Molekulyarnaya Biologiya最新文献

PAX4 (Paired Box 4) is a transcription factor that is expressed mainly in the pancreas and plays a key role in the development of insulin-producing β cells at the embryonic stage. In mature cells, PAX4 acts as a main regulator of their adaptation under pathological conditions. The importance of PAX4 to the proper function of pancreatic islets has been demonstrated in studies of the relationship between mutations of the PAX4 gene and various forms of diabetes mellitus (DM). PAX4 overexpression in adult islets stimulates β-cell proliferation and resistance to apoptosis. Taken together, the data indicate that PAX4 provides a potential target to develop new DM treatments aimed at reprogramming different cell types into insulin-producing cells and promoting their proliferation to replenish the β-cell pool lost during disease progression. The development of such methods requires knowledge of the molecular mechanisms that control expression of PAX4 and its target genes. The review summarizes the data on the structure and expression of the human PAX4 gene. Interactions of various transcription factors during differentiation of pancreatic cells and the formation of islets of Langerhans are described along with the role of PAX4 in the processes. Associations between mutations of human PAX4 and various DM forms were considered. A final part of the review examines the prospects for reprogramming cells of other types into insulin-producing cells and discusses the effects on PAX4- regulated signaling pathways as a means to develop new approaches to DM treatment.

A rapid and relatively cost-effective method was developed to isolate and purify RNA from mycelia of two plant-pathogenic fungal species of the genus Fusarium, F. graminearum and F. coffeatum, with different morphological and biochemical properties. The method utilizes a guanidine hydrochloride-based buffer and spin columns from a commercial plasmid DNA extraction kit and can be applied to both mycelia grown on nutrient agar media and liquid fungal cultures. The RNA yield with the method was 4-14 μg/100 mg of mycelium dry weight, and the RNA integrity number (RIN) values were up to 8.4. Method optimization showed that preliminary freeze drying is advisable to perform in the case of liquid cultures and that an RNase inhibitor should be used in the case of late culture growth stages.

Breast cancer (BC) is a multifactorial disease that is characterized by various genetic and epigenetic changes that occur due to the effect of various factors including that of environmental etiological agents. The obtained scientific data speak volumes for epigenetic dysregulation in BC pathogenesis. Out of all epigenetic markers, various microRNA regulating a wide spectrum of biological processes in a cell could be viewed as one of the predictors of potential risk. Understanding the functional role of these molecules will provide valuable information about the complex molecular mechanisms underlying the appearance and development of BC. This review summarizes currently existing publicly available data on aberrant expression of miR-125b, miR-181a, and miR-16 in case of various cancer localizations; analyzes their role in BC pathogenesis; presents an annotation of the target-genes; and evaluates the repression potential of microRNA and their diagnostic significance in case of BC. An analysis of changes in miRNA expression during radiation exposure was conducted. Interest in examining specific miRNAs is due to the results of long-term monitoring of the health of people living in radioactively contaminated areas of the Southern Urals, as well as data on the expression profiles of miR-125b, miR-181a, and miR-16 over the long term in exposed people.

One of the promising new approaches to the treatment of HIV infection is CRISPR/Cas-mediated knockout of the CCR5 receptor gene followed by the integration of an anti-HIV gene into the break site. Numerous studies have focused on the knockout of the CCR5 gene; however, the efficiency of subsequent targeted integration of long fragments remains poorly studied. To evaluate the efficiency of this approach, we used HT1080 cells and investigated the integration of a cassette expressing the EGFP gene into the CCR5 locus using two different nucleases (SpCas9 and AsCpf1) and various donor DNA constructs delivered by recombinant adeno-associated viral vectors (rAAV). For each nuclease, we designed five variants of donor DNA differing in the length (ranging from 150 to 1000 bp) or structure of the homology arms. The efficiency of transgene integration with 150 bp homology arms was the lowest for both nucleases and differed significantly from constructs with longer homology arms. Furthermore, it was shown that the presence of nuclease cleavage sites in the donor DNA flanking the cassette with homology arms did not affect the efficiency of transgene integration during AAV delivery. We demonstrated that the AsCpf1 nuclease provided higher efficiency of EGFP transgene integration than SpCas9, despite the lower efficiency of CCR5 knockout. The maximum percentage of cells with the integrated transgene was achieved using the AsCpf1 nuclease and an expression cassette with 600 bp homology arms, reaching 59 ± 6%.

Disordered proteins are of great interest due to their structural features, as they do not have well- defined three-dimensional structures. These proteins, often called intrinsically disordered proteins or regions, play critical roles in various cellular processes and are associated with the development of a number of diseases. Our in silico research focused on the investigation of protein complexes that include both ordered proteins, such as 14-3-3γ, and proteins containing intrinsically disordered regions, such as nucleocapsid (N) of SARS-CoV-2 and p53. Our findings demonstrate, that complexes modeled by AlphaFold2 and refined using discrete molecular dynamics simulations acquire assembled structures in disordered regions. After refinement, the modeled complexes exhibit a degree of structural assembly that addresses a key challenge in studying disordered proteins-their propensity to evade stable conformations.

