Microbial Diversity Affects the Cold Tolerance of Red Swamp Crayfish (Procambarus clarkii) by Regulating Histamine Metabolism

Abstract

The red swamp crayfish (Procambarus clarkii) is one of the important freshwater aquaculture species in China, but its growth and development are greatly affected by temperature, which makes it difficult to expand its aquaculture range to the northern regions of China. The composition of gut microbes plays a vital role in resisting environmental pressure, and is also an important driving factor for amino acid metabolism in the body. However, little is known about the relationship between microorganisms, metabolism, and cold-resistance ability of P. clarkii. In this study, we performed the cold-resistance and antioxidant ability test, gut microbiota diversity analysis, quantitative analysis of histamine, and bioinformatics analysis of histamine receptor (HR) family on P. clarkii. The results showed that the cold-resistance crayfish exhibited high antioxidant ability and low gut microbiota diversity after acute cold stress. Next, we also found that there was significant correlation between the Lactobacilli genus and histamine abundance, indicating that the excellent cold tolerance ability of crayfish may stem from the degradation of histamine by Lactobacilli. Finally, it was revealed that HR genes had considerable quantity of gene copies, conservative evolution in crustacean lineages and expression differences in low-temperature tolerant populations. These results suggested that the diversity of Lactobacillus mediated changes in histamine metabolism affect antioxidant capacity, which is one of the reasons why P. clarkii exhibits cold resistance ability. This finding provided a theoretical basis for understanding the microorganism-histamine regulation mechanism of red swamp crayfish under cold stress, promoting the breeding and healthy culture of cold-resistance strain.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00248-025-02659-1.

Introduction

The red swamp crayfish, Procambarus clarkii is an important freshwater economic aquaculture species in China, with its total output ranking the fourth [1]. However, compared with the vigorous development of P. clarkii industry, the development of its breeding is lagging behind, and a large number of self-breeding have caused the germplasm degradation, such as small size, slow growth rate, and reduced stress resistance [2]. Due to the low temperature intolerance of red swamp crayfish, the development of crayfish aquaculture in the three northern regions is greatly limited compared with the middle and lower reaches of the Yangtze River. Moreover, the red swamp crayfish in main production areas is generally listed centrally with low price. But in the off-season period (like winter), although the listing time can be adjusted by building a greenhouse, it cannot be applied in a large area. Based on the above industrial needs, breeding varieties with excellent cold resistance and stable inheritance can play a key role in promoting the crayfish breeding industry, and is the basis for solving the current contradiction and developing the crayfish industry.

Water temperature is an important ecological factor that not only determines the geographical distribution of crustaceans, but also affects their growth, development and reproduction. The drop of water temperature is a common phenomenon in the process of crustacean breeding, such as the arrival of winter, the drop of temperature at night, the change of water thermocline, as well as the cold current and artificial cooling [3]. Crustaceans have different tolerance to low temperature. The critical temperature of Litopenaeus vannamei and Macrobrachium rosenbergii are 13 ℃ and 11.5 ℃, respectively [4, 5]. That of Scylla paramamosain and Cherax quadricarinatus are 10 ℃ and 9 ℃ [6, 7], respectively. And the Antarctic krill (Euphausia superba) can even live and move freely in cold waters near 0 ℃ [8]. In the red swamp crayfish, Zhu et al. can guarantee about half of crayfish surviving when the culture temperature is slowly reduced from 22 ℃ to 4 ℃ [9]. We also conducted extreme low temperature stress research on P. clarkii and found that the critical temperature of acute cooling test can be basically stabilized at about 6 ℃.

The composition of intestinal microbes plays a vital role in resisting environmental pressure and maintaining the physiological balance of the body. In crustaceans, it is mainly reflected in the digestion and absorption of nutrients and autoimmunity [10]. The genetic background of aquatic animals can also affect the intestinal microbial community structure [11]. Through targeted breeding of low-temperature overwintering for multiple generations, the cold-resistance strain of P. clarkii has outstanding cold resistance characteristics, for example, the ability of living, production and reproduction under low temperature environment. Under this low temperature pressure, the new strain may produce heritable directional selection on the structure of intestinal flora. This difference in intestinal flora caused by the inheritance of environmental tolerance has been reported in crustaceans such as L. vannamei [12, 13] and Eriocheir sinensis [14]. The intestinal microbiota produces or degrades various metabolites that play a crucial role in influencing the host’s physiology and metabolism [15]. Among them, in view of its important role in antioxidant ability [16], histidine metabolism may make a prominent contribution to the cold tolerance of P. clarkii. However, little is known about the relationship between microorganisms, histidine metabolism, and cold resistance.

