Co-expression Network Analysis Reveals Key Genes Related to Ankylosing spondylitis Arthritis Disease: Computational and Experimental Validation

Identification of a Novel Gene Expression Signature Associated with Amino Acid Metabolism (AAM) in Ankylosing Spondylitis (AS)

Background

Studies have proven that amino acid metabolism (AAM) plays an important role in ankylosing spondylitis (AS). Therefore, this study identified key AAM-related genes (AAMRGs) for the diagnosis and prediction of AS.

Methods

Firstly, the differentially expressed genes (DEGs) were identified between AS and normal groups in the GSE25101 and GSE73754 datasets downloaded from the Gene Expression Omnibus (GEO) database, and they were intersected to get common DEGs (Co-DEGs). Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify AS and AAM score-related genes (AS-AAMSRGs). Then, AAM related DEGs (AAMR DEGs) were acquired by intersection of Co-DEGs and AS-AAMSRGs. Moreover, the least absolute shrinkage and selection operator (LASSO) was implemented on AAMR DEGs to identify diagnostic genes, and Gene Set Enrichment Analysis (GSEA) was used to explore the functional pathways of diagnostic genes. By screening differential immune cells, the correlation between differential immune cells and diagnostic genes was further analyzed. Finally, miRNA-mRNA networks were constructed and drug prediction analysis was performed.

Results

By overlapping to obtain three AAMR DEGs (TP53INP1, TUBB and RBM47). The results of nomogram and decision curve analysis (DCA) suggested that three AAMR DEGs had diagnostic value for AS and significantly enriched to neutrophil activation, neutrophil degranulation. The proportion of eight kinds of immune cells in AS and normal groups was significantly different, such as activated dendritic cell, CD56 bright natural killer cell, effector memory CD4 T cell. In the miRNA-mRNA regulatory networks, three miRNAs (has-miR-429, has-miR-200c-3p, has-miR-200b-3p) could regulate TP53INP1 and TUBB. There was only one miRNA (has-miR-122-5p) could regulate RBM47. Finally, 51 target drugs (such as colchicine, vinblastine, vincristine) were associated with TUBB.

Conclusion

TP53INP1, TUBB and RBM47 might play key roles in AS and could be used as potential biomarkers of AS.

RNA-Sequencing and Bioinformatics Analysis of Exosomal Long Noncoding RNAs Revealed a Novel ceRNA Network in Stable COPD

Purpose

Exosomes are able to exchange their bioactive RNA cargo to recipient cells. In COPD, exosomes can be controlled and engineered for its use as targeted diagnostic and therapeutic tool. Our study explored novel lncRNAs and mRNAs in plasma exosomes that could be involved in the pathogenesis of COPD.

Methods

High-throughput sequencing was conducted to detect the alterations in the expression of exosomal lncRNAs and mRNAs. Gene ontology (GO) functional analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to determine the significant functions and pathways associated with differentially expressed (DE) lncRNAs. The mRNA expression profile dataset, GSE76925, and microRNA expression profile dataset, GSE70080, were obtained from the GEO database. Venn diagrams were used to find common DE mRNAs between my mRNAs dataset and GSE76925. These common DEGs were subjected to PPI analyses to identify Hub genes. Subsequently, Venn diagrams were used to identify common genes between the target genes of DE-miRNAs and Hub genes as well as DE-miRNAs and my lncRNAs dataset. Finally, a lncRNA-miRNA-mRNA co-expression network was constructed by prediction using proprietary software. The lncRNA and mRNA expressions were then validated by quantitative reverse-transcription polymerase chain reaction (qRT-PCR).

Results

We identified 1578 differentially regulated lncRNAs and 3071 differentially regulated mRNAs. GO and KEGG pathway analyses suggested that the DE lncRNAs are involved in the pathogenesis of COPD. A lncRNA-miRNA-mRNA meshwork was established to predict the potential interactions among these RNAs. RP3-329A5.8 and MRPS11 expression was then subjected to qRT-PCR for validation. Correlations between MRPS11 and clinic-pathological features were explored.

Conclusion

Our study provided a set of lncRNAs and mRNAs that may be involved in the pathogenesis of COPD, thereby highlighting the need for further research on both diagnostic biomarkers and molecular mechanisms.

Sensitive and Low-Bias Transcriptome Sequencing Using Agarose PCR

Transcriptome sequencing has emerged as an important research tool for exploring the mysteries of life at the single-cell level. However, its wide application is limited by the bias associated with the amplification reactions which is essential for library building of trace RNA. In this study, low-melting-point agarose was added to the amplification reactions to take advantage of its molecular crowding effect and polymer cross-linked structure to improve the sensitivity of the reactions and reduce bias. To further evaluate the performance of the method, it was applied to transcriptome sequencing of microregion samples from brain tissue sections of mice with Parkinson's disease at the single cell level. The results showed that agarose PCR had better performance than in-tube PCR. Further application of agarose PCR to transcriptome library sequencing could obtain data closer to that of unamplified. With the addition of low melting point agarose, the sensitivity of the amplification reaction was significantly increased, while homogeneity was increased by approximately 2-fold. Not only that, but this work also provides 11% sensitivity improvement for spatial transcriptomic study on Parkinson's disease-associated gene detection. The agarose PCR provides a new tool for efficient and homogeneous amplification of trace samples and can be widely used for spatial transcriptome library sequencing and studies.

Exploration of the Immuno-Inflammatory Potential Targets of Xinfeng Capsule in Patients with Ankylosing Spondylitis Based on Data Mining, Network Pharmacology, and Molecular Docking

Objective

This study aimed to ascertain the immuno-inflammatory molecular targets of Xinfeng capsules (XFC) in the treatment of ankylosing spondylitis (AS) based on data mining, network pharmacology, and molecular docking.

Methods

The efficacy of XFC in the treatment of AS was assessed by clinical data mining. Network pharmacology was utilized to establish a network of the targets for XFC active ingredients in the treatment of AS. The binding mode and affinity of XFC active ingredients to the key targets for AS were predicted using molecular docking.

Results

XFC significantly diminished immuno-inflammatory indicators of AS. In total, 208 targets of XFC were obtained from the TCMSP database and 629 disease targets of AS were screened from the GeneCards database, which were intersected to yield 57 targets of XFC in the treatment of AS. Protein-protein interaction, gene ontology, and Kyoto genome encyclopedia analyses showed that XFC might activate TNF and NF-κB signaling pathways. Quercetin, kaempferol, triptolide, and formononetin had free binding energies < -9 kcal/mol to inflammatory targets (TNF and PTGS2) in the molecular docking analysis of XFC-active ingredients, indicating that TNF and PTGS2 might be the targets of the action of XFC.

Conclusions

Collectively, XFC had a significant therapeutic effect on AS. Specifically, the active ingredients of XFC, including quercetin, kaempferol, triptolide, and formononetin, inhibited the inflammatory response in AS by downregulating TNF and PTGS2 in the TNF and NF-κB signaling pathways.

csdR, an R package for differential co-expression analysis

Background

Differential co-expression network analysis has become an important tool to gain understanding of biological phenotypes and diseases. The CSD algorithm is a method to generate differential co-expression networks by comparing gene co-expressions from two different conditions. Each of the gene pairs is assigned conserved (C), specific (S) and differentiated (D) scores based on the co-expression of the gene pair between the two conditions. The result of the procedure is a network where the nodes are genes and the links are the gene pairs with the highest C-, S-, and D-scores. However, the existing CSD-implementations suffer from poor computational performance, difficult user procedures and lack of documentation.

Results

We created the R-package aimed at reaching good performance together with ease of use, sufficient documentation, and with the ability to play well with other tools for data analysis. was benchmarked on a realistic dataset with 20,645 genes. After verifying that the chosen number of iterations gave sufficient robustness, we tested the performance against the two existing CSD implementations. was superior in performance to one of the implementations, whereas the other did not run. Our implementation can utilize multiple processing cores. However, we were unable to achieve more than $$\sim$$ ∼ 2.7 parallel speedup with saturation reached at about 10 cores.

Conclusion

The results suggest that is a useful tool for differential co-expression analysis and is able to generate robust results within a workday on datasets of realistic sizes when run on a workstation or compute server.