dataesr/scientific-paragraphs-categorization huggingface dataset:
| Proteins regulating melatonin synthesis in the human pineal gland Among the most important proteins known to be found in a pineal gland are the last two enzymes in the melatonin synthesis cascade, AANAT and ASMT (HIOMT). 13, 15, 88 Interestingly neither our study, nor a recently published human pineal proteome study found peptide fragments corresponding to AANAT. 89 ASMT, on the other hand and interestingly ASMTL, an ASMT-like protein with a broader expression spectrum than ASMT, were identified in both studies. Moreover, tryptophan hydroxylase isozyme type I (TPH1) and aromatic-amino-acid decarboxylase (AADC), enzymes catalyzing important steps from the aromatic amino acid tryptophan to serotonin were also detected. Thus, all components necessary to synthesize melatonin from tryptophan are found in our human pineal proteome dataset with the exception of AANAT.
One possible reason for the absence of AANAT may be its very short half-life of about 8 minutes (in rodents) where it is stabilized to some extent by threonine-31-phosphorylation and 14-3-3 binding. However, both AANAT enzyme activity and melatonin content were measurable by biochemical assays and shown to be elevated at night in human,  14, 24, 90, 91  so that so far, no explanation for these findings can be provided. Besides being ubiquitous proteins essential for many general biochemical processes, 14-3-3 proteins have been shown to heterodimerize with AANAT thus being essential for its activity and stability. 88, 92, 93 In the present human proteome study and a previously published dataset,  89  most known forms of 14-3-3 proteins were found to be present in the pineal gland (Figure  S7 ).
ACKNOWLEDGEMENTS This work was supported by the  Institut Pasteur , the  CNRS , the  INSERM , the  AP-HP , the  University Paris Diderot , the  Cognacq-Jay foundation , the  Bettencourt-Schueller foundation , the  Orange foundation , the  FondaMental foundation , the  Fondation de France , the  Conny-Maeva foundation , the  Eranet-Neuron ANR (ALTRUISM) , and the  Labex GenMed and BioPsy. GD is funded by the  Institute for Data Valorization (IVADO) ,  Montreal , and the  Fonds de recherche du Québec (FRQ). We further wish to thank  Véronique Hourdel  and  Quentin Giai Gianetto  for early help regarding MS statistics, as well as  Christian Malosse  for the first set of sample preparation.
Seroprevalence will be estimated using two ELISA-designed assays to detect specific IgG antibodies against viral S and N proteins of SARS-CoV-2 in humoral fluid, and these results will be confirmed using a virus neutralization test (VNT). The VNT will also evaluate the levels of functional neutralizing SARS-CoV-2 antibodies. This step will allow evaluation of the immune response rates to SARS-CoV-2, the impact of COVID-19 vaccine uptake on these rates, and prevalence and risk factors of SARS-CoV-2 infection. This study will be repeated from November to December 2021 (Phase 2) to evaluate the evolution of immune status achieved by vaccination and/or natural protection of PHCWs and to describe the history of exposure to SARS-CoV-2. This study was registered at https://www. clinicaltrials. gov on 17 March 2021.
Study Procedure 2. 6. 1. PHCWs Consent Procedure PHCWs will be recruited by e-mail communication from the networks mentioned above. During the two phases, potential participants will be given a brief explanation of the study and will choose to participate by following the link in the recruitment materials to a survey on the study website (SeroPRIM website). Using this platform, before the data collection, participants will complete a prescreening questionnaire and an electronic informed consent form before being deemed to be eligible. In Phase 2, PHCWs who participated in Phase 1 will be asked by e-mail to participate in the second phase of the study.
Data Collection In Phases 1 and 2, data collection will include the analysis of capillary blood collected on a dried blood spot (DBS) and an online questionnaire for PHCWs and household contacts. PHCWs and household contacts will receive at the mailing address they gave on the study website a self-sampling DBS kit that includes a DBS card, lancets, a pad, detailed printed instructions about performing the blood sampling, and a self-addressed stamped padded envelope to return the DBS card to the centralized biobank (CEPH Biobank, Paris, France). When they receive the DBS kit, participants will be invited to perform the blood sampling and complete an online questionnaire at the same time. Successful participation in the study will be defined as providing both the blood sample for serological analysis and a completed questionnaire.
