Genome-wide analysis of the role of the antibiotic biosynthesis regulator AbsA2 in Streptomyces coelicolor A3(2)

<div><p>The AbsA1-AbsA2 two component signalling system of <i>Streptomyces coelicolor</i> has long been known to exert a powerful negative influence on the production of the antibiotics actinorhodin, undecylprodiginine and the Calcium-Dependent Antibiotic (CDA). Here we report the analysis of a <i>ΔabsA2</i> deletion strain, which exhibits the classic precocious antibiotic hyper-production phenotype, and its complementation by an N-terminal triple-FLAG-tagged version of AbsA2. The complemented and non-complemented <i>ΔabsA2</i> mutant strains were used in large-scale microarray-based time-course experiments to investigate the effect of deleting <i>absA2</i> on gene expression and to identify the <i>in vivo</i> AbsA2 DNA-binding target sites using ChIP-on chip. We show that in addition to binding to the promoter regions of <i>redZ</i> and <i>actII-orfIV</i> AbsA2 binds to several previously unidentified sites within the <i>cda</i> biosynthetic gene cluster within and/or upstream of <i>SCO3215—SCO3216</i>, <i>SCO3217</i>, <i>SCO3229—SCO3230</i>, and <i>SCO3226</i>, and we relate the pattern of AbsA2 binding to the results of the transcriptomic study and antibiotic phenotypic assays. Interestingly, dual ‘biphasic’ ChIP peaks were observed with AbsA2 binding across the regulatory genes <i>actII-orfIV</i> and <i>redZ</i> and the <i>absA2</i> gene itself, while more conventional single promoter-proximal peaks were seen at the CDA biosynthetic genes suggesting a different mechanism of regulation of the former loci. Taken together the results shed light on the complex mechanism of regulation of antibiotic biosynthesis in <i>Streptomyces coelicolor</i> and the important role of AbsA2 in controlling the expression of three antibiotic biosynthetic gene clusters.</p></div

Assessing and selecting gene expression signals based upon the quality of the measured dynamics

<p>Abstract</p> <p>Background</p> <p>One of the challenges with modeling the temporal progression of biological signals is dealing with the effect of noise and the limited number of replicates at each time point. Given the rising interest in utilizing predictive mathematical models to describe the biological response of an organism or analysis such as clustering and gene ontology enrichment, it is important to determine whether the dynamic progression of the data has been accurately captured despite the limited number of replicates, such that one can have confidence that the results of the analysis are capturing important salient dynamic features.</p> <p>Results</p> <p>By pre-selecting genes based upon quality before the identification of differential expression via algorithm such as EDGE, it was found that the percentage of statistically enriched ontologies (p < .05) was improved. Furthermore, it was found that a majority of the genes found via the proposed technique were also selected via an EDGE selection though the reverse was not necessarily true. It was also found that improvements offered by the proposed algorithm are anti-correlated with improvements in the various microarray platforms and the number of replicates. This is illustrated by the fact that newer arrays and experiments with more replicates show less improvement when the filtering for quality is first run before the selection of differentially expressed genes. This suggests that the increase in the number of replicates as well as improvements in array technologies are increase the confidence one has in the dynamics obtained from the experiment.</p> <p>Conclusion</p> <p>We have developed an algorithm that quantifies the quality of temporal biological signal rather than whether the signal illustrates a significant change over the experimental time course. Because the use of these temporal signals, whether it is in mathematical modeling or clustering, focuses upon the entire time series, it is necessary to develop a method to quantify and select for signals which conform to this ideal. By doing this, we have demonstrated a marked and consistent improvement in the results of a clustering exercise over multiple experiments, microarray platforms, and experimental designs.</p

Exon Array Analysis of Head and Neck Cancers Identifies a Hypoxia Related Splice Variant of LAMA3 Associated with a Poor Prognosis

