Metagenomic analysis of the gut microbiome in atherosclerosis patients identify cross-cohort microbial signatures and potential therapeutic target

Less is more: relative rank is more informative than absolute abundance for compositional NGS data

High-throughput gene expression data have been extensively generated and utilized in biological mechanism investigations, biomarker detection, disease diagnosis and prognosis. These applications encompass not only bulk transcriptome, but also single cell RNA-seq data. However, extracting reliable biological information from transcriptome data remains challenging due to the constrains of Compositional Data Analysis. Current data preprocessing methods, including dataset normalization and batch effect correction, are insufficient to address these issues and improve data quality for downstream analysis. Alternatively, qualification methods focusing on the relative order of gene expression (ROGER) are more informative than the quantification methods that rely on gene expression abundance. The Pairwise Analysis of Gene expression method is an enhancement of ROGER, designed for data integration in either sample space or feature space. In this review, we summarize the methods applied to transcriptome data analysis and discuss their potentials in predicting clinical outcomes.

Mendelian randomization analyses support causal relationships between gut microbiome and longevity

BACKGROUND

Gut microbiome plays a significant role in longevity, and dysbiosis is indeed one of the hallmarks of aging. However, the causal relationship between gut microbiota and human longevity or aging remains elusive.

METHODS

Our study assessed the causal relationships between gut microbiome and longevity using Mendelian Randomization (MR). Summary statistics for the gut microbiome were obtained from four genome-wide association study (GWAS) meta-analysis of the MiBioGen consortium (N = 18,340), Dutch Microbiome Project (N = 7738), German individuals (N = 8956), and Finland individuals (N = 5959). Summary statistics for Longevity were obtained from Five GWAS meta-analysis, including Human healthspan (N = 300,447), Longevity (N = 36,745), Lifespans (N = 1,012,240), Parental longevity (N = 389,166), and Frailty (one of the primary aging-linked physiological hallmarks, N = 175,226).

RESULTS

Our findings reveal several noteworthy associations, including a negative correlation between Bacteroides massiliensis and longevity, whereas the genus Subdoligranulum and Alistipes, as well as species Alistipes senegalensis and Alistipes shahii, exhibited positive associations with specific longevity traits. Moreover, the microbial pathway of coenzyme A biosynthesis I, pyruvate fermentation to acetate and lactate II, and pentose phosphate pathway exhibited positive associations with two or more traits linked to longevity. Conversely, the TCA cycle VIII (helicobacter) pathway consistently demonstrated a negative correlation with lifespan and parental longevity.

CONCLUSIONS

Our findings of this MR study indicated many significant associations between gut microbiome and longevity. These microbial taxa and pathways may potentially play a protective role in promoting longevity or have a suppressive effect on lifespan.

Pairwise analysis of gene expression for oral squamous cell carcinoma via a large-scale transcriptome integration

Among all cancers occurring in the head and neck region, oral squamous cell carcinoma (OSCC) is the most common oral malignant tumours characterized by its aggressiveness and metastasis. The development of transcriptomics technology has greatly facilitated the diagnosis of various cancers. However, identifying genetic biomarkers is limited by data from a single batch of OSCC samples, and integrating analysis across different platforms remains a great challenge. In this study, we integrated five OSCC transcriptome datasets using an innovative strategy capable of mitigating batch effect, and extracting information from different datasets based on changes in the relative expression of gene pairs. By leveraging a machine learning method, we developed a prediction model including 27 differential gene pairs (DGPs) to discriminate OSCC from control samples, achieving an area under the receiver operating characteristic curve (AUC) of 0.8987 for the training set. Moreover, the model demonstrated commendable performance in four external validation sets, with AUCs of 0.9926, 0.9688, 0.8052 and 0.8565, respectively. Subsequently, a prognostic model was constructed based on six key gene pairs through univariate and multivariate Cox regression analysis. The AUCs of the model at 1-year and 3-year overall survival time prediction were 0.717 and 0.779 in an independent dataset. Our result demonstrates the effectiveness of this new method of integrating data and identifying DGPs. Using DGPs can significantly improve the performance of both diagnostic and prognostic models.

Lactiplantibacillus plantarum P470 Isolated from Fermented Chinese Chives Has the Potential to Improve In Vitro the Intestinal Microbiota and Biological Activity in Feces of Coronary Heart Disease (CHD) Patients

Traditional fermented foods are known to offer cardiovascular health benefits. However, the potential of fermented Chinese chives (FCC) in reducing coronary heart disease (CHD) remains unclear. This study employed anaerobic fermentation to investigate Lactiplantibacillus plantarum (L. plantarum) P470 from FCC. The results indicated that L. plantarum P470 enhanced hydroxyl radical scavenging and exhibited anti-inflammatory effects on RAW264.7 macrophages in the fecal fermentation supernatant of CHD patients. These effects were attributed to the modulation of gut microbiota and metabolites, including short-chain fatty acids (SCFAs). Specifically, L. plantarum P470 increased the abundance of Bacteroides and Lactobacillus while decreasing Escherichia-Shigella, Enterobacter, Veillonella, Eggerthella, and Helicobacter in CHD patient fecal samples. Furthermore, L. plantarum P470 regulated the biosynthesis of unsaturated fatty acids and linoleic acid metabolism. These findings suggest that L. plantarum P470 from FCC can improve the fecal physiological status in patients with CHD by modulating intestinal microbiota, promoting SCFA production, and regulating lipid metabolism.

Insights from metagenomics into gut microbiome associated with acute coronary syndrome therapy

Acute coronary syndrome (ACS) is a predominant cause of mortality, and the prompt and precise identification of this condition is crucial to minimize its impact. Recent research indicates that gut microbiota is associated with the onset, progression, and treatment of ACS. To investigate its role, we sequenced the gut microbiota of 38 ACS patients before and after percutaneous coronary intervention and statin therapy at three time points, examining differential species and metabolic pathways. We observed a decrease in the abundance of Parabacteroides, Escherichia, and Blautia in patients after treatment and an increase in the abundance of Gemalla, Klebsiella variicola, Klebsiella pneumoniae, and others. Two pathways related to sugar degradation were more abundant in patients before treatment, possibly correlated with disorders of sugar metabolism and risk factors, such as hyperglycemia, insulin resistance, and insufficient insulin secretion. Additionally, seven pathways related to the biosynthesis of vitamin K2 and its homolog were reduced after treatment, suggesting that ACS patients may gradually recover after therapy. The gut microbiota of patients treated with different statins exhibited notable differences after treatment. Rosuvastatin appeared to promote the growth of anti-inflammatory bacteria while reducing pro-inflammatory bacteria, whereas atorvastatin may have mixed effects on pro-inflammatory and anti-inflammatory bacteria while increasing the abundance of Bacteroides. Our research will provide valuable insights and enhance comprehension of ACS, leading to better patient diagnosis and therapy.

Analysis of differential metabolites in serum metabolomics of patients with aortic dissection

BACKGROUND

Pathogenesis and diagnostic biomarkers of aortic dissection (AD) can be categorized through the analysis of differential metabolites in serum. Analysis of differential metabolites in serum provides new methods for exploring the early diagnosis and treatment of aortic dissection.

