Corynebacterium glutamicum is one of the most important traditional industrial microorganisms and receiving more and more attention towards a novel cellular factory due to the recently rapid development in genomics and genetic operation toolboxes for Corynebacterium. However, compared to other model organisms such as Escherichia coli, there were few studies on its metabolic regulation, especially a genome-scale integrated cellular network model currently missing for Corynebacterium, which hindered the systematic study of Corynebacterium glutamicum and large-scale rational design and optimization for strains. Here, by gathering relevant information from a number of public databases, we successfully constructed an integrated cellular network, which was composed of 1384 reactions, 1276 metabolites, 88 transcriptional factors and 999 pairs of transcriptional regulatory relationships. The transcriptional regulatory sub-network could be arranged into five layers and the metabolic sub-network presented a clear bow-tie structure. We proposed a new method to extract complex metabolic and regulatory sub-network for product-orientated study taking lysine biosynthesis as an example. The metabolic and regulatory sub-network extracted by our method was more close to the real functional network than the simplex biochemical pathways. The results would be greatly helpful for understanding the high-yielding biomechanism for amino acids and the re-design of the industrial strains.
Corynebacterium glutamicum ; genetics ; metabolism ; Gene Expression Regulation, Bacterial ; Gene Regulatory Networks ; genetics ; Lysine ; biosynthesis ; Metabolic Networks and Pathways ; genetics ; Transcription Factors ; genetics ; Transcription, Genetic

Objective:To investigate the risk factors for hemorrhagic transformation (HT) after intravenous thrombolysis (IVT) in patients with acute ischemic stroke (AIS), and the predictive value of Neutrophil to lymphocyte ratio (NLR).Methods:Consecutive patients with AIS received IVT in Zhengzhou People’s Hospital from January 2021 to December 2022 were retrospectively enrolled. HT was defined as no intracranial hemorrhage was found on the first imaging examination after admission, and new intracranial hemorrhage was found on the imaging examination 24 h after IVT or when symptoms worsened. sHT was defined as HT and the National Institutes of Health Stroke Scale (NIHSS) score increased by ≥4 compared to admission or required surgical treatment such as intubation and decompressive craniectomy. The baseline clinical and laboratory data of the patients were collected, and NLR, lymphocyte to monocyte ratio (LMR), and platelet to neutrophil ratio (PNR) were calculated. Multivariate logistic regression analysis was used to identify the independent predictors of HT and sHT, and receiver operating characteristic (ROC) curve was used to analyze the predictive value of NLR for HT and sHT after IVT. Results:A total of 196 patients were included (age 65.37±13.10 years, 124 males [63.3%]). The median baseline NIHSS score was 4 (interquartile range: 2-10). Twenty patients (10.2%) developed HT, and 12 (6.1%) developed sHT. Univariate analysis showed that there were statistically significant differences in age, baseline NIHSS score, creatinine, NLR, and stroke etiology type between the HT group and the non-HT group (all P<0.05); there were statistically significant differences in age, NLR, PNR, creatinine, baseline NIHSS score, and stroke etiological type between the sHT group and the non-sHT group (all P<0.05). Multivariate logistic regression analysis showed that NLR was an independent predictor of HT (odds ratio [ OR] 1.375, 95% confidence interval [ CI] 1.132-1.670; P=0.001) and sHT ( OR 1.647, 95% CI 1.177-2.304; P=0.004) after IVT. The ROC curve analysis showed that the area under the curve for predicting HT by NLR was 0.683 (95% CI 0.533-0.833; P=0.007), the optimal cutoff value was 5.78, the sensitivity and specificity were 55.0% and 84.1%, respectively. The area under the curve for predicting sHT by NLR was 0.784 (95% CI 0.720-0.839; P=0.001), the optimal cutoff value was 5.94, the sensitivity and specificity were 66.67% and 84.24%, respectively. Conclusions:A higher baseline NLR is associated with an increased risk of HT and sHT after IVT in patients with AIS, and can serve as a biomarker for predicting HT and sHT after IVT.