Microenvironmental regulation of cancer cell metabolism: implications for experimental design and translational studies

Cancers have an altered metabolism, and there is interest in understanding precisely how oncogenic transformation alters cellular metabolism and how these metabolic alterations can translate into therapeutic opportunities. Researchers are developing increasingly powerful experimental techniques to study cellular metabolism, and these techniques have allowed for the analysis of cancer cell metabolism, both in tumors and in ex vivo cancer models. These analyses show that, while factors intrinsic to cancer cells such as oncogenic mutations, alter cellular metabolism, cell-extrinsic microenvironmental factors also substantially contribute to the metabolic phenotype of cancer cells. These findings highlight that microenvironmental factors within the tumor, such as nutrient availability, physical properties of the extracellular matrix, and interactions with stromal cells, can influence the metabolic phenotype of cancer cells and might ultimately dictate the response to metabolically targeted therapies. In an effort to better understand and target cancer metabolism, this Review focuses on the experimental evidence that microenvironmental factors regulate tumor metabolism, and on the implications of these findings for choosing appropriate model systems and experimental approaches. Keywords\: Cancer, Cancer models, Metabolism, Microenvironment, Nutrient availability, Nutrient sensingNational Institutes of Health (U.S.) (R01CA168653)National Cancer Institute (U.S.) (F32CA213810)National Cancer Institute (U.S.) (F32CA210421)Howard Hughes Medical InstituteLudwig Institute for Cancer ResearchStand Up To Cancer (SU2C-AACR-IRG 09-16)Lustgarten FoundationMIT Center for Precision Cancer Medicin

Metabolic heterogeneity and cross-feeding within isogenic yeast populations captured by DILAC

Genetically identical cells are known to differ in many physiological parameters such as growth rate and drug tolerance. Metabolic specialization is believed to be a cause of such phenotypic heterogeneity, but detection of metabolically divergent subpopulations remains technically challenging. We developed a proteomics-based technology, termed differential isotope labelling by amino acids (DILAC), that can detect producer and consumer subpopulations of a particular amino acid within an isogenic cell population by monitoring peptides with multiple occurrences of the amino acid. We reveal that young, morphologically undifferentiated yeast colonies contain subpopulations of lysine producers and consumers that emerge due to nutrient gradients. Deconvoluting their proteomes using DILAC, we find evidence for in situ cross-feeding where rapidly growing cells ferment and provide the more slowly growing, respiring cells with ethanol. Finally, by combining DILAC with fluorescence-activated cell sorting, we show that the metabolic subpopulations diverge phenotypically, as exemplified by a different tolerance to the antifungal drug amphotericin B. Overall, DILAC captures previously unnoticed metabolic heterogeneity and provides experimental evidence for the role of metabolic specialization and cross-feeding interactions as a source of phenotypic heterogeneity in isogenic cell populations

Prospects for multi-omics in the microbial ecology of water engineering

Advances in high-throughput sequencing technologies and bioinformatics approaches over almost the last three decades have substantially increased our ability to explore microorganisms and their functions – including those that have yet to be cultivated in pure isolation. Genome-resolved metagenomic approaches have enabled linking powerful functional predictions to specific taxonomical groups with increasing fidelity. Additionally, related developments in both whole community gene expression surveys and metabolite profiling have permitted for direct surveys of community-scale functions in specific environmental settings. These advances have allowed for a shift in microbiome science away from descriptive studies and towards mechanistic and predictive frameworks for designing and harnessing microbial communities for desired beneficial outcomes. Water engineers, microbiologists, and microbial ecologists studying activated sludge, anaerobic digestion, and drinking water distribution systems have applied various (meta)omics techniques for connecting microbial community dynamics and physiologies to overall process parameters and system performance. However, the rapid pace at which new omics-based approaches are developed can appear daunting to those looking to apply these state-of-the-art practices for the first time. Here, we review how modern genome-resolved metagenomic approaches have been applied to a variety of water engineering applications from lab-scale bioreactors to full-scale systems. We describe integrated omics analysis across engineered water systems and the foundations for pairing these insights with modeling approaches. Lastly, we summarize emerging omics-based technologies that we believe will be powerful tools for water engineering applications. Overall, we provide a framework for microbial ecologists specializing in water engineering to apply cutting-edge omics approaches to their research questions to achieve novel functional insights. Successful adoption of predictive frameworks in engineered water systems could enable more economically and environmentally sustainable bioprocesses as demand for water and energy resources increases.BT/Industriele Microbiologi

