Hypoxia Enhances Differentiation of Adipose Tissue-Derived Stem Cells toward the Smooth Muscle Phenotype.

Smooth muscle differentiated adipose tissue-derived stem cells are a valuable resource for regeneration of gastrointestinal tissues, such as the gut and sphincters. Hypoxia has been shown to promote adipose tissue-derived stem cells proliferation and maintenance of pluripotency, but the influence of hypoxia on their smooth myogenic differentiation remains unexplored. This study investigated the phenotype and contractility of adipose-derived stem cells differentiated toward the smooth myogenic lineage under hypoxic conditions. Oxygen concentrations of 2%, 5%, 10%, and 20% were used during differentiation of adipose tissue-derived stem cells. Real time reverse transcription polymerase chain reaction and immunofluorescence staining were used to detect the expression of smooth muscle cells-specific markers, including early marker smooth muscle alpha actin, middle markers calponin, caldesmon, and late marker smooth muscle myosin heavy chain. The specific contractile properties of cells were verified with both a single cell contraction assay and a gel contraction assay. Five percent oxygen concentration significantly increased the expression levels of α-smooth muscle actin, calponin, and myosin heavy chain in adipose-derived stem cell cultures after 2 weeks of induction (p < 0.01). Cells differentiated in 5% oxygen conditions showed greater contraction effect (p < 0.01). Hypoxia influences differentiation of smooth muscle cells from adipose stem cells and 5% oxygen was the optimal condition to generate smooth muscle cells that contract from adipose stem cells

A human glomerular SAGE transcriptome database

Background: To facilitate in the identification of gene products important in regulating renal glomerular structure and function, we have produced an annotated transcriptome database for normal human glomeruli using the SAGE approach. Description: The database contains 22,907 unique SAGE tag sequences, with a total tag count of 48,905. For each SAGE tag, the ratio of its frequency in glomeruli relative to that in 115 non-glomerular tissues or cells, a measure of transcript enrichment in glomeruli, was calculated. A total of 133 SAGE tags representing well-characterized transcripts were enriched 10-fold or more in glomeruli compared to other tissues. Comparison of data from this study with a previous human glomerular Sau3A-anchored SAGE library reveals that 47 of the highly enriched transcripts are common to both libraries. Among these are the SAGE tags representing many podocyte-predominant transcripts like WT-1, podocin and synaptopodin. Enrichment of podocyte transcript tags SAGE library indicates that other SAGE tags observed at much higher frequencies in this glomerular compared to non-glomerular SAGE libraries are likely to be glomerulus-predominant. A higher level of mRNA expression for 19 transcripts represented by glomerulus-enriched SAGE tags was verified by RT-PCR comparing glomeruli to lung, liver and spleen. Conclusions: The database can be retrieved from, or interrogated online at http://cgap.nci.nih.gov/SAGE. The annotated database is also provided as an additional file with gene identification for 9,022, and matches to the human genome or transcript homologs in other species for 1,433 tags. It should be a useful tool for in silico mining of glomerular gene expression

Next generation transcriptomes for next generation genomes using est2assembly

<p>Abstract</p> <p>Background</p> <p>The decreasing costs of capillary-based Sanger sequencing and next generation technologies, such as 454 pyrosequencing, have prompted an explosion of transcriptome projects in non-model species, where even shallow sequencing of transcriptomes can now be used to examine a range of research questions. This rapid growth in data has outstripped the ability of researchers working on non-model species to analyze and mine transcriptome data efficiently.</p> <p>Results</p> <p>Here we present a semi-automated platform '<it>est2assembly</it>' that processes raw sequence data from Sanger or 454 sequencing into a hybrid <it>de-novo </it>assembly, annotates it and produces GMOD compatible output, including a SeqFeature database suitable for GBrowse. Users are able to parameterize assembler variables, judge assembly quality and determine the optimal assembly for their specific needs. We used <it>est2assembly </it>to process <it>Drosophila </it>and <it>Bicyclus </it>public Sanger EST data and then compared them to published 454 data as well as eight new insect transcriptome collections.</p> <p>Conclusions</p> <p>Analysis of such a wide variety of data allows us to understand how these new technologies can assist EST project design. We determine that assembler parameterization is as essential as standardized methods to judge the output of ESTs projects. Further, even shallow sequencing using 454 produces sufficient data to be of wide use to the community. <it>est2assembly </it>is an important tool to assist manual curation for gene models, an important resource in their own right but especially for species which are due to acquire a genome project using Next Generation Sequencing.</p

Separation of sequences from host-pathogen interface using triplet nucleotide frequencies.

The identification of genes involved in host-pathogen interactions is important for the elucidation of mechanisms of disease resistance and host susceptibility. A traditional way to classify the origin of genes sampled from a pool of mixed cDNA is through sequence similarity to known genes from either the pathogen or host organism or other closely related species. This approach does not work when the identified sequence has no close homologues in the sequence databases. In our previous studies, we classified genes using their codon frequencies. This method, however, explicitly required the prediction of CDS regions and thus could not be applied to sequences composed from the non-coding regions of genes. In this study, we show that the use of sliding-window triplet frequencies extends the application of the algorithm to both coding and non-coding sequences and also increases the prediction accuracy of a Support Vector Machine classifier from 95.6+/-0.3 to 96.5+/-0.2. Thus the use of the triplet frequencies increased the prediction accuracy of the new method by more than 20% compared to our previous approach. A functional analysis of sequences detected gene families having significantly higher or lower probability to be correctly classified compared to the average accuracy of the method is described. The server to perform classification of EST sequences using triplet frequencies is available at (URL: http://mips.gsf.de/proj/est3)