The Plasma Mitochondrial DNA Is an Independent Predictor for Post-Traumatic Systemic Inflammatory Response Syndrome

Background and Purpose

Mitochondrial DNA (mtDNA), a newly identified damage-associated molecular pattern, has been observed in trauma patients, however, little is known concerning the relationship between plasma mtDNA levels and concrete post-traumatic complications, particularly systemic inflammatory response syndrome (SIRS). The aim of this study is to determine whether plasma mtDNA levels are associated with injury severity and cloud predict post-traumatic SIRS in patients with acute traumatic injury.

Patients and Methods

Eighty-six consecutive patients with acute traumatic injury were prospectively enrolled in this study. The plasma mtDNA concentration was measured by a real-time, quantitative PCR assay for the human ND2 gene. The study population’s clinical and laboratory data were analyzed.

Results

The median plasma mtDNA was higher in trauma patients than in healthy controls (865.196 (251.042-2565.40)pg/ml vs 64.2147 (43.9049-80.6371)pg/ml, P<0.001) and was independently correlated with the ISS score (r=0.287, P<0.001). The plasma mtDNA concentration was also significantly higher in patients who developed post-traumatic SIRS than in patients who did not (1774.03 (564.870-10901.3)pg/ml vs 500.496 (145.415-1285.60)pg/ml, P<0.001). Multiple logistic regression analysis revealed that the plasma mtDNA was an independent predictors for post-traumatic SIRS (OR, 1.183 (95%CI, 1.015-1.379), P=0.032). Further ROC analysis demonstrated that a high plasma mtDNA level predicted post-traumatic SIRS with a sensitivity of 67% and a specificity of 76%, with a cut-off value of 1.3185 µg/ml being established, and the area under the ROC curves (AUC) was 0.725 (95% CI 0.613-0.837).

Conclusions

Plasma mtDNA was an independent indictor with moderate discriminative power to predict the risk of post-traumatic SIRS.     

Hybridization assay of insect antifreezing protein gene by novel multilayered porous silicon nucleic acid biosensor

A fabrication of a novel simple porous silicon polybasic photonic crystal with symmetrical structure has been reported as a nucleic acid biosensor for detecting antifreeze protein gene in insects (Microdera puntipennis dzhungarica), which would be helpful in the development of some new transgenic plants with tolerance of freezing stress. Compared to various porous silicon-based photonic configurations, porous silicon polytype layered structure is quite easy to prepare and shows more stability; moreover, polybasic photonic crystals with symmetrical structure exhibit interesting optical properties with a sharp resonance in the reflectance spectrum, giving a higher Q factor which causes higher sensitivity for sensing performance. In this experiment, DNA oligonucleotides were immobilized into the porous silicon pores using a standard crosslink chemistry method. The porous silicon polybasic symmetrical structure sensor possesses high specificity in performing controlled experiments with non-complementary DNA. The detection limit was found to be 21.3nM for DNA oligonucleotides. The fabricated multilayered porous silicon-based DNA biosensor has potential commercial applications in clinical chemistry for determination of an antifreeze protein gene or other genes.     

靶向UL27shRNA抑制Ⅱ型单纯疱疹病毒在HEK293细胞中的复制

按照shRNA(small hairpin RNA)设计原则,针对Ⅱ型单纯疱疹病毒(herpes simplex virus type 2, HSV-2)的UL27基因序列保守区域筛选设计、合成4条干扰靶序列并构建表达UL27序列特异性siRNA(short interfering RNA)的质粒载体pGPU6/GFP/Neo．通过脂质体介导重组表达载体转染HEK293细胞 (human embryonic kidney 293 cell)再接种HSV-2．采用实时荧光定量PCR(real-time fluorescent quantitative PCR)技术检测UL27各组的mRNA转录水平,终点滴定法检测细胞上清液中的病毒滴度,四甲基偶氮唑盐(four methyl thiazolyl tetrazolium, MTT)法测定细胞存活率,Western印迹法检测蛋白表达效果．结果显示,UL27shRNA75组对UL27基因mRNA表达抑制效果最佳,同时能显著抑制感染细胞的CPE(cytopathic effect, CPE),降低上清液中的病毒感染滴度,提高细胞的生存率,抑制UL27基因的蛋白表达．提示本研究构建的pGPU6/GFP/Neo-UL27表达载体能在细胞水平上不同程度地干扰HSV-2 UL27基因表达,抑制HSV-2在HEK293细胞中复制．     

Survival prediction in patients with colon adenocarcinoma via multiomics data integration using a deep learning algorithm

The present study proposed a deep learning (DL) algorithm to predict survival in patients with colon adenocarcinoma (COAD) based on multiomics integration. The survival-sensitive model was constructed using an autoencoder for DL implementation based on The Cancer Genome Atlas (TCGA) data of patients with COAD. The autoencoder framework was compared with PCA, NMF, t-SNE, and univariable Cox-PH model for identifying survival-related features. The prognostic robustness of the inferred survival risk groups was validated using three independent confirmation cohorts. Differential expression analysis, Pearson’s correlation analysis, construction of miRNA–target gene network, and function enrichment analysis were performed. Two risk groups with significant survival differences were identified in TCGA set using the autoencoder-based model (log-rank P-value = 5.51e⁻⁰⁷). The autoencoder framework showed superior performance compared with PCA, NMF, t-SNE, and the univariable Cox-PH model based on the C-index, log-rank P-value, and Brier score. The robustness of the classification model was successfully verified in three independent validation sets. There were 1271 differentially expressed genes, 10 differentially expressed miRNAs, and 12 hypermethylated genes between the survival risk groups. Among these, miR-133b and its target genes (GNB4, PTPRZ1, RUNX1T1, EPHA7, GPM6A, BICC1, and ADAMTS5) were used to construct a network. These genes were significantly enriched in ECM–receptor interaction, focal adhesion, PI3K–Akt signaling pathway, and glucose metabolism-related pathways. The risk subgroups obtained through a multiomics data integration pipeline using the DL algorithm had good robustness. miR-133b and its target genes could be potential diagnostic markers. The results would assist in elucidating the possible pathogenesis of COAD.