Exploring extremotolerant and extremophilic microalgae opens new frontiers in sustainable biotechnological applications. These microorganisms thrive in extreme environments and exhibit specialized metabolic pathways, making them valuable for various industries. The study focuses on the ecological adaptation and biotechnological potential of these microalgae, highlighting their ability to produce bioactive compounds under stress conditions. The literature reveals that extremophilic microalgae can significantly enhance biomass production, reduce contamination risks in large-scale systems, and produce valuable biomolecules such as carotenoids, lipids, and proteins. These insights suggest that extremophilic microalgae have promising applications in food, pharmaceutical, cosmetic, and biofuel industries, offering sustainable and efficient alternatives to traditional resources. The review concludes that further exploration and utilization of these unique microorganisms can lead to innovative and environmentally friendly solutions in biotechnology.

Meso- and extremophilic microalgae and cyanobacteria have a wide range of biotechnological applications. However, the industrial demand for bioactive molecules and the redundancy of these molecules has resulted in a need for new methodologies for enhanced production and the discovery of specialized metabolites. Co-cultivation has been established as a promising approach to addressing these challenges. In this context, this work aimed to describe the state of the art of the co-cultivation method involving meso- and extremophilic photosynthetic microorganisms, as well as discuss the advantages, challenges, and limitations of this approach. Co-culture is defined as an ecology-driven method in which various symbiotic interactions involving cyanobacteria and microalgae can be used to explore new compounds and enhanced production. Promising results regarding new bioactive metabolite expression and increased production through co-cultivation-based research support that idea. Also, the metabolic diversity and evolutionary adaptations of photosynthetic microorganisms to thrive in extreme environments could improve the efficiency of co-cultivation by allowing the implementation of these microorganisms. However, the complexity of ecological interactions and lack of standardization for co-cultivation protocols are obstacles to its success and scientific validation. Further research in symbiotic interplays using -omics and genetic engineering, and predictive experimental designs for co-cultures are needed to overcome these limitations.

Oxalate oxidases (OXOs) are integral to plant physiology, facilitating the degradation of oxalate (oxalic acid) into hydrogen peroxide (H2O2) and carbon dioxide. OXOs are considered a key source of H2O2 in plant cells, especially under oxidative stress, which activates diverse physiological roles, including cell wall fortification, redox signaling, and modulation of ion homeostasis. As a result, OXOs significantly influence embryo viability, cell wall stiffening, apoplastic redox activity, and defense, especially under abiotic stresses such as drought, salinity, and temperature. Additionally, OXO-generated H2O2 can reinforce innate immunity against fungal and bacterial pathogens, highlighting its role in both abiotic and biotic stress tolerance. Recent research suggests that OXO-mediated reactive oxygen species bursts modulate cell division processes, metabolic pathways, and potentially photosynthesis, although many mechanistic details remain to be elucidated. Beyond plant physiology, OXOs hold industrial and medical interest. Degrading oxalate helps in preventing pulp and paper processing scaling, reduces oxalate in certain foods, and may support therapeutic interventions for oxalate-related diseases. Ongoing efforts to improve heterologous expression and enzyme engineering aim to enhance production efficiency, stability, and specificity. This review synthesizes current knowledge on OXO structure–function relationships, their physiological importance across multiple plant species, and the emerging avenues for biotechnological exploitation. While commercial applications show promise, deeper insight into the mechanics of OXOs is needed to harness their capabilities fully.

Microalgae are promising as prebiotics in aquaculture. Arthrospira maxima has potential nutritional value but is poorly studied. We assessed A. maxima feed supplementation in tilapia fry and evaluated its effect on growth performance and gut microbiota. Fish were cultivated in ponds under 0%, 5%, 10%, and 15% A. maxima inclusion treatments. Growth parameters and biomass proximate analysis were assessed. A meta-amplicon analysis was performed on the gut microbiota using DADA2 and PICRUST2 for functional prediction. Among treatments, the 5% supplementation group seemed to present no negative effect on growth parameters and did not compromise the nutritional quality of tilapia fry biomass. Microbial composition was characterized by Cetobacterium, Pseudomonas and Aeromonas genera, and a predominance of beneficial metabolic pathways. Microbiota of tilapia fry fed with A. maxima showed unique genera with reported beneficial functions in tilapia. The abundance of potential pathogenic taxa was significantly decreased in supplementation treatments, possibly related to valuable compounds of A. maxima. The inclusion of the microalgae supported the stability of the microbiota, favoring the growth of commensal species. This seems to have an effect on decreasing the presence of pathogenic genera in the gastrointestinal tract. Overall, our work proposes 5% feed inclusion of A. maxima to be the most suitable for tilapia fry aquaculture to maintain production rates while improving general health.

Bioinformatics and Artificial Intelligence (AI) stand as rapidly evolving tools that have facilitated the annotation of mobile genetic elements (MGEs), enabling the prediction of health risk factors in polluted environments, such as antibiotic resistance genes (ARGs). This study aims to assess the performance of four AI-based plasmid annotation tools (Plasflow, Platon, RFPlasmid, and PlasForest) by employing defined performance parameters for the identification of ARGs in the metagenome of one sediment sample obtained from the Virilla River, Costa Rica. We extracted and sequenced complete DNA from the sample, assembled the metagenome, and then performed the plasmid prediction with each bioinformatic tool, and the ARGs annotation using the Resistance Gene Identifier web portal. Sensitivity, specificity, precision, negative predictive value, accuracy, and F1score were calculated for each ARGs prediction result of the evaluated plasmidomes. Notably, Platon emerged as the highest performer among the assessed tools, exhibiting exceptional scores. Conversely, Plasflow seems to face difficulties distinguishing between chromosomal and plasmid sequences, while PlasForest has encountered limitations when handling small contigs. RFPlasmid displayed diminished specificity and was outperformed by its taxon-dependent workflow. We recommend the adoption of Platon as the preferred bioinformatic tool for resistome investigations in the taxon-independent environmental metagenomic domain. Meanwhile, RFPlasmid presents a compelling choice for taxon-dependent prediction due to its exclusive incorporation of this approach. We expect that the results of this study serve as a guiding resource in selecting AI-based tools for accurately predicting the plasmidome and its associated genes.

Soil health is essential for maintaining ecosystem balance, food security, and human well-being. Anthropogenic activities, such as climate change and excessive agrochemical use, have led to the degradation of soil ecosystems worldwide. Microbiome transplantation has emerged as a promising approach for restoring perturbed soils; however, direct soil transfer presents practical limitations for large-scale applications. An alternative strategy involves extracting microbial communities through soil washing processes, but its success highly depends on proper microbiota characterization and efficient extraction methods. This study evaluated a soil wash method using four different dispersant solutions (Tween-80, NaCl, sodium citrate, and sodium pyrophosphate) for their ability to extract the majority of microbial cells from Antarctic and Crop soils. The extracted microbiomes were analyzed using 16S rRNA gene metataxonomics to assess their diversity and abundance. We found that some treatments extracted a greater proportion of specific taxa, and, on the other hand, some extracted a lower proportion than the control treatment. In addition, these dispersant solutions showed the extraction of the relevant microbial community profile in soil samples, composed of multiple taxa, including beneficial bacteria for soil health. Our study aims to optimize DNA extraction methods for microbiome analyses and to explore the use of this technique in various biotechnological applications. The results provide insights into the effect of dispersant solutions on microbiome extractions. In this regard, sodium chloride could be optimal for Antarctic soils, while sodium citrate is suggested for the Crop soils.