Quantification Of Metabolites Research Articles

Analysis types and quantification methods applied in UHPLC-MS metabolomics research: a tutorial.

Different types of analytical methods, with different characteristics, are applied in metabolomics and lipidomics research and include untargeted, targeted and semi-targeted methods. Ultra High Performance Liquid Chromatography-Mass Spectrometry is one of the most frequently applied measurement instruments in metabolomics because of its ability to detect a large number of water-soluble and lipid metabolites over a wide range of concentrations in short analysis times. Methods applied for the detection and quantification of metabolites differ and can either report a (normalised) peak area or an absolute concentration. In this tutorial we aim to (1) define similarities and differences between different analytical approaches applied in metabolomics and (2) define how amounts or absolute concentrations of endogenous metabolites can be determined together with the advantages and limitations of each approach in relation to the accuracy and precision when concentrations are reported. The pre-analysis knowledge of metabolites to be targeted, the requirement for (normalised) peak responses or absolute concentrations to be reported and the number of metabolites to be reported define whether an untargeted, targeted or semi-targeted method is applied. Fully untargeted methods can only provide (normalised) peak responses and fold changes which can be reported even when the structural identity of the metabolite is not known. Targeted methods, where the analytes are known prior to the analysis, can also report fold changes. Semi-targeted methods apply a mix of characteristics of both untargeted and targeted assays. For the reporting of absolute concentrations of metabolites, the analytes are not only predefined but optimized analytical methods should be developed and validated for each analyte so that the accuracy and precision of concentration data collected for biological samples can be reported as fit for purpose and bereviewed by the scientific community.

Metabolomic Profiling of Adipose Tissue in Type 2 Diabetes: Associations with Obesity and Insulin Resistance

The global prevalence of Type 2 Diabetes (T2D) poses significant public health challenges due to its associated severe complications. Insulin resistance is central to T2D pathophysiology, particularly affecting adipose tissue function. This cross-sectional observational study investigates metabolic alterations in subcutaneous adipose tissue (SAT) associated with T2D to identify potential therapeutic targets. We conducted a comprehensive metabolomic analysis of SAT from 40 participants (20 T2D, 20 ND-T2D), matched for sex, age, and BMI (Body Mass Index). Metabolite quantification was performed using GC/MS and LC/MS/MS platforms. Correlation analyses were conducted to explore associations between metabolites and clinical parameters. We identified 378 metabolites, including significant elevations in TCA cycle (tricarboxylic acid cycle) intermediates, branched-chain amino acids (BCAAs), and carbohydrates, and a significant reduction in the nucleotide-related metabolites in T2D subjects compared to those without T2D. Obesity exacerbated these alterations, particularly in amino acid metabolism. Adipocyte size negatively correlated with BCAAs, while adipocyte glucose uptake positively correlated with unsaturated fatty acids and glycerophospholipids. Our findings reveal distinct metabolic dysregulation in adipose tissue in T2D, particularly in energy metabolism, suggesting potential therapeutic targets for improving insulin sensitivity and metabolic health. Future studies should validate these findings in larger cohorts and explore underlying mechanisms to develop targeted interventions.

Polar metabolomics using trichloroacetic acid extraction and porous graphitic carbon stationary phase

IntroductionAccurately identifying and quantifying polar metabolites using untargeted metabolomics has proven challenging in comparison to mid to non-polar metabolites. Hydrophilic interaction chromatography and gas chromatography–mass spectrometry are predominantly used to target polar metabolites.ObjectivesThis study aims to demonstrate a simple one-step extraction combined with liquid chromatography–mass spectrometry (LC–MS) that reliably retains polar metabolites.MethodsThe method involves a MilliQ + 10% trichloroacetic acid extraction from 6 healthy individuals serum, combined with porous graphitic carbon liquid chromatography–mass spectrometry (LC–MS). The coefficient of variation (CV) assessed retention reliability of polar metabolites with logP as low as − 9. QreSS (Quantification, Retention, and System Suitability) internal standards determined the method's consistency and recovery efficiency.ResultsThe method demonstrated reliable retention (CV < 0.30) of polar metabolites within a logP range of − 9.1 to 5.6. QreSS internal standards confirmed consistent performance (CV < 0.16) and effective recovery (70–130%) of polar to mid-polar metabolites. Quality control dilution series demonstrated that ~ 80% of annotated metabolites could be accurately quantified (Pearson’s correlation coefficient > 0.80) within their concentration range. Repeatability was demonstrated through clustering of repeated extractions from a single sample.ConclusionThis LC–MS method is better suited to covering the polar segment of the metabolome than current methods, offering a reliable and efficient approach for accurate quantification of polar metabolites in untargeted metabolomics.

Integrated proteomic and metabolomic profiling reveals novel insights on the inflammation and immune response in HFpEF

BackgroundThe precise mechanisms leading to the development of heart failure with preserved ejection fraction (HFpEF) remain incompletely defined. In this study, an integrative approach utilizing untargeted proteomics and metabolomics was employed to delineate the altered proteomic and metabolomic profiles in patients with HFpEF compared to healthy controls.Materials and methodsData were collected from a prospective cohort consisting of 30 HFpEF participants and 30 healthy controls, matched by gender and age. plasma samples were analyzed by multi-omics platforms. The quantification of plasma proteins and metabolites was performed using data-independent acquisition-based liquid chromatography-tandem mass spectrometry (LC-MS/MS) and ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS), respectively. Additionally, Proteomic and metabolomic results were analyzed separately and integrated using correlation and pathway analysis. This was followed by the execution of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment studies to elucidate the biological relevance of the observed results.ResultsA total of 46 significantly differentially expressed proteins (DEPs) and 102 differentially expressed metabolites (DEMs) were identified. Then, GO and KEGG pathway enrichment analyses were performed by DEPs and DEMs. Integrated analysis of proteomics and metabolomics has revealed Tuberculosis and African trypanosomiasis pathways that are significantly enriched and the DEPs and DEMs enriched within them, are associated with inflammation and immune response.ConclusionsIntegrated proteomic and metabolomic analyses revealed distinct inflammatory and immune response pathways in HFpEF, highlighting novel therapeutic avenues.

Sex- and age-specific reference intervals of 16 steroid metabolites quantified simultaneously by LC-MS/MS in sera from 2458 healthy subjects aged 0 to 77 years

BackgroundReference intervals covering the whole life span for all the metabolites in the steroid hormone biosynthesis quantified by sensitive and robust analytical methods are sparse or not existing. ObjectiveTo develop a state-of-the-art LC-MS/MS method for simultaneous quantification of multiple steroid metabolites and to establish detailed sex- and age-specific reference intervals for 16 steroid metabolites. Materials and methodAn isotope diluted LC-MS/MS method was developed for simultaneous quantitation of 16 steroid hormones. Serum samples from cross-sectional cohorts of healthy infants, children, adolescents, and adults aged 0.17 months to 77 years (n = 2458) were analysed. ResultsWith this novel, specific, and sensitive LC-MS/MS method, it was possible to quantify progesterone, 17-hydroxypregnenolone, 17-hydroxyprogesterone, dehydroepiandrosterone sulfate, androstenedione, testosterone, dihydrotestosterone, 11-deoxycorticosterone, corticosterone, 11-deoxycortisol, cortisol, and cortisone in ≥90 % of the samples, while estrone sulfate, aldosterone and dehydroepiandrosterone were quantified in 77 %, 75 % and 60 % of the samples, respectively. 21-deoxycortisol was only detectable in 2.5 % of samples from healthy subjects. Sex- and age-dependent fluctuations observed in minipuberty, puberty and adulthood including the menopausal transition were modelled. This enabled us to establish valid reference intervals from birth to late adult life for both males and females. ConclusionDetailed sex- and age-specific reference intervals of multiple, simultaneously quantified steroid metabolites by a novel and specific LC-MS/MS method provides a valuable tool for clinical practice and for future research.

