MVIF.1 Research Highlights
Authors: Smirnova Ekaterina , Rossi Alessandro , Das Sudip , Binyamin Dana , Kitchin Natasha , Smatana Stanislav 
Published: September 2024

Integrative analysis of gut microbiota, microbial gene expression and function coupled with metabolome to understand mechanisms that influence the development of non-alcoholic fatty liver disease

Altered gut microbiota and metabolites are important for non-alcoholic fatty liver disease (NAFLD) in children. We aimed to comprehensively examine the effects of gut metabolites on NAFLD progression. We performed integrative metabolomics (untargeted discovery and targeted validation) analysis of non-alcoholic fatty liver (NAFL), non-alcoholic steatohepatitis (NASH), and obesity in children. Fecal samples were collected from 75 subjects in the discovery cohort (25 NAFL, 25 NASH, and 25 obese control children) and 145 subjects in an independent validation cohort (53 NAFL, 39 NASH, and 53 obese control children). Among 2,491 metabolites, untargeted metabolomics revealed a complete NAFLD metabolic map containing 318 increased and 123 decreased metabolites. Then, machine learning selected 65 important metabolites that can distinguish the severity of the NAFLD. Furthermore, precision-targeted metabolomics selected 5 novel gut metabolites from 20 typical metabolites. The functionality of candidate metabolites was validated in hepatocyte cell lines. In the end, this study annotated two novel elevated pathogenic metabolites (dodecanoic acid and creatinine) and a relationship between depleted protective gut microbiota (Butyricicoccus and Alistipes), increased inflammation (IL-1β), lipid metabolism (TG), and liver function (ALT and AST). This study demonstrates the role of novel gut metabolites (dodecanoic acid and creatinine), as the fatty acid metabolism regulator contributing to NAFLD development through its influence on inflammation and liver function.

Phage induction gives insight on the Anaerobic Digestion metavirome

The viral community has the potential to influence the structure of the microbiome and thus the yield of the anaerobic digestion process. However, the virome composition in anaerobic digestion is still under-investigated. A viral induction experiment was conducted on separate batches undergoing a series of DNA-damaging stresses, in order to coerce temperate viruses to enter the lytic cycle.

Human Lung Microbiota-driven Innate Immune Landscape

Human deep lung microbiota has been associated with lung health. Our recent study show a dynamic microbial ecosystem in lung harbouring discrete pneumotypes associated with specific immune response and clinical stability (Das, S. et al. Nat Commun 2021). But despite sharing the same niche, we know little about direct interaction between lung bacteria and human macrophages.

The aging mouse microbiome has obesogenic characteristics

During aging, there is a physiological decline, an increase of morbidity and mortality, and a natural change in the gut microbiome. In this study, we investigated the influence of the gut microbiome on different metabolic parameters in adult and aged mice.

The gut microbiotas influence in the development of Foetal Alcohol Spectrum Disorders

Background: Prenatal alcohol exposure (PAE) is one of the most preventable causes of birth defects, developmental disorders and intellectual disability, yet the prevalence of Foetal Alcohol Spectrum Disorders (FASD) in the Western Cape of South Africa is estimated to be between 16% and 31%, significantly higher than the global prevalence of 0.77%. Neurocognitive development is dependent on microbial composition and the corresponding microbial metabolic outputs. Alcohol-induced microbial alterations may alter the functioning of the infant's gut microbiota, which may therefore increase the risk of FASD development in infants. This study therefore aimed to compare the gut microbial composition of infants diagnosed with and without FASD.

When 16s rRNA outperforms WMGS metagenomics

The biggest drawback of 16S rRNA gene sequencing is that the reads originate from a single short region and the resulting reads lack sufficient specificity for reliable species-level identification. Whole metagenomic sequencing (WMGS) is seen as a solution to this problem that should provide the highest degree of specificity. We argue that WMGS for metataxonomics can be inefficient since most parts of a typical microbial genome are non-specific and provide no value for species identification. Consequently, the sequencing budget is spent on useless parts of genomes and the process’s sensitivity is significantly reduced. This is a problem, especially in the low-abundant samples contaminated by eukaryotic DNA. Unlike WMGS, where, in theory, all organisms could be classified down to the species level, in 16S rRNA the set of identifiable species depends on a chosen primer combination.