dataesr/scientific-paragraphs-categorization huggingface dataset:
Scripts from the Popoolation2 package were used to filter SNPs (removing SNPs near small indels, and those with rare minor alleles that did not appear twice in each population), calculate allele frequency and F ST between strains  69, 70. We ran the CMH test to identify consistent differences in allele frequency among independent sympatric E and Z pools from a given site  36. CMH comparisons were done on individual SNPs that passed a Woolf heterogeneity test (read coverage minimum 10, maximum 200)  71. P values were corrected using a genome-wide using false discovery rate (FDR) in the fdrtool package in R  72. We also calculated the mean F ST for each of the 3 pairs of E and Z strains by population over 1 kb windows. Genome scan of positive assortative mating. Using individual genome resequencing data, we conducted a scan for two signatures of assortative mating: (a) elevated linkage disequilibrium with the autosomal signal locus pgFAR and (b) a deficit of heterozygote genotypes.
Acknowledgements We thank  Daniel Veit  for constructing the wind tunnel setup,  Tibebe Dejene  for his assistance with the EAG recordings,  Domenica Schnabelrauch  and  Heiko Vogel  for DNA sequencing,  Crista Wadsworth  for access to pupal RNA-seq data,  Abby Seaman  for collection of Bellona samples,  Leila Hatch  for assistance with animal rearing,  Mary McCarty  for artwork suggestions,  Steve Bogdanowicz  and  Henry Kunerth  for preparing NGS libraries,  Antje Schmaltz  for rearing and molecular characterization of recombinant lines, and  Maria Servedio  for discussions on assortative mating. We also thank  Jean-Paul Concordet  and  Anne De Cian  from  TacGene (Paris-France ) for providing Cas9 protein for CRISPR assays,  Emmanuelle Jacquin Joly  and her team at the  INRA of Versailles  for their advice on qPCR and CRISPR experiments, and  Pierre Adenot  and the MIMA2 platform (https://doi.
org/10. 15454/1. 5572348210007727E12 )at  INRA of Jouy en Josas  for advice and accessibility to confocal facilities. Animal photographs were provided by  Callie Musto ,  Charles Linn , and  Bela Molnar. M. U. , D. G. H. , F. K. , and A. T. G. were funded by  Max-Planck-Gesellschaft. F. K. was funded by  European Union 's  Horizon 2020 research and innovation program  under  Marie Sklodowska-Curie Grant Agreement   661322  and by an  INRA  grant  2015-2017  of the Plant Health and Environment department. TD was supported by  VR  grant  621-2014-4816. B. S. C. was funded by  United States Department of Agriculture, Agricultural Research Service  ( USDA-ARS) CRIS-5030-22000-018-00D ,  USDA-ARS Q22 CRIS-3625-22000-017-00 , and  Iowa Agriculture  and  Home Economics Experiment Station, Ames ,  IA  project  3543.
Data availability Genome data are available from GenBank under multiple BioProjects. Pooled resequencing data can be found at BioSample, PRJNA361472 (Rockspring, PA, Landisville, PA) and BioSample, PRJNA655940 (Bellona, NY). Individual resequencing data can be found at BioSample, PRJNA540833 (Landisville, PA, Z trap) and BioSample, PRJNA656178 (Landisville, PA, E trap; Rockspring, PA, E trap; and Rockspring, PA, Z trap). New RNA-seq data are available at Bioproject, PRJNA704411. Confocal images are available at BioStudies under accession number S-BSST601. Source data are provided with this paper.
lipolytica, especially when from plant or fungal origin, as extensively reviewed previously  [18]. More recently, research teams from Poznan University of Life Sciences (Poland) have used genomic and transcriptomic data mining combined to functional screening of a library of GG-built secretion cassettes to identify five new highly efficient secretion signals and to define a consensus sequence for a potentially robust synthetic secretion signal  [212]. These new GG-compatible secretion signals constitute valuable additions to the global Y. lipolytica molecular toolbox. They were notably used in a synthetic biology study in which a TU and signal peptides shuffling approach allowed the optimization of a starch-utilizing GM Y. lipolytica strain for lipid production  [189]. The development of surface display methods, for exposing recombinant secreted proteins on the cell surface (arming yeast), is relatively recent in Y.
