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By now, you might have discovered that taming your sweet tooth as a New Year's resolution is harder than you think. New research by Duke Univ. scientists suggests that a habit leaves a lasting mark on specific circuits in the brain, priming us to feed our cravings. Published online Jan. 21 in the journal Neuron, the research deepens scientists' understanding of how habits like sugar and other vices manifest in the brain and suggests new strategies for breaking them. "One day, we may be able to target these circuits in people to help promote habits that we want and kick out those that we don't want," said the study's senior investigator Nicole Calakos, M.D., Ph.D., an associate professor of neurology and neurobiology at the Duke University Medical Center. Calakos, an expert in the brain's adaptability, teamed up with Henry Yin, an expert in animal models of habit behavior in Duke's department of psychology and neuroscience. Both scientists are also members of the Duke Institute for Brain Sciences. Their groups trained otherwise healthy mice to form sugar habits of varying severity, a process that entailed pressing a lever to receive tiny sweets. The animals that became hooked kept pressing the lever even after the treats were removed. The researchers then compared the brains of mice that had formed a habit to the ones that didn't. In particular, the team studied electrical activity in the basal ganglia, a complex network of brain areas that controls motor actions and compulsive behaviors, including drug addiction. In the basal ganglia, two main types of paths carry opposing messages: One carries a 'go' signal which spurs an action, the other a 'stop' signal. Experiments by Duke neurobiology graduate student Justin O'Hare found that the stop and go pathways were both more active in the sugar-habit mice. O'Hare said he didn't expect to see the stop signal equally ramped up in the habit brains, because it has been traditionally viewed as the factor that helps prevent a behavior. The team also discovered a change in the timing of activation in the two pathways. In mice that had formed a habit, the go pathway turned on before the stop pathway. In non-habit brains, the stop signal preceded the go. These changes in the brain circuitry were so long-lasting and obvious that it was possible for the group to predict which mice had formed a habit just by looking at isolated pieces of their brains in a petri dish. Scientists have previously noted that these opposing basal ganglia pathways seem to be in a race, though no one has shown that a habit gives the go pathway a head start. O'Hare said that's because the go and stop signals had not been studied in the same brain at the same time. But new labeling strategies used by the Duke scientists allowed researchers to measure activity across dozens of neurons in both pathways simultaneously, in the same animal. "The go pathway's head start makes sense," said Calakos. "It could prime the animal to be more likely to engage in the behavior." The researchers are testing this idea, as well as investigating how the rearrangements in activity occur in the first place. Interestingly, the group observed that changes in go and stop activity occurred across the entire region of the basal ganglia they were studying as opposed to specific subsets of brain cells. O'Hare said this may relate to the observation that an addiction to one thing can make a person more likely to engage in other unhealthy habits or addictions as well. To see if they could break a habit, the researchers encouraged the mice to change their habit by rewarding them only if they stopped pressing the lever. The mice that were the most successful at quitting had weaker go cells. But how this might translate into help for humans with bad habits is still unclear. Because the basal ganglia is involved in a broad array of functions, it may be tricky to target with medicines. Calakos said some researchers are beginning to explore the possibility of treating drug addiction using transcranial magnetic stimulation or TMS, a noninvasive technique that uses magnetic pulses to stimulate the brain. "TMS is an inroad to access these circuits in more severe diseases," she said, in particular targeting the cortex, a brain area that serves as the main input to the basal ganglia. For more ordinary bad habits "simpler, behavioral strategies many of us try may also tap into similar mechanisms," Calakos added. "It may be just a matter of figuring out which of them are the most effective." Meanwhile, Calakos and her team are studying what distinguishes ordinary habits from the problematic ones that can be seen in conditions like obsessive-compulsive disorder.


