Trehalose accumulation contributes to enhanced cold stress tolerance in Telenomus remus, a dominant egg parasitoid of Spodoptera frugiperda

Abstract

Telenomus remus is a highly efficient biological control agent against Spodoptera frugiperda, owing to its capacity to successfully locate and parasitize inner layer eggs of egg masses. However, our previous studies have revealed its limited cold tolerance, a critical bottleneck in mass rearing of this wasp species. Understanding the cold tolerance mechanism of T. remus is crucial for improving rearing techniques, optimizing storage protocols, and enhancing the overall effectiveness of biological control strategies. Therefore, the overarching objectives of this study were to investigate the dynamic changes in mortality and cryoprotectant levels under different cold stress conditions, identify candidate genes and metabolites associated with cold tolerance, and elucidate the cold tolerance mechanisms in T. remus through physiological measurements, integrated transcriptomic and metabolomic analyses, and qRT-PCR validation. The results revealed that the survival rates of T. remus declined significantly with decreasing temperature. Interestingly, substantial accumulation of trehalose was observed under cold stress. Integrated multi-omics analysis indicated that the starch and sucrose metabolism pathway was crucial for mediating cold tolerance in T. remus. In this metabolic pathway, the expression levels of GAA (α-glucosidase) and GYS (glycogen synthase) exhibited a pronounced temperature-dependent upregulation. Collectively, these findings suggest that T. remus employs a cold-tolerance strategy centered on trehalose accumulation. This research advances our understanding of the molecular and biochemical foundations of cold adaptation in T. remus, and provides a theoretical basis for optimizing the storage strategies of T. remus biocontrol products.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-025-12059-x.

Introduction

Spodoptera frugiperda (Smith), a highly destructive migratory agricultural pest, invaded China in 2019 [1]. The combination of high reproductive capacity, broad host range, rapid dispersal capability, and strong adaptability makes this pest a significant threat to maize production and food security in China, with the potential to cause severe damage [2–5]. The larval stage of S. frugiperda is particularly destructive, as larvae bore into corn ears, disrupting kernel development and often inducing ear rot. These damages significantly reduce corn yield and quality [5–8]. Economically, S. frugiperda infestation can cause corn yield losses of up to 73% [9].

Extensive studies have been conducted worldwide on S. frugiperda, encompassing its biological characteristics, occurrence patterns, monitoring and early-warning technologies, outbreak mechanisms, and key control strategies [2, 9, 10]. Among the various approaches to control S. frugiperda, biological control technology, which relies on conserving and utilizing natural enemies, offers a sustainable approach to manage this invasive pest [11]. As documented in our previous studies, S. frugiperda is confronted by approximately 300 species of natural enemies [12, 13]. Among parasitoids, egg parasitoids play a crucial role because they develop within host eggs, thereby enhancing ecosystem self-regulation [11, 14, 15]. Notably, Telenomus remus Nixon has emerged as a preeminent egg parasitoid for controlling S. frugiperda, due to its unique ability to parasitize inner-layer eggs within egg masses, overcoming the physical barriers imposed by egg mass scales [16–18].

A series of studies have been conducted to optimize the mass rearing of T. remus, including screening alternative hosts [19], optimizing rearing conditions [20, 21], developing storage methods [22, 23], and assessing product quality [24]. Consequently, a mass rearing system has been successfully established using Spodoptera litura Fabricius eggs as an alternative host. However, despite these advancements, a significant limitation remains regarding the poor cold tolerance of T. remus, which represents a critical bottleneck for its large-scale rearing and widespread application [22, 25]. This issue leads to several practical challenges, including reduced shelf life, increased production costs, and limited application scope [22, 25, 26]. Therefore, elucidating the molecular and physiological mechanisms underlying cold tolerance in T. remus is essential for developing optimized storage protocols for natural enemy insects and enhancing biological control strategies. Despite its importance, no study to date-either in domestic or international literature-has investigated the cold tolerance mechanism of T. remus.

The development and survival of insects are closely influenced by environmental factors, with temperature being particularly critical [27]. Over the course of evolution, insects have developed diverse adaptive mechanisms to withstand adverse conditions, ensuring their normal development and survival. Among these, cold tolerance and diapause capacity are pivotal for successful overwintering [28]. Insect responses to cold stress and their underlying molecular and physiological mechanisms have garnered significant attention [29–32]. Understanding these adaptive mechanisms that allow insects to maintain normal growth and development under cold conditions is important for both advancing fundamental knowledge in insect ecology and improving practical pest management strategies [33]. Generally, insects exhibit remarkable physiological adaptations to either tolerate intracellular ice formation or prevent it through supercooling mechanisms [29]. Additionally, many species also undergo diapause, which enables them to conserve energy and survive unfavorable conditions [34]. Understanding the molecular and biochemical basis of these metabolic adjustments is crucial for deciphering insect survival strategies in cold environments. Furthermore, research has shown that genetic responses to cold stress also significantly influence insect adaption, with the identification of key genes providing insights into their evolutionary trajectory and adaptive potential [29]. Insects also utilize various behavioral strategies to withstand low temperatures, including seasonal migration and shelter-seeking behavior [29]. Thus, research on the mechanisms of cold tolerance in insects can contribute to the development of better protection strategies for beneficial insects and provide robust theoretical support for integrated pest management strategies.

