Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat Commun. 2014;5:3083. [PMID: 24419221 DOI: 10.1038/ncomms4083]

Human metabolic atlas: an online resource for human metabolism

Human tissue-specific genome-scale metabolic models (GEMs) provide comprehensive understanding of human metabolism, which is of great value to the biomedical research community. To make this kind of data easily accessible to the public, we have designed and deployed the human metabolic atlas (HMA) website (http://www.metabolicatlas.org). This online resource provides comprehensive information about human metabolism, including the results of metabolic network analyses. We hope that it can also serve as an information exchange interface for human metabolism knowledge within the research community. The HMA consists of three major components: Repository, Hreed (Human REaction Entities Database) and Atlas. Repository is a collection of GEMs for specific human cell types and human-related microorganisms in SBML (System Biology Markup Language) format. The current release consists of several types of GEMs: a generic human GEM, 82 GEMs for normal cell types, 16 GEMs for different cancer cell types, 2 curated GEMs and 5 GEMs for human gut bacteria. Hreed contains detailed information about biochemical reactions. A web interface for Hreed facilitates an access to the Hreed reaction data, which can be easily retrieved by using specific keywords or names of related genes, proteins, compounds and cross-references. Atlas web interface can be used for visualization of the GEMs collection overlaid on KEGG metabolic pathway maps with a zoom/pan user interface. The HMA is a unique tool for studying human metabolism, ranging in scope from an individual cell, to a specific organ, to the overall human body. This resource is freely available under a Creative Commons Attribution-NonCommercial 4.0 International License.

Modeling cancer metabolism on a genome scale

Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome-scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network-level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field.

The human cardiac and skeletal muscle proteomes defined by transcriptomics and antibody-based profiling

Background

To understand cardiac and skeletal muscle function, it is important to define and explore their molecular constituents and also to identify similarities and differences in the gene expression in these two different striated muscle tissues. Here, we have investigated the genes and proteins with elevated expression in cardiac and skeletal muscle in relation to all other major human tissues and organs using a global transcriptomics analysis complemented with antibody-based profiling to localize the corresponding proteins on a single cell level.

Results

Our study identified a comprehensive list of genes expressed in cardiac and skeletal muscle. The genes with elevated expression were further stratified according to their global expression pattern across the human body as well as their precise localization in the muscle tissues. The functions of the proteins encoded by the elevated genes are well in line with the physiological functions of cardiac and skeletal muscle, such as contraction, ion transport, regulation of membrane potential and actomyosin structure organization. A large fraction of the transcripts in both cardiac and skeletal muscle correspond to mitochondrial proteins involved in energy metabolism, which demonstrates the extreme specialization of these muscle tissues to provide energy for contraction.

Conclusions

Our results provide a comprehensive list of genes and proteins elevated in striated muscles. A number of proteins not previously characterized in cardiac and skeletal muscle were identified and localized to specific cellular subcompartments. These proteins represent an interesting starting point for further functional analysis of their role in muscle biology and disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1686-y) contains supplementary material, which is available to authorized users.

From next-generation sequencing to systematic modeling of the gut microbiome

Changes in the human gut microbiome are associated with altered human metabolism and health, yet the mechanisms of interactions between microbial species and human metabolism have not been clearly elucidated. Next-generation sequencing has revolutionized the human gut microbiome research, but most current applications concentrate on studying the microbial diversity of communities and have at best provided associations between specific gut bacteria and human health. However, little is known about the inner metabolic mechanisms in the gut ecosystem. Here we review recent progress in modeling the metabolic interactions of gut microbiome, with special focus on the utilization of metabolic modeling to infer host–microbe interactions and microbial species interactions. The systematic modeling of metabolic interactions could provide a predictive understanding of gut microbiome, and pave the way to synthetic microbiota design and personalized-microbiome medicine and healthcare. Finally, we discuss the integration of metabolic modeling and gut microbiome engineering, which offer a new way to explore metabolic interactions across members of the gut microbiota.

