Accurate Five-category Classification for Colorectal Cancer Using Gut Microbiome 16S rRNA Sequencing

2021 ◽  
Author(s):  
Liying Zhang ◽  
Jiaqi Zhu ◽  
Qiutao Ding ◽  
Yanqi Huang ◽  
Hongbo Zhang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
16S Rrna ◽  
Gut Microbiome ◽  
Large Scale ◽  
Operational Taxonomic Unit ◽  
16S Rrna Sequencing ◽  
Screening Tests ◽  
Advanced Adenoma ◽  
Sequencing Data ◽  
Rrna Sequencing

Abstract The association between the gut microbiome and the five stages of colorectal cancer (CRC) (healthy, polyposis, nonadvanced adenoma, advanced adenoma, and cancer) remains unclear. We performed 16S rRNA sequencing of the V3-V4 amplicon from 999 samples from subjects at various stages of CRC development and constructed an accurate predictive random forest model for CRC development. In the testing set, our five-category CRC prediction classifier had accuracies of 0.84 and 0.74 using the relative operational taxonomic unit (OTU) abundances and relative genus abundances, respectively. Specifically, the OTU-based classifier had a sensitivity of 0.97 and specificity of 0.97 for CRC samples, and the genus-based classifier had a sensitivity of 0.97 and specificity of 0.95 for CRC samples. Meanwhile, the gut microbiota was found to differ at all stages of CRC development. The differential abundances of closely related bacteria were used to accurately classify the five stages of CRC development. Additionally, both unannotated and annotated OTUs played important roles in classifier modelling. Our work not only provides valuable 16S rRNA sequencing data from patients and healthy individuals on a large scale but also identifies reproducible gut microbiome biomarkers for CRC staging and highlights their potential applications as noninvasive microbiome biomarkers for diagnosis and as predictive CRC screening tests.

scholarly journals Excessive Unbalanced Meat Consumption in the First Year of Life Increases Asthma Risk in the PASTURE and LUKAS2 Birth Cohorts

2021 ◽  
Vol 12 ◽  
Author(s):  
Alexander J. Hose ◽  
Giulia Pagani ◽  
Anne M. Karvonen ◽  
Pirkka V. Kirjavainen ◽  
Caroline Roduit ◽  
...  
Keyword(s):  
16S Rrna ◽  
Meat Consumption ◽  
16S Rrna Sequencing ◽  
Atopic Asthma ◽  
First Year ◽  
Sequencing Data ◽  
Food Items ◽  
Asthma Risk ◽  
Rrna Sequencing ◽  
First Year Of Life

A higher diversity of food items introduced in the first year of life has been inversely related to subsequent development of asthma. In the current analysis, we applied latent class analysis (LCA) to systematically assess feeding patterns and to relate them to asthma risk at school age. PASTURE (N=1133) and LUKAS2 (N=228) are prospective birth cohort studies designed to evaluate protective and risk factors for atopic diseases, including dietary patterns. Feeding practices were reported by parents in monthly diaries between the 4th and 12th month of life. For 17 common food items parents indicated frequency of feeding during the last 4 weeks in 4 categories. The resulting 153 ordinal variables were entered in a LCA. The intestinal microbiome was assessed at the age of 12 months by 16S rRNA sequencing. Data on feeding practice with at least one reported time point was available in 1042 of the 1133 recruited children. Best LCA model fit was achieved by the 4-class solution. One class showed an elevated risk of asthma at age 6 as compared to the other classes (adjusted odds ratio (aOR): 8.47, 95% CI 2.52–28.56, p = 0.001) and was characterized by daily meat consumption and rare consumption of milk and yoghurt. A refined LCA restricted to meat, milk, and yoghurt confirmed the asthma risk effect of a particular class in PASTURE and independently in LUKAS2, which we thus termed unbalanced meat consumption (UMC). The effect of UMC was particularly strong for non-atopic asthma and asthma irrespectively of early bronchitis (aOR: 17.0, 95% CI 5.2–56.1, p < 0.001). UMC fostered growth of iron scavenging bacteria such as Acinetobacter (aOR: 1.28, 95% CI 1.00-1.63, p = 0.048), which was also related to asthma (aOR: 1.55, 95% CI 1.18-2.03, p = 0.001). When reconstructing bacterial metabolic pathways from 16S rRNA sequencing data, biosynthesis of siderophore group nonribosomal peptides emerged as top hit (aOR: 1.58, 95% CI 1.13-2.19, p = 0.007). By a data-driven approach we found a pattern of overly meat consumption at the expense of other protein sources to confer risk of asthma. Microbiome analysis of fecal samples pointed towards overgrowth of iron-dependent bacteria and bacterial iron metabolism as a potential explanation.

