Radioprotectors.org: an open database of known and predicted radioprotectors.

The search for radioprotectors is an ambitious goal with many practical applications. Particularly, the improvement of human radioresistance for space is an important task, which comes into view with the recent successes in the space industry. Currently, all radioprotective drugs can be divided into two large groups differing in their effectiveness depending on the type of exposure. The first of these is radioprotectors, highly effective for pulsed, and some types of relatively short exposure to irradiation. The second group consists of long-acting radioprotectors. These drugs are effective for prolonged and fractionated irradiation. They also protect against impulse exposure to ionizing radiation, but to a lesser extent than short-acting radioprotectors. Creating a database on radioprotectors is a necessity dictated by the modern development of science and technology. We have created an open database, Radioprotectors.org, containing an up-to-date list of substances with proven radioprotective properties. All radioprotectors are annotated with relevant chemical and biological information, including transcriptomic data, and can be filtered according to their properties. Additionally, the performed transcriptomics analysis has revealed specific transcriptomic profiles of radioprotectors, which should facilitate the search for potent radioprotectors.

Replicative and radiation-induced aging: a comparison of gene expression profiles.

All living organisms are subject to the aging process and experience the effect of ionizing radiation throughout their life. There have been a number of studies that linked ionizing radiation process to accelerated aging, but comprehensive signalome analysis of both processes was rarely conducted. Here we present a comparative signaling pathway based analysis of the transcriptomes of fibroblasts irradiated with different doses of ionizing radiation, replicatively aged fibroblasts and fibroblasts collected from young, middle age and old patients. We demonstrate a significant concordance between irradiation-induced and replicative senescence signalome signatures of fibroblasts. Additionally, significant differences in transcriptional response were also observed between fibroblasts irradiated with high and low dose. Our data shows that the transcriptome of replicatively aged fibroblasts is more similar to the transcriptome of the cells irradiated with 2 Gy, than with 5 сGy.This work revealed a number of signaling pathways that are shared between senescence and irradiation processes and can potentially be targeted by the new generation of gero- and radioprotectors.

Aging and drug discovery.

Multiple interventions in the aging process have been discovered to extend the healthspan of model organisms. Both industry and academia are therefore exploring possible transformative molecules that target aging and age-associated diseases. In this overview, we summarize the presented talks and discussion points of the 5th Annual Aging and Drug Discovery Forum 2018 in Basel, Switzerland. Here academia and industry came together, to discuss the latest progress and issues in aging research. The meeting covered talks about the mechanistic cause of aging, how longevity signatures may be highly conserved, emerging biomarkers of aging, possible interventions in the aging process and the use of artificial intelligence for aging research and drug discovery. Importantly, a consensus is emerging both in industry and academia, that molecules able to intervene in the aging process may contain the potential to transform both societies and healthcare.

Mathematical Justification of Expression-Based Pathway Activation Scoring (PAS).

Although modeling of activation kinetics for various cell signaling pathways has reached a high grade of sophistication and thoroughness, most such kinetic models still remain of rather limited practical value for biomedicine. Nevertheless, recent advancements have been made in application of signaling pathway science for real needs of prescription of the most effective drugs for individual patients. The methods for such prescription evaluate the degree of pathological changes in the signaling machinery based on two types of data: first, on the results of high-throughput gene expression profiling, and second, on the molecular pathway graphs that reflect interactions between the pathway members. For example, our algorithm OncoFinder evaluates the activation of molecular pathways on the basis of gene/protein expression data in the objects of the interest.Yet, the question of assessment of the relative importance for each gene product in a molecular pathway remains unclear unless one call for the methods of parameter sensitivity /stiffness analysis in the interactomic kinetic models of signaling pathway activation in terms of total concentrations of each gene product.Here we show two principal points: 1. First, the importance coefficients for each gene in pathways that were obtained using the extremely time- and labor-consuming stiffness analysis of full-scaled kinetic models generally differ from much easier-to-calculate expression-based pathway activation score (PAS) not more than by 30%, so the concept of PAS is kinetically justified. 2. Second, the use of pathway-based approach instead of distinct gene analysis, due to the law of large numbers, allows restoring the correlation between the similar samples that were examined using different transcriptome investigation techniques.

Design of efficient computational workflows for in silico drug repurposing.

Here, we provide a comprehensive overview of the current status of in silico repurposing methods by establishing links between current technological trends, data availability and characteristics of the algorithms used in these methods. Using the case of the computational repurposing of fasudil as an alternative autophagy enhancer, we suggest a generic modular organization of a repurposing workflow. We also review 3D structure-based, similarity-based, inference-based and machine learning (ML)-based methods. We summarize the advantages and disadvantages of these methods to emphasize three current technical challenges. We finish by discussing current directions of research, including possibilities offered by new methods, such as deep learning.

Large-scale profiling of signalling pathways reveals an asthma specific signature in bronchial smooth muscle cells.

BACKGROUND: Bronchial smooth muscle (BSM) cells from asthmatic patients maintain in vitro a distinct hyper-reactive ("primed") phenotype, characterized by increased release of pro-inflammatory factors and mediators, as well as hyperplasia and/or hypertrophy. This "primed" phenotype helps to understand pathogenesis of asthma, as changes in BSM function are essential for manifestation of allergic and inflammatory responses and airway wall remodelling. OBJECTIVE: To identify signalling pathways in cultured primary BSMs of asthma patients and non-asthmatic subjects by genome wide profiling of differentially expressed mRNAs and activated intracellular signalling pathways (ISPs). METHODS: Transcriptome profiling by cap-analysis-of-gene-expression (CAGE), which permits selection of preferentially capped mRNAs most likely to be translated into proteins, was performed in human BSM cells from asthmatic (n=8) and non-asthmatic (n=6) subjects and OncoFinder tool were then exploited for identification of ISP deregulations. RESULTS: CAGE revealed >600 RNAs differentially expressed in asthma vs control cells (p≤0.005), with asthma samples showing a high degree of similarity among them. Comprehensive ISP activation analysis revealed that among 269 pathways analysed, 145 (p<0.05) or 103 (p<0.01) are differentially active in asthma, with profiles that clearly characterize BSM cells of asthmatic individuals. Notably, we identified 7 clusters of coherently acting pathways functionally related to the disease, with ISPs down-regulated in asthma mostly targeting cell death-promoting pathways and up-regulated ones affecting cell growth and proliferation, inflammatory response, control of smooth muscle contraction and hypoxia-related signalization. CONCLUSIONS: These first-time results can now be exploited toward development of novel therapeutic strategies targeting ISP signatures linked to asthma pathophysiology.

Signaling pathway activation drift during aging: Hutchinson-Gilford Progeria Syndrome fibroblasts are comparable to normal middle-age and old-age cells.

For the past several decades, research in understanding the molecular basis of human aging has progressed significantly with the analysis of premature aging syndromes. Progerin, an altered form of lamin A, has been identified as the cause of premature aging in Hutchinson-Gilford Progeria Syndrome (HGPS), and may be a contributing causative factor in normal aging. However, the question of whether HGPS actually recapitulates the normal aging process at the cellular and organismal level, or simply mimics the aging phenotype is widely debated. In the present study we analyzed publicly available microarray datasets for fibroblasts undergoing cellular aging in culture, as well as fibroblasts derived from young, middle-age, and old-age individuals, and patients with HGPS. Using GeroScope pathway analysis and drug discovery platform we analyzed the activation states of 65 major cellular signaling pathways. Our analysis reveals that signaling pathway activation states in cells derived from chronologically young patients with HGPS strongly resemble cells taken from normal middle-aged and old individuals. This clearly indicates that HGPS may truly represent accelerated aging, rather than being just a simulacrum. Our data also points to potential pathways that could be targeted to develop drugs and drug combinations for both HGPS and normal aging.

Pathway activation strength is a novel independent prognostic biomarker for cetuximab sensitivity in colorectal cancer patients.

