Measurement Of Metabolites Research Articles

Absolute quantification of amine metabolites in human cerebrospinal fluid via MS1-centric isotopic N,N-dimethyl leucine (iDiLeu) labeling.

Quantitative measurement of metabolites is essential to understand biological and disease processes. Absolute quantification by spiking heavyisotope-labeled internal standards and analyzing on mass spectrometry (MS) platform is a key method to determine the concentration of metabolites in biological samples. However, MS-based absolute quantification is often challenged by the commercial availability and high costs of isotope-labeled internal standards. Here, we establish an absolute quantification method for amine metabolites utilizing isotopic N,N-dimethyl leucine (iDiLeu) tagging on the LC-MS/MS platform. Absolute quantification of metabolites with excellent accuracy and precision can be achieved with five-plex iDiLeu labeling without the need of isotope-labeled internal standards. We demonstrated that iDiLeu labeling improved the separation and detection limits of polar metabolites. Particularly, detection limits for glycine, GABA, and serotonin have been improved by more than 20 folds, and valine by more than 2000 folds. With iDiLeu tagging, 87 amine-containing metabolites were identified and quantified in human cerebrospinal fluid (CSF) samples, revealing potential metabolic changes in Alzheimer's disease patients.

P1033 The decrease of 6-TGN concentrations during pregnancy is not associated with an increase in fecal calprotectin in female patients with IBD on thiopurine-treatment

Abstract Background Thiopurines, such as azathioprine (AZA), mercaptopurine (MP) and thioguanine (TG), are commonly used to maintain disease remission in inflammatory bowel disease (IBD). AZA and MP are metabolized to form the active 6-thioguanine nucleotides (6-TGN) and the 6-methylmercaptopurine ribonucleotides (6-MMPR), associated with intolerable adverse drug reactions. Previous studies report a shift in thiopurine metabolism during pregnancy, characterized by a decrease in 6-TGN and an increase in 6-MMPR levels 1,2. The influence of these changes on clinical outcomes remains unclear. This study aims to explore the association of changes in 6-TGN and 6-MMPR levels during pregnancy with disease activity and toxicity markers in women with IBD. Methods A retrospective cohort study was conducted recruiting patients with IBD from six Dutch medical centers. Adult women who were pregnant between 2017 and 2022, using thiopurines for IBD, and with at least one thiopurine metabolite measurement during pregnancy were included. Linear mixed effects models assessed 6-TGN and 6-MMPR levels during pregnancy (categorized to half trimesters) and within 6 months postpartum, compared to preconceptional levels, adjusting for dosage and repeated measurements. The influence of percentual changes in 6-TGN and 6-MMPR on disease activity (calprotectin >250μg/g), hepatotoxicity (alanine aminotransferase (ALT) >2xULN) and myelotoxicity (leukocytes <4.0x109/L) were analyzed, adjusting for dosage and trimester. Results A total of 87 women with 100 pregnancies were included, of whom 64.4% were diagnosed with Crohn’s disease and 32.2% with ulcerative colitis. An adverse pregnancy outcome occurred in 13 pregnancies (13%), with rates comparable to the healthy Dutch population. Subtherapeutic levels of 6-TGN were measured in 32 pregnancies (32%). There were no instances of leukopenia; transient hepatotoxicity occurred thrice (3%). There was active disease in 30 pregnancies, corresponding to 37 metabolite measurements. Analysis showed a significant reduction in 6-TGN levels in the second trimester (Figure 1), with non-significant increases in 6-MMPR. Though disease activity coincided with subtherapeutic 6-TGN-levels 21 times (56.8%), the reduction in 6-TGN was not associated with changes in calprotectin (Estimated marginal mean difference (EMM) =5.435, p=0.104), ALT (EMM=-0.025, p=0.858) or leukocytes (EMM=0.068, p=0.536, Table 1). Conclusion Our study results indicate that despite that 6-TGN levels decrease and 6-MMP may increase during pregnancy, these fluctuations do not seem to associate with markers of disease activity, hepatotoxicity and myelotoxicity. Larger, prospective studies are needed to re-evaluate the postulated need for thiopurine monitoring in the third trimester.

Increased circulatory Krebs cycle metabolites in sepsis is associated with increased interleukin-6 release and worse survival.

