Remarkably, the quantum capacitance provides insight into the changes of the electronic structure of graphene upon hydrogenation, which predicts well the suppression of the electrochemical activity based on the non-adiabatic theory of electron transfer. Thus, our work unravels the interplay between the quantum transport and electrochemical kinetics of graphene and suggests hydrogenated graphene as a potent material for sensing applications with performances going beyond previously reported graphene transistor-based sensors.The design of electrochemically gated graphene field-effect transistors for detecting charged species in real time, greatly depends on our ability to understand and maintain a low level of electrochemical current. Here, we exploit the interplay between the electrical in-plane transport and the electrochemical activity of graphene. We found that the addition of one H- Electrochemically-gated graphene field-effect transistors show promise for sensing of charged species in real time. Here, the authors leverage the interplay between electrical in-plane transport and electrochemical activity to explore the sensing performance of hydrogenated graphene. The creation of practical electrochemically gated GFETs for detecting charged species, however, greatly depends on our ability to understand and maintain a low level of electrochemical current. Specifically, the electrochemical current roots on the current flowing between the graphene channel and redox active molecules in the solution phase.Graphene is unique among other solid-state materials in that all carbon atoms are located on the surface, making the graphene surface highly sensitive for the detection of changes in the environment. Particularly, the concept of electrochemically gated graphene field-effect transistor (GFET) enables the label-free detection of charged molecules on a small footprint upon their bindings at/near the graphene surface:8. Among the multiple approaches used to chemically modify graphene, for example, post-growth chemical modifications using various oxidative reactions11 are effective routes to incorporate oxygen atoms, although at the cost of a poor control over the resulting functional groups . Particularly, hydrogenated or fluorinated graphene endows a large range of possibilities to progressively tweak graphene with sp3 defects without significantly pinning the lattice integrity or breaking the resilient basal plane C–C bonds15.Complementary to GFET sensors, the electrochemical current towards a redox probe in solution has been widely studied and is at the basis of graphene electrochemical (GEC) sensors. Previous studies have revealed that the electrochemical activity is largely sensitive to the intrinsic chemical structure of the graphene basal planesp3 defects are introduced into monolayer graphene using a hydrogen plasma. We found that only 1 s of plasma treatment is able to render a pristine graphene surface (with few H-sp3 defects) from the as-grown graphene (referred as untreated graphene) by removing the adsorbed hydrocarbons at the surface, as manifested by the dramatic boost in the electron transfer rate. Importantly, further addition of only one H-sp3 defect per hundred thousand carbon atoms (more than 1 s of hydrogen plasma), allows us to substantially reduce the electron transfer rate of hydrogenated graphene (H-graphene) compared to pristine graphene. Remarkably, we successfully correlated the degradation of the electrochemical kinetics of the graphene basal plane with the density of states (DOS) by tuning the density of H-sp3 defects. Although the H-sp3 termination could contribute to a higher electrochemical activity, the electronic structure (DOS) in graphene plays an even more decisive role in the rate of electron transfer between graphene and redox probes for a low defect density, indicating a non-adiabatic transfer process on the graphene basal plane.Here, a low density of H-2 plasma—particularly the defect density nD—are respectively equivalent. Importantly, the D peak at ~1340 cm−1, due to single phonon intervalley scattering events, is caused by the apparition of H-sp3 defects16. After a hydrogenation time of 10 s, a D′ peak at 1620 cm−1 appears in the Raman spectrum as a shoulder of the G peak. The D′ peak also associates with H-sp3 defects17. The values determined for I(D)/I(D′) (~10) after 30 s and 60 s of hydrogenation are consistent with a previous report and confirm the sp3 nature of hydrogenated defects 18. Meanwhile, the intensity ratio I(2D)/I(G), a sensitive parameter to graphene doping, decreases continuously from 2.2 to 1.3 upon extended hydrogenation /I(G) ratio (a quantitative indicator of point defects in graphene samples)21, the defect density nD increases linearly with the hydrogenation time from nD = (0.2 ± 0.3) × 1010 cm−2 at 0 s (untreated graphene) to nD = (3.2 ± 0.7) × 1011 cm−2 at 60 s, corresponding to a decrease in the average distance between defect sites (LD) from 122.6 nm to 10.0 nm . Notably, the Raman mapping (D peak intensity) in Supplementary Figure −1, Fig. 22. In addition, the peak broadening as hydrogenation proceeds is mainly due to the shortened lifetime of phonons caused by increasing amounts of defects22.Derived from the  0.2 ± 0. × 1010 c23. Specifically, the topside of CVD graphene (on the copper foil) was first glued on the supporting glass substrate and protected by the photopolymer of pentaerythritol tetra(3-mercaptopropionate) and triallyl-1,3,5-triazine-2,4,6-trione (PETMP–TATATO)24. After the removal of backside graphene (by oxygen plasma), the copper ends were protected by covering them with a film of cellulose acetate butyrate (CAB). Then the graphene surface was exposed by etching the copper in a solution of ammonium persulfate, followed by a series of hydrogen plasma treatments to introduce defects with controlled densities. During the procedure, we employed a low-temperature annealing process (110 °C for ~1–3 h) to ensure a good adhesion of graphene on the underlying polymeric substrate. Only the fabricated graphene devices exhibiting mobilities on the order of 1000 – 1500 cm2 V−1 s−1 went through a series of field-effect, quantum capacitance, and cyclic voltammetry (CV) experiments immediately after each hydrogenation treatment. To rule out any possible sample-to-sample variations, all the aforementioned measurements were conducted on the same graphene samples.For the device fabrication, we used high-quality, large-area CVD graphene following a facile and clean fabrication strategy as illustrated in Fig. Vg) is applied to the electrolyte solution via a Ag/AgCl reference electrode, to modulate the conductivity (G) of the GFET. Specifically, when the Vg is swept from negative to positive, the Fermi level (EF) of graphene shifts from the valence band (hole carriers) to the conduction band (electron carriers). At the so-called charge neutrality point (CNP), the concentration of hole carriers equals that of electron carriers and the conductivity of graphene reaches its minimum Gmin  curves are the measure for the carrier mobility μ, while the observed negative voltage of the CNP for untreated graphene implies an electron (n) doping induced by the underlying photopolymer substrate.Figure min Fig. . The slo25. Upon 1 s hydrogenation, graphene exhibits a slightly increased Gmin  of graphene down to ~750 cm2 V−1 s−1 (after 2 to 5 s of hydrogenation), after which μ stabilizes at 450–660 cm2 V−1 s−1 (after 5 to 30 s of hydrogenation). As a result, the introduction of only one H-sp3 defect per ~250,000 down to ~145,000 sp2 hybridized carbon atoms effectively affects the mobility of charge carriers in graphene (correspondingly LD = 45 nm down to 35 nm). In addition to the sublinear behavior of the G(Vg) curves (even after a series of hydrogenation), the remarkable decrease of Gmin also suggests that the conductivity of hydrogenated graphene is dominated by the so-called short-range scattering mechanism29. Such an observation is also confirmed by previous work in which hydrogenation introduced short-range scatterings in graphene lattice30.As hydrogenation proceeds, the CNP continuously shifts to more positive voltages, a characteristic of hole (p) doping Fig. . We attrmin Fig.  and a ra. μ Fig. , suggest31. The quantum capacitance Cq of graphene, can be directly determined as a function of the channel potential across the graphene sheet Vch using an electrochemical configuration  curves exhibit not only positive CNP shifts, but also broader and decreased minimums with increasing hydrogenation times. In nature, Cq,min is directly related to the density of effective charged impurities n* , which can reveal the global behavior of defects in graphene  are generally lower than those reported in previous studies. For example, untreated graphene presents a n* = 9.73 × 1010 cm−2, ~8 times lower than CVD graphene on Si/SiO2 wafer (n* = 8.0 × 1011 cm−2)32. Such a remarkably lowered n* can be ascribed to our clean fabrication strategy (Methods) which introduces less charged impurities, or reflects the differences between the substrates, which could lead to different degrees of charge transfer.As a direct manifestation of the Pauli exclusion principle, the quantum capacitance effect in graphene is particularly prominent due to its low DOSion Fig. , the V-sn* is proportional to the impurity density nimp, referred as the impurities at the interfaces between graphene and the substrate, or between graphene and air, or resulting from the intrinsic defects caused by the growth or transfer of CVD graphene. In Fig. nimp decreases in the first 5 s and then settles till 30 s hydrogenation, a scenario suggesting that the mild hydrogenation (within 1–5 s) sweeps away the trapped/adsorbed charge impurities at graphene interfaces. The evolution of the defect density nD and of the impurity density nimp are closely related and critical to the electron transport and electrochemical kinetics, which we will discuss in more detail below (section Correlation of nD with nimp).More importantly, the effective charged impurities 3)62+/3+, as an outer-sphere redox mediator: (i) it is surface insensitive and thus the electron transfer from the mediator to graphene (and vice versa) mainly relies on the electronic structures of the electrode and of the mediator itself and (ii) it possesses a standard potential in the vicinity of the Fermi level of graphene34.We employed cyclic voltammetry (CV) to investigate the electrochemical behavior of H-graphene. Specifically, we used the hexaammineruthenium (II)/hexaammineruthenium (III) redox couple (Sigma Aldrich), Ru(NHj) of the oxidation peak (at –170 mV) and reduction peak (at –290 mV) show that 1 s of hydrogenation is sufficient to increase the electrochemical activity of graphene by a factor of four compared to untreated graphene, while further hydrogenation brings about an immediate decrease of activity. The peak-to-peak separation (ΔEp), a qualitative indicator of the electrochemical reversibility in graphene, displays a minimum at 1 s of hydrogenation, which is in agreement with the observed maximum for the current density  between the graphene basal plane and the redox probe to quantitatively evaluate the electrochemical kinetics of graphene upon hydrogenation. Specifically, ΔEp of the quasi-reversible redox peaks are below 220 mV as the scan rate (v) increases, which meets the criteria of Nicholson’s method to estimate the kinetic parameters35  after 1 s of hydrogenation compared to untreated graphene (5.37 × 10−4 cm s−1). For longer hydrogenation times, k0 sharply drops down to ~1.70 × 10−4 cm s−1 within 5 s and stabilizes at 1.50 × 10−4 cm s−1 after 30 s hydrogenation. Such a trend is reproducible for different batches  per unit area of graphene, consists of the electrical double layer capacitance (Cdl) and the graphene quantum capacitance (Cq) connected in series36. Ctot can be obtained either by using a lock-in technique (Methods) or by