Identification of novel mutations in MLC1 responsible for megalencephalic leukoencephalopathy with subcortical cysts

Megalencephalic leukoencephalopathy with subcortical cysts (MLC) is an inherited neurologic disorder with macrocephaly before the age of one and slowly progressive deterioration of motor functions. Magnetic resonance imaging shows diffusely abnormal and swollen white matter of the cerebral hemispheres and the presence of subcortical cysts in the anterior-temporal region and often also in the frontoparietal region. Mutations in the MLC1 gene, encoding a putative membrane protein, have been recently identified as a cause for MLC. Here, we describe 14 new mutations in 18 patients. Two identified polymorphisms lead to alterations of amino acid residues. The role, suggested by others, of a mutation in the MLC1gene in catatonic schizophrenia and the possible function of the MLC1 protein as a cation channel are discussed.     

Anti-glucocorticoid effects of progesterone in vivo on rat adipose tissue metabolism

Steroid hormones seem to be important for adipose tissue metabolism and accumulation. As progesterone has been suggested to modulate the glucocorticoid effects, the interactions between glucocortioid and progesterone on adipose tissue metabolism were investigated.Forty-eight male Wistar rats were adrenectomized and divided into four groups; controls (treated with vehicle only), dexamethasone treated (10 micro g per rat), progesterone treated (5mg per rat) and the last group received both dexamethasone and progesterone.The dexamethasone-treated group had a significant loss of body weight and smaller intra-abdominal fat depots compared to the control group in addition, dexamethasone increased LPL-activity and increased catecholamine stimulated lipolysis. When progesterone was given concomitantly the dexamethasone effects on adipose tissue mass, LPL-activity and lipolysis were blocked. When given alone progesterone had no influence on body weight, amount of adipose tissue, lipolysis or LPL-activity.These data indicate that progesterone acts as an anti-glucocorticoid in adipose tissue in vivo, thus attenuating the glucocorticoid effect on adipose tissue metabolism.     

Ligand Binding and Polymerization Characteristics of Human Milk Folate Binding Protein Depend on Concentration of Purified Protein and Presence of Amphiphatic Substances

Two folate binding proteins are present in human milk; one of 27 kDa is a cleavage product of the other one (100 kDa) which possesses a hydrophobic membrane anchor. A drastic change of radioligand binding characteristics and appearance of aggregated weak-radioligand affinity forms on gel filtration occurred at low concentrations of both proteins in the absence of Triton X-100 or other amphiphatic substances, e.g. cetyltrimethylammonium and phospholipids. These findings are consistent with a model predicting association between unliganded and liganded monomers resulting in weak-ligand affinity dimers. Amphiphatic substances form micelles and lipid bilayers which could separate hydrophobic unliganded monomers from hydrophilic liganded monomers (monomers become hydrophilic in the liganded state) thereby preventing association between these monomeric forms prevailing at low concentrations of the protein. Bio-Gel P-300 chromatography of the 27 kDa protein revealed a pronounced polymerization tendency, which diminished with decreasing protein concentrations, however, not in the presence of cetyltrimethylammonium. The data could have some bearings on observations indicating that naturally occurring amphiphatic substances, cholesterol and phospholipids, are necessary for the important clustering of membrane folate receptors.     

Evaluation of five different cDNA labeling methods for microarrays using spike controls

Background

Several different cDNA labeling methods have been developed for microarray based gene expression analysis. We have examined the accuracy and reproducibility of such five commercially available methods in detection of predetermined ratio values from target spike mRNAs (A. thaliana) in a background of total RNA. The five different labeling methods were: direct labeling (CyScribe), indirect labeling (FairPlay? – aminoallyl), two protocols with dendrimer technology (3DNA^® Array 50? and 3DNA^® submicro?), and hapten-antibody enzymatic labeling (Micromax? TSA?). Ten spike controls were mixed to give expected Cy5/Cy3 ratios in the range 0.125 to 6.0. The amounts of total RNA used in the labeling reactions ranged from 5 – 50 μg.

Results

The 3DNA array 50 and CyScribe labeling methods performed best with respect to relative deviation from the expected values (16% and 17% respectively). These two methods also displayed the best overall accuracy and reproducibility. The FairPlay method had the lowest total experimental variation (22%), but the estimated values were consistently higher than the expected values (36%). TSA had both the largest experimental variation and the largest deviation from the expected values (45% and 48% respectively).

Conclusion

We demonstrate the usefulness of spike controls in validation and comparison of cDNA labeling methods for microarray experiments.

