dataesr/scientific-paragraphs-categorization huggingface dataset:
This topology is therefore also compatible with the hypothesis of the evolution of three major lineages (TcI, TcII and the ancestor of TcIII-TcIV) having later led to the hybridization of two of them (TcII and TcIII), a hypothesis also favoured by more recent sequence analysis  [61]. In both networks in the case of TcV and in the MLEE network in the case of TcVI, links to the putative parental near-clades TcII and TcIII appear simultaneously at similar thresholds (Table  1 ), and the cluster TcV-TcVI thus builds a bridge between the two divergent near-clades TcII and TcIII. Such a striking equidistance illustrates the similar amount of genetic material shared with both parental near-clades. Rather than a chaotic lateral transfer sometimes hypothesized and often observed in bacteria  [62, 63] , this topology is in agreement with the hypothesis of an origin of hybridization through polyploidization followed by progressive but rather homogeneous diploidization or aneuploidization  [10, 16, 32, 34, 57, 64] , as well as with recent estimates of private and shared alleles among these near-clades with the microsatellite dataset used here  [35].
While it is still unknown whether TcV and TcVI have derived from the same hybridization event or from two distinct hybridization events involving the same parental nearclades as suggested on the basis of the same microsatellite dataset  [35] , joint network topologies together with the amount of shared polymorphism among TcV and TcVI fits better the first hypothesis. As a matter of fact, in the case of the second hypothesis, one would expect less proximity of the two ''daughter'' near-clades and a higher variance in their connections with parental near-clades, since not necessarily the same half of parental genome would be shared (Table  1 , Table  S1 ). Besides, TcVI connects to putative parents first through TcV, suggesting either a possible founder effect at the origin of this lineage having led to a closer relationship with TcV, or a backcross origin involving TcV and TcIII early in the history of these hybrid lineages.
Both hypotheses may explain the inconsistency in the position of two TcVI strains, including TcVI CL-Brener above mentioned, which are linked either to TcVI or TcV according to the dataset considered. Finally, the numerous incompatible splits observed with NeighborNet both for MLEE and microsatellite data (Figure  S2 ), are expected under the hypothesis of an extensive number of hybridization events  [21]. In the particular case of partially clonal pathogens, we suggest that they may however be the result of a limited number of hybridization events followed by the independent evolution of lineages within each near-clade, particularly in cases where the species considered evolve through predominant clonal evolution and therefore preserve balanced proximity with both parental genome.
Origin of the stocks & Experimental conditions The stocks characterized by MLEE have been presented in reference  [26]  and include 434 isolates delivering 262 distinct MLEE discriminated using 22 allozyme loci. They have been isolated over vast geographical ranges and spans of time, from a diversified panel of hosts, including various mammal and triatomine bug species. As underlined recently  [14] , such a sampling at highest time and space scales is the best adapted to analyze the overall population structure of a given species. The microsatellite dataset from Lewis et al. [35]  was additionally analysed for comparative and corroboration purposes, since this is the most recent and complete one presently available. We chose to analyse the dataset with 19 loci published in Lewis et al. 's  [35] study, as it allowed reconstructing a network with a larger number of stocks (81), resulting in 66 distinct MLG.
Network construction The networks characterized for MLEE (262 MLGs) and microsatellites (66 MLGs) were built separately and compared after independent, yet identical, analysis based on graph theory  [65]. A network is represented by a graph consisting of two sets: nodes and links that illustrate the pairwise relationships among nodes. In this study, nodes correspond to each of the T. cruzi MLGs in the datasets for i) MLEE; and ii) microsatellites, and links represent the genetic distance between them. The Shared Allele distance (SAD) was used as genetic distance and calculated based on MLEE data. This genetic distance measures the proportion of shared alleles  [66]. For stock pairwise comparisons, the proportion of shared alleles is estimated by: P SA ~1 2n u X u S, where the number of shared alleles S is summed up over all loci u, and n u is the number of loci Distance between nodes, D SA ~1{P SA , This measure can be used to look at population substructure.
