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Gastrointestinal Microbiome

The gastrointestinal microbiome refers to the complex community of microorganisms, including bacteria, archaea, fungi, and viruses, that reside within the human gastrointestinal tract.
This diverse ecosystem plays a crucial role in maintaining gut health, digesting food, and regulating the immune system.
Alterations in the gastrointestinal microbiome have been linked to a variety of health conditions, such as inflamematory bowel disease, obesity, and metabolic disorders.
Understanding the composition and dynamics of the gastrointestinal microbiome is an area of active research, with the potential to unlock new therapeutic and preventive strategies for a wide range of diseases.

Most cited protocols related to «Gastrointestinal Microbiome»

1
Gut Microbiome Taxonomic and Functional Analysis
Human gut 16S rRNA sequences were prepared as described in Eckburg et al. and Ley et al. (2006) and are available in GenBank, accession numbers: DQ793220-DQ802819, DQ803048, DQ803139-DQ810181, DQ823640-DQ825343, AY974810-AY986384. In our experiments we assigned all 16S sequences to taxa using a naïve Bayesian classifier currently employed by the Ribosomal Database Project II (RDP) [30] (link). COG profiles of 13 human gut microbiomes were obtained from the supplementary material of Kurokawa et al.[34] (link). We acquired metabolic functional profiles of 85 metagenomes from the online supplementary materials of Dinsdale et al. (2008) (http://www.theseed.org/DinsdaleSupplementalMaterial/).
White J.R., Nagarajan N, & Pop M. (2009). Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples. PLoS Computational Biology, 5(4), e1000352.
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Publication 2009
Gastrointestinal Microbiome Homo sapiens Human Microbiome Metabolic Profile Metagenome Ribosomes RNA, Ribosomal, 16S
2
Gut Microbiome Analysis of Twins and Mothers
To illustrate the application of these ideas to a real data set we reanalysed a study of the gut microbiomes of twins and their mothers [27] (link). These comprised faecal samples from 154 different individuals characterised by family and body mass index – ‘Lean’, ‘Obese’ and ‘Overweight’. Each individual was sampled at two time points approximately two months apart. The V2 hypervariable region of the 16S rRNA gene was amplified by PCR and then sequenced using 454. We reanalysed this data set filtering the reads, denoising and removing chimeras using the AmpliconNoise pipeline [10] (link), [11] . Denoised reads were then classified to the genus level using the RDP stand-alone classifier [5] (link). This gave a total of 570,851 reads split over 278 samples since of the 308 possible some failed to possess any reads following filtering. The size of individual samples varied from just 53 to 10,585 with a median of 1,599. A total of 129 different genera were observed with a genera diversity per sample that varied from just 12 to 50 with a median of 28. One extra category ‘Unknown’ was used for those reads that failed to be classified with greater than 50% bootstrap certainty. We will refer to this as the ‘Twins’ data set.
Holmes I., Harris K, & Quince C. (2012). Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics. PLoS ONE, 7(2), e30126.
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Publication 2012
Chimera Feces Gastrointestinal Microbiome Index, Body Mass Mothers Obesity Ribosomal RNA Genes Twins
3
Consensus Conference on Fecal Microbiota Transplantation
The process of development of the consensus conference, aimed at drawing up evidence-based recommendations for the use of FMT in clinical practice, included the following steps: selection of the expert panel members; identification of main topics and generation of WGs; development of statements according to the best available evidence; development of consensus through the electronic Delphi process and face-to-face meeting to release the final version of statements.
Consensus presidents (GC, AG) and consensus secretary (GI) chose the consensus members based on their expertise in the field of FMT and/or gut microbiota. A total number of 28 experts from 10 European countries constituted the experts panel and had an active role in the development of consensus process. Each member was assigned, according to her/his expertise, to one of the following five WGs: WG-1, indications; WG-2, donor selection; WG-3, preparation of faecal material; WG-4, clinical management and faecal delivery; WG-5, basic requirements for implementing an FMT centre.
Members of each WG elected a chair to coordinate work and to liaise with the consensus presidents and secretary. For each main topic, the consensus presidents and secretary drew up three to six key issues for which members of the corresponding WG were requested to formulate statements after a systematic review of the literature. The quality of evidence and strength of recommendation for each statement were determined according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system.23 (link)
24 (link) Definition of the strength of recommendation is given in table 1 and the quality of the published evidence is defined in table 2.
