Mechanosensitive Piezo1 and Piezo2 channels transduce various forms of mechanical forces into cellular signals that play vital roles in many important biological processes in vertebrate organisms. Besides mechanical forces, Piezo1 is selectively activated by micromolar concentrations of the small molecule Yoda1 through an unknown mechanism. Here, using a combination of all-atom molecular dynamics simulations, calcium imaging and electrophysiology, we identify an allosteric Yoda1 binding pocket located in the putative mechanosensory domain, approximately 40 Å away from the central pore. Our simulations further indicate that the presence of the agonist correlates with increased tension-induced motions of the Yoda1-bound subunit. Our results suggest a model wherein Yoda1 acts as a molecular wedge, facilitating force-induced conformational changes, effectively lowering the channel’s mechanical threshold for activation. The identification of an allosteric agonist binding site in Piezo1 channels will pave the way for the rational design of future Piezo modulators with clinical value. Besides mechanical forces, the mechanosensitive channel Piezo1 is activated by the small molecule Yoda1 through an unknown mechanism. Here, using molecular dynamics simulations, calcium imaging and electrophysiology, the authors identify an allosteric Yoda1 binding pocket located in the putative mechanosensory domain. Abnormal Piezo channel activity caused by inherited mutations, genetic manipulation or physiological regulation has been linked to a variety of pathological conditions such as xerocytosis, lymphedema, arthrogryposis, abnormal vascular development, sleep apnea, allodynia and the loss of proprioception20.Mechanosensitive Piezo channels form a small protein family encompassing only two paralogs in vertebrates, Piezo1 and Piezo2. In spite of their small family size, these channels undertake many important physiological functions in adult and developing animals, including osmotic homeostasis, visceral, and somatic mechanosensation, proprioception, blood flow sensing, and epithelial homeostasis24. Recent advances in cryo-electron microscopy (cryo-EM) have enabled the capture of mouse Piezo1 (mPZ1) in non-conducting states, which presumably correspond to the canonical closed conformation populated in absence of external mechanical force27. These structures reveal a propeller-like structure with a central pore surrounded by three large peripheral domains called arms, or blades. Each of these peripheral domains is formed by a succession of helical bundles called Piezo repeats, or transmembrane helical units, and are connected to long intracellular beams that extend toward the intracellular side of the pore. The arc-shaped arms are anticipated to mediate a local curvature, or inverted dome shape, to the lipid bilayer and to act as the channel mechanosensory domains29. Although the mechanism by which tension-mediated rearrangements of the arms open the channel gate is presently unknown, a recent study suggests the arms and beams may efficiently transfer mechanical forces from lipids to the pore via a lever mechanism28.Piezo1, the best studied Piezo channel, opens upon direct physical deformations of the lipid bilayer, such as increased membrane tension, and thus obeys the so-called force-from-lipid paradigm established for several mechanosensitive ion channels50) and binding affinity to the purified Piezo1 protein in the range of 10–50 µM30. Despite the identification of mutations that abolish Yoda1-mediated activation31, the location of the Yoda1 binding site and the mechanism by which Yoda1 modulates Piezo1 remain unknown. In principle, the Yoda1 binding site(s) could be identified by solving the structure of a Yoda1-Piezo1 complex, although the resolution of current cryo-EM structures (3.5 to 4 Å) may be too low for unambiguous identification of the small, 29-atoms Yoda1 molecule. Here, to identify this binding site, we perform microseconds-long all-atom molecular dynamics (MD) simulations of mPZ1 in the presence of multiple Yoda1 ligands and in absence or presence of membrane stretch. In this context, we have taken advantage of ANTON2 supercomputer, which has been used successfully to identify small molecule binding sites and binding pathways in membrane proteins36. We report the identification of an allosteric Yoda1 binding site and propose a simple mechanism by which Yoda1 may promote Piezo1 opening in the presence of membrane stretch.Besides mechanical stimulation, several synthetic small molecule Piezo1-selective agonists have been identified using high-throughput screening. Among them, Yoda1 possesses the highest potency, with a half maximal effective concentration , Piezo repeats A–C, and intracellular beam and clasp connecting repeat C and CTD, was built based on a high-resolution cryo-EM mPZ1 structure29, a pore vestibule located below F2480 may be accessible from the cytosol via lateral fenestrations. Indeed, we observed high potassium ion density in this vestibule, while chloride ions were excluded from this region throughout our trajectory  remain pointing toward the pore lumen and occlude the putative ion permeation pathway during our simulation . Fluorescence analyses show that Yoda1-sensitivity is either totally abolished (A1718W), severely reduced (A2094W) or partially diminished (A2091W) in these mutants  were added to the simulation system to ensure effective sampling of potential binding sites Fig. . The binane Fig. , in agre094 Fig. . To valints Fig. . The secnts Fig. , is locants Fig. . Howevernts Fig. .Fig. 2Id39. To our surprise, this mutant elicits robust calcium signals in the presence of both Jedi2 and Yoda1  channel Fig. . In cont2+ signals similar to WT channel in response to osmotic shocks  and one formed by Piezo repeat A . This trajectory indicates that L13 indeed remains stably bound in absence of other Yoda1 ligands  exhibits a larger RMSD (~20 Å) compared to other arms (~16 Å)  than in the other two subunits (~65° and ~68°), confirming the notion that Yoda1 binding increased mechanically-induced protein motions  Fig. . The dirons Fig. . The secons Fig. 27. Proje26. An interesting observation was the presence of potassium ions entering an intracellular pore vestibule through lateral fenestrations anticipated from cryo-EM studies27. We observed throughout our trajectory that negatively charged chloride ions were excluded from entering this vestibule, presumably due to the presence of glutamate residues essential for the Piezo1 cation selectivity near the intracellular side of the fenestrations37. Assuming that the entry of ions into these lateral fenestrations is not prevented by protein domains missing from the cryo-EM structures such as large intracellular loops, these results suggest that cations readily move in and out the pore vestibule even when the pore is non-permeant. This would suggest the main channel gate is located above the vestibule, at the level of a narrow constriction near F2480. This hypothesis is in good agreement with a recent study showing removal of the intracellular beam-to-latch linker region located below the vestibule does not constitutively open the pore, indicating this intracellular region does not form a major permeation gate43.To shed light on the mechanism by which the Piezo1 channel interacts with its Yoda1 agonist and responds to membrane stretch, a physiological stimulus for Piezo1, we performed a long all-atom unbiased MD simulation in the presence of the Yoda1 agonist and in absence and presence of increased membrane tension. Our ~8 µs simulation recapitulates the anticipated inverted dome shape of the lipid bilayer with a curvature similar to that observed in proteoliposomes27. Additional binding events may hence theoretically be observable with longer simulations times. Mutations A1718W and A2094W in this binding site prevent or diminish the activatory effects of Yoda1. While the A2094W channel maintains its mechanosensitivity to stretch, we were not able to detect stretch-elicited currents from the A1718W mutant although this mutant responded to osmotic swelling, another mechanical stimulus for Piezo131 28 suggests the affinity of Yoda1 for its binding site is relatively independent of the functional state of the channel. This observation is consistent with our simulation showing a spontaneous binding event to a non-conducting state. These observations favor a sequential model where the binding of Yoda1 induces conformational changes leading to pore opening.Two major mechanisms are typically used to interpret the effects of allosteric agonists such as Yoda1: the concerted and the sequential models. In the so-called concerted model, the affinity of the agonist for its binding site is higher for the open state than the non-conducting state: binding of the agonist hence shifts the open/close equilibrium towards the open state. In the alternative sequential model, binding of the agonist induces a local conformational change that allosterically propagates from the binding site to the active site . Although it is still unclear whether activation by Yoda1 follows one of these mechanisms, the fact that Yoda1 interacts with the purified Piezo1 arm (mPZ1 residues 1–2190) with an affinity in the same order of magnitude as the EC30, (2) Yoda1 disrupts interactions between residue communities  as well as other large soluble loops unresolved in the current structures will be needed to create a native, full-length computational model of Piezo1. In addition, Piezo1 spontaneously opens in asymmetric, but not symmetrical, artificial bilayers and specific membrane lipids have been found to either inhibit or promote Piezo1 activity46. Hence, manipulation of the chemical composition of the membrane in computer simulations, such as creating a native-like asymmetric bilayer, may facilitate in silico spontaneous gating transitions towards the open state upon stretch. Our current simulation of a reduced Piezo1 model in a symmetric POPC membrane represent a starting point for future computational studies.Application of membrane stretch in our simulation reveals that Yoda1 binding enhances a twist-tilt-twist like opening motion of the arm. Based on the facts that (1) Yoda1 reduces the mechanical threshold for channel activation30. This suggests activation by Yoda1 and by mechanical stimuli are coupled, as reflected in our wedge model. However, many of the mutations characterized in this study and others abolish