In Silico Identification of Peanut Peptides Suitable for Allergy Immunotherapy in HLA-DRB1*03:01-Restricted Patients.

Peanut allergy, a prevalent and potentially severe condition affecting millions worldwide, has been linked to specific human leukocyte antigens (HLAs), suggesting increased susceptibility. Employing an immunoinformatic strategy, we developed a "logo model" based on amino acid frequencies in the peptide binding core and used it to predict peptides originating from 28 known peanut allergens binding to HLA-DRB1*03:01, one of the susceptibility alleles. These peptides hold promise for immunotherapy in HLA-DRB1*03:01 carriers, offering reduced allergenicity compared to whole proteins. By targeting essential epitopes, immunotherapy can modulate immune responses with minimal risk of severe reactions. This precise approach could induce immune tolerance with fewer adverse effects, presenting a safer and more effective treatment for peanut allergy and other allergic conditions.

Correction: Popova et al. Ni-Cu and Ni-Co-Modified Fly Ash Zeolite Catalysts for Hydrodeoxygenation of Levulinic Acid to γ-Valerolactone. Molecules 2024, 29, 99.

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Tumor-Derived Antigenic Peptides as Potential Cancer Vaccines.

Peptide antigens derived from tumors have been observed to elicit protective immune responses, categorized as either tumor-associated antigens (TAAs) or tumor-specific antigens (TSAs). Subunit cancer vaccines incorporating these antigens have shown promise in inducing protective immune responses, leading to cancer prevention or eradication. Over recent years, peptide-based cancer vaccines have gained popularity as a treatment modality and are often combined with other forms of cancer therapy. Several clinical trials have explored the safety and efficacy of peptide-based cancer vaccines, with promising outcomes. Advancements in techniques such as whole-exome sequencing, next-generation sequencing, and in silico methods have facilitated the identification of antigens, making it increasingly feasible. Furthermore, the development of novel delivery methods and a deeper understanding of tumor immune evasion mechanisms have heightened the interest in these vaccines among researchers. This article provides an overview of novel insights regarding advancements in the field of peptide-based vaccines as a promising therapeutic avenue for cancer treatment. It summarizes existing computational methods for tumor neoantigen prediction, ongoing clinical trials involving peptide-based cancer vaccines, and recent studies on human vaccination experiments.

Axial Spondyloarthritis: an overview of the disease.

Axial Spondyloarthritis (axSpA) is a chronic, inflammatory, immune-mediated rheumatic disease that comprises two subsets, non-radiographic and radiographic axSpA, and belongs to a heterogeneous group of spondyloarthritides (SpA). Over the years, the concept of SpA has evolved significantly, as reflected in the existing classification criteria. Considerable progress has been made in understanding the genetic and immunological basis of axSpA, in studying the processes of chronic inflammation and pathological new bone formation, which are pathognomonic for the disease. As a result, new medication therapies were developed, which bring more effective ways for disease control. This review presents a brief overview of the literature related to these aspects of disease after summarising the available information on the topic that we considered relevant. Specifically, it delves into recent research illuminating the primary pathological processes of enthesitis and associated osteitis in the context of inflammation in axSpA. The exploration extends to discussion of inflammatory pathways, with a particular focus on Th1/Th17-mediated immunity and molecular signaling pathways of syndesmophyte formation. Additionally, the review sheds light on the pivotal role of cytokine dysregulation, highlighting the significance of the IL-23/17 axis and TNF-α in this intricate network of immune responses which is decisive for therapeutic approaches in the disease.

Viral Immunogenicity Prediction by Machine Learning Methods.

Since viruses are one of the main causes of infectious illnesses, prophylaxis is essential for efficient disease control. Vaccines play a pivotal role in mitigating the transmission of various viral infections and fortifying our defenses against them. The initial step in modern vaccine design and development involves the identification of potential vaccine targets through computational techniques. Here, using datasets of 1588 known viral immunogens and 468 viral non-immunogens, we apply machine learning algorithms to develop models for the prediction of protective immunogens of viral origin. The datasets are split into training and test sets in a 4:1 ratio. The protein structures are encoded by E-descriptors and transformed into uniform vectors by the auto- and cross-covariance methods. The most relevant descriptors are selected by the gain/ratio technique. The models generated by Random Forest, Multilayer Perceptron, and XGBoost algorithms demonstrate superior predictive performance on the test sets, surpassing predictions made by VaxiJen 2.0-an established gold standard in viral immunogenicity prediction. The key attributes determining immunogenicity in viral proteins are specific fingerprints in hydrophobicity and steric properties.

Modeling Peptide-Protein Interactions by a Logo-Based Method: Application in Peptide-HLA Binding Predictions.

Peptide-protein interactions form a cornerstone in molecular biology, governing cellular signaling, structure, and enzymatic activities in living organisms. Improving computational models and experimental techniques to describe and predict these interactions remains an ongoing area of research. Here, we present a computational method for peptide-protein interactions' description and prediction based on leveraged amino acid frequencies within specific binding cores. Utilizing normalized frequencies, we construct quantitative matrices (QMs), termed 'logo models' derived from sequence logos. The method was developed to predict peptide binding to HLA-DQ2.5 and HLA-DQ8.1 proteins associated with susceptibility to celiac disease. The models were validated by more than 17,000 peptides demonstrating their efficacy in discriminating between binding and non-binding peptides. The logo method could be applied to diverse peptide-protein interactions, offering a versatile tool for predictive analysis in molecular binding studies.

Lower vitamin D levels are associated with the pathogenesis of inflammatory bowel diseases.

Vitamin D plays a role in regulating immune homeostasis, inflammation and has an impact on the pathogenesis of inflammatory bowel diseases (IBD). IBD has a multifactorial pathogenesis primarily associated with immune dysregulation, dysbiosis, structurally altered intestinal mucosa, and genetic factors. The immunomodulatory function of this vitamin is linked to its control over innate and adaptive immunity, facilitated through its nuclear vitamin D receptor, leading to the inhibition of nuclear factor kappa-B. This study aimed to investigate serum vitamin D levels in patients with IBD compared to healthy individuals and to evaluate the relationship between vitamin D and inflammatory markers. Cross-sectional study. The study included 106 participants divided into 2 groups: patients with IBD (92), and healthy controls (14). The diagnosis of IBD was based on clinical, laboratory, fecal, endoscopic, and histological findings, following the European guidelines for diagnosis and follow-up ECCO-ESGAR guidelines for diagnostic assessment of IBD from 2019. Serum vitamin D levels were measured along with laboratory tests, imaging, and endoscopic examinations. IBD activity was evaluated using the Montreal classification and clinical and endoscopic indices. Data analysis involved calculating the mean, minimum, and maximum values, standard deviation, and Pearson coefficient. The level of statistical significance for this study was set at P < .05. The study found a prevalence of vitamin D deficiency in 32.6% of patients with IBD, while 66.3% had insufficiency, as compared with healthy individuals. The mean levels of vitamin D in UC and CD were 16 ± 8.6 ng/mL, whereas in the control healthy group, they were 26 ± 9.73 ng/mL. A statistically significant reverse correlation was observed between lower vitamin D levels and higher levels of the inflammatory markers. The study concluded that IBD patients exhibit lower levels of vitamin D, which is associated with inflammation and may contribute to the pathogenesis of the disease.

Prediction of Bacterial Immunogenicity by Machine Learning Methods.

Prediction of bacterial immunogens is a prerequisite for the process of vaccine development through reverse vaccinology. The application of in silico methods allows significant reduction in time and cost for the discovery of potential vaccine candidates among proteins of a bacterial species. The steps in the prediction algorithm include collection of protein sequence datasets of known bacterial immunogens and non-immunogens, data preprocessing to transform the protein sequences into numerical matrices suitable for use as training and test sets for various machine learning methods, and derivation of predictive models. The performance of the derived models is evaluated by means of classification metrics.In this chapter, we present a protocol for predicting bacterial immunogenicity by applying machine learning methods. The protocol describes the process of model development from data collection and manipulation to training and validation of the derived models.

CEST-MRI for body oncologic imaging: are we there yet?

Chemical exchange saturation transfer (CEST) MRI has gained recognition as a valuable addition to the molecular imaging and quantitative biomarker arsenal, especially for characterization of brain tumors. There is also increasing interest in the use of CEST-MRI for applications beyond the brain. However, its translation to body oncology applications lags behind those in neuro-oncology. The slower migration of CEST-MRI to non-neurologic applications reflects the technical challenges inherent to imaging of the torso. In this review, we discuss the application of CEST-MRI to oncologic conditions of the breast and torso (i.e., body imaging), emphasizing the challenges and potential solutions to address them. While data are still limited, reported studies suggest that CEST signal is associated with important histology markers such as tumor grade, receptor status, and proliferation index, some of which are often associated with prognosis and response to therapy. However, further technical development is still needed to make CEST a reliable clinical application for body imaging and establish its role as a predictive and prognostic biomarker.

Ni-Cu and Ni-Co-Modified Fly Ash Zeolite Catalysts for Hydrodeoxygenation of Levulinic Acid to γ-Valerolactone.

Monometallic (Ni, Co, Cu) and bimetallic (Ni-Co, Ni-Cu) 10-20 wt.% metal containing catalysts supported on fly ash zeolite were prepared by post-synthesis impregnation method. The catalysts were characterized by X-ray powder diffraction, N2 physisorption, XPS and H2-TPR methods. Finely dispersed metal oxides and mixed oxides were detected after the decomposition of the impregnating salt on the relevant zeolite support. Via reduction intermetallic, NiCo and NiCu phases were identified in the bimetallic catalysts. The catalysts were studied in hydrodeoxygenation of lignocellulosic biomass-derived levulinic acid to γ-valerolactone (GVL) in a batch system by water as a solvent. Bimetallic, 10 wt.% Ni, and 10 wt.% Cu or Co containing fly ash zeolite catalysts showed higher catalytic activity than monometallic ones. Their selectivity to GVL reached 70-85% at about 100% conversion. The hydrogenation activity of catalysts was found to be stronger compared to their hydration ability; therefore, the reaction proceeds through formation of 4-hydroxy pentanoic acid as the only intermediate compound.

Assessment of Novel Proteins Triggering Celiac Disease via Docking-Based Approach.

Human leukocyte antigens (HLAs) are pivotal in antigen processing, presenting to CD4+ T cells, and are linked to autoimmune disease susceptibility. In celiac disease, HLA-DQ2.5 and HLA-DQ8.1 bind gluten peptides on APCs, some recognized by CD4+ T cells, prompting inflammation and tissue damage. While extensively studied experimentally, these alleles lack comprehensive in silico analysis. To explore peptide-HLA preferences, we used molecular docking on peptide libraries, deriving quantitative matrices (QMs) for evaluating amino acids at nine-residue peptide binding cores. Our findings tie specific residue preferences to peptide backbone conformations. Validating QMs on known binders and non-binders showed strong predictive power (89-94% accuracy). These QMs excel in screening protein libraries, even whole proteomes, notably reducing time and costs for celiac disease risk assessment in novel proteins. This computational approach aligns with European Food Safety Authority guidance, promising efficient screening for potential celiac disease triggers.

Design and Characterization of Hyperpolarized 15N-BBCP as a H2O2-Sensing Probe.

Hydrogen peroxide (H2O2) is a type of reactive oxygen species that regulates essential biological processes. Despite the central role of H2O2 in pathophysiological states, available molecular probes for assessing H2O2 in vivo are still limited. This work develops hyperpolarized 15N-boronobenzyl-4-cyanopyridinium (15N-BBCP) as a rationally designed molecular probe for detecting H2O2. The 15N-BBCP demonstrated favorable physicochemical and biochemical properties for H2O2 detection and dynamic nuclear polarization, allowing noninvasive detection of H2O2. In particular, 15N-BBCP and the products possessed long spin-lattice relaxation times and spectrally resolvable 15N chemical shift differences. The performance of hyperpolarized 15N-BBCP was demonstrated both in vitro and in vivo with time-resolved 15N-MRS. This study highlights a promising approach to designing a reaction-based 15N-labeled molecular imaging agent for detecting oxidative stress in vivo.

Virtual Screening and Hit Selection of Natural Compounds as Acetylcholinesterase Inhibitors.

Acetylcholinesterase (AChE) is one of the classical targets in the treatment of Alzheimer's disease (AD). Inhibition of AChE slows down the hydrolysis of acetycholine and increases choline levels, improving the cognitive function. The achieved success of plant-based natural drugs acting as AChE inhibitors, such as galantamine (GAL) from Galanthus genus and huperzine A from Huperzia serrate (approved drug in China), in the treatment of AD, and the fact that natural compounds (NCs) are considered as safer and less toxic compared to synthetic drugs, led us to screen the available NCs (almost 150,000) in the ZINC12 database for AChE inhibitory activity. The compounds were screened virtually by molecular docking, filtered for suitable ADME properties, and 32 ligands from 23 structural groups were selected. The stability of the complexes was estimated via 1 µs molecular dynamics simulation. Ten compounds formed stable complexes with the enzyme and had a vendor and a reasonable price per mg. They were tested for AChE inhibitory and antioxidant activity. Five compounds showed weak AChE inhibition and three of them exhibited high antioxidant activity.

UHPLC-Orbitrap-MS Tentative Identification of 51 Oleraceins (Cyclo-Dopa Amides) in Portulaca oleracea L. Cluster Analysis and MS2 Filtering by Mass Difference.

