Metabolomic and Proteomic Profiling of Athletes Performing Physical Activity under Hypoxic Conditions.

Proteomic and metabolomic research enables quantitation of the molecular profile of athletes. Multiomic profiling was conducted using plasma samples collected from 18 male athletes performing aerobic activity (running) at high altitude. Metabolomic profiling detected changes in the levels of 4-hydroxyproline, methionine, oxaloacetate, and tyrosine during the recovery period. Furthermore, proteomic profiling revealed changes in expression of proteins contributing to the function of the immune system, muscle damage, metabolic fitness and performance, as well as hemostasis. Further research should focus on developing metabolic models to monitor training intensity and athlete adaptation.

Analysis of Structural Changes in the Protein near the Phosphorylation Site.

Modification of the protein after synthesis (PTM) often affects protein function as supported by numerous studies. However, there is no consensus about the degree of structural protein changes after modification. For phosphorylation of serine, threonine, and tyrosine, which is a common PTM in the biology of living organisms, we consider topical issues related to changes in the geometric parameters of a protein (Rg, RMSD, Cα displacement, SASA). The effect of phosphorylation on protein geometry was studied both for the whole protein and at the local level (i.e., in different neighborhoods of the modification site). Heterogeneity in the degree of protein structural changes after phosphorylation was revealed, which allowed for us to isolate a group of proteins having pronounced local structural changes in the neighborhoods of up to 15 amino acid residues from the modification site. This is a comparative study of protein structural changes in neighborhoods of 3-15 amino acid residues from the modified site. Amino acid phosphorylation in proteins with pronounced local changes caused switching from the inactive functional state to the active one.

SAFoldNet: A Novel Tool for Discovering and Aligning Three-Dimensional Protein Structures Based on a Neural Network.

The development and improvement of methods for comparing and searching for three-dimensional protein structures remain urgent tasks in modern structural biology. To solve this problem, we developed a new tool, SAFoldNet, which allows for searching, aligning, superimposing, and determining the exact coordinates of fragments of protein structures. The proposed search and alignment tool was built using neural networking. Specifically, we implemented the integrative synergy of neural network predictions and the well-known BLAST algorithm for searching and aligning sequences. The proposed method involves multistage processing, comprising a stage for converting the geometry of protein structures into sequences of a structural alphabet using a neural network, a search stage for forming a set of candidate structures, and a refinement stage for calculating the structural alignment and overlap and evaluating the similarity with the starting structure of the search. The effectiveness and practical applicability of the proposed tool were compared with those of several widely used services for searching and aligning protein structures. The results of the comparisons confirmed that the proposed method is effective and competitive relative to the available modern services. Furthermore, using the proposed approach, a service with a user-friendly web interface was developed, which allows for searching, aligning, and superimposing protein structures; determining the location of protein fragments; mapping onto a protein molecule chain; and providing structural similarity metrices (expected value and root mean square deviation).

Modeling Side Chains in the Three-Dimensional Structure of Proteins for Post-Translational Modifications.

Amino acid substitutions and post-translational modifications (PTMs) play a crucial role in many cellular processes by directly affecting the structural and dynamic features of protein interaction. Despite their importance, the understanding of protein PTMs at the structural level is still largely incomplete. The Protein Data Bank contains a relatively small number of 3D structures having post-translational modifications. Although recent years have witnessed significant progress in three-dimensional modeling (3D) of proteins using neural networks, the problem related to predicting accurate PTMs in proteins has been largely ignored. Predicting accurate 3D PTM models in proteins is closely related to another fundamental problem: predicting the correct side-chain conformations of amino acid residues in proteins. An analysis of publications as well as the paid and free software packages for modeling three-dimensional structures showed that most of them focus on working with unmodified proteins and canonical amino acid residues; the number of articles and software packages placing emphasis on modeling three-dimensional PTM structures is an order of magnitude smaller. This paper focuses on modeling the side-chain conformations of proteins containing PTMs (nonstandard amino acid residues). We collected our own libraries comprising the most frequently observed PTMs from the PDB and implemented a number of algorithms for predicting the side-chain conformation at modification points and in the immediate environment of the protein. A comprehensive analysis of both the algorithms per se and compared to the common Rosetta and FoldX structure modeling packages was also carried out. The proposed algorithmic solutions are comparable in their characteristics to the well-known Rosetta and FoldX packages for the modeling of three-dimensional structures and have great potential for further development and optimization. The source code of algorithmic solutions has been deposited to and is available at the GitHub source.

