dataesr/scientific-paragraphs-categorization huggingface dataset:
3g, 4c ), which also led to double-spiking in other neurons (Supplementary Fig. 6 ). In conclusion, these results show that virus-mediated expression VirChR1s enables light-driven firing in neurons. Crystal structure of the viral rhodopsin OLPVR1. To decipher the molecular mechanism of ion channeling, the structure of viral channelrhodopsin from group 1 is essential. One crystal structure of the VirR DTS (PDB ID: 6JO0  21  ) was recently reported  21. However, from the only available structure, it is not possible to distinguish the features of the entire group. Moreover, the ionchanneling activity of VirR DTS was not demonstrated in the original work  21  ; thus structural insights of the channel of VirChR1s were not described. Here, we present a high-resolution structure of another VR1 protein, OLPVR1, at 1.
9 ). The interfacial interaction in the putative dimer occurs mainly in the TM4 helix and involves several non-conservative residues, namely E108-E108', Y111-Y111', F122-F122' with low surface interaction area (429 Å 2 ) (Supplementary Fig. 9 ). An alternative configuration of OLPVR1 dimer predicted with GalaxyHomomer server  41  showed stronger interfacial interaction (1861 Å 2 ). Therefore, the orientation of the OLPVR1 protomers in solution is unclear at the moment (Supplementary Fig. 9 ). Overall, the OLPVR1 backbone is tolerantly superimposed with that of the Med12 proteorhodopsin (PDB ID: 4JQ6 42 ) with RMSD of 2. 1 Å, whereas the alignments with the HsBR (PDB ID: 1C3W  26  ) and CrChR2 (PDB ID: 6EID 8 ) structures gives RMSD values of 4. 3 and 3. 7 Å, respectively (Fig. 1d , e and Supplementary Fig.
10 ). Interestingly, unlike other microbial rhodopsins, OLPVR1 architecture closely resembles the architecture of G-protein coupled receptors with TM3 helix protruding to the center of the protein. In particular, OLPVR1 aligns with bovine rhodopsin (PDB ID: 1U19  43  ) with RMSD of 4. 91 Å (Fig. 1f, g ), with a high similarity among helices forming ion-conducting pathway (TM1-3 and TM7). The OLPVR1 protomer has short extracellular loops, which sharply differentiates it from other channelrhodopsins that typically have large N-and C-terminal domains. Unlike in other microbial rhodopsins, helices TM3 and TM4 of OLPVR1 are connected by the loop containing the membrane-associated helix (ICL2 helix), which is composed of hydrophilic residues (Fig. 1c ). Strikingly different from other rhodopsins, the intracellular parts of TM6 and TM7 helices of OLPVR1 are significantly moved apart from each other far enough to form a pore (Supplementary Fig.
13 ). Indeed, predictably, the data showed photocurrents, which reverse their direction at approximately 0 mV, which is characteristic of rhodopsin ion channels but not of the ion pumps (Supplementary Fig. 14e ). Thus, taking into account all the data, high homology of VirChR1s, high conservation of functionally key amino acids, we suggest that the VirChR1s (including VirR DTS from giant PgV virus infecting the algae Phaeocystis) likely form a distinct group of compact light-gated Ca 2+ -blocked channels, different from the known channelrhodopsins. To the best of our knowledge, the exclusive conductivity for monovalent cations and its regulation by divalent cations have not been reported for any rhodopsin before. Recently, the cryptophyte alga Guillardia Theta has been reported to encompass genes of two different groups of channelrhodopsins, namely, anion-conducting channelrhodopsins with ~50% sequence identity to chlorophyte channelrhodopsins  9  , and also a group with an architecture unconventional for channelrhodopsins, and containing the DTD motif that is characteristic of the archaeal proton pumps  18.
Methods All experiments involving mouse materials were done in compliance with all relevant ethical regulations for animal testing and research. The study received ethical approval from the Lower Saxony State Office for Consumer Protection and Food Safety (LAVES). No experiments were randomized or blinded in this study. Sample sizes were determined based on prior literature and best practices in the field; no statistical methods were used to predetermine sample size. Metagenomic analysis. Rhodopsins of the VR1 group were retrieved from metagenomic assembled contigs through combining similarity search, protein clustering, and Hidden Markov Models. Briefly, the first dataset of bona fide rhodopsins was retrieved from TARA Ocean metagenome-assembled contigs that were downloaded from ENA (https://www.
ebi. ac. uk). In addition, a new assembly was performed for each sample starting by raw metagenomic reads. Sequencing reads were pre-processed using Trimmomatic  74  in order to remove low-quality bases (Phred quality score was set at 20, sliding windows of 4), and assembled using MEGAHIT  75  and the default parameters for generic metagenome assemblies. Coding DNA sequences were predicted from contigs longer than 2 Kb using Prodigal  76  , and annotated against the NR database of NCBI using Diamond  77. Bona fide rhodopsins were selected by screening for different keywords related to rhodopsins that must be contained in the annotation, and filtered according to 7 transmembrane domains that were predicted using Phobius  78. Selected proteins were next aligned using the R package Decipher  79  , and alignments were used to infer a phylogenetic tree through FastTree 2  80  and using default parameters.
