Dissociation energies of AgRG (RG = Ar, Kr, Xe) and AgO molecules from velocity map imaging studies

The near ultraviolet photodissociation dynamics of silver atom rare gas dimers have been studied by velocity map imaging. AgRG (RG = Ar, Kr, Xe) species generated by laser ablation are excited in the region of the C <- X continuum leading to direct, near threshold dissociation generating Ag* (2P3/2) + RG (1S0) products. Images recorded at excitation wavelengths throughout the C <- X continuum, coupled with known atomic energy levels, permit determination of the ground X (2SIGMA+) state dissociation energies of 85.9 +/- 23.4 cm-1 (AgAr), 149.3 +/- 22.4 cm-1 (AgKr) and 256.3 +/- 16.0 cm-1 (AgXe). Three additional photolysis processes, each yielding Ag atom photoproducts, are observed in the same spectral region. Two of these are markedly enhanced in intensity upon seeding the molecular beam with nitrous oxide, and are assigned to photodissociation of AgO at the two photon level. These features yield an improved ground state dissociation energy for AgO of 15965 +/- 81 cm-1, which is in good agreement with high level calculations. The third process results in Ag atom fragments whose kinetic energy shows anomalously weak photon energy dependence and is assigned tentatively to dissociative ionization of the silver dimer Ag2

Genome-wide DNA methylation analysis for diabetic nephropathy in type 1 diabetes mellitus

BACKGROUND: Diabetic nephropathy is a serious complication of diabetes mellitus and is associated with considerable morbidity and high mortality. There is increasing evidence to suggest that dysregulation of the epigenome is involved in diabetic nephropathy. We assessed whether epigenetic modification of DNA methylation is associated with diabetic nephropathy in a case-control study of 192 Irish patients with type 1 diabetes mellitus (T1D). Cases had T1D and nephropathy whereas controls had T1D but no evidence of renal disease. METHODS: We performed DNA methylation profiling in bisulphite converted DNA from cases and controls using the recently developed Illumina Infinium(R) HumanMethylation27 BeadChip, that enables the direct investigation of 27,578 individual cytosines at CpG loci throughout the genome, which are focused on the promoter regions of 14,495 genes. RESULTS: Singular Value Decomposition (SVD) analysis indicated that significant components of DNA methylation variation correlated with patient age, time to onset of diabetic nephropathy, and sex. Adjusting for confounding factors using multivariate Cox-regression analyses, and with a false discovery rate (FDR) of 0.05, we observed 19 CpG sites that demonstrated correlations with time to development of diabetic nephropathy. Of note, this included one CpG site located 18 bp upstream of the transcription start site of UNC13B, a gene in which the first intronic SNP rs13293564 has recently been reported to be associated with diabetic nephropathy. CONCLUSION: This high throughput platform was able to successfully interrogate the methylation state of individual cytosines and identified 19 prospective CpG sites associated with risk of diabetic nephropathy. These differences in DNA methylation are worthy of further follow-up in replication studies using larger cohorts of diabetic patients with and without nephropathy

Photoionization efficiency spectroscopy and density functional theory investigations of RhHo2On, (n=0-2) clusters

The experimental and theoretical adiabatic ionization energies (IEs) of the rhodium-holmium bimetallic clusters RhHo(2)O(n) (n=0-2) have been determined using photoionization efficiency spectroscopy and density functional theory (DFT) calculations. Both sets of data show the IE of RhHo(2)O to be significantly lower than the values for RhHo(2) and RhHo(2)O(2), which are found to be similar. This indicates that there are significant changes in electronic properties upon sequential addition of oxygen atoms to RhHo(2). The DFT investigations show that the lowest energy neutral structures are a C(2v) triangle for RhHo(2), a C(2v) planar structure for RhHo(2)O where the O atom is doubly bridged to the Ho-Ho bond, and a C(2v) nonplanar structure for RhHo(2)O(2), where the O(2) is dissociative and each O atom is doubly bridged to the Ho-Ho bond in the cluster above and below the RhHo(2) trimer plane. Good correlation between the experimental and computational IE data imply that the lowest energy neutral structures calculated are the most likely isomers ionized in the molecular beam. In particular, the theoretical adiabatic IE for the dissociative RhHo(2)O(2) structure is found to compare better with the experimentally determined value than the corresponding lowest energy O(2) associative structure.Alexander S. Gentleman, Matthew A. Addicoat, Viktoras Dryza, Jason R. Gascooke, Mark A. Buntine, and Gregory F. Meth

Intrinsic molecular signature of breast cancer in a population-based cohort of 412 patients

