Liver microbiome diversity and sex selection in male and female rats

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In recent research with healthy rats, we assessed the livers of male and female Sprague Dawley rats for the 16S ribosomal ribonucleic acid (rRNA) gene. The V3–V4 region of the 16S rRNA gene was amplified through polymerase chain reaction and sequenced using an Illumina NovaSeq 6000 platform. Sequences were assigned taxonomically using the Silva database, and the community diversity and relevance were analyzed. We detected 56,926.20 ± 16,991.65 effective tags of the 16S rRNA gene and clustered them into 15,845 kinds of amplicon sequence variants (2157.80 ± 461.52, 1632–2728), of which 10 rats shared only 127 kinds in common. The phylum Nitrospinota was exclusive to male rat livers, and the bacteria phyla Halanaerobiaeota, Latescibacterota, Caldisericota, and Abditibacteriota and the Archaea phylum Thermoplasmatota were exclusive to female rat livers. Female liver bacteria showed significantly higher richness but lower beta diversity than male liver bacteria ( P < 0.05). Meanwhile, the liver microbiome showed distinct bacteria biomarkers and different bacteria correlations according to linear discriminant analysis effect size (LDA 2.0) and Spearman rank correlation analysis. According to redundancy analysis and the beta nearest taxon index null model, the distribution of several bacteria in the liver microbiome differed based on sex, and differences in community structure between the two groups were dominated by homogeneous deterministic processes rather than stochastic processes. Our results suggest that bacteria in the rat liver microbiome have the intrinsic property of sexual dimorphism. liver microbiome sexual dimorphism 16S rRNA gene sequencing female and male rats Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Significance of the study Liver dysfunction and disease are leading causes of death. Gut microbiota in normal and cancerous tissues has considered pathogenic factors besides biochemical and molecular biology mechanisms. The liver is an organ with typical sex dependence (dimorphism). Our study showed different microbiota composition of male and female rat livers and the dimorphic characteristic of liver bacteria is an intrinsic feature. Compared with the nondimorphism of the gut microbiome, our results provide strong support for the idea of the liver microbiome as an intrinsic component of hepatocytes. Furthermore, this study suggested that liver microbiome may take part in the gender-associated physiological and pathological processes in the liver. Introduction The liver is a pivotal metabolic organ in the body with interesting and significant sexual dimorphism in its functions [ 1 ]. Gender differences significantly affect the morphology and structure of hepatocytes and the expression, distribution, and secretion of various enzymes and proteins [ 2 – 10 ], as well as chromatin condensation or decondensation [ 11 ]. Genetic differences reveal different metabolic functions in male and female livers. For example, male livers have a greater ability to metabolize and clear alcohol, while female livers are better at cholesterol metabolism [ 2 ]. Sexual dimorphism of liver microbiota also plays an important role in the development of liver disease [ 12 , 13 ]. Studies have shown that when administered the toxic chemical aromatic amine 2-acetylaminofluorene, male rats are more susceptible to the induction of liver cancer than females [ 14 ]. Clinically, the incidence and mortality of liver cancer have been higher among males than females in all world regions [ 15 ]. Recent research suggests that intracellular bacteria specific to tumor type may play an important role in the development of cancer [ 16 ]. Moreover, several studies in humans or mice have demonstrated the existence of tissue microbiota and circulation microbiota [ 17 – 26 ]. Although its source is still under investigation and is hypothesized to be derived from gut microbiota [ 27 ], tissue microbiota appears to play an important role in physiological and pathological processes. By using high-throughput 16S rRNA gene sequencing and detecting LPS, LTA, or 16S rRNA genes in situ with immunofluorescence, fluorescence in situ hybridization, and western blotting, we recently confirmed the existence of the liver microbiome in hepatocytes of healthy rats. By comparing its composition and diversity with the gut microbiome, we showed that the liver microbiome is possibly an intrinsic component of hepatocytes [ 28 , 29 ]. These findings indicate an important need to explore the relationship between the liver microbiome and liver diseases and to determine whether the phylogenetic diversity of the microbiome is gender-specific. The objective of the current study was to confirm the sexual dimorphism of the liver microbiome. We performed high-throughput 16S rRNA polymerase chain reaction (PCR) amplification and gene sequencing on male and female rat livers as previously reported [ 28 , 29 ] and conducted diversity and relevance analysis. We found that male and female rat livers showed different microbiota diversity and biomarkers. Materials and methods Animals We used 6-week-old Sprague-Dawley male and female rats (n = 5) from the same litter, with a body weight of 116.0–164.0 g, for 16S rRNA gene sequencing. Rats were housed in a specific-pathogen–free (SPF) environment and fed a chow diet and water. After weaning, animals were separated into two cages based on sex and fed the same chow diet for 2 weeks. All animals received humane care, and the study protocols complied with the guidelines for the ethical review of laboratory animal welfare (GB/T 35892—2018) and were approved by the Medical Ethics Committee of Lanzhou University (jcyxy20190302). This study also conformed to the guidelines for Animal Research: Reporting of In Vivo Experiments and the Replacement, Refinement and Reduction of Animals in Research. Sample collection and contamination avoidance Animals were anesthetized with 15% urethane (0.06 ml/10 g body mass) to prevent them from suffering. Livers were then sampled. For 16S rRNA gene sequencing, liver tissues were subjected to DNA extraction, PCR amplification, and sequencing integrated procedures (BMK Co., Beijing, China, accessed on 25 June 2023, BMK221213-BF330-S01-ZX01-0101). Environment contamination was avoided according to the procedure reported previously [ 28 , 29 ]. Because no paraffin was used to block tissues, no empty paraffin controls [ 16 ] were applied. Bacterial DNA extraction and amplicon sequencing We extracted total DNA from frozen liver samples (0.25–0.5 g) using a TGuide S96 Magnetic Soil and Stool DNA Kit (#DP812; Tiangen Corp., Beijing, China, https://www.tiangen.com/ ) per the kit protocol. All procedures were performed with sterile and disposable materials to avoid cross-contamination; in addition, beads and DNA extraction blank controls were used during this process. The concentration of nucleic acid was detected using an enzyme-linked immunosorbent assay (Synergy HTX, Biotek Instruments, Winooski, VT, USA). After amplification, the integrity of the PCR product was detected using 1.8% agar gel (Beijing Bomei Fuxin Technology Co., Ltd.), and the DNA concentration and purity were determined using NanoDrop 2000 (Thermo Scientific, Waltham, MA, USA). We used two-round-tailed PCR with the barcode at the end of the primer for 16S amplification and sequencing as previously reported [ 28 , 29 ]. The bacterial primer was as follows: 338F: 5'-ACTCCTACGGGAGGCAGCA-3'; 806R: 5'-GGACTACHVGGGTWTCTAAT-3' [ 30 ]. We established a negative control and used sterile water instead of DNA for PCR amplification (no-template PCR amplification controls). After quality testing on a Qsep-400 (BiOptic, Inc., New Taipei City, Taiwan, ROC) and preparation of a flow cell chip, we subjected 500 ng PCR products to paired-end sequencing on an Illumina NovaSeq 6000 platform with PE250 strategy (Illumina, Inc., San Diego, CA, USA) at Biomarker Technologies Co, Ltd. (Beijing, China) according to standard protocols. The sequencing length was 350–450 base pairs (bp). Original image data files were transformed into sequenced reads via base calling analysis. The negative control (sequencing run controls) was not sequenced because it was bandless and sequencing would have been meaningless. Amplicon sequence analysis and quality control Paired-end reads were merged according to overlapping relationships using Fast Length Adjustment of Short Reads (FLASH) software v.1.2.11 (Johns Hopkins University Center for Computational Biology, Baltimore, MD, USA; raw tags) [ 31 ]. We discarded tags with more than six mismatches. The minimum overlap length was 10 bp, and the maximum mismatch ratio allowed in the overlap region was 0.2 (default). Briefly, raw tags with an average quality score < 20 in a 50-bp sliding window were filtered using Trimmomatic software v.0.33 (USADELLAB.org) [ 32 ] (clean reads). We then used Cutadapt 1.9.1 software [ 33 ] to identify and remove primer sequences (non-primer reads). The maximum mismatch was 20% and the minimum coverage was 80%. Paired-end reads obtained from previous steps were assembled by USEARCH [ 34 ] (version 10). The minimum overlap length was 10 bp, the minimum similarity within the overlapping region was 90%, and the maximum mismatch ratio allowed in the overlap region was 5 bp (default). All reads were denoised and merged by pair-end sequence splicing using the DADA2 v2021.2.0 algorithm [ 35 ] from quantitative insights into microbial ecology 2 (QIIME2, 2020.6; http://drive5.com/usearch/manual/uchime_algo.html , accessed on 25 June 2023) (denoised reads and merged reads) [ 36 ]. Chimera sequences were then removed using UCHIME [ 37 ] (version 8.1) (non-chimeric reads) (Table S1). The high-quality reads generated from the above steps were used in the following analysis. Taxonomic and diversity analysis of amplicon sequence variants (ASVs) After discarding chimeras, reads with similarity over 97% (default) were clustered using USEARCH (OTU clustering). ASVs were inferred using the DADA2 method in QIIME2 ( https://qiime2.org/ , accessed on 25 June 2023). The conservative threshold for ASV filtration was 0.005%. We evaluated the α-diversity index of each sample and the β-diversity index using QIIME2. Degrees of similarity in species diversity between different samples were compared. We compared representative ASV sequences against the Silva microbial reference database (release 138; http://www.arb-silva.de , accessed on 25 June 2023) [ 38 ], Unite [ 39 ] (Release 8.0, https://unite.ut.ee/ , accessed on 25 June 2023), Greengenes [ 40 ] (version 13.5, http://greengenes.secondgenome.com/ , accessed on 25 June 2023), NCBI ( ftp://ftp.ncbi.nlm.nih.gov/refseq/TargetedLoci/ , accessed on 25 June 2023), fungene [ 41 ] ( http://fungene.cme.msu.edu/ , accessed on 25 June 2023), MaarjAM [ 42 ] ( http://www.maarjam.botany.ut.ee , accessed