Transcriptomic analysis delineates potential signature genes and miRNAs associated with the pathogenesis of asthma

Integrative analysis identifies gene signatures mediating the effect of DNA methylation on asthma severity and lung function

DNA methylation (DNAm) changes play a key role in regulating gene expression in asthma. To investigate the role of epigenetics and transcriptomics change in asthma, we used publicly available DNAm (asthmatics, n = 96 and controls, n = 46) and gene expression (asthmatics, n = 79 and controls, n = 39) data derived from bronchial epithelial cells (BECs). We performed differential methylation/expression and weighted co-methylation/co-expression network analyses to identify co-methylated and co-expressed modules associated with asthma severity and lung function. For subjects with both DNAm and gene expression data (asthmatics, n = 79 and controls, n = 39), machine-learning technique was used to prioritize CpGs and differentially expressed genes (DEGs) for asthma risk prediction, and mediation analysis was used to uncover DEGs that mediate the effect of DNAm on asthma severity and lung function in BECs. Finally, we validated CpGs and their associated DEGs and the asthma risk prediction model in airway epithelial cells (AECs) dataset. The asthma risk prediction model based on 18 CpGs and 28 DEGs showed high accuracy in both the discovery BEC dataset with area under the receiver operating characteristic curve (AUC) = 0.99 and the validation AEC dataset (AUC = 0.82). Genes in the three co-methylated and six co-expressed modules were enriched in multiple pathways including WNT/beta-catenin signaling and notch signaling. Moreover, we identified 35 CpGs correlated with DEGs in BECs, of which 17 CpGs including cg01975495 (SERPINE1), cg10528482 (SLC9A3), cg25477769 (HNF1A) and cg26639146 (CD9), cg17945560 (TINAGL1) and cg10290200 (FLNC) were replicated in AECs. These DEGs mediate the association between DNAm and asthma severity and lung function. Overall, our study investigated the role of DNAm and gene expression change in asthma and provided an insight into the mechanisms underlying the effects of DNA methylation on asthma, asthma severity and lung function.

The functional role of CST1 and CCL26 in asthma development

BACKGROUND

Asthma is the most common chronic disease in children with an increasing prevalence. Its development is caused by genetic and environmental factors and allergic sensitization is a known trigger. Dog allergens affect up to 30% of all children and dog dander-sensitized children show increased expression of cystatin-1 (CST1) and eotaxin-3 (CCL26) in nasal epithelium. The aim of our study was to investigate the functional mechanism of CST1 and CCL26 in the alveolar basal epithelial cell line A549.

METHODS

A549 cells were transfected with individual overexpression vectors for CST1 and CCL26 and RNA sequencing was performed to examine the transcriptomics. edgeR was used to identify differentially expressed genes (= DEG, |log2 FC | ≥ 2, FDR < 0.01). The protein expression levels of A549 cells overexpressing CST1 and CCL26 were analyzed using the Target 96 inflammation panel from OLINK (antibody-mediated proximity extension-based assay; OLINK Proteomics). Differentially expressed proteins were considered with a |log2 FC| ≥ 1, p < .05.

RESULTS

The overexpression of CST1 resulted in a total of 27 DEG (1 upregulated and 26 downregulated) and the overexpression of CCL26 in a total of 137 DEG (0 upregulated and 137 downregulated). The gene ontology enrichment analysis showed a significant downregulation of type I and III interferon signaling pathway genes as well as interferon-stimulated genes. At the protein level, overexpression of CST1 induced a significantly increased expression of CCL3, whereas CCL26 overexpression led to increased expression of HGF, and a decrease of CXCL11, CCL20, CCL3 and CXCL10.

CONCLUSION

Our results indicate that an overexpression of CST1 and CCL26 cause a downregulation of interferon related genes and inflammatory proteins. It might cause a higher disease susceptibility, mainly for allergic asthma, as CCL26 is an agonist for CCR-3-carrying cells, such as eosinophils and Th2 lymphocytes, mostly active in allergic asthma.

Genomics of Treatable Traits in Asthma

The astounding number of genetic variants revealed in the 15 years of genome-wide association studies of asthma has not kept pace with the goals of translational genomics. Moving asthma diagnosis from a nonspecific umbrella term to specific phenotypes/endotypes and related traits may provide insights into features that may be prevented or alleviated by therapeutical intervention. This review provides an overview of the different asthma endotypes and phenotypes and the genomic findings from asthma studies using patient stratification strategies and asthma-related traits. Asthma genomic research for treatable traits has uncovered novel and previously reported asthma loci, primarily through studies in Europeans. Novel genomic findings for asthma phenotypes and related traits may arise from multi-trait and specific phenotyping strategies in diverse populations.

Construction of lncRNA-miRNA-mRNA regulatory network in severe asthmatic bronchial epithelial cells: A bioinformatics study

Asthma is a chronic respiratory disease caused by environment-host interactions. Bronchial epithelial cells (BECs) are the first line of defense against environmental toxins. However, the mechanisms underlying the role of BECs in severe asthma (SA) are not yet fully understood. Long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) have been shown to play important roles in the regulation of gene expression in the pathogenesis of SA. In this study, bioinformatics was used for the first time to reveal the lncRNA-miRNA-mRNA regulatory network of BECs in SA. Five mRNA datasets of bronchial brushing samples from patients with SA and healthy controls (HC) were downloaded from the Gene Expression Omnibus (GEO) database. A combination of the Venn diagram and robust rank aggregation (RRA) method was used to identify core differentially expressed genes (DEGs). Protein-protein interaction (PPI) analysis of core DEGs was performed to screen hub genes. The miRDB, miRWalk, and ENCORI databases were used to predict the miRNA-mRNA relationships, and the ENCORI and starBase v2.0 databases were used to predict the upstream lncRNAs of the miRNA-mRNA relationships. Four core DEGs were identified: carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5), interleukin-1 receptor type 2 (IL1R2), trefoil factor 3 (TFF3), and vascular endothelial growth factor A (VEGFA). These 4 core DEGs indicated that SA was not significantly associated with sex. Enrichment analysis showed that the MAPK, Rap1, Ras, PI3K-Akt and Calcium signaling pathways may serve as the principal pathways of BECs in SA. A lncRNA-miRNA-mRNA regulatory network of the severe asthmatic bronchial epithelium was constructed. The top 10 competing endogenous RNAs (ceRNAs) were FGD5 antisense RNA 1 (FGD5-AS1), metastasis associated lung adenocarcinoma transcript 1 (MALAT1), X inactive specific transcript (XIST), HLA complex group 18 (HCG18), small nucleolar RNA host gene 16 (SNHG16), has-miR-20b-5p, has-miR-106a-5p, hsa-miR-106b-5p, has-miR-519d-3p and Fms related receptor tyrosine kinase 1 (FLT1). Our study revealed a potential mechanism for the lncRNA-miRNA-mRNA regulatory network in BECs in SA.

Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods

Asthma is a heterogeneous respiratory disease characterized by airway inflammation and obstruction. Despite recent advances, the genetic regulation of asthma pathogenesis is still largely unknown. Gene expression profiling techniques are well suited to study complex diseases including asthma. In this study, differentially expressed genes (DEGs) followed by weighted gene co-expression network analysis (WGCNA) and machine learning techniques using dataset generated from airway epithelial cells (AECs) and nasal epithelial cells (NECs) were used to identify candidate genes and pathways and to develop asthma classification and predictive models. The models were validated using bronchial epithelial cells (BECs), airway smooth muscle (ASM) and whole blood (WB) datasets. DEG and WGCNA followed by least absolute shrinkage and selection operator (LASSO) method identified 30 and 34 gene signatures and these gene signatures with support vector machine (SVM) discriminated asthmatic subjects from controls in AECs (Area under the curve: AUC = 1) and NECs (AUC = 1), respectively. We further validated AECs derived gene-signature in BECs (AUC = 0.72), ASM (AUC = 0.74) and WB (AUC = 0.66). Similarly, NECs derived gene-signature were validated in BECs (AUC = 0.75), ASM (AUC = 0.82) and WB (AUC = 0.69). Both AECs and NECs based gene-signatures showed a strong diagnostic performance with high sensitivity and specificity. Functional annotation of gene-signatures from AECs and NECs were enriched in pathways associated with IL-13, PI3K/AKT and apoptosis signaling. Several asthma related genes were prioritized including SERPINB2 and CTSC genes, which showed functional relevance in multiple tissue/cell types and related to asthma pathogenesis. Taken together, epithelium gene signature-based model could serve as robust surrogate model for hard-to-get tissues including BECs to improve the molecular etiology of asthma.

