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The protocadherin proteins are cell adhesion molecules at the crossroad of signaling pathways playing a major role in neuronal development. It is now understood that their role as signaling hubs is not only important for the normal physiology of cells but also for the regulation of hallmarks of cancerogenesis. Importantly, protocadherins form a cluster of genes that are regulated by DNA methylation. We have identified for the first time that PCDHB15 gene is DNA-hypermethylated on its unique exon in the metastatic melanoma-derived cell lines and patients’ metastases compared to primary tumors. This DNA hypermethylation silences the gene, and treatment with the DNA demethylating agent 5-aza-2′-deoxycytidine reinduces its expression. We explored the role of PCDHB15 in melanoma aggressiveness and showed that overexpression impairs invasiveness and aggregation of metastatic melanoma cells in vitro and formation of lung metastasis in vivo. These findings highlight important modifications of the methylation of the PCDHβ genes in melanoma and support a functional role of PCDHB15 silencing in melanoma aggressiveness.

The field of antisense oligonucleotide therapeutics is rapidly growing and in addition to small molecules and therapeutic antibodies, oligonucleotide-based gene expression modifiers have been developed as fully accepted therapeutics. Antisense oligonucleotides are designed to modify gene expression of their specific target genes. However, as their effect relies on Watson–Crick base pairing, they could also bind to other unintended complementary RNAs showing sufficient sequence homology, which in turn could lead to off-target effects. It is assumed that these off-target effects depend on the degree of complementarity between the antisense oligonucleotides and off-target sequences. Aim of this study was the investigation of the effects of antisense oligonucleotides on the expression of potential off-targets having a defined number of mismatches to the oligonucleotide sequence. We extend recent studies by investigating the off-target profile of two 17-mer antisense oligonucleotides in two distinct human cell lines by a whole-transcriptome study using RNA sequencing. The relatively high percentage of significantly downregulated off-target genes for which one mismatch is present corroborates the requirement for intense bioinformatic screens and stringent specificity criteria to design antisense oligonucleotides with only minimal sequence complementarity to any non-target sequence. Avoiding suppression of off-target genes by a thorough bioinformatics screen should strongly reduce the risk for toxicities caused by antisense oligonucleotide-mediated off-target RNA suppression and finally result in safer antisense oligonucleotide-based therapeutics.

We would like to thank all the families that participated in these studies for their generous contribution. Detailed acknowledgements and funding can be found in Sup plementary Material., Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41467-021-24558-y, Maternal smoking during pregnancy contributes to poor birth outcomes. Here the authors perform a meta-analysis of the associations between maternal smoking during pregnancy and placental DNA methylation and identify links between these and poor birth outcomes, which may better inform the mechanisms through which smoking impacts placental function and fetal growth. Maternal smoking during pregnancy (MSDP) contributes to poor birth outcomes, in part through disrupted placental functions, which may be reflected in the placental epigenome. Here we present a meta-analysis of the associations between MSDP and placental DNA methylation (DNAm) and between DNAm and birth outcomes within the Pregnancy And Childhood Epigenetics (PACE) consortium (N = 1700, 344 with MSDP). We identify 443 CpGs that are associated with MSDP, of which 142 associated with birth outcomes, 40 associated with gene expression, and 13 CpGs are associated with all three. Only two CpGs have consistent associations from a prior meta-analysis of cord blood DNAm, demonstrating substantial tissue-specific responses to MSDP. The placental MSDP-associated CpGs are enriched for environmental response genes, growth-factor signaling, and inflammation, which play important roles in placental function. We demonstrate links between placental DNAm, MSDP and poor birth outcomes, which may better inform the mechanisms through which MSDP impacts placental function and fetal growth., Canadian Institutes of Health Research (CIHR) MOP 115071, United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute of Environmental Health Sciences (NIEHS) P30 ES019776 - R01 ES022223 - P01 ES022832, United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute of General Medical Sciences (NIGMS) P20 GM104416, United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute of Mental Health (NIMH) R01 MH094609

Background Abnormal DNA methylation is observed as an early event in breast carcinogenesis. However, how such alterations arise is still poorly understood. microRNAs (miRNAs) regulate gene expression at the post-transcriptional level and play key roles in various biological processes. Here, we integrate miRNA expression and DNA methylation at CpGs to study how miRNAs may affect the breast cancer methylome and how DNA methylation may regulate miRNA expression. Methods miRNA expression and DNA methylation data from two breast cancer cohorts, Oslo2 (n = 297) and The Cancer Genome Atlas (n = 439), were integrated through a correlation approach that we term miRNA-methylation Quantitative Trait Loci (mimQTL) analysis. Hierarchical clustering was used to identify clusters of miRNAs and CpGs that were further characterized through analysis of mRNA/protein expression, clinicopathological features, in silico deconvolution, chromatin state and accessibility, transcription factor binding, and long-range interaction data. Results Clustering of the significant mimQTLs identified distinct groups of miRNAs and CpGs that reflect important biological processes associated with breast cancer pathogenesis. Notably, two major miRNA clusters were related to immune or fibroblast infiltration, hence identifying miRNAs associated with cells of the tumor microenvironment, while another large cluster was related to estrogen receptor (ER) signaling. Studying the chromatin landscape surrounding CpGs associated with the estrogen signaling cluster, we found that miRNAs from this cluster are likely to be regulated through DNA methylation of enhancers bound by FOXA1, GATA2, and ER-alpha. Further, at the hub of the estrogen cluster, we identified hsa-miR-29c-5p as negatively correlated with the mRNA and protein expression of DNA methyltransferase DNMT3A, a key enzyme regulating DNA methylation. We found deregulation of hsa-miR-29c-5p already present in pre-invasive breast lesions and postulate that hsa-miR-29c-5p may trigger early event abnormal DNA methylation in ER-positive breast cancer. Conclusions We describe how miRNA expression and DNA methylation interact and associate with distinct breast cancer phenotypes.

The molecular mechanisms underlying HIV-induced inflammation, which persists even during effective long-term treatment, remain incompletely defined. Here, we studied pathogenic and nonpathogenic simian immunodeficiency virus (SIV) infections in macaques and African green monkeys, respectively. We longitudinally analyzed genome-wide DNA methylation changes in CD4 + T cells from lymph node and blood, using arrays. DNA methylation changes after SIV infection were more pronounced in lymph nodes than blood and already detected in primary infection. Differentially methylated genes in pathogenic SIV infection were enriched for Th1-signaling (e.g., RUNX3, STAT4, NFKB1) and metabolic pathways (e.g., PRKCZ). In contrast, nonpathogenic SIVagm infection induced DNA methylation in genes coding for regulatory proteins such as LAG-3, arginase-2, interleukin-21 and interleukin-31. Between 15 and 18% of genes with DNA methylation changes were differentially expressed in CD4 + T cells in vivo. Selected identified sites were validated using bisulfite pyrosequencing in an independent cohort of uninfected, viremic and SIV controller macaques. Altered DNA methylation was confirmed in blood and lymph node CD4 + T cells in viremic macaques but was notably absent from SIV controller macaques. Our study identified key genes differentially methylated already in primary infection and in tissues that could contribute to the persisting metabolic disorders and inflammation in HIV-infected individuals despite effective treatment.

