Pharmacologic Inhibition of Ferroptosis Attenuates Experimental Abdominal Aortic Aneurysm Formation.

The pathogenesis of abdominal aortic aneurysm (AAA) formation involves vascular inflammation, thrombosis formation and programmed cell death leading to aortic remodeling. Recent studies have suggested that ferroptosis, an excessive iron-mediated cell death, can regulate cardiovascular diseases, including AAAs. However, the role of ferroptosis in immune cells, like macrophages, and ferroptosis-related genes in AAA formation remains to be deciphered. Single cell-RNA sequencing of human aortic tissue from AAA patients demonstrates significant differences in ferroptosis-related genes compared to control aortic tissue. Using two established murine models of AAA and aortic rupture in C57BL/6 (WT) mice, we observed that treatment with liproxstatin-1, a specific ferroptosis inhibitor, significantly attenuated aortic diameter, pro-inflammatory cytokine production, immune cell infiltration (neutrophils and macrophages), increased smooth muscle cell α-actin expression and elastic fiber disruption compared to mice treated with inactivated elastase in both pre-treatment and treatment after a small AAA had already formed. Lipidomic analysis using mass spectrometry shows a significant increase in ceramides and a decrease in intact lipid species levels in murine tissue compared to controls in the chronic AAA model on day 28. Mechanistically, in vitro studies demonstrate that liproxstatin-1 treatment of macrophages mitigated the crosstalk with aortic smooth muscle cells (SMCs) by downregulating MMP2 secretion. Taken together, this study demonstrates that pharmacological inhibition by liproxstatin-1 mitigates macrophage-dependent ferroptosis contributing to inhibition of aortic inflammation and remodeling during AAA formation.

The Lipidomics Reporting Checklist A framework for transparency of lipidomic experiments and repurposing resource data.

The rapid increase in lipidomic studies has led to a collaborative effort within the community to establish standards and criteria for producing, documenting, and disseminating data. Creating a dynamic easy-to-use checklist that condenses key information about lipidomic experiments into common terminology will enhance the field's consistency, comparability, and repeatability. Here, we describe the structure and rationale of the established Lipidomics Minimal Reporting Checklist to increase transparency in lipidomics research.

Indole-3-pyruvate: Analysis and Control of Tautomerism and Reactivity.

It is well-known in biochemistry that structure confers function, meaning that chemical structural elucidation is critical to truly understanding the function of a given metabolite. Indole-3-pyruvate (IPyA) exists in an equilibrium between the keto and enol tautomeric forms. IPyA is suggested to play a role in immune function; however, determining whether the tautomeric forms function differently can only be studied if an analytical method is capable of distinguishing between the two forms. Herein, we describe the use of UHPLC-HRMS to gain insight into the physical variables that govern IPyA tautomer equilibrium, reactivity, and detection limit. We use hydrogen-deuterium exchange (HDX) to identify enol and keto peaks, and we show that tautomers exhibit a valley of fronting followed by a tailing peak shape (though separation is still attainable) and identical MS/MS spectra. We observed drastically different ratios of keto and enol forms in different solvents, which is an important consideration for in vitro studies. IPyA was found to be highly unstable with accelerated reactivity in peroxides. Through in vitro reactivity studies, IPyA produced a myriad of known and unknown metabolites via nonenzymatic processes, many of which were mapped in vivo via the analysis of human plasma. Finally, we show that vitamin C (ascorbic acid) can slow this reactivity and enable sensitive detection in whole blood.

A global view of T cell metabolism in systemic lupus erythematosus.

