Multiple Reaction Monitoring Profiling to Assess Compliance with an Almond Consumption Intervention

Almond Consumption for 8 Weeks Altered Host and Microbial Metabolism in Comparison to a Control Snack in Young Adults

Almond consumption can improve cardiometabolic (CM) health. However, the mechanisms underlying those benefits are not well characterized. This study explored the effects of consuming a snack of almonds vs. crackers for 8 weeks on changes in metabolomic profiles in young adults (clinicaltrials.gov ID: NCT03084003).

Participants (n = 73, age: 18-19 years, BMI: 18-41 kg/m2) were randomly assigned to consume either almonds (2 oz/d, n = 38) or an isocaloric control snack of graham crackers (325 kcal/d, n = 35) daily for 8 weeks. Blood samples were collected at baseline prior to and at 4 and 8 weeks after the intervention. Metabolite abundances in the serum were quantified by hydrophilic interaction chromatography quadrupole (Q) time-of-flight (TOF) mass spectrometry (MS/MS), gas chromatography (GC) TOF MS, CSH-ESI (electrospray) QTOF MS/MS, and targeted analyses for free PUFAs, total fatty acids, oxylipins and endocannabinoids. Linear mixed model analyses with baseline-adjustment were conducted, and those results were used for enrichment and network analyses. Microbial community pathway predictions from 16S rRNA sequencing of fecal samples was done using PICRUST2.

Almond consumption enriched unsaturated triglycerides, unsaturated phosphatidylcholines, saturated and unsaturated lysophosphatidylcholines, tricarboxylic acids, and tocopherol clusters (p < 0.05). Targeted analyses reveal lower levels of omega-3 total fatty acids (TFAs) overall in the almond group compared to the cracker group (p < 0.05). Microbial amino acid biosynthesis, and amino sugar and nucleotide sugar metabolism pathways were also differentially enriched at the end of the intervention (p < 0.05).

The study demonstrates the differential effects of almonds on host tocopherol, lipid, and TCA cycle metabolism with potential changes in microbial metabolism, which may interact with host metabolism to facilitate the CM benefits.

Targeted Lipidomics Analysis of Adipose and Skeletal Muscle Tissues by Multiple Reaction Monitoring Profiling

Lipid homeostasis is critical for maintaining normal cellular functions including membrane structural integrity, cell metabolism, and signal transduction. Adipose tissue and skeletal muscle are two major tissues involved in lipid metabolism. Adipose tissue can store excessive lipids in the form of triacylglyceride (TG), which can be hydrolyzed to release free fatty acids (FFAs) under insufficient nutrition states. In the highly energy-demanding skeletal muscle, lipids serve as oxidative substrates for energy production but can cause muscle dysfunction when overloaded. Lipids undergo fascinating cycles of biogenesis and degradation depending on physiological demands, while dysregulation of lipid metabolism has been increasingly recognized as a hallmark of diseases such as obesity and insulin resistance. Thus, it is important to understand the diversity and dynamics of lipid composition in adipose tissue and skeletal muscle. Here, we describe the use of multiple reaction monitoring profiling, based on lipid class and fatty acyl chain specific fragmentation, to explore various classes of lipids in skeletal muscle and adipose tissues. We provide a detailed method for exploratory analysis of acylcarnitine (AC), ceramide (Cer), cholesteryl ester (CE), diacylglyceride (DG), FFA, phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylglycerol (PG), phosphatidylinositol (PI), phosphatidylserine (PS), sphingomyelin (SM), and TG. Characterization of lipid composition within adipose tissue and skeletal muscle under different physiological situations will provide biomarkers and therapeutic targets for obesity-related diseases.

Multiple reaction monitoring profiling (MRM profiling): Small molecule exploratory analysis guided by chemical functionality

Small molecules, including metabolites and lipids, provide information on metabolic pathways and active biological processes in living organisms. They are often diagnostic of disease. Current exploratory methods for metabolomics and lipidomics mostly rely on separation using liquid or gas chromatography (LC or GC) coupled with mass spectrometers capable of acquiring high resolution data to generate an enormous data, but at the cost of lengthy processing and data acquisition. Even though many molecules can be identified and quantified by these methods, the laborious protocols for purification, identification, and validation limit the accessible sample chemical information. To improve the speed and efficiency of exploratory metabolomics and lipidomics, multiple reaction monitoring profiling (MRM profiling) has been developed. This strategy involves a three-stage workflow which starts by considering the metabolome as a collection of functional groups. The Discovery Stage interrogates a representative sample mixture for functional groups using the functional group specific precursor ion (Prec) scans and neutral loss (NL) scans. This experiment usually uses a triple quadrupole mass spectrometer without chromatography, i.e. by direct sample infusion. In the second Screening Stage, the main features seen in the Prec and NL scans are organized into lists of precursor ion/product ion transitions (MRMs) which are then used for the fast, specific, and sensitive interrogation of each individual sample. Data analysis by univariate and multivariate statistical methods is used to identify the most informative MRMs and so classify the individual samples. The compounds (biomarkers) which are responsible for the most informative MRMs in particular sample classes can be investigated in an optional third Identification Stage i.e. in a structural identification study. MRM profiling benefits from the much smaller number of functional groups compared to the number of individual metabolites existing in biological samples (where most metabolites are still unknown), resulting in acquisition of a much smaller data set and a shorter analysis time. The application of MRM Profiling to several biological and clinical problems is used to illustrate its features.

