Biomarkers in prostate cancer : defining 'pussycat versus tiger' phenotype by proteomic modeling

Prostate cancer is the one of the major causes of morbidity and mortality in the western world. It affects the prostate gland of males with a significant increase in the disease incidence every year. Current diagnostic and prognostic markers, such as prostate specific antigen (PSA), rectal examination and Gleason grades have their own limitations in a wider context of disease treatment and prediction. There is therefore a pressing need for novel and powerful biomarkers at protein or metabolite level. This study attempts to profile and identify candidate prostate cancer stage specific markers, within a defined population of samples. The samples were classified, based on the pathological information as “aggressive” (Gleason grade > 7) and “nonaggressive “(Gleason grade < 7). The proteomic protocols standardised at the John van Geest Cancer Research Centre, were used for the initial characterisation of the samples. The MS spectra obtained from the samples were used applied to an artificial neural network (ANN) based algorithm to generate predictive ions able to classify the samples. Three ions (m/z 1268.8, 998.6, 910.4) were able to predict and classify with high specificity and sensitivity. 24 samples were immunodepleted and subjected to nano-LC fractionation and MALDI-TOF analysis, generating 80-120 protein identities per sample. The three ions predicted previously by the ANN identified as Haemopexin, Gelsolin and Apolipoprotein B 100. Using ProfileAnalysis software, this study identified Apolipoprotein isoforms, including Apolipoprotein B 100, and Afamin as the proteins which showed differential expression in between the groups. This study identifies Apolipoprotein B 100 as a potential marker using two different modeling approaches suggesting this protein as the potential biomarker candidate. The utility of high throughput proteomic platforms such as Robotic liquid handling, MALDI-TOF and LC-MALDI for serum biomarker identification in PCa has been shown during this investigation

Biomarkers in prostate cancer : defining 'pussycat versus tiger' phenotype by proteomic modeling

Prostate cancer is the one of the major causes of morbidity and mortality in the western world. It affects the prostate gland of males with a significant increase in the disease incidence every year. Current diagnostic and prognostic markers, such as prostate specific antigen (PSA), rectal examination and Gleason grades have their own limitations in a wider context of disease treatment and prediction. There is therefore a pressing need for novel and powerful biomarkers at protein or metabolite level. This study attempts to profile and identify candidate prostate cancer stage specific markers, within a defined population of samples. The samples were classified, based on the pathological information as “aggressive” (Gleason grade > 7) and “nonaggressive “(Gleason grade < 7). The proteomic protocols standardised at the John van Geest Cancer Research Centre, were used for the initial characterisation of the samples. The MS spectra obtained from the samples were used applied to an artificial neural network (ANN) based algorithm to generate predictive ions able to classify the samples. Three ions (m/z 1268.8, 998.6, 910.4) were able to predict and classify with high specificity and sensitivity. 24 samples were immunodepleted and subjected to nano-LC fractionation and MALDI-TOF analysis, generating 80-120 protein identities per sample. The three ions predicted previously by the ANN identified as Haemopexin, Gelsolin and Apolipoprotein B 100. Using ProfileAnalysis software, this study identified Apolipoprotein isoforms, including Apolipoprotein B 100, and Afamin as the proteins which showed differential expression in between the groups. This study identifies Apolipoprotein B 100 as a potential marker using two different modeling approaches suggesting this protein as the potential biomarker candidate. The utility of high throughput proteomic platforms such as Robotic liquid handling, MALDI-TOF and LC-MALDI for serum biomarker identification in PCa has been shown during this investigation

Pharmacological Evaluation of Secondary Metabolites and Their Simultaneous Determination in the Arabian Medicinal Plant Plicosepalus curviflorus Using HPTLC Validated Method