Phylogenetic distances between nodes on the tree topology were considered for clustering bona fide rhodopsins into distinct clades, each of which was used to train a Hidden Markov Model (HMM). All HMMs were finally queried against TARA Ocean assembled contigs using HMMER version v3. 1b2 (http://hmmer. org) and setting an e-value threshold of 1e-5. Proteins identified through HMMs were clustered at 100% identity using CD-HIT suite  81  to remove redundancy, and reduced to a total of 2584 Type-1 rhodopsins that were further analyzed. The dataset of group I viral rhodopsins was constructed by searching the NCBI non-redundant protein sequence databases along with the TARA metagenomic sequences using BLSTP and TBLASTN. For the sake of clarity, for Supplementary Fig. 1 , we used a reduced number of sequences.
To obtain a representative set of 16 TARA metagenomic sequences with OLPVR1 and VirChR1 sequences included, we used the CD-HIT suite with default parameters and 60% identity cut-off. Sequence alignment and phylogenetic analysis. Rhodopsin sequences were aligned using MUSCLE using UGENE software  82  with the default parameters. Type-1 rhodopsins were named according to their names in literature. The sequence alignment was created using ESPript3 online server  83. Phylogenetic tree reconstruction was conducted by PHYLIP Neighbor Joining method using UGENE software  82  with the following parameters: Jones-Taylor-Thornton model, transition/transversion ratio = 2. 0, no gamma distribution applied. Tree visualization was done using iTOL server  84. GenBank accession numbers are additionally indicated.
Cloning, expression, and purification. The E. coli codon-optimized OLPVR1 and VirChR1 genes were synthesized commercially (Eurofins). The nucleotide sequence was optimized for E. coli expression using the GeneOptimizer software (Life Technologies). The gene, together with the 5′ ribosome-binding sites and the 3′ extensions coding additional LEHHHHHH* tag, was introduced into the pEKT expression vector (Novagen) via NdeI and XhoI restriction sites and verified by sequencing. VirChR1 protein was additionally supplemented with BRIL protein on the N-terminus of the protein, to improve protein folding and expression level  29. Full plasmid and gene constructs, as well as primers used in this study can be found in Supplementary Tables  2 3 4. The proteins were expressed as described previously  7  with further modifications.
Full plasmid and gene constructs, as well as primers used in this study can be found in Supplementary Tables  2 3 4. The SH-SY5Y human neuroblastoma cells at a confluency of 80-90% were transfected with plasmid and Lipofectamine LTX according to the manufacturer's protocol (Thermo Fisher Scientific). The cells were incubated under 5% CO 2 at 37 °C. After transfection (16-24 h), electrophysical experiments were performed. Electrophysiological recordings. For the electrophysiological characterization of VirChR1, whole-cell patch-clamp recordings were performed (Scientifica LASU, Axon Digidata 1550A, Multiclamp 700B). Horizontal puller (Sutter Instrument CO, Model P-2000) was used for the fabrication of patch pipettes (borosilicate glass GB150F-8P, 3-6 MΩ). Experiments were conducted using SH-SY5Y cell line.
Hippocampal cultures were transduced at 10 DIV with AAV-PHP. eB virus carrying the VirChR1 gene (~10 10 GC/ml). Whole-cell patch-clamp measurements were performed at 16-17 DIV. The intracellular solution contained 129 mM Kgluconate, 10 mM HEPES, 10 mM KCl, 4 mM MgATP, 0. 3 mM Na 3 GTP, pH 7. 2. Extracellular solution consisted of 140 mM NaCl, 1 mM MgCl 2 , 2. 5 mM KCl, 10 mM HEPES, 1. 5 mM CaCl 2 , 15 mM glucose, pH 7. 4. 10 μM of NBQX and 50 μM of AP5 were added to the extracellular solution to block synaptic transmission. The illumination of cells was performed using 473 nm laser with saturation intensity (150 mW/cm 2 ). All experiments were conducted at room temperature. Currentclamp recordings were performed with zero current injection. The electrophysiology data was processed using in-house Python 3 scripts (Jupyter Notebook).
