Approved drugs successfully repurposed against Leishmania based on machine learning predictions.

Drug repurposing is a promising approach towards the discovery of novel treatments against Neglected Tropical Diseases, such as Leishmaniases, presenting the advantage of reducing both costs and duration of the drug discovery process. In previous work, our group developed a Machine Learning pipeline for the repurposing of FDA-approved drugs against Leishmania parasites. The present study is focused on an in vitro validation of this approach by assessing the antileishmanial effects of 10 predicted drug candidates. First, we evaluated the drugs' activity against promastigotes from two strains of L. infantum and one of L. major, which caused distinct clinical manifestations, using an MTT assay. The standard anti-Leishmania drug Amphotericin B was used as a positive control. Five molecules demonstrated anti-Leishmania effects, out of which Acebutolol, Prilocaine and Phenylephrine are described herein for the first time. When tested on promastigote growth, Acebutolol displayed IC50 values ranging from 69.28 to 145.53 µg/mL. Prilocaine exhibited IC50 values between 33.10 and 45.81 µg/mL. Phenylephrine, on the other hand, presented IC50 values >200 µg/mL. The two remaining drugs, Dibucaine and Domperidone, exhibited significantly low IC50 values varying between 0.58 and 1.05 µg/mL, and 6.30 and 8.17 µg/mL, respectively. Both compounds were previously described as anti-Leishmania agents in vivo. All five compounds demonstrated no notable cytotoxic effects on THP-1-derived macrophages at the IC50 concentrations, allowing for their testing on the intracellular form of L. major and L. infantum parasites. Interestingly, all compounds exhibited antileishmanial activity on amastigotes with enhanced IC50 values compared to the corresponding promastigotes. Noticeably, Dibucaine and Domperidone displayed IC50 values of at most 1.99 µg/mL. Acebutolol, Prilocaine and Phenylephrine showed IC50 values ranging from 13.84 to 66.81 µg/mL. Our previously published Computer-Aided repositioning pipelines of FDA-approved drugs as antileishmanial agents identified Dibucaine and Domperidone as candidates in support of previous in vivo studies. This study consolidates such findings through the in vitro validation against 2 Leishmania species, highly prevalent in Africa and Middle East, and reveals Acebutolol, Prilocaine, and Phenylephrine as novel anti-Leishmania effectors, confirming the relevance of our approach and calling for further investigations.

Retrospective Phylodynamic and Phylogeographic Analysis of the Bluetongue Virus in Tunisia.

Bluetongue virus (BTV) is an arbovirus considered as a major threat for the global livestock economy. Since 1999, Tunisia has experienced several incursions of BTV, during which numerous cases of infection and mortality have been reported. However, the geographical origin and epidemiological characteristics of these incursions remained unclear. To understand the evolutionary history of BTV emergence in Tunisia, we extracted from Genbank the segment 6 sequences of 7 BTV strains isolated in Tunisia during the period 2000 to 2017 and blasted them to obtain a final dataset of 67 sequences. We subjected the dataset to a Bayesian phylogeography framework inferring geographical origin and serotype as phylodynamic models. Our results suggest that BTV-2 was first introduced in Tunisia in the 1960s and that since 1990s, the country has witnessed the emergence of other typical and atypical BTV serotypes notably BTV-1, BTV-3 and BTV-Y. The reported serotypes have a diverse geographical origin and have been transmitted to Tunisia from countries in the Mediterranean Basin. Interserotype reassortments have been identified among BTV-1, BTV-2 and BTV-Y. This study has provided new insights on the temporal and geographical origin of BTV in Tunisia, suggesting the contribution of animal trade and environment conditions in virus spread.

Pathologic complete response to neoadjuvant imatinib of a gastric stromal tumor with concomitant mutations in KIT: A case report and literature review.

