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Rose TD, Köhler N, Falk L, Klischat L, Lazareva OE, Pauling JK. Lipid network and moiety analysis for revealing enzymatic dysregulation and mechanistic alterations from lipidomics data. Brief Bioinform 2023; 24:6966533. [PMID: 36592059 PMCID: PMC9851308 DOI: 10.1093/bib/bbac572] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 11/10/2022] [Accepted: 11/24/2022] [Indexed: 01/03/2023] Open
Abstract
Lipidomics is of growing importance for clinical and biomedical research due to many associations between lipid metabolism and diseases. The discovery of these associations is facilitated by improved lipid identification and quantification. Sophisticated computational methods are advantageous for interpreting such large-scale data for understanding metabolic processes and their underlying (patho)mechanisms. To generate hypothesis about these mechanisms, the combination of metabolic networks and graph algorithms is a powerful option to pinpoint molecular disease drivers and their interactions. Here we present lipid network explorer (LINEX$^2$), a lipid network analysis framework that fuels biological interpretation of alterations in lipid compositions. By integrating lipid-metabolic reactions from public databases, we generate dataset-specific lipid interaction networks. To aid interpretation of these networks, we present an enrichment graph algorithm that infers changes in enzymatic activity in the context of their multispecificity from lipidomics data. Our inference method successfully recovered the MBOAT7 enzyme from knock-out data. Furthermore, we mechanistically interpret lipidomic alterations of adipocytes in obesity by leveraging network enrichment and lipid moieties. We address the general lack of lipidomics data mining options to elucidate potential disease mechanisms and make lipidomics more clinically relevant.
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Affiliation(s)
| | | | - Lisa Falk
- LipiTUM, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - Lucie Klischat
- LipiTUM, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - Olga E Lazareva
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany,Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany,Junior Clinical Cooperation Unit Multiparametric methods for early detection of prostate cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany,European Molecular Biology Laboratory, Genome Biology Unit, 69117 Heidelberg, Germany
| | - Josch K Pauling
- Corresponding author. Josch K. Pauling, LipiTUM, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany. Tel: +49 8161 71 2762; E-mail:
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2
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Winkler S, Winkler I, Figaschewski M, Tiede T, Nordheim A, Kohlbacher O. De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet. BMC Bioinformatics 2022; 23:139. [PMID: 35439941 PMCID: PMC9020058 DOI: 10.1186/s12859-022-04670-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 03/29/2022] [Indexed: 12/14/2022] Open
Abstract
Background With a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem. Results We present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet can also be used to find explicitly multi-omics subgraphs which we demonstrate by presenting subgraphs with consistent methylation-transcription patterns. DeRegNet is freely available as open-source software. Conclusion The proposed algorithmic framework and its available implementation can serve as a valuable heuristic hypothesis generation tool contextualizing omics data within biomolecular networks.
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Affiliation(s)
- Sebastian Winkler
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany. .,International Max Planck Research School (IMPRS) "From Molecules to Organism", Tübingen, Germany.
| | - Ivana Winkler
- International Max Planck Research School (IMPRS) "From Molecules to Organism", Tübingen, Germany.,Interfaculty Institute for Cell Biology (IFIZ), University of Tuebingen, Tübingen, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mirjam Figaschewski
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany
| | - Thorsten Tiede
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany
| | - Alfred Nordheim
- Interfaculty Institute for Cell Biology (IFIZ), University of Tuebingen, Tübingen, Germany.,Leibniz Institute on Aging (FLI), Jena, Germany
| | - Oliver Kohlbacher
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany.,Institute for Bioinformatics and Medical Informatics, University of Tuebingen, Tübingen, Germany.,Translational Bioinformatics, University Hospital Tuebingen, Tübingen, Germany
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3
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Mechteridis K, Lauber M, Baumbach J, List M. KeyPathwayMineR: De Novo Pathway Enrichment in the R Ecosystem. Front Genet 2022; 12:812853. [PMID: 35173764 PMCID: PMC8842393 DOI: 10.3389/fgene.2021.812853] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 12/24/2021] [Indexed: 11/13/2022] Open
Abstract
De novo pathway enrichment is a systems biology approach in which OMICS data are projected onto a molecular interaction network to identify subnetworks representing condition-specific functional modules and molecular pathways. Compared to classical pathway enrichment analysis methods, de novo pathway enrichment is not limited to predefined lists of pathways from (curated) databases and thus particularly suited for discovering novel disease mechanisms. While several tools have been proposed for pathway enrichment, the integration of de novo pathway enrichment in end-to-end OMICS analysis workflows in the R programming language is currently limited to a single tool. To close this gap, we have implemented an R package KeyPathwayMineR (KPM-R). The package extends the features and usability of existing versions of KeyPathwayMiner by leveraging the power, flexibility and versatility of R and by providing various novel functionalities for performing data preparation, visualization, and comparison. In addition, thanks to its interoperability with a plethora of existing R packages in e.g., Bioconductor, CRAN, and GitHub, KPM-R allows carrying out the initial preparation of the datasets and to meaningfully interpret the extracted subnetworks. To demonstrate the package's potential, KPM-R was applied to bulk RNA-Seq data of nasopharyngeal swabs from SARS-CoV-2 infected individuals, and on single cell RNA-Seq data of aging mice tissue from the Tabula Muris Senis atlas.
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Affiliation(s)
- Konstantinos Mechteridis
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Michael Lauber
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Jan Baumbach
- Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany.,Department of Mathematics and Computer Science, Faculty of Science, University of Southern Denmark, Odense, Denmark
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
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4
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Zhang X, Li J, Pan BZ, Chen W, Chen M, Tang M, Xu ZF, Liu C. Extended mining of the oil biosynthesis pathway in biofuel plant Jatropha curcas by combined analysis of transcriptome and gene interactome data. BMC Bioinformatics 2021; 22:409. [PMID: 34407772 PMCID: PMC8375076 DOI: 10.1186/s12859-021-04319-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 08/05/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Jatropha curcas L. is an important non-edible oilseed crop with a promising future in biodiesel production. However, little is known about the molecular biology of oil biosynthesis in this plant when compared with other established oilseed crops, resulting in the absence of agronomically improved varieties of Jatropha. To extensively discover the potentially novel genes and pathways associated with the oil biosynthesis in J. curcas, new strategy other than homology alignment is on the demand. RESULTS In this study, we proposed a multi-step computational framework that integrates transcriptome and gene interactome data to predict functional pathways in non-model organisms in an extended process, and applied it to study oil biosynthesis pathway in J. curcas. Using homologous mapping against Arabidopsis and transcriptome profile analysis, we first constructed protein-protein interaction (PPI) and co-expression networks in J. curcas. Then, using the homologs of Arabidopsis oil-biosynthesis-related genes as seeds, we respectively applied two algorithm models, random walk with restart (RWR) in PPI network and negative binomial distribution (NBD) in co-expression network, to further extend oil-biosynthesis-related pathways and genes in J. curcas. At last, using k-nearest neighbors (KNN) algorithm, the predicted genes were further classified into different sub-pathways according to their possible functional roles. CONCLUSIONS Our method exhibited a highly efficient way of mining the extended oil biosynthesis pathway of J. curcas. Overall, 27 novel oil-biosynthesis-related gene candidates were predicted and further assigned to 5 sub-pathways. These findings can help better understanding of the oil biosynthesis pathway of J. curcas, as well as paving the way for the following J. curcas breeding application.
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Affiliation(s)
- Xuan Zhang
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China.,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jing Li
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China.,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Bang-Zhen Pan
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China.,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Wen Chen
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Maosheng Chen
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China.,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Mingyong Tang
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China.,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Zeng-Fu Xu
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China. .,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China. .,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.
| | - Changning Liu
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China. .,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China. .,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.
