Dissecting complex phenotypes using the genomics of twins

Genetics in the post-genomic period is shifting from structural to functional genetics or genomics. Meanwhile, the use of twins is largely expanding from traditional heritability estimation for disease phenotypes to the study of both diseases and various molecular phenotypes, such as the regulatory phenotypes in functional genomics concerning gene expression and regulation, by engaging both classical twin design and marker-based gene mapping techniques in genetic epidemiology. New research designs have been proposed for making novel uses of twins in studying the molecular basis in the epigenetics of human diseases. Besides, twins not only serve as ideal samples for disease gene mapping using conventional genetic markers but also represent an excellent model for associating DNA copy number variations, a structural genetic marker, with human diseases. It is believed that, with the rapid development in biotechniques and new advances in bioinformatics, the unique samples of twins will make new contributions to our understanding of the nature and nurture in complex disease development and in human health. This paper aims at summarizing the new uses of twins in current genetic studies and suggesting novel proposes together with useful design and analytical strategies.     

Dissecting cascade computational components in spiking neural networks

Finding out the physical structure of neuronal circuits that governs neuronal responses is an important goal for brain research. With fast advances for large-scale recording techniques, identification of a neuronal circuit with multiple neurons and stages or layers becomes possible and highly demanding. Although methods for mapping the connection structure of circuits have been greatly developed in recent years, they are mostly limited to simple scenarios of a few neurons in a pairwise fashion; and dissecting dynamical circuits, particularly mapping out a complete functional circuit that converges to a single neuron, is still a challenging question. Here, we show that a recent method, termed spike-triggered non-negative matrix factorization (STNMF), can address these issues. By simulating different scenarios of spiking neural networks with various connections between neurons and stages, we demonstrate that STNMF is a persuasive method to dissect functional connections within a circuit. Using spiking activities recorded at neurons of the output layer, STNMF can obtain a complete circuit consisting of all cascade computational components of presynaptic neurons, as well as their spiking activities. For simulated simple and complex cells of the primary visual cortex, STNMF allows us to dissect the pathway of visual computation. Taken together, these results suggest that STNMF could provide a useful approach for investigating neuronal systems leveraging recorded functional neuronal activity.     

Signaling networks and cell motility: a computational approach using a phase field description

The processes of protrusion and retraction during cell movement are driven by the turnover and reorganization of the actin cytoskeleton. Within a reaction–diffusion model which combines processes along the cell membrane with processes within the cytoplasm a Turing type instability is used to form the necessary polarity to distinguish between cell front and rear and to initiate the formation of different organizational arrays within the cytoplasm leading to protrusion and retraction. A simplified biochemical network model for the activation of GTPase which accounts for the different dimensionality of the cell membrane and the cytoplasm is used for this purpose and combined with a classical Helfrich type model to account for bending and stiffness effects of the cell membrane. In addition streaming within the cytoplasm and the extracellular matrix is taken into account. Combining these phenomena allows to simulate the dynamics of cells and to reproduce the primary phenomenology of cell motility. The coupled model is formulated within a phase field approach and solved using adaptive finite elements.     

Genome-wide identification of peroxisome proliferator response elements using integrated computational genomics

Peroxisome proliferator-activated receptor (PPAR) agonists are currently used therapeutically in humans, even though many of their direct gene targets are unknown. Because PPARs can directly regulate gene expression through peroxisome proliferator response elements (PPREs), we pursued the computational prediction of PPREs on a genome-wide scale. Contrary to current hypotheses, PPREs are not isotype-specific, nor do flanking nucleotides confer additional information. However, a position weight matrix-based search for PPREs within upstream conserved elements yielded sufficient selectivity for a genome-wide search. Additionally, a novel motif occurring with greater prevalence than PPREs was revealed. Microarray and gene ontology analyses further validated our search technique and provided new functional clusters of genes that were not previously known to be directly regulated by PPARs (e.g., chromatin remodeling, DNA damage response, Wnt, and mitogen-activated protein kinase signaling). This first genome-wide library of high-confidence predicted PPAR target genes will be a valuable resource to PPAR biologists.     

Dissecting plant genomes with the PLAZA comparative genomics platform

With the arrival of low-cost, next-generation sequencing, a multitude of new plant genomes are being publicly released, providing unseen opportunities and challenges for comparative genomics studies. Here, we present PLAZA 2.5, a user-friendly online research environment to explore genomic information from different plants. This new release features updates to previous genome annotations and a substantial number of newly available plant genomes as well as various new interactive tools and visualizations. Currently, PLAZA hosts 25 organisms covering a broad taxonomic range, including 13 eudicots, five monocots, one lycopod, one moss, and five algae. The available data consist of structural and functional gene annotations, homologous gene families, multiple sequence alignments, phylogenetic trees, and colinear regions within and between species. A new Integrative Orthology Viewer, combining information from different orthology prediction methodologies, was developed to efficiently investigate complex orthology relationships. Cross-species expression analysis revealed that the integration of complementary data types extended the scope of complex orthology relationships, especially between more distantly related species. Finally, based on phylogenetic profiling, we propose a set of core gene families within the green plant lineage that will be instrumental to assess the gene space of draft or newly sequenced plant genomes during the assembly or annotation phase.     