Impaired metabolism is recognized as an important contributor to pathogenicity of T cells in Systemic Lupus Erythematosus (SLE). Over the last two decades, we have acquired significant knowledge about the signaling and transcriptomic programs related to metabolic rewiring in healthy and SLE T cells. However, our understanding of metabolic network activity derives largely from studying metabolic pathways in isolation. Here, we argue that enzymatic activities are necessarily coupled through mass and energy balance constraints with in-built network-wide dependencies and compensation mechanisms. Therefore, metabolic rewiring of T cells in SLE must be understood in the context of the entire network, including changes in metabolic demands such as shifts in biomass composition and cytokine secretion rates as well as changes in uptake/excretion rates of multiple nutrients and waste products. As a way forward, we suggest cell physiology experiments and integration of orthogonal metabolic measurements through computational modeling towards a comprehensive understanding of T cell metabolism in lupus.

Lipoxin A 4 /FPR2 signaling mitigates ferroptosis of alveolar epithelial cells via NRF2-dependent pathway during lung ischemia-reperfusion injury.

BACKGROUND: Post-lung transplantation (LTx) injury can involve sterile inflammation due to ischemia-reperfusion injury (IRI). We investigated the cell-specific role of ferroptosis (excessive iron-mediated cell death) in mediating lung IRI and determined if specialized pro-resolving mediators such as Lipoxin A4 (LxA 4 ) can protect against ferroptosis in lung IRI. METHODS: Single-cell RNA sequencing of lung tissue from post-LTx patients was analyzed. Lung IRI was evaluated in C57BL/6 (WT), formyl peptide receptor 2 knockout ( Fpr2 -/- ) and nuclear factor erythroid 2-related factor 2 knockout ( Nrf2 -/- ) mice using a hilar-ligation model with or without LxA 4 administration. Furthermore, the protective efficacy of LxA 4 was evaluated employing a murine orthotopic LTx model and in vitro studies using alveolar type II epithelial (ATII) cells. RESULTS: Differential expression of ferroptosis-related genes was observed in post-LTx patient samples compared to healthy controls. A significant increase in the levels of oxidized lipids and reduction in the levels of intact lipids were observed in mice subjected to IRI compared to shams. Furthermore, pharmacological inhibition of ferroptosis with liproxstatin-1 mitigated lung IRI and lung dysfunction. Importantly, LxA 4 treatment attenuated pulmonary dysfunction, ferroptosis and inflammation in WT mice subjected to lung IRI, but not in Fpr2 -/- or Nrf2 -/- mice, after IRI. In the murine LTx model, LxA 4 treatment increased PaO 2 levels and attenuated lung IRI. Mechanistically, LxA 4 -mediated protection involves increase in NRF2 activation and glutathione concentration as well as decrease in MDA levels in ATII cells. CONCLUSIONS: LxA 4 /FPR2 signaling on ATII cells mitigates ferroptosis via NRF2 activation and protects against lung IRI.

Metabolomic signatures of carfilzomib-related cardiotoxicity in patients with multiple myeloma.

As a treatment for relapsed or refractory multiple myeloma (MM), carfilzomib has been associated with a significant risk of cardiovascular adverse events (CVAE). The goals of our study were to evaluate the metabolomic profile of MM patients to identify those at high risk prior to carfilzomib treatment and to explore the mechanisms of carfilzomib-CVAE to inform potential strategies to protect patients from this cardiotoxicity. Global metabolomic profiling was performed on the baseline and post-baseline plasma samples of 60 MM patients treated with carfilzomib-based therapy, including 31 who experienced CVAE, in a prospective cohort study. Baseline metabolites and post-baseline/baseline metabolite ratios that differ between the CVAE and no-CVAE patients were identified using unadjusted and adjusted methods. A baseline metabolomic risk score was created to stratify patients. We observed a lower abundance of tauroursodeoxycholic acid (T-UDCA) in CVAE patients at baseline (odds ratio [OR] = 0.47, 95% confidence interval [CI] = 0.21-0.94, p = 0.044) compared with the no-CVAE patients. A metabolite risk score was able to stratify patients into three risk groups. The area under the receiver-operating curve of the model with clinical predictors and metabolite risk score was 0.93. Glycochenodeoxycholic acid (OR = 0.56, 95% CI = 0.31-0.87, p = 0.023) was significantly lower in post-baseline/baseline ratios of CVAE patients compared with no-CVAE patients. Following metabolomic analysis, we created a baseline metabolite risk score that can stratify MM patients into different risk groups. The result also provided intriguing clues about the mechanism of carfilzomib-CVAE and potential cardioprotective strategies.