measuring the capacitive CV current for different scan rates, which is an averaged evaluation over a relatively wide potential .The total electrical capacitance  with comparable defect densities as to hydrogenated graphene /I(D′) ratios characterized using Raman spectroscopy 37, we identify that argon plasma forms vacancy defects by removing carbon atoms, while hydrogenation changes graphene hybridization from sp2 to sp3. Thus, we gain insight into the driving mechanism for the observed electrochemical behavior. Rather than the vacancy defect, the change of hybridization (in H-graphene) is closely related to the electrochemical properties of hydrogenated graphene 41, as also confirmed by the high-resolution transmission electron microscopy images of Ar-graphene (not shown here).Based on the different sp2 (284 eV), C-sp3 (285 eV), C–O (286–286.2 eV), and C = O (287.8–288 eV) in C 1s spectra, suggest that both samples contain sp2 and sp3 carbon with minor oxygen contaminants from PMMA residues (only used for XPS samples to transfer graphene onto the Si substrate). As XPS probes both the surface chemistry of graphene and its surface adsorbents, we observed a higher content of sp3 carbon from XPS analysis (Supplementary Table sp3 C in both H-graphene (6.2–8.0%) and Ar-graphene (3%) to possible surface adsorbents including PMMA residues and hydrocarbons. A trace amount of sp3 doping in the graphene lattice (up to 0.8% sp3 C in H-graphene) was, however, determined by Raman spectroscopy.In addition, we employed X-ray photoelectron spectroscopy .To understand the correlation between defect density nimp from 8.52 × 1012 cm−2 to 5.01 × 1012 cm−2 (2–5 s) can be ascribed to the hybridization change from sp2 to sp3 when nD steadily increases with hydrogenation. The presence of sp3 hybridized spots in the lattice causes the lattice to expand and to curve. The increased distance between the lattice and substrate-related impurities explains the sharp drop in nimp. Further on, nimp slightly increases (from 5.01 × 1012 cm−2 to 5.31 × 1012 cm−2), which can be ascribed to the accumulation of trapped water molecules at the graphene surface, accompanying the increasing nD (nD > (2.6 ± 0.5) × 1010 cm−2).Aside from the cleaning effect, the subsequent dramatic drop in k0 with the density of vacancy defects in monolayer graphene showed that k0 remained constant at low densities but underwent a tenfold increase at a defect density of 1012 cm−2 (I(D)/I(G) ≅ 2.95)26. However, the high density of vacancy defects lowers the electrical performance of graphene. In our work, k0 is improved up to 12-fold (to 6.77 × 10−4 cm s−1) at a low H-sp3 defect density of nD = (1.0 ± 0.1) × 1010 cm−2 (I(D)/I(G) ≅ 0.4). Then, when nD continues to rise, k0 drops sharply to stabilize between 1.5 and 1.7 × 10−4 cm s−1  compared to untreated graphene  × 1010 cm−2. With the continuous growth of the defect density up to nD = (2.0 ± 0.7) × 1010 cm−2, μ exhibits a deep drop, indicating that the carrier transport in graphene is sensitive to the existence of even low densities of H-sp3 defects (nD ≤ (2.0 ± 0.7) × 1010 cm−2 corresponding to a distance LD of ~40 nm between the defects). For higher defect densities, however, the decrease of μ is less pronounced (till nD = (1.7 ± 0.4) × 1011 cm−2, that is LD~14 nm). The minimum conductivity (Gmin) changes with nD in Fig. Separately, when G) is proportional to 1/(nD)1/2 at high carrier density (far from the CNP)43. In consequence, μ is expected to decrease with increasing nD upon hydrogenation. Meanwhile, at low carrier density (near the CNP), G is proportional to (nimp)1/2 and Gmin is expected to reduce with the decrease of nimp. The data in Fig. Gmin and μ upon the initial hydrogenation (nD = (1.0 ± 0.1) × 1010 cm−2). This can be explained by considering the cleaning of adsorbates from the graphene surface. Particularly, hydrogenation slightly introduces H-sp3 defect while also removing surface short-range scatters outweighing the effect on the conductivity and mobility of graphene. Separately, the decrease of the DOS after hydrogenation contributes to the decrease of the density of intrinsic charge carrier n instead of affecting the carrier mobility of these charge carriers in graphene.Based on the Boltzmann theory, the conductivity of graphene (EF) with respect to the electrochemical potential of the redox couple in solution44. For example, for the electron to flow from the redox probe to graphene, the graphene EF that can be tuned by varying the potential applied to the graphene electrode or by sweeping the gate voltage, should at least align with the LUMO level of the oxidative molecule to allow an efficient electron transfer. For a non-adiabatic process, the DOS in graphene decides—whether or not—a basal plane electron could tunnel to the redox probe. Practically, the electron transfer occurs when the electronic resonance between the redox molecule and graphene is reached, that is for a given value of the applied potential, and is measured by studying how fast the electron transfer reaction can reach its equilibrium (kinetics)45. In short, the electrochemical kinetics (reflected by k0) of graphene relies on the DOS on the premise of non-adiabatic electron transfer.In electrochemistry, the kinetics of electron transfer from graphene to a redox probe is dependent on the electrochemical potential of electrons in graphene  at a specific EF: ρ = Cq,min/e2, where e is the electron charge46. In Fig. k0 as hydrogenation proceeds. During the first second (within the purple region), the ADOS decreases a little, however k0 increases dramatically, which can be mainly ascribed to the volatilization of hydrocarbon contaminants. That is, the hydrogen radicals first remove the hydrocarbon adsorbates to reveal the electrochemical activity of the underlying graphene, as the kinetic process involves interface-sensitive electron tunneling. Notably, H radicals can also attack the graphene lattice during the hydrocarbon cleaning and the resulting H-sp3 defect could lead to the observed decrease in the ADOS26. Upon further hydrogenation (the beginning of the blue region), the ADOS and k0 decrease sharply, which are mainly due to the modification of the graphene basal plane by hydrogen radicals. The decay of k0 with DOS agrees with the non-adiabatic electron transfer, in which the rate depends on the electronic properties of the electrode due to the weak electronic interaction between the redox mediator and the electrode, according to the Levich–Dogonadze theory47 and Fermi’s golden rule48. We would like to note here that the decrease in k0  in terms of defining the electrochemical current for sensing application.In 2D materials like graphene, its minimal quantum capacitance, k0 Fig.  is unlikμ/μuntre, where sp3 defects towards achieving a low electrochemical activity in GFET by suppressing its DOS. Interestingly, the boosted k0 upon H2 plasma cleaning reveals a relatively high electrochemical activity of the graphene basal plane, which was often believed to be inert and inactive in electrochemistry50.Figure entclass1pt{minimasp3 defects). We postulate that in untreated graphene, the electrochemical activity was  initially blocked by the presence of hydrocarbon adsorbates which are now removed by the hydrogen plasma51  results in the decrease of the DOS, a quantity considerably sensitive to the changes of electronic and chemical properties of graphene. Additionally, further hydrogenation of the graphene basal plane largely depresses k0 down to one fifth of its original value (pristine graphene), presumably by lowering its DOS. Interestingly, however, the mobility of graphene is preserved to a large extent , the introduced H radicals mainly sweep the hydrocarbon adsorbates away from the graphene surface. In particular, within the first second of hydrogenation we observed a large enhancement of the electrochemical activity on the surface of pristine graphene sp3 defects. Interestingly, the mild hydrogenation within 1–5 s largely preserves the basic electrical mobility while effectively depresses the electrochemical kinetics k0 and lowers the DOS in graphene, manifesting as a plausible way to improve the sensitivity of GFET. For the first time, we demonstrated that the electrochemical kinetics in single layer H-graphene is highly dependent on the ADOS, which supports the theory of non-adiabatic electron transfer on graphene. Additionally, the electrochemical activity of the pristine graphene basal plane can be restored by the removal of surface-adsorbed hydrocarbons using a low dose of hydrogen radicals, a result that will further promote graphene as an electrode for electrochemical studies. The correlation between the carrier mobility and the electrochemical kinetics suggests that the electrical conductivity of H-graphene is an important parameter to consider, for example, in GEC sensors. We believe our work will inspire several research communities to consider hydrogenated graphene as a potent material for sensing applications with performances going beyond previously reported (G)FET sensors.In summary, we have systematically probed the interplay between the in-plane electron transport and the electrochemical activity of the graphene basal plane by modulating the density of H-23. PETMP–TATATO (Sigma Aldrich) is a clean and biocompatible polymer usually used for dental restorative application24. After sufficient photo-initiated crosslinking reaction at room temperature (12 h in daylight), the whole stack (glass-glue-graphene-copper) was oxidized with an O2 plasma (60 W/0.5 mbar/2 min) to remove the trace of graphene that had grown on the backside of the copper substrate (i.e. the side now facing to the air). To fabricate the source and drain electrodes, both ends of the copper substrate (a strip of copper) were protected by a polymer film of cellulose acetate butyrate . Then an ammonium persulfate solution (0.5 M) was used to etch the non-protected copper foil to reveal the clean CVD graphene supported by the photopolymer and glass substrate without any possible polymer residues. Finally, the fabricated graphene devices were exposed to a hydrogen plasma for different durations to introduce defects with controlled densities.To fabricate the GFET devices, the graphene side of the copper growth substrate  is glued to a glass slide with a PETMP–TATATO polymerCommercially available pentaerythritol tetra(3-mercaptopropionate) and triallyl-1,3,5-triazine-2,4,6-trione  are used as monomers for the thiol-ene resin formulation. 4:3 volume proportion of PETMP–TATATO were selected for the preparation of the photopolymer.Capacitively coupled plasma system with the radio-frequency (RF) of 40 kHz and 200 W power from Diener electronic (Femto) was employed at room temperature. The base pressure of this system is <0.02 mbar. The parameters used for the controlled introduction of defects were 10 W/1.0 mbar for hydrogen plasma and 8 W/0.85 mbar for argon plasma. Specifically, a Faraday cage with grid was employed to shield all the energetic hydrogen ions to form a mild radical plasma to react with graphene.54) on Si/SiO2 substrate. Raman spectra of CVD graphene on PETMP-TATATO polymer was also performed . The gate voltage was applied on a AgCl/Ag wire as the reference electrode, at a sweep rate at 100 mV sCtot of an electrolyte-gated GFET, is composed of two components in series, quantum capacitance Cq and the electric double-layer capacitance Cdl. The Cdl for the KCl solution can be approximated as 10–20 µF cm−2 for a wide range of ionic concentration >1 mM55. Cq is relatively small (~1 µF cm−2) compared to the Cdl (in series) and thus dominates the total capacitance Cq ~ Ctot32. By calculating the Cq based on 1/Ctot = 1/Cq + 1/Cdl, we get the curves of Cq vs the potential distributed on graphene channel Vch (Vch = (VgCdl)/(Cdl + Cq)) for different hydrogenation times.As illustrated in Supplementary Figure The electrochemical experiments were carried out in a homemade one-compartment three-electrode electrochemical cell at ambient conditions. The working electrode is the CVD grown graphene and the counter electrode a platinum wire. All potentials in this work are reported with respect to a saturated Ag/AgCl reference electrode. A potentiostat/galvanostat  was used for the electrochemical measurements. The electrolyte, 0.1 M KCl, was prepared from KCl  and ultrapure water . The measured current was normalized to the geometric surface area of the working electrode and not corrected for Ohmic drop as the obtained currents were very low. Prior to the experiments, the cell containing the electrolyte solution was purged with argon to remove the dissolved oxygen.The data that support the findings of this study are available from the corresponding author on request.Supplementary Information


Cis-acting transcription factor binding sites and exhibit reduced promoter activity. The 5′UTR/ORF1 SpIREs also produce nonfunctional ORF1p variants. Finally, we demonstrate that sequence changes within the L1 5′UTR over evolutionary time, which permitted L1 to evade the repressive effects of a host protein, can lead to the generation of new L1 splicing events, which, upon retrotransposition, generates a new SpIRE subfamily. We conclude that splicing inhibits L1 retrotransposition, SpIREs generally represent evolutionary “dead-ends” in the L1 retrotransposition process, mutations within the L1 5′UTR alter L1 splicing dynamics, and that retrotransposition of the resultant spliced transcripts can generate interindividual genomic variation.Human Long interspersed element-1 (L1) retrotransposons contain an internal RNA polymerase II promoter within their 5′ untranslated region (UTR) and encode two proteins, (ORF1p and ORF2p) required for their mobilization . The evolutionary success of L1 relies on the continuous retrotransposition of full-length L1 mRNAs. Previous studies identified functional splice donor (SD), splice acceptor (SA), and polyadenylation sequences in L1 mRNA and provided evidence that a small number of spliced L1 mRNAs retrotransposed in the human genome. Here, we demonstrate that the retrotransposition of intra-5′UTR or 5′UTR/ORF1 spliced L1 mRNAs leads to the generation of spliced integrated retrotransposed elements (SpIREs). We identified a new intra-5′UTR SpIRE that is ten times more abundant than previously identified SpIREs. Functional analyses demonstrated that both intra-5′UTR and 5′UTR/ORF1 SpIREs lack  Long interspersed element-1 (L1) sequences comprise about 17% of the human genome reference sequence. The average human genome contains about 100 active L1s that mobilize throughout the genome by a “copy and paste” process termed retrotransposition. Active L1s encode two proteins (ORF1p and ORF2p). ORF1p and ORF2p preferentially bind to their encoding RNA, forming a ribonucleoprotein particle (RNP). During retrotransposition, the L1 RNP translocates to the nucleus, where the ORF2p endonuclease makes a single-strand nick in target site DNA that exposes a 3′ hydroxyl group in genomic DNA. The 3′ hydroxyl group then is used as a primer by the ORF2p reverse transcriptase to copy the L1 RNA into cDNA, leading to the integration of an L1 copy at a new genomic location. The evolutionary success of L1 requires the faithful retrotransposition of full-length L1 mRNAs; thus, it was surprising to find that a small number of L1 retrotransposition events are derived from spliced L1 mRNAs. By using genetic, biochemical, and computational approaches, we demonstrate that spliced L1 mRNAs can undergo an initial round of retrotransposition, leading to the generation of spliced integrated retrotransposed elements (SpIREs). SpIREs represent about 2% of previously annotated full-length primate-specific L1s in the human genome reference sequence. However, because splicing leads to intra-L1 deletions that remove critical sequences required for L1 expression, SpIREs generally cannot undergo subsequent rounds of retrotransposition and can be considered “dead on arrival” insertions. Our data further highlight how genetic conflict between L1 and its host has influenced L1 expression, L1 retrotransposition, and L1 splicing dynamics over evolutionary time. Long interspersed element-1 (L1) is a non-long terminal repeat (non-LTR) retrotransposon that comprises approximately 17% of human genomic DNA . HoweverHuman RC-L1s are approximately six kilobases (kb) in length ,18. TheyORF1 encodes an approximately 40-kilodalton (kDa) protein (ORF1p)  that concis to form a ribonucleoprotein particle (RNP) ), but none in older  L1 subfamilies  (csc.edu)   (97/622 (comprising about 2.0% of previously annotated full-length L1s in that subfamily) and the L1PA6 subfamily contains only one SpIRE97/622 (comprising 0.1% of previously annotated full-length L1s in that subfamily)  . The L1Pbfamily) . Seven Sequences  86]..97/622 s98U99/A788G789 and G98U99/A974G975 splicing events identified by Belancio and colleagues  . We. We98U99equences  78,79]..98U99/A797/622, SpIRE97/790, and SpIRE97/976 sequences. We first examined the post-integration  site of each SpIRE in the HGR sequence. We then used the genomic sequences flanking each SpIRE to reconstruct a putative pre-integration  site. Many of the SpIRE sequences, especially those from older L1 subfamilies, have degenerate poly(A) tails at their 3′ ends, which, in some cases, made it difficult to reconstruct the putative pre-integration site to bp resolution (https://www.ncbi.nlm.nih.gov/refseq/) .,50.97/6297/622/L1.3 but not in cells transfected with the pCEP/GFP control , or pCEP/GFP control vectors . The mneoI cassette consists of an antisense copy of a neomycin phosphotransferase gene whose coding sequence is interrupted by an intron that resides in the same transcriptional orientation as the L1 )pression , but not97/976) that contains a defective promoter and, if transcribed and translated, would produce amino-terminal truncated versions of ORF1p. If the truncated version(s) of ORF1p were nonfunctional, we reasoned that the 5′UTR/ORF1 splicing event would lead to an L1 mRNA that is compromised for an initial round of retrotransposition in cis. Indeed, RNAs derived from pPL97/976/L1.3 could not retrotranspose despite expression being driven by CMV  with a series of “driver” L1 expression plasmids that lack the mneoI retrotransposition indicator cassette  or pCEP4) did not support retrotransposition in trans  to a favorable splicing context in L1PA2 and L1PA1 subfamily members  could bind within L1PA3 and L1PA4 5′UTRs to repress their expression . Intriguequences . Thus, w members .RP [accession #AF148856]) , an, an98U99activity . Consist98U99 SD, northern blotting experiments revealed that the vast majority of L1 5′UTRs are not subject to splicing  . Al788G789 SA is about 70 MY old and is conserved through the L1PA15B subfamily  subfamily  U169 recA1 endA1 hsdR17  phoA supE44 λ- thi-1 gyrA96 relA1) . Competent cells were generated as described previously ) and then twice with high stringency wash solution . The washed membranes were placed in a film cassette  and exposed to Amersham Hyperfilm ECL  overnight at −80°C. Films were developed using a JP-33 X-Ray Processor .Northern blot experiments were performed as previously described . Briefly32-UTP radiolabeled riboprobes were generated using the MAXIscript T3 system (Thermo Fisher Scientific). Briefly, oligonucleotide primers were used to PCR amplify portions of the L1.3 5′UTR [32-UTP (10 mCi/mL), and 2 μL of T3 RNA polymerase. The reaction components then were mixed and brought to a total volume of 20 μL using nuclease-free water in a 1.5-mL Eppendorf tube, which was incubated at 37°C for 10 minutes in a heating block. Unincorporated nucleotides were subsequently depleted using the Ambion NucAway Spin Columns (Thermo Fisher Scientific) following the protocol provided by the manufacturer. To generate a control β-actin riboprobe, the pTRI-β-actin-125-Human Antisense Control Template (Applied Biosystems) was used in T3 labeling reactions. Biological triplicates of each northern blot exhibited similar results.Northern blot probes were prepared as previously described . Strand-.3 5′UTR  (L1.3 nuOligonucleotide sequences were used to generate northern blot probes. A T3 RNA polymerase promoter sequence was included on the antisense (AS) primer used to generate the antisense riboprobe (underlined below):L1.3 5′UTR 7–99 Sense: 5′-GGAGCCAAGATGGCCGAATAGGAACAGCT-3′AATTAACCCTCAAAGGGACCTCAGATGGAAATGCAG-3′L1.3 5′UTR 7–99 AS: 5′-L1.3 5′UTR 103–336 Sense: 5′-GGGTTCATCTCACTAGGGAGTG-3′AATTAACCCTCACTAAAGGGTATAGTCTCGTGGTGCGCCG-3′L1.3 5′UTR 103–336 AS: 5′-Luciferase 3′ FFLuc Sense: 5′-GGCAAGATCGCCGTGAATTCTCAC-3′AATTAACCCTCACTAAAGGGCCTGGCGCTGGCGCAAGCAGC-3′Luciferase 3′ FFLuc AS: 5′-https://imagej.nih.gov/ij/download.html) [WTLUC and pPLSDmLUC lanes were determined and normalized to the actin loading control. Three independent northern blots were subject to quantification. We then computed that average intensity of the bands and calculated a standard deviation. As reported in the text (SDmLUC mRNA is about 18% the level of pPLWTLUC mRNA with a standard deviation of ±3.1%.Northern blot bands were quantified using the ImageJ software (ad.html) . The intthe text , the ste4 HeLa cells were plated into each well of a 6-well plate (BD Biosciences). Approximately 24 hours later, each well was transfected using a transfection mixture of 100 μl Opti-MEM (Life Technologies), 3 μl of FuGENE6 transfection reagent (Promega), and 1 μg plasmid DNA . Each transfection was performed as a technical duplicate . Approximately 24 hours post-transfection, the transfected cells were washed once with ice-cold 1X PBS and the cells in each well were subjected to lysis for 15 minutes at room temperature using 500 μl of the 1X Passive Lysis Buffer supplied by the manufacturer. Following homogenization of the lysate by manual pipetting, 60 μl of the lysate from each well of the 6-well tissue culture plate was distributed equally in 3 wells of a 96-well white opaque, optically transparent top plate (BD Biosciences), allowing six luminescence readings for each transfection condition . The 96-well plate then was subject to luciferase detection assays using a GloMax-Multi Detection System (Promega) following the manufacturer’s protocol. Luminescence readings from the six technical replicates were averaged to give a single normalized luminescence reading (NLR). This assay then was performed in biological triplicate, yielding three independent NLRs. The resultant data were subsequently analyzed using a Student one-tailed t test. Error bars indicate the standard deviation. Luminescence readings from lysis buffer alone and from lysates derived from untransfected cells were included used as negative controls.Luciferase assays were performed using the Dual-Luciferase Reporter Assay System  following the manufacturers protocol. Briefly, 2×10Taq DNA Polymerase (Thermo Fisher Scientific), following the manufacturer’s protocol. The REVLUC primer was used to synthesize first-strand cDNA. The FWD5′UTR and REVLUC primers then were used to amplify the resultant cDNAs (see sequences below). For RT-PCR experiments in Poly(A) selected mRNA from transfected HeLa-JVM cells in a T-175 tissue culture flask was collected as previously described for northern blots. The resultant mRNAs were subjected to targeted RT-PCR using SuperScript III One-Step RT-PCR System, with Platinum The following oligonucleotide sequences were used in the RT-PCR experiments:FWD5′UTR: 5′-GGAACAGCTCCGGTCTACAGCTCCC-3′REVLUC′ 5′-CCCTTCTTAATGTTTTTGGCATCTTCC-3′g for 30 minutes to clear the lysate. The resultant supernatant (approximately 0.4 mL) was designated as the WCL. Alternatively, the supernatant fraction was subject to RNP collection, as previously described [g for 2 hours at 4°C. Following ultracentrifugation, the supernatant was aspirated and the resultant RNP pellet was suspended in 100 μL of water