Accurate RNA folding is essential for virus replication. Rotaviruses are viruses infecting humans and animals. Rotavirus genome comprises 11 distinct RNAs, and successful replication requires the incorporation of all 11 RNAs into a virion. The RNA chaperone NSP2 binds viral transcripts, regulating their interactions with each other. NSP2 must release RNAs after they base pair prior to their packaging. Using single-molecule fluorescence tools, we dissected the individual steps of the RNA chaperone activity of NSP2. Structural proteomics and cryo-EM studies of the NSP2–RNA complex revealed that NSP2 regulates RNA unfolding and the release of the RNA using its charged C-terminal region. Some aspects of the viral RNA chaperone regulation mirror the conserved autoregulation mechanisms employed by bacterial RNA chaperones. Rotavirus genomes are distributed between 11 distinct RNA molecules, all of which must be selectively copackaged during virus assembly. This likely occurs through sequence-specific RNA interactions facilitated by the RNA chaperone NSP2. Here, we report that NSP2 autoregulates its chaperone activity through its C-terminal region (CTR) that promotes RNA–RNA interactions by limiting its helix-unwinding activity. Unexpectedly, structural proteomics data revealed that the CTR does not directly interact with RNA, while accelerating RNA release from NSP2. Cryo–electron microscopy reconstructions of an NSP2–RNA complex reveal a highly conserved acidic patch on the CTR, which is poised toward the bound RNA. Virus replication was abrogated by charge-disrupting mutations within the acidic patch but completely restored by charge-preserving mutations. Mechanistic similarities between NSP2 and the unrelated bacterial RNA chaperone Hfq suggest that accelerating RNA dissociation while promoting intermolecular RNA interactions may be a widespread strategy of RNA chaperone recycling. Selective incorporation of viral genomes into nascent virions is essential for virus replication. This process is highly challenging for RNA viruses with multisegmented genomes (including rotaviruses [RV]) since they must coordinate the selection and assembly of multiple distinct RNAs , 2. DespNSP2 is a multivalent, nonspecific RNA chaperone with high nanomolar affinity for single-stranded (ss) RNA , 7, 8. TPrevious mutational studies of NSP2 have been hindered by the lack of a robust reverse-genetics system –15. As s16Here, we have used a single-molecule fluorescence spectroscopy approach to decouple the RNA annealing and RNA unwinding activities of full-length NSP2 and NSP2-∆C. While NSP2-∆C exhibits a reduced capacity to promote RNA–RNA interactions, it possesses enhanced RNA unwinding activity. To resolve these paradoxical observations, we determined cryogenic electron microscopy (cryo-EM) structures of NSP2 and an NSP2–ribonucleoprotein (RNP) complex at global resolutions of 3.9 Å and 3.1 Å, respectively. In the RNP structure, the RNA density localized to surface-exposed, positively charged grooves, with no evidence of the CTR interacting with the RNA. To directly map the RNA-binding surfaces of NSP2, we employed complementary structural proteomics tools that revealed that all RNA–protein contacts required for nonspecific, high-affinity RNA recognition were outside the CTR.Furthermore, our data show that while the CTR does not directly interact with RNA, it contains a conserved acidic patch that is poised toward bound RNA. Mechanistically, we demonstrate that that the CTR promotes RNA release, indicating that the CTR is required for preventing the formation of a highly stable, kinetically trapped RNP complex that is not conducive to RNA–RNA annealing. To validate our model, we showed that alanine substitutions of conserved acidic residues in the CTR  abrogated RV replication, while charge-preserving mutations had no detrimental effect on virus rescue in reverse genetics experiments. Our multifaceted approach provides a mechanistic basis for RNA release from high-affinity capture by NSP2, which is required for RNA annealing, chaperone dissociation, and ultimately efficient selection and packaging of a complete RV genome.core . This NSP2-∆C construct has been previously characterized by others (The CTR of NSP2 consists of a flexible linker (residues 295 through 304) that tethers an α-helix  to NSP2y others , 20 . As expected, our cryo-EM-derived model of NSP2 revealed an octameric assembly, which was highly similar to previously solved crystal structures of NSP2  . Due to intrinsic flexibility, the linker region was poorly resolved . We also determined a 3D reconstruction of NSP2-∆C using negative-stain EM which confirmed that NSP2-∆C remains octameric, demonstrating that the CTR does not play a role in the assembly of NSP2 into functional octamers .To visualize the CTR conformation in solution, we determined a cryo-EM three-dimensional (3D) reconstruction of full-length NSP2 (henceforth referred to as NSP2) at a global resolution of 3.9 Å (1.124 Å) , 10, 20.Next, we investigated the role of the CTR in the RNA annealing activity of NSP2 using a fluorescence cross-correlation based RNA–RNA interaction assay . We chosSI Appendix, Fig. S3). In contrast, addition of NSP2 to an equimolar mixture of these two RNAs produced a high CCF amplitude, indicative of intermolecular RNA duplex formation .As the ability of NSP2 to unfold and remodel RNA structures is a prerequisite for its RNA-annealing activity , we nextEFRET = ∼0.95) (EFRET = ∼0.95) and unfolded (EFRET = ∼0.05) RNA states. No intermediate FRET populations  were observed, in agreement with previous observations of NSP2-mediated RNA-unwinding  D. Incubanwinding .EFRET = ∼0.05)  D. Furthe= ∼0.05) E. These = ∼0.05) D, it is = ∼0.05) B.SI Appendix, Fig. S5).To deduce whether the CTR directly interacts with RNA, we used a combination of structural proteomics techniques . We perfSI Appendix, Fig. S5). However, we again did not observe any RNA-linked peptides corresponding to the CTR.We further corroborated the location of RNA-binding sites on NSP2 using UV crosslinking with RBDmap , 23. In Collectively, these results reinforce the notion that the CTR is involved in the RNA chaperone activities of NSP2. Our data indicates that although the CTR does not directly interact with RNA, it is a determinant of both the RNA-unwinding and -annealing activities of NSP2.SI Appendix, Figs. S1 and S2). While the cryo-EM density corresponding to NSP2 was well resolved, there was no density that could be attributed to the RNA in the high-resolution postprocessed NSP2–RNP map . Due to the reasons outlined above, the diffuse nature of this density prevented us from modeling the ssRNA into the structure. However, we were able to visualize residue-specific NSP2–RNA contacts . The importance of these residues for RNA capture by NSP2 is strongly supported by previous biochemical studies that identified a number of solvent-exposed lysine and arginine residues  that span the periphery of the NSP2 octamer  and contribute to RNA binding . In addition, our EM reconstruction has revealed a number of other residues  that likely contribute to RNA binding, also identified by HDX and RBDmap . These residues may also participate in nonspecific RNA contacts via electrostatic interactions, hydrogen bonding, and π-π stacking, consistent with a significant nonelectrostatic contribution to the overall free energy of RNA binding to NSP2.Furthermore, the identified residues are localized to an unstructured loop within the RNA-binding groove, allowing promiscuous and flexible accommodation of alternative RNA structures with near-identical affinities by NSP2, consistent with previous reports , 28. TogKon) remain largely consistent across a range of concentrations of both NSP2 and NSP2-∆C (NSP2-∆C binds (1.5 ± 0.4)-fold faster than NSP2) . However, NSP2-∆C exhibited a (3.2 ± 0.3)-fold slower dissociation rate than NSP2, suggesting a role for the CTR in the displacement of bound RNA .Within the cryo-EM density map, the CTRs are poised below the RNA, while making limited contacts with the observed RNA density D. This sSI Appendix, Fig. S6). The acidic patch of the CTR is presented directly underneath the density attributed to bound RNA . We determined IC50 values of 208 ± 11 nM and 890 ± 160 nM for NSP2 and NSP2-∆C, respectively, confirming that NSP2-∆C undergoes approximately fourfold reduced RNA exchange, consistent with its approximately threefold slower rate of dissociation from RNA . No dissociation of RNA from NSP2-∆C was observed in the presence of 20-fold molar excess of the CTR peptide over NSP2-∆C . The distances (∼10 to 30 Å) between acidic residues within the CTR and the RNA-interacting residues are incongruent with a direct competition model. While R68 was demonstrated to directly interact with RNA . This directly demonstrates the essential role of the CTR acidic patch in RV replication.To validate our findings, we employed a reverse-genetics approach to rescue recombinant RVs with point mutations within the CTR. We assessed the effects of amino acid substitutions within the CTR on viral replication by attempting recombinant virus rescue A, suggesDuring the course of infection, RV assembly occurs in large, cytoplasmic viral replication factories, assembled from NSP2 and NSP5 and containing viral RNAs , 29–31. 