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Bacteria are exposed to reactive oxygen and nitrogen species that provoke oxidative and nitrosative stress which can lead to macromolecule damage. Coping with stress conditions involves the adjustment of cellular responses, which helps to address metabolic challenges. In this study, we performed a global transcriptomic analysis of the response of Pseudomonas extremaustralis to nitrosative stress, induced by S-nitrosoglutathione (GSNO), a nitric oxide donor, under microaerobic conditions. The analysis revealed the upregulation of genes associated with inositol catabolism; a compound widely distributed in nature whose metabolism in bacteria has aroused interest. The RNAseq data also showed heightened expression of genes involved in essential cellular processes like transcription, translation, amino acid transport and biosynthesis, as well as in stress resistance including iron-dependent superoxide dismutase, alkyl hydroperoxide reductase, thioredoxin, and glutathione S-transferase in response to GSNO. Furthermore, GSNO exposure differentially affected the expression of genes encoding nitrosylation target proteins, encompassing metalloproteins and proteins with free cysteine and /or tyrosine residues. Notably, genes associated with iron metabolism, such as pyoverdine synthesis and iron transporter genes, showed activation in the presence of GSNO, likely as response to enhanced protein turnover. Physiological assays demonstrated that P. extremaustralis can utilize inositol proficiently under both aerobic and microaerobic conditions, achieving growth comparable to glucose-supplemented cultures. Moreover, supplementing the culture medium with inositol enhances the stress tolerance of P. extremaustralis against combined oxidative-nitrosative stress. Concordant with the heightened expression of pyoverdine genes under nitrosative stress, elevated pyoverdine production was observed when myo-inositol was added to the culture medium. These findings highlight the influence of nitrosative stress on proteins susceptible to nitrosylation and iron metabolism. Furthermore, the activation of myo-inositol catabolism emerges as a protective mechanism against nitrosative stress, shedding light on this pathway in bacterial systems, and holding significance in the adaptation to unfavorable conditions.

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Bacterial P450 cytochromes (BacCYPs) are versatile heme-containing proteins responsible for oxidation reactions on a wide range of substrates, contributing to the production of valuable natural products with limitless biotechnological potential. While the sequencing of microbial genomes has provided a wealth of BacCYP sequences, functional characterization lags behind, hindering our understanding of their roles. This study employs a comprehensive approach to predict BacCYP substrate specificity, bridging the gap between sequence and function. We employed an integrated approach combining sequence and functional data analysis, genomic context exploration, 3D structural modeling with molecular docking, and phylogenetic clustering. The research begins with an in-depth analysis of BacCYP sequence diversity and structural characteristics, revealing conserved motifs and recurrent residues in the active site. Phylogenetic analysis identifies distinct groups within the BacCYP family based on sequence similarity. However, our study reveals that sequence alone does not consistently predict substrate specificity, necessitating additional perspectives. The study delves into the genetic context of BacCYPs, utilizing neighboring gene information to infer potential substrates, a method proven very effective in many cases. Molecular docking is employed to assess BacCYP-substrate interactions, confirming potential substrates and providing insights into selectivity. Finally, a comprehensive strategy is proposed for predicting BacCYP substrates, involving all the evaluated approaches. The effectiveness of this strategy is demonstrated with two case studies, highlighting its potential for substrate discovery.

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IMPORTANCE: Fusaric acid (FA) is an important virulence factor produced by several Fusarium species. These fungi are responsible for wilt and rot diseases in a diverse range of crops. FA is toxic for animals, humans and soil-borne microorganisms. This mycotoxin reduces the survival and competition abilities of bacterial species able to antagonize Fusarium spp., due to its negative effects on viability and the production of antibiotics effective against these fungi. FA biodegradation is not a common characteristic among bacteria, and the determinants of FA catabolism have not been identified so far in any microorganism. In this study, we identified genes, enzymes, and metabolic pathways involved in the degradation of FA in the soil bacterium Burkholderia ambifaria T16. Our results provide insights into the catabolism of a pyridine-derivative involved in plant pathogenesis by a rhizosphere bacterium.

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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.