Flux balance analysis predicts essential genes in clear cell renal cell carcinoma metabolism

Flux balance analysis is the only modelling approach that is capable of producing genome-wide predictions of gene essentiality that may aid to unveil metabolic liabilities in cancer. Nevertheless, a systemic validation of gene essentiality predictions by flux balance analysis is currently missing. Here, we critically evaluated the accuracy of flux balance analysis in two cancer types, clear cell renal cell carcinoma (ccRCC) and prostate adenocarcinoma, by comparison with large-scale experiments of gene essentiality in vitro. We found that in ccRCC, but not in prostate adenocarcinoma, flux balance analysis could predict essential metabolic genes beyond random expectation. Five of the identified metabolic genes, AGPAT6, GALT, GCLC, GSS, and RRM2B, were predicted to be dispensable in normal cell metabolism. Hence, targeting these genes may selectively prevent ccRCC growth. Based on our analysis, we discuss the benefits and limitations of flux balance analysis for gene essentiality predictions in cancer metabolism, and its use for exposing metabolic liabilities in ccRCC, whose emergent metabolic network enforces outstanding anabolic requirements for cellular proliferation.

1-Deoxysphingolipids Encountered Exogenously and Made de Novo: Dangerous Mysteries inside an Enigma

The traditional backbones of mammalian sphingolipids are 2-amino, 1,3-diols made by serine palmitoyltransferase (SPT). Many organisms additionally produce non-traditional, cytotoxic 1-deoxysphingoid bases and, surprisingly, mammalian SPT biosynthesizes some of them, too (e.g. 1-deoxysphinganine from l-alanine). These are rapidly N-acylated to 1-deoxy-“ceramides” with very uncommon biophysical properties. The functions of 1-deoxysphingolipids are not known, but they are certainly dangerous as contributors to sensory and autonomic neuropathies when elevated by inherited SPT mutations, and they are noticeable in diabetes, non-alcoholic steatohepatitis, serine deficiencies, and other diseases. As components of food as well as endogenously produced, these substances are mysteries within an enigma.

Metabolic Needs and Capabilities of Toxoplasma gondii through Combined Computational and Experimental Analysis

Toxoplasma gondii is a human pathogen prevalent worldwide that poses a challenging and unmet need for novel treatment of toxoplasmosis. Using a semi-automated reconstruction algorithm, we reconstructed a genome-scale metabolic model, ToxoNet1. The reconstruction process and flux-balance analysis of the model offer a systematic overview of the metabolic capabilities of this parasite. Using ToxoNet1 we have identified significant gaps in the current knowledge of Toxoplasma metabolic pathways and have clarified its minimal nutritional requirements for replication. By probing the model via metabolic tasks, we have further defined sets of alternative precursors necessary for parasite growth. Within a human host cell environment, ToxoNet1 predicts a minimal set of 53 enzyme-coding genes and 76 reactions to be essential for parasite replication. Double-gene-essentiality analysis identified 20 pairs of genes for which simultaneous deletion is deleterious. To validate several predictions of ToxoNet1 we have performed experimental analyses of cytosolic acetyl-CoA biosynthesis. ATP-citrate lyase and acetyl-CoA synthase were localised and their corresponding genes disrupted, establishing that each of these enzymes is dispensable for the growth of T. gondii, however together they make a synthetic lethal pair.

The potential clinical impact of the tissue-based map of the human proteome

Since the first draft of the human genome sequence was published, several attempts have been made to map the human proteome, the functional representation of the genome. One such initiative is the Human Protein Atlas project, which recently released a tissue-based map of the human proteome. The Human Protein Atlas is based on the combination of transcriptomics and antibody-based proteomics for mapping the human proteome down to the single cell level. The comprehensive publicly available database contains more than 13 million unique immunohistochemistry images and provides an excellent resource for exploration and investigation of future drug targets and disease biomarkers.