Cetuximab, a monoclonal antibody against epidermal growth factor receptor (EGFR), was shown to be active in colorectal cancer. Although some patients who harbor K-ras wild-type tumors benefit from cetuximab treatment, 40 to 60% of patients with wild-type K-ras tumors do not respond to cetuximab. Currently, there is no universal marker or method of clinical utility that could guide the treatment of cetuximab in colorectal cancer. Here, we demonstrate a method to predict response to cetuximab in patients with colorectal cancer using OncoFinder pathway activation strength (PAS), based on the transcriptomic data of the tumors. We first evaluated our OncoFinder pathway activation strength model in a set of transcriptomic data obtained from patient-derived xenograft (PDx) models established from colorectal cancer biopsies. Then, the approach and models were validated using a clinical trial data set. PAS could efficiently predict patients' response to cetuximab, and thus holds promise as a selection criterion for cetuximab treatment in metastatic colorectal cancer.

Signaling pathways activation profiles make better markers of cancer than expression of individual genes.

Identification of reliable and accurate molecular markers remains one of the major challenges of contemporary biomedicine. We developed a new bioinformatic technique termed OncoFinder that for the first time enables to quantatively measure activation of intracellular signaling pathways basing on transcriptomic data. Signaling pathways regulate all major cellular events in health and disease. Here, we showed that the Pathway Activation Strength (PAS) value itself may serve as the biomarker for cancer, and compared it with the "traditional" molecular markers based on the expression of individual genes. We applied OncoFinder to profile gene expression datasets for the nine human cancer types including bladder cancer, basal cell carcinoma, glioblastoma, hepatocellular carcinoma, lung adenocarcinoma, oral tongue squamous cell carcinoma, primary melanoma, prostate cancer and renal cancer, totally 292 cancer and 128 normal tissue samples taken from the Gene expression omnibus (GEO) repository. We profiled activation of 82 signaling pathways that involve ~2700 gene products. For 9/9 of the cancer types tested, the PAS values showed better area-under-the-curve (AUC) scores compared to the individual genes enclosing each of the pathways. These results evidence that the PAS values can be used as a new type of cancer biomarkers, superior to the traditional gene expression biomarkers.

Interactome analysis of myeloid-derived suppressor cells in murine models of colon and breast cancer.

In solid cancers, myeloid derived suppressor cells (MDSC) infiltrate (peri)tumoral tissues to induce immune tolerance and hence to establish a microenvironment permissive to tumor growth. Importantly, the mechanisms that facilitate such infiltration or a subsequent immune suppression are not fully understood. Hence, in this study, we aimed to delineate disparate molecular pathways which MDSC utilize in murine models of colon or breast cancer. Using pathways enrichment analysis, we completed interactome maps of multiple signaling pathways in CD11b+/Gr1(high/low) MDSC from spleens and tumor infiltrates of mice with c26GM colon cancer and tumor infiltrates of MDSC in 4T1 breast cancer. In both cancer models, infiltrating MDSC, but not CD11b+ splenic cells, have been found to be enriched in multiple signaling molecules suggestive of their enhanced proliferative and invasive phenotypes. The interactome data has been subsequently used to reconstruct a previously unexplored regulation of MDSC cell cycle by the c-myc transcription factor which was predicted by the analysis. Thus, this study represents a first interactome mapping of distinct multiple molecular pathways whereby MDSC sustain cancer progression.

Novel robust biomarkers for human bladder cancer based on activation of intracellular signaling pathways.

We recently proposed a new bioinformatic algorithm called OncoFinder for quantifying the activation of intracellular signaling pathways. It was proved advantageous for minimizing errors of high-throughput gene expression analyses and showed strong potential for identifying new biomarkers. Here, for the first time, we applied OncoFinder for normal and cancerous tissues of the human bladder to identify biomarkers of bladder cancer. Using Illumina HT12v4 microarrays, we profiled gene expression in 17 cancer and seven non-cancerous bladder tissue samples. These experiments were done in two independent laboratories located in Russia and Canada. We calculated pathway activation strength values for the investigated transcriptomes and identified signaling pathways that were regulated differently in bladder cancer (BC) tissues compared with normal controls. We found, for both experimental datasets, 44 signaling pathways that serve as excellent new biomarkers of BC, supported by high area under the curve (AUC) values. We conclude that the OncoFinder approach is highly efficient in finding new biomarkers for cancer. These markers are mathematical functions involving multiple gene products, which distinguishes them from "traditional" expression biomarkers that only assess concentrations of single genes.

