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Article

Transcriptomic and Microbiome Analyses of Procambarus clarkii Exposed to Different Doses of 20E

by
Yan Zou
1,†,
Chen-Yang Zhang
2,†,
Xiao-Tong Cao
2,
Rui-Geng Niu
2,* and
Jiang-Feng Lan
2,*
1
Marine Science Research Institute of Shandong Province, Qingdao 266104, China
2
Shandong Provincial Key Laboratory of Zoonoses, Shandong Agricultural University, Taian 271017, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Biology 2026, 15(5), 434; https://doi.org/10.3390/biology15050434
Submission received: 30 January 2026 / Revised: 15 February 2026 / Accepted: 1 March 2026 / Published: 6 March 2026
(This article belongs to the Special Issue Metabolic and Stress Responses in Aquatic Animals (2nd Edition))
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Simple Summary

Molting is a crucial physiological process for crayfish and other crustaceans, requiring them to shed their hardened old exoskeleton for growth. However, the molting period is extremely risky, and many farmed crayfish fail to survive this stage, thus limiting crustacean production. This study investigated how crayfish prepare for molting by injecting them with different doses of 20-hydroxyecdysone, simulating the early and middle stages of the premolt process. This study found that in the early stages of premolt, the crayfish body primarily focuses on degradation and resorption of the old exoskeleton. As the molting process progresses into the middle premolt stages, the crayfish promotes the formation of a new exoskeleton and enhances energy metabolism and antioxidant defense capabilities. By identifying these specific biological changes, it provides guidance for improving the survival rate and growth rate of farmed crustaceans.

Abstract

Molting determines survival and growth in cultured crustaceans, yet its specific regulatory mechanisms remain complex. This study integrated transcriptomics and microbiome analyses to elucidate molting regulation in crayfish (Procambarus clarkii). Crayfish were injected with 20-hydroxyecdysone (20E) at 20 and 250 ng/g to simulate early premolt and middle premolt, respectively. The comprehensive upregulation of nuclear receptor family genes confirmed the reliability of the in vivo 20E injection simulation. The results showed that 20 ng/g 20E stimulation induced 13,253 unique DEGs in the epidermis, mainly enriched in protein catabolism (promoting proteolysis to degrade the old exoskeleton), and induced 137 unique DEGs in hemocytes, mainly linked to ribosomal biosynthesis, while the 250 ng/g group showed 2395 unique DEGs in the epidermis, enriched in metabolic processes and biosynthetic processes (supporting the biosynthesis of the new stratum corneum), and 99 unique DEGs in hemocytes enriched in mitochondrial pathways, concomitantly enhancing energy metabolism and antioxidant defense capabilities. Notably, 20E upregulation potentially leads to the dysbiosis of pathogens, specifically Escherichia-Shigella and Vibrio. This study elucidates key biological events in the early and middle premolt of crayfish, clarifies tissue-specific regulatory mechanisms during premolt, and provides molecular-level insights into the growth regulatory network of crustaceans.

1. Introduction

Growth and development are essential physiological processes for all living organisms, spanning all stages from juvenile development to adulthood. However, for arthropods with hard exoskeletons, growth is a cyclical struggle against their own “armor”—they must periodically shed this protective yet restrictive exoskeleton to achieve bodily expansion and morphological transformation [1]. This vulnerable molting period exposes organisms to heightened risks from pathogens, cannibalism, and predation [2,3]. Crustaceans represent a significant economic aquaculture category in China, with crayfish being the primary freshwater crustacean species cultivated [4]. A major bottleneck in crayfish farming is the “Black May” phenomenon, where peak incidence and mortality occur around May each year. Notably, a key clinical manifestation during this period is molting failure [5,6], and individuals undergoing molting are highly susceptible to pathogen infection and death. Furthermore, cannibalism resulting from asynchronous molting has become a critical bottleneck limiting the profitability of high-density crayfish farming. Therefore, deciphering the regulatory mechanisms of the molting process is not only a core issue in biological growth and development but also an urgent practical need in aquaculture.
Molting is a pivotal turning point in the growth and development of arthropods, serving as a core event in their life cycle [7]. According to the classification, the molting cycle is standardized into four primary stages: intermolt, premolt, molting, and postmolt [8]. 20E serves as the central regulator of this process [9,10,11]. Its fluctuating titers govern stage-specific transitions [8,12]. The premolt stage is triggered by low levels of 20E, serving as a preparatory stage to initiate the molting process. It primarily involves the separation of the old epidermis from the stratum corneum, cell division, and the synthesis and differentiation of new epidermal substances [13,14]. The cuticle induction stage requires higher levels of 20E to stimulate epidermal formation, with protein and chitin synthesis playing crucial roles during this phase [12]. The molting stage involves the degradation of the old epidermis by proteases and chitinases, as well as the hardening of the new epidermis [15].
During the transition phase from Helicoverpa armigera larvae to adults, 20E inhibits cell proliferation by competitively binding dopamine receptors with dopamine, initiates apoptosis, and halts feeding behavior [16]. After feeding ceases, glucose levels in its hemolymph rise to sustain energy supply during metamorphosis. During insect metamorphosis, 20E upregulates the expression of Krüppel-like factor (KLF15) via nuclear receptor (EcR). KLF15 promotes larval body fat degradation through autophagy and apoptosis, facilitating glucose homeostasis reprogramming to supply energy for the insect molting and metamorphosis process [17]. After entering the cell, 20E binds to EcR. EcR further interacts with the ultraspiracle protein (USP). The 20E-EcR-USP complex regulates the transcription of early genes, thereby promoting the expression of late genes and advancing insect molting or metamorphosis [18,19]. During insect molting and metamorphosis, 20E induces intestinal remodeling, triggering apoptosis of midgut cells in the larva, causing them to contract toward the lumen while simultaneously promoting the proliferation and differentiation of new midgut epithelial cells [20]. The process of molting and metamorphosis in insects has been extensively studied. In contrast, research on crustaceans has primarily focused on the antagonistic relationship between 20E and AMPK [21], the histological and transcriptomic differences across various molting stages [22,23,24], and the influence of environmental factors on molting [25]. Despite these established frameworks, the tissue-specific molecular coordination between the epidermis and hemocytes during the 20E-mediated transition from early to middle premolt remains poorly understood. Furthermore, the interplay between 20E-regulated physiological remodeling and the dynamics of the host-associated microbiome represents a significant research gap. Elucidating these integrated mechanisms is essential to understanding the regulatory networks of crustacean molting and the practical causes of molting-related mortality.
This study compared the molecular changes during early and middle premolt in crayfish by injecting them with 20E at concentrations of 20 ng/g and 250 ng/g. By integrating epidermis and hemocyte transcriptomics and 16S rRNA sequencing data, we aimed to elucidate stage-specific regulatory transitions and host–microbe interactions. Specifically, we analyzed the functional shift in the epidermis from proteolysis to biosynthesis, the changes in hemocytes from a basal synthetic state to dynamic energy metabolism and antioxidant defense, and the changes in key microbial communities such as Vibrio. These findings identify key regulatory genes and microbial indicators, providing a theoretical basis for improving molting survival rates in crustaceans.

2. Materials and Methods

2.1. Crayfish Maintenance

Healthy male crayfish with an average body weight of approximately 10 g ± 0.5 g and a carapace length of 5.0–7.0 cm were procured from a commercial farm in Taian, Shandong Province, China. Individuals were selected based on uniform weight, intact appendages, consistent dark coloration, and active movement, objective criteria indicating good health. The selected crayfish were acclimated in a laboratory recirculating aquaculture system for two weeks prior to experimentation. During acclimation, they were maintained under controlled conditions: water temperature 28 ± 1 °C, pH 7.5–8.5, dissolved oxygen > 6 mg/L, and a 12 h light/12 h dark photoperiod. The animals were held in 80 L glass aquaria (20 crayfish per tank, yielding a density of 2.5 g/L) containing dechlorinated, fully aerated water. They were fed daily with a commercial sinking pellet diet (3% of body weight per day), and uneaten feed and waste were removed promptly to maintain water quality.
Molting stages were identified based on morphological criteria as follows: (1) intermolt (Stage C): fully hardened exoskeleton without gastrolith; (2) postmolt (Stages A/B): soft exoskeleton; and (3) premolt (Stage D): increased gastrolith size and loosening of the old exoskeleton [26]. This study was approved by the Committee on Animal Ethics of Shandong Agricultural University (No. SDAUA-2025-261). All experimental procedures were conducted in strict accordance with the approved guidelines and were designed to comply with international standards for animal welfare, specifically following the ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines.

2.2. Experimental Design

To simulate the 20E titers in hemolymph during the premolt stage, crayfish were injected with 20E (dissolved in DMSO and diluted in PBS) at doses of 20 ng/g (mimicking early premolt) and 250 ng/g (mimicking middle premolt). These doses were selected based on documented hemolymph 20E concentrations, which remain low (1–10 ng/mL) during early premolt but increase substantially during middle premolt stages, peaking at approximately 210 ng/mL [26]. The final DMSO concentration was <0.1%. Crayfish (n = 30 per group) were randomly assigned to three groups: control (PBS vehicle), 20 ng/g 20E, and 250 ng/g 20E. After injection, crayfish were maintained separately under the original feeding conditions based on the sampling time strategies of previous studies on Macrobrachium rosenbergii and Locusta migratoria [27,28]. Meanwhile, our previous qPCR experiments in crayfish showed significant changes in the expression of key molting-related genes 6 h after injection. Therefore, we anesthetized the crayfish with ice water six hours after injection. Six hours after injection, crayfish were anesthetized with ice water. Subsequently, 200 μL of hemolymph was rapidly extracted from the abdominal sinus cavity, and the epidermis was dissected. The collected tissue was immediately frozen in liquid nitrogen and transferred to −80 °C for storage.

2.3. RNA Extraction, Transcriptome Sequencing, and Analysis

To minimize the impact of individual physiological variations and biological “noise” while ensuring sufficient biomass for high-quality multi-omic extraction, a systematic pooling strategy was implemented: tissue samples were taken from 30 individuals per group. This approach ensures that the resulting transcriptomic and microbial profiles capture the representative physiological state of the population rather than individual outliers. Furthermore, individual samples from each group were preserved separately at −80 °C for subsequent qPCR validation to verify the reliability of the pooled sequencing results. Total RNA from epidermal and hemocytes was extracted using the TRIzol (Vazyme, Nanjing, China). RNA concentration and purity of RNA were measured by a NanoDrop 2000 ultramicro spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), and integrity was verified by 1% agarose gel electrophoresis. Sequencing libraries were constructed involving mRNA enrichment, fragmentation, cDNA synthesis, and adapter ligation. The final libraries were size-sorted, purified, and sequenced on an Illumina high-throughput platform (Illumina, San Diego, CA, USA).

2.4. Mfuzz Clustering Analysis

The Mfuzz R package (version 2.58.0) was used to perform fuzzy c-means clustering on the standardized expression values (z-scores) of DEGs. The optimal number of clusters was determined to be four based on the minimum centroid distance and membership values (fuzzification parameter m = 2). Genes with membership values > 0.5 were assigned to the corresponding cluster. Expression trends for each cluster were visualized as line plots. Advanced Mfuzz analysis was performed using the OmicStudio tools at https://www.omicstudio.cn/tool (accessed on 12 October 2024).

2.5. GO Enrichment Analysis

Gene expression was quantified using RSEM. Differential expression analysis was performed using DEGseq software (Version 1.18.0), and the screening criteria for significant DEGs were false discovery rate (FDR) < 0.05 and |log2 fold change (FC)| ≥ 1. All transcripts were compared to GO databases to obtain annotation information. GO enrichment analysis was performed using Goatools software (Version 0.6.5), while KEGG pathway enrichment analysis was performed using scripts based on the Scipy (v1.11.1) package in Python (version 3.13.1), with a significant enrichment threshold of corrected p < 0.05. These thresholds were selected to ensure a balance between statistical significance and biological relevance, consistent with established standards in crustacean transcriptomic studies to minimize false positives while capturing substantial expression changes.

