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News Article | May 10, 2017
Site: www.thefishsite.com

Nutriad, world leader in feed additive solutions for livestock and aquaculture, announced the appointment of Jeroen De Gussem as Marketing Director and Joke Van De Velde as Marketing Communications Manager. Operating out of the Dendermonde (Belgium) headquarters they will support Nutriad in its’ growth ambitions across geographies and species. Stated Nutriad CEO Erik Visser: “Over the past years Nutriad has significantly grown its’ market share. To ensure we keep on working closely with customers, suppliers and our own technical staff in bringing new concepts to market and reach producers across the world we constantly invest in people. Our marketing department will ensure that our commitment of being big enough to cover the world, yet small enough to care will be applied towards achieving growing customer intimacy.” Jeroen De Gussem holds a master degree in Biotechnology and Laboratory Animal Science. He will finish his MBA this year. Jeroen has extensive experience in animal health and nutrition and most recently was a partner in a company that provided independent research for the veterinary pharmaceutical and nutritional industry. Commented De Gussem: “I have always been intrigued by how science can be applied into workable solutions for producers. Joining Nutriad allows me to focus on just that. Many companies try to enter the feed additives market, but Nutriad truly stands out as their technical excellence has allowed them to introduce concepts that prove themselves in the most challenging circumstances.” Joke Van De Velde was trained as a Marketing and Communication specialist and has worked on brand and content communication in a variety of industries. NUTRIAD, a multinational feed additives producer headquartered in Belgium, delivers products and services to over 80 countries, supported by 4 application laboratories and 5 manufacturing facilities on 3 continents. Find out more at www.nutriad.com


Bras S.,University of Aveiro | Ribeiro L.,Hospital Veterinario Do Porto | Ferreira D.A.,CBIOS | Antunes L.,CITAB | And 2 more authors.
IEEE MeMeA 2014 - IEEE International Symposium on Medical Measurements and Applications, Proceedings | Year: 2014

The development of control and automatic systems is important to guarantee a stable anesthesia, with no under or over dosage, and no awareness episodes. In this study a controller for the Cerebral State Index (CSI - an electroencephalogram derived signal) was developed. This study was a simulation study, the CSI was modeled using a fuzzy logic model with two inputs the effect-site concentration of propofol and the electromyography (EMG). The controller was tested using constant and variable references in an exhaustive set of simulations. The controller developed presents a good performance in all simulations and the controlled variable seems to be influenced by electromyography level. A controller for propofol anesthesia for veterinary use is an important step towards the improvement of animal welfare. The overall aim is to improve animal safety and comfort. © 2014 IEEE.


