dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene ontology (GO) analysis A gene enrichment analysis of GO terms was performed individually for up and downregulated genes using metascape  (Zhou et al. , 2019)  (https://metascape. org/gp/index. html) using default parameters. All genes expressed in siRNA knockdown dataset were used as background. For the ATAC-seq dataset, peaks were annotated to +/À 300bp of the 5'-end of GENCODE v 27 transcripts to identify their corresponding genes. As background, genes which were annotated from all peaks were used.
Bioinformatics analysis and molecular dynamics simulations of the DUX4 protein The sequences of the human DUX family proteins were obtained from the UniProt database (The UniProt Consortium) (UniProt, 2021), whereas DUX4 sequences from other primates were retrieved from the non-redundant database of NCBI  (Pruitt et al. , 2007)  using blastp  (Johnson et al. , 2008)  with human DUX4 (UniProt ID: Q9UBX2) as the query sequence (Key resources table). Multiple sequence alignment over the full-length sequences was carried out using MAFFT  (Katoh and Standley, 2013)  with default parameters. Secondary structures, solvent accessibility and disordered regions were predicted using SCRATCH  (Cheng et al. , 2005)  and RaptorX-Property  (Wang et al. , 2016). The 9aaTAD web server (''Most Stringent Pattern''  (Piskacek et al.
, 2007) ) was used to predict 9aaTAD motifs. The crystal structure of the DUX4 HD1-linker-HD2 fragment bound to DNA (PDB: 6E8C  (Lee et al. , 2018) ) was obtained from the Protein DataBank (PDB  (Berman et al. , 2000) ). PyMOL (version 2. 4; Schro ¨dinger LLC) and Bodil  (Lehtonen et al. , 2004)  were used to analyze inter-HD interactions. For modelling the binding of the 9aaTAD peptide 371 GLLLDELLA 379 and the KBM 416 EYRALL 421 peptide of DUX4 onto the KIX domain, the NMR structure (model 1/20) of human KIX in complex with MLL and pKID peptide  (Bruschweiler et al. , 2013)  (PDB: 2LXT) was chosen as the template; the sequence 846 PSDIMDFVL 854 of MLL and 13 SYRKIL 138 of pKID were mutated in PyMOL to match the DUX4 sequences 371 GLLLDELLA 379 and 416 EYRALL 421 , respectively, and the coordinates of extra residues of the MLL and pKID peptides were removed; PDB coordinates for KIX in complex with DUX4 9aaTAD and KBM peptides in Data S1.
Dynamic exclusion was set for 30 s. Database search was performed with Proteome Discoverer 1. 4 (Thermo Scientific) using the SEQUEST search engine on the Reviewed human proteome in UniProtKB/SwissProt databases (http://www. uniprot. org, downloaded Nov. 2018). Trypsin was selected as the cleavage enzyme and maximum of 2 missed cleavages were permitted, precursor mass tolerance at G15 ppm and fragment mass tolerance at 0. 05 Da. Carbamidomethylation of cysteine was defined as a static modification. Oxidation of methionine, and in BioID samples biotinylation of lysine and N-termini were set as variable modifications. All reported data were based on high-confidence peptides assigned in Proteome Discoverer (FDR <0. 05).
Data analysis of affinity purification data Significance Analysis of INTeractome (SAINT  (Choi et al. , 2011) )-express version 3. 6. 3 (Teo et al. , 2014) and Contaminant Repository for Affinity Purification (CRAPome, http://www. crapome. org) were used to discover statistically significant interactions from the AP-MS data  (Mellacheruvu et al. , 2013). The DUX4 LC-MS data (from one experiment, 4 replicates) was analyzed using SAINTexpress alongside a large dataset of other transcription factors, as well as a large GFP control set. Significance threshold for a statistically significant interaction was set as BFDR score lower than 0. 05. Overrepresentation analysis of statistically significant interactions matching protein complex database CORUM  (Giurgiu et al. , 2019)  (https://mips. helmholtz-muenchen.
de/corum/) and Gene Ontology terms was performed using R-package enrichR  (Chen et al. , 2013). Protein interaction networks were constructed from statistical significant (BFDR <0. 05) protein-protein interactions imported to Cytoscape 3. 6. 0 (Shannon et al. , 2003). Known prey-prey interactions were obtained from the iRef database (http://irefindex. org). The negative control (GFP) samples were treated similarly as the DUX4 samples (tetracycline induction in the case of AP-MS and biotin treatment in the case of BioID-MS). The data from the negative control MS runs are summarized in Table  S4.
