Danilo Trabuco Amaral

The genus Cereus (Cactaceae), widely distributed across arid and semi-arid environments of South America, exhibits adaptive capacity that is partly associated with proteins involved in lipid metabolism and stress responses, such as non-specific lipid transfer proteins (nsLTPs). Although well-characterized in model plants, the structural diversity and functional roles of these proteins in cacti remain largely unexplored. In this study, we characterized the nsLTP repertoire of Cereus by integrating transcriptomic data, phylogenetic analyses, structural modeling, and ligand-binding site mapping. Structural comparisons revealed conservation of the typical nsLTP fold, with variations mainly localized to loop regions surrounding the binding cavity, suggesting functional plasticity without compromising scaffold stability. Phylogenetic analyses revealed frequent duplication and loss events, as well as species-specific paralogs. Several lineage-specific copies exhibited distinctive loop architectures and pocket geometries relative to their closest orthologs, suggesting rapid structural diversification following duplication. This pattern is consistent with functional fine-tuning after gene duplication. Positive selection analyses identified nine codons with significant evidence of episodic diversifying selection (p < 0.05), some of which mapped near predicted functional regions, supporting the hypothesis that selective pressures have shaped key interaction interfaces. Together, these results provide the first integrated characterization of nsLTPs in Cereus , supporting a gate-modulation hypothesis in which structural variation concentrated at tunnel entrances may contribute to functional diversification while preserving the conserved nsLTP fold (four-helix scaffold). Rather than asserting adaptation directly, our findings provide a structural basis and testable predictions for how nsLTP diversification may influence lipid trafficking processes relevant to cuticle/membrane function under abiotic stress.

Bioluminescence in click-beetles is sustained by specialized photogenic organs with high energetic and oxidative demands, yet their metabolomic organization remains largely unexplored. Here, we performed the first exploratory GC-MS-based metabolomic comparison of lantern and fat body tissues from the bioluminescent click beetle Pyrearinus micatus. Fifteen individuals were dissected to generate pooled tissue samples, and untargeted metabolomic profiles were analyzed using multivariate statistics, presence/absence analysis, UpSet plots, and pathway annotation. Exploratory principal component analysis indicated differences between the pooled lantern profile and the fat body samples, although these patterns should be interpreted cautiously given the absence of independent biological replicates for the lantern. The pooled lantern sample displayed a metabolomic profile characterized by metabolites associated with carbohydrate metabolism, glutathione metabolism, lipid turnover, and redox homeostasis, including glucose-6-phosphate, Cys-Gly, and unsaturated fatty acids. These metabolic features are consistent with the energetic and oxidative requirements of photogenic tissues. This study establishes an initial metabolomic framework for P. micatus, providing insights into tissue specialization and generating testable hypotheses for future replicated functional and evolutionary investigations of beetle bioluminescence.

Understanding how positive selection shapes molecular evolution at the protein level remains a major challenge, particularly in non-model species. Here, we integrated phylogenetic evidence from candidate genes evolving under lineage-specific positive selection with comparative structural and biophysical analyses to investigate patterns of molecular divergence in the Cereus fernambucensis-C. insularis clade, which occupies contrasting island and continental environments. Based on a previous branch-site analysis, five orthogroups were selected for downstream analyses. Orthologous protein structures were predicted and compared, variability hotspots were identified, pockets proximity were evaluated, and the stability effects of lineage-specific substitutions were estimated. Across orthogroups, global folds were largely conserved, whereas molecular divergence was primarily associated with localized structural variation in flexible loops and surface-exposed regions. Variability hotspots were significantly enriched near predicted cavities in several orthogroups, indicating that molecular diversification was concentrated in structurally permissive regions frequently associated with putative interaction surfaces. Stability analyses of reconstructed branch-specific substitutions revealed heterogeneous energetic effects, ranging from stabilizing to strongly destabilizing changes, while global protein architecture remained largely conserved. Together, these results suggest that evolutionary divergence in Cereus lineages is primarily associated with localized modifications of surface-accessible regions rather than large-scale structural innovation. More broadly, our findings demonstrate how integrating molecular evolutionary analyses with structural bioinformatics provides a robust framework for interpreting candidate adaptive genes in non-model organisms, while highlighting both the potential and limitations of structure-informed evolutionary inference.

