Linoleic acid induces metabolic stress in the intestinal microorganism Bifidobacterium breve DSM 20213

Abstract

Despite clinical and research interest in the health implications of the conjugation of linoleic acid (LA) by bifidobacteria, the detailed metabolic pathway and physiological reasons underlying the process remain unclear. This research aimed to investigate, at the molecular level, how LA affects the metabolism of Bifidobacterium breve DSM 20213 as a model for the well-known LA conjugation phenotype of this species. the mechanisms involved and the meaning of the metabolic changes caused by LA to B. breve DSM 20213 are unclear due to the lack of comprehensive information regarding the responses of B. breve DSM 20213 under different environmental conditions. Therefore, for the first time, an untargeted metabolomics-based approach was used to depict the main changes in the metabolic profiles of B. breve DSM 20213. Both supervised and unsupervised statistical methods applied to the untargeted metabolomic data allowed confirming the metabolic changes of B. breve DSM 20213 when exposed to LA. In particular, alterations to the amino-acid, carbohydrate and fatty-acid biosynthetic pathways were observed at the stationary phase of growth curve. Among others, significant upregulation trends were detected for aromatic (such as tyrosine and tryptophan) and sulfur amino acids (i.e., methionine and cysteine). Besides confirming the conjugation of LA, metabolomics suggested a metabolic reprogramming during the whole growth curve and an imbalance in redox status following LA exposure. Such redox stress resulted in the down-accumulation of peroxide scavengers such as lowmolecular-weight thiols (glutathione-and mycothiol-related compounds) and ascorbate precursors, together with the up-accumulation of oxidized (hydroxy-and epoxy-derivatives) forms of fatty acids. Consistently, growth was reduced and the levels of the oxidative stress marker malondialdehyde were higher in LA-exposed B. breve DSM 20213 than in the control.

Potential role of microbiome in Chronic Fatigue Syndrome/Myalgic Encephalomyelits (CFS/ME)

“…The untargeted profiling of the faecal samples was done using a 1290 UHPLC system coupled with a G6550 quadrupole-time-of-flight (QTOF) mass spectrometer (Agilent Technologies, Santa Clara, CA, United States), using an electrospray ionization system (JetStream dual). The comprehensive description of the optimized instrumental conditions (for both chromatography and mass spectrometry) can be found in previous works 88 , 89 . The injection volume was 6 μL, considering a Full-Scan mode in the range 100–1200 m/z , and working in positive ionization mode (ESI+).…”

“…The raw mass features from UHPLC-ESI/QTOF were processed as widely detailed in our previous works 88 , 89 , using the Profinder software (version B.07, from Agilent Technologies) to annotate molecular features following mass and retention time alignment. Thereafter, a 'find-by-formula' algorithm coupled with the isotopic profile of each mass feature was used to annotate the compounds against the comprehensive “Human Fecal Metabolome" database, containing specific information about many small molecule metabolites found in human feces 90 .…”

Metabolomics: towards acceleration of antibacterial plant-based leads discovery

“…However, it is likely that this strategy misses the targets other than selected proteins or even other than proteins. The recent research already indicated that antibacterial natural products can influence bacterial cells on different metabolic and functional levels (Božik et al 2018;Chen et al 2020;Liu et al 2020;Mack et al 2018;Senizza et al 2020). For that reason, untargeted observations of changes taking place in bacterial cells under the influence of compounds, inhibiting their growth give a better chance for identification of target points (Zampierii et al 2018) (Fig.…”

“…It detects a large number of measurable analytes in a sample and reflects the complexity of metabolic changes caused by causative agent. The application of untargeted mode is preferred since measurements of selected predefined analytes (targeted strategy) give only very narrow view on the metabolomics perturbations as it is difficult to correlate limited sets of metabolites and specific physiological states (Senizza et al 2020).…”

“…The classification strategy NMR-, GC-MS-and LC-MS-based metabolomics were already successfully applied for the characterization of antibacterial mechanisms of action of various compounds-known and new antibiotics (Hoerr et al 2016;Koena et al 2018;Vincent et al 2016, Zampieri et al 2017a), uncharacterized antimicrobial compounds (Zampieri et al 2018), human-targeted pharmacologically diverse drugs (Campos and Zampieri 2019), and also plant secondary metabolites in mixtures or as individual molecules (Božik et al 2018;Chen et al 2020;Hossain et al 2013;Liu et al 2020;Senizza et al 2020;Zhao et al 2020;Zhi et al 2008). In 2016 Dos Santos and co-workers published a review about the application of omics technologies for evaluation of antibacterial mechanisms of action of plant-derived products.…”