Information

Databases for metabolic pathways of human disease

Databases for metabolic pathways of human disease


We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

Which databases contain the metabolic pathway of human diseases? I have searched Metacyc and KEGG but didn't find the appropriate metabolic pathway.


NCBI BioSystems help file contains a list of their sources:

http://www.ncbi.nlm.nih.gov/Structure/biosystems/docs/biosystems_help.html#SourceDatabases

Please specify what you need as stated in the comments as it is almost impossible to give you more (relevant) information then this.


You can try Disgene package in Cytoscape.


Metabolic pathway databases and model repositories

The number of biological Knowledge bases/databases storing metabolic pathway information and models has been growing rapidly. These resources are diverse in the type of information/data, the analytical tools, and objectives. Here we present a review of the most popular metabolic pathway databases and model repositories, focusing on their scope, content including reactions, enzymes, compounds, and genes, and applicability. The review aims to help researchers choose a suitable database or model repository according to the information and data required, by providing an insight look of each pathway resource.

Results

Four pathways databases and three model repositories were selected on the basis of popularity and diversity. Our review showed that the pathway resources vary in many aspects, such as their scope, content, access to data and the tools. In addition, inconsistencies have been observed in nomenclature and representation of database entities. The three model repositories reviewed do not offer a brief description of the models’ characteristics such as simulation conditions.

Conclusions

The inconsistencies among the databases in representing their contents may hamper the maximal use of the knowledge accumulated in these databases in particular and the area of systems biology at large. Therefore, it is strongly recommended that the database creators and the metabolic network models developers should follow international standards for the nomenclature of reactions and metabolites. Besides, computationally generated models that could be obtained from model repositories should be utilized with manual curations as they lack some important components that are necessary for full functionality of the models.


Introduction – Metabolism pervades every aspect of biology

Metabolism is broadly defined as the sum of biochemical processes in living organisms that either produce or consume energy. It is a dauntingly large sum: more than 8,700 reactions and 16,000 metabolites are now annotated in the Kyoto Encyclopedia of Genes and Genomes (http://www.genome.jp/kegg/pathway.html). Core metabolism can be simplified to those pathways involving abundant nutrients like carbohydrates, fatty acids and amino acids, essential for energy homeostasis and macromolecular synthesis in humans ( Figure 1 ). Pathways of core metabolism can then be separated conveniently into three classes: those that synthesize simple molecules or polymerize them into more complex macromolecules (anabolism) those that degrade molecules to release energy (catabolism) and those that help eliminate the toxic waste produced by the other classes (waste disposal). These pathways are profoundly important. Stated bluntly, they are the sole source of energy that allows life to resist the urge to degrade into entropy.

A simplified view of core metabolism, focusing on the use of major nutrients (glucose, amino acids and fatty acids) to produce or store energy, and to grow.

Defining these pathways and understanding their physiological roles have been among the most fruitful pursuits in biological research. The “golden age of biochemistry” (roughly 1920s-1960s) defined most of the metabolic network responsible for nutrient utilization and energy production in humans and other organisms. These included core activities like glycolysis (Embden, Meyerhof and Parnas), respiration (Warburg), the tricarboxylic acid (TCA) and urea cycles (Krebs), glycogen catabolism (Cori and Cori), oxidative phosphorylation (Mitchell), and the supremacy of ATP in energy-transfer reactions (Lipmann). Biochemistry and the analysis of metabolic pathways dominated basic and medically-oriented research during these decades, with some fifteen Nobel Prizes in either Physiology/Medicine or Chemistry awarded for work related to energy balance or core metabolic pathways. By the end of this period it was possible to understand, at the level of enzymatic control, such complex matters as the temporal and organ-specific regulation of fuel preferences (Krebs, 1972).

Research in metabolism has been propelled by the realization that metabolic perturbations accompany common human diseases. This insight predates the formal study of metabolism by many centuries. Almost 2,000 years ago, Celsus knew that rich foods and drink precipitated attacks of gout, and Indian physicians knew that the urine of diabetic patients attracted ants, while normal urine did not (Trowell, 1982). A greater appreciation for the relationship between precise metabolic activities and disease states blossomed during the golden age, but momentum in metabolic research gradually dissipated with the advent of newer areas of biological investigation in the latter half of the 20 th century, and perhaps from the suspicion that most of what could be known about intermediary metabolism had already been discovered. The search for the genetic and molecular bases of cancer, diabetes, obesity and neurodegeneration displaced focus from understanding the altered metabolic states in these diseases. Many common diseases are now understood in terms of inherited or somatic mutations that impact gene expression, signal transduction, cellular differentiation and other processes not traditionally viewed in bioenergetic or metabolic terms.

Ironically, ongoing exploration of cell biology and disease has recently stimulated a renaissance of interest in small-molecule metabolism (McKnight, 2010). The last ten years have revealed a host of functions for metabolites and metabolic pathways that could not have been predicted from a conventional understanding of biochemistry. As a result, it is no longer possible to view metabolism merely as a self-regulating network operating independently of other biological systems. Rather, metabolism impacts, or is impacted by, virtually every other cellular process there is no longer any space in biological research that is totally free from the influence of metabolism. This is perhaps not surprising when one considers that fundamental aspects of energy metabolism are conserved throughout evolution, whereas higher levels of regulation and the complex organization of multi-cellular organisms came much later.

Recent work has identified numerous regulatory mechanisms that either link cell signaling to the orchestration of metabolic pathways, or that enable cells to sense fuel availability and transmit the information through signaling networks ( Figure 2 ). The integration of biochemical pathways in the cellular response to growth factors is a good example of this. In most mammalian cells, growth occurs only when promoted by extracellular ligands. These growth factors stimulate signal transduction pathways such as the phosphoinositol 3’-kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR) pathway. Activation of this and other pathways alter the phosphorylation states of numerous targets which together coordinate the cellular activities that culminate in cell division. But a successful transition from a resting state to growth can only occur if metabolism is reprogrammed to meet the rising demands of proliferation. Growth factor-induced signaling coordinates these functions, including maintaining a bioenergetic state permissive for growth (Lum et al, 2005). In particular, the PI3K/Akt/mTOR pathway stimulates both a rapid increase in essential nutrient uptake and the proper allocation of these nutrients into catabolic and anabolic pathways to produce energy and macromolecules, respectively (Gibbons et al., 2009). Interruption of any of these metabolic effects renders the growth factor ineffective.

In mammals, cell growth and proliferation are controlled by extracellular factors. These ligands bind to cell surface receptors and initiate signal transduction cascades, stimulating numerous cellular activities to enable growth and replicative division. Proper control of metabolism is required for these effects. One of the proximal effects of growth factor signaling is to increase surface expression of transporters for glucose and other nutrients, which provide energy and metabolic precursors to produce macromolecules. Catabolism of these nutrients (heavy arrows) produces carbon dioxide and energy. If nutrients are present in excess, so that flux through these basic catabolic pathways is satisfied, other pathways stemming from core metabolism are induced to propagate growth signals. Hexosamine biosynthesis reinforces growth signals by enabling cells to maintain surface expression of growth factor receptors and nutrient transporters. Acetyl-CoA generated by acetyl-CoA synthetases (ACS) and ATP-citrate lyase (ACL) provides substrate for the synthesis of lipids and other macromolecules, and for acetylation reactions to regulate gene expression and enzyme function. The favorable energy state during growth factor signaling also suppresses AMPK, thereby permitting cells to engage in energy-consuming biosynthetic pathways and to progress through the cell cycle.

Dynamic mechanisms also sense cellular energy status and regulate the balance between anabolism and catabolism. While the PI3K/Akt/mTOR pathway promotes anabolism and suppresses catabolism, AMP-activated protein kinase (AMPK) does the reverse ( Figure 2 ). This serine-threonine kinase is a 𠇏uel sensor” activated during compromised bioenergetic states such as acute nutrient deprivation and hypoxia (Hardie, 2011). By phosphorylating a number of key targets, AMPK inactivates energy-consuming, growth-promoting pathways like protein and lipid synthesis, and activates catabolism of fatty acids and other fuels. This enables the cell to re-balance energy supply with demand. Interestingly, AMPK also regulates a p53-dependent cell-cycle checkpoint activated by glucose deprivation in cultured cells, thereby limiting growth in energetically unfavorable states (Jones et al., 2005). AMPK also coordinates the expression of stress response genes by localizing to chromatin and phosphorylating histone H2B on serine 36, and this activity facilitates AMPK’s effects on gene expression (Bungard et al., 2010). Therefore AMPK executes a number of activities that allow cells to respond decisively and comprehensively to energy shortage.

Metabolism also affects cell signaling by providing substrates for post-translational modifications that modulate protein trafficking, localization and enzyme activity. The most obvious example is ATP, which provides the substrate for phosphorylation in kinase cascades. But other examples involve metabolites that are produced in more complex pathways stemming from core metabolism. These modifications can signal states of nutrient abundance, because generating sufficient quantities of the requisite metabolite for protein modification requires access to nutrients in excess of the levels needed to run basic bioenergetic programs. For example, both glucose and glutamine are abundant nutrients that are catabolized to produce energy in growth-factor stimulated cells. But they also collaborate in another biochemical pathway to produce hexosamines, which modify nutrient transporters and growth factor receptors, enabling their expression on the cell membrane ( Figure 2 ). Unless glucose is present in adequate amounts to supply flux through the hexosamine pathway, cells lose the ability to respond to growth factor signaling and no longer take up the full complement of nutrients required for growth (Wellen et al., 2010). Similar examples involve the post-translational modification of signaling mediators by fatty acids and other lipid-like molecules, which facilitate membrane localization and/or activation of these proteins.

Acetyl-CoA, a central metabolite at the intersection of carbohydrate, fatty acid and amino acid oxidation, exerts tremendous influence on cell signaling. Acetylation of lysine residues on the N-terminal tails of histone proteins is a major factor in chromatin dynamics and gene expression. The acetyl-CoA groups used to modify histones are predominantly produced by acetyl-CoA synthetase in yeast and ATP-citrate lyase in mammalian cells, both of which enter the nucleus to produce a localized acetyl-CoA pool for this purpose ( Figure 2 ). Loss of function of these enzymes reduces histone acetylation, with global consequences on gene expression (Takahashi et al., 2006 Wellen et al., 2009). Many other cellular proteins besides histones are acetylated, including most of the enzymes in core metabolic pathways (Zhao et al., 2010). In the liver, these modifications appear to regulate metabolic flux in synchrony with the feed/fast cycle. For example, the acetylation status of several key enzymes in gluconeogenesis and the urea cycle correlates with their stability or activity. In both mammals and bacteria, enzyme acetylation fluctuates according to which nutrients are available to the cell (Wang et al., 2010 Zhao et al., 2010). Thus both unicellular and multicellular organisms use metabolite-mediated post-translational modification to match nutrient abundance with the distribution of carbon throughout metabolic networks.