BACKGROUND: Molecular markers and the rich biological information they contain have great potential for cancer diagnosis, prognostication and therapy prediction. So far, however, they have not superseded routine histopathology and staging criteria, partly because the few studies performed on molecular subtyping have had little validation and limited clinical characterization. METHODS: We obtained gene expression and clinical data for 412 breast cancers obtained from population-based cohorts of patients from Stockholm and Uppsala, Sweden. Using the intrinsic set of approximately 500 genes derived in the Norway/Stanford breast cancer data, we validated the existence of five molecular subtypes – basal-like, ERBB2, luminal A/B and normal-like – and characterized these subtypes extensively with the use of conventional clinical variables. RESULTS: We found an overall 77.5% concordance between the centroid prediction of the Swedish cohort by using the Norway/Stanford signature and the k-means clustering performed internally within the Swedish cohort. The highest rate of discordant assignments occurred between the luminal A and luminal B subtypes and between the luminal B and ERBB2 subtypes. The subtypes varied significantly in terms of grade (p < 0.001), p53 mutation (p < 0.001) and genomic instability (p = 0.01), but surprisingly there was little difference in lymph-node metastasis (p = 0.31). Furthermore, current users of hormone-replacement therapy were strikingly over-represented in the normal-like subgroup (p < 0.001). Separate analyses of the patients who received endocrine therapy and those who did not receive any adjuvant therapy supported the previous hypothesis that the basal-like subtype responded to adjuvant treatment, whereas the ERBB2 and luminal B subtypes were poor responders. CONCLUSION: We found that the intrinsic molecular subtypes of breast cancer are broadly present in a diverse collection of patients from a population-based cohort in Sweden. The intrinsic gene set, originally selected to reveal stable tumor characteristics, was shown to have a strong correlation with progression-related properties such as grade, p53 mutation and genomic instability

Thyroid Hormone Signalling Genes Are Regulated by Photoperiod in the Hypothalamus of F344 Rats

Seasonal animals adapt their physiology and behaviour in anticipation of climate change to optimise survival of their offspring. Intra-hypothalamic thyroid hormone signalling plays an important role in seasonal responses in mammals and birds. In the F344 rat, photoperiod stimulates profound changes in food intake, body weight and reproductive status. Previous investigations of the F344 rat have suggested a role for thyroid hormone metabolism, but have only considered Dio2 expression, which was elevated in long day photoperiods. Microarray analysis was used to identify time-dependent changes in photoperiod responsive genes, which may underlie the photoperiod-dependent phenotypes of the juvenile F344 rat. The most significant changes are those related to thyroid hormone metabolism and transport. Using photoperiod manipulations and melatonin injections into long day photoperiod (LD) rats to mimic short day (SD), we show photoinduction and photosuppression gene expression profiles and melatonin responsiveness of genes by in situ hybridization; TSHβ, CGA, Dio2 and Oatp1c1 genes were all elevated in LD whilst in SD, Dio3 and MCT-8 mRNA were increased. NPY was elevated in SD whilst GALP increased in LD. The photoinduction and photosuppression profiles for GALP were compared to that of GHRH with GALP expression following GHRH temporally. We also reveal gene sets involved in photoperiodic responses, including retinoic acid and Wnt/ß-catenin signalling. This study extends our knowledge of hypothalamic regulation by photoperiod, by revealing large temporal changes in expression of thyroid hormone signalling genes following photoperiod switch. Surprisingly, large changes in hypothalamic thyroid hormone levels or TRH expression were not detected. Expression of NPY and GALP, two genes known to regulate GHRH, were also changed by photoperiod. Whether these genes could provide links between thyroid hormone signalling and the regulation of the growth axis remains to be investigated

Comparison of multiplex meta analysis techniques for understanding the acute rejection of solid organ transplants

<p>Abstract</p> <p>Background</p> <p>Combining the results of studies using highly parallelized measurements of gene expression such as microarrays and RNAseq offer unique challenges in meta analysis. Motivated by a need for a deeper understanding of organ transplant rejection, we combine the data from five separate studies to compare acute rejection versus stability after solid organ transplantation, and use this data to examine approaches to multiplex meta analysis.</p> <p>Results</p> <p>We demonstrate that a commonly used parametric effect size estimate approach and a commonly used non-parametric method give very different results in prioritizing genes. The parametric method providing a meta effect estimate was superior at ranking genes based on our gold-standard of identifying immune response genes in the transplant rejection datasets.</p> <p>Conclusion</p> <p>Different methods of multiplex analysis can give substantially different results. The method which is best for any given application will likely depend on the particular domain, and it remains for future work to see if any one method is consistently better at identifying important biological signal across gene expression experiments.</p

Assessment of a six gene panel for the molecular detection of circulating tumor cells in the blood of female cancer patients