Oleraceins are a class of indoline amide glycosides found in Portulaca oleracea L. (Portulacaceae), or purslane. These compounds are characterized by 5,6-dihydroxyindoline-2-carboxylic acid N-acylated with cinnamic acid derivatives, and many are glucosylated. Herein, hydromethanolic extracts of the aerial parts of purslane were subjected to UHPLC-Orbitrap-MS analysis, in negative ionization mode. Diagnostic ion filtering (DIF), followed by diagnostic difference filtering (DDF), were utilized to automatically filter out MS data and select plausible oleracein structures. After an in-depth MS2 analysis, a total of 51 oleracein compounds were tentatively identified. Of them, 26 had structures, matching one of the already known oleracein, and the other 25 were new, undescribed in the literature compounds, belonging to the oleracein class. Moreover, based on selected diagnostic fragment ions, clustering algorithms and visualizations were utilized. As we demonstrate, clustering methods provide valuable insights into the mass fragmentation elucidation of natural compounds in complex mixtures.

Prenatal and Early Postnatal Behavioural Programming in Laying Hens, With Possible Implications for the Development of Injurious Pecking.

Injurious pecking (IP) represents a serious concern for the welfare of laying hens (Gallus gallus domesticus). The risk of IP among hens with intact beaks in cage-free housing prompts a need for solutions based on an understanding of underlying mechanisms. In this review, we explore how behavioural programming via prenatal and early postnatal environmental conditions could influence the development of IP in laying hens. The possible roles of early life adversity and mismatch between early life programming and subsequent environmental conditions are considered. We review the role of maternal stress, egg conditions, incubation settings (temperature, light, sound, odour) and chick brooding conditions on behavioural programming that could be linked to IP. Brain and behavioural development can be programmed by prenatal and postnatal environmental conditions, which if suboptimal could lead to a tendency to develop IP later in life, as we illustrate with a Jenga tower that could fall over if not built solidly. If so, steps taken to optimise the environmental conditions of previous generations and incubation conditions, reduce stress around hatching, and guide the early learning of chicks will aid in prevention of IP in commercial laying hen flocks.

Effects of Curcumin and Ferulic Acid on the Folding of Amyloid-ß Peptide.

The polyphenols curcumin (CU) and ferulic acid (FA) are able to inhibit the aggregation of amyloid-ß (Aß) peptide with different strengths. CU is a strong inhibitor while FA is a weaker one. In the present study, we examine the effects of CU and FA on the folding process of an Aß monomer by 1 µs molecular dynamics (MD) simulations. We found that both inhibitors increase the helical propensity and decrease the non-helical propensity of Aß peptide. They prevent the formation of a dense bulk core and shorten the average lifetime of intramolecular hydrogen bonds in Aß. CU makes more and longer-lived hydrogen bonds, hydrophobic, π-π, and cation-π interactions with Aß peptide than FA does, which is in a good agreement with the observed stronger inhibitory activity of CU on Aß aggregation.

Spectral fitting strategy to overcome the overlap between 2-hydroxyglutarate and lipid resonances at 2.25 ppm.

PURPOSE: 1 H MRS provides a noninvasive tool for identifying mutations in isocitrate dehydrogenase (IDH). Quantification of the prominent 2-hydroxyglutarate (2HG) resonance at 2.25 ppm is often confounded by the lipid resonance at the same frequency in tumors with elevated lipids. We propose a new spectral fitting approach to separate these overlapped signals, therefore, improving 2HG evaluation. METHODS: TE 97 ms PRESS was acquired at 3T from 42 glioma patients. New lipid basis sets were created, in which the small lipid 2.25-ppm signal strength was preset with reference to the lipid signal at 0.9 ppm, incorporating published fat relaxation data. LCModel fitting using the new lipid bases (Fitting method 2) was conducted along with fitting using the LCModel built-in lipid basis set (Fitting method 1), in which the lipid 2.25-ppm signal is assessed with reference to the lipid 1.3-ppm signal. In-house basis spectra of low-molecular-weight metabolites were used in both fitting methods. RESULTS: Fitting method 2 showed marked improvement in identifying IDH mutational status compared with Fitting method 1. 2HG estimates from Fitting method 2 were overall smaller than those from Fitting method 1, which was because of differential assignment of the signal at 2.25 ppm to lipids. In receiver operating characteristic analysis, Fitting method 2 provided a complete distinction between IDH mutation and wild-type whereas Fitting method 1 did not. CONCLUSION: The data suggest that 1 H MR spectral fitting using the new lipid basis set provides a robust fitting strategy that improves 2HG evaluation in brain tumors with elevated lipids.

