MPA_Pathway_Tool: User-Friendly, Automatic Assignment of Microbial Community Data on Metabolic Pathways

Microbiome modeling: a beginner's guide

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Breakdown of hardly degradable carbohydrates (lignocellulose) in a two-stage anaerobic digestion plant is favored in the main fermenter

The yield and productivity of biogas plants depend on the degradation performance of their microbiomes. The spatial separation of the anaerobic digestion (AD) process into a separate hydrolysis and a main fermenter should improve cultivation conditions of the microorganisms involved in the degradation of complex substrates like lignocellulosic biomass (LCB) and, thus, the performance of anaerobic digesters. However, relatively little is known about such two-stage processes. Here, we investigated the process performance of a two-stage agricultural AD over one year, focusing on chemical and technical process parameters and metagenome-centric metaproteomics. Technical and chemical parameters indicated stable operation of the main fermenter but varying conditions for the open hydrolysis fermenter. Matching this, the microbiome in the hydrolysis fermenter has a higher dynamic than in the main fermenter. Metaproteomics-based microbiome analysis revealed a partial separation between early and common steps in carbohydrate degradation and primary fermentation in the hydrolysis fermenter but complex carbohydrate degradation, secondary fermentation, and methanogenesis in the main fermenter. Detailed metagenomics and metaproteomics characterization of the single metagenome-assembled genomes showed that the species focus on specific substrate niches and do not utilize their full genetic potential to degrade, for example, LCB. Overall, it seems that a separation of AD in a hydrolysis and a main fermenter does not improve the cleavage of complex substrates but significantly improves the overall process performance. In contrast, the remaining methanogenic activity in the hydrolysis fermenter may cause methane losses.

Metaproteomic Analysis of Biogas Plants: A Complete Workflow from Lab to Bioinformatics

Metaproteomics represents a promising and fast method to analyze the taxonomic and functional composition of biogas plant microbiomes. However, metaproteomics sample preparation and bioinformatics analysis is still challenging due to the sample complexity and contaminants. In this chapter, a tailored workflow including sampling, phenol extraction in a ball mill, amido black protein quantification, FASP digestion, LC-MS/MS measurement as well as bioinformatics and biostatistical data evaluation are here described for the metaproteomics advancements applied to biogas plant samples.

Metaproteomics to understand how microbiota function: The crystal ball predicts a promising future

In the medical, environmental, and biotechnological fields, microbial communities have attracted much attention due to their roles and numerous possible applications. The study of these communities is challenging due to their diversity and complexity. Innovative methods are needed to identify the taxonomic components of individual microbiota, their changes over time, and to determine how microoorganisms interact and function. Metaproteomics is based on the identification and quantification of proteins, and can potentially provide this full picture. Due to the wide molecular panorama and functional insights it provides, metaproteomics is gaining momentum in microbiome and holobiont research. Its full potential should be unleashed in the coming years with progress in speed and cost of analyses. In this exploratory crystal ball exercise, I discuss the technical and conceptual advances in metaproteomics that I expect to drive innovative research over the next few years in microbiology. I also debate the concepts of 'microbial dark matter' and 'Metaproteomics-Assembled Proteomes (MAPs)' and present some long-term prospects for metaproteomics in clinical diagnostics and personalized medicine, environmental monitoring, agriculture, and biotechnology.