Inactivation of RAD52 and HDF1 DNA repair genes leads to premature chronological aging and cellular instability

Pulsed magnetic field increases the effect of ultraviolet C radiation and thermal shock in aged yeasts

The study of the effects of the magnetic field (MF) on living matter continues to be a dilemma. Until now, the interaction mechanisms of MF with living matter that explain the observed phenomena are unknown. Despite the existing literature and the multiple effects described to date, there are few published articles that study the combined effect of MF with other physical agents during the cellular aging process. In this sense, the aim of this work is to study whether low frequency and intensity pulsed and sinusoidal MF exposure produce alterations in the cell killing effect of ultraviolet C (UVC) radiation and thermal shock during the chronological aging of S. cerevisiae. Yeast cells were exposed to 2.45 mT (50 Hz) sinusoidal MF and 1.5 mT (25 Hz) pulsed MF, during 40 days of aging, in combination with UVC radiation (50 J/m2) and/or thermal shock (52°C). Cell survival was evaluated by clonogenic assay. The exposure of yeast to pulsed MF produces an acceleration of aging, which is not observed in cells exposed to sinusoidal MF. The pulsed MF modifies the cellular response to damaging agents only in aged S. cerevisiae cells. In this sense, the pulsed MF applied increases the damage induced by UVC radiation and by thermal shock. In contrast, the sinusoidal MF used has no effect.

Factors and molecular mechanisms of radiation resistance in cancer cells

PURPOSE

The aim of this work is to review the published studies on radiation resistance mechanisms and molecular markers involved in different tumors. The revision has been focused in the last 5 years (2016-2021).

CONCLUSIONS

Radioresistance is a cause of concern as it causes failure of radiation therapy and subsequent tumor relapse. Combination chemotherapy and radiation therapy are clinically successful in treating many types of tumors. Despite continued improvements in cancer treatment, locoregional recurrence or metastatic spread continues to occur in a high proportion of patients after being treated with radiation therapy or combination treatments. There is strong evidence that cancer stem cells contribute to radiation resistance, contributing to treatment failure. The mechanisms of radiation resistance in different tumors are not fully understood. A better understanding of cancer stem cells and the associated signaling pathways that regulate radiation resistance will open up new strategies for treating cancer by radiation therapy. Radiation can damage malignant cells mainly by the induction of DNA double strand breaks. However, in some tumors appear resistant cells that repopulate the tumor following therapy leading over time to the failure of the treatment. Native mechanisms and induced pathways, are the cause of radiation resistance. It has been described that numerous molecular markers acting through numerous mechanisms of action involved in radiation resistance, such as apoptosis resistance, alterations of cell growth, proliferation and DNA repair, hypoxia, increase in invasiveness and migration capacity, cell cycle alterations and expression of heat shock proteins, among others. Therefore, resistance to radiation is a multifactorial phenomenon that, in different cell types, it occurs through different regulatory mechanisms in which different molecules intervene. Resistance can be acquired by altering different regulatory pathways in different tumors. The knowledge of radiation resistance markers could help in the classification and treatment of patients with more aggressive tumors.

Identification of new proteins related with cisplatin resistance in Saccharomyces cerevisiae

The aim of this study is to select a cisplatin-resistant Saccharomyces cerevisiae strain to look for new molecular markers of resistance and the identification of mechanisms/interactions involved. A resistant strain was obtained after 80 days of cisplatin exposure. Then, total protein extraction, purification, and identification were carried out, in wild-type (wt) and resistant strains, by tandem mass spectrometry using a "nano HPLC-ESI-MS/MS" ion trap system. The increase in the exponentially modified protein abundance index (emPAI) (resistant vs wt strains) was calculated to study the increase in protein expression. "Genemania" software ( http://www.Genemania.org/ ) was used to compare the effects, functions, and protein interactions. KEGG tool was used for metabolic pathway analysis. Data are available via ProteomeXchange with identifier PXD020665. The cisplatin-resistant strain showed 2.5 times more resistance than the wt strain for the inhibitory dose 50% (ID50) value (224 μg/ml vs 89.68 μg/ml) and 2.78 times more resistant for the inhibitory dose 90% (ID90) value (735.2 μg/ml vs 264.04 μg/ml). Multiple deregulated proteins were found in the glutathione and carbon metabolism, oxidative phosphorylation, proteasome, glycolysis and gluconeogenesis, glyoxylate metabolism, fatty acid degradation pathway, citric acid cycle, and ribosome. The most overexpressed proteins in the cisplatin-resistant strain were related to growth and metabolism (QCR2, QCR1, ALDH4, ATPB, ATPA, ATPG, and PCKA), cell structure (SCW10), and thermal shock (HSP26). The results suggest that these proteins could be involved in cisplatin resistance. The resistance acquisition process is complex and involves the activation of multiple mechanisms that interact together. KEY POINTS: • Identification of new proteins/genes related to cisplatin resistance • Increased expression of QCR2/QCR1/ALDH4/ATPB/ATPA/SCW10/HSP26/ATPG and PCKA proteins • Multiple molecular mechanisms that interact together are involved in resistance.

Comparison of Methods for Differential Co-expression Analysis for Disease Biomarker Prediction

In the recent past, a number of methods have been developed for analysis of biological data. Among these methods, gene co-expression networks have the ability to mine functionally related genes with similar co-expression patterns, because of which such networks have been most widely used. However, gene co-expression networks cannot identify genes, which undergo condition specific changes in their relationships with other genes. In contrast, differential co-expression analysis enables finding co-expressed genes exhibiting significant changes across disease conditions. In this paper, we present some significant outcomes of a comparative study of four co-expression network module detection techniques, namely, THD-Module Extractor, DiffCoEx, MODA, and WGCNA, which can perform differential co-expression analysis on both gene and miRNA expression data (microarray and RNA-seq) and discuss the applications to Alzheimer's disease and Parkinson's disease research. Our observations reveal that compared to other methods, THD-Module Extractor is the most effective in finding modules with higher functional relevance and biological significance.