Cluster-Rasch models for microarray gene expression data

Background

We propose two different formulations of the Rasch statistical models to the problem of relating gene expression profiles to the phenotypes. One formulation allows us to investigate whether a cluster of genes with similar expression profiles is related to the observed phenotypes; this model can also be used for future prediction. The other formulation provides an alternative way of identifying genes that are over- or underexpressed from their expression levels in tissue or cell samples of a given tissue or cell type.

Results

We illustrate the methods on available datasets of a classification of acute leukemias and of 60 cancer cell lines. For tumor classification, the results are comparable to those previously obtained. For the cancer cell lines dataset, we found four clusters of genes that are related to drug response for many of the 90 drugs that we considered. In addition, for each type of cell line, we identified genes that are over- or underexpressed relative to other genes.

Conclusions

The cluster-Rasch model provides a probabilistic model for describing gene expression patterns across samples and can be used to relate gene expression profiles to phenotypes.     

ALC: automated reduction of rule-based models

Background

Combinatorial complexity is a challenging problem for the modeling of cellular signal transduction since the association of a few proteins can give rise to an enormous amount of feasible protein complexes. The layer-based approach is an approximative, but accurate method for the mathematical modeling of signaling systems with inherent combinatorial complexity. The number of variables in the simulation equations is highly reduced and the resulting dynamic models show a pronounced modularity. Layer-based modeling allows for the modeling of systems not accessible previously.

Results

ALC (Automated Layer Construction) is a computer program that highly simplifies the building of reduced modular models, according to the layer-based approach. The model is defined using a simple but powerful rule-based syntax that supports the concepts of modularity and macrostates. ALC performs consistency checks on the model definition and provides the model output in different formats (C MEX, MATLAB, Mathematica and SBML) as ready-to-run simulation files. ALC also provides additional documentation files that simplify the publication or presentation of the models. The tool can be used offline or via a form on the ALC website.

Conclusion

ALC allows for a simple rule-based generation of layer-based reduced models. The model files are given in different formats as ready-to-run simulation files.     

Systematic integration of experimental data and models in systems biology

Background

The quality of automated gene prediction in microbial organisms has improved steadily over the past decade, but there is still room for improvement. Increasing the number of correct identifications, both of genes and of the translation initiation sites for each gene, and reducing the overall number of false positives, are all desirable goals.

Results

With our years of experience in manually curating genomes for the Joint Genome Institute, we developed a new gene prediction algorithm called Prodigal (PROkaryotic DYnamic programming Gene-finding ALgorithm). With Prodigal, we focused specifically on the three goals of improved gene structure prediction, improved translation initiation site recognition, and reduced false positives. We compared the results of Prodigal to existing gene-finding methods to demonstrate that it met each of these objectives.

Conclusion

We built a fast, lightweight, open source gene prediction program called Prodigal http://compbio.ornl.gov/prodigal/. Prodigal achieved good results compared to existing methods, and we believe it will be a valuable asset to automated microbial annotation pipelines.     

Forecasting Temporal Dynamics of Cutaneous Leishmaniasis in Northeast Brazil

Introduction

Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions.

Methodology/Principal Findings

We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation.

Significance

These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets.     

Pneumonia in patients with cirrhosis: risk factors associated with mortality and predictive value of prognostic models

Background

Cirrhosis always goes with profound immunity compromise, and makes those patients easily be the target of pneumonia. Cirrhotic patients with pneumonia have a dramatically increased mortality. To recognize the risk factors of mortality and to optimize stratification are critical for improving survival rate.

Methods

Two hundred and three cirrhotic patients with pneumonia at a tertiary care hospital were included in this retrospective study. Demographical, clinical and laboratory parameters, severity models and prognosis were recorded. Multivariate Cox regression analysis was used to identify independent predictors of 30-day and 90-day mortality. Area under receiver operating characteristics curves (AUROC) was used to compare the predictive value of different prognostic scoring systems.

Results

Patients with nosocomial acquired or community acquired pneumonia indicated similar prognosis after 30- and 90-day follow-up. However, patients triggered acute-on-chronic liver failure (ACLF) highly increased mortality (46.4% vs 4.5% for 30-day, 69.6% vs 11.2% for 90-day). Age, inappropriate empirical antibiotic therapy (HR: 2.326 p?=?0.018 for 30-day and HR: 3.126 p?<?0.001 for 90-day), bacteremia (HR: 3.037 p?=?0.002 for 30-day and HR: 2.651 p?=?0.001 for 90-day), white blood cell count (WBC) (HR: 1.452 p?<?0.001 for 30-day and HR: 1.551 p?<?0.001 for 90-day) and total bilirubin (HR: 1.059 p?=?0.002 for 90-day) were independent factors for mortality in current study. Chronic liver failure–sequential organ failure assessment (CLIF-SOFA) displayed highest AUROC (0.89 and 0.90, 95% CI: 0.83–0.95 and 0.85–0.95 for 30-day and 90-day respectively) in current study.

Conclusions

This study found age, bacteremia, WBC, total bilirubin and inappropriate empirical antibiotic therapy were independently associated with increased mortality. Pneumonia triggered ACLF remarkably increased mortality. CLIF-SOFA was more accurate in predicting mortality than other five prognostic models (model for end-stage liver disease (MELD), MELD-Na, quick sequential organ failure assessment (qSOFA), pneumonia severity index (PSI), Child-Turcotte-Pugh (CTP) score).

     

Modelling entomological-climatic interactions of Plasmodium falciparum malaria transmission in two Colombian endemic-regions: contributions to a National Malaria Early Warning System

Background

Malaria has recently re-emerged as a public health burden in Colombia. Although the problem seems to be climate-driven, there remain significant gaps of knowledge in the understanding of the complexity of malaria transmission, which have motivated attempts to develop a comprehensive model.

Methods

The mathematical tool was applied to represent Plasmodium falciparum malaria transmission in two endemic-areas. Entomological exogenous variables were estimated through field campaigns and laboratory experiments. Availability of breeding places was included towards representing fluctuations in vector densities. Diverse scenarios, sensitivity analyses and instabilities cases were considered during experimentation-validation process.

Results

Correlation coefficients and mean square errors between observed and modelled incidences reached 0.897–0.668 (P > 0.95) and 0.0002–0.0005, respectively. Temperature became the most relevant climatic parameter driving the final incidence. Accordingly, malaria outbreaks are possible during the favourable epochs following the onset of El Niño warm events. Sporogonic and gonotrophic cycles showed to be the entomological key-variables controlling the transmission potential of mosquitoes' population. Simulation results also showed that seasonality of vector density becomes an important factor towards understanding disease transmission.

Conclusion

The model constitutes a promising tool to deepen the understanding of the multiple interactions related to malaria transmission conducive to outbreaks. In the foreseeable future it could be implemented as a tool to diagnose possible dynamical patterns of malaria incidence under several scenarios, as well as a decision-making tool for the early detection and control of outbreaks. The model will be also able to be merged with forecasts of El Niño events to provide a National Malaria Early Warning System.     

Exploring variability in methods and data sensitivity in carbon footprints of feed ingredients

Purpose

Production of feed is an important contributor to life cycle greenhouse gas emissions, or carbon footprints (CFPs), of livestock products. Consequences of methodological choices and data sensitivity on CFPs of feed ingredients were explored to improve comparison and interpretation of CFP studies. Methods and data for emissions from cultivation and processing, land use (LU), and land use change (LUC) were analyzed.

Method

For six ingredients (maize, wheat, palm kernel expeller, rapeseed meal, soybean meal, and beet pulp), CFPs resulting from a single change in methods and data were compared with a reference CFP, i.e., based on IPCC Tier 1 methods, and data from literature.

