dataesr/scientific-paragraphs-categorization huggingface dataset:
Genetic structure and association analysis To validate the lack of genetic structure according to morphs, we performed a PCA  [61] , using custom scripts in R (v3. 2. 2) for allele frequencies obtained from pooled data, and the Bioconductor package SeqVarTools for GBS data  [62]. Given our sampling strategy, we expected morphs to group together in the PCA and population structure, if any, to correlate with geography. To further rule out the possibility of any neutral structure according to colour morph, we also conducted a locus by locus analysis of molecular variance (AMOVA) in ARLEQUIN v. 3. 5  [63]  on the 10 115 autosomal SNPs with less than 95% of missing data. To detect loci displaying a significant association with coloration, we performed an association analysis on the pooled RAD-seq data using the software BAYPASS (v.
2. 1)  [64]. We computed the empirical Bayesian p-value (eBPis) and Bayes factors (BF) expressed in deciban units (dB) to determine the level of association of each SNP with the grey/brown trait. Decibans are a commonly used statistic that describes here the probability that data are produced under a given model. They are often used as a unit for BF. Here, it describes the odds of association to morphs versus the null hypothesis of non-association. BAYPASS was run using default parameters under the core model. Empirical Bayesian p-values and BF were then computed for a pseudo-observed dataset containing 1 000 000 SNPs simulated from the actual data. We then compared the observed values to this distribution in order to calibrate the statistics. For GBS data, we performed a single analysis in PLINK  [65]  by correcting for population structure with a Cochran-Mantel-Haenszel test (option -mh in PLINK).
Library quality was assessed using an Agilent Bioanalyzer and libraries were quantified by QPCR using the Kapa Library Quantification Kit. Sequencing was performed in paired-end (2 × 125 pb) on an Illumina HiSeq2500 sequencer at the GeT-PlaGe core facility (INRA, Toulouse). The mean sequencing depth ranged from 6. 9× to 11. 6× after removing duplicates (9. 3× in mean, electronic supplementary material, table  S6 ). After calling with freebayes, SNPs were filtered with VCFTOOLS using the following criteria: (i) a mean sequencing depth between 6 and 20×; (ii) a minimal Phred score of 20 and a genotype quality above 20; (iii) at least eight individuals genotyped. Putative pedigrees obtained from microsatellite data were confirmed by checking the coefficients of relatedness between individuals in KING (v.
1. 4)  [66]  using 10 000 biallelic SNPs randomly sampled across autosomal scaffolds. An association analysis (including indels) taking into account family structure was performed in LAMP (v. 0. 0. 12) [67]. We present here results taking into account the putative heredity of the colour trait (brown recessive, grey dominant) as they were similar to those obtained when not taking the transmission mode into account. To assess whether any associated SNP might lead to changes in protein sequence, we performed an annotation using the software SNPdat (v. 1. 0. 5) [68]  and using gene coordinates from  [56].
Funding. This work was supported by  Fondation pour la Recherche sur la Biodiversité (FRB) ,  Agence Française pour le Développement (AFD) ,  Agence Nationale de la Recherche  ( ANR-2006-BDIV002 ),  Centre National de la Recherche Scientifique (CNRS)  through a  PEPS grant , the  National Geographic Society  and the  'Laboratoire d'Excellence' TULIP  ( ANR-10-LABX-41 ). The first author was supported by a  MESR  ( Ministère de l'Enseignement Supérieur et de la Recherche ) PhD scholarship during this study.
Data accessibility. All data are deposited on Dryad (phenotypic data, GBS, http://dx. doi. org/10. 5061/dryad. 1q9g7) [95]  and European Nucleotide Archive repositories (raw reads for whole genomes and pooled RAD-seq, accession nos. ERP020509 and ERP002555). Methods and options are further detailed in the electronic supplementary material. Scripts used in this study are freely available on GitHub at https://github. com/YannBourgeois/Scripts_Pool_ RADseq.
