A novel antibiotic class targeting the lipopolysaccharide transporter.

Carbapenem-resistant Acinetobacter baumannii (CRAB) has emerged as a major global pathogen with limited treatment options1. No new antibiotic chemical class with activity against A. baumannii has reached patients in over 50 years1. Here we report the identification and optimization of tethered macrocyclic peptide (MCP) antibiotics with potent antibacterial activity against CRAB. The mechanism of action of this molecule class involves blocking the transport of bacterial lipopolysaccharide from the inner membrane to its destination on the outer membrane, through inhibition of the LptB2FGC complex. A clinical candidate derived from the MCP class, zosurabalpin (RG6006), effectively treats highly drug-resistant contemporary isolates of CRAB both in vitro and in mouse models of infection, overcoming existing antibiotic resistance mechanisms. This chemical class represents a promising treatment paradigm for patients with invasive infections due to CRAB, for whom current treatment options are inadequate, and additionally identifies LptB2FGC as a tractable target for antimicrobial drug development.

A previously uncharacterized gene, PA2146, contributes to biofilm formation and drug tolerance across the ɣ-Proteobacteria.

Transcriptomic studies have revealed a large number of uncharacterized genes that are differentially expressed in biofilms, which may be important in regulating biofilm phenotypes such as resistance to antimicrobial agents. To identify biofilm genes of unknown function in P. aeruginosa, we made use of RNA-seq and selected 27 uncharacterized genes that were induced upon biofilm growth. Biofilms by respective mutants were subsequently analyzed for two biofilm characteristics, the biofilm architecture and drug susceptibility. The screen revealed 12 out of 27 genes to contribute to biofilm formation and 13 drug susceptibility, with 8 genes affecting both biofilm phenotypes. Amongst the genes affecting both biofilm phenotypes was PA2146, encoding a small hypothetical protein that exhibited some of the most substantial increases in transcript abundance during biofilm growth by P. aeruginosa PAO1 and clinical isolates. PA2146 is highly conserved in É£-proteobacteria. Inactivation of PA2146 affected both biofilm phenotypes in P. aeruginosa PAO1, with inactivation of homologs in Klebsiella pneumoniae and Escherichia coli having similar effects. Heterologous expression of PA2146 homologs complemented the P. aeruginosa ∆PA2146, suggesting that PA2146 homologs substitute for and play a similar role as PA2146 in P. aeruginosa.

Shared and Unique Evolutionary Trajectories to Ciprofloxacin Resistance in Gram-Negative Bacterial Pathogens.

Resistance to the broad-spectrum antibiotic ciprofloxacin is detected at high rates for a wide range of bacterial pathogens. To investigate the dynamics of ciprofloxacin resistance development, we applied a comparative resistomics workflow for three clinically relevant species of Gram-negative bacteria: Escherichia coli, Acinetobacter baumannii, and Pseudomonas aeruginosa. We combined experimental evolution in a morbidostat with deep sequencing of evolving bacterial populations in time series to reveal both shared and unique aspects of evolutionary trajectories. Representative clone characterization by sequencing and MIC measurements enabled direct assessment of the impact of mutations on the extent of acquired drug resistance. In all three species, we observed a two-stage evolution: (i) early ciprofloxacin resistance reaching 4- to 16-fold the MIC for the wild type, commonly as a result of single mutations in DNA gyrase target genes (gyrA or gyrB), and (ii) additional genetic alterations affecting the transcriptional control of the drug efflux machinery or secondary target genes (DNA topoisomerase parC or parE). IMPORTANCE The challenge of spreading antibiotic resistance calls for systematic efforts to develop more "irresistible" drugs based on a deeper understanding of dynamics and mechanisms of antibiotic resistance acquisition. To address this challenge, we have established a comparative resistomics approach which combines experimental evolution in a continuous-culturing device, the morbidostat, with ultradeep sequencing of evolving microbial populations to identify evolutionary trajectories (mutations and genome rearrangements) leading to antibiotic resistance over a range of target pathogens. Here, we report the comparative resistomics study of three Gram-negative bacteria (Escherichia coli, Acinetobacter baumannii, and Pseudomonas aeruginosa), which revealed shared and species-specific aspects of the evolutionary landscape leading to robust resistance against the clinically important antibiotic ciprofloxacin. Despite some differences between morbidostat-deduced mutation profiles and those observed in clinical isolates of individual species, a cross-species comparative resistomics approach allowed us to recapitulate all types of clinically relevant ciprofloxacin resistance mechanisms. This observation supports the anticipated utility of this approach in guiding rational optimization of treatment regimens for current antibiotics and the development of novel antibiotics with minimized resistance propensities.

Experimental evolution in morbidostat reveals converging genomic trajectories on the path to triclosan resistance.

Understanding the dynamics and mechanisms of acquired drug resistance across major classes of antibiotics and bacterial pathogens is of critical importance for the optimization of current anti-infective therapies and the development of novel ones. To systematically address this challenge, we developed a workflow combining experimental evolution in a morbidostat continuous culturing device with deep genomic sequencing of population samples collected in time series. This approach was applied to the experimental evolution of six populations of Escherichia coli BW25113 towards acquiring resistance to triclosan (TCS), an antibacterial agent in various consumer products. This study revealed the rapid emergence and expansion (up to 100% in each culture within 4 days) of missense mutations in the fabI gene, encoding enoyl-acyl carrier protein reductase, the known TCS molecular target. A follow-up analysis of isolated clones showed that distinct amino acid substitutions increased the drug IC90 in a 3-16-fold range, reflecting their proximity to the TCS-binding site. In contrast to other antibiotics, efflux-upregulating mutations occurred only rarely and with low abundance. Mutations in several other genes were detected at an earlier stage of evolution. Most notably, three distinct amino acid substitutions were mapped in the C-terminal periplasmic domain of CadC protein, an acid stress-responsive transcriptional regulator. While these mutations do not confer robust TCS resistance, they appear to play a certain, yet unknown, role in adaptation to relatively low drug pressure. Overall, the observed evolutionary trajectories suggest that the FabI enzyme is the sole target of TCS (at least up to the ~50 µm level), and amino acid substitutions in the TCS-binding site represent the main mechanism of robust TCS resistance in E. coli. This model study illustrates the potential utility of the established morbidostat-based approach for uncovering resistance mechanisms and target identification for novel drug candidates with yet unknown mechanisms of action.

Machine-learning based patient classification using Hepatitis B virus full-length genome quasispecies from Asian and European cohorts.

Chronic infection with Hepatitis B virus (HBV) is a major risk factor for the development of advanced liver disease including fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). The relative contribution of virological factors to disease progression has not been fully defined and tools aiding the deconvolution of complex patient virus profiles is an unmet clinical need. Variable viral mutant signatures develop within individual patients due to the low-fidelity replication of the viral polymerase creating 'quasispecies' populations. Here we present the first comprehensive survey of the diversity of HBV quasispecies through ultra-deep sequencing of the complete HBV genome across two distinct European and Asian patient populations. Seroconversion to the HBV e antigen (HBeAg) represents a critical clinical waymark in infected individuals. Using a machine learning approach, a model was developed to determine the viral variants that accurately classify HBeAg status. Serial surveys of patient quasispecies populations and advanced analytics will facilitate clinical decision support for chronic HBV infection and direct therapeutic strategies through improved patient stratification.

Heteroaryldihydropyrimidine (HAP) and Sulfamoylbenzamide (SBA) Inhibit Hepatitis B Virus Replication by Different Molecular Mechanisms.

Heteroaryldihydropyrimidine (HAP) and sulfamoylbenzamide (SBA) are promising non-nucleos(t)ide HBV replication inhibitors. HAPs are known to promote core protein mis-assembly, but the molecular mechanism of abnormal assembly is still elusive. Likewise, the assembly status of core protein induced by SBA remains unknown. Here we show that SBA, unlike HAP, does not promote core protein mis-assembly. Interestingly, two reference compounds HAP_R01 and SBA_R01 bind to the same pocket at the dimer-dimer interface in the crystal structures of core protein Y132A hexamer. The striking difference lies in a unique hydrophobic subpocket that is occupied by the thiazole group of HAP_R01, but is unperturbed by SBA_R01. Photoaffinity labeling confirms the HAP_R01 binding pose at the dimer-dimer interface on capsid and suggests a new mechanism of HAP-induced mis-assembly. Based on the common features in crystal structures we predict that T33 mutations generate similar susceptibility changes to both compounds. In contrast, mutations at positions in close contact with HAP-specific groups (P25A, P25S, or V124F) only reduce susceptibility to HAP_R01, but not to SBA_R01. Thus, HAP and SBA are likely to have distinctive resistance profiles. Notably, P25S and V124F substitutions exist in low-abundance quasispecies in treatment-naïve patients, suggesting potential clinical relevance.

Comparative evaluation of rRNA depletion procedures for the improved analysis of bacterial biofilm and mixed pathogen culture transcriptomes.

Global transcriptomic analysis via RNA-seq is often hampered by the high abundance of ribosomal (r)RNA in bacterial cells. To remove rRNA and enrich coding sequences, subtractive hybridization procedures have become the approach of choice prior to RNA-seq, with their efficiency varying in a manner dependent on sample type and composition. Yet, despite an increasing number of RNA-seq studies, comparative evaluation of bacterial rRNA depletion methods has remained limited. Moreover, no such study has utilized RNA derived from bacterial biofilms, which have potentially higher rRNA:mRNA ratios and higher rRNA carryover during RNA-seq analysis. Presently, we evaluated the efficiency of three subtractive hybridization-based kits in depleting rRNA from samples derived from biofilm, as well as planktonic cells of the opportunistic human pathogen Pseudomonas aeruginosa. Our results indicated different rRNA removal efficiency for the three procedures, with the Ribo-Zero kit yielding the highest degree of rRNA depletion, which translated into enhanced enrichment of non-rRNA transcripts and increased depth of RNA-seq coverage. The results indicated that, in addition to improving RNA-seq sensitivity, efficient rRNA removal enhanced detection of low abundance transcripts via qPCR. Finally, we demonstrate that the Ribo-Zero kit also exhibited the highest efficiency when P. aeruginosa/Staphylococcus aureus co-culture RNA samples were tested.

InFusion: Advancing Discovery of Fusion Genes and Chimeric Transcripts from Deep RNA-Sequencing Data.

Analysis of fusion transcripts has become increasingly important due to their link with cancer development. Since high-throughput sequencing approaches survey fusion events exhaustively, several computational methods for the detection of gene fusions from RNA-seq data have been developed. This kind of analysis, however, is complicated by native trans-splicing events, the splicing-induced complexity of the transcriptome and biases and artefacts introduced in experiments and data analysis. There are a number of tools available for the detection of fusions from RNA-seq data; however, certain differences in specificity and sensitivity between commonly used approaches have been found. The ability to detect gene fusions of different types, including isoform fusions and fusions involving non-coding regions, has not been thoroughly studied yet. Here, we propose a novel computational toolkit called InFusion for fusion gene detection from RNA-seq data. InFusion introduces several unique features, such as discovery of fusions involving intergenic regions, and detection of anti-sense transcription in chimeric RNAs based on strand-specificity. Our approach demonstrates superior detection accuracy on simulated data and several public RNA-seq datasets. This improved performance was also evident when evaluating data from RNA deep-sequencing of two well-established prostate cancer cell lines. InFusion identified 26 novel fusion events that were validated in vitro, including alternatively spliced gene fusion isoforms and chimeric transcripts that include intergenic regions. The toolkit is freely available to download from http:/bitbucket.org/kokonech/infusion.

Association study between mannose-binding lectin haplotypes and X gene mutation of hepatitis B virus from treatment naïve patients.

Mannose binding lectin (MBL) plays important role in the innate immunity of human. Mutations in the MBL2 gene can significantly change the serum level of MBL, and consequently alter the susceptibility and progression of infectious disease. However, the association between the MBL2 profile and the HBV mutation and quasispecies complexity has not yet been reported. Our approach includes the study of the MBL2 gene genotype as well as ultra-deep sequencing of the HBV viruses obtained from the plasma of 50 treatment naïve patients with chronic HBV infection. We found that the liver function was better among patients within the high MBL2 group with respect to those within the medium/low MBL2 group. Likewise, the number of mutations in the HBV X gene as well as the viral quasispecies complexity were significantly higher in medium/low MBL2 production group. Nucleotide substitution rates were also higher within the medium/low MBL2 production group in all positions described to have an influence in liver cancer development, except for A1499G. In this work we show that the MBL2 profile may have an impact on the HBV X gene mutations as well as on viral quasispecies complexity.

Combinations of Oseltamivir and T-705 Extend the Treatment Window for Highly Pathogenic Influenza A(H5N1) Virus Infection in Mice.

Current anti-influenza therapy depends on administering drugs soon after infection, which is often impractical. We assessed whether combinations of oseltamivir (a neuraminidase inhibitor) and T-705 (a nonspecific inhibitor of viral polymerases) could extend the window for treating lethal infection with highly pathogenic A(H5N1) influenza virus in mice. Combination therapy protected 100% of mice, even when delayed until 96 h postinoculation. Compared to animals receiving monotherapy, mice receiving combination therapy had reduced viral loads and restricted viral spread in lung tissues, limited lung damage, and decreased inflammatory cytokine production. Next-generation sequencing showed that virus populations in T-705-treated mice had greater genetic variability, with more frequent transversion events, than did populations in control and oseltamivir-treated mice, but no substitutions associated with resistance to oseltamivir or T-705 were detected. Thus, combination therapy extended the treatment window for A(H5N1) influenza infection in mice and should be considered for evaluation in a clinical setting.

Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data.

MOTIVATION: Detection of random errors and systematic biases is a crucial step of a robust pipeline for processing high-throughput sequencing (HTS) data. Bioinformatics software tools capable of performing this task are available, either for general analysis of HTS data or targeted to a specific sequencing technology. However, most of the existing QC instruments only allow processing of one sample at a time. RESULTS: Qualimap 2 represents a next step in the QC analysis of HTS data. Along with comprehensive single-sample analysis of alignment data, it includes new modes that allow simultaneous processing and comparison of multiple samples. As with the first version, the new features are available via both graphical and command line interface. Additionally, it includes a large number of improvements proposed by the user community. AVAILABILITY AND IMPLEMENTATION: The implementation of the software along with documentation is freely available at http://www.qualimap.org. CONTACT: meyer@mpiib-berlin.mpg.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Helicobacter pylori Infection Causes Characteristic DNA Damage Patterns in Human Cells.

Infection with the human pathogen Helicobacter pylori (H. pylori) is a major risk factor for gastric cancer. Since the bacterium exerts multiple genotoxic effects, we examined the circumstances of DNA damage accumulation and identified regions within the host genome with high susceptibility to H. pylori-induced damage. Infection impaired several DNA repair factors, the extent of which depends on a functional cagPAI. This leads to accumulation of a unique DNA damage pattern, preferentially in transcribed regions and proximal to telomeres, in both gastric cell lines and primary gastric epithelial cells. The observed pattern correlates with focal amplifications in adenocarcinomas of the stomach and partly overlaps with known cancer genes. We thus demonstrate an impact of a bacterial infection directed toward specific host genomic regions and describe underlying characteristics that make such regions more likely to acquire heritable changes during infection, which could contribute to cellular transformation.

CisMiner: genome-wide in-silico cis-regulatory module prediction by fuzzy itemset mining.