Piezo1 activation by chemical stimuli but not mechanical stimuli or vice versa. For instance, the R2135A mutation, located in the anchor region between the pore and repeat A, disrupts mechanical activation by stretch39 and swelling  from the solvent to the membrane and enters its binding site from the intracellular side of the membrane, as anticipated from excised-patch electrophysiology experiments28. The fact that the sensitivity of Piezo1 to both Yoda1 and Jedi1/2 can be simultaneously abolished by the same point mutation is puzzling. It is possible that, although acting from distinct protein regions, both agonists somehow modulate a common long-distance allosteric transduction pathway traversing a large fraction of the Piezo arm as suggested previously28.Although both Jedi2 and Yoda1 interact with the Piezo1 arm, Jedi2 is negatively charged at physiological pH and hence is thought to interact with distal extracellular loops44. It is puzzling to reconcile the stringent nature of the Yoda1-Piezo1 interaction with a relatively simple wedge-like mechanism. It is possible that the modulatory effect of Yoda1 necessitates a specific binding pose placing specific chemical groups in an optimal orientation. Structure-activity relationships suggest that the (2.6-dichlorobenzyl)thioether group of Yoda1 is critical for binding while the pyrazine and thiadiazol groups are important for its agonist effect44. The Yoda1 binding site revealed in this study will enable future simulations and structure-functions studies that will help understand the structure-activity relationships of Yoda1 and its analogs. Such studies will also be invaluable to elucidate the origin of the Piezo1 vs. Piezo2 selectivity of Yoda1.As shown in this study and others, the agonist effects of Yoda1 are highly sensitive to protein mutations as well as small modifications of the Yoda1 chemical structureIn conclusion, our study identifies a potential allosteric Yoda1 binding site in Piezo1 and suggests a mechanism wherein Yoda1 binding facilitates force-induced protein motions through a wedge-like mechanism. This work will help the rationale design of highly-sought-after isoform-selective Piezo modulators and pave the way towards understanding how Piezo channels are modulated by chemical and mechanical stimuli.47. The protein was then embedded in a phosphatidylcholine (POPC) membrane and solvated in TIP3P water molecules and 150 mM KCl  using CHARMM-GUI website48. The full atomic model built from the cryo-EM structure with bilayer and solvent contains 1 million atoms, which exceeds the size limit on ANTON2 supercomputer . Because this system was intended to capture spontaneous Yoda1 binding, we removed the most peripheral repeats D–F, which were not anticipated to contain the binding site31. In the final reduced model, each subunit of the Piezo trimer contains four segments  into our system to ensure sufficient sampling during ANTON2 simulation. This corresponds to a ligand concentration of about 5 mM, which is lower than the concentrations used in spontaneous binding simulations50. CHARMM36 parameter sets were used throughout, including the protein, ions52, POPC lipids53, and TIP3P for water54. CHARMM general force field (CGenFF)56 was used for Yoda1 molecule. The terminal amino acids of each segment are capped using acetylated N-terminus (ACE) and methylamidated C-terminus (CT3) blocking groups. We obtained a model with 602,203 atoms (recommended size for maximum efficiency on the ANTON2 machine) with a system box size of 190.1 × 190.1 × 177.5 Å3.Two recent cryo-EM structures of mouse Piezo1 (PDBID: 6BPZ and 6B3R) obtained at ~3.8 Å resolution were first aligned and compared the amino acid sidechain assignment. Since the two structures are consistent, the latter one (6B3R) was used to build the all-atom model. We added the missing loops that are shorter than 20 amino acids using the loop remodeling function of Molecular Operating Environment57. This consisted of an initial minimization followed by a six-stage thermal equilibration phase. Following the final thermal equilibration stage, 10 ns of ‘unrestrained’ equilibration was conducted. Here, unrestrained refers to the release of membrane and protein backbone-conformation restraints. A 30 Å flat-bottom harmonic restraint with spring constant of 0.12 kcal mol−1 Å−1 was added between center of mass of the residue 1273–1299 of the beam and the center of mass of the residue 2488–2543 at the bottom of the pore throughout all the simulations. This restraint is to prevent the C-terminal of the beam drifting too far due to the absence of an unstructured loop sequence from the cryo-EM data. During the first 20 ns, all 20 Yoda1 molecules quickly diffuse from water into the lipid bilayer. Since one primary goal of this simulation procedure was to identify potential binding sites for Yoda1, to ensure the sufficient sampling of Yoda1 ligand near the protein region, a 45 Å radius flat-bottom harmonic spherical restraints  were applied to limit the diffusion of six ligands around each Piezo monomer throughout all the simulations. Since the volume of these restraints was chosen to be sufficiently large such that the entire transmembrane region of the Piezo is included, these restraints are there only to ensure that Yoda1 molecules would not drift into distant regions of the surrounding membrane environment without any bias on the interaction between Piezo1 and Yoda1. Due to system size, a total of 7500 cycles of minimization were run, with 5000 steepest decent cycles followed by 2500 conjugate gradient cycles. For this stage and all following, a non-bonded cut-off of 12.0 Å plus a 10.0 Å force switching range was employed. Both positional and internal conformational restraints were applied. Positional restraints were applied to all atoms of all protein residues with a 10.0 kcal/mol/Å harmonic spring constant while phosphate atoms of membrane POPC residues were restrained with a force constant of 2.5 kcal mol−1  Å−1. Dihedral restraints were also employed to ensure retention of backbone phi-psi angles during minimization, with 250 kcal mol−1 radian−2 force constant. Following minimization, a 25 ps NVT solvent heating simulation was run to attain a target temperature of 310.15 K. For this stage through the fifth equilibration stage, a time step of 1 fs was employed. The same positional and internal restraint setup was applied as was utilized during the previous minimization stage. For all simulation stages, temperature control was accomplished using a Langevin thermostat with a gamma parameter (friction coefficient) of 1.0 ps−1. The SHAKE algorithm was used to constrain bonds involving hydrogen. The second equilibration stage followed an identical setup with the sole exception that the force constant of the protein positional restraints was reduced to 5.0 kcal mol−1 Å−1 (down from 10.0 in the previous stage). The force constant of the dihedral phi-psi restraint was also reduced to 100 kcal mol−1 radian−2 (down from 250). The first and second stage equilibrations were each run for a duration of 25 ps each. Starting with the third equilibration stage, the duration of simulations was raised to 100 ps and an NPT ensemble was employed, with pressure regulation accomplished by utilization of a semi-isotropic Monte-Carlo barostat with a target pressure of 1.0 bar and constant zero membrane surface tension. For the third stage, force constants for protein and membrane positional restraints were reduced to 2.5 and 1.0 kcal mol−1 Å−1, respectively (down from 5.0 and 2.5). The dihedral phi-psi protein backbone restraints were also dropped to 50.0 kcal mol−1 radian−2 (down from 100.0). In the fourth stage, positional restraint force constants for protein and membrane were reduced to 1.0 and 0.5 kcal mol−1 Å−1, respectively (down from 2.5 and 1.0). In the fifth stage, positional restraint force constants for protein and membrane were reduced to 0.5 and 0.1 kcal mol−1 Å−1 (down from 1.0 and 0.5) and phi-psi backbone dihedral restraint force constants were reduced to 25.0 kcal mol−1 radian−2 (down from 50.0). For the sixth and last equilibration simulation, membrane positional restraints and phi-psi backbone dihedral restraints were removed entirely while protein positional restraints were reduced to 0.1 kcal mol−1 Å−1 (down from 0.5). Simulation timestep was also raised to 2.0 fs as it would remain for all future simulations. Finally, a fully unrestrained equilibration production run was conducted for a total of 10 ns .The resulting starting structure was first run through the equilibration using pemed.CUDA module of AMBER16−1 or kcal mol−1 Å−1, this apparently is a relatively common requirement. Similarly, due to the size of this system and the relatively long timescale being simulated, a relatively low trajectory output frequency of 1.2 ns was chosen.For zero tension simulation, the endpoint from equilibration was uploaded to the ANTON2 supercomputing cluster and converted from AMBER topology and coordinate format into an equivalent DESMOND chemical model format using the viparr modeling and simulation suite. An ark file was constructed to match as closely as possible the simulation parameters employed under the AMBER equilibration stage. Due to the size of our system, nearly seventy thousand atoms, it was necessary to raise the energy and force scale limits from the default of 1000–5000 kcal molAs with the previous AMBER equilibration step, the ANTON2 simulation was run under an NPT ensemble. Pressure regulation was accomplished via the MTK barostat, set to maintain 1 bar of pressure, with a tau (piston time constant) parameter of .0416667 ps and reference temperature of 310.15 K. The barostat period was set to the default value of 480 ps per timestep. Temperature control was accomplished via the Nosé-Hoover thermostat with a tau (time constant) parameter of .0416667 ps. For the purpose of the barostat reference, the mts parameter (substepping) was set to 4 timesteps, while for temperature control purposes a value of 1 timestep was used. The thermostat interval was set to the default value of 24 ps per timestep. The timestep for the simulation was chosen to be 2 fs. This is slightly slower than the standard 2.5 fs per timestep but was found to be necessary to ensure simulation stability in prior exploratory testing for this system. Due to the presence of a lipid membrane, semi-isotropic pressure scaling was required. For this zero-tension simulation, the tension reference was set to 0.To prevent incidental drift, center of mass motion removal was turned on. Respa was employed for all ANTON2 simulations with the default bonded interval of 1 timestep, far non-bonded interval was set to 2 timesteps and non-bonded near interval set to 1 timestep. The slightly more frequent far non-bonded interval was again chosen to ensure simulation stability as the lower default frequency seemed to induce more frequent simulation restarts thus making the higher frequency yield better overall throughput due to the significant overhead in restarting simulations.