dataesr/scientific-paragraphs-categorization huggingface dataset:
4a-d ). The calculated methyltransferase activity at 37 °C on the linear portion of the curve is 0. 06039 μmol/min/ml. Experiments were repeated three independent times. Values are presented as average ± standard deviation (S. D. ). Source data are provided as a Source Data file for c and d. Effector caspase activity was assayed by monitoring the translocation of membrane-bound fluorescent reporter protein into the cytoplasm. As control, an uncleavable construct with DEVGG sequence was also tested  85. Apoptosis was induced in the control and Pimt-RNAi animals, by ectopic expression of the activated form of the Drosophila effector caspase Dcp-1 (human Caspase-3 functional homologue). Our results show that in Pimt-RNAi cells, the CD8::PARP1::Venus substrate remained at the membrane, indicating that caspase activity was insufficient to cleave epimerised DEVDG substrates in Pimt-RNAi cells (Fig.
Data were collected from at least three independent experiments, and a minimum of 12 salivary gland or gut cells per genotype and/or condition were analysed. The ratio of cytosolic and membrane GFP signal intensities was quantified and expressed ratio signal intensities (±S. D. ). [C]-GG-DEV-[L-Asp]-G- amide, [C]-GG-DEV-[D-Asp]-G-amide, [C]-GG-DEV-[iso-L-aspartate]-G- amide, [C]-GG-DEV-[iso-D-aspartate]-G-.
Quantification of the proportion of GFP-positive areas Dissected adult guts discs were imaged at 10x magnification using a Zeiss 780 confocal laser scanning microscope. Four Z-stacks were taken for each disc. After imaging, channels were split, and maximum Z-projection was analysed. To calculate the proportion of GFP-positive areas per adult gut, a line was drawn around the total gut area using DAPI channel images and measured using ImageJ. Then the sum area of GFP-positive cells was measured using the GFP channel. The threshold was adjusted with the IsoData auto threshold algorithm to subtract background and the area above the threshold was analysed. Data were collected from at least three independent experiments, and a minimum of 10 guts per genotype and/or condition were analysed. The relative occupancy of GFP positive cells was quantified and expressed as the proportion of GFP positive area per gut area (±S.
Statistics and data presentation All statistical analyses were carried out using Microsoft GraphPad Prism 9. 2. 0 (283) and Microsoft Excel (version 16. 16. 27). Confocal images belonging to the same experiment and displayed together were acquired using the same settings. ZEISS ZEN software (2019) was used for data collection/capturing images. For visualisation, the same level and channel adjustments were applied using ImageJ (2015). Of note, all quantitative analyses were carried out on unadjusted raw images or maximum projections. Comparisons between two genotypes/conditions were analysed with Mann-Whitney nonparametric two-tailed rank U-test. Values are presented as average ± standard deviation (S. D.
The investigated peptides were peptide antigens that we used to produce iso-L-, and Dspecific antibodies described. Samples were deposited between two CaF2 windows, 22 μm was chosen as the optimised pathlength for the given concentrations. Triplet spectra were acquired of peptides and their respective baselines using 1 nm bandwidth, with a 1. 2 s integration time collecting every 1 nm from 260-175 nm, then averaged and smoothed (5 nm using Savitzky-Golay algorithm). Peptide spectra were baseline subtracted normalised with (+) camphor sulfonic acid standard and standardised to Δε as described previously  102, 103. Dataanalyses including averaging, baseline subtraction, smoothing, and scaling were carried out with CDtool software  104.
Acknowledgements We thank  Masayuki Miura ,  Joaquín de Navascués ,  Eli Arama  and  Kim McCall  for reagents; the  Bloomington Drosophila Stock Centre and Vienna Drosophila RNAi Centre (VDRC)  for providing Drosophila fly lines; the  Irish Centre for High-End Computing (ICHEC)  for supercomputing resources, the iBV PRISM platform; the French national synchrotron facility -Synchrotron SOLEIL;  Pascal Meier ,  Tencho Tenev ,  Florence Besse ,  Maximilian Fürthauer ,  Maria Duca  for comments;  Jean-Marc Gambaudo  and  Sylvie Mellet  for their support, and members of the  SN laboratory  for discussions. Funding:  Université Côte d'Azur (UCA) ,  IDEX-Initiative d'excellence Grant  (A. B. ), SOLEIL Synchrotron grant, Project number  20181617  (A. B. ), SOLEIL Synchrotron grant, Project number  20180582  (A.
