Biology subjects

Marjanovic, M.

Publications and source records attributed to Marjanovic, M..

3 recordsLinked to original sources

bioRxiv2022

Targeting plasma membrane and mitochondrial instability in breast cancer cells and breast epithelial to mesenchymal transition-model cells by adamantyl diaza-crown ether ZG613

The adamantane derived diaza-crown ether ZG613 was assessed as a potential breast cancer cells and breast epithelial to mesenchymal transition (EMT)-model cells targeting agent. We postulated that ZG613 activity relies on its plasma/mitochondria membrane disruption ability based on adamantane hydrophobicity and/or crown ether related ionophoric properties. We performed molecular dynamics (MD) simulations and next generation sequencing, followed by in vitro study of cell death, membrane perturbations and ionophoric ability, as well as in vivo study of effects on the tumour growth. MD simulation and RNA sequencing pointed toward physical disruption of plasma membrane by ZG613, corroborated by measured increase in membrane permeability leading to cell death. Measurements of ion fluxes confirmed ZG613 inability to transport Na+ and K+, as predicted by MD simulation. EMT-model cells exhibit changes in mitochondrial morphology and ATP levels, successfully targeted by ZG613. ZG613 caused mild retardation of tumour growth in vivo. In conclusion, ZG613 kills breast cancer cells and breast EMT-model cells by physical disruption of plasma membrane and impairments of mitochondrial functions. Breast EMT cells represent good potential targets within the breast tumour, due to their plasma membrane and mitochondrial instability.

cancer biology↗

bioRxiv2022

Salinomycin disturbs Golgi apparatus function and specifically affects cells in epithelial-to-mesenchymal transition

Epithelial-to-mesenchymal transition (EMT) gives rise to cells with properties similar to cancer stem cells (CSCs). Targeting the EMT program to selectively eliminate CSCs is a promising way to improve cancer therapy. Salinomycin (Sal), a K+/H+ ionophore, was identified as highly selective towards CSC-like cells, but its mechanism of action and selectivity remains elusive. Here we show that Sal, similarly to monensin and nigericin, disturbs the function of the Golgi apparatus (GA). Sal alters the expression of GA-related genes and leads to marked changes in GA morphology, particularly in cells that underwent EMT. Moreover, GA disturbing agents severely affect protein post-translational modifications including protein processing, glycosylation and secretion. We discover that the alterations induced by GA disturbing agents specifically affect the viability of EMT cells. Collectively, our work identifies a new vulnerability related to the EMT, suggesting that targeting the GA is a novel therapeutic approach against CSCs.

cancer biology↗

bioRxiv2022

Weakly supervised deep learning for human breast cancer-related optical biomarker discovery in label-free virtual histopathology

With the latest advancements in optical bioimaging, rich structural and functional information has been generated from biological samples, which calls for capable computational tools to identify patterns and uncover relationships between optical characteristics and various biomedical conditions. Constrained by the existing knowledge of the novel signals obtained by those bioimaging techniques, precise and accurate ground truth annotations can be difficult to obtain. Here we present a weakly supervised Deep Learning framework for optical signature discovery based on inexact and incomplete supervision. The framework consists of a Multiple Instance Learning-based classifier for the identification of regions of interest in coarsely labeled images, and model interpretation techniques for optical signature discovery. We applied this framework to investigate human breast cancer-related optical signatures based on virtual histopathology enabled by simultaneous label-free autofluorescence multiharmonic microscopy (SLAM), with the goal to explore unconventional cancer-related optical signatures from normal-appearing breast tissues. The framework has achieved an average area under the curve (AUC) of 0.975 on the cancer diagnosis task. In addition to well-known cancer biomarkers, non-obvious cancer-related patterns were revealed by the framework, including NAD(P)H-rich extracellular vesicles observed in normal-appearing breast cancer tissue, which facilitate new insights into the tumor microenvironment and field cancerization. This framework can be further extended to diverse imaging modalities and optical signature discovery tasks.

bioinformatics↗