Eukaryotic gene control regions are known to be spread throughout non-coding DNA sequences which may appear distant from the gene promoter. Transcription factors are proteins that coordinately bind to these regions at transcription factor binding sites to regulate gene expression. Several tools allow to detect significant co-occurrences of closely located binding sites (cis-regulatory modules, CRMs). However, these tools present at least one of the following limitations: 1) scope limited to promoter or conserved regions of the genome; 2) do not allow to identify combinations involving more than two motifs; 3) require prior information about target motifs. In this work we present CisMiner, a novel methodology to detect putative CRMs by means of a fuzzy itemset mining approach able to operate at genome-wide scale. CisMiner allows to perform a blind search of CRMs without any prior information about target CRMs nor limitation in the number of motifs. CisMiner tackles the combinatorial complexity of genome-wide cis-regulatory module extraction using a natural representation of motif combinations as itemsets and applying the Top-Down Fuzzy Frequent- Pattern Tree algorithm to identify significant itemsets. Fuzzy technology allows CisMiner to better handle the imprecision and noise inherent to regulatory processes. Results obtained for a set of well-known binding sites in the S. cerevisiae genome show that our method yields highly reliable predictions. Furthermore, CisMiner was also applied to putative in-silico predicted transcription factor binding sites to identify significant combinations in S. cerevisiae and D. melanogaster, proving that our approach can be further applied genome-wide to more complex genomes. CisMiner is freely accesible at: http://genome2.ugr.es/cisminer. CisMiner can be queried for the results presented in this work and can also perform a customized cis-regulatory module prediction on a query set of transcription factor binding sites provided by the user.

Evidence for a crucial role of a host non-coding RNA in influenza A virus replication.

A growing body of evidence suggests the non-protein coding human genome is of vital importance for human cell function. Besides small RNAs, the diverse class of long non-coding RNAs (lncRNAs) recently came into focus. However, their relevance for infection, a major evolutionary driving force, remains elusive. Using two commercially available microarray systems, namely NCode™ and Sureprint™ G3, we identified differential expression of 42 ncRNAs during influenza A virus (IAV) infection in human lung epithelial cells. This included several classes of lncRNAs, including large intergenic ncRNAs (lincRNAs). As analyzed by qRT-PCR, expression of one lincRNA, which we termed virus inducible lincRNA (VIN), is induced by several IAV strains (H1N1, H3N2, H7N7) as well as vesicular stomatitis virus. However, we did not observe an induction of VIN by influenza B virus, treatment with RNA mimics, or IFNß. Thus, VIN expression seems to be a specific response to certain viral infections. RNA fractionation and RNA-FISH experiments revealed that VIN is localized to the host cell nucleus. Most importantly, we show that abolition of VIN by RNA interference restricts IAV replication and viral protein synthesis, highlighting the relevance of this lincRNA for productive IAV infection. Our observations suggest that viral pathogens interfere with the non-coding portion of the human genome, thereby guaranteeing their successful propagation, and that the expression of VIN correlates with their virulence. Consequently, our study provides a novel approach for understanding virus pathogenesis in greater detail, which will enable future design of new antiviral strategies targeting the host's non-protein coding genome.

Qualimap: evaluating next-generation sequencing alignment data.

MOTIVATION: The sequence alignment/map (SAM) and the binary alignment/map (BAM) formats have become the standard method of representation of nucleotide sequence alignments for next-generation sequencing data. SAM/BAM files usually contain information from tens to hundreds of millions of reads. Often, the sequencing technology, protocol and/or the selected mapping algorithm introduce some unwanted biases in these data. The systematic detection of such biases is a non-trivial task that is crucial to drive appropriate downstream analyses. RESULTS: We have developed Qualimap, a Java application that supports user-friendly quality control of mapping data, by considering sequence features and their genomic properties. Qualimap takes sequence alignment data and provides graphical and statistical analyses for the evaluation of data. Such quality-control data are vital for highlighting problems in the sequencing and/or mapping processes, which must be addressed prior to further analyses. AVAILABILITY: Qualimap is freely available from http://www.qualimap.org.

