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Most candidate drugs currently fail later-stage clinical trials, largely due to poor prediction of efficacy on early target selection 1 . Drug targets with genetic support are more likely to be therapeutically valid 2,3 , but the translational use of genome-scale data such as from genome-wide association studies for drug target discovery in complex diseases remains challenging 4-6 . Here, we show that integration of functional genomic and immune-related annotations, together with knowledge of network connectivity, maximizes the informativeness of genetics for target validation, defining the target prioritization landscape for 30 immune traits at the gene and pathway level. We demonstrate how our genetics-led drug target prioritization approach (the priority index) successfully identifies current therapeutics, predicts activity in high-throughput cellular screens (including L1000, CRISPR, mutagenesis and patient-derived cell assays), enables prioritization of under-explored targets and allows for determination of target-level trait relationships. The priority index is an open-access, scalable system accelerating early-stage drug target selection for immune-mediated disease.

Slc17a5 -/- mice represent an animal model for the infantile form of sialic acid storage disease (SASD). We analyzed genetic and histological time-course expression of myelin and oligodendrocyte (OL) lineage markers in different parts of the CNS, and related this to postnatal neurobehavioral development in these mice. Sialin-deficient mice display a distinct spatiotemporal pattern of sialic acid storage, CNS hypomyelination and leukoencephalopathy. Whereas few genes are differentially expressed in the perinatal stage (p0), microarray analysis revealed increased differential gene expression in later postnatal stages (p10-p18). This included progressive upregulation of neuroinflammatory genes, as well as continuous down-regulation of genes that encode myelin constituents and typical OL lineage markers. Age-related histopathological analysis indicates that initial myelination occurs normally in hindbrain regions, but progression to more frontal areas is affected in Slc17a5 -/- mice. This course of progressive leukoencephalopathy and CNS hypomyelination delays neurobehavioral development in sialin-deficient mice. Slc17a5 -/- mice successfully achieve early neurobehavioral milestones, but exhibit progressive delay of later-stage sensory and motor milestones. The present findings may contribute to further understanding of the processes of CNS myelination as well as help to develop therapeutic strategies for SASD and other myelination disorders., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

The modern drug discovery process involves multiple sources of high-dimensional data. This imposes the challenge of data integration. A typical example is the integration of chemical structure (fingerprint features), phenotypic bioactivity (bioassay read-outs) data for targets of interest, and transcriptomic (gene expression) data in early drug discovery to better understand the chemical and biological mechanisms of candidate drugs, and to facilitate early detection of safety issues prior to later and expensive phases of drug development cycles. In this paper, we discuss a joint model for the transcriptomic and the phenotypic variables conditioned on the chemical structure. This modeling approach can be used to uncover, for a given set of compounds, the association between gene expression and biological activity taking into account the influence of the chemical structure of the compound on both variables. The model allows to detect genes that are associated with the bioactivity data facilitating the identification of potential genomic biomarkers for compounds efficacy. In addition, the effect of every structural feature on both genes and pIC50 and their associations can be simultaneously investigated. Two oncology projects are used to illustrate the applicability and usefulness of the joint model to integrate multi-source high-dimensional information to aid drug discovery.

The modern process of discovering candidate molecules in early drug discovery phase includes a wide range of approaches to extract vital information from the intersection of biology and chemistry. A typical strategy in compound selection involves compound clustering based on chemical similarity to obtain representative chemically diverse compounds (not incorporating potency information). In this paper, we propose an integrative clustering approach that makes use of both biological (compound efficacy) and chemical (structural features) data sources for the purpose of discovering a subset of compounds with aligned structural and biological properties. The datasets are integrated at the similarity level by assigning complementary weights to produce a weighted similarity matrix, serving as a generic input in any clustering algorithm. This new analysis work flow is semi-supervised method since, after the determination of clusters, a secondary analysis is performed wherein it finds differentially expressed genes associated to the derived integrated cluster(s) to further explain the compound-induced biological effects inside the cell. In this paper, datasets from two drug development oncology projects are used to illustrate the usefulness of the weighted similarity-based clustering approach to integrate multi-source high-dimensional information to aid drug discovery. Compounds that are structurally and biologically similar to the reference compounds are discovered using this proposed integrative approach.