Results and discussion

Results show that using more detailed methods to compute N₂O emissions from cultivation hardly affected reference CFPs, except for methods to determine $ \mathrm{NO}_3^{-} $ leaching (contributing to indirect N₂O emissions) in which the influence is about ?7 to +12 %. Overall, CFPs appeared most sensitive to changes in crop yield and applied synthetic fertilizer N. The inclusion of LULUC emissions can change CFPs considerably, i.e., up to 877 %. The level of LUC emissions per feed ingredient highly depends on the method chosen, as well as on assumptions on area of LUC, C stock levels (mainly aboveground C and soil C), and amortization period.

Conclusions

We concluded that variability in methods and data can significantly affect CFPs of feed ingredients and hence CFPs of livestock products. Transparency in methods and data is therefore required. For harmonization, focus should be on methods to calculate $ \mathrm{NO}_3^{-} $ leaching and emissions from LULUC. It is important to consider LUC in CFP studies of food, feed, and bioenergy products.     

A proof of the DBRF-MEGN method, an algorithm for deducing minimum equivalent gene networks

Background

We previously developed the DBRF-MEGN (difference-based regulation finding-minimum equivalent gene network) method, which deduces the most parsimonious signed directed graphs (SDGs) consistent with expression profiles of single-gene deletion mutants. However, until the present study, we have not presented the details of the method's algorithm or a proof of the algorithm.

Results

We describe in detail the algorithm of the DBRF-MEGN method and prove that the algorithm deduces all of the exact solutions of the most parsimonious SDGs consistent with expression profiles of gene deletion mutants.

Conclusions

The DBRF-MEGN method provides all of the exact solutions of the most parsimonious SDGs consistent with expression profiles of gene deletion mutants.     

Predicting 30-day mortality of aortic valve replacement by the AVR score

Objectives

The objective of this study is to develop a simple risk score to predict 30-day mortality of aortic valve replacement (AVR).

Methods

In a development set of 673 consecutive patients who underwent AVR between 1990 and 1993, four independent predictors for 30-day mortality were identified: body mass index (BMI) ≥30, BMI <20, previous coronary artery bypass grafting (CABG) and recent myocardial infarction. Based on these predictors, a 30-day mortality risk score—the AVR score—was developed. The AVR score was validated on a validation set of 673 consecutive patients who underwent AVR almost two decennia later in the same hospital.

Results

Thirty-day mortality in the development set was ≤2% in the absence of any predictor (class I, low risk), 2–5% in the solitary presence of BMI ≥30 (class II, mild risk), 5–15% in the solitary presence of previous CABG or recent myocardial infarction (class III, moderate risk), and >15% in the solitary presence of BMI <20, or any combination of BMI ≥30, previous CABG or recent myocardial infarction (class IV, high risk). The AVR score correctly predicted 30-day mortality in the validation set: observed 30-day mortality in the validation set was 2.3% in 487 class I patients, 4.4% in 137 class II patients, 13.3% in 30 class III patients and 15.8% in 19 class IV patients.

Conclusions

The AVR score is a simple risk score validated to predict 30-day mortality of AVR.     

Conservation management and sustainable harvest quotas are sensitive to choice of climate modelling approach for two marine gastropods

Aim

To establish the robustness of two alternative methods for predicting the future ranges and abundances for two wild‐harvested abalone species (Haliotis rubra Donovan 1808 and H. laevigata Leach 1814): single atmosphere–ocean general circulation model (GCM) or ensemble‐averaged GCM forecasts.

Location

South Australia.

Methods

We assessed the ability of 20 GCMs to simulate observed seasonal sea surface temperature (SST) between 1980–1999, globally, and regionally for the Indian and Pacific Oceans south of the Equator. We used model rankings to characterize a set of representative climate futures, using three different‐sized GCM ensembles and two individual GCMs (the Parallel Climate Model and the Community Climate System Model, version 3.0). Ecological niche models were then coupled to physiological information to compare forecast changes in area of occupancy, population size and harvest area based on forecasts using the various GCM selection methods, as well as different greenhouse gas emission scenarios and climate sensitivities.

Results

We show that: (1) the skill with which climate models reproduce recent SST records varies considerably amongst GCMs, with multimodel ensemble averages showing closer agreement to observations than single models; (2) choice of GCM, and the decision on whether or not to use ensemble‐averaged climate forecasts, can strongly influence spatiotemporal predictions of range, abundance and fishing potential; and (3) comparable hindcasting skill does not necessarily guarantee that GCM forecasts and ecological and evolutionary responses to these forecast changes, will be similar amongst closely ranked models.

Conclusion

By averaging across an ensemble of seven highly ranked skilful GCMs, inherent uncertainties stemming from GCM differences are incorporated into forecasts of change in species range, abundance and sustainable fishing area. Our results highlight the need to make informed and explicit decisions on GCM choice, model sensitivity and emission scenarios when exploring conservation options for marine species and the sustainability of future harvests using ecological niche models.

     

Identification of glioblastoma gene prognosis modules based on weighted gene co-expression network analysis

Background

Glioblastoma multiforme, the most prevalent and aggressive brain tumour, has a poor prognosis. The molecular mechanisms underlying gliomagenesis remain poorly understood. Therefore, molecular research, including various markers, is necessary to understand the occurrence and development of glioma.

Method

Weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network in TCGA glioblastoma samples. Gene ontology (GO) and pathway-enrichment analysis were used to identify significance of gene modules. Cox proportional hazards regression model was used to predict outcome of glioblastoma patients.

Results

We performed weighted gene co-expression network analysis (WGCNA) and identified a gene module (yellow module) related to the survival time of TCGA glioblastoma samples. Then, 228 hub genes were calculated based on gene significance (GS) and module significance (MS). Four genes (OSMR + SOX21?+?MED10?+?PTPRN) were selected to construct a Cox proportional hazards regression model with high accuracy (AUC?=?0.905). The prognostic value of the Cox proportional hazards regression model was also confirmed in GSE16011 dataset (GBM: n?=?156).

Conclusion

We developed a promising mRNA signature for estimating overall survival in glioblastoma patients.

     

A novel application of capnography during controlled human exposure to air pollution

Background

A detailed contrast bolus propagation model is essential for optimizing bolus-chasing Computed Tomography Angiography (CTA). Bolus characteristics were studied using bolus-timing datasets from Magnetic Resonance Angiography (MRA) for adaptive controller design and validation.

Methods

MRA bolus-timing datasets of the aorta in thirty patients were analyzed by a program developed with MATLAB. Bolus characteristics, such as peak position, dispersion and bolus velocity, were studied. The bolus profile was fit to a convolution function, which would serve as a mathematical model of bolus propagation in future controller design.

Results

The maximum speed of the bolus in the aorta ranged from 5–13 cm/s and the dwell time ranged from 7–13 seconds. Bolus characteristics were well described by the proposed propagation model, which included the exact functional relationships between the parameters and aortic location.

Conclusion

The convolution function describes bolus dynamics reasonably well and could be used to implement the adaptive controller design.     

Image-driven modeling of the proliferation and necrosis of glioblastoma multiforme

Background

The heterogeneity of response to treatment in patients with glioblastoma multiforme suggests that the optimal therapeutic approach incorporates an individualized assessment of expected lesion progression. In this work, we develop a novel computational model for the proliferation and necrosis of glioblastoma multiforme.

Methods

The model parameters are selected based on the magnetic resonance imaging features of each tumor, and the proposed technique accounts for intrinsic cell division, tumor cell migration along white matter tracts, as well as central tumor necrosis. As a validation of this approach, tumor growth is simulated in the brain of a healthy adult volunteer using parameters derived from the imaging of a patient with glioblastoma multiforme. A mutual information metric is calculated between the simulated tumor profile and observed tumor.

