scholarly journals Gut Microbiota and Liver Fibrosis: One Potential Biomarker for Predicting Liver Fibrosis

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Zhiming Li ◽  
Ming Ni ◽  
Haiyang Yu ◽  
Lili Wang ◽  
Xiaoming Zhou ◽  
...  
Keyword(s):  
Random Forest ◽  
Liver Fibrosis ◽  
Gut Microbiota ◽  
Control Group ◽  
Rrna Gene ◽  
Linear Discriminant ◽  
Random Forest Classification ◽  
Metabolic Functions ◽  
Forest Classification ◽  
Potential Biomarker

Purpose. To investigate the relationship between gut microbiota and liver fibrosis and establish a microbiota biomarker for detecting and staging liver fibrosis. Methods. 131 Wistar rats were used in our study, and liver fibrosis was induced by carbon tetrachloride. Stool samples were collected within 72 hours after the last administration. The V4 regions of 16S rRNA gene were amplified. The sequencing data was processed using the Quantitative Insights Into Microbial Ecology (QIIME version 1.9). The diversity, principal coordinate analysis (PCoA), nonmetric multidimensional scaling (NMDS), and linear discriminant analysis (LDA) effect size (LEfSe) were performed. Random-Forest classification was performed for discriminating the samples from different groups. Microbial function was assessed using the PICRUST. Results. The Simpson in the control group was lower than that in the liver fibrosis group (p=0.048) and differed significantly among different fibrosis stages (p=0.047). The Chao1 index in the control group was higher than that in the liver fibrosis group (p<0.001). NMDS analysis showed a marked difference between the control and liver fibrosis groups (p<0.001). PCoA analysis indicated the different community composition between the control and liver fibrosis groups with variances of PC1 13.76% and PC2 5.89% and between different liver fibrosis stages with variances of PC1 10.51% and PC2 7.78%. LEfSe analysis showed alteration of gut microbiota in the liver fibrosis group. Biomarkers obtained from Random-Forest classification showed excellent diagnostic accuracy in prediction of liver fibrosis with AUROCs of 0.99. The AUROCs were 0.77~0.84 in prediction of stage F4. There were six increased and 17 decreased metabolic functions in the liver fibrosis group and 6 metabolic functions significantly differed among four liver fibrosis stages. Conclusion. Gut microbiota is a potential biomarker for detecting and staging liver fibrosis with high diagnostic accuracies.

scholarly journals FORENSIC: an Online Platform for Fecal Source Identification

mSystems ◽  
2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Adélaïde Roguet ◽  
Özcan C. Esen ◽  
A. Murat Eren ◽  
Ryan J. Newton ◽  
Sandra L. McLellan
Keyword(s):  
Water Quality ◽  
Random Forest ◽  
Source Identification ◽  
Environmental Samples ◽  
Fecal Pollution ◽  
Rrna Gene ◽  
Content Type ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Reference Signature

ABSTRACT Sewage overflows, agricultural runoff, and stormwater discharges introduce fecal pollution into surface waters. Distinguishing these sources is critical for evaluating water quality and formulating remediation strategies. With the falling costs of sequencing, microbial community-based water quality assessment tools are under development. However, their application is limited by the need to build reference libraries, which requires extensive sampling of sources and bioinformatic expertise. Here, we introduce FORest Enteric Source IdentifiCation (FORENSIC; https://forensic.sfs.uwm.edu/), an online, library-independent source tracking platform based on random forest classification and 16S rRNA gene amplicon sequences to identify in environmental samples common fecal contamination sources, including humans, domestic pets, and agricultural animals. FORENSIC relies on a broad reference signature database of Bacteroidales and Clostridiales, two predominant bacterial groups that have coevolved with their hosts. As a result, these groups demonstrate cohesive and reliable assemblage patterns within mammalian species or among species sharing the same diet/physiology. We created a scalable and extensible platform that we tested for global applicability using samples collected in distant geographic locations. This Web application offers a fast and intuitive approach for fecal source identification, particularly in sewage-contaminated waters. IMPORTANCE FORENSIC is an online platform to identify sources of fecal pollution without the need to create reference libraries. FORENSIC is based on the ability of random forest classification to extract cohesive source microbial signatures to create classifiers despite individual variability and to detect the signatures in environmental samples. We primarily focused on defining sewage signals, which are associated with a high human health risk in polluted waters. To test for fecal contamination sources, the platform only requires paired-end reads targeting the V4 or V6 regions of the 16S rRNA gene. We demonstrated that we could use V4V5 reads trimmed to the V4 positions to generate the reference signature. The systematic workflow we describe to create and validate the signatures could be applied to many disciplines. With the increasing gap between advancing technology and practical applications, this platform makes sequence-based water quality assessments accessible to the public health and water resource communities.

scholarly journals Characteristic gut microbiota and predicted metabolic functions in women with PCOS

2020 ◽  
Vol 9 (1) ◽  
pp. 63-73 ◽  
Author(s):  
Ling Zhou ◽  
Zhexin Ni ◽  
Wen Cheng ◽  
Jin Yu ◽  
Shuai Sun ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Polycystic Ovary ◽  
16S Rrna Gene Sequencing ◽  
Cross Sectional Study ◽  
Rrna Gene ◽  
Obese Women ◽  
Cross Sectional ◽  
Linear Discriminant ◽  
Metabolic Functions ◽  
And Function

