Dienstag, August 2, 2022
StartMicrobiologyProperty area mapping of Pseudomonas aeruginosa permeability to small molecules

Property area mapping of Pseudomonas aeruginosa permeability to small molecules

A 96-well filter assay for quantifying the contributions of efflux and the OM barrier to compound penetration into P. aeruginosa

The kinetics of intracellular accumulation of compounds is anticipated to be strongly affected by compound affinities to intracellular binding websites23. To check this notion, we first analyzed the buildup of the radioactively labeled antibiotics ciprofloxacin and tetracycline, that are identified to have sturdy affinities to intracellular targets in bacterial cells, in addition to the human therapeutics oxymetazoline, nepafenac, efavirenz, delavirdine and metoprolol, which have unknown affinities to bacterial cells. As anticipated, measurements of minimal inhibitory concentrations (MICs) confirmed that ciprofloxacin and tetracycline inhibit the expansion of the wild kind P. aeruginosa PAO1 at low micromolar concentrations (Desk 1). The MICs of those antibiotics improved in each the hyperporinated PAO1-Pore and the efflux-deficient PΔ3 cells, which lack the three main efflux pumps ΔmexAB, ΔmexCD, and ΔmexXY. Nevertheless, the antibacterial exercise of ciprofloxacin was largely affected by efflux, as seen from a modest, two-fold impact of hyperporination, whereas the exercise of tetracycline was affected by each the efflux and OM limitations. The “barrierless” PΔ3-Pore pressure was at the very least 8- to 16-fold extra inclined to each antibiotics than PAO1. In distinction, no measurable antibacterial actions have been noticed for the opposite compounds for concentrations beneath 1 mM, besides that efavirenz had an MIC of 25 mM in PΔ3-Pore cells.

Desk 1 Minimal inhibitory focus of antibiotics and human therapeutics in 4 P. aeruginosa strains with various permeability limitations.

We subsequent optimized situations and analyzed time- and concentration-dependent accumulation of [14C]-ciprofloxacin and [3H]-oxymetazoline in 4 strains of P. aeruginosa with variable efflux and OM permeability limitations utilizing a 96-well filter plate assay. We discovered that washing of filtered cells with the incubation medium differentially affected the retention of [14C]-ciprofloxacin and [3H]-oxymetazoline (Fig. S1), suggesting that compound variability in affinities to intracellular binding websites might strongly have an effect on the outcomes of the assay. Due to this fact, washing of filtered cells was omitted and filters have been faraway from plates to scale back non-specific background (see Strategies). As described in Strategies, beneath the situations of the assay, every filter retained 4.3 ± 0.7 µL of answer, and cells displaced 2.8 ± 0.2 µL of this quantity (67%) (Fig. S1). Thus, the assay is anticipated to report correct values for compounds that accumulate in cells however is not going to be informative for compounds unable to build up in at the very least one of many 4 strains.

To check the validity of the assay, we subsequent measured the buildup of [14C]-ciprofloxacin and [3H]-oxymetazoline into in any other case isogenic strains of P. aeruginosa that differ within the permeability of their cell envelope. Because the composition of cell membranes doesn’t change considerably with hyperporination and efflux inactivation29, we count on that non-specific binding stays the identical for various strains and the variations within the accumulation ranges between the strains replicate the variations within the intracellular accumulation. Kinetic experiments confirmed that each [14C]-ciprofloxacin and [3H]-oxymetazoline reached steady-state ranges throughout the first minutes of incubation (Figs. S2, S3). The steady-state ranges elevated linearly with growing concentrations of the compounds (Fig. 1A,B). Moreover, these accumulation ranges various between the strains and have been larger within the efflux-deficient and hyperporinated cells than within the wild kind PAO1. Efflux inactivation or hyperporination resulted in an analogous improve within the accumulation of [3H]-oxymetazoline, and the degrees have been highest in PΔ3-Pore cells. In distinction, inactivation of efflux led to considerably larger ranges of accumulation of [14C]-ciprofloxacin than did hyperporination however once more the degrees have been highest in PΔ3-Pore cells. This main contribution of efflux within the intracellular accumulation of ciprofloxacin was additionally noticed in its MICs in several P. aeruginosa strains (Desk 1).

Determine 1
figure 1

Intracellular accumulation profiles of radioactively labeled compounds in 4 strains of P. aeruginosa after 20 min of incubation: (A) [3H]-oxymetazoline (Oxy); (B) [14C]-ciprofloxacin (Cip); (C) [14C]-efavirenz (Efa); (D) [3H]-delavirdine (Del); (E) [3H]-metaprolol (Met); (F) [3H]-nepafenac (Nep); G. [3H]-tetracycline (Tet). Extracellular (out) and intracellular (in) concentrations have been calculated primarily based on particular radioactivities of compounds. (H) Fold change (represented as slope ratios) within the intracellular accumulation of compounds in hyperporinated, efflux-deficient and “barrierless” cells compared to the wild kind PAO1.

The above findings present that the developed assay permits analyses of the kinetics of drug accumulation in cells and might distinguish between the contributions of energetic efflux and the OM barrier. Importantly, within the time-course of the experiment, modifications within the intracellular ranges in wild kind PAO1 have been very small for each radioactive compounds, suggesting that the compounds permeate these cells very slowly, even on the highest concentrations thought-about. As well as, on the highest focus of [3H]-oxymetazoline (20 µM), the sign declined in the course of the first 5 min of incubation in PAO1 cells. This consequence means that when compounds don’t accumulate in cells, they are often misplaced as a result of non-specific binding to plates or aggregation.

Accumulation of different radioactively labeled compounds was additionally concentration-dependent and various between the 4 P. aeruginosa strains (Fig. 1). Hyperporination had the strongest impact on the buildup of [14C]-efavirenz, [3H]-nepafenac and [3H]-delavirdine, whereas the “barrierless” PΔ3-Pore cells collected the very best ranges of compounds (Fig. 1C,F). The buildup of delavirdine and tetracycline was equally affected by inactivation of efflux and hyperporination (Fig. 1D,G), whereas metaprolol accumulation was not affected by both of the 2 limitations (Fig. 1E). When intracellular accumulation of compounds was normalized to that of the PAO1 cells, the ratio of focus slopes even for PΔ3-Pore various between one- and eightfold (Fig. 1H). Apart from ciprofloxacin, inactivation of efflux alone and hyperporination of cells alone generated modest slope ratios of 1.5–3. Thus, the assay is delicate to variations in permeation and can be utilized with structurally various compounds.

Properties of the targeted library of compounds for analyses

Earlier research have proven that the physicochemical area of antibiotics is broader than different medication and consists of extra hydrophilic compounds, reminiscent of beta-lactams, which penetrate the cells by water-filled OM porins, in addition to some massive compounds, reminiscent of vancomycin, which act on cell surfaces and don’t must penetrate the cell envelope33. As well as, the presence of main amines and constructive fees are typically related to elevated penetration into some bacterial cells34. We subsequent analyzed a targeted library of 12,000 commercially obtainable compounds and chosen 220 compounds with PSA 50 + , MW < 2000 Da, and cLogD7.4 < 5 for additional analyses.

Roughly 10% of the 220 bought compounds possessed antibacterial actions in at the very least one of many 4 P. aeruginosa strains grown in MOPS-M9 medium and Luria–Bertani (LB) broth and these included representatives of fluoroquinolones, sulfonamides, cyclines and linezolid (Tables 2 and S2). After elimination of insoluble compounds and optimization of the LC–MS technique, 98 compounds have been chosen for additional analyses, together with six synthesized trisubstituted piperazin-2-one derivatives (Fig. 7), out of which the intracellular accumulation of 83 compounds was measured and for 66 compounds it was quantified (see beneath, Desk S1).

Desk 2 MICs of antibiotics analyzed by LC–MS in 4 strains of P. aeruginosa grown in MOPS-M9 medium.