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5
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Uzuner D, Akkoç Y, Peker N, Pir P, Gözüaçık D, Çakır T. Transcriptional landscape of cellular networks reveal interactions driving the dormancy mechanisms in cancer. Sci Rep 2021; 11:15806. [PMID: 34349126 PMCID: PMC8339123 DOI: 10.1038/s41598-021-94005-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 06/28/2021] [Indexed: 11/22/2022] Open
Abstract
Primary cancer cells exert unique capacity to disseminate and nestle in distant organs. Once seeded in secondary sites, cancer cells may enter a dormant state, becoming resistant to current treatment approaches, and they remain silent until they reactivate and cause overt metastases. To illuminate the complex mechanisms of cancer dormancy, 10 transcriptomic datasets from the literature enabling 21 dormancy–cancer comparisons were mapped on protein–protein interaction networks and gene-regulatory networks to extract subnetworks that are enriched in significantly deregulated genes. The genes appearing in the subnetworks and significantly upregulated in dormancy with respect to proliferative state were scored and filtered across all comparisons, leading to a dormancy–interaction network for the first time in the literature, which includes 139 genes and 1974 interactions. The dormancy interaction network will contribute to the elucidation of cellular mechanisms orchestrating cancer dormancy, paving the way for improvements in the diagnosis and treatment of metastatic cancer.
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Affiliation(s)
- Dilara Uzuner
- Department of Bioengineering, Gebze Technical University, 41400, Kocaeli, Turkey
| | - Yunus Akkoç
- Koç University Research Center for Translational Medicine (KUTTAM), Zeytinburnu, 34010, Istanbul, Turkey
| | - Nesibe Peker
- Koç University Research Center for Translational Medicine (KUTTAM), Zeytinburnu, 34010, Istanbul, Turkey
| | - Pınar Pir
- Department of Bioengineering, Gebze Technical University, 41400, Kocaeli, Turkey
| | - Devrim Gözüaçık
- Koç University Research Center for Translational Medicine (KUTTAM), Zeytinburnu, 34010, Istanbul, Turkey.,Koç University School of Medicine, Sarıyer , 34450, Istanbul, Turkey.,SUNUM Nanotechnology Research and Application Center, Tuzla, 34956, Istanbul, Turkey
| | - Tunahan Çakır
- Department of Bioengineering, Gebze Technical University, 41400, Kocaeli, Turkey.
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6
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Odongo R, Demiroglu-Zergeroglu A, Çakır T. A systems pharmacology approach based on oncogenic signalling pathways to determine the mechanisms of action of natural products in breast cancer from transcriptome data. BMC Complement Med Ther 2021; 21:181. [PMID: 34193143 PMCID: PMC8244196 DOI: 10.1186/s12906-021-03340-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 06/02/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Narrow spectrum of action through limited molecular targets and unforeseen drug-related toxicities have been the main reasons for drug failures at the phase I clinical trials in complex diseases. Most plant-derived compounds with medicinal values possess poly-pharmacologic properties with overall good tolerability, and, thus, are appropriate in the management of complex diseases, especially cancers. However, methodological limitations impede attempts to catalogue targeted processes and infer systemic mechanisms of action. While most of the current understanding of these compounds is based on reductive methods, it is increasingly becoming clear that holistic techniques, leveraging current improvements in omic data collection and bioinformatics methods, are better suited for elucidating their systemic effects. Thus, we developed and implemented an integrative systems biology pipeline to study these compounds and reveal their mechanism of actions on breast cancer cell lines. METHODS Transcriptome data from compound-treated breast cancer cell lines, representing triple negative (TN), luminal A (ER+) and HER2+ tumour types, were mapped on human protein interactome to construct targeted subnetworks. The subnetworks were analysed for enriched oncogenic signalling pathways. Pathway redundancy was reduced by constructing pathway-pathway interaction networks, and the sets of overlapping genes were subsequently used to infer pathway crosstalk. The resulting filtered pathways were mapped on oncogenesis processes to evaluate their anti-carcinogenic effectiveness, and thus putative mechanisms of action. RESULTS The signalling pathways regulated by Actein, Withaferin A, Indole-3-Carbinol and Compound Kushen, which are extensively researched compounds, were shown to be projected on a set of oncogenesis processes at the transcriptomic level in different breast cancer subtypes. The enrichment of well-known tumour driving genes indicate that these compounds indirectly dysregulate cancer driving pathways in the subnetworks. CONCLUSION The proposed framework infers the mechanisms of action of potential drug candidates from their enriched protein interaction subnetworks and oncogenic signalling pathways. It also provides a systematic approach for evaluating such compounds in polygenic complex diseases. In addition, the plant-based compounds used here show poly-pharmacologic mechanism of action by targeting subnetworks enriched with cancer driving genes. This network perspective supports the need for a systemic drug-target evaluation for lead compounds prior to efficacy experiments.
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Affiliation(s)
- Regan Odongo
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey
- Department of Molecular Biology and Genetics, Gebze Technical University, Gebze, Kocaeli, Turkey
| | | | - Tunahan Çakır
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey.
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7
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Hodzic E, Shrestha R, Malikic S, Collins CC, Litchfield K, Turajlic S, Sahinalp SC. Identification of conserved evolutionary trajectories in tumors. Bioinformatics 2021; 36:i427-i435. [PMID: 32657374 DOI: 10.1093/bioinformatics/btaa453] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
MOTIVATION As multi-region, time-series and single-cell sequencing data become more widely available; it is becoming clear that certain tumors share evolutionary characteristics with others. In the last few years, several computational methods have been developed with the goal of inferring the subclonal composition and evolutionary history of tumors from tumor biopsy sequencing data. However, the phylogenetic trees that they report differ significantly between tumors (even those with similar characteristics). RESULTS In this article, we present a novel combinatorial optimization method, CONETT, for detection of recurrent tumor evolution trajectories. Our method constructs a consensus tree of conserved evolutionary trajectories based on the information about temporal order of alteration events in a set of tumors. We apply our method to previously published datasets of 100 clear-cell renal cell carcinoma and 99 non-small-cell lung cancer patients and identify both conserved trajectories that were reported in the original studies, as well as new trajectories. AVAILABILITY AND IMPLEMENTATION CONETT is implemented in C++ and available at https://github.com/ehodzic/CONETT. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ermin Hodzic
- Department of Computing Science, Simon Fraser University, Burnaby, BC, Canada
| | - Raunak Shrestha
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA
| | - Salem Malikic
- Department of Computer Science, Indiana University Bloomington, Bloomington, IN, USA
| | - Colin C Collins
- Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada.,aboratory for Advanced Genome Analysis, Vancouver Prostate Centre, Vancouver, BC, Canada
| | - Kevin Litchfield
- Cancer Dynamics Laboratory, the Francis Crick institute, Genome Instability Laboratory, Francis Crick Institute, London, UK
| | - Samra Turajlic
- Cancer Dynamics Laboratory, the Francis Crick institute, Genome Instability Laboratory, Francis Crick Institute, London, UK.,Skin and Renal Units, The royal Marsden NHS Foundation Trust, London, UK
| | - S Cenk Sahinalp
- Cancer Data Science Lab., National Cancer Institute, NIH, Bethesda, MD, USA
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8
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Lazareva O, Canzar S, Yuan K, Baumbach J, Blumenthal DB, Tieri P, Kacprowski T, List M. BiCoN: network-constrained biclustering of patients and omics data. Bioinformatics 2020; 37:2398-2404. [PMID: 33367514 DOI: 10.1093/bioinformatics/btaa1076] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 11/25/2020] [Accepted: 12/15/2020] [Indexed: 12/21/2022] Open
Abstract
Abstract
Motivation
Unsupervised learning approaches are frequently used to stratify patients into clinically relevant subgroups and to identify biomarkers such as disease-associated genes. However, clustering and biclustering techniques are oblivious to the functional relationship of genes and are thus not ideally suited to pinpoint molecular mechanisms along with patient subgroups.
Results
We developed the network-constrained biclustering approach Biclustering Constrained by Networks (BiCoN) which (i) restricts biclusters to functionally related genes connected in molecular interaction networks and (ii) maximizes the difference in gene expression between two subgroups of patients. This allows BiCoN to simultaneously pinpoint molecular mechanisms responsible for the patient grouping. Network-constrained clustering of genes makes BiCoN more robust to noise and batch effects than typical clustering and biclustering methods. BiCoN can faithfully reproduce known disease subtypes as well as novel, clinically relevant patient subgroups, as we could demonstrate using breast and lung cancer datasets. In summary, BiCoN is a novel systems medicine tool that combines several heuristic optimization strategies for robust disease mechanism extraction. BiCoN is well-documented and freely available as a python package or a web interface.
