It can exploit almost all host–guest recognition motifs formed by this host family. In our previous work, an extensive computational investigation of the binding of seven commonly abused and structurally diverse drugs to the CB8 host was performed, and a general dynamic binding picture of CB8-guest interactions was obtained. Further, two widely used fixed-charge models for drug-like molecules were investigated and compared in great detail, aiming at providing guidelines in choosing an appropriate charge scheme in host-guest modelling. Iterative refitting of atomic charges leads to improved binding thermodynamics and the best root-mean-squared deviation from the experimental reference is 2.6 kcal/mol. In this work, we focus on a thorough evaluation of the remaining parts of classical force fields, i.e., the bonded interactions. The widely used general Amber force fields are assessed and refitted with generalized force-matching to improve the intra-molecular conformational preference, and thus the description of inter-molecular host–guest interactions. The interaction pattern and binding thermodynamics show a significant dependence on the modelling parameters. The refitted system-specific parameter set improves the consistency of the modelling results and the experimental reference significantly. Finally, combining the previous charge-scheme comparison and the current force-field refitting, we provide general guidelines for the theoretical modelling of host–guest binding.Atomic-level understanding of the dynamic feature of host–guest interactions remains a central challenge in supramolecular chemistry. The remarkable guest binding behavior of the Cucurbiturils family of supramolecular containers makes them promising drug carriers. Among Cucurbit[ Pharmaceutical research acts as a key component in biological and medical science. Great efforts are required to grasp some basic insights into druggable species from the universe of chemical space. By definition, drugs are small molecules that induce physiological or psychological changes in specific target organism(s). Despite their small sizes, they are able to regulate the functionality of huge biomacromolecular receptors upon binding to a specific area(s) of the targeted molecule or its biologically relevant counterparts ,2,3,4,5.n]uril (CB[n]) is formed by n glycoluril monomers linked by 2n methylene bridges. Their hydrophilic carbonyl portals on glycoluril units stabilize cationic species and act as hydrogen bond acceptors, while the central partly closed hydrophobic cavity is able to coordinate aliphatic and aromatic components. As a result, this series of heterocycles are able to form stable host–guest complexes with a wide range of neutral and cationic species with diverse structural features, which makes them promising coordinators and carriers of functional species including drugs [n] family, Cucurbit[8]uril  leads to negligible changes in the outcomes. The selection of the target level in refitting determines the accuracy of the re-parameterized model. Here, we choose two target QM Hamiltonians. The first one is the computationally efficient semi-empirical QM (SQM) Hamiltonian of the dispersion, hydrogen-bonding, and halogen-bonding corrected PM6-D3H4X , which iWe first discuss the PM6-D3H4X results. To assess the quality of the refitted parameters, we generate another 25 ns trajectory (5000 configurations) at two temperatures with the refitted parameter set FM-PM6. The first temperature is the one that we generate in the configurational ensemble in refitting, namely 600 K. This high-temperature set enables the accuracy assessment in the ensemble in that the refitting is performed. A lower temperature of 300 K is also employed to generate another set of configurations, which enables the assessment of energetics in the low-energy regions accessible at room temperature. Two error metrics including the root-mean-squared error (RMSE) and mean absolute error (MAE) are used for quality assessment.In quality assessment, we first take a detailed investigation of the parameter set for the host CB8 due to its importance in coordinating all guests. Although (S)QM Hamiltonians are more accurate than MM force fields, all of them have their own weakness in specific regions/areas. Therefore, we then cross-check the refitted parameter sets and test whether the force field refitted targeting PM6-D3H4X is able to reproduce the energetics and atomic forces at another QM level. Here, we choose another SQM Hamiltonian of RM1 . A similThe better reproduction of PM6-D3H4X energetics for the host CB8 under the refitted force fields is also valid for other molecules , the energetics correlation plots of which are provided in The comparison between RMSEs of the energetics produced by the original GAFF2 and the newly fitted parameter set with configurations sampled at 300 K is provided in Similar procedures are repeated for the other target BLYP-D4. Namely, for each molecule, we perform 110 ns unbiased sampling at 600 K in vacuo to obtain 22,000 configurations for ab initio calculations in parameterization. As for the quality assessment, as the computational cost is much higher under the ab initio level, the sampling interval is increased to 10 ps and the number of samples is set to 1000. As a result, the sampling time for each molecule becomes 10 ns in quality assessment. The validation test  is only performed at 300 K.Still, we first look at the CB8 results due to its involvement in all host–guest pairs. Two reference QM levels including the target BLYP-D4 and another QM level PM6-D3H4X  are used in this assessment. The configurations at the two levels are generated independently and thus the results are uncorrelated. The energetics comparisons are shown in The RMSEs of energetics produced by GAFF2 and FM-BLYP are shown in The comparisons for energetics and atomic forces presented above show that the refitted parameter sets reproduce the target Hamiltonians in a better way than the pre-fitted parameter sets. However, the detailed differences between different force fields remain unclear. Therefore, before performing μs-length enhanced sampling to explore the configurational space of host–guest complexes, we first assess these parameter sets in detail to understand their intrinsic behaviors and the differences between them. The host CB8 is still selected as the illustrative example. The difference between vdW parameters in the two GAFF force fields is small. Thus, we focus on the bonded terms.The torsional rearrangement is the most important influencing part for conformational preferences in bonded interactions, while the other terms  are relatively irrelevant. Therefore, we investigate the torsional/dihedral term in each force field in detail. The first comparison to look at is between the GAFF derivatives. Dihedral terms defined in GAFF derivatives that are used to describe the conformational preference (stiffness) of the host CB8 are shown in To provide further details of the dynamics of the bonded parameter sets of the CB8 host, we then perform unbiased MD simulations in explicit solvent for 20 ns with a sampling interval of 5 ps under GAFF derivatives. From the structural overlay in The radius of gyration (Rg) is often chosen as the CV to describe the open and close of the host cavity. However, the behavior of this CV is generally not investigated in detail, and thus the correctness of this selection is not justified. To analyze the detailed behavior of this CV, we compare the time series of Rg of the host ring under GAFF2 and the refitted force fields in The general picture of CB8–guest interactions observed in our previous extensive modelling is a squashed CB8 ring that holds the guest tightly . IntuitiIn the analysis of the host–guest coordination, we still start by monitoring the total host–guest contacts and its by-host-atom decomposition to check whether the space of the relative host–guest position is properly explored. The illustrative example chosen here is the complex formed by CB8 and the deprotonated G5 described by the FM-PM6 parameter set, as shown in Similar to our previous work, we still start by checking the radius-contact  of both of them can flip outside the host cavity without causing significant unfavorable interactions. Another worth-noting point observed on the free energy surface is that the wide free energy minimum under GAFF2 covers almost all energetically favorable regions under GAFF, which suggests that the GAFF binding poses are a subset of the GAFF2 one. This conclusion can also be reached by comparing the host–guest binding patterns reported in our previous work and the current GAFF2 results. The last guest under investigation, G7 Cocaine, is also found to have different behaviors under the two GAFF derivatives. Unlike the GAFF case where only a single bound conformation is preferred, under GAFF2 the CB8-G7 complex has a very complex thermodynamic landscape with multiple low-(free-)energy minima. There are basically four conformational states on the In the current patterns . By cont2+ group of the guest forms inter-molecular hydrogen bonds with the -C=O portals of the host. In the binding pose, in the middle which is about 1 kcal/mol less stable, the guest still stays in the host cavity but is more extended than in the previous pose. The host is squashed but the host–guest coordination is not as tight as in the previous pose. Inter-molecular hydrogen bonds are formed between the -NH2+ group of the guest and the -C=O portals of the host. The structure at the left has smaller host–guest and host-phenyl contacts than the other two, which suggests a looser level of inter-molecular coordination and a larger host–guest distance. As expected, in the left structure we observe that the phenyl ring of the guest still penetrates the host cavity, but the other parts become relatively far from the host center. The inter-molecular hydrogen bonds are still formed. Overall, for the CB8–G1 binding under FM-PM6, the host cavity in the bound state is more squashed and irregularly shaped than the GAFF2 case, which is in agreement with the increased flexibility observed in unbiased simulations. As for the structurally complex G2, the FM-PM6 surface also shows obvious differences compared with the initial guess GAFF2. Generally, there are four conformational states observed on the + group of the guest forms a hydrogen bond with one of the -C=O portals of the host. In the other state with smaller host–guest contacts, the whole guest clings to the host, and phenyl at the amide bond side penetrates the host. No inter-molecular hydrogen bond is formed. This conformational state is less stable than the first one, which is in accordance with the GAFF results. Note that these two large-+ side penetrates the cavity, the middle part of the guest clings to one side of the host ring and the selected phenyl ring stays away from the host. The inter-molecular -NH+ ··· -C=O hydrogen bond seems to be formed. However, the host ring is more distorted than the GAFF and GAFF2 cases. Overall, during the CB8–G2 interactions, the basic features of the interaction patterns remain unchanged, but the host cavity is more twisted than the previous GAFF and GAFF2 cases, which is in agreement with the observations in the CB8–G1 complex. As for the structurally rigid G3, only one conformational state is observed on the + and two -OH groups forming hydrogen bonds with the -C=O portals of the host. However, the host ring is more twisted and irregularly shaped compared with the other parameter sets, which is expected considering the dynamic behavior of the CB8 ring in unbiased simulations reported in the previous section. The guest G4 is structurally similar to G3, but this time under FM-PM6 it still has an obviously different 2+ group of the guest, but the angle makes it difficult to form hydrogen bonds. The second structure has larger host–guest and host-phenyl contacts than the first one. Compared with the previous center-binding pose, the host–guest coordination at this position is more compact. The host cavity is twisted to fit the guest surface in a better way, which maximizes both the host–guest and the host–phenyl contacts. The last structure has smaller contact numbers and lies at the tail of the crescent minimum. In this binding structure, the phenyl ring of the guest fluctuates outside the host cavity and becomes solvent-exposed. Overall, the basic conformational-fluctuation feature of the CB8–G5 binding observed under the GAFF derivatives remains under FM-PM6, but the host cavity is more twisted than the GAFF derivatives, which is still in agreement with the observations in the previous host–guest complexes . The situation of the deprotonated form of G5 is still similar. The basic crescent free energy landscape remains. The structure extracted at the center of the crescent depicts a center-binding pose where the twisted host holds the guest in its central cavity. The other structure locates at the extra horn with larger host–guest and host-phenyl contacts. This pose is similar to that observed in the protonated G5 case, where the distorted host is in a good shape that fits the surface of the guest, thus maximizing the host–guest coordination. As for the next structurally complex guest G6, its free energy surface under FM-PM6 is very similar to that under the initial guess GAFF2. A wide free energy minimum is observed for the CB8–G6 complex, but the spread of the FM-PM6 basin is narrower than the GAFF2 one. Following the previous GAFF2 analysis, we extract two structures at the same positions as the GAFF2 case, i.e., one at the center of the wide free energy minimum and the other at the small-C tail of the basin. In the first bound structure, the 6-membered rings of the guest except the phenyl moiety are inside the host cavity, while in the second pose, the phenyl and cyclohexyl moieties fluctuate to be solvent-exposed, and only the piperidinium ring penetrates the host cavity. Therefore, it is clear that the basic picture of the host-G6 binding remains unchanged. Namely, the distorted host still holds the guest tightly and the guest is fluctuating inside the host cavity. The CB8–G7 surface under FM-PM6 is similar to the GAFF2 one, but only three bound conformations are observed under this parameter set. We can still differentiate them by their host–phenyl contacts. The first one with the largest We then turn to the molecule-specific FM-PM6 parameter set. It has been observed in unbiased simulations that compared with the initial guess GAFF2, this refitted parameter set produces a more flexible host cavity. Thus, we expect to observe a flexible host in the bound state. Aside from the host behavior, as the guest descriptions are also altered upon force-field refitting, the host–guest coordination is expected to be changed also in many other aspects. The 2+ group forms hydrogen bonds with -C=O portals. The shape of the free energy basin or the distribution of the low-(free-)energy regions and the bound conformations extracted there are all very similar to the GAFF2 results. The situation of the host–guest interactions in the CB8–G2 case is more complex. Under FM-BLYP, the low-(free-)energy regions are significantly different from the previous GAFF, GAFF2 and FM-PM6 cases. The regions with small host–phenyl contacts that are thermodynamically favorable under GAFF2 are relatively unstable under FM-BLYP. The most stable conformational state under this parameter set has the largest + and two -OH groups of the guest forming hydrogen bonds with the -C=O portals of the host is observed. Note that the FM-BLYP binding structure is similar to the GAFF and GAFF2 ones instead of the irregularly shaped twisted FM-PM6 one, which is expected considering the host behavior observed previously in unbiased simulations. The guest G4 is structurally similar to G3. The host–guest coordination under GAFF is dominated by the typical center-binding pose, while under the GAFF2 and FM-PM6 parameter sets many other bound conformations are populated non-negligibly. The refitted FM-BLYP parameter set, instead of being similar to the initial guess GAFF2 and FM-PM6 that is generated in a similar way but targets a lower-level reference, produces dominant center-binding preferences in the CB8-G4 complex. Namely, compared with the CB8–G4 binding patterns under GAFF2 and FM-PM6, the FM–BLYP situation is more reasonable and similar to the CB8–G3 case, where the inter-molecular coordination between the macrocyclic host and the structurally rigid guest is strong/tight and locates at the typical center-binding region. However, despite the structural similarities between the G3 and G4 guests, the G4 free energy surface is still a bit different from the single-minimum G3 one. Specifically, there are three free energy minima on the G4 surface. The distribution of the three minima is similar to the conformational-fluctuation crescent in the CB8–G5 case, which suggests that the FM-BLYP CB8–G4 complex also has some levels of conformational fluctuations. Visualization of bound conformations in the three minima suggests that all of them are center-binding ones, and the differences lie in the degree of distortion to the host cavity and the position of the guest inside the host cavity. The most stable binding pose locates at the middle minimum, where the guest stays at the center of the host and the host cavity is minimally perturbed. Its neighboring small-2+ group of the guest forms inter-molecular hydrogen bonds with the -C=O portals of the host, and the fluctuations of the guest inside the host cavity make some parts of it far away from the host center. As for the deprotonated form of G5, the crescent free energy landscape due to the conformational fluctuations in the bound state is still similar to the results under the other force fields. Namely, the large-+ group forming one inter-molecular hydrogen bond with one -C=O portal of the host. As expected, this binding pose is a snapshot during the conformational fluctuations in the bound state under the initial guess GAFF2 and the FM-PM6 parameter set that is generated in a similar way. The other thermodynamically less favorable conformational state has only the piperidinium moiety penetrating the host cavity, while the other two rings of the guest are far from the host center and solvent-exposed. We then turn to the last guest G7. Overall, the CB8–G7 Finally, we check the It should be noted that the general picture of CB8 host–guest binding summarized from our extensive computational modelling is indeed observed in many experimental structures ,112. ForThe dynamic behavior of the host ring and the binding-mode investigation shown above depicts the impacts of the refitting of bonded terms. The bonded interactions determine the conformational preference of each molecule. The GAFF and refitted FM-PM6 parameter sets describe a flexible CB8 ring, which can easily form a squashed conformation in the solvent itself, i.e., in the absence of the external guest. By contrast, the GAFF2 and FM-BLYP parameter sets provide a stiff host cavity that is less energetically favorable in the squashed conformation. However, in the presence of the external guest, the host cavity could still be squashed in order to form tighter host–guest coordination. Due to the difference between the energy costs of distorting the host cavity under different bonded parameter sets and the difference between the bonded terms for the external guest, the strengths of host–guest interactions are altered. First, the binding modes differ under different bonded parameter sets, which would alter the pattern of inter-molecular interactions and thus the strength of host–guest binding. Second, even the host–guest coordination pattern is unchanged, the detailed inter-molecular atom-atom distances are altered, which would also influence the strength of inter-molecular electrostatic and vdW interactions and thus the strength of host–guest coordination. Therefore, binding affinities under different bonded parameters are expected to show some differences. Below, we present a detailed comparison between the predicted binding affinities under different bonded parameter sets and the experimental reference . Note thThe predicted binding thermodynamics and the corresponding statistical uncertainties  under the GAFF derivatives  and the refitted FM-PM6 and FM-BLYP parameter sets are summarized in We first check the results obtained with the existing general force field parameters for drug-like molecules, i.e., the GAFF derivatives. Comparing the GAFF and GAFF2 estimates, it is clear that these two GAFF force fields provide different host–guest affinities. For G1, G2, protonated and deprotonated G5, and G6, the GAFF2 estimates are higher and also closer to the experimental data than the GAFF results. By contrast, for G3, G4 and G7, the use of GAFF2 parameters leads to smaller binding affinities than the GAFF estimates. For the specific guests G4 and G7, the GAFF2 estimates are significantly worse than the GAFF ones, which is the main source of error in the whole dataset. As a result, although GAFF2 improves the results for most guests, this significant problem results in a bit higher mean errors , which suggests that the GAFF2 is actually of a bit lower performance compared with GAFF. Interestingly, the ranking coefficients are almost unchanged. The correlation between the modelling results and the experimental values is shown in We then turn to the refitted GAFF2 parameter sets. For the lower target level PM6–D3H4X, compared with the initial guess GAFF2, the predicted binding affinities are improved for G1, G2, and protonated G5, remain almost unchanged for G3, and become worse for G4, deprotonated G5, G6 and G7. Overall, the error metrics obtained under the FM-PM6 parameter set are similar to the GAFF2 results. However, obvious differences are observed for the ranking coefficients. The Kendall τ of FM-PM6 is significantly smaller than GAFF2, while the PI is only marginally lowered. The prediction-experiment correlation for this dataset is also presented in When a higher level BLYP-D4/def2-SVP is selected, we reach the FM–BLYP dataset. This target level is more accurate than the previous PM6–D3H4X, and thus this refitted FM-BLYP parameter set should be more accurate than the previous FM-PM6. As the FM-BLYP parameters are generated in a system-specific way, they are also considered to be better than the pre-fitted transferable GAFF2 parameters. Checking the statistics in Accurate descriptions of host–guest interactions require accurate inter- and intra-molecular potentials. The inter-molecular interactions are determined by non-bonded electrostatics and vdW terms, with the former  being more significant. The intra-molecular interactions involve bond stretching, angle bending, and dihedral terms, and the dihedral term plays the most crucial role in determining the conformational preference of each molecule. The interplay between the non-bonded and bonded interactions is the core of molecular recognition. A balanced description of inter- and intra-molecular interactions is needed.In our previous work focusing on the non-bonded terms, the charge schemes widely used in drug discovery are assessed in great detail and the best-performing regime is identified . The RESIn this work, we consider another important influencing factor in MM potentials, the bonded terms. The bonded interactions are not directly involved in the calculation of inter-molecular non-bonded interactions, but still play a crucial role by influencing the intra-molecular conformational preference of each molecule involved in inter-molecular recognitions. The transferable GAFF derivatives for drug-like molecules are compared and assessed, and system-specific parameters are obtained by refitting the bonded terms  with the generalized FM scheme, which considers both the atomic force, the energy, and various regularization terms to obtain a reasonable set of parameters that reproduce the properties at the target level. The target levels determine the ultimate accuracy that the refitted model can achieve. Here, we selected the dispersion, hydrogen-bonding, and halogen-bonding corrected semi-empirical level PM6-D3H4X and the frequently used and dispersion corrected ab initio level BLYP-D4. Compared with the original GAFF2, the energetics and atomic forces at the target levels are reproduced in a much better way under the refitted parameter sets. Further improvements could be expected by choosing a higher-level QM target. For instance, with more computational resources, we could use a larger basis set  and/or more accurate functionals such as composite methods and double hybrids. Further, although the refitted model outperforms the original GAFF2 parameter set in reproducing the energetics at the target QM level, obvious deviations could still be observed. On this aspect, more complicated energy functions (formula) could be employed to improve the fitting quality.With the constructed models to describe the energetics, the solvated host–guest systems could be simulated. Due to the high (free) energy cost of breaking the host–guest coordination and the complex conformational ensemble to explore, some enhanced sampling techniques need to be employed in the modelling of host–guest binding. As for the selection of the configurational space to explore, using some CVs to describe the relative position of the host and the guest seems useful in order to scan their binding patterns. Based on our series of works on host–guest modelling ,79,92, tOverall, for model construction, the combination of the RESP charges and the refitted molecule-specific BLYP-D4 bonded parameters shows the best performance in CB8 host–guest binding. For sampling the configurational space, the spherical-coordinates-biased protocol is sufficient. Thus, we recommend using a similar protocol in the modelling of CB8-related molecules and more generally similar host–guest systems.According to our series of works on CB8 host–guest binding simulations, the combination of the RESP charges and some refitted bonded terms provides a balanced accuracy-cost regime for host–guest modelling. The fixed-charge model augmented by an optional iterative refitting procedure provides accurate electrostatics and intra-molecular conformational preferences. If the force-field refitting takes too much computational resources, the GAFF derivatives are also usable. The calculated binding thermodynamics and interaction patterns with the combined model achieve a high level of accuracy within the framework of molecular simulations with classical force fields. The sampling issue is mostly solved by enhanced sampling techniques coupled with a 3D spherical CV set. Although the sampling and Hamiltonian issues are solved mostly, there are still some places where further improvements could be performed. Below, we provide some discussions about several aspects that further developments could be performed.We first consider the sampling of the configurational space. If there are some hidden barriers on orthogonal degrees of freedom, more CVs  could be added to the CV set or coupling other enhanced sampling schemes  with the current scheme could be considered. Note that this is not really a problem for the current CB8 host–guest system, which has been discussed and tested in our previous work . Aside fWe then consider the description of the system or Hamiltonian, which is actually more problematic than the sampling issue. The Hamiltonian used in the current work is the classical force field, which can be improved by using more accurate forms of potential functions. The electrostatics could be improved by introducing multipoles or fluctuating charges, which provides a better description of the polarization effects that are often claimed to be significant ,127. As The 3D structure of the host CB8 and the 2D chemical structures of the guest molecules are shown in + or Cl− parameterized for TIP3P water are added for neutralization.According to our previous work on CB8–drug systems, the reproduction of the molecular ESP is better with the restrained electrostatic potential (RESP)  charge sAs the macrocyclic host CB8 is structurally symmetric and much simpler than biomacromolecular receptors , it is often presumptive that there is only one stable binding mode in each CB8–guest complex ,72,73,74he guest . When cahe guest ,79,92. Aith atom in group A and the jth atom in group B. To monitor the closeness of the host and the guest during the exploration of the configurational space, we calculate the total contact number between the host and the guest ith atom of the host. Due to the complexity of the host–guest interactions, the total host–guest contact is observed to be insufficient to differentiate different inter-molecular interaction patterns properly [To provide a detailed analysis of the host–guest coordination pattern, we calculate the contact number between groups of atoms, which is defined as est work . The varproperly . TherefoAs has been discussed in the model construction part, the starting configuration of each host–guest complex is obtained by simply superposing the COMs of the host and the guest. Although such a bound conformation could be energetically unfavorable and the system could be far from equilibrium, we perform 5000 steps minimization, 400 ps NVT equilibration, and 2 ns NPT equilibration to let various degrees of freedom relax and find their favorable regions. After that, we initiate 1000 ns spherical-coordinates-biased metadynamics simulations. The parameters for the metadynamics setting remain the same as in our previous work ,79,92. NS 2019.6  patched S 2019.6 . The velS 2019.6  is emploS 2019.6 ,162 is uS 2019.6  method.


The DNA damage response (DDR) is intertwined with signaling pathways downstream of oncogenic receptor tyrosine kinases (RTKs). To drive research into the application of targeted therapies as radiosensitizers, a better understanding of this molecular crosstalk is necessary. We present here the characterization of a previously unreported MET RTK phosphosite, Serine 1016 (S1016) that represents a potential DDR-MET interface. MET S1016 phosphorylation increases in response to irradiation and is mainly targeted by DNA-dependent protein kinase (DNA-PK). Phosphoproteomics unveils an impact of the S1016A substitution on the overall long-term cell cycle regulation following DNA damage. Accordingly, the abrogation of this phosphosite strongly perturbs the phosphorylation of proteins involved in the cell cycle and formation of the mitotic spindle, enabling cells to bypass a G2 arrest upon irradiation and leading to the entry into mitosis despite compromised genome integrity. This results in the formation of abnormal mitotic spindles and a lower proliferation rate. Altogether, the current data uncover a novel signaling mechanism through which the DDR uses a growth factor receptor system for regulating and maintaining genome stability. Radiotherapy (RT) has long been a staple of cancer therapy, but while great advances have been made in the delivery of radiation and the management of normal tissue toxicity , there iThe growing body of evidence linking the deregulated activation of receptor tyrosine kinases (RTKs) to enhanced radioresistance has led to preclinical and clinical trials combining RT with RTK-targeting therapies . A promiSimilarly to EGFR, MET is a promising candidate for targeting in combination with RT. This potent oncogene regulates cellular functions such as proliferation, survival and motility (reviewed in refs.  and 9]))9]). ItsRecently, we have reported extensive descriptions of MET-dependent phosphoproteome in response to DNA damage , 22. In We set out to explore the function of this novel MET phosphosite in the context of oncogenic MET addiction and response to irradiation. Using cellular models expressing constitutively active variants of the MET receptor, with or without a phosphodeficient mutation at the Serine 1016 position, we show that the genetic ablation of this phosphorylation affects the response to irradiation both in vitro