© The Author(s) 2019. The present study aimed to identify biologically active secondary metabolites from the rare plant species, Pulsatilla patens subsp. patens and the cultivated P. vulgaris subsp. vulgaris. Chromatographic fractionation of the ethanolic extract of the roots of P. patens subsp. patens resulted in the isolation of two oleanane-type glycosides identified as hederagenin 3-O-β-d-glucopyranoside (2.7 mg) and hederagenin 3-O-β-d-galactopyranosyl-(1→2)-β-d-glucopyranoside (3.3 mg, patensin). HPLC analysis of the methanolic extract of the crude root of P. patens subsp. patens and P. vulgaris subsp. vulgaris revealed the presence of Pulsatilla saponin D (hederagenin 3-O-α-l-rhamnopyranosyl(1→2)-[β-d-glucopyranosyl(1→4)]-α-l-arabinopyranoside). Chromatographic analysis using GC-MS of the silylated methanolic extracts from the leaves and roots of these species identified the presence of carboxylic acids, such as benzoic, caffeic, malic, and succinic acids. The extracts from Pulsatilla species were tested for their antifungal, antimicrobial, and antimalarial activities, and cytotoxicity to mammalian cell lines. Both P. patens subsp. patens and P. vulgaris subsp. vulgaris were active against the fungus Candida glabrata with the half-maximal inhibitory concentration (IC 50 ) values of 9.37 µg/mL and 11 µg/mL, respectively. The IC 50 values for cytotoxicity evaluation were in the range of 32–38 μg/mL for P. patens subsp. patens and 35–57 μg/mL for P. vulgaris subsp. vulgaris for each cell line, indicating general cytotoxic activity throughout the panel of evaluated cancer and noncancer cells

Mass spectrometry-based selectivity and sensitivity enhancement for the quantification of protein expression changes in anti-viral response mechanisms

Multiple Reaction Monitoring (MRM) is a highly sensitive and selective mass spectrometry technique for peptide quantification in proteomics. The development of an MRM assay method is critical for highly rapid, accurate, and reproducible measurements. We developed an automated workflow to generate optimal MRM transitions based on empirical synthetic peptide libraries produced by flow injection analysis using in-house developed software called MRMOptimizer. Furthermore, the evaluation of alternative techniques to MRM, such as multiple reaction monitoring cubed (MRM³) and differential ion mobility mass spectrometry (DMS), in terms of selectivity and sensitivity was an essential part of this thesis. Finally, we applied our optimized MRM assay method to investigate human monocyte-derived dendritic cell (MDDC) protein expression during the early HIV-1 infection state

Insights into modifiers effects in differential mobility spectrometry: A data science approach for metabolomics and peptidomics

Utilizing a data-driven approach, this study investigates modifier effects on compensation voltage in differential mobility spectrometry–mass spectrometry (DMS-MS) for metabolites and peptides. Our analysis uncovers specific factors causing signal suppression in small molecules and pinpoints both signal suppression mechanisms and the analytes involved. In peptides, machine learning models discern a relationship between molecular weight, topological polar surface area, peptide charge, and proton transfer-induced signal suppression. The models exhibit robust performance, offering valuable insights for the application of DMS to metabolites and tryptic peptides analysis by DMS-MS

Caring for patients with rare diseases during the COVID-19 pandemic

Rare diseases frequently attack and weaken the immune system, increasing the patient’s vulnerability to develop severe conditions after viral infections, such as COVID-19. Many patients with rare diseases also suffer from mental retardation and disability. These rare disease phenotypes do not emerge in older people who are susceptible to COVID-19 infection, but present at a very young age or at birth. These factors must be taken in consideration when caring for this vulnerable patient population during a pandemic, such as COVID-19. Patients with a rare disease have to take their regular medication continuously to control their condition and frequently, the medications, directly or indirectly, affect their immune system. It is important for this patient population, if infected with COVID-19 or another severe form of infection, to adjust the treatment protocol by specialists, in consultation with their own medical team. Special awareness and educational programs, understandable for mentally retarded patients, must be developed to educate them about social distancing, curfew, sanitization, and sensitization to the disease and quarantine. The COVID-19 pandemic highlighted the importance to reconsider the care required by patients with a rare disease during a pandemic or disaster, a program that should be adopted by the World Health Organization and governmental institutions for consideration

Alternatively Spliced Isoforms of <i>MUC4</i> and <i>ADAM12</i> as Biomarkers for Colorectal Cancer Metastasis

