Highly reliable LC-MS lipidomics database for efficient human plasma profiling based on NIST SRM 1950.

Liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS)-based methods have become the gold standard methodology for the comprehensive profiling of the human plasma lipidome. However, both the complexity of lipid chemistry and LC-HRMS-associated data pose challenges to the characterization of this biological matrix. In accordance with the current consensus of quality requirements for LC-HRMS lipidomics data, we aimed to characterize the NIST® Standard Reference Material for Human Plasma (SRM 1950) using an LC-ESI(+/-)-MS method compatible with high-throughput lipidome profiling. We generated a highly curated lipid database with increased coverage, quality, and consistency, including additional quality assurance procedures involving adduct formation, within-method m/z evaluation, retention behavior of species within lipid chain isomers, and expert-driven resolution of isomeric and isobaric interferences. As a proof-of-concept, we showed the utility of our in-house LC-MS lipidomic database -consisting of 592 lipid entries- for the fast, comprehensive, and reliable lipidomic profiling of the human plasma from healthy human volunteers. We are confident that the implementation of this robust resource and methodology will have a significant impact by reducing data redundancy and the current delays and bottlenecks in untargeted plasma lipidomic studies.

A metagenomic approach to demystify the anaerobic digestion black box and achieve higher biogas yield: a review.

The increasing reliance on fossil fuels and the growing accumulation of organic waste necessitates the exploration of sustainable energy alternatives. Anaerobic digestion (AD) presents one such solution by utilizing secondary biomass to produce biogas while reducing greenhouse gas emissions. Given the crucial role of microbial activity in anaerobic digestion, a deeper understanding of the microbial community is essential for optimizing biogas production. While metagenomics has emerged as a valuable tool for unravelling microbial composition and providing insights into the functional potential in biodigestion, it falls short of interpreting the functional and metabolic interactions, limiting a comprehensive understanding of individual roles in the community. This emphasizes the significance of expanding the scope of metagenomics through innovative tools that highlight the often-overlooked, yet crucial, role of microbiota in biomass digestion. These tools can more accurately elucidate microbial ecological fitness, shared metabolic pathways, and interspecies interactions. By addressing current limitations and integrating metagenomics with other omics approaches, more accurate predictive techniques can be developed, facilitating informed decision-making to optimize AD processes and enhance biogas yields, thereby contributing to a more sustainable future.

Hybrid Quadrupole Mass Filter-Radial Ejection Linear Ion Trap and Intelligent Data Acquisition Enable Highly Multiplex Targeted Proteomics.

Targeted mass spectrometry (MS) methods are powerful tools for the selective and sensitive analysis of peptides identified in global discovery experiments. Selected reaction monitoring (SRM) is the most widely accepted clinical MS method due to its reliability and performance. However, SRM and parallel reaction monitoring (PRM) are limited in throughput and are typically used for assays with around 100 targets or fewer. Here we introduce a new MS platform featuring a quadrupole mass filter, collision cell, and linear ion trap architecture, capable of targeting 5000-8000 peptides per hour. This high multiplexing capability is facilitated by acquisition rates of 70-100 Hz and real-time chromatogram alignment. We present a Skyline external software tool for building targeted methods based on data-independent acquisition chromatogram libraries or unscheduled analysis of heavy labeled standards. Our platform demonstrates ∼10× lower limits of quantitation (LOQs) than traditional SRM on a triple quadrupole instrument for highly multiplexed assays, due to parallel product ion accumulation. Finally, we explore how analytical figures of merit vary with method duration and the number of analytes, providing insights into optimizing assay performance.

Illness perceptions in cochlear implant users - a longitudinal study.

PURPOSE: 'Illness perceptions' refers to the thoughts and ideas a person has about an illness. According to Leventhal's Self-Regulatory Model (SRM), changing the threatening illness perceptions of cochlear implant (CI) recipients can be a further step in optimizing hearing outcomes with the CI. The aims of the present study were to assess users' illness perceptions and to determine whether perceptions change during six months of CI rehabilitation. METHODS: One hundred and thirty-eight participants completed the Brief Illness Perception Questionnaire (Brief IPQ), assessing their illness perceptions on nine scales. Data were collected at a German CI center at first CI fitting and six-month follow-up. After first fitting, participants underwent intensive rehabilitation including auditory training, medical, audiological and psychological treatments. RESULTS: At both assessments, participants tended to view their hearing impairment as a severe threat. On the Brief IPQ, the 'consequences' assessment improved during CI rehabilitation, which can be explained by the CI-induced hearing improvement. However, 'understanding' and 'identity' assessments worsened. This could be because CI recipients only come to realize the full complexity of their hearing impairment during rehabilitation. The other scales and the total score remained unaffected. CONCLUSIONS: Current practice in CI rehabilitation seems to be insufficient to improve threatening illness perceptions (except for perceived consequences). This may be because standard information often fails to reach the patients. The development and empirical validation of an intervention program to address individual illness perceptions in CI recipients could be helpful in this context. Further research will be needed to confirm the results.

Multiplexed quantitative proteomics in prostate cancer biomarker development.

Prostate cancer (PCa) is the most common non-skin cancer among men in the United States. However, the widely used protein biomarker in PCa, prostate-specific antigen (PSA), while useful for initial detection, its use alone cannot detect aggressive PCa and can lead to overtreatment. This chapter provides an overview of PCa protein biomarker development. It reviews the state-of-the-art liquid chromatography-mass spectrometry-based proteomics technologies for PCa biomarker development, such as enhancing the detection sensitivity of low-abundance proteins through antibody-based or antibody-independent protein/peptide enrichment, enriching post-translational modifications such as glycosylation as well as information-rich extracellular vesicles, and increasing accuracy and throughput using advanced data acquisition methodologies. This chapter also summarizes recent PCa biomarker validation studies that applied those techniques in diverse specimen types, including cell lines, tissues, proximal fluids, urine, and blood, developing novel protein biomarkers for various clinical applications, including early detection and diagnosis, prognosis, and therapeutic intervention of PCa.

Polytomous Effectiveness Indicators in Complex Problem-Solving Tasks and Their Applications in Developing Measurement Model.

Recent years have witnessed the emergence of measurement models for analyzing action sequences in computer-based problem-solving interactive tasks. The cutting-edge psychometrics process models require pre-specification of the effectiveness of state transitions often simplifying them into dichotomous indicators. However, the dichotomous effectiveness becomes impractical when dealing with complex tasks that involve multiple optimal paths and numerous state transitions. Building on the concept of problem-solving, we introduce polytomous indicators to assess the effectiveness of problem states d s and state-to-state transitions Δ d s → s ' . The three-step evaluation method for these two types of indicators is proposed and illustrated across two real problem-solving tasks. We further present a novel psychometrics process model, the sequential response model with polytomous effectiveness indicators (SRM-PEI), which is tailored to encompass a broader range of problem-solving tasks. Monte Carlo simulations indicated that SRM-PEI performed well in the estimation of latent ability and transition tendency parameters across different conditions. Empirical studies conducted on two real tasks supported the better fit of SRM-PEI over previous models such as SRM and SRMM, providing rational and interpretable estimates of latent abilities and transition tendencies through effectiveness indicators. The paper concludes by outlining potential avenues for the further application and enhancement of polytomous effectiveness indicators and SRM-PEI.

The Behaviour of Stratified Fabrics of Aramid Fibres under Stabbing Conditions.

