An UPLC-qTOF-MS-based dereplication study led to the targeted isolation of seven bromoindole alkaloids from the sub-Arctic sponge Geodia barretti. This includes three new metabolites, namely geobarrettin A–C (1–3) and four known compounds, barettin (4), 8,9-dihydrobarettin (5), 6-bromoconicamin (6), and l-6-bromohypaphorine (7). The chemical structures of compounds 1–7 were elucidated by extensive analysis of the NMR and HRESIMS data. The absolute stereochemistry of geobarrettin A (1) was assigned by ECD analysis and Marfey’s method employing the new reagent l-N^α-(1-fluoro-2,4-dinitrophenyl)tryptophanamide (l-FDTA). The isolated compounds were screened for anti-inflammatory activity using human dendritic cells (DCs). Both 2 and 3 reduced DC secretion of IL-12p40, but 3 concomitantly increased IL-10 production. Maturing DCs treated with 2 or 3 before co-culturing with allogeneic CD4⁺ T cells decreased T cell secretion of IFN-γ, indicating a reduction in Th1 differentiation. Although barettin (4) reduced DC secretion of IL-12p40 and IL-10 (IC₅₀ values 11.8 and 21.0 μM for IL-10 and IL-12p40, respectively), maturing DCs in the presence of 4 did not affect the ability of T cells to secrete IFN-γ or IL-17, but reduced their secretion of IL-10. These results indicate that 2 and 3 may be useful for the treatment of inflammation, mainly of the Th1 type.

Geobarrettin D (1), a new bromoindole alkaloid, was isolated from the marine sponge Geodia barretti collected from Icelandic waters. Its structure was elucidated by 1D, and 2D NMR (including ¹H-¹⁵N HSQC, ¹H-¹⁵N HMBC spectra), as well as HRESIMS data. Geobarrettin D (1) is a new 6-bromoindole featuring an unusual purinium herbipoline moiety. Geobarrettin D (1) decreased secretion of the pro-inflammatory cytokine IL-12p40 by human monocyte derived dendritic cells, without affecting secretion of the anti-inflammatory cytokine IL-10. Thus, compound 1 shows anti-inflammatory activity.

The bryozoan Flustra foliacea produces a range of indole alkaloids, and some have shown weak antibiotic, muscle-relaxant and cytotoxic properties; however, most of them have not been tested for bioactivity. Many of these alkaloids possess a physostigmine scaffold, and physostigmine is a well-known acetylcholinesterase (AChE) inhibitor. AChE inhibitors are of interest as drug leads in neurodegenerative diseases and are currently used in symptomatic treatment of Alzheimer’s disease (AD). In this study, the AChE inhibitory activity of Flustra alkaloids was studied in vitro using the colorimetric method of Ellman and AChE from Electrophorus electricus. Twenty-five compounds isolated from the Icelandic bryozoan F. foliacea were screened at a 100 µM concentration. Two of them, flustramine E and flustramine I, showed inhibition of 48%, and flustramine Q showed 82% inhibition. For flustramine Q, the IC₅₀ was 9.6 µM. Molecular modelling and docking studies indicated that simple in silico designed derivatives of flustramine Q could have potential for increased potency. Marine natural products including brominated indole alkaloids from Flustra foliacea are an interesting new source of AChE inhibitors with potential towards central nervous system disorders, e.g., Alzheimer’s disease.

The study of bacterial metabolism holds immense significance for improving human health and advancing agricultural practices. The prospective applications of genomically encoded bacterial metabolism present a compelling opportunity, particularly in the light of the rapid expansion of genomic sequencing data. Current metabolic inference tools face challenges in scaling with large datasets, leading to increased computational demands, and often exhibit limited inter-relatability and interoperability. Here, we introduce the Integrated Biosynthetic Inference Suite (IBIS), which employs deep learning models and a knowledge graph to facilitate rapid, scalable bacterial metabolic inference. This system leverages a series of Transformer based models to generate high quality, meaningful embeddings for individual enzymes, biosynthetic domains, and metabolic pathways. These embedded representations enable rapid, large-scale comparisons of metabolic proteins and pathways, surpassing the capabilities of conventional methodologies. The examination of evolutionary and functionally conserved metabolites across diverse bacterial species is facilitated by integrating the predictive capabilities of IBIS into a graph database enriched with comprehensive metadata. The consideration of both primary and specialized metabolism, combined with an embedding logic for enzyme discovery, uniquely positions IBIS to identify potential novel metabolic pathways. With the expansion of genomic data necessitating transformative approaches to advance molecular metabolism research, IBIS delivers an AI-driven holistic investigation of bacterial metabolism.