Biological Observation and Multi-omics Analysis Reveal the Toxicity of the Bioinsecticide Matrine to Honeybees (Apis cerana)

Biological Observation and Multi-omics Analysis Reveal the Toxicity of the Bioinsecticide Matrine to Honeybees (Apis cerana) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Biological Observation and Multi-omics Analysis Reveal the Toxicity of the Bioinsecticide Matrine to Honeybees ( Apis cerana ) Yinglong Yu, Zhaonan Zhang, Yuanchan Fan, Wencai Zhou, Dan Yao, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7062509/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Honeybees are important pollinators that enhance food safety and promote biodiversity. However, honeybees are increasingly threatened by insecticide use. Matrine, a plant-derived insecticide, has been used in plants, such as pear trees, citrus, and cotton whose flowers are a honey source. Despite matrine’s economic significance, its impact on bees is rarely reported. In this study, honeybees (Apis cerana) were exposed to three concentrations (1.2, 10, and 40 mg/L) of matrine. Continuous matrine intake caused rapid death of honeybees on day 9 but had no significant effect on food consumption and body weight of the honeybees. Matrine intake caused changes in the abundance of Gilliamella, Dorea, and Lachnoclostridium in gut microbiota, resulting in the differential expression of argininosuccinic acid, spermidine, arachidonic acid, and LOC107998471 (Aldh). This finding underscored the role of these microbiotas, metabolites, and genes in honeybees under matrine stress. This study provides a comprehensive explanation of matrine toxicity to honeybees and reveals the crucial microbiota, metabolites, and genes. These findings provide important references for honeybee poisoning and the associated detoxification mechanism. Eastern honeybee Matrine Bioinsecticide Honeybee health Multiomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Honeybee is an important agricultural pollinator, playing an important role in ensuring food safety and promoting crop yield and income. In 2020, the commercial value of global pollination services ranged between $ 195 billion and $ 387 billion (Porto et al. 2020). Honeybees also play a significant ecological role in maintaining and promoting biodiversity, the conservation of rare plants, and ecosystem restoration (Borges et al. 2009). However, the health and survival of honeybees are increasingly threatened, which necessitates their protection (Oldroyd Nanork 2009; Rúa et al. 2009; Yang 2005, 2009). Notably, the use of insecticides is considered the main factor among the many factors threatening honeybees (Alaux et al. 2010; Krupke et al. 2012). Bioinsecticides are a category of insecticides processed directly from living organisms or artificially synthesized insecticides with the same structure as natural compounds (Rodrigues et al. 2021). They are easily degradable and relatively friendly to the environment. In recent years, studies and registration of bioinsecticides have increased (Rosas-García 2009). Matrine is a tetracyclo-quinoliz-indine alkaloid derived from Sophora plants such as Sophora flavescent Ait. and Sophora alopecuroides L. (Xiao et al. 1999). It interferes with acetylcholine receptors (AChR) and acetylcholinesterase (AChE) (Ali et al. 2017) and has thus been widely used to prevent and control various pests including whiteflies (Lv et al. 2009), pest mites (Zanardi et al. 2015), aphids (Luo et al. 2014), and cotton bollworms (Luo et al. 2014), and diseases such as apple anthracnose, apple tree rot, and grape downy mildew among other pests and diseases affecting fruits (Wang et al. 2016), vegetables (Yang et al. 2013), tea plants (Ye et al. 2014), traditional Chinese medicinal plants (Zhao et al. 2015), and forest trees (Ali et al. 2017; Andrade et al. 2019; Bordini et al. 2015; Marčić Međo 2014). Moreover, it promotes crop growth (Zhang et al. 2011). In China, 105 insecticide products containing matrine have been registered, ranking first among the botanical insecticides (http://www.chinapesticide.org.cn/hysj/index.jhtml). Honeybee gut microbes play important roles, such as food digestion, regulating the immune system, and resisting pathogens, thereby maintaining the health and survival of honeybees (Engel et al. 2012; Hao et al. 2018; Kwong Moran 2016). Neonicotine and organophosphorus insecticides, including nitenpyram, imidacloprid, thiacloprid, thiamethoxam, coumaphos, and fipronil disorder the abundance of Gilliamella apicola , Frischella perrara , Bifidobacteria , and Lactobacilli in honeybee gut (Chmiel et al. 2019; Liu et al. 2020; Rouze et al. 2019; Zhu et al. 2020). Common insecticides, such as glyphosate (Vazquez et al. 2020; Zhao et al. 2020), imidacloprid (Aufauvre et al. 2014; Derecka et al. 2013; Gao et al. 2020; Li et al. 2019; Wu M. et al. 2017; Wu Y. et al. 2017), thiamethoxam (Christen et al. 2018; Shi et al. 2017), and thiacloprid (Alptekin et al. 2016; Fent Schmid et al. 2020), have a significant impact on gene expression in honeybees. They down-regulate the expression of genes associated with immunity, learning, and exercise and up-regulate the detoxification genes. Cypermethrin, carbendazim, dimethoate, phenoxycarb, chlorfenitrile, fluorofuranone, chlorpyrifos, and other pesticides can affect the expression of genes and their related pathways, such as immunity, growth and development, and learning behavior (Boncristiani et al. 2012; Fent Haltiner et al. 2020; Huang et al. 2021; Kablau et al. 2020; Wang et al. 2018; Wu et al. 2021; Ye et al. 2020). Of note, there are only a few studies on the mechanisms of bioinsecticides on honeybees despite their significant negative impact on the health of honeybees (Guo et al. 2024). Transcriptomic studies of the honeybee microbiome could reveal the mechanisms of bioinsecticide on honeybees, thus informing guidelines for the proper use of insecticides and protection of honeybees. In this study, a biological observation (mortality, food consumption, weight changes) and multi-omics analysis, including microbiome, metabolome, and transcriptome (microbiota– metabolite–gene), were done to explore the holistic effect and mechanism of matrine on honeybees. Materials and Methods 2.1 Honeybee preparation and exposure to bio-insecticide Honeybees used in this study were obtained from hives kept by Guizhou Academy of Agricultural Sciences, Guiyang, China (latitude, 26°29′29″; longitude, 106°39′36″; 1074.3m). The colonies were inspected weekly for the presence of parasites or diseases and well maintained to prevent attack. The bee colonies were subsequently visually certified to be free from diseases and parasites, such as Varroa mites, Chinese Bee Sacbrood Virus, and moths prior to the experiment. Frames of 5 different healthy colonies with adequate capped worker broods covered with sterile nylon bags were kept in an artificial climate incubator set at 35°C and 60% relative humidity in the dark mode, which is a simulated condition of the hive, to capture newly emerged honeybee workers (Motta et al. 2020). Newly emerged honeybee workers within 12h (Day 1) were randomly moved to plastic rearing cages (500 ml) with an air vent on the side and lid (30 workers/cage). Honeybee workers in the cages were also kept in an artificial climate incubator set at 35°C and 60% relative humidity in dark mode. The honeybee workers were fed on sucrose solution (50% wt/wt) ad libitum using a syringe inserted vertically through the lid hole. The sucrose solution was replaced, and dead workers were removed daily for 7 consecutive days. Pollen in a suspension of freshly mixed gut homogenate from hive honeybees was provided to the caged honeybee workers to enhance the development of the honeybee workers and the colonization of honeybee gut microbiota (Martinson et al. 2012; Motta et al. 2018). Pollen mixed gut homogenate was replaced daily for 5 consecutive days, followed by normal pollen on day 6 and day 7. The mortality of the honeybee workers was subsequently calculated within 7 days. All the colonies had a mortality rate of less than 10% and were thus healthy and capable of the subsequent experiments. Three concentrations of matrine 50% sucrose solution (1.2, 10, and 40 mg/L, treatment groups) and a pure 50% sucrose solution (control group) were introduced to different cages using a syringe from day 8 for enough bio-insecticide exposure. There were 6 cages for the control group (CK), 7 cages for treatment 1 group (T1, 1.2 mg/L), 7 cages for treatment 2 group (T2, 10 mg/L), and 13 cages treatment 3 group (T3, 40 mg/L). Each cage had 30 workers. Treatment 3 had more cages to ensure that enough samples were obtained because more honeybees would be lost. Natural environments have 1.2 mg/L of matrine, which represents a possible concentration a honeybee could contact (also nearly 1/1000 of the 48h-LC 50 of matrine for honeybee). The recommended concentration of matrine used in the field varies between 10 mg/L and 40 mg/L. A minimum and maximum concentration of 10 and 40 mg/L of matrine, respectively, was thus used to represent the possible concentration honeybee could contact directly in the field (also nearly the 1/120, 1/30 of the 48h-LC 50 of matrine for honeybee). The corresponding sucrose solutions (2 mL) were replaced daily in each cage from day 8, and pollen was no longer provided to avoid interfering because pollen could modulate cytochrome P450 (CYP) detoxication enzymes (Sousa et al. 2016). The temperature and humidity of the artificial climate incubator were set at 25℃ and 60%, respectively, with the consideration that bioinsecticides are prone to degrade, and honeybees aged 8-22 days are more likely to work outside the hive. The mortality of honeybees was calculated daily from day 8 to day 22. The sucrose consumption by each honeybee was calculated daily using the formula: [daily sucrose consumption / (number survived the previous day - number died that day/2)]. The average weight of 5 honeybee workers was calculated daily using the formula: [(cage weight with honeybee – empty cage weight) / (number survived the previous day - number died that day/2)] (Liu et al ., 2020) (Liu et al. 2020). Another parallel experiment for multi-omics analysis was conducted as detailed above but with 36 cages divided into 4 groups. Honeybee mortality and sucrose consumption were also calculated for this parallel experiment. The midguts of 10 honeybees in one cage were mixed as one biological sample for multi-omics analysis. Samples were collected from the CK group on days 8, 15, and 22 and on days 15 and 22 from the T1, T2, and T3 treatment groups. 2.2 Analysis of gut microbiota The complete gut of honeybee workers was pulled using sterilized tweezers, followed by midgut removal for gut microbiota detection. Ten midguts from one cage in each treatment group were pooled together as a biological sample. There were three replicates for each group. All the midgut samples were stored in liquid nitrogen for at least 5 min to inactivate the RNA degrading enzyme and then moved to a -80℃ refrigerator for short-term preservation before the subsequent step. Genomic DNA was extracted and quantified using TIANamp Soil DNA Kits (TIANGEN Biotech) following the manufacturer’s instructions. The target V4 regions of the bacterial 16S rRNA gene were subsequently amplified (Forward primer: 50-CCTACGGNGCWGCAG-30; Reverse primer: 50- GACTACHVGGTACTATCC-30) using a Phusion® High-Fidelity PCR Master Mix (New England Biolabs) according to the manufacturer’s instructions. Sequencing libraries were generated using the NEBNext® Ultra™ IIDNA Library Prep Kit, quantified using Agilent 5400, and then sequenced on a NovaSeq6000 platform. Species annotation was performed using QIIME2 software. Species accumulation boxplot for the 16S rRNA data was performed using the vegan package in R software to visualize the richness of microbial community and sample size. Non-metric multidimensional scaling (NMDS) analysis was implemented using the ade4 package and ggplot2 package in R software to reduce data dimension and visualize complex and multidimensional data. Anosim and Adonis’s analyses were performed using the vegan package and ggplot2 package within R to analyze the difference between high-dimensional data groups. 2.3 Analysis of DEGs in the transcriptome The first strand of cDNA was synthesized in an M-MuLV reverse transcriptase system using the fragment of mRNA as a template and random oligonucleotides as primers. The RNA was subsequently degraded using RNaseH, and the second strand of cDNA was synthesized from dNTPs in a DNA polymerase I system. The purified double-stranded cDNA was subjected to terminal repair and tail addition and was then sequenced. cDNA of about 250-300 bp was screened using AMPure XP beads, followed by PCR amplification. The PCR product was purified using AMPure XP beads, and the library was subsequently prepared using the NEBNext ® Ultra ™ RNA library prep kit for Illumina ® following the manufacturer’s instructions. The gut tissues of A. c. cerana workers subjected to matrine stress and those of the corresponding untreated workers collected on day 15 and day 22 were prepared using NEBNext® Ultra™ Directional RNA Library Prep Kit and sequenced on an Illumina NovaSeq 6000 platform. The sequenced fragments were processed into sequence data (reads) using CASAVA base recognition from the image data measured using the high-throughput sequencer. The raw data containing adaptors, unknown nucleotides (N), and reads with more than 50% of low quality ( q value ≤20) were filtrated using fastp 0.23.1 software to obtain high-quality clean reads. The original data of the sequenced transcriptome has been uploaded to the SRA database (https://www.ncbi.nlm.nih.gov/sra) of NCBI. Its serial number is PRJNA1209336. The DEGs in CK15 vs. T15 and CK22 vs T22 comparison groups were screened using the edgeR software following the standard of p -value ≤ 0.05 (Robinson et al, 2010). The volcano plot of up- and downregulated genes in these comparison groups and expression analysis were generated using the Novogene platform (https://magic.novogene.com/). GO (Gene Ontology) categorization of DEGs was carried out using WEGO software. The Blastall tool was employed to conduct pathway analysis by comparing isoforms against the KEGG (Kyoto Encyclopedia of Genes and Genomes) database (https://www.kegg.jp/). The chart was subsequently drawn using relevant tools in the Novogene platform (https://magic.novogene.com/) and the OmicShare platform (https://www.omicshare.com/). Cytochrome P450, Glutathione S-transferase, UDP-glucuronosyltransferase, acetylcholinesterase, and ABC transporter were selected for further investigation based on previous studies on the immune response of honeybees under pesticide stress. Expression clustering analysis of the aforementioned DEGs was performed using the Novogene platform. Eight DEGs ( LOC107993345, LOC107994703, LOC107998471, LOC107999176, LOC108000407, LOC108001650, LOC108001900, LOC108002960, LOC108002963, LOC108002964, and LOC108002965, LOC108003566 ) were selected for real-time quantitative PCR (qPCR) assays. Specific primers were designed using Primer 5 according to the corresponding nucleic acid sequence (Table S1). Total RNA of honeybee midguts under 40 mg/L matrine treatment in another parallel experiment was isolated using RNAiso Plus RT01 Kits (Takara Biomedical Technology). cDNA was then synthesized using reverse transcriptase kits (HiScript Q RT SuperMix for qPCR (+gDNA wiper) R123-01, Vazyme Biotechnology) according to the protocols. The qPCR reaction was conducted on QuantStudio™ 5 Real-Time PCR System (Applied Biosystems) using the ChamQ SYBR qPCR Master Mix (Vazyme Biotechnology). The PCR cycling conditions were initial denaturation for 30 s at 95℃, followed by 40 cycles of denaturation, and annealing and extension at 95℃ for 10 s and 60℃ for 30 s. The real-time gene expression was calculated using the 2 −ΔΔCt method. The transcription of the b-actin housekeeping gene was used as the internal reference. The experiment was performed in triplicate, with each replicate having three independent biological samples. 2.4 Untargeted metabolomic analysis of gut in honeybees The honeybee gut samples were homogenized with prechilled 80% methanol by vortexing and then centrifuged for 20 min at 15,000 g and 4°C. The supernatants were subsequently injected into the sample vial for UHPLC-MS/MS analysis using a Vanquish UHPLC system (Thermo Fisher, Germany) coupled with an Orbitrap Q Exactive TM HF mass spectrometer (Thermo Fisher, Germany). Samples were isolated using a Hypesil Gold column (100 × 2.1 mm, i.d., 1.9 μm) at a flow rate of 0.2 mL/min. The eluents for the positive polarity mode were eluent A (0.1% formic acid in Water) and eluent B (methanol). The eluents for the negative polarity mode were eluent A (5 mM ammonium acetate, pH = 9.0) and eluent B (methanol). The procedure of eluent gradient was established as follows: 0 – 1.5 min, 98% A; 1.5 – 3 min, 98% – 15% A; 3 – 10 min, 15% – 0% A; 10 – 10.1 min, 0% – 98% A; 11min, 98% A; 12 min, 98% A. The mass spectrometer was set to the positive/negative polarity mode, spray voltage of 3.5 kV, capillary temperature of 320°C, sheath gas flow rate of 35 psi, S-lens RF level of 60, aux gas flow rate of 10 L/min, and aux gas heater temperature of 350°C. The raw data from UHPLC-MS/MS were processed using the Compound Discoverer 3.1 (CD3.1, Thermo Fisher) to perform peak alignment, peak picking, and quantitation for each metabolite. The peaks were matched with the mzCloud (https://www.mzcloud.org/), mzVault, and MassList databases to obtain the accurate qualitative and relative quantitative results of the metabolites. The metabolites were subsequently annotated using the KEGG database (https://www.genome.jp/kegg/pathway.html), HMDB database (https://hmdb.ca/ metabolites), and LIPIDMaps database (http://www.lipidmaps.org/). The statistical significance of metabolites was calculated using a t-test. Metabolites with VIP > 1, p 1.5 were considered to be differentially expressed metabolites. The metabolic pathway enrichment of differentially expressed metabolites was subsequently analyzed. Metabolic pathways with p < 0.05 were considered to be significantly enriched pathways. Statistical analyses were performed using software R (version 3.4.3), Python (version 2.7.6), and CentOS (CentOS version 6.6). 2.5 Correlation analysis between gut microbiota, transcriptomics, and metabolomics The top 20 differentially expressed metabolites and top 10 differential bacterial genera were selected based on the p -value to perform correlation analysis. The differentially expressed genes and metabolites in different comparison groups were also selected based on the transcriptomics and metabolomics results to perform correlation analysis and KEGG enrichment analysis shared by differential genes and metabolites. Results 3.1 External effect of matrine on honeybees The survival rate of honeybees exhibited a significant decrease, especially on days 16 and 17 after being fed on matrine for 14 consecutive days (Fig. 1A.). Notably, T2 treatment had the greatest impact on the survival rate of honeybees. Honeybees had no significant changes in sucrose consumption or body weight during the experimental period (Fig. 1B & C.). 3.2 Microbiota changes under matrine stress We characterized the composition of the gut microbiota at the genus level through metagenomic sequencing. 16S rRNA pyrosequencing of 27 samples based on V3-V4 region yielded 2,212,346 high-quality sequences. The relative microbial abundances were utilized to characterize honeybee gut microbial communities. Gilliamella , Snodgrassella , and Pseudomonas were the most abundant genera in both the control and treatment groups at the genus level (Fig. 2A & B). Matrine exposure had a little effect on the size of the honeybee gut microbiome on day 15. However, the relative abundances of the core genus, Gilliamella , were significantly lower in the T3_D15 group (Fig. 2A, 2C p=0.031), but not on day 22. Matrine exposure had a little effect on the size of the honeybee core gut microbiome on day 22 (Fig. 2B, 2D). The limited impact of matrine treatment on the microbiota composition on day 22 post-treatment was unexplained but indicative of other effects of matrine on honeybees. Three core gut taxa ( Gilliamella , Snodgrassella, and Lactobacillus ) were identified in both the control and treatment groups (Fig. 2), indicating that matrine does not eliminate colonization by these core taxa. Notably, Dorea and Lachnoclostridium exhibited significant differences on T2 (p=0.018, p=0.012) and T3 (p=0.039, p=0.016) groups compared to the control on day 22. The principal coordinate analysis of NMDS (Non-Metric Multi-Dimensional Scaling) revealed that the relative abundance of gut community compositions of matrine-treated honeybees did not differed from those of the controls (Fig. 2E & F). Beta diversity, which compares the composition of two different microbial communities, can also be visualized using non-metric multidimensional scaling (NMDS) analysis. Of note, NMDS analysis results were similar to those of Bray-Curtis distance analysis (Fig. 2E & F). The microbial community composition of the control group (CK_D15) and treatment groups (T1_D15, T2_D15, T3_D15) did not differ significantly from each other on day 15 (ANOSIM, p = 0.47, p=0.9, p=0.2). Similarly, there were no significant differences in microbial community composition between the control (CK_D22) and treatment groups (T1_D22, T2_D22, T3_D22) on day 22 (ANOSIM, p = 0.7, p=0.2, p=0.2). 3.3 Transcription changes under matrine stress Quality control of the sequence data yielded 24 data sets containing between 40,710,464 and 50,635,176 clean reads with a Q20 of 97.33%~97.65%. 3.3.1 Differential gene expression profile of Apis cerana workers under matrine stress A total of 196, 829, 295, 110, 145, and 189 DEGs were identified in CK_D15 vs. T1_D15, CK_D15 vs. T2_D15, CK_D15 vs. T3_D15, CK_D22 vs. T1_D22, CK_D22 vs. T2_D22, and CK_D22 vs. T3_D22 comparison groups, respectively. The numbers of up-regulated genes in the comparison groups were 48, 117, 55, 33, 36, and 49, while those of down-regulated genes were 148, 712, 240, 77, 109, and 140, respectively. Venn analysis revealed 8 and 54 shared up-regulated and down-regulated genes, respectively, in the three D15 comparison groups (Fig. 3A & B). In contrast, 2 and 4 shared up-regulated and down-regulated genes, respectively, were identified in the three D22 comparison groups (Fig. 3C & D). Notably, the number of DEGs was most abundant in the T2 treatment (10 mg/L matrine), especially on day 15. 3.3.2 Functional and pathway annotation of DEGs DEGs in CK_D15 vs. T1_D15, CK_D15 vs. T2_D15, CK_D15 vs. T3_D15, CK_D22 vs. T1_D22, CK_D22 vs. T2_D22, and CK_D22 vs. T3_D22 comparison groups were engaged in 225, 455, 259, 148, 304, and 275 GO functional terms (Fig. 4A - F). The entries of catalytic activity, acting on a protein and transport, were significantly enriched in the three treatment groups (T1, T2, T3) on day 15 (D15; p <0.05). Of note, the entries of phosphate-containing compound metabolic process and phosphate metabolic process were significantly enriched only in the two high-concentration treatment groups (T2, T3). Numerous genes were significantly enriched in the oxidation activity and oxidation-reduction process items in T1 and T2 groups on day 22 (D22). In contrast, T3 had significant enrichment of numerous genes associated with pyrophosphatase activity and hydrolase activity, acting on acid anhydrides, in phosphorus-containing process items on day 22 (D22). DEGs in CK_D15 vs. T1_D15, CK_D15 vs. T2_D15, CK_D15 vs. T3_D15, CK_D22 vs. T1_D22, CK_D22 vs. T2_D22, and CK_D22 vs. T3_D22 comparison group were associated with 47, 64, 39, 22, 38, and 35 KEGG pathways, respectively. These pathways belonged to major categories, including metabolism, organismal systems, environmental information processing, cellular processes, and genetic information processing (Fig. 5A - F). Wnt, FoxO, mTOR, and Toll and Imd signaling pathways and Oxidative phosphorylation were the five pathways with the highest number of enriched genes. Noteworthy, the Oxidative phosphorylation pathway only showed significant gene enrichment in the high-concentration treatment group (>10mg/ml) at both D15 and D22 (Fig. 5A - F). 3.3.3 Analysis of detoxification factor-associated DEGs in Apis cerana workers Further analysis conducted to explore detoxification factor-associated DEGs in the D15 and D22 comparison groups yielded 95 DEGs. Among them, 13 were extremely significantly different between groups. They included eight cytochrome P450 protein-coding genes, one Glutathione S-transferase protein-coding gene, two UDP-glucuronosyltransferase protein-coding genes, and two acetylcholinesterase protein-coding genes (Fig. 6). A majority of detoxification factor encoding genes were induced by matrine stress, whereas a minority were suppressed. 3.3.4 Real-time qPCR analysis LOC107993345, LOC107994703, LOC107998471, LOC107999176, LOC108000407, LOC108001650, LOC108001900, LOC108002960, LOC108002963, LOC108002964, LOC108002965, and LOC108003566 genes involved in Cytochrome P450, Glutathione S-transferase, Acetylcholinesterase, decomposition of spermine and spermidine, and degradation of glutathione were selected for qPCR validation of the transcriptome sequence data. The qPCR results were generally consistent with the sequence data (Fig.7.). 3.4 Metabolite changes under matrine stress The differentially expressed metabolites in T1_D15 vs. CK_D15, T2_D15 vs. CK_D15, and T3_D15 vs. CK_D15 were 52 (23 up-regulated, 29 down-regulated), 146 (49 up-regulated, 97 down-regulated), and 242 (86 up-regulated, 156 down-regulated), respectively. All PLS-DA analyses indicated a visible difference between T1_D15, T2_D15, T3_D15, and CK_D15 (Fig. 8.). The differentially expressed metabolites in T1_D22 vs. CK_D22, T2_D22 vs. CK_D22, and T3_D22 vs. CK_D22 were 83 (59 up-regulated, 24 down-regulated), 116 (60 up-regulated, 56 down-regulated), and 102 (40 up-regulated, 62 down-regulated), respectively. Similarly, all PLS-DA analyses indicated a visible difference between T1_D22, T2_D22, T3_D22, and CK_D22 (Fig. 9.). The differentially expressed metabolites between the treatment groups and the control group were mainly enriched in ABC transporters, cysteine and methionine metabolism, purine metabolism, tyrosine metabolism, drug metabolism - cytochrome P450, metabolism of xenobiotics by cytochrome P450, and glutathione metabolism. Fig. 10 and 11 and Table 1 summarize the differentially expressed metabolites and corresponding pathways common in different comparison groups. Notably, glutathione and spermidine were the most common metabolites, while glutathione metabolism, ABC transporters, and cysteine and methionine metabolism were the most common pathways. 