Results for “Porifera”

PubMed2026

Integrating genomic distance analyses in the description of a new family, genus, and species of sponge-associated antipatharians (black corals).

Antipatharians (black corals) are among the least studied coral groups, with much of their diversity still undescribed. Here, we present an integrative morphological, phylogenomic and genomic distance study of deep-sea antipatharians sampled in high seas areas of the North Pacific Ocean and from New Zealand's Exclusive Economic Zone. These corals grow on hexactinellid sponges - a unique characteristic in the order Antipatharia. Using a dataset of ultra-conserved elements and exons, combined with morphological analyses, we reconstruct phylogenomic relationships and formally describe a new family (Eidikopathidae fam. nov.), a new genus (Eidikopathesgen. nov.), and two new species (E. korallispongiasp. nov., E. zealandkoralliasp. nov.). Morphologically, the new family is distinguished by a corallum consisting of a network of loose branches that fuse with the sponge skeletal framework. Phylogenomic analyses recovered consistent topologies with strong nodal support, corroborating the distinct evolutionary placement of this sponge-associated lineage. Pairwise genomic distances estimated using the Tamura-Nei model were concordant with patristic genomic distances, identifying Pteridopathidae as the genetically closest family to Eidikopathidae fam. nov., followed by Myriopathidae and Stylopathidae, which were recovered as sister families in the phylogeny. This pattern shows that genomic distance complements, rather than simply mirrors, tree topology by quantifying accumulated sequence divergence among lineages. Together, these results provide the first genomic distance framework for Antipatharia, offering a baseline for future systematic, evolutionary, and biodiversity studies on this fundamental shallow, mesophotic and deep-sea coral group.

Animals↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