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Kim Y, Han Y, Hopper C, Lee J, Joo JI, Gong JR, Lee CK, Jang SH, Kang J, Kim T, Cho KH. A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations. CELL REPORTS METHODS 2024; 4:100773. [PMID: 38744288 PMCID: PMC11133856 DOI: 10.1016/j.crmeth.2024.100773] [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: 05/13/2023] [Revised: 03/19/2024] [Accepted: 04/19/2024] [Indexed: 05/16/2024]
Abstract
Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms.
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Affiliation(s)
- Yunseong Kim
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Younghyun Han
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Corbin Hopper
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jonghoon Lee
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jae Il Joo
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jeong-Ryeol Gong
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Chun-Kyung Lee
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Seong-Hoon Jang
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Junsoo Kang
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Taeyoung Kim
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Kwang-Hyun Cho
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea.
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2
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Kim Y, Choi SR, Cho KH. Reducing State Conflicts between Network Motifs Synergistically Enhances Cancer Drug Effects and Overcomes Adaptive Resistance. Cancers (Basel) 2024; 16:1337. [PMID: 38611015 PMCID: PMC11010870 DOI: 10.3390/cancers16071337] [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: 02/01/2024] [Revised: 03/20/2024] [Accepted: 03/26/2024] [Indexed: 04/14/2024] Open
Abstract
Inducing apoptosis in cancer cells is a primary goal in anti-cancer therapy, but curing cancer with a single drug is unattainable due to drug resistance. The complex molecular network in cancer cells causes heterogeneous responses to single-target drugs, thereby inducing an adaptive drug response. Here, we showed that targeted drug perturbations can trigger state conflicts between multi-stable motifs within a molecular regulatory network, resulting in heterogeneous drug responses. However, we revealed that properly regulating an interconnecting molecule between these motifs can synergistically minimize the heterogeneous responses and overcome drug resistance. We extracted the essential cellular response dynamics of the Boolean network driven by the target node perturbation and developed an algorithm to identify a synergistic combinatorial target that can reduce heterogeneous drug responses. We validated the proposed approach using exemplary network models and a gastric cancer model from a previous study by showing that the targets identified with our algorithm can better drive the networks to desired states than those with other control theories. Of note, our approach suggests a new synergistic pair of control targets that can increase cancer drug efficacy to overcome adaptive drug resistance.
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Affiliation(s)
| | | | - Kwang-Hyun Cho
- Laboratory for Systems Biology and Bio-Inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea; (Y.K.); (S.R.C.)
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Demko V, Belova T, Messerer M, Hvidsten TR, Perroud PF, Ako AE, Johansen W, Mayer KFX, Olsen OA, Lang D. Regulation of developmental gatekeeping and cell fate transition by the calpain protease DEK1 in Physcomitrium patens. Commun Biol 2024; 7:261. [PMID: 38438476 PMCID: PMC10912778 DOI: 10.1038/s42003-024-05933-z] [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: 06/09/2023] [Accepted: 02/19/2024] [Indexed: 03/06/2024] Open
Abstract
Calpains are cysteine proteases that control cell fate transitions whose loss of function causes severe, pleiotropic phenotypes in eukaryotes. Although mainly considered as modulatory proteases, human calpain targets are directed to the N-end rule degradation pathway. Several such targets are transcription factors, hinting at a gene-regulatory role. Here, we analyze the gene-regulatory networks of the moss Physcomitrium patens and characterize the regulons that are misregulated in mutants of the calpain DEFECTIVE KERNEL1 (DEK1). Predicted cleavage patterns of the regulatory hierarchies in five DEK1-controlled subnetworks are consistent with a pleiotropic and regulatory role during cell fate transitions targeting multiple functions. Network structure suggests DEK1-gated sequential transitions between cell fates in 2D-to-3D development. Our method combines comprehensive phenotyping, transcriptomics and data science to dissect phenotypic traits, and our model explains the protease function as a switch gatekeeping cell fate transitions potentially also beyond plant development.
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Affiliation(s)
- Viktor Demko
- Department of Plant Sciences, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432, Ås, Norway
- Department of Plant Physiology, Faculty of Natural Sciences, Comenius University in Bratislava, Ilkovicova 6, 84104, Bratislava, Slovakia
- Plant Science and Biodiversity Center, Slovak Academy of Sciences, Dubravska cesta 9, 84104, Bratislava, Slovakia
| | - Tatiana Belova
- Department of Plant Sciences, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432, Ås, Norway
- Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway
| | - Maxim Messerer
- Plant Genome and Systems Biology, Helmholtz Center Munich-Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Torgeir R Hvidsten
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
| | - Pierre-François Perroud
- Institut Jean-Pierre Bourgin, INRAE, AgroParisTech, Université Paris-Saclay, 78000, Versailles, France
| | - Ako Eugene Ako
- Department of Biotechnology, Inland Norway University of Applied Sciences, Holsetgata 31, 2318, Hamar, Norway
- School of Animal, Rural and Environmental Sciences, Nottingham Trent University, Brackenhurst Campus, Southwell, Nottinghamshire, NG25 0QF, UK
| | - Wenche Johansen
- Department of Biotechnology, Inland Norway University of Applied Sciences, Holsetgata 31, 2318, Hamar, Norway
| | - Klaus F X Mayer
- Plant Genome and Systems Biology, Helmholtz Center Munich-Research Center for Environmental Health, 85764, Neuherberg, Germany
- School of Life Sciences, Technical University Munich, 85354, Freising, Germany
| | - Odd-Arne Olsen
- Department of Plant Sciences, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432, Ås, Norway
| | - Daniel Lang
- Plant Genome and Systems Biology, Helmholtz Center Munich-Research Center for Environmental Health, 85764, Neuherberg, Germany.
- Bundeswehr Institute of Microbiology, Microbial Genomics and Bioforensics, 80937, Munich, Germany.
