The Devonian Frasnian–Famennian (F–F) boundary marks one of the five main extinction intervals of the Phanerozoic Aeon. This time was characterized by two pulses of oceanic anoxia, named the Lower and Upper Kellwasser events, during which massive marine biodiversity losses occurred. This paper presents high-resolution magnetic susceptibility, X-ray fluorescence elemental geochemistry and carbon isotope datasets obtained from the Steinbruch Schmidt F–F boundary section (Germany). These records lead to an astronomical time calibration of the environmental changes associated with the two ocean anoxia pulses. Cyclostratigraphic interpretation indicates deposition of the black argillaceous Lower and Upper Kellwasser horizons over ~ 90 and ~ 110 kyr, respectively; approximately equivalent to the duration of one short eccentricity cycle. This study confirms that the succession of events within the Upper Kellwasser event is paced by obliquity, under a low-eccentricity orbit. Hence, astronomical insolation forcing likely contributed to the expansion of ocean anoxia and other environmental perturbations associated with these two crises. The new floating chronology established for the Steinbruch Schmidt section is anchored in numerical time by means of a radio-isotopic date, obtained from a bentonite layer interbedded between the two Kellwasser horizons. After anchoring, this time scale gives a high-precision age of 371.870 ± 0.108 Ma for the F–F boundary. Classically, the sedimentary expression below and at the F–F boundary, consists of two dark shale intervals, referred to as the Lower and Upper Kellwasser shales respectively . These dark shales stratigraphically correspond to two distinct positive carbon-isotope (δ13C) excursions recorded around the globe, including at locations where black shales did not develop6. The origin of the extinction and its specific timing have been debated for a long time. Suggested causal mechanisms for the extinction include one or more extraterrestrial impacts8, large-scale volcanism10, either climate cooling11 or warming12, and enhanced continental weathering, through mountain building13 or development of terrestrial forests14. Increase continental weathering in particular would have resulted in eutrophication, planktonic algal blooming and subsequent widespread marine anoxia.The Devonian Period experienced several episodes of strong environmental and climate changes, including different pulses of extinction and global carbon-cycle perturbations. The Frasnian–Famennian (F–F) boundary event stands out among these Devonian environmental perturbations, due to its strong impact on marine ecosystems 16 is primarily based on three ash beds dated by U–Pb as middle Frasnian to late Famennian in age. These are the Belpre Ash , the bentonite from the Steinbruch Schmidt section , and a bentonite in the Piskahegan Group . The large error bars on each of these individual radio-isotopic dates and also the use of non-chemically abraded U–Pb ages leads to substantial uncertainty, and potential inaccuracy on this ~ 2,500 kyr duration estimate.The ~ 2,500 kyr estimate22. For the F–F interval, De Vleeschouwer et al.23 worked on the expression of Milankovitch cycles in the Kowala section in Poland and suggested a ~ 800 kyr time-gap between the onsets of the LKW and the UKW and a ~ 400 kyr duration for low oxygen levels during the UKW. Subsequently, De Vleeschouwer et al.6 complemented the Kowala section, with sections from Canada, U.S.A., China and Belgium compiled into a global cyclostratigraphic framework that places the onset of the positive LKW δ13C excursion ~ 800 kyr before the F–F boundary.Recently, strong efforts have been made to improve the Devonian time scale through cyclostratigraphy22 proposed a cyclostratigraphic framework for the whole Famennian through different cores from the Illinois Basin, anchored to a precise Famennian–Tournaisian boundary age based on U–Pb dating of uppermost Famennian bentonites from Poland24. This cyclostratigraphic approach resulted in a F–F boundary age of 372.4 Ma ± 0.9 Myr. No duration is proposed for the interval between the LKW and UKW. The F–F age is based on a cyclostratigraphic extrapolation from ash beds 13.5 Myr younger than the F–F boundary.Pas et al.25 re-dated zircons from the Steinbruch Schmidt ash bed located between the LKW and UKW  U–Pb geochronology techniques as well as the Earthtime spike solutions), generating a new age of 372.360 ± 0.053 Ma for the bentonite. Using this age, extrapolating the De Vleeschouwer et al.6 cyclostratigraphic framework and assuming a constant sedimentation rate for the limestones deposited between the two Kellwasser horizons the F–F boundary age was estimated to be between 371.93 and 371.78 Ma25.Recently, Percival et al.Better constraints on the rate and timing of environmental change before, during and after the extinction could resolve the diverging hypotheses. Indeed, numerous different chronologies have been constructed for the F–F extinction event  data. At the Vrije Universiteit Brussel (Belgium), a Bruker M4 Tornado, operated under near-vacuum conditions (20 mbar) was used for the μXRF measurements. The conditions used were: a 30 W Rh anode metal-ceramic X-ray tube and a 30 mm2 SSD with a resolution of 145 eV (Mn–Ka). The X-ray beam was focused by a poly-capillary lens on a spot with a diameter of 25 mm (Mo–Ka). An integration time of 60 s per point was chosen, satisfying the conditions for reaching the Time of Stable Accuracy and Time of Stable Reproducibility and providing the ideal compromise between high analytical accuracy and precision and high sample throughput . The carbon (δ13C) stable isotope ratios of bulk powered carbonate material were also determined at the Vrije Universiteit Brussel, using a NuPerspective isotope ratio mass spectrometer (IRMS) interfaced with a NuCarb automated carbonate device. Acidification of the samples took place at a temperature of 70 °C. All values are expressed relative to the Vienna Pee Dee Belemnite (‰VPDB) standard. Calibration was carried out using an in-house Marbella limestone (MAR) standard (+ 3.41 ‰VPDB) calibrated against the international NBS-19 standard30. On the basis of replicated measurements of the MAR standard, reproducibility errors on δ13C are < 0.05 ‰ (1 σ).Magnetic susceptibility was measured 3 times on each sample, with a KLY-3 (AGICO Kappabridge) at Liège University (Belgium), averaged and weighted to produce mass-specific  as calculated by35 are selected. Long and short eccentricity relate to the shape of Earth’s orbit and not to its rotational speed, hence eccentricity is not affected by tidal dissipation. Therefore, the 100 kyr and 405 kyr periodicities have remained unchanged throughout geological history. Numerical calculations by Laskar et al.37 have demonstrated the extraordinary stability of the periodicity of the 405 kyr term, often referred to as the metronome of the Phanerozoic. In this study, eccentricity periodicities of 94.9, 98.9, 123.8, 130.7 and 405 kyr are adopted37.All time-series analyses are performed on the R platform6. These authors correlated 6 globally-distributed Frasnian–Famennian sections . Those authors then applied a Monte Carlo procedure that distorts time-differences between consecutive tie-points, i.e. small-scale stretching and squeezing between tie points. Their goal was to reach the best expression of the Milankovitch cycles, under the presumption that the chronology with the clearest orbital signature advocated to be the most accurate (detailed description in6). This technique is called the Optimized Relative Timing Approach (ORTA). The new results from the Steinbruch Schmidt section are incorporated into this correlation framework and assigned relative age tie-points (with 0 kyr corresponding to the F–F boundary) identical to those in6.The eTimeOpt chronology is then compared with a time scale obtained though the age modelling protocol designed by38), obtained through the biwavelet package39 is applied. Continuous wavelet is an evolutive spectral techniques that reveals the evolution of periodicities for the studied sections and detects changes in cycle thickness and, hence, sediment accumulation rate. These CWT results are compared with the results from eTimeOpt and ORTA.Finally, the Wavelet Transform evolutive spectra  with a grid of 100 sedimentation rates between 0.15 and 0.55 cm/kyr range. The results of eTimOpt for amplitude modulation of precession, give evolving sedimentation rates  ranging in average values between 0.47 and 0.53 cm/kyr , 1,062 kyr (MS) and 1,007 kyr (logTi). However, eTimeOpt for amplitude modulation of precession cannot consider the full sedimentation rate interval as the proxy-series resolution is too low to resolve precession cycles for sedimentation rates lower than 0.26 cm/kyr. Running eTimeOpt again on all proxies, investigating the amplitude modulation of short eccentricity by long eccentricity circumvents this problem. These settings lead to eTimeOpt results for the full sedimentation rate range. Despite the fundamentally different eTimeOpt settings, the sedimentation rates remain similar , 1,085 kyr (MS) and 1,124 kyr (logTi) for the whole section , 100 kyr, 300 kyr, 500 kyr and 700 kyr  applied on the different proxies from the Steinbruch Schmidt section, lead to similar results for the duration of the whole section. These duration estimates for the section range between a minimum of 994 kyr to a maximum value of 1,129 kyr  The results from ORTA exhibit a slightly better fit with the band of strong power in the continuous wavelet transform and evolutive harmonic analysis. Nevertheless, all results are very close as the difference between the various results obtained through the different techniques and those from ORTA on magnetic susceptibility never amounts to more than 83 kyr  eTi eTi44, wproxies.  