Aetobatus narinari—we compared the skin, gill, and cloaca microbiomes of wild individuals to those at Georgia Aquarium. Whitespotted eagle ray microbiomes from Georgia Aquarium were also compared to those of cownose rays (Rhinoptera bonasus) in the same exhibit, allowing us to explore the effect of host identity on the ray microbiome.Animal-associated microbiomes can be influenced by both host and environmental factors. Comparing wild animals to those in zoos or aquariums can help disentangle the effects of host versus environmental factors, while also testing whether managed conditions foster a ‘natural’ host microbiome. Focusing on an endangered elasmobranch species—the whitespotted eagle ray Long-term veterinary monitoring indicated that the rays in managed care did not have a history of disease and maintained health parameters consistent with those of wild individuals, with one exception. Aquarium whitespotted eagle rays were regularly treated to control parasite loads, but the effects on animal health were subclinical. Microbiome α- and β-diversity differed between wild versus aquarium whitespotted eagle rays at all body sites, with α-diversity significantly higher in wild individuals. β-diversity differences in wild versus aquarium whitespotted eagle rays were greater for skin and gill microbiomes compared to those of the cloaca. At each body site, we also detected microbial taxa shared between wild and aquarium eagle rays. Additionally, the cloaca, skin, and gill microbiomes of aquarium eagle rays differed from those of cownose rays in the same exhibit. Potentially pathogenic bacteria were at low abundance in all wild and aquarium rays.For whitespotted eagle rays, managed care was associated with a microbiome differing significantly from that of wild individuals. These differences were not absolute, as the microbiome of aquarium rays shared members with that of wild counterparts and was distinct from that of a cohabitating ray species. Eagle rays under managed care appear healthy, suggesting that their microbiomes are not associated with compromised host health. However, the ray microbiome is dynamic, differing with both environmental factors and host identity. Monitoring of aquarium ray microbiomes over time may identify taxonomic patterns that co-vary with host health.The online version contains supplementary material available at 10.1186/s42523-022-00187-8. It is now widely accepted that host-associated microbiomes play critical roles in controlling host metabolism, physiology, behavior, and overall health –4. Both In marine systems, large predatory fish are sentinels of ecosystem health and are increasingly targeted as subjects for microbiome research due to their popularity in aquariums, longer lifespans, key roles in food webs, propensity for accumulating toxins in tissues and fat, and potential for carrying pathogenic microbes that can affect both human and environmental health –16. In pAetobatus narinari) are mesopredatory batoids that play a key role in tropical and warm-temperate coastal water food webs, filling an intermediate position where this species acts as both predator (consuming gastropods and other benthic mollusks) [Whitespotted eagle rays (ollusks) , 25 and ollusks) , 27. Whiollusks) –33, and Rhinoptera bonasus) to test the effect of host identity. We hypothesized that ray microbiomes would differ between aquarium and wild individuals, demonstrating an environmental influence on the ray microbiome, and between ray species in the same exhibit, demonstrating a host phylogenetic influence on the ray microbiome, with these differences varying in magnitude according to body site.Whitespotted eagle rays can be kept and studied in zoos and aquaria. At Georgia Aquarium (Atlanta), whitespotted eagle rays have been housed for over a decade in a large exhibit alongside other ray species, whale sharks, and thousands of smaller fishes. The eagle rays are charismatic and popular with aquarium guests, helping raise awareness about elasmobranchs and their environments. Georgia Aquarium also collaborates with capture-tag-release studies of wild spotted eagle rays. Access to whitespotted eagle rays under managed care at Georgia Aquarium, in combination with collections of samples from wild populations, provides a unique opportunity to compare microbial structure in aquarium versus wild individuals. This comparison can help pinpoint potentially important prokaryotes conserved between aquarium and wild populations, particularly at external (skin and gill) and internal (cloaca/gut) body sites that have been shown in other fishes to harbor unique microbiomes tied to host health , 7 AdditMicrobiome swabs were collected (described below) from a total of 18 wild whitespotted eagle rays in collaboration with Mote Marine Laboratory in Sarasota, Florida, and 15 aquarium-housed whitespotted eagle rays and 7 cownose rays in collaboration with Georgia Aquarium , hard-shell clam (Mercenaria mercenaria), Jonah crab , knobbed whelk (Busycon carica), Atlantic sea scallops (Placopecten magellanicus), whiteleg shrimp (Litopenaeus vannamei) and blue mussels (Mytilus edulis). In contrast, cownose rays are broadcast fed capelin , Atlantic herring (Clupea harengus), mackerel (Scomber scombrus), surf clam, whiteleg shrimp, and night smelt (Sprinchus starksi), which is pumped to the bottom of the exhibit once or twice a day.Georgia Aquarium’s OV exhibit is a 6.3 million gallon artificial seawater  tank containing thousands of fish representing over 50 species from the open ocean. The water from the exhibit is filtered through a system composed of foam fractionators (protein skimmers), sand filters, ozone contact towers, countercurrent heat exchangers, sulfur-based denitrification vessels, and a deaeration tower, at a rate of 130,000 gallons per min, turning over approximately 4 million gallons once per hour . PhysicoSamples were collected from aquarium eagle rays and cownose rays during routine veterinary examinations performed at least once a year or more frequently as veterinary or husbandry needs dictated. For these examinations, animals were transferred from the OV exhibit into a small holding pool with oxygenated water. Veterinary examinations included a visual health assessment and blood sampling for complete white blood cell counts and differentials, and a biochemistry panel. All parameters were within acceptable healthy ranges for all animals for the duration of this study. For microbiome sample collection, the rays were lightly restrained for the dorsal skin swab sampling and then rolled into dorsal recumbency to induce tonic immobility for gill and cloacal swab collection. Swabs were collected by gently rubbing sterile swabs along the gill, skin, or inside the cloaca, collecting mucus and microbes over the entire surface of the swab. Cloaca swabs were collected by placing the swab at least two inches into the cloaca and swirling the swab at the base of the spiral colon. Swabs were placed into 2 mL cryovials containing 1 mL of RNA/DNA stabilizing solution  and stored at − 80 °C until lab processing. After examination, the rays were released into the OV exhibit and often resumed feeding the same day. Because microbiome samples were collected during routine veterinary examinations, and the frequency of these exams varied between individuals, certain individuals were sampled more than once during the same sampling period. For wild whitespotted eagle ray sample collection, rays were circled with a seine net, brought onboard the boat, and held in a water-filled, oxygenated tank while samples were collected in a manner similar to that described for Georgia Aquarium individuals.Water samples were obtained from both the OV and wild environment to explore how seawater microbiome composition influences that of microbiomes on the ray body. Water samples from the OV water column were collected by filtration through 0.2 μm Sterivex filters and placed in RNA/DNA stabilizing solution (as described in ) as partDNA was extracted from each swab or Isopore filter (water samples) by transferring the swabs/filters directly into Powerbead tubes from the Qiagen DNeasy PowerSoil DNA extraction kit following the manufacturer’s instructions. Extraction blanks  were performed for each new kit. DNA was extracted from Sterivex filters using a phenol–chloroform method described in . For eacSequence data were analyzed using DADA2  and QIIMMollicutes. As a result, Mollicutes-affiliated amplicons were removed from all samples, and the contaminated kit was not used again. All other extraction blanks did not pass quality control, validating our quality control method (described below).Sequences classified as Chloroplast or Mitochondria were removed from all samples. Extraction blanks were processed following the procedures described above. One of these blanks was dominated by Surface swabs of marine animals tend to produce low DNA yields, which can result in higher stochasticity in taxonomic composition among replicate samples. To address this, our sample set contained biological replicates  and technical replicates . To identify instances of high-replicate stochasticity, weighted UniFrac  distanceAfter quality control and merging replicates, 139 samples remained. A summary of the samples in the final dataset and the associated metadata can be found in Additional file To identify the number of ASVs shared between different samples, Venn diagrams were constructed in Python using the package matplotlib-venn 0.11.5 with the merged, rarefied ASV table. Venn diagram calculations were performed to assess the number of ASVs shared with seawater. ASVs shared between seawater and host microbiomes were removed to assess the number of ASVs shared between body sites, animals from different environments (wild vs. aquarium), and species (whitespotted eagle ray vs. cownose ray). For each body site, we identified ASVs that differ in abundance between aquarium and wild individuals using the package DESeq2 in R  with theWhitespotted eagle ray and cownose ray behavior and feeding were closely monitored at Georgia Aquarium, and animals were handled routinely for veterinary examination. All rays appeared healthy and had normal blood panels throughout the sampling period, with the exception of one whitespotted eagle ray  and 34% (± 43) of total sequences, respectively  and absent from the gills of aquarium individuals. Only one ASV, identified as Alkanindiges illinoisensis, was significantly enriched in the skin of aquarium versus wild whitespotted eagle rays. This ASV was present in half of aquarium individuals, but at an average relative abundance of only 0.3%, and was absent in all wild individuals.Across both aquarium and wild individuals, the most abundant bacteria in the cloaca included a single ASV classified as g 23% ± 3 and 34% g 23% ± 3 and 34% g 23% ± 3 and 34% Prokaryotic richness and β-diversity of aquarium cownose rays were higher compared to those of aquarium whitespotted eagle rays for all body sites , we analyzed microbiome composition among body sites. For each ray group, cloaca microbiomes were significantly less diverse in terms of α-diversity compared to those of other body sites  [We found significantly lower α-diversity in aquarium whitespotted eagle ray microbiomes, compared to those of wild counterparts. Previous studies on marine and terrestrial animals also show decreases in α-diversity associated with human-managed environments –51. In tversity) –52. We