Bacterial secondary metabolites are a major source of antibiotics and other bioactive compounds. In microbial communities, these molecules can mediate interspecies interactions and responses to environmental change. Despite the importance of secondary metabolites in human health and microbial ecology, little is known about their roles and regulation in the context of multispecies communities. In a simplified model of the rhizosphere composed of Bacillus cereus , Flavobacterium johnsoniae , and Pseudomonas koreensis , we show that the dynamics of secondary metabolism depend on community species composition and interspecies interactions. Comparative metatranscriptomics and metametabolomics reveal that the abundance of transcripts of biosynthetic gene clusters (BGCs) and metabolomic molecular features differ between monocultures or dual cultures and a tripartite community. In both two- and three-member cocultures, P. koreensis modified expression of BGCs for zwittermicin, petrobactin, and other secondary metabolites in B. cereus and F. johnsoniae, whereas the BGC transcriptional response to the community in P. koreensis itself was minimal. Pairwise and tripartite cocultures with P. koreensis displayed unique molecular features that appear to be derivatives of lokisin, suggesting metabolic handoffs between species. Deleting the BGC for koreenceine, another P. koreensis metabolite, altered transcript and metabolite profiles across the community, including substantial up-regulation of the petrobactin and bacillibactin BGCs in B. cereus , suggesting that koreenceine represses siderophore production. Results from this model community show that bacterial BGC expression and chemical output depend on the identity and biosynthetic capacity of coculture partners, suggesting community composition and microbiome interactions may shape the regulation of secondary metabolism in nature.

Topic modeling is a popular method used to describe biological count data. With topic models, the user must specify the number of topics |$K$|⁠. Since there is no definitive way to choose |$K$| and since a true value might not exist, we develop a method, which we call topic alignment, to study the relationships across models with different |$K$|⁠. In addition, we present three diagnostics based on the alignment. These techniques can show how many topics are consistently present across different models, if a topic is only transiently present, or if a topic splits into more topics when |$K$| increases. This strategy gives more insight into the process of generating the data than choosing a single value of |$K$| would. We design a visual representation of these cross-model relationships, show the effectiveness of these tools for interpreting the topics on simulated and real data, and release an accompanying R package, alto

Mediation analysis has emerged as a versatile tool for answering mechanistic questions in microbiome research because it provides a statistical framework for attributing treatment effects to alternative causal pathways. Using a series of linked regressions, this analysis quantifies how complementary data relate to one another and respond to treatments. Despite these advances, existing software’s rigid assumptions often result in users viewing mediation analysis as a black box. We designed the multimedia R package to make advanced mediation analysis techniques accessible, ensuring that statistical components are interpretable and adaptable. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis, enabling experimentation with various mediator and outcome models while maintaining a simple overall workflow. The software includes modules for regularized linear, compositional, random forest, hierarchical, and hurdle modeling, making it well-suited to microbiome data. We illustrate the package through two case studies. The first re-analyzes a study of the microbiome and metabolome of Inflammatory Bowel Disease patients, uncovering potential mechanistic interactions between the microbiome and disease-associated metabolites, not found in the original study. The second analyzes new data about the influence of mindfulness practice on the microbiome. The mediation analysis highlights shifts in taxa previously associated with depression that cannot be explained indirectly by diet or sleep behaviors alone. A gallery of examples and further documentation can be found at https://go.wisc.edu/830110 . IMPORTANCE: Microbiome studies routinely gather complementary data to capture different aspects of a microbiome’s response to a change, such as the introduction of a therapeutic. Mediation analysis clarifies the extent to which responses occur sequentially via mediators, thereby supporting causal, rather than purely descriptive, interpretation. Multimedia is a modular R package with close ties to the wider microbiome software ecosystem that makes statistically rigorous, flexible mediation analysis easily accessible, setting the stage for precise and causally informed microbiome engineering.

Society’s capacity for algorithmic problem-solving has never been greater. Artificial Intelligence is now applied across more domains than ever, a consequence of powerful abstractions, abundant data, and accessible software. As capabilities have expanded, so have risks, with models often deployed without fully understanding their potential impacts. Interpretable and interactive machine learning aims to make complex models more transparent and controllable, enhancing user agency. This review synthesizes key principles from the growing literature in this field. We first introduce precise vocabulary for discussing interpretability, like the distinction between glass box and explainable models. We then explore connections to classical statistical and design principles, like parsimony and the gulfs of interaction. Basic explainability techniques – including learned embeddings, integrated gradients, and concept bottlenecks – are illustrated with a simple case study. We also review criteria for objectively evaluating interpretability approaches. Throughout, we underscore the importance of considering audience goals when designing interactive data-driven systems. Finally, we outline open challenges and discuss the potential role of data science in addressing them. Code to reproduce all examples can be found at https://go.wisc.edu/3k1ewe.