On metabolic networks and multi-omics integration

Cellular metabolism is a highly complex chemical system, involving thousands of interacting metabolites and reactions. The traditional approach to understanding metabolism has been that of reductionism; by isolating and carefully measuring the involved components, the goal has been to understand the whole as the sum of its parts. This reductionist approach has successfully identified most of the components of metabolism but, unfortunately, it fails to capture the long-range and complex interactions that are essential for the functionality. Systems biology is an emerging research field which uses high-throughput data generation and mathematical modelling in order to apply a holistic, or network-centric, view on metabolism. One type of modelling framework, which is in line with this thinking, is genome-scale metabolic modelling. These models, called GEMs, represent very valuable resources, but their applications have been limited due to the large manual effort required to reconstruct them. In this project, we have developed algorithms and software for streamlining the reconstruction process, as well as for novel applications of GEMs. More specifically, we here present: the RAVEN Toolbox, a software suite for automated reconstruction and quality control; the INIT algorithm, an algorithm for inferring GEMs for human cell types; an algorithm which integrates fluxomics and transcriptomics data in order to identify transcriptionally controlled metabolic reactions.The methods and software were used in a number of case studies to address real biological questions. These studies were: 1) Metabolic engineering of Saccharomyces cerevisiae for succinic acid overproduction. The predictions from the modelling were successfully validated experimentally. 2) Study of metabolic regulation in S. cerevisiae. This led to the identification of a small number of transcription factors and enzymes which were predicted to be controlling central parts of metabolism. 3). Penicillin production in Penicillium chrysogenum. This led to the reconstruction of the first GEM for P. chrysogenum, an important resource in itself, and to identification of metabolic engineering targets for more efficient production of penicillin. 4) Human cancer metabolism. This led to the identification of metabolic subnetworks which were predicted to be significantly more active in cancers, and to identification of potential drug targets for treatment. 5) Lipid metabolism in obesity. This led to new insights into the large-scale metabolic rearrangements associated with obesity, and to identification of possible therapeutic strategies. 6) Metabolism in non-alcoholic fatty liver disease. This led to the identification of serine deficiency as a central aspect of the disease, and to proposed therapeutic strategies for remedying it.The work put forward in this thesis has resulted in improvements on several important aspects of genome-scale metabolic modelling, and it has shown how the framework can be applied to gain novel biological insights. As such, it can contribute to further increase the role of the framework in modelling of human health and disease

On metabolic networks and multi-omics integration

Cellular metabolism is a highly complex chemical system, involving thousands of interacting metabolites and reactions. The traditional approach to understanding metabolism has been that of reductionism; by isolating and carefully measuring the involved components, the goal has been to understand the whole as the sum of its parts. This reductionist approach has successfully identified most of the components of metabolism but, unfortunately, it fails to capture the long-range and complex interactions that are essential for the functionality. Systems biology is an emerging research field which uses high-throughput data generation and mathematical modelling in order to apply a holistic, or network-centric, view on metabolism. One type of modelling framework, which is in line with this thinking, is genome-scale metabolic modelling. These models, called GEMs, represent very valuable resources, but their applications have been limited due to the large manual effort required to reconstruct them. In this project, we have developed algorithms and software for streamlining the reconstruction process, as well as for novel applications of GEMs. More specifically, we here present: the RAVEN Toolbox, a software suite for automated reconstruction and quality control; the INIT algorithm, an algorithm for inferring GEMs for human cell types; an algorithm which integrates fluxomics and transcriptomics data in order to identify transcriptionally controlled metabolic reactions.The methods and software were used in a number of case studies to address real biological questions. These studies were: 1) Metabolic engineering of Saccharomyces cerevisiae for succinic acid overproduction. The predictions from the modelling were successfully validated experimentally. 2) Study of metabolic regulation in S. cerevisiae. This led to the identification of a small number of transcription factors and enzymes which were predicted to be controlling central parts of metabolism. 3). Penicillin production in Penicillium chrysogenum. This led to the reconstruction of the first GEM for P. chrysogenum, an important resource in itself, and to identification of metabolic engineering targets for more efficient production of penicillin. 4) Human cancer metabolism. This led to the identification of metabolic subnetworks which were predicted to be significantly more active in cancers, and to identification of potential drug targets for treatment. 5) Lipid metabolism in obesity. This led to new insights into the large-scale metabolic rearrangements associated with obesity, and to identification of possible therapeutic strategies. 6) Metabolism in non-alcoholic fatty liver disease. This led to the identification of serine deficiency as a central aspect of the disease, and to proposed therapeutic strategies for remedying it.The work put forward in this thesis has resulted in improvements on several important aspects of genome-scale metabolic modelling, and it has shown how the framework can be applied to gain novel biological insights. As such, it can contribute to further increase the role of the framework in modelling of human health and disease

