The dynamics of molecular, immune and physiological features of the host and the gut microbiome, and their interactions before and after onset of laying in two hen strains

Abstract

Aggregation of data, including deep sequencing of mRNA and miRNA data in jejunum mucosa, abundance of immune cells, metabolites, or hormones in blood, composition of microbiota in digesta and duodenal mucosa, and production traits collected along the lifespan, provides a comprehensive picture of lifelong adaptation processes. Here, respective data from two laying hen strains (Lohmann Brown-Classic (LB) and Lohmann LSL-Classic (LSL) collected at 10, 16, 24, 30, and 60 wk of age were analyzed. Data integration revealed strain- and stage-specific biosignatures, including elements indicative of molecular pathways discriminating the strains. Although the strains performed the same, they differed in the activity of immunological and metabolic functions and pathways and showed specific gut-microbiota-interactions in different production periods. The study shows that both strains employ different strategies to acquire and maintain their capabilities under high performance conditions, especially during the transition phase. Furthermore, the study demonstrates the capacity of such integrative analyses to elucidate molecular pathways that reflect functional biodiversity. The bioinformatic reduction of the multidimensional data provides good guidance for further manual review of the data.

Keywords: Multi-omics; RNAseq; host-gut microbiota; immune cells; laying hen.

Figure 1

Flowchart of the main steps for biosignature analysis in different groups using mixOmics. (A) The features from different organs, age groups, and strains were used as input. Biosignatures were identified along the hen lifespan, between age and hen strain, and strain-specific in the transition phase to egg laying. (B) Whole gut transcripts correlated with identified specific biosignatures of immune cells and microbes differing between strains in transition period were analyzed using DAVID (version 6.8) for Gene Ontology (biological processes) and KEGG pathway enrichment analysis.

Figure 2

Sample plots for each multi-omics panel depicting a considerable separation between production periods using supervised methods in the R Package mixOmics as the basis for identify key molecular drivers from the panels.

Figure 3

Heatmap of key molecular drivers from multi-omics assays in each production period group. The features cluster into pre-layer (10–16 wk) and layer (24–60 wk) periods. Features were labeled on the left with different colors: red for miRNA, purple for mRNA, green for immune cells, black for microbes, blue for metabolites, and brown for phenotypes.

Figure 4

Circos plot displaying the significant biosignatures from multiple blocks over the three components. The selected biomarkers were represented on the side of the circos plot with the block of immune (green), metabolome (blue), mRNA (purple), miRNA (pink), microbiota (gray) and phenotype (orange). Coloured lines in the outer circle indicate expression level in each group. The yellow and black colours within the circle link features and indicate a negative or positive correlation, respectively.

Figure 5

Circos plot indicating the significant biosignatures from multiple datasets based on the Pearson correlation coefficient |r = 0.8| over the two components. The selected biomarkers were represented in the inner circle. Similarly to Figure 4, the purple, green, pink, blue, gray, orange dashed lines outside the circos indicate each data type. The black link suggests a positive correlation, while the yellow link depicts a negative correlation. The red and green lines represent the features' expression in LB and LSL at (wk-16), respectively.

Figure 6

Gene Ontology and KEGG pathways enrichment analysis of mRNAs correlated with immune cell types within LB and LSL at wk 16. (A) The bar chart indicates the number of mRNAs correlated with immune cell types at P ≤ 0.01 in LSL. The green bar shows the abundance of immune cell types in LSL, while no immune cell type was identified in LB. (B) Gene Ontology enrichment analyses (biological process) for transcripts correlated with immune cell types in LSL. The size of the dots represents the number of transcripts involved in each biological process, while the color indicates the significance. (C) KEGG pathway enrichment analysis of mRNAs correlated with immune cells in LSL. The pie charts indicate the strain-specific proportions of mRNAs correlated with immune cells to the KEGG pathways. The green ellipse depicts mRNAs correlated with immune cells in LSL. KEGG pathways and biological processes with P ≤ 0.05 were considered significant.

Figure 7

Gene Ontology and KEGG pathways enrichment analysis of mRNAs correlated with duodenal microbiota within LB and LSL at wk 16. (A) The bar chart indicates the number of mRNAs correlated with microbes at P ≤ 0.01 within LB and LSL. The green bar shows more abundant microbes in LSL and the red bars depict more abundant microbes in LB. (B and C) Gene Ontology (biological processes) enrichment analysis for the transcripts correlated with microbes in LB and LSL, respectively. The size of the dots represents the number of transcripts involved in each biological process, while the color indicates the significance. (D) KEGG pathway enrichment analysis of mRNAs correlated with microbes within LB and LSL. The pie charts indicate the strain-specific proportions of mRNAs correlated with microbiota in the KEGG pathways. The red ellipse shows mRNAs correlated with microbes in LB, and the green ellipse depict mRNAs correlated with microbes in LSL. KEGG pathways and biological processes with P ≤ 0.05 were considered significant.

Figure 8

Circos plot indicating the significant biosignatures from multiple datasets. The plot shows Pearson correlation coefficients |r = 0.8| over the two components. The selected biomarkers are represented in the inner circle. Similarly, the purple, orange, blue, green, pink, gray dashed lines outside the circos indicate each data type. The black link suggests a positive correlation, while the yellow link depicts a negative correlation. The red and green lines represent the feature expression in LB and LSL at (wk-24), respectively.

Figure 9

Gene Ontology and KEGG pathways enrichment analysis of mRNAs correlated with immune cell types for LB and LSL at wk 24. (A) The bar chart plot shows the number of mRNAs correlated with immune cell types at a significance level of P ≤ 0.01 for LB and LSL. The green bars show abundant immune cell types in LSL and the red bars indicate abundant immune cell types in LB. (B and C) Gene Ontology (biological processes) enrichment analysis for the transcripts correlated with immune cell types in LB and LSL, respectively. The size of the dots represents the number of transcripts involved in each biological process, while the color indicates its significance. (D) KEGG pathway enrichment analysis of mRNAs correlated with microbes for LB and LSL. The pie charts indicate the strain-specific proportions of mRNAs correlated with microbiota in the KEGG pathways. The red ellipse shows mRNAs correlated with immune cell types in LB, and the green ellipse depict mRNAs correlated with immune cell types in LSL. KEGG pathways and biological processes with P ≤ 0.05 were considered significant.

Figure 10

Gene Ontology and KEGG pathways enrichment analysis of mRNAs correlated with duodenal microbiota within LB and LSL at wk 24. (A) The bar chart indicates the number of mRNAs correlated with microbes at a significance of P ≤ 0.01 for LB and LSL. The green bars show more abundant microbes in LSL and the red bars depict more abundant microbes in LB. (B and C) Gene Ontology (biological processes) enrichment analysis for the transcripts correlated with immune cell types in LB and LSL, respectively. The size of the dots represents the number of transcripts involved in each biological process, while the color indicates its significance. (D) KEGG pathway enrichment analysis of mRNAs correlated with microbes within LB and LSL. The pie charts indicate the strain-specific proportions of mRNAs correlated with microbiota to the KEGG pathways. The red ellipse shows mRNAs correlated with microbes in LB and the green ellipse depict mRNAs correlated with microbes in LSL. KEGG pathways and biological processes with P ≤ 0.05 were considered significant.