Bone Phenotyping Approaches in Human, Mice and Zebrafish - Expert Overview of the EU Cost Action GEMSTONE ("GEnomics of MusculoSkeletal traits TranslatiOnal NEtwork").

A synoptic overview of scientific methods applied in bone and associated research fields across species has yet to be published. Experts from the EU Cost Action GEMSTONE ("GEnomics of MusculoSkeletal Traits translational Network") Working Group 2 present an overview of the routine techniques as well as clinical and research approaches employed to characterize bone phenotypes in humans and selected animal models (mice and zebrafish) of health and disease. The goal is consolidation of knowledge and a map for future research. This expert paper provides a comprehensive overview of state-of-the-art technologies to investigate bone properties in humans and animals - including their strengths and weaknesses. New research methodologies are outlined and future strategies are discussed to combine phenotypic with rapidly developing -omics data in order to advance musculoskeletal research and move towards "personalised medicine".

Central dogma rates and the trade-off between precision and economy in gene expression.

Steady-state protein abundance is set by four rates: transcription, translation, mRNA decay and protein decay. A given protein abundance can be obtained from infinitely many combinations of these rates. This raises the question of whether the natural rates for each gene result from historical accidents, or are there rules that give certain combinations a selective advantage? We address this question using high-throughput measurements in rapidly growing cells from diverse organisms to find that about half of the rate combinations do not exist: genes that combine high transcription with low translation are strongly depleted. This depletion is due to a trade-off between precision and economy: high transcription decreases stochastic fluctuations but increases transcription costs. Our theory quantitatively explains which rate combinations are missing, and predicts the curvature of the fitness function for each gene. It may guide the design of gene circuits with desired expression levels and noise.

Transcriptomic Landscape of von Economo Neurons in Human Anterior Cingulate Cortex Revealed by Microdissected-Cell RNA Sequencing.

The von Economo neurons (VENs) are specialized large bipolar projection neurons with restricted distribution in the human brain, and they are far more abundant in humans than in non-human primates. However, VEN functions remain elusive due to the difficulty of isolating VENs and dissecting their connections in the brain. Here, we combined laser-capture-microdissection with RNA sequencing to describe the transcriptomic profile of VENs from human anterior cingulate cortex (ACC). Using pyramidal neurons as reference cells, we identified 344 genes with VEN-associated expression differences, including 215 higher and 129 lower expression genes. Functional enrichment and protein-protein interaction network analyses showed that these genes with VEN-associated expression differences are involved in VEN morphogenesis and functions, such as dendrite branching and axon myelination, and many of them are associated with human social-emotional disorders. With the use of in situ hybridization and immunohistochemistry assays, we validated four novel VEN markers (VAT1L, CHST8, LYPD1, and SULF2). Collectively, we generated a full-spectrum expression profile of VENs from human ACC, greatly enlarging the pool of genes with VEN-associated expression differences that can help researchers to understand the role of VENs in normal and disordered human brains.

Effects of Antenatal Maternal Depressive Symptoms and Socio-Economic Status on Neonatal Brain Development are Modulated by Genetic Risk.

This study included 168 and 85 mother-infant dyads from Asian and United States of America cohorts to examine whether a genomic profile risk score for major depressive disorder (GPRSMDD) moderates the association between antenatal maternal depressive symptoms (or socio-economic status, SES) and fetal neurodevelopment, and to identify candidate biological processes underlying such association. Both cohorts showed a significant interaction between antenatal maternal depressive symptoms and infant GPRSMDD on the right amygdala volume. The Asian cohort also showed such interaction on the right hippocampal volume and shape, thickness of the orbitofrontal and ventromedial prefrontal cortex. Likewise, a significant interaction between SES and infant GPRSMDD was on the right amygdala and hippocampal volumes and shapes. After controlling for each other, the interaction effect of antenatal maternal depressive symptoms and GPRSMDD was mainly shown on the right amygdala, while the interaction effect of SES and GPRSMDD was mainly shown on the right hippocampus. Bioinformatic analyses suggested neurotransmitter/neurotrophic signaling, SNAp REceptor complex, and glutamate receptor activity as common biological processes underlying the influence of antenatal maternal depressive symptoms on fetal cortico-limbic development. These findings suggest gene-environment interdependence in the fetal development of brain regions implicated in cognitive-emotional function. Candidate biological mechanisms involve a range of brain region-specific signaling pathways that converge on common processes of synaptic development.

Time-Dependent Gene Network Modelling by Sequential Monte Carlo.

Most existing methods used for gene regulatory network modeling are dedicated to inference of steady state networks, which are prevalent over all time instants. However, gene interactions evolve over time. Information about the gene interactions in different stages of the life cycle of a cell or an organism is of high importance for biology. In the statistical graphical models literature, one can find a number of methods for studying steady-state network structures while the study of time varying networks is rather recent. A sequential Monte Carlo method, namely particle filtering (PF), provides a powerful tool for dynamic time series analysis. In this work, the PF technique is proposed for dynamic network inference and its potentials in time varying gene expression data tracking are demonstrated. The data used for validation are synthetic time series data available from the DREAM4 challenge, generated from known network topologies and obtained from transcriptional regulatory networks of S. cerevisiae. We model the gene interactions over the course of time with multivariate linear regressions where the parameters of the regressive process are changing over time.

Detection meeting control: Unstable steady states in high-dimensional nonlinear dynamical systems.

We articulate an adaptive and reference-free framework based on the principle of random switching to detect and control unstable steady states in high-dimensional nonlinear dynamical systems, without requiring any a priori information about the system or about the target steady state. Starting from an arbitrary initial condition, a proper control signal finds the nearest unstable steady state adaptively and drives the system to it in finite time, regardless of the type of the steady state. We develop a mathematical analysis based on fast-slow manifold separation and Markov chain theory to validate the framework. Numerical demonstration of the control and detection principle using both classic chaotic systems and models of biological and physical significance is provided.

Modelling and analysis of a gene-regulatory feed-forward loop with basal expression of the second regulator.

Efficient adaptation strategies to changing environmental conditions are essential for bacteria to survive and grow. Fundamental restructuring of their metabolism is usually mediated by corresponding gene regulation. Here, often several different environmental stimuli have to be integrated into a reasonable, energy-efficient response. Fast fluctuations and overshooting have to be filtered out. The gene regulatory network for the anaerobic adaptation of the pathogenic bacterium Pseudomonas aeruginosa is organized as a feed-forward loop (FFL), which is a three-gene network motif composed of two transcription factors (Anr for oxygen, NarxL for nitrate) and one target (Nar for nitrate reductase). The upstream transcription factor (Anr) induces the downstream transcription factor (NarXL). Both regulators act together positively by inducing the target (Nar) via a direct and indirect regulation path (coherent type-1 FFL). Since full promoter activity is only achieved when both transcription factors are present the target operon is expressed with a delay. Thus, in response to environmental stimuli (oxygen, nitrate), signals are mediated and processed in a way that short pulses are filtered out. In this study we analyze a special kind of FFL called FFLk by means of a family of ordinary differential equation models. The secondary FFL regulator (NarXL) is expressed constitutively but further induced in the presence of the upstream stimuli. This FFL modification has substantial influence on the response time and cost-benefit ratio mediated by environmental fluctuations. In order to find conditions where this regulatory network motif might be beneficial, we analyzed various models and environments. We describe the observed evolutional advantage of FFLk and its role in environmental adaptation and pathogenicity.

Caenorhabditis elegans metabolic gene regulatory networks govern the cellular economy.

Diet greatly impacts metabolism in health and disease. In response to the presence or absence of specific nutrients, metabolic gene regulatory networks sense the metabolic state of the cell and regulate metabolic flux accordingly, for instance by the transcriptional control of metabolic enzymes. Here, we discuss recent insights regarding metazoan metabolic regulatory networks using the nematode Caenorhabditis elegans as a model, including the modular organization of metabolic gene regulatory networks, the prominent impact of diet on the transcriptome and metabolome, specialized roles of nuclear hormone receptors (NHRs) in responding to dietary conditions, regulation of metabolic genes and metabolic regulators by miRNAs, and feedback between metabolic genes and their regulators.

GINI: from ISH images to gene interaction networks.

Accurate inference of molecular and functional interactions among genes, especially in multicellular organisms such as Drosophila, often requires statistical analysis of correlations not only between the magnitudes of gene expressions, but also between their temporal-spatial patterns. The ISH (in-situ-hybridization)-based gene expression micro-imaging technology offers an effective approach to perform large-scale spatial-temporal profiling of whole-body mRNA abundance. However, analytical tools for discovering gene interactions from such data remain an open challenge due to various reasons, including difficulties in extracting canonical representations of gene activities from images, and in inference of statistically meaningful networks from such representations. In this paper, we present GINI, a machine learning system for inferring gene interaction networks from Drosophila embryonic ISH images. GINI builds on a computer-vision-inspired vector-space representation of the spatial pattern of gene expression in ISH images, enabled by our recently developed [Formula: see text] system; and a new multi-instance-kernel algorithm that learns a sparse Markov network model, in which, every gene (i.e., node) in the network is represented by a vector-valued spatial pattern rather than a scalar-valued gene intensity as in conventional approaches such as a Gaussian graphical model. By capturing the notion of spatial similarity of gene expression, and at the same time properly taking into account the presence of multiple images per gene via multi-instance kernels, GINI is well-positioned to infer statistically sound, and biologically meaningful gene interaction networks from image data. Using both synthetic data and a small manually curated data set, we demonstrate the effectiveness of our approach in network building. Furthermore, we report results on a large publicly available collection of Drosophila embryonic ISH images from the Berkeley Drosophila Genome Project, where GINI makes novel and interesting predictions of gene interactions. Software for GINI is available at http://sailing.cs.cmu.edu/Drosophila_ISH_images/

The conscious mind and its emergent properties; an analysis based on decision theory.

