1
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Chorbadjiev L, Cokol M, Weinstein Z, Shi K, Fleisch C, Dimitrov N, Mladenov S, Xu S, Hall J, Ford S, Lee YH, Yamrom B, Marks S, Munoz A, Lash A, Volfovsky N, Iossifov I. The Genotype and Phenotypes in Families (GPF) platform manages the large and complex data at SFARI. bioRxiv 2024:2024.02.08.579330. [PMID: 38370639 PMCID: PMC10871337 DOI: 10.1101/2024.02.08.579330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
The exploration of genotypic variants impacting phenotypes is a cornerstone in genetics research. The emergence of vast collections containing deeply genotyped and phenotyped families has made it possible to pursue the search for variants associated with complex diseases. However, managing these large-scale datasets requires specialized computational tools tailored to organize and analyze the extensive data. GPF (Genotypes and Phenotypes in Families) is an open-source platform ( https://github.com/iossifovlab/gpf ) that manages genotypes and phenotypes derived from collections of families. The GPF interface allows interactive exploration of genetic variants, enrichment analysis for de novo mutations, and phenotype/genotype association tools. In addition, GPF allows researchers to share their data securely with the broader scientific community. GPF is used to disseminate two large-scale family collection datasets (SSC, SPARK) for the study of autism funded by the SFARI foundation. However, GPF is versatile and can manage genotypic data from other small or large family collections. Our GPF-SFARI GPF instance ( https://gpf.sfari.org/ ) provides protected access to comprehensive genotypic and phenotypic data for the SSC and SPARK. In addition, GPF-SFARI provides public access to an extensive collection of de novo mutations identified in individuals with autism and related disorders and to gene-level statistics of the protected datasets characterizing the genes' roles in autism. Here, we highlight the primary features of GPF within the context of GPF-SFARI.
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2
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Yan G, Luna A, Wang H, Bozorgui B, Li X, Sanchez M, Dereli Z, Kahraman N, Kara G, Chen X, Zheng C, McGrail D, Sahni N, Lu Y, Babur O, Cokol M, Lim B, Ozpolat B, Sander C, Mills GB, Korkut A. BET inhibition induces vulnerability to MCL1 targeting through upregulation of fatty acid synthesis pathway in breast cancer. Cell Rep 2022; 40:111304. [PMID: 36103824 PMCID: PMC9523722 DOI: 10.1016/j.celrep.2022.111304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 05/06/2022] [Accepted: 08/10/2022] [Indexed: 11/12/2022] Open
Abstract
Therapeutic options for treatment of basal-like breast cancers remain limited. Here, we demonstrate that bromodomain and extra-terminal (BET) inhibition induces an adaptive response leading to MCL1 protein-driven evasion of apoptosis in breast cancer cells. Consequently, co-targeting MCL1 and BET is highly synergistic in breast cancer models. The mechanism of adaptive response to BET inhibition involves the upregulation of lipid synthesis enzymes including the rate-limiting stearoyl-coenzyme A (CoA) desaturase. Changes in lipid synthesis pathway are associated with increases in cell motility and membrane fluidity as well as re-localization and activation of HER2/EGFR. In turn, the HER2/EGFR signaling results in the accumulation of and vulnerability to the inhibition of MCL1. Drug response and genomics analyses reveal that MCL1 copy-number alterations are associated with effective BET and MCL1 co-targeting. The high frequency of MCL1 chromosomal amplifications (>30%) in basal-like breast cancers suggests that BET and MCL1 co-targeting may have therapeutic utility in this aggressive subtype of breast cancer. Yan et al. show that pharmacological co-targeting of MCL1 and BET is highly effective in breast cancer cells. The proposed combination therapy may be effective for treatment of patients with aggressive subtypes of breast cancers whose tumors carry genetic aberrations associated with cell-death evasion.
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Affiliation(s)
- Gonghong Yan
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Augustin Luna
- cBio Center, Department of Data Sciences, Dana Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Heping Wang
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Behnaz Bozorgui
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Xubin Li
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Maga Sanchez
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Zeynep Dereli
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Nermin Kahraman
- Department of Experimental Therapeutics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Goknur Kara
- Department of Experimental Therapeutics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Xiaohua Chen
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Caishang Zheng
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Daniel McGrail
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Nidhi Sahni
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Epigenetics and Molecular Carcinogenesis, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Yiling Lu
- Department of Genomic Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ozgun Babur
- Computer Science, College of Science and Mathematics, University of Massachusetts Boston, Boston, MA 02125, USA
| | - Murat Cokol
- Axcella Therapeutics, Cambridge, MA 02139, USA
| | - Bora Lim
- Breast Cancer Research Program, Dan L Duncan Comprehensive Cancer Center, Houston, TX 77030, USA
| | - Bulent Ozpolat
- Department of Experimental Therapeutics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Chris Sander
- cBio Center, Department of Data Sciences, Dana Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Gordon B Mills
- Department of Cell, Development and Cancer Biology, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, USA
| | - Anil Korkut
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
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3
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Daou N, Viader A, Cokol M, Nitzel A, Chakravarthy MV, Afeyan R, Tramontin T, Marukian S, Hamill MJ. A novel, multitargeted endogenous metabolic modulator composition impacts metabolism, inflammation, and fibrosis in nonalcoholic steatohepatitis-relevant primary human cell models. Sci Rep 2021; 11:11861. [PMID: 34088912 PMCID: PMC8178416 DOI: 10.1038/s41598-021-88913-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 04/19/2021] [Indexed: 02/07/2023] Open
Abstract
Nonalcoholic steatohepatitis (NASH) is a complex metabolic disease of heterogeneous and multifactorial pathogenesis that may benefit from coordinated multitargeted interventions. Endogenous metabolic modulators (EMMs) encompass a broad set of molecular families, including amino acids and related metabolites and precursors. EMMs often serve as master regulators and signaling agents for metabolic pathways throughout the body and hold the potential to impact a complex metabolic disease like NASH by targeting a multitude of pathologically relevant biologies. Here, we describe a study of a novel EMM composition comprising five amino acids and an amino acid derivative (Leucine, Isoleucine, Valine, Arginine, Glutamine, and N-acetylcysteine [LIVRQNac]) and its systematic evaluation across multiple NASH-relevant primary human cell model systems, including hepatocytes, macrophages, and stellate cells. In these model systems, LIVRQNac consistently and simultaneously impacted biology associated with all three core pathophysiological features of NASH—metabolic, inflammatory, and fibrotic. Importantly, it was observed that while the individual constituent amino acids in LIVRQNac can impact specific NASH-related phenotypes in select cell systems, the complete combination was necessary to impact the range of disease-associated drivers examined. These findings highlight the potential of specific and potent multitargeted amino acid combinations for the treatment of NASH.
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Affiliation(s)
- Nadine Daou
- Axcella Health Inc., 840 Memorial Drive, Cambridge, MA, 02139, USA
| | | | - Murat Cokol
- Axcella Health Inc., 840 Memorial Drive, Cambridge, MA, 02139, USA
| | - Arianna Nitzel
- Axcella Health Inc., 840 Memorial Drive, Cambridge, MA, 02139, USA
| | | | | | | | | | - Michael J Hamill
- Axcella Health Inc., 840 Memorial Drive, Cambridge, MA, 02139, USA.
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4
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Geisinger E, Mortman NJ, Dai Y, Cokol M, Syal S, Farinha A, Fisher DG, Tang AY, Lazinski DW, Wood S, Anthony J, van Opijnen T, Isberg RR. Antibiotic susceptibility signatures identify potential antimicrobial targets in the Acinetobacter baumannii cell envelope. Nat Commun 2020; 11:4522. [PMID: 32908144 PMCID: PMC7481262 DOI: 10.1038/s41467-020-18301-2] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 08/13/2020] [Indexed: 11/13/2022] Open
Abstract
A unique, protective cell envelope contributes to the broad drug resistance of the nosocomial pathogen Acinetobacter baumannii. Here we use transposon insertion sequencing to identify A. baumannii mutants displaying altered susceptibility to a panel of diverse antibiotics. By examining mutants with antibiotic susceptibility profiles that parallel mutations in characterized genes, we infer the function of multiple uncharacterized envelope proteins, some of which have roles in cell division or cell elongation. Remarkably, mutations affecting a predicted cell wall hydrolase lead to alterations in lipooligosaccharide synthesis. In addition, the analysis of altered susceptibility signatures and antibiotic-induced morphology patterns allows us to predict drug synergies; for example, certain beta-lactams appear to work cooperatively due to their preferential targeting of specific cell wall assembly machineries. Our results indicate that the pathogen may be effectively inhibited by the combined targeting of multiple pathways critical for envelope growth.
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Affiliation(s)
- Edward Geisinger
- Department of Biology, Northeastern University, Boston, MA, 02115, USA.
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA, 02111, USA.
| | - Nadav J Mortman
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA, 02111, USA
| | - Yunfei Dai
- Department of Biology, Northeastern University, Boston, MA, 02115, USA
| | - Murat Cokol
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA, 02111, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
| | - Sapna Syal
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA, 02111, USA
| | - Andrew Farinha
- Department of Biology, Northeastern University, Boston, MA, 02115, USA
| | - Delaney G Fisher
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA, 02111, USA
| | - Amy Y Tang
- Department of Biology, Northeastern University, Boston, MA, 02115, USA
| | - David W Lazinski
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA, 02111, USA
| | - Stephen Wood
- Biology Department, Boston College, Chestnut Hill, MA, 02467, USA
| | - Jon Anthony
- Biology Department, Boston College, Chestnut Hill, MA, 02467, USA
| | - Tim van Opijnen
- Biology Department, Boston College, Chestnut Hill, MA, 02467, USA
| | - Ralph R Isberg
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA, 02111, USA.
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5
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Abstract
Combinations of three or more drugs are routinely used in various medical fields such as clinical oncology and infectious diseases to prevent resistance or to achieve synergistic therapeutic benefits. The very large number of possible high-order drug combinations presents a formidable challenge for discovering synergistic drug combinations. Here, we establish a guided screen to discover synergistic three-drug combinations. Using traditional checkerboard and recently developed diagonal methods, we experimentally measured all pairwise interactions among eight compounds in Erwinia amylovora, the causative agent of fire blight. Showing that synergy measurements of these two methods agree, we predicted synergy/antagonism scores for all possible three-drug combinations by averaging the synergy scores of pairwise interactions. We validated these predictions by experimentally measuring 35 three-drug interactions. Therefore, our guided screen for discovering three-drug synergies is (i) experimental screen of all pairwise interactions using diagonal method, (ii) averaging pairwise scores among components to predict three-drug interaction scores, (iii) experimental testing of top predictions. In our study, this strategy resulted in a five-fold reduction in screen size to find the most synergistic three-drug combinations.
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Affiliation(s)
- Melike Cokol-Cakmak
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Selim Cetiner
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Nurdan Erdem
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Feray Bakan
- Nanotechnology Research and Application Center, Sabanci University, Istanbul, Turkey
| | - Murat Cokol
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
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6
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Yilancioglu K, Cokol M. Design of high-order antibiotic combinations against M. tuberculosis by ranking and exclusion. Sci Rep 2019; 9:11876. [PMID: 31417151 PMCID: PMC6695482 DOI: 10.1038/s41598-019-48410-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2019] [Accepted: 08/05/2019] [Indexed: 12/20/2022] Open
Abstract
Combinations of more than two drugs are routinely used for the treatment of pathogens and tumors. High-order combinations may be chosen due to their non-overlapping resistance mechanisms or for favorable drug interactions. Synergistic/antagonistic interactions occur when the combination has a higher/lower effect than the sum of individual drug effects. The standard treatment of Mycobacterium tuberculosis (Mtb) is an additive cocktail of three drugs which have different targets. Herein, we experimentally measured all 190 pairwise interactions among 20 antibiotics against Mtb growth. We used the pairwise interaction data to rank all possible high-order combinations by strength of synergy/antagonism. We used drug interaction profile correlation as a proxy for drug similarity to establish exclusion criteria for ideal combination therapies. Using this ranking and exclusion design (R/ED) framework, we modeled ways to improve the standard 3-drug combination with the addition of new drugs. We applied this framework to find the best 4-drug combinations against drug-resistant Mtb by adding new exclusion criteria to R/ED. Finally, we modeled alternating 2-order combinations as a cycling treatment and found optimized regimens significantly reduced the overall effective dose. R/ED provides an adaptable framework for the design of high-order drug combinations against any pathogen or tumor.
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Affiliation(s)
- Kaan Yilancioglu
- Faculty of Engineering and Natural Sciences, Uskudar University, İstanbul, Turkey
| | - Murat Cokol
- Faculty of Engineering and Natural Sciences, Uskudar University, İstanbul, Turkey. .,Laboratory of Systems Pharmacology, Harvard Medical School, Boston, Massachusetts, USA. .,Axcella Health, Cambridge, Massachusetts, USA.
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7
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Abstract
Drugs may have synergistic or antagonistic interactions when combined. Checkerboard assays, where two drugs are combined in many doses, allow sensitive measurement of drug interactions. Here, we describe a protocol to measure the pairwise interactions among three antibiotics, in duplicate, in 5 days, using only two 96-well microplates and standard laboratory equipment.
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Affiliation(s)
- Melike Cokol-Cakmak
- Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, Istanbul, Turkey
| | - Murat Cokol
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA, USA. .,Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA.
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8
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Sokolov A, Ashenden S, Sahin N, Lewis R, Erdem N, Ozaltan E, Bender A, Roth FP, Cokol M. Characterizing ABC-Transporter Substrate-Likeness Using a Clean-Slate Genetic Background. Front Pharmacol 2019; 10:448. [PMID: 31105571 PMCID: PMC6494965 DOI: 10.3389/fphar.2019.00448] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 04/08/2019] [Indexed: 12/02/2022] Open
Abstract
Mutations in ATP Binding Cassette (ABC)-transporter genes can have major effects on the bioavailability and toxicity of the drugs that are ABC-transporter substrates. Consequently, methods to predict if a drug is an ABC-transporter substrate are useful for drug development. Such methods traditionally relied on literature curated collections of ABC-transporter dependent membrane transfer assays. Here, we used a single large-scale dataset of 376 drugs with relative efficacy on an engineered yeast strain with all ABC-transporter genes deleted (ABC-16), to explore the relationship between a drug’s chemical structure and ABC-transporter substrate-likeness. We represented a drug’s chemical structure by an array of substructure keys and explored several machine learning methods to predict the drug’s efficacy in an ABC-16 yeast strain. Gradient-Boosted Random Forest models outperformed all other methods with an AUC of 0.723. We prospectively validated the model using new experimental data and found significant agreement with predictions. Our analysis expands the previously reported chemical substructures associated with ABC-transporter substrates and provides an alternative means to investigate ABC-transporter substrate-likeness.
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Affiliation(s)
- Artem Sokolov
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, United States
| | - Stephanie Ashenden
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom.,Discovery Sciences, IMed Biotech Unit, AstraZeneca R&D, Cambridge, United Kingdom
| | - Nil Sahin
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul, Turkey.,Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Richard Lewis
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
| | - Nurdan Erdem
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul, Turkey
| | - Elif Ozaltan
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul, Turkey
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
| | - Frederick P Roth
- Donnelly Centre, University of Toronto, Toronto, ON, Canada.,Department of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada.,Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Canadian Institute for Advanced Research, Toronto, ON, Canada
| | - Murat Cokol
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, United States.,Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul, Turkey.,Donnelly Centre, University of Toronto, Toronto, ON, Canada.,Axcella Health, Cambridge, MA, United States
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9
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Katzir I, Cokol M, Aldridge BB, Alon U. Prediction of ultra-high-order antibiotic combinations based on pairwise interactions. PLoS Comput Biol 2019; 15:e1006774. [PMID: 30699106 PMCID: PMC6370231 DOI: 10.1371/journal.pcbi.1006774] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Revised: 02/11/2019] [Accepted: 01/10/2019] [Indexed: 11/19/2022] Open
Abstract
Drug combinations are a promising approach to achieve high efficacy at low doses and to overcome resistance. Drug combinations are especially useful when drugs cannot achieve effectiveness at tolerable doses, as occurs in cancer and tuberculosis (TB). However, discovery of effective drug combinations faces the challenge of combinatorial explosion, in which the number of possible combinations increases exponentially with the number of drugs and doses. A recent advance, called the dose model, uses a mathematical formula to overcome combinatorial explosion by reducing the problem to a feasible quadratic one: using data on drug pairs at a few doses, the dose model accurately predicts the effect of combinations of three and four drugs at all doses. The dose model has not yet been tested on higher-order combinations beyond four drugs. To address this, we measured the effect of combinations of up to ten antibiotics on E. coli growth, and of up to five tuberculosis (TB) drugs on the growth of M. tuberculosis. We find that the dose model accurately predicts the effect of these higher-order combinations, including cases of strong synergy and antagonism. This study supports the view that the interactions between drug pairs carries key information that largely determines higher-order interactions. Therefore, systematic study of pairwise drug interactions is a compelling strategy to prioritize drug regimens in high-dimensional spaces.
