Multiomics uncovers the epigenomic and transcriptomic response to viral and bacterial stimulation in turbot

  1. Oscar Aramburu
  2. Belén Gómez-Pardo
  3. Paula Rodríguez-Villamayor
  4. Andrés Blanco-Hortas
  5. Jesús Lamas
  6. Pooran Dewari
  7. Diego Perojil-Morata
  8. Pierre Boudinot
  9. Daniel J. Macqueen
  10. Carmen Bouza
  11. Paulino Martínez

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Abstract

Uncovering the epigenomic regulation of immune responses is essential for a comprehensive understanding of host defence mechanisms, though remains poorly investigated in farmed fish. We report the first annotation of the innate immune regulatory response in the turbot genome (Scophthalmus maximus), integrating RNA-Seq with ATAC-Seq and ChIP-Seq (H3K4me3, H3K27ac and H3K27me3) data from head kidney (in vivo) and primary leukocyte cultures (in vitro) 24 hours post-stimulation with viral (poly I:C) and bacterial (inactive Vibrio anguillarum ) mimics. Among the 8,797 differentially expressed genes (DEGs), we observed enrichment of transcriptional activation pathways in response to Vibrio and immune pathways - including interferon stimulated genes - for poly I:C. We identified notable differences in chromatin accessibility (20,617 in vitro, 59,892 in vivo) and H3K4me3-bound regions (11,454 in vitro, 10,275 in vivo) between stimulations and controls. Overlap of DEGs with promoters showing differential accessibility or histone mark binding revealed significant coupling of the transcriptome and chromatin state. DEGs with activation marks in their promoters were enriched for similar functions to the global DEG set, but not always, suggesting key regulatory genes being in poised state. Active promoters and putative enhancers were enriched in specific transcription factor binding motifs, many common to viral and bacterial responses. Finally, an in-depth analysis of immune response changes in chromatin state surrounding key DEGs encoding transcription factors was performed. This multi-omics investigation provides an improved understanding of the epigenomic basis for the turbot immune responses and provides novel functional genomic information, leverageable for disease resistance selective breeding.

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  1. GigaScience

    AbstractUncovering the epigenomic regulation of immune responses is essential for a comprehensive understanding of host defence mechanisms, though remains poorly investigated in farmed fish. We report the first annotation of the innate immune regulatory response in the turbot genome (Scophthalmus maximus), integrating RNA-Seq with ATAC-Seq and ChIP-Seq (H3K4me3, H3K27ac and H3K27me3) data from head kidney (in vivo) and primary leukocyte cultures (in vitro) 24 hours post-stimulation with viral (poly I:C) and bacterial (inactive Vibrio anguillarum) mimics. Among the 8,797 differentially expressed genes (DEGs), we observed enrichment of transcriptional activation pathways in response to Vibrio and immune pathways - including interferon stimulated genes - for poly I:C. We identified notable differences in chromatin accessibility (20,617 in vitro, 59,892 in vivo) and H3K4me3-bound regions (11,454 in vitro, 10,275 in vivo) between stimulations and controls. Overlap of DEGs with promoters showing differential accessibility or histone mark binding revealed significant coupling of the transcriptome and chromatin state. DEGs with activation marks in their promoters were enriched for similar functions to the global DEG set, but not always, suggesting key regulatory genes being in poised state. Active promoters and putative enhancers were enriched in specific transcription factor binding motifs, many common to viral and bacterial responses. Finally, an in-depth analysis of immune response changes in chromatin state surrounding key DEGs encoding transcription factors was performed. This multi-omics investigation provides an improved understanding of the epigenomic basis for the turbot immune responses and provides novel functional genomic information, leverageable for disease resistance selective breeding.

    This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf077), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

    Reviewer name: Aijun Ma

    In the manuscript "Multiomics uncovers the epigenomic and transcriptomic response to viral and bacterial stimulation in turbot", many investigations were applied to uncover the immune regulatory response in the turbot. This multi-omics investigation provided an improved understanding of the epigenomic basis of turbot immune response and offers novel functional genomic information. However, some aspects need to be considered in order to improve the manuscript, as indicated below. 1 Line 16: In this sentence, authors used "the innate immune regulatory response" to describe the response of these two stimuli in a tissue and cell. Innate immunity is a very strict term, and it is not appropriate to use it here. 2 Line 34-36: poly I:C and inactive Vibrio anguillarum were just like PAMP, the response to these two stimulations cannot represent the process of disease defense. The sentence "which can be leveraged for disease resistance selective breeding" was listed in conclusions, that was not accurate. Suggest moving this sentence to the outlook section. 3 Line 80-87: Head kidney is a key lymphoid organ in most marine fishes, and plays central role in fish immunity. It is inappropriate to only talk about its innate immune function. Vibrio is a common bacterium in seawater, while Vibrio anguillarum is an opportunistic pathogen. Strictly speaking, experimental fish will inevitably meet Vibrio during the breeding process before the experiment. Suggest reorganizing the sentences of this paragraph.