Determination of Heterogeneous Proteomic and Metabolomic Response in anti-TNF and anti-IL-6 Treatment of Patients with Rheumatoid Arthritis.

Reduction in tumor necrosis factor (αTNF) and interleukin-6 (IL-6) activities is a widely utilized strategy for the treatment of rheumatoid arthritis (RA) with a high success rate. Despite both schemes targeting the deprivation of inflammatory reactions caused by the excessive activity of cytokines, their mechanisms of action and the final output are still unequal. This was a comparative longitudinal study that lasted for 24 weeks and aimed to find the answer to why the two schemes of therapy can pass out of proportion in attitude of their efficiency. What are the differences in metabolic and proteomic responses among patients who were being treated by either the anti-TNF or anti-IL-6 strategy? We found increased levels of immunoglobulins A and G (more than 2-fold in anti-IL-6 and more than 4-5-fold in anti-TNF groups) at the final stage (24 weeks) of monitoring but the most profound increase was determined for µ-chains of immunoglobulins in both groups of study. Metabolomic changes displayed main alterations with regard to arginine metabolism and collagen maintenance, where arginine increased 8.86-fold (p < 0.001) in anti-TNF and 5.71-fold (p < 0.05) in anti-IL-6 groups but patients treated by the anti-TNF scheme suffered a higher depletion of arginine before the start of therapy. Some indicators of matrix and bone tissue degradation also increased 4-hydroxyproline (4-HP) more than 6-fold (p < 0.001) in anti-TNF and more than 2-fold (p < 0.05) in the anti-IL-6 group, but the growth dynamics in the anti-IL6 group was delayed (gradually raised at week 24) compared to the anti-TNF group (raised at week 12) following a smooth reduction. The ELISA analysis of IL-6 and TNFα concentration in the study population supported proteomic and metabolomic data. A positive correlation between ΔCDAI and ΔDAS28 indicators and ESR and CRP was established for the majority of patients after 24 weeks of treatment where ESR and CRP reduced by 20% and 40% finally, respectively. A regression model using the Forest Plot was estimated to elucidate the impact of the most significant clinical, biochemical, and anthropometric indicators for the evaluation of differences between considered anti-TNF and anti-IL-6 schemes of therapy.

Consolidation of metabolomic, proteomic, and GWAS data in connective model of schizophrenia.

Despite of multiple systematic studies of schizophrenia based on proteomics, metabolomics, and genome-wide significant loci, reconstruction of underlying mechanism is still a challenging task. Combination of the advanced data for quantitative proteomics, metabolomics, and genome-wide association study (GWAS) can enhance the current fundamental knowledge about molecular pathogenesis of schizophrenia. In this study, we utilized quantitative proteomic and metabolomic assay, and high throughput genotyping for the GWAS study. We identified 20 differently expressed proteins that were validated on an independent cohort of patients with schizophrenia, including ALS, A1AG1, PEDF, VTDB, CERU, APOB, APOH, FASN, GPX3, etc. and almost half of them are new for schizophrenia. The metabolomic survey revealed 18 group-specific compounds, most of which were the part of transformation of tyrosine and steroids with the prevalence to androgens (androsterone sulfate, thyroliberin, thyroxine, dihydrotestosterone, androstenedione, cholesterol sulfate, metanephrine, dopaquinone, etc.). The GWAS assay mostly failed to reveal significantly associated loci therefore 52 loci with the smoothened p < 10-5 were fractionally integrated into proteome-metabolome data. We integrated three omics layers and powered them by the quantitative analysis to propose a map of molecular events associated with schizophrenia psychopathology. The resulting interplay between different molecular layers emphasizes a strict implication of lipids transport, oxidative stress, imbalance in steroidogenesis and associated impartments of thyroid hormones as key interconnected nodes essential for understanding of how the regulation of distinct metabolic axis is achieved and what happens in the conditioned proteome and metabolome to produce a schizophrenia-specific pattern.

Influence of Sports Training in Foothills on the Professional Athlete's Immunity.