Infant microbes and metabolites point to childhood neurodevelopmental disorders.

This study has followed a birth cohort for over 20 years to find factors associated with neurodevelopmental disorder (ND) diagnosis. Detailed, early-life longitudinal questionnaires captured infection and antibiotic events, stress, prenatal factors, family history, and more. Biomarkers including cord serum metabolome and lipidome, human leukocyte antigen (HLA) genotype, infant microbiota, and stool metabolome were assessed. Among the 16,440 Swedish children followed across time, 1,197 developed an ND. Significant associations emerged for future ND diagnosis in general and for specific ND subtypes, spanning intellectual disability, speech disorder, attention-deficit/hyperactivity disorder, and autism. This investigation revealed microbiome connections to future diagnosis as well as early emerging mood and gastrointestinal problems. The findings suggest links to immunodysregulation and metabolism, compounded by stress, early-life infection, and antibiotics. The convergence of infant biomarkers and risk factors in this prospective, longitudinal study on a large-scale population establishes a foundation for early-life prediction and intervention in neurodevelopment.

Association of Tryptophan/Kynurenine Metabolites with Healing in Chronic Venous Leg Ulcers.

Objective: Chronic wound healing is a complex process that is still not well understood. The tryptophan (TRP)-l-kynurenine (KYN) pathway has recently been under increased scrutiny with regard to wound healing. The study applied metabolomics to elucidate the TRP-l-KYN pathway associated with wound healing in chronic venous leg ulcers (CVLUs). Approach: This study used a longitudinal comparative design of 60 serum samples collected from 30 older adult patients with CVLUs, receiving weekly sharp debridement at a wound clinic. The serum samples were collected at baseline and week 4 (healed wounds) or week 8 (nonhealed wounds). Liquid chromatography-mass spectrometry (LC-MS) metabolomics was used to analyze targeted metabolites. A Bayesian approach was used to examine robust correlations between changes in metabolite values and linear healing slope and to compare by group. Results: The mean age was 71.13 (±9.46 years). Half of the sample were female and the minority (17%) were Black. The mean values of evaluated metabolites for the nonhealed group were consistently lower than those for the healed group. The healed group (n = 12) had higher KYN values. Those on a healing trajectory (n = 23) had lower KYN levels and higher TRP levels at baseline and over time. There was moderate support (Bayes factor = 3.70) for a negative association between change in kynurenic acid and linear healing slope (r = -0.35, credibility intervals [CrI] = -0.62, -0.04; probability of direction [PD] = 98%). Results suggest that KYN and TRP may be markers for healing in individuals with CVLUs. Innovation and Conclusion: Gaining a better understanding of the associations between the TRP-l-KYN pathway and the healing of CVLUs may help to clarify the links of inflammation with the rate and success of wound healing. Biomarker development focused on the TRP-l-KYN pathway could be pursued, if the associations are further supported by focused research studies.

Functional and metabolomic analysis of urinary extracellular vesicles from juvenile mice with renal compensatory hypertrophy.

