dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene Expression Signatures for Low NMDAR Activity Predict Better Patient Survival in Multiple Cancer Types As a prelude to assessing prognostic associations, we performed survival trials in the PDAC GEMM using MK801 and a clinically approved, albeit less potent and less toxic, NMDAR inhibitor, memantine. Both inhibitors prolonged survival in treated animals (Figure  8A ), establishing that NMDAR inhibition is therapeutically beneficial for treating PDAC in a mouse model, as previously shown for PanNET  (Li and Hanahan, 2013). We then employed the MK801 treatment signature to query The Cancer Genome Atlas (TCGA) database for associated survival differences. A significant survival benefit was associated with PDAC patients whose tumors correlated with the MK801 treatment signature, as if tumors from these patients had been treated with MK801, compared with the rest of the patients (Figure  8B ).
We described above (Figure  7E ) a sub-signature of the MK801 treatment signature, the NMDAR-pathway low signature, which encompassed driver genes common between the C3H strain signature and the B6-MK801 treatment signature but lacked common proliferation-associated genes that were asynchronous between highly proliferative C3H and poorly proliferative MK801-treated tumors. Analysis of the same patient tumor datasets revealed that this 148 gene NMDAR-pathway low signature could also predict survival in PDAC patients (Figure  8F ) and other cancer types (Figures  8G  and S4C ), comparable with the associations revealed by the 330 genes in the full MK801 treatment signature. The results suggest that our approach for identifying phenotype-distinguishing signatures can bypass the obstacle imposed by the extensive post-transcriptional modification of the core NMDAR signaling components and their minimal transcriptional variation, thus presenting a robust and effective method for identifying tumors with high versus low NMDAR pathway activity.
The crosslink was reversed by incubating samples at 65 C overnight. DNA purification was done according to previous protocol  (Lee et al. , 2006) , and DNA concentration was measured with the Qubit dsDNA HS Assay kit (Life Technologies). Formally, utilizing input data consisting of a genes and samples matrix, ICA uses higher order moments to characterize the dataset as a linear combination of statistically independent latent variables. These latent variables represent independent components based on maximizing non-gaussianity, and can be interpreted as independent source signals that have been mixed together to form the dataset under consideration. Each component includes a weight assignment for each gene that quantifies its contribution to that component. Additionally, ICA derives a mixing matrix that describes the contribution of each sample towards the signal embodied in each component.
This mixing matrix can be used to select biologically relevant signatures among components with distinct gene expression profiles across the set of samples. The R implementation of the core JADE algorithm (Joint Approximate Diagonalization of Eigenmatrices)  (Biton et al. , 2013; Miettinen et al. , 2017; Rutledge and Jouan-Rimbaud Bouveresse, 2013)  was used along with custom R utilities. All RNA-seq analyses were conducted in the R Statistical Programming language (http://www. r-project. org/). Isoform-level differential analyses were performed using EBSeq  (Leng et al. , 2013). Gene set enrichment analysis (GSEA) was carried out using the pre-ranked mode with default settings  (Subramanian et al. , 2005). Enrichment maps were generated using the EM  (Merico et al. , 2010)  plugin for Cytoscape  (Shannon et al.
, 2003). Heatmaps were generated using the Heatplus package in R. For the derivation of common driver genes in the strain and the MK801 treatment signatures, GSEA leading edge analyses  (Subramanian et al. , 2005)  were performed by using the ranked gene list from the strain signature to interrogate against the driver genes (defined by jZj>3) in the MK801 treatment signature, and vice versa. The union of these two analyses was taken as the common driver genes.
