Célio Dias Santos-Júnior

A substantial portion of plastic waste is composed of polystyrene (PS). The polymer’s end-of-life disposal is a concern due to its recalcitrant struc

Microbial competition for scarce resources shapes biodiversity patterns and ecosystem function across global biomes, yet quantifying this process from genomic data has remained elusive. Here, we introduce CaCo, a scalable metric that transforms metagenomic carbohydrate-active enzyme profiles into precise measures of niche overlap and competition potential (Resource Partitioning Score, RPS). Analyzing 14,691 high-quality metagenome-assembled genomes spanning Ocean, freshwater, soil, and human gut microbiomes, we reveal a striking macroecological pattern: Niche overlap increases from partitioned specialists in oligotrophic oceans to overlapping generalists in carbon-rich environments, including the human gut. This gradient aligns with classic niche theory, as phylogenetic signals indicate that closely related taxa may compete most intensely. Multitiered validation, spanning BIOLOG phenotypes, synthetic cocultures, and interaction gradients, confirms CaCo’s predictive power and captures competitive exclusion. CaCo bridges genomic potential and ecological reality, providing niche-breadth metrics and enabling testable predictions of how resource availability shapes microbial competition and community structure.

Small proteins encoded by small open reading frames (smORFs; ≤ 100 aa) represent a largely unexplored dimension of microbial diversity, especially in the deep sea. By analyzing 708 metagenomes from five major deep-sea biomes (hadal trenches, cold seeps, hydrothermal vents, abyssal plains, and seamounts), we constructed the Deep-Sea Small Protein Atlas, comprising 88.7 million smORFs with exceptional novelty and strong habitat specificity. Deep-learning predictions identified 5.47 million candidate antimicrobial peptides (c_AMPs), revealing a peptide space far larger and structurally distinct from known AMPs. Deep-sea c_AMPs are longer, enriched in nonpolar and acidic residues, and exhibit low charge and high intrinsic disorder, suggesting non-membranolytic modes of action. We synthesized 131 representative peptides, of which 87% were antimicrobial, with MICs as low as 1.25 μM, broad-spectrum antibacterial and even antifungal efficacy, and minimal mammalian cytotoxicity. Transcriptomics, TEM imaging, and peptide-protein modeling showed that representative peptides preserve membrane integrity while disrupting intracellular processes such as translation and metabolism, supporting intracellular, non-lytic mechanisms. This work uncovers a vast reservoir of previously unrecognized deep-sea small proteins and structurally unconventional AMPs, providing a foundational resource for discovering next-generation peptide therapeutics.

Small open reading frames (smORFs) shorter than 100 codons are widespread and perform essential roles in microorganisms, where they encode proteins active in several cell functions, including signal pathways, stress response, and antibacterial activities. However, the ecology, distribution and role of small proteins in the global microbiome remain unknown. Here, we construct a global microbial smORFs catalog (GMSC) derived from 63,410 publicly available metagenomes across 75 distinct habitats and 87,920 high-quality isolate genomes. GMSC contains 965 million non-redundant smORFs with comprehensive annotations. We find that archaea harbor more smORFs proportionally than bacteria. We moreover provide a tool called GMSC-mapper to identify and annotate small proteins from microbial (meta)genomes. Overall, this publicly-available resource demonstrates the immense and underexplored diversity of small proteins.

Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available ’omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.

Small open reading frames (smORFs) shorter than 100 codons are widespread and perform essential roles in microorganisms, where they encode proteins active in several cell functions, including signal pathways, stress response, and antibacterial activities. However, the ecology, distribution and role of small proteins in the global microbiome remain unknown. Here, we constructed a global microbial smORFs catalogue (GMSC) derived from 63,410 publicly available metagenomes across 75 distinct habitats and 87,920 high-quality isolate genomes. GMSC contains 965 million non-redundant smORFs with comprehensive annotations. We found that archaea harbor more small proteins proportionally than bacteria. We moreover provide a tool called GMSC-mapper to identify and annotate small proteins from microbial (meta)genomes. Overall, this publicly-available resource demonstrates the immense and underexplored diversity of small proteins.

Summary: Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine learning-based approach to predict prokaryotic antimicrobial peptides (AMPs) by leveraging a vast dataset of 63,410 metagenomes and 87,920 microbial genomes. This led to the creation of AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, the majority of which were previously unknown. We observed that AMP production varies by habitat, with animal-associated samples displaying the highest proportion of AMPs compared to other habitats. Furthermore, within different human-associated microbiota, strain-level differences were evident. To validate our predictions, we synthesized and experimentally tested 50 AMPs, demonstrating their efficacy against clinically relevant drug-resistant pathogens both in vitro and in vivo. These AMPs exhibited antibacterial activity by targeting the bacterial membrane. Additionally, AMPSphere provides valuable insights into the evolutionary origins of peptides. In conclusion, our approach identified AMP sequences within prokaryotic microbiomes, opening up new avenues for the discovery of antibiotics.

With about 13,000 known species, ants are the most abundant venomous insects. Their venom consists of polypeptides, enzymes, alkaloids, biogenic amines, formic acid, and hydrocarbons. In this study, we investigated, using in silico techniques, the peptides composing a putative antimicrobial arsenal from the venom gland of the neotropical trap-jaw ant Odontomachus chelifer. Focusing on transcripts from the body and venom gland of this insect, it was possible to determine the gland secretome, which contained about 1022 peptides with putative signal peptides. The majority of these peptides (75.5%) were unknown, not matching any reference database, motivating us to extract functional insights via machine learning-based techniques. With several complementary methodologies, we investigated the existence of antimicrobial peptides (AMPs) in the venom gland of O. chelifer, finding 112 non-redundant candidates. Candidate AMPs were predicted to be more globular and hemolytic than the remaining peptides in the secretome. There is evidence of transcription for 97% of AMP candidates across the same ant genus, with one of them also verified as translated, thus supporting our findings. Most of these potential antimicrobial sequences (94.8%) matched transcripts from the ant’s body, indicating their role not solely as venom toxins.