Data Management All participants will be assigned a participant number based on their order of inclusion. Collected data from serological tests and questionnaires will be de-identified, and databases will be compiled using the unique participant ID. Only de-identified nonpersonal data will be shared among the research team for statistical analysis. Personal and nominative data, including e-mail and postal addresses, will be used exclusively for follow-up purposes and sending the DBS kit, and this information will be kept in an electronic database that will be encrypted and password protected, and access will be restricted to the study coordinator and three people responsible for the study coordination. The personal and nominative data will be stored electronically separately from the coded, de-identified research data.
Informed Consent The informed consent form of the study contains information about the study objectives and process, potential risks and benefits, and the use and storage of data and biological specimens for this study and possible further research on respiratory viruses. Participants are provided with a link to a website containing further information about the study. The forms have been reviewed by the ethics committee that authorized the trial. Electronic informed consent will be obtained before inclusion from all participants or their legal guardians and will include consent for children. The participant/legal guardian/child will be free to decline or withdraw consent at any time without providing a reason and without being subject to any resulting detriment. Children who turn 18 years old between the time of consent and the data collection period will be invited to provide their consent for the study.
Data Protection Collected data will be stored securely at the Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP), Paris, France. De-identified data for participants who consent to the use of their data for further research may be made available to other researchers beyond the protocol stipulations without additional ethics approval. Personal and nominative data will be kept in an electronic database, which will be encrypted and password protected, and access restricted to the study coordinator and three other people responsible for the study coordination. Under no circumstances will these data be made available to a third party. The personal and nominative data will be electronically stored separately from the coded de-identified research data. De-identified data will be destroyed after 20 years.
Strengths of the Study The present study will enroll a large prospective cohort of four subpopulations of PHCWs within the same geographical area (metropolitan France) and timeframe. This will mean that the seroprevalence estimates will be comparable between the studied populations as well as with the results of the SAPRIS study  [17] , which is using identical serological methods and is being conducted concomitantly among the general population in metropolitan France. The geographic spread of PHCW subpopulations across all of metropolitan France will provide details about regional variations in seroprevalence and data useful to the national vaccination strategy. The prospective approach of this study will provide an estimate of the infection rate and the identification of SARS-CoV-2 risk factors especially among vaccinated PHCWs.
Microarray analysis Total RNA (100 ng) was processed using the Ambion Affymetrix WT protocol. Biotinylated cDNA (6 ug) was hybridized to Mouse Gene 2. 0 ST GeneChip arrays according to the manufacturer's recommendations. Signals were summarized using the RMA algorithm (  21 ) and 2-way ANOVA performed using Partek Genomics Suite v6. 6 (Partek). The false discovery rate was estimated using the Benjamini-Hochberg method (  22 ). Gene set enrichment analysis (GSEA) was performed using GSEA v2. 06 software and curated gene sets downloaded from the Broad Institute  (23).
Quantification and statistical analysis Kaplan-Meier curves were prepared using PRISM software (GraphPad). Logistic regression was used to evaluate the association of genotype with gender. All tests are two-sided and no multiple testing adjustments were performed. The analysis was performed with R statistical software (Windows version R version 3. 3. 3 for 64-bit machines), the cmprsk package (version 2. 2-7), and the stats package (version 3. 3. 3). Pearson's chi square test was used to assess genotypes derived from Mdm2 -/-, p53 R334H/R334H intercrosses based on the null hypothesis that there was no difference in survival among the three expected genotypes: Mdm2 +/+ , Mdm2 +/-and Mdm2 -/-and the proportion of expected genotypes is 0. 25, 0. 50, 0. 25, respectively.
ACKNOWLEDGMENTS We thank the  St. Jude Children's Research Hospital Hartwell Center for Bioinformatics and Biotechnology, Transgenic/Gene Knockout Lab, ARC, Veterinary Pathology Core , and the  Protein Production Facility  for technical support. We thank  Charlene Henry ,  Muralidhar Reddivari  and  Melanie Lloyd  for their technical assistance. We gratefully acknowledge the support of the  NIH/NCI Comprehensive Cancer Center Support  grant  P30CA21765 ,  Centocor , " Investments for the Future"  ( ANR-11-LABX-0028-01 ),  Speer Charitable Trust  and  American Lebanese Syrian Associated Charities (ALSAC). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Transcriptomic Analysis of Colorectal Cancer Cell Lines Grown in 2D and 3D Culture Conditions We first performed gene expression analysis through RNA-Sequencing on samples prepared from our panel of four CRC cell lines grown in either 2D or 3D cell culture conditions. The RNASeq gene expression data (available at www. ncbi. nlm. nih. gov/geo/ (accessed on 12 October 2021) under the accession number: GSE185055) demonstrated that more than 2000 gene expression profiles were affected by the growth conditions for each cell lines (Log2foldchange 2Dvs3D <-0. 5 or >1. 5 with p < 0. 05). However, hierarchical clustering showed that even if 2D/3D gene expression profiles diverge for a single cell line, their expression patterns remain similar and samples prepared from either HT29, HCT116, LS174T or LS513 are grouped together (Figure  1A ).
4. 1) (https://www. bioinformatics. babraham. ac. uk/projects/trim_galore/, accessed on 12 October 2021), poly N read tails were trimmed, reads with ≤40 bases were removed, and reads with a quality of mean ≤30 were discarded. Reads were then aligned against the Homo sapiens genome from Ensembl version 91 using STAR (version 2. 6. 1b) [94]. Alignments from reads matching more than once on the reference genome were removed using the Java version of samtools  [95]. To compute gene expression, Homo sapiens GTF genome annotation version 91 from the Ensembl database was used. All overlapping regions between alignments and referenced exons were counted and aggregated by genes using HTSeq-count 0. 5. 3  [96]. The sample counts were normalized using DESeq2 1. 8. 1  [97]. Statistical treatments and differential analyses were also performed using DESeq2 1.
8. 1. Finally, the hierarchical cluster plot was performed on normalized counts with the hcust function of the "stats" package of R 3. 2. 2 using the "complete" agglomeration method. The RNASeq gene expression data and raw fastq files are available on the GEO repository (www. ncbi. nlm. nih. gov/geo/, accessed on 12 October 2021) under accession number: GSE185055.
Quantitative Real Time PCR Total RNA was extracted as described above using the RNeasy ® Plus Micro Kit (Qiagen). cDNA was prepared using RevertAid H Minus Reverse Transcriptase (ThermoFisher). Quantitative real-time PCR (qRT-PCR) was performed in duplicate using SYBR Green Gene Expression Assays (SensiFAST TM SYBR ® No-Rox Kit Bioline). Reactions were analyzed with a Biorad CFX96 Touch Real-Time PCR machine at 62 • C for 40 cycles. Data were analyzed using the comparative threshold cycle 2 -∆∆Ct method  [98] ; the expression of 36B4 (RPL0) was used to normalize data. The expression of the genes was expressed as the fold change in the samples compared to HCEC-1CT cells; 2 -∆∆Ct calculation for three independent genes is available Table  S5. All values are averages of at least four independent experiments done in duplicate.
The analyses were performed using the Eoulsan pipeline  [93] , including read filtering, mapping, alignment filtering, read quantification, normalization and differential analysis; before mapping, adapters were removed using Trim Galore! (Version 0. 4. 1) (https://www. bioinformatics. babraham. ac. uk/projects/trim_galore/), poly N read tails were trimmed, reads with ≤40 bases were removed, and reads with a quality of mean ≤30 were discarded. Reads were then aligned against the Homo sapiens genome from Ensembl version 91 using STAR (version 2. 6. 1b) [94]. Alignments from reads matching more than once on the reference genome were removed using the Java version of samtools  [95]. To compute gene expression, Homo sapiens GTF genome annotation version 91 from the Ensembl database was used. All overlapping regions between alignments and referenced exons were counted and aggregated by genes using HTSeq-count 0.
5. 3  [96]. The sample counts were normalized using DESeq2 1. 8. 1  [97]. Statistical treatments and differential analyses were also performed using DESeq2 1. 8. 1. Finally, the hierarchical cluster plot was performed on normalized counts with the hcust function of the "stats" package of R 3. 2. 2 using the "complete" agglomeration method. The RNASeq gene expression data and raw fastq files are available on the GEO repository (www. ncbi. nlm. nih. gov/geo/) under accession number: GSE185055.
Quantitative Real Time PCR Total RNA was extracted as described above using the RNeasy ® Plus Micro Kit (Qiagen). cDNA was prepared using RevertAid H Minus Reverse Transcriptase (Ther-moFisher). Quantitative real-time PCR (qRT-PCR) was performed in duplicate using SYBR Green Gene Expression Assays (SensiFAST TM SYBR ® No-Rox Kit Bioline). Reactions were analyzed with a Biorad CFX96 Touch Real-Time PCR machine at 62°C for 40 cycles. Data were analyzed using the comparative threshold cycle 2 -ΔΔCt method  [98] ; the expression of 36B4 (RPL0) was used to normalize data. The expression of the genes was expressed as the fold change in the samples compared to HCEC-1CT cells; 2 -ΔΔCt calculation for three independent genes is available table  S5. All values are averages of at least four independent experiments done in duplicate.
