The integrative multi-kingdom interaction of the gut microbiome in ulcerative colitis (UC) and Crohn's disease (CD) remains underinvestigated. Here, we perform shotgun metagenomic sequencing of feces from patients with UC and CD, and healthy controls in the Japanese 4D cohort, profiling bacterial taxa, gene functions, and antibacterial genes, bacteriophages, and fungi. External metagenomic datasets from the US, Spain, the Netherlands, and China were analyzed to validate our multi-biome findings. We found that Enterococcus faecium and Bifidobacterium spp. were enriched in both diseases. Enriched Escherichia coli was characteristic of CD and was linked to numerous antibiotic resistance genes involved in efflux pumps and adherent-invasive Escherichia coli virulence factors. Virome changes correlated with shifts in the bacteriome, including increased abundances of phages encoding pathogenic genes. Saccharomyces paradoxus and Saccharomyces cerevisiae were enriched in UC and CD, respectively. Saccharomyces cerevisiae and Escherichia coli had negative associations with short-chain fatty acid (SCFA)-producing bacteria in CD. Multi-biome signatures and their interactions in UC and CD showed high similarities between Japan and other countries. Since bacteria, phages, and fungi formed multiple hubs of intra- or trans-kingdom networks with SCFA producers and pathobionts in UC and CD, an approach targeting the interaction network may hold therapeutic promise.

The microbiota in individual habitats differ in both relative composition and absolute abundance. While sequencing approaches determine the relative abundances of taxa and genes, they do not provide information on their absolute abundances. Here, we developed a machine-learning approach to predict fecal microbial loads (microbial cells per gram) solely from relative abundance data. Applying our prediction model to a large-scale metagenomic dataset (n = 34,539), we demonstrated that microbial load is the major determinant of gut microbiome variation and is associated with numerous host factors, including age, diet, and medication. We further found that for several diseases, changes in microbial load, rather than the disease condition itself, more strongly explained alterations in patients' gut microbiome. Adjusting for this effect substantially reduced the statistical significance of the majority of disease-associated species. Our analysis reveals that the fecal microbial load is a major confounder in microbiome studies, highlighting its importance for understanding microbiome variation in health and disease.

The rising prevalence of liver diseases related to obesity and excessive use of alcohol is fuelling an increasing demand for accurate biomarkers aimed at community screening, diagnosis of steatohepatitis and significant fibrosis, monitoring, prognosis and prediction of treatment efficacy. Breakthroughs in omics methodologies and the power of bioinformatics have created an excellent opportunity to combine clinical needs with technological advancements. Omics technologies allow for advanced investigations into biological processes from the genes to transcription and regulation, to circulating protein, metabolite and lipid levels, as well as the microbiome including bacteria, viruses and fungi. We consequently find ourselves in a period of rapid progress in technology and bioinformatics that may allow for development of precision biomarkers for personalised medicine. However, there are important barriers to consider in omics biomarker discovery and validation, including the use of semi-quantitative measurements from untargeted platforms, which may exhibit high analytical, inter- and intra-individual variance. Standardising methods and the need to validate across diverse populations, presents a challenge, partly due to disease complexity and the dynamic nature of biomarker expression in different disease stages. Lack of validity causes lost opportunities when studies fail to provide the knowledge needed for regulatory approvals, all of which contributes to a delayed translation of these discoveries into clinical practice. While no omics-based biomarkers have matured to clinical implementation, the extent of data generated through omics-technologies holds the power of hypothesis-free discovery of a plethora of candidate biomarkers to be further validated. To explore the many opportunities of omics technologies, hepatologists need detailed knowledge of commonalities and differences between the various omics layers, and both the barriers to and advantages of these approaches.

Alcohol disturbs hepatic lipid synthesis and transport, but the role of lipid dysfunction in alcohol-related liver disease (ALD) is unclear. In this biopsy-controlled, prospective, observational study, we characterized the liver and plasma lipidomes in patients with early ALD.

Multi-omics analyses are used in microbiome studies to understand molecular changes in microbial communities exposed to different conditions. However, it is not always clear how much each omics data type contributes to our understanding and whether they are concordant with each other. Here, we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers (16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics and metabolomics). We find that all the omics methods with species resolution are highly consistent in estimating relative species abundances. Furthermore, different omics methods complement each other for capturing functional changes. For example, while nearly all the omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control. Metabolomics revealed a decrease in oligosaccharide uptake, likely caused by Bacteroidota depletion. Our study highlights how multi-omics datasets can be utilized to reveal complex molecular responses to external perturbations in microbial communities.

To identify gut and oral metagenomic signatures that accurately predict pancreatic ductal carcinoma (PDAC) and to validate these signatures in independent cohorts.