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López-Cortés A, Cabrera-Andrade A, Echeverría-Garcés G, Echeverría-Espinoza P, Pineda-Albán M, Elsitdie N, Bueno-Miño J, Cruz-Segundo CM, Dorado J, Pazos A, Gonzáles-Díaz H, Pérez-Castillo Y, Tejera E, Munteanu CR. Unraveling druggable cancer-driving proteins and targeted drugs using artificial intelligence and multi-omics analyses. Sci Rep 2024; 14:19359. [PMID: 39169044 PMCID: PMC11339426 DOI: 10.1038/s41598-024-68565-7] [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] [Received: 03/04/2024] [Accepted: 07/25/2024] [Indexed: 08/23/2024] Open
Abstract
The druggable proteome refers to proteins that can bind to small molecules with appropriate chemical affinity, inducing a favorable clinical response. Predicting druggable proteins through screening and in silico modeling is imperative for drug design. To contribute to this field, we developed an accurate predictive classifier for druggable cancer-driving proteins using amino acid composition descriptors of protein sequences and 13 machine learning linear and non-linear classifiers. The optimal classifier was achieved with the support vector machine method, utilizing 200 tri-amino acid composition descriptors. The high performance of the model is evident from an area under the receiver operating characteristics (AUROC) of 0.975 ± 0.003 and an accuracy of 0.929 ± 0.006 (threefold cross-validation). The machine learning prediction model was enhanced with multi-omics approaches, including the target-disease evidence score, the shortest pathways to cancer hallmarks, structure-based ligandability assessment, unfavorable prognostic protein analysis, and the oncogenic variome. Additionally, we performed a drug repurposing analysis to identify drugs with the highest affinity capable of targeting the best predicted proteins. As a result, we identified 79 key druggable cancer-driving proteins with the highest ligandability, and 23 of them demonstrated unfavorable prognostic significance across 16 TCGA PanCancer types: CDKN2A, BCL10, ACVR1, CASP8, JAG1, TSC1, NBN, PREX2, PPP2R1A, DNM2, VAV1, ASXL1, TPR, HRAS, BUB1B, ATG7, MARK3, SETD2, CCNE1, MUTYH, CDKN2C, RB1, and SMARCA4. Moreover, we prioritized 11 clinically relevant drugs targeting these proteins. This strategy effectively predicts and prioritizes biomarkers, therapeutic targets, and drugs for in-depth studies in clinical trials. Scripts are available at https://github.com/muntisa/machine-learning-for-druggable-proteins .
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Affiliation(s)
- Andrés López-Cortés
- Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador.
| | - Alejandro Cabrera-Andrade
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador
- Escuela de Enfermería, Facultad de Ciencias de la Salud, Universidad de Las Américas, Quito, Ecuador
| | - Gabriela Echeverría-Garcés
- Centro de Referencia Nacional de Genómica, Secuenciación y Bioinformática, Instituto Nacional de Investigación en Salud Pública "Leopoldo Izquieta Pérez", Quito, Ecuador
- Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Santiago, Chile
| | | | - Micaela Pineda-Albán
- Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador
| | - Nicole Elsitdie
- Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador
| | - José Bueno-Miño
- Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador
| | - Carlos M Cruz-Segundo
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, A Coruña, Spain
- Tecnológico de Estudios Superiores de Jocotitlán, Jocotitlán, Mexico
| | - Julian Dorado
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, A Coruña, Spain
- Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), University of A Coruna, A Coruña, Spain
| | - Alejandro Pazos
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, A Coruña, Spain
- Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), University of A Coruna, A Coruña, Spain
- Biomedical Research Institute of A Coruna (INIBIC), University Hospital Complex of A Coruna (CHUAC), A Coruña, Spain
| | - Humberto Gonzáles-Díaz
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, Biscay, Spain
- IKERBASQUE, Basque Foundation for Science, Biscay, Spain
| | | | - Eduardo Tejera
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador
| | - Cristian R Munteanu
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, A Coruña, Spain
- Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), University of A Coruna, A Coruña, Spain
- Biomedical Research Institute of A Coruna (INIBIC), University Hospital Complex of A Coruna (CHUAC), A Coruña, Spain
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Vendel AC, Jaroszewski L, Linnik MD, Godzik A. B- and T-Lymphocyte Attenuator in Systemic Lupus Erythematosus Disease Pathogenesis. Clin Pharmacol Ther 2024; 116:247-256. [PMID: 38676311 DOI: 10.1002/cpt.3282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024]
Abstract
B- and T-lymphocyte attenuator (BTLA; CD272) is an immunoglobulin superfamily member and part of a family of checkpoint inhibitory receptors that negatively regulate immune cell activation. The natural ligand for BTLA is herpes virus entry mediator (HVEM; TNFRSF14), and binding of HVEM to BTLA leads to attenuation of lymphocyte activation. In this study, we evaluated the role of BTLA and HVEM expression in the pathogenesis of systemic lupus erythematosus (SLE), a multisystem autoimmune disease. Peripheral blood mononuclear cells from healthy volunteers (N = 7) were evaluated by mass cytometry by time-of-flight to establish baseline expression of BTLA and HVEM on human lymphocytes compared with patients with SLE during a self-reported flare (N = 5). High levels of BTLA protein were observed on B cells, CD4+, and CD8+ T cells, and plasmacytoid dendritic cells in healthy participants. HVEM protein levels were lower in patients with SLE compared with healthy participants, while BTLA levels were similar between SLE and healthy groups. Correlations of BTLA-HVEM hub genes' expression with patient and disease characteristics were also analyzed using whole blood gene expression data from patients with SLE (N = 1,760) and compared with healthy participants (N = 60). HVEM, being one of the SLE-associated genes, showed an exceptionally strong negative association with disease activity. Several other genes in the BTLA-HVEM signaling network were strongly (negative or positive) correlated, while BTLA had a low association with disease activity. Collectively, these data provide a clinical rationale for targeting BTLA with an agonist in SLE patients with low HVEM expression.
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Affiliation(s)
| | - Lukasz Jaroszewski
- University of California Riverside School of Medicine, Riverside, California, USA
| | | | - Adam Godzik
- University of California Riverside School of Medicine, Riverside, California, USA
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Saravanan KS, Satish KS, Saraswathy GR, Kuri U, Vastrad SJ, Giri R, Dsouza PL, Kumar AP, Nair G. Innovative target mining stratagems to navigate drug repurposing endeavours. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:303-355. [PMID: 38789185 DOI: 10.1016/bs.pmbts.2024.03.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
The conventional theory linking a single gene with a particular disease and a specific drug contributes to the dwindling success rates of traditional drug discovery. This requires a substantial shift focussing on contemporary drug design or drug repurposing, which entails linking multiple genes to diverse physiological or pathological pathways and drugs. Lately, drug repurposing, the art of discovering new/unlabelled indications for existing drugs or candidates in clinical trials, is gaining attention owing to its success rates. The rate-limiting phase of this strategy lies in target identification, which is generally driven through disease-centric and/or drug-centric approaches. The disease-centric approach is based on exploration of crucial biomolecules such as genes or proteins underlying pathological cascades of the disease of interest. Investigating these pathological interplays aids in the identification of potential drug targets that can be leveraged for novel therapeutic interventions. The drug-centric approach involves various strategies such as exploring the mechanism of adverse drug reactions that can unearth potential targets, as these untoward reactions might be considered desirable therapeutic actions in other disease conditions. Currently, artificial intelligence is an emerging robust tool that can be used to translate the aforementioned intricate biological networks to render interpretable data for extracting precise molecular targets. Integration of multiple approaches, big data analytics, and clinical corroboration are essential for successful target mining. This chapter highlights the contemporary strategies steering target identification and diverse frameworks for drug repurposing. These strategies are illustrated through case studies curated from recent drug repurposing research inclined towards neurodegenerative diseases, cancer, infections, immunological, and cardiovascular disorders.
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Affiliation(s)
- Kamatchi Sundara Saravanan
- Department of Pharmacognosy, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Kshreeraja S Satish
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Ganesan Rajalekshmi Saraswathy
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India.
| | - Ushnaa Kuri
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Soujanya J Vastrad
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Ritesh Giri
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Prizvan Lawrence Dsouza
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Adusumilli Pramod Kumar
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Gouri Nair
- Department of Pharmacology, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
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Lengauer T. Yves Moreau has received the 2023 Einstein Foundation Individual Award for Promoting Quality in Research. BIOINFORMATICS ADVANCES 2024; 4:vbae039. [PMID: 38566919 PMCID: PMC10985674 DOI: 10.1093/bioadv/vbae039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 03/08/2024] [Indexed: 04/04/2024]
Affiliation(s)
- Thomas Lengauer
- Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
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Han S, Hong J, Yun SJ, Koo HJ, Kim TY. PWN: enhanced random walk on a warped network for disease target prioritization. BMC Bioinformatics 2023; 24:105. [PMID: 36944912 PMCID: PMC10031933 DOI: 10.1186/s12859-023-05227-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 03/13/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Extracting meaningful information from unbiased high-throughput data has been a challenge in diverse areas. Specifically, in the early stages of drug discovery, a considerable amount of data was generated to understand disease biology when identifying disease targets. Several random walk-based approaches have been applied to solve this problem, but they still have limitations. Therefore, we suggest a new method that enhances the effectiveness of high-throughput data analysis with random walks. RESULTS We developed a new random walk-based algorithm named prioritization with a warped network (PWN), which employs a warped network to achieve enhanced performance. Network warping is based on both internal and external features: graph curvature and prior knowledge. CONCLUSIONS We showed that these compositive features synergistically increased the resulting performance when applied to random walk algorithms, which led to PWN consistently achieving the best performance among several other known methods. Furthermore, we performed subsequent experiments to analyze the characteristics of PWN.
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Affiliation(s)
- Seokjin Han
- Standigm Inc., 70, Nonhyeon-ro 85-gil, Gangnam-gu, Seoul, 06234, Republic of Korea
| | - Jinhee Hong
- Standigm Inc., 70, Nonhyeon-ro 85-gil, Gangnam-gu, Seoul, 06234, Republic of Korea
| | - So Jeong Yun
- Standigm Inc., 70, Nonhyeon-ro 85-gil, Gangnam-gu, Seoul, 06234, Republic of Korea
| | - Hee Jung Koo
- Standigm UK Co., Ltd, 50-60 Station Road, Cambridge, CB1 2JH, UK.
| | - Tae Yong Kim
- Standigm Inc., 70, Nonhyeon-ro 85-gil, Gangnam-gu, Seoul, 06234, Republic of Korea.
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Systemic Efficacy of Sirolimus via the ERBB Signaling Pathway in Breast Cancer. Processes (Basel) 2022. [DOI: 10.3390/pr10030552] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/10/2022] Open
Abstract
Rapamycin, also known as sirolimus, inhibits the mTOR pathway in complex diseases such as cancer, and its downstream targets are ribosomal S6 kinases (RPS6K). Sirolimus is involved in regulating cell growth and cell survival through roles such as the mediation of epidermal growth factor signaling. However, the systemic efficacy of sirolimus in pathway regulation is unclear. The purpose of this study is to determine systemic drug efficacy using computational methods and drug-induced datasets. We suggest a computational method using gene expression datasets induced by sirolimus and an inverse algorithm that simultaneously identifies parameters referring to gene–gene interactions. We downloaded two sirolimus-induced microarray gene expression datasets and used a computational method to obtain the most enriched pathway, then adopted an inverse algorithm to discover the gene–gene interactions of that pathway. In the results, RPS6KB1 was a target gene of sirolimus and was associated with genes in the pathway. The common gene interactions from two datasets were a hub gene, RPS6KB1, and 10 related genes (AKT3, CBLC, MAP2K7, NRG1/2, PAK3, PIK3CD/G, PRKCG, and SHC3) in the epidermal growth factor (ERBB) signaling pathway.
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Bima AIH, Elsamanoudy AZ, Albaqami WF, Khan Z, Parambath SV, Al-Rayes N, Kaipa PR, Elango R, Banaganapalli B, Shaik NA. Integrative system biology and mathematical modeling of genetic networks identifies shared biomarkers for obesity and diabetes. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:2310-2329. [PMID: 35240786 DOI: 10.3934/mbe.2022107] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Obesity and type 2 and diabetes mellitus (T2D) are two dual epidemics whose shared genetic pathological mechanisms are still far from being fully understood. Therefore, this study is aimed at discovering key genes, molecular mechanisms, and new drug targets for obesity and T2D by analyzing the genome wide gene expression data with different computational biology approaches. In this study, the RNA-sequencing data of isolated primary human adipocytes from individuals who are lean, obese, and T2D was analyzed by an integrated framework consisting of gene expression, protein interaction network (PIN), tissue specificity, and druggability approaches. Our findings show a total of 1932 unique differentially expressed genes (DEGs) across the diabetes versus obese group comparison (p≤0.05). The PIN analysis of these 1932 DEGs identified 190 high centrality network (HCN) genes, which were annotated against 3367 GO terms and functional pathways, like response to insulin signaling, phosphorylation, lipid metabolism, glucose metabolism, etc. (p≤0.05). By applying additional PIN and topological parameters to 190 HCN genes, we further mapped 25 high confidence genes, functionally connected with diabetes and obesity traits. Interestingly, ERBB2, FN1, FYN, HSPA1A, HBA1, and ITGB1 genes were found to be tractable by small chemicals, antibodies, and/or enzyme molecules. In conclusion, our study highlights the potential of computational biology methods in correlating expression data to topological parameters, functional relationships, and druggability characteristics of the candidate genes involved in complex metabolic disorders with a common etiological basis.
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Affiliation(s)
- Abdulhadi Ibrahim H Bima
- Department of Clinical Biochemistry, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ayman Zaky Elsamanoudy
- Department of Clinical Biochemistry, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Mansoura University, Mansoura, Egypt
| | - Walaa F Albaqami
- Department of Science, Prince Sultan Military College of Health Sciences, Dhahran, Saudi Arabia
| | - Zeenath Khan
- Department of Science, Prince Sultan Military College of Health Sciences, Dhahran, Saudi Arabia
| | | | - Nuha Al-Rayes
- Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
- Princess Al-Jawhara Al-Brahim Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Prabhakar Rao Kaipa
- Department of Genetics, College of Science, Osmania University, Hyderabad, India
| | - Ramu Elango
- Princess Al-Jawhara Al-Brahim Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Babajan Banaganapalli
- Princess Al-Jawhara Al-Brahim Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Noor A Shaik
- Princess Al-Jawhara Al-Brahim Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
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Muslu O, Hoyt CT, Lacerda M, Hofmann-Apitius M, Frohlich H. GuiltyTargets: Prioritization of Novel Therapeutic Targets With Network Representation Learning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:491-500. [PMID: 32750869 DOI: 10.1109/tcbb.2020.3003830] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
The majority of clinical trials fail due to low efficacy of investigated drugs, often resulting from a poor choice of target protein. Existing computational approaches aim to support target selection either via genetic evidence or by putting potential targets into the context of a disease specific network reconstruction. The purpose of this work was to investigate whether network representation learning techniques could be used to allow for a machine learning based prioritization of putative targets. We propose a novel target prioritization approach, GuiltyTargets, which relies on attributed network representation learning of a genome-wide protein-protein interaction network annotated with disease-specific differential gene expression and uses positive-unlabeled (PU) machine learning for candidate ranking. We evaluated our approach on 12 datasets from six diseases of different type (cancer, metabolic, neurodegenerative) within a 10 times repeated 5-fold stratified cross-validation and achieved AUROC values between 0.92 - 0.97, significantly outperforming previous approaches that relied on manually engineered topological features. Moreover, we showed that GuiltyTargets allows for target repositioning across related disease areas. An application of GuiltyTargets to Alzheimer's disease resulted in a number of highly ranked candidates that are currently discussed as targets in the literature. Interestingly, one (COMT) is also the target of an approved drug (Tolcapone) for Parkinson's disease, highlighting the potential for target repositioning with our method. The GuiltyTargets Python package is available on PyPI and all code used for analysis can be found under the MIT License at https://github.com/GuiltyTargets. Attributed network representation learning techniques provide an interesting approach to effectively leverage the existing knowledge about the molecular mechanisms in different diseases. In this work, the combination with positive-unlabeled learning for target prioritization demonstrated a clear superiority compared to classical feature engineering approaches. Our work highlights the potential of attributed network representation learning for target prioritization. Given the overarching relevance of networks in computational biology we believe that attributed network representation learning techniques could have a broader impact in the future.
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Ren Y, Sivaganesan S, Clark NA, Zhang L, Biesiada J, Niu W, Plas DR, Medvedovic M. Predicting mechanism of action of cellular perturbations with pathway activity signatures. Bioinformatics 2021; 36:4781-4788. [PMID: 32653926 DOI: 10.1093/bioinformatics/btaa590] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 06/15/2020] [Accepted: 07/03/2020] [Indexed: 12/16/2022] Open
Abstract
MOTIVATION Misregulation of signaling pathway activity is etiologic for many human diseases, and modulating activity of signaling pathways is often the preferred therapeutic strategy. Understanding the mechanism of action (MOA) of bioactive chemicals in terms of targeted signaling pathways is the essential first step in evaluating their therapeutic potential. Changes in signaling pathway activity are often not reflected in changes in expression of pathway genes which makes MOA inferences from transcriptional signatures (TSeses) a difficult problem. RESULTS We developed a new computational method for implicating pathway targets of bioactive chemicals and other cellular perturbations by integrated analysis of pathway network topology, the Library of Integrated Network-based Cellular Signature TSes of genetic perturbations of pathway genes and the TS of the perturbation. Our methodology accurately predicts signaling pathways targeted by the perturbation when current pathway analysis approaches utilizing only the TS of the perturbation fail. AVAILABILITY AND IMPLEMENTATION Open source R package paslincs is available at https://github.com/uc-bd2k/paslincs. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yan Ren
- Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45267-0056, USA
| | - Siva Sivaganesan
- Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH 45221-0025, USA
| | - Nicholas A Clark
- Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45267-0056, USA
| | - Lixia Zhang
- Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45267-0056, USA
| | - Jacek Biesiada
- Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45267-0056, USA
| | - Wen Niu
- Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45267-0056, USA
| | - David R Plas
- Department of Cancer Biology, University of Cincinnati College of Medicine, Cincinnati, OH 45267-0521, USA
| | - Mario Medvedovic
- Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45267-0056, USA
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Wang M, Luciani LL, Noh H, Mochan E, Shoemaker JE. TREAP: A New Topological Approach to Drug Target Inference. Biophys J 2020; 119:2290-2298. [PMID: 33129831 DOI: 10.1016/j.bpj.2020.10.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 09/08/2020] [Accepted: 10/07/2020] [Indexed: 10/23/2022] Open
Abstract
Over 50% of drugs fail in stage 3 clinical trials, many because of a poor understanding of the drug's mechanisms of action (MoA). A better comprehension of drug MoA will significantly improve research and development (R&D). Current proposed algorithms, such as ProTINA and DeMAND, can be overly complex. Additionally, they are unable to predict whether the drug-induced gene expression or the topology of the networks used to model gene regulation primarily impacts accurate drug target inference. In this work, we evaluate how network and gene expression data affect ProTINA's accuracy. We find that network topology predominantly determines the accuracy of ProTINA's predictions. We further show that the size of an interaction network and/or selecting cell-specific networks has a limited effect on accuracy. We then demonstrate that a specific network topology measure, betweenness, can be used to improve drug target prediction. Based on these results, we create a new algorithm, TREAP, that combines betweenness values and adjusted p-values for target inference. TREAP offers an alternative approach to drug target inference and is advantageous because it is not computationally demanding, provides easy-to-interpret results, and is often more accurate at predicting drug targets than current state-of-the-art approaches.
