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Metabolomics, the comprehensive analysis of low-molecular-weight metabolites (typically below 1500 DA) in biological systems, relies heavily on mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Each technique has inherent strengths and weaknesses. MS offers high sensitivity and is commonly coupled with chromatography to analyze complex matrices, yet it is destructive, has limited reproducibility, and provides limited structural information. NMR, while less sensitive, is non-destructive and enables structural elucidation and precise quantification. Recent studies increasingly employ data fusion (DF) strategies to combine the complementary information from NMR and MS, aiming to enhance metabolomic analyses. This review summarizes DF methodologies using NMR and MS data in metabolomics studies over the past decade. A comprehensive search of SciFinder, Scopus, and Clarivate Web of Science databases was conducted to analyze fusion techniques, methods, and statistical models. The review emphasizes the growing importance of DF in metabolomics, showing its capacity to provide a more comprehensive view of biochemical processes across diverse biological systems, including clinical, plant, and food matrices.

As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. PathIntegrate is available as an open-source Python package.

Methanol is a sustainable substrate for biotechnology. In addition to natural methylotrophs, metabolic engineering has gained attention for transfer of methylotrophy. Here, we engineered Corynebacterium glutamicum for methanol-dependent growth with a sugar co-substrate. Heterologous expression of genes for methanol dehydrogenase from Bacillus methanolicus and of ribulose monophosphate pathway genes for hexulose phosphate synthase and isomerase from Bacillus subtilis enabled methanol-dependent growth of mutants carrying one of two independent metabolic cutoffs , i.e., either lacking ribose-5-phosphate isomerase or ribulose-5-phosphate epimerase. Whole genome sequencing of strains selected by adaptive laboratory evolution (ALE) for faster methanol-dependent growth was performed. Subsequently, three mutations were identified that caused improved methanol-dependent growth by (1) increased plasmid copy numbers, (2) enhanced riboflavin supply and (3) reduced formation of the methionine-analogue O-methyl-homoserine in the methanethiol pathway. Our findings serve as a foundation for the engineering of C. glutamicum to unleash the full potential of methanol as a carbon source in biotechnological processes.

Microalgae are photosynthetic microscopic organisms that serve as the primary food source in aquatic environments. Microalgae can synthesize a wide variety of molecules, such as polyunsaturated fatty acids (PUFAs) of the omega-3 and omega-6 series. Oxidative degradation of PUFA due to radical and/or enzymatic conversion leads to the formation of oxylipins, which are compounds known for their bioactive properties. In the present study, we aim to profile oxylipins from five microalgae species grown in 10-L photo-bioreactors under optimal conditions. During their exponential phase, microalgae were harvested, extracted and analyzed by LC-MS/MS to determine the qualitative and quantitative profile of oxylipins for each species. The five different selected microalgae revealed a high diversity of metabolites, up to 33 non-enzymatic and 24 enzymatic oxylipins present in different concentrations. Taken together, these findings highlight an interesting role of marine microalgae as a source of bioactive lipids mediators, which we hypothesize have an important function in preventive health measures such as amelioration of inflammation. The rich mixture of oxylipins may display advantages to biological organisms, especially by providing for human health benefits including antioxidant, anti-inflammatory, neuroprotective or immunomodulator activities. Some oxylipins are also well known for their cardiovascular properties.

This review focuses on the added value provided by a research strategy applying metabolomics analyses to assess phenotypic flexibility in response to different nutritional challenge tests in the framework of metabolic clinical studies. We discuss findings related to the Oral Glucose Tolerance Test (OGTT) and to mixed meals with varying fat contents and food matrix complexities. Overall, the use of challenge tests combined with metabolomics revealed subtle metabolic dysregulations exacerbated during the postprandial period when comparing healthy and at cardiometabolic risk subjects. In healthy subjects, consistent postprandial metabolic shifts driven by insulin action were reported (e.g., a switch from lipid to glucose oxidation for energy fueling) with similarities between OGTT and mixed meals, especially during the first hours following meal ingestion while differences appeared in a wider timeframe. In populations with expected reduced phenotypic flexibility, often associated with increased cardiometabolic risk, a blunted response on most key postprandial pathways was reported. We also discuss the most suitable statistical tools to analyze the dynamic alterations of the postprandial metabolome while accounting for complexity in study designs and data structure. Overall, the in-depth characterization of the postprandial metabolism and associated phenotypic flexibility appears highly promising for a better understanding of the onset of cardiometabolic diseases.