Identification of Molecular Markers Related to Immune Infiltration in Patients with Severe Asthma: A Comprehensive Bioinformatics Analysis Based on the Human Bronchial Epithelial Transcriptome

Background. Severe asthma (SA), a heterogeneous inflammatory disease characterized by immune cell infiltration, is particularly difficult to treat and manage. The airway epithelium is an important tissue in regulating innate and adaptive immunity, and targeting airway epithelial cell may contribute to improving the efficacy of asthma therapy. Methods. Bioinformatics methods were utilized to identify the hub genes and signaling pathways involved in SA. Experiments were performed to determine whether these hub genes and signaling pathways were affected by the differences in immune cell infiltration. Results. The weighted gene coexpression network analysis identified 14 coexpression modules, among which the blue and salmon modules exhibited the strongest associations with SA. The blue module was mainly enriched in actomyosin structure organization and was associated with regulating stem cell pluripotency signaling pathways. The salmon module was mainly involved in cornification, skin development, and glycosphingolipid biosynthesis-lacto and neolacto series. The protein-protein interaction network and module analysis identified 11 hub genes in the key modules. The CIBERSORTx algorithm revealed statistically significant differences in CD8+ T cells ( $P=0.013$ ), T follicular helper cells ( $P=0.002$ ), resting mast cells ( $P=0.004$ ), and neutrophils ( $P=0.002$ ) between patients with SA and mild-moderate asthma patients. Pearson’s correlation analysis identified 11 genes that were significantly associated with a variety of immune cells. We further predicted the utility of some potential drugs and validated our results in external datasets. Conclusion. Our results may help provide a better understanding of the relationship between the airway epithelial transcriptome and clinical data of SA. And this study will help to guide the development of SA-targeted molecular therapy.

Peripheral blood transcriptomic clusters uncovered immune phenotypes of asthma

BACKGROUND

Transcriptomic analysis has been used to elucidate the complex pathogenesis of heterogeneous disease and may also contribute to identify potential therapeutic targets by delineating the hub genes. This study aimed to investigate whether blood transcriptomic clustering can distinguish clinical and immune phenotypes of asthmatics, and microbiome in asthmatics.

METHODS

Transcriptomic expression of peripheral blood mononuclear cells (PBMCs) from 47 asthmatics and 21 non-asthmatics was measured using RNA sequencing. A hierarchical clustering algorithm was used to classify asthmatics. Differentially expressed genes, clinical phenotypes, immune phenotypes, and microbiome of each transcriptomic cluster were assessed.

RESULTS

In asthmatics, three distinct transcriptomic clusters with numerously different transcriptomic expressions were identified. The proportion of severe asthmatics was highest in cluster 3 as 73.3%, followed by cluster 2 (45.5%) and cluster 1 (28.6%). While cluster 1 represented clinically non-severe T2 asthma, cluster 3 tended to include severe non-T2 asthma. Cluster 2 had features of both T2 and non-T2 asthmatics characterized by the highest serum IgE level and neutrophil-dominant sputum cell population. Compared to non-asthmatics, cluster 1 showed higher CCL23 and IL1RL1 expression while the expression of TREML4 was suppressed in cluster 3. CTSD and ALDH2 showed a significant positive linear relationship across three clusters in the order of cluster 1 to 3. No significant differences in the diversities of lung and gut microbiomes were observed among transcriptomic clusters of asthmatics and non-asthmatics. However, our study has limitations in that small sample size data were analyzed with unmeasured confounding factors and causal relationships or function pathways were not verified.

CONCLUSIONS

Genetic clustering based on the blood transcriptome may provide novel immunological insight, which can be biomarkers of asthma immune phenotypes. Trial registration Retrospectively registered.

The Specific microRNA Profile and Functional Networks for Children with Allergic Asthma

Background

Allergic asthma is the most common type of asthma and often occurs in early life with increasing comorbidities, including atopic dermatitis and allergic rhinitis. MicroRNAs (miRNAs) are involved in the pathogenesis of numerous immune and inflammatory disorders, particularly allergic inflammation. The specific miRNA profiles of children with allergic asthma have not been fully delineated and still require in-depth study.

Objective

This study aimed to identify the expression profile of miRNAs and constructed a network of the interactions between differentially expressed miRNAs and target mRNAs to provide novel insights into understanding the pathogenesis of allergic asthma.

