeneous interaction types. Furthermore, the integrated protein–protein interaction network is used as a basis for propagation methods. Here, we present the 2022 update of ConsensusPathDB, highlighting content growth, additional functionality and improved database stability. For example, the number of human molecular interactions increased to 859 848 connecting 200 499 unique physical entities such as genes/proteins, metabolites and drugs. Furthermore, we integrated regulatory datasets in the form of transcription factor–, microRNA– and enhancer–gene target interactions, thus providing novel functionality in the context of overrepresentation and enrichment analyses. We specifically emphasize the use of the integrated protein–protein interaction network as a scaffold for network inferences, present topological characteristics of the network and discuss strengths and shortcomings of such approaches.
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Affiliation(s)
- Atanas Kamburov
- R&D Digital Technologies Department, Bayer AG, Berlin 13353, Germany
| | - Ralf Herwig
- Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin 14195, Germany
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28
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ERK inhibition in glioblastoma is associated with autophagy activation and tumorigenesis suppression. J Neurooncol 2021; 156:123-137. [PMID: 34797524 DOI: 10.1007/s11060-021-03896-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 11/04/2021] [Indexed: 10/19/2022]
Abstract
PURPOSE Autophagy-dependent tumorigenic growth is one of the most commonly reported molecular mechanisms in glioblastoma (GBM) progression. However, the mechanistic correlation between autophagy and GBM is still largely unexplored, especially the roles of autophagy-related genes involved in GBM oncogenesis. In this study, we aimed to explore the genetic alterations that interact with both autophagic activity and GBM tumorigenesis, and to investigate the molecular mechanisms of autophagy involved in GBM cell death and survival. METHOD For this purpose, we systematically explored the alterations of autophagic molecules at the genome level in human GBM samples through deep RNA sequencing. The effect of genetic and pharmacologic inhibition of ERK on GBM growth in vitro and in vivo was researched. An image-based tracking analysis of LC3 using mCherry-eGFP-LC3 plasmid, and transmission electron microscopy were utilized to monitor autophagic flux. Immunoblot analysis was used to measure the related proteins. RESULTS MAPK ERK expression was identified as one of the most probable autophagy-related transcriptional responses during GBM growth. The genetic and pharmacologic inhibition of ERK in vivo and in vitro led to cell death, demonstrating its critical role for GBM proliferation and survival. To our surprise, autophagic activities were excessively activated and resulted in cytodestructive effects on GBM cells upon ERK inhibitor treatment. Furthermore, based on the observation of downregulation of mTOR signaling, we speculated the ERK inhibitor-induced GBM cells death might depend on mTOR-mediated pathway, leading to autophagy dysregulation. Accordingly, the in vivo and in vitro experiments revealed that the mTOR inhibitor rapamycin further increased cell mortality and exhibited enhanced antitumor effect on GBM cells when co-treated with the ERK inhibitor. CONCLUSION Our data creatively demonstrated that the autophagy-related regulator ERK maintains autophagic activity during GBM tumorigenesis via mTOR signaling pathway. The pharmacologic inhibition of both mTOR and ERK signaling exhibited synergistic therapeutic effect on GBM growth in vivo and in vitro, which has certain novelty and may provide a potential therapeutic approach for GBM treatment in the future.
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29
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Thomas JP, Modos D, Korcsmaros T, Brooks-Warburton J. Network Biology Approaches to Achieve Precision Medicine in Inflammatory Bowel Disease. Front Genet 2021; 12:760501. [PMID: 34745229 PMCID: PMC8566351 DOI: 10.3389/fgene.2021.760501] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 10/08/2021] [Indexed: 12/22/2022] Open
Abstract
Inflammatory bowel disease (IBD) is a chronic immune-mediated condition arising due to complex interactions between multiple genetic and environmental factors. Despite recent advances, the pathogenesis of the condition is not fully understood and patients still experience suboptimal clinical outcomes. Over the past few years, investigators are increasingly capturing multi-omics data from patient cohorts to better characterise the disease. However, reaching clinically translatable endpoints from these complex multi-omics datasets is an arduous task. Network biology, a branch of systems biology that utilises mathematical graph theory to represent, integrate and analyse biological data through networks, will be key to addressing this challenge. In this narrative review, we provide an overview of various types of network biology approaches that have been utilised in IBD including protein-protein interaction networks, metabolic networks, gene regulatory networks and gene co-expression networks. We also include examples of multi-layered networks that have combined various network types to gain deeper insights into IBD pathogenesis. Finally, we discuss the need to incorporate other data sources including metabolomic, histopathological, and high-quality clinical meta-data. Together with more robust network data integration and analysis frameworks, such efforts have the potential to realise the key goal of precision medicine in IBD.
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Affiliation(s)
- John P Thomas
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
- Department of Gastroenterology, Norfolk and Norwich University Hospital, Norwich, United Kingdom
| | - Dezso Modos
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Tamas Korcsmaros
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Johanne Brooks-Warburton
- Department of Gastroenterology, Lister Hospital, Stevenage, United Kingdom
- Department of Clinical, Pharmaceutical and Biological Sciences, University of Hertfordshire, Hatfield, United Kingdom
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30
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Music of metagenomics-a review of its applications, analysis pipeline, and associated tools. Funct Integr Genomics 2021; 22:3-26. [PMID: 34657989 DOI: 10.1007/s10142-021-00810-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 09/25/2021] [Accepted: 10/03/2021] [Indexed: 10/20/2022]
Abstract
This humble effort highlights the intricate details of metagenomics in a simple, poetic, and rhythmic way. The paper enforces the significance of the research area, provides details about major analytical methods, examines the taxonomy and assembly of genomes, emphasizes some tools, and concludes by celebrating the richness of the ecosystem populated by the "metagenome."
