Bacillus subtilis and two prominent pathogens, Staphylococcus aureus and Streptococcus pneumoniae. Thereby, we recapitulated previously known drug interactions, but also identified ten times more novel interactions in the pathogen S. aureus, including 150 synergies. We showed that two synergies were equally effective against multidrug-resistant S. aureus clinical isolates in vitro and in vivo. Interactions were largely species-specific and synergies were distinct from those of Gram-negative species, owing to cell surface and drug uptake differences. We also tested 2,728 combinations of 44 commonly prescribed non-antibiotic drugs with 62 drugs with antibacterial activity against S. aureus and identified numerous antagonisms that might compromise the efficacy of antimicrobial therapies. We identified even more synergies and showed that the anti-aggregant ticagrelor synergized with cationic antibiotics by modifying the surface charge of S. aureus. All data can be browsed in an interactive interface (https://apps.embl.de/combact/).Drug combinations can expand options for antibacterial therapies but have not been systematically tested in Gram-positive species. We profiled ~8,000 combinations of 65 antibacterial drugs against the model species  Antibacterial combinations that are potent against Gram-positive bacteria are identified using an automated high-throughput screen. With antimicrobial resistance (AMR) posing a global threat to public health, which permeates all domains of modern medicine3, the use of drug combinations to re-sensitize resistant strains has emerged as a promising means to bypass the stagnant drug discovery pipeline4.Antibacterial agents have been used in combination for decades for different purposes: to achieve synergy , to limit resistance  and/or to broaden the spectrum of action of anti-infective treatments 10, the full potential of drug combinations for treating bacterial pathogens remains underexplored. This is because the combinatorial space is vast and drug interactions are rare and concentration-, drug-, time-, species- and even strain-specific13, making systematic testing necessary, yet highly demanding. As a result, drug interactions have not yet been systematically profiled in many clinically relevant bacterial species. In addition, with the increase of polypharmacy14, antibiotics are often prescribed in combination with other medications15. While pharmacokinetic interactions between antibiotics and non-antibiotic drugs are well-known for the host 16, they are poorly characterized at the level of bacterial physiology.Although a few antibacterial combinations are used in clinics, and screens for approved compounds as adjuvants for antibiotics have been increasingly conducted in the past decadeStaphylococcus aureus and Streptococcus pneumoniae, two of the most prominent antibiotic-resistant bacteria17 and the model organism Bacillus subtilis. Compared with previous studies18, this vastly increased the number of drugs, concentrations and strains tested. By probing all main classes of antibiotics, we could relate interaction outcomes to bacterial structural features, cellular network architecture, as well as drug target conservation. When comparing the drug interaction networks to those of three Gram-negative pathogens obtained with a similar setup11, we could highlight clear differences driven by the distinct drug permeability barriers across this divide. Moreover, we profiled the interactions of antibiotics with a large panel of non-antibiotic drugs in S. aureus to investigate the impact of commonly administered medications on antibiotic efficacy. Thereby, we uncovered both strong synergies that proved effective against multidrug-resistant clinical S. aureus isolates and widespread antagonisms that could compromise the efficacy of antibiotic treatments.Here we used an automated platform to systematically profile drug interactions against three Gram-positive bacterial species: the pathogens S. aureus, S. pneumoniae and B. subtilis  were probed to assess within-species conservation. The drug panel (n = 65) included antibiotics (n = 57) used against infections with Gram-positive bacteria, belonging to all main classes and targeting different bacterial processes, and eight other bioactive molecules, such as antifungals, drugs with human targets and food additives, depicted as non-antibiotics  in 595) as a readout. Media and shaking conditions were different for each species (50s) for each drug in the four strains, with the highest concentration corresponding to the MIC in most cases, and the intermediate and lowest concentration corresponding to half and a quarter of the highest concentration, respectively  led to very similar results, with the former being more accurate .We measured growth in a broth microdilution format in microtitre plates using optical density at 595 nm (OD species . Drug coectively . We derind yield . We condnd yield  and 0.91nd yield ). Fitnes Fig. 19 11. All i11, with highest precision (0.87) and recall (0.68) observed for a threshold on absolute effect size of >0.1 and on Benjamini–Hochberg adjusted P value of <0.05  , equally representing the four strains probed. These combinations were tested in the same growth conditions as our high-throughput screen, but over a highly resolved dose space (8 × 8 matrix) of linearly spaced concentration gradients . The preeration) . The lowctions11 .20 was inadequate for the main screen  or was unreliable 11. This could be due to technical  or biological reasons .Antagonisms and synergies were detected to be equally prevalent across the three species, accounting for ~12% of all combinations tested Fig. . This in23 and recently systematically demonstrated for Gram-negative species, with 30% of detected interactions shared between at least two of the three species tested and 5% conserved in all three species 11. In Gram-positive species, we observed an even lower conservation rate , the two were significantly correlated , a third of which (n = 39) was conserved in both strains. This confirms that the combinatorial space is a largely unexplored reservoir for improving antimicrobial efficacy.Since dge Fig. . Some 55dge Fig. . Even whS. aureus, cefepime and teicoplanin28, and validated it against several MRSA (methicillin-resistant S. aureus) clinical isolates belonging to worldwide prevalent clonal complexes and different infection sources  and depend on dosage, infection site and specific antimicrobial agents36. In contrast, fosfomycin strongly synergized with a diverse range of protein synthesis inhibitors (Supplementary Table S. aureus (see Discussion).Synergies between cell-wall-targeting drugs and translation inhibitors are cornerstones of anti-infective therapy against Gram-positive bacteriaS. aureus strains) 11. We confirmed this hypothesis by using the OM-defective E.coli mutant lptD4213, which is hyperpermeable to hydrophobic antibiotics and detergents40. Many of the interactions between macrolides and different classes of protein synthesis inhibitors became synergistic in this E. coli mutant background  of Gram-negative bacteria. We reasoned that in Gram-positive species with no such permeability bottleneck, these drugs could synergize at their target level, the ribosome, as previously shown by combinations of genetic perturbations of translationund Fig. , demonst41. Interestingly, the number and type of PBP are largely different across bacterial species42, leading us to hypothesize that this redundancy  drives the observed difference. Indeed, the number of synergies in each strain tested correlated with the number of PBPs encoded in their genomes  as antibiotics 13%) Fig. . This mo% as anti% Fig. . n screen .Fig. 4Inn = 52), we detected 140 synergies and 105 antagonisms mainly with antibiotics  and hormone analogues (n = 6)  and antagonisms (n = 17), suggesting an understudied impact that such commonly used drugs and potentially their natural counterparts, may have on the efficacy of antibacterial therapies45. For the anti-inflammatory drugs, only four interactions with acetylsalicylic acid were previously known: its synergy with cefuroxime46 and its antagonisms with ciprofloxacin, oxacillin and azithromycin49. We validated the synergy between ibuprofen and gentamicin also against MRSA clinical isolates, including a strain resistant to linezolid (a last-resort antibiotic for MRSA), in vitro and in vivo in a G. mellonella infection model  Fig. , whereas 6) Fig. . Interesdel Fig. .12 receptor50, had the highest number of interactions (n = 27) among the 44 non-antibiotics tested ectively . We obseectively  and 15 aectively . This isectively . Furtherectively , confirmS. aureus infection have not been linked so far to a direct effect of ticagrelor on S. aureus virulence. We discovered a pervasive impact of ticagrelor on S. aureus virulence determinants and regulators, many of which were downregulated and others destabilized , the coagulase Coa and the von Willebrand-factor binding protein (vWBP) NWMN_0757 (ref. 59). These effects, evident at a clinically relevant ticagrelor concentration55, offer an alternative explanation for the beneficial effect of anti-aggregant therapy as an adjuvant in S. aureus systemic infection.The clinically observed effects of ticagrelor during zed Fig.  and 15. S. aureus; Fig. G. mellonella  d-alanylation61, DltC and DltD, were destabilized in the TPP lysate data, and TagG, a subunit of the cell wall teichoic acid (WTA) translocase, was destabilized in the whole-cell sample -inducible CRISPRi knockdown mutants of both the dltABCD operon and tagG  and combinations should be tested on the particular clinical isolate they are targeted to before their use, as done in the clinic for single drugs.In this study, we systematically profiled drug combinations against three Gram-positive bacterial species. We tested multiple compounds from each of the main antibiotic classes used to treat infections caused by Gram-positive pathogens, as well as neglected antibiotics, commonly used antibiotic adjuvants and promising non-antibiotic drugs with reported antibacterial activity. Combinations were tested in a dose-dependent manner and interactions were assessed in a quantitative manner. We report a plethora of drug–drug interactions, most of which have not been reported before. Some