scholarly journals Recovery of human gut microbiota genomes with third-generation sequencing

2021 ◽  
Vol 12 (6) ◽  
Author(s):  
Yanfei Li ◽  
Yueling Jin ◽  
Jianming Zhang ◽  
Haoying Pan ◽  
Lan Wu ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Third Generation ◽  
Sequencing Technology ◽  
Bacterial Genomes ◽  
Human Gut ◽  
Human Gut Microbiota ◽  
Third Generation Sequencing ◽  
Large Numbers ◽  
Generation Sequencing

AbstractHuman gut microbiota modulates normal physiological functions, such as maintenance of barrier homeostasis and modulation of metabolism, as well as various chronic diseases including type 2 diabetes and gastrointestinal cancer. Despite decades of research, the composition of the gut microbiota remains poorly understood. Here, we established an effective extraction method to obtain high quality gut microbiota genomes, and analyzed them with third-generation sequencing technology. We acquired a large quantity of data from each sample and assembled large numbers of reliable contigs. With this approach, we constructed tens of completed bacterial genomes in which there were several new bacteria species. We also identified a new conditional pathogen, Enterococcus tongjius, which is a member of Enterococci. This work provided a novel and reliable approach to recover gut microbiota genomes, facilitating the discovery of new bacteria species and furthering our understanding of the microbiome that underlies human health and diseases.

scholarly journals Recovery of Human Gut Microbiota Genomes Substantially With Third-generation Sequencing

2020 ◽  
Author(s):  
Yanfei Li ◽  
Yueling Jin ◽  
Haoying Pan ◽  
Jianming Zhang ◽  
Lan Wu ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Genomic Dna ◽  
Extraction Method ◽  
Third Generation ◽  
Sequencing Technology ◽  
Human Gut ◽  
Third Generation Sequencing ◽  
Health And Disease ◽  
Generation Sequencing

Abstract BackgroundHuman gut microbiota modulates normal physiological functions, such as the maintenance of barrier homeostasis and the modulation of metabolism, and various chronic diseases including type 2 diabetes and gastrointestinal cancer. Despite decades of researches, the composition of the gut microbiota remains unexplored and unidentified. ResultsHere we established an effective extraction method to obtain high-quality gut microbiota genomic DNA and detected the samples with third-generation sequencing technology. We acquired a quite big data form each sample and assembled many reliable contigs. Not only enormous unknown genes, but also several new bacteria subspecies or species were identified. ConclusionsThis work provides a novel and reliable framework to recover gut microbiota genomes substantially, facilitating the understanding of the roles of the microbiome that underlie in human health and disease.

scholarly journals Apollo: a sequencing-technology-independent, scalable and accurate assembly polishing algorithm

2020 ◽  
Vol 36 (12) ◽  
pp. 3669-3679 ◽  
Author(s):  
Can Firtina ◽  
Jeremie S Kim ◽  
Mohammed Alser ◽  
Damla Senol Cali ◽  
A Ercument Cicek ◽  
...  
Keyword(s):  
Genome Analysis ◽  
Supplementary Information ◽  
Third Generation ◽  
Sequencing Technology ◽  
Base Pairs ◽  
Sequencing Technologies ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Generation Sequencing ◽  
Large Genomes

Abstract Motivation Third-generation sequencing technologies can sequence long reads that contain as many as 2 million base pairs. These long reads are used to construct an assembly (i.e. the subject’s genome), which is further used in downstream genome analysis. Unfortunately, third-generation sequencing technologies have high sequencing error rates and a large proportion of base pairs in these long reads is incorrectly identified. These errors propagate to the assembly and affect the accuracy of genome analysis. Assembly polishing algorithms minimize such error propagation by polishing or fixing errors in the assembly by using information from alignments between reads and the assembly (i.e. read-to-assembly alignment information). However, current assembly polishing algorithms can only polish an assembly using reads from either a certain sequencing technology or a small assembly. Such technology-dependency and assembly-size dependency require researchers to (i) run multiple polishing algorithms and (ii) use small chunks of a large genome to use all available readsets and polish large genomes, respectively. Results We introduce Apollo, a universal assembly polishing algorithm that scales well to polish an assembly of any size (i.e. both large and small genomes) using reads from all sequencing technologies (i.e. second- and third-generation). Our goal is to provide a single algorithm that uses read sets from all available sequencing technologies to improve the accuracy of assembly polishing and that can polish large genomes. Apollo (i) models an assembly as a profile hidden Markov model (pHMM), (ii) uses read-to-assembly alignment to train the pHMM with the Forward–Backward algorithm and (iii) decodes the trained model with the Viterbi algorithm to produce a polished assembly. Our experiments with real readsets demonstrate that Apollo is the only algorithm that (i) uses reads from any sequencing technology within a single run and (ii) scales well to polish large assemblies without splitting the assembly into multiple parts. Availability and implementation Source code is available at https://github.com/CMU-SAFARI/Apollo. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Alpinia oxyphyllaMiq. Extract Prevents Diabetes in Mice by Modulating Gut Microbiota

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yiqiang Xie ◽  
Man Xiao ◽  
Yali Ni ◽  
Shangfei Jiang ◽  
Guizhu Feng ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Sequencing Technology ◽  
Next Generation Sequencing Technology ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Generation Sequencing ◽  
Administration Protocol ◽  
Gut Microbiota Composition

Recently, the role of gut microbiota in the development of obesity and type 2 diabetes mellitus (T2DM) has been highlighted. We performed an 8-week administration protocol on T2DM (C57BL/6J db-/db-) mice and fecal samples were collected. Comparisons of fecal bacterial communities were performed between db-/db- mice and normal mice (DB/DB) and between the db-/db mice treated and untreated with AOE using next-generation sequencing technology. Our results showed that the db-/db-AOE group had improved glycemic control and renal function compared with the db-/db-H2O group. Compared with the db-/db-H2O group, AOE administration resulted in significantly increased ratio of Bacteroidetes-to-Firmicutes in db-/db- mice. In addition, the abundance ofAkkermansiawas significantly increased, whileHelicobacterwas significantly suppressed in the db-/db-AOE group compared with the db-/db-H2O group. Our data suggest that AOE treatment decreased blood glucose levels and significantly reduced damage of renal pathology in the T2DM mice by modulating gut microbiota composition.

scholarly journals The study of transcriptomes of symbiotic tissue of pea using the third-generation sequencing technology Oxford Nanopore

2020 ◽  
Author(s):  
E. S. Gribchenko
Keyword(s):  
Nanopore Sequencing ◽  
Third Generation ◽  
Nitrogen Fixing ◽  
Sequencing Technology ◽  
The Third ◽  
Third Generation Sequencing ◽  
Oxford Nanopore ◽  
Mycorrhizal Roots ◽  
Gene Isoforms ◽  
Generation Sequencing

The transcriptome profiles the cv. Frisson mycorrhizal roots and inoculated nitrogen-fixing nodules were investigated using the Oxford Nanopore sequencing technology. A database of gene isoforms and their expression has been created.

scholarly journals Enrichment of Food With Tannin Extracts Promotes Healthy Changes in the Human Gut Microbiota

2021 ◽  
Vol 12 ◽  
Author(s):  
Silvia Molino ◽  
Alberto Lerma-Aguilera ◽  
Nuria Jiménez-Hernández ◽  
María José Gosalbes ◽  
José Ángel Rufián-Henares ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Short Chain Fatty Acids ◽  
Human Gut ◽  
Human Gut Microbiota ◽  
Wood Extracts ◽  
Potential Benefits ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing ◽  
Booster Effect

Food and food bioactive components are major drivers of modulation of the human gut microbiota. Tannin extracts consist of a mix of bioactive compounds, which are already exploited in the food industry for their chemical and sensorial properties. The aim of our study was to explore the viability of associations between tannin wood extracts of different origin and food as gut microbiota modulators. 16S rRNA amplicon next-generation sequencing (NGS) was used to test the effects on the gut microbiota of tannin extracts from quebracho, chestnut, and tara associated with commercial food products with different composition in macronutrients. The different tannin-enriched and non-enriched foods were submitted to in vitro digestion and fermentation by the gut microbiota of healthy subjects. The profile of the short chain fatty acids (SCFAs) produced by the microbiota was also investigated. The presence of tannin extracts in food promoted an increase of the relative abundance of the genus Akkermansia, recognized as a marker of a healthy gut, and of various members of the Lachnospiraceae and Ruminococcaceae families, involved in SCFA production. The enrichment of foods with tannin extracts had a booster effect on the production of SCFAs, without altering the profile given by the foods alone. These preliminary results suggest a positive modulation of the gut microbiota with potential benefits for human health through the enrichment of foods with tannin extracts.

scholarly journals Human Gut Microbiota in Health and Selected Cancers

2021 ◽  
Vol 22 (24) ◽  
pp. 13440
Author(s):  
Aleksandra Sędzikowska ◽  
Leszek Szablewski
Keyword(s):  
Gut Microbiota ◽  
Metabolic Diseases ◽  
Human Gut ◽  
Vast Number ◽  
Human Gut Microbiota ◽  
Internal And External Factors ◽  
Number Of Bacteria ◽  
Gut Microbiota Dysbiosis

The majority of the epithelial surfaces of our body, and the digestive tract, respiratory and urogenital systems, are colonized by a vast number of bacteria, archaea, fungi, protozoans, and viruses. These microbiota, particularly those of the intestines, play an important, beneficial role in digestion, metabolism, and the synthesis of vitamins. Their metabolites stimulate cytokine production by the human host, which are used against potential pathogens. The composition of the microbiota is influenced by several internal and external factors, including diet, age, disease, and lifestyle. Such changes, called dysbiosis, may be involved in the development of various conditions, such as metabolic diseases, including metabolic syndrome, type 2 diabetes mellitus, Hashimoto’s thyroidis and Graves’ disease; they can also play a role in nervous system disturbances, such as multiple sclerosis, Alzheimer’s disease, Parkinson’s disease, and depression. An association has also been found between gut microbiota dysbiosis and cancer. Our health is closely associated with the state of our microbiota, and their homeostasis. The aim of this review is to describe the associations between human gut microbiota and cancer, and examine the potential role of gut microbiota in anticancer therapy.

scholarly journals Type 2 Diabetes Biomarkers of Human Gut Microbiota Selected via Iterative Sure Independent Screening Method

PLoS ONE ◽  
2015 ◽  
Vol 10 (10) ◽  
pp. e0140827 ◽  
Author(s):  
Lihua Cai ◽  
Honglong Wu ◽  
Dongfang Li ◽  
Ke Zhou ◽  
Fuhao Zou
Keyword(s):  
Type 2 Diabetes ◽  
Gut Microbiota ◽  
Screening Method ◽  
Human Gut ◽  
Human Gut Microbiota ◽  
Independent Screening

scholarly journals Discovering Potential Taxonomic Biomarkers of Type 2 Diabetes From Human Gut Microbiota via Different Feature Selection Methods

2021 ◽  
Vol 12 ◽  
Author(s):  
Burcu Bakir-Gungor ◽  
Osman Bulut ◽  
Amhar Jabeer ◽  
O. Ufuk Nalbantoglu ◽  
Malik Yousef
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Feature Selection ◽  
Gut Microbiota ◽  
Classification Model ◽  
Selection Methods ◽  
Human Gut ◽  
Human Gut Microbiota ◽  
Potential Biomarkers

Human gut microbiota is a complex community of organisms including trillions of bacteria. While these microorganisms are considered as essential regulators of our immune system, some of them can cause several diseases. In recent years, next-generation sequencing technologies accelerated the discovery of human gut microbiota. In this respect, the use of machine learning techniques became popular to analyze disease-associated metagenomics datasets. Type 2 diabetes (T2D) is a chronic disease and affects millions of people around the world. Since the early diagnosis in T2D is important for effective treatment, there is an utmost need to develop a classification technique that can accelerate T2D diagnosis. In this study, using T2D-associated metagenomics data, we aim to develop a classification model to facilitate T2D diagnosis and to discover T2D-associated biomarkers. The sequencing data of T2D patients and healthy individuals were taken from a metagenome-wide association study and categorized into disease states. The sequencing reads were assigned to taxa, and the identified species are used to train and test our model. To deal with the high dimensionality of features, we applied robust feature selection algorithms such as Conditional Mutual Information Maximization, Maximum Relevance and Minimum Redundancy, Correlation Based Feature Selection, and select K best approach. To test the performance of the classification based on the features that are selected by different methods, we used random forest classifier with 100-fold Monte Carlo cross-validation. In our experiments, we observed that 15 commonly selected features have a considerable effect in terms of minimizing the microbiota used for the diagnosis of T2D and thus reducing the time and cost. When we perform biological validation of these identified species, we found that some of them are known as related to T2D development mechanisms and we identified additional species as potential biomarkers. Additionally, we attempted to find the subgroups of T2D patients using k-means clustering. In summary, this study utilizes several supervised and unsupervised machine learning algorithms to increase the diagnostic accuracy of T2D, investigates potential biomarkers of T2D, and finds out which subset of microbiota is more informative than other taxa by applying state-of-the art feature selection methods.

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