Experimental evolution to thermal stress indicates climate resilience in a cosmopolitan arthropod

  1. Gaoke Lei
  2. Huiling Zhou
  3. Zongyao Ma
  4. Yating Duan
  5. Yanting Chen
  6. Fengluan Yao
  7. Minsheng You
  8. Liette Vasseur
  9. Geoff M Gurr
  10. Shijun You  Is a corresponding author
  1. State Key Laboratory of Agriculture and Forestry Biosecurity, Institute of Applied Ecology, Fujian Agriculture and Forestry University, China
  2. Institute of Plant Protection, Fujian Academy of Agricultural Sciences, China
  3. International Joint Research Laboratory of Ecological Pest Control, Ministry of Education, Fujian Agriculture and Forestry University, China
  4. Ministerial and Provincial Joint Innovation Centre for Safety Production of Cross-Strait Crops, Fujian Agriculture and Forestry University, China
  5. Department of Biological Sciences, Brock University, Canada
  6. Gulbali Institute, Charles Sturt University, Australia
7 figures, 1 table and 8 additional files

Figures

Figure 1 with 1 supplement
Evolved phenotypic changes in temperature-adapted strains.

(A) Stage-specific thermal tolerance responses (survival rates) of the ancestral and hot strains, with 20 individuals used in each of the six replicates for every treatment. (B) Supercooling and freezing points of pupae for the ancestral and cold strains, with 40 biologically independent samples used in each treatment. Data are presented as mean ± SEM. Statistical analyses are performed using t-tests with significant levels indicated by asterisks (*p<0.05, **p<0.01, ***p<0.001).

Figure 1—source data 1

Raw data for stage-specific thermal tolerance and pupal supercooling/freezing points of temperature-adapted strains.

https://cdn.elifesciences.org/articles/110352/elife-110352-fig1-data1-v1.xls
Figure 1—figure supplement 1
Phenotypic fitness variation in the ancestral, hot, and cold strains of P. xylostella.

The curves show the age-stage survival rate (sxj), age-specific survival rate (lx), female age-specific fecundity (fxj), and population age-specific fecundity (mx) of the ancestral, hot, and cold strains.

Figure 2 with 1 supplement
Metabolomic analysis of 3rd-instar larvae across ancestral, hot, and cold strains (AS, HS, and CS).

(A) Classification of metabolites, with a total of 781 metabolites being identified in different strains. (B) Principal component analysis (PCA) of the 781 metabolites across different strains. PC1 and PC2 represent the first and second principal components, respectively. (C) Volcano plot showing the down-regulated (green dots) and up-regulated (red dots) metabolites based on comparison between HS/CS and AS. (D) Classification of differential metabolites between HS/CS and AS. (E) Venn diagram showing the common and unique differential metabolites in HS and CS as compared to AS. (F) Fold changes and classifications of the common differential metabolites in HS and CS as compared to AS.

Figure 2—figure supplement 1
Metabolomic analysis of the 3rd-instar larvae across the ancestral, hot, and cold strains of P. xylostella.

(A) Inter-sample correlation heat map of the metabolites. Z-score standardized values for each of the metabolites were used in clustering analysis. The color bar indicates an increase in the content of each metabolite, scaling from blue to red. (B) KEGG functional classification of differential metabolites between the HS/CS and AS. (C) Correlation analysis of differential metabolites between the HS/CS and AS. Pearson’s correlation coefficient (r) was used to evaluate the biological correlation between different replicates.

Figure 3 with 1 supplement
Transcriptomic analysis of the 3rd-instar larvae across the ancestral, hot, and cold strains.

(A) Principal component analysis (PCA) of genes across different strains. PC1 and PC2 represent the first and second principal components, respectively. (B) Volcano plots of differential gene expression, showing significantly up-regulated (red dots) and down-regulated (green dots) genes between HS/CS and AS (FDR <0.05, fold change >2). (C) Cluster analysis of the transcriptome. The colors represent the Pearson correlation coefficients between samples, indicating transcriptomic similarity. (D) The number of common or unique differentially expressed genes between HS/CS and AS. (E) KEGG function classification of differentially expressed genes between HS/CS and AS.

