Genome sequencing and analysis of black flounder (Paralichthys orbignyanus) reveals new insights into Pleuronectiformes genomic size and structure.

Black flounder (Paralichthys orbignyanus, Pleuronectiformes) is a commercially significant marine fish with promising aquaculture potential in Argentina. Despite extensive studies on Black flounder aquaculture, its limited genetic information available hampers the crucial role genetics plays in the development of this activity. In this study, we first employed Illumina sequencing technology to sequence the entire genome of Black flounder. Utilizing two independent libraries-one from a female and another from a male-with 150 bp paired-end reads, a mean insert length of 350 bp, and over 35 X-fold coverage, we achieved assemblies resulting in a genome size of ~ 538 Mbp. Analysis of the assemblies revealed that more than 98% of the core genes were present, with more than 78% of them having more than 50% coverage. This indicates a somehow complete and accurate genome at the coding sequence level. This genome contains 25,231 protein-coding genes, 445 tRNAs, 3 rRNAs, and more than 1,500 non-coding RNAs of other types. Black flounder, along with pufferfishes, seahorses, pipefishes, and anabantid fish, displays a smaller genome compared to most other teleost groups. In vertebrates, the number of transposable elements (TEs) is often correlated with genome size. However, it remains unclear whether the sizes of introns and exons also play a role in determining genome size. Hence, to elucidate the potential factors contributing to this reduced genome size, we conducted a comparative genomic analysis between Black flounder and other teleost orders to determine if the small genomic size could be explained by repetitive elements or gene features, including the whole genome genes and introns sizes. We show that the smaller genome size of flounders can be attributed to several factors, including changes in the number of repetitive elements, and decreased gene size, particularly due to lower amount of very large and small introns. Thus, these components appear to be involved in the genome reduction in Black flounder. Despite these insights, the full implications and potential benefits of genome reduction in Black flounder for reproduction and aquaculture remain incompletely understood, necessitating further research.

Omics data integration facilitates target selection for new antiparasitic drugs against TriTryp infections.

Introduction: Trypanosoma cruzi, Trypanosoma brucei, and Leishmania spp., commonly referred to as TriTryps, are a group of protozoan parasites that cause important human diseases affecting millions of people belonging to the most vulnerable populations worldwide. Current treatments have limited efficiencies and can cause serious side effects, so there is an urgent need to develop new control strategies. Presently, the identification and prioritization of appropriate targets can be aided by integrative genomic and computational approaches. Methods: In this work, we conducted a genome-wide multidimensional data integration strategy to prioritize drug targets. We included genomic, transcriptomic, metabolic, and protein structural data sources, to delineate candidate proteins with relevant features for target selection in drug development. Results and Discussion: Our final ranked list includes proteins shared by TriTryps and covers a range of biological functions including essential proteins for parasite survival or growth, oxidative stress-related enzymes, virulence factors, and proteins that are exclusive to these parasites. Our strategy found previously described candidates, which validates our approach as well as new proteins that can be attractive targets to consider during the initial steps of drug discovery.

Spatial and Seasonal Variations in the Bacterial Community of an Anthropogenic Impacted Urban Stream.

Environmental changes and human activities can alter the structure and diversity of aquatic microbial communities. In this work, we analyzed the bacterial community dynamics of an urban stream to understand how these factors affect the composition of river microbial communities. Samples were taken from a stream situated in Buenos Aires, Argentina, which flows through residential, peri-urban horticultural, and industrial areas. For sampling, two stations were selected: one influenced by a series of industrial waste treatment plants and horticultural farms (PL), and the other influenced by residential areas (R). Microbial communities were analyzed by sequence analysis of 16S rRNA gene amplicons along an annual cycle. PL samples showed high nutrient content compared with R samples. The diversity and richness of the R site were more affected by seasonality than those of the PL site. At the amplicon sequence variants level, beta diversity analysis showed a differentiation between cool-season (fall and winter) and warm-season (spring and summer) samples, as well as between PL and R sites. This demonstrated that there is spatial and temporal heterogeneity in the composition of the bacterial community, which should be considered if a bioremediation strategy is applied. The taxonomic composition analysis also revealed a differential seasonal cycle of phototrophs and chemoheterotrophs between the sampling sites, as well as different taxa associated with each sampling site. This analysis, combined with a comparative analysis of global rivers, allowed us to determine the genera Arcobacter, Simplicispira, Vogesella, and Sphingomonas as potential bioindicators of anthropogenic disturbance.