MC3 Working Group: Rehan Akbani21, Pavana Anur22, Matthew H. Bailey1,2,3, Alex Buchanan9, Kami Chiotti9, Kyle Covington12,23, Allison Creason9, Li Ding1,2,3,20, Kyle Ellrott9, Yu Fan21, Steven Foltz1,2, Gad Getz8,14,15,16, Walker Hale12, David Haussler24,25, Julian M. Hess8,26, Carolyn M. Hutter27, Cyriac Kandoth28, Katayoon Kasaian29,30, Melpomeni Kasapi27, Dave Larson1 , Ignaty Leshchiner8, John Letaw31, Singer Ma32, Michael D. McLellan1,3,20, Yifei Men32, Gordon B. Mills33,34, Beifang Niu35, Myron Peto22, Amie Radenbaugh24, Sheila M. Reynolds36, Gordon Saksena8, Heidi Sofia27, Chip Stewart8, Adam J. Struck31, Joshua M. Stuart24,37, Wenyi Wang21, John N. Weinstein38, David A. Wheeler12,13, Christopher K. Wong24,39, Liu Xi12 & Kai Ye40,41 21Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 22Molecular and Medical Genetics, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 23Castle Biosciences Inc, Friendswood, TX 77546, USA. 24UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 25Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 26Massachusetts General Hospital Center for Cancer Research, Charlestown, MA 02114, USA. 27National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20894, USA. 28Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. 29Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada. 30Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 4S6, Canada. 31Computational Biology Program, School of Medicine, Oregon Health and Science University, Portland, OR 97239, USA. 32DNAnexus Inc, Mountain View, CA 94040, USA. 33Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA. 34Precision Oncology, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 35Computer Network Information Center, Chinese Academy of Sciences, Beijing, China. 36Institute for Systems Biology, Seattle, WA 98109, USA. 37Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 38Department of Bioinformatics and Computational Biology and Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 39Biomolecular Engineering Department, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 40School of Elect, PCAWG novel somatic mutation calling methods working group: Matthew H. Bailey1,2,3, Beifang Niu35, Matthias Bieg42,43, Paul C. Boutros6,44,45,46, Ivo Buchhalter43,47,48, Adam P. Butler49, Ken Chen50, Zechen Chong51, Li Ding1,2,3,20, Oliver Drechsel52,53, Lewis Jonathan Dursi6,7, Roland Eils47,48,54,55, Kyle Ellrott9, Shadrielle M. G. Espiritu6, Yu Fan21, Robert S. Fulton1,3,20, Shengjie Gao56, Josep L. l. Gelpi57,58, Mark B. Gerstein5,18,19, Gad Getz8,14,15,16, Santiago Gonzalez59,60, Ivo G. Gut52,61, Faraz Hach62,63, Michael C. Heinold47,48, Julian M. Hess8,26, Jonathan Hinton49, Taobo Hu64, Vincent Huang6, Yi Huang65,66, Barbara Hutter43,67,68, David R. Jones49, Jongsun Jung69, Natalie Jäger47, Hyung-Lae Kim70, Kortine Kleinheinz47,48, Sushant Kumar5,19, Yogesh Kumar64, Christopher M. Lalansingh6, Ignaty Leshchiner8, Ivica Letunic71, Dimitri Livitz8, Eric Z. Ma64, Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Michael D. McLellan1,3,20, Andrew Menzies49, Ana Milovanovic57, Morten Muhlig Nielsen73, Stephan Ossowski52,53,74, Nagarajan Paramasivam43,47, Jakob Skou Pedersen73,75, Marc D. Perry76,77, Montserrat Puiggròs57, Keiran M. Raine49, Esther Rheinbay8,14,72, Romina Royo57, S. Cenk Sahinalp62,78,79, Gordon Saksena8, Iman Sarrafi62,78, Matthias Schlesner47,80, Jared T. Simpson6,17, Lucy Stebbings49, Chip Stewart8, Miranda D. Stobbe52,61, Jon W. Teague49, Grace Tiao8, David Torrents57,81, Jeremiah A. Wala8,14,82, Jiayin Wang1,40,66, Wenyi Wang21, Sebastian M. Waszak60, Joachim Weischenfeldt60,83,84, Michael C. Wendl1,10,11, Johannes Werner47,85, Zhenggang Wu64, Hong Xue64, Sergei Yakneen60, Takafumi N. Yamaguchi6, Kai Ye40,41, Venkata D. Yellapantula20,86, Christina K. Yung76 & Junjun Zhang76, PCAWG Consortium: Lauri A. Aaltonen87, Federico Abascal49, Adam Abeshouse88, Hiroyuki Aburatani89, David J. Adams49, Nishant Agrawal90, Keun Soo Ahn91, Sung-Min Ahn92, Hiroshi Aikata93, Rehan Akbani21, Kadir C. Akdemir50, Hikmat Al-Ahmadie88, Sultan T. Al-Sedairy94, Fatima Al-Shahrour95, Malik Alawi96,97, Monique Albert98, Kenneth Aldape99,100, Ludmil B. Alexandrov49,101,102, Adrian Ally30, Kathryn Alsop103, Eva G. Alvarez104,105,106, Fernanda Amary107, Samirkumar B. Amin108,109,110, Brice Aminou76, Ole Ammerpohl111,112, Matthew J. Anderson113, Yeng Ang114, Davide Antonello115, Pavana Anur22, Samuel Aparicio116, Elizabeth L. Appelbaum1,117, Yasuhito Arai118, Axel Aretz119, Koji Arihiro93, Shun-ichi Ariizumi120, Joshua Armenia121, Laurent Arnould122, Sylvia Asa123,124, Yassen Assenov125, Gurnit Atwal6,126,127, Sietse Aukema112,128, J. Todd Auman129, Miriam R. Aure130, Philip Awadalla6,126, Marta Aymerich131, Gary D. Bader126, Adrian Baez-Ortega132, Matthew H. Bailey1,2,3, Peter J. Bailey133, Miruna Balasundaram30, Saianand Balu134, Pratiti Bandopadhayay8,135,136, Rosamonde E. Banks137, Stefano Barbi138, Andrew P. Barbour139,140, Jonathan Barenboim6, Jill Barnholtz-Sloan141,142, Hugh Barr143, Elisabet Barrera59, John Bartlett98,144, Javier Bartolome57, Claudio Bassi115, Oliver F. Bathe145,146, Daniel Baumhoer147, Prashant Bavi148, Stephen B. Baylin149,150, Wojciech Bazant59, Duncan Beardsmore151, Timothy A. Beck152,153, Sam Behjati49, Andreas Behren154, Beifang Niu35, Cindy Bell155, Sergi Beltran52,61, Christopher Benz156, Andrew Berchuck157, Anke K. Bergmann158, Erik N. Bergstrom101,102, Benjamin P. Berman159,160,161, Daniel M. Berney162, Stephan H. Bernhart163,164,165, Rameen Beroukhim8,14,82, Mario Berrios166, Samantha Bersani167, Johanna Bertl73,168, Miguel Betancourt169, Vinayak Bhandari6,44, Shriram G. Bhosle49, Andrew V. Biankin133,170,171,172, Matthias Bieg42,43, Darell Bigner173, Hans Binder163,164, Ewan Birney59, Michael Birrer72, Nidhan K. Biswas174, Bodil Bjerkehagen147,175, Tom Bodenheimer134, Lori Boice176, Giada Bonizzato177, Johann S. De Bono178, Arnoud Boot179,180, Moiz S. Bootwalla166, Ake Borg181, Arndt Borkhardt182, Keith A. Boroevich183,184, Ivan Borozan6, Christoph Borst185, Marcus Bosenberg186, Mattia Bosio52,53,57, Jacqueline Boultwood187, Guillaume Bourque188,189, Paul C. Boutros6,44,45,46, G. Steven Bova190, David T. Bowen49,191, Reanne Bowlby30, David D. L. Bowtell103, Sandrine Boyault192, Rich Boyce59, Jeffrey Boyd193, Alvis Brazma59, Paul Brennan194, Daniel S. Brewer195,196, Arie B. Brinkman197, Robert G. Bristow44,198,199,200,201, Russell R. Broaddus99, Jane E. Brock202, Malcolm Brock203, Annegien Broeks204, Angela N. Brooks8,24,37,82, Denise Brooks30, Benedikt Brors67,205,206, Søren Brunak207,208, Timothy J. C. Bruxner113,209, Alicia L. Bruzos104,105,106, Alex Buchanan9, Ivo Buchhalter43,47,48, Christiane Buchholz210, Susan Bullman8,82, Hazel Burke211, Birgit Burkhardt212, Kathleen H. Burns213,214, John Busanovich8,215, Carlos D. Bustamante216,217, Adam P. Butler49, Atul J. Butte218, Niall J. Byrne76, Anne-Lise Børresen-Dale130,219, Samantha J. Caesar-Johnson220, Andy Cafferkey59, Declan Cahill221, Claudia Calabrese59,60, Carlos Caldas222,223, Fabien Calvo224, Niedzica Camacho178, Peter J. Campbell49,225, Elias Campo226,227, Cinzia Cantù177, Shaolong Cao21, Thomas E. Carey228, Joana Carlevaro-Fita229,230,231, Rebecca Carlsen30, Ivana Cataldo167,177, Mario Cazzola232, Jonathan Cebon154, Robert Cerfolio233, Dianne E. Chadwick234, Dimple Chakravarty235, Don Chalmers236, Calvin Wing Yiu Chan47,237, Kin Chan238, Michelle Chan-Seng-Yue148, Vishal S. Chandan239, David K. Chang133,170, Stephen J. Chanock240, Lorraine A. Chantrill170,241, Aurélien Chateigner76,242, Nilanjan Chatterjee149,243, Kazuaki Chayama93, Hsiao-Wei Chen114,121, Jieming Chen218, Ken Chen50, Yiwen Chen21, Zhaohong Chen244, Andrew D. Cherniack8,82, Jeremy Chien245, Yoke-Eng Chiew246,247, Suet-Feung Chin222,223, Juok Cho8, Sunghoon Cho248, Jung Kyoon Choi249, Wan Choi250, Christine Chomienne251, Zechen Chong51, Su Pin Choo252, Angela Chou170,246, Angelika N. Christ113, Elizabeth L. Christie103, Eric Chuah30, Carrie Cibulskis8, Kristian Cibulskis8, Sara Cingarlini253, Peter Clapham49, Alexander Claviez254, Sean Cleary148,255, Nicole Cloonan256, Marek Cmero257,258,259, Colin C. Collins62, Ashton A. Connor255,260, Susanna L. Cooke133, Colin S. Cooper178,196,261, Leslie Cope149, Vincenzo Corbo138,177, Matthew G. Cordes1,262, Stephen M. Cordner263, Isidro Cortés-Ciriano264,265,266, Kyle Covington12,23, Prue A. Cowin267, Brian Craft24, David Craft8,268, Chad J. Creighton269, Yupeng Cun270, Erin Curley271, Ioana Cutcutache179,180, Karolina Czajka272, Bogdan Czerniak99,273, Rebecca A. Dagg274, Ludmila Danilova149, Maria Vittoria Davi275, Natalie R. Davidson276,277,278,279,280, Helen Davies49,281,282, Ian J. Davis283, Brandi N. Davis-Dusenbery284, Kevin J. Dawson49, Francisco M. De La Vega216,217,285, Ricardo De Paoli-Iseppi211, Timothy Defreitas8, Angelo P. Dei Tos286, Olivier Delaneau287,288,289, John A. Demchok220, Jonas Demeulemeester290,291, German M. Demidov52,53,74, Deniz Demircioğlu292,293, Nening M. Dennis221, Robert E. Denroche148, Stefan C. Dentro49,290,294, Nikita Desai76, Vikram Deshpande72, Amit G. Deshwar295, Christine Desmedt296,297, Jordi Deu-Pons298,299, Noreen Dhalla30, Neesha C. Dhani300, Priyanka Dhingra301,302, Rajiv Dhir303, Anthony DiBiase304, Klev Diamanti305, Li Ding1,2,3,20, Shuai Ding306, Huy Q. Dinh159, Luc Dirix307, HarshaVardhan Doddapaneni12, Nilgun Donmez62,78, Michelle T. Dow244, Ronny Drapkin308, Oliver Drechsel52,53, Ruben M. Drews223, Serge Serge49, Tim Dudderidge150,221, Ana Dueso-Barroso57, Andrew J. Dunford8, Michael Dunn309, Lewis Jonathan Dursi6,7, Fraser R. Duthie133,310, Ken Dutton-Regester311, Jenna Eagles272, Douglas F. Easton312,313, Stuart Edmonds314, Paul A. Edwards223,315, Sandra E. Edwards178, Rosalind A. Eeles178,221, Anna Ehinger316, Juergen Eils54,55, Roland Eils47,48,54,55, Adel El-Naggar99,273, Matthew Eldridge223, Kyle Ellrott9, Serap Erkek60, Georgia Escaramis53,317,318, Shadrielle M. G. Espiritu6, Xavier Estivill53,319, Dariush Etemadmoghadam103, Jorunn E. Eyfjord320, Bishoy M. Faltas280, Daiming Fan321, Yu Fan21, William C. Faquin72, Claudiu Farcas244, Matteo Fassan322, Aquila Fatima323, Francesco Favero324, Nodirjon Fayzullaev76, Ina Felau220, Sian Fereday103, Martin L. Ferguson325, Vincent Ferretti76,326, Lars Feuerbach205, Matthew A. Field327, J. Lynn Fink57,113, Gaetano Finocchiaro328, Cyril Fisher221, Matthew W. Fittall290, Anna Fitzgerald329, Rebecca C. Fitzgerald282, Adrienne M. Flanagan330, Neil E. Fleshner331, Paul Flicek59, John A. Foekens332, Kwun M. Fong333, Nuno A. Fonseca59,334, Christopher S. Foster335,336, Natalie S. Fox6, Michael Fraser6, Scott Frazer8, Milana Frenkel-Morgenstern337, William Friedman338, Joan Frigola298, Catrina C. Fronick1,262, Akihiro Fujimoto184, Masashi Fujita184, Masashi Fukayama339, Lucinda A. Fulton1 , Robert S. Fulton1,3,20, Mayuko Furuta184, P. Andrew Futreal340, Anja Füllgrabe59, Stacey B. Gabriel8, Steven Gallinger148,255,260, Carlo Gambacorti-Passerini341, Jianjiong Gao121, Shengjie Gao56, Levi Garraway82, Øystein Garred342, Erik Garrison49, Dale W. Garsed103, Nils Gehlenborg8,343, Josep L. l. Gelpi57,58, Joshy George110, Daniela S. Gerhard344, Clarissa Gerhauser345, Jeffrey E. Gershenwald346,347, Mark B. Gerstein5,18,19, Moritz Gerstung59,60, Gad Getz8,14,15,16, Mohammed Ghori49, Ronald Ghossein348, Nasra H. Giama349, Richard A. Gibbs12, Anthony J. Gill170,350, Pelvender Gill351, Dilip D. Giri348, Dominik Glodzik49, Vincent J. Gnanapragasam352,353, Maria Elisabeth Goebler354, Mary J. Goldman24, Carmen Gomez355, Santiago Gonzalez59,60, Abel Gonzalez-Perez298,299,356, Dmitry A. Gordenin357, James Gossage358, Kunihito Gotoh359, Ramaswamy Govindan3, Dorthe Grabau360, Janet S. Graham133,361, Robert C. Grant148,260, Anthony R. Green315, Eric Green27, Liliana Greger59, Nicola Grehan282, Sonia Grimaldi177, Sean M. Grimmond362, Robert L. Grossman363, Adam Grundhoff97,364, Gunes Gundem88, Qianyun Guo75, Manaswi Gupta8, Shailja Gupta365, Ivo G. Gut52,61, Marta Gut52,61, Jonathan Göke292,366, Gavin Ha8, Andrea Haake111, David Haan37, Siegfried Haas185, Kerstin Haase290, James E. Haber367, Nina Habermann60, Faraz Hach62,63, Syed Haider6, Natsuko Hama118, Freddie C. Hamdy351, Anne Hamilton267, Mark P. Hamilton368, Leng Han369, George B. Hanna370, Martin Hansmann371, Nicholas J. Haradhvala8,72, Olivier Harismendy102,372, Ivon Harliwong113, Arif O. Harmanci5,373, Eoghan Harrington374, Takanori Hasegawa375, David Haussler24,25, Steve Hawkins223, Shinya Hayami376, Shuto Hayashi375, D. Neil Hayes134,377,378, Stephen J. Hayes379,380, Nicholas K. Hayward211,311, Steven Hazell221, Yao He381, Allison P. Heath382, Simon C. Heath52,61, David Hedley300, Apurva M. Hegde38, David I. Heiman8, Michael C. Heinold47,48, Zachary Heins88, Lawrence E. Heisler152, Eva Hellstrom-Lindberg383, Mohamed Helmy384, Seong Gu Heo385, Austin J. Hepperla134, José María Heredia-Genestar386, Carl Herrmann47,48,387, Peter Hersey211, Julian M. Hess8,26, Holmfridur Hilmarsdottir320, Jonathan Hinton49, Satoshi Hirano388, Nobuyoshi Hiraoka389, Katherine A. Hoadley134,390, Asger Hobolth75,168, Ermin Hodzic78, Jessica I. Hoell182, Steve Hoffmann163,164,165,391, Oliver Hofmann392, Andrea Holbrook166, Aliaksei Z. Holik53, Michael A. Hollingsworth393, Oliver Holmes209,311, Robert A. Holt30, Chen Hong205,237, Eun Pyo Hong385, Jongwhi H. Hong394, Gerrit K. Hooijer395, Henrik Hornshøj73, Fumie Hosoda118, Yong Hou56,396, Volker Hovestadt397, William Howat352, Alan P. Hoyle134, Ralph H. Hruban149, Jianhong Hu12, Taobo Hu64, Xing Hua240, Kuan-lin Huang1,398, Mei Huang176, Mi Ni Huang179,180, Vincent Huang6, Yi Huang65,66, Wolfgang Huber60, Thomas J. Hudson272,399, Michael Hummel400, Jillian A. Hung246,247, David Huntsman401, Ted R. Hupp402, Jason Huse88, Matthew R. Huska403, Barbara Hutter43,67,68, Carolyn M. Hutter27, Daniel Hübschmann48,54,404,405,406, Christine A. Iacobuzio-Donahue348, Charles David Imbusch205, Marcin Imielinski407,408, Seiya Imoto375, William B. Isaacs409, Keren Isaev6,44, Shumpei Ishikawa410, Murat Iskar397, S. M. Ashiqul Islam244, Michael Ittmann411,412,413, Sinisa Ivkovic284, Jose M. G. Izarzugaza414, Jocelyne Jacquemier415, Valerie Jakrot211, Nigel B. Jamieson133,172,416, Gun Ho Jang148, Se Jin Jang417, Joy C. Jayaseelan12, Reyka Jayasinghe1 , Stuart R. Jefferys134, Karine Jegalian418, Jennifer L. Jennings419, Seung-Hyup Jeon250, Lara Jerman60,420, Yuan Ji421,422, Wei Jiao6, Peter A. Johansson311, Amber L. Johns170, Jeremy Johns272, Rory Johnson230,423, Todd A. Johnson183, Clemency Jolly290, Yann Joly424, Jon G. Jonasson320, Corbin D. Jones425, David R. Jones49, David T. W. Jones426,427, Nic Jones428, Steven J. M. Jones30, Jos Jonkers204, Young Seok Ju49,249, Hartmut Juhl429, Jongsun Jung69, Malene Juul73, Randi Istrup Juul73, Sissel Juul374, Natalie Jäger47, Rolf Kabbe47, Andre Kahles276,277,278,279,430, Abdullah Kahraman431,432,433, Vera B. Kaiser434, Hojabr Kakavand211, Sangeetha Kalimuthu148, Christof von Kalle405, Koo Jeong Kang91, Katalin Karaszi351, Beth Karlan435, Rosa Karlić436, Dennis Karsch437, Katayoon Kasaian29,30, Karin S. Kassahn113,438, Hitoshi Katai439, Mamoru Kato440, Hiroto Katoh410, Yoshiiku Kawakami93, Jonathan D. Kay117, Stephen H. Kazakoff209,311, Marat D. Kazanov441,442,443, Maria Keays59, Electron Kebebew444,445, Richard F. Kefford446, Manolis Kellis8,447, James G. Kench170,350,448, Catherine J. Kennedy246,247, Jules N. A. Kerssemakers47, David Khoo273, Vincent Khoo221, Narong Khuntikeo115,449, Ekta Khurana301,302,450,451, Helena Kilpinen117, Hark Kyun Kim452, Hyung-Lae Kim70, Hyung-Yong Kim415, Hyunghwan