Figure 3—figure supplement 1
Transcriptomic analysis of the 3rd-instar larvae across the ancestral, hot, and cold strains of P. xylostella.

(A) qRT-PCR validation of transcriptome data. (B) Soft threshold selection in WGCNA. Left: The soft threshold selection graph. Right: The mean connectivity of genes under different soft thresholds. (C) Correlation heat map showing the association of the 29 modules with common differential metabolites between the hot/cold and ancestral strains.

Weighted gene co-expression network analysis (WGCNA) of transcriptomes for the 3rd-instar larvae across the ancestral, hot, and cold strains (HS, CS, and AS) of P. xylostella.

(A) Hierarchical cluster tree illustrating 29 modules identified by WGCNA. (B) Numerical distribution of genes of different modules as identified by WGCNA clustering. (C) In WGCNA, module M13 shows the highest number of metabolites strongly correlated with genes. (D) Overlap of genes in module M13 from WGCNA with common differentially expressed genes between HS/CS and AS.

Figure 5 with 5 supplements
Role of PxSODC in temperature adaptation of P. xylostella.

(A) Allele frequencies of SNPs in the PxSODC gene amplified by PCR from the ancestral, hot, and cold strains (AS, HS, and CS). The analysis involves ten 4th-instar larvae from each of the strains; the dot (·) indicates identity with the reference base. (B) Frequency of amino acid translations from non-synonymous codon mutations in the PxSODC gene in different strains. (C) Stage-specific survival rates of the ancestral and mutant strains (AS, SODC-MU1, SODC-MU2, and SODC-MU3) under extreme heat conditions. (D) Supercooling and freezing points of the pupae from different strains (AS, SODC-MU1, SODC-MU2, and SODC-MU3). Data are presented as mean ± SEM; one-way ANOVA with Tukey’s test was used for comparison. Six biologically independent samples were used in (C), and significant levels between groups with the same stress duration are indicated by asterisks (*p<0.05, **p<0.01, ***p<0.001). A total of 40 biologically independent samples were used in (D), and statistical significance is indicated by different letters (p<0.05).

Figure 5—source data 1

Raw data for thermal tolerance phenotypes of PxSODC knockout strains.

https://cdn.elifesciences.org/articles/110352/elife-110352-fig5-data1-v1.xls
Figure 5—figure supplement 1
Bioinformatic prediction and analysis of the PxSODC gene sequence.

(A) The structure of the PxSODC gene, consisting of three exons and two introns. (B) Secondary structure prediction of PxSODC. Yellow represents strand, pink represents helix, and grey represents coil. (C) Unrooted Maximum Likelihood phylogenetic tree of PxSODC based on amino acid sequences. The tree was inferred using IQ-TREE with 1000 bootstrap replicates, with a blue dot marking the position of P. xylostella.

Figure 5—figure supplement 2
Spatio-temporal expression patterns of the PxSODC gene in the ancestral, hot, and cold strains of P. xylostella.

(A) Stage-specific profiling of the PxSODC expression patterns among different strains in the favorable temperature environment (26 °C), and one-way ANOVA with Tukey’s test was used for comparison (p<0.05). (B) Expression levels of PxSODC in the 3rd-instar larvae of the ancestral and hot strains after 2 hr exposure to different high temperature environments (32°, 34°, 36°, 38°, and 40 °C), and t-test was used for comparison (p<0.05). (C) Expression levels of PxSODC in the 3rd-instar larvae of the ancestral and cold strains after 2 hr exposure to different low temperature environments (12°, 10°, 8°, 6°, and 4 °C), and t-test was used for comparison (p<0.05). Data are presented as mean ± SEM, with n=3 biologically independent samples for each data point.

Figure 5—figure supplement 3
Molecular dynamics simulations of wild-type (WT) and mutant (MU) PxSODC proteins at 15 °C, 26 °C, and 32 °C.

The favorable temperature (26 °C) served as the physiological baseline. (A–C) RMSD time-series trajectories over 100 ns at 15 °C (A), 26 °C (B), and 32 °C (C). (D) Solvent accessible surface area (SASA) across three temperatures, demonstrating the more compact structure of MU at 15°C and 26°C. (E) Number of intramolecular hydrogen bonds across three temperatures. Under cold stress (15 °C), MU actively increased hydrogen bonds compared to the 26 °C baseline, whereas WT lost bonds. Under heat stress (32 °C), MU fully maintained its bond count. Data in (D) and (E) are presented as mean ± SE from 100 ns simulations.