Clinical Data Analysis Genes with standardized signature correlation scores jzj > 3 were used as gene sets to score TCGA (https://tcga-data. nci. nih. gov/ tcga/) patient datasets using ssGSEA  (Barbie et al. , 2009). Patient tumors were stratified using standardized scores and Kaplan-Meier survival analyses were conducted to compare the top scoring patients (within 3. 5 of the top Z score) with the low scoring patients (within 3. 5 of the bottom Z score) to assess differences in survival time. P values from the log-rank test are reported. Cox proportional hazards analysis was conducted across all patients in the TCGA PDAC cohort (with survival greater than 30 days to eliminate possible complications arising from surgical intervention) to asssess the prognostic significance of the signature while controlling for various clinical covariates.
ACKNOWLEDGMENTS We thank  Tyler E. Jacks  ( Massachusetts Institute of Technology [MIT] ) for his gracious support of L. L. and A. B. ;  Ehud Drori  for technical support; the  Duboule lab (Swiss Institute for Experimental Cancer Research/E ´cole Polytechnique Federale de Lausanne  [EPFL]) for assistance with ChIP experiments;  A. Necsulea  ( EPFL and Universitede Lyon ) for bioinformatics analysis; J. De-lafontaine and  J. Rougement  ( EPFL ) for transcription factor analysis; the  De Palma lab (EPFL)  for help with lentiviral infection;  M. G. H. Chun  ( University of California, San Francisco [UCSF] and Salk Institute ),  P. Olson  ( UCSF ), and  K. Shchors  ( EPFL ) for cell line and reagents;  S. Saqafi  ( EPFL ) for bioinformatic advice;  A. Jaegger  (MIT),  A. Balmain  ( UCSF ),  G.
N/A bTC-C3H This paper. N/A bTC-3 (Efrat et al. , 1988) RRID: CVCL_0172 mPDAC-2263 This paper. N/A mPDAC-4361 This paper. N/A DanG DSMZ RRID: CVCL_0243 SUIT2 JCRB RRID:CVCL_3172 Experimental Models: Organisms/Strains C57/Bl6N Charle river laboratories Strain Code: 027 C3HeB/FeJ Jackson laboratories SN : 658 RIP1-Tag2 in C57/Bl6 strain background (Chun et al. , 2010) N/A RIP1-Tag2 in C3Heb/Fe strain background (Chun et al. , 2010) N/A Ptf1a-Cre lox-stop-lox(LSL)-Kras G12D p53 R172H/+ in (Hingorani et al. , 2005). Backcrossed into N/A pure FBV/n strain background pure FBV/n background in our laboratory. Oligonucleotides GKAP/dlgap1 shRNA Openbiosystem Clone ID: TRCN0000088935 GKAP/dlgap1 (human) siRNA #1 Thermo Fisher Scientific #4392420, Assay ID s17644 GKAP/dlgap1 (human) siRNA #2 Thermo Fisher Scientific #4392420, Assay ID s52141 FMRP/fmr1 (human) siRNA #1 Thermo Fisher Scientific #4392420, Assay ID s5316 FMRP/fmr1 (mouse) siRNA #1 Thermo Fisher Scientific #4390771, Assay ID s66176 FMRP/fmr1 (mouse) siRNA #2 Thermo Fisher Scientific #4390771, Assay ID s66177 HSF1 (human) siRNA #1 Thermo Fisher Scientific #4392420, Assay ID s6951 HSF1 (human) siRNA #2 Thermo Fisher Scientific #4392420, Assay ID s6952 HSF1 (mouse) siRNA #1 Thermo Fisher Scientific #4390771, Assay ID s67870 (Continued on next page) e1 Cancer Cell 33, 736-751.
In all cases, the homoplasmic variants were identical between the donors and the lines. However, none of the heteroplasmies identified in the hESCs was found in the blood sample of the donor. In fact, none of the blood samples contained any heteroplasmic variant (data in Table  S4 ). This can be explained by the fact that heteroplasmic variants may be lost in rapidly proliferating tissues such as blood, and approximately 15% of variants may be germline mutations  (Rebolledo-Jaramillo et al. , 2014). Next, we sequenced 11 single oocytes and eight ICMs, as these cells are the closest proxy in terms of mtDNA content to the first passages after hESC derivation. The results are shown in Figures  1A  and 1B , all data in Table  S5. We detected heteroplasmic variants in 9 out of 11 oocytes, at cumulative loads ranging from 2.
