dataesr/scientific-paragraphs-categorization huggingface dataset:
Variant calling was performed using 'HaplotypeCaller' with options '-dontUseSoftClippedBases -stand_call_conf 20. 0. ' Called variants were filtered using 'VariantFiltration' with the following options: -window 35 -cluster 3 -filterName FSfilter 'FS > 30. 0' -filterName QD -filter 'QD < 2. 0'. Variants known in dbSNP, or/and in genes encode immunoglobulins were also filtered out using SNPSIFT (v4. 3) and bedtools (v2. 29. 2), respectively. Filtered variants were annotated using SNPEFF (v4. 3) and GDC. h38 GENCODE v22 gene annotation. Percentages of A to G changes by genomic regions in the SNPEFF csv summary file were extracted for sample clustering analysis in R (v4. 1). In detail, pairwise euclidean distances among samples were computed based on percentage values of A to G changes by genomic regions with R function 'dist.
' Sample clustering based on the distance matrix was performed using the Ward's hierarchical clustering method as implemented in 'hclust. ' Sample grouping was determined by cutting the resulting dendrogram tree (cuttree) into groups by specifying six clusters (k = 6). To compare editing to genomic regions content in the human genome, the sum of the percentages of SNPEFF calculated editing in 'exons,' 'transcripts,' '5'UTR,' '3'UTR' were grouped under exons. To test for sequence motifs, a region AE5 nucleotides around the editing site was analyzed using PLOGO generator (https://plogo. uconn. edu/) [22]. Gene expression values from aligned RNA-seq reads were computed using htseq-count (v1. 99. 2) with options '-a 10 -t exon -i gene_id -m intersectionnonempty. ' For the stranded FunGeST dataset, '-s reverse' was set, while '-s no' was used for all other nonstranded datasets.
FPKM and FPKM-UQ values were computed using R (v4. 1), where transcript length information was downloaded from GDC ('genecode. gene. info. v22. tsv'). Mutational status of BAP1 was extracted from TCGA  [6]  and Bueno's  [4, 23]  datasets. Clustering of Bueno's dataset samples with wild-type (wt) BAP1 in two groups was included due to the fact that recent analysis  [23]  uncovered events masked by abundant stroma. Primary pleural mesothelioma cell lines of Fun-GeST series were characterized for genetic alterations in key genes of mesothelial carcinogenesis including BAP1 using a targeted sequencing described in  [24]. Analysis of ADAR2 co-expressed genes with a positive Pearson correlation coefficient (r > 0. 296, q < 0. 05) in cBioPortal. org mesothelioma TCGA dataset was selected for transcription factor enrichment annotation using the ENRICHR  [25]  online platform.
Protein extraction and Western blotting Total protein extracts were obtained by lysing the cells with hot Laemmli buffer (60 mM Tris-HCl pH 6. 8, 100 mM DTT, 5% glycerol, 1. 7% SDS) and passed through syringes (26G). A total of 5 lg of protein extract were separated on denaturing 10%, 15%, or 10-20% SDS/PAGE gels and proteins were transferred onto PVDF membranes (0. 45 lm, Perkin Elmer, Waltham, MA, USA) and blocked with 5% BSA/TBS-T for 2 h at room temperature. Membranes were probed with the following primary antibodies, ADAR1 (Sigma-Aldrich, St. Louis, MO, USA, Cat# HPA003890, RRID:AB_1078103), ADAR2 (Sigma-Aldrich Cat# HPA018277, RRID:AB_1844591; Santa Cruz Biotechnology, Dallas, TX, USA, Cat# sc-73409, RRID: AB_2289194), Flag-tag (Sigma-Aldrich Cat# F1804, RRID:AB_262044), DHFR (Santa Cruz Biotechnology Cat# sc-377091) TYMS (Santa Cruz Biotechnology Cat# sc-33679, RRID:AB_628355), RIG-I (Cell Signaling Technology, Danvers, MA, USA, Cat# 3743, RRID: AB_2269233), IFITM1 (Novus, Centennial, CO, USA, Cat# NBP1-77171, RRID:AB_11010388), MAVS (Mouse: Santa Cruz Biotechnology Cat# sc-365333, RRID:AB_10844335, Human: Cell Signaling Technology Cat# 3993, RRID: AB_823565), STING (Cell Signaling Technology Cat# 13647, RRID:AB_2732796), ISG15 (Santa Cruz Biotechnology Cat# sc-166755, RRID: AB_2126308), and b-actin (C4, MP Bio-medicals, Santa Ana, CA, USA, MP691002 RRID:AB_2335127), overnight at 4 °C.
Cell cycle analysis Cell cycle analysis was performed with propidium iodide staining. Cells were trypsinised at 80-90% confluence about 48 h after seeding, and fixed in 70% ethanol overnight. Cells were washed and then treated with RNase (100 lgÁmL À1 ) in FBS/PBS for 20 min at room temperature before staining with PI (50 lgÁmL À1 ) at room temperature for 10 min on ice. Samples were analyzed using a Attune flow cytometer (Applied Biosystems, Zug, Switzerland) and MODFIT LT CELL CYCLE software (Topsham, ME, USA).
Flow cytometry Peritoneal lavage was collected by washing with 5 mL PBS per mouse. Lavage single cells prepared by filtering with a cell strainer (40 lm in diameter) were used to characterize immune cell populations by flow cytometry. Single cells were pelleted and supernatant was removed and stored at À80 °C for cytokine analysis. Flow cytometry analysis was carried out as previously described  [15]  with the following anti-mouse antibodies: CD45 (clone 30-F11)-PE Cyanine7, CD11b (clone M1/70)-PE Cyanine7, CD68 (LSBio, Seattle, WA, USA)-FITC, F4/80 (Clone: BM8)-APC, F4/80 (Clone: BM8)-PE. All antibodies and isotypes were purchased from eBioscience or BioLegend (San Diego, CA, USA) unless otherwise stated. Becton Dickinson LSR II Flow Cytometer (San Jose, CA, USA) and FACS DIVA TM software were used for data acquisition and FLOWJO TM software was used for data analysis.
We found that ADAR2 protein expression was significantly correlated to the A-to-I editing of COPA (Fig. S3D ). Since ADAR2 has heterogeneous expression, we aimed at understanding the transcriptional profile in the context of ADAR2 expression in mesothelioma. We therefore extracted genes significantly positively correlated with ADAR2 in the TCGA dataset using cBioportal (cBioportal. org). We retrieved 1387 (Table  S6 ) genes and transcription enrichment analysis, which focuses on binding sites for transcription factors, revealed ENCODE consensus for Forkhead Box M1 (FOXM1; Fig. 2C ), which is consistent with ADAR2 being downregulated by YAP silencing  [17] , since FOXM1 levels are increased by YAP activation  [17]. To further support this hypothesis, we investigated the overlap between the list of genes significantly positively correlated with ADAR2, and well characterized YAP-TEA domain family member 1 (TEAD) genes associated with deregulated NF2/Hippo pathway  [45] [46] [47].
We observed that 30% of the samples had only nuclear BAP1, 20% showed only cytosolic staining, BAP1 staining was absent in 39% and 11% showed both cytosolic and nuclear staining. These frequencies are similar to those observed in the larger cohort where they were first determined  [23]. ADAR2 positivity was significantly (P = 0. 0259) enriched in the samples with nuclear only BAP1 staining (Fig. 3C ). Importantly, the association of ADAR2, but not of ADAR1, expression with BAP1 status was confirmed in the Bueno  [4]  and TCGA  [6]  RNA-seq datasets (Fig. 3D ) and was further confirmed in another cohort dataset (CIT series)  [24, 49]  (Fig. S4B ). Of note, ADAR2 expression levels are higher in mesothelioma cell lines with wt BAP1 (Fig. S4C ). In addition, we observed a difference in editing in transcripts and 3'UTR according to BAP1 status (Fig.
g. , Mesothelioma Stratified Therapy clinical trial, NCT03654833). Dampening the activation of the innate immune system by ADAR2 activity is part of mesothelioma similar carcinogenesis mechanisms including mutations in the E3 ubiquitin ligase FBXW7 (F-box/WD repeat containing protein) tumor suppressor gene  [4, [87] [88] [89] , which impairs post-transcriptional stabilization of dsRNA sensors  [90]. Additional data mining (this manuscript) and experimental observations  [17]  suggest that ADAR2 expression is downstream of the activation of YAP in mesothelioma cells. YAP is a transcriptional coactivator interacting in the nucleus with TEAD family of transcription factors  [91]  resulting in the induction of genes promoting cancer  [91] [92] [93] [94] , and we had observed its activation during mesothelioma development in an experimental animal model  [15].
Pemetrexed and its polyglutamated derivatives inhibit TS, DHFR, and glycinamide ribonucleotide transformylase. All these enzymes are involved in the de novo biosynthesis of thymidine and purine nucleotides. Therefore, pemetrexed treatment induces an imbalance in the cellular nucleotide pool thereby inhibiting nucleic acid biosynthesis and arresting the proliferation of tumor cells. ADAR2 deficient cells expressed lower levels of TS and although intronic TS editing sites (Table  S10 ) are present in RADAR database  [101] , they have not yet been functionally investigated. However, decreased TS levels might also be a consequence of a slower cell cycle  [102]. Low TS protein expression and clinical response in pemetrexed/platinum agents treated mesothelioma patients has been controversial [103-105] and no correlation has been observed in vitro between TS expression and pemetrexed sensitivity in mesothelioma cell lines  [104].
Acknowledgements We thank  Dr O 0 Connell  for the kind gift of pCD3-FLAG-ADAR2S-6xHis plasmid. This work was supported by  Swiss National Science Foundation , grant  320030_182690 ,  Walter-Bruckerhoff Stiftung  and Stiftung f€ ur Angewandte Krebsforchung; SS is supported by  China Scholarship Council (CSC). MS was supported by the  HERMES (HE-reditary Risk in MESothelioma)  Project, funded by the offer of compensation to the inhabitants of  Casale Monferrato  deceased or affected by mesothelioma. LO-G was supported by AIBG contribution for research abroad 2020 (AIBG. Associazione Italiana Biologia e Genetica Generale e Molecolare)  and a financial contribution award for stays at foreign institutions. IUSS Ferrara, PhD mobility 2020; DJ is supported by  Inserm , the  Ligue Contre le Cancer (Ile de France Committee) , the  Chancellerie des Universit es de Paris (Legs POIX) , and  Canc eropôle Île-de-France (Emergence).
We improved the source characterisation of CH 4 emissions using stable isotopes and associated uncertainty, to be used in top-down studies. We emphasise that an appropriate use of the database requires the analysis of specific parameters in relation to source type and the region of interest. The final version of the European CH 4 isotope database coupled with a global inventory of fossil and non-fossil δ 13 C CH 4 and δ 2 H CH 4 source signature measurements is available at https://doi. org/10. 24416/UU01-YP43IN (Menoud et al. , 2022a).
A disadvantage of this database is that it is rather US-centred, and that the dataset is strongest for fossil fuel sources, but less robust for non-fossil sources. Therefore the database can be completed by more studies, especially concerning non-fossil sources. The MEMO 2 project (MEthane goes MObile -MEasurements and MOdelling) was a H2020 MSCA European Training Network 1  with the goal to use innovative mobile measurement and modelling tools to improve the quantification of CH 4 emissions in Europe  (Walter et al. , 2019 ). An important component of MEMO 2 was the isotopic characterisation of CH 4 sources. Two laboratories involved in MEMO 2 , at Utrecht University, the Netherlands and at the Royal Holloway University of London, UK, carried out a large number of high-precision measurements with isotope ratio mass spectrometry (IRMS).
