dataesr/scientific-paragraphs-categorization huggingface dataset:
(Sattler, 1999). Assignments of Tae1 resonances in 20 mM HEPES or NaP i ,150 mM NaCl, 100 µM EDTA at pH 7. 0 were determined by collecting a series of [ 1 H, 15 N]-HSQC spectra titrating the sample from pH 5. 5-7. 0. These assignments were confirmed by collecting an HNCO spectrum under the final conditions at pH 7. 0. NMR data were processed and analyzed using NMRPipe  (Delaglio et al. , 1995)  and NMRView  (Johnson and Blevins, 1994; Downing, 2004)  (for the residue assignments, see Figure  7 ). See All titration experiments were conducted at the Institut de Biologie Structurale in Grenoble or at the UCSF NMR facility in Genentech Hall (San Francisco). PG ligand was dialyzed against water using a float dialysis membrane device with a 100-500 Da cutoff (Spectrum Laboratories, Inc) and lyophilized before preparation of a concentrated stock solution in NMR buffer.
Our next-generation sequencing strategy comprised two stages. First, the entire library was sequenced to determine the barcode corresponding to each variant. This was accomplished using 300 bp forward and reverse reads on the MiSeq Illumina platform  (MiSeq Reagent Kit v3). Using this information, a dictionary was prepared to demonstrate the relationships between variants and barcodes. The dictionary was prepared by following previously published scripts  (Mavor et al. , 2016)  with some modification (see the GitHub directory 'ChouLab dictionary' for the steps and scripts). For each of the subsequent DMS experiments, 25 bp reads on a NextSeq Illumina platform (conducted at Chan Zuckerberg BioHub) were used to sequence only the barcode region. For data analysis and DMS scores calculations, we used Enrich2 software  (Rubin et al.
, 2017). The barcode dictionary was used to convert barcodes to variants. Please see the 'Choulab DMS data analysis' GitHub directory for detailed information on data analysis in Enrich2. For the DMS experiments in this study, the DMS library was grown for 3 hr at 37°C in M9 minimal medium (or M9 supplemented with 20 mM D-Met) before distributing across a 96-well plate. A set of 24 wells was combined for each experimental time point in order to obtain enough DNA for library amplification. Libraries were amplified for nine cycles using Phusion high-fidelity polymerase to minimize any masking of differences in abundance. The libraries were gel purified, quantified using Qubit, and their concentrations were adjusted according to Illumina instructions before sequencing. The 0 hr time point (DMS library before any growth) was chosen as the input of the 96-well plate incubation.
After growth to OD 600 0. 2, protein expression was induced with 1 mM IPTG. Cells were collected 2 hr after induction that would serve as the output time point. All of the scripts used in the article are available on GitHub at https://github. com/AtanasDRadkov/ChouLab_DMS (copy archived at swh:1:rev:e07e30f99f36b3ad-90f042c50e2f4b3c0241e7d4)  (Radkov, 2022). The data files are available on NCBI, Sequence Read Archive -PRJNA803461.
Clustering analysis K-means clustering analysis was conducted using previously published scripts  (Thompson et al. , 2020)  to group Tae1 positions into categories based on their fitness scores. We calculated spatial clusters by (1) sorting the fitness score vectors for each position, (2) trimming the vectors to match vector lengths (discarding no-data values), (3) calculating a difference by subtracting the two sorted and trimmed vectors, and (4) calculating the distance -mean of the absolute value of the vector difference. The distance between a candidate position and a cluster of positions is calculated as the average of the distance between the candidate position and the three closest nonself positions in the cluster. Clustering was performed over 10 rounds following the initial seeded round, and convergence was confirmed by observing that five repetitions gave identical clusters.
The results yielded five clusters, including an LOF phenotype cluster, which was the focus of the study. The same analysis was performed for both DMS experiments. See the GitHub directory 'ChouLab clustering' for the steps and scripts. All of the scripts used in the article are available on GitHub at https://github. com/AtanasDRadkov/ChouLab_DMS (copy archived at swh:1:rev:e07e30f99f36b3ad90f042c50e2f-4b3c0241e7d4)  (Radkov, 2022).
Isothermal titration calorimetry Isothermal titration calorimetry experiments were performed at 298 K using a Microcal ITC200 (GE Healthcare, Northampton, MA). Stock solutions of Tae1 and Tai1 were dialyzed overnight at 277 K in the same buffer (50 mM NaCl, 20 mM Tris 7. 0). The cell was filled with 200 µl of Tai1 protein at a concentration of 20 µM to which 150 µM of Tae1 protein was titrated. For 25 titrations of 1. 5 µl of Tae1 into Tai1, the heat of binding was recorded. Data were extracted and fitted using the single-site binding model following the manufacturer's instructions. Tae1 C110S crystal structure Tae1 C110S protein crystals were generated by hanging drop vapor diffusion at 25°C from a 1:1 mixture of 10 mg/ml protein (in 20 mM HEPES pH 7, 100 mM NaCl, 5% glycerol) with 0. 2 M sodium thiocyanate, 0.
