Multiple Reaction Monitoring Analysis Research Articles

Proteomic analysis of plasma proteins from patients with cardiac rupture after acute myocardial infarction using TMT-based quantitative proteomics approach

BackgroundCardiac rupture (CR) is a rare but catastrophic mechanical complication of acute myocardial infarction (AMI) that seriously threatens human health. However, the reliable biomarkers for clinical diagnosis and the underlying signaling pathways insights of CR has yet to be elucidated.MethodsIn the present study, a quantitative approach with tandem mass tag (TMT) labeling and liquid chromatography–tandem mass spectrometry was used to characterize the differential protein expression profiles of patients with CR. Plasma samples were collected from patients with CR (n = 37), patients with AMI (n = 47), and healthy controls (n = 47). Candidate proteins were selected for validation by multiple reaction monitoring (MRM) and enzyme-linked immunosorbent assay (ELISA).ResultsIn total, 1208 proteins were quantified and 958 differentially expressed proteins (DEPs) were identified. The difference in the expression levels of the DEPs was more noticeable between the CR and Con groups than between the AMI and Con groups. Bioinformatics analysis showed most of the DEPs to be involved in numerous crucial biological processes and signaling pathways, such as RNA transport, ribosome, proteasome, and protein processing in the endoplasmic reticulum, as well as necroptosis and leukocyte transendothelial migration, which might play essential roles in the complex pathological processes associated with CR. MRM analysis confirmed the accuracy of the proteomic analysis results. Four proteins i.e., C-reactive protein (CRP), heat shock protein beta-1 (HSPB1), vinculin (VINC) and growth/differentiation factor 15 (GDF15), were further validated via ELISA. By receiver operating characteristic (ROC) analysis, combinations of these four proteins distinguished CR patients from AMI patients with a high area under the curve (AUC) value (0.895, 95% CI, 0.802–0.988, p < 0.001).ConclusionsOur study highlights the value of comprehensive proteomic characterization for identifying plasma proteome changes in patients with CR. This pilot study could serve as a valid foundation and initiation point for elucidation of the mechanisms of CR, which might aid in identifying effective diagnostic biomarkers in the future.

Determination of Four Chemical Components in Qishen Yiqi Dripping Pills by UPLC-MS/MS Method

Background In this study, the ultrahigh performance liquid chromatography mass spectrometry/mass spectrometry (UPLC-MS/MS) method was established to test the contents of protocatechuic acid (C7H6O4), salvianolic acid B (C36H30O16), rosmarinic acid (C19H14O3), and cryptotanshinone (C19H20O3) in Qishen Yiqi dripping pills. Materials and Methods Methanol was chosen as the solvent according to a certain material–liquid ratio as a solution, and the ultrasonic extraction method was used to prepare the sample solution. UPLC-MS/MS was used to complete a quantitative analysis of the contents of Qishen Yiqi dripping pills. Then, the chromatographic conditions were shown as follows. The stationary phase was Agilent ZORBAX SB-C18 (2.1 mm × 100 mm, 1.8 µm), and the mobile phase was acetonitrile–0.1% formic acid water. The column temperature was 40℃, the injection volume was 3 µL, and the flow rate was 0.2 mL⋅min–1. Finally, the determination method was built to test four chemical constituents under the multiple reaction monitoring (MRM) analysis mode. Results The retention times of protocatechuic acid, salvianolic acid B, rosmarinic acid, and cryptotanshinone were 1.26, 1.32, 1.35, and 6.76 min, respectively. In addition, the concentration ranges of four compounds were 0.25–2.0 µg⋅mL–1, 10.0–50.0 µg⋅mL–1, 5.0–50.0 µg⋅mL–1, and 1.0–10.0 µg⋅mL–1, respectively. And the standard curve showed a good linear relationship in a reasonable range ( r2 ≥ 0.9950). The average recovery rate was from 96% to 124%. The contents of protocatechuic acid, salvianolic acid B, rosmarinic acid, and cryptotanshinone were 0.12%–0.022%, 17%–8.4%, and 6.5%–8.6% in seven batches of Qishen Yiqi dripping pills, which were in the range of 23%–31%. The instrument ran stably, and the method had good accuracy and repeatability (relative standard deviation [RSD] ≤ 3%). Additionally, the test result was stable within 10 h. Conclusion The contents of the four constituents were different among different batches. The quantitative method was established to determine the four chemical components in Qishen Yiqi dripping pills, which could provide a reference for quality evaluation in vivo.

High-Throughput Comprehensive Quantitative LC-MS/MS Analysis of Common Drugs and Metabolites (62 Compounds) in Human Urine.