supplemented with 1X Complete Mini EDTA-free Protease Inhibitor Cocktail (Roche Applied Science). Bradford assays  were used to determine protein concentrations. WCLs generally yielded 15–19 μg/μL of protein. RNP preparations yielded 6–10 μg/μL of protein. The protein samples were stored at −80°C.The plating and transfection of HeLa-JVM cells in T-175 tissue culture flasks was performed as detailed above in the mRNA isolation section except that HeLa-JVM cells were subjected to selection in DMEM-complete medium supplemented with 200 μg/ml of hygromycin B (Thermo Fisher Scientific) 48 hours post-transfection and the selection medium was changed every other day for 7 days. The hygromycin resistant HeLa-JVM cells were harvested 9 days post-transfection as described in the mRNA isolation section. The cell pellets were frozen at −80°C overnight. The following day, pellets were lysed for 15 minutes on ice by incubation in 0.5 mL of lysis buffer: 10% glycerol, 20 mM Tris-HCl pH 7.5, 150 mM NaCl, 0.1% NP-40 (IGPAL) , and 1X Complete Mini EDTA-free Protease Inhibitor Cocktail . The resultant protein lysates then were centrifuged at 15,000 × escribed . BrieflyWestern blot experiments were performed as previously described, with minor modifications . Briefly3 HeLa-JVM cells/well were plated in 6-well tissue culture dishes (BD Biosciences). Approximately 24 hours post-plating, transfections were performed using a mixture containing 100 μl Opti-MEM (Life Technologies), 3 μL FuGENE6 (Promega) transfection reagent, and 1 μg L1 plasmid DNA per well of a 6-well plate. Approximately 24 hours post-transfection, the media was replaced with DMEM-complete medium to stop the transfection. Three days post-transfection, the tissue culture medium was replaced and the cells were grown in DMEM-complete medium supplemented with 400 μg/mL of G418 (Life Technologies) to select for retrotransposition events. After approximately 12 days of G418 selection, the resultant G418-resistant foci were washed with ice cold 1X PBS, fixed to the tissue culture plate by treating them for 10 minutes at room temperature in a 1X PBS solution containing 2% paraformaldehyde (Sigma Aldrich) and 0.4% glutaraldehyde (Sigma Aldrich), and stained with a 0.1% crystal violet solution for 30 minutes at room temperature to visualize the G418-resistant foci. As a transfection control, parallel 6-well tissue culture dishes of HeLa-JVM cells were co-transfected with 0.5 μg of an L1 expression plasmid and 0.5 μg of a pCEP/GFP expression plasmid (Stratagene). Three days post-transfection, the transfected HeLa-JVM cells were subjected to fluorescence detection on an Accuri C6 Flow Cytometer (BD Biosciences) to determine the transfection efficiencies  for each experiment [The cultured cell retrotransposition assay was conducted as previously described ,61,126. Trans-complementation retrotransposition assay was performed as previously described with minor modifications [5 HeLa-JVM cells were plated into 60-mm dishes (BD Biosciences). Approximately 24 hours post-plating, transfections were performed using a mixture containing 93 μl of Opti-MEM (Life Technologies), 6 μl of FuGeneHD (Promega), and 2 μg plasmid DNA . Subsequent steps of the retrotransposition assay were carried out as described above. As a transfection control, parallel 60-mm tissue culture dishes of HeLa-JVM cells were co-transfected with 0.5 μg of an L1 “reporter” plasmid, 0.5 μg of an L1 “driver” plasmid, and 1 μg of a pCEP/GFP expression plasmid (Stratagene). Three days post-transfection, the transfected HeLa-JVM cells were subjected to fluorescence detection on an Accuri C6 Flow Cytometer (BD Biosciences) to determine the transfection efficiencies  for each experiment [The ications . Brieflyperiment . The tra5 cells per well. The next day, cells were transfected with 1 μg of retrotransposition reporter plasmid using 4 μL of Fugene 6 transfection reagent. Approximately 48 hours after transfection, culture media was supplemented with puromycin (5 μg/mL) to select for transfected cells. After 4 days of puromycin selection (about 6 days post-transfection), the cells were harvested using trypsin and resuspended in PBS. The percentage of GFP positive cells was determined by flow cytometry using an Accuri C6 flow cytometer (BD Biosciences). Three wells were transfected for each plasmid  and 10,000 events were analyzed from each well of transfected cells. Live gating was set using forward scatter versus side scatter profile. Events that fell within the live gate were analyzed for fluorescence. Cells transfected with the retrotransposition-dead mutant pL1RP(JM111)-EGFP were used as a negative control to set GFP gating.EGFP retrotransposition assays were carried out as previously described . BrieflyS1 Fig97/622, SpIRE97/790, and SpIRE97/976) is indicated at the top of each table. Column 1 indicates the L1 subfamily. Column 2 indicates the number of SpIREs present in the subfamily. Column 3 indicates the number of full-length L1s in each subfamily. Column 4 indicates SpIREs as a percentage (%) of full-length L1s. (B) Evolutionary conservation of L1 splice sites. The panels show the conservation of the SD site in the L1 5′UTR  as well as SA sites in the L1 5′UTR  and ORF1 . Consensus sequences and alignment of those sequences that span the L1PA1–L1PA16 subfamilies were downloaded [(A) SpIREs present in the HGR. The class of SpIRE (SpIREwnloaded  and manu(TIF)Click here for additional data file.S2 Fig97/622/L1.3 mRNA visible in the second lane of the first blot (about 2.2 kb). The orange arrowhead indicates an mRNA that may be initiated downstream of the canonical transcriptional start site within the L1 5′UTR that is detected with the luciferase probe (about 2.0 kb). Construct names are indicated above the gel lanes; UTF = untransfected HeLa-JVM cells. The probe used in the northern blot experiment is indicated below the autoradiograph. Molecular weight standards using Millenium RNA Markers (kb) are indicated to the left of the autoradiograph panels. (B) Schematic of oligonucleotides use in RT-PCR experiments. The relative positions of the oligonucleotide primers used to reverse transcribe  and then amplify  the L1/firefly luciferase cDNA products. (C) Results from control RT-PCR experiments. A 1.2% agarose gel depicting the results from a representative experiment conducted without the addition of RT. DNA from pPL97/622LUC served as a positive control for PCR amplification (yellow #). The final lane contains RT but no mRNA (−mRNA). RT-PCR assays were conducted at least 3 independent times, yielding similar results. L1, Long interspersed element-1; RT-PCR, reverse transcription PCR; UTR, untranslated region.(A) A longer exposure of the northern blots depicted in (TIF)Click here for additional data file.S3 Fig(A) Representative ORF1p western blots from RNP fractions. Molecular weight standards (kDa) are indicated to the left of the image. The predicted sizes of full-length ORF1p (black arrowhead), and the N-terminal truncated ORF1p variants (orange and blue arrowheads) are indicated in the image. Construct names are indicated above the image; pCEP/GFP = negative control. The antibodies used in the western blot experiments are indicated to the left (α-N-ORF1p) and right (α-C-ORF1p) of the images. The eIF3 protein (110 kDa) served as lysate loading control. Western blots were performed three times, yielding similar results. (B) Representative western blot from WCL and RNP fractions. Molecular weight standards (kDa) are indicated to the left of the image. Molecular weight standards in the “M” lanes likely cross-reacted with the α-T7 antibody. The predicted sizes of full-length ORF1p (black arrowhead), and the N-terminal truncated ORF1p variants (orange and blue arrowheads) detected by the α-T7 gene10 antibody (α-T7) in WCLs (left) and RNP preparations (right) are highlighted on the gel. Construct names are indicated above the image; pCEP/GFP and untagged pJM101/L1.3 served as negative controls. The eIF3 protein (110 kDa) served as loading controls. The bands at about 30 and 45 kDa are cross-reacting proteins. Western blots were performed three times, yielding similar results. α-C-ORF1p, C-terminal ORF1p antibody; α-N-ORF1p, N-terminal ORF1p antibody; eIF3, eukaryotic initiation factor 3; GFP, green fluorescent protein; ORF, open reading frame; RNP, ribonucleoprotein particle; SpIRE, spliced integrated retrotransposed element; UTR, untranslated region; WCL, whole cell lysate.(TIF)Click here for additional data file.S1 Data97/622-6), and (4) a chromosomal location indicating the first and last nucleotide of the designated “filled site” containing the SpIRE and its immediate 5′ and 3′ flaking sequences in the HGR. The designation “empty site”  site represents the hypothetical reconstructed HGR sequence prior to SpIRE integration. The 5′ plain text/bolded text junction represents the hypothetical position of the putative L1 EN cleavage site on top-strand genomic DNA. The designation “filled site”  represents the SpIRE sequence identified in the HGR. Bolded nucleotides in the “filled site” sequence represent putative TSDs flanking the SpIRE. Dark green shading indicates the first nucleotide of the SpIRE 5′UTR. Red shading indicates the splice junction in the SpIRE. Underscored and italicized nucleotides represent the poly(A) tract at the 3′ end of some SpIREs. Yellow shading indicates possible untemplated or putative transduced sequences before the 5′ or 3′ TSD, respectively. Light blue shading indicates nucleotides in the putative 5′ TSD that differ from nucleotides present in the 3′ TSD. Gray shading indicates possible inversion junctions within the SpIRE sequence. Pink shading indicates additional sequences that interrupt the insertion. EN, endonuclease; HGR, human genome reference; L1, Long interspersed element-1; poly(A), polyadenosine; SpIRE, spliced integrated retrotransposed element; TSD, target site duplication; UTR, untranslated region.Each SpIRE is annotated to contain the following: (1) a clone number, (2) the L1 subfamily, (3) the class of SpIRE and the designated clone number Click here for additional data file.S1 Table97/622)-6). Column 2 indicates the L1 subfamily. Column 3 indicates the chromosome. Column 4 indicates the first nucleotide of the “filled site” in the HGR. Column 5 indicates the length (bp). Column 6 indicates whether the insertion resides within a gene and the name of that gene. Column 7 indicates the transcriptional orientation of the SpIRE with respect to the gene transcriptional orientation . Column 8 indicates the calculated L1 EN cleavage sequence of the insertion, where (/) indicates the location of the endonucleolytic nick. Column 9 indicates the putative size of the TSD. Column 10 indicates whether the insertion contains an L1-mediated sequence transduction. Column 11 indicates the length in bp of putative untemplated, mismatched, or 3′ transduced sequences flanking the SpIRE. Column 12 indicates additional major deletions within the SpIRE and indicates the nucleotides that have been lost in reference to L1.3 (accession #L19088). Column 13 provides additional comments about each SpIRE . EN, endonuclease; HGR, human genome reference; L1, Long interspersed element-1; SpIRE; spliced integrated retrotransposed element; TSD, target site duplication.Each tab contains information for a single SpIRE. Column 1 indicates the class of SpIRE and the designated clone number Click here for additional data file.S2 TableSee (XLSX)Click here for additional data file.S3 TableSee (XLSX)Click here for additional data file.S4 TableSee (XLSX)Click here for additional data file.S5 TableSee (XLSX)Click here for additional data file.S6 TableSee (XLSX)Click here for additional data file.S7 TableRP-EGFP is set to 100% (% Rtsn.). Column H indicates the standard deviation as a percentage of the retrotransposition efficiency (Std. Dev. % Rtsn). L1, Long interspersed element-1.See (XLSX)Click here for additional data file.