Neuroplasticity and inflammation play important part in the body's adaptive reactions in response to prolonged physical activity. These processes are associated with the cross-interaction of the nervous and immune systems, which is realized through the transmission of signals from neurotransmitters and cytokines. Using the methods of flow cytometry and advanced biochemical analysis of blood humoral parameters, we showed that intense and prolonged physical activity at the anaerobic threshold, without nutritional and metabolic support, contributes to the development of exercise-induced immunosuppression in sportsmen. These athletes illustrate the following signs of a decreased immune status: fewer absolute indicators of the content of leukocytes, lowered values in the immunoregulatory index (CD4+/CD8+), and diminished indicators of humoral immunity (immunoglobulins A, M, and G, and IFN-γ). These factors characterize the functional state of cellular and humoral immunity and their reduction affects the prenosological risk criteria, indicative of the athletes' susceptibility to develop exercise-induced immunosuppression.

Molecular Profiling of Athletes Performing High-Intensity Exercises in Extreme Environments.

The aim of this study was to determine the influence of high-intensity training under extreme conditions (T = 40 °C) on the metabolism and immunological reactions of athletes. Male triathletes (n = 11) with a high level of sports training performed load testing to failure (17 ± 2.7 min) and maximum oxygen consumption (64.1 ± 6.4 mL/min/kg). Blood plasma samples were collected before and immediately after exercise. Mass spectrometric metabolomic analysis identified 30 metabolites and 6 hormones in the plasma, of which 21 and 4 changed after exercise, respectively. Changes in the intermediate products of tricarboxylic and amino acids were observed (FC > 1.5) after exercise. The obtained data can be associated with the effect of physical activity on metabolism in athletes. Therefore, constant monitoring of the biochemical parameters of athletes can help coaches identify individual shortcomings in a timely manner and track changes, especially as the volume of training increases. In addition, it was revealed that the immunological reaction (manifestation of a hyperactive reaction to food components) is personalized in nature. Therefore, it is important for coaches and sports doctors to analyze and control the eating behavior of athletes to identify food intolerances or food allergies in a timely manner and develop an individual elimination diet.

In Silico Study of the Interactions of Anle138b Isomer, an Inhibitor of Amyloid Aggregation, with Partner Proteins.

Herein, we aimed to highlight current "gaps" in the understanding of the potential interactions between the Anle138b isomer ligand, a promising agent for clinical research, and the intrinsically disordered alpha-synuclein protein. The presence of extensive unstructured areas in alpha-synuclein determines its existence in the cell of partner proteins, including the cyclophilin A chaperone, which prevents the aggregation of alpha-synuclein molecules that are destructive to cell life. Using flexible and cascaded molecular docking techniques, we aimed to expand our understanding of the molecular architecture of the protein complex between alpha-synuclein, cyclophilin A and the Anle138b isomer ligand. We demonstrated the possibility of intricate complex formation under cellular conditions and revealed that the main interactions that stabilize the complex are hydrophobic and involve hydrogen.

PSSNet-An Accurate Super-Secondary Structure for Protein Segmentation.

A super-secondary structure (SSS) is a spatially unique ensemble of secondary structural elements that determine the three-dimensional shape of a protein and its function, rendering SSSs attractive as folding cores. Understanding known types of SSSs is important for developing a deeper understanding of the mechanisms of protein folding. Here, we propose a universal PSSNet machine-learning method for SSS recognition and segmentation. For various types of SSS segmentation, this method uses key characteristics of SSS geometry, including the lengths of secondary structural elements and the distances between them, torsion angles, spatial positions of Cα atoms, and primary sequences. Using four types of SSSs (ßαß-unit, α-hairpin, ß-hairpin, αα-corner), we showed that extensive SSS sets could be reliably selected from the Protein Data Bank and AlphaFold 2.0 database of protein structures.

Tear Proteome Revealed Association of S100A Family Proteins and Mesothelin with Thrombosis in Elderly Patients with Retinal Vein Occlusion.

