MiR-3614-5p Is a Potential Novel Biomarker for Colorectal Cancer

MiR-3614-5p has been found in a variety of cancers including colorectal cancer. However, the association of miR-3614-5p with colorectal cancer is still unclear. Based on the Cancer Genome Atlas (TCGA) database, the relationship between miR-3614-5p and colorectal cancer can be proved. Wilcoxon rank-sum test was used to compare the miR-3614-5p expression in colorectal cancer tissues and under normal conditions, respectively. The logistic regression method was further employed to analyze the relationship between miR-3614-5p and clinicopathological characteristics. Also, the correlation between miR-3614-5p and survival rate was evaluated by Kaplan-Meier and Cox regression analysis. Besides, gene set enrichment analysis (GSEA) was used to investigate the biological functions of miR-3614-5p. The decrease of miR-3614-5p expression of colorectal cancer was significantly correlated with N stage (OR) = 0.7 for N1&N2 vs. N0), M stage (OR = 0.5 for M1 vs. M0), pathologic stage (OR = 0.7 for Stage III & Stage IV vs. Stage I & Stage II), neoplasm type (OR = 0.5 for rectum adenocarcinoma vs. colon adenocarcinoma), and lymphatic invasion (OR = 0.6 for YES vs. NO) (all p-values < 0.05). Kaplan-Meier survival analysis showed that colorectal cancer with low miR-3614-5p has a poorer prognosis than that of high miR-3614-5p (p = 0.005). According to univariate analysis, low miR-3614-5p was associated with poor overall survival (OS) [hazard ratio (HR) = 0.599; 95% confidence interval (CI): 0.418-0.857; p = 0.005]. In multivariate analysis, miR-3614-5p was closely related to OS (HR = 0.630; 95% CI: 0.405-0.978, p = 0.021). GSEA showed that the high expression phenotype of miR-3614-5p differentially enriches the P53 pathway. Meanwhile, the high expression phenotype of miR-3614-5p enhanced NK T cell activation, negative T cell selection, response to interleukin 2, and response to tumor cells. MiR-3614-5p is a possible prognostic marker of low survival rate for patients with colorectal cancer. Moreover, the P53 pathway and P38MAPK pathway may be the key pathways regulated by miR-3614-5p in colorectal cancer.

In vivo single-cell analysis using calcofluor - white staining detects high expression phenotype in L. lactis cultures engineered for hyaluronic acid production

ABSTRACTHyaluronic acid (HA) is a biopolymer with wide applications in the field of medicine and cosmetics. Bacterial production of HA has a huge market globally. Certain species of Streptococcus are native producers of HA but they are pathogenic. Therefore, safer organisms such as L. lactis are engineered for HA production. However, there are challenges such as low yield, low molecular weight and polydispersity of HA obtained from these cultures. Optimisation of bioprocess parameters and downstream purification parameters are being addressed to overcome these challenges. We explore these problems from the perspective of microbial heterogeneity, since variations in phenotype affect the yield and properties of the product in a bioreactor. For this perspective, a method to quantitatively assess the occurrence of heterogenous phenotypes depending on the amount of HA produced at the single-cell level is required. Here, we evaluated for the first time the use of calcofluor white staining method combined with in vivo fluorescence confocal microscopy to quantify the heterogeneity in phenotypes of L. lactis cells engineered for HA production.From the microscopy image analysis, we found that the population harbours significant heterogeneity with respect to HA production and our novel approach successfully differentiates these phenotypes. Using the fluorescence intensity levels, first we were able to confidently differentiate cells not expressing HA (Host cells without HA genes for expression) from cells with genes for HA production (GJP2) and induced for expression, as there is a consistently two-fold higher level of expression in the GJP2 cells independently of the cell size. Further, this method revealed the occurrence of two different phenotypes in GJP2 cultures, one of a high-expression phenotype (40% of the population) and the other one of a low-expression (remaining 60% of the population), and it is the high expression phenotype that contributes to the increase in the HA expression of the GJP2 population compared with the host cells. Thus, it is essential to identify the extrinsic and intrinsic factors that can favour most of the cells in the population to switch and stabilise into the high-expression phenotype state in a bioreactor, for higher yield and possibly reduced heterogeneity of the product, such as polydispersity in chain lengths. For such optimisation studies, this in vivo method serves as a promising tool for rapid detection of phenotypes in the bioreactor samples under varying conditions, allowing fine tuning of the factors to stabilise high-expression phenotypes thereby maximizing the yield.Graphical Abstractdone.Key PointsCalcofluor staining successfully differentiated the phenotypes based on HA levels.This study revealed the occurrence of significant heterogeneity in HA expression.This method will aid for rapid optimization of factors for improved HA production.

Titrating gene expression with series of systematically compromised CRISPR guide RNAs

Biological phenotypes arise from the degrees to which genes are expressed, but the lack of tools to precisely control gene expression limits our ability to evaluate the underlying expression-phenotype relationships. Here, we describe a readily implementable approach to titrate expression of human genes using series of systematically compromised sgRNAs and CRISPR interference. We empirically characterize the activities of compromised sgRNAs using large-scale measurements across multiple cell models and derive the rules governing sgRNA activity using deep learning, enabling construction of a compact sgRNA library to titrate expression of ∼2,400 genes involved in central cell biology and a genome-wide in silico library. Staging cells along a continuum of gene expression levels combined with rich single-cell RNA-seq readout reveals gene-specific expression-phenotype relationships with expression level-specific responses. Our work provides a general tool to control gene expression, with applications ranging from tuning biochemical pathways to identifying suppressors for diseases of dysregulated gene expression.