Hippocampal neurons used for immunocytochemistry were transduced at DIV 10 (final concentration 9 × 10e9 -1. 8 × 10e10 gc) and after 5-6 days of incubation fixed by 4% paraformalaldehyde (PFA) and subjected further to immunostaining. Microtubule associated protein (Map2) was used as neuronal marker and immunolabelled by primary rabbit polyclonal Map2 antibody (Abcam, ab32454, 1:750). Secondary antibody against rabbit Map2 was Alexa Fluor goat anti-rabbit 647 IgG (Invitrogen, A21244, 1:750). VirChR1 was indirectly identified by intrinsic fluorescence of Katushka. All images were obtained with Leica scanning confocal microscope SP5 and processed using FIJI software. Final images were assembled in Adobe Illustrator software. Time-resolved absorption spectroscopy. Excitation/detection systems were composed as such: Brilliant B laser with OPO Rainbow (Quantel Inc.
) was used, providing pulses of 4-ns duration at 530-nm wavelength and an energy of 2 mJ per pulse. Samples (5 × 5-mm spectroscopic quartz cuvette; Hellma GmbH & Co. ) were placed in a thermostated house between two collimated and mechanically coupled monochromators (LOT MSH150). The probing light (xenon arc lamp, 75 W, Hamamatsu) passed the first monochromator sample and arrived after a second monochromator at a photomultiplier tube (PMT) detector (R12829, Hamamatsu). The current-to-voltage converter of the PMT determines the time resolution of the measurement system of ca. 50 ns (measured as an apparent pulse width of the 5-ns laser pulse). Two digital oscilloscopes (Keysight DSO-x4022A) were used to record the traces of transient transmission changes in two overlapping time windows. The maximal digitizing rate was 10 ns per data point.
Transient absorption changes were recorded from 10 ns after the laser pulses until full completion of the phototransformation. At each wavelength, 25 laser pulses were averaged to improve the signal-to-noise ratio. The quasilogarithmic data compression reduced the initial number of data points per trace (~32,000) to ~850 points evenly distributed in a log time scale giving ~100 points per time decade. The wavelengths were varied from 330 to 700 nm in steps of 10 nm using a computer-controlled step motor. Absorption spectra of the samples were measured before and after each experiment on a standard spectrophotometer (Avantes Avaspec 2048 L). Obtained datasets were independently analyzed using the multiexponential least-squares fitting by MEXFIT software  91. The number of exponential components was incremented until the SD of weighted residuals did not further improve.
For data collection harvested crystals were incubated for 5 min in the respective precipitant solutions. Acquisition and treatment of diffraction data. X-ray diffraction data of OLPVR1 were collected at the beamlines ID30b and ID23-1 of the ESRF, Grenoble, France, using a PILATUS 6M detector. The data collection at ESRF was performed using MxCube2 software. X-ray diffraction data of the O1O2 chimera were collected at the X06SA beamline of the SLS, Villigen, Switzerland, using EIGER 16M detector. Diffraction images were processed using XDS  92. The reflection intensities were scaled using the AIMLESS software from the CCP4 program suite  93. The reflection intensities of the highest-resolution data on OLPVR1 (1. 4 Å) were also scaled using the Staraniso server  94  for the validation of the electron density maps quality.
There is no possibility of twinning for the crystals. In all cases, diffraction data from one crystal was used. The data treatment statistics are presented in Supplementary Table  1. Structure determination and refinement. Initial phases for the OLPVR1 structures were successfully obtained in the P2 1 2 1 2 and P1 space groups by molecular replacement (MR) using MOLREP  95  using the chain A of the 6SQG structure (OLPVRII protein) as a search model. Initial phases for O1O2 chimera were successfully obtained in the I121 space group by MR using the obtained structure of OLPVR1 as a search model. The initial MR models were iteratively refined using REFMAC5  96  , PHENIX  97  and Coot  98. The structure refinement statistics are presented in Supplementary Table  1. Molecular dynamics. We used the refined 1.
Acknowledgements We thank  C. Baeken  for technical assistance. We thank  D. Gerke  for technical assistance during virus purification and  S. Langer  for technical assistance during hippocampal culture preparation. We acknowledge the  Structural Biology Group of the European Synchrotron Radiation Facility (ESRF)  for granting access to the synchrotron beamlines. We also acknowledge the  Paul Scherrer Institut, Villigen, Switzerland  for provision of synchrotron radiation beamtime at beamline X06SA of the  SLS  and would like to thank  Dr. Anuschka Pauluhn  for assistance. Electrophysiological experiments were supported by  HGF-RSF  grant ( Helmholtz-RSF Joint Research Groups grant  (RSF No. 19-44-06302 )), the common program of  Agence Nationale de la Recherche (ANR), France and Deutsche Forschungsgemeinschaft (DFG) ,  Germany  ( ANR-15-CE11-0029-02/FA 301/ 11-1 ), by the  DFG Research Unit FOR 2518 (DynIon , project  P4  to JPM,  MA 7525/1-1 ), and by funding from  Frankfurt: Cluster of Excellence Frankfurt Macromolecular Complexes  by the  Max Planck Society  (to E.
B. ) and by the  Commissariat à l'Energie Atomique et aux Energies Alternatives (Institut de Biologie Structurale)-Helmholtz-Gemeinschaft Deutscher Forschungszentren (Forschungszentrum Jülich ) Special Terms and Conditions 5. 1 specific agreement. This work used the platforms of the Grenoble Instruct-ERIC center (ISBG;  UMS 3518 CNRS-CEA-UJF-EMBL ) within the  Grenoble Partnership for Structural Biology  (PSB). Platform access was supported by  FRISBI  ( ANR-10-INBS-05-02 ) and GRAL, a project of the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ). Crystallographic experiments were funded by  RFBR  and  CNRS  according to the research project №  19-52-15017. This work was supported by the project  ANR-19-CE11-0026. FRV was supported by grant  VIREVO CGL2016-76273-P [AEI/FEDER , EU], (cofounded with FEDER funds).
DZ was supported by the  Russian Foundation for Basic Research  project numbers  17-00-00164  and  20-34-90009. A. G. , K. S. and M. K. were supported by  Russian Foundation for Basic Research  project numbers  17-00-00166  and  17-00-00167. V. G. was supported by the  Ministry of Science and Higher Education of the Russian Federation  (agreement #  075-00337-20-03 , project  FSMG -2020-0003 ). Work in Göttingen was funded by the  European Research Council  through the  Advanced Grant  'OptoHear" to T. M. under the European Union's  Horizon 2020 Research and Innovation program  (grant agreement No. 670759 ) and the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy -EXC 2067/1-390729940.
METHODS This preliminary cross-sectional study was conducted between March 24, 2020, and April 14, 2020, in two clinical laboratories in Lyon (France) to which general practitioners (GPs) refer patients with a suspicion of COVID-19 for a nasopharyngeal smear (RT-PCR). Before undergoing the test, the patients responded to a preformatted questionnaire about their symptoms, built on previous literature  3, 4  and expert opinion. Data were analyzed using Stata 15, adjusting for clustering within the two laboratories. We examined the univariate and multivariate association between smell and/or taste disorders and a positive SARS-CoV-2 test, adjusting for all the other symptoms frequently reported by the patients. We also examined the performance of these smell and taste symptoms in predicting a SARS-CoV-2positive test.
Our study was initiated after the first media reports of a possible link between anosmia and COVID-19. This may have prompted patients to over-report these symptoms, thus affecting their prevalence in our sample. Since patients were asked about the symptoms before receiving the results of their test, this limits the repercussions of this potential reporting bias on our measures of association. Future studies should explore the diagnostic performance of various combinations of symptoms (i. e. , anosmia and ageusia, anosmia without rhinitis, anosmia with nasal dryness…) to increase the specificity of the clinical picture. In accordance with Wynants et al. 's recommendations,  6  these studies should be performed with a representative dataset (i. e. , primary carebased sample), an adequate description of the study population and external validation of the diagnostic tool.
Acknowledgments: We thank all patients who accepted to participate in this study. We also thank  Rémy Boussageon ,  Maud Laprée ,  Edith Corbineau ,  Jean Pascal Fournier ,  Cerballiance Part-Dieu  and  Etats-unis laboratories ' teams, the Villon microbiology platform,  Amir Moussa  for their assistance, and the Maisonneuve family for their support. Benoit Tudrej, MD, PhD  1,2  Paul Sebo, MD ,  MSc 3 Julie Lourdaux, MD 4 Clara Cuzin 1 Martin Floquet 1 Dagmar M. Haller, MD, PhD 3 Hubert Maisonneuve, MD , M. Ed 1,2,3.
Statistical Analysis Statistical analyses were performed using SPSS software (Version 24. 0, Armonk, NY: IBM Corp. ) and all measures were compared between active and sham conditions. Since not all data followed normal distribution according to Kolmogorov-Smirnov test, a non-parametric analysis of variance was run for group comparison using Friedman's test and post-hoc Dunn's test, and took into consideration the groups (active vs. sham) and the time points (T1, T2, and T3 for N-back Test and SDMT; T0 and T3 in the case of Eyes Test and Faux Pas Test). For Friedman's test, estimation of effect size was based on Kendall's W coefficient of concordance  (54) , with effect size being considered small (<0. 3), moderate (0. 3-0. 5) and large (≥ 0. 5). To test for a possible carryover effect (i. e.
Sequence Analysis Light-oxygen-voltage (LOV) domain sequences presented in Figure  1  and those from Glantz et al. [2]  were aligned using Clustal  [30]  and visualized using Jalview  [31]. Chloroflexus aurantiacus and Chloroflexus islandicus LOV protein sequences were identified using the NCBI BLAST search  [32]. The Chloroflexus aurantiacus LOV protein sequence can also be found in the dataset by Glantz et al. [2].
Acquisition and Treatment of Diffraction Data The diffraction data were collected at 100 K on the BioMAX macromolecular crystallography beamline at MAX IV Laboratory (Lund, Sweden). Diffraction images were processed using XDS  [34]. POINTLESS and AIMLESS  [35]  were used to merge, scale and assess the quality of the data, as well as to convert intensities to structure factor amplitudes and generate Free-R labels.
Molecular Dynamics (MD) Simulations The initial coordinates for the wild type CagFbFP simulations were taken from the X-ray structure (PDB ID 6RHF)  [25]. The models of the CagFbFP-A58P variant were constructed using the SWAP function in YASARA Structure Version 17. 4. 17 [39] , and optimized using the SCWRL rotamer library search  [40]. The lowest energy conformers were selected for further studies. Two sets of independent simulations for each conformer of Ala58 and Pro58 ("A" and "B") were carried out. The protonation states of titratable residues at pH 7 were assigned on the basis of pKa calculations using the PROPKA 3. 1 program  [41]  and visual inspection; all charged residues were kept at their standard protonation states. Side chains of Asn and Gln residues were checked for possible flipping.
The phosphate group of the FMN cofactor was deprotonated, carrying a charge of -2e. Consequently, CagFbFP and CagFbFP-A58P dimers had a total charge of -4e. To neutralize the systems, solvent water molecules that were at least 5. 5 Å away from any protein atoms were replaced by Na + ions. Hydrogen atoms were added, employing the tleap module of AmberTools14  [42]. Crystal water molecules were kept; the protein was solvated in a water box centered at the center of mass to ensure a water layer of 12 Å around the protein. The total size of the simulated systems was ~43,000 atoms, including ~13,200 TIP3P  [43]  water molecules. All MD simulations were carried out using the Amber14 program  [42]  with the Amber ff99SB  [44, 45]  all-atom force field for proteins, the general Amber force field (GAFF)  [46]  for flavin mononucleotide (FMN) and the TIP3P model for water  [43].
Since a proline's backbone is naturally more rigid than that of other amino acids, it can stabilize certain kinks in the protein, and consequently the overall protein  [27, 28]. Additionally, mutating a particular amino acid to the consensus one often improves the stability of the resulting protein  [26]. Consequently, mutating Ala56 and Ala58 into prolines might be beneficial for the stability of CagFbFP. Careful analysis of the sequence alignment revealed also another position, that of CagFbFP's Ala95, situated at the N-terminus of the helix Fα, which is also often occupied by prolines in other LOV proteins. Overall, 33% of the proteins from the dataset from Glantz et al. [2]  contain a proline at this or the neighboring position. Consequently, we focused on probing the effects of the mutations A56P, A58P and A95P on the stability of CagFbFP.
CagFbFP alanines that were substituted with prolines in this work are marked with asterisks. Careful analysis of the sequence alignment revealed also another position, that of CagFbFP's Ala95, situated at the N-terminus of the helix Fα, which is also often occupied by prolines in other LOV proteins. Overall, 33% of the proteins from the dataset from Glantz et al. [2]  contain a proline at   [51] , six different FbFPs from thermophilic microorganisms  [24] , CagFbFP  [25]  and CagFbFP homologs from Chloroflexus aurantiacus and Chloroflexus islandicus. CagFbFP alanines that were substituted with prolines in this work are marked with asterisks.
Acknowledgments: Atomic coordinates and structure factors for the reported crystal structures have been deposited in the Protein Data Bank under the accession codes  6Y7R  ( CagFbFP-A56P ) and  6Y7U  ( CagFbFP-A95P ). X-ray diffraction data were collected at the  BioMAX beamline at MAX IV Laboratory (Lund, Sweden ). Simulations were performed with computing resources granted by  JARA-HPC  from  RWTH Aachen University  under project  JARA0065.