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Osmanoglu Ö, Gupta SK, Almasi A, Yagci S, Srivastava M, Araujo GHM, Nagy Z, Balkenhol J, Dandekar T. Signaling network analysis reveals fostamatinib as a potential drug to control platelet hyperactivation during SARS-CoV-2 infection. Front Immunol 2023; 14:1285345. [PMID: 38187394 PMCID: PMC10768010 DOI: 10.3389/fimmu.2023.1285345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 12/06/2023] [Indexed: 01/09/2024] Open
Abstract
Introduction Pro-thrombotic events are one of the prevalent causes of intensive care unit (ICU) admissions among COVID-19 patients, although the signaling events in the stimulated platelets are still unclear. Methods We conducted a comparative analysis of platelet transcriptome data from healthy donors, ICU, and non-ICU COVID-19 patients to elucidate these mechanisms. To surpass previous analyses, we constructed models of involved networks and control cascades by integrating a global human signaling network with transcriptome data. We investigated the control of platelet hyperactivation and the specific proteins involved. Results Our study revealed that control of the platelet network in ICU patients is significantly higher than in non-ICU patients. Non-ICU patients require control over fewer proteins for managing platelet hyperactivity compared to ICU patients. Identification of indispensable proteins highlighted key subnetworks, that are targetable for system control in COVID-19-related platelet hyperactivity. We scrutinized FDA-approved drugs targeting indispensable proteins and identified fostamatinib as a potent candidate for preventing thrombosis in COVID-19 patients. Discussion Our findings shed light on how SARS-CoV-2 efficiently affects host platelets by targeting indispensable and critical proteins involved in the control of platelet activity. We evaluated several drugs for specific control of platelet hyperactivity in ICU patients suffering from platelet hyperactivation. The focus of our approach is repurposing existing drugs for optimal control over the signaling network responsible for platelet hyperactivity in COVID-19 patients. Our study offers specific pharmacological recommendations, with drug prioritization tailored to the distinct network states observed in each patient condition. Interactive networks and detailed results can be accessed at https://fostamatinib.bioinfo-wuerz.eu/.
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Affiliation(s)
- Özge Osmanoglu
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, University of Wuerzburg, Wuerzburg, Germany
| | - Shishir K. Gupta
- Evolutionary Genomics Group, Center for Computational and Theoretical Biology, University of Würzburg, Würzburg, Germany
- Institute of Botany, Heinrich Heine University, Düsseldorf, Germany
| | - Anna Almasi
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, University of Wuerzburg, Wuerzburg, Germany
| | - Seray Yagci
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, University of Wuerzburg, Wuerzburg, Germany
| | - Mugdha Srivastava
- Core Unit Systems Medicine, University of Wuerzburg, Wuerzburg, Germany
- Algorithmic Bioinformatics, Department of Computer Science, Heinrich Heine University, Düsseldorf, Germany
| | - Gabriel H. M. Araujo
- University Hospital Würzburg, Institute of Experimental Biomedicine, Würzburg, Germany
| | - Zoltan Nagy
- University Hospital Würzburg, Institute of Experimental Biomedicine, Würzburg, Germany
| | - Johannes Balkenhol
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, University of Wuerzburg, Wuerzburg, Germany
- Chair of Molecular Microscopy, Rudolf Virchow Center for Integrative and Translation Bioimaging, University of Würzburg, Würzburg, Germany
| | - Thomas Dandekar
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, University of Wuerzburg, Wuerzburg, Germany
- European Molecular Biology Laboratory (EMBL) Heidelberg, BioComputing Unit, Heidelberg, Germany
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Tiwari H, Kumar A, Barik MR, Kaur H, Mahajan S, Shukla MK, Gupta M, Yadav G, Nargotra A. Repositioning the existing drugs for neuroinflammation: a fusion of computational approach and biological validation to counter the Parkinson's disease progression. Mol Divers 2023:10.1007/s11030-023-10708-5. [PMID: 37542020 DOI: 10.1007/s11030-023-10708-5] [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: 06/05/2023] [Accepted: 07/28/2023] [Indexed: 08/06/2023]
Abstract
Parkinson's disease is caused by the deficiency of striatal dopamine and the accumulation of aggregated α-synuclein in the substantia nigra pars compacta (SNpc). Neuroinflammation associated with oxidative stress is a key factor contributing to the death of dopaminergic neurons in SNpc and advancement of Parkinson's disease. Two molecular targets, i.e., nuclear factor kappa-light-chain-enhancer (NF-kB) and α-synuclein play a substantial role in neuroinflammation progression. Therefore, the compounds targeting these neuroinflammatory targets hold a great potential to combat Parkinson's disease. Thereby, in this study, molecular docking and Connectivity Map (CMap) based gene expression profiling was utilized to reposition the approved drugs as neuroprotective agents for Parkinson's disease. With in silico screening, two drugs namely theophylline and propylthiouracil were selected for anti-neuroinflammatory activity evaluation in in vivo models of chronic neuroinflammation. The neuroinflammatory effect of the identified compounds was confirmed by quantifying the expression of three important neuroinflammatory mediators, i.e. IL-6, TNF-alpha, and IL-1 beta on brain tissue using ELISA assay. The ELISA experiment demonstrated that both compounds significantly decreased the expression of neuroinflammatory mediators, highlighting the compounds' potential in neuroinflammation management. Furthermore, the drug and disease interaction network of the two identified drugs and diseases (neuroinflammation and Parkinson's disease) suggested that the two drugs might interact with various targets namely adenosine receptors, Poly [ADP-ribose] polymerase-1, myeloperoxidase (MPO) and thyroid peroxidase through multiple pathways associated with neuroinflammation and Parkinson's disease. Computational studies suggest that a particular drug may be effective in managing Parkinson's disease associated with neuroinflammation. However, further research is needed to confirm this in biological experiments.