Figure 5—figure supplement 4
CRISPR/Cas9-mediated mutagenesis of the PxSODC gene.

Black underlines indicate the sgRNA target sequence in the second exon; yellow underlines highlight the PAM recognition site; dashes indicate deleted base sequences; and blue letters indicate inserted base sequences.

Figure 5—figure supplement 5
Comparison of the survival rates (sxj and lx) and female fecundity (fx and mx) of P. xylostella strains, showing the role of PxSODC in the temperature adaptation.

(A) Age-stage survival rates (sxj) of the ancestral and mutant strains (AS, SODC-MU1, SODC-MU2, and SODC-MU3) in different temperature environments. (B) Age-specific survival rates (lx), female age-specific fecundity (fx), and population age-specific fecundity (mx) of the ancestral and mutant strains (AS, SODC-MU1, SODC-MU2, and SODC-MU3) in different temperature environments.

SOD expression and activity and superoxide anion (O2-) levels across developmental stages and temperature environments in different strains of P. xylostella.

(A) Expression levels of the SOD genes at different developmental stages of ancestral, hot, and cold strains (AS, HS, and CS) in the favorable temperature environment (26 °C). (B) SOD enzyme activity at different developmental stages of the AS, HS, and CS in the favorable temperature environment. (C) O2- levels at different developmental stages of AS, HS, and CS in the favorable temperature environment. (D) Expression levels of the genes from the SOD family in the 3rd-instar larvae of AS, HS, and CS in the hot (32 °C, 34 °C, 36 °C) and cold (12 °C, 10 °C, 8 °C) environments. (E) SOD enzyme activity in the 3rd-instar larvae of AS, HS, and CS in the extreme temperature environments. (F) O2- levels in the 3rd-instar larvae of AS, HS, and CS in the hot and cold environments. (G) Expression levels of SOD family (excluding the PxSODC gene) at different developmental stages of the ancestral and SODC-MU strains in the favorable temperature environment. (H) SOD enzyme activity at different developmental stages of the ancestral and SODC-MU strains in the favorable temperature environment. (I) O2- levels at different developmental stages of the ancestral and SODC-MU strains in the favorable temperature environment. n=3 biologically independent samples in (A, D, G); within each of the boxes, the numerical value represents log2-fold change of the gene expression level in the treated samples with respect to the control. n=4 biologically independent samples in (B, C, E, F, H, I), with data being presented as mean ± SEM. One-way ANOVA with Tukey’s test was used for comparison in (A, B, C) and (G, H, I) (p<0.05). t-test was used for comparison in (D, E, F) (p<0.05).

Figure 6—source data 1

Raw data for SOD enzyme activity and superoxide anion (O₂⁻) levels across strains, developmental stages, and temperature conditions.

https://cdn.elifesciences.org/articles/110352/elife-110352-fig6-data1-v1.xls
Figure 7 with 2 supplements
Comparison of metabolites and DNA methylation across different strains of P. xylostella.

(A) A Venn diagram showing the metabolites that are consistently different between the ancestral and mutant strains across different developmental stages. (B) Three metabolites with persistent discrepancy across different developmental stages in the ancestral and mutant strains. (C) Expression level of the DNA methyltransferase 1 gene (PxDnmt1) in the ancestral, hot, and cold strains. n=17 biologically independent samples. (D) DNA methyltransferase activity in the ancestral, hot, and cold strains. n=11 biologically independent samples. Data are presented as mean ± SEM; one-way ANOVA with Tukey’s test was used for comparison in (C, D) (p<0.05). (E) Injection of dsDnmt1 significantly reduced the expression level of PxDnmt1 in the ancestral strain of P. xylostella. n=3 biologically independent samples. (F) Silencing of PxDnmt1 decreased 5-methylcytosine (5-mC) content in the female adults and pupae of P. xylostella. n=4 biologically independent samples. (G) Female adults with silenced PxDnmt1 exhibited a significantly decreased critical thermal maximum (CTMax). n=20 biologically independent samples. (H) Pupae with silenced PxDnmt1 displayed elevated supercooling and freezing points. n=20 biologically independent samples. Data are presented as mean ± SEM; unpaired t-test was used for comparison in (E, F, G, H) (p<0.05).

