Differential expression analysis with global network adjustment

<p>Background: Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a gene’s expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network. The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments.</p> <p>Results: We demonstrate a type of penalized regression model that can be estimated from large gene expression databases, and then applied to smaller experiments. The ridge parameter is selected by minimizing the cross-validation error of the predictions in the independent out-sample. This tends to increase the model stability and leads to a much greater degree of parameter shrinkage, but the resulting biased estimation is mitigated by a second round of regression. Nevertheless, the proposed computationally efficient “over-shrinkage” method outperforms previously used LASSO-based techniques. In two independent datasets, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio allowing more powerful inferences on differential gene expression leading to biologically intuitive findings. We also show that a large proportion of gene dependencies are conditional on the biological state, which would be impossible with standard differential expression methods.</p> <p>Conclusions: By adjusting for the effects of the global network on individual genes, both the sensitivity and reliability of differential expression measures are greatly improved.</p&gt

Design considerations and analysis planning of a phase 2a proof of concept study in rheumatoid arthritis in the presence of possible non-monotonicity

BACKGROUND: It is important to quantify the dose response for a drug in phase 2a clinical trials so the optimal doses can then be selected for subsequent late phase trials. In a phase 2a clinical trial of new lead drug being developed for the treatment of rheumatoid arthritis (RA), a U-shaped dose response curve was observed. In the light of this result further research was undertaken to design an efficient phase 2a proof of concept (PoC) trial for a follow-on compound using the lessons learnt from the lead compound. METHODS: The planned analysis for the Phase 2a trial for GSK123456 was a Bayesian Emax model which assumes the dose-response relationship follows a monotonic sigmoid "S" shaped curve. This model was found to be suboptimal to model the U-shaped dose response observed in the data from this trial and alternatives approaches were needed to be considered for the next compound for which a Normal dynamic linear model (NDLM) is proposed. This paper compares the statistical properties of the Bayesian Emax model and NDLM model and both models are evaluated using simulation in the context of adaptive Phase 2a PoC design under a variety of assumed dose response curves: linear, Emax model, U-shaped model, and flat response. RESULTS: It is shown that the NDLM method is flexible and can handle a wide variety of dose-responses, including monotonic and non-monotonic relationships. In comparison to the NDLM model the Emax model excelled with higher probability of selecting ED90 and smaller average sample size, when the true dose response followed Emax like curve. In addition, the type I error, probability of incorrectly concluding a drug may work when it does not, is inflated with the Bayesian NDLM model in all scenarios which would represent a development risk to pharmaceutical company. The bias, which is the difference between the estimated effect from the Emax and NDLM models and the simulated value, is comparable if the true dose response follows a placebo like curve, an Emax like curve, or log linear shape curve under fixed dose allocation, no adaptive allocation, half adaptive and adaptive scenarios. The bias though is significantly increased for the Emax model if the true dose response follows a U-shaped curve. CONCLUSIONS: In most cases the Bayesian Emax model works effectively and efficiently, with low bias and good probability of success in case of monotonic dose response. However, if there is a belief that the dose response could be non-monotonic then the NDLM is the superior model to assess the dose response

Discrete molecular dynamics can predict helical prestructured motifs in disordered proteins.

Intrinsically disordered proteins (IDPs) lack a stable tertiary structure, but their short binding regions termed Pre-Structured Motifs (PreSMo) can form transient secondary structure elements in solution. Although disordered proteins are crucial in many biological processes and designing strategies to modulate their function is highly important, both experimental and computational tools to describe their conformational ensembles and the initial steps of folding are sparse. Here we report that discrete molecular dynamics (DMD) simulations combined with replica exchange (RX) method efficiently samples the conformational space and detects regions populating alpha-helical conformational states in disordered protein regions. While the available computational methods predict secondary structural propensities in IDPs based on the observation of protein-protein interactions, our ab initio method rests on physical principles of protein folding and dynamics. We show that RX-DMD predicts alpha-PreSMos with high confidence confirmed by comparison to experimental NMR data. Moreover, the method also can dissect alpha-PreSMos in close vicinity to each other and indicate helix stability. Importantly, simulations with disordered regions forming helices in X-ray structures of complexes indicate that a preformed helix is frequently the binding element itself, while in other cases it may have a role in initiating the binding process. Our results indicate that RX-DMD provides a breakthrough in the structural and dynamical characterization of disordered proteins by generating the structural ensembles of IDPs even when experimental data are not available