OBJECTIVES

This study examined affected metabolic pathways to assess the diagnostic value of metabolomics biomarkers in clients with AD.

METHOD

The serum from 30 patients with AD and 30 healthy people was collected. The most diagnostic metabolite markers were determined using metabolomic analysis and related metabolic pathways were explored.

RESULTS

In total, 71 differential metabolites were identified. The altered metabolic pathways included reduced phospholipid catabolism and four different metabolites considered of most diagnostic value including N2-gamma-glutamylglutamine, PC(phocholines) (20:4(5Z,8Z,11Z,14Z)/15:0), propionyl carnitine, and taurine. These four predictive metabolic biomarkers accurately classified AD patient and healthy control (HC) samples with an area under the curve (AUC) of 0.9875. Based on the value of the four different metabolites, a formula was created to calculate the risk of aortic dissection. Risk score = (N2-gamma-glutamylglutamine × -0.684) + (PC (20:4(5Z,8Z,11Z,14Z)/15:0) × 0.427) + (propionyl carnitine × 0.523) + (taurine × -1.242). An additional metabolic pathways model related to aortic dissection was explored.

CONCLUSION

Metabolomics can assist in investigating the metabolic disorders associated with AD and facilitate a more in-depth search for potential metabolic biomarkers.

Research development on gut microbiota and vulnerable atherosclerotic plaque

The relationship between gut microbiota and its metabolites with cardiovascular disease (CVD) has been proven. In this review, we aim to conclude the potential mechanism of gut microbiota and its metabolites on inducing the formation of vulnerable atherosclerotic plaque, and to discuss the effect of intestinal metabolites, including trimethylamine-N-oxide (TMAO), lipopolysaccharide (LPS), phenylacetylglutamine (PAG), short-chain fatty acids (SCFAs) on plaque stability. Finally, we include the impact of gut microbiota and its metabolites on plaque stability, to propose a new therapeutic direction for coronary heart disease. Gut microbiota regulation intervenes the progress of arteriosclerosis, especially on coronary atherosclerosis, by avoiding or reducing the formation of vulnerable plaque, to lower the morbidity rate of myocardial infarction.

The association between serum heat shock protein 72 and intestinal permeability with intestinal microbiota and clinical severity in patients with cerebral infarction

Objectives

We aimed to compare serum heat shock protein 72 (HSP72) and intestinal permeability in patients with cerebral infarction (CI) and healthy individuals to reveal their correlations and link to gut microbiota alterations and clinical severity of CI.

Methods and results

Stool samples of 50 patients with CI and 46 healthy volunteers were analyzed through 16S rRNA gene sequencing to characterize intestinal flora profiles. Serum HSP72 and zonulin were assayed using enzyme-linked immunoassay (ELISA). The obtained data were then subjected to comparative and correlative analysis. We found that the levels of zonulin and serum HSP72 were significantly higher in the CI group compared to the healthy group. Serum HSP72 and zonulin levels were positively correlated in the CI group and correlated positively with the clinical severity of CI. β diversity showed significant differences in intestinal microbiota composition between the two groups. In the CI patient group, the abundance of bacteria Eubacterium_fissicatena_group, Eubacterium_eligens_group, and Romboutsia manifested a remarkably positive correlation with serum HSP72. The abundance of bacteria Eubacterium_fissicatena_group and Acetivibrio had a significantly positive correlation with zonulin levels.

Conclusion

Our findings indicated that an increase in serum HSP72 and zonulin levels was manifested in patients with CI and was related to specific gut microbiota alterations and the clinical severity of CI.

Gut microbiota landscape and potential biomarker identification in female patients with systemic lupus erythematosus using machine learning

Objectives

Systemic Lupus Erythematosus (SLE) is a complex autoimmune disease that disproportionately affects women. Early diagnosis and prevention are crucial for women's health, and the gut microbiota has been found to be strongly associated with SLE. This study aimed to identify potential biomarkers for SLE by characterizing the gut microbiota landscape using feature selection and exploring the use of machine learning (ML) algorithms with significantly dysregulated microbiotas (SDMs) for early identification of SLE patients. Additionally, we used the SHapley Additive exPlanations (SHAP) interpretability framework to visualize the impact of SDMs on the risk of developing SLE in females.

Methods

Stool samples were collected from 54 SLE patients and 55 Negative Controls (NC) for microbiota analysis using 16S rRNA sequencing. Feature selection was performed using Elastic Net and Boruta on species-level taxonomy. Subsequently, four ML algorithms, namely logistic regression (LR), Adaptive Boosting (AdaBoost), Random Forest (RF), and eXtreme gradient boosting (XGBoost), were used to achieve early identification of SLE with SDMs. Finally, the best-performing algorithm was combined with SHAP to explore how SDMs affect the risk of developing SLE in females.

Results

Both alpha and beta diversity were found to be different in SLE group. Following feature selection, 68 and 21 microbiota were retained in Elastic Net and Boruta, respectively, with 16 microbiota overlapping between the two, i.e., SDMs for SLE. The four ML algorithms with SDMs could effectively identify SLE patients, with XGBoost performing the best, achieving Accuracy, Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, and AUC values of 0.844, 0.750, 0.938, 0.923, 0.790, and 0.930, respectively. The SHAP interpretability framework showed a complex non-linear relationship between the relative abundance of SDMs and the risk of SLE, with Escherichia_fergusonii having the largest SHAP value.

Conclusions

This study revealed dysbiosis in the gut microbiota of female SLE patients. ML classifiers combined with SDMs can facilitate early identification of female patients with SLE, particularly XGBoost. The SHAP interpretability framework provides insight into the impact of SDMs on the risk of SLE and may inform future scientific treatment for SLE.

Importance of gut microbiota metabolites in the development of cardiovascular diseases (CVD)

Cardiovascular disease (CVD) remains the most common cause of death worldwide and has become a public health concern. The proven notable risk factors for CVD are atherosclerosis, hypertension, diabetes, dyslipidemia, inflammation, and some genetic defects. However, research has shown a correlation between metabolic health, gut microbiota, and dietary risk factors. The gut microbiota makes an important contribution to human functional metabolic pathways by contributing enzymes that are not encoded by the human genome, for instance, the breakdown of polysaccharides, polyphenols and vitamins synthesis. TMAO and SCFAs, human gut microbiota compounds, have respective immunomodulatory and pro-inflammatory effects. Choline and l-carnitine are abundant in high-fat diets and are transformed into TMA by gut bacteria. The liver's phase of metabolism then changes TMA into TMAO. In turn, TMAO promotes the activation of macrophages, damages vascular endothelium, and results in CVD-however, dysbiosis decreases SCFAs and bile acids, which raises intestinal permeability. Congestion in the portal vein, a drop in cardiac output, a reduction in intestinal perfusion, and intestinal leakage are all caused by heart failure. These factors induce systemic inflammation by increasing intestinal leakage. By raising CRP and pro-inflammatory reactions, human gut dysbiosis and elevated TMAO levels promote the development of arterial plaque, hasten the beginning of atherosclerosis, and raise the risk of CAD. A healthy symbiosis between the gut microbiota and host is a key factor in shaping the biochemical profile of the diet, therefore which are crucial for maintaining the intestinal epithelial barrier, growing mucosa, reducing inflammation, and controlling blood pressure.

