The Escherichia coli transcriptome mostly consists of independently regulated modules

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome

Influence of GB virus C on IFN-γ and IL-2 production and CD38 expression in T lymphocytes from chronically HIV-infected and HIV-HCV-co-infected patients

This study was designed to assess the effect of GB virus (GBV)-C on the immune response to human immunodeficiency virus (HIV) in chronically HIV-infected and HIV- hepatitis C virus (HCV)-co-infected patients undergoing antiretroviral therapy. A cohort of 159 HIV-seropositive patients, of whom 52 were HCV-co-infected, was included. Epidemiological data were collected and virological and immunological markers, including the production of interferon gamma (IFN-γ) and interleukin (IL)-2 by CD4, CD8 and Tγδ cells and the expression of the activation marker, CD38, were assessed. A total of 65 patients (40.8%) presented markers of GBV-C infection. The presence of GBV-C did not influence HIV and HCV replication or TCD4 and TCD8 cell counts. Immune responses, defined by IFN-γ and IL-2 production and CD38 expression did not differ among the groups. Our results suggest that neither GBV-C viremia nor the presence of E2 antibodies influence HIV and HCV viral replication or CD4 T cell counts in chronically infected patients. Furthermore, GBV-C did not influence cytokine production or CD38-driven immune activation among these patients. Although our results do not exclude a protective effect of GBV-C in early HIV disease, they demonstrate that this effect may not be present in chronically infected patients, who represent the majority of patients in outpatient clinics.Universidade Federal de São Paulo (UNIFESP) Laboratório de Virologia e Imunologia Disciplina de InfectologiaFleury Medicina DiagnósticaUNIFESP, Laboratório de Virologia e Imunologia Disciplina de InfectologiaSciEL

The inference of gray whale (Eschrichtius robustus) historical population attributes from whole-genome sequences

Commercial whaling caused extensive demographic declines in many great whale species, including gray whales that were extirpated from the Atlantic Ocean and dramatically reduced in the Pacific Ocean. The Eastern Pacific gray whale has recovered since the 1982 ban on commercial whaling, but the Western Pacific gray whale-once considered possibly extinct-consists of only about 200 individuals and is considered critically endangered by some international authorities. Herein, we use whole-genome sequencing to investigate the demographic history of gray whales from the Pacific and use environmental niche modelling to make predictions about future gene flow.Our sequencing efforts and habitat niche modelling indicate that: i) western gray whale effective population sizes have declined since the last glacial maximum; ii) contemporary gray whale genomes, both eastern and western, harbor less autosomal nucleotide diversity than most other marine mammals and megafauna; iii) the extent of inbreeding, as measured by autozygosity, is greater in the Western Pacific than in the Eastern Pacific populations; and iv) future climate change is expected to open new migratory routes for gray whales.Our results indicate that gray whale genomes contain low nucleotide diversity and have been subject to both historical and recent inbreeding. Population sizes over the last million years likely peaked about 25,000 years before present and have declined since then. Our niche modelling suggests that novel migratory routes may develop within the next century and if so this could help retain overall genetic diversity, which is essential for adaption and successful recovery in light of global environmental change and past exploitation

Antisense-induced exon skipping for duplications in Duchenne muscular dystrophy

<p>Abstract</p> <p>Background</p> <p>Antisense-mediated exon skipping is currently one of the most promising therapeutic approaches for Duchenne muscular dystrophy (DMD). Using antisense oligonucleotides (AONs) targeting specific exons the DMD reading frame is restored and partially functional dystrophins are produced. Following proof of concept in cultured muscle cells from patients with various deletions and point mutations, we now focus on single and multiple exon duplications. These mutations are in principle ideal targets for this approach since the specific skipping of duplicated exons would generate original, full-length transcripts.</p> <p>Methods</p> <p>Cultured muscle cells from DMD patients carrying duplications were transfected with AONs targeting the duplicated exons, and the dystrophin RNA and protein were analyzed.</p> <p>Results</p> <p>For two brothers with an exon 44 duplication, skipping was, even at suboptimal transfection conditions, so efficient that both exons 44 were skipped, thus generating, once more, an out-of-frame transcript. In such cases, one may resort to multi-exon skipping to restore the reading frame, as is shown here by inducing skipping of exon 43 and both exons 44. By contrast, in cells from a patient with an exon 45 duplication we were able to induce single exon 45 skipping, which allowed restoration of wild type dystrophin. The correction of a larger duplication (involving exons 52 to 62), by combinations of AONs targeting the outer exons, appeared problematic due to inefficient skipping and mistargeting of original instead of duplicated exons.</p> <p>Conclusion</p> <p>The correction of DMD duplications by exon skipping depends on the specific exons targeted. Its options vary from the ideal one, restoring for the first time the true, wild type dystrophin, to requiring more 'classical' skipping strategies, while the correction of multi-exon deletions may need the design of tailored approaches.</p

