METHODS: We retrieved 4 previously reported SMCA, performed additional immunohistochemical and targeted next-generation sequencing (NGS). We also investigated the use of NKX3.1 as a marker for SMCA in the context of its prevalence and extent (using H-score) in a mixed cohort of retrospectively and prospectively tested head and neck lesions (n = 223) and non-neoplastic tissues (n = 66).
RESULTS: NKX3.1 positivity was confirmed in normal mucous acini as well as in mucous acinar class of lesions (5/6, mean H-score: 136.7), including mucinous adenocarcinomas (3/4), SG-IPMN (1/1), and microsecretory adenocarcinoma (MSA) (1/1). All SMCA were positive. Fluorescence in situ hybridization for SS18 rearrangements were negative in all successfully tested cases (0/3). NGS was successful in two cases (cases 3 and 4). Case 3 demonstrated a PTEN c.655C>T p.Q219* mutation and a SEC16A::NOTCH1 fusion while case 4 (clinically aggressive) showed a PTEN c.1026+1G>A p.K342 splice site variant, aTP53 c.524G>A p.R175H mutation and a higher tumor mutation burden (29 per Mb). PTEN immunohistochemical loss was confirmed in both cases and a subset of tumor cells showed strong (extreme) staining for P53 in Case 4.
CONCLUSION: Despite a partial myoepithelial phenotype, SMCA, along with mucinous adenocarcinomas/SG-IPMN and MSA, provisionally constitute a mucous acinar class of tumors based on morphology and NKX3.1 expression. Like salivary mucinous adenocarcinomas/SG-IPMN, SMCA also show alterations of the PTEN/PI3K/AKT pathway and may show progressive molecular alterations. We document the first extramammary tumor with a SEC16A::NOTCH1 fusion.
METHODS: We selected TF genes within 1 Mb of the top signal at the 12 genome-wide significant risk loci. Mutual information, a form of correlation, was used to build networks of genes strongly coexpressed with each selected TF gene in the unified microarray dataset of 489 serous EOC tumors from The Cancer Genome Atlas. Genes represented in this dataset were subsequently ranked using a gene-level test based on results for germline SNPs from a serous EOC GWAS meta-analysis (2,196 cases/4,396 controls).
RESULTS: Gene set enrichment analysis identified six networks centered on TF genes (HOXB2, HOXB5, HOXB6, HOXB7 at 17q21.32 and HOXD1, HOXD3 at 2q31) that were significantly enriched for genes from the risk-associated end of the ranked list (P < 0.05 and FDR < 0.05). These results were replicated (P < 0.05) using an independent association study (7,035 cases/21,693 controls). Genes underlying enrichment in the six networks were pooled into a combined network.
CONCLUSION: We identified a HOX-centric network associated with serous EOC risk containing several genes with known or emerging roles in serous EOC development.
IMPACT: Network analysis integrating large, context-specific datasets has the potential to offer mechanistic insights into cancer susceptibility and prioritize genes for experimental characterization.
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: Ethanolic leaf extract of A. bilimbi was exposed to Myf5 lineage precursor cells to stimulate adipocyte differentiation. Protein expressions of brown adipocyte markers were determined through high content screening analysis and validated through western blotting. Mito Stress Test assay was conducted to evaluate the cellular oxygen consumption rate upon A. bilimbi treatment.
RESULTS: A. bilimbi ethanolic leaf extract exhibited an adipogenesis effect similar to a PPARgamma agonist. It also demonstrated brown adipocyte differentiation in myoblastic Myf5-positive precursor cells. Expression of UCP1 and PRDM16 were induced. The basal metabolic rate and respiratory capacity of mitochondria were increased upon A. bilimbi treatment.
CONCLUSIONS: The findings suggest that Averrhoa bilimbi ethanolic leaf extract induces adipocyte browning through PRDM16 activation and enhances mitochondria activity due to UCP1 up-regulation.
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.
METHODS: Female rats were treated with quercetin (10, 25 and 50mg/kg/day) subcutaneously beginning from day-1 pregnancy. Uterus was harvested at day-4 (following three days quercetin treatment) for morphological, ultra-structural, protein and mRNA expressional changes and plasma sex-steroid levels analyses. In another cohort of rats, implantation rate was determined at day-6 (following five days quercetin treatment).
RESULTS: Administration of 50mg/kg/day quercetin causes increased in uterine fluid volume and CFTR expression but decreased in γ-ENaC, AQP-5, AQP-9 claudin-4, occludin, E-cadherin, integrin αnβЗ, FGF, Ihh and Msx-1expression in the uterus. Pinopodes were poorly develop, tight junctions appear less complex and implantation rate decreased. Serum estradiol levels increased but serum progesterone levels decreased.
CONCLUSIONS: Interference in the fluid volume and receptivity development of the uterus during peri-implantation period by quercetin could adversely affect embryo implantation.
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.