−1 over a series of twenty 60 ns simulations. At each step in the series, tension was set to 1 mN m−1 higher than the previous step. The second stage, which started from the endpoint of the first stretching stage, consisted of a series of ten steps wherein the tension was increased by 2 mN m−1 in each consecutive 30 ns step . Finally, the last stage consisted of 600 ns of additional simulation time at the final tension of 40 mN m−1 starting from the endpoint of the second stage. All the input files and trajectories are free to download from ANTON2 repository.Simulations of membrane stretching performed on ANTON2 followed the same protocol as zero tension simulations with the exception that membrane tension tensor was set to a fixed value. Stretching was conducted over three stages, starting from the end of the 4.8-microsecond zero tension simulation. In the first stage, tension was increased from 0 to 20 mN mcpptraj program from the AMBER suite. All solvent and ion molecules were first stripped from the system in order to reduce storage and computational power requirements for processing. The autoimage command was then run using the largest segment of the first subunit of the Piezo trimer as the anchor molecule. This ensures that protein structure remains as one contiguous unit in all frames of the trajectory. This is required to accommodate the default behavior of wrapping all atoms to the central unit cell in the output trajectory data due to the periodic boundary condition, which would complicate further analysis. The resulting trajectory data containing membrane and protein components was then processed using the pytraj python API from the AMBER suite. This allowed direct access to cpptraj subroutines from within a python environment as provided by Jupyter Notebook. The center of mass for each lipid headgroup was then computed for each lipid at each frame of the trajectory and stored into a pair of arrays corresponding to the upper and lower membrane leaflets respectively. The resulting headgroup center of mass data were then analyzed to determine parameters for interpolation of the heights of membrane headgroups to a two-dimensional grid. Grid spacing was chosen to be 4.6 Å which corresponds to about twice the molecular radius of a phosphate ion as a rough proxy for the radius of lipid headgroups. The grid dimensions were then chosen such that all headgroup center of mass coordinates, as measured over all frames of trajectory, would fit within the grid with a padding of one grid spacing on each edge. This yielded a grid with 52 gridpoints along the x-axis and 50 grid points along the y-axis. This grid extending from roughly −118.8 Å to +114.8 Å in the x dimension and from −114.7 Å to +108.6 Å in the y dimension. Prior to interpolation to the grid described above, the eight adjacent x–y periodic images of the computed lipid center of mass data were computed for each frame and appended to the computed center of mass data for each given frame. This combined periodic data were then interpolated onto the two-dimensional grid described above using bi-linear interpolation. This interpolation was computed using the sp.interpolate.griddata function from the SciPy Python library. Next, the linear interpolated data was further smoothed using a two dimensional ‘local mean’ smoothing kernel with a size of 13 grid units, as implemented under the sp.ndimage.filters.uniform_filter function from the SciPy library. The above kernel size corresponds to the one quarter of the geometric mean of the grid dimensions, rounded up. The ‘wrap’ mode was employed to resolve edge conditions. This yielded a two-dimensional grid representation of membrane surface height at each frame of the trajectory for both the upper and lower leaflet. Next, a bivariate radial Gaussian was fitted to the resulting grid data at each frame using the sp.optimize.curve_fit function. The form of this fitted function was given as:z offset of the Gaussian (z0) effectively models the plateau height of the membrane, zh, the z scaling factor, models the depth of the indentation induced by piezo, rx and ry, model the location of the center of the induced indentation. σ is standard deviation of the Gaussian.Trajectory data from the microsecond MD simulations was first preprocessed using the R is the radius of curvature at the center of the fitted Gaussian and d is the evaluation of the first and second derivative of the fitted gaussian function at x = rx and y = ry. This radius of curvature data was plotted using the ggplot2 package in R to allow visual inspection of changes in membrane curvature throughout the trajectory.Finally, the radius of curvature at the peak of these fitted Gaussian distributions was computed using the formula:60.The community analysis was also done based on the Pearson correlation coefficients of alpha carbon motions from the 600 ns single Yoda1 trajectory and the Girvan-Newman algorithm implemented in the NetworkView program in VMDPrincipal component analysis was performed using the backbone alpha carbons of all simulated piezo residues for the portion of simulation corresponding to the second stage of membrane stretching. Calculation of principal component modes was carried out using the pytraj module of the AMBER modeling and simulation package. The resulting principal component data was exported to Norma Mode Wizard format for subsequent visualization using the VMD visualization program.Point mutations were introduced into the mouse Piezo1 cDNA cloned into a pCDNA3 plasmid  using Gibson Assembly or Q5 Site-Directed Mutagenesis (New England Biolabs). Primers used in this study are listed in Supplementary Note 2) in a DMEM medium supplemented with Penicillin (100 U mL−1), streptomycin (0.1 mg mL−1), 10% sterile fetal bovine serum and 1X MEM non-essential amino-acid without L-glutamine. All cell culture products were purchased from Sigma-Aldrich. Transfection was done on cells with a passage number lower than 25 using FuGene6 (Promega) using the manufacturer’s instructions. For fluorescence experiments, cells seeded on clear-bottom black 96-well plates (Costar) were transfected at ~50% confluence 2–4 days before the experiment. For electrophysiology experiments, cells seeded on Matrigel-coated coverslips were transfected at ~10% confluence 12–36 h prior recordings.HEK293T-ΔPZ1 cells  were cultured in standard conditions . Cells were then placed on an inverted fluorescence microscope (Nikon Eclipse) and illuminated by a 100 W mercury lamp through a ×20 objective. Fluorescence images were obtained using a standard GFP filter set and acquired at 1 frame s−1 using a Nikon Digital Sight camera and the Nikon Digital Element D software. Yoda1 (Sigma-Aldrich) was dissolved at 10 mM in dimethylsulfoxide (DMSO) and various Yoda1 solutions were made by diluting the Yoda1 stock solution into HBSS. During recordings, 100 µL of a 2× Yoda1 solution was added to the cells at t = 10 s. Total DMSO concentration was kept equal to or below 1% for all tested Yoda concentrations. A similar protocol was used for Jedi2 (purchased from MolPort) where the final agonist concentration was 1 mM and the stock concentration was 100 mM (in DMSO). For osmotic shock experiments, a hypotonic saline solution (where 150 mM NaCl was replaced by 5 mM NaCl) was added to the cells as described elsewhere31. Regions of interest corresponding to individual or clustered cells were selected either manually or using an in-house MATLAB script  were calculated for each selected region following application of chemical stimuli. Except for Fig. F/F0 values were averaged across the field of view for each movie and each movie was treated as an independent measurement. For Fig. To measure Piezo1-mediated calcium entry, cells were co-transfected with a pCDNA3.1 plasmid encoding the fluorescent calcium indicator GCaMP6m (GC6). Transfected cells were washed twice with Hank’s Balanced Salt Solution (HBSS) and incubated at least 10 min in an incubator . Fire-polished patch pipettes with a diameter of 2–3 µm and resistance of 1–5 MΩ were filled with a recording solution containing 130 mM NaCl, 5 mM KCl, 1 mM CaCl2, 1 mM MgCl2, 10 mM TEA-Cl and 10 mM HEPES pH 7.3 (with NaOH). Stretch-activated currents were recorded in the cell-attached configuration after seal formation using an Axopatch 200B amplifiers (Axon) and a high-speed pressure clamp . The membrane potential inside the patch was held at −80 mV. Data were recorded at a sampling frequency of 10 kHz and filtered offline using a 1–2 kHz lowpass digital filter and a digital filter to remove electrical interferences . Peak current amplitudes were determined by adjusting the baseline prior the pressure pulse. The relative peak current values and standard error are from averaging recordings from n transfected cells. Fitting the plots of I/Imax vs. pressure was done using a Boltzmann function (OriginPro 2019).During recordings, the membrane potential was zeroed using a depolarizing bath solution containing 140 mM KCl, 1 mM MgClU-tests between WT mPZ1 and each mutant/control for 100 µM Yoda1. For Fig. T-tests assuming unequal variance among tested series. The exact n values are reported in the provided Source Data file. All errors bars = s.e.m. Asterisks indicate standard p-value range. *, 0.01 < p < 0.05; **, 0.00 1 < p < 0.01; ***, p < 0.001 and n.s. (non-significant): p > 0.05.Measurements were taken from independent samples and no measurement consisted of repeats from previously measured samples. To test statistical difference between mean values, we performed two-tails Mann–Whitney Further information on research design is available in the Supplementary InformationSupplementary Movie 1Supplementary Movie 2Supplementary Movie 3Supplementary Movie 4Supplementary Movie 5Reporting SummaryDescription of Additional Supplementary FilesPeer Review FileSource Data File