3.5 Integration of metabolomic and microbial analysis The significantly related microbiota and metabolites in the same comparison group (T vs. CK) were first screened in the correlation analysis. The common microbiota and metabolites in multiple comparison groups (≥2) were then screened. These screening steps revealed 10 pairs of associated microbial and metabolite combinations, which exhibited significant correlation in the two comparison groups (T2_D22 vs. CK_D22 and T3_D22 vs. CK_D22). Notably, all were in the cationic mode. The main metabolites involved were 2-[(3S)-1-(2-Chlorobenzyl)-3-pyrrolidinyl]-5-methyl-1,3,4-oxadiazole, gamma-Glutamylleucine, Argininosuccinic acid, 1,3-dimethyl-6-(trifluoromethyl)-1H-pyrazolo[3,4-b]pyridin-4-ol, 2-[5-(2-hydroxypropyl)oxolan-2-yl]propanoic acid, 5-(2,5-dihydroxyhexyl)oxolan-2-one, 6-Pentyl-2H-pyran-2-one, N-(2-morpholinophenyl)-2-furamide, and PC (18:1/20:5). The gut microbiota involved were Dorea , and Lachnoclostridium (Table S2). 3.6 Integration of metabolomic and transcriptomic analysis KEGG Pathways were used as terms to capture the pathways that were co-enriched in the comparison pairs (T vs. CK) in metabolomics and transcriptomics. The common co-enriched terms in multiple comparison groups (≥2) were then screened again. Correlation analysis between metabolomics and transcriptomics were also conducted. A total of 7 co-enriched KEGG pathways were selected. These pathways included Arginine and proline metabolism, beta-Alanine metabolism, Cysteine and methionine metabolism, Phototransduction – fly, Purine metabolism, Starch and sucrose metabolism, and Valine, leucine and isoleucine degradation (Table S3). Notably, spermidine was arrestive in the co-enriched pair of metabolomics and transcriptomics in the two comparison groups. Spermidine and LOC107998471 ( Aldh ) were co-enriched in arginine and proline metabolism, and the beta-alanine metabolism pathway. Arachidonic acid and five transcription genes [ LOC108004131 (myosin-IIIb), LOC107998381 (inaE), LOC107995353 (trpl), LOC108001544 (Trpgamma), and LOC108003363 (CaMKII) ] were co-enriched in the Phototransduction – fly pathway. Noteworthy, myosin-IIIb, inaE, and CaMKII were correlated with arachidonic acid. Moreover, inaE and CaMKII were correlated in the two treatment groups (T2_D15 vs. CK_D15, T3_D15 vs. CK_D15) (Table S3). Discussion In this study, the lasting effects of three matrine concentrations on the biological characteristics, including the survival, food consumption, and body weight of honeybees, were examined. The effects of the three matrine concentrations on the gut microbial composition were further examined by studying the microbiome, metabolome, and transcriptome in two 7-day interval stages. Continuous intake of matrine caused rapid death of honeybees on day 9 but had no significant effect on food consumption and body weight of the honeybees. Matrine intake caused changes in the abundance of Gilliamella , Dorea , and Lachnoclostridium in gut microbiota, resulting in differential expression of argininosuccinic acid, spermidine, arachidonic acid, and LOC107998471 ( Aldh ). This study comprehensively evaluated the impact of matrine on honeybees and provides a reference for its rational use and colony avoidance methods. Multi-omics analysis revealed the essential mechanism of matrine's impact on honeybees. Notably, preventing the accumulation of vital harmful substances and supplementing beneficial substances or microbiota are potential new methods for alleviating insecticide poisoning. 4.1 Mortality of honeybees under matrine stress Matrine intake did not cause significant changes in food consumption and body weight of honeybees. This finding suggested that matrine intake did not affect the appetite of honeybees, and they did not have obvious avoidance of matrine because they still ingested sucrose mixed with matrine. Similar studies involving spinosad, a bioinsecticide based on chemical compounds found in the bacterial species Saccharopolyspora spinosa , report that low spinosad doses (0.85 μ g/ml) do not affect the body weight and food consumption but reduce the survival rate of bees (Araújo et al. 2023; Botina et al. 2024; Marques et al. 2020). This phenomenon was attributed to matrine being a natural plant component that is not avoided by honeybees during the co-evolution of honeybees and plants. However, matrine's damage to honeybees still exists, which manifested as a cliff-like decline in the survival rate on the 9th day after continuous intake of matrine. Though matrine's effect on honeybees is not perceptible during the early stage, it becomes obvious when ingested to a certain degree or time. This aspect was proved by the mass death of workers and the rapid decline of honeybee colonies. This finding can guide practical beekeeping. Specifically, honeybee keepers must leave within 9 days or face serious colony losses if they release honeybees in orchards of pear or citrus trees, and growers start spraying insecticides containing 10mg/L (T2) ~ 40mg/L (T3) matrine concentration. 4.2 Microbiota changes associated with inflammation and glucose metabolism Matrine has effects on some honeybee gut microbiomes, such as Gilliamella , Dorea and Lachnoclostridium . Matrine intake caused inflammation and sugar metabolism disorder in honeybee intestines, resulting in an immune stress response. Significant changes in the core microbiome were only noted in the T3 group on day 15, characterized by a high relative abundance of Gilliamella . However, there were no significant changes in the core microbiome on day 22. Gilliamella participates in toxic sugars metabolism (Zheng et al. 2016) and antimicrobial peptide synthesis (Lang et al. 2023). Herein, the honeybee gut microbiome exhibited some recovery between day 15 and day 22. Traditional pesticides, such as thiacloprid and nitenpyram, affect the core gut microbiota of honeybees, including Gilliamella, Bifidobacterium, Lactobacillus, Bombella, Frischella , and Bartonella (Liu et al. 2020; Zhu et al. 2020). The relatively high toxic bioinsecticide abamectin (abamectin emulsifiable concentrate LC 50 =0.002mg/L) also affects the core gut microbiota of bees including Bifidobacterium, Lactobacillus, and Gilliamella (Guo et al. 2024). However, the impact of matrine on honeybee core microbiota was relatively small compared to other insecticides, such as abamectin. Interestingly, there were changes in the minority bacteria ( Dorea and Lachnoclostridium ). Dorea and Lachnoclostridium are associated with inflammation and glucose metabolism (obesity). Dorea is believed to be closely associated with inflammation and glucose metabolism (Allin et al. 2018; Brahe et al. 2015; Del Chierico et al. 2017; Machiels et al. 2014; Murtaza et al. 2019; Naderpoor et al. 2019; Pinna et al. 2021; Zhong et al. 2020). It is a beneficial gut bacterium that has active short-chain fatty acids producers, which can reduce inflammatory reactions (Machiels et al. 2014; Zhong et al. 2020). Lachnoclostridium is associated with inflammation and obesity (Andoh et al. 2016; Kaplan et al. 2019; Kong et al. 2019; Nogal et al. 2021; Wang et al. 2020; Xu P. et al. 2017; Zhao et al. 2017; Zhou et al. 2020). Herein, we hypothesized that matrine stress maybe alters sugar metabolism in honeybees despite it not being evident in food consumption, body weight, and other indicators. 4.3 Matrine stress induces crucial metabolism Correlation and co-enrichment analyses revealed the presence of some important metabolites, including argininosuccinic acid, spermidine, and arachidonic acid. Argininosuccinic acid was correlated with two bacteria in multiple treatment groups. Spermidine was co-enriched in two KEGG pathways. Arachidonic acid was co-enriched with five transcription genes in the Phototransduction – fly. Argininosuccinic acid (AA) is associated with many diseases (Li et al. 2018; Qi et al. 2022). Herein, there was significant upregulation of AA In the treatment groups (T2_22, T3_15, T3_22), indicating that the treatment had adverse effects on honeybees. Notably, argininosuccinic acid induce lipid and protein oxidation, thereby reducing glutathione (GSH) levels (Seminotti et al. 2020). GSH is an important non-enzymatic antioxidant (Liu et al. 2014), which can clean reactive oxygen species and influence cell apoptosis in an organism (Messina Lawrence 1989). Reduced glutathione (GSH) and oxidized glutathione (GSSG) are strictly regulated to maintain cellular homeostasis (Liu et al. 2014; Messina Lawrence 1989). Intriguingly, there was an increase in GSH in the treatment groups with significantly elevated AA (T2_22, T3_15, and T3_22). This phenomenon is still unclear. AA exhibited a significant correlation with Dorea and Lachnoclostridium in two treatment groups. Dorea and Lachnoclostridium are associated with inflammation and glucose metabolism. However, their relationship with AA needs validation. Noteworthy, AA and GSH were upregulated in T3_22 treatment. Subsequent correlation analysis revealed a significant association between Dorea and GSH. Polyamines, such as spermidine, are naturally rich in honeybee products, such as pollen and honey (Vukašinović et al. 2024). Numerous studies suggest that spermidine contribute to cellular immunity and biological resistance, such as anti-inflammation, antioxidant, and anti-aging (Eisenberg et al. 2009; Haskó et al. 2000; Madeo et al. 2018; Okumura et al. 2016). Spermidine can significantly improve myocardial damage (Hu et al. 2020; Wang et al. 2021; Wei Li et al. 2016; Wei Wang et al. 2016; Xu et al. 2018; Xu Y. et al. 2017) and cognitive disorders caused by abnormal glucose metabolism or radiation (Stacchiotti Corsetti 2020). Moreover, spermidine participate in biological processes, such as cell proliferation, differentiation, injury repair, and apoptosis (Gevrekci 2017; Guo et al. 2002; Seiler Raul 2007; Wei Wang et al. 2016), especially in the development process of animal intestines (Lux et al. 1980; Pegg 2014). Spermidine and spermine, which can convert to spermidine, have thus been used as a livestock feed additive (Gerner E. 2007; Kan et al. 2017; Wu et al. 2000). It is difficult to judge the changes of spermidine using simple stress responses or beneficial detoxifying adaptation based on the co-enriched KEGG pathways. However, functional validation of spermidine under insecticide stress is essential because of its positive role in enhancing immunity, stress resistance, and repair of intestinal cells. Arachidonic acid is an important nutrient, whose appropriate intake can maintain normal growth and development in animals. Arachidonic acid intake can maintain the pupation rate and eclosion rate in honeybees (Yu et al. 2019). Notably, arachidonic acid enhances the immune ability of honeybees. Supplementation with arachidonic acid can increase the survival rate of honeybees under Escherichia coli stress and enhance the activity of immune enzymes, including phenoxidase, antitrypsin, and lysozyme (Yu et al. 2021). Despite the positive effects of arachidonic acid on the growth, development, and immunity of organisms, there are still no reports on its direct alleviation of pesticide poisoning. It is thus important to verify the role of arachidonic acid in honeybee under pesticides because of its positive effects in honeybee immunity. 