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Background Deciphering the genetic architecture of cardiac disorders is of fundamental importance but their underlying complexity is a major hurdle. Drosophila has gained importance as a useful model to study heart development and function and allows the analysis of organismal traits in a physiologically relevant and accessible system. Our aim was to (i) identify in flies the loci associated to natural variations of cardiac performances among a natural population, (ii) decipher how these variants interact with each other and with the environment to impact cardiac traits, (iii) gain insights about the molecular and cellular processes affected, (iv) determine whether the genetic architecture of cardiac disorders is conserved with humans. Methods and Results We investigated the genetic architecture of natural variations of cardiac performance in the sequenced inbred lines of the Drosophila Genetic Reference Panel (DGRP). Genome Wide Associations (GWA) for single markers and epistatic interactions identified genetic networks associated with natural variations of cardiac traits that were extensively validated in vivo. Non-coding variants were used to map potential regulatory non-coding regions which in turn were employed to predict Transcription Factors (TFs) binding sites. Cognate TFs, many of which themselves bear polymorphisms associated with variations of cardiac performance, were validated by heart specific knockdown. We also analyzed natural variations of cardiac traits variance that revealed unique features of their micro-environmental plasticity. More importantly, correlations between genes associated with cardiac phenotypes both in flies and in humans support the conserved genetic architecture of cardiac functioning from arthropods to mammals. The characteristics of natural variations in cardiac function established in Drosophila may thus guide the analysis of cardiac disorders in humans. Using human iPSC-derived cardiomyocytes, we indeed characterized a conserved function for PAX9 and EGR2 in the regulation of the cardiac rhythm Conclusion In-depth analysis of the genetic architecture of natural variations of cardiac performance in flies combined with functional validations in vivo and in human iPSC-CM represents a major achievement in understanding the mechanisms underlying the genetic architecture of these complex traits and a valuable resource for the identification of genes and mechanisms involved in cardiac disorders in humans.

Additional file 1: Fig. S1. Flowchart describing data and the different steps of the analysis leading to the identification of 89,118 miRNA-methylation Quantitative Trait Loci (mimQTLs). Examples of negative and positive correlation between methylation at a CpG and expression of a miRNA are shown as scatterplots at the bottom. Fig. S2. Overview of the 89,118 miRNA-CpG associations found significant in both cohorts. The scatterplots show a) the –log10(Bonferroni-adjusted Spearman correlation p-values) of Oslo2 (x-axis) vs. TCGA (y-axis); b) Spearman correlation coefficients in Oslo2 (x-axis) vs. TCGA (y-axis). The histograms show the distribution of the correlation coefficients (Spearman’s rho) of all significant miRNA-CpG correlations in the Oslo2 (c) and TCGA (d) cohorts. Fig. S3. Number of associations and genomic positions of mimQTL miRNAs and CpGs. a) Barplot showing the number of negative (neg) and positive (pos) CpG correlations (cor) for the three different miRNA clusters. b) mimQTL Manhattan plot with genomic coordinates of CpGs (black or gray) and miRNAs (green) displayed along the x-axis, with the negative logarithm of the Bonferroni-corrected Spearman correlation p-value from Oslo2 on the y-axis. Each dot on the plot signifies a CpG or miRNA (CpGs are shown in two colors to distinguish the chromosomes more clearly). c) In cis mimQTL Manhattan plot displaying the chromosomal location (using the position of the CpG) along the x-axis of the 5125 mimQTLs found on the same chromosome (in cis). Each dot represents one mimQTL which is color-coded according to negative (black) or positive (green) miRNA-CpG correlation. The y-axis displays the negative logarithm of the Bonferroni-corrected Spearman correlation p-value from Oslo2. Fig. S4. Barplots showing the number of associations per miRNA or CpG. a) Barplot showing the number of CpG associations per miRNA (n = 119). Note that the y-axis is on log scale. b) Barplot showing the number of miRNA associations per CpG (n = 26,746). Fig. S5. Density plots showing the degree of CpG co-methylation or miRNA co-expression between cluster members (see Fig. 1 and Additional file 3 a, b) calculated by Spearman correlation. a) Correlation between CpG