Probiotics Antimicrob Proteins. 2026 May 23. doi: 10.1007/s12602-026-11062-2. Online ahead of print.

ABSTRACT

Autoimmune hepatitis (AIH) is a chronic progressive inflammatory liver disease with a rising global incidence. The treatment of AIH remains challenging because first-line drugs show limited efficacy and systemic side effects. Gut microbiota plays a crucial role in the pathogenesis of AIH, leading to growing interest in developing probiotic-based therapies. In this study, we used multi-omics analysis to investigate the therapeutic effects of a quadruple probiotic mixture (Probiotic-quad) consisting of Bifidobacterium infantis, Lactobacillus acidophilus, Enterococcus faecalis, and Bacillus cereus in a well-established chronic AIH murine model. Our results showed that Probiotic-quad treatment significantly alleviated AIH progression, as evidenced by lower serum liver enzyme levels, ameliorated hepatic inflammatory infiltration and histopathological damage. Metagenomic sequencing results showed that gut dysbiosis in AIH mice was partially reversed after Probiotic-quad administration. Additionally, the integrity of the intestinal epithelial barrier was restored, accompanied by a reduction in serum lipopolysaccharide levels. Untargeted metabolomic and transcriptomic analysis revealed that Probiotic-quad treatment was linked to alterations in hepatic metabolism, including the citrate cycle and tryptophan metabolism, and was associated with reduced activation of the NF-κB and NOD-like receptor signaling pathways. These findings suggest that Probiotic-quad treatment ameliorates AIH severity and is potentially associated with changes in hepatic immune responses, metabolism, gut microbiota, and intestinal barrier function, highlighting its potential as an adjuvant therapy for AIH.

PMID:42176246 | DOI:10.1007/s12602-026-11062-2

Am J Hum Genet. 2026 Apr 2;113(4):828-841. doi: 10.1016/j.ajhg.2026.03.008.

ABSTRACT

Reliable reference transcriptome prediction models are key to accurate multi-ancestry transcriptome-wide association studies (TWASs). We propose three methods leveraging functionally informed variants (FIVs) for transcriptome prediction models to improve multi-ancestry TWASs. We trained models on 1,287 multi-ancestry participants from the Trans-Omics for Precision Medicine (TOPMed) program Multi-Ethnic Study of Atherosclerosis (MESA) with RNA sequencing (RNA-seq) data from peripheral blood mononuclear cells (PBMCs). We validated models' prediction accuracy on two external independent datasets, Geuvadis and Jackson Heart Study. To test robustness of our methods for TWASs, we integrated models with three multi-ancestry GWASs from blood cell, lipid, and pulmonary function traits, respectively. Our methods presented similar prediction accuracy while using a smaller and functionally informed set of variants compared to the benchmark method, elastic net (EN). Overall, our methods achieved higher power and accuracy (with average improved accuracy of 24% over EN) for TWASs. However, no single proposed method outperformed all GWAS traits. To further improve TWAS performance, we propose an omnibus approach that aggregates TWAS summary statistics from our methods. The omnibus approach yielded the highest number of Bonferroni-significant TWAS genes for all GWAS traits, and it further improved TWAS power and accuracy for blood cell traits. Additionally, the omnibus approach detected some trait-relevant important genes that the EN missed. Our study demonstrates the value of including FIVs in multi-ancestry transcriptome prediction models for improving TWAS performance. Further, the observed TWAS improvement depends on the GWAS trait's relevance to the PBMCs used to build our transcriptome prediction models.

PMID:41932314 | DOI:10.1016/j.ajhg.2026.03.008