The identification of alternatively spliced transcript variants specific to particular biological processes in tumours should increase our understanding of cancer. Hypoxia is an important factor in cancer biology, and associated splice variants may present new markers to help with planning treatment. A method was developed to analyse alternative splicing in exon array data, using probeset multiplicity to identify genes with changes in expression across their loci, and a combination of the splicing index and a new metric based on the variation of reliability weighted fold changes to detect changes in the splicing patterns. The approach was validated on a cancer/normal sample dataset in which alternative splicing events had been confirmed using RT-PCR. We then analysed ten head and neck squamous cell carcinomas using exon arrays and identified differentially expressed splice variants in five samples with high versus five with low levels of hypoxia-associated genes. The analysis identified a splice variant of LAMA3 (Laminin α 3), LAMA3-A, known to be involved in tumour cell invasion and progression. The full-length transcript of the gene (LAMA3-B) did not appear to be hypoxia-associated. The results were confirmed using qualitative RT-PCR. In a series of 59 prospectively collected head and neck tumours, expression of LAMA3-A had prognostic significance whereas LAMA3-B did not. This work illustrates the potential for alternatively spliced transcripts to act as biomarkers of disease prognosis with improved specificity for particular tissues or conditions over assays which do not discriminate between splice variants

Difference-based clustering of short time-course microarray data with replicates

<p>Abstract</p> <p>Background</p> <p>There are some limitations associated with conventional clustering methods for short time-course gene expression data. The current algorithms require prior domain knowledge and do not incorporate information from replicates. Moreover, the results are not always easy to interpret biologically.</p> <p>Results</p> <p>We propose a novel algorithm for identifying a subset of genes sharing a significant temporal expression pattern when replicates are used. Our algorithm requires no prior knowledge, instead relying on an observed statistic which is based on the first and second order differences between adjacent time-points. Here, a pattern is predefined as the sequence of symbols indicating direction and the rate of change between time-points, and each gene is assigned to a cluster whose members share a similar pattern. We evaluated the performance of our algorithm to those of K-means, Self-Organizing Map and the Short Time-series Expression Miner methods.</p> <p>Conclusions</p> <p>Assessments using simulated and real data show that our method outperformed aforementioned algorithms. Our approach is an appropriate solution for clustering short time-course microarray data with replicates.</p

HOX and PBX gene dysregulation as a therapeutic target in glioblastoma multiforme

Background: Glioblastoma multiforme (GBM) is the most common high-grade malignant brain tumour in adults and arises from the glial cells in the brain. The prognosis of treated GBM remains very poor with 5-year survival rates of 5%, a figure which has not improved over the last few decades. Currently, there is a modest 14-month overall median survival in patients undergoing maximum safe resection plus adjuvant chemoradiotherapy. HOX gene dysregulation is now a widely recognised feature of many malignancies. Methods: In this study we have focused on HOX gene dysregulation in GBM as a potential therapeutic target in a disease with high unmet need. Results: We show significant dysregulation of these developmentally crucial genes and specifically that HOX genes A9, A10, C4 and D9 are strong candidates for biomarkers and treatment targets for GBM and GBM cancer stem cells. We evaluated a next generation therapeutic peptide, HTL-001, capable of targeting HOX gene over-expression in GBM by disrupting the interaction between HOX proteins and their co-factor, PBX. HTL-001 induced both caspase-dependent and -independent apoptosis in GBM cell lines. Conclusion: In vivo biodistribution studies confirmed that the peptide was able to cross the blood brain barrier. Systemic delivery of HTL-001 resulted in improved control of subcutaneous murine and human xenograft tumours and improved survival in a murine orthotopic model

Technical Variability Is Greater than Biological Variability in a Microarray Experiment but Both Are Outweighed by Changes Induced by Stimulation

INTRODUCTION: A central issue in the design of microarray-based analysis of global gene expression is that variability resulting from experimental processes may obscure changes resulting from the effect being investigated. This study quantified the variability in gene expression at each level of a typical in vitro stimulation experiment using human peripheral blood mononuclear cells (PBMC). The primary objective was to determine the magnitude of biological and technical variability relative to the effect being investigated, namely gene expression changes resulting from stimulation with lipopolysaccharide (LPS). METHODS AND RESULTS: Human PBMC were stimulated in vitro with LPS, with replication at 5 levels: 5 subjects each on 2 separate days with technical replication of LPS stimulation, amplification and hybridisation. RNA from samples stimulated with LPS and unstimulated samples were hybridised against common reference RNA on oligonucleotide microarrays. There was a closer correlation in gene expression between replicate hybridisations (0.86-0.93) than between different subjects (0.66-0.78). Deconstruction of the variability at each level of the experimental process showed that technical variability (standard deviation (SD) 0.16) was greater than biological variability (SD 0.06), although both were low (SD<0.1 for all individual components). There was variability in gene expression both at baseline and after stimulation with LPS and proportion of cell subsets in PBMC was likely partly responsible for this. However, gene expression changes after stimulation with LPS were much greater than the variability from any source, either individually or combined. CONCLUSIONS: Variability in gene expression was very low and likely to improve further as technical advances are made. The finding that stimulation with LPS has a markedly greater effect on gene expression than the degree of variability provides confidence that microarray-based studies can be used to detect changes in gene expression of biological interest in infectious diseases