4.4 The potential mechanism of differentially expressed genes to matrine stress In this study, metabolites (spermidine) and transcription genes ( Aldh ) were arrestive in the co-enriched pair of metabolomics and transcriptomics. Spermidine, and Aldh were co-enriched in arginine and proline metabolism and beta-alanine metabolism in the transcriptomic and metabolomic association analysis. Spermidine is an important metabolite in both pathways, while Aldh is an enzyme responsible for the downstream decomposition of spermidine (Ohashi et al. 2017; Southan et al. 1994). The down-regulation of Aldh potentially suppress spermidine degradation under matrine stress in honeybees. myosin-IIIb, inaE, trpl, Trpgamma, and CaMKII genes were co enriched with arachidonic acid in the Phototransduction - fly pathway. Of note, myosin-IIIb , inaE , and CaMKII were correlated with arachidonic acid, suggesting potential involvement in phototransduction. However, it is challenging to explain the enrichment of these DEGs in phototransduction under pesticide stress, considering that this pathway is annotated in fly. The assumption made is that the pathways of these DEGs in other species are inconsistent with those in fly. However, it is necessary to enrich the annotation of this pathway in other species to understand this disparity. Conclusion This study comprehensively evaluated the toxicological effects of matrine on honeybees, which are important ecological insects. Continuous intake of matrine affect gut microbiota, transcription, and metabolism, leading to rapid death of honeybees. Multi-omics correlation and co-enrichment analyses revealed that gut microbiota ( Gilliamella , Dorea , and Lachnoclostridium ), metabolites (argininosuccinic acid, spermidine, and arachidonic acid), and genes ( Aldh ) play a crucial role in honeybees under matrine stress. Declarations Author contributions Y.Y.: Conceptualization, Methodology, Investigation, Data curation, Visualization, Supervision, Writing - original draft. Z.Z.: Methodology, Data curation, Visualization, Writing - original draft. Y.F.: Methodology, Data curation, Visualization, Writing - original draft. W.Z.: Investigation. D.Y.: Methodology, Investigation. X.H.: Investigation. Y.L.: Investigation. W.W.: Investigation. H.Z.: Investigation. X.W.: Conceptualization, Methodology, Supervision. Supplementary Information The online version contains supplementary material available at . Acknowledgements This work was supported by Guizhou Provincial Science and Technology Project (No. ZK[2024]079, No. ZK[2023]181), National Key Research and Development Program (No. 2022YFD1601703, No. 2021YFD1100309), China Agriculture Research System of MOF and MARA (No. CARS-44-SYZ 16) and Guizhou Academy of Agricultural Sciences (No. 202313). Funding Guizhou Provincial Science and Technology Project (No. ZK[2024]079, No. ZK[2023]181), National Key Research and Development Program (No. 2022YFD1601703, No. 2021YFD1100309), China Agriculture Research System of MOF and MARA (No. CARS-44-SYZ 16) and Guizhou Academy of Agricultural Sciences (No. 202313). Data availability The original data of the sequenced transcriptome has been uploaded to the SRA database (https://www.ncbi.nlm.nih.gov/sra) of NCBI (PRJNA1209336). Conflict of interest The authors declare no competing interests. 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KEGG enrichment pathways and differentially expressed metabolites between the treatment groups and the control group. Comparison KEGG pathway Differential metabolites T1_D15 vs CK_D15 ABC transporters Methyl beta-D-galactopyranoside; L-Cystine T3_D15 vs CK_D15 ABC transporters Xylitol; Glutathione; Trehalose T1_D22 vs CK_D22 ABC transporters Spermidine; Trehalose T2_D22 vs CK_D22 ABC transporters Glutathione; Spermidine T3_D22 vs CK_D22 ABC transporters Inositol; Glutathione; Spermidine; L-Serine T1_D15 vs CK_D15 Cysteine and methionine metabolism L-Cystine T2_D15 vs CK_D15 Cysteine and methionine metabolism Homocysteine T3_D15 vs CK_D15 Cysteine and methionine metabolism Glutathione; Homocysteine T2_D22 vs CK_D22 Cysteine and methionine metabolism Glutathione; O-Phospho-L-serine; S-Adenosylhomocysteine T3_D22 vs CK_D22 Cysteine and methionine metabolism Glutathione; L-Serine T2_D15 vs CK_D15 Drug metabolism - cytochrome P450 4-Hydroxytamoxifen T3_D15 vs CK_D15 Drug metabolism - cytochrome P450 Valproic acid; 4-Hydroxytamoxifen T1_D22 vs CK_D22 Drug metabolism - cytochrome P450 Valproic acid T3_D15 vs CK_D15 Glutathione metabolism Spermine; Glutathione; Oxidized glutathione T1_D22 vs CK_D22 Glutathione metabolism Spermidine; L-Ascorbate T2_D22 vs CK_D22 Glutathione metabolism Glutathione; Spermidine; L-Ascorbate T3_D22 vs CK_D22 Glutathione metabolism Glutathione; Spermidine T3_D15 vs CK_D15 Metabolism of xenobiotics by cytochrome P450 2-Naphthol T1_D22 vs CK_D22 Metabolism of xenobiotics by cytochrome P450 2-Naphthol T2_D22 vs CK_D22 Metabolism of xenobiotics by cytochrome P450 NNK T1_D15 vs CK_D15 Purine metabolism 2'-Deoxyadenosine; Xanthosine; XMP T1_D22 vs CK_D22 Purine metabolism 2'-Deoxyadenosine; Adenosine T2_D22 vs CK_D22 Purine metabolism 2'-Deoxyadenosine; Adenosine; Xanthine T3_D22 vs CK_D22 Purine metabolism 2'-Deoxyguanosine T1_D15 vs CK_D15 Tyrosine metabolism L-Dopa T2_D15 vs CK_D15 Tyrosine metabolism L-Dopa T1_D22 vs CK_D22 Tyrosine metabolism Levodopa T2_D22 vs CK_D22 Tyrosine metabolism Hydroquinone Additional Declarations No competing interests reported. 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The days on the horizontal axis represents the number of days of pesticide treatment.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7062509/v1/2a5f4e891ad04c5a7e091c8a.png"},{"id":86250182,"identity":"423741c3-d240-4112-9800-7bc2c4afb50b","added_by":"auto","created_at":"2025-07-08 12:32:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19511068,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChanges in the composition of gut microbiota following matrine exposure in honeybees with established gut microbial communities. \u003c/strong\u003e(A) and (B) Stacked column graph illustrating the relative abundances of gut bacterial species in honeybees in the control group and those treated with 1.2 mg/L, 10 mg/L, and 40 mg/L of matrine on Day 15 and Day 22. Each column represents an individual honeybee. (C) and (D) Boxplots of bacterial 16S rDNA copies of honeybees in the control group (CK_D15, CK_D22) and those in the matrine-treated groups (T1_D15, T2_D15, T3_D15; T1_D22, T2_D22, T3_D22). Samples were collected on day 15 and day 22 (n = 3 for each group and time point). The plots display the high, low, and median values, with the lower and upper edges of each box representing the first and third quartiles, respectively, based on the Wilcoxon rank sum test and Bonferroni correction. (E) and (F)Principal coordinate analysis of gut microbial community composition using NMDS (Non-Metric Multi-Dimensional Scaling).\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7062509/v1/291cb4c48740301c6739010e.png"},{"id":86251197,"identity":"b5207fe0-48a8-4cae-a1ba-3884f7401fb4","added_by":"auto","created_at":"2025-07-08 12:40:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4300419,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of DEGs in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eApis cerana\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e workers under varying matrine stress at days 15 and 22. \u003c/strong\u003eA: Venn analysis of up-regulated genes in CK_D15 vs. T1_D15, CK_D15 vs. T2_D15, and CK_D15 vs. T3_D15 comparison groups. B: Venn analysis of down-regulated genes in CK_D15 vs. T1_D15, CK_D15 vs. T2_D15, and CK_D15 vs. T3_D15 comparison groups. C: Venn analysis of up-regulated genes in CK_D22 vs. T1_D22, CK_D22 vs. T2_D22, and CK_D22 vs. T3_D22 comparison groups. D: Venn analysis of down-regulated genes in CK_D22 vs. T1_D22, CK_D22 vs. T2_D22, and CK_D22 vs. T3_D22 comparison groups.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7062509/v1/d1207effe848d69555565f3c.png"},{"id":86251198,"identity":"6bc5d57d-3d24-45c0-b764-3fb39e2cc053","added_by":"auto","created_at":"2025-07-08 12:40:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":12328976,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Go categories of DEGs in the CK_D15 vs. T1_D15 (A), CK_D15 vs. T2_D15 (B), CK_D15 vs. T3_D15 (C), CK_D22 vs. T1_D22 (D), CK_D22 vs. T2_D22 (E), and CK_D22 vs. T3_D22 (F) comparison groups.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7062509/v1/5e8fa2c5ca05a850c97656a0.png"},{"id":86250177,"identity":"c3ffda70-8c50-4913-bf9d-3e8a21786a0d","added_by":"auto","created_at":"2025-07-08 12:32:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":6159438,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKEGG pathway enrichment of DEGs in the CK_D15 vs. T1_D15 (A), CK_D15 vs. T2_D15 (B), CK_D15 vs. T3_D15 (C), CK_D22 vs. T1_D22 (D), CK_D22 vs. T2_D22 (E), and CK_D22 vs. T3_D22 (F) comparison groups.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7062509/v1/e73080ff615dc35cb9316a15.png"},{"id":86250173,"identity":"ce52037b-71b4-460b-9cbd-730c39fd2849","added_by":"auto","created_at":"2025-07-08 12:32:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":938001,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap of detoxification factor-associated DEGs shared by the CK_D15, CK_D22, T1_D15, T2_D15, T3_D15, T1_D22, T2_D22, and T3_D22 groups.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-7062509/v1/4f50c8003cad551a148fd5b1.png"},{"id":86250175,"identity":"33cd38ac-a488-4b3e-b48c-f123e54670ba","added_by":"auto","created_at":"2025-07-08 12:32:05","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3205730,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReal-time qPCR validation of transcriptome sequence data using 12 DEGs in honeybees. (A) Control group vs. 1.2 mg/L matrine, (B) Control group vs. 10 mg/L matrine, and (C) Control group vs. 40 mg/L matrine on day 22.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-7062509/v1/0108151a86a7df26e3bb958e.png"},{"id":86251196,"identity":"70fe9f76-fac6-4e63-b82a-7835650dcaaf","added_by":"auto","created_at":"2025-07-08 12:40:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":4565704,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVolcano plots and PLS-DA graphs of differentially expressed metabolites (DEMs) in the treatment groups (T1, T2, and T3) compared to the corresponding expression in the control group (CK) on day 15.\u003c/strong\u003e A: Volcano plot of DEMs between T1 and CK. B: Volcano plot of DEMs between T2 and CK. C: Volcano plot of DEMs between T3 and CK. D: PLS-DA graph of DEMs between T1 and CK. E: PLS-DA graph of DEMs between T2 and CK. F: PLS-DA graph of DEMs between T3 and CK.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-7062509/v1/68a184c0930e71b0443a9e9b.png"},{"id":86251411,"identity":"c4ec07c7-cfc8-43e2-89a1-41fa60f1d621","added_by":"auto","created_at":"2025-07-08 12:48:05","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":4488131,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVolcano plots and PLS-DA graphs of differentially expressed metabolites (DEMs) in the treatment groups (T1, T2, and T3) compared to the corresponding expression in the control group (CK) on day 22.\u003c/strong\u003e A: Volcano plot of DEMs between T1 and CK. B: Volcano plot of DEMs between T2 and CK. C: Volcano plot of DEMs between T3 and CK. D: PLS-DA graph of DEMs between T1 and CK. E: PLS-DA graph of DEMs between T2 and CK. F: PLS-DA graph of DEMs between T3 and CK.\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-7062509/v1/5ce3b26546350222ff0fd787.png"},{"id":86250181,"identity":"18bb2dd6-cdca-40fc-bc46-f6f6bfac6888","added_by":"auto","created_at":"2025-07-08 12:32:05","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":5074787,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKEGG enrichment analysis of differentially expressed metabolites and pathways in T1_D15 vs. CK_D15 (A), T2_D15 vs. CK_D15 (B), and T3_D15 vs. CK_D15 (C) comparison groups.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-7062509/v1/1bc582db8fa5c9eb55138127.png"},{"id":86250180,"identity":"a014729b-f03b-4688-a6ab-af7238c0b7fa","added_by":"auto","created_at":"2025-07-08 12:32:05","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":6169853,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKEGG enrichment analysis of differentially expressed metabolites and pathways in T1_D22 vs. CK_D22 (A), T2_D22 vs. CK_D22 (B), and T3_D22 vs. CK_D22 (C) comparison groups.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-7062509/v1/fe89cc3572f1993e55193f66.png"},{"id":86250172,"identity":"312663bc-9d2e-4092-8247-29507e5f07a8","added_by":"auto","created_at":"2025-07-08 12:32:05","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":18640,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7062509/v1/a071882cae83806aff960c80.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eBiological Observation and Multi-omics Analysis Reveal the Toxicity of the Bioinsecticide Matrine to Honeybees (\u003cem\u003eApis cerana\u003c/em\u003e)\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHoneybee is an important agricultural pollinator, playing an important role in ensuring food safety and promoting crop yield and income. In 2020, the commercial value of global pollination services ranged between $ 195 billion and $ 387 billion (Porto et al. 2020). Honeybees also play a significant ecological role in maintaining and promoting biodiversity, the conservation of rare plants, and ecosystem restoration (Borges et al. 2009). However, the health and survival of honeybees are increasingly threatened, which necessitates their protection (Oldroyd Nanork 2009; R\u0026uacute;a et al. 2009; Yang 2005, 2009). Notably, the use of insecticides is considered the main factor among the many factors threatening honeybees (Alaux et al. 2010; Krupke et al. 2012).