Predicting candidate therapeutic drugs for sepsis-induced acute respiratory distress syndrome based on transcriptome profiling

Prognostic stratification of sepsis through DNA damage response based RiskScore system: insights from single-cell RNA-sequencing and transcriptomic profiling

Background

A novel risk scoring system, predicated on DNA damage response (DDR), was developed to enhance prognostic predictions and potentially inform the creation of more effective therapeutic protocols for sepsis.

Methods

To thoroughly delineate the expression profiles of DDR markers within the context of sepsis, an analytical approach utilizing single-cell RNA-sequencing (scRNA-seq) was implemented. Our study utilized single-cell analysis techniques alongside weighted gene co-expression network analysis (WGCNA) to pinpoint the genes that exhibit the most substantial associations with DNA damage response (DDR). Through Cox proportional hazards LASSO regression, we distinguished DDR-associated genes and established a risk model, enabling the stratification of patients into high- and low-risk groups. Subsequently, we carried out an analysis to determine our model's predictive accuracy regarding patient survival. Moreover, we examined the distinct biological characteristics, various signal transduction routes, and immune system responses in sepsis patients, considering different risk categories and outcomes related to survival. Lastly, we conducted experimental validation of the identified genes through in vivo and in vitro assays, employing RT-PCR, ELISA, and flow cytometry.

Results

Both single-cell RNA sequencing (scRNA-seq) and bulk transcriptomic analyses have demonstrated a strong correlation between DNA damage response (DDR) levels and sepsis prognosis. Specific cell subtypes, including monocytes, megakaryocytes, CD4+ T cells, and neutrophils, have shown elevated DDR activity. Cells with increased DDR scores exhibited more robust and numerous interactions with other cell populations. The weighted gene co-expression network analysis (WGCNA) and single-cell analyses revealed 71 DDR-associated genes. We developed a four-gene risk scoring system using ARL4C, CD247, RPL7, and RPL31, identified through univariate COX, LASSO COX regression, and log-rank (Mantel-Cox) tests. Nomograms, calibration plots, and decision curve analyses (DCA) regarding these specific genes have provided significant clinical benefits for individuals diagnosed with sepsis. The study suggested that individuals categorized as lower-risk demonstrated enhanced infiltration of immune cells, upregulated expression of immune regulators, and a more prolific presence of immune-associated functionalities and pathways. RT-qPCR analyses on a sepsis rat model revealed differential gene expression predominantly in the four targeted genes. Furthermore, ARL4C knockdown in sepsis model in vivo and vitro caused increased inflammatory response and a worse prognosis.

Conclusion

The delineated DDR expression landscape offers insights into sepsis pathogenesis, whilst our riskScore model, based on a robust four-gene signature, could underpin personalized sepsis treatment strategies.

Multi-organ spatiotemporal information aware model for sepsis mortality prediction

BACKGROUND

Sepsis is a syndrome involving multi-organ dysfunction, and the mortality in sepsis patients correlates with the number of lesioned organs. Precise prognosis models play a pivotal role in enabling healthcare practitioners to administer timely and accurate interventions for sepsis, thereby augmenting patient outcomes. Nevertheless, the majority of available models consider the overall physiological attributes of patients, overlooking the asynchronous spatiotemporal interactions among multiple organ systems. These constraints hinder a full application of such models, particularly when dealing with limited clinical data. To surmount these challenges, a comprehensive model, denoted as recurrent Graph Attention Network-multi Gated Recurrent Unit (rGAT-mGRU), was proposed. Taking into account the intricate spatiotemporal interactions among multiple organ systems, the model predicted in-hospital mortality of sepsis using data collected within the 48-hour period post-diagnosis.