This cell/matrigel mix was transferred into a capillary (BRAND) and kept at 37 °C with 5% CO 2 for 30 minutes for polymerization. Final density of NK cells, bystander monocytes and target K562 cells were 1 × 10 7 , 1 × 10 7 and 5 × 10 6 cells/ml, respectively. Afterwards, the migration of cells was visualized with the lightsheet micoscopy (5× objective) at 37 °C with a z-step size of 1 μ m and time interval of around 30 seconds. NK cells were tracked and analyzed using Imaris 8. 1. 2 (containing Imaris, ImarisTrack, ImarisMeasurementPro, ImarisVantage; Bitplane AG, software available at http://bitplane. com). Amplex UltraRed Assay. To determine the concentration of H 2 O 2 generated during the killing process, we carried out a direct peroxidase assay using Amplex UltraRed (ThermoFisher Scientific).
Cells were kept in AIMV medium containing Alexa 488 conjugated anti-human CD107a antibody (Biolegend) and GolgiStop TM (BD Biosciences). Cells were incubated at 37 °C with 5% CO 2 for 4 hours. Afterwards cells were washed twice and stained with APC/Cy7 conjugated anti-human CD16 Antibody (Biolegend) to identify NK cells. Data were acquired using a FACSVerse ™ flow cytometer (BD Biosciences) and were analyzed with FlowJo v10 (FLOWJO, LLC). Only CD16 + cells in lymphocytes population were gated for further analysis. Microarray measurement and data analysis. Preparation of the templates and microarray analysis were performed as described before  47. Using the Agilent Feature Extraction Software the raw values from the scanned image files were extracted. In the next step, the R software was used to normalize the data (quantile normalization) and to perform a log2 transformation.
Acknowledgements We thank the  Institute for Clinical Hemostaseology  and  Transfusion Medicine  for providing donor blood;  Carmen Hässig ,  Cora Hoxha  and  Gertrud Schwär  for excellent technical help,  Eckart Meese  and  Petra Leidinger  for microarray measurements,  Zhuo Xu  for helping the analysis of NK migration, and  Adina Vultur  for her careful and critical reading. We are grateful to  Markus Hoth  for constant support, advice regarding writing of the manuscript and for interpretion of the mathematical models. This project was funded by  Sonderforschungsbereich 1027  project  A2  (to B. Q. ),  A3  (to H. R. ),  C3  (to V. H. ) and  C4  (I. B. ). The lightsheet microscope was funded by  DFG  (GZ:  INST 256/4 19-1 FUGG ).
5%) that would be more prevalent in African than non-African populations (>5-fold enriched compared to European population). Analysis of the Exome Aggregation Consortium data (ExAC) (20) revealed 21 mutations that satisfied the above criteria (Table  S2 ). We next expressed full-length human Piezo1 cDNAs containing each of the 21 mutations into Piezo1KO HEK cells, and performed an intracellular calcium assay to screen for GOF mutations. We identified two mutations (amino acid substitution A1988V and deletion E756del), that when overexpressed, showed increased calcium signals compared to the wild type Piezo1 in response to different concentrations of Yoda1 (Fig 4A  -B ). A1988V mutation only exists in African population with 0. 8% allelic frequency (inset in Fig. 4A ). Surprisingly, we found that E756del has an allelic frequency of 18% in African population (inset in Fig 4B ), such that approximately one third of the African populations carry at least one copy of the E756del mutation (Fig  4E ).
E756del, however, has high allelic frequencies (on average 18%) in all African populations (inset in Fig B  and 4E ), and a third of sampled individuals of African descent carry at least one copy of the deletion (Figure  5 ). The observed genotype frequencies at this locus are as expected for random mating and in Hardy-Weinberg equilibrium (χ 2 =0. 201, p = 0. 654). Since we did not observe any other SNPs in linkage disequilibrium with E756del in our analysis, it is difficult to directly assess signatures of selection around this variant. However, E756del has the highest degree of variance between the African and non-African populations compared to all other Piezo1 missense alleles from the 1000 Genomes database (21) (F ST for E756del = 0. 32, F ST of all other Piezo1 missense alleles = 0-0.
