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1. DNA hypermethylation associated with upregulated gene expression in prostate cancer demonstrates the diversity of epigenetic regulation

Author

Ieva Rauluseviciute, Finn Drabløs, and Morten Beck Rye

Subjects
Epigenetics, DNA methylation, Prostate cancer, Gene expression, Internal medicine, RC31-1245, Genetics, QH426-470
Abstract

Abstract Background Prostate cancer (PCa) has the highest incidence rates of cancers in men in western countries. Unlike several other types of cancer, PCa has few genetic drivers, which has led researchers to look for additional epigenetic and transcriptomic contributors to PCa development and progression. Especially datasets on DNA methylation, the most commonly studied epigenetic marker, have recently been measured and analysed in several PCa patient cohorts. DNA methylation is most commonly associated with downregulation of gene expression. However, positive associations of DNA methylation to gene expression have also been reported, suggesting a more diverse mechanism of epigenetic regulation. Such additional complexity could have important implications for understanding prostate cancer development but has not been studied at a genome-wide scale. Results In this study, we have compared three sets of genome-wide single-site DNA methylation data from 870 PCa and normal tissue samples with multi-cohort gene expression data from 1117 samples, including 532 samples where DNA methylation and gene expression have been measured on the exact same samples. Genes were classified according to their corresponding methylation and expression profiles. A large group of hypermethylated genes was robustly associated with increased gene expression (UPUP group) in all three methylation datasets. These genes demonstrated distinct patterns of correlation between DNA methylation and gene expression compared to the genes showing the canonical negative association between methylation and expression (UPDOWN group). This indicates a more diversified role of DNA methylation in regulating gene expression than previously appreciated. Moreover, UPUP and UPDOWN genes were associated with different compartments — UPUP genes were related to the structures in nucleus, while UPDOWN genes were linked to extracellular features. Conclusion We identified a robust association between hypermethylation and upregulation of gene expression when comparing samples from prostate cancer and normal tissue. These results challenge the classical view where DNA methylation is always associated with suppression of gene expression, which underlines the importance of considering corresponding expression data when assessing the downstream regulatory effect of DNA methylation.

Published
2020
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2. Promoter Methylation of PRKCB, ADAMTS12, and NAALAD2 Is Specific to Prostate Cancer and Predicts Biochemical Disease Recurrence

Author

Kristina Daniunaite, Arnas Bakavicius, Kristina Zukauskaite, Ieva Rauluseviciute, Juozas Rimantas Lazutka, Albertas Ulys, Feliksas Jankevicius, and Sonata Jarmalaite

Subjects
prostate cancer, biochemical recurrence, DNA methylation, ADAMTS12, NAALAD2, PRKCB, Biology (General), QH301-705.5, Chemistry, QD1-999
Abstract

The molecular diversity of prostate cancer (PCa) has been demonstrated by recent genome-wide studies, proposing a significant number of different molecular markers. However, only a few of them have been transferred into clinical practice so far. The present study aimed to identify and validate novel DNA methylation biomarkers for PCa diagnosis and prognosis. Microarray-based methylome data of well-characterized cancerous and noncancerous prostate tissue (NPT) pairs was used for the initial screening. Ten protein-coding genes were selected for validation in a set of 151 PCa, 51 NPT, as well as 17 benign prostatic hyperplasia samples. The Prostate Cancer Dataset (PRAD) of The Cancer Genome Atlas (TCGA) was utilized for independent validation of our findings. Methylation frequencies of ADAMTS12, CCDC181, FILIP1L, NAALAD2, PRKCB, and ZMIZ1 were up to 91% in our study. PCa specific methylation of ADAMTS12, CCDC181, NAALAD2, and PRKCB was demonstrated by qualitative and quantitative means (all p < 0.05). In agreement with PRAD, promoter methylation of these four genes was associated with the transcript down-regulation in the Lithuanian cohort (all p < 0.05). Methylation of ADAMTS12, NAALAD2, and PRKCB was independently predictive for biochemical disease recurrence, while NAALAD2 and PRKCB increased the prognostic power of multivariate models (all p < 0.01). The present study identified methylation of ADAMTS12, NAALAD2, and PRKCB as novel diagnostic and prognostic PCa biomarkers that might guide treatment decisions in clinical practice.

Published
2021
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3. Identification of transcription factor co-binding patterns with non-negative matrix factorization

Author

Ieva Rauluseviciute, Timothée Launay, Guido Barzaghi, Sarvesh Nikumbh, Boris Lenhard, Arnaud Regis Krebs, Jaime A. Castro-Mondragon, and Anthony Mathelier

Abstract

Transcription factor (TF) binding to DNA is critical to transcription regulation. Although the binding properties of numerous individual TFs are well-documented, a more detailed comprehension of how TFs interact cooperatively with DNA, forming either complex or co-binding to the same region, is required. Indeed, the combinatorial binding of TFs is essential to cell differentiation, development, and response to external stimuli. We present COBIND, a novel method based on non-negative matrix factorization (NMF) to identify TF co-binding patterns automatically. COBIND applies NMF to one-hot encoded regions flanking known TF binding sites (TFBSs) to pinpoint enriched DNA patterns at fixed distances. We applied COBIND to 8,293 TFBS datasets from UniBind for 404 TFs in seven species. The method uncovered already established co-binding patterns (e.g.,between POU5F1 and SOX2 or SOX17) and new co-binding configurations not yet reported in the literature and inferred through motif similarity and protein-protein interaction knowledge. Our extensive analyses across species revealed that 84% of the studied TFs share a co-binding motif with other TFs from the same structural family. The co-binding patterns captured by COBIND are likely functionally relevant as they harbor higher evolutionarily conservation than isolated TFBSs. Open chromatin data from matching human cell lines further supported the co-binding predictions. Finally, we used single-molecule footprinting data from mouse embryonic stem cells to confirm that the co-binding events captured by COBIND were likely occurring on the same DNA molecules.

Published
2023

4. JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles

Author

Aziz Khan, Damir Baranasic, Benoit Ballester, Ieva Rauluseviciute, Fayrouz Hammal, Klaas Vandepoele, Paul Boddie, Anthony Mathelier, Daniel Schmelter, Rafael Riudavets-Puig, Wyeth W. Wasserman, Jérémy Lucas, François Parcy, Oriol Fornes, Boris Lenhard, Laura Turchi, Romain Blanc-Mathieu, Jaime A. Castro-Mondragon, Albin Sandelin, Nicolás Manosalva Pérez, Roza Berhanu Lemma, Tiffany Y. Leung, Alejandro Aguirre, Centre for Molecular Medicine Norway (NCMM), Faculty of Medicine [Oslo], University of Oslo (UiO)-University of Oslo (UiO)-Rigshospitalet [Copenhagen], Copenhagen University Hospital-Copenhagen University Hospital, Régulateurs du développement de la fleur (Flo_RE ), Physiologie cellulaire et végétale (LPCV), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA), Stanford Cancer Institute - Stanford University School of Medicine, Department of Plant Biotechnology and Bioinformatics, Ghent University, VIBUGent Center for Plant Systems Biology, BC Children's Hospital Research Institute [Vancouver, BC, Canada] (BCCHR), University of British Columbia (UBC), Theories and Approaches of Genomic Complexity (TAGC), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), UCSC Genome Browser, University of California Santa Cruz, MRC London Institute of Medical Sciences (LMC), Institute of Clinical Sciences, MRC Clinical Sciences Centre, Faculty of Medicine, Imperial College London, The Bioinformatics Centre, Department of Biology & Biotech Research and Innovation Centre, University of Copenhagen, Department of Medical Genetics, Institute of Clinical Medicine, University of Oslo and Oslo University Hospital, NorwegianResearch Council [187615] and [288404], South-Eastern Norway Regional Health Authority (Helse Sør-Øst), Canadian Institutes of Health Research [PJT-162120], Natural Sciences andEngineeringResearch Council of Canada (NSERC) Discovery Grant [RGPIN-2017-06824], Norwegian Cancer Society [197884], NHGRI (National Human Genome Research Institute) [5U41HG002371-20], BOFgrant from Ghent University [BOF24Y2019001901], Novo Nordisk Foundation [NNF20OC0059951, NNF19OC0058262], Danish Cancer Foundation [R204A12359], Danish Independent Research Fund [611000207B, 7014-00120B], Carlsberg Foundation, ANR-17-EURE-0003,CBH-EUR-GS,CBH-EUR-GS(2017), ANR-10-LABX-0049,GRAL,Grenoble Alliance for Integrated Structural Cell Biology(2010), European Project: MSCA-ITN 2018,pHioniC, Universiteit Gent = Ghent University (UGENT), Imperial College London, University of Copenhagen = Københavns Universitet (UCPH), University of Oslo (UiO), Martin-Laffon, Jacqueline, CBH-EUR-GS - - CBH-EUR-GS2017 - ANR-17-EURE-0003 - EURE - VALID, Grenoble Alliance for Integrated Structural Cell Biology - - GRAL2010 - ANR-10-LABX-0049 - LABX - VALID, and pH and Ion Transport in Pancreatic Cancer - pHioniC - MSCA-ITN 2018 - INCOMING

