Association analyses of DNA polymorphisms in bovine SREBP-1, LXRα,FADS1 genes with fatty acid composition in Canadian commercial crossbred beef steers

Two previously reported DNA polymorphisms of sterol regulatory element binding transcription factor 1 (SREBP1) and liver X receptor alpha (LXRα) and two DNA polymorphisms of fatty acid desaturase 1 (FADS1) were evaluated for associations with fatty acids in brisket adipose tissue of Canadian cross-bred beef steers. The polymorphism of 84 bp insert/deletion in intron 5 of SREBP1 was significantly associated with the concentration of 9c C17:1 (P=0.013). The G>A single nucleotide polymorphism (SNP) in the exon 4 of LXRα gene was associated with the concentration of 9c, 11t C18:2 (P=0.04), sum of conjugated linoleic acids (CLA) (P=0.025) and 11c C20:1(P=0.042). Two DNA polymorphisms in the promoter region of FADS1, deletion/insertion of ->GTG in rs133053720 and SNP A>G in rs42187276, were significantly associated with concentrations of C17:0 iso, C17:0 ai, total branched chain fatty acids (BFA), 12t C18:1, 13t/14t C18:1, 15t C18:1, and 13c C18:1 (P<0.05). Further studies are needed to validate the associations and to delineate the roles of the gene polymorphisms in determining the fatty acid composition in beef tissues.

New Oncogenic Drivers in Hepatocellular Carcinoma: Role of the RNA Binding Protein Hu antigen R

156 p. : graf.

Role of G protein coupled inwardly rectifier K+ channels (GIRK) on the neurobiology depression

353 págs.

Prioritization of candidate cancer genes-an aid to oncogenomic studies

The development of techniques for oncogenomic analyses such as array comparative genomic hybridization, messenger RNA expression arrays and mutational screens have come to the fore in modern cancer research. Studies utilizing these techniques are able to highlight panels of genes that are altered in cancer. However, these candidate cancer genes must then be scrutinized to reveal whether they contribute to oncogenesis or are coincidental and non-causative. We present a computational method for the prioritization of candidate (i) proto-oncogenes and (ii) tumour suppressor genes from oncogenomic experiments. We constructed computational classifiers using different combinations of sequence and functional data including sequence conservation, protein domains and interactions, and regulatory data. We found that these classifiers are able to distinguish between known cancer genes and other human genes. Furthermore, the classifiers also discriminate candidate cancer genes from a recent mutational screen from other human genes. We provide a web-based facility through which cancer biologists may access our results and we propose computational cancer gene classification as a useful method of prioritizing candidate cancer genes identified in oncogenomic studies.

Birth Weight, Working Memory and Epigenetic Signatures in IGF2 and Related Genes: A MZ Twin Study

Neurodevelopmental disruptions caused by obstetric complications play a role in the etiology of several phenotypes associated with neuropsychiatric diseases and cognitive dysfunctions. Importantly, it has been noticed that epigenetic processes occurring early in life may mediate these associations. Here, DNA methylation signatures at IGF2 (insulin-like growth factor 2) and IGF2BP1-3 (IGF2-binding proteins 1-3) were examined in a sample consisting of 34 adult monozygotic (MZ) twins informative for obstetric complications and cognitive performance. Multivariate linear regression analysis of twin data was implemented to test for associations between methylation levels and both birth weight (BW) and adult working memory (WM) performance. Familial and unique environmental factors underlying these potential relationships were evaluated. A link was detected between DNA methylation levels of two CpG sites in the IGF2BP1 gene and both BW and adult WM performance. The BW-IGF2BP1 methylation association seemed due to non-shared environmental factors influencing BW, whereas the WM-IGF2BP1 methylation relationship seemed mediated by both genes and environment. Our data is in agreement with previous evidence indicating that DNA methylation status may be related to prenatal stress and later neurocognitive phenotypes. While former reports independently detected associations between DNA methylation and either BW or WM, current results suggest that these relationships are not confounded by each other.

Altered expression of Alzheimer's disease-related genes in the cerebellum of autistic patients: a model for disrupted brain connectome and therapy

Autism and Alzheimer's disease (AD) are, respectively, neurodevelopmental and degenerative diseases with an increasing epidemiological burden. The AD-associated amyloid-beta precursor protein-alpha has been shown to be elevated in severe autism, leading to the 'anabolic hypothesis' of its etiology. Here we performed a focused microarray analysis of genes belonging to NOTCH and WNT signaling cascades, as well as genes related to AD and apoptosis pathways in cerebellar samples from autistic individuals, to provide further evidence for pathological relevance of these cascades for autism. By using the limma package from R and false discovery rate, we demonstrated that 31% (116 out of 374) of the genes belonging to these pathways displayed significant changes in expression (corrected P-values <0.05), with mitochondria- related genes being the most downregulated. We also found upregulation of GRIN1, the channel-forming subunit of NMDA glutamate receptors, and MAP3K1, known activator of the JNK and ERK pathways with anti-apoptotic effect. Expression of PSEN2 (presinilin 2) and APBB1 (or F65) were significantly lower when compared with control samples. Based on these results, we propose a model of NMDA glutamate receptor-mediated ERK activation of alpha-secretase activity and mitochondrial adaptation to apoptosis that may explain the early brain overgrowth and disruption of synaptic plasticity and connectome in autism. Finally, systems pharmacology analyses of the model that integrates all these genes together (NOWADA) highlighted magnesium (Mg2+) and rapamycin as most efficient drugs to target this network model in silico. Their potential therapeutic application, in the context of autism, is therefore discussed.