  2. GigaScience

    AbstractUncovering the epigenomic regulation of immune responses is essential for a comprehensive understanding of host defence mechanisms, though remains poorly investigated in farmed fish. We report the first annotation of the innate immune regulatory response in the turbot genome (Scophthalmus maximus), integrating RNA-Seq with ATAC-Seq and ChIP-Seq (H3K4me3, H3K27ac and H3K27me3) data from head kidney (in vivo) and primary leukocyte cultures (in vitro) 24 hours post-stimulation with viral (poly I:C) and bacterial (inactive Vibrio anguillarum) mimics. Among the 8,797 differentially expressed genes (DEGs), we observed enrichment of transcriptional activation pathways in response to Vibrio and immune pathways - including interferon stimulated genes - for poly I:C. We identified notable differences in chromatin accessibility (20,617 in vitro, 59,892 in vivo) and H3K4me3-bound regions (11,454 in vitro, 10,275 in vivo) between stimulations and controls. Overlap of DEGs with promoters showing differential accessibility or histone mark binding revealed significant coupling of the transcriptome and chromatin state. DEGs with activation marks in their promoters were enriched for similar functions to the global DEG set, but not always, suggesting key regulatory genes being in poised state. Active promoters and putative enhancers were enriched in specific transcription factor binding motifs, many common to viral and bacterial responses. Finally, an in-depth analysis of immune response changes in chromatin state surrounding key DEGs encoding transcription factors was performed. This multi-omics investigation provides an improved understanding of the epigenomic basis for the turbot immune responses and provides novel functional genomic information, leverageable for disease resistance selective breeding.

    This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf077), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

    Reviewer name: Elisabeth Busch-Nentwich

    This is a careful analysis of a large and high-quality dataset that will be a very useful resource for researchers across disciplines. I commend the authors on their extensive metadata, and comprehensive and well annotated data tables, which make this a truly accessible resource. I don't have any major criticism. A few minor points:

    1. Typo in Figure 1 (it's immature, not inmature)
    2. In Fig 3 Upset plots could be a bit easier to parse
    3. Fig 5 doesn't have a legend for the blue gradient (but it's pretty self-explanatory)
  3. GigaScience

    AbstractUncovering the epigenomic regulation of immune responses is essential for a comprehensive understanding of host defence mechanisms, though remains poorly investigated in farmed fish. We report the first annotation of the innate immune regulatory response in the turbot genome (Scophthalmus maximus), integrating RNA-Seq with ATAC-Seq and ChIP-Seq (H3K4me3, H3K27ac and H3K27me3) data from head kidney (in vivo) and primary leukocyte cultures (in vitro) 24 hours post-stimulation with viral (poly I:C) and bacterial (inactive Vibrio anguillarum) mimics. Among the 8,797 differentially expressed genes (DEGs), we observed enrichment of transcriptional activation pathways in response to Vibrio and immune pathways - including interferon stimulated genes - for poly I:C. We identified notable differences in chromatin accessibility (20,617 in vitro, 59,892 in vivo) and H3K4me3-bound regions (11,454 in vitro, 10,275 in vivo) between stimulations and controls. Overlap of DEGs with promoters showing differential accessibility or histone mark binding revealed significant coupling of the transcriptome and chromatin state. DEGs with activation marks in their promoters were enriched for similar functions to the global DEG set, but not always, suggesting key regulatory genes being in poised state. Active promoters and putative enhancers were enriched in specific transcription factor binding motifs, many common to viral and bacterial responses. Finally, an in-depth analysis of immune response changes in chromatin state surrounding key DEGs encoding transcription factors was performed. This multi-omics investigation provides an improved understanding of the epigenomic basis for the turbot immune responses and provides novel functional genomic information, leverageable for disease resistance selective breeding.

    This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf077), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

    Reviewer name: Laura Caquelin

    1. Summary of the Study This study provides the first multi-omics investigation of the innate immune response in turbot (Scophthalmus maximus). By integrating RNA-Seq, ATAC-Seq, and ChIP-Seq data, researchers identified changes in gene expression, chromatin accessibility, and histone modifications after viral and bacterial stimulation. The findings reveal a significant coupling between the transcriptome and chromatin state, offering insights for the selection of disease resistance in aquaculture.

    2. Scope of reproducibility

    According to our assessment the primary objective is: Association of ATAC-Seq and ChIP-Seq data with RNA-Seq data

    ● Outcome: Overlap of promoter DARs and DHMRs with DEG promoters ● Analysis method outcome: Hypergeometric test ● Main result: "DARs and DHMRs were much more overrepresented at the promoter regions of upregulated rather than downregulated DEGs" (Table 4, Supplementary Table 11; Lines 403-405, Page 9)

    1. Availability of Materials a. Data ● Data availability: Raw data are available, but generated data from the study are shared with the journal and not yet publicly available ● Data completeness: Complete ● Access Method: Manuscript's supplementary files/Private journal dropbox ● Repository: - ● Data quality: Structured, but lacks variable definitions in supplementary files, making it difficult to interpret and use. b. Code ● Code availability: Not available for the primary result ● Programming Language(s): Excel ● Repository link: - ● License: - ● Repository status: - ● Documentation: README lacks information on hypergeometric test.

