iNAP 2.0: Harnessing metabolic complementarity in microbial network analysis

Characteristics of microbial carbon pump in the sediment of kelp aquaculture zone and its contribution to recalcitrant dissolved organic carbon turnover: insights into metabolic patterns and ecological functions

The study delves into the microbial carbon pump (MCP) within the sediments of kelp aquaculture zones, focusing on its influence on the turnover of recalcitrant dissolved organic carbon (RDOC). Following kelp harvest, significant alterations in the microbial community structure were noted, with a decrease in complexity and heterogeneity within co-occurrence networks potentially impacting RDOC production efficiency. Metabolic models constructed identified four key microbial lineages crucial for RDOC turnover, with their abundance observed to decrease post-harvest. Analysis of metabolic complementarity revealed that RDOC-degrading microorganisms exhibit broad substrate diversity and are engaged in specific resource exchange patterns, with cross-feeding interactions possibly enhancing the ecological efficiency of the MCP. Notably, the degradation of RDOC was found not to deplete the RDOC pool; as aromatic compounds break down, new ones are released into the environment, thus supporting the renewal of the RDOC pool. The research highlights the pivotal role of microbial communities in RDOC turnover and offers fresh insights into their cross-feeding behavior related to RDOC cycling, providing valuable data to support the future development and application of MCP theory.

Methodology for microbiome data analysis: An overview

It is known that microbiome and health are related, in addition, recent research has found that microbiome has potential clinical uses. These facts highlight the importance of the microbiome in actual science. However, microbiome data has some characteristics that makes its statistical study challenging. In recent years, longitudinal and non-longitudinal methods have been designed to analyze the microbiota and knowing more about the bacterial behavior. In this article in the form of a review we summarize the characteristics of microbiome data and the statistical methods most widespread to analyze it. We have taken into account if the strategies are longitudinal or not. We also classify the methods based on their specific analytical objectives and based on their mathematical characteristics. The methods are structured according to their biological goals and mathematical features, ensuring that the insights provided are both relevant and accessible to professionals in biology and statistics. We present this review as a reference for the most widely used methods in microbiome data analysis and as a foundation for identifying potential areas for future research. We want to point out that this review can be particularly useful to remark the importance of the methodology designed in order to study microbiome longitudinal datasets.

MicrobiomeStatPlots: Microbiome statistics plotting gallery for meta-omics and bioinformatics

The rapid growth of microbiome research has generated an unprecedented amount of multi-omics data, presenting challenges in data analysis and visualization. To address these issues, we present MicrobiomeStatPlots, a comprehensive platform offering streamlined, reproducible tools for microbiome data analysis and visualization. This platform integrates essential bioinformatics workflows with multi-omics pipelines and provides 82 distinct visualization cases for interpreting microbiome datasets. By incorporating basic tutorials and advanced R-based visualization strategies, MicrobiomeStatPlots enhances accessibility and usability for researchers. Users can customize plots, contribute to the platform's expansion, and access a wealth of bioinformatics knowledge freely on GitHub (https://github.com/YongxinLiu/MicrobiomeStatPlot). Future plans include extending support for metabolomics, viromics, and metatranscriptomics, along with seamless integration of visualization tools into omics workflows. MicrobiomeStatPlots bridges gaps in microbiome data analysis and visualization, paving the way for more efficient, impactful microbiome research.

Personalized gut microbial community modeling by leveraging genome-scale metabolic models and metagenomics

The impact of the gut microbiome on human health is increasingly recognized as dysbiosis has been found to be associated with a spectrum of diseases. Here, we review the databases of genome-scale metabolic models (GEMs), which have paved the way for investigations into the metabolic capabilities of gut microbes and their interspecies dynamics. We further discuss the strategies for developing community-level GEMs, which are crucial for understanding the complex interactions within microbial communities and between the microbiome and its host. Such GEMs can guide the design of synthetic microbial communities for disease treatment. Finally, we explore advances in personalized gut microbiome modeling. These advancements broaden our mechanistic understanding and hold promise for applications in precision medicine and therapeutic interventions.