Tear samples collected from patients with central retinal vein occlusion (CRVO; n = 28) and healthy volunteers (n = 29) were analyzed using a proteomic label-free absolute quantitative approach. A large proportion (458 proteins with a frequency > 0.6) of tear proteomes was found to be shared between the study groups. Comparative proteomic analysis revealed 29 proteins (p < 0.05) significantly differed between CRVO patients and the control group. Among them, S100A6 (log (2) FC = 1.11, p < 0.001), S100A8 (log (2) FC = 2.45, p < 0.001), S100A9 (log2 (FC) = 2.08, p < 0.001), and mesothelin ((log2 (FC) = 0.82, p < 0.001) were the most abundantly represented upregulated proteins, and ß2-microglobulin was the most downregulated protein (log2 (FC) = −2.13, p < 0.001). The selected up- and downregulated proteins were gathered to customize a map of CRVO-related critical protein interactions with quantitative properties. The customized map (FDR < 0.01) revealed inflammation, impairment of retinal hemostasis, and immune response as the main set of processes associated with CRVO ischemic condition. The semantic analysis displayed the prevalence of core biological processes covering dysregulation of mitochondrial organization and utilization of improperly or topologically incorrect folded proteins as a consequence of oxidative stress, and escalating of the ischemic condition caused by the local retinal hemostasis dysregulation. The most significantly different proteins (S100A6, S100A8, S100A9, MSLN, and ß2-microglobulin) were applied for the ROC analysis, and their AUC varied from 0.772 to 0.952, suggesting probable association with the CRVO.

3ß-Corner Stability by Comparative Molecular Dynamics Simulations.

This study explored the mechanisms by which the stability of super-secondary structures of the 3ß-corner type autonomously outside the protein globule are maintained in an aqueous environment. A molecular dynamic (MD) study determined the behavioral diversity of a large set of non-homologous 3ß-corner structures of various origins. We focused on geometric parameters such as change in gyration radius, solvent-accessible area, major conformer lifetime and torsion angles, and the number of hydrogen bonds. Ultimately, a set of 3ß-corners from 330 structures was characterized by a root mean square deviation (RMSD) of less than 5 Å, a change in the gyration radius of no more than 5%, and the preservation of amino acid residues positioned within the allowed regions on the Ramachandran map. The studied structures retained their topologies throughout the MD experiments. Thus, the 3ß-corner structure was found to be rather stable per se in a water environment, i.e., without the rest of a protein molecule, and can act as the nucleus or "ready-made" building block in protein folding. The 3ß-corner can also be considered as an independent object for study in field of structural biology.

Tracking Health, Performance and Recovery in Athletes Using Machine Learning.

Training and competitive periods can temporarily impair the performance of an athlete. This disruption can be short- or long-term, lasting up to several days. We analyzed the health indicators of 3661 athletes during an in-depth medical examination. At the time of inclusion in the study, the athletes were healthy. Instrumental examinations (fluorography, ultrasound examination of the abdominal cavity and pelvic organs, echocardiography, electrocardiography, and stress testing "to failure"), laboratory examinations (general urinalysis and biochemical and general clinical blood analysis), and examinations by specialists (ophthalmologist, otolaryngologist, surgeon, cardiologist, neurologist, dentist, gynecologist (women), endocrinologist, and therapist) were performed. This study analyzed the significance of determining the indicators involved in the implementation of the "catabolism" and "anabolism" phenotypes using the random forest and multinomial logistic regression machine learning methods. The use of decision forest and multinomial regression models made it possible to identify the most significant indicators of blood and urine biochemistry for the analysis of phenotypes as a characterization of the effectiveness of recovery processes in the post-competitive period in athletes. We found that the parameters of muscle metabolism, such as aspartate aminotransferase, creatine kinase, lactate dehydrogenase, and alanine aminotransferase levels, and the parameters of the ornithine cycle, such as creatinine, urea acid, and urea levels, made the most significant contribution to the classification of two types of metabolism: catabolism and anabolism.

Atomic Simulation of the Binding of JAK1 and JAK2 with the Selective Inhibitor Ruxolitinib.

Rheumatoid arthritis belongs to the group of chronic systemic autoimmune diseases characterized by the development of destructive synovitis and extra-articular manifestations. Cytokines regulate a wide range of inflammatory processes involved in the pathogenesis of rheumatoid arthritis and contribute to the induction of autoimmunity and chronic inflammation. Janus-associated kinase (JAK) and signal transducer and activator of transcription (STAT) proteins mediate cell signaling from cytokine receptors, and are involved in the pathogenesis of autoimmune and inflammatory diseases. Targeted small-molecule drugs that inhibit the functional activity of JAK proteins are used in clinical practice for the treatment of rheumatoid arthritis. In our study, we modeled the interactions of the small-molecule drug ruxolitinib with JAK1 and JAK2 isoforms and determined the binding selectivity using molecular docking. Molecular modeling data show that ruxolitinib selectively binds the JAK1 and JAK2 isoforms with a binding affinity of -8.3 and -8.0 kcal/mol, respectively. The stabilization of ligands in the cavity of kinases occurs primarily through hydrophobic interactions. The amino acid residues of the protein globules of kinases that are responsible for the correct positioning of the drug ruxolitinib and its retention have been determined.