Unilateral nephrectomy, a procedure reducing kidney mass, triggers a compensatory response in the remaining kidney, increasing its size and function to maintain a normal glomerular filtration rate (GFR). Recent research has highlighted the role of extracellular vesicles (EVs) in renal physiology and disease, although their involvement in unilateral nephrectomy has been underexplored. In this study, unilateral nephrectomy was performed on young mice, and urinary extracellular vesicles (uEVs) characterization and cargo were analyzed. Kidney volume increased significantly post-nephrectomy, demonstrating compensatory hypertrophy. Serum creatinine, cystatin C, and urinary electrolytes concentrations were similar in both nephrectomized and control groups. Western blot analysis revealed upregulation of sodium-glucose cotransporter 2 (SGLT2) and sodium chloride cotransporter (NCC), and downregulation of sodium­potassium-chloride co-transporter (NKCC2) and epithelial sodium channel (ENaC) in the nephrectomized group. Metabolomic analysis of uEVs showed an enrichment of certain metabolites, including citrate and stachydrine. Interestingly, uEVs from the nephrectomized group demonstrated a protective effect, downregulating signal transducer and activator of transcription 3 (STAT3) and reducing reactive oxygen species (ROS) in renal proximal cells, compared to uEVs from the control group. This study suggests that uEVs contain bioactive components capable of inducing protective, anti-inflammatory, anti-fibrinolytic, and antioxidative effects in renal cells. These findings contribute to our understanding of uEVs' role in renal compensatory mechanisms after unilateral nephrectomy and may hold promise for future therapeutic interventions in renal diseases.

Identification of Novel and Early Biomarkers for Cisplatin-induced Nephrotoxicity and the Nephroprotective Role of Cimetidine using a Pharmacometabolomic-based Approach Coupled with In Vitro Toxicodynamic Modeling and Simulation.

Cisplatin is widely used for the treatment of various types of cancer. However, cisplatin-induced nephrotoxicity (CIN) is frequently observed in patients receiving cisplatin therapy which poses a challenge in its clinical utility. Currently used clinical biomarkers for CIN are not adequate for early detection of nephrotoxicity, hence there is a need to identify potential early biomarkers in predicting CIN. In the current study, a combination of in vitro toxicodynamic (TD) modeling and untargeted global metabolomics approach was used to identify novel potential metabolite biomarkers for early detection of CIN. In addition, we investigated the protective role of cimetidine (CIM), an inhibitor of the organic cation transporter 2 (OCT2), in suppressing CIN. We first characterized the time-course of nephrotoxic effects of cisplatin (CIS) and the protective effects of CIM in a human pseudo-immortalized renal proximal tubule epithelial cell line (RPTEC), SA7K cell line. Secondly, we used a mathematical cell-level, in vitro TD modeling approach to quantitatively characterize the time-course effects of CIS and CIM as single agents and combination in SA7K cells. Based on the experimental and modeling results, we selected relevant concentrations of CIS and CIM for our metabolomics study. With the help of PCA (Principal Component Analysis) and PLS-DA (Projection to Latent Structure - Discriminate Analysis) analyses, we confirmed global metabolome changes for different groups (CIS, CIM, CIS+CIM vs control) in SA7K cells. Based on the criterion of a p-value ≤ 0.05 and a fold change ≥ 2 or ≤ 0.5, we identified 20 top metabolites that were significantly changed during the early phase i.e. within first 12 h of CIS treatment. Finally, pathway analysis was conducted that revealed the key metabolic pathways that were most impacted in CIN.

Liberation of host heme by Clostridioides difficile-mediated damage enhances Enterococcus faecalis fitness during infection.

IMPORTANCE: Clostridioides difficile and Enterococcus faecalis are two pathogens of great public health importance. Both bacteria colonize the human gastrointestinal tract where they are known to interact in ways that worsen disease outcomes. We show that the damage associated with C. difficile infection (CDI) releases nutrients that benefit E. faecalis. One particular nutrient, heme, allows E. faecalis to use oxygen to generate energy and grow better in the gut. Understanding the mechanisms of these interspecies interactions could inform therapeutic strategies for CDI.

miRNA-211 maintains metabolic homeostasis in medulloblastoma through its target gene long-chain acyl-CoA synthetase 4.

