scholarly journals Transient Effect of Infant Formula Supplementation on the Intestinal Microbiota

Nutrients ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 807
Author(s):  
Ning Chin ◽  
Gema Méndez-Lagares ◽  
Diana H. Taft ◽  
Victoria Laleau ◽  
Hung Kieu ◽  
...  
Keyword(s):  
16S Rrna ◽  
Gut Microbiome ◽  
Bacterial Species ◽  
Bifidobacterium Longum ◽  
The United States ◽  
16S Rrna Sequencing ◽  
T Cell Subsets ◽  
Rrna Sequencing ◽  
Stool Samples ◽  
The Impact

Breastfeeding is the gold standard for feeding infants because of its long-term benefits to health and development, but most infants in the United States are not exclusively breastfed in the first six months. We enrolled 24 infants who were either exclusively breastfed or supplemented with formula by the age of one month. We collected diet information, stool samples for evaluation of microbiotas by 16S rRNA sequencing, and blood samples for assessment of immune development by flow cytometry from birth to 6 months of age. We further typed the Bifidobacterium strains in stool samples whose 16S rRNA sequencing showed the presence of Bifidobacteriaceae. Supplementation with formula during breastfeeding transiently changed the composition of the gut microbiome, but the impact dissipated by six months of age. For example, Bifidobacterium longum, a bacterial species highly correlated with human milk consumption, was found to be significantly different only at 1 month of age but not at later time points. No immunologic differences were found to be associated with supplementation, including the development of T-cell subsets, B cells, or monocytes. These data suggest that early formula supplementation, given in addition to breast milk, has minimal lasting impact on the gut microbiome or immunity.

scholarly journals Phenotypic and phylogenetic characterization of Lactobacillus species isolated from traditional Lighvan cheese

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Haleh Forouhandeh ◽  
Sepideh Zununi Vahed ◽  
Hossein Ahangari ◽  
Vahideh Tarhriz ◽  
Mohammad Saeid Hejazi
Keyword(s):  
16S Rrna ◽  
Random Amplified Polymorphic Dna ◽  
Bacterial Species ◽  
16S Rrna Sequencing ◽  
Lactobacillus Species ◽  
Culture Methods ◽  
Phylogenetic Characterization ◽  
Rrna Sequencing ◽  
Specific Culture ◽  
Lighvan Cheese

Abstract Lighvan cheese (Lighvan panir) is among the most famous traditional cheese in Iran for its desired aroma and flavor. Undoubtedly, the lactic acid bacteria especially the genus Lactobacillus are the critical factors in developing the aroma, flavor, and texture in Lighvan cheese. In this study, the Lactobacillus population of the main Lighvan cheese was investigated. The Lactobacillus of the main Lighvan cheese was isolated using specific culture methods according to previously published Guidelines. Then, the phylogenetic features were investigated and the phenotypic characteristics were examined using specific culture methods. Twenty-eight Gram-positive bacterial species were identified belonged to the genus Lactobacillus. According to the same sequences as each other, three groups (A, B, and C) of isolates were categorized with a high degree of similarity to L. fermentum (100%) and L. casei group (L. casei, L. paracasei, and L. rhamnosus) (99.0 to 100%). Random amplified polymorphic DNA (RAPD) fingerprint analysis manifested the presence of three clusters that were dominant in traditional Lighvan cheese. Cluster І was divided into 4 sub-clusters. By the result of carbohydrate fermentation pattern and 16S rRNA sequencing, isolates were identified as L. rhamnosus. The isolates in clusters II and III represented L. paracasei and L. fermentum, respectively as they were identified by 16S rRNA sequencing and fermented carbohydrate patterns. Our result indicated that the specific aroma and flavor of traditional Lighvan cheese can be related to its Lactobacillus population including L. fermentum, L. casei, L. paracasei, and L. rhamnosus. Graphical abstract

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.

