NetControl4BioMed: a pipeline for biomedical data acquisition and analysis of network controllability

Evaluating the in vitro and in vivo effects of carvacrol zinc oxide quantum dots in breast cancer

The study investigates the molecular interactions and biological effects of carvacrol zinc oxide quantum dots (CVC-ZnO QDs) on breast cancer in vitro MCF-7 cell lines and in vivo mammary cancer models. Molecular docking using AutoDock Vina revealed binding energies of CVC with key proteins in the PI3K/AKT/mTOR pathway, including PI3K, AKT, PTEN, and mTOR. The results showed significant interaction with specific amino acids, indicating a strong binding affinity. In vitro studies demonstrated a dose-dependent cytotoxic effect of CVC-ZnO QDs on MCF-7 cells, with an IC50 of 20.02 µg/mL, while enhancing intracellular reactive oxygen species (ROS) and decreasing mitochondrial membrane potential (MMP), indicative of apoptosis induction. Antioxidant activity, lipid peroxidation, and nuclear morphological changes were assessed, revealing decreased antioxidant status and increased lipid peroxidation in treated cells. In vivo, CVC-ZnO QDs modulated the PI3K/AKT/mTOR signaling in DMBA-induced mammary cancer in rats, decreasing p-PI3K, p-AKT, and p-mTOR expression while upregulating PTEN. Immunohistochemistry, qRT-PCR, and Western blot analyses confirmed these molecular alterations. The study concludes that CVC-ZnO QDs exert cytotoxic and pro-apoptotic effects on breast cancer cells by modulating the PI3K/Akt/mTOR pathway and promoting oxidative stress, presenting a potential therapeutic strategy for breast cancer management.

Target control of linear directed networks based on the path cover problem

Securing complete control of complex systems comprised of tens of thousands of interconnected nodes holds immense significance across various fields, spanning from cell biology and brain science to human-engineered systems. However, depending on specific functional requirements, it can be more practical and efficient to focus on a pre-defined subset of nodes for control, a concept known as target control. While some methods have been proposed to find the smallest driver node set for target control, they either rely on heuristic approaches based on k-walk theory, lacking a guarantee of optimal solutions, or they are overly complex and challenging to implement in real-world networks. To address this challenge, we introduce a simple and elegant algorithm, inspired by the path cover problem, which efficiently identifies the nodes required to control a target node set within polynomial time. To practically apply the algorithm in real-world systems, we have selected several networks in which a specific set of nodes with functional significance can be designated as a target control set. The analysed systems include the complete connectome of the nematode worm C. elegans, the recently disclosed connectome of the Drosophila larval brain, as well as dozens of genome-wide metabolic networks spanning major plant lineages. The target control analysis shed light on distinctions between neural systems in nematode worms and larval brain insects, particularly concerning the number of nodes necessary to regulate specific functional systems. Furthermore, our analysis uncovers evolutionary trends within plant lineages, notably when examining the proportion of nodes required to control functional pathways.

Cu2+ -Chelatable and ROS-Scavenging MXenzyme as NIR-II-Triggered Blood-Brain Barrier-Crossing Nanocatalyst against Alzheimer's Disease

Transition-metal dyshomeostasis has been identified as a critical pathogenic factor for the aggregates of amyloid-beta (Aβ) peptide, which is associated with the onset and progression of Alzheimer's disease (AD). Excessive transition-metal ions, especially copper ion (Cu²⁺ ), catalyze the formation of reactive oxygen species (ROS), triggering neuroinflammation and neuronal cell apoptosis. Therefore, developing a robust chelating agent can not only efficiently bind toxic Cu²⁺ , but also simultaneously scavenge the over-generated ROS that is urgently needed for AD treatment. In this work, a 2D niobium carbide (Nb₂ C) MXene-based nano-chelator is constructed and its performance in suppressing Cu²⁺ -induced accumulation of aggregated Aβ peptide and acting as a nanozyme (MXenzyme) with powerful antioxidant property to scavenge excess cellular ROS is explored, and the intrinsic mechanism is revealed by computational simulation. Importantly, the benign photothermal effect of Nb₂ C MXenzyme demonstrates the facilitated permeability of the blood-brain barrier under near-infrared laser irradiation, conquering limitations of the most conventional anti-AD therapeutic agents. This work not only demonstrates a favorable strategy for combating AD by engineering Nb₂ C MXenzyme-based neuroprotective nano-chelator, but also paves a distinct insight for extending the biomedical applications of MXenes in treating transition-metal dyshomeostasis-and ROS-mediated central nervous system diseases.

NetControl4BioMed: A web-based platform for controllability analysis of protein-protein interaction networks

MOTIVATION

There is an increasing amount of data coming from genome-wide studies identifying disease-specific survivability-essential proteins and host factors critical to a cell becoming infected. Targeting such proteins has a strong potential for targeted, precision therapies. Typically however, too few of them are drug targetable. An alternative approach is to influence them through drug targetable proteins upstream of them. Structural target network controllability is a suitable solution to this problem. It aims to discover suitable source nodes (e.g., drug targetable proteins) in a directed interaction network that can control (through a suitable set of input functions) a desired set of targets.

RESULTS

We introduce NetControl4BioMed, a free open-source web-based application that allows users to generate or upload directed protein-protein interaction networks and to perform target structural network controllability analyses on them. The analyses can be customized to focus the search on drug targetable source nodes, thus providing drug therapeutic suggestions. The application integrates protein data from HGNC, Ensemble, UniProt, NCBI, and InnateDB, directed interaction data from InnateDB, Omnipath, and SIGNOR, cell-line data from COLT and DepMap, and drug-target data from DrugBank.

AVAILABILITY

The application and data are available online at https://netcontrol.combio.org/. The source code is available at https://github.com/Vilksar/NetControl4BioMed under an MIT license.