2.6. Gene Functional Enrichment and Pathway Analysis

Functional enrichment analysis was performed on the differentially expressed genes identified through screening. The analysis was conducted using the online platform Metascape (http://metascape.org, accessed on 15 October 2024). The species was set to Drosophila melanogaster to leverage its comprehensive and well-annotated functional databases, which are the most advanced among arthropods. This approach is justified by the high degree of evolutionary conservation in core biological pathways between insects and crustaceans. Enrichment analysis was conducted using the platform’s integrated Gene Ontology, KEGG, and Reactome databases. Significantly enriched pathways were identified via hypergeometric testing and corrected using the Benjamini–Hochberg method. Entries with a corrected p-value (FDR) < 0.05 were deemed significant. We note that a limitation of this strategy is the potential underrepresentation of crustacean-specific genes or recently evolved pathways due to the evolutionary distance between the two lineages.

2.7. Dynamic Radar Chart Analysis

Dynamic radar chart analysis was performed on the screened genes. This analysis was conducted on the Omicshare online platform, where relevant data such as differentially expressed gene levels were uploaded. With each sample group serving as a dimension, the polygonal regions of the radar chart illustrate their distribution patterns; metrics such as log2FC are mapped to the size and color of the outer ring markers, providing an intuitive representation of the magnitude and direction of change.

2.8. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR)

To validate transcriptomic data, qRT-PCR was performed using SYBR Green I (Vazyme, Nanjing, China) on a Archimed X4 system (ROCGENE, Beijing, China). Specific primers (Table 1) were designed with Tm of 60 ± 2 °C; their specificity was confirmed by single-peak melting curves and agarose gel electrophoresis. Each 20 μL reaction contained 10 μL of Master Mix, 0.4 μL of each primer (10 μM), and 2 μL of cDNA. The program included pre-denaturation at 95 °C (2 min) followed by 40 cycles of 95 °C (15 s), 60 °C (30 s), and 72 °C (30 s). Relative expression was quantified via the 2−ΔΔCt method using 18S rRNA as an internal control. Significant differences were determined by one-way ANOVA with Tukey’s test (p < 0.05).

2.9. Microbial Community Diversity Analysis

To minimize contamination, both intestinal and hemolymph samples were collected in a laminar flow hood. Before the procedure, the body surface was disinfected with 70% alcohol, and then hemolymph was aspirated using a sterile syringe. All dissecting instruments were sterilized before use.
For the hemolymph samples, host cell components were first removed by centrifugation to enrich the microbial community. Total bacterial DNA was then extracted using the E.Z.N.A.® Extract Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA) following the manufacturer’s instructions. For bacterial community analysis, the V3–V4 hypervariable regions of the 16S rRNA gene were amplified, as these regions provide good taxonomic resolution and are widely used in environmental microbiome studies. The primers used were 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GACTACHVGGGTWTCTAAT-3′). Sequencing libraries were generated using the NEXTFLEX Rapid DNA Seq Kit (Bioo Scientific, Austin, TX, USA) and sequenced on an Illumina NextSeq 2000 platform (PE250) (Illumina Inc., San Diego, CA, USA). Raw reads were merged using FLASH (v1.2.11) and clustered into operational taxonomic units (OTUs) with 97% similarity using UPARSE (v7.1) after chimera removal. Sequences were rarefied to a uniform depth to normalize sequencing effort. Taxonomy was assigned using the RDP classifier (v2.11) against the Silva 16S rRNA database (v138) with a confidence threshold of 0.7.

3. Results

3.1. Transcriptomic Responses of Epidermis

3.1.1. Transcriptomic Responses to Different Doses of 20E in Crayfish Epidermis

Epidermis was collected from crayfish at 6 h after injection of 20E at final dosages of 20 ng/g and 250 ng/g body weight and subjected to transcriptome sequencing (Figure 1A). Volcano plots showed that a larger number of DEGs were observed in the 20 ng/g 20E treatment group than in the 250 ng/g 20E treatment group (Figure 1B). To verify the activation of the 20E signaling pathway induced by 20E treatment in crayfish, the expression levels of core genes in the pathway were detected (Figure 1C). Compared to the control group, the transcript levels of ecdysone receptor (EcR) and nuclear transcription factors (Eip75s) were significantly upregulated in both the 20 ng/g and 250 ng/g 20E treatment groups. Randomly selected DEGs (Figure 1C) were used to validate the transcriptome data via qRT-PCR. The expression of Eip75B-X1 was significantly higher in the treatment groups than in the control group, consistent with the transcriptome sequencing results (Figure 1D).

3.1.2. Clustering of Expression Trends and GO Enrichment Analysis of DEGs in 20E-Treatment

DEGs identified in the epidermis were subjected to expression trends clustering analysis, resulting in four major clusters (Clusters 1–4). Genes in Cluster 1 exhibited continuous downregulation with increasing 20E dose (Figure 2A). Genes in Cluster 2 were upregulated at the low dose and downregulated at the high dose (Figure 2B). Genes in Cluster 3 showed continuous upregulation with increasing 20E dose (Figure 2C). Genes in Cluster 4 were downregulated at the low dose and partially recovered or upregulated at the high dose (Figure 2D). GO enrichment was performed for the DEGs of each cluster, and the top 20 enriched terms are displayed in Figure 2a–d. Cluster 1 was enriched in terms such as “membrane”, “actin filament-based process”, “actin cytoskeleton organization”, and “cytoskeletal protein binding” (Figure 2a). Cluster 2 was enriched in terms related to catalytic activity, including “catalytic activity”, “transferase activity”, and “protein kinase activity” (Figure 2b). Cluster 3 was enriched in terms such as “steroid hormone receptor activity”, “cellular response to steroid hormone stimulus”, “transcription factor activity, sequence-specific DNA binding”, and “signaling receptor activity” (Figure 2c). Cluster 4 was enriched in terms related to biosynthetic processes, including “structural molecule activity”, “organic substance biosynthetic process”, “peptide biosynthetic process”, “translation”, and “gene expression” (Figure 2d).

