Publications by authors named "Gopal Peddinti"

10 Publications

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Characterizing the Quality of Insight by Interactions: A Case Study.

IEEE Trans Vis Comput Graph 2020 Mar 2. Epub 2020 Mar 2.

Understanding the quality of insight has become increasingly important with the trend of allowing users to post comments during visual exploration, yet approaches for qualifying insight are rare. This paper presents a case study to investigate the possibility of characterizing the quality of insight via the interactions performed. To do this, we devised the interaction of a visualization tool-MediSyn-for insight generation. MediSyn supports five types of interactions: selecting, connecting, elaborating, exploring, and sharing. We evaluated MediSyn with 14 participants by allowing them to freely explore the data and generate insights. We then extracted seven interaction patterns from their interaction logs and correlated the patterns to four aspects of insight quality. The results show the possibility of qualifying insights via interactions. Among other findings, exploration actions can lead to unexpected insights; the drill-down pattern tends to increase the domain values of insights. A qualitative analysis shows that using domain knowledge to guide exploration can positively affect the domain value of derived insights. We discuss the study's implications, lessons learned, and future research opportunities.
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http://dx.doi.org/10.1109/TVCG.2020.2977634DOI Listing
March 2020

Deficient Endoplasmic Reticulum-Mitochondrial Phosphatidylserine Transfer Causes Liver Disease.

Cell 2019 05;177(4):881-895.e17

Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain; Departament de Bioquímica i Biomedicina Molecular, Facultat de Biología, Barcelona, Spain; CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain. Electronic address:

Non-alcoholic fatty liver is the most common liver disease worldwide. Here, we show that the mitochondrial protein mitofusin 2 (Mfn2) protects against liver disease. Reduced Mfn2 expression was detected in liver biopsies from patients with non-alcoholic steatohepatitis (NASH). Moreover, reduced Mfn2 levels were detected in mouse models of steatosis or NASH, and its re-expression in a NASH mouse model ameliorated the disease. Liver-specific ablation of Mfn2 in mice provoked inflammation, triglyceride accumulation, fibrosis, and liver cancer. We demonstrate that Mfn2 binds phosphatidylserine (PS) and can specifically extract PS into membrane domains, favoring PS transfer to mitochondria and mitochondrial phosphatidylethanolamine (PE) synthesis. Consequently, hepatic Mfn2 deficiency reduces PS transfer and phospholipid synthesis, leading to endoplasmic reticulum (ER) stress and the development of a NASH-like phenotype and liver cancer. Ablation of Mfn2 in liver reveals that disruption of ER-mitochondrial PS transfer is a new mechanism involved in the development of liver disease.
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http://dx.doi.org/10.1016/j.cell.2019.04.010DOI Listing
May 2019

1-Hour Post-OGTT Glucose Improves the Early Prediction of Type 2 Diabetes by Clinical and Metabolic Markers.

J Clin Endocrinol Metab 2019 04;104(4):1131-1140

Folkhälsan Research Center, Helsinki, Finland.

Context: Early prediction of dysglycemia is crucial to prevent progression to type 2 diabetes. The 1-hour postload plasma glucose (PG) is reported to be a better predictor of dysglycemia than fasting plasma glucose (FPG), 2-hour PG, or glycated hemoglobin (HbA1c).

Objective: To evaluate the predictive performance of clinical markers, metabolites, HbA1c, and PG and serum insulin (INS) levels during a 75-g oral glucose tolerance test (OGTT).

Design And Setting: We measured PG and INS levels at 0, 30, 60, and 120 minutes during an OGTT in 543 participants in the Botnia Prospective Study, 146 of whom progressed to type 2 diabetes within a 10-year follow-up period. Using combinations of variables, we evaluated 1527 predictive models for progression to type 2 diabetes.

Results: The 1-hour PG outperformed every individual marker except 30-minute PG or mannose, whose predictive performances were lower but not significantly worse. HbA1c was inferior to 1-hour PG according to DeLong test P value but not false discovery rate. Combining the metabolic markers with PG measurements and HbA1c significantly improved the predictive models, and mannose was found to be a robust metabolic marker.

