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Posts Tagged ‘lipidomics’


Liposomes, Lipidomics and Metabolism

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Building a Better Liposome

Computational models suggest new design for nanoparticles used in targeted drug delivery.

http://www.technologynetworks.com/Metabolomics/news.aspx?ID=184147

Using computational modeling, researchers at Carnegie Mellon University, the Colorado School of Mines and the University of California, Davis have come up with a design for a better liposome. Their findings, while theoretical, could provide the basis for efficiently constructing new vehicles for nanodrug delivery.

Liposomes are small containers with shells made of lipids, the same material that makes up the cell membrane. In recent years, liposomes have been used for targeted drug delivery. In this process, the membrane of a drug-containing liposome is engineered to contain proteins that will recognize and interact with complementary proteins on the membrane of a diseased or dysfunctional cell. After the drug-containing liposomes are administered, they travel through the body, ideally connecting with targeted cells where they release the drug.

liposome_853x480-min.jpg

This packaging technique is often used with highly toxic nanodrugs, like chemotherapy drugs, in an attempt to prevent the free drug from damaging non-cancerous cells. However, studies of this model of delivery have shown that in many cases less than 10 percent of the drugs transported by liposomes end up in tumor cells. Often, the liposome breaks open before it reaches a tumor cell and the drug is absorbed into the body’s organs, including the liver and spleen, resulting in toxic side effects.

“Even with current forms of targeted drug delivery, treatments like chemotherapy are still very brutal. We wanted to see how we could make targeted drug delivery better,” said Markus Deserno, professor of physics at Carnegie Mellon and a member of the university’s Center for Membrane Biology and Biophysics.

Deserno and colleagues propose that targeted drug delivery can be improved by making more stable liposomes. Using three different types of computer modeling, they have shown that liposomes can be made sturdier by incorporating a nanoparticle core made of a material like gold or iron and connecting that core to the liposome’s membrane using polymer tethers. The core and tethers act as a hub-and-spoke-like scaffold and shock-absorber system that help the liposome to weather the stresses and strains it encounters as it travels through the body to its target.

Francesca Stanzione and Amadeu K. Sum of the Colorado School of Mines conducted a fine-grained simulation that looked at how the polymer tethers anchor the liposome’s membrane at an atomistic level. Roland Faller of UC Davis did a meso-scale simulation that looked how a number of tethers held on to a small patch of membrane. Each of these simulations allowed researchers to look at smaller components of the liposome, nanoparticle core and tethers, but not the entire structure.

To see the entire structure, Carnegie Mellon’s Deserno and Mingyang Hu developed a coarse-grained model that represents groupings of components rather than individual atoms. For example, one lipid in the cell membrane might have 100 atoms. In a fine-grain simulation, each atom would be represented. In Deserno’s coarse grain simulation, those atoms might be represented by only three pieces instead of 100.

“Its unfeasible to look at the complete construct at an atomistic level. There are too many atoms to consider, and the timescale is too long. Even with the most advanced supercomputer, we wouldn’t have the power to run an atom-level simulation,” Deserno said. “But the physics that matters isn’t locally specific. It’s more like soft matter physics, which can be described at a much coarser resolution.”

Deserno’s simulation allowed the researchers to see how the entire reinforced liposome construct responded to stress and strain. They proposed that if a liposome was given the right-sized hub and tethers, its membrane would be much more resilient, bending to absorb impact and pressure.

Additionally, they were able to simulate how to best assemble the liposome, hub and tether system. They found that if the hub and tether are attached and placed in a solution of lipids, and solvent conditions are suitably chosen, a correctly sized liposome would self-assemble around the hub and tethers.

The researchers hope that chemists and drug developers will one day be able to use their simulations to determine what size core and polymer tethers they would need to effectively secure a liposome designed to deliver a specific drug or other nanoparticle. Using such simulations could narrow down the design parameters, speed up the development process and reduce costs.

 

Lipotype GmbH and NIHS Collaborate

http://www.technologynetworks.com/Metabolomics/news.aspx?ID=184363

NIHS to use the Lipotype Shotgun Lipidomics Technology for lipid analysis.