and in vivo in a mouse xenograft model. This mutation has an impact on radiosensitivity by modulating the cell cycle response, the proliferation rate and proper mitotic spindle formation. Thus, this novel MET phosphosite stands out as the first piece of evidence for a direct connection between the DDR and MET signaling.MET-amplified cell lines EBC-1 and GTL-16 [Phosphoproteomics data from MET inhibition and irradiation experiments on the MET-addicted cancer cell lines EBC-1 (lung) and GTL-16 (gastric) from  revealedd GTL-16 –26. We ad GTL-16   in a doTo confirm that DNA-PK directly phosphorylates MET S1016 we performed an in vitro kinase assay. We utilized synthetic peptides corresponding to the S1016 region with either a not modified sequence (MET), a mutation that does not disrupt the consensus SQ sequence ), or mutations that do disrupt the SQ motif . Analogous peptides (not modified H2AX) and SQ motif-disrupting ) of histone H2AX, a canonical DNA-PK substrate, were used as controls. Indeed, these data show that DNA-PK can specifically phosphorylate MET S1016 site when the consensus sequence is present (MET and MET S1-A) but not when the SQ motif is disrupted by SA (MET Q-A), AQ (MET S2-A), or AAQ (MET S1S2-AA) mutations Fig. . InteresX and SQ To assess the function of MET Serine 1016 phosphorylation, we genetically abrogated its phosphorylation in MET-dependent cellular models. We generated cell lines expressing previously described MET-mutated variants that lead to a constitutive, ligand-independent activation of the receptor and combined them with a phosphodeficient Serine 1016 to Alanine substitution. The NIH 3T3 embryonic mouse fibroblast cell line has been successfully used in the past to study the function of various MET mutations  and was While the parental cell line transfected with an empty vector expresses minimal levels of MET and no detectable active MET as assessed by the MET tyrosine phosphorylation pY1234/5 Fig. , the MT,Since we have observed that DNA-PK phosphorylates MET Serine 1016 Fig.  and S3, We next questioned how the status of MET Serine 1016 mechanistically affects the radiosensitivity. We measured the amount of DNA damage inflicted to the different cell lines by irradiation, either with a direct (comet assay) or an indirect (γH2AX foci formation) method. We could not see any significant impact of the SA substitution on the direct DNA damage infliction Fig.  or the kTherefore, we hypothesized that the radiosensitizing effect of the SA substitution might stem from an altered cellular response to DNA damage and subsequent cell death. We measured apoptosis induction after irradiation in the four cell lines by flow cytometry (Annexin V/propidium iodide staining) but could not detect a stronger induction of apoptosis in the Serine 1016-mutated cell lines. Low dose irradiation does not significantly induce apoptosis in any cell line, and high-dose irradiation causes similar levels of apoptotic cells in all of them Fig. .After excluding that MET S1016 has any direct effect on DNA damage generation or repair and apoptosis, we resorted to phosphoproteomics to further explore the mechanisms underlying the radiosensitizing effect of the SA substitution. We performed a shotgun mass spectrometry analysis of the phosphoproteome of the MT, SAMT, YH, SAYH cell lines, unchallenged and after irradiation (1 h and 7 h after a single dose of 10 Gy). We specifically looked for phosphorylation changes due to irradiation that are different between the MT/YH cell lines and the SAMT/SAYH cell lines. To identify the differentially expressed phosphopeptides, we used the Python package ProtRank which has been developed especially for the analysis of proteomic and phosphoproteomic data .A direct comparison between the unchallenged MT/YH and the SAMT/SAYH pairs revealed that the SA substitution has very little impact on the phosphoproteome in the absence of irradiation: there are only seven phosphopeptides that significantly differ  threshold of 0.25 which is the standard value that was used in our analyses) between SA and non-SA cell lines. Upon increasing the FDR threshold to 0.5, 18 significant phosphopeptides are identified Fig. . MappingHowever, considerable differences can be observed upon DNA damage in these two pairs of cell lines. MT and YH cells have a mild response 1 h after irradiation, with no identified significantly different phosphopeptides. Similarly, these two cell lines show only a slight response 7 h after irradiation with 9 significant phosphopeptides and no enrichment of relevant molecular functions or biological processes , hinting at a potential deregulation of the mitotic machinery. This coincides with our observation that cells lacking MET Serine 1016 enter mitosis earlier after irradiation but tend to form more numerous aberrant spindles. Such defects in the spindle assembly checkpoint and the formation of multipolar spindles are known to lead to aneuploidy , which cMoreover, our findings pertain mainly to MET-addicted cells with high MET amplification and MET-activating mutations, yet this level of MET dependency is often not observed in cancers with lower level of MET amplification or MET overexpression. As MET dependency has considerably affected the success and failure of MET-targeted clinical trials, additional studies are required to understand the role of MET Ser1016 phosphorylation in HGF-dependent cancers or cancers with lower levels of MET amplification.In conclusion, our results reveal that the novel DNA-PK-targeted MET phosphosite Serine 1016 is an important regulator of the cell cycle response to irradiation in cellular models featuring constitutive, ligand-independent MET receptor activation due to the presence of MET-activating mutations. Despite the apparent functional activation of checkpoint kinases, cells lacking this phosphosite seem to avoid a lasting G2 arrest and instead proceed with the cell cycle in unfavorable circumstances, leading to abnormal mitoses and a reduced proliferation rate, possibly as a result of increased genomic instability. The impact of the phosphorylation status of MET Serine 1016 in the response to irradiation is underlined by the alteration of the phosphorylation status of diverse key proteins of the cell cycle machinery, DNA repair and DNA replication. While MET inhibition could be a means to radiosensitize MET-addicted tumors, since we have shown here that genetically preventing the DNA-PK-related phosphorylation of MET Serine 1016 radiosensitizes MET-addicted cancer cells, we hypothesize that this could be a rationale to study the use of DNA-PK inhibitors to radiosensitize MET-addicted tumors (selecting patients according to the phosphorylation status of MET Serine 1016 could support a refined stratification strategy). The application of DNA-PK inhibitors to radiosensitize solid tumors poses the challenge of potentially increasing radiotoxicity for healthy tissue, but MET-addicted tumors might exhibit a stronger radiosensitization, which could favorably extend the therapeutic window of radiotherapy and other DNA-damaging agents.If not indicated otherwise, tepotinib/EMD1214063  was dissolved in DMSO and used at a final concentration of 50 nM. KU55933 (ATM inhibitor), VE-821 (ATR inhibitor) and KU57788 (PRKDC (DNA-PK) inhibitor)  were used at a final concentration (f.c.) of 10 µM if not specified otherwise. DNA-PK inhibitor peposertib/M3814  was used at final concentrations of 100 nM and 300 nM. Inhibitors were dissolved in DMSO and working solutions were prepared freshly and remained in the media for the duration of the respective experiment.TM system  along with the DNA-PKcs Kinase Enzyme System  according to manufacturer’s instructions. The following peptides corresponding to the MET Ser1016 region were used as substrates: DQFPNSSQNG (“WT”), DQFPNSSANG (“Q-A”), DQFPNASQNG (“S1-A”) DQFPNSAQNG (“S2-A”), DQFPNAAQNG (“S1S2-AA”). As a control for the reactions, we used peptides corresponding to Ser139 region of H2AX: KKATQASQEY (“WT”), KKATQASAEY (“Q-A”), and KKATQAAQEY (“S-A”). Each reaction condition was independently set up and measured three times.PRKDC (DNA-PKcs) in vitro kinase assay on synthetic peptides was performed using the ADP-GloHuman gastric carcinoma cell line GTL-16 ) and the non-small cell lung cancer cell line EBC-1 ) were cultured in RPMI medium  supplemented with 5% and 10% FCS, respectively, and antibiotic-antimycotic . NIH 3T3 cells ) were grown in DMEM medium (Gibco) supplemented with 10% FCS (Sigma), antibiotic-antimycotic  and puromycin . Profiling of the EBC-1 cell line was done by using highly polymorphic short tandem repeat loci in June 2020 (Microsynth), the GTL-16 cells and NIH 3T3 mutants have been authenticated by whole-exome sequencing and transcriptomic analysis. All cell lines have been regularly tested for mycoplasma contamination.siRNAs targeting MET (Cat. # L-003156-00-0005), ATM (Cat # L-003201-00-0005), ATR (Cat. # L-003202-00-0005), DNA-PKcs (Cat #L-005030-00-0005), and the non-targeting siRNA control pool (Cat. #D-001810-10-20) were purchased from Dharmacon.siRNAs were transfected using TransIT-X2® Dynamic Delivery System (Cat. #MIR6004) according to manufacturer’s instructions.After seeding and adherence for 24 h, EBC-1 cells were transfected with 25 nM MET, ATM, ATR, and DNA-PK siRNAs or with 25 nM of non-targeting siRNA control. The cells were harvested for western blot analysis 48 h  or 96 h (DNA-PK) post siRNAs transfection.After seeding and adherence for 24 h, GTL-16 cells were transfected with 25 nM MET siRNA or 50 nM DNA-PK siRNA and with non-targeting siRNA control, 25 nM or 50 nM, respectively. The cells were harvested for western blot analysis either 48 h (control siRNA and MET siRNA) or 72 h (control siRNA and DNA-PK siRNA) post siRNAs transfection.http://n2t.net/addgene:17493; RRID:Addgene_17493) [Site-directed mutagenesis was performed with the QuikChange Lightning kit (Stratagene) according to the manufacturer’s instructions. The mutations described in the main text were inserted into the pBabe puro c-met WT plasmid  and treated as indicated one day after plating. Six days after treatment, cells were fixed and stained with 2% crystal violet (Sigma) in acetic acid-methanol (2:1). Cell density was measured with ImageJ. Experiments were repeated three times.Cells were plated in triplicates in 24-well plates (300 cells/well) and treated as indicated one day after plating. Six days after treatment, cells were incubated with resazurin blue  for two hours before measuring the fluorescent signal . Experiments were performed in three biological replicates.The assay was performed as recommended by the manufacturer (ThermoFisher). Briefly, cells were stained with Calcein AM  and ethidium homodimer-1  48 h after a 2 Gy irradiation. Cells were imaged with a fluorescence microscope (Leica) and analyzed with ImageJ. Experiments were repeated independently three times.-/- yc-/- mice (8-12-week-old). Tumor growth was followed daily by caliper measurement of the width, length, and depth , tail intensity was measured for 50 cells/condition with the Comet assay IV program (Andor Technology). Experiments were repeated independently three times.Cells were plated on 8-chamber microscopy slides and treated as indicated. Cells were fixed with 4% formaldehyde (Sigma), permeabilized with 0.2% Triton-X100 (Sigma) and blocked with 3% goat serum (Dako). After incubation with the appropriate primary antibody, the signal was detected with fluorescent secondary antibodies (ThermoFisher), the cells were counterstained with DAPI . The coverslips were mounted with Vectashield antifade mounting medium (Vector laboratories). Imaging was performed with a fluorescence microscope (Leica). γH2AX foci formation was quantified with CellProfiler. Antibodies used: γH2AX , α-tubulin , γ-tubulin . The quantification and statistical analysis of 100 cells per condition (γH2AX foci) or a triplicate of experiments (cell division) is shown.Apoptosis induction was measured by flow cytometry with a FITC-Annexin V/propidium iodide kit according to the manufacturer’s instruction (Invitrogen). Acquisition was performed on an LSR II (BD Biosciences) and analysis was performed with FlowJo . The quantification and statistical analysis of a triplicate of experiments is presented.Cell cycle distribution was measured by flow cytometry. At the appropriate timepoint after treatment, cells were collected and fixed in 70% EtOH, washed with PBS and stained with a propidium iodide -RNAse A -Triton-X100 (0.2%) solution in PBS. Acquisition was performed on an LSR II (BD Biosciences) and the data were analyzed with FlowJo . Experiments were performed three times.Cells were stained according to the manufacturer’s instructions  before plating. One day after plating, cells were treated as indicated. Samples were collected daily for 5 days, starting on the day of treatment. Signal intensity was measured by flow cytometry with an LSR II (BD Biosciences) and analysis was performed with FlowJo . Signal intensity was plotted as a function of time, and the proliferation rate was obtained by calculating the slope of the linear regression of this function. The statistical analysis was performed on a biological triplicate of experiments.https://string-db.org/) [Cell cultures were washed, scraped in phosphate-buffered saline, and spun down for 5 min at 1000 rpm. Resulting pellets were resuspended in 8 M urea solution containing 0.1 M ammonium bicarbonate and disrupted by sonication. Supernatants were centrifuged at 12000 rpm for 10 min and protein concentration was determined by BCA Protein Assay (Pierce). Disulfide bonds were reduced with tris(2-carboxyethyl)phosphine at a final concentration of 5 mM at 37 °C for 30 min and alkylation of free thiols was performed with 10 mM iodoacetamide at room temperature for 30 min in the dark. The solution was subsequently diluted with 0.1 M ammonium bicarbonate to a final concentration of 1.5 M urea and digestion was done overnight at 37 °C by sequencing-grade modified trypsin (Promega) at a protein-to-enzyme ratio of 50:1. Acidification was performed by adding formic acid to a final pH <3 to stop protein digestion. Peptides were desalted on a C18 Sep-Pak cartridge (Waters) and one-tenth of the resulting eluate was processed individually for total proteome analysis. Phosphopeptides were enriched from 1 mg of initial peptide mass with TiO2 as previously described . The inpdb.org/) . The resCells were plated in 6-well plates and treated as indicated. Seven days after treatment, cells were stained with X-Gal as previously described  and imag137 source at a dose rate of 0.86 Gy/min . For the experiments assessing γH2AX foci formation, cells were irradiated by X-Rad225XL (Precision X-Ray) at a dose rate of 116.2cG/min and employing the 0,3 mm Cupper filter.Cells were irradiated using a CesiumThe alignment was performed on uniprot.org with the default clustalo settings: The default transition matrix is Gonnet, gap opening penalty is 6 bits, gap extension is 1 bit. Clustal-Omega uses the HHalign algorithm and its default settings as its core alignment engine. The algorithm is described in ref. .p-values < 0.05 were considered significant . If not stated otherwise, all the experiments were performed at least three times.Statistical analysis was performed using Prism 7.03 (GraphPad), Supplementary figures legendsFigure S1Figure S2Figure S3Figure S4Figure S5Figure S6Figure S7Figure S8Figure S9Figure S10Figure S11Table S1Table S2


One Health approach acknowledges the interconnectedness of human health, animal health, and the environment. It emphasizes the importance of collaboration and coordination across these sectors to tackle complex health challenges such as antibiotic resistance. In the context of One Health, antibiotic resistance refers to the ability of bacteria to withstand the efficacy of antibiotics, rendering them less effective or completely ineffective in treating infections. The emergence and spread of antibiotic-resistant bacteria pose a threat to human and animal health, as well as to the effectiveness of medical treatments and veterinary interventions. In particular, One Health recognizes that antibiotic use in human medicine, animal agriculture, and the environment are interconnected factors contributing to the development and spread of antibiotic resistance. For example, the misuse and overuse of antibiotics in human healthcare, including inappropriate prescribing and patient non-compliance, can contribute to the selection and spread of resistant bacteria. Similarly, the use of antibiotics in livestock production for growth promotion and disease prevention can contribute to the development of antibiotic resistance in animals and subsequent transmission to humans through the food chain. Addressing antibiotic resistance requires a collaborative One Health approach that involves multiple participants, including healthcare professionals, veterinarians, researchers, and policymakers.Antibiotic resistance is a significant global health concern that affects both human and animal populations. The  Antimicrobial resistance (AMR) in pathogenic bacteria can be defined in two ways: microbiologically and clinically. Microbiological resistance is determined by the presence of a genetically acquired or mutated resistance mechanism in the pathogen, which is classified as resistant, intermediate, or susceptible based on specific cut-off values in laboratory tests . ClinicaAMR in bacterial pathogens is a significant global concern associated with high rates of illness and mortality ,4. Severhttps://www.cdc.gov/onehealth/ accessed on 1 July 2023) to manage this global health challenge [The introduction of antibiotics as clinical agents radically changed the evolution and spread of resistance by providing considerable selective pressures, especially on members of the microbiota of humans and domestic animals but also in environments heavily polluted with antibiotics. This selective pressure has promoted the mobilization and horizontal transfer of a large range of antibiotic resistance genes (ARGs) among many bacterial species, particularly to those causing disease. The downstream consequences of such accumulating evolutionary events are gradually increasing the difficulty in the prevention and treatment of bacterial infections. As bacteria and mobile genetic elements often cross environments and species boundaries, it is critical to understand and acknowledge the connections between the human, animal, and environmental microbiota  , as the rates of antibiotic use increase. Assessing the extent of AMR in LMICs is complicated due to inadequate surveillance programs, though efforts like the WHO’s GLASS project 2015) have been initiated . The mis15 have bOveruse and Misuse of Antibiotics: The over-prescription and inappropriate use of antibiotics, such as using them to treat viral infections, contribute to the development of resistance ,25,26,27Incomplete Treatment: When individuals do not complete their prescribed antibiotic course, it creates an environment for the survival of the resistant bacteria, leading to the proliferation of resistant strains.Use in Agriculture: Antibiotics are extensively used in livestock and agriculture to promote growth and prevent infections in animals. This widespread use contributes to the development of resistant bacteria that can be transmitted to humans through the food chain. In addition, mobile elements conferring antibiotic resistance can move from livestock-associated lineages to human lineages ,29.Global Travel and Trade: Resistant bacteria can spread quickly across borders through international travel and trade, making antibiotic resistance a global issue .Lack of New Antibiotics: The development of new antibiotics has slowed significantly in recent decades, reducing the arsenal of drugs available to combat resistant infections .Treating antibiotic resistant infections usually requires the prolonged use of last-line antibiotics, and these approaches often fail, contributing to infection persistence . SeveralFor all these reasons, antibiotic resistance has become a global health crisis, affecting people of all ages and in all parts of the world and, according to the World Health Organization (WHO) , antibioMoreover, antibiotic resistance has profound consequences on global health such as in: (i) difficult-to-treat infections; (ii) increased morbidity and mortality; (iii) hospital-associated infections; (iv) animal health impact; and (v) environmental and food safety impact.AcinetobacterCarbapenem-resistant Clostridioides difficileCarbapenem-resistant Enterobacterales (CRE)Drug-resistant Neisseria gonorrhoeaeCDC listed four threats in 2020 as being of urgent concern in the AMR  Table 1Table 1:CStaphylococcus aureus (MRSA) [Furthermore, the following were listed as serious threats highlighting the alarming global impact of antibiotic resistance and its implications for public health: Vancomycin-Resistant Enterococci (VRE), Extensively Drug-Resistant Tuberculosis (XDR-TB), and Methicillin-Resistant s (MRSA) .Addressing these issues requires a concerted effort from governments, healthcare providers, researchers, and the public to promote responsible antibiotic use, invest in the research and development of new treatments, and improve infection prevention and control measures.Over the past few decades, the indiscriminate and inappropriate use of antibiotics has led to the emergence of increasingly resistant bacterial strains, rendering previously effective drugs ineffective in treating infections . This phDiseases that were once easily treatable with antibiotics, such as pneumonia, urinary tract infections, and skin infections, now become formidable adversaries, necessitating more aggressive and costly treatment approaches. The loss of effective antibiotics not only jeopardizes the treatment of common infections but also undermines critical medical procedures such as surgeries, organ transplants, and cancer treatments, where the availability of effective antimicrobial agents is essential ,37. SimiE. coli, a human pathogen, shared some genetic elements and determinants with human and animal bacteria [This interplay between human and animal health cannot be overlooked. The data indicate that, among the countries presently utilizing veterinary antibiotics, five nations are predicted to experience the most significant percentage growth by 2030. These countries are Myanmar and Indonesia, followed by Nigeria and Peru, with Vietnam also showing a moderate increase . Severalbacteria ,44. Respbacteria . On a smbacteria . Strengtbacteria . Furtherbacteria .p = 0.020) and (ii) human antibiotic consumption was positively linked with animal AMR  [A recent analysis investigated the direct factors contributing to the AMR in both humans and food-producing animals on a global scale. Bidirectional associations were found: (i) animal antibiotic consumption was positively linked with resistance in critical priority human pathogens  . AMR rat= 0.010) .By embracing the principles of the One Health concept and working collaboratively, it is possible to mitigate the impact of antibiotic resistance, safeguard the effectiveness of antibiotics, and secure a healthier future for both humans and animals. Through responsible antibiotic use, improved infection control practices, surveillance systems, and ongoing research and development, we can collectively combat antibiotic resistance and preserve the efficacy of these life-saving medications for generations to come, if socio-economic country-specific factors are taken into account . Erwinia amylovora [https://www.who.int/initiatives/glass, accessed on 15 July 2023), demonstrate the global effort to combat antibiotic resistance and the importance of international cooperation. The national action plan for combating antibiotic-resistant bacteria, developed by US Department of Health and Human services (HHS), is a 2020–2025 plan [Due to the increasing development of multidrug-resistant bacteria, which renders existing antimicrobials ineffective in treating illnesses caused by these bacteria, AMR is a major global concern . This prmylovora . Antimicmylovora . Companimylovora . When somylovora . Becausemylovora ,59. Antimylovora . This ap025 plan  includin025 plan . Likewis025 plan .Antibiotic resistance is an urgent worldwide health problem that endangers the well-being of both humans and animals. This challenge stems from the improper and excessive use of antibiotics in diverse fields, resulting in the emergence of bacteria that are resistant to multiple drugs. Undoubtedly, it is a major cause for concern due to its correlation with elevated mortality rates and fears of regressing to a pre-antibiotic era. Additionally, it has the potential to hinder efforts in controlling infectious diseases, to escalate healthcare expenses, and to undermine the progress achieved in healthcare . TacklinThe implementation of the One Health approach plays a pivotal role in effectively addressing antibiotic resistance . The OneIn addition, the implementation of policies, both at national and international levels, is an essential element in addressing antibiotic resistance. It is, in fact, crucial to establish comprehensive policies that effectively regulate the use of antibiotics in humans, animals, and the environment, to mitigate the factors that contribute to antibiotic resistance. These policies should be developed collaboratively with stakeholders from all pertinent sectors, ensuring diverse perspectives are considered. They must be adaptable and flexible, capable of incorporating new scientific discoveries and adjusting to evolving circumstances. By prioritizing policy implementation, we can then foster a concerted and proactive response to combat antibiotic resistance. The global approach to antimicrobial resistance surveillance, in line with the One Health concept, acknowledges the complexity of the issue ,65. HowePromoting education and raising awareness constitutes another fundamental aspect of the strategy to combat antibiotic resistance . ProvidiIn conclusion, addressing antibiotic resistance necessitates a unified, collaborative, One Health approach that engages stakeholders from various disciplines. By embracing antibiotic stewardship, interdisciplinary research, comprehensive policy implementation, and education, we can effectively combat the factors driving antibiotic resistance and safeguard the well-being of humans and animals. This cooperative strategy is crucial for global health security and the long-term sustainability of human and animal health, demanding continuous dedication and investment from all sectors of society.One Health GovernanceComprehensive Surveillance SystemsStewardship Programs and EducationResearch and DevelopmentPreventive MeasuresPublic Education and AwarenessTo address the growing problem of antibiotic resistance, it is crucial to adopt innovative, forward-thinking, and preventive approaches that encompass the interconnected realms of human, animal, and environmental well-being. Anchored in the One Health approach, these strategies should aim to proactively address the rise and dissemination of antibiotic-resistant bacteria, ultimately ensuring the long-term efficacy of antibiotics for future generations. The identified key areas of focus, in line with the One Health approach and aiming to address critical global challenges, are:“One Health Governance” encompasses the formulation and administration of policies, frameworks, and mechanisms aimed at fostering a united and harmonized approach to addressing health challenges that intersect human, animal, and environmental health. This concept aptly acknowledges the interdependencies among these spheres and underscores the imperative for cross-sectoral and interdisciplinary endeavors to competently manage and avert health hazards that go beyond the conventional confines . The acaTo effectively construct this framework, it is imperative to prioritize public participation, engaging a spectrum of stakeholders including communities, healthcare professionals, scientists, policymakers, and industries, in decision-making processes pertaining to health matters. Additionally, adhering to a consensus-driven approach in accordance with global standards, and ensuring that policies and interventions are designed to cater to the needs and rights of all individuals and communities, regardless of their socio-economic or geographical contexts, holds paramount importance in both LMICs and high-income countries.https://www.cdc.gov/narms/index.html, accessed on 15 July 2023) established by the Food and Drug Administration, the Centers for Disease Control and the US Department of Agriculture. These systems, in turn, would play a vital role in monitoring the emergence, spread, and impact of antibiotic-resistant bacteria, providing invaluable data to influence policy decisions, guide research activities, and enable the prompt identification and containment of potential outbreaks. For these reasons, strengthening reporting mechanisms and promoting data sharing at both national and international levels are crucial in reinforcing the effectiveness of surveillance efforts and fostering a unified global response to antibiotic resistance.An essential aspect involves the development and improvement in comprehensive surveillance systems, such as The National Antimicrobial Resistance Monitoring System (NARMS) (In this context, ensuring adherence and efficiency requires the establishment and implementation of stewardship programs throughout all sectors, ranging from human health and animal health to agriculture. These efforts should be reinforced by continuous education and training initiatives, as outlined in the National Plan. Local successes can serve as valuable insights for distilling a triumphant global initiative.resistome across different reservoirs [We must stress that investing in the research and development of novel antibiotics and alternative therapies is a necessity in our fight against antibiotic resistance. Indeed, given the complex and dynamic nature of antibiotic resistance, it is essential to diversify our tools against bacterial infections. This requires not only the development of new antibiotics, but also the exploration of alternative therapies such as bacteriophage therapy, immunotherapies, and probiotics. Additionally, research activities should also prioritize the development of rapid diagnostic tests capable of quickly detecting bacterial infections and accurately identifying their antibiotic resistance profiles. By doing so, we can enable more precise and effective treatment. Bioinformatic tools, and curated AMR reference databases in accurately predicting AMR from genome sequences, have a huge impact in genomic surveillance ,90,91,92servoirs ,90,91. Gservoirs .Moreover, it is necessary to prioritize preventive measures such as infection prevention and hand hygiene in healthcare settings, PPE and hand hygiene for bacterial diseases that are often associated with antibiotic resistance, and safe food handling and preparation . In factLastly, public education and awareness initiatives play a pivotal role as preventive strategies. By increasing public comprehension of antibiotic resistance and advocating for responsible use of antibiotics, we can influence behaviors and reduce the demand for unnecessary antibiotics. These campaigns should target diverse audiences, ranging from the public to healthcare professionals, and should employ a variety of communication channels to maximize their reach and impact.In summary, it is evident that prospective and preventive strategies are crucial in fighting against antibiotic resistance. By establishing robust surveillance systems, advocating for antibiotic stewardship, allocating resources to research and development, improving infection prevention, and raising public awareness, we can effectively mitigate the emergence and dissemination of antibiotic-resistant bacteria. These collective efforts will ensure the continued effectiveness of antibiotics for the well-being of both current and future generations.


Social media companies have come under increasing pressure to remove misinformation from their platforms, but partisan disagreements over what should be removed have stymied efforts to deal with misinformation in the United States. Current explanations for these disagreements center on the “fact gap”—differences in perceptions about what is misinformation. We argue that partisan differences could also be due to “party promotion”—a desire to leave misinformation online that promotes one’s own party—or a “preference gap”—differences in internalized preferences about whether misinformation should be removed. Through an experiment where respondents are shown false headlines aligned with their own or the opposing party, we find some evidence of party promotion among Democrats and strong evidence of a preference gap between Democrats and Republicans. Even when Republicans agree that content is false, they are half as likely as Democrats to say that the content should be removed and more than twice as likely to consider removal as censorship. Partisan conflict over content moderation is not only due to disagreement about facts, but also to divergence in preferences. Misinformation is seen as a major global threat by political and economic leaders around the world  relative to the other party (out-group)  differences in internal factors like identity or core values, which are deeply rooted and difficult to change, or (b) internalization of elite cues and signals, which may be more changeable. In terms of (a), people differ in identity, values, personality, cognitive processes, motives, and emotions , people internalizing cues and signals from elites in the party they identify with as their own preferences . For our analysis, we focus on English-speaking respondents who identified as Democrat or Republican, resulting in 1120 respondents, with a mean age of 53.29 (SD = 16.53) and 56.3% female (see section S1.7 for detailed descriptive statistics). The experiment and analyses were preregistered . This research was approved by the Institutional Review Boards at our respective universities.false by third-party fact checkers.).” One of the headlines aligned with the respondent’s partisanship, while the other headline was not aligned with the respondent’s partisanship. For example, one pro-Republican headline  reads: “Hours after signing an executive order on Jan. 20, 2021, U.S. President Joe Biden violated his own mask mandate.” Whether the respondent saw the aligned or misaligned headline first was randomized. Headlines were selected from a bank of 18 news headlines  that contained false claims. We provide more information on headline selection in Materials and Methods.The survey experiment relied on simple randomization at the participant and at the headline level. Each participant was shown two different false news headlines sequentially . Respondents were told that “Someone has shared the following headline on a social media site. : Whether or not the participant states that the headline should be removed by the social media company; (ii) Perception of headline removal as censorship (censorship): Whether the participant considers the removal of the headline censorship; (iii) Intent to report headline as harmful (harm): Whether the participant would report the headline as harmful content on a social media platform. We also measure a range of covariates, including perceived accuracy. To measure perceptions of accuracy, we ask respondents for their perceived accuracy of the false news headlines on a four-point scale. All measures, including control variables and indices, are described in detail in section S1.6.We measured three main outcomes: (i) iaY is the binary outcome measure for individual i and headline a. iD indicates that respondent i is a Democrat and iR indicates that respondent i is a Republican. The difference in coefficients on iD and iR reflects the preference gap or the amount overall that Democrats and Republicans disagree about whether false content should be removed controlling for alignment. aHd is an indicator of whether headline a is aligned for Democrats and aHr is an indicator of whether headline a is aligned for Republicans. The coefficients on i · HdaD and i · HraR reflect party promotion or the amount that the outcome depends on the alignment between the partisan nature of the content and the respondent for Democrats and Republicans, respectively .We analyze results using OLS regression, interacting partisanship of participants and political alignment of the headlines:We find a large and statistically significant difference between the content moderation preferences of Republicans and Democrats. Overall, the probability that Democrats say a false headline should be removed is 0.69, while the probability that Republicans say a false headline should be removed is 0.34. The probability that Democrats would report a false headline as harmful is 0.49, while for Republicans, it is 0.27. The probability that Democrats perceive the removal of false headlines as censorship is 0.29, while for Republicans, it is 0.65 .The left panels of Looking at the intent to remove headline outcome , we can While we inform respondents that the headlines have been rated as false by third-party fact-checkers, respondents rated 20.32% of headlines as either “very accurate” or “somewhat accurate.” Moreover, consistent with previous literature, the interaction terms in If some respondents still believed that the headlines were true, despite being told they were false, this poses a problem of identification. It means that we cannot fully isolate the effects of party promotion and the preference gap from the effect of the fact gap. To address this, we conducted three additional analyses, two of which were preregistered. If the results of all three tests are consistent, then it would give us further confidence that we can measure the contribution of party promotion and the preference gap to partisan disagreement over content moderation, controlling for the fact gap. For more information on each method, see Materials and Methods.In the first analysis to address this persistent fact gap, we subset to respondents who rated the headlines as inaccurate, including respondents who assessed headlines as “not very accurate” or “not at all accurate.” A shortcoming of this approach, which means we cannot fully account for the fact gap, is that Democrats and Republicans who believe the headline to be inaccurate are potentially differentially selected. We find that on observables such as gender, age, income, and education, Democrats and Republicans who assess headlines as inaccurate are not significantly different from Democrats and Republicans in the overall sample (see tables S3 and S4). However, there could still be differences in unobservables between those who assess headlines to be inaccurate and the broader respondent pool.In the second analysis, we conducted the main analyses for headlines that, on average, both Republicans and Democrats think are inaccurate and where there is little difference in accuracy perception between Democrats and Republicans (see section S2.1.5). We identify these “consensus headlines” by limiting the mean absolute difference between the average Democrat and Republican accuracy rating for headlines that both Democrats and Republicans on average rate as inaccurate to 0.5 on the four-point accuracy scale. This results in eight headlines. When we decrease this threshold, reducing the number of headlines, the substantive results remain unchanged; see section S2.1.5. We add this analysis to attempt to address the concern that the gap in support for removal observed among Democrats and Republicans in the previous analyses is driven by headlines with a large gap in perceived accuracy between Democrats and Republicans .While a limitation of this robustness check is that it selects only very few headlines and therefore may not generalize, at least for this set of headlines, we continue to observe evidence for the preference gap when we hone in on headlines Democrats and Republicans agree are inaccurate. Democrats remain nearly twice as likely as Republicans to want to remove content and to report content as harmful, while Republicans are nearly twice as likely as Democrats to consider removal censorship. We also continue to see evidence of party promotion among Democrats.In the third analysis, we examine the extent to which party promotion among Democrats and the preference gap are mediated by belief in the accuracy of the content. For the mediation effect for party promotion, we conducted a mediation analysis of the effect of alignment between respondent and headline partisanship on the outcomes for Democratic respondents. The mediation analysis was preregistered for party promotion. We also conducted a mediation analysis for the preference gap, which was not preregistered. For the mediation effect of the preference gap, we conducted a mediation analysis of the effect of Democrat partisanship on the outcomes for all respondents. For more details on the mediation analyses, see sections S2.2.1 and S2.2.2.In the main analysis , we saw Note that ACME is only identified under a sequential ignorability assumption that (i) given the observed pretreatment confounders, the treatment assignment is statistically independent of potential outcomes and potential mediators and (ii) the mediator is ignorable given observed treatment and pretreatment confounders. While treatment is randomly assigned, the second assumption may not hold because the mediator, perception of accuracy, is not. In other words, there may be characteristics of respondents that affect both whether they think a headline is accurate and whether they think a headline should be removed. To probe the robustness of the findings, we conducted a sensitivity analysis  and context  of misinformation headlines also matter , political content, a clear partisan slant, and was recent. We selected headlines that were relatively balanced in terms of the intensity of the information conveyed  and the topic. We then used a pretest to ensure that candidate headlines had a partisan alignment in the expected direction and to measure other headline characteristics including the perceived intensity . Although these headlines may differ in other dimensions that we did not assess and may not be perfectly comparable, the final selection of headlines included pro-Democrat and pro-Republican headlines with the expected ideological slant that were relatively balanced in terms of perceived intensity and topic .We identified headlines used in the experiment from the fact-checking website All outcome measures are binary with the exception of the censorship measure, which was recoded as a binary measure by considering “Yes” as 1, “No” as 0, and “Do not know” as a missing value. We deviated from the pre-analysis plan in recoding “Do not know” as a missing value instead of 0 because recoding “Do not know” as 0 would have imposed a strong assumption that undecided participants actually did not think of headline removal as censorship. We provide results for the main models with the original coding as a robustness check in the Supplementary Materials (see section S2.1.2), and find that the main results remain the same.To measure perceptions of accuracy, we asked respondents for their perceived accuracy of the false news headlines on a four-point scale. We randomized whether participants first answered the question about accuracy or the outcome questions after the headline throughout the experiment (see fig. S1). This also allows us to ensure that our results were not driven by an “accuracy nudge” (We measured a range of covariates such as news consumption and demographics as detailed in section S1.6. Control variables include age, gender, education, race, ethnicity, household income, political interest, whether social media was the most common news source, and whether a participant’s posts had ever been flagged or removed from social media. The order of response options in several questions on covariates, such as partisanship, was randomized. Some of the control variables that we include in our regressions are measured by multiple survey questions. For such questions, we used composite indices as detailed in section S1.6.


Selecting an empiric antimicrobial regimen can be difficult for early learners and misuse of antibiotics can lead to adverse events and antimicrobial resistance. There have been few interventions that have focused on improving antibiotic decision making, as a form of therapeutic reasoning, for post-graduate trainees. We describe here an approach to aid internal medicine interns in their therapeutic reasoning, particularly when it comes to diagnosing and empirically treating infections.The PEST  model was created as a four-step approach to therapeutic reasoning and choosing an appropriate antimicrobial regimen for a given infectious disease syndrome. In February 2020, we conducted two independent teaching sessions for interns on the PEST approach. We assessed pre-and post-teaching responses to five clinical vignette-based questions. Results were presented as a percentage of interns who chose an appropriate antibiotic and provided sufficient therapeutic reasoning as defined by using at least three out of the four PEST criteria. Statistical analysis occurred via Fischer’s exact test to determine the level of statistical significance between responses.Twenty-seven interns participated in the activity. At baseline, several interns had incorporated aspects of the PEST approach in their pre-teaching responses. Ten interns commented on the usefulness of such a systematic approach. While there was no statistically significant difference in antibiotic selection, the teaching session demonstrated a trend towards significance in improving therapeutic reasoning as defined by the PEST strategy.Our results suggested an improvement in using a structured cognitive tool such as the PEST approach to reinforce therapeutic reasoning, but the method did little to improve antibiotic selection. Some interns used select “PEST” concepts prior to the intervention suggesting that the PEST approach may enhance prior knowledge or clinical reasoning skills. Continued incorporation of the PEST approach using a case-based framework may solidify conceptual and practical knowledge of antimicrobial selection. Further studies are needed to assess the impact of such teaching interventions.The online version contains supplementary material available at 10.1186/s12909-023-04286-1. Understand the importance of therapeutic reasoning and selecting optimal empiric antibioticsApply a structured approach to selecting empiric antibiotics, Particularly at an early learner stageUse therapeutic reasoning to arrive at the selection of empiric antibioticsAs a goal, learners will be able to:Clostridioides difficile.Selecting an empiric antimicrobial regimen for a given syndrome can be challenging for doctors-in-training who are by and large the main prescribing physicians in academic medical centers. Overuse of the so-called “broad spectrum antibiotics” is common and can result in adverse events, selection of resistance through disruption of the gut microbiota, and subsequent superinfection with opportunistic pathogens such as Studies have shown that both residents and attending physicians in the general medicine and intensive care setting acknowledge the concern of antibiotic resistance and support the notion of antimicrobial stewardship programs . ResourcExperts have described key decision-making points in antibiotic prescribing, but to date little has been written regarding specific methods to bridge gaps in teaching appropriate antibiotic prescribing across the spectrum of learners –6. The CIn order to allow for adaptability across clinical settings , it is important to teach the therapeutic reasoning behind empiric antibiotics –10 SeverOur intervention focuses on existing resources such as antibiograms, which can help educate early trainees in selecting appropriate antibiotics. We describe here a therapeutic reasoning strategy to aid doctors who are early in their training, particularly when it comes to empirically treating infections.What is the pathology? What is the epidemiology? What is the severity of the disease? What is the treatment being chosen? The PEST model was created as a four-step approach to therapeutic reasoning and choosing an appropriate antimicrobial regimen for a given infectious disease syndrome Fig. . Parts oIn February 2020, we conducted two independent teaching sessions for interns on the PEST approach during noon conference at UCLA Ronald Reagan Medical Center (UCLA RRMC) and the Greater Los Angeles Veterans Affairs Healthcare System (GLA VAHS). Each intern completed a pre-and post-intervention assessment which included free text responses to five clinical vignette-based questions , which also helped learners navigate through the PEST algorithm when deciding on the optimal antimicrobial regimen.As is the case with real-life clinical decision making, more than one approach to prescribing antibiotics was deemed to be correct. Though not previously validated, the rubric was constructed based on a general knowledge of acceptable antibiotic choices for specific infectious disease syndromes and on an a priori construct of therapeutic reasoning , 13. TheThe teaching component and intervention during noon-conference consisted of 1.) an overview of the PEST approach and the rationale behind its use and 2.) a series of clinical vignette-style questions as a group with more in-depth discussions of how to approach each scenario with the PEST checklist kept in mind. The group was encouraged to use the institutional antibiogram to help make clinical decisions.The session was one hour in duration with fifteen minutes for both the pre- and post-assessments and approximately thirty minutes allocated for teaching. The end of the hour consisted of one to two minutes dedicated for written feedback from the interns on the quality of the session. Upon conclusion, the interns repeated the assessment and submitted voluntary written evaluations. Each intern was assigned a number to anonymously group both pre- and post-intervention assessments and evaluations.p value set to < / = 0.05. Responses were evaluated per question, and per hospital training site, to determine if the differences between interns’ responses were statistically significant pre- and post-teaching intervention.A convenience sample was used based on availability at each respective site. A Fisher’s exact test was performed to calculate the statistical significance of the responses with a Twenty-seven interns participated in the activity, with sixteen interns present at the GLA VAHS session and eleven interns present at the UCLA RRMC session. When comparing correct antibiotic responses to each clinical vignette question, the difference in the number of interns at UCLA RRMC and GLA VAHS who answered appropriately pre- and post-intervention for questions 1 through 5 are described in Table With respect to the therapeutic reasoning responses, the difference in the number of interns at UCLA RRMC and GLA VAHS who answered appropriately pre- and post-intervention for questions 1 through 5 are described in Table Interns were given the option to write in free-response format their impression of the session and the PEST approach. Ten interns commented on the usefulness of the approach. Specifically, interns appreciated the systematic approach to choosing antibiotics guided by reasoning, rather than reflexively choosing the correct antibiotic. Additionally, interns enjoyed reviewing the institution’s antibiogram both at the GLA VAHS and UCLA RRMC to help with decision making surrounding antibiotic choices. Lastly, one intern relayed that PEST provided a structured approach to what was informally taught in training, which can facilitate thoroughness when busy on clinical service.Our results showed that the PEST approach did little to improve antibiotic selection, however the results suggest an improvement in therapeutic reasoning that was not statistically significant. The lack of improvement in antibiotic selection could be due to the timing of the intervention as this study was conducted in February of intern year. Many interns were very comfortable with common infectious disease syndromes such as community acquired pneumonia, pyelonephritis and cellulitis, but less comfortable with febrile neutropenia or meningitis which are in general rarer conditions on a general medicine service. Additionally, the lack of a significant improvement in therapeutic reasoning may have been due to the small sample size.Firstline™ which includes antibiotic choices for select infectious disease syndromes in addition to details about our antibiogram [Although we initially designed the study with the goal of improving antibiotic selection, we soon realized that the true benefit may have been in improving therapeutic reasoning as a means to guide antibiotic selection across different clinical scenarios. Antibiotic selection for specific clinical syndromes may, in part, be informed by institutional and broader clinical guidance, which are perhaps easily “memorized” or accessed in different ways. At our institution, we offer some clinical decision support through a smartphone application known as ibiogram .Even though therapeutic reasoning may be an important intervention to help teach early learners how to choose antimicrobials wisely, meta-analyses have described that such focuses only provide a modest improvement in diagnostic accuracy , 12. We In this study, we used the educational strategy of small group discussion and teaching in conjunction with clinical vignettes. We created an assessment to demonstrate mastery of the material for pre- and post-intervention. We graded the therapeutic reasoning responses using a specific rubric which may be biased towards a post intervention improvement. However, as mentioned, even when taken into account, aspects of PEST were inherent in pre-intervention clinical reasoning which helps to supports the ongoing teaching to reinforce knowledge that may already taught implicitly or explicitly in an existing medical curriculum . UltimatMany clinicians do not have a specific approach to choosing antibiotics and often model behaviors based on prior experiences or training . Our stuTo resemble real life decision making in infectious diseases, we chose to not create multiple choice questions, which are often administered as a method to ascertain clinical proficiency during standardized examinations. This allowed interns to explain their answer choices and learn from the explanations, rather than by simply choosing the correct response to a question. Antibiotic choices are not always obvious, and there may be several ways to treat a clinical syndrome. Open-ended questions may allow interns to acknowledge that clinical reasoning may sometimes be more important than arriving at a singular antibiotic choice.Future studies should assess the value of interventions such as the PEST approach in a longitudinal curriculum at various points during the intern year, using a spiral curriculum approach. The spiral curriculum involves teaching a concept, in this case the PEST approach, in a longitudinal format with spaced repetition . HoweverWe acknowledge several limitations with our study. For instance, the teaching sessions consisted only of a one-time intervention at each respective site. Additionally, the intervention occurred at a snapshot in time half-way through the academic year, where doctors-in-training may have a certain amount of confidence when compared to counterparts earlier, or later, in the academic year as mentioned above. Lastly, the cohort included doctors in training within internal medicine, which may not be generalizable for the entire population of recent graduates from medical school. More frequent teaching to a larger and diverse population, spread throughout the academic year, will be needed to reliably measure the efficacy of the PEST approach.In conclusion, the PEST approach may be effective in enriching therapeutic decision-making to guide antibiotic choices for doctors in training, though further research on this cognitive reasoning tool, with perhaps a longitudinal approach and a larger sample size, may be warranted to demonstrate a significant impact. By having such a framework, medical residents can have a reasoning strategy to selecting antibiotics for varied infectious disease syndromes across diverse clinical settings. We hope to continue to assess how to best utilize this intervention throughout the continuum of medical education in an effort to improve comfort with antibiotic prescribing.Additional file 1:Appendix 1. Clinical vignette questions.Additional file 2: Appendix 2. Clinical vignette scoring rubric.