Genes and biological networks that increase long-term COVID risk

Genes and biological networks that increase long-term COVID risk

By combining causal genetics with network control theory, this study uncovers hidden drivers of long-COVID and offers new insights into why the disease affects patients so differently.

Study:Integrative multi-omics framework for causal gene discovery in Long COVID. Photo credit: Daisy Daisy/Shutterstock.com

The coronavirus disease 2019 (COVID-19) pandemic took a heavy toll on people's lives and health starting in 2020. Although the severity of the pandemic has lessened, its long-term consequences continue to plague hundreds of thousands of survivors.

A study recently published in the journalPLoS Computational Biologyis studying the genes underlying long COVID risk using multi-omics tools.

Long COVID affects millions with varying degrees of persistent symptoms

Post-acute sequelae of SARS-CoV-2 infection (PASC), also known as long-COVID, refers to persistent or new symptoms that occur after infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It affects up to 20% of people, even in subclinical form.

However, the reported prevalence varies due to different definitions by different organizations, including the World Health Organization (WHO) and the National Institute for Health and Care Excellence (NICE).

Long-term COVID symptoms include neurological (brain fog, headaches, memory problems), respiratory (difficulty breathing, chest tightness, reduced exercise capacity), musculoskeletal (persistent severe fatigue, myalgia, joint pain), cardiovascular (chest pain, rapid heartbeat, fluctuating blood pressure), and inflammatory symptoms (swollen lymph nodes, mild fever).

The known risk factors for long COVID include gender, age and the presence of a previous illness. However, the genetic basis is unclear, which motivates the current study. This knowledge would help develop more accurate diagnoses and support future personalized therapies for this widespread condition.

Multi-omics data forms the basis for a new causal gene framework

The current study used a tailored multi-omics platform that combines two analysis methods: one to identify potential genes associated with long COVID and the other to identify network “driver” genes that exert control over disease-related biological pathways.

The computational platform included multiple types of biological data and mathematical methods, which together form a comprehensive framework for analyzing the genetic causes of Long COVID.

Methods used in this integrated approach included:

• Transkriptomweite Mendelsche Randomisierung (TWMR) zur Unterstützung bei der Suche nach Genen mit Hinweisen auf kausale Auswirkungen auf das Langzeit-COVID-Risiko oder den Langzeit-COVID-Schutz
• Quantitative Expression Trait Loci (eQTLs) zur Untersuchung genetischer Varianten auf ihren Einfluss auf die Genexpression
• Genomweite Assoziationsstudien (GWAS) zur Identifizierung von Zusammenhängen zwischen genetischen Varianten und dem Risiko einer langen COVID-Erkrankung
• RNA-Sequenzierung (RNA-seq) zur Untersuchung der tatsächlichen Veränderungen der Genexpression bei Long-COVID
• Das menschliche Protein-Protein-Interaktionsnetzwerk (PPI), das untersucht, wie Proteine ​​interagieren, und mithilfe der Netzwerkkontrolltheorie wichtige regulatorische Kontrollpunkte identifiziert

The authors integrated these to form a combined score for each gene:

Final result=α⋅(TWMR score)+(1−α)⋅(CT score)

Where the parameter α allows users to weigh the contribution of direct causal inference against network controllability.

The study prioritizes 32 genes associated with long COVID

The study identified 32 candidate genes that likely cause long COVID. Of these, 19 were reported by previous researchers, supporting the current study. Thirteen have now been identified for the first time and require further investigation. This set of genes is involved in the host's response to the virus, the virus's ability to cause cancerous changes in cells, and the regulation of the host's immune response and cell cycle.

Using enrichment analyses, it became clear that the same set of genes was involved in long COVID, autoimmune and connective tissue diseases, and certain syndromes and metabolic diseases. This explains why the former occurs with such different symptoms.

The scientists classified the causative genes based on their expression profiles to identify three subtypes of Long COVID. These had different symptoms, different underlying disease pathways and different clinical features.

The researchers developed a free, open-source app on the Shiny framework to allow other users to freely explore, search, and analyze their data using their own filters and parameters. This can be used to generate lists of putative causative genes using Mendelian randomization or control theory. It also helps reproduce the results of the current study.

The combination of causality and network biology strengthens discovery

The strengths of this study include the combination of causal inference using MR with network control theory, thereby capturing both the direct effects of causal gene expression and the effects of perturbations at control points on the entire system. Second, the use of multi-omics data is superior to a study based on only a single type of data.

In addition, gene discovery was accompanied by the identification of disease subtypes, making them clinically relevant, and the development of an interactive user tool. The Shiny app allows users to find more data by determining how much focus they want to place on either directly causal genes or the impact of regulatory control on the network.

Goals for future diagnostics and therapies

“This integrative framework illuminates new causal mechanisms and therapeutic targets and advances precision medicine strategies for long-COVID,” the authors conclude, while emphasizing that these findings provide a foundation for future research.

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