3.1.3. Dose-Specific GO Enrichment Patterns in 20E-Induced DEGs

Venn diagram analysis of DEGs between the 20 ng/g 20E group and 250 ng/g 20E groups showed 13,253 DEGs unique to the 20 ng/g 20E group, 2395 DEGs unique to the 250 ng/g 20E group, and 3999 shared DEGs (Figure 3A). DEGs unique to the 20 ng/g and 250 ng/g treatment groups were subjected to separate GO-BP enrichment analysis. In the 20 ng/g group, the most significantly enriched GO terms were primarily associated with “protein modification” and “protein catabolism processes” (Figure 3B), whereas the 250 ng/g group exhibited prominent enrichment in “metabolic processes” and “biosynthetic processes” (Figure 3C). Expression profiles of genes annotated to proteolysis and developmental processes were visualized using ridge plots. The results demonstrated that the expression distributions in both the 20 ng/g and 250 ng/g groups were significantly shifted to the right and exhibited increased dispersion, with median expression levels being significantly higher than those in the control group (Figure 3D,E).

3.2. Transcriptomic Responses of Hemocytes

3.2.1. Transcriptomic Responses to Different Doses of 20E in Crayfish Hemocytes

To investigate the potential functions of hemocytes during the premolt stage of crayfish, we further performed transcriptome sequencing analysis on hemocytes from crayfish treated with different doses of 20E. Transcriptome sequencing revealed that both treatments induced substantial gene expression changes compared to the control. The 20 ng/g group exhibited 4407 DEGs (2855 upregulated, 1552 downregulated), while the high-dose group showed a similar magnitude with 4201 DEGs (2858 upregulated, 1343 downregulated) (Figure 4A). Given that the ecdysone signaling hierarchy is highly characterized in Drosophila melanogaster, we mapped the crayfish DEGs to their Drosophila homologues to assess whether the 20E-triggered response in crayfish hemocytes follows the evolutionarily conserved regulatory framework of arthropods. We first attempted to map DEGs to their Drosophila melanogaster homologues. Subsequently, we constructed a condition–gene association network using successfully mapped DEGs, confirming that the 20E-induced transcriptional response exhibits a degree of conservation (Figure 4B).
For the 20 ng/g 20E group, enriched terms were highly concentrated in core pathways governing development and morphogenesis, such as neuron differentiation, tissue development, and anatomical structure formation (Figure 4C). In contrast, the enrichment network of the 250 ng/g 20E group retained these core developmental modules but was significantly expanded to include additional processes related to cellular stress response (e.g., response to external stimulus), cytoskeletal reorganization, cell division (mitotic cytokinesis), and cell fate determination (e.g., photoreceptor cell fate determination, dorsal/ventral axis specification) (Figure 4D).

3.2.2. 20 ng/g 20E Initiates Fundamental Anabolic Programs in Crayfish Hemocytes

In order to clarify the specific functions that hemocytes may perform in different molting states, this study focused on the specific expression genes of hemocytes stimulated by two doses of 20E (Figure 5A). GO enrichment analysis of genes uniquely responsive to the 20 ng/g 20E group revealed a specific regulatory focus on fundamental biosynthetic pathways (Figure 5B). The significantly enriched biological processes clustered into two primary categories: (1) protein synthesis and ribosome biogenesis (e.g., structural constituent of ribosome, ribosomal subunit, cytosolic ribosome); and (2) nucleotide synthesis and metabolism, particularly pyrimidine nucleoside/triphosphate biosynthesis and metabolism (e.g., pyrimidine nucleoside biosynthetic process, NAD binding). This profile indicates that the 20 ng/g 20E group specifically targets the core machinery for macromolecule production in hemocytes.
For protein synthesis and ribosome-related genes, the vast majority were upregulated, including key factors such as elongation factor 1-alpha, tyrosyl-tRNA synthetase (TyrRS), eukaryotic translation initiation factor 5 (eIF5), and ribosomal proteins (eS10, eL30-like), alongside chaperones like calreticulin and Gp93. Only a minority, such as eIF3d1, were downregulated (Figure 5C). Similarly, most genes associated with nucleoside metabolism exhibited increased expression, exemplified by isocitrate dehydrogenase [NADP] cytoplasmic (Idh) and deoxynucleoside kinase, while a subset including NAD kinase and cytosolic purine 5′-nucleotidase-like was suppressed (Figure 5D). This coordinated upregulation highlights the effects of low-dose 20E on cellular anabolic processes.

3.2.3. 250 ng/g 20E Induces Genes Related to Energy Metabolism and Oxidative Stress Defense in Hemocytes

GO enrichment analysis of the DEGs specifically induced by the 250 ng/g 20E group revealed a pronounced functional focus on mitochondria-related terms (mitochondrion, mitochondrial inner membrane, and mitochondrial envelope) and oxidoreductase activity (NADH dehydrogenase activity, oxidoreductase activity) (Figure 6A). This enrichment profile suggests that high-dose 20E stimulation potently modulates cellular redox homeostasis and mitochondrial function.
Specifically, in terms of energy metabolism, key genes involved in the mitochondrial electron transport chain and ion transport were significantly upregulated, including cytochrome c, sulfide quinone oxidoreductase (Sqor), isoglutaryl-CoA dehydrogenase, and a variety of ATPases (Figure 6B). Furthermore, a large proportion of genes involved in antioxidant defense mechanisms were significantly induced, such as catalase (CAT), superoxide dismutase [Cu-Zn] (CZ-SOD), glutathione S-transferase Mu 3 (GSTM3), peroxiredoxin 4 (Prx4), and several heat shock proteins (Hsp90) (Figure 6C). Gene co-induction aligns with GO enrichment results, suggesting that 250 ng/g 20E may regulate energy metabolism and antioxidant responses. We randomly screened CZ-SOD for qPCR to validate the results of transcriptome sequencing, and the 250 ng/g 20E group significantly induced upregulation of CZ-SOD, supporting the reliability of the transcriptome data (Figure 6D).

3.3. Effects of Exogenous 20E on Intestinal and Hemolymph Microbiota Composition

3.3.1. Phylum-Level Changes

This study further analyzes the effects of 20E on the intestinal and hemolymph microbial communities of crayfish. Phylum-level results showed (Figure 7A,C) that the intestinal microbiota had Proteobacteria as the predominant phylum, followed by Firmicutes. The relative abundance of Firmicutes in the 20 ng/g 20E group significantly increased, while the relative abundance of Proteobacteria significantly decreased; in the 250 ng/g 20E group, the bacterial population’s composition was similar to that of the control group. In the hemolymph microbiota, Proteobacteria also occupied a dominant position, followed by Bacteroidota. The proportion of Proteobacteria was significantly reduced in the 20 ng/g 20E group, accompanied by a significant increase in Bacteroidota and Actinobacteriota; the community structure tended to be in a control state in the 250 ng/g 20E group, with only a slight increase in Firmicutes.

