The Effects on the Femoral Cortex of a 24 Month Treatment Compared to an 18 Month Treatment with Teriparatide: A Multi-Trial Retrospective Analysis

Background

Teriparatide (TPTD) is an anabolic agent indicated for the treatment of severely osteoporotic patients who are at high risk of fragility fractures. The originally approved duration of TPTD treatment in several regions, including Europe, was 18 months. However, studies of areal bone mineral density (aBMD) showed additional benefit when treatment is continued beyond 18 months, and the drug is currently licenced for 24 months. Improvements in cortical structure at the proximal femur have already been shown in patients given TPTD for 24 months using quantitative computed tomography (QCT). Here, we investigate whether cortical and endocortical trabecular changes differ between an 18- and 24-month treatment.

Methods

Since an 18- versus 24-month TPTD study using QCT has not been conducted, we studied combined QCT data from four previous clinical trials. Combined femoral QCT data from three 18-month TPTD studies (‘18-month group’) were compared with data from a fourth 24-month trial (‘24-month group’). Cortical parameters were measured over the entire proximal femur which allowed for a comparison of the mean changes as well as a visual comparison of the colour maps of changes after 18 and 24 months TPTD.

Results

For both the combined 18-month group and the 24-month group, overall cortical thickness and endocortical trabecular density increased, while overall cortical bone mineral density decreased. While the changes in the 24-month group were of greater magnitude compared to the 18-month group, the differences were only significant for the endocortical trabecular density (ECTD), corrected for age, weight, femoral neck T-score, total hip T-score and the baseline mean ECTD.

Conclusion

Although the combination of data from different clinical trials is not optimal, these data support the concept that the duration of TPTD in the 18–24 month phase is of clinical relevance when considering improvement in hip structure.     

Defective Cytochrome P450-Catalysed Drug Metabolism in Niemann-Pick Type C Disease

Niemann-Pick type C (NPC) disease is a neurodegenerative lysosomal storage disease caused by mutations in either the NPC1 or NPC2 gene. NPC is characterised by storage of multiple lipids in the late endosomal/lysosomal compartment, resulting in cellular and organ system dysfunction. The underlying molecular mechanisms that lead to the range of clinical presentations in NPC are not fully understood. While evaluating potential small molecule therapies in Npc1^-/- mice, we observed a consistent pattern of toxicity associated with drugs metabolised by the cytochrome P450 system, suggesting a potential drug metabolism defect in NPC1 disease. Investigation of the P450 system in the context of NPC1 dysfunction revealed significant changes in the gene expression of many P450 associated genes across the full lifespan of Npc1^-/- mice, decreased activity of cytochrome P450 reductase, and a global decrease of multiple cytochrome P450 catalysed dealkylation reactions. In vivo drug metabolism studies using a prototypic P450 metabolised drug, midazolam, confirmed dysfunction in drug clearance in the Npc1^-/- mouse. Expression of the Phase II enzyme uridinediphosphate-glucuronosyltransferase (UGT) was also significantly reduced in Npc1^-/- mice. Interestingly, reduced activity within the P450 system was also observed in heterozygous Npc1^+/- mice. The reduced activity of P450 enzymes may be the result of bile acid deficiency/imbalance in Npc1^-/- mice, as bile acid treatment significantly rescued P450 enzyme activity in Npc1^-/- mice and has the potential to be an adjunctive therapy for NPC disease patients. The dysfunction in the cytochrome P450 system were recapitulated in the NPC1 feline model. Additionally, we present the first evidence that there are alterations in the P450 system in NPC1 patients.     

Using intra‐individual variation in shrub architecture to explain population cover

Plant architecture is related to the performance of long‐lived plants; its role in promoting species coexistence and in successional patterns is now widely recognized. However, because plant architecture involves branching processes, it is highly variable at the intra‐specific level. In this paper, we address two questions: what is the best way to describe plant architecture to obtain meaningful information for explaining population cover: at the whole‐plant level, or at the level of its unitary constituent parts? Further, are there architectural designs related to populations’ success? We evaluated the relative impact of ontogeny and whole‐plant traits on the cover achieved by the populations of five shrub species developing on 25 abandoned farmlands in southwestern Québec (Canada). We compared four ways of analyzing plant architecture: 1–2) using morphological traits described at the scale of a module (an elementary architectural unit made up of all the different types of shoots), with or without taking into account the ontogeny of the whole organism, 3) using the rate of changes during ontogeny as traits, and 4) using whole‐plant traits describing branching processes at a scale larger than modules. We then used variation partitioning to discriminate the actual effects of these traits on percent cover of the species from hidden effects due to plant ontogenesis and population spatial structure. Our results suggest that the predominant variables that effectively describe population cover vary from one species to another. At the same time, whole‐plant architectural traits and the rate of change of morphological traits during ontogeny both have an important effect on population cover. These findings suggest that acknowledging the developmental pattern of woody species can clarify the impact of intra‐specific trait variation on population cover.     