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. All mouse experiments were approved by the Animal Research Committee, the Norwegian Food Safety Authority (NFDA), and conducted in accordance with the rules and regulations of the Federation of European Laboratory Animal Science Associations (FELASA). C57BL6/CBA mice were housed in IVC SealSafe Plus Greenline cages in an Aero IVC Greenline system at SPF status. They were maintained on a 12 h light, 12 h dark cycle with ad libitum access to food and water. Four- to eight-week-old donors were injected with 5 units of pregnant mare serum gonadotropin (for oocytes and 2-cells at 14:00; for 8-cells at 15:00, 100 μl of 50 I.U. (international units) ml−1 solution) followed by 5 units of human chorionic gonadotropin (hCG) (for oocytes and 2-cells at 11:00; for 8-cells, 15:00, 100 μl of 50 I.U. ml−1 solution) 45 h (for oocytes and 2-cells) or 48 h (for 8-cells) after injection of pregnant mare serum gonadotropin. For 2-cells and 8-cells collection, females were transferred to cages with males for breeding immediately after hCG injections. Donor mice were killed by cervical dislocation 18 h after hCG injection (no mating). Oviducts were transferred to a clean dish with M2 (Sigma) medium. The ampulla was identified under a stereomicroscope, and the oocytes released followed by removal of cumulus mass by room temperature incubation in M2 containing 0.3 mg ml−1 hyaluronidase. The oocytes were further washed in M2. Donor mice were killed by cervical dislocation 45 h after hCG injection. Oviducts were transferred to a clean dish with M2 medium. Infundibulum was identified and the 2-cells were released by placing a syringe containing M2 inside the infundibulum opening, followed by flushing the M2 through the whole oviduct. The 2-cells were further washed in M2 medium. Donor mice were killed by cervical dislocation 68 h after hCG injection/mating. Oviducts were transferred to a clean dish with M2 medium. Infundibulum was identified and the 8-cells were released by placing a syringe containing M2 inside the infundibulum opening, followed flushing the M2 through the whole oviduct. The 8-cells were further washed in M2 medium. The oocytes, 2-cells and 8-cells were transferred to a 150 μl drop of Acidic Tyrode’s solution (Sigma), and further transferred to a drop of M2 immediately after the zona had been removed. 5 steps of washing in M2 were carried out, and the oocytes, 2-cells and 8-cells were ready for fixation. Immature oocytes were isolated from 12-day-old and 15-day-old prepubertal CD-1 mice (RjOrl:SWISS) as follows. Ovaries were removed with fine scissors and carefully freed from surrounding tissues with a 25G needle. Batches of five ovaries were placed in 800 μl DPBS in a 60 mm culture dish, 400 μl of Trypsin-EDTA (0.05%) (Gibco) was added immediately before fine mincing of the ovaries with a scalpel. After mincing, 5 μl of DNase I (10 U μl−1) (Sigma, 04716728001) was added and the minced ovaries were incubated at 37 °C for 20 min. Next, 20 μl of Collagenase Type II (100 mg ml−1) (Sigma, C9407), 800 μl of DBPS and 400 μl of Trypsin-EDTA (0.05%) was added and the dish was incubated for 10 min at 37 °C. Mechanical dissociation with a pipette then resulted in denuded oocytes. To remove any possible traces of somatic contaminants, and to remove the zona, oocytes were washed four times in M2 medium, incubated in two consecutive drops of M2 containing 0.3 mg ml−1 hyaluronidase, washed two times in M2 medium, then in two drops of Acidic Thyrode’s solution (Sigma) and again washed four times in M2 medium. Batches of oocytes to be analysed for DNA methylation were washed once in WGBS lysis solution (20 mM Tris-HCl, 20 mM KCl, 2 mM EDTA), transferred in a volume of maximum 5 μl to a 1.5 ml tube, snap-frozen in liquid nitrogen and stored at −80 °C before further processing. Batches of oocytes for ChIP−seq were treated as described below. Mouse embryos were immunostained using an adapted protocol from ref. 31 in 96-well plates. Briefly, embryos were subjected to thinning of the zona pellucida using acidic DPBS (pH 2.5), and fixation in 2% paraformaldehyde for 30 min. Embryos were permeabilized in 0.3% BSA, 0.1% Triton X-100, 0.02% NaN PBS solution. Blocking was carried out in 0.3% BSA, 0.01% Tween-20, and 0.02% NaN in PBS. Embryos were incubated in blocking solution with 1:200 H3K4me3 antibody (Merck Millipore, 04-745) for 60 min at room temperature. After further blocking, embryos were finally incubated with goat anti-rabbit Alex Fluor 488 (Invitrogen, A-11008) or Alexa Fluor 568 for morpholino injected embryos (Invitrogen, A-21069) at 1:200 dilution and placed on a slide in SlowFade Gold with DAPI (Invitrogen). Quantitative measurements of H3K4me3 were obtained using a Zeiss Axio Observer epi-fluorescence microscope with a Coolsnap HQ2 camera. Confocal images were obtained with a Zeiss Axio Observer LSM 710 confocal microscope. Images were processed and quantified in Axiovision and ImageJ software. Isolated zygotes were injected at 0.5 dpc (days post coitum) with either fluorescein-tagged morpholino oligonucleotides (Gene-tools) targeted at Kdm5a (5'-TGACGGCCACCAAAGCCCTCTCA-3') and Kdm5b (5'-AGCACAGGGCAGGCTCCGCAACC-3') or five base mismatch control morpholinos for Kdm5a (5'-TGAaGGaCACaAAAcCCCTaTCA-3') and Kdm5b (5'-AcCAaAGGGaAGGaTCCGaAACC-3'). Embryos were cultured in G1 plus media (Vitrolife) until late 2-cell (35 h after hCG) and fixed in 2% PFA. Embryos were further treated as described above. Lysates from 132 two-cell embryos were prepared adding lysis buffer (20 mM Tris-HCl, pH 7.4, 20% glycerol, 0.5% NP40, 1 mM MgCl , 0.150 M NaCl, 1 mM EDTA, 1 mM EGTA, 1 mM DTT, and 1 mM PMSF, 1× PIC, 1% SDS) to a final volume of 7 μl. Embryos were lysed on ice for 30 min with occasional vortexing and spinning. Embryos were vortexed and spun down at the end and were frozen on dry ice. Samples were subsequently thawed, sonicated on ultrasound bath for 1 min and centrifuged at 16,000g for 10 min followed by transfer of 5 μl supernatant to a new tube. The simple western immunoblots were performed on a PeggySue (ProteinSimple) using the Size Separation Master Kit with Split Buffer (12–230 kDa) according to the manufacturer’s standard instruction, using the following abtibodies: anti-Kdm5A (CellSignalling, 3876), anti-Kdm5b (Abcam, 181089) and anti-β-actin (Abcam, ab8227). The Compass software (ProteinSimple, version 2.7.1) was used to program the PeggySue-robot and for presentation (and quantification) of the western Immunoblots. Output data was displayed from the software-calculated average of seven exposures (5–480 s). Human NCCIT pluripotent embryonal carcinoma cell line was obtained from ATCC (CRL-2073), and cultured according to ATCC specifications. Mouse E14 ES cells were obtained from a stock at passage P2 equal to what was used for the mouse ENCODE project, and cultured according to that specified by the mouse ENCODE project (https://www.encodeproject.org/biosamples/ENCBS171HGC/). Cell lines were validated by ChIP–seq confirming species and a highly conserved profile. Cell lines were never passaged passed passage 15 for the work described here. Mycoplasma testing was carried out on a regular basis and both of the cell lines were free for Mycoplasma. Cross-linking of oocytes, 2-cell or 8-cell embryos. We added 50 μl M2 medium to a 0.6-ml tube. Embryos were then added and let settle to the bottom. Volume was controlled by eye by comparing to another 0.6-ml tube with 50 μl M2 medium and adjusted with mouth pipette to 50 μl. 50 μl of PBS with 2% formaldehyde was added to get a 1% final concentration and vortexed carefully, incubated at room temperature for 8 min, and vortexed once more. 12 μl of 1.25 M glycine stock (final concentration 125 mM) was added, mixed by gentle vortexing, incubated for 5 min at room temperature, and vortexed once during the incubation step. This was centrifuged at 700g for 10 min at 4 °C in a swinging-bucket rotor with soft deceleration settings and washed twice with 400 μl ice-cold PBS. A volume of 10 μl was left after the last wash, snap-frozen in liquid nitrogen and stored at −80 °C. Binding of antibodies to paramagnetic beads. The stock of paramagnetic Dynabeads Protein A was vortexed thoroughly to ensure the suspension was homogenous before pipetting. 100 μl of Dynabeads stock solution was transferred into a 1.5-ml tube, which was placed in a magnetic rack and the beads captured on the tube wall. The buffer was discarded, and the beads washed twice in 500 μl of RIPA buffer (10 mM Tris-HCl pH 8.0, 140 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 1% Triton X-100, 0.1% SDS, 0.1% Na-deoxycholate) and resuspended in RIPA buffer to a final volume of 100 μl. 96 μl of RIPA buffer was aliquoted into 200-μl PCR tubes on ice, the washed beads were vortexed thoroughly, and 2 μl of bead suspension and 2 μl of either antibody against H3K4me3 (Merck Millipore, 04-745) or to H3K27ac (Active Motif, catalogue number AM39133) was added to each of the 200 μl PCR tubes. This was then incubated at 40 r.p.m. on a ‘head-over-tail’ tube rotator for at least 4 h at 4 °C. Chromatin preparation. The desired number of cross-linked and frozen pools of embryos was removed from −80 °C storage and placed on dry ice in an insulated box (for example, four tubes with a total number of 1,000 2-cell embryos). 10 minutes of cross-linking was carried out during thawing as follows: one tube was moved at the time from dry-ice to ice for 5 s, and any frozen droplets quick pelleted by a brief spin in a mini-centrifuge. 100 μl of 1.1% formaldehyde solution was added (PBS with 1 mM EDTA, 1.1% formaldehyde, 20 mM sodium butyrate, 1 mM PMSF and protease inhibitor cocktail). The tubes were incubated for 10 min at room temperature and vortexed gently twice. 7 μl was added of 2.5 M glycine, vortexed gently and incubated for 5 min before the tube was moved to ice. Tubes were centrifuged at 750g for 10 min at 4 °C in a swinging-bucket rotor with soft deceleration settings, then washed twice with 400 μl PBS with 1 mM EDTA, 20 mM sodium butyrate, 1 mM PMSF and protease inhibitor cocktail. A volume of 10 μl was kept after the last wash. For four tubes, a total of 120 μl of 0.8% SDS lysis buffer with 20 mM sodium butyrate, 1 mM PMSF and protease inhibitor cocktail was used. First 60 μl, then 2 × 30 μl was used for two consecutive rounds of washing through the four tubes by pipetting. The same tip was used and the entire volume (160 μl) left in the last of the four tubes. The sample was sonicated for 5 × 30 s using a UP100H Ultrasonic Processor (Hielscher) fitted with a 2-mm probe. We allowed 30 s pauses on ice between each 30 s session, using pulse settings with 0.5 s cycles and 27% power. 170 μl RIPA Dilution buffer (10 mM Tris-HCl pH 8.0, 175 mM NaCl, 1 mM EDTA, 0.625 mM EGTA, 1.25% Triton X-100, 0.125% Na-deoxycholate, 20 mM sodium butyrate, 1 mM PMSF and protease inhibitor cocktail) was added. The sample was centrifuged at 12,000g in a swinging-bucket rotor for 10 min at 4 °C and the supernatant transferred to a 1.5-ml tube. 200 μl of RIPA Dilution buffer was added to the pellet and sonicated 3 × 30 s. The sample was centrifuged at 12,000g in a swinging-bucket rotor for 8 min, then the supernatant was removed and mixed well with the first supernatant, resulting in a total volume of about 530 μl of ChIP-ready chromatin. Immunoprecipitation and washes. Pre-incubated antibody–bead complexes were washed twice in 130 μl RIPA buffer by vortexing roughly. The tubes were centrifuged in a mini-centrifuge to bring down any solution trapped in the lid and antibody–bead complexes were captured in a magnetic rack cooled on ice. 250 μl of chromatin was added to each of anti H3K4me3 or H3K27ac reactions, and 25 μl kept for input control. 2 μl of cross-linked recombinant histone octameres and 1.25 μg of non-immunized rabbit IgG was immediately added to ChIP reactions, then incubated at 4 °C, 40 r.p.m. on a ‘head-over-tail’ rotator for 30 h. The chromatin–antibody–bead complexes were washed four times in 100 μl ice-cold RIPA buffer. The concentration of SDS and NaCl was titrated for each antibody to find optimal conditions for maximized signal-to-noise ratio. For H3K4me3, we washed 1× RIPA buffer with 0.2% SDS and 300 mM NaCl, 1× RIPA buffer with 0.23% SDS and 300 mM NaCl followed by 2× RIPA buffer with 0.2% SDS and 300 mM NaCl. For H3K27ac, we washed 4× RIPA buffer with 0.1% SDS and 140 mM NaCl. Each wash involved rough vortexing on full speed, repeated twice with pauses on ice in between. Next, a wash in 1 × 100 μl TE and tube shift was carried out as previously described32, 33. DNA isolation and purification. We removed TE and added 150 μl ChIP elution buffer (20 mM Tris-HCl pH 7.5, 50 mM NaCl, 5 mM EDTA. 1% SDS, 30 μg RNase A) and incubated at 37 °C, 1 h at 1,200 r.p.m. on a Thermomixer. 1 μl of Proteinase K (20 mg ml−1 stock) was added to each tube and incubated at 68 °C, 4 h at 1,250 r.p.m. Eluate was transferred to a 1.5-ml tube. A second elution with 150 μl was performed for 5 min and pooled with the first supernatant. ChIP DNA was purified by phenol-chloroform isoamylalcohol extraction, ethanol-precipitated with 10 μl acrylamide carrier as described previously32, 33 and dissolved in 10 μl EB (10 mM Tris-HCl). Library preparation and sequencing. ChIP and input library preparations were carried out according to the ThruPLEX (Rubicon Genomics) procedure with some modifications, including increased incubation times for the library purification and size selection. 12 ChIP libraries were pooled before AMPure XP purification and allowed to bind for 10 min after extensive mixing. Increased elution time, thorough mixing and the use of a strong neodymium bar magnet allowed for high recovery in elution volumes of 25 μl buffer EB. Sequencing procedures were carried out as described previously according to Illumina protocols with minor modifications (Illumina,). We sequenced all P12, P15, oocyte, 2-cell, and 8-cell ChIP–seq libraries as paired-end and all NCCIT ChIP–seq libraries as single-end. Mouse ES cell ChIP–seq libraries were sequenced as paired-end and single-end and the results were combined after mapping for analysis. Single-end and paired-end library information for all samples have been deposited in the GEO database (GSE72784). Sequence read alignment. We aligned single- and paired-end μChIP–seq reads from H3K27ac and H3K4me3 experiments to the mm10 reference genome by using BWA-mem34. For human ChIP–seq samples performed with human NCCIT cells, we aligned reads to the hg19 reference genome using BWA-mem. Unmapped and non-uniquely mapped reads were removed. We also removed PCR duplicate reads with Picard. H3K4me3 ChIP–seq data for heart, liver, and cerebellum were downloaded from the mouse ENCODE project26. H3K4me3 ChIP–seq data for sperm was downloaded from GEO database under accession number GSE42629 (ref. 23). Culture and collection of embryos. 2-cell stage embryos (2 × 25 embryos) were transferred to 350 μl of Buffer RLT (QIAgen RNaseay) (including β-Me according to manufacturer’s description) in a 1.5 ml low-binding tube and snap-frozen in liquid nitrogen and stored at −80 °C. Zygotes were cultured in M16 medium. For α-amanitin treatment the medium contained 10 μg ml−1 α-amanitin (Sigma, A2263). RNA extraction with QIAGEN RNeasy Micro Kit. RNAs were extracted by following QIAGEN RNeasy Micro handbook. Briefly, 25 embryos were disrupted by addition of buffer RLT followed by homogenization of the lysate. One volume of 70% EtOH was added to the lysate, transferred to an RNeasy MinElute spin column, and centrifuged for 15 s. Next, 350 μl of buffer RW1 from QIAGEN RNeasy Micro Kit was added to wash the RNeasy MinElute spin column by 15 s centrifugation. 