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The accumulation time for MS-MS was 80 ms, and the CE setting principle was the same as for the IDA mode. Raw IDA data acquired from the TripleTOF 5600 were analyzed with the ProteinPilot software (version 5. 0, AB Sciex) with a Paragon algorithm search. Results were filtered with a 1% false discovery rate at both the protein and peptide levels, and the accuracy of the MS that we used was 0. 05 D for MS and 0. 1 D for MS-MS fragment ions. The result file was imported into the PeakView SWATH MicroApp (version 2. 0) software to generate a spectral library, with default parameters. The SWATH MicroApp was also used for peptide peak extraction from the SWATH data. Results were filtered with a q < 0. 01 threshold. MarkerView (version 1. 3) was used for data normalization and the quantification of relative peptide or protein peak extraction data.
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The different proteins or peptides were selected with a fold change > 1. 5. The MS proteomics data have been deposited into the ProteomeXchange Consortium database via the PRIDE partner repository (Perez-Riverol et al. , 2019) , with the data set identifier PXD015995.
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About 100 mg of protein treated with or without CIAP was subjected to a separation via 10% (w/v) SDS-PAGE with 100 mM Phos-tag (Boppard,. Mn 21 -Phos-tag SDS-PAGE preparation and immunoblotting were performed based on the protocol described in FU-JIFILM Wako Chemicals' Phos-tag guide book (https://labchem-wako. fujifilm. com/us/category/lifescience/proteomics/phostag/index. html). Proteins (10 mg) were assayed using another 10% (w/v) SDS-PAGE gel as a reference, and HA antibodies (Sigma-Aldrich, catalog no. H3 63, lot no. 066M4837V) or b-actin (CWBio, catalog no. CW0264) antibodies were used for the immunoblot analysis. The intensity of immunoblot bands was assayed with ImageJ software (https://imagej. nih. gov/ij/).
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Statistical Analysis For root architecture and symbiotic nodulation phenotype assays, gene expression analysis, and the quantification of intensity of immunoblot bands, statistical analysis was performed as described in the figure legends. Significant differences were determined with paired two-tailed Student's t test using Excel 2016 or with ANOVA and Kruskal-Wallis nonparametric testing using IBM SPSS Statistics version 25 (Supplemental Data Set 3).
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Accession Numbers Sequence data of the M. truncatula genes studied in this article can be found in Legumeinfo (https://legumeinfo. org/search/gene) under the following accession numbers: MtCRA2 (Medtr3g110840), MtEIN2 (Medtr0041s0030), and MtYUC2 (Medtr6g086870).
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ACKNOWLEDGMENTS We thank Peibin Qin and Yinghua Zhao ( Shanghai AB Sciex Analytical Instrument Trading Co. ) for help with MS analyses, Jijun Yan and Jinfang Chu ( Institute of Genetics and Developmental Biology, Chinese Academy of Sciences ) for help with the IAA quantification, Chuanen Zhou ( School of Life Sciences, Shandong University ) for providing the DR5:GUS vector, and Qijun Chen ( College of Biological Science, China Agriculture University ) for providing the CRISPR/Cas9 system. This work was supported by the National Natural Science Foundation of China (grants 31772658 and 31571587 ), the project for Extramural Scientists of the State Key Laboratory of Agrobiotechnology, China Agricultural University (grant 2018SKLAB6-22 ), and the China Postdoctoral Science Foundation (grant 2019M660878 ).
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However, in a conditional analysis, adjusting for heterozygosity at rs1799990, we found no substantive evidence of an independent association signal (Supplementary Figure 7 and 8 ); lead SNP rs12624635, P=0. 03 (heterozygous conditional model). The region of high LD surrounding rs3747957 in STX6 results in a large causal set, making identification of a single causal variant more complex (Figure 2b ). Subsequently, using eCAVIAR and GTEx 19 and other eQTL databases, we identified a strong correlation between sCJD risk and increased expression of STX6 mRNA in multiple brain regions, particularly in the caudate and putamen nuclei of the brain (Putamen STX6 rs3747957 P=2. 3 x 10 -13 , GTEx, Figure 3 ), key regions implicated in sCJD and the most frequently abnormal brain regions at diagnostic MRI brain imaging 20.
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As the GWAS signal is associated with only two SNPs at GAL3ST1 (in strong LD with each other but low LD with all other surrounding variants) these SNPs predominantly define the causal set as expected, however, these are statistically indistinguishable from each other (Figure 2c ). One of these SNPs, rs2267161, is a non-synonymous variant of GAL3ST1 p. V29M. In GTEx, neither SNP correlates with expression of genes at the locus in brain tissues. Close to p. V29M resides p. V34M (rs55674628, allele frequency=0. 02; LD with rs2267161, r 2 =0. 01, D'=1. 00, discovery P=0. 18), these being the only common non-synonymous variants in European ancestries populations. These polymorphisms form three common haplotypes, rs2267161-C/rs55674628-C (CC), CT and TC with frequencies in the combined case-control dataset of 0.
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This research study consists of an extended questionnaire, blood withdrawal from patients and relatives, and collection of data from the medical records. All patients and relatives participating in this study provided written informed consent to participate and to obtain information from their treating physicians. The patients included in this study were probable sCJD cases or definite sCJD cases based on the diagnostic criteria from the University of Edinburgh. 1 for sample sources and sizes. Control samples were chosen from genotype datasets available for biomedical research derived from countries that matched those providing case samples. In all but a single example, genotyping was done using Illumina arrays with substantial overlap with the Illumina Omniexpress array used to genotype cases.
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LD matrix was calculated from sCJD GWAS data using PLINK --r2 function. CAVIAR was run with an undefined number of causal variants and causal set probability at 95%. eCAVIAR is an extension of the CAVIAR framework to estimate the posterior probability of the same variant being causal in both GWAS and eQTL datasets 55. The STX6 locus was defined as previously and SNPs were pruned with variant inflation factor of 5 to extract distinct signals. We obtained eQTL data for 48 tissues included in the GTEx portal (v7). eQTLs for these 46 pruned variants with nominal P≤10 -5 were defined as 'eGenes' and eCAVIAR performed for each in its target tissue. Resulting colocalisation posterior probability (CLPP) was ranked to identify target gene and tissue. Gene-based analysis (MAGMA / VEGAS2) By aggregating the effects of all SNPs of a GWAS in a gene, correcting for LD and testing the joint association of all markers in the gene with the phenotype, it is possible to extend the power of detection and detect multiple weaker associations within a gene, which otherwise would be missed.
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PAINTOR In addition, we utilised PAINTOR, a Bayesian probabilistic framework, which integrates GWAS summary and LD data with functional genomic annotation data such as ENCODE and FANTOM, to calculate posterior probabilities and identify a set of likely causal variants at any given risk locus (Supplementary Table 6 ). We aimed to identify causal variants 100kB around the lead SNP rs11586493 in the STX6 locus, our prime candidate identified in the GWAS, including all available functional brain tissue annotations (n=587). Whole exome sequencing 249 sCJD exomes were captured with SureSelect and HaloPlex (Agilent) kits. Exomes were sequenced on the Illumina HiSeq2000 platform with 100 bp paired-end runs. Sequences were aligned to the human reference genome using Novoalign software. The Genome Analysis Toolkit (GATK) Unified Genotyper were used for SNP and indel calling then sequences were recalibrated with the GATK Variant Recalibrator and variants annotated with ANNOVAR.