Proteome- and transcriptome-driven reconstruction of the human myocyte metabolic network and its use for identification of markers for diabetes

Skeletal myocytes are metabolically active and susceptible to insulin resistance and are thus implicated in type 2 diabetes (T2D). This complex disease involves systemic metabolic changes, and their elucidation at the systems level requires genome-wide data and biological networks. Genome-scale metabolic models (GEMs) provide a network context for the integration of high-throughput data. We generated myocyte-specific RNA-sequencing data and investigated their correlation with proteome data. These data were then used to reconstruct a comprehensive myocyte GEM. Next, we performed a meta-analysis of six studies comparing muscle transcription in T2D versus healthy subjects. Transcriptional changes were mapped on the myocyte GEM, revealing extensive transcriptional regulation in T2D, particularly around pyruvate oxidation, branched-chain amino acid catabolism, and tetrahydrofolate metabolism, connected through the downregulated dihydrolipoamide dehydrogenase. Strikingly, the gene signature underlying this metabolic regulation successfully classifies the disease state of individual samples, suggesting that regulation of these pathways is a ubiquitous feature of myocytes in response to T2D.

Transcriptional networks implicated in human nonalcoholic fatty liver disease

The transcriptome of nonalcoholic fatty liver disease (NAFLD) was investigated in several studies. However, the implications of transcriptional networks in progressive NAFLD are not clear and mechanisms inducing transition from nonalcoholic simple fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH) are still elusive. The aims of this study were to (1) construct networks for progressive NAFLD, (2) identify hub genes and functional modules in these networks and (3) infer potential linkages among hub genes, transcription factors and microRNAs (miRNA) for NAFLD progression. A systems biology approach by combining differential expression analysis and weighted gene co-expression network analysis (WGCNA) was utilized to dissect transcriptional profiles in 19 normal, 10 NAFL and 16 NASH patients. Based on this framework, 3 modules related to chromosome organization, proteasomal ubiquitin-dependent protein degradation and immune response were identified in NASH network. Furthermore, 9 modules of co-expressed genes associated with NAFL/NASH transition were found. Further characterization of these modules defined 13 highly connected hub genes in NAFLD progression network. Interestingly, 11 significantly changed miRNAs were predicted to target 10 of the 13 hub genes. Characterization of modules and hub genes that may be regulated by miRNAs could facilitate the identification of candidate genes and pathways responsible for NAFL/NASH transition and lead to a better understanding of NAFLD pathogenesis. The identified modules and hub genes may point to potential targets for therapeutic interventions.

Estrogen regulation of phosphoserine phosphatase during regression and recrudescence of female reproductive organs

Phosphoserine phosphatase (PSPH) is a well-known mediator of l-serine biosynthesis in a variety of tissues and its dysregulation causes various diseases, specifically most cancers. However, little is known about the expression and hormonal regulation of PSPH gene in the female reproductive tract. Therefore, in the current study, we focused on relationships between PSPH expression and estrogen during growth, development, differentiation, remodeling and recrudescence of the chicken oviduct and in the progression of epithelial-derived ovarian carcinogenesis in laying hens. The results revealed that PSPH mRNA and protein levels increased in the glandular (GE) and luminal epithelial (LE) cells in the developing oviduct of chicks treated with exogenous estrogen. Additionally, PSPH mRNA and protein expression was up-regulated in GE and LE of the oviduct in response to endogenous estrogen during the recrudescence phase after induced molting. Furthermore, PSPH mRNA and protein were predominantly detected in GE of cancerous, but not normal ovaries. In conclusion, PSPH is a novel estrogen-responsive gene involved in development of the oviduct of chicks and recrudescence of the oviduct of laying hens after molting. PSPH is also a potential target molecule that may help elucidate mechanism responsible for the progression of epithelial cell-derived ovarian carcinogenesis and be of use in therapeutic applications as a biomarker for early diagnosis of epithelial cell-derived ovarian cancer in laying hen as well as women.