Cryoprotectants play a pivotal role in the anti-freezing mechanisms of insects, enabling them to survive under extremely low temperatures by inhibiting the formation of intracellular ice crystals [35]. These compounds, including sugars, lipids, and polyols, significantly enhance cold tolerance, with their concentration often correlated with the severity of cold stress [36]. Maintaining cellular integrity and metabolic functions is critical for insect survival under such conditions [37]. The physiological importance of cryoprotectants was first demonstrated by the identification of glycerol and sorbitol in diapause eggs of Bombyx mori Linnaeus [38]. Following this discovery, a multi-component cryoprotectant system comprising trehalose, glycerol, polyols, and small molecular sugars was established in 1982 [39]. Subsequent studies have demonstrated that numerous insects can dynamically modulate the types and concentrations of cryoprotectants in response to cold stress [32, 40]. For instance, the composition of cold-tolerance substances in Locusta migratoria L. exhibits geographic variation, with the Beidagang population (Tianjin) primarily synthesizing glycerol and sorbitol, while the Heshuo population (Xinjiang) predominantly produces trehalose. Notably, the Hami population in Xinjiang employs a more complex cryoprotectant system integrating sorbitol, glycerol, and trehalose [41]. These findings highlight the diversity of cryoprotectant systems evolved in insects as an adaptation to low-temperature environments, where the specific composition is influenced by species-specific traits and geographical distribution patterns [41].

Among these, trehalose, the primary blood sugar in insects, has drawn significant attention [42]. This non-reducing disaccharide, abundant in insect hemolymph, is crucial for maintaining open circulatory system integrity [29]. Trehalose’s inability to cross biomembranes prevents gut fluid loss while ensuring efficient sugar absorption and metabolic regulation of sugar substances. Furthermore, its characteristically low osmotic pressure renders it particularly suitable for biological storage and transport functions [43]. Current research suggests that trehalose concentration in insects is regulated by multiple factors, including environmental conditions, nutritional status, and physiological state [44]. Specifically, insects undergoing diapause or exposed to cold stress rapidly convert trehalose into cryoprotectants such as glycerol and sorbitol, thereby improving cold tolerance and mitigating cold-induced damage [45]. Under cold stress, trehalose forms a protective film on cell surfaces, preventing protein denaturation and maintaining essential cellular functions [36, 46, 47]. Consequently, trehalose accumulation enhances cold tolerance in insects [48]. Research on Dendroides canadensis Latreille larvae further clarified this mechanism by showing that antifreeze proteins critically inhibit trehalose crystallization [49].

To better understand the response strategies of T. remus to temperature fluctuations, this study employs an integrated approach combining transcriptomic, metabolomic, and biochemical analyses. The primary objectives are to identify cold tolerance-related genes, metabolites, and pathways, as well as to characterize the dynamics of cryoprotectants under different stress conditions in T. remus. These findings are expected to advance our comprehensive understanding of the molecular and biochemical mechanisms underlying cold tolerance in T. remus, offering theoretical support for improved storage strategies for natural enemy insect products. Moreover, this study may contribute to optimizing large-scale production systems for T. remus, ultimately enhancing its effectiveness in integrated pest management programs.

Materials and methods

Hosts and parasitoids

Spodoptera frugiperda individuals were collected from corn fields in Kunming, Yunnan Province, in 2019. These individuals were subsequently maintained in a climate-controlled chamber under standardized conditions including a temperature of 28 ± 1 °C, relative humidity (RH) of 60 ± 5%, and a 16: 8 h (light: dark) photoperiod [24].

Eggs of S. litura were obtained from the Institute of Plant Protection, Jilin Academy of Agricultural Sciences. Larvae were collectively maintained on an artificial diet [50] under identical environmental conditions and rearing methods as those used for S. frugiperda.

The parasitoid wasp T. remus was obtained from the Maize Pest Research Group at the Institute of Plant Protection, Chinese Academy of Agricultural Sciences. The wasps were laboratory-reared on S. litura eggs as hosts under controlled conditions of 26 ± 1 °C, 70 ± 5% RH, and a 14: 10 h (light: dark) photoperiod [19].

Physiological measurements of T. remus under cold stress

To evaluate the mortality rates of different developmental stages of T. remus (first instar larvae, second instar larvae, prepupae, and pupae) under cold stress, two temperatures (− 5 °C and − 10 °C) were tested with exposure durations of 4, 8, 12, and 24 h. Egg cards were prepared using approximately 80 fresh S. litura eggs (< 24 h) [24]. To ensure uniformity in the developmental stage of T. remus, the egg cards were placed in plastic tubes (2.5 cm diameter × 10 cm height) for 1 h to allow newly emerged wasps (< 24 h) to parasitize the host eggs. According to previous research [19], the parasitized egg cards were incubated at 26 °C for 48, 72, 96, and 120 h to reach the first instar larvae, second instar larvae, prepupae, and pupal stages of T. remus, respectively. Notably, all aforementioned developmental stages of T. remus occur inside the host egg. For cold stress treatment, egg cards at specific developmental stage were individually placed into 1.5 mL thin-walled plastic tubes and sealed with caps. The tubes were then immersed in a refrigerated bath circulator (Thermo Fisher Scientific SC150) to apply cold stress. After the stress period, the egg cards were returned to standard rearing conditions until adult emergence. Each treatment was replicated ten times (10 egg cards), and the number of emerged adults was recorded to calculate mortality rate.

Based on preliminary findings [22, 26], two temperatures (2 °C and 14 °C) were selected to examine the metabolic responses of T. remus pupae to cold stress, with exposure durations of 2, 4, 8, and 24 h. The control group was kept at 26 °C. The concentrations of glycogen, trehalose, glucose, sorbitol, and triglycerides in the pupae were measured using a commercial microplate ELISA kit (Suzhou Comin Biotech Co., Ltd., Suzhou, China) [51]. Each treatment consisted of three biological replicates, with three technical replicates per biological replicate to ensure experimental reliability and reproducibility.