Deacetylation reactions also regulate metabolism ( Figure 2 ). A class of deacetylases, the sirtuins, are nicotinamide adenine dinucleotide (NAD)-dependent deactylases whose targets include histones and metabolic enzymes. Sirtuins are key evolutionarily-conserved factors linking caloric restriction to longevity. Over-expression of sirtuins in model systems ameliorates a variety of age-related phenotypes including cancer, diabetes and neurodegeneration (Guarente, 2011). There is a tremendous amount of interest in identifying potent pharmacological activators of sirtuins to treat or prevent these diseases. Interestingly, several sirtuins localize to the mitochondria, where they deacetylate or otherwise modify metabolic enzymes. It is unknown whether these sirtuins serve to antagonize the effects of as-yet unidentified mitochondrial acetyltransferases or to reverse non-enzymatic acetylation, but evidence indicates their importance in regulating induction of catabolism and waste disposal during the feed/fast cycle. For example, the urea cycle enzyme carbamoyl phosphate synthetase (CPS) is deacetylated by the sirtuin SIRT5 during fasting (Nakagawa et al., 2009). This likely enables the liver to accommodate the increased amino acid degradation and ammonia production stimulated by the fasting state. CPS is one of many enzymes previously thought to be regulated primarily through allosteric mechanisms, but now recognized to be subject to additional levels of control.

Acetyl-CoA levels also regulate higher levels of organization in eukaryotes, particularly fundamental processes such as the commitment to cell growth. When budding yeast are cultured in glucose-limiting conditions, they collectively oscillate through a metabolic cycle that is linked to synchronized cell growth (Tu et al., 2005). A rise in intracellular acetyl-CoA occurs in phase with the induction of growth genes, and a bolus of extracellular nutrients that can be readily converted to acetyl-CoA causes cells to short-circuit the metabolic cycle and enter directly into growth (Cai et al., 2011). Peak levels of acetyl-CoA are accompanied by histone acetylation at specific regions of chromatin containing growth genes. Thus, in these cells, extracellular nutrients stimulate gene expression via an acetyl-CoA-transmitted signal that culminates in a commitment to cell growth/division.

Work in all of these areas emphasizes that metabolism pervades every aspect of biology from the single-cell to whole-organism level. No cellular functions occur independently of metabolism, and a metabolic perturbation at one node has ripple effects that can extend throughout the network and out into other systems. Thus metabolic disturbances have an extremely long reach, and this extends to disease phenotypes.


INTRODUCTION

Metabolomics is a newly emerging field of ‘omics’ research concerned with the high-throughput identification, quantification and characterization of the small molecule metabolites in the metabolome ( 1). The metabolome can be defined as the complete complement of all small molecule (<1500 Da) metabolites found in a specific cell, organ or organism. It is a close counterpart to the genome, the transcriptome and the proteome. Together these four ‘omes’ constitute the building blocks of systems biology. Thanks to the Human Genome Project most of the human genome, transcriptome and proteome are now known and the data are electronically available. Unfortunately, the same cannot be said of the human metabolome. To remedy this situation, the Human Metabolome Project (HMP) was launched in 2004 as part of an effort to identify and quantify all detectable metabolites (>1 μM) in the human body. In addition to experimentally identifying and quantifying hundreds of metabolites in different body fluids, this multi-year project was also formally tasked with backfilling and validating the information on all previously identified metabolites and providing this information as a freely available electronic database called the Human Metabolome Database (HMDB).

The foundation to all metabolomics research lies in the work done by hundreds of metabolic biochemists and clinical chemists over the past 70 years. The pathways, reactions and reactants that were identified by these scientists are now available in many excellent on-line metabolic pathway databases such as KEGG ( 2), BioCyc ( 3) and Reactome ( 4). More recently, information about the genes and diseases that are associated with perturbations to these pathways has come on-line through such outstanding resources as OMMBID ( 5) and OMIM ( 6). However, the information contained in these databases does not meet the unique data requirements for metabolomics researchers, especially those involved in human metabolomics. This is because human metabolomics is often concerned with rapidly identifying dozens of metabolites at a time and then using these metabolites or combinations of metabolites as disease and/or phenotypic biomarkers. As a result, metabolomics researchers need databases that can be searched using Nuclear Magnetic Resonance (NMR) spectra, mass spectra (MS), chemical structures or chemical formulas—as opposed to sequences or sequence names ( 7). Likewise metabolomics researchers routinely need to search for metabolite concentrations, properties, locations or metabolite-disease associations. Therefore, metabolomics databases require information not only about compounds and reactions but also data about compound concentrations, biofluid or tissue locations, subcellular locations, physical properties, known disease associations, nomenclature, descriptions, enzyme data, mutation data and characteristic MS or NMR spectra. These data need to be readily available, experimentally validated, fully referenced, easily searched, readily interpreted and they need to cover as much of the human metabolome as possible. In other words, metabolomics researchers need a metabolic equivalent to GenBank or SwissProt. To address these needs, and to serve as a potential model for other metabolomic resources, we have developed the HMDB.


Food Components

Food is a complex combination of numerous components which can be classified into nutrients and non-nutrients. Nutrients have been traditionally classified as macronutrients and micronutrients. Plants and animals do not have identical nutrient requirements and produce nutrient metabolites that may not be common to each other. Micronutrients, which include vitamins and minerals, are needed in only small amounts, and are required for the proper function of important proteins and enzymes. Macronutrients, which include carbohydrates, proteins, and fats, are typically needed in large amounts. The benefits of consuming macronutrients are self-evident since their subunits serve as building blocks of cellular structures and as energy substrates in all organisms. Some species are unable to synthesize key metabolites needed for survival, and thus must obtain these from other species. These essential metabolites, along with minerals, make up a class of substances referred to as essential nutrients. Non-nutrient components of food are those that cannot be categorized as either macronutrients or micronutrients. These substances include both natural and synthetic compounds. They can be beneficial (e.g. fiber, and some polyphenolic compounds produced by plants), non-beneficial (e.g. many food additives, and preservatives) or even toxic (e.g. xenobiotics, and antibiotics, also some plant-derived polyphenolic compounds) [34,35]. It has become evident that both nutrients and non-nutrients, as well as their metabolites, have the capacity to modulate gene expression, protein function and epigenome [36-38].

The potential of macronutrients and their metabolites to regulate metabolic function is typically taken for granted. For example, the monosaccharide fructose is commonly used as a sweetener in commercially prepared foods and is present in these foods at exceedingly high amounts compared to natural foods [39]. Fructose is known to stimulate de novo lipid synthesis in the liver and to induce endoplasmic reticulum stress in many cell types [40,41]. In general, excess glucose and fructose induce cellular stress which leads to the development of insulin resistance and fatty liver disease [40,42,43]. Certain amino acids have been shown to act as signaling molecules to regulate cellular growth and proliferation via mTOR (mechanistic target of rapamycin) [44,45], whose function has been implicated in many human diseases [46]. Some fatty acids from fats and oils serve as ligands for G protein-coupled receptors as well as for transcription factors belonging to the nuclear receptor family of transcription factors [47,48], and therefore regulate cellular processes and gene expression [49]. Saturated fatty acids have long been the focus of investigation as high intake of saturated fats was considered to be a risk factor for cardiovascular diseases [50], however subsequent studies have not provided strong evidence for causality [9]. This may be partly attributable to the wide range of biological activities associated with different fatty acids species [51]. Palmitic acid, a fatty acid species that is enriched in the Western-style diet, is a potent inducer of endoplasmic reticulum stress whereas oleic acid, a fatty acid prominent in the Mediterranean diet, has been shown to inhibit endoplasmic reticulum stress [52,53]. Importantly, the surplus of nutrients and energy induce endoplasmic reticulum stress and inflammatory responses that lead to systemic metabolic dysregulation [31,32].

Many metabolic diseases caused by micronutrient deficiencies can be corrected by restoring the missing micronutrients in the diet [54,55]. One critical aspect of micronutrient supplementation applied to the general population is overdose. Some of these compounds are potent modulators of nuclear receptors and have serious impacts on the activities of multiple metabolic pathways. For example, deficiency of vitamin A can lead to blindness while its excess is teratogenic. Vitamin D also modulates the expression of many genes that participate in many pathways [56], and its deficiency causes rickets. However, it is not yet known if it is possible to overdose with this micronutrient. Excessive dietary intake of minerals can be equally deleterious, as exemplified by diet-induced hypertension due to high intake of sodium [57].

Other metabolites produced by the mammalian metabolic machinery also play critical roles in metabolism. For example, cholesterol serves as a membrane component, signaling molecule, and precursor for the synthesis of steroid hormones and bile acids [58]. Bile acids aid in the absorption of dietary fats and lipid-soluble compounds, and also act as signaling molecules modulating macronutrient and energy metabolism, inflammatory responses, and detoxification through intracellular ligand-activated nuclear receptors [59]. Gut bacteria are capable of metabolizing bile acids and one of the products is a secondary bile acid referred to as ursodeoxycholic acid. It is of interest to note that this bile acid and its taurine-conjugated derivative can alleviate endoplasmic reticulum stress by promoting proteostasis [60], and has been shown to be effective in preventing cardiac fibrosis [61,62].

The non-nutrient components of food can be beneficial or non-beneficial. Beneficial ones include fiber and certain types of plant polyphenolic compounds. Dietary fiber, derived from plant-based foods, is not an effective nutrient for humans, but serves as a nutrient for gut microflora. Some of the products generated from dietary fiber include short chain fatty acids (e.g., butyric and propionic acids) that are absorbed in the lower gut and serve as both energy substrates and regulators of host metabolism [63,64]. Plant polyphenolic compounds have been popularized as anti-oxidants. However, there are numerous polyphenolic compounds present in plants, and these compounds likely have a wide range of biological activities and effects on human metabolism [65,66]. A polyphenolic-rich extract prepared from potatoes exhibits beneficial activity by attenuating weight gain in mice fed with obesity-inducing high fat diet [67]. There is also emerging evidence for the modulating effect of polyphenols on the composition and metabolic activity of gut microbiota that provides potential benefits to the host [68]. Not all polyphenolic compounds are beneficial, as some compounds such as caffeic acid and genistein may be carcinogenic or genotoxic at high dosage [34,69,70]. It is commonly assumed that synthetic food additives (colorants, preservatives, sweeteners) do not have effects on metabolism, but this assumption should be tested regularly to ensure food safety. Other xenobiotics (e.g. pollutants, drugs, and agricultural chemicals) that find their way into the food supply can influence human health directly, by disrupting normal metabolic processes, or indirectly, by influencing the composition of the gut microbiota [71]. All food components likely work together to drive metabolic processes in every cell of the body.


Tools

PCViz

Data visualization and analysis

Cytoscape app providing keyword search and retrieval of pathways from Pathway Commons along with advanced filtering and graph queries.

Use Pathway Commons from Cytoscape.

R package that faciliates interacting with BioPAX. Supports access to Pathway Commons web services.

Use Pathway Commons data within R.

Chisio BioPAX Editor (ChiBE) is an editing and visualization tool for pathway models represented in BioPAX. Provides access to pathways from Pathway Commons.

View and edit BioPAX pathway models.

Developer resources

The BioPAX Validator applies custom criteria to identify syntax and semantic errors. Rules originate from the BioPAX Level3 specification.

Validate BioPAX using Level3 specification.


Pathway analysis in metabolomics: pitfalls and best practice for the use of over-representation analysis

Over-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention in the field. We developed in-silico simulations using five publicly available datasets and illustrated that changes in parameters, such as the background set, differential metabolite selection methods, and pathway database choice, could all lead to profoundly different ORA results. The use of a non-assay-specific background set, for example, resulted in large numbers of false-positive pathways. Pathway database choice, evaluated using three of the most popular metabolic pathway databases: KEGG, Reactome, and BioCyc, led to vastly different results in both the number and function of significantly enriched pathways. Metabolomics data specific factors, such as reliability of compound identification and assay chemical bias also impacted ORA results. Simulated metabolite misidentification rates as low as 4% resulted in both gain of false-positive pathways and loss of truly significant pathways across all datasets. Our results have several practical implications for ORA users, as well as those using alternative pathway analysis methods. We offer a set of recommendations for the use of ORA in metabolomics, alongside a set of minimal reporting guidelines, as a first step towards the standardisation of pathway analysis in metabolomics.

Author summary Metabolomics is a rapidly growing field of study involving the profiling of small molecules within an organism. It allows researchers to understand the effects of biological status (such as health or disease) on cellular biochemistry, and has wide-ranging applications, from biomarker discovery and personalised medicine in healthcare to crop protection and food security in agriculture. Pathway analysis helps to understand which biological pathways, representing collections of molecules performing a particular function, are involved in response to a disease phenotype, or drug treatment, for example. Over-representation analysis (ORA) is perhaps the most common pathway analysis method used in the metabolomics community. However, ORA can give drastically different results depending on the input data and parameters used. In this work, we have established the effects of these factors on ORA results using computational simulations applied to five real-world datasets. Based on our results, we offer the research community a set of best-practice recommendations applicable not only to ORA but also to other pathway analysis methods to help ensure the reliability and reproducibility of results.


Author information

Present address: Present address: Department of Chemical Engineering, Indian Institute of Technology, Madras, India.,

Affiliations

Department of Bioengineering, University of California, San Diego, San Diego,California, USA.

Elizabeth Brunk, Daniel C Zielinski, Nathan Mih, Francesco Gatto, Anand Sastry & Bernhard O Palsson

The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark

Elizabeth Brunk, Jens Nielsen & Bernhard O Palsson

Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg

Swagatika Sahoo, German Andres Preciat Gonzalez, Maike Kathrin Aurich, Anna D Danielsdottir, Almut Heinken, Alberto Noronha, Ronan M T Fleming & Ines Thiele

RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, California, USA.

Ali Altunkaya, Andreas Prlić, Peter W Rose & Stephen K Burley

Department of Computer Science, Arizona State University, Tempe, AZ 85281, Arizona, USA

Applied Bioinformatics Group, Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, Tübingen, Germany

Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden

Francesco Gatto, Avlant Nilsson & Jens Nielsen

Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Institute for Quantitative Biomedicine, and Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA

Division of Analytical Biosciences, Leiden Academic Centre for Drug Research, Faculty of Science, University of Leiden, Leiden, the Netherlands

Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: E.B., I.T., and D.C.Z. methodology, reconstruction of metabolic network: S.S., I.T., R.M.T.F., A.D.D., A.H., and M.K.A. reconstruction of GEM-PRO: E.B., N.M., and A.S. 3D-hotspot analysis: E.B., A.P., A.S., and P.W.R. machine learning: D.C.Z. PDB visualization: A.A., A.P., A.D., R.M.T.F., and S.K.B. atom–atom mapping: G.A.P.G. and R.M.T.F. model testing and validation: I.T., R.M.T.F., S.S., M.K.A., D.C.Z., A.N., and F.G. cell-specific and infant model simulations: M.K.A., A.N., and F.G.) investigation, E.B., D.C.Z., and G.A.P.G. writing, original draft: E.B. and B.O.P. writing, review and editing: all authors funding acquisition: I.T., R.M.T.F., S.K.B., J.N., and B.O.P. resources, I.T., R.M.T.F., S.K.B., J.N., and B.O.P. supervision: I.T., R.M.T.F., S.K.B., and B.O.P.

Corresponding authors


Databases for metabolic pathways of human disease - Biology

The KEGG database has been in development by Kanehisa Laboratories since 1995, and is now a prominent reference knowledge base for integration and interpretation of large-scale molecular data sets generated by genome sequencing and other high-throughput experimental technologies.

2. The KEGG Database

KEGG is an integrated database resource consisting of sixteen databases shown below. They are broadly categorized into systems information, genomic information, chemical information and health information, which are distinguished by color coding of web pages.

Category Database Content Color
Systems
information
KEGG PATHWAY KEGG pathway maps
KEGG BRITE BRITE hierarchies and tables
KEGG MODULE KEGG modules and reaction modules
Genomic
information
KEGG ORTHOLOGY (KO) Functional orthologs
KEGG GENES Genes and proteins
KEGG GENOME KEGG organisms and viruses
Chemical
information
KEGG COMPOUND Small molecules
KEGG GLYCAN Glycans
KEGG REACTION / RCLASS Reactions and reaction class
KEGG ENZYME Enzyme nomenclature
Health
information
KEGG NETWORK Disease-related network variations
KEGG VARIANT Human gene variants
KEGG DISEASE Human diseases
KEGG DRUG / DGROUP Drugs and drug groups

These databases contain various data objects for computer representation of the biological systems. Thus, the database entry of each database is called the KEGG object, which is identified by the KEGG object identifier consisting of a database-dependent prefix and a five-digit number (see: KEGG objects).

Release Database Object Identifier Remark
1995KEGG PATHWAYmap number
KEGG GENESlocus_tag / GeneID
KEGG ENZYMEEC number
KEGG COMPOUNDC number
1998KEGG REACTIONR number
2000KEGG GENOMEorganism code / T number
2002KEGG ORTHOLOGY K numberOrtholog IDs in 2000
2003KEGG GLYCANG number
2004KEGG RPAIRRP numberDiscontinued in 2016
2005KEGG BRITEbr number
KEGG DRUGD number
2006KEGG MODULEM number
2008KEGG DISEASEH number
2010KEGG RCLASSRC number
KEGG EDRUGE numberRenamed to ENVIRON
2011KEGG ENVIRONE numberDiscontinued in 2021
2014KEGG DGROUPDG number
2017KEGG NETWORKN number / nt number
KEGG VARIANTGeneID+variant number

3. KEGG Molecular Networks

  • Pathway map - in KEGG PATHWAY (see: Pathway maps)
  • Brite hierarchy and table - in KEGG BRITE (see: Brite hierarchies)
  • Membership (logical expression) - in KEGG MODULE
  • Membership (simple list) - in KEGG DISEASE

In 1995 the concept of mapping was first introduced in KEGG for linking genomes to metabolic pathways (metabolic reconstruction) using the EC number. Once the EC numbers were assigned to enzyme genes in the genome, organism-specific pathways could be generated automatically by matching against the enzyme (EC number) networks of the KEGG reference metabolic pathways. The EC number is no longer used as an identifier in KEGG. The KEGG Orthology (KO) system is the basis for genome annotation and KEGG mapping.

Period Identifier Reference knowledge Assignment
1995-1999 EC number Metabolic pathways Domain based
2000-2002 Ortholog ID Metabolic and regulatory pathways Domain based
2003- KO Pathways and BRITE hierarchies Gene based

From a different perspective, individual instances of genes are grouped into KO entries representing functional orthologs in the molecular networks. There are two more types of such generalization in KEGG as shown below.

Network type Class Instance
All types KO (gene ortholog) Genes in KEGG GENES
Biochemical reaction RC (reaction class) Reactions in KEGG REACTION
Drug interaction DG (drug group) Drugs in KEGG DRUG

4. Network Variants

However, this generic approach is inadequate for understanding more detailed features caused by variations of genes and genomes within a species, especially for understanding disease related variations of human genes and genomes. KEGG NETWORK represents a renewed attempt by KEGG to capture knowledge on diseases and drugs in terms of network variants caused by not only gene variants, but also viruses and other factors.


Sphingolipid Metabolic Pathway: An Overview of Major Roles Played in Human Diseases

Sphingolipids, a family of membrane lipids, are bioactive molecules that participate in diverse functions controlling fundamental cellular processes such as cell division, differentiation, and cell death. Given that most of these cellular processes form the basis for several pathologies, it is not surprising that sphingolipids are key players in several pathological processes. This review discusses the role of the sphingolipid metabolic pathway in diabetes, Alzheimer’s disease, and hepatocellular carcinoma, with a special emphasis on the changes in gene expression pattern in these disease conditions. For convenience, the sphingolipid metabolic pathway is divided into hypothetical compartments (modules) with each compartment representing a physiological process and changes in gene expression pattern are mapped to each of these modules. It appears that alterations in the gene expression pattern in these disease conditions are biased to manipulate the system in order to result in a particular disease.

1. Introduction

Sphingolipids are a class of natural lipids comprised of a sphingoid base backbone, sphingosine. Sphingosine N-acylated with fatty acids forms ceramide [1], a central molecule in the sphingolipid biology. A variety of charged, neutral, phosphorylated, or glycosylated moieties are attached to ceramide further forming complex sphingolipids [2] (see Figure 1 for details). For example, phosphoryl choline attached to ceramide makes the most abundant mammalian sphingolipid, sphingomyelin. These moieties result in both polar and nonpolar regions giving the molecules an amphipathic character which accounts for their tendency to aggregate into membranous structures. Furthermore, such variations found in their chemical structures allow them to play diverse roles in cellular metabolism. Research in the past decade has clearly indicated that sphingolipids are not just the structural components of cell membrane but also act as signaling molecules controlling a majority of cellular events including signal transduction, cell growth, differentiation, and apoptosis [3–5]. Ceramide, sphingosine, sphingosine-1-phosphate (S1P), and ceramide-1-phosphate (C1P) have emerged as chief bioactive mediators in the context of sphingolipid biology.


(a)
(b)
(c)
(d)
(e)
(a)
(b)
(c)
(d)
(e) Structure of key sphingolipid molecules. All sphingolipids are comprised of a sphingoid base, and in mammals sphingosine (mainly C-18) is the major sphingoid base (a). A long chain fatty acid attached to sphingosine through amide linkage forms ceramide (b). Complex sphingolipids are obtained by replacement of hydrogen group of ceramide (H*) with various functional head groups (represented as R group in (e)). Complex sphingolipids vary in the nature of the polar head groups. For example, in sphingomyelin the head group is phosphocholine whereas in glycosphingolipids the head group could be one or more sugar residues. The phosphorylated derivatives, namely, sphingosine-1-phosphate (c) and ceramide 1 phosphate (d), are obtained by action of respective kinases on sphingosine and sphingosine-1-phosphate.

Although sphingolipids contribute to only a small proportion of the total cellular lipid pool, their accumulation in certain cells may be a trigger for pathology of many diseases. Because of the presence of a highly integrated metabolic network among various bioactive sphingolipids, it can be implicated that manipulation of one enzyme or metabolite may result in unexpected changes in metabolite levels, enzyme activities, and cellular programs [6, 7].