Lipotype GmbH and the Nestlé Institute of Health Sciences (NIHS) have collaborated to employ the innovative Lipotype Shotgun Lipidomics Technology to analyze lipids in blood for nutritional research. Recently, Lipotype and NIHS have jointly published results of the robustness of the Lipotype Technology. Lipotype envisions a future use of its technology in clinical diagnostics screens for establishing reliable lipid diagnostic biomarkers.

Innovative Lipotype Technology for lipid analysis
The purpose of this collaboration is to enable NIHS to use the Lipotype Shotgun Lipidomics Technology for lipid analysis. The mass spectrometry-based Lipotype technology covers a broad spectrum of lipid molecules and delivers quantitative results in high-throughput. The Nestlé Institute of Health Sciences uses this technology platform for nutritional research. NIHS is a specialized biomedical research institute and is part of Nestlé’s global Research & Development network.

Joint research project reveals robustness of Lipotype Technology
During the collaboration, Lipotype and NIHS conducted a joint research project and demonstrated that the Lipotype technology was robust enough to deliver data with high precision and negligible technical variation between different sites. In addition, important features are the high coverage and throughput, which were confirmed when applying the Lipotype technology.

Lipotype envisions these as important features, required for future use in clinical diagnostics screens, in order to establish and validate reliable lipid diagnostic biomarkers. The results have been published in October 2015, in the European Journal of Lipid Science and Technology (Surma et al. “An Automated Shotgun Lipidomics Platform for High Throughput, Comprehensive, and Quantitative Analysis of Blood Plasma Intact Lipids.”).

Lipids play an important role for health and disease
Lipotype is a spin-off company of the Max-Planck-Institute of Molecular Cell Biology and Genetics in Dresden, Germany. Prof. Kai Simons, CEO of Lipotype explains: “We developed a novel Shotgun-Lipidomics technology to analyze lipids in blood and other biological samples. Our analysis is quick and covers hundreds of lipid molecules at the same time. Our technology can be used to identify disease related lipid signatures.”

 

New Treatment for Obesity Developed

http://www.technologynetworks.com/Metabolomics/news.aspx?ID=183998

Researchers at the University of Liverpool, working with a global healthcare company, have helped develop a new treatment for obesity.
The treatment, which is a once-daily injectable derivative of a metabolic hormone called GLP-1 conventionally used in the treatment of type 2 diabetes, has proved successful in helping non-diabetic obese patients lose weight.

Professor John Wilding, who leads Obesity and Endocrinology research in the Institute of Ageing and Chronic Disease, investigates the pathophysiology and treatment of both obesity and type 2 diabetes and is applying his expertise in this area to work with, and often act as a consultant for, a number of large pharmaceutical companies looking to develop new treatments for obesity and diabetes.

Exciting development

Professor Wilding, said: “The biology of GLP-1 has been a focus of my research for 20 years; in particular when I was working at Hammersmith Hospital in London, I was part of the team that demonstrated that it was involved in appetite regulation; work on GLP-1 has continued during my time in Liverpool. Being involved in the development of a treatment, from the basic research right through to clinical trials in patients is very exciting”.

“It is likely that the treatment will be used initially in very specific situations, such as helping patients who are severely obese. It differs from current treatments used for diabetes, as it has stronger appetite regulating effects but no greater effect on glucose control.”

In 2014 more than 1.9 billion adults worldwide were classed as obese by the World Health Organisation; in the UK numbers have more than tripled since 1980. This Obesity can lead to other serious health-related illnesses including type 2 diabetes, hypertension and obstructive sleep apnoea as well as increasing the risk for many common cancers.

The drug has been approved in the European Union, but has not yet launched in the UK.

Professor Wilding added: “Consultancy like this can help relationship and reputation building and informs my research keeping it at the forefront of developments. It also brings many other benefits such as publications and income generation, which can help support other research, for example by such as funding for pilot projects that can lead to grant applications and investigator-initiated trials funded by the company”.

 

Evidence of How Incurable Cancer Develops

http://www.technologynetworks.com/Metabolomics/news.aspx?ID=184346

Researchers in the West Midlands have made a breakthrough in explaining how an incurable type of blood cancer develops from an often symptomless prior blood disorder.

The findings could lead to more effective treatments and ways to identify those most at risk of developing the cancer.

All patients diagnosed with myeloma, a cancer of the blood-producing bone marrow, first develop a relatively benign condition called ‘monoclonal gammopathy of undetermined significance’ or ‘MGUS’.