Transcriptome sequencing and data analysis

Considering the feasibility aspects of dissection and sampling, T. remus pupae were choosen for transcriptomic analysis. Following an established protocol [22], two cold stress temperatures (2 °C and 14 °C) were implemented, with 26 °C serving as the control temperature. All samples underwent a 4-hour cold storage treatment. After the cold stress, the parasitized eggs were carefully dissected using a 00# insect pin under a stereomicroscope (SZX10, Olympus Corporation, Japan) [52, 53]. The dissected pupae were promptly transferred into pre-chilled (in liquid nitrogen) 1.5 mL centrifuge tubes for preservation. To maintain consistency, the dissection for each egg card was completed within a strict 5-minute timeframe. Samples were then stored at -80 °C for RNA extraction. Three biological replicates were performed for each treatment, with each replicate containing 1,000 T. remus pupae. The 2 °C, 14 °C, and 26 °C treatment groups were labeled as Tr_T2, Tr_T14, and Tr_T26, respectively.

Total RNA was extracted using the Trizol reagent kit (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s instructions. RNA quantity and integrity were evaluated with the RNA Nano 6000 Assay Kit on a Bioanalyzer 2100 system (Agilent Technologies, CA, USA). Only RNA samples with an RNA Integrity Number (RIN) of ≥ 7.0 were used for subsequent analysis. cDNA libraries were prepared from 1 µL of each RNA sample using the Illumina NEBNext Ultra RNA Library Prep Kit (New England Biolabs, Ipswich, MA, USA). After quality assessment, libraries were pooled according to their effective concentration and desired sequencing depth, followed by sequencing on the Illumina NovaSeq 6000 platform (Illumina, USA).

Clean reads were obtained by removing sequences containing adapters, N base, or low-quality sequences (Q_phred ≤ 20 bases), where low-quality bases comprised over 50% of the total read length. These clean reads were assembled into a reference transcriptome using Trinity software (v2.6.6). Gene and protein functions were annotated using the Nr, Nt, KEGG, GO, KOG, Pfam, and Swiss-Prot databases. Gene expression levels were quantified as fragments per kilobase of transcript per million mapped reads (FPKM). Differentially expressed genes (DEGs) between groups (Tr_T2 vs. Tr_T26 and Tr_T14 vs. Tr_T26) were identified using DESeq2 R package (v1.20.0), with significant thresholds set at P < 0.05 and |log₂(foldchange, FC)| >1. GO functional and KEGG pathway enrichment analyses of DEGs were performed using GOseq (v1.10.0) and KOBAS (v2.0.12), respectively.

Metabolite profiling analysis

Sample preparation and collection were conducted as described in section "Transcriptome sequencing and data analysis". Metabolomic analysis followed an established protocol [54]. Briefly, 100 mg of each sample was homogenized in liquid nitrogen and resuspended in pre-chilled 80% methanol with vigorous vortexing. After incubation on ice for 5 min, the samples were centrifuged at 15,000 g and 4 °C for 20 min. The supernatant was diluted to 53% methanol using LC-MS grade water, transferred to fresh tubes, and centrifuged again under the same conditions. The final supernatant was analyzed by LC-MS/MS. Each treatment included six biological replicates, with 1,000 T. remus pupae per replicate.

UHPLC-MS/MS analyses were performed on a Vanquish UHPLC system (ThermoFisher, Germany) coupled with an Orbitrap Q Exactive™ HF mass spectrometer (Thermo Fisher). Raw data files were processed using Compound Discoverer 3.1 (Thermo Fisher) for peak alignment, peak picking, and metabolite quantitation. Peak intensities were normalized to total spectral intensity, and the normalized data were used to predict molecular formulas based on additive ions, molecular ion peaks, and fragment ions. The detected peaks were matched against the mzCloud, mzVault, and MassList databases to ensure accurate qualitative and relative quantitative identification. Statistical analyses were carried out using R (v3.4.3), Python (v2.7.6), and CentOS (v6.6).

Metabolites annotation was conducted using the KEGG (https://www.genome.jp/kegg/pathway.html), HMDB (https://hmdb.ca/metabolites), and LIPIDMaps (http://www.lipidmaps.org/) databases. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were performed with metaX software. Differentially accumulated metabolites (DAMs) were identified, with significant thresholds set at variable importance in projection (VIP) score > 1, P-value < 0.05, and fold change (FC) ≥ 2.0 or ≤ 0.5.

Integrative analysis of the transcriptome and metabolome

Three biological replicates from the metabolome were selected for correlation analysis with the transcriptome. Pearson’s correlation analysis was conducted to assess the relationship between DEGs and DAMs. All DEGs and DAMs from each comparison group were co-mapped to the KEGG database to identify their commonly enriched metabolic pathways.

Cold-tolerance related genes quantitation by qRT-PCR

Two differently expressed sequences, GAA (α-glucosidase) and GYS (glycogen synthase), were identified through transcriptomic and metabolomic analyses. Total RNA was extracted using TRIzol reagent following the protocol in section "Transcriptome sequencing and data analysis". cDNA synthesis and gene cloning were carried out according to the manufacturer’s instructions (Transgen, Beijing, China). Gene-specific primers were designed with the PrimerQuest Tool (Table 1). The amplified fragments were subsequently cloned into the pEASY-T5 Vector (Transgen, Beijing, China) and verified by sequencing (TSINGKE Biological Technology).

Table 1.