MATERIAL AND METHODS

Multiple parallel GRU sub-models were formulated to investigate the temporal physiological variations of single organ systems. Meanwhile, a GAT structure featuring a memory unit was constructed to capture spatiotemporal connections among multi-organ systems. Additionally, an attention-injection mechanism was employed to govern the data flowing within the network pertaining to multi-organ systems. The proposed model underwent training and testing using a dataset of 10,181 sepsis cases extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. To evaluate the model's superiority, it was compared with the existing common baseline models. Furthermore, ablation experiments were designed to elucidate the rationale and robustness of the proposed model.

RESULTS

Compared with the baseline models for predicting mortality of sepsis, the rGAT-mGRU model demonstrated the largest area under the receiver operating characteristic curve (AUROC) of 0.8777 ± 0.0039 and the maximum area under the precision-recall curve (AUPRC) of 0.5818 ± 0.0071, with sensitivity of 0.8358 ± 0.0302 and specificity of 0.7727 ± 0.0229, respectively. The proposed model was capable of delineating the varying contribution of the involved organ systems at distinct moments, as specifically illustrated by the attention weights. Furthermore, it exhibited consistent performance even in the face of limited clinical data.

CONCLUSION

The rGAT-mGRU model has the potential to indicate sepsis prognosis by extracting the dynamic spatiotemporal interplay information inherent in multi-organ systems during critical diseases, thereby providing clinicians with auxiliary decision-making support.

Exploring m6A-RNA methylation as a potential therapeutic strategy for acute lung injury and acute respiratory distress syndrome

N6-methyladenosine (m6A) is the most common methylation modification in mammalian messenger RNA (mRNA) and noncoding RNAs. m6A modification plays a role in the regulation of gene expression and deregulation of m6A methylation has been implicated in many human diseases. Recent publications suggest that exploitation of this methylation process may possess utility against acute lung injury (ALI). ALI and its more severe form, acute respiratory distress syndrome (ARDS) are acute, inflammatory clinical syndromes characterized by poor oxygenation and diffuse pulmonary infiltrates. This syndrome is associated with microvascular endothelial dysfunction, subsequent pulmonary hypertension and may ultimately lead to mortality without rigorous and acute clinical intervention. Over the years, many attempts have been made to detect novel therapeutic avenues for research without much success. The urgency for the discovery of novel therapeutic agents has become more pronounced recently given the current pandemic infection of coronavirus disease 2019 (COVID-2019), still ongoing at the time that this review is being written. We review the current landscape of literature regarding ALI and ARDS etiology, pathophysiology, and therapeutics and present a potential role of m6A methylation. Additionally, we will establish the axiomatic principles of m6A methylation to provide a framework. In conclusion, METTL3, or methyltransferase-like 3, the selective RNA methyltransferase for m6A, is a hub of proinflammatory gene expression regulation in ALI, and using a modern drug discovery strategy will identify new and effective ALI drug candidates targeting METTTL3.

The effect of N6-methyladenosine (m6A) factors on the development of acute respiratory distress syndrome in the mouse model

Acute respiratory distress syndrome (ARDS) can cause loss of alveolar-capillary membrane integrity and life-threatening immune responses. The underlying molecular mechanisms of ARDS remain unclear. N6-methyladenosine (m6A)-RNA modification plays an important part in many biological processes. However, it is not clear whether ARDS alters RNA methylation in lung tissue. We tried to investigate the changes of m6A-RNA methylation in lung tissues of lipopolysaccharide (LPS)-induced ARDS mice. Lung tissue samples were collected to detect the expression of m6A factors through hematoxylin and eosin (HE) staining, quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR), immunohistochemical analysis and western blot. The overall m6A levels in lung tissue of ARDS in mouse were detected by UPLC-UV-MS. HE staining showed that the degree of the inflammatory response was more severe in the LPS-3 h group. The mRNA expression of YTHDF1, YTHDC1 and IGFBP3 was remarkably up-regulated at, respectively, 6, 6 and 12 h after LPS treatment. The mRNA expression of METTL16, FTO, METTL3, KIAA1429, RBM15, ALKBH5, YTHDF2, YTHDF3, YTHDC2 and IGFBP2 was significantly down-regulated at 24 h after LPS treatment. The protein expression of METTL16 and FTO increased, YTHDC1, IGFBP3 YTHDF1 and YTHDF3 showed a down-regulation trend after LPS induction. Overall m6A-RNA methylation levels were significantly increased at 6 h after LPS induction. In ARDS mice, LPS-induced m6A methylation may be involved in the expression regulation of inflammatory factors and may play important roles in the occurrence and development of lung tissue. It is suggested that m6A modification may be a promising therapeutic target for ARDS.