    2. Computational environment of reproduction analysis

    ● Operating system for reproduction: MacOS 14.7.4 ● Programming Language(s): Excel ● Code implementation approach: Excel formulas based on methodology description provided by authors ● Version environment for reproduction: Excel version 16.94

    1. Results

    5.1 Original study results ● Results 1: Table 4 and supplementary table 11

    5.3 Steps for reproduction

     Reproduce supplementary table 11 to perform hypergeometric test

    • Issue 1: No code or instructions for constructing Table 4 in manuscript and README text. ▪ Resolved: Authors shared methodology upon request Authors' Clarification: The hypergeometric test wasn't carried out with any particular script but with the following public online tool, that can be replicated in excel: https://systems.crump.ucla.edu/hypergeometric/ The tool basically runs the following excel formulas: Cumulative distribution function (CDF) of the hypergeometric distribution in Excel =IF(k>=expected,1-HYPGEOM.DIST(k-1,s,M,N,TRUE),HYPGEOM.DIST(k,s,M,N,TRUE)) =IF(k>=((sM)/N),1-HYPGEOM.DIST(k-1,s,M,N,TRUE),HYPGEOM.DIST(k,s,M,N,TRUE)) expected = (sM)/N direction =IF(k=expected,"match",IF(k<expected,"de-enriched","enriched")) fold change =IF(k<expected,expected/k,k/expected)

    where k is the number of successes (intersection of DAR/DHMR in promoters + DEG), s the sample size (DEG), M the number of successes in the population (DAR/DHMR in promoters) and N the population size (28.602 genes). For each condition, the count of downregulated and upregulated DEG (s) was taken from supplementary table 4. Similarly, the count of downregulated and upregulated DAR/DHMR (M) was taken from supplementary table 10, considering only differential peaks that are annotated as "promoter-TSS" in the annotation column (column M). The population size (N) was the total list of genes that were DEG, DAR or DHMR (combining the data on supplementary tables 4 and 11, eliminating duplicates). Finally, the intersection of of DAR and DEG (k) for each condition was retrieved with the following venn diagram online tool: https://bioinformatics.psb.ugent.be/webtools/Venn/"

    • Issue 2: Discrepancies in DEG counts from supplementary table 11 ▪ Resolved: Investigated variable definitions (using the wrong variable - strand), confirmed that log2FoldChange determines up/down-regulation
    • Issue 3: Filling in DAR/DHMR values ▪ Unresolved: Unclear correspondence between "promoters" rows and excel file sheets. Does H3K27me3 correspond to the promoters?
    • Issue 4: Using the Venn diagram tool to find intersections ▪ Unresolved: Worked for one condition (ATC vivo poly (down)) but failed for ATAC vitro-vibrio and ATAC-vivo-vibrio. Tool returns a "Request Entity Too Large" error.
    • Issue 5: Define the population size ▪ Unresolved: The instructions for defining the population size are not clear. In supplementary table 4, it seems that the variable "Gene ID (ENSEMBL)" should be used, but in supplementary table 10, should the variable "Nearest PromoterID" or "Gene symbol" be used?  Using supplementary table 11 values to perform hypergeometric test Having failed to obtain the values required to reproduce supplementary table 11, the data already provided were used to obtain the "enrichment" and "p-value" values using the excel function provided.
    • Issue 1: Comparison of p-values ▪ Resolved: For Up condition, extremely small p-values are not displayed correctly due to Excel's limitations in scientific notation. Excel may either display them as zero or in an incomplete scientific format (e.g., 0.00E+00). Using the tool on the web.

    5.4 Statistical comparison Original vs Reproduced results ● Results: Based on the available data in supplementary table 11, the "enrichment" and "p-value" values have been successfully reproduced in most cases. ● Comments: The full table could not be reproduced, particularly the data corresponding to DAR/DHMR, DAR/DHMR+DEG and population size values, due to missing information or unclear definitions in the supplementary files. ● Errors detected: The enrichment value for the Up condition of promoters-vitro-vibrio was incorrectly reported in the manuscript/table. Based on the Excel formula and the online tool used, the correct value appears to be 2.28 instead of 2.82. ● Statistical Consistency: All the values that could be reproduced from the available data matched the original results, except for the detected error.

    1. Conclusion

    • Summary of the computational reproducibility review The study's results were partially reproduced. Key values such as enrichment and p-values were successfully replicated, but some dataset elements (DAR/DHMR, DAR/DHMR+DEG, and size population) could not be verified due to insufficient methodological details provided in the manuscript. An error in the enrichment value for the Up condition of promoters-vitro-vibrio was identified (2.28 instead of 2.82). The p values used for statistical inference were however successfully reproduced.

    • Recommendations for authors o Improve data documentation: Define variables in supplementary files. o Provide all code and scripts: Share the excel formulas used for table 4/supplementary table 11. o Clarify statistical methodology: Include detailed methods description for the hypergeometric test. o Enhance reproducibility workflow: Provide a structured README with all necessary steps.

  4. Version published to 10.1101/2024.02.15.580452 on bioRxiv