Subjects
AcademicSubjects/SCI00010, Genome browser, Biology, computer.software_genre, MOUSE, Genome, Bioconductor, Mice, 03 medical and health sciences, 0302 clinical medicine, ENRICHMENT ANALYSIS, Databases, Genetic, [SDV.BBM] Life Sciences [q-bio]/Biochemistry, Molecular Biology, Genetics, Animals, Humans, Database Issue, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Taxonomic rank, MOTIF DATABASE, Transcription factor, TFBSSHAPE, 030304 developmental biology, TOOLS, 0303 health sciences, Binding Sites, Database, Computational Biology, Biology and Life Sciences, Genomics, Structural classification, Plants, GENE, DNA SHAPE-FEATURES, Vertebrates, computer, Software, 030217 neurology & neurosurgery, Protein Binding, Transcription Factors
Abstract

JASPAR (http://jaspar.genereg.net/) is an open-access database containing manually curated, non-redundant transcription factor (TF) binding profiles for TFs across six taxonomic groups. In this 9th release, we expanded the CORE collection with 341 new profiles (148 for plants, 101 for vertebrates, 85 for urochordates, and 7 for insects), which corresponds to a 19% expansion over the previous release. We added 298 new profiles to the Unvalidated collection when no orthogonal evidence was found in the literature. All the profiles were clustered to provide familial binding profiles for each taxonomic group. Moreover, we revised the structural classification of DNA binding domains to consider plant-specific TFs. This release introduces word clouds to represent the scientific knowledge associated with each TF. We updated the genome tracks of TFBSs predicted with JASPAR profiles in eight organisms; the human and mouse TFBS predictions can be visualized as native tracks in the UCSC Genome Browser. Finally, we provide a new tool to perform JASPAR TFBS enrichment analysis in user-provided genomic regions. All the data is accessible through the JASPAR website, its associated RESTful API, the R/Bioconductor data package, and a new Python package, pyJASPAR, that facilitates serverless access to the data.

Published
2021

5. Promoter Methylation of PRKCB, ADAMTS12, and NAALAD2 Is Specific to Prostate Cancer and Predicts Biochemical Disease Recurrence

Author

Feliksas Jankevičius, Juozas R. Lazutka, Kristina Daniunaite, Arnas Bakavicius, Kristina Zukauskaite, Albertas Ulys, Sonata Jarmalaite, and Ieva Rauluseviciute

Subjects
0301 basic medicine, Oncology, Glutamate Carboxypeptidase II, Male, Microarray, PRKCB, Prostatic Hyperplasia, NAALAD2, Disease, Prostate cancer, 0302 clinical medicine, ADAMTS Proteins, Prostate, biochemical recurrence, ADAMTS12, DNA methylation, prostate cancer, Biology (General), Promoter Regions, Genetic, Spectroscopy, Aged, 80 and over, Intracellular Signaling Peptides and Proteins, General Medicine, Methylation, Middle Aged, Computer Science Applications, Gene Expression Regulation, Neoplastic, Chemistry, medicine.anatomical_structure, 030220 oncology & carcinogenesis, Biochemical recurrence, Adult, medicine.medical_specialty, QH301-705.5, Catalysis, Article, Inorganic Chemistry, 03 medical and health sciences, Internal medicine, Protein Kinase C beta, medicine, Humans, Physical and Theoretical Chemistry, Molecular Biology, Gene, QD1-999, Aged, business.industry, Organic Chemistry, Prostatic Neoplasms, medicine.disease, 030104 developmental biology, Neoplasm Recurrence, Local, business, Transcription Factors
Abstract