Genome-wide differential genetic profiling characterizes colorectal cancers with genetic instability and specific routes to HLA class I loss and immune escape.

AIM: We compared the expression of genes related to inflammatory and cytotoxic functions between MSI and MSS (HLA-class I-negative and HLA-class I-positive) colorectal cancers (CRCs), seeking evidence of differences in inflammatory mediators and cytotoxic T-cell responses. Twenty-two CRCs were divided into three study groups as a function of HLA class I expression and MSI phenotype: 8 MSI tumours, 6 MSS/HLA- tumours and 6 MSS/HLA+ tumours (controls). FINDINGS: A first comparison between eight MSI and six MSS/HLA-positive (control) cancers, based on microarray analysis on an Affymetrix(®) HG-U133-Plus-PM plate, identified 1974 differentially expressed genes (P < 0.05). We grouped genes in Gene Ontology functional categories: apoptotic programme (72 genes, P = 5.5·10(-3)), leucocyte activation (43 genes, P = 1.8·10(-5)), T-cell activation (24 genes, P = 6.3·10(-4)), inflammatory response (40 genes, 2.3·10(-2)) and cytokine production (10 genes, P = 1.9·10(-2)). Real-time PCR and immunohistochemical evaluation were used to validate the data, finding that increased mRNA levels of pro-inflammatory cytokines and cytotoxic mediators were associated with greater infiltration by CD8+T lymphocytes in the MSI group (P < 0.001). Finally, HLA-class I-negative tumours were not grouped together but rather in accordance with features of the gene expression profile of MSI or MSS tumours. As expected, genes associated with antigen processing machinery and MHC class I molecules (TAP2, B2m) were downregulated in MSS/HLA-class I-negative CRCs (n = 6) in comparison to controls. CONCLUSIONS: In conclusion, microarray and immunohistochemical data may be useful to comprehensively assess tumour-host interactions and differentiate MSI from MSS cancers. The two types of tumour, MSI/HLA-class I-negative and MSS/HLA-class I-negative, showed marked differences in the composition and intensity of infiltrating leucocytes, suggesting that their immune escape strategies involve distinct pathways.

Transdifferentiation of MALME-3M and MCF-7 Cells toward Adipocyte-like Cells is Dependent on Clathrin-mediated Endocytosis.

Enforced cell transdifferentiation of human cancer cells is a promising alternative to conventional chemotherapy. We previously identified albumin-associated lipid- and, more specifically, saturated fatty acid-induced transdifferentiation programs in human cancer cells (HCCLs). In this study, we further characterized the adipocyte-like cells, resulting from the transdifferentiation of human cancer cell lines MCF-7 and MALME-3M, and proposed a common mechanistic approach for these transdifferentiating programs. We showed the loss of pigmentation in MALME-3M cells treated with albumin-associated lipids, based on electron microscopic analysis, and the overexpression of perilipin 2 (PLIN2) by western blotting in MALME-3M and MCF-7 cells treated with unsaturated fatty acids. Comparing the gene expression profiles of naive melanoma MALME-3M cells and albumin-associated lipid-treated cells, based on RNA sequencing, we confirmed the transcriptional upregulation of some key adipogenic gene markers and also an alternative splicing of the adipogenic master regulator PPARG, that is probably related to the reported up regulated expression of the protein. Most importantly, these results also showed the upregulation of genes responsible for Clathrin (CLTC) and other adaptor-related proteins. An increase in CLTC expression in the transdifferentiated cells was confirmed by western blotting. Inactivation of CLTC by chlorpromazine (CHP), an inhibitor of CTLC mediated endocytosis (CME), and gene silencing by siRNAs, partially reversed the accumulation of neutral lipids observed in the transdifferentiated cells. These findings give a deeper insight into the phenotypic changes observed in HCCL to adipocyte-like transdifferentiation and point towards CME as a key pathway in distinct transdifferentiation programs. DISCLOSURES: Simon C and Aguilar-Gallardo C are co-inventors of the International Patent Application No. PCT/EP2011/004941 entitled "Methods for tumor treatment and adipogenesis differentiation".

