系统生物学

Many small molecules require derivatization to increase their volatility and to be amenable to gas chromatographic (GC) separation. Derivatization is usually time-consuming, and typical batch-wise procedures increase sample variability. Sequential automation of derivatization via robotic liquid handling enables the overlapping of sample preparation and analysis, maximizing time efficiency and minimizing variability. Herein, a protocol for the fully automated, two-stage derivatization of human blood–based samples in line with GC–[Orbitrap] mass spectrometry (MS)-based metabolomics is described. The protocol delivers a sample-to-sample runtime of 31 min, being suitable for better throughput routine metabolomic analysis.

Capturing produced, consumed, or exchanged metabolites (metabolomics) and the result of gene expression (transcriptomics) require the extraction of metabolites and RNA. Multi-omics approaches and, notably, the combination of metabolomics and transcriptomic analyses are required for understanding the functional changes and adaptation of microorganisms to different physico-chemical and environmental conditions. A protocol was developed to extract total RNA and metabolites from less than 6 mg of a kind of phototrophic biofilm: oxygenic photogranules. These granules are aggregates of several hundred micrometers up to several millimeters. They harbor heterotrophic bacteria and phototrophs. After a common step for cell disruption by bead-beating, a part of the volume was recovered for RNA extraction, and the other half was used for the methanol- and dichloromethane-based extraction of metabolites. The solvents enabled the separation of two phases (aqueous and lipid) containing hydrophilic and lipophilic metabolites, respectively. The ¹H nuclear magnetic resonance (NMR) analysis of these extracts produced spectra that contained over a hundred signals with a signal-to-noise ratio higher than 10. The quality of the spectra enabled the identification of dozens of metabolites per sample. Total RNA was purified using a commercially available kit, yielding sufficient concentration and quality for metatranscriptomic analysis. This novel method enables the co-extraction of RNA and metabolites from the same sample, as opposed to the parallel extraction from two samples. Using the same sample for both extractions is particularly advantageous when working with inherently heterogeneous complex biofilm. In heterogeneous systems, differences between samples may be substantial. The co-extraction will enable a holistic analysis of the metabolomics and metatranscriptomics data generated, minimizing experimental biases, including technical variations and, notably, biological variability. As a result, it will ensure more robust multi-omics analyses, particularly by improving the correlation between metabolic changes and transcript modifications.

Stable-isotope resolved metabolomics (SIRM) is a powerful approach for characterizing metabolic states in cells and organisms. By incorporating isotopes, such as ¹³C, into substrates, researchers can trace reaction rates across specific metabolic pathways. Integrating metabolomics data with gene expression profiles further enriches the analysis, as we demonstrated in our prior study on glioblastoma metabolic symbiosis. However, the bioinformatics tools for analyzing tracer metabolomics data have been limited. In this protocol, we encourage the researchers to use SIRM and transcriptomics data and to perform the downstream analysis using our software tool DIMet. Indeed, DIMet is the first comprehensive tool designed for the differential analysis of tracer metabolomics data, alongside its integration with transcriptomics data. DIMet facilitates the analysis of stable-isotope labeling and metabolic abundances, offering a streamlined approach to infer metabolic changes without requiring complex flux analysis. Its pathway-based "metabologram" visualizations effectively integrate metabolomics and transcriptomics data, offering a versatile platform capable of analyzing corrected tracer datasets across diverse systems, organisms, and isotopes. We provide detailed steps for sample preparation and data analysis using DIMet through its intuitive, web-based Galaxy interface. To showcase DIMet's capabilities, we analyzed LDHA/B knockout glioblastoma cell lines compared to controls. Accessible to all researchers through Galaxy, DIMet is free, user-friendly, and open source, making it a valuable resource for advancing metabolic research.