The molecular diversity of prostate cancer (PCa) has been demonstrated by recent genome-wide studies, proposing a significant number of different molecular markers. However, only a few of them have been transferred into clinical practice so far. The present study aimed to identify and validate novel DNA methylation biomarkers for PCa diagnosis and prognosis. Microarray-based methylome data of well-characterized cancerous and noncancerous prostate tissue (NPT) pairs was used for the initial screening. Ten protein-coding genes were selected for validation in a set of 151 PCa, 51 NPT, as well as 17 benign prostatic hyperplasia samples. The Prostate Cancer Dataset (PRAD) of The Cancer Genome Atlas (TCGA) was utilized for independent validation of our findings. Methylation frequencies of ADAMTS12, CCDC181, FILIP1L, NAALAD2, PRKCB, and ZMIZ1 were up to 91% in our study. PCa specific methylation of ADAMTS12, CCDC181, NAALAD2, and PRKCB was demonstrated by qualitative and quantitative means (all p &lt, 0.05). In agreement with PRAD, promoter methylation of these four genes was associated with the transcript down-regulation in the Lithuanian cohort (all p &lt, 0.05). Methylation of ADAMTS12, NAALAD2, and PRKCB was independently predictive for biochemical disease recurrence, while NAALAD2 and PRKCB increased the prognostic power of multivariate models (all p &lt, 0.01). The present study identified methylation of ADAMTS12, NAALAD2, and PRKCB as novel diagnostic and prognostic PCa biomarkers that might guide treatment decisions in clinical practice.

Published
2021

6. DNA hypermethylation associated with upregulated gene expression in prostate cancer demonstrates the diversity of epigenetic regulation

Author

Morten Beck Rye, Finn Drabløs, and Ieva Rauluseviciute

Subjects
Male, lcsh:Internal medicine, lcsh:QH426-470, Biology, Epigenesis, Genetic, Transcriptome, Gene expression, Genetics, medicine, Humans, Epigenetics, lcsh:RC31-1245, Gene, Genetics (clinical), DNA methylation, Prostate cancer, Prostatic Neoplasms, Cancer, DNA, Neoplasm, Methylation, medicine.disease, Up-Regulation, Gene Expression Regulation, Neoplastic, lcsh:Genetics, DNA microarray, Research Article
Abstract

Background Prostate cancer (PCa) has the highest incidence rates of cancers in men in western countries. Unlike several other types of cancer, PCa has few genetic drivers, which has led researchers to look for additional epigenetic and transcriptomic contributors to PCa development and progression. Especially datasets on DNA methylation, the most commonly studied epigenetic marker, have recently been measured and analysed in several PCa patient cohorts. DNA methylation is most commonly associated with downregulation of gene expression. However, positive associations of DNA methylation to gene expression have also been reported, suggesting a more diverse mechanism of epigenetic regulation. Such additional complexity could have important implications for understanding prostate cancer development but has not been studied at a genome-wide scale. Results In this study, we have compared three sets of genome-wide single-site DNA methylation data from 870 PCa and normal tissue samples with multi-cohort gene expression data from 1117 samples, including 532 samples where DNA methylation and gene expression have been measured on the exact same samples. Genes were classified according to their corresponding methylation and expression profiles. A large group of hypermethylated genes was robustly associated with increased gene expression (UPUP group) in all three methylation datasets. These genes demonstrated distinct patterns of correlation between DNA methylation and gene expression compared to the genes showing the canonical negative association between methylation and expression (UPDOWN group). This indicates a more diversified role of DNA methylation in regulating gene expression than previously appreciated. Moreover, UPUP and UPDOWN genes were associated with different compartments — UPUP genes were related to the structures in nucleus, while UPDOWN genes were linked to extracellular features. Conclusion We identified a robust association between hypermethylation and upregulation of gene expression when comparing samples from prostate cancer and normal tissue. These results challenge the classical view where DNA methylation is always associated with suppression of gene expression, which underlines the importance of considering corresponding expression data when assessing the downstream regulatory effect of DNA methylation. © The Author(s). 2020 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Published
2020

7. DNA methylation data by sequencing: experimental approaches and recommendations for tools and pipelines for data analysis

Author

Finn Drabløs, Morten Beck Rye, and Ieva Rauluseviciute

Subjects
Data Analysis, Computer science, Bisulfite sequencing, Hydroxymethylation, Review, Computational biology, Genome, Epigenesis, Genetic, Genetics, Humans, Epigenetics, Molecular Biology, Gene, Genetics (clinical), DNA methylation, Genome, Human, Computational Biology, Sequence Analysis, DNA, Methylation, Human genetics, Computational pipelines, Human genome, Software, Developmental Biology
Abstract