Exacerbation of Nanoparticle-Induced Acute Pulmonary Inflammation in a Mouse Model of Metabolic Syndrome

Nanotechnology has the capacity to revolutionize numerous fields and processes, however, exposure-induced health effects are of concern. The majority of nanoparticle (NP) safety evaluations have been performed utilizing healthy models and have demonstrated the potential for pulmonary toxicity. A growing proportion of individuals suffer diseases that may enhance their susceptibility to exposures. Specifically, metabolic syndrome (MetS) is increasingly prevalent and is a risk factor for the development of chronic diseases including type-2 diabetes, cardiovascular disease, and cancer. MetS is a combination of conditions which includes dyslipidemia, obesity, hypertension, and insulin resistance. Due to the role of lipids in inflammatory signaling, we hypothesize that MetS-associated dyslipidemia may modulate NP-induced immune responses. To examine this hypothesis, mice were fed either a control diet or a high-fat western diet (HFWD) for 14-weeks. A subset of mice were treated with atorvastatin for the final 7-weeks to modulate lipids. Mice were exposed to silver NPs (AgNPs) via oropharyngeal aspiration and acute toxicity endpoints were evaluated 24-h post-exposure. Mice on the HFWD demonstrated MetS-associated alterations such as increased body weight and cholesterol compared to control-diet mice. Cytometry analysis of bronchoalveolar lavage fluid (BALF) demonstrated exacerbation of AgNP-induced neutrophilic influx in MetS mice compared to healthy. Additionally, enhanced proinflammatory mRNA expression and protein levels of monocyte chemoattractant protein-1, macrophage inflammatory protein-2, and interleukin-6 were observed in MetS mice compared to healthy following exposure. AgNP exposure reduced mRNA expression of enzymes involved in lipid metabolism, such as arachidonate 5-lipoxygenase and arachidonate 15-lipoxygenase in both mouse models. Exposure to AgNPs decreased inducible nitric oxide synthase gene expression in MetS mice. An exploratory lipidomic profiling approach was utilized to screen lipid mediators involved in pulmonary inflammation. This assessment indicates the potential for reduced levels of lipids mediators of inflammatory resolution (LMIR) in the MetS model compared to healthy mice following AgNP exposure. Statin treatment inhibited enhanced inflammatory responses as well as alterations in LMIR observed in the MetS model due to AgNP exposure. Taken together our data suggests that MetS exacerbates the acute toxicity induced by AgNPs exposure possibly via a disruption of LMIR leading to enhanced pulmonary inflammation.

An Integrative Proteomic/Lipidomic Analysis of the Gold Nanoparticle Biocorona in Healthy and Obese Conditions

Introduction: When nanoparticles (NPs) enter a physiological environment, a coating of biomolecules or biocorona (BC) forms on the surface. Formation of the NP-BC is dependent on NP properties, the physiological environment, and time. The BC influences NP properties and biological interactions such as cellular internalization, immune responses, biodistribution, and others, leading to pharmacological and toxicological consequences. To date, examination of the NP-BC has focused primarily on protein components and healthy conditions. Therefore, we evaluated the protein and lipid content of BCs that formed on physicochemically distinct gold nanoparticles (AuNPs) under healthy and obese conditions. A comprehensive understanding of the NP-BC is necessary for the translation of in vitro toxicity assessments to clinical applications. Materials and Methods: AuNPs with two coatings (poly-N-vinylpyrrolidone [PVP] or citrate) and diameters (20 or 100 nm) were incubated in pooled human serum, and an integrated proteomic/lipidomic approach was used to evaluate BC composition. Macrophages were utilized to evaluate differential immune responses due to variations in the AuNP-BC. Results: AuNPs form distinct BCs based on physicochemical properties and the surrounding environment, with the obese BC containing more proteins and fewer lipids than the healthy BC. Differential macrophage inflammatory responses were observed based on AuNP properties and BC composition. Discussion and Conclusion: Overall, these findings demonstrate that AuNP size and coating, as well as physiological environment, influence the protein and lipid composition of the BC, which impacts cellular responses following exposure. These findings demonstrate that incorporation of BCs representing distinct physiological conditions may enhance the translatability of nanosafety in vitro studies.

Multiple reaction monitoring (MRM)-profiling with biomarker identification by LC-QTOF to characterize coronary artery disease

Metabolite profiling by mass spectrometry (MS) is an area of interest for disease diagnostics, biomarker discovery, and therapeutic evaluation. A recently developed approach, multiple reaction monitoring (MRM)-profiling, searches for metabolites with precursor (Prec) and neutral loss (NL) scans in a representative sample and creates a list of ion transitions. These are then used in an MRM method for fast screening of individual samples and discrimination between healthy and diseased. A large variety of functional groups are considered and all signals discovered are recorded in the individual samples, making this a largely unsupervised method. MRM-profiling is described here and then demonstrated with data for over 900 human plasma coronary artery disease (CAD) samples. Representative pooled samples for each condition were interrogated using a library of over a hundred Prec and NL scans on a triple quadrupole MS. The data from the Prec and NL experiments were converted into ion transitions, initially some 1266 transitions. Each ion transition was examined in the individual samples on a time scale of milliseconds per transition, which allows for rapid screening of large sample sets (<5 days for 1000 samples). Use of univariate and multivariate statistics allowed classification of the sample set with high accuracy. The metabolite profiles classified the CAD female, CAD male, and peripheral artery disease (PAD) samples relative to controls with an accuracy of 90%, 78%, and 85%, respectively. The compounds responsible for informative ion transitions were identified by chromatography and high resolution MS; some have been previously reported and found to be associated with coronary artery disease metabolism, indicating that the methodology generates a meaningful metabolite profile while being faster than traditional methodologies.