The disordered PCI‐binding human proteins CSNAP and DSS1 have diverged in structure and function

Intrinsically disordered proteins (IDPs) regularly constitute components of larger protein assemblies contributing to architectural stability. Two small, highly acidic IDPs have been linked to the so‐called PCI complexes carrying PCI‐domain subunits, including the proteasome lid and the COP9 signalosome. These two IDPs, DSS1 and CSNAP, have been proposed to have similar structural propensities and functions, but they display differences in their interactions and interactome sizes. Here we characterized the structural properties of human DSS1 and CSNAP at the residue level using NMR spectroscopy and probed their propensities to bind ubiquitin. We find that distinct structural features present in DSS1 are completely absent in CSNAP, and vice versa, with lack of relevant ubiquitin binding to CSNAP, suggesting the two proteins to have diverged in both structure and function. Our work additionally highlights that different local features of seemingly similar IDPs, even subtle sequence variance, may endow them with different functional traits. Such traits may underlie their potential to engage in multiple interactions thereby impacting their interactome sizes.     

Contrasting demographic histories of the neighboring bonobo and chimpanzee

The Pleistocene epoch was a period of dramatic climate change that had profound impacts on the population sizes of many animal species. How these species were shaped by past events is often unclear, hindering our understanding of the population dynamics resulting in present day populations. We analyzed complete mitochondrial genomes representing all four recognized chimpanzee subspecies and the bonobo to infer the recent demographic history and used simulations to exclude a confounding effect of population structure. Our genus-wide Bayesian coalescent-based analysis revealed surprisingly dissimilar demographic histories of the chimpanzee subspecies and the bonobo, despite their overlapping habitat requirements. Whereas the central and eastern chimpanzee subspecies were inferred to have expanded tenfold between around 50,000 and 80,000 years ago and today, the population size of the neighboring bonobo remained constant. The changes in population size are likely linked to changes in habitat area due to climate oscillations during the late Pleistocene. Furthermore, the timing of population expansion for the rainforest-adapted chimpanzee is concurrent with the expansion of the savanna-adapted human, which could suggest a common response to changed climate conditions around 50,000–80,000 years ago.     

Herp regulates Hrd1-mediated ubiquitylation in a ubiquitin-like domain-dependent manner

Accumulation of aberrant proteins in the endoplasmic reticulum (ER) triggers the unfolded protein response pathway that helps the cell to survive under these stress conditions. Herp is a mammalian ubiquitin domain protein, which is strongly induced by the unfolded protein response. It is involved in ER-associated protein degradation (ERAD) and interacts directly with the ubiquitin ligase Hrd1, which is found in high molecular mass complexes of the ER membrane. Here we present the first evidence that Herp regulates Hrd1-mediated ubiquitylation in a ubiquitin-like (UBL) domain-dependent manner. We found that upon exposure of cells to ER stress, elevation of Herp steady state levels is accompanied by an enhanced association of Herp with pre-existing Hrd1. Hrd1-associated Herp is rapidly degraded and substituted by de novo synthesized Herp, suggesting a continuous turnover of the protein at Hrd1 complexes. Further analysis revealed the presence of multiple Hrd1 copies in a single complex enabling binding of a variable number of Herp molecules. Efficient ubiquitylation of the Hrd1-specific ERAD substrate α1-antitrypsin null Hong Kong (NHK) required the presence of the Herp UBL domain, which was also necessary for NHK degradation. In summary, we propose that binding of Herp to Hrd1-containing ERAD complexes positively regulates the ubiquitylation activity of these complexes, thus permitting survival of the cell during ER stress.     

Protection against Atlantic halibut nodavirus in turbot is induced by recombinant capsid protein vaccination but not following DNA vaccination

Fish nodaviruses (betanodaviruses) are small, non-enveloped icosahedral single-stranded positive-sense RNA viruses that can cause viral encephalopathy and retinopathy (VER) in a number of cultured marine teleost species, including Atlantic halibut (Hippoglossus hippoglossus). A recombinant protein vaccine and a DNA vaccine were produced, based on the same capsid-encoding region of the Atlantic halibut nodavirus (AHNV) genome, and tested for protection in juvenile turbot (Scophthalmus maximus). Vaccine efficacy was demonstrated in the fish vaccinated with recombinant capsid protein but not in the DNA-vaccinated fish, despite the fact that in vivo expression of the DNA vaccine-encoded antigen was confirmed by RNA in situ hybridisation and immunohistochemistry. Combined DNA and recombinant vaccine administration did not improve the effect of the latter. Surprisingly, fish vaccinated with 50 microg recombinant protein demonstrated a threefold lower survival rate than the two groups that received 10 microg recombinant protein. Neither the recombinant protein vaccine nor the DNA vaccine induced anti-viral antibodies 9 weeks after immunisation, while antibodies reactive with the recombinant protein were detectable mainly in fish vaccinated with 50 microg recombinant protein. The study also demonstrates evidence of viral replication inside the myocytes of intramuscularly challenged fish.     

A Model-Based Approach for Identifying Signatures of Ancient Balancing Selection in Genetic Data

While much effort has focused on detecting positive and negative directional selection in the human genome, relatively little work has been devoted to balancing selection. This lack of attention is likely due to the paucity of sophisticated methods for identifying sites under balancing selection. Here we develop two composite likelihood ratio tests for detecting balancing selection. Using simulations, we show that these methods outperform competing methods under a variety of assumptions and demographic models. We apply the new methods to whole-genome human data, and find a number of previously-identified loci with strong evidence of balancing selection, including several HLA genes. Additionally, we find evidence for many novel candidates, the strongest of which is FANK1, an imprinted gene that suppresses apoptosis, is expressed during meiosis in males, and displays marginal signs of segregation distortion. We hypothesize that balancing selection acts on this locus to stabilize the segregation distortion and negative fitness effects of the distorter allele. Thus, our methods are able to reproduce many previously-hypothesized signals of balancing selection, as well as discover novel interesting candidates.     

