Mutant Phenotype Data Research Articles

CHD7 variants associated with hearing loss and enlargement of the vestibular aqueduct

Enlargement of the endolymphatic sac, duct, and vestibular aqueduct (EVA) is the most common inner ear malformation identified in patients with sensorineural hearing loss. EVA is associated with pathogenic variants in SLC26A4. However, in European–Caucasian populations, about 50% of patients with EVA carry no pathogenic alleles of SLC26A4. We tested for the presence of variants in CHD7, a gene known to be associated with CHARGE syndrome, Kallmann syndrome, and hypogonadotropic hypogonadism, in a cohort of 34 families with EVA subjects without pathogenic alleles of SLC26A4. In two families, NM_017780.4: c.3553A > G [p.(Met1185Val)] and c.5390G > C [p.(Gly1797Ala)] were detected as monoallelic CHD7 variants in patients with EVA. At least one subject from each family had additional signs or potential signs of CHARGE syndrome but did not meet diagnostic criteria for CHARGE. In silico modeling of these two missense substitutions predicted detrimental effects upon CHD7 protein structure. Consistent with a role of CHD7 in this tissue, Chd7 transcript and protein were detected in all epithelial cells of the endolymphatic duct and sac of the developing mouse inner ear. These results suggest that some CHD7 variants can cause nonsyndromic hearing loss and EVA. CHD7 should be included in DNA sequence analyses to detect pathogenic variants in EVA patients. Chd7 expression and mutant phenotype data in mice suggest that CHD7 contributes to the formation or function of the endolymphatic sac and duct.

Genome-Wide Analysis of Sugar Transporters Identifies the gtsA Gene for Glucose Transportation in Pseudomonas stutzeri A1501.

Pseudomonas stutzeri A1501 possesses an extraordinary number of transporters which confer this rhizosphere bacterium with the sophisticated ability to metabolize various carbon sources. However, sugars are not a preferred carbon source for P. stutzeri A1501. The P. stutzeri A1501 genome has been sequenced, allowing for the homology-based in silico identification of genes potentially encoding sugar-transport systems by using established microbial sugar transporters as a template sequence. Genomic analysis revealed that there were 10 sugar transporters in P. stutzeri A1501, most of which belong to the ATP-binding cassette (ABC) family (5/10); the others belong to the phosphotransferase system (PTS), major intrinsic protein (MIP) family, major facilitator superfamily (MFS) and the sodium solute superfamily (SSS). These systems might serve for the import of glucose, galactose, fructose and other types of sugar. Growth analysis showed that the only effective medium was glucose and its corresponding metabolic system was relatively complete. Notably, the loci of glucose metabolism regulatory systems HexR, GltR/GtrS, and GntR were adjacent to the transporters ABCMalEFGK, ABCGtsABCD, and ABCMtlEFGK, respectively. Only the ABCGtsABCD expression was significantly upregulated under both glucose-sufficient and -limited conditions. The predicted structure and mutant phenotype data of the key protein GtsA provided biochemical evidence that P. stutzeri A1501 predominantly utilized the ABCGtsABCD transporter for glucose uptake. We speculate that gene absence and gene diversity in P. stutzeri A1501 was caused by sugar-deficient environmental factors and hope that this report can provide guidance for further analysis of similar bacterial lifestyles.

Kinetic ensemble model of gas fermenting Clostridium autoethanogenum for improved ethanol production

Developing autotrophic, acetogenic bacteria strains as gas fermentation platforms is a promising avenue for converting industrial waste gas streams into valuable chemical products. One such strain, Clostridium autoethanogenum, naturally converts CO, CO2, and H2 gases into ethanol and acetate. Currently, lowering the acetate to ethanol production ratio is a key strategy for accomplishing large-scale industrial application of C. autoethanogenum gas fermentation. Unfortunately, the limited availability and time-intensive implementation of genetic engineering tools for clostridia strains greatly hinders metabolic engineering efforts toward this goal. To alleviate the lack of sufficient mutant phenotype data interrogating the pathways of interest, computational tools are needed to resolve experimental observations and predict engineering targets to help minimize experimental characterization in the lab. While stoichiometric models of C. autoethanogenum metabolism are available, they are unable to provide insight into regulatory relationships, rate-limiting steps, or the effects of altering enzyme expression. In this work, we offer the first kinetic representation of C. autoethanogenum core metabolism developed using the Ensemble Modeling (EM) framework. We have adapted the existing method to enable the usage of non-genetic perturbation data, specifically the effects of changing biomass concentration, to sample and train our kinetic parameter sets. Our final kinetic parameter ensemble accurately predicts intracellular metabolite concentrations and engineering strategies for improved ethanol production.