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 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 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 The Limits and Avoidance of Biases in Metagenomic Analyses of Human Fecal Microbiota

2020 ◽  
Vol 8 (12) ◽  
pp. 1954
Author(s):  
Emma Bergsten ◽  
Denis Mestivier ◽  
Iradj Sobhani
Keyword(s):  
16S Rrna ◽  
Dna Extraction ◽  
Fecal Microbiota ◽  
16S Rrna Sequencing ◽  
Marker Genes ◽  
Bacteria Identification ◽  
16S Rna ◽  
Rrna Sequencing ◽  
The Impact ◽  
Identification And Quantification

An increasing body of evidence highlights the role of fecal microbiota in various human diseases. However, more than two-thirds of fecal bacteria cannot be cultivated by routine laboratory techniques. Thus, physicians and scientists use DNA sequencing and statistical tools to identify associations between bacterial subgroup abundances and disease. However, discrepancies between studies weaken these results. In the present study, we focus on biases that might account for these discrepancies. First, three different DNA extraction methods (G’NOME, QIAGEN, and PROMEGA) were compared with regard to their efficiency, i.e., the quality and quantity of DNA recovered from feces of 10 healthy volunteers. Then, the impact of the DNA extraction method on the bacteria identification and quantification was evaluated using our published cohort of sample subjected to both 16S rRNA sequencing and whole metagenome sequencing (WMS). WMS taxonomical assignation employed the universal marker genes profiler mOTU-v2, which is considered the gold standard. The three standard pipelines for 16S RNA analysis (MALT and MEGAN6, QIIME1, and DADA2) were applied for comparison. Taken together, our results indicate that the G’NOME-based method was optimal in terms of quantity and quality of DNA extracts. 16S rRNA sequence-based identification of abundant bacteria genera showed acceptable congruence with WMS sequencing, with the DADA2 pipeline yielding the highest congruent levels. However, for low abundance genera (<0.5% of the total abundance) two pipelines and/or validation by quantitative polymerase chain reaction (qPCR) or WMS are required. Hence, 16S rRNA sequencing for bacteria identification and quantification in clinical and translational studies should be limited to diagnostic purposes in well-characterized and abundant genera. Additional techniques are warranted for low abundant genera, such as WMS, qPCR, or the use of two bio-informatics pipelines.

scholarly journals Machine Learning Approach Identified Multi-Platform Factors for Caries Prediction in Child-Mother Dyads

2021 ◽  
Vol 11 ◽  
Author(s):  
Tong Tong Wu ◽  
Jin Xiao ◽  
Michael B. Sohn ◽  
Kevin A. Fiscella ◽  
Christie Gilbert ◽  
...  
Keyword(s):  
Machine Learning ◽  
Environmental Factors ◽  
16S Rrna ◽  
Bacterial Species ◽  
Fungal Species ◽  
Oral Microbiome ◽  
16S Rrna Sequencing ◽  
Tooth Decay ◽  
Impact Machine ◽  
Rrna Sequencing

Untreated tooth decays affect nearly one third of the world and is the most prevalent disease burden among children. The disease progression of tooth decay is multifactorial and involves a prolonged decrease in pH, resulting in the demineralization of tooth surfaces. Bacterial species that are capable of fermenting carbohydrates contribute to the demineralization process by the production of organic acids. The combined use of machine learning and 16s rRNA sequencing offers the potential to predict tooth decay by identifying the bacterial community that is present in an individual’s oral cavity. A few recent studies have demonstrated machine learning predictive modeling using 16s rRNA sequencing of oral samples, but they lack consideration of the multifactorial nature of tooth decay, as well as the role of fungal species within their models. Here, the oral microbiome of mother–child dyads (both healthy and caries-active) was used in combination with demographic–environmental factors and relevant fungal information to create a multifactorial machine learning model based on the LASSO-penalized logistic regression. For the children, not only were several bacterial species found to be caries-associated (Prevotella histicola, Streptococcus mutans, and Rothia muciloginosa) but also Candida detection and lower toothbrushing frequency were also caries-associated. Mothers enrolled in this study had a higher detection of S. mutans and Candida and a higher plaque index. This proof-of-concept study demonstrates the significant impact machine learning could have in prevention and diagnostic advancements for tooth decay, as well as the importance of considering fungal and demographic–environmental factors.

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