Publications by authors named "Daniel Ziemek"

34 Publications

Prediction of response of methotrexate in patients with rheumatoid arthritis using serum lipidomics.

Sci Rep 2021 Mar 31;11(1):7266. Epub 2021 Mar 31.

Versus Arthritis Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK.

Methotrexate (MTX) is a common first-line treatment for new-onset rheumatoid arthritis (RA). However, MTX is ineffective for 30-40% of patients and there is no way to know which patients might benefit. Here, we built statistical models based on serum lipid levels measured at two time-points (pre-treatment and following 4 weeks on-drug) to investigate if MTX response (by 6 months) could be predicted. Patients about to commence MTX treatment for the first time were selected from the Rheumatoid Arthritis Medication Study (RAMS). Patients were categorised as good or non-responders following 6 months on-drug using EULAR response criteria. Serum lipids were measured using ultra-performance liquid chromatography-mass spectrometry and supervised machine learning methods (including regularized regression, support vector machine and random forest) were used to predict EULAR response. Models including lipid levels were compared to models including clinical covariates alone. The best performing classifier including lipid levels (assessed at 4 weeks) was constructed using regularized regression (ROC AUC 0.61 ± 0.02). However, the clinical covariate based model outperformed the classifier including lipid levels when either pre- or on-treatment time-points were investigated (ROC AUC 0.68 ± 0.02). Pre- or early-treatment serum lipid profiles are unlikely to inform classification of MTX response by 6 months with performance adequate for use in RA clinical management.
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http://dx.doi.org/10.1038/s41598-021-86729-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8012618PMC
March 2021

Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data.

BMC Bioinformatics 2020 Mar 20;21(1):119. Epub 2020 Mar 20.

Unlearn.AI, Inc., San Francisco, CA, USA.

Background: The ability to confidently predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. Yet, the goal of developing actionable, robust, and reproducible predictive signatures of phenotypes such as clinical outcome has not been attained in almost any disease area. Here, we report a comprehensive analysis spanning prediction tasks from ulcerative colitis, atopic dermatitis, diabetes, to many cancer subtypes for a total of 24 binary and multiclass prediction problems and 26 survival analysis tasks. We systematically investigate the influence of gene subsets, normalization methods and prediction algorithms. Crucially, we also explore the novel use of deep representation learning methods on large transcriptomics compendia, such as GTEx and TCGA, to boost the performance of state-of-the-art methods. The resources and findings in this work should serve as both an up-to-date reference on attainable performance, and as a benchmarking resource for further research.

Results: Approaches that combine large numbers of genes outperformed single gene methods consistently and with a significant margin, but neither unsupervised nor semi-supervised representation learning techniques yielded consistent improvements in out-of-sample performance across datasets. Our findings suggest that using l-regularized regression methods applied to centered log-ratio transformed transcript abundances provide the best predictive analyses overall.

Conclusions: Transcriptomics-based phenotype prediction benefits from proper normalization techniques and state-of-the-art regularized regression approaches. In our view, breakthrough performance is likely contingent on factors which are independent of normalization and general modeling techniques; these factors might include reduction of systematic errors in sequencing data, incorporation of other data types such as single-cell sequencing and proteomics, and improved use of prior knowledge.
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http://dx.doi.org/10.1186/s12859-020-3427-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085143PMC
March 2020

Identification of novel pheno-groups in heart failure with preserved ejection fraction using machine learning.

Heart 2020 03 7;106(5):342-349. Epub 2020 Jan 7.

Department of Medicine Solna, Unit of Cardiology, Karolinska Institute, Stockholm, Sweden.

Objective: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome. We aimed to derive HFpEF phenotype-based groups ('phenogroups') based on clinical and echocardiogram data using machine learning, and to compare clinical characteristics, proteomics and outcomes across the phenogroups.

Methods: We applied model-based clustering to 32 echocardiogram and 11 clinical and laboratory variables collected in stable condition from 320 HFpEF outpatients in the Karolinska-Rennes cohort study (56% female, median 78 years (IQR: 71-83)). Baseline proteomics and the composite end point of all-cause mortality or heart failure (HF) hospitalisation were used in secondary analyses.

Results: We identified six phenogroups, for which significant differences in the prevalence of concomitant atrial fibrillation (AF), anaemia and kidney disease were observed (p<0.05). Fifteen out of 86 plasma proteins differed between phenogroups (false discovery rate, FDR<0.05), including biomarkers of HF, AF and kidney function. The composite end point was significantly different between phenogroups (log-rank p<0.001), at short-term (100 days), mid-term (18 months) and longer-term follow-up (1000 days). Phenogroup 2 was older, with poorer diastolic and right ventricular function and higher burden of risk factors as AF (85%), hypertension (83%) and chronic obstructive pulmonary disease (30%). In this group a third experienced the primary outcome to 100 days, and two-thirds to 18 months (HR (95% CI) versus phenogroups 1, 3, 4, 5, 6: 1.5 (0.8-2.9); 5.7 (2.6-12.8); 2.9 (1.5-5.6); 2.7 (1.6-4.6); 2.1 (1.2-3.9)).

Conclusions: Using machine learning we identified distinct HFpEF phenogroups with differential characteristics and outcomes, as well as differential levels of inflammatory and cardiovascular proteins.
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http://dx.doi.org/10.1136/heartjnl-2019-315481DOI Listing
March 2020

Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data.

Sci Rep 2019 12 30;9(1):20353. Epub 2019 Dec 30.

Pfizer, Worldwide Research and Development, Berlin, 10785, Germany.

