Kim, O. T. P. | bioRxiv Science

bioRxiv2026

DETECTION OF BIOSYNTHETIC GENE CLUSTERS FROM METAGENOME OF NATURAL HONEY COLLECTED IN VIETNAM

The antimicrobial properties of natural honey are partly attributed to bioactive secondary metabolites produced by its associated microbial communities, yet the biosynthetic capacity of these communities remains poorly characterized. Here, we applied a metagenomic approach to investigate the biosynthetic gene cluster (BGC) diversity of bacteria associated with Apis cerana honey from the Northwest mountainous region of Vietnam - a biogeographically distinct and underexplored ecosystem. A total of 366 BGCs spanning 38 compound classes were identified, with terpenes, nonribosomal peptide synthetases (NRPS), and ribosomally synthesized and post-translationally modified peptides (RiPPs) being the most prevalent. Strikingly, 304 BGCs (>83%) lacked close matches in the MIBiG reference database, indicating a high degree of biosynthetic novelty relative to previously characterized natural product repertoires. Among the identified clusters, an azole-containing RiPP BGC recovered from a metagenome-assembled genome (MAG) assigned to Atlantibacter hermannii was predicted to exhibit strong antibacterial activity, with a probability score of 74.5%, representing a prioritized target for heterologous expression and bioactivity validation. These findings establish the Apis cerana honey microbiome as a tractable and largely untapped reservoir for applied microbial research, with direct implications for the discovery of novel antimicrobial agents from underexplored environmental niches. ImportanceAntimicrobial resistance represents one of the most pressing challenges in modern medicine, driving urgent demand for novel bioactive compounds from underexplored microbial sources. Honey-associated bacterial communities are recognized contributors to the antimicrobial properties of natural honey, yet their biosynthetic capacity remains poorly characterized at the metagenomic level. This study demonstrates that the microbiome of Apis cerana honey from a biogeographically distinct region of Vietnam harbors extensive and largely novel biosynthetic gene cluster diversity, with over 83% (304/366) of identified BGCs lacking database references. These findings position honey microbiomes as a tractable and underutilized reservoir for applied microbial research, with direct relevance to the discovery of new antimicrobial agents. The identification of a candidate antibacterial RiPP BGC in Atlantibacter hermannii provides a concrete target for future cultivation-based and heterologous expression studies, bridging metagenomic discovery with applied biotechnological pipelines.

microbiology↗

bioRxiv2021

Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms

Assessments of genomic prediction accuracies using artificial intelligence (AI) algorithms (i.e., machine and deep learning methods) are currently not available or very limited in aquaculture species. The principal aim of this study was to examine the predictive performance of these new methods for disease resistance to Edwardsiella ictaluri in a population of striped catfish Pangasianodon hypophthalmus and to make comparisons with four common methods, i.e., pedigree-based best linear unbiased prediction (PBLUP), genomic-based best linear unbiased prediction (GBLUP), single-step GBLUP (ssGBLUP) and a non-linear Bayesian approach (notably BayesR). Our analyses using machine learning (i.e., ML-KAML) and deep learning (i.e., DL-MLP and DL-CNN) together with the four common methods (PBLUP, GBLUP, ssGBLUP and BayesR) were conducted for two main disease resistance traits (i.e., survival status coded as 0 and 1 and survival time, i.e., days that the animals were still alive after the challenge test) in a pedigree consisting of 560 individual animals (490 offspring and 70 parents) genotyped for 14,154 Single Nucleotide Polymorphism (SNPs). The results using 6470 SNPs after quality control showed that AI methods outperformed PBLUP, GBLUP and ssGBLUP, with the increases in the prediction accuracies for both traits by 9.1 - 15.4%. However, the prediction accuracies obtained from AI methods were comparable to those estimated using BayesR. Imputation of missing genotypes using AlphaFamImpute increased the prediction accuracies by 5.3 - 19.2% in all the methods and data used. On the other hand, there were insignificant decreases (0.3 - 5.6%) in the prediction accuracies for both survival status and survival time when multivariate models were used in comparison to univariate analyses. Interestingly, the genomic prediction accuracies based on only highly significant SNPs (P < 0.00001, 318 - 400 SNPs for survival status and 1362 - 1589 SNPs for survival time) were somewhat lower (0.3 to 15.6%) than those obtained from the whole set of 6,470 SNPs. In most of our analyses, the accuracies of genomic prediction were somewhat higher for survival time than survival status (0/1 data). It is concluded that there are prospects for the application of genomic selection to increase disease resistance to Edwardsiella ictaluri in striped catfish breeding programs.

genetics↗