Zebrafish map9 is expressed early during embryo development To obtain a full-length zebrafish map9 cDNA clone, we screened the IMAGE library using the sequence of the predicted ENSDART00000133937 transcript (Ensembl), which encodes a 678-aa protein, and the partial sequence BC124374 (GenBank), which was identified as zebrafish map9. The clone IMAGp998O0412053Q (IMAGE ID 5413083) was ordered and fully sequenced. This 2520-bp cDNA sequence (GenBank accession number JQ768417) includes a 2037 nucleotide-long ORF that encodes a 678-aa protein with very few amino acid changes compared with the ENSDART00000133937 sequence. We also cloned and sequenced the map9 cDNA from the zebrafish golden mutant. The predicted protein sequence was identical to the one encoded by the JQ768417 clone. Protein sequence comparisons show that the zebrafish Map9 protein is ~50% similar (~27% protein identity) to the human and mouse MAP9 proteins.
Also, the MITlike domain  19  (microtubule interacting and trafficking domain) is conserved in fish (amino acids 455-533) with most of its characteristic features. In addition, experiments using a YFP-ΔCterMap9 construct lacking the MAP domain showed a loss of fiber-like distribution of Map9 in mitotic cells, suggesting that this domain is required for zebrafish Map9 to localize to microtubules, as we previously showed in human  9  (Fig. 1D ). The nuclear localization signals (NLS) found in the human MAP domain are not conserved in fish. However another bipartite NLS was found in the same region using the pSORTII software (amino acids 570-587 and 598-614).
Comparison of the qPCR curves (Cp) between morphants and controls indicated that β-actin expression did not change between morphants and controls (not shown). Also, we checked that the apparent increase of gene expression was not due to the repression of miR-430 expression in morphants. Indeed, miR-430 is expressed at the onset of zygotic transcription and promotes clearance of maternal mRNAs. 5] [46]  By screening the Targetscan database (http://www. targetscan. org/fish_62/), we found that only plk4 is a potential miR-430 target, and we show here that it displays a significant increase in morphants (~×1. 62). However, eomesa (×3. 9) and aurka (×2. 2) are not miR-430 targets, even considering weak sites (Targetscan). Furthermore, rescue experiments of mutants defective for miR-430 show that the expression of these genes is not modified, indicating that they are most likely not regulated by the expression of other miR-430dependent genes (GEO profiles, GDS1771 from Giraldez lab, http://www.
Cloning and sequencing zebrafish map9 The full-length zebrafish map9 ORF (clone IMAGp998O0412053Q) was ordered from ImaGenes GmbH, sequenced, and the sequence deposited in the GenBank database under the accession number JQ768417. A fragment with the same sequence was amplified by RT-PCR using RNA from the golden zebrafish line used in the laboratory. The map9 ORF and a map9ΔCter cDNA (lacking the MAP domain corresponding to amino acids 451-678) were cloned in phase with YFP in the pCS2+ vector. mRNA was in vitro transcribed using the mMessage mMachine Kit (Ambion, Life Technologies Corporation).
Acknowledgments The authors are grateful to  Drs A Lander ,  A Muto ,  A Bruce ,  M Halpern ,  T Lepage , and  M Poulain  for providing the sox17, sox32, oep, and cyc cDNA clones, to  C Gonzalez ,  Drs M N'Guyen  and  K Kissa  for advice and help in manipulating zebrafish embryos, to  Dr Chris Jopling  for help in map9 ISH experiments, and to  Dr A Renucci  for helpful discussions. We also would like to thank  Montpellier Rio Imaging  facility for microscopy support. We thank  Drs E Andermarcher  and  R Kiernan  for the careful reading of the manuscript and the English language corrections. This work was supported by the  CNRS  and grants from  Association de la Recherche contre le Cancer  ( ARC N°4027 ),  Ligue Nationale contre le Cancer  ( Comité Languedoc-Roussillon 2009 ), and the  European Community 's  Seventh Framework Programme  [ FP7-PEOPLE-2011-ITN]  under grant agreement no.