MGUS is fairly common in the older population and only progresses to cancer in approximately one in 100 cases. However, currently there is no way of accurately predicting which patients with MGUS are likely to go on to get myeloma.

Myeloma is diagnosed in around 4,000 people each year in the UK. It specifically affects antibody-producing white blood cells found in the bone marrow, called plasma cells. The researcher team from the University of Birmingham, New Cross and Heartlands Hospitals compared the cellular chemistry of bone marrow and blood samples taken from patients with myeloma, patients with MGUS and healthy volunteers.

Surprisingly, the researchers found that the metabolic activity of the bone marrow of patients with MGUS was significantly different to plasma from healthy volunteers, but there were very few differences at all between the MGUS and myeloma samples. The research was funded by the blood cancer charity Bloodwise, which changed its name from Leukaemia & Lymphoma in September.

The findings suggest that the biggest metabolic changes occur with the development of the symptomless condition MGUS and not with the later progression to myeloma.

Dr Daniel Tennant, who led the research at the University of Birmingham, said, “Our findings show that very few changes are required for a MGUS patient to progress to myeloma as we now know virtually all patients with myeloma evolve from MGUS. A drug that interferes with these specific initial metabolic changes could make a very effective treatment for myeloma, so this is a very exciting discovery.”

The research team found over 200 products of metabolism differed between the healthy volunteers and patients with MGUS or myeloma, compared to just 26 differences between MGUS patients and myeloma patients. The researchers believe that these small changes could drive the key shifts in the bone marrow required to support myeloma growth.

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Lipid Classification System

Curator: Larry H. Bernstein, MD, FCAP

Lipid Classification, Nomenclature and Structure Drawing

 

The LIPID MAPS consortium has developed a comprehensive classification, nomenclature, and chemical representation system for lipids, the details of which are described in the May 2009 issue of the Journal of Lipid Research:

Fahy E, Subramaniam S, Murphy R, Nishijima M, Raetz C, Shimizu T, Spener F, van Meer G, Wakelam M and Dennis E.A.,Update of the LIPID MAPS comprehensive classification system for lipids. J. Lipid Res. (2009) 50: S9-S14.PubMed ID:19098281.

Fahy E, Subramaniam S, Brown H, Glass C, Merrill JA, Murphy R, Raetz C, Russell D, Seyama Y, Shaw W, Shimizu T, Spener F, van Meer G, Vannieuwenhze M, White S, Witztum J and Dennis E.A.,A comprehensive classification system for lipids. J. Lipid Res. (2005) 46: 839-861.PubMed ID:15722563.

http://www.lipidmaps.org/resources/tutorials/lipid_cns.html

Lipid Classification System

The LIPID MAPS Lipid Classification System is comprised of eight lipid categories, each with its own sublassification hierarchy.

All lipids in the LIPID MAPS Structure Database (LMSD) have been classified using this system and have been assigned LIPID MAPS ID’s (LM_ID) which reflects their position in the classification hierarchy.

LMSD can be searched by lipid class, common name, systematic name or synonym, mass, InChIKey or LIPID MAPS ID with the “Quick Search” tool on the home page, or alternatively, by

LIPID MAPS ID, systematic or common name, mass, formula, category, main class, subclass data, or structure or sub-structure with one of the search interfaces in the LMSD database section.

Each LMSD record contains an image of the

  • molecular structure,
  • common and systematic names,
  • links to external databases,
  • Wikipedia pages (where available),
  • other annotations and links to structure viewing tools.

In addition to LMSD search interfaces, you can drill down through the classification hierarchy below to the LMSD record for an individual lipid.

 

Lipid Classes
Fatty Acyls [FA] Fatty Acids and Conjugates [FA01]Octadecanoids [FA02]Eicosanoids [FA03]

Docosanoids [FA04]

Fatty alcohols [FA05]

Fatty aldehydes [FA06]

Fatty esters [FA07]

Fatty amides [FA08]

Fatty nitriles [FA09]

Fatty ethers [FA10]

Hydrocarbons [FA11]

Oxygenated hydrocarbons [FA12]

Fatty acyl glycosides [FA13]

Other Fatty Acyls [FA00]

Glycerophospholipids [GP] Glycerophosphocholines [GP01]Glycerophosphoethanolamines [GP02]Glycerophosphoserines [GP03]

Glycerophosphoglycerols [GP04]

Glycerophosphoglycerophosphates [GP05]

Glycerophosphoinositols [GP06]

Glycerophosphoinositol monophosphates [GP07]

Glycerophosphoinositol bisphosphates [GP08]

Glycerophosphoinositol trisphosphates [GP09]

Glycerophosphates [GP10]

Glyceropyrophosphates [GP11]

Glycerophosphoglycerophosphoglycerols [GP12]

CDP-Glycerols [GP13]

Glycosylglycerophospholipids [GP14]

Glycerophosphoinositolglycans [GP15]

Glycerophosphonocholines [GP16]

Glycerophosphonoethanolamines [GP17]

Di-glycerol tetraether phospholipids (caldarchaeols) [GP18]

Glycerol-nonitol tetraether phospholipids [GP19]

Oxidized glycerophospholipids [GP20]

Other Glycerophospholipids [GP00]

Glycerolipids [GL] Monoradylglycerols [GL01]Diradylglycerols [GL02]Triradylglycerols [GL03]

Glycosylmonoradylglycerols [GL04]

Glycosyldiradylglycerols [GL05]

Other Glycerolipids [GL00]

Sphingolipids [SP] Sphingoidbases [SP01]Ceramides [SP02]Phosphosphingolipids [SP03]

Phosphonosphingolipids [SP04]

Neutral glycosphingolipids [SP05]

Acidic glycosphingolipids [SP06]

Basic glycosphingolipids [SP07]

Amphoteric glycosphingolipids [SP08]

Arsenosphingolipids [SP09]

Other Sphingolipids [SP00]

Sterol Lipids [ST] Sterols [ST01]Steroids [ST02]Secosteroids [ST03]

Bile acids and derivatives [ST04]

Steroid conjugates [ST05]

Other Sterol lipids [ST00]

Prenol Lipids [PR] Isoprenoids [PR01]Quinones andhydroquinones [PR02]Polyprenols [PR03]

Hopanoids [PR04]

Other Prenol lipids [PR00]

Saccharolipids [SL] Acylaminosugars [SL01]Acylaminosugarglycans [SL02]Acyltrehaloses [SL03]

Acyltrehalose glycans [SL04]

Other acyl sugars [SL05]

Other Saccharolipids [SL00]

Polyketides [PK] Linearpolyketides [PK01]Halogenatedacetogenins [PK02]Annonaceae acetogenins [PK03]

Macrolides and lactone polyketides [PK04]

Ansamycins and related polyketides [PK05]

Polyenes [PK06]

Linear tetracyclines [PK07]

Angucyclines [PK08]

Polyether polyketides [PK09]

Aflatoxins and related substances [PK10]

Cytochalasins [PK11]

Flavonoids [PK12]

Aromatic polyketides [PK13]

Non-ribosomal peptide/polyketide hybrids [PK14]

Other Polyketides [PK00]

 

 

LIPID MAPS Structure Database (LMSD)

 

The LIPID MAPS Structure Database (LMSD) is a relational database encompassing structures and annotations of biologically relevant lipids. As of May 3, 2013, LMSD contains over 37,500 unique lipid structures, making it the largest public lipid-only database in the world. Structures of lipids in the database come from several sources:

  • LIPID MAPS Consortium’s core laboratories and partners;
  • lipids identified by LIPID MAPS experiments;
  • biologically relevant lipids manually curated from LIPID BANK, LIPIDAT, Lipid Library, Cyberlipids, ChEBI and other public sources;
  • novel lipids submitted to peer-reviewed journals;
  • computationally generated structures for appropriate classes.

All the lipid structures in LMSD adhere to the structure drawing rules proposed by the LIPID MAPS consortium. A number of structure viewing options are offered: gif image (default), Chemdraw (requires Chemdraw ActiveX/Plugin), MarvinView (Java applet) and JMol (Java applet).

All lipids in the LMSD have been classified using the LIPID MAPS Lipid Classification System. Each lipid structure has been assigned a LIPID MAPS ID (LM_ID) which reflects its position in the classification hierarchy. In addition to a classification-based retrieval of lipids, users can search LMSD using either text-based or structure-based search options.