Automated Liquid Handling Extraction and Rapid Quantification of Underivatized Amino Acids and Tryptophan Metabolites from Human Serum and Plasma Using Dual-Column U(H)PLC-MRM-MS and Its Application to Prostate Cancer Study.

Amino acids (AAs) and their metabolites are important building blocks, energy sources, and signaling molecules associated with various pathological phenotypes. The quantification of AA and tryptophan (TRP) metabolites in human serum and plasma is therefore of great diagnostic interest. Therefore, robust, reproducible sample extraction and processing workflows as well as rapid, sensitive absolute quantification are required to identify candidate biomarkers and to improve screening methods. We developed a validated semi-automated robotic liquid extraction and processing workflow and a rapid method for absolute quantification of 20 free, underivatized AAs and six TRP metabolites using dual-column U(H)PLC-MRM-MS. The extraction and sample preparation workflow in a 96-well plate was optimized for robust, reproducible high sample throughput allowing for transfer of samples to the U(H)PLC autosampler directly without additional cleanup steps. The U(H)PLC-MRM-MS method, using a mixed-mode reversed-phase anion exchange column with formic acid and a high-strength silica reversed-phase column with difluoro-acetic acid as mobile phase additive, provided absolute quantification with nanomolar lower limits of quantification within 7.9 min. The semi-automated extraction workflow and dual-column U(H)PLC-MRM-MS method was applied to a human prostate cancer study and was shown to discriminate between treatment regimens and to identify metabolites responsible for discriminating between healthy controls and patients on active surveillance.

Extraction, Quantification and Anti-Cancer Activity of Important Secondary Metabolite “Shatavarin-IV” from in vitro Callus Culture of Asparagus racemosus Willd.

Extraction, Quantification and Anti-Cancer Activity of Important Secondary Metabolite “Shatavarin-IV” from in vitro Callus Culture of Asparagus racemosus Willd.

Quantification of gut microbiome metabolites using chemical isotope derivatization strategy combined with LC-MS/MS: Application in neonatal hypoxic-ischemic encephalopathy rat model

The gut microbiome plays pivotal roles in various physiological and pathological processes, with key metabolites including short chain fatty acids (SCFAs), bile acids (BAs), and tryptophan (TRP) derivatives gaining significant attention for their diverse physiological roles. However, quantifying these metabolites presents challenges due to structural similarity, low abundance, and inherent technical limitations in traditional detection methods. In this study, we developed a precise and sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method utilizing a chemical isotope derivatization technique employing 4-(aminomethyl)-N,N-dimethylaniline-d0/d6 (4-AND-d0/d6) reagents to quantify 37 typical gut microbiome-derived metabolites. This method achieved an impressive 1500-fold enhancement in sensitivity for detecting metabolites, compared to methods using non-derivatized, intact molecules. Moreover, the quantitative accuracy of our chemical isotope derivatization strategy proved comparable to the stable isotope labeled internal standards (SIL-IS) method. Subsequently, we successfully applied this newly developed method to quantify target metabolites in plasma, brain, and fecal samples obtained from a neonatal hypoxic-ischemic encephalopathy (HIE) rat model. The aim was to identify crucial metabolites associated with the progression of HIE. Overall, our sensitive and reliable quantification method holds promise in elucidating the role of gut microbiome metabolites in the pathogenesis of various diseases.

Attempting to Create a Pathway to 15-Deacetylcalonectrin with Limited Accumulation in Cultures of Fusarium Tri3 Mutants: Insight into Trichothecene Biosynthesis Machinery.

