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Lodi, Italy

Genomics Platform

Lodi, Italy

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Courtois A.,The Surgical Center | Courtois A.,University of Liège | Coppieters W.,Genomics Platform | Bours V.,Laboratory of Human Genetics | And 4 more authors.
European Journal of Medical Genetics | Year: 2017

Heterozygous mutations in the SMAD3 gene were recently described as the cause of a form of non-syndromic familial aortic thoracic aneurysm and dissection (FTAAD) transmitted as an autosomal dominant disorder and often associated with early-onset osteoarthritis. This new clinical entity, called aneurysms-osteoarthritis syndrome (AOS) or Loeys-Dietz syndrome 3 (LDS3), is characterized by aggressive arterial damages such as aneurysms, dissections and tortuosity throughout the arterial tree. We report, here, the case of a 45 year-old man presenting multiple visceral arteries and abdominal aortic aneurysms but without dissection of the thoracic aorta and without any sign of osteoarthritis. Exome-sequencing revealed a new frameshift heterozygous c.455delC (p.Pro152Hisfs*34) mutation in the SMAD3 gene. This deletion is located in the exon 3 coding for the linker region of the protein and causes a premature stop codon at positions 556-558 in the exon 4. The same mutation was found in the proband's mother and sister who had open surgery for abdominal aortic aneurysm and in one of his children who was 5 year-old and did not present aneurysm yet. © 2017 Elsevier Masson SAS.