\u003c/p\u003e\n\u003cp\u003eBioinsecticides are a category of insecticides processed directly from living organisms or artificially synthesized insecticides with the same structure as natural compounds (Rodrigues et al. 2021). They are easily degradable and relatively friendly to the environment. In recent years, studies and registration of bioinsecticides have increased (Rosas-Garc\u0026iacute;a 2009). Matrine is a tetracyclo-quinoliz-indine alkaloid derived from \u003cem\u003eSophora\u003c/em\u003e plants such as \u003cem\u003eSophora flavescent\u003c/em\u003e Ait. and \u003cem\u003eSophora alopecuroides\u003c/em\u003e L. (Xiao et al. 1999). It interferes with acetylcholine receptors (AChR) and acetylcholinesterase (AChE) (Ali et al. 2017) and has thus been widely used to prevent and control various pests including whiteflies (Lv et al. 2009), pest mites (Zanardi et al. 2015), aphids (Luo et al. 2014), and cotton bollworms (Luo et al. 2014), and diseases such as apple anthracnose, apple tree rot, and grape downy mildew among other pests and diseases affecting fruits (Wang et al. 2016), vegetables (Yang et al. 2013), tea plants (Ye et al. 2014), traditional Chinese medicinal plants (Zhao et al. 2015), and forest trees (Ali et al. 2017; Andrade et al. 2019; Bordini et al. 2015; Marčić Međo 2014). Moreover, it promotes crop growth (Zhang et al. 2011). In China, 105 insecticide products containing matrine have been registered, ranking first among the botanical insecticides (http://www.chinapesticide.org.cn/hysj/index.jhtml).\u003c/p\u003e\n\u003cp\u003eHoneybee gut microbes play important roles, such as food digestion, regulating the immune system, and resisting pathogens, thereby maintaining the health and survival of honeybees (Engel et al. 2012; Hao et al. 2018; Kwong Moran 2016). Neonicotine and organophosphorus insecticides, including nitenpyram, imidacloprid, thiacloprid, thiamethoxam, coumaphos, and fipronil disorder the abundance of \u003cem\u003eGilliamella apicola\u003c/em\u003e, \u003cem\u003eFrischella perrara\u003c/em\u003e, \u003cem\u003eBifidobacteria\u003c/em\u003e, and \u003cem\u003eLactobacilli\u003c/em\u003e in honeybee gut (Chmiel et al. 2019; Liu et al. 2020; Rouze et al. 2019; Zhu et al. 2020). Common insecticides, such as glyphosate (Vazquez et al. 2020; Zhao et al. 2020), imidacloprid (Aufauvre et al. 2014; Derecka et al. 2013; Gao et al. 2020; Li et al. 2019; Wu M. et al. 2017; Wu Y. et al. 2017), thiamethoxam (Christen et al. 2018; Shi et al. 2017), and thiacloprid (Alptekin et al. 2016; Fent Schmid et al. 2020), have a significant impact on gene expression in honeybees. They down-regulate the expression of genes associated with immunity, learning, and exercise and up-regulate the detoxification genes. Cypermethrin, carbendazim, dimethoate, phenoxycarb, chlorfenitrile, fluorofuranone, chlorpyrifos, and other pesticides can affect the expression of genes and their related pathways, such as immunity, growth and development, and learning behavior (Boncristiani et al. 2012; Fent Haltiner et al. 2020; Huang et al. 2021; Kablau et al. 2020; Wang et al. 2018; Wu et al. 2021; Ye et al. 2020). Of note, there are only a few studies on the mechanisms of bioinsecticides on honeybees despite their significant negative impact on the health of honeybees (Guo et al. 2024). Transcriptomic studies of the honeybee microbiome could reveal the mechanisms of bioinsecticide on honeybees, thus informing guidelines for the proper use of insecticides and protection of honeybees. In this study, a biological observation (mortality, food consumption, weight changes) and multi-omics analysis, including microbiome, metabolome, and transcriptome (microbiota\u0026ndash; metabolite\u0026ndash;gene), were done to explore the holistic effect and mechanism of matrine on honeybees.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch2\u003e2.1 Honeybee preparation and exposure to bio-insecticide\u003c/h2\u003e\n\u003cp\u003eHoneybees used in this study were obtained from hives kept by Guizhou Academy of Agricultural Sciences, Guiyang, China (latitude, 26\u0026deg;29\u0026prime;29\u0026Prime;; longitude, 106\u0026deg;39\u0026prime;36\u0026Prime;; 1074.3m). The colonies were inspected weekly for the presence of parasites or diseases and well maintained to prevent attack. The bee colonies were subsequently visually certified to be free from diseases and parasites, such as Varroa mites, Chinese Bee Sacbrood Virus, and moths prior to the experiment. \u003c/p\u003e\n\u003cp\u003eFrames of 5 different healthy colonies with adequate capped worker broods covered with sterile nylon bags were kept in an artificial climate incubator set at 35\u0026deg;C and 60% relative humidity in the dark mode, which is a simulated condition of the hive, to capture newly emerged honeybee workers (Motta et al. 2020). Newly emerged honeybee workers within 12h (Day 1) were randomly moved to plastic rearing cages (500 ml) with an air vent on the side and lid (30 workers/cage).\u003c/p\u003e\n\u003cp\u003eHoneybee workers in the cages were also kept in an artificial climate incubator set at 35\u0026deg;C and 60% relative humidity in dark mode. The honeybee workers were fed on sucrose solution (50% wt/wt) ad libitum using a syringe inserted vertically through the lid hole. The sucrose solution was replaced, and dead workers were removed daily for 7 consecutive days. Pollen in a suspension of freshly mixed gut homogenate from hive honeybees was provided to the caged honeybee workers to enhance the development of the honeybee workers and the colonization of honeybee gut microbiota (Martinson et al. 2012; Motta et al. 2018). Pollen mixed gut homogenate was replaced daily for 5 consecutive days, followed by normal pollen on day 6 and day 7. The mortality of the honeybee workers was subsequently calculated within 7 days. All the colonies had a mortality rate of less than 10% and were thus healthy and capable of the subsequent experiments. \u003c/p\u003e\n\u003cp\u003eThree concentrations of matrine 50% sucrose solution (1.2, 10, and 40 mg/L, treatment groups) and a pure 50% sucrose solution (control group) were introduced to different cages using a syringe from day 8 for enough bio-insecticide exposure. There were 6 cages for the control group (CK), 7 cages for treatment 1 group (T1, 1.2 mg/L), 7 cages for treatment 2 group (T2, 10 mg/L), and 13 cages treatment 3 group (T3, 40 mg/L). Each cage had 30 workers. Treatment 3 had more cages to ensure that enough samples were obtained because more honeybees would be lost. Natural environments have 1.2 mg/L of matrine, which represents a possible concentration a honeybee could contact (also nearly 1/1000 of the 48h-LC 50 of matrine for honeybee). The recommended concentration of matrine used in the field varies between 10 mg/L and 40 mg/L. A minimum and maximum concentration of 10 and 40 mg/L of matrine, respectively, was thus used to represent the possible concentration honeybee could contact directly in the field (also nearly the 1/120, 1/30 of the 48h-LC 50 of matrine for honeybee). The corresponding sucrose solutions (2 mL) were replaced daily in each cage from day 8, and pollen was no longer provided to avoid interfering because pollen could modulate cytochrome P450 (CYP) detoxication enzymes (Sousa et al. 2016). The temperature and humidity of the artificial climate incubator were set at 25℃ and 60%, respectively, with the consideration that bioinsecticides are prone to degrade, and honeybees aged 8-22 days are more likely to work outside the hive. The mortality of honeybees was calculated daily from day 8 to day 22. The sucrose consumption by each honeybee was calculated daily using the formula: [daily sucrose consumption / (number survived the previous day - number died that day/2)]. The average weight of 5 honeybee workers was calculated daily using the formula: [(cage weight with honeybee \u0026ndash; empty cage weight) / (number survived the previous day - number died that day/2)] (Liu \u003cem\u003eet al\u003c/em\u003e., 2020) (Liu et al. 2020).\u003c/p\u003e\n\u003cp\u003eAnother parallel experiment for multi-omics analysis was conducted as detailed above but with 36 cages divided into 4 groups. Honeybee mortality and sucrose consumption were also calculated for this parallel experiment. \u003c/p\u003e\n\u003cp\u003eThe midguts of 10 honeybees in one cage were mixed as one biological sample for multi-omics analysis. Samples were collected from the CK group on days 8, 15, and 22 and on days 15 and 22 from the T1, T2, and T3 treatment groups. \u003c/p\u003e\n\u003ch2\u003e2.2 Analysis of gut microbiota\u003c/h2\u003e\n\u003cp\u003eThe complete gut of honeybee workers was pulled using sterilized tweezers, followed by midgut removal for gut microbiota detection. Ten midguts from one cage in each treatment group were pooled together as a biological sample. There were three replicates for each group. All the midgut samples were stored in liquid nitrogen for at least 5 min to inactivate the RNA degrading enzyme and then moved to a -80℃ refrigerator for short-term preservation before the subsequent step. Genomic DNA was extracted and quantified using TIANamp Soil DNA Kits (TIANGEN Biotech) following the manufacturer\u0026rsquo;s instructions. The target V4 regions of the bacterial 16S rRNA gene were subsequently amplified (Forward primer: 50-CCTACGGNGCWGCAG-30; Reverse primer: 50- GACTACHVGGTACTATCC-30) using a Phusion\u0026reg; High-Fidelity PCR Master Mix (New England Biolabs) according to the manufacturer\u0026rsquo;s instructions. Sequencing libraries were generated using the NEBNext\u0026reg; Ultra\u0026trade; IIDNA Library Prep Kit, quantified using Agilent 5400, and then sequenced on a NovaSeq6000 platform. Species annotation was performed using QIIME2 software.\u003c/p\u003e\n\u003cp\u003eSpecies accumulation boxplot for the 16S rRNA data was performed using the vegan package in R software to visualize the richness of microbial community and sample size. \u003c/p\u003e\n\u003cp\u003eNon-metric multidimensional scaling (NMDS) analysis was implemented using the ade4 package and ggplot2 package in R software to reduce data dimension and visualize complex and multidimensional data. \u003c/p\u003e\n\u003cp\u003eAnosim and Adonis\u0026rsquo;s analyses were performed using the vegan package and ggplot2 package within R to analyze the difference between high-dimensional data groups.\u003c/p\u003e\n\u003ch2\u003e2.3 Analysis of DEGs in the transcriptome\u003c/h2\u003e\n\u003cp\u003eThe first strand of cDNA was synthesized in an M-MuLV reverse transcriptase system using the fragment of mRNA as a template and random oligonucleotides as primers. The RNA was subsequently degraded using RNaseH, and the second strand of cDNA was synthesized from dNTPs in a DNA polymerase I system. The purified double-stranded cDNA was subjected to terminal repair and tail addition and was then sequenced. cDNA of about 250-300 bp was screened using AMPure XP beads, followed by PCR amplification. The PCR product was purified using AMPure XP beads, and the library was subsequently prepared using the NEBNext\u003csup\u003e\u0026reg;\u003c/sup\u003e Ultra \u0026trade; RNA library prep kit for Illumina\u003csup\u003e\u0026reg;\u003c/sup\u003e following the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\n\u003cp\u003eThe gut tissues of \u003cem\u003eA. c. cerana\u003c/em\u003e workers subjected to matrine stress and those of the corresponding untreated workers collected on day 15 and day 22 were prepared using NEBNext\u0026reg; Ultra\u0026trade; Directional RNA Library Prep Kit and sequenced on an Illumina NovaSeq 6000 platform. The sequenced fragments were processed into sequence data (reads) using CASAVA base recognition from the image data measured using the high-throughput sequencer. The raw data containing adaptors, unknown nucleotides (N), and reads with more than 50% of low quality (\u003cem\u003eq\u003c/em\u003e value \u0026le;20) were filtrated using fastp 0.23.1 software to obtain high-quality clean reads. The original data of the sequenced transcriptome has been uploaded to the SRA database (https://www.ncbi.nlm.nih.gov/sra) of NCBI. Its serial number is PRJNA1209336.\u003c/p\u003e\n\u003cp\u003eThe DEGs in CK15 \u003cem\u003evs.\u003c/em\u003e T15 and CK22 \u003cem\u003evs\u003c/em\u003e T22 comparison groups were screened using the edgeR software following the standard of \u003cem\u003ep\u003c/em\u003e-value \u0026le; 0.05 (Robinson et al, 2010). The volcano plot of up- and downregulated genes in these comparison groups and expression analysis were generated using the Novogene platform (https://magic.novogene.com/). GO (Gene Ontology) categorization of DEGs was carried out using WEGO software. The Blastall tool was employed to conduct pathway analysis by comparing isoforms against the KEGG (Kyoto Encyclopedia of Genes and Genomes) database (https://www.kegg.jp/). The chart was subsequently drawn using relevant tools in the Novogene platform (https://magic.novogene.com/) and the OmicShare platform (https://www.omicshare.com/).