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31
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Csabai L, Fazekas D, Kadlecsik T, Szalay-Bekő M, Bohár B, Madgwick M, Módos D, Ölbei M, Gul L, Sudhakar P, Kubisch J, Oyeyemi OJ, Liska O, Ari E, Hotzi B, Billes VA, Molnár E, Földvári-Nagy L, Csályi K, Demeter A, Pápai N, Koltai M, Varga M, Lenti K, Farkas IJ, Türei D, Csermely P, Vellai T, Korcsmáros T. SignaLink3: a multi-layered resource to uncover tissue-specific signaling networks. Nucleic Acids Res 2021; 50:D701-D709. [PMID: 34634810 PMCID: PMC8728204 DOI: 10.1093/nar/gkab909] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/16/2021] [Accepted: 09/22/2021] [Indexed: 12/26/2022] Open
Abstract
Signaling networks represent the molecular mechanisms controlling a cell's response to various internal or external stimuli. Most currently available signaling databases contain only a part of the complex network of intertwining pathways, leaving out key interactions or processes. Hence, we have developed SignaLink3 (http://signalink.org/), a value-added knowledge-base that provides manually curated data on signaling pathways and integrated data from several types of databases (interaction, regulation, localisation, disease, etc.) for humans, and three major animal model organisms. SignaLink3 contains over 400 000 newly added human protein-protein interactions resulting in a total of 700 000 interactions for Homo sapiens, making it one of the largest integrated signaling network resources. Next to H. sapiens, SignaLink3 is the only current signaling network resource to provide regulatory information for the model species Caenorhabditis elegans and Danio rerio, and the largest resource for Drosophila melanogaster. Compared to previous versions, we have integrated gene expression data as well as subcellular localization of the interactors, therefore uniquely allowing tissue-, or compartment-specific pathway interaction analysis to create more accurate models. Data is freely available for download in widely used formats, including CSV, PSI-MI TAB or SQL.
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Affiliation(s)
- Luca Csabai
- Earlham Institute, Norwich NR4 7UZ, UK.,Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | - Dávid Fazekas
- Earlham Institute, Norwich NR4 7UZ, UK.,Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | - Tamás Kadlecsik
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | | | - Balázs Bohár
- Earlham Institute, Norwich NR4 7UZ, UK.,Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | - Matthew Madgwick
- Earlham Institute, Norwich NR4 7UZ, UK.,Gut Microbes and Health Programme, Quadram Institute Bioscience, Norwich, NR4 7UQ, UK
| | - Dezső Módos
- Earlham Institute, Norwich NR4 7UZ, UK.,Gut Microbes and Health Programme, Quadram Institute Bioscience, Norwich, NR4 7UQ, UK
| | - Márton Ölbei
- Earlham Institute, Norwich NR4 7UZ, UK.,Gut Microbes and Health Programme, Quadram Institute Bioscience, Norwich, NR4 7UQ, UK
| | - Lejla Gul
- Earlham Institute, Norwich NR4 7UZ, UK
| | - Padhmanand Sudhakar
- Earlham Institute, Norwich NR4 7UZ, UK.,Translational Research in GastroIntestinal Disorders, Leuven BE-3000, Belgium
| | - János Kubisch
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | | | - Orsolya Liska
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary.,HCEMM-BRC Metabolic Systems Biology Lab, Szeged H-6726, Hungary.,Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Eötvös Loránd Research Network (ELKH), Szeged H-6726, Hungary.,Doctoral School in Biology, University of Szeged, Szeged H-6720 Hungary
| | - Eszter Ari
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary.,HCEMM-BRC Metabolic Systems Biology Lab, Szeged H-6726, Hungary.,Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Eötvös Loránd Research Network (ELKH), Szeged H-6726, Hungary
| | - Bernadette Hotzi
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | - Viktor A Billes
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary.,ELKH/MTA-ELTE Genetics Research Group, Budapest H-1117, Hungary
| | - Eszter Molnár
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | - László Földvári-Nagy
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary.,Department of Morphology and Physiology, Semmelweis University, Budapest H-1088, Hungary
| | - Kitti Csályi
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | - Amanda Demeter
- Earlham Institute, Norwich NR4 7UZ, UK.,Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | - Nóra Pápai
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary.,Institute of Molecular Biotechnology, Vienna A-1030, Austria
| | - Mihály Koltai
- Centre for the Mathematical Modelling of Infectious Diseases (CMMID), London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Máté Varga
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | - Katalin Lenti
- Department of Morphology and Physiology, Semmelweis University, Budapest H-1088, Hungary
| | - Illés J Farkas
- Citibank Europe plc Hungarian Branch Office, Budapest H-1133, Hungary
| | - Dénes Türei
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Péter Csermely
- Department of Molecular Biology, Semmelweis University, Budapest H-1094, Hungary
| | - Tibor Vellai
- Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary.,ELKH/MTA-ELTE Genetics Research Group, Budapest H-1117, Hungary
| | - Tamás Korcsmáros
- Earlham Institute, Norwich NR4 7UZ, UK.,Department of Genetics, ELTE Eötvös Loránd University, Budapest H-1117, Hungary.,Gut Microbes and Health Programme, Quadram Institute Bioscience, Norwich, NR4 7UQ, UK
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Touré V, Flobak Å, Niarakis A, Vercruysse S, Kuiper M. The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling. Brief Bioinform 2021; 22:bbaa390. [PMID: 33378765 PMCID: PMC8294520 DOI: 10.1093/bib/bbaa390] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 11/26/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022] Open
Abstract
Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. Logical regulatory networks can be used to predict biological and cellular behaviors by system perturbations and in silico simulations. Today, broad sets of causal interactions are available in a variety of biological knowledge resources. However, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. It can therefore be challenging to efficiently explore various resources of causal interaction and maintain an overview of recorded contextual information that ensures valid use of the data. This review lists the different types of public resources with causal interactions, the different views on biological processes that they represent, the various data formats they use for data representation and storage, and the data exchange and conversion procedures that are available to extract and download these interactions. This may further raise awareness among the targeted audience, i.e. logical modelers and other scientists interested in molecular causal interactions, but also database managers and curators, about the abundance and variety of causal molecular interaction data, and the variety of tools and approaches to convert them into one interoperable resource.