In recent years a multitude of LC-MS/MS assays have been widely reported in commercial and clinical literature demonstrating the simultaneous analyses of dozens of drugs of abuse in human samples. The utility of such assays is meant to supplant the indirect detection based on the classical spectral library approach. Direct and simultaneous analysis via LC-MS/MS technology is made possible by fast acquisition rates in multiple reaction monitoring, as well as sensitivity and high selectivity of the technology for each individual analyte in a complex mixture. Hence, unlike immunoassays, which are not well-suited for the analyses of mixtures, and which may also be prone to false positives from potential interferences, quantitative LC-MS/MS analyses are feasible for complex patient mixtures of drugs of abuse. We hereby present a robust clinical LC-MS/MS assay for the simultaneous and semi-quantitative analysis of up to 62 drugs of abuse in human urine, representing major classes that include opiates, benzodiazepines, amphetamines, etc. The assay utilizes dilute and shoot, whereby the sample is diluted ten times in internal standard reagent and thereafter submitted to the LC-MS instrument, i.e., reversed-phase liquid chromatography coupled to the electrospray ionization multiple reaction monitoring analysis, via the TSQ Endura triple-quadrupole instrument. The assay employs stable isotope-labeled internal standards with a linear response in the 30-300ng/mL range, effectively semi-quantitative, since this analytical range is well within typical immunoassay cutoffs for most drugs.

Food allergen detection by mass spectrometry: From common to novel protein ingredients.

Food allergens are molecules, mainly proteins, that trigger immune responses in susceptible individuals upon consumption even when they would otherwise be harmless. Symptoms of a food allergy can range from mild to acute; this last effect is a severe and potentially life-threatening reaction. The European Union (EU) has identified 14 common food allergens, but new allergens are likely to emerge with constantly changing food habits. Mass spectrometry (MS) is a promising alternative to traditional antibody-based assays for quantifying multiple allergenic proteins in complex matrices with high sensitivity and selectivity. Here, the main allergenic proteins and the advantages and drawbacks of some MS acquisition protocols, such as multiple reaction monitoring (MRM) and data-dependent analysis (DDA) for identifying and quantifying common allergenic proteins in processed foodstuffs are summarized. Sections dedicated to novel foods like microalgae and insects as new sources of allergenic proteins are included, emphasizing the significance of establishing stable marker peptides and validated methods using database searches. The discussion involves the in-silico digestion of allergenic proteins, providing insights into their potential impact on immunogenicity. Finally, case studies focussing on microalgae highlight the value of MS as an effective analytical tool for ensuring regulatory compliance throughout the food control chain.

Serum Protein Profiling Reveals a Decrease in Apolipoprotein A-IV During a Clinical Depressive Mood State.

Depressive mood is a major psychiatric symptom that causes serious disturbances in daily life. Unlike physical symptoms, psychiatric symptoms are more difficult to evaluate objectively. Therefore, we aimed to discover biomarkers that reflect changes in serum protein metabolism during a clinical depressive mood. Serum protein profiling was conducted in participants who were not experiencing a current depressive episode (healthy individuals and patients in remission). Serum proteins were identified and quantified using liquid chromatography-tandem mass spectrometry. Differentially expressed proteins with a p-value <0.05 were selected, and candidate biomarkers were verified using multiple reaction monitoring analysis for absolute quantification. Apolipoprotein A-IV levels were lower in the group with a current episode of depression than in the remission and healthy control groups. Further, fibronectin levels were also lower in the group with a current episode of depression than in the healthy control group but not in the remission group. We found that apolipoprotein A-IV-mediated inflammation is involved in clinical depressive moods, possibly by inducing neurological changes in the brain. Therefore, apolipoprotein A-IV and fibronectin levels may be explored as potentially novel biomarkers for detecting a current episode of depression.

Metabolomic characterization benefits the identification of acute lung injury in patients with type A acute aortic dissection.

Introduction: Acute aortic dissection (AAD) often leads to the development of acute lung injury (ALI). However, the early detection and diagnosis of AAD in patients with ALI pose significant challenges. The objective of this study is to investigate distinct metabolic alterations in the plasma samples of AAD patients with ALI, AAD patients without ALI, and healthy individuals. Method: Between September 2019 and September 2022, we retrospectively collected data from 228 AAD patients who were diagnosed with ALI through post-surgery chest X-ray and PaO2/FiO2 assessments. Univariate analysis was employed to identify pre-surgery risk factors for ALI. Additionally, we conducted high-throughput target metabolic analysis on 90 plasma samples, comprising 30 samples from AAD patients with ALI, 30 from patients with AAD only, and 30 from healthy controls. After LC-MS spectral processing and metabolite quantification, the recursive feature elimination with cross-validation (RFECV) analysis based on the random forest was used to select the optimal metabolites as a diagnostic panel for the detection of AAD patients with ALI. The support vector machines (SVM) machine learning model was further applied to validate the diagnostic accuracy of the established biomarker panel. Results: In the univariate analysis, preoperative β-HB and TNF-α exhibited a significant association with lung injury (OR = 0.906, 95% CI 0.852-0.965, p = 0.002; OR = 1.007, 95% CI 1.003-1.011, p < 0.0001). The multiple-reaction monitoring analysis of 417 common metabolites identified significant changes in 145 metabolites (fold change >1.2 or <0.833, p < 0.05) across the three groups. Multivariate statistical analysis revealed notable differences between AAD patients and healthy controls. When compared with the non-ALI group, AAD patients with ALI displayed remarkable upregulation in 19 metabolites and downregulation in 4 metabolites. Particularly, combining citric acid and glucuronic acid as a biomarker panel improved the classification performance for distinguishing between the ALI and non-ALI groups. Discussion: Differentially expressed metabolites in the ALI group were primarily involved in amino acids biosynthesis, carbohydrate metabolism (TCA cycle), arginine and proline metabolism, and glucagon signaling pathway. These findings demonstrate a great potential of the targeted metabolomic approach for screening, routine surveillance, and diagnosis of pulmonary injury in patients with AAD.

Validation of the Ultra-Performance Liquid Chromatography with Tandem Mass Spectrometry Method for Simultaneous Analysis of Eighteen Compounds in the Traditional Herbal Prescription, Sanjoin-Tang

Sanjoin-tang (SJIT) is an ancient oriental medicine prescription listed in the Jinguiyaolue that is mainly used for the treatment of primary insomnia. This study was conducted to develop and validate an ultra-performance liquid chromatography with tandem mass spectrometry (UPLC–MS/MS) simultaneous analysis method for the quality control of SJIT using 18 target compounds. The 18 analytes were separated on an Acquity UPLC BEH C18 column maintained at 45 °C using a mobile phase composed of distilled water and acetonitrile. The MS system was used to simultaneously detect all analytes using the multiple reaction monitoring (MRM) method of Xevo TQ-XS coupled with an electrospray ionization source. The concentrations of the 18 analytes investigated in the SJIT samples ranged from below the limit of detection to 9.553 mg/g. In conclusion, the validated UPLC–MS/MS MRM analysis method can be used to obtain basic data to establish chemical-nonclinical linkage efficacy and for the clinical research and quality evaluation of SJIT.

A retrospective screening method for carbamate toxicant exposure based on butyrylcholinesterase adducts in human plasma with ultra-high performance liquid chromatography–tandem mass spectrometry

Carbamate pesticides are extensively used in agriculture for their inhibition to acetylcholinesterase and damages to the insects’ neural systems. Because of their toxicity, human poisoning incidents caused by carbamate pesticide exposure have occurred from time to time. What’s more, some lethally toxic carbamate toxicants known as carbamate nerve agents (CMNAs) have been supplemented in Schedule 1 of the Annex on Chemicals in the Chemical Weapons Convention (CWC) by Organisation of the Prohibition of Chemical Weapons (OPCW) from 2020. And some other carbamates, like physostigmine, have been used in clinical treatment as anticholinergic drugs and their misuse may also cause damages to the body. Similar to organophosphorus toxicants, carbamate toxicants would react with butyrylcholinesterase (BChE) in plasma when entering the human body, resulting in the BChE adducts, based on which the exposure of carbamate toxicants could be detected retrospectively. In this study, methylcarbamyl nonapeptide and dimethylcarbamyl nonapeptide from pepsin digestion of BChE adducts were identified with ultra-high performance liquid chromatography–tandem mass spectrometry (UHPLC–MS/MS) in product ion scan mode. Carbofuran was chosen as the target to establish the detection method of carbamate toxicant exposure based on methylcarbamyl nonapeptide digested from methylcarbamyl BChE. Procainamide-gel affinity purification, pepsin digestion and UHPLC–MS/MS analysis in multiple reaction monitoring (MRM) mode were applied. Under the optimized conditions of sample preparation and UHPLC–MS/MS MRM analysis, the limits of detection (LODs) reached 10.0 ng/mL of plasma exposed to carbofuran with satisfactory specificity. The quantitation approach was established with d3-carbofuran-exposed plasma as the internal standard (IS) and the linearity range was 30.0–1.00 × 103 nmol/L (R2 >0.998) with the accuracy of 95.6%–107% and precision of ≤9% relative standard deviation (RSD). The applicability was also evaluated by N,N-dimethyl-carbamates with the LODs of 30.0 nmol/L for pirimicarb-exposed plasma based on dimethylcarbamyl nonapeptide. Because most of carbamate toxicants has methylcarbamyl or dimethylcarbamyl groups, this approach could be applied on the retrospective screening of carbamate toxicant exposure including CMNAs, carbamate pesticides or carbamate drugs. This study could provide an effective means in the fields of CWC verification, toxicological mechanism investigation and down-selection of potential treatment options.

Targeted MRM Quantification of Urinary Proteins in Chronic Kidney Disease Caused by Glomerulopathies.

Glomerulopathies with nephrotic syndrome that are resistant to therapy often progress to end-stage chronic kidney disease (CKD) and require timely and accurate diagnosis. Targeted quantitative urine proteome analysis by mass spectrometry (MS) with multiple-reaction monitoring (MRM) is a promising tool for early CKD diagnostics that could replace the invasive biopsy procedure. However, there are few studies regarding the development of highly multiplexed MRM assays for urine proteome analysis, and the two MRM assays for urine proteomics described so far demonstrate very low consistency. Thus, the further development of targeted urine proteome assays for CKD is actual task. Herein, a BAK270 MRM assay previously validated for blood plasma protein analysis was adapted for urine-targeted proteomics. Because proteinuria associated with renal impairment is usually associated with an increased diversity of plasma proteins being present in urine, the use of this panel was appropriate. Another advantage of the BAK270 MRM assay is that it includes 35 potential CKD markers described previously. Targeted LC-MRM MS analysis was performed for 69 urine samples from 46 CKD patients and 23 healthy controls, revealing 138 proteins that were found in ≥2/3 of the samples from at least one of the groups. The results obtained confirm 31 previously proposed CKD markers. Combination of MRM analysis with machine learning for data processing was performed. As a result, a highly accurate classifier was developed (AUC = 0.99) that enables distinguishing between mild and severe glomerulopathies based on the assessment of only three urine proteins (GPX3, PLMN, and A1AT or SHBG).

The use of data independent acquisition based proteomic analysis and machine learning to reveal potential biomarkers for autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurological developmental disorder in children, and is associated with social isolation and restricted interests. The etiology of this disorder is still unknown. There is neither any confirmed laboratory test nor any effective therapeutic strategy to diagnose or cure it. We performed data independent acquisition (DIA) and multiple reaction monitoring (MRM) analysis of plasma from children with ASD and controls. The result showed that 45 differentially expressed proteins (DEPs) were identified between autistic subjects and controls. Among these, only one DEP was down-regulated in ASD; other DEPs were up-regulated in ASD children's plasma. These proteins are found associated with complement and coagulation cascades, vitamin digestion and absorption, cholesterol metabolism, platelet degranulation, selenium micronutrient network, extracellular matrix organization and inflammatory pathway, which have been reported to be related to ASD. After MRM verification, five key proteins in complement pathway (PLG, SERPINC1, and A2M) and inflammatory pathway (CD5L, ATRN, SERPINC1, and A2M) were confirmed to be significantly up-regulated in ASD group. Through the screening of machine learning model and MRM verification, we found that two proteins (biotinidase and carbonic anhydrase 1) can be used as early diagnostic markers of ASD (AUC = 0.8, p = 0.0001). SignificanceASD is the fastest growing neurodevelopmental disorder in the world and has become a major public health problem worldwide. Its prevalence has been steadily increasing, with a global prevalence rate of 1%. Early diagnosis and intervention can achieve better prognosis. In this study, data independent acquisition (DIA) and multiple reaction monitoring (MRM) analysis was applied to analyze the plasma proteome of ASD patients (31 (±5) months old), and 378 proteins were quantified. 45 differentially expressed proteins (DEPs) were identified between the ASD group and the control group. They mainly were associated with platelet degranulation, ECM proteoglycar, complement and coagulation cascades, selenium micronutrient network, regulation of insulin-like growth factor (IGF) transport and uptake by insulin-like growth factor binding proteins (IGFBPs), cholesterol metabolism, vitamin metabolism, and inflammatory pathway. Through the integrated machine learning methods and the MRM verification of independent samples, it is considered that biotinidase and carbon anhydrase 1 have the potential to become biomarkers for the early diagnosis of ASD. These results complement proteomics database of the ASD patients, broaden our understanding of ASD, and provide a panel of biomarkers for the early diagnosis of ASD.

Proteomic profiling of urinary small extracellular vesicles in children with pneumonia: a pilot study.

Small extracellular vesicles (sEV) play a crucial role in immune responses to viral infection. However, the composition of sEV derived from children with viral pneumonia remains ill defined. First, we performed mass spectrometry-based label-free proteomic analysis of urinary sEV in 7 children with viral pneumonia, 4 children with Mycoplasma pneumoniae pneumonia and 20 healthy children. Then a total of 33 proteins were selected to validate by multiple reaction monitoring analysis in an independent cohort of 20 healthy children and 29 children with pneumonia. In the discovery phase, a total of 1621 proteins were identified, while 260 proteins have differential expression in children with viral pneumonia compared to healthy children. Biological pathways primarily associated with neutrophil degranulation, carbohydrate metabolism and endocytosis were enriched in children with viral pneumonia. Finally, the abundance of eight proteins was verified to be significantly higher in children with viral pneumonia than in healthy children. This pilot study with proteomic profiles of urinary sEV provided insights to the host response to viral pathogen exposure and potential diagnostic biomarkers for children with viral pneumonia, and served as the basis for understanding the fundamental biology of infection. There were significant differences in the proteomic features of urinary sEV between children with viral pneumonia and those with Mycoplasma pneumoniae pneumonia. Many viral infection-related proteins were identified in urinary sEV and overrepresented in children with viral pneumonia, which facilitates our understanding of the fundamental biology of viral infection. A total of eight proteins (ANPEP, ASAH1, COL11A1, EHD4, HEXB, LGALS3BP, SERPINA1 and SERPING1) were verified as potential biomarkers for the diagnosis of viral pneumonia in children.

Simultaneous Quantification of Apolipoprotein C-III O-Glycoforms by Protein-MRM.

Apolipoprotein C-III (APOC-III) regulates triglyceride levels, associated with a risk of cardiovascular disease. One gene generates several proteoforms, each with a different molecular mass and a unique function. Unlike peptide multiple reaction monitoring (MRM), protein-MRM without digestion is required to analyze clinically relevant individual proteoforms. We developed a protein-MRM method without digestion to individually quantify APOC-III proteoforms in human serum. We optimized the protein-MRM method following 60% acetonitrile extraction with C18 filtration. Bovine serum and myoglobin served as supporting cushions and the internal standard during sample preparation, respectively. Furthermore, we evaluated the LOD, lower limit of quantification, linearity, accuracy, and precision. Good correlation compared with turbidimetric immunoassay (TIA) and peptide-MRM was observed using 30 clinical sera. Individual APOC-III O-glycoforms were identified by top-down proteomics and simultaneously quantified using the protein-MRM method. The sum abundance of APOC-III proteoforms was significantly correlated with TIA and peptide-MRM. Our protein-MRM method provides an affordable and rapid quantification of potential disease-specific proteoforms. Precise quantification of each proteoform allows investigators to identify novel biological roles potentially related to cardiovascular disease or novel biomarkers. We expect our protein-oriented method to be more clinically useful than antibody-based immunoassays and peptide-oriented MRM analysis, especially for quantification of a biomarker proteoform with certain post-translational modifications.

Qualitative Profiling and Quantitative Analysis of Major Constituents in Jinmu-tang by UHPLC-Q-Orbitrap-MS and UPLC-TQ-MS/MS.

Jinmu-tang (JMT) is a traditional herbal medicine consisting of five herbal medicines: Poria cocos Wolf, Paeonia lactiflora Pallas, Zingiber officinale Roscoe, Atractylodes japonica Koidzumi, and Aconitum carmichaeli Debeaux. In this study, the JMT components were profiled using UHPLC-Q-Orbitrap-MS, and 23 compounds were identified and characterized. In addition, UPLC-TQ-MS/MS analysis was performed in the positive and negative ion modes of an electrospray ionization source for the simultaneous quantification of the identified compounds. The multiple reaction monitoring (MRM) method was established to increase the sensitivity of the quantitative analysis, and the method was verified through linearity, recovery, and precision. All analytes showed good linearity (R2 ≤ 0.9990). Moreover, the recovery and the relative standard deviation of precision were 86.19-114.62% and 0.20-8.00%, respectively. Using the established MRM analysis method, paeoniflorin was found to be the most abundant compound in JMT. In conclusion, these results provide information on the constituents of JMT and can be applied to quality control and evaluation.

Plasma proteomic signature predicts who will get persistent symptoms following SARS-CoV-2 infection.

BackgroundThe majority of those infected by ancestral Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) during the UK first wave (starting March 2020) did not require hospitalisation. Most had a short-lived mild or asymptomatic infection, while others had symptoms that persisted for weeks or months. We hypothesized that the plasma proteome at the time of first infection would reflect differences in the inflammatory response that linked to symptom severity and duration.MethodsWe performed a nested longitudinal case-control study and targeted analysis of the plasma proteome of 156 healthcare workers (HCW) with and without lab confirmed SARS-CoV-2 infection. Targeted proteomic multiple-reaction monitoring analysis of 91 pre-selected proteins was undertaken in uninfected healthcare workers at baseline, and in infected healthcare workers serially, from 1 week prior to 6 weeks after their first confirmed SARS-CoV-2 infection. Symptom severity and antibody responses were also tracked. Questionnaires at 6 and 12 months collected data on persistent symptoms.FindingsWithin this cohort (median age 39 years, interquartile range 30–47 years), 54 healthcare workers (44% male) had PCR or antibody confirmed infection, with the remaining 102 (38% male) serving as uninfected controls. Following the first confirmed SARS-CoV-2 infection, perturbation of the plasma proteome persisted for up to 6 weeks, tracking symptom severity and antibody responses. Differentially abundant proteins were mostly coordinated around lipid, atherosclerosis and cholesterol metabolism pathways, complement and coagulation cascades, autophagy, and lysosomal function. The proteomic profile at the time of seroconversion associated with persistent symptoms out to 12 months. Data are available via ProteomeXchange with identifier PXD036590.InterpretationOur findings show that non-severe SARS-CoV-2 infection perturbs the plasma proteome for at least 6 weeks. The plasma proteomic signature at the time of seroconversion has the potential to identify which individuals are more likely to suffer from persistent symptoms related to SARS-CoV-2 infection.Funding informationThe COVIDsortium is supported by funding donated by individuals, charitable Trusts, and corporations including Goldman Sachs, Citadel and Citadel Securities, The Guy Foundation, GW Pharmaceuticals, Kusuma Trust, and Jagclif Charitable Trust, and enabled by Barts Charity with support from University College London Hospitals (UCLH) Charity. This work was additionally supported by the Translational Mass Spectrometry Research Group and the Biomedical Research Center (BRC) at Great Ormond Street Hospital.

