Construction and analysis of a spinal cord injury competitive endogenous RNA network based on the expression data of long noncoding, micro‑ and messenger RNAs

Spinal Cord Injury: From MicroRNAs to Exosomal MicroRNAs

Spinal cord injury (SCI) is an unfortunate experience that may generate extensive sensory and motor disabilities due to the destruction and passing of nerve cells. MicroRNAs are small RNA molecules that do not code for proteins but instead serve to regulate protein synthesis by targeting messenger RNA's expression. After SCI, secondary damage like apoptosis, oxidative stress, inflammation, and autophagy occurs, and differentially expressed microRNAs show a function in these procedures. Almost all animal and plant cells release exosomes, which are sophisticated formations of lipid membranes. These exosomes have the capacity to deliver significant materials, such as proteins, RNAs and lipids, to cells in need, regulating their functions and serving as a way of communication. This new method offers a fresh approach to treating spinal cord injury. Obviously, the exosome has the benefit of conveying the transported material across performing regulatory activities and the blood-brain barrier. Among the exosome cargoes, microRNAs, which modulate their mRNA targets, show considerable promise in the pathogenic diagnosis, process, and therapy of SCI. Herein, we describe the roles of microRNAs in SCI. Furthermore, we emphasize the importance of exosomal microRNAs in this disease.

of Potential Noncoding RNAs Related to Spinal Cord Injury Based on Competing Endogenous RNAs

This study aims to elucidate the key regulatory molecules, specifically messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), and microRNAs (miRNAs) and their roles in the development and progression of spinal cord injury (SCI). Expression profiles (GSE45006, GSE19890, and GSE125630) for SCI were sourced from the Gene Expression Omnibus (GEO) database. By comparing rats with SCI at various time points against those without SCI, we identified differentially expressed mRNAs (DEmRNAs), lncRNAs (DElncRNAs), and miRNAs (DEmiRNAs). The GSE45006 dataset facilitated the production of DEmRNAs, which were then clustered using Mfuzz. Subsequently, we constructed a protein-protein interaction (PPI) network and anticipated interaction pairs between miRNA-mRNA and lncRNA-mRNA. These pairs were instrumental in forming a regulatory network involving lncRNA-miRNA-mRNA interactions. Additionally, we conducted functional enrichment studies on the DEmRNAs within these gene networks. A total of 2313 DEmRNAs were identified using the GSE45006 dataset, alongside 111 DEmiRNAs from GSE19890. From GSE125630, we extracted 154 DElncRNAs and 2322 DEmRNAs. Our analysis revealed 294 up-regulated DEmRNAs, grouped into the up-cluster, and 407 down-regulated DEmRNAs, forming the down-cluster. Key hub genes in the PPI network, such as Rhof, Vav1, Lyz2, Rab3a, Lyn, Cyfip1, Gns, and Nckap1l, were identified. Additionally, the study successfully constructed a competing endogenous RNA (ceRNA) network, revealing 55 unique lncRNA-miRNA-mRNA link pairs. Our research established a ceRNA network associated with SCI, identifying several critical lncRNA-miRNA-mRNA connection pairs integral to the disease's onset and progression. Notably, significant associations, including the AABR07041411.1-miR-125a-5p-Slc4a7 and the Smg1-rno-miR-331-3p-Tlr4 pairs, were observed to exert a significant influence within this biological context.

A future blood test for acute traumatic spinal cord injury

Acute spinal cord injury (SCI) requires prompt diagnosis and intervention to minimize the risk of permanent neurologic deficit. Presently, SCI diagnosis and interventional planning rely on magnetic resonance imaging (MRI), which is not always available or feasible for severely injured patients. Detection of disease-specific biomarkers in biofluids via liquid biopsy may provide a more accessible and objective means of evaluating patients with suspected SCI. Cell-free DNA, which has been used for diagnosing and monitoring oncologic disease, may detect damage to spinal cord neurons via tissue-specific methylation patterns. Other types of biomarkers, including proteins and RNA species, have also been found to reflect neuronal injury and may be included as part of a multi-analyte assay to improve liquid biopsy performance. The feasibility of implementing liquid biopsy into current practices of SCI management is supported by the relative ease of blood sample collection as well as recent advancements in droplet digital polymerase chain reaction technology. In this review, we detail the current landscape of biofluid biomarkers for acute SCI and propose a framework for the incorporation of a putative blood test into the clinical management of SCI.

