Spatial metabolomics using mass-spectrometry imaging to decipher the impact of high red meat diet on the colon metabolome in rat

Colorectal cancer (CRC) is the third most common cancer in the world with a higher prevalence in the developed countries, mainly caused by environmental and lifestyle factors such as diet, particularly red meat consumption. The metabolic impact of high red meat consumption on the epithelial part of the colon was investigated using Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MSI), to specifically analyze the epithelial substructure. Ten colons from rats fed for 100 days high red or white meat diet were subjected to untargeted MSI analyses using two spatial resolutions (100 μm and 10 μm) to evaluate metabolite changes in the epithelial part and to visualize the distribution of metabolites of interest within the epithelium crypts. Our results suggest a specific effect of red meat diet on the colonic epithelium metabolism, as evidenced by an increase of purine catabolism products or depletion in glutathione pool, reinforcing the hypothesis of increased oxidative stress with red meat diet. This study also highlighted cholesterol sulfate as another up-regulated metabolite, interestingly localized at the top of the crypts. Altogether, this study demonstrates the feasibility and the added value of using MSI to decipher the effect of high red meat diet on the colonic epithelium.

Urinary Metabolome Analysis Reveals Potential Microbiota Alteration and Electrophilic Burden Induced by High Red Meat Diet: Results from the French NutriNet-Santé Cohort and an In Vivo Intervention Study in Rats

SCOPE

High red and processed meat consumption is associated with several adverse outcomes such as colorectal cancer and overall global mortality. However, the underlying mechanisms remain debated and need to be elucidated.

METHODS AND RESULTS

Urinary untargeted Liquid Chromatography-Mass Spectrometry (LC-MS) metabolomics data from 240 subjects from the French cohort NutriNet-Santé are analyzed. Individuals are matched and divided into three groups according to their consumption of red and processed meat: high red and processed meat consumers, non-red and processed meat consumers, and at random group. Results are supported by a preclinical experiment where rats are fed either a high red meat or a control diet. Microbiota derived metabolites, in particular indoxyl sulfate and cinnamoylglycine, are found impacted by the high red meat diet in both studies, suggesting a modification of microbiota by the high red/processed meat diet. Rat microbiota sequencing analysis strengthens this observation. Although not evidenced in the human study, rat mercapturic acid profile concomitantly reveals an increased lipid peroxidation induced by high red meat diet.

CONCLUSION

Novel microbiota metabolites are identified as red meat consumption potential biomarkers, suggesting a deleterious effect, which could partly explain the adverse effects associated with high red and processed meat consumption.

Maternal heme-enriched diet promotes a gut pro-oxidative status associated with microbiota alteration, gut leakiness and glucose intolerance in mice offspring

Maternal environment, including nutrition and microbiota, plays a critical role in determining offspring's risk of chronic diseases such as diabetes later in life. Heme iron requirement is amplified during pregnancy and lactation, while excessive dietary heme iron intake, compared to non-heme iron, has shown to trigger acute oxidative stress in the gut resulting from reactive aldehyde formation in conjunction with microbiota reshape. Given the immaturity of the antioxidant defense system in early life, we investigated the extent to which a maternal diet enriched with heme iron may have a lasting impact on gut homeostasis and glucose metabolism in 60-day-old C3H/HeN mice offspring.

As hypothesized, the form of iron added to the maternal diet differentially governed the offspring's microbiota establishment despite identical fecal iron status in the offspring. Importantly, despite female offspring was unaffected, oxidative stress markers were however higher in the gut of male offspring from heme enriched-fed mothers, and were accompanied by increases in fecal lipocalin-2, intestinal para-cellular permeability and TNF-α expression. In addition, male mice displayed blood glucose intolerance resulting from impaired insulin secretion following oral glucose challenge. Using an integrated approach including an aldehydomic analysis, this male-specific phenotype was further characterized and revealed close covariations between unidentified putative reactive aldehydes and bacterial communities belonging to Bacteroidales and Lachnospirales orders.

Our work highlights how the form of dietary iron in the maternal diet can dictate the oxidative status in gut offspring in a sex-dependent manner, and how a gut microbiota-driven oxidative challenge in early life can be associated with gut barrier defects and glucose metabolism disorders that may be predictive of diabetes development.

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Maternal heminic vs. non-heminic iron intake differentially and persistently imprints the offspring's fecal microbiota.

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Males from heme-fed dams exhibit increased gut lumen reactive aldehydes in absence of direct dietary exposure to heme iron.

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Some of the increased reactive aldehydes closely covariated with Orders belonging to Bacteroidales and Lachnospirales.

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Maternal exposure to dietary heme iron impairs gut barrier and glucose tolerance in male offspring.

Osmolality-based normalization enhances statistical discrimination of untargeted metabolomic urine analysis: results from a comparative study

INTRODUCTION

Because of its ease of collection, urine is one of the most commonly used matrices for metabolomics studies. However, unlike other biofluids, urine exhibits tremendous variability that can introduce confounding inconsistency during result interpretation. Despite many existing techniques to normalize urine samples, there is still no consensus on either which method is most appropriate or how to evaluate these methods.

OBJECTIVES

To investigate the impact of several methods and combinations of methods conventionally used in urine metabolomics on the statistical discrimination of two groups in a simple metabolomics study.

METHODS

We applied 14 different strategies of normalization to forty urine samples analysed by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). To evaluate the impact of these different strategies, we relied on the ability of each method to reduce confounding variability while retaining variability of interest, as well as the predictability of statistical models.

RESULTS

Among all tested normalization methods, osmolality-based normalization gave the best results. Moreover, we demonstrated that normalization using a specific dilution prior to the analysis outperformed post-acquisition normalization. We also demonstrated that the combination of various normalization methods does not necessarily improve statistical discrimination.

CONCLUSIONS

This study re-emphasized the importance of normalizing urine samples for metabolomics studies. In addition, it appeared that the choice of method had a significant impact on result quality. Consequently, we suggest osmolality-based normalization as the best method for normalizing urine samples.

TRIAL REGISTRATION NUMBER

NCT03335644.

Global Profiling of Toxicologically Relevant Metabolites in Urine: Case Study of Reactive Aldehydes

Among the numerous unknown metabolites representative of our exposure, focusing on toxic compounds should provide more relevant data to link exposure and health. For that purpose, we developed and applied a global method using data independent acquisition (DIA) in mass spectrometry to profile specifically electrophilic compounds originating metabolites. These compounds are most of the time toxic, due to their chemical reactivity toward nucleophilic sites present in biomacromolecules. The main line of cellular defense against these electrophilic molecules is conjugation to glutathione, then metabolization into mercapturic acid conjugates (MACs). Interestingly, MACs display a characteristic neutral loss in MS/MS experiments that makes it possible to detect all the metabolites displaying this characteristic loss, thanks to the DIA mode, and therefore to highlight the corresponding reactive metabolites. As a proof of concept, our workflow was applied to the toxicological issue of the oxidation of dietary polyunsaturated fatty acids, leading in particular to the formation of toxic alkenals, which lead to MACs upon glutathione conjugation and metabolization. By this way, dozens of MACs were detected and identified. Interestingly, multivariate statistical analyses carried out only on extracted HRMS signals of MACs yield a better characterization of the studied groups compared to results obtained from a classic untargeted metabolomics approach.