Background Fiber-rich feed components possess prebiotic potential to enhance pig health and are considered a potential solution to the high prevalence of post-weaning diarrhea in pig production under the phased suspension of antibiotics and zinc oxide use. Methods We screened the gut microbiota modulatory properties of pectin substrates prepared from sugar beet within the freshly weaned piglet gut microbiome using an in vitro colon model, the CoMiniGut. We focused on testing a variety (13) of sugar beet-derived pectin substrates with defined structures, as well as known prebiotics such as inulin, fructooligosaccharide (FOS) and galactooligosaccharide (GOS), to gain insights on the structure–function related properties of specific substrates on the weaner gut microbial composition as well as shortchain fatty acid production (SCFA). Results Sugar beet-derived pectin and rhamnogalacturonan-I selectively increased the relative abundance of Bacteroidetes, specifically Prevotella copri, Bacteroides ovatus, Bacteroides acidificiens, and an unclassified Bacteroides member. The degree of esterification impacted the relative abundance of these species and the SCFA production during the in vitro fermentations. Modified arabinans derived from sugar beet promoted the growth of Blautia, P. copri, Lachnospiraceae members and Limosilactobacillus mucosae and amongst all oligosaccharides tested yielded the highest amount of total SCFA produced after 24 h of fermentation. Sugar beet-derived substrates yielded higher total SCFA concentrations (especially acetic and propionic acid) relative to the known prebiotics inulin, FOS and GOS. Conclusion Our results indicate that the molecular structures of pectin, that can be prepared form just one plant source (sugar beet) can selectively stimulate different GM members, highlighting the potential of utilizing pectin substrates as targeted GM modulatory ingredients.

Most studies of the microbiota in the human gut focus on the bacterial part, but increasing information shows that intestinal fungi are also important for maintaining health. This can be either by directly influencing the host or by indirectly influencing the gut bacteria that link to host health. Studies of fungal communities in large cohorts are scarce; therefore, this study aims at obtaining more insight into the mycobiome of healthy individuals and how this mycobiome interacts with the bacterial component of the microbiome. For this purpose, ITS2 and 16S rRNA gene amplicon sequencing was performed on fecal samples from 163 individuals which were available from two separate studies to analyze the fungal and bacterial microbiome, respectively, as well as the cross-kingdom interactions. The results showed a much lower fungal, as compared to bacterial, diversity. Ascomycota and Basidiomycota were the dominant fungal phyla across all the samples, but levels varied enormously between individuals. The ten most abundant fungal genera were Saccharomyces, Candida, Dipodascus, Aureobasidium, Penicillium, Hanseniaspora, Agaricus, Debaryomyces, Aspergillus, and Pichia, and here also extensive inter-individual variation was observed. Correlations were made between bacteria and fungi, and only positive correlations were observed. One of the correlations was between Malassezia restricta and the genus Bacteroides, which have both been previously described as alleviated in IBD. Most of the other correlations found were with fungi that are not known as gut colonizers but originate from food and the environment. To further investigate the importance of the observed correlations found, more research is needed to discriminate between gut colonizers and transient species.

Most research on the human microbiome focuses on the bacterial component, and this has led to a lack of information about the fungal component (mycobiota) and how this can influence human health, e.g., by modulation through the diet. The validated, dynamic computer-controlled model of the colon (TIM-2) is an in vitro model to study the microbiome and how this is influenced by interventions such as diet. In this study, it was used to the study the gut fungal-community. This was done in combination with next-generation sequencing of the ITS2 region for fungi and 16S rRNA for bacteria. Different dietary interventions (control diet (SIEM), high-carbohydrate, high-protein, glucose as a carbon source) were performed, to see if diet could shape the mycobiome. The mycobiome was investigated after the adaptation period, and throughout the intervention period which lasted 72 h, and samples were taken every 24 h. The fungal community showed low diversity and a greater variability when compared to bacteria. The mycobiome was affected most in the first hours of the adaptation period. Taxonomic classification showed that at the phylum-level Ascomycota and Basidiomycota dominated, while Agaricus, Aspergillus, Candida, Penicillum, Malassezia, Saccharomyces, Aureobasidium, Mycosphaerella, Mucor and Clavispora were the most abundant genera. During the intervention period, it was shown that the change of diet could influence the diversity. Clustering of samples for different time points was analyzed using Bray–Curtis dissimilarities. Samples of t0 clustered together, and samples of all other time points clustered together. The Bray–Curtis-dissimilarity analysis also showed that for the different dietary interventions, samples treated with glucose clustered together and were different from the other groups (p < 0.05, PERMANOVA). Taxonomic classification showed that the genera Alternaria, Thanatephorus, Candida and Dekkera differentially changed for the various diet groups (p < 0.05, Kruskal–Wallis). These results show that the mycobiota could be modelled in TIM-2; however, the low diversity and high variability make studying fungal, as compared to bacterial, communities, much more challenging. Future research should focus on the optimization of the stability of the fungal community to increase the strength of the results.

The complex microbial community found in the human gut consist of members of multiple kingdoms, among which are bacteria and fungi. Microbiome research mainly focuses on the bacterial part of the microbiota, thereby neglecting interactions that can take place between bacteria and fungi. With the rise of sequencing techniques, the possibilities to study cross-kingdom relationships has expanded. In this study, fungal-bacterial relationships were investigated using the complex, dynamic computer-controlled in vitro model of the colon (TIM-2). Interactions were investigated by disruption of either the bacterial or fungal community by the addition of antibiotics or antifungals to TIM-2, respectively, compared to a control without antimicrobials. The microbial community was analyzed with the use of next generation sequencing of the ITS2 region and the 16S rRNA. Moreover, the production of SCFAs was followed during the interventions. Correlations between fungi and bacteria were calculated to investigate possible cross-kingdom interactions. The experiments showed that no significant differences in alpha-diversity were observed between the treatments with antibiotics and fungicide. For beta-diversity, it could be observed that samples treated with antibiotics clustered together, whereas the samples from the other treatments were more different. Taxonomic classification was done for both bacteria and fungi, but no big shifts were observed after treatments. At the level of individual genera, bacterial genus Akkermansia was shown to be increased after fungicide treatment. SCFAs levels were lowered in samples treated with antifungals. Spearman correlations suggested that cross-kingdom interactions are present in the human gut, and that fungi and bacteria can influence each other. Further research is required to gain more insights in these interactions and their molecular nature and to determine the clinical relevance.

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