1 M HEPES pH 7. 5, 20% PEG3350 for 24 hr (same conditions that were used for initial structure of Tae1  [LaCourse et al. , 2018] ). Crystals were directly used for diffraction data collection at the Lawrence Berkeley National Laboratory Advanced Light Source Beamline 8. 3. 1  (MacDowell et al. , 2004). Coot  (Emsley and Cowtan, 2004) , and maximum-likelihood  (Adams et al. , 2010)  refinement with PHENIX were used for iterative building and refinement. Structure was validated by MOLProbity  (Davis et al. , 2004)  and visualized with Chimera  (Pettersen et al. , 2004). Coordinates and structure factors were deposited in the Protein Data Bank (PDB ID: 7TVH). See Figure  6 -source data 1 for validation report, as well as the maps and coordinates of the deposited structure.
Acknowledgements We thank  Dr Doug Fowler  ( University of Washington ) and  Dr Eric Chow  ( University of California -San Francisco ) for their help with setting up the deep mutational scanning pipeline. We are also grateful to  Dr David Mavor  ( University of Massachusetts Chan Medical School ),  Dr Robert Newberry  ( University of Texas -Austin ),  Dr Martin Kampmann  ( University of California -San Francisco ), and  Dr James Fraser  ( University of California -San Francisco ) for their assistance with sequencing data analysis. We thank  Dr Kari Herrington  and  Dr SoYeon Kim  ( University of California -San Francisco ) for their contribution with microscopy data collection and analysis. We also thank  Dr George Meigs  and  Dr James Holton  ( Lawrence Berkeley National Lab ), and  Dr Michael Thompson  ( University of California -Merced ), for their help with X-ray crystallography data collection and analysis.
We are grateful to  Dr Mark Kelly  for his help with setting up NMR data collection ( University of California -San Francisco ). We also thank  Dr Parnian Lak  ( University of California -San Francisco ) for her help with setting up the ITC experiments. Lastly, we greatly appreciate the input regarding the experiments and the manuscript from members of the  Chou lab at the University of California -San Francisco  ( Kristine Trotta ,  Calla Martyn ,  Fauna Yarza ,  Elizabeth A Martinez-Bond ,  Patrick Grimes ,  Dr Domokos Lauko ). AS received a  Life Sciences Research Foundation  fellowship from the  SVCF-Wave Fund. Financial support from the  IR-RMN-THC FR3050   CNRS  for conducting the research on the 950 MHz NMR spectrometer is gratefully acknowledged. We would also like to thank the  Chan Zuckerberg Biohub  as well as the  Sanghvi-Agarwal Innovation Award  for their financial support.
Data availability -Deep mutational scanning dataset has been deposited to NCBI, Sequence Read Archive -identifier: PRJNA803461 -X-ray structure crystallography dataset has been deposited to PDB -ID: 7TVH -NMR resonance peak assignments have been directly provided in the manuscript -Custom scripts for DMS analyses have been uploaded at GitHub at https://github. com/AtanasDRadkov/ChouLab_DMS (Copy archived at swh:1:rev:e07e30f99f36b3ad90f042c50e2f4b3c0241e7d4) -All other data generated or analyzed in this study are included in the manuscript and supporting files.
The following datasets were generated: Author(s) Year Dataset title Dataset URL Database and Identifier Radkov AD 2022 T6S amidase effector 1 https://www. ncbi. nlm. NCBI BioProject, deep mutational scanning nih. gov/ bioproject/ PRJNA803461 PRJNA803461 Radkov A, Saunders 2022 Hyperlytic variant of https://www. rcsb. org/ RCSB Protein Data Bank, H, Chou S Tae1, Type VI secretion structure/ unreleased/ 7TVH amidase effector 1, from 7TVH Pseudomonas aeruginosa (Cys110Ser) Radkov et al. eLife 2022;11:e79796. DOI: https://doi. org/10. 7554/eLife. 79796 24 of 26.
