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News Article | April 17, 2017
Site: www.eurekalert.org

Cholera cases in East Africa increase by roughly 50,000 during El Niño, the cyclical weather occurrence that profoundly changes global weather patterns, new Johns Hopkins Bloomberg School of Public Health research suggests. The findings, researchers say, could help health ministries anticipate future cholera surges during El Niño years and save lives. The researchers, reporting April 10 in the Proceedings of the National Academy of Sciences, used sophisticated mapping to pinpoint the location of clusters of cholera cases before, during and after El Niño years. Cholera is an infectious and often fatal bacterial disease, typically contracted from infected water supplies and causing severe vomiting and diarrhea. Africa has the most cholera deaths in the world. "We usually know when El Niño is coming six to 12 months before it occurs," says study leader Justin Lessler, an associate professor of epidemiology at the Bloomberg School. "Knowing there is elevated cholera risk in a particular region can help reduce the number of deaths that result. If you have cholera treatment centers available, fast, supportive care can reduce the fatality rate from cholera from as high as 30 percent to next to nothing." The total number of cases of cholera across Africa as a whole were about the same in El Niño years as compared to non-El Niño years, the researchers found, but the geographic distribution of illnesses was fundamentally different. El Niño conditions in the equatorial Pacific region strongly impact weather conditions globally, including increasing rainfall in East Africa and decreasing rainfall in drier areas of northern and southern Africa. During the years classified as El Niño between 2000 and 2014, cholera incidence increased threefold in regions such as East Africa that had the strongest association between El Niño and cholera, with 177 million people living in areas that experienced an increase in cholera cases during a time of additional rainfall. At the same time, there were 30,000 fewer cases in southern Africa during El Niño where there was less rainfall than normal. Parts of central West Africa, however, saw significantly fewer cases of cholera, but with little change in rainfall patterns. While El Niño brings wetter and warmer weather to East Africa, rainfall is not the only variable that appears to impact cholera rates, Lessler says. Cholera is almost always linked to vulnerable water systems. In some areas, massive rainfall can overrun sewer systems and contaminate drinking water. In other locations, however, dry conditions can mean that clean water sources aren't available and people must consume water from sources known to be contaminated. "Countries in East Africa, including Tanzania and Kenya, have experienced several large cholera outbreaks in recent decades," says study author Sean Moore, PhD, a post-doctoral fellow in the Bloomberg School's Department of Epidemiology. "Linking these outbreaks to El Niño events and increased rainfall improves our understanding of the environmental conditions that promote cholera transmission in the region and will help predict future outbreaks." For the study, Lessler, Moore and their colleagues collected data on cholera cases in Africa from 360 separate data sets, analyzing 17,000 annual observations from 3,710 different locations between 2000 and 2014. The researchers note that there were weak El Niño years from 2004 to 2007, while 2002-2003 and 2009-2010 were classified as moderate-to-strong El Niño years. They say that 2015-2016 was also an El Niño year with the largest cholera outbreak since the 1997-1998 El Niño occurring in Tanzania. Using this knowledge of a link between cholera and El Niño could allow countries to prepare for outbreaks long before they start, Lessler says. Currently, there is an approved vaccine for cholera, but its effects are not lifelong and there are not enough doses for everyone in areas that could be impacted by El Niño. Once there is more vaccine, he says, it can be another tool for health officials to use as they try to prevent deadly cholera in their nations. As climate change continues, disease patterns will continue to change as well, Lessler says. Often, the story is that climate change will put more people at risk for more types of diseases. "But what the link between cholera and El Niño tells us is that changes may be subtler than that," he says. "There will be winners and losers. It's not a one-way street." "El Niño and the Shifting Geography of Cholera in Africa" was written by Sean Moore, Andrew Azman, Benjamin Zaitchik, Eric Mintz, Joan Brunkard, Dominique Legros, Alexandra Hill, Heather McKay, Francisco Luquero, David Olson and Justin Lessler. The research was supported by a grant from the Bill and Melinda Gates Foundation and the National Science Foundation. Cholera data was provided by the Ministries of Health of Benin, Democratic Republic of Congo, Mozambique, South Sudan and Nigeria as well as Médecins Sans Frontières and MSF/Epicentre, the World Health Organization and the United Nations Relief Agency.


Human embryonic kidney 293 (HEK293) cells, HEK293FT, OVCAR5, A375, HeLa, and mouse embryonic fibroblasts (MEFs) were maintained in Dulbecco’s Modified Eagle’s Medium (DMEM) containing 10% fetal bovine serum (FBS), 100 units of penicillin and 100 mg ml−1 streptomycin. Gβl+/+ and Gβl−/− MEFs were generous gifts from D. M. Sabatini (Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology). Traf2+/+ and Traf2−/− MEFs were obtained from Y. Sun (Department of Radiation Oncology, University of Michigan). Otud7b+/+ and Otud7b−/− MEFs have been described previously29. All the cell lines were routinely tested negative for mycoplasma contamination. Transfection was performed using lipofectamine 2000 reagent as described previously9, 10. For serum starvation, 32 h post-transfection, cells were washed with PBS twice and cultured in FBS-free DMEM for 14–16 h. To initiate growth factor signalling, the medium was added with EGF (Sigma E9644, 100 ng ml−1) or insulin (Invitrogen 41400-045,100 nM) for indicated period of times. Rabbit polyclonal antibody against human OTUD7B was purchased from Cell Signaling Technology (CST). Anti-mouse Otud7b antibody was obtained from Proteintech. Anti-SIN1 antibodies were purchased from CST (12860) or generated (K87) in the B. Su laboratory (Department of Immunobiology and the Vascular Biology and Therapeutics Program, Yale University). Primary antibodies against TRAF2 (4724), RICTOR (9476), RPTOR (2280), mTOR (2983), AKT (pS473) (4051 and 4060), AKT(pT308) (2965), S6K(pT389) (9205), pFOXO1(Thr24)/FOXO3a(Thr32) (9464), AKT (4691), S6K (2708), FOXO1 (9454), GST tag (2625) and Myc Tag (2278 and 2276) were purchased from CST. Primary antibodies against GβL were purchased from CST (3274) and Bethyl (A300-679A). Rabbit polyclonal anti-HA antibody (MMS-101P) was purchased from BioLegend. Mouse monoclonal anti-HA antibody (sc-805) was obtained from Santa Cruz. Rabbit antibody against the Flag epitope (F7425), mouse monoclonal anti-Flag antibody (F3165), mouse monoclonal anti-Flag M2 affinity agarose beads (A2220), mouse monoclonal anti-HA agarose beads (A2095), anti-tubulin antibody (T5168), peroxidase-conjugated secondary anti-mouse (A4416) and anti-rabbit (A4914) antibodies were purchased from Sigma. These primary antibodies were used at a 1:1,000 dilution and secondary antibodies were diluted at 1:3,000 in 5% non-fat milk for immunoblotting analysis. For immunoblot analysis of mouse normal lung and tumour tissues, antibodies for Akt1 (B-1), HSP60 (H1) were purchased from Santa Cruz Biotechnology, Inc. Expression vectors CMV-GST-GβL (amino acid (aa) 1–326), CMV-GST-GβL-ΔWD6 (deleting aa 211–262), CMV-GST-GβL-ΔWD7 (aa 1–271), CMV-GST-GβL-WD6 (aa 211–271), CMV-GST-GβL-WD7 (aa 263–326), CMV-GST-GβL-WD6+7 (aa 211–326) and CMV-GST-UCH-L1 were generated by subcloning the corresponding cDNAs into the pCMV-GST vector via BamHI/BglII and XhoI/SalI sites. HA-GβL (aa 1–326), HA-GβL-ΔWD6 (deleting aa 211–262), HA-GβL-ΔWD7 (aa 1–271), HA-GβL-ΔWD6+7 (aa 1–211), HA-GβL-ΔW297 (aa 1–297), HA-SIN1, HA-RICTOR, HA-Rptor, HA-mTOR, HA-TRAF1, HA-TRAF2 and HA-TRAF3 were constructed by cloning the corresponding cDNAs into pcDNA3-HA vector via BamHI and XhoI sites. Flag-TRAF2 and Flag-TRAF2-ΔRING were kindly gifted by Y. Sun (Department of Radiation Oncology, University of Michigan). Myc-RPTOR, Myc-TRAF2, Myc-TRAF6 or Flag-TRAF6, Flag-SKP2, Flag-RNF168 and Flag-SIN1 were constructed by cloning the corresponding cDNAs into pcDNA3-Myc or pcDNA3-Flag vector via BamHI and XhoI sites. Flag-Myc-OTUB1, Flag-Myc-OTUB2, Flag-Myc-OTUD3, Flag-Myc-OTUD4, Flag-Myc-OTUD5, Flag-Myc-OTUD6A, Flag-Myc-OTUD6B, Flag-Myc-OTUD7A and Flag-Myc-OTUD7B constructs have been described previously31. The HA-GβL(K305R), HA-GβL(K313R), HA-GβL(K305R/K313R) (KRKR), HA-GβL(P265A/E267A) (PEAA), Flag-Myc-OTUD7B(C194A) mutants29, 32 were constructed using the Site-Directed Mutagenesis Kit (Stratagene) following the manufacturer’s instructions. His-Ub, His-Ub(K6R), His-Ub(K11R), His-Ub(K27R), His-Ub(K29R), His-Ub(K33R), His-Ub(K48R), His-Ub(K63R), His-Ub(K0), His-Ub(K48) only and His-Ub(K63) only vectors were provided by P. P. Pandolfi (Beth Israel Deaconess Medical Center, Harvard Medical School). GβL shRNA vectors were purchased from GE Healthcare Dharmacon (Clone ID: TRCN0000039758, TRCN0000039759, TRCN0000039760, TRCN0000039761, TRCN0000039762). Lentiviral shRNA vectors depleting human OTUD7B were from an shRNA library targeting human de-ubiquitinating enzymes (OBS Catalogue number: RHS6054), purchased from Thermo Scientific Open Biosystems. The target sequences are OTUD7B shRNA #1: 5′-CGGCGGAAGGAGAAGTCAA-3′ and OTUD7B shRNA #2: 5′-ACGTCTTTGTCCTTGCTCA-3′. For lentiviral shRNA infection, HEK293FT cells were transfected with shGFP or GβL shRNA or OTUD7B shRNA plenti-puro vectors, together with packing vectors (Δ8.9 and VSVG plasmids) using lipofectamine 2000 reagent as previously described9, 10. To restore GβL expression, GβL mutants resistant to shRNA (GβL-shRes) were constructed from HA-GβL, HA-GβL(PEAA) and HA-GβL(KRKR) vectors using the following primer sets: 5′-CAATAGCACCGGCAACTGCTACGTATGGAATCTGACG-3′ (sense) and 5′-CGTCAGATTCCATACGTAGCAGTTGCCGGTGCTATTG-3′ (antisense). The shRes HA-GβL, HA-GβL(PEAA) and HA-GβL(KRKR) were subcloned into pBabe-hygro retroviral vectors33 and co-transfected with packaging vectors (Retro-VSVG, JK3, and CMV-TAT plasmids) into HEK293FT cells using lipofectamine 2000 reagent. All the constructs were confirmed by DNA sequencing. The cells were infected with various virus particles and selected with medium containing puromycin and/or hygromycin for at least three days. Cellular ubiquitination assays were performed as described previously34. In brief, HEK293 cells were co-transfected with His-Ub and the indicated vectors for 48 h and lysed in denaturing condition (buffer A: 6 M guanidine-HCl, 0.1 M Na HPO /NaH PO , 10 mM imidazole (pH 8.0)). After sonication, the poly-ubiquitinated proteins were purified by incubation with nickel-nitrilotriacetic acid (Ni-NTA) matrices (QIAGEN) for 3 h at room temperature. Histidine pull-down products were washed sequentially once in buffer A, twice in buffer A/TI mixture (buffer A:buffer TI = 1:3), and once in buffer TI (25 mM Tris-HCl and 20 mM imidazole (pH 6.8)). The poly-ubiquitinated proteins were separated by SDS–PAGE for immunoblot analysis. HEK293 cells were transfected with CMV-GST-GβL and ubiquitin expression constructs. Thirty-two hours post-transfection, the cells were serum starved for 16 h, with or without insulin stimulation, and lysed using Triton buffer for GST pull-down. The GST pull-down products were eluted with glutathione-containing buffer and then subjected to Ubiquitin Absolute Quantification (UB-AQUA) mass spectrometry (MS) analysis of ubiquitin chain linkage. For UB-AQUA/PRM, samples were subject to TCA precipitation. Samples were digested first with Lys-C (in 100 mM tetraethylammonium bromide (TEAB), 0.1% Rapigest (Waters Corporation), 10% (vol/vol) acetonitrile (ACN)) for 2 h at 37 °C, followed by the addition of trypsin and further digested overnight. Digests were acidified with an equal volume of 5% (vol/vol) formic acid (FA) to a pH of approximately 2 for 30 min, dried down, and resuspended in 1% (vol/vol) FA. UB-AQUA/PRM was performed largely as described previously but with several modifications35, 36, 37. A collection of 16 heavy-labelled reference peptides36, each containing a single 13C/15N-labelled amino acid, was produced at Cell Signaling Technologies and quantified by amino acid analysis. UB-AQUA peptides from working stocks (in 5% (vol/vol) FA) were diluted into the digested sample (in 1% (vol/vol) FA) to be analysed to an optimal final concentration predetermined for individual peptide. Samples and AQUA peptides were oxidized with 0.05% hydrogen peroxide for 20 min, subjected to C18 StageTip and resuspended in 1% (vol/vol) FA. MS data were collected sequentially by liquid chromatography (LC)/MS on a Q Exactive mass spectrometer (Thermo Fisher Scientific) coupled with a Famos Autosampler (LC Packings) and an Accela600 LC pump (Thermo Fisher Scientific). Peptides were separated on a 100 μm i.d. microcapillary column packed with around 0.5 cm of Magic C4 resin (5 μm, 100 Å; Michrom Bioresources) followed by approximately 20 cm of Accucore C18 resin (2.6 μm, 150 Å; Thermo Fisher Scientific). Peptides were separated using a 45 min gradient of 3–25% ACN in 0.125% FA with a flow rate of about 300 nl min−1. The scan sequence began with an Orbitrap full MS1 spectrum with the following parameters: resolution of 70,000, scan range of 200–1,000 Thomson (Th), AGC target of 1 × 106, maximum injection time of 250 ms, and profile spectrum data type. This scan was followed by 12 targeted MS2 scans selected from a scheduled inclusion list with a 8-min retention time window. Each targeted MS2 scan consisted of high-energy collision dissociation (HCD) with the following parameters: resolution of 17,500, AGC of 1 × 105, maximum injection time of 200 ms, isolation window of 1 Th, normalized collision energy (NCE) of 23, and profile spectrum data type. Raw files were searched, and precursor and fragment ions were quantified using Skyline version 3.5 (ref. 38). Data generated from Skyline were exported into an Excel spreadsheet for further analysis as previously described36. Total UB was determined as the average of the total UB calculated for each individual locus, unless specified otherwise. Samples were subjected to reduction (10 mM TCEP) and alkylation (20 mM chloroacetamide) followed by TCA precipitation. Samples were digested overnight at 37 °C with Lys-C and trypsin (in 100 mM TEAB, 0.1% Rapigest, 10% (vol/vol) acetonitrile (ACN)). Digests were acidified with an equal volume of 5% (vol/vol) formic acid (FA) to a pH of approximately 2 for 30 min, dried down, resuspended in 1% (vol/vol) FA before C18 StageTip (packed with Empore C18; 3M Corporation) desalting. Eluted peptide were resuspended in 1% FA and mass spectrometry data were collected using a Qexactive mass spectrometer (Thermo Fisher Scientific, San Jose, CA) with a Famos Autosampler (LC Packings) and an Accela600 liquid chromatography (LC) pump (Thermo Fisher Scientific). Peptides were separated on a 100 μm inner diameter microcapillary column packed with around 0.5 cm of Magic C4 resin (5 μm, 100 Å, Michrom Bioresources) followed by about 20 cm of Accucore C18 resin (2.6 μm, 150 Å, Thermo Fisher Scientific). Peptides were separated using an 80 min gradient of 3 to 35% acetonitrile in 0.125% formic acid with a flow rate of approximately 300 nl min−1. The scan sequence began with an MS1 spectrum (Orbitrap analysis; resolution 70,000; mass range 300−1,500 m/z; automatic gain control (AGC) target 1 × 106; maximum injection time 250 ms). Precursors for MS2 analysis were selected using a Top20 most abundant peptides. MS2 analysis consisted of high-energy collision-induced dissociation (quadrupole ion trap analysis; AGC 1 × 105; normalized collision energy (NCE) 25; maximum injection time 60 ms; resolution 17,500). Sequest-based identification using a Human UNIPROT database followed by a target decoy-based linear discriminant analysis was used for peptide and protein identification as described39. Parameters used for database searching include: 50 p.p.m. Precursor mass tolerance; 0.03 Da product ion mass tolerance; tryptic digestion with up to three missed cleavages; carboxyamidomethylation of Cys was set as a fixed modification, while oxidation of Met was set as variable modifications. Localization of diGly sites used a modified version of the A-score algorithm as described40, 41. A-scores of 13 were considered localized. The GβLKRKR knock-in cell line was generated following the protocol described previously42. The sgRNA (5′-TCCAGCTTCCTCGGACAACC-3′) targeting the genomic sequence close to the GβL K305/K313 site was designed using the CRISPR design tool (http://crispr.mit.edu) and cloned into GeneArt CRISPR nuclease vector with OFP reporter (Life Technologies, A21174). A 167-nt single-stranded oligodeoxynucleotides (ssODNs) was used as the template with KRKR mutation and a silent change to the PAM site that do not alter the amino acid sequence. The sgRNA construct and the ssODNs were co-transfected into HEK293 cells. Forty-eight hours post-transfection, the OFP-positive cells were enriched by FACS sorting and seeded into a 96-well plate with one cell per well. The genomic DNA of individual clone was extracted using the Quick Extract DNA Extraction Solution (Epicentre, Q09050) and used as the template to amplify the DNA fragment containing the K305/K313 site. The PCR products were cut by BspEI (NEB, R0540L) to screen the potential correct clones. Finally, knock-in mutations were verified by the Sanger sequencing method. The primers for amplification of the genomic DNA were: forward, 5′-GCAGCTTCCCCTCTGCTG-3′; reverse, 5′-AGGGGAGGGTCTGCTCTG-3′. ssODNs: 5′-GCACCAGGCAGTCCCGAGGGGTCACAGGCTAGCCCAGCACACTGTCATTGAAGGCCAGGCAGACAACAGCCCGCTGGTGGCCGCCATACTCTCTCCGGATCTCTCCAGTCTCCACACACCAGAGCCGGGCGAGGTTGTCCGAGGAAGCTGGAGGGGGAGATTGTGCA-3′. To measure the half-life of GβL protein in cells, a cycloheximide (CHX)-based assay was performed following our previously described experimental procedures34. Traf2−/− MEFs, GβLKRKR knock-in HEK293 cells, Otud7b−/− MEFs, and corresponding wild-type cells, or GβL-depleted A375 cells stably expressing GβL or GβL(ΔW297) were cultured in serum-containing medium were washed with phosphate-buffered saline, lysed in 0.5 ml of CHAPS buffer supplemented with protease inhibitors (Complete Mini, Roche) and phosphatase inhibitors (phosphatase inhibitor cocktail set I and II, Calbiochem). Alternatively, HEK293 cells were serum starved for 16 h and stimulated with or without insulin (100 nM) for 15 min, and lysed using CHAPS buffer. The gel filtration chromatography assays were performed as described previously9. In brief, whole-cell lysates (WCL) were filtered through a 0.45 μm syringe filter and protein concentration was adjusted to 8 mg ml−1 with CHAPS buffer. Afterward, 500 μl of the WCL was injected onto a Superdex 200 10/300 GL column (GE Lifesciences cat. no. 17-5175-01). Chromatography was performed on the AKTA-FPLC (GE Lifesciences cat. no. 18-1900-26) with CHAPS buffer. One column volume of eluates was fractionated with 500 μl in each fraction, at the elution speed of 0.3 ml min−1. Aliquots (25 μl each) of each fraction were resolved by SDS–PAGE gels and detected with indicated antibodies. CHAPS buffer (40 mM Tris, pH 7.5, 120 mM NaCl, 1 mM EDTA, 0.3% CHAPS)11, 43, Triton buffer (40 mM Tris, pH 7.5, 120 mM NaCl, 1 mM EDTA, 1% Triton X-100) or EBC buffer (50 mM Tris, pH 7.5, 120 mM NaCl, 0.5% NP-40) was added with protease inhibitors (Complete Mini, Roche) and phosphatase inhibitors (cocktail set I and II, Calbiochem). When analysing mTOR complex formation, cells were lysed using CHAPS buffer to preserve mTOR complex integrity. Under other experimental conditions, WCL were collected using EBC, CHAPS or Triton buffer as indicated. Protein concentrations were measured using Bio-Rad protein assay kit in a spectrophotometer (Beckman Coulter DU-800). To perform immunoprecipitation, same amounts of WCL were incubated with the primary antibodies (1–2 μg) for 4 h at 4 °C. The incubation tubes were added with Protein A/G sepharose beads (GE Healthcare) to incubate for 1 h and washed four times with NETN buffer (20 mM Tris, pH 8.0, 100 mM NaCl, 1 mM EDTA, 0.5% NP-40) or CHAPS buffer. For western blot analysis, equal amounts of WCL or immunoprecipitate were separated by SDS–PAGE and immunoblotted with indicated antibodies. To examine cell viability, cells were seeded at 3,000 per well in 96-well plates overnight and treated with DMEM medium containing indicated doses of etoposide (Sigma, E1383) or cisplatin (Selleck S1166) for 24 h. The viability was measured using a CellTiter-Glo Luminescent Cell Viability Assay Kit (Promega), following the manufacturer’s instructions. The indicated tumour cells were plated in six-well plates (300 or 600 cells per well) and maintained for up to 10–12 days. Visible colonies were washed with PBS and fixed with 10% acetic acid/10% methanol for 30 min, and then stained with 0.4% crystal violet/20% ethanol. After washing with distilled water and air-dried, the colonies were quantified and analysed. The anchorage-independent growth capacity of tumour cells was examined by soft agar assays, as described previously9, 10. In brief, each well of six-well plates was coated with the bottom layer noble agar (0.8%). The single tumour cells were seeded into the top layer medium containing 0.4% agar. Specifically, 3 × 104 OVCAR5 cells or 1 × 104 A375 cells were plated in the top layer of each well. The wells were added with 500 μl complete DMEM medium every 3 days. Four weeks later, the cells were stained with iodonitrotetrazolium chloride and visible colonies were counted. The assays were performed in triplicates. All animal experiments were approved by the Beth Israel Deaconess Medical Center IACUC Committee review board. GβL-depleted OVCAR5 cells stably expressing HA-GβL or HA-GβL(KRKR) were mixed with matrigel (1:1) and inoculated into the flank of female nude mice (2.5 × 106 cells per injection, 6 mice for each group). Alternatively, GβL-depleted A375 cells stably expressing HA-GβL or HA-GβL(ΔW297) were injected subcutaneously into female nude mice (5 × 106 cells per mice and 7 mice for each cell line). After tumour establishment, the palpable xenograft nodules were measured for the longest diameter (L) and the shortest diameter (W) every three days using a calliper. The tumour volumes were calculated with the formula: L × W2 × 0.5. After indicated days, the mice were killed humanely. None of the xenograft tumours reached the maximal tumour volume permitted by the institutional IACUC Committee. The xenograft tumours were dissected and weighted. Otud7b−/− mice were generated in B6.129sv genetic background and subsequently backcrossed for four generations to the C57BL/6 background29. KrasLA2 mice (in B6.129s2 background) were described previously44 and provided by the NCI Mouse Repository. The KrasLA2 mice develop tumours in the lung as a result of spontaneous recombination that generates the oncogenic Kras mutant KrasG12D. Otud7b+/− heterozygous mice were crossed with KrasLA2 mice to generate age-matched Otud7b+/+KrasLA2 and Otud7b−/−KrasLA2 mice for experiments. Mice were maintained in specific-pathogen-free facility of the University of Texas MD Anderson Cancer Center, and the experiments were performed according to the Institutional Animal Care and Use Committee. Age-matched Otud7b+/+KrasLA2 and Otud7b−/−KrasLA2 mice killed humanely at the indicated ages for analyses of lung tumours. Following perfusion with PBS, the lungs were removed and fixed in 10% phosphate-buffered formalin (pH 7.4) for histology analyses. For the survival study, Otud7b+/+ and Otud7b−/− mice with KrasG12D were housed in the animal core facility and the monitored every three days for the indicated time period to calculate survival rate. Haematoxylin and eosin (H&E)-stained lung tissue slides were analysed for tumour numbers and size. Total tissue lysates from the lung or dissected tumours were also prepared and subjected to IB assays. Kaplan–Meier survival curves were generated using the Kaplan–Meier Plotter website for lung cancer (Version 2015, http://kmplot.com)45 and statistical significance was determined by the log–rank test. Gene expression and patient survival data were downloaded from public databases including Gene Expression Omnibus (GEO), European Genome–Phenome Archive (EGA), and The Cancer Genome Atlas (TCGA). The majority of experiments were repeated at least three times to obtain data for indicated statistical analyses. No statistical method was used to calculate sample size. Group variation was not estimated before experiments. The experiments were not randomized in the animal studies and investigators were not blind during experiments and outcome assessment. For western blotting data, representative images from 3–5 biological replicate experiments were shown. For quantification analysis, the original western blot images were quantified using ImageJ software to measure the intensity of some key blots for statistical analysis. The number of mice per group was described in the corresponding figure legends and none of the animals was excluded from the experiment. All quantitative data were presented as mean ± s.d. Results were analysed by a two-tailed unpaired or paired Student’s t-test or two-way ANOVA, as appropriate. *P < 0.05; **P < 0.01; ***P < 0.001. For survival analysis, the Kaplan–Meier survival curves were compared using the log–rank test. Uncropped images for immunoblots are provided in Supplementary Fig. 1. Mouse model data are also provided in the Source Data. All other relevant data are available from the corresponding author upon reasonable request.


News Article | May 24, 2017
Site: www.sciencemag.org

The Democratic Republic of the Congo has moved a step closer to using an unlicensed vaccine to battle an Ebola outbreak that began last month in a remote northeastern part of the country. Yesterday, the country's government submitted a formal vaccine trial protocol, developed with Epicentre, the Paris-based research arm of Doctors Without Borders (MSF), to an ethical review board. If the plan gets the green light, the first doses of the vaccine could go into the arms of people at risk within 2 weeks, according to an official at the World Health Organization (WHO) in Geneva, Switzerland. WHO today issued a “donor alert,” urgently requesting a 6-month budget of $10.5 million to support the vaccine study (which may require 5000 doses), as well as surveillance, treatment, and conventional prevention and control efforts. But whether the shots will actually be needed is unclear. So far, there have been only two confirmed Ebola cases and 41 suspected or probable cases. More than 350 contacts of cases were being monitored. But samples from several dozen suspected cases tested negative on Monday, raising the possibility that the outbreak may be quite small, and perhaps already nearing the end. The outbreak is in the northeastern Bas-Uélé province, about 500 kilometers north of Kisangani, a city of 1.6 million people. The location slows spread but poses huge challenges. Poor and conflict-ridden, the area has few passable roads and bridges. Helicopters carry teams and equipment to the town of Likati, where motorbikes take over. Workers set up two mobile labs, but a generator failed in one and had to be replaced. The vaccine, made by Merck and stored in the United States, was tested in 2015, during the massive outbreak in West Africa that left more than 11,000 dead. WHO and MSF set up a trial in Guinea with an unusual “ring vaccination” design that selectively gave shots to people most likely to have had contact with a known case. People in a control group, also potentially exposed, received shots 3 weeks later. The results showed 100% protection 10 days after immunization, but the unconventional approach led Merck to put off applying for regulatory approval so it could gather more safety and immune data from other studies. For the moment, the vaccine can only be used in experimental settings. Epicentre and the DRC’s Ministry of Health (MoH) have written a protocol for a new ring vaccination study in the DRC. The trial would carefully evaluate safety, but this time there will be no control group because withholding the vaccine from some participants is no longer seen as ethical. As a result, the trial cannot evaluate the vaccine’s efficacy. “We’ll try to bring more data in to help with licensing, but we’re using the vaccine as a public health intervention,” says MSF’s Micaela Serafini in Geneva, Switzerland. If approved, the protocol could also be used in any future outbreaks. The MoH did not respond to emailed questions about why it didn’t request the vaccine sooner. One reason, says Epicentre Director of Research Rebecca Grais, is that the outbreak’s extent remains so unclear. “It’s not like they were dragging their feet,” she says. DRC officials may also feel confident they can stop the outbreak without vaccines, as they have seven times in the past, says Peter Piot, who heads the London School of Hygiene & Tropical Medicine and was part of the team that responded to the first known Ebola outbreak, near Likati, in 1976. “We should really leave some of the decision-making to people on the ground,” Piot says. But Michael Osterholm, director of the Center for Infectious Disease Research and Policy at the University of Minnesota in Minneapolis, says authorities should have been prepared to deploy the vaccine more quickly. Every African country at risk of Ebola by now should have approved a study protocol, he says, and the DRC should keep the vaccine ready in a freezer in Kinshasa. Under a WHO emergency-use status, the vaccine could also have been deployed without trials, Osterholm notes. A Merck application for that status filed in December 2015 is in limbo; a WHO spokesperson says “there was not necessarily sufficient data to enable a full assessment.” Even without the vaccine, Ebola experts don’t expect the outbreak to explode as it did in West Africa. “My gut feeling,” says Piot, “is this is going to be more like the outbreaks we had before in DRC,” the largest of which had 318 cases. “Proper isolation of patients and care plus contact tracing and quarantine should really bring this epidemic under control—except if someone gets to Kisangani or Kinshasa.” There's another reason to be optimistic: The international response to the outbreak so far has been overwhelming. Acutely aware of its failings in Liberia, Guinea, and Sierra Leone in 2014 and 2015, the international community is determined to help end the outbreak as soon as possible. Matshidiso Rebecca Moeti, WHO's regional director for Africa, immediately traveled to Kinshasa from her office in Brazzaville, in the neighboring Republic of the Congo, to help coordinate the battle. The United Nations dispatched cargo planes and helicopters, and DRC government officials began holding daily coordinating committee meetings attended by representatives from international aid, and development organizations, WHO, and the U.S. Centers for Disease Control and Prevention. “All those actors have strengthened their presence because of what happened in West Africa,” says epidemiologist Yap Boum, Epicentre’s Africa representative. “People are afraid.”


