{'fraction_non_alphanumeric': 0.03195714973519499, 'fraction_numerical': 0.04730380356283101, 'mean_word_length': 4.694653872515421, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 1, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Identification of novel anti-bacterial compounds from a chemically highly diverse pathways-based library using phenotypic screens in amoebae host models

Legionella pneumophila and tubercular Mycobacteria are the causative agents of potentially fatal diseases due to their pathogenesis but also to the emergence of antibiotic resistance that limits treatment strategies. The aim of our study is to explore the antimicrobial activity of a small ligand-based chemical library of 1,255 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 an additional anti-virulence “plaque” assay for M. marinum. We set up these assays using two amoeba strains, the genetically tractable Dictyostelium discoideum and the free-living amoeba Acanthamoeba castellanii. In summary, sixty-four compounds showed anti-infective/anti-virulence activity in at least one of the 3 assays. The intracellular assays hit rate varied between 1.7% (n=22) for M. marinum and 2.8% (n=35) for L pneumophila with 7 compounds in common between both pathogens. In parallel, 1.2 % (n= 15) of the tested compounds were able to restore D. discoideum growth in presence of M. marinum spiked in a lawn of Klebsiella pneumoniae. We also validated the generality of the hit compounds identified using the A. castellanii-M. marinum anti-infective screen in the powerful 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. Our studies underline the relevance of using a combination of low-cost and low-complexity assays with full 3R compliance associated with a rationalized focused library of compounds to help identifying new chemical scaffolds and dissect some of their properties prior to run further compounds development steps.


INTRODUCTION 46
Nowadays, the burden of antibiotic-resistant bacteria is reaching a critical point. Classical strategies to identify new antibiotics, based on their inhibitory effect on bacterial growth in broth, outside 8 concentrate on the validated hits selected from the different screens. The analysis of the drug likeness 184 of the compounds composing the library and the comparison with the known drug-likeness standards 185 (Lipinski and Hopkins 2004;Lipinski 2016;Lipinski et al. 1997) reveals that the waste majority of the 186 compounds have the properties to be drug-like (Figure 3).

188
Screens characteristics 189 1,255 compounds were assayed using our three biological phenotypic screens (see materials and 190 methods). The categories of pharmacophore queries are indicated in Table 1, and the hallmarks of the 191 various screens are summarized in Table 2.

192
Each of the three assays was established and optimized separately prior to testing the 1,255 193 compounds (Harrison et al. 2013;Kicka et al. 2014;Froquet et al. 2009). The three screens were run 194 simultaneously using the same compounds batch. The compounds' concentration was adjusted to 30 195 µM for the two cell infection screening assays, taking into account the shielding effect of A. castellanii 196 cells on intracellular bacteria (Tosetti, Croxatto, and Greub 2014), whereas a final concentration of 10 197 µM was used in the anti-virulence assay monitoring D. discoideum growth on bacteria lawns (Table 2).

198
Considering screen stringency variations, the threshold for hit detection was arbitrarily fixed at a 199 minimum of 20% reduction of the intracellular mycobacteria growth, and 40% reduction for intracellular  Figure 4A and B respectively for M. marinum and L. pneumophila. Normalization and 209 analysis of the data generated in these two screens were performed as previously reported for the A. 9 pneumophila (Harrison et al. 2013) host-pathogen assays, respectively. In brief, normalization of 212 bacterial growth in presence of each compound was calculated related to the DMSO carrier (=1). For 213 the phagocytic plaque assay, a semi-quantitative visual inspection and scoring of the compounds was 214 applied, as described previously (Froquet et al. 2009). As shown in Figure 4C, the potency of hit 215 compounds to restore D. discoideum growth was evaluated to range from non-hit molecules (=0) to hits 216 that fully restore host growth (=4). Following their identification in their respective initial screen, 217 candidate hit compounds were independently validated at least three times in their respective assay.

219
Hit frequency and overlap between screens 220 The two intracellular growth assays used for screening all 1,255 compounds resulted in a broad 221 range of inhibitory activities. The hit rate was considerably higher for L. pneumophila, where 2.8% 222 (n=35) of the compounds tested at 30 M showed a growth inhibition of at least 40%, compared to 1.7%

223
(n= 22) with at least a 20% inhibition for M. marinum (Fig. 5 A and B.). The M. marinum -D. 224 discoideum phagocytic plaque assay showed that 1.2% (n=15) of the compounds at 10 M are valuable 225 hits and were able to restore host growth ( fig. 5C). In comparison to infection assays, full restoration of 226 amoeba growth on a bacteria lawn containing pathogenic mycobacteria appeared to be more restrictive 227 and identified only 15 hits from the library. Taken together, sixty-four compounds showed anti-infective 228 activity in one of the intracellular growth assay or did attenuate mycobacterial virulence. As depicted in 229 the Venn diagram, the A. castellanii -L. pneumophila screen shared 8 hit compounds with the other 230 two screens, among these 7 are common between the two infection assays and only 1 compound between 231 the L. pneumophila -A castellanii model and the phagocytic plaque assay (Fig. 5D). Surprisingly, no 232 hit was common to the two assays using M. marinum as pathogenic bacterium. Notably, we also 233 identified a certain number of pro-infective compounds, namely chemicals that lead to a remarkable 234 increase of the intracellular bacterial mass compared to the DMSO control. The A. castellanii -L.