identified synergies were also effective against multiple MDR clinical isolates that we tested and during infections in vivo. It should be noted that this does not mean that such synergies will be effective against all MDR isolates . Fosfomycin synergies that are strong and conserved across the Gram-positive/-negative divide are good candidates, as they are more likely to be broadly effective. Fosfomycin is increasingly used in clinics11. The confidence and depth level of these comparisons are high, since the two studies have similar experimental and data analysis design, including the drugs tested. As for Gram-negative species, we found that drug interactions were largely species-specific for Gram-positive species, with synergies tending to be more conserved than antagonisms and driven by antibiotics sharing general cellular targets. In contrast, antagonisms were more common between antibiotics of different cellular targets and are less conserved, presumably because they are driven by interactions at the level of drug concentration11. Overall, only a small number of interactions is conserved across Gram-positive and -negative species. Such interactions are more likely to be driven by interactions at the drug target and may thus hold true for strains and species not tested here, providing a high-confidence set for future exploitation. Differences in cell surface organization  or in the degree of redundancy in cell-wall-building enzymes can explain some of the strong synergies observed specifically in Gram-positive or Gram-negative species. Our ability to replicate Gram-positive-specific synergies in hyperpermeable Gram-negative bacteria, together with previous evidence on the strong dependence of antagonisms on drug permeability11, suggests that synergies rely more on drug targets and antagonisms more on drug intracellular concentrations. The exact degree to which this is true and whether this makes antagonisms less conserved (drug cellular targets are more conserved than their transport mechanisms) remain to be systematically assessed. In any case, this is presumably the reason why antagonisms were more frequent than synergies in Gram-negative species11 but not here: synergies based on drug target are easier to detect in Gram-positive bacteria, and antagonisms are less common, as there are fewer permeability bottlenecks to overcome.We compared results with those we obtained previously using a similar screen with three Gram-negative speciesS. aureus. Although the interaction potential was lower for drugs without antibacterial activity, the vast majority of synergies that we detected were previously unknown. Although non-antibiotic drugs have been proposed as anti-infective adjuvants for decades5, their in vivo efficacy and molecular basis of action are only known for a few examples67. We focused on the anti-aggregant ticagrelor, whose repurposing as an anti-infective adjuvant for Gram-positive bacteria has been recently proposed68. While the in vivo benefit of ticagrelor for systemic infections has been documented55, we identified 13 additional synergies with antibiotics in S. aureus, and provided molecular insights into how ticagrelor affects S. aureus physiology and potentiates positively charged antibiotics, such as aminoglycosides or nisin.We also assessed combinations of approved non-antibiotic drugs with antibiotics in a dose-dependent manner in 14. Although known pharmacokinetic interactions that have been documented in humans are routinely avoided, it is assumed that interactions between drugs in bacteria will not impact anti-infective efficacy. We detected both synergies and antagonisms between commonly administered non-antibiotic drugs and antibiotics against S. aureus. These antagonisms could potentially decrease the efficacy of antibiotic therapies and increase the probability of emergence of resistance. Overall, it is important that drug interactions are investigated not only at the level of growth inhibition, but also at the levels of killing and clearing of an infection, as the interaction outcome might differ10.Drugs are regularly combined to treat patients in the clinic, not only in rationally designed therapeutic schemes, but also extemporarily in poly-treated patients69. In our screen, loperamide had the most interactions with antibiotics. Although its potential use as an adjuvant for specific antibiotics and its antibacterial mode-of-action are known5, we detected an additional broad antagonism with macrolides. Loperamide and macrolides are often co-administered for travellers’ diarrhoea66, which is caused by Gram-negative enteric pathogens. It is tempting to speculate that part of the beneficial effect of this combination results from the protection of Gram-positive commensal gut species from macrolide action.It has recently been proposed that the attenuation of antibiotic efficacy (antagonism) could be used to reduce the collateral damage of antibiotics on commensal bacteriaIn summary, we present a systematic and quantitative account of drug interactions against important Gram-positive species. Thereby, we discovered a number of potent synergistic combinations that are effective against clinical MDR isolates. We also investigated mechanisms of selected interactions. In an era where novel antibiotic development faces technical and economic hurdles, and new antimicrobial strategies are urgently needed, we propose that systematic drug interaction profiling might offer alternative solutions to treat bacterial infections. Extending the systematic testing of drug (antibiotic or non-antibiotic) interactions to additional bacterial species will improve our understanding of drug interaction conservation and mechanisms, and inform tailored treatments for bacterial infections.B. subtilis subsp. subtilis 168 (ref. 70) was kindly provided by C. A. Gross, all MRSA clinical isolates by S. Göttig, S. pneumoniae D39V71 by J.-W. Veening and S. aureus USA300 by D. Lopez. Staphylococcus aureus subsp. aureus Newman72 was purchased from NCTC (NCTC 8178) and DSM 20231 (ref. 73)  from DSMZ.All strains used in this study are listed in Supplementary Table S. aureus strains were grown in tryptic soy broth , B. subtilis in LB Lennox and S. pneumoniae in CY medium, as adapted from ref. 74. All species were grown at 37 °C with vigorous shaking (850 r.p.m.), except for S. pneumoniae, which was grown without shaking. The ticagrelor purine supplementation experiments in S. aureus Newman were conducted in SSM9PR-defined medium supplemented with 1% glucose75.For all experiments and unless otherwise specified, S. aureus Newman76. In these strains, dcas was expressed from an IPTG-inducible promoter on plasmid pLOW, while single guide RNAs (sgRNAs) were expressed from a constituted promoter on a plasmid derived from pCG248. The sgRNA target sequences were TGTCTAACAGCAATGCTTTG for dltABCD and AAACCATAATTTGCATAACA for tagG, and ATAGAGGATAGAATGGCGCC for the non-target control MM76 . Plates were sealed with breathable membranes and incubated at 37 °C. OD595 was measured every 30 min for 14 h using a Filtermax F5 multimode plate reader (Molecular Devices). OD595 values were background subtracted . The timepoint corresponding to entry into stationary phase in no-drug control wells was selected for each strain: 8 h for both S. aureus strains, 5.5 h for B. subtilis and 3.7 h for S. pneumoniae. For each drug concentration and strain, the OD595 value at this timepoint was then divided by the robust mean77 of the corresponding values of the no-drug controls for each strain. For each drug, IC50s were then calculated after fitting a four-parameter log-logistic model using the R package drm78. The MIC was considered as the lowest concentration at which growth was inhibited. Experiments were conducted in biological duplicates.MICs and ICB. subtilis and S. aureus DSM 20231; co-amoxiclav, clavulanic acid, pseudomonic acid and cefuroxime in S. aureus Newman. All donor drugs were tested in two biological replicates. Control wells were included in each plate . After the addition of donor drugs, plates were inoculated with cells. Handling, inoculation, growth conditions, plate incubation and OD595 measurements were performed as in MIC determination.Sixty-two drugs, hereafter designated as recipients, were arrayed in flat, clear-bottom 384-well plates in three 2-fold serial dilutions and 2 technical replicates . Concentrations were selected according to MICs, with the highest concentration corresponding to the MIC, and the intermediate and lowest concentration corresponding to half and a quarter of MIC, respectively  estimated from the wells not affected by such artefacts within the same plate. When more than one in the first four timepoints was affected, those wells were identified as non-monotonically increasing OD595 values across the first four timepoints, and their background was estimated as the median first-timepoint OD595 of artefact-free wells .Data analysis was adapted from ref. 595 value was selected at the transition between exponential and stationary phase as in the IC50 determination and used to derive fitness measurements that captured effects both on growth rate and maximum yield. OD-based endpoints correlated well with AUC-based fitness measurements (Pearson correlation 0.96)  correlated worse with either AUC- or endpoint OD-based measurements  , obtaining 3 fitness measures for each drug concentration pair: f1, fitness upon exposure to drug 1; f2, fitness upon exposure to drug 2; and f1,2, fitness in presence of drug 1 + drug 2. On the basis of these values, further quality control was again performed, correcting fitness increase artefacts (maximum fitness was set to 1) and removing plates with poor technical replicate correlation (Pearson correlation <0.7). f1, f2 and f1,2 were used to calculate interaction scores using the Bliss model19. The choice of this model over other available quantification methods was driven by the following considerations: (1) the three measurements obtained for drug dose responses are not sufficient for accurate quantification using alternative models  and (2) the Bliss model can more accurately account for single drugs with no effect (such as most non-antibiotic drugs included in the screen).This value was then divided, per plate, by the robust meanε) scores were