5B-C ), also similar to RBCs from known xerocytosis patients. This suggests that RBC dehydration (due to expression of Piezo1 GOF allele) is unexpectedly common in African populations. Next, we infected both non-carrier and E756del carrier RBCs with P. falciparum in vitro (genotypes blinded from experimenters)  (23). Our data showed that parasitemia was significantly lower for E756del heterozygous carriers relative to non-carriers at both 48hr and 96hr post infection (Fig. 5D ). In addition, we used a more quantitative method based on SYBR green labeling of parasite DNA to measure the parasitemia. Our results show that parasitemia (normalized to the non-carrier control) for carriers' blood was significantly lower than controls at 48hr, 96hr and 144hr post infection (Fig. 5E ), consistent with Giemsa staining data.
Together, our data demonstrate that E756del is a common Piezo1 GOF mutation in African populations and that it confers resistance to RBC infection by P. falciparum at least in vitro. Here, we generated a mouse xerocytosis model and demonstrate that overactive mechanically activated Piezo1 channel in blood cells strongly protects animals from Plasmodium infection by significantly reducing RBC infection and preventing cerebral malaria. Strikingly, we identified a Piezo1 GOF mutation prevalent in populations of African descent that causes dehydrated RBCs, similar to HX patients. This is surprising, as HX is thought to be a very rare disorder that mainly afflicts Caucasians  (6, 7, 8). The donors that carry this allele are apparently asymptomatic as they were registered as healthy blood donors.
Also, E756del is in a region with multiple short tandem repeats so that imputation of this mutation into current GWAS datasets is not straightforward. In the future, analysis of epidemiological data will reveal whether this Piezo1 variant is indeed associated with protection against severe malaria  (25). In addition, clinical characterization of individuals with the E756del allele will shed further light on the range of phenotypes that are associated with Piezo1 GOF expression, including anemia, splenomegaly, as well as various aspects of cardiovascular function  (26, 27, 28). Figure legends.
Parasitemia Determination Asynchronous P. falciparum parasites (Dd2 strain) were cultured in standard conditions (as described above), then synchronized twice via sorbitol  (31)  and grown to 7% parasitemia at the late trophozoite/schizont stage. Patient blood was obtained from TSRI Normal blood donor service, washed and centrifuged three times (at 800 x g for 5 min at 4 0 C) with RPMI only and once with complete media (as above)  (32) , with any visible buffy coat being removed after each spin. All blood samples were given a numerical designation and allele status was not determined until after all data collection was completed. Parasite growth was determined in two independent ways: absolute parasitemia determination via thin-blood smear after Giemsa staining, and inferred parasite growth via the SYBR Green I-based fluorescence assay  (33).
Acknowledgements We thank  Ali Torkamani  for advice on human genomics,  Dominic Kwiatkowski  for discussions, and  Lisa Stowers  for reading the manuscript. This work was partly supported by the following grants:  NIH   R01 DE022358  to  A. P  and  AI090141  and  AI103058  to EAW. S. M is supported by  Calibr-GHDDI Gates postdoctoral fellowship. GLM is supported by an  A. P. Giannini  postdoctoral fellowship. A. P. is an investigator of the  Howard Hughes Medical Institute. The authors declare no competing financial interests.
