Developmental mechanisms and experimental models to understand forebrain malformative diseases

The development of the central nervous system can be divided into a number of phases, each of which can be subject of genetic or epigenetic alterations that may originate particular developmental disorders. In recent years, much progress has been made in elucidating the molecular and cellular mechanisms by which the vertebrate forebrain develops. Therefore, our understanding of major developmental brain disorders such as cortical malformations and neuronal migration disorders has significantly increased. In this review, we will describe the major stages in forebrain morphogenesis and regionalization, with special emphasis on developmental molecular mechanisms derailing telencephalic development with subsequent damage to cortical function. Because animal models, mainly mouse, have been fundamental for this progress, we will also describe some characteristic mouse models that have been capital to explore these molecular mechanisms of malformative diseases of the human brain. Although most of the genes involved in the regulation of basic developmental processes are conserved among vertebrates, the extrapolation of mouse data to corresponding gene expression and function in humans needs careful individual analysis in each functional system.     

Vertebrate animal models of glioma: Understanding the mechanisms and developing new therapies

Glioblastoma Multiforme (GBM) is recognized as one of the most deadly cancers characterized by cellular atypia, severe necrosis, and high rate of angiogenesis. In this review, we discuss a diversified group of GBM xenograft models and compare them with the genetically engineered mouse (GEM) model systems. Next, we describe common genetic defects observed in GBM and numerous GEM models that recapitulate these abnormalities. Finally, we focus on the clinical value of other vertebrate animal models such as the canine model by examining their contributions to GBM research.     

On integrating experimental and theoretical models to determine physical mechanisms of morphogenesis

Researchers in developmental biology are increasingly recognizing the value of theoretical models in studies of morphogenesis. However, creating and testing realistic quantitative models for morphogenetic processes can be an extremely challenging task. The focus of this paper is on models for the mechanics of morphogenesis. Models for these problems often must include large changes in geometry, leading to highly nonlinear problems with the possibility of multiple solutions that must be sorted out using experimental data. Here, we illustrate our approach to these problems using the specific example of head fold formation in the early chick embryo. The interplay between experimental and theoretical results is emphasized throughout, as the model is gradually refined. Some of the limitations inherent in theoretical/computational modeling of biological systems are also discussed.     

From microarrays to mechanisms of brain development and function

Microarray technology provides a powerful approach to understand complex biological systems. The most common application of microarray technology is to document gene expression profiles of all genes within a genome in response to specific conditions such as disease, drug application, or genotype. One result of this technology is the ability to ascribe activities to genes with unknown functions - such rationale is the basis behind ‘functional genomics’. This approach is particularly well-suited to studies of the brain because roughly one third to one half of all genes in vertebrate genomes are expressed in the brain. However, less than half of such genes have any defined function. While a large number of studies have applied microarray technology to the brain, few studies have followed up the expression profiling approach with functional characterization of the genes identified. In this review, I highlight recent research that reflects the initial promise of functional genomics in the brain. I focus on neural differentiation with particular emphasis on synapse development.     

Exposing the confounding in experimental designs to understand and evaluate them,and formulating linear mixed models for analyzing the data from a designed experiment

Comparative experiments involve the allocation of treatments to units, ideally by randomization. This necessarily confounds treatment information with unit information, which we distinguish from the other forms of information blending, in particular aliasing and marginality. We outline a factor-allocation paradigm for describing experimental designs with the aim of (i) exhibiting the confounding in a design, using analysis-of-variance-like tables, so as to understand and evaluate the design and (ii) formulating a linear mixed model based on the factor allocation that the design involves. The approach exhibits the dispersal of treatments information between units sources, allows designers a choice in the strategy that they adopt for including block-treatment interactions, clarifies differences between experiments, accommodates systematic allocation of factors, and provides a consolidated analysis of nonorthogonal designs. It provides insights into the process of designing experiments and issues that commonly arise with designs. The paradigm has pedagogical advantages and is implemented using the R package dae .     

Promotion time models with time-changing exposure and heterogeneity: application to infectious diseases

Promotion time models have been recently adapted to the context of infectious diseases to take into account discrete and multiple exposures. However, Poisson distribution of the number of pathogens transmitted at each exposure was a very strong assumption and did not allow for inter-individual heterogeneity. Bernoulli, the negative binomial, and the compound Poisson distributions were proposed as alternatives to Poisson distribution for the promotion time model with time-changing exposure. All were derived within the frailty model framework. All these distributions have a point mass at zero to take into account non-infected people. Bernoulli distribution, the two-component cure rate model, was extended to multiple exposures. Contrary to the negative binomial and the compound Poisson distributions, Bernoulli distribution did not enable to connect the number of pathogens transmitted to the delay between transmission and infection detection. Moreover, the two former distributions enable to account for inter-individual heterogeneity. The delay to surgical site infection was an example of single exposure. The probability of infection was very low; thus, estimation of the effect of selected risk factors on that probability obtained with Bernoulli and Poisson distributions were very close. The delay to nosocomial urinary tract infection was a multiple exposure example. The probabilities of pathogen transmission during catheter placement and catheter presence were estimated. Inter-individual heterogeneity was very high, and the fit was better with the compound Poisson and the negative binomial distributions. The proposed models proved to be also mechanistic. The negative binomial and the compound Poisson distributions were useful alternatives to account for inter-individual heterogeneity.     

Binding of cationic dyes to DNA: distinguishing intercalation and groove binding mechanisms using simple experimental and numerical models

Abstract

Simple methods for predicting intercalation or groove binding of dyes and analogous compounds with double stranded DNA are described. The methods are based on a quantitative assessment of the aspect (width to length) ratio of the dyes. The procedures were validated using a set of 38 cationic dyes of varied chemical structures binding to well oriented DNA fibers and assessing binding orientation by linear dichroism and polarized fluorescence. We demonstrated that low aspect ratio dyes bound by intercalation, whereas more rod-like dyes were groove binders. Some problems that result and possible applications are discussed briefly.     

CUX1 transcription factors: from biochemical activities and cell-based assays to mouse models and human diseases

Oxidored nitro domain containing protein 1 (NOR1) is usually restrictively expressed in the brain and testis. Detection of altered NOR1 expression could help us to identify its functions in cell growth, differentiation, metabolism, or even carcinogenesis. In this study, NOR1 homologues were identified in multiple species through GenBank search. NOR1 is a novel protein conserved in multiple species. Mouse NOR1 shared high homology with human NOR1. Furthermore, NOR1 expression was analyzed in mouse tissues by using RT-PCR, Western blot, and immunohistochemistry. The data showed that NOR1 is broadly expressed in neurons of mouse brain and the expression profile changes during postnatal development of the mouse brain. Moreover, in non-nervous tissues, strong immunostaining for NOR1 protein was observed in the testis, epididymis and trachea. In addition, expression of human NOR1 protein in different normal and cancerous human tissues was analyzed via search of the human RNA and protein databases; the data showed that although most malignant cells weakly stained or were negative for NOR1 expression, the liver cancer cells displayed moderate to strong expression of NOR1. These data suggested that NOR1 might serve as a cancer/testis/brain antigen in cells, and that altered NOR1 expression in liver cancer may help us to elucidate the functions of NOR1 protein in liver carcinogenesis.     

Nocturnal light pollution and underexposure to daytime sunlight: Complementary mechanisms of circadian disruption and related diseases

Routine exposure to artificial light at night (ALAN) in work, home, and community settings is linked with increased risk of breast and prostate cancer (BC, PC) in normally sighted women and men, the hypothesized biological rhythm mechanisms being frequent nocturnal melatonin synthesis suppression, circadian time structure (CTS) desynchronization, and sleep/wake cycle disruption with sleep deprivation. ALAN-induced perturbation of the CTS melatonin synchronizer signal is communicated maternally at the very onset of life and after birth via breast or artificial formula feedings. Nighttime use of personal computers, mobile phones, electronic tablets, televisions, and the like – now epidemic in adolescents and adults and highly prevalent in pre-school and school-aged children – is a new source of ALAN. However, ALAN exposure occurs concomitantly with almost complete absence of daytime sunlight, whose blue-violet (446–484?nm λ) spectrum synchronizes the CTS and whose UV-B (290–315?nm λ) spectrum stimulates vitamin D synthesis. Under natural conditions and clear skies, day/night and annual cycles of UV-B irradiation drive corresponding periodicities in vitamin D synthesis and numerous bioprocesses regulated by active metabolites augment and strengthen the biological time structure. Vitamin D insufficiency and deficiency are widespread in children and adults in developed and developing countries as a consequence of inadequate sunlight exposure. Past epidemiologic studies have focused either on exposure to too little daytime UV-B or too much ALAN, respectively, on vitamin D deficiency/insufficiency or melatonin suppression in relation to risk of cancer and other, e.g., psychiatric, hypertensive, cardiac, and vascular, so-called, diseases of civilization. The observed elevated incidence of medical conditions the two are alleged to influence through many complementary bioprocesses of cells, tissues, and organs led us to examine effects of the totality of the artificial light environment in which humans reside today. Never have chronobiologic or epidemiologic investigations comprehensively researched the potentially deleterious consequences of the combination of suppressed vitamin D plus melatonin synthesis due to life in today’s man-made artificial light environment, which in our opinion is long overdue.     

Construction of a risk model through the fusion of experimental data and finite element modeling: Application to car crash-induced TBI

This article introduces a new approach for the construction of a risk model for the prediction of Traumatic Brain Injury (TBI) as a result of a car crash. The probability of TBI is assessed through the fusion of an experiment-based logistic regression risk model and a finite element (FE) simulation-based risk model. The proposed approach uses a multilevel framework which includes FE simulations of vehicle crashes with dummy and FE simulations of the human brain. The loading conditions derived from the crash simulations are transferred to the brain model thus allowing the calculation of injury metrics such as the Cumulative Strain Damage Measure (CSDM). The framework is used to propagate uncertainties and obtain probabilities of TBI based on the CSDM injury metric. The risk model from FE simulations is constructed from a support vector machine classifier, adaptive sampling, and Monte-Carlo simulations. An approach to compute the total probability of TBI, which combines the FE-based risk assessment as well as the risk prediction from the experiment-based logistic regression model is proposed. In contrast to previous published work, the proposed methodology includes the uncertainty of explicit parameters such as impact conditions (e.g., velocity, impact angle), and material properties of the brain model. This risk model can provide, for instance, the probability of TBI for a given assumed crash impact velocity.     

A combination of experimental measurement,constitutive damage model,and diffusion tensor imaging to characterize the mechanical properties of the human brain

Understanding the mechanical properties of the human brain is deemed important as it may subject to various types of complex loadings during the Traumatic Brain Injury (TBI). Although many studies so far have been conducted to quantify the mechanical properties of the brain, there is a paucity of knowledge on the mechanical properties of the human brain tissue and the damage of its axon fibers under the various types of complex loadings during the Traumatic Brain Injury (TBI). Although many studies so far have been conducted to quantify the mechanical properties of the brain, there is a paucity of knowledge on the mechanical properties of the human brain tissue and the damage of its axon fibers under the frontal lobe of the human brain. The constrained nonlinear minimization method was employed to identify the brain coefficients according to the axial and transversal compressive data. The pseudo-elastic damage model data was also well compared with that of the experimental data and it not only up to the primary loading but also the discontinuous softening could well address the mechanical behavior of the brain tissue.     

Evaluation of different models to describe egg and pupal development of Xyleborus fornicatus Eichh, （Coleoptera： Scolytidae）, the shot-hole borer of tea in Sri Lanka

Development data of eggs and pupae ofXyleborusfornicatus Eichh. （Coleoptera： Scolytidae）, the shot-hole borer of tea in Sri Lanka, at constant temperatures were used to evaluate a linear and seven nonlinear models for insect development. Model evaluation was based on fit to data （residual sum of squares and coefficient of determination or coefficient of nonlinear regression）, number of measurable parameters, the biological value of the fitted coefficients and accuracy in the estimation of thresholds. Of the nonlinear models, the Lactin model fitted experimental data well and along with the linear model, can be used to describe the temperature-dependent development of this species.