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/).
Gonzalez-Recio O.,INIA |
Forni S.,Genus Plc
Genetics Selection Evolution | Year: 2011
Background: Genomic selection has gained much attention and the main goal is to increase the predictive accuracy and the genetic gain in livestock using dense marker information. Most methods dealing with the large p (number of covariates) small n (number of observations) problem have dealt only with continuous traits, but there are many important traits in livestock that are recorded in a discrete fashion (e.g. pregnancy outcome, disease resistance). It is necessary to evaluate alternatives to analyze discrete traits in a genome-wide prediction context. Methods. This study shows two threshold versions of Bayesian regressions (Bayes A and Bayesian LASSO) and two machine learning algorithms (boosting and random forest) to analyze discrete traits in a genome-wide prediction context. These methods were evaluated using simulated and field data to predict yet-to-be observed records. Performances were compared based on the models' predictive ability. Results: The simulation showed that machine learning had some advantages over Bayesian regressions when a small number of QTL regulated the trait under pure additivity. However, differences were small and disappeared with a large number of QTL. Bayesian threshold LASSO and boosting achieved the highest accuracies, whereas Random Forest presented the highest classification performance. Random Forest was the most consistent method in detecting resistant and susceptible animals, phi correlation was up to 81% greater than Bayesian regressions. Random Forest outperformed other methods in correctly classifying resistant and susceptible animals in the two pure swine lines evaluated. Boosting and Bayes A were more accurate with crossbred data. Conclusions: The results of this study suggest that the best method for genome-wide prediction may depend on the genetic basis of the population analyzed. All methods were less accurate at correctly classifying intermediate animals than extreme animals. Among the different alternatives proposed to analyze discrete traits, machine-learning showed some advantages over Bayesian regressions. Boosting with a pseudo Huber loss function showed high accuracy, whereas Random Forest produced more consistent results and an interesting predictive ability. Nonetheless, the best method may be case-dependent and a initial evaluation of different methods is recommended to deal with a particular problem. © 2011 González-Recio and Forni; licensee BioMed Central Ltd.
Rodriguez Martin J.A.,I.N.I.A |
Ramos-Miras J.J.,University of Almeria |
Boluda R.,University of Valencia |
Gil C.,University of Almeria
Geoderma | Year: 2013
This study characterises and compares Cr, Ni, Pb, Cu, Zn and Cd (HMs) contents and the main edaphic parameters in arable soils (AS) from western areas of the Andalusian Autonomous Community (SE Spain) with greenhouse soils (GS) from the province of Almería, one of the most productive agricultural systems in Europe. We explored 199 GS and 142 AS, representing local and regional scales of variation in this important Mediterranean area. The hazardousness of HMs was particularly relevant in GS where agricultural practices, which centre on maximising production, end up with products that finally enter the human food chain directly. Despite their similar edaphic characteristics, the main differences between AS and GS were nutrients and HM contents such as P, K, Cd, Pb and Zn, suggesting the widespread use of agrochemicals in greenhouse farming. Cd concentration in GS tripled that in AS. Here, we conclude that despite anthropogenic HM input, the association patterns of these elements were similar on the two spatial variability scales. Cd, Pb and Zn contents, and partly those of Cu, were related with agricultural practices. On the short spatial scale, grouping these HMs gave very high contents in GS. The associations found with Cr and Ni suggest a lithogenic influence combined with a paedogenic effect on spatial maps; this natural origin input becomes more marked on the long spatial scale represented by AS, where the main Cr and Ni contents were found in the vicinity of Mountain areas not influenced by human activities. © 2013 Elsevier B.V.
Combined treatments of high-pressure with the lactoperoxidase system or lactoferrin on the inactivation of Listeria monocytogenes, Salmonella Enteritidis and Escherichia coli O157: H7 in beef carpaccio
Bravo D.,INIA |
de Alba M.,INIA |
Food Microbiology | Year: 2014
The effect of high hydrostatic pressure (HHP) treatments in combination with the lactoperoxidase system (LPOS) or activated lactoferrin (ALF) on Listeria monocytogenes, Salmonella enterica subsp. enterica serovar Enteritidis and Escherichia coli O157:H7 was investigated in cured beef carpaccio stored at 8°C or 22°C during 7d. HHP (450MPa for 5min) reduced pathogen levels by 1-3 log units and the antimicrobial effect remained during 7d of storage under temperature abuse conditions at 8°C and at 22°C. The individual application of LPOS and ALF did not affect the survival of the three pathogens studied during storage. However, a synergistic bactericidal interaction between LPOS and HHP was observed against S. Enteritidis and E.coli O157:H7. Combined treatments of HHP with LPOS would be useful to reduce the intensity of pressurization treatments diminishing changes in the quality of meat products. © 2014 Elsevier Ltd.
