Institute for Research in Immunology and Cancer
Institute for Research in Immunology and Cancer
Patenaude J.,Institute for Research in Immunology and Cancer |
Patenaude J.,University of Montréal |
Perreault C.,Institute for Research in Immunology and Cancer |
Perreault C.,University of Montréal
Journal of Immunology | Year: 2016
In order to understand the role of mesenchymal cells (MCs) in the adult thymus, we performed whole transcriptome analyses of primary thymic, bone, and skin MCs. These three MC populations shared expression of 2850 core MC genes involved in generic processes including interactions with tissue-resident macrophages. Moreover, we discovered that 2036 genes were differentially expressed, by at least 5-fold, in the threeMCpopulations. Genes preferentially expressed in thymicMCs are instrumental in clearance of apoptotic thymocytes by macrophages, maintenance of a noninflammatory milieu, and attraction-expansion of thymocyte progenitors. Thymic and boneMCs share other sets of differentially expressed genes implicated in resolution of inflammation and expansion of hematolymphoid progenitors. Consistent with the fact that thymic and skin MCs have to support epithelial cells, they express at higher levels genes mediating epithelial cell adhesion to basement membrane and mesenchymal-epithelial cross-talk. Differentially expressed genes preferentially expressed by bone MCs are connected to formation and remodeling of bone, whereas those preferentially expressed in skin MCs are involved in skin and hair follicle homeostasis. We conclude that MCs from different organs display substantial heterogeneity and that the transcriptome of thymic MCs is exquisitely suited for interactions with epithelial and hematolymphoid cells in an environment with a high apoptosis rate. Copyright © 2016 by The American Association of Immunologists, Inc.
Capalbo L.,University of Cambridge |
D'Avino P.P.,University of Cambridge |
Archambault V.,University of Cambridge |
Archambault V.,Institute for Research in Immunology and Cancer |
Glover D.M.,University of Cambridge
Proceedings of the National Academy of Sciences of the United States of America | Year: 2011
The small GTPase Rab5 is a conserved regulator of membrane trafficking; it regulates the formation of early endosomes, their transport along microtubules, and the fusion to the target organelles. Although several members of the endocytic pathway were recently implicated in spindle organization, it is unclear whether Rab5 has any role during mitosis. Here, we describe that Rab5 is required for proper chromosome alignment during Drosophila mitoses. We also found that Rab5 associated in vivo with nuclear Lamin and mushroom body defect (Mud), the Drosophila counterpart of nuclear mitotic apparatus protein (NuMA). Consistent with this finding, Rab5 was required for the disassembly of the nuclear envelope at mitotic entry and the accumulation of Mud at the spindle poles. Furthermore, Mud depletion caused chromosome misalignment defects that resembled the defects of Rab5 RNAi cells, and double-knockdown experiments indicated that the two proteins function in a linear pathway. Our results indicate a role for Rab5 in mitosis and reinforce the emerging view of the contributions made by cell membrane dynamics to spindle function.
Piekny A.J.,Concordia University at Montréal |
Maddox A.S.,Institute for Research in Immunology and Cancer
Seminars in Cell and Developmental Biology | Year: 2010
Anillin is a highly conserved multidomain protein that interacts with cytoskeletal components as well as their regulators. Throughout phylogeny, Anillins contribute to cytokinesis, the cell shape change that occurs at the end of meiosis and mitosis to separate a cell into daughter cells. Failed cytokinesis results in binucleation, which can lead to genomic instability. Study of Anillin in several model organisms has provided us with insight into how the cytoskeleton is coordinated to ensure that cytokinesis occurs with high fidelity. Here we review Anillin's interacting partners and the relevance of these interactions in vivo. We also discuss questions of how these interactions are coordinated, and finally provide some perspective regarding Anillin's role in cancer. © 2010 Elsevier Ltd.
Vincent K.,Institute for Research in Immunology and Cancer |
Vincent K.,University of Montréal |
Roy D.-C.,University of Montréal |
Perreault C.,Institute for Research in Immunology and Cancer |
Perreault C.,University of Montréal
Blood | Year: 2011
Allogeneic hematopoietic cell transplantation led to the discovery of the allogeneic GVL effect, which remains the most convincing evidence that immune cells can cure cancer in humans. However, despite its great paradigmatic and clinical relevance, induction of GVL by conventional allogeneic hematopoietic cell transplantation remains a quite rudimentary form of leukemia immunotherapy. It is toxic and its efficacy is far from optimal. It is therefore sobering that since the discovery of the GVL effect 3 decades ago, the way GVL is induced and manipulated has practically not changed. Preclinical and clinical studies suggest that injection of T cells primed against a single Ag present on neoplastic cells could enhance the GVL effect without causing any GVHD. We therefore contend that Ag-targeted adoptive T-cell immunotherapy represents the future of leukemia immunotherapy, and we discuss the specific strategies that ought to be evaluated to reach this goal. Differences between these strategies hinge on 2 key elements: the nature of the target Ag and the type of Ag receptor expressed on T cells. © 2011 by The American Society of Hematology.