Both approaches have the advantage of giving an exhaustive list of potential paratope residues at the expense of a large number of false positive positions, since residues in direct contact within the antibody-antigen interface do not necessarily participate to the binding free energy  [21]. In this work, we focused on a quantitative and statistical analysis of side-chain contributions in a large set of antibody-antigen complexes. We first established a database of 227 non-redundant structures and we calculated for each residue in the binding interface its contribution to the antibody-antigen interaction free energy using a computational alanine scanning procedure. To compare the results between structures, we numbered all antibodies using a previously established numbering scheme that minimizes the average deviation from the averaged structure of the aligned domains  [17].
Aligned database of antibody side-chain binding free energies to antigens For the purpose of this study, we focused on protein and peptide antigens since haptens are known to have a different mode of binding that would require an independent analysis to derive general characteristic rules. We first recovered 506 structures of antibody-antigen complexes from the IMGT database (Supplementary Table  1 ), removed mis-annotated structures, and then filtered for sequence redundancy. We independently extracted VH and VL domains from the structures together with the interacting molecules of the antigen. Finally, we renamed antibody and antigen chains using a common naming scheme and renumbered variable domains using AHo numbering  [17]. The choice of AHo numbering was dictated by its design, based on the spatial alignment of known three-dimensional structures of immunoglobulin domains.
The final database contains 227 VH and 206 VL non-redundant renumbered structures and their corresponding interacting antigen molecules (Supplementary Table  2 ), representing 206 Fv and 21 VHH. To evaluate the contribution of each antibody side chain present in the binding interface to the free energy of binding, we performed computational alanine scanning experiments using the FoldX program  [22]. The contribution of each residue side chain to the antibody-antigen binding free energy for the 433 complexes is given in Supplementary File complexes. xlsm.
Positions were ranked according to their average Δ ΔG, and the percentage of the total ΔΔG as a function of the number of these fixed residues is shown in Fig. 3. This showed, for example, that 80% of the total ΔΔG localized among 30 positions, 27 in the CDRs and 3 in FRs, for more than half of the structures, representing only a fraction of the total CDR positions (less than half of the CDR positions according to Kabat's definition). Furthermore, several of these positions were not always occupied and their number could be easily reduced for most antibodies. The results presented in Figs. 2  and 3  were obtained for the whole database that contains mainly κ light chains. If analyzed independently, the best positions were slightly dependent on the light chain class, λ light chains having a stronger propensity than κ light chains to bind the antigen using residue L109 and κ light chains having frequently longer L1 loops making contacts with residues L32-L36 (Supplementary Fig.
5 ). Among the 7 most frequent residues of these two groups, representing 75% of the used residues, only two residues were in common, G and S. Tyrosine, the most frequent residue at hot spot positions with a 24% frequency, was only present with a 2. 7% frequency at non-paratope surface positions. This general trend was found at most of the positions of the paratope but H39, L40, and L137 showed a somewhat different distribution, the two former being strongly enriched in Tyr and the latter in Pro and Leu (Supplementary Fig. 4 ). We took advantage of the quantitative nature of our database to also calculate the frequency of the positive contributions of each residue of the paratope to the free energy of binding (black bars in Fig. 5 ). The ranking of the residues was clearly different and two classes could be identified: residues that make frequent interactions with the antigen, essentially aromatics, and neutral residues that were frequently found in the paratope but did not play any role in the binding free energy, as G and S.
Universality of the characteristic rules, a structure case study Most of the antibodies present in the current Protein Data Bank (PDB) and analyzed above have been obtained from immunized animals and antibody phage libraries made from human donors. In both cases, the diversity of the paratope is natural and originates from V gene diversity, V(D)J recombination and somatic hypermutation events. We demonstrated that these antibodies interact with their antigen using a very limited number of residues, both in position and in nature. Such a restricted diversity could originate from constraints imposed either by the immune system or by the structure of the antibody paratope itself. To address this question, we isolated a single chain Fv (scFv) from a synthetic strongly biased antibody library by panning on Gankyrin antigen.
6a ). Computational alanine scanning analysis further specified that 12 antibody residues located in 5 CDRs and 1 FR were implicated in the stability of the complex (Fig. 6b ). Eleven of these residues were located within the 30 best hot spot positions determined above from the antibody-antigen database (Table  2  and Fig. 3 ) and their binding free energy profiles fitted well with the average values obtained in the database (filled triangles in Fig. 1 ). The missed residue is N167 (L39) with a ΔΔG of 0. 9 kcal/mol. This position has a low average energy in the database due to its low usage in κ light chains. However, if we only consider Fv domains with a light chain of the same λ class than the scFv F5, L39 ranks 18th with an average ΔΔG of 0. 6 kcal/mol (Supplementary Fig. 2 ). Furthermore, the CDR contributions to the GkF5 complex were very similar to the averaged values obtained from the database (filled triangle in Fig.
4a ). The binding free energy of the two diversified CDRs (H3 and L3) added up to 32% of the total ΔΔG, whereas the three interacting but fixed CDRs (H1, H2, and L1) and the FRs contributed 68% to the total ΔΔG. This higher contribution of the fixed part of the paratope in the binding free energy is also found in the number of contacts since the two CDR3s and the non-diversified parts of the scFv (other CDRs and FRs) formed 4 and 8 contacts with the antigen, respectively. Finally, 83% of the total binding free energy is due to only 8 residues as in natural antibodies (Fig. 1c ), 6 of them being Tyr. To confirm this result using a different approach, we determined antibody contacts using the Crystallography and NMR System software suite that uses geometric rules to identify interactions (Table  2 )  [36, 37].
Ten of the twelve residues identified using computational alanine scanning were also present in this analysis, the two missed residues being Y34 and Y185. In addition, among the 19 identified residues, 14 are in the list of the 30 most contributing positions presented in Fig. 3. Altogether, this demonstrated using two approaches that, despite its synthetic and biased origin, scFv F5 paratope conforms well to the general rules defined from our database analysis.
Universality of the characteristic rules, prediction of important paratope residues We next tested if our database can be used to predict important paratope residues in the absence of structural and experimental data. As a reference, we used the experimental data generated by Muller et al. [38]  on a humanized anti-VEGF Fab. In this publication, authors mutated one by one the 68 CDR residues to alanine and measured the relative affinity of the resulting mutant Fab. We grouped the CDR residues in three categories: 8 major contributors that affected the affinity by more than a 150-fold factor, 11 residues decreasing the affinity by a more moderate factor (between 5-and 66-fold), and 49 neutral or weak contributors. These residues are displayed with dark green, light green, and white backgrounds, respectively, in the line Muller1998 in Fig.
7. If we compare the location of these residues with the positions identified as the main hot spots in our study (Fig. 2 ), 7 of the 8 Muller's major contributors have an average binding free energy higher than 0. 8 kcal/mol. Since only 12 hot spot positions have such a high average binding free energy, this means that using our data and targeting mutations to these 12 residues would have identified 7 (88%) out of 8 of the Fab major paratope residues. If we decrease the binding free energy cutoff value to 0. 4 kcal/mol, our database identified 27 potential interaction sites in the Fab paratope (red and pink backgrounds in Fig. 7 ). Among these 27 sites, 15 positions were also identified (moderate or major contributors) in Muller's data. In that case, experimental alanine scanning of only 27 positions would have identified 15 (79%) out of 19 of the paratope residues.
Discussion The aim of this study was to describe quantitatively at the molecular level the mode of binding of antibody molecules to their cognate antigens, identify the critical residues of the paratope both in position and in nature, and derive general rules to help in antibody engineering and library design. Large-scale alanine scanning analysis on an aligned non-redundant structural database of antibodyantigen complexes enabled the first quantitative description of the role of all the antibody residues in the free energy of binding. These data provide a quantitative description of paratopes from which characteristic rules can be derived. (1) Eighty percent of the binding energy is due to only a few significant residues, typically 8 (between 4 and 13). (2) These residues localize among 25-30 fixed hot spot positions that contribute to 75-80% of the energy in more than half of the complexes.
VL, VH, ANK4, ANK5, and ANK6 repeats are colored magenta, cyan, yellow, orange, and brown, respectively. Oxygen and nitrogen atoms are colored red and blue, respectively. Red and black dotted lines indicate ionic and hydrogen bonds, respectively. mutagenesis and the effects precisely quantified. With the use of different models, it has been shown that the interaction involves between 5 and 10 main residue contributors  [3, 21]. Even in the case of an antibody with a very high affinity of 50 pM for its antigen, only 4-5 residues of the light chain contributed significantly to the binding energy  [39] , in good agreement with the values obtained from our database (Fig. 1b ). Some known antibodies deviate however from this general rule as the anti-idiotypic antibody E5. 2 in which most of the contact residues play a significant role in the binding free energy  [3, 40].
87 kcal/mol). In addition, we used surface residues of antibody molecules as a reference whereas Collis used loop a Rank of the residue as defined in Fig. 3. b Nature of the residue in scFv F5. Numbering scheme is the sequential numbering used in the PDB ID: 4NIK structure. Equivalence between the different numbering schemes is given in Supplementary Table  3. c ΔΔG of the residue in the scFv F5-Gankyrin complex, determined by computational alanine scanning, in kilocalories per mole. Only ΔΔG N 0. 8 kcal/mol are shown. d Type of contact. e Interacting residue in Gankyrin determined using contact. inp command in CNS. ANKn is the ankyrin repeat to which the following residue belongs. f Position occupied in less than 40% of the antibody sequences and excluded from the ranking in Fig. 3. The rank given is that of the residue with the closest ΔΔG and present in more than 40% of the sequences.
regions from the CATH database. Despite these differences, we share the main observation that Tyr and Trp are strongly enriched in the paratope of antibodies. However, this enrichment is larger in our case presumably because of the prominent role of these residues in the free energy of binding. There are also subtle discordances that may be due to the different sets of residues analyzed. We noted an enrichment of the two other aromatic residues (F and H), Asp, and Asn whereas Collis did not see any significant enrichment of these amino acids in the paratope. As for Tyr and Trp, this discrepancy could be attributed to the important contribution to the free binding energy of these side chains in our analysis. The discontinuous nature of the paratope presented here is different from the classical CDR definition.
Indeed, most of the main contributing residues are part of Kabat's hypervariable regions but the reverse is not true, since only 25% of these regions contribute to more than 70% of the binding free energy. However, many of the non-interacting residues play an important structural role in avoiding steric clashes and in shaping the paratope  [9, 13]. The importance of these residues in the antigen binding affinity has been demonstrated in several studies. However, they did not show up in our analysis for two main reasons. First, the used FoldX script restrained the analysis to the residues present in the interface between the antibody and the antigen. Second, the importance of these residues is highly variable between antibody molecules and we averaged the energy on the whole database of 227 structures.
This has been one of the main arguments in favor of synthetic and semisynthetic antibody libraries over natural ones to derive antibodies against self-proteins. However, there is no experimental evidence showing that such synthetic libraries are indeed more capable of generating such binders than natural repertoires, and several anti-self antibodies have been successfully selected from libraries made from human donors. We directly tested the possibility of escaping the natural antibody mode of binding by selecting an scFv from a synthetic and strongly biased library and solving the structure of the complex. Contrary to usual expectations, most of the binding free energy (68%) is due to non-diversified positions. The antibody almost perfectly fulfilled the rules defined above since most of the interacting residues were located at the hot spot positions identified in this study, and the CDR contributions were similar to the averaged values obtained from the database.
In a recent study, Persson et al. isolated a series of 10 clones sharing the same H3 sequence despite interacting with different antigens  [51]. As in our case, they showed that, in 80% of these clones, several conserved invariant H3 side chains interacted with the antigen. They also isolated two antibodies in which the H3 loop was not involved in the interaction with the antigen, in good agreement with the frequency of 10% of such antibodies in our database (Fig. 4c ). The main conclusion of their study is that antibody libraries can be designed to give a dominant role to the L3 loop. Although this remains true but taken individually, such antibodies exist in nature. Indeed, as shown in Fig. 4a , in some antibody-antigen complexes, the L3 loop contributes to more than 40% of the binding energy.