Dataset Information

Insulin Resistance-Associated Bifidobacterium longum Ameliorates Prediabetes via the 6-ketoLCA/TGR5/GLP-1 axis

ABSTRACT:

Background Prediabetes, a global health concern characterized by insulin resistance (IR) and closely linked to obesity, was investigated in this study to identify key gut microbiota characteristics. The development of insulin resistance is associated with gut microbiota. Therefore, identifying key bacterial species linked to the progression of insulin resistance and modulating the gut microbiota to intervene in this process represent a strategy for mitigating prediabetes.

Results Using machine learning and other bioinformatics methods, we compared obese participants with normal glucose tolerance (NGT, n=28), prediabetes (preDM, n=41), and type 2 diabetes (T2D, n=35). Results revealed that Bifidobacterium longum (B. longum) and Streptococcus salivarius (S. salivarius) were the key species distinguishing among obese populations with varying degrees of glucose intolerance. The efficacy of B. longum and S. salivarius in alleviating prediabetes was subsequently validated in mice. Supplementation with B. longum restored IR and regulated bile acid metabolism in prediabetic mice. Further treatment with the bile acid derivative 6-ketoLCA demonstrated its role in activating the TGR5/GLP-1 signaling pathway to ameliorate prediabetes.

Conclusion These findings highlight that B. longum plays a critical role in the onset of T2D, and intervention with B. longum and 6-ketoLCA can prevent prediabetes from progressing to type 2 diabetes.

INSTRUMENT(S): Liquid Chromatography MS - alternating - hilic, Liquid Chromatography MS - positive - hilic, Liquid Chromatography MS - negative - hilic

PROVIDER: MTBLS13684 | MetaboLights | 2026-01-14

REPOSITORIES: MetaboLights

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	a_MTBLS13684_LC-MS_alternating_hilic_metabolite_profiling-1.txt	Txt
	a_MTBLS13684_LC-MS_alternating_hilic_metabolite_profiling-2.txt	Txt
	a_MTBLS13684_LC-MS_alternating_hilic_metabolite_profiling.txt	Txt
	a_MTBLS13684_LC-MS_negative_hilic_metabolite_profiling-1.txt	Txt
	a_MTBLS13684_LC-MS_negative_hilic_metabolite_profiling-2.txt	Txt

Items per page:

1 - 5 of 21

Similar Datasets

Project description:The obese people with abnormal BMI are predisposed to insulin resistance and diabetes. At the same time, human subjects with obesity and high BMI that are otherwise insulin sensitive are an interesting group to study the underlying gene expression patterns which provide them with such protective phenotype. Objective: Insulin resistance (IR) is one of the earliest predictors of type 2 diabetes. However, diagnosis of IR is limited. High fat fed mouse models provide key insights into IR. We hypothesized that early features of IR are associated with persistent changes in gene expression (GE) and endeavoured to (a) develop novel methods for improving signal:noise in analysis of human GE using mouse models; (b) identify a GE motif that accurately diagnoses IR in humans; and (c) identify novel biology associated with IR in humans. Methods: We integrated human muscle GE data with longitudinal mouse GE data and developed an unbiased three-level cross-species analysis platform (single-gene, gene-set and networks) to generate a gene expression motif (GEM) indicative of IR. A logistic regression classification model validated GEM in 3 independent human datasets (n =115). Results: This GEM of 93 genes substantially improved diagnosis of IR compared to routine clinical measures across multiple independent datasets. Individuals misclassified by GEM possessed other metabolic features raising the possibility that they represent a separate metabolic subclass. The GEM was enriched in pathways previously implicated in insulin action and revealed novel associations between Î²-catenin and Jak1 and IR. Functional analyses using small molecule inhibitors showed an important role for these proteins in insulin action. Conclusions: This study shows that systems approaches for identifying molecular signatures provides a powerful way to stratify individuals into discrete metabolic groups. Moreover, we speculate that the Î²-catenin pathway may represent a novel biomarker for IR in humans that warrant future investigation. Gene expression from muscle biopsies of Lean, Obese insulin sensitive (OIS), Obese insulin resistant (OIR) and obese T2D patients (T2D) were compared for differential expression between the groups (n=7 in each group)

Project description:Pancreatic cancer is the 3rd most prevalent cause of cancer related deaths in United states alone, with over 55000 patients being diagnosed in 2019 alone and nearly as many succumbing to it. Late detection, lack of effective therapy and poor understanding of pancreatic cancer systemically contributes to its poor survival statistics. Obesity and high caloric intake linked co-morbidities like type 2 diabetes (T2D) have been attributed as being risk factors for a number of cancers including pancreatic cancer. Studies on gut microbiome has shown that lifestyle factors as well as diet has a huge effect on the microbial flora of the gut. Further, modulation of gut microbiome has been seen to contribute to effects of intensive insulin therapy in mice on high fat diet. In another study, abnormal gut microbiota was reported to contribute to development of diabetes in Db/Db mice. Recent studies indicate that microbiome and microbial dysbiosis plays a role in not only the onset of disease but also in its outcome. In colorectal cancer, Fusobacterium has been reported to promote therapy resistance. Certain intra-tumoral bacteria have also been shown to elicit chemo-resistance by metabolizing anti-cancerous agents. In pancreatic cancer, studies on altered gut microbiome have been relatively recent. Microbial dysbiosis has been observed to be associated with pancreatic tumor progression. Modulation of microbiome has been shown to affect response to anti-PD1 therapy in this disease as well. However, most of the studies in pancreatic cancer and microbiome have remained focused om immune modulation. In the current study, we observed that in a T2D mouse model, the microbiome changed significantly as the hyperglycemia developed in these animals. Our results further showed that, tumors implanted in the T2D mice responded poorly to Gemcitabine/Paclitaxel (Gem/Pac) standard of care compared to those in the control group. A metabolomic reconstruction of the WGS of the gut microbiota further revealed that an enrichment of bacterial population involved in drug metabolism in the T2D group.

Dataset Information

Insulin Resistance-Associated Bifidobacterium longum Ameliorates Prediabetes via the 6-ketoLCA/TGR5/GLP-1 axis

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets