LGC Genomics GmbH

Berlin, Germany

LGC Genomics GmbH

Berlin, Germany
Time filter
Source Type

Kuppusamy T.,University of Western Australia | Giavalisco P.,Max Planck Institute of Molecular Plant Physiology | Arvidsson S.,Max Planck Institute of Molecular Plant Physiology | Arvidsson S.,LGC Genomics GmbH | And 7 more authors.
Plant Physiology | Year: 2014

Hakea prostrata (Proteaceae) is adapted to severely phosphorus-impoverished soils and extensively replaces phospholipids during leaf development. We investigated how polar lipid profiles change during leaf development and in response to external phosphate supply. Leaf size was unaffected by a moderate increase in phosphate supply. However, leaf protein concentration increased by more than 2-fold in young and mature leaves, indicating that phosphate stimulates protein synthesis. Orthologs of known lipid-remodeling genes in Arabidopsis (Arabidopsis thaliana) were identified in the H. prostrata transcriptome. Their transcript profiles in young and mature leaves were analyzed in response to phosphate supply alongside changes in polar lipid fractions. In young leaves of phosphate-limited plants, phosphatidylcholine/phosphatidylethanolamine and associated transcript levels were higher, while phosphatidylglycerol and sulfolipid levels were lower than in mature leaves, consistent with low photosynthetic rates and delayed chloroplast development. Phosphate reduced galactolipid and increased phospholipid concentrations in mature leaves, with concomitant changes in the expression of only four H. prostrata genes, GLYCEROPHOSPHODIESTER PHOSPHODIESTERASE1, N-METHYLTRANSFERASE2, NONSPECIFIC PHOSPHOLIPASE C4, and MONOGALACTOSYLDIACYLGLYCEROL3. Remarkably, phosphatidylglycerol levels decreased with increasing phosphate supply and were associated with lower photosynthetic rates. Levels of polar lipids with highly unsaturated 32:x (x = number of double bonds in hydrocarbon chain) and 34:x acyl chains increased.We conclude that a regulatory network with a small number of central hubs underpins extensive phospholipid replacement during leaf development in H. prostrata. This hardwired regulatory framework allows increased photosynthetic phosphorus use efficiency and growth in a low-phosphate environment. This may have rendered H. prostrata lipid metabolism unable to adjust to higher internal phosphate concentrations. © 2014 American Society of Plant Biologists. All rights reserved.