 

The text-based search implementation supports data retrieval by any combination of these data fields: LIPID MAPS ID, systematic or common name, mass, formula, category, main class, and subclass data fields. The structure-based search, in conjunction with optional data fields, provides the capability to perform a substructure search or exact match for the structure drawn by the user. Search results, in addition to structure and annotations, also include relevant links to external databases.

Statistics

(as of 10/8/14)

Number of lipids per category

Fatty acyls          5869

Glycerolipids       7541

Glycerophospholipids       8002

Sphingolipids      4338

Sterol lipids         2715

Prenol lipids        1259

Sacccharolipids  1293

Polyketides         6742

TOTAL  37,759 structures

References

Sud M, Fahy E, Cotter D, Brown A, Dennis EA, Glass CK, Merrill AH Jr, Murphy RC, Raetz CR, Russell DW, Subramaniam S. LMSD: LIPID MAPS structure database Nucleic Acids Research 35: p. D527-32. PMID:17098933 [http://dx.doi.org:/10.1093/nar/gkl838]     PMID: 17098933

Fahy E, Sud M, Cotter D & Subramaniam S. LIPID MAPS online tools for lipid research Nucleic Acids Research (2007) 35: p. W606-12.PMID:17584797 [http://dx.doi.org:/10.1093/nar/gkm324] PMID: 17584797

 

Proteome Database (LMPD)

– over 2,400 lipid-associated proteins from human and mouse

Pathways

– manually curated lipid metabolism and signaling pathways

MS analysis tools

– tools for searching various lipid classes by precursor or product ion

Structure Drawing Tools

– draw and save lipid structures using online menus

 

References

Time-varying causal inference from phosphoproteomic measurements in macrophage cells.

IEEE Trans Biomed Circuits Syst. 2014 Feb;8(1):74-86.
http://dx.doi.org:/10.1109/TBCAS.2013.2880235.

 

 

research highlights icon Modeling of eicosanoid fluxes reveals functional coupling between cyclooxygenases and terminal synthases.

Biophys J. 2014 Feb 18;106(4):966-75.
http://dx.doi.org:/10.1016/j.bpj.2014.01.015.

 

Lipid Classification

Starting from a lipid category, the user can navigate through the hierarchy by clicking on the “[+]” icon next to a main class name.

This will expand that item to reveal its sub classes.

Clicking on hyperlinks to the right of main classes, sub classes or level 4 classes will display a tabular listing of all lipids corresponding to that particular subset in the LMSD database.

Finally, clicking on the LM_ID hyperlink displays the LMSD record for an individual lipid, which contains

  • an image of the molecular structure,
  • common and systematic names,
  • links to external databases,
  • Wikipedia pages (where available),
  • other annotations and links to structure viewing tools.

LIPID MAPS classification hierarchy

Category (Example: Prenol lipids [LMPR])

Main class (Example: Isoprenoids [LMPR01])

Sub class (where applicable) (Example: C15 Isoprenoids (sesquiterpenes) [LMPR0103])

Level 4 class (where applicable) (Example: Bisabolane sesquiterpenoids [LMPR010306])

Pathways

We have carefully constructed these lipid pathways based on LIPID MAPS experimental data and data from the literature. LIPID MAPS experimental data obtained from our lipid time course experiments and microarray experiments on macrophagese were mapped to corresponding lipids and genes, respectively.

Pathway maps created using VANTED

VANTED is a tool for the visualization and analysis of networks with related experimental data. For more information on VANTED, please refer to: Björn H. Junker, Christian Klukas and Falk Schreiber (2006): VANTED: A system for advanced data analysis and visualization in the context of biological networks. BMC Bioinformatics, 7:109 (http://www.biomedcentral.com/1471-2105/7/109)

References

Fahy E, Subramaniam S, Murphy R, Nishijima M, Raetz C, Shimizu T, Spener F, van Meer G, Wakelam M and Dennis E.A.,Update of the LIPID MAPS comprehensive classification system for lipids. J. Lipid Res. (2009) 50: S9-S14.PubMed ID:19098281.

Fahy E, Subramaniam S, Brown H, Glass C, Merrill JA, Murphy R, Raetz C, Russell D, Seyama Y, Shaw W, Shimizu T, Spener F, van Meer G, Vannieuwenhze M, White S, Witztum J and Dennis E.A.,A comprehensive classification system for lipids. J. Lipid Res. (2005) 46: 839-861.PubMed ID:15722563.