10 μl DNase I mix was added to the RNeasy MinElute spin column membrane (10 μl DNase + 70 μl buffer RDD) and incubated at room temperature for 15 min. The spin column membrane was washed twice with 500 μl buffer RPE from the QIAGEN RNeasy Micro Kit followed by 500 μl of 80% EtOH. 14 μl RNase-free water was added directly to the centre of the spin column membrane and centrifuged to elute RNA. RNA amplification with NuGEN Ovation RNA-seq system V2. Extracted RNA was amplified by following the NuGEN ovation RNA-seq handbook. Briefly, step 1 was first-strand cDNA synthesis. 2 μl of First Strand Primer Mix from NuGEN Ovation RNA-seq system V2 was added to a PCR tube followed by addition of 5 μl of total RNA sample, and the thermal cycler for primer annealing was run (65 °C for 2 min, held at 4 °C). 3 μl of the First Strand Master Mix from NuGEN Ovation RNA-seq system V2 was added to each tube and thermal cycler for first strand synthesis was run (4 °C for 1 min; 25 °C for 10 min; 42 °C for 10 min, 70 °C for 15 min, held at 4 °C). Step 2 was second-strand cDNA synthesis. 10 μl of the second-strand mix from NuGEN Ovation RNA-seq system V2 was added to each first-strand reaction tube and the thermal cycler was run (4 °C for 1 min; 25 °C for 10 min; 50 °C for 30 min; 80 °C for 20 min; hold at 4 °C). Step 3 was purification of cDNA with Agencourt RNAClean XP beads. Step 4 was SPIA amplification. 40 μl of the SPIA master Mix from NuGEN Ovation RNA-seq system V2 was added to each tube containing the double-stranded cDNA bound to the Agencourt RNAClean XP beads, and the thermal cycler was run to amplify double-stranded cDNA (4 °C for 1 min; 47 °C for 60 min; 80 °C for 20 min, held at 4 °C). The tubes were transferred to the magnet and 40 μl of the supernatant containing the SPIA cDNA was transferred to a new tube, followed by SPIA cDNA purification with QIAGEN MinElute reaction cleanup kit. Library preparation and sequencing. The volume and concentration of purified SPIA cDNA to 500 ng in 100 μl and sonicated SPIA cDNA with Covaris M220 ultrasonicator was adjusted to 400 bp DNA fragment size. Library preparation was carried out according to TruSeq library preparation. Sequencing procedures were carried out as described previously according to Illumina HiSeq2500 protocols with minor modifications (Illumina). DNA methylation libraries of growing oocytes obtained from day 12 (P12) and day 15 (P15) mice were constructed with a modified library protocol from ref. 2. Briefly, 100-500 embryos were lysed in 5 μl lysis buffer (20 mM Tris, 2 mM EDTA, 20 mM KCl, 1 mg ml−1 proteinase K (QIAGEN)) for 1.5 h at 56 °C. followed by heat-inactivation for 30 min at 75 °C. 45 μl nuclease-free water and 0.5% Lamda DNA (Promega) spike-in was added into the lysate. DNA was fragmented with Covaris M220 ultrasonicator and incubated at 37 °C to reduce volume to 30 μl. The fragmented DNA was end-repaired by incubating with 5 μl end-repair enzyme mixture (3.5μl T4 DNA ligase buffer (NEB), 0.35 μl 10 mM dNTP, 1.15 μl NEBNext End Repair Enzyme Mix (NEB)) for 30 min at 20 °C, followed by heat-inactivation for 30 min at 75 °C. Then, 5 μl of dA-tailing mixture (0.5 μl T4 DNA ligase buffer, 1 μl Klenow exo- (NEB), 0.5 μl 100 mM dATP and 3 μl nuclease free water) was added and incubated for 30 min at 37 °C, followed by heat-inactivation for 30 min at 75 °C. Finally, 10 μl ligation mixture (1 μl T4 DNA ligase buffer, 0.5 μl 100 mM ATP, 1.5 μl 50 mM cytosine methylated Illumina adaptor, 2 μl T4 DNA ligase (NEB) and 5 μl nuclease-free water) was added and incubated at 16 °C overnight. 100 ng Carrier RNA (Ambion) was added into the tube. Bisulfite conversion reaction was performed with the EZ DNA methylation-Gold Kit (Zymo Research) according to the manufacturer’s instructions. The purified DNA was then amplified with 6 cycles PCR by using KAPA HiFi HotStart Uracil+ DNA polymerase (KAPA). Amplified DNA was purified with Ampure XP beads (Beckman) to discard the short fragments and adaptor-self ligations. Then, another round of 6–8 cycles of PCR was performed to obtain sufficient molecules for sequencing. Sequencing procedures were carried out as described previously according to Illumina HiSeq2500 protocols with minor modifications (Illumina). Reads were trimmed by Trimmomatic35 with default parameters to remove the reads containing adapters and showing low quality. Trimmed reads were aligned by using Bismark (V12.5)36 Bisulfite Mapper against the mouse reference genome mm10 with parameters: -N 1 –score_min L,0,-0.6. Duplicate reads were removed with Picard after splitting aligned reads into Watson and Crick strands. CpG methylation level was extracted with Samtools mpileup. Strands were merged to calculate the CpG methylation level per dinucleotide CpG site. Methylation level was calculated for each site spanned by at least 4 reads. During RNA-seq data analysis we used GENCODE gene annotation v3. We considered all level 1 and 2 genes and included level 3 protein-coding genes. To define gene expression levels, mouse oocytes, 2-cell, and 8-cell stage embryos RNA-seq data sets were downloaded from the GEO database with accession number GSE44183 (ref. 8). Mouse ES cell RNA-seq data were downloaded from GEO database with accession number GSE39619. RNA-seq reads were aligned to the mm10 reference genome using BWA-mem. Unmapped and non-uniquely mapped reads were removed. Gene expression values were obtained based on GENCODE annotation v3 and normalized to fragments per kilobase of transcript per million mapped (FPKM) values using Cufflinks37. Whole-genome bisulfite sequencing (WGBS) data from sperm was obtained from GEO database with accession number GSE56697 (ref. 2). Oocyte DNA methylation data was obtained from GEO database under accession number GSE56879 (ref. 19). We combined all data from 12 individual MII oocytes and the bulk oocyte sample. Deeply sequenced results were used for MII oocyte with number 2 and 5. WGBS data for GVO and NGO stage oocytes were obtained through personal communication with the authors22. We performed broad peak calling for H3Kme3 in oocytes based on MACS2 broad peak calling algorithm with default parameters (–format = BAM -g mm -m 5 50 -p 1e-5 –broad) followed by combining adjacent peaks within 5 kb. We determined the optimal distance to combine adjacent peaks on the basis of the number of broad H3K4me3 domains at varying distance thresholds. At 5 kb distance threshold, the number of broad domains became stable as shown in Extended Data Fig. 5b. On the basis of the location of transcription start sites (GENCODE v3), we classified broad H3K4me3 domains into two groups as TSS-containing and non-TSS-containing domains. The basic idea of RPKM values is to calculate relative ChIP signal enrichment for a given genomic region compared to the entire genome to normalize different sequencing depth between samples. This approach is reasonable when the total amount of ChIP DNA is similar between samples, and in general the fraction of genomic regions covered by each histone modification mark is similar between samples such as that H3K4me3 marks around 1–3% of the human genome in cells/tissues assessed to date. Therefore by using RPKM values, one can simply avoid a potential bias caused by different sequencing depth. However, if a sample shows an extraordinary ChIP signal distribution, the sample with much larger genomic regions covered with, for example, H3K4me3 tends to show relatively lower RPKM values owing to the large amount of total ChIP signal. In oocytes, we observed such notably broadly distributed H3K4me3 signals, resulting in lower RPKM values than other samples when we consider the top-ranked promoter regions in terms of H3K4me3 signal (Extended Data Fig. 5d). In this regard, to compare H3K4me3 signals fairly between samples, we need to adjust H3K4me3 RPKM values in each cell type. In order to adjust H3K4me3 RPKM values between samples, we used the top-5,000 ranked promoters in terms of H3K4me3 level as internal control regions during H3K4me3 normalization. We calculated H3K4me3-adjustment scaling factors on the basis of the H3K4me3 ChIP signals at the top 