MetaSVs: A pipeline combining long and short reads for analysis and visualization of structural variants in metagenomes

Structural Variations in Ulcerative Colitis-associated Escherichia coli Reduce Fructose Utilization and Aggravate Inflammation Under High-Fructose Diet

BACKGROUND AND AIMS

Structural variations (SVs) have significant effects on microbial phenotypes. The underlying mechanism of functional changes caused by gut microbial SVs in the development of ulcerative colitis (UC) need further investigation.

METHODS

We performed long-read (Oxford Nanopore Technology-based) and short-read (Illumina-based) metagenomic sequencing on stool samples from 93 patients with UC and 100 healthy controls (HCs) and analyzed microbial SVs. A total of 648 Escherichia coli strains from fecal samples of patients with UC (UC-strains) and HCs (HC-strains) were isolated. SV-associated scrK gene deletion was verified via whole-genome sequencing or targeted polymerase chain reaction. Then, representative UC-strains, HC-strains, and scrK-knockout E coli were used for the in vitro and in vivo experiments to investigate the effects of specific SVs in E coli on fructose utilization ability and colitis.

RESULTS

E coli in UC with the highest fold change had SV-affected functional differences on fructose metabolism to that of HCs. The fructose utilization gene deletion was common in UC-strains, ostensibly reducing fructose utilization in vitro and leading to fructose-dependent aggravation of colitis in murine models. UC-strains and HC-strains induced comparable colitis under low fructose. However, high fructose exacerbated colitis severity exclusively in UC-strain-colonized mice, with elevated intestinal fructose residues, significant microbiome/metabolome changes, increased inflammation, and gut barrier disruption. These changes were mechanistically dependent on the deletion of the fructose utilization gene scrK.

CONCLUSIONS

SV-caused difference in fructose utilization and proinflammatory properties in E coli from patients with UC influence the development of UC, emphasizing the importance of fine-scale metagenomic studies in disease.

Pangenome and genome variation analyses of pigs unveil genomic facets for their adaptation and agronomic characteristics

The development of a comprehensive pig graph pangenome assembly encompassing 27 genomes represents the most extensive collection of pig genomic data to date. Analysis of this pangenome reveals the critical role of structural variations in driving adaptation and defining breed-specific traits. Notably, the study identifies BTF3 as a key candidate gene governing intramuscular fat deposition and meat quality in pigs. These findings underscore the power of pangenome approaches in uncovering novel genomic features underlying economically important agricultural traits. Collectively, these results demonstrate the value of leveraging large-scale, multi-genome analyses for advancing our understanding of livestock genomes and accelerating genetic improvement.

Reference-free structural variant detection in microbiomes via long-read co-assembly graphs

MOTIVATION

The study of bacterial genome dynamics is vital for understanding the mechanisms underlying microbial adaptation, growth, and their impact on host phenotype. Structural variants (SVs), genomic alterations of 50 base pairs or more, play a pivotal role in driving evolutionary processes and maintaining genomic heterogeneity within bacterial populations. While SV detection in isolate genomes is relatively straightforward, metagenomes present broader challenges due to the absence of clear reference genomes and the presence of mixed strains. In response, our proposed method rhea, forgoes reference genomes and metagenome-assembled genomes (MAGs) by encompassing all metagenomic samples in a series (time or other metric) into a single co-assembly graph. The log fold change in graph coverage between successive samples is then calculated to call SVs that are thriving or declining.

RESULTS

We show rhea to outperform existing methods for SV and horizontal gene transfer (HGT) detection in two simulated mock metagenomes, particularly as the simulated reads diverge from reference genomes and an increase in strain diversity is incorporated. We additionally demonstrate use cases for rhea on series metagenomic data of environmental and fermented food microbiomes to detect specific sequence alterations between successive time and temperature samples, suggesting host advantage. Our approach leverages previous work in assembly graph structural and coverage patterns to provide versatility in studying SVs across diverse and poorly characterized microbial communities for more comprehensive insights into microbial gene flux.

AVAILABILITY AND IMPLEMENTATION

rhea is open source and available at: https://github.com/treangenlab/rhea.

Reference-free Structural Variant Detection in Microbiomes via Long-read Coassembly Graphs

Bacterial genome dynamics are vital for understanding the mechanisms underlying microbial adaptation, growth, and their broader impact on host phenotype. Structural variants (SVs), genomic alterations of 10 base pairs or more, play a pivotal role in driving evolutionary processes and maintaining genomic heterogeneity within bacterial populations. While SV detection in isolate genomes is relatively straightforward, metagenomes present broader challenges due to absence of clear reference genomes and presence of mixed strains. In response, our proposed method rhea, forgoes reference genomes and metagenome-assembled genomes (MAGs) by encompassing a single metagenome coassembly graph constructed from all samples in a series. The log fold change in graph coverage between subsequent samples is then calculated to call SVs that are thriving or declining throughout the series. We show rhea to outperform existing methods for SV and horizontal gene transfer (HGT) detection in two simulated mock metagenomes, which is particularly noticeable as the simulated reads diverge from reference genomes and an increase in strain diversity is incorporated. We additionally demonstrate use cases for rhea on series metagenomic data of environmental and fermented food microbiomes to detect specific sequence alterations between subsequent time and temperature samples, suggesting host advantage. Our innovative approach leverages raw read patterns rather than references or MAGs to include all sequencing reads in analysis, and thus provide versatility in studying SVs across diverse and poorly characterized microbial communities for more comprehensive insights into microbial genome dynamics.