ChIP-sequencing analyses of AHR binding sites in human cells AHR ChIP-sequencing data were retrieved from NCBI GEO and fastq files downloaded from EMBL-EBI ENA website (https://www. ebi. ac. uk/ena) on the Galaxy webinterface  (29). We used Bowtie2 to map sequencing reads against human reference genome hg19 and produce bam files and ChIP-sequencing peaks  (30). ChIP-sequencing datasets were previously published and prepared as follows. AHR ChIPs in MCF-7 breast cancer cells were performed using an AHR antibody (Santa Cruz, H-211) in triplicate, starting from MCF-7 treated with 10 nM TCDD  (2,3,7,8-tetrachlorodibenzodioxin)  or DMSO (dimethylsulfoxide) for 45 min and 24 h (GSE90550)  (5). AHR ChIPs in GM17212 EBV-immortalised lymphocytes (GSE116638) were performed using an AHR antibody (Cell Signaling Technology (CST); D5S6H) in duplicate, starting from GM17212 cells treated with 1 M 3-MC  (3methylcholanthrene)  or DMSO for 24 h  (6).
AHR ChIPs in HepG2 hepatocarcinoma cells were produced using an antibody directed against Flag-Tag (Sigma-Aldrich) from cells engineered to express an AHR flag-tagged protein (GSE127649; ENCSR412ZDC, released on 10 February 2018)  (31). Gene classification and gene-ontology (GO) enrichment were performed using the PANTHER interface (version 16. 0 released 2020-12-01) using Fisher's exact t-test and false discovery rate  (32). The UCSC Genome Browser and Integrative Genome Viewer (IGV 2. 3. 92) were used to visualize the ChIPsequencing data as well as to prepare the different panels in the manuscript  (33). The UCSC CpG Islands, GeneHancer and RefSeq gene tracks were used to locate promoters and enhancers as well as genes in the human genome (reference hg19)  (33). ENCODE datasets, including transcription factors ChIP-sequencing data and DNAseI sites, are freely usable and were retrieved from the ENCODE download portal  (31).
Histone ChIP-sequencing data are freely usable and were retrieved from the ChIP-Atlas database (https://chip-atlas. org/)  (34). A search for classical recognition motif of the AHR/ARNT complex (5 -GCGTG-3 ; 3 -CACGC-5 and its longer version 5 -TNGCGTG-3 ; 3 -CACGCNA-5 ) was performed manually on the sequence of HK2 gene (coordinates: chromosome 2:75059782-75120481 on human reference genome hg19).
The Comparative Toxicogenomics Database (CTD) The Comparative Toxicogenomics Database (CTD) was interrogated on 1 September 2021 on an updated version of the database including 14 036 unique chemicals  (38). Using the search engine 'chemical-gene interaction' tool, 2284 chemical interactions with AHR in human (taxon:9606) were retrieved among which 748 interactions modulate AHR activity (either 'increase', 'decrease' or 'affect with degree unspecified'). Using a similar approach, we identified 180 chemical interactions with HK2 and 158 chemical interactions regulating HK2 expression in human. We manually curated the lists (to remove duplications) and then com-pared them to identify the chemicals common to the regulation of HK2 expression and AHR activity, and we assessed the resulting overlap by hypergeometric test.
Digestion U2OS-GFP-HK2 and U2OS cell pellets (four samples per group) were lysed during 5 min at 95 • C in 100 mM Tris/HCl pH8. 5, 2% SDS. Protein concentration was determined using SDS PAGE of an aliquot and Imagelab software (Bio-Rad Laboratories). 50 g of proteins from each lysate were reduced and alkylated with 10 mM tris(2carboxyethyl)phosphine (TCEP) and 50 mM chloroacetamide for 5 min at 95 • C. After cooling to room temperature, extracts were diluted with 300 l 8 M urea, 50mM Tris/HCl pH 8. 5, transferred onto 30 kDa centrifugal filters and prepared for FASP digestion as described previously  (39). Proteins were digested overnight at 37 • C with 1g trypsin (V511A; Promega).
LC-MS/MS After speed-vacuum drying, fractions were solubilized in 10 l of 0. 1% TFA, 10% ACN. Liquid chromatography and mass spectrometry analyses were performed on an U3000 RSLC nanoflow-HPLC system coupled to a Q-Exactive Orbitrap mass spectrometer (both from Thermo Fisher Scientific). 1 l of each fraction were concentrated and washed on a C18 reverse-phase precolumn (3 m particle size, 100 Å pore size, 75 m inner diameter, 2 cm length, Thermo Fischer Scientific), then separated using a C18 reverse-phase analytical column (2 m particle size, 100 Å pore size, 75 m inner diameter, 25 cm length from Thermo Fischer Scientific) with a 3 h gradient starting from 99% of solvent A (0. 1% formic acid) to 55% of solvent B (80% ACN and 0. 085% formic acid). The mass spectrometer acquired data throughout the elution process and operated in a datadependent scheme with full MS scans acquired, followed by up to 10 successive MS/MS HCD-fragmentations on the most abundant ions detected.