The Design of FluxML: A Universal Modeling Language for 13C Metabolic Flux Analysis

13C metabolic flux analysis (MFA) is the method of choice when a detailed inference of intracellular metabolic fluxes in living organisms under metabolic quasi-steady state conditions is desired. Being continuously developed since two decades, the technology made major contributions to the quantitative characterization of organisms in all fields of biotechnology and health-related research. 13C MFA, however, stands out from other “-omics sciences,” in that it requires not only experimental-analytical data, but also mathematical models and a computational toolset to infer the quantities of interest, i.e., the metabolic fluxes. At present, these models cannot be conveniently exchanged between different labs. Here, we present the implementation-independent model description language FluxML for specifying 13C MFA models. The core of FluxML captures the metabolic reaction network together with atom mappings, constraints on the model parameters, and the wealth of data configurations. In particular, we describe the governing design processes that shaped the FluxML language. We demonstrate the utility of FluxML to represent many contemporary experimental-analytical requirements in the field of 13C MFA. The major aim of FluxML is to offer a sound, open, and future-proof language to unambiguously express and conserve all the necessary information for model re-use, exchange, and comparison. Along with FluxML, several powerful computational tools are supplied for easy handling, but also to maintain a maximum of flexibility. Altogether, the FluxML collection is an “all-around carefree package” for 13C MFA modelers. We believe that FluxML improves scientific productivity as well as transparency and therewith contributes to the efficiency and reproducibility of computational modeling efforts in the field of 13C MFA

Peptide based 13C MFA

<p>A peptide-based method for 13C Metabolic Flux analysis in microbial communities</p

Mapping Metabolic Interactions between S. cerevisiae and Lactic Acid Bacteria

Microorganisms in nature live in interconnected communities, where the language of biochemistry creates means for communicating, fighting, and cooperating with each other. This work investigates one of the ways for microbial interactions - nutrient exchange. It is focus ed primarily on co - metabolism of Saccharomyces cerevisiae and lactic acid bacteria - Lactococcus la ctis and Lactobacillus plantarum - community whose composition was inspired by co - occurrence of yeast and LAB in a multitude of naturally fermented foods. Specifically, I w as interested in detecting metabolic interactions between budding yeast and lactic a cid bacteria, identifying transferred molecules, exploring metabolic mechanisms of their biosynthesis and excretion, and understanding possible causes behind them. A c ombination of experimental and computational methods was used to understand how nutritio nal dependencies shape communit ies of microorganisms. First step involved compos ing a synthetic community of common laboratory strains of yeast and lactic acid bacteria . Following a series of experiments with chemically defined media, LAB revealed their me tabolic dependency on yeast for growth and survival. This m ixed species community appears to be stable and is sustained through the flow of small nitrogenous molecule s from yeast to bacteria. Nutrient cross feeding was found to be a result of overflow meta bolis m in yeast, which release excess catabolit es under particular combinations of available nitrogen sources. The o bserved nutrient excretion involve s a set of genes that regulate yeast nitrogen metabolism when depleted of preferred nitrogen sources. We h ave recreated co - metabolism of yeast - LAB community, as well as multiple natural bacterial communities, with multi - species genome - scale metabolic modeling. Simulation results demonstrated a link between metabolic cross - feeding and species co - occurrence, and proved its high potential of the method for predicting metabolite exchange in microbial communities. In this project, the inter - kingdom model community of wild type microorganisms has been established and characterized. Peculiarities of yeast regulatory n etwork, in certain media compositions, cause “wasteful” excretion of amino acids and other metabolites. This in turn cr e ates a stable niche for growth of lactic acid bacteria , which are auxotro phic for multiple amino acids. D escribed scenario of metabolic dependency between yeast and lactic acid bacteria demonstrates how survival of one species can be driven by metabo lic idiosyncrasy of the other. The y east - LAB interaction is established instantly, and thus can serve as a first step in evolution of cooperat ion

Development of 13C Fingerprint Tool and Its Application for Exploring Carbon and Energy Metabolism in Cyanobacterium Synechocystis sp. PCC 6803