cluster members in the Oslo2 data. b) Correlation between CpG cluster members in the TCGA data. c) Correlation between miRNA cluster members in the Oslo2 data. d) Correlation between miRNA cluster members in the TCGA data. The dotted lines represent density plots of corresponding correlations expected by chance, i.e. correlations observed after randomly permuting the same data before performing correlation analyses. Fig. S6. Density plots showing the distribution of Spearman correlation coefficients between miRNA expression and selected variables for members of each of the miRNA clusters. a, b) miRNA expression-immune infiltration score [34] correlations for the Oslo2 (a) and TCGA (b) cohorts. c, d) miRNA expression-fibroblast infiltration score [35] correlations for the Oslo2 (c) and TCGA (d) cohorts. e, f) miRNA expression-ESR1 mRNA expression correlations for the Oslo2 (e) and TCGA (f) cohorts. Fig. S7. Heatmaps showing hierarchical clustering of miRNA expression levels (rows) from tumors (columns) of the Oslo2 (top) and TCGA (bottom) cohort. Clustering was performed using Euclidean distance and average linkage. Tumors are annotated with the following clinical/molecular classifications: PAM50 molecular subtypes (Luminal A (LumA), Luminal B (LumB), Basal-like (Basal), HER2-enriched (Her2), Normal-like (Normal); Lymphocyte infiltration (LI) group where tumors were divided into quartiles: 1 (low) – 4 (high); Fibroblast infiltration group (Fibro) where tumors were divided into quartiles: 1 (low) – 4 (high); Human epidermal growth factor receptor 2 (HER2) status; Estrogen receptor (ER) status. a, b) Clustering of miRNA cluster A expression (n = 23); c, d) Clustering of miRNA cluster B expression (n = 59); e, f) Clustering of miRNA cluster C expression (n = 37). Fig. S8. Heatmaps showing hierarchical clustering of methylation levels of CpG cluster 1 (a; n = 14,040) and CpG cluster 2 (b; n = 12,706) in the TCGA cohort (CpGs in rows and tumors in columns). Clustering was performed using Euclidean distance and average linkage. Tumors are annotated with the following clinical/molecular classifications: PAM50 molecular subtypes (Luminal A (LumA), Luminal B (LumB), Basal-like (Basal), HER2-enriched (Her2), Normal-like (Normal); Lymphocyte infiltration (LI) group where tumors were divided into quartiles: 1 (low) – 4 (high); Human epidermal growth factor receptor 2 (HER2) status; Estrogen receptor (ER) status. The CpGs are annotated according to overlap with regions annotated as “active intergenic enhancer” from ChromHMM of subtype-specific cell lines [37] with corresponding subtype colors. Fig. S9. Boxplot showing average DNA methylation of CpGs from cluster 1 in PAM50 subtypes of the Oslo2 cohort (Luminal A (LumA), Luminal B (LumB), Basal-like (Basal), HER2-enriched (Her2)). b) Boxplot showing average DNA methylation of CpGs from cluster 2 in the TCGA cohort when tumors were separated into quartile lymphocyte infiltration groups from low (1) to high (4) infiltration. c) Boxplot showing average DNA methylation of CpGs from cluster 2 in normal breast tissue (reduction mammoplasty, n = 17) or estrogen receptor (ER) positive (pos) or negative (neg) tumors of the Oslo2 cohort. d) Boxplot showing average DNA methylation of CpGs from cluster 2 in normal breast tissue (normal adjacent breast tissue, n = 97) or ER positive or negative tumors of the TCGA cohort. P-values resulting from Kruskal-Wallis tests indicated. Fig. S10. Boxplot showing DNA methylation of the hub CpG of miRNA cluster A (cg14270581; y-axis)) in ER positive (pos) and negative (neg) breast cancer cell lines and from different immune cell types (x-axis); B-cells, leukocytes (leuko), monocytes (mono) and T-cells. P-value resulting from Wilcoxon rank-sum test between cancer cell lines vs. immune cells is indicated. Fig. S11. Density plot showing the distribution of the Global Methylation Alteration (GMA) score in normal adjacent breast tissue (green), tumors (black) and tumors separated into estrogen receptor (ER) positive (pos) and negative (neg). Data from TCGA. Fig. S12. Top panel: Scatterplots showing on the x-axis mRNA expression of DNMT3A (left), DNMT3B (middle) and DNMT1 (right) vs. hsa-miR-29c-5p expression (y-axis) measured in 377 samples of the Oslo2 cohort. Bottom panel: Scatterplots showing on the x-axis protein expression of DNMT3A (left), DNMT3B (middle) and DNMT1 (right) vs. hsa-miR-29c-5p expression (y-axis) measured in 45 samples of the Oslo2 cohort. Each dot represents a tumor color-coded according to PAM50 subtype (Luminal A (LumA): dark blue; Luminal B (LumB): light blue; Basal-like (Basal): red; HER2-enriched (Her2): pink; Normal-like: green). Spearman correlation coefficient and p-value are indicated for each plot.

Additional file 1: Table S1. Overview of the permissive collection. Table providing the number of datasets, TFs, cell / tissue types, and TFBSs in the permissive collection of UniBind. The number of TFBSs was computed as the number of unique instances of genomic loci bound by a TF. Table S2. Overview of the robust collection. Table providing the number of datasets, TFs, cell /tissue types, and TFBSs in the robust collection of UniBind. The number of TFBSs was computed as the number of unique instances of genomic loci bound by a TF. Figure S1. Visual overview of the permissive collection. (A) Barplots showing the number of TFs (dark orange), TFBSs (green), datasets (blue), and cell and tissue types (light orange) stored in the permissive collection of UniBind for each analyzed species. All values are log10-transformed. (B) Distribution of the percentages of the genomes covered by robust TFBSs in each species (one color per species, see legend). Figure S2. Relationship between number of datasets and genome coverage. Scatter plots representing the percentage of genome coverage (y-axes) with respect to the number of datasets in the permissive (A) and robust (C) collections or the number of TFs in the permissive (B) and robust (D) collection (x-axes). Each colored point in each panel represents the data associated to one species (see legend for color coding). Figure S3. The UniBind 2021 compressed and robust tracks with all TFBSs from the robust human collection. An example of a random genomic locus showing the comparison between the original and archetypal TFBSs. The tracks shown are, from top to bottom: RefSeq track with the first intron of the human TTC6 gene, the UniBind compressed track with archetypal TFBSs, and the UniBind robust track showing all TFBSs at the same location. Figure S4. Evolutionary conservation at human and mouse robust CRMs. Distributions of the average base-pair evolutionary conservation scores (phyloP and phastCons scores using multi-species genome alignments, see legend) at regions centered around UniBind human (A) and mouse (B) CRMs from the robust collection. Conservation of random CRMs was obtained by shuffling the original CRMs and obtaining the conservation score of the new regions. Figure S5. Enrichment analysis for A. thaliana TFBSs in genomic regions. Barplots representing the expected (grey bars) versus observed (blue bars) overlap lengths (A) or number of intersections (B) between A. thaliana TFBSs from the robust collection and genomic annotations (x-axis). The plots and computed p-values (green: enrichment; orange: depletion) were obtained using the OLOGRAM command of the GTF toolkit. Figure S6. Enrichment analysis for C. elegans TFBSs in genomic regions. Barplots representing the expected (grey bars) versus observed (blue bars) overlap lengths (A) or number of intersections (B) between C. elegans TFBSs from the robust collection and genomic annotations (x-axis). The plots and computed p-values (green: enrichment; orange: depletion) were obtained using the OLOGRAM command of the GTF toolkit. Figure S7. Enrichment analysis for D. rerio TFBSs in genomic regions. Barplots representing the expected (grey bars) versus observed (blue bars) overlap lengths (A) or number of intersections (B) between D. rerio TFBSs from the robust collection and genomic annotations (x-axis). The plots and computed p-values (green: enrichment; orange: depletion) were obtained using the OLOGRAM command of the GTF toolkit. Figure S8. Enrichment analysis for D. melanogaster TFBSs in genomic regions. Barplots representing the expected (grey bars) versus observed (blue bars) overlap lengths (A) or number of intersections (B) between D. melanogaster TFBSs from the robust collection and genomic annotations (x-axis). The plots and computed p-values (green: enrichment; orange: depletion) were obtained using the OLOGRAM command of the GTF toolkit. Figure S9. Enrichment analysis for R. norvegicus TFBSs in genomic regions. Barplots representing the expected (grey bars) versus observed (blue bars) overlap lengths (A) or number of intersections (B) between R. norvegicus TFBSs from the robust collection and genomic annotations (x-axis). The plots and computed p-values (green: enrichment; orange: depletion) were obtained using the OLOGRAM command of the GTF toolkit. Figure S10. Enrichment analysis for S. cerevisae TFBSs in genomic regions. Barplots representing the expected (grey bars) versus observed (blue bars) overlap lengths (A) or number of intersections (B) between S. cerevisae TFBSs from the robust collection and genomic annotations (x-axis). The plots and computed p-values (green: enrichment; orange: depletion) were obtained using the OLOGRAM command of the GTF toolkit. Figure S11. Analysis of the overlap of robust TFBSs with respect to genomic annotations in all species in UniBind. Fraction of TFBSs in the UniBind robust collection (y-axis) with respect to increasing relative distances (x-axis) from different genomic regions computed using the bedtools reldist command. When two genomic tracks are not spatially related, one expects the fraction of relative distance distribution to be uniform. Figure S12. Genomic distribution of TFBSs in A. thaliana, C. elegans and D. rerio. Distribution of the proportion of A. thaliana, C. elegans and D. rerio UniBind robust TFBSs overlapping with different types of genomic regions (colors; see legend) across TFs (columns). Figure S13. Genomic distribution of TFBSs in D. melanogaster and H. sapiens. Distribution of the proportion of D. melanogaster and H. sapiens UniBind robust TFBSs overlapping with different types of genomic regions (colors; see legend) across TFs (columns). Figure S14. Genomic distribution of TFBSs in H. sapiens (continued) and M. musculus. Distribution of the proportion of H. sapiens (continued) and M. musculus UniBind robust TFBSs overlapping with different types of genomic regions (colors; see legend) across TFs (columns). Figure S15. Genomic distribution of TFBSs in M. musculus (continued). Distribution of the proportion of M. musculus (continued) UniBind robust TFBSs overlapping with different types of genomic regions (colors; see legend) across TFs (columns). Figure S16. Genomic distribution of TFBSs in R. norvegicus and S. cerevisiae. Distribution of the proportion of R. norvegicus and S. cerevisiae UniBind robust TFBSs overlapping with different types of genomic regions (colors; see legend) across TFs (columns). Figure S17. Enrichment analysis for H. sapiens TFBSs in ENCODE cCREs. Barplots representing the expected (grey bars) versus observed (blue bars) overlap lengths (A) or number of intersections (B) between H. sapiens TFBSs from the robust collection and ENCODE cCREs (x-axis). The plots and computed p-values (green: enrichment; orange: depletion) were obtained using the OLOGRAM command of the GTF toolkit. Figure S18. Enrichment analysis for M. musculus TFBSs in ENCODE cCREs. Barplots representing the expected (grey bars) versus observed (blue bars) overlap lengths (A) or number of intersections (B) between M. musculus TFBSs from the robust collection and ENCODE cCREs (x-axis). The plots and computed p-values (green: enrichment; orange: depletion) were obtained using the OLOGRAM command of the GTF toolkit. Figure S19. Enrichment analysis for H. sapiens CRMs in ENCODE cCREs. Barplots representing the expected (grey bars) versus observed (blue bars) overlap lengths (A) or number of intersections (B) between H. sapiens CRMs from the robust collection and ENCODE cCREs (x-axis). The plots and computed p-values (green: enrichment; orange: depletion) were obtained using the OLOGRAM command of the GTF toolkit. Figure S20. Enrichment analysis for M. musculus CRMs in ENCODE cCREs. Barplots representing the expected (grey bars) versus observed (blue bars) overlap lengths (A) or number of intersections (B) between M. musculus CRMs from the robust collection and ENCODE cCREs (x-axis). The plots and computed p-values (green: enrichment; orange: depletion) were obtained using the OLOGRAM command of the GTF toolkit. Figure S21. Relative distance distributions between CRMs and ENCODE cCREs. Fraction of CRMs in the UniBind robust collection (y-axis) with respect to increasing relative distances (x-axis) from ENCODE cCREs computed using the bedtools reldist command for human (A) and mouse (B). When two genomic tracks are not spatially related, one expects the fraction of relative distance distribution to be uniform. Figure S22. Correlation between enhancer activity and TF binding. For each enhancer predicted using Cap Analysis of Gene Expression (CAGE) by the FANTOM5 consortium, we computed the number of TFs with overlapping TFBSs in the robust collection of UniBind (x-axis). The figure provides, for each value of the number of TFs found to bind in enhancers, the median (blue line) together with the 10th to 90th percentiles (grey area) of tissue specific activity of these enhancers. The expression measures were derived from CAGE (capturing enhancer RNA expression). The specificity of activity (y-axis) is provided within the [0; 1] range with 0 representing ubiquitous enhancer activity and 1 exclusive expression activity.