EVI1 modulates metabolism in AML cells To characterize the transcriptional consequences of EVI1 overexpression in AML, we generated an EVI1-dependent gene signature from murine bone marrow Lin Low cells infected with a retroviral construct encoding Evi1 (GSE 34729  16  ). We then used this signature to conduct an open-ended enrichment analysis of 4725 curated gene sets from the c2 collection assembled from various sources such as online pathway databases, publications in PubMed, and knowledge of domain experts available from MSigDB (Figure  1A ). We confirmed the findings of other investigators that gene sets related to cell proliferation and differentiation were enriched in genes whose expression is suppressed by enforced Evi1 overexpression  [16] [17] [18]. Multiple cell metabolism-related gene sets were also significantly enriched in an unbiased search of the complete c2 collection.
Taken together, these results show that EVI1 represses RUNX1 expression by direct binding to its promoter. This, in turn, eliminates repressive RUNX1 binding at the CKMT1 promoter and thereby promotes CKMT1 expression. Moreover, we also observed that RUNX1-directed shRNAs attenuated by 17-fold (based on the average bioluminescence across the two hairpins) the reduced disease burden caused by EVI1 depletion (Figure  3M ). Finally, we explored the relationship between EVI1 and RUNX1 expression with CKMT1 mRNA levels in two AML transcriptional datasets (GSE14468 and GSE10358). We divided these cohorts into four subgroups: EVI1 high /RUNX1 low , EVI1 high /RUNX1 high , EVI1 low / RUNX1 low , and EVI1 low /RUNX1 high. Consistent with our mechanistic analysis, AML samples with high CKMT1 levels were significantly more represented within the EVI1 high / RUNX1 low subgroup than in the three other subgroups (Figure  3N ).
Plasmids and shRNA Constructs Empty and Evi GCDN vectors were a kind gift from Drs. Tomohiko Sato and Mineo Kurokawa (The University of Tokyo, Tōkyō, Japan). Empty and HA-tagged pHAGE-CMV-CKMT1 and pHAGE-CMV-RUNX1 vectors were generated by enzyme-mediated sitespecific recombination between pENTR223-CKMT1 (Harvard PlasmID database, HsCD00372650) and pENTR223-RUNX1 (Harvard PlasmID database, HsCD00378663) donor vectors and pHAGE-CMV-HA destination vector (gift from Dr. J. Wade Harper, Harvard Medical School, Boston, MA, USA). shRNA constructs targeting human CKMT1 (shCKMT1_1 and shCKMT1_2), EVI1 (shEVI1_1, shEVI1_2, shEVI1_4), CS (shCS_1, shCS_2, shCS3), SDHA (shSDHA_1, shSDHA_2, shSDHA_3), or RUNX1 (shRUNX1_2, shRUNX1_6) -sequences listed below -were designed by adapting BIOPREDsi small interfering RNA predictions.
Hairpins were amplified in multiple 50 μl reactions using HotStar Taq (Qiagen). After PCR amplification, samples were pooled and prepared for sequencing with Illumina's genomic adaptor kit. At least 41 bases of the PCR product were sequenced with an Illumina HiSeq 2000 machine. shRNAs with less than 100 reads in the input sample were excluded from further analysis, and read numbers for each shRNA were normalized to the total read numbers per sample to allow for cross-comparison between samples. We then compared using an EdgeR/RIGER method the hairpin representation between day 0 and day 36 and thus identified genes whose depletion blocked or enhanced cell proliferation. EdgeR (R/ Bioconductor v. 3. 3 package  38  ) provided the statistical analysis of count data from the day 0 versus day 36 time-points per cell lines.
Some metabolites were targeted in both positive and negative ion mode for a total of 391 SRM transitions using pos/neg polarity switching. ESI voltage was +4900V in positive ion mode and -4500V in negative ion mode. The dwell time was 3 ms per SRM transition and the total cycle time was 1. 55 seconds. Approximately 10-14 data points were acquired per detected metabolite. Samples were delivered to the MS via normal phase chromatography using a 4. 6 mm i. d × 10 cm Amide Xbridge HILIC column (Waters Corp. ) at 350 μL/min. Gradients were run starting from 85% buffer B (HPLC grade acetonitrile) to 42% B from 0-5 minutes; 42% B to 0% B from 5-16 minutes; 0% B was held from 16-24 minutes; 0% B to 85% B from 24-25 minutes; 85% B was held for 7 minutes to re-equilibrate the column. Buffer A was comprised of 20 mM ammonium hydroxide/20 mM ammonium acetate (pH=9.
