Merkesvik, J. | bioRxiv Science

bioRxiv2026

In vitro model reveals structural and metabolic insights to the porcine caecal microbiota in response to β-mannan exposure

The gastrointestinal microbiota plays a pivotal role in shaping host physiology and health. By selectively promoting bacteria associated with improved host health, microbiota-directed fibres offer a strategy to enhance the beneficial functions of the microbiota. In this work, we developed a pH-controlled in vitro fermentation system (InVitSim) as a model to evaluate the effects of such a fibre - acetylated galactoglucomannan from Norway spruce - on the composition and functionality of porcine caecal microbial communities. We validated the experimental outcomes by comparing the response of the in vitro model to a previous in vivo feeding trial utilising the same {beta}-mannan fibres. Long-read sequencing with Oxford Nanopore, metatranscriptomics, and short-chain fatty acid measurements were undertaken to survey microbial community dynamics and functionality. Microbial communities in pigs and InVitSim responded similarly to {beta}-mannan supplementation, with taxa like Prevotella, Catenibacterium, and Faecalibacterium increasing in abundance. Intriguingly, some taxa were observed to be more affected by {beta}-mannan supplementation in InVitSim than in vivo. These taxa included several bacterial species that were not previously known to utilise {beta}-mannan, yet exhibited upregulated genes encoding carbohydrate-active enzymes involved in the degradation of this substrate. ImportanceIn this study, we establish a fermenter system able to preserve more than 70% of over 300 distinct microbial taxa identified in the porcine caecal gut. The in vitro model and the functional omic data generated from it enabled us to identify relevant microbial populations that responded to the presence of AcGGM by upregulating {beta}-mannan-specific polysaccharide utilisation loci. Our results highlight the value of in vitro approaches as a complementary tool to in vivo trials for learning about the gastrointestinal microbiomes response to dietary interventions on the host level. Description of supplementary filesO_LIIn-depth analyses for in vitro model validation and investigations. C_LIO_LICommon taxa between in vivo and in vitro systems exposed to {beta}-mannans. C_LIO_LIDifferential abundance analysis result visualisations. C_LIO_LIShort-chain fatty acid concentrations and correlation with microbial abundances. C_LI

microbiology↗

bioRxiv2025

Integrative holo-omic data analysis predicts interactions across the host-microbiome axis

Understanding the interplay between host organisms and their microbiomes is central to the development of sustainable food systems. However, high dimensionality and spurious associations remain major obstacles to extracting meaningful biological insight from multi-omic host-associated microbiome data; a challenge further exacerbated when "holo-omic" analyses across the host-microbiome boundary is considered. Here, we show that a computational method designed for multi-omic analysis in eukaryotes can be leveraged to integrate and analyse five layers of holo-omic data from porcine hosts and their gut microbiomes. We collected caecal tissue and digesta samples during a feeding trial that tested the impact of microbiota-directed fibres (acetylated galactoglucomannan) at critical developmental stages. From 800,000 features including microbial and host genes, metagenome-assembled genomes, and metabolites from caecal tissue and digesta, we used multiset correlation and factor analysis to select the most relevant features for capturing coordinated patterns across omic layers. From these features, we predicted over 2,000 putative host-microbiome interactions based on co-occurrence. Some of them reflected previously known relationships between animal and microbiome features, such as microbial genes for carbohydrate metabolism being linked to glycoside abundances in host tissue. Other predicted co-occurrences included features that were not detected in single-omic analysis and offer new hypotheses of host-microbiome interactions that warrant future investigation. Hence, we showcase an application of holo-omic analysis that avoids common pitfalls in high-dimensional data analysis; identifies known interactions as a form of validation; and most importantly, predicts new leads for understanding host-microbiome symbiosis. ImportanceWhile study systems involving mammalian hosts and their microbiomes are inherently complex, multi- and holo-omic analyses promise to provide interpretable results with translational value for the animal production industry. Unfortunately, computational methods capable of this kind of integration are currently scarce, as most existing multi-omics approaches have been developed for analysis of data layers within a single multicellular organism. We propose to adapt existing multi-omic methods for holo-omics by combining feature selection and interaction inference. This two-step analysis approach addresses common challenges in data-driven studies and can be implemented with a variety of tools for feature selection and interaction modelling. Through this holistic approach, we show that both known and novel relationships across the holobiont axis can be identified in a data-driven manner, offering new targets for the continued study of host-microbiome interactions and the effect of dietary interventions on production animals.

bioinformatics↗