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Affiliation(s)
- Vasundra Touré
- Department of Biology of the Norwegian University of Science and Technology
| | | | - Anna Niarakis
- Department of Biology, Univ Evry, University of Paris-Saclay, affiliated with the laboratory GenHotel in Genopole campus, and a delegate at the Lifeware Group, INRIA Saclay
| | - Steven Vercruysse
- Researcher in computer science and computational biology and focuses on building a bridge between human and computer understanding
| | - Martin Kuiper
- systems biology at the Department of Biology of the Norwegian University of Science and Technology
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33
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Wang M, Withers JB, Ricchiuto P, Voitalov I, McAnally M, Sanchez HN, Saleh A, Akmaev VR, Ghiassian SD. A systems-based method to repurpose marketed therapeutics for antiviral use: a SARS-CoV-2 case study. Life Sci Alliance 2021; 4:e202000904. [PMID: 33593923 PMCID: PMC7893815 DOI: 10.26508/lsa.202000904] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 02/04/2021] [Accepted: 02/05/2021] [Indexed: 01/02/2023] Open
Abstract
This study describes two complementary methods that use network-based and sequence similarity tools to identify drug repurposing opportunities predicted to modulate viral proteins. This approach could be rapidly adapted to new and emerging viruses. The first method built and studied a virus-host-physical interaction network; a three-layer multimodal network of drug target proteins, human protein-protein interactions, and viral-host protein-protein interactions. The second method evaluated sequence similarity between viral proteins and other proteins, visualized by constructing a virus-host-similarity interaction network. Methods were validated on the human immunodeficiency virus, hepatitis B, hepatitis C, and human papillomavirus, then deployed on SARS-CoV-2. Comparison of virus-host-physical interaction predictions to known antiviral drugs had AUCs of 0.69, 0.59, 0.78, and 0.67, respectively, reflecting that the scores are predictive of effective drugs. For SARS-CoV-2, 569 candidate drugs were predicted, of which 37 had been included in clinical trials for SARS-CoV-2 (AUC = 0.75, P-value 3.21 × 10-3). As further validation, top-ranked candidate antiviral drugs were analyzed for binding to protein targets in silico; binding scores generated by BindScope indicated a 70% success rate.
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Affiliation(s)
| | | | | | | | | | | | - Alif Saleh
- Scipher Medicine Corporation, Waltham, MA, USA
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34
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Ravichandran S, Banerjee U, Dr GD, Kandukuru R, Thakur C, Chakravortty D, Balaji KN, Singh A, Chandra N. VB 10, a new blood biomarker for differential diagnosis and recovery monitoring of acute viral and bacterial infections. EBioMedicine 2021; 67:103352. [PMID: 33906069 PMCID: PMC8099739 DOI: 10.1016/j.ebiom.2021.103352] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 04/04/2021] [Accepted: 04/07/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Precise differential diagnosis between acute viral and bacterial infections is important to enable appropriate therapy, avoid unnecessary antibiotic prescriptions and optimize the use of hospital resources. A systems view of host response to infections provides opportunities for discovering sensitive and robust molecular diagnostics. METHODS We combine blood transcriptomes from six independent datasets (n = 756) with a knowledge-based human protein-protein interaction network, identifies subnetworks capturing host response to each infection class, and derives common response cores separately for viral and bacterial infections. We subject the subnetworks to a series of computational filters to identify a parsimonious gene panel and a standalone diagnostic score that can be applied to individual samples. We rigorously validate the panel and the diagnostic score in a wide range of publicly available datasets and in a newly developed Bangalore-Viral Bacterial (BL-VB) cohort. FINDING We discover a 10-gene blood-based biomarker panel (Panel-VB) that demonstrates high predictive performance to distinguish viral from bacterial infections, with a weighted mean AUROC of 0.97 (95% CI: 0.96-0.99) in eleven independent datasets (n = 898). We devise a new stand-alone patient-wise score (VB10) based on the panel, which shows high diagnostic accuracy with a weighted mean AUROC of 0.94 (95% CI 0.91-0.98) in 2996 patient samples from 56 public datasets from 19 different countries. Further, we evaluate VB10 in a newly generated South Indian (BL-VB, n = 56) cohort and find 97% accuracy in the confirmed cases of viral and bacterial infections. We find that VB10 is (a) capable of accurately identifying the infection class in culture-negative indeterminate cases, (b) reflects recovery status, and (c) is applicable across different age groups, covering a wide spectrum of acute bacterial and viral infections, including uncharacterized pathogens. We tested our VB10 score on publicly available COVID-19 data and find that our score detected viral infection in patient samples. INTERPRETATION Our results point to the promise of VB10 as a diagnostic test for precise diagnosis of acute infections and monitoring recovery status. We expect that it will provide clinical decision support for antibiotic prescriptions and thereby aid in antibiotic stewardship efforts. FUNDING Grand Challenges India, Biotechnology Industry Research Assistance Council (BIRAC), Department of Biotechnology, Govt. of India.
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Affiliation(s)
| | - Ushashi Banerjee
- Department of Biochemistry, Indian Institute of Science, Bangalore 560012, India
| | - Gayathri Devi Dr
- Department of Microbiology, M S Ramaiah Medical College, Bangalore 560054, Karnataka, India
| | - Rooparani Kandukuru
- Department of Microbiology, M S Ramaiah Medical College, Bangalore 560054, Karnataka, India
| | - Chandrani Thakur
- Department of Biochemistry, Indian Institute of Science, Bangalore 560012, India
| | - Dipshikha Chakravortty
- Centre for Biosystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India; Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore 560012, India
| | | | - Amit Singh
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore 560012, India; Centre for Infectious Disease Research, Indian Institute of Science, Bangalore 560012, India
| | - Nagasuma Chandra
- IISc Mathematics Initiative, Indian Institute of Science, Bangalore 560012, India; Department of Biochemistry, Indian Institute of Science, Bangalore 560012, India; Centre for Biosystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India.