This paper presents research on several factors influencing the stabbing behaviour of stratified panels made of aramid fabric Twaron® SRM509 Teijin Aramid BV (Arnhem, The Netherlands). The inputs in the test campaign were the number of layers, the impact energy, and the sample size. Tests were performed on small samples (130 mm × 130 mm) on an Instron® CEAST 9350 drop-tower impact system (Norwood, MA, USA) and on larger samples (400 mm × 400 mm) using a test installation with the same values of the impact energy. Knife type S1 was used, with the geometry recommended in NIJ Standard 0115.00 Stab Resistance of Body Armor SEM, and macro photography investigations revealed the failure mechanisms of panel, layers and fibres. A very important conclusion of this study regarding the stabbing performance of fabric Twaron® SRM 509 in particular, but also in general for panels for body protection is that a research study could start on small size samples, with an accurately instrumented machine, in order to establish the influence of significant factors of stab resistance (energy level, number of layers in a panel, etc.), as these samples are less expensive and less time consuming, but the study should be continued to examine larger size samples. The obtained data are useful for the prototype.

SRRM2 splicing factor modulates cell fate in early development.

Embryo development is an orchestrated process that relies on tight regulation of gene expression to guide cell differentiation and fate decisions. The Srrm2 splicing factor has recently been implicated in developmental disorders and diseases, but its role in early mammalian development remains unexplored. Here, we show that Srrm2 dosage is critical for maintaining embryonic stem cell pluripotency and cell identity. Srrm2 heterozygosity promotes loss of stemness, characterised by the coexistence of cells expressing naive and formative pluripotency markers, together with extensive changes in gene expression, including genes regulated by serum-response transcription factor (SRF) and differentiation-related genes. Depletion of Srrm2 by RNA interference in embryonic stem cells shows that the earliest effects of Srrm2 heterozygosity are specific alternative splicing events on a small number of genes, followed by expression changes in metabolism and differentiation-related genes. Our findings unveil molecular and cellular roles of Srrm2 in stemness and lineage commitment, shedding light on the roles of splicing regulators in early embryogenesis, developmental diseases and tumorigenesis.

Effect of lesion dimension on survival in patients with T1a renal cell carcinoma who underwent deferred surgery.

BACKGROUND: Small renal masses (SRMs) have been shown to have low malignant potential. Active surveillance (AS), typically characterized by regular follow-up and delayed nephrectomy if necessary, is recommended as an option for frail patients with SRMs. Nevertheless, the impact of tumor size on survival in T1a RCC patients undergoing delayed nephrectomy for SRMs remains unclear. METHODS: Patients diagnosed with non-metastatic T1a RCC who underwent nephrectomy were identified from the Surveillance, Epidemiology, and End Results (SEER) database and divided into immediate (< 6 months) and delayed nephrectomy (≥ 6 months) groups based on the duration from diagnosis to nephrectomy. After propensity score matching (PSM), overall survival (OS) and cancer-specific survival (CSS) were estimated by K-M curves and compared with log-rank test. RESULTS: A total of 27,502 patients were enrolled, of whom 26,915 (97.9%) received immediate nephrectomy and 587 (2.1%) received delayed nephrectomy. After PSM, 1174 patients who underwent immediate nephrectomy and 587 patients who underwent delayed nephrectomy were included. With a median delay of 7 months, delayed nephrectomy resulted in non-inferior OS for RCC tumors sized 0.1-2.0 cm (HR = 1.12, p = 0.636). However, for RCC tumors sized 2.1-3.0 cm (HR = 1.60, p = 0.008) and 3.1-4.0 cm (HR = 1.89, p < 0.001), delayed nephrectomy showed inferior OS compared to immediate nephrectomy. Delayed nephrectomy did not result in significantly worse CSS than immediate nephrectomy in all tumor size subgroups (all p > 0.05), however this may be due to sample size limiting statistical power. CONCLUSION: Based on the SEER database, we found that with a median delay of 7 months, 2 cm may be an appropriate cut-off point of delayed nephrectomy for patients diagnosed with non-metastatic T1a RCC.

Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control.