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Affiliation(s)
- Muying Wang
- Department of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Lauren L Luciani
- Department of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Heeju Noh
- Department of Systems Biology, Columbia University, New York, New York; Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
| | - Ericka Mochan
- Department of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania; Department of Mathematics and Data Analytics, Carlow University, Pittsburgh, Pennsylvania
| | - Jason E Shoemaker
- Department of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania; Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania; The McGowan Institute for Regenerative Medicine, Pittsburgh, Pennsylvania.
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A New Computational Approach to Evaluating Systemic Gene–Gene Interactions in a Pathway Affected by Drug LY294002. Processes (Basel) 2020. [DOI: 10.3390/pr8101230] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
In this study, we investigate how drugs systemically affect genes via pathways by integrating information from interactions between chemical compounds and molecular expression datasets, and from pathway information such as gene sets using mathematical models. First, we adopt drug-induced gene expression datasets; then, employ gene set enrichment analysis tools for selecting candidate enrichment pathways; and lastly, implement the inverse algorithm package for identifying gene–gene regulatory networks in a pathway. We tested LY294002-induced datasets of the MCF7 breast cancer cell lines, and found a CELL CYCLE pathway with 101 genes, ERBB signaling pathway consisting of 82 genes, and MTOR pathway consisting of 45 genes. We consider two interactions: quantity strength depending on number of interactions, and quality strength depending on weight of interaction as positive (+) and negative (−) interactions. Our methods revealed ANAPC1-CDK6 (−0.412) and ORC2L- CHEK1(0.951) for the CELL CYCLE pathway; INS-RPS6 (−3.125) and PRKAA2-PRKAA2 (+1.319) for the MTOR pathway; and CBLB-RPS6KB1 (−0.141), RPS6KB1-CBLC (+0.238) for the ERBB signaling pathway to be top quality interactions. Top quantity interactions discovered include 12; the CDC (−,+) gene family for the CELL CYCLE pathway, 20; PIK3 (−), 23; PIK3CG (+) for the MTOR pathway, 11; PAK (−), 10; PIK3 (+) for the ERBB signaling pathway.
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12
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Chang HC, Chu CP, Lin SJ, Hsiao CK. Network hub-node prioritization of gene regulation with intra-network association. BMC Bioinformatics 2020; 21:101. [PMID: 32164570 PMCID: PMC7069025 DOI: 10.1186/s12859-020-3444-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 03/06/2020] [Indexed: 11/10/2022] Open
Abstract
Background To identify and prioritize the influential hub genes in a gene-set or biological pathway, most analyses rely on calculation of marginal effects or tests of statistical significance. These procedures may be inappropriate since hub nodes are common connection points and therefore may interact with other nodes more often than non-hub nodes do. Such dependence among gene nodes can be conjectured based on the topology of the pathway network or the correlation between them. Results Here we develop a pathway activity score incorporating the marginal (local) effects of gene nodes as well as intra-network affinity measures. This score summarizes the expression levels in a gene-set/pathway for each sample, with weights on local and network information, respectively. The score is next used to examine the impact of each node through a leave-one-out evaluation. To illustrate the procedure, two cancer studies, one involving RNA-Seq from breast cancer patients with high-grade ductal carcinoma in situ and one microarray expression data from ovarian cancer patients, are used to assess the performance of the procedure, and to compare with existing methods, both ones that do and do not take into consideration correlation and network information. The hub nodes identified by the proposed procedure in the two cancer studies are known influential genes; some have been included in standard treatments and some are currently considered in clinical trials for target therapy. The results from simulation studies show that when marginal effects are mild or weak, the proposed procedure can still identify causal nodes, whereas methods relying only on marginal effect size cannot. Conclusions The NetworkHub procedure proposed in this research can effectively utilize the network information in combination with local effects derived from marker values, and provide a useful and complementary list of recommendations for prioritizing causal hubs.
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Affiliation(s)
- Hung-Ching Chang
- Division of Biostatistics, Institute of Epidemiology and Preventive Medicine, National Taiwan University, No. 17, Xu-Zhou Road, Taipei, 10055, Taiwan
| | - Chiao-Pei Chu
- Division of Biostatistics, Institute of Epidemiology and Preventive Medicine, National Taiwan University, No. 17, Xu-Zhou Road, Taipei, 10055, Taiwan
| | - Shu-Ju Lin
- Institute of Statistical Science, Academia Sinica, Taipei, 11529, Taiwan
| | - Chuhsing Kate Hsiao
- Division of Biostatistics, Institute of Epidemiology and Preventive Medicine, National Taiwan University, No. 17, Xu-Zhou Road, Taipei, 10055, Taiwan. .,Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, 10055, Taiwan.
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13
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Abstract
The abundance of high-throughput data and technical refinements in graph theories have allowed network analysis to become an effective approach for various medical fields. This chapter introduces co-expression, Bayesian, and regression-based network construction methods, which are the basis of network analysis. Various methods in network topology analysis are explained, along with their unique features and applications in biomedicine. Furthermore, we explain the role of network embedding in reducing the dimensionality of networks and outline several popular algorithms used by researchers today. Current literature has implemented different combinations of topology analysis and network embedding techniques, and we outline several studies in the fields of genetic-based disease prediction, drug-target identification, and multi-level omics integration.
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14
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Network-based method for drug target discovery at the isoform level. Sci Rep 2019; 9:13868. [PMID: 31554914 PMCID: PMC6761107 DOI: 10.1038/s41598-019-50224-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 09/06/2019] [Indexed: 02/06/2023] Open
Abstract
Identification of primary targets associated with phenotypes can facilitate exploration of the underlying molecular mechanisms of compounds and optimization of the structures of promising drugs. However, the literature reports limited effort to identify the target major isoform of a single known target gene. The majority of genes generate multiple transcripts that are translated into proteins that may carry out distinct and even opposing biological functions through alternative splicing. In addition, isoform expression is dynamic and varies depending on the developmental stage and cell type. To identify target major isoforms, we integrated a breast cancer type-specific isoform coexpression network with gene perturbation signatures in the MCF7 cell line in the Connectivity Map database using the ‘shortest path’ drug target prioritization method. We used a leukemia cancer network and differential expression data for drugs in the HL-60 cell line to test the robustness of the detection algorithm for target major isoforms. We further analyzed the properties of target major isoforms for each multi-isoform gene using pharmacogenomic datasets, proteomic data and the principal isoforms defined by the APPRIS and STRING datasets. Then, we tested our predictions for the most promising target major protein isoforms of DNMT1, MGEA5 and P4HB4 based on expression data and topological features in the coexpression network. Interestingly, these isoforms are not annotated as principal isoforms in APPRIS. Lastly, we tested the affinity of the target major isoform of MGEA5 for streptozocin through in silico docking. Our findings will pave the way for more effective and targeted therapies via studies of drug targets at the isoform level.
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15
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Noh H, Shoemaker JE, Gunawan R. Network perturbation analysis of gene transcriptional profiles reveals protein targets and mechanism of action of drugs and influenza A viral infection. Nucleic Acids Res 2019; 46:e34. [PMID: 29325153 PMCID: PMC5887474 DOI: 10.1093/nar/gkx1314] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Accepted: 12/22/2017] [Indexed: 12/12/2022] Open
Abstract
Genome-wide transcriptional profiling provides a global view of cellular state and how this state changes under different treatments (e.g. drugs) or conditions (e.g. healthy and diseased). Here, we present ProTINA (Protein Target Inference by Network Analysis), a network perturbation analysis method for inferring protein targets of compounds from gene transcriptional profiles. ProTINA uses a dynamic model of the cell-type specific protein-gene transcriptional regulation to infer network perturbations from steady state and time-series differential gene expression profiles. A candidate protein target is scored based on the gene network's dysregulation, including enhancement and attenuation of transcriptional regulatory activity of the protein on its downstream genes, caused by drug treatments. For benchmark datasets from three drug treatment studies, ProTINA was able to provide highly accurate protein target predictions and to reveal the mechanism of action of compounds with high sensitivity and specificity. Further, an application of ProTINA to gene expression profiles of influenza A viral infection led to new insights of the early events in the infection.