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Affiliation(s)
- Itay Katzir
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Murat Cokol
- Axcella Health, Cambridge, MA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston MA
| | - Bree B. Aldridge
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston MA
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA
- Department of Biomedical Engineering, Tufts University School of Engineering, Medford, MA
- * E-mail: (BBA); (UA)
| | - Uri Alon
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
- * E-mail: (BBA); (UA)
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10
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Cokol M, Li C, Chandrasekaran S. Chemogenomic model identifies synergistic drug combinations robust to the pathogen microenvironment. PLoS Comput Biol 2018; 14:e1006677. [PMID: 30596642 PMCID: PMC6329523 DOI: 10.1371/journal.pcbi.1006677] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 01/11/2019] [Accepted: 11/27/2018] [Indexed: 01/31/2023] Open
Abstract
Antibiotics need to be effective in diverse environments in vivo. However, the pathogen microenvironment can have a significant impact on antibiotic potency. Further, antibiotics are increasingly used in combinations to combat resistance, yet, the effect of microenvironments on drug-combination efficacy is unknown. To exhaustively explore the impact of diverse microenvironments on drug-combinations, here we develop a computational framework—Metabolism And GENomics-based Tailoring of Antibiotic regimens (MAGENTA). MAGENTA uses chemogenomic profiles of individual drugs and metabolic perturbations to predict synergistic or antagonistic drug-interactions in different microenvironments. We uncovered antibiotic combinations with robust synergy across nine distinct environments against both E. coli and A. baumannii by searching through 2556 drug-combinations of 72 drugs. MAGENTA also accurately predicted the change in efficacy of bacteriostatic and bactericidal drug-combinations during growth in glycerol media, which we confirmed experimentally in both microbes. Our approach identified genes in glycolysis and glyoxylate pathway as top predictors of synergy and antagonism respectively. Our systems approach enables tailoring of antibiotic therapies based on the pathogen microenvironment. The antibiotic resistance epidemic has created a pressing need to understand factors that influence antibiotic efficacy. An often-overlooked factor in the search for new treatments is the pathogen environment. Understanding the differences in pathogen sensitivity to antibiotics in lab conditions versus inside the host is necessary for translating new discoveries into the clinic. Hence, we experimentally measured the sensitivity of E. coli to drugs and drug combinations in different metabolic conditions. Our data revealed that the environment dramatically changes treatment potency. Each antibiotic class was affected uniquely by each metabolic condition. The large number of metabolic conditions inside the host greatly complicates the identification of effective therapies. To address this challenge, we present a computational approach called MAGENTA that accurately predicted efficacy of antibiotic regimens in different conditions, which we confirmed experimentally. Furthermore, we show that MAGENTA can be applied to other bacterial pathogens such as A. baumannii and M. tuberculosis without the need for generating expensive data in each organism. MAGENTA accurately predicted efficacy in the pathogen A. baumannii using data from E. coli by identifying genes that are common between the two bacteria. Our study revealed the significant yet predictable impact of environment on drug combination potency.
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Affiliation(s)
- Murat Cokol
- Axcella Health, Cambridge, Massachusetts, United States of America
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, Massachusetts, United States of America
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
- * E-mail: (SC); (MC)
| | - Chen Li
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
- * E-mail: (SC); (MC)
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11
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Weinstein ZB, Kuru N, Kiriakov S, Palmer AC, Khalil AS, Clemons PA, Zaman MH, Roth FP, Cokol M. Modeling the impact of drug interactions on therapeutic selectivity. Nat Commun 2018; 9:3452. [PMID: 30150706 PMCID: PMC6110842 DOI: 10.1038/s41467-018-05954-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2016] [Accepted: 07/27/2018] [Indexed: 01/12/2023] Open
Abstract
Combination therapies that produce synergistic growth inhibition are widely sought after for the pharmacotherapy of many pathological conditions. Therapeutic selectivity, however, depends on the difference between potency on disease-causing cells and potency on non-target cell types that cause toxic side effects. Here, we examine a model system of antimicrobial compound combinations applied to two highly diverged yeast species. We find that even though the drug interactions correlate between the two species, cell-type-specific differences in drug interactions are common and can dramatically alter the selectivity of compounds when applied in combination vs. single-drug activity-enhancing, diminishing, or inverting therapeutic windows. This study identifies drug combinations with enhanced cell-type-selectivity with a range of interaction types, which we experimentally validate using multiplexed drug-interaction assays for heterogeneous cell cultures. This analysis presents a model framework for evaluating drug combinations with increased efficacy and selectivity against pathogens or tumors.
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Affiliation(s)
- Zohar B Weinstein
- Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, Boston, MA, 02115, USA
| | - Nurdan Kuru
- Faculty of Engineering and Natural Sciences, Sabanci University, 34956, Istanbul, Turkey
| | - Szilvia Kiriakov
- Program in Molecular Biology, Cell Biology & Biochemistry, Boston University, Boston, MA, 02215, USA
- Biological Design Center, Boston University, Boston, MA, 02215, USA
| | - Adam C Palmer
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
| | - Ahmad S Khalil
- Biological Design Center, Boston University, Boston, MA, 02215, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
| | - Paul A Clemons
- Chemical Biology & Therapeutics Science Program, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Muhammad H Zaman
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
- Howard Hughes Medical Institute, Boston University, Boston, MA, 02215, USA
| | - Frederick P Roth
- Donnelly Centre and Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Lunenfeld-Tanenbaum Research Institute, Toronto, ON, M5S 3E1, Canada
- Canadian Institute for Advanced Research, Toronto, ON, M5S 3E1, Canada
| | - Murat Cokol
- Faculty of Engineering and Natural Sciences, Sabanci University, 34956, Istanbul, Turkey.
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA.
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12
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Abstract
A synergistic drug combination has a higher efficacy compared to the effects of individual drugs. Checkerboard assays, where drugs are combined in many doses, allow sensitive measurement of drug interactions. However, these assays are costly and do not scale well for measuring interaction among many drugs. Several recent studies have reported drug interaction measurements using a diagonal sampling of the traditional checkerboard assay. This alternative methodology greatly decreases the cost of drug interaction experiments and allows interaction measurement for combinations with many drugs. Here, we describe a protocol to measure the three pairwise interactions and one three-way interaction among three antibiotics in duplicate, in five days, using only three 96-well microplates and standard laboratory equipment. We present representative results showing that the three-antibiotic combination of Levofloxacin + Nalidixic Acid + Penicillin G is synergistic. Our protocol scales up to measure interactions among many drugs and in other biological contexts, allowing for efficient screens for multi-drug synergies against pathogens and tumors.
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Affiliation(s)
| | - Feray Bakan
- Nanotechnology Research and Application Center, Sabanci University
| | - Selim Cetiner
- Faculty of Engineering and Natural Sciences, Sabanci University
| | - Murat Cokol
- Faculty of Engineering and Natural Sciences, Sabanci University; Nanotechnology Research and Application Center, Sabanci University; Laboratory of Systems Pharmacology, Harvard Medical School;
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13
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Logsdon MM, Ho PY, Papavinasasundaram K, Richardson K, Cokol M, Sassetti CM, Amir A, Aldridge BB. A Parallel Adder Coordinates Mycobacterial Cell-Cycle Progression and Cell-Size Homeostasis in the Context of Asymmetric Growth and Organization. Curr Biol 2017; 27:3367-3374.e7. [PMID: 29107550 DOI: 10.1016/j.cub.2017.09.046] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Revised: 08/24/2017] [Accepted: 09/20/2017] [Indexed: 12/13/2022]
Abstract
In model bacteria, such as E. coli and B. subtilis, regulation of cell-cycle progression and cellular organization achieves consistency in cell size, replication dynamics, and chromosome positioning [1-3]. Mycobacteria elongate and divide asymmetrically, giving rise to significant variation in cell size and elongation rate among closely related cells [4, 5]. Given the physical asymmetry of mycobacteria, the models that describe coordination of cellular organization and cell-cycle progression in model bacteria are not directly translatable [1, 2, 6-8]. Here, we used time-lapse microscopy and fluorescent reporters of DNA replication and chromosome positioning to examine the coordination of growth, division, and chromosome dynamics at a single-cell level in Mycobacterium smegmatis (M. smegmatis) and Mycobacterium bovis Bacillus Calmette-Guérin (BCG). By analyzing chromosome and replisome localization, we demonstrated that chromosome positioning is asymmetric and proportional to cell size. Furthermore, we found that cellular asymmetry is maintained throughout the cell cycle and is not established at division. Using measurements and stochastic modeling of mycobacterial cell size and cell-cycle timing in both slow and fast growth conditions, we found that well-studied models of cell-size control are insufficient to explain the mycobacterial cell cycle. Instead, we showed that mycobacterial cell-cycle progression is regulated by an unprecedented mechanism involving parallel adders (i.e., constant growth increments) that start at replication initiation. Together, these adders enable mycobacterial populations to regulate cell size, growth, and heterogeneity in the face of varying environments.
