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Sahoo A, Pechmann S. Functional network motifs defined through integration of protein-protein and genetic interactions. PeerJ 2022; 10:e13016. [PMID: 35223214 PMCID: PMC8877332 DOI: 10.7717/peerj.13016] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 02/06/2022] [Indexed: 01/11/2023] Open
Abstract
Cells are enticingly complex systems. The identification of feedback regulation is critically important for understanding this complexity. Network motifs defined as small graphlets that occur more frequently than expected by chance have revolutionized our understanding of feedback circuits in cellular networks. However, with their definition solely based on statistical over-representation, network motifs often lack biological context, which limits their usefulness. Here, we define functional network motifs (FNMs) through the systematic integration of genetic interaction data that directly inform on functional relationships between genes and encoded proteins. Occurring two orders of magnitude less frequently than conventional network motifs, we found FNMs significantly enriched in genes known to be functionally related. Moreover, our comprehensive analyses of FNMs in yeast showed that they are powerful at capturing both known and putative novel regulatory interactions, thus suggesting a promising strategy towards the systematic identification of feedback regulation in biological networks. Many FNMs appeared as excellent candidates for the prioritization of follow-up biochemical characterization, which is a recurring bottleneck in the targeting of complex diseases. More generally, our work highlights a fruitful avenue for integrating and harnessing genomic network data.
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Affiliation(s)
- Amruta Sahoo
- Département de Biochimie, Université de Montréal, Montréal, QC, Canada
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2
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Lüönd F, Pirkl M, Hisano M, Prestigiacomo V, Kalathur RK, Beerenwinkel N, Christofori G. Hierarchy of TGFβ/SMAD, Hippo/YAP/TAZ, and Wnt/β-catenin signaling in melanoma phenotype switching. Life Sci Alliance 2021; 5:5/2/e202101010. [PMID: 34819356 PMCID: PMC8616544 DOI: 10.26508/lsa.202101010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 11/11/2021] [Accepted: 11/12/2021] [Indexed: 12/13/2022] Open
Abstract
TGFβ, YAP/TAZ, and canonical Wnt/β-catenin signaling functionally interact in a hierarchical manner to induce the switching of melanoma cells from proliferative-to-invasive cell phenotype. In melanoma, a switch from a proliferative melanocytic to an invasive mesenchymal phenotype is based on dramatic transcriptional reprogramming which involves complex interactions between a variety of signaling pathways and their downstream transcriptional regulators. TGFβ/SMAD, Hippo/YAP/TAZ, and Wnt/β-catenin signaling pathways are major inducers of transcriptional reprogramming and converge at several levels. Here, we report that TGFβ/SMAD, YAP/TAZ, and β-catenin are all required for a proliferative-to-invasive phenotype switch. Loss and gain of function experimentation, global gene expression analysis, and computational nested effects models revealed the hierarchy between these signaling pathways and identified shared target genes. SMAD-mediated transcription at the top of the hierarchy leads to the activation of YAP/TAZ and of β-catenin, with YAP/TAZ governing an essential subprogram of TGFβ-induced phenotype switching. Wnt/β-catenin signaling is situated further downstream and exerts a dual role: it promotes the proliferative, differentiated melanoma cell phenotype and it is essential but not sufficient for SMAD or YAP/TAZ–induced phenotype switching. The results identify epistatic interactions among the signaling pathways underlying melanoma phenotype switching and highlight the priorities in targets for melanoma therapy.
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Affiliation(s)
- Fabiana Lüönd
- Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Martin Pirkl
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Mizue Hisano
- Department of Biomedicine, University of Basel, Basel, Switzerland
| | | | - Ravi Kr Kalathur
- Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
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3
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Green AJ, Anchang B, Akhtari FS, Reif DM, Motsinger-Reif A. Extending the lymphoblastoid cell line model for drug combination pharmacogenomics. Pharmacogenomics 2021; 22:543-551. [PMID: 34044623 DOI: 10.2217/pgs-2020-0160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Combination drug therapies have become an integral part of precision oncology, and while evidence of clinical effectiveness continues to grow, the underlying mechanisms supporting synergy are poorly understood. Immortalized human lymphoblastoid cell lines (LCLs) have been proven as a particularly useful, scalable and low-cost model in pharmacogenetics research, and are suitable for elucidating the molecular mechanisms of synergistic combination therapies. In this review, we cover the advantages of LCLs in synergy pharmacogenomics and consider recent studies providing initial evidence of the utility of LCLs in synergy research. We also discuss several opportunities for LCL-based systems to address gaps in the research through the expansion of testing regimens, assessment of new drug classes and higher-order combinations, and utilization of integrated omics technologies.
