Development of a low allergenic product for patients with milk allergy and assessment of its specific IgE reactivity.

BACKGROUND: Milk oral immunotherapy is the riskiest and most unpredictable form of oral immunotherapy. We aimed to produce a low allergenic product than conventional once baked-cake/muffin, to develop indirect in-house ELISA to check the tolerance status with milk products and evaluate IgE reactivity of patients' sera via western blotting (WB) and indirect in-house ELISA. METHOD: A low allergenic product named biscotti-twice baked-cake was developed, and the total protein concentration was determined. The protein content was studied by SDS-PAGE and proteomics. Milk-specific IgE (sIgE) binding assays were performed by WB and indirect in-house ELISA by using patients' sera. RESULTS: Casein band intensity was observed to be lower in the biscotti-twice baked-cake than in the once baked-cake (p = .014). Proteomics analysis and αS1-casein measurement showed that the lowest intensity of casein was found in biscotti. The low binding capacity of milk sIgE to biscotti compared with once baked-cake was shown by WB (p = .0012) and by indirect in-house ELISA (p = .0001). In the ROC analysis, the area under the curve (AUC) of the in-house ELISA IgE was comparable with Uni-CAP milk and casein sIgE. The AUC of the in-house ELISA IgE for cake (0.96) and biscotti (1) was slightly better than Uni-CAP milk sIgE (0.94; 0.97) and casein sIgE (0.96; 0.97), respectively. CONCLUSION: The low allergenicity of the newly developed low allergenic product "biscotti-twice baked-cake" has been demonstrated by in vitro experiments. Biscotti could be a safe treatment option than once baked-cake/muffin in patients who are reactive to once baked-milk products.

Bioactive and chemically defined hydrogels with tunable stiffness guide cerebral organoid formation and modulate multi-omics plasticity in cerebral organoids.

Organoids are an emerging technology with great potential in human disease modelling, drug development, diagnosis, tissue engineering, and regenerative medicine. Organoids as 3D-tissue culture systems have gained special attention in the past decades due to their ability to faithfully recapitulate the complexity of organ-specific tissues. Despite considerable successes in culturing physiologically relevant organoids, their real-life applications are currently limited by challenges such as scarcity of an appropriate biomimetic matrix. Peptide amphiphiles (PAs) due to their well-defined chemistry, tunable bioactivity, and extracellular matrix (ECM)-like nanofibrous architecture represent an attractive material scaffold for organoids development. Using cerebral organoids (COs) as exemplar, we demonstrate the possibility to create bio-instructive hydrogels with tunable stiffness ranging from 0.69 kPa to 2.24 kPa to culture and induce COs growth. We used orthogonal chemistry involving oxidative coupling and supramolecular interactions to create two-component hydrogels integrating the bio-instructive activity and ECM-like nanofibrous architecture of a laminin-mimetic PAs (IKVAV-PA) and tunable crosslinking density of hyaluronic acid functionalized with tyramine (HA-Try). Multi-omics technology including transcriptomics, proteomics, and metabolomics reveals the induction and growth of COs in soft HA-Tyr hydrogels containing PA-IKVAV such that the COs display morphology and biomolecular signatures similar to those grown in Matrigel scaffolds. Our materials hold great promise as a safe synthetic ECM for COs induction and growth. Our approach represents a well-defined alternative to animal-derived matrices for the culture of COs and might expand the applicability of organoids in basic and clinical research. STATEMENT OF SIGNIFICANCE: Synthetic bio-instructive materials which display tissue-specific functionality and nanoscale architecture of the native extracellular matrix are attractive matrices for organoids development. These synthetic matrices are chemically defined and animal-free compared to current gold standard matrices such as Matrigel. Here, we developed hydrogel matrices with tunable stiffness, which incorporate laminin-mimetic peptide amphiphiles to grow and expand cerebral organoids. Using multi-omics tools, the present study provides exciting data on the effects of neuro-inductive cues on the biomolecular profiles of brain organoids.

