Adipose Triglyceride Lipase Loss Promotes a Metabolic Switch in A549 Non-Small Cell Lung Cancer Cell Spheroids.

Cancer cells undergo complex metabolic adaptations to survive and thrive in challenging environments. This is particularly prominent for solid tumors, where cells in the core of the tumor are under severe hypoxia and nutrient deprivation. However, such conditions are often not recapitulated in the typical 2D in vitro cancer models, where oxygen as well as nutrient exposure is quite uniform. The aim of this study was to investigate the role of a key neutral lipid hydrolase, namely adipose triglyceride lipase (ATGL), in cancer cells that are exposed to more tumor-like conditions. To that end, we cultured lung cancer cells lacking ATGL as multicellular spheroids in 3D and subjected them to comprehensive proteomics analysis and metabolic phenotyping. Proteomics data are available via ProteomeXchange with identifier PXD021105. As a result, we report that loss of ATGL enhanced growth of spheroids and facilitated their adaptation to hypoxia, by increasing the influx of glucose and endorsing a pro-Warburg effect. This was followed by changes in lipid metabolism and an increase in protein production. Interestingly, the observed phenotype was also recapitulated in an even more "in vivo like" setup, when cancer spheroids were grown on chick chorioallantoic membrane, but not when cells were cultured as a 2D monolayer. In addition, we demonstrate that according to the publicly available cancer databases, an inverse relation between ATGL expression and higher glucose dependence can be observed. In conclusion, we provide indications that ATGL is involved in regulation of glucose metabolism of cancer cells when grown in 3D (mimicking solid tumors) and as such could be an important factor of the treatment outcome for some cancer types. Finally, we also ratify the need for alternative cell culture models, as the majority of phenotypes observed in 3D and spheroids grown on chick chorioallantoic membrane were not observed in 2D cell culture.

The glyphosate formulation Roundup® LB plus influences the global metabolome of pig gut microbiota in vitro.

Glyphosate is the world's most widely used herbicide, and its potential side effects on the intestinal microbiota of various animals, from honeybees to livestock and humans, are currently under discussion. Pigs are among the most abundant livestock animals worldwide and an impact of glyphosate on their intestinal microbiota function can have serious consequences on their health, not to mention the economic effects. Recent studies that addressed microbiota-disrupting effects focused on microbial taxonomy but lacked functional information. Therefore, we chose an experimental design with a short incubation time in which effects on the community structure are not expected, but functional effects can be detected. We cultivated intestinal microbiota derived from pig colon in chemostats and investigated the acute effect of 228 mg/d glyphosate acid equivalents from Roundup® LB plus, a frequently applied glyphosate formulation. The applied glyphosate concentration resembles a worst-case scenario for an 8-9 week-old pig and relates to the maximum residue levels of glyphosate on animal fodder. The effects were determined on the functional level by metaproteomics, targeted and untargeted meta-metabolomics, while variations in community structure were analyzed by 16S rRNA gene profiling and on the single cell level by microbiota flow cytometry. Roundup® LB plus did not affect the community taxonomy or the enzymatic repertoire of the cultivated microbiota in general or on the expression of the glyphosate target enzyme 5-enolpyruvylshikimate-3-phosphate synthase in detail. On the functional level, targeted metabolite analysis of short chain fatty acids (SCFAs), free amino acids and bile acids did not reveal significant changes, whereas untargeted meta-metabolomics did identify some effects on the functional level. This multi-omics approach provides evidence for subtle metabolic effects of Roundup® LB plus under the conditions applied.

Metabolomics reveals impact of seven functional foods on metabolic pathways in a gut microbiota model.

Functional food defined as dietary supplements that in addition to their nutritional values, can beneficially modulate body functions becomes more and more popular but the reaction of the intestinal microbiota to it is largely unknown. In order to analyse the impact of functional food on the microbiota itself it is necessary to focus on the physiology of the microbiota, which can be assessed in a whole by untargeted metabolomics. Obtaining a detailed description of the gut microbiota reaction to food ingredients can be a key to understand how these organisms regulate and bioprocess many of these food components. Extracts prepared from seven chief functional foods, namely green tea, black tea, Opuntia ficus-indica (prickly pear, cactus pear), black coffee, green coffee, pomegranate, and sumac were administered to a gut consortium culture encompassing 8 microbes which are resembling, to a large extent, the metabolic activities found in the human gut. Samples were harvested at 0.5 and 24 h post addition of functional food extract and from blank culture in parallel and analysed for its metabolites composition using gas chromatography coupled to mass spectrometry detection (GC-MS). A total of 131 metabolites were identified belonging to organic acids, alcohols, amino acids, fatty acids, inorganic compounds, nitrogenous compounds, nucleic acids, phenolics, steroids and sugars, with amino acids as the most abundant class in cultures. Considering the complexity of such datasets, multivariate data analyses were employed to classify samples and investigate how functional foods influence gut microbiota metabolisms. Results from this study provided a first insights regarding how functional foods alter gut metabolism through either induction or inhibition of certain metabolic pathways, i.e. GABA production in the presence of higher acidity induced by functional food metabolites such as polyphenols. Likewise, functional food metabolites i.e., purine alkaloids acted themselves as direct substrate in microbiota metabolism.