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Affiliation(s)
- Harshita Tiwari
- Discovery Informatics, NPMC Division, CSIR-Indian Institute of Integrative Medicine, Jammu, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Amit Kumar
- Mutagenicity Laboratory, Pharmacology Division, CSIR- Indian Institute of Integrative Medicine, Jammu, 180001, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Manas Ranjan Barik
- Discovery Informatics, NPMC Division, CSIR-Indian Institute of Integrative Medicine, Jammu, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Harjot Kaur
- Discovery Informatics, NPMC Division, CSIR-Indian Institute of Integrative Medicine, Jammu, India
| | - Shubham Mahajan
- Discovery Informatics, NPMC Division, CSIR-Indian Institute of Integrative Medicine, Jammu, India
| | - Monu Kumar Shukla
- PK-PD Toxicology Division, CSIR-Indian Institute of Integrative Medicine, Jammu, India
| | - Monika Gupta
- Discovery Informatics, NPMC Division, CSIR-Indian Institute of Integrative Medicine, Jammu, India
| | - Govind Yadav
- Mutagenicity Laboratory, Pharmacology Division, CSIR- Indian Institute of Integrative Medicine, Jammu, 180001, India
| | - Amit Nargotra
- Discovery Informatics, NPMC Division, CSIR-Indian Institute of Integrative Medicine, Jammu, India.
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Wicik Z, Nowak A, Jarosz-Popek J, Wolska M, Eyileten C, Siller-Matula JM, von Lewinski D, Sourij H, Filipiak KJ, Postuła M. Characterization of the SGLT2 Interaction Network and Its Regulation by SGLT2 Inhibitors: A Bioinformatic Analysis. Front Pharmacol 2022; 13:901340. [PMID: 36046822 PMCID: PMC9421436 DOI: 10.3389/fphar.2022.901340] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 06/22/2022] [Indexed: 11/29/2022] Open
Abstract
Background: Sodium–glucose cotransporter 2 (SGLT2), also known as solute carrier family 5 member 2 (SLC5A2), is a promising target for a new class of drugs primarily established as kidney-targeting, effective glucose-lowering agents used in diabetes mellitus (DM) patients. Increasing evidence indicates that besides renal effects, SGLT2 inhibitors (SGLT2i) have also a systemic impact via indirectly targeting the heart and other tissues. Our hypothesis states that the pleiotropic effects of SGLT2i are associated with their binding force, location of targets in the SGLT2 networks, targets involvement in signaling pathways, and their tissue-specific expression. Methods: Thus, to investigate differences in SGLT2i impact on human organisms, we re-created the SGLT2 interaction network incorporating its inhibitors and metformin and analyzed its tissue-specific expression using publicly available datasets. We analyzed it in the context of the so-called key terms ( autophagy, oxidative stress, aging, senescence, inflammation, AMPK pathways, and mTOR pathways) which seem to be crucial to elucidating the SGLT2 role in a variety of clinical manifestations. Results: Analysis of SGLT2 and its network components’ expression confidence identified selected organs in the following order: kidney, liver, adipose tissue, blood, heart, muscle, intestine, brain, and artery according to the TISSUES database. Drug repurposing analysis of known SGLT2i pointed out the influence of SGLT1 regulators on the heart and intestine tissue. Additionally, dapagliflozin seems to also have a stronger impact on brain tissue through the regulation of SGLT3 and SLC5A11. The shortest path analysis identified interaction SIRT1-SGLT2 among the top five interactions across six from seven analyzed networks associated with the key terms. Other top first-level SGLT2 interactors associated with key terms were not only ADIPOQ, INS, GLUT4, ACE, and GLUT1 but also less recognized ILK and ADCY7. Among other interactors which appeared in multiple shortest-path analyses were GPT, COG2, and MGAM. Enrichment analysis of SGLT2 network components showed the highest overrepresentation of hypertensive disease, DM-related diseases for both levels of SGLT2 interactors. Additionally, for the extended SGLT2 network, we observed enrichment in obesity (including SGLT1), cancer-related terms, neuroactive ligand–receptor interaction, and neutrophil-mediated immunity. Conclusion: This study provides comprehensive and ranked information about the SGLT2 interaction network in the context of tissue expression and can help to predict the clinical effects of the SGLT2i.
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Affiliation(s)
- Zofia Wicik
- Center for Preclinical Research and Technology CEPT, Department of Experimental and Clinical Pharmacology, Medical University of Warsaw, Warsaw, Poland
| | - Anna Nowak
- Center for Preclinical Research and Technology CEPT, Department of Experimental and Clinical Pharmacology, Medical University of Warsaw, Warsaw, Poland
- Doctoral School, Medical University of Warsaw, Warsaw, Poland
| | - Joanna Jarosz-Popek
- Center for Preclinical Research and Technology CEPT, Department of Experimental and Clinical Pharmacology, Medical University of Warsaw, Warsaw, Poland
- Doctoral School, Medical University of Warsaw, Warsaw, Poland
| | - Marta Wolska
- Center for Preclinical Research and Technology CEPT, Department of Experimental and Clinical Pharmacology, Medical University of Warsaw, Warsaw, Poland
- Doctoral School, Medical University of Warsaw, Warsaw, Poland
| | - Ceren Eyileten
- Center for Preclinical Research and Technology CEPT, Department of Experimental and Clinical Pharmacology, Medical University of Warsaw, Warsaw, Poland
- Genomics Core Facility, Centre of New Technologies, University of Warsaw, Warsaw, Poland
| | - Jolanta M. Siller-Matula
- Center for Preclinical Research and Technology CEPT, Department of Experimental and Clinical Pharmacology, Medical University of Warsaw, Warsaw, Poland
- Department of Cardiology, Medical University of Vienna, Vienna, Austria
| | - Dirk von Lewinski
- Department of Internal Medicine, Division of Cardiology, Medical University of Graz, Graz, Austria
| | - Harald Sourij
- Division of Endocrinology and Diabetology, Interdisciplinary Metabolic Medicine Trials Unit, Medical University of Graz, Graz, Austria
| | | | - Marek Postuła
- Center for Preclinical Research and Technology CEPT, Department of Experimental and Clinical Pharmacology, Medical University of Warsaw, Warsaw, Poland
- *Correspondence: Marek Postuła,
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Bell PA, Scheuermann S, Renner F, Pan CL, Lu HY, Turvey SE, Bornancin F, Régnier CH, Overall CM. Integrating knowledge of protein sequence with protein function for the prediction and validation of new MALT1 substrates. Comput Struct Biotechnol J 2022; 20:4717-4732. [PMID: 36147669 PMCID: PMC9463181 DOI: 10.1016/j.csbj.2022.08.021] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 08/07/2022] [Accepted: 08/08/2022] [Indexed: 11/30/2022] Open
Affiliation(s)
- Peter A. Bell
- Centre for Blood Research, Life Sciences Centre, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
- Department of Oral Biological and Medical Sciences, Faculty of Dentistry, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Sophia Scheuermann
- Centre for Blood Research, Life Sciences Centre, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
- Department of Oral Biological and Medical Sciences, Faculty of Dentistry, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
- Department of Immunology, Eberhard Karl University Tübingen, 72076 Tübingen, Germany
- Department of Hematology and Oncology, University Hospital Tübingen, Children's Hospital, 72076 Tübingen, Germany
| | - Florian Renner
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4056 Basel, Switzerland
- Molecular Targeted Therapy - Discovery Oncology, Roche Pharma Research & Early Development, F. Hoffmann-La Roche Ltd, 4070 Basel, Switzerland
| | - Christina L. Pan
- Centre for Blood Research, Life Sciences Centre, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
- Department of Oral Biological and Medical Sciences, Faculty of Dentistry, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Henry Y. Lu
- Department of Pediatrics, British Columbia Children's Hospital, The University of British Columbia, Vancouver, BC V5Z 4H4, Canada
- Department of Experimental Medicine, Faculty of Medicine, The University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Stuart E. Turvey
- Department of Pediatrics, British Columbia Children's Hospital, The University of British Columbia, Vancouver, BC V5Z 4H4, Canada
- Department of Experimental Medicine, Faculty of Medicine, The University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Frédéric Bornancin
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4056 Basel, Switzerland
| | - Catherine H. Régnier
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4056 Basel, Switzerland
| | - Christopher M. Overall
- Centre for Blood Research, Life Sciences Centre, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
- Department of Oral Biological and Medical Sciences, Faculty of Dentistry, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
- Corresponding author at: Centre for Blood Research, Life Sciences Centre, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
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Liang X, Li L, Fan Y. Diagnostic, Prognostic, and Immunological Roles of HELLS in Pan-Cancer: A Bioinformatics Analysis. Front Immunol 2022; 13:870726. [PMID: 35774795 PMCID: PMC9237247 DOI: 10.3389/fimmu.2022.870726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 05/17/2022] [Indexed: 11/25/2022] Open
Abstract
Background Inappropriate repair of DNA damage drives carcinogenesis. Lymphoid-specific helicase (HELLS) is an important component of the chromatin remodeling complex that helps repair DNA through various mechanisms such as DNA methylation, histone posttranslational modification, and nucleosome remodeling. Its role in human cancer initiation and progression has garnered recent attention. Our study aims to provide a more systematic and comprehensive understanding of the role of HELLS in the development and progression of multiple malignancies through analysis of HELLS in cancers. Methods We explored the role of HELLS in cancers using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) database. Multiple web platforms and software were used for data analysis, including R, Cytoscape, HPA, Archs4, TISIDB, cBioPortal, STRING, GSCALite, and CancerSEA. Results High HELLS expression was found in a variety of cancers and differentially expressed across molecular and immune subtypes. HELLS was involved in many cancer pathways. Its expression positively correlated with Th2 and Tcm cells in most cancers. It also correlated with genetic markers of immunomodulators in various cancers. Conclusions Our study elucidates the role HELLS plays in promotion, inhibition, and treatment of different cancers. HELLS is a potential cancer diagnostic and prognostic biomarker with immune, targeted, or cytotoxic therapeutic value. This work is a prerequisite to clinical validation and treatment of HELLS in cancers.
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Affiliation(s)
- Xiao Liang
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, China
| | - Linji Li
- Department of Anesthesiology, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College, Nanchong, China
| | - Yuchao Fan
- Department of Anesthesiology, Sichuan Cancer Center, Sichuan Cancer Hospital & Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- *Correspondence: Yuchao Fan,
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9
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De Marinis I, Lo Surdo P, Cesareni G, Perfetto L. SIGNORApp: a Cytoscape 3 application to access SIGNOR data. Bioinformatics 2022; 38:1764-1766. [PMID: 34954787 DOI: 10.1093/bioinformatics/btab865] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 12/20/2021] [Accepted: 12/23/2021] [Indexed: 02/03/2023] Open
Abstract
SUMMARY SIGNORApp is a Cytoscape 3 (3.8 and later) application that provides access to causal interactions annotated in the SIGNOR resource. The application builds networks that can be represented as weighted, signed, directed graphs, where nodes are interacting biological entities and edges represent causal interactions captured by expert curators from experiments reported in peer reviewed journals. Users can query the SIGNOR dataset with (i) single or multiple entity name(s) or identifier(s) and optionally they may require to include in the output network their interacting partners, (ii) browse pathways that are annotated in the SIGNOR resource and (iii) extract the entire causal interactome. The app offers two visualizations modes: one only displaying entity interactions and a second emphasizing the post-translational modifications occurring as a consequence of the interaction. In addition, users can click on nodes and edges to access entity and interaction annotations. Causal information is available for three model organisms: Homo sapiens, Mus musculus and Rattus norvegicus. AVAILABILITY AND IMPLEMENTATION SIGNORApp has been developed for Cytoscape 3 (3.8 and later) in the Java programming language. The latest source code and the plugin can be found at: https://github.com/SIGNORcysAPP/signor-app and https://apps.cytoscape.org/apps/signorapp, respectively. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ilaria De Marinis
- Department of Biology, University of Rome Tor Vergata, 00133 Rome, Italy
| | - Prisca Lo Surdo
- Department of Biology, University of Rome Tor Vergata, 00133 Rome, Italy.,Department of Biology, Fondazione Human Technopole, 20157 Milan, Italy
| | - Gianni Cesareni
- Department of Biology, University of Rome Tor Vergata, 00133 Rome, Italy
| | - Livia Perfetto
- Department of Biology, University of Rome Tor Vergata, 00133 Rome, Italy.,Department of Biology, Fondazione Human Technopole, 20157 Milan, Italy
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10
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Pierrelée M, Reynders A, Lopez F, Moqrich A, Tichit L, Habermann BH. Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs). Sci Rep 2021; 11:13691. [PMID: 34211067 PMCID: PMC8249521 DOI: 10.1038/s41598-021-93128-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 06/18/2021] [Indexed: 12/13/2022] Open
Abstract
Integrating -omics data with biological networks such as protein-protein interaction networks is a popular and useful approach to interpret expression changes of genes in changing conditions, and to identify relevant cellular pathways, active subnetworks or network communities. Yet, most -omics data integration tools are restricted to static networks and therefore cannot easily be used for analyzing time-series data. Determining regulations or exploring the network structure over time requires time-dependent networks which incorporate time as one component in their structure. Here, we present a method to project time-series data on sequential layers of a multilayer network, thus creating a temporal multilayer network (tMLN). We implemented this method as a Cytoscape app we named TimeNexus. TimeNexus allows to easily create, manage and visualize temporal multilayer networks starting from a combination of node and edge tables carrying the information on the temporal network structure. To allow further analysis of the tMLN, TimeNexus creates and passes on regular Cytoscape networks in form of static versions of the tMLN in three different ways: (i) over the entire set of layers, (ii) over two consecutive layers at a time, (iii) or on one single layer at a time. We combined TimeNexus with the Cytoscape apps PathLinker and AnatApp/ANAT to extract active subnetworks from tMLNs. To test the usability of our app, we applied TimeNexus together with PathLinker or ANAT on temporal expression data of the yeast cell cycle and were able to identify active subnetworks relevant for different cell cycle phases. We furthermore used TimeNexus on our own temporal expression data from a mouse pain assay inducing hindpaw inflammation and detected active subnetworks relevant for an inflammatory response to injury, including immune response, cell stress response and regulation of apoptosis. TimeNexus is freely available from the Cytoscape app store at https://apps.cytoscape.org/apps/TimeNexus .