Recent studies have proposed that Krebs cycle metabolites may serve as potential biomarkers for prognosis in sepsis. However, whether these metabolites are associated with disease severity and can be applied to improve the effectiveness of current prognosis assessment in sepsis remains unclear and is explored in this study. This prospective multicenter cohort study was conducted in medical intensive care units (ICUs). From December 2019 to September 2022, consecutive patients admitted to medical ICUs for sepsis were screened and recruited. Plasma samples were obtained for measurements of cytokines and Krebs cycle metabolites, including citrate/isocitrate, cis-aconitate, alpha-ketoglutarate, succinate, fumarate and malate. In total, 97 patients admitted for sepsis were enrolled in the study. The 28-day mortality rate was 17.5%, and non-survivors exhibited significantly increased plasma lactate levels and Sequential Organ Failure Assessment (SOFA) scores. Plasma levels of Krebs cycle metabolites were significantly correlated with both plasma lactate and interleukin-6 levels. Except for citrate/isocitrate, all Krebs cycle metabolites were significantly elevated in patients with acute kidney injury. Multivariate Cox proportional hazard models, adjusted for plasma lactate levels and SOFA scores, revealed that plasma levels of alpha-ketoglutarate (adjusted hazard ratio [HR]: 2.404, P = 0.002), fumarate (adjusted HR: 1.904, P = 0.001) and malate (adjusted HR: 1.327, P = 0.019) were associated with increased risk of 28-day mortality. Study findings indicate that Krebs cycle metabolites, particularly alpha-ketoglutarate, fumarate, and malate, when applied with SOFA score, might enhance prognostic assessment in patients with sepsis.

Quantification of multi-pathway metabolites related to folate metabolism and application in natural population with MTHFR C677T polymorphism.

Folate, serving as a crucial micronutrient, plays an important role in promoting human growth and supporting transformations to a variety of metabolic pathways including one-carbon, pyrimidine, purine, and homocysteine metabolism. The 5,10-methylenetetrahydrofolate reductase (MTHFR) enzyme is pivotal in the folate metabolic pathway. Polymorphism in the MTHFR gene, especially C677T, was associated with decreased enzyme activity and disturbance of folate metabolism, which is linked to various diseases including birth defects in newborns and neural tube abnormalities. However, the detailed metabolic disturbance induced by MTHFR C677T polymorphism is still elusive. In this study, a novel liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed for the precise quantification of 93 metabolites from six important metabolic pathways related to folate metabolism. The method characteristics demonstrated high accuracy and precision, with r2 values ranging from 0.981 to 1.000 for all metabolites. Then the impact of the MTHFR C677T polymorphism on folate metabolism was further investigated, revealing a significant reduction in the level of 5-methyltetrahydrofolate and abnormal levels of metabolites associated with DNA synthesis pathways in individuals carrying the mutation. These data highlight the pivotal role of folic acid supplementation for individuals with the MTHFR C677T polymorphism to mitigate health risks and show the value of precision measurement of folate-related metabolites.

DNEA: an R package for fast and versatile data-driven network analysis of metabolomics data

BackgroundMetabolomics is a high-throughput technology that measures small molecule metabolites in cells, tissues or biofluids. Analysis of metabolomics data is a multi-step process that involves data processing, quality control and normalization, followed by statistical and bioinformatics analysis. The latter step often involves pathway analysis to aid biological interpretation of the data. This approach is limited to endogenous metabolites that can be readily mapped to metabolic pathways. An alternative to pathway analysis that can be used for any classes of metabolites, including unknown compounds that are ubiquitous in untargeted metabolomics data, involves defining metabolite-metabolite interactions using experimental data. Our group has developed several network-based methods that use partial correlations of experimentally determined metabolite measurements. These were implemented in CorrelationCalculator and Filigree, two software tools for the analysis of metabolomics data we developed previously. The latter tool implements the Differential Network Enrichment Analysis (DNEA) algorithm. This analysis is useful for building differential networks from metabolomics data containing two experimental groups and identifying differentially enriched metabolic modules. While Filigree is a user-friendly tool, it has certain limitations when used for the analysis of large-scale metabolomics datasets.ResultsWe developed the DNEA R package for the data-driven network analysis of metabolomics data. We present the DNEA workflow and functionality, algorithm enhancements implemented with respect to the package’s predecessor, Filigree, and discuss best practices for analyses. We tested the performance of the DNEA R package and illustrated its features using publicly available metabolomics data from the environmental determinants of diabetes in the young. To our knowledge, this package is the only publicly available tool designed for the construction of biological networks and subsequent enrichment testing for datasets containing exogenous, secondary, and unknown compounds. This greatly expands the scope of traditional enrichment analysis tools that can be used to analyze a relatively small set of well-annotated metabolites.ConclusionsThe DNEA R package is a more flexible and powerful implementation of our previously published software tool, Filigree. The modular structure of the package, along with the parallel processing framework built into the most computationally extensive steps of the algorithm, make it a powerful tool for the analysis of large and complex metabolomics datasets.

The promising role of proteomes and metabolomes in defining the single-cell landscapes of plants.

The plant community has a strong track record of RNA sequencing technology deployment, which combined with the recent advent of spatial platforms (e.g. 10× genomics) has resulted in an explosion of single-cell and nuclei datasets that can be positioned in an in situ context within tissues (e.g. a cell atlas). In the genomics era, application of proteomics technologies in the plant sciences has always trailed behind that of RNA sequencing technologies, largely due in part to upfront cost, ease-of-use, and access to expertise. Conversely, the use of early analytical tools for characterizing small molecules (metabolites) from plant systems predates nucleic acid sequencing and proteomics analysis, as the search for plant-based natural products has played a significant role in improving human health throughout history. As the plant sciences field now aims to fully define cell states, cell-specific regulatory networks, metabolic asymmetry and phenotypes, the measurement of proteins and metabolites at the single-cell level will be paramount. As a result of these efforts, the plant community will unlock exciting opportunities to accelerate discovery and drive toward meaningful translational outcomes.

Current practice in the management of paediatric autoimmune liver disease in Europe

AbstractObjectivePaediatric autoimmune liver disease (pAILD) is a rare condition with serious health implications. Notwithstanding treatment advancements, areas of uncertainty and knowledge gaps still exist. We here investigated the real‐life approach to pAILD management in Europe.MethodsA survey was distributed to members of the RARE‐LIVER ERN and the ESPGHAN Hepatology Interest Group. Information was gathered regarding clinical activity, medications used, and access to paediatric drug formulations at each site.ResultsThirty‐six centres from 22 European countries responded to the survey. The majority are exclusively paediatric units (86%). Among participants, 80% follow <50 children with pAILD, of which 25%–50% are <10 years old in 44% of centres. All centres use predniso(lo)ne as first‐line therapy, alone (15/36) or with azathioprine (21/36). Azathioprine and mycophenolate are the preferred second‐line options in centres using first‐line steroid monotherapy (11/15) or combined steroid‐azathioprine (19/21), respectively. Tacrolimus is used as third‐line agent in 15/36 centres. Proactive measurement of drug metabolites and target levels vary widely among centres. Paediatric predniso(lo)ne formulations are commercially available in 7/22 European countries, azathioprine in 3, mycophenolate in 14, tacrolimus in 15 and ursodeoxycholic acid in 14. When paediatric formulations are unavailable, children are treated with magisterial preparations or ‘solid’ formulations (crushed or intact).ConclusionsTreatment of pAILD in Europe varies widely in terms of medications used and treatment monitoring. Availability of paediatric drug formulations across Europe is limited. Collaborative initiatives are needed to define evidence‐based strategies for management of pAILD and to promote an equal, age‐appropriate treatment for affected children.

Resazurin dye is an invivo sensor of kidney tubular function.

Glomerular filtration rate (GFR) is the main functional index of kidney health and disease. Currently, no methods are available to directly measure tubular mass and function. Here, we report a serendipitous finding that the in vitro cell viability dye resazurin can be used in mice as an exogenous sensor of tubular function. Intravenously injected resazurin exhibited significant plasma protein binding and was found to mainly undergo tubular secretion. Mechanistic studies showed that the blue-colored, weakly fluorescent resazurin is taken up by tubular cells through organic anion transporters, followed by conversion to a highly fluorescent, pink-colored resorufin by mitochondrial and cytosolic reductases, converted to an orange-colored β-d-glucuronide with subsequent efflux into the urine. Here we report a simple method in which the intravenous injection of resazurin is followed by the measurement of fluorescent metabolites in the urine, providing a sensitive readout of tubular function. Three mouse models of acute kidney injury (rhabdomyolysis, bilateral ischemia-reperfusion injury, and cisplatin nephrotoxicity) were tested and the resazurin-based method was able to sensitively detect the loss of tubular function much earlier than the increase in serum creatinine levels. Strikingly, in mice with unilateral ischemia-reperfusion injury and genetic mutation-linked kidney hypoplasia (oligosyndactylism, a genetic model for congenital kidney hypoplasia), the resazurin-based method was able to detect loss of tubular mass and function despite normal GFR levels. Collectively, our findings establish the preclinical utility of resazurin as a sensitive exogenous marker of tubular function and support future examination in larger animals for potential clinical translation.

