Datasets Construction and Development of QSAR Models for Predicting Micronucleus In Vitro and In Vivo Assay Outcomes.

In silico (quantitative) structure-activity relationship modeling is an approach that provides a fast and cost-effective alternative to assess the genotoxic potential of chemicals. However, one of the limiting factors for model development is the availability of consolidated experimental datasets. In the present study, we collected experimental data on micronuclei in vitro and in vivo, utilizing databases and conducting a PubMed search, aided by text mining using the BioBERT large language model. Chemotype enrichment analysis on the updated datasets was performed to identify enriched substructures. Additionally, chemotypes common for both endpoints were found. Five machine learning models in combination with molecular descriptors, twelve fingerprints and two data balancing techniques were applied to construct individual models. The best-performing individual models were selected for the ensemble construction. The curated final dataset consists of 981 chemicals for micronuclei in vitro and 1309 for mouse micronuclei in vivo, respectively. Out of 18 chemotypes enriched in micronuclei in vitro, only 7 were found to be relevant for in vivo prediction. The ensemble model exhibited high accuracy and sensitivity when applied to an external test set of in vitro data. A good balanced predictive performance was also achieved for the micronucleus in vivo endpoint.

Incidence and Risk Factors of Acute Leukemias in Armenia: A Population-Based Study.

OBJECTIVE: Leukemia represents a serious public health concern as the incidence is increasing worldwide. In this study we aimed to describe the epidemiological profile of acute lymphoblastic (ALL) and myeloid (AML) leukemia, identify disease clusters and find association with possible risk factors. METHODS: Data on leukemia cases were provided by the National Institute of Health of the Republic of Armenia for the period of 2012-2018. Age-standardized incidence rate was calculated using Segi World Population. SaTScan purely spatial analysis was applied to find leukemia clusters. To find association between leukemia and agricultural and mining activities and demographic data Poisson regression model was used. RESULTS: During the studied period 259 new cases of ALL and 478 AML were recorded. The age-standardized incidence rate was 1.5 and 1.9 per 100,000 inhabitants with male to female ratio of 0.97 and 1.1 for ALL and AML, respectively. No significant changes in ALL or AML incidence trends were found. For ALL significant cluster encompassing Shirak, Lori, Tavush and Armavir provinces of Armenia was identified, while Kotayk and Ararat was provinces with the highest incidence of AML. We found significant positive association of ALL with crop density, while no elevated risk estimates were found between AML and exposure variables. CONCLUSION: Altogether, our results suggested that acute leukemias incidence in Armenia follows the pattern described for developing countries.

Improving VAE based molecular representations for compound property prediction.

Collecting labeled data for many important tasks in chemoinformatics is time consuming and requires expensive experiments. In recent years, machine learning has been used to learn rich representations of molecules using large scale unlabeled molecular datasets and transfer the knowledge to solve the more challenging tasks with limited datasets. Variational autoencoders are one of the tools that have been proposed to perform the transfer for both chemical property prediction and molecular generation tasks. In this work we propose a simple method to improve chemical property prediction performance of machine learning models by incorporating additional information on correlated molecular descriptors in the representations learned by variational autoencoders. We verify the method on three property prediction tasks. We explore the impact of the number of incorporated descriptors, correlation between the descriptors and the target properties, sizes of the datasets etc. Finally, we show the relation between the performance of property prediction models and the distance between property prediction dataset and the larger unlabeled dataset in the representation space.

Ochratoxin A induces global DNA hypomethylation and oxidative stress in neuronal cells in vitro.

Recently, it was reported that ochratoxin A (OTA) mycotoxin, produced by a number of Aspergillus and Penicillium fungal species, may cause neuropsychological impairment or mental and emotional disorders but the mechanism of neurotoxicity remains unknown. Adverse effects of OTA in human (SHSY5Y) and mouse (HT22) neuronal cell lines were studied in vitro. OTA was found to be non-cytotoxic in both cell lines at concentrations 2.5-30 µmol/l, which are above the levels reported for human and animal plasma. OTA led to slightly elevated chromosomal instability in HT22 cells at concentrations of 15-30 µmol/l after 48 h, while in SHSY5Y cells, no evidence for genotoxic effects was observed at concentrations of 2.5-30 µmol/l. OTA treatment at 10 µmol/l resulted in elevated levels of unmethylated cytosines in CpG dinucleotides (up to 1.4-fold), elevated levels of intracellular reactive oxygen species (up to 1.6-fold), and in elevated levels of oxidized DNA purines (up to 2.2-fold) in both cell lines. Detected global DNA hypomethylation and oxidative stress were found to be reversible in 96 h and 24-72 h, respectively. In general, the observed pattern of OTA-induced effects in both cell lines was similar, but HT22 cells exhibited higher sensitivity, as well as better repair capacity in response to OTA toxicity. In conclusion, the results suggest that oxidative stress and epigenetic changes are directly involved in OTA-induced neurotoxicity, while cytotoxicity and genotoxicity cannot be considered as primary cause of toxicity in neuronal cells in vitro.