Preparation, Characterization and In Vitro Stability of a Novel ACE-Inhibitory Peptide from Soybean Protein.

A soy protein isolate was hydrolyzed with Alcalase®, Flavourzyme® and their combination, and the resulting hydrolysates (A, F and A + F) were ultrafiltered and analyzed through SDS-PAGE. Fractions with MW < 1 kDa were investigated for their ACE-inhibitory activity, and the most active one (A < 1 kDa) was purified by semi-preparative RP-HPLC, affording three further subfractions. NMR analysis and Edman degradation of the most active subfraction (A1) enabled the identification of four putative sequences (ALKPDNR, VVPD, NDRP and NDTP), which were prepared by solid-phase synthesis. The comparison of their ACE-inhibitory activities suggested that the novel peptide NDRP might be the main agent responsible for A1 fraction ACE inhibition (ACE inhibition = 87.75 ± 0.61%; IC50 = 148.28 ± 9.83 μg mL−1). NDRP acts as a non-competitive inhibitor and is stable towards gastrointestinal simulated digestion. The Multiple Reaction Monitoring (MRM) analysis confirmed the presence of NDRP in A < 1 kDa.

Combination treatment with hENT1 and miR-143 reverses gemcitabine resistance in triple-negative breast cancer

BackgroundTriple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer and is susceptible to develop gemcitabine (GEM) resistance. Decreased expression of human equilibrative nucleoside transporter 1 (hENT1) accompanied by compensatory increase of glycolysis is strongly associated with GEM resistance in TNBC. In this study, we investigated the treatment feasibility of combined hENT1 upregulation and miR-143-mediated inhibition of glycolysis for reversing GEM resistance in TNBC.MethodsExperiments were performed in vitro and in vivo to compare the efficacy of GEM therapies. In this study, we established stable drug-resistant cell line, GEM-R cells, from parental cells (MDA-MB-231) through exposure to GEM following a stepwise incremental dosing strategy. Then GEM-R cells were transfected by lentiviral plasmids and GEM-R cells overexpressing hENT1 (GEM-R-hENT1) were established. The viability and apoptosis of wild-type (MDA-MB-231), GEM-R, and GEM-R-hENT1 cells treated with GEM or GEM + miR-143 were analyzed by CCK8 assay and flow cytometry. The RNA expression and protein expression were measured by RT-PCR and western blotting respectively. GEM uptake was determined by multiple reaction monitoring (MRM) analysis. Glycolysis was measured by glucose assay and 18F-FDG uptake. The antitumor effect was assessed in vivo in a tumor xenograft model by evaluating toxicity, tumor volume, and maximum standardized uptake value in 18F-FDG PET. Immunohistochemistry and fluorescence photography were taken in tumor samples. Pairwise comparisons were performed using Student’s t-test.ResultsOur results represented that overexpression of hENT1 reversed GEM resistance in GEM-R cells by showing lower IC50 and higher rate of apoptosis. MiR-143 suppressed glycolysis in GEM-R cells and enhanced the effect of reversing GEM resistance in GEM-R-hENT1 cells. The therapeutic efficacy was validated using a xenograft mouse model. Combination treatment decreased tumor growth rate and maximum standardized uptake value in 18F-FDG PET more effectively.ConclusionsCombined therapy of exogenous upregulation of hENT1 expression and miR-143 mimic administration was effective in reversing GEM resistance, providing a promising strategy for treating GEM-resistant TNBC.