In many research areas scientists are interested in clustering objects within small datasets while making use of prior knowledge from large reference datasets. We propose a method to apply the machine learning concept of transfer learning to unsupervised clustering problems and show its effectiveness in the field of single-cell RNA sequencing (scRNA-Seq). The goal of scRNA-Seq experiments is often the definition and cataloguing of cell types from the transcriptional output of individual cells. To improve the clustering of small disease- or tissue-specific datasets, for which the identification of rare cell types is often problematic, we propose a transfer learning method to utilize large and well-annotated reference datasets, such as those produced by the Human Cell Atlas. Our approach modifies the dataset of interest while incorporating key information from the larger reference dataset via Non-negative Matrix Factorization (NMF). The modified dataset is subsequently provided to a clustering algorithm. We empirically evaluate the benefits of our approach on simulated scRNA-Seq data as well as on publicly available datasets. Finally, we present results for the analysis of a recently published small dataset and find improved clustering when transferring knowledge from a large reference dataset. Implementations of the method are available at https://github.com/nicococo/scRNA.
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http://dx.doi.org/10.1038/s41598-019-56911-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6937257PMC
December 2019

Molecular Profiling of Ulcerative Colitis Subjects from the TURANDOT Trial Reveals Novel Pharmacodynamic/Efficacy Biomarkers.

J Crohns Colitis 2019 May;13(6):702-713

Pfizer, Cambridge, MA, USA.

Background And Aims: To define pharmacodynamic and efficacy biomarkers in ulcerative colitis [UC] patients treated with PF-00547659, an anti-human mucosal addressin cell adhesion molecule-1 [MAdCAM-1] monoclonal antibody, in the TURANDOT study.

Methods: Transcriptome, proteome and immunohistochemistry data were generated in peripheral blood and intestinal biopsies from 357 subjects in the TURANDOT study.

Results: In peripheral blood, C-C motif chemokine receptor 9 [CCR9] gene expression demonstrated a dose-dependent increase relative to placebo, but in inflamed intestinal biopsies CCR9 gene expression decreased with increasing PF-00547659 dose. Statistical models incorporating the full RNA transcriptome in inflamed intestinal biopsies showed significant ability to assess response and remission status. Oncostatin M [OSM] gene expression in inflamed intestinal biopsies demonstrated significant associations with, and good accuracy for, efficacy, and this observation was confirmed in independent published studies in which UC patients were treated with infliximab or vedolizumab. Compared with the placebo group, intestinal T-regulatory cells demonstrated a significant increase in the intermediate 22.5-mg dose cohort, but not in the 225-mg cohort.

Conclusions: CCR9 and OSM are implicated as novel pharmacodynamic and efficacy biomarkers. These findings occur amid coordinated transcriptional changes that enable the definition of surrogate efficacy biomarkers based on inflamed biopsy or blood transcriptomics data.ClinicalTrials.gov identifierNCT01620255.
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http://dx.doi.org/10.1093/ecco-jcc/jjy217DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6535501PMC
May 2019

An Unbiased Lipid Phenotyping Approach To Study the Genetic Determinants of Lipids and Their Association with Coronary Heart Disease Risk Factors.

J Proteome Res 2019 06 26;18(6):2397-2410. Epub 2019 Apr 26.

Department of Biochemistry and Cambridge Systems Biology Centre , University of Cambridge , Cambridge CB2 1GA , U.K.

Direct infusion high-resolution mass spectrometry (DIHRMS) is a novel, high-throughput approach to rapidly and accurately profile hundreds of lipids in human serum without prior chromatography, facilitating in-depth lipid phenotyping for large epidemiological studies to reveal the detailed associations of individual lipids with coronary heart disease (CHD) risk factors. Intact lipid profiling by DIHRMS was performed on 5662 serum samples from healthy participants in the Pakistan Risk of Myocardial Infarction Study (PROMIS). We developed a novel semi-targeted peak-picking algorithm to detect mass-to-charge ratios in positive and negative ionization modes. We analyzed lipid partial correlations, assessed the association of lipid principal components with established CHD risk factors and genetic variants, and examined differences between lipids for a common genetic polymorphism. The DIHRMS method provided information on 360 lipids (including fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, and sterol lipids), with a median coefficient of variation of 11.6% (range: 5.4-51.9). The lipids were highly correlated and exhibited a range of associations with clinical chemistry biomarkers and lifestyle factors. This platform can provide many novel insights into the effects of physiology and lifestyle on lipid metabolism, genetic determinants of lipids, and the relationship between individual lipids and CHD risk factors.
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http://dx.doi.org/10.1021/acs.jproteome.8b00786DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6558644PMC
June 2019

SUBSTRA: Supervised Bayesian Patient Stratification.

Bioinformatics 2019 09;35(18):3263-3272

Computational Systems Immunology, Pfizer Worldwide R&D, Berlin, Germany.

Motivation: Patient stratification methods are key to the vision of precision medicine. Here, we consider transcriptional data to segment the patient population into subsets relevant to a given phenotype. Whereas most existing patient stratification methods focus either on predictive performance or interpretable features, we developed a method striking a balance between these two important goals.

Results: We introduce a Bayesian method called SUBSTRA that uses regularized biclustering to identify patient subtypes and interpretable subtype-specific transcript clusters. The method iteratively re-weights feature importance to optimize phenotype prediction performance by producing more phenotype-relevant patient subtypes. We investigate the performance of SUBSTRA in finding relevant features using simulated data and successfully benchmark it against state-of-the-art unsupervised stratification methods and supervised alternatives. Moreover, SUBSTRA achieves predictive performance competitive with the supervised benchmark methods and provides interpretable transcriptional features in diverse biological settings, such as drug response prediction, cancer diagnosis, or kidney transplant rejection.

Availability And Implementation: The R code of SUBSTRA is available at https://github.com/sahandk/SUBSTRA.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btz112DOI Listing
September 2019

Profiling of Gene Expression Biomarkers as a Classifier of Methotrexate Nonresponse in Patients With Rheumatoid Arthritis.

Arthritis Rheumatol 2019 05 19;71(5):678-684. Epub 2019 Mar 19.

Manchester University NHS Foundation Trust, Manchester, UK.

Objective: Approximately 30-40% of rheumatoid arthritis (RA) patients who are initially started on low-dose methotrexate (MTX) will not benefit from the treatment. To date, no reliable biomarkers of MTX inefficacy in RA have been identified. The aim of this study was to analyze whole blood samples from RA patients at 2 time points (pretreatment and 4 weeks following initiation of MTX), to identify gene expression biomarkers of the MTX response.