scholarly journals Characterizing the Composition of the Pediatric Gut Microbiome: A Systematic Review

Nutrients ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 16 ◽  
Author(s):  
Kane E. Deering ◽  
Amanda Devine ◽  
Therese A. O’Sullivan ◽  
Johnny Lo ◽  
Mary C. Boyce ◽  
...  
Keyword(s):  
Systematic Review ◽  
16S Rrna ◽  
Relative Abundance ◽  
Gut Microbiome ◽  
Geographical Location ◽  
Short Chain Fatty Acids ◽  
16S Rrna Sequencing ◽  
Short Chain ◽  
Α Diversity ◽  
Rrna Sequencing

The consortium of trillions of microorganisms that live inside the human gut are integral to health. Little has been done to collate and characterize the microbiome of children. A systematic review was undertaken to address this gap (PROSPERO ID: CRD42018109599). MEDLINE and EMBASE were searched using the keywords: “healthy preadolescent children” and “gut microbiome” to 31 August 2018. Of the 815 journal articles, 42 met the inclusion criteria. The primary outcome was the relative abundance of bacteria at the phylum, family, and genus taxonomic ranks. α-diversity, short chain fatty acid concentrations, diet, 16S rRNA sequencing region, and geographical location were documented. The preadolescent gut microbiome is dominated at the phylum level by Firmicutes (weighted overall average relative abundance = 51.1%) and Bacteroidetes (36.0%); genus level by Bacteroides (16.0%), Prevotella (8.69%), Faecalibacterium (7.51%), and Bifidobacterium (5.47%). Geographic location and 16S rRNA sequencing region were independently associated with microbial proportions. There was limited consensus between studies that reported α-diversity and short chain fatty acids. Broadly speaking, participants from non-Western locations, who were less likely to follow a Westernized dietary pattern, had higher α-diversity and SCFA concentrations. Confirmatory studies will increase the understanding of the composition and functional capacity of the preadolescent gut microbiome.

scholarly journals LogMPIE, pan-India profiling of the human gut microbiome using 16S rRNA sequencing

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Ashok Kumar Dubey ◽  
Niyati Uppadhyaya ◽  
Pravin Nilawe ◽  
Neeraj Chauhan ◽  
Santosh Kumar ◽  
...  
Keyword(s):  
16S Rrna ◽  
Gut Microbiome ◽  
16S Rrna Sequencing ◽  
Human Gut ◽  
Human Gut Microbiome ◽  
Rrna Sequencing

scholarly journals Noise exposure-induced intestinal flora dysbiosis disrupts homeostasis of oxi-inflamm-barrier in the gut–brain axis of APP/PS1 mice: implications for early onset Alzheimer's disease

2020 ◽  
Author(s):  
Bo Cui ◽  
Huimin Chi ◽  
Wa Cao ◽  
Donghong Su ◽  
Honglian Yang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
16S Rrna ◽  
Gut Microbiome ◽  
Early Onset ◽  
Noise Exposure ◽  
Intestinal Flora ◽  
Environmental Noise ◽  
16S Rrna Sequencing ◽  
Rrna Sequencing