The prognosis of childhood medulloblastoma (MB) is often poor, and it usually requires aggressive therapy that adversely affects quality of life. microRNA-211 (miR-211) was previously identified as an important regulator of cells that descend from neural cells. Since medulloblastomas primarily affect cells with similar ontogeny, we investigated the role and mechanism of miR-211 in MB. Here we showed that miR-211 expression was highly downregulated in cell lines, PDXs, and clinical samples of different MB subgroups (SHH, Group 3, and Group 4) compared to normal cerebellum. miR-211 gene was ectopically expressed in transgenic cells from MB subgroups, and they were subjected to molecular and phenotypic investigations. Monoclonal cells stably expressing miR-211 were injected into the mouse cerebellum. miR-211 forced expression acts as a tumor suppressor in MB both in vitro and in vivo, attenuating growth, promoting apoptosis, and inhibiting invasion. In support of emerging regulatory roles of metabolism in various forms of cancer, we identified the acyl-CoA synthetase long-chain family member (ACSL4) as a direct miR-211 target. Furthermore, lipid nanoparticle-coated, dendrimer-coated, and cerium oxide-coated miR-211 nanoparticles were applied to deliver synthetic miR-211 into MB cell lines and cellular responses were assayed. Synthesizing nanoparticle-miR-211 conjugates can suppress MB cell viability and invasion in vitro. Our findings reveal miR-211 as a tumor suppressor and a potential therapeutic agent in MB. This proof-of-concept paves the way for further pre-clinical and clinical development.

Effect of statin treatment on metabolites, lipids and prostanoids in patients with Statin Associated Muscle Symptoms (SAMS).

BACKGROUND: Between 5-10% of patients discontinue statin therapy due to statin-associated adverse reactions, primarily statin associated muscle symptoms (SAMS). The absence of a clear clinical phenotype or of biomarkers poses a challenge for diagnosis and management of SAMS. Similarly, our incomplete understanding of the pathogenesis of SAMS hinders the identification of treatments for SAMS. Metabolomics, the profiling of metabolites in biofluids, cells and tissues is an important tool for biomarker discovery and provides important insight into the origins of symptomatology. In order to better understand the pathophysiology of this common disorder and to identify biomarkers, we undertook comprehensive metabolomic and lipidomic profiling of plasma samples from patients with SAMS who were undergoing statin rechallenge as part of their clinical care. METHODS AND FINDINGS: We report our findings in 67 patients, 28 with SAMS (cases) and 39 statin-tolerant controls. SAMS patients were studied during statin rechallenge and statin tolerant controls were studied while on statin. Plasma samples were analyzed using untargeted LC-MS metabolomics and lipidomics to detect differences between cases and controls. Differences in lipid species in plasma were observed between cases and controls. These included higher levels of linoleic acid containing phospholipids and lower ether lipids and sphingolipids. Reduced levels of acylcarnitines and altered amino acid profile (tryptophan, tyrosine, proline, arginine, and taurine) were observed in cases relative to controls. Pathway analysis identified significant increase of urea cycle metabolites and arginine and proline metabolites among cases along with downregulation of pathways mediating oxidation of branched chain fatty acids, carnitine synthesis, and transfer of acetyl groups into mitochondria. CONCLUSIONS: The plasma metabolome of patients with SAMS exhibited reduced content of long chain fatty acids and increased levels of linoleic acid (18:2) in phospholipids, altered energy production pathways (ß-oxidation, citric acid cycle and urea cycles) as well as reduced levels of carnitine, an essential mediator of mitochondrial energy production. Our findings support the hypothesis that alterations in pro-inflammatory lipids (arachidonic acid pathway) and impaired mitochondrial energy metabolism underlie the muscle symptoms of patients with statin associated muscle symptoms (SAMS).

Niclosamide as a chemical probe for analyzing SARS-CoV-2 modulation of host cell lipid metabolism.