Conclusions: The 1-hour PG, alone or in combination with metabolic markers, is a robust predictor for determining the future risk of type 2 diabetes, outperforms the 2-hour PG, and is cheaper to measure than metabolites. Metabolites add to the predictive value of PG and HbA1c measurements. Shortening the standard 75-g OGTT to 1 hour improves its predictive value and clinical usability.
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http://dx.doi.org/10.1210/jc.2018-01828DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6382453PMC
April 2019

Diacetyl control during brewery fermentation via adaptive laboratory engineering of the lager yeast Saccharomyces pastorianus.

J Ind Microbiol Biotechnol 2018 Dec 10;45(12):1103-1112. Epub 2018 Oct 10.

VTT Technical Research Centre of Finland Ltd, Tietotie 2, VTT, P.O. Box 1000, FI-02044, Espoo, Finland.

Diacetyl contributes to the flavor profile of many fermented products. Its typical buttery flavor is considered as an off flavor in lager-style beers, and its removal has a major impact on time and energy expenditure in breweries. Here, we investigated the possibility of lowering beer diacetyl levels through evolutionary engineering of lager yeast for altered synthesis of α-acetolactate, the precursor of diacetyl. Cells were exposed repeatedly to a sub-lethal level of chlorsulfuron, which inhibits the acetohydroxy acid synthase responsible for α-acetolactate production. Initial screening of 7 adapted isolates showed a lower level of diacetyl during wort fermentation and no apparent negative influence on fermentation rate or alcohol yield. Pilot-scale fermentation was carried out with one isolate and results confirmed the positive effect of chlorsulfuron adaptation. Diacetyl levels were over 60% lower at the end of primary fermentation relative to the non-adapted lager yeast and no significant change in fermentation performance or volatile flavor profile was observed due to the adaptation. Whole-genome sequencing revealed a non-synonymous SNP in the ILV2 gene of the adapted isolate. This mutation is known to confer general tolerance to sulfonylurea compounds, and is the most likely cause of the improved tolerance. Adaptive laboratory evolution appears to be a natural, simple and cost-effective strategy for diacetyl control in brewing.
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http://dx.doi.org/10.1007/s10295-018-2087-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6267509PMC
December 2018

Validation and Automation of a High-Throughput Multitargeted Method for Semiquantification of Endogenous Metabolites from Different Biological Matrices Using Tandem Mass Spectrometry.

Metabolites 2018 Aug 5;8(3). Epub 2018 Aug 5.

Metabolomics Unit, Institute for Molecular Medicine Finland (FIMM), University of Helsinki, HiLIFE, Tukholmankatu 8, Biomedicum 2U, 00290 Helsinki, Finland.

The use of metabolomics profiling to understand the metabolism under different physiological states has increased in recent years, which created the need for robust analytical platforms. Here, we present a validated method for targeted and semiquantitative analysis of 102 polar metabolites that cover major metabolic pathways from 24 classes in a single 17.5-min assay. The method has been optimized for a wide range of biological matrices from various organisms, and involves automated sample preparation and data processing using an inhouse developed R-package. To ensure reliability, the method was validated for accuracy, precision, selectivity, specificity, linearity, recovery, and stability according to European Medicines Agency guidelines. We demonstrated an excellent repeatability of retention times (CV < 4%), calibration curves (R² ≥ 0.980) in their respective wide dynamic concentration ranges (CV < 3%), and concentrations (CV < 25%) of quality control samples interspersed within 25 batches analyzed over a period of one year. The robustness was demonstrated through a high correlation between metabolite concentrations measured using our method and the NIST reference values (R² = 0.967), including cross-platform comparability against the BIOCRATES AbsoluteIDQp180 kit (R² = 0.975) and NMR analyses (R² = 0.884). We have shown that our method can be successfully applied in many biomedical research fields and clinical trials, including epidemiological studies for biomarker discovery. In summary, a thorough validation demonstrated that our method is reproducible, robust, reliable, and suitable for metabolomics studies.
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http://dx.doi.org/10.3390/metabo8030044DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6161248PMC
August 2018

Drug Target Commons: A Community Effort to Build a Consensus Knowledge Base for Drug-Target Interactions.