(E) Heatmaps showing Z score normalized RNA-seq expression levels (Yan et al. , 2013)  (GEO: GSE36552) for significantly upregulated (left) and downregulated (right) genes from (C). Upregulated genes (left) are mainly expressed in oocyte, zygote and 2-cell stages while downregulated genes (right) are expressed from 8-cell stage onwards. See also FigureS6.
, 2002; Radhakrishnan et al. , 1997; Wang et al. , 2012). The position of the 9aaTAD (blue letters) and KBM (KIX binding motif; red letters) sequences are indicated by black bars. (C) Modelled interactions of the human KIX domain (PDB: 2LXT) with DUX4 9aaTAD (cyan) and KBM (salmon). (D) Replicate wells of HEK-GripTite 293 MSR cells were transfected with either wildtype DUX4 (DUX4 WT) or KIX-binding motif mutant DUX4 (DUX4-KBM mut) both tagged with HA, and with either LACZ or MED15 both tagged with V5. The cell lysates were precipitated for HA-tag. Immunoblots are shown for V5 (above) and HA (below) antibodies. See also Figures S7-S9 and Video S5.
ACKNOWLEDGMENTS We are grateful to the couples that have donated their surplus zygotes or embryos for this project. We thank the  IVF  nurses at the  Reproductive Medicine Unit of the Helsinki University Hospital  for recruiting the couples to the oocyte and embryo donation program,  Dr. Piero Carninci  for helpful discussions and critical reading of the manuscript, and  Dr. Diego Balboa-Alonso  for the pB-ires-EmGFP-pA-PGK-Puro plasmid. We acknowledge the  Biomedicum Imaging Unit (Helsinki) ,  Functional Genomics Unit (Helsinki), Biomedicum Flow Cytometry Unit (Helsinki), Bioinformatics and Expression Analysis Core Facility (Stockholm)  for skilled technical assistance. We thank  Dr. Jukka Lehtonen  for scientific IT support ( Biocenter Finland Bioinformatics network ) and  CSCIT Center for Science  for supercomputing.
This project was supported by  Jane and Aatos Erkko Foundation  (to T. O. , J. K. , and J. T. ),  Sigrid Juselius Foundation  and  Academy of Finland , grant # 295760  (to J. T. ),  University of Helsinki   Three-year research grant  (to S. V. ), and  Helsinki University Hospital funds  (to T. O. and J. T. ). S. V. was supported by  Jane and Aatos Erkko Foundation  and I. K. , S. N. , P. P. , and S. V. were supported by  Sigrid Juselius Foundation. S. B. was supported by the  RIKEN International Program Associate program. M. Y. was supported by the  Scandinavia-Japan Sasakawa Foundation , the  Japan Eye Bank Association , the  Astellas Foundation for Research on Metabolic Disorders , and the  Japan Society for the Promotion of Science Overseas Research Fellowships. Purchase of Imaris Software was supported by  ARC Foundation  (G.
Data and code availability Accession numbers for data generated in this paper and weblinks to the code have been listed in the key resources table. The ATAC-seq, CAGE/NET-CAGE, and bulk STRT data have been deposited in Gene Expression Omnibus (GEO: GSE171803). Cloned transcript sequences have been deposited in European Nucleotide Archive (ENA: LR694082-LR694089). Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
However, relative differences in T 1 relaxation values may be anticipated to follow similar trends as in D 2 O. Overall, CD 3 OD as a dissolution solvent compared to D 2 O showcases superior performance by offering better line shape, which translates into improved signal sensitivity of our model metabolite mixture sample. However, for the wide range of biological samples, a lack of chemical shift database of metabolites and the inefficient solubility of metabolites in CD 3 OD could impose additional challenges. On the one hand, the chemical shift assignment challenge in CD 3 OD could be overcome by "spiking" experiments. On the other hand, previous studies showed that aqueous-solvent-based dissolution techniques can be improved using back-pressure techniques  (Kouřil et al. , 2021 (Kouřil et al.
The effective pathlength is measured for each flight using periodic in-flight measurements of cavity ringdown time. Table  1  gives 1-s precision of H 2 O of 0. 05 ppmv. For isotopic ratio measurements at 10-s integration, realized precision is 18 ppbv and 80 pptv for H 2 O and HDO, respectively. We report here on 1-Hz data but note that cell mixing timescales are ≈ 2-3 s,.