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Affiliation(s)
- Michaël Pierrelée
- Aix-Marseille University, CNRS, IBDM UMR 7288, Computational Biology Team, Turing Centre for Living Systems (CENTURI), Marseille, France
| | - Ana Reynders
- Aix-Marseille University, CNRS, IBDM UMR 7288, Team Chronic Pain: Molecular and Cellular Mechanisms, Turing Centre for Living systems (CENTURI), Marseille, France
| | - Fabrice Lopez
- Aix-Marseille University, INSERM, TAGC U 1090, Marseille, France
| | - Aziz Moqrich
- Aix-Marseille University, CNRS, IBDM UMR 7288, Team Chronic Pain: Molecular and Cellular Mechanisms, Turing Centre for Living systems (CENTURI), Marseille, France
| | - Laurent Tichit
- Aix-Marseille University, CNRS, I2M UMR 7373, Turing Centre for Living Systems (CENTURI), Marseille, France
| | - Bianca H Habermann
- Aix-Marseille University, CNRS, IBDM UMR 7288, Computational Biology Team, Turing Centre for Living Systems (CENTURI), Marseille, France. .,Aix-Marseille University, CNRS, IBDM UMR 7288, Turing Center for Living Systems (CENTURI), Parc Scientifique de Luminy, Case 907, 163, Avenue de Luminy, 13009, Marseille, France.
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11
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Mousavian Z, Khodabandeh M, Sharifi-Zarchi A, Nadafian A, Mahmoudi A. StrongestPath: a Cytoscape application for protein-protein interaction analysis. BMC Bioinformatics 2021; 22:352. [PMID: 34187355 PMCID: PMC8244221 DOI: 10.1186/s12859-021-04230-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 06/02/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND StrongestPath is a Cytoscape 3 application that enables the analysis of interactions between two proteins or groups of proteins in a collection of protein-protein interaction (PPI) network or signaling network databases. When there are different levels of confidence over the interactions, the application is able to process them and identify the cascade of interactions with the highest total confidence score. Given a set of proteins, StrongestPath can extract a set of possible interactions between the input proteins, and expand the network by adding new proteins that have the most interactions with highest total confidence to the current network of proteins. The application can also identify any activating or inhibitory regulatory paths between two distinct sets of transcription factors and target genes. This application can be used on the built-in human and mouse PPI or signaling databases, or any user-provided database for some organism. RESULTS Our results on 12 signaling pathways from the NetPath database demonstrate that the application can be used for indicating proteins which may play significant roles in a pathway by finding the strongest path(s) in the PPI or signaling network. CONCLUSION Easy access to multiple public large databases, generating output in a short time, addressing some key challenges in one platform, and providing a user-friendly graphical interface make StrongestPath an extremely useful application.
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Affiliation(s)
- Zaynab Mousavian
- Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran.
| | - Mehran Khodabandeh
- School of Computing Science, Simon Fraser University, Burnaby, BC, Canada
| | - Ali Sharifi-Zarchi
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.,Department of Stem cells and Developmental Biology at the Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
| | - Alireza Nadafian
- Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Alireza Mahmoudi
- Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
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12
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Corrêa T, Feltes BC, Gonzalez EA, Baldo G, Matte U. Network Analysis Reveals Proteins Associated with Aortic Dilatation in Mucopolysaccharidoses. Interdiscip Sci 2021; 13:34-43. [PMID: 33475959 DOI: 10.1007/s12539-020-00406-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 11/25/2020] [Accepted: 12/03/2020] [Indexed: 06/12/2023]
Abstract
Mucopolysaccharidoses are caused by a deficiency of enzymes involved in the degradation of glycosaminoglycans. Heart diseases are a significant cause of morbidity and mortality in MPS patients, even in conditions in which enzyme replacement therapy is available. In this sense, cardiovascular manifestations, such as heart hypertrophy, cardiac function reduction, increased left ventricular chamber, and aortic dilatation, are among the most frequent. However, the downstream events which influence the heart dilatation process are unclear. Here, we employed systems biology tools together with transcriptomic data to investigate new elements that may be involved in aortic dilatation in Mucopolysaccharidoses syndrome. We identified candidate genes involved in biological processes related to inflammatory responses, deposition of collagen, and lipid accumulation in the cardiovascular system that may be involved in aortic dilatation in the Mucopolysaccharidoses I and VII. Furthermore, we investigated the molecular mechanisms of losartan treatment in Mucopolysaccharidoses I mice to underscore how this drug acts to prevent aortic dilation. Our data indicate that the association between the TGF-b signaling pathway, Fos, and Col1a1 proteins can play an essential role in aortic dilation's pathophysiology and its subsequent improvement by losartan treatment.