News Article | November 29, 2016
Site: www.newsmaker.com.au

With a CAGR of 10.1%, global market value for PCR Products/Tools market is anticipated to be worth US$12 billion by 2020. On a global scale, Europe accounts for more than 25% of the market. While USA accounts for the largest share of the global market value on a country basis, Asia-Pacific is the fastest growing region in terms of growth rate anticipated in the near future and leads the world. PCR Machines account for the largest share of the entire market, driving a CAGR of 9.5% during the analysis period 2014-2020. PCR Reagents and PCR Detection Kits/Assays accounts for more than 40% of the market share and fastest growing segment with a CAGR approximately 10.8% and 10.5% by 2020 respectively. The report “Polymerase Chain Reaction (PCR) - Products/Tools - Global Trends, Estimates and Forecasts, 2014-2020” reviews the latest PCR market trends with a perceptive attempt to disclose the near-future growth prospects. An in-depth analysis on a geographic basis provides strategic business intelligence for life science sector investments. The study reveals profitable investment strategies for pharmaceutical manufacturers, biotechnology companies, laboratories, Contract Research  Organizations (CROs) and many more in preferred locations. The report primarily focuses on: Estimates are based on online surveys using customized questionnaires by our research team. Besides information from government databases, company websites, press releases & published research reports are also used for estimates. The analysis primarily deals with major PCR product/tools market. Further, the subdivided categories include: The period considered for the PCR Products/Tools market analysis is 2014-2020. The region wise distribution of the market consists of North America (USA and Canada), Europe (Germany, France, United Kingdom, Italy, Spain and Rest of Europe), Asia- Pacific (Japan, China, India, South Korea and Rest of Asia-Pacific), Latin America (Brazil, Columbia, Argentina and Rest of Latin America) and Rest of the World. The market growth rate in the major economies such as the U.S., Japan, China etc. are estimated individually for the upcoming years. More than 435 leading market players are identified and 45 key companies that project improved market activities in the near future are profiled. The report consists of 91 data charts describing the market shares, sales forecasts and growth prospects. Moreover, key strategic activities in the market including mergers/acquisitions, collaborations/partnerships, product launches/developments are discussed. Abbott Laboratories, Affymetrix, Inc., Agilent Technologies, Inc., BD Biosciences, Bio-Rad Laboratories, Inc., Complete Genomics, Inc., Epicentre® Biotechnologies, GE Healthcare (Life Sciences), Illumina, Qiagen, Inc., Dna Landmarks, Inc., Roche Diagnostics, Eppendorf AG, Cytocell Ltd, Shimadzu Biotech, Dnavision SA, Exiqon, Hokkaido System Science Co., Ltd., Ocimum Biosolutions, Ltd., HY Laboratories LTD., PerkinElmer Life Sciences & many more… History Of Polymerase Chain Reaction  All About PCR Specimen Preparation 1. Isolating The Target DNA - Denaturation 2. Binding PrimerstoThe DNA Chain - Annealing 3. Making A Replica – Extension PCR Variations Of Polymerase Chain Reaction Basic PCR Technique’s Fluctuations Allele-Precise PCR Pca (Polymerase Cycling Assembly)orAssembly PCR Asymmetric PCR Helicase-Reliant Amplification Hot–Start PCR Intersequence-Specific PCR Reverse PCR Ligation Mediated PCR Methylation Specific PCR Miniprimer PCR Multiplex Ligation-Reliant Probe Amplification Multiplex PCR Nested PCR Overlap-Extension PCR Quantitative PCR Rt-PCR Solid-Phase PCR Tail-PCR Touchdown PCR Pan-Ac Universal Fast Walking Parameters For Successful PCR I) Metal Ion Cofactors And PCR Ii) Substrates And Substrate Analogs For PCR Iii) Buffers And Salts For PCR Iv) Cosolvents Theory And Methodology Of Polymerase Chain Reaction Methodology Of Use PCR – Improvised Technique For Testing Nucleic Acids Formatting Step Denaturation Step Annealing Step Extension/Elongation Step PCR Reagents Role Of PCR Reagents Gotaq® PCR Mix Primers PCR Reagents Next Generation PCR Reagents For Clinical Diagnostics PCR Reagent Market Trends Other PCR Software PCR Robotics The Automation And Usage Of Robotics In Amplification Assays Pre-PCR Robotic System The Post-PCR Robotic System Integrated Robotic System For High Sample Throughput Within A DNA Databasing Unit PCR Arrays Need Of PCR Arrays Doctrine Of Assay Corroboration For Nucleic Acid Diagnostic Tests Assay Corroboration – An Introduction 1. Selecting An Assay Fitting Its Intended Purpose Considerations Towards Primitive Assay Developments A) Care And Restraints B) Protections For Avoiding False-Positive Results C) Safeguards For Avoiding Negative Outcomes. D) Standards’ Preparation


News Article | September 28, 2016
Site: www.nature.com

No statistical methods were used to predetermine sample size. These experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. Between 10 October 2009 and 12 December 2011, 90 samples were collected at 45 locations throughout the world’s oceans (Supplementary Table 1) through the Tara Oceans expedition32. These included samples from the following range of depths: surface, deep chlorophyll maximum, bottom of mixed layer when no deep chlorophyll maximum was observed (stations 123, 124, and 125), and mesopelagic samples. The sampling stations were located in 7 oceans and seas, 4 different biomes and 14 Longhurst oceanographic provinces (Supplementary Table 1). For Tara station 100, two different peaks of chlorophyll were observed, so two samples were taken at the shallow (100_DCM) and deep (100_dDCM) chlorophyll maximum. For each sample, 20 l of seawater was 0.22 μm-filtered and viruses were concentrated from the filtrate using iron chloride flocculation33 followed by storage at 4 °C. After resuspension in ascorbic-EDTA buffer (0.1 M EDTA, 0.2 M Mg, 0.2 M ascorbic acid, pH 6.0), viral particles were concentrated using Amicon Ultra 100 kDa centrifugal devices (Millipore), treated with DNase I (100 U ml−1) followed by the addition of 0.1 M EDTA and 0.1 M EGTA to halt enzyme activity and extracted as previously described34. In brief, viral particle suspensions were treated with Wizard PCR Preps DNA Purification Resin (Promega) at a ratio of 0.5 ml sample to 1 ml resin, and eluted with Tris-EDTA buffer (10 mM Tris, pH 7.5, 1 mM EDTA) using Wizard Minicolumns. Extracted DNA was Covaris-sheared and size-selected to 160–180 bp sequence lengths, followed by amplification and ligation according to standard Illumina protocol. Sequencing was performed with a HiSeq 2000 system (101 bp, paired end reads). Temperature, salinity, and oxygen data were collected from each station using an SBE 911plus CTD with Searam recorder and an SBE 43 dissolved oxygen sensor (Sea-Bird Electronics). Nutrient concentrations were determined using segmented flow analysis35 and included nitrite, phosphate, nitrite-plus-nitrate, and silica. Nutrient concentrations below the detection limit (0.02 μmol kg−1) are reported as 0.02 μmol kg−1. All data from the Tara Oceans expedition are available from the European Nucleotide Archive (ENA) (for nucleotide data) and from PANGAEA (for environmental, biogeochemical, taxonomic and morphological data)36, 37, 38. Thirteen bathypelagic samples and one mesopelagic sample were collected between 19 April 2011 and 11 July 2011 during the Malaspina 2010 global circumnavigation expedition covering the Pacific and the North Atlantic Oceans. All samples were taken at 4,000 m depth with the exception of two samples from stations 81 and 82, which were collected at 3,500 m and 2,150 m, respectively (Supplementary Table 1). Additionally, station M114 was sampled at the OMZ region at 294 m depth. For each sample, 80 l of seawater was 0.22 μm-filtered and viruses were concentrated from the filtrate using iron chloride flocculation33, followed by storage at 4 °C. More details about sampling and additional variables used in the Malaspina expedition can be found in ref. 39. Further processing was performed as for the Tara Oceans samples other than Illumina sequencing (151 bp, paired end reads). An overview of the contig-generation process is provided in Supplementary Fig. 8. The first step involved the generation of a set of contigs using as many reads as possible from the 104 oceanic viromes. These viromes including 74 epipelagic and 16 mesopelagic samples from the Tara Oceans expedition5 and 1 mesopelagic and 13 bathypelagic samples from the Malaspina expedition6. This set of contigs was generated through an iterative cross-assembly12, using MOCAT40 and Idba_ud41, (Supplementary Fig. 8) as follows: (i) high-quality reads were first assembled sample-by-sample with the MOCAT pipeline as described previously18; (ii) all reads not mapping (Bowtie 2 (ref. 42), options: -sensitive, -X 2000, -non-deterministic, other parameters at default) to a MOCAT contig (by which we denote ‘scaftigs’, that is, contigs that were extended and linked using the paired-end information of sequencing read42) were assembled sample-by-sample with Idba_ud (iterative k-mer assembly, with k-mer length increasing from 20 to 100 bp in steps of 20); (iii) all reads that remained unmapped to any contig were then pooled by Longhurst province (that is, unmapped reads from samples corresponding to the same Longhurst province were gathered) and assembled with Idba_ud (with the same parameters as above); and (iv) all remaining reads unmapped from every sample were gathered for a final cross-assembly (using Idba_ud). This resulted in 10,845,515 contigs (Supplementary Fig. 8b). As the contigs assembled from the marine viral metagenomes could still contain redundant sequences derived from the same (or closely related) populations, we set out to merge contigs derived from the same population into clusters representing population genomes. To this end, contig sequences were first clustered at 95% global average nucleotide identity (ANI) with cd-hit-est43 (options: -c 0.95 -G 1 -n 10 -mask NX) (Supplementary Fig. 8b), resulting in 10,578,271 non-redundant genome fragments. Next, we used co-abundance (that is, the correlation between abundance profiles estimated by reads mapping) and nucleotide-usage profiles of the non-redundant contigs to further identify contigs derived from the same populations using Metabat44. In brief, Metabat uses Pearson correlation between coverage profiles (determined from the mapping of high-quality reads of each sample to the contigs with Bowtie 2 (ref. 42), options: -sensitive, -X 2000, -non-deterministic, other parameters at default) and tetranucleotide frequencies to identify contigs originating from the same genome (Metabat parameters: 98% minimum correlation, mode ‘sensitive’; see Supplementary Text for more detail about the selection of these parameters). The 8,744 bins generated, including 3,376,683 contigs, were further analysed, alongside 623,665 contigs that were not included in any genome bin but were ≥1.5 kb. In an attempt to better assemble these genome bins, two additional sets of contigs were generated for each genome bin (beyond the set of initial contigs binned by Metabat44). These were based on the de novo assembly of: (i) all reads mapping to the contigs in the genome bin, and (ii) only reads from the sample displaying the highest coverage for the genome bin (both assemblies with Idba_ud41; Supplementary Fig. 8c). The latter assembly might be expected to lead to the ‘cleanest’ genome assembly because it includes the minimum between-sample sequence variation, lowering the probability of generating a chimaeric contig45. The former assembly may be necessary if the virus is locally rare, so that sequences from multiple metagenomes are needed to achieve complete genome coverage. Thus, if the assembly from the single ‘highest-coverage’ sample was improved or equivalent to the initial assembly (that is, the longest contig in the new assembly representing ≥95% of the longest contig in the initial assembly), this set of contigs was selected as the sequence for this bin (n = 6,423). This optimal single-sample assembly was thus privileged compared to a cross-assembly (either based on the initial contigs or on the re-assembly of all sequences aligned to that bin). Otherwise, the ‘all samples’ bin re-assembly was selected if it was equivalent to or better than the initial assembly (longest contig representing ≥95% of the longest initial contig, n = 999). The assumption that cross-assembly would be needed for locally rare viruses without a high-coverage sample was confirmed by the comparison between the highest coverage of these two types of bins. On average, bins for which the ‘optimal’ assembly was selected displayed a maximum coverage of 5.47× per Gb of metagenome, while the bins for which the ‘cross-assembly’ was selected displayed a maximum coverage of 1.37× per Gb of metagenome (Supplementary Table 2). Finally, if both re-assemblies yielded a longest contig smaller (<95%) than the one in the initial assembly, the bin was considered to be a false-positive (that is, binning of contigs from multiple genomes, n = 1,356), and contigs from the initial assembly were considered as ‘unbinned’ (263,006 contigs, added to the 623,665 contigs ≥1.5 kb initially retained as ‘unbinned’). Despite efforts to remove cellular DNA completely during sample preparation, the resulting viral metagenomic datasets can only ever be enriched for viruses46. Thus, assembled sequences in the GOV dataset were in silico filtered a posteriori to identify and remove any clearly non-viral signal. In this way, our purification methods should have greatly enriched for viruses, but the in silico decontamination step served as a back-up for problematic samples. Together these two filters mean that virtually no known cellular signal should have been considered in our analyses. For the in silico cleaning step, VirSorter47 was used to identify and remove microbial contigs using the ‘virome decontamination’ mode, with every contig ≥10 kb that was not identified as viral considered to be a microbial contig. Sequences predicted to be from prophages were manually curated to distinguish actual prophages (that is, viral regions within a microbial contig) from contigs that belonged to a viral genome and were wrongly predicted as a prophage. Contigs originating from a eukaryotic virus were identified based on best BLAST hit affiliation of the contig-predicted genes against NCBI RefseqVirus (see Supplementary Text). The genome bins were affiliated as microbial (if 1 or more contigs were identified as microbial, n = 1,763), eukaryotic virus (if contigs affiliated as eukaryotic virus comprised more than 10 kb or more than 25% of the genome bin total length, n = 962) or viral (that is, archaean and bacterial viruses, n = 4,341), with the 356 remaining bins that lacked a contig long enough for an accurate affiliation considered as ‘unknown’ (see Supplementary Text). Viral bins were then refined to evaluate whether they corresponded to a single viral population or to a mix. To that end, the Pearson correlation and Euclidean distance between abundance profiles (that is, the profile of the average coverage depth of a contig across the 104 samples) of bin members and the bin seed (that is, the largest contig) were computed, and a single-copy viral marker gene (terL) was identified in binned contigs (Supplementary Fig. 8e). Thresholds were chosen to maximize the number of bins with exactly one terL gene and minimize the number of bins with multiple terL genes (Supplementary Fig. 8g). For each bin, contigs with a Pearson correlation coefficient to the bin seed of <0.96 or a Euclidean distance to the seed of >1.05 were removed from the bin, and added to the pool of unbinned contigs. Eventually, every bin still displaying multiple terL genes after this refinement step were split and all corresponding contigs added to the pool of ‘unbinned’ contigs (Supplementary Fig. 8e). The final set of contigs was formed by compiling: (i) all contigs belonging to a viral bin, (ii) ‘unbinned’ viral contigs (that is, contigs affiliated to archaeal and bacterial virus and not part of any genome bin), and (iii) viral contigs identified in microbial or eukaryote virus bins (considered as ‘unbinned’ contigs, Supplementary Fig. 8f). Within this set of contigs, all viral bins were considered as viral populations, as well as every unbinned viral contig of ≥10 kb, leading to a total of 15,222 epipelagic and mesopelagic populations, and 58 bathypelagic populations (Supplementary Fig. 1, Supplementary Table 2 and Supplementary Information). In this study, we focus only on the 15,222 epipelagic and mesopelagic populations, totaling 24,353 contigs. For the detection of AMGs, we added to these populations all short epipelagic and mesopelagic unbinned viral contigs (<10 kb), totalling 298,383 contigs. Genomes of viruses associated with a bacterial or archaeal host were downloaded from NCBI RefSeq (1,680 sequences, v70, 05-26-2015; http://www.ncbi.nlm.nih.gov/refseq/). To complete this dataset of reference genomes, viral genomes and genome fragments available in GenBank (http://www.ncbi.nlm.nih.gov/genbank/) but not in RefSeq were downloaded (July 2015) and manually curated to select only bacterial and archaeal viruses (1,017 sequences). These included viral genomes not yet added to RefSeq, as well as genome fragments from fosmid libraries generated from seawater samples9, 10. Mycophage sequences (available at http://phagesdb.org48) were downloaded in July 2015 and included as well if not already in RefSeq (734 sequences). Finally, 12,498 viral genome fragments from the VirSorter Curated Dataset, identified in publicly available microbial genome sequencing projects, were added to the database8. Proteins predicted from 14,650 large GOV contigs (≥10 kb and ≥10 genes), were added to all proteins from the publicly available viral genomes and genomes fragments gathered, and compared through all-vs-all blastp, with a threshold of 10−5 for E-value and 50 for bit score. Protein clusters were then defined using MCL (Markov Cluster Algorithm, using default parameters for clustering of proteins, similarity scores as log-transformed E-value, and 2 for MCL inflation49). We then used vContact (https://bitbucket.org/MAVERICLab/vcontact) to first calculate a similarity score between every pair of genomes and/or contigs based on the number of protein clusters shared between the two sequences (as in refs 7, 8), and then compute an MCL clustering of the genomes/contigs based on these similarity scores (thresholds of 1 for similarity score, MCL inflation of 2). The resulting viral clusters (clusters including ≥2 contigs and/or genomes), consistent with a clustering based on whole-genome BLAST comparison, corresponded approximately to genus-level taxonomy, with rare cases closer to subfamily-level taxonomy (Extended Data Fig. 2 and Supplementary Information). A total of 1,259 viral clusters were obtained, with 867 including at least one GOV sequence. Notably, however, automatically defined viral clusters serve only as a starting point for assigning viral taxonomy. Current ICTV convention for formal taxonomic consideration of these viral clusters would require the manual comparison of genomes and genome fragments to identify signature genes, compare phylogenetic signals and, ideally, observe morphological features of corresponding viruses, although this process is currently being reviewed as advanced computational analytics and genome datasets, such as those presented here, are being developed. A functional annotation of all GOV-predicted proteins was based on a comparison to the PFAM domain database v.27 (ref. 50) with HmmSearch51 (threshold of 30 for bit score and 10−3 for E-value). Additional putative structural proteins were identified through a BLAST comparison to the protein clusters detected in the viral metaproteomics dataset52. This metaproteomics dataset led to the annotation of 13,547 hypothetical proteins lacking a PFAM annotation. A taxonomic annotation of the predicted proteins was performed based on a blastp against proteins from archaeal and bacterial viruses from NCBI RefSeq and GenBank (threshold of 50 for bit score and an E-value of 10−3). Viral clusters were affiliated based on isolate genome members, where available. When multiple isolates were included in the viral cluster, the viral cluster was affiliated to the corresponding subfamily or genus of these isolates (excluding all ‘unclassified’ cases). This was the case for VC_2 (T4 superfamily14, 15), and VC_9 (T7 virus16). When only one, or a handful of, affiliated isolate genomes were included in the viral cluster and lacked genus-level classification, a candidate name was derived from the isolate (if there were several isolates it was derived from the first one isolated). This was the case for VC_5 (Cbaphi381virus; ref. 53), VC_12 (P12024virus; ref. 54), VC_14 (MED4-117virus), VC_19 (HMO-2011virus; ref. 55), VC_31 (RM378virus; ref. 56), VC_36 (GBK2virus; ref. 57), VC_47 (Cbaphi142virus; ref. 53) and VC_277 (vB_RglS_P106Bvirus; ref. 58). Otherwise, viral clusters were considered as ‘new viral clusters’. All publicly available complete genomes (see above), all complete (circular) and near-complete (extrachromosomal genome fragment >50 kb with a terminase) from the VirSorter Curated Dataset and all complete and near-complete GOV contigs were compared to generate a phage proteomic tree, as previously described9, 59. In brief, a proteomic similarity score was calculated for each pair of genome based on an all-versus-all tblastx similarity as the sum of bit scores of significant hits between two genomes (E ≤ 0.001, bit score ≥ 30, identity percentage ≥ 30). To normalize for different genome sizes, each genome was also compared to itself to generate a self-score, and the distance between two different genomes was calculated as a Dice coefficient as previously9. That is, for two genomes A and B with a proteomic similarity score of AB, the corresponding distance d would be: d = 1 − (2 × AB)/(AA + BB); with AA and BB being the self-score of genomes A and B respectively. For clarity, the tree displayed in Extended Data Fig. 2 includes only non-GOV sequences found in a viral cluster with GOV sequence(s) or within a distance d < 0.5 to a GOV sequence, totalling 1,522 reference sequences. iTOL60, 61 was used to visualize and display the tree. Detection and estimation of abundance for viral contigs and populations The presence and relative abundance of a viral contig in a sample was determined based on the mapping of high-quality reads to the contig sequences, computed with Bowtie 2 (options: -sensitive, -X 2000, -non-deterministic, default parameters otherwise62), as previously described4. A contig was considered to be detected in a metagenome if more than 75% of its length was covered by aligned reads derived from the corresponding sample. A normalized coverage for the contig was then computed as the average contig coverage (that is, the number of nucleotides mapped to the contig divided by the contig length) normalized by the total number of base pairs sequenced in this sample. The detection and relative abundance of a viral population was based on the coverage of its contigs; that is, a population was considered as detected in a sample if more than 75% of its cumulated length was covered, and its normalized coverage was computed as the average normalized coverage of its contigs. The relative abundance of viral clusters was calculated based on the coverage of its members within the 15,222 viral populations identified. If a population included contigs that were all linked to the same viral cluster, or that were linked to a single viral cluster (except for unclustered contigs owing to short length), this population coverage was added to the total of the corresponding viral cluster. In the rare cases where the link between population and viral cluster was ambiguous because different contigs within a population pointed towards different viral clusters (n = 475, that is, 3.1% of the populations), the population coverage was equally split between these viral clusters. Finally, if no contig in the population belonged to any viral cluster (n = 2,605, 17% of the populations), the population coverage was added to the ‘unclustered’ category. Eventually, for each sample, the cumulative coverage of a viral cluster was normalized by the total coverage of all populations to calculate a relative abundance of the viral cluster among viral populations. The selection of abundant viral clusters within a sample was based on the contribution of the viral cluster to the sample diversity as measured by the Simpson index. For each sample, the overall Simpson index was first calculated with all viral clusters. Following this, viral clusters were sorted by decreasing relative abundance and progressively added to a new calculation of the Simpson index. Viral clusters considered as abundant were the ones which, once cumulated, represented 80% of the sample diversity (that is, a Simpson index ≥80% of the sample total Simpson index; Extended Data Fig. 1c). The 38 viral clusters that were identified as abundant in at least 2 different stations were selected as ‘recurrently abundant viral clusters in the GOV dataset’ (Fig. 2 and Extended Data Fig. 3). Three different approaches were used to link viral contigs and putative host genomes: blastn similarity, CRISPR spacer similarity and tetranucleotide frequency similarities. An overview of the contig-generation process is provided in Supplementary Fig. 8, and an extended discussion about the efficiency and raw results of these host prediction methods is provided in Supplementary Information, Supplementary Table 4, and ref. 63. A list of all host predictions by viral sequence is available in Supplementary Table 5. A genome database of putative hosts for the epipelagic and mesopelagic GOV viruses was generated, including all archaeal and bacterial genomes annotated as ‘marine’ from NCBI RefSeq and WGS (both times only sequences ≥5 kb, 184,663 sequences from 4,452 genomes, downloaded in August 2015), and all contigs ≥5 kb from the 139 Tara Oceans microbial metagenomes corresponding to the bacterial and archaeal size fraction (791,373 sequences)18. For these microbial metagenomic contigs, a first blastn alignment was computed to compare with all GOV contigs, and exclude from the putative host dataset all metagenomic contigs with a significant similarity to a viral GOV sequence (thresholds of 50 for bit score, 0.001 for E-value, and 70% for identity percentage) on ≥90% of their length, as these are likely to be sequences of viral origin sequenced in the bacteria and archaea size fraction (these represented 2.2% of the contigs in the assembled microbial metagenomes). The taxonomic affiliation of NCBI genomes was taken from the NCBI taxonomy. For Tara Oceans contigs, a last common ancestor (LCA) affiliation was generated for each contig based on genes affiliation18, if three or more genes on the contig were affiliated. All GOV viral contigs were compared to all archaeal and bacterial genomes and genome fragments with a blastn (threshold of 50 for bit score and 0.001 for E-value), to identify regions of similarity between a viral contig and a microbial genome, indicative of a prophage integration or horizontal gene transfer63. A host prediction was made when: (i) a NCBI genomes displayed a region similar to a GOV viral contig ≥5 kb at ≥70% identity, or (ii) when a Tara Oceans microbial metagenomic contig (≥5 kb) displayed a region similar to a GOV viral contig ≥2.5 kb at ≥70% identity. CRISPR arrays were predicted for all putative host genomes and genome fragments (NCBI microbial genomes and Tara Oceans microbial metagenomic contigs) with MetaCRT64, 65. CRISPR spacers were extracted, and all spacers with ambiguous bases or low complexity (that is, consisting of 4–6 bp repeat motifs) were removed. All remaining spacers were matched to viral contigs with fuzznuc66, with no mismatches allowed, which, although rarely, observed yields highly accurate host predictions63 (Supplementary Table 4). Bacterial and archaeal viruses tend to have a genome composition close to the genome composition of their host, a signal that can be used to predict viral–host pairs8, 63, 67. Here, canonical tetranucleotide frequencies were observed for all viral and host sequences using Jellyfish68 and mean absolute error (that is, the average of absolute differences) between tetranucleotide-frequency vectors were computed with in-house Perl and Python scripts for each pair of viral and host sequence as previously reported8. A GOV viral contig was then assigned to the closest sequence (that is, lowest distance ‘d’) from the pool of NCBI genomes if d < 0.001 (because both the tetranucleotide-frequency signal and the taxonomic affiliation of these complete genomes are more robust than for metagenomic contigs), and otherwise assigned to the closest (that is, lowest distance) Tara Oceans microbial contig if d < 0.001. Overall, 3,675 GOV contigs could be linked to a putative host group among the 24,353 GOV contigs associated with an epipelagic or mesopelagic viral population. To summarize these affiliations at the viral cluster level, a Poisson distribution was used to estimate the number of expected false-positive associations for each viral cluster–host group combination based on: (i) the global probability of obtaining a host prediction across all pairs of viral and host sequences tested and for all methods (5.8 × 10−8), (ii) the number of potential predictions generated for the viral cluster, corresponding to 3 times the number of sequences in the viral cluster (to take into account the three methods) and (iii) the number of sequences from the host group in the database (Supplementary Fig. 2). By comparing the number of links observed between a viral cluster and a host group to this expected value, which takes into account the bias in database (that is, some host groups will be over- or under-represented in our set of archaeal and bacterial genomes and genome fragments) and the bias linked to the variable number of sequences in viral clusters, we can determine if the number of associations observed for any combination of viral cluster and host group is likely to be due to chance alone (and calculate the associated P value). Diversity and richness indices for putative host populations were based on the OTU abundance matrix generated from the analysis of TAGs in Tara Oceans microbial metagenomes18. These indexes were computed for each host group at the same taxonomic level as the host prediction (that is, the phylum level, except for Proteobacteria where the class level is used). The R package vegan69 was used to estimate for each group: (i) a global Chao index (that is, including all OTUs from all samples) through the function estaccumR, (ii) a sample-by-sample Chao index with the function estimateR, and (iii) Sorensen indexes between all pairs of samples with the function betadiver. Diversity indices presented in Extended Data Fig. 4 are based solely on epipelagic samples as the 38 viral clusters identified as abundant were mostly retrieved in epipelagic samples. Candidate division OP1 was excluded from this analysis because no OTU affiliated to this phylum was identified. Predicted proteins from all GOV viral contigs were compared to the PFAM domain database (hmmsearch51, threshold of 40 for bit score and 0.001 for E-value), and all PFAM domains detected were classified into 8 categories: ‘structural’, ‘DNA replication, recombination, repair, nucleotide metabolism’, ‘transcription, translation, protein synthesis’, ‘lysis’, ‘membrane transport, membrane-associated’, ‘metabolism’, ‘other’, and ‘unknown’ (as in ref 20). Four AMGs (similar to a domain from the ‘metabolism’ category) were then selected for further study owing to their central role in sulfur (dsrC and soxYZ) or nitrogen (P-II, amoC) cycle, and the fact that these had never been detected in a surface ocean viral genome thus far (dsrC/tusE-like genes have been detected in deep water viruses11, 21). To evaluate if an AMG was ‘known’, a list of PFAM domain detected in NCBI RefSeqVirus and Environmental Phages was computed based on a similar hmmsearch comparison (threshold of 40 for bit score and 0.001 for E-value), and augmented by manual annotation of AMGs from refs 20, 70. These corresponded, for the most part, to photosynthesis and carbon metabolism AMGs previously described in cyanophages71, 72, 73, 74, 75. The complete list of PFAM domains detected in GOV viral contigs is available in Supplementary Table 6. Sequences similar to the four AMGs described in the previous paragraph were recruited from the Tara Oceans microbial metagenomes18, based on a blastp of all predicted proteins from microbial metagenome to the viral AMGs identified (threshold of 100 for bit score, 10−5 for E-value, except for P-II where a threshold of 170 for bit score was used because of the high number of sequences recruited). The viral AMG sequences were also compared to NCBI nr database (blastp, threshold of 50 for bit score and 10−3 for E-value) to recruit relevant reference sequences (up to 20 for each viral AMG sequence). These sets of viral AMGs and related protein sequences were then aligned with Muscle76, the alignment manually curated to remove poorly aligned positions with Jalview77, and two trees were computed from the same curated alignment: a maximum-likelihood tree with FastTree (v2.7.1, model WAG, other parameters set to default78) and a bayesian tree with MrBayes (v3.2.5, mixed evolution models, other parameters set to default, 2 MCMC chains were run until the average standard deviation of split frequencies was <0.015, relative burn-in of 25% used to generate the consensus tree79). In all cases except for AmoC, the mixed model used by MrBayes was 100% WAG, confirming that this model was well suited for archaeal and bacterial virus protein trees. Manual inspection revealed only minor differences between each pair of trees, so a Shimodaira–Hasegawa (SH) test was used to determine which tree best fitted the sequence alignment, using the R library phangorn80. Itol60 was used to visualize and display these trees, in which branches with supports <40% were collapsed. Annotated interactive trees are available online at http://itol.embl.de/shared/Siroux. Contigs map comparison were generated with Easyfig81, following the same method used for the viral clusters (see Supplementary Information). Conserved motifs were identified on the different AMGs based on the literature: dsrC-conserved motifs were obtained from ref. 24, soxYZ conserved residues were identified from the PFAM domains PF13501 and PF08770, and P-II conserved motifs identified from PROSITE documentation PDOC00439. A 3D structure could also be predicted for P-II AMGs by I-TASSER82 (default parameters), the quality of these predictions being confirmed with ProSA web server83. To further confirm the functionality of these genes, selective constraint on these AMGs was evaluated through pN/pS calculation, as previously84. In brief, synonymous (pS) and non-synonymous (pN) SNPs were observed in each AMG, and compared to expected ratio of synonymous and non-synonymous SNPs under a neutral evolution model for these genes. The interpretation of pN/pS is similar as for dN/dS analyses, with the operation of purifying selection leading to pN/pS values <1. Finally, AMG transcripts were searched in metatranscriptomic datasets, generated by the Tara Oceans consortium (ENA Id ERS1092158, ERS488920, and ERS494518). To generate these metatranscriptomes, bacterial rRNA depletion was carried out on 240–500 ng total RNA using Ribo-Zero Magnetic Kit for Bacteria (Epicentre) for 0.2–1.6 μm and 0.22–3 μm filters. The Ribo-Zero depletion protocol was modified to be adapted to low RNA input amounts85. Depleted RNA was used to synthetize cDNA with SMARTer Stranded RNA-Seq Kit (Clontech)85. Metatranscriptomic libraries were quantified by quantitive PCR using the KAPA Library Quantification Kit for Illumina Libraries (KapaBiosystems) and library profiles were assessed using the DNA High Sensitivity LabChip kit on an Agilent Bioanalyzer (Agilent Technologies). Libraries were sequenced on Illumina HiSeq2000 instrument (Illumina) using 100-base-length read chemistry in a paired-end mode. High-quality reads were then mapped to viral contigs containing dsrC, soxYZ, P-II, or amoC genes with SOAPdenovo242 within MOCAT40 (options ‘screen’ and ‘filter’ with length and identity cutoffs of 45% and 95%, respectively, and paired-end filtering set to ‘yes’), and coverage was defined for each gene as the number of base pairs mapped divided by gene length (including only those reads mapped to the predicted coding strand). The distribution and relative abundance of AMGs was based on the readmapping and normalized coverage of the contig that included the AMG. To get a range of temperature and nutrient concentrations for the widespread AMGs (those detected in >5 stations) that takes into account both the samples in which these AMGs were detected and the differences in normalized coverage, a set of samples was selected through a weighted random selection with replacement, with the weight of each sample corresponding to the normalized coverage of the AMG. This ensured that a range of temperature or nutrient concentration values associated with the distribution and abundance of the AMG could be generated for each AMG and each environmental parameter tested. The number of samples randomly selected for each AMG was the same as the total number of samples for which a value of this parameter was available. Scripts used in this manuscript are available on the Sullivan laboratory bitbucket under project GOV_Ecogenomics (http://bitbucket.org/MAVERICLab/gov_ecogenomics/overview). Scripts used in the assessment of microbial diversity are gathered in the directory Host_diversity, the ones used for host predictions are in Host_prediction, and the scripts used to identify abundant viral clusters are in Virus_clusters_prevalence. All raw reads are available through ENA (Tara Oceans) or IMG (Malaspina) using the dataset identifiers listed in Supplementary Table 1. Processed data are available through iVirus (http://mirrors.iplantcollaborative.org/browse/iplant/home/shared/iVirus/GOV/), including all sequences from assembled contigs, lists of viral populations and associated annotated sequences as GenBank files, viral clusters composition and characteristics, map comparisons of genomes and contigs of the 38 abundant viral clusters and host predictions for viral contigs.