Availability and implementation
PyPI package: https://pypi.org/project/bicon.
Web interface
https://exbio.wzw.tum.de/bicon.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Olga Lazareva
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Weihenstephan, 80333 Munich, Germany
| | - Stefan Canzar
- Gene Center, Ludwig-Maximilians-University of Munich, 81377 Munich, Germany
| | - Kevin Yuan
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Weihenstephan, 80333 Munich, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Weihenstephan, 80333 Munich, Germany
| | - David B Blumenthal
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Weihenstephan, 80333 Munich, Germany
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome 00185, Italy
- La Sapienza University of Rome, Rome 00185, Italy
| | - Tim Kacprowski
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Weihenstephan, 80333 Munich, Germany
- Division of Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, Brunswick 38106, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Weihenstephan, 80333 Munich, Germany
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9
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Pusa T, Ferrarini MG, Andrade R, Mary A, Marchetti-Spaccamela A, Stougie L, Sagot MF. MOOMIN - Mathematical explOration of 'Omics data on a MetabolIc Network. Bioinformatics 2019; 36:514-523. [PMID: 31504164 PMCID: PMC9883724 DOI: 10.1093/bioinformatics/btz584] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 07/16/2019] [Accepted: 08/19/2019] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Analysis of differential expression of genes is often performed to understand how the metabolic activity of an organism is impacted by a perturbation. However, because the system of metabolic regulation is complex and all changes are not directly reflected in the expression levels, interpreting these data can be difficult. RESULTS In this work, we present a new algorithm and computational tool that uses a genome-scale metabolic reconstruction to infer metabolic changes from differential expression data. Using the framework of constraint-based analysis, our method produces a qualitative hypothesis of a change in metabolic activity. In other words, each reaction of the network is inferred to have increased, decreased, or remained unchanged in flux. In contrast to similar previous approaches, our method does not require a biological objective function and does not assign on/off activity states to genes. An implementation is provided and it is available online. We apply the method to three published datasets to show that it successfully accomplishes its two main goals: confirming or rejecting metabolic changes suggested by differentially expressed genes based on how well they fit in as parts of a coordinated metabolic change, as well as inferring changes in reactions whose genes did not undergo differential expression. AVAILABILITY AND IMPLEMENTATION github.com/htpusa/moomin. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Taneli Pusa
- To whom correspondence should be addressed. or
| | - Mariana Galvão Ferrarini
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne 69622, France,Univ Lyon, INSA-Lyon, INRA, BF2i, UMR0203, F-69621, Villeurbanne 69622, France
| | - Ricardo Andrade
- INRIA Grenoble Rhône-Alpes, Montbonnot-Saint-Martin 38334, France,Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne 69622, France
| | - Arnaud Mary
- INRIA Grenoble Rhône-Alpes, Montbonnot-Saint-Martin 38334, France,Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne 69622, France
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10
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Hodzic E, Shrestha R, Zhu K, Cheng K, Collins CC, Cenk Sahinalp S. Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks. Gigascience 2019; 8:giz024. [PMID: 30978274 PMCID: PMC6458499 DOI: 10.1093/gigascience/giz024] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 12/12/2018] [Accepted: 02/21/2019] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Advances in large-scale tumor sequencing have led to an understanding that there are combinations of genomic and transcriptomic alterations specific to tumor types, shared across many patients. Unfortunately, computational identification of functionally meaningful and recurrent alteration patterns within gene/protein interaction networks has proven to be challenging. FINDINGS We introduce a novel combinatorial method, cd-CAP (combinatorial detection of conserved alteration patterns), for simultaneous detection of connected subnetworks of an interaction network where genes exhibit conserved alteration patterns across tumor samples. Our method differentiates distinct alteration types associated with each gene (rather than relying on binary information of a gene being altered or not) and simultaneously detects multiple alteration profile conserved subnetworks. CONCLUSIONS In a number of The Cancer Genome Atlas datasets, cd-CAP identified large biologically significant subnetworks with conserved alteration patterns, shared across many tumor samples.
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Affiliation(s)
- Ermin Hodzic
- Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, 2660 Oak St, Vancouver, BC, V6H 3Z6, Canada
- School of Computing Science, Simon Fraser University, 8888 University Dr, Burnaby, BC, V5A 1S6, Canada
| | - Raunak Shrestha
- Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, 2660 Oak St, Vancouver, BC, V6H 3Z6, Canada
- Department of Urologic Sciences, University of British Columbia, 2775 Laurel St, Vancouver, BC, V5Z 1M9, Canada
| | - Kaiyuan Zhu
- Department of Computer Science, Indiana University Bloomington, 700 N. Woodlawn Ave, Bloomington, IN, 47408, USA
| | - Kuoyuan Cheng
- Center for Bioinformatics and Computational Biology, University of Maryland, 8125 Paint Branch Dr, College Park, MD, 20742, USA
| | - Colin C Collins
- Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, 2660 Oak St, Vancouver, BC, V6H 3Z6, Canada
- Department of Urologic Sciences, University of British Columbia, 2775 Laurel St, Vancouver, BC, V5Z 1M9, Canada
| | - S Cenk Sahinalp
- Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, 2660 Oak St, Vancouver, BC, V6H 3Z6, Canada
- Department of Computer Science, Indiana University Bloomington, 700 N. Woodlawn Ave, Bloomington, IN, 47408, USA
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11
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Barel G, Herwig R. Network and Pathway Analysis of Toxicogenomics Data. Front Genet 2018; 9:484. [PMID: 30405693 PMCID: PMC6204403 DOI: 10.3389/fgene.2018.00484] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 09/28/2018] [Indexed: 12/20/2022] Open
Abstract
Toxicogenomics is the study of the molecular effects of chemical, biological and physical agents in biological systems, with the aim of elucidating toxicological mechanisms, building predictive models and improving diagnostics. The vast majority of toxicogenomics data has been generated at the transcriptome level, including RNA-seq and microarrays, and large quantities of drug-treatment data have been made publicly available through databases and repositories. Besides the identification of differentially expressed genes (DEGs) from case-control studies or drug treatment time series studies, bioinformatics methods have emerged that infer gene expression data at the molecular network and pathway level in order to reveal mechanistic information. In this work we describe different resources and tools that have been developed by us and others that relate gene expression measurements with known pathway information such as over-representation and gene set enrichment analyses. Furthermore, we highlight approaches that integrate gene expression data with molecular interaction networks in order to derive network modules related to drug toxicity. We describe the two main parts of the approach, i.e., the construction of a suitable molecular interaction network as well as the conduction of network propagation of the experimental data through the interaction network. In all cases we apply methods and tools to publicly available rat in vivo data on anthracyclines, an important class of anti-cancer drugs that are known to induce severe cardiotoxicity in patients. We report the results and functional implications achieved for four anthracyclines (doxorubicin, epirubicin, idarubicin, and daunorubicin) and compare the information content inherent in the different computational approaches.
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Affiliation(s)
| | - Ralf Herwig
- Department Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
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12
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Alexander-Dann B, Pruteanu LL, Oerton E, Sharma N, Berindan-Neagoe I, Módos D, Bender A. Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data. Mol Omics 2018; 14:218-236. [PMID: 29917034 PMCID: PMC6080592 DOI: 10.1039/c8mo00042e] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Accepted: 05/08/2018] [Indexed: 12/12/2022]
Abstract
The toxicogenomics field aims to understand and predict toxicity by using 'omics' data in order to study systems-level responses to compound treatments. In recent years there has been a rapid increase in publicly available toxicological and 'omics' data, particularly gene expression data, and a corresponding development of methods for its analysis. In this review, we summarize recent progress relating to the analysis of RNA-Seq and microarray data, review relevant databases, and highlight recent applications of toxicogenomics data for understanding and predicting compound toxicity. These include the analysis of differentially expressed genes and their enrichment, signature matching, methods based on interaction networks, and the analysis of co-expression networks. In the future, these state-of-the-art methods will likely be combined with new technologies, such as whole human body models, to produce a comprehensive systems-level understanding of toxicity that reduces the necessity of in vivo toxicity assessment in animal models.
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Affiliation(s)
- Benjamin Alexander-Dann
- University of Cambridge
, Centre for Molecular Informatics
, Department of Chemistry
,
Lensfield Road
, Cambridge CB2 1EW
, UK
.
;
| | - Lavinia Lorena Pruteanu
- University of Cambridge
, Centre for Molecular Informatics
, Department of Chemistry
,
Lensfield Road
, Cambridge CB2 1EW
, UK
.
;
- Babeş-Bolyai University
, Institute for Doctoral Studies
,
1 Kogălniceanu Street
, Cluj-Napoca 400084
, Romania
- University of Medicine and Pharmacy “Iuliu Haţieganu”
, MedFuture Research Centre for Advanced Medicine
,
23 Marinescu Street/4-6 Pasteur Street
, Cluj-Napoca 400337
, Romania
| | - Erin Oerton
- University of Cambridge
, Centre for Molecular Informatics
, Department of Chemistry
,
Lensfield Road
, Cambridge CB2 1EW
, UK
.
;
| | - Nitin Sharma
- University of Cambridge
, Centre for Molecular Informatics
, Department of Chemistry
,
Lensfield Road
, Cambridge CB2 1EW
, UK
.
;
| | - Ioana Berindan-Neagoe
- University of Medicine and Pharmacy “Iuliu Haţieganu”
, MedFuture Research Centre for Advanced Medicine
,
23 Marinescu Street/4-6 Pasteur Street
, Cluj-Napoca 400337
, Romania
- University of Medicine and Pharmacy “Iuliu Haţieganu”
, Research Center for Functional Genomics
, Biomedicine and Translational Medicine
,
23 Marinescu Street
, Cluj-Napoca 400337
, Romania
- The Oncology Institute “Prof. Dr Ion Chiricuţă”
, Department of Functional Genomics and Experimental Pathology
,
34-36 Republicii Street
, Cluj-Napoca 400015
, Romania
| | - Dezső Módos
- University of Cambridge
, Centre for Molecular Informatics
, Department of Chemistry
,
Lensfield Road
, Cambridge CB2 1EW
, UK
.
;
| | - Andreas Bender
- University of Cambridge
, Centre for Molecular Informatics
, Department of Chemistry
,
Lensfield Road
, Cambridge CB2 1EW
, UK
.
;
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13
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Hassan SS, Jamal SB, Radusky LG, Tiwari S, Ullah A, Ali J, Behramand, de Carvalho PVSD, Shams R, Khan S, Figueiredo HCP, Barh D, Ghosh P, Silva A, Baumbach J, Röttger R, Turjanski AG, Azevedo VAC. The Druggable Pocketome of Corynebacterium diphtheriae: A New Approach for in silico Putative Druggable Targets. Front Genet 2018; 9:44. [PMID: 29487617 PMCID: PMC5816920 DOI: 10.3389/fgene.2018.00044] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Accepted: 01/30/2018] [Indexed: 01/20/2023] Open
Abstract
Diphtheria is an acute and highly infectious disease, previously regarded as endemic in nature but vaccine-preventable, is caused by Corynebacterium diphtheriae (Cd). In this work, we used an in silico approach along the 13 complete genome sequences of C. diphtheriae followed by a computational assessment of structural information of the binding sites to characterize the “pocketome druggability.” To this end, we first computed the “modelome” (3D structures of a complete genome) of a randomly selected reference strain Cd NCTC13129; that had 13,763 open reading frames (ORFs) and resulted in 1,253 (∼9%) structure models. The amino acid sequences of these modeled structures were compared with the remaining 12 genomes and consequently, 438 conserved protein sequences were obtained. The RCSB-PDB database was consulted to check the template structures for these conserved proteins and as a result, 401 adequate 3D models were obtained. We subsequently predicted the protein pockets for the obtained set of models and kept only the conserved pockets that had highly druggable (HD) values (137 across all strains). Later, an off-target host homology analyses was performed considering the human proteome using NCBI database. Furthermore, the gene essentiality analysis was carried out that gave a final set of 10-conserved targets possessing highly druggable protein pockets. To check the target identification robustness of the pipeline used in this work, we crosschecked the final target list with another in-house target identification approach for C. diphtheriae thereby obtaining three common targets, these were; hisE-phosphoribosyl-ATP pyrophosphatase, glpX-fructose 1,6-bisphosphatase II, and rpsH-30S ribosomal protein S8. Our predicted results suggest that the in silico approach used could potentially aid in experimental polypharmacological target determination in C. diphtheriae and other pathogens, thereby, might complement the existing and new drug-discovery pipelines.
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Affiliation(s)
- Syed S Hassan
- Department of Chemistry, Islamia College University Peshawar, Peshawar, Pakistan
| | - Syed B Jamal
- PG Program in Bioinformatics, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Leandro G Radusky
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Sandeep Tiwari
- PG Program in Bioinformatics, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Asad Ullah
- Department of Chemistry, Islamia College University Peshawar, Peshawar, Pakistan
| | - Javed Ali
- Department of Chemistry, Kohat University of Science and Technology, Kohat, Pakistan
| | - Behramand
- Department of Chemistry, Islamia College University Peshawar, Peshawar, Pakistan
| | - Paulo V S D de Carvalho
- PG Program in Bioinformatics, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Rida Shams
- Department of Chemistry, Islamia College University Peshawar, Peshawar, Pakistan
| | - Sabir Khan
- Department of Analytical Chemistry, Institute of Chemistry, São Paulo State University, São Paulo, Brazil
| | - Henrique C P Figueiredo
- AQUACEN, National Reference Laboratory for Aquatic Animal Diseases, Ministry of Fisheries and Aquaculture, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Debmalya Barh
- PG Program in Bioinformatics, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Brazil.,Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology, Purba Medinipur, India
| | - Preetam Ghosh
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States
| | - Artur Silva
- Institute of Biological Sciences, Federal University of Pará, Belém, Brazil
| | - Jan Baumbach
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Richard Röttger
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Adrián G Turjanski
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina.,INQUIMAE/UBA-CONICET, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Vasco A C Azevedo
- PG Program in Bioinformatics, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Brazil
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14
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Alcaraz N, List M, Batra R, Vandin F, Ditzel HJ, Baumbach J. De novo pathway-based biomarker identification. Nucleic Acids Res 2017; 45:e151. [PMID: 28934488 PMCID: PMC5766193 DOI: 10.1093/nar/gkx642] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 07/13/2017] [Indexed: 02/07/2023] Open
Abstract
Gene expression profiles have been extensively discussed as an aid to guide the therapy by predicting disease outcome for the patients suffering from complex diseases, such as cancer. However, prediction models built upon single-gene (SG) features show poor stability and performance on independent datasets. Attempts to mitigate these drawbacks have led to the development of network-based approaches that integrate pathway information to produce meta-gene (MG) features. Also, MG approaches have only dealt with the two-class problem of good versus poor outcome prediction. Stratifying patients based on their molecular subtypes can provide a detailed view of the disease and lead to more personalized therapies. We propose and discuss a novel MG approach based on de novo pathways, which for the first time have been used as features in a multi-class setting to predict cancer subtypes. Comprehensive evaluation in a large cohort of breast cancer samples from The Cancer Genome Atlas (TCGA) revealed that MGs are considerably more stable than SG models, while also providing valuable insight into the cancer hallmarks that drive them. In addition, when tested on an independent benchmark non-TCGA dataset, MG features consistently outperformed SG models. We provide an easy-to-use web service at http://pathclass.compbio.sdu.dk where users can upload their own gene expression datasets from breast cancer studies and obtain the subtype predictions from all the classifiers.
