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Article

Metabolome and Transcriptome Analysis Reveals the Effects of Host Shift on Dendrolimus houi Lajonquière Larvae

by
Xinyuan Fang
1,†,
Zhenghao Chen
1,†,
Zhenhong Chen
2,
Jian Chen
1,2,
Zhenhui Zhao
1,2,
Peilin Wu
1,2,
Hongmin Wu
1,
Feiping Zhang
2 and
Guanghong Liang
2,*
1
Forestry College, Fujian Agriculture and Forestry University, Fuzhou 350002, China
2
Provincial Key Laboratory of Integrated Pest Management in Ecological Forests, Fujian Agriculture and Forestry University, Fuzhou 350002, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2023, 14(7), 1307; https://doi.org/10.3390/f14071307
Submission received: 25 May 2023 / Revised: 13 June 2023 / Accepted: 16 June 2023 / Published: 26 June 2023
(This article belongs to the Special Issue Diagnostics of Forest Pest Insects)
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Abstract

:
Dendrolimus houi Lajonquière is one of the most prevalent pine caterpillars in southern China, causing overwhelming forest infestation. It can overcome the negative impact of host shift from the original host, Cryptomeria fortune, to other tree species to complete its life cycle. In this work, D. houi larvae (1st to 3rd instar), originally feeding on C. fortunei needles, were used to determine the changes in the growth and development after they moved to needles of Cupressus funebris and Pinus yunnanensis during their 4th to 5th instar. Metabonomics and transcriptomics were conducted to evaluate the effects of the host shift on metabolite accumulation and gene expression of D. houi larvae. The results showed that the host shift significantly inhibited the pest growth and development by extending developmental duration and minifying the body length, body weight and head shell width. Besides, we found that the host shift influenced steroid hormone biosynthesis in D. houi larvae, which might lead to their abnormal development. The results may help to understand the response of D. houi larvae to host shift, and their adaptation mechanism to different hosts during multiple life cycles, providing a new plantation mode for mixed forests to suppress D. houi as well.

1. Introduction

Host plants are important for the survival of herbivorous insects, and the quality differing in host species greatly influences the growth and development of herbivorous insects [1,2,3,4,5]. Since the decrease of host quality due to environmental changes occurs frequently, insects adapt to them in various ways. Feeding on other hosts through short-term migration is a common survival strategy for polyphagous insects [6]. For example, Monolepta hieroglyphica migrates to the shoots of various host plants as crops grow [7,8]; Henosepilachna vigintioctopunctata can continuously expand and colonize new hosts, feeding on Solanum tuberosum L., S. melongena L. and S. nigrum L. successively in spring and summer, and feeding entirely on S. nigrum L. in autumn to complete the host shift [9]; Myzus persicae overwinters on Brassica napus L. in southern Shaanxi, China, and then shifts to Nicotiana tabacum to feed and reproduce in mid-April of the next year [10]. Insects are bound to face defensive interference from new hosts after host shift, such as volatile compounds and other secondary metabolites, which are reflected in the individual growth and development of insects [11,12,13,14]. Previous studies have shown that insects are strongly affected by the second host after host shift. The growth, development and digestive enzyme activities of Hyphantria cunea larvae have shown changes due to host shift [15]; Acyrthosiphon pisum Harris exhibit small body size, low reproductive rate, slow population growth and ontogeny, and long lifespan after shift to nonprimitive hosts [16]; and when the larvae of Spilosoma virginica shift from Taraxacum officinale free of iridoid glycosides, to Plantago lanceolata with high levels of iridoid glycosides, the β-glucosidase activity of the larvae has been significantly reduced [17].
Dendrolimus houi Lajonquière is a polyphagous lepidopteran defoliator that is widely distributed in southern China, India, Myanmar, Sri Lanka and Indonesia [18,19,20,21]. The pests cause serious damage to Cryptomeria fortunei, Pinus massoniana, P. yunnanensis, P. kesiya var. langbianensis and Cupressus funebris [22,23,24]. The food shortage forces the pests to transfer to other conifer species to complete generation development, indicating that D. houi has the ability of short-term adaptation to host shift. Thus, clarifying the effects of host shift on D. houi aids in understanding the adaptation mechanisms of the pests to the new hosts, providing insights into integrated pest management.
Several omics tools have greatly promoted research on interaction mechanism between phytophagous insects and host plants in recent years, allowing our understanding of interior physiology and biochemistry to be expanded beyond appearance [25]. In earlier studies, just one omics approach was used to determine a group of samples. Fortunately, an increasing number of studies are moving toward the combined analysis of several omics technologies, leading to the broad use of multi-omics technology as a frequent analytic strategy [26], which can disclose the underlying mechanisms of insects on how they adapt to variable living environments under molecular level. In this work, we examined the effects of host shifting on the growth of D. houi larvae and combined transcriptomes and metabolomics to analyze the biological, physiological and biochemical effects of host shift on D. houi after the host shifting and to reveal the adaptation mechanism of D. houi. This finding is supposed to provide the theoretical foundation for a new mixed forest model to control the dispersal of D. houi.