Sequencing technologies have changed not only our approaches to classical genetics, but also the field of epigenetics. Specific methods allow scientists to identify novel genome-wide epigenetic patterns of DNA methylation down to single-nucleotide resolution. DNA methylation is the most researched epigenetic mark involved in various processes in the human cell, including gene regulation and development of diseases, such as cancer. Increasing numbers of DNA methylation sequencing datasets from human genome are produced using various platforms—from methylated DNA precipitation to the whole genome bisulfite sequencing. Many of those datasets are fully accessible for repeated analyses. Sequencing experiments have become routine in laboratories around the world, while analysis of outcoming data is still a challenge among the majority of scientists, since in many cases it requires advanced computational skills. Even though various tools are being created and published, guidelines for their selection are often not clear, especially to non-bioinformaticians with limited experience in computational analyses. Separate tools are often used for individual steps in the analysis, and these can be challenging to manage and integrate. However, in some instances, tools are combined into pipelines that are capable to complete all the essential steps to achieve the result. In the case of DNA methylation sequencing analysis, the goal of such pipeline is to map sequencing reads, calculate methylation levels, and distinguish differentially methylated positions and/or regions. The objective of this review is to describe basic principles and steps in the analysis of DNA methylation sequencing data that in particular have been used for mammalian genomes, and more importantly to present and discuss the most pronounced computational pipelines that can be used to analyze such data. We aim to provide a good starting point for scientists with limited experience in computational analyses of DNA methylation and hydroxymethylation data, and recommend a few tools that are powerful, but still easy enough to use for their own data analysis. © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Published
2019