They constructed a library of more than 500,000 insertion mutants that was cultivated for 80 generations on either glucose or glycerol as sole carbon source, before being submitted to massive sequencing in order to identify the mutants present in the surviving population. A statistical analysis of the resulting data identified 22% of Y. lipolytica genes as essential and allowed to measure the contributions of non-essential genes to growth on each carbon source  [249]. These data were used to evaluate and sometimes correct two previously established genome-scale models of Y. lipolytica metabolism  [250, 251]. A selection of the insertion mutants in which lipid accumulation was enhanced was performed by combining Bodipy lipid staining and fluorescence-activated cell sorting (FACS). The identification of the corresponding genes provided new insights into Y.
Kildare, Ireland). This software pipeline can be used to construct a pan-genome from a set of genomes from any eukaryotic microbial species, using a PanOCT (Pan-genome Ortholog Clustering Tool) approach, and be applied to various functional characterisation and visualisation analyses of this pan-genomic data. These authors used Pangloss to design a Y. lipolytica pan-genome based on a set of seven sequences selected from wild-type isolates of various origins  [257]. The Y. lipolytica pan-genome still contains a large majority of ORFs which function remains unknown or only putative (http://gryc. inra. fr/). The recent development in Y. lipolytica of genome-wide high-throughput analysis technics, such as Tn-seq or CRISPR-based ones (CRISPR-directed knocking-out, interference or activation) is expected to allow reducing this number in a near future.
MEA data acquisition and analysis Cells were grown on MEA plates [Multi Channel Systems (MCS), 120MEA200/30iR-Ti] containing 120 titanium nitride (TiN) electrodes with 4 internal reference electrodes. Each electrode's diameter is 30 μm and they are spaced on a 12X12 grid (24 spaces in the 4 corners did not contain electrodes), spaced 200 μm apart. Data was recorded by either a MEA2100-System (MCS) with a chamber that maintained 37°C and 5% CO2, or a MEA1200-mini-system (MCS) that was constantly placed inside an incubator. Raw data was collected at 10 kHz, with a hardware high-pass filter of 1 Hz and an upper cut off of 3. 3 kHz for the MEA2100-system and 3. 5 kHz for the MEA2100-mini-system. Using MCS data analyzer software offline, raw data were filtered by a Butterworth 2 nd order highpass filter at 200 Hz.
Patch clamp whole-cell recordings and analysis Experiments were performed at room temperature in a RC-26G recording chamber (Warner instrument LCC, USA) on the stage of FV300 inverted confocal microscope (Olympus, Japan) using a Multiclamp 700B amplifier and a Digidata 1440A digitizer (Molecular devices, LLC, USA). All experiments were carried out using extracellular Tyrode solution containing (in mM): NaCl, 145; KCl, 3; glucose, 15; HEPES, 10; MgCl2, 1. 2; CaCl2, 1. Intrinsic excitability protocols: small DC current was injected in current-clamp mode to maintain membrane potential at -65 mV. For I-F curve: positive currents from 40 to 600 pA were injected in 40 pA increments for 500 ms. Input resistance (Rin) was measured by calculating the slope of the voltage change in response to increasing current injections from -80 to +20 mV in 20 mV increments.
For single AP measurements, 2 ms currents were injected at 40 pA increments. For mEPSCs recordings, neurons were voltage-clamped at -65 mV. Neurons were excluded from the analysis if no dendritic spines were observed, serial resistance was > 15 MΩ, serial resistance changed by >20% during recording, or if Rin was < 75 MΩ. Signals were recorded at 10 kHz, and low-pass filtered with Bessel filter 2 kHz. Electrophysiological data were analyzed using pClamp (Molecular Devices LLC, USA) and MiniAnalysis (Synaptosoft, Decatur, Georgia, USA) for mEPSC. For distributions of mEPSCs, 90 first events were taken from each neuron to prevent over-representation of high-frequency neurons.
Infection with AAV1/2-CBAP-Cre-Cerulean or AAV1/2-CBAP-Cerulean was verified with a 440 nm laser. Imaging was done with a 60X lens and X1. 5 digital magnification at ~12. 5 frames per second with 488 nm and 561 nm lasers, and emission spectra of 505 -540 nm and 575 -675 nm for GCaMP8m and jRGECO1a, respectively. Field stimulation was given using a SIU-102 stimulation unit (Warner Instruments LLC, USA) connected to an Axon Digidata 1440A digitizer (Molecular devices, LLC, USA). Each imaged neuron was stimulated by a single stimulus and bursts of 3, 5 and 10 stimuli at 50 Hz. Some neurons had more than one clear response to 3, 5 or 10 stimuli and thus excluded from analysis. Analysis were done using ImageJ.
