New Approach for Studying of Isoforms and High-Homology Proteins in Mammalian Cells.

In mammals, a large number of proteins are expressed as more than one isoform, resulting in the increased diversity of their proteome. Understanding the functions of isoforms is very important, since individual isoforms of the same protein can have oncogenic or pathogenic properties, or serve as disease markers. The high homology of isoforms with ubiquitous expression makes it difficult to study them. In this work, we propose a new approach for the study of protein isoforms in mammalian cells, which makes it possible to individually detect and investigate the functions of an individual isoform. The approach was developed to study the functions of isoforms of the PHF10 protein, a chromatin subunit of the PBAF remodeling complex. We demonstrated the possibility of induced simultaneous suppression of all endogenous PHF10 isoforms and the expression of a single recombinant FLAG-tagged isoform. For this purpose, we created constructs based on the pSLIK plasmid with a cloned cassette containing the recombinant gene of interest and miR30 with the corresponding shRNAs. The doxycycline-induced activation of the cassette allows on and off switching. Using this construct, we achieved the preferential expression of only one recombinant PHF10 isoform with a simultaneously reduced number of all endogenous isoforms. Our approach can be used to study the role of point mutations, the functions of individual domains and important sites, or to individually detect untagged isoforms with knockdown of all endogenous isoforms.

Targeted metabolomic profiling as a tool for diagnostics of patients with non-small-cell lung cancer.

Lung cancer is referred to as the second most common cancer worldwide and is mainly associated with complex diagnostics and the absence of personalized therapy. Metabolomics may provide significant insights into the improvement of lung cancer diagnostics through identification of the specific biomarkers or biomarker panels that characterize the pathological state of the patient. We performed targeted metabolomic profiling of plasma samples from individuals with non-small cell lung cancer (NSLC, n = 100) and individuals without any cancer or chronic pathologies (n = 100) to identify the relationship between plasma endogenous metabolites and NSLC by means of modern comprehensive bioinformatics tools, including univariate analysis, multivariate analysis, partial correlation network analysis and machine learning. Through the comparison of metabolomic profiles of patients with NSCLC and noncancer individuals, we identified significant alterations in the concentration levels of metabolites mainly related to tryptophan metabolism, the TCA cycle, the urea cycle and lipid metabolism. Additionally, partial correlation network analysis revealed new ratios of the metabolites that significantly distinguished the considered groups of participants. Using the identified significantly altered metabolites and their ratios, we developed a machine learning classification model with an ROC AUC value equal to 0.96. The developed machine learning lung cancer model may serve as a prototype of the approach for the in-time diagnostics of lung cancer that in the future may be introduced in routine clinical use. Overall, we have demonstrated that the combination of metabolomics and up-to-date bioinformatics can be used as a potential tool for proper diagnostics of patients with NSCLC.

Water Effective Diffusion Coefficient in Dairy Powder Calculated by Digital Image Processing and through Machine Learning Algorithms of CLSM Micrographs.

Rehydration of dairy powders is a complex and essential process. A relatively new quantitative mechanism for monitoring powders' rehydration process uses the effective diffusion coefficient. This research focused on modifying a previously used labor-intensive method that will be able to automatically measure the real-time water diffusion coefficient in dairy powders based on confocal microscopy techniques. Furthermore, morphological characteristics and local hydration of individual particles were identified using an imaging analysis procedure written in Matlab©-R2023b and image analysis through machine learning algorithms written in Python™-3.11. The first model includes segmentation into binary images and labeling particles during water diffusion. The second model includes the expansion of data set selection, neural network training and particle markup. For both models, the effective diffusion follows Fick's second law for spherical geometry. The effective diffusion coefficient on each particle was computed from the dye intensity during the rehydration process. The results showed that effective diffusion coefficients for water increased linearly with increasing powder particle size and are in agreement with previously used methods. In summary, the models provide two independent machine measurements of effective diffusion coefficient based on the same set of micrographs and may be useful in a wide variety of high-protein powders.

Stability of the PHF10 subunit of PBAF signature module is regulated by phosphorylation: role of ß-TrCP.

The PBAF chromatin-remodeling complexes are multi-protein machines, regulating expression of genes involved in proliferation and differentiation. PHF10 is a subunit of the PBAF essential for its association with chromatin. Mammalian PHF10 is expressed as four ubiquitous isoforms, which are alternatively incorporated in the complex and differ by their influence on transcription of target genes. PHF10 have different domain structure and two of them (PHF10-S isoforms) lack C-terminal PHD domains, which enables their phosphorylation by CK-1. Here we have found that PBAF subunits have low turnover rate, except for PHF10 which has much lower half-life, and is degraded by ß-TrCP. The ß-TrCP knockdown stabilizes PBAF core subunits - BRG1 and BAF155 and specific subunits - PHF10, BAF200, BAF180 and BRD7. PHF10 isoforms contain two non-canonical ß-TrCP degrons and are degraded by ß-TrCP in a phospho-dependent manner. But phosphorylation of PHF10-S degrons by CK-1, contrary to previously described degrons, prevents their degradation. Targeted molecular docking demonstrated that phosphorylated forms of PHF10 bind to ß-TrCP with much lower affinity than non-phosphorylated ones, contrary to previously described degrons. This unorthodox mechanism proposes that phosphorylation of ß-TrCP degrons by CK-1 could not only degrade a set of proteins, but also stabilize a different set of targets.

Synthesis, structure and affinity of novel 3-alkoxy-1,2-dihydro-3H-1,4-benzodiazepin-2-ones for CNS central and peripheral benzodiazepine receptors.

A series of novel 3-alkoxy-1,2-dihydro-3H-1,4-benzodiazepin-2-ones (7-15) was synthesized and their in vitro affinity for both the central benzodiazepine receptor (CBR) and the peripheral benzodiazepine receptor (PBR) of rat brain was studied. Racemic mixture of 7-bromo-3-(2-methoxy)ethoxy-5-phenyl-1,2-dihydro-3H-1,4-benzodiazepin-2-one (13) was separated into enantiomers 14, 15 by chiral HPLC. Absolute configuration of R-enantiomer 15 was determined by the method of X-ray diffraction analysis. The affinity of S-enantiomer 14 for CBR ( IC50)=245 nM) is 20-fold higher than the affinity of R-enantiomer 15 (IC50)=4,930 nM). A high selectivity for CBR versus PBR (IC50) (PBR)>10,000 nM) was shown by all reported compounds. Compound 12 was revealed as a potent (IC50)=9 nM) and selective CBR ligand among the synthesized 3-alkoxy-1,2-dihydro-3H-1,4-benzodiazepin-2-ones.