Gareth A. Palidwor

Gene function in early mouse embryonic stem cell differentiation

BackgroundLittle is known about the genes that drive embryonic stem cell differentiation. However, such knowledge is necessary if we are to exploit the therapeutic potential of stem cells. To uncover the genetic determinants of mouse embryonic stem cell (mESC) differentiation, we have generated and analyzed 11-point time-series of DNA microarray data for three biologically equivalent but genetically distinct mESC lines (R1, J1, and V6.5) undergoing undirected differentiation into embryoid bodies (EBs) over a period of two weeks.ResultsWe identified the initial 12 hour period as reflecting the early stages of mESC differentiation and studied probe sets showing consistent changes of gene expression in that period. Gene function analysis indicated significant up-regulation of genes related to regulation of transcription and mRNA splicing, and down-regulation of genes related to intracellular signaling. Phylogenetic analysis indicated that the genes showing the largest expression changes were more likely to have originated in metazoans. The probe sets with the most consistent gene changes in the three cell lines represented 24 down-regulated and 12 up-regulated genes, all with closely related human homologues. Whereas some of these genes are known to be involved in embryonic developmental processes (e.g. Klf4, Otx2, Smn1, Socs3, Tagln, Tdgf1), our analysis points to others (such as transcription factor Phf21a, extracellular matrix related Lama1 and Cyr61, or endoplasmic reticulum related Sc4mol and Scd2) that have not been previously related to mESC function. The majority of identified functions were related to transcriptional regulation, intracellular signaling, and cytoskeleton. Genes involved in other cellular functions important in ESC differentiation such as chromatin remodeling and transmembrane receptors were not observed in this set.ConclusionOur analysis profiles for the first time gene expression at a very early stage of mESC differentiation, and identifies a functional and phylogenetic signature for the genes involved. The data generated constitute a valuable resource for further studies. All DNA microarray data used in this study are available in the StemBase database of stem cell gene expression data [1] and in the NCBIs GEO database.

A general model of codon bias due to GC mutational bias.

Background In spite of extensive research on the effect of mutation and selection on codon usage, a general model of codon usage bias due to mutational bias has been lacking. Because most amino acids allow synonymous GC content changing substitutions in the third codon position, the overall GC bias of a genome or genomic region is highly correlated with GC3, a measure of third position GC content. For individual amino acids as well, G/C ending codons usage generally increases with increasing GC bias and decreases with increasing AT bias. Arginine and leucine, amino acids that allow GC-changing synonymous substitutions in the first and third codon positions, have codons which may be expected to show different usage patterns. Principal Findings In analyzing codon usage bias in hundreds of prokaryotic and plant genomes and in human genes, we find that two G-ending codons, AGG (arginine) and TTG (leucine), unlike all other G/C-ending codons, show overall usage that decreases with increasing GC bias, contrary to the usual expectation that G/C-ending codon usage should increase with increasing genomic GC bias. Moreover, the usage of some codons appears nonlinear, even nonmonotone, as a function of GC bias. To explain these observations, we propose a continuous-time Markov chain model of GC-biased synonymous substitution. This model correctly predicts the qualitative usage patterns of all codons, including nonlinear codon usage in isoleucine, arginine and leucine. The model accounts for 72%, 64% and 52% of the observed variability of codon usage in prokaryotes, plants and human respectively. When codons are grouped based on common GC content, 87%, 80% and 68% of the variation in usage is explained for prokaryotes, plants and human respectively. Conclusions The model clarifies the sometimes-counterintuitive effects that GC mutational bias can have on codon usage, quantifies the influence of GC mutational bias and provides a natural null model relative to which other influences on codon bias may be measured.

Study of stem cell function using microarray experiments

DNA Microarrays are used to simultaneously measure the levels of thousands of mRNAs in a sample. We illustrate here that a collection of such measurements in different cell types and states is a sound source of functional predictions, provided the microarray experiments are analogous and the cell samples are appropriately diverse. We have used this approach to study stem cells, whose identity and mechanisms of control are not well understood, generating Affymetrix microarray data from more than 200 samples, including stem cells and their derivatives, from human and mouse. The data can be accessed online (StemBase; http://www.scgp.ca:8080/StemBase/).

