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. 2023 Jan 6:13:1053655.
doi: 10.3389/fgene.2022.1053655. eCollection 2022.

Comparative transcriptome analyses of immune responses to LPS in peripheral blood mononuclear cells from the giant panda, human, mouse, and monkey

Shun Li  1 , Caiwu Li  2 , Lixiang Chen  1 , Hua Yang  1 , Xiaonan Ren  1 , Chunhua Xu  1 , Bin Wu  1 , Chao Wang  1 , Yun Ling  3 , Yinzhong Shen  4 , Hongzhou Lu  5 , Weiping Liu  2 , Xiaohui Zhou  1
Affiliations

Comparative transcriptome analyses of immune responses to LPS in peripheral blood mononuclear cells from the giant panda, human, mouse, and monkey

Shun Li et al. Front Genet. .

Abstract

Gram-negative bacteria are major pathogens that can cause illnesses in giant pandas. Lipopolysaccharides (LPS), components of Gram-negative bacteria, can activate immune responses in mammals (i.e., humans and mice) through recognition by toll-like receptors (TLRs). However, the giant pandas' immune response to LPS stimulation and the differences between the giant panda and other mammals are not fully known. In this study, we administrated peripheral blood mononuclear cells (PBMCs) from giant pandas, humans, C57BL/6 mice, and rhesus monkeys by LPS treatment at 6 h followed by RNA sequencing (RNA-seq), respectively, with control of non-stimulation. KEGG analyses of differentially expressed genes (DEGs) pathways indicated that LPS could activate the classic signaling pathway of NF-κB in PBMCs from those four tested species. Thus, similar to the other three species, NF-κB is an LPS-responsive regulator of innate immune responses in giant pandas. Furthermore, the expression patterns of adapter genes, inflammatory cytokine genes, chemokines, interferon genes, cytokine genes related to cell growth and development, costimulatory molecules, Th1/Th2 cytokine genes, Th17 cytokine genes, Th9, and Th22 cytokine genes were compared among giant pandas and three other species. Our data indicated that in addition to the similar expression patterns of certain genes among giant pandas and other species, the unique expression pattern response to LPS in giant pandas was also discovered. Furthermore, Th9, Th17, and Th22 cells might be involved in the response to LPS in giant pandas at this tested time point. This study reveals that LPS-induced immune responses have different sensitivities and response timelines in giant pandas compared with other mammals. This study facilitates further understanding of the role of the TLR signaling pathway and the immune system in giant pandas, which might be helpful for disease prevention and protection.

Keywords: LPS; PBMCs; comparative transcriptome analyses; giant panda; immune responses.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Alignment and analysis of TLR-4 amino acid sequences among different species. (A) Conserved domains of giant panda TLR4 protein analysis, showing it contains the conserved domains of LRRs (leucine-rich repeats) and TIR (toll/interleukin 1-receptor). (B) Bioinformatic prediction of the transmembrane regions for giant panda TLR4 protein. (C) Three-dimensional crystal structure of giant panda TLR4 protein constructed by SWISS-MODEL.
FIGURE 2
FIGURE 2
Transcriptomic profiles of LPS-stimulated and LPS-unstimulated PBMCs from giant pandas by RNA-seq. (A) Flow diagram of the experimental design. (B) Volcano plot shows the DEGs by comparing the LPS-stimulated PBMCs and LPS-unstimulated PBMCs from giant pandas at the time point of 6 h. Significantly differentially expressed genes were defined by both p-value (less than and equal to 0.05) and fold change (greater than and equal to 2). (C) GO and KEGG enrichments of upregulated DEGs for the group of LPS-stimulated PBMCs vs. LPS-unstimulated PBMCs from giant pandas by RNA-seq. (D) GO and KEGG enrichments of downregulated DEGs for the group of LPS-stimulated PBMCs vs. LPS-unstimulated PBMCs from giant pandas by RNA-seq.
FIGURE 3
FIGURE 3
Transcriptomic profiles of LPS stimulated and LPS-unstimulated PBMCs from humans by RNA-seq. (A) Flow diagram of the experimental design. (B) Volcano plot shows the DEGs by comparing the LPS-stimulated PBMCs and LPS-unstimulated PBMCs from humans at the time point of 6 h. Significantly differentially expressed genes were defined by both p-value (less than and equal to 0.05) and fold change (greater than and equal to 2). (C) GO and KEGG enrichments of upregulated DEGs for the group of LPS-stimulated PBMCs vs. LPS-unstimulated PBMCs from humans by RNA-seq. (D) GO and KEGG enrichments of downregulated DEGs for the group of LPS-stimulated PBMCs vs. LPS-unstimulated PBMCs from humans by RNA-seq.
FIGURE 4
FIGURE 4
Venn diagram of DEGs among four species and KEGG enrichments of giant panda’s unique DEGs. (A) Overlapping upregulated and downregulated DEGs among giant pandas, humans, mice, and monkeys. (B) KEGG enrichments of giant panda’s unique upregulated and downregulated DEGs.
FIGURE 5
FIGURE 5
Expression tendency of selected gene expression among giant pandas, humans, mice, and monkeys.
FIGURE 6
FIGURE 6
Heat map of representative gene expression across all samples from the four species.
FIGURE 7
FIGURE 7
Network of protein–protein interaction analysis of immune-related genes.
FIGURE 8
FIGURE 8
Graphic overview of the main signaling pathways and conclusions for the present study. LBP (LPS-binding protein); MD2 (myeloid differentiation factor 2); MyD88 (myeloid differentiation primary response 88); TRIF (TIR-domain-containing adapter-inducing interferon-β); IRF (IFN-regulatory factor); UD (undetected); NS (non-significant differential expression); ↑(significantly upregulated); ↓(significantly downregulated).
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