dataesr/scientific-paragraphs-categorization huggingface dataset:
were identified and one of these grouped with T. annulata sequences from Pakistan (GenBank: JQ743630) and Turkey (GenBank: MK918607), whereas the second sequence clustered with that of B. occultans from South Africa (GenBank: U09834) (Fig. 5 ). For Rickettsia spp. , only one unique gltA sequence (GenBank: MN728990) was found which grouped with R. aeschlimannii (Gen-Bank: DQ235776) from Russia, R. rhipicephali (GenBank: U59721) from USA and Rickettsia sp. Bar (GenBank: U59720) from Spain (Fig. 6 ).
There is no surveillance and/or diagnostic system in place for borreliosis in animals or humans in Pakistan; thereby it remained unknown until now. Further testing is required to characterise Borrelia spp. and to assess their pathogenic and zoonotic potential. We found that one Hy. hussaini and three Hy. anatolicum ticks contained DNA of R. massiliae, whereas, five ticks (one Hy. hussaini and four Hy. anatolicum) were positive for the DNA of unidentified Rickettsia spp. DNA sequencing followed by phylogenetic analyses revealed that gltA sequences of Rickettsia spp. determined herein available on GenBank. The sequence data (618 bp) were analysed using Neighbour Joining (NJ), Maximum Likelihood (ML) and Bayesian Inference (BI) methods. There was a concordance among the topology of the BI, ML and NJ trees (not shown) and only NJ tree is presented here.
Chlorocebus sabaeus and human protein sequences were aligned, and phosphorylation sites and protein identifiers were mapped to their respective human protein orthologs. Phosphorylation fold changes calculated using the 0-or 24-h mock control were highly comparable (correlation coefficient r = 0. 77); therefore, the 0-h mock control was used for all subsequent comparisons. Quality control filtering of the data was performed, and two samples from each of the phosphorylation and protein abundance datasets were removed because of poor correlation with their respective replicates  (Figures S1A and S1B). Principal component analysis (PCA) of the remaining samples revealed good separation of mock and infected samples as well as high quantitative reproducibility between biological replicates  (Figures 1B, 1C, and S1C).
See also Figure  S1. opposed to transcriptional regulation, which would influence protein abundance. Phosphorylation of SARS-CoV-2 Viral Proteins by the Host Proteome Viral protein phosphorylation within the host cell may play a role in sensing and responding to cell state. We detected 25 phosphorylation sites in SARS-CoV-2 viral proteins that we combined with another proteomics dataset  (Davidson et al. , 2020)  to amass a total of 49 sites detected across seven viral proteins (Table  S2 ). Of note, this analysis does not distinguish cleaved from uncleaved viral proteins in the assignment of viral phosphorylation sites. The degree of conservation, indicative of functional constraint, was estimated for each residue position (Figure  2A ;  Ng and Henikoff 2003) , and the sites were mapped to positions within structured regions for five proteins, with the majority observed in accessible positions (i.
This evolutionary pattern suggests that a negative charge in this region may play a functional role, reminiscent of other multi-site phosphorylation events  (Serber and Ferrell 2007). To identify phosphorylation sites that may regulate proteinprotein interactions, all sites were mapped to 3D structures, and solvent accessibility based protein-protein interface identification and recognition (SPPIDER) was used to assess whether sites resided within interface regions  (Porollo and Meller 2007; Figure   2A ; Table  S2 ). The single phosphorylation site in Nsp9 was predicted to be at an interface region (''True''), which was supported by inspection of the homodimer structure (PDB: 6W4B). Additional phosphorylation sites were predicted to be at interface residues within the S protein (Figure  2A ).
Some of the changes in kinase activity can be directly linked to host-viral protein interactions (Figure  4C ). Among the 10 interacting kinases detected in a virus-host protein-protein interaction map  (Gordon et al. , 2020) , an increase in activity for CK2 and a decrease for MARK2 and PRKACA were observed (Figure  4C ). Of note, although we predict decreased activity for PRKACA based on phosphorylation of its substrates, we simultaneously detected a significant increase in T198 phosphorylation (8, 12, and 24 h post infection) within its activation loop, suggesting an increase in PRKACA activity. It is possible that Nsp13 is sequestering active PRKACA away from its typical substrates. To better understand the signaling states of cells over the course of infection, we compared our data with a compilation of public phosphoproteomics datasets of other conditions  (Ochoa et al.
