Gut microbiota, lipids, immunity, and liver disease: Insights from Mendelian Randomization and murine models

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Gut microbiota, lipids, immunity, and liver disease: Insights from Mendelian Randomization and murine models

https://doi.org/10.21203/rs.3.rs-4961377/v1

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Liver diseases, including NAFLD and cirrhosis, are significant health issues. This study used Mendelian Randomization (MR) to identify causal links between gut microbiota, lipidomes, immunophenotypes, inflammatory factors, and liver diseases, while also evaluating the therapeutic potential of Lactobacillus salivarius in NAFLD murine models. Summary statistics from GWAS for gut microbiota traits, lipidome components, immune cell counts, inflammatory cytokines, and liver disease traits were analyzed. MR analysis identified causal relationships, with sensitivity analyses confirming the results. Notable findings included 53 gut microbiota taxa, 28 lipidome components, 92 immune cell types, and 8 cytokines linked to liver diseases. Key taxa like Megamonas funiformis increased liver disease risk, while phosphatidylcholines (PCs) were protective. Immune markers such as CD33 on CD33dim HLA-DR- correlated with higher risk, while CD3 on CD39 + resting Tregs and LIF/LIF-R were protective. In vivo, Lactobacillus salivarius treatment significantly improved metabolic and hepatic parameters. The study emphasizes the causal role of gut microbiota and related factors in liver diseases and highlights Lactobacillus salivarius as a promising therapeutic option for NAFLD.

Biological sciences/Microbiology/Communities/Microbiome

Biological sciences/Biotechnology/Biologics

Mendelian randomization

gut microbiota

liver diseases

Lactobacillus salivarius

NAFLD

Liver diseases pose a global health burden, ranging from metabolic disorders like non-alcoholic fatty liver disease (NAFLD) to severe conditions like liver fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). NAFLD, characterized by fat accumulation in liver cells, is the most common chronic liver condition and can progress to NASH, fibrosis, and cirrhosis¹. Cirrhosis, marked by scarring and impaired function, often results from chronic injuries due to viral hepatitis, alcohol abuse, and metabolic syndrome². Understanding the pathogenesis, including roles of liposomes, immunophenotypes, and cytokines, is crucial for developing effective diagnostic and therapeutic strategies.

Recent research highlights a significant connection between gut microbiota and liver diseases. The gut-liver axis is crucial for liver health, with dysbiosis often leading to conditions like NAFLD, liver fibrosis, and cirrhosis. Dysbiosis can disrupt the intestinal barrier, allowing bacterial products like lipopolysaccharides to enter the portal circulation, promoting liver inflammation and fibrosis. Key mechanisms include bacterial translocation and endotoxemia, triggering inflammatory responses in the liver³. Modulating gut microbiota through probiotics like Lactobacillus and Bifidobacterium, prebiotics, and dietary interventions holds promise as a therapeutic approach for liver diseases⁴.

Liposomes, nano-sized vesicles, can encapsulate drugs, enhancing their delivery and efficacy while minimizing side effects. In diseases like NAFLD and liver fibrosis, liposome-based drug delivery systems show promise in targeting hepatic cells and modulating pathological processes. Liposomal formulations of curcumin have demonstrated anti-inflammatory and anti-fibrotic effects in experimental liver disease models⁵. Liposomes can be engineered to improve the bioavailability and stability of therapeutic agents, making them valuable in liver disease treatment⁶.

Immune cells and pro-inflammatory cytokines are key drivers of inflammation and fibrosis in liver diseases.In NAFLD, liver fibrosis, and cirrhosis, immune cells like macrophages, T cells, and hepatic stellate cells (HSCs) mediate inflammation and fibrosis. Pro-inflammatory cytokines like TNF-α, IL-6, and IL-1β exacerbate liver injury by promoting inflammation, apoptosis, and fibrogenesis⁷. Kupffer cells, liver-resident macrophages, activate HSCs through cytokine signaling, leading to fibrosis⁸. Targeting these pathways offers therapeutic avenues, as evidenced by studies on cytokine inhibitors and immunomodulatory therapies⁹.

Mendelian Randomization (MR), based on genetic variations, overcomes confounding biases and reverse causality in traditional epidemiological studies. MR uses genetic variants as instrumental variables to infer causal relationships between exposures and diseases, extensively applied in etiological research¹⁰. This study systematically analyzed causal relationships between gut microbiota, liposomes, immunophenotypes, inflammatory factors, and liver diseases using MR to uncover mechanisms in liver disease onset and progression. Mediation analysis identified pathways and interactions of these biological factors in disease progression. We used Lactobacillus salivarius to investigate its effects on NAFLD in mice. This provides experimental evidence for developing novel microbial-based liver disease therapies.

