It is acknowledged that cancer cells are able to undergo senescence in response to clinically used chemotherapeutics. Moreover, recent years have provided evidence that some drugs can selectively remove senescent cells. Therefore, it is essential to properly identify and characterize senescent cells, especially when it comes to cancer. Senescence was induced in various cancer cell lines (A549, SH-SY-5Y, HCT116, MDA-MB-231, and MCF-7) following treatment with doxorubicin, irinotecan, methotrexate, 5-fluorouracil, oxaliplatin, or paclitaxel. Treatment with tested chemotherapeutics resulted in upregulation of p21 and proliferation arrest without cytotoxicity. A comparative analysis with the use of common senescence markers (i.e., morphology, SA-β-galactosidase, granularity, secretory phenotype, and the level of double-stranded DNA damage) revealed a large diversity in response to the chemotherapeutics used. The strongest senescence inducers were doxorubicin, irinotecan, and methotrexate; paclitaxel had an intermediate effect and oxaliplatin and 5-fluorouracil did not induce senescence. In addition, different susceptibility of cancer cells to senescence was observed. A statistical analysis aimed at finding any relationship between the senescence markers applied did not show clear correlations. Moreover, increased SA-β-gal activity coupled with p21 expression proved not to be an unequivocal senescence marker. This points to a need to simultaneously analyze multiple markers, given their individual limitations.

Bioenergetic failure, oxidative stress, and changes in mitochondrial morphology are common pathologic hallmarks of amyotrophic lateral sclerosis (ALS) in several cellular and animal models. Disturbed mitochondrial physiology has serious consequences for proper functioning of the cell, leading to the chronic mitochondrial stress. Mitochondria, being in the center of cellular metabolism, play a pivotal role in adaptation to stress conditions. We found that mitochondrial dysfunction and adaptation processes differ in primary fibroblasts derived from patients diagnosed with either sporadic or familial forms of ALS. The evaluation of mitochondrial parameters such as the mitochondrial membrane potential, the oxygen consumption rate, the activity and levels of respiratory chain complexes, and the levels of ATP, reactive oxygen species, and Ca2+ show that the bioenergetic properties of mitochondria are different in sporadic ALS, familial ALS, and control groups. Comparative statistical analysis of the data set (with use of principal component analysis and support vector machine) identifies and distinguishes 3 separate groups despite the small number of investigated cell lines and high variability in measured parameters. These findings could be a first step in development of a new tool for predicting sporadic and familial forms of ALS and could contribute to knowledge of its pathophysiology.—Walczak, J., Dębska‐Vielhaber, G., Vielhaber, S., Szymański, J., Charzyńska, A., Duszyński, J., Szczepanowska, J. Distinction of sporadic and familial forms of ALS based on mitochondrial characteristics. FASEB J. 33, 4388–4403 (2019). http://www.fasebj.org

The hypoxia-inducible factors (HIF) are transcription factors that activate the adaptive hypoxic response when oxygen levels are low. The HIF transcriptional program increases oxygen delivery by inducing angiogenesis and by promoting metabolic reprograming that favors glycolysis. The two major HIFs, HIF-1 and HIF-2, mediate this response during prolonged hypoxia in an overlapping and sequential fashion that is referred to as the HIF switch. Both HIF proteins consist of an unstable alpha chain and a stable beta chain. The instability of the alpha chains is mediated by prolyl hydroxylase (PHD) activity during normoxic conditions, which leads to ubiquitination and proteasomal degradation of the alpha chains. During normoxic conditions, very little HIF-1 or HIF-2 alpha–beta dimers are present because of PHD activity. During hypoxia, however, PHD activity is suppressed, and HIF dimers are stable. Here we demonstrate that HIF-1 expression is maximal after 4 h of hypoxia in primary endothelial cells and then is dramatically reduced by 8 h. In contrast, HIF-2 is maximal at 8 h and remains elevated up to 24 h. There are differences in the HIF-1 and HIF-2 transcriptional profiles, and therefore understanding how the transition between them occurs is important and not clearly understood. Here we demonstrate that the HIF-1 to HIF-2 transition during prolonged hypoxia is mediated by two mechanisms: (1) the HIF-1 driven increase in the glycolytic pathways that reactivates PHD activity and (2) the much less stable mRNA levels of HIF-1α (HIF1A) compared to HIF-2α (EPAS1) mRNA. We also demonstrate that the alpha mRNA levels directly correlate to the relative alpha protein levels, and therefore to the more stable HIF-2 expression during prolonged hypoxia.