Cyanobacteria are important microbial cell factories that are widely used in the biotechnology filed nowadays. They can use light as the sole energy source to fix CO2, accumulate biomass, and produce various valuable bio-products. Engineered cyanobacterial species can uptake nutrients from wastes to further reduce the cost. Recently, it is reported that cyanobacteria will provide much higher carbon yield than heterotrophs by co-utilizing organic carbons and CO2. However, the quantitative information of such `photo-fermentation\u27 process is still limited. Decoding the carbon metabolism of cyanobacteria during the photo-fermentation process can reveal the functional pathways, carbon distribution, and the energy requirement, all of which will provide guidelines for rational design of metabolic engineering strategies. The emerging of multiple omics tools, e.g. genomics, transcriptomics, proteinomics, and metabolomics analysis, allowed the comprehensive determination of microbial metabolisms. This dissertation describes the development of 13C fingerprint-based method to characterize the carbon metabolic network in cyanobacteria model species Synechocystis sp. PCC 6803 and the integration of this method with metabolic flux analysis and transcriptomics analysis to quantify the diverse carbon and energy metabolism regulation under different internal or external stimuli. The project mainly consists of four aspects: (1) developing the GC-MS based low-cost 13C fingerprint method; (2) exploring the carbon metabolic network structure and quantifying the central carbon metabolism under different environmental conditions; (3) determining the energy requirement for cell maintenance in cyanobacteria; (4) investigating the effects of light conditions on cyanobacterial carbon metabolism. These new findings not only improve our understandings of the flexible carbon metabolism employed by cyanobacteria, but also offer evolutionary insight into photosynthesis and potential applications of photo-fermentation

Multi-scale metabolism: from the origin of life to microbial ecology

Metabolism is a key attribute of life on Earth at multiple spatial and temporal scales, involved in processes ranging from cellular reproduction to biogeochemical cycles. While metabolic network modeling approaches have enabled significant progress at the cellular-scale, extending these techniques to address questions at both the ecosystem and planetary-scales remains highly unexplored. In this thesis, I integrate various multi-scale metabolic network modeling approaches to address key questions with regard to both the long-term evolution of metabolism in the biosphere and the metabolic processes that take place in complex microbial communities. The first portion of my thesis work, focused on the evolution of ancient metabolic networks, attempts to model the emergence of ecosystem-level metabolism from simple geochemical precursors. By integrating network-based algorithms, physiochemical constraints, and geochemical estimates of ancient Earth, I explored whether a complex metabolic network could have emerged without phosphate, a key molecular component in modern-day living systems, known to be poorly available at the onset of life. We found that phosphate may have not been essential in early living systems, and that thioesters may have been the primitive energy currency in ancient metabolic networks. By generalizing this approach to explore the scope of geochemical scenarios that could have given rise to living systems, I found that other key biomolecules, including fixed nitrogen, may have not been required at the earliest stages in biochemical evolution. The second portion of my thesis deals with a different aspect of ecosystem-level metabolism, namely the role of metabolism in shaping the structure of microbial communities. I studied the relationship between metabolism and microbial community assembly using microbial communities grown in synthetic laboratory environments. We found that a generalized statistical consumer-resource model recapitulates the emergent phenomena observed in these experiments. Future work could seek to better clarify the connection between the fundamental rules that led to life’s emergence over 4 billion years ago and the laws that shape microbial ecosystems today. An ecosystems-level metabolic perspective may aid in our understanding of both the emergence and maintenance of the biosphere

Molecular multiplexing methods for genome-scale measurements.

University of Minnesota Ph.D. dissertation. June 2018. Major: Plant and Microbial Biology. Advisor: Peter Tiffin. 1 computer file (PDF); ix, 106 pages.I present the utility of unique DNA barcodes to tag distinct genotypes and subsequently link them to phenotypes. Such molecular tagging allowed us to perform multiplexed phenotype analysis of thousands of genotypes using next-generation sequencing (NGS) technologies. Four projects are discussed in this thesis - 1) 2D Tn-Seq, a massively multiplexed experimental approach to interrogate genetic interactions of a microbe at the genome scale 2) pFluxSeq, a molecular tool that will enable peptide-based 13C metabolic flux analysis (MFA) of a mixed population of microbial cells 3) Deep mutational scanning of phenotype arrays and 4) Barcode-based lineage tracking to measure CRISPR/dCas9 RNA interference efficacy in bacteria