Metabolomic Markers for Predicting Preeclampsia in the First Trimester of Pregnancy: A Retrospective Study.

We sought to identify the characteristic metabolite profile of blood plasma samples obtained from patients with preeclampsia. Direct high-resolution mass spectrometry was used to analyze samples from 79 pregnant women, 34 of whom had preeclampsia. We performed a comparative analysis of the metabolite profiles and found that they differed between pregnant women with and without preeclampsia. Lipids and sugars were identified as components of the metabolite profile that are likely to be associated with the development of preeclampsia. While PE was established only in the third trimester, a set of metabolites specific for the third trimester, including 2-(acetylamino)-1,5-anhydro-2-deoxy-4-O-b-D-galactopyranosyl-D-arabino-Hex-1-enitol, N-Acetyl-D-glucosaminyldiphosphodolichol, Cer(d18:0/20:0), and allolithocholic acid, was already traced in the first trimester. These components are also likely involved in lipid metabolism disorders and the development of oxidative stress.

Proteomic and electron microscopy study of myogenic differentiation of alveolar mucosa multipotent mesenchymal stromal cells in three-dimensional culture.

Myocyte differentiation is featured by adaptation processes, including mitochondria repopulation and cytoskeleton re-organization. The difference between monolayer and spheroid cultured cells at the proteomic level is uncertain. We cultivated alveolar mucosa multipotent mesenchymal stromal cells in spheroids in a myogenic way for the proper conditioning of ECM architecture and cell morphology, which induced spontaneous myogenic differentiation of cells within spheroids. Electron microscopy analysis was used for the morphometry of mitochondria biogenesis, and proteomic was used complementary to unveil events underlying differences between two-dimensional/three-dimensional myoblasts differentiation. The prevalence of elongated mitochondria with an average area of 0.097 µm2 was attributed to monolayer cells 7 days after the passage. The population of small mitochondria with a round shape and area of 0.049 µm2 (p < 0.05) was observed in spheroid cells cultured under three-dimensional conditions. Cells in spheroids were quantitatively enriched in proteins of mitochondria biogenesis (DNM1L, IDH2, SSBP1), respiratory chain (ACO2, ATP5I, COX5A), extracellular proteins (COL12A1, COL6A1, COL6A2), and cytoskeleton (MYL6, MYL12B, MYH10). Most of the Rab-related transducers were inhibited in spheroid culture. The proteomic assay demonstrated delicate mechanisms of mitochondria autophagy and repopulation, cytoskeleton assembling, and biogenesis. Differences in the ultrastructure of mitochondria indicate active biogenesis under three-dimensional conditions.

Chromatomass-Spectrometric Method for the Quantitative Determination of Amino- and Carboxylic Acids in Biological Samples.

A highly sensitive method for the qualitative and quantitative determination of amino- and carboxylic acids, as well as a number of urea and methionine cycle metabolites in the studied solutions, is presented. Derivatives (esterification) were obtained for amino acids by their reaction in a solution of 3 N of hydrochloric acid in n-butanol for 15 min at 65 °C and for carboxylic acids by their reaction with phenol in ethyl acetate with 3 N of hydrochloric acid for 20 min at 65 °C. Experimental work on the determination of individual metabolites was carried out using the HPLC-MS/MS method and included the creation of a library of spectra of the analyzed compounds and their quantitative determination. Multiplex methods have been developed for the quantitative analysis of the desired metabolites in a wide range of concentrations of 3-4 orders of magnitude. The approach to the analysis of metabolites was developed based on the method of the dynamic monitoring of multiple reactions of the formation of fragments for a mass analyzer with a triple quadrupole (QQQ). The effective chromatographic separation of endogenous metabolites was carried out within 13 min. The calibration curves of the analyzed compounds were stable throughout the concentration range and had the potential to fit below empirical levels. The developed methods and obtained experimental data are of interest for a wide range of biomedical studies, as well as for monitoring the content of endogenous metabolites in biological samples under various pathological conditions. The sensitivity limit of the methods for amino acids was about 4.8 nM and about 0.5 µM for carboxylic acids. Up to 19 amino- and up to 12 carboxy acids and about 10 related metabolites can be tested in a single sample.