Cell Chem Biol 2018 02 21;25(2):224-229.e2. Epub 2017 Dec 21.

Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014 Helsinki, Finland; Department of Mathematics and Statistics, University of Turku, 20014 Turku, Finland. Electronic address:

Knowledge of the full target space of bioactive substances, approved and investigational drugs as well as chemical probes, provides important insights into therapeutic potential and possible adverse effects. The existing compound-target bioactivity data resources are often incomparable due to non-standardized and heterogeneous assay types and variability in endpoint measurements. To extract higher value from the existing and future compound target-profiling data, we implemented an open-data web platform, named Drug Target Commons (DTC), which features tools for crowd-sourced compound-target bioactivity data annotation, standardization, curation, and intra-resource integration. We demonstrate the unique value of DTC with several examples related to both drug discovery and drug repurposing applications and invite researchers to join this community effort to increase the reuse and extension of compound bioactivity data.
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http://dx.doi.org/10.1016/j.chembiol.2017.11.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5814751PMC
February 2018

Early metabolic markers identify potential targets for the prevention of type 2 diabetes.

Diabetologia 2017 09 8;60(9):1740-1750. Epub 2017 Jun 8.

Institute for Molecular Medicine Finland (FIMM), Nordic EMBL Partnership for Molecular Medicine, University of Helsinki, Helsinki, Finland.

Aims/hypothesis: The aims of this study were to evaluate systematically the predictive power of comprehensive metabolomics profiles in predicting the future risk of type 2 diabetes, and to identify a panel of the most predictive metabolic markers.

Methods: We applied an unbiased systems medicine approach to mine metabolite combinations that provide added value in predicting the future incidence of type 2 diabetes beyond known risk factors. We performed mass spectrometry-based targeted, as well as global untargeted, metabolomics, measuring a total of 568 metabolites, in a Finnish cohort of 543 non-diabetic individuals from the Botnia Prospective Study, which included 146 individuals who progressed to type 2 diabetes by the end of a 10 year follow-up period. Multivariate logistic regression was used to assess statistical associations, and regularised least-squares modelling was used to perform machine learning-based risk classification and marker selection. The predictive performance of the machine learning models and marker panels was evaluated using repeated nested cross-validation, and replicated in an independent French cohort of 1044 individuals including 231 participants who progressed to type 2 diabetes during a 9 year follow-up period in the DESIR (Data from an Epidemiological Study on the Insulin Resistance Syndrome) study.

Results: Nine metabolites were negatively associated (potentially protective) and 25 were positively associated with progression to type 2 diabetes. Machine learning models based on the entire metabolome predicted progression to type 2 diabetes (area under the receiver operating characteristic curve, AUC = 0.77) significantly better than the reference model based on clinical risk factors alone (AUC = 0.68; DeLong's p = 0.0009). The panel of metabolic markers selected by the machine learning-based feature selection also significantly improved the predictive performance over the reference model (AUC = 0.78; p = 0.00019; integrated discrimination improvement, IDI = 66.7%). This approach identified novel predictive biomarkers, such as α-tocopherol, bradykinin hydroxyproline, X-12063 and X-13435, which showed added value in predicting progression to type 2 diabetes when combined with known biomarkers such as glucose, mannose and α-hydroxybutyrate and routinely used clinical risk factors.

Conclusions/interpretation: This study provides a panel of novel metabolic markers for future efforts aimed at the prevention of type 2 diabetes.
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http://dx.doi.org/10.1007/s00125-017-4325-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5552834PMC
September 2017

Seed-effect modeling improves the consistency of genome-wide loss-of-function screens and identifies synthetic lethal vulnerabilities in cancer cells.

Genome Med 2017 06 1;9(1):51. Epub 2017 Jun 1.

Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.