Functionalities of expressed messenger RNAs revealed from mutant phenotypes.

Total messenger RNAs mRNAs that are produced from a given gene under a certain set of conditions include both functional and nonfunctional transcripts. The high prevalence of nonfunctional mRNAs that have been detected in cells has raised questions regarding the functional implications of mRNA expression patterns and divergences. Phenotypes that result from the mutagenesis of protein-coding genes have provided the most straightforward descriptions of gene functions, and such data obtained from model organisms have facilitated investigations of the functionalities of expressed mRNAs. Mutant phenotype data from mouse tissues have revealed various attributes of functional mRNAs, including tissue-specificity, strength of expression, and evolutionary conservation. In addition, the role that mRNA expression evolution plays in driving morphological evolution has been revealed from studies designed to exploit morphological and physiological phenotypes of mouse mutants. Investigations into yeast essential genes (defined by an absence of colony growth after gene deletion) have further described gene regulatory strategies that reduce protein expression noise by mediating the rates of transcription and translation. In addition to the functional significance of expressed mRNAs as described in the abovementioned findings, the functionalities of other type of RNAs (i.e., noncoding RNAs) remain to be characterized with systematic mutations and phenotyping of the DNA regions that encode these RNA molecules. WIREs RNA 2016, 7:416-427. doi: 10.1002/wrna.1329 For further resources related to this article, please visit the WIREs website.

Visualizing septins in early Drosophila embryos.

Functional studies in Drosophila have been key for establishing a role for the septin family of proteins in animal cell division and thus extending for the first time observations from the budding yeast to animal cells. Visualizing the distribution of specific septins in different Drosophila tissues and, in particular, in the Drosophila embryo, together with biochemical and mutant phenotype data, has contributed important advances to our understanding of animal septin biology, suggesting roles in processes other than in cytokinesis. Septin localization using immunofluorescence assays has been possible due to the generation of antibodies against different Drosophila septins. The recent availability of lines expressing fluorescent protein fusions of specific septins further promises to facilitate studies on septin dynamics. Here, we provide protocols for preparing early Drosophila embryos to visualize septins using immunofluorescence assays and live fluorescence microscopy. The genetic tractability of the Drosophila embryo together with its amenability to high-resolution fluorescence microscopy promises to provide novel insights into animal septin structure and function.

Using the pathogen-host interactions database (PHI-base) to investigate plant pathogen genomes and genes implicated in virulence.

New pathogen-host interaction mechanisms can be revealed by integrating mutant phenotype data with genetic information. PHI-base is a multi-species manually curated database combining peer-reviewed published phenotype data from plant and animal pathogens and gene/protein information in a single database.

YAGM: a web tool for mining associated genes in yeast based on diverse biological associations

BackgroundInvestigating association between genes can be used in understanding the relations of genes in biological processes. STRING and GeneMANIA are two well-known web tools which can provide a list of associated genes of a query gene based on diverse biological associations such as co-expression, co-localization, co-citation and so on. However, the transcriptional regulation association and mutant phenotype association have not been used in these two web tools. Since the comprehensive transcription factor (TF)-gene binding data, TF-gene regulation data and mutant phenotype data are available in yeast, we developed a web tool called YAGM (Yeast Associated Genes Miner) which constructed the transcriptional regulation association, mutant phenotype association and five commonly used biological associations to mine a list of associated genes of a query yeast gene.DescriptionIn YAGM, we collected seven kinds of datasets including TF-gene binding (TFB) data, TF-gene regulation (TFR) data, mutant phenotype (MP) data, functional annotation (FA) data, physical interaction (PI) data, genetic interaction (GI) data, and literature evidence (LE) data. Then by using the hypergeometric test to calculate the association scores of all gene pairs in yeast, we constructed seven biological associations including two transcriptional regulation associations (TFB association and TFR association), MP association, FA association, PI association, GI association, and LE association. Moreover, the expression profile association from SPELL database was also included in YAGM. When using YAGM, users can input a query gene and choose any possible subsets of the eight biological associations, then a list of associated genes of the query gene will be returned based on the chosen biological associations.ConclusionsIn this study, we presented the YAGM which provides eight biological associations for mining associated genes of a query gene in yeast. Among the eight biological associations constructed in YAGM, three (TFB association, TFR association, and MP association) are novel ones. By comparing the query results of two well-known web tools (STRING and GeneMANIA), we found that YAGM can find out distinct associated genes of a query gene. That is, YAGM can provide alternative candidates of associated genes for biologists to do further experimental investigation. We believe that YAGM will be a useful web tool for yeast biologists. YAGM is available online at http://cosbi3.ee.ncku.edu.tw/yagm/.

Gene network inference by probabilistic scoring of relationships from a factorized model of interactions.

Motivation: Epistasis analysis is an essential tool of classical genetics for inferring the order of function of genes in a common pathway. Typically, it considers single and double mutant phenotypes and for a pair of genes observes whether a change in the first gene masks the effects of the mutation in the second gene. Despite the recent emergence of biotechnology techniques that can provide gene interaction data on a large, possibly genomic scale, few methods are available for quantitative epistasis analysis and epistasis-based network reconstruction.Results: We here propose a conceptually new probabilistic approach to gene network inference from quantitative interaction data. The approach is founded on epistasis analysis. Its features are joint treatment of the mutant phenotype data with a factorized model and probabilistic scoring of pairwise gene relationships that are inferred from the latent gene representation. The resulting gene network is assembled from scored pairwise relationships. In an experimental study, we show that the proposed approach can accurately reconstruct several known pathways and that it surpasses the accuracy of current approaches.Availability and implementation: Source code is available at http://github.com/biolab/red.Contact: blaz.zupan@fri.uni-lj.siSupplementary information: Supplementary data are available at Bioinformatics online.

PT-Flax (phenotyping and TILLinG of flax): development of a flax (Linum usitatissimumL.) mutant population and TILLinG platform for forward and reverse genetics

BackgroundFlax (Linum usitatissimum L.) is an economically important fiber and oil crop that has been grown for thousands of years. The genome has been recently sequenced and transcriptomics are providing information on candidate genes potentially related to agronomically-important traits. In order to accelerate functional characterization of these genes we have generated a flax EMS mutant population that can be used as a TILLinG (Targeting Induced Local Lesions in Genomes) platform for forward and reverse genetics.ResultsA population of 4,894 M2 mutant seed families was generated using 3 different EMS concentrations (0.3%, 0.6% and 0.75%) and used to produce M2 plants for subsequent phenotyping and DNA extraction. 10,839 viable M2 plants (4,033 families) were obtained and 1,552 families (38.5%) showed a visual developmental phenotype (stem size and diameter, plant architecture, flower-related). The majority of these families showed more than one phenotype. Mutant phenotype data are organised in a database and can be accessed and searched at UTILLdb (http://urgv.evry.inra.fr/UTILLdb). Preliminary screens were also performed for atypical fiber and seed phenotypes. Genomic DNA was extracted from 3,515 M2 families and eight-fold pooled for subsequent mutant detection by ENDO1 nuclease mis-match cleavage. In order to validate the collection for reverse genetics, DNA pools were screened for two genes coding enzymes of the lignin biosynthesis pathway: Coumarate-3-Hydroxylase (C3H) and Cinnamyl Alcohol Dehydrogenase (CAD). We identified 79 and 76 mutations in the C3H and CAD genes, respectively. The average mutation rate was calculated as 1/41 Kb giving rise to approximately 9,000 mutations per genome. Thirty-five out of the 52 flax cad mutant families containing missense or codon stop mutations showed the typical orange-brown xylem phenotype observed in CAD down-regulated/mutant plants in other species.ConclusionsWe have developed a flax mutant population that can be used as an efficient forward and reverse genetics tool. The collection has an extremely high mutation rate that enables the detection of large numbers of independant mutant families by screening a comparatively low number of M2 families. The population will prove to be a valuable resource for both fundamental research and the identification of agronomically-important genes for crop improvement in flax.

New mutant phenotype data curation system in the Saccharomyces Genome Database

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) organizes and displays molecular and genetic information about the genes and proteins of baker's yeast, Saccharomyces cerevisiae. Mutant phenotype screens have been the starting point for a large proportion of yeast molecular biological studies, and are still used today to elucidate the functions of uncharacterized genes and discover new roles for previously studied genes. To greatly facilitate searching and comparison of mutant phenotypes across genes, we have devised a new controlled-vocabulary system for capturing phenotype information. Each phenotype annotation is represented as an ‘observable’, which is the entity, or process that is observed, and a ‘qualifier’ that describes the change in that entity or process in the mutant (e.g. decreased, increased, or abnormal). Additional information about the mutant, such as strain background, allele name, conditions under which the phenotype is observed, or the identity of relevant chemicals, is captured in separate fields. For each gene, a summary of the mutant phenotype information is displayed on the Locus Summary page, and the complete information is displayed in tabular format on the Phenotype Details Page. All of the information is searchable and may also be downloaded in bulk using SGD's Batch Download Tool or Download Data Files Page. In the future, phenotypes will be integrated with other curated data to allow searching across different types of functional information, such as genetic and physical interaction data and Gene Ontology annotations.Database URL: http://www.yeastgenome.org/