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35
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Touré V, Vercruysse S, Acencio ML, Lovering RC, Orchard S, Bradley G, Casals-Casas C, Chaouiya C, Del-Toro N, Flobak Å, Gaudet P, Hermjakob H, Hoyt CT, Licata L, Lægreid A, Mungall CJ, Niknejad A, Panni S, Perfetto L, Porras P, Pratt D, Saez-Rodriguez J, Thieffry D, Thomas PD, Türei D, Kuiper M. The Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST). Bioinformatics 2021; 36:5712-5718. [PMID: 32637990 PMCID: PMC8023674 DOI: 10.1093/bioinformatics/btaa622] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 06/06/2020] [Accepted: 06/30/2020] [Indexed: 12/30/2022] Open
Abstract
Motivation A large variety of molecular interactions occurs between biomolecular components in cells. When a molecular interaction results in a regulatory effect, exerted by one component onto a downstream component, a so-called ‘causal interaction’ takes place. Causal interactions constitute the building blocks in our understanding of larger regulatory networks in cells. These causal interactions and the biological processes they enable (e.g. gene regulation) need to be described with a careful appreciation of the underlying molecular reactions. A proper description of this information enables archiving, sharing and reuse by humans and for automated computational processing. Various representations of causal relationships between biological components are currently used in a variety of resources. Results Here, we propose a checklist that accommodates current representations, called the Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST). This checklist defines both the required core information, as well as a comprehensive set of other contextual details valuable to the end user and relevant for reusing and reproducing causal molecular interaction information. The MI2CAST checklist can be used as reporting guidelines when annotating and curating causal statements, while fostering uniformity and interoperability of the data across resources. Availability and implementation The checklist together with examples is accessible at https://github.com/MI2CAST/MI2CAST Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Vasundra Touré
- Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway
| | - Steven Vercruysse
- Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway
| | - Marcio Luis Acencio
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway
| | - Ruth C Lovering
- Functional Gene Annotation, Preclinical and Fundamental Science, Institute of Cardiovascular Science, UCL, University College London, London WC1E 6JF, UK
| | - Sandra Orchard
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Glyn Bradley
- Computational Biology, Functional Genomics, GSK, Stevenage SG1 2NY, UK
| | | | - Claudine Chaouiya
- Aix Marseille Univ, CNRS, Centrale Marseille, I2M Marseille 13331, France
| | - Noemi Del-Toro
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Åsmund Flobak
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway.,The Cancer Clinic, St. Olav's Hospital, Trondheim University Hospital, Trondheim 7030, Norway
| | - Pascale Gaudet
- SIB Swiss Institute of Bioinformatics, Geneva 1211, Switzerland
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | | | - Luana Licata
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 00133 Rome, Italy
| | - Astrid Lægreid
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway
| | - Christopher J Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Anne Niknejad
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, Amphipole Building, 1015 Lausanne, Switzerland
| | - Simona Panni
- Department of Biology, Ecology and Earth Sciences, University of Calabria, Ecology and Earth Science, Via Pietro Bucci Cubo 6/C, Rende 87036, CS, Italy
| | - Livia Perfetto
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Pablo Porras
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Dexter Pratt
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Julio Saez-Rodriguez
- Institute of Computational Biomedicine, Heidelberg University, Faculty of Medicine, 69120 Heidelberg, Germany.,Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, Aachen 52062, Germany
| | - Denis Thieffry
- Institut de Biologie de l'ENS (IBENS), Département de Biologie, École Normale Supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France
| | - Paul D Thomas
- Division of Bioinformatics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90007, USA
| | - Dénes Türei
- Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, Aachen 52062, Germany
| | - Martin Kuiper
- Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway
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36
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Liu C, Han Z, Zhang ZK, Nussinov R, Cheng F. A network-based deep learning methodology for stratification of tumor mutations. Bioinformatics 2021; 37:82-88. [PMID: 33416857 PMCID: PMC8034530 DOI: 10.1093/bioinformatics/btaa1099] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 11/23/2020] [Accepted: 12/28/2020] [Indexed: 12/13/2022] Open
Abstract
MOTIVATION Tumor stratification has a wide range of biomedical and clinical applications, including diagnosis, prognosis and personalized treatment. However, cancer is always driven by the combination of mutated genes, which are highly heterogeneous across patients. Accurately subdividing the tumors into subtypes is challenging. RESULTS We developed a network-embedding based stratification (NES) methodology to identify clinically relevant patient subtypes from large-scale patients' somatic mutation profiles. The central hypothesis of NES is that two tumors would be classified into the same subtypes if their somatic mutated genes located in the similar network regions of the human interactome. We encoded the genes on the human protein-protein interactome with a network embedding approach and constructed the patients' vectors by integrating the somatic mutation profiles of 7344 tumor exomes across 15 cancer types. We firstly adopted the lightGBM classification algorithm to train the patients' vectors. The AUC value is around 0.89 in the prediction of the patient's cancer type and around 0.78 in the prediction of the tumor stage within a specific cancer type. The high classification accuracy suggests that network embedding-based patients' features are reliable for dividing the patients. We conclude that we can cluster patients with a specific cancer type into several subtypes by using an unsupervised clustering algorithm to learn the patients' vectors. Among the 15 cancer types, the new patient clusters (subtypes) identified by the NES are significantly correlated with patient survival across 12 cancer types. In summary, this study offers a powerful network-based deep learning methodology for personalized cancer medicine. AVAILABILITY AND IMPLEMENTATION Source code and data can be downloaded from https://github.com/ChengF-Lab/NES. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chuang Liu
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, China
| | - Zhen Han
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, China
| | - Zi-Ke Zhang
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, China
- College of Media and International Culture, Zhejiang University, Hangzhou 310028, China
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD 21702, USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
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37
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Wang Y, Yang H, Chen L, Jafari M, Tang J. Network-based modeling of herb combinations in traditional Chinese medicine. Brief Bioinform 2021; 22:6217717. [PMID: 33834186 PMCID: PMC8425426 DOI: 10.1093/bib/bbab106] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 03/10/2021] [Accepted: 03/11/2021] [Indexed: 12/12/2022] Open
Abstract
Traditional Chinese medicine (TCM) has been practiced for thousands of years for treating human diseases. In comparison to modern medicine, one of the advantages of TCM is the principle of herb compatibility, known as TCM formulae. A TCM formula usually consists of multiple herbs to achieve the maximum treatment effects, where their interactions are believed to elicit the therapeutic effects. Despite being a fundamental component of TCM, the rationale of combining specific herb combinations remains unclear. In this study, we proposed a network-based method to quantify the interactions in herb pairs. We constructed a protein–protein interaction network for a given herb pair by retrieving the associated ingredients and protein targets, and determined multiple network-based distances including the closest, shortest, center, kernel, and separation, both at the ingredient and at the target levels. We found that the frequently used herb pairs tend to have shorter distances compared to random herb pairs, suggesting that a therapeutic herb pair is more likely to affect neighboring proteins in the human interactome. Furthermore, we found that the center distance determined at the ingredient level improves the discrimination of top-frequent herb pairs from random herb pairs, suggesting the rationale of considering the topologically important ingredients for inferring the mechanisms of action of TCM. Taken together, we have provided a network pharmacology framework to quantify the degree of herb interactions, which shall help explore the space of herb combinations more effectively to identify the synergistic compound interactions based on network topology.