Comparative Metabolomic Profiles of Vascular Involvement in Behçet's Disease.

BACKGROUND: Behçet's disease is a systemic, inflammatory disease affecting multiple organs. Vascular involvement is the main cause of morbidity and mortality in Behçet's disease patients. Though clinically well-defined, there is limited information related to disease pathogenesis and vascular incidence in this patient group. The aim of this study is to investigate the unique metabolic signatures of Behçet's disease patients with vascular involvement. METHODS: Metabolomic profiling was performed on serum samples of 48 Behçet's disease patients (18 with vascular involvement) and 40 healthy controls using gas chromatography-mass spectrometrybased untargeted metabolomics analysis. Multivariate and univariate statistical analyses were performed to find altered metabolites and pathways. RESULTS: Untargeted metabolomics results showed that a total of 168 metabolites were identified. The comparison between the groups of Behçet's disease, vascular involvement in Behçet's disease, and the healthy control group showed that altered amino acid and oxidative stress pathways, especially with glutathione synthesis, could be an important stage for developing Behçet's disease. CONCLUSION: In the present work, the untargeted metabolomics approach provided new molecular insights for a better understanding of Behçet's disease pathogenesis and also developing vascular involvement in Behçet's disease at the metabolite level. The results showed that vascular involvement in Behçet's disease could be highly linked with amino acid metabolism and also the antioxidant system, and these disease-related pathways could be evaluated with further experiments for diagnosis and prognosis of Behçet's disease and also for vascular involvement in Behçet's disease.

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer.

Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features.

Tuning the Cell-Adhesive Properties of Two-Component Hybrid Hydrogels to Modulate Cancer Cell Behavior, Metastasis, and Death Pathways.

This work presents a polysaccharide and protein-based two-component hybrid hydrogel integrating the cell-adhesive gelatin-tyramine (G-Tyr) and nonadhesive hyaluronic acid-tyramine (HA-Tyr) through enzyme-mediated oxidative coupling reaction. The resulting HA-Tyr/G-Tyr hydrogel reflects the precise chemical and mechanical features of the cancer extracellular matrix and is able to tune cancer cell adhesion upon switching the component ratio. The cells form quasi-spheroids on HA-Tyr rich hydrogels, while they tend to form an invasive monolayer culture on G-Tyr rich hydrogels. The metastatic genotype of colorectal adenocarcinoma cells (HT-29) increases on G-Tyr rich hydrogels which is driven by the material's adhesive property, and additionally confirmed by the suppressed gene expressions of apoptosis and autophagy. On the other hand, HA-Tyr rich hydrogels lead the cells to necrotic death via oxidative stress in quasi-spheroids. This work demonstrates the ideality of HA-Tyr/G-Tyr to modulate cancer cell adhesion, which also has potential in preventing primary metastasis after onco-surgery, biomaterials-based cancer research, and drug testing.

Integrative proteomics and metabolomics approach to elucidate the antimicrobial effect of simvastatin on Escherichia coli.

Globally, simvastatin is one of the most commonly used statin drugs. Its antimicrobial properties have been investigated against various pathogens. However, its effect on biological processes in bacteria has been unclear. This study focused on altered biological and metabolic processes at protein and metabolite levels induced by simvastatin. MS-based proteomics and metabolomics were used to investigate the altered proteins and metabolites between experimental groups. Proteomics results showed that simvastatin induced various antimicrobial targets such as chaperon protein DnaK and cell division protein FtsZ. Metabolomics results revealed phenotypic changes in cells under simvastatin stress. Integrated proteomics and metabolomics result indicated that various metabolic processes were altered to adapt to stress conditions. Energy metabolism (glycolysis, tricarboxylic acid cycle, etc.), amino acid synthesis and ribosomal proteins, and purine and pyrimidine synthesis were induced by the effect of simvastatin. This study will contribute to the understanding of antimicrobial properties of statin drugs.