Garcia-Valcarcel A.I.,INIA |
Journal of Separation Science | Year: 2011
A simple and rapid analytical method for the determination of 16 azoles in sewage sludge has been developed and validated. The method was based on ultrasound-assisted extraction followed by dispersive solid-phase extraction cleanup and liquid chromatography-electrospray tandem mass spectrometric detection. The azoles were selected by their intensive usage as biocides (tebuconazole, propiconazole, cyproconazole and thiabendazole), antimycotic pharmaceuticals (ketoconazole, econazole, fluconazole and clotrimazole) or fungicides in agriculture (difenoconazole, flusilazole, hexaconazole, prochloraz, bromuconazole, epoxiconazole and triticonazole). The recoveries of these compounds through the method were between 71.9 and 115.8%, with relative standard deviations lower than 20%. Detection limits were in the range of 0.5-5.0 ng/g. The developed method was applied to the analysis of azoles in sewage sludge samples collected from 19 Spanish wastewater treatment plants. Although azoles used as biocides or agriculture fungicides were present in a few sludge samples, the pharmaceuticals ketoconazole, econazole and clotrimazole were present in all of the analyzed sludge samples, being ketoconazole the one found at the highest level, representing the 68.6% of the total azole content found in the 19 sludge samples studied. © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Genetics, selection, evolution : GSE | Year: 2010
BACKGROUND: The aim of this work was to study the effects on litter size of variants of the porcine genes RBP4, ESR1 and IGF2, currently used in genetic tests for different purposes. Moreover, we investigated a possible effect of the interaction between RBP4-MspI and ESR1-PvuII polymorphisms. The IGF2-intron3-G3072A polymorphism is actually used to select lean growth, but other possible effects of this polymorphism on reproductive traits need to be evaluated. METHODS: Detection of polymorphisms in the genomic and cDNA sequences of RBP4 gene was carried out. RBP4-MspI and IGF2-intron3-G3072A were genotyped in a hyperprolific Chinese-European line (Tai-Zumu) and three new RBP4 polymorphisms were genotyped in different pig breeds. A bivariate animal model was implemented in association analyses considering the number of piglets born alive at early (NBA12) and later parities (NBA3+ ) as different traits. A joint analysis of RBP4-MspI and ESR1-PvuII was performed to test their possible interaction. In the IGF2 analysis, paternal or maternal imprinting effects were also considered. RESULTS: Four different RBP4 haplotypes were detected (TGAC, GGAG, GAAG and GATG) in different pig breeds and wild boars. A significant interaction effect between RBP4-MspI and ESR1-PvuII polymorphisms of 0.61 +/- 0.29 piglets was detected on NBA3+. The IGF2 analysis revealed a significant increase on NBA3+ of 0.74 +/- 0.37 piglets for the paternally inherited allele A. CONCLUSIONS: All the analyzed pig and wild boar populations shared one of the four detected RBP4 haplotypes. This suggests an ancestral origin of the quoted haplotype. The joint use of RBP4-MspI and ESR1-PvuII polymorphisms could be implemented to select for higher prolificacy in the Tai-Zumu line. In this population, the paternal allele IGF2-intron3-3072A increased litter size from the third parity. The non-additive effects on litter size reported here should be tested before implementation in other pig breeding schemes.
Society of Reproduction and Fertility supplement | Year: 2010
Despite apparent progress in reproductive technology as applied to wild ruminants, the success achieved in terms of the number of offspring that become healthy adults has remained low. Difficulties often arise through a lack of knowledge regarding appropriate cryopreservation techniques, and indeed through a lack of detailed information on the reproductive physiology of the species in question. The Spanish ibex (Capra pyrenaica) is a wild caprid found exclusively in the mountains of Iberia; only two of the original four subspecies still exist. Great efforts need to be made to preserve this species. The endocrine and environmental mechanisms that control its seasonal reproduction need to be properly understood, reproductive technologies (particularly the cryopreservation of gametes) optimised, and genetic resource banks developed. The experience obtained with the Spanish ibex may be useful in ex situ conservation strategies designed to preserve other threatened Mediterranean wild ruminants.