oversity) , these cTyzzerella sp. bacteria was over 300 times higher in the cloaca of aquarium versus wild whitespotted eagle rays. Tyzzerella sp. have been found in the gut microbiota of diverse animals [Tyzzerella have been associated with low-fiber diets containing high levels of processed meat, fat, sugar, and sodium [While the cloaca and fecal microbiome can be influenced by habitat changes , 55, thi animals –59 and a animals , 60. In d sodium , althougHelcococcus were substantially enriched (> 10 ×) on whitespotted eagle rays in the aquarium, where this genus was also enriched in aquarium water compared to the natural seawater. After removing ASVs shared with seawater, the only ASV that differed significantly in abundance between the skin microbiome of aquarium and wild whitespotted eagle rays was classified as Alkanindiges illinoisensis, an alkane-degrading bacterium not previously reported in marine ecosystems [Alkanindiges illnoisensis was found in lake sediments [Kistimonas, was significantly more abundant in the gills of wild compared to aquarium rays. Kistimonas and Kistimonas-like species have been identified in marine invertebrate gill [Kistimonas includes pathogenic species [Previous studies have shown that fish external surfaces such as the skin and gills can be strongly influenced by environmental processes , 63. Amoosystems –69. Addiediments . Its rolate gill  and skinate gill  microbio species , we haveBetaproteobacteriales (incertae-sedis) ASV was present only in the cloaca of cownose rays, while Tyzzerella sp. was only present in whitespotted eagle rays. Such variation may be driven by diet or intestinal physiological differences between hosts, notably as these two ray species consume different, though nutritionally similar, prey items in the aquarium. In contrast, skin and gill microbiomes are relatively similar between ray species. After excluding ASVs shared with seawater, no ASVs with a relative abundance above 1% differed in representation between whitespotted eagle rays and cownose rays, raising the possibility that shared environmental conditions may drive convergence in external microbiomes [Microbiome composition was broadly similar between co-habitating whitespotted eagle rays and cownose rays at all body sites, but also contained species-specific signatures, similar to that observed in teleost fishes . Speciesrobiomes , 75. IntFlavobacteriales was the most abundant, contributing up to 99% of microbiome sequences in certain samples. Flavobacteriales have been recognized as part of the intestinal microbiome of trout [Flavobacterium species have also been associated with disease in fish [Flavobacteriales in the cloaca microbiome of whitespotted eagle rays.We defined shared ASVs as those ASVs detected in at least one wild and one aquarium individual. The number of ASVs shared between the cloaca of wild and aquarium whitespotted eagle rays was fewer compared to those shared between the cloaca and seawater. However, those few shared ASVs were dominant members of the community in both wild and aquarium individuals. This suggests that fewer ASVs with higher relative abundances compose the microbiome of the cloaca in whitespotted eagle rays, while less abundant environmental ASVs from the water column may be transient in the cloaca microbiome. For example, the shared cloaca microbe of trout  and otheof trout , 78. Cer in fish , 80, altPhotobacterium damselae also dominated the cloaca microbial community of both wild and aquarium rays. It is important to point out that Photobacterium harbors multiple copies of the 16S rRNA gene [RNA gene , which mRNA gene , rays apRNA gene –85.The Anna-Karenina principle may also be useful in estimating host health and/or dysbiosis. This principle suggests that microbiome perturbations translate into a variety of unstable states  and not a single dysbiotic state . We did Betaproteobacteriales was particularly abundant in wild whitespotted eagle rays. Members of the Betaproteobacteriales are ubiquitous in various aquatic environments [Helcococcus was particularly enriched in aquarium individuals. This ASV shared 98% identity with an uncultured bacterium clone from California sea lion rectal swabs [Helcococcus has been associated with disease [Helcococcus commonly colonizes diverse body sites in marine animals including the respiratory tract of dolphins [Cephalopholis urodeta, a carnivorous coral reef fish [Helcococcus to host health or physiology, if any, remains unknown.Among the ASVs composing the shared gill microbiome, one ASV classified as ronments , 88 and ronments . Finallyal swabs , as wellal swabs . In huma disease . Howeverdolphins , 93 and dolphins , and theeef fish . The conThe microbiomes of whitespotted eagle rays under managed care at Georgia Aquarium had lower α-diversity and differed in community structure (β-diversity) compared to microbiomes of wild rays. However, the magnitude of these differences varied by body site niche. Compared to microbiomes of the gill and skin, the cloaca microbiome differed the least between aquarium and wild whitespotted eagle rays, suggesting that the diet of aquarium animals  is either similar to that of wild animals or that the effect of host physiology prevents a major change in cloaca microbiome structure. While wild individuals appeared healthy, their health status was not medically determined, nor was their age, and these factors may contribute to some of the variation we observe in this study. However, it is likely that all aquarium and wild animals sampled spanned diverse age groups, suggesting that the patterns observed are primarily driven by other parameters such as changes in environmental conditions. Finally, the microbiome of aquarium whitespotted eagle rays was different from that of cownose rays in the same exhibit, highlighting the influence of host related factors in ray microbial community assembly.Our findings provide a framework for interpreting future data on ray microbiomes, helping identify signatures of a healthy microbiome as well as the extent to which such signatures vary due to environmental versus host factors. Future research should build upon this framework, for example by examining microbiome function and extending microbiome characterizations across gradients of host and ecosystem health. Such studies may identify microbial markers that can be used to guide managed care and conservation strategies.Additional file 1: Figures S1–S6 and Tables S1–S3 reporting sample identity and collection information, and additional microbiome diversity statistics.


Our study thus clarifies the phase behaviour of the high-pressure ices and reveals peculiar solid–solid transition mechanisms not known in other systems.Most experimentally known high-pressure ice phases have a body-centred cubic (bcc) oxygen lattice. Our large-scale molecular-dynamics simulations with a machine-learning potential indicate that, amongst these bcc ice phases, ices VII, VII′ and X are the same thermodynamic phase under different conditions, whereas superionic ice VII″ has a first-order phase boundary with ice VII′. Moreover, at about 300 GPa, the transformation between ice X and the P Many experimentally known high-pressure ice phase are structurally very similar. Here authors elucidate the phase behaviour of the high-pressure insulating ices and reveal solid-solid transition mechanisms not known in other systems. Intriguingly, at high pressures between 2 GPa to 200 GPa, with the exception of one face-centred cubic (fcc) superionic phase (ice XVIII)3, all known phases of ices have a body-centred cubic (bcc) lattice of oxygen atoms4, with subtle differences in the positions and the dynamics of the hydrogen atoms.Water ice exhibits remarkable structural diversity: there are currently 20 experimentally confirmed polymorphs with distinct atomic arrangementsbcm phase, with a distorted hexagonal close-packed oxygen lattice, to be stable5. It has been proposed that Pbcm forms due to a dynamic instability in ice X above ~400 GPa6.Ice VII is the proton-disordered counterpart of the antiferroelectric phase ice VIII, and hydrogen atoms can occupy any of the half-diagonals of the bcc unit cell. Ice VII′ forms upon compression (above ~40 GPa at 300 K) during which hydrogen atoms exhibit substantial translational movements along the bcc half-diagonal, leading to a bimodal distribution in the hydrogen positions between two oxygen atoms. Ice VII″ has a stronger delocalisation of protons compared to VII′, and is the solid phase that coexists with the liquid above ~1000 K. Above ~80 GPa, the bimodal distribution of hydrogen positions becomes unimodal and peaks at the centre: this is ice X. Between ~300 GPa and 700 GPa, density functional theory (DFT) calculations based on the PBE functional predict the P7 found a first-order transition between VII′ and VII″ using DFT-MD, based on the sudden changes in the diffusivity, the delocalisation of the H atoms, internal energy and elastic constants. The prediction of this isostructual first-order transition was later corroborated by the experimental evidence of a kink on the melting line Tm at 14.6 GPa, which has been interpreted to correspond to the VII′–VII″ transition line meeting Tm8. By contrast, for the VII–VII′–X transition sequence, experiments did not find any first-order features in the equations of state11. Similarly, a VII/X equation of state parameterised from DFT-MD simulations suggests no first-order transition occurs, but does not exclude a possible second-order transition12, and effective potentials for the energy as a function of the proton position between oxygen atoms, constructed from DFT simulations, have been shown to convert continuously from a double well (ice VII) to a single well (ice X)13. Some recent work proposed a theoretical framework based on dynamic partition functions to gain insight into the transition between ice VII and its analogues with faster proton dynamics14. In DFT simulations, X was observed to transform continuously into Pbcm upon increased pressure at 0 K without a noticeable barrier starting at about 350 GPa via a transformation path analogous to that proposed for the bcc–hcp transitions in many metals5.We have only rather limited understanding of the thermodynamic distinctions and the transitions between these high-pressure ice phases. For example, it is not fully resolved whether some of these phase transitions are of first order. The problem is partly due to the limited resolution of the experimental measurements at high pressures, and partly because the system size and timescale accessible to DFT molecular-dynamics (MD) simulations are far from what is required to approach the thermodynamic limit of ice systems. Hernandez and Caracas16.From a thermodynamic point of view, it is thus not clear which of these ice phases are actually different phases of water , rather than the same thermodynamic phase exhibiting different behaviours at different conditions. Determining the nature of these phase boundaries in theoretical calculations entails looking for signs of hysteresis or discontinuities in density or energy during phase transitions, or computing the form of the chemical potentials of the phases. Both approaches require long simulations of large system sizes in order to approach the thermodynamic limit, and consequently standard DFT simulations are generally not feasible because of their severe system size and timescale limitations. Furthermore, the transition pathways amongst the high-pressure ice phases remain largely elusive. Understanding how such transitions occur and in particular the magnitude of the kinetic barrier provides additional insights into the structural relationships between the ice phases, and is also crucial for designing and interpreting high-pressure experiments17 approximation to the exchange-correlation functional, as well as a recently constructed machine-learning potential (MLP)18 fitted to the PBE reference that has been employed to predict the phase behaviour of superionic water. The MLP reproduces accurately the free energies of the superionic water phases of the underlying PBE reference with very small residual error18. We first compute the phase diagram of ice at P < 100 GPa for the MLP and probe the nature of the phase boundaries between all the ice phases that share the bcc oxygen lattice, and then investigate the X–Pbcm solid–solid transition mechanism at higher pressures. Crucially, the combination of first-principles methods, MLP, free-energy estimations and enhanced sampling methods enables us to provide both a thermodynamic picture of the high-pressure ice phases and a detailed mechanistic view of the transitions between them. We not only account for the influence of thermal and quantum fluctuations on the free energies, but also model the dynamic transitions between the ice phases.In this study, we use a first-principles description of high-pressure water based on the Perdew–Burke–Ernzerhof (PBE)3, all the relevant ice phases  have a bcc lattice of oxygen atoms and differ by the position and the dynamics of the hydrogen atoms. In this section, we investigate whether there are structural differences between these bcc-based ices or if any differences arise entirely from the dynamics of the H atoms, and whether phase transitions between them are of first order. Unlike previous studies, here we use a thermodynamic approach by focusing on chemical potentials of sufficiently large systems rather than solely relying on the equations of states computed from DFT.Between 2 GPa and 200 GPa and below the melting line, except for ice XVIII19 [Supplementary Fig. DH > 10–8 m2 s–1) used as a proxy for the superionic transition by Cheng et al.18. The background colour in Fig. 20: we classify phases according to the frequency of displacements of protons after 0.4 ps by considering where the mode of the displacement distribution is and the skewness of the distribution [Fig. 19 line beyond which the protons exhibit only one peak in the O–H pair correlation function at distances smaller than the nearest O–O distance, which serves to delineate ice X from ice VII.The thermodynamic approach is made feasible by employing the MLP, which has first-principles accuracy but with orders-of-magnitude lower cost. To validate the MLP, we first compare the lattice constants, potential energy and diffusivities computed from the MLP MD simulations with previous first-principles molecular-dynamics (FPMD) simulations20 and Queyroux et al.19. The presence of an apparent discontinuity in density and enthalpy in the superionic transition may suggest that this is a first-order transition, although it is often in practice difficult to distinguish between a discontinuous and merely a rapidly changing function. Moreover, for the remaining phases, based on dynamic criteria alone it is not immediately obvious whether they are thermodynamically distinct. To ascertain the nature of the phase transitions in this relatively low-pressure region of the phase diagram, we computed the liquid–solid coexistence curve using thermodynamic integration along isotherms and isobars23 of the appropriate solid and liquid phases starting from an initial coexistence point determined by direct coexistence, and verified by direct-coexistence simulations24 at several other points. We then determined the VII′–VII″ coexistence line by thermodynamic integration along isobars starting from the liquid–solid coexistence curve. The resulting thermodynamic coexistence curve [Fig. T–P coexistence curve corresponds to the change in the volume per particle Δv and the entropy per particle Δs via the Clapeyron equation, ∂T/∂P = Δv/Δs = TΔv/Δh, where Δh is the enthalpy change per particle. The smaller gradient for the VII′–VII″ transition compared to that of the VII″–liquid and VII′–liquid transitions could arise either from a larger denominator or a smaller numerator, or both. Using the data plotted in Supplementary Fig. v is about 30% larger for the VII″–VII′ transition than for the VII″–liquid transition at their respective coexistence points, Δh is even larger, leading to an overall lower ∂T/∂P. It therefore appears that the enthalpy loss, and, in turn, the entropy gain of the highly superionic phase VII″ is the driving force for the VII′–VII″ phase transition, and is the reason that the melting point of the solid is considerably higher than it might otherwise be. However, the liquid–solid coexistence line does not appear to have a significant change in gradient precisely at the triple point; indeed close to the triple point, all three phases have similar densities and enthalpies.The qualitative phase behaviour we observe is rather similar to that studied by Zhuang et al.10 and previous simulation work13. Moreover, none of these phases appears to be metastable with respect to the others in this class. It is therefore not possible to determine the order of such phase transitions by computing chemical potentials, determining where they cross over and computing their gradients at the binodal point. Instead, we compute the chemical potential by thermodynamic integration along isotherms and isobars across all phases of interest. In principle, thermodynamic integration can only be performed along reversible paths, i.e., without any first-order phrase transitions along the way23. If we are able to integrate the chemical potential along a ‘closed loop’ in T–P space, we therefore expect that, should any first-order transitions occur along the way, the integral would in principle have a discontinuity and when integrated numerically will result in different values for the chemical potential for the same point when arrived at along different pathways. We have computed the chemical potential along several such pathways, including that indicated by the example route in Fig. T–P space and over any combination of the phases shown in yellow, red and green in Fig. For the remaining ice phases , VII′ (R), X) shown in Fig. bcm is believed, based on geometry optimisation at zero temperature using DFT, to become stable over X at pressures higher than ~350 GPa18. However, the precise location of the phase boundary at finite temperatures with the inclusion of nuclear quantum effects (NQEs) is unknown, and the mechanism of this X–Pbcm transition is not clear. Here we first provide a static-lattice picture of the ice phases at the DFT level, and then exploit the MLP, thermodynamic integration and enhanced sampling to provide both thermodynamic and kinetic descriptions of the X–Pbcm transition.Below the superionic transition temperature, P25. We computed the 0 K enthalpy at pressures from 100 GPa to 1100 GPa for different ice phases predicted by the PBE DFT functional. The MLP and revPBE0-D3 results are shown in Supplementary Fig. First we present the 0 K description for different ice phases predicted by the PBE DFT functional. We considered all the ice structures reported in ref. 2/nnm (ice X(orthorhombic)), Pnm (ice X(cubic)), Pbca, Pbcm, P21212 and P42212 phases, whose structures are illustrated in Fig. 1212 and P42212 structures in Fig. bcm phase. Both Pbcm and Pbca have a distorted hcp oxygen lattice and differ only in the position of hydrogen atoms at P > 800 GPa. In what follows, we focus just on the Pbcm phase at 100 < P/GPa < 800, as Pbca, P21212 and P42212 are either the same or simply have a different stacking sequence in this pressure range.In Fig. nm), and above 300 GPa the cell shape is orthorhombic (space group P42/nnm). We use ‘X’ to refer to the phase with the cubic–orthorhombic transition at 300 GPa. The breaking of the cubic symmetry of ice X probably arises from the dynamic instability revealed by the imaginary phonons, which was previously reported in ref. 27. Although ref. bcm transition, here we suggest that the cubic–orthorhombic transition is an alternative way of gaining dynamical stability.X(orthorhombic) and X(cubic) both have a body-centred oxygen lattice; however, the cubicity of their unit cells differs above 300 GPa. Our phonon calculations [Supplementary Fig. bcm structures in Fig. P < 300 GPa, the cell parameters of the Pbcm and the X(orthorhombic) structure indeed correspond to those of X(cubic). Above 300 GPa, the shape of the cell, which is related to the configuration of the ice structures, exhibits a sharp, first-order-like change at the transition. By contrast, the enthalpy  at the bifurcation point. To the best of our knowledge, such a bifurcation point has not been observed before in any other systems.In the pressure range from 100 GPa to 300 GPa, all the phases considered collapse to the cubic ice-X structure, while the enthalpy difference gradually increases at higher pressures. To probe this collapse further, we show the change in lattice constants as a function of pressure for the X(cubic), X(orthorhombic) and Plpy Fig. b, the pobcm phase transition, we investigated its behaviour at finite temperatures. To this end, we ran well-tempered metadynamics28 simulations with adaptive bias29 employing the MLP in the NPT ensemble at a temperature of 1000 K, with all the simulation supercell parameters allowed to fluctuate. We employed an orthorhombic supercell of 64 molecules, illustrated in Fig. bcm ice structures, as well as other Pbcm-like structures with mixed stacking. We used two collective variables (CVs): lxx,  lxy,  lxz),  and . It is worth noting that, even if the geometry of the supercell is carefully selected, when probing solid–solid transitions using metadynamics, there can be some hysteresis which affects the computed free-energy profile. Each set of metadynamics simulations lasted 3.5 ns, which is considerably beyond the timescale accessible in DFT simulations, and is only made tractable by using the MLP. To illustrate the validity of the MLP for describing transition processes during metadynamics simulations, we recomputed the PBE DFT energies for 2,500 snapshots collected from metadynamics simulations and demonstrated that the PBE and the MLP energies agree for these snapshots  corresponding to structures similar to Pbcm but with different stacking sequences. The Pbcm phase has two minima on the FES because it can fit into the simulation box in two different ways due to its specific crystal structure. SF1 and SF2 have higher 0 K enthalpies compared to the Pbcm structure, and are analogous to the P21212 and P42212 structures illustrated in Fig. The computed free-energy surfaces (FES) at 200 GPa, 300 GPa, 400 GPa and 600 GPa and a temperature of 1000 K are presented in Fig. P = 600 GPa, the FES still has the five regions that correspond to the four distinct ice structures, although they are more separated and the transition states have much higher free energy (~0.18 eV per f.u.). At P = 300 GPa, the five regions are instead connected with no transition barrier. At P = 200 GPa, only one minimum is explored in our metadynamics simulations even when a substantial amount of a biasing potential has been added. This minimum corresponds to the X phase, and the main variance within the minimum is associated with the elongation of the supercell. In addition, we use green diamond symbols to indicate the location of X(cubic) in the space of the CVs in Fig. During the metadynamics run, the system had frequent transitions between the five states. The regions that can be considered as transition states are wide and only have a free-energy excess of ~0.03 eV per formula unit (f.u.). At P ≥ 300 GPa), there are distinct phases including Pbcm, X and the phases with stacking faults, while at low pressures (P ≤ 300 GPa), all the phases collapse into X. These solid–solid transitions are more facile at lower pressures, as suggested by the lack of activation barriers at 300 GPa and the lower barriers at 400 GPa compared to 600 GPa. The transition pathway from X to Pbcm involves a highly collective shuffling of the {100} planes in X, similar to the bcc–hcp transitions in many metals26, and therefore does not entail a nucleation step. We also performed metadynamics simulations using system sizes 8 times as large , or along isotherms and isobars to calculate the chemical-potential differences at other pressure and temperature conditions. It is worth noting that, as the X(cubic)–X(orthorhombic) and X(cubic)–Pbcm transitions at about ~300 GPa are peculiar and accompanied by sharp jumps in the lattice parameter  discontinuities of the enthalpy and density in simulations using system sizes large enough to investigate thermodynamic behaviour. We have explicitly computed their chemical potentials to confirm that the VII′–VII″ phase transition is of first order, and we determined the associated thermodynamic coexistence curve between them. We speculate that one possible reason for the various different reported triple points is that in our simulations, it does not appear that the solid–liquid coexistence curve changes gradient significantly precisely at the triple point; there is a more gradual change of gradient as higher pressures and temperatures are reached, as the supercritical ice phase becomes progressively more different from its low-temperature less-disordered analogue.The superionic transition in bcc ice phases has been studied in both theoretical and experimental work10. We confirm here that ices VII, VII′ (T), VII′ (R) and X show no clear discontinuities in thermodynamic quantities including density and enthalpy, and in turn the chemical potentials. The only differences between them come from the positions and the dynamics of the hydrogen atoms. We thus regard these phases to be merely different manifestations of the same thermodynamic phase under different conditions.In X-ray diffraction experiments, no clear distinction has been seen between the other bcc ice phases, including VII and Xbca, Pbcm, P21212 and P42212) collapse into ice X below 300 GPa. This means that there is no distinction between them at pressures from 100 GPa to 300 GPa. Moreover, the transition barriers between X, Pbcm and the structures with stacking faults increase as the pressure increases. Above 300 GPa, X and Pbcm are thermodynamically distinct phases, and we have computed their relative chemical potentials, fully accounting for thermal and nuclear fluctuations. Pbcm has not yet been experimentally observed, despite past and on-going dynamical compression experiments. Our computed phase diagram in Fig. bcm and X is largely independent of temperature. By contrast, as suggested in ref. bcm phase becomes superionic and its distorted hexagonal close-packed (hcp) oxygen lattice becomes fully hcp at the superionic transition temperature Ts. This Ts, which is marked by the green curve in Fig. 3) and the superionic hcp phase were predicted to have similar chemical potentials and were both regarded as thermodynamically stable between about 100 GPa to 800 GPa18. This means, if feasible, keeping the temperature low (at least below the superionic transition temperature) is essential for making the Pbcm phase in compression experiments. (ii) Between 300 GPa and 800 GPa, the chemical-potential difference μX − μbcmP increases with increasing pressure, although the magnitude of the chemical-potential difference remains rather small even at 800 GPa. This means the equilibrium driving force favouring the thermodynamic stability of the Pbcm phase is greater at higher pressures. On the other hand, the activation barrier for the X–Pbcm transition also becomes larger, meaning that the kinetic transition rate is smaller at higher pressures. This implies that, in compression experiments that aim to make the Pbcm phase, instead of targeting the highest pressure achievable, it may be advantageous to aim for pressures that are moderately above the X–Pbcm transition line in order to utilise the lower kinetic barrier. (iii) If the Pbcm phase, or a mixed-stacking phase, is indeed made during compression experiments, it will not remain stable and will instead reversibly transform into X whenever the external pressure drops to below 300 GPa.For the insulating ice phases that have been theoretically predicted for the pressure range between 100 GPa and 800 GPa, we find that all of them (X(orthorhombic), X(cubic), Pbcm, which sheds light on the viability of the experimental synthesis of the as yet experimentally undetected, but long predicted, Pbcm phase. Beyond the water ice system, our methodological framework, which combines first-principles methods, machine-learning potentials, free-energy methods and enhanced sampling, can be applied to study the phase diagrams and phase transitions of other polymorphic systems, such as high-pressure solid hydrogen38, perovskites39 and two-dimensional materials40.In summary, our study provides a thermodynamic view of the high-pressure phase diagram of ice below the superionic transition line. Our simulation results suggest that the various bcc ice phases, including ice VII, ice VII′ (T/R) and ice X, are indeed the same thermodynamic phase that behaves differently at different conditions. Our simulations also reveal the peculiar bifurcation and transition pathway between X and P41. Pulsed internal heating and alternating-current calorimetry in the diamond anvil cell (DAC) is a promising technique for investigating the nature of phase transitions experimentally43. A combination of resistive heating, near- and mid-infrared laser heating in the DAC to study the structure of dense ice at high temperature with X-ray diffraction has provided evidence for phase transformations consistent with two superionic ices having either a bcc or a fcc oxygen sublattice. CO2 laser heating in the DAC has also recently been shown to release deviatoric stresses and enable collection of high-quality powder X-ray diffraction and Raman scattering44. Finally, laser-driven dynamic compression has enabled the investigation of the thermodynamics and atomic structure to several hundred GPa and revealed evidence for superionic ice XVIII15. With the rapid evolution and improvement of experimental methods to investigate material properties at extreme conditions, we expect that this work will provide insights into the interpretation of experimental data as well as input for new experiments.Experimental investigations on the high-temperature and high-pressure water ices are notoriously difficult, but recent technical breakthroughs could open new opportunities to test the predictions from this work in the future. For example, neutron scattering was used to investigate the atomic structure of ice VII at room temperature up to ~100 GPa and revealed subtle pressure-induced changes of the proton disorder45 as well as more distant water-rich exoplanets49, as the presence (or absence) of discontinuous phase transitions can induce (or suppress) thermal boundaries, which would affect mass and heat flows and have a significant role in shaping the internal structure and evolution of these bodies.The precise nature of phase transformations in dense ices at high temperatures could also affect our understanding of large icy worlds in our solar system such as Ganymede, Titan and Callistok points were chosen automatically using a Γ-point-centred grid with a resolution of 0.3 Å−1. We considered nine structures with the space groups P42/nnm (ice X(orthorhombic)), P3221, P4322, P21212, P42212, Pbca, Pbcm and Pnm (ice X(cubic)), which were taken from a previous study18. That study predicted the structures with AIRSS25 using CASTEP to perform the DFT calculations and applying various exchange-correlation functionals. Their identified PBE structures were reoptimised in this study with VASP at constant pressure to remove any residual forces. We considered a pressure range between 100 GPa and 1100 GPa and checked that the initial space group for each considered structure was maintained after reoptimisation. The energies were typically converged to 10–8 eV and the forces to 10−6 eV Å−1. Subsequently, the structures were optimised similarly with the revPBE0-D3 exchange-correlation functional to investigate the influence of a hybrid functional on the structural stability. Finally, we calculated the phonons for the P42/nnm, Pbcm and Pnm structures at the PBE level to identify possible pressure ranges of dynamical instability. Those calculations were performed with Phonopy50 on 2 × 2 × 2 supercells in 25 GPa steps between 100 GPa and 400 GPa. The phonon densities of states were evaluated with at least 71 mesh q points in each direction.The DFT calculations were carried out with VASP 5.4 using the hard PAW pseudopotentials and an energy cutoff of 1000 eV. The NPT MD simulations for bcc ice systems with LAMMPS51 with an MLP implementation52. Bulk phases were simulated in the NPT ensemble using the Nosé–Hoover isotropic barostat53 and a timestep of 0.5 fs, with typical MD simulation runs of between 20 ps and 60 ps at each set of conditions considered. Bulk-simulation system sizes ranged between 128 and 2048 water molecules to check for finite-size effects; for direct-coexistence simulations, we used system sizes of between 2916 and 3453 molecules. We have repeated simulations of ice VII with several different choices of proton disorder. In simulations along isotherms and isobars, we gradually changed the pressure and the temperature in steps of between 0.3 GPa and 4 GPa and between 5 K and 60 K, respectively, with step sizes chosen to be smaller closer to the limits of metastability of each phase. Since each simulation was started from an equilibrated system very close in pressure and temperature, the initial equilibration prior to data collection could be brief.We performed 24 of liquid water in contact with ice and determined the coexistence temperature at pressures of 20 GPa and 26 GPa, and the coexistence pressure at 1800 K. These points match very well the coexistence curve reported in ref. 23 the Gibbs–Duhem relation, dμ = v dP − s dT, at constant temperature to give the chemical potential as a function of pressure v is the volume per formula unit, and the Gibbs–Helmholtz equation at constant pressure to give the chemical potential as a function of temperature, β = 1/kBT and H is the enthalpy. This enthalpy includes kinetic energy contributions, i.e., the de Broglie thermal wavelength’s temperature dependence is accounted for in the chemical potential54. In each case, we fitted the integrands to a polynomial to be able to extrapolate them slightly beyond the regions of metastability. We confirmed that starting from any of the direct-coexistence starting points, we can recover the other two, and others, by this thermodynamic integration route. To find other coexistence points, we computed the chemical potentials for all phases that are metastable at a given temperature and pressure, and determined numerically the point at which the chemical potentials of different phases cross over.To compute chemical potentials and in turn the phase diagram, we started by considering a large direct-coexistence simulation23, and consistency checks are often performed instead55, such as using direct-coexistence simulations and computing the chemical potential at a given point arrived at via several independent pathways. In addition to these, we have estimated numerical errors in thermodynamic integration by computing mean prediction bands for polynomial fits to the data points at a 95% confidence level and propagating the upper and lower band through the subsequent integration. In the vast majority of cases, these confidence bands are narrower than the line widths, but can be larger whenever extrapolations are needed, as shown in Fig. In free-energy calculations, it is often difficult to determine the errors in chemical potentials systematicallybcm and X using a system size of 512 formula units. We performed geometry optimisation for each structure at different pressures. Where a local minimum was found, the hessian matrices of the structures were computed using the programme i-PI56 with a finite-difference method. The hessian matrix enables us to construct a reference harmonic crystal in which the forces between the atoms are defined by the phonon modes. After that, we ran hamiltonian TI between the reference crystal and the classical physical system. We used TI along isotherms and isobars to calculate the chemical-potential differences at other pressure and temperature conditions. To validate that we can reach consistent estimates of the chemical potential using different TI pathways, we therefore performed multiple TI calculations: (i) λ-TI at different temperatures  at pressure P0 , (ii) integrate along T between 100 K and 2000 K, and finally (iii) integrate along pressure between 100 GPa and 800 GPa. As shown in Supplementary Fig. We first computed the classical Gibbs energy for both Pμ − μMLP computed using the free-energy perturbation method, which removes the small residual errors in the MLP partly due to its lack of long-range electrostatics32. This correction term at different pressure and temperature conditions is illustrated in Supplementary Fig. 2O formula units using LAMMPS in conjunction with the i-PI code56. These PIMD simulations were performed employing the MLP, with 32 beads for all phases considered at the relevant constant pressure and temperature conditions. NQEs on the chemical potentials were taken into account by integrating the quantum centroid virial kinetic energy m59, In addition, we promoted the MLP results to the PBE level by adding bcm, and Pbcm-like structures with different stacking sequences were distinguished on the FES. The length of each metadynamics run was 2 × 106 time steps. For each 400 time steps, an adaptive Gaussian bias29, which covers the space of 400 time steps in the collective variables, was added. The height of the Gaussian bias decayed with the well-tempered scheme28 using a bias factor of 100.The CVs (the anisotropy and the tilt of the simulation supercell) were chosen such that the X(orthorhombic), X(cubic), PSupplementary InformationPeer Review File


CK2 is a ubiquitous protein kinase with an anti-apoptotic role and is found to be overexpressed in multiple cancer types. To this end, the inhibition of CK2 is of great interest with regard to the development of novel anti-cancer therapeutics. ATP-site inhibition of CK2 is possible; however, this typically results in poor selectivity due to the highly conserved nature of the catalytic site amongst kinases. An alternative methodology for the modulation of CK2 activity is through allosteric inhibition. The recently identified αD site represents a promising binding site for allosteric inhibition of CK2α. The work presented herein describes the development of a series of CK2α allosteric inhibitors through iterative cycles of X-ray crystallography and enzymatic assays, in addition to both fragment growing and fragment merging design strategies. The lead fragment developed, fragment 8, exhibits a high ligand efficiency, displays no drop off in activity between enzymatic and cellular assays, and successfully engages CK2α in cells. Furthermore, X-ray crystallographic analysis provided indications towards a novel mechanism of allosteric inhibition through αD site binding. Fragments described in this paper therefore represent promising starting points for the development of highly selective allosteric CK2 inhibitors. Development of a novel CK2α inhibitor from a fragment-based screen with a proposed novel mechanism of action. The role of CK2α is to recruit substrates and to control the intracellular localisation of the kinase.2 CK2 has been found to be overexpressed in numerous cancer cells, and many of its roles in cancer, such as modulation of cell proliferation and cell death, are attributable to its activity in the nucleus.3 There are no known mutations in CK2α implicated in cancer, in line with its constitutively active role, and rather than an oncoprotein, it is considered a facilitator that enhances tumorigenesis.4CK2 is a pleiotropic, constitutively active Ser/Thr kinase with one of the largest sets of target proteins of eukaryotic protein kinases.5 As a pro-survival kinase, overexpression of CK2α appears to both facilitate the growth of tumour cells and provide a mechanism for the cells to avoid apoptosis. Therefore, cells rely on CK2α's activity to survive, becoming addicted to its non-oncogenic activity which is typically enhanced by increasing CK2α expression. This is particularly true for cells that are challenged with chemotherapeutic agents and the most promising uses for CK2α inhibitors stem from these observations.6One of the key roles of CK2α is to promote anti-apoptotic pathways through activation of the NF-κB and Wnt signalling pathways.7–9 Indeed, inhibition of CK2α has been demonstrated to help overcome resistance to specific chemotherapeutic agents. For example, imatinib-resistant CML cells were shown to become sensitive to the drug again when co-administered with CK2α inhibitors.10–13Drug resistance is a serious problem for cancer therapies, and agents that can reverse or prevent the emergence of drug resistance are desperately needed. By suppressing survival signals, CK2α inhibition shows great potential as a combination therapy agent that could be used with tumour-specific agents to both augment their efficacy and minimise the development of resistance, or even reverse it.14There are also examples of tumours where CK2α could be effective as a monotherapy target. For example, glioblastomas, irrespective of their stage or p53 status, have been shown to be particularly vulnerable to CK2α inhibition. Glioblastomas are traditionally hard-to-treat cancers, highly malignant and with poor prognosis and thus novel treatments for these tumours is of great importance. On the contrary, non-cancerous glial cells appear to be unaffected by CK2α inhibition suggesting that selectivity for cancer cell death could be achieved with CK2-inhibiting therapies.15–17 Despite its limited selectivity, CX-4945 is well-tolerated, suggesting that inhibition of CK2α activity is a viable therapeutic target, notwithstanding the ubiquitous roles of CK2 in cells.18 However, promiscuous inhibitors are likely to have more serious side effects if used therapeutically and thus it is desirable to have more selective kinase inhibitors to probe the true potential of CK2α inhibition in the clinic.ATP competitive inhibitors of CK2α have been extensively studied, but they tend to suffer from relatively poor kinase specificity, as exemplified by the clinical candidate CX-4945.6,19,20 This pocket provides an exciting opportunity for the development of more specific CK2α inhibitors with novel mechanisms of action. We initially utilised this new pocket to develop a proof-of-concept molecule by growing a fragment hit from the αD pocket into the ATP site by linking it to a low-affinity active site “warhead”. The resulting molecule (CAM4066) is a nanomolar CK2α inhibitor with better selectivity than any other previously described CK2α inhibitor and inhibits the growth of several cancer cell lines.6,19A promising strategy for the development of more selective CK2α inhibitors is to target binding pockets outside of the conserved ATP binding site. Previously, we have used a fragment-based approach to discover a new, cryptic pocket located next to the ATP site, termed the αD site.In this work, using a fragment-based approach, we discovered novel molecules that could inhibit the activity of CK2α through binding in the αD pocket. The work described herein showcases how CK2 inhibition can be achieved without relying on interactions with the highly conserved ATP binding site and, therefore, paves the way towards the development of highly selective CK2α inhibitors.6 The elaboration of fragment 1 into the inhibitor CAM4066 has been reported previously.6,19The original fragment screens that led to the discovery of the αD pocket identified five fragments which bound in the αD pocket .6 The elX-ray crystallography showed that fragments 2, 4 and 5 bound in both the αD site and ATP site and so were of limited use for probing the αD site . FragmenTo this end, a virtual secondary screen was conducted whereby a series of commercially available and in-house analogues of 3 were docked using glide and the promising compounds from this virtual screen were tested against CK2α. This was performed initially as a crystallographic screen, after which any compounds that bound solely in the αD site were followed up by the kinase activity assay. From this process, three more fragments were discovered to bind only in the αD pocket  whilst also exhibiting promising ligand efficiencies of 0.44 and 0.47, respectively (50 = 86 ± 24 μM) which again led to a promising ligand efficiency of 0.44  and maintained high ligand efficiencies (≥0.50). The bromine of fragment 8 was also systematically varied with a range of common substituents  as determined by the phosphorylation assay and similar binding affinities as determined by ITC  μM of 8 .As expected, the binding of fragment 8 was largely inhibited by the αD-binder, indicating that fragment 8 does indeed bind in the αD site .vice versa, which is likely if 8 and its analogues affect CK2α through an allosteric mechanism. Further study would be needed to explain this observation.The result from the experiment with CX4945 was less clear. In the presence of CX4945, the binding of fragment 8 was significantly reduced . This coThe crystal structures of all the key fragments studied herein clearly showed that these compounds bound to CK2α in the αD pocket and appear to inhibit the enzyme through allostery. The crystal structures yield several clues as to the possible mechanism of the allosteric inhibition.1)et al. to be a crucial residue: when Asn118 was deleted, CK2α was no longer active.22 Asn118 is replaced upon its movement by a conserved water which sits at the mouth of the αD site where it interacts with the backbone carbonyls of Thr119 and Ile164.Firstly, ATP binding may be altered by changes in the hinge region caused by the movement of the αD loop. Specifically, the movement of the hinge region causes Asn118 to rotate out from the mouth of the αD pocket into a position where it participates in a water bridge to ATP. Asn118 has previously been shown by Srinivasan 2)6,23 When ATP or its analogues are bound, the loop normally adopts an open conformation, but when the ATP site is empty, or occupied by a compound not interacting with the hinge region in the same way as ATP, the loop adopts a closed conformation. From this it can be inferred that when ATP leaves the ATP site, the loop will then adopt the closed conformation; the transition to the closed conformation may be important for causing ADP to leave the ATP site. However, when the αD site is occupied by the fragments presented herein, this transition cannot occur, possibly stalling the phosphorylation mechanism.A second possible mechanism of inhibition could be the blocking of the transition between the open and closed form. As has been discussed previously, the αD loop is uniquely flexible in CK2α and has been observed in multiple conformations, most commonly the open and closed forms in crystallography.19 This could be attributed to a number of different factors. Firstly, many of the previous fragments bound very weakly so inhibition would not be observable in the assay. Secondly, in the structures of many of these previous fragments (e.g. PDB entry 5CS6) the αD loop is displaced and enters a more disordered open conformation. On the contrary, when the allosteric fragments bind, the αD loop is locked in a more rigid, ordered conformation which is also reflected in lower B-factors for this loop . Furthermore, fragment 8 also showed promising inhibition of cell growth through inhibition of phosphorylation by CK2α (GI50 = 88 ± 7 μM). This fragment-sized tool molecule, alongside its derivatives, confirmed that the αD site can be successfully exploited to achieve inhibition of CK2. Although 8 is unlikely to display high kinase selectivity due to its small size, it opens the potential for further development to identify a selective tool compound for CK2. The binding site was confirmed in solution by ligand-based NMR and the crystal structures of the various fragments bound gave several indications as to the mechanism of allosteric inhibition, but more detailed analyses are needed to eliminate any orthosteric effect on the ATP site by 8. These molecules and the information we have presented herein represent a promising starting point for future development and structure-based design of allosteric CK2α inhibitors which operate by a novel mechanism of action.We have presented five diverse fragments bound in the αD site. One of these fragments (3) bound solely in the αD site and showed weak inhibition of the kinase activity of CK2α. This weakly binding fragment was optimised into a higher affinity molecule, fragment 8, that bound solely in the αD site and showed clear inhibition of CK2α (ICThere are no conflicts to declare.MD-013-D2MD00161F-s001


Inhibition of mTOR phosphorylation was performed by rapamycin. Phosphorylation of mTOR complex 1 (MTORC1) downstream substrates 4E-BP1(Thr37/46) and P70S6K(Thr389), and MTORC2 downstream substrate Akt(Ser473) as well as the PDK1-dependent phosphorylation of Akt(Thr308) was investigated. Markers for extracellular matrix remodeling, cell differentiation and degenerative changes were analyzed by Western blot analysis, semi-quantitative real-time PCR and colorimetric assays. Hyperinsulinemia and hyperglycemia lead to alterations of VIC activation, differentiation and matrix remodeling as well as to an abrogation of mTOR phosphorylation. Inhibition of mTOR signaling by rapamycin leads to a general downregulation of matrix molecules, but to an upregulation of α-smooth muscle actin expression and alkaline phosphatase activity. Comparison of expression patterns upon diabetic conditions and rapamycin treatment reveal a possible regulation of particular matrix components and key degeneration markers by MTORC1 downstream signaling. The present findings broaden the understanding of mitogenic signaling pathways in VIC triggered by hyperinsulinemia and hyperglycemia, supporting the quest for developing strategies of prevention and tailored treatment of CAVD in diabetic patients.Diabetes and its major key determinants insulin resistance and hyperglycemia are known risk factors for calcific aortic valve disease (CAVD). The processes leading to molecular and structural alterations of the aortic valve are yet not fully understood. In previous studies, we could show that valvular interstitial cells (VIC) display canonical elements of classical insulin signaling and develop insulin resistance upon hyperinsulinemia and hyperglycemia accompanied by impaired glucose metabolism. Analyses of cultured VIC and aortic valve tissue revealed extracellular matrix remodeling and degenerative processes. Since PI3K signaling through mammalian target of rapamycin (mTOR) is involved in fibrotic processes of the heart, we aim at further functional investigation of this particular Akt-downstream signaling pathway in the context of diabetes-induced CAVD. Primary cultures of VIC were treated with hyperinsulinemia and hyperglycemia. Phosphorylation of mTOR(Ser Early disturbances in insulin sensitivity and hyperglycemia (HG) , 2 are rIn previous work, we could show that valvular interstitial cells (VIC) are sensitive to short-term treatment by diabetic conditions such as hyperinsulinemia (HI) and HG . Here, iPI3K signaling through mammalian target of rapamycin (mTOR) is known to be involved in fibrotic processes of several organs and tissues including the myocardium . Here, en = 7) derived from a local abattoir as described before  or not (group: without acute insulin stimulus). To evaluate the optimal insulin concentration cells were initially treated either with 10 nM or with 100 nM insulin for 10 min. In the further process, 10 nM insulin was used for the acute stimulus. Afterward, cells were washed twice with cold PBS and were lysed for SDS-PAGE and Western blot as described before .2448) (cat. no. 2971); 5′-adenosine monophosphate (AMP)-activated protein kinase ; phospho-AMPKα(Thr172) (cat. no. 2535); ribosomal protein S6 kinase beta-1 ; phospho-P70S6K(Thr389) (cat. no. 9234); 4E-BP1 (cat. no. 9452); phospho-4E-BP1(Thr37/46) (cat. no. 9459); Akt (cat. no. 4691), phospho-Akt(Ser473) (cat. no. 4060), and phospho-Akt(Thr308) (cat. no. 13038). Detection of housekeeping protein GAPDH (cat. no. 2118) or β-actin (cat. no. 4967) on the according membranes was used for normalization of protein signals. Signals of primary antibodies were detected by using the following secondary antibodies: Goat IgG anti-rabbit IgG (H + L)-HRP  and goat IgG anti-mouse IgG & IgM (H + L)-HRP . Molecular weight was determined by using a PageRuler Prestained Protein ladder . Detection of protein signals was performed by using an Amersham Imager 600  and intensity of protein bands was analyzed by Image Quant TL software . Detection of 4E-BP1 and Akt as well as the corresponding phosphorylated antibodies was performed with IRDye 800 CW goat anti rabbit  and IRDye 680 LT goat anti mouse  as secondary antibodies using an Odyssey scanner (LI-COR biosciences).Lysates were conducted to SDS-PAGE and separated proteins were blotted on nitrocellulose. Protein signals were detected using the following primary antibodies purchased from Cell Signaling: mTOR (cat. no. 2983); phospho-mTOR, elastin , biglycan , decorin , α-smooth muscle actin , osteopontin , alkaline phosphatase , matrix metalloproteinase 2 , hyaluronic acid synthase 2 , and matrix metalloprotei nase 9 . Relative mRNA expression analysis was performed by 2Ct–ΔΔ method using CT means of the following reference genes: β-tubulin , 60S ribosomal protein L13a , and 60S ribosomal protein L29 . In the following, gene names are written in italics.Isolation of total RNA, cDNA synthesis as well as semi-quantitative real-time PCR was performed as previously described . Primer n = 3) was measured using supernatants of the media before inhibitor treatment and then every second day. The assay was performed according to the manufacturers’ instructions using a microplate reader (Infinite M1000 Pro) by measuring the absorbance at 405 nm.Using the PierceTM PNPP Substrate Kit , the concentration of cell released alkaline phosphatase  was used to determine the amount of lactate dehydrogenase (LDH) released into the media of the cells. Therefore, supernatants were collected at the day of cell harvest and the colorimetric assay was performed according to the manufacturer’s instructions using a microplate reader (Infinite M1000 Pro). Data was normalized to total protein content.post hoc test. For comparison of two groups, Mann–Whitney U test or Wilcoxon signed rank test was applied. p-Values < 0.05 were considered as statistically significant.Statistical analysis was performed using GraphPad Prism version 7 . Data is presented as mean ± SEM. Statistical analysis of data originating from Western blot analysis was performed using two-way ANOVA with Sidak’s multiple comparison test. Statistical analysis of data originating from semi-quantitative real-time PCR, ALP and LDH analysis was performed using Kruskal–Wallis test with Dunn’s 2448) phosphorylation (p = 0.012) under NG, an effect which was abrogated by treatment with HI or HG as well as by the combination of both treatments. Upon acute insulin stimulus, HI inhibited phosphorylation of mTOR(Ser2448) under NG treatment by 43% (p = 0.003) as well as under HG treatment by 48% (p = 0.009) (2448) phosphorylation upon acute insulin stimulus by 57% (p < 0.0001). HG treatment alone did not lead to a significant decrease of mTOR(Ser2448) phosphorylation upon acute insulin stimulus compared to the appropriate NG condition. Basal phosphorylation  . Comparerylation  as well rylation  was not rylation .2448)   whilst c37/46) phosphorylation both under basal conditions (p = 0.0006) and upon acute insulin stimulus (p = 0.0006). Acute insulin stimulus led to a significant increase of 4E-BP1(Thr37/46) under NG (p = 0.007) as well as under HG (p = 0.038) conditions compared to the corresponding basal conditions, whereas HI treatment abrogated this effect. Rapamycin treatment led to attenuated inducible 4E-BP1(Thr37/46) phosphorylation by acute insulin (37/46) both under NG (p = 0.038) and NG + HI (p = 0.073) conditions as well as under HG (p = 0.018) and HG + HI (p = 0.014) conditions compared to the corresponding untreated group (389) was not altered by rapamycin treatment  under NG (p = 0.002) and HG (p = 0.002) treatment (473) and led to a significantly reduced Akt(Ser473) phosphorylation under NG + HI (p = 0.0022) as well as under HG + HI treatment (p = 0.0026) upon acute insulin stimulus compared to NG and HG alone. A similar effect was observed upon rapamycin treatment; here, phosphorylation was increased under NG (p = 0.004) and HG (p = 0.002) treatment upon acute insulin stimulus compared to basal conditions and was abrogated by HI treatment. Generally, treatment with rapamycin led to a significantly increased phosphorylation level of Akt(Ser473) upon acute insulin stimulus under NG (p = 0.002), NG + HI (p = 0.004), HG (p = 0.002) and HG + HI (p = 0.002) conditions  phosphorylation proved to be difficult due to generally low expression levels and unspecific