This paper presents a cutting-edge Sustainable Power Management System for Light Electric Vehicles (LEVs) using a Hybrid Energy Storage Solution (HESS) integrated with Machine Learning (ML)-enhanced control. The system's central feature is its ability to harness renewable energy sources, such as Photovoltaic (PV) panels and supercapacitors, which overcome traditional battery-dependent constraints. The proposed control algorithm orchestrates power sharing among the battery, supercapacitor, and PV sources, optimizing the utilization of available renewable energy and ensuring stringent voltage regulation of the DC bus. Notably, the ML-based control ensures precise torque and speed regulation, resulting in significantly reduced torque ripple and transient response times. In practical terms, the system maintains the DC bus voltage within a mere 2.7% deviation from the nominal value under various operating conditions, a substantial improvement over existing systems. Furthermore, the supercapacitor excels at managing rapid variations in load power, while the battery adjusts smoothly to meet the demands. Simulation results confirm the system's robust performance. The HESS effectively maintains voltage stability, even under the most challenging conditions. Additionally, its torque response is exceptionally robust, with negligible steady-state torque ripple and fast transient response times. The system also handles speed reversal commands efficiently, a vital feature for real-world applications. By showcasing these capabilities, the paper lays the groundwork for a more sustainable and efficient future for LEVs, suggesting pathways for scalable and advanced electric mobility solutions.

A comprehensive UHPLC-MS/MS method for metabolomics profiling of signaling lipids: Markers of oxidative stress, immunity and inflammation.

Signaling lipids (SLs) play a crucial role in various signaling pathways, featuring diverse lipid backbone structures. Emerging evidence showing the biological significance and biomedical values of SLs has strongly spurred the advancement of analytical approaches aimed at profiling SLs. Nevertheless, the dramatic differences in endogenous abundances across lipid classes as well as multiple isomers within the same lipid class makes the development of a generic analytical method challenging. A better analytical method that combines comprehensive coverage and high sensitivity is needed to enable us to gain a deeper understanding of the biochemistry of these molecules in health and disease. In this study, we developed a fast and comprehensive targeted ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method for profiling SLs. The platform enables analyses of 260 metabolites covering oxylipins (isoprostanes, prostaglandins and other oxidized lipids), free fatty acids, lysophospholipids, sphingoid bases (C16, C18), platelet activating factors (C16, C18), endocannabinoids and bile acids. Various validation parameters including linearity, limit of detection, limit of quantification, extraction recovery, matrix effect, intra-day and inter-day precision were used to characterize this method. Metabolite quantitation was successfully achieved in both NIST Standard Reference Material for human plasma (NIST SRM 1950) and pooled human plasma, with 109 and 144 metabolites quantitated. The quantitation results in NIST SRM 1950 plasma demonstrated good correlations with certified or previously reported values in published literature. This study introduced quantitative data for 37 SLs for the first time. Metabolite concentrations measured in NIST SRM 1950 will serve as essential reference data for facilitating interlaboratory comparisons. The methodology established here will be the cornerstone for in-depth profiling of signaling lipids across diverse biological samples and contexts.

Prospective exploration of the role of combined internalizing symptoms in self-reported memory among older adults during the COVID-19 pandemic.

OBJECTIVES: A growing literature suggests depression and anxiety increase risk of cognitive decline. However, few studies have examined their combined effects on cognition, among older adults, especially during periods of high stress. METHOD: Based on a sample of community dwelling older adults (N = 576), we evaluated the effects of pre-pandemic anxiety and depressive symptoms, obtained in September 2018, to changes in self-reported memory (SRM) assessed 3 months into the COVID-19 pandemic. RESULTS: In separate models, we found participants with depression scores at least 1-SD above the mean and participants with anxiety scores at least 2-SD above the mean to report a significant decline in SRM. Moderation analyses revealed those with high depressive symptoms (at or above the mean) showed a decrease in SRM regardless of anxiety. The extent to which high pre-pandemic anxiety symptoms influenced SRM is dependent on whether pre-pandemic depression was at or above the mean. CONCLUSIONS: Pre-pandemic depression predicted a decline in SRM regardless of anxiety. Moderation analyses revealed that the extent to which anxiety symptoms influenced SRM was dependent on depression being at or above the mean. Those with high anxiety and depression are at highest risk of experiencing cognitive consequences related to stressful exposures like COVID-19.

