dataesr/scientific-paragraphs-categorization huggingface dataset:
Rv0269c/Rv0268c is interesting since it is in a T->A configuration, which is unusual for the PhdYeFM antitoxin. Homologies suggest that Rv0269c is related to proteins with a DNA polymerase/primase/ligase domain. Therefore Rv0269c/Rv0268c is a puzzling pair worth deeper investigation. Whether these two systems are bona fide TA pairs remains to be investigated. Rv0367c/Rv0366c (A->T) is a putative TA couple where both loci are hit by TASmania HMM profiles belonging to the A123 (nearest Pfam ParD_like) and T70 (nearest Pfam Zeta_toxin) clusters, respectively. The combination A123. T70 (nearest Pfam ParD_like. Zeta_toxin) could represent a new TAS family, since the canonical zeta toxin is described in the literature as the cognate of epsilon antitoxin. In the TASmania database, most of T70 clusters hits appear as paired with A49 and A123 clusters (both with nearest Pfam ParD_like).
Finally, in the case of Rv2016 (T144 nearest Pfam HicA_toxin), which is highly toxic when expressed in M. smegmatis, we could also detect an effective but very limited suppression of toxicity in the presence of the putative antitoxin gene Rv2017 (A32 nearest Pfam HTH_3). Whether this is due to the genetic organization with the toxin and/or to the lack of a chaperone partner is unknown  [48]. All together, these experimental validations of TASmania in silico predictions show how our database can be a very powerful tool in discovering unexpected TAS families.
Some other interesting x antitoxins are the ones with nearest Pfam annotations of Colicin_Pyocin (A � 190, as in Colicin_Pyocin. YafQ_toxin-. A � 190. T4), VraX (A � 371, as in VraX. PemK_toxin-A � 371_T143, specific to Staphylococcus), Glyoxalase (A � 77, as in YafQ_toxin. Glyoxalase-T32. A � 77), Antirestrict (A � 237, as in Antirestrict. CbtA_toxin-A � 237. T3) and Response_reg (T5, as in Cpta_toxin. Response_reg-T5. A � 2). VraX (IPR035374) and Glyoxalase (IPR004360) are both involved in antibiotics resistance pathways. The VraX-like putative antitoxins seem to originally be derived from a phage protein. Intriguingly, the VraX. PemK pair is not found in the reference Staphylococcus aureus subsp. aureus NCTC 8325 while it is present in other S. aureus strains (S4 Fig). Colicin_Pyocin and Respon-se_reg families could potentially give some clues in the evolution of the TAS.
The Colicin_Pyocin (IPR000290) family contains the immunity proteins and/namely members of the effectorimmunity system, which is a two-component genetic system (TCS) similar to the TAS but where both cognates are secreted in order to protect the bacteria itself and its clonemates  [50]. Response_reg (IPR001789) belongs to another two-component genetic system called "twocomponent signal transduction system", which also presents similarities with the TAS. Previous publications have already suggested potential interplay and/or homology between different TCS  [51, 52]. Finally, annotations from other x antitoxins indicate that many more popTx could be promising candidates: Ap_endonuc_2 (as in AP_endonuc_2. ParE_toxin) and Pha-ge_integrase (as in CcdB. Phage_integrase, Phage_integrase. PemK_toxin or Phage_integrase.
Intriguingly the next neighbour gene P9303_20031 is also a Clp protease. Overall, TASmania data indicate that even species previously considered as "TAS-free" in the literature might actually contain TAS loci, but whether these are expressed in vivo and are biologically functional would require to be investigated in further experimental analysis. By avoiding any assumption in the TA protein length and the type of operon-TASmania includes orphan TA loci and TAS hits from multigene pseudo-operons-our database opens up to new TAS families and possible networks. In parallel, we use our large database to apply a meaningful analysis of the biology of the TAS by looking at their organisation in pairs. Our results highlight the modularity of the TA cognates and the issues raised by the conventional misleading family annotations of the TAS.
Building reference TAS HMM profiles The global strategy is to build an updated list of toxin and antitoxin HMM profiles and scan a local version of the EnsemblBacteria database (N>41K assemblies) with thoses HMM profiles. To achieve this, we have downloaded EnsemblBacteria (release 33, November 2016)  [55] , updated its InterPro (IPR) (version Nov 2016)  [56]  annotation and applied a pseudo-operon annotation with arbitrary definition where a maximal intergenic distance of 100 bp is applied, as shown in Fig 1. A) Building the reference TAS IPR list. An initial reference TAS IPR database is built as following. Based on a keyword search in UniProtKb ("toxin+antitoxin"), a set of N*44K proteins is extracted. These hits are filtered to keep only the hits corresponding to the Bacteria superkingdom (id = 2) and annotated with at least 1 IPR, giving N*37K proteins.