Introduction: SARS-CoV-2 subverts host cell processes to facilitate rapid replication and dissemination, and this leads to pathological inflammation. Methods: We used niclosamide (NIC), a poorly soluble anti-helminth drug identified initially for repurposed treatment of COVID-19, which activates the cells' autophagic and lipophagic processes as a chemical probe to determine if it can modulate the host cell's total lipid profile that would otherwise be either amplified or reduced during SARS-CoV-2 infection. Results: Through parallel lipidomic and transcriptomic analyses we observed massive reorganization of lipid profiles of SARS-CoV-2 infected Vero E6 cells, especially with triglycerides, which were elevated early during virus replication, but decreased thereafter, as well as plasmalogens, which were elevated at later timepoints during virus replication, but were also elevated under normal cell growth. These findings suggested a complex interplay of lipid profile reorganization involving plasmalogen metabolism. We also observed that NIC treatment of both low and high viral loads does not affect virus entry. Instead, NIC treatment reduced the abundance of plasmalogens, diacylglycerides, and ceramides, which we found elevated during virus infection in the absence of NIC, resulting in a significant reduction in the production of infectious virions. Unexpectedly, at higher viral loads, NIC treatment also resulted in elevated triglyceride levels, and induced significant changes in phospholipid metabolism. Discussion: We posit that future screens of approved or new partner drugs should prioritize compounds that effectively counter SARS-CoV-2 subversion of lipid metabolism, thereby reducing virus replication, egress, and the subsequent regulation of key lipid mediators of pathological inflammation.

An Automated Workflow Composition System for Liquid Chromatography-Mass Spectrometry Metabolomics Data Processing.

Liquid chromatography-mass spectrometry (LC-MS) metabolomics studies produce high-dimensional data that must be processed by a complex network of informatics tools to generate analysis-ready data sets. As the first computational step in metabolomics, data processing is increasingly becoming a challenge for researchers to develop customized computational workflows that are applicable for LC-MS metabolomics analysis. Ontology-based automated workflow composition (AWC) systems provide a feasible approach for developing computational workflows that consume high-dimensional molecular data. We used the Automated Pipeline Explorer (APE) to create an AWC for LC-MS metabolomics data processing across three use cases. Our results show that APE predicted 145 data processing workflows across all the three use cases. We identified six traditional workflows and six novel workflows. Through manual review, we found that one-third of novel workflows were executable whereby the data processing function could be completed without obtaining an error. When selecting the top six workflows from each use case, the computational viable rate of our predicted workflows reached 45%. Collectively, our study demonstrates the feasibility of developing an AWC system for LC-MS metabolomics data processing.

Untargeted Metabolomic Analysis of Lactation-Stage-Matched Human and Bovine Milk Samples at 2 Weeks Postnatal.

Epidemiological data demonstrate that bovine whole milk is often substituted for human milk during the first 12 months of life and may be associated with adverse infant outcomes. The objective of this study is to interrogate the human and bovine milk metabolome at 2 weeks of life to identify unique metabolites that may impact infant health outcomes. Human milk (n = 10) was collected at 2 weeks postpartum from normal-weight mothers (pre-pregnant BMI < 25 kg/m2) that vaginally delivered term infants and were exclusively breastfeeding their infant for at least 2 months. Similarly, bovine milk (n = 10) was collected 2 weeks postpartum from normal-weight primiparous Holstein dairy cows. Untargeted data were acquired on all milk samples using high-resolution liquid chromatography-high-resolution tandem mass spectrometry (HR LC-MS/MS). MS data pre-processing from feature calling to metabolite annotation was performed using MS-DIAL and MS-FLO. Our results revealed that more than 80% of the milk metabolome is shared between human and bovine milk samples during early lactation. Unbiased analysis of identified metabolites revealed that nearly 80% of milk metabolites may contribute to microbial metabolism and microbe-host interactions. Collectively, these results highlight untargeted metabolomics as a potential strategy to identify unique and shared metabolites in bovine and human milk that may relate to and impact infant health outcomes.

Expanding Per- and Polyfluoroalkyl Substances Coverage in Nontargeted Analysis Using Data-Independent Analysis and IonDecon.

