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| KEGG pathways (level 3) predicted by PICRUSt. (A,B) Differential KEGG pathways with top relative abundance were visualized as heatmap and box plot. Warmer color indicates higher relative abundance, whereas colder color indicates lower relative abundance.
Source publication
Background
Carotid atherosclerosis (CAS) is an important cause of stroke. Although interactions between the gut microbiome and metabolome have been widely investigated with respect to the pathogenesis of cardiovascular diseases, information regarding CAS remains limited.
Materials and Methods
We utilized 16S ribosomal DNA sequencing and untargeted...
Contexts in source publication
Context 1
... total, 65 of 265 differentially enriched KEGG pathways (level 3) were identified (Supplementary Table 3) with a threshold of p < 0.05, of which 39 were enriched in the CAS group, whereas 26 were enriched in healthy controls. Different pathways with the highest relative abundance were visualized by heatmap and boxplot (Figure 4). ...
Citations
... Recent years have witnessed a burgeoning body of research establishing an association between the composition and structure of the gut microbial community and atherosclerotic conditions. A 2021 study of 64 participants pinpointed an excess of inflammation-associated gut microbes, like Acidaminococcus, in patients with carotid atherosclerosis, whereas beneficial bacteria, such as anaerobes and butyrate-producing bacteria like Clostridium XVIII/ XlVa/XlVb, prevailed at elevated levels in healthy individuals (Ji et al., 2021). An experimental model employing mice on a high-fat diet unveiled that modulating the gut flora, by upregulating Bacteroides and downregulating Bacillota, could ameliorate lipid metabolism and attenuate atherosclerosis (Zhang et al., 2021). ...
Introduction
The relationship between gut microbiota and peripheral artery disease (PAD) remains understudied. While traditional risk factors like smoking and hyperlipidemia are well-understood, our study aims to determine the potential causative association of gut microbiota with PAD using Mendelian Randomization.
Methods
Data from the International MiBioGen Consortium and the FinnGen research project were used to study 211 bacterial taxa. Instrumental variables, comprising 2079 SNPs, were selected based on significance levels and linkage disequilibrium. Analyses were conducted utilizing the inverse-variance weighted (IVW) method and other statistical MR techniques to mitigate biases, processed in R (v4.3.1) with the TwosampleMR package.
Results
Three bacterial taxa, namely genus Coprococcus2, RuminococcaceaeUCG004, and RuminococcaceaeUCG010, emerged as protective factors against PAD. In contrast, family. FamilyXI and the genus Lachnoclostridium and LachnospiraceaeUCG001 were identified as risk factors.
Conclusion
Our findings hint at a causative association between certain gut microbiota and PAD, introducing new avenues for understanding PAD’s etiology and developing effective treatments. The observed associations now warrant further validation in varied populations and detailed exploration at finer taxonomic levels.
... [5,6] A major portion of the current research is focused on understanding the role of the gut microbiome in the pathogenesis of atherosclerosis, including the correlation between the presence of harmful inflammation-associated microbes and a decreased level of some beneficial bacteria. [7,8] Direct manipulation of the mouse gut microbiome reportedly retards the progression of atherosclerotic plaque. [9] Moreover, a series of metabolites generated by the gut microbiota participate in the pathogenesis of cardiovascular diseases. ...
... [57] Subsequent studies using high-throughput sequencing and omics techniques have reported that gut microbiota associated with inflammation (eg, Acidaminococcus) was increased in patients with carotid atherosclerosis, whereas the levels of beneficial bacteria (eg, Anaerostipes) and butyrate-producing bacteria (eg, Clostridium XVIII/XlVa/XlVb) were higher in healthy controls. [8] Inflammation is a well-known key contributor to the pathogenesis of atherosclerosis. Indeed, a recent study demonstrated that the pathogenic bacteria Porphyromonas gingivalis (P. ...
Cardiovascular diseases, including heart failure, coronary artery disease, atherosclerosis, aneurysm, thrombosis, and hypertension, are a great economic burden and threat to human health and are the major cause of death worldwide. Recently, researchers have begun to appreciate the role of microbial ecosystems within the human body in contributing to metabolic and cardiovascular disorders. Accumulating evidence has demonstrated that the gut microbiota is closely associated with the occurrence and development of cardiovascular diseases. The gut microbiota functions as an endocrine organ that secretes bioactive metabolites that participate in the maintenance of cardiovascular homeostasis, and their dysfunction can directly influence the progression of cardiovascular disease. This review summarizes the current literature demonstrating the role of the gut microbiota in the development of cardiovascular diseases. We also highlight the mechanism by which well-documented gut microbiota-derived metabolites, especially trimethylamine N-oxide, short-chain fatty acids, and phenylacetylglutamine, promote or inhibit the pathogenesis of cardiovascular diseases. We also discuss the therapeutic potential of altering the gut microbiota and microbiota-derived metabolites to improve or prevent cardiovascular diseases.
... Several studies in recent years have verified the presence of bacterial deoxyribonucleic acid (DNA) in atherosclerotic plaques, and the amount of DNA linked with the presence of many leukocytes in the plaque, which may contribute to the CVD development (Koren et al., 2011;Torres et al., 2015). Furthermore, researchers discover that AS patients have abnormalities in their GM when compared to persons without AS (Koren et al., 2011;Baragetti et al., 2021;Ji et al., 2021;Szabo et al., 2021). The impact of GM on the development of atherosclerotic lesions is investigated in a few experiments. ...
... Phylum Proteobacteria, genus Escherichia is abundant in individuals with subclinical carotid AS and CAD, providing a novel predictor in the AS progression Baragetti et al., 2021). Phylum Bacillota, genus Acidaminococcus is formerly often found in people with inflammatory disorders and a pro-inflammatory diet, also more prevalent in the AS patients (Ji et al., 2021). Mitra et al. (2015) observed that the GM in patients with unstable plaque have more abundant Helicobacteraceae and Neisseriaceae than individuals with stable plaques. ...
The gut microbiota (GM) has become recognized as a crucial element in preserving human fitness and influencing disease consequences. Commensal and pathogenic gut microorganisms are correlated with pathological progress in atherosclerosis (AS). GM may thus be a promising therapeutic target for AS. Natural products with cardioprotective qualities might improve the inflammation of AS by modulating the GM ecosystem, opening new avenues for researches and therapies. However, it is unclear what components of natural products are useful and what the actual mechanisms are. In this review, we have summarized the natural products relieving inflammation of AS by regulating the GM balance and active metabolites produced by GM.
... To compare the differences in plasma metabolites between AAA and AS, 32 AAA and 32 AS patients were included in this study. The clinical characteristics of these participants are shown in Table 1 (Ji et al., 2021). Patients with AAA were slightly older (P=0.019), ...
Abdominal aortic aneurysm (AAA) and atherosclerosis (AS) have considerable similarities in clinical risk factors and molecular pathogenesis. The aim of our study was to investigate the differences between AAA and AS from the perspective of metabolomics, and to explore the potential mechanisms of differential metabolites via integration analysis with transcriptomics. Plasma samples from 32 AAA and 32 AS patients were applied to characterize the metabolite profiles using untargeted liquid chromatography-mass spectrometry (LC-MS). A total of 18 remarkably different metabolites were identified, and a combination of seven metabolites could potentially serve as a biomarker to distinguish AAA and AS, with an area under the curve (AUC) of 0.93. Subsequently, we analyzed both the metabolomics and transcriptomics data and found that seven metabolites, especially 2'-deoxy-D-ribose (2dDR), were significantly correlated with differentially expressed genes. In conclusion, our study presents a comprehensive landscape of plasma metabolites in AAA and AS patients, and provides a research direction for pathogenetic mechanisms in atherosclerotic AAA.
