Recon3D enables a three-dimensional view of gene variation in human metabolism

Brunk, Elizabeth; Sahoo, Swagatika; Zielinski, Daniel C.; Altunkaya, Ali; Dräger, Andreas; Mih, Nathan; Gatto, Francesco; Nilsson, Anders; Gonzalez, Germán Andrés; Aurich, Maike Kathrin; Prlić, Andreas; Sastry, Anand V.; Daníelsdóttir, Anna Dröfn; Heinken, Almut; Noronha, Alberto; Rose, Peter W.; Burley, S.K.; Fleming, Ronan M. T.; Nielsen, Jens; Thiele, Ines; Palsson, Bernhard Ø.

doi:10.1038/nbt.4072

Cited by 625 publications

(767 citation statements)

References 67 publications

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“…As the ability to model complex biological interactions improves and is validated through laboratory experimentation, future modeling efforts may serve as even better predictors for metabolic interactions and disease conditions. Models and the tools built around these models are rapidly growing and improving and may eventually account for elements of host [82,83], diet, and transcriptional regulation. Many community modeling approaches like MICOM assess flux, and here we use flux to approximate metabolite production.…”

Section: Discussionmentioning

confidence: 99%

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer

Hale

Jeraldo

Mundy

et al. 2018

Methods

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Multi-omic data and genome-scale microbial metabolic models have allowed us to examine microbial communities, community function, and interactions in ways that were not available to us historically. Now, one of our biggest challenges is determining how to integrate data and maximize data potential. Our study demonstrates one way in which to test a hypothesis by combining multi-omic data and community metabolic models. Specifically, we assess hydrogen sulfide production in colorectal cancer based on stool, mucosa, and tissue samples collected on and off the tumor site within the same individuals. 16S rRNA microbial community and abundance data were used to select and inform the metabolic models. We then used MICOM, an open source platform, to track the metabolic flux of hydrogen sulfide through a defined microbial community that either represented on-tumor or off-tumor sample communities. We also performed targeted and untargeted metabolomics, and used the former to quantitatively evaluate our model predictions. A deeper look at the models identified several unexpected but feasible reactions, microbes, and microbial interactions involved in hydrogen sulfide production for which our 16S and metabolomic data could not account. These results will guide future in vitro, in vivo, and in silico tests to establish why hydrogen sulfide production is increased in tumor tissue.

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Section: Discussionmentioning

confidence: 99%

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer

Hale

Jeraldo

Mundy

et al. 2018

Methods

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“…Optimizing one function at a time gives a clear understanding of the entire metabolic capacity of the cell. Similar tests have been performed both for global human metabolic network [18] as well as cell-specific network of small intestine [44].…”

Section: Retina Function Testsmentioning

confidence: 99%

“…Constraint-based reconstruction and analysis (COBRA) remains the preferred systems biology tool to gain fundamental knowledge on the genomics and biochemistry of the target organism [15]. In line with this, the human metabolic reconstruction, that is, Recon [16][17][18], has been published accounting for metabolic reactions and their associated enzymes/genes operating in any given human cell. Recon serves as a knowledge base for interrogating human metabolism within any given disease context [19,20].…”

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confidence: 99%

Metabolite systems profiling identifies exploitable weaknesses in retinoblastoma

et al. 2018

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Retinoblastoma (RB) is a childhood eye cancer. Currently, chemotherapy, local therapy, and enucleation are the main ways in which these tumors are managed. The present work is the first study that uses constraint‐based reconstruction and analysis approaches to identify and explain RB‐specific survival strategies, which are RB tumor specific. Importantly, our model‐specific secretion profile is also found in RB1‐depleted human retinal cells in vitro and suggests that novel biomarkers involved in lipid metabolism may be important. Finally, RB‐specific synthetic lethals have been predicted as lipid and nucleoside transport proteins that can aid in novel drug target development.

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“…pancreata), tissues, and sera. Innovative computation workflows such as Recon3D would also help us understand cancer metabolism from the various layers of metabolic regulation . Recon3D includes 3D (https://www.vmh.life/#home) metabolite and protein structure data that enables integrated analyses of metabolic functions in humans.…”

Section: New Approaches and Technologies For Future Directionsmentioning

confidence: 99%

Challenges and Opportunities in Cancer Metabolomics

Kumar

Misra

2019

Proteomics

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Challenges in metabolomics for a given spectrum of disease are more or less comparable, ranging from the accurate measurement of metabolite abundance, compound annotation, identification of unknown constituents, and interpretation of untargeted and analysis of high throughput targeted metabolomics data leading to the identification of biomarkers. However, metabolomics approaches in cancer studies specifically suffer from several additional challenges and require robust ways to sample the cells and tissues in order to tackle the constantly evolving cancer landscape. These constraints include, but are not limited to, discriminating the signals from given cell types and those that are cancer specific, discerning signals that are systemic and confounded, cell culture‐based challenges associated with cell line identities and media standardizations, the need to look beyond Warburg effects, citrate cycle, lactate metabolism, and identifying and developing technologies to precisely and effectively sample and profile the heterogeneous tumor environment. This review article discusses some of the current and pertinent hurdles in cancer metabolomics studies. In addition, it addresses some of the most recent and exciting developments in metabolomics that may address some of these issues. The aim of this article is to update the oncometabolomics research community about the challenges and potential solutions to these issues.

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Recon3D enables a three-dimensional view of gene variation in human metabolism

Cited by 625 publications

References 67 publications

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer

Metabolite systems profiling identifies exploitable weaknesses in retinoblastoma

Challenges and Opportunities in Cancer Metabolomics

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