Comparison between short-term stress and long-term adaptive responses reveal common paths to molecular adaptation

Abstract

The phenotypic plasticity in responses to short-term stress can provide clues for understanding the adaptive fixation mechanism of genetic variation during long-term exposure to extreme environments. However, few studies have compared short-term stress responses with long-term evolutionary patterns; in particular, no interactions between the two processes have been evaluated in high-altitude environment. We performed RNA sequencing in embryo fibroblasts derived from great tits and mice to explore transcriptional responses after exposure to simulated high-altitude environmental stresses. Transcriptional changes of genes associated with metabolic pathways were identified in both bird and mice cells after short-term stress responses. Genomic comparisons among long-term highland tits and mammals and their lowland relatives revealed similar pathways (e.g., metabolic pathways) with that initiated under short-term stress transcriptional responses in vitro. These findings highlight the indicative roles of short-term stress in the long-term adaptation, and adopt common paths to molecular adaptation in mouse and bird cells.

Keywords: Biological sciences; Cell biology; Genetics; Genomics.

Graphical abstract

Figure 1

Transcriptional patterns detected in GEF and MEF cell lines after high-altitude simulated conditions were significantly associated with metabolic pathways (A) Schematic for cold, UVR exposure, and hypoxia treatment designs. (B) The left panel shows the weighted correlation network analysis of GEFs after stimulation. Gene expression modules are shown that exhibited correlations with cold exposure over 1 (GC1) or 4 h (GC4), 4 (GH4) or 24 h (GH24) of hypoxia stress, and 0 (GU0) or 1 h (GU1) after UVR exposure. The blue and light cyan modules are specifically listed and discussed in the text. Positive correlations are indicated in red coloring, while negative correlations are indicated in blue, based on the scale to the right. The right panel shows the weighted correlation network analysis of MEFs after treatment with cold exposure over 1 (MC1) or 4 h (MC4), 4 (MH4) or 24 h (MH24) of hypoxia stress, and 0 (MU0) or 1 h (MU1) after UVR exposure. The pink and turquoise modules are highlighted and discussed in the text. (C) The most significant pathways enriched for four modules, including two from the GEF and two from the MEF networks. (D) The most highly abundant transcriptional factor binding motifs present within genes within the four modules of panel c (blue and light cyan modules are from the GEF profile, while the pink and turquoise modules are from the MEF profile). See also Figures S1–S5, Tables S1, S2, and S6.

Figure 2

Differentially expressed genes in GEF and MEF cells after short-term stimuli (A) Gene set enrichment analysis (GSEA) plots showing representatives of the two most significantly enriched metabolic pathways in GEFs. (B) Enrichment of KEGG signaling pathways of different gene sets from GSEA in GEFs. (C) GSEA plots showing two representative significantly enriched pathways related to metabolism in MEFs. (D) The ten most significantly enriched pathways comprising different gene sets from GSEA in MEFs. (E) Abundances of differentially expressed genes (DEGs) in GEFs under different treatment with cold exposure over 1 (GC1) or 4 h (GC4), 4 (GH4) or 24 h (GH24) of hypoxia stress, and 0 (GU0) or 1 h (GU1) after UVR exposure. GC1, GC4, and GH4 groups do not have differential expression genes. (F) Abundances of DEGs in MEFs under different treatment with cold exposure over 1 (MC1) or 4 h (MC4), 4 (MH4) or 24 h (MH24) of hypoxia stress, and 0 (MU0) or 1 h (MU1) after UVR exposure. MC1 group does not have differential expression genes. (G) Comparison of DEG numbers between GEFs and MEFs under different treatment conditions. ∗∗∗ indicates p <0.001. Data are represented as mean ± SEM (H) The most significantly enriched pathways of DEGs between GEFs and MEFs under different treatment conditions. See also Figures S6–S9 and Table S7.

Figure 3

Identification of selective sweeps in great tit genomes (A) Genomic landscape of the XP-CLR values in the genome of highland and lowland great tit. (B) Genomic landscape of the XP-EHH values in the genome of highland and lowland great tit. (C) The EHHs at varying distances around the core haplotype at PARD3 and LOC107202990. See also Table S8.

Figure 4

Relationships of gene expression from different cell treatments and selective sweep genes from high-altitude great tit and low-altitude great tit (A) The overlap of the selected sweep genes in the XP-CLR and XP-EHH analysis. (B) Connectivity of the selected genes and non-selected genes by XP-CLR and XP-EHH analysis. (C) Connectivity between differentially expressed genes and genes with no differential expression. (D) The expression of the selected genes and non-selected genes by XP-CLR analysis. (E) The expression of the selected genes and non-selected genes by XP-EHH analysis. (F) Module membership (MM) and gene significance (GS) for each gene of the blue module from the GH24. (G) Module membership (MM) and gene significance (GS) for each gene of the brown module from the GU0. Data are represented as mean ± SEM Statistically significant associations are those marked with asterisks. ∗∗∗p <0.001, ∗∗p <0.01, ∗p <0.05. See also Figure S10, Tables S3–S5.

Figure 5

Functional enrichment analyses of PSGs in cattle, goat, pig, and six tit species (A) GO terms are grouped into five major categories including cellular process (dark blue), cellular component (green), localization (pink), metabolic processes (purple), and phenotypic abnormality (yellow). (B) Functional enrichments of the KEGG pathways for positive selected genes (PSGs) and convergent genes in six tit species from high- and low-altitude. The number represents the number of genes belonging to metabolism in KEGG_B_class.

Figure 6

Working model for the functional enrichment of short-term stress and long-term adaptation analysis