Theodore J. Perkins

Strategies for cellular decision-making

Stochasticity pervades life at the cellular level. Cells receive stochastic signals, perform detection and transduction with stochastic biochemistry, and grow and die in stochastic environments. Here we review progress in going from the molecular details to the information‐processing strategies cells use in their decision‐making. Such strategies are fundamentally influenced by stochasticity. We argue that the cellular decision‐making can only be probabilistic and occurs at three levels. First, cells must infer from noisy signals the probable current and anticipated future state of their environment. Second, they must weigh the costs and benefits of each potential response, given that future. Third, cells must decide in the presence of other, potentially competitive, decision‐makers. In this context, we discuss cooperative responses where some individuals can appear to sacrifice for the common good. We believe that decision‐making strategies will be conserved, with comparatively few strategies being implemented by different biochemical mechanisms in many organisms. Determining the strategy of a decision‐making network provides a potentially powerful coarse‐graining that links systems and evolutionary biology to understand biological design.

Systematic Discovery of Rab GTPases with Synaptic Functions in Drosophila

BACKGROUNDnNeurons require highly specialized intracellular membrane trafficking, especially at synapses. Rab GTPases are considered master regulators of membrane trafficking in all cells, and only very few Rabs have known neuron-specific functions. Here, we present the first systematic characterization of neuronal expression, subcellular localization, and function of Rab GTPases in an organism with a brain.nnnRESULTSnWe report the surprising discovery that half of all Drosophila Rabs function specifically or predominantly in distinct subsets of neurons in the brain. Furthermore, functional profiling of the GTP/GDP-bound states reveals that these neuronal Rabs are almost exclusively active at synapses and the majority of these synaptic Rabs specifically mark synaptic recycling endosomal compartments. Our profiling strategy is based on Gal4 knockins in large genomic fragments that are additionally designed to generate mutants by ends-out homologous recombination. We generated 36 large genomic targeting vectors and transgenic rab-Gal4 fly strains for 25 rab genes. Proof-of-principle knockout of the synaptic rab27 reveals a sleep phenotype that matches its cell-specific expression.nnnCONCLUSIONSnOur findings suggest that up to half of all Drosophila Rabs exert specialized synaptic functions. The tools presented here allow systematic functional studies of these Rabs and provide a method that is applicable to any large gene family in Drosophila.

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.

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.

UTX inhibition as selective epigenetic therapy against TAL1-driven T-cell acute lymphoblastic leukemia

T-cell acute lymphoblastic leukemia (T-ALL) is a heterogeneous group of hematological tumors composed of distinct subtypes that vary in their genetic abnormalities, gene expression signatures, and prognoses. However, it remains unclear whether T-ALL subtypes differ at the functional level, and, as such, T-ALL treatments are uniformly applied across subtypes, leading to variable responses between patients. Here we reveal the existence of a subtype-specific epigenetic vulnerability in T-ALL by which a particular subgroup of T-ALL characterized by expression of the oncogenic transcription factor TAL1 is uniquely sensitive to variations in the dosage and activity of the histone 3 Lys27 (H3K27) demethylase UTX/KDM6A. Specifically, we identify UTX as a coactivator of TAL1 and show that it acts as a major regulator of the TAL1 leukemic gene expression program. Furthermore, we demonstrate that UTX, previously described as a tumor suppressor in T-ALL, is in fact a pro-oncogenic cofactor essential for leukemia maintenance in TAL1-positive (but not TAL1-negative) T-ALL. Exploiting this subtype-specific epigenetic vulnerability, we propose a novel therapeutic approach based on UTX inhibition through in vivo administration of an H3K27 demethylase inhibitor that efficiently kills TAL1-positive primary human leukemia. These findings provide the first opportunity to develop personalized epigenetic therapy for T-ALL patients.