Background: Genome-wide loss-of-function profiling is widely used for systematic identification of genetic dependencies in cancer cells; however, the poor reproducibility of RNA interference (RNAi) screens has been a major concern due to frequent off-target effects. Currently, a detailed understanding of the key factors contributing to the sub-optimal consistency is still a lacking, especially on how to improve the reliability of future RNAi screens by controlling for factors that determine their off-target propensity.

Methods: We performed a systematic, quantitative analysis of the consistency between two genome-wide shRNA screens conducted on a compendium of cancer cell lines, and also compared several gene summarization methods for inferring gene essentiality from shRNA level data. We then devised novel concepts of seed essentiality and shRNA family, based on seed region sequences of shRNAs, to study in-depth the contribution of seed-mediated off-target effects to the consistency of the two screens. We further investigated two seed-sequence properties, seed pairing stability, and target abundance in terms of their capability to minimize the off-target effects in post-screening data analysis. Finally, we applied this novel methodology to identify genetic interactions and synthetic lethal partners of cancer drivers, and confirmed differential essentiality phenotypes by detailed CRISPR/Cas9 experiments.

Results: Using the novel concepts of seed essentiality and shRNA family, we demonstrate how genome-wide loss-of-function profiling of a common set of cancer cell lines can be actually made fairly reproducible when considering seed-mediated off-target effects. Importantly, by excluding shRNAs having higher propensity for off-target effects, based on their seed-sequence properties, one can remove noise from the genome-wide shRNA datasets. As a translational application case, we demonstrate enhanced reproducibility of genetic interaction partners of common cancer drivers, as well as identify novel synthetic lethal partners of a major oncogenic driver, PIK3CA, supported by a complementary CRISPR/Cas9 experiment.

Conclusions: We provide practical guidelines for improved design and analysis of genome-wide loss-of-function profiling and demonstrate how this novel strategy can be applied towards improved mapping of genetic dependencies of cancer cells to aid development of targeted anticancer treatments.
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http://dx.doi.org/10.1186/s13073-017-0440-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5452371PMC
June 2017

C-SPADE: a web-tool for interactive analysis and visualization of drug screening experiments through compound-specific bioactivity dendrograms.

Nucleic Acids Res 2017 07;45(W1):W495-W500

Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland.

The advent of polypharmacology paradigm in drug discovery calls for novel chemoinformatic tools for analyzing compounds' multi-targeting activities. Such tools should provide an intuitive representation of the chemical space through capturing and visualizing underlying patterns of compound similarities linked to their polypharmacological effects. Most of the existing compound-centric chemoinformatics tools lack interactive options and user interfaces that are critical for the real-time needs of chemical biologists carrying out compound screening experiments. Toward that end, we introduce C-SPADE, an open-source exploratory web-tool for interactive analysis and visualization of drug profiling assays (biochemical, cell-based or cell-free) using compound-centric similarity clustering. C-SPADE allows the users to visually map the chemical diversity of a screening panel, explore investigational compounds in terms of their similarity to the screening panel, perform polypharmacological analyses and guide drug-target interaction predictions. C-SPADE requires only the raw drug profiling data as input, and it automatically retrieves the structural information and constructs the compound clusters in real-time, thereby reducing the time required for manual analysis in drug development or repurposing applications. The web-tool provides a customizable visual workspace that can either be downloaded as figure or Newick tree file or shared as a hyperlink with other users. C-SPADE is freely available at http://cspade.fimm.fi/.
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http://dx.doi.org/10.1093/nar/gkx384DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5570255PMC
July 2017

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data.

Lancet Oncol 2017 01 16;18(1):132-142. Epub 2016 Nov 16.

Department of Pharmacology and Computational Biosciences Program, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA; University of Colorado Comprehensive Cancer Center, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA. Electronic address:

Background: Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease.

Methods: Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone.

Findings: 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·39-4·62, p<0·0001; reference model: 2·56, 1·85-3·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified predictive clinical variables and revealed aspartate aminotransferase as an important, albeit previously under-reported, prognostic biomarker.

Interpretation: Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.

Funding: Sanofi US Services, Project Data Sphere.
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http://dx.doi.org/10.1016/S1470-2045(16)30560-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5217180PMC
January 2017