Fernandez D.,INIA |
Garcia-Gomez C.,INIA |
Science of the Total Environment | Year: 2013
Zinc oxide nanoparticles (ZnO-NPs) are inevitably released into the environment and are potentially dangerous for aquatic life. However, the potential mechanisms of cytotoxicity of zinc nanoparticles remain unclear. Studying the toxicity of ZnO-NPs with In vitro systems will help to determine their interactions with cellular biomolecules. The aim of this study was to evaluate the cytotoxic potentials of ZnO-NPs in established fish cell lines (RTG-2, RTH-149 and RTL-W1) and compare them with those of bulk ZnO and Zn2+ ions. Membrane function (CFDA-AM assay), mitochondrial function (MTT assay), cell growth (KBP assay), cellular stress (β-galactosidase assay), reductase enzyme activity (AB assay), reactive oxygen species (ROS), total glutathione cellular content (tGSH assay) and glutathione S-transferase (GST) activities were assessed for all cell lines. ZnO-NPs cytotoxicity was greater than those of bulk ZnO and Zn2+. ZnO-NPs induced oxidative stress is dependent on their dose. Low cost tests, such as CFDA-AM, ROS, GST activity and tGSH cell content test that use fish cell lines, may be used to detect oxidative stress and redox status changes. Particle dissolution of the ZnO-NPs did not appear to play an important role in the observed toxicity in this study. © 2013.
De Cara M.A.R.,INIA |
Villanueva B.,INIA |
Toro M.A.,Technical University of Madrid |
Molecular Ecology | Year: 2013
Conservation programmes aim at maximizing the survival probability of populations, by minimizing the loss of genetic diversity, which allows populations to adapt to changes, and controlling inbreeding increases. The best known strategy to achieve these goals is optimizing the contributions of the parents to minimize global coancestry in their offspring. Results on neutral scenarios showed that management based on molecular coancestry could maintain more diversity than management based on genealogical coancestry when a large number of markers were available. However, if the population has deleterious mutations, managing using optimal contributions can lead to a decrease in fitness, especially using molecular coancestry, because both beneficial and harmful alleles are maintained, compromising the long-term viability of the population. We introduce here two strategies to avoid this problem: The first one uses molecular coancestry calculated removing markers with low minor allele frequencies, as they could be linked to selected loci. The second one uses a coancestry based on segments of identity by descent, which measures the proportion of genome segments shared by two individuals because of a common ancestor. We compare these strategies under two contrasting mutational models of fitness effects, one assuming many mutations of small effect and another with few mutations of large effect. Using markers at intermediate frequencies maintains a larger fitness than using all markers, but leads to maintaining less diversity. Using the segment-based coancestry provides a compromise solution between maintaining diversity and fitness, especially when the population has some inbreeding load. © 2013 John Wiley & Sons Ltd.
Journal of applied toxicology : JAT | Year: 2010
High-resolution gas chromatography/mass spectrometry (HRGC/MS) is the standard method for analysing dioxin, furan and polybrominated retardants in hazardous waste. Determination of dioxin-like compounds using in vitro bioassays such as ethoxyresorufin-O-deethylase (EROD) is an important tool to evaluate their Ah receptor-mediated toxic effects, because it detects all arylhydrocarbon receptor ligands in a variety of sample matrices. In the present work, we compared RTG-2 cell line EROD bioassay with HRGC/MS for assessing waste samples (liquid and solid) contaminated with polychlorinated dibenzo-p-dioxins and dibenzofurans, polychlorinated biphenyls (dioxin-like PCBs) and other xenobiotics. For liquid samples, HRGC/MS-toxic equivalent (HRGC/MS-TEQ) values ranged from 273.26 to 5.84 ng TEQ l(-1) and correlated well (correlation coefficient 0.99) with values obtained by EROD-TEQ, which ranged from 128 to 2.5 ng TEQ l(-1). For solid samples, HRGC/MS-TEQ values ranged from 3.44 to 0.49 ng TEQ g(-1) and correlated less well than liquid samples (correlation coefficient 0.64) with values obtained by EROD-TEQ ranging from 2.27 to 0.93 ng TEQ g(-1). The overestimation of RTG-2 EROD-TEQ (1.2 +/- 0.92 of values established by HRGC/MS) and the absence of false-negative results may limit analytical costs by eliminating the need for follow-up GC/MS analysis on the negative samples. We suggest that RTG-2 EROD bioassay is an inexpensive means for preliminary dioxin and furan positive screenings of waste samples. (c) 2010 John Wiley & Sons, Ltd.