A framework for exhaustively mapping functional missense variants

Although we now routinely sequence human genomes, we can confidently identify only a fraction of the sequence variants that have a functional impact. Here, we developed a deep mutational scanning framework that produces exhaustive maps for human missense variants by combining random codon mutagenesis and multiplexed functional variation assays with computational imputation and refinement. We applied this framework to four proteins corresponding to six human genes: UBE2I (encoding SUMO E2 conjugase), SUMO1 (small ubiquitin‐like modifier), TPK1 (thiamin pyrophosphokinase), and CALM1/2/3 (three genes encoding the protein calmodulin). The resulting maps recapitulate known protein features and confidently identify pathogenic variation. Assays potentially amenable to deep mutational scanning are already available for 57% of human disease genes, suggesting that DMS could ultimately map functional variation for all human disease genes.     

Pathways database system: an integrated system for biological pathways

MOTIVATION: During the next phase of the Human Genome Project, research will focus on functional studies of attributing functions to genes, their regulatory elements, and other DNA sequences. To facilitate the use of genomic information in such studies, a new modeling perspective is needed to examine and study genome sequences in the context of many kinds of biological information. Pathways are the logical format for modeling and presenting such information in a manner that is familiar to biological researchers. RESULTS: In this paper we present an integrated system, called Pathways Database System, with a set of software tools for modeling, storing, analyzing, visualizing, and querying biological pathways data at different levels of genetic, molecular, biochemical and organismal detail. The novel features of the system include: (a) genomic information integrated with other biological data and presented from a pathway, rather than from the DNA sequence, perspective; (b) design for biologists who are possibly unfamiliar with genomics, but whose research is essential for annotating gene and genome sequences with biological functions; (c) database design, implementation and graphical tools which enable users to visualize pathways data in multiple abstraction levels, and to pose predetermined queries; and (d) an implementation that allows for web(XML)-based dissemination of query outputs (i.e. pathways data) to researchers in the community, giving them control on the use of pathways data. AVAILABILITY: Available on request from the authors.     

Reduced anxiety-like and depression-related behavior in neuropeptide Y Y4 receptor knockout mice

Neuropeptide Y (NPY) acting through Y1 receptors reduces anxiety- and depression-like behavior in rodents, whereas Y2 receptor stimulation has the opposite effect. This study addressed the implication of Y4 receptors in emotional behavior by comparing female germ line Y4 knockout (Y4−/−) mice with control and germ line Y2−/− animals. Anxiety- and depression-like behavior was assessed with the open field (OF), elevated plus maze (EPM), stress-induced hyperthermia (SIH) and tail suspension tests (TST), respectively. Learning and memory were evaluated with the object recognition test (ORT). In the OF and EPM, both Y4−/− and Y2−/− mice exhibited reduced anxiety-related behavior and enhanced locomotor activity relative to control animals. Locomotor activity in a familiar environment was unchanged in Y4−/− but reduced in Y2−/− mice. The basal rectal temperature exhibited diurnal and genotype-related alterations. Control mice had temperature minima at noon and midnight, whereas Y4−/− and Y2−/− mice displayed only one temperature minimum at noon. The magnitude of SIH was related to time of the day and genotype in a complex manner. In the TST, the duration of immobility was significantly shorter in Y4−/− and Y2−/− mice than in controls. Object memory 6 h after initial exposure to the ORT was impaired in Y2−/− but not in Y4−/− mice, relative to control mice. These results show that genetic deletion of Y4 receptors, like that of Y2 receptors, reduces anxiety-like and depression-related behavior. Unlike Y2 receptor knockout, Y4 receptor knockout does not impair object memory. We propose that Y4 receptors play an important role in the regulation of behavioral homeostasis.     

Prioritizing causal disease genes using unbiased genomic features

Background

Cardiovascular disease (CVD) is the leading cause of death in the developed world. Human genetic studies, including genome-wide sequencing and SNP-array approaches, promise to reveal disease genes and mechanisms representing new therapeutic targets. In practice, however, identification of the actual genes contributing to disease pathogenesis has lagged behind identification of associated loci, thus limiting the clinical benefits.