Modelling the effect of SPION size in a stent assisted magnetic drug targeting system with interparticle interactions

Cancer is a leading cause of death worldwide and it is caused by the interaction of genomic, environmental, and lifestyle factors. Although chemotherapy is one way of treating cancers, it also damages healthy cells and may cause severe side effects. Therefore, it is beneficial in drug delivery in the human body to increase the proportion of the drugs at the target site while limiting its exposure at the rest of body through Magnetic Drug Targeting (MDT). Superparamagnetic iron oxide nanoparticles (SPIONs) are derived from polyol methods and coated with oleic acid and can be used as magnetic drug carrier particles (MDCPs) in an MDT system. Here, we develop a mathematical model for studying the interactions between the MDCPs enriched with three different diameters of SPIONs (6.6, 11.6, and 17.8 nm) in the MDT system with an implanted magnetizable stent using different magnetic field strengths and blood velocities. Our computational analysis allows for the optimal design of the SPIONs enriched MDCPs to be used in clinical applications.

Identifying anti-growth factors for human cancer cell lines through genome-scale metabolic modeling

Human cancer cell lines are used as important model systems to study molecular mechanisms associated with tumor growth, hereunder how genomic and biological heterogeneity found in primary tumors affect cellular phenotypes. We reconstructed Genome scale metabolic models (GEMs) for eleven cell lines based on RNA-Seq data and validated the functionality of these models with data from metabolite profiling. We used cell line-specific GEMs to analyze the differences in the metabolism of cancer cell lines, and to explore the heterogeneous expression of the metabolic subsystems. Furthermore, we predicted 85 antimetabolites that can inhibit growth of, or even kill, any of the cell lines, while at the same time not being toxic for 83 different healthy human cell types. 60 of these antimetabolites were found to inhibit growth in all cell lines. Finally, we experimentally validated one of the predicted antimetabolites using two cell lines with different phenotypic origins, and found that it is effective in inhibiting the growth of these cell lines. Using immunohistochemistry, we also showed high or moderate expression levels of proteins targeted by the validated antimetabolite. Identified anti-growth factors for inhibition of cell growth may provide leads for the development of efficient cancer treatment strategies.

Liver enzymes, metabolomics and genome-wide association studies: From systems biology to the personalized medicine

For several decades, serum levels of alanine (ALT) and aspartate (AST) aminotransferases have been regarded as markers of liver injury, including a wide range of etiologies from viral hepatitis to fatty liver. The increasing worldwide prevalence of metabolic syndrome and cardiovascular disease revealed that transaminases are strong predictors of type 2 diabetes, coronary heart disease, atherothrombotic risk profile, and overall risk of metabolic disease. Therefore, it is plausible to suggest that aminotransferases are surrogate biomarkers of “liver metabolic functioning” beyond the classical concept of liver cellular damage, as their enzymatic activity might actually reflect key aspects of the physiology and pathophysiology of the liver function. In this study, we summarize the background information and recent findings on the biological role of ALT and AST, and review the knowledge gained from the application of genome-wide approaches and “omics” technologies that uncovered new concepts on the role of aminotransferases in human diseases and systemic regulation of metabolic functions. Prediction of biomolecular interactions between the candidate genes recently discovered to be associated with plasma concentrations of liver enzymes showed interesting interconnectivity nodes, which suggest that regulation of aminotransferase activity is a complex and highly regulated trait. Finally, links between aminotransferase genes and metabolites are explored to understand the genetic contributions to the metabolic diversity.

Human genes with a greater number of transcript variants tend to show biological features of housekeeping and essential genes

Human genes with a greater number of transcript variants are more likely to play functionally important roles such as cellular maintenance and survival.

