Background: Diabetic cystopathy (DCP) is a chronic complication of diabetes mainly within the submucosal and muscular layers of the bladder due to the hyperglycemia-induced ischemia. As no effective therapies are currently available, the administration of optimized mesenchymal stem cells (MSCs) provides a potential treatment of DCP. Thus far, new strategy, such as genetic modification of MSCs, has been developed and has shown promising outcomes of various disorders. Methods: This study was conducted using integrin-linked kinase (ILK) gene-modified bone marrow-derived stem cells (BMSCs) for streptozotocin (STZ)-induced diabetic cystopathy in a rat model. In total, 68 male Sprague-Dawley rats were randomized into five groups: sham control (control group, n = 10); DCP model alone (DM group, n = 10); DCP rats intravenously treated with BMSCs (BMSC group, n = 16); DCP rats accepted adenoviral vector-infected BMSCs (Ad-null-BMSC group, n = 16) and DCP rats accepted ILK adenoviral vector-infected BMSCs (Ad-ILK-BMSC group, n = 16). Diabetic rats accepted cell transplantation in the experimental group (2 rats per group) were sacrificed for the bladder tissue on the third day, 7th day, and 14th day of treatment respectively ahead of schedule. At 4 weeks after treatment, all rats in five groups accepted urodynamic studies to evaluate bladder function and were sacrificed for bladder tissue. Results: Our data showed that the underactive bladder function was significantly improved in DCP rats intravenously treated with ILK gene-modified BMSCs compared to those in the DM, BMSCs, and Ad-null-BMSC group. Meanwhile, we found that gene-modified BMSC treatment significantly promoted the activation of the AKT/ GSK-3β pathway by increasing phosphorylation and led to the enhancement of survival. In addition, the expression levels of angiogenesis-related protein vascular endothelial growth factor (VEGF), basic fibroblast growth factor (bFGF), and stromal cell-derived factor-1 (SDF-1) were significantly higher in the Ad-ILK-BMSC group than that in the DM, BMSCs, and Ad-null-BMSC group as assessed by enzyme-linked immunosorbent assay and western blot. As two indicators of vascular endothelial cell markers, the expression of von Willebrand factor (vWF) and CD31 by

The huge body of publicly available RNA-sequencing (RNA-seq) libraries is a treasure of functional information allowing to quantify the expression of known or novel transcripts in tissues. However, transcript quantification commonly relies on alignment methods requiring a lot of computational resources and processing time, which does not scale easily to large datasets. K-mer decomposition constitutes a new way to process RNA-seq data for the identification of transcriptional signatures, as k-mers can be used to quantify accurately gene expression in a less resource-consuming way. We present the Kmerator Suite, a set of three tools designed to extract specific k-mer signatures, quantify these k-mers into RNA-seq datasets and quickly visualize large dataset characteristics. The core tool, Kmerator, produces specific k-mers for 97% of human genes, enabling the measure of gene expression with high accuracy in simulated datasets. KmerExploR, a direct application of Kmerator, uses a set of predictor gene-specific k-mers to infer metadata including library protocol, sample features or contaminations from RNA-seq datasets. KmerExploR results are visualized through a user-friendly interface. Moreover, we demonstrate that the Kmerator Suite can be used for advanced queries targeting known or new biomarkers such as mutations, gene fusions or long non-coding RNAs for human health applications.

Background RNA-seq data are increasingly used to derive prognostic signatures for cancer outcome prediction. A limitation of current predictors is their reliance on reference gene annotations, which amounts to ignoring large numbers of non-canonical RNAs produced in disease tissues. A recently introduced kind of transcriptome classifier operates entirely in a reference-free manner, relying on k-mers extracted from patient RNA-seq data. Methods In this paper, we set out to compare conventional and reference-free signatures in risk and relapse prediction of prostate cancer. To compare the two approaches as fairly as possible, we set up a common procedure that takes as input either a k-mer count matrix or a gene expression matrix, extracts a signature and evaluates this signature in an independent dataset. Results We find that both gene-based and k-mer based classifiers had similarly high performances for risk prediction and a markedly lower performance for relapse prediction. Interestingly, the reference-free signatures included a set of sequences mapping to novel lncRNAs or variable regions of cancer driver genes that were not part of gene-based signatures. Conclusions Reference-free classifiers are thus a promising strategy for the identification of novel prognostic RNA biomarkers.