Carolina H. Chung

Differences in microbiome composition profoundly influence drug response, yet methods to model the metabolic interplay between tumors, microbes, and therapeutics remain limited. We present a generalizable framework combining machine-learning and genome-scale metabolic modeling to prioritize combination therapies for colorectal cancer (CRC) in the presence of Fusobacterium nucleatum ( Fn ) and other pathogenic, probiotic, and commensal microbes. Trained on 6,514 drug combinations in microbe-free CRC cell lines, the model predicted synergistic combinations in both microbe-free and microbe-associated contexts and generalized to immunotherapy-associated conditions. Predictions were validated using an asymmetric co-culture system that mimics the colon’s normoxic–anaerobic gradient, confirming synergistic combinations in HCT116 cells with Fn , including drugs not typically used in CRC therapy. Mechanistic analysis and targeted pharmacological perturbations revealed phospho-inositol metabolism and cysteine transport as key determinants of Fn -dependent drug synergy. Together, this work introduces a scalable, microbiome-aware framework to enable discovery of context-specific combination therapies.

Anti‐estrogens have had a limited impact on breast cancer (BC) prevention. Novel agents with better tolerability, and efficacy beyond estrogen receptor (ER) positive BC are needed. We studied licochalcone A (LicA) for ER‐agnostic BC prevention. We demonstrated that LicA significantly reduced proliferation in seven human breast cell lines and suppressed ER+ and ER− xenograft tumors in mice. We confirmed these observations ex vivo in the contralateral unaffected breast (CUB) of women with unilateral sporadic BC, and BC cell lines using RNA sequencing, metabolism flux modeling, confirmatory NanoString nCounter metabolic pathway panel analysis in independent sets of specimens, proteomics, and western blots. We found that LicA targets sterol regulatory element binding protein 1 (SREBP1) with subsequent metabolic‐inflammatory changes, lowering spatiotemporally resolved cholesterol levels inside malignant cells to the levels in normal mammary cells. Mechanistically, in CUBs we observed that LicA downregulated PI3K‐AKT‐SREBP1‐dependent lipogenesis, NF‐kB‐dependent inflammation, and de novo nucleotide biosynthesis, stalling proliferation. Studies in cell lines showed suppression of PI3K and AKT phosphorylation, SREBP1 protein expression, and the SREBP1‐dependent enzymes such as ACAT2, ACLY, FASN, SCD, consistent with reduced NEDD8 required for SREBP1 stabilization. We found a significant reduction in NF‐kB expression, its nuclear translocation mediator karyopherin β 1, and prostaglandin E2 synthesis. We demonstrated a reduction in PRPS1‐catalyzed de novo nucleotide biosynthesis, and downregulation of proliferative markers MKI67, RRM2, and the survival marker BCL2. LicA reduces pro‐tumorigenic aberrations in lipid homeostasis and inflammation through SREBP1. It is a promising non‐endocrine candidate for BC prevention. Future studies in immunocompetent BC prevention models are warranted.

Machine learning (ML) algorithms are necessary to efficiently identify potent drug combinations within a large candidate space to combat drug resistance. However, existing ML approaches cannot be applied to emerging and under-studied pathogens with limited training data. To address this, we developed a transfer learning and crowdsourcing framework (TACTIC) to train ML models on data from multiple bacteria. TACTIC was built using 2,965 drug interactions from 12 bacterial strains and outperformed traditional ML models in predicting drug interaction outcomes for species that lack training data. Top TACTIC model features revealed genetic and metabolic factors that influence cross- species and species-specific drug interaction outcomes. Upon analyzing ∼600,000 predicted drug interactions across 9 metabolic environments and 18 bacterial strains, we identified a small set of drug interactions that are selectively synergistic against Gram- negative (e.g., A. baumannii ) and non-tuberculous mycobacteria (NTM) pathogens. We experimentally validated synergistic drug combinations containing clarithromycin, ampicillin, and mecillinam against M. abscessus , an emerging pathogen with growing levels of antibiotic resistance. Lastly, we leveraged TACTIC to propose selectively synergistic drug combinations to treat bacterial eye infections (endophthalmitis).

