Extensive transcriptional responses are co-ordinated by microRNAs as revealed by Exon-Intron Split Analysis (EISA).

Epithelial-mesenchymal transition (EMT) has been a subject of intense scrutiny as it facilitates metastasis and alters drug sensitivity. Although EMT-regulatory roles for numerous miRNAs and transcription factors are known, their functions can be difficult to disentangle, in part due to the difficulty in identifying direct miRNA targets from complex datasets and in deciding how to incorporate 'indirect' miRNA effects that may, or may not, represent biologically relevant information. To better understand how miRNAs exert effects throughout the transcriptome during EMT, we employed Exon-Intron Split Analysis (EISA), a bioinformatic technique that separates transcriptional and post-transcriptional effects through the separate analysis of RNA-Seq reads mapping to exons and introns. We find that in response to the manipulation of miRNAs, a major effect on gene expression is transcriptional. We also find extensive co-ordination of transcriptional and post-transcriptional regulatory mechanisms during both EMT and mesenchymal to epithelial transition (MET) in response to TGF-ß or miR-200c respectively. The prominent transcriptional influence of miRNAs was also observed in other datasets where miRNA levels were perturbed. This work cautions against a narrow approach that is limited to the analysis of direct targets, and demonstrates the utility of EISA to examine complex regulatory networks involving both transcriptional and post-transcriptional mechanisms.

Transcriptome profiling of caspase-2 deficient EµMyc and Th-MYCN mouse tumors identifies distinct putative roles for caspase-2 in neuronal differentiation and immune signaling.

Caspase-2 is a highly conserved cysteine protease with roles in apoptosis and tumor suppression. Our recent findings have also demonstrated that the tumor suppression function of caspase-2 is context specific. In particular, while caspase-2 deficiency augments lymphoma development in the EµMyc mouse model, it leads to delayed neuroblastoma development in Th-MYCN mice. However, it is unclear how caspase-2 mediates these differential outcomes. Here we utilized RNA sequencing to define the transcriptomic changes caused by caspase-2 (Casp2-/-) deficiency in tumors from EµMyc and Th-MYCN mice. We describe key changes in both lymphoma and neuroblastoma-associated genes and identified differential expression of the EGF-like domain-containing gene, Megf6, in the two tumor types that may contribute to tumor outcome following loss of Casp2. We identified a panel of genes with altered expression in Th-MYCN/Casp2-/- tumors that are strongly associated with neuroblastoma outcome, with roles in melanogenesis, Wnt and Hippo pathway signaling, that also contribute to neuronal differentiation. In contrast, we found that key changes in gene expression in the EµMyc/Casp2-/- tumors, are associated with increased immune signaling and T-cell infiltration previously associated with more aggressive lymphoma progression. In addition, Rap1 signaling pathway was uniquely enriched in Casp2 deficient EµMyc tumors. Our findings suggest that Casp2 deficiency augments immune signaling pathways that may be in turn, enhance lymphomagenesis. Overall, our study has identified new genes and pathways that contribute to the caspase-2 tumor suppressor function and highlight distinct roles for caspase-2 in different tissues.

Modeling biological tissue growth: discrete to continuum representations.

There is much interest in building deterministic continuum models from discrete agent-based models governed by local stochastic rules where an agent represents a biological cell. In developmental biology, cells are able to move and undergo cell division on and within growing tissues. A growing tissue is itself made up of cells which undergo cell division, thereby providing a significant transport mechanism for other cells within it. We develop a discrete agent-based model where domain agents represent tissue cells. Each agent has the ability to undergo a proliferation event whereby an additional domain agent is incorporated into the lattice. If a probability distribution describes the waiting times between proliferation events for an individual agent, then the total length of the domain is a random variable. The average behavior of these stochastically proliferating agents defining the growing lattice is determined in terms of a Fokker-Planck equation, with an advection and diffusion term. The diffusion term differs from the one obtained Landman and Binder [J. Theor. Biol. 259, 541 (2009)] when the rate of growth of the domain is specified, but the choice of agents is random. This discrepancy is reconciled by determining a discrete-time master equation for this process and an associated asymmetric nonexclusion random walk, together with consideration of synchronous and asynchronous updating schemes. All theoretical results are confirmed with numerical simulations. This study furthers our understanding of the relationship between agent-based rules, their implementation, and their associated partial differential equations. Since tissue growth is a significant cellular transport mechanism during embryonic growth, it is important to use the correct partial differential equation description when combining with other cellular functions.

Generalized index for spatial data sets as a measure of complete spatial randomness.