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Affiliation(s)
- Thiago Corrêa
- Gene Therapy Center, Hospital de Clínicas de Porto Alegre, Rua Ramiro Barcelos, 2350, Porto Alegre, 90035-903, Brazil
- Postgraduation Program on Genetics and Molecular Biology, Federal University of Rio Grande Do Sul, Porto Alegre, RS, Brazil
| | - Bruno César Feltes
- Institute of Informatics, Federal University of Rio Grande Do Sul, Porto Alegre, RS, Brazil
| | - Esteban Alberto Gonzalez
- Gene Therapy Center, Hospital de Clínicas de Porto Alegre, Rua Ramiro Barcelos, 2350, Porto Alegre, 90035-903, Brazil
- Postgraduation Program on Genetics and Molecular Biology, Federal University of Rio Grande Do Sul, Porto Alegre, RS, Brazil
| | - Guilherme Baldo
- Gene Therapy Center, Hospital de Clínicas de Porto Alegre, Rua Ramiro Barcelos, 2350, Porto Alegre, 90035-903, Brazil
- Postgraduation Program on Genetics and Molecular Biology, Federal University of Rio Grande Do Sul, Porto Alegre, RS, Brazil
| | - Ursula Matte
- Gene Therapy Center, Hospital de Clínicas de Porto Alegre, Rua Ramiro Barcelos, 2350, Porto Alegre, 90035-903, Brazil.
- Postgraduation Program on Genetics and Molecular Biology, Federal University of Rio Grande Do Sul, Porto Alegre, RS, Brazil.
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13
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Feltes BC, Poloni JDF, Nunes IJG, Faria SS, Dorn M. Multi-Approach Bioinformatics Analysis of Curated Omics Data Provides a Gene Expression Panorama for Multiple Cancer Types. Front Genet 2020; 11:586602. [PMID: 33329726 PMCID: PMC7719697 DOI: 10.3389/fgene.2020.586602] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 10/09/2020] [Indexed: 12/19/2022] Open
Abstract
Studies describing the expression patterns and biomarkers for the tumoral process increase in number every year. The availability of new datasets, although essential, also creates a confusing landscape where common or critical mechanisms are obscured amidst the divergent and heterogeneous nature of such results. In this work, we manually curated the Gene Expression Omnibus using rigorous filtering criteria to select the most homogeneous and highest quality microarray and RNA-seq datasets from multiple types of cancer. By applying systems biology approaches, combined with machine learning analysis, we investigated possible frequently deregulated molecular mechanisms underlying the tumoral process. Our multi-approach analysis of 99 curated datasets, composed of 5,406 samples, revealed 47 differentially expressed genes in all analyzed cancer types, which were all in agreement with the validation using TCGA data. Results suggest that the tumoral process is more related to the overexpression of core deregulated machinery than the underexpression of a given gene set. Additionally, we identified gene expression similarities between different cancer types not described before and performed an overall survival analysis using 20 cancer types. Finally, we were able to suggest a core regulatory mechanism that could be frequently deregulated.
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Affiliation(s)
- Bruno César Feltes
- Laboratory of Structural Bioinformatics and Computational Biology, Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Joice de Faria Poloni
- Laboratory of Structural Bioinformatics and Computational Biology, Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | | | - Sara Socorro Faria
- Laboratory of Immunology and Inflammation, Department of Cell Biology, University of Brasilia, Brasilia, Brazil
| | - Marcio Dorn
- Laboratory of Structural Bioinformatics and Computational Biology, Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
- Center of Biotechnology, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
- National Institute of Science and Technology - Forensic Science, Porto Alegre, Brazil
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14
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Sarmah DT, Bairagi N, Chatterjee S. Tracing the footsteps of autophagy in computational biology. Brief Bioinform 2020; 22:5985288. [PMID: 33201177 PMCID: PMC8293817 DOI: 10.1093/bib/bbaa286] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 09/29/2020] [Accepted: 09/30/2020] [Indexed: 12/11/2022] Open
Abstract
Autophagy plays a crucial role in maintaining cellular homeostasis through the degradation of unwanted materials like damaged mitochondria and misfolded proteins. However, the contribution of autophagy toward a healthy cell environment is not only limited to the cleaning process. It also assists in protein synthesis when the system lacks the amino acids’ inflow from the extracellular environment due to diet consumptions. Reduction in the autophagy process is associated with diseases like cancer, diabetes, non-alcoholic steatohepatitis, etc., while uncontrolled autophagy may facilitate cell death. We need a better understanding of the autophagy processes and their regulatory mechanisms at various levels (molecules, cells, tissues). This demands a thorough understanding of the system with the help of mathematical and computational tools. The present review illuminates how systems biology approaches are being used for the study of the autophagy process. A comprehensive insight is provided on the application of computational methods involving mathematical modeling and network analysis in the autophagy process. Various mathematical models based on the system of differential equations for studying autophagy are covered here. We have also highlighted the significance of network analysis and machine learning in capturing the core regulatory machinery governing the autophagy process. We explored the available autophagic databases and related resources along with their attributes that are useful in investigating autophagy through computational methods. We conclude the article addressing the potential future perspective in this area, which might provide a more in-depth insight into the dynamics of autophagy.