Utilization and validation of dried blood spot-based metabolomics in plasma-derived diagnostic models

Metabolomics has rapidly advanced in life sciences, enhancing our understanding of physiological conditions. Plasma stands out as a predominant sample type in metabolomics studies. Dried blood spot (DBS) has been recognized as a valuable strategy due to its unique characteristics, such as minimal invasiveness, small sample volume, and easy transport. Most large cohort studies construct the diagnostic model using plasma-based metabolomics rather than DBS. However, the comparability of metabolite measurements between DBS and plasma samples, as well as the integration of DBS-based metabolomics into established plasma-derived diagnostic models, remains unclear. We collected plasma and DBS samples from the same 12 healthy controls under fasting and non-fasting conditions and performed ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS)-based metabolomics. We identified 277 metabolites present in both plasma and DBS samples, with 229 (82.7 %) showing a strong correlation between the two sample types. Furthermore, pre-processing datasets from plasma and DBS samples before combining them effectively diminished inconsistencies between plasma- and DBS-based metabolomics without compromising the inherent classification within the data. For the clinical application, the plasma samples were obtained from the gastric cancer patients (n = 204) and the healthy controls (n = 128) to serve as the training cohorts. Additionally, DBS samples were collected from different participants of gastric cancer (n = 19) and healthy controls (n = 12) to validate the diagnostic model. Finally, DBS-based metabolomics were integrated into established plasma-derived gastric cancer diagnostic models, yielding an area under the ROC curve (AUC) of 0.934. Overall, incorporating DBS-based metabolomics into an established plasma-derived diagnostic model holds promise, offering valuable opportunities and insights for diagnostic research and clinical practice.

Longitudinal Metabolomics Reveals Metabolic Dysregulation Dynamics in Patients with Severe COVID-19.

Background/Objective: A dysregulated metabolism has been studied as a key aspect of the COVID-19 pathophysiology, but its longitudinal progression in severe cases remains unclear. In this study, we aimed to investigate metabolic dysregulation over time in patients with severe COVID-19 requiring mechanical ventilation (MV). Methods: In this single-center, prospective, observational study, we obtained 236 serum samples from 118 adult patients on MV in an ICU. The metabolite measurements were performed using capillary electrophoresis Fourier transform mass spectrometry, and we categorized the sampling time points into three time zones to align them with the disease progression: time zone 1 (T1) (the hyperacute phase, days 1-3 post-MV initiation), T2 (the acute phase, days 4-14), and T3 (the chronic phase, days 15-30). Using volcano plots and enrichment pathway analyses, we identified the differential metabolites (DMs) and enriched pathways (EPs) between the survivors and non-survivors for each time zone. The DMs and EPs were further grouped into early-stage, late-stage, and consistent groups based on the time zones in which they were detected. Results: With the 566 annotated metabolites, we identified 38 DMs and 17 EPs as the early-stage group, which indicated enhanced energy production in glucose, amino acid, and fatty acid metabolisms in non-survivors. As the late-stage group, 84 DMs and 10 EPs showed upregulated sphingolipid, taurine, and tryptophan-kynurenine metabolisms with downregulated steroid hormone synthesis in non-survivors. Three DMs and 23 EPs in the consistent group showed more pronounced dysregulation in the dopamine and arachidonic acid metabolisms across all three time zones in non-survivors. Conclusions: This study elucidated the temporal differences in metabolic dysregulation between survivors and non-survivors of severe COVID-19, offering insights into its longitudinal progression and disease mechanisms.

UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.

Comprehensively studying metabolism requires the measurement of metabolite levels. However, in contrast to the broad availability of gene expression data, metabolites are rarely measured in large molecularly-defined cohorts of tissue samples. To address this basic barrier to metabolic discovery, we propose a Bayesian framework ("UnitedMet") which leverages the empirical strength of RNA-metabolite covariation to impute otherwise unmeasured metabolite levels from widely available transcriptomic data. We demonstrate that UnitedMet is equally capable of imputing whole pool sizes as well as the outcomes of isotope tracing experiments. We apply UnitedMet to investigate the metabolic impact of driver mutations in kidney cancer, identifying a novel association between BAP1 and a highly oxidative tumor phenotype. We similarly apply UnitedMet to determine that advanced kidney cancers upregulate oxidative phosphorylation relative to early-stage disease, that oxidative metabolism in kidney cancer is associated with inferior outcomes to combination therapy, and that kidney cancer metastases themselves demonstrate elevated oxidative phosphorylation relative to primary tumors. UnitedMet therefore enables the assessment of metabolic phenotypes in contexts where metabolite measurements were not taken or are otherwise infeasible, opening new avenues for the generation and evaluation of metabolite-centered hypotheses. UnitedMet is open source and publicly available ( https://github.com/reznik-lab/UnitedMet ).

