Systematic diet composition swap in a mouse genome-scale metabolic model reveals determinants of obesogenic diet metabolism in liver cancer

Novel analysis based on Raman spectroscopy in nutrition science

Modern research in nutrition science is transitioning from classical methodologies to advanced analytical strategies, in which Raman spectroscopy plays a crucial role. Raman spectroscopy and its derived techniques are gaining recognition in nutrition science for their features, such as high-speed, non-destructive analysis, label-free multiple detection and high sensitivity. Raman-enhancing techniques have further improved the sensitivity of Raman spectroscopy and widely extended its detection and imaging applications in nutrient analysis, as well as in ancillary tasks for nutrition research, such as nutrient status evaluation, nutrient interaction and metabolism studies. Further development of Raman-based analytical approaches lies in the improvement of instruments with higher precision, as well as the incorporation of other analytical techniques and advanced data analysis tools. This paper provides a comprehensive review of the application of nanoscience and nanotechnology, with a specific focus on Raman technology, in the field of food and nutrition science research. Instead of delving into the quantitative or qualitative detection capabilities of Raman technology, we highlight the remarkable food analysis and nutrition research methods established by this technology. Generally, this review introduces the characteristics and applications of Raman technology in nutrition analysis and discusses the limitations and future prospects of Raman spectroscopy for nutrition monitoring.

Untargeted Metabolomic Profiling Reveals Differentially Expressed Serum Metabolites and Pathways in Type 2 Diabetes Patients with and without Cognitive Decline: A Cross-Sectional Study

Diabetes is recognized as a risk factor for cognitive decline, but the underlying mechanisms remain elusive. We aimed to identify the metabolic pathways altered in diabetes-associated cognitive decline (DACD) using untargeted metabolomics. We conducted liquid chromatography-mass spectrometry-based untargeted metabolomics to profile serum metabolite levels in 100 patients with type 2 diabetes (T2D) (54 without and 46 with DACD). Multivariate statistical tools were used to identify the differentially expressed metabolites (DEMs), and enrichment and pathways analyses were used to identify the signaling pathways associated with the DEMs. The receiver operating characteristic (ROC) analysis was employed to assess the diagnostic accuracy of a set of metabolites. We identified twenty DEMs, seven up- and thirteen downregulated in the DACD vs. DM group. Chemometric analysis revealed distinct clustering between the two groups. Metabolite set enrichment analysis found significant enrichment in various metabolite sets, including galactose metabolism, arginine and unsaturated fatty acid biosynthesis, citrate cycle, fructose and mannose, alanine, aspartate, and glutamate metabolism. Pathway analysis identified six significantly altered pathways, including arginine and unsaturated fatty acid biosynthesis, and the metabolism of the citrate cycle, alanine, aspartate, glutamate, a-linolenic acid, and glycerophospholipids. Classifier models with AUC-ROC > 90% were developed using individual metabolites or a combination of individual metabolites and metabolite ratios. Our study provides evidence of perturbations in multiple metabolic pathways in patients with DACD. The distinct DEMs identified in this study hold promise as diagnostic biomarkers for DACD patients.

Predicting microbial interactions with approaches based on flux balance analysis: an evaluation

BACKGROUND

Given a genome-scale metabolic model (GEM) of a microorganism and criteria for optimization, flux balance analysis (FBA) predicts the optimal growth rate and its corresponding flux distribution for a specific medium. FBA has been extended to microbial consortia and thus can be used to predict interactions by comparing in-silico growth rates for co- and monocultures. Although FBA-based methods for microbial interaction prediction are becoming popular, a systematic evaluation of their accuracy has not yet been performed.

RESULTS

Here, we evaluate the accuracy of FBA-based predictions of human and mouse gut bacterial interactions using growth data from the literature. For this, we collected 26 GEMs from the semi-curated AGORA database as well as four previously published curated GEMs. We tested the accuracy of three tools (COMETS, Microbiome Modeling Toolbox and MICOM) by comparing growth rates predicted in mono- and co-culture to growth rates extracted from the literature and also investigated the impact of different tool settings and media. We found that except for curated GEMs, predicted growth rates and their ratios (i.e. interaction strengths) do not correlate with growth rates and interaction strengths obtained from in vitro data.

CONCLUSIONS

Prediction of growth rates with FBA using semi-curated GEMs is currently not sufficiently accurate to predict interaction strengths reliably.

Dietary fat and lipid metabolism in the tumor microenvironment

Metabolic reprogramming has been considered a core hallmark of cancer, in which excessive accumulation of lipids promote cancer initiation, progression and metastasis. Lipid metabolism often includes the digestion and absorption of dietary fat, and the ways in which cancer cells utilize lipids are often influenced by the complex interactions within the tumor microenvironment. Among multiple cancer risk factors, obesity has a positive association with multiple cancer types, while diets like calorie restriction and fasting improve health and delay cancer. Impact of these diets on tumorigenesis or cancer prevention are generally studied on cancer cells, despite heterogeneity of the tumor microenvironment. Cancer cells regularly interact with these heterogeneous microenvironmental components, including immune and stromal cells, to promote cancer progression and metastasis, and there is an intricate metabolic crosstalk between these compartments. Here, we focus on discussing fat metabolism and response to dietary fat in the tumor microenvironment, focusing on both immune and stromal components and shedding light on therapeutic strategies surrounding lipid metabolic and signaling pathways.