Paired microbiome and metabolome analyses associate bile acid changes with colorectal cancer progression.

Colorectal cancer (CRC) is driven by genomic alterations in concert with dietary influences, with the gut microbiome implicated as an effector in disease development and progression. While meta-analyses have provided mechanistic insight into patients with CRC, study heterogeneity has limited causal associations. Using multi-omics studies on genetically controlled cohorts of mice, we identify diet as the major driver of microbial and metabolomic differences, with reductions in α diversity and widespread changes in cecal metabolites seen in high-fat diet (HFD)-fed mice. In addition, non-classic amino acid conjugation of the bile acid cholic acid (AA-CA) increased with HFD. We show that AA-CAs impact intestinal stem cell growth and demonstrate that Ileibacterium valens and Ruminococcus gnavus are able to synthesize these AA-CAs. This multi-omics dataset implicates diet-induced shifts in the microbiome and the metabolome in disease progression and has potential utility in future diagnostic and therapeutic developments.

BIRDMAn: A Bayesian differential abundance framework that enables robust inference of host-microbe associations.

Quantifying the differential abundance (DA) of specific taxa among experimental groups in microbiome studies is challenging due to data characteristics (e.g., compositionality, sparsity) and specific study designs (e.g., repeated measures, meta-analysis, cross-over). Here we present BIRDMAn (Bayesian Inferential Regression for Differential Microbiome Analysis), a flexible DA method that can account for microbiome data characteristics and diverse experimental designs. Simulations show that BIRDMAn models are robust to uneven sequencing depth and provide a >20-fold improvement in statistical power over existing methods. We then use BIRDMAn to identify antibiotic-mediated perturbations undetected by other DA methods due to subject-level heterogeneity. Finally, we demonstrate how BIRDMAn can construct state-of-the-art cancer-type classifiers using The Cancer Genome Atlas (TCGA) dataset, with substantial accuracy improvements over random forests and existing DA tools across multiple sequencing centers. Collectively, BIRDMAn extracts more informative biological signals while accounting for study-specific experimental conditions than existing approaches.

Quantifying Tumor Heterogeneity via MRI Habitats to Characterize Microenvironmental Alterations in HER2+ Breast Cancer.

This study identifies physiological habitats using quantitative magnetic resonance imaging (MRI) to elucidate intertumoral differences and characterize microenvironmental response to targeted and cytotoxic therapy. BT-474 human epidermal growth factor receptor 2 (HER2+) breast tumors were imaged before and during treatment (trastuzumab, paclitaxel) with diffusion-weighted MRI and dynamic contrast-enhanced MRI to measure tumor cellularity and vascularity, respectively. Tumors were stained for anti-CD31, anti-ɑSMA, anti-CD45, anti-F4/80, anti-pimonidazole, and H&E. MRI data was clustered to identify and label each habitat in terms of vascularity and cellularity. Pre-treatment habitat composition was used stratify tumors into two "tumor imaging phenotypes" (Type 1, Type 2). Type 1 tumors showed significantly higher percent tumor volume of the high-vascularity high-cellularity (HV-HC) habitat compared to Type 2 tumors, and significantly lower volume of low-vascularity high-cellularity (LV-HC) and low-vascularity low-cellularity (LV-LC) habitats. Tumor phenotypes showed significant differences in treatment response, in both changes in tumor volume and physiological composition. Significant positive correlations were found between histological stains and tumor habitats. These findings suggest that the differential baseline imaging phenotypes can predict response to therapy. Specifically, the Type 1 phenotype indicates increased sensitivity to targeted or cytotoxic therapy compared to Type 2 tumors.

Nonalcoholic Steatohepatitis and HCC in a Hyperphagic Mouse Accelerated by Western Diet.

BACKGROUND & AIMS: How benign liver steatosis progresses to nonalcoholic steatohepatitis (NASH), fibrosis, and hepatocellular carcinoma (HCC) remains elusive. NASH progression entails diverse pathogenic mechanisms and relies on complex cross-talk between multiple tissues such as the gut, adipose tissues, liver, and the brain. Using a hyperphagic mouse fed with a Western diet (WD), we aimed to elucidate the cross-talk and kinetics of hepatic and extrahepatic alterations during NASH-HCC progression, as well as regression. METHODS: Hyperphagic mice lacking a functional Alms1 gene (Foz/Foz) and wild-type littermates were fed WD or standard chow for 12 weeks for NASH/fibrosis and for 24 weeks for HCC development. NASH regression was modeled by switching back to normal chow after NASH development. RESULTS: Foz+WD mice were steatotic within 1 to 2 weeks, developed NASH by 4 weeks, and grade 3 fibrosis by 12 weeks, accompanied by chronic kidney injury. Foz+WD mice that continued on WD progressed to cirrhosis and HCC within 24 weeks and had reduced survival as a result of cardiac dysfunction. However, NASH mice that were switched to normal chow showed NASH regression, improved survival, and did not develop HCC. Transcriptomic and histologic analyses of Foz/Foz NASH liver showed strong concordance with human NASH. NASH was preceded by an early disruption of gut barrier, microbial dysbiosis, lipopolysaccharide leakage, and intestinal inflammation. This led to acute-phase liver inflammation in Foz+WD mice, characterized by neutrophil infiltration and increased levels of several chemokines/cytokines. The liver cytokine/chemokine profile evolved as NASH progressed, with subsequent predominance by monocyte recruitment. CONCLUSIONS: The Foz+WD model closely mimics the pathobiology and gene signature of human NASH with fibrosis and subsequent HCC. Foz+WD mice provide a robust and relevant preclinical model of NASH, NASH-associated HCC, chronic kidney injury, and heart failure.