The resu25, who reported a weighted mean age of 372.360 ± 0.053 Ma. Combining this age with the present cyclostratigraphy provides an integrated time scale for the Frasnian–Famennian boundary interval  are all in phase  and the Frasnian termination is between 690 and 843 kyr ). Percival et al.25 proposed different uncertainties, one 2σ at ± 53 kyr, which represents only the measurement uncertainty and allows comparison with ash bed dates obtained through the same technique and one 2σ at ± 410 kyr, which includes the measurement, tracer and also decay constant uncertainties and must be employed when making comparisons between U–Pb ages and argon-argon (Ar–Ar) ages. Consideration of those uncertainties gives a Frasnian–Famennian boundary age of 371.870 ± 0.108 Ma for the uncertainty to be compared with other U–Pb ages and 371.870 ± 0.420 Ma to be compared with Ar–Ar ages.Uncertainty assessment on cyclostratigraphic results is a challenging endeavoural Table . The sta48. However, two contrasting hypotheses exist regarding the exact nature of this link (synthesis in49): one in which organic matter accumulation is favoured during eccentricity maxima and one during eccentricity minima. The eccentricity maxima hypothesis involves a context of strong seasonal contrast between dry and wet seasons, allowing strong fluvial discharge, productivity blooms and organic-matter accumulation. This model is classically invoked to explain high-productivity dark sapropel layers from the Pliocene of the Mediterranean region51. The eccentricity minima hypothesis invokes a scenario of enhanced preservation, involves low seasonal contrast, associated with stable conditions, with water mass stratification allowing organic matter preservation and persistent anoxia. This model is used to explain the Cretaceous Oceanic Anoxic Events52.The newly constructed integrated and anchored time scale obtained for the Steinbruch Schmidt section also allows for the evaluation of the impact of astronomical forcing on the Kellwasser environmental perturbations. A connection between orbital forcing and organic carbon accumulation has been frequently postulated6 reported maximum obliquity power in the δ13C proxy starting a few tens of thousands of years before the F–F boundary. They identified that the obliquity signal is strong in this interval because it represents a 2.4 Myr minimum in eccentricity. In such an astronomical configuration, the amplitude of precession is muted, and obliquity is the dominant astronomical parameter that directly influences the distribution of insolation over the globe and over the seasons. This scenario is similar to that proposed for Cretaceous Ocean Anoxic Event 2 during the Cenomanian–Turonian interval52 and fits with the eccentricity minima enhanced preservation model. The Steinbruch Schmidt section δ13C proxy series tests this hypothesis. The δ13C signal is long enough to includes sufficient amount of short eccentricity to focus on the evolution of obliquity and eccentricity power, to confirm or refute the minima eccentricity hypothesis.De Vleeschouwer et al.13C record at Steinbruch Schmidt documents similar trends to those of De Vleeschouwer et al.6, with an obliquity power reaching maximum values slightly below the F–F boundary  δ47.To summarise, both Kellwasser events occur over approximately only a single short eccentricity cycle and are initiated during the early (low-eccentricity) phase of a 405 kyr cycle. An extreme eccentricity minimum, during which a 2.4 Myr and 405 kyr minima culminated, likely initiated the Upper Kellwasser Event. This eccentricity minimum 2.4 Myr configuration potentially explains why the Upper Kellwasser appears to have been more severe than the lower Kellwasser. Such low-eccentricity configuration decreases seasonality and reduces circulation and overturns ocean basins, facilitating the accumulation of organic matterFor the first time, an astrochronology is compiled for a Devonian section that also comprises a high-precision radio-isotopic date. This unique integration of chrono- and cyclostratigraphy results in a precisely time-calibrated sequence of events across the F–F boundary, well-anchored in numerical time.6 that obliquity, under a low-eccentricity orbit make the Earth system more prone to developing marine anoxia, helping the development of the low-oxygen Kellwasser events preceding the F–F boundary mass extinction. However, the main result of this study is a new F–F boundary age of 371.870 ± 0.108 Ma (to be compared with U–Pb ages).The Steinbruch Schmidt section  is ideal for this combined cyclostratrigraphy/absolute age approach because it is characterized by the rare combination of well-expressed dark shale Kellwasser intervals, reliable environmental proxies and a datable bentonite ash. The carbon isotopes, logTi and magnetic susceptibility series of the section provide new insight in the astronomical signature of environmental change across the F–F boundary and build a cyclostratigraphy for the transition. Both Kellwasser anoxic episodes are interpreted to have lasted for about one short eccentricity cycle and to have begun during a 405 kyr eccentricity minima. The Upper Kellwasser appears more severe in its expression as it corresponds to a 2.4 Myr minimum in eccentricity . This study confirms the hypothesis put forward byThis study demonstrates that even in the Palaeozoic a < 100 kyr time scale resolution is within reach when cyclostratigraphy can be combined with high-precision absolute ages. However, such chronologies can only be obtained through optimal proxy selection and multiproxy approach for robustness, careful comparison of different techniques for time-series analysis and an integrated stratigraphy approach.Supplementary Information


Early recognition of anthracycline-induced cardiomyopathy may reduce morbidity and mortality in children, but risk stratification tools are lacking. This study evaluates whether electrocardiogram (ECG) changes precede echocardiographic abnormalities in children with anthracycline-induced cardiomyopathy.We performed a retrospective analysis of 589 pediatric cancer patients who received anthracyclines at a tertiary referral center. ECG endpoints were sum of absolute QRS amplitudes in the 6 limb leads (ΣQRS(6 L)) and corrected QT interval (QTc). Cardiomyopathy was defined by echocardiogram as ejection fraction < 50%, shortening fraction < 26%, or left ventricular end-diastolic diameter z-score > 2.5.p = 0.003) and 1.098  respectively. Kaplan-Meier estimates showed a lower chance of cardiomyopathy-free survival for QTc ≥ 440 ms and ΣQRS(6 L) ≤ 3.2 mV over time. After controlling for confounders, total anthracycline dose predicted a decrease in ΣQRS(6 L) and an increase in QTc independent of cardiomyopathy status (p = 0.01 and p < 0.001 respectively). Cardiotoxic radiation did not predict changes in ECG parameters. Cardiomyopathy was associated with increased mortality .Median age at start of therapy was 7.8 years (IQR 3.7–13.6); median follow-up time was 3.6 years (IQR 1.1–5.8). 19.5% of patients met criteria for cardiomyopathy. Male sex, race, older age at first dose, and larger body surface area were associated with development of cardiomyopathy. A 0.6 mV decrease in ΣQRS(6 L) and 10 ms increase in QTc were associated with an increased risk of developing cardiomyopathy with hazard ratios of 1.174  and QTc prolongation are associated with increased risk of developing cardiomyopathy. ECG is a potential non-invasive risk stratification tool for prediction of anthracycline-induced cardiomyopathy and requires prospective validation. Anthracyclines are an important component of chemotherapy in pediatrics; however, they carry cardiotoxic risk . PreventAlthough there are long-term cardiotoxicity screening guidelines for survivors of childhood, adolescent and young adult cancers developed by The Children’s Oncology Group (COG), there continues to be debate on the diagnostic modalities to use and frequency of monitoring. Additionally, there are no current guidelines on the timing or type of cardiac testing outside of echocardiograms that should be done for children during anthracycline treatment and in the acute and early exposure period –3, 7. MyThis study aims to evaluate ECG findings in children at a tertiary care institution who have received anthracycline therapy and determine if ECG changes precede echocardiographic findings of cardiomyopathy. This may help answer whether ECGs can serve as a non-invasive tool to screen for cardiomyopathy during and after anthracycline therapy.This was a retrospective cohort study at a tertiary care pediatric referral center using electronic medical records and the institution’s oncology pharmacy database. Institutional review board approval was obtained in this HIPAA-compliant study. Informed consent was not required. All pediatric patients from 2006 to 2014 who received anthracyclines as part of their chemotherapy regimen were included in the study population. Echocardiograms were typically obtained by the patient’s oncology team based on COG guidelines unless additional testing was deemed necessary based