HSK31679 alleviates MASLD by suppressing monoglucosylation of gut microbial sphingolipids

Description

Background: As the first approved medication for metabolic dysfunction-associated steatohepatitis (MASH), thyroid hormone receptor β (THR-β) agonist MGL-3196 (Resmetirom) has been highly spotlighted as the liver-directed, bioactive oral drug. However, it was also identified with remarkable heterogeneity of individual clinical efficacy and its interference with gut microbiota in host hepatoenteral circulation was still undocumented. Methods: We compared MASH attenuation by MGL-3196 and its derivative drug HSK31679 in germ-free (GF) and specific-pathogen free (SPF) mice to evaluate the role of gut microbiota. Then deep cross-omics analyses of microbial metagenome, metabolome and single-cell RNA-sequencing were clinically applied into the randomized, double-blind, placebo-controlled multiple-ascending-dose (MAD) cohort of HSK31679 treatment (n = 50), to comprehensively investigate the altered gut microbiota metabolism and circulating immune signatures. Results: HSK31679 outperformed MGL-3196 in ameliorating MASH diet-induced steatohepatitis of SPF mice but not GF mice. In the MAD cohort of HSK31679, relative abundance of B. thetaiotaomicron was significantly enriched to impair glucosylceramide synthase (GCS)-catalyzed monoglucosylation of microbial Cer(d18:1/16:0) and Cer(d18:1/24:1). In stark contrast to the non-inferiority MASH resolution between MGL-3196 and HSK31679 for GFBT△GCS mice, HSK31679 manifested superior steatohepatitis alleviation than MGL-3196 for GFBTWT mice, which may attribute to its steric hindrance with R123 and Y401 of gut microbial GCS. In stool samples with high GCS activity, the administration of 160 mg HSK31679 has induced a shift in peripheral compartments towards an immunosuppressive niche, characterized by the down-regulation of CD8α+ dendritic cells and MINCLE+ macrophages. Conclusions: This study has provided novel insights into the indispensable gut microbiota for HSK31679 treatment, which revealed microbial GCS may serve as its prognostic biomarker of MASH treatment, as well as the new target for further strategies of microbiota-based MASH therapeutics.

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Metagenomics According to manufacturer’s instructions (Biomarker Technologies), total fecal DNA was extracted and sequencing libraries were generated using the illumina Nextera DNA Flex Library Prep Kit. After the genomic DNA qualification, the metagenomic libraries were sequenced on the illumina HiSeq 250 platform after PCR amplification and DNA purification. Reads of sequenced metagenomes were cleaned using the software Fastp (version 0.23.2) to remove low-quality reads (quality score and length cut-off of Q20 and 75 bp, respectively). Metagenomic assembly of microbiome was performed using the software MEGAHIT (version 1.2.9), filtering for Contig reads < 300 bp. Redundant gene sets were removed using the software MMseq2 with the threshold of 95% similarity and 90% gene length coverage. Metagenomic species taxonomical annotation of microbiome was performed by comparing the sequences of non-redundant genes and Non-Redundant Protein Database using NCBI blast with expected-value 1*e-5. Metabolomics Data were obtained using the TOF full-scan method in the positive and negative ion mode with the collision energy set at 35 ± 15 eV. 211,468 metabolites containing specific source information were compared with MetOrigin database (http://metorigin.met-bioinformatics.cn/) to be classified into different categories of host (mammals), microbiota (archaea, fungi, bacteria), co-metabolism (shared by both host and microbiota), food (food & plant), drug, and environment (toxins & pollutants). Isotopically-labeled internal standards (IS) were spiked into the samples for metabolite quantitation. Metabolite levels of microbiota were normalized based on the following rules: (1) Correct peak areas of metabolites with their corresponding IS; (2) When Step (1) was not feasible due to the unavailability of commercial standards, the peak area was corrected with IS of the same category, peak intensities that were comparable, and/or proximity of retention times; (3) Results from Step (2) were evaluated according to the relative standard deviation (RSD) values before and after IS correction of each metabolite. If RSD values of corrected peak areas were smaller than which of original areas in quality control samples, the corrected peak areas were adopted. All metabolite levels were labeled and compared with “intensity” for simplicity.

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