3.3.2. Genus-Level Changes

Genus-level analysis further revealed that 20E-induced compositional shifts mainly occurred in the intestine (Figure 7B,D). In the 20 ng/g 20E group, the relative abundance of Citrobacter significantly decreased; in the 250 ng/g 20E group, Citrobacter significantly recovered, while the abundance of Escherichia-Shigella significantly decreased. Hemolymph community structure was generally maintained between treatments. Notably, significant changes in the abundance of Vibrio, which is highly harmful to aquaculture, were observed in both the intestine and hemolymph.

4. Discussion

4.1. Stage-Specific Epidermal Regulation Mediated by 20E

Molting is the rate-limiting gateway to growth, weight gain, and market-size achievement in crustaceans [29]. However, this process represents a critical window of physiological vulnerability. Coinciding with the “Black May” disease season, molting crayfish are highly susceptible to pathogens like WSSV and Vibrio parahaemolyticus, leading to mass mortality and substantial economic losses [5,6]. Consequently, deciphering the regulatory mechanisms of molting is essential for improving survival and production efficiency [30]. Recent studies have further elucidated the complex regulatory networks in crustaceans. For instance, Benrabaa et al. [31] characterized the expression of ecdysteroid-responsive genes during the molting cycle, while Yu et al. [32] highlighted the critical role of gut microbiota homeostasis in the growth and stress adaptation of P. clarkii. While the ecdysone signaling cascade involving EcR and Eip75 is well-characterized in insects [12,33,34] and evolutionarily conserved [35], the specific molecular mechanisms in crustaceans remain understudied [36,37]. Hemolymph 20E titers in P. clarkii are typically low during intermolt and early premolt (~1–10 ng/mL) and increase substantially during middle premolt (peaking at approximately 210 ng/mL) [27]. This stage-dependent fluctuation pattern parallels observations in other decapods, such as Cancer magister [38], Chionoecetes bairdi [39], and Paralithodes camtschaticus [40], reinforcing the physiological relevance of the dosage gradient used in our simulation. By simulating early and middle premolt via differential 20E injections, we demonstrated that 20E functions as a dose-dependent rheostat. The successful activation of the signaling pathway was confirmed by the significant upregulation of Eip75 (Figure 1C). This progressive regulation is further substantiated by Mfuzz Cluster 3, where steroid signaling genes exhibited sustained upregulation, confirming that the canonical cascade tracks with increasing hormone titers to coordinate distinct phases of epidermal biology.
Our results revealed that the 20 ng/g 20E treatment induced a significantly higher number of epidermal unique DEGs (13,253) compared to the 250 ng/g group. This quantitative discrepancy suggests that early premolt (20 ng/g) acts as a comprehensive preparatory phase involving wide-ranging cellular remodeling, while middle premolt (250 ng/g) shifts toward a highly specialized regulatory mode. Meanwhile, these catabolic features observed, via GO enrichment entries, may reflect a direct guiding role of 20E signaling or a preparatory step for subsequent cell dissociation and tissue remodeling (Figure 3B). We propose that this is mediated by low-dose 20E signaling through the EcR/RXR complex, which likely activates catabolic programs (e.g., proteolysis and autophagy) for tissue detachment, analogous to the EcR-E75 regulated destructive phase in insects. This aligns with the physiological requirements of apolysis, where the epidermis must secrete enzymes to digest the endocuticle and detach from the old exoskeleton, a pattern consistent with the peak transcriptomic activity observed during the intermolt to premolt transition in crabs [41]. Furthermore, the specific downregulation of cytoskeletal genes in Cluster 1 (Figure 2A) supports this model. In contrast, the shift to an anabolic “building” program at higher 20E doses (250 ng/g) may involve a switch in transcriptional output, promoting biosynthetic pathways for new cuticle formation.
In contrast, the transition to middle premolt (250 ng/g 20E) marks a shift toward an “anabolic” or “constructive” program. Although the total number of DEGs decreased, the functional focus shifted significantly toward metabolic and biosynthetic processes (Figure 3C), fitting the profile of cuticulogenesis. The expression pattern of Cluster 4 is particularly illuminating: genes related to translation and peptide biosynthesis were suppressed at the low dose but recovered or were upregulated at the high dose (Figure 2D). This “suppression-then-recovery” pattern suggests that the epidermis may transiently pause general protein synthesis during the chaotic remodeling of early premolt, only to reactivate the translational machinery (ribosomes and elongation factors) when high-level production of new cuticular proteins is required.

4.2. Functional Shift in Hemocytes Regulated by 20E

In the 20 ng/g treatment group, genes specifically differentially expressed in hemocytes were predominantly enriched in GO terms associated with protein synthesis, ribosomal biogenesis, and nucleotide metabolism (Figure 5B). Nucleotides constitute the fundamental building blocks of nucleic acid synthesis [42], whilst ribosomes serve as the factories for protein synthesis [43]. Their active states provide the material and energy foundation for epidermal remodeling and tissue differentiation required during early premolt [44,45]. The enrichment of differentially expressed genes in developmental and morphogenesis pathways (Figure 4B) further corroborates the central role of 20E as a developmental hormone. Conversely, genes associated with the 250 ng/g treatment group predominantly clustered within mitochondrial function and redox enzyme activity-related entries (Figure 6A). Mitochondria serve as cellular powerhouses, and their functional state is critical for efficient energy supply [17,46]. Redox enzymes are closely associated with antioxidant defense systems that scavenge reactive oxygen species [47]. This finding suggests that 20E may help organisms maintain homeostasis by optimizing hemocyte energy metabolism and antioxidant defense capabilities, thereby aiding adaptation to the intense physiological challenges of middle premolt [48,49].

4.3. Host–Microbiome Interplay During Molting

Molting is accompanied by significant microbiome restructuring, which has been validated in crustaceans [50,51]. Microbial imbalance has been proven to be closely associated with high mortality during ecdysis. Dominant phyla such as Proteobacteria and Firmicutes exhibited significant fluctuations, indicating that hormonal signals directly influence microbial stability across host compartments. Of particular concern is the dysbiosis related to opportunistic pathogens. We observed significant fluctuations in Escherichia-Shigella and, notably, an enrichment of Vibrio in both the intestine and hemolymph in the 250 ng/g group. However, the observed association does not establish causality. It remains unclear whether 20E-mediated host physiological changes (e.g., metabolic or immune modulation) directly drive the microbial community shifts, or whether the altered microbiome conversely influences host physiology and molting success. This proposed interaction requires direct functional validation through targeted manipulation of host pathways or specific microbial constituents to dissect the causal mechanisms and to evaluate the direct contribution of microbiome dysbiosis to molting-associated mortality.

4.4. Limitations of This Study

Limitations of this study include the following: (1) Sampling at a single 6 h post-injection timepoint captures acute transcriptional responses but does not allow us to capture the dynamic gene expression profile throughout the premolt period. This limits our understanding of how the transcriptional cascade unfolds over time relative to morphological changes. Future studies should employ multi-timepoint sampling to resolve the full temporal sequence of 20E-regulated gene networks. (2) We did not directly measure hemolymph 20E titers following injection. Therefore, the actual physiological hormone concentration achieved is uncertain, making the relationship between injected dose and biological effect inferential. Future experiments should include direct hemolymph titer measurements to calibrate the injected dose to endogenous physiological levels. (3) The observed shifts in the microbiome are correlated with host physiological responses, but this correlation does not establish causality. To move beyond correlation, future functional studies are needed, such as in vitro validation of key host genes or controlled manipulation of specific microbial taxa, to test whether microbial changes are drivers or consequences of the molt process. (4) The experimental design involved pooling multiple individuals for RNA-seq and microbiome analysis. While this approach increases sample homogeneity and sequencing depth, it inherently precludes the assessment of individual variation and prohibits statistical analysis at the individual level. Future studies should employ designs with independent, nonpooled biological replicates to enable statistical comparisons and to capture the range of individual responses, which is crucial for understanding population-level resilience and variability in molting.

5. Conclusions

This study elucidates the regulatory mechanisms of molting in P. clarkii by integrating transcriptomic and microbiome analyses. We demonstrate that low-dose 20E (simulating early premolt) primarily triggers epidermal tissue remodeling and hemocyte biosynthesis, accumulating essential molecular substrates. Conversely, high-dose 20E (simulating middle premolt) shifts the epidermal program toward new cuticle formation while enhancing mitochondrial bioenergetics and antioxidant defenses in hemocytes to sustain metabolic demands.
These distinct host physiological shifts are coordinated by specific regulators (e.g., Eip75) and effector genes (e.g., CZ-SOD), accompanied by significant microbiota reorganization and increased abundance of opportunistic pathogens (e.g., Vibrio). These findings suggest a potential physiological tradeoff where the allocation of resources required for growth and metabolism may coincide with transient microbiota dysbiosis and a window of infection risk. Therefore, the identified molecular and microbial markers (e.g., CZ-SOD expression and Vibrio abundance) provide specific targets for monitoring molting processes and health status in crustacean aquaculture.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biology15050434/s1, Table S1: Raw data for Figure 1: The activating effect of 20E on the molting signaling pathway in crayfish. Table S2: Raw data for Figure 2: Expression trends of differentially expressed epidermal genes (DEGs) at different 20E titers, clustered and functional enrichment analysis. Table S3: Raw data for Figure 3: Venn diagram and GO-BP enrichment analysis of DEGs in response to different doses of 20E in crayfish. Table S4: Raw data for Figure 4: Transcriptomic landscape of hemocytes under different 20E treatments. Table S5: Raw data for Figure 5: Transcriptomic response of hemocytes to low-dose (20 ng/g) 20E. Table S6: Raw data for Figure 6: Transcriptome analysis and gene expression validation under 250 ng/g 20E treatment. Table S7: Raw data for Figure 7: Effects of exogenous 20E on intestinal and hemolymph microbiota composition in crayfish.

Author Contributions

Y.Z.: Software, data curation, methodology, formal analysis. C.-Y.Z.: Software, data curation, methodology, formal analysis. X.-T.C.: Conceptualization, writing—review and editing draft, investigation, supervision. R.-G.N.: Data curation, methodology, formal analysis, writing—review and editing draft, investigation. J.-F.L.: Conceptualization, data curation, methodology, formal analysis, funding acquisition, investigation, project administration, resources, supervision, validation, visualization, writing—review and editing draft. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by grants from the Shandong Modern Agricultural Industry Technology System (SDAIT 13-03, 13-04).

Institutional Review Board Statement

This study was approved by the Committee on Animal Ethics of Shandong Agricultural University (No. SDAUA-2025-261). All experimental procedures were conducted in strict accordance with the approved guidelines and were designed to comply with international standards for animal welfare, specifically following the ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines.

Informed Consent Statement

Not applicable.

Data Availability Statement

Further inquiries can be directed to the corresponding author.

Acknowledgments

The authors thank all the staff and participants of this study for their contributions to the research.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
DEGsdifferentially expressed genes
20E20-hydroxyecdysone
EcRecdysone receptor
USPultraspiracle protein
RXRretinoid X receptor
Eip75ecdysone-induced protein 75
KLF15Krüppel-like factor 15
AMPKAMP-activated protein kinase
GOGene Ontology
KEGGKyoto Encyclopedia of Genes and Genomes
RSEMRNA-Seq by Expectation-Maximization
FDRfalse discovery rate
FCfold change
qRT-PCRquantitative real-time polymerase chain reaction
OTUsoperational taxonomic units
RDPRibosomal Database Project
WSSVwhite spot syndrome virus
TyrRStyrosyl-tRNA synthetase
eIF5eukaryotic translation initiation factor 5
eIF3d1eukaryotic translation initiation factor 3 subunit D1
eS10eukaryotic small ribosomal subunit protein eS10
eL30-likeeukaryotic large ribosomal subunit protein eL30-like
Gp93glycoprotein 93
Idhisocitrate dehydrogenase
Sqorsulfide quinone oxidoreductase
CATCatalase
CZ-SODcopper–zinc superoxide dismutase
GSTM3glutathione S-transferase Mu 3
Prx4peroxiredoxin 4
Hsp90heat shock protein 90