Identification and quantification The mass spectrometry data were analyzed using Maxquant version 1. 6. 2. 6  (41). The database used was a concatenation of human sequences from the Uniprot-Swissprot database (Uniprot, release 2018-06) and an incremented list of contaminants. The enzyme specificity was trypsin. The precursor mass tolerance was set to 4. 5 ppm and the fragment mass tolerance to 20 ppm. Carbamidomethylation of cysteins was set as constant modification and acetylation of protein N-terminus and oxidation of methionines were set as variable modifications. Second peptide search was allowed and minimal length of peptides was set at seven amino acids. False discovery rate (FDR) was kept below 1% on both peptides and proteins. Label-free protein quantification (LFQ) was done using both unique and razor peptides.
Proteome data analysis Using Perseus software (version 1. 6. 2. 3) (42) false proteins discovery were filtered out, to wit proteins that match with contaminant, to the reverse database and proteins identified only with modified peptide, leading to a matrix of 7278 proteins. LFQ intensity data were transformed into log 2 and a Student's t-test was performed using the Benjamini-Hochberg false discovery rate to identify the differentially expressed proteins. Proteins present in at least 3/4 replicates only in U2OS or U2OS-GFP-HK2 cells were manually given the scores: P-value = 0 and |log ratio| = 10 (i. e. log 2 (mean LFQ intensity in U2OS) -log 2 (mean LFQ intensity in U2OS-GFP-HK2)). Then Canonical Pathways and Upstream Regulator analyses were generated through the use of Ingenuity Pathway Analysis software IPA (QI-AGEN, https://www.
Cytoscape visualization The list including the differentially regulated proteins belonging to the AHR signalling pathway, the Upstream Regulators predicted in this pathway and their target proteins was submitted into Cytoscape software (v. 3. 8. 2) for network visualisation  (43). Proteins of the 'AHR signalling' were manually highlighted by bold lines. Proteins were colour coded according to their log ratio between U2OS and U2OS-GFP-HK2 cells.
Cancer Cell Line Encyclopedia (CCLE) data RNA sequencing data were downloaded from the authors' manuscript through the cBioPortal web interface (https://www. cbioportal. org) and retrieved in reads per kilobase per million mapped reads (RPKM)  (35, 45). Illumina 450K methylation data and reduced-representation bisulfite sequencing (RRBS) data of the AHR promoter in osteosarcoma cell lines were retrieved using the CellMiner Cross-Database (CDB) web interface (https://discover. nci. nih. gov/cellminercdb/)  (46). Only CpG located in the AHR CpG islands with coverage >5 were exported for RRBS data.
The Cancer Genome Atlas (TCGA) data Gene expression, DNA methylation and clinical data in 33 cancer types were obtained from the TCGA and cBio-Portal data portal  (45, 47, 48). Correlations were evaluated with Pearson coefficient values. High and low HK2 expression groups were defined using the median expression value of HK2 in each TCGA cancer type. High and low AHR expression and promoter methylation level groups were defined using the same criteria. All data used in this study were freely available, freely re-usable and patients' information anonymized. Adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), acute myeloid leukemia (LAML), brain lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), mesothelioma (MESO), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), sarcoma (SARC), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), testicular germ cell tumors (TGCT), thyroid carcinoma (THCA), thymoma (THYM), uterine corpus endometrial carcinoma (UCEC), uterine carcinosarcoma (UCS) and uveal melanoma (UVM).
We identified 1314 AHR-bound genes common between the different ChIP-sequencing datasets and experimental conditions (Figure  1A ). We performed a functional annotation of this list of genes using the PANTHER GO-Slim Biological Process database  (32)  and observed that a large proportion of AHR target genes encode proteins involved in metabolic pathways and processes (Figure  1B  and Supplementary Table S1). We notably observed that HK2, a key cancer gene, is an AHR target in all experimental conditions of the three studies which led us to further explore this AHR/HK2 axis. In MCF-7 cells, AHR binding is detectable in a cluster of cis-regulatory elements in the vicinity of HK2 exon 3 (i. e. introns 2 and 3; that we called A, B, C and D cisregulatory elements hereafter) and the binding is enhanced after 45 min of TCDD exposure (Figure  1C ).
These observations support the hypothesis that these AHR-bound genomic sites are functional cis-regulatory elements of HK2. To confirm that HK2 is a bona-fide target of AHR, we treated U2OS human osteosarcoma cells with 10 nM TCDD or vehicle for 1 h and 24 h before assessing the binding of AHR at genomic sites nearby exon 3 (i. e. regions A, B, C and D), at the HK2 promoter as well as at negative control regions located ±2 kb from the promoter. As an additional control we also monitored AHR binding at the CYP1A1 promoter, commonly assessed as a readout of AHR transcriptional activity (3). Importantly, using histone marks ChIP-sequencing data produced in U2OS cells, we could confirm the location of HK2 enhancers, although only AHR-binding sites A, B and C are enriched for enhancer-associated histone marks (Supplementary Figure  S1C  and D ).
We also investigated whether HK2 expression was regulated upon TCDD exposure in vivo, in tissues expressing high levels of AHR, such as the lung  (18). To do that, we studied Hk2 expression by RT-qPCR and western blot in lung tissues from adult male mice exposed to TCDD in utero and during lactation. We observed significant higher Hk2 mRNA and protein levels compared to vehicle-treated mice tissues (Figure  2H ). Hk2 is thus also regulated by TCDD exposure in vivo. Finally, to gain a broader insight into the association between modulators of AHR activity and the expression of HK2, we interrogated the Comparative Toxicogenomics Database (CTD)  (38). Among the 266 chemicals (or mixtures) interactions reported to modulate AHR activity in human cells, we identified 18 compounds that also regulate HK2 expression (fold enrichment: 8.
05; P(X = 18) = 8. 06 × 10 -12 ) (Supplementary Figure  S2E  and Table  S2 ). Among these associations, we retrieved complex chemical mixtures such as cigarette smoke as well as several HAH and PAH compounds. This data mining analysis further supports that HK2 is transcriptionally regulated by several modulators of AHR pathway in human cells, although it appears context (agonist-and cell type-) dependent.
Following downstream statistical analyses, we identified the differentially expressed proteins (|log ratio| > 1 and P-value < 0. 001) between the two cell lines. A functional annotation of this list using the QIAGEN Ingenuity Pathway Analysis (IPA) software revealed several pathways deregulated upon HK2 over-expression, among which 'AHR signalling' (P = 8. 32 × 10 -8 ) with 29 associated proteins. This list includes proteins involved in phase-I and phase-II detoxification mechanisms (ALDH1B1, ALDH3A1, ALDH3A2, ALDH6A1, GSTK1, GSTM3, GSTP1, GSTZ1, NQO1 and NQO2); cell cycle regulators (CCND2, CCND3, CCNE1, CDK4, CDK6, CDKN2A, CHK2 and RB); chromatin factors (EP300, JUN, NCOA3 and NFKB2) and signalling proteins (HSPB1, MAPK1, MAPK8, RXRA, SRC, TGFB2 and TGM2) (Figure  3B ). Of note, additional proteins of the AHR network were detected in our proteomic analysis although not robustly enough to be considered by IPA, including most of the CYP enzymes and AHR itself (Supplementary Table  S3 ).
As an orthogonal validation of the proteomic data, we performed western blot analyses. We validated that HK2 overexpression leads to the down-regulation of HK1 and JUN and the up-regulation of LDHA, CHK2 and NQO1 at pro-tein levels in U2OS-GFP-HK2 compared to U2OS-GFP cells (Figure  3C ). Our analysis, using the IPA software, also indicated that 12 of these 29 proteins are predicted as 'upstream regulators', suggesting that they potentially regulate additional proteins identified as mis-regulated by HK2 over-expression (Supplementary Table  S4 ). For instance, CDKN2A is predicted to regulate 32 proteins regulated by HK2 overexpression in our analysis (P = 1. 52 × 10 -8 ). This complementary analysis led us to identify 166 downstream proteins including 16 which are annotated as belonging to 'AHR signalling' (Figure  3D, E ).
Importantly, by western blot, we confirmed higher levels of AHR protein in U2OS-GFP-HK2 cells compared to control cells (Figure  5C ). As AHR promoter contains CpG sequences susceptible to DNA methylation  (19) , we investigated whether DNA methylation was involved in the regulation of AHR expression. We first assessed the methylation status and the expression level of AHR in a panel of osteosarcoma cells us-ing the Cancer Cell Line Encyclopedia (CCLE) datasets  (35, 46). We observed that the methylation of AHR promoter is negatively correlated with AHR mRNA expression in this set of cell lines and that in U2OS cells AHR promoter is highly methylated and AHR mRNA expression is low (Figure  5D ) (data are not available for the 143B cell line in this database). The methylation of AHR promoter in U2OS cells was further confirmed by interrogating single CpG methylation analyses from the CCLE (Figure  5E ).