Exposure to Streptococcus anginosus facilitates lipid metabolism disorder in obese mouse model

Exposure to Streptococcus anginosus facilitates lipid metabolism disorder in obese mouse model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exposure to Streptococcus anginosus facilitates lipid metabolism disorder in obese mouse model Xian-Long Shu, Jia-Ling Xie, Xi Li, Jie Tang, Guo Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7055704/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Oct, 2025 Read the published version in World Journal of Microbiology and Biotechnology → Version 1 posted 11 You are reading this latest preprint version Abstract Background Mendelian Randomization (MR) analysis can link the host gut microbiome to cardiovascular diseases. Streptococcus anginosus ( S. anginosus ) has been found to be positively correlated with early atherosclerosis, but its role in lipid metabolism remains to be explored. Methods We employed five MR analysis methods to examine the association between gut microbiota and disorders of lipoprotein metabolism. Inducing obesity in mouse models by using a high-fat diet. Throughout the experiment, we measured alterations in serum concentrations of total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). Hematoxylin and eosin (H&E) staining was utilized to assess pathological changes in adipose tissue and liver, while reverse transcription quantitative polymerase chain reaction (RT-qPCR) was conducted to evaluate changes in the expression of genes associated with triglyceride metabolism and synthesis. Results The findings from MR analysis indicate that ten specific gut microbial taxa, including Streptococcaceae , Parabacteroides goldsteinii , and Ruminococcus , exhibit a causal relationship with disorders of lipoprotein metabolism. Notably, Streptococcaceae has been identified as a risk factor for it, this result has been validated in another GWAS study. Furthermore, the presence of S. anginosus has been shown to elevate serum TG levels, diminish the accumulation of lipid droplets in both hepatic and adipose tissues, and downregulate the expression of genes associated with TG metabolism and the enzymes involved in TG synthesis. Conclusion these evidences suggest that Streptococcaceae as a risk factor for lipoprotein metabolism disorders, while S. anginosus induces TG metabolism disorders by impairing the utilization of dietary triglycerides. Streptococcus anginosus triglycerides lipid metabolism lipoprotein lipase Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cardiovascular disease (CVD) represents the foremost cause of mortality globally(Chong et al 2024 , Roth et al 2020 ). Data from the World Heart Federation indicate that in 2021, CVD-related fatalities reached 20.5 million, constituting approximately one-third of all deaths (Mensah et al 2023 ). CVD encompasses a range of chronic conditions characterized by a shared pathological mechanism of atherosclerosis (AS), predominantly impacting the coronary arteries, cerebral arteries, aorta, and peripheral arterial system. The etiology of CVD is multifaceted, with established risk factors including hypertension(Fuchs and Whelton 2020 , Kjeldsen 2018 ), poor dietary habits(Kaminsky et al 2022 ), tobacco use (Teo et al 2006 ), and alcohol consumption (Minzer et al 2020 ). Recent clinical cohort studies have identified lipid metabolism disorders as significant contributors to both AS and CVD(Ginsberg et al 2021 , Imano et al 2011 , Sarwar et al 2007 , Wilson et al 2019 ). These disorders, which primarily involve abnormalities in triglyceride and cholesterol levels, can result in hyperlipidemia, non-alcoholic fatty liver disease, insulin resistance, and obesity(Hegele et al 2014 ). The accumulation of chylomicrons and low-density lipoproteins within the vascular endothelium precipitates the formation of atherosclerotic lesions, thereby heightening the risk of ischemic heart disease (Lawler et al 2017 ). In addition to genetic variations and unhealthy dietary practices, recent research has identified the gut microbiota as a significant factor in the regulation of host metabolic homeostasis, which in turn influences the progression of atherosclerosis (AS) (Jie et al 2017 , Wang and Zhao 2018 , Witkowski et al 2020 ). Clinical investigations have revealed notable alterations in the gut microbiota composition of individuals with hyperlipidemia when compared to healthy controls, characterized by a decrease in both the richness and diversity of bacterial communities(Rebolledo et al 2017 ). Numerous studies have indicated that the abundance of certain gut microbiota, particularly the genus Streptococcus , is markedly elevated in the intestines of AS patients relative to healthy individual(Drapkina et al 2022 , Jie 2017, Koren et al 2011 ). The Swedish Cardiopulmonary Bioimage Study (SCPIS), which involved 8,973 patients with early-stage AS, performed metagenomic sequencing of fecal microbiota. The findings demonstrated that Streptococcus anginosus ( S. anginosus ) exhibited the most pronounced positive correlation with coronary artery calcification among patients and had the highest detection rate in the fecal samples of all participants(Sayols-Baixeras et al 2023 ). Genome-wide association studies (GWAS) have elucidated human genetic variations that exhibit significant correlations with both diseases and the composition and abundance of the host gut microbiota(Lopera-Maya et al 2022 , Qi et al 2022 , Qin et al 2022 ). This advancement facilitates the exploration of the relationship between gut microbiota and diseases through Mendelian randomization (MR) analysis (Smith and Ebrahim 2003 ). Specifically, host genetic variations can modulate the composition and abundance of specific gut microbial populations(Hughes et al 2020 , Kolde et al 2018 , Luca et al 2018 ), while concurrently influencing the onset and progression of various diseases. By leveraging this interplay, MR can establish connections between disease phenotypes and exposure factors(Chen et al 2024a ). MR represents an innovative analytical approach frequently employed to deduce causal relationships between exposure factors and disease outcomes(Benn and Nordestgaard 2018 , Levin and Burgess 2024 , Taylor et al 2019 ). By utilizing genetic variations as instrumental variables, statistical methodologies can be applied to infer causal relationships between exposure factors and outcome variables(Sekula et al 2016 ). In comparison to traditional randomized controlled trials, MR analysis benefits from more readily accessible data and larger sample sizes, thereby serving as a robust and effective analytical tool for investigating the associations between host gut microbiota and diseases(Larsson et al 2023 ). In this research, we put forth the hypothesis that S. anginosus may contribute to the development of lipid metabolism disorders. To investigate this, we employed the GWAS dataset pertaining to lipoprotein metabolism disorders from the FinnGen database, alongside the microbiome GWAS (mbGWAS) data gathered by the Dutch Microbiome Project (DMP). Through a two-sample MR analysis, we identified that microorganisms belonging to the family Streptococcaceae are associated with an increased risk of lipoprotein metabolism disorders. We then validated this result in the hyperlipidemia GWAS study of the Korean National Biobank, and subsequently found that Streptococcaceae and Streptococcus are also risk factors for lipid metabolism disorders. Following this, we established an obesity model in C57BL/6J mice by administering a high-fat diet. After depleting the gut microbiota using an antibiotic cocktail, we subsequently colonized the mice with S. anginosus to investigate its role in lipid metabolism and to corroborate the findings from the MR analysis. Materials and Methods Mendelian Randomization Analysis The dataset utilized for the exposure factor was derived from the Dutch Microbiome Project (DMP), which employed shotgun metagenomic sequencing to analyze the gut microbiome of a cohort comprising 7,738 individuals of European descent. This investigation yielded 207 taxonomic classifications, encompassing 5 phyla, 10 classes, 13 orders, 26 families, 48 genera, and 105 species. The robustness of the sequencing depth, the substantial size of the study population, and the relatively uniform ethnic background render the DMP study a high-quality resource for MR analysis(Zhernakova et al 2024 ). (Data access link: https://github.com/GRONINGEN-MICROBIOME-CENTRE/Groningen-Microbiome/tree/master/Projects/DMP ). The outcome data were sourced from GWAS) on lipoprotein metabolism disorders conducted by FinnGen(Kurki et al 2023 ), which included a total of 37,742 participants, with male individuals constituting 56.75% of the sample. (Data access link: https://storage.googleapis.com/finngen-public-data-r10/summary_stats/finngen_R10_E4_LIPOPROT.gz ). The validation cohort comes from the GWAS study of hyperlipidemia conducted by the Korean National Biobank (Data access link: KoGES PheWeb). Through MR analysis and OR analysis with mbGWAS, the effect of Streptococcaceae on lipoprotein metabolism disorders is validated in different cohorts. Mendelian randomization analysis fundamentally employs linear regression techniques, utilizing genetic variations, specifically SNPs, as instrumental variables (IVs) to investigate the causal relationships between outcome variables and exposure factors. This approach effectively addresses the issue of endogeneity commonly encountered in regression analysis. We filtered the mbGWAS data according to the following criteria: 1. SNPs must demonstrate a strong association with the gut microbiome ( P < 5 × 10 − 8 ). 