Improving Brain Metabolite Detection with a Combined Low-Rank Approximation and Denoising Diffusion Probabilistic Model Approach.

In vivo proton magnetic resonance spectroscopy (MRS) is a noninvasive technique for monitoring brain metabolites. However, it is challenged by a low signal-to-noise ratio (SNR), often necessitating extended scan times to compensate. One of the conventional techniques for noise reduction is signal averaging, which is inherently time-consuming and can lead to participant discomfort, thus posing limitations in clinical settings. This study aimed to develop a hybrid denoising strategy that integrates low-rank approximation and denoising diffusion probabilistic model (DDPM) to enhance MRS data quality and shorten scan times. Using publicly available 1H MRS datasets from 15 subjects, we applied the Casorati SVD and DDPM to obtain baseline and functional data during a pain stimulation task. This method significantly improved SNR, resulting in outcomes comparable to or better than averaging over 32 signals. It also provided the most consistent metabolite measurements and adequately tracked temporal changes in glutamate levels, correlating with pain intensity ratings after heating. These findings demonstrate that our approach enhances MRS data quality, offering a more efficient alternative to conventional methods and expanding the potential for the real-time monitoring of neurochemical changes. This contribution has the potential to advance MRS techniques by integrating advanced denoising methods to increase the acquisition speed and enhance the precision of brain metabolite analyses.

Abstract 4133122: Integrative Metabolomic Analysis of Triglyceride-Independent Lipoprotein Lipase Pathway Mechanisms for Coronary Artery Disease

Background: Strong genetic evidence supports the associations between key genes in triglyceride (TG) metabolism via the lipoprotein lipase (LPL) pathway (i.e., LPL , APOC3 , and ANGPTL3 ) and coronary artery disease (CAD). Given sparse human genetic or clinical trial evidence for other TG-modulating pathways, it is unclear if CAD risk reduction is due to reductions in TG levels alone or a secondary mechanism of action. Aim: To determine if there are TG-independent mechanisms of CAD risk reduction specific to the LPL pathway using genomics and metabolomics. Methods: We assessed the genetic effects of LPL pathway genes (67, 46, and 17 SNPs for LPL , APOC3 , and ANGPTL3 , respectively) and non-LPL pathway genes (3462 SNPs) for TG levels and 168 metabolites. We identified metabolites with associated effects that were greater than the associated effects with TG levels for LPL pathway genes and showed heterogeneity compared to the effects for non-LPL pathway genes. Cox proportional-hazards models were used to evaluate the association of candidate metabolites measured at baseline with incident CAD in participants of the UK Biobank. Models were adjusted for age, sex, TG levels, BMI, prevalent type 2 diabetes, statin, and ten PCs of genetic ancestry. Results: Among 233,719 UK Biobank participants free of prevalent CAD and with metabolite measurements, the mean (SD) age was 57.1 (8.0) years, 105,111 (45.0%) were male, and all self-reported as white. Over a median (IQR) follow-up of 11.2 (10.5-11.9) years, there were 7992 (3.4%) incident CAD cases. There were 30, 6, and 93 metabolites with significantly greater genetic effects for LPL , APOC3 , and ANGPTL3 than what was observed with TG levels, respectively. Among 20 metabolites with heterogeneity for ≥2 LPL pathway genes compared to non-LPL pathway genes, multi-variable Cox regression revealed 19 significantly associated with CAD (P<3x10 -4 ). Conclusion: LPL pathway genes have convergent and distinct impacts across metabolites. While LPL and ANGPTL3 have large genetic effects on VLDL metrics, APOC3 may have genetic effects beyond lipoprotein metabolism that impact CAD risk. The extent to which these metabolites influence CAD risk beyond TG alterations requires further study.