\u003c/p\u003e\n\u003cp\u003eCytochrome P450, Glutathione S-transferase, UDP-glucuronosyltransferase, acetylcholinesterase, and ABC transporter were selected for further investigation based on previous studies on the immune response of honeybees under pesticide stress. Expression clustering analysis of the aforementioned DEGs was performed using the Novogene platform. \u003c/p\u003e\n\u003cp\u003eEight DEGs (\u003cem\u003eLOC107993345, LOC107994703, LOC107998471, LOC107999176, LOC108000407, LOC108001650, LOC108001900, LOC108002960, LOC108002963, LOC108002964, \u003c/em\u003eand\u003cem\u003e LOC108002965, LOC108003566\u003c/em\u003e) were selected for real-time quantitative PCR (qPCR) assays. Specific primers were designed using Primer 5 according to the corresponding nucleic acid sequence (Table S1). Total RNA of honeybee midguts under 40 mg/L matrine treatment in another parallel experiment was isolated using RNAiso Plus RT01 Kits (Takara Biomedical Technology). cDNA was then synthesized using reverse transcriptase kits (HiScript Q RT SuperMix for qPCR (+gDNA wiper) R123-01, Vazyme Biotechnology) according to the protocols. The qPCR reaction was conducted on QuantStudio\u0026trade; 5 Real-Time PCR System (Applied Biosystems) using the ChamQ SYBR qPCR Master Mix (Vazyme Biotechnology). The PCR cycling conditions were initial denaturation for 30 s at 95℃, followed by 40 cycles of denaturation, and annealing and extension at 95℃ for 10 s and 60℃ for 30 s. The real-time gene expression was calculated using the 2\u003csup\u003e\u0026minus;\u0026Delta;\u0026Delta;Ct\u003c/sup\u003e method. The transcription of the b-actin housekeeping gene was used as the internal reference. The experiment was performed in triplicate, with each replicate having three independent biological samples.\u003c/p\u003e\n\u003ch2\u003e2.4 Untargeted metabolomic analysis of gut in honeybees\u003c/h2\u003e\n\u003cp\u003eThe honeybee gut samples were homogenized with prechilled 80% methanol by vortexing and then centrifuged for 20 min at 15,000 g and 4\u0026deg;C. The supernatants were subsequently injected into the sample vial for UHPLC-MS/MS analysis using a Vanquish UHPLC system (Thermo Fisher, Germany) coupled with an Orbitrap Q Exactive\u003csup\u003eTM\u003c/sup\u003e HF mass spectrometer (Thermo Fisher, Germany). Samples were isolated using a Hypesil Gold column (100 \u0026times; 2.1 mm, i.d., 1.9 \u0026mu;m) at a flow rate of 0.2 mL/min. The eluents for the positive polarity mode were eluent A (0.1% formic acid in Water) and eluent B (methanol). The eluents for the negative polarity mode were eluent A (5 mM ammonium acetate, pH = 9.0) and eluent B (methanol). The procedure of eluent gradient was established as follows: 0 \u0026ndash; 1.5 min, 98% A; 1.5 \u0026ndash; 3 min, 98% \u0026ndash; 15% A; 3 \u0026ndash; 10 min, 15% \u0026ndash; 0% A; 10 \u0026ndash; 10.1 min, 0% \u0026ndash; 98% A; 11min, 98% A; 12 min, 98% A. The mass spectrometer was set to the positive/negative polarity mode, spray voltage of 3.5 kV, capillary temperature of 320\u0026deg;C, sheath gas flow rate of 35 psi, S-lens RF level of 60, aux gas flow rate of 10 L/min, and aux gas heater temperature of 350\u0026deg;C.\u003c/p\u003e\n\u003cp\u003eThe raw data from UHPLC-MS/MS were processed using the Compound Discoverer 3.1 (CD3.1, Thermo Fisher) to perform peak alignment, peak picking, and quantitation for each metabolite. The peaks were matched with the mzCloud (https://www.mzcloud.org/), mzVault, and MassList databases to obtain the accurate qualitative and relative quantitative results of the metabolites. The metabolites were subsequently annotated using the KEGG database (https://www.genome.jp/kegg/pathway.html), HMDB database (https://hmdb.ca/ metabolites), and LIPIDMaps database (http://www.lipidmaps.org/). The statistical significance of metabolites was calculated using a t-test. Metabolites with VIP \u0026gt; 1, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, and fold change \u0026gt; 1.5 were considered to be differentially expressed metabolites. The metabolic pathway enrichment of differentially expressed metabolites was subsequently analyzed. Metabolic pathways with \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 were considered to be significantly enriched pathways. Statistical analyses were performed using software R (version 3.4.3), Python (version 2.7.6), and CentOS (CentOS version 6.6).\u003c/p\u003e\n\u003ch2\u003e2.5 Correlation analysis between gut microbiota, transcriptomics, and metabolomics\u003c/h2\u003e\n\u003cp\u003eThe top 20 differentially expressed metabolites and top 10 differential bacterial genera were selected based on the \u003cem\u003ep\u003c/em\u003e-value to perform correlation analysis. The differentially expressed genes and metabolites in different comparison groups were also selected based on the transcriptomics and metabolomics results to perform correlation analysis and KEGG enrichment analysis shared by differential genes and metabolites.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e3.1 External effect of matrine on honeybees\u003c/h2\u003e\n\u003cp\u003eThe survival rate of honeybees exhibited a significant decrease, especially on days 16 and 17 after being fed on matrine for 14 consecutive days (Fig. 1A.). Notably, T2 treatment had the greatest impact on the survival rate of honeybees. Honeybees had no significant changes in sucrose consumption or body weight during the experimental period (Fig. 1B \u0026amp; C.).\u003c/p\u003e\n\u003ch2\u003e3.2 Microbiota changes under matrine stress\u003c/h2\u003e\n\u003cp\u003eWe characterized the composition of the gut microbiota at the genus level through metagenomic sequencing. 16S rRNA pyrosequencing of 27 samples based on V3-V4 region yielded \u003cstrong\u003e2,212,346\u003c/strong\u003e high-quality sequences.\u003c/p\u003e\n\u003cp\u003eThe relative microbial abundances were utilized to characterize honeybee gut microbial communities. \u003cem\u003eGilliamella\u003c/em\u003e, \u003cem\u003eSnodgrassella\u003c/em\u003e, and \u003cem\u003ePseudomonas\u003c/em\u003e were the most abundant genera in both the control and treatment groups at the genus level (Fig. 2A \u0026amp; B). Matrine exposure had a little effect on the size of the honeybee gut microbiome on day 15. However, the relative abundances of the core genus, \u003cem\u003eGilliamella\u003c/em\u003e, were significantly lower in the T3_D15 group (Fig. 2A, 2C p=0.031), but not on day 22.\u003c/p\u003e\n\u003cp\u003eMatrine exposure had a little effect on the size of the honeybee core gut microbiome on day 22 (Fig. 2B, 2D). The limited impact of matrine treatment on the microbiota composition on day 22 post-treatment was unexplained but indicative of other effects of matrine on honeybees. Three core gut taxa (\u003cem\u003eGilliamella\u003c/em\u003e, \u003cem\u003eSnodgrassella,\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e) were identified in both the control and treatment groups (Fig. 2), indicating that matrine does not eliminate colonization by these core taxa. Notably, \u003cem\u003eDorea\u003c/em\u003e and \u003cem\u003eLachnoclostridium \u003c/em\u003eexhibited significant differences on T2 (p=0.018, p=0.012) and T3 (p=0.039, p=0.016) groups compared to the control on day 22.\u003c/p\u003e\n\u003cp\u003eThe principal coordinate analysis of NMDS (Non-Metric Multi-Dimensional Scaling) revealed that the relative abundance of gut community compositions of matrine-treated honeybees did not differed from those of the controls (Fig. 2E \u0026amp; F). Beta diversity, which compares the composition of two different microbial communities, can also be visualized using non-metric multidimensional scaling (NMDS) analysis. Of note, NMDS analysis results were similar to those of Bray-Curtis distance analysis (Fig. 2E \u0026amp; F). The microbial community composition of the control group (CK_D15) and treatment groups (T1_D15, T2_D15, T3_D15) did not differ significantly from each other on day 15 (ANOSIM, p = 0.47, p=0.9, p=0.2). Similarly, there were no significant differences in microbial community composition between the control (CK_D22) and treatment groups (T1_D22, T2_D22, T3_D22) on day 22 (ANOSIM, p = 0.7, p=0.2, p=0.2).\u003c/p\u003e\n\u003ch2\u003e3.3 Transcription changes under matrine stress\u003c/h2\u003e\n\u003cp\u003eQuality control of the sequence data yielded 24 data sets containing between 40,710,464 and 50,635,176 clean reads with a Q20 of 97.33%~97.65%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.1 Differential gene expression profile of \u003cem\u003eApis cerana\u003c/em\u003e workers under matrine stress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 196, 829, 295, 110, 145, and 189 DEGs were identified in CK_D15 vs. T1_D15, CK_D15 vs. T2_D15, CK_D15 vs. T3_D15, CK_D22 vs. T1_D22, CK_D22 vs. T2_D22, and CK_D22 vs. T3_D22 comparison groups, respectively. The numbers of up-regulated genes in the comparison groups were 48, 117, 55, 33, 36, and 49, while those of down-regulated genes were 148, 712, 240, 77, 109, and 140, respectively. Venn analysis revealed 8 and 54 shared up-regulated and down-regulated genes, respectively, in the three D15 comparison groups (Fig. 3A \u0026amp; B). In contrast, 2 and 4 shared up-regulated and down-regulated genes, respectively, were identified in the three D22 comparison groups (Fig. 3C \u0026amp; D). Notably, the number of DEGs was most abundant in the T2 treatment (10 mg/L matrine), especially on day 15.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.2 Functional and pathway annotation of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDEGs in CK_D15 vs. T1_D15, CK_D15 vs. T2_D15, CK_D15 vs. T3_D15, CK_D22 vs. T1_D22, CK_D22 vs. T2_D22, and CK_D22 vs. T3_D22 comparison groups were engaged in 225, 455, 259, 148, 304, and 275 GO functional terms (Fig. 4A - F). The entries of catalytic activity, acting on a protein and transport, were significantly enriched in the three treatment groups (T1, T2, T3) on day 15 (D15; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05). Of note, the entries of phosphate-containing compound metabolic process and phosphate metabolic process were significantly enriched only in the two high-concentration treatment groups (T2, T3). Numerous genes were significantly enriched in the oxidation activity and oxidation-reduction process items in T1 and T2 groups on day 22 (D22). In contrast, T3 had significant enrichment of numerous genes associated with pyrophosphatase activity and hydrolase activity, acting on acid anhydrides, in phosphorus-containing process items on day 22 (D22).\u003c/p\u003e\n\u003cp\u003eDEGs in CK_D15 vs. T1_D15, CK_D15 vs. T2_D15, CK_D15 vs. T3_D15, CK_D22 vs. T1_D22, CK_D22 vs. T2_D22, and CK_D22 vs. T3_D22 comparison group were associated with 47, 64, 39, 22, 38, and 35 KEGG pathways, respectively. These pathways belonged to major categories, including metabolism, organismal systems, environmental information processing, cellular processes, and genetic information processing (Fig. 5A - F). Wnt, FoxO, mTOR, and Toll and Imd signaling pathways and Oxidative phosphorylation were the five pathways with the highest number of enriched genes. Noteworthy, the Oxidative phosphorylation pathway only showed significant gene enrichment in the high-concentration treatment group (\u0026gt;10mg/ml) at both D15 and D22 (Fig. 5A - F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.3 Analysis of detoxification factor-associated DEGs in \u003cem\u003eApis cerana \u003c/em\u003eworkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther analysis conducted to explore detoxification factor-associated DEGs in the D15 and D22 comparison groups yielded 95 DEGs. Among them, 13 were extremely significantly different between groups. They included eight cytochrome P450 protein-coding genes, one Glutathione S-transferase protein-coding gene, two UDP-glucuronosyltransferase protein-coding genes, and two acetylcholinesterase protein-coding genes (Fig. 6). A majority of detoxification factor encoding genes were induced by matrine stress, whereas a minority were suppressed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.4 Real-time qPCR analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLOC107993345, LOC107994703, LOC107998471, LOC107999176, LOC108000407, LOC108001650, LOC108001900, LOC108002960, LOC108002963, LOC108002964, LOC108002965, \u003c/em\u003eand \u003cem\u003eLOC108003566\u003c/em\u003e genes involved in Cytochrome P450, Glutathione S-transferase, Acetylcholinesterase, decomposition of spermine and spermidine, and degradation of glutathione were selected for qPCR validation of the transcriptome sequence data. The qPCR results were generally consistent with the sequence data (Fig.7.).\u003c/p\u003e\n\u003ch2\u003e3.4 Metabolite changes under matrine stress\u003c/h2\u003e\n\u003cp\u003eThe differentially expressed metabolites in T1_D15 vs. CK_D15, T2_D15 vs. CK_D15, and T3_D15 vs. CK_D15 were 52 (23 up-regulated, 29 down-regulated), 146 (49 up-regulated, 97 down-regulated), and 242 (86 up-regulated, 156 down-regulated), respectively. All PLS-DA analyses indicated a visible difference between T1_D15, T2_D15, T3_D15, and CK_D15 (Fig. 8.). The differentially expressed metabolites in T1_D22 vs. CK_D22, T2_D22 vs. CK_D22, and T3_D22 vs. CK_D22 were 83 (59 up-regulated, 24 down-regulated), 116 (60 up-regulated, 56 down-regulated), and 102 (40 up-regulated, 62 down-regulated), respectively. Similarly, all PLS-DA analyses indicated a visible difference between T1_D22, T2_D22, T3_D22, and CK_D22 (Fig. 9.).\u003c/p\u003e\n\u003cp\u003eThe differentially expressed metabolites between the treatment groups and the control group were mainly enriched in ABC transporters, cysteine and methionine metabolism, purine metabolism, tyrosine metabolism, drug metabolism - cytochrome P450, metabolism of xenobiotics by cytochrome P450, and glutathione metabolism. Fig. 10 and 11 and Table 1 summarize the differentially expressed metabolites and corresponding pathways common in different comparison groups. Notably, glutathione and spermidine were the most common metabolites, while glutathione metabolism, ABC transporters, and cysteine and methionine metabolism were the most common pathways.