Long-Lasting Cerebral Vasospasm,Microthrombosis, Apoptosis and Paravascular Alterations Associated with Neurological Deficits in a Mouse Model of Subarachnoid Hemorrhage

Subarachnoid hemorrhage (SAH) is a devastating disease with high mortality and morbidity. Long-term cognitive and sensorimotor deficits are serious complications following SAH but still not well explained and described in mouse preclinical models. The aim of our study is to characterize a well-mastered SAH murine model and to establish developing pathological mechanisms leading to cognitive and motor deficits, allowing identification of specific targets involved in these long-term troubles. We hereby demonstrate that the double blood injection model of SAH induced long-lasting large cerebral artery vasospasm (CVS), microthrombosis formation and cerebral brain damage including defect in potential paravascular diffusion. These neurobiological alterations appear to be associated with sensorimotor and cognitive dysfunctions mainly detected 10 days after the bleeding episode. In conclusion, this characterized model of SAH in mice, stressing prolonged neurobiological pathological mechanisms and associated sensitivomotor deficits, will constitute a validated preclinical model to better decipher the link between CVS, long-term cerebral apoptosis and cognitive disorders occurring during SAH and to allow investigating novel therapeutic approaches in transgenic mice.     

Gait analysis in a murine model of collagen-induced arthritis

Murine collagen-induced arthritis (CIA) has become a valuable animal model for elucidating pathogenic mechanisms and evaluating therapeutic effects for rheumatoid arthritis. Recent advances in digital imaging and computer technology have enabled gait analysis to develop into a powerful tool for objectively detecting functional deficits in human and animal models. The present study explored the use of non-invasive video-capture gait analysis in the evaluation of a murine CIA model. CIA was induced in 45 female DBA/1LacJ mice (8 to 10 weeks old) by immunization with lyophilized bovine articular type II collagen. Gait parameters were determined by ventral plane videography and were correlated to traditional arthritis clinical scores. Our results showed that increases in clinical scores that measure the severity of CIA corresponded to changes in multiple gait parameters that reflect both morphologic (increases in paw area) and functional (increase in stride frequency, decrease in stride length, hind-limb paw placement angle, as well as stride, stance, and braking times) deficits. Our work indicated that the non-invasive video-capture device may be used as a simple and objective data acquisition system for quantifying gait disturbances in CIA mice for the investigation of mechanisms and the evaluation of therapeutic agents.     

Bayesian meta-analysis models for microarray data: a comparative study

Background

With the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies. Two common approaches for meta-analysis of microarrays include either combining gene expression measures across studies or combining summaries such as p-values, probabilities or ranks. Here, we compare two Bayesian meta-analysis models that are analogous to these methods.

Results

Two Bayesian meta-analysis models for microarray data have recently been introduced. The first model combines standardized gene expression measures across studies into an overall mean, accounting for inter-study variability, while the second combines probabilities of differential expression without combining expression values. Both models produce the gene-specific posterior probability of differential expression, which is the basis for inference. Since the standardized expression integration model includes inter-study variability, it may improve accuracy of results versus the probability integration model. However, due to the small number of studies typical in microarray meta-analyses, the variability between studies is challenging to estimate. The probability integration model eliminates the need to model variability between studies, and thus its implementation is more straightforward. We found in simulations of two and five studies that combining probabilities outperformed combining standardized gene expression measures for three comparison values: the percent of true discovered genes in meta-analysis versus individual studies; the percent of true genes omitted in meta-analysis versus separate studies, and the number of true discovered genes for fixed levels of Bayesian false discovery. We identified similar results when pooling two independent studies of Bacillus subtilis. We assumed that each study was produced from the same microarray platform with only two conditions: a treatment and control, and that the data sets were pre-scaled.

Conclusion

The Bayesian meta-analysis model that combines probabilities across studies does not aggregate gene expression measures, thus an inter-study variability parameter is not included in the model. This results in a simpler modeling approach than aggregating expression measures, which accounts for variability across studies. The probability integration model identified more true discovered genes and fewer true omitted genes than combining expression measures, for our data sets.     

Spatial variability of the vertical migration of fallout 137Cs in the soil of a pasture, and consequences for long-term predictions

Various transport models are presently used to predict the long-term migration behaviour of fallout radiocesium on the soil. To examine to what extent the uncertainty of these predictions is influenced by the spatial variability of the migration rates, we determined the depth profiles of Chernobyl-derived ¹³⁷Cs at 100 plots in a 100 m×100 m pasture. These data were used to obtain the frequency distributions of the characteristic transport parameters of three widely used transport models (e.g. dispersion-convection model, residence time model, and back-flow model). The results show that these transport parameters are generally log-normally distributed with a coefficient of variation of about 80%. Finally, each transport model was employed to predict the resulting frequency distribution of the ¹³⁷Cs inventory in the main root layer (0–7 cm) of the pasture, 20, 50, and 100 years after the deposition. If only the spatial variability of the transport parameters is taken into account, this analysis revealed that the dispersion-convection model and the back-flow model always predicted rather similar, but significantly higher median inventories than those obtained with the residence time model. If, in addition, the spatial variability of the amount of ¹³⁷Cs deposited is also taken into account, the frequency distributions of the ¹³⁷Cs inventories in the root layer become so wide that differences in the median inventories predicted by the three models become statistically significant only after 100 years. Several statistically significant correlations between the transport parameters of the three models were also detected. Received: 28 February / Accepted: 8 June 2000     

Multivariate analysis of microarray data: differential expression and differential connection

Background  

Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically allows for correlation between genes. As a result we combine gene network ideas with linear models and differential expression.     

Association between the Functional Gait Assessment and spatiotemporal gait parameters in individuals with obesity compared to normal weight controls: A proof-of-concept study

Objectives:Obesity is a significant global health concern that involves motor impairment, including deficits in gait and balance. A simple tool would be useful to capture gait and balance impairment in obesity. We assessed whether the Functional Gait Assessment (FGA) captures impairment in individuals with obese BMI (≥30 kg/m²) and whether impairment was related to spatiotemporal gait parameters.Methods:Fourteen individuals with obese BMI and twenty individuals of normal weight underwent the FGA. Spatiotemporal gait parameters were collected while participants walked on a pressure sensitive walkway under five conditions: pre-baseline (flat ground walking), crossing small, medium, and high obstacles, and final-baseline (flat ground walking).Results:Individuals with obesity had lower scores on the FGA (p≤0.001) and showed less efficient spatiotemporal gait parameters than healthy controls, particularly when crossing over obstacles (all ps≤0.05). For participants with obesity, lower FGA scores were associated with decreased gait velocity, but only during obstacle crossing (p≤0.05).Conclusions:The FGA may be a useful tool to capture gait impairment in populations with obesity. Obstacles may help reveal meaningful gait impairments. To our knowledge, this is the first study to examine the FGA in individuals with obesity, and represents a proof-of-concept that motivates further validation studies.     

Physiologically Shrinking the Solution Space of a Saccharomyces cerevisiae Genome-Scale Model Suggests the Role of the Metabolic Network in Shaping Gene Expression Noise

Sampling the solution space of genome-scale models is generally conducted to determine the feasible region for metabolic flux distribution. Because the region for actual metabolic states resides only in a small fraction of the entire space, it is necessary to shrink the solution space to improve the predictive power of a model. A common strategy is to constrain models by integrating extra datasets such as high-throughput datasets and C¹³-labeled flux datasets. However, studies refining these approaches by performing a meta-analysis of massive experimental metabolic flux measurements, which are closely linked to cellular phenotypes, are limited. In the present study, experimentally identified metabolic flux data from 96 published reports were systematically reviewed. Several strong associations among metabolic flux phenotypes were observed. These phenotype-phenotype associations at the flux level were quantified and integrated into a Saccharomyces cerevisiae genome-scale model as extra physiological constraints. By sampling the shrunken solution space of the model, the metabolic flux fluctuation level, which is an intrinsic trait of metabolic reactions determined by the network, was estimated and utilized to explore its relationship to gene expression noise. Although no correlation was observed in all enzyme-coding genes, a relationship between metabolic flux fluctuation and expression noise of genes associated with enzyme-dosage sensitive reactions was detected, suggesting that the metabolic network plays a role in shaping gene expression noise. Such correlation was mainly attributed to the genes corresponding to non-essential reactions, rather than essential ones. This was at least partially, due to regulations underlying the flux phenotype-phenotype associations. Altogether, this study proposes a new approach in shrinking the solution space of a genome-scale model, of which sampling provides new insights into gene expression noise.     

Variation in the large-scale organization of gene expression levels in the hippocampus relates to stable epigenetic variability in behavior

Background

Despite sharing the same genes, identical twins demonstrate substantial variability in behavioral traits and in their risk for disease. Epigenetic factors–DNA and chromatin modifications that affect levels of gene expression without affecting the DNA sequence–are thought to be important in establishing this variability. Epigenetically-mediated differences in the levels of gene expression that are associated with individual variability traditionally are thought to occur only in a gene-specific manner. We challenge this idea by exploring the large-scale organizational patterns of gene expression in an epigenetic model of behavioral variability.

Methodology/Findings

To study the effects of epigenetic influences on behavioral variability, we examine gene expression in genetically identical mice. Using a novel approach to microarray analysis, we show that variability in the large-scale organization of gene expression levels, rather than differences in the expression levels of specific genes, is associated with individual differences in behavior. Specifically, increased activity in the open field is associated with increased variance of log-transformed measures of gene expression in the hippocampus, a brain region involved in open field activity. Early life experience that increases adult activity in the open field also similarly modifies the variance of gene expression levels. The same association of the variance of gene expression levels with behavioral variability is found with levels of gene expression in the hippocampus of genetically heterogeneous outbred populations of mice, suggesting that variation in the large-scale organization of gene expression levels may also be relevant to phenotypic differences in outbred populations such as humans. We find that the increased variance in gene expression levels is attributable to an increasing separation of several large, log-normally distributed families of gene expression levels. We also show that the presence of these multiple log-normal distributions of gene expression levels is a universal characteristic of gene expression in eurkaryotes. We use data from the MicroArray Quality Control Project (MAQC) to demonstrate that our method is robust and that it reliably detects biological differences in the large-scale organization of gene expression levels.

Conclusions

Our results contrast with the traditional belief that epigenetic effects on gene expression occur only at the level of specific genes and suggest instead that the large-scale organization of gene expression levels provides important insights into the relationship of gene expression with behavioral variability. Understanding the epigenetic, genetic, and environmental factors that regulate the large-scale organization of gene expression levels, and how changes in this large-scale organization influences brain development and behavior will be a major future challenge in the field of behavioral genomics.     

Analyses of gene frequencies of mates

A genic analysis of variance of data on mate pairs for a codominant gene is developed. This analysis provides estimators of the correlation, F, of genes within individuals, of the correlation, Θ, of genes between mates, and of various variances—all relative to the correlation or variation among genes of nonmates. The data are manipulated into marginal distributions to produce another method of obtaining the same estimators. Several examples are given of how assumptions about the model and parameters modify the estimators and which were utilized in constructing χ² tests of hypotheses concerning F and Θ.—A recessive gene is also considered. Only the frequency of recessive genotypes and the correlation of recessive mates are estimable in this case unless one makes very demanding assumptions about the model.—Numerical examples of the analysis of variance and estimators are given for both a codominant and recessive gene.     

Analysis of pelvic strain in different gait configurations in a validated cohort of computed tomography based finite element models

The pelvis functions to transmit upper body loads to the lower limbs and is critical in human locomotion. Semi-automated, landmark-based finite element (FE) morphing and mapping techniques eliminate the need for segmentation and have shown to accelerate the generation of multiple specimen-specific pelvic FE models to enable the study of pelvic mechanical behaviour. The purpose of this research was to produce an experimentally validated cohort of specimen-specific FE models of the human pelvis and to use this cohort to analyze pelvic strain patterns during gait. Using an initially segmented specimen-specific pelvic FE model as a source model, four more specimen-specific pelvic FE models were generated from target clinical CT scans using landmark-based morphing and mapping techniques. FE strains from the five models were compared to the experimental strains obtained from cadaveric testing via linear regression analysis, (R² values ranging from 0.70 to 0.93). Inter-specimen variability in FE strain distributions was seen among the five specimen-specific pelvic FE models. The validated cohort of specimen-specific pelvic FE models was utilized to examine pelvic strains at different phases of the gait cycle. Each validated specimen-specific FE model was reconfigured into gait cycle phases representing heel-strike/heel-off and midstance/midswing. No significant difference was found in the double-leg stance and heel-strike/heel-off models (p = 0.40). A trend was observed between double-leg stance and midstance/midswing models (p = 0.07), and a significant difference was found between heel-strike/heel-off models and midstance/midswing models (p = 0.02). Significant differences were also found in comparing right vs. left models (heel-strike/heel-off p = 0.14, midstance/midswing p = 0.04).     

TRAF3 mediates neuronal apoptosis in early brain injury following subarachnoid hemorrhage via targeting TAK1-dependent MAPKs and NF-κB pathways

Neuronal apoptosis has an important role in early brain injury (EBI) following subarachnoid hemorrhage (SAH). TRAF3 was reported as a promising therapeutic target for stroke management, which covered several neuronal apoptosis signaling cascades. Hence, the present study is aimed to determine whether downregulation of TRAF3 could be neuroprotective in SAH-induced EBI. An in vivo SAH model in mice was established by endovascular perforation. Meanwhile, primary cultured cortical neurons of mice treated with oxygen hemoglobin were applied to mimic SAH in vitro. Our results demonstrated that TRAF3 protein expression increased and expressed in neurons both in vivo and in vitro SAH models. TRAF3 siRNA reversed neuronal loss and improved neurological deficits in SAH mice, and reduced cell death in SAH primary neurons. Mechanistically, we found that TRAF3 directly binds to TAK1 and potentiates phosphorylation and activation of TAK1, which further enhances the activation of NF-κB and MAPKs pathways to induce neuronal apoptosis. Importantly, TRAF3 expression was elevated following SAH in human brain tissue and was mainly expressed in neurons. Taken together, our study demonstrates that TRAF3 is an upstream regulator of MAPKs and NF-κB pathways in SAH-induced EBI via its interaction with and activation of TAK1. Furthermore, the TRAF3 may serve as a novel therapeutic target in SAH-induced EBI.Subject terms: Apoptosis, Neuro-vascular interactions     

Gait in Very Preterm School-Aged Children in Dual-Task Paradigms

Objective

The control of gait requires executive and attentional functions. As preterm children show executive and attentional deficits compared to full-term children, performing concurrent tasks that impose additional cognitive load may lead to poorer walking performance in preterm compared to full-term children. Knowledge regarding gait in preterm children after early childhood is scarce. We examined straight walking and if it is more affected in very preterm than in full-term children in dual-task paradigms.

Study design

Twenty preterm children with very low birth-weight (≤ 1500 g), 24 preterm children with birth-weight > 1500 g, and 44 full-term children, born between 2001 and 2006, were investigated. Gait was assessed using an electronic walkway system (GAITRite) while walking without a concurrent task (single-task) and while performing one concurrent (dual-task) or two concurrent (triple-task) tasks. Spatio-temporal gait parameters (gait velocity, cadence, stride length, single support time, double support time), normalized gait parameters (normalized velocity, normalized cadence, normalized stride length) and gait variability parameters (stride velocity variability, stride length variability) were analyzed.

Results

In dual- and triple-task conditions children showed decreased gait velocity, cadence, stride length, as well as increased single support time, double support time and gait variability compared to single-task walking. Further, results showed systematic decreases in stride velocity variability from preterm children with very low birth weight (≤ 1500 g) to preterm children with birth weight > 1500 g to full-term children. There were no significant interactions between walking conditions and prematurity status.

Conclusions

Dual and triple tasking affects gait of preterm and full-term children, confirming previous results that walking requires executive and attentional functions. Birth-weight dependent systematic changes in stride velocity variability indicate poorer walking performance in preterm children who were less mature at birth.     

Mutation of the Diamond-Blackfan Anemia Gene Rps7 in Mouse Results in Morphological and Neuroanatomical Phenotypes

The ribosome is an evolutionarily conserved organelle essential for cellular function. Ribosome construction requires assembly of approximately 80 different ribosomal proteins (RPs) and four different species of rRNA. As RPs co-assemble into one multi-subunit complex, mutation of the genes that encode RPs might be expected to give rise to phenocopies, in which the same phenotype is associated with loss-of-function of each individual gene. However, a more complex picture is emerging in which, in addition to a group of shared phenotypes, diverse RP gene-specific phenotypes are observed. Here we report the first two mouse mutations (Rps7^Mtu and Rps7^Zma) of ribosomal protein S7 (Rps7), a gene that has been implicated in Diamond-Blackfan anemia. Rps7 disruption results in decreased body size, abnormal skeletal morphology, mid-ventral white spotting, and eye malformations. These phenotypes are reported in other murine RP mutants and, as demonstrated for some other RP mutations, are ameliorated by Trp53 deficiency. Interestingly, Rps7 mutants have additional overt malformations of the developing central nervous system and deficits in working memory, phenotypes that are not reported in murine or human RP gene mutants. Conversely, Rps7 mouse mutants show no anemia or hyperpigmentation, phenotypes associated with mutation of human RPS7 and other murine RPs, respectively. We provide two novel RP mouse models and expand the repertoire of potential phenotypes that should be examined in RP mutants to further explore the concept of RP gene-specific phenotypes.     

Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments

Background  

The small sample sizes often used for microarray experiments result in poor estimates of variance if each gene is considered independently. Yet accurately estimating variability of gene expression measurements in microarray experiments is essential for correctly identifying differentially expressed genes. Several recently developed methods for testing differential expression of genes utilize hierarchical Bayesian models to "pool" information from multiple genes. We have developed a statistical testing procedure that further improves upon current methods by incorporating the well-documented relationship between the absolute gene expression level and the variance of gene expression measurements into the general empirical Bayes framework.