Succinate dehydrogenase inhibition leads to epithelial-mesenchymal transition and reprogrammed carbon metabolism

BACKGROUND

Succinate dehydrogenase (SDH) is a mitochondrial metabolic enzyme complex involved in both the electron transport chain and the citric acid cycle. SDH mutations resulting in enzymatic dysfunction have been found to be a predisposing factor in various hereditary cancers. Therefore, SDH has been implicated as a tumor suppressor.

RESULTS

We identified that dysregulation of SDH components also occurs in serous ovarian cancer, particularly the SDH subunit SDHB. Targeted knockdown of Sdhb in mouse ovarian cancer cells resulted in enhanced proliferation and an epithelial-to-mesenchymal transition (EMT). Bioinformatics analysis revealed that decreased SDHB expression leads to a transcriptional upregulation of genes involved in metabolic networks affecting histone methylation. We confirmed that Sdhb knockdown leads to a hypermethylated epigenome that is sufficient to promote EMT. Metabolically, the loss of Sdhb resulted in reprogrammed carbon source utilization and mitochondrial dysfunction. This altered metabolic state of Sdhb knockdown cells rendered them hypersensitive to energy stress.

CONCLUSIONS

These data illustrate how SDH dysfunction alters the epigenetic and metabolic landscape in ovarian cancer. By analyzing the involvement of this enzyme in transcriptional and metabolic networks, we find a metabolic Achilles' heel that can be exploited therapeutically. Analyses of this type provide an understanding how specific perturbations in cancer metabolism may lead to novel anticancer strategies.

Kiwi: a tool for integration and visualization of network topology and gene-set analysis

BACKGROUND

The analysis of high-throughput data in biology is aided by integrative approaches such as gene-set analysis. Gene-sets can represent well-defined biological entities (e.g. metabolites) that interact in networks (e.g. metabolic networks), to exert their function within the cell. Data interpretation can benefit from incorporating the underlying network, but there are currently no optimal methods that link gene-set analysis and network structures.

RESULTS

Here we present Kiwi, a new tool that processes output data from gene-set analysis and integrates them with a network structure such that the inherent connectivity between gene-sets, i.e. not simply the gene overlap, becomes apparent. In two case studies, we demonstrate that standard gene-set analysis points at metabolites regulated in the interrogated condition. Nevertheless, only the integration of the interactions between these metabolites provides an extra layer of information that highlights how they are tightly connected in the metabolic network.

CONCLUSIONS

Kiwi is a tool that enhances interpretability of high-throughput data. It allows the users not only to discover a list of significant entities or processes as in gene-set analysis, but also to visualize whether these entities or processes are isolated or connected by means of their biological interaction. Kiwi is available as a Python package at http://www.sysbio.se/kiwi and an online tool in the BioMet Toolbox at http://www.biomet-toolbox.org.

SteatoNet: the first integrated human metabolic model with multi-layered regulation to investigate liver-associated pathologies

Current state-of-the-art mathematical models to investigate complex biological processes, in particular liver-associated pathologies, have limited expansiveness, flexibility, representation of integrated regulation and rely on the availability of detailed kinetic data. We generated the SteatoNet, a multi-pathway, multi-tissue model and in silico platform to investigate hepatic metabolism and its associated deregulations. SteatoNet is based on object-oriented modelling, an approach most commonly applied in automotive and process industries, whereby individual objects correspond to functional entities. Objects were compiled to feature two novel hepatic modelling aspects: the interaction of hepatic metabolic pathways with extra-hepatic tissues and the inclusion of transcriptional and post-transcriptional regulation. SteatoNet identification at normalised steady state circumvents the need for constraining kinetic parameters. Validation and identification of flux disturbances that have been proven experimentally in liver patients and animal models highlights the ability of SteatoNet to effectively describe biological behaviour. SteatoNet identifies crucial pathway branches (transport of glucose, lipids and ketone bodies) where changes in flux distribution drive the healthy liver towards hepatic steatosis, the primary stage of non-alcoholic fatty liver disease. Cholesterol metabolism and its transcription regulators are highlighted as novel steatosis factors. SteatoNet thus serves as an intuitive in silico platform to identify systemic changes associated with complex hepatic metabolic disorders.

Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer

Utilizing molecular data to derive functional physiological models tailored for specific cancer cells can facilitate the use of individually tailored therapies. To this end we present an approach termed PRIME for generating cell-specific genome-scale metabolic models (GSMMs) based on molecular and phenotypic data. We build >280 models of normal and cancer cell-lines that successfully predict metabolic phenotypes in an individual manner. We utilize this set of cell-specific models to predict drug targets that selectively inhibit cancerous but not normal cell proliferation. The top predicted target, MLYCD, is experimentally validated and the metabolic effects of MLYCD depletion investigated. Furthermore, we tested cell-specific predicted responses to the inhibition of metabolic enzymes, and successfully inferred the prognosis of cancer patients based on their PRIME-derived individual GSMMs. These results lay a computational basis and a counterpart experimental proof of concept for future personalized metabolic modeling applications, enhancing the search for novel selective anticancer therapies.

Personalizing care for nonalcoholic fatty liver disease patients: what are the research priorities?

Nonalcoholic fatty liver disease (NAFLD) is a common chronic liver disease whose prevalence has reached global epidemic proportions, not only in adults but also in children. From a clinical point of view, NAFLD stems a myriad of challenges to physicians, researchers and patients. In this study, we revise the current knowledge and recent insights on NAFLD pathogenesis and diagnosis in the context of a personalized perspective with special focus on the following issues: noninvasive biomarkers for the evaluation of disease severity and progression, lifestyle-related patients' recommendations, risk prediction of disease by genetic testing, management of NAFLD-associated comorbidities and patient-oriented therapeutic intervention strategies.

Defining the human gallbladder proteome by transcriptomics and affinity proteomics

Global protein analysis of human gallbladder tissue is vital for identification of molecular regulators and effectors of its physiological activity. Here, we employed a genome-wide deep RNA sequencing analysis in 28 human tissues to identify the genes overrepresented in the gallbladder and complemented it with antibody-based immunohistochemistry in 48 human tissues. We characterized human gallbladder proteins and identified 140 gallbladder-specific proteins with an elevated expression in the gallbladder as compared to the other analyzed tissues. Five genes were categorized as enriched, with at least fivefold higher levels in gallbladder, 60 genes were categorized as group enriched with elevated transcript levels in gallbladder shared with at least one other tissue and 75 genes were categorized as enhanced with higher expression than the average expression in other tissues. We explored the localization of the genes within the gallbladder through cell-type specific antibody-based protein profiling and the subcellular localization of the genes through immunofluorescent-based profiling. Finally, we revealed the biological processes and metabolic functions carried out by these genes through the use of GO, KEGG Pathway, and HMR2.0 that is compilation of the human metabolic reactions. We demonstrated the results of the combined analysis of the transcriptomics and affinity proteomics.

Defining the human adipose tissue proteome to reveal metabolic alterations in obesity

White adipose tissue (WAT) has a major role in the progression of obesity. Here, we combined data from RNA-Seq and antibody-based immunohistochemistry to describe the normal physiology of human WAT obtained from three female subjects and explored WAT-specific genes by comparing WAT to 26 other major human tissues. Using the protein evidence in WAT, we validated the content of a genome-scale metabolic model for adipocytes. We employed this high-quality model for the analysis of subcutaneous adipose tissue (SAT) gene expression data obtained from subjects included in the Swedish Obese Subjects Sib Pair study to reveal molecular differences between lean and obese individuals. We integrated SAT gene expression and plasma metabolomics data, investigated the contribution of the metabolic differences in the mitochondria of SAT to the occurrence of obesity, and eventually identified cytosolic branched-chain amino acid (BCAA) transaminase 1 as a potential target that can be used for drug development. We observed decreased glutaminolysis and alterations in the BCAAs metabolism in SAT of obese subjects compared to lean subjects. We also provided mechanistic explanations for the changes in the plasma level of BCAAs, glutamate, pyruvate, and α-ketoglutarate in obese subjects. Finally, we validated a subset of our model-based predictions in 20 SAT samples obtained from 10 lean and 10 obese male and female subjects.