Whilst the scientific literature has been enriched by articles focusing on structural diversity and cellular metabolism, this review focuses on how alterations in expression of genes involved in sphingolipid metabolism could result in the progression of severe diseases. We chose three most prevalent diseases: type 2 diabetes, Alzheimer’s disease, and hepatocellular carcinoma and analyzed the nature of gene expression changes in these disease conditions. The gene expression changes were further translated into possible physiological effects and these effects were analyzed to check for any correlation to the key pathology of the disease in question. It turns out that, under these disease conditions at least, the sphingolipid metabolic pathway is modulated in such a way that the resultant changes in the physiology act as a significant contributor to the disease pathology.

2. Sphingolipid Metabolism: An Overview

The process of metabolism of sphingolipids has been studied extensively and most of the biochemical pathways of synthesis and degradation, including all the enzymes involved, have been determined successfully [8]. Sphingolipid metabolic pathway is an important cellular pathway that represents a highly coordinated system linking together various pathways, where ceramide occupies a central position in both biosynthesis and catabolism, thereby crafting a metabolic hub [9]. The reaction sequences involved in the formation of ceramide and other sphingolipids are represented in Figure 2.


Key reactions involved in the sphingolipid metabolic pathway. Ceramide is produced in the ER and later transported to the Golgi complex for further conversion to complex sphingolipids. In addition to de novo synthesis, ceramide is also generated by hydrolysis of sphingomyelin. Ceramide is subject to conversion to various other sphingolipid intermediates like ceramide-1-phosphate, sphingosine, and sphingosine-1-phosphate. Cellular compartments are represented by boxes and enzymes are italicized. CDASE: ceramidase CERK: ceramide kinase CERS: ceramide synthase CERT: ceramide transfer protein DEGS: dihydroceramide desaturase ER: endoplasmic reticulum GBA: glucosyl ceramidase GC: Golgi complex KDSR: 3-keto dihydrosphinganine reductase PM: plasma membrane PPAP2A/B/C: phosphatidic acid phosphatase 2A/B/C SGMS: sphingomyelin synthase SGPP: sphingosine-1-phosphate phosphatase SMPD: sphingomyelin phosphodiesterase SPHK: sphingosine kinase SPT: serine palmitoyll transferase UGCG: UDP-glucose ceramide glucosyltransferase.
2.1. De Novo Synthesis

The first step in the de novo biosynthesis of sphingolipids is the condensation of serine and palmitoyl CoA, a reaction catalyzed by the rate-limiting enzyme, serine palmitoyltransferase (SPT, EC 2.3.1.50), to produce 3-ketodihydrosphingosine [10, 11]. Among various organisms, several metabolic divergences appear after the formation of sphinganine (dihydrosphingosine). In fungi and higher plants, sphinganine thus formed is first hydroxylated to phytosphingosine and then acylated to produce phytoceramide, whereas in animal cells sphinganine it is acylated to dihydroceramide which is later desaturated to form ceramide [12]. These reactions leading to generation of ceramide starting from serine take place in the endoplasmic reticulum. Ceramide thus generated needs to be transported to the Golgi complex, where it serves as a substrate for production of complex sphingolipids like sphingomyelin and glycosphingolipids (Figure 2). Both vesicular and nonvesicular transport mechanisms can mediate this process. The non-vesicular transport is mediated by the ceramide transfer protein (CERT) in mammals, in an ATP-dependent manner [13]. Once transported to the Golgi complex, several different head groups can be added to ceramide to form different classes of complex sphingolipids [14]. These complex sphingolipids will traverse different cellular locations mainly through vesicular transport.

2.2. Ceramide Homeostasis

Ceramide is considered as a molecule central to sphingolipid metabolic pathway and it serves as a branch point in the pathway. It acts as substrate not only for complex sphingolipids but also for the generation of ceramide-1-phosphate (C1P) and sphingosine, and sphingosine can be further converted into sphingosine-1-phosphate (S1P) (reactions are outlined in Figure 2). Various secondary signaling intermediates produced by further conversion of ceramide can participate in diametrically opposite cellular processes for example, ceramide and sphingosine are proapoptotic while their phosphorylated derivatives, C1P and S1P, are involved in progrowth activities [15]. Several studies have established that ceramide and sphingosine function as tumor-suppressor lipids mediating apoptosis, growth arrest, senescence, and differentiation. On the other hand, S1P and C1P are regarded as a tumor-promoting lipids involved in cell proliferation, migration, transformation, inflammation, and angiogenesis [16–19].

In addition to de novo biosynthesis, ceramide can be generated in the cell through hydrolysis of complex sphingolipids. The hydrolytic pathway controls the regeneration of ceramide from the complex sphingolipid pool, for example, from glycosphingolipids (GSLs) and sphingomyelin (SM), through the action of specific hydrolases and phosphodiesterases. Regeneration of ceramide from sphingomyelin is carried out by the plasma membrane bound enzyme SMPD (sphingomyelin phosphodiesterase) as presented in Figure 2. Ceramide regeneration from complex glycosphingolipids can be regulated by either lysosomal or nonlysosomal degradation. In lysosomal degradation, catabolism of GSLs occurs by cleavage of sugar residues which leads to the formation of glucosyl ceramide and galactosylceramide. Thereafter, specific β-glucosidases and galactosidases hydrolyze these lipids to produce ceramide that can later be deacylated by an acid ceramidase to form sphingosine [9, 16]. The sphingosine thus produced can further be salvaged to form ceramide (the intricate details of reactions involved in the degradative pathway of complex sphingolipids are well covered in the literature and are not discussed in this review interested readers are recommended to refer to [16] from this article and relevant citations from this reference). Defects in the function of these enzymes lead to a variety of lysosomal storage disorders such as Gaucher, Sandhoff, and Tay-Sachs diseases [20]. Degradation of sphingolipids is also an indispensable component of lipid homeostasis consequently SM levels are maintained by the catabolic action of sphingomyelinases (also called sphingomyelin phosphodiesterases, SMPD), releasing ceramide and the corresponding head group, phosphorylcholine.

2.2.1. Compartmental View of Sphingolipid Metabolic Pathway

It is evident that sphingolipid metabolic pathway is complex and involves several reactions and cellular organelles. Outcomes of these reactions in different organelles could lead to serous physiological consequences and this forms the basis for the role of sphingolipids in disease pathogenesis. In this review we have presented sphingolipid metabolic pathway as a combination of four sets of modules as depicted in Figure 3. (i) The de novo biosynthesis of ceramide which occurs in ER is represented by the compartment C1. (ii) The conversion of ceramide into complex sphingolipid like SM and glycosphingolipids is represented by compartment C2. (iii) Hydrolysis of SM which produces ceramide is presented as compartment C3. (iv) Conversion of ceramide into bioactive molecules such as C1P and S1P is represented by compartment C4.


Hypothetical compartmentalization of sphingolipid metabolism. Reactions in the sphingolipid metabolic pathway can be isolated into different hypothetical compartments with each compartment representing ceramide either as a substrate or product of the reactions. Compartment C-1 is a de novo ceramide synthesis channel, compartment C-2 represents reactions leading to synthesis of complex sphingolipids, compartment C-3 is comprised of reaction leading to hydrolytic generation of ceramide from sphingomyelin, and compartment C-4 represents reactions in which ceramide is converted into other intermediates like sphingosine-1-phosphate and ceramide-1-phosphate. Activities/status of each compartment can be translated into different physiological events accumulation of ceramide due to increased production of ceramide in C-1 would result in ER stress, increased generation of ceramide-1 phosphate, and sphingosine-1-phosphate in compartment C-4 results in progrowth and proinflammatory scenario in the cell. In different disease conditions, the status of each of these compartments contributes to the overall pathology of the disease.

Activities in each of these compartments can be mapped to physiological processes for example, overall increase in the enzymes of de novo ceramide biosynthesis and hence increased levels of ceramide in compartment C1 can be designated as a proapoptotic scenario. Increased ceramide production in the ER followed by decreased CERT expression would result in an accumulation of ceramide in this organelle and hence can result in ER stress [21]. Decreased expression of enzymes involved in hydrolysis of complex sphingolipids as indicated in C3 can be characterized as sphingolipid storage. Increase in the components of sphingolipid rheostat (sphingosine kinase (SPHK1) and ceramide kinase (CERK)) thereby increasing the levels of C1P and S1P in compartment C4 can be taken to represent progrowth situations. Compartment C2 provides a channel wherein proapoptotic ceramide is converted into inert complex sphingolipids and this compartment can be considered as an adaptive channel since this channel is employed by drug resistant cancer cells as an adaptive mechanism. This approach provides a more explanatory vision for finding links between severe pathological diseases and sphingolipid metabolism.

3. Sphingolipid Metabolism in Pathogenesis of Human Diseases

Human diseases resulting due to impaired sphingolipid metabolism are generally the outcome of defect in enzymes that degrade the sphingolipids [22]. In general, they are a group of relatively rare inborn errors of metabolism resulting in accumulation of sphingolipids (sphingolipidosis) caused by defects in the genes coding for proteins taking part in the lysosomal degradation of sphingolipids [23]. Historically, the pathological significance of sphingolipid related diseases is discussed with reference to sphingolipidosis. There has been a tremendous progress in the sphingolipid research in the recent past and it is now clear that sphingolipids can play a major role in pathogenesis of several diseases apart from traditionally studied sphingolipidosis. Deregulation of sphingolipid homeostasis is established as a key factor in the pathogenesis of several disorders like metabolic, neuronal, and proliferative disorders.

Recent studies have established the role of altered sphingolipid metabolism in brain cells in Alzheimer’s disease [24]. The key role of sphingomyelinases in this disease is to promote apoptosis in neuronal cells through generation of proapoptotic molecule, ceramide [25]. In addition, serine palmitoyl transferase (SPT) has been shown to be downregulated by the amyloid precursor protein [26]. This exclusive physiological function of the amyloid precursor protein suggests the involvement of SPT and sphingolipid metabolism in Alzheimer’s disease pathology.

It has been established for decades that patients with cirrhosis had increased levels of plasma long chain fatty acids (LCFA) as compared to controls, and the level of these substances is augmented with greater severity of the disease [27]. Furthermore, ceramide has been known as the prototype of sphingolipids that provokes cell death and its levels increase in response to apoptotic stimuli such as ionizing radiation or chemotherapy. In the liver, accumulation of ceramide may contribute to a variety of complications, leading to the substitution of steatosis to steatohepatitis [28], which can further develop into cirrhosis and hepatocellular carcinoma (HCC). Different studies have shown that either pharmacologic ceramide accumulation or systemic intravenous administration of liposomal ceramide is an effective approach against HCC [29].

Though a lot of research has already been done to emphasize the association of sphingolipids with several disorders, an understanding of the respective pathology in more detail with reference to sphingolipid metabolic pathway genes would be desirable. An analysis of the expression levels of genes involved in sphingolipid metabolic pathway presented below throws more light on the predisposition of sphingolipid pathway genes to execute the disease process.

Here we discuss the role of sphingolipid metabolism in three different disease conditions—type 2 diabetes mellitus (T2DM), Alzheimer’s disease (AD), and hepatocellular carcinoma (HCC). For this purpose, we obtained the patterns in expression profiles of genes involved in sphingolipid metabolism under each of the diseased state from publicly available databases and mapped them to different physiological processes visualized through hypothetical compartments (Figure 3). Once the sphingolipid gene expression pattern from each study was mapped to a physiological process, possible effects of changes in the enzymes were predicted under diseased conditions. This is discussed in detail in the subsequent sections.

3.1. Role of Sphingolipid Metabolism in Pathogenesis of Type 2 Diabetes Mellitus (T2DM)

T2DM is a metabolic disease characterized by insulin resistance primarily in adipose, liver, and muscle tissues. Earlier reports suggesting that sphingolipids might play a role in insulin signaling came from experiments indicating accumulation of ceramides in insulin-resistant tissues. Zucker obese rats, a common model to study insulin resistance, were found to have elevated ceramide levels within the liver and skeletal muscles [30]. Ceramide can influence insulin signaling pathway by two autonomous mechanisms: (1) through activation of protein phosphatase 2A (PP2A), which in turn can dephosphorylate and hence inhibit Akt/PKB [31] and (2) by inhibiting the translocation and activation of Akt/PKB through protein kinase C [32]. Ceramide analogs have been shown to inhibit insulin stimulated glucose uptake, GLUT4 translocation, and glycogen synthesis in cultured cells. They inhibit Akt/PKB in cultured muscle cells [33–35], adipocytes [36], and hepatocytes [37]. Decreasing the levels of ceramide by using inhibitors can restore the insulin sensitivity in lipid induced insulin resistant cell culture [33, 34]. Ceramide levels have also been reported to increase in the muscles or liver of insulin-resistant rodents [30] or human [38, 39]. S1P having progrowth properties often opposes ceramide action, allowing researchers to purport the existence of a ceramide: S1P rheostat that controls cellular responses [40]. Sphingosine kinase (SPHK) hence is an important lipid kinase that maintains the balance between progrowth and proapoptotic precursors. Recently, it has been reported that such a rheostat is very important for both islet function and beta cell survival and may act as a possible therapeutic target to protect the beta cell from diabetes related complications and to improve pancreatic islet function [41].

We analyzed gene expression data obtained from separate studies (GSE15653, GSE22435, and GSE22309) and Figure 4 presents the status of different hypothetical compartments C1, C2, C3, and C4 in T2DM. Genes involved in de novo biosynthesis of ceramide (compartment C1) show an increased level of expression in T2DM which apparently indicate that ceramide levels are increased. In general, ceramide thus formed must be transported to the Golgi apparatus with the help of CERT protein in mammals [13, 14]. However, in T2DM, genes coding for CERT protein were found to be downregulated. Overall, this might present a scenario wherein there is an increased de novo ceramide biosynthesis in the ER and a decreased transport of ceramide due to decreased levels of CERT. Ceramide thus accumulated in the ER is a contributing factor for ER stress. ER stress in turn is known to inhibit insulin receptor signaling through the activation of c-Jun N-terminal kinase (JNK) and subsequent serine phosphorylation of insulin receptor substrate-1 (IRS-1) [42]. Studies indicate that mice deficient in X-box-binding protein-1 (XBP-1), a transcription factor that controls the ER stress response, develop insulin resistance [43]. Apparently elevated ceramide in the ER and subsequent ER stress contributes to insulin resistance—a major pathology of T2DM.


Status of different compartments in T2DM. In case of T2DM, the enzymes of the de novo ceramide synthesis, sphingomyelinase (SMPD), and ceramide kinase (CERK) genes are upregulated (⚫). CERT gene which is responsible for the transport of ceramide from ER to Golgi complex is downregulated (x). Consequently, this scenario might result in increased ER stress, insulin resistance, and inflammation, all of which are key pathologies associated with T2DM.

In addition to its de novo route, ceramide can also be produced by hydrolysis of sphingomyelin in the membrane. This reaction is carried out by sphingomyelinase. Several cellular insults have been shown to trigger the sphingomyelin hydrolysis leading to generation of ceramide, which in turn would bring about the subsequent effects. Two proinflammatory cytokines, tumor necrosis factor alpha (TNF-α) and interleukin 1 beta (IL-1β), play an important role in hydrolytic generation and accumulation of ceramide [44, 45]. TNF-α has been shown to be associated with the stimulation of insulin resistance [46–48]. Neutral and acid SMPD is reported to be elevated in adipose tissue of obese rodents, possibly through a TNF-α-regulated mechanism [49]. In T2DM, the genes responsible for hydrolytic generation of ceramide (SMPD) are upregulated in the compartment C3. This may again result in increased ceramide levels and hence concomitantly lead to insulin resistance.

Inflammation is one of the major components of the pathogenesis of T2DM and immunomodulatory strategies targeting inflammation are proposed as a therapeutic approach [50, 51]. Sphingolipids act as potential players in the process of inflammation and clinical data suggest a correlation between ceramide, inflammation, and insulin resistance [52, 53]. Studies have demonstrated that many of the proinflammatory effects of ceramide can be attributed to its phosphorylated derivative ceramide-1-phosphate [54]. Ceramide-1-phosphate can bind directly to phospholipase-A2 (PLA2) [55] and allosterically activate the enzyme leading to release of arachidonic acid and subsequent prostaglandin formation [56]. Ceramide kinase (CERK), a key gene involved in the generation of ceramide-1-phosphate, is upregulated in T2DM condition (Figure 4). With obesity/T2DM being a case of chronic low grade inflammation, it is possible that increased CERK activity (and consequent increase in C1P levels) would serve to execute a proinflammatory scenario contributing towards pathology. Recently, the CERK null mice have been shown to be resistant to diet-induced obesity and glycemic dysregulation [57].

Overall in the context of T2DM, sphingolipid metabolic pathway contributes to ER stress and inflammation, two major contributors for insulin resistance.

3.2. Role of Sphingolipid Metabolism in Pathogenesis of Alzheimer’s Disease

Sphingolipids form the integral component of the brain and a proper sphingolipid homeostasis is essential for the normal functioning of neurons. Several neurological disorders like Niemann-Pick disease (type I), Gaucher’s disease, and Tay-Sacks disease result due to impaired activities of the enzymes that handle complex sphingolipids [14]. Studies suggest that even minor changes in sphingolipid balance may play significant roles in the development of neurodegenerative diseases including Alzheimer’s disease [58], amyotrophic lateral sclerosis [59], Parkinson’s disease [60], and dementia [61].

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by a progressive decline in cognitive processes gradually leading to dementia. Extracellular deposition of Aβ peptide and neurofibrillary tangles are the well-known histopathological markers of AD [62]. Accumulation of abnormally folded Aβ in association with inability to catabolize Aβ peptide triggers the neuronal degeneration and this event is critical to the development of AD [63]. Sphingolipids are known to contribute to the development of AD at various steps during the progression of the disease.

We analyzed data obtained from gene expression studies (GSE29652, GSE5281, GSE16759, GDS1979, GSE34879, GSE30945, GSE15222, GSE4757, and GSE28146) and as depicted in Figure 5, de novo synthesis of ceramide (indicated by compartment C1) is increased significantly and CERT gene which is involved in transport of ceramide from ER to Golgi complex is downregulated. The overall effect may lead to ER stress due to ceramide accumulation in the ER. ER is an organelle involved in proper folding and sorting of proteins. Impaired protein folding and subsequent accumulation of neurotoxic peptides are major pathological factors in AD. Neuronal cells are vulnerable to perturbations which affect the homeostasis of ER and disturbances in redox and Ca2+ balances [64]. A number of studies have demonstrated ER stress as a major pathological driver in several neurodegenerative diseases [65–68]. Immunohistochemical studies have indicated that neurons of AD patients show prominent expression of ER stress markers [66]. The presence of oxidative stress, accumulation of neurofibrillary tangles, and intraneuronal amyloid-β aggregates [69] in AD point out the role of ER stress in this disease [70, 71]. It has been established that higher concentration of ceramide promotes the Aβ biogenesis by stabilizing the APP cleaving enzyme [72, 73], but diminished concentration of ceramide leads to a reduction in the secretion of APP and Aβ in human neuroblastoma cells [74]. Thus, it is imperative that ceramide and Aβ may work together to encourage neuronal death in AD.


Status of different compartments in Alzheimer’s disease. In Alzheimer’s disease condition, there is an upregulation of genes involved in the de novo ceramide synthesis (⚫) and downregulation of CERT (x). This contributes towards ER stress. CERK gene which generates C1P a progrowth molecule is downregulated (x). Down regulation of ceramidase might lead to accumulation of ceramide which is a proapoptotic molecule. Overall this scenario would result in ER stress and proapoptotic phenotype.

CERK and ceramidase (CDASE) are also downregulated in AD (compartment C4), which indicate decreased levels of progrowth molecule ceramide 1 phosphate and accumulation of ceramide. Diminished levels of C1P hence might reinforce a proapoptotic/degenerative scenario—a hall mark of AD. The decreased activity of ceramidase can reflect a scenario wherein ceramide accumulation in the lysosomes could lead to lysosomal dysfunction. In fact, lysosomal storage defect (LSD) is considered as one of the early histological changes associated with AD [75]. Accumulation of sphingolipids not only underlies the pathogenesis of LSDs but also elicits increased generation of Aβ and contributes to neurodegeneration in AD. Experimental evidences indicate that accumulation of sphingolipids decreases the lysosome dependent degradation of APP-CTFs (amyloid precursor protein c-terminal fragments) and increases γ-secretase activity. Both these activities result in increased generation of intracellular and secreted Aβ [74].

Thus, from sphingolipid perceptive, AD may be envisaged as an ER stress and lysosomal storage disorder arising through increased ceramide synthesis and decreased catabolism. Increased ceramide production due to increased de novo biosynthesis of ceramide can be a contributing factor for neurodegeneration. An earlier study reports that genes controlling de novo synthesis of ceramide are upregulated at early stages in AD disease progression [76]. Sphingosine, a proapoptotic sphingolipid is known to be accumulated in AD brain [77, 78]. One study has specified decreased S1P levels in cytosolic fractions of grey matter from the frontotemporal areas of AD patients [78].

4. Role of Sphingolipid Metabolism in Pathogenesis of Hepatocellular Carcinoma

Impaired sphingolipid homeostasis is a common theme for most of the cancers [79] and overexpression of SPHK1 has been identified in multiple cancer cells derived from breast, colon, lung, ovary, stomach, uterus, kidney, and rectum [80]. Central to the role of sphingolipids in cancer is the fact that ceramide generated through either the de novo/hydrolytic pathway is a proapoptotic molecule and sphingosine-1-phosphate and ceramide 1 phosphate resist the action of ceramide and promote cell proliferation [81]. Owing to the significant role of sphingolipids in cell death/survival regulation, the alteration in sphingolipid metabolism has a profound impact on cancer biology and therapy. While increased levels of progrowth sphingolipids would enhance the cell proliferation, channelizing the proapoptotic ceramide into other sphingolipid molecules would augment resistance to drug induced cell death. In fact many cancer cells suppress their ceramide biosynthetic machinery while upregulating the biosynthesis of progrowth sphingolipids like S1P. This modulation would serve to increase proliferation of these cells. Besides maintaining a proliferative phenotype, many cancer cell types resist therapy in several ways including escape from therapy-induced apoptosis [82]. Decreasing the levels of proapoptotic ceramide in the cells is one of the adaptive means to resist therapy-induced apoptosis.