Research Progress of Long Non-coding RNAs in Spinal Cord Injury

Spinal cord injury (SCI) can result in a partial or complete loss of motor and sensory function below the injured segment, which has a significant impact on patients' quality of life and places a significant social burden on them. Long non-coding RNA (LncRNA) is a 200-1000 bp non-coding RNA that has been shown to have a key regulatory role in the progression of a variety of neurological illnesses. Many studies have demonstrated that differentially expressed LncRNAs following spinal cord injury can participate in inflammatory damage, apoptosis, and nerve healing by functioning as competitive endogenous RNA (ceRNA); at the same time, it has a significant regulatory effect on sequelae such neuropathic pain. As a result, we believe that LncRNAs could be useful as a molecular regulatory target in the diagnosis, treatment, and prognosis of spinal cord injury.

MiRNAs as Promising Translational Strategies for Neuronal Repair and Regeneration in Spinal Cord Injury

Spinal cord injury (SCI) represents a devastating injury to the central nervous system (CNS) that is responsible for impaired mobility and sensory function in SCI patients. The hallmarks of SCI include neuroinflammation, axonal degeneration, neuronal loss, and reactive gliosis. Current strategies, including stem cell transplantation, have not led to successful clinical therapy. MiRNAs are crucial for the differentiation of neural cell types during CNS development, as well as for pathological processes after neural injury including SCI. This makes them ideal candidates for therapy in this condition. Indeed, several studies have demonstrated the involvement of miRNAs that are expressed differently in CNS injury. In this context, the purpose of the review is to provide an overview of the pre-clinical evidence evaluating the use of miRNA therapy in SCI. Specifically, we have focused our attention on miRNAs that are widely associated with neuronal and axon regeneration. “MiRNA replacement therapy” aims to transfer miRNAs to diseased cells and improve targeting efficacy in the cells, and this new therapeutic tool could provide a promising technique to promote SCI repair and reduce functional deficits.

Analysis of the Efficacy and Mechanism of Action of Xuebijing Injection on ARDS Using Meta-Analysis and Network Pharmacology

Objective

Acute respiratory distress syndrome (ARDS) is defined as the acute onset of noncardiogenic edema and subsequent gas-exchange impairment due to a severe inflammatory process known as cytokine storm. Xuebijing injection (hereinafter referred to as Xuebijing) is a patent drug that was used to treat ARDS or severe pneumonia (SP) in China. However, its efficacy and mechanism of actions remain unclear. In this study, we used meta-analysis and network pharmacology to assess these traits of Xuebijing.

Methods

We searched PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), and Wanfang databases for randomized controlled trials (RCTs) that evaluated Xuebijing therapy for ARDS or SP. The outcomes were total mortality, intensive care unit (ICU) stay time, and TNF-α and IL-6 levels. We performed a meta-analysis using RevMan 5.3 software. The putative targets, top 10 proteins, and possible pathway of Xuebinjing on ARDS were analyzed by network pharmacology. TNF-α and IL-6 were further docked with the six main active components of Xuebinjing using AutoDock 4.2.6 and PyMol 1.5.0.3 software.

Results

Fifteen RCTs involving 2778 patients (13 ARDS and 2 SP) were included. Compared with the control, Xuebijing treatment significantly reduced the mortality rate (risk ratio, 0.64 (95% credible interval (CrI), 0.54-0.77)), reduced the ICU stay time (mean difference (MD), -4.51 (95% CrI, -4.97--4.06)), reduced the TNF-α ((MD), -1.23 (95% CrI, -1.38--1.08)) and IL-6 ((MD), -1.15 (95% CrI, -1.52--0.78)) levels. The 56 putative targets, top 10 proteins (MAPK1 (mitogen-activated protein kinase 1), MAPK8 (mitogen-activated protein kinase 8), RELA (transcription factor p65), NFKB1 (nuclear factor NF-kappa-B p105 subunit), JUN (transcription factor AP-1), SRC (proto-oncogene tyrosine-protein kinase), TNF (tumor necrosis factor), HRAS (GTPase HRas), IL6 (interleukin-6), and APP (amyloid-beta A4 protein)), and possible pathways (Ret tyrosine kinase, IL2-mediated signaling events, CD4+/CD8+ T cell-related TCR signaling, p75(NTR)-mediated signaling, CXCR4-mediated signaling events, LPA receptor-mediated events, IL12-mediated signaling events, FAS (CD95) signaling pathway, and immune system) of Xuebinjing's action on ARDS were obtained. The molecular docking results showed that all the six components of Xuebinjing docked with TNF-α, and two components docked with IL-6 got the binding energies lower than -5.