Comparing phosphoproteomics profiles of SARS-CoV-2-infected cells with a database of phosphorylation profiles collected at specific cell cycle stages, viral infection was most highly correlated with cells arrested at the S/G2 transition and was negatively correlated with profiles of cells in mitosis (Figure  6G ). We also observed SARS-CoV-2-dependent regulation of CDK2 T14/Y15 phosphorylation, initially increased in response to SARS-CoV-2 infection at 2 h, followed by a decrease over the remainder of the infection time course  (Figure 6H, left). CDK2 activity promotes transition from the G2 phase of the cell cycle into mitosis and is inhibited by phosphorylation at positions T14 and Y15 by kinases WEE1 and MYT1, preventing premature entry into mitosis  (Parker and Piwnica-Worms 1992; Mueller et al.
Mass spectrometry data acquisition Digested samples were analyzed on an Orbitrap Exploris 480 mass spectrometry system (Thermo Fisher Scientific) equipped with an Easy nLC 1200 ultra-high pressure liquid chromatography system (Thermo Fisher Scientific) interfaced via a Nanospray Flex nanoelectrospray source. For all analyses, samples were injected on a C18 reverse phase column (25 cm x 75 mm packed with ReprosilPur 1. 9 mm particles). Mobile phase A consisted of 0. 1% FA, and mobile phase B consisted of 0. 1% FA/80% ACN. Peptides were separated by an organic gradient from 5% to 30% mobile phase B over 112 minutes followed by an increase to 58% B over 12 minutes, then held at 90% B for 16 minutes at a flow rate of 350 nL/minute. Analytical columns were equilibrated with 6 mL of mobile phase A.
To build a spectral library, one sample from each set of biological replicates was acquired in a data dependent manner. Data dependent analysis (DDA) was performed by acquiring a full scan over a m/z range of 400-1000 in the Orbitrap at 60,000 resolving power (@200 m/z) with a normalized AGC target of 300%, an RF lens setting of 40%, and a maximum ion injection time of 60 ms. Dynamic exclusion was set to 60 seconds, with a 10 ppm exclusion width setting. Peptides with charge states 2-6 were selected for MS/MS interrogation using higher energy collisional dissociation (HCD), with 20 MS/MS scans per cycle. For phosphopeptide enriched samples, MS/MS scans were analyzed in the Orbitrap using isolation width of 1. 3 m/z, normalized HCD collision energy of 30%, normalized AGC of 200% at a resolving power of 30,000 with a 54 ms maximum ion injection time.
Similar settings were used for data dependent analysis of samples used to determine protein abundance, with an MS/MS resolving power of 15,000 and a 22 ms maximum ion injection time. Data-independent analysis (DIA) was performed on all samples. An MS scan at 60,000 resolving power over a scan range of 390-1010 m/z, a normalized AGC target of 300%, an RF lens setting of 40%, and a maximum injection time of 60 ms was acquired, followed by DIA scans using 8 m/z isolation windows over 400-1000 m/z at a normalized HCD collision energy of 27%. Loop control was set to All. For phosphopeptide enriched samples, data were collected using a resolving power of 30,000 and a maximum ion injection time of 54 ms. Protein abundance samples were collected using a resolving power of 15,000 and a maximum ion injection time of 22 ms.
Spectral library generation and raw data processing Raw mass spectrometry data from each DDA dataset were used to build separate libraries for DIA searches using the Pulsar search engine integrated into Spectronaut version 13. 12. 200217. 43655 (Bruderer et al. , 2015)  by searching against a database of Uniprot Chlorocebus sequences  (19,136 proteins, downloaded April 3, 2020)  and 29 SARS-CoV-2 protein sequences translated from genomic sequence downloaded from GISAID (accession EPI_ISL_406596, downloaded April 7, 2020) with two mutations (G22661T Spike V367F and G26144T ORF3a G251V) detected by RNASeq analysis of virus stocks. For protein abundance samples, data were searched using the default BGS settings, variable modification of methionine oxidation, static modification of carbamidomethyl cysteine, and filtering to a final 1% false discovery rate (FDR) at the peptide, peptide spectrum match (PSM), and protein level  (Elias and Gygi 2007).
For phosphopeptide enriched samples, BGS settings were modified to include phosphorylation of S, T, and Y as a variable modification. The generated search libraries were used to search the DIA data. For protein abundance samples, default BGS settings were used, with no data normalization performed. For phosphopeptide enriched samples, the Significant PTM default settings were used, with no data normalization performed, and the DIA-specific PTM site localization score in Spectronaut was applied.
