Posts Tagged ‘Biomarker discovery’

Summary of Genomics and Medicine: Role in Cardiovascular Diseases

Summary of Genomics and Medicine: Role in Cardiovascular Diseases

Author: Larry H. Bernstein, MD, FCAP

The articles within Chapters and Subchapters you have just read have been organized into four interconnected parts.
  1. Genomics and Medicine
  2. Epigenetics – Modifyable Factors Causing CVD
  3. Determinants of CVD – Genetics, Heredity and Genomics Discoveries
  4. Individualized Medicine Guided by Genetics and Genomics Discoveries
The first part established the
  • rapidly evolving science of genomics
  • aided by analytical and computational tools for the identification of nucleotide substitutions, or combinations of them
that have a significant association with the development of
  • cardiovascular diseases,
  • hypercoagulable state,
  • atherosclerosis,
  • microvascular disease,
  • endothelial disruption, and
  • type-2DM, to name a few.
These may well be associated with increased risk for stroke and/or peripheral vascular disease in some cases,
  • essentially because the involvement of the circulation is systemic in nature.

Part 1

establishes an important connection between RNA and disease expression.  This development has led to
  • the necessity of a patient-centric approach to patient-care.
When I entered medical school, it was eight years after Watson and Crick proposed the double helix.  It was also
  • the height of a series of discoveries elucidating key metabolic pathways.
In the period since then there have been treatments for some of the important well established metabolic diseases of
  • carbohydrate,
  • protein, and
  • lipid metabolism,
such as –  glycogen storage disease, and some are immense challenges, such as
  • Tay Sachs, or
  • Transthyretin-Associated amyloidosis.
But we have crossed a line delineating classical Mendelian genetics to
  • multifactorial non-linear traits of great complexity and
involving combinatorial program analyses to resolve.
The Human Genome Project was completed in 2001, and it has opened the floodgates of genomic discovery.  This resulted in the identification of
genomic alterations in
  • cardiovascular disease,
  • cancer,
  • microbial,
  • plant,
  • prion, and
  • metabolic diseases.
This has also led to
  • the identification of genomic targets
  • that are either involved in transcription or
  • are involved with cellular control mechanisms for targeted pharmaceutical development.
In addition, there is great pressure on the science of laboratory analytics to
  • codevelop with new drugs,
  • biomarkers that are indicators of toxicity or
  • of drug effectiveness.
I have not mentioned the dark matter of the genome. It has been substantially reduced, and has been termed dark because
  • this portion of the genome is not identified in transcription of proteins.
However, it has become a lightning rod to ongoing genomic investigation because of
  • an essential role in the regulation of nuclear and cytoplasmic activities.
Changes in the three dimensional structure of these genes due to
  • changes in Van der Waal forces and internucleotide distances lead to
  • conformational changes that could have an effect on cell activity.

Part 2

is an exploration of epigenetics in cardiovascular diseases.  Epigenetics is
  • the post-genomic modification of genetic expression
  • by the substitution of nucleotides or by the attachment of carbohydrate residues, or
  • by alterations in the hydrophobic forces between sequences that weaken or strengthen their expression.
This could operate in a manner similar to the conformational changes just described.  These changes
  • may be modifiable, and they
  • may be highly influenced by environmental factors, such as
    1. smoking and environmental toxins,
    2. diet,
    3. physical activity, and
    4. neutraceuticals.
While neutraceuticals is a black box industry that evolved from
  • the extraction of ancient herbal remedies of agricultural derivation
    (which could be extended to digitalis and Foxglove; or to coumadin; and to penecillin, and to other drugs that are not neutraceuticals).

The best examples are the importance of

  • n-3 fatty acids, and
  • fiber
  • dietary sulfur (in the source of methionine), folic acid, vitamin B12
  • arginine combined with citrulline to drive eNOS
  • and of iodine, which can’t be understated.
In addition, meat consumption involves the intake of fat that contains

  • the proinflammatory n-6 fatty acid.

The importance of the ratio of n-3/n-6 fatty acids in diet is not seriously discussed when

  • we look at the association of fat intake and disease etiology.
Part 2 then leads into signaling pathways and proteomics. The signaling pathways are
  • critical to understanding the inflammatory process, just as
  • dietary factors tie in with a balance that is maintained by dietary intake,
    • possibly gut bacteria utilization of delivered substrate, and
    • proinflammatory factors in disaease.
These are being explored by microfluidic proteomic and metabolomic technologies that were inconceivable a half century ago.
This portion extended into the diagnosis of cardiovascular disease, and
  • elucidated the relationship between platelet-endothelial interaction in the formation of vascular plaque.
It explored protein, proteomic, and genomic markers
  1. for identifying and classifying types of disease pathobiology, and
  2. for following treatment measures.

Part 3

connected with genetics and genomic discoveries in cardiovascular diseases.

Part 4

is the tie between life style habits and disease etiology, going forward with
  • the pursuit of cardiovascular disease prevention.
The presentation of walking and running, and of bariatric surgery (type 2DM) are fine examples.
It further discussed gene therapy and congenital heart disease.  But the most interesting presentations are
  • in the pharmacogenomics for cardiovascular diseases, with
    1. volyage-gated calcium-channels, and
    2. ApoE in the statin response.

This volume is a splendid example representative of the entire collection on cardiovascular diseases.

Read Full Post »

Introduction to Genomics and Epigenomics Roles in Cardiovascular Diseases

Introduction to Genomics and Epigenomics Roles in Cardiovascular Diseases

Author and Curator: Larry H Bernstein, MD, FCAP

This introduction is to a thorough evaluation of a rich source of research literature on the genomic influences, which may have variable strength in the biological causation of atherosclerosis, microvascular disease, plaque formation, not necessarily having expressing, except in a multivariable context that includes the environment, dietary factors, level of emotional stress, sleep habits, and the daily activities of living for affected individuals.  The potential of genomics is carried in the DNA, copied to RNA, and this is most well studied in the micro RNAs (miRNA).  The miRNA has been explored for the appearance in the circulation of specific miRNAs that might be associated with myocyte or endothelial cell injury, and they are also being used as targets for therapeutics by the creation of silencing RNAs (siRNA).  The extent to which there is evidence of success in these studies is limited, but is being translated from animal studies to human disease.  There is also a long history of the measurement of  circulating enzymes and isoenzymes (alanine amino transferase, creatine kinase, and lactate dehydrogenase, not to leave out the adenylate kinase species specific to myocardium), and more recently the release of troponins I and T, and the so far still not fully explored ischemia modified albumin, or of miRNAs for the diagnosis of myocardial infarction.

There is also a significant disagreement about the value of measuring high sensitivity C reactive protein (hs-CRP), which has always been a marker for systemic inflammatory disease, in both chronic rheumatic and infectious diseases having a broad range, so that procalcitonin has appeared to be better for that situation, and for early diagnosis of sepsis. The hs-CRP has been too easily ignored because of

1. the ubiquitous elevations in the population
2. the expressed concerns that one might not be inclined to treat a mild elevation without other risk factors, such as, LDL cholesterolemia, low HDL, absent diabetes or obesity.  Nevertheless, hs-CRP raises an reasonable argument for preventive measures, and perhaps the use of a statin.

There has been a substantial amount of work on the relationship of obesity to both type 2 diabetes mellitus (T2DM) and to coronary vascular disease and stroke.  Here we bring in the relationship of the vascular endothelium, adipose tissue secretion of adiponectin, and platelet activation.  A whole generation of antiplatelet drugs addresses the mechanism of platelet activation, adhession, and interaction with endothelium.   Very interesting work has appeared on RESISTIN, that could bear some fruit in the treatment of both obesity and T2DM.

It is important to keep in mind that epigenomic gene rearrangements or substitutions occur throughout life, and they may have an expression late in life.  Some of the known epigenetic events occur with some frequency, but the associations are extremely difficult to pin down, as well as the strength of the association.  In a population that is not diverse, epigenetic changes are passed on in the population in the period of childbearing age.  The establishment of an epigenetic change is diluted in a diverse population.  There have been a number of studies with different findings of association between cardiovascular disease and genetic mutations in the Han and also in the Uyger Chinese populations, which are distinctly different populations that is not part of this discussion.

This should be sufficient to elicit broad appeal in reading this volume on cardiovascular diseases, and perhaps the entire series.  Below is a diagram of this volume in the series.

PART 1 – Genomics and Medicine
Introduction to Genomics and Medicine (Vol 3)
Genomics and Medicine: The Physician’s View
Ribozymes and RNA Machines
Genomics and Medicine: Genomics to CVD Diagnoses
Establishing a Patient-Centric View of Genomic Data
VIDEO:  Implementing Biomarker Programs ­ P Ridker PART 2 – Epigenetics – Modifiable
Factors Causing CVD
Diseases Etiology
   Environmental Contributors
Implicated as Causing CVD
   Diet: Solids and Fluid Intake
and Nutraceuticals
   Physical Activity and
Prevention of CVD
   Psychological Stress and
Mental Health: Risk for CVD
   Correlation between
Cancer and CVD
PART 3  Determinants of CVD – Genetics, Heredity and Genomics Discoveries
    Why cancer cells contain abnormal numbers of chromosomes (Aneuploidy)
     Functional Characterization of CV Genomics: Disease Case Studies @ 2013 ASHG
     Leading DIAGNOSES of CVD covered in Circulation: CV Genetics, 3/2010 – 3/2013
     Commentary on Biomarkers for Genetics and Genomics of CVD
PART 4 Individualized Medicine Guided by Genetics and Genomics Discoveries
    Preventive Medicine: Cardiovascular Diseases
    Walking and Running: Similar Risk Reductions for Hypertension, Hypercholesterolemia,
DM, and possibly CAD
    Prevention of Type 2 Diabetes: Is Bariatric Surgery the Solution?
Gene-Therapy for CVD
Congenital Heart Disease/Defects
   Medical Etiologies: EBM – LEADING DIAGNOSES, Risks Pharmacogenomics for Cardio-
vascular Diseases
   Signaling Pathways     Response to Rosuvastatin in
Patients With Acute Myocardial Infarction:
Hepatic Metabolism and Transporter Gene
Variants Effect
   Proteomics and Metabolomics      Voltage-Gated Calcium Channel and Pharmaco-
genetic Association with Adverse Cardiovascular
Outcomes: Hypertension Treatment with Verapamil
SR (CCB) vs Atenolol (BB) or Trandolapril (ACE)
      SNPs in apoE are found to influence statin response
significantly. Less frequent variants in
PCSK9 and smaller effect sizes in SNPs in HMGCR

Read Full Post »

Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets

Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets

Curator, Reporter, EAW:  Larry H Bernstein, MD, FCAP


This discussion is a continuation of an earlier piece on the technologic framework for , proteomics, nutrigenomics, and translational medicine. The last decade has seen the emergence of a genomic science that is changing the trajectory of biological sciences and medicine. It has not resolved all of our problems by any means, but it has begun to redraw the map, which began with the elucidation of major metabolic pathways in the first half of the 20th century, was then captured by the transformation of genetics with the discovery of the “Watson-Crick Model”, and then later was recharged with the discovery of the Toll-like receptor and the drawing of “signaling pathways”. What we have seen in an unraveling of protein-genome interactions, small peptide regulators, and dynamic changes in pathway dominance, bloackage, and reentry, depending on genetic, dietary, and environmental conditions, mostly expressed in what we refer to as “oxidative stress”.

Unraveling the multitude of nutrigenomic, proteomic, and metabolomic patterns that arise from the ingestion of foods or their bioactive food components will not be simple but is likely to provide insights into a tailored approach to diet and health. The use of new and innovative technologies, such as microarrays, RNA interference, and nanotechnologies, will provide needed insights into molecular targets for specific bioactive food components and how they harmonize to influence individual phenotypes. A challenging aspect of omic technologies is the refined analysis of quantitative dynamics in biological systems.

In recent years, nutrition research has moved from classical epidemiology and physiology to molecular biology and genetics. The new era of nutrition research translates empirical knowledge to evidence-based molecular science. Following this trend, Nutrigenomics has emerged as a novel and multidisciplinary research field in nutritional science that aims to elucidate how diet can influence human health. It is already well known that bioactive food compounds can interact with genes affecting transcription factors, protein expression and metabolite production. The study of these complex interactions requires the development of advanced analytical approaches combined with bioinformatics.
The Institute of Medicine recently convened a workshop to review the state of the various domains of nutritional genomics research and policy and to provide guidance for further development and translation of this knowledge into nutrition practice and policy. Nutritional genomics holds the promise to revolutionize both clinical and public health nutrition practice and facilitate the establishment of

  1.  genome-informed nutrient and food-based dietary guidelines for disease prevention and healthful aging,
  2.  individualized medical nutrition therapy for disease management, and
  3.  better targeted public health nutrition interventions (including micronutrient fortification and supplementation) that maximize benefit and minimize adverse outcomes within genetically diverse human populations.

For metabolomics, gas and liquid chromatography coupled to mass spectrometry are well suited for coping with high sample numbers in reliable measurement times with respect to both technical accuracy and the identification and quantitation of small-molecular-weight metabolites. This potential is a prerequisite for the analysis of dynamic systems. Thus, metabolomics is a key technology for systems biology.
The bioavailability of bioactive food constituents as well as dose-effect correlations are key information to understand the impact of food on defined health outcomes. Both strongly depend on appropriate analytical tools to identify and quantify minute amounts of individual compounds in highly complex matrices–food or biological fluids–and to monitor molecular changes in the body in a highly specific and sensitive manner. Based on these requirements, mass spectrometry has become the analytical method of choice with broad applications throughout all areas of nutrition research.

Dynamic Construct of the –Omics

Metabolomics is a term that encompasses several types of analyses, including

  1. metabolic fingerprinting, which measures a subset of the whole profile with little differentiation or quantitation of metabolites;
  2. metabolic profiling, the quantitative study of a group of metabolites, known or unknown, within or associated with a particular metabolic pathway; and
  3. target isotope-based analysis, which focuses on a particular segment of the metabolome by analyzing only a few selected metabolites that comprise a specific biochemical pathway.

Any unifying concept of the metabolome was incomplete or debatable in the first 30 years of the 20th century. It was only known that insulin is anabolic and that insulin deficiency (or resistance) would have consequences in the point of entry into the citric acid cycle, which generates 28-32 ATPs. In fat catabolism, triglycerides are hydrolyzed to break them into fatty acids and glycerol. In the liver the glycerol can be converted into glucose via dihydroxyacetone phosphate and glyceraldehyde-3-phosphate by way of gluconeogenesis. In the case of this cycle there is a tie in with both catabolism and anabolism.