Molecular aspects of development and regulation of endometriosis.

Endometriosis is a common and painful condition affecting women of reproductive age. While the underlying pathophysiology is still largely unknown, much advancement has been made in understanding the progression of the disease. In recent years, a great deal of research has focused on non-invasive diagnostic tools, such as biomarkers, as well as identification of potential therapeutic targets. In this article, we will review the etiology and cellular mechanisms associated with endometriosis as well as the current diagnostic tools and therapies. We will then discuss the more recent genomic and proteomic studies and how these data may guide development of novel diagnostics and therapeutics. The current diagnostic tools are invasive and current therapies primarily treat the symptoms of endometriosis. Optimally, the advancement of "-omic" data will facilitate the development of non-invasive diagnostic biomarkers as well as therapeutics that target the pathophysiology of the disease and halt, or even reverse, progression. However, the amount of data generated by these types of studies is vast and bioinformatics analysis, such as we present here, will be critical to identification of appropriate targets for further study.

A role for G-CSF and GM-CSF in nonmyeloid cancers.

Granulocyte colony-stimulating factor (G-CSF) and granulocyte-macrophage colony-stimulating factor (GM-CSF) modulate progression of certain solid tumors. The G-CSF- or GM-CSF-secreting cancers, albeit not very common are, however, among the most rapidly advancing ones due to a cytokine-mediated immune suppression and angiogenesis. Similarly, de novo angiogenesis and vasculogenesis may complicate adjuvant use of recombinant G-CSF or GM-CSF thus possibly contributing to a cancer relapse. Rapid diagnostic tools to differentiate G-CSF- or GM-CSF-secreting cancers are not well developed therefore hindering efforts to individualize treatments for these patients. Given an increasing utilization of adjuvant G-/GM-CSF in cancer therapy, we aimed to summarize recent studies exploring their roles in pathophysiology of solid tumors and to provide insights into some complexities of their therapeutic applications.

Genetics and epigenetics of aging and longevity.

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.

The OncoFinder algorithm for minimizing the errors introduced by the high-throughput methods of transcriptome analysis.

The diversity of the installed sequencing and microarray equipment make it increasingly difficult to compare and analyze the gene expression datasets obtained using the different methods. Many applications requiring high-quality and low error rates cannot make use of available data using traditional analytical approaches. Recently, we proposed a new concept of signalome-wide analysis of functional changes in the intracellular pathways termed OncoFinder, a bioinformatic tool for quantitative estimation of the signaling pathway activation (SPA). We also developed methods to compare the gene expression data obtained using multiple platforms and minimizing the error rates by mapping the gene expression data onto the known and custom signaling pathways. This technique for the first time makes it possible to analyze the functional features of intracellular regulation on a mathematical basis. In this study we show that the OncoFinder method significantly reduces the errors introduced by transcriptome-wide experimental techniques. We compared the gene expression data for the same biological samples obtained by both the next generation sequencing (NGS) and microarray methods. For these different techniques we demonstrate that there is virtually no correlation between the gene expression values for all datasets analyzed (R (2) < 0.1). In contrast, when the OncoFinder algorithm is applied to the data we observed clear-cut correlations between the NGS and microarray gene expression datasets. The SPA profiles obtained using NGS and microarray techniques were almost identical for the same biological samples allowing for the platform-agnostic analytical applications. We conclude that this feature of the OncoFinder enables to characterize the functional states of the transcriptomes and interactomes more accurately as before, which makes OncoFinder a method of choice for many applications including genetics, physiology, biomedicine, and molecular diagnostics.