Phosphopeptide intensity data were summarized at the peptide ion level along with confident localization of phosphorylation as described in the previous section. Quantification of phosphorylation based on peptide ions were processed using artMS as a wrapper around MSstats, via functions artMS::doSiteConversion and artMS::artmsQuantification with default settings. All peptides containing the same set of phosphorylated sites were grouped and quantified together into phosphorylation site groups. For both phosphopeptide and protein abundance MSstats pipelines, MSstats performs normalization by median equalization, imputation of missing values and median smoothing to combine intensities for multiple peptide ions or fragments into a single intensity for their protein or phosphorylation site group, and statistical tests of differences in intensity between infected and control time points.
Identifying significantly regulated proteins When not explicitly indicated, we used defaults for MSstats for adjusted p values, even in cases of N = 2. By default, MSstats uses Student's t test for p value calculation and Benjamini-Hochberg method of FDR estimation to adjust p values. Phosphorylation fold change data were filtered for quality based on consistency of observations between treatment and controls by requiring the MSstats reported value missingPercentage < 0. 60. This value is the proportion of features (i. e. , peptide ions) that are missing across the treatment and control replicates for a specific fold change computation. MSstats phosphorylation results had to be further simplified to effects at single sites. The results of artMS/MSstats are fold changes of specific phosphorylation site groups detected within peptides, so one phosphorylation site can have multiple measurements if it occurs in different phosphorylation site groups (see Table  S1  for examples).
This complex dataset was reduced to a single fold change per site and time point by choosing (per time point) the fold change with the lowest p value, favoring those detected in both treatment and control, i. e. , non-infinite log 2 fold change values. This single-site dataset, further reduced to those with human orthology (Table  S1 ), was used as the input for kinase activity analysis. Significance filters of adjusted p value < 0. 05 and absolute log 2 fold change (infected/mock) > 1 were applied to both phosphorylation and abundance datasets. Additionally, for protein abundance data, if a protein was detected in only infected but not mock, or vice versa, we required detection in 3 replicates or by 3 peptide ion fragments in at least 2 replicates. These significance-filtered data were used for enrichment analyses and counts of significant effects (Figure  1 ).
GO enrichment analysis Sets of genes with significant up and down effects were tested for enrichment of Gene Ontology (GO Biological Process, Molecular Function and Cellular Component) terms. Sets of genes were either combined across time points (Figure  1I ) or collected separately per time point (Figures  S1F  and S1G ). The over-representation analysis (ORA) was performed using the enricher function of cluster-Profiler package (version 3. 12. 0) in R with default parameters. The gene ontology terms were obtained from the c5 category of Molecular Signature Database (MSigDBv7. 1) (Subramanian et al. , 2005). Significant GO terms (adjusted p value < 0. 01) were identified and further refined to select non-redundant terms. In order to select non-redundant gene sets, we first constructed a GO term tree based on distances (1-Jaccard Similarity Coefficients of shared genes in MSigDB) between the significant terms.
, 2012). The pathway terms were obtained from the c2 category (Reactome) of Molecular Signature Database (MSigDBv6. 1). Estimation of kinase activities in the time-course experiment Kinase activities were estimated using known kinase-substrate relationships in literature  (Bachman et al. , 2019). The resource comprises of a comprehensive collection of phosphosite annotations of direct substrates of kinases obtained from six databases, Phos-phoSitePlus, SIGNOR, HPRD, NCI-PID, Reactome, and the BEL Large Corpus, and using three text-mining tools, REACH, Sparser, and RLIMS-P. Kinase activities were inferred as a -log 10 (p value) of Z-test from the comparison of fold changes in phosphosite measurements of the known substrates against the overall distribution of fold changes across the sample.
Comparison of phosphorylation profiles with other conditions In addition to the time-course experiment, we also estimated kinase activities of cell line perturbation in a range of biological conditions using the K-test based approach, from a large resource of previously published phosphoproteomics datasets comprising of 435 biological conditions which include several drug treatments (chlorzoxazone, paclitaxel), inhibitors (EGFRi, AKTi) and cell cycle states  (Ochoa et al. , 2016). After selecting for kinase activities derived from 3 or more substrates, a total of 214 kinases had activity estimates for at least one biological condition including all 97 kinases which were estimated in the time-course experiment. Of the 435 conditions, 309 conditions with at least one kinase with significantly changing activity (-log 10 (p value) from Z-test > 2.
Phosphorylation of protein complexes Annotations for protein complexes were obtained from CORUM v3. 0 database  (Giurgiu et al. , 2019). Overall significance of changes of phosphosites of a protein complex was inferred from the log 2 fold change of individual phosphosites of protein members of the complex using the same Z-test based approach described for estimation of kinase activities. Among the 3512 complexes, 1070 complexes with phosphorylation changes obtained from 4 or more phosphosites were considered. Of these, 67 complexes had significant phosphorylation change (-log 10 (p value) from Z-test > 2. 5) in at least one time point. In order to select non-redundant complexes, we first constructed a complex term tree based on distances (1-Jaccard Similarity Coefficients of shared genes) between the significant complexes.
Transcription factor activity after SARS-CoV-2 infection To evaluate the effect of SARS-CoV-2 infection at the Transcription Factor (TF) level, we applied DoRothEA  (Garcia-Alonso et al. , 2019)  to RNA-seq datasets of different human lung cell lines from a recent study (GSE147507)  (Blanco-Melo et al. , 2020). DoRothEA is a comprehensive resource containing a curated collection of TF-target interactions. Each TF-target interaction is associated with a confidence level based on the number of supporting evidence. Here we selected the most reliable interactions (A, B, and C levels) and computed TF activities based on the normalized expression of their targets using the VIPER algorithm  (Alvarez et al. , 2016). For the TF activity enrichment analysis, VIPER was executed with the Wald statistic resulting from the differential expression analysis at the gene level between controls and SARS-CoV-2 infected cells using the DESeq2 package  (Love et al.
Asterisks indicate PHs. (E) Alignment of M protein phosphorylation clusters across different coronaviruses. Asterisks indicate PHs. (F) Surface electrostatic potential of non-phosphorylated (left) and phosphorylated (right) RNA-binding domains of the N protein (PDB: 6M3M). Positions of PHs are indicated by arrows. Blue denotes a positive charge potential, and red indicates a negative charge potential. Electrostatic potential was computed with the Advanced Poisson-Boltzmann Solver (APBS) tool after preparation with the PDB2PQR tool.
G. S. ); by supplements to  NIAID  grant  U19AI135972  and  DoD  grant  W81XWH-19-PRMRP-FPA  (to A. G. S. ); and by the generous support of the  JPB Foundation , the  Open Philanthropy Project  (research grant  2020-215611 [5384 ]), and other philanthropic donations (to A. G. S. ); by the  Laboratoire d'Excellence ''Integrative Biology of Emerging Infectious Diseases''  grant  ANR-10-LABX-62-IBEID  (to M. V. ); by the  DFG under Germany's Excellence Strategy  ( EXC-2189 , project  ID   390939984  to R. G. ); by a  Starting Grant Award  from the  European Research Council  ( ERC-2014-STG 638884 PhosFunc  to P. B. ); by the  Federal Ministry of Education and Research (BMBF, Computational Life Sciences  grant  031L0181B  to J. S. R. ); by the  Intramural Research Program  of the  NIH, National Institute of Allergy and Infectious Diseases  (to E.
Data and Code Availability The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD019113  (Perez-Riverol et al. , 2019). An interactive version of phosphorylation data can be found at https://kroganlab. ucsf. 2 1 3 3 3 5 3  1 2 2 1 2 4  1 1  3 2 2 2 3  3 3 3 3 5  4 3 3 3 4  1 1 3 3 3 3 3 1  1 3 5 4 5 5 6  6 6 6 6 7  1 1 1 1 1  1  4 4 4 4 6  2 1 8 8 9 9 12  1 12 11 11 11 12  1 1 1 1 1  4 4 4 4 4  16 15 17 17 20  4 4 4 4 5  1 7 6 8   Pearson's correlation -1 0 1.
Epitope Mapping The epitope mapping of C6 and D5 antibodies was predicted using MabTope software  31  , as described in Supporting Informations 1. The amino acids that were identified as highly probable epitope residues were mutated to alanine to create one mutated HER4 construct for each initially identified region. The wild type (HER4_WT) and mutated constructs (HER4_P1m, HER4_P2m, HER4_P3m and HER4_P4m) were N-terminally Flag-tagged to monitor antigen expression in HEK293 cells. The binding of C6 and D5 antibodies on wild type and mutated HER4-transfected HEK293 cells were performed by flow cytometry as described in Supporting Informations 1.
Immunohistochemistry (IHC) Four-micron formalin-fixed, paraffin-embedded (FFPE) tissue sections from tumor xenografts were placed on poly-L-lysine coated slides, deparaffinized in xylene, and rehydrated in graded alcohols. The antigen retrieval was performed by heating tissues at 95°C in 10mM citrate buffer for 30min. The endogenous peroxidase activity was inhibited by Dual Endogenous Enzyme-Blocking Reagent for 15min (Dako, Glostrup, Denmark) and the nonspecific binding was reduced by the serum-free protein blocking (Dako) for 20min. The sections were then incubated with anti-cleaved caspase-3 antibody (Cell Signaling Technology, Beverly, MA) diluted at 1:5000, for 1h. Antibody binding was revealed using the StreptaABComplex/HRP Duet kit (Dako). Quantification was done with the Aperio ImageScope software (Leica Biosystems.
D5 and F4 binding to HER4 was partially inhibited by 10 nM NRG1 (60% and 40% inhibition, respectively), but not C6 and H2 binding (Fig. 4B ). F4 and H2, but not C6 and D5, still maintained around 40% of residual binding at high NRG1 concentration (100 nM). We predicted the HER4 epitopes of the D5 and C6 antibodies using the MabTope software  31  (Fig. S5A-B ). We identified four predicted areas that belong to the D5 and C6 putative epitopes: P1 606-623, P2 573-593, P3 260-279 and P4 622-642 (Fig. S5B ), according to the numbering of the UniProtKB-Q15303 HER4 sequence. We designed HER4 mutants within the four predicted areas (HER4_P1m to P4m) (Fig. 4C ) and transfected them in HEK293 cells. We mutated into alanine only surface residues, ensuring that HER4 structure was not altered. Flow cytometry analysis (Fig.
Cells were transfected with HER4 JMa/CYT1 (left panel) or JMa/CYT2 (right panel) plasmids for 5h, serum-starved for 19h, and then stimulated or not with NRG1 and incubated with 20 g/ml anti-HER4 antibodies for another 6h. After subcellular fractionation, the cytosolic and mitochondrial fractions were confirmed with antibodies against -tubulin, and VDAC1 or TIM23, respectively. HER4 and 4ICD localizations were detected using the anti-HER4 antibody E200 (Abcam). WCL: Whole Cell Lysate. D5 and C6 3D-structures were modeled, docked on HER4 (PDB:2AHX), and 5x10 8 conformational positions were generated for the complexes. The view of the top 30 ranked predicted conformations for the complex between C6 and D5 (in colors) and the HER4 structure (in grey) is presented. (B) Probability for HER4 residue implication in the D5 and C6 epitopes.
ACKNOWLEDGMENTS We thank  S. Bousquié  ( IRCM ) for cell culture and  T. David  ( IRCM ) for antibody production. The staff members at the  IRCM animal facility , the  GenAc  and  MRI platforms , and the RHEM histology facility are greatly acknowledged. Y. Yarden  ( Weizmann Institute ) is also greatly acknowledged for providing the anti-HER4 antibody Ab77. This work was supported by the program " Investissement d'Avenir " (grant agreement:  Labex MAbImprove ,  ANR-10-LABX-53-01 ;  A. Pèlegrin ), by  INSERM Transfert  ( CoPoC grant   HER4Valid ;  T. Chardès ). R. Lanotte was a recipient of a PhD studentship from the  LabEx MAbImprove , and from the  Ligue Nationale contre le Cancer. N. Gaborit  was a recipient of a post-doctoral fellowship from the  LabEx MAbImprove. DISCLOSURE STATEMENT R.
POPULATION AND METHOD We conducted an epidemiological, multicentric, and retrospective study and reviewed the files of PLWHA at Grenoble University Hospital  (2,200 beds)  and Annecy Hospital  (1, 450  beds). The study population was made of all patients who had been referred for an HIV diagnosis at these two centers between January 1, 1997 and December 31, 2015, and for whom relevant data was available. We used the DOMEVIH® software, commonly used for the follow-up of HIV-infected subjects, to extract the date of diagnosis (first positive HIV serology), the country of birth, and the following clinical and biological variables at diagnosis: age; mode of contamination; CD4+ T lymphocyte count, and CD8+ T lymphocyte count (allowing the computation of the CD4/CD8 ratio); and plasma HIV viral load.
RESULTS From January 1997 to December 2015, 1,427 new cases of HIV infection were recorded in the DOMEVIH database in Grenoble and Annecy: -902 were diagnosed at Grenoble Hospital; among those, 88 were excluded because of non-exploitable data or missing data, or because they were not newly diagnosed with HIV infection; -525 were diagnosed at Annecy Hospital; among those, 279 were excluded because of non-exploitable data, or because they were not newly diagnosed with HIV infection. The mean diagnosis rate was 76 patients per year (47. 5 diagnoses per year in Grenoble and 27. 6 per year in Annecy).