Bioactive peptides are an important source of therapeutic molecules and molecular scaffolds involved in defense, signaling, and immune regulation. Despite the extraordinary diversity of Coleoptera, the structural landscape of beetle-derived bioactive peptides remains largely unexplored, limiting our understanding of their evolutionary diversity and biotechnological potential. Here, we performed a large-scale structural survey of predicted toxin-like peptide scaffolds across publicly available Coleoptera transcriptomes by integrating transcriptome mining, peptide maturation prediction, physicochemical characterization, AlphaFold 3 structural modeling, structural similarity analyses, and interpretable machine learning. We identified 291 candidate peptides, of which 155 contained canonical signal peptides and 273 produced mature peptides within the expected size range of known bioactive peptides. Structural analyses revealed that, despite extensive sequence diversity, many candidates were organized into a comparatively restricted repertoire of compact cysteine-rich architectures, indicating that structural similarity is retained across peptides exhibiting substantial primary-sequence variation. Comparative structural analyses further identified recurrent protein architectures shared across multiple beetle lineages, while machine learning prioritization integrated structural and biochemical descriptors to identify high-confidence candidates for future functional characterization. These analyses establish the first structural atlas of predicted toxin-like peptides across Coleoptera and demonstrate that structure-guided transcriptome mining provides a powerful framework for uncovering recurrent bioactive peptide scaffolds that would remain largely undetected using sequence-based approaches alone. Beyond expanding our understanding of peptide evolution in beetles, this resource is a foundation for future structural, functional, and biotechnological exploration of bioactive peptides in underexplored animal groups.

Insects display adaptive diversity in their secretory systems, which mediate ecological interactions ranging from defence to adhesion. Within Diptera, the co‐occurrence of silk and venom represents a rare functional combination. Here, we integrate multi‐species transcriptomics, coexpression network analysis and interpretable deep learning to investigate the physiological and evolutionary architecture underlying these traits. Using de novo assemblies from 11 Bibionomorpha species, we identified thousands of orthogroups and modelled their expression across species with Weighted Gene Coexpression Network Analysis and categorical neural classifiers. The models achieved near‐perfect discrimination of venom and silk phenotypes (AUC >0.95), enabling functional inference at the orthogroup level. Venom‐associated genes were enriched in proteolytic and inhibitory domains (trypsin‐like, WAP, Kunitz, serpin), while silk‐related genes exhibited chitin‐binding CBM14/peritrophin motifs typical of adhesive matrices, supporting their physiological specialisation. AlphaFold3 structural predictions confirmed these domain‐level signatures and revealed conserved disulfide‐stabilized folds, supporting extracellular secretion. The minimal overlap between silk‐ and venom‐associated modules indicates independent evolutionary origins of these secretory systems, exemplifying adaptive molecular convergence. Our findings provide molecular, structural and physiological insights into the adaptive evolution of secretory systems in Diptera, offering new perspectives for understanding the evolution of insect physiological innovations.

