Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

We report for the first time the genomics of a nuclear compartment of the eukaryotic cell. 454 sequencing and microarray analysis revealed the pattern of nucleolus-associated chromatin domains (NADs) in the linear human genome and identified different gene families and certain satellite repeats as the major building blocks of NADs, which constitute about 4% of the genome. Bioinformatic evaluation showed that NAD–localized genes take part in specific biological processes, like the response to other organisms, odor perception, and tissue development. 3D FISH and immunofluorescence experiments illustrated the spatial distribution of NAD–specific chromatin within interphase nuclei and its alteration upon transcriptional changes. Altogether, our findings describe the nature of DNA sequences associated with the human nucleolus and provide insights into the function of the nucleolus in genome organization and establishment of nuclear architecture.

OBJECTIVEObesity-associated insulin resistance is characterized by a state of chronic, low-grade inflammation that is associated with the accumulation of M1 proinflammatory macrophages in adipose tissue. Although different evidence explains the mechanisms linking the expansion of adipose tissue and adipose tissue macrophage (ATM) polarization, in the current study we investigated the concept of lipid-induced toxicity as the pathogenic link that could explain the trigger of this response.RESEARCH DESIGN AND METHODSWe addressed this question using isolated ATMs and adipocytes from genetic and diet-induced murine models of obesity. Through transcriptomic and lipidomic analysis, we created a model integrating transcript and lipid species networks simultaneously occurring in adipocytes and ATMs and their reversibility by thiazolidinedione treatment.RESULTSWe show that polarization of ATMs is associated with lipid accumulation and the consequent formation of foam cell–like cells in adipose tissue. Our study reveals that early stages of adipose tissue expansion are characterized by M2-polarized ATMs and that progressive lipid accumulation within ATMs heralds the M1 polarization, a macrophage phenotype associated with severe obesity and insulin resistance. Furthermore, rosiglitazone treatment, which promotes redistribution of lipids toward adipocytes and extends the M2 ATM polarization state, prevents the lipid alterations associated with M1 ATM polarization.CONCLUSIONSOur data indicate that the M1 ATM polarization in obesity might be a macrophage-specific manifestation of a more general lipotoxic pathogenic mechanism. This indicates that strategies to optimize fat deposition and repartitioning toward adipocytes might improve insulin sensitivity by preventing ATM lipotoxicity and M1 polarization.

Babelomics is a response to the growing necessity of integrating and analyzing different types of genomic data in an environment that allows an easy functional interpretation of the results. Babelomics includes a complete suite of methods for the analysis of gene expression data that include normalization (covering most commercial platforms), pre-processing, differential gene expression (case-controls, multiclass, survival or continuous values), predictors, clustering; large-scale genotyping assays (case controls and TDTs, and allows population stratification analysis and correction). All these genomic data analysis facilities are integrated and connected to multiple options for the functional interpretation of the experiments. Different methods of functional enrichment or gene set enrichment can be used to understand the functional basis of the experiment analyzed. Many sources of biological information, which include functional (GO, KEGG, Biocarta, Reactome, etc.), regulatory (Transfac, Jaspar, ORegAnno, miRNAs, etc.), text-mining or protein–protein interaction modules can be used for this purpose. Finally a tool for the de novo functional annotation of sequences has been included in the system. This provides support for the functional analysis of non-model species. Mirrors of Babelomics or command line execution of their individual components are now possible. Babelomics is available at http://www.babelomics.org.