Cell Viability Assay Cell viability was assessed by CellTiter Glo Luminescent Viability assay (Promega). At J0, 1500 cells were seeded in a white 96-well plate. At J1, cells were treated with ATP, ATPγS or adenosine at the indicated concentrations. After four days of treatment, a CellTiter Glo assay was performed as recommended by Promega. Luminescence was measured in a microplate reader, Infinite 200Pro (TECAN). All values are averages of at least three independent experiments done in duplicate. The IC50 values were calculated with the Graphpad Prism software using a nonlinear regression curve.
Spheroid Growth Cells were seeded in a low attachment plate (Corning), specific for spheroid formation; 2000 cells were plated, and growth was assessed after eight days by microscopy (Evos, inverted microscope). Cells were treated after 24 h of culture with ATP (250 µM) and adenosine (250 µM). Image analysis was performed using ImageJ software to determine the area growth of the spheroids.
γ-H2AX Immunolabeling At J0, 5 × 10 4 cells were seeded in a 24-well plate with 500µL of medium. At J1, cells were treated with ATP (250µM) or adenosine (250µM) for 48 h. The DNA double stranded breaks (DSBs) formation was then assessed by the measurement of Ser-139 phosphorylation of the histone variant H2AX with an indirect labeling protocol. Cells were fixed and permeabilized by PFA (4%) and then ice-cold methanol, and labeled by the primary antibody anti-γ-H2AX (ab26350-Abcam, 1µg/10 6 cells diluted in PBS-BSA 1%). Then, cells were incubated with an anti-mouse FITC-conjugated secondary antibody (concentration 1/250), and the signal was measured by flow cytometry using a cytoFLEX (Beckman coulter). Data were analyzed using FlowJo software and the percentage of γ-H2AX positive cells determined.
Cell Cycle Analysis Cell cycle analysis was performed by using the APC BrdU Flow kit (BD biosciences). Briefly, 10 5 cells were treated by ATP (250 µM) for 24 h and incubated with 10 µM bromodeoxyuridine (BrdU) for 1 h at 37 • C. Cells were then fixed and stained with an APCconjugated anti-BrdU antibody and 7-aminoactinomycin D (7-AAD) according to the manufacturer's instructions. Two-color flow cytometry was performed in a cytoFLEX (Beckman Coulter) and data analyzed to identify the percentage of cells actively synthesizing DNA (BrdU incorporation) and their cell cycle position (i. e. , G0/1, S, or G2/M phase defined by 7-AAD staining intensities).
We found a concentration-dependent increase in the number of H2AX foci after exposure to either olaparib or talazoparib, an effect which was more pronounced in PBRM1-KO cells (Fig. 3C, D ; Fig. S7A-D ; Fig. S8A-D ). We also found that the preexisting number of RAD51 and RPA foci was increased in PBRM1-KO cells, and that this phenotype was further exacerbated by PARPi exposure (Fig. 3E , F; Fig. S7E-J ; Fig. S8E-I ). We also noted that: (i) mRNA and protein levels of the key HR genes BRCA1, BRCA2 and RAD51 were similar in PBRM1-WT and -KO cells (Fig. S9A, B ); (ii) PBRM1-KO cells were as sensitive as PBRM1-WT cells to ionizing radiation (Fig. S10A-C ) and to the DNA DSB-inducing agents etoposide (Fig. S10D-F ) and doxorubicin (Fig. S10G-I ); and (iii) the number of loss-of-heterozygosity (LOH) events, a correlate genetic marker of HR deficiency, was not increased in PBRM1mutant tumors from the TCGA dataset (Fig.
3K , L; ≈ 2-fold change, p < 0. 0001, two-way ANOVA). Exposure to olaparib or talazoparib exacerbated this effect (Fig. 3K , L; Fig. S13A-C ), to a degree that was at least equivalent to that caused by the clastogen mitomycin C (used here as a positive control). To investigate the potential sources of micronuclei, we performed timelapse confocal video-microscopy using an mCherry-labeled histone H2B PBRM1isogenic cells. We found that PBRM1-KO cells exhibited a delayed metaphase plate formation, an increased average mitosis duration (45 vs. 70 min in PBRM1-WT and -KO cells, respectively, p < 0. 0001, two-way ANOVA; Fig. S14A ; Videos S1-4) and a significantly higher number of anaphase bridges and lagging chromosomes compared to PBRM1-WT cells (Fig. S14B-D ). Together, our data suggested that loss of PBRM1 leads to increased DNA damage, replication stress and micronuclei formation, effects which were exacerbated by exposure to PARPi.
Because several clinical ATRi are currently being developed, notably in combination with ICI (NCT02264678), assessing whether ATRi-dependent immunomodulation also occurs in genetic contexts where ATRi elicit synthetic lethality  (50, 51)  could have direct clinical implications. Interestingly, PBRM1 loss-of-function has recently been associated with enhanced tumor cell sensitivity to interferon- and increased type I interferon response  (10). PBRM1 defects were further linked to better survival upon anti-PD-1 and anti-CTLA-4 therapy in mice  (10) , as well as enhanced efficacy of anti-PD-(L)1 therapy in patients with ccRCC  (9, 63). Controversially, other studies reported that Pbrm1 inactivation resulted in a non-immunogenic tumor microenvironment favoring resistance to anti-PD-1 therapy in murine ccRCC models  (64) , and that PBRM1 mutation did not correlate with clinical benefit in patients treated with ICI  (18, 64, 65).
Investigating whether the inconstant sensitivity of PBRM1-deficient tumors to ICI results from variable cytosolic DNA sensing capacities within tumor cells, notably that of the cGAS/STING pathway as recently described in mismatch repair deficient contexts  (48) , is an important question that warrants further exploration. In conclusion, our findings shed light on the genetic vulnerabilities associated with loss of PBRM1 related to its role in maintaining genome integrity. We are currently exploring the PARPi plus anti-PD-L1 combination therapy in patients with ccRCC, in an investigator-initiated academic phase II study developed on the basis of our preclinical findings (EudraCT N° 2018-001744-62). Figure Legends.
Acknowledgements This work was funded by programme grants to SPV from  Institut National de la Santé et de la Recherche Médicale  ( INSERM ATIP-Avenir Programme Funding ),  Site de Recherche Intégrée sur le Cancer   SOCRATE-2 (INCa-DGOS-INSERM_12551 ),  Cancéropôle Ile-de-France  ( 2017-1-EMERG-72 ), and  Association pour la Recherche contre le Cancer  ( PGA1 RF 20190208576 ), as well as programme grants to CJL from  Cancer Research UK  ( CRUK Programme Funding   C30061/A24439 ) and  Breast Cancer Now  as part of programme funding to the  Breast Cancer Now Toby Robins Research Centre. RMC  received award funding from  Fondation Bettencourt-Schueller ,  Institut Servier ,  Fondation des Treilles  and  Cancéropôle Ile-De-France. DM received funding from the  INSERM ITMO Cancer  grant. This study represents independent research supported by the  National Institute for Health Research (NIHR) Biomedical Research Centre at The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London.
Microbiota analysis Raw sequences were demultiplexed using the MrDNA tool fastqSplitter (https://www. mrdnalab. com/mrdnafreesoftware/fastq-splitter. html). Demultiplexed sequences were treated using the DADA2  (Callahan et al. , 2016)  package available in the RStudio software  (RStudio Team, 2020). Briefly, sequences were filtered using a maximum expected error of 2. Filtered sequences were used to estimate error rates and dereplication. Dereplicated samples and error rates were implemented in the DADA2 error model in order to correct sequencing errors and construct Amplicon Sequence Variants (ASVs) as described in  Callahan et al. (2016)  and  Callahan et al. (2016). Paired-end reads were merged and chimeras were removed using the consensus method. Taxonomy was finally assigned using the Silva 138 database  (Quast et al.
, 2013). Data were normalized with the Counts Per Million (CPM) method using the cpm function available in the edgeR package under RStudio  (Robinson, McCarthy & Smyth, 2009). All libraries were normalized to 1,000,000 reads prior to microbiota analysis. A dendrogram based on a Bray-Curtis dissimilarity matrix and Ward method was obtained using the vegan, ggplot2, dplyr and dendextend packages in RStudio  (Galili, 2015; Oksanen et al. , 2020; Wickham, 2016; Wickham et al. , 2021). Histogram tables were obtained using the dplyr package in RStudio  (Wickham et al. , 2021). Venn diagrams were made using the open-source component for web environment jvenn (http://jvenn. toulouse. inra. fr/app/example. html) (Bardou et al. , 2014). All the 16S rRNA data are available in the NCBI SRA repository (Submission ID SUB9828953, BioProject ID PRJNA736535).