Introduction to lipids

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Introduction to Lipid Metabolism

Author, Curator: Larry H. Bernstein, MD, FCAP 

Introduction to Lipid Metabolism

This series of articles is concerned with lipid metabolism. These discussions lay
the groundwork to proceed to discussions that will take on a somewhat different
approach, but they are critical to developing a more complete point of view of life
processes.  I have indicated that there are protein-protein interactions or protein-membrane interactions and associated regulatory features, but the focus of the
discussion or points made were different, and will be returned to.  The role of
lipids in circulating plasma proteins as biomarkers for coronary vascular disease
can be traced to the early work of Frederickson and the classification of lipid disorders.  The very critical role of lipids in membrane structure in health and
disease has had much less attention, despite the enormous importance,
especially in the nervous system.

This portion of the discussions of metabolism will have several topics on lipid
metabolism.  The first is concerned with the basic types of lipids -which are defined structurally and have different carbon chain length, and have
two basic types of indispensible fatty acid derivations – along pro-inflammatory
and anti-inflammatory pathways:

  1. Alpha-linolenic acid (ALA) and LA, n-3 polyunsaturated fatty acids LCPUFAs (EPA, DHA, and AA), eicosanoids,
    delta-3-desaturase, prostaglandins, and leukotrienes.
  2. the role of the mitochondrial electron transport chain in hydrogen transfers
    and oxidative phosphorylation with respect to the oxidation of fatty acids
    and fatty acid synthesis.
  3. The membrane structures of the cell, including
  • the cytoskeleton, essential organelles, and the intercellular matrix, which
    is a critical consideration for
  • cell motility, membrane conductivity, flexibility, and  signaling.
  • The membrane structure involves aggregation of lipids with proteins,
  • and is associated with hydrophobicity.
  1. The pathophysiology of systemic circulating lipid disorders.
  2. The fifth is the pathophysiology of cell structures under oxidative
    stress.
  3. Lipid disposal and storage diseases.

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A Future for Plasma Metabolomics in Cardiovascular Disease Assessment

Curator: Larry H Bernstein, MD, FCAP

 

 

Plasma metabolomics reveals a potential panel of biomarkers for early diagnosis
in acute coronary syndrome  

CM. Laborde, L Mourino-Alvarez, M Posada-Ayala,
G Alvarez-Llamas, MG Serranillos-Reus, et al.
Metabolomics – manuscript draft

In this study, analyses of peripheral plasma from Non-ST Segment Elevation
Acute Coronary Syndrome patients and healthy controls by gas chromatography-
mass spectrometry permitted the identification of 15 metabolites with statistical
differences (p<0.05) between experimental groups.
In our study, 6 amino acids were found decreased in NSTEACS patients when
compared with healthy control group suggesting either a decrease in anabolic
activity of these metabolites or an increase in the catabolic pathways. Of both
possibilities, the increased catabolism of the amino acids can be explained
considering simultaneously the capacity of glycogenic and ketogenic amino
acids along with the gradual hypoxic condition to which cardiac muscle cells
have been exposed.

Additionally, validation by gas chromatography-mass spectrometry and liquid
chromatography-mass spectrometry permitted us to identify a potential panel
of biomarkers formed by 5-OH tryptophan, 2-OH-butyric acid and 3-OH-butyric
acid. Oxidative stress conditions dramatically increase the rate of hepatic
synthesis of glutathione. It is synthesized from the amino acids cysteine, glutamic
acid and glycine. Under these conditions of metabolic stress, the supply of cysteine
for glutathione synthesis become limiting and homocysteine is used to form
cystathionine, which is cleaved to cysteine and 2-OH-butyric acid. Thus elevated
plasma levels of 2-OH-butyric acid can be a good biomarker of cellular oxidative
stress for the early diagnosis of ACS.  Another altered metabolite of similar
structure was 3-OH-butyric acid, a ketone body together with the acetoacetate,
and acetone. Elevated levels of ketone bodies in blood and urine mainly occur
in diabetic ketoacidosis. Type 1 diabetes mellitus (DMI) patients have decreased
levels of insulin in the blood that prevent glucose enter cells so these cells use
the catabolism of fats as energy source that produce ketones as final products.
This panel of biomarkers reflects the oxidative stress and the hypoxic state that
disrupts the myocardial cells and consequently constitutes a metabolomic
signature that could be used for early diagnosis of acute coronary syndrome.
We hypothesize that the hypoxia situation comes to “mimic” the physiological
situation that occurs in DMI. In this case, the low energy yield of glucose
metabolism “forces” these cells to use fat as energy source (through catabolism
independent of aerobic/anaerobic conditions) occurring ketones as final
products. In our experiment, the 3-OH-butyric acid was strongly elevated in
NSTEACS patients.