Kim250, Jaegil Kim8, Jihoon Kim453, Jong K. Kim454, Youngwook Kim455,456, Tari A. King457,458,459, Wolfram Klapper128, Kortine Kleinheinz47,48, Leszek J. Klimczak460, Stian Knappskog49,461, Michael Kneba437, Bartha M. Knoppers424, Youngil Koh462,463, Jan Komorowski305,464, Daisuke Komura410, Mitsuhiro Komura375, Gu Kong415, Marcel Kool426,465, Jan O. Korbel59,60, Viktoriya Korchina12, Andrey Korshunov465, Michael Koscher465, Roelof Koster466, Zsofia Kote-Jarai178, Antonios Koures244, Milena Kovacevic284, Barbara Kremeyer49, Helene Kretzmer164,165, Markus Kreuz467, Savitri Krishnamurthy99,468, Dieter Kube469, Kiran Kumar8, Pardeep Kumar221, Sushant Kumar5,19, Yogesh Kumar64, Ritika Kundra114,121, Kirsten Kübler8,14,72, Ralf Küppers470, Jesper Lagergren383,471, Phillip H. Lai166, Peter W. Laird472, Sunil R. Lakhani473, Christopher M. Lalansingh6, Emilie Lalonde6, Fabien C. Lamaze6, Adam Lambert351, Eric Lander8, Pablo Landgraf474,475, Luca Landoni115, Anita Langerød130, Andrés Lanzós230,231,423, Denis Larsimont476, Erik Larsson477, Mark Lathrop189, Loretta M. S. Lau478, Chris Lawerenz55, Rita T. Lawlor177, Michael S. Lawrence8,72,183, Alexander J. Lazar99,108, Xuan Le479, Darlene Lee30, Donghoon Lee5, Eunjung Alice Lee480, Hee Jin Lee417, Jake June-Koo Lee264,266, Jeong-Yeon Lee481, Juhee Lee482, Ming Ta Michael Lee340, Henry Lee-Six49, Kjong-Van Lehmann276,277,278,279,430, Hans Lehrach483, Dido Lenze400, Conrad R. Leonard209,311, Daniel A. Leongamornlert49,178, Ignaty Leshchiner8, Louis Letourneau484, Ivica Letunic71, Douglas A. Levine88,485, Lora Lewis12, Tim Ley486, Chang Li56,396, Constance H. Li6,44, Haiyan Irene Li30, Jun Li21, Lin Li56, Shantao Li5, Siliang Li56,396, Xiaobo Li56,396, Xiaotong Li5, Xinyue Li56, Yilong Li49, Han Liang21, Sheng-Ben Liang234, Peter Lichter68,397, Pei Lin8, Ziao Lin8,487, W. M. Linehan488, Ole Christian Lingjærde489, Dongbing Liu56,396, Eric Minwei Liu88,301,302, Fei-Fei Liu201,490, Fenglin Liu381,491, Jia Liu492, Xingmin Liu56,396, Julie Livingstone6, Dimitri Livitz8, Naomi Livni221, Lucas Lochovsky5,19,110, Markus Loeffler467, Georgina V. Long211, Armando Lopez-Guillermo493, Shaoke Lou5,19, David N. Louis72, Laurence B. Lovat117, Yiling Lu38, Yong-Jie Lu162,494, Youyong Lu495,496,497, Claudio Luchini167, Ilinca Lungu144,148, Xuemei Luo152, Hayley J. Luxton117, Andy G. Lynch223,315,498, Lisa Lype36, Cristina López111,112, Carlos López-Otín499, Eric Z. Ma64, Yussanne Ma30, Gaetan MacGrogan500, Shona MacRae501, Geoff Macintyre223, Tobias Madsen73, Kazuhiro Maejima184, Andrea Mafficini177, Dennis T. Maglinte166,502, Arindam Maitra174, Partha P. Majumder174, Luca Malcovati232, Salem Malikic62,78, Giuseppe Malleo115, Graham J. Mann211,246,503, Luisa Mantovani-Löffler504, Kathleen Marchal505,506, Giovanni Marchegiani115, Elaine R. Mardis1,193,507, Adam A. Margolin31, Maximillian G. Marin37, Florian Markowetz223,315, Julia Markowski403, Jeffrey Marks508, Tomas Marques-Bonet61,81,386,509, Marco A. Marra30, Luke Marsden351, John W. M. Martens332, Sancha Martin49,510, Jose I. Martin-Subero81,511, Iñigo Martincorena49, Alexander Martinez-Fundichely301,302,451 Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Charlie E. Massie223, Thomas J. Matthew37, Lucy Matthews178, Erik Mayer221,512, Simon Mayes513, Michael Mayo30, Faridah Mbabaali272, Karen McCune514, Ultan McDermott49, Patrick D. McGillivray19, Michael D. McLellan1,3,20, John D. McPherson148,272,515, John R. McPherson179,180, Treasa A. McPherson260, Samuel R. Meier8, Alice Meng516, Shaowu Meng134, Andrew Menzies49, Neil D. Merrett115,517, Sue Merson178, Matthew Meyerson8,14,82, William U. Meyerson4,5, Piotr A. Mieczkowski518, George L. Mihaiescu76, Sanja Mijalkovic284, Ana Mijalkovic Mijalkovic-Lazic284, Tom Mikkelsen519, Michele Milella253, Linda Mileshkin103, Christopher A. Miller1 , David K. Miller113,170, Jessica K. Miller272, Gordon B. Mills33,34, Ana Milovanovic57, Sarah Minner520, Marco Miotto115, Gisela Mir Arnau267, Lisa Mirabello240, Chris Mitchell103, Thomas J. Mitchell49,315,352, Satoru Miyano375, Naoki Miyoshi375, Shinichi Mizuno521, Fruzsina Molnár-Gábor522, Malcolm J. Moore300, Richard A. Moore30, Sandro Morganella49, Quaid D. Morris127,490, Carl Morrison523,524, Lisle E. Mose134, Catherine D. Moser349, Ferran Muiños298,299, Loris Mularoni298,299, Andrew J. Mungall30, Karen Mungall30, Elizabeth A. Musgrove133, Ville Mustonen525,526,527, David Mutch528, Francesc Muyas52,53,74, Donna M. Muzny12, Alfonso Muñoz59, Jerome Myers529, Ola Myklebost461, Peter Möller530, Genta Nagae89, Adnan M. Nagrial170, Hardeep K. Nahal-Bose76, Hitoshi Nakagama531, Hidewaki Nakagawa184, Hiromi Nakamura118, Toru Nakamura388, Kaoru Nakano184, Tannistha Nandi532, Jyoti Nangalia49, Mia Nastic284, Arcadi Navarro61,81,386, Fabio C. P. Navarro19, David E. Neal223,352, Gerd Nettekoven533, Felicity Newell209,311, Steven J. Newhouse59, Yulia Newton37, Alvin Wei Tian Ng534, Anthony Ng535, Jonathan Nicholson49, David Nicol221, Yongzhan Nie321,536, G. Petur Nielsen72, Morten Muhlig Nielsen73, Serena Nik-Zainal49,281,282,537, Michael S. Noble8, Katia Nones209,311, Paul A. Northcott538, Faiyaz Notta148,539, Brian D. O’Connor76,540, Peter O’Donnell541, Maria O’Donovan282, Sarah O’Meara49, Brian Patrick O’Neill542, J. Robert O’Neill543, David Ocana59, Angelica Ochoa88, Layla Oesper544, Christopher Ogden221, Hideki Ohdan93, Kazuhiro Ohi375, Lucila Ohno-Machado244, Karin A. Oien523,545, Akinyemi I. Ojesina546,547,548, Hidenori Ojima549, Takuji Okusaka550, Larsson Omberg551, Choon Kiat Ong552, Stephan Ossowski52,53,74, German Ott553, B. F. Francis Ouellette76,554, Christine P’ng6, Marta Paczkowska6, Salvatore Paiella115, Chawalit Pairojkul523, Marina Pajic170, Qiang Pan-Hammarström56,555, Elli Papaemmanuil49, Irene Papatheodorou59, Nagarajan Paramasivam43,47, Ji Wan Park385, Joong-Won Park556, Keunchil Park557,558, Kiejung Park559, Peter J. Park264,266, Joel S. Parker518, Simon L. Parsons124, Harvey Pass560, Danielle Pasternack272, Alessandro Pastore276, Ann-Marie Patch209,311, Iris Pauporté251, Antonio Pea115, John V. Pearson209,311, Chandra Sekhar Pedamallu8,14,82, Jakob Skou Pedersen73,75, Paolo Pederzoli115, Martin Peifer270, Nathan A. Pennell561, Charles M. Perou129,518, Marc D. Perry76,77, Gloria M. Petersen562, Myron Peto22, Nicholas Petrelli563, Robert Petryszak59, Stefan M. Pfister426,465,564, Mark Phillips424, Oriol Pich298,299, Hilda A. Pickett478, Todd D. Pihl565, Nischalan Pillay566, Sarah Pinder567, Mark Pinese170, Andreia V. Pinho568, Esa Pitkänen60, Xavier Pivot569, Elena Piñeiro-Yáñez95, Laura Planko533, Christoph Plass345, Paz Polak8,14,15, Tirso Pons570, Irinel Popescu571, Olga Potapova572, Aparna Prasad52, Shaun R. Preston573, Manuel Prinz47, Antonia L. Pritchard311, Stephenie D. Prokopec6, Elena Provenzano574, Xose S. Puente499, Sonia Puig176, Montserrat Puiggròs57, Sergio Pulido-Tamayo505,506, Gulietta M. Pupo246, Colin A. Purdie575, Michael C. Quinn209,311, Raquel Rabionet52,53,576, Janet S. Rader577, Bernhard Radlwimmer397, Petar Radovic284, Benjamin Raeder60, Keiran M. Raine49, Manasa Ramakrishna49, Kamna Ramakrishnan49, Suresh Ramalingam578, Benjamin J. Raphael579, W. Kimryn Rathmell580, Tobias Rausch60, Guido Reifenberger475, Jüri Reimand6,44, Jorge Reis-Filho348, Victor Reuter348, Iker Reyes-Salazar298, Matthew A. Reyna579, Sheila M. Reynolds36, Esther Rheinbay8,14,72, Yasser Riazalhosseini189, Andrea L. Richardson323, Julia Richter111,128, Matthew Ringel581, Markus Ringnér181, Yasushi Rino582, Karsten Rippe405, Jeffrey Roach583, Lewis R. Roberts349, Nicola D. Roberts49, Steven A. Roberts584, A. Gordon Robertson30, Alan J. Robertson113, Javier Bartolomé Rodriguez57, Bernardo Rodriguez-Martin104,105,106, F. Germán Rodríguez-González83,332, Michael H. A. Roehrl44,123,148,234,585,586, Marius Rohde587, Hirofumi Rokutan440, Gilles Romieu588, Ilse Rooman170, Tom Roques262, Daniel Rosebrock8, Mara Rosenberg8,72, Philip C. Rosenstiel589, Andreas Rosenwald590, Edward W. Rowe221,591, Romina Royo57, Steven G. Rozen179,180,592, Yulia Rubanova17,127, Mark A. Rubin423,593,594,595,596, Carlota Rubio-Perez298,299,597, Vasilisa A. Rudneva60, Borislav C. Rusev177, Andrea Ruzzenente598, Gunnar Rätsch276,277,278,279,280,430, Radhakrishnan Sabarinathan298,299,599, Veronica Y. Sabelnykova6, Sara Sadeghi30, S. Cenk Sahinalp62,78,79, Natalie Saini357, Mihoko Saito-Adachi440, Gordon Saksena8, Adriana Salcedo6, Roberto Salgado600, Leonidas Salichos5,19, Richard Sallari8, Charles Saller601, Roberto Salvia115, Michelle Sam272, Jaswinder S. Samra115,602, Francisco Sanchez-Vega114,121, Chris Sander276,603,604, Grant Sanders134, Rajiv Sarin605, Iman Sarrafi62,78, Aya Sasaki-Oku184, Torill Sauer489, Guido Sauter520, Robyn P. M. Saw211, Maria Scardoni167, Christopher J. Scarlett170,606, Aldo Scarpa177, Ghislaine Scelo194, Dirk Schadendorf68,607, Jacqueline E. Schein30, Markus B. Schilhabel589, Matthias Schlesner47,80, Thorsten Schlomm84,608, Heather K. Schmidt1 , Sarah-Jane Schramm246, Stefan Schreiber609, Nikolaus Schultz121, Steven E. Schumacher8,323, Roland F. Schwarz59,403,405,610, Richard A. Scolyer211,448,602, David Scott428, Ralph Scully611, Raja Seethala612, Ayellet V. Segre8,613, Iris Selander260, Colin A. Semple434, Yasin Senbabaoglu276, Subhajit Sengupta614, Elisabetta Sereni115, Stefano Serra585, Dennis C. Sgroi72, Mark Shackleton103, Nimish C. Shah352, Sagedeh Shahabi234, Catherine A. Shang329, Ping Shang211, Ofer Shapira8,323, Troy Shelton271, Ciyue Shen603,604, Hui Shen615, Rebecca Shepherd49, Ruian Shi490, Yan Shi134, Yu-Jia Shiah6, Tatsuhiro Shibata118,616, Juliann Shih8,82, Eigo Shimizu375, Kiyo Shimizu617, Seung Jun Shin618, Yuichi Shiraishi375, Tal Shmaya285, Ilya Shmulevich36, Solomon I. Shorser6, Charles Short59, Raunak Shrestha62, Suyash S. Shringarpure217, Craig Shriver619, Shimin Shuai6,126, Nikos Sidiropoulos83, Reiner Siebert112,620, Anieta M. Sieuwerts332, Lina Sieverling205,237, Sabina Signoretti202,621, Katarzyna O. Sikora177, Michele Simbolo138, Ronald Simon520, Janae V. Simons134, Jared T. Simpson6,17, Peter T. Simpson473, Samuel Singer115,458, Nasa Sinnott-Armstrong8,217, Payal Sipahimalani30, Tara J. Skelly390, Marcel Smid332, Jaclyn Smith622, Karen Smith-McCune514, Nicholas D. Socci276, Heidi J. Sofia27, Matthew G. Soloway134, Lei Song240, Anil K. Sood623,624,625, Sharmila Sothi626, Christos Sotiriou244, Cameron M. Soulette37, Paul N. Span627, Paul T. Spellman22, Nicola Sperandio177, Andrew J. Spillane211, Oliver Spiro8, Jonathan Spring628, Johan Staaf181, Peter F. Stadler163,164,165, Peter Staib629, Stefan G. Stark277,279,618,630, Lucy Stebbings49, Ólafur Andri Stefánsson631, Oliver Stegle59,60,632, Lincoln D. Stein6,126, Alasdair Stenhouse633, Chip Stewart8, Stephan Stilgenbauer634, Miranda D. Stobbe52,61, Michael R. Stratton49, Jonathan R. Stretch211, Adam J. Struck31, Joshua M. Stuart24,37, Henk G. Stunnenberg396,635, Hong Su56,396, Xiaoping Su99, Ren X. Sun6, Stephanie Sungalee60, Hana Susak52,53, Akihiro Suzuki89,636, Fred Sweep637, Monika Szczepanowski128, Holger Sültmann67,638, Takashi Yugawa617, Angela Tam30, David Tamborero298,299, Benita Kiat Tee Tan639, Donghui Tan518, Patrick Tan180,532,592,640, Hiroko Tanaka375, Hirokazu Taniguchi616, Tomas J. Tanskanen641, Maxime Tarabichi49,290, Roy Tarnuzzer220, Patrick Tarpey642, Morgan L. Taschuk152, Kenji Tatsuno89, Simon Tavaré223,643, Darrin F. Taylor113, Amaro Taylor-Weiner8, Jon W. Teague49, Bin Tean Teh180,592,640,644,645, Varsha Tembe246, Javier Temes104,105, Kevin Thai76, Sarah P. Thayer393, Nina Thiessen30, Gilles Thomas646, Sarah Thomas221, Alan Thompson221, Alastair M. Thompson633, John F. Thompson211, R. Houston Thompson647, Heather Thorne103, Leigh B. Thorne176, Adrian Thorogood424, Grace Tiao8, Nebojsa Tijanic284, Lee E. Timms272, Roberto Tirabosco648, Marta Tojo106, Stefania Tommasi649, Christopher W. Toon170, Umut H. Toprak48,650, David Torrents57,81, Giampaolo Tortora651,652, Jörg Tost653, Yasushi Totoki118, David Townend654, Nadia Traficante103, Isabelle Treilleux655,656, Jean-Rémi Trotta61, Lorenz H. P. Trümper469, Ming Tsao124,539, Tatsuhiko Tsunoda183,657,658,659, Jose M. C. Tubio104,105,106, Olga Tucker660, Richard Turkington661, Daniel J. Turner513, Andrew Tutt323, Masaki Ueno376, Naoto T. Ueno662, Christopher Umbricht151,213,663, Husen M. Umer305,664, Timothy J. Underwood665, Lara Urban59,60, Tomoko Urushidate616, Tetsuo Ushiku339, Liis Uusküla-Reimand666,667, Alfonso Valencia57,81, David J. Van Den Berg166, Steven Van Laere307, Peter Van Loo290,291, Erwin G. Van Meir668, Gert G. Van den Eynden307, Theodorus Van der Kwast123, Naveen Vasudev137, Miguel Vazquez57,669, Ravikiran Vedururu267, Umadevi Veluvolu518, Shankar Vembu490,670, Lieven P. C. Verbeke506,671, Peter Vermeulen307, Clare Verrill351,672, Alain Viari177, David Vicente57, Caterina Vicentini177, K. Vijay Raghavan365, Juris Viksna673, Ricardo E. Vilain674, Izar Villasante57, Anne Vincent-Salomon635, Tapio Visakorpi190, Douglas Voet8, Paresh Vyas311,351, Ignacio Vázquez-García49,86,675,676, Nick M. Waddell209, Nicola Waddell209,311, Claes Wadelius677, Lina Wadi6, Rabea Wagener111,112, Jeremiah A. Wala8,14,82, Jian Wang56, Jiayin Wang1,40,66, Linghua Wang12, Qi Wang465, Wenyi Wang21, Yumeng Wang21, Zhining Wang220, Paul M. Waring523, Hans-Jörg Warnatz483, Jonathan Warrell5,19, Anne Y. Warren352,678, Sebastian M. Waszak60, David C. Wedge49,294,679, Dieter Weichenhan345, Paul Weinberger680, John N. Weinstein38, Joachim Weischenfeldt60,83,84, Daniel J. Weisenberger166, Ian Welch681, Michael C. Wendl1,10,11, Johannes Werner47,85, Justin P. Whalley61,682, David A. Wheeler12,13, Hayley C. Whitaker117, Dennis Wigle683, Matthew D. Wilkerson518, Ashley Williams244, James S. Wilmott211, Gavin W. Wilson6,148, Julie M. Wilson148, Richard K. Wilson1,684, Boris Winterhoff685, Jeffrey A. Wintersinger17,127,384, Maciej Wiznerowicz686,687, Stephan Wolf688, Bernice H. Wong689, Tina Wong1,30, Winghing Wong690, Youngchoon Woo250, Scott Wood209,311, Bradly G. Wouters44, Adam J. Wright6, Derek W. Wright133,691, Mark H. Wright217, Chin-Lee Wu72, Dai-Ying Wu285, Guanming Wu692, Jianmin Wu170, Kui Wu56,396, Yang Wu179,180, Zhenggang Wu64, Liu Xi12, Tian Xia693, Qian Xiang76, Xiao Xiao66, Rui Xing497, Heng Xiong56,396, Qinying Xu209,311, Yanxun Xu694, Hong Xue64, Shinichi Yachida118,695, Sergei Yakneen60, Rui Yamaguchi375, Takafumi N. Yamaguchi6, Masakazu Yamamoto120, Shogo Yamamoto89, Hiroki Yamaue376, Fan Yang490, Huanming Yang56, Jean Y. Yang696, Liming Yang220, Lixing Yang697, Shanlin Yang306, Tsun-Po Yang270, Yang Yang369, Xiaotong Yao408,698, Marie-Laure Yaspo483, Lucy Yates49, Christina Yau156, Chen Ye56,396, Kai Ye40,41, Venkata D. Yellapantula20,86, Christopher J. Yoon249, Sung-Soo Yoon463, Fouad Yousif6, Jun Yu699, Kaixian Yu700, Willie Yu701, Yingyan Yu702, Ke Yuan223,510,703, Yuan Yuan21, Denis Yuen6, Takashi Yugawa617, Christina K. Yung76, Olga Zaikova704, Jorge Zamora49,104,105,106, Marc Zapatka397, Jean C. Zenklusen220, Thorsten Zenz67, Nikolajs Zeps705,706, Cheng-Zhong Zhang8,707, Fan Zhang381, Hailei Zhang8, Hongwei Zhang494, Hongxin Zhang121, Jiashan Zhang220, Jing Zhang5, Junjun Zhang76, Xiuqing Zhang56, Xuanping Zhang66,369, Yan Zhang5,708,709, Zemin Zhang381,710, Zhongming Zhao711, Liangtao Zheng381, Xiuqing Zheng381, Wanding Zhou615, Yong Zhou56, Bin Zhu240, Hongtu Zhu700,712, Jingchun Zhu24, Shida Zhu56,396, Lihua Zou713, Xueqing Zou49, Anna deFazio246,247,714, Nicholas van As221, Carolien H. M. van Deurzen715, Marc J. van de Vijver523, L. van’t Veer716 & Christian von Mering433,717, The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.