2. The physical distance between any two SNPs must exceed 10,000 kb, with a correlation coefficient (r 2 ) of less than 0.01. Following this, we employed the F-test to identify SNPs with an F-statistic greater than 20 to be utilized as IVs. The formula for calculating the F-statistic is as follows: \(\:\text{F=}\frac{\text{N-k-1}}{\text{k}}\text{×}\frac{{\text{R}}^{\text{2}}}{\text{1-}{\text{R}}^{\text{2}}}\) (1–1) The specific calculation method for R 2 is as follows: \(\:{\text{R}}^{\text{2}}\text{=2×}\left(\text{1-MAF}\right)\text{×MAF×}{\text{β}}^{\text{2}}\) (1–2) In this context, “N” signifies the sample size utilized in the GWAS data; “k” represents the number of IVs employed; R 2 reflects the degree to which the IVs account for the exposure factors. The term MAF refers to the minor allele frequency, while β denotes the effect size of the SNPs on the exposure factors. Subsequently, five prevalent statistical methodologies in MR analysis are applied to deduce causal relationships between exposure factors and outcome variables. These methods include IVW analysis, MR-Egger regression, weighted median estimation (WME), penalized weighted median, and mode-based estimation. The robustness of the model is evaluated through a leave-one-out sensitivity analysis, followed by a quality control assessment of the MR analysis results. This includes testing for heterogeneity and horizontal pleiotropy of the instrumental variables to ensure adherence to the assumptions of MR. The methodological framework of the MR analysis is illustrated in Fig. 1 . Establishment of a Lipid Metabolism Disorder Model in Mice In the present investigation, male C57BL/6J mice, aged six weeks and weighing approximately 16 ± 2 grams, were utilized to develop a model of lipid metabolism disorder through the administration of a high-fat diet comprising 60% caloric content from fat (XieTong Biotechnology Co., Ltd., China). The experimental animals were procured from STJ Laboratory Animal Co., Ltd. (Changsha, China) and were maintained at the Central South University Experimental Animal Center. Following a one-week acclimatization period on a standard diet, the mice were weighed and subsequently assigned to experimental groups based on their body weight, as illustrated in Fig. 5 -a. Throughout the duration of the study, measurements of blood lipid levels and body weight were conducted biweekly, and fecal samples were collected. At the conclusion of the experiment, all subjects were euthanized via CO2 anesthesia, and liver and adipose tissues were harvested. The animal experimentation protocols received ethical approval from the Central South University Experimental Animal Ethics Review Board (Ethics Review Number: CSU-2024-0045). Bacterial Culture S. anginosus (ATCC9895) was obtained from the American Type Culture Collection. Following its revival, the strain was cultured anaerobically in Brain Heart Infusion (BHI) medium (Thermo Fisher Oxoid, USA) at a temperature of 37°C, with subsequent passaging conducted at a dilution ratio of 5%. The concentration of bacteria was quantified by calculating the colony-forming units (CFU) through the dilution plating technique. The bacterial suspension was subjected to centrifugation at 4000× g for 10 minutes at 4°C, resulting in the collection of the supernatant, which contained the S. anginosus metabolites. The metabolites, along with the bacterial pellet, were then resuspended in an appropriate volume of phosphate-buffered saline (PBS) for oral gavage administration in mice. Lipid Profile Measurement in Serum To conduct serum lipid profile testing, collect 100 µL of mice blood utilizing a centrifuge tube. Following the coagulation of the blood, centrifuge the sample at 1000× g and 4°C for a duration of 10 minutes. Subsequently, aspirate the upper serum layer and employ a lipid testing kit (Nan Jing Jian Cheng Biotechnology Co., Ltd, Nan Jing, China) to quantify the levels of total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) in the serum via the chemiluminescence method. For detailed procedural guidelines, please consult the instructions provided in the kit (A110-1-1, A111-1-1, A112-1-1, and A113-1-1). Histopathology Following the washing of liver and adipose tissue with PBS, the specimens were subsequently fixed in a 4% paraformaldehyde solution. After undergoing dehydration and embedding processes, the tissues were sectioned into 4µm thick slices utilizing a microtome (Leica Instrument Co., Ltd. RM2016, Germany). The resulting sections were stained with a solution comprising 2.5% hematoxylin and 0.5% eosin (H&E). Upon mounting with neutral resin, the stained sections were examined using a pathology scanning and image analysis system (Akoya Biosciences PhenoImager HT, USA). Nucleic Acid Extraction and qRT-PCR Mouse fecal DNA was extracted using a commercial kit (Omega-Biotek, America) following the instructions provided. RNA from liver and adipose tissues was extracted using Trizol (Accurate Biotechnology Co., Ltd, China). After tissue lysis, RNA was extracted with chloroform, precipitated with isopropanol, and washed with 75% ethanol. The RNA pellet was then resuspended in an appropriate amount of DEPC water and the nucleic acid concentration was measured using a nucleic acid quantification analyzer (Thermofisher NanoDrop, America). After reverse transcription of RNA to cDNA, the reaction system was prepared using SYBR Green qPCR Master Mix (Selleck, America), with all primer sequences used in the reaction listed in Supplementary materia1. The amplification of the target gene was monitored using a real-time fluorescence quantitative PCR system (Thermofisher QuantStudio™, America), and the qRT-PCR reaction conditions were consistent with the kit instructions and the recommended annealing temperatures of the primers. Statistical Analysis The analysis was conducted utilizing R Studio (version 4.3.0) to read and filter the exposure data. We employed the analysis packages VariantAnnotation (version 1.48.1), gwasglue (version 0.0.9000), and TwoSampleMR (version 0.5.11) to extract outcome data corresponding to the SNPs identified in the exposure data through the extract outcome data function, subsequently performing MR analysis. Additionally, one-way ANOVA was executed on the mean and standard deviation of the animal experiment data using GraphPad Prism 5 software (GraphPad Software, San Diego, CA), with a significance threshold set at P < 0.05. The results are presented as mean ± standard deviation, and graphical representations were generated using the same software. Results Gut microbiota in Streptococcaceae are risk factor in lipoprotein metabolism disorders MR analysis was performed utilizing several methodologies, including the IVW method, MR-Egger regression, weighted median method, penalized weighted median method, and pattern-based estimation method, to statistically infer the causal link between gut microbiota and lipoprotein metabolism disorders. The analysis revealed that specific microorganisms, including Streptococcaceae , unclassified Bilophila , Bilophila , Escherichia coli , Escherichia , Parabacteroides goldsteinii , Bacteroides cellulosilyticus , Ruminococcus , Bacilli , and Bacteroides finegoldii , exhibit a significant causal association with lipoprotein metabolism disorders (Table1). The linear regression fitting graphs corresponding to the five MR analyses are presented in Figure 2. The odds ratio (OR) analysis further identifies Streptococcaceae , Bacteroides cellulosilyticus , and Bacilli as risk factors for lipoprotein metabolism disorders, in the subsequent GWAS study on hyperlipidemia conducted by the Korean National Biobank, we also observed that Streptococcaceae and Streptococcus are risk factor for hyperlipidemia, as illustrated in Table2 and Figure 3a-b. Results from the pleiotropy test and Q test indicate that the SNPs employed in the MR analysis do not exhibit significant horizontal pleiotropy (Table 3) or heterogeneity (Table 4). Additionally, the leave-one-out sensitivity analysis demonstrates minimal variation in effect values among the SNPs utilized for MR analysis, affirming the robustness and reliability of the findings (Figure 4). S. anginosus reduced the body weight of mice and altered their blood lipid profile. Following treatment with an antibiotic cocktail, the subsequent gavage of S. anginosus led to a marked increase in the concentration of S. anginosus in the feces of the mice (Figure 5-b), suggesting successful colonization of S. anginosus within the intestinal tract. By the eighth week of the study, mice receiving S. anginosus via gavage exhibited a notable decrease in body weight (Figure 5-c), alongside a significantly elevated serum TG concentration compared to Control group (Figure 5-e). However, no significant changes were observed in the levels of TC, LDL-C, or HDL-C (Figures 5-d, 5-F, and 5-g). S. anginosus impairs the utilization and metabolism of dietary lipids in the mice. The liver and adipose tissue are recognized as primary sites for dietary lipid metabolism. Histopathological analysis via hematoxylin and eosin (HE) staining revealed a significant accumulation of lipid droplets in the liver and adipocytes of mice subjected to a high-fat diet. In contrast, following the gavage of S. anginosus , there was a reduction in the accumulation of lipid droplets within the liver (Figure 6-a) and a decrease in adipocyte tissue (Figure 6-b). These findings align with the observed changes in serum TG concentrations, indicating that the gavage of S. anginosus led to diminished utilization of dietary TG, resulting in an increased accumulation of TG in the bloodstream rather than its utilization by peripheral tissues. Utilizing reverse transcription quantitative polymerase chain reaction (RT-qPCR), the expression levels of key enzymatic systems and transcription factors associated with TG synthesis and metabolism in the adipose tissue and liver of mice were assessed. The findings indicated a significant downregulation of lipoprotein lipase (LPL), a critical enzyme in the metabolism of dietary TG within adipose tissue. Additionally, the expression of enzymes responsible for TG synthesis, specifically fatty acid synthase (FASN) and diacylglycerol O-acyltransferase 2 (DGAT2), was markedly reduced in both adipose tissue and liver. Furthermore, the expression of other transcription