Spatial data sets, generated from a wide range of physical systems can be analyzed by counting the number of objects in a set of bins. Previous work has been limited to equal-sized bins, which are inappropriate for some domains (e.g., circular). We consider a nonequal size bin configuration whereby overlapping or nonoverlapping bins cover the domain. A generalized index, defined in terms of a variance between bin counts, is developed to indicate whether or not a spatial data set, generated from exclusion or nonexclusion processes, is at the complete spatial randomness (CSR) state. Limiting values of the index are determined. Using examples, we investigate trends in the generalized index as a function of density and compare the results with those using equal size bins. The smallest bin size must be much larger than the mean size of the objects. We can determine whether a spatial data set is at the CSR state or not by comparing the values of a generalized index for different bin configurations-the values will be approximately the same if the data is at the CSR state, while the values will differ if the data set is not at the CSR state. In general, the generalized index is lower than the limiting value of the index, since objects do not have access to the entire region due to blocking by other objects. These methods are applied to two applications: (i) spatial data sets generated from a cellular automata model of cell aggregation in the enteric nervous system and (ii) a known plant data distribution.

Aggregation patterns from nonlocal interactions: Discrete stochastic and continuum modeling.

Conservation equations governed by a nonlocal interaction potential generate aggregates from an initial uniform distribution of particles. We address the evolution and formation of these aggregating steady states when the interaction potential has both attractive and repulsive singularities. Currently, no existence theory for such potentials is available. We develop and compare two complementary solution methods, a continuous pseudoinverse method and a discrete stochastic lattice approach, and formally show a connection between the two. Interesting aggregation patterns involving multiple peaks for a simple doubly singular attractive-repulsive potential are determined. For a swarming Morse potential, characteristic slow-fast dynamics in the scaled inverse energy is observed in the evolution to steady state in both the continuous and discrete approaches. The discrete approach is found to be remarkably robust to modifications in movement rules, related to the potential function. The comparable evolution dynamics and steady states of the discrete model with the continuum model suggest that the discrete stochastic approach is a promising way of probing aggregation patterns arising from two- and three-dimensional nonlocal interaction conservation equations.

On the role of differential adhesion in gangliogenesis in the enteric nervous system.

A defining characteristic of the normal development of the enteric nervous system (ENS) is the existence of mesoscale patterned entities called ganglia. Ganglia are clusters of neurons with associated enteric neural crest (ENC) cells, which form in the simultaneously growing gut wall. At first the precursor ENC cells proliferate and gradually differentiate to produce the enteric neurons; these neurons form clusters with ENC scattered around and later lying on the periphery of neuronal clusters. By immunolabelling neural cell-cell adhesion molecules, we infer that the adhesive capacity of neurons is greater than that of ENC cells. Using a discrete mathematical model, we test the hypothesis that local rules governing differential adhesion of neuronal agents and ENC agents will produce clusters which emulate ganglia. The clusters are relatively stable, relatively uniform and small in size, of fairly uniform spacing, with a balance between the number of neuronal and ENC agents. These features are attained in both fixed and growing domains, reproducing respectively organotypic in vitro and in vivo observations. Various threshold criteria governing ENC agent proliferation and differentiation and neuronal agent inhibition of differentiation are important for sustaining these characteristics. This investigation suggests possible explanations for observations in normal and abnormal ENS development.

The evolution of developmental timing in natural enemy systems.

Natural parasitoid systems exhibit considerable variation in their life history properties yet little is known about the effects of development time on parasitoid fitness or of the conditions that might select for rapid development at the expense of reduced parasitoid growth. In this study the techniques of adaptive dynamics are applied to a discrete time host-parasitoid model to examine the evolution of parasitoid life history strategies. In particular, we explore the conditions that select for variation in parasitoid traits, such as, the timing of parasitoid attack and emergence from the host. The process of evolutionary branching, leading to dimorphism, can occur when the benefits to reproduction of early parasitoid attack are bought at a cost in terms of mortality of late parasitoid emergence from the host. We also find that trends in parasitoid life history traits depend critically on the nature of the underlying population dynamics. Increases in the strength of host density-dependence acts to select for shorter parasitoid development time and lower searching efficiency when the underlying population dynamics are at equilibrium. This trend is reversed when the underlying population dynamics exhibit fluctuations. Here, fluctuations in host density driven by parasitism become more extreme as the strength of host density-dependence decreases and so the parasitoid selects early emergence to avoid the mortality experienced at outbreak host densities. Our results are consistent with the general principle that parasitoids facing high mortality risk favour short development times over size and high searching efficiency, whereas species facing low mortality risks favour size at the cost of increased development time.