Functional characterisation of naturally occurring mutations in human melanopsin

Melanopsin is a blue light-sensitive opsin photopigment involved in a range of non-image forming behaviours, including circadian photoentrainment and the pupil light response. Many naturally occurring genetic variants exist within the human melanopsin gene (OPN4), yet it remains unclear how these variants affect melanopsin protein function and downstream physiological responses to light. Here, we have used bioinformatic analysis and in vitro expression systems to determine the functional phenotypes of missense human OPN4 variants. From 1242 human OPN4 variants collated in the NCBI Short Genetic Variation database (dbSNP), we identified 96 that lead to non-synonymous amino acid substitutions. These 96 missense mutations were screened using sequence alignment and comparative approaches to select 16 potentially deleterious variants for functional characterisation using calcium imaging of melanopsin-driven light responses in HEK293T cells. We identify several previously uncharacterised OPN4 mutations with altered functional properties, including attenuated or abolished light responses, as well as variants demonstrating abnormal response kinetics. These data provide valuable insight into the structure–function relationships of human melanopsin, including several key functional residues of the melanopsin protein. The identification of melanopsin variants with significantly altered function may serve to detect individuals with disrupted melanopsin-based light perception, and potentially highlight those at increased risk of sleep disturbance, circadian dysfunction, and visual abnormalities.     

Volatility forecasting with bivariate multifractal models

This paper examines volatility linkages and forecasting for stock and foreign exchange markets from a novel perspective by utilizing a bivariate Markov-switching multifractal model that accounts for possible interactions between stock and foreign exchange markets. Examining daily data from major advanced and emerging nations, we show that generalized autoregressive conditional heteroskedasticity models generally offer superior volatility forecasts for short horizons, particularly for foreign exchange returns in advanced markets. Multifractal models, on the other hand, offer significant improvements for longer horizons, consistently across most markets. Finally, the bivariate multifractal model provides superior forecasts compared to the univariate alternative in most advanced markets and more consistently for currency returns, while its benefits are limited in the case of emerging markets.     

TMEM70 mutations cause isolated ATP synthase deficiency and neonatal mitochondrial encephalocardiomyopathy

We carried out whole-genome homozygosity mapping, gene expression analysis and DNA sequencing in individuals with isolated mitochondrial ATP synthase deficiency and identified disease-causing mutations in TMEM70. Complementation of the cell lines of these individuals with wild-type TMEM70 restored biogenesis and metabolic function of the enzyme complex. Our results show that TMEM70 is involved in mitochondrial ATP synthase biogenesis in higher eukaryotes.     

Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs

Accurate and complete measurement of single nucleotide (SNP) and copy number (CNV) variants, both common and rare, will be required to understand the role of genetic variation in disease. We present Birdsuite, a four-stage analytical framework instantiated in software for deriving integrated and mutually consistent copy number and SNP genotypes. The method sequentially assigns copy number across regions of common copy number polymorphisms (CNPs), calls genotypes of SNPs, identifies rare CNVs via a hidden Markov model (HMM), and generates an integrated sequence and copy number genotype at every locus (for example, including genotypes such as A-null, AAB and BBB in addition to AA, AB and BB calls). Such genotypes more accurately depict the underlying sequence of each individual, reducing the rate of apparent mendelian inconsistencies. The Birdsuite software is applied here to data from the Affymetrix SNP 6.0 array. Additionally, we describe a method, implemented in PLINK, to utilize these combined SNP and CNV genotypes for association testing with a phenotype.     

SNP and haplotype mapping for genetic analysis in the rat

The laboratory rat is one of the most extensively studied model organisms. Inbred laboratory rat strains originated from limited Rattus norvegicus founder populations, and the inherited genetic variation provides an excellent resource for the correlation of genotype to phenotype. Here, we report a survey of genetic variation based on almost 3 million newly identified SNPs. We obtained accurate and complete genotypes for a subset of 20,238 SNPs across 167 distinct inbred rat strains, two rat recombinant inbred panels and an F2 intercross. Using 81% of these SNPs, we constructed high-density genetic maps, creating a large dataset of fully characterized SNPs for disease gene mapping. Our data characterize the population structure and illustrate the degree of linkage disequilibrium. We provide a detailed SNP map and demonstrate its utility for mapping of quantitative trait loci. This community resource is openly available and augments the genetic tools for this workhorse of physiological studies.     