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Affiliation(s)
- Heeju Noh
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich 8093, Switzerland.,Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Jason E Shoemaker
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA.,Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Rudiyanto Gunawan
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich 8093, Switzerland.,Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
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16
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Napolitano F, Carrella D, Mandriani B, Pisonero-Vaquero S, Sirci F, Medina DL, Brunetti-Pierri N, di Bernardo D. gene2drug: a computational tool for pathway-based rational drug repositioning. Bioinformatics 2019; 34:1498-1505. [PMID: 29236977 DOI: 10.1093/bioinformatics/btx800] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Accepted: 12/10/2017] [Indexed: 02/06/2023] Open
Abstract
Motivation Drug repositioning has been proposed as an effective shortcut to drug discovery. The availability of large collections of transcriptional responses to drugs enables computational approaches to drug repositioning directly based on measured molecular effects. Results We introduce a novel computational methodology for rational drug repositioning, which exploits the transcriptional responses following treatment with small molecule. Specifically, given a therapeutic target gene, a prioritization of potential effective drugs is obtained by assessing their impact on the transcription of genes in the pathway(s) including the target. We performed in silico validation and comparison with a state-of-art technique based on similar principles. We next performed experimental validation in two different real-case drug repositioning scenarios: (i) upregulation of the glutamate-pyruvate transaminase (GPT), which has been shown to induce reduction of oxalate levels in a mouse model of primary hyperoxaluria, and (ii) activation of the transcription factor TFEB, a master regulator of lysosomal biogenesis and autophagy, whose modulation may be beneficial in neurodegenerative disorders. Availability and implementation A web tool for Gene2drug is freely available at http://gene2drug.tigem.it. An R package is under development and can be obtained from https://github.com/franapoli/gep2pep. Contact dibernardo@tigem.it. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Francesco Napolitano
- Systems and Synthetic Biology Lab, Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA) 80078, Italy
| | - Diego Carrella
- Systems and Synthetic Biology Lab, Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA) 80078, Italy
| | - Barbara Mandriani
- Systems and Synthetic Biology Lab, Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA) 80078, Italy
| | - Sandra Pisonero-Vaquero
- Systems and Synthetic Biology Lab, Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA) 80078, Italy
| | - Francesco Sirci
- Systems and Synthetic Biology Lab, Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA) 80078, Italy
- Institute for Research in Biomedicine (IRB Barcelona), C/ Baldiri Reixac 10, 08028 Barcelona, Spain
| | - Diego L Medina
- Systems and Synthetic Biology Lab, Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA) 80078, Italy
| | - Nicola Brunetti-Pierri
- Systems and Synthetic Biology Lab, Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA) 80078, Italy
- Department of Translational Medicine, Federico II University, 80131 Naples, Italy
| | - Diego di Bernardo
- Systems and Synthetic Biology Lab, Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA) 80078, Italy
- Department of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, 80125 Naples, Italy
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17
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Ma J, Wang J, Ghoraie LS, Men X, Haibe-Kains B, Dai P. A Comparative Study of Cluster Detection Algorithms in Protein-Protein Interaction for Drug Target Discovery and Drug Repurposing. Front Pharmacol 2019; 10:109. [PMID: 30837876 PMCID: PMC6389713 DOI: 10.3389/fphar.2019.00109] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Accepted: 01/28/2019] [Indexed: 12/29/2022] Open
Abstract
The interactions between drugs and their target proteins induce altered expression of genes involved in complex intracellular networks. The properties of these functional network modules are critical for the identification of drug targets, for drug repurposing, and for understanding the underlying mode of action of the drug. The topological modules generated by a computational approach are defined as functional clusters. However, the functions inferred for these topological modules extracted from a large-scale molecular interaction network, such as a protein–protein interaction (PPI) network, could differ depending on different cluster detection algorithms. Moreover, the dynamic gene expression profiles among tissues or cell types causes differential functional interaction patterns between the molecular components. Thus, the connections in the PPI network should be modified by the transcriptomic landscape of specific cell lines before producing topological clusters. Here, we systematically investigated the clusters of a cell-based PPI network by using four cluster detection algorithms. We subsequently compared the performance of these algorithms for target gene prediction, which integrates gene perturbation data with the cell-based PPI network using two drug target prioritization methods, shortest path and diffusion correlation. In addition, we validated the proportion of perturbed genes in clusters by finding candidate anti-breast cancer drugs and confirming our predictions using literature evidence and cases in the ClinicalTrials.gov. Our results indicate that the Walktrap (CW) clustering algorithm achieved the best performance overall in our comparative study.
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Affiliation(s)
- Jun Ma
- National Engineering Research Center for Miniaturized Detection Systems, Northwest University, Xi'an, China.,Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Jenny Wang
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | | | - Xin Men
- Shaanxi Microbiology Institute, Xi'an, China
| | | | - Penggao Dai
- National Engineering Research Center for Miniaturized Detection Systems, Northwest University, Xi'an, China
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18
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Pabon NA, Xia Y, Estabrooks SK, Ye Z, Herbrand AK, Süß E, Biondi RM, Assimon VA, Gestwicki JE, Brodsky JL, Camacho CJ, Bar-Joseph Z. Predicting protein targets for drug-like compounds using transcriptomics. PLoS Comput Biol 2018; 14:e1006651. [PMID: 30532261 PMCID: PMC6300300 DOI: 10.1371/journal.pcbi.1006651] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 12/19/2018] [Accepted: 11/13/2018] [Indexed: 01/07/2023] Open
Abstract
An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation of set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions. Bioactive compounds often disrupt cellular gene expression in ways that are difficult to predict. While the correlation between a cellular response after treatment with a small molecule and the knockdown of its target protein should be simple to establish, in practice this goal has been difficult to achieve. The main challenges are that data are noisy, drugs are not intended to be active in all cell types, and signals from a bona fide target(s) may be obscured by correlations with knockdowns of other proteins in the same pathway(s). Here, we find that a random forest classification model can detect meaningful correlational patterns when gene expression profiles after compound treatment and gene knockdowns in four or more cell lines are compared. When this approach is enriched by a structure-based screen, novel drug-target interactions can be predicted. We then validated new ligand-protein interactions for four difficult targets. Although the initial compounds are not especially potent in vitro, they are capable of disrupting their target pathway in the cell to an extent that generates a significant and characteristic gene expression profile. Collectively, our studies provide insight on cell-level transcriptomic responses to pharmaceutical intervention and the use of these patterns for target identification. In addition, the method provides a novel drug discovery pipeline to test chemistries without a priori knowledge of their target(s).
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Affiliation(s)
- Nicolas A. Pabon
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Yan Xia
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Samuel K. Estabrooks
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Zhaofeng Ye
- School of Medicine, Tsinghua University, Beijing, China
| | - Amanda K. Herbrand
- Department of Internal Medicine I, Universitätsklinikum Frankfurt, Frankfurt, Germany
| | - Evelyn Süß
- Department of Internal Medicine I, Universitätsklinikum Frankfurt, Frankfurt, Germany
| | - Ricardo M. Biondi
- Department of Internal Medicine I, Universitätsklinikum Frankfurt, Frankfurt, Germany
| | - Victoria A. Assimon
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, United States of America
| | - Jason E. Gestwicki
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, United States of America
| | - Jeffrey L. Brodsky
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Carlos J. Camacho
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- * E-mail: (CJC); (ZBJ)
| | - Ziv Bar-Joseph
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- * E-mail: (CJC); (ZBJ)
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19
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Ji J, He D, Feng Y, He Y, Xue F, Xie L. JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data. Bioinformatics 2018; 33:3080-3087. [PMID: 28582486 DOI: 10.1093/bioinformatics/btx360] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 06/01/2017] [Indexed: 12/26/2022] Open
Abstract
Motivation A complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application. Results We propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data. Availability and implementation R scripts available at https://github.com/jijiadong/JDINAC. Contact lxie@iscb.org. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jiadong Ji
- Department of Mathematical Statistics, School of Statistics, Shandong University of Finance and Economics, Jinan 250014, China
| | - Di He
- Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY 10016, USA
| | - Yang Feng
- Department of Statistics, Columbia University, New York, NY 10027, USA
| | - Yong He
- Department of Mathematical Statistics, School of Statistics, Shandong University of Finance and Economics, Jinan 250014, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, China
| | - Lei Xie
- Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY 10016, USA.,Department of Computer Science, Hunter College, The City University of New York, NY 10065, USA
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20
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Fu J, Yu L, Luo J, Huo R, Zhu B. Paeonol induces the apoptosis of the SGC‑7901 gastric cancer cell line by downregulating ERBB2 and inhibiting the NF‑κB signaling pathway. Int J Mol Med 2018; 42:1473-1483. [PMID: 29845222 PMCID: PMC6089764 DOI: 10.3892/ijmm.2018.3704] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Accepted: 05/11/2018] [Indexed: 12/28/2022] Open
Abstract
The purpose of the present study was to analyze the association between paeonol and the known genes related to gastric cancer (GC) using bioinformatics methods, and to investigate the role of paeonol in the potential impact on the nuclear factor‑κB (NF‑κB) signaling pathway, in order to provide a theoretical basis for further elucidating the effect of paeonol on cancer cells. Cell viability, morphology and apoptosis were detected using an MTT assay, an inverted microscope, and flow cytometry, respectively. The correlation between drugs and genes was analyzed using the Search Tool for Interactions of Chemicals (STITCH) gene‑drug interaction network. The expression levels of related mRNA and proteins were determined using reverse transcription‑quantitative polymerase chain reaction analysis and enzyme‑linked immunosorbent assay. The changes in protein expression were examined using western blot analysis. The correlation network between target genes directly affected by paeonol and known GC genes was determined by analyzing the association between the compounds and genes recorded in the STITCH database. The GC‑related epidermal growth factor receptor 2 (ERBB2) gene was at the core position of the paeonol interaction network and may be an important potential target gene for the effect of paeonol on cancer cells. The effect of paeonol on the viability of the SGC‑7901 GC cell line was detected using an MTT assay, which showed that the inhibitory effect occurred in a time‑ and dose‑dependent manner. The observations of cell morphology demonstrated that the cells were floating, abnormal in shape, had unclear boundaries and were sparse in arrangement following paeonol treatment. Flow cytometry indicated that paeonol significantly accelerated the apoptotic rate of the SGC‑7901 GC cells. The examination of clinical samples suggested that ERBB2 was expressed at a high level in GC samples, and was significantly downregulated following the addition of paeonol. The western blot analysis revealed that downregulating ERBB2 affected the activation of the NF‑κB signaling pathway, thereby upregulating the pro‑apoptotic factor B‑cell lymphoma‑associated X protein. Taken together, paeonol significantly downregulated ERBB2 and inhibited the activation of the NF‑κB signaling pathway, thereby inhibiting the proliferation of SGC‑7901 cells and inducing apoptosis.
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Affiliation(s)
- Jun Fu
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui 233004, P.R. China
| | - Luhua Yu
- Department of Otolaryngology‑Head and Neck Surgery, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui 233004, P.R. China
| | - Jie Luo
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui 233004, P.R. China
| | - Rui Huo
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui 233004, P.R. China
| | - Bing Zhu
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui 233004, P.R. China
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21
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Musa A, Ghoraie LS, Zhang SD, Glazko G, Yli-Harja O, Dehmer M, Haibe-Kains B, Emmert-Streib F. A review of connectivity map and computational approaches in pharmacogenomics. Brief Bioinform 2018; 19:506-523. [PMID: 28069634 PMCID: PMC5952941 DOI: 10.1093/bib/bbw112] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.
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Affiliation(s)
- Aliyu Musa
- Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Laleh Soltan Ghoraie
- Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Shu-Dong Zhang
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, University of Ulster, C-TRIC Building, Altnagelvin Area Hospital, Glenshane Road, Derry/Londonderry, Northern Ireland, UK
| | - Galina Glazko
- University of Rochester Department of Biostatistics and Computational Biology, Rochester, New York, USA
| | - Olli Yli-Harja
- Computational Systems Biology, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Matthias Dehmer
- Institute for Bioinformatics and Translational Research, UMIT- The Health and Life Sciences University, Eduard Wallnoefer Zentrum 1, Hall in Tyrol, Austria
| | - Benjamin Haibe-Kains
- Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Ontario Institute of Cancer Research, Toronto, ON, Canada
| | - Frank Emmert-Streib
- Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
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22
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Abstract
Background Finding potential drug targets is a crucial step in drug discovery and development. Recently, resources such as the Library of Integrated Network-Based Cellular Signatures (LINCS) L1000 database provide gene expression profiles induced by various chemical and genetic perturbations and thereby make it possible to analyze the relationship between compounds and gene targets at a genome-wide scale. Current approaches for comparing the expression profiles are based on pairwise connectivity mapping analysis. However, this method makes the simple assumption that the effect of a drug treatment is similar to knocking down its single target gene. Since many compounds can bind multiple targets, the pairwise mapping ignores the combined effects of multiple targets, and therefore fails to detect many potential targets of the compounds. Results We propose an algorithm to find sets of gene knock-downs that induce gene expression changes similar to a drug treatment. Assuming that the effects of gene knock-downs are additive, we propose a novel bipartite block-wise sparse multi-task learning model with super-graph structure (BBSS-MTL) for multi-target drug repositioning that overcomes the restrictive assumptions of connectivity mapping analysis. Conclusions The proposed method BBSS-MTL is more accurate for predicting potential drug targets than the simple pairwise connectivity mapping analysis on five datasets generated from different cancer cell lines. Availability The code can be obtained at http://gr.xjtu.edu.cn/web/liminli/codes.
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23
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Gao YF, Mao XY, Zhu T, Mao CX, Liu ZX, Wang ZB, Li L, Li X, Yin JY, Zhang W, Zhou HH, Liu ZQ. COL3A1 and SNAP91: novel glioblastoma markers with diagnostic and prognostic value. Oncotarget 2018; 7:70494-70503. [PMID: 27655637 PMCID: PMC5342568 DOI: 10.18632/oncotarget.12038] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Accepted: 08/26/2016] [Indexed: 01/15/2023] Open
Abstract
Although patients with glioblastoma (GBM) have grave prognosis, significant variability in patient outcome is observed. This study aims to identify novel targets for GBM diagnosis and therapy. Microarray data (GSE4290, GSE7696, and GSE4412) obtained from the Gene Expression Omnibus was used to identify the differentially expressed genes (DEGs) by significant analysis of microarray (SAM). Intersection of the identified DEGs for each profile revealed 46 DEGs in GBM. A subset of common DEGs were validated by real-time reverse transcription quantitative PCR (qPCR). The prognostic value of some of the markers was also studied. We determined that RRM2 and COL3A1 were increased and directly correlated with glioma grade, while SH3GL2 and SNAP91 were decreased in GBM and inversely correlated with glioma grade. Kaplan-Meir analysis of GSE7696 revealed that COL3A1 and SNAP91 correlated with survival, suggesting that COL3A1 and SNAP91 may be suitable biomarkers for diagnostic or therapeutic strategies for GBM.
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Affiliation(s)
- Yuan-Feng Gao
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha 410008, P. R. China.,Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, P. R. China
| | - Xiao-Yuan Mao
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha 410008, P. R. China.,Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, P. R. China
| | - Tao Zhu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha 410008, P. R. China.,Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, P. R. China
| | - Chen-Xue Mao
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha 410008, P. R. China.,Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, P. R. China
| | - Zhi-Xiong Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha 410008, P. R. China
| | - Zhi-Bin Wang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha 410008, P. R. China.,Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, P. R. China
| | - Ling Li
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha 410008, P. R. China.,Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, P. R. China
| | - Xi Li
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha 410008, P. R. China.,Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, P. R. China
| | - Ji-Ye Yin
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha 410008, P. R. China.,Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, P. R. China
| | - Wei Zhang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha 410008, P. R. China.,Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, P. R. China
| | - Hong-Hao Zhou
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha 410008, P. R. China.,Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, P. R. China
| | - Zhao-Qian Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha 410008, P. R. China.,Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, P. R. China
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Brown N, Cambruzzi J, Cox PJ, Davies M, Dunbar J, Plumbley D, Sellwood MA, Sim A, Williams-Jones BI, Zwierzyna M, Sheppard DW. Big Data in Drug Discovery. PROGRESS IN MEDICINAL CHEMISTRY 2018; 57:277-356. [PMID: 29680150 DOI: 10.1016/bs.pmch.2017.12.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.
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Affiliation(s)
| | | | | | | | | | | | | | - Aaron Sim
- BenevolentAI, London, United Kingdom
| | | | - Magdalena Zwierzyna
- BenevolentAI, London, United Kingdom; Institute of Cardiovascular Science, University College London, London, United Kingdom
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25
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Jalili M, Gebhardt T, Wolkenhauer O, Salehzadeh-Yazdi A. Unveiling network-based functional features through integration of gene expression into protein networks. Biochim Biophys Acta Mol Basis Dis 2018; 1864:2349-2359. [PMID: 29466699 DOI: 10.1016/j.bbadis.2018.02.010] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 01/31/2018] [Accepted: 02/13/2018] [Indexed: 02/02/2023]
Abstract
Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype-phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers.
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Affiliation(s)
- Mahdi Jalili
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran; Hematologic Malignancies Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Tom Gebhardt
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany
| | - Ali Salehzadeh-Yazdi
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany.
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26
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Brown KK, Hann MM, Lakdawala AS, Santos R, Thomas PJ, Todd K. Approaches to target tractability assessment - a practical perspective. MEDCHEMCOMM 2018; 9:606-613. [PMID: 30108951 DOI: 10.1039/c7md00633k] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 02/13/2018] [Indexed: 01/21/2023]
Abstract
The assessment of the suitability of novel targets to intervention by different modalities, e.g. small molecules or antibodies, is increasingly seen as important in helping to select the most progressable targets at the outset of a drug discovery project. This perspective considers differing aspects of tractability and how it can be assessed using in silico and experimental approaches. We also share some of our experiences in using these approaches.
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Affiliation(s)
- Kristin K Brown
- Computational and Modelling Sciences , Platform Technology and Sciences , GlaxoSmithKline , 1250 S. Collegeville Road , Collegeville , Pennsylvania 19426 , USA
| | - Michael M Hann
- NCE Molecular Discovery , Platform Technology and Sciences , GlaxoSmithKline Medicines Research Centre , Gunnels Wood Road, Stevenage , Hertfordshire , SG1 2NY , UK .
| | - Ami S Lakdawala
- In vitro/In vivo Translation Sciences , Platform Technology and Sciences , GlaxoSmithKline , 1250 S. Collegeville Road , Collegeville , Pennsylvania 19426 , USA
| | - Rita Santos
- Target Sciences Computational Biology , GlaxoSmithKline Medicines Research Centre , Gunnels Wood Road, Stevenage , Hertfordshire , SG1 2NY , UK
| | - Pamela J Thomas
- Computational and Modelling Sciences , Platform Technology and Sciences , GlaxoSmithKline Medicines Research Centre , Gunnels Wood Road, Stevenage , Hertfordshire , SG1 2NY , UK
| | - Kieran Todd
- Computational and Modelling Sciences , Platform Technology and Sciences , GlaxoSmithKline Medicines Research Centre , Gunnels Wood Road, Stevenage , Hertfordshire , SG1 2NY , UK
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27
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Ferrero E, Dunham I, Sanseau P. In silico prediction of novel therapeutic targets using gene-disease association data. J Transl Med 2017; 15:182. [PMID: 28851378 PMCID: PMC5576250 DOI: 10.1186/s12967-017-1285-6] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Accepted: 08/22/2017] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Target identification and validation is a pressing challenge in the pharmaceutical industry, with many of the programmes that fail for efficacy reasons showing poor association between the drug target and the disease. Computational prediction of successful targets could have a considerable impact on attrition rates in the drug discovery pipeline by significantly reducing the initial search space. Here, we explore whether gene-disease association data from the Open Targets platform is sufficient to predict therapeutic targets that are actively being pursued by pharmaceutical companies or are already on the market. METHODS To test our hypothesis, we train four different classifiers (a random forest, a support vector machine, a neural network and a gradient boosting machine) on partially labelled data and evaluate their performance using nested cross-validation and testing on an independent set. We then select the best performing model and use it to make predictions on more than 15,000 genes. Finally, we validate our predictions by mining the scientific literature for proposed therapeutic targets. RESULTS We observe that the data types with the best predictive power are animal models showing a disease-relevant phenotype, differential expression in diseased tissue and genetic association with the disease under investigation. On a test set, the neural network classifier achieves over 71% accuracy with an AUC of 0.76 when predicting therapeutic targets in a semi-supervised learning setting. We use this model to gain insights into current and failed programmes and to predict 1431 novel targets, of which a highly significant proportion has been independently proposed in the literature. CONCLUSIONS Our in silico approach shows that data linking genes and diseases is sufficient to predict novel therapeutic targets effectively and confirms that this type of evidence is essential for formulating or strengthening hypotheses in the target discovery process. Ultimately, more rapid and automated target prioritisation holds the potential to reduce both the costs and the development times associated with bringing new medicines to patients.