Another method, using cavity ringdown spectroscopy (CRDS) was developed for the mobile measurements of ambient CH 4 isotopic composition. Several research groups were involved in field work with mobile measurements that targeted specific sources or environments in several European countries. Using this network, air samples from numerous CH 4 sources could be measured for isotopic composition. The resulting isotopic source signatures were gathered in a publicly available database, with the first version made accessible on 1 October 2020  (Menoud et al. , 2020a) , and described in a publicly available report  (Menoud et al. , 2020b). The European data were used in several publications over the past two years by  Menoud et al. (2020c Menoud et al. ( , 2021 Menoud et al. ( , 2022b)) ,  Maazallahi et al.
(2020) ,  Defratyka et al. (2021) ,  Bakkaloglu et al. (2021 ), Fernandez et al. (2022) ,  Bakkaloglu et al. (2022). These studies emphasised the benefits from regional estimates of source CH 4 isotopic composition. The last update of the MEMO 2 isotopic data was compiled into the European methane isotope database (EMID). The present study provides an in-depth analysis of the EMID, a comparison with the global data, and the implications for the global understanding of CH 4 source isotopic composition. To this purpose, we compiled all the CH 4 isotopic source signatures from MEMO 2 with the latest version of the  Sherwood et al. (2017 Sherwood et al. ( , 2021) )  global database. We also searched the literature for more measured CH 4 source signatures to add to the dataset.
Sampling The data were collected by the research teams of eight universities and research institutes: Utrecht University (UU), the Royal Holloway University of London (RHUL), the Laboratoire des Sciences du Climat et de l'Environnement (LSCE), Heidelberg University (UHEI), AGH University of Science and Technology (AGH), Lund University (LU), the University of Groningen (UG), and the Netherlands Organisation for Applied Scientific Research (TNO). They participated in several campaigns in the Netherlands, the United Kingdom, France, Germany, Poland, Sweden, Romania, and Turkey. Several other teams collaborated in two intensive campaigns: the CoMet 2 campaign in the Upper Silesian Coal Basin (USCB) in Poland  (Fiehn et al. , 2020; Gałkowski et al. , 2021) , and the ROMEO campaign in Romania 3  (Röckmann, 2020).
02 ‰ and 0. 04 ‰. They were within the analytical error reported by the two laboratories, so that the isotopic results obtained within the MEMO 2 project are consistent across the laboratories. The inter-comparison exercise is presented in detail in a MEMO 2 deliverable report, and publicly available  (Lowry et al. , 2018). The UHEI and LSCE groups performed isotopic measurements using CRDS instruments (G2201-i, Picarro inc. , USA). Their measurement and calibration methods are described in  Hoheisel et al. (2019)  and  Defratyka et al. (2021). In the database, the method of isotopic measurements is specified by the "measurement type" parameter, as either "IRMS" or "CRDS". The laboratory where the measurements were performed is specified in the column "measurement lab".
Reported variables The analytical parameters reported in the database are δ 13 C CH 4 and δ 2 H CH 4 , which are defined as δX = R sample R standard -1 , with R = 13 C 12 C for X= 13 C or R = 2 H 1 H for X= 2 H. δ values are reported in per mille (‰), relative to the international standard materials Vienna Peedee Belemnite (VPDB) for δ 13 C, and Vienna Standard Mean Ocean Water (VSMOW) for δ 2 H.
Structure of the database to include previous and new measurements We used the same parameters as in the database of  Sherwood et al. (2017 Sherwood et al. ( , 2021) )  for non-fossil data. That is because our objectives concern only values for δ 13 C and δ 2 H of emitted CH 4 , and do not include measurements of other gases or isotope signatures that  Sherwood et al. (2017)  reported in the fossil fuel database. The variables of interest are listed in Table 1 and include the site description (country, region, group, category, and sub-category), and the δ 13 C and δ 2 H of CH 4. There are two types of values: -Single measurement values, as from the characterisation of one emission event. Most fossil fuel data from Sherwood et al. (  2021 ) are single measurements, and the entries in the EMID.