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Latin America is one of the regions in which the COVID-19 pandemic has a stronger impact, with more than 72 million reported infections and 1.6 million deaths until June 2022. Since this region is ecologically diverse and is affected by enormous social inequalities, efforts to identify genomic patterns of the circulating SARS-CoV-2 genotypes are necessary for the suitable management of the pandemic. To contribute to the genomic surveillance of the SARS-CoV-2 in Latin America, we extended the number of SARS-CoV-2 genomes available from the region by sequencing and analyzing the viral genome from COVID-19 patients from seven countries (Argentina, Brazil, Costa Rica, Colombia, Mexico, Bolivia, and Peru). Subsequently, we analyzed the genomes circulating mainly during 2021 including records from GISAID database from Latin America. A total of 1,534 genome sequences were generated from seven countries, demonstrating the laboratory and bioinformatics capabilities for genomic surveillance of pathogens that have been developed locally. For Latin America, patterns regarding several variants associated with multiple re-introductions, a relatively low percentage of sequenced samples, as well as an increment in the mutation frequency since the beginning of the pandemic, are in line with worldwide data. Besides, some variants of concern (VOC) and variants of interest (VOI) such as Gamma, Mu and Lambda, and at least 83 other lineages have predominated locally with a country-specific enrichments. This work has contributed to the understanding of the dynamics of the pandemic in Latin America as part of the local and international efforts to achieve timely genomic surveillance of SARS-CoV-2.

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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.

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INTRODUCTION: Coinfection with two SARS-CoV-2 viruses is still a very understudied phenomenon. Although next generation sequencing methods are very sensitive to detect heterogeneous viral populations in a sample, there is no standardized method for their characterization, so their clinical and epidemiological importance is unknown. MATERIAL AND METHODS: We developed VICOS (Viral COinfection Surveillance), a new bioinformatic algorithm for variant calling, filtering and statistical analysis to identify samples suspected of being mixed SARS-CoV-2 populations from a large dataset in the framework of a community genomic surveillance. VICOS was used to detect SARS-CoV-2 coinfections in a dataset of 1,097 complete genomes collected between March 2020 and August 2021 in Argentina. RESULTS: We detected 23 cases (2%) of SARS-CoV-2 coinfections. Detailed study of VICOS's results together with additional phylogenetic analysis revealed 3 cases of coinfections by two viruses of the same lineage, 2 cases by viruses of different genetic lineages, 13 were compatible with both coinfection and intra-host evolution, and 5 cases were likely a product of laboratory contamination. DISCUSSION: Intra-sample viral diversity provides important information to understand the transmission dynamics of SARS-CoV-2. Advanced bioinformatics tools, such as VICOS, are a necessary resource to help unveil the hidden diversity of SARS-CoV-2.

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Identifying high-yield genotypes under low water availability is essential for soybean climate-smart breeding. However, a major bottleneck lies in phenotyping, particularly in selecting cost-efficient markers associated with stress tolerance and yield stabilization. Here, we conducted in-depth phenotyping experiments in two soybean genotypes with contrasting drought tolerance, MUNASQA (tolerant) and TJ2049 (susceptible), to better understand soybean stress physiology and identify/statistically validate drought-tolerance and yield-stabilization traits as potential breeding markers. Firstly, at the critical reproductive stage (R5), the molecular differences between the genotype's responses to mild water deficit were explored through massive analysis of cDNA ends (MACE)-transcriptomic and gene ontology. MUNASQA transcriptional profile, compared to TJ2049, revealed significant differences when responding to drought. Next, both genotypes were phenotyped under mild water deficit, imposed in vegetative (V3) and R5 stages, by evaluating 22 stress-response, growth, and water-use markers, which were subsequently correlated between phenological stages and with yield. Several markers showed high consistency, independent of the phenological stage, demonstrating the effectiveness of the phenotyping methodology and its possible use for early selection. Finally, these markers were classified and selected according to their cost-feasibility, statistical weight, and correlation with yield. Here, pubescence, stomatal density, and canopy temperature depression emerged as promising breeding markers for the early selection of drought-tolerant soybeans.

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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.

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SARS-CoV-2 variants with concerning characteristics have emerged since the end of 2020. Surveillance of SARS-CoV-2 variants was performed on a total of 4,851 samples from the capital city and 10 provinces of Argentina, during 51 epidemiological weeks (EWs) that covered the end of the first wave and the ongoing second wave of the COVID-19 pandemic in the country (EW 44/2020 to EW 41/2021). The surveillance strategy was mainly based on Sanger sequencing of a Spike coding region that allows the identification of signature mutations associated with variants. In addition, whole-genome sequences were obtained from 637 samples. The main variants found were Gamma and Lambda, and to a lesser extent, Alpha, Zeta, and Epsilon, and more recently, Delta. Whereas, Gamma dominated in different regions of the country, both Gamma and Lambda prevailed in the most populated area, the metropolitan region of Buenos Aires. The lineages that circulated on the first wave were replaced by emergent variants in a term of a few weeks. At the end of the ongoing second wave, Delta began to be detected, replacing Gamma and Lambda. This scenario is consistent with the Latin American variant landscape, so far characterized by a concurrent increase in Delta circulation and a stabilization in the number of cases. The cost-effective surveillance protocol presented here allowed for a rapid response in a resource-limited setting, added information on the expansion of Lambda in South America, and contributed to the implementation of public health measures to control the disease spread in Argentina.