Putative Antimicrobial Peptides Within Bacterial Proteomes Affect Bacterial Predominance: A Network Analysis Perspective

Bacterial Wars-a tool for the prediction of bacterial predominance based on network analysis measures

Bacterial Wars (BW) is a network-based tool that applies a two-step pipeline to display information on the competition of bacterial species found in the same microbiome. It utilizes antimicrobial peptide (AMP) sequence similarities to obtain a relationship between species. The working hypothesis (putative AMP defense) is that friendly species share sequence similarity among the putative AMPs of their proteomes and are therefore immune to their AMPs. This may not happen in competing bacterial species with dissimilar putative AMPs. Similarities in the putative AMPs of bacterial proteomes may be thus used to predict predominance. The tool provides insights as to which bacterial species are more likely to 'die' in a competing environmental niche.

The adaptive microbiome hypothesis and immune interactions in amphibian mucus

The microbiome is known to provide benefits to hosts, including extension of immune function. Amphibians are a powerful immunological model for examining mucosal defenses because of an accessible epithelial mucosome throughout their developmental trajectory, their responsiveness to experimental treatments, and direct interactions with emerging infectious pathogens. We review amphibian skin mucus components and describe the adaptive microbiome as a novel process of disease resilience where competitive microbial interactions couple with host immune responses to select for functions beneficial to the host. We demonstrate microbiome diversity, specificity of function, and mechanisms for memory characteristic of an adaptive immune response. At a time when industrialization has been linked to losses in microbiota important for host health, applications of microbial therapies such as probiotics may contribute to immunotherapeutics and to conservation efforts for species currently threatened by emerging diseases.

Beyond Basic Diversity Estimates-Analytical Tools for Mechanistic Interpretations of Amplicon Sequencing Data

Understanding microbial ecology through amplifying short read regions, typically 16S rRNA for prokaryotic species or 18S rRNA for eukaryotic species, remains a popular, economical choice. These methods provide relative abundances of key microbial taxa, which, depending on the experimental design, can be used to infer mechanistic ecological underpinnings. In this review, we discuss recent advancements in in situ analytical tools that have the power to elucidate ecological phenomena, unveil the metabolic potential of microbial communities, identify complex multidimensional interactions between species, and compare stability and complexity under different conditions. Additionally, we highlight methods that incorporate various modalities and additional information, which in combination with abundance data, can help us understand how microbial communities respond to change in a typical ecosystem. Whilst the field of microbial informatics continues to progress substantially, our emphasis is on popular methods that are applicable to a broad range of study designs. The application of these methods can increase our mechanistic understanding of the ongoing dynamics of complex microbial communities.