Primers used in this study

Primer	Primer sequence (5’ to 3’)	Amplification efficiency
Gene cloning
GAA-F	GCTGATCAACCACCCATAGAA
GAA-R	TTGCCAACAAACCCTCATTAAC
GYS-F	GAAGTGCAGCGTGGAAATTAG
GYS-R	ACTCGTATCGACCAGCAATAAA
qRT-PCR
GAA-F	GGAGGAAATTGTCACGGAGTT	1.14
GAA-R	CCGCCAATCGTTCTGTATGT
GYS-F	GAAGTGCAGCGTGGAAATTAG	1.02
GYS-R	CTCGACATCGAGATGAGGAATC
RPL12-F	GAGGTGTGTTGGTGGAGAA	0.96
RPL12-R	TAAGAGAGGCAGCAGAAGG

Quantitative real-time PCR (qRT-PCR) was employed to measure the expression levels of GAA and GYS under different temperatures (2 and 14 °C) and exposure durations (2, 4, 8, and 24 h). The reactions were carried out using cDNA and primers with TOROGreen^® 5G qPCR Premix (Toroid Technology Limited) on a LightCycler^® 96 Instrument (Roche, Switzerland) (Table 1). The thermal cycling protocol consisted of an initial denaturation at 95 ℃ for 5 min, followed by 50 cycles of 95 ℃ for 10 s and 55–59 ℃ for 20 s. A dissociation step (95 ℃ for 10 s, 65 ℃ for 60 s, and 97 °C for 1 s) was included for melting curve analysis to verify amplification specificity [55]. RPL12, which exhibits stable expression in T. remus [56], served as the reference gene for data normalization. The relative gene expression levels were calculated using the 2^−ΔΔCt method [57]. Each experimental group comprised four independent biological replicates, with three technical replicates per sample to ensure data reliability and reproducibility.

Statistical analysis

The effects of stress temperature and duration on the mortality rates of T. remus were analyzed using two-way ANOVA. Significant differences in mortality rates between exposure durations and temperatures were assessed using Tukey’s test and t-test, respectively (P < 0.05). The median lethal time (LT₅₀) was calculated by fitting the relationship between mortality rate (Y) and exposure duration (X) using logistic regression in Origin 2022 (OriginLab Corp., Northampton, MA, USA) [58]. Tukey’s test was also applied to analyse significant differences in glycogen, trehalose, glucose, sorbitol, and triglyceride levels in T. remus. Similarly, differences in target gene expression levels under varying cold durations and temperatures were evaluated using Tukey’s test and t-test (P < 0.05). All statistical analyses were conducted in SPSS 19.0 (IBM Corp., Chicago, IL, USA), and graphs were generated using GraphPad Prism 8.0 (GraphPad Software, Inc., San Diego, CA, USA).

Results

Low-temperature mortality rate and concentration of cold tolerance substances

The results indicated that temperature, exposure duration, and their interaction significantly affected on the mortality rates of T. remus (Table S1). Mortality increased progressively with lower temperatures and longer exposure durations (Fig. 1). At any given exposure duration, the mortality rate of T. remus at -10 °C was significantly higher than that at -5 °C. For first instar larvae exposed to -5 °C for 4 and 8 h, no significant difference in mortality rates was observed, but these rates were significantly lower than those at 12 and 24 h. A similar trend occurred in the second instar larvae (Fig. 1). The LT₅₀ values of T. remus at -5 °C were presented in Table 2, with prepupae and pupae showing nearly identical values, while second instar larvae exhibited the lowest cold tolerance (LT₅₀ = 19.7 h). The model indicated a high fit with a correlation coefficient of (R²) > 0.92.

Fig. 1.

The effects of cold stress temperature and duration on the mortality rate of Telenomus remus. Data are expressed as mean ± SE. Different lowercase letters indicate significant differences among exposure durations (Tukey’s test, P < 0.05). Asterisks indicate significant differences between two exposure temperatures (t-test, P < 0.05)

Table 2.

Median lethal time (LT₅₀) for various developmental stages of Telenomus remus exposed to -5 ℃

Stage	Equation	LT 50 (h)	R 2
First instar larvae	Y = e(−2.395+0.067*X)/(1 + e(−2.395+0.067*X))	35.6	0.9676
Second instar larvae	Y = e(−1.356+0.069*X)/(1 + e(−1.356+0.069*X))	19.7	0.9295
Prepupae	Y = e(−1.728+0.046*X)/(1 + e(−1.728+0.046*X))	37.3	0.9380
Pupae	Y = e(−1.184+0.030*X)/(1 + e(−1.184+0.030*X))	38.8	1.0000

Triglycerides concentrations after cold stress at 2 °C for 2, 4, 8, and 24 h were significantly higher than those at 14 °C (Fig. 2A). The sorbitol concentration in the control group (20.915 mg/g) was significantly lower than that in the 2 °C and 14 °C treatment groups. Sorbitol levels showed a gradual increase with prolonged stress duration and lower temperatures (Fig. 2B). In contrast, glucose concentrations showed no significant differences between the low-temperature stress groups and the control (Fig. 2C). Trehalose concentrations increased with greater cold stress intensity (Fig. 2D), while glycogen levels remained unchanged across all treatment groups (Fig. 2E).

Fig. 2.

Effects of cold stress on the concentrations of cold tolerance substances in Telenomus remus. Data are expressed as mean ± SE. Different lowercase letters indicate significant differences among different treatments (Tukey’s test, P < 0.05). A, B, C, D, and E represent the concentration of triglyceride, sorbitol, glucose, trehalose, and glycogen, respectively

Transcriptome profiles of T. remus in response to cold stress

The raw RNA-seq data generated in this study are available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under accession number PRJNA1260750. Quality assessment demonstrated R² values exceeding 0.87 across all samples (Table S2; Figure S1), with transcriptome sequencing of nine samples yielding 58.8 Gb of clean reads. Each sample produced over 6 Gb of clean reads, exhibiting Q₂₀ and Q₃₀ contents above 96.3% and 90.46%, respectively. Reference sequence alignment showed mapping rates surpassing 85.51% for all samples (Table S3). Analysis identified 19,121 transcripts collectively (Table S4). In the absence of T. remus genomic data, the Unigenes were aligned to multiple public databases (NR, NT, KO, SwissProt, PFAM, GO, and KOG) for functional annotation, resulting in annotation of 9,629 Unigenes (50.35%) in at least one database (Table S5).