Spontaneous cytosolic calcium in neuronal soma Experiments were performed on a FV-1000 (Olympus, Japan) system. Cultures were plated on #0 glass bottom 24 well (Cellvis, USA) and imaged in a heated (34-35°C) Stage-Top Incubator System TC, connected to a CU-501 temperature controller and a humidifier delivering 5% CO2 air (Live Cell Instruments, Republic of Korea). Imaging parameters were the same as for the evoked imaging. Each neuron was recorded 3 times for 3 minutes, with a 3-minute interval to prevent bleaching and phototoxicity. Analysis was done using ImageJ (for extraction of florescence intensity over time) and a custom-written MATLAB script (for dF/F calculation and event identification and quantification).
Statistical analysis Experiments were repeated at least three times (three independent cultures) for each group. Data are shown as mean ± standard error of the mean (SEM). All statistical analyses were carried out using GraphPad Prism 8. 0 (GraphPad software, USA), and the specific tests of each figure, along with their P-values and total number of cells/repetitions per experimental group (n/N) are specified in the figure legends. were obtained for all MCUc spots, both in the MCUc and in the IGF1R channels. The ROIs were then overlaid, which provides a visual indication of the presence of IGF1R in relation to MCUc spots. The arrows point to the ROI centers, where the MCUc spots are located, and where an enrichment of IGF1R is also observed. To analyze these images, line scan profiles of intensity for IGF1R (magenta) and the four MCUc subunits (green) were performed across the ROIs, and are shown as means ± SEM from 8 samples for each MCUc subunit.
Acknowledgments We thank  Tim O' Leary  and  Lee Susman  for comments on the manuscript and all members of the  Slutsky Lab  for discussions. This work was supported by research grants to I. S. from the  European Research Council  ( 724866 ), the  Israel Science Foundation  ( 1663/18 ),  Rosetrees Trust  ( A2590 ), the  Volkswagen Foundation  and the  Ministry of Science and Culture of Lower Saxony  grant (with  Dr. Silvio O. Rizzoli ), the  Deutsche Forschungsgemeinschaft  (440813539 with  Dr. Silvio Rizzoli ), and  BIRAX Regenerative Medicine Initiative  (with  Dr. Tara Keck ). S. A. and M. H. received research funds from  INSERM  ( Institut National de la Santé et de la Recherche Médicale ) and  Sorbonne University. I. S. is grateful to  Sheila and Denis Cohen Charitable Trust and Rosetrees Trust  of the UK for their support.
5% (Fig. 2d ). The frequency of ZFP36 gene family mutations ranged between 9. 1 and 10%, among the three bladder carcinoma cohorts (Fig. 2e ); notably, mutations of ZFP36L1 Fig. 2  Distribution of mutations affecting ZFP36 family genes in UTUC and several public bladder carcinoma datasets. Lollipops represent mutations of a ZFP36L1, b ZFP36L2, and c ZFP36 genes. d Distribution of mutation frequency of ZFP36L1 in different public bladder carcinoma cohorts using the cBioPortal database. e Distribution of mutation frequency of ZFP36 family in different bladder carcinoma cohorts using the cBioPortal database. f Box plot representing the mutational load in UTUC samples according to ZFP36 family gene mutations. g Box plot representing mutational load in bladder carcinoma TCGA cohort according to ZFP36 family mutations.
h Kaplan-Meier curves for overall survival of patients with bladder carcinomas in TCGA cohort according to ZFP36 family gene mutations status. i Kaplan-Meier curves for progression-free survival of patients with bladder carcinomas in TCGA cohort according to ZFP36 family mutations and ZFP36L2 were mutually co-occurring (P = 0. 02; FDR = 0. 06). This was also significant using all TCGA samples (P < 0. 001; FDR = 0. 002). UTUC samples with ZFP36 gene family mutations harbored a higher mutational load as compared to others (P = 0. 008; Fig. 2f ). This observation was validated in the TCGA bladder cancer dataset (P = 0. 0009; Fig. 2g ). Next, we then looked at the expression of ZFP36L1 in the FANTOM5 dataset and Human Protein Atlas and discovered that the bladder urothelium is one of the tissues with the highest mRNA and protein expression levels, highly suggesting the functional significance of this gene (Additional file 2: Fig.
As a zinc-finger RNA-binding protein regulating several cytoplasmic AU-rich elements (ARE), ZFP36L1 ultimately attenuates protein synthesis through the degradation of several mRNA transcripts. Using loss-of-function experiments, we observed increased cell motility of a bladder cancer cell line, likely through EMT, we did not detect any effect on cell proliferation and apoptosis. These data are consistent with recently reported functional analysis using in vitro and in vivo experiments, demonstrating that ZFP36L1 suppress hypoxia and cell cycle signaling in bladder carcinomas  [15]. Thus, ZFP36L1 mutations might lead to an increase in cell migration, bolstering tumor progression. In addition, UTUC cases harboring ZFP36 family gene mutations displayed higher tumor mutational loads, an observation which we validated in the TCGA bladder carcinoma dataset.