on 25 June 2023). The classification information of each ASV was obtained by comparison, and the ASV was annotated using the naive Bayes classifier-based method in QIIME2 [ 43 ]. Species annotation was processed with classify-sklearn. The classifier was trained before use to “learn” which features to use for classification. The confidence interval of the classifier was 0.7. Next, we counted the community composition of each sample at the phylum, class, order, family, genus, and species levels. Species richness at different taxonomic levels was assessed using QIIME, and the community structure diagram of each taxonomic level was drawn using R software v3.1.1 (R Foundation for Statistical Computing, Vienna, Austria). linear discriminant analysis with effect size (LEfSe) analysis ( http://huttenhower.sph.harvard.edu/lefse/ , accessed on 25 June 2023) was used for high-dimensional biomarker discovery and to identify significant differences in species abundance between female and male rats. LDA combines non-parametric Kruskal Wallis and Wilcoxon rank sum tests with LDA effect size, which can determine indicator taxa with statistical differences between different groups. For this study, the LDA threshold score was set to 2.0. Functional composition prediction Functional composition prediction was performed according to a Kyoto Encyclopedia of Genes and Genomes (KEGG) database comparison, using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2) algorithm and the tax4fun package in R [ 44 ]. The phenotypes of male and female liver microbiota were predicted using BugBase [ 45 ], and the metabolism and ecological roles were inferred using Functional Annotation of Prokaryotic Taxa (FAPROTAX) [ 46 ]. Correlation analysis Based on the abundance of each species in each sample, Spearman rank correlation analysis was conducted, and data with a correlation greater than 0.1 and a p -value less than 0.05 were selected to construct a species correlation network, which was diagrammed using R software (v3.6.1 (psych-v2.1.9, igraph-v1.2.5, visNetwork-v2.1.0)). The network graph made it possible to analyze the coexistence, interaction relationship, species pattern information, and microbiome formation mechanism of phenotypic differences between samples. The network was composed of edges and nodes, with an edge connected by two nodes. The nodes number, edges number, modularity and modules number, network diameter and density, average path length, and average clustering coefficient were described as the characteristics of the network. Node features were characterized by degree, clustering coefficient, tight centrality, intermediate centrality, within-module connectivity (Zi), and among-module connectivity (Pi). The higher the value, the higher the importance of the node. Based on Zi and Pi values, nodes were divided into four categories to demonstrate the importance of the nodes in the network: peripheral nodes (Zi ≤ 2.5, Pi ≤ 0.62, with few edges and connected only to nodes inside the module); connectors (Zi ≤ 2.5, Pi > 0.62, connecting different modules); module hubs (Zi > 2.5, Pi ≤ 0.62, highly connected to many nodes in their module); and network hubs (Zi > 2.5, Pi > 0.62, highly connected to many nodes in their module and connecting to different modules). To visualize the relationship between microbe and sex, canonical correspondent analysis (CCA) and redundancy analysis (RDA) [ 47 ] were designed at the genus and phylum level using R (v3.1.1, vegan v2.3-0). Before conducting analysis, the species community data were subjected to a detrended correspondence analysis according to the maximum value of lengths of gradient, CCA (value > 4), RDA (value < 3), or CCA/RDA (value between 3 and 4). Null Models By using the R package, the beta nearest taxon index (βNTI) was calculated to quantify the deviation between the absolute phylogenetic distance and the random phylogenetic distance of a community (v3.1.1, picante v1.8.2, vegan v2.3-0). The βNTI indicates the extent to which differences between grouped communities are influenced by deterministic or stochastic factors. The greater the deviation, the greater the impact of deterministic factors on changes in community structure. βNTI values are given on the z-score scale. Typically, values greater than + 2 (variable selection) and below − 2 (homogeneous selection) are interpreted as a dominance of deterministic selection processes, and βNTI values between − 2 and + 2 indicate the dominance of stochastic processes [ 48 ]. Statistics Data were expressed as mean ± standard deviation (SD). Comparisons of α- and β-diversity indices between male and female livers were conducted using a Student’s t -test with R software (v3.1.1). Community dissimilarities were analyzed via permutational multivariate analysis of variance (PERMANOVA). Differences were considered statistically significant when p < 0.05. Results Male and female rat livers exhibited different microbiome richness and diversity We sequenced the V3–V4 region of the 16S rRNA gene in liver tissues, obtaining 56,926.20 ± 16,991.65 effective tags. These tags were further clustered into 15,845 kinds of ASVs (2157.80 ± 461.52, 1632–2728) (Fig. 1 A), of which 10 rats shared only 127 kinds of ASVs (Fig. 1 B). The number of ASVs was lower in male rats than in females (1840.40 ± 260.62 vs. 2475.20 ± 399.35, P < 0.05), indicating higher abundance of bacteria in the livers of females versus males. They shared 1510 ASVs (15.29% in females and 20.19% in males) and 905 kinds of microbiota in the genus (72.11% in females and 81.53% in males). The phylum Nitrospinota was exclusive to male rat livers, and the bacteria phyla Halanaerobiaeota, Latescibacterota, Caldisericota, and Abditibacteriota and Archaea phylum Thermoplasmatota were exclusive to female rat livers. Table 1 summarizes the shared and exclusive bacteria in female and male rat livers. Species distribution was the same between female and male rats (Fig. 1 C and D). The top 10 dominant bacterial taxa were Proteobacteria, Firmicutes, Actinobacteriota, Acidobacteriota, unclassified bacteria, Bacteroidota, Gemmatimonadota, Methylomirabilota, Patescibacteria, and Chloroflexi. The alpha diversity analysis showed that the Chao1 and ACE index were higher in female rats than in male rats while the Shannon and Simpson indices were the same at the genus level (Fig. 2 ). The results suggest the same evenness of species between female and male livers, while the liver microbiome of female rats has higher abundance and alpha diversity than that of males. Table 1 Shared and gender-exclusive rat liver bacteria Shared Exclusive Female Male Phylum 38 5 (11%) 1 (2.5%) Class 85 17 (17%) 6 (6.5%) Order 249 49 (16%) 19 (7%) Family 502 151 (23%) 81 (14%) Genus 905 350 (28%) 250 (21.6%) Species 1030 573 (36%) 336 (24.6%) According to the binary Jaccard distance, the liver microbiome of male rats exhibited higher β-diversity than that of female rats in the ASV, order, or species level ( P 0.05), Fig. 3 . Microbiota biomarker analysis LEfSe analysis (LDA 2.0) from phylum to species resulted in a total of 91 bacteria that can be interpreted as biomarkers of female and male liver microbiota, of which 52 were specific to females and 39 to males (Fig. 4 ). The phylum Myxococcota, phylum Proteobacteria and its downward family Caulobacteraceae, genus Brevundimonas , and species unclassified Brevundimonas were indicator taxa of female rat livers, while the class Nitrospiria (phylum Nitrospirota) and its downward order Nitrospirales, family Nitrospiraceae, genus Nitrospira , and species unclassified Nitrospira ; the class Negativicutes (within the phylum Firmicutes); and family Veillonellaceae (within the phylum Firmicutes) represented indicator species for males. Functional analysis of the liver microbiota The main functional role of the genomes of annotated liver bacteria was related to metabolism (78.43 ± 0.08%). Genetic information processing (7.61 ± 0.02%), environmental information processing (6.41 ± 0.05%), and cellular processes (3.40 ± 0.03%) were less dominant and showed no obvious harm to the body (2.75 ± 0.02%). Moreover, the organismal system function (1.40 ± 0.00%) was low (Table S5). According to BugBase analysis, the liver bacteria possessed diverse phenotypes. The Gram-negative phenotype (72.42 ± 1.04%) was approximately 1.6 times more common than the Gram-positive (27.58 ± 1.04%) one, and the aerobic (29.56 ± 0.90%) and anaerobic (32.90 ± 0.80%) phenotypes were nearly equally common. Moreover, liver microbiota showed high expression of mobile elements (0.31 ± 0.09%), biofilm formation (0.45 ± 0.22%), and stress tolerance (8.07 ± 0.38%), as well as potentially pathogenic (0.54 ± 0.22%) phenotypes. According to FAPROTAX analysis, the liver bacteria were mainly heterotrophic with chemoheterotrophy (30.89 ± 0.49%), fermentation (17.04 ± 0.30%), and aerobic chemoheterotrophy (13.80 ± 0.55%) functions. There was no significant difference in functional composition and phenotype between female and male liver microbiomes ( P > 0.05). Bacteria correlation differed between female and male microbiomes In specific microbial habitats, bacteria interact and coexist to maintain a relatively stable environment. To determine the interactions and correlations of bacteria in the liver microbiome, we performed Spearman rank correlation analysis and selected data with a correlation greater than 0.1 and a p -value less than 0.05 (Fig. 6 ). Table S2 summarizes the network properties. A total of 63 bacteria were identified, 44 with negative relationships (correlation coefficient − 0.94 to − 0.77, P < 0.01) and 56 with positive relationships (correlation coefficient 0.77–0.98, P < 0.01) (Table S3). Each type of bacteria was influenced by others and produced in differing abundance. The most complicated Achromobacter negatively regulated unclassified Rokubacteriales , unclassified Gemmatimonadaceae , uncultured gamma proteobacterium , and unclassified Actinobacteriota and positively regulated unclassified Oxalobacteraceae , unclassified LWQ8 , Citrifermentans , Prevotella 9 , and unclassified BSV26 . Meanwhile, Achromobacter also was negatively regulated by unclassified Geminicoccaceae and positively regulated by Bacteroides and unclassified Gaiellales . The unclassified Geminicoccaceae negatively regulated Flavobacterium , unclassified BSV26 , Prevotella 9 , and Achromobacter and positively regulated unclassified Vicinamibacteraceae , MND1 , unclassified Gemmatimonadaceae , uncultured gamma proteobacterium , and unclassified Actinobacteriota . The unclassified Geminicoccaceae was also positively influenced by the Lachnospiraceae NK4A136 group (Fig. 6 A). Female and male rat livers showed very different bacteria correlations (Fig. 6 B, C; Table S4, S5). Among the top 100 genera with the highest correlation, 15 were identical with female rats, 14 were identical with male rats, and 4 were identical with both female and male rats. Positive correlations (r = 1.0) in both sexes were uncultured Desulfuromonadaceae bacterium to Bacillus and Megasphaera to Bifidobacterium . Unclassified BSV26 was negatively correlated (r = − 1) with unclassified Gemmatimonadaceae in both sexes. Terrisporobacter to Ligilactobacillus was negatively correlated in female rats but positively correlated in male rats ( P < 0.01). In female rats, the unclassified Gemmatimonadaceae , in phylum Gemmatimonadota, was the module hub (Zi = 2.9, Pi = 0). The Sulfurifustis , in phylum Proteobacteria, was the connector (Zi = 0, Pi = 0.73), and all other nodes were peripheral. In male rats, all nodes showed peripheral properties. Sex-associated bacteria analysis in rat liver microbiome RDA on the liver microbiome at the phylum and genus level revealed differences in the distribution of several types of bacteria in males versus females. At the phylum level, Firmicutes, Bacteroidota, Actinobacteriota, Acidobacteriota, Gemmatimonadota, and Methylomirabilota were positively related to male rats. Chloroflexi, Patescibacteria, unclassified Bacteria, and Proteobacteria were positively related to females. At the genus level, unclassified Acidobacteriales , MND1 , and unclassified Bacteria were positively related to females, and Megasphaera , Bifidobacterium , unclassified Vicinamibacterales , Lactobacillus , unclassified Vicinamibacteraceae , unclassified Gemmatimonadaceae , and Pseudomonas were positively related to males (Fig. 7 ). Phylogenetic diversity and inference of selection processes between female and male rats The null model βNTI index tool was calculated to analyze whether differences in the community structure between groups were dominated by deterministic processes or stochastic processes. The results indicated that the community composition between grouped communities was more similar than would be expected with randomization. The βNTI values of both female and male rat liver microbiomes were clearly below the significance threshold (− 2), indicating strong homogeneous deterministic selection with consistent selection pressure (Fig. 8 ). Discussion The liver is the central organ of metabolic regulation and plays pivotal physiological and biochemical roles in maintaining the homeostasis of the body [ 49 ]. Liver dysfunction and disease are leading causes of death. Much research has revealed the biochemical and molecular biology mechanisms of various liver disorders, including hepatic carcinoma, viral hepatitis, alcohol and drug toxicity, and metabolic chaos. Recent research involving gut microbiota in normal and cancerous tissues [ 16 ] has considered pathogenic factors in the gut microbiome. Many studies have reported on variations in the gut microbiota of healthy individuals versus people with various organ diseases, including obesity and diabetes [ 50 ], autism and mood disorders [ 51 ], retinal diseases [ 52 ], atherosclerosis [ 53 ], liver disease [ 54 , 55 ], and kidney disease [ 56 ]. Gut leakage has been considered the source of tissue or circulation microbiota [ 57 ]. The liver is intimately involved in interchange with the gut, including by the liver-gut pathway through the bile duct system [ 58 , 59 ] and the gut-liver pathway through the gut leakage and portal vein system [ 60 – 62 ]. It therefore seems natural that microbes would be found in liver tissue. In a recent study of the microbiomes of liver tissue from healthy rats, we found that bacterial communities in the liver were different from gut microbiomes, suggesting the possibility of microbiota as an intrinsic component of hepatocytes [ 28 ]. The liver microbiome characteristics, and the effect and mechanisms of microbial host interaction in regulating homeostasis, are thus key areas for further research. Additionally, the liver is an organ with typical sex dependence (dimorphism) [ 63 , 64 ] and its pathological or physiological processes are associated with sex differences to a large extent [ 65 ]. However, the molecular mechanism of sex-related genetics and epigenetics in the liver is still to be uncovered. We thus conducted 16S rRNA gene sequencing to explore liver microbes as a component of hepatocytes and to identify dimorphic characteristics of the liver microbiome. Our study showed different microbiota composition of male and female rat livers. First, female livers had higher bacterial abundance and alpha diversity than those of males, while male livers exhibited higher β-diversity. Second, the female rats showed more gender-specific microbes than male rats. The phylum Nitrospinota was absent in females, while the male rats lacked the bacteria phyla Halanaerobiaeota, Latescibacterota, Caldisericota, and Abditibacteriota and the Archaea phylum Thermoplasmatota. Third, based on LEfSe analysis at the LDA 2.0 level, male and female rat livers had different bacteria biomarkers, including the components of phyla Myxococcota and Proteobacteria for females and components of phyla Nitrospirota and Firmicutes for males. Fourth, male and female rat livers showed different bacteria correlations. For example, Terrisporobacter to Ligilactobacillus was negatively correlated in female rats (r = − 1.0) but positively correlated in male rats (r = 1.0). In female rats, unclassified Gemmatimonadaceae was the module hub, and Sulfurifustis was the connector, while in male rats, all nodes showed peripheral properties. We also performed RDA and calculated the null model βNTI index. Some gender-specific bacterial distribution was found in the liver microbiome, and the results indicated that the composition of grouped communities was based on homogeneous deterministic selection (βNTI < − 2) rather than stochastic selection. These results suggest dimorphism in the liver microbiome of rats. Recently, several studies have reported on the sexual dimorphism of gut microbiota from adolescents in pre-puberty and puberty [ 66 ], men and pre-menopausal women [ 67 ], C57BL/6J mice aged 6–8 weeks or 12–13 weeks [ 68 , 69 ], and juvenile Chinese alligators [ 70 ]. Our research also found sexual differences in the gut microbiota from young SD rats without food ingestion (Beijing Biomarker Technologies Co., Ltd., Beijing, China, www.biomarker.com.cn , Project Number: BMK230904–BO604–ZX01–0101). However, the relationship of this sexual differentiation between liver microbiota and gut microbiota is still unclear. Based on our comparison of the gut microbiota and liver bacteria, as well as the result of the null model, we conclude that the dimorphic characteristic of liver bacteria is an intrinsic feature. Although there were no significant differences in functional composition and phenotype between female and male liver microbiomes, the different microbe community structures and interactions may contribute to sex-dependent pathological or physiological processes in the host liver. Disagreement remains about the scientific validity of the conclusion that an abundant microbiota is intrinsic to the liver. Although the detecting process was strictly controlled and we used the most advanced 16S sequencing techniques, some reviewers question the possibility of environmental pollution during sampling. This is based on the long-held assumption that microbes live outside the cell and outside the body in the environment and the feces. In addition, some have questioned whether the 16S sequencing process [ 71 ] could substitute for the culturing process in vitro. Despite the success of metagenomic sequencing in discovering and analyzing microbiota, many questions remain. For example, most of the microbes have still not been identified and verified by in vitro culture [ 72 ], and their relationship to health and pathology is still unclear. With the optimization of sequencing techniques, many findings may be further confirmed or denied [ 73 ]. Our findings may challenge traditional knowledge of microbiology based on traditional technology. However, new technology offers the possibility of uncovering new, groundbreaking knowledge about health and disease in fields such as aging and cancer. In conclusion, based on our research showing the liver microbiome as an intrinsic component of hepatocytes, this study further demonstrates its dimorphic characteristics. Compared with the nondimorphism of the gut microbiome, our results provide strong support for the idea of the liver microbiome as an intrinsic component of hepatocytes. Many opportunities for further research remain around the dimorphic features, such as the mechanism for the development of gender-specific microbiota, and the influence of liver microbe communities on physiological and pathological processes in the liver. Abbreviations ASVs: amplicon sequence variants; βNTI: beta nearest taxon indices; CCA: canonical correspondent analysis; DNA: deoxyribonucleic acid; FAPROTAX: functional annotation of prokaryotic taxa; FLASH: fast length adjustment of short reads; KEGG: kyoto encyclopedia of genes and genomes; LDA: linear discriminant analysis; LEfSe: linear discriminant analysis with effect size; NMDS: non-metric multidimensional scaling; PCA: principal component analysis; PCoA: principal coordinate analysis; PCR: polymerase chain reaction; PERMANOVA: permutational multivariate analysis of variance; PICRUSt2: phylogenetic investigation of communities by reconstruction of unobserved states 2; QIIME: quantitative insights into microbial ecology; RDA: redundancy analysis; rRNA: ribosomal ribonucleic acid; SD: Sprague Dawley; standard deviation; SPF: specific pathogen free. Declarations D ata a vailability The raw datasets generated during the current study are available in the NCBI repository (https://www.ncbi.nlm.nih.gov/, accessed on 20 September 2023), BioProject: PRJNA1019449. Conflict of interest The authors declare that there are no conflicts of interest regarding the publication of this article. Funding statement This work was supported by the National Natural Science Foundation of China [Grant Nos. 81670776 and 81970734] to JGZ. Authors’ contributions JGZ contributed to the study concept and design. XWS and JGZ contributed to the analysis and interpretation of data and drafted the manuscript. All authors contributed to the acquisition of data and critical revision of the manuscript. All authors approved the final manuscript prior to submission. YMH, ZYL and JXD are shared co-first authors, and XWS and JGZ are co-corresponding authors. Acknowledgments We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript. Author details Yu-Meng Hao, Pathology Institute, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China. E-mail: [email protected] . Zhao-Yang Li, Pathology Institute, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China. E-mail: [email protected] Jing-Xin Deng, Pathology Institute, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China. E-mail: [email protected] . Xiao-Wei Sun, Pathology Institute, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China. Tel: 86 13519316382; E-mail: [email protected] . Jian-Gang Zhang, Pathology Institute, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China. Tel: 86 15095387695; E-mail: [email protected] . 