Methods: RA patients who were about to commence treatment with MTX were selected from the Rheumatoid Arthritis Medication Study. Using European League Against Rheumatism (EULAR) response criteria, 42 patients were categorized as good responders and 43 as nonresponders at 6 months following the initation of MTX treatment. Data on whole blood transcript expression were generated, and supervised machine learning methods were used to predict a EULAR nonresponse. Models in which transcript levels were included were compared to models in which clinical covariates alone (e.g., baseline disease activity, sex) were included. Gene network and ontology analysis was also performed.

Results: Based on the ratio of transcript values (i.e., the difference in log -transformed expression values between 4 weeks of treatment and pretreatment), a highly predictive classifier of MTX nonresponse was developed using L2-regularized logistic regression (mean ± SEM area under the receiver operating characteristic [ROC] curve [AUC] 0.78 ± 0.11). This classifier was superior to models that included clinical covariates (ROC AUC 0.63 ± 0.06). Pathway analysis of gene networks revealed significant overrepresentation of type I interferon signaling pathway genes in nonresponders at pretreatment (P = 2.8 × 10 ) and at 4 weeks after treatment initiation (P = 4.9 × 10 ).

Conclusion: Testing for changes in gene expression between pretreatment and 4 weeks post-treatment initiation may provide an early classifier of the MTX treatment response in RA patients who are unlikely to benefit from MTX over 6 months. Such patients should, therefore, have their treatment escalated more rapidly, which would thus potentially impact treatment pathways. These findings emphasize the importance of a role for early treatment biomarker monitoring in RA patients started on MTX.
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http://dx.doi.org/10.1002/art.40810DOI Listing
May 2019

ProGeM: a framework for the prioritization of candidate causal genes at molecular quantitative trait loci.

Nucleic Acids Res 2019 01;47(1):e3

MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.

Quantitative trait locus (QTL) mapping of molecular phenotypes such as metabolites, lipids and proteins through genome-wide association studies represents a powerful means of highlighting molecular mechanisms relevant to human diseases. However, a major challenge of this approach is to identify the causal gene(s) at the observed QTLs. Here, we present a framework for the 'Prioritization of candidate causal Genes at Molecular QTLs' (ProGeM), which incorporates biological domain-specific annotation data alongside genome annotation data from multiple repositories. We assessed the performance of ProGeM using a reference set of 227 previously reported and extensively curated metabolite QTLs. For 98% of these loci, the expert-curated gene was one of the candidate causal genes prioritized by ProGeM. Benchmarking analyses revealed that 69% of the causal candidates were nearest to the sentinel variant at the investigated molecular QTLs, indicating that genomic proximity is the most reliable indicator of 'true positive' causal genes. In contrast, cis-gene expression QTL data led to three false positive candidate causal gene assignments for every one true positive assignment. We provide evidence that these conclusions also apply to other molecular phenotypes, suggesting that ProGeM is a powerful and versatile tool for annotating molecular QTLs. ProGeM is freely available via GitHub.
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http://dx.doi.org/10.1093/nar/gky837DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6326795PMC
January 2019

From hype to reality: data science enabling personalized medicine.

BMC Med 2018 08 27;16(1):150. Epub 2018 Aug 27.

Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, SI-1000, Ljubljana, Slovenia.

Background: Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of 'big data' and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future.

Conclusions: There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.
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http://dx.doi.org/10.1186/s12916-018-1122-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6109989PMC
August 2018

A Genome-Wide Association Study of Diabetic Kidney Disease in Subjects With Type 2 Diabetes.

Diabetes 2018 07 27;67(7):1414-1427. Epub 2018 Apr 27.

Department of Medicine & Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.

Identification of sequence variants robustly associated with predisposition to diabetic kidney disease (DKD) has the potential to provide insights into the pathophysiological mechanisms responsible. We conducted a genome-wide association study (GWAS) of DKD in type 2 diabetes (T2D) using eight complementary dichotomous and quantitative DKD phenotypes: the principal dichotomous analysis involved 5,717 T2D subjects, 3,345 with DKD. Promising association signals were evaluated in up to 26,827 subjects with T2D (12,710 with DKD). A combined T1D+T2D GWAS was performed using complementary data available for subjects with T1D, which, with replication samples, involved up to 40,340 subjects with diabetes (18,582 with DKD). Analysis of specific DKD phenotypes identified a novel signal near (rs9942471, = 4.5 × 10) associated with microalbuminuria in European T2D case subjects. However, no replication of this signal was observed in Asian subjects with T2D or in the equivalent T1D analysis. There was only limited support, in this substantially enlarged analysis, for association at previously reported DKD signals, except for those at and , both associated with estimated glomerular filtration rate. We conclude that, despite challenges in addressing phenotypic heterogeneity, access to increased sample sizes will continue to provide more robust inference regarding risk variant discovery for DKD.
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http://dx.doi.org/10.2337/db17-0914DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6014557PMC
July 2018

Robust phenotype prediction from gene expression data using differential shrinkage of co-regulated genes.

Sci Rep 2018 01 19;8(1):1237. Epub 2018 Jan 19.

Computational Sciences, Pfizer Worldwide Research & Development, Cambridge, MA, 02139, USA.

Discovery of robust diagnostic or prognostic biomarkers is a key to optimizing therapeutic benefit for select patient cohorts - an idea commonly referred to as precision medicine. Most discovery studies to derive such markers from high-dimensional transcriptomics datasets are weakly powered with sample sizes in the tens of patients. Therefore, highly regularized statistical approaches are essential to making generalizable predictions. At the same time, prior knowledge-driven approaches have been successfully applied to the manual interpretation of high-dimensional transcriptomics datasets. In this work, we assess the impact of combining two orthogonal approaches for the discovery of biomarker signatures, namely (1) well-known lasso-based regression approaches and its more recent derivative, the group lasso, and (2) the discovery of significant upstream regulators in literature-derived biological networks. Our method integrates both approaches in a weighted group-lasso model and differentially weights gene sets based on inferred active regulatory mechanism. Using nested cross-validation as well as independent clinical datasets, we demonstrate that our approach leads to increased accuracy and generalizable results. We implement our approach in a computationally efficient, user-friendly R package called creNET. The package can be downloaded at https://github.com/kouroshz/creNethttps://github.com/kouroshz/creNet and is accompanied by a parsed version of the STRING DB data base.
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http://dx.doi.org/10.1038/s41598-018-19635-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5775343PMC
January 2018

A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

BMC Bioinformatics 2017 Dec 19;18(1):565. Epub 2017 Dec 19.

Department of Mathematics, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, 0212, MA, USA.

Background: Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model.

Results: We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database.

Conclusion: In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.
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http://dx.doi.org/10.1186/s12859-017-1984-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5735940PMC
December 2017

From the Cover: Fenretinide, Troglitazone, and Elmiron Add to Weight of Evidence Support for Hemangiosarcoma Mode-of-Action From Studies in Mice.

Toxicol Sci 2018 01;161(1):58-75

Pfizer Worldwide Research and Development, Pfizer, Drug Safety Research and Development, Groton, Connecticut 06340.

Pharmaceuticals and chemicals produce hemangiosarcomas (HS) in mice, often by nongenotoxic, proliferative mechanisms. A mode-of-action (MOA) for hemangiosarcoma was proposed based on information presented at an international workshop (Cohen et al., Hemangiosarcoma in rodents: Mode-of-action evaluation and human relevance. Toxicol. Sci. 111, 4-18.). Five key elements of the MOA were articulated and included hypoxia, macrophage activation, increased angiogenic growth factors, dysregulated angiogenesis/erythropoiesis, and endothial cell proliferation. The goal of the current study was to add to the weight-of-evidence for the proposed MOA by assessing these key elements with 3 different compounds of varying potency for HS induction: fenretinide (high), troglitazone (intermediate), and elmiron (low). Multiple endpoints, including hypoxia (hyproxyprobe, transcriptomics), endothelial cell (EC) proliferation, and clinical and anatomic pathology, were assessed after 2, 4, and 13-weeks of treatment in B6C3F1 mice. All 3 compounds demonstrated strong evidence for dysregulated erythropoiesis (decrease in RBC and a failure to increase reticulocytes) and macrophage activation (4- to 11-fold increases); this pattern of hematological changes in mice might serve as an early biomarker to evaluate EC proliferation in suspected target organs for potential HS formation. Fenretinide demonstrated all 5 key elements, while troglitazone demonstrated 4 and elmiron demonstrated 3. Transcriptomics provided support for the 5 elements of the MOA, but was not any more sensitive than hypoxyprobe immunohistochemistry for detecting hypoxia. The overall transcriptional evidence for the key elements of the proposed MOA was also consistent with the potency of HS induction. These data, coupled with the previous work with 2-butoxyethanol and pregablin, increase the weight-of-evidence for the proposed MOA for HS formation.
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http://dx.doi.org/10.1093/toxsci/kfx195DOI Listing
January 2018

Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease.

PLoS Genet 2017 Apr 3;13(4):e1006706. Epub 2017 Apr 3.

Department of Systems Biology, Technical University of Denmark, Copenhagen, Denmark.

Recent advances in highly multiplexed immunoassays have allowed systematic large-scale measurement of hundreds of plasma proteins in large cohort studies. In combination with genotyping, such studies offer the prospect to 1) identify mechanisms involved with regulation of protein expression in plasma, and 2) determine whether the plasma proteins are likely to be causally implicated in disease. We report here the results of genome-wide association (GWA) studies of 83 proteins considered relevant to cardiovascular disease (CVD), measured in 3,394 individuals with multiple CVD risk factors. We identified 79 genome-wide significant (p<5e-8) association signals, 55 of which replicated at P<0.0007 in separate validation studies (n = 2,639 individuals). Using automated text mining, manual curation, and network-based methods incorporating information on expression quantitative trait loci (eQTL), we propose plausible causal mechanisms for 25 trans-acting loci, including a potential post-translational regulation of stem cell factor by matrix metalloproteinase 9 and receptor-ligand pairs such as RANK-RANK ligand. Using public GWA study data, we further evaluate all 79 loci for their causal effect on coronary artery disease, and highlight several potentially causal associations. Overall, a majority of the plasma proteins studied showed evidence of regulation at the genetic level. Our results enable future studies of the causal architecture of human disease, which in turn should aid discovery of new drug targets.
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http://dx.doi.org/10.1371/journal.pgen.1006706DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5393901PMC
April 2017

GWAS of self-reported mosquito bite size, itch intensity and attractiveness to mosquitoes implicates immune-related predisposition loci.

Hum Mol Genet 2017 04;26(7):1391-1406

Pfizer WRD, Human Genetics and Computational Biomedicine, The Portway Building, Granta Park, Cambridge CB21 6GS, UK.

Understanding the interaction between humans and mosquitoes is a critical area of study due to the phenomenal burdens on public health from mosquito-transmitted diseases. In this study, we conducted the first genome-wide association studies (GWAS) of self-reported mosquito bite reaction size (n = 84,724), itchiness caused by bites (n = 69,057), and perceived attractiveness to mosquitoes (n = 16,576). In total, 15 independent significant (P < 5×10-8) associations were identified. These loci were enriched for immunity-related genes that are involved in multiple cytokine signalling pathways. We also detected suggestive enrichment of these loci in enhancer regions that are active in stimulated T-cells, as well as within loci previously identified as controlling central memory T-cell levels. Egger regression analysis between the traits suggests that perception of itchiness and attractiveness to mosquitoes is driven, at least in part, by the genetic determinants of bite reaction size.Our findings illustrate the complex genetic and immunological landscapes underpinning human interactions with mosquitoes.
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http://dx.doi.org/10.1093/hmg/ddx036DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5390679PMC
April 2017

The Genetic Landscape of Renal Complications in Type 1 Diabetes.