Background Efficient treatments against metastatic melanoma dissemination are still lacking. Here, we report that low-cytotoxic concentrations of 5-aza-2′-deoxycytidine, a DNA demethylating agent, prevent in vitro 3D invasiveness of metastatic melanoma cells and reduce lung metastasis formation in vivo. Results We unravelled that this beneficial effect is in part due to MIR-199A2 re-expression by promoter demethylation. Alone, this miR showed an anti-invasive and anti-metastatic effect. Throughout integration of micro-RNA target prediction databases with transcriptomic analysis after 5-aza-2′-deoxycytidine treatments, we found that miR-199a-3p downregulates set of genes significantly involved in invasion/migration processes. In addition, analysis of data from melanoma patients showed a stage- and tissue type-dependent modulation of MIR-199A2 expression by DNA methylation. Conclusions Thus, our data suggest that epigenetic- and/or miR-based therapeutic strategies can be relevant to limit metastatic dissemination of melanoma. Electronic supplementary material The online version of this article (10.1186/s13148-018-0600-2) contains supplementary material, which is available to authorized users.

In asthma and allergy genetics, a trend towards a few main topics developed over the last 2 years. First, a number of studies have been published recently which focus on overlapping and/or very specific phenotypes: within the allergy spectrum but also reaching beyond, looking for common genetic traits shared between different diseases or disease entities. Secondly, an urgently needed focus has been put on asthma and allergy genetics in populations genetically different from European ancestry. This acknowledges that the majority of new asthma patients today are not white and asthma is a truly worldwide disease. In epigenetics, recent years have seen several large-scale epigenome-wide association studies (EWAS) being published and a further focus was on the interaction between the environment and epigenetic signatures. And finally, the major trends in current asthma and allergy genetics and epigenetics comes from the field of pharmacogenetics, where it is necessary to understand the susceptibility for and mechanisms of current asthma and allergy therapies while at the same time, we need to have scientific answers to the recent availability of novel drugs that hold the promise for a more individualized therapy.