Histamine is the core substance of histidine metabolism. Histamine is produced by various types of cells widely distributed in mammals, reptiles, fish and crustaceans, affecting a series of physiological and pathological processes [17, 18]. It was suggested that injection of histamine could significantly inhibit the total blood cell count (THC) and induce the superoxide dismutase (SOD) activity of E. sinensis [19]. Histamine receptors (HRs) are a class of biogenic amine receptors that can specifically bind to histamine. They belong to G protein-coupled receptors (GPCR) superfamily, with different binding affinities to histamine [17, 20, 21]. HRs had unique expression pattern and overlapping functions, displayed constitutive activity as well [22, 23]. However, there are few studies on the effects of HRs on extreme environmental tolerance in crustaceans.

Hence, in the present study, we firstly evaluated the difference of cold-resistance ability and antioxidant level between cold resistance group and common group of P. clarkii. Then using 16 S rRNA technology, the gut microbiota structure was investigated to identify the genus associated with low temperature tolerance traits. Next, the abundance of core metabolites in histidine metabolism, histamine, were also identified and compared between the two groups, while the relationship between the bacterium genus and histamine were also explored. In addition, bioinformatics analysis was also performed to investigate the classification feature, conserved structure and expression differences of histamine receptor gene family. This study aimed to gain insight into the interaction between microorganisms, histidine metabolism, and cold resistance of P. clarkii. This will provide a theoretical basis for understanding the microorganism-histamine regulation mechanism under cold stress, promoting the breeding and healthy culture of cold-resistance crayfish strain.

Material and Method

Sample Preparation

The red swamp crayfish were obtained from the crayfish breeding and culture center of Freshwater Fisheries Research Center (FFRC), Chinese Academy of Fishery Sciences (CAFS) in Shenyang, Liaoning Province. A total of 120 uninjured and vibrant individuals with the similar size (the body weight is 15.70 ± 4.60 g) were randomly selected for the cold stress experiments. Among them, sixty individuals were defined as cold-resistance strains (RCS) which randomly selected from generational selection work, these individuals successfully overwintered in the Northeast region of China and can crawl to eat at extremely low temperature. While the other sixty individuals were named as control population (CP).

Cold Stress Experiment

Each individual was marked and then placed in cold water at 6℃ for cold stress testing (Fig. 1A). The special observation will be taken to those individuals remaining standing or even crawling, and the time taken for each test crayfish to fall down will be recorded. Testing will last five minutes. The crayfish showed standing and crawling within first five minutes under low temperature, they were noted as cold resistance (RC) group. While those who fell down and even die in the testing were noted as common (CT) group. Six crayfish were randomly selected from each group, and each individual were dissected to obtain hemolymph, intestine and hepatopancreas tissue for subsequent analysis.

Fig. 1.

A Schematic diagram on cold resistance testing and classification of red swamp crayfish. B Survival curves of different crayfish populations fallen down under low temperature stress at 6 degrees Celsius. CP, common population; RCS, cold-resistance strain. C to F Effects of cold stress on antioxidant indexes (C, T-AOC level; D, SOD activity; E, CAT activity; F, MDA content) in hemolymph of red swamp crayfish. Results are expressed as mean ± standard deviation. “*”, “**” and “***” represented the significant difference (p-value < 0.05, p-value < 0.01 and p-value < 0.001, respectively) between each two groups. “ns” represented the insignificant difference (p-value > 0.05) between two groups. Blank, test group without cold stress; CT, common group; RC, cold-resistance group

Measurement of Antioxidant Levels

The hemolymph was used to measure the antioxidant level. After extracting blood, it was placed in a centrifuge tube and centrifuged at 4 °C, 5000 r/min for 10 min. The activity of superoxide dismutase (SOD), catalase (CAT), total antioxidant capacity (T-AOC), and content of malondialdehyde (MDA) were determined using commercially available kits (Jiancheng Ltd., Nanjing, China) according to the manufacturer’s instructions. Measure the OD value of each reaction using a UV spectrophotometer and calculate the content of each indicator using a standard curve.

DNA Extraction and 16 S rRNA Sequence

Bacterial DNA was extracted from twelve samples of intestine according to the instructions of the DNA extraction kit (Tiangen, Beijing) kit, and the concentration was checked and verified using NanoDrop 2000 spectrophotometer and 1% agarose gel electrophoresis. Afterwards, the purified DNA was used as a template for PCR amplification of the V4 region using Tks Gflex DNA polymerase (Takara) with primers 343 F and 798R. For library construction, the quality of amplification products was detected by gel electrophoresis, and the final amplification products were quantified by Qubit dsDNA assay kit (Thermo). Finally, the mixed and purified amplification products were sequenced using Illumina NovaSeq6000 platform.

Amplicon Analysis

The raw reads were preprocessed using QIIME 2 (https://library.qiime2.org/). Then data is checked for reads quality by FastQC (version 0.11.5), and the unqualified reads were removed using Fastp (version 0.14.0) [24]. After filtering, the QIIME 2 was used to analyze the data according to the following steps: (1) Use the pick_otus and usearch61 script to select operational taxonomic units (OTUs), and define sequences with a similarity greater than 97% as the same bacterium; (2) Use the assign_taxonomy and ucluster script to annotate taxonomy based on the SILVA database with an 80% confidence threshold; (3) Use the filter_otus_from_otu_table script to filter high abundance sequences, with the parameter min count action greater than 0.0001; (4) Use the pick_closed_reference_otus script to annotate with reference to the Greenines database; (5) Calculate the alpha and beta diversity using the alpha_diversity and beta-diversity_through_plots scripts, respectively. Then, in R 4.0 environment, Principal Component Analysis (PCA) was plotted based on the β-diversity Euclidean distance. Besides, the Unweighted Pair-group Method with Arithmetic Mean (UPGMA) tree was also plotted based on unweighted unifrac distance.

