Ezetimibe: A biomarker for efficacy of liver directed UGT1A1 gene therapy for inherited hyperbilirubinemia

As recently demonstrated in patients with factor IX deficiency, adeno-associated virus (AAV)-mediated liver-directed therapy is a viable option for inherited metabolic liver disorders. Our aim is to treat Crigler-Najjar syndrome type I (CN I), an inherited severe unconjugated hyperbilirubinemia, as a rare recessive inherited disorder. Because the number of patients eligible for this approach is small, the efficacy can only be demonstrated by a beneficial effect on the pathophysiology in individual patients. Serum bilirubin levels in potential candidates have been monitored since birth, providing an indication of their pathophysiology. Adjuvant phototherapy to prevent brain damage reduces serum unconjugated bilirubin (UCB) levels in CN I patients to the level seen in the milder form of the disease, CN type II. This therapy increases the excretion of UCB, thereby complicating the use of UCB and conjugated bilirubin levels in serum as biomarkers for the gene therapy we try to develop. Therefore, a suitable biomarker that is not affected by phototherapy is currently needed. To this end, we have investigated whether estradiol, ethinylestradiol or ezetimibe could be used as markers for uridine 5'-di-phospho-glucuronosyltransferase isoform 1A1 (UGT1A1) activity restored by AAV gene therapy in Gunn rats, a relevant animal model for CN I. Of these compounds, ezetimibe appeared most suitable because its glucuronidation rate in untreated control Gunn rats is low. Subsequently, ezetimibe glucuronidation was studied in both untreated and AAV-treated Gunn rats and the results suggest that it may serve as a useful serum marker for restored hepatic UGT1A1 activity.     

Indirect genetic effects and the spread of infectious disease: are we capturing the full heritable variation underlying disease prevalence?

Reducing disease prevalence through selection for host resistance offers a desirable alternative to chemical treatment. Selection for host resistance has proven difficult, however, due to low heritability estimates. These low estimates may be caused by a failure to capture all the relevant genetic variance in disease resistance, as genetic analysis currently is not taylored to estimate genetic variation in infectivity. Host infectivity is the propensity of transmitting infection upon contact with a susceptible individual, and can be regarded as an indirect effect to disease status. It may be caused by a combination of physiological and behavioural traits. Though genetic variation in infectivity is difficult to measure directly, Indirect Genetic Effect (IGE) models, also referred to as associative effects or social interaction models, allow the estimation of this variance from more readily available binary disease data (infected/non-infected). We therefore generated binary disease data from simulated populations with known amounts of variation in susceptibility and infectivity to test the adequacy of traditional and IGE models. Our results show that a conventional model fails to capture the genetic variation in infectivity inherent in populations with simulated infectivity. An IGE model, on the other hand, does capture some of the variation in infectivity. Comparison with expected genetic variance suggests that there is scope for further methodological improvement, and that potential responses to selection may be greater than values presented here. Nonetheless, selection using an index of estimated direct and indirect breeding values was shown to have a greater genetic selection differential and reduced future disease risk than traditional selection for resistance only. These findings suggest that if genetic variation in infectivity substantially contributes to disease transmission, then breeding designs which explicitly incorporate IGEs might help reduce disease prevalence.     

On the relation between gene flow theory and genetic gain

In conventional gene flow theory the rate of genetic gain is calculated as the summed products of genetic selection differential and asymptotic proportion of genes deriving from sex-age groups. Recent studies have shown that asymptotic proportions of genes predicted from conventional gene flow theory may deviate considerably from true proportions. However, the rate of genetic gain predicted from conventional gene flow theory was accurate. The current note shows that the connection between asymptotic proportions of genes and rate of genetic gain that is embodied in conventional gene flow theory is invalid, even though genetic gain may be predicted correctly from it.     