No statistical methods were used to predetermine sample size. All cells were tested for mycoplasma and included for analysis only upon testing negative. The identity of all cell lines was confirmed by whole-exome sequencing and SNP array analysis. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. As a source of hES cells for this study, we focused on those that had been voluntarily listed by research institutions on the registry of hES cell lines maintained by the US National Institutes of Health (NIH) (http://grants.nih.gov/stem_cells/registry/current.htm). As of 8 July 2015, a total of 307 hES cell lines were listed on this registry. Of these, we requested viable frozen stocks of the 182 lines annotated to be available for distribution and to lack known karyotypic abnormalities or disease-causing mutations. During our effort to obtain these cell lines, we found that 45 were subject to overly restrictive material transfer agreements that precluded their use in our studies and 11 could not be readily obtained as frozen stocks owing to differences in human subjects research regulations between the US and the UK. Nine cell lines were unavailable upon request or were overly difficult to import, and three could not be cultured despite repeated attempts. Further details on the availability of cell lines can be found in Supplementary Table 1. The generation of hES cells used in this study was previously approved by the institutional review boards (IRBs) of all providing institutions. Use of the hES cells for sequencing at Harvard was further approved and determined not to constitute Human Subjects Research by the Committee on the Use of Human Subjects in Research at Harvard University. A protocol for the adaptation of hES cell lines from diverse culture conditions can be found at Protocol Exchange30. In brief, we considered that different laboratories employ different methods to culture hES cells, raising the question of how best to thaw and culture the cell lines we obtained from multiple sources. Traditionally, hES cells are maintained on gelatinized plates and co-cultured with replication-incompetent mouse embryonic fibroblast (MEF) feeder cells in tissue culture medium containing knockout serum replacement (KOSR). More recently, hES cells have been cultured on a substrate of cell-line-derived basal membrane proteins known by the trade names of Matrigel (BD Biosciences) or Geltrex (Life Technologies), in mTeSR1 (ref. 31), E8 (ref. 32) or similar in the absence of feeder cells. In previous work, we found that a medium containing an equal volume of KOSR-based hES cell medium (KSR) and mTeSR1 (STEMCELL Technologies) (KSR–mTeSR1) robustly supports the pluripotency of hES cells undergoing antibiotic selection during the course of gene-targeting experiments under feeder-free conditions33. To minimize stress to hES cells previously cultured and frozen under diverse conditions, cell lines were thawed in the presence of 10 μM Y-27632 (DNSK International) into two wells of a 6-well plate, one of which contained KSR–mTeSR1 on a substrate of Matrigel, and the other containing KOSR-based hES cell medium on a monolayer of irradiated MEFs. After 24 h, Y-27632 was removed and cells were fed daily with the aforementioned media in the absence of any antibiotics. All cultures were tested for the presence of mycoplasma and cultured in a humidified 37 °C tissue culture incubator in the presence of 5% CO and 20% O . Colonies of cells with hES cell morphology and with a diameter of approximately 400 μm were transferred into KSR–mTeSR1 medium containing 10 μM Y-27632 on a substrate of Matrigel by manual picking under a dissecting microscope. Cells with differentiated morphology were removed from plates by aspiration during feeding. Once cultures consisting of cells with homogeneous pluripotent stem cell morphology had been established, they were passaged by brief (2–10 min) incubation in 0.5 mM EDTA in PBS followed by gentle trituration in KSR–mTeSR1 medium containing 10 μM Y-27632 and re-plating. Once cultures had reached approximately 90% confluence in one well of a six-well plate, they were passaged with ETDA onto a Matrigel-coated 10 cm plate. Upon reaching approximately 90% confluence, cell lines were dissociated with EDTA as described above and banked for later use in cryoprotective medium containing 50% KSR–mTeSR1, 10 μM Y-27632, 10% DMSO, and 40% fetal bovine serum (HyClone). A subset of hES cell lines (Supplementary Table 1) were passaged enzymatically with TrypLE Express (Life Technologies), expanded onto two 15 cm plates, and frozen down in 25 cryovials. Cell pellets of approximately 1–5 million cells were generated from banked cryovials of research-grade hES cell lines, or were obtained directly from institutions providing GMP-grade hES cell lines. Cell pellets were digested overnight at 50 °C in 500 μl lysis buffer containing 100 μg ml−1 proteinase K (Roche), 10 mM Tris (pH 8.0), 200 mM NaCl, 5% w/v SDS, 10 mM EDTA, followed by phenol:chloroform precipitation, ethanol washes, and resuspension in 10 mM Tris buffer (pH 8.0). Genomic DNA was then transferred to the Genomics Platform at the Broad Institute of MIT and Harvard for Illumina Nextera library preparation, quality control, and sequencing on the Illumina HiSeq X10 platform. Sequencing reads (150 bp, paired-end) were aligned to the hg19 reference genome using the BWA alignment program. Genotypes from WES data for the cell lines were computed using best practices from GATK software34 compiled on 31 July 2015. Sequencing quality and coverage were analysed using Picard tool metrics. Cross sample contamination was estimated using VerifyBamID (v1.1.2)35, and none was detected. Data from each cell line were independently processed with the HaplotypeCaller walker and further aggregated with the CombineGVCFs and GenotypeGVCFs walkers to generate a combined variant call format (VCF) file. Genotyped sites were finally filtered using the ApplyRecalibration walker. To determine whether lines with or without acquired TP53 mutations showed other chromosomal aberrations or smaller regional changes in copy number, additional genotyping of the 140 hES cell lines was performed using a custom high density SNP array (‘Human Psych array’) that contains more than half a million SNPs across the genome. CNVs larger than 500 kb were identified using the PennCNV (v1.0.0)36 tool (http://penncnv.openbioinformatics.org). All CNVs were manually reviewed and are shown in Supplementary Table 6. To identify candidate mosaic variants, a table of heterozygous variants was generated from the VCF (Supplementary Table 2). To limit the frequency of false positive calls due to sequencing artefacts and PCR errors, variants were included if they had a variant read depth of at least 10, if they were either flagged as a ‘PASS’ site or were not reported in the Exome Aggregation Consortium (ExAC) database11, and if they were not located in regions of the genome with low sequence complexity, common large insertions and segmental duplications, as described by Genovese and colleagues5. Multiallelic sites were split, left-aligned, and normalized. The resulting list of 2.1 million ‘high-quality heterozygous variants’ was further refined to include sites that were covered by at least 60 unique reads and had a high confidence variant score (‘PASS’) as ascertained by GATK’s Variant Quality Score Recalibration software (840,222 variants). To exclude common inherited variants, we selected variants present in less than 0.01% of the (ExAC) control population and restricted our analysis to only singleton or doubleton variants (9,490 variants present in 1–2 of the 140 samples). Coverage was calculated by summing reference and alternate allele counts for each variant. Allelic fraction was calculated by dividing the alternate allele count by the total read coverage (both alleles) of the site. Although the allelic fraction of inherited heterozygous variants is expected to be 50%, reference capture bias (a tendency of hybrid selection to capture the reference allele more efficiently than alternative alleles) causes the actual expected allele fraction for SNPs and indels to be closer to 45% and 35%, respectively5. To account for these technical biases, we used a binomial test with a null model centred at 45% allelic fraction for inherited SNPs and 35% for inherited indels. Variants for which this binomial test was nominally significant (P < 0.01) were deemed to be candidate mosaic variants. The nominal P-value threshold of 0.01 was chosen as an inclusive threshold in order to screen sensitively for potentially mosaic variants, at the expense of also capturing false positives for which low allelic fractions represented statistical sampling fluctuations. For this reason, we considered it important to further evaluate putative mosaic variants by independent molecular methods that deeply sample alleles at the nominated sites (Fig. 3). A more stringent computational screen based on a P-value threshold of 1 × 10−7 identified three of the six TP53 variants, and TP53 was also the only gene with multiple putatively mosaic variants in this screen. We also identified all high quality heterozygous variants that passed the inclusive statistical threshold of (P < 0.01) in our binomial test and could potentially be mosaic (n = 36,396). These data are included in Supplementary Table 2. Variant annotation was performed using SnpEff with GRCh37.75 Ensembl gene models. Variants with moderate effect were classified as damaging by a consensus model based on seven in silico prediction algorithms37. We turned to the ExAC database11 that compiles the whole-exome sequences of over 60,000 individuals to assess the frequency at which the amino acid residues we observed to be mutated in some hES cells were affected in the general population. We then consulted the COSMIC12 (http://cancer.sanger.ac.uk/cosmic/gene/analysis?ln=TP53), ICGC13 (https://dcc.icgc.org/), and IARC P53 (ref. 14) (http://p53.iarc.fr/TP53SomaticMutations.aspx) databases and plotted the percentage of tumours carrying a mutation in each codon (Fig. 2d, Extended Data Fig. 2b). To visualize the spatial location of the amino acid residues affected by TP53 mutations observed in hES cells by WES on the P53 protein, we downloaded the 1.85 Angstrom X-ray diffraction-based structure file from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (file 2AHI) and built the model protein/DNA system (chain IDs D, G, and H) to visualize the secondary structure of a P53 monomer complexed to DNA as a ribbon diagram. DNA was illustrated as a space-filling model. Water molecules were discarded when building the wild-type model and minimized in two steps using the AMBER 16 package38. Affected residues were indicated as space-filling model superimposed on the ribbon diagram of P53 and highlighted in blue (wild-type) or red (mutated) without consideration of how the mutations might affect the secondary or tertiary structure of the protein. We assayed the allelic fraction of the four distinct TP53 mutations identified by WES (Supplementary Table 3) in the 140 hES cell lines by droplet digital PCR (ddPCR). Each ddPCR analysis incorporated a custom TaqMan assay (IDT). Assays were designed with Primer3Plus and consisted of a primer pair and a 5′ fluorescently labelled probe (HEX or FAM) with 3′ quencher (Iowa Black with Zen) for either the control (reference) or mutant (alternative) base for each identified P53 variant (Supplementary Table 4). Genomic DNA from each hES cell line was analysed by ddPCR according to the manufacturer’s protocol (BioRad). The frequency of each allele for a given sample was estimated first by Poisson correction of the endpoint fluorescence reads21. These corrected counts were then converted to fractional abundance estimates of the mutant allele and multiplied by two to determine the fraction of cells carrying the variant allele. To assess how the allelic fraction of TP53 mutations might change over time in culture, hES cell lines CHB11 (passage 22 or 25), WA26 (passage 13 or 15), and ESI035 (passage 36 in two separate experiments) were serially passaged in mTeSR1 media (STEMCELL Technologies) at a density of approximately 30,000 cells cm−2 in the presence of 10 μM Y-27632 on the day of passaging. Cells were fed daily with mTeSR1 and passaged with Accutase (Innovative Cell Technologies Inc.) at approximately 90% confluence. To monitor changes in allelic fractions, genomic DNA from cells at the indicated passages were analysed by ddPCR. To calculate the relative expansion rate of mutant relative to wild-type cells, we applied the following formula: where R is defined as the ratio of (variant positive cells)/(variant negative cells) after some number of starting passages and R and R represent the aforementioned ratios measured on the same sample at T and T  > T passages respectively. From this equation, the estimation of variant positive cells after T passages from starting ratio R can be defined as R egT. Note that this equation estimates the relative growth rate of cells carrying the variant allele with a round of passaging as unit of time, with both relative survival and growth being incorporated. These data are included in Supplementary Table 5. For the subsequent calculation of the earliest passage at which these mutations might have become detectable, the detection thresholds (R ) for WES and ddPCR was assumed to be 0.1 (10 / 100 reads) and 0.001 (1 per 1,000 droplets), respectively. In order to identify TP53 mutations in hPS cells, we analysed 256 publicly available high-throughput RNA sequencing samples of hPS cells from the SRA database39 (http://www.ncbi.nlm.nih.gov/sra). Data accession numbers for SRA (and GEO, where applicable) are provided in Supplementary Table 7. 5 of these 256 samples were not considered further as they were from single cells rather than cell lines. Following sequence alignment to the hg19 human reference genome with Tophat2 (ref. 40), single nucleotides divergent from the reference genome were identified using GATK HaplotypeCaller34. As sufficient sequencing depth is required to deduce sequence mutation, a threshold of 25 reads per nucleotide was set. Under this criterion, 43 samples (40 hES cell lines and 3 hiPS cell lines) had a missense mutation in TP53. 10 of the 40 hES cell samples (WA09) carried two separate mutations (Supplementary Table 7). Upon the identification of cell lines carrying mutant reads, RNA sequencing data from studies containing differentiated samples were included for analysis. In order to evaluate TP53 alleles, we assessed the level of polymorphism by calculating the ratio between the minor and major alleles across chromosome 17. So as to minimize sequencing noise and errors, we included SNPs covered by more than 10 reads and that are located in the dbSNP build 142 database41. The resulted wig files were then plotted using Integrative Genomics Viewer (IGV)42 (Extended Data Fig. 4). In order to quantify the difference in polymorphism between samples, we converted the wig files to BigWig using UCSC Genome Browser utilities43 and summed the allelic ratios between the distal part of the short arm of chromosome 17 (17p), the proximal side of this arm and the long arm of chromosome 17 (17q). The allelic ratio sum was then divided by the region’s length (bp), which resulted in the proportion of SNPs, followed by one-sided Z-score test for two population proportion to compare between the chromosome 17 areas within each sample. Whereas most samples with mutations in TP53 showed a comparable, non-significant rate of polymorphic sites along the chromosome, WIBR3 samples with H193R mutations and WA09 samples with both P151S and R248Q mutations had a significantly different proportion (P < 0.001) of polymorphic sites, in the distal part of the short arm of the chromosome (first 16 × 106 base pairs), including the TP53 site. Unlike the three mutant WIBR3 samples, the wild-type WIBR3 sample had a normal distribution of polymorphic sites with no significant difference between the short and long arms. Sequence data from cell lines listed on the NIH hES cell registry have been deposited in the NCBI database of Genotypes and Phenotypes (dbGaP) under accession number phys001343.v1.p1 (at https://www.ncbi.nlm.nih.gov/gap/?term=phys001343.v1.p1). Sequence data from the remaining cell lines reported in our study have been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001002400 (at https://www.ebi.ac.uk/ega/search/site/EGAS00001002400).


PubMed | Center for the Development of Therapeutics and., Imaging Platform., Genomics Platform., Center for the Science of Therapeutics and 3 more.
Type: Journal Article | Journal: Proceedings of the National Academy of Sciences of the United States of America | Year: 2014

High-throughput screening has become a mainstay of small-molecule probe and early drug discovery. The question of how to build and evolve efficient screening collections systematically for cell-based and biochemical screening is still unresolved. It is often assumed that chemical structure diversity leads to diverse biological performance of a library. Here, we confirm earlier results showing that this inference is not always valid and suggest instead using biological measurement diversity derived from multiplexed profiling in the construction of libraries with diverse assay performance patterns for cell-based screens. Rather than using results from tens or hundreds of completed assays, which is resource intensive and not easily extensible, we use high-dimensional image-based cell morphology and gene expression profiles. We piloted this approach using over 30,000 compounds. We show that small-molecule profiling can be used to select compound sets with high rates of activity and diverse biological performance.


News Article | April 27, 2016
Site: www.nature.com

NOD/SCID Il2rgnull mice (Jackson Laboratory) were bred and maintained in the Stem Cell Unit animal barrier facility at McMaster University. All procedures were approved by the Animal Research Ethics Board at McMaster University. All patient samples were obtained with informed consent and with the approval of local human subject research ethics boards at McMaster University. Human umbilical cord blood mononuclear cells were collected by centrifugation with Ficoll-Paque Plus (GE), followed by red blood cell lysis with ammonium chloride (StemCell Technologies). Cells were then incubated with a cocktail of lineage-specific antibodies (CD2, CD3, CD11b, CD11c, CD14, CD16, CD19, CD24, CD56, CD61, CD66b, and GlyA; StemCell Technologies) for negative selection of Lin− cells using an EasySep immunomagnetic column (StemCell Technologies). Live cells were discriminated on the basis of cell size, granularity and, as needed, absence of viability dye 7-AAD (BD Biosciences) uptake. All flow cytometry analysis was performed using a BD LSR II instrument (BD Biosciences). Data acquisition was conducted using BD FACSDiva software (BD Biosciences) and analysis was performed using FlowJo software (Tree Star). To quantify MSI2 expression in human HSPCs, Lin− cord blood cells were stained with the appropriate antibody combinations to resolve HSC (CD34+ CD38− CD45RA− CD90+), MPP (CD34+ CD38− CD45RA− CD90−), CMP (CD34+ CD38+ CD71−) and EP (CD34+ CD38+ CD71+) fractions as similarly described previously18, 19 with all antibodies from BD Biosciences: CD45RA (HI100), CD90 (5E10), CD34 (581), CD38 (HB7) and CD71 (M-A712). Cell viability was assessed using the viability dye 7AAD (BD Biosciences). All cell subsets were isolated using a BD FACSAria II cell sorter (BD Biosciences) or a MoFlo XDP cell sorter (Beckman Coulter). HemaExplorer20 analysis was used to confirm MSI2 expression in human HSPCs and across the hierarchy. For all qRT–PCR determinations total cellular RNA was isolated with TRIzol LS reagent according to the manufacturer’s instructions (Invitrogen) and cDNA was synthesized using the qScript cDNA Synthesis Kit (Quanta Biosciences). qRT–PCR was done in triplicate with PerfeCTa qPCR SuperMix Low ROX (Quanta Biosciences) with gene-specific probes (Universal Probe Library (UPL), Roche) and primers: MSI2 UPL-26, F-GGCAGCAAGAGGATCAGG, R-CCGTAGAGATCGGCGACA; HSP90 UPL-46, F-GGGCAACACCTCTACAAGGA, R-CTTGGGTCTGGGTTTCCTC; CYP1B1 UPL-20, F-ACGTACCGGCCACTATCACT, R-CTCGAGTCTGCACATCAGGA; GAPDH UPL-60, F-AGCCACATCGCTCAGACAC, R-GCCCAATACGACCAAATCC; ACTB (UPL Set Reference Gene Assays, Roche). The mRNA content of samples compared by qRT–PCR was normalized based on the amplification of GAPDH or ACTB. MSI2 shRNAs were designed with the Dharmacon algorithm (http://www.dharmacon.com). Predicted sequences were synthesized as complimentary oligonucleotides, annealed and cloned downstream of the H1 promoter of the modfied cppt-PGK-EGFP-IRES-PAC-WPRE lentiviral expression vector18. Sequences for the MSI2 targeting and control RFP targeting shRNAs were as follows: shMSI2, 5′-GAGAGATCCCACTACGAAA-3′; shRFP, 5′-GTGGGAGCGCGTGATGAAC-3′. Human MSI2 cDNA (BC001526; Open Biosystems) was subcloned into the MA bi-directional lentiviral expression vector21. Human CYP1B1 cDNA (BC012049; Open Biosystems) was cloned in to psMALB22. All lentiviruses were prepared by transient transfection of 293FT (Invitrogen) cells with pMD2.G and psPAX2 packaging plasmids (Addgene) to create VSV-G pseudotyped lentiviral particles. All viral preparations were titrated on HeLa cells before use on cord blood. Standard SDS–PAGE and western blotting procedures were performed to validate the effects of knockdown on transduced NB4 cells (DSMZ) and overexpression on 293FT cells. Immunoblotting was performed with anti-MSI2 rabbit monoclonal IgG (EP1305Y, Epitomics) and β-actin mouse monoclonal IgG (ACTBD11B7, Santa Cruz Biotechnology) antibodies. Secondary antibodies used were IRDye 680 goat anti-rabbit IgG and IRDye 800 goat anti-mouse IgG (LI-COR). 293FT and NB4 cell lines tested negative for mycoplasma. NB4 cells were authenticated by ATRA treatment before use. Cord blood transductions were conducted as described previously18, 23. Briefly, thawed Lin− cord blood or flow-sorted Lin− CD34+ CD38− or Lin− CD34+ CD38+ cells were prestimulated for 8–12 h in StemSpan medium (StemCell Technologies) supplemented with growth factors interleukin 6 (IL-6; 20 ng ml−1, Peprotech), stem cell factor (SCF; 100 ng ml−1, R&D Systems), Flt3 ligand (FLT3-L; 100 ng ml−1, R&D Systems) and thrombopoietin (TPO; 20 ng ml−1, Peprotech). Lentivirus was then added in the same medium at a multiplicity of infection of 30–100 for 24 h. Cells were then given 2 days after transduction before use in in vitro or in vivo assays. For in vitro cord blood studies biological (experimental) replicates were performed with three independent cord blood samples. Human clonogenic progenitor cell assays were done in semi-solid methylcellulose medium (Methocult H4434; StemCell Technologies) with flow-sorted GFP+ cells post transduction (500 cells per ml) or from day seven cultured transduced cells (12,000 cells per ml). Colony counts were carried out after 14 days of incubation. CFU-GEMMs can seed secondary colonies owing to their limited self-renewal potential24. Replating of MSI2-overexpressing and control CFU-GEMMs for secondary CFU analysis was performed by picking single CFU-GEMMs at day 14 and disassociating colonies by vortexing. Cells were spun and resuspended in fresh methocult, mixed with a blunt-ended needle and syringe, and then plated into single wells of a 24-well plate. Secondary CFU analysis for shMSI2- and shControl-expressing cells was performed by harvesting total colony growth from a single dish (as nearly equivalent numbers of CFU-GEMMs were present in each dish), resuspending cells in fresh methocult by mixing vigorously with a blunt-ended needle and syringe and then plating into replicate 35-mm tissue culture dishes. In both protocols, secondary colony counts were done following incubation for 10 days. For primary and secondary colony forming assays performed with the AHR agonist FICZ (Santa Cruz Biotechnology), 200 nM FICZ or 0.1% DMSO was added directly to H4434 methocult medium. Two-way ANOVA analysis was performed to compare secondary CFU output and FICZ treatment for MSI2-overexpressing or control conditions. Colonies were imaged with a Q-Colour3 digital camera (Olympus) mounted to an Olympus IX5 microscope with a 10× objective lens. Image-Pro Plus imaging software (Media Cybernetics) was used to acquire pictures and subsequent image processing was performed with ImageJ software (NIH). Transduced human Lin− cord blood cells were sorted for GFP expression and seeded at a density of 105 cells per ml in IMDM 10% FBS supplemented with human growth factors IL-6 (10 ng ml−1), SCF (50 ng ml−1), FLT3-L (50 ng ml−1), and TPO (20 ng ml−1) as previously described25. To generate growth curves, every seven days cells were counted, washed, and resuspended in fresh medium with growth factors at a density of 105 cells per ml. Cells from suspension cultures were also used in clonogenic progenitor, cell cycle and apoptosis assays. Experiments performed on transduced Lin− CD34+ cord blood cells used serum-free conditions as described in the cord blood transduction subsection of Methods. For in vitro cord blood studies, biological (experimental) replicates were performed with three independent cord blood samples. Cell cycle progression was monitored with the addition of BrdU to day 10 suspension cultures at a final concentration of 10 μM. After 3 h of incubation, cells were assayed with the BrdU Flow Kit (BD Biosciences) according to the manufacturer’s protocol. Cell proliferation and quiescence were measured using Ki67 (BD Bioscience) and Hoechst 33342 (Sigma) on day 4 suspension cultures after fixing and permeabilizing cells with the Cytofix/Cytoperm kit (BD Biosciences). For apoptosis analysis, Annexin V (Invitrogen) and 7-AAD (BD Bioscience) staining of day 7 suspension cultures was performed according to the manufacturer’s protocol. Lin− cord blood cells were initially stained with anti-CD34 PE (581) and anit-CD38 APC (HB7) antibodies (BD Biosciences) then fixed with the Cytofix/Cytoperm kit (BD Biosciences) according to the manufacturer’s instructions. Fixed and permeabilized cells were immunostained with anti-MSI2 rabbit monoclonal IgG antibody (EP1305Y, Abcam) and detected by Alexa-488 goat anti-rabbit