Abstract 4113263: Serum Metabolomics Adds Predictive Value to Clinical and Polygenic Risk Scores for Atrial Fibrillation: A UK Biobank study of >240,000 Patients

Background: Atrial fibrillation (AF) poses substantial morbidity and mortality burdens globally. It is critical to identify individuals at high-risk for AF to implement both lifestyle modifications and close monitoring for initiation of oral anticoagulants. Currently, risk prediction in AF relies on clinical scores such as CHARGE-AF. Small molecule metabolites are thought to play a role in AF pathogenesis, but the additional predictive value of metabolomics over clinical and genetic risk factors is unknown. Using the UK Biobank (UKBB), we sought to perform this evaluation. Methods: We included all UKBB participants with complete 1 H-NMR metabolite measurements. We excluded participants with prior history of AF or with incomplete AF Polygenic Risk Score (AF-PRS) or CHARGE-AF components. The final cohort included 240,628 patients. In-patient records, primary care notes, and death registries were queried to identify patients who had developed incident AF within 5 years of enrollment. The cohort was divided into a 80/20:Train/Test split. We compared the performance of an Elastic-Net regularized Cox Proportional Hazards (EN-CPH) model trained on CHARGE-AF, AF-PRS, and the full 170 metabolite panel to a CPH model trained only on CHARGE-AF and AF-PRS. Results: Within 5 years, 4,174 (1.7%) patients developed AF. Compared to the control population, the incident AF cases were more likely to be older (62 vs. 56, p<0.001), male (65% vs 45%, p<0.001), and have higher CHARGE-AF and AF-PRS scores (p<0.001). After training the EN-CPH model on CHARGE-AF, AF-PRS, and 170 metabolites, the final model included 8 metabolites on top of CHARGE-AF (HR: 1.28) and AF-PRS (HR: 1.11). After adjusting for CHARGE-AF and AF-PRS, creatinine level was associated with increased risk of AF (HR: 1.01) while linoleic acid level (HR: 0.985) was associated with decreased risk. Furthermore, total cholesterol, esterified cholesterol, free cholesterol, cholesteryl esters in IDL, omega-6 fatty acids, and cholesterol in large LDL were associated with HR 0.993-0.999. The EN-CPH model out-performed the CPH model without metabolomics on the test set (AUC 0.786 vs 0.753, p < 0.001). Conclusions: The addition of metabolomics to clinical and genomic risk scores improves prediction of 5-year incident AF within a general population. This study highlights the significance of the metabolome as an independent prognosticator of AF risk. Further study of the mechanisms by which selected metabolites alter AF risk is needed.

Association between urinary sodium-to-potassium ratio, elevated blood pressure phenotypes and microalbuminuria: Tehran Lipid and Glucose Study

This cross-sectional study investigated the associations between urinary sodium (UNa) to potassium (UK) ratio, different phenotypes of elevated blood pressure (BP), and microalbuminuria (MAU) in a cohort of the Tehran Lipid and Glucose Study (TLGS). Adult participants (n = 1782, mean age of 43.0 ± 13.7 years and 46.0% were men) were recruited (2015–2017) for measurements of spot urinary metabolites, i.e., Na, K, creatinine (Cr), microalbumin, and BP. Multinomial logistic regression was used to estimate the relative risk ratios (RRR) of elevated BP phenotypes [i.e., isolated systolic (ISH), diastolic (IDH), and systolic-diastolic (SDH) hypertension], and binary logistic regression was used to estimate odds ratios (ORs) of MAU across quintile categories and per each SD-increment of UNa-K ratio. Mean UNa, UK, and its ratio was 137 ± 57.4, 72.1 ± 36.6 mmol/L, and 2.31 ± 1.41, respectively. Subjects with UNa-K > 3.14 had higher prevalence of ISH (3.4 vs. 1.1%), SDH (11.0 vs. 6.2%), and MAU (14.1 vs. 6.2%) (P for all < 0.05). Highest compared to the lowest UNa-K ratio values (> 3.14 vs. <1.23) was associated with an increased probability of SDH (RRR = 1.79, 95% CI 1.09–3.19) and MAU (OR = 2.53, 95% CI 1.23–5.20). Every 1 SD-increment of the UNa-K ratio was associated with a 29 and 38% increased chance of having SDH and MAU, respectively. Our findings imply that a high UNa-K ratio may be a potential risk factor for elevated BP and renal dysfunction.

Hypometric genetics: Improved power in genetic discovery by incorporating quality control flags.

