Batrachochytrium dendrobatidis (Bd) when tested against nine Bd isolates from two distinct lineages. Though we achieved strong growth inhibition between 70 and 100% for seven Bd isolates, two isolates appeared consistently resistant to inhibition, irrespective of probiotic strategy employed. We found no evidence that genomic relatedness of the chytrid predicted similarity of inhibition scores, nor that increasing the genetic diversity of the bacterial consortia could offer stronger inhibition of pathogen growth, even for the two resistant isolates. Our findings have important consequences for the application of probiotics to mitigate wildlife diseases in the face of extensive pathogen genomic variation.Host-associated microbes form an important component of immunity that protect against infection by pathogens. Treating wild individuals with these protective microbes, known as probiotics, can reduce rates of infection and disease in both wild and captive settings. However, the utility of probiotics for tackling wildlife disease requires that they offer consistent protection across the broad genomic variation of the pathogen that hosts can encounter in natural settings. Here we develop multi-isolate probiotic consortia with the aim of effecting broad-spectrum inhibition of growth of the lethal amphibian pathogen  Emerging infectious diseases pose a significant threat to human and wildlife health, and are responsible for driving declines in biodiversity around the globe . The lasBatrachochytrium dendrobatidis (Bd), which causes the lethal disease chytridiomycosis in amphibians  and one isolate of the more distantly related BdCAPE  probiotic consortia with higher mean genetic diversity will more likely elicit broad-spectrum inhibition of multiple pathogen variants; and (iii) Bd isolates that are more genetically similar will have more similar inhibition profiles across the panel of isolates tested.Here we examine patterns of inhibition of both individual bacterial isolates and consortia tested against a panel of nine isolates of Bd , comprisCAPE see . We seekAgalychnis spp. frogs at Las Cuevas Research Station in the Maya Mountains of Belize by permission of the Belize Forestry Department  based on preliminary screening of inhibitory capabilities; medium to strong inhibitors pf Bd’s growth were selected to maximize the potential inhibitory capabilities of consortia , two (“double probiotic”), or three (“triple probiotic”). Double probiotics were always constructed from two unique congeneric bacterial isolates. Triple probiotics were constructed from three bacteria either within the same genus  or across three different genera . These were grown in 1% tryptone broth until maximum zoospore production was observed . Three flasks per Bd isolate were grown and combined for in vitro challenges. Bd zoospores were separated from sporangia by filtering through 20 μl sterile filters . Bacterial probiotics were filtered  to isolate bacterial products in the filtrate, which were kept on ice until Bd challenges were conducted.Nine in vitro challenge assays, 50 μl of bacterial filtrate and 50 μl of Bd suspension were combined in 96-well plates. Each Bd -bacteria combination was run in triplicate. Positive controls were included using 50 μl 1% tryptone instead of bacterial filtrate. Negative controls were included using 50 μl sterile tryptone and 50 μl of heat-treated Bd for each isolate. The optical density (OD) was measured at 492 nm filter on a spectrophotometer on initial construction of the challenge assays, and every 24 h after for four subsequent days. For each measurement, data were transformed using the equation Ln[OD/(1-OD)], and a regression analysis was used to gain the slope values for each sample over time  = [1−(slope of sample/slope of control)] × 100 to give an “inhibition score.” A positive inhibition score represents inhibition of Bd growth and a negative score indicates enhanced growth of Bd. We did not use a nutrient depleted control in our experiments , which produced 125 + 125 bp paired-end sequenced reads using the Illumina HiSeqTM high output V4 chemistry. Data analysis was done using a previously defined Bd specific alignment pipeline and stringent variant discovery conditions .Extracted high molecular weight DNA of six nditions . A pairwbrms , Bd isolate and bacterial genus on inhibition scores, we fitted a linear mixed effects model (LMM) with inhibition as the response, probiotic type as a fixed effect, and Bd isolate and bacterial genus as random intercepts. As the dataset contained both positive and negative inhibition values, where a negative value represents facilitation of Bd growth, we used a Gaussian error structure to avoid having to remove the informative negative values. Ideally, we would have fitted a 2-stage Hurdle model to such data, simultaneously modeling the factors influencing the probability of Bd growth facilitation, and the magnitude of Bd inhibition | a positive inhibition value. However, these models were regrettably too complex for this dataset as the negative values were sparse and clustered within certain Bd isolates. Using a Gaussian model allowed us to capture these same patterns in a simpler model. We used the R package pheatmap . As so many inhibition values were near the upper bound (100% inhibition), we logit transformed inhibition scores after converting % scores to the scale , and used a Gaussian error structure  = 28.4% (16.1–40.7); bacterial genus mean = 21.1% (7.9–36.3)].There was strong variation in patterns of inhibition among both probiotic types  and amonic types . When coBd isolates (JEL423 and MG04) irrespective of the probiotic type applied  = −2.77 ; ΔLOO effect of genetic distance = −0.2; We found that consortia containing isolates from three different bacterial genera had higher average between-isolate distance than those containing two or three bacterial isolates from the same genus . HoweverBd isolates in determining overall inhibition . The high variance among Bd isolates is evidenced by three Bd isolates  displaying highly unstable inhibition dynamics as probiotic complexity increased  and three (MG04) of the bacterial consortia, respectively. Consistent with this, there was a negative correlation across all Bd isolates between the mean inhibition and variance in inhibition across genera for three-isolate probiotics  = 3.66 ; ncreased . WhereasV = 514, p = 0.004). Only in 19% of cases (7/36) did the inhibition score exceed the score of the isolate with strongest inhibition in the probiotic  the probiotic exhibited an inhibition score that was higher than the mean of the single-isolate inhibition scores for the isolates that probiotic contained  appeared to resist inhibition irrespective of the probiotic type being employed. Consistent with previous research, treatments comprising just a single bacterial isolate performed worse on average when tested across a range of Bd isolates  we might expect the diversity-function relationship to re-emerge.There is now growing evidence that multi-strain probiotic consortia offer both increased inhibition potential for individual isolates , and a hus study , we founhibition . HoweverBd. For three isolates in particular , increasing the species-richness of the probiotic destabilized inhibition dynamics and increased variance in inhibition among bacterial genera. It is noteworthy that two of these isolates were those that were most resistant to inhibition; we observed the same negative mean-variance relationship for consortia comprising isolates from three bacterial genera as we did for single-genus consortia. Similarly, it was rare to observe inhibition values for triple consortia that exceeded that of the strongest single isolate inhibitor in the community, indicating no evidence for strong synergistic effects when co-culturing. Taken together, these results suggest that the emergent functional properties of these microbial communities, specifically the efficacy of the repertoire of anti-fungal metabolites they produce when co-cultured, vary in concert with the genotype of the pathogen. These patterns highlight the difficulties associated with predicting microbial community dynamics and function purely from species composition  and B. dendrobatidis-Brazil, which clustered together and separately from six BdGPL isolates. We detected no such distinction in our panel of Bds, where we might have expected the BdCAPE lineage to cluster independently of isolates belonging to BdGPL. Whilst our data do not support a linear trend between relatedness and inhibition, we cannot discount the possibility that there may be clusters of resistant Bd genotypes throughout the broader Bd phylogeny. Bd can rapidly evolve resistance to inhibition by antimicrobial peptides through changes in genomic traits such as chromosomal copy number (Bd lineages, including Bsal, to test this possibility. It would be particularly interesting to investigate whether clusters of inhibition-resistant genotypes cluster spatially and/or align with particular environmental conditions. Recent in vitro work has highlighted that abiotic conditions such as temperature can both affect the efficacy of bacterial probiotics against Bd (Bd genotypes even more challenging.We found no evidence that genetic similarity of y number , and theainst Bd  and viruainst Bd . These sBd. Though challenges of this nature provide compelling evidence of the ability of certain bacterially derived metabolites to prevent Bd growth, they are not necessarily accurate reflections of natural interactions among host-associated microbes, or between microbes and the pathogen. For example, the phenotypic traits of bacteria grown in planktonic form, as we have cultured them, can differ vastly from those same bacteria grown in biofilms (e.g., Bd. Concordant with our results, Bd is a function of increased bacterial community complexity (see also In our study, we filtered metabolites derived from bacterial co-culture to be used in inhibition challenges against ms e.g., . Likewisms e.g., . Such assee also . Future In summary, developing effective probiotic strategies to mitigate wildlife disease is clearly complicated by the ability of the pathogen to evolve resistance to potential probiotics , the modhttps://doi.org/10.6084/m9.figshare.c.4666922.All code and data to reproduce the results in this manuscript are available on Figshare at DOI The animal study was reviewed and approved by the University of Salford Ethical Approval Committee.Bd isolates and calculated Bd relatedness data. XH analyzed the data.RA and XH conceived the study and wrote the manuscript with input from all authors. RA collected the bacterial inhibition data. TS and MF sequenced the The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