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Affiliation(s)
| | - Nandadulal Bairagi
- Centre for Mathematical Biology and Ecology, Department of Mathematics, Jadavpur University, Kolkata, India
| | - Samrat Chatterjee
- Translational Health Science and Technology Institute, Faridabad, India
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15
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Saxe GN, Ma S, Morales LJ, Galatzer-Levy IR, Aliferis C, Marmar CR. Computational causal discovery for post-traumatic stress in police officers. Transl Psychiatry 2020; 10:233. [PMID: 32778671 PMCID: PMC7417525 DOI: 10.1038/s41398-020-00910-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 06/15/2020] [Accepted: 06/18/2020] [Indexed: 11/09/2022] Open
Abstract
This article reports on a study aimed to elucidate the complex etiology of post-traumatic stress (PTS) in a longitudinal cohort of police officers, by applying rigorous computational causal discovery (CCD) methods with observational data. An existing observational data set was used, which comprised a sample of 207 police officers who were recruited upon entry to police academy training. Participants were evaluated on a comprehensive set of clinical, self-report, genetic, neuroendocrine and physiological measures at baseline during academy training and then were re-evaluated at 12 months after training was completed. A data-processing pipeline-the Protocol for Computational Causal Discovery in Psychiatry (PCCDP)-was applied to this data set to determine a causal model for PTS severity. A causal model of 146 variables and 345 bivariate relations was discovered. This model revealed 5 direct causes and 83 causal pathways (of four steps or less) to PTS at 12 months of police service. Direct causes included single-nucleotide polymorphisms (SNPs) for the Histidine Decarboxylase (HDC) and Mineralocorticoid Receptor (MR) genes, acoustic startle in the context of low perceived threat during training, peritraumatic distress to incident exposure during first year of service, and general symptom severity during training at 1 year of service. The application of CCD methods can determine variables and pathways related to the complex etiology of PTS in a cohort of police officers. This knowledge may inform new approaches to treatment and prevention of critical incident related PTS.
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Affiliation(s)
- Glenn N. Saxe
- grid.137628.90000 0004 1936 8753Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY USA
| | - Sisi Ma
- grid.17635.360000000419368657Institute of Health Informatics, University of Minnesota School of Medicine, Minneapolis, MN USA
| | - Leah J. Morales
- grid.137628.90000 0004 1936 8753Perlmutter Cancer Center, New York University School of Medicine, New York, NY USA
| | - Isaac R. Galatzer-Levy
- grid.137628.90000 0004 1936 8753Department of Psychiatry, New York University School of Medicine, New York, NY USA
| | - Constantin Aliferis
- grid.17635.360000000419368657Institute of Health Informatics, University of Minnesota School of Medicine, Minneapolis, MN USA
| | - Charles R. Marmar
- grid.137628.90000 0004 1936 8753Department of Psychiatry, New York University School of Medicine, New York, NY USA
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16
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Corrêa T, Poswar F, Feltes BC, Riegel M. Candidate Genes Associated With Neurological Findings in a Patient With Trisomy 4p16.3 and Monosomy 5p15.2. Front Genet 2020; 11:561. [PMID: 32625234 PMCID: PMC7311770 DOI: 10.3389/fgene.2020.00561] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 05/11/2020] [Indexed: 12/12/2022] Open
Abstract
In this report, we present a patient with brain alterations and dysmorphic features associated with chromosome duplication seen in 4p16.3 region and chromosomal deletion in a critical region responsible for Cri-du-chat syndrome (CdCS). Chromosomal microarray analysis (CMA) revealed a 41.1 Mb duplication encompassing the band region 4p16.3-p13, and a 14.7 Mb deletion located between the bands 5p15.33 and p15.1. The patient's clinical findings overlap with previously reported cases of chromosome 4p duplication syndrome and CdCS. The patient's symptoms are notably similar to those of CdCS patients as she presented with a weak, high-pitched voice and showed a similar pathogenicity observed in the brain MRI. These contiguous gene syndromes present with distinct clinical manifestations. However, the phenotypic and cytogenetic variability in affected individuals, such as the low frequency and the large genomic regions that can be altered, make it challenging to identify candidate genes that contribute to the pathogenesis of these syndromes. Therefore, systems biology and CMA techniques were used to investigate the extent of chromosome rearrangement on critical regions in our patient's phenotype. We identified the candidate genes PPARGC1A, CTBP1, TRIO, TERT, and CCT5 that are associated with the neuropsychomotor delay, microcephaly, and neurological alterations found in our patient. Through investigating pathways that associate with essential nodes in the protein interaction network, we discovered proteins involved in cellular differentiation and proliferation, as well as proteins involved in the formation and disposition of the cytoskeleton. The combination of our cytogenomic and bioinformatic analysis provided these possible explanations for the unique clinical phenotype, which has not yet been described in scientific literature.
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Affiliation(s)
- Thiago Corrêa
- Post-Graduate Program in Genetics and Molecular Biology, Genetics Department, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Fabiano Poswar
- Medical Genetics Service, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Bruno César Feltes
- Department of Theoritical Informatics, Institute of Informatics, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Mariluce Riegel
- Post-Graduate Program in Genetics and Molecular Biology, Genetics Department, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Medical Genetics Service, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
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17
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Zahoránszky-Kőhalmi G, Sheils T, Oprea TI. SmartGraph: a network pharmacology investigation platform. J Cheminform 2020; 12:5. [PMID: 33430980 PMCID: PMC6974502 DOI: 10.1186/s13321-020-0409-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Accepted: 01/07/2020] [Indexed: 11/18/2022] Open
Abstract
Motivation Drug discovery investigations need to incorporate network pharmacology concepts while navigating the complex landscape of drug-target and target-target interactions. This task requires solutions that integrate high-quality biomedical data, combined with analytic and predictive workflows as well as efficient visualization. SmartGraph is an innovative platform that utilizes state-of-the-art technologies such as a Neo4j graph-database, Angular web framework, RxJS asynchronous event library and D3 visualization to accomplish these goals. Results The SmartGraph framework integrates high quality bioactivity data and biological pathway information resulting in a knowledgebase comprised of 420,526 unique compound-target interactions defined between 271,098 unique compounds and 2018 targets. SmartGraph then performs bioactivity predictions based on the 63,783 Bemis-Murcko scaffolds extracted from these compounds. Through several use-cases, we illustrate the use of SmartGraph to generate hypotheses for elucidating mechanism-of-action, drug-repurposing and off-target prediction. Availability https://smartgraph.ncats.io/.
