Complex phenotypes

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Incorporating Gene Annotation into Genomic Prediction of Complex Phenotypes

Incorporating Gene Annotation into Genomic Prediction of Complex Phenotypes

ABSTRACT Today, genomic prediction (GP) is an established technology in plant and animal breeding programs. Current standard methods are purely based on statistical considerations but do not make use of the abundant biological knowledge, which is easily available from public databases. Major questions that have to be answered before biological prior information can be used routinely in GP approaches are which types of information can be used, and at which points they can be incorporated into prediction methods. In this study, we propose a novel strategy to incorporate gene annotation into GP of complex phenotypes by defining haploblocks according to gene positions. Haplotype effects are then modeled as categorical or as numerical allele dosage variables. The underlying concept of this approach is to build the statistical model on variables representing the biologically functional units. We evaluate the new methods with data from a heterogeneous stock mouse population, the Drosophila Genetic Reference Panel (DGRP), and a rice breeding population from the Rice Diversity Panel. Our results show that using gene annotation to define haploblocks often leads to a comparable, but for some traits to a higher, predictive ability compared to SNP-based models or to haplotype models that do not use gene annotation information. Modeling gene interaction effects can further improve predictive ability. We also illustrate that the additional use of markers that have not been mapped to any gene in a second separate relatedness matrix does in many cases not lead to a relevant additional increase in predictive ability when the fi rst matrix is based on haploblocks de fi ned with gene annotation data, suggesting that intergenic markers only provide redundant information on the considered data sets. Therefore, gene annotation information seems to be appropriate to perceive the importance of DNA segments. Finally, we discuss the effects of gene annotation quality, marker density, and linkage disequilibrium on the performance of the new methods. To our knowledge, this is the first work that incorporates epistatic interaction or gene annotation into haplotype-based prediction approaches.

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Retinal nerve fibre layer loss in hereditary spastic paraplegias is restricted to complex phenotypes

Retinal nerve fibre layer loss in hereditary spastic paraplegias is restricted to complex phenotypes

In contrast, patients with complex forms of HSP where the disease spreads beyond the long fibre tracts presented significant thinning of RNFL especially in temporal and temporal inferior sectors. These changes reached signifi- cance despite the small number of individuals with com- plex HSP included in this study. However, these changes were not statistically significant after correction for mul- tiple comparisons and need to be reproduced in a larger cohort of patients with complex HSP. This may also help to depict genotype specific effects. So far we found patients with mutations in the nuclear-encoded mitochon- drial AAA metalloproteinase paraplegin causing SPG7 [23] to present with retinal changes. Interestingly, within the small group of SPG7-patients abnormal thinning of RNFL was restricted to the two patients with complex phenotypes.

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Sharing of Genes and Pathways Across Complex Phenotypes: A Multilevel Genome-Wide Analysis

Sharing of Genes and Pathways Across Complex Phenotypes: A Multilevel Genome-Wide Analysis

ABSTRACT Evidence from genome-wide association studies (GWAS) suggest that pleiotropic effects on human complex phenotypes are very common. Recently, an atlas of genetic correlations among complex phenotypes has broadened our understanding of human diseases and traits. Here, we examine genetic overlap, from a gene-centric perspective, among the same 24 phenotypes previously investigated for genetic correlations. After adopting the multilevel pipeline (freely available at http://grass.cgs.hku.hk/limx/kgg/), which includes intragenic single nucleotide polymorphisms (SNPs), genes, and gene-sets, to estimate genetic similarities across phenotypes, a large amount of sharing of several biologically related phenotypes was con fi rmed. In addition, signi fi cant genetic overlaps were also found among phenotype pairs that were previously unidenti fi ed by SNP-level approaches. All these pairs with new genetic links are supported by earlier epidemiological evidence, although only a few of them have pleiotropic genes in the GWAS Catalog. Hence, our gene and gene-set analyses are able to provide new insights into cross-phenotype connections. The investigation on genetic sharing at three different levels presents a complementary picture of how common DNA sequence variations contribute to disease comorbidities and trait manifestations.