IgG antibody (Invitrogen). CD34+ cells were transduced with an MSI2-overexpression or MSI2-knockdown lentivirus along with their corresponding controls and sorted for GFP expression 3 days later. Transductions for MSI2 overexpression or knockdown were each performed on two independent cord blood samples. Total RNA from transduced cells (>1 × 105) was isolated using TRIzol LS as recommended by the manufacturer (Invitrogen), and then further purified using RNeasy columns (Qiagen). Sample quality was assessed using Bioanalyzer RNA Nano chips (Agilent). Paired-end, barcoded RNA-seq sequencing libraries were then generated using the TruSeq RNA Sample Prep Kit (v2) (Illumina) following the manufacturer’s protocols starting from 1 μg total RNA. The quality of library generation was then assessed using a Bioanalyzer platform (Agilent) and Illumina MiSeq-QC run was performed or quantified by qPCR using KAPA quantification kit (KAPA Biosystems). Sequencing was performed using an Illumina HiSeq2000 using TruSeq SBS v3 chemistry at the Institute for Research in Immunology and Cancer’s Genomics Platform (University of Montreal) with cluster density targeted at 750,000 clusters per mm2 and paired-end 2 × 100-bp read lengths. For each sample, 90–95 million reads were produced and mapped to the hg19 (GRCh37) human genome assembly using CASAVA (version 1.8). Read counts generated by CASAVA were processed in EdgeR (edgeR_3.12.0, R 3.2.2) using TMM normalization, paired design, and estimation of differential expression using a generalized linear model (glmFit). The false discovery rate (FDR) was calculated from the output P values using the Benjamini–Hochberg method. The fold change of logarithm of base 2 of TMM normalized data (logFC) was used to rank the data from top upregulated to top downregulated genes and FDR (0.05) was used to define significantly differentially expressed genes. RNA-seq data have been deposited in NCBI’s Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number GSE70685. iRegulon26 was used to retrieve the top 100 AHR predicted targets with a minimal occurrence count threshold of 5. The data were analysed using GSEA27 with ranked data as input with parameters set to 2,000 gene-set permutations. The GEO dataset GSE28359, which contains Affymetrix Human Genome U133 Plus 2.0 Array gene expression data for CD34+ cells treated with SR1 at 30 nM, 100 nM, 300 nM and 1,000 nM was used to obtain lists of genes differentially expressed in the treated samples compared to the control ones (0 nM)2. Data were background corrected using Robust Multi-Array Average (RMA) and quantile normalized using the expresso() function of the affy Bioconductor package (affy_1.38.1, R 3.0.1). Lists of genes were created from the 150 top upregulated and downregulated genes from the SR1-treated samples at each dose compared to the non-treated samples (0 nM). The data were analysed using GSEA with ranked data as input with parameters set to 2,000 gene-set permutations. The normalized enrichment score (NES) and false discovery rate (FDR) were calculated for each comparison. The GEO data set GSE24759, which contains Affymetrix GeneChip HT-HG_U133A Early Access Array gene expression data for 38 distinct haematopoietic cell states4, was compared to the MSI2 overexpression and knockdown data. GSE24759 data were background corrected using Robust Multi-Array Average (RMA), quantile normalized using the expresso() function of the affy Bioconductor package (affy_1.38.1, R 3.0.1), batch corrected using the ComBat() function of the sva package (sva_3.6.0) and scaled using the standard score. Bar graphs were created by calculating for significantly differentially expressed genes the number of scaled data that were above (>0) or below (<0) the mean for each population. Percentages indicating for how long the observed value (set of up- or downregulated genes) was better represented in that population than random values were calculated from 1,000 trials. A unique list of genes closest to AHR-bound regions previously identified from TCDD-treated MCF7 ChIP–seq data14 was used to calculate the overlap with genes showing >1.5-fold downregulation in response to treatment with UM171 (35 nM) or SR1 (500 nM) relative to DMSO-treated samples3 as well as with genes significantly downregulated in MSI2-overexpressing versus control treated samples (FDR < 0.05). The percentage of downregulated genes with AHR-bound regions was then plotted for each gene set. P values were generated with Fisher’s exact test for comparisons between gene lists. AHR transcription factor binding sites in downregulated gene sets were identified with oPOSSUM-328. Genes showing >1.5-fold downregulation in response to treatment with UM171 (35 nM) or SR1 (500 nM) relative to DMSO-treated samples3 were used along with significantly downregulated genes (FDR < 0.05) with EdgeR-analysed MSI2-overexpressing versus control-treated samples. The three gene lists were uploaded into oPOSSUM-3 and the AHR:ARNT transcription factor binding site profile was used with the matrix score threshold set at 80% to analyse the region 1,500 bp upstream and 1,000 bp downstream of the transcription start site. The percentage of downregulated genes with AHR-binding sites in their promoters was then plotted for each gene set. Fisher’s exact test was used to identify significant overrepresentation of AHR-binding sites in gene lists relative to background. Eight- to 12-week-old male or female NSG mice were sublethally irradiated (315 cGy) one day before intrafemoral injection with transduced cells carried in IMDM 1% FBS at 25 μl per mouse. Injected mice were analysed for human haematopoietic engraftment 12–14 weeks after transplantation or at 3 and 6.5 weeks for STRC experiments. Mouse bones (femurs, tibiae and pelvis) and spleen were removed and bones were crushed with a mortar and pestle then filtered into single-cell suspensions. Bone marrow and spleen cells were blocked with mouse Fc block (BD Biosciences) and human IgG (Sigma) and then stained with fluorochrome-conjugated antibodies specific to human haematopoietic cells. For multilineage engraftment analysis, cells from mice were stained with CD45 (HI30) (Invitrogen), CD33 (P67.6), CD15 (HI98), CD14 (MφP9), CD19 (HIB19), CD235a/GlyA (GA-R2), CD41a (HIP8) and CD34 (581) (BD Biosciences). For MSI2 knockdown in HSCs, 5.0 × 104 and 2.5 × 104 sorted Lin− CD34+ CD38− cells were used per short-hairpin transduction experiment, leading to transplantation of day zero equivalent cell doses of 10 × 103 and 6.25 × 103, respectively, per mouse. For STRC LDA transplantation experiments, 105 sorted CD34+CD38+ cells were used per control or MSI2-overexpressing transduction. After assessing levels of gene transfer, day zero equivalent GFP+ cell doses were calculated to perform the LDA. Recipients with greater than 0.1% GFP+CD45+/− cells were considered to be repopulated. For STRC experiments that read out extended engraftment at 6.5 weeks, 2 × 105 CD34+ CD38+ cells were used per overexpressing or control transduction to allow non-limiting 5 × 104 day zero equivalent cell doses per mouse. For HSC expansion and LDA experiments, CD34+CD38− cells were sorted and transduced with MSI2-overexpressing or control vectors (50,000 cells per condition) for 3 days and then analysed for gene-transfer levels (% GFP+/−) and primitive cell marker expression (% CD34 and CD133). To ensure that equal numbers of GFP+ cells were transplanted into both control and MSI2-overexpressing recipient mice, we added identically cultured GFP− cells to the MSI2 culture to match the % GFP+ of the control culture (necessary owing to the differing efficiency of transduction). The adjusted MSI2-overexpressing culture was recounted and aliquoted (63,000 cells) to match the output of half of the control culture. Three day 0 equivalent GFP+ cell doses (1,000, 300 and 62 cells) were then transplanted per mouse to perform the D3 primary LDA. A second aliquot of the adjusted MSI2-overexpressing culture was then taken and put into culture in parallel with the remaining half of the control culture to perform another LDA after 7 days of growth (10 days total growth, D10 primary LDA). Altogether, four cell doses were transplanted; when converted back to day 0 equivalents these equalled approximately 1,000, 250, 100, and 20 GFP+ cells per mouse, respectively. Pooled bone marrow from six engrafted primary mice that received D10 cultured control or MSI2-overexpressing cells (from the two highest doses transplanted) was aliquoted into five cell doses of 15 million, 10 million, 6 million, 2 million and 1 million cells. The numbers of GFP+ cells within primary mice was estimated from nucleated cell counts obtained from NSG femurs, tibias and pelvises and from Colvin et al.29. The actual numbers of GFP+ cells used for determining numbers of GFP+ HSCs and the number of mice transplanted for all LDA experiments is shown in Supplementary Tables 3–5. The cut-off for HSC engraftment was a demonstration of multilineage reconstitution that was set at bone marrow having >0.1% GFP+ CD33+ and >0.1% GFP+ CD19+ cells. HSC and STRC frequency was assessed using ELDA software30. For all mouse transplantation experiments, mice were age- (6–12 week) and sex-matched. All transplanted mice were included for analysis unless mice died from radiation sickness before the experimental endpoint. No randomization or blinding was performed for animal experiments. Approximately 3–6 mice were used per cell dose for each cord blood transduction and transplantation experiment. CLIP–seq was performed as previously described15. Briefly, 25 million NB4 cells (a transformed human cell line of haematopoietic origin) were washed in PBS and UV-cross-linked at 400 mJ cm−2 on ice. Cells were pelleted, lysed in wash buffer (PBS, 0.1% SDS, 0.5% Na-deoxycholate, 0.5% NP-40) and DNase-treated, and supernatants from lysates were collected for immunoprecipitation. MSI2 was immunoprecipitated overnight using 5 μg of anti-MSI2 antibody (EP1305Y, Abcam) and Protein A Dynabeads (Invitrogen). Beads containing immunoprecipated RNA were washed twice with wash buffer, high-salt wash buffer (5× PBS, 0.1% SDS, 0.5% Na-Deoxycholate, 0.5% NP-40), and PNK buffer (50 mM Tris-Cl pH 7.4, 10 mM MgCl , 0.5% NP-40). Samples were then treated with 0.2 U MNase for 5 min at 37° with shaking to trim immunopreciptated RNA. MNase inactivation was then carried out with PNK + EGTA buffer (50 mM Tris-Cl pH 7.4, 20 mM EGTA, 0.5% NP-40). The sample was dephosphorylated using alkaline phosphatase (CIP, NEB) at 37° for 10 min followed by washing with PNK+EGTA, PNK buffer, and then 0.1 mg ml−1 BSA in nuclease-free water. 