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Affiliation(s)
- Gergely Zahoránszky-Kőhalmi
- National Center for Advancing Translational Sciences, Rockville, MD, USA. .,Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA.
| | - Timothy Sheils
- National Center for Advancing Translational Sciences, Rockville, MD, USA
| | - Tudor I Oprea
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA.,UNM Comprehensive Cancer Center, Albuquerque, NM, USA.,Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy At University of Gothenburg, Gothenburg, Sweden.,Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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18
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Ramos PIP, Arge LWP, Lima NCB, Fukutani KF, de Queiroz ATL. Leveraging User-Friendly Network Approaches to Extract Knowledge From High-Throughput Omics Datasets. Front Genet 2019; 10:1120. [PMID: 31798629 PMCID: PMC6863976 DOI: 10.3389/fgene.2019.01120] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 10/16/2019] [Indexed: 11/13/2022] Open
Abstract
Recent technological advances for the acquisition of multi-omics data have allowed an unprecedented understanding of the complex intricacies of biological systems. In parallel, a myriad of computational analysis techniques and bioinformatics tools have been developed, with many efforts directed towards the creation and interpretation of networks from this data. In this review, we begin by examining key network concepts and terminology. Then, computational tools that allow for their construction and analysis from high-throughput omics datasets are presented. We focus on the study of functional relationships such as co-expression, protein-protein interactions, and regulatory interactions that are particularly amenable to modeling using the framework of networks. We envisage that many potential users of these analytical strategies may not be completely literate in programming languages and code adaptation, and for this reason, emphasis is given to tools' user-friendliness, including plugins for the widely adopted Cytoscape software, an open-source, cross-platform tool for network analysis, visualization, and data integration.
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Affiliation(s)
- Pablo Ivan Pereira Ramos
- Center for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
| | - Luis Willian Pacheco Arge
- Laboratório de Genética Molecular e Biotecnologia Vegetal, Centro de Ciências da Saúde, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | | | - Kiyoshi F. Fukutani
- Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Fundação José Silveira, Salvador, Brazil
| | - Artur Trancoso L. de Queiroz
- Center for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
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19
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Kabir MH, O'Connor MD. Stems cells, big data and compendium-based analyses for identifying cell types, signalling pathways and gene regulatory networks. Biophys Rev 2019; 11:41-50. [PMID: 30684132 DOI: 10.1007/s12551-018-0486-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 11/15/2018] [Indexed: 01/31/2023] Open
Abstract
Identification of new drug and cell therapy targets for disease treatment will be facilitated by a detailed molecular understanding of normal and disease development. Human pluripotent stem cells can provide a large in vitro source of human cell types and, in a growing number of instances, also three-dimensional multicellular tissues called organoids. The application of stem cell technology to discovery and development of new therapies will be aided by detailed molecular characterisation of cell identity, cell signalling pathways and target gene networks. Big data or 'omics' techniques-particularly transcriptomics and proteomics-facilitate cell and tissue characterisation using thousands to tens-of-thousands of genes or proteins. These gene and protein profiles are analysed using existing and/or emergent bioinformatics methods, including a growing number of methods that compare sample profiles against compendia of reference samples. This review assesses how compendium-based analyses can aid the application of stem cell technology for new therapy development. This includes via robust definition of differentiated stem cell identity, as well as elucidation of complex signalling pathways and target gene networks involved in normal and diseased states.
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Affiliation(s)
- Md Humayun Kabir
- School of Medicine, Western Sydney University, Campbelltown, NSW, Australia.,Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - Michael D O'Connor
- School of Medicine, Western Sydney University, Campbelltown, NSW, Australia. .,Medical Sciences Research Group, Western Sydney University, Campbelltown, NSW, Australia.
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20
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Kabir MH, Patrick R, Ho JWK, O'Connor MD. Identification of active signaling pathways by integrating gene expression and protein interaction data. BMC SYSTEMS BIOLOGY 2018; 12:120. [PMID: 30598083 PMCID: PMC6311899 DOI: 10.1186/s12918-018-0655-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Background Signaling pathways are the key biological mechanisms that transduce extracellular signals to affect transcription factor mediated gene regulation within cells. A number of computational methods have been developed to identify the topological structure of a specific signaling pathway using protein-protein interaction data, but they are not designed for identifying active signaling pathways in an unbiased manner. On the other hand, there are statistical methods based on gene sets or pathway data that can prioritize likely active signaling pathways, but they do not make full use of active pathway structure that link receptor, kinases and downstream transcription factors. Results Here, we present a method to simultaneously predict the set of active signaling pathways, together with their pathway structure, by integrating protein-protein interaction network and gene expression data. We evaluated the capacity for our method to predict active signaling pathways for dental epithelial cells, ocular lens epithelial cells, human pluripotent stem cell-derived lens epithelial cells, and lens fiber cells. This analysis showed our approach could identify all the known active pathways that are associated with tooth formation and lens development. Conclusions The results suggest that SPAGI can be a useful approach to identify the potential active signaling pathways given a gene expression profile. Our method is implemented as an open source R package, available via https://github.com/VCCRI/SPAGI/. Electronic supplementary material The online version of this article (10.1186/s12918-018-0655-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Md Humayun Kabir
- School of Medicine, Western Sydney University, Campbelltown, NSW, Australia.,Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia.,Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - Ralph Patrick
- Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia.,St. Vincent's Clinical School, University of New South Wales, Sydney, NSW, Australia.,Stem Cells Australia, Melbourne Brain Centre, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Joshua W K Ho
- Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia. .,St. Vincent's Clinical School, University of New South Wales, Sydney, NSW, Australia. .,School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, SAR, China.
| | - Michael D O'Connor
- School of Medicine, Western Sydney University, Campbelltown, NSW, Australia. .,Molecular Medicine Research Group, Western Sydney University, Campbelltown, NSW, Australia.
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21
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Abstract
PathLinker is a graph-theoretic algorithm originally developed to reconstruct the interactions in a signaling pathway of interest. It efficiently computes multiple short paths within a background protein interaction network from the receptors to transcription factors (TFs) in a pathway. Since December 2015, PathLinker has been available as an app for Cytoscape. This paper describes how we automated the app to use the CyRest infrastructure and how users can incorporate PathLinker into their software pipelines.
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Affiliation(s)
- Li Jun Huang
- Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Jeffrey N Law
- Genetics, Bioinformatics, and Computational Biology Ph.D. program, Virginia Tech, Blacksburg, VA, 24061, USA
| | - T M Murali
- Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA.,ICTAS Center for Systems Biology of Engineered Tissues, Virginia Tech, Blacksburg, VA, 24061, USA
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