Crystal structure of an Hsp90-nucleotide-p23/Sba1 closed chaperone complex

Hsp90 (heat shock protein of 90 kDa) is a ubiquitous molecular chaperone responsible for the assembly and regulation of many eukaryotic signalling systems and is an emerging target for rational chemotherapy of many cancers. Although the structures of isolated domains of Hsp90 have been determined, the arrangement and ATP-dependent dynamics of these in the full Hsp90 dimer have been elusive and contentious. Here we present the crystal structure of full-length yeast Hsp90 in complex with an ATP analogue and the co-chaperone p23/Sba1. The structure reveals the complex architecture of the 'closed' state of the Hsp90 chaperone, the extensive interactions between domains and between protein chains, the detailed conformational changes in the amino-terminal domain that accompany ATP binding, and the structural basis for stabilization of the closed state by p23/Sba1. Contrary to expectations, the closed Hsp90 would not enclose its client proteins but provides a bipartite binding surface whose formation and disruption are coupled to the chaperone ATPase cycle.     

Clusters of iron-rich cells in the upper beak of pigeons are macrophages not magnetosensitive neurons

Understanding the molecular and cellular mechanisms that mediate magnetosensation in vertebrates is a formidable scientific problem. One hypothesis is that magnetic information is transduced into neuronal impulses by using a magnetite-based magnetoreceptor. Previous studies claim to have identified a magnetic sense system in the pigeon, common to avian species, which consists of magnetite-containing trigeminal afferents located at six specific loci in the rostral subepidermis of the beak. These studies have been widely accepted in the field and heavily relied upon by both behavioural biologists and physicists. Here we show that clusters of iron-rich cells in the rostro-medial upper beak of the pigeon Columbia livia are macrophages, not magnetosensitive neurons. Our systematic characterization of the pigeon upper beak identified iron-rich cells in the stratum laxum of the subepidermis, the basal region of the respiratory epithelium and the apex of feather follicles. Using a three-dimensional blueprint of the pigeon beak created by magnetic resonance imaging and computed tomography, we mapped the location of iron-rich cells, revealing unexpected variation in their distribution and number--an observation that is inconsistent with a role in magnetic sensation. Ultrastructure analysis of these cells, which are not unique to the beak, showed that their subcellular architecture includes ferritin-like granules, siderosomes, haemosiderin and filopodia, characteristics of iron-rich macrophages. Our conclusion that these cells are macrophages and not magnetosensitive neurons is supported by immunohistological studies showing co-localization with the antigen-presenting molecule major histocompatibility complex class II. Our work necessitates a renewed search for the true magnetite-dependent magnetoreceptor in birds.     

The worldwide leaf economics spectrum

Bringing together leaf trait data spanning 2,548 species and 175 sites we describe, for the first time at global scale, a universal spectrum of leaf economics consisting of key chemical, structural and physiological properties. The spectrum runs from quick to slow return on investments of nutrients and dry mass in leaves, and operates largely independently of growth form, plant functional type or biome. Categories along the spectrum would, in general, describe leaf economic variation at the global scale better than plant functional types, because functional types overlap substantially in their leaf traits. Overall, modulation of leaf traits and trait relationships by climate is surprisingly modest, although some striking and significant patterns can be seen. Reliable quantification of the leaf economics spectrum and its interaction with climate will prove valuable for modelling nutrient fluxes and vegetation boundaries under changing land-use and climate.     

A Machine Learning Approach for Mechanism Selection in Complex Negotiations

Automated negotiation mechanisms can be helpful in contexts where users want to reach mutually satisfactory agreements about issues of shared interest, especially for complex problems with many interdependent issues. A variety of automated negotiation mechanisms have been proposed in the literature. The effectiveness of those mechanisms, however, may depend on the characteristics of the underlying negotiation problem (e.g. on the complexity of participant’s utility functions, as well as the degree of conflict between participants). While one mechanism may be a good choice for a negotiation problem, it may be a poor choice for another. In this paper, we pursue the problem of selecting the most effective negotiation mechanism given a particular problem by (1) defining a set of scenario metrics to capture the relevant features of negotiation problems, (2) evaluating the performance of a range of negotiation mechanisms on a diverse test suite of negotiation scenarios, (3) applying machine learning techniques to identify which mechanisms work best with which scenarios, and (4) demonstrating that using these classification rules for mechanism selection enables significantly better negotiation performance than any single mechanism alone.