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Affiliation(s)
| | - Hongbin Yang
- Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Linxiao Chen
- Department of Mathematics and Statistics, University of Helsinki, Finland
| | | | - Jing Tang
- Faculty of Medicine of the University of Helsinki and Group Leader of Network Pharmacology for Precision Medicine group, Finland
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38
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Banerjee U, Baloni P, Singh A, Chandra N. Immune Subtyping in Latent Tuberculosis. Front Immunol 2021; 12:595746. [PMID: 33897680 PMCID: PMC8059438 DOI: 10.3389/fimmu.2021.595746] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 03/16/2021] [Indexed: 12/13/2022] Open
Abstract
Latent tuberculosis infection (LTBI) poses a major roadblock in the global effort to eradicate tuberculosis (TB). A deep understanding of the host responses involved in establishment and maintenance of TB latency is required to propel the development of sensitive methods to detect and treat LTBI. Given that LTBI individuals are typically asymptomatic, it is challenging to differentiate latently infected from uninfected individuals. A major contributor to this problem is that no clear pattern of host response is linked with LTBI, as molecular correlates of latent infection have been hard to identify. In this study, we have analyzed the global perturbations in host response in LTBI individuals as compared to uninfected individuals and particularly the heterogeneity in such response, across LTBI cohorts. For this, we constructed individualized genome-wide host response networks informed by blood transcriptomes for 136 LTBI cases and have used a sensitive network mining algorithm to identify top-ranked host response subnetworks in each case. Our analysis indicates that despite the high heterogeneity in the gene expression profiles among LTBI samples, clear patterns of perturbation are found in the immune response pathways, leading to grouping LTBI samples into 4 different immune-subtypes. Our results suggest that different subnetworks of molecular perturbations are associated with latent tuberculosis.
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Affiliation(s)
- Ushashi Banerjee
- Department of Biochemistry, Indian Institute of Science, Bangalore, India
| | - Priyanka Baloni
- Department of Biochemistry, Indian Institute of Science, Bangalore, India
| | - Amit Singh
- Centre for Infectious Disease Research, Indian Institute of Science, Bangalore, India
| | - Nagasuma Chandra
- Department of Biochemistry, Indian Institute of Science, Bangalore, India.,Center for Biosystems Science and Engineering, Indian Institute of Science, Bangalore, India
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39
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Vlachavas EI, Bohn J, Ückert F, Nürnberg S. A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research. Int J Mol Sci 2021; 22:2822. [PMID: 33802234 PMCID: PMC8000236 DOI: 10.3390/ijms22062822] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 03/05/2021] [Accepted: 03/05/2021] [Indexed: 02/06/2023] Open
Abstract
Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.
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Affiliation(s)
- Efstathios Iason Vlachavas
- Medical Informatics for Translational Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (J.B.); (F.Ü.)
| | - Jonas Bohn
- Medical Informatics for Translational Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (J.B.); (F.Ü.)
| | - Frank Ückert
- Medical Informatics for Translational Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (J.B.); (F.Ü.)
- Applied Medical Informatics, University Hospital Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Sylvia Nürnberg
- Medical Informatics for Translational Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (J.B.); (F.Ü.)
- Applied Medical Informatics, University Hospital Hamburg-Eppendorf, 20251 Hamburg, Germany
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40
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Türei D, Valdeolivas A, Gul L, Palacio‐Escat N, Klein M, Ivanova O, Ölbei M, Gábor A, Theis F, Módos D, Korcsmáros T, Saez‐Rodriguez J. Integrated intra- and intercellular signaling knowledge for multicellular omics analysis. Mol Syst Biol 2021; 17:e9923. [PMID: 33749993 PMCID: PMC7983032 DOI: 10.15252/msb.20209923] [Citation(s) in RCA: 112] [Impact Index Per Article: 37.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 02/11/2021] [Accepted: 02/15/2021] [Indexed: 12/12/2022] Open
Abstract
Molecular knowledge of biological processes is a cornerstone in omics data analysis. Applied to single-cell data, such analyses provide mechanistic insights into individual cells and their interactions. However, knowledge of intercellular communication is scarce, scattered across resources, and not linked to intracellular processes. To address this gap, we combined over 100 resources covering interactions and roles of proteins in inter- and intracellular signaling, as well as transcriptional and post-transcriptional regulation. We added protein complex information and annotations on function, localization, and role in diseases for each protein. The resource is available for human, and via homology translation for mouse and rat. The data are accessible via OmniPath's web service (https://omnipathdb.org/), a Cytoscape plug-in, and packages in R/Bioconductor and Python, providing access options for computational and experimental scientists. We created workflows with tutorials to facilitate the analysis of cell-cell interactions and affected downstream intracellular signaling processes. OmniPath provides a single access point to knowledge spanning intra- and intercellular processes for data analysis, as we demonstrate in applications studying SARS-CoV-2 infection and ulcerative colitis.
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Affiliation(s)
- Dénes Türei
- Faculty of Medicine and Heidelberg University HospitalInstitute of Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Alberto Valdeolivas
- Faculty of Medicine and Heidelberg University HospitalInstitute of Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | | | - Nicolàs Palacio‐Escat
- Faculty of Medicine and Heidelberg University HospitalInstitute of Computational BiomedicineHeidelberg UniversityHeidelbergGermany
- Faculty of MedicineJoint Research Centre for Computational Biomedicine (JRC‐COMBINE)RWTH Aachen UniversityAachenGermany
- Faculty of BiosciencesHeidelberg UniversityHeidelbergGermany
| | - Michal Klein
- Institute of Computational BiologyHelmholtz Zentrum MünchenNeuherbergGermany
| | - Olga Ivanova
- Faculty of Medicine and Heidelberg University HospitalInstitute of Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Márton Ölbei
- Earlham InstituteNorwichUK
- Quadram Institute BioscienceNorwichUK
| | - Attila Gábor
- Faculty of Medicine and Heidelberg University HospitalInstitute of Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Fabian Theis
- Institute of Computational BiologyHelmholtz Zentrum MünchenNeuherbergGermany
- Department of MathematicsTechnical University of MunichGarchingGermany
| | - Dezső Módos
- Earlham InstituteNorwichUK
- Quadram Institute BioscienceNorwichUK
| | | | - Julio Saez‐Rodriguez
- Faculty of Medicine and Heidelberg University HospitalInstitute of Computational BiomedicineHeidelberg UniversityHeidelbergGermany
- Faculty of MedicineJoint Research Centre for Computational Biomedicine (JRC‐COMBINE)RWTH Aachen UniversityAachenGermany
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41
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do Valle IF, Roweth HG, Malloy MW, Moco S, Barron D, Battinelli E, Loscalzo J, Barabási AL. Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols. NATURE FOOD 2021; 2:143-155. [PMID: 37117448 DOI: 10.1038/s43016-021-00243-7] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 02/16/2021] [Indexed: 04/30/2023]
Abstract
Polyphenols, natural products present in plant-based foods, play a protective role against several complex diseases through their antioxidant activity and by diverse molecular mechanisms. Here we develop a network medicine framework to uncover mechanisms for the effects of polyphenols on health by considering the molecular interactions between polyphenol protein targets and proteins associated with diseases. We find that the protein targets of polyphenols cluster in specific neighbourhoods of the human interactome, whose network proximity to disease proteins is predictive of the molecule's known therapeutic effects. The methodology recovers known associations, such as the effect of epigallocatechin-3-O-gallate on type 2 diabetes, and predicts that rosmarinic acid has a direct impact on platelet function, representing a novel mechanism through which it could affect cardiovascular health. We experimentally confirm that rosmarinic acid inhibits platelet aggregation and α-granule secretion through inhibition of protein tyrosine phosphorylation, offering direct support for the predicted molecular mechanism. Our framework represents a starting point for mechanistic interpretation of the health effects underlying food-related compounds, allowing us to integrate into a predictive framework knowledge on food metabolism, bioavailability and drug interaction.