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Affiliation(s)
- Michelle M Logsdon
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA 02111, USA; Sackler School of Graduate Biomedical Sciences, Tufts University School of Medicine, Boston, MA 02111, USA
| | - Po-Yi Ho
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Kadamba Papavinasasundaram
- Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worchester, MA 01655, USA
| | - Kirill Richardson
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA 02111, USA
| | - Murat Cokol
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA 02111, USA; Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Christopher M Sassetti
- Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worchester, MA 01655, USA
| | - Ariel Amir
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Bree B Aldridge
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA 02111, USA; Sackler School of Graduate Biomedical Sciences, Tufts University School of Medicine, Boston, MA 02111, USA; Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA; Department of Biomedical Engineering, Tufts University School of Engineering, Medford, MA 02155, USA.
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14
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Cokol M, Kuru N, Bicak E, Larkins-Ford J, Aldridge BB. Efficient measurement and factorization of high-order drug interactions in Mycobacterium tuberculosis. Sci Adv 2017; 3:e1701881. [PMID: 29026882 PMCID: PMC5636204 DOI: 10.1126/sciadv.1701881] [Citation(s) in RCA: 80] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 09/19/2017] [Indexed: 05/03/2023]
Abstract
Combinations of three or more drugs are used to treat many diseases, including tuberculosis. Thus, it is important to understand how synergistic or antagonistic drug interactions affect the efficacy of combination therapies. However, our understanding of high-order drug interactions is limited because of the lack of both efficient measurement methods and theoretical framework for analysis and interpretation. We developed an efficient experimental sampling and scoring method [diagonal measurement of n-way drug interactions (DiaMOND)] to measure drug interactions for combinations of any number of drugs. DiaMOND provides an efficient alternative to checkerboard assays, which are commonly used to measure drug interactions. We established a geometric framework to factorize high-order drug interactions into lower-order components, thereby establishing a road map of how to use lower-order measurements to predict high-order interactions. Our framework is a generalized Loewe additivity model for high-order drug interactions. Using DiaMOND, we identified and analyzed synergistic and antagonistic antibiotic combinations against Mycobacteriumtuberculosis. Efficient measurement and factorization of high-order drug interactions by DiaMOND are broadly applicable to other cell types and disease models.
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Affiliation(s)
- Murat Cokol
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA 02111, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
- Corresponding author. (M.C.); (B.B.A.)
| | - Nurdan Kuru
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
| | - Ece Bicak
- Master of Science Program in Biotechnology, Brandeis University, Waltham, MA 02453, USA
| | - Jonah Larkins-Ford
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA 02111, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
- Sackler School of Graduate Biomedical Sciences, Tufts University School of Medicine, Boston, MA 02111, USA
| | - Bree B. Aldridge
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA 02111, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
- Department of Biomedical Engineering, Tufts University School of Engineering, Medford, MA 02155, USA
- Corresponding author. (M.C.); (B.B.A.)
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15
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16
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Bakan F, Kara G, Cokol Cakmak M, Cokol M, Denkbas EB. Synthesis and characterization of amino acid-functionalized calcium phosphate nanoparticles for siRNA delivery. Colloids Surf B Biointerfaces 2017; 158:175-181. [PMID: 28689100 DOI: 10.1016/j.colsurfb.2017.06.028] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2017] [Revised: 06/18/2017] [Accepted: 06/20/2017] [Indexed: 01/05/2023]
Abstract
Small interfering RNAs (siRNA) are short nucleic acid fragments of about 20-27 nucleotides, which can inhibit the expression of specific genes. siRNA based RNAi technology has emerged as a promising method for the treatment of a variety of diseases. However, a major limitation in the therapeutic use of siRNA is its rapid degradation in plasma and cellular cytoplasm, resulting in short half-life. In addition, as siRNA molecules cannot penetrate into the cell efficiently, it is required to use a carrier system for its delivery. In this work, chemically and morphologically different calcium phosphate (CaP) nanoparticles, including spherical-like hydroxyapatite (HA-s), needle-like hydroxyapatite (HA-n) and calcium deficient hydroxyapatite (CDHA) nanoparticles were synthesized by the sol-gel technique and the effects of particle characteristics on the binding capacity of siRNA were investigated. In order to enhance the gene loading efficiency, the nanoparticles were functionalized with arginine and the morphological and their structural characteristics were analyzed. The addition of arginine did not significantly change the particle sizes; however, it provided a significantly increased binding of siRNA for all types of CaP nanoparticles, as revealed by spectrophotometric measurements analysis. Arginine functionalized HA-n nanoparticles showed the best binding behavior with siRNA among the other nanoparticles due to its high, positive zeta potential (+18.8mV) and high surface area of Ca++ rich "c" plane. MTT cytotoxicity assays demonstrated that all the nanoparticles tested herein were biocompatible. Our results suggest that high siRNA entrapment in each of the three modified non-toxic CaP nanoparticles make them promising candidates as a non-viral vector for delivering therapeutic siRNA molecules to treat cancer.
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Affiliation(s)
- Feray Bakan
- Sabanci University Nanotechnology Research and Application Center (SUNUM), 34956, Istanbul, Turkey.
| | - Goknur Kara
- Hacettepe University, Department of Chemistry, Biochemistry Division, 06800, Ankara, Turkey
| | - Melike Cokol Cakmak
- Sabanci University, Molecular Biology, Genetics and Bioengineering Program, 34956, Istanbul, Turkey
| | - Murat Cokol
- Tufts University School of Medicine, Department of Molecular Biology and Microbiology, Harvard Medical School, Laboratory of Systems Pharmacology, Boston, MA, USA
| | - Emir Baki Denkbas
- Hacettepe University, Department of Chemistry, Biochemistry Division, 06800, Ankara, Turkey
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17
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Mason DJ, Stott I, Ashenden S, Weinstein ZB, Karakoc I, Meral S, Kuru N, Bender A, Cokol M. Prediction of Antibiotic Interactions Using Descriptors Derived from Molecular Structure. J Med Chem 2017; 60:3902-3912. [PMID: 28383902 DOI: 10.1021/acs.jmedchem.7b00204] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Combination antibiotic therapies are clinically important in the fight against bacterial infections. However, the search space of drug combinations is large, making the identification of effective combinations a challenging task. Here, we present a computational framework that uses substructure profiles derived from the molecular structures of drugs and predicts antibiotic interactions. Using a previously published data set of 153 drug pairs, we showed that substructure profiles are useful in predicting synergy. We experimentally measured the interaction of 123 new drug pairs, as a prospective validation set for our approach, and identified 37 new synergistic pairs. Of the 12 pairs predicted to be synergistic, 10 were experimentally validated, corresponding to a 2.8-fold enrichment. Having thus validated our methodology, we produced a compendium of interaction predictions for all pairwise combinations among 100 antibiotics. Our methodology can make reliable antibiotic interaction predictions for any antibiotic pair within the applicability domain of the model since it solely requires chemical structures as an input.