B. ),  Science Foundation Ireland (SFI)  grant, award number  12/RC/2275_P2  (DT),  ERC  starting grant CellSex, Grant number: ERC-2019-STG# 850934  (B. H. ). Work in SN laboratory is supported by  Agence Nationale pour la Recherche  ( ANR-17-CE13-0024 ;  ANR-20-CE13-0004 ),  Fondation pour la Recherche Médicale  (FRM;  EQU201903007825 ),  Université Côte d'Azur (UCA) ,  Centre National pour la Recherche Scientifique (CNRS) ,  Institut National pour la Recherche Médicale (Inserm) ,  LABEX SIGNALIFE  ( ANR-11-LABX-0028-01 ).
Data availability All data is available in the main text or the supplementary data. Source data are provided with this paper. Materials generated for the study are available from the corresponding author on reasonable request. Article https://doi. org/10. 1038/s41467-022-34516-x Nature Communications | (2022) 13:7059 summaries and residual decomposition of binding energies, distance profiles, and interaction energies) have been deposited in the Zenodo database and is freely available under accession code: https://doi. org/10. 5281/zenodo. 7199808 [https://zenodo. org/ record/7199808#. Y0wKKOzMJ_Q].
Results ARID1A-inactivating mutations in bladder cancers. To assess the prevalence of ARID1A mutations in bladder cancer, we performed in silico mutation analysis via cBioPortal (http://www. cbioportal. org/), using the Memorial Sloan Kettering Cancer Center bladder cancer sequencing data set and The Cancer Genome Atlas (TCGA) data set  (2). This analysis indicated that up to 29% of bladder cancers have nonsense or truncating mutations in ARID1A (Supplemental Figure  1A ; supplemental material available online with this article; https://doi. org/10. 1172/jci. insight. 155899DS1). Furthermore, ARID1A-mutated (herein referred to as ARID1Amut) bladder cancers expressed high levels of EZH2, consistent with previous findings that EZH2 JCI Insight 2022;7(16):e155899 https://doi. org/10. 1172/jci.
insight. 155899 expression is upregulated in bladder cancer (17) (Supplemental Figure  1B ). Our analysis of the COSMIC cell line data set (https://cancer. sanger. ac. uk/cosmic) (Wellcome Trust Sanger Institute) suggested that various bladder cancer cell lines harbored mutations in ARID1A. Among these, HT1376 had a frameshift deletion, and VM-CUB1 had a nonsense substitution mutation. Other bladder cancer cells (T24, RT-112, and 5637) did not have mutations in the ARID1A gene. Furthermore, cell lines harboring truncating mutations in ARID1A showed lower levels of the ARID1A protein, without differences in EZH2 or EZH2 methyltransferase activity (Figure  1A ). ARID1A mutation is associated with sensitivity to EZH2 inhibitors in vitro and in vivo. GSK-126 is a specific small-molecule inhibitor of EZH2.
This could explain the differences between these results. Pharmacologic inhibitors of EZH2 are currently being investigated in a variety of tumor types, including lymphomas, sarcomas, and advanced treatment-resistant solid tumors (reviewed in refs. 20). B cell lymphomas often have activating mutations in EZH2, and some subtypes of sarcomas have mutations in SWI-SNF subunits SMARCB1 (BAF47) or SMARCA4 (BRG1). In fact, the EZH2 inhibitor tazemetostat was approved by the FDA in 2020 for the treatment of advanced epithelioid sarcoma  (42). Thus, there is a rationale to repurpose EZH2 inhibitors for the pharmacologic treatment of patients with bladder cancers harboring somatic truncating mutations in ARID1A. A phase I/II trial is currently investigating the combination of tazemetostat/pembrolizumab in patients with molecularly unselected, advanced urothelial carcinoma (Clinicaltrials.
gov NCT03854474) and another trial is investigating tazemetostat alone in advanced solid tumors harboring an ARID1A mutation (Clinicaltrials. gov NCT05023655). The results herein suggest that subgroup analyses of these and other EZH2 inhibitor trials should focus on patients with bladder cancer with ARID1A-deficient tumors. We discovered that ARID1A mutations and/or deficiency leads to a dependency on PI3K/AKT/ mTOR signaling via upregulation of PIK3R3 and downregulation of MAPK signaling. The PI3K/AKT/ mTOR pathway is involved in bladder tumorigenesis, as a substantial proportion of these tumors have activating mutations in PIK3CA (2). Among the regulatory subunits, PIK3R3 is least characterized. Little is understood about how it preferentially interacts with the various catalytic subunits, how it competes with other regulatory subunits, and how heterodimers containing PIK3R3 function in the presence of PI3K pharmacologic inhibitors.