Streptococcus Species Abundance in the Gut Is Linked to Subclinical Coronary Atherosclerosis in 8973 Participants From the SCAPIS Cohort

BACKGROUND

Gut microbiota have been implicated in atherosclerotic disease, but their relation with subclinical coronary atherosclerosis is unclear. This study aimed to identify associations between the gut microbiome and computed tomography-based measures of coronary atherosclerosis and to explore relevant clinical correlates.

METHODS

We conducted a cross-sectional study of 8973 participants (50 to 65 years of age) without overt atherosclerotic disease from the population-based SCAPIS (Swedish Cardiopulmonary Bioimage Study). Coronary atherosclerosis was measured using coronary artery calcium score and coronary computed tomography angiography. Gut microbiota species abundance and functional potential were assessed with shotgun metagenomics sequencing of fecal samples, and associations with coronary atherosclerosis were evaluated with multivariable regression models adjusted for cardiovascular risk factors. Associated species were evaluated for association with inflammatory markers, metabolites, and corresponding species in saliva.

RESULTS

The mean age of the study sample was 57.4 years, and 53.7% were female. Coronary artery calcification was detected in 40.3%, and 5.4% had at least 1 stenosis with >50% occlusion. Sixty-four species were associated with coronary artery calcium score independent of cardiovascular risk factors, with the strongest associations observed for Streptococcus anginosus and Streptococcus oralis subsp oralis (P<1×10-5). Associations were largely similar across coronary computed tomography angiography-based measurements. Out of the 64 species, 19 species, including streptococci and other species commonly found in the oral cavity, were associated with high-sensitivity C-reactive protein plasma concentrations, and 16 with neutrophil counts. Gut microbial species that are commonly found in the oral cavity were negatively associated with plasma indole propionate and positively associated with plasma secondary bile acids and imidazole propionate. Five species, including 3 streptococci, correlated with the same species in saliva and were associated with worse dental health in the Malmö Offspring Dental Study. Microbial functional potential of dissimilatory nitrate reduction, anaerobic fatty acid β-oxidation, and amino acid degradation were associated with coronary artery calcium score.

CONCLUSIONS

This study provides evidence of an association of a gut microbiota composition characterized by increased abundance of Streptococcus spp and other species commonly found in the oral cavity with coronary atherosclerosis and systemic inflammation markers. Further longitudinal and experimental studies are warranted to explore the potential implications of a bacterial component in atherogenesis.

Co-expression module analysis reveals high expression homogeneity for both coding and non-coding genes in sepsis

Sepsis is a life-threatening condition characterized by a harmful host response to infection with organ dysfunction. Annually about 20 million people are dead owing to sepsis and its mortality rates is as high as 20%. However, no studies have been carried out to investigate sepsis from the system biology point of view, as previous research predominantly focused on individual genes without considering their interactions and associations. Here, we conducted a comprehensive exploration of genome-wide expression alterations in both mRNAs and long non-coding RNAs (lncRNAs) in sepsis, using six microarray datasets. Co-expression networks were conducted to identify mRNA and lncRNA modules, respectively. Comparing these sepsis modules with normal modules, we observed a homogeneous expression pattern within the mRNA/lncRNA members, with the majority of them displaying consistent expression direction. Moreover, we identified consistent modules across diverse datasets, consisting of 20 common mRNA members and two lncRNAs, namely CHRM3-AS2 and PRKCQ-AS1, which are potential regulators of sepsis. Our results reveal that the up-regulated common mRNAs are mainly involved in the processes of neutrophil mediated immunity, while the down-regulated mRNAs and lncRNAs are significantly overrepresented in T-cell mediated immunity functions. This study sheds light on the co-expression patterns of mRNAs and lncRNAs in sepsis, providing a novel perspective and insight into the sepsis transcriptome, which may facilitate the exploration of candidate therapeutic targets and molecular biomarkers for sepsis.

The potential of tailoring the gut microbiome to prevent and treat cardiometabolic disease

Despite milestones in preventive measures and treatment, cardiovascular disease (CVD) remains associated with a high burden of morbidity and mortality. The protracted nature of the development and progression of CVD motivates the identification of early and complementary targets that might explain and alleviate any residual risk in treated patients. The gut microbiota has emerged as a sentinel between our inner milieu and outer environment and relays a modified risk associated with these factors to the host. Accordingly, numerous mechanistic studies in animal models support a causal role of the gut microbiome in CVD via specific microbial or shared microbiota-host metabolites and have identified converging mammalian targets for these signals. Similarly, large-scale cohort studies have repeatedly reported perturbations of the gut microbial community in CVD, supporting the translational potential of targeting this ecological niche, but the move from bench to bedside has not been smooth. In this Review, we provide an overview of the current evidence on the interconnectedness of the gut microbiome and CVD against the noisy backdrop of highly prevalent confounders in advanced CVD, such as increased metabolic burden and polypharmacy. We further aim to conceptualize the molecular mechanisms at the centre of these associations and identify actionable gut microbiome-based targets, while contextualizing the current knowledge within the clinical scenario and emphasizing the limitations of the field that need to be overcome.

Gut Microbiota in Children with Hand Foot and Mouth Disease on 16S rRNA Gene Sequencing

Hand foot and mouth disease (HFMD) is a contagious and seasonal viral disease in children. The gut microbiota of HFMD children is not clear now. The study aimed to explore the gut microbiota of HFMD children. The 16S rRNA gene of the gut microbiota of ten HFMD patients and ten healthy children were sequenced on the NovaSeq and PacBio platforms respectively. There were significant differences in gut microbiota between the patients and healthy children. The diversity and abundance of gut microbiota in HFMD patients were lower than that in healthy children. The species Roseburia inulinivorans and Romboutsia timonensis were more abundant in healthy children than those in HFMD patients, which suggests that the two species may be used as probiotics for adjusting the gut microbiota of HFMD patients. Meanwhile, the results of 16S rRNA gene sequences from the two platforms were different. The NovaSeq platform identified more microbiota and has the characteristics of high throughput, short time and low price. However, the NovaSeq platform has low resolution at the species level. The PacBio platform has high resolution based on its long reads length, which is more suitable for species-level analysis. But, the shortcomings of the high price and low throughput of PacBio still need to be overcome. With the development of sequencing technology, the reduction in sequencing price and the increase in throughput will promote the third-generation sequencing technology used in the study of gut microbes.