No statistical methods were used to predetermine sample size. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. Metaphase cells were obtained by treating cells with Karyomax (Gibco) at a final concentration of 0.01 μg ml−1 for 1–3 h. Cells were collected, washed in PBS, and resuspended in 0.075 M KCl for 15–30 min. Carnoy’s fixative (3:1 methanol:glacial acetic acid) was added dropwise to stop the reaction. Cells were washed an additional three times with Carnoy’s fixative, before being dropped onto humidified glass sides for metaphase cell preparations. For ECdetect analyses, DAPI was added to the slides. Images in the main figures were captured with an Olympus FV1000 confocal microscope. All other images were captured at a magnification of 1,000× with an Olympus BX43 microscope equipped with a QiClick cooled camera. FISH was performed by adding the appropriate DNA FISH probe onto the fixed metaphase spreads. A coverslip was added and sealed with rubber cement. DNA denaturation was carried out at 75 °C for 3–5 min and the slides were allowed to hybridize overnight at 37 °C in a humidified chamber. Slides were subsequently washed in 0.4× SSC at 50 °C for 2 min, followed by a final wash in 2× SSC containing 0.05% Tween-20. Metaphase cells and interphase nuclei were counterstained with DAPI, a coverslip was applied and images were captured. The NCI-60 cell line panel (gift from A. Shiau, obtained from NCI) was grown in RPMI-1640 with 10% FBS under standard culture conditions. Cell lines were not authenticated, as they were obtained from the NCI. The PDX cell lines were cultured in DMEM/F-12 medium supplemented with glutamax, B27, EGF, FGF and heparin. Lymphoblastoid cells (gift from B. Ren) were grown in RPMI-1640, supplemented with 2 mM glutamine and 15% FBS. IMR90 and ALS6-Kin4 (gift from J. Ravits and D. Cleveland) cells were grown in DMEM/F-12 supplemented with 20% FBS. Normal human astrocytes (NHA) and normal human dermal fibroblasts (NHDF) were obtained from Lonza and cultured according to Lonza-specific recommendations. Cell lines were not tested for mycoplasma contamination. Tissues were obtained from the Moores Cancer Center Biorepository Tissue Shared Resource with IRB approval (#090401). All samples were de-identified and patient consent was obtained. Additional tissue samples that were obtained were approved by the UCSD IRB (#120920). DNA was sonicated to produce 300–500 bp fragments. DNA end repair was performed using End-it (Epicentre), DNA library adapters (Illumina) were ligated and the DNA libraries were amplified. Paired-end next-generation sequencing was performed and samples were run on the Illumina Hi-Seq using 100 cycles. Cells were collected and washed with 1 × cold PBS. Cell pellets were resuspended in buffer 1 (50 mM Tris pH 7.5, 10 mM EDTA, 50 μg ml−1 RNase A), and incubated in buffer 2 (1.2% SDS) for 5 min on ice. DNA was acidified by the addition of buffer 3 (3 M CsCl, 1 M potassium acetate, 0.67 M acetic acid) and incubated for 15 min on ice. Samples were centrifuged at 14,000g for 15 min at 4 °C. The supernatant was added to a Qiagen column and briefly centrifuged. The column was washed (60% ethanol, 10 mM Tris pH 7.5, 50 μM EDTA, 80 mM potassium acetate) and eluted in water. Metaphase cells were dropped onto slides and visualized with DAPI. Coverslips were removed and slides washed in 2 × SSC, and subsequently treated with 2.5% trypsin, and incubated at 25 °C for 3 min. Slides were then washed in 2 × SSC, DNase solution (1 mg ml−1) was applied to the slide and cells were incubated at 37 °C for 3 h. Slides were washed in 2 × SSC and DAPI was again applied to the slide to visualize DNA. In Fig. 2a, b the violin plots represent the distribution of ecDNA counts in different sample types. In order to compare the ecDNA counts between the different samples, we use a one-sided Wilcoxon rank-sum test, where the null hypothesis assumes that the mean ecDNA-count ranks of the compared sample types are equal. There is a wide variation in the number of ecDNA across different samples and within metaphases of the same sample. We want to estimate and compare the frequency of samples containing ecDNA for each sample type. We label a sample as being ecDNA positive by using the pathology standard: a sample is deemed to be ecDNA positive if we observe ≥ 2 ecDNA in ≥ 2 out of 20 metaphase images. Therefore, we ensure that every sample contains at least 20 metaphases. We define indicator variable X  = 1 if metaphase image j in sample i has ≥2 ecDNA elements, X  = 0 otherwise. Let n be the number of metaphase images acquired for sample i. We assume that X is the outcome of the jth Bernoulli trial, where the probability of success P is drawn at random from a beta distribution with parameters determined by ∑ X . Formally, We model the likelihood of observing k successes in n = 20 trials using the binomial density function as: Finally, the predictive distribution p(k), is computed using the product of the binomial likelihood and beta prior, modelled as a ‘beta–binomial distribution’29. We model the probability of sample i being ecDNA positive with the random variable Y so that: The expected value of Y is: Let T be the set of samples belonging to a certain sample type t, for example, immortalized samples. We estimate the frequency of samples under sample t containing ecDNA (bar heights on Fig. 2c, d) as assuming independence among samples i ∈ T. For any α or β  = 0, we assign them a sufficiently small ε. For more detail, please see Supplementary Information 1. We construct binary ecDNA-presence distributions, based on the ecDNA counts, such that an image with ≥ 2 ecDNA is represented as a 1, and 0 otherwise. In order to compare the ecDNA presence between the different samples, we use a one-sided Wilcoxon rank-sum test using the binary ecDNA-presence distributions, where the null hypothesis assumes the mean ranks of the compared sample types are equal. The software applies an initial coarse adaptive thresholding30, 31 on the DAPI images to detect the major components in the image with a window size of 150 × 150 pixels, and T = 10%. Components over 3,000 pixels and 80% of solidity are masked, and small components discarded. Weakly connected components of the remaining binary image are computed to find the separate chromosomal regions. Connected components over a cumulative pixel count of 5,000 are considered as candidate search regions, and their convex hull with a dilation of 100 pixels are added into the ecDNA search region. Following the manual masking and verification of the ecDNA search region, a second finer adaptive thresholding with a window size of 20 × 20 pixels and T = 7% is performed. Components that are greater than 75 pixels are designated as non-ecDNA structures and their 15-pixel neighbourhood is removed from the ecDNA search region. Any component detected with a size less than or equal to 75 pixels and greater than or equal to 3 pixels inside the search region is detected as ecDNA. For more detail, please see Supplementary Information 2. We sequenced 117 tumour samples including 63 cell lines, 19 neurospheres and 35 cancer tissues with coverage ranging from 0.6× to 3.89× and an additional 8 normal tissues as controls. See Extended Data Fig. 4 for the coverage distribution across samples. We mapped the sequencing reads from each sample to the hg19 (GRCh37) human reference genome32 from the UCSC genome browser33 using BWA software version 0.7.9a (ref. 34). We inferred an initial set of copy-number variants (CNVs) from these mapped sequence samples using the ReadDepth CNV software35 version 0.9.8.4 with parameters FDR = 0.05 and overDispersion = 1. We downloaded CNV calls for 11,079 paired tumour–normal samples covering 33 different tumour types from TCGA. We applied similar filtering criteria to ReadDepth output and TCGA calls to eliminate false copy number amplification calls from repetitive genomic regions and hotspots for mapping artefacts. We used the filtered set of CNV calls from ReadDepth as input probes for AmpliconArchitect which revealed the final set of amplified intervals and the architectures of the amplicons. See Supplementary Information 3 for more details. We developed a novel tool AmpliconArchitect, to automatically identify connected amplified genomic regions and reconstruct plausible amplicon architectures. For each sample, AmpliconArchitect takes as input an initial list of amplified intervals and whole-genome sequencing paired-end reads aligned to the human reference. It implements the following steps to reconstruct the one or more architectures for each amplicon present in the sample: (1) use discordant read-pair alignments and coverage information to iteratively visit and extend connected genomic regions with high copy numbers; (2) for each set of connected amplified regions, segment the regions based on depth of coverage using a mean-shift segmentation to detect copy-number changes and discordant read-pair clusters to identify genomic breaks; (3) construct a breakpoint graph connecting segments using discordant read-pair clusters; (4) compute a maximum-likelihood network to estimate copy counts of genomic segments; and (5) report paths and cycles in the graph that identify the dominant linear and circular structures of the amplicon (see also Supplementary Information 3). We compared our sample set against TCGA samples to test the assumption that the genomic intervals amplified in our sample set are broadly representative of a pan-cancer dataset, by comparing against TCGA samples. Here, we deal with an abstract notation to represent different datasets and describe a generic procedure to compare amplified regions. Consider a set of K samples. For any k ∈ [1,..., K], let S denote the set of amplified intervals in sample k. Let c be the cancer subtype for sample k. We compare S against TCGA samples with subtype c. Let T denote the set of all genomic regions which are amplified in at least 1% of TCGA samples of subtype c. For each interval t ∈ T, let f denote its frequency in TCGA samples of subtype c. We define a match score The cumulative match score for all samples is defined as: To compute the significance of statistic D, we do a permutation test. We generate N random permutations of the TCGA intervals for subtype c and estimate the distribution of match scores of our sample set against the random permutations. We choose a random assignment of locations of all intervals in T, while retaining their frequencies. For the jth permuted set T , we computed the cumulative match score D relative to our sample set. Thus the significance of overlap between amplified intervals in our sample set and the TCGA set is estimated by the fraction of random permutations with D  > D. Computing 1 million random permutations generated exactly one permutation breaching the TCGA score D, implying a P ≤ 10−6. We compared the rank correlation of the most frequent oncogenes in our sample set with the top oncogenes as reported by TCGA pan-cancer analysis in ref. 20. We identified 14 oncogenes occurring in 2 or more samples of our sample set and compared these to the top 10 oncogenes from the TCGA pan-cancer analysis. We found that 7 out of the top 10 oncogenes were represented in our list of 14 oncogenes. Considering 490 oncogenes in the COSMIC database, the significance of observing 7 or more oncogenes in common in the two datasets is given by the hypergeometric probability We found high similarity between amplicon structures of biological replicates (for example, Extended Data Fig. 8). We estimate the probability of common origin between two samples by measuring the pairwise similarity between amplicon structures. In reconstructing the structures (Supplementary Information 3), we identify a set of locations representing change in copy number and we use the locations of change in copy number to estimate the similarity in amplicon structures. Let L be the total length of amplified intervals. These intervals are binned into windows of size r, resulting in bins. We use a segmentation algorithm that determines if there is a change in copy number in any bin, within a resolution of r = 10,000 bp (see meanshift in coverage: Supplementary Information 3.2.). Note that this is an overestimate, because with split-reads and high-density sequencing data, we can often get the resolution down to a few base pairs. Let S and S represent the set of bins with copy-number changes in the two samples, respectively. S and S are selected from a candidate set of locations N . Under the null hypothesis that S is random with respect to S , we expect I = S  ∩ S to be small. Let m = min {|S1|, |S2|}, and M = max {|S |, |S |}. A P value is computed as follows: When looking at GBM39 replicates (Extended Data Fig. 8), we find that all replicates displaying EGFR ecDNA are similar to each other. Comparing replicates in row 1 and row 2 among |N | = 129 bins (1.29 Mb), |S1| = 5 corresponding to row 1 (ecDNA sample), |S2| = 6 corresponding to row 2 (ecDNA sample) and intersection set size |I| = 5, we compute that the P value for observing such structural similarity by random chance is 2.18 × 10−8, which is the highest P value among all ecDNA replicate pairs. In addition, we compare the replicates containing EGFR in ecDNA with the culture containing EGFR in HSR. Among |N | = 129 bins, |S1| = 6 corresponding to row 2 (ecDNA), |S2| = 4 corresponding to row 4 (HSR), the intersection set has size |I| = 4 intervals giving a P value of 1.98 × 10−5, which gives the highest P value among the 3 ecDNA replicates compared to the HSR culture, suggesting a common origin. Consider an initial population of N cells, of which N cells contain a single extra copy of an oncogene. We model the population using a discrete generation Galton–Watson branching process23. In this simplified model, each cell in the current generation containing k amplicons (amplifying an oncogene) either replicates with probability b to create the next generation, or dies with probability 1 − b to create the next generation. We set the selective advantage In other words, cells with k copies of the amplicon stop dividing after reaching a limit of M amplicons. Otherwise, they have a selective advantage for 0 < k ≤ M , where the strength of selection is described by f (k), as follows: Here, s denotes the selection coefficient, and parameters m and α are the ‘mid-point’, and ‘steepness’ parameters of the logistic function, respectively. Initially, f (k) grows linearly, reaching a peak value of f (k) = 1 for k = M . As the viability of cells with large number of amplicons is limited by available nutrition36, f (k) decreases logistically in value for k > M reaching f (k) → 0 for k ≥ M . We model the decrease by a sigmoid function with a single mid-point parameter m so that f (m) = 0.5. The ‘steepness’ parameter α is automatically adjusted to ensure that max{1 – f (M ), f (M )} → 0. The copy-number change is affected by different mechanisms for extrachromosomal (ecDNA) and intrachromosomal (HSR) models. In the ecDNA model, the available k amplicons are on ecDNA elements which replicate and segregate independently. We assume complete replication of ecDNA elements so that there are 2k copies which are partitioned into the two daughter cells via independent segregation. Formally, the daughter cells end up with k and k amplicons respectively, where By contrast, in the intrachromosomal model, the change in copy number happens via mitotic recombination, and the daughter cell of a cell with k amplicons will acquire either k + 1 amplicons or k − 1 amplicons, each with probability P . With probability 1−2P , the daughter cell retains k amplicons. See Supplementary Information 4 for more details. AmpliconArchitect is available for use online at: https://github.com/virajbdeshpande/AmpliconArchitect. ECdetect will be available upon request. Whole-genome sequencing data are deposited in the NCBI Sequence Read Archive (SRA) under Bioproject (accession number: PRJNA338012). DAPI and FISH metaphase images are available for download on figshare at https://figshare.com/s/ab6a214738aa43833391.


