Transcriptional Regulatory Network Analysis to Reveal the Key Genes Involved in Skeletal Muscle Injury

Targeting Hub Genes Involved in Muscle Injury Induced by Jumping Load Based on Transcriptomics

The purpose of this study was to find hub genes that may play key roles in skeletal muscle injury induced by jumping load. Twelve female Sprague Dawley rats were divided into the normal control (NC) group and the jumping-induced muscle injury (JI) group. After 6 weeks of jumping, transmission electron microscopy, hematoxylin-eosin staining, transcriptomics sequencing and genes analysis, interaction network prediction of multiple proteins, real-time PCR detection, and Western blotting were performed on gastrocnemius muscles from NC and JI groups. As compared with NC rats, excessive jumping can result in notable structural damage and inflammatory infiltration in JI rats. A total of 112 differentially expressed genes were confirmed in NC rats versus JI rats, with 59 genes upregulated and 53 genes downregulated. Using the online String database, four hub genes in the transcriptional regulatory network were targeted, including FOS, EGR1, ATF3, and NR4A3. All expression levels of FOS, EGR1, ATF3, and NR4A3 mRNAs were decreased in JI rats compared with NC rats (p < 0.05 or p < 0.01). All expression levels of c-Fos, EGR1, ATF3, and NOR1 proteins were upregulated in JI rats (p < 0.01, p < 0.05, p > 0.05, and p < 0.01, respectively). Collectively, these findings indicate that FOS, EGR1, ATF3, and NR4A3 genes may be functionally important in jumping-induced muscle injury.

Novel Prediction Method Applied to Wound Age Estimation: Developing a Stacking Ensemble Model to Improve Predictive Performance Based on Multi-mRNA

(1) Background: Accurate diagnosis of wound age is crucial for investigating violent cases in forensic practice. However, effective biomarkers and forecast methods are lacking. (2) Methods: Samples were collected from rats divided randomly into control and contusion groups at 0, 4, 8, 12, 16, 20, and 24 h post-injury. The characteristics of concern were nine mRNA expression levels. Internal validation data were used to train different machine learning algorithms, namely random forest (RF), support vector machine (SVM), multilayer perceptron (MLP), gradient boosting (GB), and stochastic gradient descent (SGD), to predict wound age. These models were considered the base learners, which were then applied to developing 26 stacking ensemble models combining two, three, four, or five base learners. The best-performing stacking model and base learner were evaluated through external validation data. (3) Results: The best results were obtained using a stacking model of RF + SVM + MLP (accuracy = 92.85%, area under the receiver operating characteristic curve (AUROC) = 0.93, root-mean-square-error (RMSE) = 1.06 h). The wound age prediction performance of the stacking models was also confirmed for another independent dataset. (4) Conclusions: We illustrate that machine learning techniques, especially ensemble algorithms, have a high potential to be used to predict wound age. According to the results, the strategy can be applied to other types of forensic forecasts.

Investigating Transcriptional Dynamics Changes and Time-Dependent Marker Gene Expression in the Early Period After Skeletal Muscle Injury in Rats

Following skeletal muscle injury (SMI), from post-injury reaction to repair consists of a complex series of dynamic changes. However, there is a paucity of research on detailed transcriptional dynamics and time-dependent marker gene expression in the early stages after SMI. In this study, skeletal muscle tissue in rats was taken at 4 to 48 h after injury for next-generation sequencing. We examined the transcriptional kinetics characteristics during above time periods after injury. STEM and maSigPro were used to screen time-correlated genes. Integrating 188 time-correlated genes with 161 genes in each time-related gene module by WGCNA, we finally identified 18 network-node regulatory genes after SMI. Histological staining analyses confirmed the mechanisms underlying changes in the tissue damage to repair process. Our research linked a variety of dynamic biological processes with specific time periods and provided insight into the characteristics of transcriptional dynamics, as well as screened time-related biological indicators with biological significance in the early stages after SMI.