Abstract Background: Environmental noise exposure and genetic risk factors are thought to be associated with gut microbiome that exacerbates Alzheimer’s disease (AD) pathology. However, the role and mechanism of environmental risk factors in early-onset AD (EOAD) pathogenesis remain unclear. Methods: We established APP/PS1 Tg and C57BL/6 (wild type [WT]) mouse models to evaluate the molecular pathways underlying EOAD pathophysiology following environmental noise exposure. 16S rRNA sequencing analyses were used for intestinal flora measurements and Tax4Fun were used to predict the metagenome content from 16S rRNA sequencing results; and assessment of the flora dysbiosis-triggered dyshomeostasis of oxi-inflamm-barrier and the effects of the CNS end of the gut–brain axis were conducted to explore the underlying pathological mechanisms. Results: Both WT and APP/PS1 mice showed statistically significant relationship between environmental noise and the taxonomic composition of the corresponding gut microbiome. Bacterial-encoded functional categories in noise-exposed WT and APP/PS1 mice included phospholipid and galactose metabolism, oxidative stress, and cell senescence. These alterations corresponded with imbalanced intestinal oxidation and anti-oxidation systems and low-grade systemic inflammation after noise exposure. Mechanistically, axis-series experiments demonstrated that after noise exposure, intestinal and hippocampal tight junction proteins levels reduced, whereas serum levels of inflammatory mediator were elevated. With regard to APP/PS1 overexpression, noise-induced abnormalities in the gut–brain axis may contribute to aggravation of neuropathology in the presymptomatic stage of EOAD mice model. Conclusions: Our results demonstrate that noise exposure has deleterious effects on the homeostasis of oxi-inflamm-barrier in the microbiome–gut–brain axis. Therefore, at least in a genetic context, chronic noise may aggravate the progression of EOAD.

scholarly journals HyperEx: A Tool to Extract Hypervariable Regions from 16S rRNA Sequencing Data

2021 ◽  
Author(s):  
Anicet Ebou ◽  
Dominique Koua ◽  
Adolphe Zeze
Keyword(s):  
16S Rrna ◽  
Ribosomal Rna ◽  
High Throughput Sequencing ◽  
16S Ribosomal Rna ◽  
16S Rrna Sequencing ◽  
Taxonomic Resolution ◽  
Sequencing Data ◽  
Pairwise Sequence Alignment ◽  
Hypervariable Regions ◽  
Rrna Sequencing

The 16S ribosomal RNA gene is one of the most studied genes in biology. This 16S ribosomal RNA importance is due to its wide application in phylogenetics and taxonomic elucidation of bacteria and archaea. Indeed, 16S ribosomal RNA is present in almost all bacteria and archaea and has, among many other useful characteristics, a low mutation rate. The 16S ribosomal RNA is composed of nine hypervariable regions which are commonly targeted by high throughput sequencing technologies in identification or community studies like metabarcoding studies. Unfortunately, the hypervariable regions do not have the same taxonomic resolution among all bacteria taxa. This requires a preliminary in silico analysis to determine the best hypervariable regions to target in a particular study. Nevertheless, to the best of our knowledge, no automated primer-based open-source tool exists to extract hypervariable regions from complete or near-complete 16S rRNA sequencing data. Here we present HyperEx which efficiently extracts the hypervariable region of interest based on embedded primers or user-given primers. HyperEx implements the Myers algorithm for the exact pairwise sequence alignment. HyperEx is freely available under the MIT license as an operating system independent Rust command-line tool at https://github.com/Ebedthan/hyperex and https://crates.io.

scholarly journals Gut microbiota modification suppresses the development of pulmonary arterial hypertension in an SU5416/hypoxia rat model

2020 ◽  
Vol 10 (3) ◽  
pp. 204589402092914
Author(s):  
Takayuki J. Sanada ◽  
Koji Hosomi ◽  
Hiroki Shoji ◽  
Jonguk Park ◽  
Akira Naito ◽  
...  
Keyword(s):  
Pulmonary Arterial Hypertension ◽  
16S Rrna ◽  
Gut Microbiota ◽  
Arterial Hypertension ◽  
Right Ventricular ◽  
Rat Model ◽  
Gut Microbiome ◽  
16S Rrna Sequencing ◽  
Rrna Sequencing ◽  
Pulmonary Arterial