calculated as follows:fd1d2 corresponds to the observed fitness in the presence of the drug combination, and fd1 and fd2 correspond to the fitness in the presence of drug 1 and drug 2, respectively.Bliss  < 0.05.When no data were discarded upon quality control, the number of Bliss scores obtained for each combination was 72, composed of 3 × 3 (in the 2D concentration space) × 2 technical replicates × 2 biological replicates × 2 replicates with drugs tested as donor or recipient. Hit calling was performed using a resampling procedure with 10,000 repetitions for each combination tested, where the 11, we coupled this significance threshold to an effect-size threshold. For each combination, we defined a cumulative score using the quartiles of its distribution of ε scores. We tested the performance of different thresholds in precision and recall upon screen benchmarking, and identified |0.1| as the best threshold, with precision of 0.87 and recall of 0.68 , meeting significance and effect-size thresholds in one strain, but with lower effect size in the other strain. A cut-off of |0.08| allowed us to maintain the same precision and increase the recall to 0.72.As before81 and RStudio (v.2021.09.1)82, and networks were created with Cytoscape (v.3.8.2)83.Data analysis was implemented in R (v.4.1.2)11. Synergies could not be observed when the expected fitness of a drug combination  as a response variable, dose responses of monotherapies were fit with a four-parameter logistic regression, with upper and lower boundaries set at 1 and 0, respectively. The null model for the expected response surface was the alternative Loewe generalization84. The prediction covariance matrix was estimated using 200 bootstrap iterations, and the interaction sign and significance were assessed for each concentration combination using the average deviation from the null model as previously described84. The errors for drug combination effects were assumed to be normally distributed. For 24 combinations where one of the drugs had no effect, Loewe could not be applied. Combinations for which the Loewe model could be applied but was unreliable were removed from later analyses. The model was deemed unreliable when: (1) an interaction effect was estimated outside of its confidence interval (due to bias in the Shannon entropy estimate85) and (2) the majority of individual dose combinations deviating from the Loewe null hypothesis disagreed with the direction of an overall global deviation. A separate analysis was carried out to visualize Loewe isoboles. At each concentration of one of the drugs in combination, an individual four-parameter log-logistic model was fit to the dose response of the other drug. To visualize the lines of additive (according to Loewe’s model) and experimentally observed effects, for each combination, an appropriate magnitude of drug effect was chosen according to the relevant effect sizes  . The concatenated sequences  were used to calculate percentage nucleotide sequence identity with vsearch89 and to create a phylogenetic tree. To this end, a multiple sequence alignment was created using MUSCLE (v.3.8.1551)90 with default parameters. Finally, a maximum-likelihood phylogenetic tree was constructed using the online tool PhymL (v.3.0)91 with default parameters. To evaluate interaction conservation, only the 46 drugs tested both in Gram-positive and Gram-negative species  at their final instar larval stage were used for evaluation of selected drug combinations to assess their efficacy against MRSA in vivo. Larvae were purchased from UK Waxworms and Mucha Terra. Stock solutions of cefepime, gentamicin, ibuprofen, teicoplanin and trimethoprim were freshly prepared as described for the in vitro experiments  of the larvae after 24 h . Ten larvae per condition were injected with 10 µl of the bacterial cell suspension or PBS (referred to as uninfected control) into the haemocoel via the last left proleg using Hamilton precision syringes. After 1 h, 10 µl of single drug combinations or vehicle were injected into the last right proleg at the following drug concentrations: teicoplanin 1 µg ml−1, trimethoprim 250 µg ml−1, cefepime 0.025 µg ml−1, gentamicin 2 µg ml−1, ibuprofen 4 µg ml−1, ticagrelor 100 µg ml−1. The survival of Galleria larvae was monitored at the indicated timepoints by two observers independently. Each strain–drug combination was evaluated in three independent experiments.Larvae of the greater wax moth , gentamicin (1.5 µg ml−1), their combination or without drugs. Of serial 10-fold dilutions of cultures, 50 µl were plated on TSA plates every 30 min for 2 h. Cell viability was determined by counting CFUs after plates were incubated overnight in four independent experiments.Overnight cultures of 578 ~ 0.6. Ticagrelor at the desired concentrations  or vehicle was added and cultures were incubated at 37 °C for 10 min. Cells were then pelleted at 4,000 × g for 5 min, washed with 10 ml PBS containing the drug at the appropriate concentrations, resuspended in the same buffer to an OD578 of 10 and aliquoted to a PCR plate. The plate was then exposed to a temperature gradient for 3 min in a PCR machine (Agilent SureCycler 8800), followed by 3 min at room temperature. Cells were lysed with lysis buffer , 250 U ml−1 benzonase and 1 mM MgCl2 in PBS) for 20 min with shaking at room temperature, followed by five freeze–thaw cycles. Protein aggregates were then removed by centrifuging the plate at 2,000 × g and filtering the supernatant at 500 × g through a 0.45 µm filter plate for 5 min at 4 °C. Protein digestion, peptide labelling and MS-based proteomics were performed as previously described57.Bacterial cells were grown overnight at 37 °C in TSB and diluted 1,000‐fold into 50 ml of fresh medium. Cultures were grown at 37 °C with shaking until OD56. Raw MS files were processed using isobarQuant92. Peptide and protein identification were performed using Mascot 2.4 (Matrix Science) against the S. aureus Newman strain Uniprot FASTA (Proteome ID: UP000006386), modified to include known contaminants and the reversed protein sequences. Data analysis was performed in R using the package TPP2D93 as previously described94. Briefly, to identify stability changes, a null model allowing the soluble protein fraction to depend only on temperature, and an alternative model corresponding to a sigmoidal dose-response function for each temperature step were fitted to the data. For each protein, the residual sums of squares (RSS) of the two models were compared to obtain an F-statistic. FDR control was performed with a bootstrap procedure as previously described94. The abundance or thermal stability effect size was calculated for each protein as follows:κ is the slope of the dose-response model fitted across temperatures and drug concentrations, and RSS0 and RSS1 correspond to the residual sums of squares of the null  and alternative models, respectively95.Data were pre-processed and normalized as previously describedS. aureus Newman proteome was annotated using KEGG96 . Proteins with missing KEGG annotation were preliminarily removed. A one-sided Fisher’s exact test was then used to test the enrichment of input protein sets (hits corresponding to FDR < 0.05) against the background  for each term. The P values were corrected for multiple testing using the Benjamini–Hochberg procedure. The analysis was performed in R using the packages KEGGREST97, EnrichmentBrowser98 and clusterProfiler99.The S. aureus Newman as described above in SSM9PR-defined medium supplemented with 1% glucose75 in flat, clear-bottom 384-well plates with a final volume of 30 µl. Adenine and inosine were added at 20 and 100 µg ml−1, respectively, or in combination, both at 100 µg ml−1. Experiments were conducted in four biological replicates. A single-timepoint OD595 at the transition between exponential and stationary phase (13.5 h) was used to derive dose-response curves after normalization to the respective no-drug control for each condition.Ticagrelor  MIC was measured upon purine supplementation in dltABCD and tagG IPTG-inducible knockdown mutants (−1) and chloramphenicol (10 µg ml−1) for plasmid maintenance. IPTG (500 µM) was used to achieve maximal dCas9 expression and thereby, knockdown of the targeted gene. The parent S. aureus Newman and the control strain MM76 (containing the two vectors with dCas9 and a non-targeting sgRNA) were included in all experiments and experiments were conducted in four biological replicates in 384-well plates. For each plate, we identified the timepoint when the control strain MM76  reached plateau, defined as the first timepoint before no increase was detected in log10(OD595) values of two consecutive timepoints. This timepoint was then used for all wells to derive dose-response curves after normalization to the respective no-drug control for each strain and biological replicate. Full growth curves annotated with the timepoint used for the dose-response curves and dose-response curves with all controls are included in For gentamicin and nisin MIC measurements,  mutants  were groc binding assay was conducted as previously described100. Briefly, overnight cultures of S. aureus Newman were diluted 1:1,000 in 20 ml of TSB medium and grown in flasks at 37 °C with continuous shaking until they reached OD578 of ~0.45. Samples were then incubated in the same conditions with or without 10 and 5 µg ml−1 ticagrelor for 20 min. Samples were centrifuged at 10,000 g for 15 min at room temperature, washed twice with 20 mM MOPS buffer (pH 7) and concentrated to reach a final A578 of 10 in a 96-well plate  containing cytochrome c  or MOPS buffer (Fig. c in the supernatant was determined spectrophotometrically at OD410. Two-fold dilutions of cytochrome c in the same plate, starting from 256 µg ml−1, were used to obtain a standard curve onto which a linear model was fitted to calculate cytochrome c concentrations in the other wells. Results are expressed as unbound cytochrome c fraction in the supernatants. Experiments were conducted in four biological replicates.The cytochrome fer Fig. . The plaFurther information on research design is available in the Supplementary InformationSupplementary Figs. 1–7.Reporting SummarySupplementary TablesSupplementary Table 1 Strains used in this study. Supplementary Table 2 Drugs tested in