on the patient’s clinical status. Other cardiac testing was obtained by the patient’s care team based on clinical judgement, so patients did not undergo a pre-specified ECG protocol. Patients without an ECG or echocardiogram were excluded from analysis.All ECGs were recorded and analyzed on a digital system . ECGs were digitized at 500 Hz, and measurements were verified manually by a single investigator (LD) blinded to knowledge of the echocardiographic findings. Two ECG endpoints were pre-specified: the sum of absolute QRS amplitudes in the 6 limb leads (ΣQRS(6 L)) and the corrected QT (QTc) interval. These endpoints were chosen based on preliminary data from previously published work , 11. AmpDue to lack of clear consensus on echocardiographic parameters for definition of cardiomyopathy, cardiomyopathy was defined based on clinically actionable thresholds both at our institution and in the literature , 13–17. p-value < 0.05 was considered statistically significant, and no adjustments for multiplicity were made.Patient characteristics were summarized using descriptive statistics including mean, standard deviations, median values and interquartile ranges as appropriate. Categorical variables were summarized using counts and percentages. Comparisons between groups by cardiomyopathy status were based on either two-sample t-tests or Wilcoxon’s rank sum test (for continuous variables) and the Chi-square or Fisher’s exact test . A two-sided Cox regression models with time-dependent covariates explored associations between ECG measures and time from first ECG to development of cardiomyopathy. Subjects were included if they had at least one ECG prior to the echocardiogram identifying cardiomyopathy, and for censored subjects at least one ECG prior to the last negative echocardiogram. A 0.6 mV change in ΣQRS(6 L) was chosen as the initial unit for evaluation because each vertical box represents 0.1 mV on a standard ECG, and an average amplitude change of less than 1 box per lead is difficult to measure in clinical practice. A 10 ms change in QTc interval was chosen as the initial unit for evaluation because it is typically the minimum clinically relevant interval.> 440 ms and < 440 ms and ΣQRS (6 L) ≤ 3.2 mV and > 3.2 mV (on ECGs prior to development of cardiomyopathy or last negative echocardiogram) both from the time of ECG and from the time of therapy completion.Receiver operating characteristic curves of QTc and ΣQRS (6 L) values on the ECGs prior to the echocardiograms showing cardiomyopathy were created to determine Youden’s cutoff for each measure - the point where sensitivity and specificity of the measure is maximized. This cutoff was determined to be 440 ms for the QTc  and 3.2 mV  for the ΣQRS (6 L). These cutoffs were then used for Kaplan-Meier estimates to demonstrate the probability of developing cardiomyopathy in patients stratified by QTc intervals Differences in ΣQRS(6 L) and QTc interval between ECGs before and after anthracycline therapy were compared for the subset of patients with available data. Multiple linear regression models were used to assess the effect of anthracycline dose and cardiotoxic radiation on post-therapy ECG measures adjusting for potential confounders including sex, age at first dose, body surface area (BSA), race , time between pre- and post-therapy ECGs, and pre-therapy ECG measurements. Cardiotoxic radiation was calculated as the total radiation dose a patient received to body fields that are reported to increase the risk of cardiotoxicity by COG . Lastly,There were 691 eligible pediatric patients in the study period, of which 102 patients were removed because an ECG was not available. The flow diagram in Fig. p < 0.001).The median age at the start of anthracycline therapy was 7.8 years IQR 3.7–13.6). The median follow-up time after completion of anthracycline therapy to last ECG or echocardiogram was 3.6 years (IQR 1.1–5.8). There were 115 patients (19.5%) who met criteria for cardiomyopathy. Male sex, race, older age at first dose, larger BSA and total anthracycline dose were associated with development of cardiomyopathy . Among the 87 patients who developed cardiomyopathy in the acute/early period, 25 patients developed cardiomyopathy before the last dose of anthracycline was administered.More patients met criteria for cardiomyopathy based on systolic dysfunction parameters versus left ventricular dilation (79% versus 19%) with 2% meeting criteria for both. The median time to development of cardiomyopathy after initiation of anthracycline therapy was 0.1 years (0–0.7). A majority of patients developed cardiomyopathy in the acute/early period of ≤ 1 year after start of therapy (p = 0.003) for a 0.6 mv decrease in ΣQRS(6 L) and a hazard ratio of 1.098  for a 10 ms increase in QTc interval , and the median time from the first available ECG to next echocardiogram was 91 days IQR 19–336). The median time from the start of treatment to the first available ECG was − 1 day (IQR − 34 to 38). The median time from the start of treatment to the next available ECG was 110 days (IQR 31–364). Figure 36. The mp = 0.01) and a 4 ms average increase in QTc interval (p = 0.02) after completion of therapy (Table p < 0.001 for both). Multiple linear regression models adjusting for additional confounders including sex, age at first dose, BSA, race, and time between pre- and post-therapy ECG measures showed the total anthracycline dose remained a modest but statistically significant predictor for a decrease in ΣQRS(6 L) and an increase in QTc interval   or the QTc interval. This was true in both simple and multiple linear regression models and was also true when radiation was stratified as a binary variable of radiation exposure versus no radiation exposure.There were 62 patients 25%) in this sub-group who met criteria for cardiomyopathy with 11 patients developing cardiomyopathy before completing therapy. When comparing the change in ECG parameters before and after anthracycline therapy in the cardiomyopathy versus no cardiomyopathy group, there was a greater decrease in ΣQRS(6 L) in the cardiomyopathy group  and prolongation of the QTc interval, are associated with an increased risk of developing anthracycline-induced cardiomyopathy. As survival rates have improved in pediatric oncology, focus has shifted on reducing associated morbidities like cardiotoxicity to ensure long-term quality of life . Reducti L and prExisting literature has attempted to show a relationship between ECG measures after anthracycline therapy and echocardiographic findings. An initial study analyzed ECGs for pediatric patients treated with Adriamycin at specified intervals after completion of chemotherapy: 2 months, 1 year, and 3 years . AlthougA more recent analysis by Markman et al. attempted a similar approach to our study to determine if an ECG finding, specifically the QTc interval, precedes development of left ventricular dysfunction defined by shortening fraction . A KaplaOur study expands on the concept presented by prior literature by analyzing a larger patient cohort, including a more consistent and comprehensive definition of cardiomyopathy, evaluating an additional ECG parameter of ΣQRS (6 L), determining cutoffs for QTc and ΣQRS (6 L) using a receiver operating characteristic curve, and using only ECG values that precede development of cardiomyopathy for analysis. Additionally, we show how changes to ECG measures over time affect the risk of developing cardiomyopathy.Anthracycline therapy has been shown to prolong the QTc interval in prior small studies in adult survivors of either pediatric or adult malignancies , 9, 11. n = 87) with some patients even developing toxicity before the last dose of an anthracycline was given (n = 25/87). The patients who developed cardiomyopathy before their last dose of anthracyclines may have the most potential to benefit from ECG-based risk stratification, since heart failure therapy could be initiated and treatment protocols modified before echocardiographic changes are noted [A large percentage of patients in this study met echocardiographic criteria for cardiomyopathy in the acute/early period . This was somewhat mitigated by our inclusion of both patients with and without cardiomyopathy in the Cox and Kaplan-Meier analyses with a consistent median number of ECGs for patients with and without development of cardiomyopathy. Additionally, dichotomization of the primary predictor variable (presence or absence of cardiomyopathy) can be a subjective process, especially when performed using only echocardiographic means without clinical correlation. There is suggestion in small pediatric studies that diastolic dysfunction can occur as part of anthracycline related cardiotoxicity even at lower anthracycline doses , 24. DiaA decrease in ΣQRS(6 L) or prolongation of the QTc interval increases the risk of developing cardiomyopathy in pediatric patients receiving anthracycline therapy. Higher total anthracycline doses were associated with decreases in the ΣQRS(6 L) and prolongation of the QTc interval. The decrease in ΣQRS(6 L) after therapy was greater in patients who developed cardiomyopathy. Cardiotoxic radiation was not associated with changes in the ECG parameters measured. ECGs are a potential non-invasive, inexpensive tool for prediction of anthracycline-induced cardiomyopathy and require prospective validation. Incorporation of ECG evaluation in cardio-oncology monitoring regimens in pediatric patients should be carefully considered.