2.1 Causal relationships between gut microbiota and liver diseases

Using two-sample MR, we identified 53 associations (P__IVW < 0.05) between gut microbiota and liver diseases, with 13, 16, 13, and 11 taxa linked to NAFLD, alcoholic liver disease, fibrosis and cirrhosis, and cirrhosis, respectively (Fig. 2; Supplementary Table S3). Lactobacillus B salivarius and Genus.Parabacteroides were inversely associated with liver disease. Notably, CAG-488 negatively correlated with fibrosis and cirrhosis, while Megamonas funiformis and Rubneribacter sp002159915 positively correlated with these conditions. Sensitivity analyses confirmed these results( Supplementary Table S4), and reverse MR showed a negative correlation between NAFLD and Prevotella bivia (Supplementary Table S5-6, OR = 0.972, P = 0.00974).

2.2 Causal relationships between lipidosome and liver diseases

MR identified 28 significant lipid-liver disease associations: 14 with alcoholic liver disease, 4 with fibrosis and cirrhosis, and 10 with cirrhosis (Fig. 3; Supplementary Table S7). Triacylglycerol (48:2) was a risk factor, while phosphatidylcholines and phosphatidylethanolamine (16:0_18:2) were protective. After FDR correction, only Phosphatidylcholine (15:0_18:2) remained inversely associated with cirrhosis(FDR = 0.0158). Sensitivity analyses showed no heterogeneity or pleiotropy(Supplementary Table S8).

2.3 Causal influence of immunophenotypes and inflammatory factors on liver diseases

IVW analysis identified 92 significant links between immunophenotypes and liver diseases, with 24, 23, 24, and 21 associated with NAFLD, alcoholic liver disease, fibrosis and cirrhosis, and cirrhosis, respectively (Fig. 4; Supplementary Table S9). Ten immune cell features had consistent effects across multiple liver diseases. Six traits, like CD25 + + CD8br AC and Naive DN (CD4-CD8-) %T cell, increased disease risk, while four traits, including CD20 on CD20- CD38- and HLA DR on CD33dim HLA DR + CD11b+, were protective. Sensitivity analyses confirmed these findings, showing no heterogeneity or pleiotropy (Supplementary Table S10).

MR analysis also found 14 cytokine-liver disease associations, with LIF-R emerging as a common protective factor, particularly against fibrosis and cirrhosis (Fig. 5; Supplementary Table S11). FDR correction confirmed LIF-R’s inverse association with cirrhosis (FDR = 0.00412) and fibrosis/cirrhosis (FDR = 0.00434). Sensitivity analyses supported these results, showing no heterogeneity or pleiotropy (Supplementary Table S12).

2.4 Mediation analysis results

Mediation analysis explored causal pathways involving gut microbiota, lipids, immunophenotypes, and inflammatory factors in liver diseases. Using two-sample MR, we identified 16 associations between gut microbiota and lipids, 44 with immunophenotypes, and 5 with cytokines (Supplementary Tables S13). Sensitivity analyses validated the MR findings, with no heterogeneity or pleiotropy detected (Supplementary Table S14). The analysis revealed a negative mediation effect of LIF-R on the link between Rhodococcus and cirrhosis (β = -0.146, 95% CI [-0.281, -0.01141], P = 0.0335), accounting for 14.01% of the total effect.

2.5 Lactobacillus salivarius enhances glycolipid metabolism and hepatic function

In the AVNM study, antibiotic treatment resulted in no significant differences in body weight across groups (Fig. 6B). After 20 weeks on a high-fat diet (HFD), the HFD group had significant increases in body weight, liver weight and fasting glucose indices compared to the normal diet (ND) group, with similar trends in the PBS group. In contrast, the L. salivarius group showed marked improvements in these parameters (Figs. 6C-F). L. salivarius significantly mitigated insulin resistance seen in the HFD and PBS groups (Figs. 6G). ALT and AST levels, which are markers of liver damage, were reduced by L.salivarius (Figs. 6H-I). Elevated serum TG and TC levels in the HFD and PBS groups were also lowered by L. salivarius, with corresponding improvements in hepatic TG and TC levels, reducing steatosis and lipid deposition (Figs. 6J-M).