Massive intoxication due to cocaine adulterated with carfentanil

To identify the causal agent of the massive intoxication occurred in February 2022 in Buenos Aires, with 98 hospitalized victims and 24 deaths. The first victims presented the following signs: miosis, shock, sensory depression, respiratory distress (bradypnea), psychomotor excitement, seizures, and cardiorespiratory arrest. Some showed a typical opiomimetic sign: “wood chest”. Given the parasympathetic signs, the victims were treated with Naloxone at high doses, responding favourably and with a continuous infusion drip of 10 mg in 250 mL at a flow rate of 11 mL/h. The latter to prevent them from deteriorating when they waked up. All the cases had in common having inhaled a line of cocaine acquired from the same dealer. Seized powder samples were preliminarily analysed by HPTLC and UPLC-DAD (Acquity System-Waters Corp, USA). A cocaine standard (LGC Std, Lot. 979337, USA) was used. Subsequently, for the detailed analysis of components, a fractionation was made by extracting the soluble components in chloroform and the soluble and insoluble fractions were analysed by 1H and 13 C NMR on a Bruker A Neo 500 spectrometer (1H 500 MHz, 13C 125 MHz). The chloroform insoluble fraction was further analysed by LC-MS with exact mass detection on a Bruker micrOTOF-Q II spectrometer coupled to an Agilent 1200 HPLC. Structure confirmation was performed by MRM (multiple reaction monitoring), and MS/MS fragmentation. UPLC-DAD analysis showed a purity of 51.6% for COC, the rest being mostly sorbitol as cutting material (confirmed by NMR, as noted below). In the soluble fraction, where the spectra were measured in deuterochloroform solution, the NMR spectra showed the characteristic signals of cocaine hydrochloride and small amounts of cinnamyl cocaine (CIN) that usually accompanies cocaine hydrochloride and truxillines. (The latter very important to infer the origin of cocaine). In the insoluble fraction the main component was identified as sorbitol from its 1H and 13C NMR spectra (in DMSO-d6). A small remnant of cocaine and cinnamyl cocaine (CIN) was also observed. On the other hand, this fraction was analysed by LC-MS, observing 7 relevant peaks: cocaine, benzoylecgonine, cinnamyl cocaine (cis and trans), truxillines and a minor component of m/z 395.2316 that corresponds to the formula C24H31N2O3 (calculated 395,2329) coincident with the [M + H]+ quasi-molecular ion of carfentanil. Structure confirmation was performed by MRM, and fragmentation by MS/MS of the M + H ion, observing the characteristic fragments of the carfentanil structure including the two confirmatory ions of m/z 335.2123 (calculated for C22H27N2O+ 335,2118) and m/z 246.1489 (calculated for C15H20NO2+ 246,1489). Quantitative analysis was performed by NMR for the COC/CIN ratio and from LC-MS for the CIN/Carfentanil ratio. Giving an estimated result of 30 μg/100 mg of pure cocaine. The toxic dose for humans is unknown, although would be less than 1 mg based on animal studies and known fentanyl lethal cases (estimated lethal dose is 20 μg) (Casale et al., 2017). The therapeutic treatment followed by the victims and the results observed allowed us to focus on opiomimetics, as the most relevant factor in the cause of the 24 deaths. The high-resolution methods applied allowed us to confirm cocaine in a percentage of purity close to half of the total powder. Carfentanil was confirmed by MRM analysis and in an amount of approximately 30 μg/100 mg of pure cocaine. Based on the data collected in the case, the consumption of cocaine was about 200 mg, therefore, in the proportion of purity, each line contained about 30 μg of carfentanil. So, for 400 mg of powder consumed, carfentanil would be found at 60 μg. Therefore, we estimated the toxic dose for this episode between 30–60 μg.

Metabolomic Characterization of Acute Ischemic Stroke Facilitates Metabolomic Biomarker Discovery.

Acute ischemic stroke (AIS) is characterized by a sudden blockage of one of the main arteries supplying blood to the brain, leading to insufficient oxygen and nutrients for brain cells to function properly. Unfortunately, metabolic alterations in the biofluids with AIS are still not well understood. In this study, we performed high-throughput target metabolic analysis on 44 serum samples, including 22 from AIS patients and 22 from healthy controls. Multiple-reaction monitoring analysis of 180 common metabolites revealed a total of 29 metabolites that changed significantly (VIP > 1, p < 0.05). Multivariate statistical analysis unraveled a striking separation between AIS patients and healthy controls. Comparing the AIS group with the control group, the contents of argininosuccinic acid, beta-D-glucosamine, glycerophosphocholine, L-abrine, and L-pipecolic acid were remarkably downregulated in AIS patients. Twenty-nine out of 112 detected metabolites enriched in disturbed metabolic pathways, including aminoacyl-tRNA biosynthesis, glycerophospholipid metabolism, lysine degradation, phenylalanine, tyrosine, and tryptophan biosynthesis metabolic pathways. Collectively, these results will provide a sensitive, feasible diagnostic prospect for AIS patients.