Before the flight, the instrument is ventilated for several hours using dry air (< 1 ppmv) and the inlet is kept sealed until the aircraft reaches 250 hPa to avoid chamber contamination with moist tropospheric air. The turnover time of air in the measurement chamber is 0. 19 s and during the StratoClim flights FLASH reported measurements averaged to a 1 Hz sampling frequency. The precision on the 1 Hz data in the stratosphere is 0. 2 ppmv with a detection limit of 0. 1 ppmv for a 5 s integration time. FLASH-A was calibrated against a reference MBW-373LX frostpoint hygrometer before and after the aircraft deployment as well as during the campaign using FISH calibration facility  (Zöger et al. , 1999). During the StratoClim campaign, FLASH reported measurements for all eight scientific flights as well as during the transfer flight to Kathmandu.
Temperature and pressure Full meteorological data (pressure, temperature, altitude) were measured on-board the Geophysica by the aircraft aeronautical system (UCSE)  (Stefanutti et al. , 1999)  and temperature and pressure by a separate scientific instrument, the Rosemount thermodynamic complex (TDC)  (Shur et al. , 2006). In the field, systematic differences between the temperatures measured by UCSE and TDC at high altitudes appeared to be driven by. discrepancies in the calculated Mach number. During postprocessing the TDC temperature was recalculated using the Mach number from UCSE. Temperature and pressure measurements are used in this analysis to calculate the saturation vapor pressure, saturation specific humidity, and relative humidity with respect to ice (RH ice ) according to  Murphy and Koop (2005).
MLS The Microwave Limb Sounder (MLS) instrument, operating onboard the NASA Aura satellite, measures vertical profiles of temperature and several trace gas species. Here we use 118 water vapor profiles spatially and temporally co-located with the StratoClim flights as a point of comparison (shown in Fig. 1a ). We use the version 4. 3 profiles and data screening criteria described by  Livesey et al. (2015) , who report a vertical resolution of 2. 8-3. 2 km and accuracy of 4-9% in the lower-middle https://doi. org/10. 5194/amt-2022-13 Preprint. Discussion started: 31 January 2022 c Author(s) 2022. CC BY 4. 0 License. stratosphere. We interpolate the water vapor profiles onto a potential temperature grid using the MLS temperature product provided at the same pressure levels.
Excluded data The intercomparison is primarily based on measurements made from the three in situ hygrometers on-board the Geophysica aircraft during the StratoClim campaign. Comparisons are restricted to F2-F4 and F6-F8 because only the FLASH instrument reported data for F1 and F5. This paper focuses on comparisons in the UTLS so we restrict our comparison to measurements between 12 and 20 km. We also remove ascent and descent periods where the aircraft was moving vertically at a rate faster than 10 m s -1 because these fast changes in altitude exacerbate small differences in the timing of measurements and the location of the instruments on the aircraft due to sharp vertical gradients in H 2 O. ChiWIS was designed to operate with the cell pressure at approximately 40 mbar to maintain a 2 s flush time.
05-1. 07 with very few in-cloud measurements above the homogeneous nucleation threshold from  Koop et al. (2000)  at temperatures < 190 K. Campaign-mean profiles of UTLS H 2 O from the airborne in situ hygrometers agreed with the balloon CFH and remotely-sensed MLS profiles to within 5%. The agreement between the in situ hygrometers is remarkably good (mean percentage differences of < 1. 5%) and exceeds that found by previous intercomparison studies (2. 5 -30%)  (Vömel et al. , 2007; Weinstock et al. , 2009; Rollins et al. , 2014; Meyer et al. , 2015; Kaufmann et al. , 2018). This agreement is the result of fastidious engineering before the flight campaign and robust communication between instrument teams during subsequent data analysis. For the new ChiWIS instrument, extra care was taken during construction to prevent, as much as possible, adsorption and desorption of water vapor in the inlet plumbing.
We also benefited from improved spectroscopic data in the HITRAN2016 database  (Gordon et al. , 2017 ) that reduced the line strength uncertainty from ≈ 10% down to only 2 -3%. Finally, in the field and during subsequent data analysis, a strong collaboration and robust communication between instrument teams helped discover and resolve individual instrument calibration challenges. These consistent in situ measurements of UTLS water vapor represent major progress in the field to improve the accuracy of instrumentation in this difficult measurement regime. The agreement found here allows for unambiguous scientific interpretation and detailed process-level studies that will clarify the role of the ASM on the global UTLS water vapor budget. During StratoClim, diverse conditions of the monsoon were sampled in a single campaign, as noted by the two characteristic profile shapes shown in Figure  8 , which were previously only seen in different geographic regions  (Schiller et al.