0) in 95:5 water:acetonitrile. Peak areas from the total ion current for each metabolite SRM transition were integrated using MultiQuant v2. 0 software (AB/SCIEX). For stable isotope labeling experiments, custom SRMs were created for expected  13  C incorporation in various forms for targeted LC/MS/MS. Peak areas of metabolites detected by mass spectrometry were normalized to median and then normalized to protein concentrations. A Student's t-test was performed to assess significance of all pairwise comparisons. All data are log 2transformed and normalized against the average control condition. No or small variations in metabolite production compared to average control condition appears closest to the white color on the heatmap. Decreased or increased metabolite production compared to the average control condition is represented by a gradient of color from blue to red respectively.
ChIP-sequencing and ChIP-qPCR A Runx1 gene track generated by ChIP-sequencing for the analysis of murine Evi1 binding peaks was obtained from Dr. Archibald S. Perkins (University of Rochester Medical Center, New-York, NY, USA). The human RUNX1 gene track was obtained from publically available ChIP-sequencing data generated by Bard-Chapeau et al. 19. The conservation score between species was computed based on phasCons data for hg18 and mm9  41. Gene tracks and EVI1-binding peaks were aligned using IGV software  42. ChIP-qPCR was performed as described by Bernt et al. 43.
Genome-wide Expression Analysis Genome Profiling of Evi1-overexpressing Murine Progenitor Cells-eGFPpositive control and Evi1-overexpressing progenitor cells were sorted and total RNA was extracted and profiled using the GeneChip Mouse Gene 1. 0 ST Array from Affymetrix (GSE39103  16  ). Comparative Marker and GSEA Analyses: The 6 samples in the GSE39103 data available from the Gene Expression Omnibus (GEO) repository (http:// www. ncbi. nlm. nih. gov/geo/) were separated into 2 groups: control versus Evi1overexpressing samples. The Comparative Marker Selection module from GenePattern v3. 9. 3  45  was used to identify individual genes differentially expressed between the two groups. A permutation p value ≤ 0. 05, a Benjamini-Hochberg false discovery rate (FDR) ≤ 0. 05 accounting for multiple hypothesis testing, and an absolute fold change ≥ 2 served as cutoff for significant genes.
The Gene Set Enrichment Analysis (GSEA) v. 2. 1. 0 software  46, 47  was used to identify pathways, or groups of functionally related genes, deregulated by Evi1 overexpression. The goal of GSEA is to identify groups of genes sharing common biologic function (gene sets), which are distributed at the top or at the bottom of the ranked list of differentially expressed genes. The significance of the enrichment score is estimated on the basis of a permutation p value and adjusted for multiple hypotheses testing through FDR. The set of differentially expressed genes accounting for the enrichment signal is called the leading edge. GSEA was run on the c2 collection of 4725 curated gene sets from MSigDB v. 5. 0 (http://software. broadinstitute. org/gsea/msigdb/). Gene sets with less than 15 genes or more than 500 genes were excluded from the analysis.
Gene sets with a nominal p value ≤ 0. 05 and FDR ≤ 0. 25 were considered significant. The gene ranking metric in the weighted enrichment score was the 2-sided SNR, and the p values were calculated using 1,000 permutations of the phenotype. Metabolic Network: Each metabolic gene deregulated upon Evi1 overexpression was assigned to a functional group based on the c2 collection from MSigDB (http:// www. genomespace. org). The enrichment results were visualized with the Enrichment Map module,  48, 49  which organizes the significant gene sets into a network called an "enrichment map. " In the enrichment map, the nodes correspond to gene sets and the edges reflect significant overlap between the nodes according to a two-tailed Fisher exact test. The hubs correspond to collections of gene sets with a unifying class label according to gene ontology (GO) biologic processes.