Gluten-Free Bread Enriched with Artichoke Leaf Extract In Vitro Exerted Antioxidant and Anti-Inflammatory Properties

Due to its high nutritional value and broad beneficial effects, the artichoke plant (Cynara cardunculus L.) is an excellent healthy food candidate. Additionally, the artichoke by-products are usually discarded even though they still contain a huge concentration of dietary fibers, phenolic acids, and other micronutrients. The present work aimed to characterize a laboratory-made gluten-free bread (B) using rice flour supplemented with a powdered extract from artichoke leaves (AEs). The AE, accounting for the 5% of titratable chlorogenic acid, was added to the experimental gluten-free bread. Accounting for different combinations, four different bread batches were prepared. To evaluate the differences, a gluten-free type-II sourdough (tII-SD) was added in two doughs (SB and SB-AE), while the related controls (YB and YB-AE) did not contain the tII-SD. Profiling the digested bread samples, SB showed the lowest glycemic index, while SB-AE showed the highest antioxidant properties. The digested samples were also fermented in fecal batches containing viable cells from fecal microbiota samples obtained from healthy donors. Based on plate counts, no clear tendencies emerged concerning the analyzed microbial patterns; by contrast, when profiling volatile organic compounds, significant differences were observed in SB-AE, exhibiting the highest scores of hydrocinnamic and cyclohexanecarboxylic acids. The fecal fermented supernatants were recovered and assayed for healthy properties on human keratinocyte cell lines against oxidative stress and for effectiveness in modulating the expression of proinflammatory cytokines in Caco-2 cells. While the first assay emphasized the contribution of AE to protect against stressor agents, the latter enlightened how the combination of SB with AE decreased the cellular TNF-α and IL1-β expression. In conclusion, this preliminary study suggests that the combination of AE with sourdough biotechnology could be a promising tool to increase the nutritional and healthy features of gluten-free bread.

Key Stratification of Microbiota Taxa and Metabolites in the Host Metabolic Health-Disease Balance

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 pathophysiology 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 microbiota 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 phosphatidylethanolamine; 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 microbiota 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 metabolites 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.

Flavonifractor plautii Protects Against Elevated Arterial Stiffness

BACKGROUND

Dysbiosis of gut microbiota plays a pivotal role in vascular dysfunction and microbial diversity was reported to be inversely correlated with arterial stiffness. However, the causal role of gut microbiota in the progression of arterial stiffness and the specific species along with the molecular mechanisms underlying this change remain largely unknown.

METHODS

Participants with elevated arterial stiffness and normal controls free of medication were matched for age and sex. The microbial composition and metabolic capacities between the 2 groups were compared with the integration of metagenomics and metabolomics. Subsequently, Ang II (angiotensin II)-induced and humanized mouse model were employed to evaluate the protective effect of Flavonifractor plautii (F plautii) and its main effector cis-aconitic acid.

RESULTS

Human fecal metagenomic sequencing revealed a significantly high abundance and centrality of F plautii in normal controls, which was absent in the microbial community of subjects with elevated arterial stiffness. Moreover, blood pressure only mediated part of the effect of F plautii on lower arterial stiffness. The microbiome of normal controls exhibited an enhanced capacity for glycolysis and polysaccharide degradation, whereas, those of subjects with increased arterial stiffness were characterized by increased biosynthesis of fatty acids and aromatic amino acids. Integrative analysis with metabolomics profiling further suggested that increased cis-aconitic acid served as the main effector for the protective effect of F plautii against arterial stiffness. Replenishment with F plautii and cis-aconitic acid improved elastic fiber network and reversed increased pulse wave velocity through the suppression of MMP-2 (matrix metalloproteinase-2) and inhibition of MCP-1 (monocyte chemoattractant protein-1) and NF-κB (nuclear factor kappa-B) activation in both Ang II-induced and humanized model of arterial stiffness.

CONCLUSIONS

Our translational study identifies a novel link between F plautii and arterial function and raises the possibility of sustaining vascular health by targeting gut microbiota.

GPGPS: a robust prognostic gene pair signature of glioma ensembling IDH mutation and 1p/19q co-deletion

MOTIVATION

Many studies have shown that IDH mutation and 1p/19q co-deletion can serve as prognostic signatures of glioma. Although these genetic variations affect the expression of one or more genes, the prognostic value of gene expression related to IDH and 1p/19q status is still unclear.

RESULTS

We constructed an ensemble gene pair signature for the risk evaluation and survival prediction of glioma based on the prior knowledge of the IDH and 1p/19q status. First, we separately built two gene pair signatures IDH-GPS and 1p/19q-GPS and elucidated that they were useful transcriptome markers projecting from corresponding genome variations. Then, the gene pairs in these two models were assembled to develop an integrated model named Glioma Prognostic Gene Pair Signature (GPGPS), which demonstrated high area under the curves (AUCs) to predict 1-, 3- and 5-year overall survival (0.92, 0.88 and 0.80) of glioma. GPGPS was superior to the single GPSs and other existing prognostic signatures (avg AUC = 0.70, concordance index = 0.74). In conclusion, the ensemble prognostic signature with 10 gene pairs could serve as an independent predictor for risk stratification and survival prediction in glioma. This study shed light on transferring knowledge from genetic alterations to expression changes to facilitate prognostic studies.

AVAILABILITY AND IMPLEMENTATION

Codes are available at https://github.com/Kimxbzheng/GPGPS.git.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Increased abundance of Ruminococcus gnavus in gut microbiota is associated with moyamoya disease and non-moyamoya intracranial large artery disease

Moyamoya disease (MMD) is a rare cerebrovascular disease endemic in East Asia. The p.R4810K mutation in RNF213 gene confers a risk of MMD, but other factors remain largely unknown. We tested the association of gut microbiota with MMD. Fecal samples were collected from 27 patients with MMD, 7 patients with non-moyamoya intracranial large artery disease (ICAD) and 15 control individuals with other disorders, and 16S rRNA were sequenced. Although there was no difference in alpha diversity or beta diversity between patients with MMD and controls, the cladogram showed Streptococcaceae was enriched in patient samples. The relative abundance analysis demonstrated that 23 species were differentially abundant between patients with MMD and controls. Among them, increased abundance of Ruminococcus gnavus > 0.003 and decreased abundance of Roseburia inulinivorans < 0.002 were associated with higher risks of MMD (odds ratio 9.6, P = 0.0024; odds ratio 11.1, P = 0.0051). Also, Ruminococcus gnavus was more abundant and Roseburia inulinivorans was less abundant in patients with ICAD than controls (P = 0.046, P = 0.012). The relative abundance of Ruminococcus gnavus or Roseburia inulinivorans was not different between the p.R4810K mutant and wildtype. Our data demonstrated that gut microbiota was associated with both MMD and ICAD.

Gut microbiome metabolites as key actors in atherosclerosis co-depression disease

Cardiovascular diseases, mainly characterized by atherosclerosis (AS), and depression have a high comorbidity rate. However, previous studies have been conducted under a single disease, and there is a lack of studies in comorbid states to explore the commonalities in the pathogenesis of both diseases. Modern high-throughput technologies have made it clear that the gut microbiome can affect the development of the host's own disorders and have shown that their metabolites are crucial to the pathophysiology of AS and depression. The aim of this review is to summarize the current important findings on the role of gut microbiome metabolites such as pathogen-associated molecular patterns, bile acids, tryptophan metabolites, short-chain fatty acids, and trimethylamine N -oxide in depression and AS disease, with the aim of identifying potential biological targets for the early diagnosis and treatment of AS co-depression disorders.