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Affiliation(s)
- Adrian J Green
- Department of Biological Sciences & the Bioinformatics Research Center, NC State University, Raleigh, NC, USA
| | - Benedict Anchang
- Biostatistics & Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Farida S Akhtari
- Biostatistics & Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - David M Reif
- Department of Biological Sciences & the Bioinformatics Research Center, NC State University, Raleigh, NC, USA
| | - Alison Motsinger-Reif
- Biostatistics & Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
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4
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Zhang Y, Zhu L, Wang X. NEM-Tar: A Probabilistic Graphical Model for Cancer Regulatory Network Inference and Prioritization of Potential Therapeutic Targets From Multi-Omics Data. Front Genet 2021; 12:608042. [PMID: 33968127 PMCID: PMC8100334 DOI: 10.3389/fgene.2021.608042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 03/22/2021] [Indexed: 11/13/2022] Open
Abstract
Targeted therapy has been widely adopted as an effective treatment strategy to battle against cancer. However, cancers are not single disease entities, but comprising multiple molecularly distinct subtypes, and the heterogeneity nature prevents precise selection of patients for optimized therapy. Dissecting cancer subtype-specific signaling pathways is crucial to pinpointing dysregulated genes for the prioritization of novel therapeutic targets. Nested effects models (NEMs) are a group of graphical models that encode subset relations between observed downstream effects under perturbations to upstream signaling genes, providing a prototype for mapping the inner workings of the cell. In this study, we developed NEM-Tar, which extends the original NEMs to predict drug targets by incorporating causal information of (epi)genetic aberrations for signaling pathway inference. An information theory-based score, weighted information gain (WIG), was proposed to assess the impact of signaling genes on a specific downstream biological process of interest. Subsequently, we conducted simulation studies to compare three inference methods and found that the greedy hill-climbing algorithm demonstrated the highest accuracy and robustness to noise. Furthermore, two case studies were conducted using multi-omics data for colorectal cancer (CRC) and gastric cancer (GC) in the TCGA database. Using NEM-Tar, we inferred signaling networks driving the poor-prognosis subtypes of CRC and GC, respectively. Our model prioritized not only potential individual drug targets such as HER2, for which FDA-approved inhibitors are available but also the combinations of multiple targets potentially useful for the design of combination therapies.
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Affiliation(s)
- Yuchen Zhang
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - Lina Zhu
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - Xin Wang
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China.,Key Laboratory of Biochip Technology, Biotech and Health Centre, Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China
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5
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Pirkl M, Beerenwinkel N. Inferring perturbation profiles of cancer samples. Bioinformatics 2021; 37:2441-2449. [PMID: 33617647 PMCID: PMC8388028 DOI: 10.1093/bioinformatics/btab113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 11/20/2020] [Accepted: 02/18/2021] [Indexed: 11/25/2022] Open
Abstract
Motivation Cancer is one of the most prevalent diseases in the world. Tumors arise due to important genes changing their activity, e.g. when inhibited or over-expressed. But these gene perturbations are difficult to observe directly. Molecular profiles of tumors can provide indirect evidence of gene perturbations. However, inferring perturbation profiles from molecular alterations is challenging due to error-prone molecular measurements and incomplete coverage of all possible molecular causes of gene perturbations. Results We have developed a novel mathematical method to analyze cancer driver genes and their patient-specific perturbation profiles. We combine genetic aberrations with gene expression data in a causal network derived across patients to infer unobserved perturbations. We show that our method can predict perturbations in simulations, CRISPR perturbation screens and breast cancer samples from The Cancer Genome Atlas. Availability and implementation The method is available as the R-package nempi at https://github.com/cbg-ethz/nempi and http://bioconductor.org/packages/nempi. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Martin Pirkl
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, 4058, Switzerland.,Swiss Institute of Bioinformatics, Basel, 4058, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, 4058, Switzerland.,Swiss Institute of Bioinformatics, Basel, 4058, Switzerland
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6