Key clinical message: We report the first case of pathologic complete response (pCR) to neoadjuvant imatinib in a gastric stromal tumor harboring KIT mutations in both exons 11 and 9. The significance of this co-occurrence is unknown and might increase the responsiveness of gastrointestinal stromal tumors (GISTs) to imatinib. Abstract: pCR of GIST to neoadjuvant imatinib is rare. We report a case of pCR to neoadjuvant imatinib in a gastric stromal tumor that harbored co-occurrence of multiple KIT mutations in exons 11 and 9. This co-occurrence in exons 9 and 11 is the first to be reported in the English literature.

Enzymatic and Molecular Characterization of Anti-Leishmania Molecules That Differently Target Leishmania and Mammalian eIF4A Proteins, LieIF4A and eIF4AMus.

Previous investigations of the Leishmania infantum eIF4A-like protein (LieIF4A) as a potential drug target delivered cholestanol derivatives inhibitors. Here, we investigated the mode of action of cholesterol derivatives as a novel scaffold structure of LieIF4A inhibitors on the RNA-dependent ATPase activity of LieIF4A and its mammalian ortholog (eIF4AI). We compared their biochemical effects on RNA-dependent ATPase activities of both proteins and investigated if rocaglamide, a known inhibitor of eIF4A, could affect LieIF4A as well. Kinetic measurements were conducted at different concentrations of ATP, of the compound and in the presence of saturating whole yeast RNA concentrations. Kinetic analyses showed different ATP binding affinities for the two enzymes as well as different sensitivities to 7-α-aminocholesterol and rocaglamide. The 7-α-aminocholesterol inhibited LieIF4A with a higher binding affinity relative to cholestanol analogs. Cholesterol, another tested sterol, had no effect on the ATPase activity of LieIF4A or eIF4AI. The 7-α-aminocholesterol demonstrated an anti-Leishmania activity on L. infantum promastigotes. Additionally, docking simulations explained the importance of the double bond between C5 and C6 in 7-α-aminocholesterol and the amino group in the C7 position. In conclusion, Leishmania and mammalian eIF4A proteins appeared to interact differently with effectors, thus making LieIF4A a potential drug against leishmaniases.

Longitudinal and Comparative Analysis of Gut Microbiota of Tunisian Newborns According to Delivery Mode.

Microbiota colonization is a dynamic process that impacts the health status during an individual's lifetime. The composition of the gut microbiota of newborns is conditioned by multiple factors, including the delivery mode (DM). Nonetheless, the DM's influence remains uncertain and is still the subject of debate. In this context, the medical indication and the emergency of a cesarean delivery might have led to confounding conclusions regarding the composition and diversity of the neonatal microbiome. Herein, we used high-resolution shotgun sequencing to decipher the composition and dynamics of the gut microbiota composition of Tunisian newborns. Stool samples were collected from 5 elective cesarean section (ECS) and 5 vaginally delivered (VD) newborns at the following time points: Day 0, Day 15, and Day 30. The ECS and VD newborns showed the same level of bacterial richness and diversity. In addition, our data pointed to a shift in microbiota community composition during the first 2 weeks, regardless of the DM. Both ECS and VD showed a profile dominated by Proteobacteria, Actinobacteria, and Firmicutes. However, ECS showed an underrepresentation of Bacteroides and an enrichment of opportunistic pathogenic species of the ESKAPE group, starting from the second week. Besides revealing the intestinal microbiota of Tunisian newborns, this study provides novel insights into the microbiota perturbations caused by ECS.

Applied Machine Learning Toward Drug Discovery Enhancement: Leishmaniases as a Case Study.