The NIH-funded LINCS program has been initiated to generate a library of integrated, network-based, cellular signatures (LINCS). A novel high-throughput gene-expression profiling assay known as L1000 was the main technology used to generate more than a million transcriptional profiles. The profiles are based on the treatment of 14 cell lines with one of many perturbation agents of interest at a single concentration for 6 and 24 hours duration. In this study, we focus on the chemical compound treatments within the LINCS data set. The experimental variables available include number of replicates, cell lines, and time points. Our study reveals that compound characterization based on three cell lines at two time points results in more genes being affected than six cell lines at a single time point. Based on the available LINCS data, we conclude that the most optimal experimental design to characterize a large set of compounds is to test them in duplicate in three different cell lines. Our conclusions are constrained by the fact that the compounds were profiled at a single, relative high concentration, and the longer time point is likely to result in phenotypic rather than mechanistic effects being recorded.

During drug discovery and development, the early identification of adverse effects is expected to reduce costly late-stage failures of candidate drugs. As risk/safety assessment takes place rather late during the development process and due to the limited ability of animal models to predict the human situation, modern unbiased high-dimensional biology readouts are sought, such as molecular signatures predictive for in vivo response using high-throughput cell-based assays. In this theoretical proof of concept, we provide findings of an in-depth exploration of a single chemical core structure. Via transcriptional profiling, we identified a subset of close analogues that commonly downregulate multiple tubulin genes across cellular contexts, suggesting possible spindle poison effects. Confirmation via a qualified toxicity assay (in vitro micronucleus test) and the identification of a characteristic aggregate-formation phenotype via exploratory high-content imaging validated the initial findings. SAR analysis triggered the synthesis of a new set of compounds and allowed us to extend the series showing the genotoxic effect. We demonstrate the potential to flag toxicity issues by utilizing data from exploratory experiments that are typically generated for target evaluation purposes during early drug discovery. We share our thoughts on how this approach may be incorporated into drug development strategies.

With substantial numbers of breast tumors showing or acquiring treatment resistance, it is of utmost importance to develop new agents for the treatment of the disease, to know their effectiveness against breast cancer and to understand their relationships with other drugs to best assign the right drug to the right patient. To achieve this goal drug screenings on breast cancer cell lines are a promising approach. In this study a large-scale drug screening of 37 compounds was performed on a panel of 42 breast cancer cell lines representing the main breast cancer subtypes. Clustering, correlation and pathway analyses were used for data analysis. We found that compounds with a related mechanism of action had correlated IC50 values and thus grouped together when the cell lines were hierarchically clustered based on IC50 values. In total we found six clusters of drugs of which five consisted of drugs with related mode of action and one cluster with two drugs not previously connected. In total, 25 correlated and four anti-correlated drug sensitivities were revealed of which only one drug, Sirolimus, showed significantly lower IC50 values in the luminal/ERBB2 breast cancer subtype. We found expected interactions but also discovered new relationships between drugs which might have implications for cancer treatment regimens.

