DeepCBA: a deep learning framework for gene expression prediction in maize based on DNA sequence and chromatin interaction.

Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome, which has an important impact on gene expression, transcriptional regulation, and phenotypic traits. To date, several methods have been developed for predicting gene expression. However, existing methods do not take into consideration the impact of chromatin interactions on target gene expression, thus potentially reduces the accuracy of gene expression prediction and mining of important regulatory elements. In this study, a highly accurate deep learning-based gene expression prediction model (DeepCBA) based on maize chromatin interaction data was developed. Compared with existing models, DeepCBA exhibits higher accuracy in expression classification and expression value prediction. The average Pearson correlation coefficients (PCC) for predicting gene expression using gene promoter proximal interactions, proximal-distal interactions, and proximal and distal interactions were 0.818, 0.625, and 0.929, respectively, representing an increase of 0.357, 0.16, and 0.469 over the PCC of traditional methods that only use gene proximal sequences. Some important motifs were identified through DeepCBA and were found to be enriched in open chromatin regions and expression quantitative trait loci (eQTL) and have the molecular characteristic of tissue specificity. Importantly, the experimental results of maize flowering-related gene ZmRap2.7 and tillering-related gene ZmTb1 demonstrate the feasibility of DeepCBA in exploring regulatory elements that affect gene expression. Moreover, the promoter editing and verification of two reported genes (ZmCLE7, ZmVTE4) demonstrated new insights of DeepCBA in precise designing of gene expression and even future intelligent breeding. DeepCBA is available at http://www.deepcba.com/ or http://124.220.197.196/.

Pyroptosis in Spinal Cord Injury: Implications for Pathogenesis and Therapeutic Approaches.

Traumatic spinal cord injury (SCI) is a serious disease of the central nervous system. Aside from the limited intrinsic regenerative capacity of neurons, complex microenvironmental disturbances can also lead to further cellular damage and growth inhibition. Programmed cell death regulated by pyroptosis has an important role in the pathogenesis of SCI. While there has been a wealth of new knowledge regarding cellular pyroptosis, a detailed understanding of its role in SCI and possible therapeutic strategies is still lacking. This review summarizes current advances in the regulatory role of pyroptosis-regulated cell death and inflammasome components in the inhibitory microenvironment following SCI, as well as recent therapeutic advances.

Comprehensive analysis of a pyroptosis-related gene signature of clinical and biological values in spinal cord injury.

Background: Since some of the clinical examinations are not suitable for patients with severe spinal cord injury (SCI), blood biomarkers have been reported to reflect the severity of SCI. The objective of this study was to screen out the potential biomarkers associated with the diagnosis of SCI by bioinformatics analysis. Methods: The microarray expression profiles of SCI were obtained from the Gene Expression Omnibus (GEO) database. Core genes correlated to pyroptosis were obtained by crossing the differential genes, and module genes were obtained by WGCNA analysis and lasso regression. The immune infiltration analysis and GSEA analysis revealed the essential effect of immune cells in the progression of SCI. In addition, the accuracy of the biomarkers in diagnosing SCI was subsequently evaluated and verified using the receiver operating characteristic curve (ROC) and qRT-PCR. Results: A total of 423 DEGs were identified, among which 319 genes were upregulated and 104 genes were downregulated. Based on the WGCNA analysis, six potential biomarkers were screened out, including LIN7A, FCGR1A, FGD4, GPR27, BLOC1S1, and GALNT4. The results of ROC curves demonstrated the accurate value of biomarkers related to SCI. The immune infiltration analysis and GSEA analysis revealed the essential effect of immune cells in the progression of SCI, including macrophages, natural killer cells, and neutrophils. The qRT-PCR results verified that FGD4, FCAR1A, LIN7A, BLOC1S1, and GPR27 were significantly upregulated in SCI patients. Conclusion: In this study, we identified and verified five immune pyroptosis-related hub genes by WGCNA and biological experiments. It is expected that the five identified potential biomarkers in peripheral white blood cells may provide a novel strategy for early diagnosis.