Differential expression in RNA-seq: a matter of depth.

Next-generation sequencing (NGS) technologies are revolutionizing genome research, and in particular, their application to transcriptomics (RNA-seq) is increasingly being used for gene expression profiling as a replacement for microarrays. However, the properties of RNA-seq data have not been yet fully established, and additional research is needed for understanding how these data respond to differential expression analysis. In this work, we set out to gain insights into the characteristics of RNA-seq data analysis by studying an important parameter of this technology: the sequencing depth. We have analyzed how sequencing depth affects the detection of transcripts and their identification as differentially expressed, looking at aspects such as transcript biotype, length, expression level, and fold-change. We have evaluated different algorithms available for the analysis of RNA-seq and proposed a novel approach--NOISeq--that differs from existing methods in that it is data-adaptive and nonparametric. Our results reveal that most existing methodologies suffer from a strong dependency on sequencing depth for their differential expression calls and that this results in a considerable number of false positives that increases as the number of reads grows. In contrast, our proposed method models the noise distribution from the actual data, can therefore better adapt to the size of the data set, and is more effective in controlling the rate of false discoveries. This work discusses the true potential of RNA-seq for studying regulation at low expression ranges, the noise within RNA-seq data, and the issue of replication.

Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data.

MOTIVATION: The development of the omics technologies such as transcriptomics, proteomics and metabolomics has made possible the realization of systems biology studies where biological systems are interrogated at different levels of biochemical activity (gene expression, protein activity and/or metabolite concentration). An effective approach to the analysis of these complex datasets is the joined visualization of the disparate biomolecular data on the framework of known biological pathways. RESULTS: We have developed the Paintomics web server as an easy-to-use bioinformatics resource that facilitates the integrated visual analysis of experiments where transcriptomics and metabolomics data have been measured on different conditions for the same samples. Basically, Paintomics takes complete transcriptomics and metabolomics datasets, together with lists of significant gene or metabolite changes, and paints this information on KEGG pathway maps. AVAILABILITY: Paintomics is freely available at http://www.paintomics.org.

Genome-wide gene expression analysis in mouse embryonic stem cells.

Embryonic stem cell studies have generated great interest, due to their ability to form a wide variety of matured cells. However, there remains a poor understanding of mechanisms regulating the cell state of embryonic stem cells (ESCs) and of the genes they express during early differentiation. Gene expression analysis may be a valuable tool to elucidate either the molecular pathways involved in self-renewal and pluripotency, or early differentiation and to identify potential molecular therapy targets. The aim of this study was to characterize at the molecular level the undifferentiated mouse ESC state and the early development towards embryoid bodies. To attempt this issue, we performed CodeLink Mouse Uniset I 20K bioarrays in a well-characterized mouse ESC line, MES3, 3- and 7 day-old embryoid bodies and we compared our findings with those in adult tissue cells. Gene expression results were subsequently validated in a commercial stem cell line, CGR8 (ATCC). Significance Analysis of Microarrays (SAM) was used to identify statistically significant changes in microarray data. We identified 3664 genes expressed at significantly greater levels in MES3 stem cells than in adult tissue cells, which included 611 with 3-fold higher gene expression levels versus the adult cells. We also investigated the gene expression profile during early embryoid body formation, identifying 2040 and 2243 genes that were up-regulated in 3- and 7- day-old embryoid bodies, respectively. Our gene expression results in MES3 cells were partially confirmed in CGR8 cells, showing numerous genes that are expressed in both mouse stem cells. In conclusion, our results suggest that commonly expressed genes may be strong candidates for involvement in the maintenance of a pluripotent and undifferentiated phenotype and in early development.