News Article
Site: http://www.materialstoday.com/news/

The recipient of the 2017 Acta Materialia Gold Medal is Dr. John J. Jonas, Henry Birks Professor Emeritus, Department of Mining and Materials Engineering, McGill University, Montreal, Canada.   Dr. Jonas was born in Montreal and graduated from McGill University with a bachelor’s degree in Metallurgical Engineering in 1954.  After working for one year at the Steel Company of Wales in Port Talbot, he attended Cambridge University on an Athlone Fellowship and received a Ph.D. degree in Mechanical Sciences in 1960.  On returning to Montreal, he began teaching “mechanical metallurgy” at McGill and built up a research laboratory that includes a number of specialized testing machines and is particularly well equipped for experimental investigations in the field of high temperature deformation. Professor Jonas’ most important scientific contributions are related to determining what happens to sheet steel when it is red hot and flying through a rolling mill at 100 km/hr. The basic phenomena involved include dynamic and post-dynamic recrystallization, dynamic transformation and retransformation, and the dynamic and strain-induced precipitation of carbonitrides.  He and his co-workers have made seminal contributions to all three of these areas of research.  An important related innovation was establishment of the laboratory method of determining the T  (temperature of no-recrystallization) during rolling, a procedure that is now employed in rolling mills worldwide.  This work has resulted in major improvements in the understanding and control of the microstructural changes taking place during steel processing and has led to more accurate computer models for the control of industrial rolling mills. In addition to his research in ferrous metallurgy, Professor Jonas has made numerous contributions to the understanding of the deformation behavior of non-ferrous metals. These have included explanations of variant selection of twins in Mg and Ti, of the causes of plastic instability and flow localization during metal forming, and of texture development during deformation, annealing and phase transformation. He has received numerous awards for this work, including the Réaumur and Gold Medals of the French Metallurgical Society, the Hatchett Medal of the Metals Society (U.K.), the Airey, Dofasco and Alcan Awards of the Canadian Institute of Mining and Metallurgy, the Gold Medal of the Canadian Metal Physics Association, the NSERC Award of Excellence, the Killam Prize for Engineering, the Michael Tenenbaum Award of the American Institute of Metallurgical Engineers, the Hunt Silver Medal of the US Iron and Steel Society, the Barrett Silver Medal and G. Macdonald Young Award of the American Society for Metals, the Alexander von Humboldt Research Award (Germany), and the Yukawa Silver Medal and two Sawamura Bronze Medals of the Iron and Steel Institute of Japan. Professor Jonas has been elected a Fellow of the American Society for Metals, Royal Society of Canada, Canadian Academy of Engineering, Canadian Institute of Mining and Metallurgy, and Hungarian Academy of Sciences.  He is an Honorary Member of the Iron and Steel Institute of Japan and of the Indian Institute of Metals.  He was made an Officer of the Order of Canada in 1993, a Chevalier of the Order of Quebec in 2000, and received the Quebec prize for science (Prix du Québec - Marie Victorin) in 1995.  He has served as a visiting professor in numerous countries, including Argentina, Australia, Belgium, Brazil, Britain, China, France, Germany, Holland, Hungary, India, Iran, Israel, Japan, Mexico, South Africa, South Korea, Spain, Taiwan, the USA and the USSR. In 1985, Dr. Jonas was appointed to the CSIRA/NSERC Chair of Steel Processing at McGill, a position which was funded jointly by the Canadian Steel Industry Research Association and the Natural Sciences and Engineering Research Council of Canada.  In this capacity, he worked closely with the Canadian steel industry, and collaborated in the solution of a number of important processing problems.  He and his colleagues have been granted five sets of international patents associated with steel rolling, three of which have been assigned to the sponsoring companies. He has trained over 200 students and research fellows in the specializations outlined above and he and his students have published more than 800 papers, 100 of them in Acta and Scripta Materialia.  His current h-index (Hirsch number) is 83 and he has more than 25,000 citations to his credit. The Acta Materialia Gold Medal, established in 1972, is awarded annually by the Board of Governors of Acta Materialia, Inc., with partial financial support from Elsevier, Ltd.  Nominees are solicited each year from the Cooperating Societies and Sponsoring Societies of Acta Materialia, Inc., based on demonstrated ability and leadership in materials research.  Dr. Jonas will receive the Gold Medal at the TMS Annual Meeting in San Diego in March 2017.


News Article
Site: http://www.materialstoday.com/news/

The recipient of the 2017 Acta Materialia Silver Medal is Jing-yang Wang, the distinguished professor and division head in the High-performance Ceramic Division at the Shenyang National Laboratory for Materials Science and Institute of Metal Research, Chinese Academy of Sciences. He is also the assistant director of Shenyang National Laboratory for Materials Science. Jingyang Wang received the B.A. degree in Physics in 1992 from Peking University, M.A. degree in 1995 and Ph.D. degree in 1998, both in Materials Physics from Institute of Metal Research, Chinese Academy of Sciences. He joined the faculty in Institute of Metal Research where he became the assistant professor in 1998, associate professor in 2002, and full professor in 2006. He was the visiting scientist at International Centre for Theoretical Physics (Italy) in 2001, University of Trento (Italy) in 2001, and International Center for Young Scientists (ICYS) at National Institute of Materials Science (Japan) in 2007. Professor Wang focused over 15 years of research activities in the area of materials science of advanced engineering ceramics. He has published more than 180 peer-reviewed SCI papers (H-index factor 36), including 30 in Acta Materialia and Scripta Materialia, and has 17 patents in the field of ceramics. In addition, he presented ~50 keynote/invited talks and served 25 advisory board members and symposium organizers in international conferences. He is internationally recognized for his scientific contributions and leadership in high-throughput materials design and modeling, novel methods for processing bulk, low-dimensional and porous ceramic materials, and multi-scale structure-property relationship of high performance structural ceramics. His recent notable research contributions are: His contributions have been recognized on many scientific advisory boards and committees of the American Ceramic Society (ACerS) and the American Society of Metals International (ASM Int.) and serves on the International Advisory Board of UK CAFFE consortium (University of Cambridge, Imperial College London and University of Manchester) on ceramics for nuclear applications. He also served as the volume editor ofCeramic Engineering and Science Proceedings and is the book editor ofDevelopments in Strategic Materials and Computational Design, both published by John Wiley & Sons, Inc., and is the Executive editor ofJournal of Materials Science and Technology published by Elsevier. Professor Wang’s scientific career has also been recognized with many awards and honors, including ASM-IIM Visiting Lecturer Award in 2016, Distinguished Professor of CAS Distinguished Research Fellow Program from Chinese Academy of Sciences (CAS) in 2016, National Leading Talent of Young and Middle-aged Scientist Award from the Ministry of Science and Technology of China in 2015, DisLate Shri Sardar Pratap Singh Memorial Award from the Indian Ceramic Society in 2015, JACerS Author Loyalty Recognition Award in 2014 and the Global Star Award Society in 2012 from the ACerS, Second Prize in 2012 and First Prize in 2011 for Science and Technology Progress Award from China and First Prize for Natural Science Award from Liaoning Province in 2005. The Acta Materialia Silver Medal honors and recognizes scientific contributions and leadership from academic, industry and public sector leaders in materials research in the midst of their careers.  The Silver Medal was established in 2016 and nominees are solicited each year from the Cooperating Societies and Sponsoring Societies of Acta Materialia. Inc.  Professor Wang will receive the Silver Medal at the TMS Annual Meeting in San Diego in March 2017.