factors implicated in TG metabolism, including CCAAT/enhancer-binding protein alpha (CEBPα), sterol regulatory element-binding protein 1c (SREBP-1C), and peroxisome proliferator-activated receptor gamma (PPARγ), also exhibited significant downregulation. Conversely, no notable differences were observed in the gene expression of enzymatic systems related to endogenous TG transport and metabolism (refer to Figure 6-c and Figure 6-d). Discussion This research utilizes MR analysis to explore the association between gut microbiota and disorders of lipoprotein metabolism, employing human genetic variations as instrumental variables. The findings indicate that Streptococcus serves as a risk factor for lipoprotein metabolism disorders(95%CI: 1.014093 ~ 1.252069, P <0.01). In the validation cohort, it was also found that Streptococcus (95%CI༚0.948 ~ 1.120, P<0.05) and Streptococcaceae ༈95%CI༚0.948 ~ 1.152, P <0.05༉ are risk factors for hyperlipidemia. These results align with clinical observations, as numerous studies have reported an increased presence of Streptococcaceae in the intestines of patients with CVD. The SCAPIS study further demonstrated a significant positive correlation between S. anginosus and early atherosclerosis, based on extensive metagenomic sequencing conducted within CVD populations. Previous research has identified various gut microbiota as risk factors for CVD through MR analysis, including Ruminococcaceae , which is associated with atrial fibrillation, and Oxalobacter and Clostridium , which may be linked to coronary heart disease(Hu et al 2024 ). Additionally, Terrisporobacter has been associated with elevated levels of host LDL-C and TC(Guo et al 2023 ). Furthermore, Streptococcaceae has been recognized as a risk bacterium for rheumatic valvular disease in several MR studies(Chen et al 2024b ), suggesting that Streptococcus may play a significant role as a risk bacterium in the context of cardiovascular diseases. In a subsequent study, an obese mouse model was employed to further elucidate the role of S. anginosus in dyslipidemia. The findings indicated that S. anginosus contributes to an altered lipid profile in mice, primarily by diminishing the utilization of dietary TG, which leads to an accumulation of TG in the bloodstream. Notably, there was a reduction in the gene expression of enzymes associated with TG synthesis in adipose tissue and the liver, specifically acetyl-CoA carboxylase (ACC), FASN, and DGAT2, as well as transcription factors such as PPARγ, SREBP-1C, and CEBP-α. Conversely, the expression of genes involved in endogenous TG transport (apolipoprotein B100, APOB100) and metabolism (hormone-sensitive lipase, HSL) remained relatively unchanged. These results imply that the impact of S. anginosus on serum TG levels is primarily attributable to the inhibition of dietary TG absorption, with minimal influence on the synthesis of TG by the body. Dietary lipids primarily consist of triglycerides, cholesterol, phospholipids, and sphingolipids, with triglycerides accounting for over 95% of the total dietary lipid content. In the gastrointestinal tract, triglycerides are hydrolyzed by pancreatic lipase into glycerol and free fatty acids. These components are subsequently reassembled into triglycerides within the small intestine and packaged into chylomicrons, facilitated by APOB48, for transport into the bloodstream via the lymphatic system(Malick et al 2023 ). Lipoprotein lipase (LPL), located on the surface of adipose tissue, capillaries, cardiac muscle, and skeletal muscle, hydrolyzes chylomicrons into free fatty acids and triglycerides. The free fatty acids generated can serve as an energy source for oxidative tissues or can be taken up by adipocytes for storage as triglycerides, thereby acting as energy reserves(Morigny et al 2021 ). The remnants of chylomicrons, following hydrolysis, are directed to the liver for clearance(Malick 2023). It is noteworthy that the expression of LPL, which is the rate-limiting enzyme in the hydrolysis of dietary triglycerides, is significantly downregulated. Recent studies have indicated that a reduction in LPL expression may contribute to hypertriglyceridemia(Akivis et al 2024 , Huang et al 2024 , Jin et al 2023 , Larouche et al 2023 ), which could elucidate the observed increase in serum triglyceride levels in mice following S. anginosus treatment, as well as the diminished accumulation of lipid droplets in both liver and adipose tissue. In experimental animal studies, S. anginosus has been shown to induce metabolic disorders related to TG in murine models by inhibiting the hydrolysis of chylomicrons. This finding is consistent with prior MR analyses that suggest the family Streptococcaceae is a risk factor to lipoprotein metabolic disorders. However, due to the heterogeneity present within the GWAS cohort, the MR analysis results may not accurately establish a causal relationship between exposure factors and outcomes(Gupta et al 2017 , Lovegrove et al 2024 ). Therefore, it is imperative to corroborate MR analysis findings through real-world research designs. For instance, certain studies have employed MR analysis to elucidate the causal relationship between gut microbiota and pulmonary hypertension, identifying relevant genes at specific SNP loci through bioinformatics tools, and subsequently validating the expression of these genes in a hypoxia-induced pulmonary hypertension model in mice(Su et al 2025 ). This study also utilized a high-fat diet-induced obesity mouse model to validate the MR analysis outcomes. Nonetheless, this study is not without its limitations. Firstly, the absence of a normal diet control group in the animal experiments restricts our ability to observe the TG metabolic disorders induced by a high-fat diet and to evaluate the specific contribution of S. anginosus to these disorders. Furthermore, lipoproteins encompass a range of types, including chylomicrons, very low-density lipoproteins, low-density lipoproteins, intermediate-density lipoproteins, and high-density lipoproteins(Bhargava et al 2022 ). The use of an obesity model does not adequately replicate the full phenotype of lipoprotein metabolic disorders, necessitating a broader assessment of S. anginosus 's role across various lipid metabolism models. Lastly, the influence of S. anginosus on the expression of enzymes involved in TG synthesis and metabolism warrants further validation at the protein level. Conclusion This research employed GWAS data derived from extensive population sequencing to identify the gut microbiota in family Streptococcaceae as a contributing risk factor for disorders in lipoprotein metabolism. Additionally, it was observed that specific strains within the Streptococcaceae , particularly S. anginosus , can suppress the expression of LPL, thereby diminishing the capacity of mice to utilize dietary TGand resulting in disturbances in TG metabolism. Declarations Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contribution XianLong-Shu.: Investigation, Methodology, Formal analysis, Visualization and Writing - Original Draft; JiaLing-Xie.: Investigation, Resources, Writing-Original Draft; Xi Li.: Investigation and Validation; Jie Tang.: Supervision, Funding Acquisition and Writing - Review & Editing; Guo Wang.: Project Administration and Writing - Review & Editing. All authors reviewed the manuscript. 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Nat Genet 54:134–142. https://doi.org/10.1038/s41588-021-00991-z Smith GD, Ebrahim S (2003) Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol 32:1–22. https://doi.org/10.1093/ije/dyg070 Hughes DA, Bacigalupe R, Wang J, Rühlemann MC, Tito RY, Falony G et al (2020) Genome-wide associations of human gut microbiome variation and implications for causal inference analyses. Nat Microbiol 5:1079–1087. https://doi.org/10.1038/s41564-020-0743-8 Kolde R, Franzosa EA, Rahnavard G, Hall AB, Vlamakis H, Stevens C et al (2018) Host genetic variation and its microbiome interactions within the Human Microbiome Project. Genome Med 10:6. https://doi.org/10.1186/s13073-018-0515-8 Luca F, Kupfer SS, Knights D, Khoruts A, Blekhman R (2018) Functional Genomics of Host-Microbiome Interactions in Humans. 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Genome Med 11:6. https://doi.org/10.1186/s13073-019-0613-2 Sekula P, Del Greco MF, Pattaro C, Köttgen A (2016) Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J Am Soc Nephrol 27:3253–3265. https://doi.org/10.1681/asn.2016010098 Larsson SC, Butterworth AS, Burgess S (2023) Mendelian randomization for cardiovascular diseases: principles and applications. Eur Heart J 44:4913–4924. https://doi.org/10.1093/eurheartj/ehad736 Zhernakova DV, Wang D, Liu L, Andreu-Sánchez S, Zhang Y, Ruiz-Moreno AJ et al (2024) Host genetic regulation of human gut microbial structural variation. Nature 625:813–821. https://doi.org/10.1038/s41586-023-06893-w Kurki MI, Karjalainen J, Palta P, Sipilä TP, Kristiansson K, Donner KM et al (2023) FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613:508–518. https://doi.org/10.1038/s41586-022-05473-8 Hu XZ, Fu LL, Ye B, Ao M, Yan M, Feng HC (2024) Gut microbiota and risk of coronary heart disease: a two-sample Mendelian randomization study. Front Cardiovasc Med 11:1273666. https://doi.org/10.3389/fcvm.2024.1273666 Guo G, Wu Y, Liu Y, Wang Z, Xu G, Wang X et al (2023) Exploring the causal effects of the gut microbiome on serum lipid levels: A two-sample Mendelian randomization analysis. Front Microbiol 14:1113334. https://doi.org/10.3389/fmicb.2023.1113334 Chen X, Hu G, Ning D, Wang D (2024b) Exploring gut microbiota's role in rheumatic valve disease: insights from a Mendelian randomization study and mediation analysis. Front Immunol 15:1362753. https://doi.org/10.3389/fimmu.2024.1362753 Malick WA, Do R, Rosenson RS (2023) Severe hypertriglyceridemia: Existing and emerging therapies. Pharmacol Ther 251:108544. https://doi.org/10.1016/j.pharmthera.2023.108544 Morigny P, Boucher J, Arner P, Langin D (2021) Lipid and glucose metabolism in white adipocytes: pathways, dysfunction and therapeutics. Nat Rev Endocrinol 17:276–295. https://doi.org/10.1038/s41574-021-00471-8 Akivis Y, Alkaissi H, McFarlane SI, Bukharovich I (2024) The Role of Triglycerides in Atherosclerosis: Recent Pathophysiologic Insights and Therapeutic Implications. Curr Cardiol Rev 20:39–49. https://doi.org/10.2174/011573403x272750240109052319 Huang Y, Chen L, Li L, Qi Y, Tong H, Wu H et al (2024) Downregulation of adipose LPL by PAR2 contributes to the development of hypertriglyceridemia. JCI Insight. 9.https://doi.org/10.1172/jci.insight.173240 Jin J, Huangfu B, Xing F, Xu W, He X (2023) Combined exposure to deoxynivalenol facilitates lipid metabolism disorder in high-fat-diet-induced obesity mice. Environ Int 182:108345. https://doi.org/10.1016/j.envint.2023.108345 Larouche M, Khoury E, Brisson D, Gaudet D (2023) Inhibition of Angiopoietin-Like Protein 3 or 3/8 Complex and ApoC-III in Severe Hypertriglyceridemia. Curr Atheroscler Rep 25:1101–1111. https://doi.org/10.1007/s11883-023-01179-y Gupta V, Walia GK, Sachdeva MP (2017) Mendelian randomization': an approach for exploring causal relations in epidemiology. Public Health 145:113–119. https://doi.org/10.1016/j.puhe.2016.12.033 Lovegrove CE, Howles SA, Furniss D, Holmes MV (2024) Causal inference in health and disease: a review of the principles and applications of Mendelian randomization. J Bone Min Res 39:1539–1552. https://doi.org/10.1093/jbmr/zjae136 Su L, Wang X, Lin Y, Zhang Y, Yao D, Pan T et al (2025) Exploring the Causal Relationship Between Gut Microbiota and Pulmonary Artery Hypertension: Insights From Mendelian Randomization. J Am Heart Assoc 14:e038150. https://doi.org/10.1161/jaha.124.038150 Bhargava S, de la Puente-Secades S, Schurgers L, Jankowski J (2022) Lipids and lipoproteins in cardiovascular diseases: a classification. Trends Endocrinol Metab 33:409–423. https://doi.org/10.1016/j.tem.2022.02.001 Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files table1.docx table2.docx table3.docx table4.docx SupplementaryMaterials.docx Cite Share Download PDF Status: Published Journal Publication published 28 Oct, 2025 Read the published version in World Journal of Microbiology and Biotechnology → Version 1 posted Editorial decision: Revision requested 28 Jul, 2025 Reviews received at journal 27 Jul, 2025 Reviews received at journal 27 Jul, 2025 Reviews received at journal 17 Jul, 2025 Reviewers agreed at journal 16 Jul, 2025 Reviewers agreed at journal 13 Jul, 2025 Reviewers agreed at journal 13 Jul, 2025 Reviewers invited by journal 13 Jul, 2025 Editor assigned by journal 07 Jul, 2025 Submission checks completed at journal 07 Jul, 2025 First submitted to journal 06 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7055704","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486832579,"identity":"a0ad5109-902a-42a7-9b10-cce58211ec88","order_by":0,"name":"Xian-Long Shu","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Xian-Long","middleName":"","lastName":"Shu","suffix":""},{"id":486832583,"identity":"880d884f-9ca1-4c7e-8df0-749910876bc3","order_by":1,"name":"Jia-Ling Xie","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jia-Ling","middleName":"","lastName":"Xie","suffix":""},{"id":486832584,"identity":"462e8843-1943-4a5f-a861-07107a7e6bf1","order_by":2,"name":"Xi Li","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Li","suffix":""},{"id":486832585,"identity":"ddbed229-7278-4bc6-a4b2-7c04e7427c5d","order_by":3,"name":"Jie Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACxmYILQeh2IjTwtgApI2J1wLSBdKS2EC0FuZ25uMPfu6oTe/vP2PA8KHsMAP/7AZCDmNLbOw9czx3xo0cA8YZ5w4zSNw5QEgLj2EDb9ux3A0SPAbMvG2HGQwkEghp4f/Y+LftWLoB/xkD5r/EaeFhbOZtq0kwYMgxYGYkTgub4WzZtgOGM26kFRzsOZfOI3GDgBbD/sMPPr5tq5Pn7z+88cGPMms5/hmEtDSAqcNg8gAQ8+BXDwTyEKqOoMJRMApGwSgYwQAAbVNCqKDQC8wAAAAASUVORK5CYII=","orcid":"","institution":"Central South University","correspondingAuthor":true,"prefix":"","firstName":"Jie","middleName":"","lastName":"Tang","suffix":""},{"id":486832586,"identity":"9ad760a3-f4b2-470f-bf80-4111765720b5","order_by":4,"name":"Guo Wang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Guo","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-07-06 04:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7055704/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7055704/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11274-025-04558-6","type":"published","date":"2025-10-28T15:57:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87016255,"identity":"9be651ce-b36a-4acc-b5fa-2be00f2c8c85","added_by":"auto","created_at":"2025-07-18 10:16:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3130054,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResearch Design of Mendelian Randomization Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003embGWAS: microbiota GWAS in Dutch Microbiota Project; SNPs: single nucleotide polymorphism; IVW: inverse-variance weighted; WME: weighted median; DMP: Dutch Microbiota Project.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7055704/v1/49a439255dd8f7d0cb3df5f8.png"},{"id":87016253,"identity":"178f7dd0-ce13-414e-9413-2e6bf2bbd555","added_by":"auto","created_at":"2025-07-18 10:16:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3856424,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatter plots of MR analyses between gut microbiota and\u003c/strong\u003e \u003cstrong\u003edisorder of lipoprotein metabolism\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e5 two‐sample MR methods (IVW method, MR-Egger regression, WME, penalized weighted median, and pattern-based estimation) were used to examine the causal relationship between gut microbiota and disorder of lipoprotein metabolism. ten groups of intestinal microorganisms were associated with lipid metabolism disorder. IVW: inverse variance weighted; MR: Mendelian randomization; SNP: single nucleotide polymorphism.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7055704/v1/1648b856a50a0487a5492871.png"},{"id":87017203,"identity":"bc06b050-f726-4d33-89d1-8b1d0d53030a","added_by":"auto","created_at":"2025-07-18 10:24:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1708374,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMR results of gut microbiota associated with lipoprotein metabolism\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea.\u003c/strong\u003eThe statistical results and the odds ratio data of IVW analysis of 10 species of bacteria that may have causal relationship with disorder of lipoprotein metabolism were presented. Odds ratio data are presented in logarithmic form; \u003cstrong\u003eb.\u003c/strong\u003e The OR analysis results of \u003cem\u003eStreptococcus\u003c/em\u003e and hyperlipidemia GWAS from the Korean National Biobank. IVW: inverse variance weighted; MR: Mendelian randomization; SNP: single‐nucleotide polymorphism.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7055704/v1/a5b2bbefd2aadbe8b1f709ea.png"},{"id":87016260,"identity":"ec1214ff-b1d7-4627-89be-19cf6b53c303","added_by":"auto","created_at":"2025-07-18 10:16:20","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":559807,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLeave-one-out stability tests causal estimates of exposure (gut microbiota) on outcome (disorder of lipoprotein metabolism)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe vertical axis on the left side of the graph shows the names of each SNP, while the horizontal axis represents the magnitude of the MR analysis effect estimates. The black line indicates the range of MR effect estimates for the remaining SNPs after excluding that SNP, the red line represents the range of combined effect estimates for all SNPs, and the dots represent the average values of the estimates.\u003c/p\u003e","description":"","filename":"figure4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7055704/v1/c43dbd57196025b9601dcb47.jpeg"},{"id":87017207,"identity":"0451b8d9-fce7-479b-b635-1e781a04216e","added_by":"auto","created_at":"2025-07-18 10:24:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1948216,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTreatment of experimental animals and dynamic changes in body weight and blood lipids\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea: \u003c/strong\u003eGrouping and treatment of animal experiments; \u003cstrong\u003eb:\u003c/strong\u003e Relative abundance of \u003cem\u003eS. anginosus\u003c/em\u003e in the feces of each group of mice after 2 weeks; \u003cstrong\u003ec:\u003c/strong\u003eDynamic changes in body weight of mice over 12 weeks; \u003cstrong\u003ed-g: \u003c/strong\u003eDynamic changes in serum total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) concentrations in mice over 12 weeks. Data are presented as means ± SD (n = 8 per group), the different lowercase letters signify significant differences (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Antibiotics cocktail: Ampicillin 100 mg/kg/d, Neomycin sulfate 100 mg/kg/d, Metronidazole 100 mg/kg/d, and Vancomycin 50 mg/kg/d; \u003cem\u003eip\u003c/em\u003e.: intraperitoneal; \u003cem\u003eig\u003c/em\u003e.: intragastric irrigation.