Regina A.,AngioChem |
Demeule M.,AngioChem |
Tripathy S.,AngioChem |
Tripathy S.,Institute for Research in Immunology and Cancer |
And 6 more authors.
Molecular Cancer Therapeutics | Year: 2015
Anti-HER2 monoclonal antibodies (mAb) have been shown to reduce tumor size and increase survival in patients with breast cancer, but they are ineffective against brain metastases due to poor brain penetration. In previous studies, we identified a peptide, known as Angiopep-2 (An2), which crosses the blood-brain barrier (BBB) efficiently via receptor-mediated transcytosis, and, when conjugated, endows small molecules and peptides with this property. Extending this strategy to higher molecular weight biologics, we now demonstrate that a conjugate between An2 and an anti-HER2 mAb results in a new chemical entity, ANG4043, which retains in vitro binding af finity for the HER2 receptor and antiproliferative potency against HER2-positive BT-474 breast ductal carcinoma cells. Unlike the native mAb, ANG4043 binds LRP1 clusters and is taken up by LRP1-expressing cells. Measuring brain exposure after intracarotid delivery, we demonstrate that the new An2-mAb conjugate penetrates the BBB with a rate of brain entry (Kin) of 1.6 x 10-3 mL/g/s. Finally, in mice with intracranially implanted BT-474 xenografts, systemically administered ANG4043 increases survival. Overall, this study demonstrates that the incorporation of An2 to the anti-HER2 mAb confers properties of increased uptake in brain endothelial cells as well as BBB permeability. These characteristics of ANG4043 result in higher exposure levels in BT-474 brain tumors and prolonged survival following systemic treatment. Moreover, the data further validate the An2-drug conjugation strategy as a way to create brain-penetrant biologics for neuro-oncology and other CNS indications. © 2014 American Association for Cancer Research.
Labrie M.,INRS Institute Armand Frappier |
Vladoiu M.C.,INRS Institute Armand Frappier |
Grosset A.-A.,INRS Institute Armand Frappier |
Gaboury L.,Institute for Research in Immunology and Cancer |
St-Pierre Y.,INRS Institute Armand Frappier
Oncotarget | Year: 2014
There is a critical need to develop effective new strategies for diagnosis and treatment of ovarian cancer. In the present work, we investigated the expression of galectin-7 (gal-7) in epithelial ovarian cancer (EOC) cells and studied its functional relevance. Immunohistochemical analysis of gal-7 expression in tissue microarrays showed that while gal-7 was not detected in normal ovarian tissues, positive cytoplasmic staining of gal-7 was detected in epithelial cells in all EOC histological subtypes but was more frequent in high grade tumors and metastatic samples. Gal-7 expression correlated with a significant difference in the overall survival of patients with ovarian serous cystadenocarcinoma. Furthermore, using human EOC cell lines, we found that gal-7 expression was induced by mutant p53. Mechanistically, Matrigel invasion assays and live cell imaging showed that gal-7 increased the invasive behavior of ovarian cancer cells by inducing MMP-9 and increasing cell motility. EOC cells can also secrete gal-7. Recombinant human gal-7 kills Jurkat T cells and human peripheral T cells, suggesting that gal-7 also has immunosuppressive properties. Taken together, our study validates the clinical significance of gal-7 overexpression in ovarian cancer and provides a rationale for targeting gal-7 to improve the outcome of patients with this disease.
Lacombe J.,Institute for Research in Immunology and Cancer
Blood | Year: 2013
SCL/TAL1, a tissue-specific transcription factor of the basic helix-loop-helix family, and c-Kit, a tyrosine kinase receptor, control hematopoietic stem cell survival and quiescence. Here we report that SCL levels are limiting for the clonal expansion of Kit+ multipotent and erythroid progenitors. In addition, increased SCL expression specifically enhances the sensitivity of these progenitors to steel factor (KIT ligand) without affecting interleukin-3 response, whereas a DNA-binding mutant antagonizes KIT function and induces apoptosis in progenitors. Furthermore, a twofold increase in SCL levels in mice bearing a hypomorphic Kit allele (W41/41) corrects their hematocrits and deficiencies in erythroid progenitor numbers. At the molecular level, we found that SCL and c-Kit signaling control a common gene expression signature, of which 19 genes are associated with apoptosis. Half of those were decreased in purified megakaryocyte/erythroid progenitors (MEPs) from W41/41 mice and rescued by the SCL transgene. We conclude that Scl operates downstream of Kit to support the survival of MEPs. Finally, higher SCL expression upregulates Kit in normal bone marrow cells and increases chimerism after bone marrow transplantation, indicating that Scl is also upstream of Kit. We conclude that Scl and Kit establish a positive feedback loop in multipotent and MEPs.
