A temporal switch model for estimating transcriptional activity in gene expression

Motivation: The analysis and mechanistic modelling of time series gene expression data provided by techniques such as microarrays, NanoString, reverse transcription–polymerase chain reaction and advanced sequencing are invaluable for developing an understanding of the variation in key biological processes. We address this by proposing the estimation of a flexible dynamic model, which decouples temporal synthesis and degradation of mRNA and, hence, allows for transcriptional activity to switch between different states. Results: The model is flexible enough to capture a variety of observed transcriptional dynamics, including oscillatory behaviour, in a way that is compatible with the demands imposed by the quality, time-resolution and quantity of the data. We show that the timing and number of switch events in transcriptional activity can be estimated alongside individual gene mRNA stability with the help of a Bayesian reversible jump Markov chain Monte Carlo algorithm. To demonstrate the methodology, we focus on modelling the wild-type behaviour of a selection of 200 circadian genes of the model plant Arabidopsis thaliana. The results support the idea that using a mechanistic model to identify transcriptional switch points is likely to strongly contribute to efforts in elucidating and understanding key biological processes, such as transcription and degradation

The genetic architecture underlying the evolution of a rare piscivorous life history form in brown trout after secondary contact and strong introgression

Identifying the genetic basis underlying phenotypic divergence and reproductive isolation is a longstanding problem in evolutionary biology. Genetic signals of adaptation and reproductive isolation are often confounded by a wide range of factors, such as variation in demographic history or genomic features. Brown trout ( ) in the Loch Maree catchment, Scotland, exhibit reproductively isolated divergent life history morphs, including a rare piscivorous (ferox) life history form displaying larger body size, greater longevity and delayed maturation compared to sympatric benthivorous brown trout. Using a dataset of 16,066 SNPs, we analyzed the evolutionary history and genetic architecture underlying this divergence. We found that ferox trout and benthivorous brown trout most likely evolved after recent secondary contact of two distinct glacial lineages, and identified 33 genomic outlier windows across the genome, of which several have most likely formed through selection. We further identified twelve candidate genes and biological pathways related to growth, development and immune response potentially underpinning the observed phenotypic differences. The identification of clear genomic signals divergent between life history phenotypes and potentially linked to reproductive isolation, through size assortative mating, as well as the identification of the underlying demographic history, highlights the power of genomic studies of young species pairs for understanding the factors shaping genetic differentiation

Foxp1 and lhx1 coordinate motor neuron migration with axon trajectory choice by gating Reelin signalling.

Topographic neuronal maps arise as a consequence of axon trajectory choice correlated with the localisation of neuronal soma, but the identity of the pathways coordinating these processes is unknown. We addressed this question in the context of the myotopic map formed by limb muscles innervated by spinal lateral motor column (LMC) motor axons where the Eph receptor signals specifying growth cone trajectory are restricted by Foxp1 and Lhx1 transcription factors. We show that the localisation of LMC neuron cell bodies can be dissociated from axon trajectory choice by either the loss or gain of function of the Reelin signalling pathway. The response of LMC motor neurons to Reelin is gated by Foxp1- and Lhx1-mediated regulation of expression of the critical Reelin signalling intermediate Dab1. Together, these observations point to identical transcription factors that control motor axon guidance and soma migration and reveal the molecular hierarchy of myotopic organisation

A subset of methylated CpG sites differentiate psoriatic from normal skin.

Psoriasis is a chronic inflammatory immune-mediated disorder affecting the skin and other organs including joints. Over 1,300 transcripts are altered in psoriatic involved skin compared with normal skin. However, to our knowledge, global epigenetic profiling of psoriatic skin is previously unreported. Here, we describe a genome-wide study of altered CpG methylation in psoriatic skin. We determined the methylation levels at 27,578 CpG sites in skin samples from individuals with psoriasis (12 involved, 8 uninvolved) and 10 unaffected individuals. CpG methylation of involved skin differed from normal skin at 1,108 sites. Twelve mapped to the epidermal differentiation complex, upstream or within genes that are highly upregulated in psoriasis. Hierarchical clustering of 50 of the top differentially methylated (DM) sites separated psoriatic from normal skin samples with uninvolved skin exhibiting intermediate methylation. CpG sites where methylation was correlated with gene expression are reported. Sites with inverse correlations between methylation and nearby gene expression include those of KYNU, OAS2, S100A12, and SERPINB3, whose strong transcriptional upregulation is an important discriminator of psoriasis. Pyrosequencing of bisulfite-treated DNA from skin biopsies at three DM loci confirmed earlier findings and revealed reversion of methylation levels toward the non-psoriatic state after 1 month of anti-TNF-α therapy

Microarray time-series data clustering via gene expression profile alignment

Clustering gene expression data given In terms of time-series is a challenging problem that imposes its own particular constraints, namely, exchanging two or more time points is not possible as it would deliver quite different results and would lead to erroneous biological conclusions. In this thesis, clustering methods introducing the concept of multiple alignment of natural cubic spline representations of gene expression profiles are presented. The multiple alignment is achieved by minimizing the sum of integrated squared errors over a time-interval, defined on a set of profiles. The proposed approach with flat clustering algorithms like k-means and EM are shown to cluster microarray time-series profiles efficiently and reduce the computational time significantly. The effectiveness of the approaches is experimented on six data sets. Experiments have also been carried out in order to determine the number of clusters and to determine the accuracies of the proposed approaches

Deep mixture of linear mixed models for complex longitudinal data

Mixtures of linear mixed models are widely used for modelling longitudinal data for which observation times differ between subjects. In typical applications, temporal trends are described using a basis expansion, with basis coefficients treated as random effects varying by subject. Additional random effects can describe variation between mixture components, or other known sources of variation in complex experimental designs. A key advantage of these models is that they provide a natural mechanism for clustering, which can be helpful for interpretation in many applications. Current versions of mixtures of linear mixed models are not specifically designed for the case where there are many observations per subject and a complex temporal trend, which requires a large number of basis functions to capture. In this case, the subject-specific basis coefficients are a high-dimensional random effects vector, for which the covariance matrix is hard to specify and estimate, especially if it varies between mixture components. To address this issue, we consider the use of recently-developed deep mixture of factor analyzers models as the prior for the random effects. The resulting deep mixture of linear mixed models is well-suited to high-dimensional settings, and we describe an efficient variational inference approach to posterior computation. The efficacy of the method is demonstrated on both real and simulated data