With a cutoff of P < 0.05 and |log₂ FC| >1, 1,380 differentially expressed genes (DEGs) were identified between Tr_T2 (2 °C) and Tr_T26 (26 °C), including 578 up-regulated and 802 down-regulated genes. In the Tr_T14 (14 °C) vs. Tr_T26 comparison, 380 up-regulated and 599 down-regulated genes were detected (Table S6). The number of both up- and down-regulated DEGs increased with the severity of cold stress (Fig. 3A). Cluster analysis showed that Tr_T14_1 and Tr_T14_3 grouped together, while Tr_T2_2 and Tr_T2_3, Tr_T26_1 and Tr_T26_2 formed a distinct cluster. Meanwhile, Tr_T14_2 and Tr_T2_1 clustered together and subsequently merged with Tr_T26_3. The heatmap reflected that the DEG expression pattern at 2 °C and 14 °C was more similar to each other than to that at 26 °C (Fig. 3B). Venn diagram analysis revealed 246 common up-regulated and 352 common down-regulated DEGs shared by both comparison groups. Furthermore, the Tr_T2 vs. Tr_T26 comparison had a higher number of both up- and down-regulated DEGs than the Tr_T14 vs. Tr_T26 comparison (Fig. 3C and D).

Fig. 3.

Differentially expressed genes analysis in Telenomus remus after cold stress. A, B, C and D represent the number of up- and down-regulated DEGs, the clustering heatmap of DEGs, the Venn diagram of up-regulated and down-regulated DEGs in different comparisons, respectively

To investigate functional annotations, GO term analysis was performed on DEGs from the Tr_T2 vs. Tr_T26 and Tr_T14 vs. Tr_T26 comparisons (Table S7). A total of 20 enriched GO terms were identified (Figure S2). In the Tr_T2 vs. Tr_T26 comparison, DEGs in the biological process (BP) category were primarily associated with cellular nitrogen compound metabolism process and chromosome organization. For molecular function (MF) and cellular component (CC), the dominant terms were DNA binding and nucleus, respectively. In the Tr_T14 vs. Tr_T26 comparison, DEGs in the BP category were mainly enriched in biosynthetic process and cellular amino acid metabolism, while DNA binding and nucleoplasm were the most prominent terms in the MF and CC categories, respectively. Additionally, up-regulated DEGs outnumbered down-regulated DEGs in individual terms, and more DEGs were enriched in the Tr_T2 vs. Tr_T26 comparison than in Tr_T14 vs. Tr_T26 (Figure S2).

To elucidate the biological functions of DEGs in T. remus under cold stress, pathway enrichment analysis was performed using the KEGG database. The analysis identified 282 and 247 significantly enriched pathways in the Tr_T2 vs. Tr_T26 and Tr_T14 vs. Tr_T26 comparisons, respectively (Fig. 4; Table S8). In the Tr_T2 vs. Tr_T26 comparison, DEGs were significantly enriched in the MAPK signaling pathway, FoxO signaling pathway, mTOR signaling pathway, and glycerophospholipid metabolism pathway. For the Tr_T14 vs. Tr_T26 comparison, significant enrichment was observed for DEGs related to FoxO signaling pathway and glycerophospholipid metabolism pathway (Fig. 4; Table S8).

Fig. 4.

KEGG enrichment analysis of DEGs in Telenomus remus comparisons for Tr_T2 vs. Tr_T26 (A) and Tr_T14 vs. Tr_T26 (B)

Metabolomic changes after cold stress of T. remus

The metabolomics data generated in this study were deposited in the MetaboLights database (MTBLS12473). The relative quantification values of metabolites showed R² values > 0.991 for all samples, confirming detection stability for subsequent analyses (Figure S3A. Principal component analysis (PCA) displayed distinct separation among temperature conditions, with the six biological replicates from each treatment forming tight clusters, demonstrating low intra-group variability and excellent reproducibility (Figure S3B).

Based on the criteria of VIP > 1.0, P < 0.05, and |FC| ≥ 2 or ≤ 0.5, a significant number of DAMs were identified in both comparisons. The Tr_T2 vs. Tr_T26 comparison yielded 325 DAMs, comprising 100 up-regulated and 225 down-regulated metabolites. Similarly, the Tr_T14 vs. Tr_T26 comparison resulted in 237 DAMs, comprising 71 up-regulated and 166 down-regulated metabolites (Fig. 5A and B; Table S9). In both comparisons, down-regulated DAMs outnumbered up-regulated DAMs. Furthermore, the Tr_T2 vs. Tr_T26 comparison had more up- and down-regulated DAMs than the Tr_T14 vs. Tr_T26 comparison. Venn diagram analysis identified 41 up-regulated and 66 down-regulated DAMs common to both comparisons (Fig. 5C and D).

Fig. 5.

Differentially accumulated metabolites (DAMs) analysis in Telenomus remus after cold stress. A, B, C and D represent the number of up- and down-regulated DAMs, the volcano plots of DAMs, the Venn diagram of up-regulated and down-regulated DAMs in different comparisons, respectively

Enrichment analysis revealed that the DAMs in the Tr_T2 vs. Tr_T26 and Tr_T14 vs. Tr_T26 comparisons were associated with 76 and 49 pathways, respectively (Table S10). The top 20 KEGG pathways for each comparison are presented in Fig. 6. In the Tr_T2 vs. Tr_T26 comparison, the five most enriched pathways were global metabolic pathways (50 DAMs), amino acid biosynthesis (10 DAMs), pyrimidine metabolism (9 DAMs), ABC transporters (9 DAMs), and purine metabolism (8 DAMs) (Fig. 6A). For the Tr_T14 vs. Tr_T26 comparison, the top pathways included global metabolic pathways (39 DAMs), pyrimidine metabolism (6 DAMs), vitamin digestion and absorption pathway(6 DAMs), galactose metabolism pathway (5 DAMs), and tryptophan metabolism (4 DAMs) (Fig. 6B). Starch and sucrose metabolism and glycerolipid metabolism pathways were commonly enriched in both comparisons. The DAMs involved in starch and sucrose metabolism comprised D-glucose 6-phosphate, sucrose, trehalose-6-phosphate (T-6-P), UDP-glucose, and D-fructose 6-phosphate, while UDP-glucose was concurrently enriched in glycerolipid metabolism.