2. Materials and Methods

2.1. The Tested Insects

All the tested insects were collected from Jiangle County, Fujian Province, China (E: 117°35′12.17″, N: 26°37′49.54″), and their original hosts were C. fortunei (LL). The eggs were brought indoors and placed on wet filter paper to keep moisture in a Petri dish (D = 9 cm) in an artificial climate chamber (MGC-300H, Shanghai Yiheng Scientific Instrument Co., Ltd., Shanghai, China); the condition was temperature 22 ± 1 °C, 75% ± 5% RH and 14L: 10D. After hatching, the newly hatched larvae of D. houi were reared in a plastic bento box (17.2 cm × 11.6 cm × 5.2 cm) with 20 larvae per box. The 1st to 4th instar larvae were routinely reared by fresh branches of C. fortunei. Then the 4th instar larvae hatched with the same growth schedule were transferred to the rearing cage (75 cm × 75 cm × 75 cm). They were divided into three groups, of which two groups were subsequently fed with C. funebris (LL-LB) or P. yunanensis (LL-LY), respectively, and the other group with C. fortune as the control (LL-LL) with 30 larvae per cage for each group. The larvae and host plants were simultaneously placed in an artificial climate chamber (same rearing conditions as the 4th instar larvae), and the larvae were reared until the 6th instar [27]. The growth and development indexes including body length, body weight, head shell width and developmental duration were measured at the 4th and 6th instar, respectively [28]. Then, the larvae were collected and stored in a centrifuge tube (50 mL), rapidly quenched in liquid nitrogen for 15–20 min and then stored at −80 °C. Each group had three replicates with three larvae per replicate.

2.2. Sample Preparation for HPLC-Mass

A 50 mg sample in an EP tube was extracted in 1000 μL of solvent containing inner label (acetonitrile/methanol/water = 2:2:1, with an internal standard 2 mg/L, 2-Chloro-L-phenylalanine). Subsequent to vortex for 30 s, homogenization proceeded at 45 Hz for 10 min, followed by sonication for 10 min at 4 °C. The samples were set at −20 °C for 1 h, and centrifuged at 12,000 rpm at 4 °C for 15 min. The 500 μL supernatant was removed from the sample and dried in a vacuum concentrator. A total 160 μL extract solution (acetonitrile/water = 1:1) was added to the dried metabolites for resolution, vortex for 30 s, ultrasonic in the ice water bath for 10 min and centrifuge the samples at 4 °C for 15 min (12,000 rpm/min) [29]. Finally, 120 μL supernatant was carefully taken from the sample and put into a 2 mL injection bottle. From each sample, 10 μL were extracted and mixed into quality control samples for the detection.

2.3. Metabolite Profiling

Metabolomic analysis was performed using a liquid–mass system consisting of an ultra-high performance liquid phase (Waters Acquity I-Class PLUS, Waters, Framingham, MA, USA) tandem high-resolution mass spectrometer (Waters Xevo G2-XS QTof, Waters, Framingham, MA, USA). AcquityUPLC HSS T3 column (1.8 μm × 2.1 mm × 100 mm), liquid phase separation condition: mobile phase A (0.1% formic acid aqueous solution) and mobile phase B (0.1% formic acid acetonitrile) gradient elution [30]. The elution process started with 2% mobile phase B and was maintained for 0.25 min. The mobile phase B increased uniformly from 0.25–10 min to 98% and maintained for 3 min. Return to the initial condition of 2% mobile phase B and hold for 2 min, then analyze the next sample. The flow rate was 0.4 mL/min and the sample volume was 1 μL. The mass spectrometer detection conditions were as follows: capillary voltage: 2000 V (positive ion modes) or −1500 V (negative ion modes), cone hole voltage: 30 V, ion source temperature: 150 °C, desolvation temperature: 500 °C, the flow rate of back blowing: 50 L/h, and desolvent gas flow rate: 800 L/h.