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04:14:48","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11038,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7751900/v1/285b76bcf00c45c300ef2874.png"},{"id":92912293,"identity":"0ce40211-f229-4c9a-aa28-4d3752155bb5","added_by":"auto","created_at":"2025-10-07 04:14:49","extension":"xml","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":181956,"visible":true,"origin":"","legend":"","description":"","filename":"rs77519000structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7751900/v1/650c21530b4649b50bd889b4.xml"},{"id":92912292,"identity":"13ab9255-894a-4614-931b-f63186dbc2b6","added_by":"auto","created_at":"2025-10-07 04:14:49","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":194863,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7751900/v1/72c85ab05ae5c6a6dd883ab1.html"},{"id":92912267,"identity":"ec46c7e7-9cbd-4a19-9ec4-e6504b3b3f4b","added_by":"auto","created_at":"2025-10-07 04:14:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":804667,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eASV distribution and taxonomic components\u0026nbsp;of microbiota.\u003c/strong\u003e\u0026nbsp;(A) Number of sample ASVs, generated with Python 2 and Matplotlib 1.4.3. (B) Venn diagram, created using R v3.1.1 and Venn diagram v1.6.9. (C) Species distribution and components (genera), visualized with Python 2, Matplotlibv1.5.1, and Circos v0.66-7. (D) Abundance heatmap (genera), generated with R v3.1.1 and pheatmap v1.0.2.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7751900/v1/45282e33f1eb123b9a616e14.jpg"},{"id":92912445,"identity":"5cc3797c-01a6-4c91-9693-5c4bfeca9e58","added_by":"auto","created_at":"2025-10-07 04:22:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":616970,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLiver bacteria alpha\u0026nbsp;diversity\u003c/strong\u003e. Female liver bacteria show\u0026nbsp;significantly higher richness than male liver\u0026nbsp;bacteria. (A) MEGAN\u0026nbsp;tree of female and male liver microbiomes shows\u0026nbsp;higher richness in females\u0026nbsp;than in males, generated with MEGAN v5.8.1. (B) Indices of α-diversity. *: Compared\u0026nbsp;with male rat liver, \u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.05, analyzed using R v3.1.1 and Picante v1.8.2. (C) Rarefaction curve and rank abundance curve of\u0026nbsp;female and male rat\u0026nbsp;liver bacteria, visualized with Mother v1.22.2 and Python v2.7.8 (Matplotlib v1.4.3).\u0026nbsp;The significance of the α-diversity index differences was verified through Student’s \u003cem\u003et\u003c/em\u003e-test.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7751900/v1/f67ddcdabfff9caf801f3a77.jpg"},{"id":92912274,"identity":"6419b284-9665-4b10-b52c-ef4165f4f012","added_by":"auto","created_at":"2025-10-07 04:14:48","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":467654,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBeta\u0026nbsp;diversity in bacteria composition (ASV\u0026nbsp;level). \u003c/strong\u003eSample\u0026nbsp;distances were calculated using the binary Jaccard method.\u0026nbsp;(A) Principal component analysis (PCA), performed with Python 2 and Scikit-learn 0.17.1. (B) Principal coordinate analysis (PCoA), conducted using QIIME 1.8.0 and principal coordinates.py. (C) Non-metric multidimensional scaling\u0026nbsp;(NMDS), with stress = 0.1373, executed using QIIME 1.9.1, nmds.py. (D) Sample distance analysis (PERMANOVA), analyzed with R v3.1.1 and Vegan v2.3-0.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7751900/v1/775e9229be058dd7a32a5c24.jpg"},{"id":92912282,"identity":"3091bbf3-fa39-4564-ba3e-18f6733fb6b9","added_by":"auto","created_at":"2025-10-07 04:14:48","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1130096,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLiver\u0026nbsp;microbiome markers of female and male rats. \u003c/strong\u003eA total of\u0026nbsp;91 bacteria, ranging from phylum to species,\u0026nbsp;(52 female and 39 male) showed significant differences (LDA score = 2.0), analyzed using Python 2 and LEfSe.\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7751900/v1/08e5271e83220477a2c7a80e.jpg"},{"id":92912446,"identity":"134080ac-a14e-4429-b3d2-c4bc00496db9","added_by":"auto","created_at":"2025-10-07 04:22:48","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":844073,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of microbiome functional\u0026nbsp;characteristics. \u003c/strong\u003eThere were no significant functional differences in\u0026nbsp;liver microbiome between female and male rats\u0026nbsp;(\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026gt; 0.05).\u0026nbsp;(A) Functional features, analyzed with PICRUSt2 using the KEGG database v2.3.0.\u0026nbsp;(B) Metabolic\u0026nbsp;features, analyzed with FAPROTA v1.2.6.\u0026nbsp;(C) Phenotype composition, analyzed with BugBase v0.1.0.\u0026nbsp;(D) Comparison of functional, metabolic, and phenotype features\u0026nbsp;between female and male microbiomes.\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7751900/v1/46a39092847a8cf6eeceda09.jpg"},{"id":92912288,"identity":"31b9e846-5714-4864-9786-783d652c5af2","added_by":"auto","created_at":"2025-10-07 04:14:48","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1583081,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpecies correlation network. \u003c/strong\u003eLines between two species represent a correlation, with line thickness and depth representing the strength of the correlation. Red lines represent positive correlation; green lines represent negative correlation. Nodes represent species, and node size represents the average abundance of the species. Data can be found in Tables S2–S5. (A) Bacteria correlation of rat liver microbiome. (B) Liver microbiome correlation of female rats. (C) Liver microbiome correlation of male rats, analyzed using R v3.6.1 with packages Psych v2.1.9, igraph v1.2.5, and visNetwork v2.1.0.\u003c/p\u003e","description":"","filename":"Fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7751900/v1/7697870f6a11512f98a7663c.jpg"},{"id":92912291,"identity":"00de4939-63ce-44aa-a265-4a283cd88988","added_by":"auto","created_at":"2025-10-07 04:14:48","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":409305,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRedundancy\u0026nbsp;analysis of liver microbiome of female and male rats. \u003c/strong\u003eArrow\u0026nbsp;length represents the strength of the correlation between rat gender\u0026nbsp;and the microbes. The longer the arrow length, the stronger the correlation. Perpendicular\u0026nbsp;distance between microbes and environmental variable axes in the plot reflects correlation strength. The smaller the distance, the stronger the correlation.\u0026nbsp;The “A” and “B” samples indicate male and female rats, respectively, analyzed using R v3.1.1 and Vegan v2.3-0.\u003c/p\u003e","description":"","filename":"Fig.71.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7751900/v1/9ddd15496721980e77188f22.jpg"},{"id":92912277,"identity":"9fa8f7fb-00b0-4ddb-ad17-b71a7740e6d5","added_by":"auto","created_at":"2025-10-07 04:14:48","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":125017,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBeta nearest taxon indices (βNTI)\u003c/strong\u003e \u003cstrong\u003eof rat liver microbiota\u0026nbsp;groups.\u003c/strong\u003e\u0026nbsp;The horizontal axis represents sample names, and the vertical axis represents the nearest beta classification index value (βNTI). Dashed lines mark the significance thresholds (βNTI \u0026gt; 2 and βNTI \u0026lt; −2) for deterministic selection processes.\u0026nbsp;In general, it is believed that βNTI \u0026gt; 2 indicates variable deterministic selection and that βNTI \u0026lt; −2 indicates homogeneous deterministic selection. βNTI-values between the dashed lines (−2 \u0026lt; βNTI \u0026lt; 2) are interpreted as stochastic selection. In the diagram, both female (B7–B11) and male (A2–A6) communities indicate strong homogeneous deterministic community selection. R v3.1.1 with Picante v1.8.2, and Vegan v2.3-0.\u003c/p\u003e","description":"","filename":"Fig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-7751900/v1/fc3f9cf67a5ac5adaa0464f1.png"},{"id":92913495,"identity":"7f3cf1d5-2d73-48d0-960a-c4aeaf3520b6","added_by":"auto","created_at":"2025-10-07 04:38:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6918510,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7751900/v1/dd8dba07-9117-48a5-8979-6b49da2d71bb.pdf"},{"id":92912269,"identity":"42233e26-01fc-4613-9061-bcb5c7961fda","added_by":"auto","created_at":"2025-10-07 04:14:48","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":660992,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.doc","url":"https://assets-eu.researchsquare.com/files/rs-7751900/v1/244c042893985afcf6e79343.doc"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eLiver microbiome diversity and sex selection in male and female rats\u003c/p\u003e","fulltext":[{"header":"Significance of the study","content":"\u003cp\u003eLiver dysfunction and disease are leading causes of death. Gut microbiota in normal and cancerous tissues has considered pathogenic factors besides biochemical and molecular biology mechanisms. The liver is an organ with typical sex dependence (dimorphism). Our study showed different microbiota composition of male and female rat livers and the dimorphic characteristic of liver bacteria is an intrinsic feature. Compared with the nondimorphism of the gut microbiome, our results provide strong support for the idea of the liver microbiome as an intrinsic component of hepatocytes. Furthermore, this study suggested that liver microbiome may take part in the gender-associated physiological and pathological processes in the liver.