245 research outputs found
Investigating Metabolism-Based Pharmacologic and Dietary Strategies of Nutrient Restriction to Impact Health and Disease
No full textMetabolism is essential for life and is involved in disease. By studying metabolism, we can understand basic cellular and organismal physiology, and try to modulate metabolism to treat diseases where it is implicated. Nutrient restriction, through both pharmacologic and dietary methods, is one way metabolism can be modulated because nutrients feed metabolism. In this dissertation, I examine both pharmacologic and dietary strategies of limiting different nutrients, and the impact on metabolism, physiology, and on models of disease. I use different model systems including mammalian cell culture, flies, and mice and examine their overall health and/or growth under different types of nutrient restriction. I examine metabolism using a liquid chromatography-mass spectrometry-based metabolomics method, which allows the simultaneous measurement of the relative levels of hundreds of polar and semi-polar metabolites. By using pharmacologic inhibitors that target glycolysis and connected pathways, my colleagues and I found that cancer cell viability and growth are reduced and showed how these metabolic pathways are used to benefit cancer growth. I also used pharmacologic and dietary strategies to target cysteine metabolism and found an unexpected connection with altered nucleotide metabolism when cysteine was limited in cancer cells and flies. Finally, I examined the impact of restrictive diets in mice, and my preliminary findings show that glucose metabolism is altered in these mice. This work collectively shows how metabolism can be altered by different methods of nutrient restriction, and how these strategies could be useful for treating diseases like cancer and promoting health and longevity. </p
On the optimality of the enzyme–substrate relationship in bacteria
No full textMuch recent progress has been made to understand the impact of proteome allocation on bacterial growth; much less is known about the relationship between the abundances of the enzymes and their substrates, which jointly determine metabolic fluxes. Here, we report a correlation between the concentrations of enzymes and their substrates in Escherichia coli. We suggest this relationship to be a consequence of optimal resource allocation, subject to an overall constraint on the biomass density: For a cellular reaction network composed of effectively irreversible reactions, maximal reaction flux is achieved when the dry mass allocated to each substrate is equal to the dry mass of the unsaturated (or "free") enzymes waiting to consume it. Calculations based on this optimality principle successfully predict the quantitative relationship between the observed enzyme and metabolite abundances, parameterized only by molecular masses and enzyme-substrate dissociation constants (Km). The corresponding organizing principle provides a fundamental rationale for cellular investment into different types of molecules, which may aid in the design of more efficient synthetic cellular systems
Computational approaches for understanding one-carbon metabolism in cancer
No full textCancer metabolism is an emerging research area in cancer biology and therapeutics. One of the major metabolic pathways known to play important roles in the pathogenesis of cancer is one-carbon (1-C) metabolism. 1-C metabolism integrates the status of many dietary nutrients as inputs, and in turn regulates a variety of cellular processes including de novo nucleotide synthesis, lipid metabolism, protein biosynthesis, redox metabolism, transsulfuration, and epigenetics. As the regulation of these cellular processes is critical to cells, the tuning of the activity of 1-C metabolism plays important roles in cancer. Previous studies have established implications of genetic and dietary perturbations of multiple components of 1-C metabolism in human cancers. However, the heterogeneity among cancer types and subtypes with respect to the usage and flux distribution of 1-C metabolism has not been systematically quantified. There remain great potentials in deciphering how 1-C metabolism plays different roles in different human cancers, especially since this metabolic pathway is targeted by a number of the existing antimetabolite chemotherapeutic agents.