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Optimizing complex phenotypes through model guided multiplex genome engineering

Optimizing complex phenotypes through model guided multiplex genome engineering

Leveraging recent improvements in the cost and speed of microbial whole-genome sequencing (WGS), we present a method for identifying precise genomic changes that optimize complex phenotypes, combining multiplex genome engineering, genotyping, and predict- ive modeling (Fig. 1). Multiple rounds of genome editing are used to generate a population enriched with com- binatorial diversity at the targeted loci. Throughout the editing process, clones from the population are subject to WGS and are screened for phenotype. The genotype and phenotype data are used to update a model which predicts the effects of individual alleles. These steps are repeated on a reduced set of candidate alleles informed by the model or on a new set of targets. Finally, the highest impact alleles are rationally introduced into the original organism, minimizing alterations to the organ- ism’s original genotype while optimizing the desired phenotype.

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Quantitative Genomics of 30 Complex Phenotypes in Wagyu x Angus F1 Progeny

Quantitative Genomics of 30 Complex Phenotypes in Wagyu x Angus F1 Progeny

plants do not synthesize CLA [66]. Three genes, EFEMP1, PLTP, and DSEL, were involved in the CLA network. PLTP is a lipid transfer protein that belongs to the lipopolysaccharide family. Previous reports revealed that plasma PLTP activity is elevated in type 2 diabetes mellitus, and obesity, with a decrease in PLTP being observed after weight loss [67-68]. Higher PLTP activity could contribute to elevated cardio- vascular risk in the presence of obesity and insulin resistance [69]. Recently, a genome-wide association study showed a SNP locus of PLTP is significantly associated with HDL-cholesterol level in human, which is a risk factor of coronary heart disease [70-71]. In the present study, our association result also im- plied that PLTP may affect lipid level by regulating CLA. This is a good clue for improving the level of CLA in beef production by using genomic markers if we consider PLTP as a good candidate for decreasing the risk of coronary heart disease. DSEL acts as a chondroitin-glucuronate C5 epimerase, converting D-glucuronic acid to L-iduronic acid, and catalyzing the formation of dermatan sulfate from chondroitin sulfate [72]. Our previous study found DSEL has an overdominant effect on R2 (calculated as (16:1/16:0) × 100%) [11]. Now a different role has been discovered for the relationship between DSEL and CLA. Inter- estingly, the same genetic network is also responsible for both CLA and palmitoleic acid (C16:1n7). Overall, we discovered three different effects, an overdomi- nant effect for PLTP and dominant effects for both EFEMP1 and DSEL, which are involved in the same CLA network, suggesting the regulation of CLA may be more complex.

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Manganese homeostasis: from rare single gene disorders to complex phenotypes and diseases

Manganese homeostasis: from rare single gene disorders to complex phenotypes and diseases