Tyrosine kinase inhibitors reprogramming immunity in renal cell carcinoma: rethinking cancer immunotherapy

Review article[Abstract] The immune system regulates angiogenesis in cancer by way of both pro- and antiangiogenic activities. A bidirectional link between angiogenesis and the immune system has been clearly demonstrated. Most antiangiogenic molecules do not inhibit only VEGF signaling pathways but also other pathways which may affect immune system. Understanding of the role of these pathways in the regulation of immunosuppressive mechanisms by way of specific inhibitors is growing. Renal cell carcinoma (RCC) is an immunogenic tumor in which angiogenesis and immunosuppression work hand in hand, and its growth is associated with impaired antitumor immunity. Given the antitumor activity of selected TKIs in metastatic RCC (mRCC), it seems relevant to assess their effect on the immune system. The confirmation that TKIs improve cell cytokine response in mRCC provides a basis for the rational combination and sequential treatment of TKIs and immunotherapy

Linking Proteomic and Transcriptional Data through the Interactome and Epigenome Reveals a Map of Oncogene-induced Signaling

Cellular signal transduction generally involves cascades of post-translational protein modifications that rapidly catalyze changes in protein-DNA interactions and gene expression. High-throughput measurements are improving our ability to study each of these stages individually, but do not capture the connections between them. Here we present an approach for building a network of physical links among these data that can be used to prioritize targets for pharmacological intervention. Our method recovers the critical missing links between proteomic and transcriptional data by relating changes in chromatin accessibility to changes in expression and then uses these links to connect proteomic and transcriptome data. We applied our approach to integrate epigenomic, phosphoproteomic and transcriptome changes induced by the variant III mutation of the epidermal growth factor receptor (EGFRvIII) in a cell line model of glioblastoma multiforme (GBM). To test the relevance of the network, we used small molecules to target highly connected nodes implicated by the network model that were not detected by the experimental data in isolation and we found that a large fraction of these agents alter cell viability. Among these are two compounds, ICG-001, targeting CREB binding protein (CREBBP), and PKF118–310, targeting β-catenin (CTNNB1), which have not been tested previously for effectiveness against GBM. At the level of transcriptional regulation, we used chromatin immunoprecipitation sequencing (ChIP-Seq) to experimentally determine the genome-wide binding locations of p300, a transcriptional co-regulator highly connected in the network. Analysis of p300 target genes suggested its role in tumorigenesis. We propose that this general method, in which experimental measurements are used as constraints for building regulatory networks from the interactome while taking into account noise and missing data, should be applicable to a wide range of high-throughput datasets.National Science Foundation (U.S.) (DB1-0821391)National Institutes of Health (U.S.) (Grant U54-CA112967)National Institutes of Health (U.S.) (Grant R01-GM089903)National Institutes of Health (U.S.) (P30-ES002109

NK105, a paclitaxel-incorporating micellar nanoparticle, is a more potent radiosensitising agent compared to free paclitaxel