Effect of α-tocopherol in alleviating the lipopolysaccharide-induced acute lung injury via inhibiting nuclear factor kappa-B signaling pathways

Acute respiratory distress syndrome (ARDS) leads to the acute lung injury (ALI), a form of diffused alveolars injury, accompanied by severe inflammation and oxidative damage of alveolar epithelial cells. α-Tocopherol (α-TOH), one of the eight isoforms of vitamin E, is a natural antioxidant-free radical. We aimed to understand the effect of α-TOH and mechanism involved in inducing the ALI. Lipopolysaccharide (LPS) is injected into the trachea of mice to generate ALI mouse models. α-TOH was used to administrate the mice intragastrically to detect the expression of inflammatory factors and antioxidant molecules by enzyme linked immunosorbent assay, hematoxylin–eosin staining and immunohistochemical staining. Mouse alveolar epithelial cell line (MLE-12 cells) was used to determine the effect of α-TOH on alveolar epithelial cells. Inflammatory factors such as, interleukin (IL)-1β, IL-6, and tumor necrosis factor (TNF)-α shows significant increase in the lung tissues of the mice induced by LPS and reduction in the expressions of superoxide dismutase (SOD)1/2 and glutathione peroxidase (GSH-Px). After treatment with α-TOH, the inflammation and oxidative stress levels shows substantial reduction in the lung tissues of the mice. Moreover, α-TOH also increases the proliferation ability of MLE-12 cells in vitro and reduces apoptosis level. In addition, α-TOH reduces p65 phosphorylation and nuclear translocation in alveolar epithelial cells in vivo and in vitro, thus, inhibiting the activity of the nuclear factor kappa-B (NF-κB) signaling pathway. α-TOH reduces the inflammation and oxidative stress of lung tissue by inhibiting the NF-κB signaling pathway, thereby alleviating the LPS-induced ALI.

Special electromagnetic field-treated water and far-infrared radiation alleviates lipopolysaccharide-induced acute respiratory distress syndrome in rats by regulating haptoglobin

Special electromagnetic field-treated water (SEW) and far-infrared radiation (FIR) can reduce acute respiratory distress syndrome (ARDS) in rats inflicted by lipopolysaccharides (LPSs). However, little is known about its underlying molecular mechanism. Differentially expressed proteins (DEPs) of SEW and FIR interventions were obtained from a proteomics database. A total of 89 DEPs were identified. Enrichment analysis of DEPs was performed using the Database for Annotation, Visualization, and Integrated Discovery. These DEPs were associated with the responses to LPSs, acute inflammation, extracellular exosomes, glucocorticoids, and electrical stimuli. The protein-protein interaction network was set up using the STRING database. Modular analysis was performed using MCODE in the Cytoscape software. Proteins Haptoglobin, Apolipoprotein B, Transthyretin, and Fatty acid binding protein 1 were among the core networks. A tail vein injection of LPS was used to establish the rat model with ARDS. Parallel reaction monitoring confirmed Hp protein expression. Inflammatory pathway factors were detected using an enzyme-linked immunosorbent assay. This indicates that SEW and FIR can be considered as potential clinical treatment methods for ARDS treatment and that their functional mechanisms are related to the ability of alleviating lung inflammation through Hp protein adjustment.