News Article | September 26, 2016
Researchers funded in part by NIBIB have recently shown that magnetic bacteria are a promising vehicle for more efficiently delivering tumor-fighting drugs. They reported their results in the August 2016 issue of Nature Nanotechnology. One of the biggest challenges in cancer therapy is being able to sufficiently deliver chemotherapy drugs to tumors without exposing healthy tissues to their toxic effects. One way researchers have attempted to overcome this is by developing nanocarriers—extremely small particles packed with drugs. The nanocarriers are designed so they’re only taken up by cancer cells, thereby preventing the drugs from being absorbed by healthy tissues as they travel through the body’s circulation. Yet while nanocarriers do a good job protecting healthy tissues, the amount of drug successfully delivered to tumors remains low. The main reasons for this shortcoming are that nanocarriers rely on the circulation system to carry them to the tumor, so a large percentage are filtered out of the body before ever reaching their destination. In addition, differences in pressure between the tumor and its surrounding tissue prevent nanocarriers from penetrating deep inside the tumor. As a result, nanocarriers aren’t able to reach the tumor’s hypoxic zones, which are regions of active cell division that are characterized by low oxygen content. “Only a very small proportion of drugs reach the hypoxic zones, which are believed to be the source of metastasis. Therefore, targeting the low-oxygen regions will most likely decrease the rate of metastasis while maximizing the effect of a therapy,” says Sylvain Martel, Ph.D., Director of the Polytechnique Montréal NanoRobotics Laboratory and lead researcher of the study. Martel and his research team were attempting to develop robotic nanocarriers that would travel to hypoxic zones when they realized nature may have already created one in the form of a bacteria called magnetococcus marinus or MC-1. MC-1 cells thrive in deep waters where oxygen is sparse. In order to find these areas, the bacteria rely on a two-part navigation system. The first part involves a chain of magnetic nanocrystals within MC-1 that acts like a compass needle and causes the bacteria to swim in a north direction when in the Northern Hemisphere. The second part consists of sensors that allow the bacteria to detect changes in oxygen levels. This unique navigation system helps the bacteria migrate to and maintain their position at areas of low oxygen. With funding support from NIBIB and others, Martel’s research team conducted a series of experiments to show that the bacteria’s unique navigation system could be exploited to more efficiently deliver drugs to tumors. In an initial experiment, mice that had been given human colorectal tumors were injected with either live MC-1 cells, dead MC-1 cells, or as a control group, non-magnetic beads (roughly the same size as the bacteria). The injection was made into the tissue directly adjacent to the tumors after which the mice were exposed to a computer-programmed magnetic field, meant to direct the cells or beads into the tumor. Upon examination of the tumors, the researchers found minimal penetration of the dead bacterial cells and the beads into the tumor, whereas the live bacterial cells were found deep within the tumor and especially in regions with low oxygen content. “When they get inside the tumor, we switch off the magnetic field and the bacteria automatically rely on the oxygen sensors to seek out the hypoxic areas,” says Martel. “We constrain them to the tumor and then let nature do the rest.” Next, the researchers wanted to see whether attaching vesicles loaded with drugs to the cells would affect their movement into the tumors. They attached approximately 70 drug-containing vesicles to each bacterial cell. The cells were then injected into another set of mice with colorectal tumors and exposed to the magnet. After examining the tumors of those mice, the researchers estimated that on average, 55% of the injected bacterial cells with attached vesicles made it into the tumor. For comparison, some researchers estimate that only approximately 2% of drugs delivered via current nanocarriers make it into tumors. "This proof-of-concept work shows the potential to tap into the intricate and optimized cell machinery of single celled organisms such as bacteria," said Richard Conroy, Ph.D., director of the Division of Applied Sciences and Technology at NIBIB. "The ability to actively and precisely target drug delivery to a tumor will help reduce side effects and potentially improve the efficacy of treatments." The next step for Martel’s team is to determine the effects of the drug-loaded bacterial cells on reducing tumor size. They would also like to test whether the bacteria can be used to deliver other types of cancer-killing medicines such as molecules that instruct the immune system to attack tumors. In addition, the team is working to expand the types of tumors the bacteria could be used for. Currently, the bacteria have to be injected very close to the tumor because, if injected into arteries, the excessive blood flow and the distance needed to travel would impact the number of bacteria that reach the tumor. This limits the drug delivery approach to cancers that are easily accessible such as colorectal, prostate, and potentially breast cancer. However, Martel’s team has shown in animals