Response to Therapeutic Sleep Deprivation: A Naturalistic Study of Clinical and Genetic Factors and Post-treatment Depressive Symptom Trajectory

Research has shown that therapeutic sleep deprivation (SD) has rapid antidepressant effects in the majority of depressed patients. Investigation of factors preceding and accompanying these effects may facilitate the identification of the underlying biological mechanisms. This exploratory study aimed to examine clinical and genetic factors predicting response to SD and determine the impact of SD on illness course. Mood during SD was also assessed via visual analogue scale. Depressed inpatients (n = 78) and healthy controls (n = 15) underwent ~36 h of SD. Response to SD was defined as a score of ≤ 2 on the Clinical Global Impression Scale for Global Improvement. Depressive symptom trajectories were evaluated for up to a month using self/expert ratings. Impact of genetic burden was calculated using polygenic risk scores for major depressive disorder. In total, 72% of patients responded to SD. Responders and non-responders did not differ in baseline self/expert depression symptom ratings, but mood differed. Response was associated with lower age (p = 0.007) and later age at life-time disease onset (p = 0.003). Higher genetic burden of depression was observed in non-responders than healthy controls. Up to a month post SD, depressive symptoms decreased in both patients groups, but more in responders, in whom effects were sustained. The present findings suggest that re-examining SD with a greater focus on biological mechanisms will lead to better understanding of mechanisms of depression

Can a systems approach produce a better understanding of mood disorders?

Background: One in twenty-five people suffer from a mood disorder. Current treatments are sub-optimal with poor patient response and uncertain modes-of-action. There is thus a need to better understand underlying mechanisms that determine mood, and how these go wrong in affective disorders. Systems biology approaches have yielded important biological discoveries for other complex diseases such as cancer, and their potential in affective disorders will be reviewed. Scope of review: This review will provide a general background to affective disorders, plus an outline of experimental and computational systems biology. The current application of these approaches in understanding affective disorders will be considered, and future recommendations made. Major conclusions: Experimental systems biology has been applied to the study of affective disorders, especially at the genome and transcriptomic levels. However, data generation has been slowed by a lack of human tissue or suitable animal models. At present, computational systems biology has only be applied to understanding affective disorders on a few occasions. These studies provide sufficient novel biological insight to motivate further use of computational biology in this field. General significance: In common with many complex diseases much time and money has been spent on the generation of large-scale experimental datasets. The next step is to use the emerging computational approaches, predominantly developed in the field of oncology, to leverage the most biological insight from these datasets. This will lead to the critical breakthroughs required for more effective diagnosis, stratification and treatment of affective disorders

Diurnal Regulation of Lipid Metabolism and Applications of Circadian Lipidomics

AbstractThe circadian timing system plays a key role in orchestrating lipid metabolism. In concert with the solar cycle, the circadian system ensures that daily rhythms in lipid absorption, storage, and transport are temporally coordinated with rest-activity and feeding cycles. At the cellular level, genes involved in lipid synthesis and fatty acid oxidation are rhythmically activated and repressed by core clock proteins in a tissue-specific manner. Consequently, loss of clock gene function or misalignment of circadian rhythms with feeding cycles (e.g., in shift work) results in impaired lipid homeostasis. Herein, we review recent progress in circadian rhythms research using lipidomics, i.e., large-scale profiling of lipid metabolites, to characterize circadian-regulated lipid pathways in mammals. In mice, novel regulatory circuits involved in fatty acid metabolism have been identified in adipose tissue, liver, and muscle. Extensive diversity in circadian regulation of plasma lipids has also been revealed in humans using lipidomics and other metabolomics approaches. In future studies, lipidomics platforms will be increasingly used to better understand the effects of genetic variation, shift work, food intake, and drugs on circadian-regulated lipid pathways and metabolic health