Oral microbiome alterations in epilepsy and after seizure control.

Background: The existing diagnostic methods of epilepsy such as history collection and electroencephalogram have great limitations in practice, so more reliable and less difficult diagnostic methods are needed. Methods: By characterizing oral microbiota in patients diagnosed with epilepsy (EPs) and patients whose seizures were under control (EPRs), we sought to discover biomarkers for different disease states. 16S rRNA gene sequencing was performed on 480 tongue swabs [157 EPs, 22 EPRs, and 301 healthy controls (HCs)]. Results: Compared with normal individuals, patients with epilepsy exhibit increased alpha diversity in their oral microbiota, and the oral microbial communities of the two groups demonstrate significant beta diversity differences. EPs exhibit a significant increase in the abundance of 26 genera, including Streptococcus, Granulicatella, and Kluyvera, while the abundance of 14 genera, including Peptostreptococcus, Neisseria, and Schaalia, is significantly reduced. The area under the receiver operating characteristic curve (AUC) of oral microbial markers in the training cohort and validation cohort was 98.85% and 97.23%, respectively. Importantly, the AUC of the biomarker set achieved 92.44% of additional independent validation sets. In addition, EPRs also have their own unique oral community. Conclusion: This study describes the characterization of the oral microbiome in EP and EPR and demonstrates the potential of the specific microbiome as a non-invasive diagnostic tool for epilepsy.

Microbial community and antibiotic resistance gene distribution in food waste, anaerobic digestate, and paddy soil.

The study assessed the occurrence and distribution of microbial community and antibiotic resistance genes (ARGs) in food waste, anaerobic digestate, and paddy soil samples, and revealed the potential hosts of ARGs and factors influencing their distribution. A total of 24 bacterial phyla were identified, of which 16 were shared by all samples, with Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria accounting for 65.9-92.3 % of the total bacterial community. Firmicutes was the most abundant bacteria in food waste and digestate samples, accounting for 33-83 % of the total microbial community. However, in paddy soil samples with digestate, Proteobacteria had the highest relative abundance of 38-60 %. Further, 22 ARGs were detected in food waste and digestate samples, with multidrug, macrolide-lincosamide-streptogramin (MLS), bacitracin, aminoglycoside, tetracycline, vancomycin, sulfonamide, and rifamycin resistance genes being the most abundant and shared by all samples. The highest total relative abundance of ARGs in food waste, digestate, and soil without and with digestate was detected in samples from January 2020, May 2020, October 2019, and May 2020, respectively. The MLS, vancomycin, tetracycline, aminoglycoside, and sulfonamide resistance genes had higher relative abundance in food waste and anaerobic digestate samples, whereas multidrug, bacteriocin, quinolone, and rifampin resistance genes were more abundant in paddy soil samples. Redundancy analysis demonstrated that aminoglycoside, tetracycline, sulfonamide, and rifamycin resistance genes were positively correlated with total ammonia nitrogen and pH of food waste and digestate samples. Vancomycin, multidrug, bacitracin, and fosmidomycin resistance genes had positive correlations with potassium, moisture, and organic matter in soil samples. The co-occurrence of ARG subtypes with bacterial genera was investigated using network analysis. Actinobacteria, Proteobacteria, Bacteroidetes, and Acidobacteria were identified as potential hosts of multidrug resistance genes.

Oral Microbiome Characteristics in Patients With Autoimmune Hepatitis.

