Introduction

The microbiota of human gastrointestinal tract is a complex and dynamic population of microorganisms and plays a major role in the maintenance of gut health and disease conditions. The microbial population, structure and function vary considerably throughout the upper and lower gut, but specific population and its diversity for each segment of GI tract is still not well described.

Aims & Methods

Aim: The retrospective evaluation of microorganism population and its diversity separately for upper and lower GI tract by cultivation of tissue samples that were obtained at routine endoscopy of 271 patients indicated to examination for different reasons.
Methods: Biopsies were taken using sterilized standard endoscopic biopsy forceps. Two pieces of tissue were obtained without the removal of forceps from the duodenum, from the antrum of the stomach and in same way from different segments of large intestine. Samples were placed into a 10% glucose solution for microbial cultivation.

Results

Overall, 271 biopsy specimens from the upper and lower GI tract were cultivated, specifically in 235 patients samples were obtained from antrum of stomach and duodenum, in 127 patients from large intestine and in 36 patients from both parts of GI tract. The presence of bacteria in upper GI tract was found in 70,21% and was negative in 29,79% of cases, while in lower GI tract bacteria was found in 93,70% and was negative 6,30% of cases. Tissue samples from both parts of GI tract was obtained from 36 patients and the same genera was found in 44,44%. In upper GI tract E. coli was present in 15,74%, followed by Enterobacter cloacae in 10,64%, Pseudomonas aeruginosa in 8,51%, Enterococcus species in 7,66%, Klebsiella pneumonie subs pneumoniae in 5,11%, Candida albicans in 4,26%, Helicobacter pylori and Klebsiella pneumoniae in 2,98%, Proteus mirabilis in 2,55%, Stenotrophomonas maltofilia in 1,70%, Neisseria subflava and E. coli ESBL positive in 0,85% and 0,43% belongs to Klebsiella pneumoniae ESBL positive, Streptococcus salivarius, Acinetobacter baumani, Staphylococcus warneri, Enterococcus faecalis, Streptococcus maltofilia, Enterococcus hirae and Candida complex. In lower GI tracts E. coli was present in 64,57%, followed by Klebsiella pneumoniae in 8,66%, Candida albicans, Proteus mirabilis and Enterobacter cloacae in 3,15%, Enterococcus species and Klebsiella pneumoniae subs pneumoniae in 2,36%, E. coli ESBL positive, Enterococcus faecalis and Pseudomonas aeruginosa in 1,57%, Candida non albicans and Citrobacter koseri in 0,79%.
The diversity of upper and lower GI tract was measured by Shannon diversity index, and it was 2.48 for upper GI tract and 1.29 for lower GI tract.

Conclusion

Most of studies which reveals fundamental differences between microbiota of upper and lower GI tract is based on examination of luminal content. We are presenting our results of the diversity of microbiota based on cultivation of biopsy samples.

Introduction

Understanding the intra- and inter-individual variability of gut microbiota is crucial for advancing microbiota research and its clinical applications. Gut microbial markers play essential roles in maintaining intestinal health, with functional groups including opportunistic, proteolytic, equilibrium, immunoprotective, mucosal homeostasis, proinflammatory, and methanogenic microbes.

Aims & Methods

This study aimed to evaluate the temporal stability of 15 microbial markers and identify potential transient disruptions associated with lifestyle and dietary factors.
A longitudinal observational study was conducted with 6 participants, including 4 healthy individuals and 2 with mild IBS, each providing 7 fecal samples (42 samples total) over a one-month period on randomly selected days. Microbial DNA was extracted from fecal samples and analyzed using a qPCR kit of a specific microbiota test, which includes a panel of 15 microbial markers. Participants completed questionnaires on each sampling day, documenting dietary habits, lifestyle factors, and digestive health. To assess the stability of the microbial markers in relation to differences among individuals, the Intraclass Correlation Coefficient (ICC) was calculated after adjusting a mixed effects model for each marker. These metrics provide numerical insights into the consistency and variability of the microbial populations over time. However, it is essential to contrast these numbers with the nature or behaviour of each marker, allowing for a more accurate and contextualized interpretation of the results, as the observed variability and stability may be influenced by intrinsic factors to each type of microbial marker.

Results

Distinct individual-specific microbiota patterns were observed, with clear inter-individual variability in baseline distributions. Transient alterations were observed in some cases, with many of them associated with dietary factors, such as specific food types or eating habits reported prior to sample collection. Notably, indicative markers of intestinal health, such as Faecalibacterium prausnitzii, Roseburia, and Ruminococcus, remained stable. In contrast, modifications in Akkermansia muciniphila and Methanobrevibacter smithii levels were observed coincident with variations in dietary intake suggesting that these could be an influent factor. Particularly, these disruptions were generally short-lived, with markers returning to baseline levels within a few days. The ICC has demonstrated its effectiveness in refining results and quantifying the relationship among intra- and inter-personal variability of markers. For example, Faecalibacterium prausnitzii exhibited a low ICC (0.112), indicating minimal differences among participants, whereas Methanobrevibacter smithii had a high ICC (0.921), reflecting significant variability among participants.

Conclusion

This study provides preliminary insights into the stability and individual variability of gut microbiota markers analyzed using the qPCR kit of this microbiota test. These findings highlight the importance of establishing personal baselines for accurate diagnostic interpretation and underscore the resilience of the gut microbiota in response to daily lifestyle fluctuations Moreover, the stability of intestinal health indicators markers supports their potential as reliable indicators of gut health. These results provide valuable data for a deeper understanding of gut health dynamics and underscore the importance of personalized approaches in microbiota diagnostics and management.

Disclosure

This work has been previously presented at an international meeting.
Authors: Lia Oliver, Marta Malagón, Mariona Serra-Pagès, and Daniel Rius are employees of GoodGut SLU. Xavier Aldeguer has also collaborated with GoodGut as a medical advisor.
Funding: This project has been partially funded by the AGAUR grant for Industrial Doctorates.

Introduction

The modulation of gut microbiota through bioactive nutraceuticals offers promising avenues for managing gastrointestinal diseases such as ulcerative colitis (UC). In this study, we investigated the effects of two nutraceutical formulations, Biointestil® and Clovedin®, both rich in natural bioactive compounds, on the gut microbiota of healthy individuals and UC patients.

Aims & Methods

A standardized in vitro simulation of human gastrointestinal digestion and colonic fermentation was conducted using the MICODE (Multi-Unit Colon Model) platform, which through the replication of intestinal conditions including pH and O₂ milieu and specific gut microbiota enables conduct precise Risk/Benefit Assessment by omics means.
Biointestil® or Clovedin®. were subjected to 48-hour colonic fermentation with two different gut microbiota models: that from healthy and that from UC donors in biological duplicates.
Microbial composition was monitored using quantitative PCR (qPCR) targeting key pro-inflammatory and beneficial bacterial taxa. Changes in microbiota structure were evaluated through ecological indicators (i.e. prebiotic index, Firmicutes/Bacteroidota, etc), providing insight into gut microbial stability and resilience.
In addition, a comprehensive volatilome analysis was performed using SPME GC-MS to assess the metabolic output of the microbial communities, focusing on short-chain fatty acids (SCFAs), indoles, phenols, and other microbial metabolites.

Results

In addition, a comprehensive volatilome analysis was performed using SPME GC-MS to assess the metabolic output of the microbial communities, focusing on short-chain fatty acids (SCFAs), indoles, phenols, and other microbial metabolites. Treatment with Biointestil® and Clovedin® resulted in a reduction of some opportunistic and pro-inflammatory bacteria (Clostridiaceae, Collinsella aerofaciens, Desulfovibrio spp.) and a concurrent increase in some beneficial taxa (Bifidobacteriaceae, Ruminococcaceae, Faecalibacerium prausnitzii). These shifts were accompanied by an enhanced SCFA profile and a decreased production of toxic volatiles (p-cresol and skatole), particularly in the UC-derived microbiota.

Conclusion

The combined use of qPCR, volatilomics, and ecological modeling within the MICODE in vitro system provides a robust framework for assessing microbiota-targeted interventions and supports the therapeutic potential of Biointestil® and Clovedin® in inflammatory bowel disorders by remission of gut microbiota dysbiosis.