To determine if PIK3R3 upregulation in ARIDA-deficient (ARID1Adef) cells is specific to bladder cancer, we performed an in silico meta-analysis focused on the differential expression of PI3KR3 in all publicly available transcriptomic data sets comparing ARID1A-deficient and WT cells (see Supplemental Figure  8  for accession numbers). Using 6 separate data sets from 6 different cancer cell cells in 50 μL of incomplete media without FBS and 50 μL Matrigel, 354277, Corning). Tumor growth was measured with Vernier calipers and recorded weekly. Tumor volume was calculated with the formula: 0. 5 × tumor length × tumor width 2. Animals were randomized prior to treatment initiation. GSK-126 was administered intraperitoneally at a dose of 100 mg/kg daily after tumor volume reached 150 to 200 mm 3.
Acknowledgments The research reported in this article was supported in part by:  Research Development Award  from the  U. S. Department of Veterans Affairs BLRD service  (to JEF),  U. S. Department of Veterans Affairs Merit Review  Award # 1I01 BX003840  (to CP),  U. S. Department of Defense  Award # W81XWH-19-1-0588  (to SV),  NIH  Award # U54 CA233306  (to CP), and  NIH  Award # U54 CA118948  (to SV and UM). The contents do not represent the views of the  U. S. Department of Veterans Affairs  or the  United States Government. The graphical abstract was created with BioRender. com,with appropriate license release. The authors would like to thank  Don Hill  for editing the manuscript.
Data availability. The RNA-Seq data generated in this study are publicly available in Gene Expression Omnibus (GEO) at GSE183777. Statistics. All results are representative of at least 3 biologic replicates, unless otherwise specified. All data points are shown as the mean ± SD and are representative of at least 3 technical replicates, unless otherwise specified. Two-way ANOVA or 2-tailed Student's t tests were performed using GraphPad Prism v7. 03 to determine significant differences between control and experimental groups, as mentioned in the figure legends. P values of less than 0. 05 were considered statistically significant. Study approval. Studies of human bladder cancer lysates were performed after IRB approval at the University of Alabama at Birmingham (X120917005) and obtaining written informed consent from patients.
Methods Further information can be found in Supplemental Methods. Experimental design Cell lines and reagents. Bladder cancer cell lines HT1376 (RRID CVCL_1292), T24 (CVCL_0554), and 5637 (CVCL_0126) were obtained from ATCC, and RT112 (CVCL_1670) and VM-CUB1 (CVCL_1786) were obtained from DSMZ. Cell lines were authenticated by the vendors and tested for mycoplasma prior to use. These were grown in Dulbecco's 90% MEM (4. 5 g/L glucose) with penicillin/streptomycin (100 U/ mL) and 10% fetal bovine serum (MilliporeSigma) in 5% CO 2 incubators. GSK-126 (HY-13470), alpelisib (HY-15244), and dactolisib (HY-50673) were obtained from MedChem Express. Pictilisib (S1065), MG132 (S2619), MAK-683, EPZ-6438, and CPI-1205 were obtained from Selleckchem. Captisol (RC-0C7-020) was obtained from CyDex Pharmaceuticals.
Intrinsically disordered regions (IDRs) are abundant in the proteome of RNA viruses. The multifunctional properties of these regions are widely documented and their structural flexibility is associated with the low constraint in their amino acid positions. Therefore, from an evolutionary stand point, these regions could have a greater propensity to accumulate non-synonymous mutations (NS) than highly structured regions (ORs, or 'ordered regions'). To address this hypothesis, we compared the distribution of non-synonymous mutations (NS), which we relate here to mutational robustness, in IDRs and ORs in the genome of potyviruses, a major genus of plant viruses. For this purpose, a simulation model was built and used to distinguish a possible selection phenomenon in the biological datasets from randomly generated mutations.
