Transcriptomic Profiling Reveals Altered Expression of Genes Involved in Metabolic and Immune Processes in NDV-Infected Chicken Embryos

Abstract

Objective: The poultry industry is significantly impacted by viral infections, particularly Newcastle Disease Virus (NDV), which leads to substantial economic losses. It is essential to comprehend how the sequence of development affects biological pathways and how early exposure to infections might affect immune responses.

Methods: This study employed transcriptome analysis to investigate host-pathogen interactions by analyzing gene expression changes in NDV-infected chicken embryos' lungs.

Result: RNA-Seq reads were aligned with the chicken reference genome (Galgal7), revealing 594 differentially expressed genes: 264 upregulated and 330 downregulated. The most overexpressed genes, with logFC between 8.15 and 8.75, included C8A, FGG, PIT54, FETUB, APOC3, and FGA. Notably, downregulated genes included BPIFB3 (-4.46 logFC) and TRIM39.1 (-4.26 logFC). The analysis also identified 29 novel transcripts and 20 lncRNAs that were upregulated. Gene Ontology and KEGG pathways' analyses revealed significant alterations in gene expression related to immune function, metabolism, cell cycle, nucleic acid processes, and mitochondrial activity due to NDV infection. Key metabolic genes, such as ALDOB (3.27 logFC), PRPS2 (2.66 logFC), and XDH (2.15 logFC), exhibited altered expression patterns, while DCK2 (-1.99 logFC) and TK1 (-2.11 logFC) were also affected. Several immune-related genes showed significant upregulation in infected lung samples, including ALB (6.15 logFC), TLR4 (1.86 logFC), TLR2 (2.79 logFC), and interleukin receptors, such as IL1R2 (3.15 logFC) and IL22RA2 (1.37 logFC). Conversely, genes such as CXCR4 (-1.49 logFC), CXCL14 (-2.57 logFC), GATA3 (-1.51 logFC), and IL17REL (-2.93 logFC) were downregulated. The higher expression of HSP genes underscores their vital role in immune responses.

Conclusion: Comprehension of these genes' interactions is essential for regulating viral replication and immune responses during infections, potentially aiding in the identification of candidate genes for poultry breed improvement amidst NDV challenges.

Keywords: ALDOB; APOC3; BPI; NDV; TRIM3 HSP; chicken; immune genes; metabolic genes.

Figure 1

The proportion of differently expressed genes from the respective chicken chromosomes. (a) The amount of lnRNA and protein-coding RNA transcripts, (b) the overall total amount of RNA from each chromosome, and (c) the quantity of transcripts expressed from each chromosome.

Figure 1

The proportion of differently expressed genes from the respective chicken chromosomes. (a) The amount of lnRNA and protein-coding RNA transcripts, (b) the overall total amount of RNA from each chromosome, and (c) the quantity of transcripts expressed from each chromosome.

Figure 2

Bar diagram illustrating the proportionate number of exons (a) and transcripts (b) per gene.

Figure 3

Volcano plot showing differential expression profiles of genes. Red indicates absolute log2 fold change ≥1 and adjusted p-value ≤ 0.01. As the first three comparisons did not have any significantly expressed genes, the graphical results are provided only for the fourth comparison (1C, 2C, and 3C vs. 1T, 2T, and 3T).

Figure 4

Hierarchical cluster and expression profile of (a) 594 differently expressed genes (p < 0.002) and (b) the highly significant (p < 10 ⁻⁷) differentially expressed top 100 genes across the samples. The heat maps were generated from the normalized expression values of each sample for a given gene.

Figure 4

Hierarchical cluster and expression profile of (a) 594 differently expressed genes (p < 0.002) and (b) the highly significant (p < 10 ⁻⁷) differentially expressed top 100 genes across the samples. The heat maps were generated from the normalized expression values of each sample for a given gene.

Figure 5

The bar graph depicts the five transcripts that were chosen at random and validated by RT-qPCR. The fold change indicates the variation in transcript amounts between infected and control samples.

Figure 6

Control vs. treated groups (COMP4) bubble plot of the overrepresented GO terms using significantly expressed genes. The orange line represents the p-value threshold (Benjamini–Hochberg p < 0.05). The size of the bubble is proportionate to the number of genes involved in the GO term.

Figure 7

The Lollypop graph displays the results of the functional fold enrichment study conducted on the target genes of predicted known RNAs pertaining to various different (a) biological, (b) metabolic, and (c) cellular processes.

Figure 8

A hierarchical clustering tree of (a) biological, (b) metabolic, and (c) cellular processes, summarizing the correlation among significant pathways listed in the enrichment tab. Pathways with many shared genes are clustered together. Bigger dots indicate more significant p-values.

Figure 9

KEGG pathway enrichment analysis of transcripts with differential expression against NDV. (a) A bubble graphic illustrates the degree of enrichment and the number of genes in KEGG pathways. The top 20 significant KEGG keywords in metabolic processes are displayed in a chord plot (b).

Figure 9

KEGG pathway enrichment analysis of transcripts with differential expression against NDV. (a) A bubble graphic illustrates the degree of enrichment and the number of genes in KEGG pathways. The top 20 significant KEGG keywords in metabolic processes are displayed in a chord plot (b).

Figure 10

Potential networks of protein interactions encoded by genes related to different biological and cellular mechanics of cell functions. We drew the interaction networks using STRING functional protein association networks (https://string-db.org, accessed on 16 July 2024). Proteins with known or projected three-dimensional structures have clusters indicated by their color.