5,000 ranked promoters, with the assumption that the promoters with the highest H3K4me3 levels in each cell type represent fully H3K4me3-modified promoters and have similar H3K4me3 signal levels. In support of this assumption, all oocyte and embryo samples represent highly homogenous cell populations, thus it is plausible that most or all cells carry the H3K4me3 mark at the cell-type-specific top-ranked promoters. Furthermore, ChIP conditions were kept the same for all samples. As expected, we observed very similar H3K4me3 signals between samples exhibiting only canonical H3K4me3 patterns when we consider the same number of top-ranked promoters (Extended Data Fig. 5e). On the basis of this observation, we calculated H3K4me3 RPKM adjustment scaling factors for different numbers of the top-ranked promoters (Extended Data Fig. 5f). The scaling factors were calculated by dividing median H3K4me3 RPKM values at the top-ranked promoters in each sample by median H3K4me3 RPKM value at the top-ranked promoters in mES cells. Importantly, the scaling factors are very robust regardless of the number of promoters analysed, indicating that there is a systematic bias caused by different genomic coverage of H3K4me3. Indeed, the adjustment scaling factors are supported by the qPCR-quantified amount of ChIP DNA that is precipitated in each experiment (Supplementary Table 2). Therefore, in this study, we defined the scaling factors based on the top 5,000 most highly ranked promoters. We downloaded a list of maternally expressed genes from GEO database under accession number GSE45719 (ref. 7). We excluded all genes expressed in oocytes to allow us to distinguish maternally expressed genes in the early embryo. We considered genes with less than 0.3 FPKM values as not expressed. On the basis of the extracted genes, we tested whether maternally expressed genes are enriched within broad H3K4me3 domains. The number of genes expected by chance was calculated on the basis of the fraction of all genes located within broad H3K4me3 domains. The significance of enrichment of maternally expressed genes was calculated by Fisher-exact tests. P values were 1.9−8, 2.9−9, 1.6−4, 8.2−6, 2.8−4, 8.0−4 for zygote, early 2-cell-, mid 2-cell-, late 2-cell-, 4-cell-, and 8-cell-stage embryos, respectively. We used a predefined ZGA gene list obtained from a previous study5. The list of oocyte-specific genes (denoted as maternal RNA) was also obtained from the same study after excluding any genes showing less than 0.3 FPKM values in oocytes. Visualization and preceding analysis was done using EaSeq and its integrated tools38. Heat maps were generated using the ‘HeatMap’ tool, and superimposed tracks were generated using the ‘FillTrack’ tool. Data were imported using default settings and all values were normalized to FPKM and scaled as described above (see ‘An adjustment of H3K4me3 RPKM values’). Distances from and orientation of each TSS to the nearest domain centre were calculated using the ‘Colocalize’ tool, and the ‘Sort’ tool was used to order the TSS in the heat maps according to these distances or for ordering heat maps according to domain size. We only considered broad H3K4me3 domains that span more than 5 kbp DNA to avoid any overlapping information at domain boundaries between 5′ and 3′ends of domains. Clustering of domain boundaries was carried out by: (1) quantifying normalized and scaled H3K4me3 RPKM values for P12, P15, and oocyte samples at a set of regions corresponding to the most proximal 2 kbp within the boundary and average DNA methylation frequency for NGO, P12, P15, GVO and oocyte samples at a set of regions corresponding to the most proximal 2 kbp outside of the domain boundaries using the EaSeq (ref. 38) ‘Quantify’-tool (settings: ‘Start = Center, offset = -1000, Fixed width’, ‘End = Center, offset = 1000, Fixed width’, ‘Normalize to reads pr. million, checked’, ‘Normalize to signal size of, unchecked’, ‘Normalized counts to fragments, checked’, ‘Present values as Z-scores, unchecked’); then (2) clustering the boundaries based on this quantified signal using EaSeq’s ‘ClusterP’-tool (settings: ‘Log-Transform, unchecked’, ‘Normalize parameters to average signal’, ‘k-means clustering, checked’, ‘k = 10’, ‘g = 0’). The order of the clusters was changed manually. Distances from each boundary to nearest CGI were calculated using a set of CGIs downloaded from the UCSC table browser and ‘Colocalize’-tool. We predicted distal cis-regulatory elements on the basis of H3K27ac μChIP–seq results. We combined two biological replicates for each cell type and called H3K27ac peaks using MACS2 with the following parameters (–format = BAM -g mm -m 5 50 -p 1e-5). To directly compare the activity of distal cis-regulatory elements between cell types, we defined putative distal cis-regulatory elements by combining all H3K27ac peaks from oocytes, 2-cell and 8-cell embryos and ES cells after excluding chrY, chrM, and any peaks within 2.5 kb from known transcription start sites (GENCODE v3). The activity for each distal cis-regulatory element in each cell type was defined by taking the log ratio between H3K27ac ChIP–seq and input RPKM values. On the basis of the activity of distal cis-regulatory elements, we performed k-means clustering. 20 clusters were defined with Euclidian distance metric followed by reordering clusters manually. On the basis of the clustered patterns, we identified stage-restricted distal cis-regulatory elements. We defined nearby genes of each cRE when the distance between gene TSS and each distal cRE is less than 15 kb. Similarly, in order to define nearby distal cREs for ZGA genes, we combined all distal cREs within 15 kb from each ZGA gene TSS. We used HOMER to find enriched transcription factor motif sequences in distal cREs for each developmental stage. We also performed GREAT39 analysis for each class of stage-restricted distal cREs using the settings ‘single nearest gene’, ‘within 300 kb’ of the enriched H3K27ac region, and no curated regions. In order to identify genes with a certain transcription factors in nearby cREs, we carried out STORM40 motif search with –f –t 0.9 parameters for nearby cREs within 15 kb from each TSS. Each transcription factor motif position weight matrix was obtained from HOMER motif search41 results. The genes with a certain transcription factor in nearby cREs were called if any cREs within 15 kb from the TSS matched with the corresponding transcription factor motif sequence. We called downregulated genes between Kdm5a and Kdm5b MO injected and control MO injected 2-cell embryos when gene FPKM values were 1.5-fold or more reduced in both of the two biological replicates. Additionally, we called experimental stage specific ZGA genes when gene FPKM values were twofold or more reduced in α-amanitin treated embryos as compared to control MO injected embryos. The rational for identifying the experimental stage-specific ZGA genes comes from the observation that the composition of the transcriptome changes dramatically and rapidly during the 2-cell stage7. Although α-amanitin-treated embryos blocked polymerase II transcription from the early 1-cell stage onwards, de novo transcription-independent degradation of maternal RNA may still occur. Therefore, they provide a well-suited control for defining the experimental stage-specific ZGA genes when compared to the control morpholino-injected embryos. As a result, we identified 7,132 putative experimental stage-specific ZGA genes and these genes are significantly overlapped with the ZGA gene list obtained from a previous study5 (hypergeometric P value is 0). KDM5A- and KDM5B-depleted embryos showed that 1,303 ZGA genes are downregulated among 7,132 experimental stage-specific ZGA genes, whereas 980 non-ZGA genes are downregulated among 25,155 genes. We visualized ChIP–seq and RNA-seq data on the basis of raw read depth after converting aligned bam files to wig files using genomeCoverageBed and wigTobigWig utilities.