3′RNA linker ligation was performed at 16° overnight with the following adaptor: 5′P-UGGAAUUCUCGGGUGCCAAGG-puromycin. Samples were then washed with PNK buffer, radiolabelled using P32-y-ATP (Perkin Elmer), run on a 4–12% Bis-Tris gel and then transferred to a nitrocellulose membrane. The nitrocellulose membrane was developed via autoradiography and RNA–protein complexes 15–20 kDa above the molecular weight of MSI2 were extracted with proteinase K followed by RNA extraction with acid phenol-chloroform. A 5′RNA linker (5′HO-GUUCAGAGUUCUACAGUCCGACGAUC-OH) was ligated to the extracted RNA using T4 RNA ligase (Fermentas) for 2 h and the RNA was again purified using acid phenol-chloroform. Adaptor ligated RNA was re-suspended in nuclease-free water and reverse transcribed using Superscript III reverse transcriptase (Invitrogen). Twenty cycles of PCR were performed using NEB Phusion Polymerase using a 3′PCR primer that contained a unique Illumina barcode sequence. PCR products were run on an 8% TBE gel. Products ranging between 150 and 200 bp were extracted using the QIAquick gel extraction kit (Qiagen) and re-suspended in nuclease-free water. Two separate libraries were prepared and sent for single-end 50-bp Illumina sequencing at the Institute for Genomic Medicine at the University of California, San Diego. 47,098,127 reads from the first library passed quality filtering, of which 73.83% mapped uniquely to the human genome. 57,970,220 reads from the second library passed quality filtering, of which 69.53% mapped uniquely to the human genome. CLIP-data reproducibility was verified through high correlation between gene RPKMs and statistically significant overlaps in the clusters and genes within replicates. CLIP–seq data have been deposited in NCBI’s GEO and are accessible through GEO Series accession number GSE69583. Before sequence alignment of CLIP–seq reads to the human genome was performed, sequencing reads from libraries were trimmed of polyA tails, adapters, and low quality ends using Cutadapt with parameters–match-read-wildcards–times 2 -e 0 -O 5–quality-cutoff' 6 -m 18 -b TCGTATGCCGTCTTCTGCTTG -b ATCTCGTATGCCGTCTTCTGCTTG -b CGACAGGTTCAGAGTTCTACAGTCCGACGATC -b TGGAATTCTCGGGTGCCAAGG -b AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA-b TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT. Reads were then mapped against a database of repetitive elements derived from RepBase (version 18.05). Bowtie (version 1.0.0) with parameters -S -q -p 16 -e 100 -l 20 was used to align reads against an index generated from Repbase sequences31. Reads not mapped to Repbase sequences were aligned to the hg19 human genome (UCSC assembly) using STAR (version 2.3.0e)32 with parameters–outSAMunmapped Within –outFilterMultimapNmax 1 –outFilterMultimapScoreRange 1. To identify clusters in the genome of significantly enriched CLIP–seq reads, reads that were PCR replicates were removed from each CLIP–seq library using a custom script of the same method as in ref. 33; otherwise, reads were kept at each nucleotide position when more than one read’s 5′-end was mapped. Clusters were then assigned using the CLIPper software with parameters–bonferroni–superlocal–threshold-34. The ranked list of significant targets was calculated assuming a Poisson distribution, where the observed value is the number of reads in the cluster, and the background is the number of reads across the entire transcript and or across a window of 1000 bp ± the predicted cluster. Transcriptomic regions and gene classes were defined using annotations found in gencode v17. Depending on the analysis, clusters were associated by the Gencode-annotated 5′UTR, 3′UTR, CDS or intronic regions. If a cluster overlapped multiple regions, or a single part of a transcript was annotated as multiple regions, clusters were iteratively assigned first as CDS, then 3′UTR, 5′UTR and finally as proximal (<500 bases from an exon) or distal (>500 bases from an exon) introns. Overlapping peaks were calculated using bedtools and pybedtools35, 36. Significantly enriched gene ontology (GO) terms were identified using a hypergeometric test that compared the number of genes that were MSI2 targets in each GO term to genes expressed in each GO term as the proper background. Expressed genes were identified using the control samples in SRA study SRP012062. Mapping was performed identically to CLIP–seq mapping, without peak calling and changing the STAR parameter outFilterMultimapNmax to 10. Counts were calculated with featureCounts37 and RPKMs were then computed. Only genes with a mean RPKM > 1 between the two samples were used in the background expressed set. Randomly located clusters within the same genic regions as predicted MSI2 clusters were used to calculate a background distribution for motif and conservation analyses. Motif analysis was performed using the HOMER algorithm as in ref. 34. For evolutionary sequence conservation analysis, the mean (mammalian) phastCons score for each cluster was used. CD34+ cells (>5 × 104) were transduced with an MSI2-overexpression or control lentivirus. Three days later, GFP+ cells were sorted and then put back in to StemSpan medium containing growth factors IL-6 (20 ng ml−1), SCF (100 ng ml−1), FLT3-L (100 ng ml−1) and TPO (20 ng ml−1). A minimum of 10,000 cells were used for immunostaining at culture days 3 and 7 after GFP sorting. Cells were fixed in 2% PFA for 10 min, washed with PBS and then cytospun on to glass slides. Cytospun cells were then permeabilized (PBS, 0.2% Triton X-100) for 20 min, blocked (PBS, 0.1% saponin, 10% donkey serum) for 30 min and stained with primary antibodies (CYP1B1 (EPR14972, Abcam); HSP90 (68/hsp90, BD Biosciences)) in PBS with 10% donkey serum for 1 h. Detection with secondary antibody was performed in PBS 10% donkey serum with Alexa-647 donkey anti-rabbit antibody or Alexa-647 donkey anti-mouse antibodies for 45 min. Slides were mounted with Prolong Gold Antifade containing DAPI (Invitrogen). Several images (200–1,000 cells total) were captured per slide at 20× magnification using an Operetta HCS Reader (Perkin Elmer) with epifluorescence illumination and standard filter sets. Columbus software (Perkin Elmer) was used to automate the identification of nuclei and cytoplasm boundaries in order to quantify mean cell fluorescence. A 271-bp region of the CYP1B1 3′UTR that flanked CLIP–seq-identified MSI2-binding sites was cloned from human HEK293FT genomic DNA using the forward primer GTGACACAACTGTGTGATTAAAAGG and reverse primer TGATTTTTATTATTTTGGT AATGGTG and placed downstream of renilla luciferase in the dual-luciferase reporter vector pGL4 (Promega). A 271-bp geneblock (IDT) with 6 TAG > TCC mutations was cloned in to pGL4 using XbaI and NotI. The HSP90 3′UTR was amplified from HEK293FT genomic DNA with the forward primer TCTCTGGCTGAGGGATGACT and reverse primer TTTTAAGGCCAAGGAATTAAGTGA and cloned into pGL4. A geneblock of the HSP90 3′UTR (IDT) with 14 TAG > TCC mutations was cloned in to pGL4 using SfaAI and NotI. Co-transfection of wild-type or mutant luciferase reporter (40 ng) and control or MSI2-overexpressing lentiviral expression vector (100 ng) was performed in the NIH-3T3 cell line, which does not express MSI1 or MSI2 (50,000 cells per co-transfection). Reporter activity was measured using the Dual-Luciferase Reporter Assay System (Promega) 36–40 h later. For MSI2-overexpressing cultures with the AHR antagonist SR1, Lin− CD34+ cells were transduced with MSI2-overexpression or control lentivirus in medium supplemented with SR1 (750 nM; Abcam) or DMSO vehicle (0.1%). GFP+ cells were isolated (20,000 cells per culture) and allowed to proliferate with or without SR1 for an additional 7 days at which point they were counted and immunophenotyped for CD34 and CD133 expression. For MSI2-overexpressing cultures with the AHR agonist FICZ, Lin− CD34+ cells were transduced with MSI2-overexpression or control lentivirus. GFP+ cells were isolated (20,000 cells per culture) and allowed to proliferate with FICZ (200 nM; Santa Cruz Biotechnology) or DMSO (0.1%) for an additional 3 days, at which point they were immunophenotyped for CD34 and CD133 expression. Lin− CD34+ cells were cultured for 72 h (lentiviral treated but non-transduced flow-sorted GFP− cells) in StemSpan medium containing growth factors IL-6 (20 ng ml−1), SCF (100 ng ml−1), FLT3-L (100 ng ml−1) and TPO (20 ng ml−1) before the addition of the CYP1B1 inhibitor TMS (Abcam) at a concentration of 10 μM or mock treatment with 0.1% DMSO. Equal numbers of cells (12,000 per condition) were then allowed to proliferate for 7 days at which point they were counted and immunophenotyped for CD34 and CD133 expression. Unless stated otherwise (that is, analysis of RNA–seq and CLIP–seq data sets), all statistical analysis was performed using GraphPad Prism (GraphPad Software version 5.0). Unpaired student t-tests or Mann–Whitney tests were performed with P < 0.05 as the cut-off for statistical significance. No statistical methods were used to predetermine sample size.


