Deficiency for the ubiquitin ligase UBE3B in a blepharophimosis-ptosis-intellectual-disability syndrome.

Ubiquitination plays a crucial role in neurodevelopment as exemplified by Angelman syndrome, which is caused by genetic alterations of the ubiquitin ligase-encoding UBE3A gene. Although the function of UBE3A has been widely studied, little is known about its paralog UBE3B. By using exome and capillary sequencing, we here identify biallelic UBE3B mutations in four patients from three unrelated families presenting an autosomal-recessive blepharophimosis-ptosis-intellectual-disability syndrome characterized by developmental delay, growth retardation with a small head circumference, facial dysmorphisms, and low cholesterol levels. UBE3B encodes an uncharacterized E3 ubiquitin ligase. The identified UBE3B variants include one frameshift and two splice-site mutations as well as a missense substitution affecting the highly conserved HECT domain. Disruption of mouse Ube3b leads to reduced viability and recapitulates key aspects of the human disorder, such as reduced weight and brain size and a downregulation of cholesterol synthesis. We establish that the probable Caenorhabditis elegans ortholog of UBE3B, oxi-1, functions in the ubiquitin/proteasome system in vivo and is especially required under oxidative stress conditions. Our data reveal the pleiotropic effects of UBE3B deficiency and reinforce the physiological importance of ubiquitination in neuronal development and function in mammals.

Optimising experimental design for high-throughput phenotyping in mice: a case study.

To further the functional annotation of the mammalian genome, the Sanger Mouse Genetics Programme aims to generate and characterise knockout mice in a high-throughput manner. Annually, approximately 200 lines of knockout mice will be characterised using a standardised battery of phenotyping tests covering key disease indications ranging from obesity to sensory acuity. From these findings secondary centres will select putative mutants of interest for more in-depth, confirmatory experiments. Optimising experimental design and data analysis is essential to maximise output using the resources with greatest efficiency, thereby attaining our biological objective of understanding the role of genes in normal development and disease. This study uses the example of the noninvasive blood pressure test to demonstrate how statistical investigation is important for generating meaningful, reliable results and assessing the design for the defined research objectives. The analysis adjusts for the multiple-testing problem by applying the false discovery rate, which controls the number of false calls within those highlighted as significant. A variance analysis finds that the variation between mice dominates this assay. These variance measures were used to examine the interplay between days, readings, and number of mice on power, the ability to detect change. If an experiment is underpowered, we cannot conclude whether failure to detect a biological difference arises from low power or lack of a distinct phenotype, hence the mice are subjected to testing without gain. Consequently, in confirmatory studies, a power analysis along with the 3Rs can provide justification to increase the number of mice used.

The fallacy of ratio correction to address confounding factors.

Scientists aspire to measure cause and effect. Unfortunately confounding variables, ones that are associated with both the probable cause and the outcome, can lead to an association that is true but potentially misleading. For example, altered body weight is often observed in a gene knockout; however, many other variables, such as lean mass, will also change as the body weight changes. This leaves the researcher asking whether the change in that variable is expected for that change in weight. Ratio correction, which is often referred to as normalization, is a method used commonly to remove the effect of a confounding variable. Although ratio correction is used widely in biological research, it is not the method recommended in the statistical literature to address confounding factors; instead regression methods such as the analysis of covariance (ANCOVA) are proposed. This method examines the difference in means after adjusting for the confounding relationship. Using real data, this manuscript demonstrates how the ratio correction approach is flawed and can result in erroneous calls of significance leading to inappropriate biological conclusions. This arises as some of the underlying assumptions are not met. The manuscript goes on to demonstrate that researchers should use ANCOVA, and discusses how graphical tools can be used readily to judge the robustness of this method. This study is therefore a clear example of why assumption testing is an important component of a study and thus why it is included in the Animal Research: Reporting of In Vivo Experiment (ARRIVE) guidelines.