Molecular and Immunological Signatures of Long COVID: Implications for Diagnosis and Personalized Treatment Strategies

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to an emerging health challenge known as "long COVID," also known as PASC, post-acute sequelae of COVID-19, characterized by symptoms that persist beyond the acute phase of infection. While acute COVID-19 has been extensively studied, the molecular and immunological mechanisms underlying long COVID remain poorly understood. This study aims to investigate these mechanisms by examining the presence of the viral nucleocapsid (N) and spike (S) genes, their mRNA expression, associated immunoglobulins (IgG), and immune regulation via IDO-2 activity in blood of individuals suspected of long COVID. Here we show that a unique pattern of test results contributes to a better understanding of the underlying mechanisms of long COVID, ultimately leading to improved diagnostic and therapeutic strategies for this condition. This study focuses on four key objectives: detecting viral or vaccin induced genetic material, quantifying mRNA expression of the N and S genes, profiling immunoglobulin levels, and measuring IDO-2 activity. These objectives aim to differentiate long COVID from other post-infectious conditions and provide insights into prolonged symptoms. The study population comprised 72 participants, 31 of whom were suspected of having long COVID based on defined symptomatology. Viral genetic material was detected in both symptomatic and asymptomatic individuals, Immunoglobulin levels varied, with symptomatic males exhibiting lower anti-Spike IgG levels than females, suggesting possible gender differences in immune response. Logistic regression models revealed that mRNA spike data alone in this small group was insufficient to predict symptoms presence, but the inclusion of immunoglobulins and inflammatory markers significantly improved predictive accuracy. Overall, this study highlights the complexity of long COVID and suggests that a multi-variable approach, combining mRNA and genomic spike data with inflammatory markers and demographic factors, provides a basis for effective prediction of symptoms, helping refine diagnostic and therapeutic strategies for long COVID