Managing of Unassigned Mass Spectrometric Data by Neural Network for Cancer Phenotypes Classification.

Mass spectrometric profiling provides information on the protein and metabolic composition of biological samples. However, the weak efficiency of computational algorithms in correlating tandem spectra to molecular components (proteins and metabolites) dramatically limits the use of "omics" profiling for the classification of nosologies. The development of machine learning methods for the intelligent analysis of raw mass spectrometric (HPLC-MS/MS) measurements without involving the stages of preprocessing and data identification seems promising. In our study, we tested the application of neural networks of two types, a 1D residual convolutional neural network (CNN) and a 3D CNN, for the classification of three cancers by analyzing metabolomic-proteomic HPLC-MS/MS data. In this work, we showed that both neural networks could classify the phenotypes of gender-mixed oncology, kidney cancer, gender-specific oncology, ovarian cancer, and the phenotype of a healthy person by analyzing 'omics' data in 'mgf' data format. The created models effectively recognized oncopathologies with a model accuracy of 0.95. Information was obtained on the remoteness of the studied phenotypes. The closest in the experiment were ovarian cancer, kidney cancer, and prostate cancer/kidney cancer. In contrast, the healthy phenotype was the most distant from cancer phenotypes and ovarian and prostate cancers. The neural network makes it possible to not only classify the studied phenotypes, but also to determine their similarity (distance matrix), thus overcoming algorithmic barriers in identifying HPLC-MS/MS spectra. Neural networks are versatile and can be applied to standard experimental data formats obtained using different analytical platforms.

Sports Nutrition: Diets, Selection Factors, Recommendations.

An athlete's diet is influenced by external and internal factors that can reduce or exacerbate exercise-induced food intolerance/allergy symptoms. This review highlights many factors that influence food choices. However, it is important to remember that these food choices are dynamic, and their effectiveness varies with the time, location, and environmental factors in which the athlete chooses the food. Therefore, before training and competition, athletes should follow the recommendations of physicians and nutritionists. It is important to study and understand the nutritional strategies and trends that athletes use before and during training or competitions. This will identify future clinical trials that can be conducted to identify specific foods that athletes can consume to minimize negative symptoms associated with their consumption and optimize training outcomes.

Severe types of fetopathy are associated with changes in the serological proteome of diabetic mothers.

ABSTRACT: Pregestational or gestational diabetes are the main risk factors for diabetic fetopathy. There are no generalized signs of fetopathy before the late gestational age due to insufficient sensitivity of currently employed instrumental methods. In this cross-sectional observational study, we investigated several types of severe diabetic fetopathy (cardiomyopathy, central nervous system defects, and hepatomegaly) established in type 2 diabetic mothers during 30 to 35 gestational weeks and confirmed upon delivery. We examined peripheral blood plasma and determined a small proportion of proteins strongly associated with a specific type of fetopathy or anatomical malfunction. Most of the examined markers participate in critical processes at different stages of embryogenesis and regulate various phases of morphogenesis. Alterations in CDCL5 had a significant impact on mRNA splicing and DNA repair. Patients with central nervous system defects were characterized by the greatest depletion (ca. 7% of the basal level) of DFP3, a neurotrophic factor needed for the proper specialization of oligodendrocytes. Dysregulation of noncanonical wingless-related integration site signaling pathway (Wnt) signaling guided by pigment epithelium-derived factor (PEDF) and disheveled-associated activator of morphogenesis 2 (DAAM2) was also profound. In addition, deficiency in retinoic acid and thyroxine transport was exhibited by the dramatic increase of transthyretin (TTHY). The molecular interplay between the identified serological markers leads to pathologies in fetal development on the background of a diabetic condition. These warning serological markers can be quantitatively examined, and their profile may reflect different severe types of diabetic fetopathy, producing a beneficial effect on the current standard care for pregnant women and infants.