TDP-43 is a primarily nuclear RNA-binding protein, whose abnormal phosphorylation and cytoplasmic aggregation characterizes affected neurons in patients with amyotrophic lateral sclerosis and frontotemporal dementia. Here, we report that physiological nuclear TDP-43 in mouse and human brain forms homo-oligomers that are resistant to cellular stress. Physiological TDP-43 oligomerization is mediated by its N-terminal domain, which can adopt dynamic, solenoid-like structures, as revealed by a 2.1 Å crystal structure in combination with nuclear magnetic resonance spectroscopy and electron microscopy. These head-to-tail TDP-43 oligomers are unique among known RNA-binding proteins and represent the functional form of the protein in vivo, since their destabilization results in loss of alternative splicing regulation of known neuronal RNA targets. Our findings indicate that N-terminal domain-driven oligomerization spatially separates the adjoining highly aggregation-prone, C-terminal low-complexity domains of consecutive TDP-43 monomers, thereby preventing low-complexity domain inter-molecular interactions and antagonizing the formation of pathologic aggregates. TDP-43 aggregation is observed in amyotrophic lateral sclerosis. Here the authors combine X-ray crystallography, nuclear magnetic resonance and electron microscopy studies and show that physiological oligomerization of TDP-43 is mediated through its N-terminal domain, which forms functional and dynamic oligomers antagonizing pathologic aggregation. In ALS the loss of motor neurons leads to fatal paralysis typically within 1–5 years of onset, whereas FTD causes severe atrophy of the frontal and temporal lobes leading to behavioral and language dysfunction5. The RNA-binding protein (RBP) TDP-43 (Trans-activation response element (TAR) DNA-binding protein 43) is mislocalized and aggregated into neuronal cytoplasmic ubiquitinated inclusions in the vast majority of ALS, as well as approximately half of FTD cases2. Even though the loss of normal nuclear localization and cytoplasmic TDP-43 aggregation correlates with neurodegeneration, the exact mechanisms of neurotoxicity remain elusive. Moreover, the molecular mechanisms triggering TDP-43 pathology in ALS and FTD remain poorly understood, in part due to lack of high-resolution structural information of TDP-43 in the physiological and pathological state.Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are two intimately related adult-onset neurodegenerative disorders8. TDP-43 also contains a C-terminal prion-like or low-complexity domain (LCD)9, mediating protein–protein interactions10 and also its incorporation into stress granules11, potentially via its property to phase separate13. Furthermore, the TDP-43 LCD is crucially involved in disease, since it is proteolytically cleaved and abnormally phosphorylated, leading to its cytoplasmic accumulation in complex with the full-length protein14. Finally, TDP-43 contains an N-terminal region spanning its first 80 amino acids, whose role in the function and/or malfunction of the protein remains unclear. In fact, due to the lack of sequence homology of this region with any known structures, the N-terminal domain (NTD) of TDP-43 was initially thought to be unstructured. However, it was recently reported that monomeric TDP-43 NTD can adopt a Ubiquitin-like15 or DIshevelled and aXin (DIX)-domain-like16 fold in solution. Intriguingly, while several recent studies highlighted the importance of NTD for functional TDP-43 dimerization17 and nucleic acid interaction21, others argued that the same domain promoted pathologic cytoplasmic aggregation and neurotoxicity23. Moreover, a small fraction of TDP-43 was reported to exist as dimers in cells, which led to speculation that TDP-43 dimers may initiate or “seed” the formation of high molecular weight pathologic TDP-43 aggregates17.TDP-43 is a nucleo-cytoplasmic shuttling protein comprised of two RNA recognition motifs (RRMs) that bind TG-/UG-repeat nucleic acids in a sequence-specific fashion and are indispensable for its roles in RNA metabolism24 involved in Wnt signaling, is unique among RBPs and broadens our understanding of TDP-43 function. Most excitingly, our findings indicate that stabilization of functional TDP-43 oligomers could have therapeutic potential by counteracting pathologic aggregation and restoring nuclear function.Here, we show that physiological TDP-43 exists as nuclear oligomers that are distinct from cytoplasmic complexes formed upon cellular stress or pathologic aggregates. To elucidate the molecular basis of physiological TDP-43 oligomerization, we determined the crystal structure of TDP-43 NTD at 2.1 Å resolution, which revealed an unprecedented mode of head-to-tail interactions between monomers generating solenoid-like polymers. Consistent with the crystal structure, solution NMR spectroscopy confirmed the dynamic nature of inter-molecular and low micromolar affinity electrostatic interactions that stabilize these polymers. Destabilizing oligomerization by point mutations resulted in loss of TDP-43 regulation of alternative splicing of known neuronal RNA targets, indicating that these dynamic TDP-43 oligomers are the functional form of the protein in vivo. Tripartite GFP complementation experiments in cells illustrate that physiological TDP-43 oligomerization prevents LCD intermolecular interactions. Importantly, we show that NTD-driven TDP-43 oligomerization antagonizes pathologic aggregation. This dynamic head-to-tail polymerization of TDP-43, which is reminiscent of DIX domains25. We performed DSG cross-linking in normal human fibroblasts and brain tissue, followed by nucleo-cytoplasmic fractionation and immunoblotting, in order to analyze the subcellular localization of TDP-43 oligomers and monomers. Upon increasing the DSG concentration, a specific ladder of slowly migrating TDP-43 complexes is detected, consistent with increasing multimeric species of TDP-43 in human fibroblasts , which is known to exist exclusively as cytoplasmic homodimers28, showed only the presence of dimeric protein without any non-specific cross-linking, even at the highest DSG concentration used both in human fibroblasts and mouse brain slices , a membrane-permeable cross-linker, which allows preservation of native protein–protein interactionssts Fig.  and humasts Fig. . The slists Fig.  is due tsts Fig. .The highsts Fig. . To furtsts Fig. . Howeversts Fig. . Interessts Fig. . This is31. In order to determine if normal nuclear TDP-43 oligomerization is changed upon cellular stress, we treated mouse cortico-hippocampal organotypic slice cultures with sodium arsenite and then performed in situ cross-linking by DSG followed by nucleo-cytoplasmic fractionation  in amounts amenable for structural analysis  that diffracted to 2.1 Å resolution  failed. However, crystals were grown in sodium cacodylate buffer and consequently starting phases were obtained by single-wavelength anomalous dispersion making use of the strong anomalous signal that was generated by the arsenic K-edge  are found in the asymmetric unit, whose structures are virtually identical, indicated by a root-mean square deviation (r.m.s.d) of 0.612 Å (Cα atoms of residues 1–80). Crystallographic symmetry generated three intertwined superhelices of TDP-43 NTDs packed against each other, building a tubular superstructure  of TDP-43 NTD is positively charged and interactions are made by residues mainly from β3- (Asn45), β4- , β5- (Asn76) strands and the β3-β4 loop (Ser48) Fig. . In cont2 at the interface between Mol-A and Mol-B within the asymmetric unit and 648.1 Å2 between Mol-B and Mol-A’ between two adjacent asymmetric units . This area is comparable to the buried surface area in the oligomeric structures formed by the DIX domains of both Axin-1 (768 Å2) and Dishevelled Segment Polarity Protein-2 (425–470 Å2).Thus, the two NTD molecules in the asymmetric unit interact extensively with one another in head-to-tail fashion Fig. , burying1H-15N Heteronuclear Single Quantum Coherence (HSQC) spectrum  rum Fig. . Interesrum Fig. . Sequencrum Fig.  revealedrum Fig. . These arum Fig. . Furtherrum Fig. . InteresTo directly visualize the TDP-43 NTD polymers under native conditions, we used transmission electron microscopy (TEM) on negatively stained samples of purified recombinant protein in the buffer conditions used for NMR experiments. TDP-43 NTD formed fibrillar structures of variable length and diameter, even at low protein concentrations of 100 μM Fig. . Some fi1H-15NHSQC spectra of mutant proteins compared to wild-type NTD occurred, due to disruption of the interaction interface , Dnajc5 (DnaJ homolog subfamily C member 5), Poldip3 (polymerase delta-interacting protein 3), Eif4h (eukaryotic translation initiation factor 4H), Sema3 (semaphorin-3F), and Tsn (translin), which was rescued by co-transfection with wild type TDP-43  or the 11th (T11) β-strand of GFP, which when in close proximity to each other can reconstitute fluorescence of the remaining GFP1–9 protein, which by itself lacks fluorescence ability40 44.TDP-43 is a multifunctional and essential RBP, whose abnormal cytoplasmic misaccumulation is integrally linked to the pathogenesis of ALS and FTD24. Interestingly, despite very low sequence homology  domains undergo similar head-to-tail interactions to generate not only homo- but also heteropolymers that have been implicated in modulating signalosome assemblyogy Fig. , TDP-43 ogy Fig. , and theogy Fig. , while oNTD Fig. . Neverth1 and possesses high propensity to self-associate29, phase separate13, and aggregate2. Our current data strongly support the hypothesis that formation of physiological NTD-mediated, nuclear TDP-43 oligomers can counteract cytoplasmic aggregation  and its subsequent inhibition by Trypsin inhibitor (Gibco). The cells were washed twice with phosphate-buffered saline  and then resuspended in 100 μL PBS. Fresh 20 mM DSG (Sigma) stocks were prepared in dimethylsulphoxide. The reaction was started by adding an appropriate volume of the DSG stock to the 100 μL cell suspension (with Roche protease inhibitor cocktail in PBS) at a final DSG concentration of 50, 100, 250, 500, and 1000 μM. The reaction was incubated at room temperature with shaking for 30 min and then quenched by addition of Tris base  for 15 min at room temperature (RT).Cross-linking in cells and tissues was performed by using DSGg for 5 min at RT. Supernatants were rejected, and the pellets underwent cross-linking by addition of 1000 μL of 1 mM DSG in PBS/PI for 100 mg tissue. The samples were incubated at 37 °C for 30 min with shaking followed by quenching with 20 mM Tris base  for 15 min.For cross-linking in human and mouse brain , tissue samples were chopped twice on a Tissue Chopper . In between each chopping, the sample was rotated by 90° to ensure fine mincing of the brain tissue. The finely minced brain samples were then transferred to 15 mL polypropylene tubes (GreinerBio-One)and resuspended in 5 mL PBS/PI by gentle pipetting (3–4 times). From this tissue suspension, homogeneous aliquots were transferred to 1.5 mL eppendorf tubes , that were centrifuged at 1500x37. After centrifugation at 3000xg for 5 min at 4 °C, the supernatant was collected as the cytoplasmic fraction and the pellet as the nuclear fraction.Intact cell pellet or human and mouse tissue pellet following cross-linking were lysed in lysis buffer ) by incubating 5 min on ice45. Two slices each were cultured in Millicell-CM inserts (Millipore) in forebrain culture medium ). Slice cultures were kept in culture for 3 weeks  before the start of the experiment to stabilize them after the cutting procedure. To induce oxidative stress, 0.25 mM sodium (meta)arsenite (Sigma) was added to the medium for 2 h. For crosslinking experiments, slices were subsequently scraped into PBS and a homogenous cell suspension was prepared by pipetting up and down. The cross-linking procedure was performed as described above with 1 mM DSG. Anti-TDP-43 antibody . Bacterial cultures were induced at OD600 = 1.0 by adding 1 mM isopropyl β-d-thiogalactoside  at 25 °C for 30 h in minimal M9 medium containing 15NH4Cl