News Article | March 1, 2017
The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. ARC-Net, University of Verona: approval number 1885 from the Integrated University Hospital Trust (AOUI) Ethics Committee (Comitato Etico Azienda Ospedaliera Universitaria Integrata) approved in their meeting of 17 November 2010, documented by the ethics committee 52070/CE on 22 November 2010 and formalized by the Health Director of the AOUI on the order of the General Manager with protocol 52438 on 23 November 2010. APGI: Sydney South West Area Health Service Human Research Ethics Committee, western zone (protocol number 2006/54); Sydney Local Health District Human Research Ethics Committee (X11-0220); Northern Sydney Central Coast Health Harbour Human Research Ethics Committee (0612-251M); Royal Adelaide Hospital Human Research Ethics Committee (091107a); Metro South Human Research Ethics Committee (09/QPAH/220); South Metropolitan Area Health Service Human Research Ethics Committee (09/324); Southern Adelaide Health Service/Flinders University Human Research Ethics Committee (167/10); Sydney West Area Health Service Human Research Ethics Committee (Westmead campus) (HREC2002/3/4.19); The University of Queensland Medical Research Ethics Committee (2009000745); Greenslopes Private Hospital Ethics Committee (09/34); North Shore Private Hospital Ethics Committee. Baylor College of Medicine: Institutional Review Board protocol numbers H-29198 (Baylor College of Medicine tissue resource), H-21332 (Genomes and Genetics at the BCM-HGSC), and H-32711(Cancer Specimen Biobanking and Genomics). Patients were recruited and consent obtained for genomic sequencing through the ARC-Net Research Centre at Verona University, Australian Pancreatic Cancer Genome Initiative (APGI), and Baylor College of Medicine as part of the ICGC (www.icgc.org). A patient criterion for admission to the study was that they were clinically sporadic. This information was acquired through direct interviews with participants and a questionnaire regarding their personal history and that of relatives with regard to pancreas cancers and any other cancers during anamnesis. Clinical records were also used to clarify familial history based on patient indications. Samples were prospectively and consecutively acquired through institutions affiliated with the Australian Pancreatic Cancer Genome Initiative. Samples from the ARC-Net biobank are the result of consecutive collections from a single centre. All tissue samples were processed as previously described5151. Representative sections were reviewed independently by at least one additional pathologist with specific expertise in pancreatic diseases. Samples either had full face frozen sectioning performed in optimal cutting temperature (OCT) medium, or the ends excised and processed in formalin to verify the presence of tumour in the sample to be sequenced and to estimate the percentage of neoplastic cells in the sample relative to stromal cells. Macrodissection was performed if required to excise areas that did not contain neoplastic epithelium. Tumour cellularity was determined using SNP arrays (Illumina) and the qpure tool9. PanNET is a rare tumour type and the samples were collected via an international network. We estimate that with 98 unique patients in the discovery cohort, we will achieve 90% power for 90% of genes to detect mutations that occur at a frequency of ~10% above the background rate for PanNET (assuming a somatic mutation frequency of more than 2 per Mb)52. Cancer and matched normal colonic mucosa were collected at the time of surgical resection from the Royal Brisbane and Women’s Hospital and snap frozen in liquid nitrogen. A biallelic germline mutation in the MUTYH gene was detected by restriction fragment length polymorphism analysis and confirmed by automated sequencing to be the G382D mutation (or ENST00000450313.5 G396D, ClinVar#5294) in both alleles53. The primary antibodies used for immunohistochemical staining were: cytokeratin 8/18 (5D3, Novocastra), chromogranin A (DAK-A3, Dako), and CD99 (O13, Biolegend). Antibodies and staining conditions have been described elsewhere39. Whole-genome sequencing with 100-bp paired reads was performed with a HiSEQ2000 (Illumina). Sequence data were mapped to a GRCh37 using BWA and BAM files are available in the EGA (accession number: EGAS00001001732). Somatic mutations and germline variants were detected using a previously described consensus mutation calling strategy11. Mutations were annotated with gene consequence using SNPeff. The pathogenicity of germline variants was predicted using cancer-specific and locus-specific genetic databases, medical literature, computational predictions with ENSEMBL Variant Effect Predictor (VEP) annotation, and second hits identified in the tumour genome. Intogen27 was used to find somatic genes that were significantly mutated. Somatic structural variants were identified using the qSV tool as previously described10, 11, 17. Coding mutations are included in supplementary tables and all mutations have been uploaded to the International Cancer Genome Consortium Data Coordination Center. Mutational signatures were predicted using a published framework14. Essentially, the 96-substitution classification was determined for each sample. The signatures were compared to other validated signatures and the prevalence of each signature per megabase was determined. Somatic copy number was estimated using high density SNP arrays and the GAP tool12. Arm level copy number data were clustered using Ward’s method, Euclidian distance. GISTIC13 was used to identify recurrent regions of copy number change. The whole genome sequence data was used to determine the length of the telomeres in each sample using the qMotif tool. Essentially, qMotif determines telomeric DNA content by calculating the number of reads that harbour the telomere motif (TTAGG), and then estimates the relative length of telomeres in the tumour compared to the normal. qMotif is available online (http://sourceforge.net/projects/adamajava). Telomere length was validated by qPCR as previously described54. RNASeq library preparation and sequencing were performed as previously described55. Essentially, sequencing reads were mapped to transcripts corresponding to ensemble 70 annotations using RSEM. RSEM data were normalized using TMM (weighted trimmed mean of M-values) as implemented in the R package ‘edgeR’. For downstream analyses, normalized RSEM data were converted to counts per million (c.p.m.) and log transformed. Genes without at least 1 c.p.m. in 20% of the sample were excluded from further analysis55. Unsupervised class discovery was performed using consensus clustering as implemented in the ConsensusClusterPlus R package56. The top 2,000 most variable genes were used as input. Differential gene expression analysis between representative samples was performed using the R package ‘edgeR’57. Ontology and pathway enrichment analysis was performed using the R package ‘dnet’58. PanNET class enrichment using published gene signatures44 was performed using Gene Set Variation Analysis (GSVA) as described previously55. Two strategies were used to verify fusion transcripts. For verification of EWSR1–BEND2 fusions, cDNAs were synthesized using the SuperScript VILO cDNA synthesis kit (Thermofisher) with 1 μg purified total RNA. For each fusion sequence, three samples were used: the PanNET sample containing the fusion, the PanNET sample without that fusion, and a non-neoplastic pancreatic sample. The RT–PCR product were evaluated on the Agilent 2100 Bioanalyzer (Agilent Technologies) and verified by sequencing using the 3130XL Genetic Analyzer (Life Technologies). Primers specific for EWSR1–BEND2 fusion genes are available upon request. To identify the EWSR1 fusion partner in the case ITNET_2045, a real-time RT–PCR translocation panel for detecting specific Ewing sarcoma fusion transcripts was applied as described59. Following identification of the fusion partner, PCR amplicons were subjected to sequencing using the 3130XL Genetic Analyzer. EWSR1 rearrangements were assayed on paraffin-embedded tissue sections using a commercial split-signal probe (Vysis LSI EWSR1 (22q12) Dual Colour, Break Apart Rearrangement FISH Probe Kit) that consists of a mixture of two FISH DNA probes. One probe (~500 kb) is labelled in SpectrumOrange and flanks the 5′ side of the EWSR1 gene, extending through intron 4, and the second probe (~1,100 kb) is labelled in SpectrumGreen and flanks the 3′ side of the EWSR1 gene, with a 7-kb gap between the two probes. With this setting, the assay enables the detection of rearrangements with breakpoints spanning introns 7–10 of the EWSR1 gene. Hybridization was performed according to the manufacturer’s instructions and scoring of tissue sections was assessed as described elsewhere60, counting at least 100 nuclei per slide. Recurrently mutated genes identified by whole-genome sequencing were independently evaluated in a series of 62 PaNETs from the ARC-Net Research Centre, University of Verona. Four Ion Ampliseq Custom panels (Thermofisher) were designed to target the entire coding regions and flanking intron–exon junctions of the following genes: MEN1, DAXX, ATRX, PTEN and TSC2 (panel 1); DEPDC5, TSC1 and SETD2 (panel 2); ARID1A and MTOR (panel 3); CHEK2 and MUTYH (panel 4). Twenty nanograms of DNA were used per multiplex PCR amplification. The quality of the obtained libraries was evaluated by the Agilent 2100 Bioanalyzer on chip electrophoresis. Emulsion PCR was performed with the OneTouch system (Thermofisher). Sequencing was run on the Ion Torrent Personal Genome Machine (PGM, Thermofisher) loaded with 316 or 318 chips. Data analysis, including alignment to the hg19 human reference genome and variant calling, was done using Torrent Suite Software v4.0 (Thermofisher). Filtered variants were annotated using a custom pipeline based on the Variant Effector Predictor (VEP) software. Alignments were visually verified with the Integrative Genomics Viewer: IGV v2.3 (Broad Institute). There is no contiguous structure available for CHEK2, so we produced a model of isoform C using PDBid 3i6w61 as a template for predicting the structure of sequence O96017. Modelling was carried out within the YASARA suite of programs62 and consisted of an initial BLAST search for suitable templates followed by alignment, building of loops not present in selected template structure and energy minimization in explicit solvent. Modelling was carried out in the absence of a phosphopeptide ligand, which was added on completion by aligning the model with structure 1GXC and merging the ligand contained therein with the model structure. Similarly, MUTYH is represented by discontinuous structures and so this too was modelled using PDBids 3N5N and 4YPR as templates together with sequence NP_036354.1. Having constructed both models, amino acid substitutions were carried out to make the wild-type sequences conform to the variants described above. Each substitution was carried out independently and the resulting variant structures were subject to simulated annealing energy minimization using the AMBER force field. The resulting energy-minimized structures formed the basis of the predictions. CHEK2 site mutants were generated by site-directed mutagenesis of wild-type pCMV–FLAG CHEK2 (primer sequences in Supplementary Table 16). Proteins were expressed in HEK293T, a highly transfectable derivative of HEK293 cells that were retrieved from the cell culture bank at the QIMR Berghofer medical research institute. Cells were authenticated by STR profiling and were negative for mycoplasma. Transfected cells were lysed in NP-40 modified RIPA with protease and phosphatase inhibitors. Protein expression levels were analysed by western blotting with anti-FLAG antibodies and imaging HRP luminescent signal on a CCD camera (Fuji) and quantifying in MultiGauge software (Fuji). Kinase assays were performed using recombinant GST–CDC25C (amino acids 200–256) as substrate, essentially as described63. Kinase assay quantification was performed by scintillation counting of excised gel bands in OptiPhase scintillant (Perkin Elmer) using a Tri-Carb 2100TR beta counter (Packard). Counts for each reaction set were expressed as a fraction of the wild type. All experiments were performed at least three times. The date of diagnosis and the date and cause of death for each patient were obtained from the Central Cancer Registry and treating clinicians. Median survival was estimated using the Kaplan–Meier method and the difference was tested using the log-rank test. P values of less than 0.05 were considered statistically significant. The hazard ratio and its 95% confidence interval were estimated using Cox proportional hazard regression modelling. The correlation between DAXX or ATRX mutational status and other clinico-pathological variables was calculated using the χ2 test. Statistical analysis was performed using StatView 5.0 Software (Abacus Systems). Disease-specific survival was used as the primary endpoint. Genome sequencing data presented in this study have been submitted to the European Genome-Phenome Archive under accession number EGAS00001001732 (https://www.ebi.ac.uk/ega/search/site/EGAS00001001732).