The pharmaceutical industry is faced with steadily declining R&D efficiency which results in fewer drugs reaching the market despite increased investment. A major cause for this low efficiency is the failure of drug candidates in late-stage development owing to safety issues or previously undiscovered side-effects. We analyzed to what extent gene expression data can help to de-risk drug development in early phases by detecting the biological effects of compounds across disease areas, targets and scaffolds. For eight drug discovery projects within a global pharmaceutical company, gene expression data were informative and able to support go/no-go decisions. Our studies show that gene expression profiling can detect adverse effects of compounds, and is a valuable tool in early-stage drug discovery decision making., (Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Integrating gene expression profiles with certain proteins can improve our understanding of the fundamental mechanisms in protein-ligand binding. This paper spotlights the integration of gene expression data and target prediction scores, providing insight into mechanism of action (MoA). Compounds are clustered based upon the similarity of their predicted protein targets and each cluster is linked to gene sets using Linear Models for Microarray Data. MLP analysis is used to generate gene sets based upon their biological processes and a qualitative search is performed on the homogeneous target-based compound clusters to identify pathways. Genes and proteins were linked through pathways for 6 of the 8 MCF7 and 6 of the 11 PC3 clusters. Three compound clusters are studied; (i) the target-driven cluster involving HSP90 inhibitors, geldanamycin and tanespimycin induces differential expression for HSP90-related genes and overlap with pathway response to unfolded protein. Gene expression results are in agreement with target prediction and pathway annotations add information to enable understanding of MoA. (ii) The antipsychotic cluster shows differential expression for genes LDLR and INSIG-1 and is predicted to target CYP2D6. Pathway steroid metabolic process links the protein and respective genes, hypothesizing the MoA for antipsychotics. A sub-cluster (verepamil and dexverepamil), although sharing similar protein targets with the antipsychotic drug cluster, has a lower intensity of expression profile on related genes, indicating that this method distinguishes close sub-clusters and suggests differences in their MoA. Lastly, (iii) the thiazolidinediones drug cluster predicted peroxisome proliferator activated receptor (PPAR) PPAR-alpha, PPAR-gamma, acyl CoA desaturase and significant differential expression of genes ANGPTL4, FABP4 and PRKCD. The targets and genes are linked via PPAR signalling pathway and induction of apoptosis, generating a hypothesis for the MoA of thiazolidinediones. Our analysis show one or more underlying MoA for compounds and were well-substantiated with literature.

It has recently been shown that disease associated gene signatures can be identified by profiling tissue other than the disease related tissue. In this paper, we investigate gene signatures for Irritable Bowel Syndrome (IBS) using gene expression profiling of both disease related tissue (colon) and surrogate tissue (rectum). Gene specific joint ANOVA models were used to investigate differentially expressed genes between the IBS patients and the healthy controls taken into account both intra and inter tissue dependencies among expression levels of the same gene. Classification algorithms in combination with feature selection methods were used to investigate the predictive power of gene expression levels from the surrogate and the target tissues. We conclude based on the analyses that expression profiles of the colon and the rectum tissue could result in better predictive accuracy if the disease associated genes are known.

Bedaquiline (BDQ), an ATP synthase inhibitor, is the first drug to be approved for treatment of multidrug-resistant tuberculosis in decades. Though BDQ has shown excellent efficacy in clinical trials, its early bactericidal activity during the first week of chemotherapy is minimal. Here, using microfluidic devices and time-lapse microscopy of Mycobacterium tuberculosis, we confirm the absence of significant bacteriolytic activity during the first 3-4 days of exposure to BDQ. BDQ-induced inhibition of ATP synthesis leads to bacteriostasis within hours after drug addition. Transcriptional and proteomic analyses reveal that M. tuberculosis responds to BDQ by induction of the dormancy regulon and activation of ATP-generating pathways, thereby maintaining bacterial viability during initial drug exposure. BDQ-induced bacterial killing is significantly enhanced when the mycobacteria are grown on non-fermentable energy sources such as lipids (impeding ATP synthesis via glycolysis). Our results show that BDQ exposure triggers a metabolic remodelling in mycobacteria, thereby enabling transient bacterial survival.

Illumina bead arrays are microarrays that contain a random number of technical replicates (beads) for every probe (bead type) within the same array. Typically around 30 beads are placed at random positions on the array surface, which opens unique opportunities for quality control. Most preprocessing methods for Illumina bead arrays are ported from the Affymetrix microarray platform and ignore the availability of the technical replicates. The large number of beads for a particular bead type on the same array, however, should be highly correlated, otherwise they just measure noise and can be removed from the downstream analysis. Hence, filtering bead types can be considered as an important step of the preprocessing procedure for Illumina platform. This paper proposes a filtering method for Illumina bead arrays, which builds upon the mixed model framework. Bead types are called informative/non-informative (I/NI) based on a trade-off between within and between array variabilities. The method is illustrated on a publicly available Illumina Spike-in data set (Dunning et al., 2008) and we also show that filtering results in a more powerful analysis of differentially expressed genes.

