A specific microbiota signature is associated to various degrees of ulcerative colitis as assessed by a machine learning approach

Barberio, Brigida; Facchin, Sonia; Patuzzi, Ilaria; Ford, Alexander C; Massimi, Davide; Valle, Giorgio; Sattin, Eleonora; Simionati, Barbara; Bertazzo, Elena; Zingone, Fabiana; Savarino, Edoardo

doi:10.1080/19490976.2022.2028366

Cited by 43 publications

(30 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Butyricicoccus is known to prevent cytokine-induced epithelial integrity losses and promote the proliferation of beneficial flora (such as Bifidobacterium and Lactobacillus ) ( Eeckhaut et al., 2013 ; Devriese et al., 2017 ). In addition, we found no significant difference in Faecalibacterium prausnitzii abundance among the three groups, similar to the finding of Brigida et al ( Barberio et al., 2022 ), but in contrast to the vast majority of previous studies ( Sokol et al., 2009 ; Machiels et al., 2014 ). This may be related to the ethnic and geographical differences of UC patients, and follow-up of a large number of Asian UC patients will be necessary to explore this issue.…”

Section: Discussionsupporting

confidence: 88%

Composition and diverse differences of intestinal microbiota in ulcerative colitis patients

Zhu

Han

Liu

et al. 2022

Front. Cell. Infect. Microbiol.

View full text Add to dashboard Cite

ObjectiveTo explore the composition of the intestinal microbiota in ulcerative colitis (UC) patients and to identify differences in the microbiota between patients with active disease and those in remission.MethodsBetween September 2020 and June 2021, we enrolled into our study, and collected stool samples from, patients with active UC or in remission and healthy control subjects. The diagnosis of UC was based on clinical, endoscopic, radiological, and histological findings. The composition of the intestinal microbiota was determined by sequencing of the 16S rRNA V3–V4 region and by bioinformatic methods. The functional composition of the intestinal microbiota was predicted using PICRUSt 2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) software.ResultsWe found that the intestinal flora was significantly less rich and diverse in UC patients than in healthy control subjects. Beta diversity analysis revealed notable differences in the intestinal flora compositions among the three groups, but there was no statistical difference in alpha diversity between UC patients with active disease and those in remission. At the phylum level, the relative abundances of Proteobacteria and Patescibacteria were significantly higher, and the relative abundances of Desulfobacterota and Verrucomicrobiota were lower, in UC patients with active disease than in the healthy control group. Higher levels of potential pathogens and lower levels of butyrate-producing bacteria were also detected in UC patients with active disease. Linear discriminant analysis Effect Size (LefSe) revealed that 71 bacterial taxa could serve as biomarkers, with 26 biomarkers at the genus level. In addition, network analysis showed that there was a positive correlation between Roseburia and Lachnospira. Functional predictions indicated that gene functions involving the metabolism of some substances, such as methane, lipopolysaccharide, geraniol, and ansamycins, were significantly different among the three groups.ConclusionThe richness and diversity of the intestinal microbiota differed significantly among the three groups. Richness describes the state of being rich in number of intestinal bacteria, whereas diversity is the number of different species of intestinal bacteria. Different bacterial taxa could be used as biomarkers, expanding our understanding of the relationship between the intestinal microbiota microenvironment and UC in the future.

show abstract

Section: Discussionsupporting

confidence: 88%

Composition and diverse differences of intestinal microbiota in ulcerative colitis patients

Zhu

Han

Liu

et al. 2022

Front. Cell. Infect. Microbiol.

View full text Add to dashboard Cite

show abstract

“…Three core microbes were higher in fCD infants, Erysipelatoclostridium , Haemophilus , and Lachnospiraceae NK4A136 group. Erysipelatoclostridium and Haemophilus are opportunistic pathogens correlated with disease ( Shao et al., 2017 ; Aranaz et al., 2021 ; Barberio et al., 2022 ; Cao et al., 2021 ; Liu et al., 2021 ; Milosavljevic et al., 2021 ). Lachnospiraceae NK4A136 group is a butyrate producer correlated with both health and disease ( Ma et al., 2020 ; Stadlbauer et al., 2020 ; Park et al., 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Gut microbiome markers in subgroups of HLA class II genotyped infants signal future celiac disease in the general population: ABIS study

Milletich

Ahrens²,

Russell³

et al. 2022

Front. Cell. Infect. Microbiol.

View full text Add to dashboard Cite

Although gut microbiome dysbiosis has been illustrated in celiac disease (CD), there are disagreements about what constitutes these microbial signatures and the timeline by which they precede diagnosis is largely unknown. The study of high-genetic-risk patients or those already with CD limits our knowledge of dysbiosis that may occur early in life in a generalized population. To explore early gut microbial imbalances correlated with future celiac disease (fCD), we analyzed the stool of 1478 infants aged one year, 26 of whom later acquired CD, with a mean age of diagnosis of 10.96 ± 5.6 years. With a novel iterative control-matching algorithm using the prospective general population cohort, All Babies In Southeast Sweden, we found nine core microbes with prevalence differences and seven differentially abundant bacteria between fCD infants and controls. The differences were validated using 100 separate, iterative permutations of matched controls, which suggests the bacterial signatures are significant in fCD even when accounting for the inherent variability in a general population. This work is the first to our knowledge to demonstrate that gut microbial differences in prevalence and abundance exist in infants aged one year up to 19 years before a diagnosis of CD in a general population.

show abstract

“…It often requires that the dataset creators be contacted for missing information, which is especially a lengthy task when not only one but multiple datasets are re-used. Due to advancements in big data analyses and artificial intelligence, which are occurring in parallel to microbiome research [ 3 , 11 ], it will become increasingly important to create added value by combining both. Integrating large numbers of available datasets to newly developed algorithms based on machine learning with improved analytical capacity can help to improve our understanding and therapeutic possibilities of poorly understood diseases that might be linked to the microbiota, for example inflammatory bowel disease in humans [ 9 ].…”

Section: Selected Use Cases Demonstrating Added Value Of Re-using Mic...mentioning

confidence: 99%

“…Although comparisons of different datasets are still rare, tools based on artificial intelligence approaches and especially machine learning, which can be applied, develop fast. This would allow nowadays assessments of large datasets and predictions for microbiome assembly as shown for the soil and gut microbiome [ 3 , 11 ]. In addition, comparative analyses of datasets obtained by different technologies are rare although especially combinations of different methods can increase the accuracy of results in microbiome research [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Metadata harmonization–Standards are the key for a better usage of omics data for integrative microbiome analysis

Cernava

Rybakova²,

Buscot³

et al. 2022

Environmental Microbiome

View full text Add to dashboard Cite

Background Tremendous amounts of data generated from microbiome research studies during the last decades require not only standards for sampling and preparation of omics data but also clear concepts of how the metadata is prepared to ensure re-use for integrative and interdisciplinary microbiome analysis. Results In this Commentary, we present our views on the key issues related to the current system for metadata submission in omics research, and propose the development of a global metadata system. Such a system should be easy to use, clearly structured in a hierarchical way, and should be compatible with all existing microbiome data repositories, following common standards for minimal required information and common ontology. Although minimum metadata requirements are essential for microbiome datasets, the immense technological progress requires a flexible system, which will have to be constantly improved and re-thought. While FAIR principles (Findable, Accessible, Interoperable, and Reusable) are already considered, international legal issues on genetic resource and sequence sharing provided by the Convention on Biological Diversity need more awareness and engagement of the scientific community. Conclusions The suggested approach for metadata entries would strongly improve retrieving and re-using data as demonstrated in several representative use cases. These integrative analyses, in turn, would further advance the potential of microbiome research for novel scientific discoveries and the development of microbiome-derived products.

show abstract

A specific microbiota signature is associated to various degrees of ulcerative colitis as assessed by a machine learning approach

Cited by 43 publications

References 33 publications

Composition and diverse differences of intestinal microbiota in ulcerative colitis patients

Composition and diverse differences of intestinal microbiota in ulcerative colitis patients

Gut microbiome markers in subgroups of HLA class II genotyped infants signal future celiac disease in the general population: ABIS study

Metadata harmonization–Standards are the key for a better usage of omics data for integrative microbiome analysis

Contact Info

Product

Resources

About