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Affiliation(s)
- Daniel J Mason
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge , Cambridge CB2 1EW, United Kingdom
| | - Ian Stott
- Unilever Research and Development , Port Sunlight, Wirral CH63 3JW, United Kingdom
| | - Stephanie Ashenden
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge , Cambridge CB2 1EW, United Kingdom
| | - Zohar B Weinstein
- Boston University School of Medicine , Boston, Massachusetts 02118, United States
| | - Idil Karakoc
- Faculty of Engineering and Natural Sciences, Sabanci University , Tuzla, Istanbul 34956, Turkey
| | - Selin Meral
- Faculty of Engineering and Natural Sciences, Sabanci University , Tuzla, Istanbul 34956, Turkey
| | - Nurdan Kuru
- Faculty of Engineering and Natural Sciences, Sabanci University , Tuzla, Istanbul 34956, Turkey
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge , Cambridge CB2 1EW, United Kingdom
| | - Murat Cokol
- Faculty of Engineering and Natural Sciences, Sabanci University , Tuzla, Istanbul 34956, Turkey.,Department of Molecular Biology and Microbiology, Tufts University School of Medicine , Boston, Massachusetts 02111, United States.,Laboratory of Systems Pharmacology, Harvard Medical School , Boston, Massachusetts 02115, United States
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18
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Jeannot V, Busser B, Vanwonterghem L, Michallet S, Ferroudj S, Cokol M, Coll JL, Ozturk M, Hurbin A. Synergistic activity of vorinostat combined with gefitinib but not with sorafenib in mutant KRAS human non-small cell lung cancers and hepatocarcinoma. Onco Targets Ther 2016; 9:6843-6855. [PMID: 27877053 PMCID: PMC5108607 DOI: 10.2147/ott.s117743] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Development of drug resistance limits the efficacy of targeted therapies. Alternative approaches using different combinations of therapeutic agents to inhibit several pathways could be a more effective strategy for treating cancer. The effects of the approved epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (gefitinib) or a multi-targeted kinase inhibitor (sorafenib) in combination with a histone deacetylase inhibitor (vorinostat) on cell proliferation, cell cycle distribution, apoptosis, and signaling pathway activation in human lung adenocarcinoma and hepatocarcinoma cells with wild-type EGFR and mutant KRAS were investigated. The effects of the synergistic drug combinations were also studied in human lung adenocarcinoma and hepatocarcinoma cells in vivo. The combination of gefitinib and vorinostat synergistically reduced cell growth and strongly induced apoptosis through inhibition of the insulin-like growth factor-1 receptor/protein kinase B (IGF-1R/AKT)-dependent signaling pathway. Moreover, the gefitinib and vorinostat combination strongly inhibited tumor growth in mice with lung adenocarcinoma or hepatocarcinoma tumor xenografts. In contrast, the combination of sorafenib and vorinostat did not inhibit cell proliferation compared to a single treatment and induced G2/M cell cycle arrest without apoptosis. The sorafenib and vorinostat combination sustained the IGF-1R-, AKT-, and mitogen-activated protein kinase-dependent signaling pathways. These results showed that there was synergistic cytotoxicity when vorinostat was combined with gefitinib for both lung adenocarcinoma and hepatocarcinoma with mutant KRAS in vitro and in vivo but that the combination of vorinostat with sorafenib did not show any benefit. These findings highlight the important role of the IGF-1R/AKT pathway in the resistance to targeted therapies and support the use of histone deacetylase inhibitors in combination with EGFR-tyrosine kinase inhibitors, especially for treating patients with mutant KRAS resistant to other treatments.
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Affiliation(s)
- Victor Jeannot
- INSERM U1209, Department Cancer Targets and Experimental Therapeutics, Grenoble, France; University Grenoble Alpes, Institute for Advanced Biosciences, Grenoble, France
| | - Benoit Busser
- INSERM U1209, Department Cancer Targets and Experimental Therapeutics, Grenoble, France; University Grenoble Alpes, Institute for Advanced Biosciences, Grenoble, France; Department of Biochemistry, Toxicology and Pharmacology, Grenoble University Hospital, Grenoble, France
| | - Laetitia Vanwonterghem
- INSERM U1209, Department Cancer Targets and Experimental Therapeutics, Grenoble, France; University Grenoble Alpes, Institute for Advanced Biosciences, Grenoble, France
| | - Sophie Michallet
- INSERM U1209, Department Cancer Targets and Experimental Therapeutics, Grenoble, France; University Grenoble Alpes, Institute for Advanced Biosciences, Grenoble, France
| | - Sana Ferroudj
- INSERM U1209, Department Cancer Targets and Experimental Therapeutics, Grenoble, France; University Grenoble Alpes, Institute for Advanced Biosciences, Grenoble, France
| | - Murat Cokol
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Jean-Luc Coll
- INSERM U1209, Department Cancer Targets and Experimental Therapeutics, Grenoble, France; University Grenoble Alpes, Institute for Advanced Biosciences, Grenoble, France
| | - Mehmet Ozturk
- INSERM U1209, Department Cancer Targets and Experimental Therapeutics, Grenoble, France; University Grenoble Alpes, Institute for Advanced Biosciences, Grenoble, France; Faculty of Medicine, Dokuz Eyul University, Izmir Biomedicine and Genome Center, Izmir, Turkey
| | - Amandine Hurbin
- INSERM U1209, Department Cancer Targets and Experimental Therapeutics, Grenoble, France; University Grenoble Alpes, Institute for Advanced Biosciences, Grenoble, France
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19
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Chandrasekaran S, Cokol-Cakmak M, Sahin N, Yilancioglu K, Kazan H, Collins JJ, Cokol M. Chemogenomics and orthology-based design of antibiotic combination therapies. Mol Syst Biol 2016; 12:872. [PMID: 27222539 PMCID: PMC5289223 DOI: 10.15252/msb.20156777] [Citation(s) in RCA: 74] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical–genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli. Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug‐interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less‐studied pathogens by leveraging chemogenomics data in model organisms.
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Affiliation(s)
- Sriram Chandrasekaran
- Harvard Society of Fellows, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA Broad Institute of MIT and Harvard, Cambridge, MA, USA Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, USA
| | - Melike Cokol-Cakmak
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Nil Sahin
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Kaan Yilancioglu
- Department of Molecular Biology and Genetics, Uskudar University, Istanbul, Turkey
| | - Hilal Kazan
- Department of Computer Engineering, Antalya International University, Antalya, Turkey
| | - James J Collins
- Broad Institute of MIT and Harvard, Cambridge, MA, USA Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, USA Department of Biological Engineering, Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA, USA
| | - Murat Cokol
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA, USA Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
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20
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Seashore-Ludlow B, Rees MG, Cheah JH, Cokol M, Price EV, Coletti ME, Jones V, Bodycombe NE, Soule CK, Gould J, Alexander B, Li A, Montgomery P, Wawer MJ, Kuru N, Kotz JD, Hon CSY, Munoz B, Liefeld T, Dančík V, Bittker JA, Palmer M, Bradner JE, Shamji AF, Clemons PA, Schreiber SL. Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset. Cancer Discov 2015; 5:1210-23. [PMID: 26482930 DOI: 10.1158/2159-8290.cd-15-0235] [Citation(s) in RCA: 439] [Impact Index Per Article: 48.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2015] [Accepted: 07/21/2015] [Indexed: 12/15/2022]
Abstract
UNLABELLED Identifying genetic alterations that prime a cancer cell to respond to a particular therapeutic agent can facilitate the development of precision cancer medicines. Cancer cell-line (CCL) profiling of small-molecule sensitivity has emerged as an unbiased method to assess the relationships between genetic or cellular features of CCLs and small-molecule response. Here, we developed annotated cluster multidimensional enrichment analysis to explore the associations between groups of small molecules and groups of CCLs in a new, quantitative sensitivity dataset. This analysis reveals insights into small-molecule mechanisms of action, and genomic features that associate with CCL response to small-molecule treatment. We are able to recapitulate known relationships between FDA-approved therapies and cancer dependencies and to uncover new relationships, including for KRAS-mutant cancers and neuroblastoma. To enable the cancer community to explore these data, and to generate novel hypotheses, we created an updated version of the Cancer Therapeutic Response Portal (CTRP v2). SIGNIFICANCE We present the largest CCL sensitivity dataset yet available, and an analysis method integrating information from multiple CCLs and multiple small molecules to identify CCL response predictors robustly. We updated the CTRP to enable the cancer research community to leverage these data and analyses.