In this article, we discuss methods to select three different types of genes (treatment related, response related, or both) and investigate whether they can serve as biomarkers for a binary outcome variable. We consider an extension of the joint model introduced by Lin et al. (2010) and Tilahun et al. (2010) for a continuous response. As the model has certain drawbacks in a binary setting, we also present a way to use classical selection methods to identify subgroups of genes, which are treatment and/or response related. We evaluate their potential to serve as biomarkers by applying DLDA to predict the response level.

Motivation: Biclustering of transcriptomic data groups genes and samples simultaneously. It is emerging as a standard tool for extracting knowledge from gene expression measurements. We propose a novel generative approach for biclustering called 'FABIA: Factor Analysis for Bicluster Acquisition'. FABIA is based on a multiplicative model, which accounts for linear dependencies between gene expression and conditions, and also captures heavy-tailed distributions as observed in real-world transcriptomic data. The generative framework allows to utilize well-founded model selection methods and to apply Bayesian techniques., Results: On 100 simulated datasets with known true, artificially implanted biclusters, FABIA clearly outperformed all 11 competitors. On these datasets, FABIA was able to separate spurious biclusters from true biclusters by ranking biclusters according to their information content. FABIA was tested on three microarray datasets with known subclusters, where it was two times the best and once the second best method among the compared biclustering approaches., Availability: FABIA is available as an R package on Bioconductor (http://www.bioconductor.org). All datasets, results and software are available at http://www.bioinf.jku.at/software/fabia/fabia.html., Supplementary Information: Supplementary data are available at Bioinformatics online.

Multitargeted kinase inhibitors have shown clinical efficacy in a range of cancer types. However, two major problems associated with these drugs are the low fraction of patients for which these treatments provide initial clinical benefit and the occurrence of resistance during prolonged therapy. Several types of predictive biomarkers have been suggested, such as expression level and phosphorylation status of the major targeted kinase(s), mutational status of the kinases involved and of key components of the downstream signaling cascades, and gene expression signatures. In this work, we describe the development of a response prediction platform that does not require prior knowledge of the relevant kinases targeted by the inhibitor; instead, a phosphotyrosine peptide profile using peptide arrays with a kinetic readout is derived in lysates in the presence and absence of a kinase inhibitor. We show in a range of cell lines and in xenograft tumors that this approach allows for the stratification of responders and nonresponders to a multitargeted kinase inhibitor.

One of multiple testing problems in drug finding experiments is the comparison of several treatments with one control. In this paper we discuss a particular situation of such an experiment, i.e., a microarray setting, where the many-to-one comparisons need to be addressed for thousands of genes simultaneously. For a gene-specific analysis, Dunnett's single step procedure is considered within gene tests, while the FDR controlling procedures such as Significance Analysis of Microarrays (SAM) and Benjamini and Hochberg (BH) False Discovery Rate (FDR) adjustment are applied to control the error rate across genes. The method is applied to a microarray experiment with four treatment groups (three microarrays in each group) and 16,998 genes. Simulation studies are conducted to investigate the performance of the SAM method and the BH-FDR procedure with regard to controlling the FDR, and to investigate the effect of small-variance genes on the FDR in the SAM procedure.