News Article | February 15, 2017
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. We sequenced Chenopodium quinoa Willd. (quinoa) accession PI 614886 (BioSample accession code SAMN04338310; also known as NSL 106399 and QQ74). DNA was extracted from leaf and flower tissue of a single plant, as described in the “Preparing Arabidopsis Genomic DNA for Size-Selected ~20 kb SMRTbell Libraries” protocol (http://www.pacb.com/wp-content/uploads/2015/09/Shared-Protocol-Preparing-Arabidopsis-DNA-for-20-kb-SMRTbell-Libraries.pdf). DNA was purified twice with Beckman Coulter Genomics AMPure XP magnetic beads and assessed by standard agarose gel electrophoresis and Thermo Fisher Scientific Qubit Fluorometry. 100 Single-Molecule Real-Time (SMRT) cells were run on the PacBio RS II system with the P6-C4 chemistry by DNALink (Seoul). De novo assembly was conducted using the smrtmake assembly pipeline (https://github.com/PacificBiosciences/smrtmake) and the Celera Assembler, and the draft assembly was polished using the quiver algorithm. DNA was also sequenced using an Illumina HiSeq 2000 machine. For this, DNA was extracted from leaf tissue of a single soil-grown plant using the Qiagen DNeasy Plant Mini Kit. 500-bp paired-end (PE) libraries were prepared using the NEBNext Ultra DNA Library Prep Kit for Illumina. Sequencing reads were processed with Trimmomatic (v0.33)42, and reads <75 nucleotides in length after trimming were removed from further analysis. The remaining high-quality reads were assembled with Velvet (v1.2.10)43 using a k-mer of 75. High-molecular-weight DNA was isolated and labelled from leaf tissue of three-week old quinoa plants according to standard BioNano protocols, using the single-stranded nicking endonuclease Nt.BspQI. Labelled DNA was imaged automatically using the BioNano Irys system and de novo assembled into consensus physical maps using the BioNano IrysView analysis software. The final de novo assembly used only single molecules with a minimum length of 150 kb and eight labels per molecule. PacBio-BioNano hybrid scaffolds were identified using IrysView’s hybrid scaffold alignment subprogram. Using the same DNA prepared for PacBio sequencing, a Chicago library was prepared as described previously10. The library was sequenced on an Illumina HiSeq 2500. Chicago sequence data (in FASTQ format) was used to scaffold the PacBio-BioNano hybrid assembly using HiRise, a software pipeline designed specifically for using Chicago data to assemble genomes10. Chicago library sequences were aligned to the draft input assembly using a modified SNAP read mapper (http://snap.cs.berkeley.edu). The separations of Chicago read pairs mapped within draft scaffolds were analysed by HiRise to produce a likelihood model, and the resulting likelihood model was used to identify putative mis-joins and score prospective joins. A population was developed by crossing Kurmi (green, sweet) and 0654 (red, bitter). Homozygous high- and low-saponin F lines were identified by planting 12 F seeds derived from each F line, harvesting F seed from these F plants, and then performing foam tests on the F seed. Phenotyping was validated using gas chromatography/mass spectrometry (GC/MS). RNA was extracted from inflorescences containing a mixture of flowers and seeds at various stages of development from the parents and 45 individual F progeny. RNA extraction and Illumina sequencing were performed as described above. Sequencing reads from all lines were trimmed using Trimmomatic and mapped to the reference assembly using TopHat44, and SNPs were called using SAMtools mpileup (v1.1)45. For linkage mapping, markers were assigned to linkage groups on the basis of the grouping by JoinMap v4.1. Using the maximum likelihood algorithm of JoinMap, the order of the markers was determined; using this as start order and fixed order, regression mapping in JoinMap was used to determine the cM distances. Genes differentially expressed between bitter and sweet lines and between green and red lines were identified using default parameters of the Cuffdiff function of the Cufflinks program46. A second mapping population was developed by crossing Atlas (sweet) and Carina Red (bitter). Bitter and sweet F lines were identified by performing foam and taste tests on the F seed. DNA sequencing was performed with DNA from the parents and 94 sweet F lines, as described above, and sequencing reads were mapped to the reference assembly using BWA. SNPs were called in the parents and in a merged file containing all combined F lines. Genotype calls were generated for the 94 F genotypes by summing up read counts over a sliding window of 500 variants, at all variant positions for which the parents were homozygous and polymorphic. Over each 500-variant stretch, all reads with Atlas alleles were summed, and all reads with the Carina Red allele were summed. Markers were assigned to linkage groups using JoinMap, with regression mapping used to obtain the genetic maps per linkage group. The Kurmi × 0654 and Atlas × Carina Red maps were integrated with the previously published quinoa linkage map13, with the Kurmi × 0654 map being used as the reference for the positions of anchor markers and scaling. We selected markers from the same scaffold that were in the same 10,000-bp bin in the assembly. The anchor markers on the alternative map received the position of the Kurmi × 0654 map anchor marker in the integrated map. This process was repeated with anchor markers at the 100,000-bp bin level. The assumption is that at the 100,000-bp bin level recombination should essentially be zero. On this level, a regression of cM position on both maps yielded R2 values >0.85 and often >0.9, so the regression line can easily be used for interpolating the positions of the alternative map towards the corresponding position on the Kurmi × 0654 map. All Kurmi × 0654 markers went into the integrated map on their original position. Pseudomolecules were assembled by concatenating scaffolds based on their order and orientation as determined from the integrated linkage map. An AGP (‘A Golden Path’) file was made that describes the positions of the scaffold-based assembly in coordinates of the pseudomolecule assembly, with 100 ‘N’s inserted between consecutive scaffolds. Based on these coordinates, custom scripts were used to generate the pseudomolecule assembly and to recoordinate the annotation file. DNA was extracted from C. pallidicaule (PI 478407) and C. suecicum (BYU 1480) and was sent to the Beijing Genomic Institute (BGI, Hong Kong) where one 180-bp PE library and two mate-pair libraries with insert sizes of 3 and 6 kb were prepared and sequenced on the Illumina HiSeq platform to obtain 2 × 100-bp reads for each library. The generated reads were trimmed using the quality-based trimming tool Sickle (https://github.com/najoshi/sickle). The trimmed reads were then assembled using the ALLPATHS-LG assembler47, and GapCloser v1.1248 was used to resolve N spacers and gap lengths produced by the ALLPATHS-LG assembler. Repeat families found in the genome assemblies of quinoa, C. pallidicaule and C. suecicum (see Supplementary Information 3) were first independently identified de novo and classified using the software package RepeatModeler49. RepeatMasker50 was used to discover and identify repeats within the respective genomes. AUGUSTUS51 was used for ab initio gene prediction, using model training based on coding sequences from Amaranthus hypochondriacus, Beta vulgaris, Spinacia oleracea and Arabidopsis thaliana. RNA-seq and isoform sequencing reads generated from RNA of different tissues were mapped onto the reference genome using Bowtie 2 (ref. 52) and GMAP53, respectively. Hints with locations of potential intron–exon boundaries were generated from the alignment files with the software package BAM2hints in the MAKER package54. MAKER with AUGUSTUS (intron–exon boundary hints provided from RNA-seq and isoform sequencing) was then used to predict genes in the repeat-masked reference genome. To help guide the prediction process, peptide sequences from B. vulgaris and the original quinoa full-length transcript (provided as EST evidence) were used by MAKER during the prediction. Genes were characterized for their putative function by performing a BLAST search of the peptide sequences against the UniProt database. PFAM domains and InterProScan ID were added to the gene models using the scripts provided in the MAKER package. The following quinoa accessions were chosen for DNA re-sequencing: 0654, Ollague, Real, Pasankalla (BYU 1202), Kurmi, CICA-17, Regalona (BYU 947), Salcedo INIA, G-205-95DK, Cherry Vanilla (BYU 1439), Chucapaca, Ku-2, PI 634921 (Ames 22157), Atlas and Carina Red. The following accessions of C. berlandieri were sequenced: var. boscianum (BYU 937), var. macrocalycium (BYU 803), var. zschackei (BYU 1314), var. sinuatum (BYU 14108), and subsp. nuttaliae (‘Huauzontle’). Two accessions of C. hircinum (BYU 566 and BYU 1101) were also sequenced. All sequencing was performed with an Illumina HiSeq 2000 machine, using either 125-bp (Atlas and Carina Red) or 100-bp (all other accessions) paired-end libraries. Reads were trimmed