Materials and Methods

In this study, we performed high-throughput sequencing of peripheral blood mononuclear cells (PBMCs) from children in the acute phase of asthma. Bioinformatics approaches, including miRanda, Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, were employed to predict novel therapeutic and diagnostic targets for allergic asthma. Real-time quantitative PCR was conducted to detect the expression of aberrantly expressed miRNAs.

Results

One hundred and sixty-one differentially expressed miRNAs were identified in children with allergic asthma, including 140 conserved miRNAs and 21 novel miRNAs. A total of 8929 targeted mRNAs (44,186 transcripts) associated with differentially expressed miRNAs were predicted and significantly enriched in the cGMP-PKG signalling pathway, cholinergic synapse, and salivary secretion. We also found that miRNA-370-3p targeted PKG and MLCP molecules in the cGMP-PKG signalling pathway and was involved in the pathogenesis of allergic asthma.

Conclusion

We identified the miRNA profile of PBMCs in children with allergic asthma and also found that miRNA-370-3p targeted PKG and MLCP molecules in the cGMP-PKG signalling pathway, which provides a novel insight into understanding the pathogenesis of allergic asthma and investigating new targets for the treatment of allergic asthma in children.

Identification of pyroptosis-related subtypes and establishment of prognostic model and immune characteristics in asthma

Background

Although studies have shown that cell pyroptosis is involved in the progression of asthma, a systematic analysis of the clinical significance of pyroptosis-related genes (PRGs) cooperating with immune cells in asthma patients is still lacking.

Methods

Transcriptome sequencing datasets from patients with different disease courses were used to screen pyroptosis-related differentially expressed genes and perform biological function analysis. Clustering based on K-means unsupervised clustering method is performed to identify pyroptosis-related subtypes in asthma and explore biological functional characteristics of poorly controlled subtypes. Diagnostic markers between subtypes were screened and validated using an asthma mouse model. The infiltration of immune cells in airway epithelium was evaluated based on CIBERSORT, and the correlation between diagnostic markers and immune cells was analyzed. Finally, a risk prediction model was established and experimentally verified using differentially expressed genes between pyroptosis subtypes in combination with asthma control. The cMAP database and molecular docking were utilized to predict potential therapeutic drugs.

Results

Nineteen differentially expressed PRGs and two subtypes were identified between patients with mild-to-moderate and severe asthma conditions. Significant differences were observed in asthma control and FEV1 reversibility between the two subtypes. Poor control subtypes were closely related to glucocorticoid resistance and airway remodeling. BNIP3 was identified as a diagnostic marker and associated with immune cell infiltration such as, M2 macrophages. The risk prediction model containing four genes has accurate classification efficiency and prediction value. Small molecules obtained from the cMAP database that may have therapeutic effects on asthma are mainly DPP4 inhibitors.

Conclusion

Pyroptosis and its mediated immune phenotype are crucial in the occurrence, development, and prognosis of asthma. The predictive models and drugs developed on the basis of PRGs may provide new solutions for the management of asthma.

Integrated transcriptomic and regulatory network analyses uncovers the role of let-7b-5p, SPIB, and HLA-DPB1 in sepsis

Sepsis has affected millions of populations of all age groups, locations, and sexes worldwide. Immune systems, either innate or adaptive are dysregulated due to the infection. Various biomarkers are present to date, still sepsis is a primary cause of mortality. Globally, post-operative body infections can cause sepsis and septic shock in ICU. Abnormal antigen presentation to T-cells leads to a dysregulated immune system. miRNAs are sparkly evolved as biomarkers due to their high sensitivity and efficiency. In this work, we analyzed high-throughput mRNA data collected from Gene Expression Omnibus (GEO) and linked it to significant miRNAs and TFs using a network-based approach. Protein–protein interaction (PPI) network was constructed using sepsis-specific differentially expressed genes (DEGs) followed by enrichment analyses and hub module detection. Sepsis-linked decrease transcription of the classical HLA gene such as HLA-DPB1 and its interplay with miR-let-7b-5p and transcription factor SPIB was observed. This study helped to provide innovative targets for sepsis.