News Article | November 30, 2016
Site: www.nature.com

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. The C. marinus laboratory stocks were bred according to Neumann1, care was provided by the MFPL aquatic facility. Briefly, C. marinus were kept in 20 × 20 × 5 cm plastic containers with sand and natural seawater diluted to 15‰ with desalted water, fed diatoms (Phaeodactylum tricornutum, strain UTEX 646) in early larval stages and nettle powder in later stages. Temperature in the climate chambers was set to 20 °C and the light–dark cycle was 12:12 (except where noted differently). Moonlight was simulated with an incandescent flashlight bulb (about 1 lx), which was switched on all night for four successive nights every 30 days. The genome assembly process (Extended Data Fig. 9a) was based on three sequencing libraries (Supplementary Table 10): a 0.2-kb insert library was prepared from a single adult male of the Jean laboratory strain (established from field samples taken at St. Jean-de-Luz, France, in 2007; >12 generations in the laboratory), which was starved and kept in seawater with penicillin (60 units per ml), streptomycin (60 μg ml−1) and neomycin (120 μg ml−1) during the last 2 weeks of development. DNA was extracted with a salting-out method46, sheared on a Covaris S2 sonicator (frequency sweeping mode; 4 °C; duty cycle, 10%; intensity, 7; cycles per burst, 300; microTUBE AFA fibre 6 × 16 mm; 30 s) and prepared for Illumina sequencing with standard protocols. A 2.2-kb and a 7.6-kb insert library were prepared from a polymorphic DNA pool of >300 field-caught Jean adult males by Eurofins MWG Operon (Ebersberg, Germany) according to the manufacturer’s protocol. Each library was sequenced in one lane of an Illumina HiSeq2000 with 100-bp paired-end reads at the Next Generation Sequencing unit of the Vienna Biocenter Core Facilities (http://vbcf.ac.at). Reads were filtered for read quality, adaptor and spacer sequences with cutadapt47 (−b −n 3 −e 0.1 −O 8 −q 20 −m 13) and duplicates were removed with fastq-mcf from ea-utils48 (−D 70). Read pairs were interleaved with ngm-utils49, leaving only paired reads. Contamination with human DNA found in the 0.2-kb library was removed by deleting reads matching the human genome at a phred-scaled quality score ≥ 20 (alignment with BWA50). Assembly into contigs with Velvet51 (scaffolding disabled; 57-bp kmers as determined by VelvetOptimiser52) was based solely on the less polymorphic 0.2-kb library. About 600 remaining adaptor sequences at the ends of assembled contigs were trimmed with cutadapt (−O 8 −e 0.1 −n 3). For assembly statistics see Supplementary Table 11. Scaffolding of the contigs was based on all three libraries and performed with SSPACE53 in two iterations, that is, scaffolds from the first round were scaffolded again. Using different parameters in the iterations (Supplementary Table 12) allowed different connections to be made and thus increased scaffold connectivity (Supplementary Table 13). The effect is probably owing to the polymorphic nature of the 2.2-kb and 7.6-kb libraries; it results in a ‘population-consensus most common arrangement of the scaffolds’. The iterative scaffolding process was performed with and without applying a size cut-off excluding contigs <1 kb, resulting in two independent assemblies (CLUMA_0.3 and CLUMA_0.4; see Extended Data Fig. 9a), which differed in overall connectivity and sequence content (Supplementary Table 11), but also in the identity and structure of the large scaffolds. In order to combine both connectivity and sequence content, and in order to resolve the contradictions in the structure of the largest scaffolds, the two assemblies were compared and reconciled in a manual super-scaffolding process, as detailed in Supplementary Method 1. Briefly, the overlap of scaffolds from the two assemblies was tested with BLAST searches and represented in a graphical network structure. Scaffolds with congruent sequence content in both assemblies would result in a linear network, whereas scaffolds with contradictory sequence content would result in branching networks. At the same time, both assemblies were subject to genetic linkage mapping based on genotypes obtained from restriction-site-associated DNA sequencing (RAD sequencing) of a published mapping family6 (Supplementary Method 2). The resulting genetic linkage information served to resolve the branching networks into the longest possible unambiguous linear sub-networks with consistent genetic linkage information (see scheme A in Supplementary Method 1). Finally, the structure of the resulting super-scaffolds was coded in YAML format and translated into DNA sequence with Scaffolder54, resulting in 75 mapped super-scaffolds. The remaining small and unmapped scaffolds were filtered for fragments of the mitochondrial genome, the histone gene cluster and 18S/28S ribosomal rDNA gene cluster, which were assembled separately (Supplementary Method 3; Extended Data Fig. 10). Unmapped scaffolds were also filtered for obvious contamination from other species (Supplementary Method 3). The degree to which the remaining unmapped scaffolds are leftover polymorphic variants of parts of the mapped super-scaffolds was estimated by blasting the former against the latter (Supplementary Method 3 and Supplementary Table 14). All scaffolds were subject to gap closing with GapFiller55 and repeated edges, that is, gaps with almost identical sequences at both sides that are generally not closed because of genetic polymorphisms, were assessed and if possible removed with a custom script (Supplementary Method 4; code available supplied as Source Data File). The final assembly CLUMA_1.0 was submitted under project PRJEB8339 (75 mapped scaffolds; 23,687 unmapped scaffolds ≥100 bp). The assembly and further information can also be obtained from ClunioBase (http://cluniobase.cibiv.univie.ac.at). Genetic linkage information for the final 75 super-scaffolds was obtained by repeating read mapping to genotype calling for the RAD sequencing experiment as described above (Supplementary Method 2), but now with assembly CLUMA_1.0 as a reference. This allowed us to place and orient super-scaffolds along the genetic linkage map (Fig. 1a and Extended Data Fig. 2). The positions of the recombination events within a scaffold were approximated as the middle between the positions of the two RAD markers between which the marker pattern changed from one map location to the next. The published genetic linkage map was refined and revised (Supplementary Method 5 and Extended Data Fig. 2). Based on the refined linkage map, QTL analysis of the published mapping family was repeated as described6 (Supplementary Table 4 and Supplementary Note 5). Using the correspondence between the reference assembly and the genetic linkage map, we were able to directly identify the genomic regions corresponding to the confidence intervals of the QTLs (Fig. 1 and Extended Data Fig. 5a, b). Assembled transcripts of a normalized cDNA library of all life stages and various C. marinus strains (454 sequencing) were available from previous experiments and RNA sequencing data was available for Jean strain adults (Illumina sequencing). Furthermore, specifically for genome annotation, RNA from 80 third instar larvae from the Jean and Por laboratory strains each was prepared for RNA sequencing according to standard protocols (Supplementary Method 6). Each sample was sequenced on a single lane of an Illumina HiSeq 2000. All transcript reads were submitted to the European Nucleotide Archive (ENA) under project PRJEB8339. For the adult and larval RNA sequencing data, raw reads were quality checked with fastqc56, trimmed for adaptors quality with cutadapt47 and filtered to contain only read pairs using the interleave command in ngm-utils49. Reads were assembled separately for larvae and adults with Trinity57 (path_reinforcement_distance: 25; maximum paired-end insert size: 1,500 bp; otherwise default parameters). Automated annotation was performed with MAKER258. Repeats were masked based on all available databases in repeatmasker. MAKER2 combined evidence from assembled transcripts (see above), mapped protein data sets from Culex quinquefasciatus (CpipJ1), Anopheles gambiae (AgamP3), Drosophila melanogaster (BDGP5), Danaus plexippus (DanPle_1.0), Apis mellifera (Amel4.0), Tribolium castaneum (Tcas3), Strigamia maritima (Smar1) and Daphnia pulex (Dappu1) and ab initio gene predictions with AUGUSTUS59 and SNAP60 into gene models. AUGUSTUS was trained for C. marinus based on assembled transcripts from the normalized cDNA library. SNAP was run with parameters for A. mellifera, which had the highest congruence with known C. marinus genes in preliminary trials (Supplementary Method 7). MAKER was set to infer gene models from all evidence combined (not transcripts only) and gene predictions without transcript evidence were allowed. Splice variant detection was enabled, single-exon genes had to be larger than 250 bp and intron size was limited to a maximum of 10 kb. All gene models within the QTL confidence intervals, as well as all putative circadian clock genes and light receptor genes were manually curated: exon–intron boundaries were corrected according to transcript evidence (approximately 500 gene models), chimeric gene models were separated into the underlying individual genes (approximately 100 gene models separated into around 300 gene models) and erroneously split gene models were joined (approximately 15 gene models). Finally, this resulted in 21,672 gene models, which were given IDs from CLUMA_CG000001 to CLUMA_CG021672 (‘CLUMA’ for Clunio marinus, following the controlled vocabulary of species from the UniProt Knowledgebase; CG for ‘computated gene’). Splice variants of the same gene (detected in 752 gene models) were identified by the suffix ‘-RA’, ‘-RB’ and so on, and the corresponding proteins by the suffix ‘-PA’, ‘-PB’ and so forth. Gene models were considered as supported if they overlapped with mapped transcripts or protein data (Supplementary Table 1). Gene counts for D. melanogaster were retrieved from BDGP5, version 75.546 and for A. gambiae from AgamP3, version 75.3. The putative identities of the C. marinus gene models were determined in reciprocal BLAST searches, first against UniProtKB/Swiss-Prot (8,379 gene models assigned) and if no hit was found, second against the non-redundant protein sequences (nr database) at NCBI (1,802 additional genes assigned). Reciprocal best hits with an e value < 1 × 10−10 were considered putative orthologues (termed ‘putative gene X’), non-reciprocal hits with the same e value were considered paralogues (termed ‘similar to’). All remaining gene models were searched against the PFAM database of protein domains (111 gene models assigned; termed ‘gene containing domain X’). If still no hit was found, the gene models were left unassigned (‘NA’). Genome-wide synteny between the C. marinus, D. melanogaster and A. gambiae genomes was assessed based on reciprocal best BLAST hits (e value < 10 × 10-10) between the three protein data sets (Ensembl Genomes, Release 22, for D. melanogaster and A. gambiae). Positions of pairwise orthologous genes were retrieved from the reference genomes (BDGP5, AgamP3 and CLUMA_1.0) and plotted with Circos61. C. marinus chromosome arms were delimited based on centromeric and telomeric signatures in genetic diversity and linkage disequilibrium (Extended Data Fig. 3c and Supplementary Table 3; for data source see ‘strain re-sequencing’ below). Homologues for C. marinus chromosome arms were assigned based on enrichment with putative orthologous genes from specific chromosome arms in D. melanogaster and A. gambiae (Extended Data Figs 3, 4 and Supplementary Table 3). Additionally, for the 5,388 detected putative 1:1:1 orthologues (C. marinus:D. melanogaster:A. gambiae), microsynteny was assessed by testing if all pairs of directly adjacent genes in one species were also directly adjacent in the other species. The degree of microsynteny was then calculated as the fraction of conserved adjacencies among all pairs of adjacent genes. From this fraction the relative levels of chromosomal rearrangements in the evolutionary lineage leading to C. marinus were estimated (Supplementary Note 3 and Extended Data Fig. 4). Genetic variation in five C. marinus strains (Extended Data Fig. 1) was assessed based on pooled-sequencing data from field-caught males from the strains of St. Jean-de-Luz (Jean; Basque Coast, France; sampled in 2007; n = 300), Port-en-Bessin (Por; Normandie, France; 2007; n = 300), as well as Vigo (Spain; 2005; n = 100), Helgoland (He; Germany; 2005; n = 300) and Bergen (Ber; Norway; 2005; n = 100). Samples from Vigo and Bergen, were provided by D. Neumann and C. Augustin, respectively. For each strain we chose the largest available number of individuals to obtain the best possible resolution of allele frequencies. Females are not available, because they are virtually invisible in the field. For an overview of the experimental procedure, see Extended Data Fig. 9b. DNA was extracted with a salting-out method46 from sub-pools of 50 males, the DNA pools were mixed at equal DNA amounts, sheared and prepared as described above and sequenced on four lanes of an Illumina HiSeq2000 with paired-end 100-bp reads (Ber and Vigo combined in one lane, distinguished by index reads). All reads were submitted to the European Nucleotide Archive (ENA) under project PRJEB8339. Sequencing reads were filtered for read quality and adaptor sequences with cutadapt47 (−b −n 2 −e 0.1 −O 8 −q 13 −m 15), interleaved with ngm-utils49 and duplicates were removed with fastq-mcf from ea-utils48 (−D 70). Reads were aligned to the mapped super-scaffolds of assembly CLUMA_1.0 with BWA50 (aln and sampe; maximal insert size (bp): −a 1500). Based on the unfiltered alignments, the samples from Por and Jean were screened for genomic inversions and indels relative to the reference sequence with the multi-sample version of DELLY62. Paired-end information was only considered if the mapping quality was high (q ≥ 20) (see also Supplementary Note 3). For population genomic analysis (Extended Data Fig. 9b), the alignments of the pool-sequencing (pool–seq) data from Vigo, Jean, Por, He and Ber were filtered for mapping quality (q ≥ 20), sorted, merged and indexed with SAMtools63. Reads were re-aligned around indels with the RealignerTargetCreator and the IndelRealigner in GATK64. The resulting coverage per strain is given in Supplementary Table 5. For identification of SNPs, a pileup file was created with the mpileup command of SAMtools63. Base Alignment Quality computation was disabled (−B); instead, after creating a synchronized file with the mpileup2sync script in PoPoolation265, indels that occurred more than ten times were masked (including 3 bp upstream and downstream) with the identify-indel-regions and filter-sync-by-gtf scripts of PoPoolations2. F values were determined with the fst-sliding script of PoPoolation2, applying a minimum allele count of 10 (so that any false-positive SNPs resulting from the remaining unmasked indels were effectively excluded) and a minimum coverage of 40× for the comparison between Por and Jean or 10× for the comparison of all five strains. F was calculated at a single base resolution, as well as in windows of 5 kb (step size, 1 kb). Individual SNPs were only considered for further analyses or plotted if they were significantly differentiated as assessed by Fisher’s exact test (fisher-test in PoPoolation2). Average genome-wide genetic differentiation between timing strains, as obtained by averaging over 5-kb sliding-windows, was compared to the respective timing differences and geographic distances (see Supplementary Table 8) in Mantel tests (Pearson’s product moment correlation; 9,999 permutations), as implemented in the vegan package in the R statistical programming environment (ref. 66). Geographic distances and circadian timing differences were determined as described previously67 (see Supplementary Table 8). For determination of lunar timing differences when comparing lunar with semilunar rhythms see Supplementary Note 6. In order to find genomic regions for which genetic differentiation is correlated with the timing differences between strains, the Mantel test was then applied to 5-kb genomic windows every 1 kb along the reference sequence. 5 kb is roughly the average size of a gene locus in C. marinus. Windows with a correlation coefficient of r ≥ 0.5 were tested for significance (999 permutations). For each genomic position the number of overlapping significantly correlated 5-kb windows was enumerated, resulting in a correlation score (CS; ranging from 0 to 5). Genetic diversity, measured as Watterson’s theta (θ ), for each strain was assessed with PoPoolation1.1.2 (ref. 68) in 20-kb windows with 10-kb steps. In order to save computing time, the pileup files of Jean, Por and He were linearly downscaled to 100× coverage with the subsample-pileup script (‘fraction’ option), positions below 100× coverage were discarded. Indel regions were excluded (default in PoPoolation 1.1.2) and a minimum of 66% of a sliding window needed to be covered. SNPs were only considered in θ calculations if present ≥2 times, leading to slight inconsistencies in θ estimates between strains due to differing coverage, but not affecting diversity comparisons within strains. Linkage disequilibrium between the SNPs was determined for the Por and Jean strains with LDx69, assuming physical linkage between alleles on the same read or read pairs. r2 was determined by a maximum likelihood estimator, minimum and maximum read depths corresponded to the 2.5% and 97.5% coverage depths for each population (Jean, 111–315; Por, 98–319), total insert distance was limited to 600 bp, minimum phred-scaled base quality was 20, minimum allele frequency was 0.1 and a minimum coverage per pair of SNPs was 11. SNPs were binned by their physical distance for the plots (0–200 bp, 200–400 bp, 400–600 bp), with the mean value plotted. Finally, small indels (<30 bp) in the Por and Jean strains were detected with the UnifiedGenotyper (−glm INDEL) in GATK64 for positions with more than 20× coverage. Genetic differentiation for indels was calculated with the classical formula F  = (H −H )/H , where H is the average expected heterozygosity according to Hardy–Weinberg Equilibrium (HWE) in the two subpopulations and H is the expected heterozygosity in HWE of the hypothetical combined total population. If more than two alleles were present, only the two most abundant alleles were considered in the calculation of F . Gene models from the automated annotation were considered candidate genes, if they fulfilled the following criteria. (1) The gene was located within the reference sequence corresponding to the QTL confidence intervals as determined for the Por and Jean strains. (2) The gene contained a strongly differentiated SNP or small indel or it was directly adjacent to such a SNP or small indel (F  ≥ 0.8 for Por versus Jean, that is, the strains used in QTL mapping). This resulted in a preliminary list of 133 genes based on the comparison between Por and Jean (Supplementary Table 6). These candidate genes were narrowed down based on their overlap with genomic 5-kb windows, for which genetic differentiation between five European timing strains correlated with their timing differences (Fig. 1a, Extended Data Fig. 5a, b and Supplementary Table 9). The location and putative effects of the SNPs and indels relative to the gene models were assessed with SNPeff70 (−ud 0, otherwise default parameters; Extended Data Fig. 5c, d and Supplementary Tables 6, 9). For Gene Ontology (GO) term analysis, all C. marinus gene models with putative orthologues in the UniProtKB/Swiss-Prot and non-redundant protein sequences (nr) databases based on reciprocal best BLAST hits (see above) were annotated with the GO terms of their detected orthologues (6,837 gene models). Paralogues were not annotated. The enrichment of candidate SNPs and indels (F  ≥ 0.8 between Por and Jean) in specific GO terms was tested with SNP2GO71 (min.regions = 1, otherwise default parameters). Hyper-geometric sampling was applied to test if individual genes of a GO term or a whole pathway of genes are enriched for SNPs (Supplementary Table 7). RNA-seq data of the Por and Jean strains for CaMKII.1 were obtained from the larval RNA sequencing experiment described above. Besides four assembled full-length transcripts (RA–RD) from RNA-seq and assembled EST libraries, additional partial transcripts (RE–RO) were identified by PCR amplification (for PCR primers see Supplementary Table 15), gel extraction (QIAquick Gel Extraction Kit, Qiagen), cloning with the CloneJET PCR Cloning Kit (Thermo Scientific) and Sanger sequencing with pJET1.2 primers (LGC Genomics & Microsynth). cDNA was prepared from RNA extracted from third instar larvae of the Por and Jean laboratory strains (RNA extraction with RNeasy Plus Mini Kit, Qiagen; reverse transcription with QuantiTect Reverse Transcription Kit, Qiagen). qPCR was performed with variant-specific primers and actin was used as a control gene (Supplementary Table 16). cDNA was obtained from independent pools of 20 third instar larvae of the Por and Jean strains. Sample size was ten pools per strain to cover different time points during the day and to test for reproducibility (two samples each at zeitgeber times 0, 4, 8, 16 and 20; for one Por sample extraction failed; RNA extraction and reverse transcription as above). qPCR was performed with Power SYBR Green PCR Master Mix on a StepOnePlus Real Time System (both Applied Biosystems). Fold-changes were calculated according to ref. 72 in a custom excel sheet. The assumption of equal variance was violated for the RD comparison (F-test) and the assumption of normal distribution was violated for the data of RA and RC in the Por strain (Shapiro–Wilk normality test), possibly reflecting circadian effects in the samples from different times of day. Thus, expression differences were assessed for significance in a two-tailed Wilcoxon rank-sum test (wilcox.test in R66). Holm correction73 was used for multiple testing (default in p.adjust function of R). PCR fragments containing the CaMKII.1 linker region (exons 10–15) were amplified from genomic Por or Jean DNA, respectively, with primers CaMKII-Sc61-F-344112 and CaMKII-Sc61-R-351298 (Supplementary Table 15), cloned with the CloneJET PCR Cloning Kit (Thermo Scientific), transferred into the pcDNA3.1+ vector using NotI and XbaI (Thermo Scientific). These constructs were transfected into D. melanogaster S2R+ cells and RNA was prepared 48 h after transfection. After DNase digestion, isoform expression was analysed by radioactive, splicing-sensitive RT–PCR (primers in Supplementary Table 17) and phosphorimager quantification as described74. Identity of isoforms is based on size and sequencing of PCR products. To test for reproducibility, there were seven biological replicates (raw data in Supplementary Table 18). As the assumptions of equal variance (F-test) and normal distribution of data (Shapiro–Wilk normality test) were not violated, the significance of expression differences was assessed in unpaired, two-sided two-sample t-tests. Holm correction73 was used for multiple testing (default in p.adjust function of R). S2R+ cells were obtained from the laboratory of S. Sigrist, regularly authenticated by morphology and routinely tested for absence of mycoplasma contamination. The entire experiment was reproduced several months later with three biological replicates (raw data in Supplementary Table 18). Firefly luciferase is driven from a period 3X69 promoter under control of the CLOCK and CYCLE protein19, 21. The D. melanogaster pAc–clk construct was obtained from F. Rouyer, pCopia–Renilla luciferase and period 3X69–luc reporter constructs from M. Rosbash, a [Ca2+]-independent mouse CaMKIIT286D was provided by M. Mayford. The CaMKII inhibitor KN-93 was purchased from Abcam (#ab120980). C. marinus Cyc, C. marinus Clk and C. marinus CaMKII.1–RD were cloned into the pAc5.1/V5–His A plasmid (Invitrogen) with stop codons before the tag. The Q5 Site-Directed Mutagenesis Kit (NEB) was used to make kinase-dead and [Ca2+]-independent versions of C. marinus CaMKII.1–RD (for primers, see Supplementary Table 17). D. melanogaster S2 cells (Invitrogen) were cultured at 25 °C in Schneider’s D. melanogaster medium (Lonza) supplemented with fetal bovine serum (FBS, 10%, heat-inactivated), penicillin (100 U ml−1), streptomycin (100 μg ml−1) and 2 mM l-glutamine; Sigma). Cells were seeded into 24-well plates (800,000 cells per well) and transfected with Effectene transfection reagent (Qiagen) according to the manufacturer’s instructions. Experiment with mouse [Ca2+]-independent CaMKII: 25 ng pCopia–Renilla, 10 ng period 3X69–luc, 0.5 ng D. melanogaster pAc–clk, 200 ng mouse pAc–CaMKIIT286D. Experiment with CaMKII inhibitor KN-93: 25 ng pCopia–Renilla, 10 ng period 3X69–luc, 0.5 ng D. melanogaster pAc–clk, various amounts of KN-93. Experiment with C. marinus genes: 25 ng pCopia–Renilla, 10 ng period 3X69–luc, 100 ng C. marinus pAc–cyc, 100 ng C. marinus pAc–clk, 200 ng C. marinus CaMKII.1–RDK42R or 200 ng C. marinus CaMKII.1–RDT286D. In all experiments, the transfection mix was filled up with empty pAc5.1/V5–His A vector to a total of 435 ng DNA per well. After 48 h, cells were washed with PBS and lysed with Passive Lysis Buffer (Promega). Luciferase activities were determined on a Synergy H1 plate reader (Biotek) using a Dual-Luciferase Reporter Assay System (Promega). For each biological replicate three independent cell lysates were measured and their mean value determined. Firefly luciferase activity was normalized to Renilla luciferase activity and values were normalized to controls transfected with D. melanogaster pAc–clk or C. marinus pAc–clk and C. marinus pAc–cyc, respectively. S2 cells (Invitrogen/Life Technologies, Cat.no. R690-07) were regularly authenticated by morphology and routinely tested for absence of mycoplasma contamination (Lonza MycoAlert). Sample size was chosen to test for reproducibility. For circadian free-run experiments, culture boxes of the Por, He and Jean strains were transferred from light–dark cycle (16:8) to constant dim light (light–light cycle, about 100 lx). Emerging adults were collected in 1-h intervals by a custom made C. marinus fraction collector (similar to those described in ref. 75) and counted once a day. Because collection was automated, the experimenter had no influence on the results and blinding was not necessary. As the circalunar clock restricts adult emergence to a few days, the circadian emergence rhythm can only be assessed over a few days. Several culture boxes were transferred to a light–light cycle at different time points. The resulting emergence data were combined for each strain using the switch to a light–light cycle as a common reference point. We used the maximum number of available individuals. Free-running period was calculated as the mean interval between subsequent emergence peaks, weighting each peak by the number of individuals. All sequence data are deposited in the European Nucleotide Archive (ENA) under PRJEB8339. The reference genome is also on ClunioBase (http://cluniobase.cibiv.univie.ac.at). Machine readable super-scaffolding data and the computer source code for the removal of repeated edges are supplied as source data files.

Bernt M.,University of Leipzig | Bleidorn C.,University of Leipzig | Braband A.,LGC Genomics GmbH | Dambach J.,Center for Molecular Biodiversity Research | And 24 more authors.
Molecular Phylogenetics and Evolution | Year: 2013

About 2800 mitochondrial genomes of Metazoa are present in NCBI RefSeq today, two thirds belonging to vertebrates. Metazoan phylogeny was recently challenged by large scale EST approaches (phylogenomics), stabilizing classical nodes while simultaneously supporting new sister group hypotheses. The use of mitochondrial data in deep phylogeny analyses was often criticized because of high substitution rates on nucleotides, large differences in amino acid substitution rate between taxa, and biases in nucleotide frequencies. Nevertheless, mitochondrial genome data might still be promising as it allows for a larger taxon sampling, while presenting a smaller amount of sequence information. We present the most comprehensive analysis of bilaterian relationships based on mitochondrial genome data. The analyzed data set comprises more than 650 mitochondrial genomes that have been chosen to represent a profound sample of the phylogenetic as well as sequence diversity. The results are based on high quality amino acid alignments obtained from a complete reannotation of the mitogenomic sequences from NCBI RefSeq database. However, the results failed to give support for many otherwise undisputed high-ranking taxa, like Mollusca, Hexapoda, Arthropoda, and suffer from extreme long branches of Nematoda, Platyhelminthes, and some other taxa. In order to identify the sources of misleading phylogenetic signals, we discuss several problems associated with mitochondrial genome data sets, e.g. the nucleotide and amino acid landscapes and a strong correlation of gene rearrangements with long branches. © 2013 Elsevier Inc.

Bernt M.,University of Leipzig | Braband A.,LGC Genomics GmbH | Middendorf M.,University of Leipzig | Misof B.,Molekulare Biodiversitatsforschung | And 6 more authors.
Molecular Phylogenetics and Evolution | Year: 2013

In this review we provide an overview of various bioinformatics methods and tools for the analysis of metazoan mitochondrial genomes. We compare available dedicated databases and present current tools for accurate genome annotation, identification of protein coding genes, and determination of tRNA and rRNA models.We also evaluate various tools and models for phylogenetic tree inference using gene order or sequence based data. As for gene order based methods, we compare rearrangement based and gene cluster based methods for gene order rearrangement analysis. As for sequence based methods, we give special emphasis to substitution models or data treatment that reduces certain systematic biases that are typical for metazoan mitogenomes such as within genome and/or among lineage compositional heterogeneity. © 2012 Elsevier Inc.

Bernt M.,University of Leipzig | Braband A.,LGC Genomics GmbH | Schierwater B.,ITZ | Schierwater B.,American Museum of Natural History | And 6 more authors.
Molecular Phylogenetics and Evolution | Year: 2013

Many years of extensive studies of metazoan mitochondrial genomes have established differences in gene arrangements and genetic codes as valuable phylogenetic markers. Understanding the underlying mechanisms of replication, transcription and the role of the control regions which cause e.g. different gene orders is important to assess the phylogenetic signal of such events. This review summarises and discusses, for the Metazoa, the general aspects of mitochondrial transcription and replication with respect to control regions as well as several proposed models of gene rearrangements. As whole genome sequencing projects accumulate, more and more observations about mitochondrial gene transfer to the nucleus are reported. Thus occurrence and phylogenetic aspects concerning nuclear mitochondrial-like sequences (NUMTS) is another aspect of this review. © 2012 Elsevier Inc.

Lenz D.,Max Planck Institute of Molecular Plant Physiology | Lenz D.,LGC Genomics GmbH | May P.,University of Luxembourg | May P.,Institute for Systems Biology | Walther D.,Max Planck Institute of Molecular Plant Physiology
BMC Research Notes | Year: 2011

Background: MicroRNA (miRNA) mediated regulation of gene expression has been recognized as a major posttranscriptional regulatory mechanism also in plants. We performed a comparative analysis of miRNAs and their respective gene targets across four plant species: Arabidopsis thaliana (Ath), Medicago truncatula(Mtr), Brassica napus (Bna), and Chlamydomonas reinhardtii (Cre). Results: miRNAs were obtained from mirBase with 218 miRNAs for Ath, 375 for Mtr, 46 for Bna, and 73 for Cre, annotated for each species respectively. miRNA targets were obtained from available database annotations, bioinformatic predictions using RNAhybrid as well as predicted from an analysis of mRNA degradation products (degradome sequencing) aimed at identifying miRNA cleavage products. On average, and considering both experimental and bioinformatic predictions together, every miRNA was associated with about 46 unique gene transcripts with considerably variation across species. We observed a positive and linear correlation between the number miRNAs and the total number of transcripts across different plant species suggesting that the repertoire of miRNAs correlates with the size of the transcriptome of an organism. Conserved miRNA-target pairs were found to be associated with developmental processes and transcriptional regulation, while species-specific (in particular, Ath) pairs are involved in signal transduction and response to stress processes. Conserved miRNAs have more targets and higher expression values than non-conserved miRNAs. We found evidence for a conservation of not only the sequence of miRNAs, but their expression levels as well. Conclusions: Our results support the notion of a high birth and death rate of miRNAs and that miRNAs serve many species specific functions, while conserved miRNA are related mainly to developmental processes and transcriptional regulation with conservation operating at both the sequence and expression level. © 2010 Walther et al; licensee BioMed Central Ltd.

Shen H.,Humboldt University of Berlin | Shen H.,Nanjing Agricultural University | Braband A.,Humboldt University of Berlin | Braband A.,LGC Genomics GmbH | Scholtz G.,Humboldt University of Berlin
Journal of Zoological Systematics and Evolutionary Research | Year: 2015

We sequenced the complete mitogenomes of three species of Decapoda, Astacidea, comprising Astacida (freshwater crayfish) and Homarida (marine clawed lobsters): 1. Procambarus fallax f. virginalis (Astacida, Astacoidea), 2. Homarus gammarus (Homarida, Nephropoidea) and 3. Enoplometopus occidentalis (Homarida, Enoplometopoidea). Together with the available species in GenBank, the taxon Astacidea is covered with at least one representative for each of the four main subtaxa. Astacidea show unexpectedly diverse genomic organizations. Ten different gene arrangements have been observed in the 28 investigated species. Compared with the decapod ground pattern, a huge inversion, involving more than half of the mitogenome, has been found in four freshwater crayfish species of Astacoidea and convergently in one lobster species. Surprisingly, this inversion can also be observed in the distantly related Priapulida. This multiple convergent evolution suggests a relative ease in the evolution of great similarities in mitochondrial gene order. In addition, a partial or complete loss of the protein-coding gene nad2 has been found in E. occidentalis and H. gammarus but not in Nephrops norvegicus, Homarus americanus and Enoplometopus debelius. A reversal of the strand asymmetry has been found in five astacideans which is supposed to be caused by the inversion of a replication origin in the control region. © 2015 Blackwell Verlag GmbH.