In this dissertation, I quantitatively characterize various aspects of 1-C metabolism across human cancers. I first investigate the between-cancer-type variation in the usage of serine by 1-C metabolism using flux distribution analyses and find substantial heterogeneity. I also show that a common feature across cancers is correlated activation of nucleotide and redox metabolism. Next I assess the link between 1-C metabolism and DNA methylation using computational modeling and machine-learning. I find significant contribution from particular enzymes within 1-C metabolism— such as methionine adenosyltransferases— in explaining the within- cancer-type (inter-individual) variation in DNA methylation. My results provide evidence that misregulation of 1-C metabolism is at least in part responsible for disrupted DNA methylation profiles in tumors leading to epigenetic instability and higher malignancy. Given evidence for the role of 1-C metabolism and the methionine cycle in methylation dynamics, I next evaluate the potential for dietary intervention using the amino acid methionine. To this end, I model human serum methionine levels and quantify the contribution of various factors in determining the concentration of methionine. I discover that dietary factors could together explain nearly 30% of overall variation in methionine concentrations, and also provide evidence that the relationship between 1-C metabolism and methylation exists at physiological concentrations of methionine. Finally, I use a novel approach to identify gene expression markers of tumor response to 5-FU and Gemcitabine —two of the commonly used antimetabolite chemotherapies that target enzymes in 1-C metabolism. I discover that response to these agents is to a large degree determined by the metabolic state of tumors and the expression levels of specific target pathways of each of these agents. Together, my findings provide quantitative information about the heterogeneity among tumors with respect to the usage of 1-C metabolism, and delineate some of the ways this information can be translated into clinical decision- making
Design principles of mammalian signaling networks : emergent properties at modular and global scales
No full textThesis (Ph. D.)--Massachusetts Institute of Technology, Biological Engineering Division, 2008.Includes bibliographical references (leaves 244-249).This thesis utilizes modeling approaches rooted in statistical physics and physical chemistry to investigate several aspects of cellular signal transduction at both the modular and global levels. Design principles of biological networks and cell signaling processes pertinent to disease progression emerge from these studies. It is my hope that knowledge of these principles may provide new mechanistic insights and conceptual frameworks for thinking about therapeutic intervention into diseases such as cancer and diabetes that arise from aberrant signaling. Areas of interest have emphasized the role of scaffold proteins in protein kinase cascades, modeling relevant biophysical processes related to T cell activation, design principles of signal transduction focusing on multisite phosphorylation, quantifying the notion of signal duration and the time scale dependence of signal detection, and entropy based models of network architecture inferred from proteomics data. These problems are detailed below. The assembly of multiple signaling proteins into a complex by a scaffold protein guides many cellular decisions. Despite recent advances, the overarching principles that govern scaffold function are not well understood. We carried out a computational study using kinetic Monte Carlo simulations to understand how spatial localization of kinases on a scaffold may regulate signaling under different physiological condition. Our studies identify regulatory properties of scaffold proteins that allow them to both amplify and attenuate incoming signals in different biological contexts. In a further, supplementary study, simulations also indicate that a major effect that scaffolds exert on the dynamics of cell signaling is to control how the activation of protein kinases is distributed over time[2].(cont.) Scaffolds can influence the timing of kinase activation by allowing for kinases to become activated over a broad range of times, thus allowing for signaling across a broad spectrum of time scales. T cells orchestrate the adaptive immune response and are central players in maintenance of functioning immune system. Recent studies have reported that T cells can integrate signals between interrupted encounters with Antigen Presenting Cells (APCs) in such a way that the process of signal integration exhibits a form of memory. We carried out a computational study using a simple mathematical model of T cell activation to investigate the ramifications of interrupted T cell-APC contacts on signal integration. We considered several mechanisms of how signal integration at these time scales may be achieved. In another study, we investigated the role of spatially localizing signaling components of the T cell signaling pathway into a structure known as the immunological synapse. We constructed a minimal mathematical model that offers a mechanism for how antigen quality can regulate signaling dynamics in the immunological synapse These studies involving the analysis of signaling dynamics led us to investigate how differences in signal duration might be detected. Signal duration (e.g. the time scales over which an active signaling intermediate persists) is a key regulator of biological decisions in myriad contexts such as cell growth, proliferation, and developmental lineage commitments. Accompanying differences in signal duration are numerous downstream biological processes that require multiple steps of biochemical regulation. We present an analysis that investigates how simple biochemical motifs that involve multiple stages of regulation can be constructed to differentially process signals that persist at different time scales[3].