Background & Aims: Irritable bowel syndrome (IBS) has been associated with mucosal dysfunction, mild inflammation, and altered colonic bacteria. We used microarray expression profiling of sigmoid colon mucosa to assess whether there are stably expressed sets of genes that suggest there are objective molecular biomarkers associated with IBS., Methods: Gene expression profiling was performed using Human Genome U133 Plus 2.0 (Affymetrix) GeneChips with RNA from sigmoid colon mucosal biopsy specimens from 36 IBS patients and 25 healthy control subjects. Real-time quantitative polymerase chain reaction was used to confirm the data in 12 genes of interest. Statistical methods for microarray data were applied to search for differentially expressed genes, and to assess the stability of molecular signatures in IBS patients., Results: Mucosal gene expression profiles were consistent across different sites within the sigmoid colon and were stable on repeat biopsy over approximately 3 months. Differentially expressed genes suggest functional alterations of several components of the host mucosal immune response to microbial pathogens. The most strikingly increased expression involved a yet uncharacterized gene, DKFZP564O0823. Identified specific genes suggest the hypothesis that molecular signatures may enable distinction of a subset of IBS patients from healthy controls. By using 75% of the biopsy specimens as a validation set to develop a gene profile, the test set (25%) was predicted correctly with approximately 70% accuracy., Conclusions: Mucosal gene expression analysis shows there are relatively stable alterations in colonic mucosal immunity in IBS. These molecular alterations provide the basis to test the hypothesis that objective biomarkers may be identified in IBS and enhance understanding of the disease.

Probe-level data from Affymetrix GeneChips can be summarized in many ways to produce probe-set level gene expression measures (GEMs). Disturbingly, the different approaches not only generate quite different measures but they could also yield very different analysis results. Here, we explore the question of how much the analysis results really do differ, first at the gene level, then at the biological process level. We demonstrate that, even though the gene level results may not necessarily match each other particularly well, as long as there is reasonably strong differentiation between the groups in the data, the various GEMs do in fact produce results that are similar to one another at the biological process level. Not only that the results are biologically relevant. As the extent of differentiation drops, the degree of concurrence weakens, although the biological relevance of findings at the biological process level may yet remain.

Motivation: DNA microarray technology typically generates many measurements of which only a relatively small subset is informative for the interpretation of the experiment. To avoid false positive results, it is therefore critical to select the informative genes from the large noisy data before the actual analysis. Most currently available filtering techniques are supervised and therefore suffer from a potential risk of overfitting. The unsupervised filtering techniques, on the other hand, are either not very efficient or too stringent as they may mix up signal with noise. We propose to use the multiple probes measuring the same target mRNA as repeated measures to quantify the signal-to-noise ratio of that specific probe set. A Bayesian factor analysis with specifically chosen prior settings, which models this probe level information, is providing an objective feature filtering technique, named informative/non-informative calls (I/NI calls)., Results: Based on 30 real-life data sets (including various human, rat, mice and Arabidopsis studies) and a spiked-in data set, it is shown that I/NI calls is highly effective, with exclusion rates ranging from 70% to 99%. Consequently, it offers a critical solution to the curse of high-dimensionality in the analysis of microarray data., Availability: This filtering approach is publicly available as a function implemented in the R package FARMS (www.bioinf.jku.at/software/farms/farms.html).

Background & Aims: Visceral hypersensitivity, a hallmark of irritable bowel syndrome, is generally considered to be mechanosensitive in nature and mediated via spinal afferents. Both stress and inflammation are implicated in visceral hypersensitivity, but the underlying molecular mechanisms of visceral hypersensitivity are unknown., Methods: Mice were infected with Nippostrongylus brasiliensis (Nb) larvae, exposed to environmental stress and the following separate studies performed 3-4 weeks later. Mesenteric afferent nerve activity was recorded in response to either ramp balloon distention (60 mm Hg), or to an intraluminal perfusion of hydrochloric acid (50 mmol/L), or to octreotide administration (2 micromol/L). Intraperitoneal injection of cholera toxin B-488 identified neurons projecting to the abdominal viscera. Fluorescent neurons in dorsal root and nodose ganglia were isolated using laser-capture microdissection. RNA was hybridized to Affymetrix Mouse whole genome arrays for analysis to evaluate the effects of stress and infection., Results: In mice previously infected with Nb, there was no change in intestinal afferent mechanosensitivity, but there was an increase in chemosensitive responses to intraluminal hydrochloric acid when compared with control animals. Gene expression profiles in vagal but not spinal visceral sensory neurons were significantly altered in stressed Nb-infected mice. Decreased afferent responses to somatostatin receptor 2 stimulation correlated with lower expression of vagal somatostatin receptor 2 in stressed Nb-infected mice, confirming a link between molecular data and functional sequelae., Conclusions: Alterations in the intestinal brain-gut axis, in chemosensitivity but not mechanosensitivity, and through vagal rather than spinal pathways, are implicated in stress-induced postinflammatory visceral hypersensitivity.