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Affiliation(s)
- Nicolas Alcaraz
- Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark.,Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark.,The Bioinformatics Centre, Department of Biology, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Markus List
- Computational Biology and Applied Algorithms, Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| | - Richa Batra
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Munich, Germany.,Department of Dermatology and Allergy, Technical University of Munich, 80802 Munich, Germany
| | - Fabio Vandin
- Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark.,Department of Information and Engineering, University of Padowa, 35122 Padowa, Italy
| | - Henrik J Ditzel
- Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark.,Department of Oncology, Odense University Hospital, 5000 Odense, Denmark
| | - Jan Baumbach
- Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark.,Computational Systems Biology Group, Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
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15
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Ghanat Bari M, Ung CY, Zhang C, Zhu S, Li H. Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks. Sci Rep 2017; 7:6993. [PMID: 28765560 PMCID: PMC5539301 DOI: 10.1038/s41598-017-07481-5] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 06/27/2017] [Indexed: 12/25/2022] Open
Abstract
Emerging evidence indicates the existence of a new class of cancer genes that act as "signal linkers" coordinating oncogenic signals between mutated and differentially expressed genes. While frequently mutated oncogenes and differentially expressed genes, which we term Class I cancer genes, are readily detected by most analytical tools, the new class of cancer-related genes, i.e., Class II, escape detection because they are neither mutated nor differentially expressed. Given this hypothesis, we developed a Machine Learning-Assisted Network Inference (MALANI) algorithm, which assesses all genes regardless of expression or mutational status in the context of cancer etiology. We used 8807 expression arrays, corresponding to 9 cancer types, to build more than 2 × 108 Support Vector Machine (SVM) models for reconstructing a cancer network. We found that ~3% of ~19,000 not differentially expressed genes are Class II cancer gene candidates. Some Class II genes that we found, such as SLC19A1 and ATAD3B, have been recently reported to associate with cancer outcomes. To our knowledge, this is the first study that utilizes both machine learning and network biology approaches to uncover Class II cancer genes in coordinating functionality in cancer networks and will illuminate our understanding of how genes are modulated in a tissue-specific network contribute to tumorigenesis and therapy development.
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Affiliation(s)
- Mehrab Ghanat Bari
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN, 55905, USA
| | - Choong Yong Ung
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN, 55905, USA
| | - Cheng Zhang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN, 55905, USA
| | - Shizhen Zhu
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN, 55905, USA
| | - Hu Li
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN, 55905, USA.
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16
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Scarpa JR, Jiang P, Losic B, Readhead B, Gao VD, Dudley JT, Vitaterna MH, Turek FW, Kasarskis A. Systems Genetic Analyses Highlight a TGFβ-FOXO3 Dependent Striatal Astrocyte Network Conserved across Species and Associated with Stress, Sleep, and Huntington's Disease. PLoS Genet 2016; 12:e1006137. [PMID: 27390852 PMCID: PMC4938493 DOI: 10.1371/journal.pgen.1006137] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Accepted: 05/31/2016] [Indexed: 12/22/2022] Open
Abstract
Recent systems-based analyses have demonstrated that sleep and stress traits emerge from shared genetic and transcriptional networks, and clinical work has elucidated the emergence of sleep dysfunction and stress susceptibility as early symptoms of Huntington's disease. Understanding the biological bases of these early non-motor symptoms may reveal therapeutic targets that prevent disease onset or slow disease progression, but the molecular mechanisms underlying this complex clinical presentation remain largely unknown. In the present work, we specifically examine the relationship between these psychiatric traits and Huntington's disease (HD) by identifying striatal transcriptional networks shared by HD, stress, and sleep phenotypes. First, we utilize a systems-based approach to examine a large publicly available human transcriptomic dataset for HD (GSE3790 from GEO) in a novel way. We use weighted gene coexpression network analysis and differential connectivity analyses to identify transcriptional networks dysregulated in HD, and we use an unbiased ranking scheme that leverages both gene- and network-level information to identify a novel astrocyte-specific network as most relevant to HD caudate. We validate this result in an independent HD cohort. Next, we computationally predict FOXO3 as a regulator of this network, and use multiple publicly available in vitro and in vivo experimental datasets to validate that this astrocyte HD network is downstream of a signaling pathway important in adult neurogenesis (TGFβ-FOXO3). We also map this HD-relevant caudate subnetwork to striatal transcriptional networks in a large (n = 100) chronically stressed (B6xA/J)F2 mouse population that has been extensively phenotyped (328 stress- and sleep-related measurements), and we show that this striatal astrocyte network is correlated to sleep and stress traits, many of which are known to be altered in HD cohorts. We identify causal regulators of this network through Bayesian network analysis, and we highlight their relevance to motor, mood, and sleep traits through multiple in silico approaches, including an examination of their protein binding partners. Finally, we show that these causal regulators may be therapeutically viable for HD because their downstream network was partially modulated by deep brain stimulation of the subthalamic nucleus, a medical intervention thought to confer some therapeutic benefit to HD patients. In conclusion, we show that an astrocyte transcriptional network is primarily associated to HD in the caudate and provide evidence for its relationship to molecular mechanisms of neural stem cell homeostasis. Furthermore, we present a unified systems-based framework for identifying gene networks that are associated with complex non-motor traits that manifest in the earliest phases of HD. By analyzing and integrating multiple independent datasets, we identify a point of molecular convergence between sleep, stress, and HD that reflects their phenotypic comorbidity and reveals a molecular pathway involved in HD progression.
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Affiliation(s)
- Joseph R. Scarpa
- Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Peng Jiang
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, Illinois, United States of America
| | - Bojan Losic
- Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Ben Readhead
- Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Vance D. Gao
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, Illinois, United States of America
| | - Joel T. Dudley
- Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Martha H. Vitaterna
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, Illinois, United States of America
| | - Fred W. Turek
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, Illinois, United States of America
| | - Andrew Kasarskis
- Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
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17
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Alcaraz N, List M, Dissing-Hansen M, Rehmsmeier M, Tan Q, Mollenhauer J, Ditzel HJ, Baumbach J. Robust de novo pathway enrichment with KeyPathwayMiner 5. F1000Res 2016; 5:1531. [PMID: 27540470 PMCID: PMC4965696 DOI: 10.12688/f1000research.9054.1] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/22/2016] [Indexed: 01/26/2023] Open
Abstract
Identifying functional modules or novel active pathways, recently termed de novo pathway enrichment, is a computational systems biology challenge that has gained much attention during the last decade. Given a large biological interaction network, KeyPathwayMiner extracts connected subnetworks that are enriched for differentially active entities from a series of molecular profiles encoded as binary indicator matrices. Since interaction networks constantly evolve, an important question is how robust the extracted results are when the network is modified. We enable users to study this effect through several network perturbation techniques and over a range of perturbation degrees. In addition, users may now provide a gold-standard set to determine how enriched extracted pathways are with relevant genes compared to randomized versions of the original network.
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Affiliation(s)
- Nicolas Alcaraz
- Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark; Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark
| | - Markus List
- Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark; Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark; Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, University of Southern Denmark, 5000 Odense, Denmark; Institute of Clinical Research, University of Southern Denmark, 5000 Odense, Denmark; Max Planck Institute for Informatics, 66123 Saarbrucken, Germany
| | - Martin Dissing-Hansen
- Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark
| | - Marc Rehmsmeier
- Integrated Research Institute (IRI) for the Life Sciences and Department of Biology, Humboldt-Universitat zu Berlin, 10099 Berlin, Germany
| | - Qihua Tan
- Institute of Clinical Research, University of Southern Denmark, 5000 Odense, Denmark; Epidemiology, Biostatistics and Biodemography, Institute of Public Health, University of Southern Denmark, 5000 Odense, Denmark
| | - Jan Mollenhauer
- Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark; Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, University of Southern Denmark, 5000 Odense, Denmark
| | - Henrik J Ditzel
- Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark; Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, University of Southern Denmark, 5000 Odense, Denmark; Department of Oncology, Odense University Hospital, 5000 Odense, Denmark
| | - Jan Baumbach
- Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark; Max Planck Institute for Informatics, 66123 Saarbrucken, Germany
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18
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List M, Alcaraz N, Dissing-Hansen M, Ditzel HJ, Mollenhauer J, Baumbach J. KeyPathwayMinerWeb: online multi-omics network enrichment. Nucleic Acids Res 2016; 44:W98-W104. [PMID: 27150809 PMCID: PMC4987922 DOI: 10.1093/nar/gkw373] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2016] [Accepted: 04/25/2016] [Indexed: 01/25/2023] Open
Abstract
We present KeyPathwayMinerWeb, the first online platform for de novo pathway enrichment analysis directly in the browser. Given a biological interaction network (e.g. protein–protein interactions) and a series of molecular profiles derived from one or multiple OMICS studies (gene expression, for instance), KeyPathwayMiner extracts connected sub-networks containing a high number of active or differentially regulated genes (proteins, metabolites) in the molecular profiles. The web interface at (http://keypathwayminer.compbio.sdu.dk) implements all core functionalities of the KeyPathwayMiner tool set such as data integration, input of background knowledge, batch runs for parameter optimization and visualization of extracted pathways. In addition to an intuitive web interface, we also implemented a RESTful API that now enables other online developers to integrate network enrichment as a web service into their own platforms.