Dissecting the links between reward and loss,decision-making,and self-reported affect using a computational approach

Links between affective states and risk-taking are often characterised using summary statistics from serial decision-making tasks. However, our understanding of these links, and the utility of decision-making as a marker of affect, needs to accommodate the fact that ongoing (e.g., within-task) experience of rewarding and punishing decision outcomes may alter future decisions and affective states. To date, the interplay between affect, ongoing reward and punisher experience, and decision-making has received little detailed investigation. Here, we examined the relationships between reward and loss experience, affect, and decision-making in humans using a novel judgement bias task analysed with a novel computational model. We demonstrated the influence of within-task favourability on decision-making, with more risk-averse/‘pessimistic’ decisions following more positive previous outcomes and a greater current average earning rate. Additionally, individuals reporting more negative affect tended to exhibit greater risk-seeking decision-making, and, based on our model, estimated time more poorly. We also found that individuals reported more positive affective valence during periods of the task when prediction errors and offered decision outcomes were more positive. Our results thus provide new evidence that (short-term) within-task rewarding and punishing experiences determine both future decision-making and subjectively experienced affective states.     

Dissecting the DNA damage response using functional genomics approaches in S. cerevisiae

The faithful transmission of genetic information from a mother to daughter cells can only occur if the integrity of the genome is preserved. DNA transactions within cells are tightly regulated to prevent DNA damage. When events that challenge genome integrity do occur, a complex web of DNA damage response pathways comes into play. Studies in model organisms have contributed significantly to the understanding of these pathways. In the last decade, the development of functional genomics techniques in S.cerevisiae has ushered in systematic approaches for the study of complex cellular processes. These methods have enabled the systematic interrogation of the DNA damage response.     

Validation of computational methods in genomics

High-throughput technologies for genomics provide tens of thousands of genetic measurements, for instance, gene-expression measurements on microarrays, and the availability of these measurements has motivated the use of machine learning (inference) methods for classification, clustering, and gene networks. Generally, a design method will yield a model that satisfies some model constraints and fits the data in some manner. On the other hand, a scientific theory consists of two parts: (1) a mathematical model to characterize relations between variables, and (2) a set of relations between model variables and observables that are used to validate the model via predictive experiments. Although machine learning algorithms are constructed to hopefully produce valid scientific models, they do not ipso facto do so. In some cases, such as classifier estimation, there is a well-developed error theory that relates to model validity according to various statistical theorems, but in others such as clustering, there is a lack of understanding of the relationship between the learning algorithms and validation. The issue of validation is especially problematic in situations where the sample size is small in comparison with the dimensionality (number of variables), which is commonplace in genomics, because the convergence theory of learning algorithms is typically asymptotic and the algorithms often perform in counter-intuitive ways when used with samples that are small in relation to the number of variables. For translational genomics, validation is perhaps the most critical issue, because it is imperative that we understand the performance of a diagnostic or therapeutic procedure to be used in the clinic, and this performance relates directly to the validity of the model behind the procedure. This paper treats the validation issue as it appears in two classes of inference algorithms relating to genomics - classification and clustering. It formulates the problem and reviews salient results.     

Dissecting cell adhesion architecture using advanced imaging techniques

Cell adhesion to extracellular matrix proteins or to other cells is essential for the control of embryonic development, tissue integrity, immune function and wound healing. Adhesions are tightly spatially regulated structures containing over one hundred different proteins that coordinate both dynamics and signaling events at these sites. Extensive biochemical and morphological analysis of adhesion types over the past three decades has greatly improved understanding of individual protein contributions to adhesion signaling and, in some cases, dynamics. However, it is becoming increasingly clear that these diverse macromolecular complexes contain a variety of protein sub-networks, as well as distinct sub-domains that likely play important roles in regulating adhesion behavior. Until recently, resolving these structures, which are often less than a micron in size, was hampered by the limitations of conventional light microscopy. However, recent advances in optical techniques and imaging methods have revealed exciting insight into the intricate control of adhesion structure and assembly. Here we provide an overview of the recent data arising from such studies of cell:matrix and cell:cell contact and an overview of the imaging strategies that have been applied to study the intricacies and hierarchy of proteins within adhesions.Key words: adhesion, migration, microscopy, dynamics, cytoskeleton, photobleaching, super-resolution imaging, fluorescence