8. Reproducibility of Fluorescent Expression from Engineered Biological Constructs in E. coli

Author

Jacob, Beal, Trac Haddock Angellii, Markus, Gershater, Kim de Mora, Meagan, Lizarazo, Jim, Hollenhorst, Randy, Rettberg, Philipp, Demling, Rene, Hanke, Michae, Osthegel, Anna, Schechtel, Suresh, Sudarsan, Arne, Zimmermann, Bartosz, Gabryelczyk, Martina, Ikonen, Minnamari, Salmela, Muradıye, Acar, Muhammed Fatih Aktas, Furkan, Bestepe, Furkan Sacit Ceylan, Sadık, Cigdem, Mikail, Dohan, Mustafa, Elitok, Mehmet, Gunduz, Esra, Gunduz, Omer Faruk Hatipoglu, Turan, Kaya, Orhan, Sayin, Safa, Tapan, Osman Faruk Tereci, Abdullah, Uçar, Mustafa, Yilmaz, Jeffrey, Barrick, Alex, Gutierrez, Dennis, Mishler, Jordan, Monk, Kate, Mortensen, Nathan, Shin, Ella, Watkins, Yintong, Chen, Yuji, Jin, Yuanjie, Shi, Haoqian Myelin Zhang, Bruno, Ono, Ieda Maria Martinez Paino, Lais, Ribovski, Ivan, Silva, Danilo Keiji Zampronio, Nils, Birkholz, Rudiger Frederik Busche, Oliver, Konzock, Steffen, Lippold, Carsten, Ludwig, Melanie, Philippi, Lukas, Platz, Christian, Sigismund, Susanne, Weber, Maren, Wehrs, Niels, Werchau, Anna, Wronska, Zen Zen Yen, Yash, Agarwal, Evan, Appleton, Douglas, Densmore, Ariela, Esmurria, Kathleen, Lewis, Alan, Pacheco, Marcel, Bruchez, Danielle, Peters, Cheryl, Telmer, Lena, Wang, Silvia Canas Duarte, Daniel Giraldo Perez, Camilo Gomez Garzon, Jorge Madrid Wolff, Nathaly Marin Medina, Valentina, Mazzanti, Laura Rodriguez Forero, Eitan, Scher, Robin, Dowell, Samantha, O’Hara, Cloe Simone Pogoda, Kendra, Shattuck, Ali, Altintas, Anne Pihl Bali, Rasmus, Bech, Anne, Egholm, Anne Sofie Laerke Hansen, Kristian, Jensen, Kristian Barreth Karlsen, Caroline, Mosbech, Sophia, Belkhelfa, Noemie, Berenger, Romain, Bodinier, Cecile, Jacry, Laura Matabishi Bibi, Pierre, Parutto, Julie, Zaworski, Andries de Vries, Freek de Wijs, Rick, Elbert, Lisa, Hielkema, Chandhuru, Jagadeesan, Bayu, Jayawardhana, Oscar, Kuipers, Anna, Lauxen, Thomas, Meijer, Sandra, Mous, Renske van Raaphorst, Aakanksha, Saraf, Otto, Schepers, Oscar, Smits, Jan Willem Veening, Ruud Detert Oude Weme, Lianne, Wieske, Catherine, Ainsworth, Xenia Spencer Milnes, Alejandro, Gómezávila, Eddie Cano Gamez, Ana Laura Torres Huerta, Carlos Alejandro Meza Ramirez, Philipp, Popp, Jara, Radeck, Anna, Sommer, Xiangkai, Li, Qi, Wu, Hongxia, Zhao, Ruixue, Zhao, Irem, Bastuzel, Yasemin, Ceyhan, Mayda, Gursel, Burak, Kizil, Ilkem, Kumru, Yasemin, Kuvvet, Helin, Tercan, Seniz, Yuksel, Luiza, Niyazmetova, Timothy, Ang, Lucas, Black, Ciaran, Kelly, George, Wadhams, Clovis, Basier, Urszula, Czerwinska, Cindy Suci Ananda, Muhammad Al Azhar, Adelia, Elviantari, Maya, Fitriana, Arief Budi Witarto, Yuliant, Jia Fangxing, Qingfeng, Hou, Wan, Pei, Chen, Rifei, Wang, Rong, Huang, Wei, Zhang, Yushan, Jianguo, He, Dengwen, Lai, Pai, Li, Jianheng, Liu, Chunyang, Ni, Qianbin, Zhang, Cinthya, Cadenas, Zardain Canabal, Eduardo J., Claudia Nallely Alonso Cantu, Mercedes Alejandra Vazquez Cantu, Eduardo Cepeda Canedo, Cesar Miguel Valdez Cordova, Jose Alberto de la Paz Espinosa, Carlos Enrique Alavez Garcia, Ana Laura Navarro Heredia, Adriana, Hernandez, Sebastian Valdivieso Jauregui, Eduardo Ramirez Montiel, Eduardo Serna Morales, Yamile Minerva Castellanos Morales, Omar Alonso Cantu Pena, Ramirez Rodríguez, Eduardo A., Elizabeth Vallejo Trejo, Jesus Gilberto Rodriguez Ceja, Jesus Eduardo Martinez Hernandez, Mario Alberto Pena Hernandez, Enrique Amaya Perez, Rebeca Paola Torres Ramirez, Cla, J., Martin, Hanzel, Sarah Mohand Said, Shihab, Sawar, Dylan, Siriwardena, Alex, Tzahristos, Nils, Anlind, Martin, Friberg, Erik, Gullberg, Stephanie, Herman, Dallin, Christensen, Sara, Gertsch, Cody, Maxfield, Charles, Miller, Ryan, Putman, Christine, Bauerl, Estelles Lopez, Lucia T., Estefania Huet Trujillo, Marta Vazquez Vilar, Marlène Sophie Birk, Nico, Claassens, Walter de Koster, Rik van Rosmalen, Wen Ying Wu, Sian, Davies, Dan, Goss, William, Rostain, Chelsey, Tye, Waqar, Yousaf, Natalie, Farny, Chloe, Lajeunesse, Alex, Turland, Chen, An, Jielin, Chen, Yahong, Chen, Zehua, Che, Baishan, Fang, Xiaotong, Fu, Xifeng, Guo, Yue, Jiang, Yiying, Lei, Jianqiao, Li, Zhe, Li, Chang, Liu, Weibing, Liu, Yang, Li, Yizhu, Lv, Qingyu, Ruan, Yue, Su, Chun, Tang, Yushen, Wang, Fan, Wu, Xiaoshan, Yan, Ruihua, Zhang, Tangduo, Zhang, Farren, Isaacs, Ariel Leyva Hernandez, Natalie, Ma, Stephanie, Mao, Yamini, Naidu, Tuukka, Miinalainen, Marion Aruann, Daniel Calendini, Yoann Chabert, Gael Chambonnier, Myriam, Choukour, Ella de Gaulejac, Camille, Houy, Axel, Levier, Loreen, Logger, Sebastien, Nin, Valerie, Prima, Sturgis, James N., Beibei, Fang, Sadik, Cigdem, Abdullah, Ucar, Alejandro, Gutierrez, Revanth, Poondla, Sanjana, Reddy, Tyler, Rocha, Natalie, Schulte, Devin, Wehle, Marta Eva Jackowski, Sean Ross Craig, Ariana Mirzarafie Ahi, Elliott, Parris, Luba, Prout, Barbara, Steijl, Rachel, Wellman, Zhao, Fan, Zhang, Jing, Yang, Wei, Yang, Yuanzhan, Wen, Zhaosen, Evan, Appletion, Jeffrey, Chen, Abha, Patil, Shaheer, Priracha, Kate, Ryan, Nick, Salvador, John, Viola, Boralli, Camila Maria S., Camila Barbosa Bramorski, Juliana Cancino Bernardi, Ana Laura de Lima, Paula Maria Pincela Lins, Cristiane Casonato Melo, Deborah Cezar