International Journal of Microbiology

Background. Colistin (Polymyxin E) has reemerged in the treatment of MDR Gram-negative infections. Traditional Colistin AST methods have long turnaround times and are cumbersome for routine use. We present a SEM-AST technique enabling rapid detection of Colistin resistance through direct observation of morphological and quantitative changes in bacteria exposed to Colistin. Methods. Forty-four Gram-negative reference organisms were chosen based on their Colistin susceptibility profiles. Bacterial suspensions of ∼10⁷ CFU/mL were exposed to Colistin at EUCAST-ECOFF, with controls not exposed, incubated at 37°C, and then sampled at 0, 15, 30, 60, and 120 minutes. Phosphotungstic Acid (PTA) staining was applied, followed by SEM imaging using Hitachi TM4000PlusII-Tabletop-SEM at ×2000, ×5000 and ×7000 magnifications. Bacterial viability analysis was performed for all conditions by quantifying viable and dead organisms based on PTA-staining and morphologic changes. Results. We identified a significant drop in the percentage of viable organisms starting 30 minutes after exposure in susceptible strains, as compared to nonsignificant changes in resistant strains across all tested organisms. The killing effect of Colistin was best observed after 120 minutes of incubation with the antibiotic, with significant changes in morphologic features, including bacterial inflation, fusion, and lysis, observed as early as 30 minutes. Our observation matched the results of the gold standard-based broth microdilution method. Conclusions. We provide an extended application of the proof of concept for the utilization of the SEM-AST assay for Colistin for a number of clinically relevant bacterial species, providing a rapid and reliable susceptibility profile for a critical antibiotic.

Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.

Pseudomonas aeruginosa is an opportunistic pathogen found in a wide variety of environments, including soil, water, and habitats associated with animals, humans, and plants. From a One Health perspective, which recognizes the interconnectedness of human, animal, and environmental health, it is important to study the virulence characteristics and antibiotic susceptibility of environmental bacteria. In this study, we compared the virulence properties and the antibiotic resistance profiles of seven isolates collected from the Gulf of Mexico with those of seven clinical strains of P. aeruginosa. Our results indicate that the marine and clinical isolates tested exhibit similar virulence properties; they expressed different virulence factors and were able to kill Galleria mellonella larvae, an animal model commonly used to analyze the pathogenicity of many bacteria, including P. aeruginosa. In contrast, the clinical strains showed higher antibiotic resistance than the marine isolates. Consistently, the clinical strains exhibited a higher prevalence of class 1 integron, an indicator of anthropogenic impact, compared with the marine isolates. Thus, our results indicate that the P. aeruginosa marine strains analyzed in this study, isolated from the Gulf of Mexico, have similar virulence properties, but lower antibiotic resistance, than those from hospitals.

The development of anthropic activities during recent years has led to an increase in nutrient fluxes in the Río Grande de Comitán and Montebello Lakes National Park, Mexico. In turn, this has modified the dynamics of the biotic community, specifically favoring the presence of cyanobacteria tolerant to contamination. The continual and massive presence of Planktothrix species (spp.) in the system suggests a potential detrimental impact for economic issues and human health. In this study, we identify the morphological and molecular characteristics of Planktothrix populations from seven tropical (1,380–1,740 masl, 23.0–25.5°C) and calcareous lakes and two ponds from a water treatment plant. We also assess the ecological drivers that could be related to the presence of cyanotoxins in the system. The ecological preferences, morphology, 16S rRNA structure, and 16S-23S rRNA internal transcribed spacer found evidence for three species: P. agardhii distributed in neutral to slightly basic water (pH = 7.7–8.7), and P. spiroides and Planktothrix sp. in alkaline waters (pH = 9.1). The presence of the mcyE gene and its validation by liquid chromatography confirmed the presence of two microcystin variants (MC-RR and MC-LR) in at least three populations of P. agardhii. These microcystins put the health of the ecosystem and its inhabitants at risk, a condition that should be addressed and resolved with a water management and detoxification strategy in the basin.

Arbuscular mycorrhizal fungi (AMF) and some rhizobacteria are known as plant growth-promoting microorganism (PGPM) as they play significant roles in improving soil fertility structure, plant nutrition, growth, and health. However, little is known about the PGPM potential of AMF and rhizobacteria native to the Rift Valley and highland regions of Ethiopia. Hence, this study aimed to investigate the PGPM effect of single and co-inoculation of AMF and the Bacillus subtilis ALCR46 strain, on tomato (Lycopersicum esculentum L.), onion (Allium cepa L.), and squash (Cucurbita pepo L.) plants. The experimental setup was a randomized complete block design with three replications of the following treatments: (i) inoculation with a consortium of AMF, (ii) co-inoculation with a consortium of AMF and the Bacillus subtilis, (iii) inoculation with Rhizophagus clarus, (iv) co-inoculation with R. clarus and B. subtilis, (v) inoculation with B. subtilis, (vi) plants without inoculation (negative control), and (vii) plants treated with chemical fertilizer (positive control). Plants were maintained in a greenhouse for 60 days, and after harvest, plant growth parameters, percentage of AMF root colonization, and spore number were analyzed. The result shows that the growth of crops significantly increased by co-inoculation with the consortium of AMF and B. subtilis. AMF spore density and root colonization rate were also increased in co-inoculated plants. Highest root colonization, spore number, and mycorrhizal dependency were observed in A. cepa. Our results suggest that there is a synergistic effect between the AMF and B. subtilis ALCR46, and between AMF inoculants. However, the application of present findings under field conditions is required to be confirmed by further studies.

With Bacillus species, about 30% of extracellular proteins are translocated through the cytoplasmic membrane, coordinated by the Sec translocase. This system mainly consists of the cytoplasmic ATPase SecA and the membrane-embedded SecYEG channel. The purpose of this work was to investigate the effects of the SecYEG export system on the production of industrial biomolecules, such as biosurfactants, proteases, amylases, and cellulases. Fifty-two isolates of Bacillus species were obtained from traditional fermented foods and then characterized using molecular microbiology methods. The isolates secreted exoenzymes that included cellulases, amylases, and proteases. We present evidence that a biosurfactant-like molecule requires the SecA ATPase and the SecYEG membrane channel for its secretion. In addition, we showed that biomolecules involved in biofilm formation required the SecYEG pathway. This work presents a novel seven-target fragment multiplex PCR assay capable of identification at the species level of Bacillus through a unique SecDF chromosomal gene. The bacterial membrane protein SecDF allowed the discrimination of Bacillus subtilis, B. licheniformis, B. amyloliquefaciens, and B. sonorensis. SecA was able to interact with AprE, AmyE, and TasA. The Rose Bengal inhibitor of SecA crucially affected the interaction of AprE, AmyE, TapA, and TasA with recombinant Gst-SecA. The Rose Bengal prevented Bacillus species from secreting and producing proteases, cellulases, amylases, and biosurfactant-like molecules. It also inhibited the formation of biofilm cell communities. The data support, for the first time, that the SecYEG translocon mediates the secretion of a biosurfactant-like molecule.