Analysis of human disease genes in the context of gene essentiality

The characteristics of human disease genes were investigated through a comparative analysis with mouse mutant phenotype data. Mouse orthologs with mutations that resulted in discernible phenotypes were separated from mutations with no phenotypic defect, listing ‘ phenotype’ and ‘ no phenotype’ genes. First, we showed that phenotype genes are more likely to be disease genes compared to no phenotype genes. Phenotype genes were further divided into ‘ embryonic lethal’, ‘ postnatal lethal’, and ‘ non-lethal phenotype’ groups. Interestingly, embryonic lethal genes, the most essential genes in mouse, were less likely to be disease genes than postnatal lethal genes. These findings indicate that some extremely essential genes are less likely to be disease genes, although human disease genes tend to display characteristics of essential genes. We also showed that, in lethal groups, non-disease genes tend to evolve slower than disease genes indicating a strong purifying selection on non-disease genes in this group. In addition, phenotype and no phenotype groups showed differing types of disease mutations. Disease genes in the no phenotype group displayed a higher frequency of regulatory mutations while those in the phenotype group had more frequent coding mutations, indicating that the types of disease mutations vary depending on gene essentiality. Furthermore, missense disease mutations in no phenotype genes were found to be more radical amino acid substitutions than those in phenotype genes.

Probabilistic Protein Function Prediction from Heterogeneous Genome-Wide Data

Dramatic improvements in high throughput sequencing technologies have led to a staggering growth in the number of predicted genes. However, a large fraction of these newly discovered genes do not have a functional assignment. Fortunately, a variety of novel high-throughput genome-wide functional screening technologies provide important clues that shed light on gene function. The integration of heterogeneous data to predict protein function has been shown to improve the accuracy of automated gene annotation systems. In this paper, we propose and evaluate a probabilistic approach for protein function prediction that integrates protein-protein interaction (PPI) data, gene expression data, protein motif information, mutant phenotype data, and protein localization data. First, functional linkage graphs are constructed from PPI data and gene expression data, in which an edge between nodes (proteins) represents evidence for functional similarity. The assumption here is that graph neighbors are more likely to share protein function, compared to proteins that are not neighbors. The functional linkage graph model is then used in concert with protein domain, mutant phenotype and protein localization data to produce a functional prediction. Our method is applied to the functional prediction of Saccharomyces cerevisiae genes, using Gene Ontology (GO) terms as the basis of our annotation. In a cross validation study we show that the integrated model increases recall by 18%, compared to using PPI data alone at the 50% precision. We also show that the integrated predictor is significantly better than each individual predictor. However, the observed improvement vs. PPI depends on both the new source of data and the functional category to be predicted. Surprisingly, in some contexts integration hurts overall prediction accuracy. Lastly, we provide a comprehensive assignment of putative GO terms to 463 proteins that currently have no assigned function.

Roles of a fimbrin and an alpha-actinin-like protein in fission yeast cell polarization and cytokinesis.

Eukaryotic cells contain many actin-interacting proteins, including the alpha-actinins and the fimbrins, both of which have actin cross-linking activity in vitro. We report here the identification and characterization of both an alpha-actinin-like protein (Ain1p) and a fimbrin (Fim1p) in the fission yeast Schizosaccharomyces pombe. Ain1p localizes to the actomyosin-containing medial ring in an F-actin-dependent manner, and the Ain1p ring contracts during cytokinesis. ain1 deletion cells have no obvious defects under normal growth conditions but display severe cytokinesis defects, associated with defects in medial-ring and septum formation, under certain stress conditions. Overexpression of Ain1p also causes cytokinesis defects, and the ain1 deletion shows synthetic effects with other mutations known to affect medial-ring positioning and/or organization. Fim1p localizes both to the cortical actin patches and to the medial ring in an F-actin-dependent manner, and several lines of evidence suggest that Fim1p is involved in polarization of the actin cytoskeleton. Although a fim1 deletion strain has no detectable defect in cytokinesis, overexpression of Fim1p causes a lethal cytokinesis defect associated with a failure to form the medial ring and concentrate actin patches at the cell middle. Moreover, an ain1 fim1 double mutant has a synthetical-lethal defect in medial-ring assembly and cell division. Thus, Ain1p and Fim1p appear to have an overlapping and essential function in fission yeast cytokinesis. In addition, protein-localization and mutant-phenotype data suggest that Fim1p, but not Ain1p, plays important roles in mating and in spore formation.