We analyzed several short-term experimental evolution datasets. An analysis was also performed on the natural diversity of three different species of potyviruses reflecting their long-term evolution. We observed that the mutational robustness of IDRs is significantly higher than that of ORs. Moreover, the substitutions in the ORs are very constrained by the conservation of the physico-chemical properties of the amino acids. This feature is not found in the IDRs where the substitutions tend to be more random. This reflects the weak structural constraints in these regions, wherein an amino acid polymorphism is naturally conserved. In the course of evolution, potyvirus IDRs and ORs follow different evolutive paths with respect to their mutational robustness. These results have forced the authors to consider the hypothesis that IDRs and their associated amino acid polymorphism could constitute a potential adaptive reservoir.
e. , a virus ability to increase its fitness over time in the course of an evolution process)). To evaluate the impact of disordered versus ordered regions on potyvirus proteinsmutational robustness at a higher scale of evolution, genomic sequences from TuMV, TEV, and PVY's natural diversity were also retrieved. To prevent bias in our analysis of the structural determinant of potyvirus evolution, a third dataset corresponding to simulated data was also obtained. Briefly, potyvirus genomes were artificially mutated in silico according to their viral replicase features to mimic the genetic diversity obtained in the absence of natural complex phenomena shaping the virus mutational landscape such as selection, genetic drift, or complementation within the viral population. Viruses 2022,  14, 1959 3 of 13  Adaptive parameters of the resulting mutants were thus obtained and compared to those from the biological data.
Experimental dataset The study of  Cuevas et al. 2015 (TEV 2015) ,  [13]  evolved the TEV (Tobacco etch virus) on two different host, Nicotiana tabacum and Capsicum annuum. The mutations occurring within both hosts after passages 1, 4, 6, 9, 12, and 15 were pooled and compared to the initial TEV strain. For the two others studies, namely  Kutnjak et al. 2015 Kutnjak et al. , 2017 [11,12] [11,12]  (PVY 2015 and PVY 2017), using the PVY (Potato virus Y) on Solanum tuberosum, mutations were compiled for a single three weeks residence and for passages from 1 to 6, respectively. They were compared to the initial strain. Table S1 compiles all of the resulting mutations of experimental datasets.
Simulation The distribution of mutations in the virus sequence results from from the sum of the contribution of viral polymerase errors and the subsequent complex interplay between selection, genetic drift, and possible complementation between variants which cannot be simply discriminated. In order to uncouple these two components, we built an algorithm to mimic the distribution of synonymous and non-synonymous mutations introduced by the low fidelity virus RNA polymerase during genome replication (DOI: 10. 5281/zenodo. 6396239). It was hypothesized that mutations could be randomly introduced all along the genome during its replication. Consequently, if IDRs and ORs were equally susceptible to mutations, NS and S were expected to be homogenously distributed in each of the two regions before virus submission to the selection pressure and other evolutionary forces.
The simulation also takes into consideration the specificity of viral polymerase on transversion/transition mutations calculated from TEV experimental data  [17]. We generated 1000 variants from the original potyvirus sequence, with each variant bearing one SNP. The genome sequences used for simulations were as follows: GenBank accession numbers TuMV: D83184, PVY: AJ439544. 2, and TEV: DQ986288. 1.
Adaptive components tested The collections of sequences generated from experimental evolution, natural diversity, and simulated experiments where then analyzed with respect to the number of S and NS mutations (DOI: 10. 5281/zenodo. 6396239) and for each viral protein, their location either in the ORs or IDRs. BLOSUM-based scores of each NS mutations were also used to determine the potential of IDRs and ORs to cope with amino acid substitutions (DOI: 10. 5281/zenodo. 6396239). Finally, the characteristic of naturally occurring substitutions were analyzed in term of maintenance versus disturbance of disorder. It was reported that ORs and IDRs possess distinct sequence biases. Disorder-promoting scores, ranging from 0 to 1 (1 being the highest disorder-promoting score for disorder) were adapted from a previously published classification [18] and associated with each amino acids (Table  S3 ).
Codon volatility assessment Codon volatility values, corresponding to the 61 sense codons under the simplest mutation model, were retrieved from  [19]  and used to calculate the mean volatility for the three TuMV, PVY and TEV reference genomes using a script previously published in  [6]. Codon volatility values obtained for IDRs and ORs were compared within each viral species using a non-parametric Mann-Whitney U-test and mean values. For each viral species, simulated codon volatility data were also obtained by generating 1000 random nucleotide sequences encoding the same amino acid sequence but varying by one base at each codon (see 10. 5281/zenodo. 7032281 for the corresponding script). The mean codon volatilities of each variant randomly generated were assessed as described previously and compared to the values obtained for the corresponding viral species.
