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Neuroblastoma (NB) has a low frequency of recurrent mutations compared to other cancers, which hinders the development of targeted therapies and novel risk stratification strategies. Multikinase inhibitors have shown potential in treating high-risk NB, but their efficacy is likely impaired by the cancer cells’ ability to adapt to these drugs through the employment of alternative signaling pathways. Based on the expression of 48 growth factor-related genes in 1189 NB tumors, we have developed a model for NB patient survival prediction. This model discriminates between stage 4 NB tumors with favorable outcomes (>80% overall survival) and very poor outcomes (<10%) independently from MYCN-amplification status. Using signaling pathway analysis and gene set enrichment methods in 60 NB patients with known therapy response, we identified signaling pathways, including EPO, NGF, and HGF, upregulated in patients with no or partial response. In a therapeutic setting, we showed that among six selected growth factors, EPO, and NGF showed the most pronounced protective effects in vitro against several promising anti-NB multikinase inhibitors: imatinib, dasatinib, crizotinib, cabozantinib, and axitinib. Mechanistically kinase inhibitors potentiated NB cells to stronger ERK activation by EPO and NGF. The protective action of these growth factors strongly correlated with ERK activation and was ERK-dependent. ERK inhibitors combined with anticancer drugs, especially with dasatinib, showed a synergistic effect on NB cell death. Consideration of growth factor signaling activity benefits NB outcome prediction and tailoring therapy regimens to treat NB.

Interferons (IFNs) are critical determinants in immune-competence and autoimmunity, and are endogenously regulated by a low-level constitutive feedback loop. However, little is known about the functions and origins of constitutive IFN. Recently, lipopolysaccharide (LPS)-induced IFN was implicated as a driver of necroptosis, a necrotic form of cell death downstream of receptor-interacting protein (RIP) kinase activation and executed by mixed lineage kinase like-domain (MLKL) protein. We found that the pre-established IFN status of the cell, instead of LPS-induced IFN, is critical for the early initiation of necroptosis in macrophages. This pre-established IFN signature stems from cytosolic DNA sensing via cGAS/STING, and maintains the expression of MLKL and one or more unknown effectors above a critical threshold to allow for MLKL oligomerization and cell death. Finally, we found that elevated IFN-signaling in systemic lupus erythematosus (SLE) augments necroptosis, providing a link between pathological IFN and tissue damage during autoimmunity.

ABSTRACTPersonalized medicine implies that distinct treatment methods are prescribed to individual patients according several features that may be obtained from, e.g., gene expression profile. The majority of machine learning methods suffer from the deficiency of preceding cases, i.e. the gene expression data on patients combined with the confirmed outcome of known treatment methods. At the same time, there exist thousands of various cell lines that were treated with hundreds of anti-cancer drugs in order to check the ability of these drugs to stop the cell proliferation, and all these cell line cultures were profiled in terms of their gene expression.Here we present a new approach in machine learning, which can predict clinical efficiency of anti-cancer drugs for individual patients by transferring features obtained from the expression-based data from cell lines. The method was validated on three datasets for cancer-like diseases (chronic myeloid leukemia, as well as lung adenocarcinoma and renal carcinoma) treated with targeted drugs – kinase inhibitors, such as imatinib or sorafenib.

ABSTRACTHigh throughput technologies opened a new era in biomedicine by enabling massive analysis of gene expression at both RNA and protein levels. Unfortunately, expression data obtained in different experiments are often poorly compatible, even for the same biologic samples. Here, using experimental and bioinformatic investigation of major experimental platforms, we show that aggregation of gene expression data at the level of molecular pathways helps to diminish cross- and intra-platform bias otherwise clearly seen at the level of individual genes. We created a mathematical model of cumulative suppression of data variation that predicts the ideal parameters and the optimal size of a molecular pathway. We compared the abilities to aggregate experimental molecular data for the 5 alternative methods, also evaluated by their capacity to retain meaningful features of biologic samples. The bioinformatic method OncoFinder showed optimal performance in both tests and should be very useful for future cross-platform data analyses.

ABSTRACTResponses to human cytomegalovirus (HCMV) infection are largely individual and cell type specific. We investigated molecular profiles in 2 primary cell cultures of human fibroblasts, which are highly or marginally sensitive to HCMV infection, respectively. We screened expression of genes and microRNAs (miRs) at the early (3 hours) stage of infection. To assess molecular pathway activation profiles, we applied bioinformatic algorithms OncoFinder and MiRImpact. In both cell types, pathway regulation properties at mRNA and miR levels were markedly different. Surprisingly, in the infected highly sensitive cells, we observed a “freeze” of miR expression profiles compared to uninfected controls. Our results evidence that in the sensitive cells, HCMV blocks intracellular regulation of microRNA expression already at the earliest stage of infection. These data suggest somewhat new functions for HCMV products and demonstrate dependence of miR expression arrest on the host-encoded factors.