NK105 is a micellar nanoparticle formulation designed to enhance the delivery of paclitaxel (PTX) to solid tumours. It has been reported to exert antitumour activity in vivo and to have reduced neurotoxicity as compared to that of free PTX. The purpose of this study was to investigate the radiosensitising effect of NK105 in comparison with that of PTX. Lewis lung carcinoma (LLC)-bearing mice were administered a single intravenous (i.v.) injection of PTX or NK105; 24 h after the drug administration, a proportion of the mice received radiation to the tumour site or lung fields. Then, the antitumour activity and lung toxicity were evaluated. In one subset of mice, the tumours were excised and specimens were prepared for analysis of the cell cycle distribution by flow cytometry. Combined NK105 treatment with radiation yielded significant superior antitumour activity as compared to combined PTX treatment with radiation (P=0.0277). On the other hand, a histopathological study of lung sections revealed no significant difference in histopathological changes between mice treated with PTX and radiation and those treated with NK105 and radiation. Flow-cytometric analysis showed that NK105-treated LLC tumour cells showed more severe arrest at the G2/M phase as compared to PTX-treated tumour cells. The superior radiosensitising activity of NK105 was thus considered to be attributable to the more severe cell cycle arrest at the G2/M phase induced by NK105 as compared to that induced by free PTX. The present study results suggest that further clinical trials are warranted to determine the efficacy and feasibility of combined NK105 therapy with radiation

In Vivo Transcription Dynamics of the Galactose Operon: A Study on the Promoter Transition from P1 to P2 at Onset of Stationary Phase

Quantitative analyses of the 5′ end of gal transcripts indicate that transcription from the galactose operon P1 promoter is higher during cell division. When cells are no longer dividing, however, transcription is initiated more often from the P2 promoter. Escherichia coli cells divide six times before the onset of the stationary phase when grown in LB containing 0.5% galactose at 37°C. Transcription from the two promoters increases, although at different rates, during early exponential phase (until the third cell division, OD600 0.4), and then reaches a plateau. The steady-state transcription from P1 continues in late exponential phase (the next three cell divisions, OD600 3.0), after which transcription from this promoter decreases. However, steady-state transcription from P2 continues 1 h longer into the stationary phase, before decreasing. This longer steady-state P2 transcription constitutes the promoter transition from P1 to P2 at the onset of the stationary phase. The intracellular cAMP concentration dictates P1 transcription dynamics; therefore, promoter transition may result from a lack of cAMP-CRP complex binding to the gal operon. The decay rate of gal-specific transcripts is constant through the six consecutive cell divisions that comprise the exponential growth phase, increases at the onset of the stationary phase, and is too low to be measured during the stationary phase. These data suggest that a regulatory mechanism coordinates the synthesis and decay of gal mRNAs to maintain the observed gal transcription. Our analysis indicates that the increase in P1 transcription is the result of cAMP-CRP binding to increasing numbers of galactose operons in the cell population

Waist circumference and risk of elevated blood pressure in children: a cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Increasing childhood obesity has become a major health threat. This cross-sectional study reports associations between schoolchildren's waist circumference (WC) and risk of elevated blood pressure.</p> <p>Methods</p> <p>We measured height, weight, neck and waist circumference, and blood pressure in regular health examinations among children in grade 1 (ages 6-7 years) at six elementary schools in Taipei County, Taiwan. Elevated blood pressure was defined in children found to have mean systolic or diastolic blood pressure greater than or equal to the gender-, age-, and height-percentile-specific 95th-percentile blood pressure value.</p> <p>Results</p> <p>All 2,334 schoolchildren were examined (response rate was 100% in the six schools). The mean of systolic and diastolic blood pressure increased as WC quartiles increased (p < 0.0001). The prevalence of elevated blood pressure for boys and girls within the fourth quartile of waist circumference was 38.9% and 26.8%, respectively. In the multivariate logistic regression analyses, the adjusted odds ratios of elevated blood pressure were 1.78 (95% confidence interval [CI] = 1.13-2.80), 2.45 (95% CI = 1.56-3.85), and 6.03 (95% CI = 3.59-10.1) for children in the second, third, and fourth waist circumference quartiles compared with the first quartile. The odds ratios for per-unit increase and per increase of standard deviation associated with elevated blood pressure were 1.14 (95% CI = 1.10-1.18) and 2.22 (95% CI = 1.76-2.78), respectively.</p> <p>Conclusions</p> <p>Elevated blood pressure in children was associated with waist circumference. Not only is waist circumference easier to measure than blood pressure, but it also provides important information on metabolic risk. Further research is needed on effective interventions to identify and monitor children with increased waist circumference to reduce metabolic and blood pressure risks.</p