The analyzed 66 compounds fluctuate in molecular weight (MW) between 200 and 750 Da and have cLogD7.4 values between 5.0 and -3.5 (Fig. 2A). We subsequent calculated 9 physico-chemical properties together with the molecular weight, logP, the variety of hydrogen bond donors and acceptors, clogD7.4, the topological polar floor space, the fraction of sp3 hybridized carbon atoms (Fsp3), the heavy atom rely, and the variety of rotatable bonds for the analyzed compounds. These properties have been additionally calculated for the SPARK compound library35, FDA-approved medication36, and compounds whose accumulation in E. coli was analyzed in earlier research15,20,34. Principal element evaluation confirmed that the chemical area coated on this work is broader than the area coated by earlier research15,20,34 (Fig. 2B-C). The distribution of properties of this library surrounds that of Iyer et al.20 in each function (Fig. S4) and that of Richter et al.34 in principal element 1 (PC1), which is represented by molecular weight, variety of hydrogen bond donors and acceptors, topological polar floor space, rotatable bounds, and heavy atom rely (Fig. 2C and S4). The distribution of the compounds analyzed by Richter et al.34 is wider within the Fsp3 property area however narrower in logP and logD, leading to a barely narrower distribution in principal element 2 (PC2). The partial overlap in PC1 between our compounds and the Richter et al. set is because of an offset within the vary of values for the topological polar floor space and the variety of hydrogen bond acceptors. For instance, the variety of hydrogen bond acceptors ranges from 0–8 within the Richter et al. set and from 2–12 within the current research.

Determine 2
figure 2

Properties of the library of 66 compounds analyzed on this research. (A) Measurement and lipophilicity of the analyzed compounds as represented by MW and cLogD7.4 descriptors. Antibiotics are highlighted in orange. (B) Principal element evaluation of variety within the chemical area of molecules from SPARK library35, Richter et al.16, Davis et al.15, Iyer et al.20, FDA- authorised medication (extracted from36) and this research. The outermost knowledge factors from the 4 research are linked to point out the relative measurement of the sampled area. (C) Zoom-in view of (B) and the loading vectors for this plot. Knowledge factors for the SPARK library and FDA-approved medication are hidden for readability. Molecular weight, hydrogen bond donors and acceptors, topological polar floor space, rotatable bounds and heavy atom rely are the key contributing elements to PC1, whereas LogP, logD and the fraction of sp3 hybridized carbon atoms (Fsp3) are main contributors to PC2.

Quantification of the impact of efflux and the OM barrier in compound penetration into P. aeruginosa

The 96-well filter assay described above was subsequent used for LC–MS-based quantification of compound penetration into P. aeruginosa cells. To cut back variability, non-physiological responses, and matrix results, cells have been grown within the optimized M9 minimal medium (see Strategies) and all subsequent steps have been carried out at room temperature.

Compound accumulation was analyzed at 4 concentrations: 10, 20, 40 and 80 µM. Kinetic analyses of the radioactively labeled compounds described above confirmed that compounds fluctuate within the time wanted to succeed in steady-state ranges, with some compounds reaching the regular state in all 4 strains throughout the first 1–2 min of incubation and others requiring longer incubation occasions, particularly within the case of PAO1 cells (Fig. 3). In distinction, for some compounds longer incubation occasions led to a lower in compound focus (Fig. S2). Due to this fact, we selected to research the compound library accumulation in cells incubated with compounds for 1 min and 40 min.

Determine 3
figure 3

Intracellular accumulation of the analyzed library of 66 compounds. (A) Slopes of intracellular accumulation of compounds at 1 min and 40 min incubation occasions. (B) Plot of the slopes of measured intracellular accumulation within the “barrierless” pressure as a perform of the fold distinction in accumulation in PAO1 and “barrierless” pressure. (C) and (D) Comparability of fold change within the compound accumulation for the PΔ3-Pore/PAO1 (Whole barrier) and PΔ3-Pore/PAO1-Pore (Efflux) or PΔ3-Pore/PΔ3 (OM).

The LC–MS technique developed right here enabled concentration-dependent quantification of 83 compounds together with 22 antibiotics in 4 strains of P. aeruginosa. After subtraction of indicators from the excluded quantity of empty filters, the constructive focus values have been obtained for 66 compounds, which have been additional analyzed (Desk S1). For a lot of the compounds, the calculated focus slopes of compound accumulation in 4 strains at 1 and 40 min correlated effectively with one another (Fig. 3A). At every focus, the buildup ranges various by orders of magnitude relying on the particular compound. Nevertheless, with few exceptions, the variations between the strains have been comparatively small. Total, we discovered that the buildup ranges within the “barrierless” PΔ3-Pore cells at 1 and 40 min trended positively with the ratios of focus slopes obtained in PΔ3-Pore and PAO1 cells (Fig. 3B). This consequence means that for sure compounds the excessive accumulation ranges in PΔ3-Pore are as a result of elimination of the permeation limitations.