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7055704/v1/5e5a4b4eb4de517f8c8897b9.png"},{"id":87017205,"identity":"b07dad12-7e80-472a-b927-c64a7ad510df","added_by":"auto","created_at":"2025-07-18 10:24:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":11612746,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathological changes in adipose tissue and the liver, as well as changes in gene expression of TG metabolism and synthesis enzyme systems\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea:\u003c/strong\u003eH\u0026amp;E staining of liver sections; \u003cstrong\u003eb:\u003c/strong\u003e H\u0026amp;E staining of EP adipose. \u003cstrong\u003ec:\u003c/strong\u003eChanges in gene expression of dietary triglyceride metabolism (LPL) enzyme, endogenous TG synthesis enzymes and transcription factors (FASN, DGAT2, CEBP-α, SREBP-1C, and PPAR-γ), and endogenous TG hydrolysis enzyme (HSL) in adipose tissue. \u003cstrong\u003ed:\u003c/strong\u003e Changes in gene expression of endogenous TG synthesis enzymes (ACC, FASN, and DGAT2) and transcription factors (SREBP-1C and PPARγ), and endogenous TG transport protein (APOB100) in the liver. Data are presented as means ± SD (n = 3 per group), the different lowercase letters signify significant differences (P \u0026lt; 0.05). LPL: lipoprotein lipase; FASN: fatty acid synthase; DGAT2: diacylglycerol O-acyltransferase 2; CEBP-α: CCAAT/enhancer-binding protein alpha; PPARγ: peroxisome proliferator-activated receptor gamma; HSL: hormone-sensitive lipase; ACC: acetyl-CoA carboxylase; SREBP-1C: sterol regulatory element-binding protein 1c; APOB100: apolipoprotein B100\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7055704/v1/4ba644683c09cb0ceed01591.png"},{"id":95039906,"identity":"94895473-bf5c-4bb7-8892-d0356121652f","added_by":"auto","created_at":"2025-11-03 16:05:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":22433749,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7055704/v1/83be0ef1-c038-4c78-911b-8cf2654716cd.pdf"},{"id":87016259,"identity":"4b7d220a-7297-4f4f-9505-14d8c7ce2a3e","added_by":"auto","created_at":"2025-07-18 10:16:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":28021,"visible":true,"origin":"","legend":"","description":"","filename":"table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7055704/v1/8614ef948f803e9db495429e.docx"},{"id":87016262,"identity":"2347b7d2-52ca-4f2e-a543-395186bac8f4","added_by":"auto","created_at":"2025-07-18 10:16:20","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18110,"visible":true,"origin":"","legend":"","description":"","filename":"table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7055704/v1/1923c28b265a91865fd0ea6f.docx"},{"id":87017202,"identity":"c7b3ef87-3964-49e4-96f4-a66f4827c7c5","added_by":"auto","created_at":"2025-07-18 10:24:20","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17796,"visible":true,"origin":"","legend":"","description":"","filename":"table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7055704/v1/6576d9634c1739342433497c.docx"},{"id":87016265,"identity":"8e717182-7e17-4ce8-ac79-cf994ae07e90","added_by":"auto","created_at":"2025-07-18 10:16:20","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":19214,"visible":true,"origin":"","legend":"","description":"","filename":"table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-7055704/v1/6dc88d2ebd847350905824a9.docx"},{"id":87016264,"identity":"05c88a5a-571b-438a-a441-e6f93fc72680","added_by":"auto","created_at":"2025-07-18 10:16:20","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":18836,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7055704/v1/646d47534910d279e25c7e35.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exposure to Streptococcus anginosus facilitates lipid metabolism disorder in obese mouse model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular disease (CVD) represents the foremost cause of mortality globally(Chong et al \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Roth et al \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Data from the World Heart Federation indicate that in 2021, CVD-related fatalities reached 20.5\u0026nbsp;million, constituting approximately one-third of all deaths (Mensah et al \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). CVD encompasses a range of chronic conditions characterized by a shared pathological mechanism of atherosclerosis (AS), predominantly impacting the coronary arteries, cerebral arteries, aorta, and peripheral arterial system. The etiology of CVD is multifaceted, with established risk factors including hypertension(Fuchs and Whelton \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Kjeldsen \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), poor dietary habits(Kaminsky et al \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), tobacco use (Teo et al \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and alcohol consumption (Minzer et al \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Recent clinical cohort studies have identified lipid metabolism disorders as significant contributors to both AS and CVD(Ginsberg et al \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Imano et al \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Sarwar et al \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Wilson et al \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These disorders, which primarily involve abnormalities in triglyceride and cholesterol levels, can result in hyperlipidemia, non-alcoholic fatty liver disease, insulin resistance, and obesity(Hegele et al \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The accumulation of chylomicrons and low-density lipoproteins within the vascular endothelium precipitates the formation of atherosclerotic lesions, thereby heightening the risk of ischemic heart disease (Lawler et al \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn addition to genetic variations and unhealthy dietary practices, recent research has identified the gut microbiota as a significant factor in the regulation of host metabolic homeostasis, which in turn influences the progression of atherosclerosis (AS) (Jie et al \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Wang and Zhao \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Witkowski et al \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Clinical investigations have revealed notable alterations in the gut microbiota composition of individuals with hyperlipidemia when compared to healthy controls, characterized by a decrease in both the richness and diversity of bacterial communities(Rebolledo et al \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Numerous studies have indicated that the abundance of certain gut microbiota, particularly the genus \u003cem\u003eStreptococcus\u003c/em\u003e, is markedly elevated in the intestines of AS patients relative to healthy individual(Drapkina et al \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Jie 2017, Koren et al \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The Swedish Cardiopulmonary Bioimage Study (SCPIS), which involved 8,973 patients with early-stage AS, performed metagenomic sequencing of fecal microbiota. The findings demonstrated that \u003cem\u003eStreptococcus anginosus\u003c/em\u003e (\u003cem\u003eS. anginosus\u003c/em\u003e) exhibited the most pronounced positive correlation with coronary artery calcification among patients and had the highest detection rate in the fecal samples of all participants(Sayols-Baixeras et al \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGenome-wide association studies (GWAS) have elucidated human genetic variations that exhibit significant correlations with both diseases and the composition and abundance of the host gut microbiota(Lopera-Maya et al \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Qi et al \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Qin et al \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This advancement facilitates the exploration of the relationship between gut microbiota and diseases through Mendelian randomization (MR) analysis (Smith and Ebrahim \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Specifically, host genetic variations can modulate the composition and abundance of specific gut microbial populations(Hughes et al \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Kolde et al \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Luca et al \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), while concurrently influencing the onset and progression of various diseases. By leveraging this interplay, MR can establish connections between disease phenotypes and exposure factors(Chen et al \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). MR represents an innovative analytical approach frequently employed to deduce causal relationships between exposure factors and disease outcomes(Benn and Nordestgaard \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Levin and Burgess \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Taylor et al \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). By utilizing genetic variations as instrumental variables, statistical methodologies can be applied to infer causal relationships between exposure factors and outcome variables(Sekula et al \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In comparison to traditional randomized controlled trials, MR analysis benefits from more readily accessible data and larger sample sizes, thereby serving as a robust and effective analytical tool for investigating the associations between host gut microbiota and diseases(Larsson et al \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this research, we put forth the hypothesis that \u003cem\u003eS. anginosus\u003c/em\u003e may contribute to the development of lipid metabolism disorders. To investigate this, we employed the GWAS dataset pertaining to lipoprotein metabolism disorders from the FinnGen database, alongside the microbiome GWAS (mbGWAS) data gathered by the Dutch Microbiome Project (DMP). Through a two-sample MR analysis, we identified that microorganisms belonging to the family \u003cem\u003eStreptococcaceae\u003c/em\u003e are associated with an increased risk of lipoprotein metabolism disorders. We then validated this result in the hyperlipidemia GWAS study of the Korean National Biobank, and subsequently found that \u003cem\u003eStreptococcaceae\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e are also risk factors for lipid metabolism disorders. Following this, we established an obesity model in C57BL/6J mice by administering a high-fat diet. After depleting the gut microbiota using an antibiotic cocktail, we subsequently colonized the mice with \u003cem\u003eS. anginosus\u003c/em\u003e to investigate its role in lipid metabolism and to corroborate the findings from the MR analysis.