CO2 absorption into the oceans creating particular concern for calcifying species. In addition to ocean acidification, near shore marine habitats are exposed to the deleterious effects of runoff from acid sulfate soils which also decreases environmental pH. This coastal acidification is being exacerbated by climate change‐driven sea‐level rise and catchment‐driven flooding. In response to reduction in habitat pH by ocean and coastal acidification, mollusks are predicted to produce thinner shells of lower structural integrity and reduced mechanical properties threatening mollusk aquaculture. Here, we present the first study to examine oyster biomineralization under acid sulfate soil acidification in a region where growth of commercial bivalve species has declined in recent decades. Examination of the crystallography of the shells of the Sydney rock oyster, Saccostrea glomerata, by electron back scatter diffraction analyses revealed that the signal of environmental acidification is evident in the structure of the biomineral. Saccostrea glomerata, shows phenotypic plasticity, as evident in the disruption of crystallographic control over biomineralization in populations living in coastal acidification sites. Our results indicate that reduced sizes of these oysters for commercial sale may be due to the limited capacity of oysters to biomineralize under acidification conditions. As the impact of this catchment source acidification will continue to be exacerbated by climate change with likely effects on coastal aquaculture in many places across the globe, management strategies will be required to maintain the sustainable culture of these key resources.Ocean acidification is occurring globally through increasing  Three oysters were sampled per replicate site, 3 were used for SEM analyses and 3 were used for isotope analyses.Sydney rock oysters were sampled from three commercial oyster leases in Wallis Lake and Port Stephens, New South Wales, Australia in the first week of May 2017. Port Stephens was sampled with a tidal height of 0.60–0.70 meters , and at Wallis Lake, the tidal height was 0.50 meters (Min 0.4 m to max 1.50 m). The sites were chosen to reflect different exposures to acidification, but all had oysters grown from the same oyster family (F31). Sites in Wallis Lake were a “control” site “Cockatoo Island,” with estuarine salinities and good oyster growth; and the Upper Wallamba “acidified site 1,” a site which receives runoff from identified areas of acid sulfate soil in the Wallamba river and has poor oyster growth. In Port Stephens, the coastal acidified site was Tilligerry Creek, “acidified site 2,” also a site which receives runoff from acid sulfate soils. The oysters collected from each site , an assessment of the quality of the pattern indexing where <1 is considered good, was 0.78  for calcite. The data were noise reduced using Oxford Instruments HKL software Channel 5 by a wildspike correction that removes isolated datapoints followed by a 6‐point nearest neighbor zero solution. This procedure aids in defining grains without creating substantial artifacts. Crystallographic preferred orientations for calcite grains are defined by the two major crystallographic axis (with the following standard notation): {0001} and {101¯0}. Subsets of each data set were created for texturally distinct regions within the oyster shells. The poles to the major crystallographic planes in these subsets were plotted in a lower hemisphere stereographic projection using the mambo module in Oxford Instruments HKL software Channel 5.Oysters were dissected, shells rinsed with freshwater and air dried. The left “cupped” valve was embedded in epoxy resin, sliced longitudinally with a diamond trim slow saw and polished for scanning electron microscopy. In brief, the exposed cut shell section was polished using grit papers , using polishing cloths with alpha alumina 1 μm and alpha alumina 0.3 μm, and finally using colloidal silica for 1 hr using a Vibromat. Shells were finished with distilled water and methanol. Polished shell sections were analyzed using SEM combined with EBSD. The same midsection of the shell from the outer calcitic layer to the inner chalky layers was selected for each individual. Specimens were examined under low vacuum mode (∼50 Pa) with an accelerating voltage of 20 kV using a Carl Zeiss Sigma variable pressure field emission gun SEM at the School of Geographical and Earth sciences, University of Glasgow, UK. The stage was tilted to 70° to examine backscatter Kikuchi patterns  from November 2016 to March 2017, while the acidified site 2 near Tilligerry Creek had a mean pH 7.63   and for Tilligerry Creek a mean of 33.40 ppt . At the acidified site there was a correlation between declining pH and decreased salinity  compared to the average DO for Port Stephens lake of 95.71% saturation .The Port Stephens Lake control site had a mean pH 7.98  Figure . Salinity Figure . The DO SE = 0.14, range of pH 7.47–7.92, n = 12) compared to a mean pH of 8.08  in the “control” site. The salinity also followed a similar trend declining alongside the pH at the “acidified site 1” with a mean of 32.95 ppt  compared to the “control” site with a mean of 35.40 . The DO again was much lower in the “acidified site 1” with a mean of 90.29% saturation  compared to the “control” site with a mean DO of 99.19% saturation  in Wallis Lake  where brighter areas produced better EBSP and shows the crystal structure from the ornamented calcite on the outer shell (bottom of the left image). The “feather like” calcite prisms more commonly imaged using EBSD, the inner most chalk layers  during EBSD data acquisition Figure . The cryIn comparison the shells of the Sydney rock oysters grown under coastal acidification in Wallis Lake at “acidified site 1” appear disordered throughout the outer prismatic layer .The shells of the Sydney rock oysters grown at “acidified site 2” in Port Stephens show similarities in the chalky layer crystallographic growth compared to oysters grown in “control” conditions at Cockatoo Island Figures  and 6. T3.2.1The m‐index values for each of the specimens illustrated in Figures 3.2.2All the oysters examined had a similar levels of sulfur (<0.2 wt%) in their shells as shown by the peak in the mass spectrum, albeit at the detection limit of the SEM‐EDS. There was no visual increase in sulfur levels in oyster shells grown at acidified sites 1 or 2, Representative SEM‐EDS sulfur maps of the shells are provided in the Supporting Informations  can be explained by a disruption to the biomineralization process, reducing the ability of the oyster to control the crystallographic layering of the calcite folia and chalky layers.Coastal acidification is a widespread concern for coastal aquaculture  sites does not resemble that reported for the Pacific oyster Magallana gigas (previously Crassostrea gigas) also grown in good estuarine conditions   these are at similar pH levels to the coastal acidification in this study. Experimental acidification studies report a reduction in the structural integrity of mussel and oyster shells leading to reduced mechanical properties of the shells , declines in water quality (including acid sulfate soils) and changes to single‐seed oyster production  is expected by 2100  could be indicative of oxidation reactions caused by sulfate soil acidification or be due to organic matter remineralization in the water column. The fDOM data  acidification and coastal (acid sulfate soil and humic/tannic) acidification. Coastal acidification could increase as sea‐level rise results in tidal pumping of groundwater from greater areas of acid sulfate soil and increased runoff due to altered storm intensity  and Maria Byrne (MB) designed and undertook field ecosystem experiments. SCF, MB, Sergio Torres Gabarda (STG), Brian Hughes (BH), and Michael Dove (MD) were all involved in the acquisition of field data. SF and Luke Daly (LD) analyzed field samples and interpreted the microscopy data. SF interpreted the data and drafted the manuscript with MS. Jaimie Potts (JP) and Peter Scanes (PS) collected field water quality data for the revised manuscript. SF, MS, BH, MD, JP, PS and Wayne O'Connor (WO) revised the work critically for important intellectual content and approved the final version to be published.https://doi.org/10.5061/dryad.cn006gh.Crystallographic orientation data from inverse pole figure maps are archived at the Dryad Digital Repository:  Click here for additional data file.


Brain butyrylcholinesterase (BChE) is an attractive target for drugs designed for the treatment of Alzheimer’s disease (AD) in its advanced stages. It also potentially represents a biomarker for progression of this disease. Based on the crystal structure of previously described highly potent, reversible, and selective BChE inhibitors, we have developed the fluorescent probes that are selective towards human BChE. The most promising probes also maintain their inhibition of BChE in the low nanomolar range with high selectivity over acetylcholinesterase. Kinetic studies of probes reveal a reversible mixed inhibition mechanism, with binding of these fluorescent probes to both the free and acylated enzyme. Probes show environment-sensitive emission, and additionally, one of them also shows significant enhancement of fluorescence intensity upon binding to the active site of BChE. Finally, the crystal structures of probes in complex with human BChE are reported, which offer an excellent base for further development of this library of compounds. Alzheimer’s disease (AD) is the most common form of senile dementia, and its prevalence is expected to increase further in the coming decades due to ageing of the populations in the Western worldThe pathogenesis of AD is complex. It includes accumulation of the amyloid β protein (Aβ) and abnormal modifications and accumulation of the hyperphosphorylated protein tau, which is accompanied by oxidative stress. This leads to synaptic loss, selective neuronal death, and decreased brain concentrations of specific neurotransmitters, most notably of acetylcholine (ACh)Cholinergic synapses are particularly affected by Aβ neurotoxicity, and the synaptic loss induces decreased brain ACh concentrations, which correlate in time with the cognitive impairment that is characteristic of AD. ACh is the neurotransmitter at cholinergic synapses, where its activity can be terminated by two cholinesterases: acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). The activity of BChE is increased in certain regions of the brain of patients upon AD progression,,Future development of better treatments and more rapid and accurate diagnostic tools, and ultimately the prevention of AD, depends on a full understanding of the pathophysiology of AD, especially during the period before the onset of symptoms. The cholinesterases have important roles in the pathogenesis of AD, although their exact roles and their interplay in relation to the other hallmarks of AD remain poorly understood. New molecular tools to study cholinesterases might provide new insights into the pathophysiology of AD. Fluorescent probes are particularly useful here, due to the high sensitivity of fluorescence techniques and the potential for visualisation through various fluorescence microscopy techniques. Numerous AChE-specific fluorescent inhibitors have been reported, some of which are irreversible and some of which are competitive2A, 3A, and 3B. Moreover, significantly increased fluorescent intensity was observed upon binding of probe 2A to BChE. The crystal structures of these three best probes in complex with human BChE (huBChE) confirm the binding mode that was anticipated in the design phase.In the present study, we report on the two BChE-specific fluorescent probes that were derived from previously described highly potent and reversible BChE-selective inhibitorsDetailed schemes, synthetic procedures, and characterisation of each probe are given in the Supporting Information.0v) and presence (iv) of the test compounds were calculated. The inhibitory potencies are expressed as the residual activities (RAs), according to Equation (1):b is the