2.6 Lactobacillus salivarius alleviates HFD-Induced hepatic steatosis

HE staining showed normal liver structures in the ND group, while the HFD and PBS groups had disrupted architecture, hepatocyte swelling, and lipid droplets, consistent with NAFLD. L. salivarius treatment reduced hepatocyte swelling and lipid droplets, indicating less steatosis (Fig. 7A). Oil Red O staining corroborated these findings, showing fewer lipid droplets in the L. salivarius group, resembling the ND pattern (Fig. 7B). Protein analysis revealed higher levels of SREBP1, ChREBP, ACC, and FAS in the HFD group, which were reduced by L. salivarius, indicating decreased lipid deposition (Fig. 7C).

2.7 Lactobacillus salivarius reduces inflammatory cytokine secretion

The HFD and PBS groups had increased serum levels of TNF-α, IL-6, and IL-17A and reduced IL-10, consistent with NAFLD. L. salivarius reduced pro-inflammatory cytokines and increased IL-10 compared to the PBS group, indicating its anti-inflammatory effects (Figs. 7D-G).

This study investigates the potential roles and interactions of gut microbiota, lipidomes, immunophenotypes, and inflammatory factors in the onset and progression of liver diseases using an advanced Mendelian randomization analysis approach. Our findings reveal the complex network relationships among these biological factors and provide new insights into the pathophysiological mechanisms underlying liver diseases. Specifically, we identified possible causal links involving 53 bacterial signatures, 28 lipidome components, 92 immune cell types, and 8 inflammatory factors in relation to liver diseases. Additionally, mediation analysis highlighted the intermediary role of the inflammatory factor LIF-R in the pathogenesis of liver diseases, influenced by the gut microbiome, particularly Rhodococcus. We will further explore the specific mechanisms driving these findings, their clinical implications, and directions for future research.

In our study, Lactobacillus salivarius emerged as a potent therapeutic candidate for improving non-alcoholic fatty liver disease (NAFLD), particularly when driven by a high-fat diet (HFD). NAFLD is strongly linked with metabolic syndrome, insulin resistance, and systemic inflammation, which contribute to the development of hepatic steatosis and fibrosis¹¹. Administering Lactobacillus salivarius in our study resulted in notable improvements in various metabolic and hepatic parameters. Specifically, Lactobacillus salivarius treatment led to significant reductions in body weight, liver weight, fasting blood glucose, insulin resistance, ALT and AST levels, and blood lipid levels compared to HFD and PBS control groups. Mechanistically, Lactobacillus salivarius modulated key lipid metabolism pathways by upregulating genes associated with fatty acid oxidation and downregulating those linked to lipogenesis, thereby reducing hepatic lipid accumulation and enhancing liver function¹². Furthermore, Lactobacillus salivarius enhances gut barrier integrity and reduces endotoxin translocation, leading to decreased hepatic inflammation. Additionally, Lactobacillus salivarius modulates immune responses by reducing pro-inflammatory cytokines such as TNF-α, IL-6, and IL-17A, and increasing the anti-inflammatory cytokine IL-10. This anti-inflammatory effect is vital in preventing the progression of NAFLD to more severe forms, such as non-alcoholic steatohepatitis (NASH) and fibrosis¹³. These findings underscore the potential of Lactobacillus salivarius as a viable probiotic intervention for preventing and managing NAFLD, offering a promising therapeutic strategy to mitigate the adverse effects of high-fat diet-induced liver diseases.

We also discovered that Megamonas funiformis and members of the order Actinomycetales significantly increase the risk of liver diseases. Megamonas funiformis affects hepatic lipid metabolism and intestinal permeability by participating in carbohydrate metabolism and short-chain fatty acid production, thereby worsening non-alcoholic fatty liver disease (NAFLD) through heightened endotoxemia and inflammation¹⁴. Similarly, Actinomycetales members produce bioactive compounds that regulate immune responses and inflammation, promoting pro-inflammatory cytokines and contributing to liver inflammation and fibrosis¹⁵.

Our analysis further underscores the protective role of various phosphatidylcholines (PCs) in mitigating liver disease risk. PCs are vital for lipid metabolism and liver health, improving membrane integrity, facilitating lipid transport, and decreasing oxidative stress¹⁶. Clinical studies indicate that PC supplementation significantly enhances liver function and reduces inflammation in patients with non-alcoholic fatty liver disease (NAFLD)¹⁷. Essential phospholipids (EPLs), which are rich in PC, consistently reduce liver steatosis and improve liver function markers¹⁸. These findings underscore the therapeutic potential of PCs in preventing and managing liver diseases by improving hepatic lipid metabolism and reducing liver inflammation.

