Identification of Anti- Mycobacterium and Anti- Legionella Compounds With Potential Distinctive Structural Scaffolds From an HD-PBL Using Phenotypic Screens in Amoebae Host Models

Abstract

Tubercular Mycobacteria and Legionella pneumophila are the causative agents of potentially fatal respiratory diseases due to their intrinsic pathogenesis but also due to the emergence of antibiotic resistance that limits treatment options. The aim of our study was to explore the antimicrobial activity of a small ligand-based chemical library of 1255 structurally diverse compounds. These compounds were screened in a combination of three assays, two monitoring the intracellular growth of the pathogenic bacteria, Mycobacterium marinum and L. pneumophila, and one assessing virulence of M. marinum. We set up these assays using two amoeba strains, the genetically tractable social amoeba Dictyostelium discoideum and the free-living amoeba Acanthamoeba castellanii. In summary, 64 (5.1%) compounds showed anti-infective/anti-virulence activity in at least one of the three assays. The intracellular assays hit rate varied between 1.7% (n = 22) for M. marinum and 2.8% (n = 35) for L. pneumophila with seven compounds in common for both pathogens. In parallel, 1.2% (n = 15) of the tested compounds were able to restore D. discoideum growth in the presence of M. marinum spiked in a lawn of food bacteria. We also validated the generality of the hits identified in the A. castellanii-M. marinum anti-infective screen using the D. discoideum-M. marinum host-pathogen model. The characterization of anti-infective and antibacterial hits in the latter infection model revealed compounds able to reduce intracellular growth more than 50% at 30 μM. Moreover, the chemical space and physico-chemical properties of the anti-M. marinum hits were compared to standard and candidate Mycobacterium tuberculosis (Mtb) drugs using ChemGPS-NP. A principle component analysis identified separate clusters for anti-M. marinum and anti-L. pneumophila hits unveiling the potentially new physico-chemical properties of these hits compared to standard and candidate M. tuberculosis drugs. Our studies underscore the relevance of using a combination of low-cost and low-complexity assays with full 3R compliance in concert with a rationalized focused library of compounds to identify new chemical scaffolds and to dissect some of their properties prior to taking further steps toward compound development.

Keywords: ChemGPS; Legionella; Mycobacterium; amoebae; anti-infective.

FIGURE 1

(A) Schematic representation of the LVS workflow applied per each pathway. (B) A visualization of the Ligand-based virtual screen of the ZINC lead-like database; (i) ranking of the 25,000 best virtually screened hits according to the ROCS TanimotoCombo score; (ii) selection of the first hit and then each next one if structurally dissimilar to already chosen ones by using the Lingo method; (iii) we chose at most two analogs per series from each screened pathway saving 100 selected compounds to the pool of potential hits composing the physical library for the experimental screen.

FIGURE 2

The heat map of pathways-based library. The Z-matrix was calculated according to T_sim values: T_sim = 0 for total dissimilarity (blue); T_sim = 1 for total similarity (red).

FIGURE 3

Physico-chemical prediction of the HD-PBL. The prediction descriptors of the library were performed using the cheminformatics package Canvas to evaluate the drug-likeness properties of library’s compounds (Ghose et al., 1999). MW: molecular weight, logP: partition coefficient, PSA: polar surface area, nRotB: number of rotatable bonds, nHBA: number of hydrogen bond acceptors, nHBD: number of hydrogen bond donors.

FIGURE 4

A representative selection of the HD-PBL compounds tested in the three different assays. Phagocytic host cell A castellanii was infected with (A) M. marinum msp12:GFP and (B) GFP-expressing L. pneumophila, 5 × 10⁴ infected cells were transferred to each well of a 96-well plate. The course of infection at 25°C was monitored by measuring fluorescence for 72 h in the presence of 30 μM of corresponding compounds compared to DMSO control (green) and 10 μM rifabutin—only for M. marinum assay (black). (C) Virulence assay. Each compound (10 μM) was added on SM-agar medium followed by the addition of K. pneumoniae and M. marinum mixture; 1000 D. discoideum cells were deposited in the center of the well and plates were incubated for 5–9 days at 25°C and the formation of phagocytic plaques was monitored visually.

FIGURE 5

Percentage hit rate (A–C) and hits overlap of the three screens. All 1255 compounds were tested for potential intracellular growth inhibition. Library compounds were plotted based on their anti-infective properties. Compounds resulting in decreased intracellular M. marinum and L. pneumophila replication by over 20 and 40%, respectively, at the screening concentration (30 μM) were defined as hit candidates. (C) Compounds score from the “phagocytic plaque assay.” The potency of each compound to restore D. discoideum growth was evaluated, hits were determined as molecules that fully restore host growth (=4). (D) Venn diagram representing summarized results of the primary hits identified from the three assays. The analyzed set included compounds that passed the aforementioned cut-offs.

FIGURE 6

Characterization of cytotoxic and growth inhibitory activities of primary hits. Primary hits come from the three different screens [(A) AcMm refers to A. castellanii–M. marinum, (B) AcLp refers to A. castellanii–L. pneumophila, and (C) DdMm refers to D. discoideum–M. marinum]. Two different assays were compared, a cytotoxicity test in A. castellanii and a growth inhibition assay in D. discoideum. Compound cytotoxicity (y-axis in A–C) against A. castellanii was determined using the Alamar blue reagent. The corresponding data are presented in Supplementary Table S2 column K. The growth inhibitory activity of compounds on D. discoideum GFP-ABD was measured with a fluorescence plate reader (x-axis in A–C). The corresponding data are presented in Supplementary Table S2 column I. The compounds were tested at 30 mM and values were normalized to the DMSO carrier control (=1).

FIGURE 7

Intracellular and extracellular growth of the pathogenic bacteria in presence of the primary hits. Growth of M. marinum (A) and L. pneumophila (B) in the presence of hits (30 μM) was determined for both intracellular (A. castellanii) and extracellular replication (broth). The graphs indicate the fluorescence measurements normalized to 1 (vehicle control). (C) Antibiotic assay. Compounds (10 μM) identified in phagocytic plaques assay were added to 7H11 medium in each well and then 1000 M. marinum bacteria were deposited in the well. Growth of mycobacteria was monitored after 6 days at 30°C.

FIGURE 8

Comparison of two amoeba host models. (A) Comparison of anti-infective properties of primary hits on intracellular M. marinum growth in D. discoideum and A. castellanii host cells. (B) Selected anti-virulence hits plotted based on their anti-infective (20% inhibition) and anti-bacterial (40%) activity in D. discoideum–M. marinum model. 10⁵ D. discoideum cells infected with M. marinum msp12:GFP were transferred to the wells, hits were added at 30 μM. The fluorescence intensities were measured for 72 h every 3 h.

FIGURE 9

ChemGPS-NP analysis of anti-Mtb hits. (A) ChemGPS-NP-based analysis of the chemical space occupied by 211,000 compounds extracted from ISDB (gray background), the standard and candidate anti-Mtb drugs (black dots), the HD-PBL (orange cloud = non-hits, orange dots = anti-M. marinum hits, blue dots = anti-L. pneumophila hits, orange dots with a blue center = hits active against both M. marinum and L. pneumophila), and the GSK TB set (green cloud = non-hits, green dots = anti-M. marinum hits). To improve visualization, 2D t-distributed stochastic neighbor embedding (tSNE) was applied to the eight output PCs of ChemGPS-NP. (B) Hierarchical classification of anti-mycobacterial compounds computed using the Euclidian distance in the Chem-GPS-NP space with average linkage: standard and candidate anti-Mtb drugs (black); GSK TB set hits (green); HD-PBL hits (orange). The dendrogram illustrates the arrangement of the clusters produced by the complete linkage clustering method.