Theoretical Minimum Number of Mutations Required for an Accurate Estimation of S and NS Distribution in the Genome Datasets generated from the 3 experimental evolutions represent between 115 and 317 mutations. We hypothesized that the number of mutations considered could be too low to lead to robust conclusions. Consequently, our first consideration was to estimate the average number of mutations required to be significant. A total of 4 independent generations of 100, 300, 500, 750, 1000 and 1250 mutations were thus randomly introduced along the TEV genome sequence. Assuming that such random mutagenesis should not be impacted by any structural or protein determinants, the number of mutations (synonymous and non-synonymous) should be equally distributed along the genome, regardless of the corresponding proteome intrinsic disorder.
Thus, the distribution of NS and S mutations among IDRs were determined (Figure  1 ). Above 600 mutations, an equal distribution of mutations among either ORs and IDRs was observed. Therefore, in order to ensure representative values for further analysis, the results of 4 independent simulations with 1000 mutations each will be used. This threshold of 1000 mutations, which is required for a robust analysis, was confirmed by monitoring the evolution of the R 2 correlation coefficient between the number of mutations and viral protein length (see the following subsection) as a function of the mutation number. Whether for NS or S, below 750 mutations, the R 2 greatly fluctuates (Figure  S1 ). This result confirms that the limited number of mutations available from the experimental datasets is likely to make our analysis less robust.
To increase the size of the dataset and extend our observations to larger scales of viral evolution, the natural diversity of TEV ND , PVY ND , and TuMV ND isolates was also analyzed by retrieving complete genomes available in Genbank. We identified 1296, 4646, and 7528 mutations in the corresponding TEV ND , PVY ND and TuMV ND datasets. These data should allow us to assess whether there is a significant difference in mutational robustness between IDRs and ORs. This result confirms that the limited number of mutations available from the perimental datasets is likely to make our analysis less robust. To increase the size o dataset and extend our observations to larger scales of viral evolution, the natural d sity of TEVND, PVYND, and TuMVND isolates was also analyzed by retrieving com genomes available in Genbank.
Correlation Assessment between Protein Length and Number of Mutations Several factors can influence protein propensity to accumulate amino acid subs tions. One of them could be intrinsic disorder, which is highly heterogeneous betw potyviral proteins  [9]. To investigate the link between ID and mutational robustne the protein level, we first assessed the propensity of each potyvirus protein to accumu non-synonymous (NS) versus synonymous (S) mutations. The percentage of NS mutations observed in each protein coding sequence was calculated as a function o total amount of NS or S mutations in the genome for each of the experimental evolu natural diversity, and simulated datasets (Figure  2 ). At the short-term evolution scale, the longer the protein, the higher the number mutations, with a significant correlation between protein length and percentage mutations.
Correlation Assessment between Protein Length and Number of Mutations Several factors can influence protein propensity to accumulate amino acid substitutions. One of them could be intrinsic disorder, which is highly heterogeneous between potyviral proteins  [9]. To investigate the link between ID and mutational robustness at the protein level, we first assessed the propensity of each potyvirus protein to accumulate non-synonymous (NS) versus synonymous (S) mutations. The percentage of NS or S mutations observed in each protein coding sequence was calculated as a function of the total amount of NS or S mutations in the genome for each of the experimental evolution, natural diversity, and simulated datasets (Figure  2 ).
By contrast, for the experimental data, the number of S and NS mutations is equivalent. Assuming that there is little or no selection pressure on S mutations, we can extrapolate the number of NS mutations before selection. So, in the TEV 2015 experiment  [13] , the total number of mutations before selection would be 300 mutations distributed in 75 S and 225 NS mutations, 278 mutations for the PVY 2015 experiment  [11] , and 1077 mutations for the PVY 2017 experiment  [12]. We can see that for two datasets, before selection, the minimum required of 1000 mutations previously defined to obtain a robust analysis was not reached. Although not statistically significant, these two datasets reflect true natural selection processes from a larger set of mutations on different pathosystems and are presented as such.