Bioactive peptides are an important source of therapeutic molecules and molecular scaffolds involved in defense, signaling, and immune regulation. Despite the extraordinary diversity of Coleoptera, the structural landscape of beetle-derived bioactive peptides remains largely unexplored, limiting our understanding of their evolutionary diversity and biotechnological potential. Here, we performed a large-scale structural survey of predicted toxin-like peptide scaffolds across publicly available Coleoptera transcriptomes by integrating transcriptome mining, peptide maturation prediction, physicochemical characterization, AlphaFold 3 structural modeling, structural similarity analyses, and interpretable machine learning. We identified 291 candidate peptides, of which 155 contained canonical signal peptides and 273 produced mature peptides within the expected size range of known bioactive peptides. Structural analyses revealed that, despite extensive sequence diversity, many candidates converged toward a comparatively restricted repertoire of compact cysteine-rich architectures, suggesting that structural conservation exceeds primary sequence conservation during peptide diversification. Comparative structural analyses further identified recurrent protein architectures shared across multiple beetle lineages, while machine learning prioritization integrated structural and biochemical descriptors to identify high-confidence candidates for future functional characterization. These analyses establish the first structural atlas of predicted toxin-like peptides across Coleoptera and demonstrate that structure-guided transcriptome mining provides a powerful framework for uncovering evolutionarily conserved bioactive peptide scaffolds that would remain largely undetected using sequence-based approaches alone. Beyond expanding our understanding of peptide evolution in beetles, this resource is a foundation for future structural, functional, and biotechnological exploration of bioactive peptides in underexplored animal groups.

Understanding how protein cavities accommodate small polar molecules without strict ligand-specific recognition remains an important challenge in molecular modeling. Here, we used creatinine and uric acid as molecular probes to investigate permissive protein-metabolite interaction environments in transcriptome-derived structural models from Cereus jamacaru. An integrative computational workflow combining comparative structural modeling, inverse virtual screening, interaction profiling, pocket characterization, molecular dynamics simulations, and binding free-energy estimation was applied to 1,996 high-confidence protein models. The screening identified structurally and functionally distinct proteins exhibiting reproducible accommodation of both probe molecules. Detailed analyses of purple acid phosphatase, phosphoglucan phosphatase DSP4, and beta-mannosidase showed that stable interaction patterns emerged despite substantial differences in cavity geometry, polarity, and surface composition. Molecular dynamics simulations indicated that the predicted complexes remained structurally stable over 50 ns, with binding behavior primarily determined by protein-specific pocket properties rather than by ligand identity. Uric acid generally displayed more favorable interaction energies, consistent with its greater hydrogen-bonding capacity, whereas differences between ligands were smaller than those observed among protein environments. These findings indicate that structurally unrelated protein cavities can converge toward permissive physicochemical environments capable of accommodating small polar metabolites. More broadly, the study demonstrates how transcriptome-scale molecular modeling can reveal structural principles underlying low-specificity protein–metabolite interactions in undercharacterized proteomes.

Antimicrobial resistance (AMR) represents a major global public health challenge, arising from the ability of microorganisms such as bacteria, fungi, and viruses to develop mechanisms that render them insensitive to conventional antibiotics. A key factor contributing to this problem is the production of β-lactamase enzymes, which confer resistance to multiple antibiotic classes and are associated with multidrug-resistant pathogens such as Pseudomonas aeruginosa, Acinetobacter baumannii, and Klebsiella pneumoniae, responsible for severe and often fatal hospital-acquired infections. In addition, the phospholipid membrane model was generated using CHARMM-GUI to provide insight into peptide-membrane interactions and their potential to induce membrane disruption. In this context, developing new therapeutic strategies, such as antimicrobial peptides (AMPs), has emerged as a promising alternative capable of acting against resistant microorganisms. This study investigates, through in silico analyses using tools such as Fpocket, FTsite, FTmap, and HADDOCK, to evaluate the interactions between five AMPs derived from Neotropical Cereus (mandacaru) species against β-lactamases from K. pneumoniae and A. baumannii. As a result, all five Cereus-derived AMPs showed favorable predicted interactions with β-lactamase interaction sites, and CF267 and CJ149 exhibited stable engagement with the phospholipid bilayer, supporting their potential to disrupt membranes in the simulated system. Docking analyses indicated favorable affinities for all peptides, with CF267 × 3RXX and CJ149 × 3RXX emerging as the most promising complexes due to their highly negative energy scores. These findings suggest favorable predicted interactions with β-lactamase binding regions, indicating potential molecular compatibility against AMR mechanisms in A. baumannii and K. pneumoniae. Upcoming in vitro assays will be essential to validate these predictions.