binding (p = 0.011), whereas phosphorylation was not inducible under NG (p = 0.620), NG + HI (p = 0.805) and HG + HI (p = 0.710) conditions. Under rapamycin treatment, acute insulin stimulus led to an increased Akt(Thr308) phosphorylation compared to the according basal condition under NG (p = 0.018) as well as under HG (p = 0.007) conditions. HI treatment abrogated this effect (473), rapamycin treatment led to generally higher expression levels of phosphorylated Akt(Thr308) upon acute insulin stimulus compared to the according untreated groups . Total Akt abundance was not influenced by acute insulin stimulus compared to basal conditions. However, rapamycin led to generally higher Akt expression levels compared to the untreated groups (Detection of Akt(Thr binding . Acute is effect . Similard groups .2448)   whilst cUpon stress or tissue damage, VIC change their quiescent fibroblastoidal phenotype toward an activated myofibroblastoidal phenotype with higher contractility, which is indicated by an increased expression of ACTA2 –21. The ACTA2 gene expression in VIC under NG (p = 0.016) as well as under HG conditions (p = 0.047) compared to NG control conditions. HG treatment alone had no effect (p = 0.469). Inhibition of mTOR signaling by rapamycin treatment led to a significant upregulation of ACTA2 gene expression under NG (p = 0.016) as well as under HG conditions (p = 0.031) compared to NG control conditions. HI treatment seemed to mitigate these effects. Comparison between rapamycin and HI treatment revealed opposed effects with highly significant difference  and with HI (p = 0.047) when compared to NG conditions . Gene expression of ELN was significantly downregulated by HG + HI treatment with similar effect upon rapamycin treatment alone (p = 0.016) as well as in combination with HI (p = 0.031) when compared to NG control conditions (ELN gene expression in comparison to NG control conditions (p = 0.078). MMP2 gene expression was significantly upregulated under HI conditions (p = 0.016) and by a slight trend under HG + HI conditions , whereas comparison between HI and HI + rapamycin showed a significant opposed effect (p = 0.018). MMP9 gene expression levels were not detectable (not shown).nditions . CombineBGN gene expression upon HI treatment (p = 0.031) as well as upon HG + HI treatment . Comparison between HI and rapamycin treatment showed significantly different gene expression of BGN (p = 0.010). Interestingly, when the subset of rapamycin-containing treatments is considered, rapamycin entirely abolished the effects of diabetic conditions on the expression of BGN as observed within the subset of conditions without rapamycin. DCN gene expression was in general upregulated by diabetic conditions when compared to NG control conditions . Comparison between HI and rapamycin treatment showed significantly different gene expression of DCN (p = 0.008). Similar to the observations on BGN gene expression, rapamycin led to a general abrogation of the elevating effect of diabetic conditions on DCN gene expression. HAS2 gene expression was significantly upregulated by diabetic conditions either by HI (p = 0.003), HG (p = 0.019) or combined treatment with HI + HG . Comparison between HI and rapamycin treatment showed significantly different HAS2 gene expression levels (p = 0.0004), with an overall inhibition of the stimulatory impact of diabetic conditions on HAS2 gene expression.Analysis of glycosylated matrix molecules revealed a significant upregulation of OPN gene expression was significantly upregulated by HG treatment (p = 0.023). Rapamycin treatment did not alter OPN gene expression. However, OPN gene expression levels upon HI or HI + HG treatment were comparable to those under rapamycin treatment  except for the combination with HG + HI which led to a trend toward higher ALP activity (p = 0.063). Comparison between HI and rapamycin treatment revealed significantly opposed effects on ALP activity (p = 0.027). Collectively, rapamycin abrogated the inducing effect of HG on OPN gene expression and had a strong enhancing effect on ALP activity that contrasts the effects observed under HI.reatment . HI and The present work shows that chronic insulin treatment leads to decreased mTOR signaling by abrogating mTOR phosphorylation in VIC. Chronic insulin exposure as well as HG conditions in turn lead to VIC activation, chondro-osteogenic differentiation, and matrix remodeling. Inhibition of mTOR signaling by rapamycin altered the impact of diabetic conditions with respect to various components of the extracellular matrix as well as of markers of activation and chondro-osteogenic differentiation. Remarkably, the effect of mTOR inhibition in front of effects observed for diabetic conditions was not uniform: Rapamycin treatment resulted in a modulation of VIC regulation, partly resembling an amplification of HI or HG induced effects whilst also reflecting a marked inhibitory impact on other factors. These results suggest that mTOR is involved in intracellular transmission of diabetic stimulus on VIC but is not the only signaling pathway by which diabetic conditions regulate aortic valve morphology.37/46) for phosphorylation upon acute insulin stimulus. This might be indicative for an impaired MTORC1 downstream signaling in VIC upon chronic diabetic conditions, as it has been described before for HUVECS and smooth muscle cells in vitro. Here, impaired phosphorylation of 4E-BP1 has been observed even after 5 min of HI (37/46), a decrease of P70S6K(Thr389) phosphorylation has been described in vitro in HUVECS and smooth muscle cells after a short-time incubation with HI (389) did not show significant changes under diabetic conditions in VIC which might be related to specific differences in short-term vs. long-term HI treatments. Time course experiments as well as examination of valvular tissue of diabetics might therefore lead to a better understanding of diabetes-induced P70S6K(Thr389) phosphorylation in VIC. However, it has been shown that P70S6K acts via different phosphorylation sites by MTORC1 activation alone or in combination with other kinases (389).Hyperinsulinemia led to a decreased susceptibility of 4E-BP1 signaling under NG as well as under HG conditions (308) phosphorylation is not as sensitive to HI as Akt(Ser473) phosphorylation. Moreover, Akt(Thr308) phosphorylation seems to be mainly impaired by HG conditions. Susceptibility of both Akt(Ser473) and Akt(Thr308) phosphorylation to impaired signaling upon HI is more pronounced under rapamycin treatment. Here, Akt(Thr308) phosphorylation shows a similar pattern as Akt(Ser473) phosphorylation. This effect here seen in VIC might probably be due to MTORC1 inhibition by rapamycin and a resulting inhibition of the negative feedback loop, which then enhances activation of Akt(Ser473) signaling (308) signaling. However, enhanced susceptibility of Akt(Thr308) phosphorylation by prior Akt(Ser473) activation has been shown already by Sarbassov et al.  by rapamycin seems to be unstable in long-term treatment (48 h +) and is particularly described to be cell type dependent (37/46) phosphorylation in VIC upon acute insulin stimulus even after 5 days of treatment. In addition, rapamycin also reduces the susceptibility of 4E-BP1 to inducible phosphorylation by acute insulin stimulus.Rapamycin was initially considered as a potent inhibitor of translation and thus, e.g., inhibiting tumor growth through inhibition of MTORC1 complex and downstream inhibition of 4E-BP1 and P70S6K signaling . Howeverependent . Howeverependent . Our dat389) was not influenced by rapamycin treatment in VIC. This might be due to the relatively low rapamycin concentration chosen for our experiments (10 nM), whereas others also reported cell line dependent responsiveness with this concentration  und Akt(Thr308). Since Akt(Thr308) is not mTOR dependent, we did not expect a decrease here, but would have excepted an impaired Akt(Ser473) phosphorylation. Increase in Akt signaling in VIC might therefore be due to a not yet sufficient inhibition of MTORC2 together with the inhibition of the negative feedback loop of MTORC1 leading to enhanced activation of Akt signaling (308) phosphorylation depends on Akt(Ser473) phosphorylation (473) phosphorylation when compared to the enhanced activation due to a loss of MTORC1 negative feedback. In conclusion, interplay of MTORC1/MTORC2 in the balance of Akt phosphorylation is delicate and cell type specific, even more so with respect to the question of the impact of long-term usage of rapamycin.Based on this, it is not clear whether MTORC2 is impaired by 5 days treatment with rapamycin in our approach, since we do not see impaired Akt signaling but rather increased Akt phosphorylation of both Akt, providing heterogeneous or even contrary findings, e.g., for proteoglycans  activation seems to be cell type dependent as well as susceptible to different mTOR inhibitors. For example, an increase in α-SMA expression was reported for vascular smooth muscle cells ] cell culture bears several limitations, e.g., the influence of the interaction with other cell types of the tissue of origin, the lack of cell–matrix interactions or, especially in the case of the aortic valve, the lack of shear stress-induced mechanisms. Thus, findings of in vitro experiments have to be regarded with great attention concerning their comparability with whole tissue examinations and need to be confirmed by three-dimensional analysis and in translational approaches. Nevertheless, in vitro culture environments offer advantages for basic studies of isolated parameters if used in a reproducible and stable model.The use of The use of a non-human  VIC cell source is also afflicted with limitations, since these cells will differ from the corresponding human source in relation to mechanics, physiology, and immunology. Although ovine VIC have advanced to a reliable model for functional studies on CAVD, direct comparability to human-derived VIC is restricted and validation in human-derived cell lines therefore is inevitable. Our approach using ovine VIC is mainly owed to the lack of human healthy donor valves and a heterogeneous population of patients concerning age, medication and co-morbidities. Ovine VIC therefore represent a “healthy” phenotype that is hardly achievable with human specimen and allows analysis without patient-related side effects.in vitro, since it is inferior in completely blocking MTORC1 together with MTORC2 in comparison to, e.g., torin (Although rapamycin is a clinically proven agent for the treatment of heart failure and post-infarction cardiac remodeling, it might lead to conflicting or confounding results ., torin , 61. ThuIn the present investigations, DMSO was used as a solvent for rapamycin. DMSO is known to have unwarranted effects on cell proliferation and differentiation, which we sought to minimize by using concentrations of 0.1% DMSO.The original contributions presented in this study are included in the article/MB and JS: conceptualization, formal analysis, and project administration. MB, JS, DO, and PA: methodology. JS, MB, and PA: validation. HK, CK, FK, and EA: investigation. AL: resources. HK and MB: data curation and visualization. MB and PA: writing—original draft preparation. MB, JS, AL, DO, SB, and PA: writing—review and editing. JS, MB, AL, and PA: supervision. JS: data curation and visualization. All authors contributed to the article and approved the submitted version.


To evaluate the clinical efficacy of Gandakang tablets plus methylprednisolone in patients with systemic lupus erythematosus (SLE).  From February 2015 to February 2019, 60 eligible patients with SLE were recruited and assigned via the random number table method at a ratio of 1 : 1 to receive either methylprednisolone (control group) or Gandakang tablets plus methylprednisolone (observation group). The primary endpoint was clinical efficacy, and the secondary endpoints included Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) score, immunoglobulin (Ig), inflammatory factor levels, and adverse events. P < 0.05). Gandakang tablets plus methylprednisolone resulted in significantly lower SLEDAI scores and lower levels of IgG, IgM, IgA, tumor necrosis factor-α (TNF-α), interleukin-4 (IL-4), and interleukin-6 (IL-6) versus single medication of methylprednisolone (P < 0.05). The two groups showed a similar incidence of adverse events (P > 0.05). Patients given Gandakang tablets plus methylprednisolone had higher mental health, emotional role, physical role, social functioning, and bodily pain scores versus those receiving the monotherapy of methylprednisolone (P < 0.05).  Gandakang tablets plus methylprednisolone were associated with a significantly higher treatment efficacy versus methylprednisolone alone ( Gandakang tablets plus methylprednisolone is effective in the treatment of SLE by enhancing the patients' immunity, mitigating the inflammatory response, eliminating negative emotions, and improving their quality of life. Systemic lupus erythematosus (SLE) is a connective tissue disease secondary to autoimmune system disorders  and is aP > 0.05), as shown in From February 2015 to February 2019, 60 eligible patients with SLE were recruited and assigned via the random number table method at a ratio of 1 : 1 to a control group or an observation group. The two groups showed similar baseline features (Inclusion criteria : ① patiePatients in the control group were given methylprednisolone powder injection (Pfizer Manufacturing Belgium NV) intravenously at 15–30 mg/kg and repeated every 4–6 h. The treatment spanned 48 hours. Patients in the observation group were given methylprednisolone powder  plus Gandakang tablets orally, 3 g/d, 3 times/d. The efficacy was determined after 12 weeks of treatment in all groups.Clinical Efficacy. Markedly effective: facial pigmentation completely disappears. Effective: facial pigmentation is significantly alleviated. Ineffective: no improvement or even aggravation of facial pigmentation is found. The total clinical efficacy = (the number of markedly effective cases + the number of effective cases)/total number of cases ∗ 100%.Immunoglobulins (Ig). Five ml of morning fasting venous blood was collected from the patients of both groups and centrifuged to determine the levels of IgG, IgM, and IgA in both groups using the immunoturbidimetric method.Inflammatory Factor Level. Three milliliters of morning venous blood was collected from the patients of both groups and serum was obtained by centrifugation. The levels of tumor necrosis factor-α (TNF-α), interleukin-4 (IL-4), and interleukin-6 (IL-6) were determined using an enzyme-linked immunosorbent assay.Adverse Events. Adverse events such as headaches were recorded in both groups.The degree of disease was assessed before and after treatment using the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI), which includes 24 items such as epilepsy, psychiatric symptoms, visual impairment, organic brain syndrome, arthritis, vasculitis, and myositis. The higher the score, the more severe the condition.The MOS 36-item short-form health survey (SF-36) was used to assess the quality of life of the patients. The evaluation indexes of the scale included mental health, emotional role, physical role, social functioning, and bodily pain, with a full score of 100 points.t-test, and the count data were expressed as [n(%)] using the chi-square test. Differences are considered statistically significant at P < 0.05.SPSS25.0 statistical software was adopted for processing data in this study. The measurement data were expressed as (mean ± standard error) using the independent paired P < 0.05)  .P < 0.05)  .P > 0.05). Gandakang tablets plus methylprednisolone resulted in significantly lower SLEDAI scores and lower levels of IgG, IgM, and IgA versus the single medication of methylprednisolone (P < 0.05)  .α, IL-4, and IL-6 in the two groups (P > 0.05). The patients receiving combined treatment had lower levels of TNF-α, IL-4, and IL-6 versus monotherapy of methylprednisolone (P < 0.05)  .P > 0.05) . In the observation group, there were 2 cases of headache and 2 cases of vomiting, and the incidence of adverse reactions was 13.33% (4/30). No significant differences were observed in the incidence of adverse events between the two groups ( > 0.05) .P < 0.05) (Gandakang tablets plus methylprednisolone were associated with a better remission rate versus methylprednisolone ( < 0.05) .P > 0.05). Patients given Gandakang tablets plus methylprednisolone had higher mental health, emotional role, physical role, social functioning, and bodily pain scores versus those receiving the monotherapy of methylprednisolone (P < 0.05)  .Systemic lupus erythematosus is a common disease in rheumatology  and an iα, IL-4, and IL-6 versus methylprednisolone, indicating that the combined treatment can effectively reduce the level of inflammatory factors in SLE patients, regulate their immune function, and facilitate rapid recovery. Methylprednisolone is a mild glucocorticoid with strong anti-inflammatory effects and is widely used in the treatment of rheumatic diseases and endocrine disorders [The results of this study showed that Gandakang tablets plus methylprednisolone were associated with higher efficacy and lower levels of IgG, IgM, IgA, TNF-isorders . As mentisorders . Nonetheisorders , 19. TheThe results of this study showed better quality of life for patients given Gandakang tablets plus methylprednisolone versus those receiving methylprednisolone alone. Furthermore, patients' negative emotions can be mitigated after treatment, which contributes to the recovery of mental health, and the improvement of quality of life.In conclusion, Gandakang tablets plus methylprednisolone are effective in the treatment of SLE, which improves the immunity of patients, mitigates inflammatory responses, eliminates their negative emotions, and enhances their quality of life. The proposed use of Gandakang tablets is simultaneous and additional to methylprednisolone therapy.


Transcriptome-wide detection of binding sites of RNA-binding proteins is achieved using Individual-nucleotide crosslinking and immunoprecipitation (iCLIP) and its derivative enhanced CLIP (eCLIP) sequencing methods. Here, we introduce htseq-clip, a python package developed for preprocessing, extracting and summarizing crosslink site counts from i/eCLIP experimental data. The package delivers crosslink site count matrices along with other metrics, which can be directly used for filtering and downstream analyses such as the identification of differential binding sites.https://github.com/EMBL-Hentze-group/htseq-clip and documentation is available under https://htseq-clip.readthedocs.io/en/latest.The Python package htseq-clip is available via pypi (python package index), bioconda and the Galaxy Tool Shed under the open source MIT License. The code is hosted at  In recent years, a 4-figure number of novel RNA-binding proteins (RBPs) was discovered, many of them containing disease-associated mutations  or into sliding windows over these features. Thus, htseq-clip allows a user to have granular control over the information extracted from i/eCLIP datasets.htseq-clip is written in Python 3 and builds upon the htseq package  files after polymerase chain reaction (PCR) duplicate removal using unique molecular identifiers (UMIs). Ideally, the experiment should be performed in replicates with proper size-matched input controls.htseq-clip can be grouped into three steps:The workflow implemented in Prepare annotation: flatten the gene annotations to unique annotations for chromosomal positions, create sliding windows from flattened annotation and a mapping file for downstream analyses.Extract crosslink sites: extract truncation, deletion, insertion sites and summarize them as counts per sliding window.Count and summarize crosslink sites: summarize counts of multiple samples to a count matrix for further analysis.The first step in processing i/eCLIP data using htseq-clip is to prepare the annotation file. This step requires a .gff3 formatted gene annotation file as input. The supplied annotation file is flattened into BED6 format and is further used to create sliding windows. Users can set the width and step (slide) of the windows. The gene feature boundaries are taken into consideration at this step such that the sliding windows do not span multiple gene features (e.g. a window beginning in an exon of a gene and ending in the neighboring intron). The last step is to generate a mapping file. This file contains all the metadata related to a sliding window such as: window id, window size, step size, corresponding gene, gene feature, gene type, gene name and gene id. This information is essential for downstream filtering, quality control and analyses.In this step, alignment files are processed and crosslink sites are extracted in BED6 format. The parameters used in this step give the user flexibility to define the read/mate, crosslink type and offset length parameters which can be used to convert an RT stop position into a crosslink site position. Additionally, users also have the flexibility to choose alignment quality, minimum and maximum lengths of reads and whether only primary alignments or both primary and secondary alignments of a read can be used for crosslink site counting. An additional advantage of extracting crosslink sites in BED6 format is that users gain flexibility to convert the crosslink site file into bigWig or bedGraph format, for visualization using a genome browser.In this step, a sliding window file created in the first step is used to count and summarize the crosslink sites into sliding windows. Along with the total number of sites per window, additional information such as length of the window, number of positions in a window with crosslink sites, maximum site count in the window and crosslink site position density (ratio of number of nucleotides with crosslink sites compared to the length of the window) are also given. In downstream analysis, users may filter the sliding windows based on this information. As an additional step, crosslink site counts from multiple samples can be concatenated into one matrix, creating a site count matrix file. This step is intended to create an R-friendly output matrix file, as R/Bioconductor packages are the default choice for statistical analysis in bioinformatics pipelines.htseq-clip provides an easy-to-use solution for the pre-processing of i/eCLIP alignment files using custom gene annotations. The parameter combinations available in htseq-clip enable users to work with data from a wide variety of common CLIP protocols. The output crosslink site matrix and other metrics can be used for downstream analysis such as the R/Bioconductor Package DEWSeq.