<p>Abstract</p> <p>Background</p> <p>The presence of circulating tumor cells (CTC) in the peripheral blood of cancer patients has been described for various solid tumors and their clinical relevance has been shown. CTC detection based on the analysis of epithelial antigens might be hampered by the genetic heterogeneity of the primary tumor and loss of epithelial antigens. Therefore, we aimed to identify new gene markers for the PCR-based detection of CTC in female cancer patients.</p> <p>Methods</p> <p>Gene expression of 38 cancer cell lines (breast, ovarian, cervical and endometrial) and of 10 peripheral blood mononuclear cell (PBMC) samples from healthy female donors was measured using microarray technology (Applied Biosystems). Differentially expressed genes were identified using the maxT test and the 50% one-sided trimmed maxT-test. Confirmatory RT-qPCR was performed for 380 gene targets using the AB TaqMan^®Low Density Arrays. Then, 93 gene targets were analyzed using the same RT-qPCR platform in tumor tissues of 126 patients with primary breast, ovarian or endometrial cancer. Finally, blood samples from 26 healthy women and from 125 patients (primary breast, ovarian, cervical, or endometrial cancer, and advanced breast cancer) were analyzed following OncoQuick enrichment and RNA pre-amplification. Likewise, <it>hMAM </it>and <it>EpCAM </it>gene expression was analyzed in the blood of breast and ovarian cancer patients. For each gene, a cut-off threshold value was set at three standard deviations from the mean expression level of the healthy controls to identify potential markers for CTC detection.</p> <p>Results</p> <p>Six genes were over-expressed in blood samples from 81% of patients with advanced and 29% of patients with primary breast cancer. <it>EpCAM </it>gene expression was detected in 19% and 5% of patients, respectively, whereas <it>hMAM </it>gene expression was observed in the advanced group (39%) only. Multimarker analysis using the new six gene panel positively identified 44% of the cervical, 64% of the endometrial and 19% of the ovarian cancer patients.</p> <p>Conclusions</p> <p>The panel of six genes was found superior to <it>EpCAM </it>and <it>hMAM </it>for the detection of circulating tumor cells in the blood of breast cancer, and they may serve as potential markers for CTC derived from endometrial, cervical, and ovarian cancers.</p

pcaGoPromoter - An R Package for Biological and Regulatory Interpretation of Principal Components in Genome-Wide Gene Expression Data

Analyzing data obtained from genome-wide gene expression experiments is challenging due to the quantity of variables, the need for multivariate analyses, and the demands of managing large amounts of data. Here we present the R package pcaGoPromoter, which facilitates the interpretation of genome-wide expression data and overcomes the aforementioned problems. In the first step, principal component analysis (PCA) is applied to survey any differences between experiments and possible groupings. The next step is the interpretation of the principal components with respect to both biological function and regulation by predicted transcription factor binding sites. The robustness of the results is evaluated using cross-validation, and illustrative plots of PCA scores and gene ontology terms are available. pcaGoPromoter works with any platform that uses gene symbols or Entrez IDs as probe identifiers. In addition, support for several popular Affymetrix GeneChip platforms is provided. To illustrate the features of the pcaGoPromoter package a serum stimulation experiment was performed and the genome-wide gene expression in the resulting samples was profiled using the Affymetrix Human Genome U133 Plus 2.0 chip. Array data were analyzed using pcaGoPromoter package tools, resulting in a clear separation of the experiments into three groups: controls, serum only and serum with inhibitor. Functional annotation of the axes in the PCA score plot showed the expected serum-promoted biological processes, e.g., cell cycle progression and the predicted involvement of expected transcription factors, including E2F. In addition, unexpected results, e.g., cholesterol synthesis in serum-depleted cells and NF-κB activation in inhibitor treated cells, were noted. In summary, the pcaGoPromoter R package provides a collection of tools for analyzing gene expression data. These tools give an overview of the input data via PCA, functional interpretation by gene ontology terms (biological processes), and an indication of the involvement of possible transcription factors

Advanced Computational Biology Methods Identify Molecular Switches for Malignancy in an EGF Mouse Model of Liver Cancer

The molecular causes by which the epidermal growth factor receptor tyrosine kinase induces malignant transformation are largely unknown. To better understand EGFs' transforming capacity whole genome scans were applied to a transgenic mouse model of liver cancer and subjected to advanced methods of computational analysis to construct de novo gene regulatory networks based on a combination of sequence analysis and entrained graph-topological algorithms. Here we identified transcription factors, processes, key nodes and molecules to connect as yet unknown interacting partners at the level of protein-DNA interaction. Many of those could be confirmed by electromobility band shift assay at recognition sites of gene specific promoters and by western blotting of nuclear proteins. A novel cellular regulatory circuitry could therefore be proposed that connects cell cycle regulated genes with components of the EGF signaling pathway. Promoter analysis of differentially expressed genes suggested the majority of regulated transcription factors to display specificity to either the pre-tumor or the tumor state. Subsequent search for signal transduction key nodes upstream of the identified transcription factors and their targets suggested the insulin-like growth factor pathway to render the tumor cells independent of EGF receptor activity. Notably, expression of IGF2 in addition to many components of this pathway was highly upregulated in tumors. Together, we propose a switch in autocrine signaling to foster tumor growth that was initially triggered by EGF and demonstrate the knowledge gain form promoter analysis combined with upstream key node identification