A Robust and Generalizable Immune-Related Signature for Sepsis Diagnostics

High-throughput sequencing can detect tens of thousands of genes in parallel, providing opportunities for improving the diagnostic accuracy of multiple diseases including sepsis, which is an aggressive inflammatory response to infection that can cause organ failure and death. Early screening of sepsis is essential in clinic, but no effective diagnostic biomarkers are available yet. Here, we present a novel method, Recurrent Logistic Regression, to identify diagnostic biomarkers for sepsis from the blood transcriptome data. A panel including five immune-related genes, LRRN3, IL2RB, FCER1A, TLR5, and S100A12, are determined as diagnostic biomarkers (LIFTS) for sepsis. LIFTS discriminates patients with sepsis from normal controls in high accuracy (AUROC = 0.9959 on average; IC = [0.9722-1.0]) on nine validation cohorts across three independent platforms, which outperforms existing markers. Our analysis determined an accurate prediction model and reproducible transcriptome biomarkers that can lay a foundation for clinical diagnostic tests and biological mechanistic studies.

Iron metabolism-related genes reveal predictive value of acute coronary syndrome

Iron deficiency has detrimental effects in patients with acute coronary syndrome (ACS), which is a common nutritional disorder and inflammation-related disease affects up to one-third people worldwide. However, the specific role of iron metabolism in ACS progression is opaque. In this study, we construct an iron metabolism-related genes (IMRGs) based molecular signature of ACS and to identify novel iron metabolism gene markers for early stage of ACS. The IMRGs were mainly collected from Molecular Signatures Database (mSigDB) and two relevant studies. Two blood transcriptome datasets GSE61144 and GSE60993 were used for constructing the prediction model of ACS. After differential analysis, 22 IMRGs were differentially expressed and defined as DEIGs in the training set. Then, the 22 DEIGs were trained by the Elastic Net to build the prediction model. Five genes, PADI4, HLA-DQA1, LCN2, CD7, and VNN1, were determined using multiple Elastic Net calculations and retained to obtain the optimal performance. Finally, the generated model iron metabolism-related gene signature (imSig) was assessed by the validation set GSE60993 using a series of evaluation measurements. Compared with other machine learning methods, the performance of imSig using Elastic Net was superior in the validation set. Elastic Net consistently scores the higher than Lasso and Logistic regression in the validation set in terms of ROC, PRC, Sensitivity, and Specificity. The prediction model based on iron metabolism-related genes may assist in ACS early diagnosis.

Assessment of the safety and probiotic properties of Roseburia intestinalis: A potential “Next Generation Probiotic”

Roseburia intestinalis is an anaerobic bacterium that produces butyric acid and belongs to the phylum Firmicutes. There is increasing evidence that this bacterium has positive effects on several diseases, including inflammatory bowel disease, atherosclerosis, alcoholic fatty liver, colorectal cancer, and metabolic syndrome, making it a potential “Next Generation Probiotic.” We investigated the genomic characteristics, probiotic properties, cytotoxicity, oral toxicity, colonization characteristics of the bacterium, and its effect on the gut microbiota. The genome contains few genes encoding virulence factors, three clustered regularly interspaced short palindromic repeat (CRISPR) sequences, two Cas genes, no toxic biogenic amine synthesis genes, and several essential amino acid and vitamin synthesis genes. Seven prophages and 41 genomic islands were predicted. In addition to a bacteriocin (Zoocin A), the bacterium encodes four metabolic gene clusters that synthesize short-chain fatty acids and 222 carbohydrate-active enzyme modules. This bacterium is sensitive to antibiotics specified by the European Food Safety Authority, does not exhibit hemolytic or gelatinase activity, and exhibits some acid resistance. R. intestinalis adheres to intestinal epithelial cells and inhibits the invasion of certain pathogens. In vitro experiments showed that the bacterium was not cytotoxic. R. intestinalis did not affect the diversity or abundance of the gut flora. Using the fluorescent labelling method, we discovered that R. intestinalis colonizes the cecum and mucus of the colon. An oral toxicity study did not reveal any obvious adverse effects. The lethal dose (LD)50 of R. intestinalis exceeded 1.9 × 10⁹ colony forming units (CFU)/kg, whereas the no observed adverse effect level (NOAEL) derived from this study was 1.32 × 10⁹ CFU/kg/day for 28 days. The current research shows that, R. intestinalis is a suitable next-generation probiotic considering its probiotic properties and safety.

Qing-Xin-Jie-Yu Granule alleviates atherosclerosis by reshaping gut microbiota and metabolic homeostasis of ApoE-/- mice

BACKGROUND

Atherosclerosis (AS) is a key pathological factor in cardiovascular disease (CVD) and is characterized by high mortality and morbidity worldwide. Metabolic disorders, including pathoglycemia and dyslipidemia that lead to chronic inflammation, represent the prominent pathological characteristics of atherosclerotic CVD, Qing-Xin-Jie-Yu Granule (QXJYG) is a Chinese traditional decoction that has been clinically proven to be effective for patients with CVD. However, the underlying mechanisms have not been completely elucidated.

PURPOSE

To investigate the protective effects of QXJYG against AS and its potential mechanisms.

METHODS

QXJYG was orally administered at doses of 1.664 and 4.992 g·kg^-1·d^-1 in a high-fat diet (HFD)-induced AS model using ApoE-/- mice. Histopathological and immunohistochemical analyses, ELISA, untargeted and targeted metabolomics analysis, 16S rRNA analysis, and RT-qPCR were performed to identify the therapeutic effects and mechanisms of QXJYG in treating HFD-induced AS.

RESULTS

QXJYG retarded HFD-induced weight gain and reduced the increased serum levels of total cholesterol, triglycerides, and low-density lipoprotein-cholesterol, whereas high-dose QXJYG increased the serum level of high-density lipoprotein-cholesterol in HFD-fed ApoE-/- mice. Meanwhile, QXJYG reduced the serum levels, as well as aortas mRNA levels of the inflammatory cytokines, IL-1β and IL-6, which indicates that QXJYG is effective against metaflammation. Mechanistically, QXJYG reshaped the gut microbiota and its associated bile acids (BAs) metabolomic phenotype, partly by increasing the levels of BA synthesis enzymes, hepatic CYP7A1, and CYP27A1, while decreasing ileal FGF15 and β-Klotho mRNA expression, favoring facilitated de novo BAs synthesis and thereby driving cholesterol catabolic excretion.

CONCLUSION

Our findings indicate that QXJYG is effective against HFD-triggered chronic inflammation, and contributes to the alleviation of AS development, and the antiatherogenic properties of QXJYG may be partly due to the remodeling of the gut microbiota and BA metabolism. Although the results are encouraging, further clinical studies of anti-AS herbal medicines are required to elucidate the full potential of the gut microbiota and BA metabolism.

Vascular Calcification and the Gut and Blood Microbiome in Chronic Kidney Disease Patients on Peritoneal Dialysis: A Pilot Study

Vascular calcification (VC) is a frequent condition in chronic kidney disease (CKD) and a well-established risk factor for the development of cardiovascular disease (CVD). Gut dysbiosis may contribute to CVD and inflammation in CKD patients. Nonetheless, the role of gut and blood microbiomes in CKD-associated VC remains unknown. Therefore, this pilot study aimed to explore the link between gut and blood microbiomes and VC in CKD patients on peritoneal dialysis (CKD-PD). Our results showed relative changes in specific taxa between CKD-PD patients with and without VC, namely Coprobacter, Coprococcus 3, Lactobacillus, and Eubacterium eligens group in the gut, and Cutibacterium, Pajaroellobacter, Devosia, Hyphomicrobium, and Pelomonas in the blood. An association between VC and all-cause mortality risk in CKD-PD patients was also observed, and patients with higher mortality risk corroborate the changes of Eubacterium eligens in the gut and Devosia genus in the blood. Although we did not find differences in uremic toxins, intestinal translocation markers, and inflammatory parameters among CKD-PD patients with and without VC, soluble CD14 (sCD14), a nonspecific marker of monocyte activation, positively correlated with VC severity. Therefore, gut Eubacterium eligens group, blood Devosia, and circulating sCD14 should be further explored as biomarkers for VC, CVD, and mortality risk in CKD.

Urine and Fecal 1H-NMR Metabolomes Differ Significantly between Pre-Term and Full-Term Born Physically Fit Healthy Adult Males

Preterm birth (before 37 weeks gestation) accounts for ~10% of births worldwide and remains one of the leading causes of death in children under 5 years of age. Preterm born adults have been consistently shown to be at an increased risk for chronic disorders including cardiovascular, endocrine/metabolic, respiratory, renal, neurologic, and psychiatric disorders that result in increased death risk. Oxidative stress was shown to be an important risk factor for hypertension, metabolic syndrome and lung disease (reduced pulmonary function, long-term obstructive pulmonary disease, respiratory infections, and sleep disturbances). The aim of this study was to explore the differences between preterm and full-term male participants' levels of urine and fecal proton nuclear magnetic resonance (¹H-NMR) metabolomes, during rest and exercise in normoxia and hypoxia and to assess general differences in human gut-microbiomes through metagenomics at the level of taxonomy, diversity, functional genes, enzymatic reactions, metabolic pathways and predicted gut metabolites. Significant differences existed between the two groups based on the analysis of ¹H-NMR urine and fecal metabolomes and their respective metabolic pathways, enabling the elucidation of a complex set of microbiome related metabolic biomarkers, supporting the idea of distinct host-microbiome interactions between the two groups and enabling the efficient classification of samples; however, this could not be directed to specific taxonomic characteristics.

Diet-gut microbiota interactions on cardiovascular disease

Cardiovascular diseases (CVD) are a group of disorders of the heart and blood vessels and remain the leading cause of morbidity and mortality worldwide. Over the past decades, accumulating studies indicated that the gut microbiota, an indispensable "invisible organ", plays a vital role in human metabolism and disease states including CVD. Among many endogenous and exogenous factors that can impact gut microbial communities, the dietary nutrients emerge as an essential component of host-microbiota relationships that can be involved in CVD susceptibility. In this review, we summarize the major concepts of dietary modulation of the gut microbiota and the chief principles of the involvement of this microbiota in CVD development. We also discuss the mechanisms of diet-microbiota crosstalk that regulate CVD progression, including endotoxemia, inflammation, gut barrier dysfunction and lipid metabolism dysfunction. In addition, we describe how metabolites produced by the microbiota, including trimethylamine-N-oxide (TMAO), secondary bile acids (BAs), short chain fatty acids (SCFAs) as well as aromatic amino acids (AAAs) derived metabolites play a role in CVD pathogenesis. Finally, we present the potential dietary interventions which interacted with gut microbiota as novel preventive and therapeutic strategies for CVD management.

Gut Microbial Signatures of Distinct Trimethylamine N-Oxide Response to Raspberry Consumption

The aim of this exploratory study was to evaluate the gut microbial signatures of distinct trimethylamine N-oxide (TMAO) responses following raspberry consumption. Investigations were carried out in 24 subjects at risk of developing metabolic syndrome who received 280 g/day of frozen raspberries for 8 weeks. Blood and stool samples were collected at weeks 0 and 8. Inter-individual variability in plasma TMAO levels was analyzed, 7 subjects were excluded due to noninformative signals and 17 subjects were kept for analysis and further stratified according to their TMAO response. Whole-metagenome shotgun sequencing analysis was used to determine the impact of raspberry consumption on gut microbial composition. Before the intervention, the relative abundance of Actinobacteriota was significantly higher in participants whose TMAO levels increased after the intervention (p = 0.03). The delta TMAO (absolute differences of baseline and week 8 levels) was positively associated with the abundance of gut bacteria such as Bilophila wadsworthia (p = 0.02; r² = 0.37), from the genus Granulicatella (p = 0.03; r² = 0.48) or the Erysipelotrichia class (p = 0.03; r² = 0.45). Changes in the gut microbial ecology induced by raspberry consumption over an 8-week period presumably impacted quaternary amines-utilizing activity and thus plasma TMAO levels.

Tongxinluo May Alleviate Inflammation and Improve the Stability of Atherosclerotic Plaques by Changing the Intestinal Flora

Intestinal flora plays an important role in atherosclerosis. Tongxinluo, as a multi-target Chinese medicine to improve atherosclerosis, whether it can improve atherosclerosis by affecting the intestinal flora is worth exploring. We established a vulnerable plaque model of atherosclerosis in New Zealand white rabbits by high cholesterol diet and balloon injury (HCB), and performed Tongxinluo intervention. We detected the level of inflammation by immunohistochemistry, Western Blot, and ELISA, analyzed plaque characteristics by calculating the vulnerability index, and analyzed the changes of gut microbiota and metabolites by 16S rRNA gene sequencing and untargeted metabolomic sequencing. The results showed that Tongxinluo intervention improved plaque stability, reduced inflammatory response, inhibited NLRP3 inflammatory pathway, increased the relative abundance of beneficial bacteria such as Alistipes which reduced by HCB, and increased the content of beneficial metabolites such as trans-ferulic acid in feces. Through correlation analysis, we found that some metabolites were significantly correlated with some bacteria and some inflammatory factors. In particular, the metabolite trans-ferulic acid was also significantly positively correlated with plaque stability. Our further studies showed that trans-ferulic acid could also inhibit the NLRP3 inflammatory pathway. In conclusion, Tongxinluo can improve plaque stability and reduce inflammation in atherosclerotic rabbits, which may be achieved by modulating intestinal flora and intestinal metabolism. Our study provides new views for the role of Tongxinluo in improving atherosclerotic vulnerable plaque, which has important clinical significance.

The Contribution of Gut Microbiota and Endothelial Dysfunction in the Development of Arterial Hypertension in Animal Models and in Humans

The maintenance of the physiological values of blood pressure is closely related to unchangeable factors (genetic predisposition or pathological alterations) but also to modifiable factors (dietary fat and salt, sedentary lifestyle, overweight, inappropriate combinations of drugs, alcohol abuse, smoking and use of psychogenic substances). Hypertension is usually characterized by the presence of a chronic increase in systemic blood pressure above the threshold value and is an important risk factor for cardiovascular disease, including myocardial infarction, stroke, micro- and macro-vascular diseases. Hypertension is closely related to functional changes in the endothelium, such as an altered production of vasoconstrictive and vasodilator substances, which lead to an increase in vascular resistance. These alterations make the endothelial tissue unresponsive to autocrine and paracrine stimuli, initially determining an adaptive response, which over time lead to an increase in risk or disease. The gut microbiota is composed of a highly diverse bacterial population of approximately 10¹⁴ bacteria. A balanced intestinal microbiota preserves the digestive and absorbent functions of the intestine, protecting from pathogens and toxic metabolites in the circulation and reducing the onset of various diseases. The gut microbiota has been shown to produce unique metabolites potentially important in the generation of hypertension and endothelial dysfunction. This review highlights the close connection between hypertension, endothelial dysfunction and gut microbiota.

