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Poly-hydroxybutyrate (PHB) is an environmentally friendly alternative for conventional fossil fuel-based plastics that is produced by various microorganisms. Large-scale PHB production is challenging due to the comparatively higher biomanufacturing costs. A PHB overproducer is the haloalkaliphilic bacterium Halomonas campaniensis, which has low nutritional requirements and can grow in cultures with high salt concentrations, rendering it resistant to contamination. Despite its virtues, the metabolic capabilities of H. campaniensis as well as the limitations hindering higher PHB production remain poorly studied. To address this limitation, we present HaloGEM, the first high-quality genome-scale metabolic network reconstruction, which encompasses 888 genes, 1528 reactions (1257 gene-associated), and 1274 metabolites. HaloGEM not only displays excellent agreement with previous growth data and experiments from this study, but it also revealed nitrogen as a limiting nutrient when growing aerobically under high salt concentrations using glucose as carbon source. Among different nitrogen source mixtures for optimal growth, HaloGEM predicted glutamate and arginine as a promising mixture producing increases of 54.2% and 153.4% in the biomass yield and PHB titer, respectively. Furthermore, the model was used to predict genetic interventions for increasing PHB yield, which were consistent with the rationale of previously reported strategies. Overall, the presented reconstruction advances our understanding of the metabolic capabilities of H. campaniensis for rationally engineering this next-generation industrial biotechnology platform. KEY POINTS: A comprehensive genome-scale metabolic reconstruction of H. campaniensis was developed. Experiments and simulations predict N limitation in minimal media under aerobiosis. In silico media design increased experimental biomass yield and PHB titer.

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Mycolicibacterium fortuitum, a fast-growing nontuberculous mycobacterium, is a significant pathogen in healthcare-associated infections, encompassing skin, soft tissue, and pulmonary diseases. In this study, we present draft genome sequences from 12 M. fortuitum strains isolated from sputum samples from patients diagnosed with pulmonary infections in Mexico.

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Genetic variation in tuberculosis is influenced by the host environment, patients with comorbidity, and tuberculosis-type 2 diabetes mellitus (TB-T2DM) and implies a higher risk of treatment failure and development of drug resistance. Considering the above, this study aimed to evaluate the influence of T2DM on the dynamic of polymorphisms related to antibiotic resistance in TB. Fifty individuals with TB-T2DM and TB were initially characterized, and serial isolates of 29 of these individuals were recovered on day 0 (diagnosis), 30, and 60. Genomes were sequenced, variants related to phylogeny and drug resistance analyzed, and mutation rates calculated and compared between groups. Lineage X was predominant. At day 0 (collection), almost all isolates from the TB group were sensitive, apart from four isolates from the TB-T2DM group showing the mutation katG S315T, from which one isolate had the mutations rpoB S450L, gyrA A90G, and gyrA D94G. This pattern was observed in a second isolate at day 30. The results provide a first overview of the dynamics of mutations in resistance genes from individuals with TB-T2DM, describing an early development of resistance to isoniazid and a rapid evolution of resistance to other drugs. Although preliminary, these results help to explain the increased risk of drug resistance in individuals with TB and T2DM.

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We report the draft genomes of four Kluyveromyces marxianus isolates obtained from the elaboration process of henequen (Agave fourcroydes) mezcal, a Mexican alcoholic beverage. The average nucleotide identity analysis revealed that isolates derived from agave plants are distinct from those from other environments, including agave fermentations.

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INTRODUCTION: Tuberculosis (TB) is among the deadliest diseases worldwide, and its impact is mainly due to the continuous emergence of resistant isolates during treatment due to the laborious process of resistance diagnosis, nonadherence to treatment and circulation of previously resistant isolates of Mycobacterium tuberculosis. In this study, we evaluated the performance and functionalities of web-based tools, including Mykrobe, TB-profiler, PhyResSE, KvarQ, and SAM-TB, for detecting resistance in 88 Ecuadorian isolates of Mycobacterium tuberculosis drug susceptibility tested previously. Statistical analysis was used to determine the correlation between genomic and phenotypic analysis. Our results showed that with the exception of KvarQ, all tools had the highest correlation with the conventional drug susceptibility test (DST) for global resistance detection (98% agreement and 0.941 Cohen's kappa), while SAM-TB, PhyResSE, TB-profiler and Mykrobe had better correlations with DST for first-line drug analysis individually. We also identified that in our study, only 50% of mutations characterized by the web-based tools in the rpoB, katG, embB, pncA, gyrA and rrs regions were canonical and included in the World Health Organization (WHO) catalogue. Our findings suggest that SAM-TB, PhyResSE, TB-profiler and Mykrobe were efficient in determining canonical resistance-related mutations, but more analysis is needed to improve second-line detection. Improving surveillance programs using whole-genome sequencing tools for first-line drugs, MDR-TB and XDR-TB is essential to understand the molecular epidemiology of TB in Ecuador. IMPORTANCE: Tuberculosis, an infectious disease caused by Mycobacterium tuberculosis, most commonly affects the lungs and is often spread through the air when infected people cough, sneeze, or spit. However, despite the existence of effective drug treatment, patient adherence, long duration of treatment, and late diagnosis have reduced the effectiveness of therapy and increased drug resistance. The increase in resistant cases, added to the impact of the COVID-19 pandemic, has highlighted the importance of implementing efficient and timely diagnostic methodologies worldwide. The significance of our research is in evaluating and identifying a more efficient and user-friendly web-based tool to characterize resistance in Mycobacterium tuberculosis by whole-genome sequencing, which will allow more routine application to improve TB strain surveillance programs locally.

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Oligosaccharides are significant in mammalian milk, where they serve as prebiotics that promote the growth of beneficial gut bacteria in infants. Comprehensive research of milk oligosaccharides requires precise and validated analytical methods for compositional studies. To address this need, the focus of our study was to develop and validate an analytical method using UPLC-MS/MS to quantify seven specific oligosaccharides found in mammalian milk. The developed and optimized method has adequate linearity, accuracy, and precision parameters. The detection (LOD) and quantification (LOQ) limits for the seven compounds ranged from 0.0018 to 0.0030 µg/mL and 0.0054-0.0063 µg/mL, respectively. The sample preparation method yielded recovery rates above 90.5 %. Furthermore, no significant matrix effect was observed. The validated method was successfully applied to human, goat, and bovine milk samples, demonstrating its proficiency in identifying variances in the concentration of oligosaccharides across different mammals. This versatile method will allow future research about factors affecting oligosaccharide composition.

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The natural products (NPs) biosynthetic gene clusters (BGCs) represent the adapting biochemical toolkit for microorganisms to thrive different microenvironments. Despite their high diversity, particularly at the genomic level, detecting them in a shake-flask is challenging and remains the primary obstacle limiting our access to valuable chemicals. Studying the molecular mechanisms that regulate BGC expression is crucial to design of artificial conditions that derive on their expression. Here, we propose a phylogenetic analysis of regulatory elements linked to biosynthesis gene clusters, to classify BGCs to regulatory mechanisms based on protein domain information. We utilized Hidden Markov Models from the Pfam database to retrieve regulatory elements, such as histidine kinases and transcription factors, from BGCs in the MIBiG database, focusing on actinobacterial strains from three distinct environments: oligotrophic basins, rainforests, and marine environments. Despite the environmental variations, our isolated microorganisms share similar regulatory mechanisms, suggesting the potential to activate new BGCs using activators known to affect previously characterized BGCs.

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Genomics has significantly revolutionized pathogen surveillance, particularly in epidemiological studies, the detection of drug-resistant strains, and disease control. Despite its potential, the representation of Latin American countries in the genomic catalogues of Mycobacterium tuberculosis (Mtb), the bacteria responsible for Tuberculosis (TB), remains limited. In this study, we present a whole genome sequencing (WGS)-based analysis of 85 Mtb clinical strains from 17 Mexican states, providing insights into local adaptations and drug resistance signatures in the region. Our results reveal that the Euro-American lineage (L4) accounts for 94% of our dataset, showing 4.1.2.1 (Haarlem, n = 32), and 4.1.1.3 (X-type, n = 34) sublineages as the most prevalent. We report the presence of the 4.1.1.3 sublineage, which is endemic to Mexico, in six additional locations beyond previous reports. Phenotypic drug resistance tests showed that 34 out of 85 Mtb samples were resistant, exhibiting a variety of resistance profiles to the first-line antibiotics tested. We observed high levels of discrepancy between phenotype and genotype associated with drug resistance in our dataset, including pyrazinamide-monoresistant Mtb strains lacking canonical variants of drug resistance. Expanding the Latin American Mtb genome databases will enhance our understanding of TB epidemiology and potentially provide new avenues for controlling the disease in the region.

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Calculations predict that testing of 5 000-10 000 molecules and >1 billion US dollars (£0.8 billion, £1=$1.2) are required for one single drug to come to the market. A solution to this problem is to establish more efficient protocols that reduce the high rate of re-isolation and continuous rediscovery of natural products during early stages of the drug development process. The study of 'rare actinobacteria' has emerged as a possible approach for increasing the discovery rate of drug leads from natural sources. Here, we define a simple genomic metric, defined as biosynthetic novelty index (BiNI), that can be used to rapidly rank strains according to the novelty of the subset of encoding biosynthetic clusters. By comparing a subset of high-quality genomes from strains of different taxonomic and ecological backgrounds, we used the BiNI score to support the notion that rare actinobacteria encode more biosynthetic gene cluster (BGC) novelty. In addition, we present the isolation and genomic characterization, focused on specialized metabolites and phenotypic screening, of two isolates belonging to genera Lentzea and Actinokineospora from a highly oligotrophic environment. Our results show that both strains harbour a unique subset of BGCs compared to other members of the genera Lentzea and Actinokineospora. These BGCs are responsible for potent antimicrobial and cytotoxic bioactivity. The experimental data and analysis presented in this study contribute to the knowledge of genome mining analysis in rare actinobacteria and, most importantly, can serve to direct sampling efforts to accelerate early stages of the drug discovery pipeline.

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BACKGROUND: Breastmilk is a dynamic fluid whose initial function is to provide the most adapted nutrition to the neonate. Additional attributes have been recently ascribed to breastmilk, with the evidence of a specific microbiota and the presence of various components of the immune system, such as cytokines and leukocytes. The composition of breastmilk varies through time, according to the health status of mother and child, and altogether contributes to the future health of the infant. Obesity is a rising condition worldwide that creates a state of systemic, chronic inflammation including leukocytosis. Here, we asked whether colostrum, the milk produced within the first 48 h post-partum, would contain a distinct leukocyte composition depending on the body mass index (BMI) of the mother. METHODS: We collected peripheral blood and colostrum paired samples from obese (BMI > 30) and lean (BMI < 25) mothers within 48 h post-partum and applied a panel of 6 antibodies plus a viability marker to characterize 10 major leukocyte subpopulations using flow cytometry. RESULTS: The size, internal complexity, and surface expression of CD45 and CD16 of multiple leukocyte subpopulations were selectively regulated between blood and colostrum irrespective of the study groups, suggesting a generalized cell-specific phenotype alteration. In obesity, the colostrum B lymphocyte compartment was significantly reduced, and CD16+ blood monocytes had an increased CD16 expression compared to the lean group. CONCLUSIONS: This is the first characterization of major leukocyte subsets in colostrum of mothers suffering from obesity and the first report of colostrum leukocyte subpopulations in Latin America. We evidence various significant alterations of most leukocyte populations between blood and colostrum and demonstrate a decreased colostrum B lymphocyte fraction in obesity. This pioneering study is a stepping stone to further investigate active immunity in human breastmilk.

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