the antibiotic screen. Supplementary Table 3 Interactions detected in the antibiotic screen. Supplementary Table 4 Benchmarked interactions from the antibiotic screen. Supplementary Table 5 Concordance between Loewe and Bliss models on assessing the interactions in the benchmarked set. Supplementary Table 6 Conserved interactions across Gram-positive and Gram-negative species. Supplementary Table 7 Known and novel interactions detected in S. aureus. Supplementary Table 8 Number of PBPs and synergies in Gram-positive and Gram-negative species. Supplementary Table 9 Beta-lactams considered in each Gram-positive or Gram-negative species. Supplementary Table 10 Correlation between number of PBPs and synergies considering only one strain per species. Supplementary Table 11 Non-antibiotic drugs tested in S. aureus DSM 20231, their therapeutic class and concentration chosen. Supplementary Table 12 Interactions detected in the non-antibiotic drug screen performed in S. aureus DSM 20231. Supplementary Table 13 Benchmarked interactions from non-antibiotic screen performed in S. aureus DSM 20231. Supplementary Table 14 2D-TPP hits of ticagrelor treatment of S. aureus Newman. Supplementary Table 15 KEGG annotation of 2D-TPP hits of ticagrelor treatment of S. aureus Newman.Supplementary DataSupplementary Data 1 Source data for Supplementary Fig. 1, Supplementary Data 2 Source data for Supplementary Fig. 2, Supplementary Data 3 Source data for Supplementary Fig. 3, Supplementary Data 4 Source data for Supplementary Fig. 4, Supplementary Data 5 Source data for Supplementary Fig. 5, Supplementary Data 6 Source data for Supplementary Fig. 6, Supplementary Data 7 Source data for Supplementary Fig. 7.Source Data Fig. 1–5, Source Data Extended Data Fig. 1–9Statistical source data


Haemophilus influenzae has become increasingly important as a causative agent of invasive diseases following vaccination against H. influenzae type b. The emergence of antibiotic resistance underscores the necessity to investigate typeable non-b carriage and non-typeable H. influenzae (NTHi) in children.Non-typeable H. influenzae strains were determined phenotypically. Mutations in the ftsI gene were explored in 129 strains that were resistant or had reduced susceptibility to beta-lactam antibiotics. Results were compared with data obtained during overlapping time periods from 94 children experiencing invasive disease.Nasopharyngeal swab samples were taken over a three-year period (2016–2018) from 336 children (6–30 months of age) attending daycare centers (DCCs) in Belgium, and from 218 children with acute otitis media (AOM). Biotype, serotype, and antibiotic resistance of ftsI mutations were found in the AOM group than the DCC group (21.6 vs. 14.9% – p = 0.056). Even more so, the proportion of biotype III strains that carried a ftsI mutation was higher in AOM compared to DCC (50.0 vs. 26.3% – p < 0.01) and invasive group.Overall, NTHi was most frequently present in both carriage  and invasive group. This was followed by serotype “f” (2.2%) and “e” (1.4%) in carriage, and “b” (16.0%), “f” (11.7%), and “a” (4.3%) in invasive strains. Biotype II was most prevalent in all studied groups, followed by biotype III in carriage and I in invasive strains. Strains from both groups showed highest resistance to ampicillin (26.7% in carriage vs. 18.1% in invasive group). A higher frequency of ftsI mutations was found in children with AOM compared to DCC and invasive group. This data helps understanding the H. influenzae carriage in Belgian children, as such information is scarce.In both groups, NTHi was most frequently circulating, while specific encapsulated serotypes for carriage and invasive group were found. Biotypes I, II and III were more frequently present in the carriage and invasive group. The carriage group had a higher resistance-frequency to the analyzed antibiotics than the invasive group. Interestingly, a higher degree of  Haemophilus influenzae is one of the most important bacterial colonizers of the nasopharynx and is a frequent cause of acute otitis media (AOM) and sinusitis (H. influenzae can exist without a capsule (non-typeable H. influenzae) or can be capsulated by six different polysaccharide capsules . Up until the introduction of the H. influenzae vaccine — that included the Hib capsule — into the national childhood vaccination programs in 1993  cases as well as non-b-serotype cases to increase  and children with acute otitis media (AOM), and compares it to Belgian children experiencing invasive disease caused by both non-typeable and encapsulated H. influenzae.The existence of antibiotic resistance of The study protocol, the informed consent forms, questionnaires and sample collection were approved by the ethics committee of University of Antwerp (UAntwerpen) and the Antwerp University Hospital . Written informed consent was obtained from the infants’ parents or legal representatives.Haemophilus influenzae carriage strains were collected from children sampled in the ongoing Belgian nasopharyngeal carriage study (Np carriage study) during the sample collection periods 2016–2018 (016–2018 . Samples016–2018 . No dataH. influenzae strains present from children suffering from AOM (n = 218), from the same collection period. To make an adequate comparison between samples taken from healthy children attending DCCs and AOM-children, samples were stratified on age, sex, vaccination status and AB treatment within the two groups were selected. The selected children aged between 6 to 30 months.A total number of 554 strains, collected between 2016 and 2018 for the NP carriage study, were analyzed. These strains include a stratified selection of strains from children attending DCC (n = 336) and all Haemophilus influenzae including biotype, serotype, ftsI sequencing (when performed), AST to ampicillin, amoxicillin-clavulanic-acid, cefotaxime and ciprofloxacin.Microbiological results for all invasive strains  isolated in Belgian children under five between 2015 and 2018 (referred to as the “invasive group”) were provided by the Belgian National Reference Centre (NRC) for Parents or legal representatives were asked to fill out a questionnaire before sampling. A single nasopharyngeal (NP) swab was taken with a flocked nylon swab and transported in 1 ml STGG (Skim milk – Tryptone – Glucose – Glycerol) at 2–8° C and cultured or stored at-80° C within 24 h.Haemophilus influenzae. Before testing, strains were subcultured twice onto chocolate agar (in-house preparation).Analyses were performed at the Belgian National Reference Center (NRC) for H. influenzae strains were determined using biochemical tests based on the production of indole, urease and ornithine decarboxylase  and Difco Haemophilus antiserum agglutination kit , respectively, and following manufacturer’s instructions.All isolates were identified by MALDI-TOF mass spectrometry using a Microflex LT spectrometer and the Biotyper 4.2.80 database . Biotypes and serotypes of Antimicrobial susceptibility testing were performed following EUCAST V9.0 guidelines and clinical breakpoints. A disk diffusion test with benzylpenicillin 1 unit disk and a beta-lactamase detection using BBL paper disks (Becton Dickinson) were performed in parallel with the susceptibility testing for ampicillin, amoxicillin-clavulanic acid and cefuroxime; ciprofloxacin was also tested. All minimum inhibitory concentrations (MIC) were determined by gradient strips antimicrobial susceptibility testing .ftsI gene was sequenced as described by The transpeptidase domain of the H. influenzae strains obtained from children with AOM during the period 2016–2018 (n = 218). Similar numbers (n = 336) of H. influenzae strains obtained from children attending DCC during the period 2016–2018 were selected.Sample size was based on the number of 2) Test was used to assess significant differences between different analyzed groups (α = 0.05). On serotype level Chi2 Test is performed on NTHi versus encapsulated H. influenzae. On biotype level Chi2 Test is performed on (in our study) frequently circulating biotypes  versus less frequently circulating biotypes .The Chi-Square . Children are defined as healthy if they either do not have a disease condition or they have a disease that is fully controlled which allows the child to attend the DCC. Results from the carriage groups are compared to the “invasive” group, consisting of 94 strains.H. influenzae was most prevalent in both carriage (96.4%) and invasive strains , biotype III (24.2%), and biotype I 15.3%; . The disH. influenzae strains from the carriage group showed resistance to ampicillin (26.7%), cefuroxime (12.8%), amoxicillin-clavulanic acid (7.2%), and ciprofloxacin (0.4%). No difference was observed in resistance of H. influenzae strains across the different seasons, nor between the AOM and DCC group. Also for the invasive group, resistance was found to ampicillin (18.1%), amoxicillin-clavulanic acid (2.1%), and ciprofloxacin (1.1%). No data was provided for cefuroxime in the invasive group .H. influenzae strains in the carriage and invasive group, respectively (n = 132) as well as the invasive group (n = 15), all of the beta-lactamase positive strains showed resistance to ampicillin, while 3.0% (4/132) and 6.7% (1/15) of the strains were resistant to amoxicillin-clavulanic acid in the carriage and invasive group, respectively. Resistance to cefuroxime was found in 10.6% (14/132) of the beta-lactamase positive strains in the carriage group.Beta-lactamases were present in 23.8 and 16.0% of the ectively . In the ftsI gene. In 81.0% (104/129) of the strains, a mutation in the ftsI gene was found. No difference was observed in the incidence of the ftsI mutation over the different time periods. Of all ftsI mutated strains, 93.7% corresponded to the Ubukata-group 2 and a small proportion (3.1%) to the Ubukata-group 3. A higher frequency of ftsI mutations was found in the strains of children with AOM compared to children attending DCCs (21.6 vs. 14.9% – p = 0.056). Moreover, a higher proportion of biotype III strains carried a mutated ftsI genein the strains of children with AOM compared to children attending DCCs (50.0 vs. 26.3% – p < 0.01), while no difference in mutation frequency in other biotypes was seen. All strains carrying a mutated ftsI gene were non-typeable.From