Search for New Candidate Genes Involved in the Comorbidity of Asthma and Hypertension Based on Automatic Analysis of Scientific Literature

Saik, Olga V, Demenkov PS, Ivanisenko, Timofey V, et al. Search for New Candidate Genes Involved in the Comorbidity of Asthma and Hypertension Based on Automatic Analysis of Scientific Literature. JOURNAL OF INTEGRATIVE BIOINFORMATICS. 2018;15(4): 20180054.Comorbid states of diseases significantly complicate diagnosis and treatment. Molecular mechanisms of comorbid states of asthma and hypertension are still poorly understood. Prioritization is a way for identifying genes involved in complex phenotypic traits. Existing methods of prioritization consider genetic, expression and evolutionary data, molecular-genetic networks and other. In the case of molecular-genetic networks, as a rule, protein-protein interactions and KEGG networks are used. ANDSystem allows reconstructing associative gene networks, which include more than 20 types of interactions, including protein-protein interactions, expression regulation, transport, catalysis, etc. In this work, a set of genes has been prioritized to find genes potentially involved in asthma and hypertension comorbidity. The prioritization was carried out using well-known methods (ToppGene and Endeavor) and a cross-talk centrality criterion, calculated by analysis of associative gene networks from ANDSystem. The identified genes, including ILIA, CD40LG, STAT3, IL15, FAS, APP, TLR2, C3, IL13 and CXCL10, may be involved in the molecular mechanisms of comorbid asthma/hypertension. An analysis of the dynamics of the frequency of mentioning the most priority genes in scientific publications revealed that the top 100 priority genes are significantly enriched with genes with increased positive dynamics, which may be a positive sign for further studies of these genes

Diagnostic and Prognostic Markers for Pancreatitis and Pancreatic Ductal Adenocarcinoma

Diagnostic markers are desperately needed for the early detection of pancreatic ductal adenocarcinoma (PDA). We describe sets of markers expressed in temporal order in mouse models during pancreatitis, PDA initiation and progression. Cell type specificity and the differential expression of PDA markers were identified by screening single cell (sc) RNAseq from tumor samples of a mouse model for PDA (KIC) at early and late stages of PDA progression compared to that of a normal pancreas. Candidate genes were identified from three sources: (1) an unsupervised screening of the genes preferentially expressed in mouse PDA tumors; (2) signaling pathways that drive PDA, including the Ras pathway, calcium signaling, and known cancer genes, or genes encoding proteins that were identified by differential mass spectrometry (MS) of mouse tumors and conditioned media from human cancer cell lines; and (3) genes whose expression is associated with poor or better prognoses (PAAD, oncolnc.org). The developmental progression of PDA was detected in the temporal order of gene expression in the cancer cells of the KIC mice. The earliest diagnostic markers were expressed in epithelial cancer cells in early-stage, but not late-stage, PDA tumors. Other early markers were expressed in the epithelium of both early- and late-state PDA tumors. Markers that were expressed somewhat later were first elevated in the epithelial cancer cells of the late-stage tumors, then in both epithelial and mesenchymal cells, or only in mesenchymal cells. Stromal markers were differentially expressed in early- and/or late-stage PDA neoplasia in fibroblast and hematopoietic cells (lymphocytes and/or macrophages) or broadly expressed in cancer and many stromal cell types. Pancreatitis is a risk factor for PDA in humans. Mouse models of pancreatitis, including caerulein treatment and the acinar-specific homozygous deletion of differentiation transcription factors (dTFs), were screened for the early expression of all PDA markers identified in the KIC neoplasia. Prognostic markers associated with a more rapid decline were identified and showed differential and cell-type-specific expression in PDA, predominately in late-stage epithelial and/or mesenchymal cancer cells. Select markers were validated by immunohistochemistry in mouse and human samples of a normal pancreas and those with early- and late-stage PDA. In total, we present 2165 individual diagnostic and prognostic markers for disease progression to be tested in humans from pancreatitis to late-stage PDA