The study additionally highlights critical immune cell markers linked with liver disease progression. CD33 on CD33dim HLA-DR- and CD33 on Im MDSCs were positively correlated with liver conditions, indicating that these myeloid-derived suppressor cells (MDSCs) contribute to hepatic inflammation and fibrosis¹⁹. Conversely, markers such as CD3 on CD39 + resting Tregs and CD3 on HLA-DR + CD4 + T cells were negatively associated with liver diseases, indicating protective roles. Regulatory T cells (Tregs) expressing CD39 reduce inflammation by hydrolyzing ATP to adenosine, an anti-inflammatory molecule²⁰. Similarly, HLA-DR + CD4 + T cells enhance immune surveillance and contribute to a strong anti-inflammatory response, thereby supporting liver health. These findings highlight potential therapeutic targets in liver disease management: reducing MDSC activity while enhancing the functions of Tregs and HLA-DR + CD4 + T cells could provide a balanced strategy for mitigating liver inflammation and fibrosis²¹.

Finally, our research suggests that Leukemia Inhibitory Factor (LIF) and its receptor (LIF-R) may play a crucial protective role against liver diseases. LIF-R is an essential receptor involved in the JAK/STAT, MAPK, and PI3K signaling pathways, which regulate inflammation and fibrosis²². Activation of LIF-R enhances anti-inflammatory responses and reduces fibrogenesis by inhibiting the production of pro-inflammatory cytokines and collagen synthesis in hepatic stellate cells²³. These findings highlight the therapeutic potential of targeting LIF-R signaling to mitigate liver inflammation and fibrosis.

However, certain limitations are recognized in this study. First, the lack of demographic information such as age and gender in the initial studies restricts further subgroup analyses. Second, the predominantly European ancestry of the GWAS participants limits the applicability of our findings to other populations. Additionally, the underlying mechanisms of action require further validation through complementary experiments and clinical studies.

4.1 Study design

The study design is shown in Fig. 1. We first collected GWAS summary statistics on gut microbiota composition, lipidome traits, immunophenotypes, inflammatory cytokines, and liver disease status (Supplementary Table S1). A two-sample Mendelian Randomization (MR) analysis was conducted to assess causal relationships among these factors and liver diseases. Additionally, a two-step MR analysis was performed to identify the mediating roles of these factors between gut microbiota and liver diseases. Our MR study followed STROBE-MR guidelines²⁴ (Supplementary Table S2) to ensure the reliability and reproducibility of our findings.

4.2 Data sources

GWAS summary statistics for gut microbiota were obtained from the largest multi-ethnic meta-analysis in the GWAS Catalog (GCST90032172 to GCST90032644), covering 2801 microbial taxa and 7,967,866 genetic variants in 5959 individuals from the FR02 cohort. We used 471 summary statistics across 11 phyla, 19 classes, 24 orders, 62 families, 146 genera, and 209 species²⁵. Lipidome data were retrieved from the GWAS Catalog (GCST90277238 to GCST90277416) involving 7174 Finnish individuals, examining 179 lipids across 13 classes and 4 categories. Immune cell trait data from 3757 Europeans were accessed via the GWAS Catalog (GCST0001391 to GCST0002121) ²⁶and included 731 immune phenotypes²⁷. Cytokine data were derived from the largest pQTL study, including 91 summary statistics from 14,824 participants in 11 cohorts²⁸. Liver disease data were sourced from FinnGen, including NAFLD, alcoholic liver disease, fibrosis, and cirrhosis, with GWAS data from approximately 700,000 Europeans. The diagnosis of NAFLD, alcoholic liver disease, liver fibrosis and cirrhosis is based on International Classification of Diseases (ICD) codes.

4.3 Selection of instrumental variables (IVs)

To estimate causal effects using genetic variations, instrumental variables (IVs) must meet three criteria: association with the exposure, independence from confounders, and influencing the outcome only through the exposure. This study strictly adhered to these criteria. When genome-wide significant loci were insufficient, SNPs were selected with a relaxed threshold (p < 1 × 10⁻⁵ or p < 5 × 10⁻⁸). Outcome-associated SNPs (P < 0.05) were excluded to prevent bias. Clumping ensured SNP independence (r2 < 0.01, 500 kb or r2 < 0.001, 10000 kb). The MR-PRESSO test removed pleiotropic effects by excluding outliers (p > 0.05)²⁹. IV strength was assessed with the F-statistic, excluding those with F < 10³⁰. Steiger filtering removed IVs more correlated with the outcome than the exposure, ensuring accurate mediation. This rigorous selection process strengthens the reliability and validity of our causal inferences.