See Aerobic glucose and acetate metabolism. (from dos Santos MM, et al. EUKARYOTIC CELL 2003; 2:599–608)

For bypass of the Pyruvate Kinase reaction of Glycolysis, cleavage of 2 ~P bonds is required. The free energy change associated with cleavage of one ~P bond of ATP is insufficient to drive synthesis of phosphoenolpyruvate (PEP), since PEP has a higher negative DG of phosphate hydrolysis than ATP.
The two enzymes that catalyze the reactions for bypass of the Pyruvate Kinase reaction are the following:

  • Pyruvate Carboxylase (Gluconeogenesis) catalyzes pyruvate + HCO3- + ATP — oxaloacetate + ADP + Pi
  • PEP Carboxykinase (Gluconeogenesis) catalyzes: oxaloacetate + GTP —- phosphoenolpyruvate + GDP + CO2

Many high throughput methods have been employed to get some insight into the whole process and several examples of successful research. Proteomics and metabolomics need to encompass large numbers of experiments and linked data. Due to the nature of the proteins, as well as due to the properties of various metabolites, experimental approaches require the use of comprehensive high throughput methods and a sufficiency of analysed tissue or body fluids.

Ovesná J, Slabý O, Toussaint O, Kodícek M, et al. High throughput ‘omics’ approaches to assess the effects of phytochemicals in human health studies. Br J Nutr. 2008;99 E Suppl 1:ES127-34.

An important and revolutionary aspect of  ‘The 2010 Project’ is that it implicitly endorses the allocation of resources to attempts to assign function to genes that have no known function. This represents a significant departure from the common practice of defining and justifying a scientific goal based on the biological phenomena. The rationale for endorsing this radical change is that for the first time it is feasible to envision a whole-systems approach to gene and protein function. I shall not discuss the emerging field of bioinformatics that makes this possible.
In this review, the end-of-the line “detector will be considered having been covered. The entire focus proceeds to a discussion of separation methods. Separation methods have always been tricky, time consuming, and a multiple step process that depended on using anionic and cationic resins as intermediate steps in bulk separation, and then molecular size separation.  Therapeutic Targets will be identified as they are seen.

Affinity Chromatography
The rapid development of biotechnology and biomedicine requires more reliable and efficient separation technologies for the isolation and purification of biopolymers such as therapeutic proteins, antibodies, enzymes and nucleic acids. In particular, monoclonal antibodies are centrally important as therapeutics for the treatment of cancer and other diseases, leading to recombinant monoclonal antibodies that dominate today’s biopharmaceutical pipeline. The large-scale production of therapeutic biopolymers requires

  • a manufacturing process that delivers reliability and in high-yield, as well as
  • an effective purification process affording extremely pure products.

Because of its high selectivity, affinity chromatography has been used extensively to isolate a variety of biopolymers. The retention of solutes is based on specific, reversible interactions found in biological systems, such as the binding of an enzyme with an inhibitor or an antibody with an antigen. These interactions are exploited in affinity chromatography by immobilizing an affinity ligand onto a support, and using this as a stationary phase.
Non-porous particles having an average diameter of 2.1 mm were prepared by co-polymerization of styrene, methyl methacrylate and glycidyl methacrylate, which was abbreviated as P(S–MMA–GMA). The particles were mechanically stable due to the presence of benzene rings in the backbone of polymer chains, and could withstand high pressures when a column packed with these particles was operated in the HPLC mode.

The polymer particles were advantaged by immobilization of ligands via the epoxy groups on the particle surface that were introduced by one of the monomers, glycidyl methacrylate. As a model system, Cibacron Blue 3G-A was covalently immobilized onto the non-porous copolymer beads. The dye-immobilized P(S–MMA–GMA) particles were slurry packed into a 1.0 cm30.46 cm I.D. column. This affinity column was effective for the separation of turkey egg white lysozyme from a protein mixture. The bound lysozyme could be eluted to yield a sharp peak by using a phosphate buffer containing 1 M NaCl. For a sample containing up to 8 mg of lysozyme, the retained portion of proteins could be completely eluted without any slit peak. Due to the use of a shorter column, the analysis time was shorter in comparison with other affinity systems reported in the literature. The retention time could be reduced significantly by increasing the flow-rate, while the capacity factor remained at the same level.
CH Chen, WC Lee. Affinity chromatography of proteins on non-porous copolymerized particles of styrene, methyl methacrylate and glycidyl methacrylate. Journal of Chromatography A 2001; 921: 31–37.

Affinity separation membranes, consisting of electrospun nanofibers, have been developed recently. Affinity ligands are attached to the surface of the constituent fibers, offering a potential solution to some of the problems of traditional, column-based, affinity chromatography. Electrospun fibers are good candidates for use in affinity separation because of their

  • unique characteristics of high surface area to volume ratio, resulting in
  • high ligand loading, and
  • their large porosity, resulting in
  • high throughput operation.

A number of polymers have been used for electrospun fiber mesh-based affinity membrane separations including poly (ether-urethane-urea), cellulose, poly(ethylene terephthalate, polysulphone, and polyacrlonitrile. Typically, very thin electrospun fiber meshes are produced by electrostatically collecting negatively charged fibers on a collector electrode. These very thin 2D electrospun fiber mesh mats provide excellent solution permeability as compared to 3D column packed with affinity beads.
M Miyauchi, J Miao, TJ Simmons, JS Dordick and RJ Linhardt. Flexible Electrospun Cellulose Fibers as an Affinity Packing Material for the Separation of Bovine Serum Albumin. J Chromatograph Separat Techniq 2011; 2:2 http://dx.doi.org/10.4172/2157-7064.1000110

Dye Affinity Chromatography
Biomimetic Dyes
Affinity adsorbents based on immobilized triazine dyes offer important advantages circumventing many of the problems associated with biological ligands. The main drawback of dyes is their moderate selectivity for proteins. Rational attempts to tackle this problem are realized through the biomimetic dye concept according to which new dyes, the biomimetic dyes, are designed to mimic natural ligands. Biomimetic dyes are expected to exhibit increased affinity and purifying ability for the targeted proteins.

Biocomputing offers a powerful approach to biomimetic ligand design. The successful exploitation of contemporary computational techniques in molecular design requires the knowledge of the three-dimensional structure of the target protein, or at least, the amino acid sequence of the target protein and the three-dimensional structure of a highly homologous protein. From such information one can then design, on a graphics workstation,

  • the model of the protein and also
  • a number of suitable synthetic ligands which mimic natural biological ligands of the protein.

There are several examples of enzyme purifications

  • trypsin
  • urokinase
  • kallikrein
  • alkaline phosphatase
  • malate dehydrogenase
  • formate dehydrogenase
  • oxaloacetate decarboxylase
  • lactate dehydrogenase

where synthetic biomimetic dyes have been used successfully as affinity chromatography tools.
YD Clonis, NE Labrou, VPh Kotsira, C Mazitsos, et al. Biomimetic dyes as affinity chromatography tools in enzyme purification. Journal of Chromatography A 2000; 891: 33–44.

Interactions between Cibacron Blue F3GA (CB F3GA), as a model of triazine dye, and 2-hydroxypropyl-b-cyclodextrin (HP-b-CD), as a model of cyclodextrin, were investigated by monitoring the spectral shift that accompanies the binding phenomena. Matrix analysis of the difference spectral titration of CB F3GA with HP-b-CD revealed only two absorbing species, indicating a host–guest ratio of 1:1. The dissociation constant for this HP-b-CD–CB F3GA complex, K , was found d to be 0.43 mM. The data for HP-b-CD forming inclusion complexes with CB F3GA were used to develop the concept of competitive elution by inclusion complexes in dye-affinity chromatography.
When this concept was applied to the elution of L-lactate dehydrogenase from a CB F3GA affinity matrix, it was shown to be an effective elution strategy. It provided a 15-fold purification factor with 89% recovery and sharp elution profile (0.8 column volumes for 80% recovery), which is as good as that obtained by specific elution with NADH (16-fold, 78% recovery and 1.8 column volumes). In addition, the new elution strategy showed a better purification factor and sharper elution profile than traditional non-specific.
JA Lopez-Mas, SA Streitenberger, F Garcıa-Carmona, AA Sanchez-Ferrer. Cyclodextrin biospecific-like displacement in dye-affinity chromatography. Journal of Chromatography A 2001; 911: 47–53.

Affinity chromatography uses biospecific binding usually between an antibody and an antigen, an enzyme and a substrate or other pairs of key-lock type of matching molecules. Due to its high selectivity, it is able to purify proteins and other macromolecules from very dilute solutions. In this work, a general rate model for affinity chromatography was used for scale-up studies. Parameters for the model were estimated from existing correlations, or from experimental results obtained on a small column with the same packing material. As anexample, Affi-Gel with 4.5mol cm−3 Cibacron Blue F-3GA as immobilized ligands covalently attached to cross-linked 6% agarose was used for column packing. Cibacron Blue F-3GA was also used as a soluble ligand in the elution stage. Satisfactory scale-up predictions were obtained for a 98.2 ml column and a 501 ml column based on a few experimental data obtained on a 7.85 ml small column.
T. Gu, K.-H. Hsu and M.-J. Syu, “Scale-Up of Affinity Chromatography for Purification of Enzymes and Other Proteins.” Enzyme and Microbial Technology 2003; 33:433-437.

Affinity Column with AAAA as a Model Sense Ligand
The degeneracy of antisense peptides was studied by high-performance affinity chromatography. A model sense peptide (AAAA) and its antisense peptides (CGGG, GGGG, RGGG, SGGG) were designed and synthesized according to the degeneracy of genetic codes. An affinity column with AAAA as the ligand was prepared. The affinity chromatographic behaviors of antisense peptides on the column were evaluated. The results indicated that model antisense peptides have clear retention on the immobilized AAAA affinity column. RGGG showed the strongest affinity interaction.
R Zhao, X Yu, H Liu, L Zhai, S Xiong, et al. Study on the degeneracy of antisense peptides using affinity chromatography. Journal of Chromatography A 2001; 913: 421–428.

Frontal AC for Biomolecular Interactions
Frontal affinity chromatography is a method for quantitative analysis of biomolecular interactions. We reinforced it by incorporating various merits of a contemporary liquid chromatography system. As a model study, the interaction between an immobilized Caenorhabditis elegans galectin (LEC-6) and fluorescently labeled oligosaccharides (pyridylaminated sugars) was analyzed. LEC-6 was coupled to N-hydroxysuccinimide-activated Sepharose 4 Fast Flow (100 mm diameter), and packed into a miniature column (e.g., 1034.0 mm, 0.126 ml). The volume of the elution front (V) determined graphically for each sample was compared with that obtained in the presence of an excess amount of hapten saccharide, lactose (V ); and the dissociation constant, K , was calculated according to the literature. This system also proved to be useful for an inverse confirmation; that is, application of galectins to an immobilized glycan column (in the present case, asialofetuin was immobilized on Sepharose 4 Fast Flow), and the elution profiles were monitored by fluorescence based on tryptophan. The newly constructed system proved to be extremely versatile. It enabled rapid (analysis time 12 min/ cycle) and sensitive (20 nM for pyridylaminated derivatives, and 1 mg/ml for protein) analyses of lectin–carbohydrate interactions.
J Hirabayashi, Y Arata, K Kasai. Reinforcement of frontal affinity chromatography for effective analysis of lectin–oligosaccharide interactions. Journal of Chromatography A 2000; 890:261–271.

Immobilized Metal Ion Affinity
New immobilized metal ion affinity chromatography (IMAC) matrices containing a high concentration of metal–chelate moieties and completely coated with inert flexible and hydrophilic dextrans are here proposed to improve the purification of polyhistidine (poly-His) tagged proteins. The purification of an interesting recombinant multimeric enzyme (a thermoresistant b-galactosidase from Thermus sp. strain T2) has been used to check the performance of these new chromatographic media.

IMAC supports with a high concentration (and surface density) of metal chelate groups promote a rapid adsorption of poly-His tagged proteins during IMAC. However, these supports also favor the promotion of undesirable multi-punctual adsorptions and problems may arise for the simple and effective purification of poly-His tagged proteins. For example, desorption of the pure enzyme from the support may become quite difficult (e.g., it is not fully desorbed from the support even using 200 mM of imidazole).

The coating of these IMAC supports with dextrans greatly reduces these undesired multi-point adsorptions. However, this dextran coating of chromatographic matrices seems to allow the formation of strong one-point adsorptions that involve small areas of the protein and support surface, but the dextran coating seems to have dramatic effects for the prevention of weak or strong multipoint interactions that should involve a high geometrical congruence between the enzyme and the support surface.
C Mateo , G Fernandez-Lorente , BCC Pessela , A Vian, et al. Affinity chromatography of polyhistidine tagged enzymes. New dextran-coated immobilized metal ion affinity chromatography matrices for prevention of undesired multipoint adsorptions. Journal of Chromatography A 2001; 915:97–106.
The underlying principle of immobilized metal ion affinity chromatography (IMAC) of proteins is the coordination between the electron donor groupings on a protein surface (histidine, tryptophan, cysteine) and chelated (iminodiacetate; IDA) transition metal ions [IDA-M(II)].  This principle of immobilized metal ion affinity (IMA) has been presented by now in some detail. The practice of IMAC in the purification of proteins has had its empirical phase. There is now a need, from the body of data, to establish somewhat more detailed ground rules that would allow for the use of IMAC in a more predictive manner.
Immobilized metal ion affinity chromatography (IMAC) has been explored as a probe into the topography of histidyl residues of a protein molecule. An evaluation of the chromatographic behavior of selected model proteins-

  • thioredoxin
  • ubiquitin
  • calmodulin
  • lysozyme
  • cytochrome c
  • myoglobin

on immobilized transition metal ions

  • Co2+
  • Ni2+
  • Cu2+
  • Zn2

-allows establishment of the following facets of the histidyl side chain distribution:

  1. either interior or surface;
  2. when localized on the surface, accessible or unaccessible for coordination;
  3. single or multiple;
  4. When multiple, either distant or vicinal.

Moreover, proteins displaying single histidyl side chains on their surfaces may, in some instances, be resolved by IMAC; apparently, the microenvironments of histidyl residues are sufficiently diverse to result in different affinities for the immobilized metal ions. IMAC, previously introduced as an approach to the fractionation of proteins, has become also, upon closer examination, a facile probe into the topography of histidyl residues.
This is possible because of the inherent versatility of IMAC; an appropriate metal ion (M2+) can be selected to suit the analytical purpose and a particular chromatographic protocol can be applied (isocratic pH, falling pH, and imidazole elution). We now report that IMAC may be exploited as an analytical tool in addition to its use as a protein purification technique. IMAC can be used to ascertain several facets of the status of a histidyl residue(s) in a protein molecule:

  1. localization (interior vs. surface)
  2. coordination potential as defined by the steric accessibility and the state of protonation
  3. single vs. multiple
  4. surface density.