Dose-response studies are commonly used in experiments in pharmaceutical research in order to investigate the dependence of the response on dose, i.e., a trend of the response level toxicity with respect to dose. In this paper we focus on dose-response experiments within a microarray setting in which several microarrays are available for a sequence of increasing dose levels. A gene is called differentially expressed if there is a monotonic trend (with respect to dose) in the gene expression. We review several testing procedures which can be used in order to test equality among the gene expression means against ordered alternatives with respect to dose, namely Williams' (Williams 1971 and 1972), Marcus' (Marcus 1976), global likelihood ratio test (Bartholomew 1961, Barlow et al. 1972, and Robertson et al. 1988), and M (Hu et al. 2005) statistics. Additionally we introduce a modification to the standard error of the M statistic. We compare the performance of these five test statistics. Moreover, we discuss the issue of one-sided versus two-sided testing procedures. False Discovery Rate (Benjamni and Hochberg 1995, Ge et al. 2003), and resampling-based Familywise Error Rate (Westfall and Young 1993) are used to handle the multiple testing issue. The methods above are applied to a data set with 4 doses (3 arrays per dose) and 16,998 genes. Results on the number of significant genes from each statistic are discussed. A simulation study is conducted to investigate the power of each statistic. A R library IsoGene implementing the methods is available from the first author.

Vagal afferent neurons are thought to convey primarily physiological information, whereas spinal afferents transmit noxious signals from the viscera to the central nervous system. To elucidate molecular identities for these different properties, we compared gene expression profiles of neurons located in nodose ganglia (NG) and dorsal root ganglia (DRG) in mice. Intraperitoneal administration of Alexa Fluor-488-conjugated cholera toxin B allowed enrichment for neurons projecting to the viscera. Fluorescent neurons in DRG (from T10 to T13) and NG were isolated using laser-capture microdissection. Gene expression profiles of these afferent neurons, obtained by microarray hybridization, were analyzed using multivariate spectral map analysis, significance analysis of microarrays (SAM) algorithm, and fold-difference filtering. A total of 1,996 genes were differentially expressed in DRG vs. NG, including 41 G protein-coupled receptors and 60 ion channels. Expression profiles obtained on laser-captured neurons were contrasted to those obtained on whole ganglia, demonstrating striking differences and the need for microdissection when studying visceral sensory neurons because of dilution of the signal by somatic sensory neurons. Furthermore, we provide a detailed catalog of all adrenergic and cholinergic, GABA, glutamate, serotonin, and dopamine receptors; voltage-gated potassium, sodium, and calcium channels; and transient receptor potential cation channels present in afferents projecting to the peritoneal cavity. Our genome-wide expression profiling data provide novel insight into molecular signatures that underlie both functional differences and similarities between NG and DRG sensory neurons. Moreover, these findings will offer novel insight into mode of action of pharmacological agents modulating visceral sensation.

When small biological samples are collected by microdissection or other methods, amplification techniques are required to provide sufficient target for hybridization to expression arrays. One such technique is to perform two successive rounds of T7-based in vitro transcription. However the use of random primers, required to regenerate cDNA from the first round of transcription, results in shortened copies of cDNA from which the 5' end is missing. In this paper we describe an experiment designed to compare the quality of data obtained from labeling small RNA samples using the Affymetrix Two-Cycle Eukaryotic. Target Labeling procedure to that of data obtained using the One-Cycle Eukaryotic Target Labeling protocol. We utilized different preprocessing algorithms to compare the data generated using both labeling methods and present a new algorithm that improves upon existing ones in this setting.