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003eMendelian Randomization Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe dataset utilized for the exposure factor was derived from the Dutch Microbiome Project (DMP), which employed shotgun metagenomic sequencing to analyze the gut microbiome of a cohort comprising 7,738 individuals of European descent. This investigation yielded 207 taxonomic classifications, encompassing 5 phyla, 10 classes, 13 orders, 26 families, 48 genera, and 105 species. The robustness of the sequencing depth, the substantial size of the study population, and the relatively uniform ethnic background render the DMP study a high-quality resource for MR analysis(Zhernakova et al \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). (Data access link: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/GRONINGEN-MICROBIOME-CENTRE/Groningen-Microbiome/tree/master/Projects/DMP\u003c/span\u003e\u003cspan address=\"https://github.com/GRONINGEN-MICROBIOME-CENTRE/Groningen-Microbiome/tree/master/Projects/DMP\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The outcome data were sourced from GWAS) on lipoprotein metabolism disorders conducted by FinnGen(Kurki et al \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which included a total of 37,742 participants, with male individuals constituting 56.75% of the sample. (Data access link: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://storage.googleapis.com/finngen-public-data-r10/summary_stats/finngen_R10_E4_LIPOPROT.gz\u003c/span\u003e\u003cspan address=\"https://storage.googleapis.com/finngen-public-data-r10/summary_stats/finngen_R10_E4_LIPOPROT.gz\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The validation cohort comes from the GWAS study of hyperlipidemia conducted by the Korean National Biobank (Data access link: KoGES PheWeb). Through MR analysis and OR analysis with mbGWAS, the effect of \u003cem\u003eStreptococcaceae\u003c/em\u003e on lipoprotein metabolism disorders is validated in different cohorts.\u003c/p\u003e\u003cp\u003eMendelian randomization analysis fundamentally employs linear regression techniques, utilizing genetic variations, specifically SNPs, as instrumental variables (IVs) to investigate the causal relationships between outcome variables and exposure factors. This approach effectively addresses the issue of endogeneity commonly encountered in regression analysis. We filtered the mbGWAS data according to the following criteria: 1. SNPs must demonstrate a strong association with the gut microbiome (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e). 2. The physical distance between any two SNPs must exceed 10,000 kb, with a correlation coefficient (r\u003csup\u003e2\u003c/sup\u003e) of less than 0.01. Following this, we employed the F-test to identify SNPs with an F-statistic greater than 20 to be utilized as IVs. The formula for calculating the F-statistic is as follows:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{F=}\\frac{\\text{N-k-1}}{\\text{k}}\\text{\u0026times;}\\frac{{\\text{R}}^{\\text{2}}}{\\text{1-}{\\text{R}}^{\\text{2}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1\u0026ndash;1)\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\u003eThe specific calculation method for R\u003csup\u003e2\u003c/sup\u003e is as follows:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{R}}^{\\text{2}}\\text{=2\u0026times;}\\left(\\text{1-MAF}\\right)\\text{\u0026times;MAF\u0026times;}{\\text{\u0026beta;}}^{\\text{2}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1\u0026ndash;2)\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\u003eIn this context, \u0026ldquo;N\u0026rdquo; signifies the sample size utilized in the GWAS data; \u0026ldquo;k\u0026rdquo; represents the number of IVs employed; R\u003csup\u003e2\u003c/sup\u003e reflects the degree to which the IVs account for the exposure factors. The term MAF refers to the minor allele frequency, while β denotes the effect size of the SNPs on the exposure factors.\u003c/p\u003e\u003cp\u003eSubsequently, five prevalent statistical methodologies in MR analysis are applied to deduce causal relationships between exposure factors and outcome variables. These methods include IVW analysis, MR-Egger regression, weighted median estimation (WME), penalized weighted median, and mode-based estimation. The robustness of the model is evaluated through a leave-one-out sensitivity analysis, followed by a quality control assessment of the MR analysis results. This includes testing for heterogeneity and horizontal pleiotropy of the instrumental variables to ensure adherence to the assumptions of MR. The methodological framework of the MR analysis is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEstablishment of a Lipid Metabolism Disorder Model in Mice\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the present investigation, male C57BL/6J mice, aged six weeks and weighing approximately 16\u0026thinsp;\u0026plusmn;\u0026thinsp;2 grams, were utilized to develop a model of lipid metabolism disorder through the administration of a high-fat diet comprising 60% caloric content from fat (XieTong Biotechnology Co., Ltd., China). The experimental animals were procured from STJ Laboratory Animal Co., Ltd. (Changsha, China) and were maintained at the Central South University Experimental Animal Center. Following a one-week acclimatization period on a standard diet, the mice were weighed and subsequently assigned to experimental groups based on their body weight, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e5\u003c/span\u003e-a. Throughout the duration of the study, measurements of blood lipid levels and body weight were conducted biweekly, and fecal samples were collected. At the conclusion of the experiment, all subjects were euthanized via CO2 anesthesia, and liver and adipose tissues were harvested. The animal experimentation protocols received ethical approval from the Central South University Experimental Animal Ethics Review Board (Ethics Review Number: CSU-2024-0045).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eBacterial Culture\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eS. anginosus\u003c/em\u003e (ATCC9895) was obtained from the American Type Culture Collection. Following its revival, the strain was cultured anaerobically in Brain Heart Infusion (BHI) medium (Thermo Fisher Oxoid, USA) at a temperature of 37\u0026deg;C, with subsequent passaging conducted at a dilution ratio of 5%. The concentration of bacteria was quantified by calculating the colony-forming units (CFU) through the dilution plating technique. The bacterial suspension was subjected to centrifugation at 4000\u0026times;\u003cem\u003eg\u003c/em\u003e for 10 minutes at 4\u0026deg;C, resulting in the collection of the supernatant, which contained the \u003cem\u003eS. anginosus\u003c/em\u003e metabolites. The metabolites, along with the bacterial pellet, were then resuspended in an appropriate volume of phosphate-buffered saline (PBS) for oral gavage administration in mice.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLipid Profile Measurement in Serum\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo conduct serum lipid profile testing, collect 100 \u0026micro;L of mice blood utilizing a centrifuge tube. Following the coagulation of the blood, centrifuge the sample at 1000\u0026times;\u003cem\u003eg\u003c/em\u003e and 4\u0026deg;C for a duration of 10 minutes. Subsequently, aspirate the upper serum layer and employ a lipid testing kit (Nan Jing Jian Cheng Biotechnology Co., Ltd, Nan Jing, China) to quantify the levels of total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) in the serum via the chemiluminescence method. For detailed procedural guidelines, please consult the instructions provided in the kit (A110-1-1, A111-1-1, A112-1-1, and A113-1-1).\u003c/p\u003e\u003cp\u003e\u003cb\u003eHistopathology\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFollowing the washing of liver and adipose tissue with PBS, the specimens were subsequently fixed in a 4% paraformaldehyde solution. After undergoing dehydration and embedding processes, the tissues were sectioned into 4\u0026micro;m thick slices utilizing a microtome (Leica Instrument Co., Ltd. RM2016, Germany). The resulting sections were stained with a solution comprising 2.5% hematoxylin and 0.5% eosin (H\u0026amp;E). Upon mounting with neutral resin, the stained sections were examined using a pathology scanning and image analysis system (Akoya Biosciences PhenoImager HT, USA).\u003c/p\u003e\u003cp\u003e\u003cb\u003eNucleic Acid Extraction and qRT-PCR\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMouse fecal DNA was extracted using a commercial kit (Omega-Biotek, America) following the instructions provided. RNA from liver and adipose tissues was extracted using Trizol (Accurate Biotechnology Co., Ltd, China). After tissue lysis, RNA was extracted with chloroform, precipitated with isopropanol, and washed with 75% ethanol. The RNA pellet was then resuspended in an appropriate amount of DEPC water and the nucleic acid concentration was measured using a nucleic acid quantification analyzer (Thermofisher NanoDrop, America). After reverse transcription of RNA to cDNA, the reaction system was prepared using SYBR Green qPCR Master Mix (Selleck, America), with all primer sequences used in the reaction listed in Supplementary materia1. The amplification of the target gene was monitored using a real-time fluorescence quantitative PCR system (Thermofisher QuantStudio\u0026trade;, America), and the qRT-PCR reaction conditions were consistent with the kit instructions and the recommended annealing temperatures of the primers.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eThe analysis was conducted utilizing R Studio (version 4.3.0) to read and filter the exposure data. We employed the analysis packages VariantAnnotation (version 1.48.1), gwasglue (version 0.0.9000), and TwoSampleMR (version 0.5.11) to extract outcome data corresponding to the SNPs identified in the exposure data through the extract outcome data function, subsequently performing MR analysis. Additionally, one-way ANOVA was executed on the mean and standard deviation of the animal experiment data using GraphPad Prism 5 software (GraphPad Software, San Diego, CA), with a significance threshold set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The results are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and graphical representations were generated using the same software.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGut microbiota in \u003cem\u003eStreptococcaceae\u003c/em\u003e are risk factor in lipoprotein metabolism disorders\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMR analysis was performed utilizing several methodologies, including the IVW method, MR-Egger regression, weighted median method, penalized weighted median method, and pattern-based estimation method, to statistically infer the causal link between gut microbiota and lipoprotein metabolism disorders. The analysis revealed that specific microorganisms, including\u003cem\u003e\u0026nbsp;Streptococcaceae\u003c/em\u003e, \u003cem\u003eunclassified Bilophila\u003c/em\u003e, \u003cem\u003eBilophila\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eEscherichia\u003c/em\u003e, \u003cem\u003eParabacteroides goldsteinii\u003c/em\u003e, \u003cem\u003eBacteroides cellulosilyticus\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eBacilli\u003c/em\u003e, and \u003cem\u003eBacteroides finegoldii\u003c/em\u003e, exhibit a significant causal association with lipoprotein metabolism disorders (Table1). The linear regression fitting graphs corresponding to the five MR analyses are presented in Figure 2. The odds ratio (OR) analysis further identifies \u003cem\u003eStreptococcaceae\u003c/em\u003e, \u003cem\u003eBacteroides cellulosilyticus\u003c/em\u003e, and \u003cem\u003eBacilli\u003c/em\u003e as risk factors for lipoprotein metabolism disorders, in the subsequent GWAS study on hyperlipidemia conducted by the Korean National Biobank, we also observed that \u003cem\u003eStreptococcaceae and Streptococcus\u003c/em\u003e are risk factor for hyperlipidemia, as illustrated in Table2 and Figure 3a-b. Results from the pleiotropy test and Q test indicate that the SNPs employed in the MR analysis do not exhibit significant horizontal pleiotropy (Table 3) or heterogeneity (Table 4). Additionally, the leave-one-out sensitivity analysis demonstrates minimal variation in effect values among the SNPs utilized for MR analysis, affirming the robustness and reliability of the findings (Figure 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eS. anginosus\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;reduced the body weight of mice and altered their blood lipid profile.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing treatment with an antibiotic cocktail, the subsequent gavage of \u003cem\u003eS. anginosus\u003c/em\u003e led to a marked increase in the concentration of \u003cem\u003eS. anginosus\u003c/em\u003e in the feces of the mice (Figure 5-b), suggesting successful colonization of \u003cem\u003eS. anginosus\u003c/em\u003e within the intestinal tract. By the eighth week of the study, mice receiving \u003cem\u003eS. anginosus\u003c/em\u003e via gavage exhibited a notable decrease in body weight (Figure 5-c), alongside a significantly elevated serum TG concentration compared to Control group (Figure 5-e). However, no significant changes were observed in the levels of TC, LDL-C, or HDL-C (Figures 5-d, 5-F, and 5-g).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eS. anginosus\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;impairs the utilization and metabolism of dietary lipids in the mice.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe liver and adipose tissue are recognized as primary sites for dietary lipid metabolism. Histopathological analysis via hematoxylin and eosin (HE) staining revealed a significant accumulation of lipid droplets in the liver and adipocytes of mice subjected to a high-fat diet. In contrast, following the gavage of \u003cem\u003eS. anginosus\u003c/em\u003e, there was a reduction in the accumulation of lipid droplets within the liver (Figure 6-a) and a decrease in adipocyte tissue (Figure 6-b). These findings align with the observed changes in serum TG concentrations, indicating that the gavage of \u003cem\u003eS. anginosus\u003c/em\u003e led to diminished utilization of dietary TG, resulting in an increased accumulation of TG in the bloodstream rather than its utilization by peripheral tissues.\u003c/p\u003e\n\u003cp\u003eUtilizing reverse transcription quantitative polymerase chain reaction (RT-qPCR), the expression levels of key enzymatic systems and transcription factors associated with TG synthesis and metabolism in the adipose tissue and liver of mice were assessed. The findings indicated a significant downregulation of lipoprotein lipase (LPL), a critical enzyme in the metabolism of dietary TG within adipose tissue. Additionally, the expression of enzymes responsible for TG synthesis, specifically fatty acid synthase (FASN) and diacylglycerol O-acyltransferase 2 (DGAT2), was markedly reduced in both adipose tissue and liver. Furthermore, the expression of other transcription factors implicated in TG metabolism, including CCAAT/enhancer-binding protein alpha (CEBPα), sterol regulatory element-binding protein 1c (SREBP-1C), and peroxisome proliferator-activated receptor gamma (PPARγ), also exhibited significant downregulation. Conversely, no notable differences were observed in the gene expression of enzymatic systems related to endogenous TG transport and metabolism (refer to Figure 6-c and Figure 6-d).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis research utilizes MR analysis to explore the association between gut microbiota and disorders of lipoprotein metabolism, employing human genetic variations as instrumental variables. The findings indicate that \u003cem\u003eStreptococcus\u003c/em\u003e serves as a risk factor for lipoprotein metabolism disorders(95%CI: 1.014093\u0026thinsp;~\u0026thinsp;1.252069, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01). In the validation cohort, it was also found that \u003cem\u003eStreptococcus\u003c/em\u003e(95%CI༚0.948\u0026thinsp;~\u0026thinsp;1.120, P\u0026lt;0.05) and \u003cem\u003eStreptococcaceae\u003c/em\u003e༈95%CI༚0.948\u0026thinsp;~\u0026thinsp;1.152, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05༉ are risk factors for hyperlipidemia. These results align with clinical observations, as numerous studies have reported an increased presence of \u003cem\u003eStreptococcaceae\u003c/em\u003e in the intestines of patients with CVD. The SCAPIS study further demonstrated a significant positive correlation between \u003cem\u003eS. anginosus\u003c/em\u003e and early atherosclerosis, based on extensive metagenomic sequencing conducted within CVD populations. Previous research has identified various gut microbiota as risk factors for CVD through MR analysis, including \u003cem\u003eRuminococcaceae\u003c/em\u003e, which is associated with atrial fibrillation, and \u003cem\u003eOxalobacter\u003c/em\u003e and \u003cem\u003eClostridium\u003c/em\u003e, which may be linked to coronary heart disease(Hu et al \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, \u003cem\u003eTerrisporobacter\u003c/em\u003e has been associated with elevated levels of host LDL-C and TC(Guo et al \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, \u003cem\u003eStreptococcaceae\u003c/em\u003e has been recognized as a risk bacterium for rheumatic valvular disease in several MR studies(Chen et al \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e), suggesting that \u003cem\u003eStreptococcus\u003c/em\u003e may play a significant role as a risk bacterium in the context of cardiovascular diseases.\u003c/p\u003e\u003cp\u003eIn a subsequent study, an obese mouse model was employed to further elucidate the role of \u003cem\u003eS. anginosus\u003c/em\u003e in dyslipidemia. The findings indicated that \u003cem\u003eS. anginosus\u003c/em\u003e contributes to an altered lipid profile in mice, primarily by diminishing the utilization of dietary TG, which leads to an accumulation of TG in the bloodstream. Notably, there was a reduction in the gene expression of enzymes associated with TG synthesis in adipose tissue and the liver, specifically acetyl-CoA carboxylase (ACC), FASN, and DGAT2, as well as transcription factors such as PPARγ, SREBP-1C, and CEBP-α. Conversely, the expression of genes involved in endogenous TG transport (apolipoprotein B100, APOB100) and metabolism (hormone-sensitive lipase, HSL) remained relatively unchanged. These results imply that the impact of \u003cem\u003eS. anginosus\u003c/em\u003e on serum TG levels is primarily attributable to the inhibition of dietary TG absorption, with minimal influence on the synthesis of TG by the body. Dietary lipids primarily consist of triglycerides, cholesterol, phospholipids, and sphingolipids, with triglycerides accounting for over 95% of the total dietary lipid content. In the gastrointestinal tract, triglycerides are hydrolyzed by pancreatic lipase into glycerol and free fatty acids. These components are subsequently reassembled into triglycerides within the small intestine and packaged into chylomicrons, facilitated by APOB48, for transport into the bloodstream via the lymphatic system(Malick et al \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Lipoprotein lipase (LPL), located on the surface of adipose tissue, capillaries, cardiac muscle, and skeletal muscle, hydrolyzes chylomicrons into free fatty acids and triglycerides. The free fatty acids generated can serve as an energy source for oxidative tissues or can be taken up by adipocytes for storage as triglycerides, thereby acting as energy reserves(Morigny et al \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The remnants of chylomicrons, following hydrolysis, are directed to the liver for clearance(Malick 2023). It is noteworthy that the expression of LPL, which is the rate-limiting enzyme in the hydrolysis of dietary triglycerides, is significantly downregulated. Recent studies have indicated that a reduction in LPL expression may contribute to hypertriglyceridemia(Akivis et al \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Huang et al \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Jin et al \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Larouche et al \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which could elucidate the observed increase in serum triglyceride levels in mice following \u003cem\u003eS. anginosus\u003c/em\u003e treatment, as well as the diminished accumulation of lipid droplets in both liver and adipose tissue.