ABSTRACTFibrosis, a progressive accumulation of extracellular matrix components, encompasses a wide spectrum of distinct organs, and accounts for an increasing burden of morbidity and mortality worldwide. Despite the tremendous clinical impact, the mechanisms governing the fibrotic process are not yet understood, and to date, no clinically reliable therapies for fibrosis have been discovered. Here we applied Regeneration Intelligence, a new bioinformatics software suite for qualitative analysis of intracellular signaling pathway activation using transcriptomic data, to assess a network of molecular signaling in lung and liver fibrosis. In both tissues, our analysis detected major conserved signaling pathways strongly associated with fibrosis, suggesting that some of the pathways identified by our algorithm but not yet wet-lab validated as fibrogenesis related, may be attractive targets for future research. While the majority of significantly disrupted pathways were specific to histologically distinct organs, several pathways have been concurrently activated or downregulated among the hepatic and pulmonary fibrosis samples, providing new evidence of evolutionary conserved pathways that may be relevant as possible therapeutic targets. While future confirmatory studies are warranted to validate these observations, our platform proposes a promising new approach for detecting fibrosis-promoting pathways and tailoring the right therapy to prevent fibrogenesis.

ABSTRACTMicroRNAs (miRs) are short noncoding RNA molecules that regulate expression of target mRNAs. Many published sources provide information about miRs and their targets. However, bioinformatic tools elucidating higher level impact of the established total miR profiles, are still largely missing. Recently, we developed a method termed OncoFinder enabling quantification of the activities of intracellular molecular pathways basing on gene expression data. Here we propose a new technique, MiRImpact, which enables to link miR expression data with its estimated outcome on the regulation of molecular pathways, like signaling, metabolic, cytoskeleton rearrangement, and DNA repair pathways. MiRImpact uses OncoFinder rationale for pathway activity calculations, with the major distinctions that (i) it deals with the concentrations of miRs - known regulators of gene products participating in molecular pathways, and (ii) miRs are considered as negative regulators of target molecules, if other is not specified. MiRImpact operates with 2 types of databases: for molecular targets of miRs and for gene products participating in molecular pathways. We applied MiRImpact to compare regulation of human bladder cancer-specific signaling pathways at the levels of mRNA and miR expression. We took 2 most complete alternative databases of experimentally validated miR targets – miRTarBase and DianaTarBase, and an OncoFinder database featuring 2725 gene products and 271 signaling pathways. We showed that the impact of miRs is orthogonal to pathway regulation at the mRNA level, which stresses the importance of studying posttranscriptional regulation of gene expression. We also report characteristic set of miR and mRNA regulation features linked with bladder cancer.

AbstractEndogenous retroviruses (ERVs) and LTR retrotransposons (LRs) occupy ∼8% of human genome. Deep sequencing technologies provide clues to understanding of functional relevance of individual ERVs/LRs by enabling direct identification of transcription factor binding sites (TFBS) and other landmarks of functional genomic elements. Here, we performed the genome-wide identification of human ERVs/LRs containing TFBS according to the ENCODE project. We created the first interactive ERV/LRs database that groups the individual inserts according to their familial nomenclature, number of mapped TFBS and divergence from their consensus sequence. Information on any particular element can be easily extracted by the user. We also created a genome browser tool, which enables quick mapping of any ERV/LR insert according to genomic coordinates, known human genes and TFBS. These tools can be used to easily explore functionally relevant individual ERV/LRs, and for studying their impact on the regulation of human genes. Overall, we identified ∼110,000 ERV/LR genomic elements having TFBS. We propose a hypothesis of “domestication” of ERV/LR TFBS by the genome milieu including subsequent stages of initial epigenetic repression, partial functional release, and further mutation-driven reshaping of TFBS in tight coevolution with the enclosing genomic loci.

Evolutionary theories of aging predict the existence of certain genes that provide selective advantage early in life with adverse effect on lifespan later in life (antagonistic pleiotropy theory) or longevity insurance genes (disposable soma theory). Indeed, the study of human and animal genetics is gradually identifying new genes that increase lifespan when overexpressed or mutated: gerontogenes. Furthermore, genetic and epigenetic mechanisms are being identified that have a positive effect on longevity. The gerontogenes are classified as lifespan regulators, mediators, effectors, housekeeping genes, genes involved in mitochondrial function, and genes regulating cellular senescence and apoptosis. In this review we demonstrate that the majority of the genes as well as genetic and epigenetic mechanisms that are involved in regulation of longevity are highly interconnected and related to stress response.

Transposable elements (TEs), which occupy nearly 40% of eukaryotic DNA, are selfish repetitive sequences, able to proliferate in the host genomes via either their DNA copies or RNA intermediates utilizing the mechanism termed 'reverse transcription' and the RNA-dependant DNA polymerase enzyme, called reverse transcriptase. The newly formed DNA copy of the element then integrates into the genome, using a combination of host and self-encoded proteins, depending on the transposable element origin. Being important model objects for the study of many fundamental molecular biology processes and by actively participating in the gene regulation network, TEs are of great interest for basic researches in molecular genetics and genomics. Their practical use, however, is limited now to some fields of forensic sciences, phylogenetic studies and population genetics. In this mini-review I have tried to put together both theoretical, experimental and speculative data on the use of the transposable elements as tools for the gene delivery into the host eukaryotic genomes, producing stable transgene transformants. The strength of the TE-based constructions as compared with popular viral vectors would be the predictable, genomic target sequence-specific transgene integration, mediated by the enzymatic machinery of some TEs. These and other implications of transposable elements in biomedical sciences will be discussed.

To investigate transcriptional alterations in human semen samples associated with COVID-19 infection.