Use of genome-scale metabolic models for understanding microbial physiology

The exploitation of microorganisms in industrial, medical, food and environmental biotechnology requires a comprehensive understanding of their physiology. The availability of genome sequences and accumulation of high-throughput data allows gaining understanding of microbial physiology at the systems level, and genome-scale metabolic models represent a valuable framework for integrative analysis of metabolism of microorganisms. Genome-scale metabolic models are reconstructed based on a combination of genome sequence information and detailed biochemical information, and these reconstructed models can be used for analyzing and simulating the operation of metabolism in response to different stimuli. Here we discuss the requirement for having detailed physiological insight in order to exploit microorganisms for production of fuels, chemicals and pharmaceuticals. We further describe the reconstruction process of genome-scale metabolic models and different algorithms that can be used to apply these models to gain improved insight into microbial physiology. (C) 2010 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved

Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling

Synopsis Personalized GEMs for six hepatocellular carcinoma patients are reconstructed using proteomics data and a task-driven model reconstruction algorithm. These GEMs are used to predict antimetabolites preventing tumor growth in all patients or in individual patients. The presence of proteins encoded by 15,841 genes in tumors from 27 HCC patients is evaluated by immunohistochemistry. Personalized GEMs for six HCC patients and GEMs for 83 healthy cell types are reconstructed based on HMR 2.0 and the tINIT algorithm for task-driven model reconstruction. 101 antimetabolites are predicted to inhibit tumor growth in all patients. Antimetabolite toxicity is tested using the 83 cell type-specific GEMs. Genome-scale metabolic models (GEMs) have proven useful as scaffolds for the integration of omics data for understanding the genotype-phenotype relationship in a mechanistic manner. Here, we evaluated the presence/absence of proteins encoded by 15,841 genes in 27 hepatocellular carcinoma (HCC) patients using immunohistochemistry. We used this information to reconstruct personalized GEMs for six HCC patients based on the proteomics data, HMR 2.0, and a task-driven model reconstruction algorithm (tINIT). The personalized GEMs were employed to identify anticancer drugs using the concept of antimetabolites; i.e., drugs that are structural analogs to metabolites. The toxicity of each antimetabolite was predicted by assessing the in silico functionality of 83 healthy cell type-specific GEMs, which were also reconstructed with the tINIT algorithm. We predicted 101 antimetabolites that could be effective in preventing tumor growth in all HCC patients, and 46 antimetabolites which were specific to individual patients. Twenty-two of the 101 predicted antimetabolites have already been used in different cancer treatment strategies, while the remaining antimetabolites represent new potential drugs. Finally, one of the identified targets was validated experimentally, and it was confirmed to attenuate growth of the HepG2 cell line

Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling

Synopsis Personalized GEMs for six hepatocellular carcinoma patients are reconstructed using proteomics data and a task-driven model reconstruction algorithm. These GEMs are used to predict antimetabolites preventing tumor growth in all patients or in individual patients. The presence of proteins encoded by 15,841 genes in tumors from 27 HCC patients is evaluated by immunohistochemistry. Personalized GEMs for six HCC patients and GEMs for 83 healthy cell types are reconstructed based on HMR 2.0 and the tINIT algorithm for task-driven model reconstruction. 101 antimetabolites are predicted to inhibit tumor growth in all patients. Antimetabolite toxicity is tested using the 83 cell type-specific GEMs. Genome-scale metabolic models (GEMs) have proven useful as scaffolds for the integration of omics data for understanding the genotype-phenotype relationship in a mechanistic manner. Here, we evaluated the presence/absence of proteins encoded by 15,841 genes in 27 hepatocellular carcinoma (HCC) patients using immunohistochemistry. We used this information to reconstruct personalized GEMs for six HCC patients based on the proteomics data, HMR 2.0, and a task-driven model reconstruction algorithm (tINIT). The personalized GEMs were employed to identify anticancer drugs using the concept of antimetabolites; i.e., drugs that are structural analogs to metabolites. The toxicity of each antimetabolite was predicted by assessing the in silico functionality of 83 healthy cell type-specific GEMs, which were also reconstructed with the tINIT algorithm. We predicted 101 antimetabolites that could be effective in preventing tumor growth in all HCC patients, and 46 antimetabolites which were specific to individual patients. Twenty-two of the 101 predicted antimetabolites have already been used in different cancer treatment strategies, while the remaining antimetabolites represent new potential drugs. Finally, one of the identified targets was validated experimentally, and it was confirmed to attenuate growth of the HepG2 cell line

Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease

Several liver disorders result from perturbations in the metabolism of hepatocytes, and their underlying mechanisms can be outlined through the use of genome-scale metabolic models (GEMs). Here we reconstruct a consensus GEM for hepatocytes, which we call iHepatocytes2322, that extends previous models by including an extensive description of lipid metabolism. We build iHepatocytes2322 using Human Metabolic Reaction 2.0 database and proteomics data in Human Protein Atlas, which experimentally validates the incorporated reactions. The reconstruction process enables improved annotation of the proteomics data using the network centric view of iHepatocytes2322. We then use iHepatocytes2322 to analyse transcriptomics data obtained from patients with non-alcoholic fatty liver disease. We show that blood concentrations of chondroitin and heparan sulphates are suitable for diagnosing non-alcoholic steatohepatitis and for the staging of non-alcoholic fatty liver disease. Furthermore, we observe serine deficiency in patients with NASH and identify PSPH, SHMT1 and BCAT1 as potential therapeutic targets for the treatment of non-alcoholic steatohepatitis

In vivo screen identifies a SIK inhibitor that induces beta cell proliferation through a transient UPR

It is known that beta cell proliferation expands the beta cell mass during development and under certain hyperglycemic conditions in the adult, a process that may be used for beta cell regeneration in diabetes. Here, through a new high-throughput screen using a luminescence ubiquitination-based cell cycle indicator (LUCCI) in zebrafish, we identify HG-9-91-01 as a driver of proliferation and confirm this effect in mouse and human beta cells. HG-9-91-01 is an inhibitor of salt-inducible kinases (SIKs), and overexpression of Sik1 specifically in beta cells blocks the effect of HG-9-91-01 on beta cell proliferation. Single-cell transcriptomic analyses of mouse beta cells demonstrate that HG-9-91-01 induces a wave of activating transcription factor (ATF)6-dependent unfolded protein response (UPR) before cell cycle entry. Importantly, the UPR wave is not associated with an increase in insulin expression. Additional mechanistic studies indicate that HG-9-91-01 induces multiple signalling effectors downstream of SIK inhibition, including CRTC1, CRTC2, ATF6, IRE1 and mTOR, which integrate to collectively drive beta cell proliferation. A high-throughput chemical screen identifies the salt-inducible kinase inhibitor HG-9-91-01 as a driver of beta cell proliferation, acting through an ATF6-dependent unfolded protein response

Archaeal community changes in Lateglacial lake sediments: Evidence from ancient DNA

The Lateglacial/early Holocene sediments from the ancient lake at H\ue4sseldala Port, southern Sweden provide an important archive for the environmental and climatic shifts at the end of the last ice age and the transition into the present Interglacial. The existing multi-proxy data set highlights the complex interplay of physical and ecological changes in response to climatic shifts and lake status changes. Yet, it remains unclear how microorganisms, such as Archaea, which do not leave microscopic features in the sedimentary record, were affected by these climatic shifts. Here we present the metagenomic data set of H\ue4sseldala Port with a special focus on the abundance and biodiversity of Archaea. This allows reconstructing for the first time the temporal succession of major Archaea groups between 13.9 and 10.8 ka BP by using ancient environmental DNA metagenomics and fossil archaeal cell membrane lipids. We then evaluate to which extent these findings reflect physical changes of the lake system, due to changes in lake-water summer temperature and seasonal lake-ice cover. We show that variations in archaeal composition and diversity were related to a variety of factors (e.g., changes in lake water temperature, duration of lake ice cover, rapid sediment infilling), which influenced bottom water conditions and the sediment-water interface. Methanogenic Archaea dominated during the Aller\uf8d and Younger Dryas pollen zones, when the ancient lake was likely stratified and anoxic for large parts of the year. The increase in archaeal diversity at the Younger Dryas/Holocene transition is explained by sediment infilling and formation of a mire/peatbog