Cancer cells rewire their metabolism, which creates targetable metabolic vulnerabilities. Previous analyses of metabolic vulnerabilities in cancer cells have been limited to the analysis of individual genes or metabolites. However, metabolic pathways exhibit significant cross talk and compensation for one another. We developed a computational method to answer the question: when a metabolic pathway’s activity is high, which other metabolic pathways become more essential or less essential? By integrating genetic screen data with drug response data from FDA approved drugs, we identified cancer cell line dependence on metabolic pathways as opposed to individual genes. For example, we found that identifying key regulators of metabolic pathways, such as the Pentose Phosphate Pathway, may serve as a biomarker to identify which patients may benefit from antifolate chemotherapies (e.g. methotrexate, 5-fluorouracil). The efforts outlined here serve as the first characterization of the landscape of metabolic pathway vulnerabilities in cancer cell lines. Our results demonstrate the benefit of analyzing dependencies on metabolic pathways as opposed to metabolic genes.

Elucidating the complex relationship between nutrient-induced signaling and metabolism represents a key in understanding the onset of many different human diseases like obesity, type 3 diabetes, cancer, and many neurological disorders. In this work we proposed a hybrid modeling approach, combining Boolean representation of signaling pathways, like Snf1, TORC1, and PKA with the enzyme constrained model of metabolism linking them via the regulatory network. This allowed us to improve individual model predictions and elucidate how single components in the dynamic signaling layer affect steady-state metabolism. The model has been tested under respiration and fermentation, revealing novel connections and further reproducing the regulatory effects that are associated with the Crabtree effect and glucose repression. Finally, we show a connection between Snf1 signaling and chronological lifespan.

The mechanisms by which TP53, the most frequently mutated gene in human cancer, suppresses tumorigenesis remain unclear. p53 modulates various cellular processes, such as apoptosis and proliferation, which has led to distinct cellular mechanisms being proposed for p53-mediated tumor suppression in different contexts. Here, we asked whether during tumor suppression p53 might instead regulate a wide range of cellular processes. Analysis of mouse and human oncogene-expressing wild-type and p53-deficient cells in physiological oxygen conditions revealed that p53 loss concurrently impacts numerous distinct cellular processes, including apoptosis, genome stabilization, DNA repair, metabolism, migration, and invasion. Notably, some phenotypes were uncovered only in physiological oxygen. Transcriptomic analysis in this setting highlighted underappreciated functions modulated by p53, including actin dynamics. Collectively, these results suggest that p53 simultaneously governs diverse cellular processes during transformation suppression, an aspect of p53 function that would provide a clear rationale for its frequent inactivation in human cancer.

Metabolic flux analysis in disease biology is opening up new avenues for therapeutic interventions. Numerous diseases lead to disturbance in the metabolic homeostasis and it is becoming increasingly important to be able to quantify the difference in interaction under normal and diseased condition. While genome-scale metabolic models have been used to study those differences, there are limited methods to probe into the differences in flux between these two conditions. Our method of conducting a differential flux analysis can be leveraged to find which reactions are altered between the diseased and normal state. We applied this to study the altered reactions in the case of SARS-CoV-2 infection. We further corroborated our results with other multi-omics studies and found significant agreement.

Understanding deregulations of metabolism based on isolated measures of gene expression or protein or metabolite concentrations is a challenging task due to the interconnection of multiple processes. One solution is to extract, from generic genome-scale metabolic networks, the specific sub-network which is modulated in the studied condition. Many algorithms have been proposed for such context-specific network extraction based on experimental measurements. However, this process is subject to some randomness and variability, since multiple metabolic networks can model the metabolic state in a similarly adequate manner for the same experimental data. This means that for a given data and reconstruction method, there are usually multiple solutions that satisfy the same constraints and with the same quality, but only one solution is returned by the commonly used reconstruction methods. Here, we formalize this problem and we propose and analyze different methods to obtain diverse samples of metabolic sub-networks. We evaluate them by performing an extensive comparison and we show how the different sets of optimal networks discovered by the different methods are biological meaningful by constructing ensembles of networks to improve the prediction of essential genes in Saccharomyces cerevisiae and to detect enriched metabolic pathways in four different human cancer cell lines.

Vaccinia virus (VACV) is a large DNA virus with an acute and increasing demand for energy and macromolecules to build hundreds and thousands of viral particles in a single cell within hours of infection. The demand postulates reprogramming of the TCA cycle, as it is the central metabolic hub of a cell that generates metabolites for energy production and macromolecule synthesis. We show that VACV infection reprograms cellular metabolism globally, elevating the TCA cycle intermediate levels and modulating related cell metabolism. The elevation of the TCA cycle intermediates depends on the virus-encoded growth factor that stimulates non-canonical STAT3 signaling during VACV infection. Our results provide the metabolic foundation of viral growth factor to boost VACV infection.

Glycolysis is a central pathway in cellular energy metabolism that breaks down glucose to produce ATP, yet it can sometimes fail to start up properly after cells have experienced a period of starvation. This puzzling failure occurs when a sudden increase in glucose concentration throws a cell into a self-sustaining imbalanced state in which upper and lower glycolysis work at different rates. As a result, glycolytic intermediates accumulate in the cell, and it is unable to maintain high ATP concentration needed to support cellular functions. Here, we perform individual-based eco-evolutionary simulations of a simplified yeast glycolysis pathway and show that this apparently costly vulnerability allows for faster growth in environments with scarce or fluctuating resource availability. Accordingly, we propose that vulnerability to metabolic imbalance can be interpreted as a manifestation of an evolutionary trade-off between performance in rich, stable environments and poor, fluctuating ones. Furthermore, we show that when resource availability fluctuates, imbalanced dynamics itself can be advantageous: when glucose is abundant, imbalanced pathways can quickly accumulate glycolytic intermediates as intracellular storage that is used to sustain growth during periods of starvation. Finally, we find that in variable environments, competition for glucose can support stable coexistence of balanced and imbalanced cells in the population, as well as repeated cycles of population crashes and recoveries. Overall, our results show that ecological and evolutionary mechanisms provide a fruitful context for interpreting seemingly flawed aspects of cellular metabolism.

An adjuvant is the pharmacological agent that is added to vaccines to boost immune responses. Currently, there are only seven FDA-approved adjuvants for human use, and vaccines based on these adjuvants have mainly been evaluated for elicitation of antibody-based immunity. However, vaccines need to also stimulate T cell-mediated immunity to protect against diseases such as AIDS, TB and Malaria. Hence, there is a critical need to develop adjuvants that stimulate protective T cell immunity. Here, we identified an adjuvant (Adjuplex; ADJ) that safely induces strong T cell immunity and protects against virus and intracellular bacteria. We also found that ADJ stimulated T cell immunity by unique mechanisms that did not include metabolic activation of antigen-presenting dendritic cells. Instead, ADJ induced a low metabolic state and engaged mechanisms including lipid pathways and induction of reactive oxygen species to promote activation of T cells by dendritic cells, following vaccination. These data not only provide new mechanistic insights into the mechanisms driving activation of T cells by ADJ, it provides a blue print for what adjuvants need to do to induce protection against infections that require T cell immunity.

Spatiotemporally regulated targeted gene manipulation is a common way to study the effect of gene variants on phenotypic traits, but the Cre/loxP and Tet-On/Tet-Off systems can affect whole-organism physiology and function due to off-target effects. We highlight some of these adverse effects, including whole-body endocrinology and disturbances in the gut microbiome and in mitochondrial and metabolic function.