System analysis of surface CD markers during the process of granulocytic differentiation.

Plasma membrane proteins with extracellular-exposed domains are responsible for transduction of extracellular signals into intracellular responses, and their accessibility to therapeutic molecules makes them attractive targets for drug development. In this work, using omics technologies and immunochemical methods, we have studied changes in the content of markers of clusters of differentiation (CD markers) of neutrophils (CD33, CD97, CD54, CD38, CD18, CD11b, CD44, and CD71) at the level of transcripts and proteins in NB4, HL-60 and K562 cell lines, induced by the treatment with all-trans-retinoic acid (ATRA). Transcriptomic analysis revealed the induction of CD38, CD54, CD11b, and CD18 markers as early as 3 h after the addition of the inducer in the ATRA-responsive cell lines HL-60 and NB4. After 24 h, a line-specific expression pattern of CD markers could be observed in all cell lines. Studies of changes in the content of CD antigens by means of flow cytometry and targeted mass spectrometry (MS) gave similar results. The proteomic profile of the surface markers (CD38, CD54, CD11b, and CD18), characteristic of the NB4 and HL-60 lines, reflects different molecular pathways for the implementation of ATRA-induced differentiation of leukemic cells into mature neutrophils.

A Swiss-Roll-Type Methanol Mini-Steam Reformer for Hydrogen Generation with High Efficiency and Long-Term Durability.

This paper proposes a Swiss-roll-type mini-reformer employing a copper-zinc catalyst for high-efficient SRM process. Although the commercially available copper-zinc catalysts commonly used in cylindrical-type reformers provide decent conversion rates in the short term, their long-term durability still requires improvement, mainly due to temperature variations in the reformer, catalyst loading, and thermal sintering issues. This Swiss-roll-shaped mini-reformer is designed to improve thermal energy preservation/temperature uniformity by using dual spiral channels to improve the long-term durability while maintaining methanol-reforming efficiency. It was fabricated on a copper plate that was 80 mm wide, 80 mm long, and 4 mm high with spiral channels that were 2 mm deep, 4 mm wide, and 350 mm long. To optimize the design and reformer operation, the catalyst porosity, gas hourly speed velocity (GHSV), operation temperature, and fuel feeding rate are investigated. Swiss-roll-type reformers may require higher driving pressures but can provide better thermal energy preservation and temperature uniformity, posing a higher conversion rate for the same amount of catalyst when compared with other geometries. By carefully adjusting the catalyst bed porosity, locations, and catalyst loading amount as well as other conditions, an optimized gas hourly space velocity (GHSV) can be obtained (14,580 mL/g·h) and lead to not only a high conversion rate (96%) and low carbon monoxide generation rate (0.98%) but also a better long-term durability (decay from 96% to 88.12% after 60 h operation time) for SRM processes. The decay rate, 0.13%/h, after 60 h of operation, is five-folds lower than that (0.67%/h, 0.134%/h) of a commercial cylindrical-type fixed-bed reactor with a commercial catalyst.

Characterization of recombinant photoconverting green fluorescent Akanes.