Differential Requirement for Utrophin in the Induced Pluripotent Stem Cell Correction of Muscle versus Fat in Muscular Dystrophy Mice

Duchenne muscular dystrophy (DMD) is an incurable degenerative muscle disorder. We injected WT mouse induced pluripotent stem cells (iPSCs) into mdx and mdx∶utrophin mutant blastocysts, which are predisposed to develop DMD with an increasing degree of severity (mdx <<< mdx∶utrophin). In mdx chimeras, iPSC-dystrophin was supplied to the muscle sarcolemma to effect corrections at morphological and functional levels. Dystrobrevin was observed in dystrophin-positive and, at a lesser extent, utrophin-positive areas. In the mdx∶utrophin mutant chimeras, although iPSC-dystrophin was also supplied to the muscle sarcolemma, mice still displayed poor skeletal muscle histopathology, and negligible levels of dystrobrevin in dystrophin- and utrophin-negative areas. Not only dystrophin-expressing tissues are affected by iPSCs. Mdx and mdx∶utrophin mice have reduced fat/body weight ratio, but iPSC injection normalized this parameter in both mdx and mdx∶utrophin chimeras, despite the fact that utrophin was compromised in the mdx∶utrophin chimeric fat. The results suggest that the presence of utrophin is required for the iPSC-corrections in skeletal muscle. Furthermore, the results highlight a potential (utrophin-independent) non-cell autonomous role for iPSC-dystrophin in the corrections of non-muscle tissue like fat, which is intimately related to the muscle

Biologic Phenotyping of the Human Small Airway Epithelial Response to Cigarette Smoking

BACKGROUND: The first changes associated with smoking are in the small airway epithelium (SAE). Given that smoking alters SAE gene expression, but only a fraction of smokers develop chronic obstructive pulmonary disease (COPD), we hypothesized that assessment of SAE genome-wide gene expression would permit biologic phenotyping of the smoking response, and that a subset of healthy smokers would have a "COPD-like" SAE transcriptome. METHODOLOGY/PRINCIPAL FINDINGS: SAE (10th-12th generation) was obtained via bronchoscopy of healthy nonsmokers, healthy smokers and COPD smokers and microarray analysis was used to identify differentially expressed genes. Individual responsiveness to smoking was quantified with an index representing the % of smoking-responsive genes abnormally expressed (I(SAE)), with healthy smokers grouped into "high" and "low" responders based on the proportion of smoking-responsive genes up- or down-regulated in each smoker. Smokers demonstrated significant variability in SAE transcriptome with I(SAE) ranging from 2.9 to 51.5%. While the SAE transcriptome of "low" responder healthy smokers differed from both "high" responders and smokers with COPD, the transcriptome of the "high" responder healthy smokers was indistinguishable from COPD smokers. CONCLUSION/SIGNIFICANCE: The SAE transcriptome can be used to classify clinically healthy smokers into subgroups with lesser and greater responses to cigarette smoking, even though these subgroups are indistinguishable by clinical criteria. This identifies a group of smokers with a "COPD-like" SAE transcriptome

Stereotyping of medical disability claimants' communication behaviour by physicians: towards more focused education for social insurance physicians

Background: Physicians who hold medical disability assessment interviews (social insurance physicians) are probably influenced by stereotypes of claimants, especially because they have limited time available and they have to make complicated decisions. Because little is known about the influences of stereotyping on assessment interviews, the objectives of this paper were to qualitatively investigate: (1) the content of stereotypes used to classify claimants with regard to the way in which they communicate; (2) the origins of such stereotypes; (3) the advantages and disadvantages of stereotyping in assessment interviews; and (4) how social insurance physicians minimise the undesirable influences of negative stereotyping. Methods: Data were collected during three focus group meetings with social insurance physicians who hold medical disability assessment interviews with sick-listed employees (i.e. claimants). The participants also completed a questionnaire about demographic characteristics. The data were qualitatively analysed in Atlas.ti in four steps, according to the grounded theory and the principle of constant comparison. Results: A total of 22 social insurance physicians participated. Based on their responses, a claimant's communication was classified with regard to the degree of respect and acceptance in the physician-claimant relationship, and the degree of dominance. Most of the social insurance physicians reported that they classify claimants in general groups, and use these classifications to adapt their own communication behaviour. Moreover, the social insurance physicians revealed that their stereotypes originate from information in the claimants' files and first impressions. The main advantages of stereotyping were that this provides a framework for the assessment interview, it can save time, and it is interesting to check whether the stereotype is correct. Disadvantages of stereotyping were that the stereotypes often prove incorrect, they do not give the complete picture, and the claimant's behaviour changes constantly. Social insurance physicians try to minimise the undesirable influences of stereotypes by being aware of counter transference, making formal assessments, staying neutral to the best of their ability, and being compassionate. Conclusions: We concluded that social insurance physicians adapt their communication style to the degree of respect and dominance of claimants in the physician-claimant relationship, but they try to minimise the undesirable influences of stereotypes in assessment interviews. It is recommended that this issue should be addressed in communication skills trainin