as nitrogen source and/or 13C-D-glucose as a carbon source for labeled protein samples. For unlabeled protein samples, bacterial cultures were grown in Luria-Bertani (LB) medium (Invitrogen). Following cell harvesting, the cell pellets were resuspended in lysis buffer that comprised of 50 mM sodium phosphate, pH 8.0, 300 mM NaCl, 10 mM imidazole, 1 mM dithiothreitol (DTT), 1 mM phenylmethylsulphonyl fluoride. Cell pellets were resuspended in 20 mL of lysis buffer per liter of bacterial culture pellet. The resuspended cells were then lysed by microfluidizer. Total cell lysates were then centrifuged . Pellet was discarded and the supernatant containing soluble protein was used for further purification. Protein purification was achieved on ÄKTA Prime purification system (Amersham Biosciences) where the N-terminal His-tagged protein was purified with Ni2+-affinity chromatography under native conditions. The bound protein was eluted from Ni2+-columns using a linear gradient of imidazole in elution buffer . The fractions containing pure protein  were pooled and dialyzed overnight in a buffer without imidazole . Finally, the protein was concentrated using a Vivaspin 5000 MWCO concentrator (Sartorius Stedium Biotech). Further protein purification was achieved by size exclusion chromatography using a Superdex75 column . The purified protein was aliquoted and stored at −20 °C.For bacterial recombinant protein expression, the DNA nucleotide sequence coding for full-length human TDP-43 (414 amino acids) was PCR amplified and cloned into the expression vector—pET-28a(+). Subsequently, the region corresponding to the NTD (amino acids 1–80) was sub-cloned into the pET28a(+) expression vector. Point mutations were incorporated by site-directed mutagenesis PCR using high fidelity Phusion DNA Polymerase (New England biolabs) with primers introducing DNA base substitutions (Supplementary Table −1 was mixed in a 3:1 (protein:mother liquor) ratio, dispersed on a 96 well Intelli-Plate 96-3 LVR crystallization plate (Art Robbins) using a Phoenix crystallization robot (Art Robbins) and incubated at 4 °C against 50 µL reservoir solution. An initial crystallization hit was found in 50 mM Tris/HCl pH 8.5, 8%(w/v) PEG 8000. These crystals were crushed and used for matrix microseeding in Crystal Screen 1&2 from Hampton Research . This led to new crystals in 0.1 M sodium cacodylate pH 6.5 and 1.4 M sodium acetate and a fine screen with differing pH and molarity of the sodium acetate was set-up, which resulted in crystals with good diffraction quality.To screen for initial crystallization conditions, commercially available sparse matrix screens from Hampton Research  and Molecular Dimensions  were used to set up sitting-drop vapor diffusion experiments. For this, purified protein at a concentration of 10 mg mL46. Due to the formation of weak ice-rings, data between 2.235 and 2.245 Å resolution were excluded. The outer resolution limit was estimated using the CC1/247 and I/σ(I) criteria48. According to these criteria diffraction data were cut at 2.1 Å resolution with CC1/2 and I/σ(I) of 59.2% and 1.6% in the outer resolution shell, respectively.For data collection, crystals were incubated in mother liquor containing 30% ethylene glycol and flash-cooled in liquid nitrogen. Data were collected at beamline X06DA at the Swiss Light Source  with a wavelength of 1 Å using the PILATUS 2M-F detector system  and processed with XDS49 programs in the software suite hkl2map50. An initial search in SHELX-D revealed four cacodylate ions and SHELX-E was used to build an initial poly-alanine trace with 20 cycles of density modification and a solvent content of 50%. After that, phenix autobuild52 was used to build the initial model. Further model building was done in COOT53 and refinement was done with REFMAC554 in the CCP4-suite55, phenix refine56 and BUSTER . Table 57, buried surface area was calculated with PISA58, and the interacting residues were drawn with Ligplot+59. To analyze the superhelical parameters of the structure we used the make_symmdef_file.pl script from the Rosetta Symmetry framework60 in combination with the Cα model from the asymmetric unit. All structural figures were generated in Pymol61.Using the anomalous signal from the dimethyl arsinic anion (cacodylate) linked to cysteine residues 39 and 50, phases and an initial poly-alanine model were obtained using the SHELX-C/D/Ehttp://www.cgl.ucsf.edu/home/sparky). For TDP-43 NTD backbone assignments, the following NMR experiments were measured (in H2O)—two-dimensional (2D) 15N–1H HSQC, 2D 13C–1H HSQC, three-dimensional (3D) HNCA, 3D HNCOCA, 3D CACBCONH, 3D HCcH TOCSY, 3D hCCH TOCSY, 3D 15N-, and 13C-edited Nuclear Overhauser SpectroscopY (NOESYs).The following NMR spectrometers were used for measuring the NMR experiments in this study—Bruker AVIII-500 MHz, AVIII-600 MHz, AVIII-700 MHz, and Avance-900 MHz that were equipped with cryoprobes. TopSpin2.1/TopSpin3.0 (Bruker) was used to perform data processing while the analysis was done in Sparky (Purified wild type and mutant TDP-43 NTD (100–500 μM) were applied to carbon-coated grids, stained with 2% (w/v) uranyl acetate and analyzed at various magnifications on 100 kV transmission electron microscope (Philips CM 100).2. Mouse multipotent neural progenitor or stem-like C17.2 cells  were grown on cell culture dishes in DMEM supplemented with 10% fetal bovine serum and 1% NEAA, at 37 °C in an atmosphere of 5% CO2. Both NSC-34 and C17.2 cell lines were authenticated by the respective sources and were checked for Mycoplasma contamination in our laboratory.Mouse motor neuron-like hybrid cell line  were grown on cell culture dishes coated with matrigel in Dulbecco’s modified Eagle’s/F-12 medium (DMEM) supplemented with 10% fetal bovine serum, at 37 °C in an atmosphere of 5% CO6cells) was obtained with Lipofectamine 2000 (Life technologies) according to the manufacturer’s instruction. After 12 h incubation with transfection reagents, the medium was replaced with normal growth medium. Depletion of endogenous mouse TDP-43 was performed by RNA interference as described previously38. The sequence of TDP-43 siRNA used is 5′-AGGAAUCAGCGUGCAUAUAUU-3′ and were used at 20 nM concentration. The sequence of control siRNA used is 5′-GUGCACAUGAGUGAGAUUU-3′.For RNA splicing rescue experiments, transfections for transient over-expression of each vector  and the plate was kept for at least 24 h in the dark before imaging. For the bipartite and tripartite GFP complementation assays, C17.2 cells were transiently transfected directly on poly-d-lysine-coated 8-well microscope slides with Lipofectamine 3000 and a total of 0.3 µg of DNA per well. Cells were fixed 24 h later with 4% PFA followed by treatment with 0.3% Triton X-100 and 5% normal goat serum in PBS. Immunostaining was performed with anti-human TDP-43 primary monoclonal antibody and secondary anti-mouse IgG Alexa594. Reconstituted GFP was directly visualized by fluorescence emission.For quantification of phosphorylated TDP-43 (pTDP-43), NSC-34 cells were plated on 24-well (ibidi) cell culture-plates at a density of ~ 5*10Total RNA from NSC-34 cells was obtained using TRIzol (Life Technologies) according to the manufacturer’s instructions. Complementary DNA was generated from the total RNA (extracted from NSC-34 cells) using oligo-dT primers and Superscript III reverse transcriptase (Life technologies) as instructed by the manufacturer. Candidate TDP-43 splicing targets were tested by RT-PCR amplification using 24–27 cycles with specific primers ) before, and at each solubilization phase of SIA. For each step, samples were centrifuged for 30 min at 16,000×g at 4 °C. After removing the supernatant , pellets  were suspended in SB 1% v/v Nonidet P40 Substitute , SB 2% w/v dodecyl-ß-d-maltoside (Sigma), SB 2% w/v N-Lauroylsarcosine  and SB 1% w/v sodium dodecyl sulfate  sequentially. SDS insoluble material was suspended in SB 1x SDS blue loading buffer (Life Technologies).One 10 cm petri dish of cells transfected as described above was used for each sequential extraction of proteins with buffers of increasing stringency. Cells were harvested by scraping in solubilization buffer . Total protein concentrations were measured and 1 mg of total protein was used for the SIA. Samples were treated with Benzonase  under the accession number 27072. All other data are available from the corresponding author upon reasonable request.Supplementary InformationPeer Review file


Reaction time data have long been collected in order to gain insight into the underlying mechanisms involved in language processing. Means analyses often attempt to break down what factors relate to what portion of the total reaction time. From a dynamic systems theory perspective or an interaction dominant view of language processing, it is impossible to isolate discrete factors contributing to language processing, since these continually and interactively play a role. Non-linear analyses offer the tools to investigate the underlying process of language use in time, without having to isolate discrete factors. Patterns of variability in reaction time data may disclose the relative contribution of automatic (grapheme-to-phoneme conversion) processing and attention-demanding (semantic) processing. The presence of a fractal structure in the variability of a reaction time series indicates automaticity in the mental structures contributing to a task. A decorrelated pattern of variability will indicate a higher degree of attention-demanding processing. A focus on variability patterns allows us to examine the relative contribution of automatic and attention-demanding processing when a speaker is using the mother tongue (L1) or a second language (L2). A word naming task conducted in the L1 (Dutch) and L2 (English) shows L1 word processing to rely more on automatic spelling-to-sound conversion than L2 word processing. A word naming task with a semantic categorization subtask showed more reliance on attention-demanding semantic processing when using the L2. A comparison to L1 English data shows this was not only due to the amount of language use or language dominance, but also to the difference in orthographic depth between Dutch and English. An important implication of this finding is that when the same task is used to test and compare different languages, one cannot straightforwardly assume the same cognitive sub processes are involved to an equal degree using the same task in different languages. Reaction time experiments such as the word naming task are widely used to explore differences between L1 and L2 language processing. However, there continues to be debate about how to interpret naming latencies. It appears to be a major challenge to relate any of the many factors that are involved in the naming of a word  to a particular portion of the total naming time . Even thTherefore, keeping in mind that the contribution of all the different factors involved in naming will be variable across tasks, languages, and participants, the current study aims at clarifying the degree to which automatic and controlled processing play a role in naming from an interaction-dominant perspective. Rather than trying to isolate the factors of interest in L1 and L2 naming, automatic grapheme-to-phoneme conversion and semantic involvement, the current study will use different tasks in which the relative contribution of these processes is assumed to be different. Analyzing the variability of naming latencies (RTs) during the task rather than the overall time it takes to name the words presents the opportunity to look at the relative contribution of automatic (grapheme-to-phoneme conversion) processes and more conscious, attention-demanding processes (semantic involvement) in L1 (Dutch) and L2 (English) word naming. With the possible exception of the most advanced speakers of an L2, word naming results consistently show language performance to be slower in an L2; however, longer naming latencies in the L2 do not necessarily indicate L2 naming to be qualitatively different from L1 naming . FocusinA possible explanation why L2 language performance is usually slower in reaction time experiments could be simply the degree of use and practice. The underlying processing may be qualitatively similar in L1 and L2 language use in advanced L2 speakers, but due to less practice in using the language these processes could be less automatized and thus slower. However, another explanation could be that L1 and L2 language processing are qualitatively different, even for advanced L2 speakers. Using a language successfully requires the interaction of several components, such as mapping graphemes to phonemes, articulating sounds, and semantics. The relative contribution of these different components could be different between languages depending on proficiency and frequency of use. Looking at the speed of processing alone is not enough to distinguish between these accounts.The central aim of the current study is to disclose the underlying processing involved in naming words in the L1 and L2, and to see if these processes are qualitatively different. A secondary question is if any differences that are found in the underlying processing, are attributable to the languages being a speaker’s L1 or L2, or are to do with differences between the languages. In order to answer these questions, the focus will be on the degree of automatization in naming words in an L1 and L2 as is shown from patterns of variability, and on assessing the relative contribution of semantic involvement in the naming process.Since automatization and semantic involvement are part of language processing and take place over time, this study will use non-linear analyses to shed light on the underlying processes involved in naming. Originating in biology and ecology, but now also used extensively in other fields of cognitive science, spectral analysis is used to look at variability in response time data that has traditionally been regarded as irrelevant ‘noise’ but has been found to reveal the degree of automatization of mental processes . LookingThe word naming task is a quick and undemanding task suitable for gathering language production data of multilingual speakers, without the drawback of confounding lexical processing with discrimination, as could be the case for the lexical decision task . For theA word naming task is a suitable method for obtaining long, continuous data series in either the L1 or L2, and thus provides the opportunity to look at language processing over time. The different components (production and understanding) that contribute to natural language use are present, but a word naming task draws heavily on the production part of language use. In regular word naming, an automatic orthography-to-pronunciation link could be used in which semantics plays a minor role  model  or the cRC model , which iA possible alternative to dual models of word recognition is offered by parallel distributed processing (PDP) models, such as the PDP triangle model by In models such as the ones discussed above, semantic representations are one of many components of stored lexical information. Lexical access in this view entails finding a simplified orthographic/phonological representation, whereas access to meaning is a later stage in word processing that can occur only after the lexical representation has been retrieved. The dissociation between lexical and semantic representations has long been taken as a necessary distinction to account for fast word processing. However, in the past decade this idea has been challenged. Elman points to the problem of where to draw the boundary between the lexicon and other types of linguistic knowledge. He found that many contextual factors  influence expectations regarding the argument a verb will take. It would not do to list separate entries for every individual occurrence of a word, but any decision on what to include in the lexicon would also be rather arbitrary. He suggests “words do not have meaning, they are cues to meaning” , 2011. SModels such as the dual route models of word recognition do not specify if and how the word recognition process would be modulated for multilingual language use. A lot of research has focused on differences between L1 and L2 language processing, such as the influence of the L1 on L2 processing, but as That L1 and L2 language processing are different, has been made visible by a range of neuroimaging studies. A marked difference is the level of attention or control needed for bilingual language processing. In an fMRI study looking at age of acquisition and proficiency effects in L1 and L2 processing, component dominant view. In this view, any changes in the system can be traced back to isolated, independent information-processing modules, as in the dual-route models mentioned above. This view has inspired a lot of empirical studies and has generated many (modular) models, and produced many insights. However, when it comes to objects of study that are very complex, consisting of many interacting variables, as is the case in biological and psychological systems, a modular and linear model can also lead to oversimplification. When many variables continuously interact, trying to isolate them in a study can produce mixed results and studies that are hard to replicate. In the language system, as well as the cognitive system as a whole, isolating components that contribute to change or development proves to be very difficult. Human behavior is often the result of many interacting factors and is very hard to explain by one or several separate factors.An essential question to pose before embarking on research into language processing and the role of semantics in processing, is the question what kind of underlying system it is we are confronting. In cognitive science, including language research, the computer metaphor of the mind has been hugely influential. The human mind being considered as too complex to study as a whole, it was decomposed into different parts that could be studied separately, giving rise to a modular view of the mind . Anotherinteraction-dominant dynamics. In interaction-dominant dynamics, the behavior of the system emerges from the non-linear interaction between many interdependent components that are nested within each other , . There iThe idea that practice and development lead to a system being more optimally organized and thus exhibiting pink noise has been applied to language tasks in the L1 and L2. In a longitudinal study carried out by The different factors involved in language processing have been found to be numerous, and their contribution to the processing either inconclusive or inconsistent between languages and across individuals and different moments of testing. Therefore, rather than looking at the exact contribution of factors involved in language processing at any specific moment in time, it is more useful to look at the relative contribution of factors involved in language processing over time. Taking into account the complex, variable and adaptable nature of language processing, the current study takes a processing perspective to look at the relative contribution over time of two major factors (automatic grapheme-to-phoneme conversion and attention-demanding semantic processing) in reading out loud. The results will provide insight in the contribution of these factors in L1 and L2 language processing, and will provide an answer as to whether or not L1 and L2 processing are qualitatively different.The discussion on the role of meaning in L1 and L2 language processing thus far has been rather dichotomous. Semantic involvement has been found to play a more limited role in single word naming than in other experimental paradigms such as lexical decision or picture naming. The dual route processing models feed into this dichotomous discussion, by allowing processing to travel along a ‘semantic’ route, or to bypass semantics altogether. In order to determine whether semantic involvement plays any role in regular word naming, two versions of a word naming experiment were devised. One regular version in the L1 and L2, the only instruction of which was to name the words correctly, and as fast as possible. The other, semantic condition version of the exact same experiment had the added instruction of the participant being required to press a button whenever the word appearing on the screen denoted an animal. The resulting response time series were analyzed using spectral analysis, which is a type of analysis that yields a slope line. The steepness of this line gives information about the pattern of variability in the time series; a slope line of zero indicates a random pattern of variability, whereas a slope line of -1 indicates pink noise, or a fractal pattern of variability. A pink noise pattern indicates optimal coordination and a high degree of automatization in the sub-processes contributing to a task , 2012.Comparing the slope statistics of standard L1 word naming to standard L2 word naming will show if the degree of automatization in language processing is different in L1 and L2 naming. Since the L1 is the dominant language for the participants, subcomponents contributing to language output are expected to be more tightly coupled and be more efficiently and optimally coordinated. Therefore, slope statistics closer to the scaling exponent of pink noise are expected in the Dutch L1 standard naming condition. The L1 and L2 semantic naming condition are compared to gain insight into the relative degree of automatic vs. attention-demanding semantic processing in L1 and L2 word naming. More attention-demanding semantic processing is expected to decorrelate the variability pattern, and yield slopes that are closer to random variability. Of interest is whether processing in both languages is affected to an equal degree, which will give information about the relative degree of automatic vs. attention-demanding processes in L1 and L2 word naming.There is one other factor that will be considered in this study. Since the studies mentioned above  that havThe analysis used here to distinguish a fractal structure in the data (coordinated behavior) from random variation is spectral analysis. Spectral analysis can be used to look at the variation in a signal. It transforms a data series from the time domain (milliseconds) into the frequency domain (Hz) through a Fast-Fourier-Transformation  and random variation when analyzing the randomized data.To conduct a spectral analysis, some preprocessing of the raw data is required . ExtremeN = 42) were students of the English Language and Culture BA program at the University of Groningen in the Netherlands. Most of them were female . Ages ranged from 19 to 43 years . Participation was a course requirement. Participants were asked for information on language and academic background. All participants were native Dutch speakers with near-native English proficiency (CEFR level = C2). In the academic context, these students almost exclusively use English; all classes, assignments and course materials in this BA program are in English. The participants in this study had started learning English as a foreign language in formal education between the ages of 10 and 13 years old. Dutch is the dominant language in the Netherlands in everyday life. Even so, English acquisition may have started earlier due to the presence of the English language in the Netherlands in media, music, internet, and games  . ResponsFigure 2. The order of presentation of the test items was randomized automatically by the computer program for each participant. A practice block of 10 items preceded the experimental block to familiarize participants with the task requirements.Participants were tested individually in a soundproof room. The English and Dutch version of the experiment were conducted consecutively. Half the participants started with the Dutch language version, the others with the English language version. Participants were randomly assigned to the standard or the semantic condition. The button that had to be pressed in the semantic condition was a red button in a black box, connected to the computer with a USB cable; button presses were not recorded. The participant sat facing a computer screen, with a microphone placed on the table in front of him. The test items appeared in black, lowercase Courier New (24 points) on a white background. The experiment started with an instruction slide informing the participant to pronounce the words appearing on the screen as quickly and accurately as possible. In the semantic condition, instructions were the same, with the added instruction to press a red button if the word appearing on the screen denoted an animal. Each target was preceded by a fixation point in the middle of the screen for 500 ms. The target would appear on the screen, and remain there for maximally 5,000 ms, or until a response was recorded. The target would remain on the screen for 300 ms after the participant had started to pronounce the word, to ensure that it was still on the screen during the pronunciation. The screen would be blank for 1,000 ms, after which the next fixation point would appear. The procedure is visually represented in Table 1. If the voice key was set off by accident or if no response was recorded within 1,000 ms, the data point was counted as an error and removed before any analyses were carried out.The results are summarized in Table 1. Mean RTs on the Dutch sessions are faster than on the English sessions. The effect of language is found to be significant, F = 57.363, p < 0.001. There was no significant effect found for the semantic vs. the standard condition, and the interaction between naming condition and language was also found not to be significant.The data were analyzed using a repeated measures ANOVA. Results are summarized in Spectral analyses were carried out to establish whether, regardless of how fast or slow the naming performance, naming is more automatized and optimally coordinated in the L1 than in the L2, and whether the stronger role of semantics in the semantic categorization condition would lead to less reliance on automatic grapheme-to-phoneme conversion and more attention-demanding semantic processing in naming. Spectral analyses were run on all individual sessions, as well as on the aggregate data of each of the conditions.Before the spectral analyses were carried out, the data was truncated to 512 data points. On average, 35 data points were removed from the beginning of the time series. Linear trends were removed see , p. 288 M = -0.38. Spectral slopes for L2 English standard naming ranged from -0.47 to 0.04, M = -0.19. Spectral slopes found for L1 Dutch naming in the semantic condition ranged from -0.50 to 0.00, M = -0.28, spectral slopes for L2 English naming in the semantic condition ranged from -0.40 to 0.00, M = -0.19. Table 2 shows the mean spectral slopes for each of the conditions.For each of the time series, 64 frequencies were calculated. To estimate the spectral slope, a slope line was fit to 50% of the frequencies. Spectral slopes found for L1 Dutch standard naming were steepest, showing the strongest fractal pattern, ranging from -0.58 to 0.04, F = 13.189, p = 0.002. In comparing the semantic vs. standard naming condition no significant effect was found for the main effect of condition; however, the interaction between naming condition and language was found to be significant, F = 4.857, p < 0.04, showing that there was a larger decrease of the spectral slopes for L1 compared to L2. Figure 3 graphically depicts this interaction.Again, the data were analyzed using a repeated measures ANOVA. As is the case for the RTs analysis, the main effect of language was significant, Figure 4 shows this comparison for the aggregate data of the Dutch standard naming data.To test the validity of the spectral slopes found on all conditions, data was compared to randomized versions of the same data, but with the time ordered sequence of the data disrupted. Spectral slopes on all conditions were found to differ significantly from slopes fit to the randomized data. The present study aimed to investigate the relative contribution of automatic processes (grapheme-to-phoneme conversion) and attention-demanding processes (semantic involvement) in L1 and L2 language production using a standard word naming task and one that required semantic categorization. Spectral analysis was used to look at the degree to which the L1 and L2 language subsystems were optimally coordinated and automatized during word naming, and the degree to which semantic processing played a role in word naming.A fractal pattern of variability was found for both L1 Dutch and L2 English word naming. The spectral slopes found for L1 Dutch standard word naming (-0.38) were significantly steeper than the spectral slopes found for the English standard naming condition (-0.19). It is important to stress that the steepness of the spectral slopes is not related to the speed of processing; even though L2 naming is consistently slower, this does not lead to a relative difference in the steepness of spectral slopes. Speed of processing and scaling are independent of one another. As indicated, a fractal pattern of variability is associated with optimal coordination and automatization of the mental subsystems contributing to a task. The fractal structure in the L2 data shows a more decorrelated pattern closer to random variation. The stronger fractal pattern in the L1 data indicates a more optimal coordination of mental subsystems when naming L1 words than when naming L2 words. In other words, the appearance of a string of letters on the screen that correspond to an L1 word leads to a highly automatized, smooth response that is consistent with an account of automatic spelling-to-sound conversion in the L1. The same degree of automatization does not seem available in the L2.The same naming task in the semantic condition was conducted to ensure that the processing that was being investigated was not only governed by automatic spelling-to-sound-conversion, but also semantic processing. Comparing this task to the standard word naming allows us to infer the degree of semantic processing present in standard word naming; if no difference between the conditions were found, the same degree of semantic processing must already be present in standard word naming. However, the semantic categorization condition did have a differentiating effect in the L1 and L2; a comparison of the L1 standard naming condition with the L1 semantic naming condition shows a more decorrelated fractal pattern of variability in the L1 semantic naming condition (-0.38 for L1 standard naming and -0.28 for L1 semantic naming). The same effect was not found in L2 naming; both standard L2 naming and L2 semantic naming generated the same mean spectral slope of -0.19. This finding is in line with the observation that L1 naming relies more than L2 naming on automatic spelling-to-sound conversion. The semantic categorization condition would have forced more attention-demanding lexical involvement than regularly used in the reading aloud of L1 words, leading to a variability pattern that was less fractal than the one observed for L1 standard naming. If automatic spelling-to-sound conversion is less available in the L2, as suggested by 1. For easier reference, the Figure 5 combines the results obtained in the present study, and those found by To find out whether the observed difference in slope statistics comes down to a difference between L1 and L2 processing, or between Dutch and English language processing  it is useful to compare the languages in the semantic categorization condition, and to also compare these results to L1 English standard naming, as obtained by Van Orden et al. (2003)The spectral slope found for the Dutch semantic condition was less steep than the slope found in the standard naming condition, but at -0.28, it was still steeper than the spectral slope of -0.19 found for the English semantic condition. So, even if the degree of lexical involvement is manipulated for both languages, a language effect remains intact, indicating more automatized processing in the L1. However, these results do support the orthographic depth hypothesis, which classifies English as having a deep orthography with an inconsistent spelling-to-sound correspondence that requires lexical mediation for correct pronunciation. The slope of -0.28 found for the L1 Dutch semantic condition corresponds almost exactly to the mean spectral slope of -0.29 reported by This leads to another observation on account of the advanced L2 learners participating in the current study. Even though they do not achieve the optimal coordination of underlying subsystems when using the L2, they are adopting language processing strategies with a higher relative contribution of lexical mediation than is usual in their L1, thus adopting native-like language processing strategies, albeit less optimally organized than native speakers.The goal of this investigation was to look at the relative contribution of automatic grapheme to phoneme conversion processes and more conscious, attention-demanding processes  in L1 Dutch and L2 English word naming. As hypothesized, the relative contribution of automatic vs. attention-demanding processes in the L1 and L2 was not equivalent, even though the learners participating in this study were near-native L2 speakers. The scaling relation found for L1 naming as compared to L2 naming was closer to the scaling relation indicating automatic, optimal coordination of mental processes, probably achieved through more regular use and practice of the L1. This conclusion is in line with In addition to, and related to the relative contribution of automatic spelling-to-sound conversion, the focus of interest was the relative contribution of more attention-demanding, semantic processing. The semantic condition was found to affect patterns of variability in the L1 only, leading to the conclusion that L2 speakers rely more on lexical involvement in naming words. Even when forcing more contribution of semantic processing in the L1, the spectral slopes still indicated a higher contribution of automatic spelling-to-sound conversion processes in L1 naming than in L2 naming. We would therefore like to propose an amendment to the conclusion reached by This study is not the first to find L2 processing is less automatized than L1 processing . HoweverThis differential degree of automatized and controlled processing in L1 and L2 naming has implications for how to interpret the extensive body of research into multilingualism that uses this task to look at word processing differences between languages in multilinguals. Earlier research already indicated care should be taken in interpreting evidence on word recognition that may be confounded with task-specific processing , since fThese results taken together indicate caution is needed when interpreting studies that are generalized over individuals, monolinguals and bilinguals, languages or tasks. If word naming in the L2 tests ‘deeper’ processing than naming in the L1, caution is needed when comparing the results of multilingual naming. When a task such as word naming is used in bilingual research, the aim is to be able to generalize about a variable that has been manipulated. The assumption is that apart from this manipulated variable, the same task in different languages is testing the same underlying processes. The  individual’s adaptability to task, circumstance and environment is underestimated in such direct comparisons; this study suggests caution is needed when comparing cross-linguistic results of word naming tasks.The study was exempt from ethical considerations. The experiment concerned a non-invasive reaction time experiment. Participants were adult, healthy university students, who participated voluntarily and received a small reward. For some participants, participation was part of course credit.RP, principal author, carried out the study, analyzed the results, and wrote most of the paper. WL, promotor and daily advisor, has been involved in designing the study and providing advice and feedback along the way. KdB, co-promotor, was involved in the design of the study and giving advice and also offering feedback during the writing of the paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.