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Affiliation(s)
- Italo F do Valle
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA, USA
| | - Harvey G Roweth
- Division of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Michael W Malloy
- Division of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Sofia Moco
- Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | - Denis Barron
- Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | - Elisabeth Battinelli
- Division of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Joseph Loscalzo
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Albert-László Barabási
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA, USA.
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Network and Data Science, Central European University, Budapest, Hungary.
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42
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Integrating Patient-Specific Information into Logic Models of Complex Diseases: Application to Acute Myeloid Leukemia. J Pers Med 2021; 11:jpm11020117. [PMID: 33578936 PMCID: PMC7916657 DOI: 10.3390/jpm11020117] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 02/05/2021] [Accepted: 02/05/2021] [Indexed: 12/12/2022] Open
Abstract
High throughput technologies such as deep sequencing and proteomics are increasingly becoming mainstream in clinical practice and support diagnosis and patient stratification. Developing computational models that recapitulate cell physiology and its perturbations in disease is a required step to help with the interpretation of results of high content experiments and to devise personalized treatments. As complete cell-models are difficult to achieve, given limited experimental information and insurmountable computational problems, approximate approaches should be considered. We present here a general approach to modeling complex diseases by embedding patient-specific genomics data into actionable logic models that take into account prior knowledge. We apply the strategy to acute myeloid leukemia (AML) and assemble a network of logical relationships linking most of the genes that are found frequently mutated in AML patients. We derive Boolean models from this network and we show that by priming the model with genomic data we can infer relevant patient-specific clinical features. Here we propose that the integration of literature-derived causal networks with patient-specific data should be explored to help bedside decisions.
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43
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Agapito G, Pastrello C, Jurisica I. Comprehensive pathway enrichment analysis workflows: COVID-19 case study. Brief Bioinform 2020. [PMCID: PMC7799312 DOI: 10.1093/bib/bbaa377] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19) outbreak due to the novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been classified as a pandemic disease by the World Health Organization on the 12th March 2020. This world-wide crisis created an urgent need to identify effective countermeasures against SARS-CoV-2. In silico methods, artificial intelligence and bioinformatics analysis pipelines provide effective and useful infrastructure for comprehensive interrogation and interpretation of available data, helping to find biomarkers, explainable models and eventually cures. One class of such tools, pathway enrichment analysis (PEA) methods, helps researchers to find possible key targets present in biological pathways of host cells that are targeted by SARS-CoV-2. Since many software tools are available, it is not easy for non-computational users to choose the best one for their needs. In this paper, we highlight how to choose the most suitable PEA method based on the type of COVID-19 data to analyze. We aim to provide a comprehensive overview of PEA techniques and the tools that implement them.
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Affiliation(s)
| | - Chiara Pastrello
- Krembil Research Institute, University Health Network, Toronto, Canada
| | - Igor Jurisica
- Departments of Medical Biophysics and Computer Science, University of Toronto, Canada
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44
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Li M, Jiang WT, Li J, Ji WC. Exercise protects against spinal cord injury through miR-21-mediated suppression of PDCD4. Am J Transl Res 2020; 12:5708-5718. [PMID: 33042450 PMCID: PMC7540147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 07/26/2020] [Indexed: 06/11/2023]
Abstract
Spinal cord injury (SCI) can lead to different levels of paraplegia. Studies have shown that exercise exerts wide protective effects against various diseases, and microRNAs (miRNAs) are involved in its beneficial effects. However, the specific role of miRNAs in the protective effects of exercise against SCI remains unclear. Here, we showed that exercise exerted protective effects against SCI as evidenced by increased locomotor activity and spinal cord cell survival in rats with SCI. Exercise upregulated circulating miR-21, detected by miRNA microarray, in rats with SCI. Treating SCI rats with agomiR-21 upregulated circulating miR-21 and exerted protective effects against SCI. Additionally, downregulating miR-21 using antagomir-21 abolished the protective effects of exercise on SCI. Programmed cell death protein 4 (PDCD4) was found to be the target of miR-21. These results suggested that exercise protects against SCI, at least partly, through miR-21-mediated suppression of PDCD4.