Thus, SLC30A10 and SLC39A8 appear to act as the primary regulators of biliary Mn excretion and systemic Mn homeosta- sis, with SLC30A10 exporting Mn into the bile and SLC39A8 reclaiming it from the bile (Figure 1). In this view, it is perhaps no surprise that these two genes were the top genetic loci associated with blood Mn levels on GWAS (15). These common vari- ant studies indicate that subtle changes in expression or function of these two trans- porters can influence Mn homeostasis and blood concentrations. In the case of the highly pleiotropic SLC39A8, reduced Mn concentrations due to the Ala391Thr allele appear to directly influence a wide variety of phenotypic traits and disease risks. Altered Mn homeostasis may influ- ence phenotypes through modulation of the activity of certain Mn-dependent enzymes including transferases, hydro- lases, lyases, isomerases, and oxidoreduc- tases (2). For example, Slc39a8 deletion in mice resulted in a significant decrease in the tissue activity of arginase, a well- recognized Mn-dependent enzyme, which could explain the association of this variant with reduced blood pressure (17). Furthermore, both humans and mice with genetically reduced SLC39A8 activity have lower levels of protein N- glycosylation due to reduced activity of certain Mn-dependent glycosyltransfer- ases (6, 17), which could affect a variety of phenotypic traits. A hypothesis — not yet confirmed — is that the pleiotropic effects of modestly genetically reduced SLC39A8 activity lead to reduced blood and tissue Mn concentrations, which in turn cause reduced activity of specific Mn-dependent enzymes in specific tis- sues. It remains a mystery why genetic variants at the SLC30A10 locus that are also associated with blood Mn levels lack the pleiotropic associations with pheno- types and disease risk that SLC39A8 variants show. The common variants at SLC30A10 may have a quantitatively reduced effect on Mn concentrations and thus less of an effect on phenotype, or there could be tissue-specific effects of genetic variation on these transporters.

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The Long Way from Complex Phenotypes to Genes: The Story of Rat Chromosome 4 and Its Behavioral Effects

The Long Way from Complex Phenotypes to Genes: The Story of Rat Chromosome 4 and Its Behavioral Effects

Despite the complexity and limitations intrinsic to QTLs studies, the remarkable strength of Anxrr16 has in- spired our team to persevere in the study of this genome region and its associated phenotypes. Doubts have been raised regarding the replicability, the pattern of inheritance, and the ethological meaning of this QTL [28] [32]. A series of subsequent studies have helped us to look more closely at these matters. A study carried out by Mor- mède et al. [37] used F4 and F5 generations from a LEW x SHR intercross, selected through molecular markers at both Anxrr16 and Anxrr17 (another QTL for OF inner locomotion, located on Chr. 7 and previously called Ofil2). This selection led to a “high line”—which had alleles increasing inner locomotion in the OF, i.e. LEW alleles at Anxrr16 and SHR alleles at Anxrr17—and a “low line”—with the inverse phenotype/genotype. They found that the high line was more active than the low line in the OF. It was also observed that the inhibition of locomotor activity in the low line (as compared to the high line) was directly related to the aversiveness of the situation (larger in the center than in the periphery of the OF, and under strong than under dim light conditions), and this effect was more intense in males than in females [37]. This study was the first one to reproduce Anxrr16’s effects in the OF after its description in 1999, despite its limitation in dissociating the magnitude of each of the QTLs’ effect (Anxrr16 and Anxrr17). It has also revealed a potential effect of Anxrr16 in males, which wasn’t originally observed by Ramos et al. [9]. Using a similar methodology, Vendruscolo et al. [38] have confirmed this QTL’s effects on OF inner locomotion in rats deriving from an F2 intercross between the LEW and the SHR strains. Identically to Ramos et al. [9] (but not to Mormède et al. [37]), they have found that this effect was restricted to females. Interestingly, this study has also revealed an effect of Anxrr16 on ethanol 10% intake of female rats, with SHR alleles increasing the alcohol consumption (in the expected sense, since SHR rats have a higher ethanol intake than LEW rats). This work suggested that the Anxrr16 region could con- tain either linked genes with independent influences on anxiety-related responses and ethanol consumption, or a pleiotropic gene with simultaneous effects on both traits.

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Yu, Zhonghao
  

(2013):


	Metabolomics analyses to better understand complex phenotypes.


Dissertation, LMU München: Medizinische Fakultät

Yu, Zhonghao (2013): Metabolomics analyses to better understand complex phenotypes. Dissertation, LMU München: Medizinische Fakultät

The nature of the data acquired in the metabolomics studies is similar to those in other ‘omics’ studies: high in dimension with a relatively small number of observations. The major goal in metabolomics studies related to life science research is to identify biomarkers and to understand the mechanistic basis for biological difference (e.g. healthy vs. diseased). The machine learning methods which have been applied for years are suitable for this purpose with such data property. Both unsupervised (e.g. principle component analysis (PCA), clustering) and supervised methods (e.g. random forest, partial least square (PLS)) can be used to find the features, which are crucial to the phenotypes (e.g. the development of the disease) but which have been buried under the huge amount of data.

