RESULTS: In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer's disease; 2. ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors.
CONCLUSION: By merely leveraging gene expression data, CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions. The constructed networks are helpful in the identification of important regulators for complex diseases.
OBJECTIVE: We previously identified AtWRKY50 as a transcriptional activator of SAR gene PR1. Although PR1 accumulates to high levels in plants after attack by pathogens, its function is still elusive. Here we investigated the effects of overexpression of several WRKY proteins, including AtWRKY50, on the metabolome of Arabidopsis thaliana.
METHODS: The influence of overexpression of WRKY proteins on the metabolites of Arabidopsis was investigated by using an NMR spectroscopy-based metabolomic approach. The 1H NMR data was analysed using the multivariate data analysis methods, such as principal component analysis, hierarchical cluster analysis and partial least square-discriminant analysis.
RESULTS: The results showed that the metabolome of transgenic Arabidopsis seedlings overexpressing AtWRKY50 was different from wild type Arabidopsis and transgenic Arabidopsis overexpressing other WRKY genes. Amongst other metabolites, sinapic acid and 1-O-sinapoyl-β-D-glucose especially appeared to be the most prominent discriminating metabolites, accumulating to levels 2 to 3 times higher in the AtWRKY50 overexpressor lines.
CONCLUSION: Our results indicate a possible involvement of AtWRKY50 in secondary metabolite production in Arabidopsis, in particular of hydroxycinnamates such as sinapic acid and 1-O-sinapoyl-β-D-glucose.
PATIENTS AND METHODS: We report two siblings of a healthy but consanguineous Malaysian family presenting with severe short stature caused by CPHD with a variable phenotype. Importantly, at the beginning the girl presented with isolated GHD, whereas the boy was hypothyroid. As the most common gene alterations responsible for CPHD are within either the PROP-1- or the POU1F1- (PIT-1)-gene these two genes were further studied.
RESULTS: Subsequent sequencing of the six exons of the POU1F1-gene allowed the identification of a new N-terminal mutation (Q4ter) in these two children. A substitution of C to T induced a change from a glutamine (CAA) to a stop codon (TAA) in exon 1 of the PIT-1 protein. Both affected children were homozygous for the mutation, whereas the mother and father were heterozygous.
CONCLUSION: We describe two children with autosomal recessive inherited CPHD caused by a new N-terminal located mutation within the PUO1F1-gene. The clinical history of these two children underline the phenotypic variability and support the fact that children with any isolated and/or combined PHD need to be closely followed as at an any time other hormonal deficiencies may occur. In addition, molecular analysis of the possible genes involved might be most helpful for the future follow-up.
METHODS: Seven single-nucleotide polymorphisms (SNPs) in IKZF1, three SNPs in DDC, two SNPs in CDKN2A, two SNPs in CEBPE, and three SNPs in LMO1 were genotyped in 289 Yemeni children (136 cases and 153 controls), using the nanofluidic Dynamic Array (Fluidigm 192.24 Dynamic Array). Logistic regression analyses were used to estimate ALL risk, and the strength of association was expressed as odds ratios with 95% confidence intervals.
RESULTS: We found that the IKZF1 SNP rs10235796 C allele (p = 0.002), the IKZF1 rs6964969 A>G polymorphism (p = 0.048, GG vs. AA), the CDKN2A rs3731246 G>C polymorphism (p = 0.047, GC+CC vs. GG), and the CDKN2A SNP rs3731246 C allele (p = 0.007) were significantly associated with ALL in Yemenis of Arab-Asian descent. In addition, a borderline association was found between IKZF1 rs4132601 T>G variant and ALL risk. No associations were found between the IKZF1 SNPs (rs11978267; rs7789635), DDC SNPs (rs3779084; rs880028; rs7809758), CDKN2A SNP (rs3731217), the CEBPE SNPs (rs2239633; rs12434881) and LMO1 SNPs (rs442264; rs3794012; rs4237770) with ALL in Yemeni children.
CONCLUSION: The IKZF1 SNPs, rs10235796 and rs6964969, and the CDKN2A SNP rs3731246 (previously unreported) could serve as risk markers for ALL susceptibility in Yemeni children.