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Introduction: The purpose of this investigation was to elucidate the functions of TRIM11 and HOXB9 in the pathogenesis of sepsis, focusing on their influence on inflammation, apoptosis, and the NF-κB signaling pathway. Material and methods: Through public databases, TRIM family genes related to sepsis were screened, and TRIM11 was evaluated as a sepsis biomarker through ROC analysis. The UbiBrowser database screened TRIM11 downstream genes and identified HOXB9 as an essential target. THP-1 cells were stimulated by Lipopolysaccharide (LPS) to induce inflammation and simulate sepsis. Flow cytometry, Enzyme-linked immunosorbent assay, and Western blot experiments were used to detect changes in cell apoptosis rate, apoptosis-related proteins, and inflammatory cytokines after TRIM11 and HOXB9 were silenced. Additionally, we investigated the ubiquitination interaction between TRIM11 and HOXB9 and their effects on the NF-κB signaling pathway. Results: Our findings demonstrated that sepsis patient samples had elevated levels of TRIM11 expression and had high clinical diagnostic value. Functional experiments showed that the knockdown of TRIM11 significantly alleviated LPS-induced THP-1 cell apoptosis and inflammation, while the knockdown of HOXB9 did the opposite. The simultaneous downregulation of TRIM11 and HOXB9 balanced these responses, suggesting they play a key role in regulating sepsis-associated inflammation and apoptosis. In addition, TRIM11 regulated the NF-κB signaling pathway by reversing HOXB9-induced activation through ubiquitination, suggesting a novel regulatory mechanism in the pathogenesis of sepsis. Conclusions: Our findings highlight the interaction between TRIM11 and HOXB9 in regulating inflammation and apoptosis pathways, providing new insights into sepsis treatment. [ABSTRACT FROM AUTHOR]

RNA velocities and generalizations emerge as powerful approaches for extracting time-resolved information from high-throughput snapshot single-cell data. Yet, several inherent limitations restrict applying the approaches to genes not suitable for RNA velocity inference due to complex transcriptional dynamics, low expression, or lacking splicing dynamics, or data of non-transcriptomic modality. Here, we present GraphVelo, a graph-based machine learning procedure that uses as input the RNA velocities inferred from existing methods and infers velocity vectors lying in the tangent space of the low-dimensional manifold formed by the single cell data. GraphVelo preserves vector magnitude and direction information during transformations across different data representations. Tests on multiple synthetic and experimental scRNA-seq data including viral-host interactome and multi-omics datasets demonstrate that GraphVelo, together with downstream generalized dynamo analyses, extends RNA velocities to multi-modal data and reveals quantitative nonlinear regulation relations between genes, virus and host cells, and different layers of gene regulation.

RNA velocities and generalizations emerge as powerful approaches for extracting time-resolved information from high-throughput snapshot single-cell data. Yet, several inherent limitations restrict applying the approaches to genes not suitable for RNA velocity inference due to complex transcriptional dynamics, low expression, or lacking splicing dynamics, or data of non-transcriptomic modality. Here, we present GraphVelo, a graph-based machine learning procedure that uses as input the RNA velocities inferred from existing methods and infers velocity vectors lying in the tangent space of the low-dimensional manifold formed by the single cell data. GraphVelo preserves vector magnitude and direction information during transformations across different data representations. Tests on multiple synthetic and experimental scRNA-seq data including viral-host interactome and multi-omics datasets demonstrate that GraphVelo, together with downstream generalized dynamo analyses, extends RNA velocities to multi-modal data and reveals quantitative nonlinear regulation relations between genes, virus and host cells, and different layers of gene regulation.