Cognitive dysfunction in NF1 knock-out mice may result from altered vesicular trafficking of APP/DRD3 complex

BACKGROUND: It has been estimated that more than 50% of patients with Neurofibromatosis type 1 (NF1) have neurobehavioral impairments which include attention deficit/hyperactivity disorder, visual/spatial learning disabilities, and a myriad of other cognitive developmental problems. The biological mechanisms by which NF1 gene mutations lead to such cognitive deficits are not well understood, although excessive Ras signaling and increased GABA mediated inhibition have been implicated. It is proposed that the cognitive deficits in NF1 are the result of dysfunctional cellular trafficking and localization of molecules downstream of the primary gene defect. RESULTS: To elucidate genes involved in the pathogenic process, gene expression analysis was performed comparing the expression profiles in various brain regions for control and Nf1(+/- )heterozygous mice. Gene expression analysis was performed for hippocampal samples dissected from postnatal day 10, 15, and 20 mice utilizing the Affymetrix Mouse Genome chip (Murine 430 2.0). Analysis of expression profiles between Nf1(+/-)and wild-type animals was focused on the hippocampus because of previous studies demonstrating alterations in hippocampal LTP in the Nf1(+/- )mice, and the region's importance in visual/spatial learning. Network analysis identified links between neurofibromin and kinesin genes, which were down regulated in the Nf1(+/- )mice at postnatal days 15 and 20. CONCLUSION: Through this analysis, it is proposed that neurofibromin forms a binding complex with amyloid precursor protein (APP) and through filamin proteins interacts with a dopamine receptor (Drd3). Though the effects of these interactions are not yet known, this information may provide novel ideas about the pathogenesis of cognitive defects in NF1 and may facilitate the development of novel targeted therapeutic interventions

SNP imputation in association studies

The rationale that underlies imputation methods is that even though the causal SNP may not have been genotyped in the study at hand, it may have been genotyped in the reference population. In this case, simulations have revealed that the imputation of SNPs that appear in the reference population facilitates detection of association. Imputation methods are also invaluable when multiple data sets, the same haplotype distribution for every set of SNPs. Thus, the structure of the linkage disequilibrium in the reference population, in conjunction with the structure of the linkage disequilibrium of the observed SNPs within both the cases and the controls, can be used to impute the alleles of a hidden SNP. Imputed SNPs can then be tested for association using an appropriate statistical test. T he large amount of data generated in whole-genome association studies, involving hundreds of thousands of SNPs genotyped in thousands of individuals, complicates the statistical and computational analysis of that data. The correlation between SNPs (linkage disequilibrium) enables much of the variation to be captured despite the inability to genotype all SNPs, and our previous primer 1 described how tagSNPs and haplotypes have been used as proxies for neighboring associations. However, especially with the advent of high-throughput genotyping technologies, the key challenge has started to shift from identifying tagSNPs that best capture genetic variation in the population to the ability to interrogate SNPs not covered by these technologies. Moreover, how does one consolidate distinct data sets when subsets of the same population are genotyped with slightly different technologies that have different capacities? Imputation methods address these problems by using the linkage disequilibrium structure in a region to infer the alleles of SNPs not directly genotyped in the study (hidden SNPs). The starting point of imputation methods is a reference data set such as the HapMap, in which a large set of SNPs is being genotyped. The underlying assumption is that the reference samples, the cases and the controls are all sampled from the same population. Under this assumption, the three populations share the same linkage disequilibrium structure and Every circle is a state, each column corresponds to a SNP and each row corresponds to an ancestral haplotype. According to this model, a haplotype is generated by a random walk on the Markov chain from left to right, where the transition probabilities from one haplotype to another (denoted by the dashed arrows) are determined by the recombination rate and physical distance between the two SNPs. At each position, there is a small probability that the resulting haplotype will be mutated further. A genotype is generated at the conjunction of two such haplotypes. (b) A perfect phylogeny tree explaining the genealogy of the haplotypes, and leading to a test of the hidden SNP 6. Each node in the tree corresponds to a haplotype, and each edge corresponds to a mutating position. A perfect phylogeny model assumes no recurrent mutations or recombination events. The dashed line corresponds to an unobserved SNP (at position 6), which can be tested for association by testing the haplotypes spanned by SNPs 4 and 5. SNP imputation in association studies P r i m e

Investigations on residues of XenTari® (Bacillus thuringiensis subspec. aizawai) on greenhouse tomatoes

