Gut microbiomes of fish species consist of thousands of bacterial taxa that interact among each other, their environment, and the host. These complex networks of interactions are regulated by a diverse range of factors, yet little is known about the hierarchy of these interactions. Here, we introduce SAMBA (Structure-Learning of Aquaculture Microbiomes using a Bayesian Approach), a computational tool that uses a unified Bayesian network approach to model the network structure of fish gut microbiomes and their interactions with biotic and abiotic variables associated with typical aquaculture systems. SAMBA accepts input data on microbial abundance from 16S rRNA amplicons as well as continuous and categorical information from distinct farming conditions. From this, SAMBA can create and train a network model scenario that can be used to (i) infer information of how specific farming conditions influence the diversity of the gut microbiome or pan-microbiome, and (ii) predict how the diversity and functional profile of that microbiome would change under other variable conditions. SAMBA also allows the user to visualize, manage, edit, and export the acyclic graph of the modelled network. Our study presents examples and test results of Bayesian network scenarios created by SAMBA using data from a microbial synthetic community, and the pan-microbiome of gilthead sea bream (Sparus aurata) in different feeding trials. It is worth noting that the usage of SAMBA is not limited to aquaculture systems as it can be used for modelling microbiome–host network relationships of any vertebrate organism, including humans, in any system and/or ecosystem.

Motivation: Deciphering phenotype-specific regulatory mechanisms is key to understanding the molecular basis of complex diseases and traits. However, constructing multi-omic regulatory networks (MO-RNs) is challenging, as it requires integrating heterogeneous omics data, incorporating biological context, and detecting regulatory mechanisms that vary across conditions. The R package MORE (Multi-Omics REgulation) addresses these challenges by applying robust statistical models to infer phenotype-specific regulatory networks from multi-omics data. However, the use of MORE typically requires programming expertise, limiting its accessibility to non-specialist users. To democratize access to advanced multi-omics modeling tools, we present MOREshiny, an interactive web application built on Shiny that extends the module of pathway enrichment analysis and automatically guides the choice of statistical methods. Results: MOREshiny enables users to upload multi-omic data, configure their models, and interpret results through a user-friendly interface-without the need for coding skills. MOREshiny also allows users to download MORE results for their later exploration and study. To demonstrate the utility of MOREshiny, we showcase its functionalities on a multi-omic ovarian cancer dataset to understand regulatory differences between patients who did or did not require chemotherapy. Availability and implementation: MOREshiny is freely available for download as a dockerized R Shiny package at https://github.com/BiostatOmics/MOREshiny.

In aquaculture systems, microbiomes of farmed fishes may contain thousands of bacterial taxa that establish complex networks of interactions among each other and among the host and the environment. Gut microbiomes in many fish species consist of thousands of bacterial taxa that interact among each other, their environment, and the host. These complex networks of interactions are regulated by a diverse range of factors, yet little is known about the hierarchy of these interactions. Here, we introduce SAMBA (Structure-Learning of Aquaculture Microbiomes using a Bayesian Approach), a computational tool that uses a unified Bayesian network approach to model the network structure of fish gut microbiomes and their interactions with biotic and abiotic variables associated with typical aquaculture systems. SAMBA accepts input data on microbial abundance from 16S rRNA amplicons as well as continuous and categorical information from distinct farming conditions. From this, SAMBA can create and train a network model scenario that can be used to: i) infer information how specific farming conditions influence the diversity of the gut microbiome or pan-microbiome, and ii) predict how the diversity and functional profile of that microbiome would change under other experimental variables. SAMBA also allows the user to visualize, manage, edit, and export the acyclic graph of the modelled network. Our study presents examples and test results of bayesian network scenarios created by SAMBA using data from: a) a microbial synthetic experiment; and b) the pan-microbiome of the gilthead sea bream ( Sparus aurata ) under different experimental feeding trials. It is worth noting that the usage of SAMBA is not limited to aquaculture systems and can be used for modelling microbiome-host network relationships in any vertebrate organism, including humans, in any system and/or ecosystem.

Obesity represents a significant risk factor in the development of type 2 diabetes (T2D), a chronic metabolic disorder characterized by elevated blood glucose levels. Significant sex differences have been identified in the prevalence, development, and pathophysiology of obesity and T2D; however, the underlying molecular mechanisms remain unclear. This study aims to identify sex-specific biomarkers in obesity and T2D and enhance our understanding of the underlying mechanisms associated with sex differences by integrating expression data. A systematic review, individual transcriptomic analysis, gene-level meta-analysis, and functional characterization were performed to achieve this aim. Eight studies and 236 subcutaneous adipose tissue samples were analyzed, identifying common and sex-specific biomarkers, many of which were previously associated with obesity or T2D. The obesity meta-analysis yielded nineteen differentially-expressed biomarkers from a sex-specific perspective (e.g., SPATA18, KREMEN1, NPY4R , and PRM3 ), while a comparison of the expression profiles between sexes in T2D prompted the identification and validation of specific transcriptomic signatures in males ( SAMD9, NBPF3, LDHD , and EHD3 ) and females ( RETN, HEY1, PLPP2 , and PM20D2 ). At the functional level, we highlighted the fundamental role of the Wnt pathway in the development of obesity and T2D in females and the roles of more significant mitochondrial damage and free fatty acids in males. Overall, our sex-specific meta-analyses supported the detection of differentially expressed genes in males and females associated with the development of obesity and T2D, emphasizing the relevance of sex-based information in biomedical data and opening new avenues for research. Highlights: First meta-analysis on WAT with sex as a central perspective in obesity and T2D This study identifies 19 sex-differential biomarkers in obesity, highlighting NPY4 Sex specific transcriptional signatures in SAT in the development of T2D Wnt pathway genes show sex-specific roles in obesity and T2D, notably in females Obesity increases mitogenesis in male, mediated by SPATA18, with an increased role of free fatty acids in T2D