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Affiliation(s)
| | - Matthew G Rees
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts
| | - Jaime H Cheah
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts
| | - Murat Cokol
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Edmund V Price
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts
| | - Matthew E Coletti
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts
| | - Victor Jones
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts
| | - Nicole E Bodycombe
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts
| | - Christian K Soule
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts
| | - Joshua Gould
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts
| | - Benjamin Alexander
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts
| | - Ava Li
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts
| | - Philip Montgomery
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts
| | - Mathias J Wawer
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts
| | - Nurdan Kuru
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Joanne D Kotz
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts
| | - C Suk-Yee Hon
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts
| | - Benito Munoz
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts
| | - Ted Liefeld
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts
| | - Vlado Dančík
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts
| | - Joshua A Bittker
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts
| | - Michelle Palmer
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts
| | - James E Bradner
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts. Cancer Biology and Medical Oncology, Harvard Medical School, Boston, Massachusetts
| | - Alykhan F Shamji
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts.
| | - Paul A Clemons
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts.
| | - Stuart L Schreiber
- Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts
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21
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Weinstein Z, Kuru N, Clemons P, Roth F, Cokol M. Synergy and Selectivity of Antifungal Small Molecule Combinations. FASEB J 2015. [DOI: 10.1096/fasebj.29.1_supplement.773.13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Zohar Weinstein
- Pharmacology and Experimental TherapeuticsBoston University School of MedicineUnited States
| | | | - Paul Clemons
- Center for the Science of Therapeutics Broad Institute of Harvard and MITUnited States
| | - Frederick Roth
- Molecular Genetics & Computer Science University of TorontoCanada
- Lunenfeld‐Tanenbaum Research Institute Mt. Sinai HospitalCanada
| | - Murat Cokol
- Faculty of Engineering and Natural Sciences Sabanci UniversityTurkey
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22
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Yilancioglu K, Weinstein ZB, Meydan C, Akhmetov A, Toprak I, Durmaz A, Iossifov I, Kazan H, Roth FP, Cokol M. Target-independent prediction of drug synergies using only drug lipophilicity. J Chem Inf Model 2014; 54:2286-93. [PMID: 25026390 PMCID: PMC4144720 DOI: 10.1021/ci500276x] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
![]()
Physicochemical
properties of compounds have been instrumental
in selecting lead compounds with increased drug-likeness. However,
the relationship between physicochemical properties of constituent
drugs and the tendency to exhibit drug interaction has not been systematically
studied. We assembled physicochemical descriptors for a set of antifungal
compounds (“drugs”) previously examined for interaction.
Analyzing the relationship between molecular weight, lipophilicity,
H-bond donor, and H-bond acceptor values for drugs and their propensity
to show pairwise antifungal drug synergy, we found that combinations
of two lipophilic drugs had a greater tendency to show drug synergy.
We developed a more refined decision tree model that successfully
predicted drug synergy in stringent cross-validation tests based on
only lipophilicity of drugs. Our predictions achieved a precision
of 63% and allowed successful prediction for 58% of synergistic drug
pairs, suggesting that this phenomenon can extend our understanding
for a substantial fraction of synergistic drug interactions. We also
generated and analyzed a large-scale synergistic human toxicity network,
in which we observed that combinations of lipophilic compounds show
a tendency for increased toxicity. Thus, lipophilicity, a simple and
easily determined molecular descriptor, is a powerful predictor of
drug synergy. It is well established that lipophilic compounds (i)
are promiscuous, having many targets in the cell, and (ii) often penetrate
into the cell via the cellular membrane by passive diffusion. We discuss
the positive relationship between drug lipophilicity and drug synergy
in the context of potential drug synergy mechanisms.
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Affiliation(s)
- Kaan Yilancioglu
- Faculty of Engineering and Natural Sciences, Biological Sciences and Bioengineering Program, ⊥Faculty of Engineering and Natural Sciences, Computer Science and Engineering Program, and ▽Nanotechnology Research and Application Center, Sabanci University , Istanbul 34956, Turkey
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23
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Oz T, Guvenek A, Yildiz S, Karaboga E, Tamer YT, Mumcuyan N, Ozan VB, Senturk GH, Cokol M, Yeh P, Toprak E. Strength of selection pressure is an important parameter contributing to the complexity of antibiotic resistance evolution. Mol Biol Evol 2014; 31:2387-401. [PMID: 24962091 PMCID: PMC4137714 DOI: 10.1093/molbev/msu191] [Citation(s) in RCA: 164] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Revealing the genetic changes responsible for antibiotic resistance can be critical for developing novel antibiotic therapies. However, systematic studies correlating genotype to phenotype in the context of antibiotic resistance have been missing. In order to fill in this gap, we evolved 88 isogenic Escherichia coli populations against 22 antibiotics for 3 weeks. For every drug, two populations were evolved under strong selection and two populations were evolved under mild selection. By quantifying evolved populations’ resistances against all 22 drugs, we constructed two separate cross-resistance networks for strongly and mildly selected populations. Subsequently, we sequenced representative colonies isolated from evolved populations for revealing the genetic basis for novel phenotypes. Bacterial populations that evolved resistance against antibiotics under strong selection acquired high levels of cross-resistance against several antibiotics, whereas other bacterial populations evolved under milder selection acquired relatively weaker cross-resistance. In addition, we found that strongly selected strains against aminoglycosides became more susceptible to five other drug classes compared with their wild-type ancestor as a result of a point mutation on TrkH, an ion transporter protein. Our findings suggest that selection strength is an important parameter contributing to the complexity of antibiotic resistance problem and use of high doses of antibiotics to clear infections has the potential to promote increase of cross-resistance in clinics.
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Affiliation(s)
- Tugce Oz
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Aysegul Guvenek
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Sadik Yildiz
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Enes Karaboga
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Yusuf Talha Tamer
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Nirva Mumcuyan
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Vedat Burak Ozan
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Gizem Hazal Senturk
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Murat Cokol
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Pamela Yeh
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles
| | - Erdal Toprak
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
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24
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Abstract
Chemogenomic experiments, where genetic and chemical perturbations are combined, provide data for discovering the relationships between genotype and phenotype. Traditionally, analysis of chemogenomic datasets has been done considering the sensitivity of the deletion strains to chemicals, and this has shed light on drug mechanism of action and detecting drug targets. Here, we computationally analyzed a large chemogenomic dataset, which combines more than 300 chemicals with virtually all gene deletion strains in the yeast S. cerevisiae. In addition to sensitivity relation between deletion strains and chemicals, we also considered the deletion strains that are resistant to chemicals. We found a small set of genes whose deletion makes the cell resistant to many chemicals. Curiously, these genes were enriched for functions related to RNA metabolism. Our approach allowed us to generate a network of drugs and genes that are connected with resistance or sensitivity relationships. As a quality assessment, we showed that the higher order motifs found in this network are consistent with biological expectations. Finally, we constructed a biologically relevant network projection pertaining to drug similarities, and analyzed this network projection in detail. We propose this drug similarity network as a useful tool for understanding drug mechanism of action.
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Affiliation(s)
- Nermin Pinar Karabulut
- Computer Science and Engineering Program, Sabanci University, Tuzla-Istanbul 34956, Turkey , Department of Genome Oriented Bioinformatics, TUM, Freising 85354, Germany
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25
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Cokol M, Weinstein ZB, Yilancioglu K, Tasan M, Doak A, Cansever D, Mutlu B, Li S, Rodriguez-Esteban R, Akhmedov M, Guvenek A, Cokol M, Cetiner S, Giaever G, Iossifov I, Nislow C, Shoichet B, Roth FP. Large-scale identification and analysis of suppressive drug interactions. ACTA ACUST UNITED AC 2014; 21:541-551. [PMID: 24704506 DOI: 10.1016/j.chembiol.2014.02.012] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2013] [Revised: 01/26/2014] [Accepted: 02/07/2014] [Indexed: 11/29/2022]
Abstract
One drug may suppress the effects of another. Although knowledge of drug suppression is vital to avoid efficacy-reducing drug interactions or discover countermeasures for chemical toxins, drug-drug suppression relationships have not been systematically mapped. Here, we analyze the growth response of Saccharomyces cerevisiae to anti-fungal compound ("drug") pairs. Among 440 ordered drug pairs, we identified 94 suppressive drug interactions. Using only pairs not selected on the basis of their suppression behavior, we provide an estimate of the prevalence of suppressive interactions between anti-fungal compounds as 17%. Analysis of the drug suppression network suggested that Bromopyruvate is a frequently suppressive drug and Staurosporine is a frequently suppressed drug. We investigated potential explanations for suppressive drug interactions, including chemogenomic analysis, coaggregation, and pH effects, allowing us to explain the interaction tendencies of Bromopyruvate.