Hepatocellular carcinoma is a common type of liver cancer resulting due to either viral causes or cirrhosis. We analyzed gene expression data obtained from several studies (GSE21362, GSE6764, GSE14323, GSE5975, GSE25097, GSE10459, and GSE14323) and Figure 6 demonstrates the status of four compartments in hepatocellular carcinoma (HCC). The expression of genes involved in the de novo ceramide synthesis is unaltered as depicted by compartment C1 and levels of CERT are upregulated. In compartment C2, markedly, there is an upregulation of the enzymes involved in the synthesis of complex sphingolipids. In compartment C3, hydrolytic generation of ceramide from sphingomyelin is downregulated once again ensuring lesser levels of ceramide.


Status of different compartments in hepatocellular carcinoma. Hepatocellular carcinoma is a case of proliferative scenario mediated through downregulation of ceramide production (in compartment C-3(x)) and upregulation (⚫) of complex sphingolipid biosynthesis in order to quench the proapoptotic ceramide.

The digression of proapoptotic ceramide to progrowth molecule S1P may modulate the future of a cell in response to cancer therapy [83, 84]. Ceramidases promote carcinogenesis by disturbing the ceramide/S1P ratio, permitting phosphorylation of sphingosine by SPHKs, and determine the efficacy of cancer therapy. For instance, inhibition of an acid CDASE by a newly developed ceramide analogue, B13, induces apoptosis in cultured human colon cancer cells and prevents liver metastases in vivo [85]. Concurrent to these findings, our analysis also revealed that, in compartment C4, the expression of CDASEs and SPHK, enzymes involved in the conversion of ceramide to sphingosine and later to sphingosine-1-phosphate are upregulated. Sphingosine-1-phosphate phosphatase (SGPP), an enzyme involved in dephosphorylation of progrowth molecule sphingosine-1-phosphate to proapoptotic sphingosine, was found to be downregulated. Apparently in HCC, sphingolipid metabolic pathway is driven towards decreasing the levels of ceramide and increasing progrowth sphingolipids. Increased expression of enzymes involved in the complex sphingolipid biosynthesis in compartment C2 serves as an adaptive channel to quench the proapoptotic ceramide. Elevated expression of CERT apparently will augment the transfer of ceramide from ER to Golgi complex, providing it as a substrate for complex sphingolipid biosynthesis. Earlier studies employing different cancer cell types have established the relationship between glucosyl ceramide synthase and chemoresistance. In studies comparing the nature of lipid intermediates between drug-sensitive and resistant cancer cell lines, glucosyl ceramide levels were found to be higher in drug-resistant cells [86, 87]. The role of GCS (glucosyl ceramide synthase) is further confirmed in cell model where overexpression of GCS resulted in resistance of HL-60 to doxorubicin-induced apoptosis [88].

In summary, in HCC sphingolipid, metabolism is manipulated at 3 levels. (1) Channels generating the proapoptotic ceramide through hydrolytic machinery are decreased. (2) The channel converting the proapoptotic ceramide into progrowth molecule is upregulated. (3) Adaptive channel which converts proapoptotic ceramide into less effective complex sphingolipids is activated.

While the first two changes ensure that cell assumes a proliferative mode favoring the cancer phenotype, the adaptive channel in which proapoptotic ceramide is quenched into less effective complex sphingolipids contributes towards drug resistance.

5. Conclusions

Sphingolipids affect various aspects of cell physiology like cell proliferation, cell death, differentiation, and cell signaling and are known to contribute to key cellular pathologies like ER stress, insulin resistance, inflammation, and drug resistance. Analyzing the gene expression pattern in three different disease conditions—T2DM, Alzheimer’s disease, and hepatocellular carcinoma, indicates that under different disease conditions sphingolipid metabolic pathway may employ slightly different routes to contribute towards the pathology. For example, in diabetes, sphingolipid metabolic pathway genes are manipulated in such a way that it leads to ER stress and inflammation, while Alzheimer’s disease condition is a case of increased ceramide mediated apoptosis coupled with inability to degrade ceramide. In hepatocellular carcinoma, sphingolipid pathway is manipulated in such a way that reactions leading to quenching of proapoptotic molecule ceramide into inert complex sphingolipids and those leading to generation of progrowth sphingolipids are upregulated. Although a bias for the sphingolipid metabolic pathway is apparent under these disease conditions, further analysis is required to verify if such a phenomenon is universally applicable. Understanding the nature of changes in the sphingolipid metabolic pathway and their overall pathological effect under different disease conditions would be very useful in order to design therapeutic strategies.

6. Highlights

(i) The sphingolipid metabolic pathway is a major player in the pathology of human diseases. (ii) This review focuses on changes in the sphingolipid pathway in 3 different diseases. (iii) In metabolic disorder, this pathway contributes to endoplasmic reticulum stress (ER stress) and inflammation. (iv) In Alzheimer’s disease, the pathway contributes to ER stress and apoptosis. (v) In hepatocellular carcinoma, the pathway contributes to proproliferative scenario.

Abbreviation

AD:Alzheimer’s disease
APP:Amyloid precursor protein
Aβ:Amyloid beta
C1P: Ceramide-1-phosphate
CDASE:Ceramidase
CERK:Ceramide kinase
CERT:Ceramide transfer protein
COX-2:Cyclooxygenase-2
ER:Endoplasmic reticulum
GLUT-4:Glucose transporter-4
GSL:Glycosphingolipid
HCC:Hepatocellular carcinoma
IL-1β:Interleukin-1β
IRS-1:Insulin receptor substrate-1
JNK:c-Jun N-terminal kinase
LCFA:Long chain fatty acids
LSD:Lysosomal storage disorder
PGE-2:Prostaglandin E-2
PKB:Protein kinase B
PLA-2:Phospholipase A-2
PP2A:Protein phosphatase 2A
S1P:Sphingosine-1-phosphate
SL:Sphingolipid
SM:Sphingomyelin
SMPD:Sphingomyelin phosphodiesterase
SPHK:Sphingosine kinase
SPT:Serine palmitoyltransferase
T2DM:Type 2 diabetes mellitus
XBP-1:X-box binding protein-1.

Conflict of Interests

The authors declare that there is no conflict of interests relevant to this article.

Authors’ Contribution

Raghavendra, Nandita, and Mathiyazhagan were involved in collecting the gene expression data and analysis. Neeti was involved in collection of gene expression data. Raghavendra, Anup, and Jagannath were involved in the analysis of the data and writing.

Acknowledgments

The authors sincerely thank Dr. Suri Venkatachalam for the valuable comments and helpful discussions and Dr. Nishta Jain for scientific writing services offered during the preparation of this review article.