XenTari® (Bacillus thuringiensis subspecies aizawai) ist ein bedeutendes biologisches Pflanzenschutzmittel zur Bekämpfung von Noctuidenraupen im Tomatenanbau unter Glas. Da B. thuringiensis (B.t.) zur Gruppe der präsumptiven Bacillus cereus-Arten gezählt wird, in der Lebensmittelüberwachung im Allgemeinen aber kein Unterschied zwischen B.t. und B. cereus gemacht wird und für präsumptive B. cereus ein Grenzwert von 105 Koloniebildende Einheiten (KbE)/g Frischgewicht (FG) gilt, wurde experimentell überprüft, welche maximalen KbE-Konzentrationen an Gewächshaustomaten bei Anwendung von B.t.-Präparaten erreicht werden können. In Gewächshausversuchen mit fünf XenTari® Anwendungen im wöchentlichen Abstand wurden Rückstände von 4,9 × 104 bis 8,5 × 104 KbE/g FG ermittelt. Somit wurden in keinem der Versuche die Richtwerte für präsumptive B. cereus-Konzentrationen von 105 KbE/g FG erreicht, obwohl eine praxisunübliche und sehr enge Spritzfolge appliziert wurde. Ergänzende Labor- und Praxisversuche bekräftigten diese Ergebnisse. Wurde die Persistenz der Sporen auf dem Erntegut untersucht, so nahm die Sporenkonzen­tration innerhalb der ersten Woche nach Applikation auf 46% bis 77% der anfänglichen Konzentration ab. Durch Spritzdüseneinstellungen nur auf das obere beblätterte Pflanzensegment – unter Aussparung der unten hängenden unbeblätterten erntereifen Früchte – konnte die Keimbelastung des Ernteguts nach einmaliger Anwendung von XenTari® von 2,05 × 104 KbE/g FG auf 1,85 × 103 KbE/g FG reduziert werden. Daher könnten anwendungstechnische Maßnahmen, wie die Nichtbehandlung erntbarer Früchte – die entsprechende Applikationstechnik ist in der modernen Tomatenproduktion mittlerweile Standard – als ergänzende Maßnahmen dienen, die Belastung des Ernteguts mit B.t. weiter zu reduzieren. DOI: 10.5073/JfK.2014.09.04, https://doi.org/10.5073/JfK.2014.09.04XenTari® (Bacillus thuringiensis (B.t.) subspecies aizawai) is an important biological plant protection agent for the control of Noctuidae larva on tomato fruits in greenhouses and belongs to the group of presumptive Bacillus cereus species. In general, food control agencies do not routinely differentiate between B.t. and B. cereus and a threshold of 105 colony forming units (cfu)/g fresh weight is applied for presumptive B. cereus in official food control. As no data exists on the expected residues of B.t. spores after application, residual experiments were conducted on tomatoes in greenhouses. In the greenhouse experiment, five applications of XenTari® were applied at weekly intervals. The concentration of B.t. spores on the tomato fruits ranged in all experiments between 4.9 × 104 and 8.5 × 104 cfu/g fresh weight. For single application of B.t., a maximum spore concentration of 4.7 × 104 cfu/g fresh weight was measured. None of the experiments reached the threshold for B. cereus of 1 × 105 cfu/g, although treatments were applied in a very narrow window. The findings were confirmed by additional laboratory experiments and by experiments conducted on a commercial tomato farm. To prove the degradation of B.t. spores under protected greenhouse conditions over time, a series of samples was taken after the last application over one week. Over all, the experiments demonstrated that the concentration of B.t. spores was reduced within one week to between 46% and 77% of the initial spore concentration. Therefore, in comparison to open field condition the degradation of B.t. spores under greenhouse condition was limited. When only the upper parts of the tomato plant were treated with XenTari® a distinct reduction of B.t. spores of up to 90% of B.t. spores with a concentration of 1.85 × 103 cfu/g fresh weight on the marketable tomatoes was achieved. DOI: 10.5073/JfK.2014.09.04, https://doi.org/10.5073/JfK.2014.09.0

Inter-Platform comparability of microarrays in acute lymphoblastic leukemia

BACKGROUND: Acute lymphoblastic leukemia (ALL) is the most common pediatric malignancy and has been the poster-child for improved therapeutics in cancer, with life time disease-free survival (LTDFS) rates improving from <10% in 1970 to >80% today. There are numerous known genetic prognostic variables in ALL, which include T cell ALL, the hyperdiploid karyotype and the translocations: t(12;21)[TEL-AML1], t(4;11)[MLL-AF4], t(9;22)[BCR-ABL], and t(1;19)[E2A-PBX]. ALL has been studied at the molecular level through expression profiling resulting in un-validated expression correlates of these prognostic indices. To date, the great wealth of expression data, which has been generated in disparate institutions, representing an extremely large cohort of samples has not been combined to validate any of these analyses. The majority of this data has been generated on the Affymetrix platform, potentially making data integration and validation on independent sample sets a possibility. Unfortunately, because the array platform has been evolving over the past several years the arrays themselves have different probe sets, making direct comparisons difficult. To test the comparability between different array platforms, we have accumulated all Affymetrix ALL array data that is available in the public domain, as well as two sets of cDNA array data. In addition, we have supplemented this data pool by profiling additional diagnostic pediatric ALL samples in our lab. Lists of genes that are differentially expressed in the six major subclasses of ALL have previously been reported in the literature as possible predictors of the subclass. RESULTS: We validated the predictability of these gene lists on all of the independent datasets accumulated from various labs and generated on various array platforms, by blindly distinguishing the prognostic genetic variables of ALL. Cross-generation array validation was used successfully with high sensitivity and high specificity of gene predictors for prognostic variables. We have also been able to validate the gene predictors with high accuracy using an independent dataset generated on cDNA arrays. CONCLUSION: Interarray comparisons such as this one will further enhance the ability to integrate data from several generations of microarray experiments and will help to break down barriers to the assimilation of existing datasets into a comprehensive data pool