The compound 15-deacetylcalonectrin (15-deCAL) is a common pathway intermediate in the biosynthesis of Fusarium trichothecenes. This tricyclic intermediate is metabolized to calonectrin (CAL) by trichothecene 15-O-acetyltransferase encoded by Tri3. Unlike other trichothecene pathway Tri gene mutants, the Δtri3 mutant produces lower amounts of the knocked-out enzyme's substrate 15-deCAL, and instead, accumulates higher quantities of earlier bicyclic intermediate and shunt metabolites. Furthermore, evolutionary studies suggest that Tri3 may play a role in shaping the chemotypes of trichothecene-producing Fusarium strains. To better understand the functional role of Tri3p in biosynthesis and evolution, we aimed to develop a method to produce 15-deCAL by using transgenic Fusarium graminearum strains derived from a trichothecene overproducer. Unfortunately, introducing mutant Tri3, encoding a catalytically impaired but structurally intact acetylase, did not improve the low 15-deCAL production level of the ΔFgtri3 deletion strain, and the bicyclic products continued to accumulate as the major metabolites of the active-site mutant. These findings are discussed in light of the enzyme responsible for 15-deCAL production in trichothecene biosynthesis machinery. To efficiently produce 15-deCAL, we tested an alternative strategy of using a CAL-overproducing transformant. By feeding a crude CAL extract to a Fusarium commune strain that was isolated in this study and capable of specifically deacetylating C-15 acetyl, 15-deCAL was efficiently recovered. The substrate produced in this manner can be used for kinetic investigations of this enzyme and its possible role in chemotype diversification.

A UPLC-MS/MS method for quantification of metabolites in the ethylene biosynthesis pathway and its biological validation in Arabidopsis.

The plant hormone ethylene is of vital importance in the regulation of plant development and stress responses. Recent studies revealed that 1-aminocyclopropane-1-carboxylic acid (ACC) plays a role beyond its function as an ethylene precursor. However, the absence of reliable methods to quantify ACC and its conjugates malonyl-ACC (MACC), glutamyl-ACC (GACC), and jasmonyl-ACC (JA-ACC) hinders related research. Combining synthetic and analytical chemistry, we present the first, validated methodology to rapidly extract and quantify ACC and its conjugates using ultra-high-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS). Its relevance was confirmed by application to Arabidopsis mutants with altered ACC metabolism and wild-type plants under stress. Pharmacological and genetic suppression of ACC synthesis resulted in decreased ACC and MACC content, whereas induction led to elevated levels. Salt, wounding, and submergence stress enhanced ACC and MACC production. GACC and JA-ACC were undetectable in vivo; however, GACC was identified in vitro, underscoring the broad applicability of the method. This method provides an efficient tool to study individual functions of ACC and its conjugates, paving the road toward exploration of novel avenues in ACC and ethylene metabolism, and revisiting ethylene literature in view of the recent discovery of an ethylene-independent role of ACC.

The repertoire and levels of secondary metabolites in microbial cocultures depend on the inoculation ratio: a case study involving Aspergillus terreus and Streptomyces rimosus

ObjectiveThe aim of this study was to determine the influence of the inoculation volume ratio on the production of secondary metabolites in submerged cocultures of Aspergillus terreus and Streptomyces rimosus.ResultsThe shake flask cocultures were initiated by using 23 inoculum variants that included different volumes of A. terreus and S. rimosus precultures. In addition, the axenic controls were propagated in parallel with the cocultures. UPLC‒MS analysis revealed the presence of 15 secondary metabolites, 12 of which were found both in the “A. terreus vs. S. rimosus” cocultures and axenic cultures of either A. terreus or S. rimosus. The production of the remaining 3 molecules was recorded solely in the cocultures. The repertoire and quantity of secondary metabolites were evidently dependent on the inoculation ratio. It was also noted that detecting filamentous structures resembling typical morphological forms of a given species was insufficient to predict the presence of a given metabolite.ConclusionsThe modification of the inoculation ratio is an effective strategy for awakening and enhancing the production of secondary metabolites that are not biosynthesized under axenic conditions.

Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal.

Magnetic Resonance Spectroscopy (MRS) is an important technique for biomedical detection. However, it is challenging to accurately quantify metabolites with proton MRS due to serious overlaps of metabolite signals, imperfections because of non-ideal acquisition conditions, and interference with strong background signals mainly from macromolecules. The most popular method, LCModel, adopts complicated non-linear least square to quantify metabolites and addresses these problems by designing empirical priors such as basis-sets, imperfection factors. However, when the signal-to-noise ratio of MRS signal is low, the solution may have large deviation. Linear Least Squares (LLS) is integrated with deep learning to reduce the complexity of solving this overall quantification. First, a neural network is designed to explicitly predict the imperfection factors and the overall signal from macromolecules. Then, metabolite quantification is solved analytically with the introduced LLS. In our Quantification Network (QNet), LLS takes part in the backpropagation of network training, which allows the feedback of the quantification error into metabolite spectrum estimation. This scheme greatly improves the generalization to metabolite concentrations unseen in training compared to the end-to-end deep learning method. Experiments show that compared with LCModel, the proposed QNet, has smaller quantification errors for simulated data, and presents more stable quantification for 20 healthy in vivo data at a wide range of signal-to-noise ratio. QNet also outperforms other end-to-end deep learning methods. This study provides an intelligent, reliable and robust MRS quantification. QNet is the first LLS quantification aided by deep learning.

Application of chitosan nanopriming on plant growth and secondary metabolites of Pancratium maritimum L

BackgroundNanotechnology has demonstrated its vital significance in all aspects of daily life. Our research was conducted to estimate the potential of primed seed with chitosan nanoparticles in seed growth and yield by inducing plant secondary metabolism of Pancratium maritimum L. one of the important medicinal plants. Petri dish and pot experiments were carried out. Seeds of Pancratium maritimum L. were soaked in Nano solution (0.1, 0.5, 1 mg/ ml) for 4, 8, 12 h. Germination parameters (germination percentage, germination velocity, speed of germination, germination energy, germination index, mean germination time, seedling shoot and root length, shoot root ratio, seedling vigor index, plant biomass and water content), alkaloids and antioxidant activity of Pancratium maritimum L. were recorded and compared between coated and uncoated seeds.ResultsOur results exhibited that chitosan nanopriming had a positive effect on some growth parameters, while it fluctuated on others. However, the data showed that most germination parameters were significantly affected in coated seeds compared to uncoated seeds. GC-MS analysis of Pancratium maritimum L. with different nanopriming treatments showed that the quantity of alkaloids decreased, but the amount of pancratistatin, lycorine and antioxidant content increased compared with the control.ConclusionsApplying chitosan nanoparticles in priming seeds might be a simple and effective way to improve the quantity of secondary metabolites of Pancratium maritimum L. valuable medicinal plant.

Bioprocess monitoring applications of an innovative ATR-FTIR spectroscopy platform.

Pharmaceutical manufacturing is reliant upon bioprocessing approaches to generate the range of therapeutic products that are available today. The high cost of production, susceptibility to process failure, and requirement to achieve consistent, high-quality product means that process monitoring is paramount during manufacturing. Process analytic technologies (PAT) are key to ensuring high quality product is produced at all stages of development. Spectroscopy-based technologies are well suited as PAT approaches as they are non-destructive and require minimum sample preparation. This study explored the use of a novel attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy platform, which utilises disposable internal reflection elements (IREs), as a method of upstream bioprocess monitoring. The platform was used to characterise organism health and to quantify cellular metabolites in growth media using quantification models to predict glucose and lactic acid levels both singularly and combined. Separation of the healthy and nutrient deficient cells within PC space was clearly apparent, indicating this technique could be used to characterise these classes. For the metabolite quantification, the binary models yielded R 2 values of 0.969 for glucose, 0.976 for lactic acid. When quantifying the metabolites in tandem using a multi-output partial least squares model, the corresponding R 2 value was 0.980. This initial study highlights the suitability of the platform for bioprocess monitoring and paves the way for future in-line developments.