For each passage, we collected 20 single cells, which were analyzed using only one of the two different long-range PCRs for mtDNA enrichment (set 1 or set 2). This means that ten cells were studied individually for each specific region; the results have not been pooled. The data are summarized in Figure  3 , all data can be found in Tables  S7-S9. Overall, the results show a remarkable level of diversity in the mtDNA variants among individual cells. Most of the The two variants represented by blue (m. 203G>A) and red (m. 11155C>A) mitochondria are passenger mutations of cells that acquired a chromosomal abnormality conferring growth advantage and resulted in clonal culture takeover. For the variant m. 203G>A, we observe how one bottleneck (first ''hit,'' red star) causes the appearance of the variant in passage 66 and an expansion until a homoplasmic level at passage 125.
Importantly, Figure  3. Heteroplasmies Observed in Single Cells of Early and Late hESC Lines Single cells from three VUB lines (VUB03, VUB04 and VUB14) were sequenced after mtDNA PCR amplification using set 1 (A-C) or set 2 (D-F). The y axis represents the load of the variants observed; the single variants are defined as a single bar with the height proportional to their frequency. All the variants from one sample are stacked to obtain the cumulative load. The variants in white are unique to one cell, while the ones recurring in more than one cell are color-coded as indicated in the legends. For every line, the pathogenicity results are reported (G-I); a threshold of 0. 611 defines variants with a high probability of being pathogenic. All data can be found in Tables  S7-S9. the pathogenicity scores of the variants in the later passages were higher as compared with the early ones (Figures  3G-3I ).
High-Load mtDNA Variants in hiPSCs Originate from the Source Fibroblasts and Stably Transmit during Differentiation We studied the mitochondrial genome of early-passage clonal hiPSC lines derived from two different fibroblast cultures at two different institutions, named STBG-iPSCs (nine clones) and VUBi004 (eight clones). The STBG-iPSC lines were generated with different reprogramming methods, which did not appear to have a clear differential impact on the mtDNA integrity. For VUBi004, we sequenced passage 5 of all the lines and passage 10 of four clones. Data are shown in Figure  4  and the complete data in Tables  S10  and S11. The first striking point is that (low-passage) hiPSCs very frequently harbored variants with high cumulative loads (up to 172. 6% in the STBG-iPSC lines and 239.
For instance, m. 12071T>C is present at a frequency of 9. 9% in the bulk DNA sample, but is nearly homoplasmic in 2 out of the 11 tested fibroblast cells, while the rest of the cells are only carrying wild-type sequence. In line with this, when comparing these results with those of the VUBi004 clones, we find very similar patterns. For instance, the variant m. 2760A>G has a load of 5. 8% in the bulk fibroblast culture, but is present in one of the single fibroblast at 95. 8% and in one of the clonal hiPSC lines at 72. 2%. Finally, we assessed the transmission and maintenance of the heteroplasmic load of these variants during short-term differentiation. For this, we differentiated six hiPSC clones into definitive endoderm and ectoderm. We characterized the cells by immunostaining for SOX17 and PAX6, and real-time PCR for SOX17, FOXA2, PAX6, SOX1, and POU5F1 (part of the data is shown in Figures  5A  and 5B , the rest can be found in Figure  S1 ).
mRNA Quantification Total RNA was extracted using the RNeasy Micro Kit (Qiagen), and reverse transcription was performed using the First-Strand cDNA Synthesis Kit (GE Healthcare) following the manufacturer's instructions. Quantitative real-time PCR was performed using the ViiA 7 thermocycler (Thermo Fisher Scientific) and ViiA 7 software v1. 2 (Thermo Fisher Scientific), using the standard settings. Quantitative real-time PCR was carried out on 40 ng of cDNA, using the qPCR MasterMix Plus Low ROX (Eurogentec), and TaqMan gene expression assays for SOX17, FOXA2 (endoderm), SOX1, PAX6 (ectoderm), and GUSB (Housekeeping) or 1. 8 mM primer mix (IDT) and 250 nM probe (Thermo Fisher Scientific) for POU5F1 (pluripotency) and UBC (housekeeping). Fold changes were calculated with the ddCt method and UBC and GUSB were used as endogenous control.
