Tumor markers for lipid metabolism-related genes: Based on small cell lung cancer and bronchial asthma dual analysis

Numerous studies have elucidated the intricate relationship between bronchial asthma and small cell lung cancer (SCLC), as well as the role lipid metabolism genes play in transitioning from bronchial asthma to SCLC. Despite this, the predictive power of single gene biomarkers remains insufficient and necessitates the development of more accurate prognostic models. In our study, we downloaded and preprocessed scRNA-seq of SCLC from the GEO database GSE164404 and severe asthma scRNA-seq from GSE145013 using the Seurat package. Using the MSigDB database and geneCard database, we selected lipid metabolism-related genes and performed scRNA-seq data analysis from the gene expression GEO database, aiming to uncover potential links between immune signaling pathways in bronchial asthma and SCLC. Our investigations yielded differentially expressed genes based on the scRNA-seq dataset related to lipid metabolism. We executed differential gene analysis, gene ontology, and Kyoto Encyclopedia of Genes and Genomes analyses. In-depth GSEA pathway activation analysis, crucial target gene predictions via protein-protein interactions, and key cluster gene evaluations for differential and diagnostic ROC values correlation analysis confirmed that key cluster genes are significant predictors for the progression of bronchial asthma to SCLC. To validate our findings, we performed wet laboratory experiments using real-time quantitative PCR to assess the expression of these relevant genes in SCLC cell lines. In conclusion, this research proposes a novel lipid metabolism-related gene marker that can offer comprehensive insights into the pathogenesis of bronchial asthma leading to SCLC. Although this study does not directly focus on senescence-associated molecular alterations, our findings in the lipid metabolism genes associated with inflammation and cancer progression offer valuable insights for further research targeting senescence-related changes in treating inflammatory diseases.

Comparative Transcriptomic Analyses for the Optimization of Thawing Regimes during Conventional Cryopreservation of Mature and Immature Human Testicular Tissue

Cryopreservation of human testicular tissue, as a key element of anticancer therapy, includes the following stages: saturation with cryoprotectants, freezing, thawing, and removal of cryoprotectants. According to the point of view existing in "classical" cryobiology, the thawing mode is the most important consideration in the entire process of cryopreservation of any type of cells, including cells of testicular tissue. The existing postulate in cryobiology states that any frozen types of cells must be thawed as quickly as possible. The technologically maximum possible thawing temperature is 100 °C, which is used in our technology for the cryopreservation of testicular tissue. However, there are other points of view on the rate of cell thawing, according to how thawing should be carried out at physiological temperatures. In fact, there are morphological and functional differences between immature (from prepubertal patients) and mature testicular tissue. Accordingly, the question of the influence of thawing temperature on both types of tissues is relevant. The purpose of this study is to explore the transcriptomic differences of cryopreserved mature and immature testicular tissue subjected to different thawing methods by RNA sequencing. Collected and frozen testicular tissue samples were divided into four groups: quickly (in boiling water at 100 °C) thawed cryopreserved mature testicular tissue (group 1), slowly (by a physiological temperature of 37 °C) thawed mature testicular tissue (group 2), quickly thawed immature testicular tissue (group 3), and slowly thawed immature testicular tissue (group 4). Transcriptomic differences were assessed using differentially expressed genes (DEG), the Kyoto Encyclopedia of Genes and Genomes (KEGG), gene ontology (GO), and protein-protein interaction (PPI) analyses. No fundamental differences in the quality of cells of mature and immature testicular tissue after cryopreservation were found. Generally, thawing of mature and immature testicular tissue was more effective at 100 °C. The greatest difference in the intensity of gene expression was observed in ribosomes of cells thawed at 100 °C in comparison with cells thawed at 37 °C. In conclusion, an elevated speed of thawing is beneficial for frozen testicular tissue.

Towards In Silico Identification of Genes Contributing to Similarity of Patients' Multi-Omics Profiles: A Case Study of Acute Myeloid Leukemia

We propose a computational framework for selecting biologically plausible genes identified by clustering of multi-omics data that reveal patients' similarity, thus giving researchers a more comprehensive view on any given disease. We employ spectral clustering of a similarity network created by fusion of three similarity networks, based on mRNA expression of immune genes, miRNA expression and DNA methylation data, using SNF_v2.1 software. For each cluster, we rank multi-omics features, ensuring the best separation between clusters, and select the top-ranked features that preserve clustering. To find genes targeted by DNA methylation and miRNAs found in the top-ranked features, we use chromosome-conformation capture data and miRNet2.0 software, respectively. To identify informative genes, these combined sets of target genes are analyzed in terms of their enrichment in somatic/germline mutations, GO biological processes/pathways terms and known sets of genes considered to be important in relation to a given disease, as recorded in the Molecular Signature Database from GSEA. The protein-protein interaction (PPI) networks were analyzed to identify genes that are hubs of PPI networks. We used data recorded in The Cancer Genome Atlas for patients with acute myeloid leukemia to demonstrate our approach, and discuss our findings in the context of results in the literature.

In Vivo Quantitative Estimation of DNA-Dependent Interaction of Sox2 and Oct4 Using BirA-Catalyzed Site-Specific Biotinylation

Protein–protein interactions of core pluripotency transcription factors play an important role during cell reprogramming. Cell identity is controlled by a trio of transcription factors: Sox2, Oct4, and Nanog. Thus, methods that help to quantify protein–protein interactions may be useful for understanding the mechanisms of pluripotency at the molecular level. Here, a detailed protocol for the detection and quantitative analysis of in vivo protein–protein proximity of Sox2 and Oct4 using the proximity-utilizing biotinylation (PUB) method is described. The method is based on the coexpression of two proteins of interest fused to a biotin acceptor peptide (BAP)in one case and a biotin ligase enzyme (BirA) in the other. The proximity between the two proteins leads to more efficient biotinylation of the BAP, which can be either detected by Western blotting or quantified using proteomics approaches, such as a multiple reaction monitoring (MRM) analysis. Coexpression of the fusion proteins BAP-X and BirA-Y revealed strong biotinylation of the target proteins when X and Y were, alternatively, the pluripotency transcription factors Sox2 and Oct4, compared with the negative control where X or Y was green fluorescent protein (GFP), which strongly suggests that Sox2 and Oct4 come in close proximity to each other and interact.

PCLPred: A Bioinformatics Method for Predicting Protein-Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation

Protein–protein interactions (PPI) are key to protein functions and regulations within the cell cycle, DNA replication, and cellular signaling. Therefore, detecting whether a pair of proteins interact is of great importance for the study of molecular biology. As researchers have become aware of the importance of computational methods in predicting PPIs, many techniques have been developed for performing this task computationally. However, there are few technologies that really meet the needs of their users. In this paper, we develop a novel and efficient sequence-based method for predicting PPIs. The evolutionary features are extracted from the position-specific scoring matrix (PSSM) of protein. The features are then fed into a robust relevance vector machine (RVM) classifier to distinguish between the interacting and non-interacting protein pairs. In order to verify the performance of our method, five-fold cross-validation tests are performed on the Saccharomyces cerevisiae dataset. A high accuracy of 94.56%, with 94.79% sensitivity at 94.36% precision, was obtained. The experimental results illustrated that the proposed approach can extract the most significant features from each protein sequence and can be a bright and meaningful tool for the research of proteomics.