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

Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life

Samþætting lífefnafræðimælinga og efnaskiptaneta

The appearance of omics data sets has contributed to the rapid development of systems biology, which seeks the understanding of complex biological systems. Constraint-based modeling is one modeling formalism applied in systems biology, which relies on genome-scale network reconstructions. Metabolic reconstructions are increasingly used to understand normal cellular and disease states, which often involves the generation of cell-line or tissue-specific metabolic models through the integration of omics data. Metabolomic data can be easily obtained. Yet, methods for the generation of condition-specific metabolic models are less well developed. In this thesis, a workflow is established for the generation of condition-specific models from extracellular metabolomic data and the human metabolic model. The analysis of the models enables the investigation of metabolic phenotypes among cancer cell line specific models, based on model predictions of ATP yield, and the robustness of the models towards environmental and genetic perturbation. The models are built through a rigid reduction of exchange reactions, which emphasizes the detected metabolite concentration changes. However, the internal pathway redundancy remains widely preserved. Integration of transcriptomic reduces the internal pathway redundancy. Hence, in a following study, two lymphoblastic leukemia cells line models are generated, combining metabolomic and transcriptomic data. The models explain distinctive concentration changes in the spent medium of the two cancer cell lines by different utilization of glycolysis and oxidative phosphorylation. Analysis further reveals the accumulation of differential gene regulation and alternative splicing events at key steps of central metabolic pathways. Metabolism is closely intertwined with other cellular processes, namely signaling pathways, which play a key role in diseases like cancer. Hence, a contextualization procedure for signaling networks was developed, opening yet another avenue for omics data analysis. This approach is demonstrated through the contextualization of the Toll-like receptor (TLR) signaling network towards a generic monocyte TLR signaling network at first, and subsequently towards an LPS activated TLR signaling network. Taken together, my work extends the scope of omics data integration within the COBRA field. The inference of internal network states from extracellular measurements, as demonstrated herein, holds great potential for personalized medicine. However, further development is needed for the interpretation of metabolomic data derived from bio-fluids. Additionally, contextualization of signaling and metabolic networks can become crucial to understand the interplay between different cellular processes that collectively give rise to complex diseases.Tilkoma mengjagagna hefur ýtt undir hraða þróun kerfislíffræði, fræðigreinar sem miðar að því að auka skilning á flóknum líffræðilegum kerfum. Meðal þeirra líkana sem eru notuð í kerfislíffræði eru skorðuð líkön af efnaskiptanetum, sem ná yfir stóran hluta af genamengjum lífvera. Líkön af efnaskiptanetum eru notuð í sífellt meiri mæli til að skilja hegðun fruma í heilbrigðu eða sjúku ástandi. Það felur oft í sér smíði sérhæfðra líkana af ákveðinni frumulínu eða vefjagerð við ákveðin skilyrði. Slík skilyrða-sérhæfð líkön má smíða með því að tvinna saman mengjagögn og almenn líkön. Utanfrumumælingar á efnaskiptaefnamengi fruma við tiltekin skilyrði má nota til að smíða sérhæfð efnaskiptalíkön. Auðvelt að nálgast slíkar mælingar, en aðferðir til að smíða líkön út frá þeim hafa hingað til ekki verið nægilega þróaðar. Þessi ritgerð mun kynna verkferli til að smíða skilyrðasérhæfð efnaskiptalíkön út frá utanfrumumælingum af efnaskiptaefnamengjum og almennu líkani af efnaskiptaneti manna. Sérhæfð líkön fyrir krabbameinsfrumulínur má nota í rannsóknum á efnaskiptasvipgerðum slíkra frumulína til að spá fyrir um ATP nýtni og næmni fyrir umhverfis- og genabreytingum. Líkönin eru smíðuð með því að fækka víxlunarefnahvörfum í samræmi við mældar breytingar á styrkleika efnaskiptaefna. Þessi fækkun ein og sér leiðir ekki til mikillar minnkunar á umfremd innri efnaskiptaferla. Minnkun á umfremd innri efnaskiptaferla fæst fram með viðbótargögnum um umritamengi frumulínanna. Í rannsókn sem hér er lýst tvinnuðum við saman gögnum um bæði efnaskiptaefnamengi og umritamengi til að smíða líkön af tveimur frumulínum úr hvítblæði í eitilfrumum. Líkönin skýra mismun á styrkbreytingum í ræktunarvökva þessarra tveggja frumulína með mismunandi notkun á sykurrofi og oxunarfosfórun. Greining okkar leiddi einnig í ljós uppsöfnun á mismunandi genastýringaratburðum og breytilegri splæsingu við lykilskref í miðlægum efnaskiptaferlum. Efnaskipti eru náið samtengd öðrum frumuferlum, sérstaklega boðefnaferlum sem leika lykilhlutverk í sjúkdómum eins og krabbameini. Við þróuðum því aðferð til að aðlaga boðefnanet og opnuðum þar með á enn aðra leið til að greina mengjagögn. Við sýnum þessa aðferð með því að aðlaga boðefnanet fyrir Toll-líka viðtaka (TLR net), fyrst að almennu TLR neti í einkjörnungum, svo að LPS virkjuðu TLR neti. Vinna mín í heild sinni eykur við umfang samtvinnunar mengjagagna innan kerfislíffræði. Aðferðir til að draga ályktanir um innri ástand efnaskiptaneta út frá utanfrumumælingum opna á mikla möguleika fyrir einstaklingsmiðaðar lækningar, eins og sýnt er fram á hér. Þó er þörf á frekari þróun aðferða til að túlka gögn um efnaskiptaefnamengi sem fengin eru úr lífvökva. Þar að auki getur aðlögun boðefna- og efnaskiptaneta orðið lykilatriði í að skilja samspil mismunandi frumuferla sem saman valda flóknum sjúkdómum