Mendonca, Thiago, Mosqueiro, Everton, Silva, Graziele, Vasconcelos, Ruchi, Asthana, Donna, Lee, Michelle, Yu, Peter, Choi, Effie, Lau, Kenneth, Lau, Oscar, Ying, Brandon, Malone, Paul, Young, Aidan, Ceney, Dakota, Hawthorne, Sharon, Lian, Sam, Mellentine, Dylan, Miller, Barbara Castro Moreira, Christie, Peebles, Olivia, Smith, Kevin, Walsh, Allison, Zimont, Michael, Brasino, Michael, Donovan, Hannah, Young, Jan, Bejvl, Daniel, Georgiev, Hynek, Kasl, Katerina Pechotova, Vaclav Pelisek, Anna, Sosnova, Pavel, Zach, Anthony, Ciesla, Benjamin, Hoover, Elliott, Chapman, Jon Marles Wright, Vicky, Moynihan, Liusaidh, Owen, Brooke Rothschild Mancinelli, Emilie, Cuillery, Joseph, Heng, Vincent, Jacquot, Paola, Malsot, Rocco, Meli, Cyril, Pulver, Ari, Sarfatis, Loic, Steiner, Victor, Steininger, Nina van Tiel, Gregoire, Thouvenin, Axel, Uran, Lisa, Baumgartner, Anna, Fomitcheva, Daniel, Gerngross, Verena, Jagger, Michael, Meier, Anja, Michel, Jasmine, Bird, Bradley, Brown, Todd, Burlington, Daniel, Herring, Joseph, Slack, Georgina, Westwood, Emilia, Wojcik, Julian, Bender, Julia, Donauer, Ramona, Emig, Rabea, Jesser, Julika, Neumann, Lara, Stuhn, Takema, Hasegawa, Tomoya, Kozakai, Haruka, Maruyama, Sean, Colloms, Charlotte, Flynn, Vilija, Lomeikaite, James, Provan, Kang, Ning, Shuyan, Tang, Guozhao, Wu, Yunjun, Yang, Zhi, Zeng, Zhan, Yi, Pan, Chu, Jun, Li, Keji, Yan, Athale, Chaitanya A., Swapnil, Bodkhe, Manasi, Gangan, Harsh, Gakhare, Yash, Jawale, Snehal, Kadam, Prachiti, Moghe, Gayatri, Mundhe, Neha, Khetan, Ira, Phadke, Prashant, Uniyal, Siddhesh, Zadey, Ines, Cottignie, Eline, Deprez, Astrid, Deryckere, Jasper, Janssens, Frederik, Jonnaert, Katarzyna, Malczewska, Thomas, Pak, Johan, Robben, Ovia Margaret Thirukkumaran, Vincent Van Deuren, Laurens, Vandebroek, Laura Van Hese, Laetitia Van Wonterghem, Leen, Verschooten, Moritz, Wolter, Joss, Auty, Richard, Badge, Liam, Crawford, Raymond, Dalgleish, Amy, Evans, Cameron, Grundy, Charlie, Kruczko, Payal, Karia, Graeme, Glaister, Rhys, Hakstol, Seme, Mate, Karin, Otero, Dustin, Smith, Jeff, Tingley, Hans Joachim Wieden, Haotian, Wang, Ningning, Yao, Matthias, Franz, Anna, Knoerlein, Nicolas, Koutsoubelis, Loechner, Anne C., Max, Mundt, Alexandra, Richter, Oliver, Schauer, Marjorie, Buss, Sivateja, Tangirala, Brian, Teague, Tianyi, Huang, Xinhao, Song, Yibing, Wei, Zhaoran, Zhang, Longzhi, Cao, Cheng Li, Kang Yang, Zhiqin, Chen, Yuxing, Fang, Libo, Sun, Weiyi, Wang, Yang, Yang, David, Adams, Joshua, Colls, Joshua, Timmons, David, Urick, Julia Anna Adrian, Madina, Akan, Youssef, Chahibi, Rahmi, Lale, Typhaine Le Doujet, Marit Vaagen Roee, Altynay, Abdirakhmanova, Askarbek, Orakov, Azhar, Zhailauova, Jinyang, Liang, Yu, Ma, Qikai, Qin, Yetian, Su, Ju Yeon Han, Raphaella, Hull, Wei Chung Kong, Li Chieh Lu, Duke, Quinton, Pauline, Aubert, Johan, Bourdarias, Olivier, Bugaud, Coralie 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The specific roles of these authors are articulated in the author contributions section, The authors wish to thank Sarah Munro and Marc Salit of NIST for help in designing this study. Consortium authors include all persons self-identified by contributing teams as deserving co-authorship credit. Contributors are listed alphabetically within team, and teams alphabetically and by year. Note that some persons may be credited as contributing in both years. Team names are given as identified in iGEM records: full details of each team’s institution and additional members may be found online in the iGEM Foundation archives at:http://year.igem.org/Team:name e.g.: full information on the 2015 ETH_Zurich team may be found at: http://2015.igem.org/Team:ETH_Zurich, BBN Technologies, IGEM Foundation, Synthace Ltd., Agilent Technologies, Department of Biotechnology and Chemical Technology, Helsinki Institute for Information Technology HIIT, ATOMS Turkiye, Boston University, Carnegie Mellon University, Technical University of Denmark, Ludwig Maximilian University of Munich, Middle East Technical University, Sumbawa University of Technology, Southern University of Science and Technology, Sun Yat-Sen University, Tecnológico de Monterrey, Universidad Autonoma de Nuevo Leon, University of Ottawa, Universitat Politècnica de València, Wageningen University and Research Centre, Worcester Polytechnic Institute, Xiamen University, University of Texas at Austin, Bielefeld University, Birkbeck University of London, USP-Brazil, City University of Hong Kong, Colorado State University, CU Boulder, Swiss Federal Institute of Technology Lausanne, Swiss Federal Institute of Technology Zurich, University of Exeter, Indian Institute of Science Education and Research, KU Leuven, Massachusetts Institute of Technology, Northeast Agricultural University, Nanjing Agricultural University, Norwegian University of Science and Technology, Ocean University of China, University of Southern Denmark, Shenzhen Middle School - SZMS 15, Tokyo Institute of Technology, Trinity College Dublin, Delft University of Technology, Eindhoven University of Technology, Tuebingen, University of California Los Angeles, University of California San Diego, University of Maryland, University of Trento, Vanderbilt University, College of William and Mary, Department of Bioproducts and Biosystems, Aalto-yliopisto, Aalto University, Jones, D Dafydd, Discrete Technology and Production Automation, and ​Robotics and image-guided minimally-invasive surgery (ROBOTICS)

