Decoding host-microbiome interactions through co-expression network analysis within the non-human primate intestine.

The gut microbiome affects the health status of the host through complex interactions with the host's intestinal wall. These host-microbiome interactions may spatially vary along the physical and chemical environment of the intestine, but these changes remain unknown. This study investigated these intricate relationships through a gene co-expression network analysis based on dual transcriptome profiling of different intestinal sites-cecum, transverse colon, and rectum-of the primate common marmoset. We proposed a gene module extraction algorithm based on the graph theory to find tightly interacting gene modules of the host and the microbiome from a vast co-expression network. The 27 gene modules identified by this method, which include both host and microbiome genes, not only produced results consistent with previous studies regarding the host-microbiome relationships, but also provided new insights into microbiome genes acting as potential mediators in host-microbiome interplays. Specifically, we discovered associations between the host gene FBP1, a cancer marker, and polysaccharide degradation-related genes (pfkA and fucI) coded by Bacteroides vulgatus, as well as relationships between host B cell-specific genes (CD19, CD22, CD79B, and PTPN6) and a tryptophan synthesis gene (trpB) coded by Parabacteroides distasonis. Furthermore, our proposed module extraction algorithm surpassed existing approaches by successfully defining more functionally related gene modules, providing insights for understanding the complex relationship between the host and the microbiome.IMPORTANCEWe unveiled the intricate dynamics of the host-microbiome interactions along the colon by identifying closely interacting gene modules from a vast gene co-expression network, constructed based on simultaneous profiling of both host and microbiome transcriptomes. Our proposed gene module extraction algorithm, designed to interpret inter-species interactions, enabled the identification of functionally related gene modules encompassing both host and microbiome genes, which was challenging with conventional modularity maximization algorithms. Through these identified gene modules, we discerned previously unrecognized bacterial genes that potentially mediate in known relationships between host genes and specific bacterial species. Our findings underscore the spatial variations in host-microbiome interactions along the colon, rather than displaying a uniform pattern throughout the colon.

Intraintestinal Analysis of the Functional Activity of Microbiomes and Its Application to the Common Marmoset Intestine.

The intestinal microbiome is closely related to host health, and metatranscriptomic analysis can be used to assess the functional activity of microbiomes by quantifying microbial gene expression levels, helping elucidate the interactions between the microbiome and the environment. However, the functional changes in the microbiome along the host intestinal tract remain unknown, and previous analytical methods have limitations, such as potentially overlooking unknown genes due to dependence on existing databases. The objective of this study is to develop a computational pipeline combined with next-generation sequencing for spatial covariation analysis of the functional activity of microbiomes at multiple intestinal sites (biogeographic locations) within the same individual. This method reconstructs a reference metagenomic sequence across multiple intestinal sites and integrates the metagenome and metatranscriptome, allowing the gene expression levels of the microbiome, including unknown bacterial genes, to be compared among multiple sites. When this method was applied to metatranscriptomic analysis in the intestinal tract of common marmosets, a New World monkey, the reconstructed metagenome covered most of the expressed genes and revealed that the differences in microbial gene expression among the cecum, transverse colon, and feces were more dynamic and sensitive to environmental shifts than the abundances of the genes. In addition, metatranscriptomic profiling at three intestinal sites of the same individual enabled covariation analysis incorporating spatial relevance, accurately predicting the function of a total of 10,856 unknown genes. Our findings demonstrate that our proposed analytical method captures functional changes in microbiomes at the gene resolution level. IMPORTANCE We developed an analysis method that integrates metagenomes and metatranscriptomes from multiple intestinal sites to elucidate how microbial function varies along the intestinal tract. This method enables spatial covariation analysis of the functional activity of microbiomes and accurate identification of gene expression changes among intestinal sites, including changes in the expression of unknown bacterial genes. Moreover, we applied this method to the investigation of the common marmoset intestine, which is anatomically and pharmacologically similar to that of humans. Our findings indicate the expression pattern of the microbiome varies in response to changes in the internal environment along the intestinal tract, and this microbial change may affect the intestinal environment.

Incomplete Selective Sweeps of Microcystis Population Detected by the Leader-End CRISPR Fragment Analysis in a Natural Pond.

The freshwater cyanobacterium Microcystis aeruginosa frequently forms toxic massive blooms and exists in an arms race with its infectious phages in aquatic natural environments, and as a result, has evolved extremely diverse and elaborate antiviral defense systems, including the clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated genes (Cas) system. Here, to assess Microcystis population dynamics associated with exogenous mobile genetic elements such as phages and plasmids, we examined the temporal variation in CRISPR genotypes (CTs) by analyzing spacer sequences detected in a natural pond between June and October 2013 when a cyanobacterial bloom occurred. A total of 463,954 high-quality leader-end CRISPR sequences were obtained and the sequences containing spacers were classified into 31 previously reported CTs and 68 new CTs based on the shared order of the leader-end spacers. CT19 was the most dominant genotype (32%) among the 16 most common CTs, followed by CT52 (14%) and CT58 (9%). Spacer repertoires of CT19 showed mainly two different types; CT19origin, which was identical to the CT19 spacer repertoire of previously isolated strains, and CT19new+, which contained a new spacer at the leader-end of the CRISPR region of CT19origin, which were present in almost equal abundance, accounting for up to 99.94% of CT19 sequences. Surprisingly, we observed the spacer repertoires of the second to tenth spacers of CT19origin at the most leader-end of proto-genotype sequences of CT19origin. These were observed during the sampling in this study and our previous study at the same ecosystem in 2010 and 2011, suggesting these CTs persisted from 2011 to 2013 in spite of phage pressure. The leader-end variants were observed in other CT genotypes. These findings indicated an incomplete selective sweep of Microcystis populations. We explained the phenomenon as follow; the abundance of Microcystis varied seasonally and drastically, resulting that Microcystis populations experience a bottleneck once a year, and thereby founder effects following a bottleneck mean that older CTs have an equal chance of increasing in prevalence as the CTs generated following acquisition of newer spacers.