Under natural environments, light quality and quantity are extremely varied. To respond and acclimate to such changes, plants have developed a multiplicity of molecular regulatory mechanisms. Non-photochemical quenching of chlorophyll fluorescence (NPQ) and thylakoid protein phosphorylation are two mechanisms that protect vascular plants. To clarify the role of thylakoid protein phosphorylation in energy-dependent quenching of chlorophyll fluorescence (qE) in rice plants, we used a direct Western blot assay after BN-PAGE to detect all phosphoproteins by P-Thr antibody as well as by P-Lhcb1 and P-Lhcb2 antibodies. Isolated thylakoids in either the dark- or the light-adapted state from wild type (WT) and PsbS-KO rice plants were used for this approach to detect light-dependent interactions between PsbS, PSII, and LHCII proteins. We observed that the bands corresponding to the phosphorylated Lhcb1 and Lhcb2 as well as the other phosphorylated proteins were enhanced in the PsbS-KO mutant after illumination. The qE relaxation became slower in WT plants after 10 min HL treatment, which correlated with Lhcb1 and Lhcb2 protein phosphorylation in the LHCII trimers under the same experimental conditions. Thus, we concluded that light-induced phosphorylation of PSII core and Lhcb1/Lhcb2 proteins is enhanced in rice PsbS-KO plants which might be due to more reactive-oxygen-species production in this mutant. Sunlight is the primary and key energy source for photosynthetic organisms; however, its quality and quantity are extremely variable in natural environments. Therefore, plants have to respond and acclimate to fluctuating light conditions by evolving a diversity of molecular regulatory mechanisms. These mechanisms control the photosynthetic apparatus’s structure and redistribute excitation energy between photosystems . Diversephot2 14], qI, −2 s−1) for 1 h. Thylakoid protein phosphorylation has been related to sustained NPQ [We have previously shown that the absence of the PsbS protein does not significantly affect the zeaxanthin accumulation and the state transition . On the ined NPQ , but we Arabidopsis thaliana T-DNA knockout plants lacking Lhcb3 (koLhcb3) do not display a noteworthy alteration in PSII efficiency and/or qE type of NPQ, but the rate of transition from state 1 to state 2, increases in koLhcb3, suggesting that Lhcb3 modulates the rate of state transitions [−2 s−1) and almost completely dephosphorylated after 1 h of the same light-intensity treatment. While there was no detected phosphorylated Lhcb2 band in LHCII trimer from dark-adapted samples, after 10 min of light treatment, the LHCII trimer was highly enriched with Lhcb2 spots. It was reported that strong Lhcb2 phosphorylation in L-LHCII trimers is a prerequisite for energy transfer from LHCII to PSI [The major LHCII of land plants consists of three different Lhcb proteins (Lhcb1-3). Our data reveal the principal interaction of PsbS with PSII core and LHCII phosphoproteins in the dark-adapted state and enhanced interaction with P-Lhcb1 in the qE-active light state. It is well known that PsbS-dependent qE is a very rapid process, taking place in seconds to minutes, however, in most of our experiments, the major alterations in phosphorylation were observed after 1 h. Thus we cannot exclude the possibility of the phosphorylation response to the impaired qE. There is evidence that at least part of the increased phosphorylation in light-treated PsbS-KO is in the PSII core complex . This wonsitions . UnfortuI to PSI ,66. CompIn addition, phosphorylation of CP29 and NPQ are two processes which are considered to be independent of one another. However, a connection between NPQ and phosphorylation of CP29 in monocots was also shown ; the autAltogether, our data allow us to conclude that Lhcb1 and Lhcb2 phosphorylation is somehow involved in qE relaxation kinetics. On the other hand, based on the accumulation of P-Lhcb1 in SCs in HL 1 h-treated PsbS-KO leaves and in both SC and LHCII trimers and monomers in LL 1 h-treated PsbS-KO leaves, we can suggest that some defects shown in PsbS-KO leaves may inhibit the dephosphorylation of Lhcb1. A candidate for this triggering effect would be more ROS generated in the PsbS-KO leaves under high light conditions . HoweverOryza sativa L.) WT Hwayoung cultivars and PsbS-KO mutant were grown in a greenhouse at 30/26 °C (day/night), with a light photoperiod of 16 h of light/8 h of dark. The middle parts of the leaves of one-month-old seedlings were used for experiments.Rice .To analyze light-dependent changes of thylakoid membrane proteins, WT and PsbS-deficient mutant plants were dark-adapted overnight (12 h) before being exposed to light of different intensities . The homogenate was filtered through four layers of Miracloth  followed by centrifugation at 5500× g for 6 min at 4 °C. The resulting pellet was washed and suspended in a shocking buffer (50 mM Hepes/KOH (pH 7.5), 5 mM sorbitol, 5 mM MgCl2, and 10 mM NaF) and then centrifuged at 5500× g for 6 min at 4 °C. The pellet was resuspended in a storage buffer (50 mM Hepes/KOH (pH 7.5), 100 mM sorbitol, 10 mM MgCl2, and 10 mM NaF) followed by centrifugation at 5500× g for 6 min at 4 °C. Finally, the thylakoid pellet was suspended into a small aliquot of storage buffer. All the stages in thylakoid preparation were performed on ice under dim light. The chlorophyll concentration was determined from the samples as described in [Thylakoid membranes were isolated from one-month-old rice leaves as described by  with somribed in .g for 6 min at 4 °C. The resulting pellet was resuspended into 10 µL ice-cold buffer containing 25 mM BisTris/HCl (pH 7.0), 20% (w/v) glycerol, 10 mM NaF, and 0.25 mg ml−1 Pefabloc. An equal volume of detergent solution (which was diluted in this buffer) was added to a final concentration of 1.0% (w/v) β-DM . The thylakoid membranes were incubated in darkness for 5 min on ice; insolubilized material was removed by centrifugation at 13,000 rpm for 10 min at 4 °C. Prior to loading, the samples were supplemented with a one-tenth volume of Serva Blue G buffer (100 mM BisTris/HCl (pH 7.0), 0.5 M amino-n-caproic acid, and 0.25 mg ml−1 Pefabloc, 30% (w/v) sucrose, and 50 mg·ml−1 Serva Blue G) to introduce a negative charge and to increase the solubility of the sample. BN-PAGE was performed with a linear gradient of 4.5–13.5% separation gel and 4% (w/v) stacking gel [w/v) Serva Blue G stain (Coomassie) and, as the blue running front had moved about a half in the cathode, a buffer without Coomassie. The BN-PAGE was stopped when the Coomassie blue ran out of the bottom of the gel. The same anode buffer (50 mM BisTris/HCl (pH 7.0)) was used during all native PAGE.The BN-PAGE was performed according to  with somking gel . The sam−2 s−1, 1 h at 1500 µL photons m−2 s−1) rice WT and mutant plants lacking PsbS protein of PSII according to [Thylakoid membranes were isolated from dark-adapted (12 h darkness) and light-adapted  using an actinic light intensity of 700 µL photons meviously . The NPQeviously .t-test was employed to define the statistical significance (p < 0.01 with one asterisk and p < 0.05 with two asterisks) of the alterations between the means. The number of biological and analytical replicates was at least three. All digitized data shown as Microsoft Excel software (Microsoft Office 2016 for Windows) was used for statistical analyses (one-way ANOVA). All BN-PAGE and Western blot images were digitized using ImageJ software. Data are stated as mean values ± standard deviation. The NPQ and thylakoid protein phosphorylation are two important mechanisms that protect vascular plants. Therefore, the detailed characterization of these processes using plants lacking the energy-dependent (qE) part of NPQ is important for a reliable identification of functionally relevant phosphoproteins during quenching processes. Although it is clear that NPQ and the thylakoid protein phosphorylation work together in synchrony, their molecular mechanism is still unclear. Thus, the identification of thylakoid protein phosphorylation during quenching processes will help to better understand the underlying molecular mechanisms. Furthermore, analysis of the phosphorylated proteins will help to detect novel marker candidates for NPQ and the origin of their action. The reorganization of plant thylakoid membranes during light intensity changes depends on PsbS interactions with the LHCII and PSII core proteins as well as with their phosphorylation. Therefore, we conclude that more ROS production in rice PsbS-KO may cause enhanced light-induced phosphorylation of PSII core and antenna proteins.