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

Polybrene, bafilomycin A, nocodazole and cyclohexamide were purchased from Sigma. CLAAAP (protease inhibitor cocktail) and PhosStop (phosphatase inhibitor) were purchased from Roche. Cytochalasin D was obtained from Fluka. Each antibody was obtained as follows: α-tubulin (B-5-1-2, Santa Cruz Biotechnology), AIF (sc-13116, Santa Cruz Biotechnology), β-actin (AC-74, Sigma), calnexin (ADI-SPA-860, ENZO Life Sciences), catalase (219010, Millipore), Drp1 (611113, BD Transduction Laboratories), FLAG (M2, Sigma), GFP for western blots (JL-8, Clontech), GFP (anti FP) for electron microscopy and immunoprecipitation (A6455, Life Technology), IP R1 (8568, Cell Signalling), KDEL (ab50601, Abcam), Lamp1b (H5G11, Santa Cruz Biotechnology), MCU (HPA016480, Sigma), MUL1 (HPA017681, Sigma), myc (4A6, Upstate), PEX14 (ABC142, Millipore), PMP70 (sab4200181, Sigma), PRDX3 (ref. 30), Tom20 (FL-145, Santa Cruz Biotechnology), ubiquitin (P4D1-A11, Millipore), VDAC1 (20B12AF2, Abcam), Vps35 (2D3, Novus). The human peroxisomal biogenesis-deficient fibroblast cell line PBD400-T1 was derived from a patient with Zellweger syndrome carrying a single nucleotide insertion (c542insT) leading to a premature stop codon in the core peroxin gene PEX3 (called Pex3mut), a gift from P. Kim (Univ. Toronto, Canada). Pex16mut cells were derived from a patient with Zellweger syndrome carrying a terminating mutation in PEX16, R176ter (GM06231 cells, Coriell Institute, called Pex16mut), which were immortalized as described in ref. 31. Control human fibroblasts (cell line MCH64) were obtained from Montreal Children’s Hospital. The cell lines were validated using qRT–PCR to confirm the loss of Pex3 or Pex16 (data shown in Extended Data Fig. 1a). Cells were maintained in DMEM (GIBCO) supplied with 10% fetal bovine serum (Wisent Bio Products) and non-essential amino acids (GIBCO) in 5.0% CO at 37 °C. Cells were tested for mycoplasma contamination using MycoAlert Mycoplasma Detection kit (Lonza). Transfection with plasmid DNA or siRNA was performed with Lipofectamine 3000, Nucleofector (Lonza) or RNAiMAX (Invitrogen) following the manufacturer’s instructions. Addition of drugs to monitor peroxisome biogenesis was performed using the following conditions: 0.02% DMSO, 20 nM bafilomycin A, 2 μM MG132, 1 μM cytochalasin D or 1.5 μM nocodazole for 14 h. Series of ON-TARGETplus siRNAs were purchased from Dharmacon. Non-targeting control pool (D-001810-10), targeted sequences are as follows: ON-TARGETplus DNML1 siRNA smart pool (J-012092-09 – 12, GUUAACCCGUGGAUGAUAA, CGUAAAAGGUUGCCUGUUA, CAUCAGAGAUGUUUACCA, GGAGCCAGCUAGAUAUUAA), ON-TARGETplus smart pool siVps35 (GAACAUAUUGCUACCAGUA, GAAAGAGCAUGAGUUGUUA, GUUGUAAACUGUAGGGAUG, GAACAAAUUUGGUGCGCCU), ON-TARGETplus siPEX19 (J-012594-05, J-012594-06, J-012594-07, J-012594-08) C-terminal YFP-fused Pex3 (UniProtKB: P56589) and Pex16 (UniprtoKB: Q9Y5Y5) (obtained from P. Kim12, GFP tag switched for YFP using standard procedures) were subcloned into D2-MCS viral vector (BioVector) under the control of a CMV promoter. Ad-GFP, Ad-Pex3–YFP and Ad-Pex16–YFP were used to infect cells at 50, 500 and 200 pfu per cell, respectively, in the presence of 4 μg/ml polybrene. Medium was replaced 1 day after infection. Cells were fixed with pre-warmed 5% PFA that was added directly to cells just after removing the culture medium without PBS wash. After incubation at 37 °C for 15 min, PFA was quenched with 50 mM NH Cl/PBS for 10 min at room temperature. Cells were permeabilized with 0.1% Triton X-100/PBS (v/v) for 10 min at room temperature and blocked with 5% FBS/PBS for 10 min at room temperature. Cells were incubated with appropriate primary antibodies for 2 h. After the wash with PBS, cells were incubated with secondary antibodies for 1 h. Cells were observed with spinning confocal microscopy (Olympus IX81 with Andor/Yokogawa spinning disk system (CSU-X), sCMOS camera and 100× or 60× objective lenses (NA1.4)). For quantification analysis, more than 30 cells in each condition were randomly chosen and counted based on the definitions on main figures (stages of de novo synthesis: Fig. 1a; localization of Pex3–YFP: Fig. 2b, left). Cells plated in a glass-bottom cell culture dish (MatTek) were infected with adenoviruses. Twenty-four hours after infection, cells were incubated with 100 nM MitoTracker Deep Red FM (Molecular Probes) for 20 min at 37 °C. Cells were washed in DMEM and observed in DMEM containing no phenol red (31053028, GIBCO) supplied with 10% FBS and 2 mM l-glutamine, NEAA and 10 mM HEPES pH 7.4 using a spinning disk confocal microscope (described above) with a 100× objective and EMCCD camera. For long-term imaging (40–48 h), infected cells in phenol red-free medium were monitored with Viva View FL Incubator microscope fitted with a 40× objective (Olympus) beginning 24 h after initial infection. Pex3mut and Pex16mut cells were transfected with Pex16–mRFP and infected with Ad-Pex3–YFP. Sixteen hours later, cells were trypsinized and co-plated into a glass bottom cell culture dish (MatTek). One day after that, cells were fused with 50% (w/v) PEG (Fluka, MW: 1,500 Da) in MEM (Invitrogen) containing medium for 1 min. After extensive washing (5×) with DMEM, cells were monitored with the spinning disk confocal microscope beginning 1 h after whole-cell fusion32. Cells prepared for electron microscopy were infected as indicated for 1 day before processing to capture the early events in peroxisomal biogenesis. As previously described33, cells were fixed with 5% PFA and 1.6% glutaraldehyde (GA) in 0.1 M sodium cacodylate buffer (pH 7.4) for 10 min at room temperature, then further fixed at 4 °C in the same buffer overnight. After washing with 0.1 M cacodylate buffer, cells were fixed with 1% osmium tetroxide for 60 min at 4 °C. Cells were washed with water, stained with saturated aqueous uranyl acetate for 45 min at room temperature, and then gradually dehydrated with a series of increasing concentrations of ethanol (70–100%). After dehydrating with 100% acetone, cells were gradually embedded in Spurr’s resin, and polymerized for 48 h at 60 °C. Samples were sectioned to a 100-nm thickness and sections were mounted on 200-mesh copper grids. Sections were imaged at 120 kV using a FEI Tecnai 12 TEM outfitted with an AMT XR80C CCD Camera System, housed in the Facility for Electron Microscopy Research (FEMR) at McGill University. The sizes of pre-peroxisomes on mitochondria were measured with ImageJ (NIH). For immuno-gold labelling, cells infected with Ad-Pex3–YFP or Ad-Pex16–YFP for 24 h were fixed in 5% PFA and 0.1% GA in PBS for 15 min at 37 °C. After washing with PBS, aldehydes were quenched with 50 mM glycine in PBS. Cells were permeabilized with 0.1% saponin and 5% BSA in PBS for 30 min at room temperature. Cells were incubated with anti GFP-antibody for 1 h at room termperature. After washing with 1% BSA in PBS, cells were incubated with 1.4 nm nanogold-conjugated goat anti-rabbit IgG for one hour at room temperature. After washing with PBS, cells were post-fixed with 1.6% GA in PBS for 10 min at room temperature. Cells were washed with water, then nanogold particles were enhanced using the HQ Silver Enhancement Kit (Nanoprobes) according to the manufacturer’s instructions. Cells were stored in 1.6% GA in 0.1 M sodium cacodylate at 4 °C overnight and processed as for conventional TEM (described above). Cells resuspended in ice-cold homogenization buffer (HB, 10 mM HEPES-KOH pH 7.4, 220 mM manitol, 70 mM sucrose, protease inhibitor cocktail) were homogenized with a 27-G needle (BD). Post-nuclear supernatants after centrifugation at 800g for 10 min were centrifuged at 2,300g for 10 min. Supernatants were further centrifuged at 23,000g for 15 min and at 100,000g for 1 h. After each centrifugation, pellets were resuspended in HB. Protein concentrations were measured by the Bradford method and analysed by immunoblotting. Twenty-five micrograms of 2.3 K for mitochondrial or 23 K for peroxisomal fractions suspended in 40 μl of mitochondrial isolation buffer (MIB, 10 mM HEPES pH 7.4, 68 mM sucrose, 80 mM KCl, 0.5 mM EDTA, 2 mM Mg(CH COO) ) containing varying amounts of trypsin (Sigma) were incubated on ice for 20 min. Digestion was terminated by adding soybean trypsin inhibitor (2.5 mg/ml, Sigma). For alkaline carbonate extraction, 50 μg of each fraction suspended in 50 μl of 0.1 M Na CO pH 11.5 was incubated on ice for 30 min. Soluble and membrane fractions were separated by centrifugation at 200,000g for 15 min at 4 °C. Cell-free mitochondrial import assays were performed as previously described34, with some modifications. In brief, Pex3– and Pex16–myc-His inserted into pCDNA3.1 myc-His (-) B (Invitrogen) were linearized by AglII restriction enzyme (NEB) before in vitro transcription. Capped RNA was synthesized in vitro using T7 polymerase (Promega). For the co-translational import assay, synthesized RNA was incubated with rabbit reticulocyte lysate (RRL, Promega) and 14.4 μg of canine pancreas microsomes35, 36 for 30 min at 30 °C. Microsomes were collected by centrifugation at 100,000g for 15 min after washing with MIB twice. For post-translational import into isolated mitochondria, synthesized RNA was incubated with RRL for 30 min at 30 °C. Reaction products were incubated with 50 μg mitochondria isolated from mouse heart34 in 50 μl reaction mix (10 mM HEPES pH 7.4, 110 mM Mannitol, 68 mM sucrose, 80 mM KCl, 0.5 mM EGTA, 2 mM Mg(CH COO) , 0.5 mM GTP, 2 mM K HPO , 1 mM ATP (K+), 0.08 mM ADP, 5 mM sodium succinate) for 30 min at 30 °C. Mitochondria were washed with MIB twice and subjected to further analysis for suborganellular localization as described above or directly analysed by immunoblotting. Pex3mut cells infected with Ad-GFP or Ad-Pex3–YFP for 10 h were further treated with or without 500 nM MG132 for 14 h. Cells were lysed with 0.1% SDS lysis buffer (50 mM Tris-HCl pH 7.4, 150 mM NaCl, 5 mM EDTA, 0.1% SDS, 1% Triton-X 100, protease inhibitor cocktail and PhosStop). Soluble fractions were obtained by centrifugation at 20,000g for 20 min at 4 °C. Non-specific binding proteins were removed by rotating with protein-G sepharose (GE Healthcare) for 1 h at 4 °C. Lysates were subjected to immunoprecipitation using rabbit polyclonal anti-GFP antibodies (Invitrogen). Immmunoprecipitates were eluted by adding SDS–PAGE sample buffer and analysed by immunoblotting. Total RNA was isolated from each cell using RNeasy kit (QIAGEN). qRT–PCR and data analysis were performed at IRIC Genomics Platform (University de Montreal). Primers are as follows: ACTB (endogenous control) (Fw: attggcaatgagcggttc, Rv: tgaaggtagtttcgtggatgc), GAPDH (endogenous control) (Fw: agccacatcgctcagacac, Rv: gcccaatacgaccaaatcc), Pex19 (Fw: gcaagtcggaggtagcaaga, Rv: ctttatcgaaatcatcaagagcac), Pex3 (Fw: aaccagaggacttgcaatatgac, Rv: tgctgcattaaggcctctct), Pex16 (Fw: aggtgtggggtgaagtgg, Rv: caggagcatccgcagtaca). The means of each condition were calculated from three independent experiments, counting at least 30 cells per condition to generate enough power for statistical significance. P values for data comparison were calculated by Student’s t-test. 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 data shown here have been reproduced at least three times by the authors. All data supporting the findings of this study are available within the paper and the supplementary information files.