References

  1. Y. Chen, Y. Liu, M. C. Sullards, and A. H. Merrill Jr., “An introduction to sphingolipid metabolism and analysis by new technologies,” NeuroMolecular Medicine, vol. 12, no. 4, pp. 306–319, 2010. View at: Publisher Site | Google Scholar
  2. A. H. Merrill Jr., M. D. Wang, M. Park, and M. C. Sullards, “(Glyco)sphingolipidology: an amazing challenge and opportunity for systems biology,” Trends in Biochemical Sciences, vol. 32, no. 10, pp. 457–468, 2007. View at: Publisher Site | Google Scholar
  3. Y. A. Hannun and L. M. Obeid, “The ceramide-centric universe of lipid-mediated cell regulation: stress encounters of the lipid kind,” Journal of Biological Chemistry, vol. 277, no. 29, pp. 25847–25850, 2002. View at: Publisher Site | Google Scholar
  4. J. Ohanian and V. Ohanian, “Sphingolipids in mammalian cell signalling,” Cellular and Molecular Life Sciences, vol. 58, no. 14, pp. 2053–2068, 2001. View at: Google Scholar
  5. J. C. M. Holthuis, T. Pomorski, R. J. Raggers, H. Sprong, and G. van Meer, “The organizing potential of sphingolipids in intracellular membrane transport,” Physiological Reviews, vol. 81, no. 4, pp. 1689–1723, 2001. View at: Google Scholar
  6. S. E. Brice and L. A. Cowart, “Sphingolipid metabolism and analysis in metabolic disease,” Advances in Experimental Medicine and Biology, vol. 721, pp. 1–17, 2011. View at: Publisher Site | Google Scholar
  7. F. Alvarez-Vasquez, K. J. Sims, L. A. Cowart, Y. Okamoto, E. O. Voit, and Y. A. Hannun, “Simulation and validation of modelled sphingolipid metabolism in Saccharomyces cerevisiae,” Nature, vol. 433, no. 7024, pp. 425–430, 2005. View at: Publisher Site | Google Scholar
  8. Y. A. Hannun and C. Luberto, “Lipid metabolism: ceramide transfer protein adds a new dimension,” Current Biology, vol. 14, no. 4, pp. R163–R165, 2004. View at: Publisher Site | Google Scholar
  9. Y. A. Hannun and L. M. Obeid, “Principles of bioactive lipid signalling: lessons from sphingolipids,” Nature Reviews Molecular Cell Biology, vol. 9, no. 2, pp. 139–150, 2008. View at: Publisher Site | Google Scholar
  10. K. Hanada, “Serine palmitoyltransferase, a key enzyme of sphingolipid metabolism,” Biochimica et Biophysica Acta, vol. 1632, no. 1𠄳, pp. 16–30, 2003. View at: Publisher Site | Google Scholar
  11. U. Acharya and J. K. Acharya, “Enzymes of sphingolipid metabolism in Drosophila melanogaster,” Cellular and Molecular Life Sciences, vol. 62, no. 2, pp. 128–142, 2005. View at: Publisher Site | Google Scholar
  12. Y. Sugimoto, H. Sakoh, and K. Yamada, “IPC synthase as a useful target for antifungal drugs,” Current Drug Targets, vol. 4, no. 4, pp. 311–322, 2004. View at: Publisher Site | Google Scholar
  13. K. Hanada, K. Kumagai, N. Tomishige, and T. Yamaji, “CERT-mediated trafficking of ceramide,” Biochimica et Biophysica Acta, vol. 1791, no. 7, pp. 684–691, 2009. View at: Publisher Site | Google Scholar
  14. R. P. Rao and J. K. Acharya, “Sphingolipids and membrane biology as determined from genetic models,” Prostaglandins and Other Lipid Mediators, vol. 85, no. 1-2, pp. 1–16, 2008. View at: Publisher Site | Google Scholar
  15. Y. H. Zeidan and Y. A. Hannun, “Translational aspects of sphingolipid metabolism,” Trends in Molecular Medicine, vol. 13, no. 8, pp. 327–336, 2007. View at: Publisher Site | Google Scholar
  16. T. Kolter and K. Sandhoff, “Principles of lysosomal membrane digestion: stimulation of sphingolipid degradation by sphingolipid activator proteins and anionic lysosomal lipids,” Annual Review of Cell and Developmental Biology, vol. 21, pp. 81–103, 2005. View at: Publisher Site | Google Scholar
  17. C. F. Snook, J. A. Jones, and Y. A. Hannun, “Sphingolipid-binding proteins,” Biochimica et Biophysica Acta, vol. 1761, no. 8, pp. 927–946, 2006. View at: Publisher Site | Google Scholar
  18. S. A. Summers, “Ceramides in insulin resistance and lipotoxicity,” Progress in Lipid Research, vol. 45, no. 1, pp. 42–72, 2006. View at: Publisher Site | Google Scholar
  19. D. S. Menaldino, A. Bushnev, A. Sun et al., “Sphingoid bases and de novo ceramide synthesis: enzymes involved, pharmacology and mechanisms of action,” Pharmacological Research, vol. 47, no. 5, pp. 373–381, 2003. View at: Publisher Site | Google Scholar
  20. H. Schulze and K. Sandhoff, “Lysosomal lipid storage diseases,” Cold Spring Harbor Perspectives in Biology, vol. 3, no. 6, 2011. View at: Google Scholar
  21. X. Wang, R. P. Rao, T. Kosakowska-Cholody et al., “Mitochondrial degeneration and not apoptosis is the primary cause of embryonic lethality in ceramide transfer protein mutant mice,” Journal of Cell Biology, vol. 184, no. 1, pp. 143–158, 2009. View at: Publisher Site | Google Scholar
  22. T. Kolter and K. Sandhoff, “Sphingolipid metabolism diseases,” Biochimica et Biophysica Acta, vol. 1758, no. 12, pp. 2057–2079, 2006. View at: Publisher Site | Google Scholar
  23. A. Raas-Rothschild, I. Pankova-Kholmyansky, Y. Kacher, and A. H. Futerman, “Glycosphingolipidoses: beyond the enzymatic defect,” Glycoconjugate Journal, vol. 21, no. 6, pp. 295–304, 2004. View at: Publisher Site | Google Scholar
  24. N. J. Haughey, V. V. R. Bandaru, M. Bae, and M. P. Mattson, “Roles for dysfunctional sphingolipid metabolism in Alzheimer's disease neuropathogenesis,” Biochimica et Biophysica Acta, vol. 1801, no. 8, pp. 878–886, 2010. View at: Publisher Site | Google Scholar
  25. A. Jana and K. Pahan, “Fibrillar amyloid-β-activated human astroglia kill primary human neurons via neutral sphingomyelinase: implications for Alzheimer's disease,” Journal of Neuroscience, vol. 30, no. 38, pp. 12676–12689, 2010. View at: Publisher Site | Google Scholar
  26. M. O. W. Grimm, S. Grösgen, T. L. Rothhaar et al., “Intracellular APP domain regulates serine-palmitoyl-CoA transferase expression and is affected in Alzheimer's disease,” International Journal of Alzheimer's Disease, vol. 2011, Article ID 695413, 8 pages, 2011. View at: Publisher Site | Google Scholar
  27. H. G. Wilcox, G. D. Dunn, and S. Schenker, “Plasma long chain fatty acids and esterified lipids in cirrhosis and hepatic encephalopathy,” American Journal of the Medical Sciences, vol. 276, no. 3, pp. 293–303, 1978. View at: Google Scholar
  28. L. Longato, K. Ripp, M. Setshedi et al., “Insulin resistance, ceramide accumulation, and endoplasmic reticulum stress in human chronic alcohol-related liver disease,” Oxidative Medicine and Cellular Longevity, vol. 2012, Article ID 479348, 17 pages, 2012. View at: Publisher Site | Google Scholar
  29. A. Morales, M. Marí, C. Garc໚-Ruiz, A. Colell, and J. C. Fernández-Checa, “Hepatocarcinogenesis and ceramide/cholesterol metabolism,” Anti-Cancer Agents in Medicinal Chemistry, vol. 12, no. 4, pp. 364–375, 2012. View at: Google Scholar
  30. J. Turinsky, D. M. O'Sullivan, and B. P. Bayly, “1,2-diacylglycerol and ceramide levels in insulin-resistant tissues of the rat in vivo,” Journal of Biological Chemistry, vol. 265, no. 28, pp. 16880–16885, 1990. View at: Google Scholar
  31. S. Resjö, O. Göransson, L. Härndahl, S. Zolnierowicz, V. Manganiello, and E. Degerman, “Protein phosphatase 2A is the main phosphatase involved in the regulation of protein kinase B in rat adipocytes,” Cellular Signalling, vol. 14, no. 3, pp. 231–238, 2002. View at: Publisher Site | Google Scholar
  32. D. J. Powell, E. Hajduch, G. Kular, and H. S. Hundal, “Ceramide disables 3-phosphoinositide binding to the pleckstrin homology domain of protein kinase B (PKB)/Akt by a PKCζ-dependent mechanism,” Molecular and Cellular Biology, vol. 23, no. 21, pp. 7794–7808, 2003. View at: Publisher Site | Google Scholar
  33. J. A. Chavez, T. A. Knotts, L.-P. Wang et al., “A role for ceramide, but not diacylglycerol, in the antagonism of insulin signal transduction by saturated fatty acids,” Journal of Biological Chemistry, vol. 278, no. 12, pp. 10297–10303, 2003. View at: Publisher Site | Google Scholar
  34. D. J. Powell, S. Turban, A. Gray, E. Hajduch, and H. S. Hundal, “Intracellular ceramide synthesis and protein kinase Cζ activation play an essential role in palmitate-induced insulin resistance in rat L6 skeletal muscle cells,” Biochemical Journal, vol. 382, no. 2, pp. 619–629, 2004. View at: Publisher Site | Google Scholar
  35. C. Schmitz-Peiffer, D. L. Craig, and T. J. Biden, “Ceramide generation is sufficient to account for the inhibition of the insulin-stimulated PKB pathway in C2C12 skeletal muscle cells pretreated with palmitate,” Journal of Biological Chemistry, vol. 274, no. 34, pp. 24202–24210, 1999. View at: Publisher Site | Google Scholar
  36. S. A. Summers, L. A. Garza, H. Zhou, and M. J. Birnbaum, “Regulation of insulin-stimulated glucose transporter GLUT4 translocation and Akt kinase activity by ceramide,” Molecular and Cellular Biology, vol. 18, no. 9, pp. 5457–5464, 1998. View at: Google Scholar
  37. W. L. Holland, T. A. Knotts, J. A. Chavez, L.-P. Wang, K. L. Hoehn, and S. A. Summers, “Lipid mediators of insulin resistance,” Nutrition Reviews, vol. 65, no. 6, pp. S39–S46, 2007. View at: Google Scholar
  38. J. M. Adams II, T. Pratipanawatr, R. Berria et al., “Ceramide content is increased in skeletal muscle from obese insulin-resistant humans,” Diabetes, vol. 53, no. 1, pp. 25–31, 2004. View at: Publisher Site | Google Scholar
  39. M. Straczkowski, I. Kowalska, A. Nikolajuk et al., “Relationship between insulin sensitivity and sphingomyelin signaling pathway in human skeletal muscle,” Diabetes, vol. 53, no. 5, pp. 1215–1221, 2004. View at: Publisher Site | Google Scholar
  40. B. T. Bikman and S. A. Summers, “Ceramides as modulators of cellular and whole-body metabolism,” Journal of Clinical Investigation, vol. 121, no. 11, pp. 4222–4230, 2011. View at: Publisher Site | Google Scholar
  41. C. F. Jessup, C. S. Bonder, S. M. Pitson, and P. T. H. Coates, “The sphingolipid rheostat: a potential target for improving pancreatic islet survival and function,” Endocrine, Metabolic and Immune Disorders, vol. 11, no. 4, pp. 262–272, 2011. View at: Google Scholar
  42. Y. H. Lee, J. Giraud, R. J. Davis, and M. F. White, “c-Jun N-terminal kinase (JNK) mediates feedback inhibition of the insulin signaling cascade,” Journal of Biological Chemistry, vol. 278, no. 5, pp. 2896–2902, 2003. View at: Publisher Site | Google Scholar
  43. U. Özcan, Q. Cao, E. Yilmaz et al., “Endoplasmic reticulum stress links obesity, insulin action, and type 2 diabetes,” Science, vol. 306, no. 5695, pp. 457–461, 2004. View at: Publisher Site | Google Scholar
  44. K. A. Dressler, S. Mathias, and R. N. Kolesnick, “Tumor necrosis factor-α activates the sphingomyelin signal transduction pathway in a cell-free system,” Science, vol. 255, no. 5052, pp. 1715–1618, 1992. View at: Google Scholar
  45. K. Wiegmann, S. Schütze, T. Machleidt, D. Witte, and M. Krönke, “Functional dichotomy of neutral and acidic sphingomyelinases in tumor necrosis factor signaling,” Cell, vol. 78, no. 6, pp. 1005–1015, 1994. View at: Publisher Site | Google Scholar