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Understanding the Genetic Etiology of Complex Phenotypes using Bayesian Neural Networks.

Understanding the Genetic Etiology of Complex Phenotypes using Bayesian Neural Networks.

previously developed and applied for other tasks [Lisboa et al., 2003, Baesens et al., 2002, Neal, 1995, Neal, 1992] they have yet to see significant usage in bioinformatics and computational biology. Like most complex Bayesian models, BNNs require stochastic sampling techniques that draw samples from the posterior distribution, because direct or deterministic calculation of the posterior distribution is often intractable. These posterior samples are then used to make inferences about the parameters of the model or used to make predictions for new data. Standard MCMC methods that employ a random walk such as the Metropolis-Hastings (RW- MH) algorithm [Metropolis et al., 1953, Hastings, 1970] (which is the algorithm that forms the core of BEAM [Zhang and Liu, 2007]) explores the posterior distribution very slowly when the number of predictors is large. If d is the number of parameters in a model, the number of iterations needed to obtain a nearly independent sample is O(d 2 ) [Neal, 2011] for RW-MH. This makes the RW-MH algorithm unsuitable for neural network models in high-dimensions, so the Hamiltonian Monte Carlo (HMC) algorithm is instead used to generate samples from the posterior. HMC has more favorable scaling properties, as the number of iterations needed is only O(d 5/4 ) [Neal, 2011]. HMC achieves this favorable scaling by using information about the

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Statistical Methods for Identifying X-linked Genes Associated with Complex Phenotypes

Statistical Methods for Identifying X-linked Genes Associated with Complex Phenotypes

Complex disorders that are caused by mutations in genes on the X chromo- some are described as X-linked. X-linked recessive traits such as hemophilia A & B, and red-green color blindness, are not expressed in all heterogametics, but only in those homogametics that are homozygous for the recessive allele. X-linked disease genes are usually passed from female carriers to their ill sons and carrier daughters. Therefore, the incidence of X-linked recessive phenotypes in females is the square of that in males. A few examples of X-linked dominant diseases, such as vitamin D resis- tant rickets (OMIM: 307800) and Rett syndrome (OMIM: 312750), are manifest both in the hemizygous male and in the heterozygous female. An affected heterozygous female transmits the condition on average to half of the chance of her sons and to half of the chance of her daughters. If affected males and females show normal fertility, then the incidence in females will be approximately twice the incidence in males, as females possess two X chromosomes. However, sometimes, males are more severely affected than females because of the protection afforded to females by X chromosome inactivation (XCI).

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Reliability of genomic predictions of complex human phenotypes

Reliability of genomic predictions of complex human phenotypes

Genomic prediction (GP) refers to the use of genomic information for predicting an individual ’ s phenotype [1]. Several different approaches have been developed with the purpose of performing GP, such as marker-assisted selection (MAS) and genomic-best linear unbiased predic- tion methods (G-BLUP) [2]. MAS approaches have been widely successful when single genomic variants affect the trait of interest, but remain limited in their predictive cap- abilities for complex phenotypes [3]. Evidence suggests that complex traits are influenced by many genes, with effects that often fall below statistical significance thresholds [4]. As a consequence, the combined effects of variants identi- fied through association only explains a small portion of the interindividual phenotypic differences [5]. G-BLUP – based

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Associations among Antibiotic and Phage Resistance Phenotypes in Natural and Clinical Escherichia coli Isolates

Associations among Antibiotic and Phage Resistance Phenotypes in Natural and Clinical Escherichia coli Isolates