A 16-Channel 13C Array Coil for Magnetic Resonance Spectroscopy of the Breast at 7T.

OBJECTIVE: Considering the reported elevation of ω-6/ω-3 fatty acid ratios in breast neoplasms, one particularly important application of 13C MRS could be in more fully understanding the breast lipidome's relationship to breast cancer incidence. However, the low natural abundance and gyromagnetic ratio of the 13C isotope lead to detection sensitivity challenges. Previous 13C MRS studies have relied on the use of small surface coils with limited field-of-view and shallow penetration depths to achieve adequate signal-to-noise ratio (SNR), and the use of receive array coils is still mostly unexplored. METHODS: This work presents a unilateral breast 16-channel 13C array coil and interfacing hardware designed to retain the surface sensitivity of a single small loop coil while improving penetration depth and extending the field-of-view over the entire breast at 7T. The coil was characterized through bench measurements and phantom 13C spectroscopy experiments. RESULTS: Bench measurements showed receive coil matching better than -17 dB and average preamplifier decoupling of 16.2 dB with no evident peak splitting. Phantom MRS studies show better than a three-fold increase in average SNR over the entirety of the breast region compared to volume coil reception alone as well as an ability for individual array elements to be used for coarse metabolite localization without the use of single-voxel or spectroscopic imaging methods. CONCLUSION: Our current study has shown the benefits of the array. Future in vivo lipidomics studies can be pursued. SIGNIFICANCE: Development of the 16-channel breast array coil opens possibilities of in vivo lipidomics studies to elucidate the link between breast cancer incidence and lipid metabolics.

Two Faces of Milk Proteins Peptides with Both Allergenic and Multidimensional Health Beneficial Impact- Integrated In Vitro/In Silico Approach.

The main food-origin antigens that the infant's body is in contact with are cow's milk proteins (CMP). Still, CMP are one of the main sources of beneficial biologically active peptides that play a role in treatment of non-communicable diseases. Safe methods to quickly predict the sensitizing potential of food proteins among their range of health-promoting properties are essential. The aim of study was to adapt an integrated approach combining several in silico (IS) studies and in vitro (IV) assays to screen the multifunctionality of CMP-derived peptides. Major histocompatability complex type II MHC II-binders, interleukin-4 and -10 inducers, interferon γ -inducers and immunobioactivity tools were used to predict the peptide-power of inducing allergies or tolerance. A comparison of the peptide profiless revealed the presence of one identical and one overlapping sequence in IS and IV hydrolysate. By IS analysis, four of 24 peptides were found to have high affinity and stimulate IL-4 expression, and by IV, one of seven peptides had this potential (Bos d9 peptide DIPNPIGSENSEK (195-208)). Three IV peptides may induce IL-10 expression. The IV/IS assessment seems promising agents for peptides' potential determination dedicated only to preliminary screening of peptides. The IV verification is still crucial in further steps of studies.

Combining inhomogeneous magnetization transfer and multipoint Dixon acquisition: Potential utility and evaluation.

PURPOSE: The recently introduced inhomogeneous magnetization transfer (ihMT) method has predominantly been applied for imaging the central nervous system. Future applications of ihMT, such as in peripheral nerves and muscles, will involve imaging in the vicinity of adipose tissues. This work aims to systematically investigate the partial volume effect of fat on the ihMT signal and to propose an efficient fat-separation method that does not interfere with ihMT measurements. METHODS: First, the influence of fat on ihMT signal was studied using simulations. Next, the ihMT sequence was combined with a multi-echo Dixon acquisition for fat separation. The sequence was tested in 9 healthy volunteers using a 3T human scanner. The ihMT ratio (ihMTR) values were calculated in regions of interest in the brain and the spinal cord using standard acquisition (no fat saturation), water-only, in-phase, and out-of-phase reconstructions. The values obtained were compared with a standard fat suppression method, spectral presaturation with inversion recovery. RESULTS: Simulations showed variations in the ihMTR values in the presence of fat, depending on the TEs used. The IhMTR values in the brain and spinal cord derived from the water-only ihMT multi-echo Dixon images were in good agreement with values from the unsuppressed sequence. The ihMT-spectral presaturation with inversion recovery combination resulted in 24%-35% lower ihMTR values compared with the standard non-fat-suppressed acquisition. CONCLUSION: The presence of fat within a voxel affects the ihMTR calculations. The IhMT multi-echo Dixon method does not compromise the observable ihMT effect and can potentially be used to remove fat influence in ihMT.