4.4 Experimental validation in mice

4.4.1 Preparation of bacterial strains and animal experiments

Lactobacillus salivarius Li01 (CGMCC 7045) was originally isolated from the fecal samples of healthy individuals and subsequently preserved by the China General Microbiological Culture Collection Center (CGMCC). Due to its strong probiotic properties, this strain has been widely used in various studies. In our research, we obtained this strain from the CGMCC repository, ensuring the authenticity and purity of its culture. Upon receipt, we cultured the strain anaerobically in de Man, Rogosa, and Sharpe (MRS) broth to maintain its viability and functionality.

Forty female SPF-grade C57BL/6 mice were obtained from Beijing Hua Fukang Biotechnology Co., Ltd., and housed in a specific pathogen-free (SPF) facility with controlled temperature and humidity, with free access to food and water. After a 7-day acclimatization, the mice were randomly divided into four groups of ten: ND (normal diet), HFD (high-fat diet), L. salivarius (antibiotic + HFD + L. salivarius), and PBS (antibiotic + HFD + PBS). The HFD contained 40% fat. Before administration, the L. salivarius and PBS groups were made pseudo-germ-free using an antibiotic cocktail (ampicillin, vancomycin, neomycin, and metronidazole) in drinking water, replaced every two days for four weeks. Subsequently, L. salivarius and PBS groups received intragastric administrations of bacterial suspension or PBS (10^9 CFU/ml, 0.2 ml per mouse) three times a week for eight weeks. Continuous feeding for 20 weeks established the NAFLD model in the PBS group. The experimental design is shown in Fig. 6A.

4.4.2 Serological analysis

Serum ALT and AST levels were measured using commercial kits from Nanjing Jiancheng Bioengineering Institute. Hepatic triglycerides were quantified using a triglyceride assay kit (Nanjing Jiancheng Bioengineering Institute), with results normalized to liver homogenate protein concentration measured by a BCA protein assay kit (Beyotime Biotechnology).

4.4.3 Histological analysis

Liver tissues were fixed in 4% paraformaldehyde, paraffin-embedded, and sectioned at 5 µm thickness. H&E staining assessed hepatic steatosis, inflammation, and fibrosis. Oil Red O staining detected lipid accumulation. Steatosis and fibrosis were quantified based on lipid droplets and fibrotic tissue distribution.

4.4.4 Western blot analysis

Total protein extracts were prepared using a lysis buffer with phosphatase inhibitors. Lysates were resolved by SDS-PAGE and transferred to PVDF membranes. After blocking, membranes were incubated with primary antibodies targeting Acetyl-CoA carboxylase (ACC), fatty acid synthase (FAS), sterol regulatory element-binding protein 1 (SREBP-1), followed by secondary antibody incubation. Chemiluminescent detection was performed, and densitometry was analyzed using ImageJ.

4.5 Statistical analysis

4.5.1 Two-Sample Mendelian Randomization

We used Mendelian Randomization (MR) to assess causal relationships among gut microbiota, lipidome, immunophenotypes, inflammatory cytokines, and liver diseases. The Wald ratio estimator was used for single IVs, while methods like Inverse-Variance Weighted (IVW), maximum likelihood, MR-Egger, weighted median, and weighted mode were applied for multiple IVs. IVW, our primary method, integrates Wald ratio estimates using meta-analysis³¹. Sensitivity analyses, including MR-Egger and MR-PRESSO, checked for pleiotropy³². Heterogeneity was assessed with Cochran's Q test, and the Steiger filter was used to exclude IVs more associated with outcomes than exposures. The Benjamini-Hochberg procedure corrected the false discovery rate (FDR) for multiple comparisons³³. All analyses were conducted in R (version 4.3.3) .

4.5.2 Reverse Mendelian Randomization analysis

Reverse Mendelian Randomization (RMR) was performed to assess if liver diseases have a causal effect on identified gut microbiota (P_IVW< 0.05). SNPs associated with liver diseases were used as IVs, treating liver diseases as exposures and gut microbiota taxa as outcomes. RMR followed standard MR methodologies.

4.5.3 Mediation analysis

We performed mediation analysis to identify pathways through which gut microbiota, lipidome traits, immunophenotypes, and inflammatory factors impact liver diseases. Using two-sample MR, we estimated the causal effect of gut microbiota on these mediators (β(A)), calculated the mediation effect as β(A) × β(B) (where β(B) is the effect of the mediator on liver disease), and derived the direct effect by subtracting the mediation effect from the total effect³⁴. The mediation proportion was determined as a percentage of the total effect, and mediators with significant effects (p < 0.05) were classified as strong evidence mediators. This analysis clarified the roles of these factors in liver disease, highlighting potential therapeutic targets.

4.6.4 Mice experiment

Data were analyzed using GraphPad Prism 9.0 and presented as mean ± SEM. Student's t-test was used for normally distributed data with homogeneity of variance; Welch's t-test was applied otherwise. One-way ANOVA was used for multiple group comparisons. The Kruskal-Wallis test identified significant differences in microbial communities, with statistical significance set at p < 0.05.

By integrating various methodologies, this study has thoroughly elucidated the complex interactions among gut microbiota, lipoproteins, immunophenotypes, and inflammatory factors in liver diseases. Our findings provide new insights into the pathological mechanisms of liver diseases and lay a theoretical foundation for developing novel therapeutic strategies focusing on microbial and immune modulation. However, additional experimental and clinical studies are necessary to confirm the broad applicability and clinical feasibility of these findings.

Competing interests

The author(s) declare no competing interests.

Ethics Statement

All animal experiments were approved by the Ethics Committee of Guangxi Zhuang Autonomous Region People's Hospital (Approval Number: KY-GZR-2024-093) and performed in accordance with their guidelines, including adherence to the ARRIVE guidelines. Mice were anesthetized with isoflurane and euthanized with carbon dioxide to minimize pain and ensure a humane end.

Publisher's Note

The views expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated institutions, the publisher, editors, or reviewers. The publisher does not guarantee or endorse any products that may be evaluated in this article or any claims made by their manufacturers.

Funding Statement

The authors declare that the research, writing, and/or publication of this article were supported by funding. This study was funded by the Guangxi Natural Science Foundation (Grant No. 2024GXNSFBA010057) ,the Self-raised Scientific Research Project of the Health Commission of Guangxi Zhuang Autonomous Region (Grant No. Z-A20230033), the Guangxi Science and Technology Major Project [grant number: AA22096018],the Guangxi Classification of Project [grant number: AB22080094], the Guangxi Natural Science Foundation (Grant No. 2024GXNSFBA010267) .

Author Contribution

LHL: Investigation, Methodology, Software, Writing - Original Draft, Formal Analysis. JR Y: Methodology, Software, Validation, Visualization, Writing - Original Draft, Formal Analysis. WL: Data Curation, Resources, Writing - Original Draft. XY: Conceptualization, Funding Acquisition, Methodology, Supervision, Writing - Original Draft, Project Administration. WS: Conceptualization, Methodology, Writing - Review & Editing. QYL, JLL,XZH,LXP: Conceptualization, Funding Acquisition, Writing - Review & Editing.

Acknowledgement

The authors express their gratitude to the GWAS catalog FinnGen for generously sharing their GWAS summary data publicly, which significantly facilitated the conduct of this study.

Data Availability

The original data pertaining to this research are included within the article and its supplementary materials. For any further inquiries, please contact the corresponding author.

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Gut microbiota, lipids, immunity, and liver disease: Insights from Mendelian Randomization and murine models

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Abstract

Figures

1. Introduction

2. Results

2.1 Causal relationships between gut microbiota and liver diseases

2.2 Causal relationships between lipidosome and liver diseases

2.3 Causal influence of immunophenotypes and inflammatory factors on liver diseases

2.4 Mediation analysis results

2.5 Lactobacillus salivarius enhances glycolipid metabolism and hepatic function

2.6 Lactobacillus salivarius alleviates HFD-Induced hepatic steatosis

2.7 Lactobacillus salivarius reduces inflammatory cytokine secretion

3. Discussion

4. Method

4.1 Study design

4.2 Data sources

4.3 Selection of instrumental variables (IVs)

4.4 Experimental validation in mice

4.4.1 Preparation of bacterial strains and animal experiments

4.4.2 Serological analysis

4.4.3 Histological analysis

4.4.4 Western blot analysis

4.5 Statistical analysis

4.5.1 Two-Sample Mendelian Randomization

4.5.2 Reverse Mendelian Randomization analysis

4.5.3 Mediation analysis

4.6.4 Mice experiment

5. Conclusion

Declarations

Competing interests

Ethics Statement

Publisher's Note

Funding Statement

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

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