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Figure 1. The activating effect of 20E on the molting signaling pathway in crayfish. (A) Crayfish were injected with 20E at dosages of 20 ng/g and 250 ng/g, with PBS injection as the control (n = 30 per group). At 6 h post-injection, epidermis was collected for RNA sequencing. (B) Volcano plots showing DEGs in 20 ng/g 20E vs. control (left) and 250 ng/g 20E vs. control (right). Red dots indicate significantly upregulated genes, and blue dots indicate significantly downregulated genes. DEGs were false discovery rate (FDR) < 0.05 and | log2 fold change (FC)| ≥ 1. (C) Heatmap displaying the expression profiles of core molting-related genes. The color scale indicates the Z-score normalized expression levels (log2-transformed). Hierarchical clustering was performed using Euclidean distance and complete linkage. (D) Expression analysis of Eip75B X1 in epidermis. Data are presented as mean ± SD. Statistical differences were analyzed using one-way ANOVA followed by Tukey’s post hoc test (*** p < 0.001). (Statistical data source: Supplementary Table S1).
Figure 1. The activating effect of 20E on the molting signaling pathway in crayfish. (A) Crayfish were injected with 20E at dosages of 20 ng/g and 250 ng/g, with PBS injection as the control (n = 30 per group). At 6 h post-injection, epidermis was collected for RNA sequencing. (B) Volcano plots showing DEGs in 20 ng/g 20E vs. control (left) and 250 ng/g 20E vs. control (right). Red dots indicate significantly upregulated genes, and blue dots indicate significantly downregulated genes. DEGs were false discovery rate (FDR) < 0.05 and | log2 fold change (FC)| ≥ 1. (C) Heatmap displaying the expression profiles of core molting-related genes. The color scale indicates the Z-score normalized expression levels (log2-transformed). Hierarchical clustering was performed using Euclidean distance and complete linkage. (D) Expression analysis of Eip75B X1 in epidermis. Data are presented as mean ± SD. Statistical differences were analyzed using one-way ANOVA followed by Tukey’s post hoc test (*** p < 0.001). (Statistical data source: Supplementary Table S1).
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Figure 2. Expression trends of differentially expressed epidermal genes (DEGs) at different 20E titers, clustered and functional enrichment analysis. (A–D) Line plots show four different expression patterns of DEGs. The y−axis represents normalized expression change (expression change relative to the control group). (a–d) Enrichment analysis of the top 20 GO entries in each cluster. Statistical significance and parameters: DEGs were identified using DESeq2 as |log2 fold change| ≥ 1.0 and false discovery rate (FDR) < 0.05. GO enrichment analysis was performed using Goatools software (Version 0.6.5). (Statistical data source: Supplementary Table S2).
Figure 2. Expression trends of differentially expressed epidermal genes (DEGs) at different 20E titers, clustered and functional enrichment analysis. (A–D) Line plots show four different expression patterns of DEGs. The y−axis represents normalized expression change (expression change relative to the control group). (a–d) Enrichment analysis of the top 20 GO entries in each cluster. Statistical significance and parameters: DEGs were identified using DESeq2 as |log2 fold change| ≥ 1.0 and false discovery rate (FDR) < 0.05. GO enrichment analysis was performed using Goatools software (Version 0.6.5). (Statistical data source: Supplementary Table S2).
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Figure 3. Venn diagram and GO-BP enrichment analysis of DEGs in response to different doses of 20E in crayfish. (A) Venn diagram showing the overlap of DEGs detected in the two 20E treatments relative to the control. Group I: 20 ng/g 20E vs. control; Group II: 250 ng/g 20E vs. control (n = 30 per group). (B) Top 20 enriched GO-biological processes for Group I. (C) Top 20 enriched GO-biological processes for Group II. (D) Expression profiles of genes annotated under the term “proteolysis”. (E) Expression profiles of genes annotated under the term “developmental process”. DEGs were defined by |log2 fold change| ≥ 1 and FDR < 0.05. GO enrichment analysis was performed using Goatools software (Version 0.6.5). (Statistical data source: Supplementary Table S3).
Figure 3. Venn diagram and GO-BP enrichment analysis of DEGs in response to different doses of 20E in crayfish. (A) Venn diagram showing the overlap of DEGs detected in the two 20E treatments relative to the control. Group I: 20 ng/g 20E vs. control; Group II: 250 ng/g 20E vs. control (n = 30 per group). (B) Top 20 enriched GO-biological processes for Group I. (C) Top 20 enriched GO-biological processes for Group II. (D) Expression profiles of genes annotated under the term “proteolysis”. (E) Expression profiles of genes annotated under the term “developmental process”. DEGs were defined by |log2 fold change| ≥ 1 and FDR < 0.05. GO enrichment analysis was performed using Goatools software (Version 0.6.5). (Statistical data source: Supplementary Table S3).
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Figure 4. Transcriptomic landscape of hemocytes under different 20E treatments. (A) Statistics of DEGs in the 20 ng/g and 250 ng/g groups. DEGs were identified using DESeq2 as |log2 fold change| ≥ 1.0 and false discovery rate (FDR) < 0.05. (B) Circos plot of DEGs mapped to Drosophila melanogaster orthologs. (C,D) Functional enrichment networks for the 20 ng/g (C) and 250 ng/g (D) groups. Nodes represent enriched terms (GO/KEGG/Reactome) and are colored by cluster ID; nodes sharing the same cluster ID are spatially grouped. Significantly enriched pathways were identified via hypergeometric testing and corrected using the Benjamini–Hochberg method. Entries with a corrected FDR < 0.05 were deemed significant. (Statistical data source: Supplementary Table S4).