Balancing the tradeoff between quantity and quality of phenotypic data is critical in omics studies. Measurements below the limit of quantification (BLQ) are often tagged in quality control fields, but these flags are currently underutilized in human genetics studies. Extreme phenotype sampling is advantageous for mapping rare variant effects. We hypothesize that genetic drivers, along with environmental and technical factors, contribute to the presence of BLQ flags. Here, we introduce "hypometric genetics" (hMG) analysis and uncover a genetic basis for BLQ flags, indicating an additional source of genetic signal for genetic discovery, especially from phenotypic extremes. Applying our hMG approach to n= 227,469UK Biobank individuals with metabolomic profiles, we reveal more than 5% heritability for BLQ flags and report biologically relevant associations, for example, at APOC3, APOA5, and PDE3B loci. For common variants, polygenic scores trained only for BLQ flags predict the corresponding quantitative traits with 91% accuracy, validating the genetic basis. For rare coding variant associations, we find an asymmetric 65.4% higher enrichment of metabolite-lowering associations for BLQ flags, highlighting the impact of putative loss-of-function variants with large effects on phenotypic extremes. Joint analysis of binarized BLQ flags and the corresponding quantitative metabolite measurements improves power in Bayesian rare variant aggregation tests, resulting in an average of 181% more prioritized genes. Our approach is broadly applicable to omics profiling. Overall, our results underscore the benefit of integrating quality control flags and quantitative measurements and highlight the advantage of joint analysis of population-based samples and phenotypic extremes in human genetics studies.

A Non-targeted Proteomics Newborn Screening Platform for Inborn Errors of Immunity

PurposeNewborn screening using dried blood spot (DBS) samples for the targeted measurement of metabolites and nucleic acids has made a substantial contribution to public healthcare by facilitating the detection of neonates with genetic disorders. Here, we investigated the applicability of non-targeted quantitative proteomics analysis to newborn screening for inborn errors of immunity (IEIs).MethodsDBS samples from 40 healthy newborns and eight healthy adults were subjected to non-targeted proteomics analysis using liquid chromatography-mass spectrometry after removal of the hydrophilic fraction. Subsequently, DBS samples from 43 IEI patients were analyzed to determine whether patients can be identified by reduced expression of disease-associated proteins.ResultsDBS protein profiling allowed monitoring of levels of proteins encoded by 2912 genes, including 1110 listed in the Online Mendelian Inheritance in Man database, in healthy newborn samples, and was useful in identifying patients with IEIs by detecting reduced levels of disease causative proteins and their interacting proteins, as well as cell-phenotypical alterations.ConclusionOur results indicate that non-targeted quantitative protein profiling of DBS samples can be used to identify patients with IEIs and develop a novel newborn screening platform for genetic disorders.

8415 Rapid Generation Of An In Vivo Loss-Of-Function Genetic Model Of sdhb Zebrafish: Functional And Metabolic Characterization

Abstract Disclosure: S. Parisien-La Salle: None. F. Nobilleau: None. J. Lamontagne: None. S. Éric: None. I. Bourdeau: None. Introduction: Animal research has been limited in pheochromocytomas and paragangliomas (PPGLs). Recently, Dona et al., described a sdhb mutated zebrafish model by introducing a 13 bp frameshift mutation at the sdhb exon1—intron1 boundary into the zebrafish sdhb gene that recapitulated features of PPGLs. Objective: To generate a new model of mutated sdhb zebrafish to further investigate the underlying metabolic and biochemical perturbations in vivo. Methods: We took advantage of the genetic accessibility of zebrafish embryo to generate loss-of-function model for sdhb using CRISPR/Cas9 technology. Three guide RNAs were designed and microinjected in one-cell stage zebrafish embryos to target the coding sequence of zebrafish sdhb gene. In order to control for the stress brought on by injections, we injected a group of zebrafish with Cas9 endonuclease as a control. At five days post fertilization, mutant and control zebrafish were flash frozen and sent for liquid chromatography mass spectrometry measurements of Krebs cycle metabolites and normetanephrines/metanephrines. All results were normalized to the control groups consisting of wild-type or Cas9 injected larvae. Sdhb expression was analyzed by RT-qPCR in sdhb mutants and wild-type zebrafish larvae. We also monitored survival and heart rate in sdhb mutant zebrafish, compared to wild-type and Cas9. Results: Firstly, we confirmed the significant reduction of sdhb expression in CRISPR-injected larvae compared to wild-type larvae at 5 dpf (0.0613 vs 1.000 p&amp;lt;0.0001). Our metabolic panel showed that succinate was up to 26 times more elevated in sdhb mutants than in wild-type fish extracts (sdhb: 26.51 p&amp;lt;0.0001). Lactate (sdhb: 5.27 p&amp;lt;0.0001), leucine (sdhb: 2.88 p&amp;lt;0.0001), arginine (sdhb: 1.50 p=0.0034) and HMG-CoA (sdhb: 1.36 p=0.0033) were also higher in sdhb mutants. Whereas aspartate (sdhb: 0.244 p&amp;lt;0.0001) and oxidized gluthiatone (sdhb: 0.64 p=0.0011) were significantly lower in sdhb mutants than in wild-types. In regards to functionality, normetanephrine and metanephrine levels were at least four folds higher in sdhb mutants than in Cas9 injected larvaes (4.06 p&amp;lt;0.0001 and 3.42 p&amp;lt;0.0001 respectively). Interestingly, this difference was more noticeable in the fish bathing medium than in larvae tissue extracts, suggesting an increased hormonal excretion (normetanephrines: 4.81 p&amp;lt;0.0001 and metanephrines: 9.40 p&amp;lt;0.0001). Finally, sdhb mutated zebrafish presented with a higher heart rate (148 vs 133 p&amp;lt;0.0001) and reduced lifespan (p&amp;lt;0.0001) when compared to the wild-type/Cas9 group. Conclusion: These findings confirm that the zebrafish is an ideal model for studying PPGLs and their associated genetic defects. Our approach allows the in vivo assessment of a large metabolic panel, opening avenues on new potential clinical metabolic biomarkers in a loss-of-function of the SDHB gene. Ref: Dona M, et al. Endocr Relat Cancer. 2021 Presentation: 6/1/2024