Aim: To assess whether DNA methylation levels account for the noninherited phenotypic variations observed among cystic fibrosis (CF) patients. Patients & methods: Using the 450 K BeadChip, we profiled DNA methylation in nasal epithelial cells collected from 32 CF patients and 16 controls. Results: We detected substantial DNA methylation differences up to 55% (median β change 0.13; IQR: 0.15–0.11) between CF patients and controls. DNA methylation levels differed between mild and severe CF patients and correlated with lung function at 50 CpG sites. Conclusion: In CF samples, dynamic changes of DNA methylation occurred in genes responsible for the integrity of the epithelium and the inflammatory and immune responses, were prominent in transcriptionally active genomic regions and were over-represented in enhancers active in lung tissues. ( Clinicaltrials.gov NCT02884622).

Osteosarcoma (OS) is an aggressive bone tumor primarily affecting children and adolescents. The etiology of OS is not fully understood. Thus, there is a great need to obtain a better understanding of OS development and progression. Alterations in miRNA expression contribute to the required molecular alterations for neoplastic initiation and progression. This study is the first to investigate miRNA expression in OS in a large discovery and validation cohort comprising a total of 101 OS samples. We established the signature of altered miRNA expression in OS by profiling the expression level of 752 miRNAs in 23 OS samples using sensitive LNA-enhanced qPCR assays. The identified miRNA expression changes were correlated with gene expression in the same samples. Furthermore, miRNA expression changes were validated in a second independent cohort consisting of 78 OS samples. Analysis of 752 miRNAs in the discovery cohort led to the identification of 33 deregulated miRNAs in OS. Twenty-nine miRNAs were validated with statistical significance in the second cohort comprising 78 OS samples. miRNA/mRNA targets were determined, and 361 genes with an inverse expression of the target miRNA were identified. Both the miRNAs and the identified target genes were associated with multiple pathways related to cancer as well as bone cell biology, thereby correlating the deregulated miRNAs with OS tumorigenesis. An analysis of the prognostic value of the 29 miRNAs identified miR-221/miR-222 to be significantly associated with time to metastasis in both cohorts. This study contributes to a more profound understanding of OS tumorigenesis, by substantiating the importance of miRNA deregulation. We have identified and validated 29 deregulated miRNAs in the – to our knowledge – largest discovery and validation cohorts used so far for miRNA analyses in OS. Two of the miRNAs showed a promising potential as prognostic biomarkers for the aggressiveness of OS.

Epigenetic modifications, including DNA methylation, represent a potential mechanism for environmental impacts on human disease. Maternal smoking in pregnancy remains an important public health problem that impacts child health in a myriad of ways and has potential lifelong consequences. The mechanisms are largely unknown, but epigenetics most likely plays a role. We formed the Pregnancy And Childhood Epigenetics (PACE) consortium and meta-analyzed, across 13 cohorts (n = 6,685), the association between maternal smoking in pregnancy and newborn blood DNA methylation at over 450,000 CpG sites (CpGs) by using the Illumina 450K BeadChip. Over 6,000 CpGs were differentially methylated in relation to maternal smoking at genome-wide statistical significance (false discovery rate, 5%), including 2,965 CpGs corresponding to 2,017 genes not previously related to smoking and methylation in either newborns or adults. Several genes are relevant to diseases that can be caused by maternal smoking (e.g., orofacial clefts and asthma) or adult smoking (e.g., certain cancers). A number of differentially methylated CpGs were associated with gene expression. We observed enrichment in pathways and processes critical to development. In older children (5 cohorts, n = 3,187), 100% of CpGs gave at least nominal levels of significance, far more than expected by chance (p value < 2.2 × 10−16). Results were robust to different normalization methods used across studies and cell type adjustment. In this large scale meta-analysis of methylation data, we identified numerous loci involved in response to maternal smoking in pregnancy with persistence into later childhood and provide insights into mechanisms underlying effects of this important exposure. The BAMSE cohort was supported by The Swedish Research Council, The Swedish Heart-Lung Foundation, Freemason Child House Foundation in Stockholm, MeDALL (Mechanisms of the Development of ALLergy), a collaborative project conducted within the European Union (grant agreement No. 261357), Centre for Allergy Research, Stockholm County Council (ALF), Swedish foundation for strategic research (SSF, RBc08-0027, EpiGene project), the Strategic Research Programme (SFO) in Epidemiology at Karolinska Institutet, The Swedish Research Council Formas and the Swedish Environment Protection Agency.

