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Liver diseases are a group of major metabolic and immune or inflammation related diseases caused due to various reasons including infection, abnormalities in immune system, genetic defects, and lifestyle habits. However, the cause-effect relationship is not completely understood in liver disease. The role of microbiome, particularly, the role of gut and oral microbiome in liver diseases has been extensively studied in recent years. More interestingly, the presence of blood microbiome and tissue microbiome has been identified in many liver diseases. The translocation of microbes from the gut into the portal circulation has been attributed to be the major reason for the presence of blood microbial components and its clinical implications in liver disorders. Besides microbial translocation, Pathogen associated Molecular Patterns (PAMPs) derived from gut microbiota might also translocate. The presence of blood microbiome in liver disease has been reviewed earlier. However, the role of blood microbiome as a biomarker and therapeutic target in liver diseases has not been analysed earlier. In this review, we confabulate the origin and physiology of blood microbiome and blood microbial components in relation to the progression and pathogenesis of liver disease. In conclusion, we discuss the translational perspectives targeting the blood microbial components in the diagnosis and therapy of liver disease.

Background: It is difficult to distinguish between acute myocardial infarction (AMI) and unstable angina (UA) due to their similar clinical features. In recent years, studies have shown that microbiomes have great potential in distinguishing diseases. The purpose of this study is to describe the composition of serum microbiome in the AMI and UA by 16S rDNA sequencing., Methods: Based on the high-throughput detection platform and 16S rDNA amplification sequencing technology, this study detected the blood microbial composition of 55 patients with AMI and 62 patients with UA. Alpha diversity and Beta diversity analysis were used to compare the differences in microbial composition and bacterial colony structure between AMI and UA groups. We perform PCoA (Principal Co-ordinates Analysis) based on Unweighted Unifrac distance. In addition, various statistical methods were employed to examine the significance of differences in microbial composition and genus between the two groups. PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) was employed to predict KEGG (Kyoto Encyclopedia of Genes and Genomes) function from 16S sequencing data. Random forest was applied to identify biomarkers and construct the diagnostic model. Subsequently, the stability of the model was verified by 10-fold cross and the diagnostic effectiveness was evaluated through ROC (Receiver Operating Characteristic)., Results: In this study, we found that alpha diversity index of serum microbiome in AMI group was significantly higher than in UA group. T-test analysis demonstrated that the UA group presented a higher abundance of Ralstonia, Faecalibaculum and Gammaproteobacteria, while Bacteroides, Sphingomonas, Faecalibaculum, Haemophilus, Serratia, Bifidobacterium and Chloroplast were more abundant in the AMI group. The LefSe (LDA Effect Size) analysis showed that the Gammaproteobacteria, Proteobacteria, Ralstonia pickettli, Ralstonia, Burkholderiaceae and Burkholderiales were enriched in UA group, and Bacteroidales, Bacteroidia, Bacteroidota, Clostridia and Firmicutes were more abundant in the AMI group. Ten bacterial diagnostic models were constructed in the random forest. The area under the curve (AUC) in the training set was 88.01%, and the AUC value in the test set was 95.04%., Conclusion: In this study, the composition of blood microorganisms in the groups of patients with AMI and UA has been analyzed, providing novel insights for understanding the pathogenesis of AMI; Blood microbiome may serve as novel diagnostic biomarkers of AMI., Competing Interests: 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., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective: Few symptoms persist for a long time after patients recover from COVID-19, called "long COVID". We explored the potential microbial risk factors for COVID-19 for a deeper understanding and assistance in the follow-up treatment of these sequelae., Methods: Microbiome re-annotation was performed using whole blood RNA-Seq data collected from recovered COVID-19 patients and healthy controls at multiple time points. Subsequently, a series of downstream analyses were conducted to reveal the microbial characteristics of patients who recovered from SARS-CoV-2 infection., Results: The blood microbiome at 12 weeks post-infection was most evidently disturbed, including an increasing ratio of Bacillota/Bacteroidota and a higher microbial alpha diversity. In addition, a group of pathogenic microbes at 12 weeks post-infection were identified, including Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa, which were positively associated with host genes involved in immune regulatory and olfactory transduction pathways. Several microbes, such as Streptococcus pneumoniae were associated with infiltrating immune cells, such as M2 macrophages., Conclusion: This study provides insights into the relationship between the blood microbiome and COVID-19 sequelae. Several pathogenic microbes were enriched in recovered COVID-19 patients and thus affected host genes participating in the immune and olfactory transduction pathways, which play critical roles in COVID-19 sequelae., Competing Interests: 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., (Copyright © 2023 Elsevier Ltd. All rights reserved.)