2.4. Qualitative and Quantitative Analyses of Metabolites

The metabolites were identified and quantified based on the online METLIN database of Progenesis QI software and the self-built database of Beijing BioMaker Technologies Co., (Beijing, China), and the theoretical fragments were identified at the same time. An OPLS-DA model was employed with the first principal component of VIP (variable importance in the projection) ≥1, combined with Students’ t-test (p < 0.05) and FC (Foldchange) >1.5 [31], to identify differentially accumulated metabolites. Principal component analysis (PCA) was used to analyze the samples to investigate the variable composition, and the HMDB database was used to classify the screened differential expression metabolites (DEMs).

2.5. RNA Extraction and Transcriptome Sequencing Analysis

Total RNA of each sample was extracted following the manufacturer’s instructions for TRIzol Reagent (Invitrogen, Waltham, MA, USA), which was qualified using an ultramicro biochemical spectrophotometer (NanoDrop2000, Thermo, Waltham, MA, USA). The mRNA was enriched with Oligo (dT) magnetic beads, and Fragmentation Buffer was added. The first cDNA strand was synthesized using mRNA as the template with six-base random hexamers. The second cDNA chain was synthesized by adding buffer solution, dNTPs, RNase H and DNA polymerase I, and the cDNA was purified by AMPure XP beads. The purified double-stranded cDNA was subjected to end repair, A-tail addition and sequencing adapter ligation; then, AMPure XP beads were used for segment size selection, and cDNA libraries were constructed by PCR enrichment. Agilent 2100 Bioanalyzer and ABI Step One Plus Real-Time PCR System were used to detect the quality and yield, and the Q-PCR method was used to accurately quantify the library’s effective concentration (library effective concentration >2 nM). After the library is qualified, different libraries are pooled based on the amount of offline data required and sequenced on the Illumina platform.
Clean data was obtained by removing Reads containing adapters and low-quality Reads (Reads with a proportion of N greater than 10% or Reads with a quality value of Q ≤10 accounting for more than 50% of the whole Read) from the offline data. According to the expression level of genes in different experimental groups, Fold Change ≥ 2 and p value < 0.05 [32] were used as the screening criteria to screen differential expression genes (DEGs), and the KEGG database [33] was used for pathway enrichment analysis.

2.6. Combined Analysis of Metabolome and Transcriptome

Venn diagram analysis of differential metabolites and differential genes was performed to screen the same metabolic differential genes and differential metabolites after both host shift treatments. These obtained common DEMs and DEGs were analyzed using Pearson correlation analysis [34]. |r| > 0.90 was used as the standard to screen the combination of DEMs and DEGs with extremely high correlation. Subsequently, these combinations were further analyzed by KEGG pathway annotation.

2.7. Data Processing and Analysis

The body length (mm), body weight (g) and head shell width (mm) of various treatments were compared using the SPSS (v16.0) and one-factor ANOVA (Fisher’s LSD method). R 4.1.3 was used to process and normalize the metabolome and transcriptome data, and the biocloud platform (biocloud.net, accessed on 1 November 2022), and Origin 2022 were used for analysis and plotting.

3. Results

3.1. The Effects of Host Shift on the Growth and Development of D. houi Larvae

After host shift, the growth of body length, body weight and head shell width of the 4th–6th larvae fed on C. funebris or P. yunnanensis were significantly lower than that of the control (p < 0.05), whereas the developmental duration was significantly longer (p < 0.05), indicating that host shift slowed down the larval development. Host shift had greater negative effects on larval body weight than body length or head shell width. However, there were significant differences in body weight between the larvae fed on P. yunnanensis and C. funebris respectively, indicating that host shift to the two hosts had little impact on the body weight of D. houi larvae when they were transferred from original C. fortunei to P. yunnanensis and C. funebris.

3.2. The Effects of Host Shift on the Metabolome of D. houi Larvae

PCA analysis showed that host shift of LL-LB or LL-LY were significantly different from LL-LL (the control), and different host shift mode had significantly different effects on the metabolism of D. houi larvae in both positive and negative ion modes. However, there was a non-remarkable difference between LL-LB and LL-LY, suggesting that host shift might induce similar metabolites in these two groups (Figure 1a,b, Table 1).
Differential metabolites (DEMs) were detected in positive (4140) and negative (4308) modes (Supplementary Table S1). LL-LB had 280 and 150 DEMs in positive and negative ion modes, respectively, and 177 and 110 DEMs in LL-LY. The amount of DEMs in LL-LB was significantly higher than that in LL-LY regardless of the pattern (Figure 2), indicating that the shift of D. houi larvae from C. fortunei to C. funebris (LL-LB) had greater effects on their metabolite biosynthesis than LL-LY and control.
In the positive ion mode, LL-LB had more DEMs of fatty acyls (3 DEMs) and glycerophospholipids (3 DEMs). In negative ion mode, there were more metabolites in glycerophospholipids (4 DEMs) and carboxylic acids and their derivatives (3 DEMs). LL-LY had more DEMs in carboxylic acids and their derivatives (3 DEMs), glycerophospholipids (3 DEMs), steroids and steroid derivatives (3 DEMs) under the positive ion mode, and more DEMs in glycerophospholipids (3 DEMs) under the negative ion mode (Supplementary Figure S1). In total, the DEMs were mainly relevant to glycerophospholipids, carboxylic acids and their derivatives.

3.3. The Effects of Host Shift on the Transcriptome of D. houi Larvae

Clean data (58.31 GB) were obtained; the clean data of all samples reached 5.76 GB; and the proportion of Q30 base was above 93% (Supplementary Table S2), which shows that the transcriptome data were of high quality and could meet the requirements for further analysis. There were 622 DEGs in LL-LB, of which 342 DEGs were up-regulated and 280 DEGs down-regulated. There were 1386 DEGs in LL-LY, containing 750 up-regulated genes and 636 down-regulated genes. The number of up-regulated and down-regulated genes of LL-LB was significantly lower than that of LL-LY, and the expression differential factor of LL-LY was also significantly higher than that of LL-LB (Figure 3a,b), indicating that the gene differential expression induced by the host shift to P. yunnanensis was more significant.
Ribosome had the largest number of DEGs in LL-LB, with a total of 17 DEGs, followed by Lysosome and Peroxisome (Figure 3c), with 10 and 9 DEGs, respectively. Ribosome also had the largest number of DEGs in LL-LY, with a total of 35 DEGs followed by Spliceosome and Peroxisome, with 18 and 20 DEGs, respectively (Figure 3d). The number of DEGs from the two groups was the largest and most significant in Ribosome.

3.4. Integrated Metabolome and Transcriptome Analysis

Totally, 99 common DEMs and 411 common DEGs were screened by the Venn diagram (Figure 4a,b). A good clustering distribution in the Pearson correlation heatmap and 190 DEGs were highly correlated with the majority of the DEMs (Supplementary Figure S2), showing that these DEGs may be closely related to abnormal expression of DEMs. The results of the KEGG pathway revealed that multiple highly correlated (|r| > 0.9) DEMs and DEGs combinations were annotated in the steroid biosynthesis pathway (ko00100) and insect hormone biosynthesis pathway (ko00981), indicating that the steroid hormone biosynthesis of D. houi was greatly impacted by host shift, which may be a key factor causing the D. houi’s abnormal growth.
In this study, the insect molting hormone biosynthesis pathway was mapped to further clarify the regulation of steroid hormone biosynthesis within D. houi larvae under host shift treatments. The results showed that the expression of Sad and Shd gene in the pathway was significantly up-regulated after host shift, and the expression of 7-Dehydrocholesterol was also significantly up-regulated (Figure 4c). These two genes and metabolite may be important targets for regulating the growth of D. houi larvae under host shift conditions.