It is generally accepted that the existing set of proteinogenic amino acids encoded by the standard genetic code was formed step by step in the course of evolution. Most studies name Ala, Asp, Glu, Gly, Ile, Leu, Pro, Ser, Thr, and Val as early amino acids, presumably of extraterrestrial origin. However, other studies have chosen a consensus list of early amino acids in which Ile is replaced by Arg. We compared the differences between early and late amino acids for the lists with Ile and with Arg based on their physicochemical properties (AAindex database). The point-biserial correlation coefficient rpb, Student's t-test, and its reliability, the p-value, were calculated between the binary lists with Ile and Arg and each AA index. Since in total 2x553 p-values were obtained, the problem of multiple comparisons was solved using the Bonferroni correction and the Benjamini-Hochberg method. Next, we used the 2B-PLS method, which is applied to two different sets of variables related to the same objects, to find information common to both sets. The first set was the binary lists of Trifonov (Arg) and Wong (Ile), and the second set was 553 AA indexes. The maximum correlation with both the list with Ile and with Arg (1.0 and 0.8, respectively) was demonstrated by the binary AA index CHAM830108, which characterizes the ability of an amino acid to be a charge donor: late amino acids are capable of being donors, while early ones are not. Apparently, this is due to the differences in the conditions under which the standard set of amino acids evolved: prebiotic and biotic. The results of the 2B-PLS analysis also show that in the list of ten evolutionarily early amino acids, Ile appears preferable to Arg. The allocation of the last six amino acids (Cys, His, Met, Phe, Trp, and Tyr) obtained on the basis of the reduction of the HOMO-LUMO gap in a separate, third stage of the evolution of the set of standard amino acids is confirmed. A compact arrangement on the 2B-PLS plane of the physicochemical properties of three groups of amino acids, in which adenine, thymine, and cytosine are located in the second position of the codons, respectively, as well as the maximum dispersion of amino acids with guanine in the second position of the codons, is revealed.

Numerous histological studies have demonstrated that impaired placentation processes are involved in the key pathogenetic mechanisms of great obstetrical syndromes (GOSs). However, the molecular basis of this discovery is still unclear. Therefore, the objectives of this work were to characterize the molecular mechanisms and to search for new genetic markers of pregnancy complications via an integrative analysis of the data obtained by genome-wide expression profiling of placental tissue in preeclampsia, intrauterine growth restriction (IUGR), premature labor (PL), and physiological pregnancy (PP). Oxidative stress, ferroptosis, and disordered intercellular interactions in placenta were assumed to be common pathogenetic mechanisms of GOSs. A total of 64 genes were found to be significantly dysregulated in at least two pregnancy complications. Maternal endothelial cells and syncytiotrophoblast cells were the most significant cell populations enriched in these genes. A computational analysis and the topology of the protein-protein interaction network identified SOD1, ACTG1, TXNRD1, TKT, GCLM, GOT1, ACO1, and UBB as hub genes. A set of key regulators that trigger the reaction cascades involving the differentially expressed genes was found to include MAPK3, MID1, LCMT1, DUSP10, TOPS, SOX10, EGFR, TFAP2A, GLIS1, NR2F1, NR2F2, PAX5, HSF1, and BCL6. The genes were overrepresented in the MAP kinase and interferon-γ response signaling pathways. The above genes and their products were assumed to provide the most promising biomarkers for developing new approaches to risk factor assessment and targeted therapy in GOSs. Further studies should be aimed at clarifying their functional and diagnostic significance in pregnancy complications.

High-throughput transcriptomic research methods provide the assessment of a vast number of factors valuable for researchers. At the same time, "curse of dimensionality" issues arise, which lead to increasing the requirements on data processing and analysis methods. In this study, we propose a new algorithm that combines Monte Carlo methods and machine learning. This algorithm will enable feature space reduction by highlighting genes most likely associated with the investigated diseases. Our approach allows one not only to generate a set of "interesting" genes but also to assign weight to each gene, indicating its "importance." This measure can be used in subsequent statistical analysis, visualization, and interpretation of results. Algorithm performance was demonstrated on open transcriptomic data of patients with HCM (GSE36961 and GSE1145). The analysis revealed genes MYH6, FCN3, RASD1, and SERPINA3, which is in good agreement with the available literature.

Immune response gene expression analysis is an important task in studies of interactions between a host and an infectious agent. Many approaches to this task have been developed, but despite significant progress, the problem of selecting a single standard for data normalization remains unsolved. In the present work, HPRT1, SDHA, GAPDH, and TBP were selected as candidate reference genes with stable expression, and a system based on multiplex real-time RT-PCR was developed for their analysis. Calculations using the geNorm and BestKeeper algorithms made it possible to create a stable index based on two genes, HPRT1 and SDHA. The index was used to normalize the expression levels of the target Toll-like receptor genes (TLRs) TLR1, TLR2, TLR4, TLR6, and TLR8. A high stability and positive mutual correlations were observed for expression values of the TLR genes (except TLR6) in a sample of healthy individuals. The finding suggested common mechanisms of expression regulation and confirmed that the multiplex system is suitable for analyzing expression of immune response genes.

According to classic ideas, new genes emerge from old genes by duplication or horizontal transfer. Analyses of a large number of genomes in recent decades have shown that some genes have no visible homologs and have presumably emerged de novo from previously noncoding sequences. The review considers possible mechanisms of de novo gene formation, the properties of protein sequences encoded by such genes, and features of their expression and selection. The problem of identification of de novo arising gene is discussed separately.