Quantification of glyphosate and aminomethylphosphonic acid from microbiome reactor fluids.

RATIONALE: Glyphosate is one of the most widely used herbicides and it is suspected to affect the intestinal microbiota through inhibition of aromatic amino acid synthesis via the shikimate pathway. In vitro microbiome bioreactors are increasingly used as model systems to investigate effects on intestinal microbiota and consequently methods for the quantitation of glyphosate and its degradation product aminomethylphosphonic acid (AMPA) in microbiome model systems are required. METHODS: An optimized protocol enables the analysis of both glyphosate and AMPA by simple extraction with methanol:acetonitrile:water (2:3:1) without further enrichment steps. Glyphosate and AMPA are separated by liquid chromatography on an amide column and identified and quantified with a targeted tandem mass spectrometry method using a QTRAP 5500 system (AB Sciex). RESULTS: Our method has a limit of detection (LOD) in extracted water samples of <2 ng/mL for both glyphosate and AMPA. In complex intestinal medium, the LOD is 2 and 5 ng/mL for glyphosate and AMPA, respectively. These LODs allow for measurement at exposure-relevant concentrations. Glyphosate levels in a bioreactor model of porcine colon were determined and consequently it was verified whether AMPA was produced by porcine gut microbiota. CONCLUSIONS: The method presented here allows quantitation of glyphosate and AMPA in complex bioreactor fluids and thus enables studies of the impact of glyphosate and its metabolism on intestinal microbiota. In addition, the extraction protocol is compatible with an untargeted metabolomics analysis, thus allowing one to look for other perturbations caused by glyphosate in the same sample.

Following the community development of SIHUMIx - a new intestinal in vitro model for bioreactor use.

Diverse intestinal microbiota is frequently used in in vitro bioreactor models to study the effects of diet, chemical contaminations, or medication. However, the reproducible cultivation of fecal microbiota is challenging and the resultant communities behave highly dynamic. To approach the issue of reproducibility in in vitro models, we established an intestinal microbiota model community of reduced complexity, SIHUMIx, as a valuable model for in vitro use. The development of the SIHUMIx community was monitored over time with methods covering the cellular and the molecular level. We used microbial flow cytometry, intact protein profiling and terminal restriction fragment length polymorphism analysis to assess community structure. In parallel, we analyzed the functional level by targeted analysis of short-chain fatty acids and untargeted metabolomics. The stability properties constancy, resistance, and resilience were approached both on the structural and functional level of the community. We show that the SIHUMIx community is highly reproducible and constant since day 5 of cultivation. Furthermore, SIHUMIx has the ability to resist and recover from a pulsed perturbation, with changes in community structure recovered earlier than functional changes. Since community structure and function changed divergently, both levels need to be monitored at the same time to gain a full overview of the community development. All five methods are highly suitable to follow the community dynamics of SIHUMIx and indicated stability on day five. This makes SIHUMIx a suitable in vitro model to investigate the effects of e.g. medical, chemical, or dietary interventions.

The Simplified Human Intestinal Microbiota (SIHUMIx) Shows High Structural and Functional Resistance against Changing Transit Times in In Vitro Bioreactors.

Many functions in host-microbiota interactions are potentially influenced by intestinal transit times, but little is known about the effects of altered transition times on the composition and functionality of gut microbiota. To analyze these effects, we cultivated the model community SIHUMIx in bioreactors in order to determine the effects of varying transit times (TT) on the community structure and function. After five days of continuous cultivation, we investigated the influence of different medium TT of 12 h, 24 h, and 48 h. For profiling the microbial community, we applied flow cytometric fingerprinting and revealed changes in the community structure of SIHUMIx during the change of TT, which were not associated with changes in species abundances. For pinpointing metabolic alterations, we applied metaproteomics and metabolomics and found, along with shortening the TT, a slight decrease in glycan biosynthesis, carbohydrate, and amino acid metabolism and, furthermore, a reduction in butyrate, methyl butyrate, isobutyrate, valerate, and isovalerate concentrations. Specifically, B. thetaiotaomicron was identified to be affected in terms of butyrate metabolism. However, communities could recover to the original state afterward. This study shows that SIHUMIx showed high structural stability when TT changed-even four-fold. Resistance values remained high, which suggests that TTs did not interfere with the structure of the community to a certain degree.