... More importantly, without homeostatic control urine have the potential to detect the small and early pathological changes (Wu and Gao, 2015;Wu et al., 2021). Mass spectrum-based omics methods have been used in the biomarker discovery of CAS, and the sample source mainly contained blood or atherosclerosis plaque (Vorkas et al., 2016;Li et al., 2017;Lee et al., 2019;Ji et al., 2021). Until now, there is still no available urinary biomarker for CAS. ...
Background: Carotid artery stenosis (CAS) is caused by the formation of atherosclerotic plaques inside the arterial wall and accounts for 20–30% of all strokes. The development of an early, noninvasive diagnostic method and the identification of high-risk patients for ischemic stroke is essential to the management of CAS in clinical practice.
Methods: We used the data-independent acquisition (DIA) technique to conduct a urinary proteomic study in patients with CAS and healthy controls. We identified the potential diagnosis and risk stratification biomarkers of CAS. And Ingenuity pathway analysis was used for functional annotation of differentially expressed proteins (DEPs). Furthermore, receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic values of DEPs.
Results: A total of 194 DEPs were identified between CAS patients and healthy controls by DIA quantification. The bioinformatics analysis showed that these DEPs were correlated with the pathogenesis of CAS. We further identified 32 DEPs in symptomatic CAS compared to asymptomatic CAS, and biological function analysis revealed that these proteins are mainly related to immune/inflammatory pathways. Finally, a biomarker panel of six proteins (ACP2, PLD3, HLA-C, GGH, CALML3, and IL2RB) exhibited potential diagnostic value in CAS and good discriminative power for differentiating symptomatic and asymptomatic CAS with high sensitivity and specificity.
Conclusions: Our study identified novel potential urinary biomarkers for noninvasive early screening and risk stratification of CAS.
... In recent years, several studies have investigated the effects of the gut microbiota on atherosclerosis and proved that there are some connections between cardiovascular events and atherosclerotic plaque characteristics (9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19). However, the roles of the gut microbiota in the stability of plaque are still unclear. ...
... In recent years, several studies have confirmed the presence of bacterial DNA in atherosclerotic plaque which may contribute to the development of cardiovascular disease (52). In addition, researchers have also found that compared with people without atherosclerosis, the patients with atherosclerosis ( Table 1) showed some differences in the gut microbiota (9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)53). Garshick et al. donated aortas of Apoe-/-mice with atherosclerosis into normolipidemic wild-type mice, then feeding antibiotic. ...
... Acidaminococcus was once often enriched in patients with several inflammatory diseases and positively correlated with a proinflammatory diet, which may indicate that Acidaminococcus was a proinflammatory microbiota and represent inflammatory status in the development of AS (18). In atherosclerotic plaques, phylum Proteobacteria dominated and the phylum Firmicutes, predominantly found in the gut, is also present in atherosclerotic plaques. ...
Cardiovascular diseases (CVDs) are major causes of mortality and morbidity in the modern society. The rupture of atherosclerotic plaque can induce thrombus formation, which is the main cause of acute cardiovascular events. Recently, many studies have demonstrated that there are some relationships between microbiota and atherosclerosis. In this review, we will focus on the effect of the microbiota and the microbe-derived metabolites, including trimethylamine-N-oxide (TMAO), short-chain fatty acids (SCFAs), and lipopolysaccharide (LPS), on the stability of atherosclerotic plaque. Finally, we will conclude with some therapies based on the microbiota and its metabolites.
In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.