The in vitro hybrid test system comprised “curtains” of human hair mounted onto sections of excised porcine skin within a modified diffusion cell. The results demonstrated that hair substantially reduced underlying scalp skin contamination and that hair may provide a limited decontamination effect by removing contaminants from the skin surface. This hybrid test system may have application in the development of improved chemical incident response processes through the evaluation of various hair and skin decontamination strategies.Most casualty or personnel decontamination studies have focused on removing contaminants from the skin. However, scalp hair and underlying skin are the most likely areas of contamination following airborne exposure to chemicals. The aim of this study was to investigate the interactions of contaminants with scalp hair and underlying skin using a hybrid  The emergency services’ response to incidents involving large numbers of individuals is commonly termed “mass casualty decontamination”2; one such protocol is the “Ladder Pipe System” of decontamination (LPS), during which a pair of fire engines park in parallel to deliver a high-volume, low-pressure water mist into a corridor through which casualties pass 1. The vast majority of previous studies that evaluated the efficacy of various decontamination systems focused on removing contaminants from the skin surface10. In contrast, relatively few studies have investigated hair decontamination and there is currently no in vitro model to quantify the risk associated with spreading contamination from hair to the underlying scalp or lower body surfaces during wet decontamination processes (e.g. LPS). The limited number of previous hair decontamination studies utilised pig or human scalp skin16. Although anatomically correct, such models do not accurately reproduce the normal geometric coverage of hair over the underlying scalp skin. This is because hair naturally falls under the influence of gravity and so the number of hairs covering the scalp will tend to increase from the top of the head down. Therefore, a model incorporating a “curtain” of hair laid over a skin surface would seem more representative.The adverse health effects of exposure to hazardous materials can be mitigated by decontamination: the timely removal of contaminants that may be on or near to body surfacesin vitro diffusion cell model that reproduces LPS hydrodynamics, allows the skin to be placed in a more realistic  geometry during showering and has a relatively large (~20 cm2) area to investigate the spreading of contaminants over the skin surface17. A logical step to develop this model further is the inclusion of human hair. Here we describe a hybrid in vitro model comprising excised pig skin partially overlaid with a curtain of human hair. The time-resolved, compartmental distribution of four chemicals—a curcumin and methyl salicylate mixture (CMX), sodium fluoroacetate (SFA), potassium cyanide (KCN) and phorate (PHR)—was investigated using the modified in vitro system. CMX has previously been validated for use as a simulant for medium volatility chemical warfare agents such as sulphur mustard18. The other contaminants were selected as being representative of toxic industrial chemicals.We have previously developed an A clear, consistent outcome was the predominant recovery of contaminants from within the hair Fig. . When avThe second greatest proportion of applied dose was recovered from the hair surface (hair swabs): the time-averaged recoveries were SFA 11.5% (range 6.0–16.0), KCN 5.4% (1.3–7.9), PHR 3.0% (2.2–4.6) and CMX 2.2% (1.6–2.4), with SFA and CMX being statistically different . When subcategorised by lipophilicity or hydrophilicity, the hair surface recoveries were attributable to solubility (P < 0.0001) and time .Given the statistically significant time-dependencies identified above, the hair-to-hair surface ratios (H:HS) of each contaminant were further investigated Fig. . There w14C-CMX, 14C-PHR and 14C-KCN was consistently and significantly (P < 0.05) greater on the hair curtains than on the underlying skin  and 14C-PHR (P = 0.0002) with time  did not significantly vary over the duration of the study , indicating that lateral diffusion was complete before the first time point (8 minutes). The spreading of each contaminant over the hair surfaces was consistently greater than the spreading over the underlying skin  with the exception of SFA Fig. .Figure 6in vitro skin model that integrates a realistic “curtain” of human hair to investigate the time-dependent distribution of contaminants in different compartments . Several methods have previously been reported for investigating skin decontamination using human scalp12, decontamination of human hair in the absence of skin14, and combined hair and skin decontamination using excised pig scalp skin13. Hair strands tend to fall under the influence of gravity to form a sheet over the underlying scalp skin. Therefore, a human hair curtain laid over dermatomed pig skin would appear to represent a rational approach for developing a more accurate hybrid hair/skin model.To our knowledge, this is the first description of an −2 19. Correspondingly, the linear arrangement of hair follicles would be √350 = 18.7 cm−1. The number of hair strands present over a section of scalp skin of similar dimensions to the hair curtain (3 × 1.7 cm) would thus be (3 × 18.7) × (1.7 × 18.7) = 1785. This calculation is based on the understanding that it is not the hair follicle density that dictates the amount of hair per unit area, but the accumulation of hair resting against the side of an individual’s head  as this can affect dermal penetration21. This hybrid system uses previously frozen porcine skin, making this model slightly more conservative as frozen skin is more permeable to chemicals than fresh human skin21.The method for creating hair curtains was reproducible, with each swatch containing ~1785 hairs (±6%). The average number of hair follicles on a human scalp has been estimated at 350 cmin vitro study  reported a hair recovery of ~10% of the applied dose, with the majority (75%) of the chemical distributed on or within the skin13. Since phorate and VX have similar physicochemical properties, this disparity may be explained by the different amounts and/or orientation of hair: the porcine scalp skin model mainly comprised vertical strands of hair at a density of 82 cm−2. In contrast, the human hair curtain in this current study was orientated horizontally and so provided greater coverage over the underlying skin. A recent human volunteer study reported a hair-to-skin ratio of ~20 for CMX at one hour post exposure22. This is of the same order of magnitude as the ratio of ~36 derived at the same time point for CMX in this current study. It should be noted that the volunteer study data would tend to underestimate the hair-to-skin ratio, as the hair was not excised and subjected to solvent extraction. Therefore, the in vitro model arguably provides a more realistic representation of the distribution of a contaminant between the hair and underlying skin. Collectively, these data indicate that scalp hair provides a significant degree of protection, a practical implication of which is that casualties exposed to toxic chemicals may be able to focus on other, less protected anatomical areas which may be more time-critical for effective decontamination.A surprising outcome of this study was the level of protection afforded by the hair, which retained the majority (53–89%) of the applied dose of each contaminant. In contrast, a previous 23, these do not appear to influence the distribution of contaminants following gross contamination.No statistically significant differences were observed in the distribution of contaminants between the different hair types used in this study. Whilst subtle differences in hair types have previously been identified24.An interesting observation was the dynamic redistribution of aqueous-based contaminants (SFA and KCN) between the hair and hair surface  proportion of contaminants was recovered from within the hair, subsequent diffusion via the transappendageal pathways may be of toxicological relevance and so further work is required to assess the significance of this route.A hybrid model has been developed that combines dermatomed pig skin with a partial overlay of human hair. The hair layer provides a substantial degree of protection against exposure of the underlying skin, which is in agreement with a previous human volunteer study. Analysis of the temporal distribution of contaminants within the different experimental compartments has indicated a dynamic redistribution that is both time- and solubility-dependent. The two chemicals applied in aqueous solution (SFA and KCN) gradually partitioned into the hair over the 4-hour exposure period. In contrast, transfer from the hair surface to the hair was extremely rapid (<8 minutes) for the two lipophilic substances (CMX and phorate). There was evidence to suggest that hair may also have a limited capacity to remove contaminants from the skin surface. The hybrid system will be used in future studies to assess the effects of various dry and LPS decontamination strategies.14C) MS (70 mCi mMol−1), PHR (50 mCi mMol−1), SFA (50 mCi mMol−1) and KCN (58 mCi mMol−1) were purchased from ARC . Their non-radioactive analogues of MS and PHR were added in an appropriate proportion to give a final working solution with a nominal activity of 0.5 µCi µL−1 respectively. Curcumin was added to 14C-MS (10 mg mL−1) to produce the 14C-CMX solution. Saturated solutions of SFA and KCN were prepared by the addition of 2.5 mCi of each chemical to 5 mL water, with the subsequent addition of 5 g (SFA) or 3.5 g (KCN) unlabelled chemical.Methyl salicylate  and curcumin  were purchased from Acros Organics, UK. PHR (95%) and SFA (99%) were custom synthesised by American Radiolabelled Chemicals . KCN (>98%) was purchased from Sigma Aldrich . Ethanol (Absolute), methanol (HPLC grade), propan-2-ol (HPLC grade), acetonitrile (HPLC grade), glacial acetic acid and formic acid (>99%) were obtained from Fisher Scientific, UK. Ultra-pure water (>18.2 MΩ) was filtered from the municipal supply via a MilliQ Integral 3 . Compressed air, helium and nitrogen for thermal desorption gas chromatography were supplied by BOC UK Ltd. Nitrogen for the liquid chromatography mass spectrometer was supplied from a nitrogen generator manufactured by Peak