Figure 7—source data 1

Raw data for PxDnmt1 expression, DNA methyltransferase activity, and effects of PxDnmt1 silencing on thermal tolerance.

https://cdn.elifesciences.org/articles/110352/elife-110352-fig7-data1-v1.xls
Figure 7—figure supplement 1
Metabolomic analysis of different developmental stages in the ancestral and mutant strains of P. xylostella.

(A) Classification of metabolites. A total of 608 metabolites were detected in the ancestral and mutant strains (AS and SODC-MU1/SODC-MU2). (B) Principal component analysis (PCA) of the 608 metabolites in the ancestral and mutant strains (AS and SODC-MU1/SODC-MU2). PC1 and PC2 represent the first and second principal components, respectively. (C) Volcano plot showing down-regulated (blue dots) and up-regulated (red dots) metabolites identified in the mutant strains compared to the ancestral strain. (D) Venn diagram showing common and unique differential metabolites between the ancestral and mutant strains at different developmental stages.

Figure 7—figure supplement 2
Differential metabolite classification and functional analysis in the ancestral and mutant strains across different developmental stages of P. xylostella.

(A) Classification of differential metabolites between the ancestral and mutant strains at different developmental stages. (B) KEGG function classification of differential metabolites in the mutant strains compared to the ancestral strain. The vertical axis represents the names of the KEGG pathways, and the horizontal axis indicates the number of differential metabolites annotated to each pathway and their proportion of the total metabolites annotated to that pathway.

Tables

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Strain, strain background (Plutella xylostella)Ancestral strain (AS)Field collection, Fuzhou, China (26°05'N, 119°18'E)Collected July 2012; maintained at 26 °C
Strain, strain background (P. xylostella)Hot strain (HS)This paperEvolved at 32 °C/27 °C (12 h/12 h);~75 generations
Strain, strain background (P. xylostella)Cold strain (CS)This paperEvolved at 15 °C/10 °C (12 h/12 h);~15 generations
Genetic reagent (P. xylostella)SODC-MU1This paperPxSODC knockout;+1 bp insertion; homozygous at G5
Genetic reagent (P. xylostella)SODC-MU2This paperPxSODC knockout;+2 bp insertion; homozygous at G5
Genetic reagent (P. xylostella)SODC-MU3This paperPxSODC knockout; −1 bp deletion; homozygous at G5
Strain, strain background (E. coli)DH5α competent cellsYEASEN, Shanghai, ChinaCloning host
Gene (P. xylostella)PxSODCP. xylostella genomeCu/Zn SOD; ORF 633 bp, 210 aa
Gene (P. xylostella)PxDnmt1P. xylostella genomeDNA methyltransferase 1
Recombinant DNA reagentpESI-Blunt vectorYEASEN, Shanghai, ChinaCloning vector
Recombinant proteinCas9 proteinGenCrispr, Nanjing, China200 ng/μL in injection mixture
Sequence-based reagentPxSODC sgRNAThis paperSee Supplementary file 7
Sequence-based reagentdsDnmt1This paperdsRNA for PxDnmt1 RNAi; see Supplementary file 7
Sequence-based reagentdsEGFPThis paperControl dsRNA; see Supplementary file 7
Sequence-based reagentAll primersThis paperFull list in Supplementary file 7
Commercial assay or kitEastep Super Total RNA Extraction KitPromega, Shanghai, ChinaRNA extraction
Commercial assay or kitReverse-Transcription System KitPromega, Shanghai, ChinacDNA synthesis
Commercial assay or kit2×Taq Pro Universal qPCR Master MixVazyme, Nanjing, ChinaqRT-PCR
Commercial assay or kitPhanta Max Super-Fidelity DNA PolymeraseVazyme, ChinaHigh-fidelity PCR
Commercial assay or kitHieff Clone Zero TOPO-Blunt Simple Cloning KitYEASEN, Shanghai, ChinaTA cloning
Commercial assay or kitTiANamp Genomic DNA KitTIANGEN, ChinagDNA extraction
Commercial assay or kitHiScribe T7 Quick High Yield RNA Synthesis KitNew England Biolabs, USAsgRNA in vitro transcription
Commercial assay or kitT7 High Yield RNA Transcription KitVazyme, Nanjing, ChinadsRNA synthesis for RNAi
Commercial assay or kitGel Extraction KitOmega Bio-tek, USAPCR product purification
Commercial assay or kitSOD activity assay kitComin Biotechnology, ChinaSOD enzyme activity
Commercial assay or kitO₂⁻ assay kitComin Biotechnology, ChinaSuperoxide anion levels
Commercial assay or kit5-mC ELISA kitShanghai Enzyme-linked Biotechnology, ChinaDNA methylation detection
Chemical compound, drugEthylene glycolSigma-AldrichSupercooling/freezing point assay
Software, algorithmTWOSEX-MSChartChi et al., 2020Life table analysis; bootstrap 100,000
Software, algorithmHISAT2 V2.1.0Kim et al., 2019RRID:SCR_015530RNA-seq alignment
Software, algorithmDESeq2BioconductorRRID:SCR_015687Differential expression analysis
Software, algorithmWGCNA v2.0Langfelder and Horvath, 2008RRID:SCR_003302Co-expression network analysis
Software, algorithmGROMACS 2022.3RRID:SCR_014565MD simulations; Amber99sb-ildn
Software, algorithmAlphaFold2DeepMindRRID:SCR_025089Protein structure prediction
Software, algorithmIQ-TREERRID:SCR_017254ML phylogenetics; bootstrap 1,000
Software, algorithmSPSS v23.0IBMRRID:SCR_002865Statistical analysis
OtherIllumina HiSeq4000Illumina, San Diego, USARNA-seq platform
OtherQuantStudio Real-Time PCR SystemThermo Fisher, USAqRT-PCR
OtherOlympus SZX16 microinjection systemOlympus, JapanCRISPR egg injection
OtherNanoliter 2000 microinjectorWorld Precision Instruments, USAdsRNA injection