A birdcage transmit, 24-channel conformal receive array coil for sensitive 31P magnetic resonance spectroscopic imaging of the human brain at 7 T.

Phosphorus (31P) magnetic resonance spectroscopic imaging (MRSI) can serve as a critical tool for more direct quantification of brain energy metabolism, tissue pH, and cell membrane turnover. However, the low concentration of 31P metabolites in biological tissue may result in low signal-to-noise ratio (SNR) in 31P MRS images. In this work, we present an innovative design and construction of a 31P radiofrequency coil for whole-brain MRSI at 7 T. Our coil builds on current literature in ultra-high field 31P coil design and offers complete coverage of the brain, including the cerebellum and brainstem. The coil consists of an actively detunable volume transmit (Tx) resonator and a custom 24-channel receive (Rx) array. The volume Tx resonator is a 16-rung high-pass birdcage coil. The Rx coil consists of a 24-element phased array composed of catered loop shapes and sizes built onto a custom, close-fitting, head-shaped housing. The Rx array was designed to provide complete coverage of the head, while minimizing mutual coupling. The Rx configuration had a mean reflection coefficient better than -20 decibels (dB) when the coil was loaded with a human head. The mean mutual coupling ( ) among Rx elements, when loaded with a human head, was -16 dB. In phantom imaging, the phased array produced a central SNR that was 4.4-fold higher than the corresponding central SNR when operating the 31P birdcage as a transceiver. The peripheral SNR was 12-fold higher when applying the optimized phased array. In vivo 3D 31P MRSI experiments produced high-quality spectra in the cerebrum gray and white matter, as well as in the cerebellum. Characteristic phosphorus metabolites related to adenosine triphosphate metabolism and cell membrane turnover were distinguishable across all brain regions. In summary, our results demonstrate the potential of our novel coil for accurate, whole-brain 31P metabolite quantification.

Single versus multiple metabolite quantification of in vitro starch digestion: A comparison for the case of pulse cotyledon cells

Different quantification methods for in vitro amylolysis were compared for individual chickpea and lentil cotyledon cells (ICC) as a relevant case study. For the first time, much-applied spectrophotometric methods relying on the quantification of certain functional groups (i.e., DNS, GOPOD) were compared to chromatographic quantification of starch metabolites (HPLC-ELSD). The estimated rate constant and linked initial rates of amylolysis were highly correlated for DNS, GOPOD, and HPLC-ELSD. However, absolute amylolysis levels depended on the applied method and sample-specific metabolite formation patterns. Multiresponse modelling was employed to further investigate HPLC-ELSD metabolite formation patterns. This delivered insight into the relative importance of different amylolysis reactions during in vitro digestion of pulse ICC, proving that maltotriose and maltose formation determined the overall amylolysis rate in this case. Multiresponse reaction rate constants of maltotriose and maltose formation were highly correlated to single response amylolysis rate constants (and initial rates) obtained for all three quantification methods.

Differential impact of sex on regulation of skeletal muscle mitochondrial function and protein homeostasis by hypoxia-inducible factor-1α in normoxia.