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe liver is a pivotal metabolic organ in the body with interesting and significant sexual dimorphism in its functions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Gender differences significantly affect the morphology and structure of hepatocytes and the expression, distribution, and secretion of various enzymes and proteins [\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7 CR8 CR9\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], as well as chromatin condensation or decondensation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Genetic differences reveal different metabolic functions in male and female livers. For example, male livers have a greater ability to metabolize and clear alcohol, while female livers are better at cholesterol metabolism [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Sexual dimorphism of liver microbiota also plays an important role in the development of liver disease [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Studies have shown that when administered the toxic chemical aromatic amine 2-acetylaminofluorene, male rats are more susceptible to the induction of liver cancer than females [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Clinically, the incidence and mortality of liver cancer have been higher among males than females in all world regions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent research suggests that intracellular bacteria specific to tumor type may play an important role in the development of cancer [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Moreover, several studies in humans or mice have demonstrated the existence of tissue microbiota and circulation microbiota [\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Although its source is still under investigation and is hypothesized to be derived from gut microbiota [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], tissue microbiota appears to play an important role in physiological and pathological processes. By using high-throughput 16S rRNA gene sequencing and detecting LPS, LTA, or 16S rRNA genes \u003cem\u003ein situ\u003c/em\u003e with immunofluorescence, fluorescence \u003cem\u003ein situ\u003c/em\u003e hybridization, and western blotting, we recently confirmed the existence of the liver microbiome in hepatocytes of healthy rats. By comparing its composition and diversity with the gut microbiome, we showed that the liver microbiome is possibly an intrinsic component of hepatocytes [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These findings indicate an important need to explore the relationship between the liver microbiome and liver diseases and to determine whether the phylogenetic diversity of the microbiome is gender-specific.\u003c/p\u003e\u003cp\u003eThe objective of the current study was to confirm the sexual dimorphism of the liver microbiome. We performed high-throughput 16S rRNA polymerase chain reaction (PCR) amplification and gene sequencing on male and female rat livers as previously reported [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and conducted diversity and relevance analysis. We found that male and female rat livers showed different microbiota diversity and biomarkers.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eAnimals\u003c/h2\u003e\u003cp\u003eWe used 6-week-old Sprague-Dawley male and female rats (n\u0026thinsp;=\u0026thinsp;5) from the same litter, with a body weight of 116.0\u0026ndash;164.0 g, for 16S rRNA gene sequencing. Rats were housed in a specific-pathogen\u0026ndash;free (SPF) environment and fed a chow diet and water. After weaning, animals were separated into two cages based on sex and fed the same chow diet for 2 weeks. All animals received humane care, and the study protocols complied with the guidelines for the ethical review of laboratory animal welfare (GB/T 35892\u0026mdash;2018) and were approved by the Medical Ethics Committee of Lanzhou University (jcyxy20190302). This study also conformed to the guidelines for Animal Research: Reporting of In Vivo Experiments and the Replacement, Refinement and Reduction of Animals in Research.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSample collection and contamination avoidance\u003c/h3\u003e\n\u003cp\u003eAnimals were anesthetized with 15% urethane (0.06 ml/10 g body mass) to prevent them from suffering. Livers were then sampled. For 16S rRNA gene sequencing, liver tissues were subjected to DNA extraction, PCR amplification, and sequencing integrated procedures (BMK Co., Beijing, China, accessed on 25 June 2023, BMK221213-BF330-S01-ZX01-0101). Environment contamination was avoided according to the procedure reported previously [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Because no paraffin was used to block tissues, no empty paraffin controls [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] were applied.\u003c/p\u003e\n\u003ch3\u003eBacterial DNA extraction and amplicon sequencing\u003c/h3\u003e\n\u003cp\u003eWe extracted total DNA from frozen liver samples (0.25\u0026ndash;0.5 g) using a TGuide S96 Magnetic Soil and Stool DNA Kit (#DP812; Tiangen Corp., Beijing, China, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.tiangen.com/\u003c/span\u003e\u003cspan address=\"https://www.tiangen.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) per the kit protocol. All procedures were performed with sterile and disposable materials to avoid cross-contamination; in addition, beads and DNA extraction blank controls were used during this process. The concentration of nucleic acid was detected using an enzyme-linked immunosorbent assay (Synergy HTX, Biotek Instruments, Winooski, VT, USA). After amplification, the integrity of the PCR product was detected using 1.8% agar gel (Beijing Bomei Fuxin Technology Co., Ltd.), and the DNA concentration and purity were determined using NanoDrop 2000 (Thermo Scientific, Waltham, MA, USA). We used two-round-tailed PCR with the barcode at the end of the primer for 16S amplification and sequencing as previously reported [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The bacterial primer was as follows: 338F: 5'-ACTCCTACGGGAGGCAGCA-3'; 806R: 5'-GGACTACHVGGGTWTCTAAT-3' [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. We established a negative control and used sterile water instead of DNA for PCR amplification (no-template PCR amplification controls).\u003c/p\u003e\u003cp\u003eAfter quality testing on a Qsep-400 (BiOptic, Inc., New Taipei City, Taiwan, ROC) and preparation of a flow cell chip, we subjected 500 ng PCR products to paired-end sequencing on an Illumina NovaSeq 6000 platform with PE250 strategy (Illumina, Inc., San Diego, CA, USA) at Biomarker Technologies Co, Ltd. (Beijing, China) according to standard protocols. The sequencing length was 350\u0026ndash;450 base pairs (bp). Original image data files were transformed into sequenced reads via base calling analysis. The negative control (sequencing run controls) was not sequenced because it was bandless and sequencing would have been meaningless.\u003c/p\u003e\n\u003ch3\u003eAmplicon sequence analysis and quality control\u003c/h3\u003e\n\u003cp\u003ePaired-end reads were merged according to overlapping relationships using Fast Length Adjustment of Short Reads (FLASH) software v.1.2.11 (Johns Hopkins University Center for Computational Biology, Baltimore, MD, USA; raw tags) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. We discarded tags with more than six mismatches. The minimum overlap length was 10 bp, and the maximum mismatch ratio allowed in the overlap region was 0.2 (default). Briefly, raw tags with an average quality score\u0026thinsp;\u0026lt;\u0026thinsp;20 in a 50-bp sliding window were filtered using Trimmomatic software v.0.33 (USADELLAB.org) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] (clean reads). We then used Cutadapt 1.9.1 software [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] to identify and remove primer sequences (non-primer reads). The maximum mismatch was 20% and the minimum coverage was 80%. Paired-end reads obtained from previous steps were assembled by USEARCH [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] (version 10). The minimum overlap length was 10 bp, the minimum similarity within the overlapping region was 90%, and the maximum mismatch ratio allowed in the overlap region was 5 bp (default). All reads were denoised and merged by pair-end sequence splicing using the DADA2 v2021.2.0 algorithm [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] from quantitative insights into microbial ecology 2 (QIIME2, 2020.6; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://drive5.com/usearch/manual/uchime_algo.html\u003c/span\u003e\u003cspan address=\"http://drive5.com/usearch/manual/uchime_algo.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 25 June 2023) (denoised reads and merged reads) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Chimera sequences were then removed using UCHIME [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] (version 8.1) (non-chimeric reads) (Table S1). The high-quality reads generated from the above steps were used in the following analysis.\u003c/p\u003e\n\u003ch3\u003eTaxonomic and diversity analysis of amplicon sequence variants (ASVs)\u003c/h3\u003e\n\u003cp\u003eAfter discarding chimeras, reads with similarity over 97% (default) were clustered using USEARCH (OTU clustering). ASVs were inferred using the DADA2 method in QIIME2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://qiime2.org/\u003c/span\u003e\u003cspan address=\"https://qiime2.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 25 June 2023). The conservative threshold for ASV filtration was 0.005%. We evaluated the α-diversity index of each sample and the β-diversity index using QIIME2. Degrees of similarity in species diversity between different samples were compared.\u003c/p\u003e\u003cp\u003eWe compared representative ASV sequences against the Silva microbial reference database (release 138; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.arb-silva.de\u003c/span\u003e\u003cspan address=\"http://www.arb-silva.de\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 25 June 2023) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], Unite [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] (Release 8.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://unite.ut.ee/\u003c/span\u003e\u003cspan address=\"https://unite.ut.ee/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 25 June 2023), Greengenes [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] (version 13.5, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://greengenes.secondgenome.com/\u003c/span\u003e\u003cspan address=\"http://greengenes.secondgenome.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 25 June 2023), NCBI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eftp://ftp.ncbi.nlm.nih.gov/refseq/TargetedLoci/\u003c/span\u003e\u003cspan address=\"http://ftp://ftp.ncbi.nlm.nih.gov/refseq/TargetedLoci/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 25 June 2023), fungene [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://fungene.cme.msu.edu/\u003c/span\u003e\u003cspan address=\"http://fungene.cme.msu.