that they can transport the bacteria through arteries and sufficiently close to the tumor by first encapsulating them in magnetic carriers and propelling them by the magnetic field of an MRI scanner. The bacteria can then be released from the carriers, like torpedoes from a submarine, once close to the tumor. This multi-step approach could potentially open the door for using the bacteria to deliver drugs to tumors deeper in the body. Martel says that preliminary test results of the bacteria in mice and rats and the fact that the bacteria die within 30 minutes of being injected, suggest that they could potentially be safe in humans. “These bacteria are really the perfect machine. They replicate, they’re cheap, and we can inject hundreds of millions or more at a time,” says Martel. This research was funded by the Consortium québécois sur la découverte du médicament (Québec consortium for drug discovery – CQDM), the Canada Research Chairs, the Natural Sciences and Engineering Research Council of Canada (NSERC), the Research Chair in Nanorobotics of Polytechnique Montréal, Mitacs, the Canada Foundation for Innovation (CFI) and the National Institute of Biomedical Imaging and Bioengineering (EB 007506). Montréal’s Jewish General Hospital, the McGill University Health Centre (MUHC), the Institute for Research in Immunology and Cancer (IRIC), and the Rosalind and Morris Goodman Cancer Research Centre also took part in this promising research work
News Article | August 16, 2016
Researchers from Polytechnique Montréal, Université de Montréal, and McGill University have just achieved a spectacular breakthrough in cancer research. They have developed new nanorobotic agents capable of navigating through the bloodstream to administer a drug with precision by specifically targeting the active cancerous cells of tumors. This way of injecting medication ensures the optimal targeting of a tumor and avoids jeopardizing the integrity of organs and surrounding healthy tissues. As a result, the drug dosage that is highly toxic for the human organism could be significantly reduced. This scientific breakthrough has just been published in the prestigious journal Nature Nanotechnology in an article titled “Magneto-aerotactic bacteria deliver drug-containing nanoliposomes to tumor hypoxic regions.” The article notes the results of the research done on mice, which were successfully administered nanorobotic agents into colorectal tumors. “These legions of nanorobotic agents were actually composed of more than 100 million flagellated bacteria — and therefore self-propelled — and loaded with drugs that moved by taking the most direct path between the drug’s injection point and the area of the body to cure,” explains Professor Sylvain Martel, holder of the Canada Research Chair in Medical Nanorobotics and Director of the Polytechnique Montréal Nanorobotics Laboratory, who heads the research team’s work. “The drug’s propelling force was enough to travel efficiently and enter deep inside the tumors.” When they enter a tumor, the nanorobotic agents can detect in a wholly autonomous fashion the oxygen-depleted tumor areas, known as hypoxic zones, and deliver the drug to them. This hypoxic zone is created by the substantial consumption of oxygen by rapidly proliferative tumor cells. Hypoxic zones are known to be resistant to most therapies, including radiotherapy. But gaining access to tumors by taking paths as minute as a red blood cell and crossing complex physiological micro-environments does not come without challenges. So Professor Martel and his team used nanotechnology to do it. To move around, bacteria used by Martel’s team rely on two natural systems. A kind of compass created by the synthesis of a chain of magnetic nanoparticles allows them to move in the direction of a magnetic field, while a sensor measuring oxygen concentration enables them to reach and remain in the tumor’s active regions. By harnessing these two transportation systems and by exposing the bacteria to a computer-controlled magnetic field, researchers showed that these bacteria could perfectly replicate artificial nanorobots of the future designed for this kind of task. “This innovative use of nanotransporters will have an impact not only on creating more advanced engineering concepts and original intervention methods, but it also throws the door wide open to the synthesis of new vehicles for therapeutic, imaging and diagnostic agents,” Martel adds. “Chemotherapy, which is so toxic for the entire human body, could make use of these natural nanorobots to move drugs directly to the targeted area, eliminating the harmful side effects while also boosting its therapeutic effectiveness.” The work by Professor Martel obtained the very valuable support of the Consortium québécois sur la découverte du médicament (Québec consortium for drug discovery — CQDM), the Canada Research Chairs, the Natural Sciences and Engineering Research Council of Canada (NSERC), the Research Chair in Nanorobotics of Polytechnique Montréal, Mitacs, the Canada Foundation for Innovation (CFI), and the National Institutes of Health (NIH). Montréal’s Jewish General Hospital, the McGill University Health Centre (MUHC), the Institute for Research in Immunology and Cancer (IRIC), and the Rosalind and Morris Goodman Cancer Research Centre also took part in this promising research work.
News Article | April 27, 2016
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.