Cross-talk of four types of RNA modification writers defines the immune microenvironment in severe asthma

Adenine modifications, including m⁶ A, m¹ A, APA, and A-to-I modifications, are the most impactful RNA modifications. These modifications are primarily produced by enzymes called writers. The main purpose of this study was to explore the cross-talk and potential roles of these writers in severe asthma. We found 13 RNA writers potentially related to severe asthma and three RNA modification patterns. Cluster 3 showed predominant neutrophil infiltration and C-type lectin receptor signaling; cluster 1 showed predominant innate immune cell infiltration and ubiquitin-proteasome system activation; and cluster 2 did not show obvious immune infiltration characteristics. We found that RNA modification writers modified immune cell-related genes and led to both accumulation of different immune cells in the airways and activation of a series of biological processes, which ultimately leads to severe asthma. TRMT6, WTAP, and TRMT6A were included in a random forest model as predictors. Cromoglicic acid, thioperamide, and fluvastatin were potential drugs for clusters 1, 2, and 3, respectively. We found that cross-talk of RNA modifications is significant in severe asthma, which provides insight into severe asthma pathogenesis and possible treatment avenues.

Asthma and Post-Asthmatic Fibrosis: A Search for New Promising Molecular Markers of Transition from Acute Inflammation to Pulmonary Fibrosis

Asthma is a heterogeneous pulmonary disorder, the progression and chronization of which leads to airway remodeling and fibrogenesis. To understand the molecular mechanisms of pulmonary fibrosis development, key genes forming the asthma-specific regulome and involved in lung fibrosis formation were revealed using a comprehensive bioinformatics analysis. The bioinformatics data were validated using a murine model of ovalbumin (OVA)-induced asthma and post-asthmatic fibrosis. The performed analysis revealed a range of well-known pro-fibrotic markers (Cat, Ccl2, Ccl4, Ccr2, Col1a1, Cxcl12, Igf1, Muc5ac/Muc5b, Spp1, Timp1) and a set of novel genes (C3, C3ar1, Col4a1, Col4a2, Cyp2e1, Fn1, Thbs1, Tyrobp) mediating fibrotic changes in lungs already at the stage of acute/subacute asthma-driven inflammation. The validation of genes related to non-allergic bleomycin-induced pulmonary fibrosis on asthmatic/fibrotic lungs allowed us to identify new universal genes (Col4a1 and Col4a2) associated with the development of lung fibrosis regardless of its etiology. The similarities revealed in the expression profiles of nodal fibrotic genes between asthma-driven fibrosis in mice and nascent idiopathic pulmonary fibrosis in humans suggest a tight association of identified genes with the early stages of airway remodeling and can be considered as promising predictors and early markers of pulmonary fibrosis.

Hsa-Mir-320c, Hsa-Mir-200c-3p, and Hsa-Mir-449c-5p as Potential Specific miRNA Biomarkers of COPD: A Pilot Study

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory disease commonly induced by cigarette smoke. The expression of miRNAs can be altered in patients with COPD and could be used as a biomarker. We aimed to identify a panel of miRNAs in bronchoalveolar lavage (BAL) to differentiate COPD patients from smokers and non-smokers with normal lung function. Accordingly, forty-five subjects classified as COPD, smokers, and non-smokers (n = 15 per group) underwent clinical, functional characterization and bronchoscopy with BAL. The mean age of the studied population was 61.61 ± 12.95 years, BMI 25.72 ± 3.82 Kg/m2, FEV1/FVC 68.37 ± 12.00%, and FEV1 80.07 ± 23.63% predicted. According to microarray analysis, three miRNAs of the most upregulated were chosen: miR-320c, miR-200c-3p, and miR-449c-5p. These miRNAs were validated by qPCR and were shown to be differently expressed in COPD patients. ROC analysis showed that these three miRNAs together had an area under the curve of 0.89 in differentiating COPD from controls. Moreover, in silico analysis of candidate miRNAs by DIANA-miRPath showed potential involvement in the EGFR and Hippo pathways. These results suggest a specific 3-miRNA signature that could be potentially used as a biomarker to distinguish COPD patients from smokers and non-smoker subjects.