Results

To aid in localizing causal genes, we develop a machine learning approach, Objective Prioritization for Enhanced Novelty (OPEN), which quantitatively prioritizes gene-disease associations based on a diverse group of genomic features. This approach uses only unbiased predictive features and thus is not hampered by a preference towards previously well-characterized genes. We demonstrate success in identifying genetic determinants for CVD-related traits, including cholesterol levels, blood pressure, and conduction system and cardiomyopathy phenotypes. Using OPEN, we prioritize genes, including FLNC, for association with increased left ventricular diameter, which is a defining feature of a prevalent cardiovascular disorder, dilated cardiomyopathy or DCM. Using a zebrafish model, we experimentally validate FLNC and identify a novel FLNC splice-site mutation in a patient with severe DCM.

Conclusion

Our approach stands to assist interpretation of large-scale genetic studies without compromising their fundamentally unbiased nature.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0534-8) contains supplementary material, which is available to authorized users.     

Using spray-dried and encapsulated Nannochloropsis oculata biomasses in white spread

Journal of Applied Phycology - In this study, dried or encapsulated Nannochloropsis oculata microalgal biomass was used in spread samples (0.00–0.75 g (100 g)?1...     

A critical assessment of <Emphasis Type="Italic">Mus musculus</Emphasis>gene function prediction using integrated genomic evidence

Background:

Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated.

Results:

In this study, a standardized collection of mouse functional genomic data was assembled; nine bioinformatics teams used this data set to independently train classifiers and generate predictions of function, as defined by Gene Ontology (GO) terms, for 21,603 mouse genes; and the best performing submissions were combined in a single set of predictions. We identified strengths and weaknesses of current functional genomic data sets and compared the performance of function prediction algorithms. This analysis inferred functions for 76% of mouse genes, including 5,000 currently uncharacterized genes. At a recall rate of 20%, a unified set of predictions averaged 41% precision, with 26% of GO terms achieving a precision better than 90%.

Conclusion:

We performed a systematic evaluation of diverse, independently developed computational approaches for predicting gene function from heterogeneous data sources in mammals. The results show that currently available data for mammals allows predictions with both breadth and accuracy. Importantly, many highly novel predictions emerge for the 38% of mouse genes that remain uncharacterized.

     

Association of PARP-1, NF-κB,NF-κBIA and IL-6, IL-1β and TNF-α with Graves Disease and Graves Ophthalmopathy

Background

Graves Disease (GD) is an autoimmune disorder affected by an interaction of multiple genes such as Nuclear Factor-κB (NF-κB), Nuclear Factor-κB Inhibitor (NF-κBIA), Poly (ADP-ribose) polymerase-1 (PARP-1) and cytokines like Interleukin-1β (IL-1β), Interleukin-6 (IL-6) and Tumor Necrosis Factor-α (TNF-α) and mostly accompanied by an ocular disorder, Graves Ophthalmopathy (GO). We hypothesize that there is a relationship between GD, GO, polymorphisms of inflammatory related genes and their association with cytokines, which may play important roles in autoimmune and inflammatory processes.

Subjects and methods

To confirm our hypothesis, we studied the polymorphisms and cytokine levels of 120 patients with GD and GO using PCR-RFLP and ELISA methods, respectively.

Results

We found that patients with GG genotype and carriers of G allele of PARP-1 G1672A polymorphism are at risk in the group having GD (p = 0.0007) while having GA genotype may be protective against the disease. PARP-1 C410T polymorphism was found to be associated with GO by increasing the risk by 1.7 times (p = 0.004). Another risk factor for development of GO was the polymorphism of del/ins of NFkB1 gene (p = 0.032) that increases the risk by 39%. Levels of cytokines were also elevated in patients with GD, but no association was found between levels of cytokines and the development of GO as there was no change in levels of cytokines.

Conclusions

We suggest that, PARP-1 and NFkB1 gene polymorphisms may be risk factors for developing Graves Disease and Ophthalmopathy.