using Trimmomatic and mapped to the reference assembly using BWA (v0.7.10)55. Read alignments were manipulated with SAMtools, and the mpileup function of SAMtools was used to call SNPs. Orthologous and paralogous gene clusters were identified using OrthoMCL28. Recommended settings were used for all-against-all BLASTP comparisons (Blast+ v2.3.056) and OrthoMCL analyses. Custom Perl scripts were used to process OrthoMCL outputs for visualization with InteractiVenn57. Using OrthoMCL, orthologous gene sets containing two copies in quinoa and one copy each in C. pallidicaule, C. suecicum, and B. vulgaris were identified. In total, 7,433 gene sets were chosen, and their amino acid sequences were aligned individually for each set using MAFFT58. The 7,433 alignments were converted into PHYLIP format files by the seqret command in the EMBOSS package59. Individual gene trees were then constructed using the maximum likelihood method using proml in PHYLIP60. In addition, the genomic variants of all 25 sequenced taxa (Supplementary Data 5) relative to the reference sequence were called based on the mapped Illumina reads in 25 BAM files using SAMtools. To call variants in the reference genome (PI 614886), Illumina sequencing reads were mapped to the reference assembly. Variants were then filtered using VCFtools61 and SAMtools, and the qualified SNPs were combined into a single VCF file which was used as an input into SNPhylo62 to construct the phylogenetic relationship using maximum likelihood and 1,000 bootstrap iterations. To identify FT homologues, the protein sequence from the A. thaliana flowering time gene FT was used as a BLAST query. Filtering for hits with an E value <1 × e−3 and with RNA-seq evidence resulted in the identification of four quinoa proteins. One quinoa protein (AUR62013052) appeared to be comprised of two tandem repeats which were separated for the purposes of phylogenetic analysis. For the construction of the phylogenetic tree, protein sequences from these five quinoa FT homologues were aligned using Clustal Omega63 along with two B. vulgaris (gene models: BvFT1-miuf.t1, BvFT2-eewx.t1) and one A. thaliana (AT1G65480.1) homologue. Phylogenetic analysis was performed with MEGA64 (v6.06). The JTT model was selected as the best fitting model. The initial phylogenetic tree was estimated using the neighbour joining method (bootstrap value = 50, Gaps/ Missing Data Treatment = Partial Deletion, Cutoff 95%), and the final tree was estimated using the maximum likelihood method with a bootstrap value of 1,000 replicates. The syntenic relationships between the coding sequences of the chromosomal regions surrounding these FT genes were visualized using the CoGE65 GEvo tool and the Multi-Genome Synteny Viewer66. The alignment of bHLH domains was performed with Clustal Omega63, using sequences from Mertens et al.39. The phylogeny was inferred using the maximum likelihood method based on the JTT matrix-based model67. Initial trees for the heuristic search were obtained automatically by applying Neighbour-Join and BioNJ algorithms to a matrix of pairwise distances estimated using a JTT model, and then selecting the topology with superior log likelihood value. All positions containing gaps and missing data were eliminated. Trimmed PE Illumina sequencing reads that were used for the de novo assembly of C. suecicum and C. pallidicaule were mapped onto the reference quinoa genome using the default settings of BWA. For every base in the quinoa genome, the depth coverage of properly paired reads from the C. suecicum and C. pallidicaule mapping was calculated using the program GenomeCoverage in the BEDtools package68. A custom Perl script was used to calculate the percentage of each scaffold with more than 5× coverage from both diploids. Scaffolds were assigned to the A or B sub-genome if >65% of the bases were covered by reads from one diploid and <25% of the bases were covered by reads from the other diploid. The relationship between the quinoa sub-genomes and the diploid species C. pallidicaule and C. suecicum was presented in a circle proportional to their sizes using Circos69. Orthologous regions in the three species were identified using BLASTN searches of the quinoa genome against each diploid genome individually. Single top BLASTN hits longer than 8 kb were selected and presented as links between the quinoa genome assembly (arranged in chromosomes, see Supplementary Information 7.3) and the two diploid genome assemblies on the Circos plot (Fig. 2a). Sub-genome synteny was analysed by plotting the positions of homoeologous pairs of A- and B-sub-genome pairs within the context of the 18 chromosomes using Circos. Synteny between the sub-genomes and B. vulgaris was assessed by first creating pseudomolecules by concatenating scaffolds which were known to be ordered and oriented within each of the nine chromosomes. Syntenic regions between these B. vulgaris chromosomes and those of quinoa were then identified using the recommended settings of the CoGe SynMap tool70 and visualized using MCScanX71 and VGSC72. For the purposes of visualization, quinoa chromosomes CqB05, CqA08, CqB11, CqA15 and CqB16 were inverted. Quinoa seeds were embedded in a 2% carboxymethylcellulose solution and frozen above liquid nitrogen. Sections of 50 μm thickness were obtained using a Reichert-Jung Frigocut 2800N, modified to use a Feather C35 blade holder and blades at −20 °C using a modified Kawamoto method73. A 2,5-dihydroxybenzoic acid (Sigma-Aldrich) matrix (40 mg ml−1 in 70% methanol) was applied using a HTX TM-Sprayer (HTX Technologies LLC) with attached LC20-AD HPLC pump (Shimadzu Scientific Instruments). Sections were vacuum dried in a desiccator before analysis. The optical image was generated using an Epson 4400 Flatbed Scanner at 4,800 d.p.i. For mass spectrometric analyses, a Bruker SolariX XR with 7T magnet was used. Images were generated using Bruker Compass FlexImaging 4.1. Data were normalized to the TIC, and brightness optimization was employed to enhance visualization of the distribution of selected compounds. Individual spectra were recalibrated using Bruker Compass DataAnalysis 4.4 to internally lock masses of known DHB clusters: C H O = 273.039364 and C H O = 409.055408 m/z. Accurate mass measurements for individual saponins and identified compounds were run using continuous accumulation of selected ions (CASI) using mass windows of 50–100 m/z and a transient of 4 megaword generating a transient of 2.93 s providing a mass resolving power of approximately 390,000 at 400 m/z. Lipids were putatively assigned by searching the LipidMaps database74 (http://www.lipidmaps.org) and lipid class confirmed by collision-induced dissociation using a 10 m/z window centred around the monoisotopic peak with collision energy of between 15–20 V. Quinoa flowers were marked at anthesis, and seeds were sampled at 12, 16, 20 and 24 days after anthesis. A pool of five seeds from each time point was analysed using GC/MS. Quantification of saponins was performed indirectly by quantifying oleanolic acid (OA) derived from the hydrolysis of saponins extracted from quinoa seeds. Derivatized solution was analysed using single quadrupole GC/MS system (Agilent 7890 GC/5975C MSD) equipped with EI source at ionisation energy of 70 eV. Chromatography separation was performed using DB-5MS fused silica capillary column (30m × 0.25 mm I.D., 0.25 μm film thickness; Agilent J&W Scientific), chemically bonded with 5% phenyl 95% methylpolysiloxane cross-linked stationary phase. Helium was used as the carrier gas with constant flow rate of 1.0 ml min−1. The quantification of OA in each sample was performed using a standard curve based on standards of OA. Specific, individual saponins were identified in quinoa using a preparation of 20 mg of seeds performed according a modified protocol from Giavalisco et al.75. Samples were measured with a Waters ACQUITY Reversed Phase Ultra Performance Liquid Chromatography (RP-UPLC) coupled to a Thermo-Fisher Exactive mass spectrometer, which consists of an electrospray ionisation source and an Orbitrap mass analyser. A C18 column was used for the hydrophilic measurements. Chromatograms were recorded in full-scan MS mode (mass range, 100 −1,500). Extraction of the LC/MS data was accomplished with the software REFINER MS 7.5 (GeneData). SwissModel76 was used to produce homology models for the bHLH region of AUR62017204, AUR62017206 and AUR62010677. RaptorX77 was used for prediction of secondary structure and disorder. QUARK78 was used for ab initio modelling of the C-terminal domain, and the DALI server79 was used for 3D homology searches of this region. Models were manually inspected and evaluated using the PyMOL program (http://pymol.org). The genome assemblies and sequence data for C. quinoa, C. pallidicaule and C. suecicum were deposited at NCBI under BioProject codes PRJNA306026, PRJNA326220 and PRJNA326219, respectively. Additional accessions numbers for deposited data can be found in Supplementary Data 9. The quinoa genome can also be accessed at http://www.cbrc.kaust.edu.sa/chenopodiumdb/ and on the Phytozome database (http://www.phytozome.net/).