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No statistical methods were used to predetermine sample size. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. E14 mouse ES cells were cultured in high-glucose DMEM (Invitrogen) supplemented with 15% FBS (Millipore), 0.1 mM non-essential amino acids (Invitrogen), 1 mM sodium pyruvate (Invitrogen), 0.1 mM 2-mercaptoethanol, 1500 U ml−1 LIF (Millipore), 25 U ml−1 penicillin, and 25 μg ml−1 streptomycin. The cells were mycoplasma free. Generation of Dnmt3b−/− ES cells was performed using TALEN technology. Cells were transfected with the two TALEN constructs targeting exon 17 of murine Dnmt3b (corresponding to the start of the catalytic domain) and after 16 h were seeded as a single cell. After ten days, clones were screened by western blot analysis. Positive clones were analysed by genomic sequencing. For half-life measurements and Pol II elongation inhibition, wild-type and Dnmt3b−/− ES cells were treated with DRB at the concentration of 75 μM for the indicated times. For total cell extracts, cells were resuspended in F-buffer (10 mM Tris-HCl pH 7.0, 50 mM NaCl, 30 mM Na-pyrophosphate, 50 mM NaF, 1% Triton X-100, anti-proteases) and sonicated for three pulses. Extracts were quantified using BCA assay (Pierce) and were run on SDS-polyacrylamide gels at different percentages, transferred to nitrocellulose membranes and incubated with specific primary antibodies overnight. Nuclear protein extractions were performed as described in ref. 41. In brief, cells were harvested in PBS 1× and resuspended in isotonic buffer (20 mM HEPES pH 7.5, 100 mM NaCl, 250 mM sucrose, 5 mM MgCl , 5 μM ZnCl ). Successively, cells were resuspended in isotonic buffer supplemented with 1% NP-40 to isolate nuclei. The isolated nuclei were resuspended in digestion buffer (50 mM Tris-HCl pH 8.0, 100 mM NaCl, 250 mM sucrose, 0.5 mM MgCl , 5 mM CaCl , 5 μM ZnCl ) and treated with Microccocal Nuclease (NEB) at 30 °C for 10 min. Nuclear proteins from about 1 × 107 cells were incubated with 3 μg of specific antibody overnight at 4 °C. Immunocomplexes were incubated with protein-G-conjugated magnetic beads (DYNAL, Invitrogen) for 2 h at 4 °C. Samples were washed four times with digestion buffer supplemented with 0.1% NP-40 at RT. Proteins were eluted by incubating with 0.4 M NaCl TE buffer for 30 min and were analysed by western blotting. Custom shRNAs against SetD2, Dis3 and Rrp6 were constructed using the TRC hairpin design tool (http://www.broadinstitute.org/rnai/public/seq/search), and designed to target the 3′ UTR. shRNAs with more than 14 consecutive matches to non-target transcripts were avoided. Hairpins were cloned into pLKO.1 vector (Addgene: 10878) and each construct was verified by sequencing. Dnmt3b construct was obtained by PCR amplification and cloned into pEF6/V5-His vector (Invitrogen). The Dnmt3b mutant constructs (V725G, S277P and VW-RR) were generated by introducing a site-specific mutation in the DNA sequence corresponding to Val725 to mutate it into a glycine, or Ser277 to mutate it into a proline, or Val236Trp237 to mutate it to Arg–Arg, using QuickChange XL Site-Directed Mutagenesis Kit (Agilent Technologies). Transfections of mouse ES cells were performed using Lipofectamine 2000 Transfection Reagent in according to manufacturer’s protocol using equal amounts of each plasmid in multiple transfections. For SetD2 knockdown, cells were transfected with 5 μg of the specific shRNA construct, and maintained in medium with puromycin selection (1 μg ml−1) for 48 h. To investigate the distribution of the endogenous Dnmt3b we tested different antibodies and found one that was able to immunoprecipitate the endogenous Dnmt3b cross-linked to chromatin, which showed no background signal in Dnmt3b−/− (Extended Data Fig. 1g–i). The ChIP-seq data were validated by ChIP–qPCR, using several biological replicates, on target genomic regions and by crosslinked co-immunoprecipitation experiments between Dnmt3b and H3K36me3 in wild-type or Dnmt3b−/− ES cells (Extended Data Fig. 1o, p). For Dnmt3b ChIP-seq, approximately 2 × 107 cells were cross-linked by addition of formaldehyde to 1% for 10 min at RT, quenched with 0.125 M glycine for 5 min at RT, and then washed twice with cold PBS. The cells were resuspended in lysis buffer 1 (50 mM HEPES-KOH pH 7.5, 140 mM NaCl, 1 mM EDTA, 10% glycerol, 0.5% NP-40, 0.25% Triton X-100 and protease inhibitor) to disrupt the cell membrane and in lysis buffer 2 (10 mM Tris-HCl pH 8.0, 200 mM NaCl, 1 mM EDTA, 0.5 mM EGTA and protease inhibitor) to isolate nuclei. The isolated nuclei were then resuspended in SDS ChIP Buffer (20 mM Tris-HCl pH 8.0, 10 mM EDTA, 1% SDS and protease inhibitors). Extracts were sonicated using the BioruptorH Twin (Diagenode) for two runs of ten cycles (30 s on, 30 s off) at high-power setting. Cell lysate was centrifuged at 12,000g for 10 min at 4 °C. The supernatant was diluted with ChIP dilution buffer (20 mM Tris-HCl pH 8.0, 150 mM NaCl, 2 mM EDTA, 1% Triton) before the immunoprecipitation step. Magnetic beads (Dynabeads rat anti-mouse IgM for anti-Pol II-phospho-S5, Dynabeads Protein G for all the other ChIPs, Life Technologies) were saturated with PBS/1% BSA and the samples were incubated with 2 μg of antibody overnight at 4 °C on a rotator. Next day samples were incubated with saturated beads for two hours at 4 °C on a rotator. Successively immunoprecipitated complexes were washed five times with RIPA buffer (50 mM HEPES-KOH pH 7.6, 500 mM LiCl, 1 mM EDTA, 1% NP-40, 0.7% Na-Deoxycholate) at 4 °C for 5 min each on a rotator. For other ChIP-seq, ChIP-seq was performed as described previously42. Elution buffer was added and incubated at 65 °C for 15 min. The de-crosslinking was performed at 65 °C overnight. De-crosslinked DNA was purified using QiaQuick PCR Purification Kit (Quiagen) according to the manufacture’s instruction. MeDIP was performed using MeDIP kit (Active Motif), according to the manufacturer’s protocol. DNA was analysed by quantitative real-time PCR by using SYBR GreenER kit (Invitrogen). All experiment values were normalized to input. The data shown represent triplicate real-time quantitative PCR measurements of the immunoprecipitated DNA. The data are expressed as a percentage of the DNA inputs. Error bars represent standard deviation determined from triplicate experiments. Oligonucleotide sequences are reported in Supplementary Table 1. Genomic DNA was extracted from cells using DNeasy Blood and Tissue kit (Qiagen). For dot-blot analysis, extracted genomic DNA was sonicated using the BioruptorH Twin (Diagenode) for two runs of ten cycles (30 s on, 30 s off) at high-power setting, in order to obtain 300-bp fragments, denatured with 0.4 M NaOH and incubated for 10 min at 95 °C before being spotted onto HybondTM-N+ (GE Healthcare). Membranes were saturated with 5% milk and incubated with the specific antibodies overnight. Approximately 10 ng of purified ChIP DNA were end-repaired, dA-tailed, and adaptor-ligated using the NEBNext ChIP-seq Library Prep Master Mix Set (NEB), following the manufacturer’s instructions. For whole-genome bisulphite-seq library preparation, 2.5 μg of ES cells genomic DNA, were spiked-in with 1 ng of Escherichia coli genomic DNA, and sheared using a Bioruptor Twin sonicator (Diagenode) for three runs of ten cycles (30 s on, 30 s off) at high-power setting. Fragmented/digested DNA was then end-repaired, dA-tailed, and ligated to methylated adapters, using the Illumina TruSeq DNA Sample Prep Kit, following manufacturer instructions. DNA was loaded on EGel Size select 2% agarose pre-cast gel (Invitrogen), and a fraction corresponding to fragments ranging from 180 bp to 350 bp was recovered. Purified DNA was then subjected to bisulphite conversion using the EpiTect Bisulphite Kit (Qiagen). Bisulphite-converted DNA was finally enriched by 15 cycles of PCR using Pfu Turbo Cx HotStart Taq (Agilent). Total RNA was extracted as previously described43 using TRIzol reagent (Invitrogen). Real-time PCR was performed using the SuperScript III Platinum One-Step Quantitative RT–PCR System (Invitrogen) following the manufacturer’s instructions. Ribo-RNA-seq library preparation was performed as described previously44. In brief, 2.5 μg of total RNA were depleted of ribosomal RNA using the RiboMinus Eukaryote System v2 kit (Invitrogen), following manufacturer instructions. Ribo-RNA was resuspended in 17 μl of EFP buffer (Illumina), heated to 94 °C for 8 min, and used as input for first strand synthesis, using the TruSeq RNA Sample Prep kit, following manufacturer instructions. Poly(A) RNA-seq library was performed by using the TruSeq RNA Sample Prep kit, following the manufacturer’s instructions. For immunoprecipitation of mRNA for CAP-Seq experiments, 30 μg of total RNA were fragmented by alkaline hydrolysis in ~200-nt fragments and incubated with 5 μg of mouse anti-CAP antibody (anti-m3G-cap, m7g-cap, Clone H20, Millipore MABE419) (or IgG) overnight at 4 °C in 0.5 ml of IP buffer (10 mM Tris-HCl pH 7.5; 150 mM NaCl; 0.1% Triton X-100) supplemented with 50 U ml−1 RNaseOUT (Invitrogen), 50 U ml−1 SuperaseIN (Invitrogen), and 50 U ml−1 RNase Inhibitor (Ambion). 25 μl of Dynabeads Protein G (Invitrogen) were saturated overnight at 4 °C in IP buffer supplemented with 150 μg of Sonicated Salmon Sperm DNA (Qiagen). Following incubation, beads were washed two times in IP buffer and incubated with the preformed RNA-antibody complexes at 4 °C. After 3 h, beads were washed four times with IP buffer. Specific elution of recovered fragments were obtained by incubation of beads with 100 μl elution buffer (5 mM Tris pH 7.5; 1 mM EDTA; 0.05% SDS; 0.3 mg ml−1 Proteinase K) for 1.5 h at 50 °C. Fragments were then purified by addition of 1 ml of TRIzol reagent (Invitrogen), and subjected to random-primed reverse transcription using the SuperScript III Reverse Trancriptase (Invitrogen) at 50 °C for 1 h. Resulting cDNAs were then used as input for the TruSeq RNA Sample Prep kit (Illumina), starting from the ‘second strand synthesis’ step, to produce the sequencing library, following the manufacturer’s instruction. To map the transcriptional start sites at single-base resolution we used an enzymatic-based approach by the use of the RNA 5′ pyrophosphohydrolase (RppH) enzyme to decap eukaryotic mRNAs21. We validated the specificity of this technique in a pilot experiment by comparing RppH-treated RNA versus untreated or T4 polynucleotide kinase (PNK)-treated RNA (Extended Data Fig. 6a–e). When required the total RNA was depleted from small nuclear RNAs (snRNAs) by using the following protocol. 5 μg of total RNA was resuspended in snRNA-depletion buffer (20 mM HEPES pH 7.5, 80 mM KCl, 1 mM DTT), 1 μl RNase inhibitor (Ambion), 2 μM oligo mix (designed against snRNAs sequences, primers sequences in Supplementary Table 1) in a final volume of 50 μl, heated to 70 °C for 5 min and immediately put on ice. After that it was added 25 μl snRNA-depletion buffer 2 × (40 mM HEPES pH 7.5, 160 mM KCl, 10 mM MgCl , 2 mM DTT), supplemented with 1 μl RNase inhibitor (Ambion) and 1 μl of RNAse H (NEB) to a final volume of 100 μl. Incubated for 30 min at 37 °C. snRNA-depleted RNA were purified by RNA Clean and Concentration kit (Zymo Research) and DNaseI digestion was performed following the manufacturer’s instructions. snRNA-depleted RNAs were further depleted from ribosomal RNA by using the RiboMinus Eukaryote System v2 kit (Invitrogen). The RNA obtained from previous depletions (or poly(A)+ RNA enriched using NEBNext Poly(A) mRNA Magnetic Isolation Module kit (NEB), following the manufacturer’s instructions) was chemically fragmented by using first strand buffer of the SuperScript II Reverse Transcriptase (Invitrogen). The fragmented RNA was dephosphorylated of natural 5′ and fragmentation-derived 3′ phosphate by using Antarctic Phosphatase (AP, NEB). Dephosphorylated RNA was then treated with RNA 5′ pyrophosphohydrolase (RppH, NEB) in 1 × Thermopol buffer (NEB) (for decapping and pyrophosphate removal from the 5′ end of RNA to leave a 5′ monophosphate RNA). For positive and negative control, the dephosphorylated RNA was treated with the T4 polynucleotide kinase (PNK, NEB) (for 5′ phosphorylation of all RNA fragments) or was performed without adding the enzyme. 5′ RNA adaptor ligation was carried out by using the TruSeq Small RNA Sample Preparation Kit (Illumina). Reverse transcription was performed with SuperScript III enzyme (Invitrogen) and Illumina 3′ Adaptor Rev-Comp Random Hexamers (RC3N6). The RNA was size selected on TBE-Urea 10% PAGE gel and PCR amplification was carried out by using the TruSeq Small RNA Sample Preparation Kit (Illumina). Ribosome profiling was performed using the ARTseq/TruSeq Ribo Profile (Illumina), with minor changes to the manufacturer protocol. In brief, around 3 × 107 cells were treated with 0.1 μg μl−1 final cycloheximide for 5 min at 37 °C. Cells were then washed twice and harvested with ice-cold PBS (supplemented with 0.1 μg μl−1 final cycloheximide). Cells were lysed in 1 ml of mammalian lysis buffer (supplemented with 0.5% final concentration of NP-40) at 4 °C for 10 min on a rotator. The lysate was then treated with 50 U of ART-seq nuclease for 45 min at 25 °C, with moderate shaking. 400 μl of the digested lysate were then layered on the top of a 2.5 ml sucrose cushion, and centrifuged at 265,000g for 5 h at 4 °C. After completely removing the supernatant, the pellet was resuspended in 100 μl nuclease-free water, and purified on RNA Clean & Concentrator-5 columns (Zymo Research). 5 μg of the recovered monosomal RNA was then subjected to two consecutive rounds of rRNA depletion using the Ribo-Zero Gold Kit (Human/Mouse/Rat, Epicentre), and then run on a 10% TBE-Urea PAGE gel for 25 min at 200 V. A gel slice corresponding to 28–30 nt was then cut, crushed, and RNA was recovered by passive diffusion at 4 °C for 16 h. The eluted RNA fragments were then end-repaired, ligated to the 3′ adaptor, and reverse-transcribed. The cDNA was run on 10% TBE-Urea PAGE gel for 30 min at 180 V, and a gel slice corresponding to fragments of approximately 70–80 nt was cut, crushed, and cDNA was recovered by passive diffusion at 37 °C for 16 h with vigorous shaking. The eluted cDNA was then subjected to circularization, and the final library was obtained by ten cycles of PCR. The final library was inspected on the Fragment Analyzer (Advanced Analytical), revealing a single sharp peak around 150 bp. Samples were sequenced on the HiScanSQ or Next500 platforms (Illumina). All of the analysed datasets were mapped to a recently published variant of the mm9 genome assembly that includes single-nucleotide variants from E14 ES cells45. Prior to mapping, sequencing reads were trimmed on the basis of low-quality scores and clipped from the adaptor sequence by using FASTX toolkit (http://hannonlab.cshl.edu/fastx_toolkit/). For RNA-seq data analysis, reads were mapped using TopHat v2.0.6 (ref. 46) and mRNA quantification was performed using Cuffdiff v2.0.2 (ref. 47). For ChIP-seq data analysis, reads were mapped Bowtie version 0.12.7 (ref. 48), reporting only unique hits with up to two mismatches (parameters: -m 1 -v 2). For bisulphite-seq data analysis, reads were mapped using BSMAP v2.74 (ref. 49). Unmapped reads from the first mapping round were trimmed by 10 nt at their 5′ end, and 15 nt at their 3′ end using fastx_trimmer tool from the FASTX toolkit, and subjected to a second round of mapping. Reads failing this second mapping round were mapped to the Escherichia coli strain K-12 substrain DH10B genome (NCBI accession: NC_010473), in order to estimate bisulphite conversion efficiency. RNA-seq correlation analyses were performed by using Pearson correlation coefficient and by plotting RPKM value calculated on RefFlat gene annotation. Intragenic transcription initiation analysis was performed on a non-redundant gene annotation built starting from the RefFlat annotation, by keeping only the longest isoform for each gene, with at least 1 RPKM of expression and at least 5 exons. RPKM on each exon was calculated by counting reads falling in the exon (normalizing on the exon length in kb and on the total mapped reads of the experiment in millions) using custom script and then the ratio was calculated as the log fold-change of second, third and last exon RPKM over the first exon RPKM for each gene. For the ratio of intermediate to first exons, averages of the RPKM value of all the other exons (from fourth to penultimate) were used. Alternative promoter analysis was performed on a non-redundant gene annotation built starting from the RefFlat annotation by keeping only the genes that had at least two isoforms transcribed from known different promoters. RPKM of the first exon of the isoforms transcribed from alternative promoters was calculated with a custom script. The log ratio between the first exons transcribed from the first over the second promoter was plotted by using the heatscatter function (on R) and correlation was quantified with Pearson’s coefficient. Alternative promoter analysis was calculated on the same reference as above. The log ratio was calculated as the RPKM value of the first exon transcribed from each class of different alternative promoters over the RPKM of the whole transcript, in order to normalize differentially expressed genes in wild-type and Dnmt3b−/− cells. For DECAP-seq only intragenic mapped reads were used for further analysis. We used a RefSeq-based genic reference containing only the annotated longest isoforms and deprived from all the genes overlapping other genes or ncRNAs on the same strand. Since DECAP-seq is a technique capable of single-base resolution and the first base of the sequenced reads corresponds to the base having the cap signal, only the first position of the mapped read was used to calculate a count per million of mapped reads (RPM). All the analyses were performed on the genes belonging to the third or fourth quartiles of expression. Venn diagram overlap is calculated at single-base resolution. Logo analysis of the sequence enrichment was performed by using WebLogo (http://weblogo.berkeley.edu/). Motif discovery was performed by using HOMER Motif Analysis (http://homer.salk.edu/homer/motif/). For CAPIP-seq only intragenic mapped reads were used for further analysis. RPKM of each genomic feature were calculated as described above by using custom script. Enrichment was calculated as the log fold change of RPKM value from CAP immunoprecipitated samples over the RPKM from input samples for each genomic feature. As for DECAP-seq, the intragenic CAPIP-seq signal ratio between wild-type and Dnmt3b−/− cells was calculated as the fold change of the intragenic enrichment (from 2 kb downstream TSS to TES) in wild-type over Dnmt3b−/− cells. The ratio gene-body to TSS was defined as the log fold change of gene-body enrichment (derived from intronic and intermediate exonic regions) over the enrichment calculated on the first 200 nt of the transcripts. All the analyses were performed on genes belonging to the third or fourth quartiles of expression. Poly(A)+ enriched RNA-seq analyses were performed from RNA derived from DRB-treated wild-type and Dnmt3b−/− ES cells. For half-life calculation, gene quantifications performed with CuffDiff (see above) were normalized on the average of the top ten genes showing less degradation rate following DRB treatment having at least 10 RPKM in ES cells. Degradation rate has been defined as the ratio of RPKM value of the sample at time 0 h of DRB treatment over the average RPKM value of the samples treated for 3, 6 and 12 h with DRB. The top ten genes are Tmsb10, Mt1, Mt2, Rps14, Rplp2, 4930412F15Rik, Rpl38, Rplp1, Tomm7 and Cox6a1. Only genes with a RPKM > 1 were used for further analysis and a constant of 0.1 pseudo-RPKM was introduced to reduce sampling noise. Half-life (t ) was calculated by using the following formula50: where k is the decay rate constant obtained by fitting data (gene RPKM value for each time point) with an exponential function. Half-life on introns was measured as calculated for mature mRNAs, but gene quantification (RPKM) was performed counting the reads on introns and normalizing for intron length (kb) and for the number of total intragenic mapped reads (millions). For introns and exons quantification, reads were treated as above (see RNA-seq analysis). Analysis of ART-seq experiments were performed as previously described31. Differently from the other sequencing data, for ribosome profiling, only adaptor containing reads were used in order to avoid total RNA contamination. Reads were clipped from adapters and mapped on rRNAs and tRNAs. Only reads not mapping on rRNA/tRNA genes were used for downstream analysis. Quantification (RPKM) of the reads derived from different transcript parts or genomic features was performed as described above. Following mapping, reads with the same start mapping coordinates were collapsed using custom Perl scripts, and peak calling was performed using MACS version 1.4.1 (ref. 51). ChIP-seq signal log enrichment was calculated as previously described10, with some modifications. In brief, the mouse genome was partitioned into 500-bp bins. Bins overlapping with satellite repeats and with an insufficient coverage in WGBS (less than 50% of all CpGs covered at least 10×) were removed resulting in 2,708,724 bins. Signal enrichment was calculated as the log of ChIP-seq over input RPKM. These whole-genome log enrichment values were used for clustering, correlation, box plot and scatter plot analysis by using custom scripts. For genomic binning by H3K36me3, the above bins were divided in ten equal-size groups rank-ordered by their log enrichment for H3K36me3. Heat map representations of ChIP-seq peaks and plots were performed with respect to annotated RefSeq genes, sorted by their expression level, according to RNA-seq data. Plots of Dnmt3b and H3K36me3 distribution on genes clustered in quartiles of expression revealed an almost identical distribution for both features. For the analysis of Dnmt3b intragenic binding in Setd2 knockdown ES cells and Dnmt3b-re-expressing Dnmt3b−/− ES cells, a non-redundant gene annotation was built starting from the RefFlat annotation, by keeping only the longest isoform for each gene. After calling H3K36me3 peaks in wild-type ES cells using MACS 1.4.1 (parameters: -p 1e-8 –nolambda), the genes from the RefFlat annotation that overlap an H3K36me3 peak were marked as H3K36me3-positive, while genes lacking any overlap were marked as H3K36me3-negative. For each gene in the two datasets, the normalized Dnmt3b signal (RPKM) in control and treated ES cells was calculated as: where n is the number of Dnmt3b reads overlapping a gene’s coordinates, TSS and TES are respectively the start and end coordinate of the gene annotation, and N is the total number of mapped reads in the ChIP-seq experiment. P values were calculated using a one-tailed paired Wilcoxon rank-sum test. Methylation calling was performed using the methratio.py script provided with the BSMAP tool and comparative analyses were performed by using only CpG covered at least 5× in both wild-type and Dnmt3b−/− cells. Heat maps and comparative analysis were performed using custom Perl scripts. Datasets used for comparative analysis were obtained from Gene Expression Omnibus by downloading the following datasets: GSE12241, GSE11172, GSE31039, GSE44642, GSE44566, GSE55660, GSE57413, GSE44566. Antibodies were purchased from Abcam (anti-Dnmt3b; anti-H3K36me3; anti-single-strand DNA; anti-H3 pan; anti-Tbp; anti-TIIb), from Imgenex (anti-Dnmt3a; anti-Dnmt3b; anti-Dnmt1), from Diagenode (anti-5-methylcytidine), from Millipore (anti-H3K27me3; anti-m3G-cap, anti-m7G-cap; anti-Elk1), from Upstate (anti-H3K4me3), from Covance (anti-Pol II-phospho-Ser5), from SantaCruz (anti-pan Pol II, anti-Sp1; anti-Elf1), from Upstate (anti-H3K4me3; anti-H3ac). Anti-Dnmt3l was provided by S. Yamanaka. The raw data that support the findings of this study have been deposited at Gene Expression Omnibus under the accession code GSE72856.


