dataesr/scientific-paragraphs-categorization huggingface dataset:
Herein, IFNβ-induced ISG-response was maintained in necroptotic TC1 +DCs co-cultures but was potentiated in apoptotic TC1 +DCs co-cultures (Fig. 2J ). Moreover, TNF-driven apoptosis/necroptosis was better at preserving or potentiating ISGresponse than TRAIL (Fig. 2J ). Overall, above data made us prioritize TNF-driven cell death, DCs and IFNβ co-cultures as putative DCvax-IT formulations. Next, we checked for macrophages-like or (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted March 17, 2023. ; https://doi. org/10. 1101/2023. 03. 14. 532534 doi: bioRxiv preprint mregDCs-like phenotypes. Analyses for PDL1 + PDL2 + CD200 + CD11c + mregDC  (Maier et al. , 2020)  (Suppl.
PDL1 and TAMs co-association is a negative prognostic niche in human cancers that specifically predicts clinical response to anti-PDL1 ICBs Above observations deserved further physiological or clinical validation. Herein, we wished to use above TC1-tumour derived data as putative 'biomarker profiles' for clinical validation. Accordingly, a large-scale bioinformatics analysis with 21 existing single-cell (sc)RNAseq datasets spanning 13 diverse cancer-types, 287 patients and 2 species (humans or mice) showed that, at single-cell resolution, TAMs where the most dominant source of CD274 (PDL1) (Fig. 7A ), amongst various immune, stromal or cancer cells. Next, we wanted to understand if CD274 indeed dominantly co-associated with M2-like TAMs in T celldepleted human tumours. For this, we interrogated CD274's correlation to M1 vs.
ACKNOWLEDGEMENTS: We are immensely thankful to  Prof. Roos Vandenbroucke  ( VIB-UGent ) for providing us with the Ifnar1 -/-C57BL/6 mice as well as  Dr. Andrew Brown  and  Prof. Bart Lambrechts  ( VIB-UGent, Belgium ) for providing us with the Ccr7 -/-C57BL/6 mice. We acknowledge  Prof. Patrizia Agostinis ,  Prof. Geert Bultynck , and  Prof. Peter Vangheluwe  ( KU Leuven, Belgium ) for supporting our research by sharing lab space and/or cell culture facilities. We would also like to thank  Prof. Stefan Krautwald  ( Germany ),  Prof. Steven Verhelst  ( KU Leuven ),  Prof. An Coosemans  ( KU Leuven ) and  Prof. James Murphy  (Australia) for providing us with various cell lines, tools or helping us with some experiments that unfortunately could not be accommodated in the final manuscript. We thank  Prof.
Pierre Coulie  ( UCL, Belgium ),  Prof. Adrian Liston  ( Babraham Institute, UK ) and  Elselien Frijlink  ( LUMC, The Netherlands ) for attentively reading the manuscript and supplying valuable suggestions and feedback. This study is supported by  Research Foundation Flanders (FWO)  ( Fundamental Research Grant ,  G0B4620N  to ADG;  Excellence of Science/EOS  grant,  30837538 , for ' DECODE ' consortium, for ADG, JB),  KU Leuven  ( C1  grant,  C14/19/098 ;  C3  grant,  C3/21/037 ; and  POR  award funds,  POR/16/040  to ADG),  Kom op Tegen Kanker  ( KOTK/2018/11509/1  to SDV, ADG; and  KOTK/2019/11955/1  to ADG) and  VLIR-UOS  ( iBOF grant ,  iBOF/21/048 , for ' MIMICRY ' consortium to ADG and ST). IV is supported by. JS is funded by  Kom op tegen Kanker (Stand up  to Cancer), the  Flemish cancer society via Emmanuel van der Schueren (EvDS)   PhD fellowship  (projectID:  12699 ).
Determination of Tick Species Ticks were first morphologically identified under a stereomicroscope using standard morphological keys  [14]. To confirm the identification, we took advantage of the concomitant sequencing of tick transcriptomes and used the Barcode of Life Data Systems (BOLD) as previously described  [15]. Briefly, all trimmed reads were mapped onto the Ixodidae Cytochrome Oxydase sub-unit I (COI) sequences included in the BOLD database. Mapped reads were de novo assembled, and the resulting contigs were uploaded to the BOLD Identification System (https://www. boldsystems. org/index. php/IDS_OpenIdEngine) (accessed on 30 September 2021) for species identification. Results were finally confirmed by BlastN, (https://blast. ncbi. nlm. nih. gov/Blast. cgi, accesed on 30 September 2021).