4. Discussion

Our findings demonstrated that the growth and development including body length, body weight and head shell width of D. houi were significantly negatively affected by host shift. Some previous studies have revealed that insects will inevitably encounter plant secondary metabolites such as phenols, tannins and alkaloids in the process of selecting hosts, which play negative roles in their growth and development [35,36,37,38], and the contents of total phenols and flavonoids within C. funebris and P. yunnanensis were significantly higher than those within C. fortune. C. fortune was the preferred host plant for D. houi larvae among these three plants [27,39]. The decreasing growth and development of D. houi larvae may be related to the secondary metabolites of these host plants.
In KEGG pathway enrichment analysis, we noted that DEGs in both treatments were significantly enriched in the ribosome pathway, indicating that genes involved in the ribosome pathway may be significantly impacted by host shift. Ribosomes and their associated proteins are closely related to cell survival and growth of organisms [40,41], which is a large nuclear protein particle that provides places for protein translation in eukaryotes, and protein translation requires not only ribosomes themselves but also extra protein factors, many of which are GTPase activated by ribosomes [42]. Reduced translation levels of ribosomal RNA (rRNA) can extend the lifespan of mice, nematodes and flies [43,44,45], and the absence or reduced ribosome function may lead to the absence of key protein synthesis in flies during development, ultimately resulting in the occurrence of body abnormalities [46]. The results of transcriptome analysis may indicate that the growth and development of D. houi were significantly affected by the second host plant.
We discovered that host shift affected steroid hormone biosynthesis in D. houi by a combined analysis of the transcriptome and metabolome. Many studies have proven that insect metamorphosis is mediated by the secretion of steroid hormones, which can coordinate the gene expression profile in insects and are closely related to the growth and development of insects [47,48,49,50,51]. For instance, ecdysone is involved in the feedback regulation of the production and secretion of neurosecretions by nerve cells in the insect brain [52,53]. The 20-Hydroxyecdysone can inhibit ovarian maturation and oviposition of adult houseflies, and it can bind with dopamine receptors to inhibit feeding and promote pupation [54,55]. Additionally, some studies have indicated that insect ecdysone can indirectly control the size of insect development. Some insects will stop the development of larvae in advance in case of environmental deterioration or shortage of food resources, and instar pupating when they reach the minimum size required for pupation, to ensure the normal reproduction of offspring [56,57,58,59]. The detrimental impacts of host shift on the development of D. houi may cause abnormal expression of steroid hormones, and thus affect the normal growth and development of D. houi.
D. houi may use different strategies at different time points in the process of adapting to new hosts. Our future work needs to clarify this important question to fully understand the adaptation mechanism of D. houi to changes in their living environment. C. fortunei, P. yunnanensis and C. funebris are major afforestation trees in the high-altitude area of southern China. The rapid adaptation of D. houi may explain why it infested the plants in several regions. Our findings provide better understanding on how and why the D. houi larvae moved to different host plants under food shortage stress and a probe to reveal the rapid adaptation mechanism of phytophagous insects with the host shift to a new host.

5. Conclusions

D. houi is a polyphagous lepidopteran defoliator. In previous studies, D. houi has shown strong adaptability to a variety of hosts. Our data suggest that enabling a shift to other hosts during the development of D. houi can cause significant negative effects on the growth and development of the caterpillars. At the same time, two genes and one metabolite in the insect molting hormone biosynthesis pathway were significantly up-regulated in response to the host shift, which could be potential control targets in the future.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f14071307/s1, Figure S1: HMDB classification maps of DEMs in positive and negative ion modes for the LL-LB and LL-LY, respectively; Figure S2: Pearson correlation heatmap of common DEMs with common DEGs; Table S1: List of metabolites in the positive and negative ion modes; Table S2: Quality and base content of RNA-seq in D. houi.

Author Contributions

Conceptualization, X.F., Z.C. (Zhenghao Chen) and G.L.; data curation, Z.C. (Zhenghao Chen), J.C. and Z.Z.; investigation, X.F., P.W., H.W. and Z.C. (Zhenhong Chen); writing—original draft preparation, X.F. and G.L.; writing—review and editing, F.Z. and G.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Fund of China (No. 31870641), the guided project of Fujian Provincial Science and Technology Department (No. 2021N0002), the Fuzhou Forestry Science and Technology Research Project (No. 2021FZLY01), the Forestry Peak Discipline Construction Project of Fujian Agriculture and Forestry University (No. 72202200205).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

In this study, we thank the Forestry Bureau of Jiangle County for providing support for insect source collection.

Conflicts of Interest

The authors declare no conflict of interest.

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Forests 14 01307 g001 550
Figure 1. (a) PCA score plot of metabolite profiles of LL, LB and LY in positive ion mode; (b) PCA score plot of metabolite profiles of LL, LB and LY in negative ion mode.
Figure 1. (a) PCA score plot of metabolite profiles of LL, LB and LY in positive ion mode; (b) PCA score plot of metabolite profiles of LL, LB and LY in negative ion mode.
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Figure 2. Differential metabolites of LL-LB and LL-LY in positive and negative ion mode.
Figure 2. Differential metabolites of LL-LB and LL-LY in positive and negative ion mode.
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Figure 3. (a) Volcano Plot of the LL-LB differentially expressed genes; (b) Volcano Plot of the LL-LY differentially expressed genes; (c) KEGG enrichment pathway map of differentially expressed genes in LL-LB; (d) KEGG enrichment pathway map of differentially expressed genes in LL-LY.
Figure 3. (a) Volcano Plot of the LL-LB differentially expressed genes; (b) Volcano Plot of the LL-LY differentially expressed genes; (c) KEGG enrichment pathway map of differentially expressed genes in LL-LB; (d) KEGG enrichment pathway map of differentially expressed genes in LL-LY.
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Figure 4. (a) The DEMs Venn diagram of LL-LB and LL-LY; (b) the DEGs Venn diagram of LL-LB and LL-LY; (c) the pathway of insect molting hormone biosynthesis. The boxes represent genes, and the circles represent metabolites; the red color represents up-regulation, and the blue color represents inconsistent up and down regulation of these two treatments; the EO refers to Ecdysone Oxidase, and the R08141 refers to the process of 3-Dehydroecdysone + NADH + H+ <=> Ecdysone + NAD+.
Figure 4. (a) The DEMs Venn diagram of LL-LB and LL-LY; (b) the DEGs Venn diagram of LL-LB and LL-LY; (c) the pathway of insect molting hormone biosynthesis. The boxes represent genes, and the circles represent metabolites; the red color represents up-regulation, and the blue color represents inconsistent up and down regulation of these two treatments; the EO refers to Ecdysone Oxidase, and the R08141 refers to the process of 3-Dehydroecdysone + NADH + H+ <=> Ecdysone + NAD+.
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Table
Table 1. The effects of host shift on the growth and development of the 4th–6th instar larvae of D. houi.
Table 1. The effects of host shift on the growth and development of the 4th–6th instar larvae of D. houi.
InstarsC. fortunei–C. fortunei (LL)C. fortunei–C. funebris (LB)C. fortunei–P. yunnanensis (LY)
body length/mm4th46.09 ± 2.88 a48.95 ± 4.10 a48.30 ± 2.89 a
6th90.93 ± 5.18 a73.27 ± 13.18 b67.26 ± 10.60 b
body weight/g4th0.77 ± 0.12 b0.80 ± 0.27 b0.94 ± 0.24 a
6th6.28 ± 1.13 a3.54 ± 1.56 b2.84 ± 1.58 b
head shell width/mm4th4.03 ± 0.19 a4.10 ± 0.21 a4.15 ± 0.21 a
6th6.97 ± 0.11 a6.52 ± 0.61 b5.80 ± 0.79 b
development duration/d4–6th32.00 ± 0.34 b37.78 ± 0.87 a38.11 ± 1.13 a
Note: Different lowercase letters indicated significant differences in length, weight and head shell width (p < 0.05). LL-LB means host shift from C. fortunei to C. funebris; LL-LB means host shift from C. fortune to P. yunanensis (LL-LY); and the control means invariable host of C. fortunei (LL-LL), same below.
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Fang, X.; Chen, Z.; Chen, Z.; Chen, J.; Zhao, Z.; Wu, P.; Wu, H.; Zhang, F.; Liang, G. Metabolome and Transcriptome Analysis Reveals the Effects of Host Shift on Dendrolimus houi Lajonquière Larvae. Forests 2023, 14, 1307. https://doi.org/10.3390/f14071307

AMA Style

Fang X, Chen Z, Chen Z, Chen J, Zhao Z, Wu P, Wu H, Zhang F, Liang G. Metabolome and Transcriptome Analysis Reveals the Effects of Host Shift on Dendrolimus houi Lajonquière Larvae. Forests. 2023; 14(7):1307. https://doi.org/10.3390/f14071307

Chicago/Turabian Style

Fang, Xinyuan, Zhenghao Chen, Zhenhong Chen, Jian Chen, Zhenhui Zhao, Peilin Wu, Hongmin Wu, Feiping Zhang, and Guanghong Liang. 2023. "Metabolome and Transcriptome Analysis Reveals the Effects of Host Shift on Dendrolimus houi Lajonquière Larvae" Forests 14, no. 7: 1307. https://doi.org/10.3390/f14071307

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