Additional files

Supplementary file 1

Life table parameters of the ancestral, hot, and cold strains of P. xylostella at the favorable temperature (26 °C).

https://cdn.elifesciences.org/articles/110352/elife-110352-supp1-v1.docx
Supplementary file 2

Candidate genes linked to thermal adaptation in DBM.

https://cdn.elifesciences.org/articles/110352/elife-110352-supp2-v1.docx
Supplementary file 3

Population fitness parameters of the ancestral strain (AS) and SODC-mutant strains of DBM under the constant favorable environment (26 °C).

https://cdn.elifesciences.org/articles/110352/elife-110352-supp3-v1.docx
Supplementary file 4

Population fitness parameters of the ancestral strain (AS) and mutant strains of DBM under the hot environment (32 °C/27 °C:12 hr/12 hr).

https://cdn.elifesciences.org/articles/110352/elife-110352-supp4-v1.docx
Supplementary file 5

Population fitness parameters of the ancestral strain (AS) and mutant strains of DBM under the cold environment (15 °C/10 °C:12 hr/12 hr).

https://cdn.elifesciences.org/articles/110352/elife-110352-supp5-v1.docx
Supplementary file 6

Information on the PxSODC homologous genes identified in the transcriptome.

https://cdn.elifesciences.org/articles/110352/elife-110352-supp6-v1.docx
Supplementary file 7

The primers used in this study.

https://cdn.elifesciences.org/articles/110352/elife-110352-supp7-v1.docx
MDAR checklist
https://cdn.elifesciences.org/articles/110352/elife-110352-mdarchecklist1-v1.docx

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  1. Gaoke Lei
  2. Huiling Zhou
  3. Zongyao Ma
  4. Yating Duan
  5. Yanting Chen
  6. Fengluan Yao
  7. Minsheng You
  8. Liette Vasseur
  9. Geoff M Gurr
  10. Shijun You
(2026)
Experimental evolution to thermal stress indicates climate resilience in a cosmopolitan arthropod
eLife 15:RP110352.
https://doi.org/10.7554/eLife.110352.3