Vossmann S.,Bernhard Nocht Institute for Tropical Medicine | Vossmann S.,LGC Genomics GmbH | Wieseler J.,University of Bonn | Kerber R.,Bernhard Nocht Institute for Tropical Medicine | And 2 more authors.
Journal of Virology | Year: 2015

The flavivirus NS2A protein is involved in the assembly of infectious particles. To further understand its role in this process, a charged-to-alanine scanning analysis was performed on NS2A encoded by an infectious cDNA clone of yellow fever virus (YFV). Fifteen mutants containing single, double, or triple charged-to-alanine changes were tested. Five of them did not produce infectious particles, whereas efficient RNA replication was detectable for two of the five NS2A mutants (R22A-K23A-R24A and R99AE100A- R101A mutants). Prolonged cultivation of transfected cells resulted in the recovery of pseudorevertants. Besides suppressor mutants in NS2A, a compensating second-site mutation in NS3 (D343G) arose for the NS2A R22A-K23A-R24A mutant. We found this NS3 mutation previously to be suppressive for the NS2Aα cleavage site Q189S mutant, also deficient in virion assembly. In this study, the subsequently suggested interaction between NS2A and NS3 was proven by coimmunoprecipitation analyses. Using selectively permeabilized cells, we could demonstrate that the regions encompassing R22A-K23A-R24A and Q189S in NS2A are localized to the cytoplasm, where NS3 is also known to reside. However, the defect in particle production observed for the NS2A R22A-K23A-R24A and Q189S mutants was not due to a defect in physical interaction between NS2A and NS3, as the NS2A mutations did not interrupt NS3 interaction. In fact, a region just upstream of R22-K23-R24 was mapped to be critical for NS2A-NS3 interaction. Taken together, these data support a complex interplay between YFV NS2A and NS3 in virion assembly and identify a basic cluster in the NS2A N terminus to be critical in this process. © 2015, American Society for Microbiology.

PubMed | U.S. Department of Energy, Leibniz Institute DSMZ German Collection of Microorganisms and Cell Cultures, Helmholtz Center for Infection Research, LGC Genomics GmbH and King Abdulaziz University
Type: | Journal: Standards in genomic sciences | Year: 2015

Although Escherichia coli is the most widely studied bacterial model organism and often considered to be the model bacterium per se, its type strain was until now forgotten from microbial genomics. As a part of the G enomic E ncyclopedia of B acteria and A rchaea project, we here describe the features of E. coli DSM 30083(T) together with its genome sequence and annotation as well as novel aspects of its phenotype. The 5,038,133 bp containing genome sequence includes 4,762 protein-coding genes and 175 RNA genes as well as a single plasmid. Affiliation of a set of 250 genome-sequenced E. coli strains, Shigella and outgroup strains to the type strain of E. coli was investigated using digital DNA:DNA-hybridization (dDDH) similarities and differences in genomic G+C content. As in the majority of previous studies, results show Shigella spp. embedded within E. coli and in most cases forming a single subgroup of it. Phylogenomic trees also recover the proposed E. coli phylotypes as monophyla with minor exceptions and place DSM 30083(T) in phylotype B2 with E. coli S88 as its closest neighbor. The widely used lab strain K-12 is not only genomically but also physiologically strongly different from the type strain. The phylotypes do not express a uniform level of character divergence as measured using dDDH, however, thus an alternative arrangement is proposed and discussed in the context of bacterial subspecies. Analyses of the genome sequences of a large number of E. coli strains and of strains from > 100 other bacterial genera indicate a value of 79-80% dDDH as the most promising threshold for delineating subspecies, which in turn suggests the presence of five subspecies within E. coli.

PubMed | LGC Genomics GmbH and SNSB
Type: Journal Article | Journal: PloS one | Year: 2016

The German Barcoding initiatives BFB and GBOL have generated a reference library of more than 16,000 metazoan species, which is now ready for applications concerning next generation molecular biodiversity assessments. To streamline the barcoding process, we have developed a meta-barcoding pipeline: We pre-sorted a single malaise trap sample (obtained during one week in August 2014, southern Germany) into 12 arthropod orders and extracted DNA from pooled individuals of each order separately, in order to facilitate DNA extraction and avoid time consuming single specimen selection. Aliquots of each ordinal-level DNA extract were combined to roughly simulate a DNA extract from a non-sorted malaise sample. Each DNA extract was amplified using four primer sets targeting the CO1-5 fragment. The resulting PCR products (150-400bp) were sequenced separately on an Illumina Mi-SEQ platform, resulting in 1.5 million sequences and 5,500 clusters (coverage 10; CD-HIT-EST, 98%). Using a total of 120,000 DNA barcodes of identified, Central European Hymenoptera, Coleoptera, Diptera, and Lepidoptera downloaded from BOLD we established a reference sequence database for a local CUSTOM BLAST. This allowed us to identify 529 Barcode Index Numbers (BINs) from our sequence clusters derived from pooled Malaise trap samples. We introduce a scoring matrix based on the sequence match percentages of each amplicon in order to gain plausibility for each detected BIN, leading to 390 high score BINs in the sorted samples; whereas 268 of these high score BINs (69%) could be identified in the combined sample. The results indicate that a time consuming presorting process will yield approximately 30% more high score BINs compared to the non-sorted sample in our case. These promising results indicate that a fast, efficient and reliable analysis of next generation data from malaise trap samples can be achieved using this pipeline.

Loading LGC Genomics GmbH collaborators
Loading LGC Genomics GmbH collaborators