Gene expression meta-analysis identifies metastatic pathways and transcription factors in breast cancer

<p>Abstract</p> <p>Background</p> <p>Metastasis is believed to progress in several steps including different pathways but the determination and understanding of these mechanisms is still fragmentary. Microarray analysis of gene expression patterns in breast tumors has been used to predict outcome in recent studies. Besides classification of outcome, these global expression patterns may reflect biological mechanisms involved in metastasis of breast cancer. Our purpose has been to investigate pathways and transcription factors involved in metastasis by use of gene expression data sets.</p> <p>Methods</p> <p>We have analyzed 8 publicly available gene expression data sets. A global approach, "gene set enrichment analysis" as well as an approach focusing on a subset of significantly differently regulated genes, GenMAPP, has been applied to rank pathway gene sets according to differential regulation in metastasizing tumors compared to non-metastasizing tumors. Meta-analysis has been used to determine overrepresentation of pathways and transcription factors targets, concordant deregulated in metastasizing breast tumors, in several data sets.</p> <p>Results</p> <p>The major findings are up-regulation of cell cycle pathways and a metabolic shift towards glucose metabolism reflected in several pathways in metastasizing tumors. Growth factor pathways seem to play dual roles; EGF and PDGF pathways are decreased, while VEGF and sex-hormone pathways are increased in tumors that metastasize. Furthermore, migration, proteasome, immune system, angiogenesis, DNA repair and several signal transduction pathways are associated to metastasis. Finally several transcription factors e.g. E2F, NFY, and YY1 are identified as being involved in metastasis.</p> <p>Conclusion</p> <p>By pathway meta-analysis many biological mechanisms beyond major characteristics such as proliferation are identified. Transcription factor analysis identifies a number of key factors that support central pathways. Several previously proposed treatment targets are identified and several new pathways that may constitute new targets are identified.</p

Three Essential Ribonucleases—RNase Y, J1, and III—Control the Abundance of a Majority of Bacillus subtilis mRNAs

Bacillus subtilis possesses three essential enzymes thought to be involved in mRNA decay to varying degrees, namely RNase Y, RNase J1, and RNase III. Using recently developed high-resolution tiling arrays, we examined the effect of depletion of each of these enzymes on RNA abundance over the whole genome. The data are consistent with a model in which the degradation of a significant number of transcripts is dependent on endonucleolytic cleavage by RNase Y, followed by degradation of the downstream fragment by the 5′–3′ exoribonuclease RNase J1. However, many full-size transcripts also accumulate under conditions of RNase J1 insufficiency, compatible with a model whereby RNase J1 degrades transcripts either directly from the 5′ end or very close to it. Although the abundance of a large number of transcripts was altered by depletion of RNase III, this appears to result primarily from indirect transcriptional effects. Lastly, RNase depletion led to the stabilization of many low-abundance potential regulatory RNAs, both in intergenic regions and in the antisense orientation to known transcripts

The HIV Matrix Protein p17 Subverts Nuclear Receptors Expression and Induces a STAT1-Dependent Proinflammatory Phenotype in Monocytes

BACKGROUND: Long-term remission of HIV-1 disease can be readily achieved by combinations of highly effective antiretroviral therapy (HAART). However, a residual persistent immune activation caused by circulating non infectious particles or viral proteins is observed under HAART and might contribute to an higher risk of non-AIDS pathologies and death in HIV infected persons. A sustained immune activation supports lipid dysmetabolism and increased risk for development of accelerated atehrosclerosis and ischemic complication in virologically suppressed HIV-infected persons receiving HAART. AIM: While several HIV proteins have been identified and characterized for their ability to maintain immune activation, the role of HIV-p17, a matrix protein involved in the viral replication, is still undefined. RESULTS: Here, we report that exposure of macrophages to recombinant human p17 induces the expression of proinflammatory and proatherogenic genes (MCP-1, ICAM-1, CD40, CD86 and CD36) while downregulating the expression of nuclear receptors (FXR and PPARγ) that counter-regulate the proinflammatory response and modulate lipid metabolism in these cells. Exposure of macrophage cell lines to p17 activates a signaling pathway mediated by Rack-1/Jak-1/STAT-1 and causes a promoter-dependent regulation of STAT-1 target genes. These effects are abrogated by sera obtained from HIV-infected persons vaccinated with a p17 peptide. Ligands for FXR and PPARγ counteract the effects of p17. CONCLUSIONS: The results of this study show that HIV p17 highjacks a Rack-1/Jak-1/STAT-1 pathway in macrophages, and that the activation of this pathway leads to a simultaneous dysregulation of immune and metabolic functions. The binding of STAT-1 to specific responsive elements in the promoter of PPARγ and FXR and MCP-1 shifts macrophages toward a pro-atherogenetic phenotype characterized by high levels of expression of the scavenger receptor CD36. The present work identifies p17 as a novel target in HIV therapy and grounds the development of anti-p17 small molecules or vaccines

Switching to dual/monotherapy determines an increase in CD8+ in HIV-infected individuals: An observational cohort study

Background: The CD4/CD8 ratio has been associated with the risk of AIDS and non-AIDS events. We describe trends in immunological parameters in people who underwent a switch to monotherapy or dual therapy, compared to a control group remaining on triple antiretroviral therapy (ART). Methods: We included patients in Icona who started a three-drug combination ART regimen from an ART-naïve status and achieved a viral load ≤ 50 copies/mL; they were subsequently switched to another triple or to a mono or double regimen. Standard linear regression at fixed points in time (12-24 months after the switch) and linear mixed model analysis with random intercepts and slopes were used to compare CD4 and CD8 counts and their ratio over time according to regimen types (triple vs. dual and vs. mono). Results: A total of 1241 patients were included; 1073 switched to triple regimens, 104 to dual (72 with 1 nucleoside reverse transcriptase inhibitor (NRTI), 32 NRTI-sparing), and 64 to monotherapy. At 12 months after the switch, for the multivariable linear regression the mean change in the log10 CD4/CD8 ratio for patients on dual therapy was -0.03 (95% confidence interval (CI) -0.05, -0.0002), and the mean change in CD8 count was +99 (95% CI +12.1, +186.3), taking those on triple therapy as reference. In contrast, there was no evidence for a difference in CD4 count change. When using all counts, there was evidence for a significant difference in the slope of the ratio and CD8 count between people who were switched to triple (points/year change ratio = +0.056, CD8 = -25.7) and those to dual regimen (ratio = -0.029, CD8 = +110.4). Conclusions: We found an increase in CD8 lymphocytes in people who were switched to dual regimens compared to those who were switched to triple. Patients on monotherapy did not show significant differences. The long-term implications of this difference should be ascertained

Gender differences in the use of cardiovascular interventions in HIV-positive persons; the D:A:D Study

Peer reviewe

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules