A Gram-negative pink-pigmented bacillus (named 2A) was isolated from Solanum tuberosum L. cv. Desirée plants that were strikingly more developed, presented increased root hair density, and higher biomass than other potato lines of the same age. The 16S ribosomal DNA sequence, used for comparative gene sequence analysis, indicated that strain 2A belongs to the genus Methylobacterium. Nucleotide identity between Methylobacterium sp. 2A sequenced genome and the rest of the species that belong to the genus suggested that this species has not been described so far. In vitro, potato plants inoculated with Methylobacterium sp. 2A had a better performance when grown under 50 mM NaCl or when infected with Phytophthora infestans. We inoculated Methylobacterium sp. 2A in Arabidopsis thaliana roots and exposed these plants to salt stress (75 mM NaCl). Methylobacterium sp. 2A-inoculated plants, grown in control or salt stress conditions, displayed a higher density of lateral roots (p < 0.05) compared to noninoculated plants. Moreover, under salt stress, they presented a higher number of leaves and larger rosette diameter. In dual confrontation assays, Methylobacterium sp. 2A displayed biocontrol activity against P. infestans, Botrytis cinerea, and Fusarium graminearum, but not against Rhizoctonia solani, and Pythium dissotocum. In addition, we observed that Methylobacterium sp. 2A diminished the size of necrotic lesions and reduced chlorosis when greenhouse potato plants were infected with P. infestans. Methylobacterium sp. 2A produces indole acetic acid, solubilizes mineral phosphate and is able to grow in a N 2 free medium. Whole-genome sequencing revealed metabolic pathways associated with its plant growth promoter capacity. Our results suggest that Methylobacterium sp. 2A is a plant growth-promoting rhizobacteria (PGPR) that can

Decades of successful use of antibiotics is currently challenged by the emergence of increasingly resistant bacterial strains. Novel drugs are urgently required but, in a scenario where private investment in the development of new antimicrobials is declining, efforts to combat drug-resistant infections become a worldwide public health problem. Reasons behind unsuccessful new antimicrobial development projects range from inadequate selection of the molecular targets to a lack of innovation. In this context, increasingly available omics data for multiple pathogens has created new drug discovery and development opportunities to fight infectious diseases. Identification of an appropriate molecular target is currently accepted as a critical step of the drug discovery process. Here, we review how diverse layers of multi-omics data in conjunction with structural/functional analysis and systems biology can be used to prioritize the best candidate proteins. Once the target is selected, virtual screening can be used as a robust methodology to explore molecular scaffolds that could act as inhibitors, guiding the development of new drug lead compounds. This review focuses on how the advent of omics and the development and application of bioinformatics strategies conduct a “big-data era” that improves target selection and lead compound identification in a cost-effective and shortened timeline.

BACKGROUND Carrion's disease (CD) is a neglected biphasic illness caused by Bartonella bacilliformis, a Gram-negative bacteria found in the Andean valleys. The spread of resistant strains underlines the need for novel antimicrobials against B. bacilliformis and related bacterial pathogens. OBJECTIVE The main aim of this study was to integrate genomic-scale data to shortlist a set of proteins that could serve as attractive targets for new antimicrobial discovery to combat B. bacilliformis. METHODS We performed a multidimensional genomic scale analysis of potential and relevant targets which includes structural druggability, metabolic analysis and essentiality criteria to select proteins with attractive features for drug discovery. FINDINGS We shortlisted seventeen relevant proteins to develop new drugs against the causative agent of Carrion's disease. Particularly, the protein products of fabI, folA, aroA, trmFO, uppP and murE genes, meet an important number of desirable features that make them attractive targets for new drug development. This data compendium is freely available as a web server (http://target.sbg.qb.fcen.uba.ar/). MAIN CONCLUSION This work represents an effort to reduce the costs in the first phases of B. bacilliformis drug discovery.

Background Whole-genome sequencing has shown that the Mycobacterium tuberculosis infection process can be more heterogeneous than previously thought. Compartmentalized infections, exogenous reinfections, and microevolution are manifestations of this clonal complexity. The analysis of the mechanisms causing the microevolution —the genetic variability of M. tuberculosis at short time scales— of a parental strain into clonal variants with a patient is a relevant issue that has not been yet completely addressed. To our knowledge, a whole genome sequence microevolution analysis in a single patient with inadequate adherence to treatment has not been previously reported. Case presentation In this work, we applied whole genome sequencing analysis for a more in-depth analysis of the microevolution of a parental Mycobacterium tuberculosis strain into clonal variants within a patient with poor treatment compliance in Argentina. We analyzed the whole-genome sequence of 8 consecutive Mycobacterium tuberculosis isolates obtained from a patient within 57-months of intermittent therapy. Nineteen mutations (9 short-term, 10 fixed variants) emerged, most of them associated with drug resistance. The first isolate was already resistant to isoniazid, rifampicin, and streptomycin, thereafter the strain developed resistance to fluoroquinolones and pyrazinamide. Surprisingly, isolates remained susceptible to the pro-drug ethionamide after acquiring a frameshift mutation in ethA, a gene required for its activation. We also found a novel variant, (T-54G), in the 5′ untranslated region of whiB7 (T-54G), a region allegedly related to kanamycin resistance. Notably, discrepancies between canonical and phage-based susceptibility testing to kanamycin were previously found for the isolate harboring this mutation. In our patient, microevolution was mainly driven by drug selective pressure. Rare short-term mutations fixed together with resistance-conferring mutations during therapy. Conclusions This report highlights the relevance of whole-genome sequencing analysis in the clinic for characterization of pre-XDR and MDR resistance profile, particularly in patients with incomplete and/or intermittent treatment.

Carbapenem-resistant Klebsiella pneumoniae (CR-KP) represents an emerging threat to public health. CR-KP infections result in elevated morbidity and mortality. This fact, coupled with their global dissemination and increasingly limited number of therapeutic options, highlights the urgency of novel antimicrobials. Innovative strategies linking genome-wide interrogation with multi-layered metabolic data integration can accelerate the early steps of drug development, particularly target selection. Using the BioCyc ontology, we generated and manually refined a metabolic network for a CR-KP, K. pneumoniae Kp13. Converted into a reaction graph, we conducted topological-based analyses in this network to prioritize pathways exhibiting druggable features and fragile metabolic points likely exploitable to develop novel antimicrobials. Our results point to the aptness of previously recognized pathways, such as lipopolysaccharide and peptidoglycan synthesis, and casts light on the possibility of targeting less explored cellular functions. These functions include the production of lipoate, trehalose, glycine betaine, and flavin, as well as the salvaging of methionine. Energy metabolism pathways emerged as attractive targets in the context of carbapenem exposure, targeted either alone or in conjunction with current therapeutic options. These results prompt further experimental investigation aimed at controlling this highly relevant pathogen.

Listeriamonocytogenes (Lm) is a Gram-positive bacillus responsible for listeriosis in humans. Listeriosis has become a major foodborne illness in recent years. This illness is mainly associated with the consumption of contaminated food and ready-to-eat products. Recently, Lm has developed resistances to a broad range of antimicrobials, including those used as the first choice of therapy. Moreover, multidrug-resistant strains have been detected in clinical isolates and settings associated with food processing. This scenario punctuates the need for novel antimicrobials against Lm. On the other hand, increasingly available omics data for diverse pathogens has created new opportunities for rational drug discovery. Identification of an appropriate molecular target is currently accepted as a critical step of this process. In this work, we generated multiple layers of omics data related to Lm, aiming to prioritize proteins that could serve as attractive targets for antimicrobials against L. monocytogenes. We generated genomic, transcriptomic, metabolic, and protein structural information, and this data compendium was integrated onto a freely available web server (Target Pathogen). Thirty targets with desirable features from a drug development point of view were shortlisted. This set of target proteins participates in key metabolic processes such as fatty acid, pentose, rhamnose, and amino acids metabolism. Collectively, our results point towards novel targets for the control of Lm and related bacteria. We invite researchers working in the field of drug discovery to follow up experimentally on our revealed targets.

Leishmaniasis is a public health disease that requires the development of more effective treatments and the identification of novel molecular targets. Since blocking the PI3K/AKT pathway has been successfully studied as an effective anticancer strategy for decades, we examined whether the same approach would also be feasible in Leishmania due to their high amount and diverse set of annotated proteins. Here, we used a best reciprocal hits protocol to identify potential protein kinase homologues in an annotated human PI3K/AKT pathway. We calculated their ligandibility based on available bioactivity data of the reported homologues and modelled their 3D structures to estimate the druggability of their binding pockets. The models were used to run a virtual screening method with molecular docking. We found and studied five protein kinases in five different Leishmania species, which are AKT, CDK, AMPK, mTOR and GSK3 homologues from the studied pathways. The compounds found for different enzymes and species were analysed and suggested as starting point scaffolds for the design of inhibitors. We studied the kinases’ participation in protein–protein interaction networks, and the potential deleterious effects, if inhibited, were supported with the literature. In the case of Leishmania GSK3, an inhibitor of its human counterpart, prioritized by our method, was validated in vitro to test its anti-Leishmania activity and indirectly infer the presence of the enzyme in the parasite. The analysis contributes to improving the knowledge about the presence of similar signalling pathways in Leishmania, as well as the discovery of compounds acting against any of these kinases as potential molecular targets in the parasite.

Phenotypic screening is a powerful technique that allowed the discovery of antimicrobials to fight infectious diseases considered deadly less than a century ago. In high throughput phenotypic screening assays, thousands of compounds are tested for their capacity to inhibit microbial growth in-vitro. After an active compound is found, identifying the molecular target is the next step. Knowing the specific target is key for understanding its mechanism of action, and essential for future drug development. Moreover, this knowledge allows drug developers to design new generations of drugs with increased efficacy and reduced side effects. However, target identification for a known active compound is usually a very difficult task. In the present work, we present a powerful reverse virtual screening strategy, that can help researchers working in the drug discovery field, to predict a set of putative targets for a compound known to exhibit antimicrobial effects. The strategy combines chemical similarity methods, with target prioritization based on essentiality data, and molecular-docking. These steps can be tailored according to the researchers’ needs and pathogen’s available information. Our results show that using only the chemical similarity approach, this method is capable of retrieving potential targets for half of tested compounds. The results show that even for a low chemical similarity threshold whenever domains are retrieved, the correct domain is among those retrieved in more than 80% of the queries. Prioritizing targets by an essentiality criteria allows us to further reduce, up to 3–4 times, the number of putative targets. Lastly, docking is able to identify the correct domain ranked in the top two in about two thirds of cases. Bias docking improves predictive capacity only slightly in this scenario. We expect to integrate the presented strategy in the context of Target Pathogen database to make it available for the wide community of researchers working in antimicrobials discovery.