 

Current Methods Used in the Protein Carbonyl Assay
Nicoleta Carmen Purdel, Denisa Margina and Mihaela Ilie.
Ann Res & Rev in Biol 2014; 4(12): 2015-2026.
http://www.sciencedomain.org/download.php?f=Purdel4122013ARRB8763-1

The attack of reactive oxygen species on proteins and theformation of
protein carbonyls were investigated only in the recent years. Taking into
account that protein carbonyls may play an important role in the early
diagnosis of pathologies associated with reactive oxygen species
overproduction, a robust and reliable method to quantify the protein
carbonyls in complex biological samples is also required. Oxidative
stress represents the aggression produced at the molecular level by
the imbalance between pro-oxidant and antioxidant agents, in favor of
pro-oxidants, with severe functional consequences in all organs and
tissues. An overproduction of ROS results in oxidative damages
especially to proteins (the main target of ROS), as well as in lipids,or
DNA. Glycation and oxidative stress are closely linked, and both
phenomena are referred to as ‘‘glycoxidation’’. All steps of glycoxidation
generate oxygen-free radical production, some of them being common
with lipidic peroxidation pathways.
The initial glycation reaction is followed by a cascade of chemical
reactions resulting in the formation of intermediate products (Schiff base,
Amadori and Maillard products) and finally to a variety of derivatives
named advanced glycation end products (AGEs). In hyperglycemic
environments and in natural aging, AGEs are generated in increased
concentrations; their levels can be evaluated in plasma due to the fact
that they are fluorescent compounds. Specific biomarkers of oxidative
stress are currently investigated in order to evaluate the oxidative status
of a biological system and/or its regenerative power. Generaly, malondi-
aldehyde, 4-hydroxy-nonenal (known together as thiobarbituric acid
reactive substances – TBARS), 2-propenal and F2-isoprostanes are
investigated as markers of lipid peroxidation, while the measurement
of protein thiols, as well as S-glutathionylated protein are assessed
as markers of oxidative damage of proteins. In most cases, the
oxidative damage of the DNA has 8-hydroxy-2l-deoxyguanosine
(8-OHdG) as a marker.  The oxidative degradation of proteins plays an
important role in the early diagnosis of pathologies associated with
ROS overproduction. Oxidative modification of the protein structure
may take a variety of forms, including the nitration of tyrosine residues,
carbonylation, oxidation of methionine, or thiol groups, etc.

The carbonylation of protein represents the introduction of carbonyl
groups (aldehyde or ketone) in the protein structure, through several
mechanisms: by direct oxidation of the residues of lysine, arginine,
proline and threonine residues from the protein chain, by interaction
with lipid peroxidation products with aldehyde groups (such as 4-
hydroxy-2-nonenal, malondialdehyde, 2-propenal), or by the
interaction with the compounds with the carbonyl groups resulting
from the degradation of the lipid or glycoxidation. All of these
molecular changes occur under oxidative stress conditions.
There is a pattern of carbonylation, meaning that only certain
proteins can undergo this process and protein structure determines
the preferential sites of carbonylation. The most investigated
carbonyl derivates are represented by gamma-glutamic
semialdehyde (GGS) generated from the degradation of arginine
residue and α-aminoadipic semialdehyde (AAS) derived from lysine.

A number of studies have shown that the generation of protein
carbonyl groups is associated with normal cellular phenomena like
apoptosis, and cell differentiation and is dependent on age, species
and habits (eg. smoking) or severe conditions’ exposure (as
starvation or stress). The formation and accumulation of protein
carbonyls is increased in various human diseases, including –
diabetes and cardiovascular disease.

