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Temperature-sensitive (ts) mutations are mutations that exhibit a mutant phenotype at high or low temperatures and a wild-type phenotype at normal temperature. Temperature-sensitive mutants are valuable tools for geneticists, particularly in the study of essential genes. However, finding ts mutations typically relies on generating and screening many thousands of mutations, which is an expensive and labor-intensive process. Here we describe an in silico method that uses Rosetta and machine learning techniques to predict a highly accurate \"top 5\" list of ts mutations given the structure of a protein of interest. Rosetta is a protein structure prediction and design code, used here to model and score how proteins accommodate point mutations with side-chain and backbone movements. We show that integrating Rosetta relax-derived features with sequence-based features results in accurate temperature-sensitive mutation predictions.

This analysis comprehensively compares methods for gene regulatory network inference submitted through the DREAM5 challenge. It demonstrates that integration of predictions from multiple methods shows the most robust performance across data sets. Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli , Staphylococcus aureus , Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus , each comprising ∼1,700 transcriptional interactions at a precision of ∼50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli , of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The tools of computer science can be a tremendous help to the working biologist. Two broad areas where this is particularly true are visualization and prediction. In visualization, the size of the data involved often makes meaningful exploration of the data and discovery of salient features difficult and time-consuming. Similarly, intelligent prediction algorithms can greatly reduce the lab time required to achieve significant results, or can reduce an intractable space of potential experiments to a tractable size. We describe two specific projects designed with these precepts in mind. The first, a software program called Sungear (Poultney et al., 2007; Poultney and Shasha, 2009), is a visualization tool that shows the outcomes of N experiments on M entities. Sungear displays sets of entities—the intersections of these outcomes—within a generalization of the traditional Venn diagram, and provides an interactive, visual system to answer queries about the data. The second project addresses the issue of rational design of temperature-sensitive mutants. Temperature-sensitive mutations are a tremendously valuable research tool, but to date, most methods for generating such mutants involve large-scale random mutations followed by an intensive screening and characterization process. Surprisingly little work has been done in the area of predicting these mutations given the importance they play in many genetic and functional studies of gene function. Furthermore, the existing methods are based entirely on secondary sequence, and do not use 3-dimensional structure. We describe a system that, given the structure of a protein of interest, uses a combination of protein structure prediction and machine learning to provide a ranked list of likely candidates for temperature-sensitive mutations.

Cutinases have been exploited for a broad range of reactions, from hydrolysis of soluble and insoluble esters to polymer synthesis. To further expand the biotechnological applications of cutinases for synthetic polyester degradation, we perform a comparative activity and stability analysis of five cutinases from Alternaria brassicicola (AbC), Aspergillus fumigatus (AfC), Aspergillus oryzae (AoC), Humicola insolens (HiC), and the well-characterized Fusarium solani (FsC). Of the cutinases, HiC demonstrated enhanced poly([straight epsilon]-caprolactone) hydrolysis at high temperatures and under all pH values, followed by AoC and AfC. Both AbC and FsC are least stable and function poorly at high temperatures as well as at acidic pH conditions. Surface charge calculations and phylogenetic analysis reveal two important modes of cutinase stabilization: 1. an overall neutral surface charge within the crowning area by the active site and 2. additional disulfide bond formation. These studies provide insights useful for reengineering such enzymes with improved function and stability for a wide range of biotransformations. [PUBLICATION ABSTRACT]

Survey of postzygotic mosaic mutations (PZMs) in 5,947 trios with autism spectrum disorders (ASD) discovers differences in mutational properties between germline mutations and PZMs. Spatiotemporal analyses of the PZMs also revealed the association of the amygdala with ASD and implicated risk genes, including recurrent potential gain-of-function mutations in SMARCA4 . We systematically analyzed postzygotic mutations (PZMs) in whole-exome sequences from the largest collection of trios (5,947) with autism spectrum disorder (ASD) available, including 282 unpublished trios, and performed resequencing using multiple independent technologies. We identified 7.5% of de novo mutations as PZMs, 83.3% of which were not described in previous studies. Damaging, nonsynonymous PZMs within critical exons of prenatally expressed genes were more common in ASD probands than controls ( P < 1 × 10 −6 ), and genes carrying these PZMs were enriched for expression in the amygdala ( P = 5.4 × 10 −3 ). Two genes ( KLF16 and MSANTD2 ) were significantly enriched for PZMs genome-wide, and other PZMs involved genes ( SCN2A , HNRNPU and SMARCA4 ) whose mutation is known to cause ASD or other neurodevelopmental disorders. PZMs constitute a significant proportion of de novo mutations and contribute importantly to ASD risk.

Elise Robinson and colleagues present the polygenic transmission disequilibrium test (pTDT) for evaluating transmission of polygenic risk in family-based study designs. The authors apply pTDT to a cohort of autism spectrum disorder (ASD) families and find that common polygenic variation acts additively with de novo variants to contribute to ASD risk. Autism spectrum disorder (ASD) risk is influenced by common polygenic and de novo variation. We aimed to clarify the influence of polygenic risk for ASD and to identify subgroups of ASD cases, including those with strongly acting de novo variants, in which polygenic risk is relevant. Using a novel approach called the polygenic transmission disequilibrium test and data from 6,454 families with a child with ASD, we show that polygenic risk for ASD, schizophrenia, and greater educational attainment is over-transmitted to children with ASD. These findings hold independent of proband IQ. We find that polygenic variation contributes additively to risk in ASD cases who carry a strongly acting de novo variant. Lastly, we show that elements of polygenic risk are independent and differ in their relationship with phenotype. These results confirm that the genetic influences on ASD are additive and suggest that they create risk through at least partially distinct etiologic pathways.

Background Over the past decade genome-wide association studies (GWAS) have been applied to aid in the understanding of the biology of traits. The success of this approach is governed by the underlying effect sizes carried by the true risk variants and the corresponding statistical power to observe such effects given the study design and sample size under investigation. Previous ASD GWAS have identified genome-wide significant (GWS) risk loci; however, these studies were of only of low statistical power to identify GWS loci at the lower effect sizes (odds ratio (OR) <1.15). Methods We conducted a large-scale coordinated international collaboration to combine independent genotyping data to improve the statistical power and aid in robust discovery of GWS loci. This study uses genome-wide genotyping data from a discovery sample (7387 ASD cases and 8567 controls) followed by meta-analysis of summary statistics from two replication sets (7783 ASD cases and 11359 controls; and 1369 ASD cases and 137308 controls). Results We observe a GWS locus at 10q24.32 that overlaps several genes including PITX3 , which encodes a transcription factor identified as playing a role in neuronal differentiation and CUEDC2 previously reported to be associated with social skills in an independent population cohort. We also observe overlap with regions previously implicated in schizophrenia which was further supported by a strong genetic correlation between these disorders (Rg = 0.23; P  = 9 × 10 −6 ). We further combined these Psychiatric Genomics Consortium (PGC) ASD GWAS data with the recent PGC schizophrenia GWAS to identify additional regions which may be important in a common neurodevelopmental phenotype and identified 12 novel GWS loci. These include loci previously implicated in ASD such as FOXP1 at 3p13, ATP2B2 at 3p25.3, and a ‘neurodevelopmental hub’ on chromosome 8p11.23. Conclusions This study is an important step in the ongoing endeavour to identify the loci which underpin the common variant signal in ASD. In addition to novel GWS loci, we have identified a significant genetic correlation with schizophrenia and association of ASD with several neurodevelopmental-related genes such as EXT1 , ASTN2 , MACROD2 , and HDAC4.