29Since all our attempts to rescue NSP2-AAA mutants were unsuccessful, we also examined whether these mutations disrupted the formation of the NSP2/NSP5-rich viroplasms. As described in Long RNAs adopt an ensemble of diverse stable structures that limit spontaneous RNA–RNA interactions through the sequestration of sequences required for intermolecular base pairing , 33–36. 3337In order to function as an RNA chaperone, NSP2 must capture, unwind, anneal, and release complementary RNA sequences , 40, 41.Escherichia coli StpA and HIV-1 NC . Moreover, CTR removal results in a ∼2.4-fold increase in the RNA-unwinding activity of NSP2-∆C as well as an approximately twofold decrease in its RNA-annealing activity. Additionally, smFRET data reveal that binding to NSP2-∆C energetically favors low-FRET (unfolded) RNA conformations, resulting in remodelling of structured RNAs , suggesting a conserved role of the CTR in NSP2 function. This model is further supported by our observation that removal of the unstructured region downstream of the ampholytic CTR . Indeed, this partial truncation has been previously shown to support viral replication . Nevertheless, the CTR modulates the kinetics and thermodynamics of NSP2–RNP complex formation to accomplish RNA chaperone recycling. We propose that this may represent a conserved mechanistic feature of multimeric RNA chaperones that capture RNA with high affinity and require auto-regulation to assist RNA dissociation in order to promote efficient matchmaking.The described principle of CTR-assisted RNA dissociation from NSP2 is strikingly similar to that of the bacterial RNA chaperone protein Hfq –48. Hfq  binding , 51. It SI Appendix, Table S2. RNA sequences S5, S6, and S11 were produced and labeled using previously described in vitro transcription and labeling protocols  stem-loop RNA was purchased from IBA Life Sciences.NSP2 and NSP2-∆C (RVA strains SA11 and RF) were expressed and purified as described previously . RNAs usrotocols . UnstrucFor negative-stain grid preparation, 4 µL of sample (at various concentrations ranging from 100 to 500 nM) was incubated on glow-discharged (using PELCO easiGlow), carbon-coated Formvar 300-mesh Cu grid (Agar Scientific) for 90 s prior to blotting, stained twice with 20 µL 2% uranyl acetate , and allowed to dry. Micrographs were collected on a FEI Tecnai 12 transmission electron microscope operated at 120 kV and equipped with a Gatan UltraScan 4000 charge-coupled device camera operated at a nominal magnification of 30,000 × (giving a 3.74 Å/pixel sampling on the object level). From 23 micrograph images taken with a nominal defocus of −3 µm, 14,740 particles were picked using template-based autopicking within Relion 3. Multiple rounds of two-dimensional and 3D classification resulted in the selection of a subset of 2,864 particles. These particles were used to determine an ∼22 Å resolution NSP2-∆C reconstruction with D4 symmetry applied.SI Appendix, SI Methods.NSP2 RNP complex was assembled by incubating NSP2 with 40mer RNA. A range of different NSP2: RNA ratios were tested, but ratios above 1 RNA: 2 NSP2 monomer resulted in immediate complex precipitation. A 1 RNA: 2 NSP2 molar ratio gives a stoichiometry of 1 RNA per binding site on NSP2, thereby ensuring saturation of NSP2 with RNA. To establish optimal cryo-EM grid conditions, grids with a range of NSP2 RNP concentrations were made. RNP complex was incubated at room temperature (25 °C) for 30 min prior to application to Quantifoil 1.2/1.3 holey carbon grids and vitrification. At 25 μM RNP complex (∼1 mg/mL), no particles were present in the ice; this was probably due to preferentially resting on the carbon instead of distributing evenly throughout the ice. This was ameliorated by a twofold increase in the RNP concentration (while maintaining a 1 RNA: 2 NSP2 monomer molar ratio), producing a suitable particle distribution within the ice. Further details on sample preparation, data acquisition and analysis are given in An atomic model of NSP2 published by Jayaram et al.  PDB 1L9 and subjSI Appendix, Table S4). Sequences of avian strains and of rearranged RNA segments of mammalian strains were excluded from the analysis. Protein sequence conservation and multiple-sequence alignment (MSA) was performed using the online ConSurf . All experiments were performed in SPR buffer . RNAs were immobilized on a streptavidin (SA) sensor chip  with an analyte Rmax of ∼20 resonance units (RU). Analyte measurements were performed at 25 °C and a flow rate of 40 µL/min. The chip surface was regenerated between protein injections with a 40 µL 0.05% sodium dodecyl sulfate (SDS) injection. Data were analyzed using BIAevaluation 3.1 software . The kinetic parameters were derived assuming a binding stoichiometry of 1:1.A Biacore 3000 was used to analyze the binding kinetics of NSP2 and NSP2-∆C to 5′biotinylated-10mer RNA  in binding buffer . Fluorescence anisotropy measurements were performed in the presence of various concentrations of unlabeled 20mer RNA in low-volume Greiner 384-well plates. Data were recorded at 25 °C in a PHERAstar Plus multidetection plate reader (BMG Labtech) equipped with a fluorescence polarization optical module . The data were normalized and binding curves were fitted in Origin 9.0 using a Hill binding curve resulting in R2 values of 0.997 and 0.991 for NSP2 and NSP2-∆C respectively.A total 250 nM NSP2 and NSP2-∆C (RF) was preincubated with 10 nM 20mer AlexaFluor488-labeled RNA . A total 25 nM AlexaFluor488-labeled RNA was incubated with 20-fold excess NSP2-∆C . After 30 min at room temperature (∼25 °C), the CTR peptide was added in 20-fold excess of NSP2-∆C . To investigate direct CTR–RNA interactions, 25 nM 10mer RNA was also coincubated with 10 µM CTR peptide. Fluorescence anisotropy measurements of RNA alone, RNA-NSP2-∆C, RNA:NSP2-∆C:CTR, and RNA:CTR were performed in triplicate as described above for RNA competition assays.SI Appendix, Table S2). A total 30 µl of protein-containing solution was added to 135 μL deuterated buffer  and incubated at 4 °C for 0.5, 2, 30, or 120 min. After labeling, HDX was quenched by adding 100 μL of quench buffer  to 50 μL of the labeling reaction. A total 50 μL of the quenched sample was passed through immobilized pepsin and aspergillopepsin columns  connected in series (20 °C), and the peptides were trapped on a VanGuard Precolumn  for 3 min. The peptides were separated using a C18 column  by gradient elution of 0 to 40% (vol/vol) acetonitrile (0.1% vol/vol formic acid) in H2O (0.3% vol/vol formic acid) over 7 min at 40 μL/min−1. Peptides were detected using a Synapt G2Si mass spectrometer . The mass spectrometer was operated in mobility-assisted data-independent analysis with the dynamic range extension enabled (HDMSE) mode were used to separate peptides prior to collision-induced dissociation (CID) fragmentation in the transfer cell. CID data were used for peptide identification, and uptake quantification was performed at the peptide level (as CID results in deuterium scrambling). Data were analyzed using PLGS (version 3.0.2) and DynamX  coupled to an Acquity M-Class liquid chromatography and HDX manager  was used for all HDX-MS experiments. Differential HDX-MS of NSP2 was performed using NSP2 (10 µM) or preincubated NSP2-RNP complexes –IMS–MS spectra were acquired with an Orbitrap UHMR mass spectrometer (Thermo Fisher Scientific), operated as previously described  in a UVP CL-1000 UV Crosslinker (Scientifix). Crosslinked RNP complexes were digested by LysC  (500 ng per crosslinked RNP sample) overnight at room temperature. Enrichment and identification of cross-linked peptides were performed using the in vitro adaptation of the RBDmap protocol, as described in ref. A total 10 µM NSP2 was incubated with 5′-AMA104 cell line  and its derivatives stably expressing NSP5-EGFP (MA-NSP5-EGFP) and NSP5 (MA-NSP5)  were culSI Appendix, SI Methods.Recombinant simian RV strain SA11 NSP2 mutants were rescued using plasmids encoding the wild-type SA11 (G3P[2]) virus with modifications, as previously described . FurtherSI Appendix, SI Methods.MA104-NSP5-EGFP cells were seeded into Ibidi 8-well µ-slides and allowed to reach 90% confluency prior to the infection. Confluent cell monolayers were infected with trypsin-activated RV stocks, as described in ref. SI Appendix, SI Methods) using Lipofectamine 3000 (Sigma-Aldrich), following the manufacturer’s instructions. The cells were maintained in DMEM supplemented with 10% FBS for 3 d, after which the cells were fixed with 4% (vol/vol) paraformaldehyde in PBS for 15 min at room temperature then rinsed twice with PBS and incubated for 10 min in a 0.1 M solution of glycine in PBS pH 7.4. Samples were then permeabilized with 0.2% Triton X-100  for 10 min, rinsed with PBS, and incubated in PBS containing 3% molecular biology grade bovine serum albumin (BSA) (Sigma-Aldrich) for 1 h at room temperature. Cells were then incubated with guinea pig NSP5 antibodies (MA-NSP5 cells were seeded on glass coverslips (IBIDI) 24 h prior to transfection with pcDNA3-NSP2-AAA construct (tibodies  diluted Widefield imaging was carried out on an ONI Nanoimager S with an Olympus 100× super apochromatic oil immersion objective (NA1.4). Dye excitation was performed with an ONI laser illumination system at the wavelengths 405 nm (DAPI), 488 nm (eGFP), 561 nm (AlexaFluor550), and 640 nm (Atto647N), with laser intensities set to 10% (405 nm), 2% (488 nm), 5% (561 nm), and 7% (641 nm). Fluorescent signals were recorded with a sCMOS camera with a pixel size of 0.117 µm. Images were acquired over a field of view of the camera chip resulting in a total imaging region of 50 µm × 80 µm. Exposure times were adjusted accordingly to the signal intensity to avoid pixel saturation. Typical exposure times were 30 ms for all channels. Images were recorded consecutively for each channel, from the lowest to the highest energy excitation wavelength. All images were processed in ImageJ , and figSupplementary File