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Affiliation(s)
- Murat Cokol
- Biological Sciences and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey; Nanotechnology Research and Application Center, Sabanci University, Istanbul 34956, Turkey.
| | - Zohar B Weinstein
- Biological Sciences and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey; Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Kaan Yilancioglu
- Biological Sciences and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey; Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Murat Tasan
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Allison Doak
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Dilay Cansever
- Biological Sciences and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey; Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Beste Mutlu
- Biological Sciences and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey; Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Siyang Li
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Raul Rodriguez-Esteban
- Department of Computational Biology, Boehringer Ingelheim Pharmaceuticals, Ridgefield, CT 06877, USA
| | - Murodzhon Akhmedov
- Biological Sciences and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
| | - Aysegul Guvenek
- Biological Sciences and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
| | - Melike Cokol
- Biological Sciences and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
| | - Selim Cetiner
- Biological Sciences and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
| | - Guri Giaever
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Pharmaceutical Sciences, University of British Columbia, 2405 Wesbrook Mall, Vancouver, BC V6T 1Z3, Canada
| | - Ivan Iossifov
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Corey Nislow
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Pharmaceutical Sciences, University of British Columbia, 2405 Wesbrook Mall, Vancouver, BC V6T 1Z3, Canada
| | - Brian Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Frederick P Roth
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Center for Cancer Systems Biology, Dana-Farber Cancer Institute, One Jimmy Fund Way, Boston, MA 02215, USA; Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON M5G 1X5, Canada; Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada.
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26
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Yilancioglu K, Cokol M, Pastirmaci I, Erman B, Cetiner S. Oxidative stress is a mediator for increased lipid accumulation in a newly isolated Dunaliella salina strain. PLoS One 2014; 9:e91957. [PMID: 24651514 PMCID: PMC3961284 DOI: 10.1371/journal.pone.0091957] [Citation(s) in RCA: 152] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2013] [Accepted: 02/17/2014] [Indexed: 01/08/2023] Open
Abstract
Green algae offer sustainable, clean and eco-friendly energy resource. However, production efficiency needs to be improved. Increasing cellular lipid levels by nitrogen depletion is one of the most studied strategies. Despite this, the underlying physiological and biochemical mechanisms of this response have not been well defined. Algae species adapted to hypersaline conditions can be cultivated in salty waters which are not useful for agriculture or consumption. Due to their inherent extreme cultivation conditions, use of hypersaline algae species is better suited for avoiding culture contamination issues. In this study, we identified a new halophilic Dunaliella salina strain by using 18S ribosomal RNA gene sequencing. We found that growth and biomass productivities of this strain were directly related to nitrogen levels, as the highest biomass concentration under 0.05 mM or 5 mM nitrogen regimes were 495 mg/l and 1409 mg/l, respectively. We also confirmed that nitrogen limitation increased cellular lipid content up to 35% under 0.05 mM nitrogen concentration. In order to gain insight into the mechanisms of this phenomenon, we applied fluorometric, flow cytometric and spectrophotometric methods to measure oxidative stress and enzymatic defence mechanisms. Under nitrogen depleted cultivation conditions, we observed increased lipid peroxidation by measuring an important oxidative stress marker, malondialdehyde and enhanced activation of catalase, ascorbate peroxidase and superoxide dismutase antioxidant enzymes. These observations indicated that oxidative stress is accompanied by increased lipid content in the green alga. In addition, we also showed that at optimum cultivation conditions, inducing oxidative stress by application of exogenous H2O2 leads to increased cellular lipid content up to 44% when compared with non-treated control groups. Our results support that oxidative stress and lipid overproduction are linked. Importantly, these results also suggest that oxidative stress mediates lipid accumulation. Understanding such relationships may provide guidance for efficient production of algal biodiesels.
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Affiliation(s)
- Kaan Yilancioglu
- Faculty of Engineering and Natural Sciences, Sabanci University, Orhanlı, Istanbul, Turkey
| | - Murat Cokol
- Faculty of Engineering and Natural Sciences, Sabanci University, Orhanlı, Istanbul, Turkey
- Sabanci University Nanotechnology Research and Application Center, Orhanlı, Istanbul, Turkey
| | - Inanc Pastirmaci
- Faculty of Engineering and Natural Sciences, Sabanci University, Orhanlı, Istanbul, Turkey
| | - Batu Erman
- Faculty of Engineering and Natural Sciences, Sabanci University, Orhanlı, Istanbul, Turkey
| | - Selim Cetiner
- Faculty of Engineering and Natural Sciences, Sabanci University, Orhanlı, Istanbul, Turkey
- * E-mail:
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27
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Abstract
Small molecules with selective efficacy can be used as drugs. Drugs typically have a therapeutic dose range where the benefit from intended effects outweighs the cost of unintended (side) effects. Herein, I use case scenarios to illustrate potential advantages and pitfalls of treatment with drugs alone or in combination. Combinations of drugs may show surprising effects given the effects of individual drugs, in phenomena known as drug interactions. Drug interactions can be classified as synergistic or antagonistic, if the effect of the combination is higher or lower than expected, respectively. A better understanding of drug interactions and their relationship to phenotypes offers hope in finding drug combinations that have high therapeutic values.
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Affiliation(s)
- Murat Cokol
- Sabanci University, Faculty of Engineering and Natural Sciences, Biological Sciences and Bioengineering Program, and Nanotechnology Research and Application Center, Istanbul 34956, Turkey.
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28
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Ceran C, Cokol M, Cingoz S, Tasan I, Ozturk M, Yagci T. Novel anti-HER2 monoclonal antibodies: synergy and antagonism with tumor necrosis factor-α. BMC Cancer 2012; 12:450. [PMID: 23033967 PMCID: PMC3517359 DOI: 10.1186/1471-2407-12-450] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2012] [Accepted: 09/11/2012] [Indexed: 11/13/2022] Open
Abstract
Background One-third of breast cancers display amplifications of the ERBB2 gene encoding the HER2 kinase receptor. Trastuzumab, a humanized antibody directed against an epitope on subdomain IV of the extracellular domain of HER2 is used for therapy of HER2-overexpressing mammary tumors. However, many tumors are either natively resistant or acquire resistance against Trastuzumab. Antibodies directed to different epitopes on the extracellular domain of HER2 are promising candidates for replacement or combinatorial therapy. For example, Pertuzumab that binds to subdomain II of HER2 extracellular domain and inhibits receptor dimerization is under clinical trial. Alternative antibodies directed to novel HER2 epitopes may serve as additional tools for breast cancer therapy. Our aim was to generate novel anti-HER2 monoclonal antibodies inhibiting the growth of breast cancer cells, either alone or in combination with tumor necrosis factor-α (TNF-α). Methods Mice were immunized against SK-BR-3 cells and recombinant HER2 extracellular domain protein to produce monoclonal antibodies. Anti-HER2 antibodies were characterized with breast cancer cell lines using immunofluorescence, flow cytometry, immunoprecipitation, western blot techniques. Antibody epitopes were localized using plasmids encoding recombinant HER2 protein variants. Antibodies, either alone or in combination with TNF-α, were tested for their effects on breast cancer cell proliferation. Results We produced five new anti-HER2 monoclonal antibodies, all directed against conformational epitope or epitopes restricted to the native form of the extracellular domain. When tested alone, some antibodies inhibited modestly but significantly the growth of SK-BR-3, BT-474 and MDA-MB-361 cells displaying ERBB2 amplification. They had no detectable effect on MCF-7 and T47D cells lacking ERBB2 amplification. When tested in combination with TNF-α, antibodies acted synergistically on SK-BR-3 cells, but antagonistically on BT-474 cells. A representative anti-HER2 antibody inhibited Akt and ERK1/2 phosphorylation leading to cyclin D1 accumulation and growth arrest in SK-BR-3 cells, independently from TNF-α. Conclusions Novel antibodies against extracellular domain of HER2 may serve as potent anti-cancer bioactive molecules. Cell-dependent synergy and antagonism between anti-HER2 antibodies and TNF-α provide evidence for a complex interplay between HER2 and TNF-α signaling pathways. Such complexity may drastically affect the outcome of HER2-directed therapeutic interventions.