Dietary saturated fatty acids (SFAs) are upregulated in the blood circulation following digestion. A variety of circulating lipid species have been implicated in metabolic and inflammatory diseases; however, due to the extreme variability in serum or plasma lipid concentrations found in human studies, established reference ranges are still lacking, in addition to lipid specificity and diagnostic biomarkers. Mass spectrometry is widely used for identification of lipid species in the plasma, and there are many differences in sample extraction methods within the literature. We used ultra-high performance liquid chromatography (UPLC) coupled to a high-resolution hybrid triple quadrupole-time-of-flight (QToF) mass spectrometry (MS) to compare relative peak abundance of specific lipid species within the following lipid classes: free fatty acids (FFAs), triglycerides (TAGs), phosphatidylcholines (PCs), and sphingolipids (SGs), in the plasma of mice fed a standard chow (SC; low in SFAs) or ketogenic diet (KD; high in SFAs) for two weeks. In this protocol, we used Principal Component Analysis (PCA) and R to visualize how individual mice clustered together according to their diet, and we found that KD-fed mice displayed unique blood profiles for many lipid species identified within each lipid class compared to SC-fed mice. We conclude that two weeks of KD feeding is sufficient to significantly alter circulating lipids, with PCs being the most altered lipid class, followed by SGs, TAGs, and FFAs, including palmitic acid (PA) and PA-saturated lipids. This protocol is needed to advance knowledge on the impact that SFA-enriched diets have on concentrations of specific lipids in the blood that are known to be associated with metabolic and inflammatory diseases.

Key features

• Analysis of relative plasma lipid concentrations from mice on different diets using R.

• Lipidomics data collected via ultra-high performance liquid chromatography (UPLC) coupled to a high-resolution hybrid triple quadrupole-time-of-flight (QToF) mass spectrometry (MS).

• Allows for a comprehensive comparison of diet-dependent plasma lipid profiles, including a variety of specific lipid species within several different lipid classes.

• Accumulation of certain free fatty acids, phosphatidylcholines, triglycerides, and sphingolipids are associated with metabolic and inflammatory diseases, and plasma concentrations may be clinically useful.

Graphical overview

Magnetic resonance spectroscopy (MRS) can be used to measure in vivo concentrations of neurometabolites. This information can be used to identify neurotransmitter involvement in healthy (e.g., perceptual and cognitive processes) and unhealthy brain function (e.g., neurological and psychiatric illnesses). The standard approach for analyzing MRS data is to combine spectral transients acquired over a ~10 min scan to yield a single estimate that reflects the average metabolite concentration during that period. The temporal resolution of metabolite measurements is sacrificed in this manner to achieve a sufficient signal-to-noise ratio to produce a reliable estimate. Here we introduce two analyses that can be used to increase the temporal resolution of neurometabolite estimates produced from MRS measurements. The first analysis uses a sliding window approach to create a smoothed trace of neurometabolite concentration for each MRS scan. The second analysis combines transients across participants, rather than time, producing a single “group trace” with the highest possible temporal resolution achievable with the data. These analyses advance MRS beyond the current “static” application by allowing researchers to measure dynamic changes in neurometabolite concentration and expanding the types of questions that the technique can be used to address.

Macrophages are highly plastic immune cells that are capable of adopting a wide array of functional phenotypes in response to environmental stimuli. The changes in macrophage function are often supported and regulated by changes in cellular metabolism. Capturing a comprehensive picture of metabolism is vital for understanding the role of metabolic rewiring in the immune response. Here we present a method for systematically quantifying the abundance of metabolites and lipids in primary murine bone marrow derived macrophages (BMDMs). This method simultaneously extracts polar metabolites and lipids from BMDMs using a rapid two-phase extraction procedure. The polar metabolite fraction and lipid fraction are subsequently analyzed by separate liquid chromatography-mass spectrometry (LC-MS) methods for optimized coverage and quantification. This allows for a comprehensive characterization of cellular metabolism that can be used to understand the impact of a variety of environmental stimuli on macrophage metabolism and function.