Hypoxia-inducible factor (HIF)-1α is continuously synthesized and degraded in normoxia. During hypoxia, HIF1α stabilization restricts cellular/mitochondrial oxygen utilization. Cellular stressors can stabilize HIF1α even during normoxia. However, less is known about HIF1α function(s) and sex-specific effects during normoxia in the basal state. Since skeletal muscle is the largest protein store in mammals and protein homeostasis has high energy demands, we determined HIF1α function at baseline during normoxia in skeletal muscle. Untargeted multiomics data analyses were followed by experimental validation in differentiated murine myotubes with loss/gain of function and skeletal muscle from mice without/with post-natal muscle-specific Hif1a deletion (Hif1amsd). Mitochondrial oxygen consumption studies using substrate, uncoupler, inhibitor, titration protocols; targeted metabolite quantification by gas chromatography-mass spectrometry; and post-mitotic senescence markers using biochemical assays were performed. Multiomics analyses showed enrichment in mitochondrial and cell cycle regulatory pathways in Hif1a deleted cells/tissue. Experimentally, mitochondrial oxidative functions and ATP content were higher with less mitochondrial free radical generation with Hif1a deletion. Deletion of Hif1a also resulted in higher concentrations of TCA cycle intermediates and HIF2α proteins in myotubes. Overall responses to Hif1amsd were similar in male and female mice, but changes in complex II function, maximum respiration, Sirt3 and HIF1β protein expression and muscle fibre diameter were sex-dependent. Adaptive responses to hypoxia are mediated by stabilization of constantly synthesized HIF1α. Despite rapid degradation, the presence of HIF1α during normoxia contributes to lower mitochondrial oxidative efficiency and greater post-mitotic senescence in skeletal muscle. In vivo responses to HIF1α in skeletal muscle were differentially impacted by sex. KEY POINTS: Hypoxia-inducible factor -1α (HIF1α), a critical transcription factor, undergoes continuous synthesis and proteolysis, enabling rapid adaptive responses to hypoxia by reducing mitochondrial oxygen consumption. In mammals, skeletal muscle is the largest protein store which is determined by a balance between protein synthesis and breakdown and is sensitive to mitochondrial oxidative function. To investigate the functional consequences of transient HIF1α expression during normoxia in the basal state, myotubes and skeletal muscle from male and female mice with HIF1α knockout were studied using complementary multiomics, biochemical and metabolite assays. HIF1α knockout altered the electron transport chain, mitochondrial oxidative function, signalling molecules for protein homeostasis, and post-mitotic senescence markers, some of which were differentially impacted by sex. The cost of rapid adaptive responses mediated by HIF1α is lower mitochondrial oxidative efficiency and post-mitotic senescence during normoxia.

PFAS and their association with the increased risk of cardiovascular disease in postmenopausal women.

Cardiovascular diseases (CVDs) are one of the major causes of death globally. In addition to traditional risk factors such as unhealthy lifestyles (smoking, obesity, sedentary) and genetics, common environmental exposures, including persistent environmental contaminants, may also influence CVD risk. Per- and polyfluoroalkyl substances (PFASs) are a class of highly fluorinated chemicals used in household consumer and industrial products known to persist in our environment for years, causing health concerns that are now linked to endocrine disruptions and related outcomes in women, including interference of the cardiovascular and reproductive systems. In postmenopausal women, higher levels of PFAS are observed than in premenopausal women due to the cessation of menstruation, which is crucial for PFAS excretion. Because of these findings, we explored the association between perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), and perfluorobutanesulfonic acid in postmenopausal women from our previously established CVD study. We used liquid chromatography with tandem mass spectrometry, supported by machine learning approaches, and the detection and quantification of serum metabolites and proteins. Here, we show that PFOS can be a good predictor of coronary artery disease, whereas PFOA can be an intermediate predictor of coronary microvascular disease. We also found that the PFAS levels in our study are significantly associated with inflammation-related proteins. Our findings may provide new insight into the potential mechanisms underlying the PFAS-induced risk of CVDs in this population. This study shows that exposure to PFOA and PFOS is associated with an increased risk of cardiovascular disease in postmenopausal women. PFOS and PFOA levels correlate with amino acids and proteins related to inflammation. These circulating biomarkers contribute to the etiology of CVD and potentially implicate a mechanistic relationship between PFAS exposure and increased risk of cardiovascular events in this population.