Phylogenetic distribution of resistance phenotypes. Bacterial isolates varied in their resistance to antibiotics and phages (Fig. 1), although for phages most isolate- phage interactions did not result in inhibition of bacterial growth. This is consistent with the idea that phages are usually specific for particular host genotypes or strains. For both phage resistance and antibiotic resistance, more closely related isolates tended to have more similar resistance profiles (Euclidean distance between resistance scores across antibiotics or phages; Mantel test for correlation with patristic genetic distances: antibiotic resistance correlation ⫽ 0.05, P ⬍ 0.05; phage resistance correla- tion ⫽ 0.11, P ⬍ 0.01). Despite this, it was not the case that phylogenetically close isolates had similar overall resistance measured as average resistance across all antibi- otics (P [Mantel] ⬎ 0.1; P [Pagel’s ␭ ] ⬎ 0.5) or phages (P [Mantel] ⬎ 0.1; P [Pagel’s ␭ ] ⬎ 0.5), suggesting that our isolates do not fall into generally more or less resistant clades for either type of stressor. For individual stressors, associations with phylogenetic distance were rare after accounting for multiple testing (Materials and Methods). This was also the case when using an alternative (Pasteur) MLST scheme (10) (see Fig. S1 in the supplemental material).

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Heritability patterns in hand osteoarthritis: the role of osteophytes

Heritability patterns in hand osteoarthritis: the role of osteophytes

In an attempt to analyze heritability, the subdivision of the population into the phenotypes of joint location and clinical and radiographic features may lead to smaller sam- ple sizes for some groups, which may limit the utility of a given analysis. With the dearth of information available regarding carefully characterized phenotypes in hand OA in the literature, however, even an analysis of the smaller sample will help guide the phenotypes upon which to focus future genetic analysis. Genetic studies of hand OA to date have used clinical phenotypes, radiographic pheno- types or anatomic location for genetic studies. It is possible that the dissimilarity of analyzed OA features has lead to a difficulty in reproducing genotype associations in different studies [15,16]. The plan for the present study going forward is therefore to characterize the population for clinical, radiographic and anatomic phenotypes to deter- mine those features showing the greatest heritability.

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Biochemical basis of prolidase deficiency  Polypeptide and RNA phenotypes and the relation to clinical phenotypes

Biochemical basis of prolidase deficiency Polypeptide and RNA phenotypes and the relation to clinical phenotypes

Cultured skin fibroblasts or lymphoblastoid cells from eight patients with clinical symptoms of prolidase deficiency were analyzed in terms of enzyme activity, presence of material crossreacting with specific antibodies, biosynthesis of the polypeptide, and mRNA corresponding to the enzyme. There are at least two enzymes that hydrolyze imidodipeptides in these cells and these two enzymes could be separated by an immunochemical procedure. The specific assay for prolidase showed that the enzyme activity was virtually absent in six cell strains and was markedly reduced in two (less than 3% of controls). The activities of the labile enzyme that did not immunoprecipitate with the anti-prolidase antibody were decreased in the cells (30-60% of controls). Cell strains with residual activities of prolidase had immunological polypeptides crossreacting with a Mr 56,000, similar to findings in the normal enzyme. The polypeptide biosynthesis in these cells and the controls was similar. Northern blot analyses revealed the presence of mRNA in the polypeptide-positive cells, yet it was absent in the polypeptide-negative cells. The substrate specificities analyzed in the partially purified enzymes from the polypeptide- positive cell strains differed, presumably due to different mutations. Thus, there seems to be a molecular heterogeneity in prolidase deficiency. There was no apparent relation between the clinical symptoms and the biochemical phenotypes, except that mental retardation was present in the polypeptide-negative patients. The […]

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Evidence for an ecological cost of enhanced herbicide metabolism in Lolium rigidum

