Haploinsufficiency of a spliceosomal GTPase encoded by EFTUD2 causes mandibulofacial dysostosis with microcephaly

Mandibulofacial dysostosis with microcephaly (MFDM) is a rare sporadic syndrome comprising craniofacial malformations, microcephaly, developmental delay, and a recognizable dysmorphic appearance. Major sequelae, including choanal atresia, sensorineural hearing loss, and cleft palate, each occur in a significant proportion of affected individuals. We present detailed clinical findings in 12 unrelated individuals with MFDM; these 12 individuals compose the largest reported cohort to date. To define the etiology of MFDM, we employed whole-exome sequencing of four unrelated affected individuals and identified heterozygous mutations or deletions of EFTUD2 in all four. Validation studies of eight additional individuals with MFDM demonstrated causative EFTUD2 mutations in all affected individuals tested. A range of EFTUD2-mutation types, including null alleles and frameshifts, is seen in MFDM, consistent with haploinsufficiency; segregation is de novo in all cases assessed to date. U5-116kD, the protein encoded by EFTUD2, is a highly conserved spliceosomal GTPase with a central regulatory role in catalytic splicing and post-splicing-complex disassembly. MFDM is the first multiple-malformation syndrome attributed to a defect of the major spliceosome. Our findings significantly extend the range of reported spliceosomal phenotypes in humans and pave the way for further investigation in related conditions such as Treacher Collins syndrome.     

A spatial analytical approach for selecting reintroduction sites for burbot in English rivers

1. Availability of suitable habitat is a prerequisite for species reintroduction success, and to ensure population persistence, investigations of a species’ habitat utilisation throughout its life history should be conducted as part of a feasibility study. 2. Habitat utilisation models for burbot, Lota lota, developed using data from field studies conducted in France and Germany and information from the literature were used to assess the feasibility of reintroducing burbot into rivers of its former native range in eastern England. 3. Per cent tree roots, aquatic vegetation and flow types were important predictors of adult burbot abundance. Furthermore, the habitat utilisation models were supplemented with information from the literature, which suggested that off‐channel habitat such as wetlands and backwaters is important for spawning and nursery stages. 4. An assessment of the habitat availability in the rivers of the burbot’s former native range using variables related to spawning and nursery and adult life stages showed that although adult habitat was widely distributed, the availability of spawning and nursery habitat was less abundant, potentially limiting successful reestablishment. 5. Potential suitable habitat was concentrated in the central and southern areas of the species’ former English distribution. Overall, rivers of the burbot’s former range potentially afford suitable habitat to sustain a reintroduced population. However, sites should be preferentially selected on the basis of having appropriate spawning and nursery areas.     

Evolution of extrafloral nectaries: adaptive process and selective regime changes from forest to savanna

Much effort has been devoted to understanding the function of extrafloral nectaries (EFNs) for ant–plant–herbivore interactions. However, the pattern of evolution of such structures throughout the history of plant lineages remains unexplored. In this study, we used empirical knowledge on plant defences mediated by ants as a theoretical framework to test specific hypotheses about the adaptive role of EFNs during plant evolution. Emphasis was given to different processes (neutral or adaptive) and factors (habitat change and trade‐offs with new trichomes) that may have affected the evolution of ant–plant associations. We measured seven EFN quantitative traits in all 105 species included in a well‐supported phylogeny of the tribe Bignonieae (Bignoniaceae) and collected field data on ant–EFN interactions in 32 species. We identified a positive association between ant visitation (a surrogate of ant guarding) and the abundance of EFNs in vegetative plant parts and rejected the hypothesis of phylogenetic conservatism of EFNs, with most traits presenting K‐values < 1. Modelling the evolution of EFN traits using maximum likelihood approaches further suggested adaptive evolution, with static‐optimum models showing a better fit than purely drift models. In addition, the abundance of EFNs was associated with habitat shifts (with a decrease in the abundance of EFNs from forest to savannas), and a potential trade‐off was detected between the abundance of EFNs and estipitate glandular trichomes (i.e. trichomes with sticky secretion). These evolutionary associations suggest divergent selection between species as well as explains K‐values < 1. Experimental studies with multiple lineages of forest and savanna taxa may improve our understanding of the role of nectaries in plants. Overall, our results suggest that the evolution of EFNs was likely associated with the adaptive process which probably played an important role in the diversification of this plant group.     

Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes?

Aim Species distribution models and geographical information system (GIS) technologies are becoming increasingly important tools in conservation planning and decision‐making. Often the rich data bases of museums and herbaria serve as the primary data for predicting species distributions. Yet key assumptions about the primary data often are untested, and violation of such assumptions may have consequences for model predictions. For example, users of primary data assume that sampling has been random with respect to geography and environmental gradients. Here we evaluate the assumption that plant voucher specimens adequately sample the climatic gradient and test whether violation of this assumption influences model predictions. Location Bolivia and Ecuador. Methods Using 323,711 georeferenced herbarium collections and nine climatic variables, we predicted the distribution of 76 plant species using maximum entropy models (MAXENT) with training points that sampled the climate environments randomly and training points that reflected the climate bias in the herbarium collections. To estimate the distribution of species, MAXENT finds the distribution of maximum entropy (i.e. closest to uniform) subject to the constraint that the expected value for each environmental variable under the estimated distribution matches its empirical average. The experimental design included species that differed in geographical range and elevation; all species were modelled with 20 and 100 training points. We examined the influence of the number of training points and climate bias in training points, elevation and range size on model performance using analysis of variance models. Results We found that significant parts of the climatic gradient were poorly represented in herbarium collections for both countries. For the most part, existing climatic bias in collections did not greatly affect distribution predictions when compared with an unbiased data set. Although the effects of climate bias on prediction accuracy were found to be greater where geographical ranges were characterized by high spatial variation in the degree of climate bias (i.e. ranges where the bias of the various climates sampled by collections deviated considerably from the mean bias), the greatest influence on model performance was the number of presence points used to train the model. Main conclusions These results demonstrate that predictions of species distributions can be quite good despite existing climatic biases in primary data found in natural history collections, if a sufficiently large number of training points is available. Because of consistent overprediction of models, these results also confirm the importance of validating models with independent data or expert opinion. Failure to include independent model validation, especially in cases where training points are limited, may potentially lead to grave errors in conservation decision‐making and planning.