dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We gratefully acknowledge  Julio Ortiz  and  Tat Cheung Cheng  for their help during cryoEM data collection. This research was supported by  ATIP-avenir  ( CDP-0B1INSB-HS9ADO1051 ) as well as  LabEx start up  ( ANR-10-LABEX-30-HS ) grants to HS and a  Region Grand Est jeunes chercheurs fellowship  to PS. HS also acknowledges the support of the  MRC Laboratory of Molecular Biology  during the initial stages of the project. This study was further supported by the grant  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02. The authors acknowledge the support and the use of resources of the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INBS-05  and of Instruct-ERIC.
Data availability The atomic coordinates for the Rea1 AAA+ ring and the Rea1 linker in the ADP state have been deposited with PDB IDs 6HYP and 6HYD, respectively. The accession codes for the Rea1 and Rea1_DAAA2L-H2a models in the AMPPNP state are 6I26 and 6I27, respectively. The accession codes for the cryoEM maps of the Rea1 AAA +ring and the Rea1 linker in the ADP state are EMD-0309 and EMD-0308, respectively. The accession code for the unsharpened cryoEM map of the Rea1 AAA +ring in the ADP state is EMD-0330. The cryoEM maps of the Rea1 and Rea1_DAAA2L-H2a AMPPNP states have the EMD accession codes EMD-0328 and EMD-0329, respectively.
Data availability The atomic coordinates for the Rea1 AAA+ ring and the Rea1 linker in the ADP state have been deposited with PDB IDs 6HYP and 6HYD, respectively. The accession codes for the Rea1 and Rea1_DAAA2L-H2a models in the AMPPNP state are 6I26 and 6I27, respectively. The accession codes for the cryoEM maps of the Rea1 AAA+ ring and the Rea1 linker in the ADP state are EMD-0309 and EMD-0308, respectively. The accession code for the unsharpened cryoEM map of the Rea1 AAA+ ring in the ADP state is EMD-0330. The cryoEM maps of the Rea1 and Rea1_DAAA2L-H2a AMPPNP states have the EMD accession codes EMD-0328 and EMD-0329, respectively. The following datasets were generated:.
Results Overview of the Aplysina aerophoba microbial metaproteome. Four individuals of Aplysina aerophoba (16, 17, 19, 25) and four samples of surrounding seawater (5, 6, 7, 8) were collected in the Mediterranean Sea (see Methods). Sponge individuals 16 and 19 were processed in technical replicates (Table  1 ). De novo peptide sequencing generated between 1,609 and 4,560 peptide sequences for each of the sponge samples. A total of 2,536 peptide sequences were obtained in the pooled seawater samples (Table  1 ). The resulting peptides were matched against predicted proteins from two metagenomic assemblies, one per habitat. The number of peptides per sponge sample with a match in the database with predicted proteins in assembled metagenomes from A. aerophoba (782,998 predicted proteins) and surrounding seawater (370,455 predicted proteins) was in a range of 72.
3% to 76. 7% of the sequenced peptides. In contrast, the number of peptides with a match in the database for seawater samples was rather low, between 9. 9 and 27. 9%. From this initial set of expressed proteins, we could match between 245 and 757 protein sequences from each of the sponge samples to the corresponding metagenomics datasets (Table  1 ), adding up to a total of 3,226 proteins. Of these, 383 proteins were shared among all individual sponge samples, and up to 185 proteins were shared between any two samples from the same individual (Fig. 1 ). In the seawater, a total of 222 common proteins was identified in the four samples. In order to explore the differences on the expressed protein profiles between the sponge and seawater, we performed a clustering analysis  19  using the detected and pooled proteins in each habitat.