Identification of disease causing loci using an array-based genotyping approach on pooled DNA

BACKGROUND: Pooling genomic DNA samples within clinical classes of disease followed by genotyping on whole-genome SNP microarrays, allows for rapid and inexpensive genome-wide association studies. Key to the success of these studies is the accuracy of the allelic frequency calculations, the ability to identify false-positives arising from assay variability and the ability to better resolve association signals through analysis of neighbouring SNPs. RESULTS: We report the accuracy of allelic frequency measurements on pooled genomic DNA samples by comparing these measurements to the known allelic frequencies as determined by individual genotyping. We describe modifications to the calculation of k-correction factors from relative allele signal (RAS) values that remove biases and result in more accurate allelic frequency predictions. Our results show that the least accurate SNPs, those most likely to give false-positives in an association study, are identifiable by comparing their frequencies to both those from a known database of individual genotypes and those of the pooled replicates. In a disease with a previously identified genetic mutation, we demonstrate that one can identify the disease locus through the comparison of the predicted allelic frequencies in case and control pools. Furthermore, we demonstrate improved resolution of association signals using the mean of individual test-statistics for consecutive SNPs windowed across the genome. A database of k-correction factors for predicting allelic frequencies for each SNP, derived from several thousand individually genotyped samples, is provided. Lastly, a Perl script for calculating RAS values for the Affymetrix platform is provided. CONCLUSION: Our results illustrate that pooling of DNA samples is an effective initial strategy to identify a genetic locus. However, it is important to eliminate inaccurate SNPs prior to analysis by comparing them to a database of individually genotyped samples as well as by comparing them to replicates of the pool. Lastly, detection of association signals can be improved by incorporating data from neighbouring SNPs

Therapeutic targets for HIV-1 infection in the host proteome

BACKGROUND: Despite the success of HAART, patients often stop treatment due to the inception of side effects. Furthermore, viral resistance often develops, making one or more of the drugs ineffective. Identification of novel targets for therapy that may not develop resistance is sorely needed. Therefore, to identify cellular proteins that may be up-regulated in HIV infection and play a role in infection, we analyzed the effects of Tat on cellular gene expression during various phases of the cell cycle. RESULTS: SOM and k-means clustering analyses revealed a dramatic alteration in transcriptional activity at the G1/S checkpoint. Tat regulates the expression of a variety of gene ontologies, including DNA-binding proteins, receptors, and membrane proteins. Using siRNA to knock down expression of several gene targets, we show that an Oct1/2 binding protein, an HIV Rev binding protein, cyclin A, and PPGB, a cathepsin that binds NA, are important for viral replication following induction from latency and de novo infection of PBMCs. CONCLUSION: Based on exhaustive and stringent data analysis, we have compiled a list of gene products that may serve as potential therapeutic targets for the inhibition of HIV-1 replication. Several genes have been established as important for HIV-1 infection and replication, including Pou2AF1 (OBF-1), complement factor H related 3, CD4 receptor, ICAM-1, NA, and cyclin A1. There were also several genes whose role in relation to HIV-1 infection have not been established and may also be novel and efficacious therapeutic targets and thus necessitate further study. Importantly, targeting certain cellular protein kinases, receptors, membrane proteins, and/or cytokines/chemokines may result in adverse effects. If there is the presence of two or more proteins with similar functions, where only one protein is critical for HIV-1 transcription, and thus, targeted, we may decrease the chance of developing treatments with negative side effects

PPP2R5C couples hepatic glucose and lipid homeostasis

In mammals, the liver plays a central role in maintaining carbohydrate and lipid homeostasis by acting both as a major source and a major sink of glucose and lipids. In particular, when dietary carbohydrates are in excess, the liver converts them to lipids via de novo lipogenesis. The molecular checkpoints regulating the balance between carbohydrate and lipid homeostasis, however, are not fully understood. Here we identify PPP2R5C, a regulatory subunit of PP2A, as a novel modulator of liver metabolism in postprandial physiology. Inactivation of PPP2R5C in isolated hepatocytes leads to increased glucose uptake and increased de novo lipogenesis. These phenotypes are reiterated in vivo, where hepatocyte specific PPP2R5C knockdown yields mice with improved systemic glucose tolerance and insulin sensitivity, but elevated circulating triglyceride levels. We show that modulation of PPP2R5C levels leads to alterations in AMPK and SREBP-1 activity. We find that hepatic levels of PPP2R5C are elevated in human diabetic patients, and correlate with obesity and insulin resistance in these subjects. In sum, our data suggest that hepatic PPP2R5C represents an important factor in the functional wiring of energy metabolism and the maintenance of a metabolically healthy state