The linear discriminant analysis effect size (LEfSe) (version 1.0) [25] was performed to detect bacterial taxa that responded to cold resistance, with a threshold of 4.0. Based on the random matrix theory (RMT), the microbial community interactions between different groups were evaluated. Based on the abundance table at the genus level, the top 30 bacterial genus in each group were selected to construct a correlation network. The Spearman method was used to calculate the correlation coefficient r. When the absolute value of r > 0.6 and p < 0.005, it is considered that there is a significant correlation between the two bacterial genera. Microbial phenotypes at the genus level were predicted based on Bugbase (https://bugbase.cs.umn.edu/), and Kruskal-Wallis was used to detect test inter-group phenotype differences. Finally, the abundance of gene family for each sample was determined using PICRUSt2 (http://huttenhower.sph.harvard.edu/galaxy), and then compare it with KEGG databases for functional prediction.

Histamine Identification Using Liquid Chromatography-Mass Spectrometry

The hepatopancreas of each group were used to identify histamine using Liquid Chromatography-Mass Spectrometry (LC-MS). Briefly, take 100 mg of sample and place it in an EP tube, then add 500 µL of 80% methanol solution and perform vortex shaking. Next, the sample was frozen in an ice bath for 5 min, then centrifuged at 15,000 r and 4℃ for 20 min. The supernatant was extracted, and dilute the supernatant with mass spectrometry grade water to a methanol content of 53%. After that, centrifuge again under the above conditions, and collect the supernatant. Take 20 µL of each sample and mix it into QC samples. The samples were tested using Vanquish ultra-high performance liquid chromatography (UHPLC) tandem Q Exactive ™ HF-X high-resolution mass spectrometer (Thermo Fisher, Germany) with HPLC C18 chromatographic column and the mass spectrometry scanning nucleus ratio of 100–1500. The metabolite composition from two groups of samples were identified by performing mass spectrometry detection in positive and negative ion scanning mode on the above extraction solution.

The raw data (. raw) were processed using CD 3.1 tool. After simple screening of parameters such as retention time and mass to charge ratio of metabolites, set a retention time deviation of 0.2 min and a mass deviation of 5 ppm to align the peaks of the samples for more accurate identification results. Then, further set mass deviation of 5 ppm, signal intensity deviation of 30%, signal-to-noise ratio of 3, minimum signal intensity, and additive ions for peak extraction. Quantify peak area, and integrate target ions, then predict metabolite molecular formulas through molecular ion peaks and fragment ions, and map them on mzCloud (https://www.mzcloud.org/) with mzVault and Masslist databases, to obtain identification and relative quantification results of metabolites. Annotate identified metabolites using KEGG (http://www.genome.jp/kegg/) and HMDB (http://www.hmdb.ca/) database, and then find out the relative abundance of histamine. Based on Pearson correlation coefficient, correlation analysis and visualization between the relative abundance of top ten bacterial genera and histamine were performed using the “ggstatsprot” package [26].

Identification of Histamine Receptor Genes in P. Clarkii Genome

In order to identify the HR genes in P. clarkii genome (GCA_020424385.2), the Hidden Markov Model (HMM) profiles of G protein-coupled receptors (GPCR, PF00001) domains were obtained from the InterPro database [27]. The hmmsearch tool in HMMER (version 3.3.2) (http://www.hmmer.org/) were used to identify the GPCR proteins in these species with the e-value ≤ 1e-5. Besides, the animo acid sequences of four human HR genes named HRH1 (Accession number: ENSP00000380247), HRH2 (Accession number: ENSP00000231683), HRH3 (Accession number: ENSP00000342560), HRH4 (Accession number: ENSP00000256906) were also downloaded from NCBI database. BLASTP [28] tool was used to identify each subtype of HRs in the GPCR proteins. The assignment of HR genes for putative hits obtained from our searches was considered as the corresponding subtypes only if it had known HRs as top identity in a reciprocal BLAST search. All candidate protein sequences were confirmed on the NCBI Conserved Domain Database (CDD) [29] and Simple Modular Architecture Research Tool (SMART) database [30]. The sequence genomic positions and structural features of the HR gene family were extracted from the genome annotation file (GFF) of P. clarkii. The physicochemical parameters, molecular weight (kDa) and isoelectric point (pI) of the HRs were calculated by ExPASy [31].

Multiple Sequence Alignment and Phylogenetic Analysis

The same workflow was also applied to identify HR protein sequences in Apis mellifera, Artemia franciscana, Daphnia pulex, Drosophila melanogaster, E. sinensis, Homarus americanus, Macrobrachium nipponense, Penaeus vannamei and Tetranychus urticae. The identified HR amino acid sequences from above species were aligned by MUSCLE with default parameters after splicing the respective HR sequences of species together [32]. The phylogenetic tree was constructed using ML method with IQ-Tree (version 2.4.0) [33]. Bootstrapping with 1,000 replications was applied to estimate the support rate of branch nodes. The best fitting model (VT + F + I + R3 for HRH1 and HRH2, VT + F + R4 for HRH3, VT + F + I + G4 for HRH4, and Blosum62 + F + R4 for all HRs) was selected via ModelFinder in IQ-Tree according to bayesian information criterion (BIC). The phylogenetic tree was visualized using the online iToL platform (https://itol.embl.de).

Conserved Structure Analysis

For each animo acid sequence of P. clarkii HR gene, the conserved domains were identified based on the Pfam database. The conserved motif of the HR gene family was predicted using MEME (version 5.5.8) using the 0-order model of sequences [34]. In this workflow, ten motifs were totally identified with the width of each motif ranged from six to fifty. The conserved domains and motifs visualization were carried out using TBtools (version 2.301) [35].

Expression Analysis of Histamine Receptor Genes in Response To Cold Stress

The hepatopancreas of each cold-resistance group were also used to analyze the expression of HR genes. All samples were stored in RNAstore (TIANGEN, Beijing, China) for transportation. Total mRNA was extracted with TRlzol reagent (Invitrogen, USA) according to the manufacturer’s instructions. Illumina RNA-seq libraries were prepared and sequenced on Illumina NovaSeq6000 platform with a PE150 model. After quality control, high quality clean reads were mapped on the P. clarkii genome with Hisat2 (version 2.2.1) [36]. The gene expression level was counted by calculating the fragments Per Kilobase of exon model per Million mapped fragments (FPKM) using Featurecounts (version 2.0.3) [37]. The expression data was visualized as heatmap using “pheatmap” R package.

Data Statistics

The data were expressed as mean ± standard deviation and analyzed using SPSS 22.0 software (SPSS Inc., IBM, Chicago, IL, USA). Student’s t test was used to test for significant differences between the experimental groups. A p-value of less than 0.05 was considered as significant difference. The data were visualized using GraphPad Prism 5.0 software (GraphPad, San Diego, CA, USA).

Results

Differences in Cold Tolerance and Antioxidant Level of Red Swamp Crayfish

Survival curves were used to evaluate the difference in activity between cold resistance strain and common strain under low temperature stress of six degrees Celsius. As shown in Fig. 1B, the cold-resistance strain exhibited significantly higher survival rate than common population (p < 0.0001). At the first minute, 76% of common individuals fell down, while 45% of individuals in cold-resistance strain survived. At the third minute, 38.3% of cold-resistance individuals survived, while all individuals in the common population fell down.

Next, antioxidant enzyme activities were used to analyze the changes in antioxidant levels of cold resistant and non-cold resistant individuals in cold-resistance strain under low temperature stress. The results showed that the T-AOC level significantly decreased after cold stress (p < 0.001), while it exhibited higher level in cold-resistance group than in common group (Fig. 1C). The SOD activity significantly increased after cold stress (p < 0.001), while it exhibited higher level in cold-resistance group than in common group (Fig. 1D). The CAT activity just significantly increased in cold-resistance group after cold stress (p < 0.05), and it also exhibited higher level in cold-resistance group than in common group (Fig. 1E). The MDA content also significantly increased in cold-resistance group (p < 0.001), and it also exhibited higher level than in common group (Fig. 1F).

The Alpha and Beta Diversity of Gut Microbiota in Different Cold Tolerance Groups

The sequence quality was shown in supplemental Table S1. The results showed that a total of 887,142 effective sequences were obtained from twelve samples of red swamp crayfish, with an average of 73,928.5, and the content of effective sequences exceeded 80%. The sequencing depth of the two groups was 99.60% and 99.50%, respectively. Besides, the dilution curve tends to be flat, indicating that the amount of data is gradually reasonable (Fig. S1A).

Alpha diversity analysis showed that indexes of cold-resistance group were higher than those of common group (Fig. 2A and D). Notably, the Shannon and Simpson exhibited insignificant difference (p > 0.05), while the Chao1 and ACE exhibited significant difference (p < 0.05). Furthermore, the ranking curve of the cold-resistance group was wider than that of the common group, indicating that the microbial diversity level in cold-resistance group was higher (Fig. S1B). Then, we used PCA analysis to evaluate the beta diversity of each sample. The two groups exhibited obvious separation (Fig. 2E). The similar results can be shown in the UPGMA tree using unweighted-unifrac distance which calculated by relative abundance in Phylum level (Fig. 2F).

Fig. 2.