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Affiliation(s)
- Meng Li
- Department of Orthopaedics, The First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061, China
| | - Wan-Ting Jiang
- Department of Ultrasound Diagnosis, The Fourth Hospital of Xi’anXi’an 710004, China
| | - Jia Li
- Department of Orthopaedics, The First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061, China
| | - Wen-Chen Ji
- Department of Orthopaedics, The First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061, China
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45
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Rubanova N, Pinna G, Kropp J, Campalans A, Radicella JP, Polesskaya A, Harel-Bellan A, Morozova N. MasterPATH: network analysis of functional genomics screening data. BMC Genomics 2020; 21:632. [PMID: 32928103 PMCID: PMC7491077 DOI: 10.1186/s12864-020-07047-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 09/01/2020] [Indexed: 12/18/2022] Open
Abstract
Background Functional genomics employs several experimental approaches to investigate gene functions. High-throughput techniques, such as loss-of-function screening and transcriptome profiling, allow to identify lists of genes potentially involved in biological processes of interest (so called hit list). Several computational methods exist to analyze and interpret such lists, the most widespread of which aim either at investigating of significantly enriched biological processes, or at extracting significantly represented subnetworks. Results Here we propose a novel network analysis method and corresponding computational software that employs the shortest path approach and centrality measure to discover members of molecular pathways leading to the studied phenotype, based on functional genomics screening data. The method works on integrated interactomes that consist of both directed and undirected networks – HIPPIE, SIGNOR, SignaLink, TFactS, KEGG, TransmiR, miRTarBase. The method finds nodes and short simple paths with significant high centrality in subnetworks induced by the hit genes and by so-called final implementers – the genes that are involved in molecular events responsible for final phenotypic realization of the biological processes of interest. We present the application of the method to the data from miRNA loss-of-function screen and transcriptome profiling of terminal human muscle differentiation process and to the gene loss-of-function screen exploring the genes that regulates human oxidative DNA damage recognition. The analysis highlighted the possible role of several known myogenesis regulatory miRNAs (miR-1, miR-125b, miR-216a) and their targets (AR, NR3C1, ARRB1, ITSN1, VAV3, TDGF1), as well as linked two major regulatory molecules of skeletal myogenesis, MYOD and SMAD3, to their previously known muscle-related targets (TGFB1, CDC42, CTCF) and also to a number of proteins such as C-KIT that have not been previously studied in the context of muscle differentiation. The analysis also showed the role of the interaction between H3 and SETDB1 proteins for oxidative DNA damage recognition. Conclusion The current work provides a systematic methodology to discover members of molecular pathways in integrated networks using functional genomics screening data. It also offers a valuable instrument to explain the appearance of a set of genes, previously not associated with the process of interest, in the hit list of each particular functional genomics screening.
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Affiliation(s)
- Natalia Rubanova
- Institut des Hautes Etudes Scientifiques, Le Bois-Marie 35 rte de Chartres, 91440, Bures-sur-Yvette, France. .,Université Paris Diderot, Paris, France. .,Skolkovo Institute of Science and Technology, Skolkovo, Russia.
| | - Guillaume Pinna
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91198, Gif-sur-Yvette cedex, France
| | - Jeremie Kropp
- Institut des Hautes Etudes Scientifiques, Le Bois-Marie 35 rte de Chartres, 91440, Bures-sur-Yvette, France
| | - Anna Campalans
- Institute of Molecular and Cellular Radiobiology, Institut François Jacob, CEA, F-92265, Fontenay-aux-Roses, France.,INSERM, U967, bâtiment 56 PC 103 18 route du Panorama, BP6 92265, Fontenay-aux-Roses Cedex, France.,Université Paris Sud, U967, bâtiment 56 PC 103 18 route du Panorama, BP6 92265, Fontenay-aux-Roses Cedex, France
| | - Juan Pablo Radicella
- Institute of Molecular and Cellular Radiobiology, Institut François Jacob, CEA, F-92265, Fontenay-aux-Roses, France.,INSERM, U967, bâtiment 56 PC 103 18 route du Panorama, BP6 92265, Fontenay-aux-Roses Cedex, France.,Université Paris Sud, U967, bâtiment 56 PC 103 18 route du Panorama, BP6 92265, Fontenay-aux-Roses Cedex, France
| | - Anna Polesskaya
- Ecole Polytechnique, Université Paris-Saclay, CNRS UMR 7654, Laboratoire de Biochimie, Ecole Polytechnique, 91128, Palaiseau, France
| | - Annick Harel-Bellan
- Institut des Hautes Etudes Scientifiques, Le Bois-Marie 35 rte de Chartres, 91440, Bures-sur-Yvette, France
| | - Nadya Morozova
- Institut des Hautes Etudes Scientifiques, Le Bois-Marie 35 rte de Chartres, 91440, Bures-sur-Yvette, France.,Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91198, Gif-sur-Yvette cedex, France
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46
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Predicting and affecting response to cancer therapy based on pathway-level biomarkers. Nat Commun 2020; 11:3296. [PMID: 32620799 PMCID: PMC7335104 DOI: 10.1038/s41467-020-17090-y] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Accepted: 06/12/2020] [Indexed: 12/15/2022] Open
Abstract
Identifying robust, patient-specific, and predictive biomarkers presents a major obstacle in precision oncology. To optimize patient-specific therapeutic strategies, here we couple pathway knowledge with large-scale drug sensitivity, RNAi, and CRISPR-Cas9 screening data from 460 cell lines. Pathway activity levels are found to be strong predictive biomarkers for the essentiality of 15 proteins, including the essentiality of MAD2L1 in breast cancer patients with high BRCA-pathway activity. We also find strong predictive biomarkers for the sensitivity to 31 compounds, including BCL2 and microtubule inhibitors (MTIs). Lastly, we show that Bcl-xL inhibition can modulate the activity of a predictive biomarker pathway and re-sensitize lung cancer cells and tumors to MTI therapy. Overall, our results support the use of pathways in helping to achieve the goal of precision medicine by uncovering dozens of predictive biomarkers.