Sequencing was carried out on a Hiseq 1500 (Illumina) platform with an average sequencing depth of 75003. The files were first aligned to the mitochondrial revised Cambridge Reference sequence (NC 012920. 1) using BWA-MEM and sorted  (Li, 2013; McKenna et al. , 2010). This was followed by GATK realignment around indels and recalibration. Finally, variant calling itself was done using Mutect v2. 0  (Cibulskis et al. , 2013) , with a lower frequency threshold of 1. 5% for bulk DNA and oocytes and 2% for single cells and ICMs. The data were also analyzed using the mtDNA server  (Weissensteiner et al. , 2016) , and only SNVs identified by both methods were considered. Variants found recurrently in all the samples or in long homopolymeric stretches were not considered. (Excluded variants and further details can be found in Supplemental Experimental Procedures.
) In case of a discordant heteroplasmy level, the frequency retrieved with mtDNA server was considered. Pathogenic potential of nonsynonymous changes was assessed using Mutpred2  (Pejaver et al. , 2017)  4 3 44K Human Genome Array-CGH We used the protocol for oligo-arrays as implemented in the ISO 15189 laboratory of the Center for Medical Genetics Universitair Ziekenhuis Brussel (UZ Brussel, Belgium), based on the protocol provided by Agilent Technologies, as previously described in  Jacobs et al. (2014). In brief, the DNA was purified using Amicon Ultra 30K centrifugal filter tubes (Merck Chemicals); 400 ng of DNA was labeled using the BioPrime aCGH Labeling module (Life Technologies). Blocking and hybridization was carried out using Cot-1 DNA (Life Technologies) and Blocking Agent and hybridization buffer (Agilent Technologies).
The samples are hybridized on the microarray slide (4 3 44K Human Genome CGH Microarray, Agilent Technologies) for 24 hr. Slides were washed and dried with Oligo Wash Stabilization and Drying solution following the manufacturer's protocol (Agilent Technologies). Scanning of the slides was done using an Agilent dual laser DNA microarray scanner G2566AA (Agilent Technologies). The data were extracted using Agilent Feature Extraction software and analyzed with the ''ArrayCGHbase'' analysis platform (http://medgen. ugent. be/arrayCGHbasecmgg/) (Menten et al. , 2005).
ACKNOWLEDGMENTS The authors would like to acknowledge  Lise Barbe  ´for providing DNA samples from the VUBi004 lines. The work was funded by the  Wetenschappelijke Fonds Willy Gepts (UZ Brussel) , the  FWO-Vlaanderen  ( 1506616N ), and the Methusalem grant of the  Vrije Universiteit Brussel  to K. S.
milii, which included a CNE in an intron of the Tbx4 gene (Fig. 2b ) previously reported as the core lung mesenchyme-specific enhancer  9. Its presence in a cartilaginous fish that lacks a lung homologue prompts a reexamination of its evolutionary significance. This finding also highlights the problem of using only a single holocephalan species as a representative of chondrichthyans, whether the CNEs missing in this species are lost during evolution or masked in gaps in the genome assembly. We also searched for elasmobranch homologues of human long noncoding RNAs (lncRNAs), which again revealed more candidate homologues than in teleost fishes (Supplementary Note 14). These were screened for transcript evidence in bamboo shark RNA-seq data and absence of homology to coding sequences. This screening resulted in the identification of 38 transcript contigs with variable degrees of spatial expression biases (Fig.