To normalize for variations within the affinities of compounds for intracellular targets, we additionally used: (i) the efflux ratios expressed because the ratios of measured concentrations or focus slopes in efflux-deficient and efflux-proficient cells with the hyperporinated OM (PΔ3-Pore/PAO1-Pore), (ii) the OM ratios expressed because the respective ratios in efflux-deficient cells with regular and hyperporinated OMs (PΔ3-Pore/PΔ3), and (iii) the full barrier (PΔ3-Pore/PAO1). No vital correlation was discovered between the efflux and the full barrier ratios, suggesting that the contribution of efflux various broadly between compounds, and was not the key contributor to the intracellular accumulation (Fig. 3C). In distinction, the OM ratios clearly correlated with the full barrier (Fig. 3D). This consequence means that the permeation throughout the OM is the dominating issue for intracellular accumulation of the analyzed compounds.

Classification of compounds

The measured ranges of compound accumulation have been analyzed utilizing machine studying to find out which options of the compounds are most necessary for enabling their mobile accumulation and to find out their optimum values. For modeling, we used compound focus slopes of the buildup ranges noticed after 1 min (A1) and 40 min (A2) incubations, which have been approximated as straight traces with y-intercept set to zero. To intensify the permeability facet, the buildup slopes in efflux-deficient and hyperporinated strains have been normalized to these within the parental PAO1 pressure. In a number of instances, the slopes have been decrease within the permeabilized cells than in wild kind cells, presumably as a result of knowledge scatter or low sign (Desk S1). Such modifications can’t be attributed to the porination state or efflux deficiency of the cell. Due to this fact, we attributed such instances to the dearth of a rise in permeability and set these ratios equal to 1.

To assist visualize the info, we carried out a principal element (PC) decomposition of the info. The primary two principal parts have been cut up virtually equally between efflux and hyperporination ratios (PC 1) or dominated by absolutely the ranges of compound accumulation (PC 2; Desk S3). Elbow evaluation of the unexplained variance prompt the existence of 5 clusters, which have been effectively separated within the principal element view (Fig. 4A) and in native coordinates (Fig. 4B). All clusters comprised compounds which are affected to a numerous extent by P. aeruginosa permeability limitations. To formalize additional evaluation, we outlined because the goal subgroup these compounds that (i) accumulate at or above extracellular ranges, (ii) exhibit comparatively low sign on empty filters; and (iii) whose uptake doesn’t improve by greater than 30% by hyperporination mixed with efflux inactivation (boxed in Fig. 4B). A complete of 25 compounds happy these standards and have been labeled nearly as good permeators. A lot of the good permeators have been present in clusters 1 and three.

Determine 4
figure 4

Distribution of uptake knowledge within the exercise area in PC (A) and native (B) coordinates. Crosses mark facilities of clusters decided utilizing okay-means clustering. Violet rectangle encompasses compounds outlined nearly as good permeators. Stars mark antibiotics.

Random Forest classification

The dataset of 66 compounds is comparatively small, however it’s corresponding to earlier research16,20. We first skilled a random forest binary classification mannequin utilizing the above LC-MS knowledge and 27 2D and 3D physicochemical descriptors for every compound that have been pre-selected from a bigger descriptor pool by clustering evaluation (see Strategies). The mannequin was designed to tell apart between permeators and non-permeators as outlined from the principal element evaluation (Fig. 4B). The mannequin achieved solely 61% accuracy, 48% recall and 46% precision when examined on leave-one-out cross-validation (Fig. 5A). The world beneath the receiver-operator attribute (AUROC) curve was solely 0.53. The broad confidence interval (Fig. 5B) signifies that the efficiency of the mannequin is essentially dictated by which compounds are included within the coaching set for a given cross-validation step. This habits implies {that a} bigger compound set would seemingly considerably enhance the precision of the mannequin. Descriptors derived from molecular dynamics (MD) simulations are prevalent among the many prime ten, suggesting that molecular form, conformational flexibility, and dynamics are necessary for distinguishing permeators from non-permeators (Fig. 5C). Examples embrace shape-based descriptors such because the Corridor-Kier kappa3 molecular form descriptor, the typical acylindricity and asphericity, i.e., the deviation from cylindrical and spherical symmetries, and the radius of gyration (common Rg and common smallest principal Rg). Different prime descriptors related to favorable uptake embrace lipophilicity (MolLogP) and properties associated to molecule topology (second principal second of inertia, minimal absolute E-state index, and Morgan fingerprint density).

Gnomic classification tree

One of many limitations of the RF evaluation is its propensity to make use of all offered descriptors with none regard for potential overlap between them. To handle this subject, we developed a classification algorithm, Gnomic, that helps cut back the variety of synonymous descriptors within the constructed classification mannequin and finally constructs a single, distinctive classification tree. The descriptors recognized in the course of the development of the tree outline a property area that can be utilized for mapping compounds with various actions.

The algorithm employs a number of rounds of descriptor choice (Fig. 6A). First, the descriptors are clustered as described in Strategies, and a single consultant descriptor is chosen from every cluster. These representatives are then ranked within the order of their predictability by the opposite descriptors, utilizing a regression tree, in an iterative process that sequentially removes essentially the most predictable descriptor from the checklist. On this process, the misclassification error of the regression tree defines the extent of the data overlap between the examined descriptors, i.e., the smaller the error, the bigger the synonymity. The misclassification error additionally provides a pure measure of the stringency of the process. In consequence, the synonymous descriptors are faraway from the set previous to modeling, and the stringency of the filtering could be various just by altering the choice threshold. Thereafter, a classification tree could be skilled on the uptake knowledge at numerous ranges of stringency, thereby finalizing the checklist of descriptors. To cut back the bias brought on by the preliminary rating of the descriptors, the order of descriptors was randomly permuted 100 occasions in the course of the development of the classification tree at every stringency degree.

Utilizing Gnomic, we constructed fashions that predict the 5 clusters outlined by the permeability of the compounds (Fig. 6). We then chosen the mannequin that yields the very best general precision, 79%. The accuracy of the fashions various between 85 and 97% and recall charges between 58% for cluster 4 and 95% for cluster 1 (Fig. 6B, C). The mannequin operated with 7 descriptors, together with these reporting on the form of the molecule, cost and its distribution, topology of the compound, variety of rotatable bonds and the presence of fragrant rings (Fig. 6D).

We subsequent visualized the mannequin by plotting the info within the descriptor area (Fig. 6E). Regardless of the excessive precision of the mannequin, there was no clear set of descriptor values that outlined any of the clusters. Knowledge factors from most clusters coated a sprawling, irregularly formed chemical area that unfold all through the complete sampled area. In distinction, the separation between the clusters was a lot smaller than their attain.

We subsequent mapped the subset of fine permeators outlined in Fig. 4B to the property area. The 25 compounds discovered on this set shaped six clusters, certainly one of which included solely a single compound (Fig. 6E). Hereafter, we refer to those clusters within the property area as nodes to tell apart them from the clusters within the exercise area outlined in Fig. 4. A single cluster within the permeability area normally remodeled into a number of nodes when considered within the property area (Fig. 6E) and vice versa. The nodes comprising a number of compounds have been largely devoid of non-permeating molecules, indicating that the constructed map represents at the very least a fraction of the area attainable to good permeators.

Notably, some identified antibiotics have been discovered inside or within the neighborhood of some, however not all recognized areas. Node 5 includes six fluoroquinolones and linezolid, whereas one other fluoroquinolone prulifloxacin is in node 3. Node 4 is sulfamethazine. For these antibiotics, the efflux (PAO1-Pore/PΔ3-Pore) and the OM (PΔ3/PΔ3-Pore) MIC ratios various broadly (Desk 2). This consequence agrees with earlier research that confirmed a scarcity of easy correlations between permeation and the antibacterial actions of antibiotics20,23,24. Compounds from the opposite nodes haven’t been beforehand related to environment friendly permeation into micro organism. Thus, this research identifies new property areas wherein excessive permeability into P. aeruginosa is anticipated. The optimum values of the descriptors that outline these areas are listed in Desk 3.


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