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Affiliation(s)
- Markus List
- Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, University of Southern Denmark, 5000 Odense, Denmark Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark Institute of Clinical Research, University of Southern Denmark, 5000 Odense, Denmark Max Planck Institute for Informatics, 66123 Saarbrücken, Germany
| | - Nicolas Alcaraz
- Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark
| | - Martin Dissing-Hansen
- Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark
| | - Henrik J Ditzel
- Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, University of Southern Denmark, 5000 Odense, Denmark Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark Department of Oncology, Odense University Hospital, 5000 Odense, Denmark
| | - Jan Mollenhauer
- Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, University of Southern Denmark, 5000 Odense, Denmark Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark
| | - Jan Baumbach
- Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark Max Planck Institute for Informatics, 66123 Saarbrücken, Germany
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19
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Fonseca A, Gubitoso MD, Reis MS, de Souza SJ, Barrera J. A New Approach for Identification of Cancer-related Pathways using Protein Networks and Genomic Data. Cancer Inform 2016; 14:139-49. [PMID: 27158220 PMCID: PMC4854218 DOI: 10.4137/cin.s30800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Revised: 02/11/2016] [Accepted: 02/17/2016] [Indexed: 11/11/2022] Open
Abstract
Cancer cells have anomalous development and proliferation due to disturbances in their control systems. The study of the behavior of cellular control system requires high-throughput dynamical data. Unfortunately, this type of data is not largely available. This fact motivates the main issue of this article: how to use static omics data and available biological knowledge to get new information about the elements of the control system in cancer cells. Two important measures to access the state of the cellular control system are the gene expression profile and the signaling pathways. This article uses a combination of these two static omics data to gain insights on the states of a cancer cell. To extract information from this kind of data, a statistical computational model was formalized and implemented. In order to exemplify the application of some aspects of the developed conceptual framework, we verified the hypothesis that different types of cancer cells have different disturbed signaling pathways. To this end, we developed a method that recovers small protein networks, called motifs, which are differentially represented in some subtypes of breast cancer. These differentially represented motifs are enriched with specific gene ontologies as well as with new putative cancer genes.
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Affiliation(s)
| | | | | | | | - Junior Barrera
- Institute of Mathematics and Statistics, USP, São Paulo, Brazil.; LETA, CeTICS, Butantan Institute, São Paulo, Brazil
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20
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Chandrasekaran S, Bonchev D. Network analysis of human post-mortem microarrays reveals novel genes, microRNAs, and mechanistic scenarios of potential importance in fighting huntington's disease. Comput Struct Biotechnol J 2016; 14:117-130. [PMID: 27924190 PMCID: PMC5128196 DOI: 10.1016/j.csbj.2016.02.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Revised: 01/28/2016] [Accepted: 02/02/2016] [Indexed: 01/18/2023] Open
Abstract
Huntington's disease is a progressive neurodegenerative disorder characterized by motor disturbances, cognitive decline, and neuropsychiatric symptoms. In this study, we utilized network-based analysis in an attempt to explore and understand the underlying molecular mechanism and to identify critical molecular players of this disease condition. Using human post-mortem microarrays from three brain regions (cerebellum, frontal cortex and caudate nucleus) we selected in a four-step procedure a seed set of highly modulated genes. Several protein-protein interaction networks, as well as microRNA-mRNA networks were constructed for these gene sets with the Elsevier Pathway Studio software and its associated ResNet database. We applied a gene prioritizing procedure based on vital network topological measures, such as high node connectivity and centrality. Adding to these criteria the guilt-by-association rule and exploring their innate biomolecular functions, we propose 19 novel genes from the analyzed microarrays, from which CEBPA, CDK1, CX3CL1, EGR1, E2F1, ERBB2, LRP1, HSP90AA1 and ZNF148 might be of particular interest for experimental validation. A possibility is discussed for dual-level gene regulation by both transcription factors and microRNAs in Huntington's disease mechanism. We propose several possible scenarios for experimental studies initiated via the extra-cellular ligands TGFB1, FGF2 and TNF aiming at restoring the cellular homeostasis in Huntington's disease.
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Affiliation(s)
- Sreedevi Chandrasekaran
- Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA, USA
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Ippolito DL, AbdulHameed MDM, Tawa GJ, Baer CE, Permenter MG, McDyre BC, Dennis WE, Boyle MH, Hobbs CA, Streicker MA, Snowden BS, Lewis JA, Wallqvist A, Stallings JD. Gene Expression Patterns Associated With Histopathology in Toxic Liver Fibrosis. Toxicol Sci 2015; 149:67-88. [DOI: 10.1093/toxsci/kfv214] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
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Czerwinska U, Calzone L, Barillot E, Zinovyev A. DeDaL: Cytoscape 3 app for producing and morphing data-driven and structure-driven network layouts. BMC SYSTEMS BIOLOGY 2015; 9:46. [PMID: 26271256 PMCID: PMC4535771 DOI: 10.1186/s12918-015-0189-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2014] [Accepted: 07/22/2015] [Indexed: 01/17/2023]
Abstract
Background Visualization and analysis of molecular profiling data together with biological networks are able to provide new mechanistic insights into biological functions. Currently, it is possible to visualize high-throughput data on top of pre-defined network layouts, but they are not always adapted to a given data analysis task. A network layout based simultaneously on the network structure and the associated multidimensional data might be advantageous for data visualization and analysis in some cases. Results We developed a Cytoscape app, which allows constructing biological network layouts based on the data from molecular profiles imported as values of node attributes. DeDaL is a Cytoscape 3 app, which uses linear and non-linear algorithms of dimension reduction to produce data-driven network layouts based on multidimensional data (typically gene expression). DeDaL implements several data pre-processing and layout post-processing steps such as continuous morphing between two arbitrary network layouts and aligning one network layout with respect to another one by rotating and mirroring. The combination of all these functionalities facilitates the creation of insightful network layouts representing both structural network features and correlation patterns in multivariate data. We demonstrate the added value of applying DeDaL in several practical applications, including an example of a large protein-protein interaction network. Conclusions DeDaL is a convenient tool for applying data dimensionality reduction methods and for designing insightful data displays based on data-driven layouts of biological networks, built within Cytoscape environment. DeDaL is freely available for downloading at http://bioinfo-out.curie.fr/projects/dedal/.
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Affiliation(s)
- Urszula Czerwinska
- Institut Curie, 26 rue d'Ulm, Paris, France. .,INSERM U900, Paris, France. .,Mines Paris Tech, Fontainebleau, France.
| | - Laurence Calzone
- Institut Curie, 26 rue d'Ulm, Paris, France. .,INSERM U900, Paris, France. .,Mines Paris Tech, Fontainebleau, France.
| | - Emmanuel Barillot
- Institut Curie, 26 rue d'Ulm, Paris, France. .,INSERM U900, Paris, France. .,Mines Paris Tech, Fontainebleau, France.
| | - Andrei Zinovyev
- Institut Curie, 26 rue d'Ulm, Paris, France. .,INSERM U900, Paris, France. .,Mines Paris Tech, Fontainebleau, France.
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23
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Abstract
The growing body of transcriptomic, proteomic, metabolomic and genomic data generated from disease states provides a great opportunity to improve our current understanding of the molecular mechanisms driving diseases and shared between diseases. The use of both clinical and molecular phenotypes will lead to better disease understanding and classification. In this study, we set out to gain novel insights into diseases and their relationships by utilising knowledge gained from system-level molecular data. We integrated different types of biological data including genome-wide association studies data, disease-chemical associations, biological pathways and Gene Ontology annotations into an Integrated Disease Network (IDN), a heterogeneous network where nodes are bio-entities and edges between nodes represent their associations. We also introduced a novel disease similarity measure to infer disease-disease associations from the IDN. Our predicted associations were systemically evaluated against the Medical Subject Heading classification and a statistical measure of disease co-occurrence in PubMed. The strong correlation between our predictions and co-occurrence associations indicated the ability of our approach to recover known disease associations. Furthermore, we presented a case study of Crohn's disease. We demonstrated that our approach not only identified well-established connections between Crohn's disease and other diseases, but also revealed new, interesting connections consistent with emerging literature. Our approach also enabled ready access to the knowledge supporting these new connections, making this a powerful approach for exploring connections between diseases.