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Immunohistology We used rabbit polyclonal anti-syntaxin-6 (Atlas Antibodies, HPA038558) and PDIA4 (Atlas Antibodies) to immunostain cases of sporadic CJD from archives of the Division of Neuropathology, University College London Hospitals NHS Foundation Trust. Immunostaining was performed on control cases and sCJD cases. Controls (non-neurodegenerative disease deaths, processed similarly to sCJD) were obtained from the Division of Neuropathology. A proportion of the samples originated from frozen material, and these were fixed by immersion into formalin, formic acid treated and then processed into a Formalin Fixed Paraffin Embedded (FFPE) sample. On all cases, frontal cortex and cerebellum (including the dentate nucleus) were stained. Measurement of reference and GWAS significant gene expression level in cerebellum by reverse transcription quantitative PCR (RT-qPCR) RNA was extracted from 50-100 mg post-mortem cerebellum (10 sCJD cases and 10 neurologically normal controls) using the Zymo Direct-zol™ DNA/RNA Miniprep kit (Zymo Research, Irvine, US) according to the manufacturer's instructions followed by DNAse I digestion and concentration using RNA Clean & Concentrator-5 columns (Zymo Research, Irvine, US) according to manufacturer's instructions.
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Acknowledgements We thank Richard Newton for support with images. This study makes use of data generated by the Wellcome Trust Case-Control Consortium. A full list of the investigators who contributed to the generation of the data is available from www. wtccc. org. uk. Funding for the project was provided by the Wellcome Trust and Medical Research Council. We would like to thank patients, their families and carers, UK neurologists and other referring physicians, co-workers at the National Prion Clinic , our colleagues at the National Creutzfeldt-Jakob Disease Research and Surveillance Unit, Edinburgh. We thank the Director of the Papua New Guinea Institute of Medical Research , Professor Willie Pomat , and former Directors Professors Peter Siba and John Reeder , the staff of the PNGIMR, especially the kuru project field team, and the communities of the kuru-affected region for their generous support.
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Data availability Summary statistics will be made available through the GWAS catalogue at NHGRI-EBI. Accession number not yet available.
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of Central Nervous System Studies of the National Institutes of Health, Bethesda, USA for archiving and sharing old kuru samples. The kuru studies were initially funded by a Wellcome Trust Principal Research Fellowship in the Clinical Sciences to JC, and since 2001, and all other aspects of the work by the Medical Research Council. Several authors at UCL/UCLH receive funding from the Department of Health's NIHR Biomedical Research Centres funding scheme. Some of this work was supported by the Department of Health funded National Prion Monitoring Cohort study. SJC receives an NHMRC Practitioner Fellowship (ID# APP1105784). Tze How Mok is supported by a Fellowship award from Alzheimer's Society, UK (grant number 341 (AS-CTF-16b-007)) and CJD Support Network UK Research Support Grants. Funding for the collection of Polish samples for study was provided Medical University of Lodz.
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The Italian national surveillance of Creutzfeldt-Jakob disease and related disorders is partially supported by the Ministero della Salute, Italy. The German National Reference Centre for TSE is funded by grants from the Robert-Koch-Institute. The Dutch National Prion Disease Registry is funded by the National Institute for Public Health and the Environment (RIVM), which is part from the Ministry for Health, Welfare and Sports, The Netherlands. PS-J was supported by Instituto de Salud Carlos III [Fondo de Investigación Sanitaria, PI16/01652] Accion Estrategica en Salud integrated in the Spanish National I+D+i Plan and financed by Instituto de Salud Carlos III (ISCIII) -Subdireccion General de Evaluacion and the Fondo Europeo de Desarrollo Regional (FEDER -"Una Manera de Hacer Europa"). We thank Inés Santiuste and the Valdecilla Biobank (PT17/0015/0019), integrated in the Spanish Biobank Network, for their support and collaboration in sample collection and management.
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The study on Italian controls was supported by the Ministero dell'Istruzione, dell'Università e della Ricerca -MIUR project "Dipartimenti di Eccellenza 2018 -2022" (n° D15D18000410001) to the Department of Medical Sciences, University of Torino (G. M. ) and the AIRC -Associazione Italiana per la Ricerca sul Cancro (IG 2018 Id. 21390 to G. M. ). The Three-City Study was performed as part of a collaboration between the Institut National de la Santé et de la Recherche Médicale (Inserm), the Victor Segalen Bordeaux II University and Sanofi-Synthélabo. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The 3C Study was also funded by the Caisse Nationale Maladie des Travailleurs Salariés, Direction Générale de la Santé, MGEN, Institut de la Longévité, Agence Française de Sécurité Sanitaire des Produits de Santé, the Aquitaine and Bourgogne Regional Councils, Agence Nationale de la Recherche, ANR supported the COGINUT and COVADIS projects.
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Fondation de France and the joint French Ministry of Research/INSERM «Cohortes et collections de données biologiques» programme. Lille Génopôle received an unconditional grant from Eisai. The Three-city biological bank was developed and maintained by the laboratory for genomic analysis LAG-BRC -Institut Pasteur de Lille. This work was also funded by the Pasteur Institut de Lille, the Lille Métropole Communauté Urbaine, the Haut-de France council, the European Community (FEDER) and the French government's LABEX DISTALZ program (development of innovative strategies for a transdisciplinary approach to Alzheimer's disease). The French National Surveillance Network for Creutzfeldt-Jakob disease is supported by Santé Publique France. rs1799990 rs3747957 rs2267161 rs9065 rs6498552 Nearest gene PRNP STX6 GAL3ST1 PDIA4 BMERB1 Location (GRCh37) 20:4680251 1:180953853 22:30953295 7:148700849 16:15539901 Type of mutation Missense Synonymous Missense 3' UTR Intronic Exonic Exonic Exonic Exonic Risk allele A A C T T Minor allele G A T T T Number Cases 4110 4110 4110 4110 4110 of samples Controls 13569 13569 13569 13569 13569 s t a g e D i s c o v e r y MAF P-value Cases Controls 0.
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The best-fitted nucleotide substitution model was evaluated in MEGA 7. 0. 26 based both on the corrected Akaike information criterion (AICc) and the maximized log likelihood (lnL). Maximum likelihood phylogeny was constructed in MEGA 7. 0. 26 [33] using the Generalized Time Reversible substitution model (GTR) with a gamma distribution of rate variation among sites (shape parameter = 4) and acceptance of invariant sites (I). Branch support was assessed with 500 bootstrap replicates. X. populi CFBP 1817 T was chosen as an outgroup. All the sequences generated were deposited in the National Center for Biotechnology Information GenBank under accession numbers MK610462 to MK610526 for gapA, MK610527 to MK610591 for gyrB and MK610592 to MK610656 for rpoD. The full concatenated alignment is provided along with the supplementary material.
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Genome sequencing, assembly, annotation and acquisition Genomic DNAs were extracted and purified from 25 ml 1/10 th TSB overnight cultures using the standard phenol-chloroform method [55] [73] with a minimum contig size of 200 bp, then annotated with PROKKA v1. 14 [58]. Additionally, 46 genome assemblies representing the taxonomic diversity of Xanthomonas clade II as reported by Jacques et al. [28] with the emended propositions of Constantin et al. [13] were acquired from the NCBI Genbank database. Genome characteristics, GenBank accession numbers and quality statistics are listed in Table S2.
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Phylogenomic analysis Phylogenomic tree reconstruction was performed on all the genomes of type, pathotype and representative strains of Xanthomonas clade II using PHYLOPHLAN v1. 10 [59]. Type strain of Stenotrophomonas maltophilia CFBP 3035 T was selected as an outgroup. A core of ~350 single-copy full-length predicted proteins from the PhyloPhlan database were retrieved in the genomes with USEARCH v8. 0. 1623 [16] , then aligned and concatenated with MUSCLE v3. 8. 31 [17]. Finally, a phylogenomic tree was built with FASTTREE 2. 1. 10 SSE3 [44] and robustness was locally assessed by the Shimodaira-Hasegawa test (SH) using 1,000 resamples. Additionally, a phylogeny was also constructed on all the available genomes of X. campestris pv. vitians type B, X. hortorum and X. cynarae with X.
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2 µm filters, then added in the inoculation fluid only for PM03B microplates at a 1x final concentration. Fluids were then inoculated following the same procedure as for the GEN III microplates to obtain to transmittance values of 85 %. Plates were subsequently filled with 100 µL per well and incubated 5 days at 25°C in an OmniLog system. Raw data were imported in R and analyzed using the opm package [70]. The maximum heights of the curves (parameter A) were discretized using empirical cutoffs of 75 and 125 omnilog arbitrary units for weak and positive reaction respectively. Some of the phenotypic features reported in Brown's first description of the pathogen responsible for bacterial leaf spot of lettuce [7] were also tested to check the authenticity of the pathotype strain. Gram staining were performed by staining with crystal violet for 1 min followed by Lugol for 1 min, then decolorized with 70 % ethanol for 20 s and finally stained with safranine for 1 min.
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Pathogenicity assays All strains were tested for their pathogenicity on two lettuce cultivars (oakleaf cv. Kirinia and leaf lettuce cv. Météore), and 11 strains on tomato cv. Marmande. Strains able to induce a mean disease severity ≥ 2 on the 8 plants at the end of the three-week monitoring period were considered pathogenic, whereas strains were determined non-pathogenic when mean disease severity was < 2. When mean disease severity was ≥ 2, few variations of disease severity were observed between strains or cultivars using our five-point scale disease index (data not shown). All strains labelled as X. hortorum pv. vitians were able to induce typical BLSL symptoms on the two lettuce cultivars (Table 1 ), and therefore considered pathogenic on lettuce. Consistent with previous reports [3, 53] , pathotype strain of X.
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hortorum presents highly variable profiles of nitrogen sources utilization. The type strain is CFBP 5858 T = CFBP 4925 T = LMG 733 T = NCPPB 939 T = ICMP 453 T. The GenBank accession number for its genome assembly is GCA_002940005. 1 and its 16S rRNA gene accession number is NR_026386. The species has a G+C mole % value between 63. 3 to 63. 9 %. The species includes, so far, the following pathovars based on their phytopathogenic specialization: X. hortorum pv. hederae, X. hortorum pv. pelargonii, X. hortorum pv. carotae, X. hortorum pv. taraxaci, X. hortorum pv. cynarae, X. hortorum pv. gardneri and X. hortorum pv. vitians. The closest species described is X. populi (ex-Ridé 1958) van den Mooter and Swings 1990 [71]. According to the characteristics of X. populi described in the valid description, in Ridé and Ridé 1992 [48] and Vauterin et al.
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The primary host is the common artichoke (Cynara scolymus L. ). The pathotype strain is CFBP 4188 PT = ICMP 16775 PT , the former type strain of X. cynarae Trébaol 2000 emend. The description is the same as the species. Additionally, the Biolog GEN III and PM02A MicroPlates revealed the ability of strain CFBP 8163 PT The primary hosts are tomato (Solanum lycopersicon L. ) and pepper (Capsicum annuum L. ). The pathotype strain is CFBP 8163 PT = NCPPB 881 PT = ATCC 19865 PT. The GenBank accession numbers for the two versions its genome assembly are SMDW00000000 and GCA_000192065. 2. In order to provide taxonomic data ready to use and easily comparable, and because the original protologues may be difficult to find, we recall hereby the characteristics of the other pathovars of X. hortorum complemented with data from our study: The description is the same as the species.
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The primary host is the Kazakh dandelion (Taraxacum kok-saghyz Rodin). The pathotype strain is CFBP 410 PT = LMG 870 PT = NCPPB 940 PT = ICMP 579 PT = ATCC 19318 PT. The GenBank accession number for its genome assembly is SMDY00000000. Two different nomenclatures may be displayed in order to respect validly published names and assure continuity through literature by adding the ones sometimes still used by researchers * Cancellation of CFBP 2538 as the pathotype strain of the vitians pathovar and proposal of LMG 938 as the neopathotype were submitted by letter to the Committee on the Taxonomy of Plant Pathogenic Bacteria + Representative strain CFBP 7900 of X. hortorum pv. carotae was chosen as the actual pathotype CFBP 4997 is known to be inconsistent ¥ Pathogenicity assays conducted in this study: + = pathogenic, -= non-pathogenic, w = weakly pathogenic, NA = non-tested.
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The primary host is the ivy-leaved geranium (Pelargonium peltatum L'Hér. ). The pathotype strain is CFBP 2533 PT = LMG 7314 PT = NCPPB 2985 PT = ICMP 4321 PT. The GenBank accession number for its genome assembly is SMDX00000000. X. hortorum pv. carotae (Kendrick 1934) Vauterin et al. 1995 = X. campestris pv. carotae (Kendrick 1934) Dye 1978.
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Acknowledgments The first author would like to thank Laurène Mirabel from the Microbial Ecology lab for her precious technical assistance, Martial Briand from the IRHS lab and Danis Abrouk from the iBio platform of UCBL for their teachings of bioinformatics. The authors are thankful to Elise Lacroix from the greenhouse platform of the UCBL for her careful management of plants, to lettuce producers of the SERAIL who guided us through their fields, and to the FNX network for the meeting opportunities it offered.
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The primary host is the ivy-leaved geranium (Pelargonium peltatum L'Hér. ). The pathotype strain is CFBP 2533 PT = LMG 7314 PT = NCPPB 2985 PT = ICMP 4321 PT. The GenBank accession number for its genome assembly is SMDX00000000. X. hortorum pv. carotae (Kendrick 1934) Vauterin et al. 1995 = X. campestris pv. carotae (Kendrick 1934) Dye 1978.
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hortorum pv. vitians pv. nov. The GenBank accession number for its genome assembly is SMED00000000. X. hortorum pv. cynarae (Trébaol et al. 2000) comb. nov. = X. cynarae Trébaol et al. 2000 = X. cynarae pv. cynarae (Trébaol et al. 2000) Timilsina et al. 2019.
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hortorum pv. gardneri (Jones et al. 2006) comb. nov. Timilsina et al. 2019. The GenBank accession number for its genome assembly is GCA_002939985. 1. X. = X. gardneri (ex-Sutić 1957) Jones et al. 2006 = X. cynarae pv. gardneri (Jones et al. 2006) Timilsina et al. 2019.
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Moreover, it remains to be determined whether ADAM10 functions as an intimate ADAM10/ TspanC8 complex or whether TspanC8s are merely modulators of ADAM10 trafficking. This study focuses on Tspan15 as a model TspanC8. Human Protein Atlas mRNA data indicate that Tspan15 is expressed within most tissue types, but only by approximately one-third of cancer cell lines, and not by most blood cell types (17, 18). Tspan15 is best-characterized by its capacity to promote ADAM10 cleavage of N-cadherin in cell lines and in vivo (8, 12, 13, 16). Tspan15 is also up-regulated and is a marker of poor prognosis in certain cancers (19 -21) , and it promotes cancer progression in a mouse model (21). The aims of this study were 2-fold: first, to develop a novel strategy for tetraspanin mAb generation to make and characterize the first Tspan15 mAbs, and second, to use these mAbs to test four hypotheses that would support the theory that Tspan15 and ADAM10 exist together as a scissor complex: 1) that endogenous Tspan15 and ADAM10 co-localize on the cell surface; 2) that ADAM10 is the principal Tspan15-interacting protein; 3) that Tspan15 expression requires ADAM10; and 4) that covalently linking Tspan15 and ADAM10 together as a single fusion protein yields a functional scissor.
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Tspan15 immunoprecipitation followed by ADAM10 Western blotting showed that each mAb effectively co-immunoprecipitated ADAM10 (Fig. 1D ). Therefore, these data validate the four new mAbs as specific mouse anti-human Tspan15 reagents capable of immunoprecipitating Tspan15/ADAM10 complexes. To determine whether our novel immunogen strategy may be useful for other tetraspanins, the same strategy was used for Tspan14, another member of the TspanC8 subgroup. This generated five mouse anti-human Tspan14 monoclonal antibodies, which were selected by flow cytometry screening of HEK-293T cells transiently overexpressing Tspan14 (Fig. S1 ). However, these mAbs did not detect overexpressed Tspan14 by Western blotting and did not detect endogenous Tspan14 by flow cytometry in several cell lines tested (data not shown); the selected antibodies may be low-affinity.
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expression constructs (except for Tspan10, which was of mouse origin) or an empty vector control (Ϫ), lysed in 1% Triton X-100 lysis buffer and Western blotted with tissue culture supernatants for each of the four Tspan15 hybridomas, or a positive control FLAG antibody. Blots are representative of three independent experiments. D, human platelets were lysed in 1% digitonin lysis buffer and subjected to immunoprecipitation (i. p. ) with the four Tspan15 mAbs or a negative control mouse IgG1, followed by anti-ADAM10 and anti-Tspan15 (5D4) Western blotting (top panels). The faint additional band in the 5F4 lane corresponds to light chain from the immunoprecipitating mAb (data not shown). To quantitate the data, the amount of ADAM10 co-immunoprecipitated was normalized to the amount of immunoprecipitated Tspan15 with each antibody (bottom).
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Tspan15 is an essential subunit of an ADAM10 scissor complex ϳ50%, and a titration effect was observed with lower concentrations (Fig. 3 , Bi and Bii). In contrast, mAbs 5D4 and 5F4 had no effect (Fig. 3 , Biii and Biv). The reason for the different effects of the mAbs is not known, but it could be related to subtle differences in epitope, given that 1C12 and 4A4 more efficiently detected mouse Tspan15 by flow cytometry (Fig. S2 ). Nevertheless, these data show that certain Tspan15 mAbs can inhibit ADAM10/Tspan15 activity.
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Tspan15 and ADAM10 co-localize on the cell surface Epitope-tagged Tspan15 is known to co-localize with ADAM10 in transfected cells (6, 8) , but this has yet to be confirmed for endogenous proteins. To address this, endogenous Tspan15 and ADAM10 were visualized on the surface of A549 cells by total internal reflection fluorescence (TIRF) microscopy. Tspan15 showed substantial co-localization with ADAM10, in contrast to the non-TspanC8 tetraspanin CD9, which was used as a control (Fig. 4Ai ). Quantitation of the imaging data using Manders' coefficient showed that significantly more of the Tspan15/ADAM10 overlapping signal was present within total ADAM10 signal compared to the proportion of CD9/ADAM10 overlapping signal within total ADAM10 signal (M1) (Fig. 4Aii ). Consistent with this, significantly more of the Tspan15/ADAM10 signal was present within total Tspan15 signal than CD9/ADAM10 was within total CD9 signal (M2) (Fig.
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5 ). These data suggest that ADAM10 is the principal Tspan15-interacting protein in HEK-293T cells.
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6C ). Importantly, the loss of Tspan15 protein was not a consequence of reduced Tspan15 mRNA expression in the absence of ADAM10, as this was unaffected in ADAM10knockout cell lines as measured by quantitative RT-PCR (qRT-PCR) (Fig. 6D ). To investigate the mechanism underlying the requirement of ADAM10 for Tspan15 expression, A549 cells were treated with the lysosomal inhibitor ammonium chloride. This significantly increased Tspan15 protein expression in ADAM10-knockout cells (Fig. 6E ), whereas the proteasome inhibitor MG132 had no effect (data not shown). Interestingly, the molecular weight of Tspan15 was increased in ADAM10knockout cells following lysosomal inhibition (Fig. 6Ei ). This increase was due to aberrant glycosylation, because all Tspan15 bands on the blot were reduced to the same molecular weight following deglycosylation with peptide:N-glycosidase F (data not shown), indicating that ADAM10 affects Tspan15 glycosylation as has previously been reported for Tspan5 (15).
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Finally, lysosomal inhibition did not rescue cell surface expression of Tspan15 in the absence of ADAM10 (Fig. S3 ), indicating that an ADAM10/Tspan15 complex is required for trafficking to the cell surface. Together, these data demonstrate that ADAM10 and Tspan15 are each required for expression of the other, pro-.
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Tspan15 is an essential subunit of an ADAM10 scissor complex ding to a level comparable with that of ADAM10 and Tspan15 transfected as individual constructs, in terms of both basal shedding and shedding induced by the ADAM10 activator NEM (Fig. 9Ei ). Betacellulin shedding was partially dependent on Tspan15, because ADAM10 transfection alone was not sufficient to fully restore shedding (Fig. 9Ei ). To confirm expression of the constructs used in these experiments, ADAM10 flow cytometry was used; the fusion protein was expressed at ϳ2-fold greater levels than ADAM10 individually with Tspan15 (Fig. 9Eii ). These fusion protein data provide further evidence that ADAM10/Tspan15 exists as a functional scissor complex, because expression and function of the fusion protein are similar to when ADAM10 and Tspan15 are individually co-expressed.
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Generation of CRISPR/Cas9-knockout cell lines Two guide RNA sequences were selected for each of human ADAM10 and Tspan15 using the Wellcome Trust Sanger Institute's CRISPR Finder tool (47). The following primer pairs were used to encode these sequences: ADAM10 guide 1 (5Ј-CACC-GCGTCTAGATTTCCATGCCCA-3Ј and 5Ј-AAACTGGGC-ATGGAAATCTAGACGC-3Ј); ADAM10 guide 2 (5Ј-CACC-GATACCTCTCATATTTACAC-3Ј and 5Ј-AAACGTGTAA-ATATGAGAGGTATC-3Ј); Tspan15 guide 1 (5Ј-CAC-CGGCGCGCGCTTCTCCTACCTC-3Ј and 5Ј-AAACGAGG-TAGGAGAAGCGCGCGCC-3Ј); Tspan15 guide 2 (5Ј-CAC-CGAGCGCCCAGGATGCCGCGCG-3Ј and 5Ј-AAACCG-CGCGGCATCCTGGGCGCTC-3Ј). Each primer pair was annealed and cloned into the pSpCas9 (BB)-2A-Puro (PX459) plasmid (a gift from Feng Zhang, Addgene plasmid 62988) (48). Cells were transfected with either of the guide constructs; clonal transfectants were selected using 1, 2.
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Protein structure prediction and computational modeling The structure of human Tspan15 (UniProt accession number O95858) was predicted and modeled using Phyre2 (51) under intensive mode and visualized with PyMOL. The model generated yielded 100% confidence for 78% of the total protein length, based on the crystal structure of human CD81 (Protein Data Bank code 5TCX) (5). In particular, residues 16 -190 and 209 -261 were built with 100% confidence, whereas the remainder of the residues, primarily located on the loops, were built ab initio, corresponding to lower-confidence regions.
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Data analysis and label-free quantification The raw data were analyzed by Maxquant software (Max-Planck Institute Munich) version 1. 6. 6. 0 (53). The MS data were searched against a fasta database of Homo sapiens from UniProt (download: June 12, 2019; 20,962 entries). Trypsin was defined as the protease. Two missed cleavages were allowed for the database search. The option first search was used to recalibrate the peptide masses within a window of 20 ppm. For the main search, peptide and peptide fragment mass tolerances were set to 4. 5 and 20 ppm, respectively. Carbamidomethylation of cysteine was defined as static modification. Acetylation of the protein N terminus and oxidation of methionine were set as variable modifications. The false discovery rate for both peptides and proteins was adjusted to less than 1%.
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Label-free quantification of proteins required at least two ratio counts of unique peptides. Only unique peptides were used for quantification. The option "match between runs" was enabled with a matching time of 1 min. The protein label-free quantification intensities were log 2 -transformed, and a two-sided Student's t test was applied to evaluate the significance of proteins with changed abundance between the TSPAN15 immunoprecipitation from WT samples and the control immunoprecipitation from Tspan15-knockout samples. Additionally, a permutationbased false discovery rate estimation was used (54). The MS data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository (55) with the data set identifier PXD016508.
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Blots are representative of three independent experiments. D, human platelets were lysed in 1% digitonin lysis buffer and subjected to immunoprecipitation (i. p. ) with the four Tspan15 mAbs or a negative control mouse IgG1, followed by anti-ADAM10 and anti-Tspan15 (5D4) Western blotting (top panels). The faint additional band in the 5F4 lane corresponds to light chain from the immunoprecipitating mAb (data not shown). To quantitate the data, the amount of ADAM10 co-immunoprecipitated was normalized to the amount of immunoprecipitated Tspan15 with each antibody (bottom). Error bars, S. E. from three independent experiments.
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Edited by Peter Cresswell This work was funded by a British Heart Foundation Ph. D. Studentship Grant FS/18/9/33388 and COMPARE (to C. Z. K. ), Biotechnology and Biological Sciences Research Council Project Grant BB/P00783X/1 (to N. H. ), British Heart Foundation Project Grant PG/13/92/30587 (to P. J. N. ), Biotechnology and Biological Sciences Research Council Ph. D. Studentships (to J. S. and A. L. M. ), and a Biochemical Society Summer Vacation Studentship (to H. T. H. N. ). This work was also supported by the Deutsche Forschungsgemeinschaft (German Research Foundation ) within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy , project ID 390857198 ) and by the BMBF through CLINSPECT-M (to S. F. L. ). Further support was provided by Deutsche Forschungsgemeninschaft Grant DFG-SFB877-A3 (to P.
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Blots are representative of three independent experiments. D, human platelets were lysed in 1% digitonin lysis buffer and subjected to immunoprecipitation (i. p. ) with the four Tspan15 mAbs or a negative control mouse IgG1, followed by anti-ADAM10 and anti-Tspan15 (5D4) Western blotting (top panels). The faint additional band in the 5F4 lane corresponds to light chain from the immunoprecipitating mAb (data not shown). To quantitate the data, the amount of ADAM10 co-immunoprecipitated was normalized to the amount of immunoprecipitated Tspan15 with each antibody (bottom). Error bars, S. E. from three independent experiments.
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Relevant P-values are highlighted directly in the figure (*< 0. 05; **< 0. 01, ***< 0. 001), and all P-values can be found in Table EV8. B Genetic interactions of nlpI with EPase genes. Strains were arrayed using a Rotor HDA replicator on Lennox LB agar plates and incubated for 12 h at 37°C. Each plate contained 384 colonies, 96 from the wild type, single mutants and double mutants. An example of a 384-well plate is shown. Double mutants were made twice, swapping the resistance markers to the two single mutants. Colony integral opacity was quantified as a fitness readout, using the image analysis software Iris (Kritikos et al, 2017). Bar plots show the averaged values of 2 biological experiments, each having 96 technical replicates (i = 2, n = 192). The error bars represent the 95% confidence interval.
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5% (wt/vol) blocking reagents (Boehringer, Mannheim, Germany) in PBS) was used, and the samples were incubated for 30 min at 37°C. For immunolocalization, cells were immobilized on 1% agarose in water slabs coated object glasses as described (Koppelman et al, 2004) and photographed with an Orca Flash 4. 0 (Hamamatsu) CCD camera mounted on an Olympus BX-60 fluorescence microscope through a 100×/N. A. 1. 35 oil objective. Images were taken using the program ImageJ with MicroManager (https:// www. micro-manager. org). SIM images were obtained with a Nikon Ti Eclipse microscope and captured using a Hamamatsu Orca-Flash 4. 0 LT camera. Phasecontrast images were acquired with a Plan APO 100×/1. 45 Ph3 oil objective. SIM images were obtained with a SR APO TIRF 100×/1. 49 oil objective, using 3D-SIM illumination with a 488 nm laser, and were reconstructed with Nikon-SIM software using the values 0.
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115. Binding curves and kinetic parameters were plotted and estimated using NT Analysis 1. 5. 41 and MO. Affinity Analysis (x64) software.
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Fixed ligand concentration MST assays for trimeric complexes For fixed ligand concentration MST assays, labelled proteins were titrated against a fixed concentration of unlabelled proteins, respectively. In fixed concentration assays with labelled MepS or LpoA, unlabelled PBP4-NlpI or PBP7-NlpI complexes were preformed by incubating NlpI (3 lM) with excess PBP4 or PBP7 (30 lM), on ice for 10 min. In fixed concentration assays with labelled PBP1A, unlabelled PBP4-NlpI complex was pre-formed by incubating PBP4 (0. 5 lM) with NlpI (1 lM), on ice for 10 min. Thermophoresis or fluorescence of labelled protein in the presence of unlabelled ligands was determined using NT Analysis 1. 5. 41 and MO Affinity Analysis (×64) software.
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Analytical ultracentrifugation Purified NlpI was dialysed O/N against 25 mM HEPES/NaOH, 150 mM NaCl, pH 7. 5, in preparation for AUC. AUC sedimentation velocity (SV) experiments were carried out in a Beckman Coulter (Palo Alto, CA, USA) ProteomeLab XL-I analytical ultracentrifuge using absorbance at 280 nm and interference optics. All AUC runs were carried out at a rotation speed of 45,000 rpm at 20°C using an 8-hole AnTi50 rotor and double-sector aluminium-Epon centrepieces. The sample volume was 400 ll and the sample concentrations ranged between 0. 3 and 1. 2 mg/ml. The partial specific volumes ( v) for the proteins were calculated from the amino acid sequence of NlpI, using the program SEDNTERP (Laue et al, 1992). Sedimentation velocity profiles were treated using the size-distribution c(s) model implemented in the program SEDFIT14.
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1 (Schuck, 2000). The experimental values of the sedimentation coefficient were corrected for the viscosity and density of the solvent, relative to that of H 2 O at 20°C (s 20,w ). The atomic coordinates from the published crystal structure of (Wilson et al, 2005) were used to calculate the sedimentation coefficient values for the monomer and dimer of NlpI using the program SoMo (Brookes et al, 2010). In vitro PG digestion assays PG digestion assays and subsequent muropeptide composition analysis were carried out as previously described (Glauner, 1988). 10% (v/v) substrate isolated from E. coli strain MC1061 was utilized in digestion reactions as follows: MepM (2 lM) AE NlpI (4 lM) incubated against intact sacculi for 4 h, MepS (5 lM) AE NlpI (10 lM) incubated against muropeptides O/N, PBP4 (2 lM) AE NlpI (4 lM) incubated against sacculi for 4 h, PBP7 (2 lM) AE NlpI (4 lM) incubated against muropeptides for 4 h.
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A lock-mass correction with a background ion (mass/ charge ratio, 445. 12003) was applied. The raw mass spectrometry data were processed with MaxQuant (v1. 5. 2. 8; Cox & Mann, 2008) and searched against an Uniprot E. coli K12 proteome database. The search parameters were as following: carbamidomethyl (C) (fixed), acetyl (N-term) and oxidation (M) (variable) were used as modifications. For the full-scan MS spectra (MS1), the mass error tolerance was set to 20 ppm and for the MS/MS spectra (MS2) to 0. 5 Da. Trypsin was selected as protease with a maximum of two missed cleavages. For protein identification, a minimum of one unique peptide with a peptide length of at least seven amino acids and a false discovery rate below 0. 01 were required on the peptide and protein level. The match between runs function was enabled, and a time window of one minute was set.
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Label-free quantification was selected using iBAQ (calculated as the sum of the intensities of the identified peptides and divided by the number of observable peptides of a protein) (Schwanhausser et al, 2011) , with the log fit function enabled. The proteinGroups. txt file, an output of MaxQuant, was loaded into R (ISBN 3-900051-07-0) for further analysis. The iBAQ values of the MaxQuant output were first batch-corrected using the limma package (Ritchie et al, 2015) and then normalized with the vsn package (Huber et al, 2002). Individual normalization coefficients were estimated for each biological condition separately. Limma was used again to test the normalized data for differential expression. Proteins were classified as a "hit" with a log 2 fold change higher than 4 and a "candidate" with a log 2 fold change higher than 2.
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Colony integral opacity as fitness readout was quantified using the image analysis software Iris (Kritikos et al, 2017). Doublemutant genetic interaction scores were calculated as previously described. Briefly, fitness ratios are calculated for all mutants by dividing their fitness values by the respective WT fitness value. The product of single mutant fitness ratios (expected) is compared to the double mutant fitness ratio (observed) across replicates. The probability that the two means (expected and observed) are equal across replicates is obtained by a Student's two-sample t-test.
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Microscopy, cell width measurements For phase imaging and cell shape measurements, cells were grown and collected at steady state at 30°C at OD 600 %0. 1. Cells were concentrated 20 times, and 0. 4 ll was transferred to a 1% agarose pad (UltraPure Agarose; Invitrogen) prepared with LB and preheated at 30°C. The pad was supplemented with Carb 100 lg/ml and L-arabinose 0. 2% or glucose 0. 2% if specified. Phase images were obtained with an inverted epi-fluorescence Eclipse Ti microscope (Nikon), equipped with a 100× phase contrast objective (CFI PlanApo LambdaDM100X 1. 4NA, Nikon). Images were acquired using a sCMOS camera (Orca Flash 4. 0, Hamamatsu, Japan) with an effective pixel size of 65 nm. Cell boundaries were detected from phase-contrast microscopy images using the MATLAB-based cell segmentation tool Morphometrics (SimTK) (Ursell et al, 2017).
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Acknowledgements We thank Manjula Reddy for fruitful discussions and providing reagents. This work was supported by the Sofja Kovalevskaja Award of the Alexander von Humboldt Foundation and EMBL core funding to A. T. , the Wellcome Trust ( 101824/Z/13/Z ) and Medical Research Council ( MR/N002679/1 ) to V. W. M. B. is funded by the Royal Society ( RGS\R1\191041 ). A. M. is supported by a fellowship from the EMBL Interdisciplinary Postdoc ( EI3POD ) programme under Marie Skłodowska-Curie Actions COFUND (grant number 664726 ). M. W. was supported by a Humboldt postdoctoral fellowship. S. v. T received support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme [Grant Agreement No. (679980)], the French Government 's Investissement d'Avenir program Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases" ( ANR-10-LABX-62-IBEID ), the Mairie de Paris "Emergence(s) " programme and the Volkswagen Foundation.
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Data availability The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD016825 (http://www. ebi. ac. uk/pride/arc.
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This attenuated light was then compared to exo-atmospheric values that were recorded when the light source was sufficiently high above the atmosphere. This technique allows for high-precision measurements on the order of 5 %, as reported in the level 2 data product, for Published by Copernicus Publications on behalf of the European Geosciences Union. SAGE aerosol extinction in the main aerosol layer. In general, stratospheric aerosol extinction measurements are challenging due to the paucity of aerosols under background conditions and the ephemeral nature of ash and particulates injected directly from volcanic eruptions. However, occultation observations have the benefit of long path lengths (on the order of 100-1000 km, dependent on altitude). Further, due to the self-calibrating nature of this method, SAGE measurements are inherently stable (i.
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Ground lidar Ground lidar data from three stations were used within this study. To allow intercomparison with both SAGE II and SAGE III, candidate ground stations with a long-duration data record were preferred. Further, data quality is likewise important. The Network for Detection of Atmospheric Composition Change (NDACC, https://www. ndacc. org, last access: 7 August 2020) was founded to observe long-term stratospheric trends by making long-term, high-quality atmospheric measurements. Therefore, stations within this network were selected for comparison. We identified three stations that satisfied the requirements of this analysis: Table Mountain Facility, Mauna Loa Observatory, and Observatoire de Haute-Provence. A brief description of the instruments and their algorithms is provided below.
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Mauna Loa Observatory The NOAA Mauna Loa Observatory (MLO; 19. 5 • N, 155. 6 • W; alt. 3. 4 km) is located on the Big Island of Hawai'i. The dataset used here comes from the elastic and inelastic (Raman) backscatter channels of the JPL ozone DIAL that began measurements in 1993 (McDermid et al. , 1995; Mc-Dermid, 1995). Just like the JPL-TMF system, the lidar used the third harmonic of an Nd:YAG laser to record the elastic backscatter at 355 nm, followed by correction for ozone and NO 2 absorption, as well as Rayleigh extinction. The corrected backscatter was then used to calculate the aerosol backscatter coefficient from the backscatter ratio using the 387 nm channel as the purely molecular component in the BSR as described in Chouza et al. (2020). The BSR was normalized to 1 at a constant altitude of 35 km where it was assumed that the aerosol backscatter contribution was negligible.
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Methodology Extinction and backscatter observations cannot be directly compared. In order to evaluate the agreement between backscatter measurements and extinction coefficient measurements, the data types must be converted to a common parameter, thereby requiring a conversion algorithm. As previously mentioned, this is usually done by converting backscatter to extinction coefficients using conversion factors from sources independent of either instrument (e. g. , constant lidar ratio). Herein, we derive a process to infer this relationship based on the spectral dependence of SAGE II/III aerosol extinction coefficient measurements and only make basic assumptions on the character of the underlying aerosol. Indeed, this EBC method is proposed to act as a bridge between aerosol extinction and backscatter observations.
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2. Though this figure only shows data for one combination of extinction and backscatter wavelengths, similar figures were generated for each combination (not shown), with the 520/1020 combination providing the best combination of linearity, atmospheric penetration depth, and wavelength overlap between SAGE II and SAGE III. This figure elucidates the relationship between the inverted lidar ratio (β/k, hereafter referred to as S -1 ), extinction ratio, and distribution width. Indeed, this figure provided the nexus between extinction and backscatter observations and between theory and observation since SAGEobserved extinctions were imported into this model to derive β 355. To do this, SAGE extinction ratios (k 520 /k 1020 ) were used to define the abscissa value, followed by identifying the ordinate value (S -1 ) according to the line drawn in Fig.
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78-1. 02) and mean R 2 (0. 87-0. 98) had broad ranges, as did the corresponding standard deviations. However, when the dataset was limited to 15-31 km (values in parentheses) the range of mean slopes (0. 94-0. 99) and mean R 2 (0. 95-0. 98) decreased significantly, as did the corresponding standard deviations. It was observed that when the filtering criteria were in place the standard deviation significantly narrowed, in some cases by more than an order of magnitude. By considering only the mean slope and mean R 2 values the impact of the filtering criteria is partially masked. The influence of these criteria is better observed by considering the minimum and maximum values for both slope and R 2 and by considering its impact on the 95th percentile (P 95 ). Here, P 95 was calculated using a nontraditional method.
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Uncertainties As with any study that involves modeling PSDs, the dominant sources of uncertainty are in the assumptions of aerosol composition and distribution parameters. Here, the particle number density and mode radius play a minor role. However, as seen in Fig. 2 , the selection of σ g has a variable impact. The statistics presented in Sect. 3. 1 were calculated using σ g = 1. 5 but are not influenced by the selection of σ g since changing the selection of σ g will shift all datasets up or down equally. On the other hand, the accuracy of the method is highly dependent on σ g. As an example, setting σ g = 1. 5 leads to a +32/ -16 % uncertainty (compared to σ = 1. 8 and σ = 1. 2, respectively) when the extinction ratio equals 6. Since > 90 % of the stratospheric extinction ratios do not exceed 6, we consider +32/ -16 % to act as a reasonable upper limit of expected uncertainty for this analysis.
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Method application The EBC method was applied to SAGE II and SAGE III datasets for intercomparison with ground-based lidar products. A discussion of the results of each SAGE mission follows.
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6, 7 , and 8 for Table Mountain, Mauna Loa, and OHP, respectively. The spread in the β SAGE value, due to varying results in solving Eq. ( 4 ) for differing values of σ g , is represented by the black shaded time series data. It is noted that, most of the time, this shaded area is indistinguishable from the black line thickness. Error bars in Figs. 6, 7 , and 8 represent the standard error (error on mean). We observed that the datasets were in qualitatively good agreement at all altitudes, especially when the atmosphere was impacted by the Pinatubo eruption (June 1991 (June -1998)). Statistics for the time series data are presented in Table 2. The data were broken into two time periods: (1) when the signal was perturbed by the Pinatubo eruption (labeled PE in the table, June 1991-December 1997), as defined by Deshler et al.
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Mauna Loa Similar to OHP, the MLO record did not begin until ≈ 2. 5 years into the Pinatubo recovery. Beginning in June 1995 the two datasets began to diverge at 20 km (Fig. 8 ), with the lidar record flattening out. In contrast, β SAGE continued with a quasi-exponential decay until January 1998, in agreement with the other two sites and previously published studies (e. g. , Deshler et al. , 2003; Thomason et al. , 2018). In January 1998 the lidar signal experienced an anomaly wherein the signal decreased by approximately an order of magnitude. After this time, β SAGE was consistently larger than β Lidar. The discrepancy from June 1995 to January 1998 at 20 km is currently not understood. However, the sudden change in January 1998 coincides with a new lidar instrument setup. The statistics in Table 2 show the MLO comparison to be the worst of the three stations (excluding the -29.
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SAGE III/ISS To date, the SAGE III mission has made observations under relatively clean stratospheric conditions similar to conditions at the end of the SAGE II mission. Due to the limited data record (3 years since launch), the comparison between SAGE III and the Mauna Loa and OHP lidars will be cursory. Data from the Table Mountain Facility have not been released for this time period; therefore, Table Mountain was excluded from the current analysis. The SAGE III and lidar backscatter coefficients show similar qualitative agreement at both Mauna Loa and OHP (Figs. 9 and 10, respectively), similar to what was observed in the SAGE II comparison (vide supra). During the SAGE III mission the atmosphere has been relatively stable, with a minor increase in backscatter and aerosol extinction in late 2017 due to a significant pyrocumulonimbus (pyroCB, indicated by the vertical line in the figures) event in northwestern Canada, which was comparable to a moderate volcanic eruption (Peterson et al.
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However, further evaluation involving major volcanic eruptions is required to better understand whether this agreement is fortuitous or the EBC algorithm is actually insensitive to aerosol composition and shape. The calculated S at each site was in good agreement with values calculated by Jäger et al. (1995). Immediately prior to the eruption S was approximately 40-45 for the lowermost altitudes (tropopause-20 km) and slightly higher (50-60) in the 25-30 km altitudes. This was followed by a quick decrease after the eruption of Pinatubo, down to values of 20 in the Jäger dataset, with our calculated value being slightly lower. Overall, the calculated S shows good agreement with the Jäger dataset in both magnitude and trend with altitude. Other studies that did not overlap with either SAGE II or SAGE III have shown similar S values to those calculated here (Bingen et al.
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g. , small differences such as 2 × 10 -10 for small numbers such as 1 × 10 -10 yield large percent differences -here, 100 %). Another consideration is that the SAGE III record, to date, is short compared to SAGE II, and the lidar coverage within the SAGE III time period is approximately 1 year, further limiting the intercomparison. As the record expands (possibly including observations of moderate to major volcanic events) we expect the comparison with the lidar data to improve. A potential application of this method is informing lidar ratio (S) selection for lidar observations. As an example, processing for the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar currently assumes a static lidar ratio (50 sr) for all latitudes and all altitudes. As was recently shown by Kar et al.
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Aggregate statistics for line-of-best-fit slope and R 2 for the SAGE II and SAGE III products compared to β S(520). Values in parentheses were calculated after restricting the dataset to altitudes between 15 and 31 km. The last column was calculated using data from both missions and backscatter values calculated using both β S(385) and β S(385). The slope P 95 data show the range of slopes (centered on the mean).
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Data availability. SAGE data used within this study are available at NASA's Atmospheric Science Data Center (https://eosweb. larc. nasa. gov/project/SAGEIII-ISS, NASA's Atmospheric Science Data Center, 2020). The lidar data used in this study are available from the NDACC archive (https://www. ndacc. org/, NDACC, 2020).
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0654 • a 1. 45 dg + 0. 2 (1) A similar local calibration method has provided good results with other water quality parameters in earlier studies such as Koponen et al. (2007) ; Attila et al. (2013). The data processing and extraction are done in a Calvalus massive parallel processing system (http://www. brockmannconsult. de/calvalus, last access: 22 September 2020). Data extraction areas are manually defined in the vicinity of the mouths of 69 rivers that represent ERGOM input locations (Fig. 1 ). The areas are designed so that islands, mixed pixels and shallow areas are excluded. All valid pixels (not masked as land or cloud by the pixel classification processor Idepix) within each area and image are collected and analyzed, and the 75th percentile value is chosen to represent the river a CDOM.
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Cases in which the number of valid pixels is less than 50% of all available pixels from an area are removed from the analysis. Assumedly, these represent cases with partial cloud cover and they are discarded to keep only estimates with highest quality and low uncertainty. The arithmetic means of the 75th percentile pixel values of all valid days within each calendar month during years 2017-2019 are then computed for each extraction area. We are aiming at providing the ecosystem model ERGOM with an annual cycle of CDOM loads based on monthly data. Since optical EO methods cannot provide a CDOM estimates in darkness and throughout times with ice coverage, the values for the winter months have been interpolated. As a result, the dataset contains a CDOM value for each month for each of the areas under investigation (69 extraction areas in total).
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CDOM absorption In Fig. 3a , we show the simulated CDOM absorption at the sea surface. The snapshot clearly illustrates the spatial patterns. Strong absorption is visible in the northern Baltic and the river mouths. The difference to the salinity based estimate (Eq. 3) is depicted in the right panel. Strongest differences appear in the Gulf of Bothnia and the Gulf of Finland while in the central Baltic differences are small. Strong differences are also pronounced in river estuaries. Owing to the low salinity, the salt-CDOM Both datasets were compared against in situ data collected from monitoring stations in coastal waters of Finland and from the Northern coast of Sweden. As shown in Fig. 4 , the improvement becomes obvious. With the salinity method, the correlation is low and there are some clear overestimates while most data points are underestimated.
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The impact on water temperature is small (order of 0. 01K, not shown). The effect is a temperature increase in the surface layer and a lower temperature below. We demonstrate changes in the biogeochemistry with a climatology of surface nutrient concentrations at three stations in CDOM are shown in black, based on a conversion from simulated salinity in green, and red diamonds are observations. The map was created using the software package GrADS 2. 1. 1. b0 (http://cola. gmu. edu/grads/), using published bathymetry data (Seifert et al. , 2008). Gulf of Finland at station KAS-11, the spring bloom related nutrient depletion is delayed by 2 weeks (Fig. 7a and b ). Sufficient PAR intensity, initiating a bloom, is available later in the season. The winter nutrient concentrations are elevated compared to the salinity model version.
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Figure1. Location of model rivers (black squares). The green line is the coastline of the 3nm model. We refer to the labeled river later in the text. The map was created using the software package GrADS 2. 1. 1. b0 (http://cola. gmu. edu/grads/), using published bathymetry data (Seifert et al. , 2008).
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Acknowledgements. This work was supported by ESA Contract No. 40000126233/18/I-BG (BALTIC+ SeaLaBio ). Computational power was provided by the North-German Supercomputing Alliance (HLRN).
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Code and data availability. In situ absorption observations are available from http://eo. ymparisto. fi/data/water/Baltic_SeaLaBio/. Monthly CDOM absortion data are available from http://eo. ymparisto. fi/data/water/Baltic_SeaLaBio/CDOM_input_to_ERGOM/. Model data can be accessed via https://thredds-iow. io-warnemuende. de/thredds/catalogs/projects/SeaLaBio/catalog_sealabio. html. The code of the biogeochemical model is available at www. ergom. net (last access: 22 September 2020). The ocean model "Modular Ocean Model MOM 5-1", used in this study, is available from the developers respository https://github. com/mom-ocean/MOM5 (last access: 1 December 2020). The meteorological forcing is archived at https://cera-www. dkrz. de/WDCC/ui/cerasearch/entry?acronym=coastDat-2_COSMO-CLM (last access: 1 Decenber 2020).
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The version of the model code used to produce the results in this study is archived on Zenodo at https://doi. org/10. 5281/zenodo. 4299873 (last access: 1 December 2020). In addition to the source code, the archive includes initial fields and boundary conditions exept the meteorological forcing. Sample availability. Simulated CDOM data: https://wwwi4. ymparisto. fi/i4/eng/tarkka_beta/index. html?type=ERGOM_CDOM&date=2019-12-01&lang=en&zoom=5&lat=61. 46508&lon=32. 98851.
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FN_BoB_13Figure 1. Figure 2. 12 Figure1. Location of model rivers (black squares). The green line is the coastline of the 3nm model. We refer to the labeled river later in the text. The map was created using the software package GrADS 2. 1. 1. b0 (http://cola. gmu. edu/grads/), using published bathymetry data (Seifert et al. , 2008).
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Cells were maintained at 37°C with a heating chamber. Protein recruitment was quantified using a custom-made MATLAB (MathWorks) routine. For FRAP experiments, we bleached using a 490-nm laser, a 20 × 20 pixel square located within the area previously irradiated at 405 nm to induce DNA damage. Images were collected at a frequency of 4 frames/s. After background subtraction, the fluorescence recovery kinetics were obtained by dividing the signal within the bleached area to the one measured in the unbleached part of the damaged region. FRAP curves were fitted with the following equation: I(t) = (1 -FI) × (1 -exp(-t/)) , where is the characteristic recovery time of the fast population and FI is the immobile fraction.
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8 or a Plan-Apochromat 63×/1. 4 oil objective and controlled by ZEN 2. 3 software. Fluorescence excitation was performed using diode lasers at 405, 488, and 561 nm. Images were analyzed after generating the maximum intensity projections of the z-stacks. For Rad51 foci formation, cells were treated with olaparib (10 M) for 48 hours before fixation and staining. The percentage of cells with >5 cells per nuclei was quantified. An average of 100 cells per condition were counted.
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HR and NHEJ reporter assays HeLa pGC or HeLa pEJ cells were transfected with siCtrl, siBRCA1, siKu70, or siALC1 siRNAs by DharmaFECT or mCherry-ALC1 plasmid construct by Xfect (Takara). Twenty-four hours after transfection, cells were transfected a second time with I-SceI plasmid constructs using Xfect (Takara) according to the manufacturer's instructions. Forty-eight hours after I-SceI transfection, cells were fixed with 3% PFA for 3 min and stained with Hoechst. Z-stacks of images were acquired on an LSM800 confocal setup with a Plan-Apochromat 20×/0. 8 or a Plan-Apochromat 63×/1. 4 oil objective and controlled by ZEN 2. 3 software. Fluorescence excitation was performed using diode lasers at 405, 488, and 561 nm. The raw images were analyzed in CellProfiler (58) after generating the maximum intensity projections of the z-stacks in Fiji (ImageJ) (59).
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Cell cycle analysis by flow cytometry Cells were incubated with or without 1 M olaparib for 24 hours. Cells were then collected and fixed with 70% EtOH for 30 min on ice. Cells were washed with PBS and treated with ribonuclease A solution (10 g/ml; Sigma-Aldrich) for 20 min at RT. DNA content of the cells were stained with propidium iodide solution [0. 1% Triton X-100 and propidium iodide (500 g/ml); Sigma-Aldrich] for 10 min. G 0 -G 1 , S phase, and G 2 -M were differentiated by a flow cytometer (BD FACSCalibur), using BD CellQuest Pro version 6. 0, ModFit LT software.
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Visualization of the chromatin fraction of PARP1-mCherry in cells HeLa-PARP1-mCherry cells were transfected with siCtrl, siBRCA1, or siALC1 siRNAs, and then 24 hours later, cells were plated onto glass-bottom cell culture chambers (Greiner). Forty-eight hours after the transfection, cells were incubated with or without 1 M olaparib for 1 hour and then washed with pre-extraction buffer (10 mM tris-HCl, 2. 5 mM MgCl 2 , 0. 5% NP-40, and protease inhibitor cocktail 1:100) for 3 min and fixed with 3% PFA for 5 min. The nuclei were stained with Hoechst. At least 6000 cells per condition were captured using an LSM800 confocal setup with a Plan-Apochromat 20×/0. 8 or a Plan-Apochromat 63×/1. 4 oil objective and controlled by ZEN 2. 3 software. Fluorescence excitation was performed using diode lasers at 405, 488, and 561 nm.
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1% tris-buffered saline, and incubated with primary antibodies overnight at 4°C. Membranes were then washed and incubated with horseradish peroxidase-conjugated secondary antibodies for 1 hour. Nitrocellulose membranes were developed with enhanced chemiluminesence using UviTec machine and Alliance Q9 Advanced software.
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Quantification and statistical analysis All data were derived from at least n = 3 replicates, and for each experiment, at least 40 nuclei or chromosome spreads were analyzed. A minimum of 20 cells per condition were examined in live-cell imaging experiments. P values from live-cell imaging experiments (Fig. 4 and fig. S4 ) were calculated using an unpaired Student's t test with Bonferroni correction (*P < 0. 05, **P < 0. 01, and ***P < 0. 001). Statistical analysis was performed using R statistical software (version 3. 6). Linear regression models or linear mixed effect models were fitted using lm (stats package) or lmer (lme4 package version 1. 1-23 and lmerTest package version 3. 1-2) functions, respectively. Dose-response curves were analyzed similarly as previously published (61). Specifically linear, quadratic, or cubic regression models were fitted using orthogonal polynomials of degree 1, 2, or 3, respectively (stats package poly function).
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The final model was chosen by the Akaike information criterion (bbmle package version 1. 0. 23. ) and by diagnostics of the residuals (DHARMa package version 0. 3. 1). Plots were generated by ggplot2 package (version 3. 3. 1). Asterisks represent P values, which correspond to the significance of regression coefficients (*P < 0. 05, **P < 0. 01, and ***P < 0. 001). For second-or third-degree polynomial models, the lowest P value is chosen from linear, quadratic, or cubic terms, as a significant difference in any of these coefficients indicates different curve characteristics. Model summaries are provided in table S2. Detailed analysis code is available on GitHub: (https://github. com/tschauer/Juhasz_ etal_2020).
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Acknowledgments: We thank Microscopy Rennes Imaging Center (BIOSIT, Université Rennes 1) for technical assistance. We also thank F. Zhang for the GeCKOv2 library, D. Trono for psPAX2, and G. Schotta for the pLP-eco env plasmid and the HeLa-mCAT1 cells. We specifically thank the technical assistance of A. Hegele , A. Kószó , and Z. Nacsa. We thank S. Krebs and the Laboratory for Functional Genome Analysis (LAFUGA) for sequencing. Last, we thank W. Mansour for the pGC and pEJ reporter cell lines and S. Kern for the BRCA2 cells. Funding: This work was supported by the Hungarian Academy of Sciences ( LP2017-11/2017 to G. T. ), the National Research Development and Innovation Office ( K128239 to G. T. and.
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eu/), and the Multi Experiment Data & Operation Center (MEDOC; https://idoc. ias. u-psud. fr/MEDOC), as well as the space-weather team in Toulouse (Solar-Terrestrial Observations and Modelling Service; STORMS; https: //stormsweb. irap. omp. eu/). This includes funding for the data mining tools AMDA (http://amda. cdpp. eu/), CLWEB (clweb. cesr. fr/), and the propagation tool (http://propagationtool. cdpp. eu). The work of A. P. R. and N. P. was funded by the ERC SLOW SOURCE project (SLOW SOURCE -DLV-819189). A. K. also acknowledges financial support from the COROSHOCK (ANR-17-CE31-0006-01) and FP7 HELCATS project https://www. helcats-fp7. eu/. P. H. , R. H. , G. S. , and A. V. acknowledge support from the NASA PSP program office. The toroidal current of the FR is simplified to a current loop (ring) of major radius R, and both toroidal and poloidal currents are allowed to flow inside a minor radius a, just as in Chen (1989 Chen ( , 1996)).
|
10.3847/1538-4365/ab6610
|
article
|
en
| 2,020
| true
| true
| true
| false
|
Physics and Astronomy
|
https://openalex.org/fields/31
|
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