, 2009). See  Khaykin et al. (2021)  for an analysis of the dual role of convection in hydration and dehydration. Confidence in this analysis is made possible by this rigorous intercomparison effort. Furthermore, this intercomparison serves as robust validation for the new ChiWIS flight instrument which has made the first measurements of water isotopologues in the ASM UTLS. Finally, the extreme conditions sampled during the StratoClim campaign, especially the very cold and dry conditions of the later flights can provide rare in situ data to check new theories of homogeneous nucleation and cirrus cloud formation.
allowing for possible adsorption of H 2 O onto the cavity walls and subsequent desorption, which may have artificially increased the reported values. Due to this, the main intercomparison excludes ChiWIS measurements made when the cell pressure was below 30 mbar. For completeness, periods where the cell pressure was between 20 and 30 mbar are shown in Figs. S1-S4, and several key figures in the subsequent analysis are duplicated in the supplement including all periods where cell pressure was above 20 mbar. Data from FISH taken only during periods of clear-sky are included in the intercomparison. Since FISH measures total water (vapor and condensed phases), while ChiWIS and FLASH measure only water vapor, a one-to-one comparison can only be done between the three hygrometers during flight periods of clear-sky.
Data availability. The flight data will soon be available (by February 2022) from the HALO database at https://halo-db. pa. op. dlr. de/mission/ 101 (German Aerospace Center, 2021), or are currently available in Krämer et al. (2020a). Balloon data are available in the supplement of  Brunamonti et al. (2019). MLS data are publicly available at http://disc. sci. gsfc. nasa. gov/Aura/data-holdings/MLS. Analysis and plotting scripts for this paper are available at https://github. com/claresinger/StratoClim_H2O_Intercomparison.
Activation-induced cytidine deaminase, AICDA or AID, is a driver of somatic hypermutation and class-switch recombination in immunoglobulins. In addition, this deaminase belonging to the APOBEC family may have off-target effects genome-wide, but its effects at pan-cancer level are not well elucidated. Here, we used different pan-cancer datasets, totaling more than 50,000 samples analyzed by whole-genome, whole-exome, or targeted sequencing. AID mutations are present at pan-cancer level with higher frequency in hematological cancers and higher presence at transcriptionally active TAD domains. AID synergizes initial hotspot mutations by a second composite mutation. AID mutational load was found to be independently associated with a favorable outcome in immune-checkpoint inhibitors (ICI) treated patients across cancers after analyzing 2000 samples.
Landscape of AID-related mutations at pan-cancer level We identified the mutations induced by c-AID activity by tracking the C to G/T mutations within its specific WRCY motifs. We discovered AID-related mutations in the great majority of malignancies investigated while evaluating the PCAWG data (ICGC; 2775 cancer patients and 35 cancer types). Overall, AIDrelated mutations were detected in 5. 2% (5. 1-5. 3% at 95% confidence interval [CI]), while APOBEC mutations (SBS2 + SBS13) were found in 6. 5% (6. 4-6. 7% at 95% CI; Fig. 1b ) of all singlenucleotide variants (SNV) mutations. Using the TCGA, MSKCC cohorts, and various pediatric datasets, we observed similar results at the pan-cancer level (Supplementary Fig. 1  and Supplementary Fig. 2 ). Conversely, as expected, the frequency of AID-related mutations was slightly higher in hematological cancers at ~8%, specially for B-cell malignancies, like diffuse large B-cell lymphoma (DLBCL), which showed a 10.
Interestingly, within the driver genes context, hematological cancers (i. e. , non-Hodgkin's lymphoma (Lymph-BNHL), DLBCL) and MB had the highest signature contribution of AID provoked mutations. Furthermore, among the involved targets, TP53, in all cohorts; IDH1, in hematological cancers, GBM and low-grade glioma (LGG); and PIK3 genes (TCGA and ICGC cohorts), were recurrently altered (Supplementary Fig. 9 ). These results were also confirmed using a selection intensity approach, i. e. , of every somatic mutation within the ICGC dataset. Selection intensity, introduced by Cannataro et al. , is defined as a metric of proliferative advantage of a specific mutation under a model of somatic selection  25. We found a higher selection intensity of PIK3CA, NFE2L2 but also in "minor" IDH1 mutations (i.
On the other hand, R-loops, a hybrid structure of B-form doublestranded DNA and A-form dsRNA, are formed during transcription which increases DNA exposure and has been linked to AID activity  26, 27. By using genomic coordinates of R-loop associated regions and the ICGC cohort (WGS data), we attempted to answer if, within the tumoral context, AID mutations were more localized in or out these regions compared to either APOBEC mutations (COSMIC SBS2/SBS13), other mutational processes, or c-AID mutations expected by chance (from the simulated data). The number of expected c-AID mutations falling "in R-loops" from simulated data was significantly lower than those c-AID mutations observed in real data (0. 13% vs 0. 18%, p-value <0. 001), which is probably the consequence of having globally less c-AID mutations by chance (Supplementary Fig.