Drug discovery (DD) research is a complex field with a high attrition rate. Machine learning (ML) approaches combined to chemoinformatics are of valuable input to this field. We, herein, focused on implementing multiple ML algorithms that shall learn from different molecular fingerprints (FPs) of 65 057 molecules that have been identified as active or inactive against Leishmania major promastigotes. We sought to build a classifier able to predict whether a given molecule has the potential of being anti-leishmanial or not. Using the RDkit library, we calculated 5 molecular FPs of the molecules. Then, we implemented 4 ML algorithms that we trained and tested for their ability to classify the molecules into active/inactive classes based on their chemical structure, encoded by the molecular FPs. Best performers were random forest (RF) and support vector machine (SVM), while atom-pair and topology torsion FPs were the best embedding functions. Both models were further assessed on different stratification levels of the dataset and showed stable performances. At last, we used them to predict the potential of molecules within the Food and Drug Administration (FDA)-approved drugs collection to present anti-Leishmania effects. We ranked these drugs according to their anti-Leishmanial probability and obtained in total seven anti-Leishmania agents, previously described in the literature, within the top 10 of each model. This validates the robustness of the approach, the algorithms, and FPs choices as well as the importance of the dataset size and content. We further engaged these molecules into reverse docking experiments on 3D crystal structures of seven well-studied Leishmania drug targets and could predict the molecular targets for 4 drugs. The results bring novel insights into anti-Leishmania compounds.

Deep Learning Algorithms Achieved Satisfactory Predictions When Trained on a Novel Collection of Anticoronavirus Molecules.

Drug discovery and repurposing against COVID-19 is a highly relevant topic with huge efforts dedicated to delivering novel therapeutics targeting SARS-CoV-2. In this context, computer-aided drug discovery is of interest in orienting the early high throughput screenings and in optimizing the hit identification rate. We herein propose a pipeline for Ligand-Based Drug Discovery (LBDD) against SARS-CoV-2. Through an extensive search of the literature and multiple steps of filtering, we integrated information on 2,610 molecules having a validated effect against SARS-CoV and/or SARS-CoV-2. The chemical structures of these molecules were encoded through multiple systems to be readily useful as input to conventional machine learning (ML) algorithms or deep learning (DL) architectures. We assessed the performances of seven ML algorithms and four DL algorithms in achieving molecule classification into two classes: active and inactive. The Random Forests (RF), Graph Convolutional Network (GCN), and Directed Acyclic Graph (DAG) models achieved the best performances. These models were further optimized through hyperparameter tuning and achieved ROC-AUC scores through cross-validation of 85, 83, and 79% for RF, GCN, and DAG models, respectively. An external validation step on the FDA-approved drugs collection revealed a superior potential of DL algorithms to achieve drug repurposing against SARS-CoV-2 based on the dataset herein presented. Namely, GCN and DAG achieved more than 50% of the true positive rate assessed on the confirmed hits of a PubChem bioassay.

Dipeptidyl peptidase III as a DNA marker to investigate epidemiology and taxonomy of Old World Leishmania species.

BACKGROUND: Dipeptidyl peptidase III (DPPIII) member of M49 peptidase family is a zinc-dependent metallopeptidase that cleaves dipeptides sequentially from the N-terminus of its substrates. In Leishmania, DPPIII, was reported with other peptidases to play a significant role in parasites' growth and survival. In a previous study, we used a coding sequence annotated as DPPIII to develop and evaluate a PCR assay that is specific to dermotropic Old World (OW) Leishmania species. Thus, our objective was to further assess use of this gene for Leishmania species identification and for phylogeny, and thus for diagnostic and molecular epidemiology studies of Old World Leishmania species. METHODOLOGY: Orthologous DDPIII genes were searched in all Leishmania genomes and aligned to design PCR primers and identify relevant restriction enzymes. A PCR assays was developed and seventy-two Leishmania fragment sequences were analyzed using MEGA X genetics software to infer evolution and phylogenetic relationships of studied species and strains. A PCR-RFLP scheme was also designed and tested on 58 OW Leishmania strains belonging to 8 Leishmania species and evaluated on 75 human clinical skin samples. FINDINGS: Sequence analysis showed 478 variable sites (302 being parsimony informative). Test of natural selection (dN-dS) (-0.164, SE = 0.013) inferred a negative selection, characteristic of essential genes, corroborating the DPPIII importance for parasite survival. Inter- and intra-specific genetic diversity was used to develop universal amplification of a 662bp fragment. Sequence analyses and phylogenies confirmed occurrence of 6 clusters congruent to L. major, L. tropica, L. aethiopica, L. arabica, L. turanica, L. tarentolae species, and one to the L. infantum and L. donovani species complex. A PCR-RFLP algorithm for Leishmania species identification was designed using double digestions with HaeIII and KpnI and with SacI and PvuII endonucleases. Overall, this PCR-RFLP yielded distinct profiles for each of the species L. major, L. tropica, L. aethiopica, L. arabica and L. turanica and the L. (Sauroleishmania) L. tarentolae. The species L. donovani, and L. infantum shared the same profile except for strains of Indian origin. When tested on clinical samples, the DPPIII PCR showed sensitivities of 82.22% when compared to direct examination and was able to identify 84.78% of the positive samples. CONCLUSION: The study demonstrates that DPPIII gene is suitable to detect and identify Leishmania species and to complement other molecular methods for leishmaniases diagnosis and epidemiology. Thus, it can contribute to evidence-based disease control and surveillance.