Evidence for an ecological cost of enhanced herbicide metabolism in Lolium rigidum

of several herbicides mediated by the cytochrome P450 enzymatic complex (R phenotype) (Christopher et al. 1994, Preston & Powles 1998, Christopher et al. 1991) and that 10% of individuals are herbicide susceptible (S phenotype) (Vila-Aiub et al. 2005b). Random mating in controlled field conditions under relaxed selection (no herbicide) for several generations was allowed before a plant cloning technique was used to identify and isolate discrete lines of the S phenotype and the P450-based herbicide resistant (R) phenotype within this one SLR31 L. rigidum population (Vila-Aiub et al. 2005b). Both lines were grown and produced seed under the same experimental conditions. These phenotypic lines enable resistance cost comparisons of herbicide resistant vs. susceptible individuals within a single population in a relatively homogeneous genetic background.

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Analysis of arterial intimal hyperplasia: review and hypothesis

Analysis of arterial intimal hyperplasia: review and hypothesis

To explain disease-related hyperplasia that is not preceded by changes in hemodynamics and shear stress, I hypothe- size that the arterial blood-tissue interface itself (as a top- ological entity) imposes properties that support the development of intimal phenotypes, initiating mecha- nisms of cell selection and intimal morphogenesis. This morphogenesis could be directed to the formation of either a single-cell-layer intima ("ideal intima") or multi- layer cellular compartment (intimal hyperplasia). We already know that cells of different origin can form inti- mal hyperplasia. The same is true for single-cell-layer intima. The hypothesis suggests that any cells capable of colonizing the arterial blood-tissue interface, naturally or in remodeling, acquire by default the capacity to activate genes that are necessary for producing intimal pheno- types. Note that "arterial blood-tissue interface" is defined differently from the traditional "blood-tissue interface", i.e. endothelium [272]. In my model, the term denotes the topological area where blood flow meets surrounding structures, and it includes descriptions such as "basement membrane on which the inner cell lining of vessels rests" or "proteins, glycoproteins and other molecules, includ- ing artificial ones, that appeared in fixed positions and form structures in contact with the moving blood. This includes dead vessel wall, prosthetic vascular grafts, autol- ogous and allogeneic vascular grafts, and naïve arterial vessels in any location.

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High-Frequency Generation of Conditional Mutations Affecting Drosophila melanogaster Development and Life Span

High-Frequency Generation of Conditional Mutations Affecting Drosophila melanogaster Development and Life Span

Figure 2.—Phenotypes of conditional, dominant muta- tions affecting development. Selected control and DOX-cul- tured flies were examined by scanning electron microscopy. (A) Control flies containing PdL insert 3B2 in the pumilio gene and the rtTA transactiva- tor, genotype w;PdL(3)3B2/ rtTA(3)E2. (B) Experimental flies containing PdL insert 3B2 in the pumilio gene and the rtTA transactivator, genotype w;PdL(3)3B2/rtTA(3)E2. The flies were cultured throughout larval and pupal development in the presence of 250 ␮g/ml DOX, in parallel with the con- trol flies in A. (C) Experimen- tal flies containing PdL inserts and the rtTA transactivator, genotype w;PdL(3)35B1/rtTA(3)E2, cultured throughout larval and pupal development in the presence of 250 ␮g/ml DOX. Control flies cultured without DOX were wild type and were identical to those in A (data not shown). (D) Compound eye of control flies containing PdL insert 19B3 in the dGMII gene and the rtTA transactivator, genotype w;PdL(3)19B3/ rtTA(3)E2. (E) Compound eye of experimental flies containing PdL insert 19B3 in the dGMII gene and the rtTA transactivator, genotype w;PdL(3)19B3/rtTA(3)E2. The flies were cultured throughout larval and pupal development in the presence of 250 ␮g/ ml DOX, in parallel with the control flies in D.