TLP), leaf vulnerability to embolism (P50), and the hydraulic safety margin (HSM P50). Semillon exhibited the highest maximum transpiration (Emax), and lowest sensitivity of canopy stomatal conductance (Gc) to vapor pressure deficit (VPD), followed by Syrah and Grenache. Increasing Emax was correlated with more negative water potential at which stomata close (Pgs90), π TLP, and P50, suggesting that increasing water use is associated with hydraulic traits allowing gas exchange under more negative water potentials. Nevertheless, all the cultivars closed their stomata prior to leaf embolism formation. Modeling simulations demonstrated that despite a narrower HSM, Grenache takes longer to reach thresholds of hydraulic failure due to its conservative water use. This study demonstrates that the relationships between leaf hydraulic traits are complex and interactive, stressing the importance of integrating multiple traits in characterizing drought tolerance.Adapting agriculture to climate change is driving the need for the selection and breeding of drought-tolerant crops. The aim of this study was to identify key drought tolerance traits and determine the sequence of their water potential thresholds across three grapevine cultivars with contrasting water use behaviors, Grenache, Syrah, and Semillon. We quantified differences in water use between cultivars and combined this with the determination of other leaf-level traits (e.g. leaf turgor loss point, π  The relationships between traits including stomatal regulation, leaf embolism thresholds, and survival time under drought are complex and interactive, stressing the importance of integrating multiple traits in characterizing drought tolerance. In the context of climate change, the identification of key traits responsible for drought-tolerant species/genotypes has become a major challenge in plant research. There has been a huge emphasis placed on genetics and genomics, but both of these approaches rely upon accurate identification and phenotyping of the underlying physiological traits (TLP) is defined as the water potential at which cells lose turgor  curve and is related to other parameters correlated to drought tolerance such as the relative water content (RWCTLP), the osmotic potential at full hydration (π 0), the elastic modulus of the cell wall (ɛ), and the tissue capacitance (C)  (gs90) and P50, may be more relevant in explaining plant mortality under drought than P50 alone  . VulneraTLP and P50, and the leaf HSM. To do so, we characterized each cultivar’s dynamic water use by continuously monitoring whole-plant transpiration and stomatal regulation using a greenhouse mini-lysimeter platform. Each cultivar’s corresponding leaf turgor loss point and its related parameters such as osmotic potential, modulus of elasticity, and leaf capacitance were determined through the construction of p–v curves. The leaf vulnerability to embolism was quantified in intact plants using the non-invasive optical technique  were planted in 7 liter pots containing 1 kg of gravel and 5.5 kg of commercial potting medium . Plants were grown outside under well-watered conditions for ~2 months before the experiment started. The plants were drip irrigated with nutritive solution  to avoid any deficiency during their development, and the surface of the pots were covered with a plastic bag to prevent water loss by soil evaporation.One-year-old plants of own rooted n=5) per cultivar and 23 August on another set of five plants (each drying cycle lasted between 12 d and 15 d). A set of five well-watered vines per cultivar were kept as controls concomitantly with the water-stressed plants during each drying cycle. Air temperature, relative humidity, and radiation conditions were automatically monitored every 20 min at three different positions in the greenhouse. Air temperature was maintained approximately below 25 °C by the cooling system of the greenhouse to avoid any heat stress. The experimental design within the platform was completely randomized.On 20 July, 15 plants from each cultivar were transferred to an automated mini-lysimeter greenhouse phenotyping platform where pots were continuously weighed on individual scales  and watered daily based on the plant weight loss by transpiration. The day before the experiment started, all the pots were irrigated up to their pot capacity weight and allowed to drain overnight. A dry-down experiment was imposed on 10 vines per cultivar by stopping irrigation on two different dates: 16 August on a set of five plants (E in mmol m−2 s−1) was calculated as:The transpiration per leaf area (w is the change in weight within the considered period (g s−1), AL is the leaf area (m2), and MWw the molecular weight of water (18 g mol−1).where Δ Gc (mmol m−2 s−1) was calculated using the simplification suggested by The canopy stomatal conductance KG(T) is the conductance coefficient in kPa m3 kg−1 (E is the transpiration (mmol m−2 s−1), and D is the vapor pressure deficit  calculated from the temperature and relative humidity data, as indicated in the infrared gas analyzer manual WALZ GFS-3000.Where  m3 kg−1 , E is thGc values obtained were filtered by light in order to always have saturating values of radiation [photosynthetic photon flux density (PPDF) >800 μmol m−2 s−1].The PD) on 3–4 plants per cultivar and treatment. Measurements were performed in a basal fully expanded leaf prior any light exposure (between 05.00 h and 06.00 h) every 2–3 d using a Scholander pressure chamber .The water status of the plants during the experiment was monitored by measuring the pre-dawn leaf water potential  for each cultivar using ~200 leaves per cultivar. The leaf midrib length was measured weekly on all the leaves of each plant, and the respective total leaf area per plant was then calculated using the equations obtained.Additional measurements of stomatal conductance were taken in fully expanded, mature leaves (8th–10th node) from all the cultivars and treatments. The measurements were taken at 11.00 h durng the 15 d of the experiment with a porometer .n=4) were selected to construct the p–v curves by progressively drying leaves on a laboratory bench , one healthy mature leaf (8th–10th leaf from the shoot base) was cut at the base with a razor blade, sealed in a plastic bag (Whirl-Pak), and its leaf water potential (ψ leaf) was measured with a Scholander pressure chamber (Precis 2000). The leaf mass was immediately registered in an analytical balance and the leaf was placed on a bench at room temperature (23 °C) to let it dehydrate until the next ψ leaf and mass measurements. The time elapsed between measurements attempted to capture intervals of ψ leaf of 0.2–0.3 MPa until at least five points were obtained beyond the point at which zero turgor was attained.From the same set of plants, four plants per cultivar (leaf) against the relative water content (RWC) which facilitated the determination of the turgor loss point as the point of transition between linear and non-linear portions , osmotic potential at full turgor (π 0), and modulus of elasticity (ε) were calculated according to leaf) was calculated from the change in volume per change in water potential at full turgor (CFT), and below the turgor loss point (CTLP).The p–v curves were constructed by plotting the inverse of leaf water potential . A relationship between leaf mass and time allowed us to determine that initial water loss rates are high and after some time a constant flow is achieved. The minimum conductance (gmin) is calculated by using the measured VPD according to the equation E=gmin×D/P where D is the VPD in kPa and P is the atmospheric pressure expressed in mmol m−2 s−1  was determined in eight leaves from the three cultivars by using the ‘mass loss of detached leaves’ technique (n=4) by monitoring changes in light transmission through the xylem  were made using psychrometers  properly installed on the main stem adjacent to the scanned leaf. The ψ stem values were automatically recorded every 30 min and the accuracy of the readings was confirmed by ψ leaf measurements on basal leaves previously bagged with aluminum foil (2 h at least) using a Scholander pressure bomb (Precis 2000).Simultaneous measurements of stem water potential . In these series, white pixels represented leaf embolism. Noise was removed using the ImageJ outlier removal, and pixel thresholding was used to extract embolism from any background noise remaining. The embolism area per image was calculated as the sum of non-zero pixels and expressed as cumulative embolisms, a percentage of total embolism area in the sequence.The stack of images captured at the end of the experiment comprised between 1800 and 2000 scans per leaf and they were analyzed using ImageJ software and following instructions from stem were fitted based on the following equation (Vulnerability curves corresponding to the percentage of embolized pixels (PEP) as a function of ψ equation :50 (MPa) is the ψ stem value at which 50% of the xylem embolisms were observed and slp (% MPa−1) is the slope of the vulnerability curve at the inflexion point. The xylem pressure inducing the 12% (P12) and 88% (P88) of embolism loss of functionality were calculated as follows: P12=50/slp+P50 and P88= –50/slp + P50. One vulnerability curve was obtained per leaf per plant (n=4 per cultivar).where Pstem over time.Finally, to visualize the dynamics of embolism spread through the leaf, spatio-temporal color maps of embolism formation were created for some of the samples by coloring the embolism area in each sequence using a color scale of ψ −2 s−1) or variable during the day . All plant parameters were identical for the three genotypes, except for the maximal stomatal conductance, the leaf osmotic potential at full turgor, the modulus of elasticity, the P50, and the stomatal response curve to leaf water potential that were adjusted to actual values measured on each genotypes. The simulations were performed at a time step of 0.01 s and stopped when 99.99 loss of hydraulic conductivity (PLC) was reached in the leaf apoplasm, thus defining the time to hydraulic failure.A soil–plant water transport model  and GraphPad Prism version 7.00 for Windows .Statistical analyses and fit were performed using R software /(saturated weight−DW). The saturated weight corresponded to the total pot weight registered after watering the plants to pot (or field) capacity at the end of the afternoon and drained overnight. The actual weight corresponded to the weight registered by the balances during the dry-down experiment, and the dry weight corresponded to the weight of a pot after placing in an oven for 48 h at 60 °C. The weight of the soil substrate was obtained by subtracting the weight of the pot and draining gravel (which was standardized between pots).Gc), depending on pre-dawn leaf water potential, was fit according to the following sigmoid function:Stomatal conductance  in R. For non-linear regressions (Gc and E versus ψ PD), the Akaike information criterion (AICc) method was used to compare different fits.Linear regressions between 50, P12, P88, and slp, and p–v results were tested with one-way ANOVA and post-hoc Tukey HSD test (<0.05). The HSM was calculated for each cultivar as the difference between the water potential at PGc90 and the water potential at stem P50.Difference in PE) in all plants increased from minimum values overnight to maximum rates (Emax) at ~14 h and were maintained for several hours ; however, these differences did not result in different rates of the dry-down between cultivars (not shown), which was mostly explained by differences in Emax. The distribution of the data also showed that Semillon had a higher frequency of high rates of E than Grenache, with Syrah being intermediate  revealed a linear positive relationship for the three cultivars  mirrored differences in Emax  of –0.80 MPa in Grenache, –1.00 MPa in Syrah, and –1.20 MPa in Semillon (Gc to VPD (the lower the slope of the relationship Gc versus VPD the more sensitive) was greatest for Grenache and lowest for Semillon but only under well-watered conditions  calculated with the porometer did not differ significantly as compared with that from the balances .The stomatal conductance values measured with the porometer were higher (around double) than those calculated from the balances data; however, the differences between cultivars were maintained in a similar way as in TLP and π 0 differed between cultivars, where Semillon showed a significantly more negative π TLP (–1.92 MPa) and π 0 (–1.55 MPa) than those of Syrah and Grenache.Different parameters were compared between cultivars from the analysis of p–v curves . Only π −2 s−1  indicated considerable variation among cultivars. Values of P12 were estimated at –1.26 MPa for Grenache, –1.41 MPa for Syrah, and –1.94 MPa for Semillon. The leaf vulnerability curves constructed from the data obtained followed a sigmoidal function for all genotypes and showed variability between individual leaves, mainly for Semillon (50 at –1.43 MPa), followed by Syrah (P50 at –1.65 MPa) and Semillon (P50 at –2.08 MPa). Finally, the water potentials at which 88% of the embolism occurred (P88) were observed at –1.8 MPa for Grenache, –2.24 MPa for Semillon, and –1.86 MPa for Syrah.Using the optical technique . The watSemillon . The watGc90 and P12 were greatest for Semillon (0.74 MPa), intermediate for Grenache (0.46 MPa), and lowest for Syrah  were greater in Semillon (0.88 MPa) followed by Syrah (0.65 MPa), and finally Grenache (0.63 MPa).The difference between PEmax under well-watered conditions was strongly correlated with several traits including PGc90, π 0, π TLP, and P50  decreased earlier or later depending on the cultivar. For instance, Eplant decreased significantly after 1.5 d in Semillon, 2.0 d in Syrah, and 2.5 d in Grenache  was also different across cultivars where Semillon reached P50 in 3.5 d, Syrah in 3.75 d, and Grenache in 4.2 d. When including variable climatic conditions (day/night simulations) in the model, the predicted time needed to reach P50 was higher for all the cultivars, being 7.3 d in Semillon, 7.6 d in Syrah, and 8.7 d in Grenache . Despite these differences, all cultivars closed their stomata prior to leaf embolism formation. The maximum water use given by Emax was strongly correlated with other drought traits  and thus the higher E in Semillon corresponded to a less vulnerable leaf exhibiting a more negative π TLP and leaf P50. There was substantial variation of the cultivars’ HSMs, and model simulations suggest that despite Grenache’s narrower HSM, its more conservative water use results in it having a longer time to hydraulic failure than the other cultivars.In this study, we integrated hydraulic traits to explain the drought responses of three contrasting grapevine varieties across a wide range of soil water deficit and VPD conditions. Cultivars differed in multiple traits examined, including −2 s−1), E responded linearly to VPD in all genotypes since VPD is a main driver of transpiration  when supplied with ample irrigation  , and our values of Pgs90 calculated from leaf-level measurements did not differ significantly from PGc90. Additionally, previous studies from our laboratory have shown a strong correlation between stomatal conductance measurements made at the leaf- and whole-plant levels  and leaf turgor loss (π TLP) relative to Grenache (TLP is related to a plant’s ability to ‘tolerate’ drought rather than to avoid it by closing the stomata or shedding leaves (TLP showed a strong association with water availability within and across habitats and that π 0 and π TLP were the only two parameters that clearly delimited species associated with wet and dry habitats (TLP among and within species were mainly driven by osmotic (π 0) rather than elastic adjustments to maintain RWCTLP and prevent cell dehydration. In our study, Semillon also showed a significantly lower π o than Syrah and Grenache, suggesting that osmotic adjustment was the main driver of its more negative turgor loss point. The ability to generate solutes or tolerate an increased symplastic solute concentration has been observed to differ between species  in the current study was consistent with the leaf P50 values in Syrah  which has been shown before for two of the same cultivars (Grenache and Syrah) within the same experimental system  after stomatal closure  also exhibited the largest HSM when compared with the other cultivars  could be considered the most drought tolerant because the time needed to reach P50 is greater than for the other cultivars  were well correlated with each other, and with Emax (discussed above), with greater Emax corresponding to a more negative PGc90, π TLP, and P50 , although the variation within this relationship was extremely large  Boxplot and (B) histogram.Fig. S4. Minimum leaf conductance for each cultivar.Fig. S5. Pearson correlations between the measured hydraulic traits.Fig. S6. Simulation with the Sur_Eau model under variable climatic conditions.eraa186_suppl_Supplementary_FiguresClick here for additional data file.


Caulobacter crescentus MreB to extract mechanical parameters for inputs into a coarse-grained biophysical polymer model that successfully predicts MreB filament properties in vivo. Our analyses indicate that MreB double protofilaments can exhibit left-handed twisting that is dependent on the bound nucleotide and membrane binding; the degree of twisting correlates with the length and orientation of MreB filaments observed in vitro and in vivo. Our molecular dynamics simulations also suggest that membrane binding of MreB double protofilaments induces a stable membrane curvature of similar magnitude to that observed in vivo. Thus, our multiscale modeling correlates cytoskeletal filament size with conformational changes inferred from molecular dynamics simulations, providing a paradigm for connecting protein filament structure and mechanics to cellular organization and function.In many rod-shaped bacteria, the actin homolog MreB directs cell-wall insertion and maintains cell shape, but it remains unclear how structural changes to MreB affect its organization in vivo. Here, we perform molecular dynamics simulations for  The actin homolog MreB directs cell-wall insertion and maintains cell shape in many rod-shaped bacteria. Here, Shi et al. perform molecular dynamics simulations for MreB to extract mechanical parameters for inputs into a coarse-grained biophysical polymer model that predicts MreB filament properties. In living bacteria, many of these cytoskeletal proteins form filaments that are highly dynamic. Structural tools such as X-ray crystallography and cryo-electron microscopy have elucidated various filament structures within the bacterial actin family, including antiparallel, straight double protofilaments of MreB5, single, polar polymers of FtsA3, and bipolar, antiparallel filaments of ParM4, suggesting that filament conformations are highly tunable and have been selected for particular physiological functions over evolutionary time. However, the links between the conformational dynamics of these proteins in vivo and the molecular mechanisms by which they organize within cells to regulate cell physiology remain unclear. Molecular dynamics (MD) simulations are a powerful tool for identifying protein structural dynamics and filament mechanics at atomic resolution, providing key information to map filament properties from the protein to the cellular scales.The actin and tubulin families of cytoskeletal proteins constitute essential components of cellular physiology in virtually all bacteria, archaea, and eukaryotes. Despite structural similarities within each of the two families, their primary functions span a diverse range of processes, including cell morphogenesis6. In rod-shaped bacteria such as Escherichia coli, cell-wall synthesis during elongation is regulated by the widely conserved actin homolog MreB7, which dictates the pattern of insertion of new cell-wall material8 and thereby maintains rod shape9. Genetic depletion and chemical inhibition of MreB lead to misshapen cells and eventually cell lysis11. Many point mutations in MreB alter cell shape in subtle ways, such as changing cell width14, curvature15, or polar morphology16 without affecting viability. In E. coli, MreB forms short filaments that move along the cell periphery1, and the localization and movement of these filaments are correlated with cell width17. E. coli MreB movement is chiral, which is correlated with twisting of cell body during elongation17. Previous MD studies of Thermotoga maritima MreB (TmMreB) showed that ATP hydrolysis and polymerization affect MreB monomer conformation, which in turn regulates the bending of MreB dimers18. The bending of a TmMreB dimer was also altered in silico by binding to the membrane protein RodZ, which directly interacts with MreB and tunes cell shape19. In E. coli, MreB forms antiparallel double protofilaments5 that can deform membranes20, and the double protofilament conformation is essential for rod-shape maintenance in E. coli5. However, it remains obscure how molecular-level changes in MreB connect to the biophysics of the double protofilament structure, and to the spatial patterning and functions of MreB in vivo.One such cellular-scale property defined by a bacterial actin homolog is cell shape, which is ultimately dictated by the rigid cell wall, a highly cross-linked mesh of peptidoglycan. During growth, cells actively remodel their cell wall while robustly maintaining their shapeCaulobacter crescentus MreB5 (CcMreB) to explore the connection between MreB structural dynamics in silico and filament conformation in vivo. We perform all-atom MD simulations for each step during CcMreB filament assembly  missing from the crystal structure was added back using the Molefacture Protein Builder plugin in VMD (Methods), and water and neutralizing ions (Na+ or Cl−) were added to the simulation system. By analogy with actin, we refer to the two subunits in an MreB dimer as the (+) and (−) ends  subunit in an ATP-bound MreB dimer maintained a closed conformation resembling the ADP-bound monomer Fig. , a confo18, we quantified ATP-binding pocket stability by calculating the buried solvent-accessible surface area (SASA) between MreB and ATP (Methods). Buried SASA quantifies the surface area of an ATP-MreB interface  isothiourea (A22) by performing MD simulations with MreB bound simultaneously to both ATP and A22 (Methods). Although A22 is known to perturb cell morphology in vivo by targeting the active site of MreBgle Fig. . We furtgle Fig. . Thus, od1–d3 axes  by analyzing the relative movements of the (+) and (−) subunits in the dimer Fig. . We simuxes Fig. . The memxes Fig. . Thus, θely Fig. , and θ3 ent Fig. . We defient Fig. . Bindinghes Fig. . We founhes Fig. , whereashes Fig. . The benhes Fig. , consistθ2 would require differential compression/stretching of the protofilament  was prominent in the double protofilament , and found that EcMreB exhibited quantitatively similar left-handed twisting in simulation , and found that the twist angles in EcMreB mutants were highly correlated with those of the corresponding CcMreB mutants , despite overall lower twist angles in EcMreB .To determine whether the mutations affect the ability of MreB to interact with the membrane, we performed simulations of membrane-bound MreB monomers. All mutants exhibited similar behavior, particularly the buried SASA at the membrane-binding interface 25, and aligned it to the RodZ-binding interface for each of the subunits in a 4 × 2 CcMreB protofilament (Methods). The ratio of MreB and RodZ levels in E. coli cells varies from ~10:1 to ~4:1 depending on growth conditions19, and hence MreB filaments are likely to experience a range of RodZ stoichiometries; modeling an MreB protofilament with partially-bound RodZ can cause substantial boundary effects. Therefore, we simulated MreB protofilaments in water with each MreB monomer bound to RodZ, and postulated that the in vivo effects of RodZ should be an intermediate between no RodZ and full RodZ binding. All-atom MD simulations showed that while RodZ binding did not substantially change either of the bending angles in a double protofilament  bound to the cytoplasmic tail of RodZ; the V53A 4 × 2 protofilament in the absence of RodZ exhibited the largest twisting in our simulations Fig. . Simulat28) forces the membrane to adopt a shape matching that of the cell wall, we treated the membrane as a rigid cylindrical surface. We first considered the case of binding to a flat membrane surface, and minimized the free energy of an MreB beam of different lengths L with intrinsic twist and curvature 29, which corresponds to a polymerization energy per unit length of µ0 = 2 kBT nm−1. In the case of a flat membrane surface, this value of µ0 corresponds to a limit length of ~160 nm . Considering that the large turgor pressure across the bacterial cell envelope . When r = 10,000 nm, much larger than the length scale of an MreB filament, our Monte Carlo calculations agreed quantitatively with the analytical solution   changed the pitch angle but not limit length  or the membrane binding potential (V) altered the limit length without substantially affecting the pitch angle  Fig. . We furt90° Fig. . SimilarE. coli strains expressing the MreB mutants . In wild-type cells, MreB formed short filaments with lengths around ~200–300 nm , and combining the resulting helix with the main crystal structure.The amphipathic helix in CcMreB (residues 1–8) was not resolved in the crystal structure, but plays an important role in membrane binding41; the 500-ns simulations in Fig. 42. All simulations used the CHARMM36 force field43, including CMAP corrections44. Water molecules were described with the TIP3P model45. Long-range electrostatic forces were evaluated by means of the particle-mesh Ewald summation approach with a grid spacing of <1 Å. An integration time step of 2 fs was used46. Bonded terms and short-range, nonbonded terms were evaluated every time step, and long-range electrostatics were evaluated every other time step. Constant temperature (T = 310 K) was maintained using Langevin dynamics47, with a damping coefficient of 1.0 ps−1. A constant pressure of 1 atm was enforced using the Langevin piston algorithm48 with a decay period of 200 fs and a time constant of 50 fs. Setup, analysis, and rendering of the simulation systems were performed with the software VMD49. Steering of the opening angle was achieved by introducing collective forces to constrain the angle to defined values through the collective variable functionality of NAMD41.All simulations except the 500-ns simulations in Fig. C. crescentus MreB bound to magnesium and ADP (PDB ID: 4CZF)5. The bound nucleotide was replaced by ATP or ADP with chelating Mg2+ ions for all simulated systems. In simulations including a membrane, patches consisting of phosphatidylethanolamine were generated using the membrane plugin in VMD. Water and neutralizing ions (Na+ or Cl−) were added around each simulated system, resulting in final simulation sizes of up to 480,000 atoms. For mean values and distributions of measurements, only the last 40 ns were used for each simulation. All simulations were run until equilibrium was reached unless specified in the text. To ensure simulations had reached equilibrium, measurement distributions were fit to a Gaussian.MD simulations performed in this study are described in Supplementary Table The centers-of-mass of the four subdomains of each protein subunit were obtained using VMD, excluding the amphiphilic helix (residues 1–8). For each time step, we calculated one opening angle from the dot product between the vector defined by the centers-of-mass of subdomains IIA and IIB and the vector defined by the centers-of-mass of subdomains IIA and IA. Similarly, we calculated a second opening angle from the dot products between the vectors defined by the centers-of-mass of subdomains IA and IB and of subdomains IA and IIA. The opening angles we report are the average of these two opening angles Fig. , left. Td1–d3)50. For single protofilaments, d3 approximately aligns to the center of mass between the two subunits, d2 is defined to be perpendicular to the membrane plane, and d1 = d3 × d2  represents twist between the bottom and top subunits (or subunit pairs). Similarly, rotations around d2 and d1 (θ2 and θ1) represent bending parallel to the membrane plane and bending toward the membrane plane, respectively  was defined using three unit vectors , and the SASA of the complex of the two molecules when interacting (denoted as A1+2). If the molecules are in contact, then the sum of the SASA of each molecule is greater than the SASA for both molecules together, and the contact area is the difference between the two values divided by two (to account for double counting):The interaction strength between two interacting molecules was estimated by calculating the contact surface area between them, which can be approximated by measuring the surface area buried between the two molecules that is not accessible to solvent when the molecules interact. This surface area is known as the buried SASA. The buried SASA between two molecules can be calculated from three quantities: the SASA of each molecule by itself (denoted as A1 + A2 − A1+2)/2.buried SASA = . The alignment results and the PDB file with the CcMreB crystal structure were processed by MODELLER to generate 10 homology models. The homology model with the lowest DOPE-HR score was used for MD simulations.Homology models were constructed using the software MODELERThe positions of each phosphate atom in the top layer of the membrane (the layer that directly interacts with MreB) were extracted and fit to a second-order polynomial. The curvature of the membrane patch was defined as the curvature at the center of the fitted surface.30L is the total length of the filament, θ and ψ are the local tilt and twist angles, respectively, θ’ and ψ’ are the corresponding derivatives with respect to filament length coordinate s, r is the radius of the cell, C is the bending modulus of the filament, K is the torsional modulus, V is the membrane binding potential, and k0 and ω0 are the intrinsic curvature and twist of the filament, respectively. Note that in the original model in Ref. 30, the membrane-binding interface was assumed to lie on two opposing sides of the filament (both the top and the bottom side), such that maximum membrane binding was achieved by both ψ = 0 and ψ = 180°. However, in the case of MreB, filaments only have one side that can bind to the membrane5. Therefore, we adjusted the model so that maximum membrane binding can only occur when ψ = 0, and no membrane-binding would occur with ψ = 180°, by modifying the membrane-binding term from The Hamiltonian of the filament isentclass1pt{minimar → ∞. In this caseH is minimized, We analytically minimized the Hamiltonian in Eq. and is equivalent toL/2 to L/2 givesIntegration of Eq. n is the number of full twist repeats in the filament.The total energy of the filament can be similarly calculated asL and E:Integrating the above equations, we obtained the final expressions for L and E in Eqs. ψm and θ0. Since MreB filaments are unlikely to exhibit a full 360° twist without breaking, we only considered the case n = 0. The limit length of the filament is dictated by μ0 is the MreB polymerization energy per unit length.With the analytical solutions of L, we minimized its Hamiltonian in Eq. θ and ψ to obtain the equilibrium filament shape. The energy was computed by discretizing the Hamiltonian into N segments, with each segment i able to adopt a distinct bending and twisting conformation described by angles θi and ψi. The discretized Hamiltonian was used to calculate the total energy of the filament asθ = 90° and ψ = 0, each Monte Carlo step t altered θi or ψi to change the filament conformation from zt to a trial conformation z’. The new conformation zt+1 was determined using the Metropolis algorithm7 Monte Carlo steps if the energy fluctuations were lower than 1% of the minimized energy across the previous 104 steps. Twenty independent replicate simulations were carried out for each parameter set to ensure that a global minimum was reached.For a filament of length K, the standard deviation, σ, of the fluctuations in the twist angle from 4 × 2 protofilament simulations was ~1.88° per monomer length. From this value, the torsional rigidity can be estimated as K = kBT∆l/σ2, where ∆l ~5 nm is the length of an MreB monomer. This estimate gives K ~4.6 × 103kBT nm. The bending modulus C can be estimated similarly. Simulations of MreB filaments revealed bending in two directions dictated by C1 and C2 were very similar . After the droplet dried, a small drop of ProLong Diamond AntiFade Mountant (Thermo Fisher) was added on top of the dried droplet, and the coverslip was mounted on a glass slide (VWR) and sealed with VALAP .Saturated overnight cultures were back-diluted 1:200 into prewarmed fresh LB and grown at 37 °C with shaking. The cultures were further diluted 1:10 into prewarmed fresh LB at 60 and 150 min after the first dilution, respectively. By 220 min, the cultures reached exponential growth with OD ~0.1. One milliliter of the cells was fixed in phosphate-buffered saline containing 3% glutaraldehyde/3% paraformaldehyde (electron microscopy sciences) at room temperature for 15 min, with 1 μg/mL FM 4-64FX membrane stain (Invitrogen) added during fixation. Cells were washed three times in cold phosphate-buffered saline, and 1 µL of the cell solution was pipetted onto a No. 1.5 coverslip (Zeiss) coated with poly-Cell samples were imaged on an OMX V4 microscope platform (GE Life Sciences) with a 100× (NA 1.42) oil-immersion objective (Nikon Instruments). Images from two channels were collected on two Evolve 512 electron-multiplying charged couple device cameras (Photometrics) using DeltaVision microscopy imaging system v. 3.70 (GE Life Sciences).z-stacks using SoftWoRx v. 6.5.2 (GE Life Sciences). The middle plane for each z-stack was segmented using the FM 4-64FX signal using Morphometrics54 to obtain individual cell contours. For each contour, a coordinate-system mesh was calculated using the pill mesh function from MicrobeTracker55. A three-dimensional surface was reconstructed from the segmentation mesh assuming rotational symmetry about the central axis, and MreB patches localized near the cell periphery were identified from the GFP channel based on intensity using the MATLAB function im2bw with default parameters. Patches smaller than the resolution limit for structured illumination microscopy (~0.02 μm2) were excluded from further quantification. Since all the patches were largely shaped as a band, the length of each patch’s major axis was defined as the filament length, and the pitch angle was defined as the orientation of the major axis.Raw images were reconstructed and aligned into 3D Supplementary InformationDescription of Additional Supplementary InformationSupplementary Movie 1Supplementary Movie 2


Direct oral anticoagulants (DOAC) are mostly prescribed to prevent cardioembolic stroke in patients with non-valvular atrial fibrillation (AF). An increasing number of guidelines recommend DOAC in AF patients with preserved renal function for the prevention of thromboembolism and an increased use of DOAC in daily practice is recorded also in elderly patients. Aging is associated with a reduction of glomerular filtration rate and impaired renal function, regardless of the cause, increases the risk of bleeding. Multiple medication use (polypharmacy) for treating superimposed co-morbidities is common in both elderly and chronic kidney disease (CKD) patients and drug-drug interaction may cause accumulation of DOAC, thereby increasing the risk of bleeding. There is uncertainty on the safety profile of DOAC in patients with CKD, particularly in those with severely impaired renal function or end stage renal disease, due to the heterogeneity of studies and the relative paucity of data. This document reports the position of three Italian scientific societies engaged in the management of patients with atrial fibrillation who are treated with DOAC and present with CKD. Vitamin K antagonists (VKA) and direct oral anticoagulants (DOAC) are widely prescribed for non-valvular atrial fibrillation (AF) and treatment/prophylaxis of venous thromboembolism. There is a large body of literature in favor of DOAC for the prevention of cardioembolic stroke in patients with preserved renal function and an increasing number of guidelines now recommend DOAC for this and other clinical indications . This haPolypharmacy is frequent in elderly individuals because of the presence of several co-morbidities and many important interactions between several drugs and VKA or DOAC have been reported. The interaction between VKA and drugs that modify cytochrome 2C9 and/or 3A4 is well known, as well as the interaction between DOAC and drugs that modify p-glycoprotein or cytochrome 3A4 . In the presence of eGFR < 50 mL/min, the doses of DOAC were reduced: from 150 to 110 mg twice daily for dabigatran, from 20 to 15 mg once a day for rivaroxaban, from 60 to 30 mg once daily for edoxaban. The dose of apixaban was reduced to 2.5 mg twice daily in the presence of serum creatinine > 1.5 mg/dL, but only if associated with a body weight < 60 kg and/or age > 80 years.post-hoc analysis results. All post-hoc analyses have shown that the performance of DOAC, both in terms of safety and efficacy, was maintained also in patients with CKD stage G1 to G3b [post-hoc analysis including 269 G4 patients from the ARISTOTLE trial, treated with apixaban [Since renal function was not considered as a randomization criterion in any RCT, data from the CKD patients subgroup recruited should be considered as 1 to G3b –17. A siapixaban . A systeapixaban  that incapixaban . Howeverapixaban . It is nA systemic review with meta-regression analysis carried out by Nielsen et al.  includedn = 12,155) with G3b stage, as the group of G4 stage consisted of only 390 patients [Statement: Data from RCTs show that DOAC have at least similar safety and efficacy profiles in patients with CKD stages G2 to G3b and in patients with normal renal function.Statement: In patients with CKD G4 DOAC should be used with caution because of lack of strong supporting evidence from RCTs. At present there are not enough data available to recommend the use of DOAC in patients with CKD G5 or on long term dialysis.A Cochrane review reported that DOAC could be more effective than warfarin (moderate certainty evidence) in reducing the incidence of stroke and systemic embolism  and major bleeding events . However, these data were obtained mainly in patients  has recommended to measure DOAC plasma levels in some special clinical circumstances including (1) active bleeding; (2) before surgery/invasive procedure when the patient has taken the drug in the previous 24 h, or longer if CKD stage 3b or higher; (3) patients with deteriorating renal function; (4) perioperative management; (5) identification of subtherapeutic or supratherapeutic levels in patients at the extremes of body weight .The plasma concentration of DOAC can be measured by relatively simple functional assays for both FIIa (dabigatran) and FXa inhibitors  . The tesSeveral reports have shown that there is a great variability among patients on DOAC, and that plasma levels poorly correlate with eGFR , 51. FurSeveral scientific societies suggest to measure DOAC prior to invasive procedures or surgery independently of eGFR. This is particularly recommended in elderly patients, in whom eGFR can vary rapidly over time , 53. HowA prospective study carried out in 422 patients on DOAC has shown that interrupting DOAC administration 3 days before a procedure was associated with a minimal pre-procedural anticoagulant effect and that both the presence of CKD stage 3b or higher and treatment with antiarrhythmic drugs should require a longer period of DOAC discontinuation .A relevant issue is the choice of the most accurate method to calculate eGFR. The reported RCTs used the Cockcroft-Gault equation, that is considered less accurate than the Modification of Diet in Renal Disease Study (MDRD) and the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations –58. In gStatement: Measuring plasma DOAC concentration is recommended in case of (1) active bleeding/thrombosis; (2) urgent/emergency surgery (3) acute states  [age 2–3) .Statement: Every hospital should have laboratory tests at their disposal for measuring DOAC plasma levels.Statement: Careful monitoring of renal function in patients taking DOAC is recommended to avoid the risk of overdosing.AF in CKD patients confers a high risk of worsening renal function and progression to ESRD , 10. On Chronic kidney disease classification according to estimated glomerular filtration rate.G1: > 90 ml/min.G2: 60–89 ml/min.G3: 30–59 ml/min- G3a: 45–49 ml/min- G3b: 30–44 ml/min.G4: 15–29 ml/min.G5: < 15 ml/min.


Some studies have reported that the capacity of humic substances to improve plant growth is dependent on their ability to increase root hydraulic conductivity. It was proposed that this effect is directly related to the structural conformation in solution of these substances. To study this hypothesis, the effects on root hydraulic conductivity and growth of cucumber plants of a sedimentary humic acid and two polymers—polyacrylic acid and polyethylene glycol—presenting a molecular conformation in water solution different from that of the humic acid have been studied. The results show that whereas the humic acid caused an increase in root hydraulic conductivity and plant growth, both the polyacrylic acid and the polyethylene glycol did not modify plant growth and caused a decrease in root hydraulic conductivity. These results can be explained by the different molecular conformation in water solution of the three molecular systems. The relationships between these biological effects and the molecular conformation of the three molecular systems in water solution are discussed. Humic substances (HS) may be considered as biomolecules formed in soil during the decomposition of plant and animal residues by chemical and biological processes  and part−1  of both polyethylene glycol (PEG) and a sedimentary humic acid caused a reduction of shoot dry weight, root hydraulic conductivity, and leaf-transpiration rate. Other studies showed that when the humic acid concentration in solution was lower (0.1–0.3 g·L−1) and closer to that present in soil solution or involved in agronomic practices, its root application stimulated plant growth  A[(Abs 440 + TBA − Abs 600 + TBA) × 0.0571] B−1) (A − B/157 000) × 106MDA equivalents (nmol·mLThe MDA concentration was measured following a described methodology . A 0.4 gThe MDA concentration was expressed as nmol MDA per gram of fresh weight (FW).Root Morphology Images Were Taken with a Bridge Camera (Canon PowerShot SX540). Cucumber roots were submerged in distilled water in order to take the photographs.p < 0.05; 0.01) among treatments were calculated by using one-way analysis of variance (ANOVA) and the LSD Fisher post hoc test. All statistical tests were performed using the statistical package Statistica 6.0 .Significant differences (


The S−300 ratio is effective for semi-quantification of soil arsenic content, and may be also used for soil arsenic pollution assessment and monitoring in similar settings of tungsten mining.Previous studies indicated serious soil arsenic (As) pollution of large spatial extent related to tungsten mining. We performed systematic analyses of magnetic parameters and As contents of a slag covered soil profile close to the abandoned tungsten mine in southern China, in order to discuss the feasibility of using sensitive, non-destructive, and cost-effective magnetic methods for monitoring the soil arsenic content in such arsenic pollution areas. The results indicate that arsenic sulfide entered from slags into the underlying soil and changed to iron arsenate and moveable arsenic ion. The arsenic ions were transported from the upper to the lower part of the soil profile, leading to more serious arsenic pollution at lower levels of the section. Pedogenesis and oxidation of the entered iron and arsenic sulfide resulted in coexistence of magnetite/maghemite and hematite, with different contributions at depths of 125–195 cm, 60–125 cm, and 0–60 cm. The arsenic content is significant positively correlated with the hematite concentration given by the magnetic parameter HIRM and negatively correlated with the S Serious heavy metal pollution frequently appeared worldwide7. Previous studies indicated that both surface and deep soils within and around metallic mine area such as the Lianhuashan abandoned tungsten mine at south China are being experienced serious arsenic pollution9. According to the “Law of the People’s Republic of China on the prevention and control of soil pollution”, soils within and around thus mine area are the key monitoring objects of heavy metal pollution. Therefore, rapid monitoring and identification of heavy metal pollution in soil within and around mining areas are a necessary prerequisite for mine environmental impact assessment and repair of mining damages. Rapid and effective methods are urgently needed for heavy metal content monitoring and pollution assessment.Soil environment has been becoming one of the hottest issues of environmental science as well as environmental protection agency. As well known, mine utilization usually seriously affects the surrounding soil environment. Particularly, exploitation of metallic ore always leads to heavy metal pollution11. Magnetic approaches were widely applied for soil heavy metal content monitoring and pollution assessment during the last decades15. Many studies indicated significant positive correlations between soil magnetic parameters  and heavy metal contents. Thus, in some studies, MS was regarded as an effective indicator for heavy metal contamination in soil18. However, direct application of environmental magnetism in heavy metal monitoring and pollution assessment is limited due to the unclear key issue of and the underlying mechanisms causing the relationship between magnetic particles and heavy metal elements.Environmental magnetic methods have several advantages such as rapidness, sensitiveness, non-destructiveness, and cost-effectiveness21. Furthermore, soil magnetism can be elevated with increasing arsenic content due to containing arsenic in magnetic mineral structure22. Therefore, magnetic properties have the potential to detect arsenic concentration in soils. In order to reveal the coexistence mechanism of arsenic and magnetic particles and the feasibility of using magnetic parameters to monitor arsenic in polluted soil, an arsenic sulfide-rich slag covered soil profile close to an abandoned tungsten mine in southern China was selected for magnetic properties and arsenic content measurements in the present study. The main purpose of this study is to reveal the magnetic response of soil arsenic pollution and the mechanism for coexistence of arsenic and magnetic minerals. Moreover, we try to investigate not only qualitative relationship but also quantitative calculation equations between soil arsenic and magnetic parameters. Based on the mentioned results, effective magnetic parameters are suggested for soil arsenic content monitoring and quantifying pollution in such mine areas.Previous studies verified that arsenic is often absorbed by iron oxides and oxyhydroxides, which form the most important and widely distributed magnetic minerals such as magnetite, hematite and goethite within soils2. The combination of mineral deposits is characterized by the coexistence of wolframite and scheelite, rich in sulfides, especially pyrite, arsenopyrite, etc. These sulfides are rich in heavy metals such as Cu, Co, Ni, As27. Subtropical monsoon climate characterized by a wet and hot summer and a dry and cool winter, with mean annual temperature of 21.1 °C and mean annual rainfall of 1444 mm predominates this area. The tungsten mine was exploited since 1957. Due to the depletion of resources, mining was stopped in 1991 and the mine was closed in 1999. Since no effective protection and treatment measures were taken during the period of mining activities and after sealing the mine, the area affected by mine development and utilization exceeded 200 km2. Previous researches indicated that soil within and around the mine area is seriously polluted by heavy metals especially As30. According to X-ray diffraction (XRD) results for tens of surface soil samples around the mine area (unpublished data), the main minerals within surface soil in this area are quartz (about 40–50%), clay minerals , and some other minerals such as gypsum, pyrite, magnetite, hematite.As one of the biggest tungsten mines in Guangdong Province, southern China, the Lianhuashan tungsten mine area  covers 2.122 km3) for magnetic measurements. Subsamples were prepared for element content measurements by sieving through a 74 μm mesh.A slag covered soil profile  located at about 2 km away from the mine area Fig.  was selelf and χhf, respectively) were measured using a Kappabridge MFK1-FA (AGICO). Frequency-dependent MS was calculated from the expression χfd (%) = [(χlf − χhf)/χlf] × 100. Volume-specific MS as a function of temperature (κ vs T) was measured from room temperature to 700°C using a CS4 high-temperature unit attached to the MFK1-FA instrument. Anhysteretic remanent magnetization (ARM), expressed as the susceptibility of ARM (χARM), was imparted with a D-2000 (ASC Scientific Ltd.) alternating demagnetizer creating a 100 mT peak AF field and a DC biasing field of 0.05 mT. Isothermal remanent magnetization (IRM) acquisition curves with fifty-six steps up to 2500 mT were acquired using an IM-10–30 pulse magnetizer (ASC Scientific) and a JR6-A spinner magnetometer (AGICO) for intensity measurements . Backfield IRM was imparted in a reverse 300 mT field. The IRM at an applied field of 2500 mT was regarded as saturation IRM (SIRM). An S-ratio S−300 was calculated using the equation [(-IRM−300 mT/SIRM) + 1]/2 according to Bloemendal et al.31. An estimate of the concentration of “hard” magnetic minerals (hematite and goethite) (HIRM) was calculated as HIRM = (SIRM + IRM−300 mT)/232. The contribution of different magnetic components was quantified using the IRMUNMIX2_2 and IRM_CLG1 routines to analyze cumulative log-Gaussian (CLG) IRM acquisition curves34. Hysteresis parameters such as coercivity (Bc) and remanence coercivity (Bcr), which are helpful to identify the type and particle size of magnetic minerals36, were measured using a MicroMag 3900 VSM (Princeton Measurements), with a maximum applied field of 1.5T at Sun Yat-sen University.A suite of mineral magnetic analyses was performed for all samples at the Guangzhou Institute of Geochemistry, Chinese Academy of Sciences. Low (976 Hz) and high (15 616 Hz) frequency MS . Then contents of arsenic (with detection limit of 0.2 mg/kg) as well as other trace elements were measured using an inductively coupled plasma mass spectrometry  at the ALS Minerals-ALS Chemex Laboratory, Guangzhou. The ALS is confident with the quality of the results since standard, duplicate, and blank samples were added during the measurement process followed by relevant specifications.The ground soil samples were dissolved in aqua regia according to the SUEPA standard method (3502)Pearson bivariate correlation analysis was performed for various variables to reveal the relationship between magnetic properties and arsenic contents within the soil. In order to quantify the relationships of arsenic content and magnetic parameters, stepwise multiple regression analyses were performed using arsenic content and magnetic parameters as dependent and independent variables, respectively. Values of arsenic contents outliers outside of 2σ (σ: standard deviation) were eliminated from the regression analysis. All statistical analyses were carried out using SPSS 16.0 software.11. The dramatically increasing κ values  inverting to weakly magnetic hematite (α-Fe2O3)41. The drop of κ in the heating curves around 580 °C  is widely used to identify the type of magnetic mineralsues Fig.  after he °C Fig. . For all °C Fig. , which c °C Fig.  reveals  °C Fig.  indicate1/2 of several hundreds mT is likely related to hematite43. The low-coercivity component (C1) probably reflects the fraction of magnetite and maghemite as the existence of these phases is suggested by the shape of the κ-T curves.The CLG analysis results show a low coercivity and a high coercivity phase throughout the soil profile, but with different contributions Fig. . The sec44, indicating a general soil arsenic pollution.The statistical description and vertical variation of magnetic properties and the arsenic content are listed and illustrated at Table lf, SIRM, HIRM, IRM_C1, IRM_C2 indicate that total magnetic mineral concentration and the concentration of the two magnetic components largely change within the soil profile. The ranges of Bc, S−300, B1/2_C1, and the SIRM contributions of C1 and C2  ranges from 33–52 mT at depths of 60–120 cm, while it is clearly lower (<30 mT) in the upper and lower parts of the section  with concentration-dependent magnetic parameters. Moreover, comparing to the “Chinese Environmental quality standard for soils (GB 15618–1995)vel Fig. .ARM/χ and Bc, while S−300 is negatively correlated (SI Table fd indicates that the lower coercivity phase (magnetite and maghemite) has a finer grain size (reaching into the SP range) in samples with higher arsenic content.From the Pearson bivariate correlation coefficients listed in SI Table SI Table . This im44, the entire studied profile is moderately to seriously polluted by arsenic. Arsenic content increases from the upper to the lower part of the section  and then were further oxidized by Oxygen and the originally existing Fe2O3 (as oxidant) through the reaction As/FexSy + O2/Fe2O3 = Fe3O4 + AsO43− + SO42−. As a result, the contributions of magnetite and hematite to SIRM are approximately equal within this part of the soil profile ile Fig. . Part oftio Fig. .−300) could be used to semi-quantify the arsenic content. This quantitative estimation may be also valid for other such areas.As to the above discussion, transportation and the existing form of arsenic are closely related with the magnetic mineral parameters and the transformation of magnetic phases. According to the observed relationships, some easily available hematite-related parameters  = −50.22*S2 (0.618) indicate that this regression equation is effective for soil arsenic content quantification.The normal P-P plot of the regression standardized residual SI Fig. , and val−300 values, predicted arsenic contents were calculated for the accepted 46 samples. A scatter plot of the predicted and measured arsenic contents is illustrated in SI Fig. Using this regression equation and the measured SMagnetic mineral composition consists of magnetite/maghemite and hematite, with different contributions at depths of 125–195 cm, 60–125 cm, and 0–60 cm. The magnetic mineral composition results from pedogenesis and oxidation of iron and arsenic sulfides that were delivered from the slag cover.The oxidation process changed part of the iron and arsenic sulfides to movable iron and arsenic ion, which were then transported from the upper to the lower part of the soil profile. As a result, arsenic pollution of the lower part of the soil profile is more serious than in the upper part.−300. A significant positive correlation is found between the arsenic content and the absolute hematite concentration measured by the parameter HIRM. Soil arsenic may coexist with fine high coercivity hematite particles. The S−300 ratio is an effective choice for semi-quantification of soil arsenic content, which may be also used for soil arsenic monitoring in similar settings of arsenic polluted areas.The arsenic content shows a significant negative correlation with the concentration dependent magnetic parameters χ and SIRM, as well as with the hematite/magnetite(+maghemite) indicative ratio SSupplementary information.