Solvent Sites Improve Docking Performance of Protein-Protein Complexes and Protein-Protein Interface-Targeted Drugs.

Protein-protein interactions (PPIs) are essential, and modulating their function through PPI-targeted drugs is an important research field. PPI sites are shallow protein surfaces readily accessible to the solvent, thus lacking a proper pocket to fit a drug, while their lack of endogenous ligands prevents drug design by chemical similarity. The development of PPI-blocking compounds is, therefore, a tough challenge. Mixed solvent molecular dynamics has been shown to reveal protein-ligand interaction hot spots in protein active sites by identifying solvent sites (SSs). Furthermore, our group has shown that SSs significantly improve protein-ligand docking. In the present work, we extend our analysis to PPI sites. In particular, we analyzed water, ethanol, and phenol-derived sites in terms of their capacity to predict protein-drug and protein-protein interactions. Subsequently, we show how this information can be incorporated to improve both protein-ligand and protein-protein docking. Finally, we highlight the presence of aromatic clusters as key elements of the corresponding interactions.

Cosolvent Sites-Based Discovery of Mycobacterium Tuberculosis Protein Kinase G Inhibitors.

Computer-aided drug discovery methods play a major role in the development of therapeutically important small molecules, but their performance needs to be improved. Molecular dynamics simulations in mixed solvents are useful in understanding protein-ligand recognition and improving molecular docking predictions. In this work, we used ethanol as a cosolvent to find relevant interactions for ligands toward protein kinase G, an essential protein of Mycobacterium tuberculosis (Mtb). We validated the hot spots by screening a database of fragment-like compounds and another one of known kinase inhibitors. Next, we performed a pharmacophore-guided docking simulation and found three low micromolar inhibitors, including one with a novel chemical scaffold that we expanded to four derivative compounds. Binding affinities were characterized by intrinsic fluorescence quenching assays, isothermal titration calorimetry, and the analysis of melting curves. The predicted binding mode was confirmed by X-ray crystallography. Finally, the compounds significantly inhibited the viability of Mtb in infected THP-1 macrophages.

Specificity and Reactivity of Mycobacterium tuberculosis Serine/Threonine Kinases PknG and PknB.

Mycobacterium tuberculosis (Mtb), the causative agent of Tuberculosis, has 11 eukaryotic-like serine/threonine protein kinases, which play essential roles in cell growth, signal transduction, and pathogenesis. Protein kinase G (PknG) regulates the carbon and nitrogen metabolism by phosphorylation of the glycogen accumulation regulator (GarA) protein at Thr21. Protein kinase B (PknB) is involved in cell wall synthesis and cell shape, as well as phosphorylates GarA but at Thr22. While PknG seems to be constitutively activated and recognition of GarA requires phosphorylation in its unstructured tail, PknB activation is triggered by phosphorylation of its activation loop, which allows binding of the forkhead-associated domain of GarA. In the present work, we used molecular dynamics and quantum-mechanics/molecular mechanics simulations of the catalytically competent complex and kinase activity assays to understand PknG/PknB specificity and reactivity toward GarA. Two hydrophobic residues in GarA, Val24 and Phe25, seem essential for PknG binding and allow specificity for Thr21 phosphorylation. On the other hand, phosphorylated residues in PknB bind Arg26 in GarA and regulate its specificity for Thr22. We also provide a detailed analysis of the free energy profile for the phospho-transfer reaction and show why PknG has a constitutively active conformation not requiring priming phosphorylation in contrast to PknB. Our results provide new insights into these two key enzymes relevant for Mtb and the mechanisms of serine/threonine phosphorylation in bacteria.

Pathway Driven Target Selection in Klebsiella pneumoniae: Insights Into Carbapenem Exposure.

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.

Geobiology of Andean Microbial Ecosystems Discovered in Salar de Atacama, Chile.