\u003c/p\u003e\u003cp\u003eIn experimental animal studies, \u003cem\u003eS. anginosus\u003c/em\u003e has been shown to induce metabolic disorders related to TG in murine models by inhibiting the hydrolysis of chylomicrons. This finding is consistent with prior MR analyses that suggest the family \u003cem\u003eStreptococcaceae\u003c/em\u003e is a risk factor to lipoprotein metabolic disorders. However, due to the heterogeneity present within the GWAS cohort, the MR analysis results may not accurately establish a causal relationship between exposure factors and outcomes(Gupta et al \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Lovegrove et al \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, it is imperative to corroborate MR analysis findings through real-world research designs. For instance, certain studies have employed MR analysis to elucidate the causal relationship between gut microbiota and pulmonary hypertension, identifying relevant genes at specific SNP loci through bioinformatics tools, and subsequently validating the expression of these genes in a hypoxia-induced pulmonary hypertension model in mice(Su et al \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This study also utilized a high-fat diet-induced obesity mouse model to validate the MR analysis outcomes.\u003c/p\u003e\u003cp\u003eNonetheless, this study is not without its limitations. Firstly, the absence of a normal diet control group in the animal experiments restricts our ability to observe the TG metabolic disorders induced by a high-fat diet and to evaluate the specific contribution of \u003cem\u003eS. anginosus\u003c/em\u003e to these disorders. Furthermore, lipoproteins encompass a range of types, including chylomicrons, very low-density lipoproteins, low-density lipoproteins, intermediate-density lipoproteins, and high-density lipoproteins(Bhargava et al \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The use of an obesity model does not adequately replicate the full phenotype of lipoprotein metabolic disorders, necessitating a broader assessment of \u003cem\u003eS. anginosus\u003c/em\u003e 's role across various lipid metabolism models. Lastly, the influence of \u003cem\u003eS. anginosus\u003c/em\u003e on the expression of enzymes involved in TG synthesis and metabolism warrants further validation at the protein level.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research employed GWAS data derived from extensive population sequencing to identify the gut microbiota in family \u003cem\u003eStreptococcaceae\u003c/em\u003e as a contributing risk factor for disorders in lipoprotein metabolism. Additionally, it was observed that specific strains within the \u003cem\u003eStreptococcaceae\u003c/em\u003e, particularly \u003cem\u003eS. anginosus\u003c/em\u003e, can suppress the expression of LPL, thereby diminishing the capacity of mice to utilize dietary TGand resulting in disturbances in TG metabolism.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eXianLong-Shu.: Investigation, Methodology, Formal analysis, Visualization and Writing - Original Draft; JiaLing-Xie.: Investigation, Resources, Writing-Original Draft; Xi Li.: Investigation and Validation; Jie Tang.: Supervision, Funding Acquisition and Writing - Review \u0026amp; Editing; Guo Wang.: Project Administration and Writing - Review \u0026amp; Editing. All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis research was supported by grants from the National Natural Scientific Foundation of China (No. 81403018, 81673516) and Natural Science Foundation of Hunan Province (No. 2015JJ3169, 2019JJ80013).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChong B, Jayabaskaran J, Jauhari SM, Chan SP, Goh R, Kueh MTW et al (2024) Global burden of cardiovascular diseases: projections from 2025 to 2050. 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Trends Endocrinol Metab 33:409\u0026ndash;423. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tem.2022.02.001\u003c/span\u003e\u003cspan address=\"10.1016/j.tem.2022.02.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-microbiology-and-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wibi","sideBox":"Learn more about [World Journal of Microbiology and Biotechnology](https://www.springer.com/journal/11274)","snPcode":"11274","submissionUrl":"https://submission.nature.com/new-submission/11274/3","title":"World Journal of Microbiology and Biotechnology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Streptococcus anginosus, triglycerides, lipid metabolism, lipoprotein lipase","lastPublishedDoi":"10.21203/rs.3.rs-7055704/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7055704/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eMendelian Randomization (MR) analysis can link the host gut microbiome to cardiovascular diseases. \u003cem\u003eStreptococcus anginosus\u003c/em\u003e (\u003cem\u003eS. anginosus\u003c/em\u003e) has been found to be positively correlated with early atherosclerosis, but its role in lipid metabolism remains to be explored.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe employed five MR analysis methods to examine the association between gut microbiota and disorders of lipoprotein metabolism. Inducing obesity in mouse models by using a high-fat diet. Throughout the experiment, we measured alterations in serum concentrations of total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). Hematoxylin and eosin (H\u0026amp;E) staining was utilized to assess pathological changes in adipose tissue and liver, while reverse transcription quantitative polymerase chain reaction (RT-qPCR) was conducted to evaluate changes in the expression of genes associated with triglyceride metabolism and synthesis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe findings from MR analysis indicate that ten specific gut microbial taxa, including \u003cem\u003eStreptococcaceae\u003c/em\u003e, \u003cem\u003eParabacteroides goldsteinii\u003c/em\u003e, and \u003cem\u003eRuminococcus\u003c/em\u003e, exhibit a causal relationship with disorders of lipoprotein metabolism. Notably, \u003cem\u003eStreptococcaceae\u003c/em\u003e has been identified as a risk factor for it, this result has been validated in another GWAS study. Furthermore, the presence of \u003cem\u003eS. anginosus\u003c/em\u003e has been shown to elevate serum TG levels, diminish the accumulation of lipid droplets in both hepatic and adipose tissues, and downregulate the expression of genes associated with TG metabolism and the enzymes involved in TG synthesis.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003ethese evidences suggest that \u003cem\u003eStreptococcaceae\u003c/em\u003e as a risk factor for lipoprotein metabolism disorders, while \u003cem\u003eS. anginosus\u003c/em\u003e induces TG metabolism disorders by impairing the utilization of dietary triglycerides.\u003c/p\u003e","manuscriptTitle":"Exposure to Streptococcus anginosus facilitates lipid metabolism disorder in obese mouse model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 10:16:15","doi":"10.21203/rs.3.rs-7055704/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-28T16:11:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-27T14:23:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-27T07:07:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-17T10:29:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"293812743600063121582477625872904707131","date":"2025-07-16T10:27:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20573227722669290951482453628666166275","date":"2025-07-13T14:00:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70190342306651015182431655089155217532","date":"2025-07-13T04:32:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-13T04:07:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-07T09:50:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-07T05:34:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"World Journal of Microbiology and Biotechnology","date":"2025-07-06T04:00:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-microbiology-and-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wibi","sideBox":"Learn more about [World Journal of Microbiology and Biotechnology](https://www.springer.com/journal/11274)","snPcode":"11274","submissionUrl":"https://submission.nature.com/new-submission/11274/3","title":"World Journal of Microbiology and Biotechnology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a00416a5-f029-45c2-b96c-a69e3ee92e86","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:00:00+00:00","versionOfRecord":{"articleIdentity":"rs-7055704","link":"https://doi.org/10.1007/s11274-025-04558-6","journal":{"identity":"world-journal-of-microbiology-and-biotechnology","isVorOnly":false,"title":"World Journal of Microbiology and Biotechnology"},"publishedOn":"2025-10-28 15:57:08","publishedOnDateReadable":"October 28th, 2025"},"versionCreatedAt":"2025-07-18 10:16:15","video":"","vorDoi":"10.1007/s11274-025-04558-6","vorDoiUrl":"https://doi.org/10.1007/s11274-025-04558-6","workflowStages":[]},"version":"v1","identity":"rs-7055704","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7055704","identity":"rs-7055704","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}