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 25 June 2023), MaarjAM [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.maarjam.botany.ut.ee\u003c/span\u003e\u003cspan address=\"http://www.maarjam.botany.ut.ee\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 25 June 2023). The classification information of each ASV was obtained by comparison, and the ASV was annotated using the naive Bayes classifier-based method in QIIME2 [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Species annotation was processed with classify-sklearn. The classifier was trained before use to \u0026ldquo;learn\u0026rdquo; which features to use for classification. The confidence interval of the classifier was 0.7. Next, we counted the community composition of each sample at the phylum, class, order, family, genus, and species levels. Species richness at different taxonomic levels was assessed using QIIME, and the community structure diagram of each taxonomic level was drawn using R software v3.1.1 (R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e\u003cp\u003elinear discriminant analysis with effect size (LEfSe) analysis (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://huttenhower.sph.harvard.edu/lefse/\u003c/span\u003e\u003cspan address=\"http://huttenhower.sph.harvard.edu/lefse/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 25 June 2023) was used for high-dimensional biomarker discovery and to identify significant differences in species abundance between female and male rats. LDA combines non-parametric Kruskal Wallis and Wilcoxon rank sum tests with LDA effect size, which can determine indicator taxa with statistical differences between different groups. For this study, the LDA threshold score was set to 2.0.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eFunctional composition prediction\u003c/h2\u003e\u003cp\u003eFunctional composition prediction was performed according to a Kyoto Encyclopedia of Genes and Genomes (KEGG) database comparison, using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2) algorithm and the tax4fun package in R [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The phenotypes of male and female liver microbiota were predicted using BugBase [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and the metabolism and ecological roles were inferred using Functional Annotation of Prokaryotic Taxa (FAPROTAX) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCorrelation analysis\u003c/h3\u003e\n\u003cp\u003eBased on the abundance of each species in each sample, Spearman rank correlation analysis was conducted, and data with a correlation greater than 0.1 and a \u003cem\u003ep\u003c/em\u003e-value less than 0.05 were selected to construct a species correlation network, which was diagrammed using R software (v3.6.1 (psych-v2.1.9, igraph-v1.2.5, visNetwork-v2.1.0)). The network graph made it possible to analyze the coexistence, interaction relationship, species pattern information, and microbiome formation mechanism of phenotypic differences between samples. The network was composed of edges and nodes, with an edge connected by two nodes. The nodes number, edges number, modularity and modules number, network diameter and density, average path length, and average clustering coefficient were described as the characteristics of the network. Node features were characterized by degree, clustering coefficient, tight centrality, intermediate centrality, within-module connectivity (Zi), and among-module connectivity (Pi). The higher the value, the higher the importance of the node. Based on Zi and Pi values, nodes were divided into four categories to demonstrate the importance of the nodes in the network: peripheral nodes (Zi\u0026thinsp;\u0026le;\u0026thinsp;2.5, Pi\u0026thinsp;\u0026le;\u0026thinsp;0.62, with few edges and connected only to nodes inside the module); connectors (Zi\u0026thinsp;\u0026le;\u0026thinsp;2.5, Pi\u0026thinsp;\u0026gt;\u0026thinsp;0.62, connecting different modules); module hubs (Zi\u0026thinsp;\u0026gt;\u0026thinsp;2.5, Pi\u0026thinsp;\u0026le;\u0026thinsp;0.62, highly connected to many nodes in their module); and network hubs (Zi\u0026thinsp;\u0026gt;\u0026thinsp;2.5, Pi\u0026thinsp;\u0026gt;\u0026thinsp;0.62, highly connected to many nodes in their module and connecting to different modules).\u003c/p\u003e\u003cp\u003eTo visualize the relationship between microbe and sex, canonical correspondent analysis (CCA) and redundancy analysis (RDA) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] were designed at the genus and phylum level using R (v3.1.1, vegan v2.3-0). Before conducting analysis, the species community data were subjected to a detrended correspondence analysis according to the maximum value of lengths of gradient, CCA (value\u0026thinsp;\u0026gt;\u0026thinsp;4), RDA (value\u0026thinsp;\u0026lt;\u0026thinsp;3), or CCA/RDA (value between 3 and 4).\u003c/p\u003e\n\u003ch3\u003eNull Models\u003c/h3\u003e\n\u003cp\u003eBy using the R package, the beta nearest taxon index (βNTI) was calculated to quantify the deviation between the absolute phylogenetic distance and the random phylogenetic distance of a community (v3.1.1, picante v1.8.2, vegan v2.3-0). The βNTI indicates the extent to which differences between grouped communities are influenced by deterministic or stochastic factors. The greater the deviation, the greater the impact of deterministic factors on changes in community structure. βNTI values are given on the z-score scale. Typically, values greater than +\u0026thinsp;2 (variable selection) and below \u0026minus;\u0026thinsp;2 (homogeneous selection) are interpreted as a dominance of deterministic selection processes, and βNTI values between \u0026minus;\u0026thinsp;2 and +\u0026thinsp;2 indicate the dominance of stochastic processes [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStatistics\u003c/h2\u003e\u003cp\u003eData were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Comparisons of α- and β-diversity indices between male and female livers were conducted using a Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test with R software (v3.1.1). Community dissimilarities were analyzed via permutational multivariate analysis of variance (PERMANOVA). Differences were considered statistically significant when \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eMale and female rat livers exhibited different microbiome richness and diversity\u003c/h2\u003e\u003cp\u003eWe sequenced the V3\u0026ndash;V4 region of the 16S rRNA gene in liver tissues, obtaining 56,926.20\u0026thinsp;\u0026plusmn;\u0026thinsp;16,991.65 effective tags. These tags were further clustered into 15,845 kinds of ASVs (2157.80\u0026thinsp;\u0026plusmn;\u0026thinsp;461.52, 1632\u0026ndash;2728) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), of which 10 rats shared only 127 kinds of ASVs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The number of ASVs was lower in male rats than in females (1840.40\u0026thinsp;\u0026plusmn;\u0026thinsp;260.62 vs. 2475.20\u0026thinsp;\u0026plusmn;\u0026thinsp;399.35, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating higher abundance of bacteria in the livers of females versus males. They shared 1510 ASVs (15.29% in females and 20.19% in males) and 905 kinds of microbiota in the genus (72.11% in females and 81.53% in males). The phylum Nitrospinota was exclusive to male rat livers, and the bacteria phyla Halanaerobiaeota, Latescibacterota, Caldisericota, and Abditibacteriota and Archaea phylum Thermoplasmatota were exclusive to female rat livers. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the shared and exclusive bacteria in female and male rat livers. Species distribution was the same between female and male rats (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and D). The top 10 dominant bacterial taxa were Proteobacteria, Firmicutes, Actinobacteriota, Acidobacteriota, unclassified bacteria, Bacteroidota, Gemmatimonadota, Methylomirabilota, Patescibacteria, and Chloroflexi.\u003c/p\u003e\u003cp\u003eThe alpha diversity analysis showed that the Chao1 and ACE index were higher in female rats than in male rats while the Shannon and Simpson indices were the same at the genus level (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results suggest the same evenness of species between female and male livers, while the liver microbiome of female rats has higher abundance and alpha diversity than that of males.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eShared and gender-exclusive rat liver bacteria\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eShared\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eExclusive\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhylum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (2.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (6.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49 (16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19 (7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e151 (23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81 (14%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e350 (28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e250 (21.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e573 (36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e336 (24.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAccording to the binary Jaccard distance, the liver microbiome of male rats exhibited higher β-diversity than that of female rats in the ASV, order, or species level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but showed no significant difference in the phylum, class, family, and genus levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eMicrobiota biomarker analysis\u003c/h2\u003e\u003cp\u003eLEfSe analysis (LDA 2.0) from phylum to species resulted in a total of 91 bacteria that can be interpreted as biomarkers of female and male liver microbiota, of which 52 were specific to females and 39 to males (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The phylum Myxococcota, phylum Proteobacteria and its downward family Caulobacteraceae, genus \u003cem\u003eBrevundimonas\u003c/em\u003e, and species \u003cem\u003eunclassified Brevundimonas\u003c/em\u003e were indicator taxa of female rat livers, while the class Nitrospiria (phylum Nitrospirota) and its downward order Nitrospirales, family Nitrospiraceae, genus \u003cem\u003eNitrospira\u003c/em\u003e, and species \u003cem\u003eunclassified Nitrospira\u003c/em\u003e; the class Negativicutes (within the phylum Firmicutes); and family Veillonellaceae (within the phylum Firmicutes) represented indicator species for males.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eFunctional analysis of the liver microbiota\u003c/h2\u003e\u003cp\u003eThe main functional role of the genomes of annotated liver bacteria was related to metabolism (78.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08%). Genetic information processing (7.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02%), environmental information processing (6.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05%), and cellular processes (3.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03%) were less dominant and showed no obvious harm to the body (2.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02%). Moreover, the organismal system function (1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00%) was low (Table S5).\u003c/p\u003e\u003cp\u003eAccording to BugBase analysis, the liver bacteria possessed diverse phenotypes. The Gram-negative phenotype (72.