Background: Anti-estrogens have had limited impact on breast cancer (BC) prevention. Novel agents with better tolerability, and efficacy beyond estrogen receptor (ER) positive BC are needed. We studied licochalcone A (LicA) for ER-agnostic BC prevention. Methods: We evaluated antiproliferative effects of LicA in seven breast cell lines and its suppression of ER+ and ER− xenograft tumors in mice. High-risk human breast tissue was treated with LicA ex vivo , followed by RNA sequencing and metabolism flux modeling. Confirmatory testing was performed in an independent specimen set and ER+/− BC cell lines using NanoString metabolic panel, proteomics, western blots, and spatiotemporally resolved cholesterol quantification in single cells. Results: LicA suppressed proliferation in vitro and xenograft tumor growth in vivo . It downregulated pivotal steps in PI3K-AKT-SREBP1-dependent lipogenesis, suppressed PI3K and AKT phosphorylation, SREBP1 protein expression, and cholesterol levels in the plasma membrane inner leaflet, to the levels in normal breast cells. LicA also suppressed prostaglandin E2 synthesis and PRPS1-catalyzed de novo nucleotide biosynthesis, stalling proliferation; further evident by reduced MKI67 and BCL2 proteins. Conclusions: LicA targets SREBP1, a central regulator of lipogenesis and immune response, reducing pro-tumorigenic aberrations in lipid homeostasis and inflammation. It is a promising non-endocrine candidate for BC prevention.

Drug combinations are a promising strategy to counter antibiotic resistance. However, current experimental and computational approaches do not account for the entire complexity involved in combination therapy design, such as the effect of pathogen metabolic heterogeneity, changes in the growth environment, drug treatment order, and time interval. To address these limitations, we present a comprehensive approach that uses genome-scale metabolic modeling and machine learning to guide combination therapy design. Our mechanistic approach (a) accommodates diverse data types, (b) accounts for time- and order-specific interactions, and (c) accurately predicts drug interactions in various growth conditions and their robustness to pathogen metabolic heterogeneity. Our approach achieved high accuracy (area under the receiver operating curve (AUROC) = 0.83 for synergy, AUROC = 0.98 for antagonism) in predicting drug interactions for Escherichia coli cultured in 57 metabolic conditions based on experimental validation. The entropy in bacterial metabolic response was predictive of combination therapy outcomes across time scales and growth conditions. Simulation of metabolic heterogeneity using population FBA identified two subpopulations of E. coli cells defined by the levels of three proteins (eno, fadB, and fabD) in glycolysis and lipid metabolism that influence cell tolerance to a broad range of antibiotic combinations. Analysis of the vast landscape of condition-specific drug interactions revealed a set of 24 robustly synergistic drug combinations with potential for clinical use.

Improved understanding of local breast biology that favors the development of estrogen receptor negative (ER−) breast cancer (BC) would foster better prevention strategies. We have previously shown that overexpression of specific lipid metabolism genes is associated with the development of ER− BC. We now report results of exposure of MCF-10A and MCF-12A cells, and mammary organoids to representative medium- and long-chain polyunsaturated fatty acids. This exposure caused a dynamic and profound change in gene expression, accompanied by changes in chromatin packing density, chromatin accessibility, and histone posttranslational modifications (PTMs). We identified 38 metabolic reactions that showed significantly increased activity, including reactions related to one-carbon metabolism. Among these reactions are those that produce S-adenosyl-L-methionine for histone PTMs. Utilizing both an in-vitro model and samples from women at high risk for ER− BC, we show that lipid exposure engenders gene expression, signaling pathway activation, and histone marks associated with the development of ER− BC.

Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein–protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms.