Improved Metabolite Prediction Using Microbiome Data-Based Elastic Net Models

Xie, Jialiu; Cho, Hunyong; Lin, Bridget M.; Pillai, Malvika; Heimisdottir, Lara H.; Bandyopadhyay, Dipankar; Zou, Fei; Roach, Jeffrey; Divaris, Kimon; Wu, Di

doi:10.3389/fcimb.2021.734416

Cited by 8 publications

(5 citation statements)

References 27 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A further limitation to fully characterize the small intestinal ecology from a functional perspective is the lack of metabolomic data, matching microbiome results in large cohort studies. To fill this gap, metabolite prediction methods have been developed to predict individual metabolites solely based on microbiome metagenomic or amplicon sequencing data (Langille et al 2013 , Xie et al 2021 ). In silico simulations, coupled with in vitro and in vivo data can contribute to mechanistically describe physiological and metabolic process, such as prediction of the glycemic index of biscuits using chicken duodenum (Priyadarshini et al 2021 ), characterization of the precipitation kinetics of drugs, and the prediction of their concentration in small intestine after human oral administration (Hens et al 2014 , Kambayashi et al 2016 ).…”

Section: Future Perspectivesmentioning

confidence: 99%

The small intestine: dining table of host–microbiota meetings

Delbaere

Roegiers

Bron

et al. 2023

FEMS Microbiology Reviews

View full text Add to dashboard Cite

Growing evidence suggests the importance of the small intestinal bacteria in the diet-host-microbiota dialogue in various facets of health and disease. Yet, this body site is still poorly explored and its ecology and mechanisms of interaction with the host are just starting to be unraveled. In this review, we describe the current knowledge on the small intestinal ecology, its composition and diversity, and how the intestinal bacteria in homeostatic conditions participate in nutrient digestion and absorption. We illustrate the importance of a controlled bacterial density and of the preservation of absorptive surface for the host's nutritional status. In particular, we discuss these aspects of the small intestinal environment in the framework of two disease conditions, namely small intestinal bacterial overgrowth (SIBO) and short bowel syndrome (SBS). We also detail in vivo, ex vivo and in vitro models developed to simulate the small intestinal environment, some applied for (diet-)host-bacteria interaction studies. Lastly, we highlight recent technological, medical and scientific advances applicable to investigate this complex and yet understudied body environment to broaden our knowledge in support of further progress in the medical practice, and to proceed towards the integration of the (small)intestinal bacteria in personalized therapeutic approaches.

show abstract

Section: Future Perspectivesmentioning

confidence: 99%

The small intestine: dining table of host–microbiota meetings

Delbaere

Roegiers

Bron

et al. 2023

FEMS Microbiology Reviews

View full text Add to dashboard Cite

show abstract

“…Many metabolites produced by gut microbiota are closely related to health ( Lu et al., 2021 ; Zhong et al., 2021 ; Xie et al., 2021 ). Short-chain fatty acids (SCFAs), as the main products of gut microbiota, have been found to change in many diseases such as cardiovascular disease and autism ( Chambers et al., 2018 ; Tran and Mohajeri, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Differential Gut Microbiota Compositions Related With the Severity of Major Depressive Disorder

Zhong

Chen

Wang

et al. 2022

Front. Cell. Infect. Microbiol.

View full text Add to dashboard Cite

ObjectiveIncreasing evidence shows a close relationship between gut microbiota and major depressive disorder (MDD), but the specific mechanisms remain unknown. This study was conducted to explore differential gut microbiota compositions related to the severity of MDD.MethodsHealthy controls (HC) (n = 131) and MDD patients (n = 130) were included. MDD patients with Hamilton Depression Rating Scale (HDRS) score <25 and ≥25 were assigned into moderate (n = 72) and severe (n = 58) MDD groups, respectively. Univariate and multivariate analyses were used to analyze the gut microbiota compositions at the genus level.ResultsThirty-six and 27 differential genera were identified in moderate and severe MDD patients, respectively. The differential genera in moderate and severe MDD patients mainly belonged to three (Firmicutes, Actinobacteriota, and Bacteroidota) and two phyla (Firmicutes and Bacteroidota), respectively. One specific covarying network from phylum Actinobacteriota was identified in moderate MDD patients. In addition, five genera (Collinsella, Eggerthella, Alistipes, Faecalibacterium, and Flavonifractor) from the shared differential genera by two MDD groups had a fair efficacy in diagnosing MDD from HC (AUC = 0.786).ConclusionsOur results were helpful for further exploring the role of gut microbiota in the pathogenesis of depression and developing objective diagnostic methods for MDD.

show abstract

“…A total of 503 named metabolites were identified through peak identification, QC, and correction for day-dependent technical variations [ 16 ]. Procedures and descriptions of the obtained metabolite data have been previously reported in detail [ 17 , 29 ].…”

Section: Methodsmentioning

confidence: 99%

BZINB Model-Based Pathway Analysis and Module Identification Facilitates Integration of Microbiome and Metabolome Data

et al. 2023

Self Cite

View full text Add to dashboard Cite

Integration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome) employ simple correlation-based network analyses; however, these methods are not always well-suited for microbiome analyses because they do not accommodate the excess zeros typically present in these data. In this paper, we introduce a bivariate zero-inflated negative binomial (BZINB) model-based network and module analysis method that addresses this limitation and improves microbiome–metabolome correlation-based model fitting by accommodating excess zeros. We use real and simulated data based on a multi-omics study of childhood oral health (ZOE 2.0; investigating early childhood dental caries, ECC) and find that the accuracy of the BZINB model-based correlation method is superior compared to Spearman’s rank and Pearson correlations in terms of approximating the underlying relationships between microbial taxa and metabolites. The new method, BZINB-iMMPath, facilitates the construction of metabolite–species and species–species correlation networks using BZINB and identifies modules of (i.e., correlated) species by combining BZINB and similarity-based clustering. Perturbations in correlation networks and modules can be efficiently tested between groups (i.e., healthy and diseased study participants). Upon application of the new method in the ZOE 2.0 study microbiome–metabolome data, we identify that several biologically-relevant correlations of ECC-associated microbial taxa with carbohydrate metabolites differ between healthy and dental caries-affected participants. In sum, we find that the BZINB model is a useful alternative to Spearman or Pearson correlations for estimating the underlying correlation of zero-inflated bivariate count data and thus is suitable for integrative analyses of multi-omics data such as those encountered in microbiome and metabolome studies.

show abstract

Improved Metabolite Prediction Using Microbiome Data-Based Elastic Net Models

Cited by 8 publications

References 27 publications

The small intestine: dining table of host–microbiota meetings

The small intestine: dining table of host–microbiota meetings

Differential Gut Microbiota Compositions Related With the Severity of Major Depressive Disorder

BZINB Model-Based Pathway Analysis and Module Identification Facilitates Integration of Microbiome and Metabolome Data

Contact Info

Product

Resources

About