Direct and indirect genetic effects in life‐history traits of flour beetles (Tribolium castaneum)

Indirect genetic effects (IGEs) are the basis of social interactions among conspecifics, and can affect genetic variation of nonsocial and social traits. We used flour beetles (Tribolium castaneum) of two phenotypically distinguishable populations to estimate genetic (co)variances and the effect of IGEs on three life‐history traits: development time (DT), growth rate (GR), and pupal body mass (BM). We found that GR was strongly affected by social environment with IGEs accounting for 18% of the heritable variation. We also discovered a sex‐specific social effect: male ratio in a group significantly affected both GR and BM; that is, beetles grew larger and faster in male‐biased social environments. Such sex‐specific IGEs have not previously been demonstrated in a nonsocial insect. Our results show that beetles that achieve a higher BM do so via a slower GR in response to social environment. Existing models of evolution in age‐structured or stage‐structured populations do not account for IGEs of social cohorts. It is likely that such IGEs have played a key role in the evolution of developmental plasticity shown by Tenebrionid larvae in response to density. Our results document an important source of genetic variation for GR, often overlooked in life‐history theory.     

Evaluation of the micronucleus assay in bone marrow and peripheral blood of rats for the determination of cigarette mainstream-smoke activity

The mammalian in vivo micronucleus assay is widely used as part of the genotoxicity testing battery required during the development of new drugs. As such, the in vivo micronucleus assay has been used in a battery of assays for the assessment of cigarette ingredients or design modifications to help ensure that there is no increase in risk or any new risk introduced by these additions or modifications. The present series of studies was conducted to optimize and evaluate this assay for the assessment of the effects of mainstream smoke on the micronucleus frequency in the bone marrow and peripheral blood of rats. In a first experiment, the optimal conditions for performing the micronucleus assay in these tissues were determined. This was done by use of two compounds known for their micronucleus-inducing activity, i.e., the clastogen cyclophosphamide and the aneugen colchicine. In a second experiment, the effects of tube restraint on untreated control rats were investigated. In a third experiment, the optimal conditions were used to assess the clastogenic/aneugenic activity of cigarette smoke in Sprague-Dawley rats. The rat micronucleus assay in both bone marrow and peripheral blood is able to detect clastogenic and aneugenic activity. The flow cytometric determination of micronucleated cells in rat blood is at least as sensitive as determinations in bone marrow. No statistically significant differences were observed in micronucleus frequencies between rats with and without the additional stress of tube restraint; however, the cautious approach would be to use a fresh-air-exposed group (with tube restraint) as the negative control in inhalation experiments. Using the conditions identified as optimal in the above-mentioned experiments, the micronucleus assay was not able to detect effects induced by smoke from conventional cigarettes. Nevertheless, the micronucleus assay will remain a valuable tool as part of a testing battery used to investigate possible adverse effects related to product modifications.     

Methods to estimate breeding values in honey bees

Background

Efficient methodologies based on animal models are widely used to estimate breeding values in farm animals. These methods are not applicable in honey bees because of their mode of reproduction. Observations are recorded on colonies, which consist of a single queen and thousands of workers that descended from the queen mated to 10 to 20 drones. Drones are haploid and sperms are copies of a drone’s genotype. As a consequence, Mendelian sampling terms of full-sibs are correlated, such that the covariance matrix of Mendelian sampling terms is not diagonal.

Results

In this paper, we show how the numerator relationship matrix and its inverse can be obtained for honey bee populations. We present algorithms to derive the covariance matrix of Mendelian sampling terms that accounts for correlated terms. The resulting matrix is a block-diagonal matrix, with a small block for each full-sib family, and is easy to invert numerically. The method allows incorporating the within-colony distribution of progeny from drone-producing queens and drones, such that estimates of breeding values weigh information from relatives appropriately. Simulation shows that the resulting estimated breeding values are unbiased predictors of true breeding values. Benefits for response to selection, compared to an existing approximate method, appear to be limited (~5%). Benefits may however be greater when estimating genetic parameters.

Conclusions

This work shows how the relationship matrix and its inverse can be developed for honey bee populations, and used to estimate breeding values and variance components.     

Optimal mass selection policies for schemes with overlapping generations and restricted inbreeding

Optimum breeding schemes for maximising the rate of genetic progress with a restriction on the rate of inbreeding (per year or per generation) are investigated for populations with overlapping generations undergoing mass selection. The optimisation is for the numbers of males and females to be selected and for their distribution over age classes. Expected rates of genetic progress (ΔG) are combined with expected rates of inbreeding (ΔF) in a linear objective function (Φ = ΔG - λΔF) which is maximised. A simulated annealing algorithm is used to obtain the solutions. The restriction on inbreeding is achieved by increasing the number of parents and, in small schemes with severe restrictions, by increasing the generation interval. In the latter case the optimum strategy for obtaining the maximum genetic gain is far from truncation selection across age classes. In most situations, the optimum mating ratio is one but the differences in genetic gain obtained with different mating ratios are small. Optimisation of schemes when restricting the rate of inbreeding per generation leads to shorter generation intervals than optimisation when restricting the rate of inbreeding per year.     

Protein interaction verification and functional annotation by integrated analysis of genome-scale data

Assays capable of determining the properties of thousands of genes in parallel present challenges with regard to accurate data processing and functional annotation. Collections of microarray expression data are applied here to assess the quality of different high-throughput protein interaction data sets. Significant differences are found. Confidence in 973 out of 5342 putative two-hybrid interactions from S. cerevisiae is increased. Besides verification, integration of expression and interaction data is employed to provide functional annotation for over 300 previously uncharacterized genes. The robustness of these approaches is demonstrated by experiments that test the in silico predictions made. This study shows how integration improves the utility of different types of functional genomic data and how well this contributes to functional annotation.     

Processing conditions analysis of Eucalyptus globulus plywood bonded with resol-tannin adhesives

Phenol-formaldehyde resol containing mimosa tannin extract was employed to produce plywood panels with two plies from Eucalyptus globulus veneers. The effect of processing conditions and tannin content on the gelation time of the adhesive in the glue line was evaluated by dynamic-mechanical analysis (DMA). These results were related with shear strength and wood failure of glue line in the final panels. Hazardous petrochemical phenol could be partially substituted in resols in industrial applications by addition of mimosa tannin extracts.     

A Morpho-Density Approach to Estimating Neural Connectivity

Neuronal signal integration and information processing in cortical neuronal networks critically depend on the organization of synaptic connectivity. Because of the challenges involved in measuring a large number of neurons, synaptic connectivity is difficult to determine experimentally. Current computational methods for estimating connectivity typically rely on the juxtaposition of experimentally available neurons and applying mathematical techniques to compute estimates of neural connectivity. However, since the number of available neurons is very limited, these connectivity estimates may be subject to large uncertainties. We use a morpho-density field approach applied to a vast ensemble of model-generated neurons. A morpho-density field (MDF) describes the distribution of neural mass in the space around the neural soma. The estimated axonal and dendritic MDFs are derived from 100,000 model neurons that are generated by a stochastic phenomenological model of neurite outgrowth. These MDFs are then used to estimate the connectivity between pairs of neurons as a function of their inter-soma displacement. Compared with other density-field methods, our approach to estimating synaptic connectivity uses fewer restricting assumptions and produces connectivity estimates with a lower standard deviation. An important requirement is that the model-generated neurons reflect accurately the morphology and variation in morphology of the experimental neurons used for optimizing the model parameters. As such, the method remains subject to the uncertainties caused by the limited number of neurons in the experimental data set and by the quality of the model and the assumptions used in creating the MDFs and in calculating estimating connectivity. In summary, MDFs are a powerful tool for visualizing the spatial distribution of axonal and dendritic densities, for estimating the number of potential synapses between neurons with low standard deviation, and for obtaining a greater understanding of the relationship between neural morphology and network connectivity.