\u003c/p\u003e\n\u003ch2\u003e3.5 Integration of metabolomic and microbial analysis\u003c/h2\u003e\n\u003cp\u003eThe significantly related microbiota and metabolites in the same comparison group (T vs. CK) were first screened in the correlation analysis. The common microbiota and metabolites in multiple comparison groups (\u0026ge;2) were then screened. These screening steps revealed 10 pairs of associated microbial and metabolite combinations, which exhibited significant correlation in the two comparison groups (T2_D22 vs. CK_D22 and T3_D22 vs. CK_D22). Notably, all were in the cationic mode.\u003c/p\u003e\n\u003cp\u003eThe main metabolites involved were 2-[(3S)-1-(2-Chlorobenzyl)-3-pyrrolidinyl]-5-methyl-1,3,4-oxadiazole, gamma-Glutamylleucine, Argininosuccinic acid, 1,3-dimethyl-6-(trifluoromethyl)-1H-pyrazolo[3,4-b]pyridin-4-ol, 2-[5-(2-hydroxypropyl)oxolan-2-yl]propanoic acid, 5-(2,5-dihydroxyhexyl)oxolan-2-one, 6-Pentyl-2H-pyran-2-one, N-(2-morpholinophenyl)-2-furamide, and PC (18:1/20:5). The gut microbiota involved were \u003cem\u003eDorea\u003c/em\u003e, and \u003cem\u003eLachnoclostridium \u003c/em\u003e(Table S2).\u003c/p\u003e\n\u003ch2\u003e3.6 Integration of metabolomic and transcriptomic analysis\u003c/h2\u003e\n\u003cp\u003eKEGG Pathways were used as terms to capture the pathways that were co-enriched in the comparison pairs (T vs. CK) in metabolomics and transcriptomics. The common co-enriched terms in multiple comparison groups (\u0026ge;2) were then screened again. Correlation analysis between metabolomics and transcriptomics were also conducted.\u003c/p\u003e\n\u003cp\u003eA total of 7 co-enriched KEGG pathways were selected. These pathways included Arginine and proline metabolism, beta-Alanine metabolism, Cysteine and methionine metabolism, Phototransduction \u0026ndash; fly, Purine metabolism, Starch and sucrose metabolism, and Valine, leucine and isoleucine degradation (Table S3). Notably, spermidine was arrestive in the co-enriched pair of metabolomics and transcriptomics in the two comparison groups. Spermidine and \u003cem\u003eLOC107998471 \u003c/em\u003e(\u003cem\u003eAldh\u003c/em\u003e) were co-enriched in arginine and proline metabolism, and the beta-alanine metabolism pathway. Arachidonic acid and five transcription genes [\u003cem\u003eLOC108004131 (myosin-IIIb), LOC107998381 (inaE), LOC107995353 (trpl), LOC108001544 (Trpgamma),\u003c/em\u003e and\u003cem\u003e LOC108003363 (CaMKII)\u003c/em\u003e] were co-enriched in the Phototransduction \u0026ndash; fly pathway. Noteworthy, \u003cem\u003emyosin-IIIb, inaE, and CaMKII \u003c/em\u003ewere correlated with arachidonic acid. Moreover, \u003cem\u003einaE \u003c/em\u003eand\u003cem\u003e CaMKII \u003c/em\u003ewere correlated in the two treatment groups (T2_D15 vs. CK_D15, T3_D15 vs. CK_D15) (Table S3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, the lasting effects of three matrine concentrations on the biological characteristics, including the survival, food consumption, and body weight of honeybees, were examined. The effects of the three matrine concentrations on the gut microbial composition were further examined by studying the microbiome, metabolome, and transcriptome in two 7-day interval stages. Continuous intake of matrine caused rapid death of honeybees on day 9 but had no significant effect on food consumption and body weight of the honeybees. Matrine intake caused changes in the abundance of\u0026nbsp;\u003cem\u003eGilliamella\u003c/em\u003e, \u003cem\u003eDorea\u003c/em\u003e, and \u003cem\u003eLachnoclostridium\u003c/em\u003e in gut microbiota, resulting in differential expression of argininosuccinic acid, spermidine, arachidonic acid, and \u003cem\u003eLOC107998471\u0026nbsp;\u003c/em\u003e(\u003cem\u003eAldh\u003c/em\u003e). This study comprehensively evaluated the impact of matrine on honeybees and provides a reference for its rational use and colony avoidance methods. Multi-omics analysis revealed the essential mechanism of matrine\u0026apos;s impact on honeybees. Notably, preventing the accumulation of vital harmful substances and supplementing beneficial substances or microbiota are potential new methods for alleviating insecticide poisoning.\u003c/p\u003e\n\u003ch2\u003e4.1 Mortality of honeybees under matrine stress\u003c/h2\u003e\n\u003cp\u003eMatrine intake did not cause significant changes in food consumption and body weight of honeybees. This finding suggested that matrine intake did not affect the appetite of honeybees, and they did not have obvious avoidance of matrine because they still ingested sucrose mixed with matrine. Similar studies involving spinosad, a bioinsecticide based on chemical compounds found in the bacterial species \u003cem\u003eSaccharopolyspora spinosa\u003c/em\u003e, report that low spinosad doses (0.85 \u0026mu; g/ml) do not affect the body weight and food consumption but reduce the survival rate of bees (Ara\u0026uacute;jo et al. 2023; Botina et al. 2024; Marques et al. 2020). This phenomenon was attributed to matrine being a natural plant component that is not avoided by honeybees during the co-evolution of honeybees and plants. However, matrine\u0026apos;s damage to honeybees still exists, which manifested as a cliff-like decline in the survival rate on the 9th day after continuous intake of matrine. Though matrine\u0026apos;s effect on honeybees is not perceptible during the early stage, it becomes obvious when ingested to a certain degree or time. This aspect was proved by the mass death of workers and the rapid decline of honeybee colonies. This finding can guide practical beekeeping. Specifically, honeybee keepers must leave within 9 days or face serious colony losses if they release honeybees in orchards of pear or citrus trees, and growers start spraying insecticides containing 10mg/L (T2) ~ 40mg/L (T3) matrine concentration.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e4.2 Microbiota changes associated with inflammation and glucose metabolism\u003c/h2\u003e\n\u003cp\u003eMatrine has effects on some honeybee gut microbiomes, such as\u0026nbsp;\u003cem\u003eGilliamella\u003c/em\u003e,\u0026nbsp;\u003cem\u003eDorea\u0026nbsp;\u003c/em\u003eand\u0026nbsp;\u003cem\u003eLachnoclostridium\u003c/em\u003e. Matrine intake caused inflammation and sugar metabolism disorder in honeybee intestines, resulting in an immune stress response. Significant changes in the core microbiome were only noted in the T3 group on day 15, characterized by a high\u0026nbsp;relative abundance of\u0026nbsp;\u003cem\u003eGilliamella\u003c/em\u003e. However, there were no significant changes in the core microbiome on day 22. \u003cem\u003eGilliamella\u003c/em\u003e participates in toxic sugars metabolism\u0026nbsp;(Zheng et al. 2016)\u0026nbsp;and antimicrobial peptide synthesis\u0026nbsp;(Lang et al. 2023). Herein, the honeybee gut microbiome exhibited some recovery between day 15 and day 22.\u0026nbsp;Traditional pesticides, such as\u0026nbsp;thiacloprid and nitenpyram,\u0026nbsp;affect the core gut microbiota of honeybees, including \u003cem\u003eGilliamella, Bifidobacterium, Lactobacillus, Bombella, Frischella\u003c/em\u003e, and \u003cem\u003eBartonella\u003c/em\u003e (Liu et al. 2020; Zhu et al. 2020). The relatively high toxic bioinsecticide abamectin (abamectin emulsifiable concentrate LC\u003csub\u003e50\u003c/sub\u003e=0.002mg/L) also\u0026nbsp;affects the core gut microbiota of bees including \u003cem\u003eBifidobacterium, Lactobacillus,\u0026nbsp;\u003c/em\u003eand \u003cem\u003eGilliamella\u003c/em\u003e (Guo et al. 2024). However, the impact of matrine on honeybee core microbiota was relatively small compared to other insecticides, such as\u0026nbsp;abamectin.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInterestingly, there were changes in the minority bacteria (\u003cem\u003eDorea\u0026nbsp;\u003c/em\u003eand \u003cem\u003eLachnoclostridium\u003c/em\u003e).\u0026nbsp;\u003cem\u003eDorea\u0026nbsp;\u003c/em\u003eand\u0026nbsp;\u003cem\u003eLachnoclostridium\u003c/em\u003e are associated with inflammation and glucose metabolism (obesity).\u0026nbsp;\u003cem\u003eDorea\u003c/em\u003e is believed to be closely associated with inflammation and glucose metabolism\u0026nbsp;(Allin et al. 2018; Brahe et al. 2015; Del Chierico et al. 2017; Machiels et al. 2014; Murtaza et al. 2019; Naderpoor et al. 2019; Pinna et al. 2021; Zhong et al. 2020). It is a beneficial gut bacterium that has active short-chain fatty acids producers, which can reduce inflammatory reactions\u0026nbsp;(Machiels et al. 2014; Zhong et al. 2020). \u003cem\u003eLachnoclostridium\u003c/em\u003e is associated with inflammation and obesity\u0026nbsp;(Andoh et al. 2016; Kaplan et al. 2019; Kong et al. 2019; Nogal et al. 2021; Wang et al. 2020; Xu P. et al. 2017; Zhao et al. 2017; Zhou et al. 2020). Herein, we hypothesized that matrine stress maybe alters sugar metabolism in honeybees despite it not being evident in food consumption, body weight, and other indicators.\u003c/p\u003e\n\u003ch2\u003e4.3 Matrine stress induces crucial metabolism\u003c/h2\u003e\n\u003cp\u003eCorrelation and co-enrichment analyses revealed the presence of some important metabolites, including argininosuccinic acid, spermidine, and arachidonic acid. Argininosuccinic acid was correlated with two bacteria in multiple treatment groups. Spermidine was co-enriched in two KEGG pathways. Arachidonic acid was co-enriched with five transcription genes in the Phototransduction \u0026ndash; fly.\u003c/p\u003e\n\u003cp\u003eArgininosuccinic acid (AA) is associated with many diseases (Li et al. 2018; Qi et al. 2022). Herein, there was significant upregulation of AA In the treatment groups (T2_22, T3_15, T3_22), indicating that the treatment had adverse effects on honeybees. Notably, argininosuccinic acid induce lipid and protein oxidation, thereby reducing glutathione (GSH) levels (Seminotti et al. 2020). GSH is an important non-enzymatic antioxidant (Liu et al. 2014), which can clean reactive oxygen species and influence cell apoptosis in an organism\u0026nbsp;(Messina Lawrence 1989). Reduced glutathione (GSH) and oxidized glutathione (GSSG) are strictly regulated to maintain cellular homeostasis\u0026nbsp;(Liu et al. 2014; Messina Lawrence 1989). Intriguingly, there was an increase in GSH in the treatment groups with significantly elevated AA (T2_22, T3_15, and T3_22). This phenomenon is still unclear. AA exhibited a significant correlation with \u003cem\u003eDorea\u003c/em\u003e and \u003cem\u003eLachnoclostridium\u003c/em\u003e in two treatment groups. \u003cem\u003eDorea\u003c/em\u003e and \u003cem\u003eLachnoclostridium\u003c/em\u003e are associated with inflammation and glucose metabolism. However, their relationship with AA needs validation. Noteworthy, AA and GSH were upregulated in T3_22 treatment. Subsequent correlation analysis revealed a significant association between \u003cem\u003eDorea\u003c/em\u003e and GSH.\u003c/p\u003e\n\u003cp\u003ePolyamines, such as spermidine, are naturally rich in honeybee products, such as pollen and honey (Vuka\u0026scaron;inović et al. 2024). Numerous studies suggest that spermidine contribute to cellular immunity and biological resistance, such as anti-inflammation, antioxidant, and anti-aging (Eisenberg et al. 2009; Hask\u0026oacute; et al. 2000; Madeo et al. 2018; Okumura et al. 2016). Spermidine can significantly improve myocardial damage (Hu et al. 2020; Wang et al. 2021; Wei Li et al. 2016; Wei Wang et al. 2016; Xu et al. 2018; Xu Y. et al. 2017) and cognitive disorders caused by abnormal glucose metabolism or radiation (Stacchiotti Corsetti 2020). Moreover, spermidine participate in biological processes, such as cell proliferation, differentiation, injury repair, and apoptosis (Gevrekci 2017; Guo et al. 2002; Seiler Raul 2007; Wei Wang et al. 2016), especially in the development process of animal intestines (Lux et al. 1980; Pegg 2014). Spermidine and spermine, which can convert to spermidine, have thus been used as a livestock feed additive (Gerner E. 2007; Kan et al. 2017; Wu et al. 2000). It is difficult to judge the changes of spermidine using simple stress responses or beneficial detoxifying adaptation based on the co-enriched KEGG pathways. However, functional validation of spermidine under insecticide stress is essential because of its positive role in enhancing immunity, stress resistance, and repair of intestinal cells.\u003c/p\u003e\n\u003cp\u003eArachidonic acid is an important nutrient, whose appropriate intake can maintain normal growth and development in animals. Arachidonic acid intake can maintain the pupation rate and eclosion rate in honeybees (Yu et al. 2019). Notably, arachidonic acid enhances the immune ability of honeybees. Supplementation with arachidonic acid can increase the survival rate of honeybees under \u003cem\u003eEscherichia coli\u003c/em\u003e stress and enhance the activity of immune enzymes, including phenoxidase, antitrypsin, and lysozyme (Yu et al. 2021). Despite the positive effects of arachidonic acid on the growth, development, and immunity of organisms, there are still no reports on its direct alleviation of pesticide poisoning. It is thus important to verify the role of arachidonic acid in honeybee under pesticides because of its positive effects in honeybee immunity.