Results

The tumor progression profile generated by the proposed model is compared with those produced by existing models and with the actual observed tumor progression. Both qualitative and quantitative analyses show that the model introduced in this work replicates the observed progression of glioblastoma more accurately relative to prior techniques.

Conclusions

This image-driven model generates improved tumor progression profiles and may contribute to the development of more reliable prognostic estimates in patients with glioblastoma multiforme.

     

Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models

Background

Network inference methods reconstruct mathematical models of molecular or genetic networks directly from experimental data sets. We have previously reported a mathematical method which is exclusively data-driven, does not involve any heuristic decisions within the reconstruction process, and deliveres all possible alternative minimal networks in terms of simple place/transition Petri nets that are consistent with a given discrete time series data set.

Results

We fundamentally extended the previously published algorithm to consider catalysis and inhibition of the reactions that occur in the underlying network. The results of the reconstruction algorithm are encoded in the form of an extended Petri net involving control arcs. This allows the consideration of processes involving mass flow and/or regulatory interactions. As a non-trivial test case, the phosphate regulatory network of enterobacteria was reconstructed using in silico-generated time-series data sets on wild-type and in silico mutants.

Conclusions

The new exact algorithm reconstructs extended Petri nets from time series data sets by finding all alternative minimal networks that are consistent with the data. It suggested alternative molecular mechanisms for certain reactions in the network. The algorithm is useful to combine data from wild-type and mutant cells and may potentially integrate physiological, biochemical, pharmacological, and genetic data in the form of a single model.     

Anti-tumor effects of a human VEGFR-2-based DNA vaccine in mouse models

Background

Vascular endothelial growth factor (VEGF) and its receptor, VEGFR-2 (Flk-1/KDR), play a key role in tumor angiogenesis. Blocking the VEGF-VEGFR-2 pathway may inhibit tumor growth. Here, we used human VEGFR-2 as a model antigen to explore the feasibility of immunotherapy with a plasmid DNA vaccine based on a xenogeneic homologue of this receptor.

Methods

The protective effects and therapeutic anti-tumor immunity mediated by the DNA vaccine were investigated in mouse models. Anti-angiogenesis effects were detected by immunohistochemical staining and the alginate-encapsulate tumor cell assay. The mechanism of action of the DNA vaccine was primarily explored by detection of auto-antibodies and CTL activity.

Results

The DNA vaccine elicited a strong, protective and therapeutic anti-tumor immunity through an anti-angiogenesis mechanism in mouse models, mediated by the stimulation of an antigen-specific response against mFlk-1.

Conclusion

Our study shows that a DNA vaccine based on a xenogeneic homologue plasmid DNA induced autoimmunity against VEGFR-2, resulting in inhibition of tumor growth. Such vaccines may be clinically relevant for cancer immunotherapy.     

Individual patient data meta-analysis of acupuncture for chronic pain: protocol of the Acupuncture Trialists' Collaboration

Objectives

To evaluate the use and reporting of adjusted analysis in randomised controlled trials (RCTs) and compare the quality of reporting before and after the revision of the CONSORT Statement in 2001.

Design

Comparison of two cross sectional samples of published articles.

Data Sources

Journal articles indexed on PubMed in December 2000 and December 2006.

Study Selection

Parallel group RCTs with a full publication carried out in humans and published in English

Main outcome measures

Proportion of articles reported adjusted analysis; use of adjusted analysis; the reason for adjustment; the method of adjustment and the reporting of adjusted analysis results in the main text and abstract.

