Introduction
Understanding immune response is of utmost relevance for all farmed animal s. In turbot aquaculture, i nfectious diseases are caused by a broad spectrum of well-studied pathogens, from viruses and bacteria to different parasites (Aramburu et al., 2022). Knowledge of the transcriptomic basis for immune responses , both at general and pathogen-specific levels, is essential for a comprehensive understanding of host defence in turbot and can be helpful for other flatfish species. Moreover, dy namic changes in chromatin accessibility influence gene expression by granting or preventing binding by transcription factors (TF) and the transcription preinitiation complex (Herrera-Uribe et al., 2020; Jiang & Mortazavi, 2018). This work, framed in the AQUA- FAANG project for the functional and regulatory annotation of the six main species of European pisciculture, aims at establishing a regulatory map of the innate immune response of turbot through the immunostimulation of live (in vivo) and head kidney-extracted primary leukocytes (in vitro ) using mimics of viral (Poly I:C) and bacterial (inactive Vibrio spp) infection.
Material and Methods
The immune challenges were carried out by i.p. injection (in vivo) and cell stimulation (in vitro ). H ead kidney and cell cultures were collected and frozen after 24 hpi, to be later used for RNAseq, ATACseq and ChIPseq procedures. Libraries were sequenced , with the resulting data being processed using nf-core pipelines (https://nf-co.re/).
A reference head kidney transcriptome was constructed from RNAseq data retaining genes when i) TPM > 5 and ii) present in at least two replicates in any of the conditions tested . The transcriptome was used as the reference to define differential expressed genes (DEGs ) using DESeq2 (P < 0.05) , and the resulting DEGs lists were subjected to gene ontology (GO) enrichment analysis (P < 0.05, GO terms with > 3 genes) using ShinyGO (http://bioinformatics.sdstate.edu/go/).
ATACseq and ChIPseq peaks were filtered following the Irreproducibility Discovery Rate method (http://homer.ucsd.edu/homer/index.html).
DEGs showing differential accessibility in their promoter regions (-1000 to -100 bp) were identified from the intersection of differential ly accessible regions and the DEGs list, for each comparison. Transcription factor motif analysis was carried out for each condition using HOMER’ss findMotifsGenome.pl function.A turbot-specific blacklist of high-signal and low-mappability regions for ChIPseq analysis was generated using the ChIPseq controls to avoid artifacts and noisy regions.
Results
W e identified 8, 797 DEG s across all in vivo and in vitro conditions from a total filtered transcriptome of 12,152 genes . A significant enrichment of transcriptional activation immune response pathways was observed, such as the IFN-gamma pathway (particularly in PolyI:C stimulation) in upregulated DEGs , and metabolic pathways and cell cycle in downregulated DEGs.
We constructed a regulomic atlas of immune stimulation in turbot using highly reproducible peaks of open chromatin regions (52,585 HR peaks) and three histone marks: H3K4me3 (14,741 peaks), H3K27ac (10,584 peaks) and H3K27me3 (33,843 peaks). Two different chromatin state models of 10 and 8 states (in vivo and in vitro respectively) were identified characterized by their transcription start site regions, potential enhancers, repressed polycomb and low signal regions. Roughly, 6.98% of the turbot genome was included in the blacklist of high ChIP signal/ low mappability regions, mostly comprised of telomeric and centromeric regions.
The differential binding analysis revealed significant differences in chromatin accessibility and H3K4me3-binded regions when comparing immune stimulated samples against the controls (particularly for Vibrio stimulation), with less differential binding for H3K27ac and H3K27me3. To identify potential genes with high DE and differential aperture, we identified the open chromatin, H3K4me3 and H3K27me3 differentially binded peaks annotated as promoter regions and compared them with the corresponding DEGs list (Hypergeometric distribution test, P < 0.05), showing high significance for the upregulated, differentially accessible genes for both marks and ATAC.
An analysis of the known transcription factor (TF) motifs identified in each of the peaks revealed a high representation of TF families associated with IFN regulation (IRF), defense and stress response (bZIP ), cell cycle and differentiation (ETS), hematopoiesis (bHLH) and angiogenesis (Homeobox).
Conclusion
O ur results provide the first chromatin state description of immune stimulated turbot as well as an overview of the genomic resources generated in the AQUA-FAANG project, providing brand new information on the regulomics of the species across different conditions.
References
Aramburu et al. (2022). Integration of host-pathogen functional genomics data into the chromosome-level genome assembly of turbot (Scophthalmus maximus ). Aquaculture 564. DOI: 10.1016/j.aquaculture.2022.739067
Herrera-Uribe et al. (2020). Changes in H3K27ac at Gene Regulatory Regions in Porcine Alveolar Macrophages Following LPS or PolyIC Exposure. Frontiers in Genetics 11. DOI: 10.3389/fgene.2020.00817
Jiang & Mortazavi (2018). Integrating ChIP-seq with other functional genomics data. Briefings in Functional Genomics 17(2); 104-15 DOI: 10.1093/bfgp/ely002
Acknowledgements: The AQUA-FAANG project received funding from the European
Union’s Horizon 2020 research and innovation programme under grant agreement No
817923 (www.aqua-faang.eu).