235
pneumophila assay identified only one compound that increased the intracellular growth more than 40% 236 when compared to the DMSO control (Fig. 5B). In contrast, five pro-infective compounds were 237 identified with at least 2-fold increase in the intracellular M. marinum bacterial load (Fig. 5A).

240
To evaluate the effect of hit compounds on host fitness, we used D. discoideum cells expressing 241 GFP-ABD to measure toxic and growth inhibitory activities of compounds (Trofimov et al. 2018). In 242 parallel, a cell viability test using Alamar blue was performed using A. castellanii 4 hours after hit 243 compound addition at a 30M concentration. For each assay, values were normalized to the DMSO 244 carrier control (=1), compounds' toxicity and growth inhibition data for each phenotypic screen are 245 represented in Figure 6 A, B and C.

246
Only one compound (ZINC01718072) was excluded because its strong fluorescence at the GFP 247 emission wavelength confounded the results. At the end, five hit compounds did not pass the set 248 threshold (more than 40% growth inhibition or 40% toxicity when compared to DMSO control) and 249 were rejected for this deleterious effect on host fitness.  To validate the generality of the anti-infective screen performed using the A. castellanii -M.
269 marinum system, we tested the previously identified 21 hit compounds using the D. discoideum-M.

270
marinum host-pathogen model. Fourteen compounds out of twenty-one exhibited at least a 20% 271 inhibitory activity on intracellular mycobacteria ( Figure 8A). Interestingly, from the hit compounds 272 identified in the "extracellular" anti-virulence plaque assay, almost 50% (7/15) of compounds presented

Discussion 280
Screen results reflect pathogen specificities and assays characteristics 281 Since mycobacterial infection is described as a complex and dynamic series of interactions 282 between multiple host and bacterial components and pathways. We selected a total of 18 host and 283 pathogen pathways as potential pharmacological targets, and an LVS of the ZINC database was 284 launched to identify ligands/metabolites known to interfere/interact with these pathways. In our study, 285 we tested 1,255 compounds from the ZINC lead-like database derived from a unique ligand-based 286 virtual screening to determine their anti-infective properties. For this purpose, a combination of three 287 phenotypic assays was used, as summarised in Table 2. Two assays to monitored the intracellular growth

293
We identified 64 compounds showing activity in at least one of the assays for intracellular 294 bacteria growth inhibition, or attenuation of mycobacterial virulence (table S1). Although the same host 12 was used for the two infection assays, only few hits were common against M. marinum and L. 296 pneumophila. The number of identified hit compounds was considerably higher for L. pneumophila 297 compared to M. marinum ( Figure 5A and 5B). While no strong antibiotic hit was identified using the A.  312 Surprisingly, the two assays used to identify anti-infective or anti-virulence compounds against 313 M. marinum did not identify any common hit. As presented in table 2B (1 st and 2 nd column), hits 314 hallmarks are linked to the intrinsic screen design. Indeed, in the two infection assays, the compounds 315 are added post-infection, and therefore, compounds that hamper bacteria uptake cannot be detected.

316
Thus, both assays detect anti-infective hits that inhibit virulence or boost host defences. On the contrary, 317 in the anti-virulence assay (third column, Table 2), the uptake efficiency of M. marinum might be 318 directly affected by compounds that either target the host phagocytosis machinery, or modify the 319 mycobacterial cell wall. In addition, eleven of the fifteen anti-virulence hits identified in the phagocytic 320 plaque assay had selective antibiotic activity against M. marinum ( Figure 7C).

321
To better understand the contrasting results of the anti-infective and the anti-virulence screens 322 on M. marinum, the hits from both primary assays were re-tested in the D. discoideum-M. marinum 13 infection assay (1 st column, Table2 and Figure 8A). Satisfyingly, two-thirds of the anti-infective hits 324 originally identified in the A. castellanii assay were confirmed using the D. discoideum infection assay, 325 likely reflecting the overall conservation of basic metabolic pathways between these two evolutionarily 326 close organisms. In contrast, only half of the anti-virulence hits showed mild but significant activity in 327 the D. discoideum infection assay (figure 8B). One possibility is the presence of an MCV membrane 328 around M. marinum that restricts the bioavailability of the compounds. In addition, it is known that M.  In conclusion, our data show that the ligand-based virtual screening efficiently selected anti-355 bacterials from the 2.5 Mio lead-like compounds of the ZINC library, giving rise to hit rates 2 to 3 times 356 superior to the 1% usually observed by random screening. The data also demonstrate that the validated 357 virtual hits are chemically diverse, suggesting that they most likely target different pathways within the 358 host pathogen system. We suggest that our combination of cost-effective, 3R compliant amoebae-based 359 phenotypic assays to screen structurally diverse chemical libraries efficiently identifies a variety of 360 promising non-toxic anti-infective compounds that then will be validated in more complex infection 361 systems such as the zebrafish or mouse models.

366
In recent years, the design of diverse libraries based on the principle of functional diversity has 367 become a major trend in library design (Shelat and Guy 2007 (Kirchmair et al. 2009) and the TanimotoCombo score. To ensure chemical diversity and maximize the 376 coverage of the chemical space of the ZINC lead-like database we applied the Lingo method program 377 (Vidal, Thormann, and Pons 2005). We screened the ZINC lead-like database composed of 2.5 million 378 compounds and finally selected 100 compounds per host and pathogen pathways for a total of 1'800 379 compounds using the workflow described in Figure 1. Thus, the selected virtual hits correspond to 380 0.07% of the whole ZINC lead-like database. The number of virtual hits was tractable by the medium 381 throughput assays described in this work.

382
The pathways-based library properties were characterized in term of chemical diversity and 383 drug likeness. To assess chemical diversity, we used the Z-matrix calculated according to Tanimoto

406
to an OD600 of 0.8-1 in 7H9 medium. Mycobacteria were centrifuged at RT at 500 g for two periods of 407 10 min onto a monolayer of Acanthamoeba cells at an MOI of 10 to promote efficient and synchronous 408 uptake, followed by an additional 20-30 min incubation. Un-ingested bacteria were washed off with 409 PYG and infected cells re-suspended in PYG containing 10 μM amikacin. 5 × 10 4 infected cells were 410 transferred to each well of a 96-well plate (Cell Carrier, black, transparent bottom from Perkin-Elmer) 411 with pre-plated compounds and controls The course of infection at 25°C was monitored by measuring 412 fluorescence in a plate reader (Synergy H1, BioTek) for 72 hours with time points taken every 3 hours.

413
Only experiments with a Z-factor > 0.6 (calculated from DMSO and rifabutin controls) were taken into 414 account for analysis. Time courses were plotted and data from all time points (using cumulative curves) 415 were used to determine the effect of compounds versus vehicle controls. The primary hit rate cut off 416 was set at 20% inhibition for M. marinum.

417
Intracellular replication of M. marinum in D. discoideum D. discoideum were cultured in HL5c medium in 10 cm Petri dishes at 25°C, and passaged the 419 day prior to infection to reach 90% confluency Mycobacteria were grown in 7H9 medium to a density 420 of OD600=0.8-1.0 (5x10 8 bacteria ml −1 ), centrifuged and re-suspended in HL5c medium and clumps 421 disrupted by passaging through a 26-gauge needle. GFP-expressing M. marinum were added at an MOI 422 of 10 and centrifuged onto the Dictyostelium cells at 500 g twice for 10 min. The cells were left at 25°C 423 for an additional 10-20 min before uningested bacteria were washed off by 3 washes with HL5c and

424
Attached cells were then re-suspended in HL5c containing 10 μM amikacin.

442
To test the effect of the compounds on M. marinum virulence, 10 ml of mid-log phase 443 mycobacterial cultures (OD600 around 0.8-1.2) were pelleted by centrifugation and re-suspended in 5 444 ml of an overnight culture of K. pneumoniae diluted to 10 −5 in LB medium (Alibaud et al. 2011). The 445 mixture was de-clumped by passaging through a 25-gauge blunt needle. In each well of a 24-well plate, 18 10 μM of each compound was added and allowed to diffuse on 2 ml of solid standard medium (SM) 447 agar supplemented with glucose followed by the addition of 50 μL of the bacterial suspension Once 448 dried, 1,000 D. discoideum cells were added in the centre of the well. Plates were incubated for 5-9 449 days at 25°C and the formation of phagocytic plaques was monitored visually, a negative control 450 (Bacteria + D. discoideum + DMSO) was included in every plate.

452
Antibiotic activity assays 453 Antibiograms used to monitor the inhibitory effect of compounds on mycobacterial growth were 454 performed as described previously (Ouertatani-Sakouhi et al. 2017), each molecule was added in a 24-455 well plate well containing 2 ml of 7H11 agar medium at 10 μM. Once dried, 1,000 bacteria were 456 deposited in each well and plates were incubated at 32°C for 7 days to allow bacterial growth.

457
To monitor M. marinum growth, GFP-expressing bacteria were cultivated in shaking at 32°C in 458 7H9 medium supplemented with OADC up to an OD600 of 0.8-1. 10 5 GFP-expressing M. marinum 459 were transferred into each well of 96-well white plates. To monitor L. pneumophila growth, a pre-culture 460 of GFP-expressing bacteria were diluted to a starting OD600 of 0.01 and grown overnight. Compounds 461 at 30 M in DMSO were added in each well, seeded with 100 µl of pre-culture, and bacterial growth 462 was monitored for at least 48 hours by measuring the fluorescence in a plate reader (Synergy H1) every