Bartelle, Benjamin B.

ABSTRACT Maternal obesity is associated with increased risk of sporadic colorectal cancer (CRC) in offspring, suggesting that early-life environmental exposures durably shape disease susceptibility. Intestinal stem cells (ISCs), long-lived drivers of epithelial renewal and tumor initiation, are well poised to mediate this effect; however, how maternal obesity influences ISC programming during development remains poorly understood. Using mouse models of diet-induced obesity, we show that exposure to a maternal high-fat Western diet (mHFD) during pre- and postnatal development stably programs colonic ISCs. Offspring exhibit increased ISC proliferation, enhanced self-renewal, a hypermetabolic state, and altered epithelial lineage composition that persists into adulthood despite dietary normalization. These changes are accompanied by increased tumor burden following loss of Apc heterozygosity. Mechanistically, we identify the pro-inflammatory cytokine IL-17A as a key extrinsic driver and PPARd/a nuclear receptors as intrinsic mediators of the mHFD phenotype, revealing an immune-epithelial axis that programs ISC function during early life. Together, our findings demonstrate that maternal metabolic environments durably enhance stem cell fitness, providing a mechanistic link between developmental exposure and adult disease risk.

ABSTRACT Magnetic Resonance Spectroscopy Imaging (MRSI) offers spatially-resolved, neurometabolic information, acquired non-invasively at whole-brain scales from human subjects. Analysis of MRSI however, is extremely challenging. The metabolic information is highly convolved, and sparsely distributed across millions of spatial-spectral datapoints, allowing for little direct human interpretation. Conversely, the overall low signal-to-noise with high-intensity artifacts can confound unsupervised machine learning approaches. These technical barriers have left much of the potential of MRSI unrealized. We acquired MRSI data from 4 human subjects with a diagnosis of multiple sclerosis (MS), incorporating experimental design into an informed machine learning approach. MRSI acquisitions were registered to anatomical MRI to label 105k spectra from brain tissue and 162 spectra from white matter hyperintensities (WMHs), an imaging biomarker associated with MS lesions. Spectral labels were then used in contrastive principal component analysis (cPCA) to filter artifacts and background features in the MRSI data from lesion salient features and clustered into statistically significant states based on features that could be interpreted from the original data. Our approach renders MRSI data into testable representations of neurometabolism, enabling the method for fundamental and clinical research. Graphical Abstract Analysis workflow for neurometabolic profiling of MS lesions. MRSI and anatomical MRI is acquired and processed in parallel for spectral data and anatomical labels. Spectra are then labeled and separated into experimental vs background data for contrastive PCA. Spectra are clustered for similarity, further labeled, and projected onto a brain atlas for a neurometabolic view.