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04%) was approximately 1.6 times more common than the Gram-positive (27.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04%) one, and the aerobic (29.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90%) and anaerobic (32.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80%) phenotypes were nearly equally common. Moreover, liver microbiota showed high expression of mobile elements (0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09%), biofilm formation (0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22%), and stress tolerance (8.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38%), as well as potentially pathogenic (0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22%) phenotypes. According to FAPROTAX analysis, the liver bacteria were mainly heterotrophic with chemoheterotrophy (30.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49%), fermentation (17.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30%), and aerobic chemoheterotrophy (13.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55%) functions. There was no significant difference in functional composition and phenotype between female and male liver microbiomes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eBacteria correlation differed between female and male microbiomes\u003c/h2\u003e\u003cp\u003eIn specific microbial habitats, bacteria interact and coexist to maintain a relatively stable environment. To determine the interactions and correlations of bacteria in the liver microbiome, we performed Spearman rank correlation analysis and selected data with a correlation greater than 0.1 and a \u003cem\u003ep\u003c/em\u003e-value less than 0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Table S2 summarizes the network properties. A total of 63 bacteria were identified, 44 with negative relationships (correlation coefficient \u0026minus;\u0026thinsp;0.94 to \u0026minus;\u0026thinsp;0.77, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and 56 with positive relationships (correlation coefficient 0.77\u0026ndash;0.98, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table S3). Each type of bacteria was influenced by others and produced in differing abundance. The most complicated \u003cem\u003eAchromobacter\u003c/em\u003e negatively regulated unclassified \u003cem\u003eRokubacteriales\u003c/em\u003e, unclassified \u003cem\u003eGemmatimonadaceae\u003c/em\u003e, uncultured \u003cem\u003egamma proteobacterium\u003c/em\u003e, and unclassified \u003cem\u003eActinobacteriota\u003c/em\u003e and positively regulated unclassified \u003cem\u003eOxalobacteraceae\u003c/em\u003e, unclassified \u003cem\u003eLWQ8\u003c/em\u003e, \u003cem\u003eCitrifermentans\u003c/em\u003e, \u003cem\u003ePrevotella 9\u003c/em\u003e, and unclassified \u003cem\u003eBSV26\u003c/em\u003e. Meanwhile, \u003cem\u003eAchromobacter\u003c/em\u003e also was negatively regulated by unclassified \u003cem\u003eGeminicoccaceae\u003c/em\u003e and positively regulated by \u003cem\u003eBacteroides\u003c/em\u003e and unclassified \u003cem\u003eGaiellales\u003c/em\u003e. The unclassified \u003cem\u003eGeminicoccaceae\u003c/em\u003e negatively regulated \u003cem\u003eFlavobacterium\u003c/em\u003e, unclassified \u003cem\u003eBSV26\u003c/em\u003e, \u003cem\u003ePrevotella 9\u003c/em\u003e, and \u003cem\u003eAchromobacter\u003c/em\u003e and positively regulated unclassified \u003cem\u003eVicinamibacteraceae\u003c/em\u003e, \u003cem\u003eMND1\u003c/em\u003e, unclassified \u003cem\u003eGemmatimonadaceae\u003c/em\u003e, uncultured \u003cem\u003egamma proteobacterium\u003c/em\u003e, and unclassified \u003cem\u003eActinobacteriota\u003c/em\u003e. The unclassified \u003cem\u003eGeminicoccaceae\u003c/em\u003e was also positively influenced by the \u003cem\u003eLachnospiraceae NK4A136 group\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eFemale and male rat livers showed very different bacteria correlations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, C; Table S4, S5). Among the top 100 genera with the highest correlation, 15 were identical with female rats, 14 were identical with male rats, and 4 were identical with both female and male rats. Positive correlations (r\u0026thinsp;=\u0026thinsp;1.0) in both sexes were uncultured \u003cem\u003eDesulfuromonadaceae bacterium\u003c/em\u003e to \u003cem\u003eBacillus\u003c/em\u003e and \u003cem\u003eMegasphaera\u003c/em\u003e to \u003cem\u003eBifidobacterium\u003c/em\u003e. Unclassified \u003cem\u003eBSV26\u003c/em\u003e was negatively correlated (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1) with unclassified \u003cem\u003eGemmatimonadaceae\u003c/em\u003e in both sexes. \u003cem\u003eTerrisporobacter\u003c/em\u003e to \u003cem\u003eLigilactobacillus\u003c/em\u003e was negatively correlated in female rats but positively correlated in male rats (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003eIn female rats, the unclassified \u003cem\u003eGemmatimonadaceae\u003c/em\u003e, in phylum Gemmatimonadota, was the module hub (Zi\u0026thinsp;=\u0026thinsp;2.9, Pi\u0026thinsp;=\u0026thinsp;0). The \u003cem\u003eSulfurifustis\u003c/em\u003e, in phylum Proteobacteria, was the connector (Zi\u0026thinsp;=\u0026thinsp;0, Pi\u0026thinsp;=\u0026thinsp;0.73), and all other nodes were peripheral. In male rats, all nodes showed peripheral properties.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eSex-associated bacteria analysis in rat liver microbiome\u003c/h2\u003e\u003cp\u003eRDA on the liver microbiome at the phylum and genus level revealed differences in the distribution of several types of bacteria in males versus females. At the phylum level, Firmicutes, Bacteroidota, Actinobacteriota, Acidobacteriota, Gemmatimonadota, and Methylomirabilota were positively related to male rats. Chloroflexi, Patescibacteria, unclassified Bacteria, and Proteobacteria were positively related to females. At the genus level, unclassified \u003cem\u003eAcidobacteriales\u003c/em\u003e, \u003cem\u003eMND1\u003c/em\u003e, and unclassified \u003cem\u003eBacteria\u003c/em\u003e were positively related to females, and \u003cem\u003eMegasphaera\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e, unclassified \u003cem\u003eVicinamibacterales\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, unclassified \u003cem\u003eVicinamibacteraceae\u003c/em\u003e, unclassified \u003cem\u003eGemmatimonadaceae\u003c/em\u003e, and \u003cem\u003ePseudomonas\u003c/em\u003e were positively related to males (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003ePhylogenetic diversity and inference of selection processes between female and male rats\u003c/h2\u003e\u003cp\u003eThe null model βNTI index tool was calculated to analyze whether differences in the community structure between groups were dominated by deterministic processes or stochastic processes. The results indicated that the community composition between grouped communities was more similar than would be expected with randomization. The βNTI values of both female and male rat liver microbiomes were clearly below the significance threshold (\u0026minus;\u0026thinsp;2), indicating strong homogeneous deterministic selection with consistent selection pressure (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe liver is the central organ of metabolic regulation and plays pivotal physiological and biochemical roles in maintaining the homeostasis of the body [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Liver dysfunction and disease are leading causes of death. Much research has revealed the biochemical and molecular biology mechanisms of various liver disorders, including hepatic carcinoma, viral hepatitis, alcohol and drug toxicity, and metabolic chaos. Recent research involving gut microbiota in normal and cancerous tissues [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] has considered pathogenic factors in the gut microbiome. Many studies have reported on variations in the gut microbiota of healthy individuals versus people with various organ diseases, including obesity and diabetes [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], autism and mood disorders [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], retinal diseases [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], atherosclerosis [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], liver disease [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], and kidney disease [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Gut leakage has been considered the source of tissue or circulation microbiota [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The liver is intimately involved in interchange with the gut, including by the liver-gut pathway through the bile duct system [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] and the gut-liver pathway through the gut leakage and portal vein system [\u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. It therefore seems natural that microbes would be found in liver tissue.\u003c/p\u003e\u003cp\u003eIn a recent study of the microbiomes of liver tissue from healthy rats, we found that bacterial communities in the liver were different from gut microbiomes, suggesting the possibility of microbiota as an intrinsic component of hepatocytes [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The liver microbiome characteristics, and the effect and mechanisms of microbial host interaction in regulating homeostasis, are thus key areas for further research. Additionally, the liver is an organ with typical sex dependence (dimorphism) [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] and its pathological or physiological processes are associated with sex differences to a large extent [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. However, the molecular mechanism of sex-related genetics and epigenetics in the liver is still to be uncovered. We thus conducted 16S rRNA gene sequencing to explore liver microbes as a component of hepatocytes and to identify dimorphic characteristics of the liver microbiome.\u003c/p\u003e\u003cp\u003eOur study showed different microbiota composition of male and female rat livers. First, female livers had higher bacterial abundance and alpha diversity than those of males, while male livers exhibited higher β-diversity. Second, the female rats showed more gender-specific microbes than male rats. The phylum Nitrospinota was absent in females, while the male rats lacked the bacteria phyla Halanaerobiaeota, Latescibacterota, Caldisericota, and Abditibacteriota and the Archaea phylum Thermoplasmatota. Third, based on LEfSe analysis at the LDA 2.0 level, male and female rat livers had different bacteria biomarkers, including the components of phyla Myxococcota and Proteobacteria for females and components of phyla Nitrospirota and Firmicutes for males. Fourth, male and female rat livers showed different bacteria correlations. For example, \u003cem\u003eTerrisporobacter\u003c/em\u003e to \u003cem\u003eLigilactobacillus\u003c/em\u003e was negatively correlated in female rats (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.0) but positively correlated in male rats (r\u0026thinsp;=\u0026thinsp;1.0). In female rats, unclassified \u003cem\u003eGemmatimonadaceae\u003c/em\u003e was the module hub, and \u003cem\u003eSulfurifustis\u003c/em\u003e was the connector, while in male rats, all nodes showed peripheral properties.