News Article | February 23, 2017
BOCA RATON, Fla.--(BUSINESS WIRE)--Zero Gravity Solutions, Inc. (“ZGSI” or the “Company”) (Pink Sheets: ZGSI), an agricultural biotechnology public company commercializing its technology derived from and designed for Space with significant applications for agriculture on Earth, announced that its research experiment using its BAM-FX micronutrient product was successfully delivered today to the International Space Station (ISS) on the SpaceX CRS-10 Dragon cargo resupply mission launched February 19, 2017 from historic pad 39A at NASA’s Kennedy Space Center. In collaboration with NASA and Intrinsyx Technologies Corporation (ITC), the BAM-FX experiments, led by two plant stress physiologists, Dr. John Freeman of ITC and Dr. David Bubenheim (NASA Biospheric Science Branch code SGE), will study the growth and nutritional effects of our patented micronutrient product BAM-FX in broccoli seedlings in microgravity. The focus of two separate, but related experiments, BAM-FX and V3PO (Vegetative Propagation of Plants in Orbit) are focused on advancing the science necessary to promote the growth of fresh, nutrient-dense food for astronauts on long-duration space missions. This experimental flight opportunity is made available by NanoRacks, LLC via its Space Act Agreement with NASA’s U.S. National Lab on the International Space Station. BAM-FX (Bio-Available Minerals-Formula X), a product manufactured and marketed by ZGSI’s wholly owned subsidiary, BAM Agricultural Solutions, is a solution of special ionic minerals, which can not only correct nutrient deficiencies, but may also increase yield, quality and nutrition with less inputs, by-products and waste. The patented platform technology was originally developed as a way to promote growth of strong plants for astronauts on long duration Space missions, but has shown dramatic results when applied to the agricultural industry here on Earth. The principal aim of the BAM-FX experiment on the ISS is to investigate the possibility to improve nutraceutical crop plant growth at zero gravity and produce large quantities of high quality Zinc bio-fortified broccoli on a space station, thereby ensuring astronauts on long mission a continuous supply of zinc enriched, fresh, anti-carcinogenic cruciferous vegetables. Zinc is important not just for plants to cope with several environmental stresses, but also for human immune system function and cancer inhibition. The V3PO project, supported by BASF, is a student research project on space farming, which will research the effects of microgravity in growing plantlet cuttings with the objective of seeing if vegetative propagation of plants is possible in space to generate fresh food for space missions without having to carry large amounts of seed. V3PO and BAM-FX will share two habitats inside a 1.5U NanoRacks facility compatible cube structure. The Intrinsyx hardware was already flown on SpX-3 named AFEx but slightly modified (introduction of second habitat) for V3PO and BAM-FX joint educational plant growth experiments. Once the BAM-FX experiment returns from the 25-30 day mission on the ISS, the BAM-FX cube with seedlings will come to NASA Ames, where it will be analyzed by Dr. John Freeman and Dr. David Bubenheim and the students involved in the experiment from the International Space Station Science Program at Valley Christian High School. Results are expected to take about a month to process. “The upcoming BAM-FX experiment on the ISS represents an opportunity to go back to our roots, literally and figuratively, to further validate this new science by significantly advancing how fresh, nutrient-dense food is grown on long-duration space missions,” stated Harvey Kaye, ZGSI’s Chairman of the Board. About Zero Gravity Solutions, Inc. Zero Gravity Solutions, Inc. (www.zerogsi.com) is an agricultural biotechnology public company commercializing its technology derived from and designed for Space with significant applications on Earth. These technologies are focused on providing valuable solutions to challenges facing world agriculture. ZGSI’s two primary categories of technologies aimed at sustainable agriculture are: 1) BAM-FX, a cost effective, ionic micronutrient delivery system for plants currently being introduced commercially into world agriculture by Zero Gravity’s wholly owned subsidiary BAM Agricultural Solutions 2) Directed Selection, utilized in the development and production, in the prolonged zero/micro gravity environment of the International Space Station, of large volumes of non-GMO, novel, patentable stem cells with unique and beneficial characteristics. Like us on Facebook This press release may contain certain forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. Investors are cautioned that such forward-looking statements involve risks and uncertainties, including without limitation, acceptance of the Company’s products, increased levels of competition for the Company, new products and technological changes, the Company’s dependence on third-party suppliers, and other risks detailed from time to time in the Company’s periodic reports filed with the Securities and Exchange Commission. Except as required by applicable law or regulation, Zero Gravity Solutions undertakes no obligation to update these forward-looking statements to reflect events or circumstances that occur after the date on which such statements were made.
News Article | February 23, 2017
A French SCA (Partnership Limited by Shares) with a capital of 56,000,000 Euros Head Office: La Woestyne 59173 Renescure, France Registred under number: 447 250 044 (Dunkerque Commercial and Companies Register) Bonduelle to acquire Ready Pac Foods, the US leader of single-serve salads bowls Bonduelle, the world leader of ready-to-eat vegetables, present in canned, frozen, fresh cut and delicatessen has executed an agreement to acquire Ready Pac Foods, the U.S. market leader in single serve salad bowls. Based in California, Ready Pac Foods is the #1 producer of single-serve salad bowls in the U.S. through its Bistro Bowl® suite of products and its legacy of innovation and culinary expertise. Ready Pac Foods is also a producer of fresh-cut produce, offering packaged salads, fresh-cut fruits, and mixed vegetables to its retail and foodservice customers. With 4 production facilities located in Irwindale (CA), Jackson (GA), Florence and Swedesboro (NJ), and employs about 3,500 full-time employees. Ready Pac Foods generates approximately $800m of revenues, with a national presence in the U.S. and a wide customer base. This milestone transaction is a key step in Bonduelle's strategic ambition VegeGo! 2025 of being "the world reference in "well living" through vegetable products". This acquisition will strengthen Bonduelle's international footprint and dramatically change its profile, making the U.S. the largest country of operations, continuing a longstanding track record of successful acquisitions in North America, in particular Aliments Carrière, Canada, in 2007 and Allens, USA in 2012, and the fresh category, its first business segment. This transaction will also offer new opportunities to Ready Pac Foods business partners and deliver significant value to Bonduelle's shareholders. This acquisition, which is fully compatible with Bonduelle's strong financial profile, perfectly fits with its strategic plan and will strengthen its leadership positions in its core business lines: Ready Pac Foods will become Bonduelle 5th's business unit, dedicated to the Fresh business in the Americas, along with Bonduelle Long Life Europe (BELL), Bonduelle Fresh Europe (BFE), Bonduelle Eurasia Markets (BEAM) and Bonduelle Americas (BAM), the latter being devoted to canned and frozen vegetables in Americas, from North to South. Christophe Bonduelle, Bonduelle's Chairman and CEO, said: "We welcome all of the Ready Pac Foods employees into the Bonduelle family. We look forward to working with the highly skilled and successful Ready Pac Foods management team in bringing together two great companies in the vegetal food industry. This acquisition shows Bonduelle's ambition to further develop as a global leader in its markets and strengthen its positions in the consumer convenience and health segments to meet consumers' needs." "We are thrilled to partner with market leader Bonduelle in our next Chapter of growth and feel we will be right at home within the Bonduelle family of companies," said Ready Pac Foods CEO, Tony Sarsam. "Beyond common business goals, both companies share a common purpose - to help people live healthier lives through innovative fresh food products. I am enthusiastic about Bonduelle's investment in our growth strategy and for the great success we will achieve together." Crédit Agricole CIB and Willkie Farr & Gallagher acted as financial and legal advisors to Bonduelle in connection with the transaction. Harris Williams & Co. and Skadden, Arps, Slate, Meagher & Flom acted as financial and legal advisors to Ready Pac Foods. Further information on the transaction will be communicated with half-year results, on March 2, 2017. Bonduelle, a family business, was established in 1853. Its mission is to be the world reference in "well-living" through vegetable products. Prioritizing innovation and long-term vision, the group is diversifying its operations and geographical presence. Its vegetable, grown over 128.000 hectares all over the world, are sold in 100 countries under various brand names and through various distribution channels and technologies. Expert in agro-industry with 54 industrial sites or own agricultural production, Bonduelle produces quality products by selecting the best crop areas close to its customers. Bonduelle is listed on Euronext compartment B Euronext indices: CAC MID & SMALL - CAC ALL TRADABLE - CAC ALL SHARES Bonduelle is part of the Gaïa non-financial performance index and employee shareholder index (I.A.S.) Code ISIN : FR0000063935 - Code Reuters : BOND.PA - Code Bloomberg : BON FP Home of the original Bistro Bowl® complete meal salad, Southern California-based Ready Pac Foods has been giving people the freedom to eat healthier for nearly 50 years as a premier producer of convenience fresh foods and fresh cut produce. With processing facilities throughout the United States, Ready Pac Foods manufactures a complete range of products featuring fresh produce and protein under the company's Bistro®, Ready Snax®, Cool Cuts® and elevAte(TM) brands. Offerings include fresh-cut salads, fruits, vegetables, snacking and complete meals available where consumers buy groceries and in restaurant chains across North America.
