To Örebro University

Background and aims: Per- and polyfluoroalkyl substances (PFAS) are emerging environmental pollutants linked to metabolic disorders, including metabolic dysfunction-associated steatotic liver disease (MASLD). This study investigates the associations between PFAS exposure and MASLD progression, focusing on lipidomic, metabolomic, and transcriptomic biomarkers, functional pathways, and gender-specific responses.

Method: Metabolomic and lipidomic analyses were performed to examine changes in metabolite and lipid composition across steatosis grades (0–3) and between MASH-positive and MASH-negative samples. Transcriptomic data were analyzed using weighted gene co-expression network analysis (WGCNA), with KEGG pathway enrichment for functional insights. Mediation analysis explored whether specific metabolites mediated the association between PFAS exposure and steatosis.

Results: Lipidomic analysis revealed significant shifts in lipidcomposition with steatosis severity, including increased TG-SFA and TG-MUFA levels in MASH-positive samples and decreased phosphatidylinositol and phosphatidylcholine levels. Gender-specific transcriptomic analysis using WGCNA identified significant modules in both females and males. These modules were significantly negatively correlated with pathways, including arginine biosynthesis, amino acid metabolism, and other related processes in both genders. Metabolomics analysis supported these findings, identifying metabolites negatively correlated with MASLD progression enriched in similar pathways, implicating a disruption in amino acid metabolism with disease progression. Positively correlated transcriptomic modules in females were linked to cell cycle, steroid biosynthesis, and fatty acid metabolism. Furthermore, significant positive correlations were observed between PFAS compounds (e.g., PFUnDA, PFDoDA) and lipids such as phosphatidylinositol, while acylcarnitines showed negative correlations. Mediation analysis showed that specific metabolites partially mediated the relationship between PFAS exposure and steatosis. Phosphatidylinositol mediated the effect of PFHxA ( p = 0.002), TG-SFA mediated PFHpA’s effect ( p = 0.028), and lactosylceramide (Laccer) mediated PFHpA’s effect ( p = 0.004).

Conclusion: This study provides insights into the molecular mechanisms linking PFAS exposure to MASLD progression, high-lighting disrupted amino acid and lipid metabolism and gender-specific responses. Identifying specific metabolites as mediators highlights new targets for addressing PFAS-related liver diseases.

Background: Growing up with siblings has been linked to numerous health outcomes and is also an important determinant for the developing microbiota. Nonetheless, research into the role of having siblings on the developing microbiota has mainly been incidental.

Results: Here, we investigate the specific effects of having siblings on the developing airway and gut microbiota using a total of 4497 hypopharyngeal and fecal samples taken from 686 children in the COPSAC2010 cohort, starting at 1 week of age and continuing until 6 years of age. Sibship was evaluated longitudinally and used for stratification. Microbiota composition was assessed using 16S rRNA gene amplicon sequencing of the variable V4 region. We found siblings in the home to be one of the most important determinants of the developing microbiota in both the airway and gut, with significant differences in alpha diversity, beta diversity, and relative abundances of the most abundant taxa, with the specific associations being particularly apparent during the first year of life. The age gap to the closest older sibling was more important than the number of older siblings. The signature of having siblings in the gut microbiota at 1 year was associated with protection against asthma at 6 years of age, while no associations were found for allergy.

Conclusions: Having siblings is one of the most important factors influencing a child's developing microbiota, and the specific effects may explain previously established associations between siblings and asthma and infectious diseases. As such, siblings should be considered in all studies involving the developing microbiota, with emphasis on the age gap to the closest older sibling rather than the number of siblings. Video abstract.

Studying respiratory illness-specific microbial signatures and their interaction with other micro-residents could provide a better understanding of lung microbial ecology. Each respiratory illness has a specific disease etiology, however, so far no study has revealed disease—specific microbial markers. The present study was designed to determine disease-specific microbial features and their interactions with other residents in chronic obstructive pulmonary diseases (stable and exacerbated), sarcoidosis, and interstitial lung diseases. Broncho-alveolar lavage samples (n = 43) were analyzed by SSU rRNA gene sequencing to study the alveolar microbiome in these diseases. A predominance of Proteobacteria followed by Firmicutes, Bacteroidetes, Actinobacteria, and Fusobacteria was observed in all the disease subsets. Shannon diversity was significantly higher in stable COPD when compared to exacerbated chronic obstructive pulmonary disease (ECOPD) (p = 0.0061), and ILD patient samples (p = 0.037). The lung microbiome of the patients with stable COPD was more diverse in comparison to ECOPD and ILD patients (p < 0.001). Lefse analysis identified 40 disease—differentiating microbial features (LDA score (log10) > 4). Species network analysis indicated a significant correlation (p < 0.05) of diseases specific microbial signature with other lung microbiome members. The current study strengthens the proposed hypothesis that each respiratory illness has unique microbial signatures. These microbial signatures could be used as diagnostic markers to differentiate among various respiratory illnesses.

Background: From early life, children are exposed to a multitude of environmental exposures, which may be of crucial importance for healthy development. Here, the environmental microbiota may be of particular interest as it represents the interface between environmental factors and the child. As infants in modern societies spend a considerable amount of time indoors, we hypothesize that the indoor bed dust microbiota might be an important factor for the child and for the early colonization of the airway microbiome. To explore this hypothesis, we analyzed the influence of environmental exposures on 577 dust samples from the beds of infants together with 542 airway samples from the Copenhagen Prospective Studies on Asthma in Childhood2010 cohort.

Results: Both bacterial and fungal community was profiled from the bed dust. Bacterial and fungal diversity in the bed dust was positively correlated with each other. Bacterial bed dust microbiota was influenced by multiple environmental factors, such as type of home (house or apartment), living environment (rural or urban), sex of siblings, and presence of pets (cat and/or dog), whereas fungal bed dust microbiota was majorly influenced by the type of home (house or apartment) and sampling season. We further observed minor correlation between bed dust and airway microbiota compositions among infants. We also analyzed the transfer of microbiota from bed dust to the airway, but we did not find evidence of transfer of individual taxa.

Conclusions: Current study explores the influence of environmental factors on bed dust microbiota (both bacterial and fungal) and its correlation with airway microbiota (bacterial) in early life using high-throughput sequencing. Our findings demonstrate that bed dust microbiota is influenced by multiple environmental exposures and could represent an interface between environment and child.

Next-Generation Sequencing (NGS) of 16S rRNA gene is now one of the most widely used application to investigate the microbiota at any given body site in research. Since NGS is more sensitive than traditional culture methods (TCMs), many studies have argued for them to replace TCMs. However, are we really ready for this transition? Here we compare the diagnostic efficiency of the two methods using a large number of samples (n = 1,748 fecal and n = 1,790 hypopharyngeal), among healthy children at different time points. Here we show that bacteria identified by NGS represented 75.70% of the unique bacterial species cultured in each sample, while TCM only identified 23.86% of the bacterial species found by amplicon sequencing. We discuss the pros and cons of both methods and provide perspective on how NGS can be implemented effectively in clinical settings.