Computational modeling of human metabolism and its application to systems biomedicine

Modern high-throughput techniques offer immense opportunities to investigate whole-systems behavior, such as those underlying human diseases. However, the complexity of the data presents challenges in interpretation, and new avenues are needed to address the complexity of both diseases and data. Constraint-based modeling is one formalism applied in systems biology. It relies on a genome-scale reconstruction that captures extensive biochemical knowledge regarding an organism. The human genome-scale metabolic reconstruction is increasingly used to understand normal cellular and disease states because metabolism is an important factor in many human diseases. The application of human genome-scale reconstruction ranges from mere querying of the model as a knowledge-base to studies that take advantage of the model’s topology, and most notably, to functional predictions based on cell- and condition-specific metabolic models built based on omics data. An increasing number and diversity of biomedical questions are being addressed using constraint-based modeling and metabolic models. One of the most successful biomedical applications to date is cancer metabolism, but constraint-based modeling also holds great potential for inborn errors of metabolism or obesity. In addition, it offers great prospects for individualized approaches to diagnostics and the design of disease prevention and intervention strategies. Metabolic models support this endeavor by providing easy access to complex high-throughput datasets. Personalized metabolic models have been introduced. Finally, constraint-based modeling can be used to model whole-body metabolism, which will enable the elucidation of metabolic interactions between organs and disturbances of these interactions as either causes or consequence of metabolic diseases. This chapter introduces constraint-based modeling and describes some of its contributions to systems biomedicine

Contextualization procedure and modeling of monocyte specific TLR signaling.

Innate immunity is the first line of defense against invasion of pathogens. Toll-like receptor (TLR) signaling is involved in a variety of human diseases extending far beyond immune system-related diseases, affecting a number of different tissues and cell-types. Computational models often do not account for cell-type specific differences in signaling networks. Investigation of these differences and its phenotypic implications could increase understanding of cell signaling and processes such as inflammation. The wealth of knowledge for TLR signaling has been recently summarized in a stoichiometric signaling network applicable for constraint-based modeling and analysis (COBRA). COBRA methods have been applied to investigate tissue-specific metabolism using omics data integration. Comparable approaches have not been conducted using signaling networks. In this study, we present ihsTLRv2, an updated TLR signaling network accounting for the association of 314 genes with 558 network reactions. We present a mapping procedure for transcriptomic data onto signaling networks and demonstrate the generation of a monocyte-specific TLR network. The generated monocyte network is characterized through expression of a specific set of isozymes rather than reduction of pathway contents. While further tailoring the network to a specific stimulation condition, we observed that the quantitative changes in gene expression due to LPS stimulation affected the tightly connected set of genes. Differential expression influenced about one third of the entire TLR signaling network, in particular, NF-kappaB activation. Thus, a cell-type and condition-specific signaling network can provide functional insight into signaling cascades. Furthermore, we demonstrate the energy dependence of TLR signaling pathways in monocytes

A systems approach reveals distinct metabolic strategies among the NCI-60 cancer cell lines

The metabolic phenotype of cancer cells is reflected by the metabolites they consume and by the byproducts they release. Here, we use quantitative, extracellular metabolomic data of the NCI-60 panel and a novel computational method to generate 120 condition-specific cancer cell line metabolic models. These condition-specific cancer models used distinct metabolic strategies to generate energy and cofactors. The analysis of the models' capability to deal with environmental perturbations revealed three oxotypes, differing in the range of allowable oxygen uptake rates. Interestingly, models based on metabolomic profiles of melanoma cells were distinguished from other models through their low oxygen uptake rates, which were associated with a glycolytic phenotype. A subset of the melanoma cell models required reductive carboxylation. The analysis of protein and RNA expression levels from the Human Protein Atlas showed that IDH2, which was an essential gene in the melanoma models, but not IDH1 protein, was detected in normal skin cell types and melanoma. Moreover, the von Hippel-Lindau tumor suppressor (VHL) protein, whose loss is associated with non-hypoxic HIF-stabilization, reductive carboxylation, and promotion of glycolysis, was uniformly absent in melanoma. Thus, the experimental data supported the predicted role of IDH2 and the absence of VHL protein supported the glycolytic and low oxygen phenotype predicted for melanoma. Taken together, our approach of integrating extracellular metabolomic data with metabolic modeling and the combination of different network interrogation methods allowed insights into the metabolism of cells

Membrane transporters in a human genome-scale metabolic knowledgebase and their implications for disease

Membrane transporters enable efficient cellular metabolism, aid in nutrient sensing, and have been associated with various diseases, such as obesity and cancer. Genome-scale metabolic network reconstructions capture genomic, physiological, and biochemical knowledge of a target organism, along with a detailed representation of the cellular metabolite transport mechanisms. Since the first reconstruction of human metabolism, Recon 1, published in 2007, progress has been made in the field of metabolite transport. Recently, we published an updated reconstruction, Recon 2, which significantly improved the metabolic coverage and functionality. Human metabolic reconstructions have been used to investigate the role of metabolism in disease and to predict biomarkers and drug targets. Given the importance of cellular transport systems in understanding human metabolism in health and disease, we analyzed the coverage of transport systems for various metabolite classes in Recon 2. We will review the current knowledge on transporters (i.e., their preferred substrates, transport mechanisms, metabolic relevance, and disease association for each metabolite class). We will assess missing coverage and propose modifications and additions through a transport module that is functional when combined with Recon 2. This information will be valuable for further refinements. These data will also provide starting points for further experiments by highlighting areas of incomplete knowledge. This review represents the first comprehensive overview of the transporters involved in central metabolism and their transport mechanisms, thus serving as a compendium of metabolite transporters specific for human metabolic reconstructions

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

Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life

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

Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life