ArticlebioRxiv : the preprint server for biology2025
Uncovering the Pathophysiological Pattern of Expression from Integrated Analysis across Uniformly Processed RNA Sequencing COVID-19 Datasets.
Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Background: Post-acute sequelae of SARS-CoV-2 infection (PASC) affects millions globally, yet the molecular mechanisms underlying acute COVID-19 and its chronic sequelae remain poorly understood. Methods: We performed an integrative transcriptomic analysis of three independent RNA-seq datasets, capturing the complete COVID-19 pathophysiology from health through acute severe infection to post-acute sequelae and mortality (n=142 total samples). We implemented a containerized analytical pipeline from data download, quantification, differential gene expression to uniformly process these three RNA-seq datasets. Results: Our analysis reveals striking molecular dichotomies contrasting disease phases with profound clinical implications. Acute severe/critical COVID-19 reveals predominant enrichment of TNF-α signaling via NF-κB pathways (normalized enrichment score >2.5, FDR <0.001), reflecting a cytokine storm pathophysiology characterized by rapid inflammatory developments involving IL-6, TNF-α, and anti-apoptotic responses. In contrast, PASC patients exhibit dominant enrichment of Myc Targets V1 and Oxidative Phosphorylation pathways (NES >2.2, FDR <0.005), indicating important shifts toward cellular adaptation. Pathway signature analysis identifies core differentially expressed genes that reliably distinguish disease phases, thereby offering objective biomarkers for precision diagnosis and monitoring. Conclusions: These findings establish a comprehensive molecular framework distinguishing acute inflammatory from chronic metabolic COVID-19 phases, with potential clinical applicability. TNF-α/NF-κB pathway signatures identify patients at risk for severe disease progression, while Myc/OXPHOS signatures allow objective PASC diagnosis, addressing current reliance on subjective and eliminative diagnosis. This integrative analytical framework has utility beyond COVID-19, offering an applicable approach for precision medicine implementation across other diseases processes. Clinical Significance: This study transforms COVID-19 from a symptom-based to a molecularly-defined disease spectrum, enabling precision diagnosis, prognostic monitoring, classification, and targeted therapeutic possibilities based on pathway-specific biomarkers rather than subjective clinical assessments.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.