ES Hemdan, YJ Zhao, E Sulkowski, J Porath. Surface topography of histidine residues: A facile probe by immobilized metal ion affinity chromatography. Proc. Natl. Acad. Sci. USA 1989; 86: 1811-1815. Biochemistry.

A novel, two-step preparative technique is described for the purification of authentic recombinant human prolactin (rhPRL) secreted into the periplasm of transformed Escherichia coli cells. The first step is based on immobilized metal ion affinity chromatography of periplasmic extract, using Ni(II) as a relatively specific ligand for hPRL in this system. It gives superior resolution and yield than established ion-exchange chromatography. Size-exclusion chromatography is used for further purification to .99.5% purity. The methodology is reproducible, leading to 77% recovery. Identity and purity of the rhPRL were demonstrated using sodium dodecylsulphate–polyacrylamide electrophoresis, isoelectric focusing, mass spectrometry (matrix-assisted laser desorption ionization time-of-flight), radioimmunoassay, RP-HPLC and high-performance size-exclusion chromatography. In the Nb2 bioassay, the hormone showed a bioactivity of 40.9 IU/mg.

EKM Ueda, PW Gout, L Morgantia. Ni(II)-based immobilized metal ion affinity chromatography of recombinant human prolactin from periplasmic Escherichia coli extracts. Journal of Chromatography A 2001; 922:165–175.

Adenosine Affinity Ligand for Glutamine Synthase
Glutamine synthetase has been purified from both procaryotic and eucaryotic sources using various types of affinity chromatography. For example, ADP-agarose has been used to purify glutamine synthetase from photosynthetic bacteria, while the related “Blue” chromatography media (e.g. Affigel Blue) have been used to purify glutamine synthetases from a variety of sources. In addition, 2’,5’-ADPSepharose 4B has been used to purify glutamine synthetase from procaryotes, plants and insects. However, this latter affinity ligand resembles NADP more than ADP, particularly with respect to the position of the phosphate moieties. This is reflected in the more general use of this affinity ligand in the purification of NADPH-dependent enzymes.
In the present report, we characterize the ability of glutamine synthetase to be purified by three different adenosine-affinity ligands: 5’-ADP-agarose (an ADP analogue), 2’,5’-ADP-Sepharose 4B (an NADP analogue) and 3’,5’-ADP-agarose (a cyclic AMP analogue). We report conditions for the successful purification of insect flight muscle glutamine synthetase using each of these three different affinity ligands.
The enzyme bound most strongly to the

  1. ADP analogue (S-ADP-agarose),
  2. followed by the NADPH analogue (2’,5’-ADP-Sepharose 4B), and least strongly to
  3. the cyclic AMP analogue (3’J’-ADP-agarose).

In all cases, binding was strongest in the presence of Mn2+ when compared to Mg”. These results suggest that the binding of glutamine synthetase to adenosine-affinity media is related to the participation of Mn. ADP in the y-glutamyl transferase reaction that is catalyzed by glutamine synthetase.
M Dowton, IR Kennedy. Purification of glutamine synthetase by adenosine-affinity chromatography. Journal of Chromatography A 1994; 664: 280-283

Aptamer Based Stationary Phase
An anti-adenosine aptamer was evaluated as a stationary phase in packed capillary liquid chromatography. Using an 21 aqueous mobile phase containing 20 mM Mg , adenosine was strongly retained on the column.  A gradient of increasing 21 Ni (to 18 mM), which is presumed to complex with nitrogen atoms in adenosine involved in binding to the aptamer, eluted adenosine in a narrow zone. The adenosine assay, which required no sample preparation, was used on microdialysis samples. Total analysis times were short so samples could be injected every 5 min.
Q Deng, CJ Watson, RT Kennedy. Aptamer affinity chromatography for rapid assay of adenosine in microdialysis samples collected in vivo. Journal of Chromatography A 2003; 1005:123–130.

We will realize the full power of proteomics only when we can measure and compare the proteomes of many individuals to identify biomarkers of human health and disease and track the blood-based proteome of an individual over time. Because the human proteome contains an estimated 20,000 proteins – plus splicing and post-translational variants – that span a concentration range of ,12 logs, identifying and quantifying valid biomarkers is a great technical challenge.
Proteomic measurements demand

  • extreme sensitivity
  • specificity
  • dynamic range
  • accurate quantification.

We describe a new class of DNA-based aptamers enabled by a versatile chemistry technology that endows nucleotides with protein-like functional groups. These modifications greatly expand the repertoire of targets accessible to aptamers.
The resulting technology provides efficient, large-scale selection of exquisite protein-binding reagents selected specifically for use in highly multiplexed proteomics arrays.
Aptamers are a class of nucleic acid-based molecules discovered twenty years ago, and have since been employed in diverse applications including

  • therapeutics
  • catalysis
  • proteomics

Aptamers are short single-stranded oligonucleotides, which fold into diverse and intricate molecular structures that bind with high affinity and specificity to

  • proteins
  • peptides
  • small molecules.

Aptamers are selected in vitro from enormously large libraries of randomized sequences by the process of Systematic Evolution of Ligands by EXponential enrichment (SELEX). A SELEX library with 40 random sequence positions has 440 (,1024) possible combinations and a typical selection screens 1014–1015 unique molecules. This is on the order of 105 times larger than standard peptide or protein combinatorial molecular libraries.

The interrogation of proteomes (‘‘proteomics’’) in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology and medicine. We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 mL of serum or plasma).

Our current assay measures 813 proteins with low limits of detection (1 pM median), 7 logs of overall dynamic range (,100 fM–1 mM), and 5% median coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding signature of DNA aptamer concentrations, which is quantified on a DNA microarray.

Our assay takes advantage of the dual nature of aptamers as both folded protein-binding entities with defined shapes and
unique nucleotide sequences recognizable by specific hybridization probes.

This is a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine.
L Gold, D Ayers, J Bertino, Christopher Bock, et al. Aptamer-Based Multiplexed Proteomic Technology for Biomarker Discovery. PlosONE 2010; 5 (12): e15004

Biomarker Discovery, Diagnostics, and Therapeutics
Progression from health to disease is accompanied by complex changes in protein expression in both the circulation and affected tissues. Large-scale comparative interrogation of the human proteome can offer insights into disease biology as well as lead to

  • the discovery of new biomarkers for diagnostics
  • new targets for therapeutics
  • can identify patients most likely to benefit from treatment.

Although genomic studies provide an increasingly sharper understanding of basic biological and pathobiological processes, they ultimately only offer a prediction of relative disease risk, whereas proteins offer an immediate assessment of “real-time” health and disease status.
We have recently developed a new proteomic technology, based on modified aptamers, for biomarker discovery that is capable of simultaneously measuring more than a thousand proteins from small volumes of biological samples such as plasma, tissues, or cells. Our technology is enabled by SOMAmers (Slow Off-rate Modified Aptamers), a new class of protein binding reagents that contain chemically modified nucleotides that greatly expand the physicochemical diversity of nucleic acid-based ligands. Such modifications introduce functional groups that are absent in natural nucleic acids but are often found in protein-protein, small molecule-protein, and antibody-antigen interactions. The use of these modifications expands the range of possible targets for SELEX (Systematic Evolution of Ligands by EXponential Enrichment), results in improved binding properties, and facilitates selection of SOMAmers with slow dissociation rates. Our assay works by transforming protein concentrations in a mixture into a corresponding DNA signature, which is then quantified on current commercial DNA microarray platforms. In essence, we take advantage of the dual nature of SOMAmers as

  • both folded binding entities with defined shapes and
  • unique nucleic acid sequences recognizable by specific hybridization probes.

Mehan MR, Ostroff R, Wilcox SK, Steele F, et al. Highly multiplexed proteomic platform for biomarker discovery, diagnostics, and therapeutics. Adv Exp Med Biol. 2013; 734:283-300.

Aptamers and Smart Drug delivery Targeting
In this review, the strategies for using functional nucleic acids in creating smart drug delivery devices will be explained, as their has been very recent progress in controlled drug release based on molecular gating achieved with aptamers. Aptamers are functional nucleic acid sequences which can bind specific targets.
An artificial combinatorial methodology can identify aptamer sequences for any target molecule, from ions to whole cells. Drug delivery systems seek to increase efficacy and reduce side-effects by concentrating the therapeutic agents at specific disease sites in the body. This is generally achieved by specific targeting of inactivated drug molecules.
Aptamers which can bind to various cancer cell types selectively and with high affinity have been exploited in a variety of drug delivery systems for therapeutic purposes. Recent progress in selection of cell-specific aptamers has provided new opportunities in targeted drug delivery. Especially functionalization of nanoparticles with such aptamers has drawn major attention in the biosensor and biomedical areas.

Nucleic acids are recognized as attractive building materials in nanomachines because of their unique molecular recognition properties and structural features. An active controlled delivery of drugs once targeted to a disease site is a major research challenge. Stimuli-responsive gating is one way of achieving controlled release of nanoparticle cargoes. Recent reports incorporate the structural properties of aptamers in controlled release systems of drug delivering nanoparticles.

Nanoparticle-encapsulated drug delivery aims to deliver the active therapeutic ingredients to the disease site in stable compartments in order to reduce premature release. This ensures that the effects of drug are maximized and the side effects are reduced. An encapsulated nanoparticle system requires a specific targeting mechanism and at the same time the retention of drugs inside the container should be high. The balance between specificity of targeting and the extent of premature leakage determines the success of a given delivery system.

Nanotechnology research approaches in drug delivery include a wide variety of nanomaterials ranging from soft hydrogels to solid polymeric particles. Large surface area, high drug loading efficiency and potential combination with other organic/inorganic materials are the main properties of hollow nanostructures that are attractive for biomedical applications.

Packaging of small-molecule drugs

  • improves their availability
  • compatibility
  • reduces toxicity

Controlling the drug release profile is the main challenge in drug delivery development when the drug is to be successfully targeted to a specific site. Stimuli-responsive materials have been created by using biological, physical and chemical properties of materials for heat-activated, light-activated or pH-activated delivery. Nucleic acids are utilized to construct rationally designed nanostructures at molecular levels for nanotechnology applications. Integration of the properties of nucleic acids can offer many opportunities for drug delivery systems, including stimuli-responsive nanogates for nanocarriers and molecular sensors. Favorable drug release kinetics can be achieved at the target sites by aptamer-based capping systems.

VC Ozalp, F Eyidogan and HA Oktem. Aptamer-Gated Nanoparticles for Smart Drug Delivery.
Pharmaceuticals 2011, 4, 1137-1157; doi:10.3390/ph4081137. ISSN 1424-8247. http://www.mdpi.com/journal/pharmaceuticals

Activity Based Profiling
Powerful strategies for the gel-free analysis of proteomes have emerged, including isotope-coded affinity tagging (ICAT) for quantitative proteomics and multidimensional protein identification technology (MudPIT) for comprehensive proteomics, both of which utilize liquid chromatography (LC) and MS for protein separation and detection, respectively.
Nonetheless, these methods, like 2DE-MS, still focus on measuring changes in protein abundance and, therefore, provide only an indirect estimate of dynamics in protein function. Indeed, several important forms of post-translational regulation, including protein–protein and protein–small-molecule interactions, may elude detection by abundance-based proteomic methods.
To facilitate the analysis of protein function, several proteomic methods have been introduced to characterize the activity of proteins on a global scale. These include large-scale yeast two-hybrid screens and epitope tagging immunoprecipitation experiments, which aim to construct comprehensive maps of protein–protein interactions, and protein microarrays, which aim to provide an assay platform for the rapid assessment of protein activities. A chemical proteomic strategy referred to as activity-based protein profiling (ABPP) has emerged that utilizes active site-directed probes to profile the functional state of enzyme families directly in complex proteomes.

Recent advances in genomic and proteomic technologies have begun to address the challenge of assigning molecular and cellular functions to the numerous protein products encoded by prokaryotic and eukaryotic genomes. In particular, chemical strategies for proteome analysis have emerged that enable profiling of protein activity on a global scale. Herein, we highlight these chemical proteomic methods and their application to the discovery and characterization of disease-related enzyme activities.

N Jessani and BF Cravatt. The development and application of methods for activity-based protein profiling. Current Opinion in Chemical Biology 2004; 8:54–59. In Proteomics and genomics, M Snyder and J Yates III, eds. 2003 Elsevier Ltd. DOI: 10.1016/ j.cbpa.2003.11.004

Cells with fundamental metabolic alterations commonly arise during tumorigenesis, and it is these types of changes that help to establish a biochemical foundation for disease progression and malignancy. A seminal example of this was discovered in the 1920s when Otto Warburg found that cancer cells consume higher levels of glucose and secrete most of the glucose carbon as lactate rather than oxidizing it completely.
Since then, studies by multiple groups have uncovered a diverse array of metabolic changes in cancer, including
alterations in

  1. glycolytic pathways
  2. the citric acid cycle
  3. glutaminolysis
  4. lipogenesis
  5. lipolysis
  6. proteolysis

These in turn modulate the levels of cellular building blocks

  1. lipids, nucleic acids and amino acids,
  2. cellular energetics,
  3. oncogenic signaling molecules
  4. the extracellular environment to confer protumorigenic and malignant properties.

Despite these advances, our current understanding of cancer metabolism is far from complete and would probably benefit from experimental strategies that are capable of profiling enzymatic pathways on a global scale. To this end, conventional genomic and proteomic methods, which comparatively quantify the expression levels of transcripts and proteins, respectively, have yielded many useful insights. These platforms are, however, limited in their capacity to identify changes in protein activity that are caused by posttranslational mechanisms.

Annotating biochemical pathways in cancer is further complicated by the potential for enzymes to carry out distinct metabolic activities in tumor cells that might not be mirrored in normal physiology. In addition, a substantial proportion of the human proteome remains functionally uncharacterized, and it is likely that at least some of these poorly understood proteins also have roles in tumorigenesis. These challenges require new proteomic technologies that can accelerate the assignment of protein function in complex biological systems, such as cancer cells and tumors.

Metabolomics has emerged as a powerful approach for investigating enzyme function in living systems. Metabolomic experiments in the context of enzyme studies typically start with

  1. the extraction of metabolites from control and enzyme-disrupted biological systems,
  2. followed by metabolite detection and comparative data analysis.

For example, lipophilic metabolites can be enriched from cells or tissues by organic extraction.
Mass spectrometry (MS) has become a primary analytical method for surveying metabolites in complex biological samples, with upfront separation accomplished by liquid chromatography (LC–MS) or gas chromatography (GC–MS). MS experiments can be carried out using

  • targeted or untargeted approaches,
  • depending on whether the objective is
  • to profile and quantitate known metabolites or
  • to broadly scan for metabolites across a large mass range, respectively.

As metabolomic experiments generate a large amount of data, powerful software tools are needed for identification and quantitation of ions in LC–MS data sets (see the figure; the mass to charge ratio (m/z) is indicated). One such program is XCMS95, which

  • aligns,
  • quantifies and
  • statistically ranks ions that are altered between two sets of metabolomic data.

This program can be used to rapidly identify metabolomic signatures of various disease states or to assess metabolic networks that are regulated by an enzyme using pharmacological or genetic tools that modulate enzyme function. Additional databases assist in metabolite structural characterization, such as HMDB96,97, METLIN98,99 and LIPID MAPS100.
In this Review, we discuss one such proteomic platform, termed activity based protein profiling (ABPP), and its implementation in the discovery and functional characterization of deregulated enzymatic pathways in cancer. We discuss the evidence that, when coupled with other large scale profiling methods, such as metabolomics and proteomics, ABPP can provide a compelling, systems level understanding of biochemical networks that are important for the development and progression of cancer.

Large-scale profiling methods have uncovered numerous gene and protein expression changes that correlate with tumorigenesis. However, determining the relevance of these expression changes and which biochemical pathways they affect has been hindered by our incomplete understanding of the proteome and its myriad functions and modes of regulation. Activity-based profiling platforms enable both the discovery of cancer-relevant enzymes and selective pharmacological probes to perturb and characterize these proteins in tumour cells. When integrated with other large-scale profiling methods, activity-based proteomics can provide insight into the metabolic and signaling pathways that support cancer pathogenesis and illuminate new strategies for disease diagnosis and treatment.

Representative activity-based probes and their application to cancer research

  • enzyme class applications in cancer
  • Serine hydrolases increased KIAA1363 and MAGL
  • aggressive human cancer lines
  • uPA and tPA serine protease aggressive cancers
  • RBBP9 activity in pancreatic carcinoma
  • Metalloproteinases neprilysin activity in melanoma cell lines
  • Cysteine proteases cathepsin cysteine protease in pancreatic islet tumours
  • Kinases Inhibitor selectivity profiling of kinase inhibitors
  • Caspases visualization of apoptosis in colon tumour-bearing mice treated with Apomab
  • Deubiquitylases Identified increased carboxy-terminal hydrolase UCHL3 and UCH37 activity in HPV cervical carcinomas
  • Cytochrome P450s Identified the aromatase inhibitor anastrazole as an inducer of CYP1A2

Serine hydrolases KIaa1363 and MaGL regulate lipid metabolic pathways that support cancer pathogenesis. Activity-based protein profiling (ABPP) identified

  • KIAA1363 and
  • monoacylglycerol (MAG) lipase (MAGL)

as being increased in aggressive human cancer cells from multiple tumour types. Pharmacological and/or RNA interference ablation of KIAA1363 and MAGL coupled with metabolomic analysis revealed specific roles for KIAA1363 and MAGL in cancer metabolism. Disruption of KIAA1363 by the small-molecule inhibitor AS115 lowered monoalkylglycerol ether (MAGE), alkyl lysophosphatidic acid (alkyl LPA) and alkyl lysophosphatidyl choline (alkyl LPC) levels in cancer cells. Disruption of MAGL by the small-molecule inhibitor JZL184 raised MAG levels and reduced free fatty acid, lysophosphatidic acid (LPA) and prostaglandin E2 (PGE2) levels in cancer cells. Disruption of KIAA1363 and MAGL leads to impairments in cancer cell aggressiveness and tumour growth, PAF, platelet-activating factor.

At a glance

• Activity-based protein profiling (ABPP) facilitates the discovery of deregulated enzymes in cancer.
• Competitive ABPP yields selective inhibitors for functional characterization of cancer enzymes.
• ABPP can be integrated with metabolomics to map deregulated enzymatic pathways in cancer.
• ABPP can be integrated with other proteomic methods to map proteolytic pathways in cancer.
• ABPP probes can be used to image tumour development in living animals.

DK Nomura, MM Dix and BF Cravatt. Activity-based protein profiling for biochemical pathway discovery in cancer. Nature Reviews. Cancer. 2010; 10: 630-638.

New methods are thus needed to accelerate the assignment of biochemical, cellular and physiological functions to these poorly annotated genes and proteins. Here we propose that the emerging chemical proteomic technology, ABPP, is distinctly suited to address this problem.

Activity-based protein profiling (ABPP), the use of active site-directed chemical probes to monitor enzyme function in complex biological systems, is emerging as a powerful post-genomic technology. ABPP probes have been developed for several enzyme classes and have been used to inventory enzyme activities en masse for a range of (patho)physiological processes.

ABPP uses active site–directed, small molecule–based covalent probes to report on the functional state of enzyme activities directly in native biological systems. ABPP probes are designed or selected to target a subset of the proteome based on shared principles of binding and/or reactivity and have been successfully developed for many enzyme classes, including

  • serine
  • cysteine,
  • aspartyl
  • metallo hydrolases
  • kinases
  • glycosidases
  • histone deacetylases and
  • oxidoreductases.

These probes have been shown to selectively label active enzymes but not their inactive precursor (zymogen) or inhibitor-bound forms, thus allowing researchers to capture functional information that is beyond the scope of standard proteomic methods.
By presenting specific examples, we show here that ABPP provides researchers with a distinctive set of chemical tools to embark on the assignment of functions to many of the uncharacterized enzymes that populate eukaryotic and prokaryotic proteomes.

Reactive group                                                 Enzyme                                                       Enzyme class

Benzophenone                                                  Presenilins                            Aspartyl protease (γ-secretase )

Bromoethyl                                           HSPC263 (OTU domain)              Deubiquitinating enzyme (DUB)

Vinyl-methylester                             UL from HSV-1                                 Deubiquitinating enzyme (DUB)

Aryl 2-deoxy-2-fluoro                    glycoside Cfx from C. fimi            Glycosidase (β-1-4-glycanase)
Fluorophosphonate                                    SAE                                             Serine hydrolase

Examples of enzymes assigned to specific mechanistic classes by ABPP

ABPP can also be implemented as a direct assay for inhibitor discovery, allowing researchers to develop potent and selective pharmacological probes for uncharacterized enzymes.

Examples of enzymes assigned to specific mechanistic classes by ABPP.

  • Probe Leu-Asp-αCA probe selectively labeled Upβ
  • Substrate the endogenous Upβ substrate, N-carbamoyl-β-alanine
  • Substrate mimicry of an ABPP probe.

Multidimensional profiling strategy for the annotation of the cancer-related enzyme KIAA1363. ABPP using fluorophosphonate probes identified KIAA1363 as a highly elevated enzyme activity in aggressive cancer cells. Competitive ABPP was then used to develop a selective KIAA1363 inhibitor (AS115). Metabolomic analysis of cancer cells treated with AS115 determined a role for this enzyme in the regulation of MAGE lipids in cancer cells. Biochemical studies confirmed that KIAA1363 acts as 2-acetyl MAGE hydrolase in a metabolic network that bridges the platelet activating factor and lysophosphatidic acid classes of signaling lipids.
Assignment of enzyme mechanism by ABPP

There are multiple levels of annotation for enzymes. The most basic level is assignment to a specific mechanistic class based on the general chemical reaction catalyzed by the enzyme (for example, hydrolase, kinase, oxidoreductase and others). Additional annotation involves determining the endogenous substrates and products for the enzyme. Finally, complete annotation requires an understanding of how the specific chemical transformation(s) catalyzed by an enzyme integrate into larger metabolic and signaling pathways to influence cell physiology and behavior.

Many of the predicted enzymes uncovered by genome sequencing projects can be assigned to a mechanistic class or ascribed a putative biochemical function based on sequence homology to well-characterized enzymes. But some enzymes have insufficient sequence relatedness for class assignment or have a function different from that predicted by sequence comparisons. ABPP has facilitated class annotation for several such uncharacterized enzymes.

KT Barglow & BF Cravatt. Activity-based protein profiling for the functional annotation of enzymes. Nature Methods 2007; 4(10): 822- 827. DOI:10.1038/NMETH1092

A principal goal of modern biomedical research is to discover, assemble, and experimentally manipulate molecular pathways in cells and organisms to reveal new disease mechanisms.

Toward this end, complete genome sequences for numerous bacteria and higher organisms, including humans, have laid the fundamental groundwork for understanding the molecular basis of life in its many forms. However, the information content of DNA sequences is limited and, on its own, cannot describe most physiological and pathological processes.

Unlike oligonucleotides, proteins are a very diverse group of biomolecules that display a wide range of chemical and biophysical features, including

  • membrane-binding,
  • hetero/homo-oligomerization, and
  • posttranslational modification.

The biochemical complexity intrinsic to protein science intimates that several complementary analytical strategies will be needed to achieve the ultimate goal of proteomics – a comprehensive characterization of the expression, modification state, interaction map, and activity of all proteins in cells and tissues.

A powerful LC-MS strategy for proteomics involves the use of isotope-coded affinity tags (ICAT). This approach enables the comparison of protein expression in proteomes by treating samples with isotopically distinct forms of a chemical labeling reagent. ICAT methods provide superior resolving power compared to gel-based methods and improve access to membrane-associated proteins. More recently, isotope-free MS methods for quantitative proteomics have emerged.

Reverse protein microarrays have also been described in which proteomes themselves are arrayed and the antibodies used for detection in a format analogous to Western blotting. In addition to increasing the throughput of proteomic experiments by integrating the protein separation and detection steps, microarrays consume much less material than conventional proteomic methods. Still, the general application of microarrays for proteomics is currently limited by the availability of high-quality capture reagents (e.g., antibodies, aptamers, etc).

These approaches, by measuring protein abundance provide, like genomics, only an indirect assessment of protein activity and may fail to detect important posttranslational events that regulate protein function, such as protein–protein or protein–small-molecule interactions. To address these limitations, complementary strategies for the functional analysis of proteins have been introduced. Prominent among these functional proteomic efforts is the use of chemistry for the design of active site-directed probes that measure enzyme activity in samples of high biological complexity.

Many post-translational modes of enzyme regulation share a common mechanistic foundation – they perturb the active site such that catalytic power and/or substrate recognition is impaired. Accordingly, it was hypothesized that chemical probes capable of reporting on the integrity of enzyme active sites directly in cells and tissues might serve as effective functional proteomic tools. These activity based protein profiling (ABPP) probes consist of at least two general elements:

  1. a reactive group for binding and covalently modifying the active sites of many members of a given enzyme class or classes
  2. a reporter tag for the detection, enrichment, and identification of probe-labeled proteins

ABPP probes have been successfully developed for more than a dozen enzyme classes, including

  • all major classes of proteases
  • kinases
  • phosphatases
  • glycosidases
  • GSTs
  • oxidoreductases.

Post-translational regulation of enzyme activity. Many enzymes are produced as inactive precursors, or zymogens, which require proteolytic processing for activation. Enzyme activity can be further regulated by interactions with endogenous protein inhibitors.
The field of proteomics aims to develop and apply technologies for the characterization of protein function on a global scale. Toward this end, synthetic chemistry has played a major role by providing new reagents to profile segments of the proteome based on activity rather than abundance. Small molecule probes for activity-based protein profiling have been created for more than a dozen enzyme classes and used to discover several enzyme activities elevated in disease states. These innovations have inspired complementary advancements in analytical chemistry, where new platforms have been introduced to augment the information content achievable in chemical proteomics experiments. Here, we will review these analytical platforms and discuss how they have exploited the versatility of chemical probes to gain unprecedented insights into the function of proteins in biological samples of high complexity.

Advanced analytical platforms utilize a range of separation and detection strategies, including LC-MS, CELIF, and antibody microarrays, to achieve an unprecedented breadth and depth of proteome coverage in ABPP investigations. The complementary strengths and weaknesses of each of these methods suggest that the selection of an appropriate analytical platform should be guided by the specific experimental question being addressed.
SA Sieber and BF Cravatt. Analytical platforms for activity-based protein profiling – exploiting the versatility of chemistry for functional proteomics. Chem. Commun. 2006, 2311–2319. http://www.rsc.org/chemcomm

Diagnostic Therapeutics in Activity Based Probes
Activity-based chemical proteomics-an emerging field involving a combination of organic synthesis, biochemistry, cell biology, biophysics and bioinformatics-allows the detection, visualisation and activity quantification of whole families or selected sub-sets of proteases based upon their substrate specificity. This approach can be applied for drug target/lead identification and validation, the fundamentals of drug discovery. The activity-based probes discussed in this review contain three key features;

  1. a ‘warhead’ (binds irreversibly but selectively to the active site),
  2. a ‘tag’ (allowing enzyme ‘handling’, with a combination of fluorescent, affinity and/or radio labels),
  3. a linker region between warhead and tag.

From the design and synthesis of the linker arise some of the latest developments discussed here; not only can the physical properties (e.g., solubility, localisation) of the probe be tuned, but the inclusion of a cleavable moiety allows selective removal of tagged enzyme from affinity beads etc.
Heal WP, Wickramasinghe SR, Tate EW. Activity based chemical proteomics: profiling proteases as drug targets. Curr Drug Discov Technol 2008; 5(3):200-12. PMID: 18690889

The genomic revolution has created a wealth of information regarding the fundamental genetic code that defines the inner workings of a cell. However, it has become clear that analyzing genome sequences alone will not lead to new therapies to fight human disease. Rather, an understanding of protein function within the context of complex cellular networks will be required to facilitate the discovery of novel drug targets and, subsequently, new therapies directed against them. The past ten years has seen a dramatic increase in technologies that allow large-scale, systems-based methods for analysis of global biological processes and disease states.

In the field of proteomics, several well-established methods persist as a means to resolve and analyze complex mixtures of proteins derived from cells and tissues. However, the resolving power of these methods is often challenged by the diverse and dynamic nature of the proteome. The field of activity-based proteomics, or chemical proteomics, has been established in an attempt to focus proteomic efforts on subsets of physiologically important protein targets. This new approach to proteomics is centered around the use of small molecules termed activity-based probes (ABPs) as a means to tag, enrich, and isolate, distinct sets of proteins based on their enzymatic activity.
Berger AB, Vitorino PM, Bogyo M. Activity-based protein profiling: applications to biomarker discovery, in vivo imaging and drug discovery. Am J Pharmacogenomics. 2004;4(6):371-81.

Recent advances in global genomic and proteomic methods have led to a greater understanding of how genes and proteins function in complex networks within a cell. One of the major limitations in these methodologies is their inability to provide information on the dynamic, post-translational regulation of enzymatic proteins. In particular proteases are often synthesized as inactive zymogens that need to be activated in order to carry out specific biological processes. Thus, methods that allow direct monitoring of protease activity in the context of a living cell or whole animal will be required to begin to understand the systems-wide functional roles of proteases. In this review, we discuss the development and applications of activity based probes (ABPs) to study proteases and their role in pathological processes. Specifically we focus on application of this technique for biomarker discovery, in vivo imaging and drug screening.