Fig. 6.

KEGG enrichment analysis of DAMs in Telenomus remus after cold stress. A and B represent the top 20 enriched KEGG pathways in Tr_T2 vs. Tr_T26 and Tr_T14 vs. Tr_T26 comparisons, respectively

Combined transcriptome and metabolome analysis of T. remus after cold stress

To investigate T. remus response pathways to cold stress at genetic and metabolomic levels, KEGG pathway analysis was performed on both DEGs and DAMs. Correlation analysis revealed strong correlations between DAMs and DEGs (Table S11). The top 15 enriched terms were shown in Fig. 7 and Table S12. The global metabolic pathway exhibited the highest gene/metabolite enrichment. Starch and sucrose metabolism and glycerolipid metabolism pathway were commonly enriched. In Tr_T2 vs. Tr_T26, the starch and sucrose metabolism pathway included three genes (Cluster-5045.4316, Cluster-5045.8030, and Cluster-5045.9795) and four metabolites (D-glucose 6-phosphate, sucrose, T-6-P, and UDP-glucose), while glycerolipid metabolism pathway comprised one metabolite (UDP-glucose) and four genes (Cluster-5045.5446, Cluster-5045.6823, Cluster-5045.7375, and Cluster-5045.5967) (Fig. 7A). For Tr_T14 vs. Tr_T26, the starch and sucrose metabolism pathway contained two genes (Cluster-5045.6745 and Cluster-5045.9795) and three metabolites (D-fructose 6-phosphate, T-6-P, UDP-glucose), whereas the glycerolipid metabolism pathway contained three genes (Cluster-5045.5446, Cluster-5045.7375, and Cluster-5045.5282) and one metabolite (UDP-glucose) (Fig. 7B).

Fig. 7.

KEGG analysis of co-enriched pathways from DAMs and DEGs. A and B represent the KEGG analysis of DAMs and DEGs in Tr_T2 vs. Tr_T26 and Tr_T14 vs. Tr_T26 comparisons, respectively

The starch and sucrose metabolism pathways were commonly enriched in both the Tr_T2 vs. Tr_T26 and Tr_T14 vs. Tr_T26 comparisons after cold stress. Four DEGs were identified in these comparisons, comprising two significantly down-regulated genes-TREH (α-trehalase, Cluster-5045.9795) and HK (hexokinase, Cluster-5045.6745)-and two significantly up-regulated genes-GAA (α-glucosidase, Cluster-5045.4316) and GYS (glycogen synthase, Cluster-5045.8030). Metabolomic analysis further revealed significant enrichment of T-6-P and sucrose in T. remus under cold stress (Fig. 8).

Fig. 8.

Integrated transcriptomic and metabolomic analysis of the starch and sucrose metabolism in Telenomus remus after cold stress. A Heat map of the expression of genes involved in the starch and sucrose metabolism pathway. TREH: α-trehalase; GAA: α-glucosidase; GYS: glycogen synthase; HK: hexokinase. B Starch and sucrose metabolism pathway. Boxes and circles represent enzymes and metabolites, and red and green indicate up-regulated and down-regulated expression, respectively

Expression levels of GAA and GYS at different stress temperatures and durations

The expression levels of GAA and GYS under different cold stress conditions were examined. At 2 °C (F_{3, 12} = 4.585, P = 0.023), GAA expression increased significantly at 2 h, decreased at 4 h and 8 h, and subsequently recovered at 24 h. In contrast, GAA at 14 °C showed no significant temporal variation (F_{3, 12} = 2.010, P = 0.166). Compared to 14 °C, GAA expression at 2 °C was 7.2-fold higher at 2 h (t = 4.235, df = 6, P = 0.005), 2.1-fold higher at 8 h (t = 3.125, df = 6, P = 0.020), and 8.4-fold higher at 24 h (t = 3.189, df = 6, P = 0.019), whereas expression at 4 h showed no significant change (t = 1.451, df = 6, P = 0.197). For GYS, maximum expression occurred at 2 h under 2 °C, while minimum level was recorded at 24 h under 14 °C (F_{3, 12} = 6.360, P = 0.008). Notably, GYS expression at 2 °C demonstrated 3.9-fold and 5.0-fold increase at 2 h (t = 4.722, df = 6, P = 0.003) and 24 h (t = 3.171, df = 6, P = 0.019), respectively, compared to 14 °C, with no significant differences at intermediate time points (Fig. 9).

Fig. 9.

Relative expression levels of GAA and GYS in Telenomus remus under varing cold stress conditions. Data are expressed as mean ± SE. Different lowercase letters indicate statistically significant differences among different cold stress durations (Tukey’s test, P < 0.05). Asterisks indicate significant differences between two cold stress temperatures (t-test, P < 0.05)

Discussions

To the best of our knowledge, this study provides the first integrated multi-omics investigation of cold tolerance mechanisms in the parasitoid wasp T. remus through combined transcriptomic, metabolomic, and physiological analyses. Notably, these findings demonstrate that starch and sucrose metabolism represent key regulatory pathway mediating cold tolerance in this species.