Sparrowe J.,Laboratory Animal Science | Jimenez L.,Laboratory Animal Science | Talavante A.,Laboratory Animal Science | Angulo I.,Malaria Therapeutic Efficacy | Martinez A.,Laboratory Animal Science
Scandinavian Journal of Laboratory Animal Science | Year: 2015

The NSG strain is one of the most immunodeficient mice available, and provides an effective model for studies where the engraftment of human cells is required. However because of their severe adaptive and innate immune deficiency, these mice must be kept under high environmental control standards. Routine health monitoring, according to FELASA guidelines, included antigen testing in NSG animals and antibody testing in sentinel animals (negative results, not shown). We found saprophytic flora, by use of classic microbiology techniques, in a variety of tissues and organs. Many of the sampled animals were found to have bacteria growing in the spinal cord, tarsal joint, heart, spleen, liver, kidney or blood. In order to rule out possible tissue contamination, we sent samples to three different external laboratories. All the samples submitted came back with the same results. We postulate that the widespread presence of these saprophytic bacteria may be due to a lack of IgA secretion at the mucosal epithelium, and the bacterial growth in these tissues and organs to the immunodeficiency including impaired macrophage activity. The potential clinical significance of these bacteria in NSG mice, if any, is not known. To explore the possible connection between the bacterial infection and animals with signs of slight limb paresthesia (numbness) and paralysis, and arthritis, seen in 0.5-1% of animals, further studies are needed. © 2015 Swedish Research Council. All Rights Reserved.