Conclusion

Our results recommended Xuebijing treatment for patients with ARDS. Xuebijing has therapeutic effects on ARDS patients partly by regulating the immune cell/cytokine pathways and thus inhibiting the cytokine storm. TNF-α is the cytokine both directly and indirectly inhibited by Xuebijing, and IL-6 is the cytokine mainly indirectly inhibited by Xuebijing.

Biomarkers mining for spinal cord injury based on integrated multi-transcriptome expression profile data

BACKGROUND

This study was aimed to discover more biomarkers associated with spinal cord injury (SCI) by constructing a competing endogenous RNA (ceRNA) network.

METHODS

The transcriptome expression profile data related to SCI (GSE45006 GSE20907) were downloaded from GEO database. The differentially expressed RNAs (DERs), including lncRNAs, miRNAs, and mRNAs, between SCI and control groups were selected, which were then performed function enrichment analyses. Following that, a SCI-related ceRNA regulatory network was constructed. PCA analysis was performed on the genes constituting the ceRNA regulatory network directly related to SCI.

RESULTS

In GSE45006 and GSE20907 datasets, there were respectively 3336 and 1453 DERs. Venn analysis showed that there were 429 DERs which had consistent differential expression direction. RGD1564534-miR-29b-5p relation pair and 103 miRNA-target regulatory pairs were integrated to construct the ceRNA regulatory network. Then a SCI-related ceRNA regulatory network including 8 mRNAs of IFNGR1, STAT2, CYBB, NFATC1, FCGR2B, HMOX1, TLR4, and HK2, a lncRNA of RGD1564534, and a miRNA of miR-29b-5p was constructed. Additionally, two pathways, osteoclast differentiation, and HIF-1 signaling pathway, were involved in this network. PCA indicated the samples before and after injury can be significantly distinguished based on the genes in the ceRNA network.

CONCLUSION

A total of 8 SCI-related mRNAs have been identified in the ceRNA network, including IFNGR1, STAT2, CYBB, NFATC1, FCGR2B, HMOX1, TLR4, and HK2. Moreover, RGD1564534 may serve as ceRNA by competitively binding to miR-29b-5p to regulate the expression of 8 SCI-related mRNAs. Therefore, these genes may serve as key biomarkers of SCI.

Genome-wide analysis of acute traumatic spinal cord injury-related RNA expression profiles and uncovering of a regulatory axis in spinal fibrotic scars

Objectives

Long non‐coding RNAs (lncRNAs) are critical for posttranscriptional and transcriptional regulation in eukaryotic cells. However, data on lncRNA expression in the lesion epicentres of spinal tissues after acute traumatic spinal cord injury (ATSCI) are scarce. We aimed to identify lncRNA expression profiles in such centres and predict latent regulatory networks.

Materials and methods

High‐throughput RNA‐sequencing was used to profile the expression and regulatory patterns of lncRNAs, microRNAs and messenger RNAs (mRNAs) in an ATSCI C57BL/6 mouse model. Chromosome distributions, open reading frames (ORFs), transcript abundances, exon numbers and lengths were compared between lncRNAs and mRNAs. Gene ontology, KEGG pathways and binding networks were analysed. The findings were validated by qRT‐PCRs and luciferase assays.

Results

Intronic lncRNAs were the most common differentially expressed lncRNA. Most lncRNAs had <6 exons, and lncRNAs had shorter lengths and lesser ORFs than mRNAs. MiR‐21a‐5p had the most significant differential expression and bound to the differentially expressed lncRNA ENSMUST00000195880. The microRNAs and lncRNAs with significant differential expression were screened, and a lncRNA/miRNA/mRNA interaction network was predicted, constructed and verified.

Conclusions

The regulatory actions of this network may play a role in the pathophysiology of ATSCI. Our findings may lead to better understanding of potential ncRNA biomarkers and confer better therapeutic strategies for ATSCIs.

Molecular networks in Network Medicine: Development and applications

Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.