News Article | February 15, 2017
We all have times of day when we are not at our best. For me, before 10am, and between 2-4pm, it’s as though my brain just doesn’t work the way it should. I labor to come up with names, struggle to keep my train of thought, and my eloquence drops to the level expected of an eight-year-old. In an effort to blame my brain for this, rather than my motivation, I reached out to a researcher in the area of sleep and circadian neuroscience. Andrea Smit, a PhD student working with Professors John McDonald and Ralph Mistlberger at Simon Fraser University in Canada, was happy to help me find excuses for why my memory is so terribly unreliable at certain times of day. Humans have daily biological rhythms, called circadian rhythms, which affect almost everything that we do. They inform our bodies when it is time to eat and sleep, and they dictate our ability to remember things. According to Smit, “Chronotype, the degree to which someone is a “morning lark” or a “night owl,” is a manifestation of circadian rhythms. In a recent study, Smit used EEG, a type of brain scan, to study the interaction between chronotypes and memory. “Testing extreme chronotypes at multiple times of day allowed us to compare attentional abilities and visual short term memory between morning larks and night owls. Night owls were worse at suppressing distracting visual information and had a worse visual short term memory in the morning as compared with the afternoon,” she says. “Our research shows that circadian rhythms interact with memories even at very early stages of processing within the brain.” As a self-identified night owl, this is exactly what I want to hear. When someone next asks why I am being unproductive; I can say that I’m not being lazy, it’s just that my brain is having trouble suppressing distracting information. Why you need to sleep more On top of being a night owl, I need so much sleep. I nap as if it’s going out of style, and if left alarm-less I can easily turn a night’s sleep into a steady 12-hour dream-a-thon. Luckily, Smit can help with excuses here too; “Research has reliably shown that memory performance is best following an episode of sleep, and that sleep deprivation disrupts the transfer of information into long-term memory.” This transfer of information into long-term memories is a process called memory consolidation (a process which I discuss at some length in my book on memory). Smit says that this means that particularly people who need to remember information for tests or presentations “will retain more information if they get a good night’s sleep.” She says that this applies particularly to teenagers, opining that “school start times are too early for adolescents (who are more likely to be night owls) which negatively affects their school performance.” As a bonus high-five to sleep, Smit says that “sleep is also important for clearing away waste from the brain, including proteins whose buildup has been linked with Alzheimer’s.” Being ridiculed for sleeping in? Just say something to the effect of “I didn’t oversleep. I consolidated, optimized my brain for this presentation, and cleared additional waste from my brain thereby preventing the onset of Alzheimer’s. What did you do this morning?” BAM. Watch out, however, for big changes in your sleep schedule. Disruptions to your schedule are bad news, says Smit. While many of us experience small changes to our bedtime, and some mornings we need to get up earlier than others, it’s big shifts in our schedule that we need to worry about. Shifts like those resulting from long-distance travel or shift-work. “Research suggests that circadian disruption, such as jetlag, accelerates memory loss. Chronic jetlag has been found to decrease cell proliferation and neurogenesis (the creation of new brain cells), and cause memory deficits like retrograde amnesia that last longer than the jetlag itself.” If you fancy yourself a world traveler, just make sure you get enough sleep and try not to shift your sleep schedule too many hours at a time or too often. Research like Smit’s is valuable not just to scare us into a regular schedule, but to allow us to optimize our brains. Understanding our chronotype allows us to harness times of day when we are programmed to be most efficient, and encourage us to overcome our limits (coffee!) when we need to perform at sub-optimal times of day. Understanding the role of chronotype and sleep in memory formation also allows us to cultivate compassion for ourselves and others. It’s ok that you don’t like mornings. If possible, try to shift more brain-draining work to times of day when you know your brain is going to be at its best. The quality of your work and your work enjoyment will thank you.
News Article | February 16, 2017
LONDON--(BUSINESS WIRE)--Kyriba Corp., the global leader in cloud-based treasury, cash and risk management solutions, congratulates client Dassault Systèmes, the 3DEXPERIENCE Company, world leader in 3D design software, 3D Digital Mock Up and Product Lifecycle Management (PLM) solutions, for winning the Treasury Management International (TMI) 2016 Corporate Innovation Award for Treasury Technology. Kyriba’s digital solutions are used across Dassault Systèmes’ treasury functions to add security, cash visibility, and scalability. “Worthy winners of this award, Dassault Systèmes’ adoption of Kyriba’s SaaS technology has given them access to the latest innovations and compliance while they have continued to extend their digital agenda with a central KYC repository, a 30 percent increase in cash visibility, a secure, cloud-based approach to bank account management (BAM) and standardised bank fee reconciliation,” said Robin Page, CEO and publisher of Treasury Management International. “We congratulate Dassault Systèmes’ treasury team for their success and for winning this prestigious award from TMI,” said Jean-Luc Robert, chairman and CEO at Kyriba. “Their Treasury Group demonstrates innovation in driving bottom line value by truly leveraging all that technology offers in order to meet a complex set of requirements.” To learn how your organization can increase the strategic function of its financial professionals or how to join Kyriba PartnerSURGE, contact us at email@example.com. Kyriba is the global leader in cloud-based treasury, cash and risk management solutions, delivering Software-as-a-Service (SaaS) financial technology to corporate CFOs and Treasurers. More than 1,500 global organizations use Kyriba to enhance their global cash visibility, improve financial controls, and increase productivity across their cash and liquidity, payments, supply chain finance and risk management operations. Kyriba is headquartered in New York, with offices in San Diego, Paris, London, Tokyo, Singapore, Dubai, Hong Kong, Shanghai and Rio de Janeiro. To learn how your organization can increase the strategic function of its financial professionals, contact us at firstname.lastname@example.org. To learn more about Kyriba PartnerSURGE or join our partner program, contact us at email@example.com.
News Article | February 26, 2017
This is the fourth essay in our series of 10 Lessons From 10 Years Of The World’s Most Innovative Companies . The National Football League is an amazingly successful enterprise, but these days it’s hard to think of it as a bastion of forward-thinking business practices. The organization’s blindness, or willful denial, of concussion risks could be a case study in mismanagement. Whether the edifice of professional football will eventually crumble under the weight of health challenges has become a reasonable debate. But here’s what’s not debatable: Football has been a popularizer of several key innovation-economy breakthroughs. Consider the now ubiquitous “yellow line” denoting first-down markers on football TV broadcasts. This was the first mass-market implementation of augmented-reality technology, which places digitized visual information into real-world situations. Pioneered by a Chicago-based company called Sportvision (one of Fast Company’s Most Innovative Company honorees in 2010), the yellow line is now just one example of in-game AR, from specific down-and-distance information in NFL games to digitally inserted advertisements on fields and backgrounds. Sports-related AR has spread well beyond football: Sportvision also provides the Pitchf/x technology that allows Major League Baseball games to track pitches and show strike zone information, as well as Racef/x that allows Nascar viewers to more easily follow specific vehicles. In fact, the intense competitiveness of the media markets in which football, baseball, and other sports operate has actually made them a hotbed of change. Look at data analytics: The Michael Lewis book Moneyball and its cinematic counterpart starring Brad Pitt was the first culture-wide illumination of the competitive business advantage that data can provide. While reeling off stats has always been a core part of sports-geek fandom, data analysis is now a central operating principle in sport that extends even to the high school level (as reflected in our highlighting of video-analytics platform Hudl in last year’s World’s Most Innovative Companies list). That cultural reach has supported a broader embrace of analytics, as the proliferation of data extends into all businesses. Sports have introduced broad audiences to other now-core modern business notions: 360-degree customer engagement (as seen in digitally connected venues from Kansas City to Sacramento); global outreach (the NBA reaching out to China; NFL games in Mexico and London); and the targeting of more diverse customers (marketing to women). Even the commercial appeal of social media emerged only after sports figures and other celebrities got involved. Major League Baseball’s digital streaming arm, BAM Tech, is such a pioneer that Disney invested $1 billion in it. And the NFL continues to move us further: By broadcasting games on Twitter, the league is accelerating the blurring of lines between TV networks, websites, and apps. Sports businesses will need to continue to take risks in order to maintain relevance. They are on the front lines of where technology and culture collide. It’s a collision that won’t hurt your head, but it will encourage change. This article is part of our coverage of the World’s Most Innovative Companies of 2017.