Control of glioblastoma tumorigenesis by feed-forward cytokine signaling

EGFRvIII-STAT3 signaling is important in glioblastoma pathogenesis. Here, we identified the cytokine receptor OSMR as a direct target gene of the transcription factor STAT3 in mouse astrocytes and human brain tumor stem cells (BTSCs). We found that OSMR functioned as an essential co-receptor for EGFRvIII. OSMR formed a physical complex with EGFRvIII, and depletion of OSMR impaired EGFRvIII-STAT3 signaling. Conversely, pharmacological inhibition of EGFRvIII phosphorylation inhibited the EGFRvIII-OSMR interaction and activation of STAT3. EGFRvIII-OSMR signaling in tumors operated constitutively, whereas EGFR-OSMR signaling in nontumor cells was synergistically activated by the ligands EGF and OSM. Finally, knockdown of OSMR strongly suppressed cell proliferation and tumor growth of mouse glioblastoma cells and human BTSC xenografts in mice, and prolonged the lifespan of these mice. Our findings identify OSMR as a critical regulator of glioblastoma tumor growth that orchestrates a feed-forward signaling mechanism with EGFRvIII and STAT3 to drive tumorigenesis.

Trichostatin A enhances vascular repair by injected human endothelial progenitors through increasing the expression of TAL1-dependent genes.

A major goal of cell therapy for vascular diseases is to promote revascularization through the injection of endothelial stem/progenitor cells. The gene regulatory mechanisms that underlie endothelial progenitor-mediated vascular repair, however, remain elusive. Here, we identify the transcription factor TAL1/SCL as a key mediator of the vascular repair function of primary human endothelial colony-forming cells (ECFCs). Genome-wide analyses in ECFCs demonstrate that TAL1 activates a transcriptional program that promotes cell adhesion and migration. At the mechanistic level, we show that TAL1 upregulates the expression of migratory and adhesion genes through recruitment of the histone acetyltransferase p300. Based on these findings, we establish a strategy that enhances the revascularization efficiency of ECFCs after ischemia through exxa0vivo priming with the histone deacetylase inhibitor TSA. Thus, small molecule epigenetics drugs are effective tools for modifying the epigenome of stem/progenitor cells prior to transplantation as a means to enhance their therapeutic potential.

Estimating the stochastic bifurcation structure of cellular networks.

High throughput measurement of gene expression at single-cell resolution, combined with systematic perturbation of environmental or cellular variables, provides information that can be used to generate novel insight into the properties of gene regulatory networks by linking cellular responses to external parameters. In dynamical systems theory, this information is the subject of bifurcation analysis, which establishes how system-level behaviour changes as a function of parameter values within a given deterministic mathematical model. Since cellular networks are inherently noisy, we generalize the traditional bifurcation diagram of deterministic systems theory to stochastic dynamical systems. We demonstrate how statistical methods for density estimation, in particular, mixture density and conditional mixture density estimators, can be employed to establish empirical bifurcation diagrams describing the bistable genetic switch network controlling galactose utilization in yeast Saccharomyces cerevisiae. These approaches allow us to make novel qualitative and quantitative observations about the switching behavior of the galactose network, and provide a framework that might be useful to extract information needed for the development of quantitative network models.

FiloDetect: automatic detection of filopodia from fluorescence microscopy images

BackgroundFilopodia are small cellular projections that help cells to move through and sense their environment. Filopodia play crucial roles in processes such as development and wound-healing. Also, increases in filopodia number or size are characteristic of many invasive cancers and are correlated with increased rates of metastasis in mouse experiments. Thus, one possible route to developing anti-metastatic therapies is to target factors that influence the filopodia system. Filopodia can be detected by eye using confocal fluorescence microscopy, and they can be manually annotated in images to quantify filopodia parameters. Although this approach is accurate, it is slow, tedious and not entirely objective. Manual detection is a significant barrier to the discovery and quantification of new factors that influence the filopodia system.ResultsHere, we present FiloDetect, an automated tool for detecting, counting and measuring the length of filopodia in fluorescence microscopy images. The method first segments the cell from the background, using a modified triangle threshold method, and then extracts the filopodia using a series of morphological operations. We verified the accuracy of FiloDetect on Rat2 and B16F1 cell images from three different labs, showing that per-cell filopodia counts and length estimates are highly correlated with the manual annotations. We then used FiloDetect to assess the role of a lipid kinase on filopodia production in breast cancer cells. Experimental results show that PI4KIII β expression leads to an increase in filopodia number and length, suggesting that PI4KIII β is involved in driving filopodia production.ConclusionFiloDetect provides accurate and objective quantification of filopodia in microscopy images, and will enable large scale comparative studies to assess the effects of different genetic and chemical perturbations on filopodia production in different cell types, including cancer cell lines.

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.