Advancing Understanding of Long COVID Pathophysiology Through Quantum Walk-Based Network Analysis

TL;DR

This study employs quantum walk-based network analysis to uncover key proteins and pathways involved in Long COVID, revealing mitochondrial dysfunction and inflammation as central mechanisms, and identifying potential therapeutic targets like VDAC1.

Contribution

It introduces quantum walk as a novel computational method for analyzing large biological networks, outperforming traditional approaches in identifying critical disease-related proteins.

Findings

VDAC1 identified as a potential biomarker and therapeutic target

Quantum walk uncovers deeper network regions than RWR

Mitochondrial dysfunction and inflammation are central in Long COVID

Abstract

Long COVID is a multisystem condition characterized by persistent symptoms such as fatigue, cognitive impairment, and systemic inflammation, following COVID-19 infection, yet its mechanisms remain poorly understood. In this study, we applied quantum walk (QW), a computational approach leveraging quantum interference, to explore large-scale SARS-CoV-2-induced protein (SIP) networks. Compared to the conventional random walk with restart (RWR) method, QW demonstrated superior capacity to traverse deeper regions of the network, uncovering proteins and pathways implicated in Long COVID. Key findings include mitochondrial dysfunction, thromboinflammatory responses, and neuronal inflammation as central mechanisms. QW uniquely identified the CDGSH iron-sulfur domain-containing protein family and VDAC1, a mitochondrial calcium transporter, as critical regulators of these processes. VDAC1 emerged as a potential biomarker and therapeutic target, supported by FDA-approved compounds such as cannabidiol. These findings highlight QW as a powerful tool for elucidating complex biological systems and identifying novel therapeutic targets for conditions like Long COVID.