J Am Soc Nephrol 2017 02 19;28(2):557-574. Epub 2016 Sep 19.

The Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland.

Diabetes is the leading cause of ESRD. Despite evidence for a substantial heritability of diabetic kidney disease, efforts to identify genetic susceptibility variants have had limited success. We extended previous efforts in three dimensions, examining a more comprehensive set of genetic variants in larger numbers of subjects with type 1 diabetes characterized for a wider range of cross-sectional diabetic kidney disease phenotypes. In 2843 subjects, we estimated that the heritability of diabetic kidney disease was 35% (P=6.4×10). Genome-wide association analysis and replication in 12,540 individuals identified no single variants reaching stringent levels of significance and, despite excellent power, provided little independent confirmation of previously published associated variants. Whole-exome sequencing in 997 subjects failed to identify any large-effect coding alleles of lower frequency influencing the risk of diabetic kidney disease. However, sets of alleles increasing body mass index (P=2.2×10) and the risk of type 2 diabetes (P=6.1×10) associated with the risk of diabetic kidney disease. We also found genome-wide genetic correlation between diabetic kidney disease and failure at smoking cessation (P=1.1×10). Pathway analysis implicated ascorbate and aldarate metabolism (P=9.0×10), and pentose and glucuronate interconversions (P=3.0×10) in pathogenesis of diabetic kidney disease. These data provide further evidence for the role of genetic factors influencing diabetic kidney disease in those with type 1 diabetes and highlight some key pathways that may be responsible. Altogether these results reveal important biology behind the major cause of kidney disease.
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http://dx.doi.org/10.1681/ASN.2016020231DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5280020PMC
February 2017

Interpreting transcriptional changes using causal graphs: new methods and their practical utility on public networks.

BMC Bioinformatics 2016 Aug 24;17(1):318. Epub 2016 Aug 24.

Department of Mathematics, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, 02125, USA.

Background: Inference of active regulatory cascades under specific molecular and environmental perturbations is a recurring task in transcriptional data analysis. Commercial tools based on large, manually curated networks of causal relationships offering such functionality have been used in thousands of articles in the biomedical literature. The adoption and extension of such methods in the academic community has been hampered by the lack of freely available, efficient algorithms and an accompanying demonstration of their applicability using current public networks.

Results: In this article, we propose a new statistical method that will infer likely upstream regulators based on observed patterns of up- and down-regulated transcripts. The method is suitable for use with public interaction networks with a mix of signed and unsigned causal edges. It subsumes and extends two previously published approaches and we provide a novel algorithmic method for efficient statistical inference. Notably, we demonstrate the feasibility of using the approach to generate biological insights given current public networks in the context of controlled in-vitro overexpression experiments, stem-cell differentiation data and animal disease models. We also provide an efficient implementation of our method in the R package QuaternaryProd available to download from Bioconductor.

Conclusions: In this work, we have closed an important gap in utilizing causal networks to analyze differentially expressed genes. Our proposed Quaternary test statistic incorporates all available evidence on the potential relevance of an upstream regulator. The new approach broadens the use of these types of statistics for highly curated signed networks in which ambiguities arise but also enables the use of networks with unsigned edges. We design and implement a novel computational method that can efficiently estimate p-values for upstream regulators in current biological settings. We demonstrate the ready applicability of the implemented method to analyze differentially expressed genes using the publicly available networks.
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http://dx.doi.org/10.1186/s12859-016-1181-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4995651PMC
August 2016

Genome-wide association analysis of self-reported events in 6135 individuals and 252 827 controls identifies 8 loci associated with thrombosis.

Hum Mol Genet 2016 05 9;25(9):1867-74. Epub 2016 Feb 9.

Cardiovascular Medicine Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden,

Thrombotic diseases are among the leading causes of morbidity and mortality in the world. To add insights into the genetic regulation of thrombotic disease, we conducted a genome-wide association study (GWAS) of 6135 self-reported blood clots events and 252 827 controls of European ancestry belonging to the 23andMe cohort of research participants. Eight loci exceeded genome-wide significance. Among the genome-wide significant results, our study replicated previously known venous thromboembolism (VTE) loci near the F5, FGA-FGG, F11, F2, PROCR and ABO genes, and the more recently discovered locus near SLC44A2 In addition, our study reports for the first time a genome-wide significant association between rs114209171, located upstream of the F8 structural gene, and thrombosis risk. Analyses of expression profiles and expression quantitative trait loci across different tissues suggested SLC44A2, ILF3 and AP1M2 as the three most plausible candidate genes for the chromosome 19 locus, our only genome-wide significant thrombosis-related locus that does not harbor likely coagulation-related genes. In addition, we present data showing that this locus also acts as a novel risk factor for stroke and coronary artery disease (CAD). In conclusion, our study reveals novel common genetic risk factors for VTE, stroke and CAD and provides evidence that self-reported data on blood clots used in a GWAS yield results that are comparable with those obtained using clinically diagnosed VTE. This observation opens up the potential for larger meta-analyses, which will enable elucidation of the genetics of thrombotic diseases, and serves as an example for the genetic study of other diseases.
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http://dx.doi.org/10.1093/hmg/ddw037DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4986325PMC
May 2016

Robust clinical outcome prediction based on Bayesian analysis of transcriptional profiles and prior causal networks.

Bioinformatics 2014 Jun;30(12):i69-77

Computational Sciences Center of Emphasis, Pfizer Worldwide R&D, Cambridge, Department of Mathematics, University of Massachusetts Boston, Boston, MA, Drug Safety Research & Development, Pfizer Worldwide R&D, Groton, CT, USA and Computational Sciences Center of Emphasis, Pfizer Worldwide R&D, Berlin, Germany.