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Holding AN, Cook HV, Markowetz F. Data generation and network reconstruction strategies for single cell transcriptomic profiles of CRISPR-mediated gene perturbations. BIOCHIMICA ET BIOPHYSICA ACTA. GENE REGULATORY MECHANISMS 2020; 1863:194441. [PMID: 31756390 DOI: 10.1016/j.bbagrm.2019.194441] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 10/01/2019] [Accepted: 10/01/2019] [Indexed: 02/05/2023]
Abstract
Recent advances in single-cell RNA-sequencing (scRNA-seq) in combination with CRISPR/Cas9 technologies have enabled the development of methods for large-scale perturbation studies with transcriptional readouts. These methods are highly scalable and have the potential to provide a wealth of information on the biological networks that underlie cellular response. Here we discuss how to overcome several key challenges to generate and analyse data for the confident reconstruction of models of the underlying cellular network. Some challenges are generic, and apply to analysing any single-cell transcriptomic data, while others are specific to combined single-cell CRISPR/Cas9 data, in particular barcode swapping, knockdown efficiency, multiplicity of infection and potential confounding factors. We also provide a curated collection of published data sets to aid the development of analysis strategies. Finally, we discuss several network reconstruction approaches, including co-expression networks and Bayesian networks, as well as their limitations, and highlight the potential of Nested Effects Models for network reconstruction from scRNA-seq data. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.
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Affiliation(s)
- Andrew N Holding
- Department of Biology, University of York, York, UK; York Biomedical Research Institute, University of York, York, UK; CRUK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, UK; The Alan Turing Institute, 96 Euston Road, Kings Cross, London, UK
| | - Helen V Cook
- Department of Biology, University of York, York, UK
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Jacobsen A, Ivanova O, Amini S, Heringa J, Kemmeren P, Feenstra KA. A framework for exhaustive modelling of genetic interaction patterns using Petri nets. Bioinformatics 2020; 36:2142-2149. [PMID: 31845959 DOI: 10.1093/bioinformatics/btz917] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2018] [Revised: 07/09/2019] [Accepted: 12/13/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Genetic interaction (GI) patterns are characterized by the phenotypes of interacting single and double mutated gene pairs. Uncovering the regulatory mechanisms of GIs would provide a better understanding of their role in biological processes, diseases and drug response. Computational analyses can provide insights into the underpinning mechanisms of GIs. RESULTS In this study, we present a framework for exhaustive modelling of GI patterns using Petri nets (PN). Four-node models were defined and generated on three levels with restrictions, to enable an exhaustive approach. Simulations suggest ∼5 million models of GIs. Generalizing these we propose putative mechanisms for the GI patterns, inversion and suppression. We demonstrate that exhaustive PN modelling enables reasoning about mechanisms of GIs when only the phenotypes of gene pairs are known. The framework can be applied to other GI or genetic regulatory datasets. AVAILABILITY AND IMPLEMENTATION The framework is available at http://www.ibi.vu.nl/programs/ExhMod. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Annika Jacobsen
- Department of Computer Science, Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands
| | - Olga Ivanova
- Department of Computer Science, Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands
| | - Saman Amini
- Princess Máxima Center for Pediatric Oncology, 3584 CS Utrecht, Netherlands.,Divison of Biomedical Genetics, Center for Molecular Medicine, University Medical Centre Utrecht, 3584 CX Utrecht, Netherlands
| | - Jaap Heringa
- Department of Computer Science, Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands
| | - Patrick Kemmeren
- Princess Máxima Center for Pediatric Oncology, 3584 CS Utrecht, Netherlands.,Divison of Biomedical Genetics, Center for Molecular Medicine, University Medical Centre Utrecht, 3584 CX Utrecht, Netherlands
| | - K Anton Feenstra
- Department of Computer Science, Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands
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Abstract
The GOSemSim package, an R-based tool within the Bioconductor project, offers several methods based on information content and graph structure for measuring semantic similarity among GO terms, gene products and gene clusters. In this chapter, I illustrate the use of GOSemSim on a list of regulators in preimplantation embryos. A step-by-step analysis was provided as well as instructions on interpretation and visualization of the results. GOSemSim is open-source and is available from https://www.bioconductor.org/packages/GOSemSim .