Our study is the first to propose two distinct routes of UTUC carcinogenesis through a crosstalk between genetic and epigenetic alterations. We used multi-omics data of a very well-annotated monocentric cohort with comprehensive integration of a wealth of data. However, our study has some limitations, including a small number of cases analyzed due to the relative rarity of tumor type, although we have been able to validate some of our observations in cohorts of bladder carcinomas. Further confirmation of our findings in larger UTUC cohorts is warranted. Future studies also need to examine the stability of the molecular profiles we identified in UTUC across matched primary and metastatic tumors. In summary, our findings define the foundation of the molecular basis of UTUC heterogeneity. We also provide a roadmap for the rational clinical development of targeted and immunotherapeutic strategies that are specific to UTUC, but also potentially applicable to other tumor types harboring FGFR3-activating molecular alterations.
Whole-exome sequencing and somatic mutation detection Exome capture was performed using Agilent SureSelect Human All Exon 50 Mb according to the manufacturer's instructions. Briefly, 3 mg of DNA from each sample was used to prepare the sequencing library through shearing of the DNA, followed by ligation of sequencing adaptors. Whole-exome sequencing was performed, and pairedend sequencing (2 × 76 bp) was carried out using the Illumina HiSeq 2000; the resulting data were analyzed with the Illumina pipeline to generate raw FASTQ files. The coverage of our germline samples and tumor samples varied between 43-80× and 79-158×, respectively. The technical details and mutation detection were done according to the pipeline we previously reported  [23]. We filtered out all known single-nucleotide variants (SNVs)/indels in the UCSC dbSNP 135 and 1000 Human Genome Project SNP databases, and kept any mutations, which are in the Catalogue of Somatic Mutations in Cancer (COSMIC) database, curated by the Wellcome Trust Sanger Institute.
The variation classification for each mutation was annotated by ANNOVAR. Each somatic mutation or indel was annotated with its functional effect by SIFT to determine whether a mutation candidate was synonymous or nonsynonymous (benign or deleterious). Mutations that failed to be annotated by ANNOVAR, labeled with "unknown," were removed first for downstream analysis. We used MutSigCV_v1. 41 (www. broadinstitute. org) to infer the most significantly mutated genes across these samples, and 10 frequently mutated (> 10%) genes that past test of significance were identified (P < 0. 05)  [24]. The mutation landscape for those 10 significantly mutated genes across 30 UTUC samples was shown by OncoPrint using R package ComplexHeatmap  [25]. The tumor mutational load per megabase was computed by summing all types of mutations, divided by 50 MB.
The resulting data were analyzed with the current Illumina pipeline to generate raw FASTQ files. The raw, paired-end reads were aligned to the human reference genome, GRCh37/hg19, using the MOSAIK alignment software. MOSAIK works with paired-end reads from Illumina HiSeq 2000 and uses both a hashing scheme and the Smith-Waterman algorithm to produce gapped optimal alignments and to map exon junction-spanning reads with a local alignment option for RNA-seq. The resulting alignments were then saved as a standard bam file. We then counted the mapped reads in mRNA annotated in GENCODE15 to generate the raw counts for each gene using the HTSeq-count script distributed with the HTSeq package. We calculated the number of fragments per kilobase of non-overlapped exon per million fragments mapped (FPKM)  [30].
After the chemical processes, BeadArrays were scanned and the raw signal intensities were extracted from the *. IDAT files using the "noob" function in the minfi R package. The "noob" function corrects for background fluorescence intensities and red-green dye bias. The beta (β) value for each probe was calculated as M/(M + U), in which M and U respectively refer to the (pre-processed) mean methylated and unmethylated probe signal intensities. Probes with measurements in which the fluorescent intensity was not statistically significantly above the background signal (detection P value > 0. 05) were removed from the dataset. Pattern discovery of the methylation EPIC profile was performed in 35 UTUC samples. Any methylation probe that was located in a sex chromosome or that had at least one NA value was removed out of the total 866,091 probes.
Data acquisition of external BLCA and UTUC cohorts We extracted three omics data from the TCGA-BLCA cohort where gene expression and DNA methylation data were downloaded from UCSC Xena (https://xena. ucsc. edu/ ), and somatic mutation data and detailed clinicopathological information were obtained from cBioPortal (http://www. cbioportal. org/) under the archive of Bladder Cancer  [33]. Overall, 412 muscle-invasive bladder carcinoma samples were used for DNA methylation and somatic mutation analysis, and 407 samples for expression data. Second, we included the Hurst cohort  [9]  which contains somatic SNVs and small insertions/deletions identified by whole-exome sequencing for 24 TaG2 bladder tumors. Third, we collected omics data from urinary tract cell lines. Corresponding DNA methylation profiling for 20 cell lines and expression array data for 18 cell lines (2 out of 20 have no data for expression) were retrieved from Genomics of Drug Sensitivity in Cancer (GDSC, https://www.
Subtype inference for UTUC and tumors We used R package consensusMIBC  [36] , which implements a nearest centroid transcriptomic classifier that assigned class labels according to the consensus molecular classification of MIBC to infer consensus subtype of UTUC and TCGA-BLCA. Use function of getConsensusClass() with normalized expression data and parameters by default, one of the following six molecular classes will be assigned due to the highest correlation coefficient, including luminal papillary (LumP), luminal non-specified (LumNS), luminal unstable (LumU), stroma-rich, basal/squamous (Ba/Sq), and neuroendocrine-like (NE-like). We also inferred the BASE47 subtypes for our UTUCs based on a 47-gene signature according to the literature  [37]  by using unsupervised hierarchical clustering with distance measurement of 1-Pearson's coefficient and linkage function of Ward.
Supervised clustering of BLCA methylation 450k profile BLCA methylation data contains DNA methylation β value of 433 BLCA samples with 412 tumor samples and 21 normal samples assessed by TCGA using the Illumina Infinium HumanMethylation450 platform. Out of 14,209 hypermethylated probes derived from UTUC EpiC-high vs EpiC-low, a total of intersected 3790 hypermethylated probes was used for supervised clustering of the TCGA-BLCA methylation 450k profile. To assess the stability of the discovered clusters, we performed a consensus hierarchical clustering. We conducted 500 runs of hierarchical clustering on the resampled data. For each run, 80% samples and 80% features were randomly chosen. The distance measurement was set as Manhattan, and the linkage function was set as Ward. D2. Based on the 500 runs, a consensus was obtained by taking the average over the connectivity matrices of every perturbed dataset.
Gene set enrichment analysis For gene set enrichment analysis based on gene expression data, R package clusterProfiler was used for the pre-ranked gene list (descending ordered log2FoldChange value) derived from differential expression analysis  [38, 39]. Molecular Signature Database gene sets were tested by using the gene set of msigdb. v6. 0. symbols. gmt downloaded from the GSEA website (http://software. broadinstitute. org/gsea/). GSEA for DNA methylation were executed by the embedded champ. GSEA() function in ChAMP with the typical Fisher's method. The enrichment scores of molecular pathways were evaluated using the gene set variation analysis method via R package GSVA  [40, 41].
Integrative analysis of genetic and epigenetic profiling Bayesian integrative clustering was performed by R package iClusterPlus by using the iCluster Bayesian method  [42]. We applied this method on three available data types: genetic mutation, DNA methylation, and copy number alteration data with 26 shared samples. Basically, the mutation data contains 10 significantly mutated genes that were revealed by the MutSigCV algorithm with a P value of less than 0. 05 and a mutational frequency greater than three cases. We further curated the mutation data by removing ARID1A but attaching SWI/SNF pathway mutation. We used the top 1% most variable probes as the methylation dataset. An under a confidence level of 0. 75, amplification or deletion of arm with a q value less than 0. 25 were selected, including 7 amplification peaks and 3 deletion peaks.
Quantify the immune and stromal level of UTUC samples The population abundance of tissue-infiltrating immune and stromal cell populations was estimated by R package MCPcounter per sample using normalized count data  [45]. The presence of infiltrating immune/stromal cells in tumor tissue was estimated by R package ESTIMATE  [46]. DNA methylation-based immune infiltration scores (MBII scores) were extracted by TCGA previous work  [47]. Additionally, the individual DNA methylation of tumor-infiltrating lymphocyte (MeTIL) score was calculated according to the literature  [48]. Cell line culture TCCSUP cell line was purchased from ATCC and authenticated. Cells were tested and were verified for free of Mycoplasma contamination. These cells were grown in (EAGLE, 10% FCS, 0. 1 mM AANE, 1 mM sodium pyruvate, 40 μg/ml gentamicin) media.