(cont.) Topological features of these networks that allow for different frequency dependent signal processing properties are identified. One role of multisite phosphorylation in cell signaling is also investigated. The utilization of multiple phosphorylation sites in regulating a biological response is ubiquitous in cell signaling. If each site contributes an additional, equivalent binding site, then one consequence of an increase in the number of phosphorylations may be to increase the probability that, upon disassociation, a ligand immediately rebinds to its receptor. How such effects may influence cell signaling systems is not well understood. A self-consistent integral equation formalism for ligand rebinding, in conjunction with Monte Carlo simulations, was employed to further investigate the effects of multiple, equivalent binding sites on shaping biological responses. Finally, this thesis also seeks to investigate cell signaling at a global scale. Advances in Mass Spectrometry based phosphoproteomics have allowed for the real-time quantitative monitoring of entire proteomes as signals propagate through complex networks in response to external signals. The trajectories of as many as 222 phosphorylated tyrosine sites can be simultaneously and reproducibly monitored at multiple time points. We develop and apply a method using the principle of maximum entropy to infer a model of network connectivity of these phosphorylation sites. The model predicts a core structure of signaling nodes, affinity dependent topological features of the network, and connectivity of signaling nodes that were hitherto unassociated with the canonical growth factor signaling network. Our combined results illustrate many complexities in the broad array of control properties that emerge from the physical effects that constrain signal propagation on complex biological networks.(cont.) It is the hope of this work that these studies bring coherence to seemingly paradoxical observations and suggest that cells have evolved design rules that enable biochemical motifs to regulate widely disparate cellular functions.by Jason W. Locasale.Ph.D
Computational investigations into the origins of short-term biochemical memory in T cell activation.
No full textRecent studies have reported that T cells can integrate signals between interrupted encounters with Antigen Presenting Cells (APCs) in such a way that the process of signal integration exhibits a form of memory. Here, we carry out a computational study using a simple mathematical model of T cell activation to investigate the ramifications of interrupted T cell-APC contacts on signal integration. We consider several mechanisms of how signal integration at these time scales may be achieved and conclude that feedback control of immediate early gene products (IEGs) appears to be a highly plausible mechanism that allows for effective signal integration and cytokine production from multiple exposures to APCs. Analysis of these computer simulations provides an experimental roadmap involving several testable predictions
STRATEGIES FOR SELECTIVE TARGETING OF THE WARBURG EFFECT IN CANCER
No full textCancer cells undergo numerous adaptive processes to sustain rapid growth and survival. One notable mechanism is by rewiring their metabolism, most prominently through a phenomenon known as the Warburg Effect (WE). The WE is defined as an increase in glucose consumption and lactate secretion in the presence or absence of oxygen. Although the WE has been extensively studied, efforts to therapeutically target it have been largely unsuccessful due to the lack of obvious metabolic biomarkers and difficulties achieving full enzyme inhibition without inducing toxicity in normal tissue. Although targeted cancer therapies that use genetics have been successful, principles for selectively targeting tumor metabolism that also depend on environmental factors remain unknown. This limitation prompted the investigation to determine whether differential control in metabolism can be exploited for therapy. In this dissertation, I first determined whether therapeutic targeting of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), an enzyme that differentially regulates the Warburg Effect in cancer, can result in anti-tumor efficacy. Using comparative metabolomics, integrated pharmacogenomics, and systems biology, I found that koningic acid (KA), a natural product produced by the Trichoderma species, is a highly specific inhibitor of GAPDH. Notably, I determined that the quantitative extent of the Warburg Effect predicts response to KA in both cancer cells and tumors. Secondly, given the efficacy of KA in cancers specifically undergoing the Warburg Effect, I next used KA as a tool to determine whether there is a biological distinction between the Warburg Effect and glycolysis. I developed an evolved resistance model to KA and confirmed using metabolomics, stable isotope tracing, and a set of pharmacological approaches that glucose metabolism can exist in multiple states in the cell with distinct metabolic outputs. Lastly, I used therapeutic and genetic interventions to target 3-phosphoglycerate dehydrogenase (PHGDH), which diverts glucose flux into serine biosynthesis, and showed that this disrupts cancer cell growth through inhibition of de novo serine synthesis and downstream serine metabolism. Together, my findings contribute to a shifting paradigm in the current understanding of metabolic cancer therapy and show the potential use of metabolic factors as predictors and important determinants of therapeutic response
Metabolic Regulation of Histone Methylation
No full textThe one carbon cycle encompasses the folate and methionine cycles to produce one carbon units for a variety of cellular processes. The methionine cycle, in particular, generates S-adenosylmethionine (SAM) from methionine, an essential amino acid. SAM is utilized by histone methyltransferases (HMTs) to methylate histone proteins. Histone methylation plays diverse roles in the establishment of chromatin states and the regulation of gene expression. Histones are methylated by histone methyltransferases (HMTs) and demethylated by histone demethylases (HDMs) the activities of which rely on molecules generated via cell metabolism, such as S-adenosylmethionine (SAM). It has been shown in vitro, physiological concentrations of SAM are close to reported Km values for histone methyltransferase enzymes. Therefore, histone methyltransferase activity is sensitive to small changes in intracellular concentrations of SAM that could arise from differences in nutrient availability.