Shah V.D.,Drug Metabolism and Pharmacokinetics | Walton B.J.,Laboratory Animal science | Culp A.G.,Drug Metabolism and Pharmacokinetics | Castellino S.,Drug Metabolism and Pharmacokinetics
Journal of the American Association for Laboratory Animal Science | Year: 2015

During the acclimation phase of a preclinical safety study involving Syrian golden hamsters, some of the cages of treatment-naïve animals were noted to contain blue-tinged bedding; the urine of these hamsters was not discolored. We sought to understand the underlying cause of this unusual finding to ensure that the study animals were healthy and free from factors that might confound the interpretation of the study. Analysis of extracts from the blue bedding by using HPLC with inline UV detection and high-resolution mass spectrometry indicated that the color was due to the presence of indigo blue. Furthermore, the indigo blue likely was formed through a series of biochemical events initiated by the intestinal metabolism of tryptophan to an indoxyl metabolite. We offer 2 hypotheses regarding the fate of the indoxyl metabolite: indigo blue formation through oxidative coupling in the liver or through urinary bacterial metabolism.


Salomons A.R.,Laboratory Animal Science | Salomons A.R.,Rudolf Magnus Institute of Neuroscience | Kortleve T.,Laboratory Animal Science | Reinders N.R.,Laboratory Animal Science | And 7 more authors.
Behavioural Brain Research | Year: 2010