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Affiliation(s)
- Kai Sun
- Department of Computing, Imperial College London, London, SW7 2AZ, UK.
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AbdulHameed MDM, Tawa GJ, Kumar K, Ippolito DL, Lewis JA, Stallings JD, Wallqvist A. Systems level analysis and identification of pathways and networks associated with liver fibrosis. PLoS One 2014; 9:e112193. [PMID: 25380136 PMCID: PMC4224449 DOI: 10.1371/journal.pone.0112193] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2014] [Accepted: 10/13/2014] [Indexed: 01/18/2023] Open
Abstract
Toxic liver injury causes necrosis and fibrosis, which may lead to cirrhosis and liver failure. Despite recent progress in understanding the mechanism of liver fibrosis, our knowledge of the molecular-level details of this disease is still incomplete. The elucidation of networks and pathways associated with liver fibrosis can provide insight into the underlying molecular mechanisms of the disease, as well as identify potential diagnostic or prognostic biomarkers. Towards this end, we analyzed rat gene expression data from a range of chemical exposures that produced observable periportal liver fibrosis as documented in DrugMatrix, a publicly available toxicogenomics database. We identified genes relevant to liver fibrosis using standard differential expression and co-expression analyses, and then used these genes in pathway enrichment and protein-protein interaction (PPI) network analyses. We identified a PPI network module associated with liver fibrosis that includes known liver fibrosis-relevant genes, such as tissue inhibitor of metalloproteinase-1, galectin-3, connective tissue growth factor, and lipocalin-2. We also identified several new genes, such as perilipin-3, legumain, and myocilin, which were associated with liver fibrosis. We further analyzed the expression pattern of the genes in the PPI network module across a wide range of 640 chemical exposure conditions in DrugMatrix and identified early indications of liver fibrosis for carbon tetrachloride and lipopolysaccharide exposures. Although it is well known that carbon tetrachloride and lipopolysaccharide can cause liver fibrosis, our network analysis was able to link these compounds to potential fibrotic damage before histopathological changes associated with liver fibrosis appeared. These results demonstrated that our approach is capable of identifying early-stage indicators of liver fibrosis and underscore its potential to aid in predictive toxicity, biomarker identification, and to generally identify disease-relevant pathways.
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Affiliation(s)
- Mohamed Diwan M. AbdulHameed
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
| | - Gregory J. Tawa
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
| | - Kamal Kumar
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
| | - Danielle L. Ippolito
- U.S. Army Center for Environmental Health Research, Fort Detrick, MD, United States of America
| | - John A. Lewis
- U.S. Army Center for Environmental Health Research, Fort Detrick, MD, United States of America
| | - Jonathan D. Stallings
- U.S. Army Center for Environmental Health Research, Fort Detrick, MD, United States of America
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
- * E-mail:
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25
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Alcaraz N, Pauling J, Batra R, Barbosa E, Junge A, Christensen AGL, Azevedo V, Ditzel HJ, Baumbach J. KeyPathwayMiner 4.0: condition-specific pathway analysis by combining multiple omics studies and networks with Cytoscape. BMC SYSTEMS BIOLOGY 2014; 8:99. [PMID: 25134827 PMCID: PMC4236746 DOI: 10.1186/s12918-014-0099-x] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2014] [Accepted: 08/13/2014] [Indexed: 12/17/2022]
Abstract
Background Over the last decade network enrichment analysis has become popular in computational systems biology to elucidate aberrant network modules. Traditionally, these approaches focus on combining gene expression data with protein-protein interaction (PPI) networks. Nowadays, the so-called omics technologies allow for inclusion of many more data sets, e.g. protein phosphorylation or epigenetic modifications. This creates a need for analysis methods that can combine these various sources of data to obtain a systems-level view on aberrant biological networks. Results We present a new release of KeyPathwayMiner (version 4.0) that is not limited to analyses of single omics data sets, e.g. gene expression, but is able to directly combine several different omics data types. Version 4.0 can further integrate existing knowledge by adding a search bias towards sub-networks that contain (avoid) genes provided in a positive (negative) list. Finally the new release now also provides a set of novel visualization features and has been implemented as an app for the standard bioinformatics network analysis tool: Cytoscape. Conclusion With KeyPathwayMiner 4.0, we publish a Cytoscape app for multi-omics based sub-network extraction. It is available in Cytoscape’s app store http://apps.cytoscape.org/apps/keypathwayminer or via http://keypathwayminer.mpi-inf.mpg.de.
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26
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Landesfeind M, Kaever A, Feussner K, Thurow C, Gatz C, Feussner I, Meinicke P. Integrative study of Arabidopsis thaliana metabolomic and transcriptomic data with the interactive MarVis-Graph software. PeerJ 2014; 2:e239. [PMID: 24688832 PMCID: PMC3961162 DOI: 10.7717/peerj.239] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2013] [Accepted: 12/17/2013] [Indexed: 11/20/2022] Open
Abstract
State of the art high-throughput technologies allow comprehensive experimental studies of organism metabolism and induce the need for a convenient presentation of large heterogeneous datasets. Especially, the combined analysis and visualization of data from different high-throughput technologies remains a key challenge in bioinformatics. We present here the MarVis-Graph software for integrative analysis of metabolic and transcriptomic data. All experimental data is investigated in terms of the full metabolic network obtained from a reference database. The reactions of the network are scored based on the associated data, and sub-networks, according to connected high-scoring reactions, are identified. Finally, MarVis-Graph scores the detected sub-networks, evaluates them by means of a random permutation test and presents them as a ranked list. Furthermore, MarVis-Graph features an interactive network visualization that provides researchers with a convenient view on the results. The key advantage of MarVis-Graph is the analysis of reactions detached from their pathways so that it is possible to identify new pathways or to connect known pathways by previously unrelated reactions. The MarVis-Graph software is freely available for academic use and can be downloaded at: http://marvis.gobics.de/marvis-graph.
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Affiliation(s)
- Manuel Landesfeind
- Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University , Göttingen , Germany
| | - Alexander Kaever
- Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University , Göttingen , Germany
| | - Kirstin Feussner
- Department for Plant Biochemistry, Albrecht-von-Haller-Institute for Plant Sciences, Georg-August-University , Göttingen , Germany
| | - Corinna Thurow
- Department for Plant Molecular Biology and Physiology, Schwann-Schleiden-Research-Center for Molecular Cell Biology, Georg-August-University , Göttingen , Germany
| | - Christiane Gatz
- Department for Plant Molecular Biology and Physiology, Schwann-Schleiden-Research-Center for Molecular Cell Biology, Georg-August-University , Göttingen , Germany
| | - Ivo Feussner
- Department for Plant Biochemistry, Albrecht-von-Haller-Institute for Plant Sciences, Georg-August-University , Göttingen , Germany
| | - Peter Meinicke
- Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University , Göttingen , Germany
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Pauling JK, Christensen AG, Batra R, Alcaraz N, Barbosa E, Larsen MR, Beck HC, Leth-Larsen R, Azevedo V, Ditzel HJ, Baumbach J. Elucidation of epithelial–mesenchymal transition-related pathways in a triple-negative breast cancer cell line model by multi-omics interactome analysis. Integr Biol (Camb) 2014; 6:1058-68. [DOI: 10.1039/c4ib00137k] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Network features discriminate between epithelial and mesenchymal phenotype in a triple-negative breast cancer cell line model.
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Affiliation(s)
- Josch K. Pauling
- Department of Biochemistry and Molecular Biology
- Faculty of Science
- University of Southern Denmark
- Odense, Denmark
| | - Anne G. Christensen
- Department of Cancer and Inflammation Research
- Institute of Molecular Medicine
- University of Southern Denmark
- Odense, Denmark
| | - Richa Batra
- Department of Mathematics and Computer Science
- University of Southern Denmark
- Faculty of Science
- Odense, Denmark
| | - Nicolas Alcaraz
- Department of Cancer and Inflammation Research
- Institute of Molecular Medicine
- University of Southern Denmark
- Odense, Denmark
- Department of Mathematics and Computer Science
| | - Eudes Barbosa
- Department of Mathematics and Computer Science
- University of Southern Denmark
- Faculty of Science
- Odense, Denmark
| | - Martin R. Larsen
- Department of Biochemistry and Molecular Biology
- Faculty of Science
- University of Southern Denmark
- Odense, Denmark
- Department of Clinical Biochemistry and Pharmacology
| | - Hans C. Beck
- Department of Clinical Biochemistry and Pharmacology
- Centre for Clinical Proteomics
- Odense University Hospital
- Odense, Denmark
| | - Rikke Leth-Larsen
- Department of Cancer and Inflammation Research
- Institute of Molecular Medicine
- University of Southern Denmark
- Odense, Denmark
| | - Vasco Azevedo
- Institute of Biological Sciences
- Laboratory of Molecular and Cellular Genetic
- Federal University of Minas Gerais
- Belo Horizonte, Brazil
| | - Henrik J. Ditzel
- Department of Cancer and Inflammation Research
- Institute of Molecular Medicine
- University of Southern Denmark
- Odense, Denmark
- Department of Oncology
| | - Jan Baumbach
- Department of Mathematics and Computer Science
- University of Southern Denmark
- Faculty of Science
- Odense, Denmark
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28
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Differential network analysis applied to preoperative breast cancer chemotherapy response. PLoS One 2013; 8:e81784. [PMID: 24349128 PMCID: PMC3857210 DOI: 10.1371/journal.pone.0081784] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2013] [Accepted: 10/16/2013] [Indexed: 12/31/2022] Open
Abstract
In silico approaches are increasingly considered to improve breast cancer treatment. One of these treatments, neoadjuvant TFAC chemotherapy, is used in cases where application of preoperative systemic therapy is indicated. Estimating response to treatment allows or improves clinical decision-making and this, in turn, may be based on a good understanding of the underlying molecular mechanisms. Ever increasing amounts of high throughput data become available for integration into functional networks. In this study, we applied our software tool ExprEssence to identify specific mechanisms relevant for TFAC therapy response, from a gene/protein interaction network. We contrasted the resulting active subnetwork to the subnetworks of two other such methods, OptDis and KeyPathwayMiner. We could show that the ExprEssence subnetwork is more related to the mechanistic functional principles of TFAC therapy than the subnetworks of the other two methods despite the simplicity of ExprEssence. We were able to validate our method by recovering known mechanisms and as an application example of our method, we identified a mechanism that may further explain the synergism between paclitaxel and doxorubicin in TFAC treatment: Paclitaxel may attenuate MELK gene expression, resulting in lower levels of its target MYBL2, already associated with doxorubicin synergism in hepatocellular carcinoma cell lines. We tested our hypothesis in three breast cancer cell lines, confirming it in part. In particular, the predicted effect on MYBL2 could be validated, and a synergistic effect of paclitaxel and doxorubicin could be demonstrated in the breast cancer cell lines SKBR3 and MCF-7.
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29
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Systems metabolic engineering in an industrial setting. Appl Microbiol Biotechnol 2013; 97:2319-26. [DOI: 10.1007/s00253-013-4738-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2012] [Revised: 01/22/2013] [Accepted: 01/23/2013] [Indexed: 10/27/2022]
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Dand N, Sprengel F, Ahlers V, Schlitt T. BioGranat-IG: a network analysis tool to suggest mechanisms of genetic heterogeneity from exome-sequencing data. ACTA ACUST UNITED AC 2013; 29:733-41. [PMID: 23361329 DOI: 10.1093/bioinformatics/btt045] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
MOTIVATION Recent exome-sequencing studies have successfully identified disease-causing sequence variants for several rare monogenic diseases by examining variants common to a group of patients. However, the current data analysis strategies are only insufficiently able to deal with confounding factors such as genetic heterogeneity, incomplete penetrance, individuals lacking data and involvement of several genes. RESULTS We introduce BioGranat-IG, an analysis strategy that incorporates the information contained in biological networks to the analysis of exome-sequencing data. To identify genes that may have a disease-causing role, we label all nodes of the network according to the individuals that are carrying a sequence variant and subsequently identify small subnetworks linked to all or most individuals. Using simulated exome-sequencing data, we demonstrate that BioGranat-IG is able to recover the genes responsible for two diseases known to be caused by variants in an underlying complex. We also examine the performance of BioGranat-IG under various conditions likely to be faced by the user, and show that its network-based approach is more powerful than a set-cover-based approach.
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Affiliation(s)
- Nick Dand
- Department of Medical and Molecular Genetics, King's College London, London SE1 9RT, UK
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31
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Paley SM, Latendresse M, Karp PD. Regulatory network operations in the Pathway Tools software. BMC Bioinformatics 2012; 13:243. [PMID: 22998532 PMCID: PMC3473263 DOI: 10.1186/1471-2105-13-243] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2012] [Accepted: 08/31/2012] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Biologists are elucidating complex collections of genetic regulatory data for multiple organisms. Software is needed for such regulatory network data. RESULTS The Pathway Tools software supports storage and manipulation of regulatory information through a variety of strategies. The Pathway Tools regulation ontology captures transcriptional and translational regulation, substrate-level regulation of enzyme activity, post-translational modifications, and regulatory pathways. Regulatory visualizations include a novel diagram that summarizes all regulatory influences on a gene; a transcription-unit diagram, and an interactive visualization of a full transcriptional regulatory network that can be painted with gene expression data to probe correlations between gene expression and regulatory mechanisms. We introduce a novel type of enrichment analysis that asks whether a gene-expression dataset is over-represented for known regulators. We present algorithms for ranking the degree of regulatory influence of genes, and for computing the net positive and negative regulatory influences on a gene. CONCLUSIONS Pathway Tools provides a comprehensive environment for manipulating molecular regulatory interactions that integrates regulatory data with an organism's genome and metabolic network. Curated collections of regulatory data authored using Pathway Tools are available for Escherichia coli, Bacillus subtilis, and Shewanella oneidensis.
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Affiliation(s)
- Suzanne M Paley
- Bioinformatics Research Group, SRI International 333 Ravenswood Ave, Menlo Park, CA 94025
| | - Mario Latendresse
- Bioinformatics Research Group, SRI International 333 Ravenswood Ave, Menlo Park, CA 94025
| | - Peter D Karp
- Bioinformatics Research Group, SRI International 333 Ravenswood Ave, Menlo Park, CA 94025
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Gal-Ben-Ari S, Kenney JW, Ounalla-Saad H, Taha E, David O, Levitan D, Gildish I, Panja D, Pai B, Wibrand K, Simpson TI, Proud CG, Bramham CR, Armstrong JD, Rosenblum K. Consolidation and translation regulation. Learn Mem 2012; 19:410-22. [PMID: 22904372 PMCID: PMC3418764 DOI: 10.1101/lm.026849.112] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
mRNA translation, or protein synthesis, is a major component of the transformation of the genetic code into any cellular activity. This complicated, multistep process is divided into three phases: initiation, elongation, and termination. Initiation is the step at which the ribosome is recruited to the mRNA, and is regarded as the major rate-limiting step in translation, while elongation consists of the elongation of the polypeptide chain; both steps are frequent targets for regulation, which is defined as a change in the rate of translation of an mRNA per unit time. In the normal brain, control of translation is a key mechanism for regulation of memory and synaptic plasticity consolidation, i.e., the off-line processing of acquired information. These regulation processes may differ between different brain structures or neuronal populations. Moreover, dysregulation of translation leads to pathological brain function such as memory impairment. Both normal and abnormal function of the translation machinery is believed to lead to translational up-regulation or down-regulation of a subset of mRNAs. However, the identification of these newly synthesized proteins and determination of the rates of protein synthesis or degradation taking place in different neuronal types and compartments at different time points in the brain demand new proteomic methods and system biology approaches. Here, we discuss in detail the relationship between translation regulation and memory or synaptic plasticity consolidation while focusing on a model of cortical-dependent taste learning task and hippocampal-dependent plasticity. In addition, we describe a novel systems biology perspective to better describe consolidation.
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Affiliation(s)
- Shunit Gal-Ben-Ari
- Sagol Department of Neurobiology, University of Haifa, Haifa 31905, Israel
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