Subjects
0106 biological sciences, 0301 basic medicine, green fluorescent protein, Laboratory Proficiency Testing, Transcription, Genetic, International Genetically Engineered Machine, [SDV]Life Sciences [q-bio], lcsh:Medicine, Protein Engineering, 01 natural sciences, Infographics, Synthetic biology, genetics, lcsh:Science, Promoter Regions, Genetic, Macromolecular Engineering, transcription initiation, Measurement, Multidisciplinary, Chemistry, Strain (biology), gene expression regulation, good laboratory practice, Research Assessment, Fluorescence, Reproducibility, 3. Good health, Bioassays and Physiological Analysis, Engineering and Technology, Educational Status, Synthetic Biology, Genetic Engineering, Transcription, Graphs, Research Article, Biotechnology, Transcriptional Activation, Computer and Information Sciences, General Science & Technology, Green Fluorescent Proteins, Bioengineering, Computational biology, iGEM Interlab Study Contributors, Research and Analysis Methods, Promoter Regions, 03 medical and health sciences, promoter region, Genetic, 010608 biotechnology, Escherichia coli, ta215, business.industry, Data Visualization, lcsh:R, Fluorescence Competition, genetic transcription, DNA structure, Reproducibility of Results, Biology and Life Sciences, protein engineering, 030104 developmental biology, Good Health and Well Being, 7 INGENIERÍA Y TECNOLOGÍA, Synthetic Bioengineering, People and Places, lcsh:Q, Population Groupings, biosynthesis, business, metabolism, Undergraduates
Abstract

We present results of the first large-scale interlaboratory study carried out in synthetic biology, as part of the 2014 and 2015 International Genetically Engineered Machine (iGEM) competitions. Participants at 88 institutions around the world measured fluorescence from three engineered constitutive constructs in E. coli. Few participants were able to measure absolute fluorescence, so data was analyzed in terms of ratios. Precision was strongly related to fluorescent strength, ranging from 1.54-fold standard deviation for the ratio between strong promoters to 5.75-fold for the ratio between the strongest and weakest promoter, and while host strain did not affect expression ratios, choice of instrument did. This result shows that high quantitative precision and reproducibility of results is possible, while at the same time indicating areas needing improved laboratory practices. Copyright: © 2016 Beal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published
2016

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