Comparative Proteomic Analysis of Escherichia coli Under Ofloxacin Stress

Objectives: In the present study, proteomics was utilized to evaluate changes in Escherichia coli proteins in response to ofloxacin to understand the mechanism of action of ofloxacin and the mechanisms of ofloxacin resistance in E. coli. Materials and Methods: Proteomics analysis of E. coli was performed by using liquid chromatography quadrupole time-of-flight mass spectrometry followed by a data processing step using MaxQuant. Functional classification and pathway analysis showed a systematic effect of ofloxacin over E. coli proteome structure. Results: In total, 649 common proteins were identified in the untreated and ofloxacin-treated groups, while 98 proteins were significantly different in the ofloxacin-treated group. Functional classification and pathway analysis showed that ofloxacin has a systematic effect over ribosomal processes, energy pathways (tricarboxylic acid cycle and glycolysis), membrane proteins, microbial targets, and biofilm formation. Conclusion: The results showed that ofloxacin affected many cellular processes and pathways. In addition, proteomic analysis revealed that E. coli develops resistance mechanism with different biological processes.

Polycationic cyclodextrin nanoparticles induce apoptosis and affect antitumoral activity in HepG2 cell line: An evaluation at the molecular level.

Hepatocellular carcinoma (HCC) is a highly metastatic primary liver cancer generating molecular alterations that end up escaping the apoptotic machinery and conferring multidrug resistance. Targeted medicines with increased and selective cytotoxicity and minimal drug resistance are essential for the treatment of HCC. In this study, a self-assembled polycationic (PC) amphiphilic ß-cyclodextrin (ßCDC6) nanoparticle formulation was characterized and its efficacy over HCC cell line HepG2 was evaluated in terms of cytotoxicity, apoptotic potential, chemosensitivity and mitochondrial balance utilizing biochemical, gene expression and proteomic approaches without encapsulating an anti-neoplastic agent. Blank PC ßCDC6 exerted an anti-proliferative effect on 3D multicellular HepG2 spheroid tumors. These nanoparticles were able to trigger apoptosis proved by caspase 3/7 activity, gene expression and flow cytometry studies. The subjection of PC restored the chemosensitivity of HepG2 cells by suppressing the function of p-glycoprotein. The proteomic studies with Q-TOF LC/MS revealed 73 proteins that are aberrantly encoded after cells were treated with the blank PC. Metabolomic analysis further confirmed the shift in certain biological pathways. Thus, we confirmed that the hepatocellular carcinoma-targeting ßCDC6 PC nanoparticles induce apoptosis, lower the rate of cell proliferation, hinder multidrug resistance and they are convenient carriers for eventual therapeutic administrations in HCC patients.

Analysis of plasma protein biomarkers in childhood onset multiple sclerosis.

Multiple sclerosis (MS) manifesting before age 18 years is defined as pediatric MS (pMS). We analysed plasma proteins in pMS by an untargeted proteomic approach. Patients with pMS (Group pMS, n = 33), patients with demyelinating disease not meeting pMS diagnostic criteria (unclassified demyelinating disease, Group U, n = 4) and age-matched healthy subjects (Group C, n = 40) were included. Plasma proteomic analysis was performed using Q-TOF LC/MS. Proteins having fold change >1.2 and found to be statistically different (p < 0.05) between the groups were identified and discussed with a clinical perspective. Group pMS had higher alpha 1B glycoprotein (A1BG), complement factor B (CFB), plasminogen (PLG), alpha-2-antiplasmin (α2-AP, SERPINF2), inter alpha trypsin inhibitor heavy chain H2 (ITIH2), and lower centrosomal protein of 290 (CEP290) and F-box/LRR-repeat protein 17 (FBXL17) concentrations than Group C. Measurements from Group U, whose definite diagnoses were established as pMS (n = 3) and myelin oligodendrocyte glycoprotein antibody-associated disease (n = 1) on follow-up after the study, were statistically close to the results of Group pMS. Plasma protein changes observed in our study were related to the inflammation, coagulation and oxidative stress pathways. If confirmed and validated in larger groups, these results may indicate potential biomarker(s) for demyelinating diseases at proteome level and could encourage studies for the development of novel diagnostic kits.

Metabolomics and proteomics profiles of some medicinal plants and correlation with BDNF activity.

BACKGROUND: Identification of the low abundance of phytochemicals in plant extracts is very difficult. Pharmacological activity observed in such plants is not due to a single compound. In most cases, plant extracts show activity based on synergistic or antagonistic effects. Therefore, the idea of a holistic approach is more rational. PURPOSE: This study was planned to compare the metabolomics and proteomics profiles of Valeriana officinalis L. (Valerianaceae), Melissa officinalis L. (Lamiaceae), Hypericum perforatum L. (Hypericaceae) and Passiflora incarnata L. (Passifloraceae) used in sedative anxiolytic and sleep disorders. Integrated omics analyses were used to provide a better understanding of the effect of plant extracts on the brain-derived neurotrophic factor (BDNF) expression levels on the SH-SY5Y cell line by a holistic approach. METHODS: Metabolomic profiling of the plants was performed using the GC-MS and LC-qTOF-MS systems, and the proteomics analysis using the LC-qTOF-MS system after trypsin digestion. The Human BDNF Quantikine ELISA kit was utilized to test BDNF expression activity on the SH-SY5Y cell line. RESULTS: The investigated plant extracts showed a significant increase in BDNF expression (p < 0.05). M. officinalis was found as the most active extract. According to the correlation analyses between BDNF activity and metabolomics or proteomics level, 94 metabolites had a positive correlation while 23 metabolites had a highly negative correlation; those for proteins are 24 and 6, respectively. CONCLUSION: The multivariate data analysis revealed a similar metabolomics profile of H. perforatum and P. incarnata, which also had a similar activity profile. Remarkably, all the primary metabolites belonging to the Krebs Cycle (citric acid, fumaric acid, succinic acid, pyruvic acid, malic acid and citramalic acid, an analog of malic acid) were positively correlated with BDNF activity. Secondary metabolites with a high BDNF expression belonged to flavonoids, xanthone, coumarines, tannin, naphtalenes, terpenoids and carotenoid skeleton. Two proteins from the cytochrome P450 family (P450 71B11 and P450 94B3) were positively correlated with BDNF activity. Employing omics technologies in the plant research area will offer a better understanding of the role of plant extracts and may lead to the discovery of new compounds with specific activity.

Analysis of the Antiproliferative Effect of Ankaferd Hemostat on Caco-2 Colon Cancer Cells via LC/MS Shotgun Proteomics Approach.

Ankaferd hemostat (ABS), a traditional herbal extract, is a hemostatic agent used for wound healing and bleeding treatment. A standardized form of plants contains many biomolecules. In recent years, previous studies have demonstrated the antineoplastic effect of ABS. In the present work, we focused on the mechanism of its antineoplastic effect over Caco-2 colon cancer cells. The LC/MS-based proteomics method was used to understand the effect of ABS at the protein level. The results were evaluated with gene ontology, protein interaction, and pathway analysis. As shown by our results, ABS altered glucose, fatty acids, and protein metabolism. Moreover, ABS affects the cell cycle machinery. Moreover, we found that ABS induced critical cancer target and suppressor proteins such as carboxyl-terminal hydrolase 1, 60S ribosomal protein L5, Tumor protein D52-like2, karyopherin alpha 2, and protein deglycase DJ-1. In conclusion, the proteomics results indicated that ABS affects various cancer targets and suppressor proteins. Moreover ABS has systematical effect on cell metabolism and cell cycle in Caco-2 cells, suggesting that it could be used as an antineoplastic agent.

Foodomics study on the effects of extracellular production of hydrogen peroxide by rosemary polyphenols on the anti-proliferative activity of rosemary polyphenols against HT-29 cells.

A number of studies have demonstrated a strong association between the antioxidant properties of rosemary polyphenols and their chemoprotective activity. However, the prooxidant effects of rosemary polyphenols have been rarely reported. In this work, a foodomics study is performed to investigate the in vitro autooxidation of carnosic acid (CA), carnosol (CS) and a polyphenol-enriched rosemary extract (SC-RE) in cell culture conditions. The results revealed that rosemary polyphenols autooxidation in culture medium generated H2 O2 at different rates. Generated H2 O2 levels by SC-RE and CA, but not CS, were correlated with intracellular reactive oxygen species (ROS) generation in HT-29 cells, and were partially involved in their anti-proliferative effect in this cell line. These compounds also induced different effects on glutathione metabolism. Results also indicated that high extracellular H2 O2 concentrations, resulting of using high (45 µg/mL) SC-RE concentration in culture media, exerted some artifactual effects related with cell cycle, but they did not influence the expression of relevant molecular biomarkers of stress.

Determination of Rivaroxaban in Human Plasma by Solid-Phase Extraction-High Performance Liquid Chromatography.

In this study, a solid-phase extraction (SPE)-high performance liquid chromatography (HPLC)-ultra violet (UV) method was developed for the determination of rivaroxaban (RIV), an oral anticoagulant drug, in human plasma samples. The concentration of RIV in plasma samples was increased 7.5 times and the interference coming from matrix components was avoided by using SPE. The extracted samples of RIV were analyzed by using an HPLC-UV method. RIV was approved in 2008 and many studies have been published in recent years in order to investigate its pharmacokinetic profile in various groups. In light of this information, it is clear that the RIV pharmacokinetic profile should be investigated in further studies; the HPLC-UV method presented in this study might be an easy method to apply, as it is a cheap and rapid alternative to HPLC-MS-MS for this purpose. A Phenomenex Luna 5-µm C18 100 Å LC column (250 × 4.6 mm) was used for the separation of RIV and prednisolone (internal standard). The total analysis time was <6 min. The method was validated according to the FDA guidelines and can be proposed for pharmacokinetic studies of RIV.

RP-HPLC method development and validation for estimation of rivaroxaban in pharmaceutical dosage forms

Rivaroxaban, an anti-clotting medication, acts at a crucial point in the blood-clotting process and stops the formation of blood clots. In this study, RP-HPLC method was developed for the determination of rivaroxaban in tablets (Xarelto® (10 mg)). Phenomenex Luna 5 µm C18 100 Å LC Column (250 x 4.6 mm) was used at 40 ºC. Isocratic elution was performed with ACN:Water (55:45 v/v) mixture. The flow rate was 1.2 mL min-1 and UV detection was at 249 nm. Internal standard (Caffeine) and rivaroxaban were eluted within 2.21 and 3.37 minutes, respectively. The developed method was validated according to the ICH guidelines and found to be linear within the range 0.005 - 40.0 µg mL-1. The method was accurate, precise, robust and rapid. Thus, it was applied successfully for the quality control assay of rivaroxaban in tablet dosage form.

Rivaroxabana, fármaco anticoagulante, atua em um ponto crucial no processo de coagulação do sangue e impede a formação de coágulos sanguíneos. Neste estudo, desenvolveu-se método de RP-HPLC para a determinação de rivaroxabana em comprimidos (Xarelto ® (10 mg)). Utilizou-se coluna LC (250 x 4,6 mm) Phenomenex Luna C18 5 mm 100 Å a 40 ºC. Realizou-se eluição isocrática com ACN: água (55:45 v/v). O fluxo foi de 1,2 mL min-1 e a detecção de UV foi a 249 nm. Padrão interno (cafeína) e rivaroxabana eluíram em 2,21 e 3,37 minutos, respectivamente. O método desenvolvido foi validado de acordo com as diretrizes do ICH e mostrou-se linear na faixa 0,005-40,0 mg mL-1. O método foi exato, preciso, robusto e rápido. Assim, foi aplicado com êxito para o ensaio de controle de qualidade da Rivaroxabana na forma de comprimidos.