Improving bulk RNA-seq classification by transferring gene signature from single cells in acute myeloid leukemia

The advances in single-cell RNA sequencing (scRNA-seq) technologies enable the characterization of transcriptomic profiles at the cellular level and demonstrate great promise in bulk sample analysis thereby offering opportunities to transfer gene signature from scRNA-seq to bulk data. However, the gene expression signatures identified from single cells are typically inapplicable to bulk RNA-seq data due to the profiling differences of distinct sequencing technologies. Here, we propose single-cell pair-wise gene expression (scPAGE), a novel method to develop single-cell gene pair signatures (scGPSs) that were beneficial to bulk RNA-seq classification to transfer knowledge across platforms. PAGE was adopted to tackle the challenge of profiling differences. We applied the method to acute myeloid leukemia (AML) and identified the scGPS from mouse scRNA-seq that allowed discriminating between AML and control cells. The scGPS was validated in bulk RNA-seq datasets and demonstrated better performance (average area under the curve [AUC] = 0.96) than the conventional gene expression strategies (average AUC$\le$ 0.88) suggesting its potential in disclosing the molecular mechanism of AML. The scGPS also outperformed its bulk counterpart, which highlighted the benefit of gene signature transfer. Furthermore, we confirmed the utility of scPAGE in sepsis as an example of other disease scenarios. scPAGE leveraged the advantages of single-cell profiles to enhance the analysis of bulk samples revealing great potential of transferring knowledge from single-cell to bulk transcriptome studies.

Completion of the gut microbial epi-bile acid pathway

Bile acids are detergent molecules that solubilize dietary lipids and lipid-soluble vitamins. Humans synthesize bile acids with α-orientation hydroxyl groups which can be biotransformed by gut microbiota to toxic, hydrophobic bile acids, such as deoxycholic acid (DCA). Gut microbiota can also convert hydroxyl groups from the α-orientation through an oxo-intermediate to the β-orientation, resulting in more hydrophilic, less toxic bile acids. This interconversion is catalyzed by regio- (C-3 vs. C-7) and stereospecific (α vs. β) hydroxysteroid dehydrogenases (HSDHs). So far, genes encoding the urso- (7α-HSDH & 7β-HSDH) and iso- (3α-HSDH & 3β-HSDH) bile acid pathways have been described. Recently, multiple human gut clostridia were reported to encode 12α-HSDH, which interconverts DCA and 12-oxolithocholic acid (12-oxoLCA). 12β-HSDH completes the epi-bile acid pathway by converting 12-oxoLCA to the 12β-bile acid denoted epiDCA; however, a gene(s) encoding this enzyme has yet to be identified. We confirmed 12β-HSDH activity in cultures of Clostridium paraputrificum ATCC 25780. From six candidate C. paraputrificum ATCC 25780 oxidoreductase genes, we discovered the first gene (DR024_RS09610) encoding bile acid 12β-HSDH. Phylogenetic analysis revealed unforeseen diversity for 12β-HSDH, leading to validation of two additional bile acid 12β-HSDHs through a synthetic biology approach. By comparison to a previous phylogenetic analysis of 12α-HSDH, we identified the first potential C-12 epimerizing strains: Collinsella tanakaei YIT 12063 and Collinsella stercoris DSM 13279. A Hidden Markov Model search against human gut metagenomes located putative 12β-HSDH genes in about 30% of subjects within the cohorts analyzed, indicating this gene is relevant in the human gut microbiome.

Can the Salivary Microbiome Predict Cardiovascular Diseases? Lessons Learned From the Qatari Population

Background: Many studies have linked dysbiosis of the gut microbiome to the development of cardiovascular diseases (CVD). However, studies assessing the association between the salivary microbiome and CVD risk on a large cohort remain sparse. This study aims to identify whether a predictive salivary microbiome signature is associated with a high risk of developing CVD in the Qatari population. Methods: Saliva samples from 2,974 Qatar Genome Project (QGP) participants were collected from Qatar Biobank (QBB). Based on the CVD score, subjects were classified into low-risk (LR < 10) (n = 2491), moderate-risk (MR = 10-20) (n = 320) and high-risk (HR > 30) (n = 163). To assess the salivary microbiome (SM) composition, 16S-rDNA libraries were sequenced and analyzed using QIIME-pipeline. Machine Learning (ML) strategies were used to identify SM-based predictors of CVD risk. Results: Firmicutes and Bacteroidetes were the predominant phyla among all the subjects included. Linear Discriminant Analysis Effect Size (LEfSe) analysis revealed that Clostridiaceae and Capnocytophaga were the most significantly abundant genera in the LR group, while Lactobacillus and Rothia were significantly abundant in the HR group. ML based prediction models revealed that Desulfobulbus, Prevotella, and Tissierellaceae were the common predictors of increased risk to CVD. Conclusion: This study identified significant differences in the SM composition in HR and LR CVD subjects. This is the first study to apply ML-based prediction modeling using the SM to predict CVD in an Arab population. More studies are required to better understand the mechanisms of how those microbes contribute to CVD.

Blood Circulating miRNA Pairs as a Robust Signature for Early Detection of Esophageal Cancer

Esophageal cancer (EC) is a common malignant tumor in the digestive system which is often diagnosed at the middle and late stages. Noninvasive diagnosis using circulating miRNA as biomarkers enables accurate detection of early-stage EC to reduce mortality. We built a diagnostic signature consisting of four miRNA pairs for the early detection of EC using individualized Pairwise Analysis of Gene Expression (iPAGE). Profiling of miRNA expression identified 496 miRNA pairs with significant relative expression change. Four miRNA pairs consistently selected from LASSO were used to construct the final diagnostic model. The performance of the signature was validated using two independent datasets, yielding both AUCs and PRCs over 0.99. Furthermore, precision, recall, and F-score were also evaluated for clinical application, when a fixed threshold is given, resulting in all the scores are larger than 0.92 in the training set, test set, and two validation sets. Our results suggested that the 4-miRNA signature is a new biomarker for the early diagnosis of patients with EC. The clinical use of this signature would have improved the detection of EC for earlier therapy and more favorite prognosis.

Gut microbiota dysbiosis in Chinese children with type 1 diabetes mellitus: An observational study

BACKGROUND

Gut microbiota dysbiosis is reportedly actively involved in autoimmune diseases such as type 1 diabetes mellitus (T1DM). However, the alterations in the gut microbiota and their correlation with fasting blood glucose (FBG) in Chinese children with T1DM remain unclear.

AIM

To investigate alterations in the gut microbiota in Chinese children with T1DM and their associations with clinical indicators.

