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Previous studies have suggested that exposure to ionizing radiation increases the risk of ischemic heart disease (IHD). The data from the Mayak nuclear worker cohort have indicated enhanced risk for IHD incidence. The goal of this study was to elucidate molecular mechanisms of radiation-induced IHD by integrating proteomics data with a transcriptomics study on post mortem cardiac left ventricle samples from Mayak workers categorized in four radiation dose groups (0 Gy, < 100 mGy, 100-500 mGy, > 500 mGy). The proteomics data that were newly analysed here, originated from a label-free analysis of cardiac samples. The transcriptomics analysis was performed on a subset of these samples. Stepwise linear regression analyses were used to correct the age-dependent changes in protein expression, enabling the separation of proteins, the expression of which was dependent only on the radiation dose, age or both of these factors. Importantly, the majority of the proteins showed only dose-dependent expression changes. Hierarchical clustering of the proteome and transcriptome profiles confirmed the separation of control and high-dose samples. Restrictive (separate p-values) and integrative (combined p-value) approaches were used to investigate the enrichment of biological pathways. The integrative method proved superior in the validation of the key biological pathways found in the proteomics analysis, namely PPAR signalling, TCA cycle and glycolysis/gluconeogenesis. This study presents a novel, improved, and comprehensive statistical approach of analysing biological effects on a limited number of samples., Competing Interests: The authors have declared that no competing interests exist.

Background: DNA microarrays are used for discovery of genes expressed differentially between various biological conditions. In microarray experiments the number of analyzed samples is often much lower than the number of genes (probe sets) which leads to many false discoveries. Multiple testing correction methods control the number of false discoveries but decrease the sensitivity of discovering differentially expressed genes. Concerning this problem, filtering methods for improving the power of detection of differentially expressed genes were proposed in earlier papers. These techniques are two-step procedures, where in the first step some pool of non-informative genes is removed and in the second step only the pool of the retained genes is used for searching for differentially expressed genes., Results: A very important parameter to choose is the proportion between the sizes of the pools of removed and retained genes. A new method, which we propose, allow to determine close to optimal threshold values for sample means and sample variances for gene filtering. The method is adaptive and based on the decomposition of the histogram of gene expression means or variances into mixture of Gaussian components., Conclusions: By performing analyses of several publicly available datasets and simulated datasets we demonstrate that our adaptive method increases sensitivity of finding differentially expressed genes compared to previous methods of filtering microarray data based on using fixed threshold values.

Purpose: Cells exposed to ionizing radiation release factors that induce deoxyribonucleic acid damage, chromosomal instability, apoptosis, and changes in the proliferation rate of neighboring unexposed cells, phenomena known as bystander effects. This work analyzes and compares changes in global transcript levels induced by direct irradiation and by bystander effects in K562 (human erythroleukemia) cells., Methods and Materials: Cells were X-irradiated with 4 Gy or transferred into culture medium collected from cells 1 h after irradiation (irradiation-conditioned medium). Global transcript profiles were assessed after 36 h of growth by use of Affymetrix microarrays (Affymetrix, Santa Clara, CA) and the kinetics of change of selected transcripts by quantitative reverse transcriptase-polymerase chain reaction., Results: The level of the majority (72%) of transcripts changed similarly (increase, decrease, or no change) in cells grown in irradiation-conditioned medium or irradiated, whereas only 0.6% showed an opposite response. Transcript level changes in bystander and irradiated cells were significantly different from those in untreated cells grown for the same amount of time and were confirmed by quantitative reverse transcriptase-polymerase chain reaction for selected genes. Signaling pathways in which the highest number of transcripts changed in both conditions were found in the following groups: neuroactive ligand-receptor, cytokine-cytokine receptor interaction, Janus Kinase-Signal Transducers and Activators of Transcription (JAK-STAT) and Mitogen-Activated Protein Kinase (MAPK) In control cells more transcripts were downregulated than in irradiated and bystander cells with transcription factors YBX1 and STAT5B, heat shock protein HSPA1A, and ribonucleic acid helicase DDX3X as examples., Conclusions: The transcriptomes of cells grown in medium from X-irradiated cells or directly irradiated show very similar changes. Signals released by irradiated cells may cause changes in the transcriptome of neighboring cells that sustain their survival.

The paper is devoted to two questions: whether distinction of causes versus effects of neoplasia leaves a signature in the cancer versus normal gene expression profiles and whether roles of genes, "causes" or "effects", can be inferred from repeated measurements of gene expressions. We model joint probability distributions of logarithms of gene expressions with the use of Bayesian networks (BN). Fitting our models to real data confirms that our BN models have the ability to explain some aspects of observational evidence from DNA microarray experiments. Effects of neoplastic transformation are well seen among genes with the highest power to differentiate between normal and cancer cells. Likelihoods of BNs depend on the biological role of selected genes, defined by Gene Ontology. Also predictions of our BN models are coherent with the set of putative causes and effects constructed based on our data set of papillary thyroid cancer.

Ageing is a continuous process that can cause neurodevelopmental changes in the body. Several studies have examined its effects, but few have focused on how time affects biological processes in the early stages of brain development. As studying the changes that occur in the early stages of life is important to prevent age-related neurological and psychiatric disorders, we aim to focus on these changes. The transcriptomic markers of ageing that are common to the analysed brain regions of C57Bl/6J mice were identified after conducting two-way ANOVA tests and effect size analysis on the time courses of gene expression profiles in various mouse brain regions. A total of 16,374 genes (59.9%) significantly changed their expression level, among which 7600 (27.8%) demonstrated tissue-dependent differences only, and 1823 (6.7%) displayed time-dependent and tissue-independent responses. Focusing on genes with at least a large effect size gives the list of potential biomarkers 12,332 (45.1%) and 1670 (6.1%) genes, respectively. There were 305 genes that exhibited similar significant time response trends (independently of the brain region). Samples from an 11-day-old mouse embryo validated the identified early-stage brain ageing markers. The overall functional analysis revealed tRNA and rRNA processing in the mitochondrion and contact activation system (CAS), as well as the kallikrein/kinin system (KKS), together with clotting cascade and defective factor F9 activation being affected by ageing. Most ageing-related pathways were significantly enriched, especially those that are strongly connected to development processes and neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer is a heterogeneous disease, and several attempts have been made to subtype it. Based on the abundance of 86 proteins, we identified six subpopulations of breast cancer patients: one basal subtype, one HER2-enriched subtype, and four luminal subtypes. We then evaluated them demographically and clinically, and we found significant differences in survival. The new luminal subpopulations vary in prognoses. One, marked as A2, showed similar or even worse survival than HER2-enriched and basal cases. The observed poorer survival cannot be explained by clinical and demographic factors, as there was no substantial association with race, ethnicity, metastasis, tumor size, cancer stage, and age at diagnosis. It suggests that the molecular profiles underlie the cohort heterogeneity rather than the patient background. The subpopulations identified may potentially complement established breast cancer classifications and, with further molecular investigation, may find application in clinical routine. As a highly heterogeneous disease, breast cancer (BRCA) demonstrates a diverse molecular portrait. The well-established molecular classification (PAM50) relies on gene expression profiling. It insufficiently explains the observed clinical and histopathological diversity of BRCAs. This study aims to demographically and clinically characterize the six BRCA subpopulations (basal, HER2-enriched, and four luminal ones) revealed by their proteomic portraits. GMM-based high variate protein selection combined with PCA/UMAP was used for dimensionality reduction, while the k-means algorithm allowed patient clustering. The statistical analysis (log-rank and Gehan–Wilcoxon tests, hazard ratio HR as the effect size ES) showed significant differences across identified subpopulations in Disease-Specific Survival (p = 0.0160) and Progression-Free Interval (p = 0.0264). Luminal subpopulations vary in prognosis (Disease-Free Interval, p = 0.0277). The A2 subpopulation is of the poorest, comparable to the HER2-enriched subpopulation, prognoses (HR = 1.748, referenced to Luminal B, small ES), while A3 is of the best (HR = 0.250, large ES). Similar to PAM50 subtypes, no substantial dependency on demographic and clinical factors was detected across Luminal subpopulations, as measured by χ2 test and Cramér's V for ES, and ANOVA with appropriate post hocs combined with η2 or Cohen's d-type ES, respectively. Progesterone receptors can serve as the potential A2 biomarker within Luminal patients. Further investigation of molecular differences is required to examine the potential prognostic or clinical applications. [ABSTRACT FROM AUTHOR]