Akanes are fluorescent proteins that have several fluorescence maxima. In this report, Akane1 and Akane3 from Scleronephthya gracillima were selected, successfully overexpressed in Escherichia coli and purified by affinity chromatography. Fluorescence spectra of the recombinant Akanes matured in darkness, or ambient light were found to have several fluorescence peaks. SDS-PAGE analysis revealed that Akanes matured in ambient light have two fragments. MS/MS analysis of Akanes digested with trypsin showed that the cleavage site is the same as observed for the photoconvertible fluorescent protein Kaede. The differences between the calculated masses from the amino acid sequence of Akane1 and the measured masses of Akane1 fragments obtained under ambient light coincided with those of Kaede. In contrast, a mass difference between the measured N-terminal Akane3 fragment and the calculated mass indicated that Akane3 is modified in the N-terminal region. These results indicate that numerous peaks in the fluorescent spectra of Akanes partly arise from isoproteins of Akanes and photoconversion. Photoconversion of Akane1 caused a fluorescence change from green to red, which was also observed for Akane3; however, the fluorescent intensity decreased dramatically when compared with that of Akane3.

Development of a targeted hydrophilic interaction liquid chromatography-tandem mass spectrometry based lipidomics platform applied to a coronavirus disease severity study.

The importance of lipids seen in studies of metabolism, cancer, the recent COVID-19 pandemic and other diseases has brought the field of lipidomics to the forefront of clinical research. Quantitative and comprehensive analysis is required to understand biological interactions among lipid species. However, lipidomic analysis is often challenging due to the various compositional structures, diverse physicochemical properties, and wide dynamic range of concentrations of lipids in biological systems. To study the comprehensive lipidome, a hydrophilic interaction liquid chromatography-tandem mass spectrometry (HILIC-MS/MS)-based screening method with 1200 lipid features across 19 (sub)classes, including both nonpolar and polar lipids, has been developed. HILIC-MS/MS was selected due to its class separation property and fatty acyl chain level information. 3D models of class chromatographic retention behavior were established and evaluations of cross-class and within-class interferences were performed to avoid over-reporting these features. This targeted HILIC-MS/MS method was fully validated, with acceptable analytical parameters in terms of linearity, precision, reproducibility, and recovery. The accurate quantitation of 608 lipid species in the SRM 1950 NIST plasma was achieved using multi-internal standards per class and post-hoc correction, extending current databases by providing lipid concentrations resolved at fatty acyl chain level. The overall correlation coefficients (R2) of measured concentrations with values from literature range from 0.64 to 0.84. The applicability of the developed targeted lipidomics method was demonstrated by discovering 520 differential lipid features related to COVID-19 severity. This high coverage and targeted approach will aid in future investigations of the lipidome in various disease contexts.

A large-scale targeted proteomics of serum and tissue shows the utility of classifying high grade and low grade meningioma tumors.

BACKGROUND: Meningiomas are the most prevalent primary brain tumors. Due to their increasing burden on healthcare, meningiomas have become a pivot of translational research globally. Despite many studies in the field of discovery proteomics, the identification of grade-specific markers for meningioma is still a paradox and requires thorough investigation. The potential of the reported markers in different studies needs further verification in large and independent sample cohorts to identify the best set of markers with a better clinical perspective. METHODS: A total of 53 fresh frozen tumor tissue and 51 serum samples were acquired from meningioma patients respectively along with healthy controls, to validate the prospect of reported differentially expressed proteins and claimed markers of Meningioma mined from numerous manuscripts and knowledgebases. A small subset of Glioma/Glioblastoma samples were also included to investigate inter-tumor segregation. Furthermore, a simple Machine Learning (ML) based analysis was performed to evaluate the classification accuracy of the list of proteins. RESULTS: A list of 15 proteins from tissue and 12 proteins from serum were found to be the best segregator using a feature selection-based machine learning strategy with an accuracy of around 80% in predicting low grade (WHO grade I) and high grade (WHO grade II and WHO grade III) meningiomas. In addition, the discriminant analysis could also unveil the complexity of meningioma grading from a segregation pattern, which leads to the understanding of transition phases between the grades. CONCLUSIONS: The identified list of validated markers could play an instrumental role in the classification of meningioma as well as provide novel clinical perspectives in regard to prognosis and therapeutic targets.

