Selection of Unique Molecules for Cancer Treatment by Distance-Based Method: Hypericin Effect on Respiratory Chain

Denis Horvath,Veronika Huntosova,Silvia Tomkova

doi:10.3390/biophysica1020017

Denis Horvath, Veronika Huntosova + Show 1 more

Open Access

https://doi.org/10.3390/biophysica1020017

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Abstract

The heterogeneous composition of tumors presents a significant obstacle to the selection of a single molecule as a potential universal inhibitor of tumor growth. Lipid signaling and cellular metabolism have become the main targets of anticancer treatment in recent years. The protein kinase C (PKC) regulators Gö6976, rottlerin, hypericin, and phorbol myristyl acetate have been identified as agents affecting cellular metabolism. Measurable parameters describing metabolism, endocytosis, and respiration were subjected to a distance-based computational procedure for higher dimensions to complement and extend the knowledge gained from experimental data. The mutual distances of the parameters of the substances applied to the cancer cells in the presence and absence of lipids were calculated within the Lp spaces. The distance-based methods and comparisons of the generalized distances suggested to us the exceptional role of hypericin in heterogeneous systems. Furthermore, our results are confirmed by Western blotting of the levels of respiratory chain proteins and enzymes active in oxidative stress defense in cancer cell monolayers and spheroids. PKCα and PKCδ have been studied for lipid-activated cell signaling. In this study, we attempt to apply the concept of parametric distance in cell signal transduction and activation where the above methods have not yet been used.

Highlights

Theoretical approaches in the field of fundamental physics have led to geometrization, i.e., the transformation of information into a geometrical form or even its embodiment in manifolds [1]
We have shown that distance-based approaches, cluster analysis of fluorescence lifetime probability vectors, can be used to reliably assess the extent of oxidative stress to glioma cells based on differences in sensor fluorescence lifetimes between five different treatments [6]
Our goal is to demonstrate the effects of a molecule identified and computationally re-evaluated using the initially proposed methods on protein kinase C (PKC) signaling under lipid deprivation conditions

Summary

Introduction

Theoretical approaches in the field of fundamental physics have led to geometrization, i.e., the transformation of information into a geometrical form or even its embodiment in manifolds [1]. In a recent mathematical formalization of superstring theory, the extra dimensions of spacetime are expected to take the form of a six-dimensional Calabi–Yau manifold [1]. Closer to our current topic is the problem of multidimensional inputs. This has become relevant, for example, in data mining. An elementary strategy that allows a better understanding of the problem of multidimensional data refers to the evaluation of a scalar quantity: the distance. The duality in determining the proximity or distance of data objects promotes reduction, compression, and visualization techniques, as seen in the dimensionality reduction [2] and clustering methods of manifold learning (see, e.g., their application in genetics [3])

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