The incidence of tuberculosis has been increasing substantially on a worldwide basis over the past decade, but no tuberculosis-specific drugs have been discovered in 40 years. We identified a diarylquinoline, R207910, that potently inhibits both drug-sensitive and drug-resistant Mycobacterium tuberculosis in vitro (minimum inhibitory concentration 0.06 mug/ml). In mice, R207910 exceeded the bactericidal activities of isoniazid and rifampin by at least 1 log unit. Substitution of drugs included in the World Health Organization's first-line tuberculosis treatment regimen (rifampin, isoniazid, and pyrazinamide) with R207910 accelerated bactericidal activity, leading to complete culture conversion after 2 months of treatment in some combinations. A single dose of R207910 inhibited mycobacterial growth for 1 week. Plasma levels associated with efficacy in mice were well tolerated in healthy human volunteers. Mutants selected in vitro suggest that the drug targets the proton pump of adenosine triphosphate (ATP) synthase.

Corticotropin-releasing factor (CRF) plays a central role in the regulation of the hypothalamic-pituitary-adrenal axis, mediating endocrine and behavioral responses to various stressors. Two high-affinity receptors for CRF have been described. Although many of the intracellular signaling pathways activated by CRF have been studied extensively, our knowledge of transcriptional responses downstream of the CRF receptor 1 (CRFR1) is still limited. To elucidate gene networks regulated by CRF and CRFR1, we applied microarray technology to explore transcriptional response to CRF stimulation. Therefore, mouse pituitary-derived AtT-20 cells were exposed continuously to CRF either in the presence or absence of the specific CRFR1 antagonist R121919. Transcriptional responses to different treatments were studied in a time course ranging from 0.5 to 24 h. Microarray data were analyzed using classic microarray data analysis tools such as correspondence factor analysis, cluster analysis, and fold-change filtering. Furthermore, spectral map analysis was applied, a recently introduced unsupervised multivariate analysis method. A broad and transient transcriptional response to CRF was identified that could be blocked by the antagonist. This way, several known CRF-induced target genes and novel CRF responsive genes were identified. These include transcription factors such as cAMP-responsive element modulator (7x increased), secreted peptides such as cholecystokinin (1.5x), and proteins involved in modulating intracellular signaling, such as regulator of G-protein signaling 2 (11x). Up-regulation of many of these genes can be explained as negative feedback, attenuating CRF-activated pathways. In addition, spectral map analysis proved to be a promising new tool for microarray data analysis.

This article describes three multivariate projection methods and compares them for their ability to identify clusters of biological samples and genes using real-life data on gene expression levels of leukemia patients. It is shown that principal component analysis (PCA) has the disadvantage that the resulting principal factors are not very informative, while correspondence factor analysis (CFA) has difficulties interpreting distances between objects. Spectral map analysis (SMA) is introduced as an alternative approach to the analysis of microarray data. Weighted SMA outperforms PCA, and is at least as powerful as CFA, in finding clusters in the samples, as well as identifying genes related to these clusters. SMA addresses the problem of data analysis in microarray experiments in a more appropriate manner than CFA, and allows more flexible weighting to the genes and samples. Proper weighting is important, since it enables less reliable data to be down-weighted and more reliable information to be emphasized.

Applying microarray technology, we have investigated the transcriptome of the small bacterium Mycoplasma pneumoniae grown at three different temperature conditions: 32, 37 and 32 degrees C followed by a heat shock for 15 min at 43 degrees C, before isolating the RNA. From 688 proposed open-reading frames, 676 were investigated and 564 were found to be expressed (P < 0.001; 606 with P < 0.01) and at least 33 (P < 0.001; 77 at P < 0.01) regulated. By quantitative real-time PCR of selected mRNA species, the expression data could be linked to absolute molecule numbers. We found M.pneumoniae to be regulated at the transcriptional level. Forty-seven genes were found to be significantly up-regulated after heat shock (P < 0.01). Among those were the conserved heat shock genes like dnaK, lonA and clpB, but also several genes coding for ribosomal proteins and 10 genes of unassigned functions. In addition, 30 genes were found to be down-regulated under the applied heat shock conditions. Further more, we have compared different methods of cDNA synthesis (random hexamer versus gene-specific primers, different RNA concentrations) and various normalization strategies of the raw microarray data.

A highly abundant and heterogeneous small RNA about 205 to 210 bases long named MP200 RNA has been identified in Mycoplasma pneumoniae. It was localized on the genome within a 319-bp-long intergenic space of the pyruvate dehydrogenase (pdh) gene cluster. A database search at the DNA level revealed the highest similarity to a sequence located within the pdh gene cluster of Mycoplasma genitalium that was also shown to be transcribed into two abundant, but smaller RNAs than the ones in Mycoplasma pneumoniae. The RNAs from both M. pneumoniae and M. genitalium have the potential to code for cysteine-rich 29- and 23-amino-acid-long peptides, but so far, these peptides have not been identified experimentally in bacterial protein extracts.

Mycoplasma pneumoniae cytadherence is mediated by a specialized, polar attachment organelle. Certain spontaneously arising cytadherence mutants (designated class I) lack HMW2, fail to localize the adhesin protein P1 to the attachment organelle, and exhibit accelerated turnover of proteins HMW1, HMW3, and P65. Insertional inactivation of hmw2 by Tn4001 results in a phenotype nearly identical to that of the class I mutants, suggesting that the latter may result from a defect in hmw2. In this study, the recombinant wild-type hmw2 allele successfully complemented a class I mutant when introduced by transposon delivery. Synthesis of recombinant HMW2 at wild-type levels resulted in reacquisition of hemadsorption and normal levels of HMW1, HMW3, and P65. Low-level production of HMW2 in some transformants resulted in only an intermediate capacity to hemadsorb. Furthermore, full restoration of HMW1 and P65, but not that of HMW3, was directly proportional to the amount of recombinant HMW2 produced, reflecting the importance of proper stoichiometry for certain cytadherence-associated proteins. The recombinant class I hmw2 allele did not restore cytadherence, consistent with a defect in hmw2 in this mutant. A frameshift was discovered in different oligoadenine tracts in hmw2 from two independent class I mutants. Finally, protein P28 is thought to be the product of internal translation initiation in hmw2. A transposon excision-deletion mutant produced a truncated HMW2 but no P28, consistent with this conclusion. However, this deletion mutant was hemadsorption positive, indicating that P28 may not be required for cytadherence.

We have identified a new gene, PFK27, that encodes a second inducible 6-phosphofructo-2-kinase in the yeast Saccharomyces cerevisiae. Sequencing shows an open reading frame of 397 amino acids and 45.3 kDa. Amino acid sequence comparisons with other bifunctional 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase isoenzymes of various organisms revealed similarities only to the kinase domains. Expression of PFK27 was induced severalfold by glucose and sucrose, but not by galactose or maltose, suggesting that sugar transport might be involved in triggering the induction signal. We have constructed a mutant strain devoid of any fructose-2,6-bisphosphate. The mutant strain grew well on several kinds and concentrations of carbon sources. The levels of hexose phosphates in the cells were increased, but flux rates for glucose utilization and ethanol production were similar to the wild-type strain. However, after the transfer of the mutant cells from respiratory to fermentative growth conditions, growth, glucose consumption and ethanol production were delayed in a transition phase. Our results show that fructose-2,6-bisphosphate is an important effector in vivo of the 6-phosphofructo-1-kinase/fructose-1 ,6-bisphosphatase enzyme pair, and is involved in the initiation of glycolysis during the transition to a fermentative mode of metabolism. Nevertheless, it can be effectively replaced by other effectors and regulatory mechanisms during growth on glucose.