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47
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Mellors T, Withers JB, Ameli A, Jones A, Wang M, Zhang L, Sanchez HN, Santolini M, Do Valle I, Sebek M, Cheng F, Pappas DA, Kremer JM, Curtis JR, Johnson KJ, Saleh A, Ghiassian SD, Akmaev VR. Clinical Validation of a Blood-Based Predictive Test for Stratification of Response to Tumor Necrosis Factor Inhibitor Therapies in Rheumatoid Arthritis Patients. NETWORK AND SYSTEMS MEDICINE 2020. [DOI: 10.1089/nsm.2020.0007] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Affiliation(s)
| | | | - Asher Ameli
- Scipher Medicine, Waltham, Massachusetts, USA
| | - Alex Jones
- Scipher Medicine, Waltham, Massachusetts, USA
| | | | - Lixia Zhang
- Scipher Medicine, Waltham, Massachusetts, USA
| | | | - Marc Santolini
- Center for Research and Interdisciplinarity (CRI), University Paris Descartes, Paris, France
| | - Italo Do Valle
- Center for Complex Network Research, Department of Physics, Northeastern University, Boston, Massachusetts, USA
| | - Michael Sebek
- Center for Complex Network Research, Department of Physics, Northeastern University, Boston, Massachusetts, USA
| | - Feixiong Cheng
- Center for Complex Network Research, Department of Physics, Northeastern University, Boston, Massachusetts, USA
| | - Dimitrios A. Pappas
- Division of Rheumatology, College of Physicians and Surgeons, Columbia University, New York, New York, USA
- CORRONA, LCC, Waltham, Massachusetts, USA
| | - Joel M. Kremer
- CORRONA, LCC, Waltham, Massachusetts, USA
- Albany Medical College, The Center for Rheumatology, Albany, New York, USA
| | - Jeffery R. Curtis
- Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | | | - Alif Saleh
- Scipher Medicine, Waltham, Massachusetts, USA
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Iida M, Iwata M, Yamanishi Y. Network-based characterization of disease-disease relationships in terms of drugs and therapeutic targets. Bioinformatics 2020; 36:i516-i524. [PMID: 32657408 PMCID: PMC7355285 DOI: 10.1093/bioinformatics/btaa439] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
MOTIVATION Disease states are distinguished from each other in terms of differing clinical phenotypes, but characteristic molecular features are often common to various diseases. Similarities between diseases can be explained by characteristic gene expression patterns. However, most disease-disease relationships remain uncharacterized. RESULTS In this study, we proposed a novel approach for network-based characterization of disease-disease relationships in terms of drugs and therapeutic targets. We performed large-scale analyses of omics data and molecular interaction networks for 79 diseases, including adrenoleukodystrophy, leukaemia, Alzheimer's disease, asthma, atopic dermatitis, breast cancer, cystic fibrosis and inflammatory bowel disease. We quantified disease-disease similarities based on proximities of abnormally expressed genes in various molecular networks, and showed that similarities between diseases could be explained by characteristic molecular network topologies. Furthermore, we developed a kernel matrix regression algorithm to predict the commonalities of drugs and therapeutic targets among diseases. Our comprehensive prediction strategy indicated many new associations among phenotypically diverse diseases. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Midori Iida
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| | - Michio Iwata
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
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Wei Z, Zhang Y, Weng W, Chen J, Cai H. Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets. Brief Bioinform 2020; 22:5856342. [PMID: 32533167 DOI: 10.1093/bib/bbaa102] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 05/02/2020] [Accepted: 05/04/2020] [Indexed: 12/20/2022] Open
Abstract
The significance of pan-cancer categories has recently been recognized as widespread in cancer research. Pan-cancer categorizes a cancer based on its molecular pathology rather than an organ. The molecular similarities among multi-omics data found in different cancer types can play several roles in both biological processes and therapeutic developments. Therefore, an integrated analysis for various genomic data is frequently used to reveal novel genetic and molecular mechanisms. However, a variety of algorithms for multi-omics clustering have been proposed in different fields. The comparison of different computational clustering methods in pan-cancer analysis performance remains unclear. To increase the utilization of current integrative methods in pan-cancer analysis, we first provide an overview of five popular computational integrative tools: similarity network fusion, integrative clustering of multiple genomic data types (iCluster), cancer integration via multi-kernel learning (CIMLR), perturbation clustering for data integration and disease subtyping (PINS) and low-rank clustering (LRACluster). Then, a priori interactions in multi-omics data were incorporated to detect prominent molecular patterns in pan-cancer data sets. Finally, we present comparative assessments of these methods, with discussion over key issues in applying these algorithms. We found that all five methods can identify distinct tumor compositions. The pan-cancer samples can be reclassified into several groups by different proportions. Interestingly, each method can classify the tumors into categories that are different from original cancer types or subtypes, especially for ovarian serous cystadenocarcinoma (OV) and breast invasive carcinoma (BRCA) tumors. In addition, all clusters of the five computational methods show notable prognostic values. Furthermore, both the 9 recurrent differential genes and the 15 common pathway characteristics were identified across all the methods. The results and discussion can help the community select appropriate integrative tools according to different research tasks or aims in pan-cancer analysis.
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Affiliation(s)
- Zhuohui Wei
- Computer Science and Engineering, South China University of Technology
| | - Yue Zhang
- School of Computer Science, Guangdong Polytechnic Normal University
| | - Wanlin Weng
- Computer Science and Engineering, South China University of Technology
| | - Jiazhou Chen
- Computer Science and Engineering, South China University of Technology
| | - Hongmin Cai
- Computer Science and Engineering, South China University of Technology
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Wang J, Yang Z, Domeniconi C, Zhang X, Yu G. Cooperative driver pathway discovery via fusion of multi-relational data of genes, miRNAs and pathways. Brief Bioinform 2020; 22:1984-1999. [PMID: 32103253 DOI: 10.1093/bib/bbz167] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 12/13/2019] [Accepted: 12/29/2019] [Indexed: 12/19/2022] Open
Abstract
Discovering driver pathways is an essential step to uncover the molecular mechanism underlying cancer and to explore precise treatments for cancer patients. However, due to the difficulties of mapping genes to pathways and the limited knowledge about pathway interactions, most previous work focus on identifying individual pathways. In practice, two (or even more) pathways interplay and often cooperatively trigger cancer. In this study, we proposed a new approach called CDPathway to discover cooperative driver pathways. First, CDPathway introduces a driver impact quantification function to quantify the driver weight of each gene. CDPathway assumes that genes with larger weights contribute more to the occurrence of the target disease and identifies them as candidate driver genes. Next, it constructs a heterogeneous network composed of genes, miRNAs and pathways nodes based on the known intra(inter)-relations between them and assigns the quantified driver weights to gene-pathway and gene-miRNA relational edges. To transfer driver impacts of genes to pathway interaction pairs, CDPathway collaboratively factorizes the weighted adjacency matrices of the heterogeneous network to explore the latent relations between genes, miRNAs and pathways. After this, it reconstructs the pathway interaction network and identifies the pathway pairs with maximal interactive and driver weights as cooperative driver pathways. Experimental results on the breast, uterine corpus endometrial carcinoma and ovarian cancer data from The Cancer Genome Atlas show that CDPathway can effectively identify candidate driver genes [area under the receiver operating characteristic curve (AUROC) of $\geq $0.9] and reconstruct the pathway interaction network (AUROC of>0.9), and it uncovers much more known (potential) driver genes than other competitive methods. In addition, CDPathway identifies 150% more driver pathways and 60% more potential cooperative driver pathways than the competing methods. The code of CDPathway is available at http://mlda.swu.edu.cn/codes.php?name=CDPathway.
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Affiliation(s)
- Jun Wang
- Professor of the School of Software, Shandong University
| | - Ziying Yang
- Professor of the School of Software, Shandong University
| | | | - Xiangliang Zhang
- Computational Bioscience Research Center (CBRC), Computer Science, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology, SA
| | - Guoxian Yu
- Computational Bioscience Research Center (CBRC), Computer Science, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology, SA.,Professor of the School of Software, Shandong University and Computational Bioscience Research Center
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