\u003c/p\u003e\u003cp\u003eWe also performed RDA and calculated the null model βNTI index. Some gender-specific bacterial distribution was found in the liver microbiome, and the results indicated that the composition of grouped communities was based on homogeneous deterministic selection (βNTI\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;2) rather than stochastic selection. These results suggest dimorphism in the liver microbiome of rats.\u003c/p\u003e\u003cp\u003eRecently, several studies have reported on the sexual dimorphism of gut microbiota from adolescents in pre-puberty and puberty [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], men and pre-menopausal women [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], C57BL/6J mice aged 6\u0026ndash;8 weeks or 12\u0026ndash;13 weeks [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], and juvenile Chinese alligators [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Our research also found sexual differences in the gut microbiota from young SD rats without food ingestion (Beijing Biomarker Technologies Co., Ltd., Beijing, China, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.biomarker.com.cn\u003c/span\u003e\u003cspan address=\"http://www.biomarker.com.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Project Number: BMK230904\u0026ndash;BO604\u0026ndash;ZX01\u0026ndash;0101). However, the relationship of this sexual differentiation between liver microbiota and gut microbiota is still unclear. Based on our comparison of the gut microbiota and liver bacteria, as well as the result of the null model, we conclude that the dimorphic characteristic of liver bacteria is an intrinsic feature.\u003c/p\u003e\u003cp\u003eAlthough there were no significant differences in functional composition and phenotype between female and male liver microbiomes, the different microbe community structures and interactions may contribute to sex-dependent pathological or physiological processes in the host liver.\u003c/p\u003e\u003cp\u003eDisagreement remains about the scientific validity of the conclusion that an abundant microbiota is intrinsic to the liver. Although the detecting process was strictly controlled and we used the most advanced 16S sequencing techniques, some reviewers question the possibility of environmental pollution during sampling. This is based on the long-held assumption that microbes live outside the cell and outside the body in the environment and the feces. In addition, some have questioned whether the 16S sequencing process [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e] could substitute for the culturing process in vitro.\u003c/p\u003e\u003cp\u003eDespite the success of metagenomic sequencing in discovering and analyzing microbiota, many questions remain. For example, most of the microbes have still not been identified and verified by in vitro culture [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], and their relationship to health and pathology is still unclear. With the optimization of sequencing techniques, many findings may be further confirmed or denied [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Our findings may challenge traditional knowledge of microbiology based on traditional technology. However, new technology offers the possibility of uncovering new, groundbreaking knowledge about health and disease in fields such as aging and cancer.\u003c/p\u003e\u003cp\u003eIn conclusion, based on our research showing the liver microbiome as an intrinsic component of hepatocytes, this study further demonstrates its dimorphic characteristics. Compared with the nondimorphism of the gut microbiome, our results provide strong support for the idea of the liver microbiome as an intrinsic component of hepatocytes. Many opportunities for further research remain around the dimorphic features, such as the mechanism for the development of gender-specific microbiota, and the influence of liver microbe communities on physiological and pathological processes in the liver.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eASVs: amplicon sequence variants; βNTI: beta nearest taxon indices; CCA: canonical correspondent analysis; DNA: deoxyribonucleic acid; FAPROTAX: functional annotation of prokaryotic taxa; FLASH: fast length adjustment of short reads; KEGG: kyoto encyclopedia of genes and genomes; LDA: linear discriminant analysis; LEfSe: linear discriminant analysis with effect size; NMDS: non-metric multidimensional scaling; PCA: principal component analysis; PCoA: principal coordinate analysis; PCR: polymerase chain reaction; PERMANOVA: permutational multivariate analysis of variance; PICRUSt2: phylogenetic investigation of communities by reconstruction of unobserved states 2; QIIME: quantitative insights into microbial ecology; RDA: redundancy analysis; rRNA: ribosomal ribonucleic acid; SD: Sprague Dawley; standard deviation; SPF: specific pathogen free.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003cstrong\u003eata\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003evailability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw datasets generated during the current study are available in the NCBI repository (https://www.ncbi.nlm.nih.gov/, accessed on 20 September 2023), BioProject: PRJNA1019449.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest regarding the publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e \u003cstrong\u003estatement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China [Grant Nos. 81670776 and 81970734] to JGZ.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJGZ contributed to the study concept and design. XWS and JGZ contributed to the analysis and interpretation of data and drafted the manuscript. All authors contributed to the acquisition of data and critical revision of the manuscript. All authors approved the final manuscript prior to submission. YMH, ZYL and JXD are shared co-first authors, and XWS and JGZ are co-corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYu-Meng Hao, Pathology Institute, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China. E-mail: [email protected].\u003c/p\u003e\n\u003cp\u003eZhao-Yang Li, Pathology Institute, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China. E-mail: [email protected]\u003c/p\u003e\n\u003cp\u003eJing-Xin Deng, Pathology Institute, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China. E-mail: [email protected].\u003c/p\u003e\n\u003cp\u003eXiao-Wei Sun, Pathology Institute, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China. Tel: 86 13519316382; E-mail: [email protected].\u003c/p\u003e\n\u003cp\u003eJian-Gang Zhang, Pathology Institute, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China. Tel: 86 15095387695; E-mail: [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMarcos R, Lopes C, Malh\u0026atilde;o F, Correia-Gomes C, Fonseca S, Lima M, Gebhardt R, Rocha E (2016) Stereological assessment of sexual dimorphism in the rat liver reveals differences in hepatocytes and Kupffer cells but not hepatic stellate cells. 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Mamm Genome 32(4):282\u0026ndash;296\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"0277f4f7-33e7-4f84-b2d5-06bdd03e0489","identifier":"10.13039/501100001809","name":"National Natural Science Foundation of China","awardNumber":"81670776","order_by":0},{"identity":"51933c61-e4a7-43bb-93e0-271916e75695","identifier":"10.13039/501100001809","name":"National Natural Science Foundation of China","awardNumber":"81970734","order_by":1}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Pathology Institute, School of Basic Medical Sciences; the Second Hospital \u0026 Clinical Medical School, Lanzhou University, Lanzhou 730000, China","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"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":"liver microbiome, sexual dimorphism, 16S rRNA gene sequencing, female and male rats","lastPublishedDoi":"10.21203/rs.3.rs-7751900/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7751900/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMales and females exhibit gender dependence in disease occurrence. In recent research with healthy rats, we assessed the livers of male and female Sprague Dawley rats for the 16S ribosomal ribonucleic acid (rRNA) gene. The V3\u0026ndash;V4 region of the 16S rRNA gene was amplified through polymerase chain reaction and sequenced using an Illumina NovaSeq 6000 platform. Sequences were assigned taxonomically using the Silva database, and the community diversity and relevance were analyzed. We detected 56,926.20\u0026thinsp;\u0026plusmn;\u0026thinsp;16,991.65 effective tags of the 16S rRNA gene and clustered them into 15,845 kinds of amplicon sequence variants (2157.80\u0026thinsp;\u0026plusmn;\u0026thinsp;461.52, 1632\u0026ndash;2728), of which 10 rats shared only 127 kinds in common. The phylum Nitrospinota was exclusive to male rat livers, and the bacteria phyla Halanaerobiaeota, Latescibacterota, Caldisericota, and Abditibacteriota and the Archaea phylum Thermoplasmatota were exclusive to female rat livers. Female liver bacteria showed significantly higher richness but lower beta diversity than male liver bacteria (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Meanwhile, the liver microbiome showed distinct bacteria biomarkers and different bacteria correlations according to linear discriminant analysis effect size (LDA 2.0) and Spearman rank correlation analysis. According to redundancy analysis and the beta nearest taxon index null model, the distribution of several bacteria in the liver microbiome differed based on sex, and differences in community structure between the two groups were dominated by homogeneous deterministic processes rather than stochastic processes. Our results suggest that bacteria in the rat liver microbiome have the intrinsic property of sexual dimorphism.\u003c/p\u003e","manuscriptTitle":"Liver microbiome diversity and sex selection in male and female rats","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-07 04:14:43","doi":"10.21203/rs.3.rs-7751900/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":"11c88601-8af9-4f21-932c-86e2195dfccd","owner":[],"postedDate":"October 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-07T04:14:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-07 04:14:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7751900","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7751900","identity":"rs-7751900","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}