Figure 4. Transcriptomic landscape of hemocytes under different 20E treatments. (A) Statistics of DEGs in the 20 ng/g and 250 ng/g groups. DEGs were identified using DESeq2 as |log2 fold change| ≥ 1.0 and false discovery rate (FDR) < 0.05. (B) Circos plot of DEGs mapped to Drosophila melanogaster orthologs. (C,D) Functional enrichment networks for the 20 ng/g (C) and 250 ng/g (D) groups. Nodes represent enriched terms (GO/KEGG/Reactome) and are colored by cluster ID; nodes sharing the same cluster ID are spatially grouped. Significantly enriched pathways were identified via hypergeometric testing and corrected using the Benjamini–Hochberg method. Entries with a corrected FDR < 0.05 were deemed significant. (Statistical data source: Supplementary Table S4).
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Figure 5. Transcriptomic response of hemocytes to low-dose (20 ng/g) 20E. (A) Upset diagram of shared and unique DEGs between treatment groups. (B) Bubble plot of the top 20 GO terms enriched in DEGs unique to the 20 ng/g group. Bubble size and color indicate gene count and significance (−log10P), respectively. Key pathways related to protein synthesis and nucleotide metabolism are labeled. (C,D) Radar charts displaying expression profiles of genes involved in protein synthesis (C) and nucleotide metabolism (D) in the 20 ng/g group. (Statistical data source: Supplementary Table S5).
Figure 5. Transcriptomic response of hemocytes to low-dose (20 ng/g) 20E. (A) Upset diagram of shared and unique DEGs between treatment groups. (B) Bubble plot of the top 20 GO terms enriched in DEGs unique to the 20 ng/g group. Bubble size and color indicate gene count and significance (−log10P), respectively. Key pathways related to protein synthesis and nucleotide metabolism are labeled. (C,D) Radar charts displaying expression profiles of genes involved in protein synthesis (C) and nucleotide metabolism (D) in the 20 ng/g group. (Statistical data source: Supplementary Table S5).
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Figure 6. Transcriptome analysis and gene expression validation under 250 ng/g 20E treatment. (A) GO functional enrichment analysis of genes differentially expressed specifically under 250 ng/g 20E treatment (top 20 terms). GO enrichment analysis was performed using Goatools software (Version 0.6.5). (B,C) Log2 fold changes in genes associated with energy metabolism and oxidative stress. DEGs were identified using (DESeq2) as |log2 fold change| ≥ 1.0 and false FDR < 0.05. (D) Validation of superoxide dismutase [Cu-Zn] (CZ-SOD) relative expression levels. Data are presented as mean ± SD. Statistical differences were analyzed using one-way ANOVA followed by Tukey’s post hoc test (*** p < 0.001). (Statistical data source: Supplementary Table S6).
Figure 6. Transcriptome analysis and gene expression validation under 250 ng/g 20E treatment. (A) GO functional enrichment analysis of genes differentially expressed specifically under 250 ng/g 20E treatment (top 20 terms). GO enrichment analysis was performed using Goatools software (Version 0.6.5). (B,C) Log2 fold changes in genes associated with energy metabolism and oxidative stress. DEGs were identified using (DESeq2) as |log2 fold change| ≥ 1.0 and false FDR < 0.05. (D) Validation of superoxide dismutase [Cu-Zn] (CZ-SOD) relative expression levels. Data are presented as mean ± SD. Statistical differences were analyzed using one-way ANOVA followed by Tukey’s post hoc test (*** p < 0.001). (Statistical data source: Supplementary Table S6).
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Figure 7. Effects of exogenous 20E on intestinal and hemolymph microbiota composition in crayfish. (A) Relative abundance of bacterial communities at the phylum level in the intestine across control, 20 ng/g 20E group, and 250 ng/g 20E groups. (B) Relative abundance of bacterial communities at the genus level in the intestine across the three groups. (C) Relative abundance of bacterial communities at the phylum level in the hemolymph across the three groups. (D) Relative abundance of bacterial communities at the genus level in the hemolymph across the three groups. (Statistical data source: Supplementary Table S7).
Figure 7. Effects of exogenous 20E on intestinal and hemolymph microbiota composition in crayfish. (A) Relative abundance of bacterial communities at the phylum level in the intestine across control, 20 ng/g 20E group, and 250 ng/g 20E groups. (B) Relative abundance of bacterial communities at the genus level in the intestine across the three groups. (C) Relative abundance of bacterial communities at the phylum level in the hemolymph across the three groups. (D) Relative abundance of bacterial communities at the genus level in the hemolymph across the three groups. (Statistical data source: Supplementary Table S7).
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Table 1. Primers used in this study.
Table 1. Primers used in this study.
qRT-PCR PrimersSequence 5′-3′
RT-Eip75B X1-FCTGAGGTTCGGGAAAGGT
RT-Eip75B X1-RGGACTGAGGCTGCCACTA
RT-CZ-SOD-FGAAATCCACCGCAGTTAT
RT-CZ-SOD-RCCAGGTCACCCTTCTCGT
RT-18S- FTCTTCTTAGAGGGATTAGCGG
RT-18S- RAAGGGGATTGAACGGGTTA
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Zou, Y.; Zhang, C.-Y.; Cao, X.-T.; Niu, R.-G.; Lan, J.-F. Transcriptomic and Microbiome Analyses of Procambarus clarkii Exposed to Different Doses of 20E. Biology 2026, 15, 434. https://doi.org/10.3390/biology15050434

AMA Style

Zou Y, Zhang C-Y, Cao X-T, Niu R-G, Lan J-F. Transcriptomic and Microbiome Analyses of Procambarus clarkii Exposed to Different Doses of 20E. Biology. 2026; 15(5):434. https://doi.org/10.3390/biology15050434

Chicago/Turabian Style

Zou, Yan, Chen-Yang Zhang, Xiao-Tong Cao, Rui-Geng Niu, and Jiang-Feng Lan. 2026. "Transcriptomic and Microbiome Analyses of Procambarus clarkii Exposed to Different Doses of 20E" Biology 15, no. 5: 434. https://doi.org/10.3390/biology15050434

APA Style

Zou, Y., Zhang, C.-Y., Cao, X.-T., Niu, R.-G., & Lan, J.-F. (2026). Transcriptomic and Microbiome Analyses of Procambarus clarkii Exposed to Different Doses of 20E. Biology, 15(5), 434. https://doi.org/10.3390/biology15050434

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