Flaviviridae members exploiting direct interactions of their RNA genomes with host miRNAs. For hepatitis C virus (HCV), binding of liver-abundant miR-122 stabilizes the viral RNA and regulates viral translation. Here, we investigate the structural basis for these activities, taking into consideration that miRNAs function in complex with Argonaute (Ago) proteins. The crystal structure of the Ago2:miR-122:HCV complex reveals a structured RNA motif that traps Ago2 on the viral RNA, masking its 5’ end from enzymatic attack. The trapped Ago2 can recruit host factor PCBP2, implicated in viral translation, while binding of a second Ago2:miR-122 competes with PCBP2, creating a potential molecular switch for translational control. Combined results reveal a viral RNA structure that modulates Ago2:miR-122 dynamics and repurposes host proteins to generate a functional analog of the mRNA cap-binding complex.microRNAs (miRNAs) form regulatory networks in metazoans. Viruses engage miRNA networks in numerous ways, with  The RNA genome of the Hepatitis C Virus binds to the liver-specific miR122. Here the authors report the crystal structure of the Ago2:miR122:HCV complex showing that the viral RNA’s structural element traps the Ago2:miR-122 complex on the 5’ end of the viral genome to protect it from degradation. Misregulation in miRNA activity is connected to developmental defects1 and cancer2.Mammalian Argonaute (Ago) proteins use ~22 nt short RNA guides to target partially complementary sites generally found in the 3’ UTRs of mRNAs, recruiting additional factors that promote post-transcriptional gene silencing through translational repression and induction of mRNA decay3, the relentless tides of viral evolution have carved out a multitude of ways in which host miRNA pathways are exploited to benefit viral fitness4. Pro-viral adjustment of the host expressome is achieved by viral-encoded miRNAs (e.g. KSHV5) or modulation of host miRNA activity (e.g. HPV6), including retargeting of host miRNA complexes to cellular mRNAs7. The most direct, yet poorly understood, mechanisms involve binding of host miRNAs to genomes of single-stranded RNA viruses of the Flaviviridae family: HCV (miR-122)9, BVDV (miR-17)10, and ZIKV (miR-21)11 exploit host miRNAs to increase viral fitness through direct interactions with regions within their RNA genomes rich with secondary structure.While the animal miRNA machinery appears to derive from a primal antiviral RNA silencing mechanism9. HCV is a positive-strand single-stranded RNA virus that affects approximately 71 million people worldwide. In most patients the acute infection progresses to chronic liver infection, negatively affecting liver function and increasing the risk for developing hepatocellular carcinoma12. The virus’ decade-long persistence in the host is maintained through a delicate balance of genome replication, translation, RNA decay, and maintenance of viral subcellular compartments13.The interaction between the HCV genome and the liver-abundant miRNA miR-122 was the first direct viral RNA-host miRNA interaction to be discovered and is the best-studied to date9, and involves the host protein Argonaute2 (Ago2)14 17. The binding of miR-122 to the HCV genome leads to de-repression of host mRNAs normally targeted by miR-122, suggesting a causal link between chronic HCV infection and increased risk of developing liver cancer18. The diminutive 5’-terminal tail of the HCV genome thus acts as a regulatory epicentre, involved in viral RNA stability and folding, replication, translational control, and perturbation of host gene expression, that ultimately determines viral pathogenesis.Stabilization of HCV RNA was reported through two tandem miR-122 complementary sites at the very 5’ end of the viral genome)14 Fig. . Additio19, the mechanisms promoting stabilization and the relationship between the two tandem sites, including their roles during the viral life-cycle, are poorly understood. Moreover, it is unclear how HCV RNA structures, not observed in characterized cellular miRNA-binding sites, might influence interactions with Ago2.While the field agrees on the stabilizing effect of Ago2:miR-122 binding to the HCV 5’-terminus, which has been identified as a therapeutic target15. Our data indicate a more nuanced model in which one copy of Ago2:miR-122 remains anchored on the viral RNA 5’ end while a second copy competes with PCBP2 for the adjacent miR-122 binding site. Altogether, these data reveal the molecular architecture of a structured miRNA recognition site, how Ago2:miR-122 binding protects the HCV genomic RNA, and a potential molecular switch for controlling the balance between viral protein translation and RNA replication.Here, we determine the structural basis for recruitment of miR-122 to the HCV 5’-terminus, and biochemically examine how recruitment shapes interactions with other host factors. The 3.0 Å resolution crystal structure of the Ago2:miR-122:HCV RNA complex reveals a structured motif in the viral RNA that exploits a previously unrecognized nucleotide-binding pocket within a conserved region of Ago2. The motif locks Ago2 in a widened conformation, which counteracts the propensity of Ago2 for lateral diffusion on target RNAs, leading to robust protection of the viral RNA 5’ end from enzymatic attack. We also examined interactions between the HCV 5’ terminus, Ago2, and host protein PCBP2, which was suggested to bind HCV RNA in competition with miR-122 and promote viral translation9 through protection from cellular exoribonucleases21, primarily exoribonuclease 1 (XRN1)22. In vitro biochemistry corroborated a role for miR-122 alone23, but it is widely recognized that Ago proteins change the binding properties of miRNAs27. We, therefore, tested whether the purified Ago2:miR-122 complex is able to protect the HCV 5’ UTR from recombinant XRN1. In a control reaction, XRN1 rapidly degraded a purified RNA representing the HCV 5’ UTR . Removal of SL1 led to an ~4-fold reduced half-life of the RNA in our nuclease assay compared WT  that physically separates predicted base-paired regions between miR-122 and the HCV RNA  viral RNA, and viral RNAs with mutations that disrupt miR-122 binding either Site-1 (MUT1) or Site-2 (MUT2). Viral RNAs were electroporated into Huh-7.5.1 cells and total RNA was isolated at time points spanning 5 days. Analysis by RT-PCR revealed all mutant RNA levels steadily declined, diminishing to ~1% of the WT RNA level after five days, demonstrating that Site-1, Site-2, and SL1 each contribute to the accumulation of viral RNA in cells , positioned between the seed region and SL1, is flipped out from the seed-paired duplex and bound in a previously unrecognized nucleotide-binding pocket on the surface of Ago2  Fig. . We, theTo distinguish between these possibilities, we designed a Site-1 RNA variant with an extended 5’ tail of 10 U residues Fig. . Our rat36. On the PAZ side, the interface is primarily formed by two rows of positively charged residues: K260 and K263 of the PAZ domain, and K354 and K355, which reside on the PAZ side of the central gate along with I353, which stacks below the ribose of HCV RNA C6. On the PIWI side, the PIWI loop inserts into the middle of the HCV-miR-122 three-way junction, making contact with nucleobases at the base of SL1  nucleotides g9–g12) Fig. 36. On thSL1 Fig. .Fig. 3SLAdjacent to SL1, HCV nucleotide t9/G21 binds a pocket formed by the PIWI loop of the central gate. The t9/G21 purine nucleobase inserts between P601 and the aliphatic portion of R814 Fig. . While akoff) of RNAs corresponding to wild type Site-1 (WT Site-1), Site-1 with SL1 replaced by an A4 bridge (Site-1 ∆SL1), and a Site-1 with an abasic nucleotide at position t9 (Site-1 t9 abasic)  Fig. . Surprisely Fig. . Thus, dSL1 Fig. .koff described above . Dephosphorylation experiments analogous to the XRN1 experiments described above showed the 5’ phosphate of extended Site-1 WT RNA was protected from CIP by Ago2:miR-122, with a half-life of >550 min  or Site-2 (MUT2). Both mutant RNAs bound PCBP2 comparably to the WT RNA Fig. . These rFinally, we tested whether PCBP2 could take the place of Ago2 at Site-2 to prevent 5’ end attack by XRN1 in the absence of SL1. Compared to Ago2:miR-122 at Site-1 ∆SL1 alone, including PCBP2 increased the half-life of the RNA significantly (from 13 min to 24 min) Fig. , but farThese data provide additional evidence for the presence of a molecular switch between Ago2 and PCBP2, but compared to the previous model, we find that this switch occurs specifically at Site-2. Furthermore, Site-1 has the ability to remain occupied by Ago2 independently of the state of Site-2 Fig. . OccupanHere we dissected the two HCV miR-122 sites with respect to protection from XRN1, finding that Site-1 is essential, and Site-2 has a synergistic effect. Importantly, the conserved SL1 within Site-1 is necessary for Ago2:miR-122-mediated protection of HCV RNA from XRN1 and CIP. The crystal structure of Site-1 in complex with Ago2:miR-122 revealed a particularly open conformation of Ago2, through the use of multiple RNA-protein interaction sites, including a previously unknown t9 purine-binding site in the Ago2 PIWI domain. We propose Site-1 represents a previously unrecognized RNA motif that combines sequence-specific pairing to the miRNA with the secondary structure of SL1 to lock Ago2 in an open conformation on the HCV RNA 5’ end.15. Our data raise the possibility that the switch between replication and translation involves occupancy of either Ago2 or PCBP2, or other RBPs, at Site-2. We propose the existence of a mixed Ago2:PCBP2 complex, which along with the multiple roles for Ago2, might explain seemingly contradictory results indicating miR-122 can both stimulate16 and inhibit15 viral protein translation  either desalted (for biochemistry) or HPLC-purified   and lysed by three passages through an M-110P microfluidizer (Microfluidics). The lysate was clarified by centrifugation at 29000 g for 15 min and initial purification by Ni affinity  was combined with Micrococcal nuclease (Takara) treatment to remove bound RNA . Ago2 was loaded with a synthetic miRNA bearing a 5’-terminal phosphate (IDT) and the His tag was removed using Tobacco Etch Virus protease. After removal of the His tag using TEV protease followed by reversed Ni affinity purification, Ago2 loaded with a specific microRNA was captured using an antisense oligonucleotide (IDT) and eluted with a DNA competitor (IDT). The protein was passed through Q Sepharose Fast Flow resin (Cytiva) to further remove nucleic acids, and purified using size exclusion on a Superdex 75 16 600 column (Cytiva) on an Äkta Pure FPLC (Cytiva) using 50 mM Tris-HCl pH 8, 1 M NaCl, 0.5 mM TCEP. After final dialysis into 30 mM Tris-HCl pH 8, 100 mM NaCl, 0.5 mM TCEP, protein concentration was determined on Nanodrop OneC (Thermo Fisher Scientific) using a calculated extinction coefficient at 280 nm of 198370 M−1cm−1. Aliquots were flash frozen in liquid nitrogen and stored at −80 °C until use.Human Ago2 was expressed in Sf9 cells using the baculovirus system according to a previously published protocol2, 100 mM Tris-HCl pH 8, yielding elongated hexagons. The crystals were prepared for freezing by transferring to a solution of 7% w/v PEG8000, 50 mM BaCl2, 100 mM Tris-HCl pH8, and 50% w/v sucrose as cryoprotectant.Ago2:miR-122 was diluted to 2 g/l (~20 µM) and mixed with synthetic HCV site 1 RNA (IDT) at 1.2x molar equivalents. Initial crystallization experiments were set up manually using 24-well plates (Hampton) using the hanging drop vapour diffusion method and JB Classic screen (Jena Bioscience). Crystal conditions were optimized iteratively using a combination of manual screening and liquid handler . Seeds were prepared using the Seed bead kit (Hampton) and used in the iterative optimization. Final crystallization conditions were 6% w/v PEG8000, 50 mM BaCl40, then scaled and merged with Aimless41. Initial phases were generated by molecular replacement using Phaser42 with the search model comprising the MID and PIWI domains of Ago2 loaded with an unrelated guide RNA (PDBID: 4w5n). Molecular replacement revealed two proteins in the crystallographic asymmetric unit. Strong electron density was observed for a target RNA paired to the miR-122 seed region, the primary interaction site of Ago2:miRNA RNPs with their targets, comprising nucleotides 2-8 of the miRNA guide. The remaining protein  and viral stem-loop1 were built by iterative cycles of refinement and modeling, taking advantage of the non-crystallographic symmetry, using Phenix.refine43, manual adjustments to the model in Coot44, and real-space refinement of the RNA using ERRASER45.Diffraction experiments were performed at SSRL 12-2, APS GM/CA 23ID-B, and ALS 5.0.2, where the final data set was collected. Diffraction data were indexed using DIALS32P-labeled ATP (Perkin Elmer) and T4 polynucleotide kinase (NEB), or at the 3’-end using α-phosphate 32P pCp (Perkin Elmer) and T4 RNA ligase (Thermo Fisher). Labelled RNAs were purified by denaturing polyacrylamide gel followed by gel extraction and ethanol precipitation. RNA concentration was determined by absorption at 260 nM on a Nanodrop OneC (Thermo Fisher Scientific) using extinction coefficients calculated by the oligo tool from the IDT website (www.idtdna.com).Synthetic RNA oligonuceotides (IDT) were radiolabeled at the 5’-end using γ-phosphate 27.Experiments to measure release of target RNAs from the Ago2:miR-122 complex were performed according a previously published protocol2, 0.5 mM TCEP). The reaction time course was then initiated by the addition of 7 µl of unlabeled target RNA  to the sample. 50 µl aliquots were passed through the dotblot apparatus at various times, with each time point followed by 50 µl wash buffer. After air drying, the membrane strips were exposed to phosphor screens  for visualization. Screens were imaged on a Typhoon phosphorimager  and the signal was quantified using ImageQuant . Dissociation rates were calculated in Prism (GraphPad Software), using a one-phase decay fit, with Y0 set to 1 and the plateau set to 0.001.Ago2:miR-122 (2 nM) was incubated with radiolabeled target (0.1 nM) in reaction buffer 2, 0.5 mM TCEP, 0.005% NP-40) with a total volume of 750 µl and incubated at room temperature for 45 min to reach equilibrium. Protein-bound and free RNAs were separated by passing samples through a Protran nitrocellulose membrane  and a Hybond N + nylon membrane  using a dotblot apparatus . Initial (zero) time point was taken by applying 50 µl to the apparatus (with vacuum) and washing with 50 µl wash buffer Kluyveromyces lactis XRN-1 was expressed in BL21 Escherichia coli with a plasmid gifted from the Tong lab46, and purified by Ni-NTA affinity chromatography followed by size exclusion chromatography on an Äkta Pure FPLC using a Superde x 75 column (Cytiva) as before.Recombinant 2, 0.5 mM TCEP, 0.005% NP-40, 0.05 g/l BSA, 0.02 U/µl RNase inhibitor (NEB)) in a total volume of 126 µl at room temperature for 45 min prior to starting the XRN-1 reaction.Ago2:miR-122 (100 nM) was incubated with radiolabeled target RNA (1 nM) in reaction buffer A timepoint 0 was taken by removing (9 µl) to a fresh tube containing 20 µl of formamide loading buffer. The reaction was started by the addition of 13 µl XRN-1 at 30 nM , and 10 µl aliquots were removed and pipetted into formamide buffer to stop the reaction at indicated timepoints.The aliquots were analyzed on a denaturing polyacrylamide gel  run in 0.5x TBE. The gels were used to expose phosphor screens  for visualization. Screens were imaged on a Typhoon phosphorimager  and signal was quantified using ImageQuant . Dissociation rates were calculated in Prism (GraphPad Software), using a one-phase decay fit, with a Y0 set to 1 and the plateau set to 0.Escherichia coli, by inducing with IPTG (1 mM) overnight at 16 °C after cultures had reached an OD600 of 0.9. Pelleted cells were resuspended in lysis buffer  and lysed by three passages through an M-110P microfluidizer (Microfluidics). The lysate was clarified by centrifugation at 29000 g for 15 min and initial purification by Ni affinity  was combined with Micrococcal nuclease (Takara) treatment to remove bound RNA . After removal of the His tag using TEV protease followed by reversed Ni affinity purification, the protein was passed through Q Sepharose Fast Flow resin (Cytiva) to further remove nucleic acids, followed by a final purification step using size exclusion on a Superdex 75 10 300 column (Cytiva) on an Äkta Pure FPLC (Cytiva) using 50 mM Tris-HCl pH 8, 1 M NaCl, 0.5 mM TCEP. After final dialysis into 30 mM Tris-HCl pH 8, 100 mM NaCl, 0.5 mM TCEP, the protein was concentrated, supplemented with 20% glycerol, flash frozen in liquid nitrogen, and stored at −80 °C until use.The coding sequence for PCBP2 was amplified from HEK293T cDNA and cloned into a pET23a vector (Novagen) encoding an N-terminal His tag followed by a recognition site for Tobacco Etch Virus (TEV) protease. The recombinant protein was expressed in BL21 5’-radiolabeled RNAs (IDT) comprising HCV miR-122 binding Site-1 and Site-2  were incubated 30 min at room temperature with the indicated concentrations of Ago2 loaded with miR-122 and PCBP2. The reactions were analyzed by native polyacrylamide gel electrophoresis at 4 °C. Gels were dried ~0.5 h on a gel drier at 80 °C to prevent cracking and then used to expose phosphorscreens overnight. The screens were imaged on a Typhoon phosphorimager  and the signal was quantified using ImageQuant .47 with an excess of GMP to facilitate 5’-radiolabeling by generating a 5’-monophosphate construct. The template was then digested using RNase-free DNase I, and the RNA was purified using denaturing polyacrylamide gel electrophoresis. Yields were determined on a nanodrop one (Thermo Fisher Scientific), using A260 and extinction coefficients obtained from the IDT oligo analyzer tool (www.idtdna.com). The extinction coefficients were calculated assuming single-strandedness as an approximation.DNA templates were prepared by PCR amplification from pUC19 plasmids containing the T7 promoter followed by nt 1-357 of the HCV genome (from H77 deltaE1p7) and a cis-acting HDV ribozyme (to produce homogeneous ends). The wt construct was ordered as a gblock (IDT) and cloned into pUC19, mutants were made using the indicated primers. Transcription reactions were performed overnight at room temperature29 (wt or mutants prepared with the indicated primers), and the respective mutants and tested by Sanger sequencing. The RNAs were prepared as described above and purified using the RNeasy kit (Qiagen). Yields in ng/µl were determined on a Nanodrop OneC (Thermo Fisher Scientific) using the formula of 40 ng/µl per unit of absorbance at 260 nm.DNA templates were prepared by PCR amplification from the plasmid H77 deltaE1p748. Huh-7.5.1 cells were washed twice with DPBS (Thermo Fisher Scientific) and trypsinized. The cells were resuspended in culture medium  and washed twice with ice-cold DPBS. The second time the resuspended cells were counted, and resuspended to 1.5 × 107 cells/ml. 5 µg RNA was used for each electroporation. 800 µl cell suspension was added to the prepared RNA. The resulting solution was mixed by pipetting twice and then transferred to an electroporation cuvette. Electroporation was performed on a Gene Pulser (BioRad) according to the published conditions . Electroporated cells were supplemented with recovery medium with 20% FBS to 10 ml and plated in 6-well plates at 0.3 ml/time point.Electroporation was performed according to a published protocol2O and quantified by UV absorbance. Total RNA was reverse transcribed with Superscript III reverse transcriptase (Invitrogen). PCR was performed with Power SYBR Green Master Mix (Applied Biosystems), with primers targeting the IRES22. Rlpl2 levels were used as internal standards with the delta Ct method. For comparison, data were fit to one-phase-decay model and decay constants were compared using the extra sum-of-squares F test22. PCR primers are listed in Supplementary Table Growth medium was removed and cells were washed once with DPBS. Cells were lysed by the addition of Trizol Reagent (Invitrogen) (0.5 ml per well of a 6-well plate) and total RNA isolated according to the manufacturer’s protocol, using glycogen to facilitate precipitation. The RNA was finally resuspended in 10–15 µl RNase-free HReporter plasmids were prepared by cloning the HCV Site-1 sequence and its mutants  into the psiCHECK-2 vector (Promega). HEK293T cells were seeded into 96-well plates. 12 h after seeding, the cells were transfected with 40 ng/well of the indicated plasmid using Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer’s protocol. miR-122 was annealed with its passenger strand and transfected 5 h later at the indicated concentrations with Lipofectamine 2000. After 48 h, the medium was removed from the cells, and luciferase activity was assayed using the Dual-Glo Luciferase Assay System (Promega) on a Synergy H1 Hybrid Reader (BioTek).www.ebi.ac.uk/Tools/msa/clustalo/).Indicated sequences were aligned using the Clustal Omega tool from the website of the EMBL-EBI in Hixton, UK . Sequences corresponding to miR-122 Site-1 were obtained from NCBI. The list was manually curated to include one sequence per genotype (using the more recent one in cases where two were published), and to remove any incomplete sequence.Genbank accession numbers for HCV genotypes were obtained from the website of the International Committee for the Taxonomy of Viruses . Alignments were performed for all remaining sequences, as well as for the sequences within each genotype. Minimum folding energy for each sequence was calculated using the Vienna RNA package49.The sequences were aligned using the Clustal Omega tool from the website of the EMBL-EBI in Hixton, UK (Sequence logos were generated using the online tool from UC Berkeley (weblogo.berkeley.edu/) with the Clustal Omega multiple sequence alignments as input.Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


The question of how children learn Function Words (FWs) is still a matter of debate among child language researchers. Are early multiword utterances based on lexically specific patterns or rather abstract grammatical relations? In this corpus study, we analyzed FWs having a highly predictable distribution in relation to Mean Length Utterance (MLU) an index of syntactic complexity in a large naturalistic sample of 315 monolingual French children aged 2 to 4 year-old. The data was annotated with a Part Of Speech Tagger (POS-T), belonging to computational tools from CHILDES. While eighteen FWs strongly correlated with MLU expressed either in word or in morpheme, stepwise regression analyses showed that subject pronouns predicted MLU. Factor analysis yielded a bifactor hierarchical model: The first factor loaded sixteen FWs among which eight had a strong developmental weight , whereas the second factor loaded complex FWs (possessive verbs and object pronouns). These findings challenge the lexicalist account and support the view that children learn grammatical forms as a complex system based on early instead of late structure building. Children may acquire FWs as combining words and build syntactic knowledge as a complex abstract system which is not innate but learned from multiple word input sentences context. Notably, FWs were found to predict syntactic development and sentence complexity. These results open up new perspectives for clinical assessment and intervention. Theories of language acquisition disagree mostly on whether infants come to this process equipped with some language-specific innate predisposition9 or whether general learning abilities can account for it14. According to the general learning theories, infants are thought to grasp the statistical regularities and transitional probabilities present in the input they receive, with the help of a critical social learning ability; the outcome of this process of abstraction is semantically specified schemas, related to each kind of meaningful situation. The nativist approach, on the contrary, insists on the innovative linguistic forms and utterances produced by infants, their rule-like patterns of errors and the lack of negative feedback in the input they get.The question of how children learn Function Words (FWs) to build syntactic knowledge is a central issue in developmental psycholinguistics and in the applied fields of education, speech-language pathology and early intervention. Since seminal work of Bloom15).Leaving aside the nativist vs. emergentist debate, the analyses presented, in this study, are in line with the so-called “syntactic connectivity” account according to which children would learn grammatical words as an interconnected system based on early rather than late structure building . According to this latter view, FWs are considered as linguistic cues to the grammatical category of other words. Exploring whether FWs may be grammatically functional early in the acquisition of language is also in line with the bottom-up driven hypothesis. Under this assumption, by attending to the recurring phonological, prosodic and distributional characteristics which FWs typically share young children could derive some useful information for (i) segmenting the continuous speech stream into a set of distinct constituents, (ii) discovering the syntactic class of words and phrases. Young children, accordingly, could be using a comprehensive strategy in sentence processing, one which incorporates not only the meaning-carrying units  but also the functional elements in language. In the learning process, children extract FWs from the input, making use of the formal distributional properties of their native language. They have a surface knowledge and use FWs to process auditory and linguistic input very early on. Doing so, they gradually construct FWs that manage the multiword sequences in an utterance. Learners do not acquire syntax in a piecemeal and isolated fashion. In such learning-based theories, the young child is sensitive to the phonological, prosodic and distributional patterns in language, and relies on general cognitive (not language-specific) mechanisms to generalize these patterns into a full grammar.Herein, we challenge the lexicalist approach or usage-based account, which assumes that children begin to produce multiword utterances before they have any grammatical knowledge (late structure building hypothesis). We postulate that acquisition of FWs and morphemes bootstraps syntactic development  will be tested in this corpus study using a stringent linguistic coding from the CHILDESThe main purpose of this study is to investigate how French-speaking children learn FWs to build syntactic knowledge. We hypothezize that acquisition of FWs and morphemes primarily bootstraps syntactic development in order to facilitate syntactic knowledge. A data-driven model will be used to support a syntactic connectivity approach raising the two following questions: (i) Which FW(s) is (are) the most predictive of MLU? and (ii) how FWs are hierarchically organized and reflect sentence complexity. Specifically, we will examine a subset of eighteen FWs since they have a highly predictable distribution from a large corpora of 315 monolingual French children aged 2 to 4.20. Thus, by tackling the issue of FWs productivity and complexity, we expected to better understand the processes underlying the grammatical relations (GRs). French-speaking children may acquire the grammatical complex features over a protracted period of time, from 2 to 4 years of age.Mean Length Utterance in words (MLU-w) or in morphemes (MLU-m) usually both serve as developmental measure for cross-linguistic comparisons and as a global index of sentence complexity. One important question is to determine whether or not there are subsets of FWs that can predict MLU-w and/or MLU-m. This first question is relevant because FWs include various subcategories which are linguistically and cognitively complex, and acquired at different time periods. Production of noun morphology occurs at an earlier stage of language development than verb morphology. Indeed, it appears that noun morphology  is easier to acquire, possibly as a consequence of the semantic and syntactic transparency of nouns. Similarly, determiners are acquired earlier than relative or reflexive pronouns, the latter being linguistically more complex. Determiners are also acquired earlier than time or space prepositions, which are conceptually more elaborate22. Some languages appear to be relatively simple with regard to their morphology, while others are viewed as highly complex. MLU-w has been shown to strongly correlate with MLU-m in several languages other than English23, including Dutch24, Irish25, Icelandic26, Cypriot-Greek27, Eastern Canadian Inuktitut28 and Basque29. MLU-w has been recommended as an unbiased measure in children who speak dialects or learn multiple languages.Another question raised in this study, is to determine whether MLU should be measured in words (MLU-w) or in morphemes (MLU-m). This matter has consequences, especially when comparing development across languages. Languages differ greatly in how different meanings are mapped into morpho-syntactic structuresil dort, ils dor-ment ‘he/they sleep’). Ninety-four percent of verbal forms with different inflections are homophonous 30. The gender/number of many nouns/adjectives is made overt only by the presence of the determiner. When audible, these inflections are formed by a vowel  or consonantal marker . All these FWs make French-speaking children to control a variety of grammatical morphemes in their early syntax .Regarding French, verbs are inflected differently for all persons, . By describing the different FWs factor loadings, it would be possible to draw inferences about the way young children must integrate FWs with a set of multiple syntactic dependencies. First, EFA can be run to search for correlations between FWs. However, because EFA remains an exploratory statistical method to represent the observed data, it does not include any formal a priori test hypothesis. The conclusions that can be drawn from EFA are therefore limited. The degree of independence of the factors identified is thus questionable. Confirmatory factor analysis (CFA), a latent variable approach, can overcome these limitations since it allows comparison between FWs factor loadings. We hypothesize that CFA could define FWs in terms of degree of complexity, thus providing a hierarchical model of the constructs underlying early grammar building. This modeling approach would offer a refined granularity level of difficulty of FWs production compared to existing markers such as MLU. So far, no CFA study has been carried out for estimating the FWs load factors in children aged 2 to 4. Further, CFA should enable prediction of clinical populations performance.31) and being a native speaker of French. 315 participants were recruited from homes and nurseries in the Paris area, France. This corpus includes a total of 32,321 utterances, 3016 word types i.e., the total number of different words, and 108,887 word tokens i.e., the total number of words, resulting in a Type/Token ratio of 0.028. Table This corpus study was approved by Institutional Review Board, from the French National Health Institute, (IRB Number 00000096) and all the research was performed in accordance with their relevant ethical guidelines and regulations. Selection of participants included passing an auditory screening test, scoring in the normal range on an age-appropriate nonverbal cognitive test (Symbolic Play TestEach child participated in a dyadic interaction with a familiar adult partner (parent or nursery teacher) either in the child's home, nursery or school. Informed written consent was obtained from both parents to videorecorded their child. As children were fairly talkative, we have retained a 20-min sample-time approach. The child and adult were seated at a small table, and the same standardized set of 22 Fisher-Price toys  was used with all children see Fig. .Figure 134, allowing for the computation of linguistic production as described in the corpus processing system CLAN . The parser is freely available in the CLAN program which can be found on the CHILDES website (http://childes.psy.cmu.edu). The automated MOR function assigned and disambiguated grammatical descriptions of all the words in these 315 transcripts. Following the running of MOR and POST, we then used the KIDEVAL command to generate spreadsheet output of each child’s language features involving specific morphological features.Two trained assistants transcribed the recorded language samples following the transcription and segmentation conventions for spoken Frenchla poupée de Marie qui est dans sa poussette est belle ‘Mary’s doll in her stroller is pretty’The output has the form of this excerpt: *CHI: la poupée de Marie qui est dans sa poussette est belle.%mor:det:art|la&f&sg n|poupée&f prep|de n:prop|Mariepro:rel|qui v:exist|être&PRES&3 s^v:aux|être&PRES&3sprep|dans det:poss|sa&f&sg n|poussette&fv:exist|être&PRES&3 s^v:aux|être&PRES&3 s adj|beau&f&sg = pretty.Tagging quality was checked by hand, as this corpus is intended to represent a reference for future syntactic analyses of children’s French language corpora. The effective tagging quality of the present corpus after checking by hand averages 97%. From this dataset, a list of 18 FWs has been selected on word tokens (≥ 100 tokens) as described in Supplementary data.https://childes.talkbank.org/access/French/MTLN.html.This dataset is available at 37 version 1.6. We used Ωnyx, a graphical SEM software for performing maximum likelihood estimation of parameters in models38 (http://onyx.brandmaier.de).First, pearson product–moment correlations were performed to examine associations among FWs and MLU-w and MLU-m. Second, a series of multiple regression model was undertaken to estimate whether FWs could predict MLU-w and MLU-m . Intercept values and regression slopes were determined for every FWs. Finally, Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were run to extract the factors and validate the factor structure of GMs by performing an oblique rotation. CFA includes multiple indices of fit, which can be used to test models previously developed through EFA conducted on every GM. The Kaiser–Meyer–Olkin (KMO) measurement was calculated to assess the adequacy of the sampling. Ideally, the KMO should be greater than 0.60. We evaluated the fit of a structural equation model (SEM) to determine the degree of adequacy using different adjustment indices: the index square root approximation error of Steiger Lind (RMSEA), the normalized adjustment index (NFI), the Tucker-Lewis index (TLI), the comparative adjustment index (CFI). These indices are interpreted according to critical thresholds. In general, values below 0.05 and 0.08 for the RMSEA are considered to indicate good and acceptable data adequacy respectively. The CFI and TLI should be greater than 0.95. All statistical analyses were performed under JAMOVIp < 0.001) as shown in Table Correlations between age, 18 FWs and MLU-w and MLU-m values was strongly significant .Tables Figures Factor analysis on FWs reflected grammatical productivity and sentence complexity.2 = 1042—df = 134, p < 0.001—Fit measures CFI = 0.844—TLI = 0.822, SRMR = 0.0467—RMSEA = 0.147, (90% CI from 0.138 to 0.155); AIC = 36,049, BIC = 36,256). Path diagram is presented in Fig. Confirmatory Factor analysis (CFA) yielded a bifactor model: the first factor accounted for 61.44% of the variance  and the second for 6.56% of the variance . A summary of the CFA is presented in Table The main purpose of this corpus study was to investigate how French-speaking children learn FWs to build syntactic knowledge. We used a data-driven model to explore in one hand which FW(s) is (are) the most predictive of sentence complexity using Mean Length Utterance expressed either in words or in morphemes (MLU-w and MLU-m), and in the other hand how FWs are hierarchically organized. Similarly to many developmental psycholinguistics, the term of FW was defined taking into account various dimensions of linguistic knowledge including phonology, semantics, syntax, distributional regularity and frequency.When analyzing a subset of the most frequent FWs i.e., eighteen selected FWs in a large corpus of 315 monolingual French children aged 2 to 4, we challenged the lexicalist view, making the hypothesis that FWs are better connected to syntactic development than Content Words (CWs) since these categories do not contribute to the constitution of the lexicon in the same manner. Nouns and predicates, i.e., CWs, are classes of high density, that strongly contribute to the diversity and enrichment of the lexicon, whereas grammatical words  are less diversified classes although being used with greater frequency. FWs learning necessarily involves multiword input and hence it necessitates the mastery of syntactic principles (early structure building hypothesis). CWs, as words possessing semantic content, can be learned from single word utterances, as their meaning is learned from the nonlinguistic context (late structure building hypothesis). Learning FWs starts very early on and consequently is a pre-requisite of syntactic development. FWs, particularly determiners and personal pronouns, do not carry a conceptually complex content. They are easy to learn because they are monosyllabic words prosodically constrained.39. This standard has indeed several advantages. First, it is widely used in the field of typical language acquisition all over the world. Second, it captures to a certain extent complexity carried by the number of words in a sentence or the number of morphemes, i.e., inflectional changes on word form. However, MLU is not universally accepted and its reliability has been questioned due to its variability across age-groups40. It is therefore important to define the conditions under which MLU is related to age. For instance, MLU has been reported to correlate with age when smaller than 3.5, provided the context of language production is strictly controlled41. Other studies also concluded that MLU is highly correlated with the development of morphological and syntactic skills but only until the age of 48 months46. In our study, a high correlation between MLU-w and MLU-m was found (r = 0.99), indicating that the two can be used indifferently. Both MLU-w and MLU-m were found to correlate with age (r = 0.69).One major finding, in this corpus study, was that all eighteen FWs were correlated to MLU-w and MLU-m. Particularly, stepwise regression analyses showed that subject-pronoun was found to be the most consistent predictor of MLU accounting for 60% of the variance in MLU-w and for 57% of the variance in MLU-m. MLU was described here as the standard for measuring children's sentence complexityAnother finding from a series of multiple regression analysis showed that productivity of FWs in the children’s earliest multiword utterances was strongly related to MLU ranging from 2.09 to 2.81 for MLU-w, and 2.49 to 3.35 for MLU-m. In the case of determiners, for example, children between age 2 and 4 already produced the full set of determiner system with gender and plural markers to the same extent as adults.la, for 91% of the children; le, for 87%; un, for 79%; les for 70%; l’ for 69%; and une for 66%. It should be noticed that at 2 years of age, 36% of children (14 out of 39) omitted determiners in front of noun category with a low MLUw value of 1.60 (SD = 0.55) and MLUm value of 1.86 (SD = 0.70), described as the criterion for identifying the two-word stage and representing the earliest stage of grammatical development47). From the age of 1;9 to 2;3 years, children enter a phase of intense development of FWs. This period lasts at least until the age of 3 to 3.5 years. At the end of this phase, the child gives, at least in his most complex productions, the impression of an almost adult language.A closer look in Fig. il ‘he’ for 91% of the children, elle ‘she’ for 66% /je ‘I’ for 63% /on ‘it’ for 60% /tu ‘you’ for 47% /ils ‘they’ for 24% /elles ‘they’ for 20%. Similar to the determiner category, considerable inter-individual differences were found in subject personal pronoun category. Young children learn subject pronouns at different paces  ‘stress’52, contrasts prosodically with English, which tends to have lexical, trochaic stress53.Regarding to the ongoing debate about how children acquire determiners and use them -i.e., whether early determiner use is lexicallyle, la, les” , or indirect object “à lui, à elle” . As a matter of fact, all these pronouns have specific prosodic forms inherent to the target language. They also have strict obligatory positions, a typical phenomenon of the dependency grammar.The same explanation could be given for children’s production of subject pronouns and other pronouns (reflexive pronouns), e.g., the development of some very frequent particular constructions, such as object pronoun ““la voiture” (the car) vs. “le bébé” (the baby) or pronoun-verb word sequences -e.g., “il va prendre sa voiture” (he is go-ing to take his car) vs. “elle va prendre sa voiture” (she is go-ing to take her car). All these utterances are mostly correctly marked for gender, number and person.Concerning the extent to which children analyze pronouns or determiners as separate elements, the strong relations found between MLU and determiners and subject pronoun provide developmental evidence that young French children process formal linguistic relationships. Indeed, morphosyntactic markings are associated with a correct gender form, upon the acquisition of determinant-noun and subject pronoun-verb word sequences -e.g., 54. These creative forms should be differentiated from routines or formulaic expressions, i.e., supra-lexical unanalyzed elements, mostly adverbs such as s’il-te-plaît ‘please’, ça-y-est ‘it’s over’ coded as pragmatic words.MLU findings led to the view that there is a dichotomy between the open-class lexicon  and the close-class lexicon  obtained from a corpus of 100 utterances, within which 56 specific language structures were identified. This underspecification of MLU has motivated, in our study, the need to refine the multiple regression analysis exploring the respective role of every FW using factor analysis to provide a hierarchical model of early grammar.Even though significant correlations were found between MLU and all 18 FWs, MLU remains a metric too underspecified to reflect GRs of syntactic structures as already suggested by ScarboroughThe results from the factor analysis also support the view according to which children are thought to be able to use syntactic knowledge very early from a range of categories of their native language. This basic learning in context consisting to learn groups of words rather than single words in different contexts  allows the learner to correctly use basic functional categories  and all morphological markers , and as such plays a crucial role in early structure building. These important findings show that the set of eighteen FWs depends not only on strong relations to the grammatical category of other words but also on their placements and projections. FWs are organized as a complex system.il/elle/on ‘he/she/it’ (iii) determiners-articles, (iv) auxiliaries avoir et être ‘to have/to be’, (v) prepositions dans ‘in’, avec ‘with’, pour ‘for’, (vi) modal verbs pouvoir ‘can’ (vii), demonstrative-pronoun ça ‘this’, and (viii) plural markers les ‘the’. The CFA second factor loads complex forms such as reflexives and object pronouns: e.g., il/elle se lave ‘he/she cleans him/herself’, il/elle le met ‘he/she puts it’.Structural equation modeling enables to determine loading factors for every FW relative to their degree of complexity: namely, higher-ranking over lower-ranking related to placement and projection. Some functional projections are present as soon as the child produces short multiword utterances. However, other functional projections may be left unspecified, yielding a transitory incomplete grammar. CFA could assign a status to every FW, thus providing a hierarchical model of the constructs underlying non-adjacent dependencies. This modeling approach focuses on the details of linguistic representation in language learners with predictions about what the child must acquire first before starting to produce complex sentences. In our model, the first factor loads sixteen FWs among which eight FWs are strongly weighted: (i) third person singular (ii) subject pronouns, 59. Two restrictions are placed on the dependency structure of a grammatical sentence: first, every word must have a head, and second, every word has only a single head. The exception is the root, namely, the highest word of the sentence, which does not have a head. Although the number of heads per dependent is restricted to one, the converse is not true, so that a head may have a theoretically unlimited number of dependents60. Dependencies governing subject-noun/verb agreement and auxiliary/inflectional morpheme relations are acquired earlier than dependencies involving more abstract constituent relationships. In our study, the following GRs were produced by more than 80% of the children. Determiner-noun la voiture ‘the car’ was produced by 96% of the children, preposition-determiner dans la ‘in the’ by 81%, subject-pronoun-auxiliary il va ‘he is’ by 89%, subject-pronoun-verb-exist il est ‘he is’ by 92%, demonstrative-pronoun-auxiliary ça c’est ‘that is’ by 91%, and auxiliary-main verb va aller ‘is go-ing’ by 81%. In contrast, dependencies involving complex GRs such as those found in subject-pronoun-object pronoun il le ‘he him’ and possessive verbs were produced by 67% and 54% of the children, respectively.This hierarchical model of early grammar reflects adjacent and non-adjacent dependencies governing both simple and complex syntactic structures. The concept of dependency stems from traditional grammar of languages. Linguists mostly state that dependencies involve binary relationship between two linguistic units, mostly the governor and the dependent61. Between age 2 to 4, French-speaking children productively use the full set of formally marked determiners-articles with correct gender and numbers: e.g., le, la, les, l’ ‘the’ un, une, des ‘a’. Similarly, they produce a large variety of prepositions early on pour ‘for’, à ‘at’, de ‘from’, dans ‘in’, sur ‘on’, avec ‘with’, and gradually define the case marking of verb argument, allowing the identification of GRs e.g., all-er à l’école, ‘go to school’ that constraint word order or introducing an oblique object of the verb e.g., fin-ir de mang-er ‘finish eat-ing’ or donner à Marie ‘give to Mary’. All these examples indicate congruent GRs within the nominal and/or verbal context. Sentences are marked by GRs: determiners-articles predict nouns while pronouns predict verbs. Words are sorted to form categories such as nouns and verbs and learning regularities over those categories is central to build syntactic knowledge63.Statistically, FWs have an extremely high token frequency but a limited set of free and bound morphemes indicating hierarchically organized grammatical relations (GRs) to reflect syntactic knowledge at different times and at different paces. FWs have features which make them easy to learn: they are extremely frequent and have a highly predictable distribution. They are constrained in their distribution due to their placement and projections relative to a sentence context64. When considering the initial stages of grammar, it is assumed that distributional regularities are readily learned by children and constitute their early generalized syntactic knowledge. According to this statistical learning theory, FWs facilitated infants’ speech segmentation under artificial language learning experiments. For example, 51⁄2- to 8-month-old infants segment speech streams based on adjacent dependencies66, whereas the ability to compute non-adjacent dependencies seems to develop at around 15 months of age67. The constraints on the learnability of non-adjacent dependencies show that statistical learning of these dependencies has greater complexity than statistical learning of adjacent dependencies. Furthermore, FWs occur frequently at the edges of utterances70, thus enable infants to easily perform word segmentation71.Our results also support the view that FWs and morphemes bootstrap syntactic development. It has been shown that infants demonstrated robust abilities to abstract both specific and general patterns of varying complexity from auditory and language‐like stimuli72.This mechanism of chunking input includes both implicit and explicit distributional learning about the particular form-meaning mapping of the ambient language. If the input from the environment plays an essential role, early grammar building can be viewed as a learning process which starts and evolves in parallel with cognitive development. FWs should be significantly involved in the child’s grammar construction. There is a great deal of evidence in child language literature that processing dependencies proceeds in a highly incremental fashion and can improve our understanding of the child syntactic knowledge. Some FWs are more heavily weighted than others. For instance, development of copula ‘be’ precedes that of possessive verb ‘have’ which in turn outpaces auxiliary ‘do’la|poupée ‘the|doll’ sa|poussette ‘her|stroller’ prepositions ‘de|Marie’, ‘Mary’s|doll’ dans|sa|poussette ‘in|her|stroller’, copula-be est|belle ‘is|pretty’, possessive verb-be est|dans|sa|poussette ‘is|in|her|stroller’ and relative-pronoun qui|est|dans|sa|poussette ‘who|is|in|her|stroller’.A key difficulty is that learning grammar does not involve dependencies just between adjacent but also non-adjacent words. In order to understand the role played by FWs on sentence complexity, computational models based on metagrammar could be directly used to parse sentences and to derive Dependency Grammar (DG). The FRench MetaGrammar (FRMG) PARSER, for instance, is an efficient and accurate solution to cover GR for French. FRMG can parse complex sentences with all FWs and their dependencies in a hierarchically organized syntactic tree see Fig. . In this74 (UD), a framework for consistent annotation of grammar . UD is an open community effort with over 300 contributors producing nearly 200 treebanks in over 100 languages. . An operational grammar like a Tree Adjoining Grammar (TAG) assigns abstract syntactic and semantic representations to an input signal that contains a string of words. It is therefore possible to assign a developmental weight to every particular FW according to DG theory, thus predicting grammar dependencies within complex sentences.To further assess learning dependencies across different languages, linguists rely on Universal Dependenciesle, la, les (the) un, une (a).Herein, the results from the factor analysis of eighteen distinctive FWs provided a model of how Typically Developing (TD) French children aged between 2 to 4 years acquire GRs. Critically, to build syntactic knowledge, the child must not only access FWs, but also recognize the crucial roles that FWs play in grammatical organization. French language is an excellent test case to explore this model because FWs show a certain morphological richness. Indeed, French has a diversified and widely used range of FWs of various types. The latter express a variety of distinct meanings, and most of them mark gender, number, and person information , resulting in a large variety of FWs within these classes. Determiners, for instance, involve a number of different definite, indefinite and partitive articles marked for gender and number Importantly, FWs must be learned as combining words and not separated words like CWs. FWs are learned from multiword input sentence context and facilitate syntactic knowledge whereas CWs are learned from single word input.75.The results from the factor analysis of eighteen distinctive FWs also provide many possible ways for comparing sentence complexity of FWs. Corpus analysis offers a golden standard showing how groups of FWs hang together. These analyses of GRs from Dependency Grammar are of great value in clinical settings, allowing clinicians and researchers to construct profiles of language by comparing small speech samples collected in a naturalistic context with large corpora. In TD children, clinicians will track syntactic dependencies in sentence structures requiring the productivity of complex FWs such as possessive verbs and object pronouns78. A meta-analysis carried out by Lammertink et al.79, indicated a robust difference between children with developmental language disorder (DLD) and those without DLD in their detection of statistical regularities in the auditory input. The detection of statistical regularities is on average, not as effective in DLD compared to TD subjects.In children with language disorders, the description of the hierarchical organization of FWs is important since the productive use of FWs and morphological markers of gender, number, tense, mood is known to be particularly limited81. It supplements standardized appraisal and yields baseline insights into the child’s strengths and weaknesses across language skills. The general parsing techniques have been shown to be effective in the present study. Further research in children with language disorders is needed to better identify atypical syntactic profiles. For instance, clinicians can undertake dependency grammar analyses, which can be harmonized with the UD tagset. Comparisons between TD children and those with language disorders are critical not only for clinical assessment, but also for developing cross-linguistic investigations.The present study shows the critical role of FWs to build syntactic knowledge as demonstrated by the strong correlations found between MLU-w, MLU-m and FWs. Indeed, FWs were found to predict syntactic development and sentence complexity. Children may learn FWs based on early rather late structure building. Doing so, they acquire FWs as combining words and build syntax as a complex system which is not innate but learned from multiword input sentences context. This study also indicates that sentence complexity is organized according to a hierarchical model of the most frequent FWs. Clinicians and speech-language pathologists could use such model in their practice. As a matter of fact, model can provide a benchmark for children aged 2 to 4 years upon which various clinical profiles can be analyzed both for diagnosis and interventions purposes. When assessing early grammar, corpus analysis offers a very high degree of ecological validitySupplementary Information.