There is a growing attention regarding the toxic effect of microplastics pollutants. However, comprehensive phenotyping- and omics-based strategies for the toxicity evaluation of microplastics on the host remain to be established. To this end, we designed an encompassing phenotyping and multi-omics analysis method to detect the molecular interference of nanopolystyrene (PS)-exposed mice. The exposure time was 28 days with 1000 μg/L PS. We found that PS induced microbial alteration and metabolic disorders, which was closely related to immune disturbances. In addition, the altered expression of some genes related to immune dysregulation was observed. Interestingly, Bifidobacterium breve M-16V (B. breve M-16V) significantly inhibited Th2 and Th17 lymphocyte subset. Simultaneously, B.breve M-16V may activate MyD88 expression and promote Th1-related cytokine IL-12 production. In addition, B. breve M-16V may partially restore the gut microbiota dysbiosis. In summary, we demonstrated that the combined phenotyping and omics-based profiling established a practical framework that allowed us to gain a deeper understanding of the maladaptive consequences of PS exposure. It can be utilized to evaluate the toxicity of other environmental microplastics pollutants. Meanwhile, we found that B. breve M-16V has certain anti-inflammatory and immunomodulatory functions through host-microbiome interactions.

Background: The incidence of pediatric inflammatory bowel disease (PIBD) has been steadily increasing globally. Delayed diagnosis of PIBD increases the risk of complications and contributes to growth retardation. To improve long-term outcomes, there is a pressing need to identify novel markers for early diagnosis of PIBD.Methods: The candidate biomarkers for PIBD were identified from the GSE117993 dataset by two machine learning algorithms, namely LASSO and mSVM-RFE, and externally validated in the GSE126124 dataset and our PIBD cohort. The role of ficolin-1 (FCN1) in PIBD and its association with macrophage infiltration was investigated using the CIBERSORT method and enrichment analysis of the single-cell dataset GSE121380, and further validated using immunoblotting, qRT-PCR, and immunostaining in colon biopsies from PIBD patients, a juvenile murine DSS-induced colitis model, and THP-1-derived macrophages.Results: FCN1 showed great diagnostic performance for PIBD in an independent clinical cohort with the AUC of 0.986. FCN1 expression was upregulated in both colorectal biopsies and blood samples from PIBD patients. Functionally, FCN1 was associated with immune-related processes in the colonic mucosa of PIBD patients, and correlated with increased proinflammatory M1 macrophage infiltration. Furthermore, single-cell transcriptome analysis and immunostaining revealed that FCN1 was almost exclusively expressed in macrophages infiltrating the colonic mucosa of PIBD patients, and these FCN1⁺macrophages were related to hyper-inflammation. Notably, proinflammatory M1 macrophages derived from THP-1 expressed high levels of FCN1 and IL-1β, and FCN1 overexpression in THP-1-derived macrophages strongly promoted LPS-induced activation of the proinflammatory cytokine IL-1β via the NLRP3-caspase-1 axis.Conclusions: FCN1 is a novel and promising diagnostic biomarker for PIBD. FCN1⁺macrophages enriched in the colonic mucosa of PIBD exhibit proinflammatory phenotypes, and FCN1 promotes IL-1β maturation in macrophages via the NLRP3-caspase-1 axis.

Background: Early diagnosis and treatment of pediatric Inflammatory bowel disease (PIBD) is challenging due to the complexity of the disease and lack of disease specific biomarkers. The novel machine learning (ML) technique may be a useful tool to provide a new route for the identification of early biomarkers for the diagnosis of PIBD.Methods: In total, 66 treatment naive PIBD patients and 27 healthy controls were enrolled as an exploration cohort. Fecal microbiome profiling using 16S rRNA gene sequencing was performed. The correlation between microbiota and inflammatory and nutritional markers was evaluated using Spearman’s correlation. A random forest model was used to set up an ML approach for the diagnosis of PIBD using 1902 markers. A validation cohort including 14 PIBD and 48 irritable bowel syndrome (IBS) was enrolled to further evaluate the sensitivity and accuracy of the model.Result: Compared with healthy subjects, PIBD patients showed a significantly lower diversity of the gut microbiome. The increased Escherichia-Shigella and Enterococcus were positively correlated with inflammatory markers and negatively correlated with nutrition markers, which indicated a more severe disease. A diagnostic ML model was successfully set up for differential diagnosis of PIBD integrating the top 11 OTUs. This diagnostic model showed outstanding performance at differentiating IBD from IBS in an independent validation cohort.Conclusion: The diagnosis penal based on the ML of the gut microbiome may be a favorable tool for the precise diagnosis and treatment of PIBD. A study of the relationship between disease status and the microbiome was an effective way to clarify the pathogenesis of PIBD.