Autoimmune hepatitis (AIH) is a common cause of liver cirrhosis. To identify the characteristics of the oral microbiome in patients with AIH, we collected 204 saliva samples including 68 AIH patients and 136 healthy controls and performed microbial MiSeq sequencing after screening. All samples were randomly divided into discovery cohorts (46 AIH and 92 HCs) and validation cohorts (22 AIH and 44 HCs). Moreover, we collected samples of 12 AIH patients from Hangzhou for cross-regional validation. We described the oral microbiome characteristics of AIH patients and established a diagnostic model. In the AIH group, the oral microbiome diversity was significantly increased. The microbial communities remarkably differed between the two groups. Seven genera, mainly Fusobacterium, Actinomyces and Capnocytophaga, were dominant in the HC group, while 51 genera, Streptococcus, Veillonella and Leptotrichia, were enriched in the AIH group. Notably, we found 23 gene functions, including Membrane Transport, Carbohydrate Metabolism, and Glycerolipid metabolism that were dominant in AIH and 31 gene functions that prevailed in HCs. We further investigated the correlation between the oral microbiome and clinical parameters. The optimal 5 microbial markers were figured out through a random forest model, and the distinguishing potential achieved 99.88% between 46 AIH and 92 HCs in the discovery cohort and 100% in the validation cohort. Importantly, the distinguishing potential reached 95.55% in the cross-regional validation cohort. In conclusion, this study is the first to characterize the oral microbiome in AIH patients and to report the successful establishment of a diagnostic model and the cross-regional validation of microbial markers for AIH. Importantly, oral microbiota-targeted biomarkers may be able to serve as powerful and noninvasive diagnostic tools for AIH.

Gut Microbiome Distinguishes Patients With Epilepsy From Healthy Individuals.

Objective: The gut microecosystem is the largest microecosystem in the human body and has been proven to be linked to neurological diseases. The main objective of this study was to characterize the fecal microbiome, investigate the differences between epilepsy patients and healthy controls, and evaluate the potential efficacy of the fecal microbiome as a diagnostic tool for epilepsy. Design: We collected 74 fecal samples from epilepsy patients (Eps, n = 24) and healthy controls (HCs, n = 50) in the First Affiliated Hospital of Zhengzhou University and subjected the samples to 16S rRNA MiSeq sequencing and analysis. We set up a train set and a test set, identified the optimal microbial markers for epilepsy after characterizing the gut microbiome in the former and built a diagnostic model, then validated it in the validation group. Results: There were significant differences in microbial communities between the two groups. The α-diversity of the HCs was higher than that of the epilepsy group, but the Venn diagram showed that there were more unique operational taxonomic unit (OTU) in the epilepsy group. At the phylum level, Proteobacteria and Actinobacteriota increased significantly in Eps, while the relative abundance of Bacteroidota increased in HCs. Compared with HCs, Eps were enriched in 23 genera, including Faecalibacterium, Escherichia-Shigella, Subdoligranulum and Enterobacteriaceae-unclassified. In contrast, 59 genera including Bacteroides, Megamonas, Prevotella, Lachnospiraceae-unclassified and Blautia increased in the HCs. In Spearman correlation analysis, age, WBC, RBC, PLT, ALB, CREA, TBIL, Hb and Urea were positively correlated with most of the different OTUs. Seizure-type, course and frequency are negatively correlated with most of the different OTUs. In addition, twenty-two optimal microbial markers were identified by a fivefold cross-validation of the random forest model. In the established train set and test set, the area under the curve was 0.9771 and 0.993, respectively. Conclusion: Our study was the first to characterize the gut microbiome of Eps and HCs in central China and demonstrate the potential efficacy of microbial markers as a noninvasive biological diagnostic tool for epilepsy.

Fecal Microbiomes Distinguish Patients With Autoimmune Hepatitis From Healthy Individuals.

Objective: The intestinal microbiome is associated with various autoimmune diseases. Regional difference is the main influencing factor of intestinal microbial difference. This study aimed to identify the differences in fecal microbiome between autoimmune hepatitis (AIH) patients and healthy controls (HCs) in Central China, and to validate the efficacy of fecal microbiome as a diagnostic tool for AIH. Design: We collected 115 fecal samples from AIH patients (N = 37) and HCs (N = 78) in Central China and performed gene sequencing. Fecal microbiomes were characterized and microbial markers for AIH were identified. Results: Fecal microbial diversity showed a downward trend in AIH compared with HCs. Fecal microbial communities significantly differed between both groups. At the phylum level, Verrucomicrobia abundance was significantly increased, while Lentisphaerae and Synergistetes were significantly decreased in the AIH patients vs. the HCs. Compared to the HCs, 15 genera, including Veillonella, Faecalibacterium, and Akkermansia, were enriched, while 19 genera, such as Pseudobutyrivibrio, Lachnospira, and Ruminococcaceae, were decreased in the AIH patients. Ten genera, including Veillonella, Faecalibacterium, and Akkermansia, predominated in the AIH patients. Five microbial biomarkers were deemed optimal diagnostic tools for AIH. The probability of disease was significantly increased in AIH group vs. HCs, achieving 83.25% value of area under the curve. Conclusion: We present the characteristics of AIH patients in Central China for the first time. Five microbial biomarkers, including Lachnospiraceae, Veillonella, Bacteroides, Roseburia, and Ruminococcaceae, achieved a high potential distinguishing AIH patients from HCs.