The remaining identified genera contributed <15 proteins each. Complementary analysis based on reads matching the 16S rRNA gene extracted from the sequenced metagenomes (see Supplementary Fig. S1 ) and gene markers from the assembled contigs (see Supplementary Fig. S2 ) showed that the vast majority of organisms included in our metagenomics database were of bacterial origin (91%). Some of the major groups found, coincided with the taxa detected using the LCA algorithm, namely Proteobacteria (36%), with a dominance of Alpha-(10%) and Gammaproteobacteria (14%), and Chloroflexi (17%). The Deltaproteobacteria and unclassified bacteria made up to 4% and 10% of the contigs of the database, respectively. Although the genus Entotheonella was not explicitly found with the gene marker analysis performed with Phylosift, two partial Entotheonellaceae 16S rRNA gene sequences were recovered from our metagenomic assembly (see Supplementary Table  S2 ).
Depending on the database used, Entotheonellaceae sequences are classified within the Deltaproteobacteria (Green Genes), as it was originally described  20  , or within the Tectomicrobia (SILVA), as it is currently accepted (see Supplementary Table  S2 ). These two fragments overlapped with 45 bp and constitute a full-length 16S rRNA marker gene, which had a relative abundance of 0. 4 ± 0. 01% in the sponge metagenomes. Phylogenetic analyses of the 16S rRNA marker gene revealed the position of this sequence within sponge-associated Tectomicrobia, not within the genus Entotheonella sensu stricto (Fig. 3 ). The A. aerophoba-derived Entotheonellaceae consensus sequence is clustered within the sponge-associated Tectomicrobia reported previously  20. Hybridization with probes designed for CARD-FISH revealed the presence of the sponge-associated Tectomicrobia scattered throughout the A.
The KO group with the highest number of proteins was transport of amino acids, sugars and ions (371), followed by carbon metabolism (114), amino acid and nucleotide metabolism (47), photosynthesis (45), nitrogen metabolism (38) and degradation and metabolism of xenobiotics (22). Within the latter category, the expressed proteins were related to the conversion of carbon monoxide (CoxL and CoxM) and the degradation of aromatic ). Eight proteins were found to be related to sulfur metabolism and one to a CasA protein within a CRISPR system. A close examination of the top-20 KO identifiers in Bacteria based on absolute abundance, in a range of ten to 140 proteins, showed that the transport of peptides/nickel (K02035), sugars (K02027), amino acids (K01999), polyamines (e. g. putrescine and spermidine) (K02055), sulfonate, nitrate and taurine (K02051) were highly represented in the A.
aerophoba (Table  4 ). These proteins were consistently found across several of the sponge individuals tested (Table  4 ). Carbohydrate-active enzymes in the metaproteome of A. aerophoba. From the 2,987 unique proteins that were uniquely detected in the sponge dataset, a total of 111 (3. 7%) was annotated as carbohydrate-active (CAZy) enzymes (see Supplementary Table  S7 ). These proteins fell within 23 different CAZy families (Fig. 5A ). The most abundant families (more than four proteins) were GH109, CE4, CBM37, CBM32, AA3, CBM9 and GH74, and are always present between three to all six samples analyzed. Five of these 23 families are involved in the metabolism of chitin and N-acetylglucosamine (GH3, GH116, CE4, CBM37 and CBM32), three in the bioconversion of glycoproteins and glycolipids (GT26, GH109, and CBM40) and four in the degradation of other complex polysaccharides like xylan and/or xyloglucan (GH95, GH74, CE3, CBM9).
In order to infer the microbial source of each CAZy family protein, all relevant proteins were analyzed by BLAST_P against the NCBI nr database (see Supplementary Table  S7 ). Based on the best hit to a CAZy domain, the proteins from family GH109 (EC 3. 2. 1. 49; α-N-acetylgalactosaminidase) were mostly affiliated to a hypothetical protein (accession number OIP04581. 1) from an Armatimonadetes bacterium (16 proteins, Fig. 5B ). In the case of family CE4 (polysaccharide deacetylases), 13 proteins were associated with four bacterial genera (Aneurinibacillus, Pantoea, Pseudomonas and Parageobacillus). In addition, eight proteins belonging to family CBM9 (binding domain associated with xylanases) were mostly affiliated to those predicted from species in the Alteromonas and Bacteroides genera. Moreover, proteins associated with similar ones from Paenibacillus were found within families GH109, CBM37 (binding domain associate with polysaccharide-degrading enzymes) and CE3.
