Transgenic expression of neuronal dystonin isoform 2 partially rescues the disease phenotype of the Dystonia musculorum mouse model of hereditary sensory autonomic neuropathy VI

A newly identified lethal form of hereditary sensory and autonomic neuropathy (HSAN), designated HSAN-VI, is caused by a homozygous mutation in the bullous pemphigoid antigen 1 (BPAG1)/dystonin gene (DST). The HSAN-VI mutation impacts all major neuronal BPAG1/dystonin protein isoforms: dystonin-a1, -a2 and -a3. Homozygous mutations in the murine Dst gene cause a severe sensory neuropathy termed dystonia musculorum (dt). Phenotypically, dt mice are similar to HSAN-VI patients, manifesting progressive limb contractures, dystonia, dysautonomia and early postnatal death. To obtain a better molecular understanding of disease pathogenesis in HSAN-VI patients and the dt disorder, we generated transgenic mice expressing a myc-tagged dystonin-a2 protein under the regulation of the neuronal prion protein promoter on the dtTg4/Tg4 background, which is devoid of endogenous dystonin-a1 and -a2, but does express dystonin-a3. Restoring dystonin-a2 expression in the nervous system, particularly within sensory neurons, prevented the disorganization of organelle membranes and microtubule networks, attenuated the degeneration of sensory neuron subtypes and ameliorated the phenotype and increased life span in these mice. Despite these improvements, complete rescue was not observed likely because of inadequate expression of the transgene. Taken together, this study provides needed insight into the molecular basis of the dt disorder and other peripheral neuropathies including HSAN-VI. \ua9 The Author 2013. Published by Oxford University Press. All rights reserved.Peer reviewed: YesNRC publication: Ye

Spatial modelling of biodiversity at the community level

1. Statistical modelling is often used to relate sparse biological survey data to remotely derived environmental predictors, thereby providing a basis for predictively mapping biodiversity across an entire region of interest. The most popular strategy for such modelling has been to model distributions of individual species one at a time. Spatial modelling of biodiversity at the community level may, however, confer significant benefits for applications involving very large numbers of species, particularly if many of these species are recorded infrequently. 2. Community-level modelling combines data from multiple species and produces information on spatial pattern in the distribution of biodiversity at a collective community level instead of, or in addition to, the level of individual species. Spatial outputs from community-level modelling include predictive mapping of community types (groups of locations with similar species composition), species groups (groups of species with similar distributions), axes or gradients of compositional variation, levels of compositional dissimilarity between pairs of locations, and various macro-ecological properties (e.g. species richness). 3. Three broad modelling strategies can be used to generate these outputs: (i) 'assemble first, predict later', in which biological survey data are first classified, ordinated or aggregated to produce community-level entities or attributes that are then modelled in relation to environmental predictors; (ii) 'predict first, assemble later', in which individual species are modelled one at a time as a function of environmental variables, to produce a stack of species distribution maps that is then subjected to classification, ordination or aggregation; and (iii) 'assemble and predict together', in which all species are modelled simultaneously, within a single integrated modelling process. These strategies each have particular strengths and weaknesses, depending on the intended purpose of modelling and the type, quality and quantity of data involved. 4. Synthesis and applications. The potential benefits of modelling large multispecies data sets using community-level, as opposed to species-level, approaches include faster processing, increased power to detect shared patterns of environmental response across rarely recorded species, and enhanced capacity to synthesize complex data into a form more readily interpretable by scientists and decision-makers. Community-level modelling therefore deserves to be considered more often, and more widely, as a potential alternative or supplement to modelling individual species