Bifenthrin at Sublethal Concentrations Suppresses Mating and Laying of Female Conogethes punctiferalis by Regulating Sex Pheromone Biosynthesis and JH Signals.

Conogethes punctiferalis, a polyphagous pest in Asia, infests various crops, causing severe economic losses. Its larvae feed inside plants, making management challenging, with conventional insecticides. This study examines sublethal bifenthrin effects on the reproductive capabilities of adult females. Findings show sublethal bifenthrin concentrations (LC1, LC10, LC20, and LC30) significantly reduce sex pheromone production and mating success in a dose-dependent manner. Furthermore, these sublethal exposures influence the expression of pheromone biosynthesis activating neuropeptide and key juvenile hormone signaling genes, including methoprene-tolerant and Krüppel-homologue 1. Enzyme activity assays and metabolite measurements indicated that sublethal bifenthrin exposure decreases trehalose and pyruvic acid levels, suppressing the enzyme activities required for sex pheromone biosynthesis. Additionally, bifenthrin exposure delays ovarian development, reduces ovary size, and decreases egg production and hatchability. These results suggest bifenthrin's potential in attract-and-kill strategies by disrupting essential pathways for pest control, offering insights for improved insecticide use and innovative pest management for C. punctiferalis.

B-280 Nmr-based biomaker measurement determines high-dose methotrexate elimination rates in lymphoma and leukemia treatment

Abstract Background The cytotoxic methotrexate (MTX) is used in high doses in lymphoma and acute lymphocytic leukemia (ALL) treatment. A therapeutic plasma dose range is crucial to minimize MTX accumulation and prolonged exposure, which can lead to myelosuppression and other adverse effects. Determining MTX elimination prior to drug administration is difficult, given its non-linear clearance. Here we present a model to predict MTX elimination rates based on metabolite measurement in patients receiving high-dose methotrexate, using nuclear-magnetic resonance (NMR) spectroscopy. Methods A series of 26 residual sera from patients receiving MTX infusions as per treatment protocols for CNS lymphoma (MATRIX protocol, 3.5 g/m2 over four hours) and ALL (GMALL protocol, 1.5 g/m2 over 24 hours) were collected from patients at the University Hospital Regensburg at four timepoints (t0, t24, t42, and t48). Serum was NMR-measured in five replicates. NMR spectra for each sample were characterized and relevant parameters extracted. First order elimination constants were modeled for two MTX dosage regimes (&amp;lt;4 g, ≥4 g) using characterized NMR features from samples measured at t0. Results Metabolomic and lipidomic biomarkers measured at baseline (t0) were able to accurately predict MTX elimination rate constant k, determined by fitting MTX levels at a t24, t42, and t48, using first order c(t)∼c0*e(-kt) kinetic function (&amp;gt;4 g: R2 = 0.475, p-value &amp;lt; 0.05; ≤4 g: R2 = 0.918, p-value &amp;lt; 0.01) (Fig. A). Results were cross-validated with five-time repeated three-fold cross-validation. Performance values are given as means of the folds. Conclusions NMR-measured biomarkers can predict dose-dependent MTX elimination rates in patients receiving high-dose MTX, presenting a novel method to predict MTX clearance, allowing for personalized dose-monitoring and minimization of adverse events. Validation with a larger cohort is required.