The process of conscious and unconscious decision making is analyzed using decision theory. An essential part of an optimum decision strategy is the assessment of values and costs associated with correct and incorrect decisions. In the case of unconscious decisions this involves an automatic process akin to computation using numerical values. But for conscious decisions the conscious mind must experience the outcome of the decision as pleasure or pain. It is suggested that the rules of behavior are programmed in our genes but modified by experience of the society in which we are reared. Our unconscious then uses the rules to reward or punish our conscious mind for the decisions it makes. This is relevant to concepts of altruism and religion in society. It is consistent with the observation that we prefer beauty to utility. The decision theory equations also explain the paradox that a single index of happiness can be applied in society. The symptoms of mental illness can be due to appropriate or inappropriate action by the unconscious. The former indicates a psychological conflict between conscious and unconscious decision making. Inappropriate action indicates that a pathological process has switched on genetic networks that should be switched off.

Probabilistic functional gene societies.

A cellular system may be viewed as a social network of genes. Genes work together to conduct physiological processes in the cells. Thus if we have a view of the functional association among genes, we may also be able to unravel the association between genotypes and phenotypes; the emergent properties of interactive activities of genes. We could have various points of view for a gene network. Perhaps the most common standpoints are protein-protein interaction networks (PPIN) and transcriptional regulatory networks (TRN). Here I introduce another type of view for the gene network; the probabilistic functional gene network (PFGN). A 'functional view' of association between genes enables us to have a holistic model of the gene society. A 'probabilistic view' makes the model of gene associations derived from noisy high-throughput data more robust. In addition, the dynamics of gene association may be presented in a single static network model by the probabilistic view. By combining the two modeling views, the probabilistic functional gene networks have been constructed for various organisms and proved to be highly useful in generating novel biological hypotheses not only for simple unicellular microbes, but also for highly complex multicellular animals and plants.

Analysis of stochastic strategies in bacterial competence: a master equation approach.

Competence is a transiently differentiated state that certain bacterial cells reach when faced with a stressful environment. Entrance into competence can be attributed to the excitability of the dynamics governing the genetic circuit that regulates this cellular behavior. Like many biological behaviors, entrance into competence is a stochastic event. In this case cellular noise is responsible for driving the cell from a vegetative state into competence and back. In this work we present a novel numerical method for the analysis of stochastic biochemical events and use it to study the excitable dynamics responsible for competence in Bacillus subtilis. Starting with a Finite State Projection (FSP) solution of the chemical master equation (CME), we develop efficient numerical tools for accurately computing competence probability. Additionally, we propose a new approach for the sensitivity analysis of stochastic events and utilize it to elucidate the robustness properties of the competence regulatory genetic circuit. We also propose and implement a numerical method to calculate the expected time it takes a cell to return from competence. Although this study is focused on an example of cell-differentiation in Bacillus subtilis, our approach can be applied to a wide range of stochastic phenomena in biological systems.

A mathematical and computational approach for integrating the major sources of cell population heterogeneity.

Several approaches have been used in the past to model heterogeneity in bacterial cell populations, with each approach focusing on different source(s) of heterogeneity. However, a holistic approach that integrates all the major sources into a comprehensive framework applicable to cell populations is still lacking. In this work we present the mathematical formulation of a cell population master equation (CPME) that describes cell population dynamics and takes into account the major sources of heterogeneity, namely stochasticity in reaction, DNA-duplication, and division, as well as the random partitioning of species contents into the two daughter cells. The formulation also takes into account cell growth and respects the discrete nature of the molecular contents and cell numbers. We further develop a Monte Carlo algorithm for the simulation of the stochastic processes considered here. To benchmark our new framework, we first use it to quantify the effect of each source of heterogeneity on the intrinsic and the extrinsic phenotypic variability for the well-known two-promoter system used experimentally by Elowitz et al. (2002). We finally apply our framework to a more complicated system and demonstrate how the interplay between noisy gene expression and growth inhibition due to protein accumulation at the single cell level can result in complex behavior at the cell population level. The generality of our framework makes it suitable for studying a vast array of artificial and natural genetic networks. Using our Monte Carlo algorithm, cell population distributions can be predicted for the genetic architecture of interest, thereby quantifying the effect of stochasticity in intracellular reactions or the variability in the rate of physiological processes such as growth and division. Such in silico experiments can give insight into the behavior of cell populations and reveal the major sources contributing to cell population heterogeneity.