The comprehensive cold stress response in T. remus involves dynamic regulation of energy reserves, enhanced synthesis of protective compounds, metabolic adjustments, and modulation of intracellular osmotic pressure. Under cold stress, T. remus dynamically remodels its energy reserves, particularly triglycerides and glycogen, to meet elevated energy demands in low-temperature environments. During moderate cold stress (14 °C), elevated energy requirements promote triglycerides catabolism, whereas severe cold stress (2 °C) induces triglyceride and glycogen conservation due to reduced energy expenditure. Condurrently, T. remus elevates sorbitol and trehalose roduction to stabilize proteins and cell membranes, thereby preventing dehydration and ice crystal formation. Moreover, T. remus optimizes its metabolic pathways, particularly glucose metabolism. Fluctuations in glucose concentrations reflect its dual function as both an energy source and a precursor for cryoprotective molecules such as trehalose, which contribute to cold adaption. Finally, T. remus regulates intracellular osmotic pressure by adjusting sorbitol and other polyol levels. This mechanism alleviates cellular dehydration caused by low temperatures, maintaining cellular functional and structural stability under cold stress.

The cold tolerance strategy of T. remus appears to be closely associated with the accumulation of specific sugars, which directly enhance its tolerance to low temperatures. While the dynamic regulation of lipid reserves, such as triglycerides, contributes to its cold response, this mechanism primarily involves energy storage management rather than direct cellular protection. In contrast, the accumulation of sugar compounds, particularly trehalose, plays a more direct role in cellular protection and cryopreservation. Several key findings support this conclusion. Firstly, trehalose has been widely recognized for its significant cellular protective effects, including the prevention of dehydration and cellular damage under low-temperature conditions [35]. In the present study, trehalose concentrations under low-temperature stress were consistently higher than those in control conditions, particularly at 2 °C, highlighting its critical role in the insect’s cold tolerance strategy. Additionally, trehalose contributes to intracellular osmotic pressure regulation, which is essential for maintaining cellular function and structural integrity during cold exposure [35]. This regulatory function is especially vital as it helps sustain physiological stability and mitigate dehydration-related damages. Glycogen, which primarily functions as an energy storage molecule, can be metabolized to meet the increased energy demands of insects under cold stress [59, 60]. In the current study, glycogen concentrations under cold stress were significantly lower than those in the control group, confirming that glycogen is catabolized primarily for energy production rather than functioning as a direct cryoprotective mechanism [60]. These results demonstrate that T. remus preferentially utilizes trehalose accumulation for cold tolerance. This strategy represents a more efficient physiological adaptation, as trehalose directly protects cellular integrity and maintains physiological functions under cold stress [61, 62].

Integrated transcriptomic and metabolomic analysis has become a robust and widely adopted approach for elucidating the molecular mechanisms of insect cold adaptation. By incorporating multi-omics data, this method enables a comprehensive understanding of the regulatory networks and biochemical processes involved in low-temperature responses. Under cold stress, the downregulation of TREH and HK in the starch and sucrose metabolism pathway may constitute an adaptive mechanism for insects to minimize energy consumption and maintain cellular integrity. TREH suppression reduces trehalose catabolism, thereby preserving trehalose levels for osmotic pressure regulation. Concurrently, HK downregulation restricts glucose utilization in glycolysis, conserving energy reserves. This coordinated response simultaneously optimizes energy conservation and osmotic protection, mitigating cold-induced metabolic stress [42, 63]. In contrast, GAA upregulation elevates amylase activity, promoting glucose generation. As a primary energy substrate, glucose supports essential metabolic functions and cellular protection under low-temperature conditions [64]. Trichogramma chilonis Ishii showed downregulation of multiple genes in the starch and sucrose metabolic pathway, including GAA, under high-temperature stress relative to controls [52]. This pattern likely reflects reduced energy demands under elevated temperatures, resulting in decreased expression of energy metabolism-related genes and lower production of energy substrates. These findings further highlight the crucial role of GAA in insect cold stress adaptation [52]. The upregulation of GYS enhances glycogen synthase activity, increasing glycogen synthesis to provide insects with an energy reserve while potentially influencing the concentration of other metabolites [65]. The significant accumulation of T-6-P and sucrose reflects the biochemical adaptation to low-temperature stress in insects. These compounds act as osmotic protectants, maintaining cellular osmotic balance and mitigating damage caused by dehydration or ice crystal formation [66]. The accumulation of T-6-P may arise indirectly from metabolic pathways affected by the upregulation of GAA and GYS. Additionally, the downregulation of TREH and HK conserves trehalose and sucrose, preventing the excessive depletion of these critical osmotic pressure protectants [67]. The coordinated regulation of these genes, along with the accumulation of T-6-P and sucrose, forms an effective biochemical defense system that enhances insect adaptation to low-temperature stress. Collectively, these findings indicate that the upregulation of GAA and GYS facilitates the accumulation of energy (glycogen) and protective compounds (sucrose and T-6-P), thereby improving cold tolerance in T. remus. This study advances the understanding of insect cold tolerance and may contribute to the theoretical framework for developing improved biological control strategies.