News Article | December 15, 2016
Site: www.eurekalert.org

Using hundreds of viral genome sequences, scientists have shown that two major groups of rabies virus have unique evolutionary tendencies. Their findings are presented in a new study published in PLOS Pathogens. Diseases that jump from other vertebrate hosts to humans are a major public health threat, but the evolutionary mechanisms behind these jumps are poorly understood. With its long history of jumping between host species, the rabies virus offers a good opportunity to identify evolutionary patterns associated with such shifts. Cécile Troupin of Institut Pasteur, Paris, and colleagues compared 321 viral genome sequences collected from 66 countries over 65 years. The analysis revealed very different evolutionary patterns for bat-related rabies, which is found in bats and some carnivores; versus dog-related rabies, which is responsible for almost all human cases of rabies and is found in both dogs and wild carnivores. The data suggest that different subgroups of bat-related rabies do not evolve uniformly, but dog-related rabies usually evolves at a steady rate. For dog-related rabies, host jumping was linked to multiple evolutionary patterns, such as parallel changes in amino acid sequences between different host species. The data also suggest that dog-related rabies may not need to evolve much to jump to new carnivore hosts. Looking deeper into dog-related rabies, the scientists found evidence to suggest that, after trade between continents began in the 15th century, dog-related rabies rapidly spread worldwide. The authors say that the particular combination of species currently infected by dog-related rabies probably arose as a combined effect of historical spread by humans and host jumping. "The data indicate that different subgroups of bat-related rabies do not evolve uniformly, but dog-related rabies usually evolves at a steady rate," the authors explain. "For dog-related rabies, host jumping was linked to multiple evolutionary patterns, such as parallel changes in amino acid sequences between different host species, suggesting that dog-related rabies may not need to evolve much to jump to new carnivore hosts." In your coverage please use this URL to provide access to the freely available article in PLOS Pathogens: http://journals. Please contact plospathogens@plos.org if you would like more information. Funding: This work was supported by European Union Seventh Framework Programme PREDEMICS (grant number 278433) and by the Agence Nationale de la Recherche (grant number BSV3-0019). The Genomics Platform is member of "France Génomique" consortium (ANR10-INBS-09-08). SD is supported by a University of Melbourne McKenzie fellowship. ECH is supported by an NHMRC Australia Fellowship. HB and CS were supported by the European Virus Archive goes Global (EVAg) project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 653316. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. Citation: Troupin C, Dacheux L, Tanguy M, Sabeta C, Blanc H, Bouchier C, et al. (2016) Large-Scale Phylogenomic Analysis Reveals the Complex Evolutionary History of Rabies Virus in Multiple Carnivore Hosts. PLoS Pathog 12(12): e1006041. doi:10.1371/journal.ppat.1006041


Bianchini V.,Instituto Zooprofilattico Sperimentale della Lombardia e dellEmilia Romagna | Recordati C.,Filarete Foundation | Borella L.,Instituto Zooprofilattico Sperimentale della Lombardia e dellEmilia Romagna | Gualdi V.,Genomics Platform | And 4 more authors.
BioMed Research International | Year: 2015

Helicobacter pylori is responsible for gastritis and gastric adenocarcinoma in humans, but the routes of transmission of this bacterium have not been clearly defined. Few studies led to supposing that H. pylori could be transmitted through raw milk, and no one investigated the presence of other Helicobacteraceae in milk. In the current work, the presence of Helicobacteraceae was investigated in the bulk tank milk of dairy cattle herds located in northern Italy both by direct plating onto H. pylori selective medium and by screening PCR for Helicobacteraceae, followed by specific PCRs for H. pylori, Wolinella spp., and "Candidatus Helicobacter bovis." Three out of 163 bulk milk samples tested positive for Helicobacteraceae, but not for the subsequent PCRs. H. pylori was not isolated in any case. However, given similar growth conditions, Arcobacter butzleri, A. cryaerophilus, and A. skirrowii were recovered. In conclusion, the prevalence of Helicobacteraceae in raw milk was negligible (1.8%), and H. pylori was not identified in any of the positive samples, suggesting that, at least in the farming conditions of the investigated area, bovine milk does not represent a potential source of infection. © 2015 Valentina Bianchini et al.


PubMed | Filarete Foundation, Genomics Platform, Instituto Zooprofilattico Sperimentale della Lombardia e dellEmilia Romagna and University of Milan
Type: | Journal: BioMed research international | Year: 2015

Helicobacter pylori is responsible for gastritis and gastric adenocarcinoma in humans, but the routes of transmission of this bacterium have not been clearly defined. Few studies led to supposing that H. pylori could be transmitted through raw milk, and no one investigated the presence of other Helicobacteraceae in milk. In the current work, the presence of Helicobacteraceae was investigated in the bulk tank milk of dairy cattle herds located in northern Italy both by direct plating onto H. pylori selective medium and by screening PCR for Helicobacteraceae, followed by specific PCRs for H. pylori, Wolinella spp., and Candidatus Helicobacter bovis. Three out of 163 bulk milk samples tested positive for Helicobacteraceae, but not for the subsequent PCRs. H. pylori was not isolated in any case. However, given similar growth conditions, Arcobacter butzleri, A. cryaerophilus, and A. skirrowii were recovered. In conclusion, the prevalence of Helicobacteraceae in raw milk was negligible (1.8%), and H. pylori was not identified in any of the positive samples, suggesting that, at least in the farming conditions of the investigated area, bovine milk does not represent a potential source of infection.

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