Fermentation Analytical Technology (FAT): Monitoring industrial E. coli fermentations using absolute quantitative 1H NMR spectroscopy

BackgroundTo perform fast, reproducible, and absolute quantitative measurements in an automated manner has become of paramount importance when monitoring industrial processes, including fermentations. Due to its numerous advantages – including its inherent quantitative nature – Proton Nuclear Magnetic Resonance (1H NMR) spectroscopy provides an ideal tool for the time-resolved monitoring of fermentations. However, analytical conditions, including non-automated sample preparation and long relaxation times (T1) of some metabolites, can significantly lengthen the experimental time and make implementation in an industrial set up unfeasible. ResultsWe present a high throughput method based on Standard Operating Procedures (SOPs) and 1H NMR, which lays the foundation for what we call Fermentation Analytical Technology (FAT). Our method was developed for the accurate absolute quantification of metabolites produced during Escherichia coli industrial fermentations. The method includes: (1) a stopped flow system for non-invasive sample collection followed by sample quenching, (2) automatic robot-assisted sample preparation, (3) fast 1H NMR measurements, (4) metabolites quantification using multivariate curve resolution (MCR), and (5) metabolites absolute quantitation using a novel correction factor (k) to compensate for the short recycle delay (D1) employed in the 1H NMR measurements. The quantification performance was tested using two sample types: buffer solutions of chemical standards and real fermentation samples. Five metabolites – glucose, acetate, alanine, phenylalanine and betaine – were quantified. Absolute quantitation ranged between 0.64 and 3.40 mM in pure buffer, and 0.71–7.76 mM in real samples. SignificanceThe proposed method is generic and can be straight forward implemented to other types of fermentations, such as lactic acid, ethanol and acetic acid fermentations. It provides a high throughput automated solution for monitoring fermentation processes and for quality control through absolute quantification of key metabolites in fermentation broth. It can be easily implemented in an at-line industrial setting, facilitating the optimization of the manufacturing process towards higher yields and more efficient and sustainable use of resources.

Ectomycorrhizal fungi alter soil food webs and the functional potential of bacterial communities.

Most of Earth's trees rely on critical soil nutrients that ectomycorrhizal fungi (EcMF) liberate and provide, and all of Earth's land plants associate with bacteria that help them survive in nature. Yet, our understanding of how the presence of EcMF modifies soil bacterial communities, soil food webs, and root chemistry requires direct experimental evidence to comprehend the effects that EcMF may generate in the belowground plant microbiome. To this end, we grew Pinus muricata plants in soils that were either inoculated with EcMF and native forest bacterial communities or only native bacterial communities. We then profiled the soil bacterial communities, applied metabolomics and lipidomics, and linked omics data sets to understand how the presence of EcMF modifies belowground biogeochemistry, bacterial community structure, and their functional potential. We found that the presence of EcMF (i) enriches soil bacteria linked to enhanced plant growth in nature, (ii) alters the quantity and composition of lipid and non-lipid soil metabolites, and (iii) modifies plant root chemistry toward pathogen suppression, enzymatic conservation, and reactive oxygen species scavenging. Using this multi-omic approach, we therefore show that this widespread fungal symbiosis may be a common factor for structuring soil food webs.IMPORTANCEUnderstanding how soil microbes interact with one another and their host plant will help us combat the negative effects that climate change has on terrestrial ecosystems. Unfortunately, we lack a clear understanding of how the presence of ectomycorrhizal fungi (EcMF)-one of the most dominant soil microbial groups on Earth-shapes belowground organic resources and the composition of bacterial communities. To address this knowledge gap, we profiled lipid and non-lipid metabolites in soils and plant roots, characterized soil bacterial communities, and compared soils amended either with or without EcMF. Our results show that the presence of EcMF changes soil organic resource availability, impacts the proliferation of different bacterial communities (in terms of both type and potential function), and primes plant root chemistry for pathogen suppression and energy conservation. Our findings therefore provide much-needed insight into how two of the most dominant soil microbial groups interact with one another and with their host plant.