Automated generation of context-specific Gene Regulatory Networks with a weighted approach in D. melanogaster

Motivation The regulation of gene expression is a key factor in the development and maintenance of life in all organisms. This process is carried out mainly through the action of transcription factors (TFs), although other actors such as ncRNAs are involved. In this work, we propose a new method to construct Gene Regulatory Networks (GRNs) depicting regulatory events in a certain context for Drosophila melanogaster. Our approach is based on known relationships between epigenetics and the activity of transcription factors. Results We developed method, Tool for Weighted Epigenomic Networks in D. melanogaster (Fly T-WEoN), which generates GRNs starting from a reference network that contains all known gene regulations in the fly. Regulations that are unlikely taking place are removed by applying a series of knowledge-based filters. Each of these filters is implemented as an independent module that considers a type of experimental evidence, including DNA methylation, chromatin accessibility, histone modifications, and gene expression. Fly T-WEoN is based on heuristic rules that reflect current knowledge on gene regulation in D. melanogaster obtained from literature. Experimental data files can be generated with several standard procedures and used solely when and if available.Fly T-WEoN is available as a Cytoscape application that permits integration with other tools, and facilitates downstream network analysis. In this work, we first demonstrate the reliability of our method to then provide a relevant application case of our tool: early development of D. melanogaster. Availability Fly T-WEoN, together with its step-by-step guide is available at https://weon.readthedocs.io Contact alberto.martin@umayor.cl

[1]  Ann Dean,et al.  Enhancer and promoter interactions-long distance calls. , 2012, Current opinion in genetics & development.

[2]  Tongbin Li,et al.  miRecords: an integrated resource for microRNA–target interactions , 2008, Nucleic Acids Res..

[3]  M. Campbell,et al.  PANTHER: a library of protein families and subfamilies indexed by function. , 2003, Genome research.

[4]  D. Bartel MicroRNAs: Target Recognition and Regulatory Functions , 2009, Cell.

[5]  Jana Krejcí,et al.  Histone Modifications and Nuclear Architecture: A Review , 2008, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[6]  Charles Kooperberg,et al.  The histone modification pattern of active genes revealed through genome-wide chromatin analysis of a higher eukaryote. , 2004, Genes & development.

[7]  Yanhui Hu,et al.  FlyBase at 25: looking to the future , 2016, Nucleic Acids Res..

[8]  I. Rebay,et al.  Master regulators in development: Views from the Drosophila retinal determination and mammalian pluripotency gene networks. , 2017, Developmental biology.

[9]  A. Sandelin,et al.  Determinants of enhancer and promoter activities of regulatory elements , 2019, Nature Reviews Genetics.

[10]  L. Morales-Nebreda,et al.  DNA methylation as a transcriptional regulator of the immune system , 2019, Translational research : the journal of laboratory and clinical medicine.

[11]  Keqin Li,et al.  Insights into the Functions of LncRNAs in Drosophila , 2019, International journal of molecular sciences.

[12]  Alberto J. Martin,et al.  LoTo: a graphlet based method for the comparison of local topology between gene regulatory networks , 2017, PeerJ.

[13]  Marcel H. Schulz,et al.  Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction , 2016, bioRxiv.

[14]  Michael W. Hall,et al.  Ananke: temporal clustering reveals ecological dynamics of microbial communities , 2017, PeerJ.

[15]  M. Gerstein,et al.  Unlocking the secrets of the genome , 2009, Nature.

[16]  Ziv Bar-Joseph,et al.  DREM 2.0: Improved reconstruction of dynamic regulatory networks from time-series expression data , 2012, BMC Systems Biology.

[17]  J. C. Yasuhara,et al.  Molecular Landscape of Modified Histones in Drosophila Heterochromatic Genes and Euchromatin-Heterochromatin Transition Zones , 2007, PLoS genetics.

[18]  Michael L. Creech,et al.  Integration of biological networks and gene expression data using Cytoscape , 2007, Nature Protocols.

[19]  Charles Blatti,et al.  Integrating motif, DNA accessibility and gene expression data to build regulatory maps in an organism , 2015, Nucleic acids research.

[20]  Sarah A Teichmann,et al.  The developmental expression dynamics of Drosophila melanogaster transcription factors , 2010, Genome Biology.

[21]  Nicholas Carriero,et al.  Mocap: large-scale inference of transcription factor binding sites from chromatin accessibility , 2016, bioRxiv.

[22]  E. Sontheimer,et al.  Origins and Mechanisms of miRNAs and siRNAs , 2009, Cell.

[23]  Canglin Wu,et al.  RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse , 2015, Database J. Biol. Databases Curation.

[24]  Y. Guan,et al.  Anchor: trans-cell type prediction of transcription factor binding sites , 2018, Genome research.

[25]  V. Corces,et al.  Insulators, long-range interactions, and genome function. , 2012, Current opinion in genetics & development.

[26]  B. Strahl,et al.  Interpreting the language of histone and DNA modifications. , 2014, Biochimica et biophysica acta.

[27]  Hsien-Da Huang,et al.  miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions , 2017, Nucleic Acids Res..

[28]  Jacob F. Degner,et al.  Sequence and Chromatin Accessibility Data Accurate Inference of Transcription Factor Binding from Dna Material Supplemental Open Access , 2022 .

[29]  David J. Arenillas,et al.  JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework , 2017, Nucleic acids research.

[30]  S. Shen-Orr,et al.  Network motifs: simple building blocks of complex networks. , 2002, Science.

[31]  Doron Lancet,et al.  GeneHancer: genome-wide integration of enhancers and target genes in GeneCards , 2017, Database J. Biol. Databases Curation.

[32]  Jacob D. Jaffe,et al.  Plasticity in patterns of histone modifications and chromosomal proteins in Drosophila heterochromatin. , 2011, Genome research.

[33]  Guillaume J. Filion,et al.  Systematic Protein Location Mapping Reveals Five Principal Chromatin Types in Drosophila Cells , 2010, Cell.

[34]  Alberto J. M. Martin,et al.  Graphlet Based Metrics for the Comparison of Gene Regulatory Networks , 2016, PloS one.

[35]  T. Hughes,et al.  The Human Transcription Factors , 2018, Cell.

[36]  Michele Ceccarelli,et al.  TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages [version 1; referees: 1 approved, 1 approved with reservations] , 2016 .

[37]  Anushya Muruganujan,et al.  Large-scale gene function analysis with the PANTHER classification system , 2013, Nature Protocols.

[38]  Gianluca Bontempi,et al.  TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages , 2016, F1000Research.

[39]  M. Snyder,et al.  A High-Resolution Whole-Genome Map of Key Chromatin Modifications in the Adult Drosophila melanogaster , 2011, PLoS genetics.

[40]  Axel Imhof,et al.  Fast signals and slow marks: the dynamics of histone modifications. , 2010, Trends in biochemical sciences.

[41]  Christian H. Holland,et al.  Benchmark and integration of resources for the estimation of human transcription factor activities. , 2019, Genome research.

[42]  Sergio Contrino,et al.  modMine: flexible access to modENCODE data , 2011, Nucleic Acids Res..

[43]  Lovelace J. Luquette,et al.  Comprehensive analysis of the chromatin landscape in Drosophila , 2010, Nature.

[44]  I. Boros,et al.  Histone modification in Drosophila. , 2012, Briefings in functional genomics.