blank value.The inhibitory potencies of these probes against cholinesterases  were determined using the method of Ellman50 measurements, serial dilutions were prepared for the compounds with RAs <50% at 100 µM. The IC50 values were calculated by plotting the RAs against the compound concentration according to a four-parameter logistic function, using the GraphPad Prism 8 software . All of the measurements were repeated twice, each of which was in triplicate.For the ICPurified huBChE was a generous gift from Drs. O. Lockridge and L. Schopfer. DTNB and BSCh were from Sigma. All other reagents used were of analytical grade. The experiments were carried out at 25 °C in 25 mM phosphate buffer, pH 7.0.2C, 3A, 3B) were tested by measuring the progress curves for BSCh hydrolysis by huBChE in the absence and presence of each of these compounds at five to seven concentrations. Assays were carried out over 10 min, in a 0.6-ml cuvette, using the method of Ellman et al2C, 0–1 µM; 3A, 0–200 µM; and 3B, 0–50 nM.Three compounds . Due to the experimental manipulation, a dead time of 10 s was added to each measurement, and all progress curves were extrapolated accordingly prior to the analysis.The concentration of purified huBChE, which was always the final addition to these assays, was ∼0.5 nM. The hydrolysis of 50 µM BSCh was followed until completion (in most cases), in the presence of 1 mM DTNB. Reaching the plateau was important to get a precise estimate of the actual added BSCh concentration. For the calculation, the molar absorption coefficient of 13800 molwww.enzo.cmm.ki.si1k of 814 s−1, the substrate specificity constant 1/Kmk of 1.0 × 108 M−1 −1s–t, and the inhibition constant of 54 µM for the binding of the thiocholine-TNB (k3/k2) in the hydrolysis of BSCh by purified huBChE were constrained, as these values had been determined previouslyThe progress-curve analysis was performed using the ENZO web application, implemented at ,2C, 3A, and 3B were solubilised in 100% DMSO at 0.1 M, and the protein complexes were obtained by co-crystallization at 1 mM final ligand concentration 2SO4, 1% DMSO; 3A: 0.2 M ammonium acetate, 12% PEG 4000, pH 7.4) using the hanging drop method at 293 K. For 2C and 3B, the crystals were cryo-protected in a solution of 0.1 M MES, pH 6.5, 2.15 M (NH4)2SO4, 20% glycerol, 1 mM ligand, 1% DMSO before flash cooling in liquid nitrogen. For 3A, cryo-protection was achieved with the mother-liquor complemented with 17% glycerol and 1 mM 3A.Recombinant huBChE was produced in eukaryotic cells and insect cells as described previously2C and 3B were collected on the ID23-1 beamline of the European Synchrotron Radiation Facility  at 100 K, with a Pilatus 6 M detector, and for 3A, on the ID23-2 beamline at 100 K with a Pilatus 2 M detector. For 2A and 3A, the detector frames were processed manually with XDS3B, automatically with autoPROC2C and 3B, ligand geometry restraints were processed using Phoenix eLBOW3A, the geometry restraints were generated using the PRODRG serverCoot2C, 3A, and 3B have been deposited in the Protein Data Bank under accession numbers 6R6V, 6RUA, and 6R6W, respectively.The X-ray diffraction data for Fluorescent probes that undergo emission changes upon binding to an enzyme active site can be used as “hot ligands”; i.e., in fluorescence-based competition binding assays1) began before its crystal structure in complex with huBChE was resolved1 was substituted with 7-(diethylamino)coumarin, to yield 1A  was designed, on the basis that compounds with a permanent ammonium cation can produce significantly stronger cation-π interactions compared with compounds with a tertiary amine moiety in the same position2A and 2B showed very low and unselective inhibition of BChE. Naphthalene was seen to be the optimal fit for the acyl binding pocket, and substitution with coumarin resulted in loss of inhibitory activity.With the discovery of the picomolar selective BChE inhibitor ibitor 2 . Additio2C ethyl moieties form crucial interactions with the active site, the indene group was replaced by a 7-(diethylamino)-3-(thiazol-2-yl)-coumarin moiety. Indeed, the indene only has non-specific interacts with the protein scaffold, and it is furthermore oriented out of the aromatic gorge, towards the bulk, which suggests more permissivity in terms of fluorescent group substitutions. Unsurprisingly, compound 2C showed lower huBChE potency when compared to parent inhibitor 2, which is known to be one of the best reversible inhibitors of huBChE. However, compound 2C maintained good affinity, with an IC50 of 88 nM.For the design of probe 2C , a more 3 is somewhat different compared to that of inhibitors 1 and 21 and 2 points towards the entrance of the aromatic active-site gorge, while for inhibitor 3, it fits into the choline-binding pocket and it is instead the methoxyethyl group that is oriented into the gorge. Therefore, elongation of the methoxymethyl group was the most straightforward approach to graft a fluorophore onto inhibitor 3, to yield 3A coumarin at the distal end. This compound retained good BChE inhibitory activity, with an IC50 of 60 nM, but instead showed poor solubility in aqueous buffers. To avoid non-specific interactions, a more hydrophilic 1–(2-hydroxyethyl)piperazine spacer was introduced, with an environment-sensitive 7-nitro-2,1,3-benzoxadiazol-4-yl (NBD) fluorophore attached to the piperazine nitrogen. The resulting 3B -catalyzed alkyne-azide cycloaddition),3C ,3C  had a sh),3C (3D  resulted2K).The actions of different concentrations of these three fluorescent inhibitors on huBChE were tested by measuring progress curves of product formation at approximately 50 µM BSCh . The ana2 (PDB code 5NN0)2C adopts a similar binding mode, whereby the naphthalene moiety is positioned in the acyl-binding pocket through a T-stacking–like interaction with Trp231 , although it retains the same orientation as in the parent sulphonamide (5DYW)3 with BChE, the sulphonamide oxygens of 3A and 3B form H-bonds with the hydroxyl group of Thr120. The piperazine ring of 3B points out of the aromatic gorge, towards the bulk; hence, as in the complex structure of BChE with 2C, no clear electron density is seen for the presumably highly flexible fluorescent NBD moiety. Similarly, no clear electron density is seen for the fluorescent coumarin and the triethyleneglycol linker of 3A, and only the first carbon attached to the sulphonamide nitrogen was modelled.When compared to the previously determined structure of huBChE in complex with parent inhibitor h Trp231 . The piph Trp231 , while tg pocket . The pip2C and 3B had both good inhibitory potencies and sufficient aqueous solubility, and so these were further evaluated for their fluorescence properties. The low solubility of probe 3A might indeed lead to non-specific interactions with huBChE and limit is use, and it was therefore not included further. On the basis that the active site appears to be more hydrophobic than the bulk, the optimal probe should show strong emission in lipophilic solvents and no emission in polar solvents. In this respect, probe 3B proved ideal, as it showed strong emission in all of the lipophilic organic solvents, except dichloromethane, and very low fluorescence intensity in phosphate buffer at pH 8  confirms this. Future development of probe 2C should thus be orientated towards lowering the lipophilicity, which should, in turn, reduce unspecific binding.The fluorescence properties of probes ntensity . This misolvents . Improve3B in the buffer was only marginally increased upon binding to huBChE, which indicated that the NBD fluorophore did not undergo significant changes in polarity. The crystal structure of probe 3B with huBChE confirmed this, as no full electron density was recorded for the NBD fluorophore and the linker of probe 3B. The NBD fluorophore would appear to lie in the polar environment at the entrance of the active site gorge, and surrounded by aqueous medium. For future development of probe 3B, it will be necessary to position the NBD fluorophore closer to the central scaffold, and to graft in additional functional groups that will position the fluorophore in a more hydrophobic environment. This will establish further interactions with the protein scaffold, thus providing the potential for a more pronounced increase in fluorescence emission upon binding to huBChE.The weak fluorescence emission of probe 2C and 3B, to evaluate these two probes under more biologically relevant conditions. Sections of rat brain with the highest BChE activity were selected and the modified Koelle − Karnovsky histochemical method was used to detect BChE activity,2C and 3B, 1 mM was needed to achieve substantial inhibition of BChE, as measured by less formation of the dark-brown/black granular BChE reaction product (2 and 3 produced similar effects at 300 µM and 10 µM, respectively, which indicated the lower inhibition constants of the parent inhibitors.The inhibition of BChE by these highly specific fluorescent probes was pursued with a view to diagnosis and monitoring of progression of AD, by labelling the brain BChE that accumulates in disease-associated Aβ plaques. Inhibition of BChE in rat brain slices was therefore examined for probes  product . The par2C, 3A, and 3B, which show good inhibition of BChE in the low nanomolar range while showing negligible inhibition of AChE. The fluorescence properties of the most promising probes, 2A and 3A, show that they both have environment-sensitive emission. Most notably, the emission of probe 2C is significantly enhanced upon binding to the active site, and thus can be used as a tool to study the activity of BChE. Additionally, inhibition of BChE activity of rat brain slices was investigated with probes 2C and 3B, as measured by the modified Koelle − Karnovsky histochemical method. Both probes successfully inhibited formation of the dark-brown/black granular BChE reaction product. Finally, the crystal structures of probes 2C, 3A, and 3B in complex with huBChE were obtained, which provide an excellent starting point for further optimisation of these fluorescent probes for BChE. In particular, the absence of electron density for the fluorophore moieties in these structures indicate that further improvement in inhibition potency and in environment sensitivity of the fluorescence emission can be provided by changes in the linker length and nature, as well as by grafting additional functional groups onto the fluorescent probes. This should provide tighter interactions with the protein scaffold, and thereby better fulfil the requirement for further evaluation as AD-specific fluorescent probes.We report here on the design, synthesis, kinetics and spectroscopic characterisation and binding modes of the two potent BChE-specific fluorescent probes. Design of these fluorescent probes was based on the crystal structures of previously described potent cholinesterase inhibitors in complex with their target, huBChE. Initially, a fluorophore was introduced in place of the naphthalene that binds to the acyl binding pocket of BChE, although this approach went no further. Instead, the scaffold was changed to ones that were already optimised to provide the crucial interactions with the active site gorge residues. The fluorophores were then introduced to be positioned to point outside the aromatic site gorge, towards the bulk. This approach was successful, as it yielded three promising probes, Click here for additional data file.