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Affiliation(s)
- Enrico Ferrero
- Computational Biology and Stats, Target Sciences, GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, SG1 2NY UK
| | - Ian Dunham
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD UK
| | - Philippe Sanseau
- Computational Biology and Stats, Target Sciences, GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, SG1 2NY UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD UK
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Xie R, Li L, Chen L, Li W, Chen B, Jiang J, Huang H, Li Y, He Y, Lv J, He W. Identification of potential drug targets based on a computational biology algorithm for venous thromboembolism. Int J Mol Med 2016; 39:463-471. [DOI: 10.3892/ijmm.2016.2829] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Accepted: 12/09/2016] [Indexed: 11/05/2022] Open
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Global vision of druggability issues: applications and perspectives. Drug Discov Today 2016; 22:404-415. [PMID: 27939283 DOI: 10.1016/j.drudis.2016.11.021] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Revised: 10/10/2016] [Accepted: 11/25/2016] [Indexed: 02/04/2023]
Abstract
During the preliminary stage of a drug discovery project, the lack of druggability information and poor target selection are the main causes of frequent failures. Elaborating on accurate computational druggability prediction methods is a requirement for prioritizing target selection, designing new drugs and avoiding side effects. In this review, we describe a survey of recently reported druggability prediction methods mainly based on networks, statistical pocket druggability predictions and virtual screening. An application for a frequent mutation of p53 tumor suppressor is presented, illustrating the complementarity of druggability prediction approaches, the remaining challenges and potential new drug development perspectives.
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30
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PPIC, EMP3 and CHI3L1 Are Novel Prognostic Markers for High Grade Glioma. Int J Mol Sci 2016; 17:ijms17111808. [PMID: 27801851 PMCID: PMC5133809 DOI: 10.3390/ijms17111808] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Revised: 10/18/2016] [Accepted: 10/25/2016] [Indexed: 12/20/2022] Open
Abstract
Current treatment methods for patients diagnosed with gliomas have shown limited success. This is partly due to the lack of prognostic genes available to accurately predict disease outcomes. The aim of this study was to investigate novel prognostic genes based on the molecular profile of tumor samples and their correlation with clinical parameters. In the current study, microarray data (GSE4412 and GSE7696) downloaded from Gene Expression Omnibus were used to identify differentially expressed prognostic genes (DEPGs) by significant analysis of microarray (SAM) between long-term survivors (>2 years) and short-term survivors (≤2 years). DEPGs generated from these two datasets were intersected to obtain a list of common DEPGs. The expression of a subset of common DEPGs was then independently validated by real-time reverse transcription quantitative PCR (qPCR). Survival value of the common DEPGs was validated using known survival data from the GSE4412 and TCGA dataset. After intersecting DEPGs generated from the above two datasets, three genes were identified which may potentially be used to determine glioma patient prognosis. Independent validation with glioma patients tissue (n = 70) and normal brain tissue (n = 19) found PPIC, EMP3 and CHI3L1 were up-regulated in glioma tissue. Survival value validation showed that the three genes correlated with patient survival by Kaplan-Meir analysis, including grades, age and therapy.
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31
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A powerful weighted statistic for detecting group differences of directed biological networks. Sci Rep 2016; 6:34159. [PMID: 27686331 PMCID: PMC5054825 DOI: 10.1038/srep34159] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Accepted: 09/08/2016] [Indexed: 12/15/2022] Open
Abstract
Complex disease is largely determined by a number of biomolecules interwoven into networks, rather than a single biomolecule. Different physiological conditions such as cases and controls may manifest as different networks. Statistical comparison between biological networks can provide not only new insight into the disease mechanism but statistical guidance for drug development. However, the methods developed in previous studies are inadequate to capture the changes in both the nodes and edges, and often ignore the network structure. In this study, we present a powerful weighted statistical test for group differences of directed biological networks, which is independent of the network attributes and can capture the changes in both the nodes and edges, as well as simultaneously accounting for the network structure through putting more weights on the difference of nodes locating on relatively more important position. Simulation studies illustrate that this method had better performance than previous ones under various sample sizes and network structures. One application to GWAS of leprosy successfully identifies the specific gene interaction network contributing to leprosy. Another real data analysis significantly identifies a new biological network, which is related to acute myeloid leukemia. One potential network responsible for lung cancer has also been significantly detected. The source R code is available on our website.
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Kibble M, Khan SA, Saarinen N, Iorio F, Saez-Rodriguez J, Mäkelä S, Aittokallio T. Transcriptional response networks for elucidating mechanisms of action of multitargeted agents. Drug Discov Today 2016; 21:1063-75. [PMID: 26979547 DOI: 10.1016/j.drudis.2016.03.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2015] [Revised: 02/15/2016] [Accepted: 03/04/2016] [Indexed: 01/08/2023]
Abstract
Drug discovery is moving away from the single target-based approach towards harnessing the potential of polypharmacological agents that modulate the activity of multiple nodes in the complex networks of deregulations underlying disease phenotypes. Computational network pharmacology methods that use systems-level drug-response phenotypes, such as those originating from genome-wide transcriptomic profiles, have proved particularly effective for elucidating the mechanisms of action of multitargeted compounds. Here, we show, via the case study of the natural product pinosylvin, how the combination of two complementary network-based methods can provide novel, unexpected mechanistic insights. This case study also illustrates that elucidating the mechanism of action of multitargeted natural products through transcriptional response-based approaches is a challenging endeavor, often requiring multiple computational-experimental iterations.
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Affiliation(s)
- Milla Kibble
- Institute for Molecular Medicine Finland (FIMM), Biomedicum Helsinki 2U, Tukholmankatu 8, University of Helsinki, Helsinki 00014, Finland.
| | - Suleiman A Khan
- Institute for Molecular Medicine Finland (FIMM), Biomedicum Helsinki 2U, Tukholmankatu 8, University of Helsinki, Helsinki 00014, Finland
| | - Niina Saarinen
- Institute of Biomedicine, Turku Center for Disease Modeling & Functional Foods Forum, University of Turku, Turku 20014, Finland
| | - Francesco Iorio
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK; Joint Research Centre for Computational Biomedicine (JRC-COMBINE) - RWTH Aachen University, Faculty of Medicine, D-52074 Aachen, Germany
| | - Sari Mäkelä
- Institute of Biomedicine, Turku Center for Disease Modeling & Functional Foods Forum, University of Turku, Turku 20014, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), Biomedicum Helsinki 2U, Tukholmankatu 8, University of Helsinki, Helsinki 00014, Finland; Department of Mathematics and Statistics, Quantum, University of Turku, Turku 20014, Finland
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Ji J, Yuan Z, Zhang X, Xue F. A powerful score-based statistical test for group difference in weighted biological networks. BMC Bioinformatics 2016; 17:86. [PMID: 26867929 PMCID: PMC4751708 DOI: 10.1186/s12859-016-0916-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Accepted: 01/29/2016] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Complex disease is largely determined by a number of biomolecules interwoven into networks, rather than a single biomolecule. A key but inadequately addressed issue is how to test possible differences of the networks between two groups. Group-level comparison of network properties may shed light on underlying disease mechanisms and benefit the design of drug targets for complex diseases. We therefore proposed a powerful score-based statistic to detect group difference in weighted networks, which simultaneously capture the vertex changes and edge changes. RESULTS Simulation studies indicated that the proposed network difference measure (NetDifM) was stable and outperformed other methods existed, under various sample sizes and network topology structure. One application to real data about GWAS of leprosy successfully identified the specific gene interaction network contributing to leprosy. For additional gene expression data of ovarian cancer, two candidate subnetworks, PI3K-AKT and Notch signaling pathways, were considered and identified respectively. CONCLUSIONS The proposed method, accounting for the vertex changes and edge changes simultaneously, is valid and powerful to capture the group difference of biological networks.
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Affiliation(s)
- Jiadong Ji
- Department of Biostatistics, School of Public Health, Shandong University, PO Box 100, Jinan, 250012, Shandong, China.
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Shandong University, PO Box 100, Jinan, 250012, Shandong, China.
| | - Xiaoshuai Zhang
- Department of Biostatistics, School of Public Health, Shandong University, PO Box 100, Jinan, 250012, Shandong, China.
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Shandong University, PO Box 100, Jinan, 250012, Shandong, China.
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Kandoi G, Acencio ML, Lemke N. Prediction of Druggable Proteins Using Machine Learning and Systems Biology: A Mini-Review. Front Physiol 2015; 6:366. [PMID: 26696900 PMCID: PMC4672042 DOI: 10.3389/fphys.2015.00366] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2015] [Accepted: 11/17/2015] [Indexed: 12/11/2022] Open
Abstract
The emergence of -omics technologies has allowed the collection of vast amounts of data on biological systems. Although, the pace of such collection has been exponential, the impact of these data remains small on many critical biomedical applications such as drug development. Limited resources, high costs, and low hit-to-lead ratio have led researchers to search for more cost effective methodologies. A possible alternative is to incorporate computational methods of potential drug target prediction early during drug discovery workflow. Computational methods based on systems approaches have the advantage of taking into account the global properties of a molecule not limited to its sequence, structure or function. Machine learning techniques are powerful tools that can extract relevant information from massive and noisy data sets. In recent years the scientific community has explored the combined power of these fields to propose increasingly accurate and low cost methods to propose interesting drug targets. In this mini-review, we describe promising approaches based on the simultaneous use of systems biology and machine learning to access gene and protein druggability. Moreover, we discuss the state-of-the-art of this emerging and interdisciplinary field, discussing data sources, algorithms and the performance of the different methodologies. Finally, we indicate interesting avenues of research and some remaining open challenges.
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Affiliation(s)
- Gaurav Kandoi
- Department of Electrical and Computer Engineering, Iowa State University Ames, IA, USA
| | - Marcio L Acencio
- Department of Physics and Biophysics, Institute of Biosciences of Botucatu, UNESP - São Paulo State University Botucatu, Brazil
| | - Ney Lemke
- Department of Physics and Biophysics, Institute of Biosciences of Botucatu, UNESP - São Paulo State University Botucatu, Brazil
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35
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Drug target prioritization by perturbed gene expression and network information. Sci Rep 2015; 5:17417. [PMID: 26615774 PMCID: PMC4663505 DOI: 10.1038/srep17417] [Citation(s) in RCA: 85] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2015] [Accepted: 10/29/2015] [Indexed: 12/27/2022] Open
Abstract
Drugs bind to their target proteins, which interact with downstream effectors and ultimately perturb the transcriptome of a cancer cell. These perturbations reveal information about their source, i.e., drugs’ targets. Here, we investigate whether these perturbations and protein interaction networks can uncover drug targets and key pathways. We performed the first systematic analysis of over 500 drugs from the Connectivity Map. First, we show that the gene expression of drug targets is usually not significantly affected by the drug perturbation. Hence, expression changes after drug treatment on their own are not sufficient to identify drug targets. However, ranking of candidate drug targets by network topological measures prioritizes the targets. We introduce a novel measure, local radiality, which combines perturbed genes and functional interaction network information. The new measure outperforms other methods in target prioritization and proposes cancer-specific pathways from drugs to affected genes for the first time. Local radiality identifies more diverse targets with fewer neighbors and possibly less side effects.
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Kibble M, Saarinen N, Tang J, Wennerberg K, Mäkelä S, Aittokallio T. Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products. Nat Prod Rep 2015; 32:1249-66. [PMID: 26030402 DOI: 10.1039/c5np00005j] [Citation(s) in RCA: 281] [Impact Index Per Article: 31.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
It is widely accepted that drug discovery often requires a systems-level polypharmacology approach to tackle problems such as lack of efficacy and emerging resistance of single-targeted compounds. Network pharmacology approaches are increasingly being developed and applied to find new therapeutic opportunities and to re-purpose approved drugs. However, these recent advances have been relatively slow to be translated into the field of natural products. Here, we argue that a network pharmacology approach would enable an effective mapping of the yet unexplored target space of natural products, hence providing a systematic means to extend the druggable space of proteins implicated in various complex diseases. We give an overview of the key network pharmacology concepts and recent experimental-computational approaches that have been successfully applied to natural product research, including unbiased elucidation of mechanisms of action as well as systematic prediction of effective therapeutic combinations. We focus specifically on anticancer applications that use in vivo and in vitro functional phenotypic measurements, such as genome-wide transcriptomic response profiles, which enable a global modelling of the multi-target activity at the level of the biological pathways and interaction networks. We also provide representative examples of other disease applications, databases and tools as well as existing and emerging resources, which may prove useful for future natural product research. Finally, we offer our personal view of the current limitations, prospective developments and open questions in this exciting field.
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Affiliation(s)
- Milla Kibble
- Institute for Molecular Medicine Finland (FIMM), Biomedicum Helsinki 2U, 00014 University of Helsinki, Finland.
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Cichonska A, Rousu J, Aittokallio T. Identification of drug candidates and repurposing opportunities through compound-target interaction networks. Expert Opin Drug Discov 2015; 10:1333-45. [PMID: 26429153 DOI: 10.1517/17460441.2015.1096926] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
INTRODUCTION System-wide identification of both on- and off-targets of chemical probes provides improved understanding of their therapeutic potential and possible adverse effects, thereby accelerating and de-risking drug discovery process. Given the high costs of experimental profiling of the complete target space of drug-like compounds, computational models offer systematic means for guiding these mapping efforts. These models suggest the most potent interactions for further experimental or pre-clinical evaluation both in cell line models and in patient-derived material. AREAS COVERED The authors focus here on network-based machine learning models and their use in the prediction of novel compound-target interactions both in target-based and phenotype-based drug discovery applications. While currently being used mainly in complementing the experimentally mapped compound-target networks for drug repurposing applications, such as extending the target space of already approved drugs, these network pharmacology approaches may also suggest completely unexpected and novel investigational probes for drug development. EXPERT OPINION Although the studies reviewed here have already demonstrated that network-centric modeling approaches have the potential to identify candidate compounds and selective targets in disease networks, many challenges still remain. In particular, these challenges include how to incorporate the cellular context and genetic background into the disease networks to enable more stratified and selective target predictions, as well as how to make the prediction models more realistic for the practical drug discovery and therapeutic applications.
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Affiliation(s)
- Anna Cichonska
- a 1 University of Helsinki, Institute for Molecular Medicine Finland FIMM , Helsinki, Finland.,b 2 Aalto University, Helsinki Institute for Information Technology HIIT, Department of Computer Science , Espoo, Finland
| | - Juho Rousu
- c 3 Aalto University, Helsinki Institute for Information Technology HIIT, Department of Computer Science , Espoo, Finland
| | - Tero Aittokallio
- d 4 University of Helsinki, Institute for Molecular Medicine Finland FIMM , Helsinki, Finland +358 5 03 18 24 26 ; .,e 5 University of Turku, Department of Mathematics and Statistics , Turku, Finland
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Wang Y, Chen X, Liu L, Chen Y, Ma H, Yang R, Liu X. Identifying the causative proteins of similar side effect pairs to explore the common molecular basis of these side effects. MOLECULAR BIOSYSTEMS 2015; 11:2060-7. [PMID: 25992869 DOI: 10.1039/c5mb00242g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Drug side effects, or adverse drug reactions (ADRs), have become a major public health concern and often cause drug development failure and withdrawal. Some ADRs always occur concomitantly. Therefore, identifying these ADRs and their common molecular basis can better promote their prevention and treatment. In this paper we predicted the potential proteins for ADR pairs with similar mechanisms based on three layers of information: (i) the drug co-occurrence between a pair of ADRs; (ii) the correlation between a protein and an ADR pair based on the co-occurrence of drugs and (iii) the interaction between these proteins within the protein-protein interaction (PPI) network. The methods of randomization and functional annotation are used to investigate and analyze the relation between causative proteins and similar ADR pairs. The prediction accuracy of the relation between similar ADR pairs and related proteins reached 80%, and it increases with the number of drugs shared by the ADR pairs. From the ADR network made of single ADRs from predicted similar ADR pairs, we found that some ADRs are involved in multiple ADR pairs. The functional analysis of these ADR-related proteins suggests that a similar molecular basis is shared by multiple ADR pairs containing the same ADR, and these ADR pairs are almost caused by the same drug sets. The results of this study are reliable and provide a theoretical basis for the better prevention and treatment of ADRs that always occur concomitantly.
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Affiliation(s)
- Yunfeng Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China.
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Laenen G, Ardeshirdavani A, Moreau Y, Thorrez L. Galahad: a web server for drug effect analysis from gene expression. Nucleic Acids Res 2015; 43:W208-12. [PMID: 25940630 PMCID: PMC4489261 DOI: 10.1093/nar/gkv436] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2015] [Accepted: 04/23/2015] [Indexed: 01/26/2023] Open
Abstract
Galahad (https://galahad.esat.kuleuven.be) is a web-based application for analysis of drug effects. It provides an intuitive interface to be used by anybody interested in leveraging microarray data to gain insights into the pharmacological effects of a drug, mainly identification of candidate targets, elucidation of mode of action and understanding of off-target effects. The core of Galahad is a network-based analysis method of gene expression. As an input, Galahad takes raw Affymetrix human microarray data from treatment versus control experiments and provides quality control and data exploration tools, as well as computation of differential expression. Alternatively, differential expression values can be uploaded directly. Using these differential expression values, drug target prioritization and both pathway and disease enrichment can be calculated and visualized. Drug target prioritization is based on the integration of the gene expression data with a functional protein association network. The web site is free and open to all and there is no login requirement.