POS1045 MASS SPECTROMETRY-BASED PROTEOMICS FOR THE IDENTIFICATION OF CANDIDATE SERUM PROTEIN BIOMARKERS THAT MAY PREDICT TREATMENT RESPONSE IN PATIENTS WITH PSORIATIC ARTHRITIS

BackgroundThe Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) has identified a need for biomarkers to predict which patients (pts) with psoriatic arthritis (PsA) are most likely to respond to a specific therapy. Failure to identify effective treatments early on results in sub-optimal PsA disease management. Tofacitinib is an oral JAK inhibitor for the treatment of PsA. The efficacy and safety of tofacitinib 5 and 10 mg twice daily (BID) in pts with PsA have been demonstrated.1,2ObjectivesTo identify protein biomarker candidates, which may identify responders (R) vs non-responders (NR) to treatment of PsA, using mass spectrometry-based proteomics.MethodsBaseline (BL) serum samples from pts with PsA receiving tofacitinib 5 or 10 mg BID, adalimumab or placebo in OPAL Broaden (NCT01877668)1 were analysed. Pts were identified as R and NR based on the Psoriatic Arthritis Disease Activity Score (PASDAS) at Month 3; pts with lowest PASDAS ≤3.2 were defined as R, those with highest PASDAS &gt;3.2 as NR. Two proteomic strategies were employed for analysis of BL serum samples: (1) targeted mass spectrometric multiple reaction monitoring analysis of an in-house panel (‘PAPRICA’) comprising of 206 proteins, originally developed to distinguish between different arthropathies, and (2) unbiased discovery liquid chromatography-tandem mass spectrometry (LC-MS/MS). PAPRICA data were normalised using two methods: normalisation to stable isotopically labelled peptide spike-ins (SIL; corrects for fluctuations in sample injections/mass spectrometry loading amounts), and normalisation to an endogenous peptide panel representing total serum protein abundance (TSPA; corrects for different amounts of total serum protein across samples). Univariate analyses (Student’s t-test) and multivariate machine learning Random Forest (RF) modelling3 were performed. Univariate analysis of the PAPRICA panel of proteins was performed on R vs NR, and within each treatment arm, with no adjustment for multiplicity.Results96 pts were identified as 47 R and 49 NR based on PASDAS scores. Of pts receiving tofacitinib 5 or 10 mg BID (data pooled), adalimumab or placebo, there were 26 R vs 26 NR, 13 R vs 13 NR and 8 R vs 10 NR, respectively. Results from univariate analysis identified 110 differentially expressed PAPRICA peptides between R vs NR (p≤0.05). RF multivariate analysis of all data (n=96) revealed a set of PAPRICA peptide signatures with the ability to differentiate between R and NR. Two RF models generated from the PAPRICA peptide data had training area under curves (AUCs) 0.956 [95% CI 0.93, 0.99] (TSPA) and 0.959 [95% CI 0.94, 0.98] (SIL). In total, 115 PAPRICA peptides representing 87 proteins were identified as potential biomarkers for predicting treatment response. Using unbiased discovery LC-MS/MS, univariate analysis of all data revealed one candidate peptide biomarker (p≤0.05). RF modelling revealed peptides that contributed to two prediction models with training AUCs of 0.903 [95% CI 0.86, 0.96] and 0.928 [95% CI 0.89, 0.96]. In total, from unbiased discovery LC-MS/MS, 66 peptides representing 39 proteins that may act as potential peptide biomarkers were identified in univariate and multivariate analyses.ConclusionUsing two complementary proteomic approaches and a combination of univariate and machine learning models, a total of 181 candidate biomarker peptides corresponding to 106 proteins have been identified that may act as potential biomarkers for predicting response to treatment of PsA. Further study is required to verify and evaluate these candidate biomarkers, and we will report how these proteins map to biological processes, pathways and networks.

Comprehensive mass spectrometry for development of proteomic biomarkers of intracranial aneurysms.

Protein biomarkers of intracranial aneurysm (IA) are essential for early detection and prediction of its rupture to facilitate the diagnosis and clinical management of the disease, monitor treatment response and detect recurrence. Here, we developed a comprehensive strategy for IA biomarker discovery by analyzing tissues from an animal model (n=4) and serum from human patients (n=60) using isobaric tandem mass tags-based quantitative proteomics. A total of 4811 and 562 proteins were identified from aneurysm tissue and serum samples, respectively. The 223 candidate protein biomarkers were further validated in an independent serum cohort (n=30) by multiple reaction monitoring analysis. Combined with a logistic regression model, we built a diagnostic classifier P2 (FCN2 & RARRES2) to differentiate IA from healthy controls with accuracy of 93.3%, as well as a diagnostic classifier P7 (ADAM12, APOL3, F9, C3, CEACAM1, ICAM3, KLHDC7A) to classify ruptured IA from unruptured IA with accuracy of 95.0%. Taken together, our results suggest a valuable strategy for biomarker discovery and patient stratification in IA.