By analyzing data and model simulations, here we show that large AA occurs only from October to April and only over areas with significant sea-ice loss. AA largely disappears when Arctic sea ice is fixed or melts away. Periods with larger AA are associated with larger sea-ice loss, and models with bigger sea-ice loss produce larger AA. Increased outgoing longwave radiation and heat fluxes from the newly opened waters cause AA, whereas all other processes can only indirectly contribute to AA by melting sea-ice. We conclude that sea-ice loss is necessary for the existence of large AA and that models need to simulate Arctic sea ice realistically in order to correctly simulate Arctic warming under increasing CO The cause of Arctic amplification is still heavily debated. Here the authors present climate change simulations to show that sea-ice loss is essential for the existence of Arctic amplification. Many mechanisms have been proposed to explain the AA, including a central role by sea-ice loss19, reduced outgoing longwave (LW) radiation due to a stable polar temperature profile21, increased downward LW heating due to increased water vapor and clouds23, increased poleward energy transport24, and other processes33. However, their relative importance is still under debate. In particular, why the largest AA occurs in the cold season  and over areas with large sea-ice loss3 has not been well explained by these mechanisms.Enhanced warming in the Arctic (north of 67°N) is seen in recent observations10  radiation, and latent and sensible heat fluxes during the cold season from October to April when the Arctic Ocean becomes a heat source to the atmosphere2 with and without fixed SIC in calculating surface water and energy fluxes using a fully coupled climate model . These models realistically simulate the mean annual cycle of SIC, Tas, and surface energy fluxes  and the 1% CO2 run with fixed SIC in calculating the surface fluxes (denoted as FixedIce run) would allow us to further quantifying the impact of sea-ice loss on AA through its impact on surface fluxes.We address these questions here by analyzing the historical 1979–2016) and future (up to year 2300) changes in Arctic sea-ice cover (SIC), surface air temperature (Tas), and energy fluxes in ERA-Interim reanalysis data and CMIP5 model simulations (see Methods). We also perform and analyze two climate change simulations with 1%-per-year increase in atmospheric CO979–2016 We found that large AA occurs only from October to April and only over areas with significant sea-ice loss in both observations and model simulations. AA largely disappears when Arctic sea ice melts away or is held fixed for calculating surface fluxes. Periods with large AA are associated with large sea-ice loss in model simulations, and models with bigger sea-ice loss produce larger AA. Increased LW radiation and latent and sensible heat fluxes from the newly exposed Arctic waters enhance surface and low-tropospheric warming and cause AA, whereas water vapor feedback, increased downward LW radiation, and other processes can only modulate the AA induced by sea-ice loss or indirectly contribute to AA by melting sea ice. Our results highlight the essential role of sea-ice loss in producing AA under GHG-induced global warming.3. The November-minus-July difference in the LW_up (~4 W/m2/decade) and SH + LH (~1.7 W/m2/decade) fluxes in Fig. 40  of the Arctic Ocean even during the cold season  between the Arctic sea-ice loss and Arctic-minus-global warming difference from 1979 to 1999 and from 2070 to 2099 among the 38 CMIP5 models we analyzed  should not be correlated spatially with SIC loss unless there exists a major role by the sea-ice loss to alter the surface warming and water vapor (and thus LW) change patterns. In other words, if SIC does not play a major role, then the warming pattern should be fairly uniform , or spatially correlated with cloudiness changes rather than with SIC loss. Thus, while increased downward LW radiation associated with increased CO2 and water vapor may play a large role for overall Arctic43 and global44 warming, it cannot produce the enhanced warming collocated with sea-ice loss and existed primarily only during the cold season that leads to large AA only in the cold season. However, a general Arctic warming, either due to increased CO2, water vapor, or clouds, can indirectly contribute to AA through melting of sea ice.Strong spatial pattern correlations were also found between Arctic sea-ice loss and surface warming and changes in surface energy fluxes Fig. , consist28 Fig. . For exa28 Fig. , while t28 Fig. . Without23, which in turn helps maintain and enhance an elevated surface temperature over the newly opened waters. In turn, the enhanced warming should accelerate the sea-ice loss, leading to a positive feedback loop. The stable Arctic atmosphere45 may also allow the extra surface heating to generate large warming in the lower troposphere by reducing LW cooling to space21. However, the fact that the AA becomes much smaller in the 23rd century  than that in the 21st (2.97) and 22nd (2.40) century , and the second run is the same except it uses fixed Arctic sea-ice cover in calculating all the surface fluxes only . Thus, the 1% CO2-minus-FixedIce difference represents the effect of Arctic sea-ice loss through its impact on surface fluxes, and a consistent dependence of the AA on sea-ice loss during these multi-century simulations would indicate a weak dependence of the AA on the mean climate state, as these simulations cover a wide range of atmospheric CO2 from 284.7 to 2950 ppm (and thus of the mean climate). Without the sea-ice loss-induced changes in surface fluxes, Arctic warming is greatly reduced while global warming weakens only slightly under the 1%-per-year CO2 increase, and this results in negligible AA for annual-mean Tas during the entire simulation  or internal processes  that can cause or enhance Arctic warming may contribute indirectly to AA through their impact on sea ice. However, without the LW, SH, LH, and other surface flux changes associated with the sea-ice loss , Arctic warming rate under increasing GHGs would be similar to the global warming rate, leading to small AA.The seasonal, temporal, inter-model, and spatial dependence of the AA on sea-ice loss presented above, coupled with the differences between the CESM1 1% CO43 and individual model simulations may also include decadal–multidecadal changes induced by internal climate variability32. It is unclear whether sea-ice loss also plays such a central role for the AA induced by internal variability, although there is no reason to prevent the mechanism presented above from working under this case. Modeling studies36 with fixed surface albedo still produced noticeable AA, in which LW_up, SH, and LH fluxes could still change as SIC declines. This further points to the key role of increased LW_up, SH, and LH fluxes associated with sea-ice loss, besides the albedo effect which occurs mainly in the warm season and thus cannot directly affect the AA, which occurs primarily in the cold season.Different from the response to GHG forcing analyzed here, Arctic warming in observations amplified warming over the Arctic compared with the rest of the world if there is no sea-ice loss. On the other hand, how important of the additional absorption of solar radiation  during the warm season . Only nine of the models had simulations extended to 2300 under the extended RCP8.5 scenario39. These are bcc-csm1-1, CCSM4, CNRM-CM5, CSIRO-Mk3-6-0, GISS-E2-H, GISS-E2-R, HadGEM2-ES, IPSL-CM5A-LR, and MPI-ESM-LR. The surface warming pattern and magnitude from 1970 to 1999 and from 2070 to 2099 are comparable between the 38-model ensemble mean and the 9-model ensemble mean, suggesting that the 9-model ensemble is a reasonable representation of the larger CMIP5 model ensemble. The recent mean annual cycles of the SIC and surface energy fluxes from the 9-model ensemble  and RCP8.5 (2006 to 2100) simulations from 38 CMIP5 models49 with the CAM4 option for its atmospheric component to make two multi-century simulations plus a 150-year pre-industrial control run. The CESM1 is a widely used fully coupled climate model that simulates the Arctic SIC and climate realistically  run. The FixedIce run is the same as the standard 1% CO2 run except that all the internally calculated ice–atmosphere, ice–ocean, and ocean–atmosphere fluxes north of 30°N were applied to the sea-ice fractional areas temporally interpolated from the monthly climatology of the CTL run, in contrast to the standard 1% CO2 run in which these fluxes were applied only to the ice faction existed at the time in the model. Over a small fraction of the Arctic sea-ice area north of 30°N  from NCARIn the FixedIce run, the coupler and the atmospheric and ocean components in the CESM1 only saw the fixed sea-ice cover interpolated from the CTL run north of the 30°N; the sea-ice fraction inside the sea-ice model was allowed to evolve dynamically with the fluxes returned from the coupler. Thus, the above changes made in the FixedIce run would also affect the rate of sea-ice loss through the modification to the surface fluxes.Our main intervention of the fully coupled system in the FixedIce run is the application of the internally calculated fluxes over a fixed sea-ice cover from the CTL run (instead of only over the ice cover existed at the time inside the sea-ice model). A secondary intervention was the use of CTL surface fluxes  over the low SIC fraction in a small number of the grid cells that initially contained sea-ice but the sea ice melted away completely. This use of the CTL fluxes was not ideal but necessary because (1) the sea-ice model did not calculate surface fluxes for grid cells without any ice, (2) the internally calculated surface temperature over these grid cells is for water surfaces and thus can’t be used directly as the surface temperature for ice surfaces; and (3) the calculations of surface fluxes over sea-ice were complicated.37, who used an artificial LW forcing to maintain a constant sea-ice cover in a warming experiment, and Blackport and Kushner50, who modified ice–albedo to study the impact of sea-ice loss on the climate system.The CTL fluxes contained only an annual cycle and were seen by the atmosphere and ocean component models; they along should not cause any long-term trends and are balanced out on an annual mean sense when applied to the small areas where ice was melted away completely , except over the areas with large SIC decreases in the FixedIce run by the 3rd CO2 doubling  as the albedo and thus SW changes are small in the FixedIce run. Thus, these TOA flux changes are expected physically based on the imposed the changes.To examine the spatial patterns, we compare the change patterns between the two runs for the TOA and surface net energy fluxes in Supplementary Figures 2 run (see Figs. 2 over the Arctic Ocean (Supplementary Figure 2 run (Supplementary Figure For the surface net energy flux (Supplementary Figure 2 run in the Arctic (Supplementary Figure 2 run over the Arctic, and they partially offset the large increases in the absorbed SW radiation (Supplementary Figure 2 run, which results in only moderate changes in the net surface energy flux (Supplementary Figure 2 and FixedIce runs. These analyses of the TOA and surface fluxes also suggest that there are no major artifacts resulting from the imposed changes in the FixedIce run.As the fixed sea-ice cover reduces upward LW radiation, surface net LW radiative heating increases more in the FixedIce run than in the 1% CO2 minus FixedIce difference, to support our conclusion that sea-ice loss is necessary for large AA to occur. These include the diminishing AA in the 23rd century in the CMIP5 models (Fig. 2 run (Fig. Please note that we have provided several lines of evidence, besides using the 1% COrun Fig.  when Arcrun Fig. , and theERA-Interim reanalysis data48, which capture the historical SIC seasonal cycle and changes fairly realistically, although the mean SIC is somewhat lower than satellite observations from NOAA (Supplementary Figure 47 they arguably represent one of our best datasets available for the Arctic region.For the historical changes from 1979 to 2016 Fig. , we usedhttp://www.cesm.ucar.edu/models/cesm1.2/.The code of the CESM1 model used here is available from Supplementary InformationPeer Review File


