Amelioration of Streptozotocin-Induced Diabetes in Mice with Cells Derived from Human Marrow Stromal Cells

Pluri-potent bone marrow stromal cells (MSCs) provide an attractive opportunity to generate unlimited glucose-responsive insulin-producing cells for the treatment of diabetes. We explored the potential for human MSCs (hMSCs) to be differentiated into glucose-responsive cells through a non-viral genetic reprogramming approach.Two HMSC lines were transfected with three genes: PDX-1, NeuroD1 and Ngn3 without subsequent selection, followed by differentiation induction in vitro and transplantation into diabetic mice. Human MSCs expressed mRNAs of the archetypal stem cell markers: Sox2, Oct4, Nanog and CD34, and the endocrine cell markers: PDX-1, NeuroD1, Ngn3, and Nkx6.1. Following gene transfection and differentiation induction, hMSCs expressed insulin in vitro, but were not glucose regulated. After transplantation, hMSCs differentiated further and approximately 12.5% of the grafted cells expressed insulin. The graft bearing kidneys contained mRNA of insulin and other key genes required for the functions of beta cells. Mice transplanted with manipulated hMSCs showed reduced blood glucose levels (from 18.9+/-0.75 to 7.63+/-1.63 mM). 13 of the 16 mice became normoglycaemic (6.9+/-0.64 mM), despite the failure to detect the expression of SUR1, a K(+)-ATP channel component required for regulation of insulin secretion.Our data confirm that hMSCs can be induced to express insulin sufficient to reduce blood glucose in a diabetic mouse model. Our triple gene approach has created cells that seem less glucose responsive in vitro but which become more efficient after transplantation. The maturation process requires further study, particularly the in vivo factors influencing the differentiation, in order to scale up for clinical purposes

Finding gene regulatory network candidates using the gene expression knowledge base

BACKGROUND: Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of ‘omics’ data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis. RESULTS: We have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions. CONCLUSIONS: Semantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-014-0386-y) contains supplementary material, which is available to authorized users

The V471A polymorphism in autophagy-related gene ATG7 modifies age at onset specifically in Italian Huntington disease patients

The cause of Huntington disease (HD) is a polyglutamine repeat expansion of more than 36 units in the huntingtin protein, which is inversely correlated with the age at onset of the disease. However, additional genetic factors are believed to modify the course and the age at onset of HD. Recently, we identified the V471A polymorphism in the autophagy-related gene ATG7, a key component of the autophagy pathway that plays an important role in HD pathogenesis, to be associated with the age at onset in a large group of European Huntington disease patients. To confirm this association in a second independent patient cohort, we analysed the ATG7 V471A polymorphism in additional 1,464 European HD patients of the “REGISTRY” cohort from the European Huntington Disease Network (EHDN). In the entire REGISTRY cohort we could not confirm a modifying effect of the ATG7 V471A polymorphism. However, analysing a modifying effect of ATG7 in these REGISTRY patients and in patients of our previous HD cohort according to their ethnic origin, we identified a significant effect of the ATG7 V471A polymorphism on the HD age at onset only in the Italian population (327 patients). In these Italian patients, the polymorphism is associated with a 6-years earlier disease onset and thus seems to have an aggravating effect. We could specify the role of ATG7 as a genetic modifier for HD particularly in the Italian population. This result affirms the modifying influence of the autophagic pathway on the course of HD, but also suggests population-specific modifying mechanisms in HD pathogenesis