Motivation: Understanding and predicting an individual's response in a clinical trial is the key to better treatments and cost-: effective medicine. Over the coming years, more and more large-scale omics datasets will become available to characterize patients with complex and heterogeneous diseases at a molecular level. Unfortunately, genetic, phenotypical and environmental variation is much higher in a human trial population than currently modeled or measured in most animal studies. In our experience, this high variability can lead to failure of trained predictors in independent studies and undermines the credibility and utility of promising high-dimensional datasets.

Methods: We propose a method that utilizes patient-level genome-wide expression data in conjunction with causal networks based on prior knowledge. Our approach determines a differential expression profile for each patient and uses a Bayesian approach to infer corresponding upstream regulators. These regulators and their corresponding posterior probabilities of activity are used in a regularized regression framework to predict response.

Results: We validated our approach using two clinically relevant phenotypes, namely acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. To demonstrate pitfalls in translating trained predictors across independent trials, we analyze performance characteristics of our approach as well as alternative feature sets in the regression on two independent datasets for each phenotype. We show that the proposed approach is able to successfully incorporate causal prior knowledge to give robust performance estimates.
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http://dx.doi.org/10.1093/bioinformatics/btu272DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4058945PMC
June 2014

An atlas of genetic influences on human blood metabolites.

Nat Genet 2014 Jun 11;46(6):543-550. Epub 2014 May 11.

European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK.

Genome-wide association scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metabolism and complex disease. Here we report the most comprehensive exploration of genetic loci influencing human metabolism thus far, comprising 7,824 adult individuals from 2 European population studies. We report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity with more than 400 metabolites in human blood. We extensively characterize the resulting in vivo blueprint of metabolism in human blood by integrating it with information on gene expression, heritability and overlap with known loci for complex disorders, inborn errors of metabolism and pharmacological targets. We further developed a database and web-based resources for data mining and results visualization. Our findings provide new insights into the role of inherited variation in blood metabolic diversity and identify potential new opportunities for drug development and for understanding disease.
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http://dx.doi.org/10.1038/ng.2982DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4064254PMC
June 2014

Brown fat determination and development from muscle precursor cells by novel action of bone morphogenetic protein 6.

PLoS One 2014 21;9(3):e92608. Epub 2014 Mar 21.

Centers for Therapeutic Innovation, Pfizer Inc., Boston, Massachusetts, United States of America.

Brown adipose tissue (BAT) plays a pivotal role in promoting energy expenditure by the virtue of uncoupling protein-1 (UCP-1) that differentiates BAT from its energy storing white adipose tissue (WAT) counterpart. The clinical implication of "classical" BAT (originates from Myf5 positive myoblastic lineage) or the "beige" fat (originates through trans-differentiation of WAT) activation in improving metabolic parameters is now becoming apparent. However, the inducers and endogenous molecular determinants that govern the lineage commitment and differentiation of classical BAT remain obscure. We report here that in the absence of any forced gene expression, stimulation with bone morphogenetic protein 6 (BMP6) induces brown fat differentiation from skeletal muscle precursor cells of murine and human origins. Through a comprehensive transcriptional profiling approach, we have discovered that two days of BMP6 stimulation in C2C12 myoblast cells is sufficient to induce genes characteristic of brown preadipocytes. This developmental switch is modulated in part by newly identified regulators, Optineurin (Optn) and Cyclooxygenase-2 (Cox2). Furthermore, pathway analyses using the Causal Reasoning Engine (CRE) identified additional potential causal drivers of this BMP6 induced commitment switch. Subsequent analyses to decipher key pathway that facilitates terminal differentiation of these BMP6 primed cells identified a key role for Insulin Like Growth Factor-1 Receptor (IGF-1R). Collectively these data highlight a therapeutically innovative role for BMP6 by providing a means to enhance the amount of myogenic lineage derived brown fat.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0092608PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3962431PMC
November 2014

GeneTopics--interpretation of gene sets via literature-driven topic models.

BMC Syst Biol 2013 9;7 Suppl 5:S10. Epub 2013 Dec 9.

Background: Annotation of a set of genes is often accomplished through comparison to a library of labelled gene sets such as biological processes or canonical pathways. However, this approach might fail if the employed libraries are not up to date with the latest research, don't capture relevant biological themes or are curated at a different level of granularity than is required to appropriately analyze the input gene set. At the same time, the vast biomedical literature offers an unstructured repository of the latest research findings that can be tapped to provide thematic sub-groupings for any input gene set.

Methods: Our proposed method relies on a gene-specific text corpus and extracts commonalities between documents in an unsupervised manner using a topic model approach. We automatically determine the number of topics summarizing the corpus and calculate a gene relevancy score for each topic allowing us to eliminate non-specific topics. As a result we obtain a set of literature topics in which each topic is associated with a subset of the input genes providing directly interpretable keywords and corresponding documents for literature research.

Results: We validate our method based on labelled gene sets from the KEGG metabolic pathway collection and the genetic association database (GAD) and show that the approach is able to detect topics consistent with the labelled annotation. Furthermore, we discuss the results on three different types of experimentally derived gene sets, (1) differentially expressed genes from a cardiac hypertrophy experiment in mice, (2) altered transcript abundance in human pancreatic beta cells, and (3) genes implicated by GWA studies to be associated with metabolite levels in a healthy population. In all three cases, we are able to replicate findings from the original papers in a quick and semi-automated manner.

Conclusions: Our approach provides a novel way of automatically generating meaningful annotations for gene sets that are directly tied to relevant articles in the literature. Extending a general topic model method, the approach introduced here establishes a workflow for the interpretation of gene sets generated from diverse experimental scenarios that can complement the classical approach of comparison to reference gene sets.
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http://dx.doi.org/10.1186/1752-0509-7-S5-S10DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4029197PMC
November 2014

Molecular causes of transcriptional response: a Bayesian prior knowledge approach.

Bioinformatics 2013 Dec 26;29(24):3167-73. Epub 2013 Sep 26.