News Article | March 2, 2017
A French SCA (Partnership Limited by Shares) with a capital of 56,000,000 Euros Head Office: La Woestyne 59173 Renescure, France Registred under number: 447 250 044 (Dunkerque Commercial and Companies Register) Activity and profitability in line with the annual objectives and Acquisition in USA Turnover growth and stable profitability Strengthening of the financial structure and decrease of the debt Acquisition of Ready Pac Foods in USA Annual growth and profitability objectives confirmed at the high end of the target range The 2016-2017 half-year financial statements were reviewed by the General Partner, then by the Supervisory Board on the 28th of February 2017 and checked by the Statutory Auditors. In an ever changing economic, financial and consumption climate and despite the difficult harvests observed in Summer 2016, the Bonduelle Group displayed its resilience with an activity growth and a profitability largely maintained. Its robust historical activities enable the group to consider, with confidence, the next development stage with the acquisition of Ready Pac Foods. The Bonduelle Group's turnover stands for the 1st half of financial year 2016-2017 at 1,025.6 million of euro, a growth of + 1.9% on a like-for-like basis* and of + 1.4% based on reported figures. Europe Zone For the first half of FY 2016-2017, the Europe zone's turnover remains virtually unchanged at - 0.8% on a like for like basis* and - 0.9% based on reported figures. The canned operating segment experienced a downturn over quarter 2 that was related to the lower promotional activities which were largely linked, in turn, to the harvest deficits registered in Summer 2016. The frozen segment achieved positive growth over the period, witnessing the recovery of the food service activity. Lastly, the fresh processed (delicatessen) and ready to eat (fresh-cut salad in bags) segment showed an overall stability in sales linked to an Italian market for fresh-cut salad in bags that continued to be difficult and the deterioration of production conditions in Spain (floods) at the end of the period. As for the delicatessen segment, a return to strong positive growth was observed in the second quarter. Non-Europe Zone The Non-Europe zone's turnover recorded a 6.6% growth on a like for like basis* and 5.3% based on reported figures over the first 6 months despite a high basis for comparison notably in Russia coupled with a consumption climate for this area showing no real signs of recovery. In North America, the activity continued to experience strong growth, notably in Canada. In South America, the repositioning of the canned range enabled the group to resume growth. The current operating result stands at 61.- million of euro with a current operating margin at 5.9% against 64.- million and 6.3% respectively on the 31st of December 2015. The additional cost related to the difficult harvests observed in summer 2016 in France, Russia and United States, recorded in part on the first half of this FY, coupled with a downturn of the activity in Russia, whose margins have nevertheless been preserved, explained the comparative evolution of profitability. After non recurrent items (- 0.7 million of euro), the operating profitability stands at 60.3 million of euro against 62.9 last FY, consistent with the annual objective disclosed. Financial charges reached 9.5 million of euro against 10.3 million of euro recorded on the 31st of December 2015, reflecting the decrease of the group's net debt and the lower average cost of debt (2.79% against 3.40%). After result of companies consolidated by equity method and corporate tax deduction with an effective tax rate of 28.5% over the period, the net income owner interest stands at 36.5 million of euro, representing 3.6% of the turnover, virtually unchanged compared with last FY. The group's net financial debt was set on the 31st of December 2016 at 584.2 million of euro, at a debt peak when considering the seasonal nature of its activity, against 668.2 million of euro on the 31st of December 2015, a decrease of 84 million of euro. The debt ratio (net financial debt to shareholders' equity) falls below parity at 0.96 against 1.26 last FY, attesting the ability of the group to generate free cash flow. With an average maturity debt of 3.7 years and a disintermediated rate at 49 %, the group's financial structure is fully compatible with the acquisition of Ready Pac Foods. Bonduelle, the world leader of ready-to-eat vegetables, present in canned, frozen, fresh cut and delicatessen has announced on the 23rd of February 2017 an agreement to acquire Ready Pac Foods, the U.S. market leader in single serve salad bowls. Based in California, Ready Pac Foods is the #1 producer of single-serve salad bowls in the U.S. through its Bistro Bowl® suite of products and its legacy of innovation and culinary expertise. Ready Pac Foods is also a producer of fresh-cut produce, offering packaged salads, fresh-cut fruits, and mixed vegetables to its retail and foodservice customers. With 4 production facilities located in Irwindale (CA), Jackson (GA), Florence and Swedesboro (NJ), and employs about 3,500 full-time employees. Ready Pac Foods generates approximately $800M of revenues, with a national presence in the U.S. and a wide customer base. This milestone transaction is a key step in Bonduelle's strategic ambition VegeGo! 2025 of being "the world reference in "well living" through vegetable products". This acquisition will strengthen Bonduelle's international footprint and dramatically change its profile, making the U.S. the largest country of operations, continuing a longstanding track record of successful acquisitions in North America, in particular Aliments Carrière, Canada, in 2007 and Allens, USA in 2012, and the fresh category, its first business segment. This transaction will also offer new opportunities to Ready Pac Foods business partners and deliver significant value to Bonduelle's shareholders. This acquisition, which is fully compatible with Bonduelle's strong financial profile, perfectly fits with its strategic plan and will strengthen its leadership positions in its core business lines: Ready Pac Foods will become Bonduelle 5th's business unit, dedicated to the Fresh business in the Americas and named Bonduelle Fresh Americas (BFA), along with Bonduelle Long Life Europe (BELL), Bonduelle Fresh Europe (BFE), Bonduelle Eurasia Markets (BEAM) and Bonduelle Americas (BAM). The latter being devoted to canned and frozen vegetables in Americas from North to South will be renamed Bonduelle Americas Long Life (BALL). The transaction agreed on the basis of a purchase price (enterprise value) of 409 million of dollars shows a multiple below 11 times adjusted EBITDA estimated for FY 2016-2017; lower than the comparable food market transaction multiples for the snacking and healthy eating operating segments. This acquisition, accretive as of 2017-2018, financed by debt, will keep to the group its investment grade profile with an estimated pro forma leverage ratio (net debt to recurring EBITDA) of 3.5 on the 30th of June 2017. Closing of the transaction is expected in the 4th quarter of FY 2016-2017, following approvals by the relevant regulatory authorities, notably the US Anti-Trust authorities. Based on the first half year performances, the group is confident in achieving targets at the higher end of the initial range, i.e. a turnover growth of 2 to 3 % and in a stable operating margin on a like for like basis*, excluding the acquisition of Ready Pac Foods. *at constant scope of consolidation and exchange rates - 2016-2017 3rd Quarter FY Turnover: 3rd of May 2017 (after stock exchange trading session) - 2016-2017 Financial Year Turnover: 2nd of August 2017 (after stock exchange trading session) - 2016-2017 Annual Results: 3rd of October 2017 (prior to stock exchange trading session) Bonduelle, a family business, was established in 1853. Its mission is to be the world reference in "well-living" through vegetable products. Prioritising innovation and long-term vision, the group is diversifying its operations and geographical presence. Its vegetable, grown over 128.000 hectares all over the world, are sold in 100 countries under various brand names and through various distribution channels and technologies. Expert in agro-industry with 54 industrial sites or own agricultural production, Bonduelle produces quality products by selecting the best crop areas close to its customers. Bonduelle is listed on Euronext compartment B Euronext indices: CAC MID & SMALL - CAC ALL TRADABLE - CAC ALL SHARES Bonduelle is part of the Gaïa non-financial performance index and employee shareholder index (I.A.S.) Code ISIN : FR0000063935 - Code Reuters : BOND.PA - Code Bloomberg : BON FP
News Article | February 22, 2017
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. At 24 clinical genetics centres within the United Kingdom National Health Service and the Republic of Ireland, 4,293 patients with severe, undiagnosed DDs and their parents (4,125 families) were recruited and systematically phenotyped. The study has UK Research Ethics Committee approval (10/H0305/83, granted by the Cambridge South Research Ethics Committee and GEN/284/12, granted by the Republic of Ireland Research Ethics Committee). Families gave informed consent for participation. Clinical data (growth measurements, family history, developmental milestones, and so on) were collected using a standard restricted-term questionnaire within DECIPHER39, and detailed developmental phenotypes for the individuals were entered using HPO terms40. Saliva samples for the whole family and blood-extracted DNA samples for the probands were collected, processed and quality controlled as previously described15. Genomic DNA (approximately 1 μg) was fragmented to an average size of 150 base pairs (bp) and a DNA library was created using established Illumina paired-end protocols. Adaptor-ligated libraries were amplified and indexed using polymerase chain reaction (PCR). A portion of each library was used to create an equimolar pool comprising eight indexed libraries. Each pool was hybridized to SureSelect RNA baits (Agilent Human All-Exon V3 Plus with custom ELID C0338371 and Agilent Human All-Exon V5 Plus with custom ELID C0338371) and sequence targets were captured and amplified in accordance with the manufacturer’s recommendations. Enriched libraries were analysed by 75-base paired-end sequencing (Illumina HiSeq) following the manufacturer’s instructions. Mapping of short-read sequences for each sequencing lanelet was carried out using the Burrows-Wheeler aligner (BWA; version 0.59)41 backtrack algorithm with the GRCh37 1000 Genomes Project phase 2 reference (also known as hs37d5). Sample-level BAM improvement was carried out using the Genome Analysis Toolkit (GATK; version 3.1.1)42 and SAMtools (version 0.1.19)43. This consisted of a realignment