Batch effect correction for genome-wide methylation data with Illumina Infinium platform

<p>Abstract</p> <p>Background</p> <p>Genome-wide methylation profiling has led to more comprehensive insights into gene regulation mechanisms and potential therapeutic targets. Illumina Human Methylation BeadChip is one of the most commonly used genome-wide methylation platforms. Similar to other microarray experiments, methylation data is susceptible to various technical artifacts, particularly batch effects. To date, little attention has been given to issues related to normalization and batch effect correction for this kind of data.</p> <p>Methods</p> <p>We evaluated three common normalization approaches and investigated their performance in batch effect removal using three datasets with different degrees of batch effects generated from HumanMethylation27 platform: quantile normalization at average β value (QNβ); two step quantile normalization at probe signals implemented in "lumi" package of R (lumi); and quantile normalization of A and B signal separately (ABnorm). Subsequent Empirical Bayes (EB) batch adjustment was also evaluated.</p> <p>Results</p> <p>Each normalization could remove a portion of batch effects and their effectiveness differed depending on the severity of batch effects in a dataset. For the dataset with minor batch effects (Dataset 1), normalization alone appeared adequate and "lumi" showed the best performance. However, all methods left substantial batch effects intact in the datasets with obvious batch effects and further correction was necessary. Without any correction, 50 and 66 percent of CpGs were associated with batch effects in Dataset 2 and 3, respectively. After QNβ, lumi or ABnorm, the number of CpGs associated with batch effects were reduced to 24, 32, and 26 percent for Dataset 2; and 37, 46, and 35 percent for Dataset 3, respectively. Additional EB correction effectively removed such remaining non-biological effects. More importantly, the two-step procedure almost tripled the numbers of CpGs associated with the outcome of interest for the two datasets.</p> <p>Conclusion</p> <p>Genome-wide methylation data from Infinium Methylation BeadChip can be susceptible to batch effects with profound impacts on downstream analyses and conclusions. Normalization can reduce part but not all batch effects. EB correction along with normalization is recommended for effective batch effect removal.</p

A flexible framework for sparse simultaneous component based data integration

<p>Abstract</p> <p>1 Background</p> <p>High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics) that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins) have to be taken into account.</p> <p>2 Results</p> <p>We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of <it>Escherichia coli </it>samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks.</p> <p>3 Conclusion</p> <p>Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such, structures can be found that are exclusively tied to one data platform (group lasso approach) as well as structures that involve all data platforms (Elitist lasso approach).</p> <p>4 Availability</p> <p>The additional file contains a MATLAB implementation of the sparse simultaneous component method.</p

Gene set-based module discovery in the breast cancer transcriptome

<p>Abstract</p> <p>Background</p> <p>Although microarray-based studies have revealed global view of gene expression in cancer cells, we still have little knowledge about regulatory mechanisms underlying the transcriptome. Several computational methods applied to yeast data have recently succeeded in identifying expression modules, which is defined as co-expressed gene sets under common regulatory mechanisms. However, such module discovery methods are not applied cancer transcriptome data.</p> <p>Results</p> <p>In order to decode oncogenic regulatory programs in cancer cells, we developed a novel module discovery method termed EEM by extending a previously reported module discovery method, and applied it to breast cancer expression data. Starting from seed gene sets prepared based on <it>cis</it>-regulatory elements, ChIP-chip data, and gene locus information, EEM identified 10 principal expression modules in breast cancer based on their expression coherence. Moreover, EEM depicted their activity profiles, which predict regulatory programs in each subtypes of breast tumors. For example, our analysis revealed that the expression module regulated by the Polycomb repressive complex 2 (PRC2) is downregulated in triple negative breast cancers, suggesting similarity of transcriptional programs between stem cells and aggressive breast cancer cells. We also found that the activity of the PRC2 expression module is negatively correlated to the expression of EZH2, a component of PRC2 which belongs to the E2F expression module. E2F-driven EZH2 overexpression may be responsible for the repression of the PRC2 expression modules in triple negative tumors. Furthermore, our network analysis predicts regulatory circuits in breast cancer cells.</p> <p>Conclusion</p> <p>These results demonstrate that the gene set-based module discovery approach is a powerful tool to decode regulatory programs in cancer cells.</p