-Average values from repeated measurements at the same location or over time. The values found in the literature are usually averages of multiple measurements. A direct comparison between these two types of values would be unbalanced and lead to the over-representation of single measurements. Therefore, to combine the different kinds of data and perform statistical analyses, we aggregated the sources reported in the EMID by region and sub-category, and in the fossil fuel database of  Sherwood et al. (2021)  per production basin. Throughout the article, the aggregated values are referred to as data locations, to distinguish them from measurements values which refer to the single events. The source categories and sub-categories from  Sherwood et al. (2017 Sherwood et al. ( , 2021) )  were kept as they were, but when the new entries from MEMO 2 measurements and published literature required it, we added additional source categories or subcategories.
The European methane isotope database (EMID) The isotopic signatures obtained within the MEMO 2 project concern 734 locations over eight countries, with δ 2 H source signatures being measured at 54 % of the sites (Table  3 ). Measurements of δ 2 H are less numerous than of δ 13 C because only the measurement system at IMAU was able to measure this isotope signature. Depending on the availability of the measurement system, the sampling location, and the timing of the campaign, it was not possible to systematically measure all samples at IMAU. Figure  1  shows the geographical distribution of the sampled sites in the different countries, according to the type of source. The number of sources we sampled does not represent the emission magnitudes. During mobile surveys, we mostly targeted anthropogenic emissions from the exploitation and use of fossil fuels and waste processing facilities (Fig.
1 ). These are the most obvious anthropogenic CH 4 sources in densely populated regions, and we acknowledge a deliberate sampling bias towards urbanised areas. No biomass burning emissions were characterised during the MEMO 2 project. The EMID partially addresses the geographical bias pointed out by  Sherwood et al. (2017) : it particularly includes a large number of measurements made in Romania, where almost no data were available before. We characterised 376 locations by both δ 13 C and δ 2 H values, and we compared the results to ranges reported in the literature in Fig. 2. The fossil fuel sources partly overlap with the range of thermogenic CH 4 , but also spread towards lower δ 13 C or higher δ 2 H. This is due to the presence of natural gas of microbial origin in the coal reservoirs of Silesia, in Poland  (Kotarba, 2001; Kotarba and Pluta, 2009; Menoud et al.
Overview and representativeness The extended global database including all literature data and the aggregated MEMO 2 data consists of 13 313 and 4337 measurements of δ 13 C and δ 2 H, respectively, from 64 countries. The map in Fig. 4  shows the partitioning of the measurement data per country, and Table  2  the number of records per CH 4 source. Table  4  contains statistics on the data from the EMID only, and the overall database including the EMID. The number of measurements made in fossil fuel reservoirs and compiled in the database by  Sherwood et al. (2021)  is comparatively larger than from studies of other CH 4 emission sources (Table  1 ), and the number of measurements is not evenly spread geographically: significantly more measurements were made in North American and European countries, Australia, Brazil, and Japan.
Unfortunately, these data are lacking in many cases. Recently, particularly large CH 4 emissions were detected in central Asia  (Varon et al. , 2019 ), or measured in Mexico (Zavala-Araiza et al. , 2021). Besides the new measurements in Romania, the EMID and additional literature we added to the global isotope database does not address the geographical representation issue. Modern microbial. The isotopic signatures of CH 4 from modern microbial sources (mainly wetlands, ruminants, waste degradation, rice paddies, and termites) are largely dependent on environmental parameters such as the type of substrate and other ecosystem conditions. Figures  A1  and A2  show that our new data confirm the trends previously observed: the δ 13 C sensitivity to C 3 or C 4 plants in ruminant diet  (Rust, 1981; Levin et al.
Global data and the EMID Statistical information on the CH 4 isotopic signatures in the complete extended database are presented in Table  4. Figure 5  shows the distribution frequency of isotope signatures for the source categories that represent the largest reported emissions  (Saunois et al. , 2020). The categories agriculture, waste, wetlands, and partly other natural are all of modern microbial origin, mostly from acetate fermentation  (Milkov and Etiope, 2018). The different categories within microbial processes generally overlap (Fig. 5 ). Some differences can however be observed, such as the wetlands mean δ 13 C CH 4 being lower in the EMID than globally (-73. 6 ± 2. 27 ‰ compared to -63. 3 ± 0. 79 ‰), because the European samples were taken at relatively high latitudes (Sect. 3. 2.
, 1981; Bergamaschi et al. , 1998; Chanton et al. , 2006). Nevertheless, further δ 2 H measurements are required to better define the isotopic dependencies to secondary processes. In  Sherwood et al. (2017 Sherwood et al. ( , 2021)) , the pyrogenic category only contained biomass burning data, and the binary distribution clearly illustrates the difference between C 3 and C 4 plants in terms of δ 13 C CH 4 signatures: the averages in the global database are -28. 4 ± 0. 65 ‰ and -18. 0 ± 1. 9 ‰, for C 3 and C 4 plants, respectively. The additional biomass burning data we added from published literature confirm the dependency of δ 13 C CH 4 on the plant metabolism (Fig. A3 ). We also added pyrogenic data from fuel combustion (burning of fossil fuel) from both our measurements and the literature.
, 2021). Fugitive CH 4 emissions from fossil fuel source locations present a wide range of isotopic signatures: δ 13 C from -77. 4 ‰ to -18. 9 ‰, and δ 2 H from -349 ‰ to -101 ‰ (Table  4 ). The average signatures of all fugitive CH 4 emissions from the exploitation of fossil fuels (excluding seeps) in the EMID were δ 13 C = -44. 6 ± 0. 4 ‰ (n = 452) and δ 2 H=-182±2. 4 ‰ (n = 259), which compares well with the global average of -44. 8 ± 0. 1 ‰ (n = 8128) calculated in  Sherwood et al. (2017). Regarding the present updated global database, the weighted averages were δ 13 C = -46. 6±1. 8 ‰ and δ 2 H = -192 ± 7. 5 ‰, weighted by the relative emissions from conventional and coal fuels production worldwide. 5 The average values from the different databases are lower than δ 13 C and δ 2 H values used in most global models (Table  5 ), and to the value of -44.
66 for conventional fuels (oil and natural gas) and 0. 34 for coal. Emission data from  Saunois et al. (2020). in the global signature of fossil fuel emissions in atmospheric models. In Sect. 3. 1, we have shown the use of δ 13 C CH 4 to distinguish fossil fuel emissions in western Europe, and the need for δ 2 H CH 4 measurements in central and eastern Europe. In the global database, most fossil fuels records (83. 5 %) have δ 2 H CH 4 values > -250 ‰. The few values of δ 2 H < -300 ‰, indicating microbial fermentation as gas origin, were found in some coal formations in the United States and Canada. Figure  5  still allows us to generally conclude that δ 2 H measurements are more suitable to distinguish fossil fuel vs. biogenic CH 4 sources at the global scale than δ 13 C only, which further emphasises the need for more δ 2 H CH 4 measurements.
Data availability The database is made freely available to the scientific community in the belief that it provides the most complete picture of the stable isotopic composition of CH 4 sources. The free availability of these data does not constitute permission for publication of the data. For research projects, if the data used are essential to the work to be published, or if the conclusion or results largely depend on the data, coauthorship should be considered. Full contact details and information on how to cite the data are given in the accompanying database. The database is currently stored in a publicly available repository: https://doi. org/10. 24416/UU01-YP43IN (Menoud et al. , 2022a).