Allostery is a pervasive mechanism that regulates protein activity through ligand binding at a site different from the orthosteric site. The universality of allosteric regulation complemented by the benefits of highly specific and potentially non-toxic allosteric drugs makes uncovering allosteric sites invaluable. However, there are few computational methods to effectively predict them. Bond-to-bond propensity analysis has successfully predicted allosteric sites in 19 of 20 cases using an energy-weighted atomistic graph. We here extended the analysis onto 432 structures of 146 proteins from two benchmarking datasets for allosteric proteins: ASBench and CASBench. We further introduced two statistical measures to account for the cumulative effect of high-propensity residues and the crucial residues in a given site. The allosteric site is recovered for 127 of 146 proteins (407 of 432 structures) knowing only the orthosteric sites or ligands. The quantitative analysis using a range of statistical measures enables better characterization of potential allosteric sites and mechanisms involved.  •State-of-the-art prediction accuracy of allosteric sites on benchmarking datasets•Multiple measures to capture different mechanistic insights in allostery•Provide guidance for molecular design using key interaction residues in silico is of great interest in expanding the chemical space of drug discovery. Here, we demonstrate how bond-to-bond propensity analysis is used to not only predict allosteric sites in large benchmarking datasets but also to shed light on the possible allosteric mechanisms involved. Our quantitative analysis of a given site with a range of statistical measures allows the identification of key residues required for allosteric signaling. The data can be harnessed for artificial-intelligence-driven drug discovery and digital molecular design, which are new areas of interest in the data science community.Proteins are the most common drug targets, and allostery plays a key role in regulating protein activities. Predicting allosteric sites  Modulation of protein activity is important for disease treatment, and challenges associated with common drugs have fueled the need for allosteric drugs that bind distant from the active site. Yet, allosteric site prediction remains challenging. In this study, we benchmark an atomistic, graph-theoretical method called bond-to-bond propensity against allosteric protein databases to illustrate its capability to accurately uncover allosteric sites. Our six scoring measures provide additional insights into potential mechanisms involved and could guide the design of drug molecules. The design of allosteric site binders could follow well-established approaches used to develop molecules that bind to orthosteric sites, such as high-throughput screening,,,,Experimental methods including tethering,,,,There are two general ways of approaching the problem of identifying putative allosteric sites computationally: (1) identifying allosteric sites without considering the communication with orthosteric sites and (2) uncovering the allosteric communication pathways between orthosteric and allosteric sites.M ,We here exemplify the method on bovine seminal ribonuclease  reported in the ASD database are incorrect (they do not form a binding site), and those of one further structure (PDB: 2ATS) do not match with the data in ASBench. From the remaining eight, six structures  obtained from the ASBench are only one part of a large and complex multimeric protein, where the effect of cooperativity might play a crucial role. Therefore, without the complete protein structure, the allosteric signaling cannot be detected. For example, it has been demonstrated with ATCase, a large dodecameric protein with six orthosteric sites, that only when at least three orthosteric sites are involved is allosteric behavior detected.Escherichia coli, phosphoglycerate dehydrogenase (PDB: 2PA3), displays a different allosteric mechanism, the flip flop mechanism,1Z8D) contains two allosteric sites,We focus here on the 12 structures with allosteric ligands where the allosteric site could not be detected by any of the measures. From those 12, the orthosteric residues of three structures (PDB: 3PJG), ASBench has incorrect orthosteric residues reported (not forming a binding pocket), so a wrong perturbation source was used. Hemoglobin (PDB: 1B86) is a well-known protein with cooperativity underpinning its activity3C1N and 3H6O) are large and complex multimeric proteins where again cooperativity would affect the results. The orthosteric sites and allosteric sites of the other three structures , similar to those of 1Z8D above, are in close proximity. The allosteric effect is not mediated by functional coupling and is thus not revealed by propensity analysis.Upon removing the allosteric ligands, allosteric sites of seven more structures could not be identified. For the structure of UDP-glucose dehydrogenase (PDB: 11BG) example, the allosteric site contains eight residues, but only four residues form direct interactions with the allosteric ligand. Therefore, these four residues are responsible for allosteric signaling as the direct interactions connecting the ligand and the protein are essentially where the perturbation starts . In the previous bovine seminal ribonuclease  is removed when carrying out bond-to-bond propensity analysis for the CASBench database.3CSM),Bond-to-bond propensity analysis was conducted for these 314 structures using the orthosteric ligand or orthosteric site residues (with orthosteric ligand removed) as the perturbation source in two separate runs. When multiple orthosteric ligands or sites are present, all of them were used as the perturbation source. Moreover, when there are multiple allosteric sites in the protein structure, each of them is investigated separately with the six statistical measures, and the average value for each of the measures is used to decide whether the allosteric sites can be detected for the protein. It is observed in some cases that some of the allosteric sites of the protein can be detected by a particular measure, whereas the other sites cannot be detected , according to at least one statistical measure. When using the orthosteric site residues as the perturbation source, the allosteric site is detected for 304 of 314 structures (32 of 33 proteins), according to at least one statistical measure. It is observed that, in general, the allosteric site of a protein structure can be identified with more statistical measures when the orthosteric ligand is set as the perturbation source.If the orthosteric ligand is selected as the perturbation source, the source bonds include the weak bonds formed by the ligand and the surrounding residues. The orthosteric site includes all residues within 5 Å of the orthosteric ligand.4R1R) is ribonucleotide reductase protein R1 (CAS0047). It is a large and complex multimeric protein, and only one orthosteric site is reported in the CASBench database. Hence, the effect of cooperativity could affect the performance of propensity analysis as previously discussed. Another two structures (PDB: 1FUQ and 1KQ7) are two out of the four structures of fumarase (CAS0085). This is also a complex multimeric protein where bond-to-bond propensity analysis may not perform well if not all orthosteric ligands are present. The remaining three structures are epoxide hydrolase (CAS0002) . We analyzed 28 structures of epoxide hydrolase in total, each with a different orthosteric ligand. Hence, different ligands, even when binding at the same orthosteric site, exert different perturbation effects on the protein.We focus here on the six structures for which the allosteric site cannot be detected by any of the measures when using orthosteric ligands as the perturbation source. One of them (PDB: 1LLD and 1LTH) of L-lactate dehydrogenase (CAS0028) were not identified. This can be partly explained by the changed perturbation effects as the allosteric sites were identified when sourcing from the orthosteric ligands. In CASBench, the orthosteric sites include residues within 5 Å from the orthosteric ligands, which leads to a large region as the perturbation source. This shows that the specific ligand-site interactions are crucial for accurate allosteric site detection. This is consistent with the overall trend since it has been shown above that successful allosteric site detection is achieved by more statistical measures using the orthosteric ligand as the perturbation source. Moreover, allosteric sites of another eight structures were not detected when only using the orthosteric site residues as the perturbation source. This further strengthens the idea that the method is sensitive to specific interactions between the ligand and the protein and holds the potential to evaluate the performance of different ligands in the orthosteric site.When orthosteric residues were used as the perturbation source, the allosteric sites of two structures  structures and the allosteric site is detected for 99 of 118 (83.9%) structures when the allosteric ligand is removed, according to at least one statistical measure. Despite the strengthening of functional coupling of the allosteric site to the orthosteric site by the allosteric ligand, propensity analysis is still able to reveal the intrinsic connectivity between the two sites. For the CASBench database, we conducted our analysis sourced from the orthosteric ligands or the orthosteric residues and managed to detect the allosteric sites according to at least one statistical measure for 308 of 314 (98.1%) structures (32 of 33 proteins) and for 304 of 314 (96.8%) structures (32 of 33 proteins), respectively. The allosteric site of a protein structure can be identified with more statistical measures when choosing the orthosteric ligand as the perturbation source. This observation suggests that using the ligand as the perturbation source confers the perturbation effect of the binding event more accurately. However, if the information on the orthosteric substrate is not available, it is viable to select the orthosteric residues as the perturbation source.Four existing computational methods have been benchmarked using the allosteric protein data from ASBench and ASD. Since the protocols for protein structure selection and collection of relevant site information are different for each method, a direct comparison cannot be fully achieved. The prediction accuracies of AllositePro,The results presented here strengthen confidence in allosteric site identification predicted by bond-to-bond propensity, which coupled with the efficiency of the method make it an attractive approach. Generally, the definition of orthosteric and allosteric residues, which would significantly affect the size and residues involved, plays an essential part when evaluating allosteric site prediction methods and was also highlighted for bond-to-bond propensity analysis. Finally, more detailed analysis would be usually required in cases where the allosteric site and the orthosteric site are in very close proximity, to elucidate the effect of cooperativity in large and complex multimeric proteins or the role of structural water molecules, which could still be possible given the computational efficiency of the approach. The introduction of the statistical measures coupled with the availability of large datasets and the efficiency of computing bond-to-bond propensity taken together strengthens our understanding of allostery and builds the groundwork to a more targeted and data-driven allosteric drug design.s.yaliraki@imperial.ac.uk).Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Sophia N. Yaliraki N is the number of nodes (atoms).Bond-to-bond propensity analysis starts by constructing a weighted atomistic graph using the three-dimensional coordinates of the atoms of the protein in the PDB files. Atoms are represented by nodes, and interactions  between the atoms are represented by edges. The weights of edges correspond to the interaction energies between the atoms with weights derived from relevant interatomic potentials. An in-depth procedure for the atomistic protein graph construction has been described in Delmotte et al. and Amor et al.M was introduced to study non-local edge-coupling in graphs,M is employed to analyze the atomistic protein graph. The element i has on edge j. M is given byn nodes and m edges; i and j, i.e., the bond energy between the atoms. L.L, which defines the diffusion dynamics on the energy-weighted graph,Bond-to-bond propensity was first introduced in Amor et al.b anywhere else in the protein, we calculate the following:To evaluate the effect of perturbations from a group of bonds This is the raw propensity of an individual bond that reflects how strongly the bond is coupled to the perturbation source. As different proteins contain different numbers of bonds, the raw propensity is normalized and the bond propensity is defined as follows:R:The residue propensity is then defined as the sum of normalized bond propensities of all the bonds of a residue, b from the perturbation source is defined as the minimum distance, b and any bond of the perturbation source:d, a linear model for the logarithm of the propensities is adopted to solve the QR minimization problem:p is the quantile, and b with propensity Bond and residue propensities naturally decrease as the distance of the bond or residue from the perturbation source increases. To determine the bonds and residues that are significant, bond and residue propensities at a similar distance from the perturbation source are compared using conditional quantile regression (QR).R is defined similarly by using the residue propensity as shown in The residue quantile score of residue 1The average bond QS of the allosteric site:2The average residue QS of the allosteric site:3The proportion of bonds in the allosteric site with bond QS greater than 0.95,i.e., 4The average reference bond QS of the allosteric site:allosteric site is the number of bonds in the allosteric site.Four statistical measures have been used to evaluate the significance of the QS by Amor et al.5The proportion of residues in the allosteric site with residue QS greater than 0.95,i.e., 6The average reference residue QS of the allosteric site:allosteric site is the number of residues in the allosteric site.For the purpose of complementing these previous measures and to investigate more aspects of allosteric site detection, two additional measures were introduced in this work:If the functional coupling is the result of a cumulative effect of the whole allosteric site, an accurate measure of the allosteric propensity would be the average QS of all bonds or residues in the allosteric site. Hence, measures 1, 2, 4, and 6 would be able to uncover the cumulative effect at both the bond and residue level for both the intrinsic propensities of the protein and the absolute propensities comparing with the SCOP reference set.11BG)To assess the significance of the average bond and residue QS


The accumulation of lipid intermediates may interfere with energy metabolism pathways and regulate cellular energy supplies. As increased levels of long-chain acylcarnitines have been linked to insulin resistance, we investigated the effects of long-chain acylcarnitines on key components of the insulin signalling pathway. We discovered that palmitoylcarnitine induces dephosphorylation of the insulin receptor (InsR) through increased activity of protein tyrosine phosphatase 1B (PTP1B). Palmitoylcarnitine suppresses protein kinase B (Akt) phosphorylation at Ser473, and this effect is not alleviated by the inhibition of PTP1B by the insulin sensitizer bis--oxovanadium (IV). This result indicates that palmitoylcarnitine affects Akt activity independently of the InsR phosphorylation level. Inhibition of protein kinase C and protein phosphatase 2A does not affect the palmitoylcarnitine-mediated inhibition of Akt Ser473 phosphorylation. Additionally, palmitoylcarnitine markedly stimulates insulin release by suppressing Akt Ser473 phosphorylation in insulin-secreting RIN5F cells. In conclusion, long-chain acylcarnitines activate PTP1B and decrease InsR Tyr1151 phosphorylation and Akt Ser473 phosphorylation, thus limiting the cellular response to insulin stimulation. Insulin signalling plays a central role in the regulation of glucose and lipid metabolism. The activation of insulin signalling begins when insulin binds to the extracellular subunits of the insulin receptor (InsR), causing conformational changes that promote the autophosphorylation of intracellular subunits, including the Tyr1151 phosphorylation site . ActivatInsulin resistance is associated with increased levels of lipid metabolism intermediates, such as DAG, ceramides, and long-chain fatty acids and their acylcarnitines . The rolH-benzo[g]indole-2,5(3H)-dione (BVT948) were used as inhibitors of PTP1B D20 -14,4 .® insulin aspart 100 units/mL , sotrastaurin  and (R)-(+)-etomoxir sodium salt  were diluted before use in sterile filtered PBS , 10 mM Na2HPO4 , 1.8 mM KH2PO4 , pH 7.4). BVT948  and wortmannin  were dissolved in dimethyl sulfoxide (DMSO) . BMOV was synthesized using the method previously described by Caravan el al. [NovoRapidThe results are reported as the means ± standard deviations, and the statistical analysis of the data was performed using GraphPad Prism computer software . The data distribution was determined using the Shapiro—Wilk normality test. The statistical significance of the experimental results was verified by one-way ANOVA followed by Dunnett’s multiple comparison test for normally distributed data. If the data were not normally distributed, the Kruskal–Wallis test followed by Dunn’s multiple comparison test was used. The results were considered statistically significant if the p-value was less than 0.05.In conclusion, palmitoylcarnitine affects the insulin signalling pathway by activating PTP1B and inducing InsR Tyr1151 dephosphorylation, which consequently results in diminished Akt Ser473 phosphorylation. Palmitoylcarnitine also affects the downstream steps of the insulin signalling pathway and reduces Akt Ser473 phosphorylation independent of its effect on InsR phosphorylation. Inhibition of PP2A and PKCs does not affect the palmitoylcarnitine-induced dephosphorylation of Akt at Ser473, indicating different mechanisms of action.


Antarctic supraglacial lakes (SGLs) have been linked to ice shelf collapse and the subsequent acceleration of inland ice flow, but observations of SGLs remain relatively scarce and their interannual variability is largely unknown. This makes it difficult to assess whether some ice shelves are close to thresholds of stability under climate warming. Here, we present the first observations of SGLs across the entire East Antarctic Ice Sheet over multiple melt seasons (2014–2020). Interannual variability in SGL volume is >200% on some ice shelves, but patterns are highly asynchronous. More extensive, deeper SGLs correlate with higher summer (December-January-February) air temperatures, but comparisons with modelled melt and runoff are complex. However, we find that modelled January melt and the ratio of November firn air content to summer melt are important predictors of SGL volume on some potentially vulnerable ice shelves, suggesting large increases in SGLs should be expected under future atmospheric warming. Antarctic supraglacial lakes (SGLs) have been linked to ice-shelf collapse and the subsequent acceleration of inland ice flow, but observations of SGLs remain relatively scarce and their interannual variability is largely unknown. This new study shows that lake area and volume vary substantially from year-to-year around the East Antarctic Ice Sheet and between ice shelves. Of concern is that SGLs are expected to become more extensive on Antarctic ice shelves, due to increases in surface melt extent and intensity in response to future atmospheric warming14. Regular, prolonged surface melt reduces the meltwater retention capacity of ice shelves by saturating their firn layer and reducing firn air content (FAC)18. Excess meltwater that cannot be stored in the firn runs off to form SGLs on the snow or ice surface, filling topographic hollows, including rifts and crevasses16. Observations and modelling have linked such SGLs on ice shelves to meltwater-induced vertical fracture propagation, termed hydrofracturing21. Some regions of ice shelves are already vulnerable to hydrofracturing20, but ice shelves around Antarctica will become more prone to hydrofracture-driven break-up as surface melt continues to lower their ability to accommodate meltwater23. Furthermore, and despite uncertainties, numerical models that attempt to capture these processes show much higher sea-level contributions from Antarctica, due to earlier ice shelf removal and loss of buttressing24. Thus, there is an urgent need to better constrain where and when SGLs develop on Antarctic ice shelves and which ice shelves are closer to potential thresholds in meltwater-induced hydrofracturing than others20.Supraglacial lakes (SGLs) have been linked to Antarctic ice-shelf disintegration23. Studies on a handful of individual ice shelves have also quantified the seasonal evolution of SGLs in East Antarctica29. However, there is limited knowledge of the interannual variability in SGLs over multiple melt seasons and across the whole ice sheet. There have also been few attempts to link the spatial and temporal variability in SGLs on ice shelves to near-surface climatic conditions31. Here, we quantify the variability in SGL distributions and volumes around the East Antarctic Ice Sheet (EAIS) during the peak of seven consecutive melt seasons (2014–2020) and investigate potential climatic controls on their development and near-surface (i.e. firn) conditions generated by ERA5 climate reanalysis and the Community Firn Model (CFM)32 forced by the regional climate model MARv3.1133.Recent assessments using satellite observations have found that SGLs are more extensive than previously thought on the world’s largest ice sheet in East Antarctica, which holds the vast majority of the Earth’s glacier ice 34 to Landsat 8 satellite imagery around the EAIS margin during January (which is known to be the peak of the melt season)29 from 2014 to 2020  36.The peak in total (EAIS-wide) SGL volume occurred in January 2017 (2331 ×10m3) Fig. , linked m3) Fig.  and Amerm3) Fig.  ice shel37  and Amery ice shelves (1731 ×106 m3), but in January 2017 on the Roi Baudouin Ice Shelf (532 ×106 m3), despite moderate mean annual surface melt rates derived from QuikSCAT radar backscatter37 of ~50–60 mm w.e. yr−1 on all three ice shelves  on some of the northernmost ice shelves experiencing the most intense mean annual surface melt (>80 mm w.e. yr−1), such as the Shackleton and Moscow University ice shelves , such as the Nansen Ice Shelf in Victoria Land , Riiser-Larsen 193%), and Shackleton (111%) ice shelves Fig. . Moreove93%, and %, Riiserves Fig. , Table 1ves Fig. , Table 1r = 0.35, r2 = 0.13, p = 0.001) between SGL areas and maximum SGL elevation. SGLs might be expected to reach higher elevations during melt seasons with more extensive surface meltwater, but our analysis suggests this is not the case , skin temperature (Ts), and net surface solar radiation   means of three climatic variables from ERA5 climate reanalysis: near-surface 2-m air temperature T9) Table , as well Table , 2m and SGL area and volume on all individual ice shelves, with the exception of the Roi Baudouin ice shelf  and West ice shelves  , but instead found a strong negative correlation between mean DJF T2m with total SGL area  and volume  and no significant correlation excluding the Amery and the Roi Baudouin ice shelves  Fig. . The neg45. This means more snowfall during the preceding winter replenishes firn pore space, so surface meltwater can percolate into the firn and be stored rather than forming as SGLs44. However, we find low correlations between both total SGL area and volume with the mean snowfall during the preceding winter and with the mean summer snowfall  might be expected to be positively correlated to SGL volumes because enhanced incoming shortwave radiation (and therefore net Srad) increases melting of the snowpack and the volume of liquid meltwater available to form SGLs39. However, we find low and negative correlations between both total SGL area and volume with mean DJF Srad  ice regions close to ice shelf grounding lines, where snow and firn have been entirely removed by persistent katabatic wind erosion, sublimation or melt29. Here, there is regular melting because the reduced albedo increases shortwave radiation absorption, and katabatic winds maintain enhanced turbulent surface heating31. This enables SGLs to form at high elevations where low-albedo exposed bedrock and blue ice are abundant23  can percolate less far into the ice before fully saturating the snowpack48. Continued surface melting over successive melt seasons gradually depletes FAC when more pore space is lost by melt and refreezing during densification than is replenished by snowfall44. At this point, meltwater can no longer percolate into and refreeze within the firn, meaning