Difference of alpha diversity indexes (A, Shannon index; B, Simpson index; C, Chao1; D, ACE) between common group and cold-resistance group of red swamp crayfish. Results are expressed as mean ± standard deviation. “*” represented the significant difference (p-value < 0.05) between each two groups. “ns” represented the insignificant difference (p-value > 0.05) between two groups. E Principal component analysis (PCA) of two groups. F The UPGMA tree using unweighted-unifrac distance which calculated by relative abundance in Phylum level

Differences in Microbial Composition among Different Cold Tolerance Groups

Based on OTU analysis, we found that there were 1,795 OTUs shared by the two groups, while 418 OTUs unique to the common group and 1,021 OTUs unique to cold-resistance group (Fig. 3A). After annotation, it was shown that the dominant bacteria phylum in common group were Firmicutes, Proteobacteria and Bacteroidota, accounting for 96.23% of the total intestinal flora, and the other were Cyanobacteria (Fig. S1C). While the dominant bacteria phylum of cold-resistance group was the same as that of common group, accounting for 91.90%, and the rest was dominated by Actinobacteria (Fig. S1C). Moreover, the relative abundance of Actinobacteria in the cold-resistance group was significantly higher than that in the common group (p < 0.05, Fig. S1D).

Fig. 3.

A Venn diagram of differences in microbial diversity between common and cold-resistance groups. B Relative abundance of different microbial communities at the genus level. C T-test analysis of species differences between groups at the genus level. D Evolutionary branch chart and LDA value distribution bar plot of LEfSe analysis in the cold-resistance group of red swamp crayfish. E The molecular ecological network of gut microbiota at the genus level. The size of the dots represents species abundance, the color of the dots represents the level of their phylum, and the color of the lines represents correlation

Besides in genus level, both dominant bacterial were Candidatus-Bacilloplasma. In addition, the Aeromonas had a higher proportion in the common group, while the Vibrio had a higher proportion in the cold-resistance group (Fig. 3B). Compared with the common group, the relative abundance of Aeromonas in the cold-resistance group was significantly decreased, but the relative abundance of Pseudomonas, Erysipelatoclostridium, Blautia, Holdemanella, Anaerostipes, and Ralstonia increased significantly (p < 0.05, Fig. 3C).

In addition, microbial communities with significant differences between the two groups were also identified. The results showed that only two genus level biomarkers were detected in the cold-resistance group, namely Vibrio and Candidatus-Hepatorch (Fig. 3D). We also constructed microbial ecological networks for each group. The network diameter (ND), clustering coefficient (CC), graph density (GD), average degree (AD), and average path length (APL) of cold-resistance group were lower than that of common group (Supplemental Table S2). The species abundance of the network for cold-resistance group was also obviously lower than that for common group (Fig. 3E), indicating that cold-resistance individuals may reduce the microbial species in the ecological network, which decrease microbial interactions.

The Effect of Gut Microbiota Difference on Function of Histidine Metabolism

Based on the KEGG database, a total of 6 primary class, 32 secondary class and 189 pathways were obtained for the prediction of intestinal microbial functions (Fig. S2A, Supplemental Table S3). After analyzing these pathways, it was found that there was a total of 39 pathways with significant differences (p-value < 0.05, Fig. 4A), most of which belonged to the primary class of metabolism. In these differential pathways, we found many metabolic process (Fig. 4B), such as “TCA cycle”(ko00020) and “Glycolysis/Gluconeogenesis” (ko00010) in Carbohydrate metabolism class, “Oxidative phosphorylation” (ko00190) in Energy metabolism class, “Primary bile acid biosynthesis” (ko00120) and “Secondary bile acid biosynthesis” (ko00121) in Lipid metabolism class, “Histidine metabolism” (ko00340) in Xenobiotics biodegradation and metabolism class, which perhaps related to the differences in cold tolerance of red swamp crayfish.

Fig. 4.

A Heatmap of KEGG pathway with significant differences between common group and cold-resistance group. B T-test analysis of pathway abundance with significant differences (p-value < 0.05) between groups at central carbon metabolism, energy metabolism, lipid metabolism, and histidine metabolism categories. C Difference of histamine abundance between common group and cold-resistance group. D Difference of Lactobacilli genus abundance between common group and cold-resistance group. E Correlation analysis between Lactobacilli genus and histamine. “p-value < 0.05” represented the significant difference

In the above results, we identified significant differences in microbial abundance in the histidine metabolism pathway. Considering that histamine is the core metabolite of histidine metabolism, we quantitatively analyzed histamine in the two groups using LC-MS. By annotating and quantitatively analyzing histamine in samples from two groups, it was found that the abundance of histamine in the cold-resistance group was significantly lower than that in the common group (p = 0.002474, Fig. 4C). Subsequently, we conducted Pearson correlation analysis between the abundance of the top ten bacterial genus and the abundance of histamine, and found a significant positive correlation between the abundance of histamine and the microbial abundance of Aeromonas (r_Pearson = 0.77, p = 3.65e-03, Fig. S2B) and Hafnia-Obesumbacterium (r_Pearson = 0.60, p = 0.04, Fig. S2C). Besides, we also found that there was a significantly negative correlation between Lactobacillus with the significantly higher abundance in cold-resistance group (p = 0.0346, Fig. 4D) and the abundance of histamine (r_Pearson = −0.62, p = 0.03, Fig. 4E), which may explain the reason why the abundance of histamine in the cold-resistance group are lower than that in the common group.