Fast gas chromatography-tandem mass spectrometry under milder electron ionization conditions for the assay of vitamin D metabolites in human serum.

The proposed research was focused on the development of a gas chromatography-tandem mass spectrometry (GC-QqQ-MS) method under milder electron ionization (EI) conditions for the assay of vitamin D metabolites in human serum. Efficiency of three different silylation agents was evaluated for the conversion of vitamin D species into trimethylsilyl (TMS) derivatives, among which N-methyl-N-(trimethylsilyl)-trifluoroacetamide (MSTFA) proved to be the most effective. Indeed, the MSTFA reagent was able to convert in TMS ether even the 25-hydroxyl vitamin D derivative that, as known, possesses steric hindrance problems. The separation of vitamin D compounds was obtained in about 11.5 min using a narrow-bore column of dimensions 30 m × 0.25 mm ID × 0.10 µm df with a poly(5% diphenyl/95% dimethyl siloxane) stationary phase. The mass spectrometry ionization of the silylated derivatives was performed under milder EI conditions (20-eV energy) that, respect to common 70-eV energy, generated scan mass spectra with higher relative and absolute intensities of high-mass diagnostic ions, along with a reduced abundance of the low-mass. The signals of the ionized compounds were acquired in multi-reaction-monitoring (MRM) mode, thus enabling the obtainment of highly-sensitive and selective quantitative data. The developed method was validated in term of linearity, accuracy, limits of detection (LoD) and quantification (LoQ). In detail, regression coefficients of the calibration curves were between 0.9959 and 0.9999; LoDs ranged from 0.06 ng mL-1 to 0.73 ng mL-1 and LoQs from 0.16 ng mL-1 to 2.45 ng mL-1. With respect to accuracy, the serum SRM 972a certified reference material (Vitamin D metabolites in frozen human serum) (Levels 1-4) was analyzed.

Multistage Detection of Tetrodotoxin Traces in Diodon hystrix Collected in El Salvador.

This study describes a multistage methodology to detect minute amounts of tetrodotoxin in fishes, a plan that may be broadened to include other marine organisms. This methodology was applied to porcupinefish (Diodon hystrix) collected in Punta Chiquirín, El Salvador. A three-stage approach along with post-acquisition processing was employed, to wit: (a) Sample screening by selected reaction monitoring (HPLC-MS/MS-SRM) analyses to quickly identify possible toxin presence via a LC/MS/MS API 3200 system with a triple quadrupole; (b) HPLC-HRFTMS-full scan analyses using an ion trap-Orbitrap spectrometer combined with an MZmine 2-enhanced dereplication-like workflow to collect high-resolution mass spectra; and (c) HPLC-HRMS2 analyses. This is the first time tetrodotoxin has been reported in D. hystrix specimens collected in El Salvador.

Elucidating Fibroblast Growth Factor-Induced Kinome Dynamics Using Targeted Mass Spectrometry and Dynamic Modeling.

Fibroblast growth factors (FGFs) are paracrine or endocrine signaling proteins that, activated by their ligands, elicit a wide range of health and disease-related processes, such as cell proliferation and the epithelial-to-mesenchymal transition. The detailed molecular pathway dynamics that coordinate these responses have remained to be determined. To elucidate these, we stimulated MCF-7 breast cancer cells with either FGF2, FGF3, FGF4, FGF10, or FGF19. Following activation of the receptor, we quantified the kinase activity dynamics of 44 kinases using a targeted mass spectrometry assay. Our system-wide kinase activity data, supplemented with (phospho)proteomics data, reveal ligand-dependent distinct pathway dynamics, elucidate the involvement of not earlier reported kinases such as MARK, and revise some of the pathway effects on biological outcomes. In addition, logic-based dynamic modeling of the kinome dynamics further verifies the biological goodness-of-fit of the predicted models and reveals BRAF-driven activation upon FGF2 treatment and ARAF-driven activation upon FGF4 treatment.