Results

In both cohorts, 25% of studies reported adjusted analysis (84/355 in 2000 vs 113/422 in 2006). Compared with articles reporting only unadjusted analyses, articles that reported adjusted analyses were more likely to specify primary outcomes, involve multiple centers, perform stratified randomization, be published in general medical journals, and recruit larger sample sizes. In both years a minority of articles explained why and how covariates were selected for adjustment (20% to 30%). Almost all articles specified the statistical methods used for adjustment (99% in 2000 vs 100% in 2006) but only 5% and 10%, respectively, reported both adjusted and unadjusted results as recommended in the CONSORT guidelines.

Conclusion

There was no evidence of change in the reporting of adjusted analysis results five years after the revision of the CONSORT Statement and only a few articles adhered fully to the CONSORT recommendations.     

Molecular network profiling of U373MG human glioblastoma cells following induction of apoptosis by novel marine-derived anti-cancer 1,2,3,4-tetrahydroisoquinoline alkaloids

Background

Glioblastoma is the most aggressive form of brain tumors showing resistance to treatment with various chemotherapeutic agents. The most effective way to eradicate glioblastoma requires the concurrent inhibition of multiple signaling pathways and target molecules involved in the progression of glioblastoma. Recently, we obtained a series of 1,2,3,4-tetrahydroisoquinoline alkaloids with potent anti-cancer activities, including ecteinascidin-770 (ET-770; the compound 1a) and renieramycin M (RM; the compound 2a) from Thai marine invertebrates, together with a 2’-N-4”-pyridinecarbonyl derivative of ET-770 (the compound 3). We attempted to characterize the molecular pathways responsible for cytotoxic effects of these compounds on a human glioblastoma cell line U373MG.

Methods

We studied the genome-wide gene expression profile on microarrays and molecular networks by using pathway analysis tools of bioinformatics.

Results

All of these compounds induced apoptosis of U373MG cells at nanomolar concentrations. The compound 3 reduced the expression of 417 genes and elevated the levels of 84 genes, while ET-770 downregulated 426 genes and upregulated 45 genes. RM decreased the expression of 274 genes and increased the expression of 9 genes. The set of 196 downregulated genes and 6 upregulated genes showed an overlap among all the compounds, suggesting an existence of the common pathways involved in induction of apoptosis. We identified the ErbB (EGFR) signaling pathway as one of the common pathways enriched in the set of downregulated genes, composed of PTK2, AKT3, and GSK3B serving as key molecules that regulate cell movement and the nervous system development. Furthermore, a GSK3B-specific inhibitor induced apoptosis of U373MG cells, supporting an anti-apoptotic role of GSK3B.

Conclusion

Molecular network analysis is a useful approach not only to characterize the glioma-relevant pathways but also to identify the network-based effective drug targets.     

Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions

Key message

Development of models to predict genotype by environment interactions, in unobserved environments, using environmental covariates, a crop model and genomic selection. Application to a large winter wheat dataset.

Abstract

Genotype by environment interaction (G*E) is one of the key issues when analyzing phenotypes. The use of environment data to model G*E has long been a subject of interest but is limited by the same problems as those addressed by genomic selection methods: a large number of correlated predictors each explaining a small amount of the total variance. In addition, non-linear responses of genotypes to stresses are expected to further complicate the analysis. Using a crop model to derive stress covariates from daily weather data for predicted crop development stages, we propose an extension of the factorial regression model to genomic selection. This model is further extended to the marker level, enabling the modeling of quantitative trait loci (QTL) by environment interaction (Q*E), on a genome-wide scale. A newly developed ensemble method, soft rule fit, was used to improve this model and capture non-linear responses of QTL to stresses. The method is tested using a large winter wheat dataset, representative of the type of data available in a large-scale commercial breeding program. Accuracy in predicting genotype performance in unobserved environments for which weather data were available increased by 11.1 % on average and the variability in prediction accuracy decreased by 10.8 %. By leveraging agronomic knowledge and the large historical datasets generated by breeding programs, this new model provides insight into the genetic architecture of genotype by environment interactions and could predict genotype performance based on past and future weather scenarios.