Recently, Nystrom [7] suggested that the carbonylation process
is associated with the physiological and not to the chronological
age of the organism and the carbonylation may be one of the causes
of aging and cell senescence; therefore it can be used as the marker
of these processes. Jha and Rizvi, [15] proposed the quantification of
protein carbonyls in the erythrocyte membrane as a biomarker of aging

PanelomiX: A threshold-based algorithm to create panels of
biomarkers

X Robin, N Turck, A Hainard, N Tiberti, F Lisacek. 
T r a n s l a t i o n a l  P r o t e o m i c s   2 0 1 3; 1: 57–64.
http://dx.doi.org/10.1016/j.trprot.2013.04.003

The computational toolbox we present here – PanelomiX – uses
the iterative combination of biomarkers and thresholds (ICBT) method.
This method combines biomarkers andclinical scores by selecting
thresholds that provide optimal classification performance. Tospeed
up the calculation for a large number of biomarkers, PanelomiX selects
a subset ofthresholds and parameters based on the random forest method.
The panels’ robustness and performance are analysed by cross-validation
(CV) and receiver operating characteristic(ROC) analysis.

Using 8 biomarkers, we compared this method against classic
combination procedures inthe determination of outcome for 113 patients
with an aneurysmal subarachnoid hemorrhage. The panel classified the
patients better than the best single biomarker (< 0.005) and compared
favourably with other off-the-shelf classification methods.

In conclusion, the PanelomiX toolbox combines biomarkers and evaluates
the performance of panels to classify patients better than single markers
or other classifiers. The ICBT algorithm proved to be an efficient classifier,
the results of which can easily be interpreted. 

Multiparametric diagnostics of cardiomyopathies by microRNA
signatures.
CS. Siegismund, M Rohde, U Kühl,  D  Lassner.
Microchim Acta 2014 Mar.
http://dx.doi.org:/10.1007/s00604-014-1249-y

MicroRNAs (miRNAs) represent a new group of stable biomarkers
that are detectable both in tissue and body fluids. Such miRNAs
may serve as cardiological biomarkers to characterize inflammatory
processes and to differentiate various forms of infection. The predictive
power of single miRNAs for diagnosis of complex diseases may be further
increased if several distinctly deregulated candidates are combined to
form a specific miRNA signature. Diagnostic systems that generate
disease related miRNA profiles are based on microarrays, bead-based
oligo sorbent assays, or on assays based on real-time polymerase
chain reactions and placed on microfluidic cards or nanowell plates.
Multiparametric diagnostic systems that can measure differentially
expressed miRNAs may become the diagnostic tool of the future due
to their predictive value with respect to clinical course, therapeutic
decisions, and therapy monitoring.

Nutritional lipidomics: Molecular metabolism, analytics, and
diagnostics
JT. Smilowitz, AM. Zivkovic, Yu-Jui Y Wan, SM. Watkins, et al.
Mol. Nutr. Food Res2013, 00, 1–17.
http://dx.doi.org:/10.1002/mnfr.201200808

The term lipidomics is quite new, first appearing in 2001. Its definition
is still being debated, from “the comprehensive analysis of all lipid
components in a biological sample” to “the full characterization of
lipid molecular species and their biological roles with respect to the
genes that encode proteins that regulate lipid metabolism”. In principle,
lipidomics is a field taking advantage of the innovations in the separation
sciences and MS together with bioinformatics to characterize the lipid
compositions of biological samples (biofluids, cells, tissues, organisms)
compositionally and quantitatively.

Biochemical pathways of lipid metabolism remain incomplete and the
tools to map lipid compositional data to pathways are still being assembled.
Biology itself is dauntingly complex and simply separating biological
structures remains a key challenge to lipidomics. Nonetheless, the
strategy of combining tandem analytical methods to perform the sensitive,
high-throughput, quantitative, and comprehensive analysis of lipid
metabolites of very large numbers of molecules is poised to drive
the field forward rapidly. Among the next steps for nutrition to understand
the changes in structures, compositions, and function of lipid biomolecules
in response to diet is to describe their distribution within discrete functional
compartments lipoproteins. Additionally, lipidomics must tackle the task
of assigning the functions of lipids as signaling molecules, nutrient sensors,
and intermediates of metabolic pathways.

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