Background Cyanobacteria receive huge interest as green catalysts. While exploiting energy from sunlight, they co-utilize sugar and CO 2 . This photomixotrophic mode enables fast growth and high cell densities, opening perspectives for sustainable biomanufacturing. The model cyanobacterium Synechocystis sp. PCC 6803 possesses a complex architecture of glycolytic routes for glucose breakdown that are intertwined with the CO 2 -fixing Calvin-Benson-Bassham (CBB) cycle. To date, the contribution of these pathways to photomixotrophic metabolism has remained unclear. Results Here, we developed a comprehensive approach for 13 C metabolic flux analysis of Synechocystis sp. PCC 6803 during steady state photomixotrophic growth. Under these conditions, the Entner-Doudoroff (ED) and phosphoketolase (PK) pathways were found inactive but the microbe used the phosphoglucoisomerase (PGI) (63.1%) and the oxidative pentose phosphate pathway (OPP) shunts (9.3%) to fuel the CBB cycle. Mutants that lacked the ED pathway, the PK pathway, or phosphofructokinases were not affected in growth under metabolic steady-state. An ED pathway-deficient mutant ( Δeda ) exhibited an enhanced CBB cycle flux and increased glycogen formation, while the OPP shunt was almost inactive (1.3%). Under fluctuating light, ∆eda showed a growth defect, different to wild type and the other deletion strains. Conclusions The developed approach, based on parallel 13 C tracer studies with GC–MS analysis of amino acids, sugars, and sugar derivatives, optionally adding NMR data from amino acids, is valuable to study fluxes in photomixotrophic microbes to detail. In photomixotrophic cells, PGI and OPP form glycolytic shunts that merge at switch points and result in synergistic fueling of the CBB cycle for maximized CO 2 fixation. However, redirected fluxes in an ED shunt-deficient mutant and the impossibility to delete this shunt in a GAPDH2 knockout mutant, indicate that either minor fluxes (below the resolution limit of 13 C flux analysis) might exist that could provide catalytic amounts of regulatory intermediates or alternatively, that EDA possesses additional so far unknown functions. These ideas require further experiments.

The impact of abiotic challenges on plant physiology reshapes plant-pathogen interactions by modulating the plant immune responses. In wheat, the development of Fusarium Head Blight (FHB) is heavily influenced by environmental conditions, especially during the pre-anthesis stage, just before fungal infection occurs. The early stages of infection are thus likely conditioned by prior environmental changes with consequences on the disease outcome that require further characterization. In this study, we aimed to assess the impact of pre-anthesis water depletion followed by rapid rehydration at inoculation on the expression of FHB-related molecular determinants with emphasis on susceptibility factors and metabolism-related processes. Water depletion altered plant physiology and its effects remained detectable after three days after rehydration, leading to significantly reduced FHB symptoms. Dual-transcriptomics, combined with untargeted metabolomics, revealed two key findings including (i) extensive metabolic changes specific to prior water stress, and (ii) the strong conservation of previously identified candidate susceptibility genes regulation. Considering the combined stress effects, a unique response signature emerged, highlighting that immune responses are strongly interwoven with physiological adjustments. Our findings provide new insights into the trade-offs that plants make under multiple challenges and identify original wheat metabolic determinants that may improve FHB resistance even in suboptimal physiological conditions.