Stochastic stability of Markovian jumping uncertain stochastic genetic regulatory networks with interval time-varying delays.

This paper investigates the robust stability problem of stochastic genetic regulatory networks with interval time-varying delays and Markovian jumping parameters. The structure variations at discrete time instances during the process of gene regulations known as hybrid genetic regulatory networks based on Markov process is proposed. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which is governed by a Markov process with discrete and finite state space. The new type of Markovian jumping matrices P(i) and Q(i) are introduced in this paper. The parameter uncertainties are assumed to be norm bounded and the discrete delay is assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are unavoidable. Based on the Lyapunov-Krasovskii functional and stochastic stability theory, delay-interval dependent stability criteria are obtained in terms of linear matrix inequalities. Some numerical examples are given to illustrate the effectiveness of our theoretical results.

Stochastic models and numerical algorithms for a class of regulatory gene networks.

Regulatory gene networks contain generic modules, like those involving feedback loops, which are essential for the regulation of many biological functions (Guido et al. in Nature 439:856-860, 2006). We consider a class of self-regulated genes which are the building blocks of many regulatory gene networks, and study the steady-state distribution of the associated Gillespie algorithm by providing efficient numerical algorithms. We also study a regulatory gene network of interest in gene therapy, using mean-field models with time delays. Convergence of the related time-nonhomogeneous Markov chain is established for a class of linear catalytic networks with feedback loops.

Optimal intervention strategies for cyclic therapeutic methods.

External control of a genetic regulatory network is used for the purpose of avoiding undesirable states such as those associated with a disease. Certain types of cancer therapies, such as chemotherapy, are given in cycles with each treatment being followed by a recovery period. During the recovery period, the side effects tend to gradually subside. In this paper, it is shown how an optimal cyclic intervention strategy can be devised for any Markovian genetic regulatory network. The effectiveness of optimal cyclic therapies is demonstrated through numerical studies for random networks. Furthermore, an optimal cyclic policy is derived to control the behavior of a regulatory model of the mammalian cell-cycle network.

Intervention in gene regulatory networks via a stationary mean-first-passage-time control policy.

A prime objective of modeling genetic regulatory networks is the identification of potential targets for therapeutic intervention. To date, optimal stochastic intervention has been studied in the context of probabilistic Boolean networks, with the control policy based on the transition probability matrix of the associated Markov chain and dynamic programming used to find optimal control policies. Dynamical programming algorithms are problematic owing to their high computational complexity. Two additional computationally burdensome issues that arise are the potential for controlling the network and identifying the best gene for intervention. This paper proposes an algorithm based on mean first-passage time that assigns a stationary control policy for each gene candidate. It serves as an approximation to an optimal control policy and, owing to its reduced computational complexity, can be used to predict the best control gene. Once the best control gene is identified, one can derive an optimal policy or simply utilize the approximate policy for this gene when the network size precludes a direct application of dynamic programming algorithms. A salient point is that the proposed algorithm can be model-free. It can be directly designed from time-course data without having to infer the transition probability matrix of the network.

Regulatory control and the costs and benefits of biochemical noise.

Experiments in recent years have vividly demonstrated that gene expression can be highly stochastic. How protein concentration fluctuations affect the growth rate of a population of cells is, however, a wide-open question. We present a mathematical model that makes it possible to quantify the effect of protein concentration fluctuations on the growth rate of a population of genetically identical cells. The model predicts that the population's growth rate depends on how the growth rate of a single cell varies with protein concentration, the variance of the protein concentration fluctuations, and the correlation time of these fluctuations. The model also predicts that when the average concentration of a protein is close to the value that maximizes the growth rate, fluctuations in its concentration always reduce the growth rate. However, when the average protein concentration deviates sufficiently from the optimal level, fluctuations can enhance the growth rate of the population, even when the growth rate of a cell depends linearly on the protein concentration. The model also shows that the ensemble or population average of a quantity, such as the average protein expression level or its variance, is in general not equal to its time average as obtained from tracing a single cell and its descendants. We apply our model to perform a cost-benefit analysis of gene regulatory control. Our analysis predicts that the optimal expression level of a gene regulatory protein is determined by the trade-off between the cost of synthesizing the regulatory protein and the benefit of minimizing the fluctuations in the expression of its target gene. We discuss possible experiments that could test our predictions.