Individual variability in response to radiation exposure is recognised and has often been reported as important in treatment planning. Despite many efforts to identify biomarkers allowing the identification of radiation sensitive patients, it is not yet possible to distinguish them with certainty before the beginning of the radiotherapy treatment. A comprehensive analysis of genome-wide single-nucleotide polymorphisms (SNPs) and a transcriptional response to ionising radiation exposure in twins have the potential to identify such an individual. In the present work, we investigated SNP profile and CDKN1A gene expression in blood T lymphocytes from 130 healthy Caucasians with a complex level of individual kinship (unrelated, mono- or dizygotic twins). It was found that genetic variation accounts for 66% (95% CI 37–82%) of CDKN1A transcriptional response to radiation exposure. We developed a novel integrative multi-kinship strategy allowing investigating the role of genome-wide polymorphisms in transcriptomic radiation response, and it revealed that rs205543 (ETV6 gene), rs2287505 and rs1263612 (KLF7 gene) are significantly associated with CDKN1A expression level. The functional analysis revealed that rs6974232 (RPA3 gene), involved in mismatch repair (p value = 9.68e−04) as well as in RNA repair (p value = 1.4e−03) might have an important role in that process. Two missense polymorphisms with possible deleterious effect in humans were identified: rs1133833 (AKIP1 gene) and rs17362588 (CCDC141 gene). In summary, the data presented here support the validity of this novel integrative data analysis strategy to provide insights into the identification of SNPs potentially influencing radiation sensitivity. Further investigations in radiation response research at the genomic level should be therefore continued to confirm these findings. [ABSTRACT FROM AUTHOR]

Background: Microsatellite and chromosomal instability have been investigated in Hodgkin lymphoma (HL). Materials and Methods: We studied seven HL cell lines (five Nodular Sclerosis (NS) and two Mixed Cellularity (MC)) and patient peripheral blood lymphocytes (100 NS-HL and 23 MC-HL). Microsatellite instability (MSI) was assessed by PCR. Chromosomal instability and telomere dysfunction were investigated by FISH. DNA repair mechanisms were studied by transcriptomic and molecular approaches. Results: In the cell lines, we observed high MSI in L428 (4/5), KMH2, and HDLM2 (3/5), low MSI in L540, L591, and SUP-HD1, and none in L1236. NS-HL cell lines showed telomere shortening, associated with alterations of nuclear shape. Small cells were characterized by telomere loss and deletion, leading to chromosomal fusion, large nucleoplasmic bridges, and breakage/fusion/bridge (B/F/B) cycles, leading to chromosomal instability. The MC-HL cell lines showed substantial heterogeneity of telomere length. Intrachromosmal double strand breaks induced dicentric chromosome formation, high levels of micronucleus formation, and small nucleoplasmic bridges. B/F/B cycles induced complex chromosomal rearrangements. We observed a similar pattern in circulating lymphocytes of NS-HL and MC-HL patients. Transcriptome analysis confirmed the differences in the DNA repair pathways between the NS and MC cell lines. In addition, the NS-HL cell lines were radiosensitive and the MC-cell lines resistant to apoptosis after radiation exposure. Conclusions: In mononuclear NS-HL cells, loss of telomere integrity may present the first step in the ongoing process of chromosomal instability. Here, we identified, MSI as an additional mechanism for genomic instability in HL. [ABSTRACT FROM AUTHOR]