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Motivation Machine learning (ML) methods are motivated by the need to automate information extraction from large datasets in order to support human users in data-driven tasks. This is an attractive approach for integrative joint analysis of vast amounts of omics data produced in next generation sequencing and other -omics assays. A systematic assessment of the current literature can help to identify key trends and potential gaps in methodology and applications. We surveyed the literature on ML multi-omic data integration and quantitatively explored the goals, techniques and data involved in this field. We were particularly interested in examining how researchers use ML to deal with the volume and complexity of these datasets. Results Our main finding is that the methods used are those that address the challenges of datasets with few samples and many features. Dimensionality reduction methods are used to reduce the feature count alongside models that can also appropriately handle relatively few samples. Popular techniques include autoencoders, random forests and support vector machines. We also found that the field is heavily influenced by the use of The Cancer Genome Atlas dataset, which is accessible and contains many diverse experiments. Availability and implementation All data and processing scripts are available at this GitLab repository: https://gitlab.com/polavieja%5flab/ml%5fmulti-omics%5freview/ or in Zenodo: https://doi.org/10.5281/zenodo.7361807. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Aims: Given the pivotal role played by the gut microbiota in regulating the host immune system, great interest has arisen in the possibility of controlling fish health by modulating the gut microbiota. Hence, there is a need to better understand of the host–microbiota interactions after disease responses to optimize the use of probiotics to strengthen disease resilience and recovery. Methods and Results: We tested the effects of a probiotic feed additive in rainbow trout and challenged the fish with the causative agent for enteric red mouth disease, Yersinia ruckeri. We evaluated the survival, host immune gene expression and the gut microbiota composition. Results revealed that provision of probiotics and exposure to Y. ruckeri induced immune gene expression in the host, which were associated with changes in the gut microbiota. Subsequently, infection with Y. ruckeri had very little effect on microbiota composition when probiotics were applied, indicating that probiotics increased stabilisation of the microbiota. Our analysis revealed potential biomarkers for monitoring infection status and fish health. Finally, we used modelling approaches to decipher interactions between gut bacteria and the host immune gene responses, indicating removal of endogenous bacteria elicited by non‐specific immune responses. Conclusions: We discuss the relevance of these results emphasizing the importance of host–microbiota interactions, including the protective potential of the gut microbiota in disease responses. Significance and Impact of the Study: Our results highlight the functional consequences of probiotic‐induced changes in the gut microbiota post infection and the resulting host immune response. [ABSTRACT FROM AUTHOR]

In nature, secondary metabolites mediate interactions between microorganisms residing in complex microbial communities. However, the degree to which community dynamics can be linked to secondary metabolite potential remains largely unknown. In this study, we address the relationship between community succession and secondary metabolism variation. We used 16S and 18S rRNA gene and adenylation domain amplicon sequencing, genome-resolved metagenomics, and untargeted metabolomics to track the taxons, biosynthetic gene clusters, and metabolome dynamics in situ of microorganisms during marine biofilm succession over 113 days. Two phases were identified during the community succession, with a clear shift around Day 29, where the alkaloid secondary metabolites, pseudanes, were also detected. The microbial secondary metabolite potential changed between the phases, and only a few community members, including Myxococotta spp., were responsible for the majority of the biosynthetic gene cluster potential in the early succession phase. In the late phase, bryozoans and benthic copepods were detected, and the microbial nonribosomal peptide potential drastically decreased in association with a reduction in the relative abundance of the prolific secondary metabolite producers. Conclusively, this study provides evidence that the early succession of the marine biofilm community favors prokaryotes with high nonribosomal peptide synthetase potential. In contrast, the late succession is dominated by multicellular eukaryotes and a reduction in bacterial nonribosomal peptide synthetase potential., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the International Society for Microbial Ecology.)