Virus Assignation Raw reads were processed as previously described  [16]  with an in-house bioinformatics pipeline, Microseek (https://research. pasteur. fr/wp-content/uploads/2022/09/ research_pasteur-microseek-a-metagenomic-pipeline-for-virus-diagnostic-and-discoverymicroseek-4. mp4; accessed on 20 August 2021)  [17] , that comprises a quality check and the trimming of raw reads followed by read normalization prior to assembly into contigs and the generation of non-assembled singletons. ORF predictions of both contigs and singletons were performed, and a taxonomic assignation of protein sequences was performed by Blast-based querying using three successive, specialized RVDBs  [18]  followed by generalist (NCBI/nr and NCBI/nt) databases.
Phylogenetic Analyses Phylogenetic reconstructions were conducted on the following genes: the conserved, non-structural, RNA-dependent RNA polymerase (RdRP) and the glycoprotein (GP). A MAFFT (Multiple Alignment using Fast Fourier Transform) aligner was used to align the complete ORFs with other viral orders/families' representative sequences under the L-INS-i (RdRP) or G-INS-I (GP) parameters  [19]. Using ATGC Smart Model Selection  [20]  implemented in http://www. atgc-montpellier. fr/phyml-sms/ (accessed on 3 October 2022) the best amino acid-substitution models that matched the data were selected by utilizing the modified Akaike information criteria. These were LG+G for amino-acid Bunyavirales phylogenies and GTR+G for nucleotide phylogenies (whatever segment considered). Based on the selected substitution model, phylogenetic trees were generated using the maximum likelihood (ML) method provided through the IQ-TREE program (http: //iqtree.
cibiv. univie. ac. at/; accessed on 3 October 2022)  [21]. The nodal support was assessed using the approximate Bayes parameter. To generate identity matrices between Bronnoya virus genomes, complete RdRP and GP nucleotide and amino-acid sequences were aligned with MAFFT with the same parameters as those used for phylogenetic reconstructions, and matrices were generated using CLC Main Workbench v. 21. 0. 4 (Qiagen, Hilden, Germany; accessed on 5 October 2022). Molecular evolutionary distances between Bunyavirales families were calculated using MEGA7 (https://www. megasoftware. net/; accessed on 5 October 2022)  [22]. Briefly, all complete RdRP amino-acid sequences included in the GenBank/RefSeq database were downloaded (MeSH terms "txid1980410 Sequences were aligned with MAFFT with the L-INS-I parameter and grouped according to their recognized family.
As the GP structure of Bronnoya virus is not available in any database, we used AlphaFold to predict the structure of the complete GP chain of both Bronnoya-like viruses identified in Tulcea County, Romania (Figure  2 ). The program produced a per-residue confidence metric, termed pLDTT, on a scale from 0 to 100. Values of pLDTT higher than 70 reflect reliable models with correct backbone predictions; values below that threshold indicate that the structural prediction is not reliable. We identified the Gc domain (residues 711-1486, average pLDTT = 78) and two sub-domains of Gn: an N-terminal region (residues 1-569) and a C-terminal region (residues 570-664, pLDTT = 85). As in other bunyavirus families, the latter turned out to be the most immunogenic, so we decided to target the C-terminal part of BroBV-like Gn (designed below as the "Gn head") for serological screenings.
All members of this family seem to lack the S-segment coding for the viral nucleoprotein (NP), although one of its members is able to replicate in a tick-derived cell line  [12]. The absence of this key viral protein is questionable and the origin of the NP must be further investigated, for example, by checking if another bunyavirus (or other virus)-infecting tick could complement the BroBV virus for the NP. Another hypothesis is that the S-segment is so distant to known viruses present in public databases that current homology-based tools (such as Blast or HMMER,  [26] ) are not able to identify the S-segment of BroBV viruses. Within the BroBV family, the comparative phylogenetic reconstructions of the evolution history of the L and M segments showed that reassortment could have been a key event in the history of Bronnoya viruses (Figure  4 ).
Conclusions This study is the first report of the comprehensive characterization of the evolution history and host spectrum of a new viral family within Bunyavirales. Members of this family represent likely tick symbionts that produce a chronic infection of the tick (since its maintenance in a tick-derived cell line was reported) and that are constitutive of the core virome specific to a given tick species and genus. This approach, beginning with the identification of viral sequences in metatranscriptomic datasets and ending with the determination of the host spectrum of newly identified viruses, should be extended to other tick-borne viruses to obtain a better overview of the core virome of ticks and to identify viruses with arbovirus potential that could be of concern for human or animal health.