5 64 , (2) BUSCO v2. 0. 1  65  and (3) a manual curation-based census of Wnt genes (Supplementary Fig. 1 ). For both CEGMA and BUSCO, we employed not only the reference gene sets provided inherently with these program pipelines, but also the core vertebrate genes introduced particularly for vertebrates, especially species in isolated lineages such as elasmobranchs  66. The assessments were executed on the gVolante web server  67  (Supplementary Note 2) using a script released by us previously  66. The genomewide census of Wnt genes was performed with TBLASTN 2. 2. 31+ 68 searches in the elasmobranch genome assemblies using manually curated amino acid sequences of the C. milii Wnt homologues as queries, followed by a fine-scale exon search using the open reading frame sequences predicted on the individual elasmobranch genomes as queries.
Nature ecology & evolutioN Information (NCBI) RMBlast v2. 2. 27, using the custom repeat library obtained above. For gene prediction, the parts of genome sequences detected as repeats are soft-masked with the options '-nolow -xsmall'. Construction of gene models. Construction of gene models on the cloudy catshark, whale shark and bamboo shark genomes was performed in this order, following the procedure previously reported  15  (Supplementary Note 5). The gene prediction program Augustus v3. 1 was employed with 'trained' species-specific parameters and hints based on RNA-seq reads and amino acid sequences of putative homologues from other vertebrates. To build homologue hints for the cloudy catshark, we used a set of 117,246 NCBI RefSeq protein sequences downloaded on 23 November 2015, including 'known' human proteins (39,582 sequences), chicken (6,189 sequences) and amniote vertebrates (44,675 sequences), as well as C.
milii (NCBI Genome version 6. 1. 3, 26,800 sequences). For constructing gene models of the whale shark, we used a sequence set combining all predicted cloudy catshark peptide sequences along with the abovementioned sequence set. Likewise, for the gene prediction of the bamboo shark, we incorporated the predicted whale shark peptide sequences into the sequence set used in gene prediction for the catshark. RNA-seq data used for exon hint construction is indicated in Supplementary Table  6. RNA-seq and transcriptome data processing. Total RNAs were extracted with Trizol reagent (Thermo Fisher Scientific). Quality control of DNase I-treated RNA was performed with Bioanalyzer 2100 (Agilent Technologies). Libraries were prepared with TruSeq RNA Sample Prep Kit (Illumina) or TruSeq Stranded mRNA LT Sample Prep Kit (Illumina) as previously described  66.
The amount of starting total RNA and numbers of PCR cycles are included in Supplementary Table  6. The obtained sequence reads were trimmed for removal of adaptor sequences and lowquality bases with TrimGalore v0. 3. 3 as outlined above, and de novo transcriptome assembly was performed with the program Trinity v2. 2. 1  71  with the parameters '--SS_lib_type RF --min_kmer_cov 3'. Alignment of the trimmed RNA-seq reads to the genome assembly employed TopHat2 v2. 0. 11, followed by gene expression quantification with Cuffdiff v2. 1. 1, while read alignment to coding sequences employed bowtie2 v2. 2. 8 and eXpress v1. 5. 1.
0). The number of best hits that were longer than 100 bp was counted for each of the two search modes. In the analysis of the enhancer in the Tbx4 locus, the program, LAST v752  74  , was used to detect conserved noncoding elements. Visualization of sequence similarity between species employed VISTA 75 using a global pairwise alignment program Shuffle-LAGAN  76. Search for long noncoding RNA (lncRNA). Human lncRNA sequences were downloaded from GENCODE database  77  (release 25; https://www. gencodegenes. org/), which included 27,692 sequences. We removed sequences of antisense RNAs that overlapped open reading frames in the lncRNA database and repetitive sequences that were masked with RepeatMasker v4. 0. 5, as described above. By using the program BLASTN 2. 5. 0+ with the dc-megablast mode, we queried the refined set of lncRNAs against the genome assemblies of other vertebrates used above in CNE detection.