Acclimation of leaf traits to fluctuating environments is a key mechanism to maximize fitness. One of the most important strategies in acclimation to changing light is to maintain efficient utilization of nitrogen in the photosynthetic apparatus by continuous modifications of between-leaf distribution along the canopy depth and within-leaf partitioning between photosynthetic functions according to local light availability. Between-leaf nitrogen distribution has been intensively studied over the last three decades, where proportional coordination between nitrogen concentration and light gradient was considered optimal in terms of maximizing canopy photosynthesis, without taking other canopy structural and physiological factors into account. We proposed a mechanistic model of protein turnover dynamics in different photosynthetic functions, which can be parameterized using leaves grown under different levels of constant light. By integrating this dynamic model into a multi-layer canopy model, constructed using data collected from a greenhouse experiment, it allowed us to test in silico the degree of optimality in photosynthetic nitrogen use for maximizing canopy carbon assimilation under given light environments.

Cancer is a disease characterized by altered metabolism, and there has been renewed interest in understanding the metabolism of tumors. Even though nutrient availability is a critical determinant of tumor metabolism, there has been little systematic study of the nutrients directly available to cancer cells in the tumor microenvironment. Previous work characterizing the metabolites present in the tumor interstitial fluid has been restricted to the measurement of a small number of nutrients such as glucose and lactate in a limited number of samples. Here we adapt a centrifugation-based method of tumor interstitial fluid isolation readily applicable to a number of sample types and a mass spectrometry-based method for the absolute quantitation of many metabolites in interstitial fluid samples. In this method, tumor interstitial fluid (TIF) is analyzed by liquid chromatography-mass spectrometry (LC/MS) using both isotope dilution and external standard calibration to derive absolute concentrations of targeted metabolites present in interstitial fluid. The use of isotope dilution allows for accurate absolute quantitation of metabolites, as other methods of quantitation are inadequate for determining nutrient concentrations in biological fluids due to matrix effects that alter the apparent concentration of metabolites depending on the composition of the fluid in which they are contained. This method therefore can be applied to measure the absolute concentrations of many metabolites in interstitial fluid from diverse tumor types, as well as most other biological fluids, allowing for characterization of nutrient levels in the microenvironment of solid tumors.

The accurate determination of metabolite distribution in subcellular compartments is still challenging in plant science. Various methodologies, such as fluorescence resonance energy transfer-based technology, nuclear magnetic resonance spectroscopy and protoplast fractionation allow the study of metabolite compartmentation. However, large changes in metabolite levels occur during such procedures. Therefore, the non-aqueous fractionation (NAF) technique is currently the best method for the study of in-vivo metabolite distribution. Our protocol presents a detailed workflow including the NAF procedure and quantification of compartment-specific markers for three subcellular compartments: ADP glucose pyrophosphorylase (AGPase) as plastidic marker, phosphoenolpyruvate carboxylase (PEPC) as cytosolic marker, and nitrate and acid invertase as vacuolar markers.

Short-Chain Fatty Acids (SCFAs) are a product of the fermentation of resistant starches and dietary fibers by the gut microbiota. The most important SCFA are acetate (C2), propionate (C3) and butyrate (C4). These metabolites are formed and absorbed in the colon and then transported through the hepatic vein to the liver. SCFAs are more concentrated in the intestinal lumen than in the serum. Butyrate is largely consumed in the gut epithelium, propionate in the liver and acetate in the periphery. SCFAs act on many cells including components of the immune system and epithelial cells by two main mechanisms: activation of G-protein coupled receptors (GPCRs) and inhibition of histone deacetylase. Considering the association between changes in SCFA concentrations and the development of diseases, methods to quantify these acids in different biological samples are important. In this study, we describe a protocol using gas chromatography to quantify SCFAs in the serum, feces and colonic luminal content. Separation of compounds was performed using a DB-23 column (60 m x 0.25 mm internal diameter [i.d.]) coated with a 0.15 µm thick layer of 80.2% 1-methylnaphatalene. This method has a good linear range (15-10,000 µg/ml). The precision (relative standard deviation [RSD]) is less than 15.0% and the accuracy (error relative [ER]) is within ± 15.0%. The extraction efficiency was higher than 97.0%. Therefore, this is cost effective and reproducible method for SCFA measurement in feces and serum.