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Inducible clindamycin and methicillin resistant Staphylococcus aureus in a tertiary care hospital, Kathmandu, Nepal

Inducible clindamycin and methicillin resistant Staphylococcus aureus in a tertiary care hospital, Kathmandu, Nepal

It is very difficult to detect the inducible clindamycin resistance in the routine laboratory as they appear erythromycin-resistant and clindamycin sensitive in vitro when not placed adjacent to each other. In such cases, in vivo therapy with clindamycin may select constitutive erm mutants leading to clinical therapeutic failure. In case of another mechanism of resistance mediated through msrA genes i.e. efflux of antibiotic, staphylococcal isolates ap- pear erythromycin-resistant and clindamycin-sensitive both in vivo and in vitro and the strain do not typically be- come clindamycin resistant during therapy [5]. Thus to avoid clinical therapeutic failure in the resistance case me- diated by erm gene, it is very important to detect inducible clindamycin resistance phenotypes in vitro which can be made by erythromycin-clindamycin disc approximation test (D-test) as its sensitivity was found 100% in different studies when compared with erm and msr gene detection by polymerase chain reaction [6–8]. There is a wide vari- ation in the rate of inducible clindamycin resistance in different places [9–13]. In Nepal, very few reports on prevalence of inducible clindamycin resistance among S. aureus have been published [14, 15]. This study was con- ducted to determine the prevalence of inducible clindamy- cin resistance among clinical S. aureus isolates and also to study their association with MRSA in our set up.

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Chimaeras and mosaics for dissecting complex mutant phenotypes

Chimaeras and mosaics for dissecting complex mutant phenotypes

This type of chimaera is one of the easiest to produce by aggregating 8-cell stage embryos. It is very useful for examining mutant vs. wild-type cell behaviour in a competitive situation and addressing the question of cell autonomous phenotypes (Rossant and Spence, 1998). Since chimaeras are made by aggregating the embryos from a heterozygous cross with wild-type embryos, it is necessary to be able to distinguish the three possible classes of chimaeras. This can be achieved by using two different mutant alleles to generate homozygous embryos, or by use of linked markers. In the case of imprinted genes, chimaera analysis is made easy by the fact that heterozygous embryos will have the mutant phenotype if they inherit the mutant allele on the non-imprinted chromosome. We took advantage of this in chimaeric analysis of Mash2 mutants. Earlier we had shown that a targeted null mutation in the Mash2 gene resulted in a midgestational embryonic lethality (Guillemot et al., 1994). The dying embryos had a severe placenta phenotype characterized by the lack of spongiotrophoblast and defective labyrinth layers. In order to address the cell autonomous nature of the phenotype we performed a chimaeric study (Tanaka et al., 1997). One component of the chimaeras was wild-type and the other was Mash2 deficient, and tagged by ubiquitous lacZ expres- sion of the Rosa26 transgene. In the chimaeras, the Mash2 deficient cells were completely excluded from the spongiotrophoblast lineage, strongly supporting the cell autonomous nature of the Mash2 defi- ciency in this lineage. On the other hand, Mash2 deficient cells could contribute at very high levels to all other layers of the placentas, including the labyrinthine layer, without showing any signs of abnor- mality. Therefore, the labyrinthine phenotype observed in the Mash2 Fig. 2. Developmental potential (green = full, red = restricted) of the

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Drosophila couch potato Mutants Exhibit Complex Neurological Abnormalities Including Epilepsy Phenotypes

Drosophila couch potato Mutants Exhibit Complex Neurological Abnormalities Including Epilepsy Phenotypes

seizure thresholds less than half that of wild-type flies. Finally, cpoflies display several neurocircuit abnormali- ties in the giant fiber (GF) system. The TTM muscles of cpo mutants exhibit long latency responses coupled with decreased following frequency. DLM muscles in cpo mutants show drastic reductions in following frequency despite exhibiting normal latency relationships. The labile sites appear to be the electrochemical GF-TTMn synapse and the chemical PSI-DLMn synapses. These complex neurological phenotypes of cpo mutants support an important role for cpo in regulating proper nervous system function, including seizure susceptibility.

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