DOI: 10.5061/dryad.zgmsbcckt
title: Cadmium exposure induces changes in intestinal microbial structure and metabolic function in long-tailed hamsters (Cricetulus longicaudatus)

description: Many studies have shown that exposure to heavy metals can disrupt the
 diversity and composition of the gut microbiota, resulting in a
 significant decrease in gut microbial richness but the broader ecological
 reality is lacking. In this study, we comprehensively evaluated the
 effects of exposure to 6 mg/L、12 mg/L、24 mg/L and 48 mg/L Cd in drinking
 water for 35 days on the intestinal microbiota of long-tailed hamsters,
 Cricetulus longicaudatus (Rodentia: Cricetidae). The results
 suggest that Cd exposure induces changes in intestinal morphology in
 Cricetulus longicaudatus that may increase intestinal
 permeability and inflammation, but their body weight instead increases.
 These changes were accompanied by significant perturbations of the gut
 microbiota, resulting in significant alterations in microbial
 abundance. Additionally, Cd exposure induced significant changes
 in the metabolic functions of the gut microbiota, with over half of the
 metabolic functions in the Cd-treated group showing a significant decrease
 compared to the control group. These changes included essential metabolic
 pathways such as cysteine, methionine, and lysine biosynthesis and
 degradation, as well as metabolic functions related to carbohydrate and
 lipid metabolism. The pathways related to disease and environmental
 information processing showed significant increases, particularly in the
 repair system and phosphotransferase system. This study can be used as a
 new approach to comprehend the stress response of wild mammals to Cd
 exposure, and to further assess the significant impact of Cd pollution on
 ecosystems by investigating the structural and functional changes in their
 digestive system and the disruption of gut microbiota.
description: # Cadmium exposure induces changes in intestinal microbial structure and
 metabolic function in long-tailed hamsters (Cricetulus longicaudatus)
 [https://doi.org/10.5061/dryad.zgmsbcckt](https://doi.org/10.5061/dryad.zgmsbcckt) In this study, we conducted a 35d Cd exposure test on *C. longicaudatus*. HE staining, 16S rDNA sequencing and metagenomic sequencing were used to investigate organ tissue damage, gut microbial diversity and composition disorders. Based on metagenomics, the study also examined changes in metabolic function and gut microbial function of *C. longicaudatus*. The results of this study are helpful for us to understand the response of wild rodents to Cd pollution and the series of changes that occur in their gut microbiota and physiological ecology. ## Description of the data and file structure The dataset comprises 16S rDNA sequencing and metagenomic sequencing. The uploaded 16S data specifically refers to the 16S rDNA sequencing results. Within the compressed folder, the labels A, B, C, D, and E correspond to the control group, 6 mg, 12 mg, 24 mg, and 48 mg treatment groups, respectively. The suffixes -1, -2, -3, -4, -5, -6, and -7 denote the time points of 0 d, 7 d, 14 d, 21 d, 28 d, and 35 d. The -diversity indices (Chao1 indices) were quantified in terms of OTU richness. To assess sample adequacy, rarefaction curves of the observed numbers of OTUs were constructed, all diversity indices were calculated with Mothur software (version 3.8.31). The OTU rarefaction curve and rank abundance curves were plotted in R (version 3.6.0). To estimate the diversity of the microbial community of the sample, multiple group comparisons were made using ANOVA test. Beta diversity assesses the differences in the microbiome between samples and generally constrains principal component analysis (PCA) dimensionality reduction methods to obtain visual representation. These analyses were visualized using R vegan package (version 2.5-6), and finally the inter-sample distances were presented as scatterplots. Difference comparison is used to identify features with significantly different abundances between groups using STAMP (version 2.1.3 ) and LefSe (version 1.1.0 ). Correlation coefficients and p-values between communities/OTUs were calculated using SparCC (version 1.1.0), and correlation matrix heatmaps were drawn using R corrplot package (version 0.84). R ggraph package (version 2.0.0)is used to build network graphs. Fastp (version 0.36) was used for evaluating the quality of sequenced data (Bolger et al., 2014). Raw reads were filtered according to several steps: (1) removing adaptor sequence; (2) removing low-quality bases from reads 3′ to 5′ (*Q *&lt; 20), using a sliding window method to remove the base value less than 20 of reads tail (window size is 4 bp); (3) finding overlap of each pair of reads and properly correcting inconsistent bases within the interval; (4) removing reads with reads length less than 35 nt and its pairing reads. The remaining clean data were used for further analysis. Megahit (version 1.2.9) was used to perform multi-sample mixed splicing to obtain preliminary spliced contig sequences. Clean reads were subsequently mapped back to the spliced results using bowite2 (version 2.1.0) to extract unmapped reads and spliced again using SPAdes (version 3.13) to obtain low abundance contigs. MetaWRAP (version1.3.2) was used to perform a series of binning and processes, including bin sorting, bin purification, bin quantification, bin reassembly, and bin identification performed in sequence. After filtering, a draft genome of a single bacteria with high integrity and low contamination was obtained. Prodigal (version 2.60) was used to predict the ORF of the splicing results, select genes with a length greater than or equal to 100 bp, and translate them into amino acid sequences. For the gene prediction results of each sample, the CDHIT (version 2.60) was used for deredundancy to obtain a non-redundant gene set. Salmon (version 1.5.0) was used to construct a specific index of non-redundant gene sets, using a dual-phase algorithm and a method of constructing a bias model to accurately quantify the abundance of genes in each sample, and calculate gene abundance based on gene length information. DIAMOND (version 0.8.20) was used to compare the gene set with Kyoto Encyclopedia of Genes and Genomes (KEGG) and other databases to obtain species annotation information and functional annotation information of genes. Screening conditions were as follows: *E*-value &lt;1e−5, Score &gt;60. Based on gene set abundance information and annotation information, species abundance and functional abundance were obtained, and multi-directional statistical analyses such as species and functional composition analysis, species and functional difference analysis, and sample comparison analysis were performed.Use DIAMOND (version 0.8.20) to compare the gene set with KEGG and other databases to obtain species annotation information and functional annotation information of genes. Screening conditions: E-value&lt;1e-5,Score&gt;60. Based on gene set abundance information and annotation information, species abundance and functional abundance are obtained, and multi-directional statistical analysis such as species and functional composition analysis, species and functional difference analysis, and sample comparison analysis is performed. ## Code/Software The experimental results were expressed as meanstandard deviation (mean SD). SPSS 26.0 software was used to calculate the relative abundance and Alpha diversity of samples among groups by repeated measurement analysis of variance.**P*&lt; 0.05, ***P*&lt; 0.001 was statistically significant.