of reads around known and discovered indels (insertions and deletions) followed by base quality score recalibration (BQSR), with both steps performed using GATK. Lastly, SAMtools calmd was applied and indexes were created. Known indels for realignment were taken from the Mills Devine and 1000 Genomes Project Gold set and the 1000 Genomes Project phase low-coverage set, both part of the GATK resource bundle (version 2.2). Known variants for BQSR were taken from dbSNP 137, also part of the GATK resource bundle. Finally, single-nucleotide variants (SNVs) and indels were called using the GATK HaplotypeCaller (version 3.2.2); this was run in multisample calling mode using the complete dataset. GATK Variant Quality Score Recalibration (VQSR) was then computed on the whole dataset and applied to the individual-sample variant calling format (VCF) files. DeNovoGear (version 0.54)44 was used to detect SNV, insertion and deletion DNMs from child and parental exome data (BAM files). Variants in the VCF were annotated with minor allele frequency (MAF) data from a variety of different sources. The MAF annotations used included data from four different populations of the 1000 Genomes Project45(American, Asian, African and European), the UK10K cohort, the NHLBI GO Exome Sequencing Project (ESP), the Non-Finnish European (NFE) subset of the Exome Aggregation Consortium (ExAC) and an internal allele frequency generated using unaffected parents from the cohort. Variants in the VCF were annotated with Ensembl Variant Effect Predictor (VEP)46 based on Ensembl gene build 76. The transcript with the most severe consequence was selected and all associated VEP annotations were based on the predicted effect of the variant on that particular transcript; where multiple transcripts shared the same most severe consequence, the canonical or longest was selected. We included an additional consequence for variants at the last base of an exon before an intron, where the final base is a guanine, since these variants appear to be as damaging as a splice-donor variant28. We categorized variants into three classes by VEP consequence: (1) protein-truncating variants (PTV): splice donor, splice acceptor, stop gained, frameshift, initiator codon and conserved exon terminus variant; (2) missense variants: missense, stop lost, inframe deletion, inframe insertion, coding sequence and protein altering variant; (3) silent variants: synonymous. We filtered candidate DNM calls to reduce the false-positive rate but to maximize sensitivity, on the basis of previous results from experimental validation by capillary sequencing of candidate DNMs15. Candidate DNMs were excluded if not called by GATK in the child, or called in either parent, or if they had a maximum MAF greater than 0.01. Candidate DNMs were excluded when the forward and reverse coverage differed between reference and alternative alleles, defined as P < 10−3 using a Fisher’s exact test of coverage from orientation by allele summed across the child and parents. Candidate DNMs were also excluded if they met two of the three following three criteria: (1) an excess of parental alternative alleles within the cohort at the DNMs position, defined as P < 10−3 under a one-sided binomial test given an expected error rate of 0.002 and the cumulative parental depth; (2) an excess of alternative alleles within the cohort in DNMs in a gene, defined as P < 10−3 under a one-sided binomial test given an expected error rate of 0.002 and the cumulative depth; or (3) both parents had one or more reads supporting the alternative allele. If, after filtering, more than one variant was observed in a given gene for a particular trio, only the variant with the highest predicted functional impact was kept (protein truncating > missense > silent). For candidate DNMs of interest, primers were designed to amplify 150–250-bp products centred around the site of interest. Default primer3 design settings were used with the following adjustments: GC clamp = 1, human mispriming library used. Site-specific primers were tailed with Illumina adaptor sequences. PCR products were generated with JumpStart AccuTaq LA DNA polymerase (Sigma Aldrich), using 40 ng genomic DNA as template. Amplicons were tagged with Illumina PCR primers along with unique barcodes enabling multiplexing of 96 samples. Barcodes were incorporated using Kapa HiFi mastermix (Kapa Biosystems). Samples were pooled and sequenced down one lane of the Illumina MiSeq, using 250 bp paired-end reads. An in-house analysis pipeline extracted the read count per site and classified inheritance status per variant using a maximum likelihood approach (see Supplementary Note). We previously screened 1,133 individuals for variants that contribute to their disorder15, 18. All candidate variants in the 1,133 individuals were reviewed by consultant clinical geneticists for relevance to the individuals’ phenotypes. Most diagnosable pathogenic variants occurred de novo in dominant genes, but a small proportion also occurred in recessive genes or under other inheritance modes. DNMs within dominant DD-associated genes were very probable to be classified as the pathogenic variant for the individuals’ disorder. Owing to the time required to review individuals and their candidate variants, we did not conduct a similar review in the remainder of the 4,293 individuals. Instead we defined probable pathogenic variants as candidate DNMs found in autosomal and X-linked dominant DD-associated genes, or candidate DNMs found in hemizygous DD-associated genes in males. 1,136 individuals in the 4,293 cohort had variants either previously classified as pathogenic15, 18, or had a probably pathogenic DNM. Gene-specific germline mutation rates for different functional classes were computed15, 23 for the longest transcript in the union of transcripts overlapping the observed DNMs in that gene. We evaluated the gene-specific enrichment of PTV and missense DNMs by computing its statistical significance under a null hypothesis of the expected number of DNMs given the gene-specific mutation rate and the number of considered chromosomes23. We also assessed clustering of missense DNMs within genes15, as expected for DNMs causing activating or dominant-negative mechanisms. We did this by calculating simulated dispersions of the observed number of DNMs within the gene. The probability of simulating a DNM at a specific codon was weighted by the trinucleotide sequence context15, 23. This allowed us to estimate the probability of the observed degree of clustering given the null model of random mutations. Fisher’s method was used to combine the significance testing of missense + PTV DNM enrichment and missense DNM clustering. We defined a gene as significantly enriched for DNMs if the PTV-enrichment P value or the combined missense P value was less than 7 × 10−7, which represents a Bonferroni corrected P value of 0.05 adjusted for 4 × 18,500 tests (2 × consequence classes tested × protein coding genes). Families were given the option to have photographs of the affected individual(s) uploaded within DECIPHER39. Using images of individuals with DNMs in the same gene we generated de-identified realistic average faces (composite faces). Faces were detected using a discriminately trained, deformable-part-model detector47. The annotation algorithm identified a set of 36 landmarks per detected face48 and was trained on a manually annotated dataset of 3,100 images24. The average face mesh was created by the Delaunay triangulation of the average constellation of facial landmarks for all patients with a shared genetic disorder. The averaging algorithm is sensitive to left–right facial asymmetries across multiple patients. For this purpose, we use a template constellation of landmarks based on the average constellations of 2,000 healthy individuals24. For each patient, we align the constellation of landmarks to the template with respect to the points along the middle of the face and compute the Euclidean distances between each landmark and its corresponding pair on the template. The faces are mirrored such that the half of the face with the greater difference is always on the same side. The dataset used for this work may contain multiple photos for one patient. To avoid biasing the average face mesh towards these individuals, we computed an average face for each patient and use these personal averages to compute the final average face. Finally, to avoid any image in the composite dominating owing to variance in illumination between images, we normalized the intensities of pixel values within the face to an average value across all faces in each average. The composite faces were assessed visually to confirm successful ablation of any individually identifiable features. Visual assessment of the composite photograph by two experienced clinical geneticists, alongside the individual patient photos, was performed for all 93 genome-wide significant DD-associated genes for which clinical photos were available for more than one patient, to remove potentially identifiable composite faces as well as quality control on the automated composite face generation process. Eighty-one composite faces were excluded leaving the twelve de-identified composite faces that are shown in Fig. 2 and Extended Data Fig. 3. Each of the twelve composite faces that passed de-identification and quality control was generated from photos of ten or more patients. We previously described a method to assess phenotypic similarity by HPO terms among groups of individuals sharing genetic defects in the same gene28. We examined whether incorporating this statistical test improved our ability to identify dominant genes at genome-wide significance. Per gene, we tested the phenotypic similarity of individuals with DNMs in the gene. We combined the phenotypic-similarity P value with the genotypic P value per gene (the minimum P value from the DDD-only and meta-analysis) using Fisher’s method. We examined the distribution of differences in P value between tests without the phenotypic-similarity P value and tests that incorporated the phenotypic-similarity P value. Many individuals (854, 20%) of the DDD cohort experience seizures. We investigated whether testing within the subset of individuals with seizures improved our ability to find associations for seizure-specific genes. A list of 102 seizure-associated genes was curated from three sources: a gene panel for Ohtahara syndrome, a currently used clinical gene panel for epilepsy and a panel derived from DD-associated genes18. The P values from the seizure subset were compared to P values from the complete cohort. We compared the expected power of exome sequencing versus genome sequencing to identify disease genes. Within the DDD cohort, 55 dominant DD-associated genes achieve genome-wide significance when testing for enrichment of DNMs within genes. We did not incorporate missense DNM