\u003c/p\u003e\n\u003ch2\u003e4.4 The potential mechanism of differentially expressed genes to matrine stress\u003c/h2\u003e\n\u003cp\u003eIn this study, metabolites (spermidine) and transcription genes (\u003cem\u003eAldh\u003c/em\u003e) were arrestive in the co-enriched pair of metabolomics and transcriptomics. Spermidine, and\u0026nbsp;\u003cem\u003eAldh\u003c/em\u003e were co-enriched in arginine and proline metabolism and beta-alanine metabolism in the transcriptomic and metabolomic association analysis. Spermidine is an important metabolite in both pathways, while\u0026nbsp;\u003cem\u003eAldh\u003c/em\u003e is an enzyme responsible for the downstream decomposition of spermidine (Ohashi et al. 2017; Southan et al. 1994). The down-regulation of\u0026nbsp;\u003cem\u003eAldh\u003c/em\u003e potentially suppress spermidine degradation under matrine stress in honeybees.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003emyosin-IIIb, inaE, trpl, Trpgamma,\u0026nbsp;\u003c/em\u003eand \u003cem\u003eCaMKII\u003c/em\u003e genes were co enriched with arachidonic acid in the Phototransduction - fly pathway. Of note, \u003cem\u003emyosin-IIIb\u003c/em\u003e, \u003cem\u003einaE\u003c/em\u003e, and \u003cem\u003eCaMKII\u003c/em\u003e were correlated with arachidonic acid, suggesting potential involvement in phototransduction. However, it is challenging to explain the enrichment of these DEGs in phototransduction under pesticide stress, considering that this pathway is annotated in fly. The assumption made is that the pathways of these DEGs in other species are inconsistent with those in fly. However, it is necessary to enrich the annotation of this pathway in other species to understand this disparity.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study comprehensively evaluated the toxicological effects of matrine on honeybees, which are important ecological insects. Continuous intake of matrine affect gut microbiota, transcription, and metabolism, leading to rapid death of honeybees. Multi-omics correlation and co-enrichment analyses revealed that gut microbiota (\u003cem\u003eGilliamella\u003c/em\u003e, \u003cem\u003eDorea\u003c/em\u003e, and \u003cem\u003eLachnoclostridium\u003c/em\u003e), metabolites (argininosuccinic acid, spermidine, and arachidonic acid), and genes (\u003cem\u003eAldh\u003c/em\u003e) play a crucial role in honeybees under matrine stress.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.Y.: Conceptualization, Methodology, Investigation, Data curation, Visualization, Supervision, Writing - original draft. Z.Z.: Methodology, Data curation, Visualization, Writing - original draft. Y.F.: Methodology, Data curation, Visualization, Writing - original draft. W.Z.: Investigation. D.Y.: Methodology, Investigation. X.H.: Investigation. Y.L.: Investigation. W.W.: Investigation. H.Z.: Investigation. X.W.: Conceptualization, Methodology, Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information\u0026nbsp;\u003c/strong\u003eThe online version contains supplementary material available at .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eThis work was supported by Guizhou Provincial Science and Technology Project (No. ZK[2024]079, No.\u0026nbsp;ZK[2023]181),\u0026nbsp;National Key Research and Development Program (No. 2022YFD1601703, No. 2021YFD1100309), China Agriculture Research System of MOF and MARA (No. CARS-44-SYZ 16) and Guizhou Academy of Agricultural Sciences (No. 202313).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eGuizhou Provincial Science and Technology Project (No. ZK[2024]079, No.\u0026nbsp;ZK[2023]181),\u0026nbsp;National Key Research and Development Program (No. 2022YFD1601703, No. 2021YFD1100309), China Agriculture Research System of MOF and MARA (No. CARS-44-SYZ 16) and Guizhou Academy of Agricultural Sciences (No. 202313).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eThe original data of the sequenced transcriptome has been uploaded to the SRA database (https://www.ncbi.nlm.nih.gov/sra) of NCBI (PRJNA1209336).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlaux C, Brunet JL, Dussaubat C, Mondet F, Tchamitchan S, Cousin M, Brillard J, Baldy A, Belzunces LP, Conte YL (2010) Interactions between Nosema microspores and a neonicotinoid weaken honeybees (Apis mellifera). 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KEGG enrichment pathways and differentially expressed metabolites between the treatment groups and the control group.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKEGG pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDifferential metabolites\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT1_D15 vs CK_D15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eABC transporters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMethyl beta-D-galactopyranoside; L-Cystine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT3_D15 vs CK_D15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eABC transporters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eXylitol; Glutathione; Trehalose\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT1_D22 vs CK_D22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eABC transporters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSpermidine; Trehalose\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT2_D22 vs CK_D22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eABC transporters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGlutathione; Spermidine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT3_D22 vs CK_D22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eABC transporters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInositol; Glutathione; Spermidine; L-Serine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT1_D15 vs CK_D15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCysteine and methionine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eL-Cystine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT2_D15 vs CK_D15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCysteine and methionine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHomocysteine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT3_D15 vs CK_D15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCysteine and methionine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGlutathione; Homocysteine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT2_D22 vs CK_D22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCysteine and methionine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGlutathione; O-Phospho-L-serine; S-Adenosylhomocysteine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT3_D22 vs CK_D22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCysteine and methionine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGlutathione; L-Serine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT2_D15 vs CK_D15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDrug metabolism - cytochrome P450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4-Hydroxytamoxifen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT3_D15 vs CK_D15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDrug metabolism - cytochrome P450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eValproic acid; 4-Hydroxytamoxifen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT1_D22 vs CK_D22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDrug metabolism - cytochrome P450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eValproic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT3_D15 vs CK_D15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGlutathione metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSpermine; Glutathione; Oxidized glutathione\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT1_D22 vs CK_D22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGlutathione metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSpermidine; L-Ascorbate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT2_D22 vs CK_D22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGlutathione metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGlutathione; Spermidine; L-Ascorbate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT3_D22 vs CK_D22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGlutathione metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGlutathione; Spermidine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT3_D15 vs CK_D15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMetabolism of xenobiotics by cytochrome P450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2-Naphthol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT1_D22 vs CK_D22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMetabolism of xenobiotics by cytochrome P450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2-Naphthol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT2_D22 vs CK_D22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMetabolism of xenobiotics by cytochrome P450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNNK\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT1_D15 vs CK_D15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePurine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u0026apos;-Deoxyadenosine; Xanthosine; XMP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT1_D22 vs CK_D22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePurine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u0026apos;-Deoxyadenosine; Adenosine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT2_D22 vs CK_D22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePurine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u0026apos;-Deoxyadenosine; Adenosine; Xanthine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT3_D22 vs CK_D22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePurine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u0026apos;-Deoxyguanosine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT1_D15 vs CK_D15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTyrosine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eL-Dopa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT2_D15 vs CK_D15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTyrosine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eL-Dopa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT1_D22 vs CK_D22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTyrosine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLevodopa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT2_D22 vs CK_D22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTyrosine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHydroquinone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Eastern honeybee, Matrine, Bioinsecticide, Honeybee health, Multiomics","lastPublishedDoi":"10.21203/rs.3.rs-7062509/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7062509/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Honeybees are important pollinators that enhance food safety and promote biodiversity. However, honeybees are increasingly threatened by insecticide use. Matrine, a plant-derived insecticide, has been used in plants, such as pear trees, citrus, and cotton whose flowers are a honey source. Despite matrine’s economic significance, its impact on bees is rarely reported. In this study, honeybees (Apis cerana) were exposed to three concentrations (1.2, 10, and 40 mg/L) of matrine. Continuous matrine intake caused rapid death of honeybees on day 9 but had no significant effect on food consumption and body weight of the honeybees. Matrine intake caused changes in the abundance of Gilliamella, Dorea, and Lachnoclostridium in gut microbiota, resulting in the differential expression of argininosuccinic acid, spermidine, arachidonic acid, and LOC107998471 (Aldh). This finding underscored the role of these microbiotas, metabolites, and genes in honeybees under matrine stress. This study provides a comprehensive explanation of matrine toxicity to honeybees and reveals the crucial microbiota, metabolites, and genes. These findings provide important references for honeybee poisoning and the associated detoxification mechanism.","manuscriptTitle":"Biological Observation and Multi-omics Analysis Reveal the Toxicity of the Bioinsecticide Matrine to Honeybees (Apis cerana)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-08 12:32:00","doi":"10.21203/rs.3.rs-7062509/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"be1ce7ac-939a-4b37-acf4-6cd9a5093ad4","owner":[],"postedDate":"July 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-02T17:08:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-08 12:32:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7062509","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7062509","identity":"rs-7062509","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}