Fonović M, Bogyo M. Activity based probes for proteases: applications to biomarker discovery, molecular imaging and drug screening. Curr Pharm Des. 2007;13(3):253-61.

Proteases, in particular, are known for their multilayered post-translational activity regulation that can lead to a significant difference between protease abundance levels and their enzyme activity. To address these issues, the field of activity-based proteomics has been established in order to characterize protein activity and monitor the functional regulation of enzymes in complex proteomes.

Fonović M, Bogyo M. Activity-based probes as a tool for functional proteomic analysis of proteases. Expert Rev Proteomics. 2008; 5(5):721-30. PMID: 18937562. PMCID: PMC2997944

As a result of the recent enormous technological progress, experimental structure determination has become an integral part of the development of drugs against disease-related target proteins. The post-translational modification of proteins is an important regulatory process in living organisms; one such example is lytic processing by peptidases. Many different peptidases represent disease targets and are being used in structure-based drug design approaches. The development of drugs such as aliskiren and tipranavir, which inhibit renin and HIV protease, respectively, testifies to the success of this approach.

Mittl PR, Grütter MG. Opportunities for structure-based design of protease-directed drugs.
Curr Opin Struct Biol 2006; 16(6):769-75. Epub 2006 Nov 16. PMID: 17112720

Presenilin is the catalytic component of γ-secretase, a complex aspartyl protease and a founding member of intramembrane-cleaving proteases. γ-Secretase is involved in the pathogenesis of Alzheimer’s disease and a top target for therapeutic intervention. However, the protease complex processes a variety of transmembrane substrates, including the Notch receptor, raising concerns about toxicity. Nevertheless, γ-secretase inhibitors and modulators have been identified that allow Notch processing and signaling to continue, and promising compounds are entering clinical trials.

Molecular and biochemical studies offer a model for how this protease hydrolyzes transmembrane domains in the confines of the lipid bilayer. Progress has also been made toward structure elucidation of presenilin and the γ-secretase complex by electron microscopy as well as by studying cysteine-mutant presenilins. The signal peptide peptidase (SPP) family of proteases are distantly related to presenilins. However, the SPPs work as single polypeptides without the need for cofactors and otherwise appear to be simple model systems for presenilin in the γ-secretase complex.

Critical clues to the identity of γ-secretase included:
(1) Genes encoding the multi-pass membrane proteins presenilin-1 and presenilin-2 are, like APP, associated with familial, early-onset Alzheimer’s disease. The disease-causing missense mutations were found to alter how γ-secretase cuts APP, leading to increased proportions of longer, more aggregation-prone forms of Aβ.
(2) Knockout of presenilin genes eliminates γ-secretase cleavage of APP.
(3) Peptidomimetics that inhibit γ-secretase contain moieties typically found in aspartyl protease inhibitors.
These findings led to the identification of two conserved transmembrane aspartates in the multi-pass presenilins that are critical for γ-secretase cleavage of APP, evidence that presenilins are aspartyl proteases.
Presenilin is endoproteolytically cleaved into two polypeptides, an N-terminal fragment (NTF) and a C-terminal fragment (CTF), the formation of which is

  • regulated
  • metabolically stable
  • part of a high-molecular weight complex

suggesting that the NTF-CTF heterodimer is the biologically active form. NTF and CTF each contribute one of the essential and conserved aspartates, and transition-state analogue inhibitors of γ-secretase, compounds designed to interact with the active site of the protease, bind directly to presenilin NTF and CTF.
Presenilins are also required for Notch signaling (Levitan and Greenwald, 1995), a pathway essential for cell differentiation during development and beyond.

The highly conserved role of γ-secretase in Notch signalling and its importance in development led to genetic screens in Caenorhabditis elegans that identified three other integral membrane proteins besides presenilin that modify Notch signaling.
Designed inhibitors have proven to be useful tools in understanding the mechanism of γ-secretase and substrate recognition – affinity labelling with transition-state analogue inhibitors showed binding at the interface between the presenilin NTF and CTF subunits, consistent with the active site residing at this interface, with each presenilin subunit contributing one of the essential aspartates.
The concept of presenilin as the catalytic component for γ-secretase was considerably strengthened when

  1. signal peptide peptidase (SPP) was found to be a similar intramembrane aspartyl protease
  2. SPP is exploited by the hepatitis C virus for the maturation of its core protein, suggesting that this protease may be a suitable target for antiviral therapy
  3. SPP was identified by affinity labeling with a peptidomimetic inhibitor, and the protein sequence displayed similarities with presenilin.
  4. SPP contains two conserved aspartates, each predicted to lie in the middle of a transmembrane domain, and the aspartate-containing sequences resemble those found in presenilins.
  5. SPP appears to be less complicated than γ-secretase.

Expression of human SPP in yeast reconstituted the protease activity, suggesting that the protein has activity on its own and does not require other mammalian protein cofactors.

Aspartyl I-CLiPs are found in all forms of life and play essential roles in biology and disease. How these enzymes carry out hydrolysis in the membrane is a fascinating question that is not entirely resolved, but evidence suggests an initial substrate docking site and a lateral gate into a pore where water and the active site aspartates reside. Designed inhibitors have been critical in elucidating these mechanisms, but inhibitors targeting γ-secretase for the treatment of Alzheimer’s disease must avoid interfering with Notch signaling.

MS Wolfe. Structure, Mechanism and Inhibition of γ-Secretase and Presenilin-Like Proteases.
Biol Chem. 2010 August; 391(8): 839–847. doi: 10.1515/BC.2010.086. PMCID: PMC2997569. NIHMSID: NIHMS254540
Study Suggests Expanding the Genetic Alphabet May Be Easier than Previously Thought
Genomics Monday, June 4, 2012
A new study led by scientists at The Scripps Research Institute suggests that the replication process for DNA—the genetic instructions for living organisms that is composed of four bases (C, G, A and T)—is more open to unnatural letters than had previously been thought.

An expanded “DNA alphabet” could carry more information than natural DNA, potentially coding for a much wider range of molecules and enabling a variety of powerful applications, from precise molecular probes and nanomachines to useful new life forms.
The new study, which appears in the June 3, 2012 issue of Nature Chemical Biology, solves the mystery of how a previously identified pair of artificial DNA bases can go through the DNA replication process almost as efficiently as the four natural bases.
“We now know that the efficient replication of our unnatural base pair isn’t a fluke, and also that the replication process is more flexible than had been assumed,” said Floyd E. Romesberg, principal developer of the new DNA bases.

Adding to the DNA Alphabet
Romesberg and his lab have been trying to find a way to extend the DNA alphabet since the late 1990s. In 2008, they developed the efficiently replicating bases NaM and 5SICS, which come together as a complementary base pair within the DNA helix, much as, in normal DNA, the base adenine (A) pairs with thymine (T), and cytosine (C) pairs with guanine (G).

The following year, Romesberg and colleagues showed that NaM and 5SICS could be efficiently transcribed into RNA. But these bases’ lack the ability to form the hydrogen bonds that join natural base pairs in DNA. Such bonds had been thought to be an absolute requirement for successful DNA replication‑—a process in which a large enzyme, DNA polymerase, moves along a single, unwrapped DNA strand and stitches together the opposing strand, one complementary base at a time.

An early structural study of a very similar base pair in double-helix DNA added to Romesberg’s concerns. The data strongly suggested that NaM and 5SICS do not even approximate the edge-to-edge geometry of natural base pairs—termed the Watson-Crick geometry, after the co-discoverers of the DNA double-helix. Instead, they join in a looser, overlapping, “intercalated” fashion. “Their pairing resembles a ‘mispair,’ such as two identical bases together, which normally wouldn’t be recognized as a valid base pair by the DNA polymerase.” Yet in test after test, the NaM-5SICS pair was efficiently replicable.

Edge to Edge
The NaM-5SICS pair maintain an abnormal, intercalated structure within double-helix DNA—but remarkably adopt the normal, edge-to-edge, “Watson-Crick” positioning when gripped by the polymerase during the crucial moments of DNA replication. “The DNA polymerase apparently induces this unnatural base pair to form a structure that’s virtually indistinguishable from that of a natural base pair.” NaM and 5SICS, lacking hydrogen bonds, are held together in the DNA double-helix by “hydrophobic” forces, which cause certain molecular structures to be repelled by water molecules, and thus to cling together in a watery medium. “It’s very possible that these hydrophobic forces have characteristics that enable the flexibility and thus the replicability of the NaM-5SICS base pair.”

An Arbitrary Choice?
The finding suggests that NaM-5SICS and potentially other, hydrophobically bound base pairs could some day be used to extend the DNA alphabet. It also hints that Evolution’s choice of the existing four-letter DNA alphabet—on this planet—may have been somewhat arbitrary. “It seems that life could have been based on many other genetic systems.” Source: The Scripps Research Institute

DNA damage response (DDR) network

Eukaryotic cells have evolved an intricate system to resolve DNA damage to prevent its transmission to daughter cells. This system, collectively known as the DNA damage response (DDR) network, includes many proteins that detect DNA damage, promote repair, and coordinate progression through the cell cycle. Because defects in this network can lead to cancer, this network constitutes a barrier against tumorigenesis. The modular BRCA1 carboxyl-terminal (BRCT) domain is frequently present in proteins involved in the DDR, can exist either as an individual domain or as tandem domains (tBRCT), and can bind phosphorylated peptides. We performed a systematic analysis of protein-protein interactions involving tBRCT in the DDR.

We identified 23 proteins containing conserved BRCT domains and generated a human protein-protein interaction network for seven proteins with tBRCT. This study also revealed previously unknown components in DNA damage signaling, such as COMMD1 and the target of rapamycin complex mTORC2. Additionally, integration of tBRCT domain interactions with DDR phosphoprotein studies and analysis of kinase-substrate interactions revealed signaling subnetworks that may aid in understanding the involvement of tBRCT in disease and DNA repair.

NT Woods, RD Mesquita, M Sweet, MA. Carvalho, et al. Charting the Landscape of Tandem BRCT Domain–Mediated Protein Interactions. Sci. Signal 2012; 5(242): rs6. DOI: 10.1126/ scisignal.2002255.

Mitochondrial ROS production

Mitochondria have various essential functions in metabolism and in determining cell fate during apoptosis. In addition, mitochondria are also important nodes in a number of signaling pathways. For example, mitochondria can modulate signals transmitted by second messengers such as calcium. Because mitochondria are also major sources of reactive oxygen species (ROS), they can contribute to redox signaling—for example, by the production of ROS such as hydrogen peroxide that can reversibly modify cysteine residues and thus the activity of target proteins. Mitochondrial ROS production is thought to play a role in hypoxia signaling by stabilizing the oxygen-sensitive transcription factor hypoxia-inducible factor–1α. New evidence has extended the mechanism of mitochondrial redox signaling in cellular responses to hypoxia in interesting and unexpected ways. Hypoxia altered the microtubule-dependent transport of mitochondria so that the organelles accumulated in the perinuclear region, where they increased the intranuclear concentration of ROS. The increased ROS in turn enhanced the expression of hypoxia-sensitive genes such as VEGF (vascular endothelial growth factor) not by reversibly oxidizing a protein, but by oxidizing DNA sequences in the hypoxia response element of the VEGF promoter. This paper and other recent work suggest a new twist on mitochondrial signaling: that the redistribution of mitochondria within the cell can be a component of regulatory pathways.

M. P. Murphy. Modulating Mitochondrial Intracellular Location as a Redox Signal. Sci Signal 2012; 5(242): p re39. DOI: 10.1126/scisignal.2002858

A challenge in the treatment of lung cancer is the lack of early diagnostics. Here, we describe the application of monoclonal antibody proteomics for discovery of a panel of biomarkers for early detection (stage I) of non-small cell lung cancer (NSCLC). We produced large monoclonal antibody libraries directed against the natural form of protein antigens present in the plasma of NSCLC patients. Plasma biomarkers associated with the presence of lung cancer were detected via high throughput ELISA. Differential profiling of plasma proteomes of four clinical cohorts, totaling 301 patients with lung cancer and 235 healthy controls, identified 13 lung cancer-associated (p < 0.05) monoclonal antibodies. The monoclonal antibodies recognize five different cognate proteins identified using immunoprecipitation followed by mass spectrometry. Four of the five antigens were present in non-small cell lung cancer cells in situ.

Guergova-Kuras M, Kurucz I, Hempel W, et al. Discovery of lung cancer biomarkers by profiling the plasma proteome with monoclonal antibody libraries. Mol Cell Proteomics. 2011 (12): M111.010298. Epub 2011 Sep 26.

Read Full Post »

SDS-PAGE with Taq DNA Polymerase. SDS-PAGE is ...

SDS-PAGE with Taq DNA Polymerase. SDS-PAGE is an useful technique to separate proteins according to their electrophoretic mobility. (Photo credit: Wikipedia)

Proteomics and Biomarker Discovery

Reporter: Larry H. Bernstein, MD, FCAP



Advanced Proteomic Technologies for Cancer Biomarker Discovery

Sze Chuen Cesar Wong; Charles Ming Lok Chan; Brigette Buig Yue Ma; Money Yan Yee Lam; Gigi Ching Gee Choi; Thomas Chi Chuen Au; Andrew Sai Kit Chan; Anthony Tak Cheung Chan

Published: 06/10/2009

This report is extracted from the article above with editing and shortening as much as possible for the reader, and updated from LCGCNA Aug 12,  2012; 8

Part I


This review will focus on four state-of-the-art proteomic technologies, namely 2D difference gel electrophoresis, MALDI imaging mass spectrometry, electron transfer dissociation mass spectrometry and reverse-phase protein array. The major advancements these techniques have brought about biomarker discovery will be presented in this review. The wide dynamic range of protein abundance, standardization of protocols and validation of cancer biomarkers, and a 5-year view of potential solutions to such problems is discussed.

English: Public domain image from cancer.gov h...

English: Public domain image from cancer.gov http://visualsonline.cancer.gov/details.cf?imageid=3483. TECAN Genesis 2000 robot preparing Ciphergen SELDI-TOF protein chips for proteomic  analysis. (Photo credit: Wikipedia)


A common method used for isolating and identifying cancer biomarkers involves the use of serum or tissue protein identification. Unfortunately, currently used tumor markers have low sensitivities and specificities.[2] Therefore, the development of novel tumor markers might be helpful in improving cancer diagnosis, prognosis and treatment.

