Freshwater ecosystems have been experiencing various forms of threats, mainly since the last century. The severity of this adverse scenario presents unprecedented challenges to human health, water supply, agriculture, forestry, ecological systems, and biodiversity, among other areas. Despite the progress made in various biomonitoring techniques tailored to specific countries and biotic communities, significant constraints exist, particularly in assessing and quantifying biodiversity and its interplay with detrimental factors. Incorporating modern techniques into biomonitoring methodologies presents a challenging topic with multiple perspectives and assertions. This review aims to present a comprehensive overview of the contemporary advancements in freshwater biomonitoring, specifically by utilizing omics methodologies such as genomics, metagenomics, transcriptomics, proteomics, metabolomics, and multi-omics. The present study aims to elucidate the rationale behind the imperative need for modernization in this field. This will be achieved by presenting case studies, examining the diverse range of organisms that have been studied, and evaluating the potential benefits and drawbacks associated with the utilization of these methodologies. The utilization of advanced high-throughput bioinformatics techniques represents a sophisticated approach that necessitates a significant departure from the conventional practices of contemporary freshwater biomonitoring. The significant contributions of omics techniques in the context of biological quality elements (BQEs) and their interpretations in ecological problems are crucial for biomonitoring programs. Such contributions are primarily attributed to the previously overlooked identification of interactions between different levels of biological organization and their responses, isolated and combined, to specific critical conditions.

Throughout evolution, pathogenic viruses have developed different strategies to evade the response of the adaptive immune system. To carry out successful replication, some pathogenic viruses encode different proteins that manipulate the molecular mechanisms of host cells. Currently, there are different bioinformatics tools for virus research; however, none of them focus on predicting viral proteins that evade the adaptive system. In this work, we have developed a novel tool based on machine and deep learning for predicting this type of viral protein named VirusHound-I. This tool is based on a model developed with the multilayer perceptron algorithm using the dipeptide composition molecular descriptor. In this study, we have also demonstrated the robustness of our strategy for data augmentation of the positive dataset based on generative antagonistic networks. During the 10-fold cross-validation step in the training dataset, the predictive model showed 0.947 accuracy, 0.994 precision, 0.943 F1 score, 0.995 specificity, 0.896 sensitivity, 0.894 kappa, 0.898 Matthew’s correlation coefficient and 0.989 AUC. On the other hand, during the testing step, the model showed 0.964 accuracy, 1.0 precision, 0.967 F1 score, 1.0 specificity, 0.936 sensitivity, 0.929 kappa, 0.931 Matthew’s correlation coefficient and 1.0 AUC. Taking this model into account, we have developed a tool called VirusHound-I that makes it possible to predict viral proteins that evade the host’s adaptive immune system. We believe that VirusHound-I can be very useful in accelerating studies on the molecular mechanisms of evasion of pathogenic viruses, as well as in the discovery of therapeutic targets.

L-asparaginase (L-ASNase) is a key enzyme in the treatment of leukemia and lymphoma, with high demand in cancer therapies. Advances in recombinant protein production have improved yields and reduced costs, enabling large-scale production. However, optimizing culture conditions remains crucial for maximizing production. This study focused on optimizing the production of double mutant L-ASNase expressed in Escherichia coli BL21 (DE3) by supplementing media with amino acids. Five amino acids were evaluated at a shake flask scale using the design of experiments, with arginine and aspartate showing the most positive effects. Under optimized conditions (14.5 mM arginine, 12.7 mM aspartate, and 0 mM cysteine), the activity model reached 12,513 U L⁻¹, experimentally validated at 10,089 U L⁻¹. The maximum specific cell growth rate was µ_x,max = 0.74 h⁻¹, with substrate–biomass conversion factor Yx/s = 1.16 g/g and cell–product conversion factor Yp/x = 13,891 U/g_cell. Amino acid supplementation resulted in a ten-fold increase in L-ASNase activity. Finally, at the bioreactor scale, adding amino acids and the inducer at the end of the exponential phase increased activity by 135% compared to conventional MD, demonstrating its potential for industrial-scale production.

Acute lymphoblastic leukemia (ALL) is the most common cancer affecting children, making up about 80% of all acute leukemia cases in the pediatric population. While treatment with L-asparaginase (ASNase) has greatly improved survival rates, its bacterial origin often causes immune reactions in some patients, which can reduce how well the therapy works. To overcome this challenge, previous in silico studies designed a humanized chimeric ASNase by swapping out the predicted immunogenic parts of the bacterial enzyme with similar, less immunogenic segments from the human version—while keeping the enzyme’s active site intact. In this study, the chimeric L-asparaginase designed was successfully cloned, expressed, and purified using the Escherichia coli Rosetta strain. The production conditions (37 °C, 0.01 mM IPTG, 2–4 h) were optimized, and we purified the enzyme in a single step with nickel-affinity chromatography. The enzyme’s activity was confirmed in vitro, showing that it is possible to produce a functional humanized variant in a bacterial system. These results lay important groundwork for future research to assess the immune response and therapeutic potential of this novel chimeric enzyme.