Hepatocellular carcinoma: genome-scale metabolic models for hepatocellular carcinoma

A new study proposes a modelling strategy to identify reactions, genes and metabolites relevant in hepatocellular carcinoma using in silico and in vivo analyses. The proposed genome-scale metabolic model integrates genomic and proteomic information, and points to statins, among others, as potential chemopreventive and anticancer drugs.

Elucidating the interactions between the human gut microbiota and its host through metabolic modeling

Increased understanding of the interactions between the gut microbiota, diet and environmental effects may allow us to design efficient treatment strategies for addressing global health problems. Existence of symbiotic microorganisms in the human gut provides different functions for the host such as conversion of nutrients, training of the immune system, and resistance to pathogens. The gut microbiome also plays an influential role in maintaining human health, and it is a potential target for prevention and treatment of common disorders including obesity, type 2 diabetes, and atherosclerosis. Due to the extreme complexity of such disorders, it is necessary to develop mathematical models for deciphering the role of its individual elements as well as the entire system and such models may assist in better understanding of the interactions between the bacteria in the human gut and the host by use of genome-scale metabolic models (GEMs). Recently, GEMs have been employed to explore the interactions between predominant bacteria in the gut ecosystems. Additionally, these models enabled analysis of the contribution of each species to the overall metabolism of the microbiota through the integration of omics data. The outcome of these studies can be used for proposing optimal conditions for desired microbiome phenotypes. Here, we review the recent progress and challenges for elucidating the interactions between the human gut microbiota and host through metabolic modeling. We discuss how these models may provide scaffolds for analyzing high-throughput data, developing probiotics and prebiotics, evaluating the effects of probiotics and prebiotics and eventually designing clinical interventions.

NAFLD. Metabolic make-up of NASH: from fat and sugar to amino acids

NAFLD is regarded unquestionably as one of the components of the metabolic syndrome. Hence, metabolic perturbations occurring in the fatty liver become a systemic metabolic derangement. The phenotypic switching from fatty liver to NASH entails a reprogramming of liver metabolism to fit a stressful metabolic environment.

The human liver-specific proteome defined by transcriptomics and antibody-based profiling

Human liver physiology and the genetic etiology of the liver diseases can potentially be elucidated through the identification of proteins with enriched expression in the liver. Here, we combined data from RNA sequencing (RNA-Seq) and antibody-based immunohistochemistry across all major human tissues to explore the human liver proteome with enriched expression, as well as the cell type-enriched expression in hepatocyte and bile duct cells. We identified in total 477 protein-coding genes with elevated expression in the liver: 179 genes have higher expression as compared to all the other analyzed tissues; 164 genes have elevated transcript levels in the liver shared with at least one other tissue type; and an additional 134 genes have a mild level of increased expression in the liver. We identified the precise localization of these proteins through antibody-based protein profiling and the subcellular localization of these proteins through immunofluorescent-based profiling. We also identified the biological processes and metabolic functions associated with these proteins, investigated their contribution in the occurrence of liver diseases, and identified potential targets for their treatment. Our study demonstrates the use of RNA-Seq and antibody-based immunohistochemistry for characterizing the human liver proteome, as well as the use of tissue-specific proteins in identification of novel drug targets and discovery of biomarkers.-Kampf, C., Mardinoglu, A., Fagerberg, L., Hallström, B. M., Edlund, K., Lundberg, E., Pontén, F., Nielsen, J., Uhlen, M. The human liver-specific proteome defined by transcriptomics and antibody-based profiling.

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

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.