Gene expression is controlled by the involvement of gene-proximal (promoters) and distal (enhancers) regulatory elements. Our previous results demonstrated that a subset of gene promoters, termed Epromoters, work as bona fide enhancers and regulate distal gene expression. Here, we hypothesized that Epromoters play a key role in the coordination of rapid gene induction during the inflammatory response. Using a high-throughput reporter assay we explored the function of Epromoters in response to type I interferon. We find that clusters of IFNa-induced genes are frequently associated with Epromoters and that these regulatory elements preferentially recruit the STAT1/2 and IRF transcription factors and distally regulate the activation of interferon-response genes. Consistently, we identified and validated the involvement of Epromoter-containing clusters in the regulation of LPS-stimulated macrophages. Our findings suggest that Epromoters function as a local hub recruiting the key TFs required for coordinated regulation of gene clusters during the inflammatory response., Some promoters, termed Epromoters, can display enhancer activity and regulate distal genes in their endogenous context. Here the authors report the broad involvement of Epromoters in the coordinated activation of interferon (IFN) response. They show activation of a number of IFN-inducible genes is a result of Epromoters regulating expression of an entire IFN response gene cluster.

12 Pags.- 1 Tabl.- 1 Fig., RSAT (Regulatory Sequence Analysis Tools) is a modular software suite for the analysis of cisregulatory elements in genome sequences. Its main applications are (i) motif discovery, appropriate to genome-wide data sets like ChIP-seq, (ii) transcription factor binding motif analysis (quality assessment, comparisons and clustering), (iii) comparative genomics and (iv) analysis of regulatory variations. Nine new programs have been added to the 43 described in the 2011 NAR Web Software Issue, including a tool to extract sequences from a list of coordinates (fetch-sequences from UCSC), novel programs dedicated to the analysis of regulatory variants from GWAS or population genomics (retrieve-variationseq and variation-scan), a program to cluster motifs and visualize the similarities as trees (matrixclustering). To deal with the drastic increase of sequenced genomes, RSAT public sites have been reorganized into taxon-specific servers. The suite is well-documented with tutorials and published protocols. The software suite is available through Web sites, SOAP/WSDL Web services, virtual machines and stand-alone programs at http://www.rsat.eu/., This work was supported by the EU-funded COST action [BM1006 "SEQAHEAD - Next Generation Sequencing Data Analysis Network"]; FP7 MICROME Collaborative Project [Microbial genomics and bio-informatics", contract number 222886-2]; Programa Euroinvestigación/Plant KBBE 2008 (EUI2008-03612) (BCM); the GIS IBiSA and France Génomique National infrastructure, funded as part of the « Investissements d’Avenir » program managed by the French Agence Nationale pour la Recherche (contract ANR-10-INBS-09) ; projects iBone & EchiNodal funded by the French Agence Nationale pour la Recherche. JCM has been supported by master fellowship from CONACyT-Mexico and a “Contrat Doctoral d'Aix-Marseille Université attribué sur Concours EDSVS ». AMR was funded by the Consejo Nacional de Ciencia y Tecnología (CONACYT) and a CIHR Training grant (GET-101831) in Genetic Epidemiology and Statistical Genetics.