The quickly evolving field of genome engineering has now also embraced epigenomics and promises to revolutionize the possibilities to functionally investigate and validate the importance of epigenetic modifications at any locus in the genome. These approaches will allow for the first time to determine if epigenetic changes are causal to observed phenotypic changes and will substantially further our understanding of the multifaceted roles of epigenetic modifications and their combinations in chromatin, which is key to the answer of many biomedical questions. Epigenome editing will help identify and evaluate the role of intergenic gene regulatory elements such as enhancers in the regulation of endogenous gene expression in a cell-type-specific context through targeted alteration of the local chromatin structure. Furthermore, and perhaps most exiting, editing of the epigenome has also the potential to provide in the near future new tools to correct disease-specific epigenetic modifications in a personalized manner by re-establishing normal gene expression programs in the targeted cells. Three major technological approaches have been pursued in the past to localize effector domains of epigenetic modifiers to a specific genomic context. They all can permanently alter the epigenetic code of the target region by depositing or removing specific epigenetic marks close to their binding site and thereby provide evidence for the role of an epigenetic mark at a locus of interest. These include transcription activator-like effectors (TALEs), zinc finger-based artificial transcription factors (ATFs) and the CRISPR/Cas9 system [1–4]. They all can be combined with a large number of effector domains, catalytically active protein fragments of epigenetic enzymes such as DNA methyltransferases, TETs and histone (de)methylases, acetyltransferases and deacetylases as well as transcriptional repressors and activators that are able to recruit chromatin modifying and remodeling complexes [4]. Initial studies using epigenome engineering tools have focused on the selective modulation of DNA methylation at specific loci using rationally designed zinc finger arrays. The clinically used DNA demethylating drugs are considered relatively safe because nonmalignant cells recover their ‘normal’ DNA methylation profile relatively rapidly. Nonetheless, they demethylate the entire genome of all cells regardless of the disease status of the cell. Therefore, strategies have been developed to target, for example, DNA demethylases such as the TET enzymes to hypermethylated promoters that are solely present in cancer cells and reactivate the corresponding gene that could have tumor suppressive properties using either zinc fingers [5] or TALEs [6]. On the other hand, genes specifically overexpressed in malignant cells can – to variable degrees – be silenced by targeting zinc finger-conjugated subunits of DNA Engineering of the epigenome: synthetic biology to define functional causality and develop innovative therapies

Enhanced-ice-COLD-PCR (E-ice-COLD-PCR) is a novel assay format that allows for the efficient enrichment and sensitive detection of all mutations in a region of interest using a chemically modified blocking oligonucleotide, which impedes the amplification of wild-type sequences. The assay is compatible with DNA extracted from tissue and cell-free circulating DNA. The main features of E-ice-COLD-PCR are the simplicity of the setup and the optimization of the assay, the use of standard laboratory equipment and the very short time to results (~4 h including DNA extraction, enrichment and sequence-based identification of mutations). E-ice-COLD-PCR is therefore a highly promising technology for a number of basic research as well as clinical applications including detection of clinically relevant mutated subclones and monitoring of treatment response or disease recurrence.

Aim: Most studies have considered gastroenteropancreatic neuroendocrine tumors (GEP-NETs) as a homogenous group of samples or distinguish only gastrointestinal from pancreatic endocrine tumors. This article investigates if DNA methylation patterns could distinguish subtypes of GEP-NETs. Materials & methods: The DNA methylation level of 807 cancer-related genes was investigated in insulinomas, gastrinomas, non-functioning pancreatic endocrine tumors and small intestine endocrine tumors. Results: DNA methylation patterns were found to be tumor type specific for each of the pancreatic tumor subtypes and identified two distinct methylation-based groups in small intestine endocrine tumors. Differences of DNA methylation levels were validated by pyrosequencing for 20 candidate genes and correlated with differences at the transcriptional level for four candidate genes. Conclusion: The heterogeneity of DNA methylation patterns in the different subtypes of gastroenteropancreatic neuroendocrine tumors suggests different underlying pathways and, therefore, these tumors should be considered as distinct entities in molecular and clinical studies.

Extensive dysregulation of chromatin-modifying genes in clear cell renal cell carcinoma (ccRCC) has been uncovered through next-generation sequencing. However, a scientific understanding of the cross-talk between epigenetic and genomic aberrations remains limited. Here we identify three ccRCC epigenetic clusters, including a clear cell CpG island methylator phenotype (C-CIMP) subgroup associated with promoter methylation of VEGF genes (FLT4, FLT1, and KDR). C-CIMP was furthermore characterized by silencing of genes related to vasculature development. Through an integrative analysis, we discovered frequent silencing of the histone H3 K36 methyltransferase NSD1 as the sole chromatin-modifying gene silenced by DNA methylation in ccRCC. Notably, tumors harboring NSD1 methylation were of higher grade and stage in different ccRCC datasets. NSD1 promoter methylation correlated with SETD2 somatic mutations across and within spatially distinct regions of primary ccRCC tumors. ccRCC harboring epigenetic silencing of NSD1 displayed a specific genome-wide methylome signature consistent with the NSD1 mutation methylome signature observed in Sotos syndrome. Thus, we concluded that epigenetic silencing of genes involved in angiogenesis is a hallmark of the methylator phenotype in ccRCC, implying a convergence toward loss of function of epigenetic writers of the H3K36 histone mark as a root feature of aggressive ccRCC. Cancer Res; 77(18); 4835–45. ©2017 AACR.

Allergic diseases are on the rise in the Western world and well-known allergy-protecting and -driving factors such as microbial and dietary exposure, pollution and smoking mediate their influence through alterations of the epigenetic landscape. Here, we review key facts on the involvement of epigenetic modifications in allergic diseases and summarize and critically evaluate the lessons learned from epigenome-wide association studies. We show the potential of epigenetic changes for various clinical applications: as diagnostic tools, to assess tolerance following immunotherapy or possibly predict the success of therapy at an early time point. Furthermore, new technological advances such as epigenome editing and DNAzymes will allow targeted alterations of the epigenome in the future and provide novel therapeutic tools.

Recent genomic studies of sporadic clear cell renal cell carcinoma (ccRCC) have uncovered novel driver genes and pathways. Given the unequal incidence rates among men and women (male:female incidence ratio approaches 2:1), we compared the genome-wide distribution of the chromosomal abnormalities in both sexes. We observed a higher frequency for the somatic recurrent chromosomal copy number variations (CNVs) of autosomes in male subjects, whereas somatic loss of chromosome X was detected exclusively in female patients (17.1%). Furthermore, somatic loss of chromosome Y (LOY) was detected in about 40% of male subjects, while mosaic LOY was detected in DNA isolated from peripheral blood in 9.6% of them, and was the only recurrent CNV in constitutional DNA samples. LOY in constitutional DNA, but not in tumor DNA was associated with older age. Amongst Y-linked genes that were downregulated due to LOY, KDM5D and KDM6C epigenetic modifiers have functionally-similar X-linked homologs whose deficiency is involved in ccRCC progression. Our findings establish somatic LOY as a highly recurrent genetic defect in ccRCC that leads to downregulation of hitherto unsuspected epigenetic factors, and suggest that different mechanisms may underlie the somatic and mosaic LOY observed in tumors and peripheral blood, respectively.

Somatic mutations bear great promise for use as biomarkers for personalized medicine, but are often present only in low abundance in biological material and are therefore difficult to detect. Many assays for mutation analysis in cancer-related genes (hotspots) have been developed to improve diagnosis, prognosis, prediction of drug resistance, and monitoring of the response to treatment. Two major approaches have been developed: mutation-specific amplification methods and methods that enrich and detect mutations without prior knowledge on the exact location and identity of the mutation. CO-amplification at Lower Denaturation temperature Polymerase Chain Reaction (COLD-PCR) methods such as full-, fast-, ice- (improved and complete enrichment), enhanced-ice, and temperature-tolerant COLD-PCR make use of a critical temperature in the polymerase chain reaction to selectively denature wild-type-mutant heteroduplexes, allowing the enrichment of rare mutations. Mutations can subsequently be identified using a variety of laboratory technologies such as high-resolution melting, digital polymerase chain reaction, pyrosequencing, Sanger sequencing, or next-generation sequencing. COLD-PCR methods are sensitive, specific, and accurate if appropriately optimized and have a short time to results. A large variety of clinical samples (tumor DNA, circulating cell-free DNA, circulating cell-free fetal DNA, and circulating tumor cells) have been studied using COLD-PCR in many different applications including the detection of genetic changes in cancer and infectious diseases, non-invasive prenatal diagnosis, detection of microorganisms, or DNA methylation analysis. In this review, we describe in detail the different COLD-PCR approaches, highlighting their specificities, advantages, and inconveniences and demonstrating their use in different fields of biological and biomedical research.

Motivation Copy number variations (CNV) include net gains or losses of part or whole chromosomal regions. They differ from copy neutral loss of heterozygosity (cn-LOH) events which do not induce any net change in the copy number and are often associated with uniparental disomy. These phenomena have long been reported to be associated with diseases and particularly in cancer. Losses/gains of genomic regions are often correlated with lower/higher gene expression. On the other hand, loss of heterozygosity (LOH) and cn-LOH are common events in cancer and may be associated with the loss of a functional tumor suppressor gene. Therefore, identifying recurrent CNV and cn-LOH events can be important as they may highlight common biological components and give insights into the development or mechanisms of a disease. However, no currently available tools allow a comprehensive whole-genome visualization of recurrent CNVs and cn-LOH in groups of samples providing absolute quantification of the aberrations leading to the loss of potentially important information. Results To overcome these limitations, we developed aCNViewer (Absolute CNV Viewer), a visualization tool for absolute CNVs and cn-LOH across a group of samples. aCNViewer proposes three graphical representations: dendrograms, bi-dimensional heatmaps showing chromosomal regions sharing similar abnormality patterns, and quantitative stacked histograms facilitating the identification of recurrent absolute CNVs and cn-LOH. We illustrated aCNViewer using publically available hepatocellular carcinomas (HCCs) Affymetrix SNP Array data (Fig 1A). Regions 1q and 8q present a similar percentage of total gains but significantly different copy number gain categories (p-value of 0.0103 with a Fisher exact test), validated by another cohort of HCCs (p-value of 5.6e-7) (Fig 2B). Availability and implementation aCNViewer is implemented in python and R and is available with a GNU GPLv3 license on GitHub https://github.com/FJD-CEPH/aCNViewer and Docker https://hub.docker.com/r/fjdceph/acnviewer/. Contact aCNViewer@cephb.fr

DNMT3L i.e. DNA (cytosine-5)-methyltransferase 3-like protein, is devoid of cytosine methyltransferase activity, despite clear homology to DNMT3A and DNMT3B, due to the mutation of key catalytic residues. However, DNMT3L participates in de novo methylation reactions through its direct interaction with DNMT3A and DNMT3B. In the present study, we investigated if DNMT3L interacts also directly with transcription factors (TFs). Using TF arrays, we identified 73 TFs that interacted with DNMT3L, 13 of which (ASH2L, ATF1, ATF3, BLZF1, CDX2, CERM, E2F3, E2F4, GCNF, GTF2I, GTF3C5, NFkB-p65 and RXRα) interacted only with DNMT3L, but not with DNMT3A/B. By focusing on the interaction with NFkB-p65, we demonstrate that DNMT3L forms a complex with DNMT3B and NFkB-p65 and that this complex is required for the control of DNA methylation at the TRAF1 promoter in the T98G glioma cell line. In addition, our experiments describe the DNA methylation at TRAF1 as being dynamic with a demethylation phase involving TET3. Thus, our data suggests that DNMT3L can address DNMT3A/B to specific sites by directly interacting with TFs that do not directly interact with DNMT3A/B. In summary, our data provide a new avenue for the direction of site-specific de novo DNA methylation catalyzed by DNMT3A/B.

Background Genomewide association and epigenetic studies found a region within the RAD50 gene on chromosome 5q31 to be associated with total serum IgE levels and asthma. In mice, this region harbors a locus control region for nearby TH2 cytokines, which is characterized by four Rad50 DNase I hypersensitive sites (RHS4–7). Among these, RHS7 seems to have the strongest impact on TH2 differentiation. We investigated whether within the human homolog of RHS7, functional polymorphisms exist, which could affect DNA methylation or gene expression in the 5q31 locus and might have an influence on asthma status or IgE regulation. Methods The human RHS7 region was fine mapped using 1000 genomes database information. In silico analysis and electrophoretic mobility shift assays were used to assess SNP function. Allele-specific effects on DNA methylation were evaluated in cord blood (n = 73) and at age of 4.5 years (n = 61) by pyrosequencing. Allele-specific effects on RAD50, IL4, and IL13 expression were analyzed in 100 subjects. Associations with asthma and IgE levels were investigated in the MAGICS/ISAAC II population (n = 1145). Results Polymorphism rs2240032 in the RHS7 region is suggestive of allele-specific transcription factor binding, affects methylation of the IL13 promoter region and influences RAD50 and IL4 expression (lowest P = 0.0027). It is also associated with total serum IgE levels (P = 0.0227). Conclusion A functional relevant polymorphism in the TH2 locus control region, equivalent to RHS7 in mice, affects DNA methylation and gene expression within 5q31 and influences total serum IgE on the population level.

Breast cancer is a heterogeneous disease for which alterations in DNA methylation patterns have been shown to be of biological and clinical importance. Here we report on the integrated analysis of molecular alterations including the methylation status of 27 gene promoters analyzed by highly quantitative pyrosequencing, and the association to gene expression, germline genotype and clinical parameters including survival. Breast cancer specific deregulation of DNA methylation (both hyper- and hypomethylation) was found in twenty genes including ACVR1, OGG1, IL8 and TFF1. The methylation level in the promoter regions was significantly negatively correlated to gene expression for twelve genes (such as MST1R, ST6GAL1 and TFF1) indicating that a gain of aberrant methylation (hypermethylation) inhibits gene expression. Multiple associations between molecular and clinical parameters were identified, and multivariate statistical analysis demonstrated that methylation was more strongly associated to clinical parameters than gene expression for the investigated genes. The methylation level of BCAP31 and OGG1 showed significant association to survival, and these associations were validated in a larger patient cohort (The Cancer Genome Atlas). Our study provides evidence for the promise of DNA methylation alterations for clinical applications.

Background Lentiviral vectors (LV) are widely used for various gene transfer or gene therapy applications. The effects of LV on target cells are expected to be limited to gene delivery. Yet, human hematopoietic CD34+ cells respond to functional LVs as well as several types of non-integrating LVs by genome-wide DNA methylation changes. Results A new algorithm for the analysis of 450K Illumina data showed that these changes were marked by de novo methylation. The same 4126 cytosines located in islands corresponding to 1059 genes were systematically methylated. This effect required cellular entry of the viral particle in the cells but not the genomic integration of the vector cassette. Some LV preparations induced only mild sporadic changes while others had strong effects suggesting that LV batch heterogeneity may be related to the extent of the epigenetic response. Conclusion These findings identify a previously uncharacterized but consistent cellular response to viral components and provide a novel example of environmentally modified epigenome. Electronic supplementary material The online version of this article (doi:10.1186/s13072-016-0077-1) contains supplementary material, which is available to authorized users.

BACKGROUND: Genetic susceptibility and environmental influences are important contributors to the development of asthma and atopic diseases. Epigenetic mechanisms may facilitate gene by environment interactions in these diseases. METHODS: We studied the rural birth cohort PASTURE (Protection against allergy: study in rural environments) to investigate (a) whether epigenetic patterns in asthma candidate genes are influenced by farm exposure in general, (b) change over the first years of life, and (c) whether these changes may contribute to the development of asthma. DNA was extracted from cord blood and whole blood collected at the age of 4.5 years in 46 samples per time point. DNA methylation in 23 regions in ten candidate genes (ORMDL1, ORMDL2, ORMDL3, CHI3L1, RAD50, IL13, IL4, STAT6, FOXP3, and RUNX3) was assessed by pyrosequencing, and differences between strata were analyzed by nonparametric Wilcoxon-Mann-Whitney tests. RESULTS: In cord blood, regions in ORMDL1 and STAT6 were hypomethylated in DNA from farmers' as compared to nonfarmers' children, while regions in RAD50 and IL13 were hypermethylated (lowest P-value (STAT6) = 0.001). Changes in methylation over time occurred in 15 gene regions (lowest P-value (IL13) = 1.57*10(-8) ). Interestingly, these differences clustered in the genes highly associated with asthma (ORMDL family) and IgE regulation (RAD50, IL13, and IL4), but not in the T-regulatory genes (FOXP3, RUNX3). CONCLUSIONS: In this first pilot study, DNA methylation patterns change significantly in early childhood in specific asthma- and allergy-related genes in peripheral blood cells, and early exposure to farm environment seems to influence methylation patterns in distinct genes

Breast cancers exhibit genome-wide aberrant DNA methylation patterns. To investigate how these affect the transcriptome and which changes are linked to transformation or progression, we apply genome-wide expression–methylation quantitative trait loci (emQTL) analysis between DNA methylation and gene expression. On a whole genome scale, in cis and in trans, DNA methylation and gene expression have remarkably and reproducibly conserved patterns of association in three breast cancer cohorts (n = 104, n = 253 and n = 277). The expression–methylation quantitative trait loci associations form two main clusters; one relates to tumor infiltrating immune cell signatures and the other to estrogen receptor signaling. In the estrogen related cluster, using ChromHMM segmentation and transcription factor chromatin immunoprecipitation sequencing data, we identify transcriptional networks regulated in a cell lineage-specific manner by DNA methylation at enhancers. These networks are strongly dominated by ERα, FOXA1 or GATA3 and their targets were functionally validated using knockdown by small interfering RNA or GRO-seq analysis after transcriptional stimulation with estrogen., Breast cancers are classified based on gene expression profiles but it is well known that DNA methylation patterns are also altered. Here the authors, by combining genome-wide DNA methylation and gene expression analysis, identify transcriptional networks regulated by DNA methylation at enhancers able to separate specific breast cancer subtypes.

Background A large number of epigenetic alterations has been found to be implicated in the etiology of gastric cancer. We have studied the DNA methylation status of 27 500 gene promoter regions in 24 gastric adenocarcinomas from a Norwegian cohort, and aimed at identifying the hypermethylated regions. We have compared our findings to the gene expression in the same tissue, and linked our results to prognosis and survival. Methods Biopsies from gastric adenocarcinomas and adjacent normal gastric mucosa were obtained from 24 patients following surgical resection of the tumor. Genome-wide DNA methylation profiling of the tumor and matched non-cancerous mucosa was performed. The results were compared to whole transcriptome cDNA microarray analysis of the same material. Results Most of the gene promoter regions in both types of tissue showed a low degree of methylation, however there was a small, but significant hypermethylation of the tumors. Hierarchical clustering showed separate grouping of the tumor and normal tissue. Hypermethylation of the promoter region of the GFRA3 gene showed a strong correlation to post-operative survival and several of the clinicopathological parameters, however no difference was found between the two main histological types of gastric cancer. There was only a modest correlation between the DNA methylation status and gene expression. Conclusions The different DNA methylation clusters of the tumors and normal tissue indicate that aberrant DNA methylation is a distinct feature of gastric cancer, although there is little difference in the overall, and low, methylation levels between the two tissue types. The GFRA3 promoter region showed marked hypermethylation in almost all tumors, and its correlation with survival and other clinicopathological parameters may have important prognostic significance.

Background: Huge progress has been made in the development of array- or sequencing-based technologies for DNA methylation analysis. The Illumina Infinium® Human Methylation 450K BeadChip (Illumina Inc., CA, USA) allows the simultaneous quantitative monitoring of more than 480,000 CpG positions, enabling large-scale epigenotyping studies. However, the assay combines two different assay chemistries, which may cause a bias in the analysis if all signals are merged as a unique source of methylation measurement. Materials & methods: We confirm in three 450K data sets that Infinium I signals are more stable and cover a wider dynamic range of methylation values than Infinium II signals. We evaluated the methylation profile of Infinium I and II probes obtained with different normalization protocols and compared these results with the methylation values of a subset of CpGs analyzed by pyrosequencing. Results: We developed a subset quantile normalization approach for the processing of 450K BeadChips. The Infinium I signals were used as ‘anchors’ to normalize Infinium II signals at the level of probe coverage categories. Our normalization approach outperformed alternative normalization or correction approaches in terms of bias correction and methylation signal estimation. We further implemented a complete preprocessing protocol that solves most of the issues currently raised by 450K array users. Conclusion: We developed a complete preprocessing pipeline for 450K BeadChip data using an original subset quantile normalization approach that performs both sample normalization and efficient Infinium I/II shift correction. The scripts, being freely available from the authors, will allow researchers to concentrate on the biological analysis of data, such as the identification of DNA methylation signatures.

Breast cancer is a heterogeneous disease that can be divided in subtypes based on histology, gene expression profiles as well as differences in genomic aberrations. Distinct global DNA methylation profiles have been reported in normal breast epithelial cells as well as in breast tumors. However, the influence of the tumor methylome on the previously described subgroups of breast cancer is not fully understood. Here we report the DNA methylation profiles of 80 breast tumors using a panel of 807 cancer related genes interrogating 1505 CpG sites. We identified three major clusters based on the methylation profiles; one consisting of mainly tumors of myoepithelial origin and two other clusters with tumors of predominantly luminal epithelial origin. The clusters were different with respect to estrogen receptor status, TP53 status, ErbB2 status and grade. The most significantly differentially methylated genes including HDAC1, TFF1, OGG1, BMP3, FZD9 and HOXA11 were confirmed by pyrosequencing. Gene Ontology analysis revealed enrichment for genes involved in developmental processes including homeobox domain genes (HOXA9, HOXA11, PAX6, MYBL2, ISL1 and IPF1) and (ETS1, HDAC1, CREBBP, GAS7, SPI1 and TBX1). Extensive correlation to mRNA expression was observed. Pathway analyses identified a significant association with canonical (curated) pathways such as hepatic fibrosis including genes like EGF, NGFR and TNF, dendritic cell maturation and the NF‐κB signaling pathway. Our results show that breast tumor expression subtypes harbor major epigenetic differences and tumors with similar gene expression profiles might belong to epigenetically different subtypes. Some of the transcription factors identified, with key roles in differentiation and development might play a role in inducing and maintaining the different phenotypes.

Aberrant DNA methylation patterns have been identified in a variety of human diseases, particularly cancer. Pyrosequencing has evolved in recent years as a sensitive and accurate method for the analysis and quantification of the degree of DNA methylation in specific target regions. However, the number of candidate genes that can be analyzed in clinical specimens is often restricted by the limited amount of sample available. Here, we present a novel screening approach that enables the rapid identification of differentially methylated regions such as promoters by pyrosequencing of etiologically homogeneous sample pools after bisulfite treatment. We exemplify its use by the analysis of five genes (CDKN2A, GSTP1, MLH1, IGF2, and CTNNB1) involved in the pathogenesis of human hepatocellular carcinoma using pools stratified for different parameters of clinical importance. Results were confirmed by the individual analysis of the samples. The screening identified all genes displaying differential methylation successfully, and no false positives occurred. Quantitative comparison of the pools and the samples in the pool analyzed individually showed a deviation of approximately 1.5%, making the method ideally suited for the identification of diagnostic markers based on DNA methylation while saving precious DNA material.