Phototrophic anaerobic sulfur-oxidizing bacteria are mainly mesophilic and have been found previously in genera of the family Rhodobacteraceae, such as Rhodobacter and Rhodovulum 41. Sulfite is also oxidized without SoxCD and studies have shown that in this case the cytochrome C reduction rate is decreased to 25% 42. We found three sulfane dehydrogenases (subunit SoxC) from the peptide dataset matching Roseovarius (Rhodobacteraceae). The rest of the Sox pathway, namely SoxACXYZ, with the exception of SoxB, were found within the set of predicted proteins from the metagenomics assembly used in this work. The protein SoxC has been designated a sulfur dehydrogenase based on the phenotype displayed by a mutant with an in-frame deletion in the corresponding gene, which was not able to grow lithotrophically with thiosulfate but was still able to oxidize thiosulfate, although at a low rate  43.
Thus, we suggest that Roseovarius may be able to grow lithotrophically with thiosulfate as the electron donor. We cannot, by means of our approach, test the functionality of these or other identified genes, but based on their rather consistent appearance across samples (Table  4 ), and their absence in the seawater dataset, they appear essential to the system. Based on the absence of expression evidence of the accompanying subunits for known complex formation that include either NorQ or SoxC proteins, it may be that other subunits were unstable, produced less abundantly, or missed due to peptide sequencing depth. Given that sponges are relatively understudied, it is also possible that the actual function of these proteins in the sponge differs from those that have been described in other systems, for instance that other proteins fulfil the respective roles.
Only the DNA and protein fractions were used for the present study. Metagenome sequencing, assembly and protein predictions. Six individual sponge samples and three seawater samples were sequenced in two lanes of Illumina HiSeq PE100 by the Research Group Genome Analytics (GMAK) at DSMZ (Braunschweig, Germany). The sequencing details of the metagenomic data accompanying the metaproteomics analysis in this study are provided in Supplementary Table  S8. Paired-end reads from individual samples were assembled using Megahit (v1. 0. 3; parameters:-preset, meta-sensitive) and examined in order to estimate the number of predicted putatively-expressed proteins using Prodigal v2. 0 53. The A. aerophoba metagenomes assembled into 273 718 contigs >1 Kb and the number of predicted proteins was 782 998.
The three seawater metagenomes assembled into 119 824 contigs >1 Kb. From these, 370 455 proteins were predicted. The proteins predicted from the metagenomics assemblies from each environment were used as a database for subsequent identification of the peptides and proteins produced from the mass-spectrometry datasets (below). We provide an assessment of the taxonomic origin of the metagenomes used for protein predictions on the basis of two approaches: based on reads matching 16S rRNA genes extracted directly from the metagenomes using SortMeRNA 54 , and from marker genes pulled out from the assembled contigs. To obtain a measure of relative abundance of Tectomicrobia, raw reads from each of the samples were mapped onto the consensus 16S rRNA marker gene sequence with stringent parameters (minimum sequence identity of 98%) using BBMap (https://jgi.
doe. gov/data-and-tools/bbtools/). For this purpose, we used Phylosift  55  , which relies on the pplacer  56  algorithm to place identified metagenomic reads and contigs onto reference phylogenies pre-built with FastTree 57. Metagenome assemblies, predicted protein datasets, and information on sequenced peptides are available athttp://hdl. handle. net/10411/PIPXST. BLASTP  63  against the NCBI nr database (downloaded February 2017) with a threshold e-value of 10 -5. For taxonomy, we calculated the lowest common ancestor (LCA) algorithm implemented within MEGAN (v5. 0)  64  for the top 50 hits for each annotated protein. In addition, the unique proteins in the sponge were functionally classified using the BlastKOALA platform  65. For proteins taxonomically assigned to the bacterial domain in the sponge datasets, we counted the total number of fragmentation spectra that map to their constitutive peptides or spectral count  66  (SC), thereby generating an abundance estimate at the phylum level.
Proteins with <10 spectra were labelled as 'low' , between 11-40 spectra as 'medium' and >40 spectra as 'high' in abundance. In order to obtain semi-quantitative data, we clustered the proteins affiliated to Bacteria by LCA using CD-HIT  67  with a threshold of 95% amino acid identity. Clusters composed of more than five protein sequences were retrieved and denoted as "abundant", as described in  60. One representative protein, chosen at random, for each "abundant" cluster was annotated by BLASTp against the NCBI nr database  63. The most frequent taxonomic assignment between the top 10 hits was used for each annotated protein. Additionally, the proteins detected unique to each habitat were affiliated with carbohydrate-active (CAZy) enzymes using the webserver dbCAN  68  and then manually annotated by BLASTp against NCBI nr database.
Acknowledgements We thank  María J. Uriz  from the  Centre d'Estudis Avançats de Blanes (CEAB) ,  CSIC, Blanes, Spain  for providing support for sample collection and access to the laboratories for prompt processing. We thank  Boyke Bunk ,  Sixing Huang  and  Joerg Overmann  at  DSMZ  for organizing the sequencing of DNA samples. Further thanks to  Dr. Hjalmar Permentier  and  Marcel de Vries  from the  Mass Spectrometry Core Facility of the University of Groningen  for liquid chromatography-tandem mass spectrometry experimental support. We are grateful to  Jörn Piel  for providing the sequences to build the phylogenetic tree of Tectomicrobia. We would also like to thank the  Center for Information Technology of the University of Groningen  for their support and providing access to the Peregrine high-performance computing cluster.
1. 1. 42] 4 1 0 1 1 1 0 K04077 Chaperonin GroEL 4 1 0 2 1 0 0 K04748 Nitric oxide reductase NorQ protein 4 0 1 1 1 1 0 K02433 GatA; glutamyl-tRNA(Gln) amidotransferase subunit A [EC:6. 3. 5. 6] 3 0 0 0 3 0 0 K02945 RP-S1; small subunit ribosomal protein S1 3 0 1 1 0 1 0 K01061 Carboxymethylenebutenolidase [EC:3. 1. 1. 45] 3 0 0 1 0 1 1 K01607 PcaC; 4-carboxymuconolactone decarboxylase [EC:4. 1. 1. 44] 3 3 0 0 0 0 0 K00059 FabG; 3-oxoacyl-[acyl-carrier protein] reductase [EC:1. 1. 1. 100] 2 0 0 0 0 2 0 K02031 ABC. PE. A; peptide/nickel transport system ATP-binding protein 2 0 1 0 0 1 0 K00821 ArgD; acetylornithine/N-succinyldiaminopimelate aminotransferase [EC:2. 6. 1. 11] 1 0 1 0 0 0 0 K00008 SorD; L-iditol 2-dehydrogenase [EC:1. 1. 1. 14] 1 0 1 0 0 0 0 K14733 LimB; limonene 1,2-monooxygenase [EC:1.
Whole-cell recordings were performed using a patch-clamp amplifier (Axoclamp 200B, Molecular Devices) connected to a Digidata (1550 LowNoise acquisition system, Molecular Devices). Currents were recorded in voltage-clamp mode at À60 mV. Signals were low pass filtered (Bessel, 2 kHz) and collected at 10 kHz using the data acquisition software pClamp 10. 5 (Molecular Devices). Electrophysiological recordings were extracted using Clampfit (Molecular Devices) and analyzed with R. To record nicotinic currents from GFP-positive Neuro2A cells, we used the following external solution (containing in mM): 140 NaCl, 2. 8 KCl, 2 CaCl 2 , 2 MgCl 2 , 10 HEPES, 12 glucose (pH 7. 3 with NaOH). We used a computer-controlled, fast-perfusion stepper system (SF-77B, Harvard Apparatus) to apply nicotine-tartrate (100 mM, Sigma-Aldrich) or carbamylcholine chloride (CCh, 1 mM, Sigma-Aldrich), with an interval of 2 min, under different light conditions.
M. Systems Inc. ). The signal was digitized (Micro-2, Cambridge Electronic Design), sampled at 12. 5 kHz and recorded using Spike2 software (CED). DA neurons were sampled in the VTA with the following coordinates: [AP: À3 to À4 mm; ML: +0. 3 to+0. 6 mm; DV: À4 to À4. 8 mm, from Bregma]. Spontaneously active pDA neurons were identified on the basis of previously established electrophysiological criteria: 1) regular firing rate; 2) firing frequency between 1 and 10 Hz; 3) half AP >1. 1 ms. After a baseline recording of at least 5 min, a saline solution (0. 9% sodium chloride) was injected into the saphenous vein, and after another 10 min, injections of nicotine-tartrate (30 mg/kg) were administered via the same route in a final volume of 10 ml and under different light conditions (Dark -390 nm -520 nm).
Spontaneously active pDA neurons were recorded at least 30 min after MAHoCh infusion and were identified on the basis of the electrophysiological criteria used for juxtacellular recordings. The optical fiber was then lowered 100-200 mm above the tetrodes. Baseline activity was recorded for 200 s in darkness, prior to applying 5 s light flashes of alternative wavelengths (390 nm / 520 nm). Electrophysiological signals were acquired with a 20 channels preamplifier included in the Mini Matrix (Thomas Recording) connected to an amplifier (Digital Lynx SX 32 channels, Neuralynx) digitized and recorded using Cheetah software (Neuralynx). Spikes were detected using a custom-written Matlab routine and sorted using a classical principal component analysis associated with a cluster cutting method (SpikeSort3D Software, Neuralynx).
Neurons were considered as responding when their change in firing rate (% Photoswitching) at the transition from violet to green light exceeded a threshold of 15%, defined as the maximal % photoswitching observed in controls. This threshold was used for all recorded neurons in every condition. To extract the spikes contained within bursting episodes (SWB) we used the same criteria described in the juxtacellular recordings section. They are represented as the frequency of SWB because of the short analysis window (5 s). All the data were analyzed with R (https://www. r-project. org) and Matlab (MathWorks).
Nicotine-induced place preference paradigm The Conditioned Place Preference (CPP) box (Imetronic, France) consists of a Y-maze with one closed arm, and two other arms with manually operated doors. Two rectangular chambers (11 Â 25 cm) with different cues (texture and color), are separated by a center triangular compartment (side of 11 cm). One pairing compartment has grey textured floor and walls and the other one has smooth black and white striped walls and floor. The first day (pretest) of the experiment, mice (n = 6-8 animals/group) explored the environment for 900 s (15 min) and the time spent in each compartment was recorded. Pretest data were used to segregate the animals with equal bias so each group has an initial preference score almost null, indicating no preference on average. On day 2, 3 and 4, animals received an i.
Trajectories and time spent on each side are calculated based upon animal detection. Place preference and locomotor activity were recorded using a video camera, connected to a video-track system, out of sight of the experimenter. A home-made software (Labview 2014, National Instruments) tracked the animal, recorded its trajectory (20 frames per s) for 15 min and sent TTL pulses to the LED controller when appropriate (pairing sessions). For optogenetic pharmacology experiments, MAHoCh (400 mM in aCSF, 500 nl in 5 min) was injected early in the morning of pairing days (2, 3 and 4) under light gas anesthesia (Isoflurane 1%). 520/390 nm light was applied during pairing sessions (day 2, 3 and 4), on both sides, through a patch cord (500 mm core, NA = 0. 5, Prizmatix, Israel) connected to the implanted ferrule with a sleeve and to the 390/520 nm combined UHP-LEDs (Prizmatix).
Acknowledgments Authors would like to thank  Nadine Mouttajagane ,  Ambre Bonnet  and  Michael Martin  for molecular biology work, and  Justine Hadjerci ,  Steve Didienne  and  Samir Takillah  for their help with electrophysiology and behavior experiments. This work was supported by the  Agence Nationale de la Recherche  ( ANR-JCJC 2014  to A. M. ), by a  NARSAD   Young Investigator Grant  from the  Brain and Behavior Research Foundation  (to A. M. ), by the  Fondation pour la Recherche Medicale (E ´quipe FRM   DEQ2013326488  to P. F. ), by the  French National Cancer Institute  Grant  TABAC-16-022  (to P. F. ) by the  Federation de la Recherche pour le Cerveau (FRC Rotary  Espoir en tete 2012 to P. F. ) and by the  Labex Bio-Psy. A. M. was recipient of a fundamental research prize from the  Medisite Foundation for Neuroscience.
R. D. C. was supported by a Ph. D. fellowship from the  DIM Cerveau and Pensee program  of the  Region Ile-de-France , and by a fourth-year Ph. D. fellowship from  FRM  ( FDT20170437427 ). D. L. was recipient of a  Labex Biopsy post-doctoral fellowship. RHK was supported by grants from the  NIH  ( U01-NS090527  and  R01-NS100911 ). P. F. and A. M. laboratory is part of the  E ´cole des Neurosciences de Paris Ile-de-France RTRA  network. P. F. is member of  LabEx Bio-Psy  and of  DHU Pepsy.
5 KCl, 1. 25 NaH 2 PO 4 , 5. 9 MgCl 2 , 26 NaHCO 3 , 25 Sucrose, 2. 5 Glucose, Sigma-Aldrich 89121404-1VL, RRID:CVCL_0470 1 Kynurenate. After 10 min at 35 ˚C for recovery, slices were transferred into oxygenated (95% CO 2 / Transfected pIRES-a4-IRES-5% O 2 ) aCSF containing (in mM): 125 NaCl, 2. 5 KCl, 1. 25 NaH 2 PO 4 , 2 CaCl 2 , 1 MgCl 2 , 26 NaHCO 3 , https://doi. org/10. 1038/ construct GFP nature03694 15 Sucrose, 10 Glucose at room temperature for the rest of the day. Slices were labeled individually (mus musculus) with MAHoCh (70 mM) in oxygenated aCSF (1 ml) for 20 min, and transferred to a recording chamber Transfected construct pIRES-b2E61C-I RES-eGFP continuously perfused at 2 ml/min with oxygenated aCSF. this paper (mus musculus) Chemical MAHoCh https://doi. org/10. 1038/ compound, nchem.
2 Ma and 1 Ma (that is, there is a change of slope from steeply positive to slightly negative). In addition, the period between 1 Ma and 0. 6 Ma is very variable, because the slope is noisy rather than straight. The cumulative sum can be found under the function name local. trend, in the R package Pastecs (package of analysis of space-time ecological series). Plant-wax δD and δ 13 C. Plant-wax analyses were carried out at MARUM. Samples were oven dried at 40 °C, homogenized and squalane was added as an internal standard before extraction. Lipids were extracted with a DIONEX accelerated colvent extractor using a 9:1 mixture of dichloromethane to methanol at 100 °C and 1,000 p. s. i. for five minutes, repeated three times. The saturated hydrocarbon fraction was obtained by elution of the dried lipid extract over a silica column with hexane and subsequent elution over AgNO 3 -coated silica to remove unsaturated hydrocarbons.
δD values were calibrated against external H 2 reference gas, the 3H+ factor was monitored daily (values vary between 6. 7 and 6. 9); δD values are reported in parts per thousand (‰) versus the Vienna Standard of Mean Ocean Water (VSMOW) standard. The internal standards yielded a precision of 2‰ on average. Repeated analysis of an external n-alkane standard between samples yielded a root-mean-squared accuracy of <1‰ and a standard deviation of on average 3‰. The results of a method to adjust the δD wax record for vegetation and ice-volume changes are given in Extended Data Fig. 4. Pollen preparation. Samples of 1. 5 to 8. 5 ml were prepared at MARUM. The volume was measured using water displacement. Samples were decalcified with diluted HCl (~12%) and treated with HF (~40%) to remove silicates.