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Affiliation(s)
- Griet Laenen
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, 3001, Belgium iMinds Medical IT Department, KU Leuven, Leuven, 3001, Belgium
| | - Amin Ardeshirdavani
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, 3001, Belgium iMinds Medical IT Department, KU Leuven, Leuven, 3001, Belgium
| | - Yves Moreau
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, 3001, Belgium iMinds Medical IT Department, KU Leuven, Leuven, 3001, Belgium
| | - Lieven Thorrez
- Interdisciplinary Research Facility Life Sciences, Department of Development and Regeneration, KU Leuven Kulak, Kortrijk, 8500, Belgium
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Khan SA, Virtanen S, Kallioniemi OP, Wennerberg K, Poso A, Kaski S. Identification of structural features in chemicals associated with cancer drug response: a systematic data-driven analysis. ACTA ACUST UNITED AC 2015; 30:i497-504. [PMID: 25161239 PMCID: PMC4147909 DOI: 10.1093/bioinformatics/btu456] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Motivation: Analysis of relationships of drug structure to biological response is key to understanding off-target and unexpected drug effects, and for developing hypotheses on how to tailor drug therapies. New methods are required for integrated analyses of a large number of chemical features of drugs against the corresponding genome-wide responses of multiple cell models. Results: In this article, we present the first comprehensive multi-set analysis on how the chemical structure of drugs impacts on genome-wide gene expression across several cancer cell lines [Connectivity Map (CMap) database]. The task is formulated as searching for drug response components across multiple cancers to reveal shared effects of drugs and the chemical features that may be responsible. The components can be computed with an extension of a recent approach called Group Factor Analysis. We identify 11 components that link the structural descriptors of drugs with specific gene expression responses observed in the three cell lines and identify structural groups that may be responsible for the responses. Our method quantitatively outperforms the limited earlier methods on CMap and identifies both the previously reported associations and several interesting novel findings, by taking into account multiple cell lines and advanced 3D structural descriptors. The novel observations include: previously unknown similarities in the effects induced by 15-delta prostaglandin J2 and HSP90 inhibitors, which are linked to the 3D descriptors of the drugs; and the induction by simvastatin of leukemia-specific response, resembling the effects of corticosteroids. Availability and implementation: Source Code implementing the method is available at: http://research.ics.aalto.fi/mi/software/GFAsparse Contact:suleiman.khan@aalto.fi or samuel.kaski@aalto.fi Supplementary Information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Suleiman A Khan
- Department of Information and Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, 00076 Espoo, Institute for Molecular Medicine Finland FIMM, University of Helsinki, 00014 Helsinki, School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, 70211 Kuopio and Department of Computer Science, Helsinki Institute for Information Technology HIIT, University Of Helsinki, 00014 Helsinki, Finland
| | - Seppo Virtanen
- Department of Information and Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, 00076 Espoo, Institute for Molecular Medicine Finland FIMM, University of Helsinki, 00014 Helsinki, School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, 70211 Kuopio and Department of Computer Science, Helsinki Institute for Information Technology HIIT, University Of Helsinki, 00014 Helsinki, Finland
| | - Olli P Kallioniemi
- Department of Information and Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, 00076 Espoo, Institute for Molecular Medicine Finland FIMM, University of Helsinki, 00014 Helsinki, School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, 70211 Kuopio and Department of Computer Science, Helsinki Institute for Information Technology HIIT, University Of Helsinki, 00014 Helsinki, Finland
| | - Krister Wennerberg
- Department of Information and Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, 00076 Espoo, Institute for Molecular Medicine Finland FIMM, University of Helsinki, 00014 Helsinki, School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, 70211 Kuopio and Department of Computer Science, Helsinki Institute for Information Technology HIIT, University Of Helsinki, 00014 Helsinki, Finland
| | - Antti Poso
- Department of Information and Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, 00076 Espoo, Institute for Molecular Medicine Finland FIMM, University of Helsinki, 00014 Helsinki, School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, 70211 Kuopio and Department of Computer Science, Helsinki Institute for Information Technology HIIT, University Of Helsinki, 00014 Helsinki, Finland Department of Information and Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, 00076 Espoo, Institute for Molecular Medicine Finland FIMM, University of Helsinki, 00014 Helsinki, School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, 70211 Kuopio and Department of Computer Science, Helsinki Institute for Information Technology HIIT, University Of Helsinki, 00014 Helsinki, Finland
| | - Samuel Kaski
- Department of Information and Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, 00076 Espoo, Institute for Molecular Medicine Finland FIMM, University of Helsinki, 00014 Helsinki, School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, 70211 Kuopio and Department of Computer Science, Helsinki Institute for Information Technology HIIT, University Of Helsinki, 00014 Helsinki, Finland Department of Information and Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, 00076 Espoo, Institute for Molecular Medicine Finland FIMM, University of Helsinki, 00014 Helsinki, School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, 70211 Kuopio and Department of Computer Science, Helsinki Institute for Information Technology HIIT, University Of Helsinki, 00014 Helsinki, Finland
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Li G, Pan W, Yang X, Miao J. Gene co-expression network and function modules in three types of glioma. Mol Med Rep 2014; 11:3055-63. [PMID: 25435164 DOI: 10.3892/mmr.2014.3014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2013] [Accepted: 04/25/2014] [Indexed: 11/06/2022] Open
Abstract
The aim of the present study was to identify the disease‑associated genes and their functions involved in the development of three types of glioma (astrocytoma, glioblastoma and oligodendroglioma) with DNA microarray technology, and to analyze their differences and correlations. First, the gene expression profile GSE4290 was downloaded from the Gene Expression Omnibus database, then the probe‑level data were pre‑processed and the differentially expressed genes (DEGs) were identified with limma package in R language. Gene functions of the selected DEGs were further analyzed with the Database for Annotation, Visualization and Integrated Discovery. After the co‑expression network of DEGs was constructed by Cytoscape, the functional modules were mined and enrichment analysis was performed, and then the similarities and differences between any two types of glioma were compared. A total of 1151 genes between normal and astrocytoma tissues, 684 genes between normal and malignant glioma tissues, and 551 genes between normal and oligodendroglioma tissues were filtered as DEGs, respectively. By constructing co‑expression networks of DEGs, a total of 77232, 455 and 987 interactions were involved in the differentially co‑expressed networks of astrocytoma, oligodendroglioma and glioblastoma, respectively. The functions of DEGs were consistent with the modules in astrocytoma, glioblastoma and oligodendroglioma, which were mainly enriched in neuron signal transmission, immune responses and synthesis of organic acids, respectively. Model functions of astrocytoma and glioblastoma were similar (mainly related with immune response), while the model functions of oligodendroglioma differed markedly from that of the other two types. The identification of the associations among these three types of glioma has potential clinical utility for improving the diagnosis of different types of glioma in the future. In addition, these results have marked significance in studying the underlying mechanisms, distinguishing between normal and cancer tissues, and examining novel therapeutic strategies for patients with glioma.
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Affiliation(s)
- Gang Li
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, P.R. China
| | - Weiran Pan
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, P.R. China
| | - Xiaoxiao Yang
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, P.R. China
| | - Jinming Miao
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, P.R. China
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Stevens A, De Leonibus C, Hanson D, Whatmore A, Murray P, Donn R, Meyer S, Chatelain P, Clayton P. Pediatric perspective on pharmacogenomics. Pharmacogenomics 2014; 14:1889-905. [PMID: 24236488 DOI: 10.2217/pgs.13.193] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The advances in high-throughput genomic technologies have improved the understanding of disease pathophysiology and have allowed a better characterization of drug response and toxicity based on individual genetic make up. Pharmacogenomics is being recognized as a valid approach used to identify patients who are more likely to respond to medication, or those in whom there is a high probability of developing severe adverse drug reactions. An increasing number of pharmacogenomic studies are being published, most include only adults. A few studies have shown the impact of pharmacogenomics in pediatrics, highlighting a key difference between children and adults, which is the contribution of developmental changes to therapeutic responses across different age groups. This review focuses on pharmacogenomic research in pediatrics, providing examples from common pediatric conditions and emphasizing their developmental context.
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Affiliation(s)
- Adam Stevens
- Institute of Human Development, Medical & Human Sciences, University of Manchester & Royal Manchester Children's Hospital, Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, 5th Floor Research, Oxford Road, Manchester, M13 9WL, UK
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Pinto JP, Machado RSR, Xavier JM, Futschik ME. Targeting molecular networks for drug research. Front Genet 2014; 5:160. [PMID: 24926314 PMCID: PMC4045242 DOI: 10.3389/fgene.2014.00160] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2014] [Accepted: 05/14/2014] [Indexed: 01/18/2023] Open
Abstract
The study of molecular networks has recently moved into the limelight of biomedical research. While it has certainly provided us with plenty of new insights into cellular mechanisms, the challenge now is how to modify or even restructure these networks. This is especially true for human diseases, which can be regarded as manifestations of distorted states of molecular networks. Of the possible interventions for altering networks, the use of drugs is presently the most feasible. In this mini-review, we present and discuss some exemplary approaches of how analysis of molecular interaction networks can contribute to pharmacology (e.g., by identifying new drug targets or prediction of drug side effects), as well as list pointers to relevant resources and software to guide future research. We also outline recent progress in the use of drugs for in vitro reprogramming of cells, which constitutes an example par excellence for altering molecular interaction networks with drugs.
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Affiliation(s)
- José P Pinto
- SysBioLab, Centre for Molecular and Structural Biomedicine, Universidade do Algarve Faro, Portugal
| | - Rui S R Machado
- SysBioLab, Centre for Molecular and Structural Biomedicine, Universidade do Algarve Faro, Portugal
| | - Joana M Xavier
- SysBioLab, Centre for Molecular and Structural Biomedicine, Universidade do Algarve Faro, Portugal
| | - Matthias E Futschik
- SysBioLab, Centre for Molecular and Structural Biomedicine, Universidade do Algarve Faro, Portugal ; Centre of Marine Sciences, Universidade do Algarve Faro, Portugal
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Jaeger S, Min J, Nigsch F, Camargo M, Hutz J, Cornett A, Cleaver S, Buckler A, Jenkins JL. Causal Network Models for Predicting Compound Targets and Driving Pathways in Cancer. ACTA ACUST UNITED AC 2014; 19:791-802. [PMID: 24518063 DOI: 10.1177/1087057114522690] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2013] [Accepted: 01/14/2014] [Indexed: 02/06/2023]
Abstract
Gene-expression data are often used to infer pathways regulating transcriptional responses. For example, differentially expressed genes (DEGs) induced by compound treatment can help characterize hits from phenotypic screens, either by correlation with known drug signatures or by pathway enrichment. Pathway enrichment is, however, typically computed with DEGs rather than "upstream" nodes that are potentially causal of "downstream" changes. Here, we present graph-based models to predict causal targets from compound-microarray data. We test several approaches to traversing network topology, and show that a consensus minimum-rank score (SigNet) beat individual methods and could highly rank compound targets among all network nodes. In addition, larger, less canonical networks outperformed linear canonical interactions. Importantly, pathway enrichment using causal nodes rather than DEGs recovers relevant pathways more often. To further validate our approach, we used integrated data sets from the Cancer Genome Atlas to identify driving pathways in triple-negative breast cancer. Critical pathways were uncovered, including the epidermal growth factor receptor 2-phosphatidylinositide 3-kinase-AKT-MAPK growth pathway andATR-p53-BRCA DNA damage pathway, in addition to unexpected pathways, such as TGF-WNT cytoskeleton remodeling, IL12-induced interferon gamma production, and TNFR-IAP (inhibitor of apoptosis) apoptosis; the latter was validated by pooled small hairpin RNA profiling in cancer cells. Overall, our approach can bridge transcriptional profiles to compound targets and driving pathways in cancer.
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Affiliation(s)
- Savina Jaeger
- Co-first authors Oncology Translational Medicine, Novartis, Cambridge, MA, USA
| | - Junxia Min
- Co-first authors ONC Target Discovery, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA
| | - Florian Nigsch
- Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Inc., Basel, Switzerland
| | - Miguel Camargo
- Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA
| | - Janna Hutz
- Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA Pfizer, Cambridge, MA, USA
| | - Allen Cornett
- Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA
| | - Stephen Cleaver
- NIBR IT, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA
| | - Alan Buckler
- Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA
| | - Jeremy L Jenkins
- Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA
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