Genomic Characteristics of Invasive Streptococcus pneumoniae Serotype 1 in New Caledonia Prior to the Introduction of PCV13.

Streptococcus pneumoniae serotype 1 is a common cause of global invasive pneumococcal disease. In New Caledonia, serotype 1 is the most prevalent serotype and led to two major outbreaks reported in the 2000s. The pneumococcal conjugate vaccine 13 (PCV13) was introduced into the vaccination routine, intending to prevent the expansion of serotype 1 in New Caledonia. Aiming to provide a baseline for monitoring the post-PCV13 changes, we performed a whole-genome sequence analysis on 67 serotype 1 isolates collected prior to the PCV13 introduction. To highlight the S. pneumoniae serotype 1 population structure, we performed a multilocus sequence typing (MLST) analysis revealing that NC serotype 1 consisted of 2 sequence types: ST3717 and the highly dominant ST306. Both sequence types harbored the same resistance genes to beta-lactams, macrolide, streptogramin B, fluoroquinolone, and lincosamide antibiotics. We have also identified 36 virulence genes that were ubiquitous to all the isolates. Among these virulence genes, the pneumolysin sequence presented an allelic profile associated with disease outbreaks and reduced hemolytic activity. Moreover, recombination hotspots were identified in 4 virulence genes and more notably in the cps locus (cps2L), potentially leading to capsular switching, a major mechanism of the emergence of nonvaccine types. In summary, this study represents the first overview of the genomic characteristics of S. pneumoniae serotype 1 in New Caledonia prior to the introduction of PCV13. This preliminary description represents a baseline to assess the impact of PCV13 on serotype 1 population structure and genomic diversity.

Infection of Human Neutrophils With Leishmania infantum or Leishmania major Strains Triggers Activation and Differential Cytokines Release.

Leishmaniases are neglected diseases, caused by intracellular protozoan parasites of the Leishmania (L.) genus. Although the principal host cells of the parasites are macrophages, neutrophils are the first cells rapidly recruited to the site of parasites inoculation, where they play an important role in the early recognition and elimination of the parasites. The nature of early interactions between neutrophils and Leishmania could influence the outcome of infection. Herein we aimed to evaluate whether different Leishmania strains, responsible for distinct clinical manifestations, could influence ex vivo functional activity of neutrophils. Human polymorphonuclear leukocytes were isolated from 14 healthy volunteers and the ex vivo infection of these cells was done with two L. infantum and one L. major strains. Infection parameters were determined and neutrophils activation was assessed by oxidative burst, degranulation, DNA release and apoptosis; cytokine production was measured by a multiplex flow cytometry analysis. Intracellular amastigotes were rescued to determine Leishmania strains survival. The results showed that L. infantum and L. major promastigotes similarly infected the neutrophils. Oxidative burst, neutrophil elastase, myeloperoxidase activity and apoptosis were significantly increased in infected neutrophils but with no differences between strains. The L. infantum-infected neutrophils induced more DNA release than those infected by L. major. Furthermore, Leishmania strains induced high amounts of IL-8 and stimulated the production of IL-1ß, TNF-α, and TGF-ß by human neutrophils. We observed that only one strain promoted IL-6 release by these neutrophils. The production of TNF-α was also differently induced by the parasites strains. All these results demonstrate that L. infantum and L. major strains were able to induce globally a similar ex vivo activation and apoptosis of neutrophils; however, they differentially triggered cytokines release from these cells. In addition, rescue of intracellular parasites indicated different survival rates further emphasizing on the influence of parasite strains within a species on the fate of infection.

The steroid derivative 6-aminocholestanol inhibits the DEAD-box helicase eIF4A (LieIF4A) from the Trypanosomatid parasite Leishmania by perturbing the RNA and ATP binding sites.

The antifungal agent 6-aminocholestanol targets the production of ergosterol, which is the principle sterol in many fungi and protozoans; ergosterol serves many of the same roles as cholesterol in animals. We found that it also is an effective inhibitor of the translation-initiation factor eIF4AI from mouse (eIF4AIMus) and the Trypanosomatid parasite Leishmania (LieIF4A). The eIF4A proteins belong to the DEAD-box family of RNA helicases, which are ATP-dependent RNA-binding proteins and RNA-dependent ATPases. DEAD-box proteins contain a commonly-shared core structure consisting of two linked domains with structural homology to that of recombinant protein A (RecA) and that contain conserved motifs that are involved in RNA and ATP binding, and in the enzymatic activity. The compound inhibits both the ATPase and helicase activities by perturbing ATP and RNA binding, and it is capable of binding other proteins containing nucleic acid-binding sites as well. We undertook kinetic analyses and found that the Leishmania LieIF4A protein binds 6-aminocholestanol with a higher apparent affinity than for ATP, although multiple binding sites were probably involved. Competition experiments with the individual RecA-like domains indicate that the primary binding sites are on RecA-like domain 1, and they include a cavity that we previously identified by molecular modeling of LieIF4A that involve conserved RNA-binding motifs. The compound affects the mammalian and Leishmania proteins differently, which indicates the binding sites and affinities are not the same. Thus, it is possible to develop drugs that target DEAD-box proteins from different organisms even when they are implicated in the same biological process.

The PEG-responding desiccome of the alder microsymbiont Frankia alni.

Actinorhizal plants are ecologically and economically important. Symbiosis with nitrogen-fixing bacteria allows these woody dicotyledonous plants to colonise soils under nitrogen deficiency, water-stress or other extreme conditions. However, proteins involved in xerotolerance of symbiotic microorganisms have yet to be identified. Here we characterise the polyethylene glycol (PEG)-responding desiccome from the most geographically widespread Gram-positive nitrogen-fixing plant symbiont, Frankia alni, by next-generation proteomics, taking advantage of a Q-Exactive HF tandem mass spectrometer equipped with an ultra-high-field Orbitrap analyser. A total of 2,052 proteins were detected and quantified. Under osmotic stress, PEG-grown F. alni cells increased the abundance of envelope-associated proteins like ABC transporters, mechano-sensitive ion channels and Clustered Regularly Interspaced Short Palindromic Repeats CRISPR-associated (cas) components. Conjointly, dispensable pathways, like nitrogen fixation, aerobic respiration and homologous recombination, were markedly down-regulated. Molecular modelling and docking simulations suggested that the PEG is acting on Frankia partly by filling the inner part of an up-regulated osmotic-stress large conductance mechanosensitive channel.

Identification of novel leishmanicidal molecules by virtual and biochemical screenings targeting Leishmania eukaryotic translation initiation factor 4A.

Leishmaniases are neglected parasitic diseases in spite of the major burden they inflict on public health. The identification of novel drugs and targets constitutes a research priority. For that purpose we used Leishmania infantum initiation factor 4A (LieIF), an essential translation initiation factor that belongs to the DEAD-box proteins family, as a potential drug target. We modeled its structure and identified two potential binding sites. A virtual screening of a diverse chemical library was performed for both sites. The results were analyzed with an in-house version of the Self-Organizing Maps algorithm combined with multiple filters, which led to the selection of 305 molecules. Effects of these molecules on the ATPase activity of LieIF permitted the identification of a promising hit (208) having a half maximal inhibitory concentration (IC50) of 150 ± 15 µM for 1 µM of protein. Ten chemical analogues of compound 208 were identified and two additional inhibitors were selected (20 and 48). These compounds inhibited the mammalian eIF4I with IC50 values within the same range. All three hits affected the viability of the extra-cellular form of L. infantum parasites with IC50 values at low micromolar concentrations. These molecules showed non-significant toxicity toward THP-1 macrophages. Furthermore, their anti-leishmanial activity was validated with experimental assays on L. infantum intramacrophage amastigotes showing IC50 values lower than 4.2 µM. Selected compounds exhibited selectivity indexes between 19 to 38, which reflects their potential as promising anti-Leishmania molecules.

Leishmania infantum 5'-Methylthioadenosine Phosphorylase presents relevant structural divergence to constitute a potential drug target.

BACKGROUND: The 5'-methylthioadenosine phosphorylase (MTAP), an enzyme involved in purine and polyamine metabolism and in the methionine salvage pathway, is considered as a potential drug target against cancer and trypanosomiasis. In fact, Trypanosoma and Leishmania parasites lack de novo purine pathways and rely on purine salvage pathways to meet their requirements. Herein, we propose the first comprehensive bioinformatic and structural characterization of the putative Leishmania infantum MTAP (LiMTAP), using a comparative computational approach. RESULTS: Sequence analysis showed that LiMTAP shared higher identity rates with the Trypanosoma brucei (TbMTAP) and the human (huMTAP) homologs as compared to the human purine nucleoside phosphorylase (huPNP). Motifs search using MEME identified more common patterns and higher relatedness of the parasite proteins to the huMTAP than to the huPNP. The 3D structures of LiMTAP and TbMTAP were predicted by homology modeling and compared to the crystal structure of the huMTAP. These models presented conserved secondary structures compared to the huMTAP, with a similar topology corresponding to the Rossmann fold. This confirmed that both LiMTAP and TbMTAP are members of the NP-I family. In comparison to the huMTAP, the 3D model of LiMTAP showed an additional α-helix, at the C terminal extremity. One peptide located in this specific region was used to generate a specific antibody to LiMTAP. In comparison with the active site (AS) of huMTAP, the parasite ASs presented significant differences in the shape and the electrostatic potentials (EPs). Molecular docking of 5'-methylthioadenosine (MTA) and 5'-hydroxyethylthio-adenosine (HETA) on the ASs on the three proteins predicted differential binding modes and interactions when comparing the parasite proteins to the human orthologue. CONCLUSIONS: This study highlighted significant structural peculiarities, corresponding to functionally relevant sequence divergence in LiMTAP, making of it a potential drug target against Leishmania.

Identification of binding sites and favorable ligand binding moieties by virtual screening and self-organizing map analysis.

BACKGROUND: Identifying druggable cavities on a protein surface is a crucial step in structure based drug design. The cavities have to present suitable size and shape, as well as appropriate chemical complementarity with ligands. RESULTS: We present a novel cavity prediction method that analyzes results of virtual screening of specific ligands or fragment libraries by means of Self-Organizing Maps. We demonstrate the method with two thoroughly studied proteins where it successfully identified their active sites (AS) and relevant secondary binding sites (BS). Moreover, known active ligands mapped the AS better than inactive ones. Interestingly, docking a naive fragment library brought even more insight. We then systematically applied the method to the 102 targets from the DUD-E database, where it showed a 90% identification rate of the AS among the first three consensual clusters of the SOM, and in 82% of the cases as the first one. Further analysis by chemical decomposition of the fragments improved BS prediction. Chemical substructures that are representative of the active ligands preferentially mapped in the AS. CONCLUSION: The new approach provides valuable information both on relevant BSs and on chemical features promoting bioactivity.