The ESpritz predictors (NMR, X-ray, Disprot) were used with the default parameters  [34]. HCA (Hydrophobic Cluster Analysis) plots were drawn with DRAWHCA  [35]. A simple scoring procedure was then used to define consensus disordered regions. For each prediction, residues predicted to be part of a disordered region were assigned a score of 0 while other residues were assigned a score of 1. A D-score for each amino acid was calculated by averaging the value from all predictions. Regions with a normalized Dscore lower than 0. 50 are considered intrinsically disordered, whereas regions with a score higher than 0. 5 are either folded domains or potential molecular recognition elements (MoRE) for partner proteins  [25, [36] [37] [38] [39]. A freely accessible GitHUB for automatically query the different servers, process the information and to calculate the D-score is now accessible at https://github.
2 mM TCEP. The column was calibrated with globular proteins of known hydrodynamic radius (Rh)  [42]  using commercially available calibration kits (Cytiva). Chromatographic separations were performed at room temperature with a flow rate of 0. 5 mL. mn -1 with on-line MALLS detection using a DAWN-HELEOS II detector (Wyatt Technology, Santa Barbara, CA, United States), and protein concentration was measured on-line by differential refractive index measurements using an Optilab T-rEX detector (Wyatt Technology) and a refractive index increment, dn/dc, of 0. 185 mL. g -1. The data were analyzed with the software ASTRA (Wyatt Technology). [43]  Small angle x-ray scattering (SAXS) data were collected at the BioSAXS beamline (SWING) of the Synchrotron Soleil. Samples were analyzed on a Superdex Increase 75 5/150 GL (Cytiva).
X-ray crystallography Two datasets were collected on the PROXIMA-1 beamline at the SOLEIL Synchrotron at the wavelength of 1. 07169 Å: a native crystal diffracting at 2. 4 Å and a platinum derivative obtained by soaking diffracting at 2. 7 Å. For both datasets, indexing and integration were performed using the XDS program suite  [46]. Initial phases were obtained with the platinum derivative dataset by the single-wavelength anomalous dispersion method using the SHELX suite through HKL2map program  [47]. The alpha carbon trace from SHELXE was then used with the native dataset to build a first model with Phenix  [48]. The model was improved and refined against the native dataset with COOT  [49]  and Ref-mac5  [50]  from the CCP4 program suite  [51].
Four experimental replicas were done. The MS data were filtered according to the following selection criteria. All pseudogenes, predicted proteins, and immunoglobulin fragments were discarded. Common contaminants in MS experiments, such as keratins and trypsins, were also eliminated. The proteins purified with the EYFP control were deleted. To further eliminate common artifacts, we utilized the CRAPome database (2) of common protein contaminants. We selected proteins found with two or more peptides and in at least two experimental replicates.
The N-terminal part of helix C/D of two protomers packs against each other in an antiparallel orientation, mainly forming polar and symmetrical interactions. In the second mode ( showing the side-by-side assembly of the tubes described in panel J with the same four protomers using the same color code. Table  1. Crystallographic Data Collection and Refinement Statistics. 1 Values in for all data set and the high-resolution shell 2 Values from MolProbity webserver. The assembly of three protomers by both modes is shown in Figure  3H. The asymmetric unit comprised 6 molecules and had a horseshoe shape (Figure  3I ). The chaining of asymmetric units leads to a helical assembly and to the formation of tubular structures with an external diameters of ~9. 0 nm and an hexagonal internal cavity of 2.
4 -3. 0 nm width (Figure  3J ). In the crystal, the tubular helices packed side-by-side (Figure  3K ). A search for structural similarities with DALI  [59]  identified the C-terminal folded domain of the Sendai virus C protein either in complex with the N-terminal domain of human STAT1 (PDB ID: 3WWT -Z-score 4. 9, 3. 3 Å rmsd over 62 CA atoms), a key intermediate in the interferon signaling pathway  [60] , or in complex with the BRO-1 like domain of the human protein Alix (PDB ID: 6KP3 -Z-score 3. 5, 2. 2 Å rmsd over 54 Cα atoms), a component of the ESCRT machinery  [61]. In both complexes, Sendai virus C protein (in green in Figure  4A  and Figure 4B ) has the shape of a flat disk formed by 6 helices arranged in two superposed layers. In both cases, the three C-terminal helices of SeV C align with the C-terminal part of helix C/D and with helices E and F of TupV C, while additional N-terminal helices are packed on the common helical core (Figure  4C ).
In solution, TupV C is well described by ensembles of conformers with a disordered N-terminal and fluctuating helices B and C To further explore the conformational diversity of C∆53, we used the Ensemble Optimization Method (EOM)  [45]. This approach consists in generating a large initial pool of independent conformers of the protein (using the software RANCH) and in selecting subensembles of conformers that collectively reproduce the experimental SAXS curve (using the software GAJOE). We generated four initial ensembles of 10,000 conformers (Figure  8A ): (i) Ensemble 1, in which folded helix B and helix C/D were conserved but the B/C loop connecting both helices was allowed to adopt random conformations, (ii) Ensemble 2, in which helix B and the B/C loop were allowed to adopt random conformations, while helix C/D was conserved in its folded form, (iii) Ensemble 3, in which helix B was conserved in its folded form while the B/C loop and helix C were allowed to adopt random conformations and (iv) Ensemble 4, in which helix B, the B/C loop and helix C were allowed to adopt random conformations.
8 σ and stick representation of the TupV C∆53 protein in the crystal structure (PDB ID: 8BJW). (D) Cartoon representation of TupV C∆53 in the crystal. The N and C-terminal residues of C∆53 are indicated and helices are named B to F. (E) Close-up of the C-terminal domain showing residues conserved among Narmoviruses (see also Figure 2A). (F) Cartoon representation of the first mode of interaction between protomers within the asymmetric crystal unit. (G) Cartoon representation of the second mode of.
The labeled residues are those of TupV C protein. STAT1 is shown in grey ribbon. (E) Sequence alignment of Narmovirus and Respirovirus C proteins. Members of the Narmovirus genus and their UniProt accession number are the same as in Figure 2A, members of the Respirovirus genus are: SeV -Sendai virus -Q38KG9; HPIV1 -human parainfluenza virus 1 -Q8QT30; HPIV3 -human parainfluenza virus 3 -Q81077; BPIV1 -bovine parainfluenza virus 3 -P06164. The orange cylinders above the MSA indicate the location of helices in the crystal structure of TupV C. The black box indicates residues of SeV C that interact with STAT1.
To test our hypothesis, we undertook 2 complementary mass spectrometry approaches. First, we undertook a discovery-based approach called "data-dependent acquisition" (DDA), to extend our earlier pilot study  32  to the whole OPTiMiSE population and to confirm the findings using the same mass spectrometric methods as previously. Second, to investigate associations between proteins and symptomatic and treatment response, we used a data-independent acquisition (DIA) mass spectrometry approach to allow us to extract data on complement and coagulation pathway proteins in a more targeted manner.
Participants The patients were participants in the OPTiMiSE study, a multi-center trial of amisulpride conducted at 20 institutions and 15 countries (Clinicaltrials. gov identifier NCT01248195). The trial protocol has been described in detail elsewhere. 33 Five-hundred patients with the first episode of schizophrenia, schizophreniform disorder, or schizoaffective disorder as defined by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) were verified with the Mini International Neuropsychiatric Interview Plus. 34 articipants were aged between 18 and 40 years, had a maximum interval of 2 years between the onset of psychosis and study entry, and had a history of antipsychotic medication of no longer than 2 weeks in the previous year or 6 weeks lifetime. 33 Ethical approval for the proteomic analyses was granted by the Research Ethics Committee of the Royal College of Surgeons Ireland [REC No: 1699].
Statistical Analyses Statistical analyses were conducted using IBM SPSS statistics version 26 and STATA IC version 16. The set of complement and coagulation proteins that were analyzed were selected based on the UNIPROT functional annotation of pathway proteins. The LFQ values of plasma proteins that were expressed in at least 70% of the samples were considered for the statistical analysis. The mass spectrometry proteomic data were log 2 transformed and standardised to z-scores for further analysis.
Associations Between Protein Levels and Remission Status We first tested whether the levels of complement and coagulation proteins at baseline were associated with remission status (remission/non-remission) after 4 weeks of treatment. For this analysis, baseline standardized plasma protein levels were measured using both DDA and DIA proteomic approaches. The proteomic data were used as the independent variable. For both DDA and DIA data, associations with remission status at 4 weeks were evaluated for each protein using logistic regression models adjusted for age, sex, BMI, smoking status (current smoker/past smoker/no smoker), and study site. P-values were corrected for multiple comparisons using the Benjamini-Hochberg method with a false discovery rate (FDR) of 5%. 40 A receiver operator curve (ROC) analysis was performed for the significant protein C4A to assess its diagnostic value to predict remission status in FEP participants.
Complement and Coagulation Proteins From DDA and DIA Proteomic Analysis Of the 534 participants initially recruited, a total of 294 participants had provided baseline samples and had also completed Phase I of the clinical trial. This subgroup was considered for the present study and the proteomic analysis was performed in baseline plasma samples from 294 participants. After the exclusion of participants who already met remission criteria at baseline, 243 participants with complete baseline and follow-up clinical data comprised the analytical sample. The baseline characteristics of the sample are given in table  1. Data-dependent acquisition (DDA) mass spectrometry identified a total of 455 plasma proteins in all samples. Of these, 111 proteins passed the quality control (identified in more than 70% of plasma samples) and the missing values were imputed using minimum LFQ values of corresponding protein.
In the current study, we restricted the MS-based proteomic analysis to samples that passed stringent quality criteria filters, excluding 16% of the original plasma samples. Several findings were validated using ELISA-based methods. The study has several limitations. 9] [50]  In addition, the large-scale discovery proteomic analysis did not find an association of complement protein C4A variant levels and treatment response, despite there being a strong association found in the DIA analysis. C4A, which was identified in the discovery of MS data in more than 40% of the samples, did not pass the stringent quality criteria applied, and therefore the label-free quantification intensities (normalized measures) of C4A were not considered for the DDA data analysis. Finally, the present study examined group-level differences in biomarkers and is not able to address whether proteomic measures can be used to make predictions about treatment response at the individual level.
Acknowledgments We are extremely grateful to the participants of the OPTiMiSE study. We acknowledge  Ms. Magdalena Hryniewiecka  for her technical support in preparation of plasma samples for DDA proteomics. We acknowledge the contribution of  Prof. Gerard Cagney  and  Dr. Eugene Dillon  at the  UCD Conway Mass Spectrometry Core Facility  for help with MS experiments. DM, MC, and  DRC  report a patent pending (UK Patent Application No. 1919155. 0 , "Biomarkers  to predict psychosis"). RDU is an inventor named in patent applications that describe the use of complement inhibitors for therapeutic purposes and the use of circulating complement protein measurement for patient stratification, and is co-founders of and shareholder in Complement Therapeutics, a company which focuses on the development of complement targeted therapeutics for chronic diseases focused on Age-related Macular Degeneration.
Funding The OPTiMiSE clinical trial was funded by  European Commission  within the  7th Program  ( HEALTH-F2-2010-242114 ). The research work was funded also by  Irish Health Research Board  grant to  David Cotter  ( HRB-ILP-2019-005 ),  Wellcome Trust   IMPETUS Innovations Flagship grant  (project  220438Z/20/Z ) D. M. is a Fellow on the  Irish Clinical Academic Training (ICAT) Programme  which is supported by the  Wellcome Trust  and the  Health Research Board  (Grant Number  203930/B/16/Z ), the  Health Service Executive National Doctors Training and Planning and the Health and Social Care, Research and Development Division, Northern Ireland. S. R. S is supported by the  Health Research Board (HRB)  under grant number  HRB/HRA/PHR/2015-1293. D. R. C and J. F. B are supported in part by  Science Foundation Ireland (SFI)  under Grant Number  16/RC/3948  and co-funded under the  European Regional Development Fund  and by FutureNeuro industry partners.
Next, cells were washed, resuspended in PBS and stored at 4°C until flow cytometric analyses were performed on the same day. All samples were acquired on Cytek Aurora equipped with four lasers (Cytek Biosciences) or by FACSCanto II cell Analyzer BD and analyzed with FlowJo V. 10. 6. 1 or V. 10. 8 software. Viable cells were determined as a fraction of single cells negative for staining with Zombie Aqua Fixable Viability Dye. The immune cell populations were analyzed in three different panels: Panel 1 (general panel), Panel 2 (myeloid panel) and Panel 3 (lymphoid panel), which are described in online supplemental materials and methods. All cells were gated on singlets, living cells and the corresponding gating strategy for each cell population.