The cell cycle is the process by which a cell duplicates its DNA during S-phase and divides its chromosomes during M-phase, creating two genetically identical daughter cells. Cell cycle events are ordered by synthesis and degradation of key cell regulators and by phosphorylation and dephosphorylation of numerous substrates. Phosphorylation can alter the activity, interactions or subcellular localization of a protein. A substrate’s phosphorylation status is the readout of competing activities of kinases and phosphatases that target each of its phosphorylation sites. In our recent study , we performed time-resolved global phosphoproteome analysis of a period during the cell cycle known as mitotic exit. During this time, numerous cell biological events happen in fast succession but in strict order. First, at the metaphase to anaphase transition, the mitotic spindle elongates to pull maximally condensed chromosomes to opposite cell halves. Shortly after that, spindles disassemble and chromosomes decondense, before finally cell division is completed by cytokinesis. Our time-resolved phosphoproteome analysis of this period in budding yeast provided a survey of the principles of phosphoregulation used to order these events. Cdc20 and later with Cdh1, APCCdh1, they cause gradual cyclin destruction. S-phase cyclins are degraded first, followed by M-phase cyclins. This allows the timely dephosphorylation of Cdk substrates during the course of mitotic exit.Cyclin-dependent kinase (Cdk) is the master cell cycle regulator. In complex with its regulatory cyclin subunits, Cdk phosphorylates numerous substrates at distinct cell cycle stages. Specificity arises from the Cdk active site that recognizes the phosphorylation site. Cdk is a proline directed serine/threonine kinase, meaning that its substrates are minimally defined by an (S/T)P motif. A basic residue at the +3 position is preferred, giving rise to what is known as the full Cdk consensus motif (S/T)Px(K/R). In addition, the cyclins add substrate specificity by using a hydrophobic patch that recognizes substrate docking motifs. These docking motifs can be found on substrates at a distance from the phosphorylation site. During the budding yeast cell cycle, nine different cyclins are synthesized during progression from G1 until mitosis, conferring substrate specificity through the different cell cycle stages. In addition to their distinct specificities, the different cyclin-Cdk complexes build up increasing overall Cdk activity as cells progress from G1 towards mitosis. Cdk activity is low during G1, increases during S phase and culminates in mitosis. As the peak in Cdk activity is reached in metaphase, the anaphase promoting complex/cyclosome (APC/C), a large ubiquitin ligase complex, is activated. First in conjunction with its coactivator Cdc20, APCPhosphatases that counteract Cdk phosphorylation are essential during mitotic exit to bring about ordered substrate dephosphorylation. The changing kinase-to-phosphatase ratio instructs each substrate to respond with dephosphorylation at their respective times. This guarantees that substrates controlling spindle elongation are dephosphorylated before substrates that ultimately promote cytokinesis. While Cdk phosphorylation and dephosphorylation provides a crucial level of control, numerous additional kinases are present and active during mitotic exit. These also phosphorylate serine and threonine residues, albeit within their own consensus motif surroundings. In budding yeast, these include Cdc5 (Polo-like kinase), Ipl1 (Aurora kinase), Dbf2-Mob1 and Cbk1-Mob2 (two members of the Nuclear Dbf2-Related (NDR) kinase family). Mutations in any of these lead to chromosome segregation failures, suggesting that mitotic exit is not only governed by Cdk substrate dephosphorylation, but that phosphorylation events controlled by these additional kinases must also be considered. To gain a global overview of phosphoregulation during mitotic exit, we wanted to measure the timing of as many as possible phosphorylation and dephosphorylation events that take place. With this, we wanted to address three questions: How does the phosphorylation status of each individual phosphosite develop over the course of mitotic exit? How do multiple phosphosites on the same protein respond with respect to each other? Can we identify phosphosite features that predict its behavior during mitotic exit?To survey phosphoproteome dynamics during budding yeast mitotic exit, we performed a time-resolved phosphoproteome analysis. We synchronized yeast cultures by depleting the APC coactivator Cdc20 under control of a galactose-inducible promoter by galactose withdrawal. This leads to a uniform cell arrest in metaphase. Galactose readdition induces Cdc20 and starts synchronous progression through mitotic exit. Ten samples taken at five minute intervals covered the mitotic exit stages from metaphase to cytokinesis and return to G1. We used Stable Isotope Labelling with Amino acids in Cell culture (SILAC) to generate a metaphase culture that was added as a common internal comparison to all ten samples. Following cell breakage and phosphopeptide enrichment, we used LC-MS/MS mass spectrometry to identify and quantify phosphosites, 3456 of which could be followed throughout the ten timepoints. 1101 unique proteins carried at least one phosphosite, corresponding to 20% of the budding yeast proteome. Of those, 346 proteins contained at least one phosphosite that changed in abundance during mitotic exit.Looking at overall abundance changes, 79% of phosphosites did not change during mitotic exit. Their constitutive phosphorylation might maintain protein conformation, or phosphorylation changes might occur at other cell cycle stages or in response to other stimuli. 11% of all phosphosites were dephosphorylated during mitotic exit, while 10% gained phosphorylation . As Cdk activity is downregulated during mitotic exit, and Polo and Aurora kinases are degraded towards the end, mitotic exit was thought to be a period of overall protein dephosphorylation. We were therefore surprised to find an almost equal number of sites that gained phosphorylation during mitotic exit. The time resolution of our experiment afforded further insight. 86 proteins carried phosphosites that first increased in abundance during anaphase and were then dephosphorylated at later timepoint towards cytokinesis. ‘Dephosphorylation’ in our analysis means reduced phosphosite abundance. This could happen by dephosphorylation or by protein destruction. To gauge the possible contribution of proteolysis to phosphosite loss, we also measured overall protein levels. Only 1% of all detected proteins decreased in abundance during mitotic exit . While the APC/C is a prominent mitotic exit regulator, this suggests that phosphoregulation affects a far greater number of proteins, compared to proteolysis. We note that our mass spectrometry approach reports on relative phosphosite abundance changes. It does not reveal what fraction of the total protein carries the modification. This important additional information is harder to obtain. Mass spectrometric methods are being developed to address this, which will be a focus of future studies.For many proteins, we detected modification on more than one residue. In some cases, multiple phosphosites were coregulated, i.e. they decreased, or increased, with similar kinetics. However, there were many cases where different phosphosites on the same protein behaved very differently. 64 proteins contained both dephosphorylated sites as well as sites that gained phosphorylation during mitotic exit. Even when looking at the 65 proteins with more than one phosphosites that were dephosphorylated, we found surprising variability. If we classify these sites into three groups based on their dephosphorylation timing , then half of all phosphosites differ in dephosphorylation timing from other sites on the same protein. This illustrates that each phosphosite is subject to its own regulation. This could include priming modifications, as well as differential targeting of individual phosphosites by their respective kinases and phosphatases.To test how the peptide sequence surrounding each phosphorylation site affects its behavior, we performed three comparative motif analyses, using IceLogo. First, we compared all stable phosphosites with all those that changed either up or down. This revealed a notable enrichment for acidic amino acids (D/E) surrounding stable phosphosites, a feature worth exploring further . In contrast, dynamic phosphosites were often threonines followed by proline (P) and positively charged amino acids (K/R), characteristics of the full Cdk consensus motif. Next, we specifically compared sites that gain phosphorylation with all stable sites. This showed that positively charged amino acids are enriched at the -2 and -3 positions, which could be rationalized by the sequence preference of late mitotic Aurora and NDR kinases. Negative charges again were characteristic for the stable sites . Finally, we compared dephosphorylated sites to their stable counterparts. This revealed that threonine residues are preferentially dephosphorylated, suggesting that a threonine-directed phosphatase(s) impacts on substrate dephosphorylation during budding yeast mitotic exit. Hydrophobic amino-acids upstream, a proline in +1 position and additional positive charges in +3, 4, 5 or 6 positions are additional features that are enriched among dephosphorylated sites . These features partly match the substrate preferences of the most studied mitotic exit phosphatase in budding yeast, Cdc14. This phosphatase accommodates a +1 proline in its active site, and prefers downstream positive charges. However, Cdc14 is a serine-directed phosphatase, threonines produce a steric clash with its active site. PP2A phosphatases in turn show a preference for threonine. PP2A plays important roles during mitotic exit in mammals, its contribution to budding yeast mitotic exit deserves further investigation.While phosphatases are thought to target broad spectrums of substrates, kinases display greater specificity during sequence motif recognition. This allowed us to address the contribution of specific kinases to phosphosite regulation during mitotic exit. Looking at all newly phosphorylated sites, a Polo-like kinase consensus motif (D/E/N)p(S/T) dominated early phosphorylated sites. Those sites often lost phosphorylation again at later times. NDR kinase Rxx(S/T) and Aurora kinase R(R/K)x(S/T) consensus motifs became phosphorylated in a second wave, many of which also declined towards the end of mitotic exit. Finally, CKII kinase consensus motifs Sxx(D/E) were phosphorylated late in anaphase and remained stable until the end of the experiment . Turning to substrate dephosphorylation also revealed kinase signatures. Full Cdk consensus sites (S/T)Px(K/R) were the first to be dephosphorylated in early anaphase, followed by minimal Cdk phosphosites. The majority of Polo consensus motifs started to become dephosphorylated only later . The special status of full Cdk consensus sites became further apparent when looking at the fraction of sites that are dephosphorylated. As we saw above, 11% of all phosphosites are dephosphorylated during mitotic exit. The fraction of dephosphorylated sites that match a minimal Cdk or a Polo consensus sites is also around 11%. Strikingly, over 60% of full Cdk sites are efficiently dephosphorylated during mitotic exit. This ascribes particular predictive power for cell cycle regulation to a full Cdk consensus motif.Our results highlight the importance of kinases and their dynamic regulation in shaping the phosphorylation landscape during mitotic exit. Phosphomotif identities, distributed amongst the many regulated substrates, confer temporal input to their modification dynamics. The phosphorylation dynamics will be passed on to the biological processes that these substrates control. In the future, it will be important to delineate the contributions of cell cycle phosphatases to phosphoproteome dynamics during eukaryotic cell cycle progression.


Saccharomyces cerevisiae, TORC2 is essential, playing a key role in plasma membrane homeostasis. In this role, TORC2 regulates diverse processes, including sphingolipid synthesis, glycerol production and efflux, polarization of the actin cytoskeleton, and endocytosis. The major direct substrate of TORC2 is the AGC-family kinase Ypk1. Ypk1 connects TORC2 signaling to actin polarization and to endocytosis via the flippase kinases Fpk1 and Fpk2. Here, we report that Fpk1 mediates TORC2 signaling to control actin polarization, but not endocytosis, via aminophospholipid flippases. To search for specific targets of these flippase kinases, we exploited the fact that Fpk1 prefers to phosphorylate Ser residues within the sequence RXS(L/Y)(D/E), which is present ∼90 times in the yeast proteome. We observed that 25 of these sequences are phosphorylated by Fpk1 in vitro. We focused on one sequence hit, the Ark/Prk-family kinase Akl1, as this kinase previously has been implicated in endocytosis. Using a potent ATP-competitive small molecule, CMB4563, to preferentially inhibit TORC2, we found that Fpk1-mediated Akl1 phosphorylation inhibits Akl1 activity, which, in turn, reduces phosphorylation of Pan1 and of other endocytic coat proteins and ultimately contributes to a slowing of endocytosis kinetics. These results indicate that the regulation of actin polarization and endocytosis downstream of TORC2 is signaled through separate pathways that bifurcate at the level of the flippase kinases.Target of rapamycin complex 2 (TORC2) is a widely conserved serine/threonine protein kinase. In the yeast  Saccharomyces cerevisiae, two genes encode the target of rapamycin (TOR)TOR1 and TOR2  background, NVP-BHS345 treatment leads to an acute loss of TORC2 activity only. Using this experimental setup, we found that TORC2-Ypk1 regulates actin organization and endocytosis via the flippase kinases Fpk1 and Fpk2  also inhibits the two TOR complexes , prompting us to characterize this compound further. CMB4563 is toxic to WT cells grown either on solid medium, preventing colony formation at concentrations of 0.8 μm, or in liquid medium with an IC50 of 0.3 μm (b and c). As observed previously with NVP-BHS345, expression of MTTOR1 partially reduced CMB4563 toxicity, whereas expression of M2286TTOR2 (MTTOR2) strongly reduced CMB4563 toxicity, consistent with Tor2 residing in both TORC1 and TORC2 . Coexpression of both MTTOR1 and MTTOR2 reduced toxicity even further. As expected, addition of CMB4563 to WT cells triggered dephosphorylation of both the TORC1 substrate Sch9  and the TORC2 substrate Ypk1 . Expression of either MTTOR1 or MTTOR2 suppressed Sch9 dephosphorylation, whereas only MTTOR2 suppressed Ypk1 dephosphorylation. TORC2 inhibition was rapid, occurring within a minute . Comparison with our previous study . We conclude that both TORC1 and TORC2 are the growth-limiting targets of CMB4563 in WT cells and that TORC2 is the growth-limiting target in MTTOR1 cells.Previously, we characterized NVP-BHS345 as an inhibitor of TORC1 and TORC2 in yeast . InitialFig. 1us study  shows thFPK1 and FPK2 modestly suppresses the toxicity and largely prevents the actin depolarization and endocytosis arrest otherwise observed upon TORC2 inhibition with NVP-BHS345 in MTTOR1 cells . As Fpk1 phosphorylates and activates Dnf1 and Dnf2, we assessed whether deletion of DNF1, DNF2, or LEM3 also conferred resistance to CMB4563. Deletion of any one of these three genes conferred partial resistance to CMB4563 (a). Then we assessed whether TORC2 regulates actin polarization and/or endocytosis via Lem3. Interestingly, we found that although LEM3 deletion partially suppressed actin depolarization  it failed to suppress the endocytosis defect as measured by the inability to accumulate Lucifer yellow (LY) in the vacuole (d and e). These results confirm that the phospholipid flippases mediate TORC2-dependent signals to polarize the actin cytoskeleton (D/E) (Table S2). DNA fragments encoding these 90 peptides  were cloned and expressed as GST fusions in Escherichia coli . Doping of crude bacterial lysates with purified Fpk1 and radioactive ATP yielded 36 potential direct substrates in 32 different proteins . These candidate substrates were then purified on GSH resin and retested; 32 peptides from 25 proteins were again found to be directly phosphorylated by Fpk1 . Among these were the known Fpk1 substrates Dnf1, Dnf2, and Ypk1 (D/E) . A searcand Ypk1 , 17, demc). Our in vitro screen suggested that Fpk1 phosphorylates Akl1 on residues Ser-960 and Ser-1072. These residues are in the C-terminal domain of Akl1 and are not obviously conserved in either Ark1 or Prk1. Fpk1 phosphorylated full-length, kinase-dead Akl1D181Y but not Akl1D181Y/S960A/S1072A, suggesting that these two amino acids are the only residues phosphorylated by Fpk1 . In similar assay conditions, we did not observe phosphorylation of Akl1 by Fpk2 .Akl1 is a kinase related to Ark1 and Prk1, known regulators of endocytosis , 29. In Fig. 3D181Y does not affect growth (e). Overexpression of Akl1S960A/S1072A that cannot be phosphorylated by Fpk1 was also toxic, suggesting that Fpk1-mediated phosphorylation is not required for Akl1 kinase activity. Actin polarization was not affected in cells overexpressing WT Akl1 or Akl1S960A/S1072A (f), but LY uptake in these cells was significantly reduced (g), suggesting that, like Ark1 and Prk1, Akl1 also regulates endocytosis.Relative to Ark1 and Prk1, the functions of Akl1 are much less well-understood. Overexpression of Ark1 or Prk1 is toxic , 30, andt growth e. OverexA/S1072A f, but LY reduced g, suggesWT and Akl1S960A/S1072A efficiently phosphorylate Ent1 (a and c) and Pan1 (b and c) in vitro, whereas the activities of phosphomimetic Akl1S960D/S1072D and kinase-dead Akl1D181Y were obviously compromised (<50% activity of WT). This is consistent with the notion that Fpk1-mediated phosphorylation inhibits kinase activity of Akl1. We determined the relative contributions of Akl1 and Prk1 to Pan1 phosphorylation in vivo (d). Deletion of either AKL1 or PRK1 led to an ∼50% drop in anti-phosphothreonine signal on Pan1, suggesting that both kinases contribute to Pan1 phosphorylation in growing cells. Importantly, TORC2 inhibition also reduced Pan1 phosphorylation by ∼50% in WT and prk1 cells but not in akl1 or alk1 prk1 cells. The data from multiple repetitions of these experiments were assessed using multiple linear regressions (e and f). From this analysis, we calculated that Akl1 contributes 19.2% (β3 + β5 = 11.7 + 7.5%) of the observed variance in Pan1 phosphorylation and that this contribution is significantly linked to TORC2 activity  < 0.05). Prk1 contributes to 13% of this variance (β2), but this variance is not significantly associated with TORC2 activity  > 0.05). These data suggest that TORC2 regulates coat protein phosphorylation via a Ypk1–Fpk1–Akl1 kinase cascade.We tested whether Akl1, like Ark1 and Prk1, can phosphorylate endocytic coat proteins. We found that Akl1ate Ent1 , a and cand Pan1 , b and c in vivo d. Deletiressions , e and fa) (b). Abp1 recruitment began with an ∼20-s delay and associated with Pan1 for ∼10 s after which time the patch budded into the cytoplasm, representing completion of a successful endocytic event. Inhibition of TORC2 greatly extended the residency times of both Pan1 and Abp1, and endocytic patches often failed to resolve/bud inward (b).Finally, we assessed in more detail the role of Akl1 phosphorylation in the regulation of endocytosis downstream of TORC2. To monitor endocytosis kinetics, we followed the recruitment and residency times of the coat protein Pan1 (tagged with GFP) and the actin-binding protein Abp1 (tagged with mCherry) to endocytic patches at the plasma membrane a 29). C. Ca) (29a) , 32, in a) b. Abp1 rd inward b.c), multiple endocytic events over time (d and e). We observed that inhibition of TORC2, but not TORC1, indeed increased the residency times of both Pan1 and Abp1 by ∼50% . Importantly, this increase was not observed in akl1 cells. To probe this effect further, we retested MT akl1TOR1 cells transformed with an empty plasmid (no insert) or encoding WT Akl1 or Akl1 that cannot be phosphorylated by Fpk1 (g and h). Again, we found that inhibition of TORC2 increased the residency times of both Pan1 and Abp1 in the presence, but not absence, of Akl1. Strikingly, in mock-treated cells expressing Akl1S960A/S1072A, the residency times of both Pan1 and Abp1 were already reduced by ∼50%, and this short residency time was unaltered by TORC2 inhibition. Furthermore, we observed that TORC2 signaling affects the number of endocytic events per cell in an Akl1-dependent manner . Specifically, inhibition of TORC2 reduces the number of endocytic membrane patches in cells expressing WT Akl1 but not in akl1 cells; cells expressing Akl1S960A/S1072A presented significantly more endocytic events, and this number was again unaffected by TORC2 inhibition. Collectively, these results demonstrate the existence of a TORC2–Ypk1–Fpk1–Akl1 kinase cascade that regulates coat protein phosphorylation and endocytosis dynamics.To better quantify this affect we followed, as a kymograph c, multipver time , d and ein silico selection followed by an in vitro screen, we found that signaling to the endocytic machinery is mediated in large part via the Ark/Prk-related kinase Akl1  as evidenced by loss of phosphorylation on the TORC2 target site Thr-662 in Ypk1. Ypk1 activity is consequently reduced; it no longer phosphorylates/inhibits Fpk1 . Upon further examination, we observed that under these conditions there is a dramatic extension in the lifetime of Pan1 and Abp1 at the endocytic patch (d). Importantly, in akl1Δ or AAAKL1 cells, TORC2 signaling to the endocytic patches is disrupted as evidenced by the observation that CMB4563 treatment no longer increases Pan1 or Abp1 patch lifetimes in these cells. Together, these data are consistent with TORC2 regulating endocytosis dynamics via regulation of Akl1 activity. When Akl1 activity is inhibited, for example upon TORC2 inhibition, an abundance of hypophosphorylated coat proteins at the endocytic site might promote incorporation of coat proteins, explaining aberrantly long-lived patches. A gradual gain of hypophosphorylated coat proteins could explain why it takes an otherwise unusually long period of time (∼30 min) to observe a delay of endocytosis upon acute TORC2 inhibition. Following this logic, an excess of hyperphosphorylated coat proteins, as might be anticipated in cells expressing Akl1AA, would be predicted to antagonize coat protein incorporation and lead to short-lived endocytic patches, which is what we observed (h). Alternatively, or additionally, TORC2 may control other pathways apart from that of Akl1-mediated coat protein phosphorylation to regulate endocytosis.Hypophosphorylated endocytic coat proteins are assembled into nascent endocytic patches during the initial steps of endocytosis. Later, when an endocytic vesicle has been pinched off from the plasma membrane, coat proteins are phosphorylated, facilitating their disassembly . We showFig. 2ic patch d. Importobserved h. AlternOur screen aiming for novel Fpk1 effectors yielded known targets of this kinase, Dnf1, Dnf2, and Ypk1 , confirmtrans-Golgi network via Dnf3  using the strategy of Gibson et al. , polyclonal goat anti-Ypk1 ; monoclonal mouse anti-phospho-Ypk1Thr-662 ; monoclonal mouse anti-HA , polyclonal rabbit anti-phosphothreonine , and the appropriate fluorescent dye–coupled secondary antibodies .Yeast cells were grown to an l et al. . WesternGAL1 promoter in BY4741 cells. Precultures were grown overnight in CSM with 2% raffinose and freshly diluted in the same medium until an A600 of 0.8. Galactose (2%) was subsequently added to the culture, and the induction was done for 2 or 4 h depending on the experiment. GST-Dnf1, GST-Orm1, and the library of GST-tagged Fpk1candidate substrates were purified from E. coli. The corresponding plasmids were transformed and expressed in BL21*. Overnight cultures grown in Terrific Broth medium complemented with 50 μg/ml kanamycin were diluted to an A600 of 0.1, 0.1 mm isopropyl 1-thio-β-d-galactopyranoside was added at an A600 of 0.6–0.8, and then cultures were grown overnight at 18 °C.GST-Akl1, -Fpk1, -Prk1, -Ypk1, -Ent1, and -Pan1 fusion proteins were expressed from multicopy plasmids under the control of m CHAPS, Complete protease inhibitor mixture , 1 mm PMSF . Cells were mechanically broken using a French press. Following GSH-affinity chromatography and in some cases removal of the GST tag by TEV protease cleavage, purified proteins were dialyzed and/or loaded onto Superdex 200 10/30  gel filtration column. The final buffer composition was 25 mm HEPES, pH 7.5, 150 mm KCl, 5 mm MgCl2, 1 mm DTT, 2.5 mm CHAPS, and for purified kinases 50% glycerol.Yeast/bacterial cell pellets were resuspended in PBS lysis buffer (10 ml/g) containing 10% glycerol, 5 mm K-HEPES, 2.5 mm CHAPS, 5 mm MgCl2, 1 mm DTT, and 300 μm ATP including 2 μCi of [γ-32P]ATP) using 0.25 μm purified Fpk1 mixed with 0.5 μm purified substrates or 5 μl of total bacterial protein extract. Reactions were incubated for 30 min at 30 °C and stopped by addition of SDS-PAGE sample buffer. Samples were resolved by 7.5 or 10% SDS-PAGE, stained with Coomassie, dried, and analyzed with a Bio-Rad Molecular Imager.Cell pellets from 5-ml cultures of bacteria expressing GST-peptides were lysed with 250 μl of Bugbuster protein extraction reagent (Novagen) supplemented with Complete protease inhibitor mixture, and protein concentrations were normalized. Protein kinase assays were performed in 15 μl of kinase assay buffer , fixed to a concanavalin A (0.5 μg/μl)–coated coverslip, incubated for 10 min, and washed once with fresh medium. The flow chamber was subsequently filled with CSM containing the appropriate treatment. Single-plane images were taken every 10 s for 2 min with a Nikon spinning-disc microscope (Eclipse C1) at 100× magnification. Collected images were treated and analyzed with Fiji ImageJ. Specifically, a six-pixel-thick curve was drawn along the plasma membrane of each selected cell. The average signals along this line were measured at each time point and fused in a final kymograph displaying both red and green channels. Endocytic events were counted, and their intensity and lifetime were measured.Strains expressing Pan1-GFP and Abp1-mCherry were grown in appropriate synthetic media overnight and diluted to an R. L., C. B., and M. P. formal analysis; R. L. funding acquisition; R. L., C. B., and M. P. writing-original draft; R. L. project administration; R. L., C. B., and S. B. H. writing-review and editing; C. B., D. R., and M. P. conceptualization; C. B., D. R., M. B., and M. P. investigation; C. B., D. R., and M. P. methodology; I. F. and S. B. H. resources; M. P. supervision.


Potential impact of sea-ice initialization on the interannual climate predictability over the Weddell Sea is investigated using a coupled general circulation model. Climate variability in the Weddell Sea is generally believed to have association with remote forcing such as El Niño-Southern Oscillation and the Southern Annual Mode. However, sea-ice variability in the Weddell Sea has been recently suggested to play additional roles in modulating local atmospheric variability through changes in surface air temperature and near-surface baroclinicity. Reforecast experiments from September 1st, in which the model’s sea-surface temperature (SST) and sea-ice concentration (SIC) are initialized with observations using nudging schemes, show improvements in predicting the observed SIC anomalies in the Weddell Sea up to four months ahead, compared to the other experiments in which only the model’s SST is initialized. During austral spring (Oct–Dec) of lower-than-normal sea-ice years in the Weddell Sea, reforecast experiments with the SST and SIC initializations reasonably predict high surface air temperature anomalies in the Weddell Sea and high sea-level pressure anomalies over the Atlantic sector of the Southern Ocean. These results suggest that accurate initialization of sea-ice conditions during austral winter is necessary for skillful prediction of climate variability over the Weddell Sea during austral spring. Although the SIE over the Weddell Sea does not present a significant trend4, it undergoes distinct interannual variability with two peaks observed during austral autumn and spring6, probably associated with a semiannual cycle of the internal atmospheric variability in the Southern Hemisphere8.Ocean and climate systems in the Weddell Sea, located east of the Antarctic Peninsula (60°W-0°), are greatly influenced by sea-ice variability in the region across different timescales. On seasonal timescales, an increase in sea-ice extent (SIE) during austral autumn and winter inhibits air-sea interaction at the ocean surface and plays a critical role in generating cold and saline water that contributes to formation of the Antarctic Bottom Water5. Recently, the interannual sea-ice variability in the Weddell Sea has been suggested to play additional roles in modulating the atmospheric variability in the Atlantic sector of the Southern Ocean6. During the sea-ice melting phase of Oct–Dec, lower-than-normal SIC anomalies in the Weddell Sea receive more incoming solar radiation due to decreased surface albedo with a consequent enhancement of the surface air temperature (SAT) in the Weddell Sea. The high SAT anomalies act to weaken meridional temperature gradient and near-surface baroclinicity to the north of the Weddell Sea, which provides favorable conditions for reducing storm track activity and maintaining anticyclonic atmospheric anomalies in the Atlantic sector of the Southern Ocean. Although it does not play a role as strong as the other dominant remote forcing of El Niño-Southern Oscillation (ENSO) and the Southern Annular Mode (SAM), realistic representation of the SIC variability in the Weddell Sea is required for skillful prediction of the atmospheric variability there.The interannual variability of sea-ice concentration (SIC) in the Weddell Sea has so far been considered as a response to the atmospheric forcing12. The sea ice in the Weddell Sea generally tends to expand further north during El Niño years (vice versa in La Niña years). The associated cyclonic atmospheric anomalies developed northeast of the Weddell Sea aid to advect cold wind anomalies equatorward from the Antarctica12. In such a scenario, precise simulation of atmospheric teleconnection from ENSO, called the Pacific-South American pattern (PSA)13, may be necessary for skillful prediction of the sea-ice variability in the Weddell Sea. However, the sea ice in the Weddell Sea experiences interannual variability even in the absence of ENSO. That happens due to local influences arising in the Weddell Gyre circulation5 and the other atmospheric forcing such as the SAM15. Considering that sea ice plays a key role in surface heat flux exchange between the ocean and atmosphere, accurate initialization of sea-ice conditions, such as the SIC, may help skillful prediction of sea-ice variability in the Weddell Sea region and hence the local atmospheric variability.Many studies have reported that interannual SIC variation in the Weddell Sea is closely linked with ENSO17, this study would further explore predictability of the regional SIC variability and the associated atmospheric variability over the Weddell Sea.Along this line, this study aims to examine the predictability of the SIC in the Weddell Sea by conducting two reforecast experiments using a coupled general circulation model (CGCM): one experiment adopts SST initialization in which the model’s SST is nudged to the observed SST using surface heat flux, while the other additionally employs SIC initialization in which the model’s SIC is restored to the observed SIC. Differences in the two reforecast experiments highlight potential impacts of SIC initialization on prediction of the SIC variability and the overlying atmospheric variability. Since previous studies have mainly focused on prediction skills of the global average of sea-ice extent in the Antarctica18. In particular, regional seas in the West Antarctic  tend to undergo larger interannual variability of the SIC than that in the East Antarctic18.Climatologically, the Antarctic sea ice heads to its maximum extent in September, then retreats until reaching its minimum in February. During the early retreat season of Oct–Dec, the SIC in the Weddell Sea and the Ross Sea remains high above 50% Fig. . The intThe prediction skills of the Antarctic SIC are evaluated using the persistence values and anomaly correlation coefficients (ACCs) between the observed SIC and the predicted SIC in the two reforecast experiments Fig. . This alTo evaluate the improvement of the SIC prediction skills in the Weddell Sea, the ACCs of the SIC averaged in the Weddell Sea for the two reforecast results from September 1st are plotted against prediction month Fig. . Here we4. The observed SIE anomalies show a pronounced interannual variability with the maximum value (4.39 × 105 km2) in 1991 and the minimum value (−7.27 × 105 km2) in 2001. While the CTR reforecast results from September 1st tend to underestimate the amplitude of the observed interannual variability, the SIR experiment exhibits a clear improvement with the SIE anomalies that follow the observed ones, for example, the positive peak in 1991 and the negative peaks in the late 1990 s. The predicted SIE anomalies in the Weddell Sea are improved with an increase of the ACC score from 0.54 in the CTR experiment to 0.65 in the SIR experiment and also with a decrease in the root-squared-error (RMS) from 2.39 × 105 km2 in the CTR experiment to 2.19 × 105 km2 in the SIR experiment, although these differences are not statistically significant. These results suggest that the SIC initialization in the Antarctica improves the predicted amplitude and phase of the SIE anomalies in the Weddell Sea by around 10%. To demonstrate the improvement in a more quantitative way, we plotted the CTR and SIR reforecast results against the observed SIE anomalies in Fig. To estimate statistical differences between the CTR and SIR experiments, time series of SIE anomalies in the Weddell Sea (60°W-0°) during Oct–Dec were illustrated in Fig. et al.6, we defined high (low) sea-ice years as ones when the observed SIC anomalies during Nov-Jan exceed one positive (negative) standard deviation. Selection of one-month ahead of Oct–Dec period is because Nov-Jan is one of the two periods when the standard deviation of the SIC anomalies in the Weddell Sea becomes large, although the local atmosphere-ocean-ice interaction takes place more effectively during the Oct–Dec period when the SIC anomalies develop6. This procedure leads to identification of five low sea-ice years  and seven high sea-ice years  in the Weddell Sea. Considering the fact that the ACC during Oct–Dec is higher than that during Nov-Jan  anomalies during Oct–Dec of low Weddell sea-ice years were calculated in Fig. est Fig. . In part6. Both CTR and SIR reforecast results from September 1st show high SLP anomalies with anticyclonic surface wind anomalies in the Atlantic sector of the Southern Ocean and atmospheric teleconnection from the tropical Pacific  condition, initializing the SIT with long-term ocean-ice reanalysis24, which has a 1° × 1° horizontal resolution. Also, we employed the monthly mean SLP, horizontal winds at 10 m, and geopotential height at 250 hPa from the ERA-Interim atmospheric reanalysis, which has the same horizontal resolution25. To calculate monthly anomalies, we subtracted monthly climatology and removed a linear trend using a least squares method.To evaluate prediction accuracy of CGCM, we utilized the monthly mean SST and SIC data during 1982–2016 from the Optimum Interpolation SST (OISST) v227. The SINTEX-F2 model is an upgraded version of SINTEX-F1 model29. The atmospheric component is based on ECHAM530, which has 31 levels in the vertical on a T106 Gaussian grid. The oceanic component of the SINTEX-F2 is Nucleus for European Modeling of the Ocean 3 (NEMO3)31, which includes the Louvain-la-Neuve Sea Ice Model 2 (LIM2) sea ice model32 and has 0.5° × 0.5° horizontal resolution of ORCA configuration (ORCA05) with 31 levels in the vertical. Both the atmospheric and oceanic fields are exchanged every 2 hours with no flux correction by means of the Ocean Atmosphere Sea Ice Soil 3 (OASIS3) coupler33.Reforecast experiments were performed using a CGCM developed under Japan-EU collaboration, called the Scale Interaction Experiment-Frontier Research Center for Global Change 2 (SINTEX-F2) model22. The other is sea-ice restoring (SIR) experiment in which the model’s SIC is additionally restored to the observed SIC over both the Antarctic and Arctic regions. After 32-yr spun-up with the observed SST climatology since 1950, the model was initialized every month from 1982 to 2015 with two initialization schemes. Then, reforecast experiments were performed from 1st date of every month from 1982 to 2015, and the model was freely integrated over 9-month period.To examine potential impacts of sea-ice initialization on the climate predictability, we conducted two reforecast experiments: one is employed as control (CTR) experiment in which the model’s SST is initialized with the observed SST through adjustment of surface heat flux every month from 1982 afterwards24 and the high-resolution daily NOAA OISST analysis34. Then, we adopted three negative feedback values  to the surface heat flux in order to restore the model’s SST to the observed SST, which corresponds to 1, 2, and 3-day relaxation time for the 50-m mixed-layer temperature, respectively. Finally, we integrated the model with and without the vertical mixing scheme introduced by Sasaki et al.35. In the scheme, the model takes into account the strong vertical mixing associated with small vertical scale structures (SVSs) within and above the equatorial thermocline. These different initial conditions generate 12 ensemble members for the CTR and SIR experiments. For the SIC initialization, we restored the model’s SIC to the observed SIC with a timescale of 5 days. For each member, we removed monthly climatology over lead months which includes the model drift (systematic error) defined as differences in monthly climatology between the model and the historical datasets. Then, we removed a linear trend over the 33-yr integration period for each lead time to obtain monthly anomalies.To estimate the model’s uncertainty related to the initial values, we prepared 12 different initial conditions: we first employed two different SST datasets of the weekly OISST v2Supplementary Figures file


Mitragyna speciosa has been widely used as an opium substitute, mainly due to its morphine-like pharmacological effects. This study investigated the effects of M. speciosa alkaloid extract (MSE) on human recombinant cytochrome P450 (CYP) enzyme activities using a modified Crespi method. As compared with the liquid chromatography-mass spectrometry method, this method has shown to be a fast and cost-effective way to perform CYP inhibition studies. The results indicated that MSE has the most potent inhibitory effect on CYP3A4 and CYP2D6, with apparent half-maximal inhibitory concentration (IC50) values of 0.78 µg/mL and 0.636 µg/mL, respectively. In addition, moderate inhibition was observed for CYP1A2, with an IC50 of 39 µg/mL, and weak inhibition was detected for CYP2C19. The IC50 of CYP2C19 could not be determined, however, because inhibition was <50%. Competitive inhibition was found for the MSE-treated CYP2D6 inhibition assay, whereas non-competitive inhibition was shown in inhibition assays using CYP3A4, CYP1A2 and CYP2C19. Quinidine (CYP2D6), ketoconazole (CYP3A4), tranylcypromine (CYP2C19) and furafylline (CYP1A2) were used as positive controls throughout the experiments. This study shows that MSE may contribute to an herb-drug interaction if administered concomitantly with drugs that are substrates for CYP3A4, CYP2D6 and CYP1A2. The extract from  Mitragyna speciosa Korth is a tropical herb plant belonging to the family Rubiaeceae found mainly in Southeast Asian countries such as Thailand and Malaysia. It is known as “Biak-biak” or “Ketum” in Malaysia, and as “Kratom” in Thailand -7-methoxy-4-methylcoumarin (AMMC), 7-benzyloxy-4-(trifluoromethyl)-coumarin (BFC) and 7-hydroxy-4-(trifluoromethyl)-coumarin (HFC); and reduced nicotinamide adenine dinucleotide phosphate (NADPH) regeneration system (NRS) were purchased from Gentest Corporation , while all the other chemicals and standard references, including 3-cyano-7-ethoxycoumarin (CEC), quinidine, ketoconazole, tranyl-cypromine and furafylline, were purchased from Sigma Aldrich .Recombinant human CYP3A4, CYP2D6, CYP1A2 and CYP2C19 enzymes; marker substrates 3-[2-, which is close to the national border between Malaysia and Thailand. A methanol-chloroform extraction method was used to extract the alkaloids. The leaves (5 kg) were dried and soaked in 4 L methanol for three days. The methanol was filtered and the filtrate was evaporated using a rotary evaporator. This extraction and evaporation procedure was repeated three times. After that, 1 part of crude methanol extract was redissolved in around 35 parts of 1:9 acetic acid-distilled water and then washed with an adequate amount of hexane. The acidic layer was made alkaline to pH 9 using ammonia hydroxide and extracted with chloroform. The collected organic layer was filtered through anhydrous sodium sulphate, and the filtrate was concentrated using a rotary evaporator to obtain 5 g of crude alkaloid extract, which was then dissolved in dimethyl sulfoxide (DMSO) and the presence of alkaloid was confirmed using the Dragendorff test [Fresh leaves of rff test .m values that had been established by Crespi et al. were used as the substrate concentrations in this assay [The fluorescence readings of the corresponding metabolites were measured at 10, 20, 30, 45, 60 and 90 min after adding the NRS solution. Kis assay . Optimalm and Vmax values for each of the CYP enzymes were determined by using Michealis-Menten plots. The concentration of DMSO did not exceed 0.2% and that of ACN did not exceed 2%. DMSO and acetonitrile (ACN) have been known to be potent inhibitors of CYP enzymes. Reactions were carried out with 10 different substrate concentrations: 1–200 µM BFC for CYP3A4, 0.04–100 µM AMMC for CYP2D6, 0.13–100 µM CEC for CYP1A2 and 0.23–300 µM CEC for CYP2C19, in three-fold dilutions with respective buffer. The stock concentration was 20 mM for all the substrates. The previously determined optimal incubation times were used throughout the experiment. KTM HB black, flat-bottom microplates from Dynex . The final concentrations of the plant extracts (0.05–1000 µg/mL) and positive controls (0.005–100 µM) were prepared by using a 3× dilution method. NRS was prepared by using 3.3 mM of glucose-6-phosphate (G6P), 0.06 U of glucose-6-phosphate dehydrogenase (G6PDH), 3.3 mM of MgCl2 and 1.3 mM of nicotinamide adenine dinucleotide phosphate (NADP+). The reaction was initiated by addition of NRS (20 µL) into a mixture of pre-warmed enzyme/substrate mix (30 µL), test compound (20 µL) and buffer (80 µL), followed by 30 min of incubation at 37 °C. Following that, the reactions were terminated by addition of stop solution . The released fluorescence was scanned using a fluorescence plate scanner, VICTORTM X5, Perkin Elmer , at the optimal wavelength of the metabolite. Positive and negative controls were run with every assay. Data were exported and analyzed with PRISM software (version 5.04). IC50 values were calculated using the relative IC50 determination. The results are presented as the mean of three replicates for at least two independent experiments and the protocol is summarized in The assays were performed by using the modified Crespi method . Incubati) values were further determined for concentrations of substrates ranging from 3.125 to 100 µM at different concentrations of MSE (0.125–300 µg/mL). Lineweaver-Burk plots, Dixon plots and secondary reciprocal plots were plotted to determine the Ki values and mode of inhibition.The apparent inhibition constant , implying a high potential risk of herb-drug interactions, especially when patients consume large amounts of extract. The extract was found to moderately inhibit CYP1A2  but there was no significant inhibition of CYP2C19 (<50% inhibition at the highest concentration of MSE). Competitive inhibition was observed for CYP2D6 and non-competitive inhibition was found for the other CYP isoforms. At present, the active constituent responsible for inhibition of the CYPs remains unknown. Further work is required to identify the inhibition properties of each of the active constituents of M. speciosa extracts.The modified Crespi CYP inhibition method used in this study is a relatively fast and cost-effective way to perform a CYP inhibition assay. In this study,