The incidence of inflammatory bowel diseases (IBD) is increasing in both Western and developing countries. IBD are multifactorial disorders involving complex interactions between genetic, immune, and environmental factors such as exposure to food contaminants. Deoxynivalenol (DON) is the most prevalent mycotoxin that contaminates staple food and induces intestinal breakdown and inflammatory response. To delineate the role of DON oral exposure in IBD, we used a Dextran sulfate sodium (DSS) colitis model in rats fed with a DON-contaminated diet or a control diet for 4 weeks. Colitis was induced in the 4th week by increasing concentrations of DSS in the drinking water (0, 2, 3 or 5%). DON exacerbated body weight loss and accelerated the appearance of symptoms in animals treated with DSS. DON increased morphological damage, pro-inflammatory markers (myeloperoxidase, CXCL-1 and IL-1β) and immune cell responses. In lamina propria of the rat with colitis, DON increased adaptive and innate immune responses after anti-CD3/28 or LPS stimulation, respectively. In the spleen, DON increased IFNγ secretion and reduced Treg populations. Interestingly, De-epoxy-DON (DOM-1) a detoxified form of DON did not have any consequences on colitis. These results suggest that DON is a risk factor in the onset of IBD.

There is scarce information on whether inhibition of rumen methanogenesis induces metabolic changes on the host ruminant. Understanding these possible changes is important for the acceptance of methane-reducing practices by producers. In this study we explored the changes in plasma profiles associated with the reduction of methane emissions. Plasma samples were collected from lactating primiparous Holstein cows fed the same diet with (Treated, n = 12) or without (Control, n = 13) an anti-methanogenic feed additive for six weeks. Daily methane emissions (CH4, g/d) were reduced by 23% in the Treated group with no changes in milk production, feed intake, body weight, and biochemical indicators of health status. Plasma metabolome analyses were performed using untargeted [nuclear magnetic resonance (NMR) and liquid chromatography-mass spectrometry (LC–MS)] and targeted (LC–MS/MS) approaches. We identified 48 discriminant metabolites. Some metabolites mainly of microbial origin such as dimethylsulfone, formic acid and metabolites containing methylated groups like stachydrine, can be related to rumen methanogenesis and can potentially be used as markers. The other discriminant metabolites are produced by the host or have a mixed microbial-host origin. These metabolites, which increased in treated cows, belong to general pathways of amino acids and energy metabolism suggesting a systemic non-negative effect on the animal.

Metabolic profiling (metabolomics) aims at measuring small molecules (metabolites) in complex samples like blood or urine for human health studies. While biomarker-based assessment often relies on a single molecule, metabolic profiling combines several metabolites to create a more complex and more specific fingerprint of the disease. However, in contrast to genomics, there is no unique metabolomics setup able to measure the entire metabolome. This challenge leads to tedious and resource consuming preliminary studies to be able to design the right metabolomics experiment. In that context, computer assisted metabolic profiling can be of strong added value to design metabolomics studies more quickly and efficiently. We propose a constraint-based modelling approach which predicts in silico profiles of metabolites that are more likely to be differentially abundant under a given metabolic perturbation (e.g. due to a genetic disease), using flux simulation. In genome-scale metabolic networks, the fluxes of exchange reactions, also known as the flow of metabolites through their external transport reactions, can be simulated and compared between control and disease conditions in order to calculate changes in metabolite import and export. These import/export flux differences would be expected to induce changes in circulating biofluid levels of those metabolites, which can then be interpreted as potential biomarkers or metabolites of interest. In this study, we present SAMBA (SAMpling Biomarker Analysis), an approach which simulates fluxes in exchange reactions following a metabolic perturbation using random sampling, compares the simulated flux distributions between the baseline and modulated conditions, and ranks predicted differentially exchanged metabolites as potential biomarkers for the perturbation. We show that there is a good fit between simulated metabolic exchange profiles and experimental differential metabolites detected in plasma, such as patient data from the disease database OMIM, and metabolic trait-SNP associations found in mGWAS studies. These biomarker recommendations can provide insight into the underlying mechanism or metabolic pathway perturbation lying behind observed metabolite differential abundances, and suggest new metabolites as potential avenues for further experimental analyses.