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Amini S, Jacobsen A, Ivanova O, Lijnzaad P, Heringa J, Holstege FCP, Feenstra KA, Kemmeren P. The ability of transcription factors to differentially regulate gene expression is a crucial component of the mechanism underlying inversion, a frequently observed genetic interaction pattern. PLoS Comput Biol 2019; 15:e1007061. [PMID: 31083661 PMCID: PMC6532943 DOI: 10.1371/journal.pcbi.1007061] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 05/23/2019] [Accepted: 04/30/2019] [Indexed: 12/21/2022] Open
Abstract
Genetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several hypothetical molecular mechanisms have been linked to different genetic interaction types. However, differences in genetic interaction patterns and their underlying mechanisms have not yet been compared systematically between different functional gene classes. Here, differences in the occurrence and types of genetic interactions are compared for two classes, gene-specific transcription factors (GSTFs) and signaling genes (kinases and phosphatases). Genome-wide gene expression data for 63 single and double deletion mutants in baker's yeast reveals that the two most common genetic interaction patterns are buffering and inversion. Buffering is typically associated with redundancy and is well understood. In inversion, genes show opposite behavior in the double mutant compared to the corresponding single mutants. The underlying mechanism is poorly understood. Although both classes show buffering and inversion patterns, the prevalence of inversion is much stronger in GSTFs. To decipher potential mechanisms, a Petri Net modeling approach was employed, where genes are represented as nodes and relationships between genes as edges. This allowed over 9 million possible three and four node models to be exhaustively enumerated. The models show that a quantitative difference in interaction strength is a strict requirement for obtaining inversion. In addition, this difference is frequently accompanied with a second gene that shows buffering. Taken together, these results provide a mechanistic explanation for inversion. Furthermore, the ability of transcription factors to differentially regulate expression of their targets provides a likely explanation why inversion is more prevalent for GSTFs compared to kinases and phosphatases.
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Affiliation(s)
- Saman Amini
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
- Center for Molecular Medicine, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Annika Jacobsen
- Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Olga Ivanova
- Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Philip Lijnzaad
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Jaap Heringa
- Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | | | - K. Anton Feenstra
- Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Patrick Kemmeren
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
- Center for Molecular Medicine, University Medical Centre Utrecht, Utrecht, The Netherlands
- * E-mail:
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Pacini C, Koziol MJ. Bioinformatics challenges and perspectives when studying the effect of epigenetic modifications on alternative splicing. Philos Trans R Soc Lond B Biol Sci 2019; 373:rstb.2017.0073. [PMID: 29685977 PMCID: PMC5915717 DOI: 10.1098/rstb.2017.0073] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/14/2017] [Indexed: 02/07/2023] Open
Abstract
It is widely known that epigenetic modifications are important in regulating transcription, but several have also been reported in alternative splicing. The regulation of pre-mRNA splicing is important to explain proteomic diversity and the misregulation of splicing has been implicated in many diseases. Here, we give a brief overview of the role of epigenetics in alternative splicing and disease. We then discuss the bioinformatics methods that can be used to model interactions between epigenetic marks and regulators of splicing. These models can be used to identify alternative splicing and epigenetic changes across different phenotypes. This article is part of a discussion meeting issue ‘Frontiers in epigenetic chemical biology’.
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Affiliation(s)
- Clare Pacini
- Wellcome Trust Cancer Research UK Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, UK.,Department of Zoology, University of Cambridge, Downing Street, Cambridge, CB2 3EJ, UK
| | - Magdalena J Koziol
- Wellcome Trust Cancer Research UK Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, UK .,Department of Zoology, University of Cambridge, Downing Street, Cambridge, CB2 3EJ, UK
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Abstract
Motivation New technologies allow for the elaborate measurement of different traits of single cells under genetic perturbations. These interventional data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous. Results We developed a mixture of Nested Effects Models (M&NEM) for single-cell data to simultaneously identify different cellular subpopulations and their corresponding causal networks to explain the heterogeneity in a cell population. For inference, we assign each cell to a network with a certain probability and iteratively update the optimal networks and cell probabilities in an Expectation Maximization scheme. We validate our method in the controlled setting of a simulation study and apply it to three data sets of pooled CRISPR screens generated previously by two novel experimental techniques, namely Crop-Seq and Perturb-Seq. Availability and implementation The mixture Nested Effects Model (M&NEM) is available as the R-package mnem at https://github.com/cbg-ethz/mnem/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Martin Pirkl
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
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