School of Life Sciences

Dendrerpeton acadianum from the Westphalian A (Upper Carboniferous) of Joggins, Nova Scotia, is a phylogenetically and chronologically early temnospondyl. Its external cranial anatomy has been used previously to suggest the presence of a tympanic membrane, and thus of an ear adapted to the perception of airborne sound. However, supporting evidence provided by stapedial and braincase morphology has so far been lacking. The braincase and middle ear region have remained almost wholly unknown. CT scanning and 3-D computer reconstruction of BMNH R.436 have been used to shed light on these important areas. Both stapes prove to be present in the specimen; the right stapes is distorted, but the left stapes lies inside the cranial cavity and is perfectly preserved. The latter resembles the stapes of the relatively few other temnospondyls in which the bone has been described and is most similar to that of Doleserpeton . The morphology and orientation of the stapes provide strong evidence for the presence of an ear adapted to the perception of airborne sound, with similarities to the extant anuran condition. The reconstructed braincase shows a high degree of similarity to that of other adequately known temnospondyls. This gives supporting evidence that D. acadianum is correctly placed in the temnospondyl phylogeny and thus demonstrates one of the earliest hearing systems adapted to the perception of airborne sound that can be homologized with the extant anuran condition.

Disentangling networks of regulation of gene expression is a major challenge in the field of computational biology. Harvesting the information contained in microarray data sets is a promising approach towards this challenge. We propose an algorithm for the optimal estimation of Bayesian networks from microarray data, which reduces the CPU time and memory consumption of previous algorithms. We prove that the space complexity can be reduced from O(n 2 · 2 n ) to O(2 n ), and that the expected calculation time can be reduced from O(n 2 · 2 n ) to O(n · 2 n ), where n is the number of genes. We make intrinsic use of a limitation of the maximal number of regulators of each gene, which has biological as well as statistical justifications. The improvements are significant for some applications in research.

Background: S/MARs are regions of the DNA that are attached to the nuclear matrix. These regions are known to affect substantially the expression of genes. The computer prediction of S/ MARs is a highly significant task which could contribute to our understanding of chromatin organisation in eukaryotic cells, the number and distribution of boundary elements, and the understanding of gene regulation in eukaryotic cells. However, while a number of S/MAR predictors have been proposed, their accuracy has so far not come under scrutiny.

The extraction of fluorescence time course data is a major bottleneck in high-throughput live-cell microscopy. Here we present an extendible framework based on the open-source image analysis software ImageJ, which aims in particular at analyzing the expression of fluorescent reporters through cell divisions. The ability to track individual cell lineages is essential for the analysis of gene regulatory factors involved in the control of cell fate and identity decisions. In our approach, cell nuclei are identified using Hoechst, and a characteristic drop in Hoechst fluorescence helps to detect dividing cells. We first compare the efficiency and accuracy of different segmentation methods and then present a statistical scoring algorithm for cell tracking, which draws on the combination of various features, such as nuclear intensity, area or shape, and importantly, dynamic changes thereof. Principal component analysis is used to determine the most significant features, and a global parameter search is performed to determine the weighting of individual features. Our algorithm has been optimized to cope with large cell movements, and we were able to semi-automatically extract cell trajectories across three cell generations. Based on the MTrackJ plugin for ImageJ, we have developed tools to efficiently validate tracks and manually correct them by connecting broken trajectories and reassigning falsely connected cell positions. A gold standard consisting of two time-series with 15,000 validated positions will be released as a valuable resource for benchmarking. We demonstrate how our method can be applied to analyze fluorescence distributions generated from mouse stem cells transfected with reporter constructs containing transcriptional control elements of the Msx1 gene, a regulator of pluripotency, in mother and daughter cells. Furthermore, we show by tracking zebrafish PAC2 cells expressing FUCCI cell cycle markers, our framework can be easily adapted to different cell types and fluorescent markers.

Background: DNA sequences called CRMs determine the precise patterns of gene expression. Results: We identify and characterize the function of six novel hes1 CRMs. Conclusion: HES1 expression is controlled by multiple distal CRMs in addition to the known promoter. Significance: A powerful combination of computational and experimental methodologies enhances our knowledge of hes1 transcriptional control.

Motivation: cis-regulatory DNA sequence elements, such as enhancers and silencers, function to control the spatial and temporal expression of their target genes. Although the overall levels of gene expression in large cell populations seem to be precisely controlled, transcription of individual genes in single cells is extremely variable in real time. It is, therefore, important to understand how these cis-regulatory elements function to dynamically control transcription at single-cell resolution. Recently, statistical methods have been proposed to back calculate the rates involved in mRNA transcription using parameter estimation of a mathematical model of transcription and translation. However, a major complication in these approaches is that some of the parameters, particularly those corresponding to the gene copy number and transcription rate, cannot be distinguished; therefore, these methods cannot be used when the copy number is unknown. Results: Here, we develop a hierarchical Bayesian model to estimate biokinetic parameters from live cell enhancer-promoter reporter measurements performed on a population of single cells. This allows us to investigate transcriptional dynamics when the copy number is variable across the population. We validate our method using synthetic data and then apply it to quantify the function of two known developmental enhancers in real time and in single cells. Availability: Supporting information is submitted with the article.

Sema3F binding analyses indicate that Npn-2-expressing primary olfactory neurons innervate the ventral-lateral npn-2 Is Expressed in the Vomeronasal Organ side in the caudal region of the MOB, complementary The molecular cues involved in guiding vomeronasal projections to their target, the AOB, are unknown. Previ-to sema3F and sema3C expression

Some recent comparative studies have revealed that regulatory regions can retain function over large evolutionary distances, even though the DNA sequences are divergent and difficult to align. It is also known that such enhancers can drive very similar expression patterns. This poses a challenge for the in silico detection of biologically related sequences, as they can only be discovered using alignment-free methods. Results: Here, we present a new computational framework called Regulatory Region Scoring (RRS) model for the detection of functional conservation of regulatory sequences using predicted occupancy levels of transcription factors of interest. We demonstrate that our model can detect the functional and/or evolutionary links between some non-alignable enhancers with a strong statistical significance. We also identify groups of enhancers that are likely to be similarly regulated. Our model is motivated by previous work on prediction of expression patterns and it can capture similarity by strong binding sites, weak binding sites and even the statistically significant absence of sites. Our results support the hypothesis that weak binding sites contribute to the functional similarity of sequences.