the firn cannot act as an absorbing buffer44. Meltwater percolation and refreezing into the firn layer can also exert a localised warming effect on ice temperatures through the release of latent heat17. When firn saturation prevents meltwater percolation and refreezing within the firn, the firn is flooded and excess surface runoff can form SGLs49. To investigate these potential controls on the interannual variability in SGL area and volume, we performed linear regressions between total SGL areas and volumes and mean November FAC, mean summer (January and DJF) FAC, mean summer total runoff and mean summer shallowest ice lens depth simulated by the CFM32. We also conducted linear regressions with mean summer surface melt simulated by the regional climate model MARv3.1133, as well as the November FAC-to-DJF surface melt ratio (Methods). November FAC reflects accumulation throughout the year and hence the firn meltwater storage capacity before the onset of summer melt. We therefore included November FAC in our regression to try and separate the effects of summer melt and firn storage capacity on interannual SGL variability. We also compared mean January FAC and DJF FAC with total SGL areas and volumes to assess the influence of FAC around the peak of the melt season29 and over the whole melt season.Depleted FAC, refrozen subsurface meltwater and high surface runoff volumes have been linked to SGL formation on East Antarctic ice shelvesr = −0.37, p < 0.001)   and is not correlated with mean summer surface melt or the November FAC-to-DJF surface melt ratio  and Riiser-Larsen  ice shelves  and a lower November FAC-to-DJF melt ratio . Previous work has suggested its vulnerability to hydrofracturing is currently low because only a small portion of SGLs form in high-tensile regions that provide buttressing22. However, we record very high total SGL area and volume across Shackleton Ice Shelf in January 2020 compared to previous years (e.g. 156% higher than in January 2017). Although this high annual variability and our relatively short time series do not confirm an increasing trend, we observe SGLs already intersected regions vulnerable to hydrofracture in 2014, 2015 and 2020  between SGL volumes and FAC-to-melt ratio, surface melt, runoff and minimum ice lens depth on the Roi Baudouin and Nivlisen ice shelves  and minimum ice lens depth , despite a stronger relationship with surface melt  on individual ice shelves and by up to ~72% across the entire ice sheet. Peak years of SGL volume are asynchronous between ice shelves, including those experiencing similar mean annual surface melt. SGL area and volume are strongly positively correlated with mean summer  air temperature on almost all individual ice shelves. Interannual SGL variability is also sensitive to mean summer firn air content (FAC). We find substantial discrepancies between observed total SGL volumes and surface runoff modelled by MAR. However, on the few ice shelves where these are significantly correlated, January surface melt and the ratio of November FAC to DJF melt are important predictors of SGL volume. This suggests potentially large increases in SGL coverage and volume should be expected under increased atmospheric warming, meaning SGLs are likely to spread to ice-shelf regions vulnerable to hydrofracture34 to all available Landsat 8 imagery (30 m resolution) from 2014 to 2020. To obtain a record of SGLs around the peak of the melt season, we selected images from January of each year, which typically coincides with the peak of the austral summer around marginal regions of East Antarctica29, that were acquired at sun elevation angles greater than 20°. We used imagery from the month of January because our aim is to produce a comprehensive, consistent comparison of maximum total surface meltwater around the EAIS over this seven-year period, rather than focusing on its temporal evolution over the entire melt season. Quantifying this interannual variability in surface meltwater is an important step in advancing process understanding of which ice shelves may be closer to potential thresholds of meltwater-induced hydrofracturing. Our work also extends previous work15 which provided the first ice sheet-wide assessment of supraglacial lake areas at the peak of the melt season for a single melt season (January 2017). Though late-onset melting on some ice shelves in some years may result in surface runoff peaking later in the melt season, we find lake areas and volumes from Landsat 8 imagery typically peak between mid-late January during our study period, after which lakes typically start freezing over. Including imagery from February is therefore unlikely to have substantially changed our maximum lake area and volume estimates. Furthermore, regional climate models have been shown to simulate peak surface runoff in January between 2005 and 2020 over the Amery Ice Shelf57 and several other studies also note that maximum meltwater extent occurs in mid-late January29. Including the rest of the melt season (December and February) in our analysis would also have been significantly more computationally expensive. We focus here on Landsat 8 (launched in February 2013) rather than including imagery from Landsat 7 or earlier sensors because the algorithm we use was fine-tuned using a training dataset of 15 Landsat 8 images covering a wide range of illumination conditions, cloud cover, geology, and spectral characteristics34. In total, we processed 2175 Landsat 8 images. On each ice shelf or coastal region where SGLs form, useable images (i.e. those not completely obscured by cloud cover) covered on average 24 days in January from 2014 to 2020, and was highest on Amery Ice Shelf (29 days) and lowest in Wilkes Land (9 days) /Blue (Band 2) ratio. Further thresholds are applied to exclude cloud shadows and shaded snow areas. Threshold values were determined by creating a training dataset based on selected Landsat 8 images. Using these thresholds, binary (i.e. meltwater and non-meltwater) masks are created for each Landsat 8 scene. Areas <5 pixels (30 m resolution) in total and linear features that are narrower than 2 pixels are removed from these masks to avoid ambiguous classification of mixed slush as surface meltwater60.The pixel-based classification combines separate threshold-based algorithms to detect (1) surface meltwater, (2) clouds, (3) exposed rock outcrop and (4) seawater. The full details are discussed comprehensively in ref. 62. To quantify cloud cover for January of each year around the EAIS margin, we extracted and combined all polygons classified as cloud and counted overlapping features in ArcGIS Pro  ‘Polygonize’ utility to create vector polygons (shapefiles) of each masked Landsat 8 scene, where regions sharing a common pixel value  are assigned this value as an attribute. For January of each year, we extracted and combined all polygons classified as SGL in the Geographic Information Systems package ArcMap. We include a small number of SGLs forming on landfast sea ice attached to ice shelves, as this is a perennial feature around the ice sheet and experiences surface melt ponding34 recorded an accuracy of >94% when validating SGLs classified using our method against manually-digitised SGLs. Finally, we created maximum lake area masks for January of each year (i.e. containing pixels that were classified as lake on at least one day in January) by stacking and merging lake outlines for all dates within January for which we were able to classify lakes. We did this to be able to calculate maximum lake volume masks (below) due to temporally varying satellite paths and/or variable cloud cover around the EAIS margin63. We used the REMA (Reference Elevation Model of Antarctica64) mosaic (200-m resolution) to extract SGL elevations. To quantify SGL recurrence, we used the ‘Count Overlapping Features’ tool in ArcGIS Pro.In the absence of in-situ validation data, we manually verified our classification results for all 2175 Landsat 8 images and removed any false positives . These false positives were often distinguishable by their ‘diffuse’ boundaries, as opposed to distinct lake objects. False positives tended to be minimal  on large ice shelves such as Amery, Roi Baudouin and Riiser-Larsen, whereas they were much higher (up to 95%) in high-elevation regions with exposed nunataks and dirty ice, in particular Victoria and Oates Land. Therefore, manual post-processing was necessary to avoid over-prediction of SGL occurrence in these regions. ref. 66. This model is based on the rate of light attenuation in water and makes a number of assumptions, including that the lake bottom has a homogenous albedo, that there is little to no particulate matter in the water column to alter its optical properties, and that there is minimal wind-induced surface roughness66. For January of each year (2014–2020), we created a maximum lake depth mask by assigning all water pixels in the maximum lake area mask a depth equal to the maximum water depth observed out of all images during January following ref. 63, using the Cell Statistics tool in ArcGIS. Spatiotemporally variable satellite image acquisition and/or variable cloud cover around the EAIS margin mean that, for different ice shelves, lake depths are calculated on different days in January  to create a final maximum lake volume mask.We calculated lake depths and volumes using a physically-based model that has been widely used in similar studies of SGLs in Greenland and Antarctica2m), skin temperature (Ts), and surface net solar radiation (amount of direct and diffuse incoming shortwave solar radiation minus the amount reflected by the Earth’s surface), as well as mean preceding winter snowfall   means of the following ERA5 reanalysis variables: 2-m air temperature (Tr) Table . ERA5 grr) Table . This wa57. The sample sizes in these preliminary correlation analyses are typically limited by the length of the satellite record, as in this study. We note that even if we had extended our observational period back in time to also include the Landsat 7 record (i.e. from 2000 to 2020 rather than from 2014 to 2020), which would require significant computational resources, the number of data points in each linear regression would still be <30.We perform parametric correlation analysis because we wanted to test the degree to which different climatic and surface conditions influence interannual variability in total SGL area and volume. Furthermore, we wanted to assess whether these variables simulated by ERA5, the CFM and MAR  can be used as a first-order prediction of SGL area and volume over Antarctic ice shelves, as this is one of the first studies to make such comparisons with this range of variables. Several previous studies have performed Pearson correlation to assess potential drivers of interannual variability in observed total lake area, including with modelled seasonal snowmelt, surface air temperature, and firn air content32): mean January firn air content in the upper 10 m (FAC), mean DJF FAC, mean November FAC, mean January and DJF total runoff, mean January and DJF shallowest ice lens depth. We also conducted individual linear regressions of total lake area and volume with mean January and DJF surface melt simulated by the regional climate model MARv3.11 (hereafter MAR33), as well as the November FAC-to-DJF surface melt ratio. For each variable, monthly means of daily firn model outputs were extracted from the closest MAR grid cell to 11 locations  and showed good performance in capturing firn air content at these calibration sites68. Full details of ArMAP are discussed in ref. 52. In situ firn density measurements can be subject to measurement uncertainty of ~10%67 which can translate into the CFM results. The CFM allows meltwater infiltration, retention and refreezing in the firn using a bucket scheme, where meltwater is allowed to percolate through the firn52. Meltwater is assumed to run off as liquid water that is available to form SGLs once it reaches the depth below which density remains greater or equal to 830 kg m3 (i.e. the pore close-off depth). This means meltwater can bypass discrete ice lenses within the firn column, representative of firn evolution on a large horizontal scale. Volumetric irreducible water content (liquid water held by capillary forces) was set to 2% of the pore volume. Meltwater is refrozen in a model layer only if the layer has sufficient pore space, or else percolates down until it reaches a layer of maximum density. Each simulation consists of a spin-up by repeating a reference climate until reaching a firn column in equilibrium. The reference climate is taken as 1979–2009 with a spin-up duration of 120 years.The CFM uses the Ar33. MAR has been extensively used to study Antarctic surface mass balance and surface melt72. An extensive description of the MAR set-up for Antarctica can be found in ref. 33. MAR is forced at its atmospheric lateral and upper boundaries with temperature from the ERA-Interim reanalysis from the ECMWF73 and is run at a 35-km horizontal resolution. Compared to previous versions, MARv3.11 uses an improved ice-sheet mask based on Bedmap274 that includes rock outcrops, enabling potential enhanced melt-albedo feedbacks around exposed rocks23. Uncertainties in MAR are difficult to quantify and propagate from ERA-Interim, which has shown a warm bias of +3–6 °C over the Antarctic plateau75. The previous model version (MARv3.10) was found to underestimate surface mass balance over ice shelves and at low elevation coastal regions of Antarctica, though less so than other regional climate models such as RACMO2.3p271. The firn model used by MAR resolves the upper 20 m of firn across 30 layers33, whereas the CFM resolves between 100 and 200 m of firn across approximately 2000 layers32. Therefore, we use the CFM to simulate FAC, runoff and ice lens depth rather than MAR because it is able to resolve snow/firn density changes at a higher vertical resolution and with an increased level of detail in the model physics.The CFM was forced with temperature, snow accumulation and melt rates from MAR76. This limitation is unfortunately inherent to the most state-of-the-art firn models to date. The firn meltwater Retention Model Intercomparison Project (RetMIP) compared densification and meltwater dynamics from simulations of nine different firn models at four weather station sites on the Greenland Ice Sheet76. The CFM took an active part in this intercomparison project. RetMIP quantified uncertainty in modelled runoff by using inter-model spread. It was shown that runoff uncertainty is strongly dependent on total melt rates. At the Dye-2 site, the runoff uncertainty was estimated at 13%76. Dye-2 shows melt rate conditions (150 m.we yr−1) and firn structural features (formation of ice lenses) comparable to conditions on East Antarctic ice shelves. Therefore, we believe that a 13% uncertainty value on CFM-computed runoff is a sensible estimate.We acknowledge that the current handling of meltwater retention, refreezing and runoff in high-melt areas is currently a limitation of firn modelsIt is important to emphasise that we do not aim to quantify absolute amounts of surface runoff, but rather year-to-year variability in runoff, surface melt, firn air content and minimum ice lens depth to allow us to investigate these as potential controls on the interannual variability in SGL area and volume. For this reason, possible model biases in these variables do not affect our interpretations if these biases are consistent in time.71 validated MAR’s performance relative to observed surface mass balance. On the ice shelves, MAR showed the lowest bias among the six climate models investigated. Also, the ice shelf surface mass balance simulated by MAR was closest to the ensemble mean, thus likely a good representation of current model estimates. We note that the standard deviation of the intercomparison in ice shelf surface mass balance was 77 Gt/yr, corresponding to 19% of the modelled surface mass balance. Although not directly comparable, this figure can be used as an estimate of melt rates model uncertainty on the East Antarctic ice shelves.MAR surface melt has yet to be specifically validated in detail over coastal East Antarctica and model validation is hindered by the scarcity of published observations of surface melt and runoff, particularly around coastal East Antarctica. In the framework of an Antarctic-specific climate model intercomparison, ref. Supplementary InformationPeer Review File


Calluna vulgaris and Holcus lanatus) at two sites in Germany; a colder and wetter upland site with high snow accumulation and a warmer and drier lowland site, with low snow accumulation. Mesocosm monocultures were set-up in spring 2010 at both sites (with soil and plants originating from the lowland site). In the following winter, an experimental warming pulse treatment was initiated by overhead infrared heaters and warming wires at the soil surface for half of the mesocosms at both sites. At the lowland site, the warming treatment resulted in a reduced number of days with soil frost as well as increased the average daily temperature amplitude. Contrary, the treatment caused no changes in these parameters at the upland site, which was in general a much more frost affected site. Soil and plant roots were sampled before and after the following growing season (spring and autumn 2011). High-throughput sequencing was used for profiling of the root-associated fungal (ITS marker) community (mycobiome). Site was found to have a profound effect on the composition of the mycobiome, which at the upland site was dominated by fast growing saprotrophs (Mortierellomycota), and at the lowland site by plant species-specific symbionts (e.g. Rhizoscyphus ericae and Microdochium bolleyi for C. vulgaris and H. lanatus respectively). The transplantation to the colder upland site and the temperature treatment at the warmer lowland site had comparable consequences for the mycobiome, implying that winter climate change resulting in higher temperature variability has large consequences for mycobiome structures regardless of absolute temperature of a given site.Winter temperatures are projected to increase in Central Europe. Subsequently, snow cover will decrease, leading to increased soil temperature variability, with potentially different consequences for soil frost depending on e.g. altitude. Here, we experimentally evaluated the effects of increased winter soil temperature variability on the root associated mycobiome of two plant species (The online version contains supplementary material available at 10.1007/s00248-020-01667-7. Calluna vulgaris and Holcus lanatus, were planted in monoculture mesocosms and winter soil temperature was experimentally manipulated  [Winter soil temperature is an important driver for many ecological and biogeochemical processes  and was  (2014b) ).C. vulgaris and H. lanatus). Such symbioses and other plant properties e.g. exudates and root morphology [Both plant species are known to form mycorrhizal associations  and increased variability of soil temperatures, but not significantly affecting the number of freeze-thaw cycles (8/7 and 5/6 cycles across winter 2010/2011 for ambient/warming treatment at the lowland and upland site respectively) . LikewisMany microbial organisms are sensitive to frost-stress , 19 withBased on these previous findings Table , we expeHolcus lanatus and 2-year-old plants of the shrub Calluna vulgaris  were planted in May 2010 in monoculture mesocosms  which were then set up at two sites; the lowland site located in the Ecological-Botanical Garden of the University of Bayreuth, Germany  and the upland site located at the Waldstein mountain in the Fichtelgebirge, Germany . The soil substrate was a natural soil consisting of homogenized loamy sand  from a sand quarry nearby the lowland site where both plant species naturally occur, with a pH = 7 and a total carbon content of 2.4%. The barrels were attached with outlet hoses at the bottom of each mesocosm, so that the mesocosms functioned as zero tension lysimeters. In the following winter, warming pulses were administrated between December 2010 and February 2011. Warming pulses were started when weather forecast predicted air frost (at both sites) for at least 48 h in which the warming pulses took place. Each factorial combination (species by warming treatment by site) was replicated five times. Soil (− 2 cm depth) and air temperature  were measured hourly by thermistores  connected to a datalogger . The upland site was generally colder, experiencing lower annual air temperatures and higher annual precipitation  . In briel. 2014b  and supp4b . In bThe experimental design and sampling is summarized in Fig. For this study, samples were collected on March 14, 2011, and December 1, 2011 (during plant dormancy before and after the growing season following the warming pulses treatment). From each mesocosm, five soil cores (2 cm diameter) were collected and fine roots were taken from the cores by hand and frozen at − 80 °C.−1 for 60 s. DNA was extracted using the Charge Switch gDNA Plant Kit (Invitrogen) for details see Kreyling et al. (2012) [A bead mixture  was added to approximately 1 cm of fine roots for homogenisation in a FastPrep Instrument  at 6 m s. (2012) .An ITS amplicon library was constructed using the fungal specific primers ITS1F  and ITS4www.hannonlab.cshl.edu/fastx_toolkit/). The trimmed ITS1 reads, with retained quality scores, were subjected to CD-HIT-OTU [www.weizhongli-lab.org/cd-hit-otu/). An OTU  table, which lists the number of reads in each cluster for each sample, was generated by applying a 97% similarity threshold. OTUs were assigned to taxa using QIIME and the UNITE database v7 [Using the QIIME pipeline  sequence-HIT-OTU  for clus-HIT-OTU   and treatment . PERMANOVA  was performed using Bray-Curtis (BC) dissimilarity matrix of the Hellinger-transformed fungal community with 1000 permutations. Experimental parameters were as follows: site (upland and lowland site), sampling time , plant species (variance ) analysiIn total, 1077 OTUs were obtained from a 97% sequence similarity clustering; of these, 963 were classified as fungal and were considered for further analysis Table . Of the p < 0.001) and treatments , ‘site’  and the experimental ‘treatment’ (warming pulses) accounting for these axis  larger fungal diversity was observed for H. lanatus compared to C. vulgaris mesocosms , among classified OTUs Sordariomycetes (15.2%) and Helotiales (11.5%) were dominant Fig. . Second Of the 922 OTUs assigned to minimum phyla-level, 651  could be assigned to a functional group using the FUNguild classifier Table . On the Mortierella, a pattern consistent when summarized at genera level  ericae [H. lanatus though no arbuscular mycorrhizal fungi was among the dominant taxa, both Microdochium bolleyi and Chaetomium sp. were dominant at the lowland site but highly reduced at the upland site. Both these taxa (M. bolleyi and Chaetomium sp.) are known as beneficial root associated fungi involved in pathogen protection for the grass family Poaceae and other herbaceous plant species [H. lanatus  ericae , 48 whic species –53. A nu grasses ) and C. y plants , 56) at Surprisingly, the plant-specific mycobiome patterns were strongly diminished at the upland site for both ambient and warming treated mesocosms Fig. . Since nThe mesocosm design allowed us to establish and contain systems in a close-to-natural fashion, making the results more relatable to natural ecosystems compared to laboratory experiments. However, being that it is an only partly controlled design, several environmental parameters will differ. Thus, it cannot be ruled out that other factors besides temperature contributed to the observed changes in the mycobiome between the upland and lowland site. For example, the mean winter precipitation is generally higher at the upland site , which wMortierella but also Pseudogymnoascus verrucosus, the latter a psychrotolerant soil saprotroph [The plant symbiont species were diminished and instead, saprotrophic fungi were dominant at the upland site, with a large dominance of members of the genus protroph . Coincidprotroph , 18). Thprotroph . This inprotroph –62.Fusarium oxysporum, a species found to be highly sensitive to freezing under laboratory conditions [The present study only targeted the fungi and thus nothing is known about the dynamics of the remaining members of the microbiota in the soils. The degree and frequency of soil freezing have been reported to explain microbial  community composition in natural  soil ecosystems –65. In onditions  and whicnditions , 67. Kufnditions  found nonditions , 60, 68.nditions  while otnditions . Likewisnditions  and finanditions  found thnditions , 73. OurC. vulgaris at the upland site, suggesting that the lack of symbiont R. ericae did not reduce the fitness of the host plant. Both C. vulgaris and H. lanatus are common plant species in temperate and sub-arctic ecosystems and should have no problem prevailing at the mountainous upland site. Although studies have shown that both arbuscular and ectomycorrhizal fungi can survive hard frost events [C. vulgaris) have been reported to decline with increased latitude and altitude [Rhododendron (Ericaceae) with increased altitude and latitude across Europe. Common for these studies, however, is that the shifts are generally seen at considerably colder regions than our upland site . Thus, the reduction in abundance of the ericoid fungi R. ericae at the upland site might be related to other parameters than temperature per se. The host plant C. vulgaris might be capable of an efficient regulation of its mycorrhizal associations under favourable environmental condition (such as N availability) allowing the plant to put its carbon resources in to growth rather than supplying the mycosymbiont. A reduction in mycorrhizal associations under high nutrient availability has previously been reported for ecto- and arbuscular mycorrhizal associations [A positive growth response was reported for t events , ericoidaltitude , 76, andaltitude  reportedciations –82. ExpeI. macrodidyma and F. oxysporum/G. zeae/G. avenacea for C. vulgaris and H. lanatus respectively) which might contribute to explaining the positive growth response previously reported for both plant species at this site [I. macrodidyma was reduced under the warming-pulse treatment at the lowland site, and also here a positive growth response for C. vulgaris was reported [H. lanatus, where the abundance of the plant-beneficial fungi M. bolleyi decreased under the warming-pulse treatment  and winter climate (not summer) have the potential to drive drastic changes in the mycobiome, with ecosystem-scale consequences for mineralisation and plant growth. The results also suggest that the mycobiome responds similarly to a combination of cold temperature stressors i.e. fluctuations (including number of freeze-thaw cycles), rate of freezing, absolute minimum temperature and duration of freezing. More research is needed to identify specific thresholds for these stressors as well as microbial community structure resilience.Supplementary Material 1Precipitation data. (XLSX 79 kb)Supplementary Material 2OTU tables. (CSV 163 kb)Supplementary Material 3DNA- and sample-based OTU accumulation curves. (PDF 253 kb)Supplementary Material 4Analysis script. (R 44 kb)Supplementary Material 5Raw results incl. parameter interactions. (TXT 37 kb)Supplementary Material 6Bar chart showing individual sample variance. (PNG 240 kb)Supplementary Material 7Heatmap of summarized community (genus level). (PDF 132 kb)


Internally displaced persons fleeing their homes due to conflict and drought are particularly at risk of morbidity and mortality from diarrhoeal diseases. Regular handwashing with soap (HWWS) could substantially reduce the risk of these infections, but the behaviour is challenging to practice while living in resource-poor, informal settlements. To mitigate these challenges, humanitarian aid organisations distribute hygiene kits, including soap and handwashing infrastructure. Our study aimed to assess the effect of modified hygiene kits on handwashing behaviours among internally displaced persons in Moyale, Ethiopia.The pilot study evaluated three interventions: providing liquid soap; scented soap bar; and the inclusion of a mirror in addition to the standard hygiene kit. The hygiene kits were distributed to four study arms. Three of the arms received one of the interventions in addition to the standard hygiene kit. Three to six weeks after distribution the change in behaviour and perceptions of the interventions were assessed through structured observations, surveys and focus group discussions.p-value = 0.348). In the mirror and scented soap bar intervention arms, HWWS prevalence was 11 and 10%, respectively. This was indicated to be significantly different from the control arm. Participants in the focus group discussions indicated that liquid soap, scented soap bar and the mirror made handwashing more desirable. In contrast, participants did not consider the soap bar normally distributed in hygiene kits as nice to use.HWWS was rare at critical times for all study arms. In the liquid soap arm, HWWS was observed for only 20% of critical times. This result was not indicated significantly different from the control arm which had a prevalence of 17% .The trial was registered at The online version contains supplementary material available at 10.1186/s13031-021-00368-3. Crisis-affected populations are at increased risk of diarrhoeal morbidity and mortality  and in cThe seemingly simple act of handwashing with soap (HWWS) is associated with a 23–47% reduction in diarrhoea morbidity and up to a 25% reduction in respiratory illness . ConveniHumanitarian crises differ from stable settings in other important ways. In a crisis, humanitarian actors typically provide hygiene items to populations rather than assuming communities can provide this themselves (as is the case in stable settings) . The SphHygiene kits aim to reduce the risk of disease transmission by encouraging the increased practice of hygiene behaviours at the household level. However, there is limited evidence about the acceptability of hygiene kits, the use of hygiene kit products by crisis-affected populations and the effect of hygiene kit distributions on behaviour or health outcomes , 21–23. Internally displaced persons (IDPs) residing outside of camps are systematically less studied due to the complexities of researching in these settings. A 2020 systematic review of all WASH literature published about crisis-affected settings, reported that only 17% related to populations residing outside of camps, and 41% relates to IDP populations . This isOur study aimed to explore the potential to increase HWWS soap at critical times among IDPs living in an out-of-camp setting using locally available and rapidly deployable hygiene kit interventions. The pilot was also designed to explore if minor modifications to the quality of hygiene kit products could make HWWS more desirable and increase the likelihood it’s practised at critical times. We tested the inclusion of a scented soap bar, liquid soap or mirrors within the hygiene kits distributed by Action Against Hunger (AAH). The soap bars tested differs from standard soap bar included in AAH’s hygiene kits in three important ways: the soap was scented (whereas their standard soap bar was not), the soap had olive oil extracts in it, which was intended to make hands feel smoother after use (according to the manufacturer) and the cost of the soap was higher than the standard soap bar. The desire to smell nice has been found to motivate HWWS and therefore this was included as an intervention in our study . Our ratThe research took place between September and November 2019 in Moyale District, southern Ethiopia. At the time of the research, the district faced a protracted and complex emergency due to prolonged drought and armed conflict . RegularA substantial influx of IDPs had settled within and around existing villages in Moyale district. There were no official displacement camps at the time of the study. As of October 2019, an estimated 110,000 IDPs were thought to be living in the area . IDPs arMoyale district was chosen as the study location due to the large influx of IDPs in the area, the identified need for a WASH intervention  that wasThis study was an exploratory pilot study with three intervention arms and one control arm. The villages for the study were purposefully selected by the Moyale District Disaster Risk Management Office who provided lists of 100 IDP households in each study site based on the following criteria: 1) at least 100 IDP households in the area, 2) the area was safe for the research team to work in. The other selection criteria proposed by the study team were not possible to fulfil. This included a selection process where the study team could access maps of IDP households to allow for clustering before intervention distribution and the provision of a full list of all IDP households in the study area. Due to study site constraints, randomisation at a household or cluster level was not possible. As a consequence of the lack of clustering, this study deviated from the registered trial design for the study and was not a cluster-randomised controlled trial but an exploratory pilot study. The four study arms were randomised to receive one of the three interventions or the control using a random number generator. Figure All four study arms received the interventions between the 18th to 24th September 2019. The contents of the hygiene kit received by each household were based on Sphere Standards for Humanitarian Action  and stanImplementation of the intervention was conducted by AAH hygiene promotion staff. These individuals were not involved in any other study procedures and were unaware of the planned process for the data collection on HWWS behaviour. A record was compiled of all households receiving the kits. Hygiene promoters were instructed to assist households in IA3-Mirror to hang up the mirror next to the handwashing facility. All households were encouraged to build a stand for the handwashing facility and to keep the soap near the facility at all times.Our study’s primary outcome was the prevalence of handwashing with soap at critical events  by IDP household members of any age. The secondary outcomes were the perceived acceptability and desirability of the interventions among the IDP population in our study area.Handwashing events were measured through three-hour long structured observation sessions. The sessions took place from either 7:30 am to 10:30 am or 8:30 am to 11:30 am (depending on varying daily security restrictions). The research assistants were trained to document critical handwashing opportunities which were defined as 1) after using the latrine or open defecation, 2) after cleaning a child’s bottom, 3) before food preparation, 4) before eating, 5) before feeding a child or serving another person food. The research assistants captured the time of the event and if soap was used. Missed opportunities for handwashing at critical times were also captured. The structured observations were conducted at 50 households (out of the 100 that received the intervention) within each study arm. Participating households were drawn randomly (using a random number generator) from the sampling frame developed by the study team of all households who received the intervention. If the selected household could not be located on follow-up, a new household was randomly selected for data collection. A household survey was administered once the observation was concluded to capture sociodemographic data, hygiene proxy indicators  and percFocus group discussions (FGDs) were conducted in parallel with the quantitative data collection. Participants were randomly selected using a random number generator from a list of eligible participants. The list included households that received a hygiene kit in any of the three intervention arms but were not selected for structured observations or surveys. The FGDs aimed to explore barriers to handwashing and the acceptability and perceived usefulness of the hygiene kit products. Three FGDs with men and three FGDs with women were carried out, each with 4–8 participants. An FGD topic guide was developed . All the data collection staff were women. The research assistants received one and a half-day training by AHT on the study methods and then practised structured observation within the classroom and in a pilot study site. The research team was not connected to the intervention delivery. All data were captured on printed paper forms. At the end of each day of data collection, the lead author (AHT) checked all surveys and structured observation forms. If AHT found any discrepancies, the team would return to the household the following day to correct any inconsistencies. One member of the data collection team acted as a field supervisor and conducted spot checks of research assistants during data collection for quality control.Data from structured observations and surveys was double entered into Microsoft Excel and cleaned. The data were checked to identify discrepant entries against original paper surveys, and consistency checks were completed. Descriptive analysis was conducted on observational data and survey data in Stata 16 . Bivariate analysis (chi-square) was used to compare intervention arms with the control arm. This deviated from the original study protocol that described regression analysis, as the data collected was not clustered due to study constraints. Handwashing prevalence was calculated as the percentage of events at which hands were washed with soap before or after a critical time .FGDs were recorded, transcribed and translated from Afaan Oromo to English. The transcripts underwent thematic analysis informed by the methods outlined by Braun and Clarke  and condThe research received ethical approval from the London School of Hygiene and Tropical Medicine Ethics Review Committee (Ref: 17604) and Oromia Regional Health Bureau (Ref: BEFO/11BTP4/79/2011). The study was also approved by the Disaster Risk Management Office and Health Office at the zonal level (Borena) and district level  through face-to-face meetings with the study coordinator and AAH representatives.In total, 400 households received a hygiene kit, of which 203 households were enrolled for structured observations and surveys. Despite the study areas being selected for their similarity, we found variation within population demographics. The control arm was a mixture of people of the Borena and Gabbra ethnic groups. In contrast, the populations in the intervention arms consisted entirely of people from the Borena ethnic group. IA3-Mirror was located 13 km from the main road in an area more affected by drought and flooding, while the other study arms were located along or within 1 km of the main road. Six of the randomly selected households in IA3-Mirror were not available for data collection as floods hindered data collectors from reaching the households. Four households in IA1-Liquid and six households in IA2-Scented were not available on follow-up because ongoing economic hardships, drought and conflict had caused them to move on. Unfortunately, the variations between study arms were not identified before the study due to security limitations in accessing the sites.Animal ownership was over 90% in the three intervention arms, but only 54% in the control arm. The control arm results showed slightly lower educational attainment rates and household income. People in the control arm spent more time queuing to access water compared to the intervention arms. The majority of participants in all study arms had received no formal education. The mean number of people per household was similar across all study arms. All households in IA1-Liquid and the control arm were Muslims, while IA2-Scented and IA3-Mirror participants were Protestants, Muslims or practiced Wakefata . The majority of respondents were displaced due to conflict, but 8 and 30% of respondents in IA2-Scented and IA3-Mirror respectively were displaced due to drought (Table Table Table p = 0.348). In IA3-Mirror and IA2-Scented, HWWS prevalence was 11 and 10%, respectively, which was lower than in the control arm, indicating that distribution of mirrors and scented soap may have had a negative effect on HWWS prevalence. Exploratory statistical analysis suggests that this negative effect may be significant when compared to the control arm (p = 0.005 in IA2-Scented and p = 0.018 in IA3-Mirror).In total, 1458 opportunities for handwashing were observed by our research team “In the old times, the problem was illiteracy. Nowadays though, everyone including the kids have the knowledge [about handwashing]. But people are different, in that some are tidy while others don’t care a lot about cleanliness. But I can generalise and say the main problem is the lack of money for soap affordability.” “There are variety of challenges, among which affording soap is an issue. People also don’t buy soaps on a regular basis in the same way they buy other home goods when they run out of it. So people also don’t look at soaps as a priority” In addition, to affordability forgetting to do HWWS or only doing it when absolutely necessary were mentioned as reasons for not washing hands regularly. Some people reported only washing their hands when they were visibly dirty or when participants had been in contact with chemicals such as paint.“Some cannot afford soaps. The other factor is people’s style of life. Some are not used to washing with soap after using the toilet, they don’t remember to wash their hands with soaps after normal routines except when we deal with some rare activities where the need of using soap become a necessity, like after painting.” IDPs shared that humanitarian organisations sometimes provide soap in hygiene kits and do hygiene promotion in the area. The irregularity of distributions appears to have created variations in handwashing behaviour, with populations often resorting to handwashing with ash or not handwashing at all, when distributions ceased. In addition, IDPs mentioned the long distances from their houses to shops and markets as barriers to purchasing soap regularly.“We do not get soap distributions regularly. We used to wash our hands properly when the supplies were given to us, but once they were done with the distributions, we could not go out and buy soap because of money issues.” Water scarcity was frequently raised, with participants explaining that water was prioritised for other household tasks rather than HWWS.“In this zone when water becomes scarce, people don’t even wash their faces, let alone washing hands, so water shortage could be a reason” “Due to drought, famine, and conflict in our area, there is a water and money shortage which means we don’t have enough water for handwashing and money for affording soap, even though we have the knowledge about cleanliness.” A total of 33 people participated in the six FGDs. When asked about current barriers to HWWS the most common challenge was the affordability of soap. Participants made it clear that knowledge was not the problem as most people knew about the importance of handwashing to protect them against disease and maintain their health.Table “This mirror is big enough to show the all of my body. This is why we say it’s so good.” “I like the way it allows me to see my whole self, what I don’t like about it is the money I lack to get such a mirror,” When asked where they would place a mirror like this, most participants said a nice mirror like this should be kept inside the house. Participants expressed concerns about keeping the mirror outside because they believed that the sun’s reflection shining onto the mirror was harmful to their health. They were also concerned that it might get stolen or that children or animals might break it.“When it is sunny, the mirror gives out a reflection which is not good for our health. It might get stolen too, cattle might break it also” “It should not be kept outside because it might get broken, it is meant to be inside the house.” The majority of participants said they would not want to keep the mirror by the handwashing facility, as this was often located near the toilet, some distance from the house.“Firstly, that place is at a distance from our house. Secondly, children might just grab it away, the other factor is that our toilet has no suitable wooden place where we can hang the mirror.” Nonetheless, participants did see that there could be some benefits by keeping the mirror next to the handwashing facility. Some participants said keeping the mirror close to the toilet would allow them to identify dirt and make cleaning themselves an easier task. Some also reported that if they had two mirrors, they would consider keeping one in the household and one by the handwashing facility.“Yeah it has a benefit and that is that after toilet usage we would stand there to see which part to clean and wash our hands and our face.” “It shows me the cleanliness of my body, for example, after toilet usage, it shows me whether I have gotten rid of the dirt or not.” “It can show you dirt. Had we had other extra mirrors, we would spare one for that spot.” The last part of the FGD aimed to understand community perceptions towards mirrors placed in close proximity to handwashing facilities. The mirror was very well received by the participants who valued the mirror’s size, reflecting that it would allow them to see their entire bodies and not just their faces. The only thing participants listed as a concern about the mirror was that they did not think it would be affordable for them to buy themselves.Our study could not detect an effect of providing modified hygiene kits compared to households who received the standard hygiene kits on handwashing behaviour in IDP populations. The HWWS prevalence data indicate a slightly higher prevalence of HWWS at critical times for the households that received liquid soap in addition to the standard hygiene kit. However, the prevalence was not significantly different to HWWS prevalence in the control arm. The standard soap bar distributed in hygiene kits, was not considered very desirable to use by the participants of the FGD. Instead, liquid soap was considered the easiest type of soap to use. It is possible that the increased prevalence of handwashing with soap observed within IA1-Liquid occurred because liquid soap together with a dedicated handwashing facility helped to cue behaviour at the right time and make it more convenient for the population to practice. This was particularly the case for handwashing after using the toilet, given that most families chose to locate their handwashing facilities near the toilet.The scented soap bar was generally well-liked by FGD participants and considered desirable and pleasant to use. However, prevalence of HWWS was significantly lower in IA2-Scented compared to the control arm. In FGDs, participants reported that they had never seen a green bar of soap before, and it is possible that this new, foreign type of soap caused participants to use it more sparingly or prioritise it for purposes other than HWWS. In refugee camp setting, reliance on soap distribution may have led to refugee households using soap sparingly, not knowing when the next distribution might occur . ParticiIn IA3-Mirror, we found that the distribution of mirrors and the placement of these above the handwashing facility did not result in prevalence of HWWS that were higher than the control arm. Similar ‘nudges’, or environmental cues in other studies have been successful in increasing handwashing with soap after toilet use. However, most of these interventions have been tested in schools or areas where there is already good quality infrastructure and a constant supply of soap and water - something not available in the IDP settlements of Moyale District , 44. ChaThe soap products distributed in this study did not come at a much higher cost; 250 g of the soap bar normally distributed in the hygiene kits would cost 0.9 USD, while a scented soap bar of the same size cost 1.65 USD and a 500 ml bottle of liquid soap is 1.48 USD making it a feasible intervention to implement by humanitarian actors.This study was designed without a baseline assessment of handwashing behaviour. This was due to logistical, budget and security constraints, but also encouraged by other behaviour change intervention studies that recommended no baseline observation to reduce reactivity bias . ReflectIt is also possible that it was the distribution of the handwashing facility itself, rather than soap or mirrors, that made a more substantial contribution to encourage handwashing behaviour (both with water only and with soap). However, this study was not designed to measure this. In other settings, the presence of a dedicated handwashing facility has been found to increase handwashing behaviour . In our Our study observed a high prevalence of handwashing with water only despite the availability of soap in households . Given that we faced limitations in drawing conclusions from our primary outcome data, it was valuable to learn from the complementary qualitative methods, which should be included in future research in such environments.We found no evidence of increased handwashing with soap prevalence among IDPs after distribution of modified hygiene kits. However, this pilot study indicates that there is likely to be some value in understanding crisis-affected populations’ hygiene product preferences and modify hygiene kits accordingly. In our study, provision of liquid soap resulted in the highest prevalence of handwashing at critical times, and the mirror and scented soap bar that was distributed was desired by participants. Given the limitations of this research, we recommend further studies into the use of hygiene kit products prior to major changes in humanitarian practice.Additional file 1. Structured Observation Data Collection form.Additional file 2. Household Survey Form.Additional file 3. Focus Group Topic Guide.Additional file 4. Observed handwashing prevalence across all study arms, disaggregated by action taken and critical time for handwashing  in a four-arm exploratory pilot study in an internally displaced population in Moyale District, Ethiopia.


Sensorimotor coordination is thought to rely on cerebellar-based internal models for state estimation, but the underlying neural mechanisms and specific contribution of the cerebellar components is unknown. A central aspect of any inferential process is the representation of uncertainty or conversely precision characterizing the ensuing estimates. Here, we discuss the possible contribution of inhibition to the encoding of precision of neural representations in the granular layer of the cerebellar cortex. Within this layer, Golgi cells influence excitatory granule cells, and their action is critical in shaping information transmission downstream to Purkinje cells. In this review, we equate the ensuing excitation–inhibition balance in the granular layer with the outcome of a precision-weighted inferential process, and highlight the physiological characteristics of Golgi cell inhibition that are consistent with such computations. It involves interactions between an agent and its environment; like when a mouse is actively gathering information with its whiskers. In order to control these interactions, the brain must be able to approximate or predict the consequences of forthcoming action. This relies on accurate estimates of behaviourally relevant states (such as whisker position) generated by an underlying model of how states relate to one another. Estimates are intrinsically uncertain, reflecting stochasticity in sensory channels and dynamics of states. Hence, when considering the neural implementation of an estimation process, it is desirable that neural circuits are capable of representing estimates conditioned on their associated uncertainty; in other words, the underlying models ought to be probabilistic.The cerebellum has long been posited to instantiate probabilistic internal models for estimation of rapidly varying external states , whetherThese ideas are long-standing, but it remains unresolved how various components of the cerebellum could specifically contribute to state estimation. In general, only activity and plasticity of Purkinje cells, the output of the cerebellar cortex, have been associated with this computation; however, inferential processes occur all the way through the hierarchy of an internal probabilistic model. Here we consider the first step in cerebellar cortical state estimation, by proposing a role for inhibition in the granular layer. This network, comprising about half of the neurons in the mammalian nervous system, relays all extracerebellar input that is directed via mossy fibres (MFs) to Purkinje cells –10 figu. The graprecision of the information driving the inferential process, so that the excitation–inhibition balance in granule cell populations becomes the result of a precision-weighting process. This view ultimately affords a new interpretation of observed inhibitory mechanisms in the granular layer.We explore this possibility by discussing the role of Golgi cell inhibition in the context of state estimation in the cerebellar cortex, and set out a link between high-level theoretical descriptions of cerebellar computations and their neural substrates. We start from the assumption that neuronal activity encodes estimates or predictions of somatic and environmental states that enable guidance, coordination and refinement of action; then, we argue that Golgi cell inhibition promotes accurate state estimation, by adaptively tuning excitatory responses encoding those estimates. Crucially, such tuning rests upon  the . 2Many aspects of brain functioning can be phrased in terms of probabilistic inference and learning processes –15. In tIn biological neural networks, precision naturally translates into population gain , which swhat is represented can be different from those signalling how it should be represented. For instance, the identity and activity pattern of upstream neurons can be related to the nature of a stimulus encoded, whereas the postsynaptic gain to the amount of information transmitted. Second, the precision of an input, realized as population gain and ultimately translated into patterns of excitation–inhibition balance, is related to the concurrent behavioural context. This can be exemplified through attentional gain modulation in visual and auditory cortex )α6δ-subunit containing GABAA receptors [In vivo, tonic inhibition minimizes granule cell responsiveness to uncorrelated, temporally scattered inputs [A substantial component of inhibition is tonic, hinging on constantly activated extrasynaptic receptors that are responsive to ambient levels of neurotransmitter concentration . This peeceptors . In vivod inputs , while md inputs . Therefod inputs , might tin vivo, this could favour, for example, selective transmission of the synchronous and invariant component of MF stimuli to Purkinje cells, by virtue of its stronger impact on postsynaptic neurons. Moreover, inhibition can preserve temporal information in granule cell output by rapidly trailing excitation and forcing a sharp integration window of couple of milliseconds for excitatory post-synaptic currents [On top of a persistent inhibitory conductance, feedforward and feedback synaptic loops enable Golgi cells to dynamically modulate granule cells by following rapid variations in network activity —althoughcurrents . Overallcurrents –64; and currents .Along with temporal features of Golgi cell inhibition, the spatial arrangement of Golgi cell processes may also play a role in the contextualization of incoming information . NotablyThis contextual modulation of granule cell excitability relies on spatial constraints of information driving Golgi and granule cell populations, which in turn depend on different anatomical properties of the network. MFs show substantial anisotropic divergence in the granular layer ,67, whicAnother important anatomical property is the presence of millimetre-long granule-Golgi cell connections mediated by parallel fibres . ParalleIn vivo, the granular layer is characterized by endogenous activity owing to spontaneous firing of MFs and Golgi cells [Finally, precision-weighting for state estimation does not depend solely on properties intrinsic to the inputs, but also on selective mechanisms modulating states of the network. Analogously, Golgi cells are both driven by the same MF inputs that elicit activity in granule cells, and are influenced by neural components located within or external to the cerebellum. gi cells ,70,75; tgi cells –78. Fromgi cells , and inhgi cells . Moreovegi cells  and noragi cells , which eIn conclusion, there appear to be a variety of mechanisms that could inform the granular layer about precision of MF input, irrespective of the extremely diversified nature of those inputs. These mechanisms condition granule cell excitation through Golgi cell inhibition, which constitutes the unique local feedback of the network. In this sense, Golgi cells emerge as a crucial hub for precise state estimation in the cerebellar cortex .Figure . 4We have considered how putative state estimation in the cerebellar cortex relies on appropriately tuning of neural dynamics within the granular layer. In this picture, Golgi cell inhibition underlies selective responsiveness or gain of subsets of granule cell population to extracerebellar activity, controlling to what extent the latter drives state estimation. This is a form of input precision-weighting, and depends on information about precision being accessible to the network. Accordingly, several mechanisms related to Golgi cell functioning could provide this information, and therefore adapt granule cell population gain to input precision. In particular, we interpret the optimal signal-to-noise ratio set by tonic inhibition as a prior over expected signal precision, possibly defined by phylogenetic and ontogenetic processes. On the other hand, rapid changes in the excitation–inhibition balance could dynamically accommodate the relative precision of the excitatory drive as a function of its intrinsic properties and top-down modulations, hence controlling precision encoded in the granular layer output . The impArguably, neural dynamics underlying state estimation in the cerebellar cortex should necessarily be sensitive to uncertainty associated with inference, as discussed more generally in the context of cortical functioning ,27,88. CTesting these ideas requires both theoretical and experimental efforts. Here we have assumed a general principle, namely, that precision in neural representations should affect their propagation across the different stages of inference—by tuning population gain. However, future work should aim at investigating the exact nature of this probabilistic encoding throughout the cerebellar circuit. From the experimental side, testing these ideas requires tracking and manipulation of spatiotemporal properties of excitation–inhibition balance in the granular layer, as MF input is transformed into parallel fibre output. The exact shape of ensuing activity patterns depends on many factors, including kinetics variability at the Golgi-granule cell synapses , which m. 5The cerebellum is deemed to support behaviour through predictive processes, adjusting and refining interactions of the organism with its environment. These processes rely on internal models that capture consistent relationships between states of the system and are probabilistic in nature, taking uncertainty into account. Many data confirm this high-level description of the cerebellum, but it remains to be understood how this can emerge from activity of neural networks. In the present work, we link Golgi cell inhibition in the granular layer with mechanisms sensitive to the relative precision of MF input, and the ensuing excitation–inhibition balance in granule cells with a precision-weighted response.Click here for additional data file.