News Article
Site: http://www.biosciencetechnology.com/rss-feeds/all/rss.xml/all

By now, you might have discovered that taming your sweet tooth as a New Year’s resolution is harder than you think. New research by Duke University scientists suggests that a habit leaves a lasting mark on specific circuits in the brain, priming us to feed our cravings. Published online Jan. 21 in the journal Neuron, the research deepens scientists’ understanding of how habits like sugar and other vices manifest in the brain and suggests new strategies for breaking them. “One day, we may be able to target these circuits in people to help promote habits that we want and kick out those that we don’t want,” said the study’s senior investigator Nicole Calakos, M.D., Ph.D., an associate professor of neurology and neurobiology at the Duke University Medical Center. Calakos, an expert in the brain’s adaptability, teamed up with Henry Yin, an expert in animal models of habit behavior in Duke’s department of psychology and neuroscience. Both scientists are also members of the Duke Institute for Brain Sciences. Their groups trained otherwise healthy mice to form sugar habits of varying severity, a process that entailed pressing a lever to receive tiny sweets. The animals that became hooked kept pressing the lever even after the treats were removed. The researchers then compared the brains of mice that had formed a habit to the ones that didn’t. In particular, the team studied electrical activity in the basal ganglia, a complex network of brain areas that controls motor actions and compulsive behaviors, including drug addiction. In the basal ganglia, two main types of paths carry opposing messages: One carries a ‘go’ signal which spurs an action, the other a ‘stop’ signal. Experiments by Duke neurobiology graduate student Justin O’Hare found that the stop and go pathways were both more active in the sugar-habit mice. O’Hare said he didn’t expect to see the stop signal equally ramped up in the habit brains, because it has been traditionally viewed as the factor that helps prevent a behavior. The team also discovered a change in the timing of activation in the two pathways. In mice that had formed a habit, the go pathway turned on before the stop pathway. In non-habit brains, the stop signal preceded the go. These changes in the brain circuitry were so long-lasting and obvious that it was possible for the group to predict which mice had formed a habit just by looking at isolated pieces of their brains in a petri dish. Scientists have previously noted that these opposing basal ganglia pathways seem to be in a race, though no one has shown that a habit gives the go pathway a head start. O’Hare said that’s because the go and stop signals had not been studied in the same brain at the same time. But new labeling strategies used by the Duke scientists allowed researchers to measure activity across dozens of neurons in both pathways simultaneously, in the same animal. “The go pathway’s head start makes sense,” said Calakos. “It could prime the animal to be more likely to engage in the behavior.” The researchers are testing this idea, as well as investigating how the rearrangements in activity occur in the first place. Interestingly, the group observed that changes in go and stop activity occurred across the entire region of the basal ganglia they were studying as opposed to specific subsets of brain cells. O’Hare said this may relate to the observation that an addiction to one thing can make a person more likely to engage in other unhealthy habits or addictions as well. To see if they could break a habit, the researchers encouraged the mice to change their habit by rewarding them only if they stopped pressing the lever. The mice that were the most successful at quitting had weaker go cells. But how this might translate into help for humans with bad habits is still unclear. Because the basal ganglia is involved in a broad array of functions, it may be tricky to target with medicines. Calakos said some researchers are beginning to explore the possibility of treating drug addiction using transcranial magnetic stimulation or TMS, a noninvasive technique that uses magnetic pulses to stimulate the brain. “TMS is an inroad to access these circuits in more severe diseases,” she said, in particular targeting the cortex, a brain area that serves as the main input to the basal ganglia. For more ordinary bad habits “simpler, behavioral strategies many of us try may also tap into similar mechanisms,” Calakos added. “It may be just a matter of figuring out which of them are the most effective.” Meanwhile, Calakos and her team are studying what distinguishes ordinary habits from the problematic ones that can be seen in conditions like obsessive-compulsive disorder.


News Article
Site: http://www.nature.com/nature/current_issue/

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.

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