Histone methylation has only recently been appreciated as a dynamic epigenetic mark. Of the numerous histone methylation modifications that occur, the importance of H3 lysine 4 trimethylation (H3K4me3), H3K9me3, and H3K27me3 in establishing and maintaining chromatin states and their influence on gene expression are well documented. To better define the relationship between SAM availability, histone methylation and downstream consequences on gene expression, I characterized the metabolic response to methionine restriction using metabolomic, transcriptomic, and epigenomic approaches. Together, we found a specific response to methionine deprivation lead to decreases in SAM and H3K4me3 providing a link between cell metabolism and epigenetics. We took this one step further, and analyzed the transcriptional response to methionine restriction to determine the effect of metabolic alterations in H3K4me3 on gene expression.
This work provides evidence for a link between metabolic status and histone methylation in cells that could give rise to changes in gene expression-whether transient or permanent-providing a molecular basis for how environmental factors, such as diet, can influence gene expression via cell metabolism
INVESTIGATING THE HETEROGENEITY OF GLUCOSE AND GLUTAMINE METABOLISM IN CANCER
No full textRapidly proliferating cancer cells have increased biosynthetic and bioenergetic needs compared to quiescent cells. Hence, they undergo a reprogramming of intermediary metabolism, mainly by upregulating the uptake and catabolism of certain nutrients. The classical example of this is the ‘Warburg effect’ or aerobic glycolysis, defined as an increase in glucose uptake coupled to lactate secretion, regardless of oxygen availability in cancer cells. One consequence of this is that the majority of glucose carbon is diverted away from the mitochondria and the tricarboxylic acid (TCA) cycle, and secreted out of the cell as lactate. This prompts some cancer cells to exhibit an increased dependence on glutamine metabolism to refill the TCA cycle. However, recent studies have uncovered widespread heterogeneity on the roles of glucose and glutamine metabolism in cancer cells, prompting a need for further clarification.
In order to examine these metabolic pathways in cancer cells, I first helped develop high-resolution LC-MS metabolomic workflows. The initial objective of my thesis focused on the Warburg effect, and specifically, the increased rate of glycolytic flux that occurs in colon cancer cells. I demonstrated that changes in glycolytic flux could modify specific histone acylation marks in a dose-dependent manner, suggesting that a possible function of the Warburg effect is to confer specific signaling effects on cancer cells.
The second objective of my thesis focused on defining the contribution of the mitochondrial glutaminase isoenzyme GLS2 on the observed variability in glutamine dependence in various breast cancer cell types. I identified GLS2 as an important metabolically active enzyme in breast cancer cells that can feed TCA cycle anaplerosis. This finding has important clinical implications due to the fact that a glutaminase inhibitor, which fails to block GLS2 activity, is currently in Phase II trials.
In summary, my dissertation work further contributes to our fundamental understanding of the metabolic programs operating in cancer cells, by uncovering novel aspects of both glucose and glutamine metabolism. This work identifies new underlying causes of the metabolic heterogeneity observed in cancer cells and may prove relevant to future improvements in cancer therapies
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