Polycystic ovary syndrome (PCOS) is a chronic endocrine and metabolic disease. Gut microbiota is closely related to many chronic diseases. In this study, we conducted a cross-sectional study and recruited 30 obese (OG) and 30 non-obese (NG) women with PCOS, 30 healthy women (NC) and 11 healthy but obese women (OC) as controls to investigate the characteristic gut microbiota and its metabolic functions in obese and non-obese patients with PCOS. The blood and non-menstrual faecal samples of all the participants were collected and analysed. As a result, the Hirsutism score, LH/FSH and serum T level in NG and OG both increased significantly compared with their controls (P < 0.05). High-throughput 16S rRNA gene sequencing revealed that the abundance and diversity of the gut microbiota changed in patients with PCOS. The linear discriminant analysis (LDA) indicated that Lactococcus was the characteristic gut microbiota in NG, while Coprococcus_2 in OG. Correlation heatmap analysis revealed that the sex hormones and insulin levels in human serum were closely related to the changes in the gut microbiota of NG and OG. Functional prediction analysis demonstrated that the citrate cycle pathway enriched both in NG and OG, and other 12 gut bacterial metabolic pathways enriched in NG. This study highlighted significant differences in the gut microbiota and predictive functions of obese and non-obese women with PCOS, thereby providing insights into the role and function of the gut microbiota that may contribute to the occurrence and development of PCOS in obese and non-obese women.

scholarly journals The change of gut microbiota in MDD patients under SSRIs treatment

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yang Shen ◽  
Xiao Yang ◽  
Gaofei Li ◽  
Jiayu Gao ◽  
Ying Liang
Keyword(s):  
Gut Microbiota ◽  
Maximum Dose ◽  
Antidepressant Treatment ◽  
Intestinal Flora ◽  
Alpha Diversity ◽  
The Other ◽  
Control Group ◽  
Depressed Patients ◽  
Metabolic Functions

AbstractThe alterations in the gut microbiota have been reported to be correlated with the development of depression. The purpose of this study was to investigate the changes of intestinal microbiota in depressed patients after antidepressant treatment. We recruited 30 MDD patients (MDD group) and 30 healthy controls (control group). The MDD group received individualized treatment with escitalopram at a maximum dose of 20 mg/day. After depressive symptoms improved to a HAMD scale score > 50%, a fecal sample was collected again and used as the follow-up group. The differences of gut microbiota between patients and controls, the characteristics of gut microbiota under treatment and the potential differences in metabolic functions were thus investigated. The Firmicutes/Bacteroidetes ratio was significantly different within three groups, and the ratio of follow-up group was significantly lower than those of the other two groups. Alpha diversity was significantly higher in MDD group than those of the other groups, and the alpha diversity was not significantly different between control and follow-up groups. The beta diversity of some patients resembled participants in the control group. The metabolic function of gut microbiota after treatment was still different from that of the control group. This study suggests that the intestinal flora of depressed patients has a tendency to return to normal under escitalopram treatment.

scholarly journals Gut Microbiome and Metabolome Profiles Associated with High-Fat Diet in Mice

Metabolites ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 482
Author(s):  
Jae-Kwon Jo ◽  
Seung-Ho Seo ◽  
Seong-Eun Park ◽  
Hyun-Woo Kim ◽  
Eun-Ju Kim ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Relative Abundance ◽  
High Fat Diet ◽  
Amplicon Sequencing ◽  
Gas Chromatography Mass Spectrometry ◽  
Control Group ◽  
Rrna Gene ◽  
High Fat ◽  
Underlying Mechanisms ◽  
Insight Into

Obesity can be caused by microbes producing metabolites; it is thus important to determine the correlation between gut microbes and metabolites. This study aimed to identify gut microbiota-metabolomic signatures that change with a high-fat diet and understand the underlying mechanisms. To investigate the profiles of the gut microbiota and metabolites that changed after a 60% fat diet for 8 weeks, 16S rRNA gene amplicon sequencing and gas chromatography-mass spectrometry (GC-MS)-based metabolomic analyses were performed. Mice belonging to the HFD group showed a significant decrease in the relative abundance of Bacteroidetes but an increase in the relative abundance of Firmicutes compared to the control group. The relative abundance of Firmicutes, such as Lactococcus, Blautia, Lachnoclostridium, Oscillibacter, Ruminiclostridium, Harryflintia, Lactobacillus, Oscillospira, and Erysipelatoclostridium, was significantly higher in the HFD group than in the control group. The increased relative abundance of Firmicutes in the HFD group was positively correlated with fecal ribose, hypoxanthine, fructose, glycolic acid, ornithine, serum inositol, tyrosine, and glycine. Metabolic pathways affected by a high fat diet on serum were involved in aminoacyl-tRNA biosynthesis, glycine, serine and threonine metabolism, cysteine and methionine metabolism, glyoxylate and dicarboxylate metabolism, and phenylalanine, tyrosine, and trypto-phan biosynthesis. This study provides insight into the dysbiosis of gut microbiota and metabolites altered by HFD and may help to understand the mechanisms underlying obesity mediated by gut microbiota.

scholarly journals Quantitative measurement of retinal ganglion cell populations via histology-based random forest classification

2016 ◽  
Vol 146 ◽  
pp. 370-385 ◽  
Author(s):  
Adam Hedberg-Buenz ◽  
Mark A. Christopher ◽  
Carly J. Lewis ◽  
Kimberly A. Fernandes ◽  
Laura M. Dutca ◽  
...  
Keyword(s):  
Random Forest ◽  
Ganglion Cell ◽  
Retinal Ganglion Cell ◽  
Quantitative Measurement ◽  
Cell Populations ◽  
Retinal Ganglion ◽  
Random Forest Classification ◽  
Forest Classification
Sign in / Sign up

Export Citation Format

Share Document