The definition,recognition, and interpretation of convergent evolution,and two new measures for quantifying and assessing the significance of convergence

Convergent evolution is an important phenomenon in the history of life. Despite this, there is no common definition of convergence used by biologists. Instead, several conceptually different definitions are employed. The primary dichotomy is between pattern‐based definitions, where independently evolved similarity is sufficient for convergence, and process‐based definitions, where convergence requires a certain process to produce this similarity. The unacknowledged diversity of definitions can lead to problems in evolutionary research. Process‐based definitions may bias researchers away from studying or recognizing other sources of independently evolved similarity, or lead researchers to interpret convergent patterns as necessarily caused by a given process. Thus, pattern‐based definitions are recommended. Existing measures of convergence are reviewed, and two new measures are developed. Both are pattern based and conceptually minimal, quantifying nothing but independently evolved similarity. One quantifies the amount of phenotypic distance between two lineages that is closed by subsequent evolution; the other simply counts the number of lineages entering a region of phenotypic space. The behavior of these measures is explored in simulations; both show acceptable Type I and Type II error. The study of convergent evolution will be facilitated if researchers are explicit about working definitions of convergence and adopt a standard toolbox of convergence measures.     

Group-based variant calling leveraging next-generation supercomputing for large-scale whole-genome sequencing studies

Motivation

Next-generation sequencing (NGS) technologies have become much more efficient, allowing whole human genomes to be sequenced faster and cheaper than ever before. However, processing the raw sequence reads associated with NGS technologies requires care and sophistication in order to draw compelling inferences about phenotypic consequences of variation in human genomes. It has been shown that different approaches to variant calling from NGS data can lead to different conclusions. Ensuring appropriate accuracy and quality in variant calling can come at a computational cost.

Results

We describe our experience implementing and evaluating a group-based approach to calling variants on large numbers of whole human genomes. We explore the influence of many factors that may impact the accuracy and efficiency of group-based variant calling, including group size, the biogeographical backgrounds of the individuals who have been sequenced, and the computing environment used. We make efficient use of the Gordon supercomputer cluster at the San Diego Supercomputer Center by incorporating job-packing and parallelization considerations into our workflow while calling variants on 437 whole human genomes generated as part of large association study.

Conclusions

We ultimately find that our workflow resulted in high-quality variant calls in a computationally efficient manner. We argue that studies like ours should motivate further investigations combining hardware-oriented advances in computing systems with algorithmic developments to tackle emerging ‘big data’ problems in biomedical research brought on by the expansion of NGS technologies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0736-4) contains supplementary material, which is available to authorized users.     

MDAT- Aligning multiple domain arrangements

Background

Proteins are composed of domains, protein segments that fold independently from the rest of the protein and have a specific function. During evolution the arrangement of domains can change: domains are gained, lost or their order is rearranged. To facilitate the analysis of these changes we propose the use of multiple domain alignments.

Results

We developed an alignment program, called MDAT, which aligns multiple domain arrangements. MDAT extends earlier programs which perform pairwise alignments of domain arrangements. MDAT uses a domain similarity matrix to score domain pairs and aligns the domain arrangements using a consistency supported progressive alignment method.

Conclusion

MDAT will be useful for analysing changes in domain arrangements within and between protein families and will thus provide valuable insights into the evolution of proteins and their domains. MDAT is coded in C++, and the source code is freely available for download at http://www.bornberglab.org/pages/mdat.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0442-7) contains supplementary material, which is available to authorized users.     

Alpha-Agarases Define a New Family of Glycoside Hydrolases, Distinct from Beta-Agarase Families

The gene encoding the α-agarase from “Alteromonas agarilytica” (proposed name) has been cloned and sequenced. The gene product (154 kDa) is unrelated to β-agarases and instead belongs to a new family of glycoside hydrolases (GH96). The α-agarase also displays a complex modularity, with the presence of five thrombospondin type 3 repeats and three carbohydrate-binding modules.     

Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment

Genotypes are frequently used to identify parentage. Such analysis is notoriously vulnerable to genotyping error, and there is ongoing debate regarding how to solve this problem. Many scientists have used the computer program cervus to estimate parentage, and have taken advantage of its option to allow for genotyping error. In this study, we show that the likelihood equations used by versions 1.0 and 2.0 of cervus to accommodate genotyping error miscalculate the probability of observing an erroneous genotype. Computer simulation and reanalysis of paternity in Rum red deer show that correcting this error increases success in paternity assignment, and that there is a clear benefit to accommodating genotyping errors when errors are present. A new version of cervus (3.0) implementing the corrected likelihood equations is available at http://www.fieldgenetics.com .     

Modelling large herbivore movement decisions: Beyond food availability as a predictor of ranging patterns

The ability of animals to adapt to their changing environment will depend in part on shifts in their ranging patterns, but when and why individuals choose to move requires detailed understanding of their decision‐making processes. We develop a simple decision‐making model accounting for resource availability in habitually used ranges. We suggest that disparities between model predictions and animal tracking data indicate additional factors influencing movement decisions, which may be identified given detailed system‐specific knowledge. The model was evaluated using movement data from satellite‐tracked elephants (Loxodonta africana) inhabiting the Amboseli basin in Kenya, moving from savannah areas with low quality but constant resource availability, to areas with temporally constrained higher nutrient availability. Overall, the model fits the data well: There was a good correlation between predicted and observed locations for the combined data from all elephants, but variation between individuals in how well the model fits. For those elephants where model predictions were less successful, additional factors likely to affect movement decisions, including reproduction, anthropogenic threats, memory and perception are suggested. This protocol for building and testing decision‐making models should contribute to success in attempts to preserve sufficient space for large herbivores in their increasingly human‐dominated ecosystems.     

Differences in Mitotic Spindle Architecture in Mammalian Neural Stem Cells Influence Mitotic Accuracy during Brain Development

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