Riboprobes were synthesized using complementary DNA amplified with gene specific primers in Supplementary Table  14  as templates. The regions for cDNA amplification were selected in untranslated or non-conserved coding parts of exons to avoid cross-hybridization between paralogues. Orthologue group construction. We employed the OMA platform  85  for producing orthologue groups composed of diverse vertebrate species including the four cartilaginous fishes (Callorhinchus milii, Chiloscyllium punctatum, R. typus and S. torazame). We first retrieved all-against-all alignment results of the predicted peptides of the 19 osteichthyans included in Fig. 1d  and sea lamprey from the OMA database (released in March 2017). OMA standalone v2. 1. 1  85  was run to perform additional all-against-all comparisons by incorporating the peptides of the four cartilaginous fishes and Arctic lamprey, which produced 31,498 hierarchical orthologous groups (HOGs).
9c  88. Phylogenetic tree inference. To reconstruct the species tree in Fig. 1d , we retrieved 935 one-to-one orthologue groups retained by all of the 25 vertebrates used in the HOGs, allowing for none or one missing orthologue for individual groups. The peptides of the individual orthologue groups were aligned with MAFFT v7. 299b 86 with the option '-linsi'. Unambiguously aligned sites were selected by trimAl v1. 4 rev15 87 with the option '-strictplus' followed by a concatenation of these alignments into one. Phylogenetic tree inference was performed with the maximum-likelihood method using the program RAxML v8. 2. 8  89  with the options '-m PROTCATWAG -f a -# 100' , assuming the partition model for individual orthologue groups (the '-q' option). To infer individual gene family trees, amino acids sequences were retrieved from aLeaves  90  incorporating Ensembl release 84.
Acknowledgements We thank  J. Donald  for critical reading of the manuscript,  K. Shirato ,  K. Yamamoto ,  S. Shibuya  and members of the  Evolutionary Morphology Laboratory ,  RIKEN  for rearing catsharks,  M. Andrabi  for assistance in sequence data analysis,  K. Tanimoto ,  H. Kiyonari ,  T. Yoshikuni ,  T. Ito ,  Y. Miyagawa  and  S. Sodeyama  for providing materials. Our gratitude extends to  K. Muguruma ,  Y. Murakami ,  F. Sugahara ,  J. Pascual-Anaya ,  R. Kusakabe ,  S. Higuchi ,  Y. Yamaguchi ,  W. Takagi ,  H. Kaiya ,  Y. Ishihama ,  S. Miyake ,  T. Kaku ,  T. Tanaka ,  D. Sipp ,  M. Tan ,  A. D. M. Dove ,  T. D. Read ,  D. Lagman ,  D. Ocampo Daza ,  D. Larhammar ,  Y. Uno  and  S. Mazan  for insightful discussion. This study was supported by  RIKEN  and  JSPS KAKENHI  Grant Numbers 26650110, 26291065 and  17H03868  to  S.
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability Genome, transcriptome, and ChIP sequence reads are deposited in the DDBJ under the accession number DRA006338. The genome assemblies of the brownbanded bamboo shark and cloudy catshark were deposited in DDBJ under the accession numbers BEZZ01000001-BEZZ01280241 and BFAA01000001-BFAA01458049, respectively. The whale shark genome assembly and the gene models of the three shark species are available at https://figshare. com/projects/sharkgenome1phyloinfokobe/28863.
Data availability Genome, transcriptome and ChIP sequence reads are deposited in the DNA Data Bank of Japan (DDBJ) under the accession number DRA006338. The genome assemblies of the brownbanded bamboo shark and cloudy catshark were deposited in DDBJ under the accession numbers BEZZ01000001-BEZZ01280241 and BFAA01000001-BFAA01458049, respectively. The whale shark genome assembly and the gene models of the three shark species are available at https://figshare. com/ projects/sharkgenome1-phyloinfokobe/28863.