Classification feature, Structure and Expression Differences of HR Family

Using the HMM model of the structural domain and BLAST workflow, 147 h were identified in P. clarkii genome, including 33 HRH1s, 65 HRH2s, 37 HRH3s and 12 HRH4s, exhibiting considerable quantity of gene copies in arthropod (Fig. 5A, Supplemental Table S4). Furthermore, we also found that there are many mRNA variants in each subfamily. The CDS fragment size of HR genes ranged from 645 bp to 3987 bp, with the number of exons ranged from 1 to 22. The molecular weight of HR proteins ranged from 16.15 kDa to 146.21 kDa, and the theoretical isoelectric point ranged from 4.63 to 10.31.

Fig. 5.

A ML Phylogenetic analysis of histamine receptor family in arthropod lineages with the count distribution each subfamily. B Motif analysis of the conserved 7 transmembrane receptor, rhodopsin family (7tm_1) domains in the histamine receptor family. C Expression difference of up-regulated genes between common group and cold-resistance group. “*” and “**” represented the significant difference (p-value < 0.05 and p-value < 0.01, respectively) between each two groups

Phylogenetic analysis showed that the HR family had a certain conserved evolution in crustacean lineages (Fig. 5A). In which, P. clarkii were closed to H. americanus, E. sinensis, P. vannamei for HRH1, HRH2 and HRH3, respectively, and only HRH4 had the significant divergence with other crustaceans (Fig. S3A). Based on the conserved domains and motif analysis, we found that all family members have conserved 7 transmembrane receptors, rhodopsin family (7tm_1) domains, which was composed of seven motifs (Motif 2: ASILTLVAISLDRYLAITHPL, Motif 3: ERKAAKTLLIIVGAFVVCWLP, Motif 4: RKLRTVTNYFJVSLAVADLLV, Motif 5: FFVPLVVMVVCYTRI, Motif 7: WLJSLLJSLPPLLG, Motif 9: WVFGDVLCKLYAFLD, and Motif 10 YKATKRRLRERAQGTKLKKLDAIGKIKVVTLINSEGQGPEGRVSRSVTPF) (Fig. 5B, Fig. S3B).

Transcriptional expression of HR genes in hepatopancreas tissue of P. clarkii after cold stress was analyzed by RNA-seq (Fig. S4A). It was showed that most of HR genes expression downregulated in cold-resistance group and approximately one-third of HR genes are highly expressed in the hepatopancreas tissue, and a quarter of family members exhibited difference between the common group and the cold-resistance group. After clustering analysis, it was found that the expression of HRH1 genes were roughly divided into three types, while HRH2 genes can be divided into four types. The expression of HRH3 and HRH4 genes approximately can be divided into two types.

Among these HR genes, we found that five genes exhibited significant differences between the common group and cold-resistance group (p-value < 0.05). Of which three down-regulated genes were annotated as HRH2 (LOC123751347) and HRH3 (LOC123747963 and LOC123753587), respectively (Fig. 5C). The two up-regulated genes were both annotated as HRH1 (LOC123764675 and LOC123764787) (Fig. S4B).

Discussion

Temperature is one of the most important parameters in these measurements, and different species have different thresholds for cold tolerance. Species from warmer habitats are reported to respond differently to cold shock than the cold-adapted species [38]. For example, in one study on Eulimnogammarus verrucosus, physiological responses to low temperature can be measured as energetic expenditures, along with different behavioral patterns [39]. In this study, we used falling-down, a behavioral change, to assess whether crayfish are cold tolerance. Low rate of falling down were observed in the cold-resistance group after treating with cold water, confirming that this is a practical and effective as an intuitive detection method of low temperature tolerance phenotype.

Nearly every organism has a compensatory adjustment mechanism that allows it to adjust to specific temperature changes through physiological and metabolic change [40]. In order to survive at extremely low temperatures, other mechanisms are also crucial for organisms, such as increased mitochondrial density at low temperature [41]. However, there is a critical limit to the temperature range that can be adjusted by metabolic and physiological changes as well as other mechanisms. Because they can adapt to a wider range of habitat temperatures through natural selection and adaptation, organisms with more flexible compensated abilities have a higher chance of surviving in an environment with drastic temperature fluctuation [42]. One crucial defense mechanism in crayfish is the antioxidant system. Reactive oxygen species dynamic balance is maintained by this system, which also guarantees that the organism’s regular life activities take place [43]. In this study, we found that T-AOC level significantly decreased after cold stress in both groups, but the cold-resistance group exhibit higher T-AOC level. This indicates that the excess oxygen free radicals created at high temperatures were too much for the increased antioxidant enzyme activity to handle, causing oxidative tissue damage, but it has less effect on the cold-resistance group. SOD and CAT are crucial enzymes in the antioxidant system [44]. The increase of antioxidant enzyme activity observed in this study was similar to the results of others [45], indicating that the crayfish preserve equilibrium between the generation of oxygen free radicals and the antioxidant system’s activity. MDA is the final product of lipid peroxidation and is considered a basic compound in cellular damage by toxins, which represents direct evidence of toxicity caused by free radicals [44, 46], also is a marker of oxidative stress [47]. In the present study, MDA content was observed higher in cold-resistance group, thus, suggesting that the metabolism of individuals was active and produced elevated levels of oxygen free radicals (induces oxidative damage). The similar results were observed in C. quadricarinatus [48], L. vannamei [4], and S. paramamosain [49] for all the antioxidant indicators.

The gut microbial balance of crustaceans is altered by environmental stresses such as temperature [50]. In the two different populations used in this study, the cold-resistance group is selected through overwintering for many years. The alpha diversity index and beta diversity analysis (PCA analysis) results showed that the two populations have a certain degree of separation, indicating that multi generation breeding makes the gut microbes of cold-resistance group produce genetic adaptation to the cold environment. Studies have shown that Bacteroidetes, Proteobacteria, actinobacteria and Firmicutes are the core bacteria in the Crustacean gut, the dominant phyla of the two populations identified in this study were similar to the results of previous studies [51], and the relative abundance of Actinobacteria in cold resistance group was significantly higher than that in common group. It was suggested that Actinomycetes can produce beneficial secondary metabolites and reduce the pathogenicity of pathogens [52], which plays an important role in maintaining intestinal homeostasis [53], and the relative abundance of Actinobacteria is also higher in the gut of populations with strong immunity [54]. Compared with the differences at the phylum level, it had more significant difference at the genus level. In cold-resistance group, the abundance of Pseudomonas, Erysipelas, Blautia, Holdmansia_Anearostipes and Ralstonia were significantly higher than those in the common group. Vibrio and Candida_Hepatoplasma were also labeled as biomarkers. It was reported that both Blautia and Anaerostipes can promote the production of short chain fatty acids, help maintain intestinal homeostasis, and reduce the production of proinflammatory cytokines [55–57]. The higher abundance of Candida_Hepatoplasma contributes to the intestinal absorption of nutrients and resistance to disease [58]. It can be seen that the cold-resistance group greatly increased the abundance of potential probiotics in the gut microbiota, but at the same time, the abundance of conditional pathogenic bacteria in the gut microbiota was also significantly increased. A large number of studies have shown that Pseudomonas is an important aquatic organism pathogen, which can cause a variety of aquatic organism diseases, including yellow gill disease of M. rosenbergii [59], body surface canker of large yellow croaker (Pseudosciaena crocea) [60] and canker of abalone (Haliotis discus) [61], etc. In addition, Erysipelas and Ralstonia have also been identified as the main opportunistic pathogens causing bacteremia [62, 63], and the Vibrio, is also main pathogenic bacteria affecting aquatic animals [64]. Hence, it can be inferred that the cold-resistance group of crayfish selected after several generations enhanced the stability of probiotics in the intestine, improving the immunity of intestinal microbes and the adaptability in the cold environment.

Notably, through the annotation on the biological functions of microorganisms, we found that in addition to the central carbon metabolism, energy metabolism, and lipid metabolism necessary for maintaining life activities under low temperature, there are also significant differences in histidine metabolism. The histidine metabolic pathway is mutually regulated by histamine content. The histamine is a common class of biogenic amines (BA) with certain toxicological effects [65]. In Crustacean, the increase of histamine might disrupt the operation of the oxygen-dependent defense mechanism, leading to elevated ROS [16]. We quantified the histamine in both groups, and the results showed that the abundance of histamine in the cold-resistance group was lower than that in the common group with significant difference. This is consistent with the stronger antioxidant capacity exhibited by cold-resistance group under low temperature stress. Besides, it was also suggested that the Lactobacillus genus can be used to degrade the histamine [66, 67]. In this study, we also found that Lactobacillus had the higher abundance in cold-resistance group with significantly negative correlation with histamine. It precisely indicated that the strong low-temperature tolerance of the cold-resistance group may stem from the high abundance of Lactobacillus in the intestine, which degrades a large amount of histamine.

Given the diverse physiological effects of histamine are mediated by activation of four metabotropic histamine-specific GPCR subtypes H1-H4 [20, 68, 70], this study identified four subtypes of HR genes in P. clarkii genome. We detected 33 HRH1s, 65 HRH2s, 37 HRH3s and 12 HRH4s. And each subtype has a certain number of fragment duplications and transcriptional variants. The earliest studies indicated that HRs were only present in annelids, mollusks, and lophotrochozoans, and speculated to be lost in arthropods, nematodes, and other ecdysozoan lineages [71, 72]. But our research found a large number of homologous in the P. clarkii genome, although their homology with human HRs is only about 30%. This finding was similar to the recent reports [73, 74]. And according to phylogenetic classification, we found all histamine receptors exhibit “inter family” sequence identity. Our conclusion is that HR was not lost in arthropods, but sporadically distributed in the genome [75]. Notably, the copy number of HRs in crustaceans, especially Decapoda, was far ahead in this study, which may benefit from whole-genome duplication (WGD) events during their evolution, because of the huge genome size for crustaceans. A large number of low homology duplicate copies may be recruited to perform additional functional roles and have already undergone selection [76]. Conservative structural analysis reveals that all members of the HRs contain conserved 7 transmembrane receptor, rhodopsin family domains. In the Rhodopsin family, except for a few receptors such as glycoprotein hormone receptors that contain large N-terminal extracellular ordered domains [77], most typically exhibit short N-terminal extracellular disordered regions to participate in ligand recognition and receptor activation [78]. This disordered region caused diversity in the binding sites between histamine and receptors.

Finally, we also performed expression analysis of HR genes between the two groups, and revealed that due to the lower histamine content in the cold-resistance group, the histamine receptor expression level is lower than that in the normal group, correspondingly. We performed a significant difference test on the expression of all HR genes and found that the downregulated HRs were annotated as H2 and H3 subtypes, while the upregulated genes were annotated as H1. Research has found that H1R triggers the activation of phospholipase C by coupling with Gq/11 protein. This lipase can produce diacylglycerol and inositol triphosphate, leading to the activation of protein kinase C (PKC), catalyzing the phosphorylation of Ser/Thr of various downstream mediators and the release of calcium ions stored in cells [79]. H2R can not only couple with Gs protein, but also with Gq/11 protein, which leads to the formation of phosphoinositide and makes the concentration of Ca²⁺ in the cell membrane increased in some cells expressing H2 receptors [80, 81]. It was also suggested that high expression of H3R-similar H4R in mouse adipocytes promotes thermogenesis and lipid degradation in response to cold stress [21]. In mast cells, high expression of H4R also promotes the flow of calcium ions from the endoplasmic reticulum to the cytoplasm [82], which suggests that histamine may also participate in the cold stress response process by regulating intracellular and extracellular calcium ion concentrations through HR genes expression. The extracellular calcium ion concentrations can be regulated by the transient receptor potential (TRP) channels [83], while the TRP can also be activated by ROS [84]. These findings indicate that the low content of histamine, owing to being degraded by Lactobacilli under cold stress, may lead to the expression decrease of HRH2 and HRH3. However, in order to meet the calcium signal transduction of normal life activities under low temperature, the oldest H1 is activated and upregulated.

Conclusion

This study firstly investigated the cold-resistance ability, antioxidant level, and gut microbiota structure of red swamp crayfish. It was found that the cold-resistance crayfish exhibited high antioxidant ability and low gut microbiota diversity after acute cold stress. Next, through the qualitative and quantitative analysis, we also found that there was significant correlation between the abundance of the Lactobacilli genus and histamine. Finally, based on the bioinformatics analysis of the HR gene family, it was revealed that the HR genes in the P. clarkii genome exhibited considerable quantity of gene copies, conservative evolution in crustacean lineages and expression differences in low-temperature tolerant populations. In summary, these results suggested that the diversity of Lactobacillus mediated changes in histamine metabolism affect antioxidant capacity, which is one of the reasons for the differences in cold-resistance ability of P. clarkii. This finding provided a theoretical basis for understanding the Microbial metabolic regulation mechanism under cold stress, promote the breeding and healthy culture of cold-resistance crayfish strain.

Supplementary Information

Below is the link to the electronic supplementary material.

Author Contributions

S.S., C.Z. and B.L. conceived and designed the experiment. H.H., S.S. and Y.Z. wrote, revised and edited the paper. H.H., Y.Z., Z.L., Y.H., Y.L., Y.Y. and J.W. contributed materials and performed the experiment. H.H. and Y.Z. contributed analysis tools and results visualization. All authors reviewed the manuscript.

Funding

This study was supported by Central Public-interest Scientific Institution Basal Research Fund, Freshwater Fisheries Research Center, CAFS (No. 2025JBFR03); “Soil Transplantation” Team Project of Panjin Talent Program (DTYZ2024003); “JBGS” Project of Seed Industry Revitalization in Jiangsu Province (JBGS [2021]123); Central Public-Interest Scientific Institution Basal Research Fund, CAFS (2023TD39).

Data Availability

No data was used for the research described in the article.

Declarations

Ethical Approval

All experimental procedures were conducted with approval from the Institutional Animal Care and Use Ethics Committee of the Freshwater Fisheries Research Center, Chinese Academy of Fishery Sciences (Wuxi, China) (Authorization NO. LAECFFRC-2023-06-1, 1 June 2023).

Competing interests

The authors declare no competing interests.