Background: Sequencing of 16S rRNA genes has become a powerful technique to study microbial communities and their responses towards changing environmental conditions in various ecosystems. Several tools have been developed for the prediction of functional profiles from 16S rRNA gene sequencing data, because numerous questions in ecosystem ecology require knowledge of community functions in addition to taxonomic composition. However, the accuracy of these tools relies on functional information derived from genomes available in public databases, which are often not representative of the microorganisms present in the studied ecosystem. In addition, there is also a lack of tools to predict functional gene redundancy in microbial communities. Results: To address these challenges, we developed Tax4Fun2, an R package for the prediction of functional profiles and functional gene redundancies of prokaryotic communities from 16S rRNA gene sequences. We demonstrate that functional profiles predicted by Tax4Fun2 are highly correlated to functional profiles derived from metagenomes of the same samples. We further show that Tax4Fun2 has higher accuracies than PICRUSt and Tax4Fun. By incorporating user-defined, habitat-specific genomic information, the accuracy and robustness of predicted functional profiles is substantially enhanced. In addition, functional gene redundancies predicted with Tax4Fun2 are highly correlated to functional gene redundancies determined for simulated microbial communities. Conclusions: Tax4Fun2 provides researchers with a unique tool to predict and investigate functional profiles of prokaryotic communities based on 16S rRNA gene sequencing data. It is easy-to-use, platform-independent and highly memory-efficient, thus enabling researchers without extensive bioinformatics knowledge or access to high-performance clusters to predict functional profiles. Another unique feature of Tax4Fun2 is that it allows researchers to calculate the redundancy of specific functions, which is a potentially important measure of how resilient a community will be to environmental perturbation. Tax4Fun2 is implemented in R and freely available at https://github.com/bwemheu/Tax4Fun2.

Many bacteria and fungi in the plant rhizosphere and endosphere are beneficial to plant nutrient acquisition, health, and growth. Although playing essential roles in ecosystem functioning, our knowledge about the effects of multiple cropping regimes on the plant microbiome and their interactions is still limited. Here, we designed a pot experiment simulating different cropping regimes. For this purpose, wheat and faba bean plants were grown under controlled greenhouse conditions in monocultures and in two intercropping regimes: row and mixed intercropping. Bacterial and fungal communities in bulk and rhizosphere soils as well as in the roots and aerial plant parts were analyzed using large-scale metabarcoding. We detected differences in microbial richness and diversity between the cropping regimes. Generally, observed effects were attributed to differences between mixed and row intercropping or mixed intercropping and monoculture. Bacterial and fungal diversity were significantly higher in bulk soil samples of wheat and faba bean grown in mixed compared to row intercropping. Moreover, microbial communities varied between crop species and plant compartments resulting in different responses of these communities towards cropping regimes. Leaf endophytes were not affected by cropping regime but bacterial and fungal community structures in bulk and rhizosphere soil as well as fungal community structures in roots. We further recorded highly complex changes in microbial interactions. The number of negative inter-domain correlations between fungi and bacteria decreased in bulk and rhizosphere soil in intercropping regimes compared to monocultures due to beneficial effects. In addition, we observed plant species-dependent differences indicating that intra- and interspecific competition between plants had different effects on the plant species and thus on their associated microbial communities. To our knowledge, this is the first study investigating microbial communities in different plant compartments with respect to multiple cropping regimes using large-scale metabarcoding. Although a simple design simulating different cropping regimes was used, obtained results contribute to the understanding how cropping regimes affect bacterial and fungal communities and their interactions in different plant compartments. Nonetheless, we need field experiments to properly quantify observed effects in natural ecosystems.

Endophytic bacteria are critical for plant growth and health. However, compositional and functional responses of bacterial endophyte communities towards agricultural practices are still poorly understood. Hence, we analyzed the influence of fertilizer application and mowing frequency on bacterial endophytes in three agriculturally important grass species. For this purpose, we examined bacterial endophytic communities in aerial plant parts of Dactylis glomerata L., Festuca rubra L., and Lolium perenne L. by pyrotag sequencing of bacterial 16S rRNA genes over two consecutive years. Although management regimes influenced endophyte communities, observed responses were grass species-specific. This might be attributed to several bacteria specifically associated with a single grass species. We further predicted functional profiles from obtained 16S rRNA data. These profiles revealed that predicted abundances of genes involved in plant growth promotion or nitrogen metabolism differed between grass species and between management regimes. Moreover, structural and functional community patterns showed no correlation to each other indicating that plant species-specific selection of endophytes is driven by functional rather than phylogenetic traits. The unique combination of 16S rRNA data and functional profiles provided a holistic picture of compositional and functional responses of bacterial endophytes in agricultural relevant grass species towards management practices.

Assessing the functional capability and redundancy of a microbial community is a major challenge in environmental microbiology. To address this challenge, we developed Tax4Fun2, a R-based tool for the rapid prediction of functional profiles and functional redundancy of prokaryotic communities from 16S rRNA gene sequences. By incorporating user-defined, habitat-specific genomic information, the accuracy and robustness of predicted functional profiles can be substantially enhanced.

Cacao (Theobroma cacao L.) is one of the most economically important crops worldwide. Despite the important role of endophytes for plant growth and health, very little is known about the effect of agroforestry management systems on the endophyte communities of T. cacao. To close this knowledge gap, we investigated the diversity, community composition, and function of bacterial and fungal endophytes in the leaves of T. cacao trees growing in five major cacao-growing regions in the central region of Cameroon using DNA metabarcoding. Fungal but not bacterial alpha diversity measures differed significantly between the agroforestry management systems. Interestingly, less managed home-garden cacao forests harbored the lowest fungal richness and diversity. Our results suggest that the composition of bacterial and fungal endophyte communities is predominantly affected by agroforestry management systems and, to a lesser extent, by environmental properties. The core microbiome detected comprised important fungal phytopathogens, such as Lasiodiplodia species. Several predicted pathways of bacterial endophytes and functional guilds of fungal endophytes differed between the agroforest systems which might be attributed to bacteria and fungi specifically associated with a single agroforest. Our results provide the basis for future studies on foliar fungal and bacterial endophytes of T. cacao and their responsiveness towards agroforestry management systems.