     

Phytochrome Regulation of Expression of mRNA Encoding the Major Light-Harvesting Chlorophyll a/b-Binding Proteins of Photosystem II in the Haploid Phase of Adiantum capillus-veneris

Light regulates development in the fern Adiantum capillus-venerisduring both the haploid gametophyte and diploid sporophyte phases.In this study, we have investigated the role of the photoreceptorphytochrome in haploid spore germination and regulation of expressionof mRNA encoding the major light-harvesting polypeptides ofphotosystem II {Lhcb mRNAs). Both of these responses appearto be induced by phytochrome and inhibited by blue light, acting.throughseparate photoreceptor(s). Characteristics of phytochrome actionwere similar for both responses, showing no induction in thevery low fluence range and an ‘escape’ from far-redreversal for 50% of the response by 6 h. Overall, these resultsshow that phytochrome regulates both a morphological and a molecularprocess in haploid fern spores, and that these processes mayshare a common signal transduction pathway. (Received February 4, 1998; Accepted April 8, 1998)     

Analysis of mannose selection used for transformation of sugar beet

Various factors affecting mannose selection for the production of transgenic plants were studied using Agrobacterium tumefaciens-mediated transformation of sugar beet (Beta vulgaris L.) cotyledonary explants. The selection system is based on the Escherichia coli phosphomannose isomerase (PMI) gene as selectable gene and mannose as selective agent. Transformation frequencies were about 10-fold higher than for kanamycin selection but were only obtained at low selection pressures (1.0–1.5 g/l mannose) where 20–30% of the explants produced shoots. The non-transgenic shoots were eliminated during the selection procedure by a stepwise increase in the mannose concentration up to 10 g/l. Analysis of the transformed shoots showed that the PMI activity varied from 2.4 mU/mg to 350 mU/mg but the expression level was independent of the selection pressure. Complete resistance to mannose of transformed shoots was observed already at low PMI activities (7.5 mU/mg). Genomic DNA blot analysis confirmed the presence of the PMI gene in all transformants analysed. The possible mode of action of mannose selection compared to other selection methods is discussed.     

Energy landscapes shape microbial communities in hydrothermal systems on the Arctic Mid-Ocean Ridge

Methods developed in geochemical modelling combined with recent advances in molecular microbial ecology provide new opportunities to explore how microbial communities are shaped by their chemical surroundings. Here, we present a framework for analyses of how chemical energy availability shape chemotrophic microbial communities in hydrothermal systems through an investigation of two geochemically different basalt-hosted hydrothermal systems on the Arctic Mid-Ocean Ridge: the Soria Moria Vent field (SMVF) and the Loki''s Castle Vent Field (LCVF). Chemical energy landscapes were evaluated through modelling of the Gibbs energy from selected redox reactions under different mixing ratios between seawater and hydrothermal fluids. Our models indicate that the sediment-influenced LCVF has a much higher potential for both anaerobic and aerobic methane oxidation, as well as aerobic ammonium and hydrogen oxidation, than the SMVF. The modelled energy landscapes were used to develop microbial community composition models, which were compared with community compositions in environmental samples inside or on the exterior of hydrothermal chimneys, as assessed by pyrosequencing of partial 16S rRNA genes. We show that modelled microbial communities based solely on thermodynamic considerations can have a high predictive power and provide a framework for analyses of the link between energy availability and microbial community composition.     

Integration Analysis of Three Omics Data Using Penalized Regression Methods: An Application to Bladder Cancer

Omics data integration is becoming necessary to investigate the genomic mechanisms involved in complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological processes. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we propose a permutation-based method to concomitantly assess significance and correct by multiple testing with the MaxT algorithm. This was applied with penalized regression methods (LASSO and ENET) when exploring relationships between common genetic variants, DNA methylation and gene expression measured in bladder tumor samples. The overall analysis flow consisted of three steps: (1) SNPs/CpGs were selected per each gene probe within 1Mb window upstream and downstream the gene; (2) LASSO and ENET were applied to assess the association between each expression probe and the selected SNPs/CpGs in three multivariable models (SNP, CPG, and Global models, the latter integrating SNPs and CPGs); and (3) the significance of each model was assessed using the permutation-based MaxT method. We identified 48 genes whose expression levels were significantly associated with both SNPs and CPGs. Importantly, 36 (75%) of them were replicated in an independent data set (TCGA) and the performance of the proposed method was checked with a simulation study. We further support our results with a biological interpretation based on an enrichment analysis. The approach we propose allows reducing computational time and is flexible and easy to implement when analyzing several types of omics data. Our results highlight the importance of integrating omics data by applying appropriate statistical strategies to discover new insights into the complex genetic mechanisms involved in disease conditions.