Computational Sciences Center of Emphasis, Pfizer Worldwide Research & Development, Cambridge, MA 02140, USA, Department of Mathematics, University of Massachusetts Boston, Boston, MA 02125, USA, Drug Safety Research & Development, Pfizer, Groton, CT 06340, USA and Neusentis, Pfizer Worldwide Research & Development, Cambridge CB21 6GS, UK.

Motivation: The abundance of many transcripts changes significantly in response to a variety of molecular and environmental perturbations. A key question in this setting is as follows: what intermediate molecular perturbations gave rise to the observed transcriptional changes? Regulatory programs are not exclusively governed by transcriptional changes but also by protein abundance and post-translational modifications making direct causal inference from data difficult. However, biomedical research over the last decades has uncovered a plethora of causal signaling cascades that can be used to identify good candidates explaining a specific set of transcriptional changes.

Methods: We take a Bayesian approach to integrate gene expression profiling with a causal graph of molecular interactions constructed from prior biological knowledge. In addition, we define the biological context of a specific interaction by the corresponding Medical Subject Headings terms. The Bayesian network can be queried to suggest upstream regulators that can be causally linked to the altered expression profile.

Results: Our approach will treat candidate regulators in the right biological context preferentially, enables hierarchical exploration of resulting hypotheses and takes the complete network of causal relationships into account to arrive at the best set of upstream regulators. We demonstrate the power of our method on distinct biological datasets, namely response to dexamethasone treatment, stem cell differentiation and a neuropathic pain model. In all cases relevant biological insights could be validated.

Availability And Implementation: Source code for the method is available upon request.
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http://dx.doi.org/10.1093/bioinformatics/btt557DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5994944PMC
December 2013

Assessing the translatability of in vivo cardiotoxicity mechanisms to in vitro models using causal reasoning.

BMC Pharmacol Toxicol 2013 Sep 6;14:46. Epub 2013 Sep 6.

Compound Safety Prediction, Pfizer Inc,, Groton, CT, USA.

Drug-induced cardiac toxicity has been implicated in 31% of drug withdrawals in the USA. The fact that the risk for cardiac-related adverse events goes undetected in preclinical studies for so many drugs underscores the need for better, more predictive in vitro safety screens to be deployed early in the drug discovery process. Unfortunately, many questions remain about the ability to accurately translate findings from simple cellular systems to the mechanisms that drive toxicity in the complex in vivo environment. In this study, we analyzed translatability of cardiotoxic effects for a diverse set of drugs from rodents to two different cell systems (rat heart tissue-derived cells (H9C2) and primary rat cardiomyocytes (RCM)) based on their transcriptional response. To unravel the altered pathway, we applied a novel computational systems biology approach, the Causal Reasoning Engine (CRE), to infer upstream molecular events causing the observed gene expression changes. By cross-referencing the cardiotoxicity annotations with the pathway analysis, we found evidence of mechanistic convergence towards common molecular mechanisms regardless of the cardiotoxic phenotype. We also experimentally verified two specific molecular hypotheses that translated well from in vivo to in vitro (Kruppel-like factor 4, KLF4 and Transforming growth factor beta 1, TGFB1) supporting the validity of the predictions of the computational pathway analysis. In conclusion, this work demonstrates the use of a novel systems biology approach to predict mechanisms of toxicity such as KLF4 and TGFB1 that translate from in vivo to in vitro. We also show that more complex in vitro models such as primary rat cardiomyocytes may not offer any advantage over simpler models such as immortalized H9C2 cells in terms of translatability to in vivo effects if we consider the right endpoints for the model. Further assessment and validation of the generated molecular hypotheses would greatly enhance our ability to design predictive in vitro cardiotoxicity assays.
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http://dx.doi.org/10.1186/2050-6511-14-46DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3846863PMC
September 2013

HDAC inhibitors attenuate the development of hypersensitivity in models of neuropathic pain.

Pain 2013 Sep 18;154(9):1668-1679. Epub 2013 May 18.

King's College London, Wolfson Centre for Age-Related Diseases, London SE1 1UL, United Kingdom Pain Research Group, Department of Surgery and Cancer, Imperial College London, London SW10 9NH, United Kingdom Neusentis, Pfizer Worldwide R&D, Cambridge CB21 6GS, United Kingdom King's College London, Institute of Psychiatry, Centre for the Cellular Basis of Behaviour, London SE5 9NU, United Kingdom UCL Genomics, UCL Cancer Institute and Wolfson Institute for Biomedical Research, London WC1E 6BT, United Kingdom Computational Sciences Center of Emphasis, Pfizer Worldwide Research and Development, Cambridge, MA, United States.

Histone deacetylase inhibitors (HDACIs) interfere with the epigenetic process of histone acetylation and are known to have analgesic properties in models of chronic inflammatory pain. The aim of this study was to determine whether these compounds could also affect neuropathic pain. Different class I HDACIs were delivered intrathecally into rat spinal cord in models of traumatic nerve injury and antiretroviral drug-induced peripheral neuropathy (stavudine, d4T). Mechanical and thermal hypersensitivity was attenuated by 40% to 50% as a result of HDACI treatment, but only if started before any insult. The drugs globally increased histone acetylation in the spinal cord, but appeared to have no measurable effects in relevant dorsal root ganglia in this treatment paradigm, suggesting that any potential mechanism should be sought in the central nervous system. Microarray analysis of dorsal cord RNA revealed the signature of the specific compound used (MS-275) and suggested that its main effect was mediated through HDAC1. Taken together, these data support a role for histone acetylation in the emergence of neuropathic pain.
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http://dx.doi.org/10.1016/j.pain.2013.05.021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3763368PMC
September 2013

Novel pancreatic endocrine maturation pathways identified by genomic profiling and causal reasoning.

PLoS One 2013 13;8(2):e56024. Epub 2013 Feb 13.

Pfizer Regenerative Medicine, Cambridge, United Kingdom.

We have used a previously unavailable model of pancreatic development, derived in vitro from human embryonic stem cells, to capture a time-course of gene, miRNA and histone modification levels in pancreatic endocrine cells. We investigated whether it is possible to better understand, and hence control, the biological pathways leading to pancreatic endocrine formation by analysing this information and combining it with the available scientific literature to generate models using a casual reasoning approach. We show that the embryonic stem cell differentiation protocol is highly reproducible in producing endocrine precursor cells and generates cells that recapitulate many aspects of human embryonic pancreas development, including maturation into functional endocrine cells when transplanted into recipient animals. The availability of whole genome gene and miRNA expression data from the early stages of human pancreatic development will be of great benefit to those in the fields of developmental biology and diabetes research. Our causal reasoning algorithm suggested the involvement of novel gene networks, such as NEUROG3/E2F1/KDM5B and SOCS3/STAT3/IL-6, in endocrine cell development We experimentally investigated the role of the top-ranked prediction by showing that addition of exogenous IL-6 could affect the expression of the endocrine progenitor genes NEUROG3 and NKX2.2.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0056024PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3572136PMC
August 2013

Genes contributing to pain sensitivity in the normal population: an exome sequencing study.

PLoS Genet 2012 20;8(12):e1003095. Epub 2012 Dec 20.

Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom.

Sensitivity to pain varies considerably between individuals and is known to be heritable. Increased sensitivity to experimental pain is a risk factor for developing chronic pain, a common and debilitating but poorly understood symptom. To understand mechanisms underlying pain sensitivity and to search for rare gene variants (MAF<5%) influencing pain sensitivity, we explored the genetic variation in individuals' responses to experimental pain. Quantitative sensory testing to heat pain was performed in 2,500 volunteers from TwinsUK (TUK): exome sequencing to a depth of 70× was carried out on DNA from singletons at the high and low ends of the heat pain sensitivity distribution in two separate subsamples. Thus in TUK1, 101 pain-sensitive and 102 pain-insensitive were examined, while in TUK2 there were 114 and 96 individuals respectively. A combination of methods was used to test the association between rare variants and pain sensitivity, and the function of the genes identified was explored using network analysis. Using causal reasoning analysis on the genes with different patterns of SNVs by pain sensitivity status, we observed a significant enrichment of variants in genes of the angiotensin pathway (Bonferroni corrected p = 3.8×10(-4)). This pathway is already implicated in animal models and human studies of pain, supporting the notion that it may provide fruitful new targets in pain management. The approach of sequencing extreme exome variation in normal individuals has provided important insights into gene networks mediating pain sensitivity in humans and will be applicable to other common complex traits.
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http://dx.doi.org/10.1371/journal.pgen.1003095DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3527205PMC
May 2013

Silencing of enzymes involved in ceramide biosynthesis causes distinct global alterations of lipid homeostasis and gene expression.

J Lipid Res 2012 Aug 23;53(8):1459-71. Epub 2012 May 23.

Department of Cardiovascular, Metabolic, and Endocrine Diseases, Pfizer Inc., Cambridge, MA, USA.

Dysregulation of ceramide synthesis has been associated with metabolic disorders such as atherosclerosis and diabetes. We examined the changes in lipid homeostasis and gene expression in Huh7 hepatocytes when the synthesis of ceramide is perturbed by knocking down serine pal mitoyltransferase subunits 1, 2, and 3 (SPTLC123) or dihydroceramide desaturase 1 (DEGS1). Although knocking down all SPTLC subunits is necessary to reduce total ceramides significantly, depleting DEGS1 is sufficient to produce a similar outcome. Lipidomic analysis of distribution and speciation of multiple lipid classes indicates an increase in phospholipids in SPTLC123-silenced cells, whereas DEGS1 depletion leads to the accumulation of sphingolipid intermediates, free fatty acids, and diacylglycerol. When cer amide synthesis is disrupted, the transcriptional profiles indicate inhibition in biosynthetic processes, downregulation of genes involved in general endomembrane trafficking, and upregulation of endocytosis and endosomal recycling. SPTLC123 silencing strongly affects the expression of genes involved with lipid metabolism. Changes in amino acid, sugar, and nucleotide metabolism, as well as vesicle trafficking between organelles, are more prominent in DEGS1-silenced cells. These studies are the first to provide a direct and comprehensive understanding at the lipidomic and transcriptomic levels of how Huh7 hepatocytes respond to changes in the inhibition of ceramide synthesis.
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http://dx.doi.org/10.1194/jlr.M020941DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3540863PMC
August 2012

Correlation set analysis: detecting active regulators in disease populations using prior causal knowledge.

BMC Bioinformatics 2012 Mar 23;13:46. Epub 2012 Mar 23.

Bioinformatics Graduate Program, and Department of Biomedical Engineering, Boston University, 44 Cummington Street, Boston, MA 02215, USA.

Background: Identification of active causal regulators is a crucial problem in understanding mechanism of diseases or finding drug targets. Methods that infer causal regulators directly from primary data have been proposed and successfully validated in some cases. These methods necessarily require very large sample sizes or a mix of different data types. Recent studies have shown that prior biological knowledge can successfully boost a method's ability to find regulators.

Results: We present a simple data-driven method, Correlation Set Analysis (CSA), for comprehensively detecting active regulators in disease populations by integrating co-expression analysis and a specific type of literature-derived causal relationships. Instead of investigating the co-expression level between regulators and their regulatees, we focus on coherence of regulatees of a regulator. Using simulated datasets we show that our method performs very well at recovering even weak regulatory relationships with a low false discovery rate. Using three separate real biological datasets we were able to recover well known and as yet undescribed, active regulators for each disease population. The results are represented as a rank-ordered list of regulators, and reveals both single and higher-order regulatory relationships.

Conclusions: CSA is an intuitive data-driven way of selecting directed perturbation experiments that are relevant to a disease population of interest and represent a starting point for further investigation. Our findings demonstrate that combining co-expression analysis on regulatee sets with a literature-derived network can successfully identify causal regulators and help develop possible hypothesis to explain disease progression.
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http://dx.doi.org/10.1186/1471-2105-13-46DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3382432PMC
March 2012