Atherosclerosis is a chronic lipid-driven inflammatory response of the innate and adaptive immune systems, and it is responsible for several cardiovascular ischemic events. The present study aimed to determine immune infiltration-related biomarkers in carotid atherosclerotic plaques (CAPs). Gene expression profiles of CAPs were extracted from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between the CAPs and control groups were screened by the “limma” package in R software. Immune cell infiltration between the CAPs and control groups was evaluated by the single sample gene set enrichment analysis. Key infiltrating immune cells in the CAPs group were screened by the Wilcoxon test and least absolute shrinkage and selection operator regression. The weighted gene co-expression network analysis was used to identify immune cell-related genes. Hub genes were identified by the protein–protein interaction (PPI) network. Receiver operating characteristic curve analysis was performed to assess the gene’s ability to differentiate between the CAPs and control groups. Finally, we constructed a miRNA-gene-transcription factor network of hub genes by using the ENCODE database. Eleven different types of immune infiltration-related cells were identified between the CAPs and control groups. A total of 1,586 differentially expressed immunity-related genes were obtained through intersection between DEGs and immune-related genes. Twenty hub genes were screened through the PPI network. Eventually, 7 genes (BTK, LYN, PTPN11, CD163, CD4, ITGAL, and ITGB7) were identified as the hub genes of CAPs, and these genes may serve as the estimable drug targets for patients with CAPs.
Background:
Emerging evidence revealed that gut microbial dysbiosis is involved in the pathogenesis of multiple neurological diseases, but there is little available data on the relationship between gut microbiota and lacunar cerebral infarction (LCI).
Methods:
Fecal samples from acute LCI patients (n = 65) and matched healthy controls (n = 65) were collected. The compositions and potential functions of the gut microbiota were estimated.
Results:
The results showed that there were significant gut microbial differences between LCI and control groups. Patients with LCI had higher abundances of genus Lactobacillus, Streptococcus, Veillonella, Acidaminococcus, Bacillus, Peptoclostridium, Intestinibacter, Alloscardovia and Cloacibacillus but lower proportions of genus Agathobacter and Lachnospiraceae_UCG-004. Investigating further these microbes such as Lactobacillus and Veillonella were correlated with clinical signs. Moreover, we found that 9 gene functions of gut microbiota were different between LCI patients and controls, which were associated with amino acid metabolism and inflammatory signal transduction. Notably, four optimal microbial markers were determined, and the combination of Streptococcus, Lactobacillus, Agathobacter, Lachnospiraceae_UCG-004 and the three risk factors achieved an area under the curve (AUC) value of 0.854 to distinguish LCI from controls.
Conclusion:
These findings revealed the characterizing of gut microbiota in LCI patients and provided potential microbial biomarkers for clinical diagnosis of LCI.
Human gut microbiota seems to drive the interaction with host metabolism through microbial metabolites, enzymes, and bioactive compounds. These components determine the host health-disease balance. Recent metabolomics and combined metabolome-microbiome studies have helped to elucidate how these substances could differentially affect the individual host pathophys-iology according to several factors and cumulative exposures, such as obesogenic xenobiotics. The present work aims to investigate and interpret newly compiled data from metabolomics and micro-biota composition studies, comparing controls with patients suffering from metabolic-related diseases (diabetes, obesity, metabolic syndrome, liver and cardiovascular diseases, etc.). The results showed, first, a differential composition of the most represented genera in healthy individuals compared to patients with metabolic diseases. Second, the analysis of the metabolite counts exhibited a differential composition of bacterial genera in disease compared to health status. Third, qualitative metabolite analysis revealed relevant information about the chemical nature of metabolites related to disease and/or health status. Key microbial genera were commonly considered overrepresented in healthy individuals together with specific metabolites, e.g., Faecalibacterium and phosphatidyl-ethanolamine; and the opposite, Escherichia and Phosphatidic Acid, which is converted into the intermediate Cytidine Diphosphate Diacylglycerol-diacylglycerol (CDP-DAG), were overrepresented in metabolic-related disease patients. However, it was not possible to associate most specific micro-biota taxa and metabolites according to their increased and decreased profiles analyzed with health or disease. Interestingly, positive association of essential amino acids with the genera Bacteroides were observed in a cluster related to health, and conversely, benzene derivatives and lipidic metab-olites were related to the genera Clostridium, Roseburia, Blautia, and Oscillibacter in a disease cluster. More studies are needed to elucidate the microbiota species and their corresponding metabolites that are key in promoting health or disease status. Moreover, we propose that greater attention should be paid to biliary acids and to microbiota-liver cometabolites and its detoxification enzymes and pathways.