METHODS

Samples from 51 children with T1DM and 47 age-matched and gender-matched healthy controls were obtained, to explore the structural and functional alterations in the fecal microbiota. The V3-V4 regions of the 16S rRNA gene were sequenced on a MiSeq instrument, and the association with FBG were analyzed.

RESULTS

We found that the bacterial diversity was significantly increased in the T1DM-associated fecal microbiota, and changes in the microbial composition were observed at different taxonomic levels. The T1DM-reduced differential taxa, such as Bacteroides vulgatus ATCC8482, Bacteroides ovatus, Bacteroides xylanisolvens, and Flavonifractor plautii, were negatively correlated with FBG, while the T1DM-enriched taxa, such as Blautia, Eubacterium hallii group, Anaerostipes hadrus, and Dorea longicatena, were positively correlated with FBG. Bacteroides vulgatus ATCC8482, Bacteroides ovatus, the Eubacterium hallii group, and Anaerostipes hadrus, either alone or in combination, could be used as noninvasive diagnostic biomarkers to discriminate children with T1DM from healthy controls. In addition, the functional changes in the T1DM-associated fecal microbiota also suggest that these fecal microbes were associated with altered functions and metabolic activities, such as glycan biosynthesis and metabolism and lipid metabolism, which might play vital roles in the pathogenesis and development of T1DM.

CONCLUSION

Our present comprehensive investigation of the T1DM-associated fecal microbiota provides novel insights into the pathogenesis of the disease and sheds light on the diagnosis and treatment of T1DM.

Evaluating the Consistency of Gene Methylation in Liver Cancer Using Bisulfite Sequencing Data

Bisulfite sequencing is considered as the gold standard approach for measuring DNA methylation, which acts as a pivotal part in regulating a variety of biological processes without changes in DNA sequences. In this study, we introduced the most prevalent methods for processing bisulfite sequencing data and evaluated the consistency of the data acquired from different measurements in liver cancer. Firstly, we introduced three commonly used bisulfite sequencing assays, i.e., reduced-representation bisulfite sequencing (RRBS), whole-genome bisulfite sequencing (WGBS), and targeted bisulfite sequencing (targeted BS). Next, we discussed the principles and compared different methods for alignment, quality assessment, methylation level scoring, and differentially methylated region identification. After that, we screened differential methylated genes in liver cancer through the three bisulfite sequencing assays and evaluated the consistency of their results. Ultimately, we compared bisulfite sequencing to 450 k beadchip and assessed the statistical similarity and functional association of differentially methylated genes (DMGs) among the four assays. Our results demonstrated that the DMGs measured by WGBS, RRBS, targeted BS and 450 k beadchip are consistently hypo-methylated in liver cancer with high functional similarity.

Long non-coding RNA pairs to assist in diagnosing sepsis

Background

Sepsis is the major cause of death in Intensive Care Unit (ICU) globally. Molecular detection enables rapid diagnosis that allows early intervention to minimize the death rate. Recent studies showed that long non-coding RNAs (lncRNAs) regulate proinflammatory genes and are related to the dysfunction of organs in sepsis. Identifying lncRNA signature with absolute abundance is challenging because of the technical variation and the systematic experimental bias.

Results

Cohorts (n = 768) containing whole blood lncRNA profiling of sepsis patients in the Gene Expression Omnibus (GEO) database were included. We proposed a novel diagnostic strategy that made use of the relative expressions of lncRNA pairs, which are reversed between sepsis patients and normal controls (eg. lncRNA_i > lncRNA_j in sepsis patients and lncRNA_i < lncRNA_j in normal controls), to identify 14 lncRNA pairs as a sepsis diagnostic signature. The signature was then applied to independent cohorts (n = 644) to evaluate its predictive performance across different ages and normalization methods. Comparing to common machine learning models and existing signatures, SepSigLnc consistently attains better performance on the validation cohorts from the same age group (AUC = 0.990 & 0.995 in two cohorts) and across different groups (AUC = 0.878 on average), as well as cohorts processed by an alternative normalization method (AUC = 0.953 on average). Functional analysis demonstrates that the lncRNA pairs in SepsigLnc are functionally similar and tend to implicate in the same biological processes including cell fate commitment and cellular response to steroid hormone stimulus.

Conclusion

Our study identified 14 lncRNA pairs as signature that can facilitate the diagnosis of septic patients at an intervenable point when clinical manifestations are not dramatic. Also, the computational procedure can be generalized to a standard procedure for discovering diagnostic molecule signatures.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07576-4.

Metabarcoding analysis of gut microbiota of healthy individuals reveals impact of probiotic and maltodextrin consumption

In a previously published double-blind, placebo-controlled study, we showed that probiotics intake exerted a positive effect on sleep quality and a general improvement across time in different aspects of the profile of mood state, like sadness, anger, and fatigue in 33 healthy individuals. The present work investigates the impact of the probiotic product, constituted of Limosilactobacillus fermentum LF16, Lacticaseibacillus rhamnosus LR06, Lactiplantibacillus plantarum LP01 (all former members of Lactobacillus genus), and Bifidobacterium longum 04, on the gut microbiota composition of the same cohort through a metabarcoding analysis. Both the placebo and probiotic treatments had a significant impact on the microbiota composition. Statistical analysis showed that the microbiota of the individuals could be clustered into three groups, or bacteriotypes, at the baseline, and, inherently, bacterial compositions were linked to different responses to probiotic and placebo intakes. Interestingly, L. rhamnosus and L. fermentum were retrieved in the probiotic-treated cohort, while a bifidogenic effect of maltodextrin, used as placebo, was observed. The present study shed light on the importance of defining bacteriotypes to assess the impact of interventions on the gut microbiota and allowed to reveal microbial components which could be related to positive effects (i.e. sleep quality improvement) to be verified in further studies.

Systematic prediction of autophagy-related proteins using Arabidopsis thaliana interactome data

Autophagy is a self-degradative process that is crucial for maintaining cellular homeostasis by removing damaged cytoplasmic components and recycling nutrients. Such an evolutionary conserved proteolysis process is regulated by the autophagy-related (Atg) proteins. The incomplete understanding of plant autophagy proteome and the importance of a proteome-wide understanding of the autophagy pathway prompted us to predict Atg proteins and regulators in Arabidopsis. Here, we developed a systems-level algorithm to identify autophagy-related modules (ARMs) based on protein subcellular localization, protein-protein interactions, and known Atg proteins. This generates a detailed landscape of the autophagic modules in Arabidopsis. We found that the newly identified genes in each ARM tend to be upregulated and coexpressed during the senescence stage of Arabidopsis. We also demonstrated that the Golgi apparatus ARM, ARM13, functions in the autophagy process by module clustering and functional analysis. To verify the in silico analysis, the Atg candidates in ARM13 that are functionally similar to the core Atg proteins were selected for experimental validation. Interestingly, two of the previously uncharacterized proteins identified from the ARM analysis, AGD1 and Sec14, exhibited bona fide association with the autophagy protein complex in plant cells, which provides evidence for a cross-talk between intracellular pathways and autophagy. Thus, the computational framework has facilitated the identification and characterization of plant-specific autophagy-related proteins and novel autophagy proteins/regulators in higher eukaryotes.