Viruses and plasmids (invasive mobile genetic elements (iMGEs)) have important roles in shaping microbial communities, but their dynamic interactions with CRISPR-based immunity remain unresolved. We analysed generation-resolved iMGE–host dynamics spanning one and a half years in a microbial consortium from a biological wastewater treatment plant using integrated meta-omics. We identified 31 bacterial metagenome-assembled genomes encoding complete CRISPR–Cas systems and their corresponding iMGEs. CRISPR-targeted plasmids outnumbered their bacteriophage counterparts by at least fivefold, highlighting the importance of CRISPR-mediated defence against plasmids. Linear modelling of our time-series data revealed that the variation in plasmid abundance over time explained more of the observed community dynamics than phages. Community-scale CRISPR-based plasmid–host and phage–host interaction networks revealed an increase in CRISPR-mediated interactions coinciding with a decrease in the dominant ‘Candidatus Microthrix parvicella’ population. Protospacers were enriched in sequences targeting genes involved in the transmission of iMGEs. Understanding the factors shaping the fitness of specific populations is necessary to devise control strategies for undesirable species and to predict or explain community-wide phenotypes.

The ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) and Clostridium difficile have been identified as the leading global cause of multidrug-resistant bacterial infections in hospitals. CRISPR–Cas systems are bacterial immune systems, empowering the bacteria with defense against invasive mobile genetic elements that may carry the antimicrobial resistance (AMR) genes, among others. On the other hand, the CRISPR–Cas systems are themselves mobile. In this study, we annotated and compared the CRISPR–Cas systems in these pathogens, utilizing their publicly available large numbers of sequenced genomes (e.g., there are more than 12 thousands of S. aureus genomes). The presence of CRISPR–Cas systems showed a very broad spectrum in these pathogens: S. aureus has the least tendency of obtaining the CRISPR–Cas systems with only 0.55% of its isolates containing CRISPR–Cas systems, whereas isolates of C. difficile we analyzed have CRISPR–Cas systems each having multiple CRISPRs. Statistical tests show that CRISPR–Cas containing isolates tend to have more AMRs for four of the pathogens (A. baumannii, E. faecium, P. aeruginosa, and S. aureus). We made available all the annotated CRISPR–Cas systems in these pathogens with visualization at a website (https://omics.informatics.indiana.edu/CRISPRone/pathogen), which we believe will be an important resource for studying the pathogens and their arms-race with invaders mediated through the CRISPR–Cas systems, and for developing potential clinical applications of the CRISPR–Cas systems for battles against the antibiotic resistant pathogens.

The human gut microbiome is composed of a diverse and dynamic population of microbial species which play key roles in modulating host health and physiology. While individual microbial species have been found to be associated with certain disease states, increasing evidence suggests that higher-order microbial interactions may have an equal or greater contribution to host fitness. To better understand microbial community dynamics, we utilize networks to study interactions through a meta-analysis of microbial association networks between healthy and disease gut microbiomes. Taking advantage of the large number of metagenomes derived from healthy individuals and patients with various diseases, together with recent advances in network inference that can deal with sparse compositional data, we inferred microbial association networks based on co-occurrence of gut microbial species and made the networks publicly available as a resource (GitHub repository named GutNet). Through our meta-analysis of inferred networks, we were able to identify network-associated features that help stratify between healthy and disease states such as the differentiation of various bacterial phyla and enrichment of Proteobacteria interactions in diseased networks. Additionally, our findings show that the contributions of taxa in microbial associations are disproportionate to their abundances and that rarer taxa of microbial species play an integral part in shaping dynamics of microbial community interactions. Network-based meta-analysis revealed valuable insights into microbial community dynamics between healthy and disease phenotypes. We anticipate that the healthy and diseased microbiome association networks we inferred will become an important resource for human-related microbiome research.

Background: Sequencing of microbiomes has accelerated the characterization of the diversity of CRISPR-Cas immune systems. However, the utilization of next generation short read sequences for the characterization of CRISPR-Cas dynamics remains limited due to the repetitive nature of CRISPR arrays. CRISPR arrays are comprised of short spacer segments (derived from invaders' genomes) interspaced between flanking repeat sequences. The repetitive structure of CRISPR arrays poses a computational challenge for the accurate assembly of CRISPR arrays from short reads. In this paper we evaluate the use of long read sequences for the analysis of CRISPR-Cas system dynamics in microbiomes. Results: We analyzed a dataset of Illumina's TruSeq Synthetic Long-Reads (SLR) derived from a gut microbiome. We showed that long reads captured CRISPR spacers at a high degree of redundancy, which highlights the spacer conservation of spacer sharing CRISPR variants, enabling the study of CRISPR array dynamics in ways difficult to achieve though short read sequences. We introduce compressed spacer graphs, a visual abstraction of spacer sharing CRISPR arrays, to provide a simplified view of complex organizational structures present within CRISPR array dynamics. Utilizing compressed spacer graphs, several key defining characteristics of CRISPR-Cas system dynamics were observed including spacer acquisition and loss events, conservation of the trailer end spacers, and CRISPR arrays' directionality (transcription orientation). Other result highlights include the observation of intense array contraction and expansion events, and reconstruction of a full-length genome for a potential invader (Faecalibacterium phage) based on identified spacers. Conclusion: We demonstrate in an in silico system that long reads provide the necessary context for characterizing the organization of CRISPR arrays in a microbiome, and reveal dynamic and evolutionary features of CRISPR-Cas systems in a microbial population.

Microbial community members exhibit various forms of interactions. Taking advantage of the increasing availability of microbiome data, many computational approaches have been developed to infer bacterial interactions from the co-occurrence of microbes across diverse microbial communities. Additionally, the introduction of genome-scale metabolic models have also enabled the inference of cooperative and competitive metabolic interactions between bacterial species. By nature, phylogenetically similar microbial species are more likely to share common functional profiles or biological pathways due to their genomic similarity. Without properly factoring out the phylogenetic relationship, any estimation of the competition and cooperation between species based on functional/pathway profiles may bias downstream applications. To address these challenges, we developed a novel approach for estimating the competition and complementarity indices for a pair of microbial species, adjusted by their phylogenetic distance. An automated pipeline, PhyloMint, was implemented to construct competition and complementarity indices from genome scale metabolic models derived from microbial genomes. Application of our pipeline to 2,815 human-gut associated bacteria showed high correlation between phylogenetic distance and metabolic competition/cooperation indices among bacteria. Using a discretization approach, we were able to detect pairs of bacterial species with cooperation scores significantly higher than the average pairs of bacterial species with similar phylogenetic distances. A network community analysis of high metabolic cooperation but low competition reveals distinct modules of bacterial interactions. Our results suggest that niche differentiation plays a dominant role in microbial interactions, while habitat filtering also plays a role among certain clades of bacterial species.