  46. G. S. Hotamisligil, N. S. Shargill, and B. M. Spiegelman, “Adipose expression of tumor necrosis factor-α: direct role in obesity-linked insulin resistance,” Science, vol. 259, no. 5091, pp. 87–91, 1993. View at: Google Scholar
  47. C. Hofmann, K. Lorenz, S. S. Braithwaite et al., “Altered gene expression for tumor necrosis factor-α and its receptors during drug and dietary modulation of insulin resistance,” Endocrinology, vol. 134, no. 1, pp. 264–270, 1994. View at: Publisher Site | Google Scholar
  48. J. M. Stephens, J. Lee, and P. F. Pilch, “Tumor necrosis factor-α-induced insulin resistance in 3T3-L1 adipocytes is accompanied by a loss of insulin receptor substrate-1 and GLUT4 expression without a loss of insulin receptor-mediated signal transduction,” Journal of Biological Chemistry, vol. 272, no. 2, pp. 971–976, 1997. View at: Publisher Site | Google Scholar
  49. F. Samad, K. D. Hester, G. Yang, Y. A. Hannun, and J. Bielawski, “Altered adipose and plasma sphingolipid metabolism in obesity: a potential mechanism for cardiovascular and metabolic risk,” Diabetes, vol. 55, no. 9, pp. 2579–2587, 2006. View at: Publisher Site | Google Scholar
  50. M. Y. Donath and S. E. Shoelson, “Type 2 diabetes as an inflammatory disease,” Nature Reviews Immunology, vol. 11, no. 2, pp. 98–107, 2011. View at: Publisher Site | Google Scholar
  51. S. E. Shoelson, J. Lee, and A. B. Goldfine, “Inflammation and insulin resistance,” Journal of Clinical Investigation, vol. 116, no. 7, pp. 1793–1801, 2006. View at: Publisher Site | Google Scholar
  52. V. D. F. de Mello, M. Lankinen, U. Schwab et al., “Link between plasma ceramides, inflammation and insulin resistance: association with serum IL-6 concentration in patients with coronary heart disease,” Diabetologia, vol. 52, no. 12, pp. 2612–2615, 2009. View at: Publisher Site | Google Scholar
  53. J. M. R. Gill and N. Sattar, “Ceramides: a new player in the inflammation-insulin resistance paradigm?” Diabetologia, vol. 52, no. 12, pp. 2475–2477, 2009. View at: Publisher Site | Google Scholar
  54. A. Gómez-Munoz, P. Gangoiti, M. H. Granado, L. Arana, and A. Ouro, “Ceramide-1-phosphate in cell survival and inflammatory signaling,” Advances in Experimental Medicine and Biology, vol. 688, pp. 118–130, 2010. View at: Publisher Site | Google Scholar
  55. P. Subramanian, M. Vora, L. B. Gentile, R. V. Stahelin, and C. E. Chalfant, “Anionic lipids activate group IVA cytosolic phospholipase A2 via distinct and separate mechanisms,” Journal of Lipid Research, vol. 48, no. 12, pp. 2701–2708, 2007. View at: Publisher Site | Google Scholar
  56. B. J. Pettus, A. Bielawska, S. Spiegel, P. Roddy, Y. A. Hannun, and C. E. Chalfant, “Ceramide kinase mediates cytokine- and calcium ionophore-induced arachidonic acid release,” Journal of Biological Chemistry, vol. 278, no. 40, pp. 38206–38213, 2003. View at: Publisher Site | Google Scholar
  57. S. Mitsutake, T. Date, H. Yokota, M. Sugiura, T. Kohama, and Y. Igarashi, “Ceramide kinase deficiency improves diet-induced obesity and insulin resistance,” FEBS Letters, vol. 586, no. 9, pp. 1300–1305, 2012. View at: Publisher Site | Google Scholar
  58. M. M. Mielke and C. G. Lyketsos, “Alterations of the sphingolipid pathway in Alzheimer's disease: new biomarkers and treatment targets?” NeuroMolecular Medicine, vol. 12, no. 4, pp. 331–340, 2010. View at: Publisher Site | Google Scholar
  59. R. G. Cutler, W. A. Pedersen, S. Camandola, J. D. Rothstein, and M. P. Mattson, “Evidence that accumulation of ceramides and cholesterol esters mediates oxidative stress-induced death of motor neurons in amyotrophic lateral sclerosis,” Annals of Neurology, vol. 52, no. 4, pp. 448–457, 2002. View at: Publisher Site | Google Scholar
  60. V. France-Lanord, B. Brugg, P. P. Michel, Y. Agid, and M. Ruberg, “Mitochondrial free radical signal in ceramide-dependent apoptosis: a putative mechanism for neuronal death in Parkinson's disease,” Journal of Neurochemistry, vol. 69, no. 4, pp. 1612–1621, 1997. View at: Google Scholar
  61. N. J. Haughey, R. G. Cutler, A. Tamara et al., “Perturbation of sphingolipid metabolism and ceramide production in HIV-dementia,” Annals of Neurology, vol. 55, no. 2, pp. 257–267, 2004. View at: Publisher Site | Google Scholar
  62. I. Y. Tamboli, H. Hampel, N. T. Tien et al., “Sphingolipid storage affects autophagic metabolism of the amyloid precursor protein and promotes Aβ generation,” Journal of Neuroscience, vol. 31, no. 5, pp. 1837–1849, 2011. View at: Publisher Site | Google Scholar
  63. M. P. Murphy and H. Levine III, “Alzheimer's disease and the amyloid-β peptide,” Journal of Alzheimer's Disease, vol. 19, no. 1, pp. 311–323, 2010. View at: Publisher Site | Google Scholar
  64. A. Salminen, A. Kauppinen, T. Suuronen, K. Kaarniranta, and J. Ojala, “ER stress in Alzheimer's disease: a novel neuronal trigger for inflammation and Alzheimer's pathology,” Journal of Neuroinflammation, vol. 6, article 41, 2009. View at: Publisher Site | Google Scholar
  65. D. Lindholm, H. Wootz, and L. Korhonen, “ER stress and neurodegenerative diseases,” Cell Death and Differentiation, vol. 13, no. 3, pp. 385–392, 2006. View at: Publisher Site | Google Scholar
  66. J. J. M. Hoozemans, E. S. van Haastert, D. A. T. Nijholt, A. J. M. Rozemuller, P. Eikelenboom, and W. Scheper, “The unfolded protein response is activated in pretangle neurons in Alzheimer's disease hippocampus,” American Journal of Pathology, vol. 174, no. 4, pp. 1241–1251, 2009. View at: Publisher Site | Google Scholar
  67. T. Katayama, K. Imaizumi, T. Manabe, J. Hitomi, T. Kudo, and M. Tohyama, “Induction of neuronal death by ER stress in Alzheimer's disease,” Journal of Chemical Neuroanatomy, vol. 28, no. 1-2, pp. 67–78, 2004. View at: Publisher Site | Google Scholar
  68. R. Resende, E. Ferreiro, C. Pereira, and C. R. Oliveira, “ER stress is involved in Aβ-induced GSK-3β activation and tau phosphorylation,” Journal of Neuroscience Research, vol. 86, no. 9, pp. 2091–2099, 2008. View at: Publisher Site | Google Scholar
  69. D. J. Selkoe, “Alzheimer's disease: genes, proteins, and therapy,” Physiological Reviews, vol. 81, no. 2, pp. 741–766, 2001. View at: Google Scholar
  70. R. E. Tanzi and L. Bertram, “Twenty years of the Alzheimer's disease amyloid hypothesis: a genetic perspective,” Cell, vol. 120, no. 4, pp. 545–555, 2005. View at: Publisher Site | Google Scholar
  71. F. M. LaFerla, K. N. Green, and S. Oddo, “Intracellular amyloid-β in Alzheimer's disease,” Nature Reviews Neuroscience, vol. 8, no. 7, pp. 499–509, 2007. View at: Publisher Site | Google Scholar
  72. S. Patil, J. Melrose, and C. Chan, “Involvement of astroglial ceramide in palmitic acid-induced Alzheimer-like changes in primary neurons,” European Journal of Neuroscience, vol. 26, no. 8, pp. 2131–2141, 2007. View at: Publisher Site | Google Scholar
  73. L. Puglielli, B. C. Ellis, A. J. Saunders, and D. M. Kovacs, “Ceramide stabilizes β-site amyloid precursor protein-cleaving enzyme 1 and promotes amyloid β-peptide biogenesis,” Journal of Biological Chemistry, vol. 278, no. 22, pp. 19777–19783, 2003. View at: Publisher Site | Google Scholar
  74. I. Y. Tamboli, K. Prager, E. Barth, M. Heneka, K. Sandhoff, and J. Walter, “Inhibition of glycosphingolipid biosynthesis reduces secretion of the β-amyloid precursor protein and amyloid β-peptide,” Journal of Biological Chemistry, vol. 280, no. 30, pp. 28110–28117, 2005. View at: Publisher Site | Google Scholar
  75. R. A. Nixon, A. M. Cataldo, and P. M. Mathews, “The endosomal-lysosomal system of neurons in Alzheimer's disease pathogenesis: a review,” Neurochemical Research, vol. 25, no. 9-10, pp. 1161–1172, 2000. View at: Google Scholar
  76. P. Katsel, C. Li, and V. Haroutunian, “Gene expression alterations in the sphingolipid metabolism pathways during progression of dementia and Alzheimer's disease: a shift toward ceramide accumulation at the earliest recognizable stages of Alzheimer's disease?” Neurochemical Research, vol. 32, no. 4-5, pp. 845–856, 2007. View at: Publisher Site | Google Scholar
  77. Y. Huang, H. Tanimukai, F. Liu, K. Iqbal, I. Grundke-Iqbal, and C.-X. Gong, “Elevation of the level and activity of acid ceramidase in Alzheimer's disease brain,” European Journal of Neuroscience, vol. 20, no. 12, pp. 3489–3497, 2004. View at: Publisher Site | Google Scholar
  78. X. He, Y. Huang, B. Li, C.-X. Gong, and E. H. Schuchman, “Deregulation of sphingolipid metabolism in Alzheimer's disease,” Neurobiology of Aging, vol. 31, no. 3, pp. 398–408, 2010. View at: Publisher Site | Google Scholar
  79. L. K. Ryland, T. E. Fox, X. Liu, T. P. Loughran, and M. Kester, “Dysregulation of sphingolipid metabolism in cancer,” Cancer Biology and Therapy, vol. 11, no. 2, pp. 138–149, 2011. View at: Publisher Site | Google Scholar
  80. W. C. Huang, C. L. Chen, Y. S. Lin, and C. F. Lin, “Apoptotic sphingolipid ceramide in cancer therapy,” Journal of Lipids, vol. 2011, Article ID 565316, 15 pages, 2011. View at: Publisher Site | Google Scholar
  81. B. Oskouian and J. D. Saba, “Cancer treatment strategies targeting sphingolipid metabolism,” Advances in Experimental Medicine and Biology, vol. 688, pp. 185–205, 2010. View at: Publisher Site | Google Scholar
  82. M. M. Gottesman, “Mechanisms of cancer drug resistance,” Annual Review of Medicine, vol. 53, pp. 615–627, 2002. View at: Publisher Site | Google Scholar
  83. R. Kolesnick, “The therapeutic potential of modulating the ceramide/sphingomyelin pathway,” Journal of Clinical Investigation, vol. 110, no. 1, pp. 3–8, 2002. View at: Publisher Site | Google Scholar
  84. B. Ogretmen and Y. A. Hannun, “Biologically active sphingolipids in cancer pathogenesis and treatment,” Nature Reviews Cancer, vol. 4, no. 8, pp. 604–616, 2004. View at: Google Scholar
  85. M. Selzner, A. Bielawska, M. A. Morse et al., “Induction of apoptotic cell death and prevention of tumor growth by ceramide analogues in metastatic human colon cancer,” Cancer Research, vol. 61, no. 3, pp. 1233–1240, 2001. View at: Google Scholar
  86. Y. Lavie, H.-T. Cao, S. L. Bursten, A. E. Giuliano, and M. C. Cabot, “Accumulation of glucosylceramides in multidrug-resistant cancer cells,” Journal of Biological Chemistry, vol. 271, no. 32, pp. 19530–19536, 1996. View at: Publisher Site | Google Scholar
  87. H. Morjani, N. Aouali, R. Belhoussine, R. J. Veldman, T. Levade, and M. Manfait, “Elevation of glucosylceramide in multidrug-resistant cancer cells and accumulation in cytoplasmic droplets,” International Journal of Cancer, vol. 94, no. 2, pp. 157–165, 2001. View at: Publisher Site | Google Scholar
  88. M. Itoh, T. Kitano, M. Watanabe et al., “Possible role of ceramide as an indicator of chemoresistance: decrease of the ceramide content via activation of glucosylceramide synthase and sphingomyelin synthase in chemoresistant leukemia,” Clinical Cancer Research, vol. 9, no. 1, pp. 415–423, 2003. View at: Google Scholar

Copyright

Copyright © 2013 Raghavendra Pralhada Rao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Watch the video: VĚDOMÍ A OSOBNOST. OD PŘEDEM MRTVÉHO K VĚČNĚ ŽIVÉMU (May 2022).