There is a pertinent need to develop prognostic biomarkers for practicing predictive, preventive and personalized medicine (PPPM) in colorectal cancer metastasis. The analysis of isoform expression data governed by alternative splicing provides a high-resolution picture of mRNAs in a defined condition. This information would not be available by studying gene expression changes alone. Hence, we utilized our prior data from an exon microarray and found ADAM12 and MUC4 to be strong biomarker candidates based on their alternative splicing scores and pattern. In this study, we characterized their isoform expression in a cell line model of metastatic colorectal cancer (SW480 & SW620). These two genes were found to be good prognostic indicators in two cohorts from The Cancer Genome Atlas database. We studied their exon structure using sequence information in the NCBI and ENSEMBL genome databases to amplify and validate six isoforms each for the ADAM12 and MUC4 genes. The differential expression of these isoforms was observed between normal, primary and metastatic colorectal cancer cell lines. RNA-Seq analysis further proved the differential expression of the gene isoforms. The isoforms of MUC4 and ADAM12 were found to change expression levels in response to 5-Fluorouracil (5-FU) treatment in a dose-, time- and cell line-dependent manner. Furthermore, we successfully detected the protein isoforms of ADAM12 and MUC4 in cell lysates, reflecting the differential expression at the protein level. The change in the mRNA and protein expression of MUC4 and ADAM12 in primary and metastatic cells and in response to 5-FU qualifies them to be studied as potential biomarkers. This comprehensive study underscores the importance of studying alternatively spliced isoforms and their potential use as prognostic and/or predictive biomarkers in the PPPM approach towards cancer

Optimization by infusion of multiple reaction monitoring transitions for sensitive quantification of peptides by liquid chromatography/mass spectrometry

RATIONALE: In peptide quantification by liquid chromatography/mass spectrometry (LC/MS), the optimization of multiple reaction monitoring (MRM) parameters is essential for sensitive detection. We have compared different approaches to build MRM assays, based either on flow injection analysis (FIA) of isotopically labelled peptides, or on the knowledge and the prediction of the best settings for MRM transitions and collision energies (CE). In this context, we introduce MRMOptimizer, an open-source software tool that processes spectra and assists the user in selecting transitions in the FIA workflow. METHODS: MS/MS spectral libraries with CE voltages from 10 to 70 V are automatically acquired in FIA mode for isotopically labelled peptides. Then MRMOptimizer determines the optimal MRM settings for each peptide. To assess the quantitative performance of our approach, 155 peptides, representing 84 proteins, were analysed by LC/MRM-MS and the peak areas were compared between: (A) the MRMOptimizer-based workflow, (B1) the SRMAtlas transitions set used \u27as-is\u27; (B2) the same SRMAtlas set with CE parameters optimized by Skyline. RESULTS: 51% of the three most intense transitions per peptide were shown to be common to both A and B1/B2 methods, and displayed similar sensitivity and peak area distributions. The peak areas obtained with MRMOptimizer for transitions sharing either the precursor ion charge state or the fragment ions with the SRMAtlas set at unique transitions were increased 1.8- to 2.3-fold. The gain in sensitivity using MRMOptimizer for transitions with different precursor ion charge state and fragment ions (8% of the total), reaches a ~ 11-fold increase. CONCLUSIONS: Isotopically labelled peptides can be used to optimize MRM transitions more efficiently in FIA than by searching databases. The MRMOptimizer software is MS independent and enables the post-acquisition selection of MRM parameters. Coefficients of variation for optimal CE values are lower than those obtained with the SRMAtlas approach (B2) and one additional peptide was detected

Clinical Impact of Portal Vein Thrombosis Prior to Liver Transplantation: A Retrospective Cohort Study

Introduction. To identify the impact of portal vein thrombosis (PVT) and associated medical and surgical factors on outcomes post liver transplant (LT).Material and methods. Two analyses were performed. Analysis One: cohort study of 505 consecutive patients who underwent LT (Alberta) between 01/2002-12/2012. PVT was identified in 61 (14%) patients. Analysis Two: cohort study of 144 consecutive PVT patients from two sites (Alberta and London) during the same period. Cox multivariable survival analysis was used to identify independent associations with post-LT mortality.Results. In Analysis One (Alberta), PVT was not associated with post-LT mortality (log rank p = 0.99). On adjusted analysis, complete/occlusive PVT was associated with increased mortality (Hazard Ratio (HR) 8.4, p < 0.001). In Analysis Two (Alberta and London), complete/occlusive PVT was associated with increased mortality only on unadjusted analysis (HR 3.7, p = 0.02). On adjusted analysis, Hepatitis C (HR 2.1, p = 0.03) and post-LT portal vein re-occlusion (HR 3.2, p = 0.01) were independently associated with increased mortality.Conclusion: Well-selected LT patients who had PVT prior to LT had similar post-LT outcomes to non-PVT LT recipients. Subgroups of PVT patients who did worse post-LT (complete/occlusive thrombosis pre-LT, Hepatitis C or post-LT portal vein re-occlusion) warrant closer evaluation in listing and management post-LT