Altered DNA methylation patterns are found in many diseases, particularly in cancer, where the analysis of DNA methylation holds the promise to provide diagnostic, prognostic and predictive information of great clinical value. Methylation of the promoter-associated CpG island of GSTP1 occurs in many hormone-sensitive cancers, has been shown to be a biomarker for the early detection of cancerous lesions and has been associated with important clinical parameters, such as survival and response to treatment. In the current manuscript, we assessed the performance of several widely-used sodium bisulfite conversion-dependent methods (methylation-specific PCR, MethyLight, pyrosequencing and MALDI mass-spectrometry) for the analysis of DNA methylation patterns in the GSTP1 promoter. We observed large discordances between the results obtained by the different technologies. Cloning and sequencing of the investigated region resolved single-molecule DNA methylation patterns and identified heterogeneous DNA methylation patterns as the underlying cause of the differences. Heterogeneous DNA methylation patterns in the GSTP1 promoter constitute a major obstacle to the implementation of DNA methylation-based analysis of GSTP1 and might explain some of the contradictory findings in the analysis of the significance of GSTP1 promoter methylation in breast cancer.

Introduction: Epigenetic marks are likely to bear the memory of early life environmental exposures and to be associated with health on short and long terms. We tested whether exposure to nitrogen d...

Introduction: Epigenetic marks are likely to bear the memory of early life environmental exposures and to be associated with health on short and long terms. In a genome-wide study, we had identifie...

Background: DNA methylation alterations are early events in tumorigenesis and important in the regulation of gene expression in cancer cells. Lung cancer patients have in general a poor prognosis, and a deeper insight into the epigenetic landscape in lung adenocarcinoma tumors and its prognostic implications is needed. Results: We determined whole‐genome DNA methylation profiles of 164 fresh frozen lung adenocarcinoma samples and 19 samples of matched normal lung tissue using the Illumina Infinium 450K array. A large number of differentially methylated CpGs in lung adenocarcinoma tissue were identified, and specific methylation profiles were observed in tumors with mutations in the EGFR‐, KRAS‐ or TP53 genes and according to the patients' smoking status. The methylation levels were correlated with gene expression and both positive and negative correlations were seen. Methylation profiles of the tumor samples identified subtypes of tumors with distinct prognosis, including one subtype enriched for TP53 mutant tumors. A prognostic index based on the methylation levels of 33 CpGs was established, and was significantly associated with prognosis in the univariate analysis using an independent cohort of lung adenocarcinoma patients from The Cancer Genome Atlas project. CpGs in the HOX B and HOX C gene clusters were represented in the prognostic signature. Conclusions: Methylation differences mirror biologically important features in the etiology of lung adenocarcinomas and influence prognosis., Highlights Extensive DNA methylation changes linked to immune signaling.DNA methylation in EGFR‐mutated tumors associated with oncogenic pathways.DNA methylation with both positive and negative correlation with mRNA expression.DNA methylation signature with prognostic information.

International audience; Pyrosequencing permits accurate quantification of DNA methylation of specific regions where the proportions of the C/T polymorphism induced by sodium bisulfite treatment of DNA reflects the DNA methylation level. The commercially available high-throughput locus-specific pyrosequencing instruments allow for the simultaneous analysis of 96 samples, but restrict the DNA methylation analysis to CpG dinucleotide sites, which can be limiting in many biological systems. In contrast to mammals where DNA methylation occurs nearly exclusively on CpG dinucleotides, plants genomes harbor DNA methylation also in other sequence contexts including CHG and CHH motives, which cannot be evaluated by these pyrosequencing instruments due to software limitations. Here, we present a complete pipeline for accurate CpG and non-CpG cytosine methylation analysis at single base-resolution using high-throughput locus-specific pyrosequencing. The devised approach includes the design and validation of PCR amplification on bisulfite-treated DNA and pyrosequencing assays as well as the quantification of the methylation level at every cytosine from the raw peak intensities of the Pyrograms by two newly developed Visual Basic Applications. Our method presents accurate and reproducible results as exemplified by the cytosine methylation analysis of the promoter regions of two Tomato genes (NOR and CNR) encoding transcription regulators of fruit ripening during different stages of fruit development. Our results confirmed a significant and temporally coordinated loss of DNA methylation on specific cytosines during the early stages of fruit development in both promoters as previously shown by WGBS. The manuscript describes thus the first high-throughput locus-specific DNA methylation analysis in plants using pyrosequencing.

The analysis of allele-specific DNA methylation patterns has recently attracted much interest as loci of allele-specific DNA methylation overlap with known risk loci for complex diseases and the analysis might contribute to the fine-mapping and interpretation of non-coding genetic variants associated with complex diseases and improve the understanding between genotype and phenotype. In the presented protocol, we present a method for the analysis of DNA methylation patterns on both alleles separately using heterozygous Single Nucleotide Polymorphisms (SNPs) as anchor for allele-specific PCR amplification followed by analysis of the allele-specific DNA methylation patterns by Pyrosequencing(®). Pyrosequencing is an easy-to-handle, quantitative real-time sequencing method that is frequently used for genotyping as well as for the analysis of DNA methylation patterns. The protocol consists of three major steps: (1) identification of individuals heterozygous for a SNP in a region of interest using Pyrosequencing; (2) analysis of the DNA methylation patterns surrounding the SNP on bisulfite-treated DNA to identify regions of potential allele-specific DNA methylation; and (3) the analysis of the DNA methylation patterns associated with each of the two alleles, which are individually amplified using allele-specific PCR. The enrichment of the targeted allele is re-enforced by modification of the allele-specific primers at the allele-discriminating base with Locked Nucleic Acids (LNA). For the proof-of-principle of the developed approach, we provide assay details for three imprinted genes (IGF2, IGF2R, and PEG3) within this chapter. The mean of the DNA methylation patterns derived from the individual alleles corresponds well to the overall DNA methylation patterns and the developed approach proved more reliable compared to other protocols for allele-specific DNA methylation analysis.

International audience; Interaction and co-occurrence of protein and DNA-based epigenetic modifi cations have become a topic of interest for many fundamental and biomedical questions. We describe within this chapter a protocol that combines two techniques in order to determine the methylation status of the DNA specifi cally associated with a protein of interest. First, DNA that directly interacts with the selected protein (such as a specifi c histone modifi cation, a transcription factor, or any other DNA-associated protein) is purifi ed by standard chromatin immunoprecipitation (ChIP). Second, the level of DNA methylation of this immunoprecipitated DNA is measured by bisulfi te conversion and Pyrosequencing, a quantitative sequencing-by-synthesis method. This procedure allows determining the methylation status of genomic DNA associated to a specifi c protein at single nucleotide resolution.

International audience; In plants, genomic DNA methylation which contributes to development and stress responses can be actively removed by DEMETER-like DNA demethylases (DMLs). Indeed, in Arabidopsis DMLs are important for maternal imprinting and endosperm demethylation, but only a few studies demonstrate the developmental roles of active DNA demethylation conclusively in this plant. Here, we show a direct cause and effect relationship between active DNA demethylation mainly mediated by the tomato DML, SlDML2, and fruit ripening- an important developmental process unique to plants. RNAi SlDML2 knockdown results in ripening inhibition via hypermethylation and repression of the expression of genes encoding ripening transcription factors and rate-limiting enzymes of key biochemical processes such as carotenoid synthesis. Our data demonstrate that active DNA demethylation is central to the control of ripening in tomato.

Reduce, Reuse, Recycle Epigenotyping has become increasingly prevalent as a result of the accumulating evidence linking aberrant DNA methylation with human disease. Specifically, methylation of CpG...

Breast cancers exhibit genome-wide aberrant DNA methylation patterns. To investigate how these affect the transcriptome and which changes are linked to transformation or progression we applied genome-wide expression-methylation quantitative trait loci (emQTL) analysis between 5meCpG and gene expression. We find that on a whole genome scale, DNA methylation and gene expression have remarkably and reproducibly conserved patterns of association, both in cis and in trans, which lead to identify distinct transcriptional networks and specific disease cell lineages deviating from the normal tissue. In three breast cancer cohorts (n=104, n=253 and n=330), we invariably identify enhancers whose DNA methylation tethers the binding of three key transcription factors in breast cancer in a lineage specific manner. Our emQTL analysis also identifies tumor infiltrating immune cell signatures. Using ChromHMM segmentation and ChIP-seq associated TF binding regions, we identify enhancers and TF binding sites around emQTLs-CpGs. We functionally validate the genes associated with these CpGs using knock-down by siRNA and applying GRO-seq analysis after transcriptional stimulation with estrogen. DNA methylation at TF binding regions is considered an early event during normal breast cell transformation into estrogen dependent breast cancer. Two distinct key gene regulatory networks reported here are prominently altered by DNA methylation. Citation Format: Thomas Fleischer, Xavier Tekpli, Anthony Mathelier, Shixiong Wang, Daniel Nebdal, Hari P. Dhakal, Kristine Kleivi Sandberg, Ellen Schliting, Oslo Breast Cancer Research Consortium (OSBREAC), Anne-Lise Børresen-Dale, Elin Borgen, Bjørn Naume, Ragnhild Eskeland, Arnoldo Frigessi, Jörg Tost, Antoni Hurtado, Vessela N. Kristensen. DNA methylation at enhancers distinguishes distinct breast cancer lineages [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-097. doi:10.1158/1538-7445.AM2017-LB-097

Background The structural organisation of the human IGF2/ ICR1 /H19 11p15 domain is very complex, and the mechanisms underlying its regulation are poorly understood. The Imprinted Center Region 1 (ICR1) contains seven binding sites for the zinc-finger protein CTCF (CBS: CTCF Binding Sites); three additional differentially methylated regions (DMR) are located at the H19 promoter ( H19 DMR) and two in the IGF2 gene (DMR0 and DMR2), respectively. Loss of imprinting at the IGF2/ ICR1 /H19 domain results in two growth disorders with opposite phenotypes: Beckwith–Wiedemann syndrome and Russell Silver syndrome (RSS). Despite the IGF2/ ICR1 /H19 locus being widely studied, the extent of hypomethylation across the domain remains not yet addressed in patients with RSS. Methods We assessed a detailed investigation of the methylation status of the 11p15 ICR1 CBS1-7, IGF2 DMR0 and H19 DMR ( H19 promoter) in a population of controls (n=50) and RSS carrying (n=104) or not (n=65) carrying a hypomethylation at the 11p15 ICR1 region. Results The methylation indexes (MI) were balanced at all regions in the control population and patients with RSS without any as yet identified molecular anomaly. Interestingly, patients with RSS with ICR1 hypomethylation showed uneven profiles of methylation among the CBSs and DMRs. Furthermore, normal MIs at CBS1 and CBS7 were identified in 9% of patients. Conclusions The hypomethylation does not spread equally throughout the IGF2/ ICR1 /H19 locus, and some loci could have normal MI, which may lead to underdiagnosis of patients with RSS with ICR1 hypomethylation. The uneven pattern of methylation suggests that some CBSs may play different roles in the tridimensional chromosomal looping regulation of this locus.

The incidence of renal cell carcinoma (RCC) is increasing worldwide, and its prevalence is particularly high in some parts of Central Europe. Here we undertake whole-genome and transcriptome sequencing of clear cell RCC (ccRCC), the most common form of the disease, in patients from four different European countries with contrasting disease incidence to explore the underlying genomic architecture of RCC. Our findings support previous reports on frequent aberrations in the epigenetic machinery and PI3K/mTOR signalling, and uncover novel pathways and genes affected by recurrent mutations and abnormal transcriptome patterns including focal adhesion, components of extracellular matrix (ECM) and genes encoding FAT cadherins. Furthermore, a large majority of patients from Romania have an unexpected high frequency of A:T>T:A transversions, consistent with exposure to aristolochic acid (AA). These results show that the processes underlying ccRCC tumorigenesis may vary in different populations and suggest that AA may be an important ccRCC carcinogen in Romania, a finding with major public health implications.