clustering owing to the large computational requirements for assessing clustering in many replicates. We assumed a cost of USD$1,000 per individual for genome sequencing. We allowed the cost of exome sequencing to vary relative to genome sequencing, from 10–100%. We calculated the number of trios that could be sequenced under these scenarios. Estimates of the improved power of genome sequencing to detect DNMs in the coding sequence are around 1.05-fold29 and we increased the number of trios by 1.0–1.2-fold to allow this. We sampled as many individuals from our cohort as the number of trios and counted which of the 55 DD-associated genes still achieved genome-wide significance for DNM enrichment. We ran 1,000 simulations of each condition and obtained the mean number of genome-wide significant genes for each condition. We tested whether phenotypes were associated with the likelihood of having a probably pathogenic DNM. We analysed all collected phenotypes which could be coded in either a binary or quantitative format. Categorical phenotypes (for example, sex coded as male or female) were tested using a Fisher’s exact test whereas quantitative phenotypes (for example, duration of gestation coded in weeks) were tested using a logistic regression, using sex as a covariate. We investigated whether having autozygous regions affected the likelihood of having a diagnostic DNM. Autozygous regions were determined from genotypes in every individual, to obtain the total length per individual. We fitted a logistic regression for the total length of autozygous regions to whether individuals had a probably pathogenic DNM. To illustrate the relationship between length of autozygosity and the occurrence of a probably pathogenic DNM, we grouped the individuals by length and plotted the proportion of individuals in each group with a DNM against the median length of the group. The effects of parental age on the number of DNMs were assessed using 8,409 high confidence (posterior probability of DNM > 0.5) unphased coding and noncoding DNMs in 4,293 individuals. A Poisson multiple regression was fit on the number of DNMs in each individual with both maternal and paternal age at child birth as covariates. The model was fit with the identity link and allowed for overdispersion. This model used exome-based DNMs, and the analysis was scaled to the whole genome by multiplying the coefficients by a factor of 50, based on approximately 2% of the genome being well covered by our data (exons + introns). We identified the threshold for posterior probability of DNM for which the number of observed candidate synonymous DNMs was equal to the number of expected synonymous DNMs. Candidate DNMs with scores below this threshold were excluded. We also examined the probable sensitivity and specificity of this threshold based on validation results for DNMs of a previous publication15 in which comprehensive experimental validation was performed on 1,133 trios that comprise a subset of the families analysed here. The numbers of expected DNMs per gene were calculated per consequence from expected mutation rates per gene and the 2,407 male and 1,886 females in the cohort. We calculated the excess of DNMs for missense and PTVs as the ratio of numbers of observed DNMs versus expected DNMs, as well as the difference of observed DNMs minus expected DNMs. We identified 150 autosomal dominant haploinsufficient genes that affect neurodevelopment within our curated DD gene set. Genes affecting neurodevelopment were identified where the affected organs included the brain; or where HPO phenotypes linked to defects in the gene included either an abnormality of brain morphology (HP:0012443) or cognitive impairment (HP:0100543) term. The 150 genes were classified for ease of clinical recognition of the syndrome from gene defects by two consultant clinical geneticists. Genes were rated from 1 (least recognizable) to 5 (most recognizable). Categories 1 and 2 contained 5 and 22 genes, respectively, and so were combined in later analyses. The remaining categories had more than 33 genes per category. The ratio of observed loss-of-function DNMs to expected loss-of-function DNMs was calculated for each recognizability category, along with 95% CIs from a Poisson distribution given observed counts. We estimated the likelihood of obtaining the observed number of PTV DNMs under two models. Our first model assumed no haploinsufficiency, and mutation counts were expected to follow baseline mutation rates. Our second model assumed fully penetrant haploinsufficiency, and scaled the baseline PTV-mutation expectations by the observed PTV enrichment in our known haploinsufficient neurodevelopmental genes, stratified by clinical recognizability into low (containing genes with our ‘low’, ‘mild’ and ‘moderate’ labels) and high categories. We calculated the likelihoods of both models per gene as the Poisson probability of obtaining the observed number of PTVs, given the expected mutation rates. We computed the Akaike’s Information Criterion for each model and ranked them by the difference between model 1 and model 2 (Δ ). The observed excess of missense/inframe indel DNMs is composed of a mixture of DNMs with loss-of-function mechanisms and DNMs with altered-function mechanisms. We found that the excess of PTV DNMs within dominant haploinsufficient DD-associated genes had a greater skew towards genes with high intolerance for loss-of-function variants than the excess of missense DNMs in dominant non-haploinsufficient genes. We binned genes by the probability of being loss-of-function intolerant30 constraint decile and calculated the observed excess of missense DNMs in each bin. We modelled this binned distribution as a two-component mixture with the components representing DNMs with a loss-of-function or altered-function mechanism. We identified the optimal mixing proportion for the loss-of-function and altered-function DNMs from the lowest goodness of fit (from a spline fitted to the sum-of-squares of the differences per decile) to missense/inframe indels in all genes across a range of mixtures. The excess of DNMs with a loss-of-function mechanism was calculated as the excess of DNMs with a VEP loss-of-function consequence, plus the proportion of the excess of missense DNMs at the optimal mixing proportion. We independently estimated the proportions for loss of function and altered function. We counted PTV and missense/inframe indel DNMs within dominant haploinsufficient genes to estimate the proportion of excess DNMs with a loss-of-function mechanism, but which were classified as missense/inframe indel. We estimated the proportion of excess DNMs with a loss-of-function mechanism as the PTV excess plus the PTV excess multiplied by the proportion of loss of function classified as missense. We estimated the birth prevalence of monoallelic DDs by using the germline-mutation model. We calculated the expected cumulative germline-mutation rate of truncating DNMs in 238 haploinsufficient DD-associated genes. We scaled this upwards based on the composition of excess DNMs in the DDD cohort using the ratio of excess DNMs (n = 1,816) to DNMs within dominant haploinsufficient DD-associated genes (n = 412). Around 10% of DDs are caused by de novo copy-number variations49, 50, which are underrepresented in our cohort as a result of previous genetic testing. If included, the excess DNM in our cohort would increase by 21%, therefore we scaled the prevalence estimate upwards by this factor. Mothers aged 29.9 and fathers aged 29.5 have children with 77 DNMs per genome on average21. We calculated the mean number of DNMs expected under different combinations of parental ages, given our estimates of the extra DNMs per year from older mothers and fathers. We scaled the prevalence to different combinations of parental ages using the ratio of expected mutations at a given age combination to the number expected at the mean cohort parental ages. To estimate the annual number of live births with DDs caused by DNMs, we obtained country population sizes, birth rates, age at first birth51, and calculated global birth rate (18.58 live births per 1,000 individuals) and age at first birth (22.62 years), weighted by population size. We calculated the mean age when giving birth (26.57 years) given a total fertility rate of 2.45 children per mother52, and a mean interpregnancy interval of 29 months53. We calculated the number of live births given our estimate of DD prevalence caused by DNMs at this age (0.00288), the global population size (7.4 billion individuals) and the global birth rate. Source code for filtering candidate DNMs, testing DNM enrichment, DNM clustering and phenotypic similarity can be found here: https://github.com/jeremymcrae/denovoFilter, https://github.com/jeremymcrae/mupit, https://github.com/jeremymcrae/denovonear and https://github.com/jeremymcrae/hpo_similarity. Exome sequencing and phenotype data are accessible via the European Genome-phenome Archive (EGA) under accession number EGAS00001000775 (https://www.ebi.ac.uk/ega/studies/EGAS00001000775). Details of DD-associated genes are available at www.ebi.ac.uk/gene2phenotype. All other data are available from the corresponding author upon reasonable request.
News Article | February 21, 2017
On the Knife's Edge: Using Therapy To Address Violence Among Teens The fight was over a pair of gym shoes. One teenager faces years in prison. The other — the 15-year-old grandson of Congressman Danny Davis — is dead. We often hear stories about murders sparked by trivial disputes. And we also hear the same solutions proposed year after year: harsher punishments, more gun control. But what if science can help us find new solutions? Can understanding how we make decisions help us prevent these tragedies? In moments of anger, it can be hard to heed the advice to take a deep breath or count to ten. But public health researcher Harold Pollack says that "regret comes almost as fast as anger," and that five minutes of reflection can make all the difference between a regular life and one behind bars. This week, Harold Pollack and Jens Ludwig tell us about the research they do at the University of Chicago's Crime Lab. They worked with a program called BAM (Becoming a Man) to look at what happens when teenagers participate in cognitive behavioral therapy, or CBT. We hear from students in the program and examine the results of Pollack and Ludwig's research. They found that changing the way we think can change the way we behave — and changing the way we behave can change our lives. This week, we put that idea to the test. Hidden Brain is hosted by Shankar Vedantam and produced by Maggie Penman, Jennifer Schmidt, Rhaina Cohen, and Renee Klahr. Our intern is Chloe Connelly, and our supervising producer is Tara Boyle. You can follow us on Twitter @hiddenbrain, and listen for Hidden Brain stories each week on your local public radio station.