BackgroundAs suggested by the origin of the word, sphingolipids are mysterious molecules with various roles in antagonistic cellular processes such as autophagy, apoptosis, proliferation and differentiation. Moreover, sphingolipids have recently been recognized as important messengers in cellular signaling pathways. Notably, sphingolipid metabolism disorders have been observed in various pathological conditions such as cancer and neurodegeneration.ResultsThe existing formal models of sphingolipid metabolism focus mainly on de novo ceramide synthesis or are limited to biochemical transformations of particular subspecies. Here, we propose the first comprehensive computational model of sphingolipid metabolism in human tissue. Contrary to the previous approaches, we use a model that reflects cell compartmentalization thereby highlighting the differences among individual organelles.ConclusionsThe model that we present here was validated using recently proposed methods of model analysis, allowing to detect the most sensitive and experimentally non-identifiable parameters and determine the main sources of model variance. Moreover, we demonstrate the usefulness of our model in the study of molecular processes underlying Alzheimer’s disease, which are associated with sphingolipid metabolism.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0176-9) contains supplementary material, which is available to authorized users.

Modeling the dynamic behavior of signal transduction pathways is an important topic in systems biology. Mathematical models complement experimental technologies used to identify the molecular components and interactions in a system of interest. In this paper, we illustrate different types of mathematical approaches that are used to model signaling network behavior. Here, we review the basic methods of sensitivity analysis and apply them to the model of the system of membrane receptors. Four such receptors are considered: growth factor epidermal, low density lipoprotein, transferrin and vitellogenin receptor. We argue that application of sensitivity analysis methods provides an insight into how a signaling system controls the cell behavior.

Despite a conceptually simple mechanism of signaling, the JAK-STAT pathway exhibits considerable behavioral complexity. Computational pathway models are tools to investigate in detail signaling process. They integrate well with experimental studies, helping to explain molecular dynamics and to state new hypotheses, most often about the structure of interactions.A relatively small amount of experimental data is available for a JAK1/2-STAT1 variant of the pathway, hence, only several computational models were developed. Here we review a dominant approach of kinetic modeling of the JAK1/2-STAT1 pathway, based on ordinary differential equations. We also give a brief overview of attempts to computationally infer topology of this pathway.

BackgroundA vast amount of microarray data on transcriptomic response to injury has been collected so far. We designed the analysis in order to identify the genes displaying significant changes in expression after wounding in different organisms and tissues. This meta-analysis is the first study to compare gene expression profiles in response to wounding in as different tissues as heart, liver, skin, bones, and spinal cord, and species, including rat, mouse and human.ResultsWe collected available microarray transcriptomic profiles obtained from different tissue injury experiments and selected the genes showing a minimum twofold change in expression in response to wounding in prevailing number of experiments for each of five wound healing stages we distinguished: haemostasis & early inflammation, inflammation, early repair, late repair and remodelling. During the initial phases after wounding, haemostasis & early inflammation and inflammation, the transcriptomic responses showed little consistency between different tissues and experiments. For the later phases, wound repair and remodelling, we identified a number of genes displaying similar transcriptional responses in all examined tissues. As revealed by ontological analyses, activation of certain pathways was rather specific for selected phases of wound healing, such as e.g. responses to vitamin D pronounced during inflammation. Conversely, we observed induction of genes encoding inflammatory agents and extracellular matrix proteins in all wound healing phases. Further, we selected several genes differentially upregulated throughout different stages of wound response, including established factors of wound healing in addition to those previously unreported  in this context such as PTPRC and AQP4.ConclusionsWe found that transcriptomic responses to wounding showed similar traits in a diverse selection of tissues including skin, muscles, internal organs and nervous system. Notably, we distinguished transcriptional induction of inflammatory genes not only in the initial response to wounding, but also later, during wound repair and tissue remodelling.Electronic supplementary materialThe online version of this article (10.1186/s12864-017-4202-8) contains supplementary material, which is available to authorized users.

Abstract:In this paper, we investigate efficient estimation of differential entropy for multivariate random variables. We propose bias correction for the nearest neighbor estimator, which yields more accurate results in higher dimensions. In order to demonstrate the accuracy of the improvement, we calculated the corrected estimator for several families of random variables. For multivariate distributions, we considered the case of independent marginals and the dependence structure between the marginal distributions described by Gaussian copula. The presented solution may be particularly useful for high dimensional data, like those analyzed in the systems biology field. To illustrate such an application, we exploit differential entropy to define the robustness of biochemical kinetic models.