The size of the nodes is correlated with the number of genes in the gene set. Gene Expression Profiling of Cyclocreatine Treatment-TF-1, UT-7 and UCSD-AML1 cells were treated in quadruplicate with either vehicle or 3 mM cyclocreatine for 24 hours. Total RNA was extracted and profiled by RNA sequencing (HiSeq, Illumina) at the BioMicroCenter, Massachusetts Institute of Technology (Cambridge, MA, USA). The total number of reads for individual samples ranged from 44 to 48 Mb. Quality control tests for the unmapped reads were performed using the FASTQC software (http:// www. bioinformatics. babraham. ac. uk/projects/fastqc/). The reads were aligned to the GRCh37 (hg19) GENCODE v17 annotated human reference genome by using TopHat2  50. FPKM scores for genes were computed based on the RSEM R software package  51.
Expression data was evaluated as log 2 (FPKM). The significance of the differential expression between the control and cyclocreatine treatment phenotypes was estimated using the EBSeq method implemented in R (Bioconductor v. 3. 3  52  ), based on the significance cutoff 0. 05 for the posterior probability. The aligned RNASeq data is available at the GEO repository (GSE86151).
Comparative Marker Analysis: The 24 samples available in the RNAseq data were separated into two groups: 12 =vehicle-treated and 12 =cyclocreatine treated. The Comparative Marker Selection module from GenePattern v. 3. 9. 3  45  was employed to identify individual genes that were differentially expressed between the two conditions. The analysis was performed by applying a 2-sided Signal-to-Noise Ratio (SNR) test followed by 1000 permutations of phenotype labels. The settings for the SNR parameters were logtransformed-data:no, complete:no, balanced:no, smooth p values: yes. A "core" cyclocreatine signature consisting of 54 genes upregulated and of 53 genes downregulated between the vehicle and cyclocreatine-treated samples was defined based on the cut-offs SNR permutation p value ≤ 0. 05, Benjamini-Hochberg false discovery rate (FDR) ≤ 0.
GSEA analysis and Scatter Plot: The Gene Set Enrichment Analysis (GSEA v2. 1. 0) software  46, 47  was used to identify functional associations of the molecular phenotypes induced by cyclocreatine with a compendia of gene signatures including the MSigDB v5. 0 47,53 collections c2 of 4,725 curated gene sets and c6 of 189 oncogenic signatures, the DMAP collection of hematopoietic lineage differentiation signatures  53  , and with four AML differentiation gene signatures available from Gene Expression Omnibus (GEO) database: Vitamin D-and PMA-induced differentiation signatures (accession no. GSE995 54 ); JQ1induced differentiation signature (accession no. GSE29799  55  ), and DOT1L inhibitorinduced differentiation signature (accession no. GSE29828  56  ). Gene sets with less than 15 genes or more than 500 genes were excluded from the analysis.
In Silico Tests for CKMT1 High Versus CKMT1 Low Expression in Primary AML Samples The in silico test used to determine the proportion of EVI1 high /RUNX1 low , EVI1 high / RUNX1 high , EVI1 low /RUNX1 low , and EVI1 low /RUNX1 high patients exhibiting high CKMT1 versus low CKMT1 expression levels was carried out on the Affymetrix U133 Plus2 gene expression data GSE144688  57  (526 AML samples) and GSE10358  58  (279 AML samples). Data were downloaded from the InSilico DB Genomic Datasets Hub (http:// www. genomespace. org). EVI1, RUNX1, and CKMT1 expression levels were computed for each sample of these two cohorts and were further transformed into a z-score by subtracting the average of the EVI1, RUNX1, and CKMT1 expression levels assigned to all other samples and by dividing the result to their standard deviation.