When anxiety-related behaviour in animals appears to lack adaptive value, it might be defined as pathological. Adaptive behaviour can be assessed for example by changes in behavioural responses over time, i.e. habituation. Thus, non-adaptive anxiety would be reflected by a lack of habituation. Recently, we found that 129P3/J mice are characterised by non-adaptive avoidance behaviour after repeated test exposure. The present study was aimed at investigating the sensitivity of the behavioural profile of these animals to exposure to a chronic mild stress (CMS) paradigm followed by repeated exposure to the modified hole board test. If the behavioural profile of 129P3/J mice mirrors pathological anxiety, their behavioural habituation under repeated test exposure conditions should be affected by CMS treatment. The results confirm the profound lack of habituation with respect to anxiety-related behaviour in both control and CMS treated mice. Additionally, CMS treated animals revealed a lower exploratory behaviour, reduced locomotor activity and increased arousal-related behaviour over time when compared to control individuals, proving an extension of their impaired habituation behaviour. Although no effects of CMS treatment on plasma corticosterone levels were found, higher immediate early gene expression in the bed nucleus of the stria terminalis and the ventrolateral periaqueductal grey in CMS treated mice indicated that 129P3/J mice are susceptible to the negative effects of CMS treatment at both the behavioural and the functional level. These results support the hypothesis that 129P3/J mice might be an interesting model for pathological anxiety. © 2010 Elsevier B.V. All rights reserved.


Morgan L.A.,Glaxosmithkline | Olzinski A.R.,Glaxosmithkline | Upson J.J.,Glaxosmithkline | Zhao S.,Laboratory Animal science | And 8 more authors.
Journal of Cardiovascular Pharmacology | Year: 2013

ABSTRACT:: Epoxyeicosatrienoic acids, substrates for soluble epoxide hydrolase (sEH), exhibit vasodilatory and antihypertrophic activities. Inhibitors of sEH might therefore hold promise as heart failure therapeutics. We examined the ability of sEH inhibitors GSK2188931 and GSK2256294 to modulate cardiac hypertrophy, fibrosis, and function after transverse aortic constriction (TAC) in rats and mice. GSK2188931 administration was initiated in rats 1 day before TAC, whereas GSK2256294 treatment was initiated in mice 2 weeks after TAC. Four weeks later, cardiovascular function was assessed, plasma was collected for drug and sEH biomarker concentrations, and left ventricle was isolated for messenger RNA and histological analyses. In rats, although GSK2188931 prevented TAC-mediated increases in certain genes associated with hypertrophy and fibrosis (α-skeletal actin and connective tissue growth factor), the compound failed to attenuate TAC-induced increases in left ventricle mass, posterior wall thickness, end-diastolic volume and pressure, and perivascular fibrosis. Similarly, in mice, GSK2256294 did not reverse cardiac remodeling or systolic dysfunction induced by TAC. Both compounds increased the sEH substrate/product (leukotoxin/leukotoxin diol) ratio, indicating sEH inhibition. In summary, sEH inhibition does not prevent cardiac remodeling or dysfunction after TAC. Thus, targeting sEH seems to be insufficient for reducing pressure overload hypertrophy. Copyright © 2013 by Lippincott Williams & Wilkins.


PubMed | Drug Metabolism and Pharmacokinetics and Laboratory Animal science
Type: Journal Article | Journal: Journal of the American Association for Laboratory Animal Science : JAALAS | Year: 2015

During the acclimation phase of a preclinical safety study involving Syrian golden hamsters, some of the cages of treatment-nave animals were noted to contain blue-tinged bedding; the urine of these hamsters was not discolored. We sought to understand the underlying cause of this unusual finding to ensure that the study animals were healthy and free from factors that might confound the interpretation of the study. Analysis of extracts from the blue bedding by using HPLC with inline UV detection and high-resolution mass spectrometry indicated that the color was due to the presence of indigo blue. Furthermore, the indigo blue likely was formed through a series of biochemical events initiated by the intestinal metabolism of tryptophan to an indoxyl metabolite. We offer 2 hypotheses regarding the fate of the indoxyl metabolite: indigo blue formation through oxidative coupling in the liver or through urinary bacterial metabolism.


News Article | February 15, 2017
Site: www.prweb.com

LabRoots, the leading provider of interactive virtual events for tech innovators, engineers, and scientists from around the world is pleased to announce the 6th annual, two-day international, Laboratory Animal Science (LAS) virtual conference which will take place on February 8th and 9th, 2017. This premier, online-only conference focused on laboratory animal science. This year’s Program Committee, led by Dr. Szczepan Baran, Global Head of Animal Welfare and Compliance Training at Novartis Pharmaceuticals, and composed of over 20 world renowned experts, decided on the theme of this conference to be Advances and Challenges in Laboratory Animal Science in 2017 with the following tracks: The LAS virtual conference will bring together scientists, students, veterinary technicians, veterinarians, technical support staff, IACUC administrators and committee members, as well as training and compliance personnel from around the world to learn about recent advances and challenges within laboratory animal science. This provides the rare opportunity to participate with such a diverse group of experts during this groundbreaking conference, which is absolutely free to all participants. Aside from attending webinars with world-renown researchers and scientist receiving continuing Education Credits, conference participants will be able to ask questions of the speakers via live video, chat with peers, browse the virtual exhibit floor and peruse the virtual poster hall. This is all set to kick off February 8; to learn more about this event, found out about the RACE Continuing Educational credits offered, or to watch live, click here. LabRoots is the leading scientific social networking website and producer of educational virtual events and webinars. Contributing to the advancement of science through content sharing capabilities, LabRoots is a powerful advocate in amplifying global networks and communities. Founded in 2008, LabRoots emphasizes digital innovation in scientific collaboration and learning, and is a primary source for current scientific news, webinars, virtual conferences, and more. LabRoots has grown into the world’s largest series of virtual events within the Life Sciences and Clinical Diagnostics community.

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