Radiation hardening and optical properties of materials based on SiO2

Preliminary studies have shown that the optical absorption spectra of radiation-colored glasses correspond to the spectral behavior of the scattering losses of an optically inhomogeneous medium. The reasons for the same optical changes in glasses of different compositions are the radiation-induced electric charge separation in the structurally nano-inhomogeneous glass volume, polarization and formation of nanometer optical inhomogeneities. The authors of this work prove that the radiation changes in the mechanical and optical properties of silicate glasses are of the same nature. The performed estimates indicate that the electric charge separation in the glasses occurs up to absorbed doses of about 1 MGy. The local electric charge separation due to the appearance of Coulomb forces leads to radiation hardening of the glasses. The estimated Coulomb hardening of the quartz glasses was ~ 107 Pa. The theoretical results were experimentally confirmed by measuring the mechanical properties of the glasses under high intensity proton irradiation as well as by testing the mechanical strength of a composite material based on quartz glass. Under proton irradiation with a dose rate of 5×103 Gy/s (energy of 8 MeV) up to threshold doses of ~ (1 – 5) ×106 Gy in the KU-1 quartz glasses, the decrement of acoustic vibrations decreased due to Coulomb hardening. After gamma irradiation with 1.34×105 Gy, the tensile strength of the composite material based on quartz glass increased by up to 20 MPa. This value is in the range of estimates of Coulomb hardening of quartz glasses. It is also shown that ionizing radiation does not affect the elastic modulus of materials based on SiO2

FunGeneNet: a web tool to estimate enrichment of functional interactions in experimental gene sets

Abstract Background Estimation of functional connectivity in gene sets derived from genome-wide or other biological experiments is one of the essential tasks of bioinformatics. A promising approach for solving this problem is to compare gene networks built using experimental gene sets with random networks. One of the resources that make such an analysis possible is CrossTalkZ, which uses the FunCoup database. However, existing methods, including CrossTalkZ, do not take into account individual types of interactions, such as protein/protein interactions, expression regulation, transport regulation, catalytic reactions, etc., but rather work with generalized types characterizing the existence of any connection between network members. Results We developed the online tool FunGeneNet, which utilizes the ANDSystem and STRING to reconstruct gene networks using experimental gene sets and to estimate their difference from random networks. To compare the reconstructed networks with random ones, the node permutation algorithm implemented in CrossTalkZ was taken as a basis. To study the FunGeneNet applicability, the functional connectivity analysis of networks constructed for gene sets involved in the Gene Ontology biological processes was conducted. We showed that the method sensitivity exceeds 0.8 at a specificity of 0.95. We found that the significance level of the difference between gene networks of biological processes and random networks is determined by the type of connections considered between objects. At the same time, the highest reliability is achieved for the generalized form of connections that takes into account all the individual types of connections. By taking examples of the thyroid cancer networks and the apoptosis network, it is demonstrated that key participants in these processes are involved in the interactions of those types by which these networks differ from random ones. Conclusions FunGeneNet is a web tool aimed at proving the functionality of networks in a wide range of sizes of experimental gene sets, both for different global networks and for different types of interactions. Using examples of thyroid cancer and apoptosis networks, we have shown that the links over-represented in the analyzed network in comparison with the random ones make possible a biological interpretation of the original gene/protein sets. The FunGeneNet web tool for assessment of the functional enrichment of networks is available at http://www-bionet.sscc.ru/fungenenet/

The New Version of the ANDDigest Tool with Improved AI-Based Short Names Recognition

The body of scientific literature continues to grow annually. Over 1.5 million abstracts of biomedical publications were added to the PubMed database in 2021. Therefore, developing cognitive systems that provide a specialized search for information in scientific publications based on subject area ontology and modern artificial intelligence methods is urgently needed. We previously developed a web-based information retrieval system, ANDDigest, designed to search and analyze information in the PubMed database using a customized domain ontology. This paper presents an improved ANDDigest version that uses fine-tuned PubMedBERT classifiers to enhance the quality of short name recognition for molecular-genetics entities in PubMed abstracts on eight biological object types: cell components, diseases, side effects, genes, proteins, pathways, drugs, and metabolites. This approach increased average short name recognition accuracy by 13%

Additional file 9: Table S3. of FunGeneNet: a web tool to estimate enrichment of functional interactions in experimental gene sets

FunGeneNet classification results for different interaction types. (XLSX 254 kb

Additional file 1: Table S1. of FunGeneNet: a web tool to estimate enrichment of functional interactions in experimental gene sets

Positive protein samples. (XLSX 1846 kb