Identification of anticancer drugs for hepatocellular carcinoma through personalized genome‐scale metabolic modeling

Genome‐scale metabolic models (GEMs) have proven useful as scaffolds for the integration of omics data for understanding the genotype–phenotype relationship in a mechanistic manner. Here, we evaluated the presence/absence of proteins encoded by 15,841 genes in 27 hepatocellular carcinoma (HCC) patients using immunohistochemistry. We used this information to reconstruct personalized GEMs for six HCC patients based on the proteomics data, HMR 2.0, and a task‐driven model reconstruction algorithm (tINIT). The personalized GEMs were employed to identify anticancer drugs using the concept of antimetabolites; i.e., drugs that are structural analogs to metabolites. The toxicity of each antimetabolite was predicted by assessing the in silico functionality of 83 healthy cell type‐specific GEMs, which were also reconstructed with the tINIT algorithm. We predicted 101 antimetabolites that could be effective in preventing tumor growth in all HCC patients, and 46 antimetabolites which were specific to individual patients. Twenty‐two of the 101 predicted antimetabolites have already been used in different cancer treatment strategies, while the remaining antimetabolites represent new potential drugs. Finally, one of the identified targets was validated experimentally, and it was confirmed to attenuate growth of the HepG2 cell line.     

Structure-revealing data fusion

Background

Analysis of data from multiple sources has the potential to enhance knowledge discovery by capturing underlying structures, which are, otherwise, difficult to extract. Fusing data from multiple sources has already proved useful in many applications in social network analysis, signal processing and bioinformatics. However, data fusion is challenging since data from multiple sources are often (i) heterogeneous (i.e., in the form of higher-order tensors and matrices), (ii) incomplete, and (iii) have both shared and unshared components. In order to address these challenges, in this paper, we introduce a novel unsupervised data fusion model based on joint factorization of matrices and higher-order tensors.

Results

While the traditional formulation of coupled matrix and tensor factorizations modeling only shared factors fails to capture the underlying structures in the presence of both shared and unshared factors, the proposed data fusion model has the potential to automatically reveal shared and unshared components through modeling constraints. Using numerical experiments, we demonstrate the effectiveness of the proposed approach in terms of identifying shared and unshared components. Furthermore, we measure a set of mixtures with known chemical composition using both LC-MS (Liquid Chromatography - Mass Spectrometry) and NMR (Nuclear Magnetic Resonance) and demonstrate that the structure-revealing data fusion model can (i) successfully capture the chemicals in the mixtures and extract the relative concentrations of the chemicals accurately, (ii) provide promising results in terms of identifying shared and unshared chemicals, and (iii) reveal the relevant patterns in LC-MS by coupling with the diffusion NMR data.

Conclusions

We have proposed a structure-revealing data fusion model that can jointly analyze heterogeneous, incomplete data sets with shared and unshared components and demonstrated its promising performance as well as potential limitations on both simulated and real data.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-239) contains supplementary material, which is available to authorized users.     

Strategies for imputation to whole genome sequence using a single or multi-breed reference population in cattle

Background

The advent of low cost next generation sequencing has made it possible to sequence a large number of dairy and beef bulls which can be used as a reference for imputation of whole genome sequence data. The aim of this study was to investigate the accuracy and speed of imputation from a high density SNP marker panel to whole genome sequence level. Data contained 132 Holstein, 42 Jersey, 52 Nordic Red and 16 Brown Swiss bulls with whole genome sequence data; 16 Holstein, 27 Jersey and 29 Nordic Reds had previously been typed with the bovine high density SNP panel and were used for validation. We investigated the effect of enlarging the reference population by combining data across breeds on the accuracy of imputation, and the accuracy and speed of both IMPUTE2 and BEAGLE using either genotype probability reference data or pre-phased reference data. All analyses were done on Bovine autosome 29 using 387,436 bi-allelic variants and 13,612 SNP markers from the bovine HD panel.

Results

A combined breed reference population led to higher imputation accuracies than did a single breed reference. The highest accuracy of imputation for all three test breeds was achieved when using BEAGLE with un-phased reference data (mean genotype correlations of 0.90, 0.89 and 0.87 for Holstein, Jersey and Nordic Red respectively) but IMPUTE2 with un-phased reference data gave similar accuracies for Holsteins and Nordic Red. Pre-phasing the reference data only lead to a minor decrease in the imputation accuracy, but gave a large improvement in computation time. Pre-phasing with BEAGLE was substantially faster than pre-phasing with SHAPEIT2 (2.5 hours vs. 52 hours for 242 individuals), and imputation with pre-phased data was faster in IMPUTE2 than in BEAGLE (5 minutes vs. 50 minutes per individual).

Conclusion

Combining reference populations across breeds is a good option to increase the size of the reference data and in turn the accuracy of imputation when only few animals are available. Pre-phasing the reference data only slightly decreases the accuracy but gives substantial improvements in speed. Using BEAGLE for pre-phasing and IMPUTE2 for imputation is a fast and accurate strategy.