Laser-Driven Ultrashort Pulsed Electron Beam Radiation at Doses of 0.5 and 1.0 Gy Induces Apoptosis in Human Fibroblasts.

Rapidly evolving laser technologies have led to the development of laser-generated particle accelerators as an alternative to conventional facilities. However, the radiobiological characteristics need to be determined to enhance their applications in biology and medicine. In this study, the radiobiological effects of ultrashort pulsed electron beam (UPEB) and X-ray radiation in human lung fibroblasts (MRC-5 cell line) exposed to doses of 0.1, 0.5, and 1 Gy are compared. The changes of γH2AX foci number as a marker of DNA double-strand breaks (DSBs) were analyzed. In addition, the micronuclei induction and cell death via apoptosis were studied. We found that the biological action of UPEB-radiation compared to X-rays was characterized by significantly slower γH2AX foci elimination (with a dose of 1 Gy) and strong apoptosis induction (with doses of 0.5 and 1.0 Gy), accompanied by a slight increase in micronuclei formation (dose of 1 Gy). Our data suggest that UPEB radiation produces more complex DNA damage than X-ray radiation, leading to cell death rather than cytogenetic disturbance.

Impaired Inflammatory Response to LPS in Type 2 Diabetes Mellitus.

Type 2 diabetes mellitus (T2DM) is a severe health problem worldwide, reaching epidemic levels. High susceptibility to infections of T2DM patients indicates dysregulated immune responses to pathogens. However, innate immune responses, including monocyte functions, in T2DM are poorly investigated. Therefore, in this study we aimed to assess lipopolysaccharide- (LPS-) induced immune responses of circulating monocytes from T2DM patients. The results showed that monocytes from T2DM were hyporesponsive to LPS challenge as reflected by significantly suppressed secretion of TNFα (p < 0.01) and expression of CD11b (p < 0.001) and TLR4 (p < 0.001) compared to those in monocytes from healthy subjects. Furthermore, LPS-induced IL-10 levels were similar in diabetic and healthy supernatants, while expression levels of CD163 were found to be downregulated on monocytes from T2DM (p < 0.001) suggesting impaired ability of monocytes to switch their phenotype to anti-inflammatory. Taken together, our results suggest compromised function of monocytes in T2DM, which may explain, at least partly, high incidence of infection in these patients.

Autoimmunity and autoinflammation: A systems view on signaling pathway dysregulation profiles.

INTRODUCTION: Autoinflammatory and autoimmune disorders are characterized by aberrant changes in innate and adaptive immunity that may lead from an initial inflammatory state to an organ specific damage. These disorders possess heterogeneity in terms of affected organs and clinical phenotypes. However, despite the differences in etiology and phenotypic variations, they share genetic associations, treatment responses and clinical manifestations. The mechanisms involved in their initiation and development remain poorly understood, however the existence of some clear similarities between autoimmune and autoinflammatory disorders indicates variable degrees of interaction between immune-related mechanisms. METHODS: Our study aims at contributing to a holistic, pathway-centered view on the inflammatory condition of autoimmune and autoinflammatory diseases. We have evaluated similarities and specificities of pathway activity changes in twelve autoimmune and autoinflammatory disorders by performing meta-analysis of publicly available gene expression datasets generated from peripheral blood mononuclear cells, using a bioinformatics pipeline that integrates Self Organizing Maps and Pathway Signal Flow algorithms along with KEGG pathway topologies. RESULTS AND CONCLUSIONS: The results reveal that clinically divergent disease groups share common pathway perturbation profiles. We identified pathways, similarly perturbed in all the studied diseases, such as PI3K-Akt, Toll-like receptor, and NF-kappa B signaling, that serve as integrators of signals guiding immune cell polarization, migration, growth, survival and differentiation. Further, two clusters of diseases were identified based on specifically dysregulated pathways: one gathering mostly autoimmune and the other mainly autoinflammatory diseases. Cluster separation was driven not only by apparent involvement of pathways implicated in adaptive immunity in one case, and inflammation in the other, but also by processes not explicitly related to immune response, but rather representing various events related to the formation of specific pathophysiological environment. Thus, our data suggest that while all of the studied diseases are affected by activation of common inflammatory processes, disease-specific variations in their relative balance are also identified.