Statements from each WG were edited by the respective chair and then uploaded to an online electronic voting system (http://armstrong.wharton.upenn.edu/delphi2/) by the consensus secretary. The Delphi method was used to achieve a consensus.25 Several rounds of statements were uploaded and sent out, and the experts' anonymous responses were collected and shared with the group after each round. The experts were allowed to adjust their answers in subsequent rounds. After multiple rounds, the Delphi method enabled achievement of the ‘correct’ response through consensus.
For each statement, consensus members were requested to rate their level of agreement, among the following: (1) agree strongly; (2) agree with reservation; (3) undecided; (4) disagree; (5) disagree strongly. If the rating was other than ‘agree strongly’, respondents were requested to add some comments to explain their reservation/disagreement, and how to improve the statement. For each statement, the pre-established threshold was reached when the overall result was ≥80%, with the experts agreeing either strongly or with reservation. All statements not reaching 80% of agreement were revised and rated again in further round of voting. To reach consensus, two rounds of electronic votings were run overall.
Afterwards, 22 of 28 (79%) consensus members gathered on 9 July 2016 in Rome for a face-to-face meeting. Before the final conference of the expert panel, each WG had an internal meeting to reach a WG agreement on statements that had not reached consensus at the electronic voting. At the final conference, the chairs presented the respective WG statements to all members in the plenary session and the expert panel voted on each statement by a show of hands. Statements not reaching consensus (<80%) were discussed and either removed or modified and voted again. Finally, the consensus secretary provided a summary of the finalised statements.
Chairs and all WG members provided commentaries and supporting literature for each statement. Finally, all 28 members of the consensus approved the final version of released statements and commentaries.
Cammarota G., Ianiro G., Tilg H., Rajilić-Stojanović M., Kump P., Satokari R., Sokol H., Arkkila P., Pintus C., Hart A., Segal J., Aloi M., Masucci L., Molinaro A., Scaldaferri F., Gasbarrini G., Lopez-Sanroman A., Link A., de Groot P., de Vos W.M., Högenauer C., Malfertheiner P., Mattila E., Milosavljević T., Nieuwdorp M., Sanguinetti M., Simren M, & Gasbarrini A. (2017). European consensus conference on faecal microbiota transplantation in clinical practice. Gut, 66(4), 569-580.
Publication 2017
Conferences Donor Selection Europeans Face Feces Gastrointestinal Microbiome Obstetric Delivery
4
Profiling Antibiotic Resistance in Gut Microbiome
We applied ShortBRED to profile antibiotic resistance (AR) in the human gut microbiome. We first produced a set of new AR marker sequences by applying ShortBRED-Identify to a combination of (i) a curated version of the ARDB which we obtained by deleting sequences no longer stored at NCBI and (ii) a set of known antibiotic resistance genes obtained from resistant bacterial libraries. We then used ShortBRED-Quantify to profile the relative abundance of corresponding AR protein families across 552 gut metagenomes: 82 from U.S. adults sampled during the Human Microbiome Project (HMP) [10 (link)], 363 from Chinese adults with and without diabetes [11 (link)], and 107 individuals from Malawi, Venezuela, and the U.S. [12 (link)]. We used the first-visit samples from multi-visit HMP subjects to avoid redundancy. For 454-based samples characterized by sub-optimal sequencing depth, we mapped reads to full-length centroid sequences to avoid compromising sensitivity.
Kaminski J., Gibson M.K., Franzosa E.A., Segata N., Dantas G, & Huttenhower C. (2015). High-Specificity Targeted Functional Profiling in Microbial Communities with ShortBRED. PLoS Computational Biology, 11(12), e1004557.
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Publication 2015
Adult Antibiotic Resistance, Microbial Bacteria Chinese Diabetes Mellitus Gastrointestinal Microbiome Genes, vif Homo sapiens Human Microbiome Hypersensitivity Metagenome Proteins
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Detailed MetaGenomics Data Processing Protocol
The three datasets were processed by the read_trim_filter step in MOCAT with length cut off set to 30 and quality cut off set to 20, using solexaqa for the mock community and the simulated metagenome, and fastx for the 124 gut metagenomes.
Estimated taxonomic compositions for the simulated metagenome and the mock community were calculated in three steps. First, quality trimmed and filtered reads from the mock community were screened against a FASTA-file with Illumina adapter sequences (Table S5), using the screen_fastafile option and e-value set to 0.01. Second, screened reads from the mock community and quality trimmed and filtered reads from the simulated metagenome were mapped and filtered against the custom-made reference databases with chromosome and plasmid sequences from the 22 mock genomes (Table S4) and 100 genomes from the simulated metagenome (Table S2 in [13] (link) and Table S3), respectively. This was done by executing the screen and filter commands with length cutoff set to 30, percentage identity set to 90 and paired_end_filtering set to yes for the simulated metagenome and set to no for the mock community. Finally, the taxonomic composition was estimated using the calculate_coverage command.
Assembly and gene prediction, on the simulated metagenome and mock community, were performed using the assembly (SOAPdenovo version 1.06) and gene_prediction (MetaGeneMark) options. Quality trimmed and filtered reads from the simulated metagenome, and adapter-screened reads from the mock community, were assembled into scaftigs 60 bp or longer. Predicted complete genes were aligned to their respective metagenomes using blastall v2.2.26 [26] (link) (program blastn, 95% sequence identity, alignment length > = 90%, and e-value 0.1) and only the best hit selected.
The 124 human gut microbiomes were processed with and without 5′ trimming. 5′ trimmed reads were assembled using SOAPdenovo 1.05, using both the Kmer determined by MOCAT and a fixed Kmer size set to 23. These assemblies were revised using SOAPdenovo 1.06 using the assembly_revision options, and genes were predicted, with MetaGeneMark as selected software, on scaftigs from both assemblies and revised assemblies. The non 5′ trimmed and 5′ trimmed reads were mapped to the assembled scaftigs using the screen option using length cutoff 30 and quality cutoff 15.
Complete commands for processing the simulated metagenome and mock community in MOCAT are bundled with the installation of the pipeline.
Kultima J.R., Sunagawa S., Li J., Chen W., Chen H., Mende D.R., Arumugam M., Pan Q., Liu B., Qin J., Wang J, & Bork P. (2012). MOCAT: A Metagenomics Assembly and Gene Prediction Toolkit. PLoS ONE, 7(10), e47656.
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Publication 2012
Chromosomes Gastrointestinal Microbiome Genes Genome Homo sapiens Human Microbiome Metagenome Plasmids

Most recents protocols related to «Gastrointestinal Microbiome»

1
Tryptophan Decarboxylase Probiotic Effects
Not available on PMC !

Example 7

Intestinal microbiota having at least one tryptophan decarboxylase enzyme (e.g., C. sporogenes and R. gnavus) is given orally (in the form of a probiotic, prebiotic, or symbiotic) to a subject. The subject is evaluated for the presence of the provided bacteria (e.g., probiotic bacteria) in the intestine, production of tryptamine in the intestine, and improved gastrointestinal epithelial function (e.g., colonic contractility). Subjects include GF, HM, 5HTR4 KO, and WT mice. Subjects also include animals (e.g., humans) having a gastrointestinal disorder.

US11878002B2. Methods and materials for using Ruminococcus gnavus or Clostridium sporogenes to treat gastrointestinal disorders (2024-01-23). Mayo Foundation for Medical Education and Research [US], The Regents of the University of California [US]. Inventors: Purna C. Kashyap [US], Michael Fischbach [US], Brianna B. Williams [US].
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Patent 2024
Animals Aromatic-L-Amino-Acid Decarboxylases Bacteria Colon Defecation Enzymes Gastrointestinal Diseases Gastrointestinal Microbiome Homo sapiens Intestines Mus Muscle Contraction Prebiotics Probiotics Symbiosis Tryptamines
2
Gut Microbiome Profiling of Slugs
Individuals were grouped as follows. Group 1 (five D. reticulatum fed a lab diet for 7 days); Group 2 (five D. reticulatum fed a lab diet for 14 days); Group 3 (five D. reticulatum fed a lab diet and infected on Day 7 with P. hermaphrodita with feces collected 7 days postinfection—14 days in total); Group 4 (three A. valentianus fed a lab diet for 7 days); Group 5 (three A. valentianus fed a lab diet for 14 days); Group 6 (three A. valentianus infected with P. hermaphrodita with feces collected 7 days postinfection—14 days in total). Feces were collected from each slug for DNA extraction.
DNA was extracted from feces using DNeasy PowerSoil Pro Kit (Qiagen) following the manufacturer's instructions. The presence of bacterial DNA was checked after extractions using PCR amplification of the hypervariable regions of the 16S rRNA gene. This was carried out using the primers 27f (5′‐AGAGTTTGATCMTGGCTCAG‐3′) and 1492r (5′‐TACGGYTACCTTGTTACGACTT‐3′) (Lane, 1991 ) with the following thermocycler conditions: 3 min at 95°C followed by 35 cycles of 15 s at 95°C, 30 s at 55°C, 1.5 min at 72°C, and a final step of 8 min at 72°C. Amplicons were visualized using agarose gel electrophoresis to confirm that PCRs had worked; in all cases, bands of the correct size were present, and no amplification of bacterial DNA could be seen in the extraction negative control or the PCR negative control.
DNA samples were sent for 16S rRNA metagenomic sequencing (Novogene). The V4 hypervariable region of the 16S rRNA gene was amplified using the primers 515F (5′‐GTGCCAGCMGCCGCGGTAA‐3′) and 806R (5′‐GGACTACHVGGGTWTCTAAT‐3′). All PCR reactions were carried out with Phusion® High‐Fidelity PCR Master Mix (New England Biolabs). Sequencing libraries were generated with NEBNext® UltraTM DNA Library Prep Kit for Illumina and quantified via Qubit and Q‐PCR. Libraries were sequenced on an Illumina NovaSeq. 6000 platform to generate 2 × 250 bp paired‐end reads.
Analysis of the raw reads occurred at Novogene using the following method. Paired‐end reads were merged using FLASH (V1.2.7) (Magoč and Salzberg, 2011 (link)). Quality filtering on the raw tags was performed under specific filtering conditions to obtain high‐quality clean tags according to the QIIME (V1.7.0) (Caporaso et al., 2010 (link)). The tags were compared with the reference database (SILVA database) using the UCHIME algorithm (Edgar et al., 2011 (link)) to detect chimera sequences. Detected chimera sequences were then removed to obtain Effective Tags. All Effective Tags were processed by UPARSE software (v7.0.1090) (Edgar, 2013 (link)). Sequences with ≥97% similarity were assigned to the same Operational Taxonomic Units (OTUs).
For each OTU, QIIME (Version 1.7.0) in the Mothur method was performed against the SSU rRNA database of SILVA Database for species annotation at each taxonomic rank (Threshold:0.8~1) (Quast et al., 2012 (link)). MUSCLE (Version 3.8.31) (Edgar, 2004 (link)) was used to obtain the phylogenetic relationship of all OTUs.
OTUs abundance information was normalized using a standard of sequence number corresponding to the sample with the least sequences. OTUs were analyzed for Alpha diversity (Wilcoxon test function) and Beta diversity (AMOVA—Analysis of Molecular Variance) to obtain richness and evenness information in samples. AMOVA was also used to compare the taxonomic compositions of infected and noninfected slugs in weighted PCoA. Analysis of Alpha and Beta diversity were all performed on the normalized data and calculated with QIIME (Version 1.7.0). Significant intragroup variation is detected via MetaStats based on their abundance.
Sheehy L., MacDonald‐Howard K., Williams C.D., Weedall G.D., Jones H, & Rae R. (2023). A parasitic nematode induces dysbiosis in susceptible but not resistant gastropod hosts. MicrobiologyOpen, 12(2), e1346.
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Publication 2023
Chimera Diet DNA, Bacterial DNA Library Electrophoresis, Agar Gel Feces Gastrointestinal Microbiome Gene Amplification Metagenome Muscle Tissue Oligonucleotide Primers Ribosomal RNA Ribosomal RNA Genes RNA, Ribosomal, 16S Slugs Vision
3
Gut Microbiota and Metabolite Analysis
Student’s t-test, nonparametric Mann-Whitney, or Pearson correlation analysis was used when appropriate. Here, four parameters (ace, chao, shannon and simpson) were calculated to evaluate the alpha diversity of gut microbiota. The principal coordinate analysis (PCoA) was used to assess the beta diversity of gut microbiota. The linear discriminant-analysis (LDA) effect size (LEfSe) was conducted to identify the differential gut microbiota between the two groups, and the phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) analysis based on Kyoto Encyclopedia of Genes and Genomes (KEGG) database was performed to predict the potential functions of the differential gut microbiota. To identify the differential microbial metabolites (variable importance in projection (VIP) > 1.0 and p-value<0.05) between the two groups, the orthogonal partial least squares (OPLS) model was built using microbial metabolites. All the analyses was carried out using SPSS 19.0, R software 4.0 and Cytoscape 5.0, and p<0.05 was considered to be statistically significant.
Xie J., Wu W.T., Chen J.J., Zhong Q., Wu D., Niu L., Wang S., Zeng Y, & Wang Y. (2023). Tryptophan metabolism as bridge between gut microbiota and brain in chronic social defeat stress-induced depression mice. Frontiers in Cellular and Infection Microbiology, 13, 1121445.
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Publication 2023
Gastrointestinal Microbiome Genome Student
4
Alzheimer's Disease Comorbidity Study in Mice
All experiments detailed herein complied with the regulations formulated by the Institutional Animal Care and Use Committee (IACUC) of the Weizmann Institute of Science (application numbers: 03960618-3, 01200121-2, 03230322-2). Female and male mice were bred and maintained by the Animal Breeding Center of the Weizmann Institute of Science. Housing conditions were: 12-hour dark/light cycle (lights on at 8 am), temperature 22 °C, humidity 30-70%. For comorbidity studies, heterozygous 5xFAD transgenic mice33 (link) (line Tg6799, The Jackson Laboratory) on a C57/BL6-SJL background and age-matched wild-type (WT) controls were used. Genotyping was performed by PCR analysis of ear clipping DNA, as previously described33 (link). Since the C57/BL6-SJL strain carries the retinal degeneration Pde6brd1 mutation, which causes visual impairment in homozygosis (https://www.jax.org/strain/100012), mice were further tested for presence of the allele, as previously described90 (link). To avoid gut microbiota-related cage effects due to coprophagia91 (link), 5xFAD and WT mice were housed together. For the study of the effects of NANA on the immune system in vivo, we used four cohorts of female and male WT mice, and specifically: three cohorts of C57/BL6-SJL mice, age 6.5, 9, and 14 mo; one cohort of C57/BL6 mice, age 11 mo. For the study of the effects of NANA on novelty discrimination, female C57/BL6-SJL 5xFAD and age-matched WT controls were used. To avoid NANA assimilation with coprophagia, NANA-injected mice were housed separately from the PBS-injected controls. All mice were provided with standard chow (calories from proteins: 24%; calories from carbohydrates: 58%; calories from fat: 18%; 2918, Teklad), placed on a hopper integrated with the cage lid, and water ad libitum, and housed in cages enriched with one paper shelter. For comorbidity studies, to induce obesity, at 6-9 weeks of age, mice were switched to a high-fat diet (HFD; calories from proteins: 18%; calories from carbohydrates: 22%; calories from fat: 60%; TD.06414, Teklad), and the food pellet checked twice a week for replenishment. Control mice were kept on standard chow (control diet, CD). Mice allocated for behavioral studies or NANA/PBS injections were switched to a 12-h reversed dark/light cycle (lights on at 8 pm) at least 7 days prior to behavior assessment, and maintained in the regimen until experimental endpoint.
Suzzi S., Croese T., Ravid A., Gold O., Clark A.R., Medina S., Kitsberg D., Adam M., Vernon K.A., Kohnert E., Shapira I., Malitsky S., Itkin M., Brandis A., Mehlman T., Salame T.M., Colaiuta S.P., Cahalon L., Slyper M., Greka A., Habib N, & Schwartz M. (2023). N-acetylneuraminic acid links immune exhaustion and accelerated memory deficit in diet-induced obese Alzheimer’s disease mouse model. Nature Communications, 14, 1293.
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Publication 2023
Alleles Animals Animals, Transgenic Carbohydrates Coprophagia Diet, High-Fat Discrimination, Psychology Females Food Gastrointestinal Microbiome Heterozygote Homozygote Humidity Institutional Animal Care and Use Committees Light Low Vision Males Mice, Laboratory Mutation Obesity Proteins Retinal Degeneration Strains System, Immune Therapy, Diet Treatment Protocols
5
Gut Microbiome in Intrahepatic Cholestasis of Pregnancy
The study was approved by the Ethics Committee of Xinqiao Hospital, Army Medical University (Approved No. 2020-146-01). Written informed consents for participating this study were obtained from all participants. The authors affirm that human research participants provided written informed consent for publication of the potentially identifiable medical data included in this article. Participants didn’t receive cash remuneration. ICP was diagnosed according to the Guidelines for diagnosis and treatment of intrahepatic cholestasis of pregnancy from China with the following criteria: unexplainable pruritus; elevated serum bile acids (≥10 μmol/L); no identifiable cause for liver dysfunction; resolution of symptoms and laboratory values postpartum. Exclusion criteria were as follows: preeclampsia, low platelets (HELLP) syndrome, acute fatty liver of pregnancy, active viral hepatitis and primary biliary cirrhosis; patients receiving any antibiotic or probiotics treatment within 1 months; patients with other pregnant complications such as pregnancy diabetes and hypertensive disorders. All pregnant women with ICP were first-visit patients and did not receive any treatment. 50 individuals with ICP and 41 age, BMI and offspring gender matched healthy pregnant women were recruited from Chongqing and Guangdong province of China. There were 30 mild (TBA range 10–39.9 μmol/L) and 20 severe (TBA ≥ 40 μmol/L) ICP patients included. All the characteristics were summarized in Supplementary Tables 1 and 2.
Age, height, body weight, gestation week, birth weight and Apgar score were recorded, and the body mass index (BMI) was calculated. The gestational weeks were strictly matched within 1 week to reduce the impact of gestational week on gut microbiota. Fecal and blood samples were collected after fasting at least 8 h. Fecal samples were stored at −80 °C immediately until further processed. Biochemical parameters were detected by autoanalyzer.
Tang B., Tang L., Li S., Liu S., He J., Li P., Wang S., Yang M., Zhang L., Lei Y., Tu D., Tang X., Hu H., Ouyang Q., Chen X, & Yang S. (2023). Gut microbiota alters host bile acid metabolism to contribute to intrahepatic cholestasis of pregnancy. Nature Communications, 14, 1305.
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Publication 2023
Acute fatty liver of pregnancy Antibiotics Apgar Score Bile Acids Birth Weight BLOOD Blood Platelets Body Weight Diagnosis Ethics Committees, Clinical Feces Gastrointestinal Microbiome Gender HELLP Syndrome Hepatitis Viruses High Blood Pressures Homo sapiens Index, Body Mass Intrahepatic Cholestasis of Pregnancy Patients Pre-Eclampsia Pregnancy Pregnancy in Diabetics Pregnant Women Primary Biliary Cholangitis Probiotics Pruritus Serum

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More about "Gastrointestinal Microbiome"

The gastrointestinal (GI) microbiome refers to the diverse community of microorganisms, including bacteria, archaea, fungi, and viruses, that reside within the human digestive tract.
This complex ecosystem plays a crucial role in maintaining gut health, aiding in food digestion, and regulating the immune system.
Alterations in the GI microbiome have been linked to a variety of health conditions, such as inflammatory bowel disease (IBD), obesity, and metabolic disorders.
Understanding the composition and dynamics of the GI microbiome is an area of active research, with the potential to unlock new therapeutic and preventive strategies for a wide range of diseases.
Researchers utilize advanced tools and platforms, such as the Illumina MiSeq system and HiSeq platform, along with software like GraphPad Prism 7 and SPSS, to analyze the microbial communities within the gut.
The QIAamp DNA Stool Mini Kit and QIAamp Fast DNA Stool Mini Kit are commonly used for extracting high-quality genomic DNA from stool samples, which is essential for downstream microbiome analysis.
The Majorbio Cloud Platform provides a powerful bioinformatics solution for processing and interpreting the vast amounts of data generated from these analyses.
Identifying the most reproducible and accurate protocols from the literature, preprints, and patents is crucial for ensuring reliable and high-quality results in gastrointestinal microbiome research.
AI-driven tools, such as PubCompare.ai, can help researchers optimize their workflow and locate the best products and methodologies to achieve their research goals.
In addition to the gut microbiome, the role of compounds like vancomycin, an antibiotic, in modulating the microbial community and its impact on health outcomes is also an area of ongoing investigation.
By leveraging the insights gained from this multifaceted research, scientists and clinicians can develop innovative strategies to maintain a healthy GI microbiome and address a wide range of gastrointestinal and systemic conditions.