The rapid development of proteomic technologies during the past 10 years has brought about a massive increase in the discovery of novel cancer biomarkers. Such biomarkers may have broad applications, such as for the detection of the presence of a disease, monitoring of disease clearance and/or progression, monitoring of treatment response and demonstration of drug targeting of a particular pathway and/or target. In general, proteomic approaches begin with the collection of biological specimens representing two different physiological conditions, cancer patients and reference subjects. Proteins or peptides are extracted and separated, and the protein or peptide profiles are compared against each other in order to detect differentially expressed proteins. Commonly, quantitative proteomics is mainly performed by protein separation using either 2DE- or liquid chromatography (LC)-based methods coupled with protein identification using mass spectrometry (MS). Limitations include inability to obtain protein profiles directly from tissue sections for correlation with tissue morphology, limited ability to analyze post-translational modifications (PTMs) and low capacity for high-throughput validation of identified markers. Progress in proteomic technologies has led to the development of 2D DIGE, MALDI imaging MS (IMS), electron transfer dissociation (ETD) MS, and reverse-phase protein array (RPA).

2D Difference Gel Electrophoresis

The 2DE method has been one of the mainstream technologies used for proteomic investigations.[3,4] In this method, proteins are separated in the first dimension according to charge by isoelectric focusing, followed by separation in the second dimension according to molecular weight, using polyacrylamide gel electrophoresis. The gels are then stained to visualize separated protein spots,[5] separating up to 1000 protein spots in a single experiment and  protein spots are then excised and identified using mass spectrometry (MS).[6,7]

We previously used a 2DE approach to compare the proteomic profiles to identify differentially expressed proteins that may be involved in the development of nasopharyngeal cancer, [8]   as well as proteins that were responsive to treatment with the chemotherapeutic agent 5-fluorouracil (5FU) in the colorectal cancer SW480 cell line. Briefly, cell lysates from SW480 cells that were either treated with 5FU or were controls were separated using 2DE. After staining and analysis of the gels, differentially expressed protein spots were excised and identified using MS. The upregulation of heat-shock protein (Hsp)-27 and peroxiredoxin 6 and the downregulation of Hsp-70 were successfully validated by immunohistochemical (IHC) staining of SW480 cells.[9]

The 2D DIGE method improved the 2DE technique. Figure 1 shows how two different protein samples (e.g., control and disease) and, optionally, one reference sample (e.g., control and disease pooled together) are labeled with one of three spectrally different fluorophores: cyanine (Cy)2, 3 or 5. They have the same charge, similar molecular weight and distinct fluorescent properties, allowing their discrimination during fluorometric scanning.[10-12]  The minimal dye causes minimal change in the electrophoretic mobility pattern of the protein, whereas the saturation dye labels all available cysteine residues but causes a shift in electrophoretic mobility labeled proteins.[13]  The same pooled reference sample used for all gels within an experiment is an internal reference for normalization and spot matching.[12] The gel is scanned at three different wavelengths yielding images for each of the different samples, and variation between gels is minimized and difficulties are reduced in correctly matching of protein spots across different gels.[10,11]  Significant advantages of the DIGE technology includes a dynamic range of over four orders of magnitude and full compatibility with MS.  However, careful validations of identified markers using alternative techniques are still needed.

In a study that compared three commonly used DIGE analysis software packages, Kang et al. concluded that although the three softwares performed satisfactorily with minimal user intervention, significant improvements in the accuracy of analysis could be achieved .[14] Moreover, it was suggested that results concerning the magnitude of differential expression between protein spots after statistical analysis by such softwares must be examined with care.[14]

Figure 1.  Procedures for performing a 2D DIGE experiment. CY: Cyanine; DIGE: Differential in gel electrophoresis.

The choice of appropriate statistical methods for the analysis of DIGE data has to be considered. Statistical methodological error can be addressed by the use of statistical methods that apply a false-discovery rate (FDR) for the determination of significance. In this method, q-values are calculated for all protein spots. The q-value of each spot corresponds to the expected proportion of false-positives incurred by a change in expression level of that protein spot found to be significant.

Despite the ease of use and enabling the researcher to select an appropriate FDR according to study requirements, this approach was found to be only applicable to DIGE experiments using a two-dye labeling scheme, as a three-dye labeling approach violated the assumption of data independence required for statistical analysis.[16] Other statistical tests that have been applied for the analysis of DIGE results include significance analysis of microarrays,[7] principal components analysis[17,18] and partial least squares discriminant analysis.[18,19] Detailed discussions of the different statistical approaches applicable to proteomic research are beyond the scope of this review and readers may refer to[18,20] for further reading.

Using 2D DIGE, Yu et al. successfully identified biomarkers that were associated with pancreatic cancer.[21] In the study, 24 upregulated and 17 downregulated proteins were identified by MS. Among those proteins, upregulation of apolipoprotein E, α-1-antichymotrypsin and inter-α-trypsin inhibitor were confirmed by western blot analysis. Furthermore, the association of those three proteins with pancreatic cancer was successfully validated in another series of 20 serum samples from pancreatic cancer patients. Using a similar approach, Huang et al. identified and confirmed the upregulation of transferrin in the sera of patients with breast cancer.[22] When Sun et al. compared the proteomic profiles between malignant and adjacent benign tissue samples from patients with hepatocellular carcinoma, they proved 2D DIGE is not limited to serum or plasma samples.[23] In their study, overexpression of Hsp70/Hsp90-organizing protein and heterogenous nuclear ribonucleoproteins C1 and C2 were identified by 2D DIGE coupled with MS analysis, and the findings were successfully validated by both western blotting and IHC staining. Next, Kondo et al. applied 2D DIGE to laser-microdissected cells from fresh patient tissues.[13] Using this protocol, a 1-mm area of an 8-12-µm-thick tissue section was shown to be sufficient. These examples demonstrate the high sensitivity and broad applicability of 2D DIGE for proteomic investigations using various types of patient samples and provide evidence that 2D DIGE is a powerful technique for biomarker discovery.

MALDI Imaging Mass Spectrometry

A deeper understanding of the complex biochemical processes occurring within tumor cells and tissues requires a knowledge of the spatial and temporal expression of individual proteins. Currently, such information is mainly obtained by IHC staining for specific proteins in patient tissues.[8,24,25] Nevertheless, IHC has limited use in high-throughput proteomic biomarker discovery because only a few proteins can be immunostained simultaneously. MALDI IMS allows researchers to analyze proteomic expression profiles directly from patient tissue sections.[26-28] The protocol begins with mounting a tissue section onto a sample plate (Figure 2). MALDI matrix is then applied onto the tissue sample, which is analyzed by MALDI MS in order to obtain mass spectra from predefined locations across the entire patient tissue section. The mass spectrum from each location is a complete proteomic profile for that particular area. All acquired mass spectra from the entire tissue are then compiled to create a 2D map for that tissue sample. This map could then be compared with those from other tissue samples to identify changes in protein or peptide expression or comparisons of the maps from different areas within the same tissue section could be performed. This technology  importantly allows the high-throughput discovery of novel protein markers. In addition, correlations between protein expression and tissue histology can also be studied easily.

Most studies using MALDI IMS have been performed on frozen tissue sections ranging from 5 to 20 µm in thickness.[26,27,29] After sectioning, a MALDI matrix is applied either by automated spraying or spotting. The matrix of choice is usually α-cyano-4-hydroxy-cinnamic acid for peptides and sinapinic acid (3,5-dimethoxy-4-hydroxycinnamic acid) for proteins.

Figure 2.  Procedures for MALDI imaging. IMS: Imaging mass spectrometry; MS: Mass spectrometry.

Spotting allows the precise application of matrix to areas of interest and minimizes the diffusion of analyte material across the sample, although the imaging resolution achieved by spotting is lower (~150 µm). A laser beam is then fired towards the area of interest on the tissue section to generate protein ions for analysis by a mass analyzer.[29] Among the different mass analyzers, TOF analyzers are the most commonly used owing to their high sensitivity, broad mass range and suitability for detection of ions generated by MALDI. Use of other mass analyzers such as TOF-TOF, quadrupole TOF (QTOF), ion traps (ITs) and Fourier transform-ion cyclotron resonance (FT-ICR) have also been reported in other studies.[30-33]

After obtaining the mass spectra, statistical analysis needs to be performed to identify statistically significant features that could have potential use as biomarkers. But before such analyses can be applied, there has to be background-noise subtraction, spectral normalization and spectral alignment.[34,35,34] Statistical methods used to identify significant differences in peak intensity are symbolic discriminant analysis and principal component analysis. Symbolic discriminant analysis determines discriminatory features and builds functions based on such features for distinguishing samples according to their classification.[36,37] Using this approach, Lemaire et al. found a putative proteomic biomarker from ovarian cancer tissues by MALDI IMS that was later identified to be the Reg-α protein, a member of the proteasome activator 11S.[37] This result was later successfully validated by western blot (protein expression found in 88.8% carcinoma cases vs 18.7% benign disease) and IHC (protein expression found in 63.6% carcinoma tissues vs 16.6% benign tissues).[37] On the other hand, principal component analysis reduces data complexity by transforming data based on peak intensities to information based on data variance, termed ‘principal components’, resulting in a list of significant peaks (principal components) ordered by decreasing variance.[35,38,39] Neither symbolic discriminant analysis or principal component analysis is capable of performing unsupervised classification. This aim requires the use of other methods such as hierarchical clustering.[39,40] In this method identified peaks are clustered as nodes in a pair-wise manner according to similarity until a dendogram is obtained, providing information as to the degree of association of all peak masses in a hierarchical fashion. Peaks that are capable of differentiating between different histological/pathological features could then be chosen for further validation of their value as tumor markers.[39]

In MALDI IMS, protein identification cannot be performed with confidence solely on the molecular weight. However, Groseclose et al. have developed a method using in situ digestion of proteins directly on tissue section.[41] They first used MALDI IMS to obtain a map of the protein and peptide spectra, then spotted a consecutive section of the same tissue sample with trypsin for protein digestion, and then spotted matrix solution onto the digested spots and the resulting peptides are identified directly from the tissue by MS/MS. This modification increases the confidence in protein identification. The time required for MALDI IMS analysis per tissue section is as follows: tissue sectioning, mounting and matrix application: 4-8 h; MALDI image acquisition: 1-2 days; spectral analysis: 1-2 h.[33,39]

Recently, in situ enzymatic digestion has been successfully applied for improving the retrieval of peptides directly from formalin-fixed, paraffin-embedded FFPE tissue samples.[27] Such development has greatly facilitated the application of MALDI IMS in FFPE tissues.[26,42] In fact, Stauber et al. identified the downregulation of ubiquitin, transelongation factor 1, hexokinase and neurofilament M from FFPE brain tissues of rat models of Parkinson disease using this modified technique.[42] The success of performing proteomic profiling using MALDI IMS directly on FFPE tissues opens up great possibility for using archival patient materials in high-throughput biomarker discovery. Novel cancer biomarkers identified using MALDI IMS still require validation by other techniques such as IHC.

Electron Transfer Dissociation MS

Post-translational modifications play important roles in the structure and function of proteins such as protein folding, protein localization, regulation of protein activity and mediation of protein-protein interaction. Two common forms of PTM that have been implicated in cancer development are phosphorylation and glycosylation. Previously, phosphoproteomic studies have led to the identification of novel tyrosine kinase substrates in breast cancer,[43] discovery of novel therapeutic targets for brain cancer[44] and increased understanding of signaling pathways involved in lung cancer formation.[45,46] Conversely, the identification of abnormally glycosylated proteins, such as mucins, has provided novel biomarkers and therapeutic targets for ovarian cancer.[47]

The study of PTM begins with digesting the target protein using enzymes such as trypsin,   introducing the fragments into MS for determination of the sites and types of modification and, at the same time, identification of the protein. The analysis is conventionally carried out using collision-induced dissociation (CID) MS, where peptides are collided with a neutral gas for cleavage of peptide bonds to produce b- and y-type ions (Figure 3). A complete series of peptides differing in length by one amino acid is produced, leading to identification of the protein by peptide-sequence determination. However, for phosphopeptides, the presence of phosphate groups would compete with the peptide backbone as the preferred cleavage site. The end result is a reduced set of peptide fragments, which hinders protein identification, and the exact location of the phosphate group on the peptide cannot be determined accurately when there are more than one possible phosphorylation sites.[48,49]

Figure 3.  Peptide bond-cleavage site for a-, b-, c-, x-, y– and z-type ions.

Electron transfer dissociation is a recently developed dissociation technique for the analysis of peptides by MS, utilizing radiofrequency quadrupole ion traps such as 2D linear IT, spherical IT and Orbitrap™ (Thermo Fisher Scientific Inc., MA, USA) mass analyzers.[48,49] In this technology, peptides are fragmented by transfer of electrons from anions to induce cleavage of Cα-N bonds along the peptide backbone, hence producing c- and z-type ions (Figure 3). In contrast to CID, ETD preserves the localization of labile PTM and also provides peptide-sequence information.[48] But ETD fails to fragment peptide bonds adjacent to proline, which are readily cleaved by CID.[50] A study that compared the performance of CID with that of ETD found that only 12% of the identified peptides were commonly detected between the two techniques. A study reported that CID successfully identified more peptides with charge states of +2 and below, whereas ETD was found to be better at identifying peptide ions with charge states of greater than +2.[51] Therefore, it is suggested that CID and ETD should be used together to complement each other.[52]  Han et al. successfully differentiated the isobaric amino acids isoleucine and leucine from one another by performing CID on the resulting z-ions after ETD. The presence of isoleucine residue was then confirmed by the detection of a specific 29-Da loss from the peptide.[53]  A clear advantage of using ETD for the analysis of phosphopeptides is a near complete series of c- and z-ions without loss of phosphoric acid,[48] greatly facilitating the determination of the phosphorylation sites and the identification of phosphopeptides. Recently, an analysis of yeast phosphoproteome using ETD successfully identified 1252 phosphorylation sites on 629 proteins, whose expression levels ranged from less than 50 to 1,200,000 copies per cell.[54] In another study using ETD, a total of 1435 phosphorylation sites were identified from human embryonic kidney 293T cells, of which 1141 (80%) were previously unidentified. Finally, a study by Molina et al. successfully identified 80% of the known phosphorylation sites in more than 1000 yeast phosphopeptides in one single study using a combination of ETD and CID.[55] In addition, ETD could be applied to investigate other forms of PTM, such as N-linked glycosylations.[56,57] N-linked glycans contain a common core with branched structures. These can be processed by stepwise addition or removal of monosaccharide residues linked by glycosidic bonds, producing highly varied forms of N-linked glycan structures.[58-60] A weakness of analyzing glycopeptides using CID is that cleavage of glycosidic bonds occurs with little peptide backbone fragmentation, so that only the glycan structure is available.[61]  Hogan et al. used CID and ETD together to overcome this problem determining the glycan structure and glycosylation site.[61] ICID was initially used for cleavage of glycosidic bonds that allowed the entire glycan structure to be inferred from the CID spectrum alone. ETD was later performed to dissociate the same peptide that resulted in a contiguous series of fragment ions with no loss of glycan molecules, allowing the identification of both the site of glycosylation and the identity of the glycoprotein.[61] Readers are strongly encouraged to refer to[49] and.[62] In a comprehensive comparison of CID versus ETD for the identification of peptides without PTMs, CID was found to identify 50% more peptides than ETD (3518 by CID vs 2235 by ETD), but ETD provided somewhat better sequence coverage (67% for CID vs 82% for ETD). It turns out that ETD produced more uniformly fragmented ions with intensities that were five- to ten-times lower than those produced by CID.[55] Finally, the best sequence coverage of up to 92% was achieved when consecutive CID and ETD were performed.[55]

This increase in sequence coverage using the combined approach is needed for studies requiring de novo peptide identifications. As such, this strategy is particularly suited for studies involved in the discovery, identification and characterization of novel peptides or proteins and their PTMs for biomarker use. A prerequisite of this technique is that the biological samples under investigation must undergo some form of fractionation before they are amenable to analysis by ETD or CID. This is achieved by the use of LC techniques, such as reverse-phase, strong cation exchange or strong anion exchange chromatography, and serves to reduce the complexity and wide dynamic range of protein-expression levels commonly found in biological specimens. Given the important roles of PTM in the function and activity of proteins, this technology paves the way for exploring the intricate cellular activities within a cancer cell.