2b ; -50 kb vs TSS, p-value: 0. 96; TSS vs +50 kb, p-value: 5. 89 × 10 -06 ). This was not observed on the positive strand (-50 kb vs TSS, pvalue: 0. 08; TSS vs +50 kb, p-value: 0. 11) nor on the APOBEC signatures at either strand (Supplementary Fig. 12 ). Next, we wondered if the AID mutations of a specific gene were produced during transcription of that same gene. To answer this we used 1130 samples (comprising 24 tumor types) from which the mutations and expression data were available (ICGC cohort) and correlated the number of c-AID mutations occurring in gene i (AID_Muts gi ) to the expression of the same gene i (Exp gi ) within each tumor type since the expression and mutations vary greatly in this context. Only 6. 6% of the total different mutated genes (21,341) were expressed from which 6.
The impact of AID-related mutations with ICI response Because several recent studies pinpointed a potential role of APOBEC related mutations on the efficacy of ICI  17, 18  , we sought to use the fraction of AID as a surrogate marker of ICI response. We used different open access datasets that had available genomic and clinical data (see Methods). We performed a random-effects meta-analysis comparing the overall survival (OS) of all these studies and comparing the impact of AID, to the APOBEC signature and the different SNV. The details of this analysis are provided in the methods. Strikingly, the AID-related mutations were associated with the best OS in all of the studies and the random-effects model showed also a favorable prognosis (median as the cutoff, Fig. 3a ). Moreover, the effect was still significant across almost all the studies independently of either decile chosen as cutoff at univariate (Supplementary Fig.
13 ) or multivariate adjusting for TMB (Supplementary Fig. 13 ). Accordingly, the APOBEC signature was associated with a favorable prognosis, but not in all datasets. However, the random-effects model also Fig. 2  Oncogenic AID activity is higher at transcriptionally active domains and differs according to transcription direction. a Average profile of AID somatic mutations accumulation in 2775 cancer samples and replication timing across 500 kb of TAD boundaries delineating active to inactive domains (left); dot plots representing the distribution of the mutations divided by the domain length in different domaintypes (heterochromatine = purple, inactive = gray, repressed = blue, low-active = orange, active = red; right; Wilcoxon rank-sum test). Error bar limits are the 25th and 75th percentiles; the center line is the median.
b Average profiles of c-AID mutations accumulation in 2775 cancer samples across 500 kb of TSS for negative-strand genes (top) or positive-strand genes (bottom). Boxplots on the right shows the mutational load comparisons within the TSS and adjacent ∓50 bins on each strand where error bar limits are the 25th and 75th percentiles; the center line is the median and whiskers define the minimal and maximum values. c Volcano plot (n = 1130) showing the genes whose expression and mutations are correlated per tumor type (p-adj < 0. 05, Spearman Rho > 0), where colors indicate genes enriched in a specific pathway by DAVID database analysis and the pie chart (inset plot) indicates the distribution across tumor types of the associated genes. All panels were produced using the ICGC cohort. indicated an overall favorable prognosis associated with APOBEC.
3c ; Supplementary Fig. 13 ). Additionally, there was practically no correlation between the fraction of AID mutations with the APOBEC signature neither globally nor by tumor type in this cohort and in the ICGC and TCGA datasets (Supplementary Fig. 14 ). Similarly, by using four additional studies across different tumor types, we also found an association of high AID mutations with improved OS after adjusting by age, gender, and TMB using the multivariate Cox model  19, 20, 32, 33. Overall, all the studies confirmed the independent prognostic value of the high fraction of AID mutations according to the median in the univariate and multivariate analyses.
Summing up the findings that globally AICDA expression and c-AID mutations were not correlated in the TCGA nor in ICGC datasets plus that mostly only the immunoglobulin genes' expression was correlated with c-AID mutations in hematological cancers, it is tempting to speculate that the genotoxic effect of AID might be due to short-term activation of AICDA, which have been seen in APOBEC  42. Indeed, in a fate-mapping study, AICDA expression was present in a fraction of non-lymphoid embryonic cells  43  and also in malignant melanoma cells from a single-cell RNA-seq study comprising 33 melanoma tumors (Supplementary Fig. 19 )  44. Furthermore, AICDA transcripts in lymphocytes have a half-life of only 1 h  45  , supporting the lack of correlation between AID-related mutations and AICDA expression.