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Affiliation(s)
- Ceyhan Ceran
- BilGen Genetics and Biotechnology Research Center, Department of Molecular Biology and Genetics, Bilkent University, Ankara, Turkey
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29
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Cokol M, Chua HN, Tasan M, Mutlu B, Weinstein ZB, Suzuki Y, Nergiz ME, Costanzo M, Baryshnikova A, Giaever G, Nislow C, Myers CL, Andrews BJ, Boone C, Roth FP. Systematic exploration of synergistic drug pairs. Mol Syst Biol 2011; 7:544. [PMID: 22068327 PMCID: PMC3261710 DOI: 10.1038/msb.2011.71] [Citation(s) in RCA: 227] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2011] [Accepted: 08/11/2011] [Indexed: 01/20/2023] Open
Abstract
Drug synergy allows a therapeutic effect to be achieved with lower doses of component drugs. Drug synergy can result when drugs target the products of genes that act in parallel pathways ('specific synergy'). Such cases of drug synergy should tend to correspond to synergistic genetic interaction between the corresponding target genes. Alternatively, 'promiscuous synergy' can arise when one drug non-specifically increases the effects of many other drugs, for example, by increased bioavailability. To assess the relative abundance of these drug synergy types, we examined 200 pairs of antifungal drugs in S. cerevisiae. We found 38 antifungal synergies, 37 of which were novel. While 14 cases of drug synergy corresponded to genetic interaction, 92% of the synergies we discovered involved only six frequently synergistic drugs. Although promiscuity of four drugs can be explained under the bioavailability model, the promiscuity of Tacrolimus and Pentamidine was completely unexpected. While many drug synergies correspond to genetic interactions, the majority of drug synergies appear to result from non-specific promiscuous synergy.
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Affiliation(s)
- Murat Cokol
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- Biological Sciences and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Hon Nian Chua
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Murat Tasan
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Beste Mutlu
- Biological Sciences and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Zohar B Weinstein
- Biological Sciences and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Yo Suzuki
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Synthetic Biology and Bioenergy, J. Craig Venter Institute, San Diego, CA, USA
| | - Mehmet E Nergiz
- Department of Computer Engineering, Faculty of Engineering, Zirve University, Gaziantep, Turkey
| | - Michael Costanzo
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Anastasia Baryshnikova
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Guri Giaever
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Department of Pharmaceutical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Corey Nislow
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Brenda J Andrews
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Charles Boone
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Frederick P Roth
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Samuel Lunenfeld Research Institute, Mt Sinai Hospital, Toronto, Ontario, Canada
- Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA, USA
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30
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Costanzo M, Baryshnikova A, Bellay J, Kim Y, Spear ED, Sevier CS, Ding H, Koh JLY, Toufighi K, Mostafavi S, Prinz J, St Onge RP, VanderSluis B, Makhnevych T, Vizeacoumar FJ, Alizadeh S, Bahr S, Brost RL, Chen Y, Cokol M, Deshpande R, Li Z, Lin ZY, Liang W, Marback M, Paw J, San Luis BJ, Shuteriqi E, Tong AHY, van Dyk N, Wallace IM, Whitney JA, Weirauch MT, Zhong G, Zhu H, Houry WA, Brudno M, Ragibizadeh S, Papp B, Pál C, Roth FP, Giaever G, Nislow C, Troyanskaya OG, Bussey H, Bader GD, Gingras AC, Morris QD, Kim PM, Kaiser CA, Myers CL, Andrews BJ, Boone C. The genetic landscape of a cell. Science 2010; 327:425-31. [PMID: 20093466 DOI: 10.1126/science.1180823] [Citation(s) in RCA: 1580] [Impact Index Per Article: 112.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for approximately 75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.
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Affiliation(s)
- Michael Costanzo
- Banting and Best Department of Medical Research, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
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31
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Boxem M, Maliga Z, Klitgord N, Li N, Lemmens I, Mana M, de Lichtervelde L, Mul JD, van de Peut D, Devos M, Simonis N, Yildirim MA, Cokol M, Kao HL, de Smet AS, Wang H, Schlaitz AL, Hao T, Milstein S, Fan C, Tipsword M, Drew K, Galli M, Rhrissorrakrai K, Drechsel D, Koller D, Roth FP, Iakoucheva LM, Dunker AK, Bonneau R, Gunsalus KC, Hill DE, Piano F, Tavernier J, van den Heuvel S, Hyman AA, Vidal M. A protein domain-based interactome network for C. elegans early embryogenesis. Cell 2008; 134:534-45. [PMID: 18692475 DOI: 10.1016/j.cell.2008.07.009] [Citation(s) in RCA: 177] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2008] [Revised: 05/20/2008] [Accepted: 07/07/2008] [Indexed: 01/08/2023]
Abstract
Many protein-protein interactions are mediated through independently folding modular domains. Proteome-wide efforts to model protein-protein interaction or "interactome" networks have largely ignored this modular organization of proteins. We developed an experimental strategy to efficiently identify interaction domains and generated a domain-based interactome network for proteins involved in C. elegans early-embryonic cell divisions. Minimal interacting regions were identified for over 200 proteins, providing important information on their domain organization. Furthermore, our approach increased the sensitivity of the two-hybrid system, resulting in a more complete interactome network. This interactome modeling strategy revealed insights into C. elegans centrosome function and is applicable to other biological processes in this and other organisms.
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Affiliation(s)
- Mike Boxem
- Center for Cancer Systems Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA.
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32
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Cokol M, Rodriguez-Esteban R. Visualizing evolution and impact of biomedical fields. J Biomed Inform 2008; 41:1050-2. [PMID: 18558511 DOI: 10.1016/j.jbi.2008.05.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2007] [Revised: 04/23/2008] [Accepted: 05/05/2008] [Indexed: 12/30/2022]
Abstract
We describe a new tool for visualization of biomedical scientific trends. The method captures variations in scientific impact over time to allow for a comparison of relative significance and evolution of fields similar to a financial market scorecard. The tool is available at SciTrends (http://www.scitrends.net), depicting the evolution of almost 200 thousand biomedical fields in time. With millions of articles on thousands of topics published in biomedicine, we envision that only with such large-scale tools researchers can objectively understand the ever-changing interests in the biomedical sciences and make more informed decisions.
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Affiliation(s)
- Murat Cokol
- Harvard Medical School, Department of Biological Chemistry and Molecular Pharmacology, Boston, MA 02115, USA
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33
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Affiliation(s)
- Murat Cokol
- Murat Cokol, Ivan Iossifov, Raul Rodriguez-Esteban & Andrey Rzhetsky are at Columbia University in New York, NY, USA.
| | - Ivan Iossifov
- Murat Cokol, Ivan Iossifov, Raul Rodriguez-Esteban & Andrey Rzhetsky are at Columbia University in New York, NY, USA.
| | - Raul Rodriguez-Esteban
- Murat Cokol, Ivan Iossifov, Raul Rodriguez-Esteban & Andrey Rzhetsky are at Columbia University in New York, NY, USA.
| | - Andrey Rzhetsky
- Murat Cokol, Ivan Iossifov, Raul Rodriguez-Esteban & Andrey Rzhetsky are at Columbia University in New York, NY, USA.
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34
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Abstract
Our analysis highlights common statistical features of high-impact articles; we also show how information flows among various publication types.
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Affiliation(s)
- Murat Cokol
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
- Center for Computational Biology and Bioinformatics, Columbia University, New York, NY 10032, USA
| | - Raul Rodriguez-Esteban
- Center for Computational Biology and Bioinformatics, Columbia University, New York, NY 10032, USA
- Department of Electrical Engineering, Columbia University, New York, NY 10025, USA
| | - Andrey Rzhetsky
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
- Center for Computational Biology and Bioinformatics, Columbia University, New York, NY 10032, USA
- Judith P. Sulzberger MD Columbia Genome Center and Department of Biological Sciences, Columbia University, New York, NY 10032, USA
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35
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Cokol M, Iossifov I, Rodriguez‐Esteban R, Rzhetsky A. Response by Cokol
et al. EMBO Rep 2007. [DOI: 10.1038/sj.embor.7401054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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36
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Affiliation(s)
- Murat Cokol
- Department of Biological Sciences, Columbia University, New York, New York 10032, USA
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37
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Abstract
A variety of nuclear localization signals (NLSs) are experimentally known although only one motif was available for database searches through PROSITE. We initially collected a set of 91 experimentally verified NLSs from the literature. Through iterated 'in silico mutagenesis' we then extended the set to 214 potential NLSs. This final set matched in 43% of all known nuclear proteins and in no known non-nuclear protein. We estimated that >17% of all eukaryotic proteins may be imported into the nucleus. Finally, we found an overlap between the NLS and DNA-binding region for 90% of the proteins for which both the NLS and DNA-binding regions were known. Thus, evolution seemed to have used part of the existing DNA-binding mechanism when compartmentalizing DNA-binding proteins into the nucleus. However, only 56 of our 214 NLS motifs overlapped with DNA-binding regions. These 56 NLSs enabled a de novo prediction of partial DNA-binding regions for approximately 800 proteins in human, fly, worm and yeast.
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Affiliation(s)
- M Cokol
- CUBIC, Columbia University, Department of Biochemistry and Molecular Biophysics, New York, NY 10032, USA
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