Analysis of GAA and GYS expression patterns under various low-temperature conditions and durations indicates distinct adaptive strategies in T. remus to maintain energy balance and cellular stability under cold stress. During the initial cold stress phase, elevated GAA expression suggests increased polysaccharide decomposition to release glucose for immediate energy utilization. At 2 °C, the secondary increase in expression levels suggests that, after an initial adaptation period, the insect elevates its demand for sugars to meet additional energy requirements or to synthesize cryoprotectants such as sorbitol and trehalose. This shift reflects a metabolic reprioritization to enhance survival under extreme cold conditions. Under normal physiological conditions, glucose-6-phosphate (G-6-P) serve as a key glycolytic intermediate, with its distribution determining the direction of glycolytic flux. Research indicats that G-6-P allocation significantly influences both glycolysis and trehalose synthesis. Typically, about two-thirds of G-6-P enters glycolysis, while 10% is diverted to the pentose phosphate pathway (PPP), and 20% is reversibly converted to glucose-1-phosphate (G-1-P). A minor portion of G-6-P is also utilized for T-6-P synthesis, a critical precursor for trehalose biosynthesis [68]. Beyond its role in trehalose production, T-6-P modulates upstream glycolytic steps, thereby regulating overall metabolic flux [69]. For instance, T-6-P can suppress the activity of key glycolytic enzymes, reducing glycolytic output and favoring trehalose accumulation. This regulatory mechanism may be linked to cellular energy status, osmotic balance, and stress responses [70]. Consequently, GAA overexpression may enhance trehalose accumulation by redistributing G-6-P toward T-6-P production, thereby promoting trehalose synthesis. However, direct evidence supporting the specific role of GAA in this process remains lacking. Future studies should investigate how GAA modulates metabolic flux to influence trehalose accumulation. Conversely, the elevated expression of the GYS gene indicats active glycogen synthesis, consistent with the insect’s strategy of storing glycogen under low-temperature conditions for future use. Following prolonged exposure to 2 °C, the demand for glycogen synthesis may decline with reduced metabolic rates. However, glycogen may subsequently reaccumulate to support potential future energy requirements, highlighting a dynamic adjustment in energy storage strategies. These findings imply that T. remus likely adopts distinct energy metabolism and cold-tolerance strategies under varying temperature conditions. Under extreme cold (2 °C), the insect prioritize utilizing existing energy reserves, followed by activating glycogen synthesis pathways to store energy for future needs. In contrast, at a milder low temperature (14 °C), although glycogen synthesis remains active, the overall trend shows a gradual decline in synthesis rates over time, possibly representing an adaptive adjustment to less severe cold stress. Collectively, the expression patterns of GAA and GYS genes reflect the intricate adaptive responses of T. remus to low-temperature stress, aligning with its fundamental requirements for maintaining energy homeostasis and cellular stability under cold conditions.

In response to cold stress, a functional interaction likely exists between GAA and GYS. GAA primarily catalyzes the decomposition of glycogen and other carbohydrates to release glucose, providing immediate energy for the insect. Under cold stress, insects may increase α-glucosidase activity to meet heightened energy demands required to sustain physiological functions. In contrast, GYS facilitates the synthesis of glycogen from glucose subunits, serving as a critical energy reserve [71]. These two enzymes play complementary roles in energy metabolism, with GAA mobilizing energy through glycogen breakdown and GYS storing surplus energy as glycogen [71, 72]. Under low-temperature conditions, insects may coordinately regulate the activities of both enzymes, elevating GAA activity during energy-demanding phases and enhancing glycogen synthase activity during periods of energy abundance. The activities of these enzymes are likely reciprocally influenced by each other and by other metabolic pathways. For example, elevated glucose levels may promote GYS-mediated glycogen synthesis. During cold adaptation, insects presumably rely on the precise regulation of GAA and GYS to ensure sufficient energy supply under extreme conditions while storing reserves when feasible. For small parasitoid wasps, rapid and efficient energy metabolism is particularly critical due to their small body size and accelerated heat loss. Consequently, GAA and GYS play pivotal roles in cold adaptation, especially in small parasitoids. The interplay and balance between these enzymes represent a key factor in insect responses to cold stress. Their activity patterns and regulatory mechanisms reflect the complex physiological adaptations and survival strategies insects employ under environmental fluctuations. Investigating these mechanisms is essential for elucidating insect survival tactics and may offer valuable insights for developing biocontrol applications.

Conclusions

This study reveals that starch and sucrose metabolism play a pivotal role in the response of T. remus to low-temperature stress. The expression levels of GAA and GYS are temperature-dependent and increase with cold intensity. These findings indicate that T. remus employs a cold-tolerance strategy involving trehalose accumulation. Trehalose functions as a direct cellular protectant against dehydration and cryodamage while supporting cellular function through osmoregulatory mechanisms. Its preferential accumulation suggests an evolutionarily optimized adaptation mechanism for maintaining cellular integrity and physiological activity under cold stress. These findings enhance the understanding of the molecular and biochemical basis of cold adaptation in T. remus. Future research should prioritize the use of CRISPR/Cas9 gene-editing technology to generate GAA- and GYS-overexpressing lines, thereby enhancing trehalose accumulation and cold tolerance. Additionally, further investigation into the tri-level nutritional transmission system (artificial diet–host egg–parasitoid) could optimize trehalose supplementation in artificial diets to improve cold tolerance [73]. These approaches provide practical strategies for enhancing the storage stability of T. remus in biocontrol applications.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

BP

Biological process

CC

Cellular component

DAMs

Differentially accumulated metabolites

DEGs

Differential expression genes

ELISA

Enzyme-linked immunosorbent assay

FC

Fold change

FPKM

Fragments per kilobase of transcript per million mapped reads

G-1-P

Glucose-1-phosphate

G-6-P

Glucose-6-phosphate

GAA

α-Glucosidase

GO

Gene ontology

GYS

Glycogen synthase

HK

Hexokinase

KEGG

Kyoto encyclopedia of genes and genomes

LT₅₀

Median lethal time

MF

Molecular function

NCBI

National center for biotechnology information

PCA

Principal components analysis

PLS-DA

Partial least squares discriminant analysis

PPP

Pentose phosphate pathway

RIN

RNA Integrity number

SRA

Sequence read archive

T-6-P

Trehalose-6-phosphate

TREH

α-Trehalase

VIP

Variable importance in the projection

Author contributions

W.B C., and L.S Z. designed the study. W.B C., and H. L. conducted the experiments. W.B C., H.L., Y.Y L., M.Q W., and J.J M. performed data analyses. W.B C. wrote the manuscript. L.S Z., and Z.J G. edited it. All authors read and approved the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (32402455), the National Key R&D Program of China (2023YFD1400600, 2021YFD1400200), the Major Projects of China National Tobacco Corporation (2024XM07).

Data availability

The raw RNA-seq data generated in this study have been deposited in the National Center for Biotechnology Information Sequence Read Archive database under accession number PRJNA1260750. The metabolomics data generated in this study have been deposited in the MetaboLights database under accession number MTBLS12473.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.