Revealing new therapeutic opportunities in hypertension through network-driven integrative genetic analysis and drug target prediction approach

Epidemiological studies have established that untreated hypertension (HTN) is a major independent risk factor for developing cardiovascular diseases (CVD), stroke, renal failure, and other conditions. Several important studies have been published to prevent and manage HTN; however, antihypertensive agents' optimal choice remains controversial. Therefore, the present study is undertaken to update our knowledge in the primary treatment of HTN, specifically in the setting of other three important diseases. MicroRNAs (miRNAs) are remarkably stable short endogenous conserved non-coding RNAs that bind to the mRNA at its (3' UTR) to regulate its gene expression by causing translational repression or mRNA degradation. Through their coordinated activities on different pathways and networks, individual miRNAs control normal and pathological cellular processes. Therefore, to identify the critical miRNA-mRNA-TF interactions, we performed systematic bioinformatics analysis. We have also employed the molecular modelling and docking approach to identify the therapeutic target that delivers novel empathies into Food and Drug Administration approved and herbal drug response physiology. Gene Expression Omnibus (GEO) was employed to identify the differentially expressed genes (DEGs) and hub genes- KNG1, HLA-DPB1, CXCL8, IL1B, and BCL2. The HTN associated feed-forward loop (FFL) network included miR-9-5p, KNG1 and AR. We employed high throughput screening to get the best interacting compounds, telmisartan and limonin, that provided a significant docking score (-13.3 and -12.0 kcal/mol) and a potential protective effect that may help to combat the impact of HTN. The present study provides novel insight into HTN etiology through the identification of mRNAs and miRNAs and associated pathways.

Vitamin D and its therapeutic relevance in pulmonary diseases

Vitamin D is customarily involved in maintaining bone and calcium homeostasis. However, contemporary studies have identified the implication of vitamin D in several cellular processes including cellular proliferation, differentiation, wound healing, repair and regulatory systems inclusive of host defence, immunity, and inflammation. Multiple studies have indicated corelations between low serum levels of vitamin D, perturbed pulmonary functions and enhanced incidences of inflammatory diseases. Almost all of the pulmonary diseases including acute lung injury, cystic fibrosis, asthma, COPD, Pneumonia and Tuberculosis, all are inflammatory in nature. Studies have displayed strong inter-relations with vitamin D deficiency and progression of lung disorders; however, the underlying mechanism is still unknown. Vitamin D has emerged to possess inhibiting effects on pulmonary inflammation while exaggerating innate immune defenses by strongly influencing functions of inflammatory cells including dendritic cells, monocyte/macrophages, T cells, and B cells along with structural epithelial cells. This review dissects the effects of vitamin D on the inflammatory cells and their therapeutic relevance in pulmonary diseases. Although, the data obtained is very limited and needs further corroboration but presents an exciting area of further research. This is because of its ease of supplementation and development of personalized medicine which could lead us to an effective adjunct and cost-effective method of therapeutic modality for highly fatal pulmonary diseases.

Identification and Validation of Potential miRNAs, as Biomarkers for Sepsis and Associated Lung Injury: A Network-Based Approach

Sepsis is a dysregulated immune response disease affecting millions worldwide. Delayed diagnosis, poor prognosis, and disease heterogeneity make its treatment ineffective. miRNAs are imposingly involved in personalized medicine such as therapeutics, due to their high sensitivity and accuracy. Our study aimed to reveal the biomarkers that may be involved in the dysregulated immune response in sepsis and lung injury using a computational approach and in vivo validation studies. A sepsis miRNA Gene Expression Omnibus (GEO) dataset based on the former analysis of blood samples was used to identify differentially expressed miRNAs (DEMs) and associated hub genes. Sepsis-associated genes from the Comparative Toxicogenomics Database (CTD) that overlapped with identified DEM targets were utilized for network construction. In total, 317 genes were found to be regulated by 10 DEMs (three upregulated, namely miR-4634, miR-4638-5p, and miR-4769-5p, and seven downregulated, namely miR-4299, miR-451a, miR181a-2-3p, miR-16-5p, miR-5704, miR-144-3p, and miR-1290). Overall hub genes (HIP1, GJC1, MDM4, IL6R, and ERC1) and for miR-16-5p (SYNRG, TNRC6B, and LAMTOR3) were identified based on centrality measures (degree, betweenness, and closeness). In vivo validation of miRNAs in lung tissue showed significantly downregulated expression of miR-16-5p corroborating with our computational findings, whereas expression of miR-181a-2-3p and miR-451a were found to be upregulated in contrast to the computational approach. In conclusion, the differential expression pattern of miRNAs and hub genes reported in this study may help to unravel many unexplored regulatory pathways, leading to the identification of critical molecular targets for increased prognosis, diagnosis, and drug efficacy in sepsis and associated organ injuries.