Transcriptional Dominance of Pax7 in Adult Myogenesis Is Due to High-Affinity Recognition of Homeodomain Motifs

Pax3 and Pax7 regulate stem cell function in skeletal myogenesis. However, molecular insight into their distinct roles has remained elusive. Using gene expression data combined with genome-wide binding-site analysis, we show that both Pax3 and Pax7 bind identical DNA motifs and jointly activate a large panel of genes involved in muscle stem cell function. Surprisingly, in adult myoblasts Pax3 binds a subset (6.4%) of Pax7 targets. Despite a significant overlap in their transcriptional network, Pax7 regulates distinct panels of genes involved in the promotion of proliferation and inhibition of myogenic differentiation. We show that Pax7 has a higher binding affinity to the homeodomain-binding motif relative to Pax3, suggesting that intrinsic differences in DNA binding contribute to the observed functional difference between Pax3 and Pax7 binding in myogenesis. Together, our data demonstrate distinct attributes of Pax7 function and provide mechanistic insight into the nonredundancy of Pax3 and Pax7 in muscle development.

Detection of Alpha-Rod Protein Repeats Using a Neural Network and Application to Huntingtin

A growing number of solved protein structures display an elongated structural domain, denoted here as alpha-rod, composed of stacked pairs of anti-parallel alpha-helices. Alpha-rods are flexible and expose a large surface, which makes them suitable for protein interaction. Although most likely originating by tandem duplication of a two-helix unit, their detection using sequence similarity between repeats is poor. Here, we show that alpha-rod repeats can be detected using a neural network. The network detects more repeats than are identified by domain databases using multiple profiles, with a low level of false positives (<10%). We identify alpha-rod repeats in approximately 0.4% of proteins in eukaryotic genomes. We then investigate the results for all human proteins, identifying alpha-rod repeats for the first time in six protein families, including proteins STAG1-3, SERAC1, and PSMD1-2 & 5. We also characterize a short version of these repeats in eight protein families of Archaeal, Bacterial, and Fungal species. Finally, we demonstrate the utility of these predictions in directing experimental work to demarcate three alpha-rods in huntingtin, a protein mutated in Huntingtons disease. Using yeast two hybrid analysis and an immunoprecipitation technique, we show that the huntingtin fragments containing alpha-rods associate with each other. This is the first definition of domains in huntingtin and the first validation of predicted interactions between fragments of huntingtin, which sets up directions toward functional characterization of this protein. An implementation of the repeat detection algorithm is available as a Web server with a simple graphical output: http://www.ogic.ca/projects/ard. This can be further visualized using BiasViz, a graphic tool for representation of multiple sequence alignments.

Towards completion of the Earth's proteome.

New protein sequences are deposited in databases at an accelerating pace; however, many of these are homologous to known proteins and could be considered redundant. If all historical releases of the protein database are analysed using the original sequence‐clustering procedure described here, the fraction of newly sequenced proteins that are redundant is increasing. We interpret this as an indication that the sequencing of the Earths proteome—the complete set of proteins on Earth—is approaching completion. We estimate the approximate size of the Earths proteome to be 5 million sequences, most of which will be identified during the next 5 years. As the Earths proteome nears completion, cluster analysis of the protein database will become essential to identify under‐explored taxa to which future sequencing efforts should be directed and to focus research on protein families without experimental characterization.

Transcriptional profiling of skeletal muscle reveals factors that are necessary to maintain satellite cell integrity during ageing.