Per- and polyfluoroalkyl substances (PFAS) are widespread, persistent environmental contaminants that have been linked to various health issues. Comprehensive PFAS analysis often relies on ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC HRMS) and molecular fragmentation (MS/MS). However, the selection and fragmentation of ions for MS/MS analysis using data-dependent analysis results in only the topmost abundant ions being selected. To overcome these limitations, All Ions fragmentation (AIF) can be used alongside data-dependent analysis. In AIF, ions across the entire m/z range are simultaneously fragmented; hence, precursor-fragment relationships are lost, leading to a high false positive rate. We introduce IonDecon, which filters All Ions data to only those fragments correlating with precursor ions. This software can be used to deconvolute any All Ions files and generates an open source DDA formatted file, which can be used in any downstream nontargeted analysis workflow. In a neat solution, annotation of PFAS standards using IonDecon and All Ions had the exact same false positive rate as when using DDA; this suggests accurate annotation using All Ions and IonDecon. Furthermore, deconvoluted All Ions spectra retained the most abundant peaks also observed in DDA, while filtering out much of the artifact peaks. In complex samples, incorporating AIF and IonDecon into workflows can enhance the MS/MS coverage of PFAS (more than tripling the number of annotations in domestic sewage). Deconvolution in complex samples of All Ions data using IonDecon did retain some false fragments (fragments not observed when using ion selection, which were not isotopes or multimers), and therefore DDA and intelligent acquisition methods should still be acquired when possible alongside All Ions to decrease the false positive rate. Increased coverage of PFAS can inform on the development of regulations to address the entire PFAS problem, including both legacy and newly discovered PFAS.

Quantitation of phenylalanine and tyrosine from dried Blood/Plasma spots with impregnated stable isotope internal standards (SIIS) by FIA-SRM.

BACKGROUND: Dried Blood Spot (DBS) analysis has been used for identification and quantification of diseases and disorders in large populations. Simply collecting blood or plasma samples on cotton paper, followed with an organic solvent extraction, many small molecules can be detected and quantified. In a typical procedure of DBS analysis in newborn screening, stable isotope internal standards (SIIS) are added to extraction solvent as a reference. However, this way of employing SIIS does not reflect extraction efficiency, or protein binding issues, nor does it reflect potential degradation that could occur. In addition, punched-out discs from larger DBS are known to have imprecision typically ≥ 15%. METHODS: We developed and tested an approach, internal quantitative DBS (iqDBS), which delivers an exact volume of whole blood or plasma to a paper disc that is impregnated with a dried concentration of SIIS for quantitation. Amino acids were derivatized to make butyl esters and measured using Flow Injection Analysis with Selected Reaction Monitoring (FIA-SRM). RESULTS: We demonstrated with phenylalanine and tyrosine improved sensitivity and accuracy by applying iqDBS. CONCLUSIONS: We established a new method for quantitative analysis of small molecules from dried blood spots that incorporates stable isotope internal standard at the time of blood collection.

Metabolomic and Lipidomic Characterization of Meningioma Grades Using LC-HRMS and Machine Learning.

Meningiomas are among the most common brain tumors that arise from the leptomeningeal cover of the brain and spinal cord and account for around 37% of all central nervous system tumors. According to the World Health Organization, meningiomas are classified into three histological subtypes: benign, atypical, and anaplastic. Sometimes, meningiomas with a histological diagnosis of benign tumors show clinical characteristics and behavior of aggressive tumors. In this study, we examined the metabolomic and lipidomic profiles of meningioma tumors, focusing on comparing low-grade and high-grade tumors and identifying potential markers that can discriminate between benign and malignant tumors. High-resolution mass spectrometry coupled to liquid chromatography was used for untargeted metabolomics and lipidomics analyses of 85 tumor biopsy samples with different meningioma grades. We then applied feature selection and machine learning techniques to find the features with the highest information to aid in the diagnosis of meningioma grades. Three biomarkers were identified to differentiate low- and high-grade meningioma brain tumors. The use of mass-spectrometry-based metabolomics and lipidomics combined with machine learning analyses to prospect and characterize biomarkers associated with meningioma grades may pave the way for elucidating potential therapeutic and prognostic targets.