According to Stratistics MRC, the Global Polymerase Chain Reaction market is accounted for $6.95 billion in 2015 and is expected to reach $12.56 billion by 2022 growing at a CAGR of 8.8% during the forecast period. Increasing investments in gene therapy and government support in R&D are some of the factors fueling the market growth. However, rising non-validated home brew test and reimbursement issues are hampering the market. Real time polymerase chain reaction instrument is one of the major challenges for the polymerase chain reaction technologies market. Academics and research organizations hold the largest share in end users segment. Clinical diagnostic labs and hospitals market is anticipated to grow at a faster pace during the forecast period. North America is the leading PCR market followed by Europe, owing to rising demand for low-cost diagnosis in healthcare. Some of the key players in Polymerase Chain Reaction market are Sigma-Aldrich Co. LLC., Thermo Fisher Scientific Inc., GE Healthcare, F. Hoffmann-La Roche Ltd., Becton, Dickinson & Company, QIAGEN, Agilent Technologies Inc., Bio-Rad Laboratories Inc., Beckman Coulter Inc., Affymetrix Inc., Abbott Laboratories, Cytocell Ltd, Shimadzu Biotech, HY LABORATORIES, Eppendorf AG, Exiqon, Dna Landmarks, Roche Diagnostics, Ocimum Biosolutions, BD Biosciences, Illumina, Complete Genomics, Dnavision SA, Epicentre® Biotechnologies and Hokkaido System Science Co. Products Covered: • Reagents and Consumables o Buffers o Consumable o Nuclease Free Water o Enzymes o Template o Primers And Probes o DNA o Master Mixes o dNTP's o Others Reagents and Consumables • Instruments  o Digital PCR Systems o Standard PCR Systems Real time PCR's • Life Sciences • Industrial Application o Animal husbandry o Environment o Biomedical research o Agricultural o Applied testing o Other PCR industry applications • Clinical Diagnostics o Infectious o Non Infectious • Others Applications o Dentistry o Pathogen Detection End Users Covered: • Academic and Research Organizations • Pharmaceutical and Biotechnology Industries • Clinical Diagnostics Labs and Hospitals • Other End Users o Blood Banks Regions Covered: • North America o US o Canada o Mexico • Europe o Germany o France o Italy o UK  o Spain   o Rest of Europe     • Asia Pacific o Japan        o China        o India        o Australia        o New Zealand       o Rest of Asia Pacific     • Rest of the World o Middle East o Brazil o Argentina o South Africa o Egypt What our report offers: - Market share assessments for the regional and country level segments - Market share analysis of the top industry players - Strategic recommendations for the new entrants - Market forecasts for a minimum of 7 years of all the mentioned segments, sub segments and the regional markets - Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations) - Strategic recommendations in key business segments based on the market estimations - Competitive landscaping mapping the key common trends - Company profiling with detailed strategies, financials, and recent developments - Supply chain trends mapping the latest technological advancements


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No statistical methods were used to predetermine sample size. Clinical samples GBM1w, GBM2w, GBM3w, GBM4w, GBM5w, GBM6w, GBM7w, AA15 m, AA16 m, AA17 m, OD18 m and AA19 m were obtained as frozen specimens from the Massachusetts General Hospital Pathology Tissue Bank, or received directly after surgical resection and flash frozen (Extended Data Table 1). All samples were acquired with Institutional Review Board approval, and were de-identified before receipt. GBM1w was obtained at autopsy; the remaining samples were surgical resections. IDH status was determined for all clinical samples by SNaPshot multiplex PCR31. PDGFRA status was confirmed by FISH analysis. Tissue (200–500 μg) was mechanically minced with a sterile razor blade before further processing. Gliomaspheres were maintained in culture as described32, 33. In brief, neurosphere cultures contain Neurobasal media supplemented with 20 ng ml−1 recombinant EGF (R and D Systems), 20 ng ml−1 FGF2 (R and D Systems), 1× B27 supplement (Invitrogen), 0.5× N2 supplement (Invitrogen), 3 mM L-glutamine, and penicillin/streptomycin. Cultures were confirmed to be mycoplasma-free via PCR methods. GSC4 and GSC6 gliomasphere lines were derived from IDH wild-type tumours resected at Massachusetts General Hospital, and have been previously described and characterized32, 33, 34. BT142 gliomasphere line (IDH1 mutant)35 was obtained from ATCC, and cultured as described above except 25% conditioned media was carried over each passage. BT142 G-CIMP status was confirmed by evaluating LINE methylation with the Global DNA Methylation Assay – LINE-1 kit (Active Motif), as described36, and by methylation-sensitive restriction digests. GSC119 was derived from an IDH1 mutant tumour (confirmed by SNaPshot) resected at Massachusetts General Hospital. We confirmed IDH1 mutant status of GSC119 by RNA-seq (82 out of 148 reads overlapping the relevant position in the transcript correspond the mutant allele). The gliomasphere models were derived from tumours of the following types: GSC4 and GSC6: primary glioblastoma; BT142: grade III oligoastrocytoma; GSC119: secondary glioblastoma, G-CIMP. Clinical specimens and models used in this study are detailed in Extended Data Table 1. ChIP-seq was performed as described previously32. In brief, cultured cells or minced tissue was fixed in 1% formaldehyde and snap frozen in liquid nitrogen and stored at −80 °C at least overnight. Sonication of tumour specimens and gliomaspheres was calibrated such that DNA was sheared to between 400 and 2,000 bp. CTCF was immunoprecipitated with a monoclonal rabbit CTCF antibody, clone D31H2 (Cell Signaling 3418). H3K27ac was immunoprecipitated with an antibody from Active Motif (39133). ChIP DNA was used to generate sequencing libraries by end repair (End-It DNA repair kit, Epicentre), 3′ A base overhang addition via Klenow fragment (NEB), and ligation of barcoded sequencing adapters. Barcoded fragments were amplified via PCR. Libraries were sequenced as 38-base paired-end reads on an Illumina NextSeq500 instrument or as 50-base single-end reads on a MiSeq instrument. Sequencing libraries are detailed in Extended Data Table 2. H3K27ac maps for GSC6 were previously deposited to the GEO under accession GSM1306340. Genomic data has been deposited into GEO as GSE70991. For sequence analysis, identical reads were collapsed to a single paired-end read to avoid PCR duplicates. To avoid possible saturation, reads were downsampled to 5% reads collapsed as PCR duplicates, or 5 million fragments. Reads were aligned to hg19 using BWA, and peaks were called using HOMER. ChIP-seq tracks were visualized using Integrative Genomics Viewer (IGV, http://www.broadinstitute.org/igv/). To detect peaks lost in IDH mutants, we called signal over all peaks in a 100-bp window centred on the peaks. To control for copy number changes, we first called copy number profiles from input sequencing data using CNVnator37. We then removed all regions where at least one sample had a strong deletion (<0.25), and normalized by copy number. To account for batch effects and difference in ChIP efficiency, we quantile normalized each data set. Peaks were scored as lost or gained if the difference in signal between a given tumour and the average of the five wild-type tumours was at least twofold lower or higher, with a signal of at least 1 in all wild-type or IDH mutant tumours. Fisher exact test confirmed that the overlap between peaks lost in the IDH mutant tumours is highly significant (P < 10−100). GC content over CTCF peaks lost (or retained) in the IDH mutant glioma specimens was averaged over 200-bp windows centred on each peak lost in IDH mutant tumours. Methylation levels were quantified over these same regions for 3 IDH mutant and 3 IDH wild-type tumours, using TCGA data generated by whole genome bisulfite sequencing10. In brief, methylation levels (percentage) based on proportion of reads with protected CpG were averaged over all CpG di-nucleotides in these regions, treating each tumour separately. Occupancy of the CTCF site in the boundary element adjacent to the PDGFRA locus was quantified by ChIP qPCR, using the following primers: PDGFRActcfF: 5′-GTCACAGTAGAACCACAGAT-3′; PDGFRActcfR: 5′-TAAGTATACTGGTCCTCCTC-3′. Equal masses of ChIP or input (WCE) DNA were used as input for PCR, and CTCF occupancy was quantified as a ratio between ChIP and WCE, determined by 2−ΔCt. CTCF peak intensity was further normalized as ratio to two invariant peaks, at PSMB1 and SPG11, using the following primers: PSMB1ctcfF: 5′-CCTTCCTAGTCACTCAGTAA-3′; PSMB1ctcfR: 5′-CAGTGTTGACTCATCCAG-3′; SPG11ctcfF: 5′-CAGTACCAGCCTCTCTAG-3′; SPG11ctcfR: 5′-CTAAGCTAGGCCTTCAAG-3′. RNA-seq data for 357 normal brain samples was downloaded from GTEx20. RNA-seq data and copy number profiles for lower grade gliomas were downloaded from TCGA23, 24. Contact domains of IMR90, GM12878, K562 and NHEK cells were obtained from published HiC data15. Genes were assigned to the inner-most domain in which their transcription start site fell within. Gene pairs were considered to be in the same domain if they were assigned to the same domain in both GM12878 and IMR90. Gene pairs were considered to span a boundary if they were assigned to different domains in both GM12878 and IMR90, and separated by a CTCF-binding site in IDH wild-type tumours. Gene pairs that did not fit either criterion were excluded from this analysis. The plot of correlation vs distance for brain GTEx samples is based on Pearson correlations for all relevant pairs, smoothed by locally weighted scatterplot smoothing with weighted linear least squares (LOESS). To assess the bias in correlation differences, we computed the difference of Pearson correlations between wild-type and IDH mutant gliomas for all gene pairs separated by <180 kb. In Fig. 1e, this difference in correlations is plotted against the significance of this difference (estimated by Fisher z-transformation). For each gene pair, we omitted samples with a deletion or amplification of one of the genes at or above threshold of the minimal arm level deletion or amplification (to avoid copy number bias). To ensure robustness, we also repeated the analysis using boundaries defined from HiC data for K562 and NHEK. This yielded similar results: 84% pairs gaining correlation cross boundary versus 71% expected (P < 8 × 10−3), 54% pairs losing correlation are within the same domain versus 29% expected (P < 3 × 10−8). Repeating the analysis with only the 14,055 genes that have expressed over 1 transcripts per million (TPM) in at least half the samples also yielded similar results (Extended Data Fig. 7): 92% pairs gaining correlation cross boundary versus 69% expected (P < 2 × 10−3), 73% pairs losing correlation are within the same domain versus 31% expected (P < 8 × 10−4). To detect boundaries deregulated in IDH mutant gliomas, we scanned for gene pairs, separated by <1 Mb, with a significant difference in correlation between wild-type and IDH mutant tumours (Fisher z-transformation, FDR <1%). We omitted amplified or deleted samples as described above. To ensure robustness to noise from lowly expressed genes, we first filtered out 6,476 genes expressed <1 TPM in more than half of the samples (keeping 14,055 genes). We considered all domains and boundaries scored in IMR90 HiC data13. Gene pairs crossing a CTCF peak and an IMR90 boundary (that is, can be assigned to different domains) that were significantly more correlated in IDH mutant tumours were considered to support the loss of that boundary. Gene pairs not crossing a boundary (that is, can be assigned to the same domain) that were significantly less correlated in IDH mutant tumours were considered to support the loss of a flanking boundary. We collated a set of deregulated boundaries, supported by at least one cross-boundary pair gaining correlation and at least one intra-domain pair losing correlation. Each was assigned a P value equal to the product of both supporting pairs (best P value was chosen if there were more supporting pairs). If both boundaries of a domain were deregulated, or if the same pair of gene pairs (one losing and one gaining correlations) were supporting more than one boundary due to overlapping domains, the entries were merged (Supplementary Table 1). This definition allows every gene pair to be considered as potential support for a boundary loss. To quantify CTCF occupancy over these deregulated boundaries, we averaged the signal over all CTCF peaks located within a 1-kb window around the boundary, using copy number and quantile normalized CTCF signals. To quantify DNA methylation over the deregulated boundaries, we averaged DNA methylation signals from TCGA data in 200-bp windows as above. Figure 2a depicts significance of disrupted domains and the fold change of genes in them that are upregulated in IDH mutant tumours (compared to median expression in wild type). In addition to PDGFRA, top-ranking genes include CHD4 (P < 10−32), a driver of glioblastoma tumour initiation38, L1CAM (P < 10−8), a regulator of the glioma stem cells and tumour growth39, and other candidate regulators (Supplementary Table 1). To ensure robustness to cell-type-specific boundaries, we repeated the analysis with GM12878-, K562- and NHEK-defined boundaries. This yielded very similar results, and again highlighted PDGFRA as an overexpressed gene adjacent to a disrupted boundary. For the correlation of FIP1L1 and PDGFRA expression, RNA-seq data from the TCGA lower grade glioma (LGG) and glioblastoma (GBM) data sets2, 24 were downloaded and segregated by IDH mutation status and subtype. Patients from the proneural subtype were divided by IDH mutation status, while patients from the mesenchymal, classical or neural subtypes (which had no IDH mutations) were classified as ‘other’. For correlation analysis, patients with copy number variation in either gene were excluded from the analysis to control for effects of co-amplification. For outcome analysis, LGG RNA-seq data and corresponding patient survival data was obtained from TCGA. Patients with sum PDGFRA and FIP1L1 expression of at least one-half of one standard deviation above the mean were classified as ‘high PDGFRA and FIP1L1 expression’ (n = 17), while all other patients were classified as ‘low PDGFRA and FIP1L1 expression’ (n = 201). Data were plotted as Kaplan–Meier curves and statistically analysed via log–rank test. HiC data15 were downloaded from GEO. 5-kb resolution intra-chromosomal contact scores for chromosome 4 for the cell lines IMR90, NHEK, KBM7, K562, HUVEC, HMEC and GM12878 were filtered to the region between 53,700 and 55,400 kb. The average interaction score at each coordinate pair for all cell lines was calculated and used to determine putative insulator elements as local maxima at the interaction point of two domain boundaries. To determine the interactions of the PDGFRA promoter, the interaction scores of all points in the region with the PDGFRA promoter (chr4: 55,090,000) were plotted as a one-dimensional trace. To view the topological domain structure of the region, HiC interaction scores were visualized using Juicebox (http://www.aidenlab.org/juicebox/)15. Data shown is from the IMR90 cell line at 5-kb resolution, normalized to coverage. DNA methylation was analysed in two ways. For gliomaspheres, genomic DNA was isolated via QiaAmp DNA minikit (Qiagen) and subjected to bisulfite conversion (EZ DNA Methylation Gold Kit, Zymo Research). Bisulfite-converted DNA specific to the CTCF-binding site (defined by JASPAR40) in the boundary adjacent to PDGFRA was amplified using the following primers forward: 5′-GAATTATAGATAATGTAGTTAGATGG-3′, reverse: 5′-AAATATACTAATCCTCCTCTCCCAAA-3′. Amplified DNA was used to prepare a sequencing library, which was sequenced as 38-base paired-end reads on a NextSeq500. For tumours, limiting DNA yields required an alternative strategy for methylation analysis. Tumour genomic DNA was isolated from minced frozen sections of tumours by QiaAmp DNA minikit (Qiagen). Genomic DNA was digested using the methylation-sensitive restriction enzyme Hin6I (Thermo) recognizing the restriction site GCGC, or subjected to mock digestion. Protected DNA was quantified by PCR using the following primer set: PDGFRAinsF: 5′-CGTGAGCTGAATTGTGCCTG-3′, PDGFRAinsR: 5′-TGGGAGGACAGTTTAGGGCT-3′, normalizing to mock digestion. 3C analysis was performed using procedures as described previously41, 42. In brief, ~10 million cell equivalents from minced tumour specimens or gliomasphere cultures were fixed in 1% formaldehyde. Fixed samples were lysed in lysis buffer containing 0.2% PMSF using a Dounce pestle. Following lysis, samples were digested with HinDIII (NEB) overnight on a thermomixer at 37 °C rotating at 950 r.p.m. Diluted samples were ligated using T4 DNA ligase (NEB) at 16 °C overnight, followed by RNase and proteinase K treatment. DNA was extracted via phenol/chloroform/isoamyl alcohol (Invitrogen). DNA was analysed via TaqMan PCR using ABI master mix. Primers and probe were synthesized by IDT with the following sequences: common PDGFRA promoter: 5′-GGTCGTGCCTTTGTTTT-3′; FIP1L1 control: 5′-CAGGGAAGAGAGGAAGTTT-3′; FIP1L1 enhancer: 5′-TTAAGTAAGCAGGTAAACTACAT-3′; intragenic enhancer: 5′-AGCCTTTGCCTCCTTTT-3′; intragenic control: 5′-CCACAGGGAGAAGGAAAT-3′; intact promoter: 5′-CAAGGAATTCGTAGGGTTC-3′; probe: 5′-/56-FAM/TTGTATGCG/ZEN/AGATAGAAGCCAGGGCAA/3IABkFQ/-3′. For the reciprocal FIP1L1 enhancer interaction interrogation, the following primer sequences were used: common enhancer primer (as FIP1L1 enhancer primer above): 5′-TTAAGTAAGCAGGTAAACTACAT-3′, PDGFRA promoter (as common PDGFRA promoter above): 5′-GGTCGTGCCTTTGTTTT-3′; SCFD2 promoter: 5′-AATACATGGTCATGATGCTC-3′; FIP1L1 promoter: 5′-AGGCATTGCTTAAACATAAC-3′; FIP1L1 control: 5′-TTATTTGTAGTAGAGGTTACTGG-3′; PDGFRA control: 5′-ATGATAACACCACCATTCAG-3′; FIP1L1 enhancer probe: 5′-/56-FAM/TATCCCAAC/ZEN/CAAATACAGGGCTTGG/3IABkFQ/-3′. To normalize primer signals, bacterial artificial chromosome (BAC) clones CTD-2022B5 and RP11-626H4 were obtained from Invitrogen. BAC DNA was purified via BACMAX DNA Purification kit (Epicentre) and quantified using two primer sets specific to the Chloramphenicol resistance gene: 1F: 5′-TTCGTCTCAGCCAATCCCTG-3′; 1R: 5′-TTTGCCCATGGTGAAAACGG-3′; 2F: GGTTCATCATGCCGTTTGTG-3′; 2R: 5′-CCACTCATCGCAGTACTGTTG-3′. BAC DNA was subjected to a similar 3C protocol, omitting steps related to cell lysis, proteinase or RNase treatment. PCR signal from tumour and gliomasphere 3C was normalized to digestion efficiency and BAC primer signal. BT142 cells were cultured in either 5 μM 5-azacytidine or equivalent DMSO (1:10,000) for 8 days, with drug refreshed every 2 days. The following CRISPR sgRNAs were cloned into the LentiCRISPR vector obtained from the Zhang laboratory43: GFP: 5′-GAGCTGGACGGCGACGTAAA-3′; insulator: 5′-GCCACAGATAATGCAGCTAGA-3′. GSC6 gliomaspheres were mechanically dissociated and plated in 5 μg ml−1 EHS laminin (Sigma) and allowed to adhere overnight, and then infected with lentivirus containing either CRISPR vector for 48 h. Cells were then selected in 1 μg ml−1 puromycin for 4 days, with puromycin-containing media refreshed every 2 days. Genomic DNA was isolated and the region of interest was amplified using the PDGFRAins primer set described above. CRISPR-mediated disruption of this amplified DNA was confirmed via Surveyor Assay (Transgenomic), with amplified uninfected GSC6 genomic DNA being added to each annealing reaction as the unmodified control. To quantify the precise CRISPR alterations, genomic DNA from each construct was amplified using a set of primers closer to the putative deletion site as follows: forward: 5′-TTTGCAATGGGACACGGAGA-3′, reverse: 5′-AGAAATGTGTGGATGTGAGCG-3′. PCR product from these primers was used to prepare a library that was sequenced as 38-base paired-end reads on the Illumina NextSeq500. Total RNA was isolated from CRISPR-infected GSC6 gliomaspheres (insulator or control GFP sgRNA) or BT142 gliomaspheres (5-aza-treated or control condition) using the RNeasy minikit (Qiagen) and used to synthesize cDNA with the SuperScriptIII system (Invitrogen). cDNA was analysed using SYBR mastermix (Applied Biosystems) on a 7500 Fast Real Time System (Applied Biosystems). PDGFRA expression was determined using the following primers: forward: 5′-GCTCAGCCCTGTGAGAAGAC-3′, reverse: 5′-ATTGCGGAATAACATCGGAG-3′, and was normalized to primers for ribosomal protein, large, P0 (RPLP0), as follows: forward: 5′-TCCCACTTGCTGAAAAGGTCA-3′, reverse: 5′-CCGACTCTTCCTTGGCTTCA-3′. Normalization was also verified by β-actin (ACTB), forward: 5′-AGAAAATCTGGCACCACACC-3′, reverse: 5′-AGAGGCGTACAGGGATAGCA-3′. Cells were incubated with PE-conjugated anti-PDGFRa (CD140a) antibody (Biolegend, clone 16A1) for 30 min at room temperature at the dilution specified in the manufacturer’s protocol. Data was analysed and visualized with FlowJo software. Single live cells were selected for analysis via side and forward scatter, and viable cells were selected by lack of an unstained channel (APC) autofluorescence. For the cell growth assay, 2,500 dissociated viable GSC6 cells expressing CRISPR and either GFP or insulator-targeting sgRNA (see above) was plated in 100 μl of media in an opaque-walled tissue culture 96-well plate, in 1 μM dasatinib, 500 nM crenolanib, or equivalent DMSO (1:10,000) as a vehicle control. Cell growth was analysed at days 3, 5 and 7 for dasatinib, or days 3, 7 and 10 for crenolanib, using CellTiter-Glo reagent (Promega) following the manufacturer’s protocol. Data were normalized across days using an ATP standard curve.


News Article | August 31, 2016
Site: www.nature.com

A devastating 6.2-magnitude earthquake in central Italy on 24 August that killed more than 290 people was the country’s largest since a magnitude-6.3 earthquake in 2009 that hit the town of L’Aquila, about 40 kilometres away. That event killed 308 people, destroyed tens of thousands of homes and a university. Controversially, it also caused six scientists to be put on trial for manslaughter. Central Italy’s complex geological and tectonic make-up creates a notorious quake risk. The Adria micro-plate dives beneath the Apennine mountain range from east to west, creating seismic strain. The mighty Eurasian and African plates also collide here, with the Eurasian plate moving northeast at 24 millimetres per year. The latest quake also injured hundreds and laid waste to historic villages in the Apennine mountains, including Amatrice (see ‘Epicentre of a quake’). It was a result of increased horizontal stress perpendicular to the mountain chain. Seismologists had expected a rupture to occur near the location at any time. Still, Giulio Selvaggi, a research director at the National Institute of Geophysics and Volcanology in Rome, and one of those initially convicted of manslaughter — all six were cleared on appeal — says he was shocked by the death and destruction wreaked by last week’s quake. The mountainous region around Amatrice is sparsely populated, but the final death toll may exceed that of more populated and urbanized L’Aquila. Selvaggi seconds a public outcry over the failure of authorities to prioritize making old buildings more earthquake-resistant and notes that his team supplies earthquake maps to them. “We scientists have made a beautiful, detailed seismic hazard map, showing clearly the areas in greatest need of preventive measures,” he says. “But public authorities don’t take enough action.” The court case over the L’Aquila earthquake came about because a local amateur researcher claimed to have evidence of an imminent, large quake. Six scientists and one government official who had publicly dismissed the amateur’s methods were accused of misinforming the public. Following an unprecedented trial, all seven were given six-year jail sentences for manslaughter, but the scientists were cleared on appeal in 2014. Computer scientist Paola Inverardi, who is rector of the university in L’Aquila, says the rebuilding of the university is nearly complete, and that research activities had resumed by 2012. Science in the region has also benefited from supporting initiatives following the quake, she says. One of these is the Gran Sasso Science Institute, an international graduate school founded in 2012 to inject young intellectual life into L’Aquila. It has been so successful that in June it was awarded university status. Unlike the earthquake in L’Aquila, which was preceded by frequent, mostly low-magnitude, tremors in the surrounding area, no seismic activity was recorded before the latest earthquake. “It came out of the blue, without the preceding tremors we experienced in ‘our’ earthquake,” says Inverardi. L’Aquila itself experienced virtually no damage, but, she says, “psychologically we were all pushed back”.

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