We extract the IPR annotations of these proteins and we obtain N = 733 unique IPR derived from this set. In order to help in the selection of the TAS-specific IPR, we fetch the IPR detailed descriptions from EBI to manually review whether each one of these 733 would be included to the initial reference TAS IPR database. We obtain a total of N = 80 reference TAS IPR list, of which N = 45 correspond to toxin and N = 35 to antitoxin IPRs.
B) Updating the IPR annotation of EnsemblBacteria initial database. Meanwhile, we update the IPR annotation of our database. Indeed, although the gene annotation for domain features (Pfam, PROSITE etc) is accurate, we discovered that their equivalent IPR was partially missing in our database (EnsemblBacteria core 33 release 33). Based on the InterPro release 61 that maps each IPR identifier to corresponding domains features (Pfam etc), we update the IPR annotations of all the genomes by linking their domain features with the new IPR release.
D) Building the HMM profiles. The TAS IPR annotated toxins (same protocol for antitoxins) proteins are then i) clustered with MMSeqs2  [57] ; ii) a MSA is calculated for each cluster size greater than 10 unique protein sequences (ClustalO version 1. 2. 4)  [58] ; iii) the MSA of each cluster is used to build an HMM profile (HMMER3 version 3. 1b2 February 2015 http://HMMer. org). E) HMM search. Finally these HMM profiles are searched against the whole N = 41'604 proteomes, as follows: hmmsearch <hmm_profiles_database> <proteome. fasta> from HMMER3 (default settings).
Combining HMM profiles into clusters In parallel, we perform HMM profiles comparison in order to reduce the number of profiles, using the Profile Comparer program PRC (v1. 5. 6)  [59]. Combining the PRC results with the NetworkAnalyzer  [60]  in CytoScape (3. 5)  [61]  network analysis, we select the first PRC E-value of 10 -12 where the number of connected components (CC) (i. e clusters of HMM profiles) is reaching the plateau. For clarity and continuity with previous TAS annotations found in the literature, each TASMANIA cluster identifier is given the nearest corresponding Pfam family names (release 31. 0) to which the TAS scientific community is used to. The "nearest" Pfam annotation is performed as follows: using the PRC program for profile-profile comparison (default settings), each TASmania HMM profile is scanned against Pfam database.
The best Pfam profile match for each TASmania HMM profile (i. e. , the lowest E-value) is selected and the identifier of this Pfam annotation is used as the Pfam equivalent of the given TASmania HMM profile. On top of the HMM profile annotation, the TASmania clusters are also attributed a Pfam annotation. For each TASmania cluster we attribute the common profile Pfam annotation when there is no ambiguity. In cases of heterogeneity (more than one Pfam annotation per cluster), the Pfam match with the smallest E-value is selected. But in all cases, the individual Pfam annotation of each TASmania HMM profile is kept and shown in S2 Table for methodology coherence. We used the word"nearest" to emphasize the potential issues of such equivalences. The final TASmania database contains: i) the putative hits from the HMM scan; ii) the genes annotated with a reference TAS IPR and that were filtered out due to the small size of their proteins clusters (less than 10 unique sequences) when building the HMM profiles; iii) the guilt-by-association "x" cognates (see S5 Fig).
The instrument was calibrated on-line by using The ESI-TOF tuning mix (Agilent Technologies, USA). Mass spectral data was acquired in positive broadband mode over the m/z range 250-2500, with 1M data points transient, 0. 2 s ion accumulation and four scans were accumulated per spectrum. Data acquisition was performed using solariXControl FTMS control application software. For the eIF3-40S and eIF5-40S complexes, samples were separated identically as eIF3 complex. However, peptide mixtures were analyzed by MS/MS analysis in data independent mode with broad isolation window (1000 m/z). Mass spectral data was acquired in positive broadband mode over the m/z range 250-2500, with 1M data points transient, 15 eV collision voltage, 0. 2 s ion accumulation, 0. 8 s MS/MS accumulation and two scans were accumulated per spectrum.
Cross-links identification For identification of cross-links from LC-MS analysis, we used in house developed software (LinX) based on the Links algorithm, previously described as Automated Spectrum Assignment Program (ASAP)  (59, 60). Because it requires input in the format of tab separated m/z and intensity values, it was necessary to program a data output script using the internal scripting language (Visual Basic) of the DataAnalysis 4. 4 software suite (Bruker Daltonics, Germany). This script uses the DataAnalysis implementation of the SNAP 2. 0 algorithm to generate deconvoluted spectra and then exports a file containing the sorted monoisotopic masses and their corresponding intensities (and other related data). LinX software identifies cross-linked peptides by matching experimental data to a theoretical library generated based on the protein sequence, protease specificity, cross-linker reactivity and composition and protein chemical modification.
5/26. 4 Å) or an 8-carbon (DSS or watersoluble variant BS 3 ; a minimal/maximal length of the C ␣ -C ␣ cross-link is 24/30. 2 Å) spacer arm, respectively  (65, 66). According to previously published observations (67), the eIF3 complex was cross-linked using either 100-or 200-fold molar excess of the cross-linker over the protein complex to avoid perturbation of protein structure. This experiment was repeated twice with each cross-linker, hence altogether we obtained eight unique datasets. As a negative control, an untreated samples (buffer only) were always run in parallel and, as expected, no signals corresponding to cross-linked peptides were detected (data not shown). To minimize false positive assignments, only experimental values differing from theoretical ones less than 2 ppm for the both isotopes were considered and multiple assignments were discarded.
Such strict criteria prohibit the misinterpretation of mass spectrometric data. To be able to precisely and synoptically map all crosslinks between surface-exposed lysine residues of individual eIF3 domains, we first refined our previously published 2D model of the ribosome-free eIF3 complex (30) (Supplementary Figure  S1 ) using the Autodesk 3Ds MAX software (Figure  4A-D ). Please note that this schematic 2D model: (i) reflects as accurately as possible all contact points among individual eIF3 subunits that have been mapped so far using various in vivo and in vitro protein binding assays and, at the same time, (ii) purposefully disregards all contacts that eIF3 subunits may establish among each other when bound to the ribosome (reviewed in  (3, 4) ; for further details please see below).
In total, we obtained 22 unique cross-links (Figure  4E ). Two cross-links out of those 22 (between the c/Nip1 middle helical region and the g/Tif35-NTD, and the a/Tif32-HLD and the g/Tif35-RRM) were obtained in all eight datasets, eight were found in both DSG and DSS, eight or three only in DSG or DSS, respectively, and five in both replicates with at least one of the cross-linkers (for the overview and count of the occurrence of individual cross-links obtained in given experimental conditions specified above please see Supplementary Figure  S2  and Table  S1 ). Interestingly, three main directions/groups can be clearly distinguished with respect to the spatial arrangement of these cross-links (Fig-  ure 4E ). The first group (A) runs roughly vertically and includes cross-links of the N-terminal domain of a/Tif32 with both halves of g/Tif35, the middle region of c/Nip1, and the extreme CTD of b/Prt1, as well as those of the b/Prt1-CTD with the c/Nip1-PCI domain and the tip of the g/Tif35-RRM (Figure  4E ).
The reason is that with the exception of the crystal structure obtained with the N-terminal 2/3 of c/Nip1 in complex with the second half of the a/Tif32 (Figure  2A ; PDB code: 4U1C)  (26) , neither of in vitro or in vivo biochemical experiments carried out with either individual subunits or the entire eIF3 (wild-type or mutant) free of the ribosome confirmed the existence of the PCIto-PCI interaction between these two proteins  (27, 30, 31). In fact, we showed earlier that the c/Nip1 residues 157-370 are both necessary and sufficient for binding to a/Tif32 in vitro and in vivo  (30, 31). Hence, as discussed below, we propose that the establishment of the PCI-to-PCI interaction materializes only upon eIF3 binding to the 40S ribosome. In favor of our model, no specific cross-links between both PCI domains were seen, and the group A cross-link detected between the a/Tif32-NTD and the helical region of c/Nip1 could not be formed in the prospective PCI-to-PCI arrangement  (26, 45)  simply because the distance between the two lysine residues involved in this cross-link exceeds the maximal length by over 2-fold (Supplementary Figure  S4A ).
It is also noteworthy that we detected one intrasubunit cross-link within the RRM domain of g/Tif35 (Supplementary Figure  S4C ). The structure of yeast g/Tif35-RRM has not been solved yet, hence Supplementary Figure  S4C  depicts the structure of the human eIF3g-RRM that is wellconserved with its yeast counterpart  (33). It is the only cross-link in all our datasets occurring in the solved structure; it connects one of the RRM ␤-sheets (␤1) with the loop between two other ␤ sheets. Since the distance between the corresponding cross-linked lysines in this crystal structure (∼20. 7 Å) fits well with the maximal length of the employed cross-linking agent (24 Å), this result serves as a proof of principle of the meaningfulness of our crosslinking approach. The a/Tif32 and c/Nip1 subunits must restructure in order to adopt their extended conformations on the 40S ribosomal surface Our compactly packed 3D cross-linking model of eIF3 in complex with eIFs 1 and 5 is in sharp contrast to all available cryo-EM structures of eIF3 in complex with the 40S ribosome  (26, 34, (45) (46) , where a/Tif32 and c/Nip1 are heterodimerized through their PCI domains, sitting near the mRNA exit pore with the c/Nip1-NTD and a/Tif32-CTD running in the opposite direction -in the anthropomorphic terms representing the eIF3 arms embracing the 40S head from the opposite sides.
Similarly, cross-links obtained between the c/Nip1-NTD and small ribosomal proteins residing at the 40S head versus right foot (Figure  8G ) illustrate the flexibility of the c/Nip1-NTD that is most probably instrumental during AUG selection process  (51, 68). Consistently, earlier cross-linking mass spectrometry data predicted an interaction of the N-terminal segment of c/Nip1 with eIF1 bound to the 40S platform  (26) , and a density presumably corresponding to the eIF3c residues 115-220 was identified not too far away from the Psite-bound eIF1 in both the py48S-closed/open complexes  (45). To conclude, the fact that eIF3 is tightly packed in the ribosome-free form suggests that this arrangement could have a regulatory role; i. e. to expose only the 40S-and eIF1and eIF5-contact domains while preventing the hidden domains from making functional interactions until they become accessible on the ribosomal surface for binding to other factors.
In addition, in this arrangement the eIF5-CTD is also ideally suited to control the start codon selection by promoting the dissociation of eIF1 from the scanning-arrested PICs as shown in  (55). Despite the obvious limitations of this approach (for example the studied eIF5-40S complex might never assemble in the cell without other eIFs), we believe that our data are relevant and, in simple terms of the molecular functioning of eIF5 in the context of the 48S PIC, entitle us to propose the following (Figure  10 ). Upon the initial docking of eIF1 and eIF5 (via its CTD) to their places underneath the Pand E-sites by the c/Nip1-NTD, respectively, and formation of the 48S PIC, the 40S ribosome starts scanning for AUG. The position of the arginine finger-containing eIF5-NTD at this stage remains to be determined, yet we think that it must be situated close to the GTP-binding pocket of eIF2␥.
ACKNOWLEDGEMENTS We are grateful to  Ralf Ficner ,  Jon R. Lorsch  and  H üseyin Besir  for material and advice. We are indebted to  Vojtěch Spiwok  and  Dalibor Trapl  for their help with modeling of protein complexes. At last, we are very thankful to  Olga Krýdová ,  Vladimíra Matoušková  and  Veronika Kovaničová  for technical and administrative assistance. We acknowledge the  Centre of molecular structure Core Facility at BIOCEV.
FUNDING Czech Science Foundation  Grants [ GA17-06238S  to L. S. V. ,  GA16-24309S  to P. N. ]; European Regional Development Funds  [ CZ. 1. 05/1. 1. 00/02. 0109BIOCEV ]; The  Czech Infrastructure for Integrative Structural Biology, MEYS CR  [LM2015043 CIISB for CMS BIOCEV; LTC17065];  European Commission   H2020  ( European Proteomics Infrastructure Consortium providing Access--EPIC-XS  Project [823839]. Y. I. was supported by the  Overseas Research Fellowships of JSPS  and the  Uehara Memorial Foundation. Funding for open access charge:  Czech Science Foundation  Grant [ GA17-06238S ].
A PERL program was then applied for rigorous dereplication (i. e. clustering of strictly identical sequences). The dereplicated reads were then aligned using Infernal alignment  (Cole et al. , 2009) , and clustered into operational taxonomic units (OTU) using a PERL program that groups rare reads to abundant ones, and does not count differences in homopolymer lengths with a threshold of 95% of sequence similarity. A filtering step was then carried out to check all singleton (reads detected only once and not clustered, which might be artifacts, such as PCR chimeras) based on the quality of their taxonomic assignments. Finally, in order to compare the datasets efficiently and avoid biased community comparisons, the reads retained were homogenized by random selection (soils: 9 600 and 8 420 reads for 16S and 18S rRNA gene sequences, respectively; Organic Waste Products (OWP): 5 080 and 8 420 reads for 16S and 18S rRNA gene sequences, respectively).
The retained high-quality reads were used for: (i) taxonomy-independent analyses, determining several biodiversity indexes (Shannon, richness and evenness) using the OTU dataset and defining global OTU matrices, and (ii) taxonomy-based analysis using similarity approaches against dedicated reference databases from SILVA  (Quast et al. , 2012). The raw data sets are available in the EBI database system under project accession number PRJEB31513.
Acknowledgments This work was supported by grants from the  Regional Council of Burgundy, University of Burgundy and AgroSup Dijon. All field experiments form part of the  SOERE-PRO  (network of long-term experiments dedicated to the study of impacts of organic waste product recycling) certified by  ALLENVI (Alliance Nationale de Recherche pour l'Environnement ) and integrated as a service of the '' Investment for future'' infrastructure AnaEE-France , overseen by the  French National Research Agency  ( ANR-11-INBS-0001 )''.
However, the city contains a significant amount of green infrastructure (39%), ranging from isolated trees to major urban parks. We used a high resolution (5 m x 5 m) land cover map developed with a combination of GIS data from the National Geographic Institute of France (BD TOPO (c) IGN, 2010) and a classification of Worldview II remote sensing data (Digitale Globe (c) -2011). This classification discriminates vegetated and impervious surfaces by using an object-based methodology (Definiens (c) Trimble) with an overall accuracy of 94%. Nine land cover classes were defined based on: i) their relevance for hedgehogs  (Rondinini and Doncaster, 2002; Driezen et al. , 2007; Braaker et al. , 2014)  and ii) their ease of acquisition by urban planners. The land cover classes were: building, large street, small street, highway, water, railway, impervious (all other asphalt surfaces, e.
Data collection and statistical analyses From April to July 2015, hedgehogs were found across the city by searching for them at night with a torch. Captured individuals were sexed and weighed. Females were immediately released to avoid disturbing breeding females. Thirty males were tagged with radiotransmitters (Biotrack, Dorset, U. K, 10 g) fixed to spines on their lower back, after shortening the spines. To control for inter-individual variation, each individual was successively translocated to two contrasting connectivity contexts (30 release points in HCC and 30 release points in UCC) in a random order (Fig. 1 , Fig. 2  and Supp. Table  2 ). Whatever the connectivity context, the hedgehog release points were mainly small herbaceous areas such as bases of trees, lawns. HCC and UCC were selected beyond the limits of species home range (a release point was located, on average, 2.
25 km from the capture site; diameter of urban home range: around 460 m;  Braaker et al. , 2014). In this way, all individuals had to explore a new environment. Tracking lasted for two nights in each connectivity context (Fig. 2 ). Individual locations were recorded every 30 min from 22:00 to 02:30. In total, 18 h of radio-tracking data was obtained per individual. After tracking sessions, all hedgehogs were recaptured, their transmitters were removed, and they were released at their original capture site. We studied path orientations to determine whether individual movement followed the predicted LCP in highly connecting contexts (HCC). The overall orientation (i. e. , between the first and last locations) of each observed exploratory path was compared to the orientation of the predicted LCP.
Connectivity context (HCC or UCC) was considered to be a fixed factor. It was associated in an additive model with individuals and nights (first and second night), which were considered as random factors (with random intercepts) to control for inter-individual and inter-night variation, respectively (R 3. 4. 3 (R Core Team 2017) and lme4 package  (Bates and al. , 2014) ). We used a likelihood ratio test to assess the effect of connectivity contexts on movement variables. Finally, we studied how resistance values and habitat type (land cover classes) were selected by hedgehogs according to connectivity contexts. The resistance values and habitats used by hedgehogs were compared to simulated locations. Simulations were based on null models, assuming the random use of resistance values and habitats (e.
Acknowledgements We thank all students and researchers for their participation with the data collection. We thank  Yan Ropert-Coudert ,  David Pinaud  and  Akiko Kato  ( UMR CNRS7372 CEBC ) for providing helpful comments on the movement analysis. We thank  Dr. Gail Schofield  of  Write Science Right  for the English revision of this manuscript. Financial support was provided by the  French Nord-Pas de Calais and Bretagne regions  ( Corbam  project). We thank the  French Direction Départementale des Territoires et de la Mer  and the  Conseil National de Protection de la Nature  for providing hedgehog capture authorization. The fieldwork was conducted in the  Zone Atelier Armorique , a  Long Term Ecological Research (LTER).