The pathogenesis of pulmonary arterial hypertension is closely associated with dysregulated inflammation. Recently, abnormal alterations in gut microbiome composition and function were reported in a pulmonary arterial hypertension experimental animal model. However, it remains unclear whether these alterations are a result or the cause of pulmonary arterial hypertension. The purpose of this study was to investigate whether alterations in the gut microbiome affected the hemodynamics in SU5416/hypoxia rats. We used the SU5416/hypoxia rat model in our study. SU5416/hypoxia rats were treated with a single SU5416 injection (30 mg/kg) and a three-week hypoxia exposure (10% O2). Three SU5416/hypoxia rats were treated with a combination of four antibiotics (SU5416/hypoxia + ABx group) for four weeks. Another group was exposed to hypoxia (10% O2) without the SU5416 treatment, and control rats received no treatment. Fecal samples were collected from each animal, and the gut microbiota composition was analyzed by 16S rRNA sequencing. The antibiotic treatment significantly suppressed the vascular remodeling, right ventricular hypertrophy, and increase in the right ventricular systolic pressure in SU5416/hypoxia rats. 16S rRNA sequencing analysis revealed gut microbiota modification in SU5416/hypoxia + ABx group. The Firmicutes-to-Bacteroidetes ratio in SU5416/hypoxia rats was significantly higher than that in control and hypoxia rats. Compared with the control microbiota, 14 bacterial genera, including Bacteroides and Akkermansia, increased, whereas seven bacteria, including Rothia and Prevotellaceae, decreased in abundance in SU5416/hypoxia rats. Antibiotic-induced modification of the gut microbiota suppresses the development of pulmonary arterial hypertension. Dysbiosis may play a causal role in the development and progression of pulmonary arterial hypertension.

scholarly journals Characterization of Bacterial Community Dynamics of the Human Mouth Throughout Decomposition via Metagenomic, Metatranscriptomic, and Culturing Techniques

2021 ◽  
Vol 12 ◽  
Author(s):  
Emily C. Ashe ◽  
André M. Comeau ◽  
Katie Zejdlik ◽  
Seán P. O’Connell
Keyword(s):  
16S Rrna ◽  
Community Dynamics ◽  
Current Knowledge ◽  
16S Rrna Gene Sequencing ◽  
Oral Microbiome ◽  
16S Rrna Sequencing ◽  
Rrna Gene ◽  
Sequencing Data ◽  
Bacterial Community Dynamics ◽  
Rrna Sequencing

The postmortem microbiome has recently moved to the forefront of forensic research, and many studies have focused on the idea that predictable fluctuations in decomposer communities could be used as a “microbial clock” to determine time of death. Commonly, the oral microbiome has been evaluated using 16S rRNA gene sequencing to assess the changes in community composition throughout decomposition. We sampled the hard palates of three human donors over time to identify the prominent members of the microbiome. This study combined 16S rRNA sequencing with whole metagenomic (MetaG) and metatranscriptomic (MetaT) sequencing and culturing methodologies in an attempt to broaden current knowledge about how these postmortem microbiota change and might function throughout decomposition. In all four methods, Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes were the dominant phyla, but their distributions were insufficient in separating samples based on decomposition stage or time or by donor. Better resolution was observed at the level of genus, with fresher samples from decomposition clustering away from others via principal components analysis (PCA) of the sequencing data. Key genera in driving these trends included Rothia; Lysinibacillus, Lactobacillus, Staphylococcus, and other Firmicutes; and yeasts including Candida and Yarrowia. The majority of cultures (89%) matched to sequences obtained from at least one of the sequencing methods, while 11 cultures were found in the same samples using all three methods. These included Acinetobacter gerneri, Comamonas terrigena, Morganella morganii, Proteus vulgaris, Pseudomonas koreensis, Pseudomonas moraviensis, Raoutella terrigena, Stenotrophomonas maltophilia, Bacillus cereus, Kurthia zopfii, and Lactobacillus paracasei. MetaG and MetaT data also revealed many novel insects as likely visitors to the donors in this study, opening the door to investigating them as potential vectors of microorganisms during decomposition. The presence of cultures at specific time points in decomposition, including samples for which we have MetaT data, will yield future studies tying specific taxa to metabolic pathways involved in decomposition. Overall, we have shown that our 16S rRNA sequencing results from the human hard palate are consistent with other studies and have expanded on the range of taxa shown to be associated with human decomposition, including eukaryotes, based on additional sequencing technologies.

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