  1. Duffy MJ, van Dalen A, Haglund C et al. Tumor markers in colorectal caner: European Group on Tumor Markers (EGTM) guidelines for clinical use. Eur. J. Cancer 43(9),1348-1360 (2007).
  2. Duffy MJ. Role of tumor markers in patients with solid cancers: a critical review. Eur. J. Intern. Med. 18(3),175-184 (2007).
  3. Bertucci F, Birnbaum D, Goncalves A. Proteomics of breast cancer: principles and potential clinical applications. Mol. Cell. Proteomics 5(10),1772-1786 (2006).
  4. Feng JT, Shang S, Beretta L. Proteomics for the early detection and treatment of hepatocellular carcinoma. Oncogene 25(27),3810-3817 (2006).
  5. Miller I, Crawford J, Gianazza E. Protein stains for proteomic applications: which, when, why? Proteomics 6(20),5385-5408 (2006).
  6. Kumarathasan P, Mohottalage S, Goegan P, Vincent R. An optimized protein in-gel digest method for reliable proteome characterization by MALDI-TOF-MS analysis. Anal. Biochem. 346(1),85-89 (2005).
  7. Meunier B, Bouley J, Piec I, Bernard C, Picard B, Hocquette JF. Data analysis methods for detection of differential protein expression in two-dimensional gel electrophoresis. Anal. Biochem. 340(2),226-230 (2005).
  8. Chan CM, Wong SC, Lam MY et al. Proteomic comparison of nasopharyngeal cancer cell lines C666-1 and NP69 identifies down-regulation of annexin II and ß2-tubulin for nasopharyngeal carcinoma. Arch. Pathol. Lab. Med. 132(4),675-683 (2008).
  9. Wong SC, Wong VW, Chan CM et al. Identification of 5-fluorouracil response proteins in colorectal carcinoma cell line SW480 by two-dimensional electrophoresis and MALDI-TOF mass spectrometry. Oncol. Rep. 20(1),89-98 (2008).
  10. Marouga R, David S, Hawkins E. The development of the DIGE system: 2D fluorescence difference gel analysis technology. Anal. BioAnal. Chem. 382(3),669-678 (2005).
  11. Timms JF, Cramer R. Difference gel electrophoresis. Proteomics 8(23-24),4886-4897 (2008).
  12. Minden J. Comparative proteomics and difference gel electrophoresis. Biotechniques 43(6),739-745 (2007).
  13. Kondo T, Hirohashi S. Application of highly sensitive fluorescent dyes (CyDye DIGE Fluor saturation dyes) to laser microdissection and two-dimensional difference gel electrophoresis (2D-DIGE) for cancer proteomics. Nat. Protoc. 1(6),2940-2986 (2007).
  14. Kang Y, Techanukul T, Mantalaris A, Nagy JM. Comparison of three commercially available DIGE analysis software packages: minimal user intervention in gel-based proteomics. J. Proteome Res. 8(2),1077-1084 (2009).
  15. Kreil DP, Karp NA, Lilley KS. DNA microarray normalization methods can remove bias from differential protein expression analysis of 2D difference gel electrophoresis results. Bioinformatics 20(13),2026-2034 (2004).
  16. Karp NA, McCormick PS, Russell MR, Lilley KS. Experimental and statistical considerations to avoid false conclusions in proteomics studies using differential in-gel electrophoresis. Mol. Cell. Proteomics 6(8),1354-1364 (2007).
  17. Kleno TG, Leonardsen LR, Kjeldal HØ, Laursen SM, Jensen ON, Baunsgaard D. Mechanisms of hydrazine toxicity in rat liver investigated by proteomics and multivariate data analysis. Proteomics 4(3),868-880 (2004).
  18. Smit S, Hoefsloot HCJ, Smilde AK. Statistical data processing in clinical proteomics. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 866(1-2),77-88 (2008).
  19. Karp NA, Griffin JL, Lilley KS. Application of partial least squares discriminant analysis to two-dimensional difference gel studies in expression proteomics. Proteomics 5(1),81-90 (2005).
  20. Grove H, Jørgensen BM, Jessen F et al. Combination of statistical approaches for analysis of 2-DE data gives complementary results. J. Proteome Res. 7(12),5119-5124 (2008).
  21. Yu KH, Rustgi AK, Blair IA. Characterization of proteins in human pancreatic serum using differential gel electrophoresis and tandem mass spectrometry. J. Proteome Res. 4(5),1742-1751 (2005).
  22. Huang H-L, Stasyk T, Morandell S et al. Biomarker discovery in breast cancer serum using 2-D differential gel electrophoresis/MALDI-TOF/TOF and data validation by routine clinical assays. Electrophoresis 27(8),1641-1650 (2006).
  23. Sun W, Xing B, Sun Y et al. Proteome analysis of hepatocellular carcinoma by two-dimensional difference gel electrophoresis. Mol. Cell. Proteomics 6(10),1798-1808 (2007).
    •• Presents a very detailed account of the procedures for 2D difference gel electrophoresis analysis.
  24. Wong SC, Chan AT, Chan JK, Lo YM. Nuclear ß-catenin and Ki-67 expression in chriocarcinoma and its pre-malignant form. J. Clin. Pathol. 59(4),387-392 (2006).
  25. Chan CM, Ma BB, Hui EP et al. Cyclooxygenase-2 expression in advanced nasopharyngeal carcinoma: a prognostic evaluation and correlation with hypoxia inducible factor 1 α and vascular endothelial growth factor. Oral Oncol. 43(4),373-378 (2007).
  26. Groseclose MR, Massion PP, Chaurand P, Caprioli RM. High throughput proteomic analysis of formalin-fixed paraffin embedded tissue microarrays using MALDI imaging mass spectrometry. Proteomics 8(18),3715-3724 (2008).
  27. Lemaire R, Desmons A, Tabet JC, Day R, Salzet M, Fournier I. Direct analysis and MALDI imaging of formalin-fixed, paraffin-embedded tissue sections. J. Proteome Res. 6(4),1295-1305 (2007).
    •• Provides a detailed account of procedures for the analysis of paraffin-embedded tissue sections using MALDI imaging mass spectrometry (MS).
  28. Meistermann H, Norris JL, Aerni HR et al. Biomarker discovery by imaging mass spectrometry: transthyretin is a biomarker for gentamicin-induced nephrotoxicity in rat. Mol. Cell Proteomics 5(10),1876-1886 (2006).
  29. Cornett DS, Reyzer ML, Chaurand P, Caprioli RM. MALDI imaging mass spectrometry: molecular snapshots of biochemical systems. Nat. Methods 4(10),828-823 (2007).
    •• Excellent review on the application of MALDI imaging MS for studying biological systems.
  30. Shimma S, Sugiura Y, Hayasaka T, Zaima N, Matsumoto M, Setou M. Mass imaging and identification of biomolecules with MALDI-QIT-TOF-based system. Anal. Chem. 80(3),878-885 (2008).
  31. Taban IM, Altelaar AFM, van der Burgt YEM et al. Imaging of peptides in the rat brain using MALDI-FTICR mass spectrometry. J. Am. Soc. Mass Spectrom. 18(1),145-151 (2007).
  32. Hsieh Y, Casale R, Fukuda E et al. Matrix-assisted laser desorption/ionization imaging mass spectrometry for direct measurement of clozapine in rat brain tissue. Rapid Commun. Mass Spectrom. 20(6),965-972 (2006).
  33. Goodwin RJA, Penington SR, Pitt AR. Protein and peptides in pictures: imaging with MALDI mass spectrometry. Proteomics 8(18),3785-3800 (2008).
  34. Chaurand P, Norris JL, Cornett DS, Mobley JA, Caprioli RM. New developments in profiling and imaging of proteins from tissue sections by MALDI mass spectrometry. J. Proteome Res. 5(11),2889-2900 (2006).
  35. Yao I, Sugiura Y, Matsumoto M, Setou M. In situ proteomics with imaging mass spectrometry and principal component analysis in the Scrapper-knockout mouse brain. Proteomics 8(18),3692-3701 (2008).
  36. Schwartz SA, Weil RJ, Thompson RC et al. Proteomic-based prognosis of brain tumor patients using direct-tissue matrix-assisted laser desorption ionization mass spectrometry. Cancer Res. 65(17),7674-7681 (2005).
  37. Lemaire R, Menguellet SA, Stauber J et al. Specific MALDI imaging and profiling for biomarler hunting and validation: fragment of the 11S proteasome activator complex, Reg α fragment, is a new potential ovary cancer biomarker. J. Proteome Res. 6(11),4127-4134 (2007).
  38. Walch A, Rauser S, Deninger SO, Höfler H. MALDI imaging mass spectrometry for direct tissue analysis: a new frontier for molecular histology. Histochem. Cell Biol. 130(3),421-434 (2008).
  39. Deninger SO, Ebert MP, Fütterer A, Gerhard M, Röcken C. MALDI imaging combined with hierarchical clustering as a new tool for the interpretation of complex human cancers. J. Proteome Res. 7(12),5230-5236 (2008).
  40. McCombie G, Staab D, Stoeckli M, Knochenmuss R. Spatial and spectral correlations in MALDI mass spectrometry images by clustering and multivariate analysis. Anal. Chem. 77(19),6118-6124 (2005).
  41. Groseclose MR, Andersson M, Hardesty WM et al. Identification of proteins directly from tissue: in situ tryptic digestions coupled with imaging mass spectrometry. J. Mass Spectrom. 42(2),254-262 (2007).
    • First report of protein identification performed directly from tissue sections.
  42. Stauber J, Lemaire R, Franck J et al. MALDI imaging of formalin-fixed paraffin-embedded tissues: application to model animals of Parkinson disease for biomarker hunting. J. Proteome Res. 7(3),969-978 (2008).
  43. Chen Y, Choong LY, Lin Q et al. Differential expression of novel tyrosine kinase substrates during breast cancer development. Mol. Cell Proteomics 6(12),2072-2087 (2007).
  44. Huang PY, Cavenee WK, Furnari FB, White FM. Uncovering therapeutic targets for glioblastoma: a systems biology approach. Cell Cycle 6(22),2750-2754 (2007).
  45. Guha U, Chaerkady R, Marimuthu A et al. Comparisons of tyrosine phosphorylated proteins in cells expressing lung cancer-specific alleles of EGFR and KRAS. Proc. Natl Acad. Sci. USA 105(37),14112-14117 (2008).
  46. Rikova K, Guo A, Zeng Q et al. Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer. Cell 131(6),1190-1203 (2007).
  47. Singh AP, Senapati S, Ponnusamy MP et al. Clinical potential of mucins in diagnosis, prognosis, and therapy of ovarian cancer. Lancet Oncol. 9(11),1076-1085 (2008).
  48. Syka JEP, Coon JJ, Schroeder MJ, Shabanowitz J, Hunt DF. Peptide and protein sequence analysis by electron transfer dissociation mass spectrometry. Proc. Natl Acad. Sci. USA 101(26),9528-9533 (2004).
    • First report of the application of electron transfer dissociation MS for the analysis of peptides and proteins.
  49. Wiesner J, Premsler T, Sickmann A. Application of electron transfer dissociation (ETD) for the analysis of posttranslational modifications. Proteomics 8(21),4466-4483 (2008).
  50. Hayakawa S, Hashimoto M, Matsubara H, Turecek F. Dissecting the proline effect: dissociations of proline radicals formed by electron transfer to protonated Pro-Gly and Gly-Pro dipeptides in the gas phase. J. Am. Chem. Soc. 129(25),7936-7949 (2007).
  51. Good DM, Wirtala M, McAlister GC, Coon JJ. Performance characteristics of electron transfer dissociation mass spectrometry. Mol. Cell Proteomics 6(11),1942-1951 (2007).
  52. Han H, Xia Y, Yang M, McLuckey SA. Rapidly alternating transmission mode electron-transfer dissociation and collisional activation for the characterization of polypeptide ions. Anal. Chem. 80(9),3492-3497 (2008).
  53. Han H, Xia Y, McLuckey SA. Ion trap collisional activation of c and z ions formed via gas-phase ion/ion electron-transfer dissociation. J. Proteome Res. 6(8),3062-3069 (2007).
  54. Chi A, Huttenhower C, Geer LY et al. Analysis of phosphorylation sites on proteins from Saccharomyces cerevisiae by electron transfer dissociation (ETD) mass spectrometry. Proc. Natl Acad. Sci. USA 104(7),2193-2198 (2007).
  55. Molina H, Matthiesen R, Kandasamy K, Pandey A. Comprehensive comparison of collision induced dissociation and electron transfer dissociation. Anal. Chem. 80(13),4825-4835 (2008).
    • Study comparing the characteristics of peptide fragmentation performed by collision-induced dissociation with that of electron transfer dissociation.
  56. Catalina MI, Koeleman CAM, Deelder AM, Wuhrer M. Electron transfer dissociation of N-glycopeptides: loss of the entire N-glycosylated asparagine side chain. Rapid Commun. Mass Spectrom. 21(6),1053-1061 (2007).
  57. Abbott KL, Aoki K, Lim JM et al. Targeted glycoproteomic identification of biomarkers for human breast carcinoma. J. Proteome Res. 7(4),1470-1480 (2008).
  58. Yan A, Lennarz WJ. Unraveling the mechanism of protein N-glycosylation. J. Biol. Chem. 280(5),3121-3124 (2005).
  59. Danielle H, Bertozzi CR. Glycans in cancer and inflammation – potential for therapeutics and diagnostics. Nat. Rev. Drug Discov. 4(6),477-488 (2005).
  60. Morelle W, Canis K, Chirat F, Faid V, Michalski J-C. The use of mass spectrometry for the proteomic analysis of glycosylation. Proteomics 6(14),3993-4015 (2006).
  61. Hogan JM, Pitteri SJ, Chrisman PA, McLuckey SA. Complementary structural information from a tryptic N-linked glycopeptide via electron transfer ion/ion reactions and collision induced dissociation. J. Proteome Res. 4(2),628-632 (2005).
  62. Mikesh LM, Ueberheide B, Chi A et al. The utility of ETD mass spectrometry in proteomic analysis. Biochim. Biophys. Acta 1764(12),1811-1822 (2006).

Advanced Proteomic Technologies for Cancer Biomarker Discovery

Part II

Reverse-phase Protein Array

One of the goals of proteomics is to identify protein changes associated with the development of diseases such as cancer.  Even with the rapid development of proteomic technologies during the past few years, analysis of patient samples is still a challenge. Difficulties arise from the fact that[63,64]:

  • Proteomic patterns differ among cell types;
  • Protein expression changes occur over time;
  • Proteins have a broad dynamic range of expression levels spanning several orders of magnitude;
  • Proteins can be present in multiple forms, such as polymorphisms and splice variants;
  • Traditional proteomic methods require relatively large amounts of protein
  • Many proteomic technologies cannot be used to study protein-protein interactions.

The principle of RPA is simple and involves the spotting of patient samples in an array format onto a nitrocellulose support (Figure 4). Hundreds of patient specimens can be spotted onto an array, allowing a comparison of a large number of samples at once.[65] Each array is incubated with one particular antibody, and signal intensity proportional to the amount of analyte in the sample spot is generated.[66] Signal detection is commonly performed by fluorescence, chemiluminescence or colorimetric methods. The results are quantified by scanning and analyzed by softwares such as P-SCAN and ProteinScan, which can be downloaded from[84] for free.[67,68]

Figure 4.  Principle of reverse-phase protein array.

Main advantages of RPA technology include[69-71]:

  • Various types of biological samples can be used;
  • The possibility of investigating PTMs;
  • Protein-protein interactions can be studied;
  • Labeling of patient samples with fluorescent dyes (e.g., 2D DIGE) or mass tags (e.g., isotope-coded affinity tag [ICAT]) are not required;
  • Any samples spotted as a dilution allows quantifying in the linear range of detection;
  • Quantitative measurement of any protein is possible compared to reference standards of known amounts on the same array.

It has been shown that RPA is extremely sensitive as it is capable of detecting up to zeptomole (1 x 10-21 mole) levels of target proteins with less than 10% variance. The analysis of few cell signaling events is known.[65,70,71] The assay sensitivity depends on antibody affinity, which depends upon antigen-antibody pairs.[68] Of course, only known proteins with available antibodies can be identified. Therefore, this method is more suitable for biomarker screening or validation than discovery of novel proteins. To assist researchers in selecting suitable antibodies, two open antibody databases show their western blot results using cell lysates.[72,73,85,86]

One application of RPA is to investigate the signaling pathways in human cancers. Zha et al. compared the survival signaling events between Bcl 2-positive and -negative lymphomas and found that survival signals, independent of Bcl 2 expression, were detected in follicular lymphoma and confirmed by validation with IHC.[71] In another study, patient-specific signaling pathways have been identified in breast cancers using RPA. Bayesian clustering of a set of 54 subjects successfully separated normal subjects from cancer patients based on an epithelial signaling signature. Principal component analysis was capable of distinguishing normal from cancer patient samples by using a signature composed of a panel of kinase substrates.[69] Differences in cell signaling between patient-matched primary and metastatic lesions have also been found using RPA. In the study, six patient-matched primary ovarian tumors probed with antibodies against signaling proteins, and the signaling profiles differed significantly between primary and metastatic tumors and upregulation of phosphor c-kit was capable of distinguishing five of the six metastatic tumors from the primary lesions.[70] These findings suggest that treatment strategies may need to target signaling events among disseminated tumor cells.

Reverse-phase protein array has also been used to validate mathematical models of cellular pathways. The p53-Mdm2 feedback loop is one of the most well-studied cellular-feedback mechanisms.[74] Normally, p53 activates transcription and expression of Mdm2, which, in turn, suppresses p53 activity. This negative-feedback loop ensures the low-level expression of p53 under normal conditions. Mathematical models have previously been used to investigate this negative-feedback loop.[67] Ramalingam et al. has shown, by using RPA, that part of the mechanism of the p53-Mdm2 feedback loop can be explained by current mathematical models.[75]

Another important application of RPA is for the identification of cancer specific antigens.  Using this method serum from 14 lung cancer patients, colon cancer patients and normal subjects were incubated and eight fractions of the cell lysate were recognized by the sera from four patients, while none of the sera from normal individuals was positive.[76] This study demonstrates the diagnostic potential of identifying cancer antigens that induce immune response in cancer patients by using RPA.

Expert Commentary and Five-year View

The development of 2D DIGE in the past few years has provided researchers with a more accurate method for relative quantification of proteins substantially reducing the number of replicates required for 2D gels and increased its applicability for high-throughput biomarker discovery. MALDI MS has immensely facilitated the direct discovery of biomarkers from patient tissue. Even though archival patient tissue samples are a potential source of materials for tumor marker research, high-throughput techniques for biomarker discovery using such samples has been problematic. With the development of MALDI IMS, investigators can now perform studies that aim to discover novel biomarkers directly from tissue sections and are able to correlate their expression with the histopathological changes of tumors. Previously, investigation into the sites of protein PTM has been difficult since MS-dissociation techniques, such as CID, would lead to preferential loss of PTM, but the use of ETD as a complementary peptide ion-dissociation method has allowed researchers to investigate the precise location and structure of the PTM, and to identify peptide sequence with higher confidence.

The rapid technological improvements in proteomic technologies will identify potential biomarkers for clinical use. Independent validation studies using clinical specimens must be performed before such markers can be applied clinically,. In this regard, RPA has added a potential for high-throughput screening or validation of newly found markers. Using this technique, it will be possible for researchers to quantitatively measure and validate novel markers on hundreds of patient samples simultaneously.

A big problem for proteomic researchers iincludes the abundance of proteins in biological samples. This could be partially solved by depletion of abundant proteins or by fractionation of protein samples according to characteristics. It is envisaged that, in the future, proteomic technologies will be developed to a stage that is capable of analyzing complex protein mixtures without preparatory fractionation. Such progress has recently been achieved in LC-MS, where the use of a high-field, asymmetric waveform, ion-mobility spectrometry device as an interface to an IT MS resulted in a more than fivefold increase in dynamic range without increasing the length of the LC-MS analysis.[77]

Another area that needs improvement is the standardization of protocols for patient-sample collection because results were found to be inconsistent among various studies using MS.[78] It is also considered that part of the reason for this inconsistency is due to the differences in sample-collection or sample-handling procedures.[78,79] The Human Proteome Organization previously published its findings on pre-analytical factors that affect plasma proteomic patterns and provides suggestions for sample handling.[80,81] In addition to the pre-analytical stages, it is imperative to stress that consistent and strict adherence to predefined procedures or standards, from sample collection, sample processing, experimentation, data analysis through to result validation, are of utmost importance to minimize variations and achieve consistent and reproducible results.

Any newly identified potential biomarker must also be validated using an independent cohort of patients in order to establish its clinical value, but the translation of results from the laboratory to the clinic has been slow. Consequently, it has been suggested that quantitative MS could be used for the detection of proteins.[82] The increasing availability of MS facilities to researchers worldwide will facilitate the detection, measurement and validation of protein biomarkers using quantitative MS techniques. Even after validation of such results in the laboratory, diagnostic tests will need to be developed for the marker and large-scale clinical trials would also have to be performed to confirm the results.  All these efforts require cooperation of personnel from various disciplines, such as scientists, medical professionals, pharmaceutical companies and governments. Finally, it is hoped that, through improved understanding of the protein expression as cancer progresses will lead to the discovery and development of useful cancer biomarkers for patient diagnosis, prognosis, monitoring and treatment.

Key Issues

  • 2DE coupled with mass spectrometry has been the main workhorse for the proteomic discovery of novel biomarkers in the past 10 years, and the development of 2D difference gel electrophoresis has substantially improved the quantification accuracy of 2DE.
  • MALDI imaging mass spectrometry has allowed the identification of novel proteomic features directly from patient tissue section for correlation with histopathological changes.
  • Electron transfer dissociation mass spectrometry has opened up the possibility of identifying the structure and localization of the post-translational modification and the peptide/protein.
  • Reverse-phase protein array is a powerful tool for the high-throughput validation of novel biomarkers across hundreds of patient samples simultaneously.


63.  States DJ, Omenn GS, Blackwell TW et al. Challenges in deriving high-confidence protein identifications from data gathered by a HUPO plasma proteome collaborative study. Nat. Biotechnol. 24(3),333-338 (2006).

64. Wulfkuhle JD, Edmiston KH, Liotta LA, Petricoin EF 3rd. Technology insight: pharmacoproteomics for cancer – promises of patient-tailored medicine using protein microarrays. Nat. Clin. Pract. Oncol. 3(5),256-268 (2006).

•• Excellent review on the clinical application of reverse-phase protein array.

65. Tibes R, Qiu Y, Lu Y et al. Reverse phase protein array: validation of a novel proteomic technology and utility for analysis of primary leukemia specimens and hematopoietic stem cells. Mol. Cancer Ther. 5(10),2512-2521 (2006).

66. LaBaer J, Ramachandran N. Protein microarrays as tools for functional proteomics. Curr. Opin. Chem. Biol. 9(1),14-19 (2005).

67. Ramalingam S, Honkanen P, Young L et al. Quantitative assessment of the p53-Mdm2 feedback loop using protein lysate microarrays. Cancer Res. 67(13),6247-6252 (2007).

68. Nishizuka S, Ramalingam S, Spurrier B et al. Quantitative protein network monitoring in response to DNA damage. J. Proteome Res. 7(2),803-808 (2008).

69. Petricoin EF 3rd, Bichsel VE, Calvert VS et al. Mapping molecular networks using proteomics: a vision for patient-tailored combination therapy. J. Clin. Oncol. 23(15),3614-3621 (2005).

70. Sheehan KM, Calvert VS, Kay EW et al. Use of reverse-phase protein microarrays and reference standard development for molecular network analysis of metastatic ovarian carcinoma. Mol. Cell Proteomics 4(4),346-355 (2005).

71. Zha H, Raffled M, Charboneau L et al. Similarities of prosurvival signals in Bcl 2-positive and Bcl 2-negative follicular lymphomas identified by reverse phase protein microarray. Lab. Invest. 84(2),235-244 (2004).

72. Major SM, Nishizuka S, Morita D et al. AbMiner: a bioinformatic resource on available monoclonal antibodies and corresponding gene identifiers for genomic, proteomic, and immunologic studies. BMC Bioinformatics 7,192 (2006).

73. Spurrier B, Washburn FL, Asin S, Ramalingam S, Nishizuka S. Antibody screening database for protein kinetic modeling. Proteomics 7(18),3259-3263 (2007).

74. Ciliberto A, Novak B, Tyson JJ. Steady states and oscillations in the p53/Mdm2 network. Cell Cycle 4(3),488-493 (2005).

75. Ma L, Wagner J, Rice JJ, Hu W, Levine AJ, Stolovitzky GA. A plausible model for the digital response of p53 to DNA damage. Proc. Natl Acad. Sci. USA 102(40),14266-14271 (2005).

76. Madoz-Gurpide J, Kuick R, Wang H, Misek DE, Hanash SM. Integral protein microarrays for the identification of lung cancer antigens in sera that induce a humoral immune response. Mol. Cell. Proteomics 7(2),268-281 (2007).

77. Canterbury JD, Yi X, Hoopmann MR, MacCoss MJ. Assessing the dynamic range and peak capacity of nanoflow LC-FAIMS-MS on an ion trap mass spectrometer for proteomics. Anal. Chem. 80(18),6888-6897 (2008).

78. Coombes KR, Morris JS, Hu J, Edmonson SR, Baggerly KA. Serum proteomics – a young technology begins to mature. Nat. Biotechnol. 23(3),291-292 (2005).

78. Hortin GL. Can mass spectrometric protein profiling meet desired standards of clinical laboratory practice? Clin. Chem. 51(1),3-5 (2005).

79. Omenn GS, States DJ, Adamski M et al. Overview of the HUPO plasma proteome project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database. Proteomics 5(13),3226-3245 (2005).

80. Rai AJ, Gelfrand CA, Haywood BC et al. HUPO plasma proteome project specimen collection and handling: towards the standardization of parameters for plasma proteome samples. Proteomics 5(13),3262-3277 (2005).

• Concise report on several pre-analytical factors that impact the results of plasma proteomic profiling.

81. Mann M. Can proteomics retire the western blot? J. Proteome Res. 7(8),3065 (2008).

Update from LC/GC North America.

Solutions for Separation Scientists. Aug 2012; 30(8).

30 years of LCGC


The key advances in separation science is covered in five areas of the discipline:

  1. sample preparation
  2. gas chromatography(GC) columns
  3. GC instrumentation
  4. liquid cheomatography (LC) columns
  5. LC instrumentation

In the first, there is automated sample preparation in kit form (QuEChERS). A short list of automated sample preparation techniques includes: supercritical fluid extraction (SFE), microwave extraction, automated solvent extraction (ASE), and solid phase extraction (SPE). A panel of experts views the bast basic method of extraction is SPE, and one uses solid phase microextraction with direct immersion and static headspace extraction, along with liquid-liquid extraction.[2] In GC incremental improvements have been made with ionic liquids, multidimentional GC, and fast GC. LC has advanced dramatically with ultra-high pressure LC and superficially porous particles. LC-MS has become standard equipment routinely used in many labs.[1]

Biomarkers have to be detected in a background of 104-106 other components of comparable concentration that also partition with the stationary phase. The partition coefficients of many species are similar, or identical to the biomarker target. The issue is how to select and resolve fewer than 100 biomarkers from a milieu of 1 million components in a complex mixture. The novel idea is to target structure instead of general properties of molecules.[3] How might this work?  A single substrate, metabolite, hormone, or toxin is identified in milliseconds by specific protein receptors. The combinatorial chemistry community has shown that synthetic polynucleotides (aptamers) can be found and amplified that have selectivities approaching antibodies.This is a method well know for years as affinity chromatography. A distinct problem has been the natural process of post translational modification (PTMs), which may create isoforms by addition of a single phosphate ester to be found in the proverbial soup.

1. Bush L. Separation Science: Past, Present and Future. LCGC NA 2012; 30(8):620.

2.McNally ME. Analysis of the State of the Art: Sample Preparation. LCGC NA 2012; 30(8):648-651.

2. Regnier FE. Plates vs Selectivity: An Emerging Issue with Complex Samples.  LCGC NA 2012; 30(8):622.

Read Full Post »

%d bloggers like this: