Epizootic rabbit enteropathy (ERE) affects young rabbits and represents 32% of the enteropathies in rabbit production farms in Mexico. The etiology of this syndrome has not been clarified yet. A metataxonomic and histopathology study of ERE was carried out to compare the gastrointestinal microbiota and histopathological lesions of healthy and positive-ERE rabbits. The metataxonomic study was done using an Illumina MiSeq (MiSeq^® system, Illumina, San Diego California, USA) massive segmentation platform, and a Divisive Amplicon Denoising Algorithm 2 (DADA2 algorithm) was used to obtain Shannon and Simpson diversity indices as well as the relative abundance of the identified communities. For the histopathological study, paraffin sections of the cecum, ileo-cecal valve, and colon were stained with eosin and hematoxylin. AxioVision 4.9 software (Carl Zeiss MicroImaging GmbH, Jena, Germany) was used to measure the crypt depths. Statistical analysis was done using PERMANOVA analysis for the metataxonomic study and ANOVA for the histopathology study. Histopathologic analysis showed smaller sizes of crypts in the colon of ERE rabbits. Differences were observed in the diversity and abundance of the gastrointestinal microbiota between the analyzed groups. The genus Clostridium and the species Cloacibacillus porcorum and Akkermansia muciniphila were associated with ERE. The results obtained from this study can provide information for future clarification of the etiology and proposals of effective treatments.

As with many other trophic interactions, the interchange of microorganisms between plants and their herbivorous insects is unavoidable. To test the hypothesis that the composition and diversity of the insect bacteriome are driven by the bacteriome of the plant, the bacteriomes of both the plant Datura inoxia and its specialist insect Lema daturaphila were characterised using 16S sRNA gene amplicon sequencing. Specifically, the bacteriomes associated with seeds, leaves, eggs, guts, and frass were described and compared. Then, the functions of the most abundant bacterial lineages found in the samples were inferred. Finally, the patterns of co-abundance among both bacteriomes were determined following a multilayer network approach. In accordance with our hypothesis, most genera were shared between plants and insects, but their abundances differed significantly within the samples collected. In the insect tissues, the most abundant genera were Pseudomonas (24.64%) in the eggs, Serratia (88.46%) in the gut, and Pseudomonas (36.27%) in the frass. In contrast, the most abundant ones in the plant were Serratia (40%) in seeds, Serratia (67%) in foliar endophytes, and Hymenobacter (12.85%) in foliar epiphytes. Indeed, PERMANOVA analysis showed that the composition of the bacteriomes was clustered by sample type (F = 9.36, p < 0.001). Functional inferences relevant to the interaction showed that in the plant samples, the category of Biosynthesis of secondary metabolites was significantly abundant (1.4%). In turn, the category of Xenobiotics degradation and metabolism was significantly present (2.5%) in the insect samples. Finally, the phyla Proteobacteria and Actinobacteriota showed a pattern of co-abundance in the insect but not in the plant, suggesting that the co-abundance and not the presence–absence patterns might be more important when studying ecological interactions.

This study aimed to investigate the effect of feeding insoluble fiber on the microbiota and metabolites of the caecum and feces of rabbits recovering from epizootic rabbit enteropathy relative to non-infected rabbits. Rabbits that had either recovered from epizootic rabbit enteropathy or ones that had never had epizootic rabbit enteropathy were fed on a diet of 32% or 36% neutral detergent fiber until they were 70 days of age. At this point, the short-chain fatty acid and ammonia levels were measured in caecotroph and fecal samples and compared using 2 × 2 ANOVA. The microbial composition of the samples was also analyzed using next-generation sequencing and compared by PERMANOVA. Caecotrophic samples from previously affected rabbits on lower fiber diets had higher short-chain fatty acid contents and higher species diversity index values for some indices (p < 0.05), although the fecal samples showed lower species diversity levels (p < 0.05). In addition, the PERMANOVA analyses demonstrated that differences were detected in the microbial composition of both fecal and caecotrophic samples, depending on the disease status at the outset of the experiment (p < 0.05). The results of this work show that, although there is some potential in the use of high-fiber diets for the treatment of rabbits that have had epizootic rabbit enteropathy, they are not able to produce the same digestive tract properties as those seen in rabbits that have never had the condition. This is true even after the rabbits have recovered from epizootic rabbit enteropathy.

The frontiers of protein design lie in the construction of amino acid sequences and the incorporation of specific motifs, such as binding sites, into receptors, ion channels, or other proteins. This approach allows the production of protein variants with specific functions and purposes. An exemplary application of these bioinformatics resources is the improvement of antimicrobial peptides (AMPs) or the design of synthetic AMPs. This development is significant because of the recent increase in emerging diseases, often caused by pathogens that are difficult to eradicate and exhibit antibiotic resistance. The consequences of this trend include the global extinction of animal species and human deaths. The rapid synthesis of novel and effective AMPs is, therefore, essential. These servers and programs can facilitate the rapid development of vaccines, AMPs, or effective treatments against these pathogens. The aim of this work was to present and propose a procedure for protein construction from sequences and to elucidate the process of using different tools to build these molecules step by step.

Background. Antimicrobial resistance (AMR) poses a significant global health threat, necessitating a deeper understanding of bacterial adaptation mechanisms. Introduction. This study investigates the genotypic and phenotypic evolutionary trajectories of Escherichia coli under meropenem and gentamicin selection, and it benchmarks these findings against florfenicol-evolved strains. Methodology. Utilizing a downsized, three-layer acrylic modified “Microbial Evolution and Growth Arena (MEGA-plate) system”—scaled to 40 × 50 cm for sterile handling and uniform 37 °C incubation—we tracked adaptation over 9–13 days, enabling real-time visualization of movement across antibiotic gradients. Results. Meropenem exposure elicited pronounced genetic heterogeneity and morphological remodeling (filamentous and circular forms), characteristic of SOS-mediated division arrest and DNA-damage response. In contrast, gentamicin exposure produced a uniform resistance gene profile and minimal shape changes, suggesting reliance on conserved defenses without major morphological adaptation. Comprehensive genomic analysis revealed a core resistome of 22 chromosomal loci shared across all three antibiotics, highlighting potential cross-resistance and the central roles of baeR, gadX, and marA in coordinating adaptive responses. Gene ontology enrichment underscored the positive regulation of gene expression and intracellular signaling as key themes in resistance evolution. Discussion. Our findings illustrate the multifaceted strategies E. coli employs—combining metabolic flexibility with sophisticated regulatory networks—to withstand diverse antibiotic pressures. This study underscores the utility of the MEGA-plate system in dissecting spatiotemporal AMR dynamics in a controlled yet ecologically relevant context. Conclusions. The divergent responses to meropenem and gentamicin highlight the complexity of resistance development and reinforce the need for integrated, One Health strategies. Targeting shared regulatory hubs may open new avenues for antimicrobial intervention and help preserve the efficacy of existing drugs.

Ecosystems can undergo abrupt, often irreversible transitions between alternative states—phenomena termed critical transitions or regime shifts—with profound consequences for biodiversity, ecosystem services, and human well-being. Early warning signals (EWSs) derived from time-series analysis offer the prospect of anticipating such transitions before they occur, potentially enabling preventive management intervention. This review provides a comprehensive synthesis of EWS methods for ecological systems, encompassing theoretical foundations, statistical indicators, empirical applications, and emerging methodological frontiers. We examine the dynamical basis of EWS in critical slowing down theory, wherein systems approaching bifurcation points exhibit characteristic statistical signatures including rising autocorrelation, increasing variance, and spectral reddening. We present a systematic overview of proposed indicators discuss moving-window frameworks for their computation, and critically evaluate preprocessing requirements and sensitivity to analytical choices. Empirical applications across major ecosystem types—including lakes, coral reefs, grasslands, forests, and marine fisheries—reveal both successes and limitations, with EWS performance depending critically on data quality, transition mechanism, and system-specific dynamics. We address recent advances including machine learning approaches, non-equilibrium thermodynamic indicators, multivariate extensions, and the important distinction between bifurcation-induced, noise-induced, and rate-induced tipping. We conclude with recommendations for specialists, emphasizing the integration of EWS within broader monitoring frameworks, systematic sensitivity analysis, and the interpretation of indicators as probabilistic assessments of changing resilience rather than deterministic predictions of imminent collapse.