The Function of the HGF/c-Met Axis in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide, leading to a large global cancer burden. Hepatocyte growth factor (HGF) and its high-affinity receptor, mesenchymal epithelial transition factor (c-Met), are closely related to the onset, progression, and metastasis of multiple tumors. The HGF/c-Met axis is involved in cell proliferation, movement, differentiation, invasion, angiogenesis, and apoptosis by activating multiple downstream signaling pathways. In this review, we focus on the function of the HGF/c-Met axis in HCC. The HGF/c-Met axis promotes the onset, proliferation, invasion, and metastasis of HCC. Moreover, it can serve as a biomarker for diagnosis and prognosis, as well as a therapeutic target for HCC. In addition, it is closely related to drug resistance during HCC treatment.

Human microbiome is a diagnostic biomarker in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the third leading cause of cancer mortality worldwide. Increasing evidence indicates a close relationship between HCC and the human microbiota. Herein, we reviewed the important potential of the human microbiota as a diagnostic biomarker of HCC. DATA SOURCES: Several innovative studies have investigated the characteristics of the gut and oral microbiomes in patients with HCC and proposed that the human microbiome has the potential to be a diagnostic biomarker of HCC. Literature from February 1999 to February 2019 was searched in the PubMed database using the keywords "microbiota" or "microbiome" or "microbe" and "liver cancer" or "hepatocellular carcinoma", and the results of clinical and experimental studies were analyzed. RESULTS: Specific changes occur in the human microbiome of patients with HCC. Moreover, the gut microbiome and oral microbiome can be used as non-invasive diagnostic biomarkers for HCC. Furthermore, they also have certain diagnostic potential for precancerous diseases of HCC. The diagnostic potential of the blood microbiota and ascites microbiota in HCC will be gradually discovered in the future. CONCLUSIONS: The human microbiome is valuable to the diagnosis of HCC and provides a novel strategy for targeted therapy of HCC. The human microbiome may be widely used in the diagnosis, treatment and prognosis for multiple system diseases or cancers in the future.

Nanoparticle Conjugation of Ginsenoside Rg3 Inhibits Hepatocellular Carcinoma Development and Metastasis.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. The prognosis of HCC remains very poor; thus, an effective treatment remains urgent. Herein, a type of nanomedicine is developed by conjugating Fe@Fe3 O4 nanoparticles with ginsenoside Rg3 (NpRg3), which achieves an excellent coupling effect. In the dimethylnitrosamine-induced HCC model, NpRg3 application significantly prolongs the survival of HCC mice. Further research indicates that NpRg3 application significantly inhibits HCC development and eliminates HCC metastasis to the lung. Notably, NpRg3 application delays HCC-induced ileocecal morphology and gut microbial alterations more than 12 weeks during HCC progression. NpRg3 administration elevates the abundance of Bacteroidetes and Verrucomicrobia, but decreases Firmicutes. Twenty-nine predicted microbial gene functions are enriched, while seven gene functions are reduced after NpRg3 administration. Moreover, the metabolomics profile presents a significant progression during HCC development, but NpRg3 administration corrects tumor-dominant metabolomics. NpRg3 administration decreases 3-indolepropionic acid and urea, but elevates free fatty acids. Importantly, NpRg3 application remodels the unbalanced correlation networks between gut microbiota and metabolism during HCC therapy. In conclusion, nanoparticle conjugation of ginsenoside Rg3 inhibits HCC development and metastasis via the remodeling of unbalanced gut microbiota and metabolism in vivo, providing an antitumor therapy strategy.