the 554 samples from the carriage group, 129 resistant or “less” susceptible strains were reanalyzed and were checked on mutations in the The highest resistance of the mutated strains in the carriage group was seen to cefuroxime , followed by amoxicillin-clavulanic acid  and ampicillin .ftsI gene, all corresponding to the Ubukata-group 2. Biotype II was most frequently  present in these strains followed by biotype III . As in the carriage group, all mutated strains were not typeable. The mutated strains in the invasive group were resistant to ampicillin  and amoxicillin-clavulanic acid .In the invasive group, 4.3% (4/94) of the strains showed a mutation in the H. influenzae strains in samples obtained from Belgian children attending DCCs and children with AOM and compares it to the susceptibility profile of H. influenzae strains responsible for invasive disease in children.This three-year study describes the carriage status and antimicrobial susceptibility profile of Haemophilus influenzae strains were predominantly present in the different subgroups of our study. Also, the NRC reports Belgium showed that mostly non-typeables were present in invasive disease . It is already known that invasive H. influenzae serotype b (Hib) strains have been replaced by non-typeable H. influenzae in many countries since the introduction of the Hib conjugate vaccines . This was followed by biotype III, biotype I and biotype V which is in line with observations from other studies . Differechanisms .H. influenzae strains is gaining importance because of the increased use of existing antibiotics as well as development of novel antibiotics. To treat H. influenzae infections, aminopenicillins and cephalosporins are used as first choice, but there are mechanisms of resistance against these aminopenicillins  was an exclusion criterium in AOM children but not in DCC children. Children included from DCC turned out to have had more AB treatments than children included with AOM. However, AOM children may have used a broad-spectrum antibiotic. Measuring trends in the antimicrobial susceptibility of icillins . As the fections . This maftsI gene was found compared to the strains obtained from DCC children. The current study helps to lay the ground to understand the dynamics of H. influenzae carriage in Belgian children, as available data on remaining H. influenzae dynamics in a vaccinated child population (in a carriage as well as an invasive group) is scarce. This study provides an important first insight into the characteristics of circulating H. influenzae strains, especially in light of emerging antibiotic resistance to beta-lactams as they may have implications for the treatment of H. influenzae infections. It is important to monitor changes in the microbiology and epidemiology of H. influenzae as it can lead to clinical failure caused by antimicrobial resistance.In conclusion, this study found that biotypes I, II and III are more frequently present in the carriage group as well as in the invasive group, while specific serotypes for carriage (serotype “e”) and invasive group (serotype “a” and “b”) were found. In addition, a higher antimicrobial resistance level for the analyzed antibiotics was observed in the carriage  group compared to the invasive group. In the strains of children suffering from AOM, a higher degree of mutations in the The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethics committee of University of Antwerp (UAntwerpen) and the Antwerp University Hospital . Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.The study is supported by a research grant from Research Foundation Flanders  and an investigator-initiated research grant from Pfizer.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The “ESTIMATE” and “pRRophetic” packages in R were used to evaluate the tumor microenvironment and the sensitivity of patients to chemotherapy drugs. Data sets from IMvigor210 were used to evaluate the efficacy of immunotherapy in cancer patients. For experimental verification, 37 endometrial cancer and 43 normal endometrial tissues samples were collected. The mRNA expression of the IRGs was measured using qRT-PCR. The effects of IRGs on the malignant biological behaviors of endometrial cancer were detected using CCK-8, colony formation, Transwell invasion, and apoptosis assays. We developed a novel prognostic signature comprising 13 IRGs, which is an independent prognostic marker for endometrial cancer. A nomogram was developed to predict patient survival accurately. Three key IRGs  were identified in this model. Furthermore, we verified the expression of the three key IRGs using qRT-PCR. Functional experiments also confirmed the influence of the three key IRGs on the malignant biological behavior of endometrial cancer. Thus, a characteristic model constructed using IRGs can predict the survival, chemotherapeutic drug sensitivity, and immunotherapy response in patients with endometrial cancer.Inflammatory response is an important feature of most tumors. Local inflammation promotes tumor cell immune evasion and chemotherapeutic drug resistance. We aimed to build a prognostic model for endometrial cancer patients based on inflammatory response-related genes (IRGs). RNA sequencing and clinical data for uterine corpus endometrial cancer were obtained from TCGA datasets. LASSO-penalized Cox regression was used to obtain the risk formula of the model: the score = e Endometrial cancer (EC) is one of the most common gynecological tumors in the United States, Europe, and other developed countries. Most patients with EC are diagnosed at an early stage and have enhanced survival with effective treatment. However, most patients with advanced EC are incurable , 2. TherThe tumor microenvironment (TME), comprising cancer cells, non-tumor cells, and the extracellular matrix, plays significant roles in intra-tumoral crosstalk . Cancer The inflammatory response is an important feature of tumors . Both loHerein, we hypothesized that IRGs could affect EC progression and patient prognosis. Thus, this study aimed to build an IRGs-based signature to predict patient prognosis and the effects of immunotherapy and chemotherapy. We also experimentally verified the effects of IRGs on the malignant biological behavior of EC cells to provide novel insights regarding the prognosis and efficacy of immunotherapy and precision medicine in EC.In this study , an IRG-(0.1286 × Exp [GABBR1]) + (0.0212 × Exp [LAMP3]) + (−0.1382 × Exp [LCK]) + (−0.1231 × Exp [LPAR1]) + (0.0951 × Exp [MEP1A]) + (0.3928 × Exp [MXD1]) + (−0.0676 × Exp [NDP]) + (−0.2482 × Exp [P2RX4]) + (0.0128 × Exp [P2RY2]) + (−0.2786 × Exp [PSEN1]) + (0.1288 × Exp [ROS1]) + (−0.1594 × Exp [SLC11A2]) + (0.1905 × Exp [TNFSF10]). Kaplan−Meier (KM) analysis of the training group showed differences in survival between the high- and low- risk groups  . We screk groups . In the k groups . Patientk groups , 1G.The risk score was an independent prognostic factor , 2B. We To verify the effectiveness of the constructed model, we also compared it with existing models. Association analysis between risk score and clinical characteristics showed that the risk score was higher in the >65 age groups (P < 0.001), tumor stage III–IV (P < 0.001), or tumor grade 3–4 (P < 0.001) than in the ≤65 age groups, tumor stage I–II, or tumor grade 1–2 –4C. More+T cells, DCs, iDCs, pDCs, T helper cells, and TIL were significantly decreased in the high-risk group  revealed that the fractions of CD8sk group . Moreovesk group .RNA stemness scores (RNAss) and DNA stemness scores (DNAss) were used to evaluate CSCs . The resTo further verify the relationship between risk score and immunotherapy sensitivity, we downloaded the dataset from the IMvigor210 database. We identified 16 differentially expressed IRGs (DEIRGs) . Figure To further verify the role of LAMP3, MEP1A, and ROS1 in EC, we knocked down the expression of LAMP3 , MEP1A , and ROSEC is one of the most common tumors affecting the female reproductive system. Currently, EC treatment mainly employs surgical intervention alone in the early stages and combines surgery with other adjuvant treatments in advanced stages . HoweverThe main risk factors for EC are exposure to endogenous and exogenous estrogen, diabetes, obesity, late-onset menopause, nulliparity, and older age . HoweverThe TCGA-UCEC dataset was downloaded for this study. Univariate analysis of TCGA-UCEC dataset identified 39 prognostic IRGs, and LASSO regression was used to build a new gene signature containing 13 IRGs. We then constructed a nomogram based on the clinical information of UCEC patients  and compared this signature with the models constructed by Cai , and Liu+T cells, and were significantly decreased in the high-risk group. This suggests that our model is sensitive to changes in immune function. Previous studies have shown that changes in these immune-related cells affect the prognosis of cancer patients [Inflammation and the inflammatory TME drive tumor growth, metastasis, progression, and initiation . Howeverpatients , 30. CSCpatients –33. Our patients , 35. TheCurrently, clinical treatments exist for only some types of endometrial carcinomas , 37; howHowever, this study has some limitations. There was no external validation due to the lack of other datasets with UCEC clinical data. In addition, the effectiveness of the model in clinical practice is unknown, and we intend to conduct further research in the future. Nonetheless, our exploration of IRGs also provides a reference for studying inflammatory responses in UCEC. Thus, these findings might have potential clinical application for better prognostic management of patients with EC. Additionally, the genes in this model may serve as molecular targets for EC therapy.The IRGs-based model that we constructed for the first time have stable predictive power for patient prognosis. This model can be used as a potential prognostic index for patients with EC. Meanwhile, we also provide a theoretical basis for the future studies on inflammatory response in EC. Overall, this study could help reveal the role of IRGs in TME, immunotherapy response, and chemotherapeutic drug resistance, which is crucial for personalized tumor treatment and precision medicine. These findings might have potential clinical application for better prognostic management of patients with EC.https://portal.gdc.cancer.gov/) database. We obtained an RNA sequencing dataset of 552 UCEC and 35 normal tissues . After excluding patients with EC with incomplete clinical information or who were lost to follow-up, 541 patients with EC were enrolled. The IRGs were selected from the Molecular Signatures database (HALLMARK_INFLAMMATORY_RESPONSE (gsea-msigdb.org)). Immunotherapy data were obtained from IMvigor210.The clinical information and RNA sequencing datasets (FPKM) of patients with UCEC were downloaded from TCGA (sum(expression of each gene × corresponding coefficient). The UCEC patient prognostic data in the training group were divided into high- and low-risk subgroups based on the median risk score as the cutoff value. The test group was used to verify the validity of the proposed model. R packages “regplot” and “rms” were used to build a nomogram by combining the clinical information of patients with EC and compared with previously established models. GSEA was used to analyze KEGG and GO enrichment in this model. Correlation analysis of immune function was based on ssGSEA [We used univariate Cox regression analysis to analyze the 200 IRGs from the Molecular Signatures database to identify prognostic IRGs associated with OS. Because only TCGA contained prognostic information for patients with EC, we could only test our model internally. The TCGA-UCEC patients were randomly and equally divided into training (n = 272) and test (n = 269) groups. We then used LASSO-penalized Cox regression analysis on the training set . Risk scn ssGSEA .The R (version 4.1.1) package “ESTIMATE” was used to analyze infiltration of immune and stromal cells . We extrThe packages in R were used to evaluate the sensitivity of patients to chemotherapy drugs. Half-maximal inhibitory concentration (IC50) was calculated to compare drug sensitivity. Wilcoxon rank test and the “ggplot” package was used to visualize the result. Data sets from IMvigor210 were used to evaluate the efficacy of immunotherapy in cancer patients.We utilized the “limma” package in R software to identify the differentially expressed IRGs (DEIRGs) among the 200 selected genes. Our selection criteria for DEIRGs were |log2 fold change (FC)| ≥ 2 and false discovery rate < 0.05. Subsequently, we generated heatmaps and volcano plots using the “pheatmap” package. We then overlapped the DEIRGs with the genes in the model to identify the key genes. These key genes not only play a significant role in the model but may also contribute to tumor development, and further experiments will be conducted to verify their importance.We obtained 43 samples of normal endometrial tissues and 37 samples of uterine corpus EC tissues from Shengjing Hospital of China Medical University, China, between 2019 to 2021. Complete clinicopathological information was obtained for 32 patients with EC. All patients provided informed consent. The pathologic type of all cases of EC was endometrial adenocarcinoma, and the pathological diagnosis was made by two experienced pathologists in accordance with the International Federation of Gynecology and Obstetrics (FIGO 2009). None of the patients received any form of hormone, radiotherapy, chemotherapy, or treatment prior to surgery.SiRNA sequences targeting LAMP3, MEP1A and ROS1, along with their respective negative control (NC) counterparts, were procured from GenePharma . Lipofectamine 3000 (Invitrogen) was used to transfect cells with the siRNA according to the manufacturer’s instructions for subsequent experiments. Sequences of siRNA are listed in −ΔΔCT method.Total RNA was extracted using TRIzol reagent . Subsequently, cDNAs were synthesized using Prime Script RT-polymerase (Vazyme). SYBR Green Premix (Vazyme) with specific PCR primers  were used to detect the expression level of corresponding gene RNA. The primer sequences are provided in The Ishikawa cells were cultured in RPMI 1640 medium , supplemented with 10% fetal bovine serum (FBS) (Gibco) and 1% penicillin–streptomycin. The cells were maintained in a humidified incubator at 37° C with 5% CO2.To investigate the impact of LAMP3, MEP1A and ROS1 expression on cell proliferation, Ishikawa cells were transfected with NC-siRNA or siRNA targeting the genes of LAMP3, MEP1A, and ROS1. Additionally, a blank group without siRNA was included. Following transfection, 1000 cells were seeded to each well of 6-well culture plates and incubated for two weeks. Afterward, the cells were stained with 0.1% crystal violet, and the number of colonies was counted using light microscopy.Ishikawa cells were seeded in 96-well plates, and to each well, CCK-8 reagent (10 μL)  was added. The cells were then incubated at 37° C with 5% CO2 for 3 h. The OD 450 nm was measured using a microplate reader at 0, 24, 48, and 72 hours after treatment to determine the cell viability.Transwell chambers  (pore size:8μm) were utilized to evaluate cell invasion ability. Prior to seeding cells, the chambers were coated with Matrigel solution , and the upper chamber was filled with 200μl serum-free medium. The lower chamber was supplemented with 10%FBS serum. After incubation for 24 hours, the invading cells that penetrated the Matrigel and reached the lower surface of the membrane were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet. Images were obtained using a fluorescence inversion microscope (200× magnification) and subsequently analyzed.6 cells from each group were washed and stained Annexin V-APC and 7AAD  at room temperature for 15 min. Flow cytometry  was used to evaluate the proportion of apoptotic cells (low right corner of the flow cytometry graph regarded as apoptotic cells).After cell transfection, 10The GraphPad Prism 8  were used for statistical analysis. Each experiment was repeated three times independently. Data are presented as mean ± standard deviation (SD). Two-way comparisons between groups were analyzed using the t-test, and multiple group comparisons were analyzed using one-way ANOVA. Statistical significance was set at p < 0.05.All data generated or analyzed during this study are included in this article.We have obtained consent to publish this paper from all the participants of this study.Supplementary FiguresSupplementary Tables


Poa pratensis L.).Evaluation of the effects of water-limited conditions on the photosynthetic characteristics and forage yield is important for enhancing the forage productivity and drought tolerance in Kentucky bluegrass , stomatal conductance (sg), transpiration rate (Tr), chlorophyll content (Chl), and photochemical efficiency (Fv/Fm)] and forage yield [fresh forage yield (FY) and dry forage yield (Dy)] traits were analyzed during the 2018 and 2019 growing seasons.In the present study, 100 Psg with a 326% decrease under drought stress conditions showed the highest reduction rate among forage yield and photosynthesis traits, respectively. Genotypic coefficients of variation (GCV) for FY were lower under drought stress. The estimates of heritability, genetic advance, and genetic advance as percentage of mean showed the predominance of additive gene action for the traits. Overall, the results showed that “Ciakhor”, “Damavand”, “Karvandan”, “Basmenj”, “Abr2”, “Abrumand”, “Borhan”, “Hezarkanian”, “LasemCheshmeh”, “Torshab”, and “DoSar” have higher forage yield production with little change between two irrigation regimes, which makes them promising candidates for developing high-yielding drought-tolerant varieties through breeding programs.Drought stress had negative effects on evaluated photosynthesis parameters and significantly reduced dry and fresh forage yields. On average, FY with a 45% decrease and  Nowadays, climate changes that severely affect plant growth shift from monsoon patterns and global warming to drought more intensely and frequently. As a detrimental abiotic stress for plant growth, drought threatens production in agriculture in most countries and geographical regions . Drought2 intake) or nonstomatal (low photosynthetic rate in mesophyll tissue) responses, or both, are considered as the main factors responsible for decreased photosynthesis during drought stress  as a perennial grass with good spring green-up and forage quality is well suited for animal grazing. The grazing tolerance of this plant species is better than other cool-season forage grasses, which makes it an ideal species for permanent pastures. Kentucky bluegrass, unlike most cool season grasses, spreads by rhizomes, which helps it fill in open areas and produce a denser sod, which makes it ideal for erosion control. In addition, P. pratensis is more drought tolerant than many other grass species, which makes it a suitable candidate forage crop in arid and semi-arid areas. Previous studies determined that P. pratensis has a native distribution that spans different climatic regions of Iran, especially in the western and northern regions along the Zagros and Alborz Mountain ranges . Each clone sample containing 10 to 15 tillers was collected from a depth of 40 cm of soil and transferred to plastic pots for clonal propagation in a greenhouse. After pre-evaluation of the whole population, 100 viable accessions were selected for growing in the field and further phenotypic evaluation.Plan material consisted of 176 wild Kentucky bluegrass ecotypes that were collected from different geographical areas in Iran . The colThe rhizomes of the selected accessions were grown in the Shiraz University field research station at Bajgah  over 2 years (2017–2018 and 2018–2019 seasons). The geographical information about the areas where the accessions collected is presented in After field establishment, the germplasm panel was subjected to two irrigation regimes, one as a control with irrigation over the crop growing cycle and one as a drought stress treatment. The experiment was established in a randomized complete block design (RCBD) with two replications in each irrigation treatment. Each plot in the RCBD design contained one clone with a distance of 80 cm between clones. The plants continued to grow in the second year. The soil information used in the current study is shown in ng Eq. 1 :wherei at field capacityn = the number of soil layersii (m)n and i are considered 1 in this equation. Field capacity data were used for the irrigation efficiency of 90%, equal to 10% water loss, which was used to gain the gross irrigation water . After stabilization in the chamber, all photosynthetic parameters of the leaves in each sample were recorded in 2-min intervals by the device All records were performed at 800 μmol m−2 s−1 photosynthetic photon flux density, which was the light saturation point for Kentucky bluegrass leaves as described by Four weeks after the rhizome establishment in the field and implementing drought stress treatment, all photosynthesis-related traits were measured in both irrigation regimes. Single-leaf A, Expanded leaves were used for leaf photochemical efficiency as the ratio between variable and maximum fluorescence (Fv/Fm) in the non-energized state accomplished by exposure to darkness. After adaptation of selected leaves to darkness for 30 min, measurements were made on intact leaves with a chlorophyll fluorescence meter . The light intensity for the readings was 3,500 μmol.−1 dry leaf weight  in dimethyl sulfoxide solution at 40°C for 48 h in plants tested under both irrigation treatments. Absorbance of the extracts was read out at 663 and 645 nm wavelength using a spectrophotometer . These are expressed as mg gf weight .Forage yield was expressed as fresh- and dry-weight yield. FY was measured as the weight of fresh herbage harvested per plot, and after drying at 72°C for 48 h, the measured weight was expressed as DY.Analysis of variance (ANOVA) for the RCBD was carried out to examine significance of the years, irrigation regime (non- and drought stress), genotype effects and their interactions. The residual and predicted values for each trait were subjected to the ANOVA assumptions test. The expected mean squares of the general linear model (GLM) were used for variance component estimation Table 2.The phenotypic, genotypic, and environmental variances were estimated according to the expected value of mean square of the sources of variations in the ANOVA table described by g, MSe, and r are genotypic mean square, error mean square, and the number of replications, respectively.where MSPhenotypic (PCV) and genotypic (GCV) coefficients of variation were estimated according to 2) which shows the contribution of the genetic variance in the phenotypic variation of a trait, was calculated according to method of where In the above equations, The genetic advance (GA) and genetic advance as percentage of trait mean (GAM) were estimated according to where the constant k is the standardized selection differential or selection intensity. The value of k at 5% proportion selected is 2.063.The phenotypic and genotypic correlation coefficients were calculated (Eqs. 11 and 12) to determine the relationship of traits.TraitStats  and Tr (309.98%). The Fv/Fm showed a lower increase (26.60%) while sg with a 305.72% decrease showed higher reduction among photosynthetic traits under drought stress in the second year, followed by A (245.01%) and Tr (235.88)  and “Noqan” (17.25 µmol m−2 s−1): “Abrumand” (17.25 µmol m−2 s−1) stood at the second and third rankings for A under non-stress treatment in 2019. The stomatal conductance ranged from 0.01 to 0.38 mol m−2 s−1. The “Ciakhor” under non-stress conditions in the second year illustrated the highest sg followed by “Liqvan” (0.37 mol m−2 s−1) and “Noqan” (0.36 mol m−2 s−1) in the second year and under a non-stress environment (−2 s−1) and “Abr2” (4.98 mmol m−2 s−1) showed the lowest and highest transpiration rate (Tr) under drought stress conditions in 2018 and non-stress in 2019, respectively. As presented in −1 dry weight to 12.02 mg g−1 dry weight. Among the assessed accessions, “Sarab” (12.02 mg g−1 dry weight) and “Abr2” (12.00 mg g−1 dry weight) showed higher Chl content in the second year and under the non-stress condition, whereas the lowest Chl belonged to “GillanTappeh” (5.75 mg g−1 dry weight) (The net photosynthesis rate (A) ranged from 0.96 to 18.22 µmol mThe mean for fresh (FY) and dry forage yields (DY) ranged from 332.50 g to 2,026.57 g (FY) and 175.54 g to 1,129.00 g (DY), respectively. “Ciakhor” in 2019 under non-stress and “GilanTappeh” under drought stress conditions in 2018 had the highest and lowest FY and DY, respectively  had the highest PCVs in both irrigation regimes in 2018 and 2019, respectively. FY  and A  ranked next for PCV under non-stress conditions and drought stress environment in 2018 and 2019, respectively. Chl demonstrated the minimum value for PCV in both years and irrigation regimes (sg) under drought treatment. In 2019, DY (19.35%) and FY (17.54%) had the highest value for GCV under non-stress conditions while sg (39.66%) and A (36.29%) had the highest GCV under drought treatments. The lowest GCV was observed for Chl  in two growing seasons (The PCV and GCV estimated under non-stress and drought stress treatments are presented in r) under non-stress treatment and from 61.22% (FY) to 97.34% (sg) under drought stress environment in 2018 (sg (r (r under non-stress environment and from 84.22% for Tr to 94.59% for A under drought stress treatment. The forage yield traits (FY and DY) showed higher heritability in 2019 compared with 2018 under both irrigation regimes (The heritability estimates ranged from 65.83% (DY) to 95.37% (T97.34% gs under dr97.34% gs under drsg (0.07) in 2018 and sg (0.08) and Fv/Fm (0.08) in 2019 under non-stress treatment. Under drought conditions, the GA ranged from 0.05 for sg to 113.10 for FY in 2018 and from 0.05 to 269.98 for the same traits in 2019. In 2018, GAM ranged from 8.32% for Chl to 35.63% for FY under non-stress conditions and from 12.10% for Chl to 94.60% for sg under drought treatment. The DY (38.22%) and sg (78.05%) demonstrated the highest GAM in 2019 under both irrigation regimes while the lowest GAM was observed in Chl  in the same year and irrigation regimes (The FY in 2018 (251.19) and 2019 (520.23) presented the highest GA, while the lowest GA belonged to p and rg > 0.70) except with Fv/Fm under normal conditions  and Chl . Tr and Fv/Fm  and Tr and Chl content showed high correlations. High genotypic and phenotypic  correlations were obtained between A and Fv/Fm. Additionally, FY was strongly correlated with DY under both irrigation regimes . Photosynthetic parameters and forage yield were significantly correlated. Although Fv/Fm had low phenotypic and genotypic correlations with FY and FD under non-stress treatment, they showed strong correlation under drought stress treatment.The correlation coefficients of forage yield traits and photosynthetic parameters under non-stress and drought stress treatments are shown in nditions Figure 3sg, and Tr were the most important contributors to FY (R2 = 64%) and DY (R2 = 67%) variances in well-watered treatment , particularly under drought stress treatment, which shows the possibility of the trait improvement through direct and indirect selections. However, the results indicated low variability for Chl and Fv/Fm, suggesting the need for improvement of base population through cross breeding for these traits  and photosynthesis in plants (2 assimilation contributes to approximately 90% of dry matter of crop plants (Festulolium braunii (K. Richt) A. Camus] and alfalfa (Medicago × varia T. Martyn). Results of the mg(mesophyll conductance) and sg affects maximizing photosynthesis in plants. In the present study, the identified correlation between photosynthetic characteristics and forage yield phenotypes under drought stress treatment suggests the possibility of successful selection for both high forage yield and higher photosynthesis potential. The result of the interrelation analysis of the tested traits indicated the higher contribution of photosynthetic parameters to the observed variation in forage yield phenotypes (FY and DY) under drought stress environment compared with normal watering treatment, which suggests the critical role of photosynthesis parameters in yield under drought stress conditions (Information on the covariance of traits is useful for predicting how the selection pressure exerted on one trait will result in trade-offs for other traits . The corn plants . The corn plants . The resn plants . It has p plants . In our nditions .P. pratensis.Clustering individuals in a population that provides information about similarities of genotypes helps for selection and crosses between different groups for expanding genetic variation and development of new segregation populations. Results of cluster analysis in our study showed that the assessed accessions were divided into distinct high and low photosynthesis and forage yield groups. The development of a segregating population through crosses between accessions of two high and low productive groups helps in mapping quantitative traits loci and identifying markers associated with traits for use in marker-assisted selection programs in P. pratensis population for the improvement of two different sets of traits. The wide variation observed for traits in the ecotypic variation sampled in the accessions tested helps to select good candidates and develop segregating populations through cross-breeding programs to breed drought-tolerant varieties with higher forage yield traits and identify information about QTLs of traits.The results of our studies showed the significant effects of the watering regime on the photosynthesis system and forge yield traits. Among the tested photosynthetic parameters, stomatal conductance showed a higher correlation with forage yield, which can be suggested as an integrative parameter for identifying drought-tolerant varieties. This work provides supporting information for two research areas. One is the interrelationship of traits and the level of genetic variation for photosynthesis and forage-related traits under two moisture regimes. The other is information on heritability and gain from selection, which shows the potential of our The original contributions presented in the study are included in the article/NS and ZS performed the experiment, conducted data analysis, and wrote the draft of article; BH conceived and designed the project, reviewed the statistical analyses, and edited the first and final draft of the manuscript; CR contributed to reviewing the analyses and provided critical advice on data analysis. All authors have read and approved the manuscript.


To conduct a systematic review and meta-analysis to investigate the clinical efficacy of acupuncture combined with active exercise training in improving pain and function of knee osteoarthritis (KOA) individuals.PubMed, EMBASE, The Cochrane Library, Web of Science, China National Knowledge Infrastructure, Wan Fang Data, Technology Periodical Database and China Biology Medicine were searched from their inceptions to April 5, 2023.I2 > 50% and p < 0.05, we performed sensitivity analysis and subgroup analysis to find the source of heterogeneity. Publication bias was studied by funnel plot and Egger’s test was used to verify it.We analyzed trials of acupuncture combined with active exercise training for KOA. The included studies were of high quality (Jadad ≥ 4) and RCTs. Study selection, data extraction, risk of bias and quality assessment were independently performed by two reviewers. We performed systematic analyses based on different outcome measures, including total efficiency rate, visual analogue scale (VAS), the Western Ontario and Mcmaster Universities Osteoarthritis Index (WOMAC), the Lysholm Knee Scale (LKS) and range of motion (ROM). We used Review Manager 5.3 and Stata/MP 14.0 to analyze the data. And it was verified by trial sequence analysis (TSA). If I2 = 0%, P = 0.70], reducing the pain level (VAS) , improving knee joint function (WOMAC)  and improving joint range of motion (ROM) . Similarly, the combined group showed significant improvements in the total effective rate , pain (VAS)  and knee function (WOMAC)  compared with the non-acupuncture group.Full 11 high-quality studies (Jadad ≥ 4) including 774 KOA individuals were included in this review for meta-analysis. The results showed that acupuncture combined with active exercise training (combined group) was superior to the acupuncture group in improving the total effective rate , identifier [No. CRD42023425823].The online version contains supplementary material available at 10.1186/s13018-023-04403-2. Knee osteoarthritis (KOA) is the most common degenerative joint disease that can cause joint pain, swelling, stiffness, limited mobility and even disability . The oveThe meta-analysis was registered on the International Prospective Register of Systematic Reviews (PROSPERO registration number: CRD42023425823). We also reported conforming to the Preferred Reporting Items for Systematic Reviews and Meta-analyses Statement criteria (PRISMA 2020)  independently searched PubMed, EMBASE, Web of science, the Cochrane Library, China National Knowledge Infrastructure (CNKI), Technology Periodical Database (VIP), Wan Fang Data and China Biology Medicine (CBM) from the earliest available date until April 5, 2023. Following keywords and their varies were used: acupuncture (AT), active exercise training (exercise), knee osteoarthritis (KOA) and randomized controlled trials (RCTs). The language was restricted to Chinese and English. The full search strategies for all databases are shown in Additional file After our review of relevant studies, based on the PICOS frameworks , the inclusion criteria for this review are as follows: (1) Population: Patients with knee osteoarthritis, regardless of gender, age, race, nationality, duration of disease, excluding patients who had knee surgery. (2) Intervention: Treatment measures in the experimental group included only acupuncture + exercise therapy, or acupuncture + exercise therapy in addition to the control group. Here, acupuncture refers to a separate stabbing method, which does not include moxibustion. And exercise therapy is active exercise training, not passive activities such as massage. (3) Comparison: The control group was treated with acupuncture or exercise therapy or western medicine or exercise therapy combined with western medicine. (4) Outcome: Outcomes must include total efficiency rate and Visual Analogue Scale (VAS) and either the Western Ontario and Mcmaster Universities Osteoarthritis Index (WOMAC) or Lysholm Knee Scale (LKS). Range of motion (ROM) can also be included. (5) Study: High-quality (Jadad ≥ 4) RCTs, published in English or Chinese.The exclusion criteria for this review are as follows: (1) the experimental group that received treatments other than acupuncture and exercise therapy, such as moxibustion and Tuina massage; (2) non-RCTs; (3) low-quality article (Jadad < 4); and (4) unable to get full text or incomplete article data.Two reviewers (J.Ch. and Y.S.W.) screened the studies and collected the data independently according to the inclusion and exclusion criteria. The information of author, publication year, demographics of participants, intervention, treatment frequency and times, duration, outcomes and Jadad score were recorded. All studies were managed with Endnote X9. Disagreements were resolved by discussion or umpired with a third reviewer (Z.X.L.).We assessed the quality of the studies using an improved Jadad scale , and only studies with high quality (Jadad ≥ 4) were included. The scores were given independently by two reviewers (J.C. and Y.S.W.). If the results were inconsistent, they were discussed with a third reviewer (Z.X.L.).Two reviewers (J.C. and Y.S.W.) also separately evaluated the risk of bias. The evaluation was based on the Cochrane Handbook for Systematic Review of Interventions, edition 5.3. Items include: (1) random sequence generation (selection bias); (2) allocation concealment (selection bias); (3) blinding of participants and personnel (performance bias); (4) blinding of outcome assessment (detection bias); (5) incomplete outcome data (attrition bias); (6) selective reporting (reporting bias); and (7) other bias. The quality of the included studies was rated as low/unclear/high risk of bias . In the case of disagreements, a third reviewer (Z.X.L.) was involved.I2 statistical tests were adopted to assess the heterogeneity among studies. A fixed-effects model was applied to combine the data if the I2 < 50% and p > 0.05. If I2 > 50% and p < 0.05 implies high heterogeneity, a random-effects model was used for meta-analysis and subgroup analysis or sensitivity analysis was considered to determine the source of heterogeneity. The total efficiency rate is dichotomous data and categorized into two levels ((1) effective and (2) inefficacious). The total efficiency rate means the percentage of the total number of participants categorized in the first two levels. In addition, we used funnel plot to explore publication bias, and then, Stata/MP 14.0 was used to perform Egger’s test on the funnel plot to verify whether publication bias existed.We used Review Manager 5.3 software provided by the Cochrane Collaboration for data analyses and presented the final result. For the continuous data, the mean differences (MD) and 95% confidence intervals (CI) were used when outcomes were assessed by the same scale. I error  , I2 = 0%, P = 0.33] ,33] Fig. C. The reI2 = 76%, P < 0.05] , I2 = 0%, P = 0.7] , which showed that knee function improved in both groups  , I2 = 0%, P = 0.71] , I2 = 10%, P = 0.33] , 33] Fig. C. The reI2 = 86%, P < 0.05] , I2 = 40%, P = 0.20] , which showed that knee function improved in both groups  Fig. C.Result of the WOMAC-dysfunction A total of two trials  , WOMAC-stiffness  and WOMAC-pain  after combined effect size, respectively. Therefore, we conducted sensitivity analysis to find the source of heterogeneity, and only the heterogeneity of pain indicators was significantly reduced , while the heterogeneity of dysfunction  and stiffness  decreased after sensitivity analysis, but it was still more than 50%, which was not suitable for subgroup analysis and other analyses due to the limitation of the number of included studies. When the combined group was compared with the non-acupuncture group, there was no statistical heterogeneity in dysfunction , stiffness  and pain , and the results were stable. Anyhow, almost all results showed that the combination therapy significantly improved functional activity, alleviated joint stiffness and reduced pain in KOA individuals.The results of the meta-analysis were generally stable, but the analysis of the WOMAC score required more discussion. The WOMAC was mainly composed of dysfunction, stiffness and pain. In addition to statistical analysis of the WOMAC total score, we also combined the effect size of dysfunction, stiffness and pain, respectively. However, when comparing the combined group with the acupuncture group, we found statistical heterogeneity in WOMAC-dysfunction . And larger, high-quality studies are needed for further analysis in future. Thirdly, the types of acupuncture in the included studies had different acupuncture points and the number of acupuncture points selected during treatment, as well as certain differences in the mode and intensity of exercise, which might be the reason for the large heterogeneity. Therefore, a more detailed meta-analysis is needed as a next step. Finally, there were differences in follow-up time for the studies included in this review. Most outcomes were measured after treatment, and only six studies reported follow-up outcomes. The effect of follow-up time on the effect of acupuncture combined with active exercise training on KOA needs to be further explored.Our review was conducted by developing strict inclusion/exclusion criteria and controlling for methodological quality. This meta-analysis searched eight electronic databases to provide a comprehensive study. Moreover, all the included studies were of high quality (Jadad ≥ 4), and the conclusions were relatively reliable. In addition, we conducted a group comparison  to more fully illustrate the effectiveness of acupuncture combined active exercise training for pain and dysfunction in KOA individuals. However, there are several potential limitations in our study. Firstly, some of the studies included in our review had methodological flaws. The most common methodological deficit was lack of blinding of participants, therapists and assessors. Secondly, most of the included studies had small sample sizes  2020 Main Checklist.Additional file 2: Search strategies of electronic databases.Additional file 3: A list of excluded studies by reading full text.Additional file 4: Quality of evidence per outcome from selected studies by the GRADE approach.