The Protein Interaction Network Analysis (PINA) platform is a comprehensive web resource, which includes a database of unified protein–protein interaction data integrated from six manually curated public databases, and a set of built-in tools for network construction, filtering, analysis and visualization. The second version of PINA enhances its utility for studies of protein interactions at a network level, by including multiple collections of interaction modules identified by different clustering approaches from the whole network of protein interactions ('interactome') for six model organisms. All identified modules are fully annotated by enriched Gene Ontology terms, KEGG pathways, Pfam domains and the chemical and genetic perturbations collection from MSigDB. Moreover, a new tool is provided for module enrichment analysis in addition to simple query function. The interactome data are also available on the web site for further bioinformatics analysis. PINA is freely accessible at http://cbg.garvan.unsw.edu.au/pina/.
Abstract Down syndrome (DS) is the most frequent cause of human congenital mental retardation. Cognitive deficits in DS result from perturbations of normal cellular processes both during development and in adult tissues, but the mechanisms underlying DS etiology remain poorly understood. To assess the ability of induced pluripotent stem cells (iPSCs) to model DS phenotypes, as a prototypical complex human disease, we generated bona fide DS and wild-type (WT) nonviral iPSCs by episomal reprogramming. DS iPSCs selectively overexpressed chromosome 21 genes, consistent with gene dosage, which was associated with deregulation of thousands of genes throughout the genome. DS and WT iPSCs were neurally converted at >95% efficiency and had remarkably similar lineage potency, differentiation kinetics, proliferation, and axon extension at early time points. However, at later time points DS cultures showed a twofold bias toward glial lineages. Moreover, DS neural cultures were up to two times more sensitive to oxidative stress-induced apoptosis, and this could be prevented by the antioxidant N-acetylcysteine. Our results reveal a striking complexity in the genetic alterations caused by trisomy 21 that are likely to underlie DS developmental phenotypes, and indicate a central role for defective early glial development in establishing developmental defects in DS brains. Furthermore, oxidative stress sensitivity is likely to contribute to the accelerated neurodegeneration seen in DS, and we provide proof of concept for screening corrective therapeutics using DS iPSCs and their derivatives. Nonviral DS iPSCs can therefore model features of complex human disease in vitro and provide a renewable and ethically unencumbered discovery platform.
Since the sequencing of the mouse and human genomes, there has been a concerted eort to define their complete transcriptional output. EST, full length cDNA sequencing, and transcriptome annotation eorts by FANTOM, ENCODE and other consortia surveyed mammalian expression space, revealing that loci on average generate 6-10 transcripts. Alternative promoters, splicing and 3’UTRs are commonplace. While these data have provided an excellent atlas of what can be generated from mammalian genomes, we have not had, until recently, the right genomic tools to place this transcriptional complexity into a biological context. Array based profiling has been an excellent tool for assessing overall gene activity, but lacks the sensitivity and resolution required to study complete transcriptome content RNA sequencing (RNAseq) has recently been demonstrated in several eukaryotic species and is redefining our understanding of mRNA transcriptome content and mRNA dynamics, all at a single nucleotide resolution. We have developed methods for performing multi-gigabase shotgun sequencing of human and mouse transcriptomes and have developed approaches to assess locus activity and demonstrated its improved sensitivity relative to the current “gold standard” array platforms. We also use RNAseq to assess the expression levels of variant transcripts via diagnostic sequences. Thirdly, we are able to perform genome-wide transcriptome discovery. Finally we have also established approaches to identify alternations to the reference sequence content, allowing us to search for expressed polymorphisms, mutations or events such as RNA editing. These data are combined with RNAseq surveys of other fractions of the transcriptome (i.e. small RNA and polysome-associated RNAs) to gain a fuller picture of coding and functional RNA content. This is being used to define, at unprecedented resolution, the transcriptional networks driving specific biological states.
'/%3e%3c/defs%3e%3cpath fill='%23012169' d='M0 0h10080v5040H0z'/%3e%3cpath stroke='%23fff' stroke-width='.6' d='m0 0 6 3m0-3L0 3' clip-path='url(%23b)' transform='scale(840)'/%3e%3cpath stroke='%23e4002b' stroke-width='.4' d='m0 0 6 3m0-3L0 3' clip-path='url(%23c)' transform='scale(840)'/%3e%3cpath stroke='%23fff' stroke-width='840' d='M2520 0v2520M0 1260h5040'/%3e%3cpath stroke='%23e4002b' stroke-width='504' d='M2520 0v2520M0 1260h5040'/%3e%3cg fill='%23fff'%3e%3cuse xlink:href='%23d' x='2520' y='3780'/%3e%3cuse xlink:href='%23a' x='7560' y='4200'/%3e%3cuse xlink:href='%23a' x='6300' y='2205'/%3e%3cuse xlink:href='%23a' x='7560' y='840'/%3e%3cuse xlink:href='%23a' x='8680' y='1869'/%3e%3cuse xlink:href='%23e' x='8064' y='2730'/%3e%3c/g%3e%3c/svg%3e)
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)