The Salar de Atacama in the Chilean Central Andes harbors unique microbial ecosystems due to extreme environmental conditions, such as high altitude, low oxygen pressure, high solar radiation, and high salinity. Combining X-ray diffraction analyses, scanning electron microscopy and molecular diversity studies, we have characterized twenty previously unexplored Andean microbial ecosystems in eight different lakes and wetlands from the middle-east and south-east regions of this salt flat. The mats and microbialites studied are mainly formed by calcium carbonate (aragonite and calcite) and halite, whereas the endoevaporites are composed predominantly of gypsum and halite. The carbonate-rich mats and microbialites are dominated by Bacteroidetes and Proteobacteria phyla. Within the phylum Proteobacteria, the most abundant classes are Alphaproteobacteria, Gammaproteobacteria and Deltaproteobacteria. While in the phylum Bacteroidetes, the most abundant classes are Bacteroidia and Rhodothermia. Cyanobacteria, Chloroflexi, Planctomycetes, and Verrucomicrobia phyla are also well-represented in the majority of these systems. Gypsum endoevaporites, on the contrary, are dominated by Proteobacteria, Bacteroidetes, and Euryarchaeota phyla. The Cyanobacteria phylum is also abundant in these systems, but it is less represented in comparison to mats and microbialites. Regarding the eukaryotic taxa, diatoms are key structural components in most of the microbial ecosystems studied. The genera of diatoms identified were Achnanthes, Fallacia, Halamphora, Mastogloia, Navicula, Nitzschia, and Surirella. Normally, in the mats and microbialites, diatoms form nano-globular carbonate aggregates with filamentous cyanobacteria and other prokaryotic cells, suggesting their participation in the mineral precipitation process. This work expands our knowledge of the microbial ecosystems inhabiting the extreme environments from the Central Andes region, which is important to ensure their protection and conservation.

From Genome to Drugs: New Approaches in Antimicrobial Discovery.

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.

Biased Docking for Protein-Ligand Pose Prediction.

The interaction between a protein and its ligands is one of the basic and most important processes in biological chemistry. Docking methods aim to predict the molecular 3D structure of protein-ligand complexes starting from coordinates of the protein and the ligand separately. They are widely used in both industry and academia, especially in the context of drug development projects. AutoDock4 is one of the most popular docking tools and, as for any docking method, its performance is highly system dependent. Knowledge about specific protein-ligand interactions on a particular target can be used to successfully overcome this limitation. Here, we describe how to apply the AutoDock Bias protocol, a simple and elegant strategy that allows users to incorporate target-specific information through a modified scoring function that biases the ligand structure towards those poses (or conformations) that establish selected interactions. We discuss two examples using different bias sources. In the first, we show how to steer dockings towards interactions derived from crystal structures of the receptor with different ligands; in the second example, we define and apply hydrophobic biases derived from Molecular Dynamics simulations in mixed solvents. Finally, we discuss general concepts of biased docking, its performance in pose prediction, and virtual screening campaigns as well as other potential applications.

A novel mutation in PSEN1 (p.Arg41Ser) in an Argentinian woman with early onset Parkinsonism.

INTRODUCTION: Mutations in presenilin-1 (PSEN1) account for the majority of cases of familial autosomal dominant early-onset Alzheimer's disease (AD) as well as in sporadic forms. Atypical presentations are reported including extrapyramidal signs. In the last years, a pleiotropic effect of some PSEN1 variants has been reported in Parkinson's disease (PD). OBJECTIVE: to report a new PSEN1 mutation characterized by early-onset Parkinsonism (EOPD) without dementia or classical AD biomarkers phenotype. PATIENT AND METHODS: An Argentinian 46 years old woman was diagnosed with EOPD at 35 years old with no family history of neurodegenerative disorders. Her medical history included iron deficiency and anemia since childhood. A brain MRI showed moderate frontal atrophy. 18FDG-PET and PiB-PET as well as CSF biomarkers were inconclusive for AD. Two neuropsychological examinations were compatible with a mild non amnestic cognitive impairment. Whole blood DNA was extracted and whole exome sequencing and analysis was performed. RESULTS AND CONCLUSION: A heterozygous novel missense PSEN1 mutation (position 14:73637540, A > T, pArg41Ser) was identified as a likely causative mutation in this patient. To the best of our knowledge, this case is the first PSEN1 mutation with a l-dopa responsive Parkinsonism lacking distinctive classical AD biomarkers. This case opens a new window to explore the pathophysiological link among PSEN1 and EOPDs and contributes to increase the phenotypes of PSEN1 variants.

MRSA dynamic circulation between the community and the hospital setting: New insights from a cohort study.

Dissemination of methicillin-resistant-Staphylococcus aureus/(MRSA) is a worldwide concern both in hospitals [healthcare-associated-(HA)-MRSA] and communities [community-associated-(CA)-MRSA]. Knowledge on when and where MRSA colonization is acquired and what clones are involved is necessary, to focus efforts for prevention of hospital-acquired MRSA-infections. METHODS: A prospective/longitudinal cohort study was performed in eight Argentina hospitals (Cordoba/ October-December/2014). Surveillance cultures for MRSA (nose-throat-inguinal) were obtained on admission and at discharge. MRSA strains were genetically typed as CA-MRSAG and HA-MRSAG genotypes. RESULTS: Overall, 1419 patients were screened and 534 stayed at hospital for ≥3 days. S. aureus admission prevalence was 30.9% and 4.2% for MRSA. Overall MRSA acquisition rate was 2.3/1000 patient-days-at-risk with a MRSA acquisition prevalence of 1.96% (95%CI: 1.0%-3.4%); 3.2% of patients were discharged back to community with MRSA. CA-MRSAG accounted for 84.6% of imported, 100.0% of hospital-acquired and 94% of discharged MRSA strains. Most imported and acquired MRSA strains belonged to two major epidemic CA-MRSA clones spread in Argentina: PFGEtypeI-ST5-IVa-t311-PVL+ and PFGEtypeN/ST30-IVc-t019-PVL+. CONCLUSIONS: CA-MRSA clones, particularly ST5-IV-PVL+ and ST30-IV-PVL+, with main reservoir in the community, not only enter but also are truly acquired within hospital, causing healthcare-associated-hospital-onset infections, having a transmission capacity greater or similar than HA-MRSAG. This information is essential to develop appropriate MRSA infection prevention-control programs, considering hospital and community.

Kinase Activation by Small Conformational Changes.

Protein kinases (PKs) are allosteric enzymes that play an essential role in signal transduction by regulating a variety of key cellular processes. Most PKs suffer conformational rearrangements upon phosphorylation that strongly enhance the catalytic activity. Generally, it involves the movement of the phosphorylated loop toward the active site and the rotation of the whole C-terminal lobe. However, not all kinases undergo such a large configurational change: The MAPK extracellular signal-regulated protein kinases ERK1 and ERK2 achieve a 50 000 fold increase in kinase activity with only a small motion of the C-terminal region. In the present work, we used a combination of molecular simulation tools to characterize the conformational landscape of ERK2 in the active (phosphorylated) and inactive (unphosphorylated) states in solution in agreement with NMR experiments. We show that the chemical reaction barrier is strongly dependent on ATP conformation and that the "active" low-barrier configuration is subtly regulated by phosphorylation, which stabilizes a key salt bridge between the conserved Lys52 and Glu69 belonging to helix-C and promotes binding of a second Mg ion. Our study highlights that the on-off switch embedded in the kinase fold can be regulated by small, medium, and large conformational changes.

Aromatic clusters in protein-protein and protein-drug complexes.

Aromatic rings are important residues for biological interactions and appear to a large extent as part of protein-drug and protein-protein interactions. They are relevant for both protein stability and molecular recognition processes due to their natural occurrence in aromatic aminoacids (Trp, Phe, Tyr and His) as well as in designed drugs since they are believed to contribute to optimizing both affinity and specificity of drug-like molecules. Despite the mentioned relevance, the impact of aromatic clusters on protein-protein and protein-drug complexes is still poorly characterized, especially in those that go beyond a dimer. In this work, we studied protein-drug and protein-protein complexes and systematically analyzed the presence and structure of their aromatic clusters. Our results show that aromatic clusters are highly prevalent in both protein-protein and protein-drug complexes, and suggest that protein-protein aromatic clusters have idealized interactions, probably because they were optimized by evolution, as compared to protein-drug clusters that were manually designed. Interestingly, the configuration, solvent accessibility and secondary structure of aromatic residues in protein-drug complexes shed light on the relation between these properties and compound affinity, allowing researchers to better design new molecules.

Cosolvent-Based Protein Pharmacophore for Ligand Enrichment in Virtual Screening.

Virtual screening of large compound databases, looking for potential ligands of a target protein, is a major tool in computer-aided drug discovery. Throughout the years, different techniques such as similarity searching, pharmacophore matching, or molecular docking have been applied with the aim of finding hit compounds showing appreciable affinity. Molecular dynamics simulations in mixed solvents have been shown to identify hot spots relevant for protein-drug interaction, and implementations based on this knowledge were developed to improve pharmacophore matching of small molecules, binding free-energy estimations, and docking performance in terms of pose prediction. Here, we proved in a retrospective manner that cosolvent-derived pharmacophores from molecular dynamics (solvent sites) improve the performance of docking-based virtual screening campaigns. We applied a biased docking scheme based on solvent sites to nine relevant target proteins that have a set of known ligands or actives and compounds that are, presumably, nonbinders (decoys). Our results show improvement in virtual screening performance compared to traditional docking programs both at a global level, with up to 35% increase in areas under the receiver operating characteristic curve, and in early stages, with up to a 7-fold increase in enrichment factors at 1%. However, the improvement in pose prediction of actives was less profound. The presented application makes use of the AutoDock Bias method and is the only cosolvent-derived pharmacophore technique that employs its knowledge both in the ligand conformational search algorithm and the final affinity scoring for virtual screening purposes.

AutoDock Bias: improving binding mode prediction and virtual screening using known protein-ligand interactions.

SUMMARY: The performance of docking calculations can be improved by tuning parameters for the system of interest, e.g. biasing the results towards the formation of relevant protein-ligand interactions, such as known ligand pharmacophore or interaction sites derived from cosolvent molecular dynamics. AutoDock Bias is a straightforward and easy to use script-based method that allows the introduction of different types of user-defined biases for fine-tuning AutoDock4 docking calculations. AVAILABILITY AND IMPLEMENTATION: AutoDock Bias is distributed with MGLTools (since version 1.5.7), and freely available on the web at http://ccsb.scripps.edu/mgltools/ or http://autodockbias.wordpress.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Solvents to Fragments to Drugs: MD Applications in Drug Design.

Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates.

Fluoromycobacteriophages Can Detect Viable Mycobacterium tuberculosis and Determine Phenotypic Rifampicin Resistance in 3-5 Days From Sputum Collection.

The World Health Organization (WHO) estimates that 40% of tuberculosis (TB) cases are not diagnosed and treated correctly. Even though there are several diagnostic tests available in the market, rapid, easy, inexpensive detection, and drug susceptibility testing (DST) of Mycobacterium tuberculosis is still of critical importance specially in low and middle-income countries with high incidence of the disease. In this work, we have developed a microscopy-based methodology using the reporter mycobacteriophage mCherrybomb ϕ for detection of Mycobacterium spp. and phenotypic determination of rifampicin resistance within just days from sputum sample collection. Fluoromycobacteriophage methodology is compatible with regularly used protocols in clinical laboratories for TB diagnosis and paraformaldehyde fixation after infection reduces biohazard risks with sample analysis by fluorescence microscopy. We have also set up conditions for discrimination between M. tuberculosis complex (MTBC) and non-tuberculous mycobacteria (NTM) strains by addition of p-nitrobenzoic acid (PNB) during the assay. Using clinical isolates of pre-XDR and XDR-TB strains from this study, we tested mCherrybomb Φ for extended DST and we compared the antibiotic resistance profile with those predicted by whole genome sequencing. Our results emphasize the utility of a phenotypic test for M. tuberculosis extended DST. The many attributes of mCherrybomb Φ suggests this could be a useful component of clinical microbiological laboratories for TB diagnosis and since only viable cells are detected this could be a useful tool for monitoring patient response to treatment.

An integrative, multi-omics approach towards the prioritization of Klebsiella pneumoniae drug targets.

Klebsiella pneumoniae (Kp) is a globally disseminated opportunistic pathogen that can cause life-threatening infections. It has been found as the culprit of many infection outbreaks in hospital environments, being particularly aggressive towards newborns and adults under intensive care. Many Kp strains produce extended-spectrum ß-lactamases, enzymes that promote resistance against antibiotics used to fight these infections. The presence of other resistance determinants leading to multidrug-resistance also limit therapeutic options, and the use of 'last-resort' drugs, such as polymyxins, is not uncommon. The global emergence and spread of resistant strains underline the need for novel antimicrobials against Kp and related bacterial pathogens. To tackle this great challenge, we generated multiple layers of 'omics' data related to Kp and prioritized proteins that could serve as attractive targets for antimicrobial development. Genomics, transcriptomics, structuromic and metabolic information were integrated in order to prioritize candidate targets, and this data compendium is freely available as a web server. Twenty-nine proteins with desirable characteristics from a drug development perspective were shortlisted, which participate in important processes such as lipid synthesis, cofactor production, and core metabolism. Collectively, our results point towards novel targets for the control of Kp and related bacterial pathogens.

Genetics and genomic medicine in Argentina.

A historical summary of genetics and genomic medicine in Argentina. We go through the achievements and difficulties in the implementation of genetic and genomic services both in academia and health care.