Skeletal muscle ageing is characterized by faulty degenerative/regenerative processes that promote the decline of its mass, strength, and endurance. In this study, we used a transcriptional profiling method to better understand the molecular pathways and factors that contribute to these processes. To more appropriately contrast the differences in regenerative capacity of old muscle, we compared it with young muscle, where robust growth and efficient myogenic differentiation is ongoing. Notably, in old mice, we found a severe deficit in satellite cells activation. We performed expression analyses on RNA from the gastrocnemius muscle of young (3-week-old) and old (24-month-old) mice. The differential expression highlighted genes that are involved in the efficient functioning of satellite cells. Indeed, the greatest number of up-regulated genes in young mice encoded components of the extracellular matrix required for the maintenance of the satellite cell niche. Moreover, other genes included Wnt inhibitors (Wif1 and Sfrp2) and Notch activator (Dner), which are putatively involved in the interconnected signalling networks that control satellite cell function. The widespread expression differences for inhibitors of TGFbeta signalling further emphasize the shortcomings in satellite cell performance. Therefore, we draw attention to the breakdown of features required to maintain satellite cell integrity during the ageing process.

StemBase: a resource for the analysis of stem cell gene expression data.

StemBase is a database of gene expression data obtained from stem cells and derivatives mainly from mouse and human using DNA microarrays and Serial Analysis of Gene Expression. Here, we describe this database and indicate ways to use it for the study the expression of particular genes in stem cells or to search for genes with particular expression profiles in stem cells, which could be associated to stem cell function or used as stem cell markers.

MaSC: mappability-sensitive cross-correlation for estimating mean fragment length of single-end short-read sequencing data

Motivation: Reliable estimation of the mean fragment length for next-generation short-read sequencing data is an important step in next-generation sequencing analysis pipelines, most notably because of its impact on the accuracy of the enriched regions identified by peak-calling algorithms. Although many peak-calling algorithms include a fragment-length estimation subroutine, the problem has not been adequately solved, as demonstrated by the variability of the estimates returned by different algorithms. Results: In this article, we investigate the use of strand cross-correlation to estimate mean fragment length of single-end data and show that traditional estimation approaches have mixed reliability. We observe that the mappability of different parts of the genome can introduce an artificial bias into cross-correlation computations, resulting in incorrect fragment-length estimates. We propose a new approach, called mappability-sensitive cross-correlation (MaSC), which removes this bias and allows for accurate and reliable fragment-length estimation. We analyze the computational complexity of this approach, and evaluate its performance on a test suite of NGS datasets, demonstrating its superiority to traditional cross-correlation analysis. Availability: An open-source Perl implementation of our approach is available at http://www.perkinslab.ca/Software.html. Contact: tperkins@ohri.ca Supplementary information: Supplementary data are available at Bioinformatics online.

Functional and Genomic Analyses of Alpha-Solenoid Proteins

Alpha-solenoids are flexible protein structural domains formed by ensembles of alpha-helical repeats (Armadillo and HEAT repeats among others). While homology can be used to detect many of these repeats, some alpha-solenoids have very little sequence homology to proteins of known structure and we expect that many remain undetected. We previously developed a method for detection of alpha-helical repeats based on a neural network trained on a dataset of protein structures. Here we improved the detection algorithm and updated the training dataset using recently solved structures of alpha-solenoids. Unexpectedly, we identified occurrences of alpha-solenoids in solved protein structures that escaped attention, for example within the core of the catalytic subunit of PI3KC. Our results expand the current set of known alpha-solenoids. Application of our tool to the protein universe allowed us to detect their significant enrichment in proteins interacting with many proteins, confirming that alpha-solenoids are generally involved in protein-protein interactions. We then studied the taxonomic distribution of alpha-solenoids to discuss an evolutionary scenario for the emergence of this type of domain, speculating that alpha-solenoids have emerged in multiple taxa in independent events by convergent evolution. We observe a higher rate of alpha-solenoids in eukaryotic genomes and in some prokaryotic families, such as Cyanobacteria and Planctomycetes, which could be associated to increased cellular complexity. The method is available at http://cbdm.mdc-berlin.de/~ard2/.