Scientific Ltd., UK. Ring-labelled (Human hair (from 58 donors) was obtained from unisex hair salons within the Hertfordshire and Hampshire areas of the UK. Individual strands of hair were separated into four main types: dark brown (Br+), light brown (Br−), dyed blonde (DB) and black (Bk). Soluene-350 and Ultima Gold liquid scintillation counting (LSC) fluid were purchased from PerkinElmer, Cambridgeshire. Cotton wool was purchased from Robinson Healthcare, Nottinghamshire, UK.Each human hair curtain comprised ~1785 individual strands cut to a length of 17 mm. These were transferred to a strip of self-adhesive tape (3 × 0.4 cm). A thin film of cyanoacrylate glue was placed over the lower half of the tape to ensure that tip of each hair strand was embedded in the adhesive before the top of the tape was folded over the tip of the hairs. The resulting hair curtains  from a reputable supplier. The skin was close clipped and removed from the dorsal aspect of each animal. The excised skin was then wrapped in aluminium foil and stored flat at −20 °C. Prior to the start of each experiment, a skin sample from one animal was removed from cold storage and thawed for approximately 24 hours. The skin was subsequently dermatomed to a thickness of 1000 μm . Once dermatomed, the skin samples were cut into 10 cm diameter discs and mounted into the diffusion cells. Hair curtains were placed onto the skin 0.5 cm from the top (centre) of the interior clamp . The temperature of each heat mat was set to achieve a skin surface temperature of 32 °C . Diffusion cells were left for 1 hour to equilibrate, after which pre-weighed 20 mL glass scintillation vials were positioned at the receptor fluid effluent port to collect serial samples of receptor chamber fluid.Skin diffusion cells and a manifold delivery system were manufactured by Protosheet Ltd.  as previously describedExperiments were performed in batches of six diffusion cells, with each allocated a specific treatment according to a pseudo-Latin square design (so that no treatment repeatedly occupied the same position within the fume cupboard). Each experiment was repeated six times to give a total of n = 6 replicates per treatment group, with each replicate being performed on skin from a separate skin donor . The hair type in each diffusion cell was randomised so that each experimental time point had at least one of each hair type. The treatment comprised exposure for a fixed duration  to one of the four test chemicals.14C-MS, 14C-PHR, 14C-SFA or 14C-KCN directly to the centre of the hair curtain surface. Air from within each donor chamber was sampled using a constant volume pump  set at a sampling volume of 75 mL min−1. Glass sorbent tubes were purchased from Markes International . Each tube glass tube was filled with 150 ± 5 mg of Tenax TA 35/60 absorbent. Filled Tenax tubes were conditioned using a TC-20 tube conditioner  in accordance with the manufacturer’s instructions.Each experiment was started by the addition of a 20 µL droplet of either After an appropriate delay (depending on the treatment group), the hair curtain was removed, placed into a pre-weighed 30 mL vial and stored at −70 °C for a maximum of 1 week. The skin and the inside surfaces of the diffusion cells were swabbed with dry cotton wool, which was placed into 10 mL ethanol; the diffusion cells were then disassembled and the skin samples were placed flat on a petri dish and stored at −70 °C for up to 1 week prior to autoradiographic analysis. The Tenax was removed from the glass sorbet tubes, placed into pre-weighed vials and then reweighed prior to the addition of 10 mL of propan-2-ol. The tubing connecting the donor chamber and the Tenax tubes were placed into pre-weighed vials containing 10 mL of propan-2-ol.−1; ARC, USA). A sheet of clear cellophane (38 μm thickness) was laid over the skin and hair samples and an erased phosphor imaging film  was placed on top. Each film was exposed for 3 hours and then processed using a confocal, variable-mode laser scanner . Each scan was set to a resolution of 25 μm per pixel and a PMT of 800. Images obtained via autoradiography were spatially calibrated and analysed using ImageJ v1.48p to assess skin surface spreading using a (software) threshold of 244–62258. Regions of interest (ROI) were placed over the exposed area and each image was analysed. An average background signal (based on a ROI derived from negative controls) was subtracted from all analysed areas.The skin and hair samples were removed from cold storage and placed into large (35 × 43 cm) autoradiography cassettes containing a radiometric calibration slide . Hair curtains were blotted using a single sheet of absorbent paper  cut into 5 × 5 cm swatches, which were then immersed in a 20 mL vial containing 15 mL propan-2-ol. Following blotting, each hair curtain and corresponding section of cellophane was placed into glass vials containing 20 mL of scintillation fluid.14C-MS, 14C-PHR, 14C-SFA or 14C-KCN by comparison to standards (measured simultaneously) that were prepared on the day of each experiment by the addition of a known amount of test chemical  to (i) cotton wool swabs in 10 mL ethanol, (ii) Tenax and tubing in 10 mL propan-2-ol, (iii) individual hair curtains, and (iv) unexposed skin tissue dissolved in 50 mL soluene-350. A standard receptor fluid solution was also prepared by the addition of 10 μL of test chemical to 990 μL of fresh receptor fluid (50% aqueous ethanol), from which a range of triplicate samples  were placed into vials containing 5 mL of LSC fluid to produce a standard  curve. Aliquots (250 μL) of each sample were taken and placed into vials containing 5 mL of liquid scintillation fluid for liquid scintillation counting. A summary of each sample type and its corresponding compartment is presented in Table Radioactivity of samples  was quantified using a PerkinElmer Tri-Carb liquid scintillation counter Model 2810 TR), employing an analysis runtime of 2 minutes per sample and a pre-set quench curve. The amounts of radioactivity in each sample were converted to quantities of 0 TR, empA normality test (Kolmogorov–Smirnov) was performed on all data. Equal proportions were not found to be normally distributed. Therefore, treatment effects were subsequently analysed by non-parametric tests using proprietary software .


These factors are important in constraining productivity in the marine biosphere, the development of Precambrian hydrocarbon source rocks, and more generally in understanding oxygenation of the ocean and atmosphere through Earth history; as all are ultimately related to organic carbon burial.Precambrian hydrocarbons and their corresponding source rocks are distinctly different from their Phanerozoic counterparts, having been deposited in persistently anoxic environments in ecosystems dominated by bacteria. Here, we show that cyclic enrichment of organic matter in the world’s oldest hydrocarbon play (ca. 1.38 Ga), is not associated with flooding surfaces and is unrelated to variations in mineralogy or changes in the relative rate of clastic to biogenic sedimentation—factors typically attributed to organic enrichment in Phanerozoic shales. Instead, the cyclic covariation of total organic carbon,  Understanding these processes, which likely vary in both space and time, are important in constraining the development of hydrocarbon source rocks and more generally in understanding oxygenation of the ocean and atmosphere through Earth history; as both are tied to organic carbon burial.High total organic carbon (TOC) content within sediments has been attributed to various factors, including high primary productivity4+, NO3−) and bio-essential trace metals12. Furthermore, it is widely appreciated that high levels of primary productivity and organic matter export can influence basin redox. In particular, Johnston et al.13 demonstrated the relationship between high levels of organic carbon export and euxinia, while Anbar and Knoll11 highlighted how basin redox, in particular euxinia, influences nutrient concentrations, in particular trace nutrients such as molybdenum, with flow on effects for bioavailable nitrogen. Furthermore, recent work has shown that the magnitude of organic matter supply also affects the rate of denitrification14 and consequently bioavailable nitrogen concentrations. Such effects on nitrogen availability naturally affect primary productivity and organic carbon burial, as biologically available nitrogen can limit photosynthesis and is often referred to as the proximal limiting nutrient10. Building upon these general observations, we present 𝛿15N and 𝛿13C data from the hydrocarbon-bearing Roper Group of the greater McArthur Basin of northern Australia. These suggest that the cyclic production of high TOC sediments in this Mesoproterozoic black shale succession is controlled by oscillations between nitrogen replete and nitrogen limited conditions. These are hypothesized to be a consequence of the coupling of elevated primary productivity, high degrees of organic carbon export, basin redox and the biogeochemical nitrogen cycle. These processes interact to impact nitrogen availability through changes in the balance between nitrogen supply via N2 fixation and nitrogen loss through denitrification.Primary productivity is generally accepted to be limited by the availability of phosphorus, dissolved inorganic nitrogen  that perform N2-fixation (i.e. diazotrophy), along with remineralised nitrogen. The loss of bioavailable nitrogen is achieved either through heterotrophic denitrification and/or anaerobic ammonium oxidation (i.e. ANAMMOX). It is widely held that diazotrophs are at a competitive disadvantage under nitrogen replete conditions. Consequently, it is only under nitrogen-limited conditions that diazotrophs are expected to fix appreciable amounts of nitrogen. As the diazotroph biomass has an average 𝛿15N of ~−1‰, and can range from −3‰ to +1‰18, any increase in N2-fixation in response to nitrogen limitation tends to decrease the 𝛿15N of DIN toward pure N2-fixation values (i.e. −3‰ to +1‰). In contrast to N2 fixation, denitrification and anaerobic ammonium oxidation preferentially return light nitrogen to the atmosphere, leaving the residual pool of bioavailable nitrogen isotopically heavier20. Nitrification and biological assimilation produce large fractionations in nitrogen isotopes22, however, these fractionations are ordinarily not expressed, as such processes are nearly quantitative. This is observed in the modern marine system through the 1:1 correspondence between 𝛿15Nsedimentary and 𝛿15Nnitrate . Therefore, nitrogen isotopes typically trace the balance between N2-fixation and heterotrophic denitrification and anaerobic ammonium oxidation, with the latter two being highly redox dependent being carried out under sub-oxic conditions26.In models for stratified oceans with only a shallow oxic layer28. Significant lateral thickness changes occur within the Roper Group. It is thin (~1–2 km) in the vicinity of the east-west trending Urapunga Fault Zone, of moderate thickness over the Broadmere Inversion Structure (~2 km29), and thinnest (<500 m) over the north–south trending Batten Fault Zone , large variations in organic carbon content, and low metamorphic grade37. Prior work has shown that the Velkerri Formation hosts some of the oldest known ‘live‘ hydrocarbon occurrences38, and continues to generate substantial interest as an unconventional gas play, with an estimated in place gas resource of 118 trillion cubic feet37. Recent production testing has resulted in the successful hydraulic fracturing of this Mesoproterozoic formation resulting in substantial gas flows39 making this one of the oldest proven unconventional hydrocarbon plays.The Velkerri Formation, a black-shale dominated unit within the Roper Group, is a key environmental archive for the early–middle Mesoproterozoic due to its exceptional thickness, well-constrained age  reveals that these organofacies are a feature of the Amungee Member at the regional scale  and oxidation ovens (to obtain the S4 peak). The pyrolysis oven was first held at 300 °C for 5 minutes and ramped at 25 °C per minute from 300 °C to 650 °C. The oxidation oven was held at 400 °C for 3 minutes, ramped at 20 °C per minute from 400 °C to 850 °C and held at 850 °C for 5 minutes. The flame ionisation detector (FID) was calibrated by running IFP standard ‘160000’ in ‘standard’ mode. The IR Analysers were calibrated against standard gas with known concentration of CO2 and CO. An analysis blank was run as ‘blank’ mode with the sample batch and the blank data was automatically subtracted from all analyses. An internal standard was also run first with each batch to ensure the instrument status. The results were processed with Optkin 3.0 software where peak heights and geochemical indices including Total organic carbon (TOC), Oxygen Index (OI), Hydrogen Index (HI) and Production Index (PI) are automatically calculated.Pyrolysis measurements were undertaken using a Weatherfords SRA. Crucibles were loaded into the carousel and heated under inert nitrogen in both the pyrolysis  analysis was undertaken on the raw powder utilising a Bruker D4 XRD. Quantification of the multiphase mixtures using Rietveld quantitative analysis was undertaken using the DIFFRAC™ software suite.2 for nitrogen and the Vienna Pee Dee Belemnite International Standard (V-PDB) for carbon. 𝛿15N were calibrated to international reference standards IAEA N1, N2, NO-3, and USGS 25, 32, 40 and 41. 𝛿13C was calibrated to IAEA CH-6 and CH-7; USGS 24, 40, and 41; and NBS-22. Standard deviation for bulk samples is 0.15 for 𝛿13C and 0.1 for 𝛿15N.1 to 2 grams of sample powder were treated with excess 3N HCl for 24 hours to remove trace carbonate minerals. Insoluble residual was centrifuged and rinsed with deionized water until supernatant achieved a pH of 4. Isotopic analyses for nitrogen and carbon were performed using a Euro EA Elemental Analyzer coupled to a Nu Horizons continuous-flow stable isotope mass spectrometer. Analyses were performed in the stable isotope laboratory at the University of Adelaide. Powdered, decarbonated samples were weighed and sealed in tin capsules for isotopic analysis and were combusted at 1050 °C. Data are reported using delta notation relative to atmospheric N15Nbulk varies from +1.04‰ to +2.67‰ while 𝛿13Corg varies from −35.2‰ to −32.4‰. Statistically significant correlations exist between 𝛿15Nbulk, 𝛿13Corg, and TOC (at α = 0.05). Atomic C:N ratios range from 17.2 to 49.8.We analysed 30 samples Figs.  and 5 fr42), it has also been shown that in high sediment accumulation zones, such as continental margins, no significant alteration of primary 𝛿15N occurs45. Furthermore, unaltered bulk sedimentary 𝛿15N generally reflects the 𝛿15N of bioavailable nitrogen23. While such work provides confidence in the fidelity of 𝛿15N of organic-rich sediments, we have investigated the possibility of alteration, focussing on relationships between 𝛿15N and thermal maturity, hydrocarbon generation, migration and devolatilisation , carbon abundance (C%)   Fig.  and K2O (%) Fig.  suggest 2 fixation (i.e. −3‰ to +1‰18), all other samples have 𝛿15N values ≥+2‰, which are values consistent with aerobic nitrogen cycling16. Significantly, these variations in 𝛿15N are not random but show statistically significant cycling at the 95% confidence level at a wavelength of ~110m (see Supplementary Information) and show consistent positive covariation with 𝛿13Corg, TOC  and basinal (n = 8) facies, however, unlike the Belt Basin, 𝛿15N values between basinal (n = 8) and shelf (n = 22) facies were statistically indistinguishable .A further mechanism for generating isotopically heavy nitrogen was proposed by Anbar and Knoll42−), however, under euxinic conditions, molybdenum reacts strongly with hydrogen sulphide to form the particle reactive thiomolybdate ion57. Thiomolybdates can be removed from seawater in organo-metallic complexes or within sulphide phases, resulting in molybdenum limitation. Recent work has shown that N2 fixation rates are reduced by ~80% at dissolved molybdenum concentrations between 1–5 nM59, such values are in the predicted range of dissolved molybdenum concentrations of Mesoproterozoic oceans50.The basis for Mo-N co-limitation is that under oxic conditions molybdenum occurs as the soluble molybdate ion  while biomarker studies62 identified dibenzothiophene within the Amungee Member which at the very least suggests sulfate reduction within the sediments, however, together support transient deep-water euxinic conditions65.While it has been argued that euxinia and nitrate are mutually exclusive36), the comparison between molybdenum and 𝛿15N reveals two distinct populations .Based on the boundaries for euxinia defined for the Velkerri Formation  and its loss via denitrification  is thought to be largely governed by basin redox conditions66. The dichotomy in the distribution of 𝛿15N and molybdenum , the cyclic variations cannot be explained by this mechanism, as it is incompatible with the detailed sedimentology of the Amungee Member and the overall high stand systems track for the Velkerri Formation.Partial assimilation and/or nitrification seem unlikely candidates controlling the variations observed in our 𝛿36, a cyclic process developed with high primary productivity and burial driving increasing levels of denitrification (i.e. ref.14). This process is suggested to have led to overall nitrogen limitation as denitrification ultimately depletes bioavailable nitrogen. However, the dichotomy in the distribution of 𝛿15N and Mo , which likely reflect the strength of primary productivity. These systematic positive covariations  can be explained, ultimately, by changes in the balance between N2 fixation and denitrification resulting from the dynamic interaction between primary productivity, basin redox and biological metabolisms associated with nitrogen cycling. This biogeochemical model does not preclude higher order controls on the observed cyclicity such as orbital forcing mechanisms, however, available age constraints do not allow for a robust testing of such a higher order mechanism.While this discussion has centred around nitrogen, carbon isotopes also support this model. For the same reasons given for variations in 𝛿2 fixation and denitrification pathways which can be related to high levels of organic carbon production, export and variations in basin redox, specifically shifts between sub-oxic to euxinic conditions. The requirement of molybdenum in biological N2 fixation along with the efficient scavenging of molybdenum under euxinic conditions results in Mo-N co-limitation and a decrease in primary productivity. These feedbacks highlight the dynamic interplay between primary producers, basin redox and biogeochemical nitrogen cycling. Such interactions are important in constraining productivity in the marine biosphere and highlight that high levels of primary productivity and euxinia are inherently self-limiting. Furthermore, such feedbacks are important in understanding the development of Precambrian hydrocarbon source rocks, and more generally in understanding oxygenation of the ocean and atmosphere through Earth history; as all are ultimately related to organic carbon burial.Cyclic behaviour in organic matter enrichment within the Mesoproterozoic Velkerri Formation is not associated with maximum flooding surfaces or to variations in mineralogy or clastic flux. Nitrogen and carbon isotopes along with molybdenum concentrations record the shifting balance between NSupplementary Information


Rhynchosporium commune and can cause up to 30–40% yield loss in susceptible cultivars. In this study, the Australian barley cultivar ‘Yerong’ was demonstrated to have resistance that differed from Turk (Rrs1 (Rh3 type)) based on seedling tests with 11 R. commune isolates. A doubled haploid population with 177 lines derived from a cross between ‘Yerong’ and the susceptible Australian cultivar ‘Franklin’ was used to identify quantitative trait loci (QTL) for scald resistance. A QTL on chromosome 3H was identified with large effect, consistent with a major gene conferring scald resistance at the seedling stage. Under field conditions, a bivariate analysis was used to model scald percentage of infected leaf area and relative maturity, the residuals from the regression were used as our phenotype for QTL analysis. This analysis identified one major QTL on chromosome 3H, which mapped to the same position as the QTL at seedling stage. The identified QTL on 3H is proposed to be different from the Rrs1 on the basis of seedling resistance against different R. commune isolates and physical map position. This study increases the current understanding of scald resistance and identifies genetic material possessing QTLs useful for the marker-assisted selection of scald resistance in barley breeding programs.The disease scald of barley is caused by the pathogen  Rhynchosporium commune. The pathogen can cause up to 30–40% yield loss in susceptible cultivars and is found in all barley-growing regions worldwide1. Control of scald disease requires a multi-facetted approach, including application of fungicides, cultural disease management, manipulation of sowing date and the use of resistant cultivars2. R. commune populations have changed rapidly in response to newly-developed fungicides and resistant plant cultivars6. One of the most sustainable strategies for R. commune management is to develop and deploy disease-resistant barley cultivars through the introgression and pyramiding of different resistance genes (major or minor). Traditional methods of phenotypic selection for complex patho-systems such as scald on barley can be improved through detailed genetic studies, which allow the implementation of marker-assisted selection (MAS) in breeding programs.Scald is a serious foliar disease in barley (Hordeum vulgare) that is caused by 8. Reduced scald symptoms in adult stage plants under field conditions might also result from disease escape through physical barriers to infection9. In terms of scald resistance in barley, flowering time, plant height and canopy structure can affect scald symptoms by physically limiting the upward spread of the splash-dispersed pathogen8. The major scald resistance genes discovered so far have been mainly identified through experiments with seedlings via inoculation with specific isolates8. The problem with using major scald resistance genes in breeding programs is a lack of durability. Quantitative genes are thought to be more durable, and it has been suggested that pyramiding these genes could reduce the ability of R. communes to rapidly acquire new virulence combinations10.Scald resistance is governed by both major and minor genes. Major resistance genes provide high levels of resistance at all plant growth stages, while minor resistance genes generally provide partial levels of resistance at the adult plant stageRrs1 locus on chromosome 3H and the Rrs2 locus on chromosome 7H11. These two loci have been detected in different mapping populations, across different environments, under glasshouse conditions after inoculation with specific isolates, and under field conditions11. Progress of our understanding of these loci has been made through fine mapping studies13. However, it remains unknown if the QTLs detected at each of these two loci are alleles of the same gene, or if they are part of a closely linked gene cluster at each locus15. For the Rrs1 locus, multiple major and minor scald resistance alleles or loci have been identified, supporting its importance in barley germplasm worldwide21. Bjornstad, et al.16 suggests that previously identified scald resistance loci Rh, Rh1, Rh3, Rh4 and Rh7 are all alleles of the Rrs1 locus. A single dominant gene Rrs1 (Rh type) was first identified in cultivar ‘Brier’22. Rrs1 (Rh4 type) was identified in cultivars ‘La Mesita’, ‘Trebi’ and ‘Osiris’, with a similar allele, termed Rh42, identified in the cultivar ‘Modoc’23. In ‘Turk’ and ‘Atlas46’, another resistance allele Rrs1 (Rh3 type) was identified closely linked to the Rrs1 (Rh4 type) from ‘Modoc’23. Rrs1 (Rh type) was the first scald resistance locus reported in barley23 with an associated RFLP marker of cMWG680, positioned at 455.3 Mb on chromosome 3H on the pseudomolecules Morex V 2.0 201919. The location of the Rrs1 (Rh4 type) locus was further fine mapped to an interval of less than 9 Mb at 448.4 Mb on the pseudomolecules Morex V 2.0 201924 and 490.0 Mb on the pseudomolecules Morex 201712 from Spanish barley landraces.A large number of QTLs for scald resistance have been discovered in barley. Two genomic regions in particular have been frequently associated with resistance; the R. commune isolates at seedling stage to demonstrate that resistance in the cultivar ‘Turk’ ) differs from that resistance in the cultivar ‘Yerong’. These results contradicted previous findings that suggested ‘Yerong’ carries a scald resistance QTL at the Rrs1 locus on chromosome 3H25. To further investigate this finding, screening of a ‘Yerong’/‘Franklin’ doubled haploid (DH) population was conducted for seedling resistance against four R. commune isolates and adult plant resistance under natural field conditions across three years. To overcome potential confounding effects of differences in plant growth stage and the onset of flowering (determined as relative maturity) on scald resistance QTL detection within the population, a linear mixed model with a bivariate approach was used to analyse the field data and derive a resistance phenotype from the linear relationship between relative maturity and percentage of infected leaf area. Using this approach, a major QTL on chromosome 3H from Yerong was detected for scald resistance at both seedling and adult plant stage. The QTL detected did not map to the Rrs1 locus, suggesting the presence of a new and useful scald resistance QTL in ‘Yerong’.In this study, we performed screening against 11 Rrs1 + Rrs2) was resistant to all the isolates used in this study, with BLUP scores under 2.3, except isolate WAI2840, which was virulent on all cultivars with a BLUP score of more than 3.5 (Table Rrs1) was resistant to all the isolates used in this study with scores under 2.4, except two isolates WAI2439 and WAI2840. Cultivars carrying Rrs2 (‘Atlas’ and ‘Atlas46’) were resistant against isolate WAI2439 (Table Rrs1 (‘Turk’ and ‘Atlas46’) showed higher level of resistance than ‘Atlas’ and ‘Yerong’ against two isolates, WAI1245 and WAI2464. Cultivars carrying either Rrs1 or Rrs2 were resistant against isolates WAI453, WAI2466, WAI2471 and WAI2636.The cultivar ‘Atlas46’ Rrs + Rrs2 wRrs1 (Rh3 type) resistance possessed by ‘Turk’ was not present in ‘Yerong’. Compared to ‘Yerong’, ‘Atlas’ showed a higher level of resistance against isolates WAI2439, WAI2463, WAI2466, WAI2470 and WAI2636, suggesting Rrs2 is absent in ‘Yerong’. ‘Yerong’ was resistant against WAI2471, and displayed moderate resistance against isolates WAI453, WAI1245, WAI2439 and WAI2473 with the scores between 3.0 and 3.4. ‘Yerong’ showed better resistance than ‘Franklin’ against isolates WAI453, WAI1245 and WAI2471with the overall disease scores of ‘Yerong’ being lower than those of ‘Franklin’. The cultivar ‘Litmus’ was susceptible to all the isolates in this study.‘Turk’ was considerably more resistant than ‘Yerong’ against five isolates and equivalent against the remaining six isolates tested. Together, these results suggested that the There were no significant differences  in resistance to the four isolates WAI2466, WAI2470, WAI2471 and WAI2473 among the two parent cultivars in the seedling stage screening experiments. However, there was phenotypic variation in resistance to the different isolates among the DH population lines. The disease scores among DH population lines against WAI2466 showed a bimodal distribution of scores between resistant and susceptible lines Fig. . The varR. commune isolates  at the same position  between scald percentage of infected leaf area and relative maturity was observed among the DH lines Fig.  in 2015 Four QTLs for relative maturity (QTLRM) were identified in the Yerong/Franklin population consistently across all three years Fig. : two on In 2015, one QTL for scald percentage (QTLSP) on chromosome 2H (QTLSP-2H-2015) was located at the same position as the 2H QTL for relative maturity (QTLRM-2H-2015) , between Rrs1 (Rh4 type) (448.4 Mb) and Rrs4 (523.0 Mb) on chromosome 3H, explaining 14.8% of phenotypic variance. QTLs were identified for DRSRRM on chromosome 3H across the three years at the same location as the QTLs detected for scald percentage. In 2017, a novel QTL for scald resistance  was identified on chromosome 7H from ‘Franklin’, locating at 69.9 Mb on the pseudomolecules Morex V 2.0 2019. This QTL explained 12.9% phenotypic variance with a LOD value of 5.8.The QTLs for DRSRRM (QTLD) in 2016 (QTLD-3H-2016) and 2017 (QTLD-3H-2017) were located at 178.7 Mb in the same position as QTL-WAIYerong-3H, and the resistant allele was also derived from the ‘Yerong’ parent. The QTL for DRSRRM (QTL-WAIYerong-3H) explained 20.7% of the phenotypic variance in 2016  allele from ‘Turk’ is not present in ‘Yerong’. ‘Yerong’ was resistant to isolate WAI2471, and had moderate levels of resistance against WAI453, WAI1245, WAI2439 and WAI2473  were unable to distinguish this QTL from ‘Yerong’ from the Rrs1 locus.Five of the 27. In our study, flanking markers of the QTL-WAIYerong-3H, bPb-0068 and Bmag0006, are located at 120.3 and 178.7 Mb respectively on the pseudomolecules Morex V 2.0 2019. The position is some distance away from previously fine mapped Rrs1 (Rh4 type) which is located at a position of 448.4 Mb on chromosome 3H , and a correlation between mature plant height and scald resistance was not observed (data not shown), and the QTL identified for plant height did not co-locate with scald resistance QTL  infection in wheat. They aimed to calculate a phenotype that captured components of resistance that did not depend on those two traits, and represented a better approximation of genetic disease resistance from the experiments in their study. The residuals from a generalized linear model in which STB percentages were fitted to all escape related traits, including heading date, plant height, leaf spacing and leaf morphology, were used as the indicators of disease resistance to analyse the STB resistance in a set of wheat lines39. Chartrain, et al.40 identified a QTL for partial resistance to STB in wheat by using residuals from multiple regression on relative maturity and plant height as their phenotype. QTLs for spot blotch resistance in wheat were detected by using residuals when fitting disease severity as a dependent variable and plant height and days to heading as independent variables in multiple regression to exclude the effects of these traits41. Most of the reported uses of maturity regression residuals phenotype pertain to STB resistance in wheat. This could be due to the well-characterised relationship between STB infection rates and both flowering time and plant height42 and recognition that these confounding traits needed to be accounted for in ascertaining true genetic resistance for STB that would be useful for varietal improvement. In general terms, for phenotypes based on deviation from the regression of a correlated trait (like our DRSRRM measure of resistance) and the approaches described above, the most resistant lines are those with the largest negative values of residuals. This allows breeders to select resistant lines with desirable relative maturity and plant height43. Further, as noted by Van Beuningen and Kohli38, this approach is especially useful where disease resistance is evaluated in experiments at a single point in time, rather than at critical development stages for each genotype, especially in genetic material with large variation for confounding traits such as maturity.The deviation from the regression of infection on important confounding traits has been employed previously as an effective phenotype for disease resistanceIn our study, a multiplicative mixed model was used to analyse the field data of scald resistance and relative maturity. When DRSRRM was utilised as a phenotype for QTL analysis, a major QTL was identified on chromosome 3H. This major QTL is co-located with a major QTL detected for scald resistance at the seedling stage in this study, QTL-WAIYerong-3H. When scald percentage of infected leaf is used as the phenotype for QTL analysis, a QTL for scald percentage on chromosome 2H were identified at the same position as QTLs for relative maturity; and the major QTL on chromosome 3H was also detected for scald percentage Fig. . Our resRrs1, as differential cultivar screening indicates the Rrs1 (Rh3 type) allele from ‘Turk’ is not present in ‘Yerong’. The flanking markers for this QTL-WAIYerong-3H are located distantly (approx. 270 Mb) from the Rrs1 (Rh4 type) locus based on the pseudomolecules Morex V 2.0 2019. To overcome the confounding effects of relative maturity on adult plant disease resistance, a bivariate approach was used to model the field data of scald resistance and relative maturity. The phenotype derived from the bivariate analysis (DRSRRM) is a more effective trait to detect disease resistance QTLs as it removes the confounding effects of relative maturity. The new QTL identified in this study is a useful resource for pyramiding different resistance genes (major or minor) in breeding programs.In conclusion, a major QTL QTL-WAIYerong-3H providing scald resistance at both seedling and adult plant growth stages was identified. Results from this study indicate that this QTL-WAIYerong-3H is not R. commune isolates from the Wagga Wagga Agricultural Institute fungal isolate collection. These isolates were collected from southern New South Wales between 2013 and 2016 (Supplementary Table R. commune isolates were grown on lima bean agar (LBA) at 20 °C under 24 hour light condition7. After 2 to 3 weeks, spores were harvested with a scalpel blade and the spore solutions for spray inoculation were adjusted to 2 × 106 spores ml−1 in distilled water using a haemocytometer. Seeds of cultivars were sown in 6-cm pots arrayed in a randomized complete block design consisting of 4 columns by 30 rows with three replicates of each genotype in each experiment for a total of 120 pots. A total of 40 cultivars were included in the differential cultivars screening, however, only the results for six key cultivars  were reported in this study (Table Rrs1), ‘Atlas’ (Rrs2) and ‘Atlas46’ (both Rrs1 and Rrs2) were compared with that of ‘Yerong’, while ‘Litmus’ was used as the susceptible check cultivar. The barley cultivars were uniformly sprayed with spore solutions at the 3-leaf-stage, and kept in a dark chamber for 48 hours at 18 °C with 100% humidity. The seedlings were scored 14 days post-inoculation with a scoring scale from 1 to 5 (1 = resistant and 5 = very susceptible) following the methods of Jackson and Webster44. For interpretation of the analysed isolate specific interactions, we define resistance as a BLUP score of up to 2.5 which is equivalent to the reactions of known major resistance genes Rrs1 and Rrs2. We define moderate resistance as BLUPs in the range 2.5–3.5 and susceptible as scores of greater than 3.5. Conversely, an isolate is determined to be virulent if the BLUP was greater than 3.5.The seedling scald resistance of different barley cultivars was tested against 11 25.A DH population of 177 lines from a cross between the cultivars ‘Yerong’ and ‘Franklin’ was used in this study. Seeds for each genotype were obtained from the University of Tasmania. ‘Franklin’ is an Australian two-rowed malting quality cultivar, and ‘Yerong’ is an Australian six-rowed feed quality cultivar. The genetic linkage map of the population comprised 28 microsatellites and 196 diversity arrays technology (DArT) markers assembled by Li and ZhouR. commune isolates, WAI2466, WAI2470, WAI2471 and WAI2473, were used in the seedling resistance screening in the glasshouse experiments  were sown in 6-cm pots arrayed in a 24 column by 25 row randomized complete block design with three replicates of each genotype for a total of 600 pots. Each genotype by isolate combination was tested with three different experiments with three technical replicates per experiment. Seedlings were scored 14 days post-inoculation with a scoring system from 1 to 4 (1 = resistant and 4 = very susceptible) following the methods of Wallwork and Grcic7.Four different R. commune infection was residual barley crop debris from the previous harvest. Overhead irrigation was used regularly to supplement rainfall throughout the growing season to enhance the development of disease. Experiments were subject to a strict weed control and crop nutrition regime to maximize yield potential. Assessment of disease was based on leaf symptoms using the percentage of infected leaf area6. Relative maturity at the time of disease assessment was determined using the Zadoks decimal score for plant development45. Final plant height was also measured at physiological maturity in the 2017 experiment.Field screening for scald resistance was conducted at Wagga Wagga Agricultural Institute  in 2015, 2016 and 2017. All field trials were sown in May with a randomised complete block design with two replicates for each genotype. Each genotype was sown in 1.2 m rows with 0.4 m spacing between each row. The primary inoculum for 46 in the R environment47. A linear mixed model following the approach of Gilmour, et al.48 was used to analyse the data for the differential cultivar screening experiments as follows (Model 1):y is the n × 1 vector of the response variable  across p = 22 experiments with each of the 11 R. commune isolates tested in a separate experiment, and that experiment repeated once. n = 2640 for the differential cultivar screening experiments as only selected cultivars were included. X The term g is the vector of genotypic random effects with associated design matrix Z used to model the genotype by experiment effects. The term All data was analysed using the software package ASReml-R version 3A model similar to Model 1 above was also used to model scald scores for the seedling inoculation experiments with 49 and the significance of random effects other than ‘replicate’ was determined using log-likelihood ratio tests50.For the field screening experiments measuring scald resistance and relative maturity, each of the three field experiments was modelled separately using a bivariate approach as follows:38 and the paired case-control study (Example 8.8) detailed in Butler, et al.46 as follows:The linear relationship between scald resistance and relative maturity was determined from the trait:genotype covariance modelling after the general approach of Van Beuningen and Kohli51. QTLs were first analysed with interval mapping (IM). The closest marker to each QTL was selected as a cofactor for multiple QTL mapping (MQM). A logarithm of the odds (LOD) threshold value of 3 was used to identify QTL and QTL intervals.The DRSRRM values for each individual field experiment were used as the phenotype to detect QTL for scald resistance adjusted for the relationship between scald resistance and relative maturity. For the phenotype analyses of scald percentage, relative maturity and plant height BLUPs were obtained from the model for each experiment. For the scald seedling-stage resistance phenotype, BLUPs were obtained from the seedling scald inoculation experiments model. Phenotypes were used for QTL analysis using the MapQTL6.0 software packageRrs1 (Rh4 type)12 were used to perform BLAST searches by using the IPK Barley BLAST Server (http://webblast.ipk-gatersleben.de/barley). The barley pseudomolecules Morex V 2.0 2019 and pseudomolecules Morex 2017, were used for the BLASTn search. Default settings were used to do the BLASTn search and the best hit was used to decide the physical position of the detected QTL.The sequences of markers associated with the identified QTL for scald resistance and fine mapped Supplementary Information.


A computational analysis of UV filtering and radical scavenging capacity of a flavanone, Eriodictyol has been performed under DFT-B3LYP/6–31+ G . Eriodictyol is nontoxic and nonirritant bitter masker used in wine and can be used for photo protection due to its potential UV filtering and radical scavenging capacity. The compound has an absorbance in the UV-A and UV-B region of electromagnetic spectrum, it can be used as a potential UV filter in sunscreen lotions and other cosmetic products. Eriodictyol is a potent antioxidant than the most commonly studied Quercetin. The most active site in the compound is 3׳ position and is confirmed by NPA, NBO and pKa value analysis. The BDE values follow the order 3′<4′<7<5. The major transitions in the UV–visible spectrum of Eriodictyol are between HOMO and HOMO-1 with LUMO level and are well explained by NBO–NLMO tool in G09. Specifications TableValue of the data•Eriodictyol can be used as potential UV filter in sunscreen lotions and other cosmetic products.•Eriodictyol is a potential antioxidant than the most commonly studied Quercetin.•The pKa values of all the possible sites are computed on the basis of free energy values.•Its BDE values follow the order 3′<4′<7<5 position in the flavanone ring.•Major transitions in the UV–visible spectrum are between HOMO and HOMO-1 with LUMO.•The UV filtering capacity can be well explained by the combined NBO–NLMO study.1The data include the structure and numbering of the optimized stable conformer of a flavanone called Eriodictyol . A Densi2via Natural Population Analysis (NPA), pKa value The stable conformer (lowest energetic) obtained from the potential energy scanning (PES) of Eriodictyol has been optimized with DFT-B3LYP/6–31G+