Statistics Show About 170 Million World Diabetes Mellitus Biology Essay

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Statistics show that about 170 million people in the world have diabetes mellitus and this number will double in the next 20 years (Wild et. al., 2004). The prevalence of type 2 diabetes is inextricably linked to the increased incidence of cardiovascular disease and obesity due to adipose tissue accumulation. Besides, diabetes mellitus arises due to the accrual defects of different tissues in the body, for example the liver, skeletal muscle and pancreas which are involved in glucose homeostasis and insulin level. Therefore, different proteomics approaches have been conducted to understand the pathophysiological processes which lead to diabetes development and identify pathways to target for diagnostic and therapeutic analyses (Parikh and Groop, 2004). Besides, transcriptomics approach such as expression profiling of mRNA has identified several candidate genes associated with diabetes: FOXC2 (Ridderstrale et al., 2002) and calpain-10 (Baier et al., 2000) to name a few.

Uncoupling proteins (UCP) are a set of proteins found in mitochondria which can dissipate H+ gradient of inner mitochondrial membrane. UCPs are hypothetically identified as candidate genes for Type 2 diabetes based on the fact that they are able to decrease membrane potential and augment thermogenesis. Among the three types of homologues of UCPs, which are UCP1, UCP2 and UCP3, studies using yeast and knock-out mice have found that UCP2 and UCP3 possess uncoupling activity (Dalgaard and Pedersen, 2001). In studies using UCP2 knockout mice, phenotypic expressions such as increased insulin secretion which leads to hyperinsulinaemia, increase of coupling activity in mitochondria and ROS-production established that UCP2 is a suitable candidate gene for Type 2 diabetes analysis ( Arsenijevic et al., 2000; Zhang et al., 2000).


Proteome is the genomic expression of proteins in a biological system at a specific time point. Since proteome is dynamic, the study of proteome, i.e. proteomics, has gained interests in the application of biomedical research. Now, proteomics is widely used in clinical application and it has major breakthrough in the discovery of diagnostic and prognostic disease biomarkers. Potential of proteomics is limitless. Proteomics is very useful and promising for biomarker discovery through the analysis of biological fluids. These biomarkers are vital for risk identification and detection of a disease in an early stage. Nonetheless, proteome of a biological system is complex since the genome can give different mRNA transcripts due to alternative splicing and a series of post-translational modification will then result in a whole series of different proteins.

In general, proteomic analyses comprise two aspects: expression proteomics and functional proteomics. Typical technologies involved in expression proteomics are separation by two-dimensional gel electrophoresis (2DE), then identification using matrix-assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF-MS). Advance research and development on proteomic analysis has seen other technologies such as surface-enhanced laser desorption ionisation (SELDI) mass spectrometry and electrospray ionisation (ESI) MS/MS being applied. Fundamentally, expression proteomics involve identification and quantification of biomarkers, which are the proteins, and also the characterisation of various post-translational modifications of proteins and cellular localisation. On the other hand, functional proteomics involve identification of different phosphoproteins to provide more insights into signalling pathways. It involves the study of protein-protein interactions protein networks (Scott et. al., 2005).

Proteomics techniques and the merits


This system is the most commonly used method in proteomics research. Complex protein mixtures in a biological fluid are separated by 2DE, which is the combination of isoelectric focusing and SDS/PAGE. Complex protein molecules are separated according to relative charge based on isoelectric point, continued by second dimension separation based on molecular mass into single detectable protein spots. The protein spots separated on the gel are often visualized with different stains, such as silver staining, Coomasie Blue and Sypro Ruby. Sypro Ruby overcame problems of low sensitivity (Coomassie Blue) and poor dynamic range (silver staining) with higher sensitivity (1-2 ng) and three order magnitude of linear dynamic range. Sometime pre-staining with Cy dye is carried out but it is not commonly used. This method usually gives more than 1000 apparent protein spots on a single gel. Different spectrum of blue and red dye indicates the expression behaviour of the proteins, either over-expressed or under-expressed. The expression profile is then further analysed using different mass spectrometry techniques. Conventionally, the protein spots of interest will be excised from the gel and in-gel-digested by proteases, often trypsin, and then the resulting fragments are examined under MALDI-TOF-MS plate. The sample are first dried, then coated with an acidic matrix and subsequently subjected to laser radiation. The mass/charge (m/z) ratio determines the peptides separation, which is based on their time of flight. The data from the analysis is then compared with online database for instance, Mascot, to identify the protein spots of interest (Poon and Mathura, 2009; Scott et. al., 2005).

2DE coupled with MALDI-TOF-MS technique has been used widely for the analysis of whole proteins and the gel can be analysed through a myriad of stains and imaging software available. 2DE is considered the best identification method of proteins where various post-translational modifications and splice variants present. Ongoing developments in the technical advances of 2DE have improved the reproducibility, sensitivity and throughput of proteome analysis. Furthermore, this method can analyze up to 1000 different target protein spots on a single gel which renders it suitable for global analysis of the expression of proteins in a biological system. On the other words, 2DE allows hundreds of proteins to be separated and displayed on a single 2-dimensional gel to enable global view of proteins at a given point in time (Scott et. al., 2005). 2DE has been employed through research in comparing the renal proteome of type 1 diabetes mellitus nephropathy and non-diabetic mice. Further identification of MALDI-TOF-MS has identified under-expression of elastase IIIB and over-expression of monocyte neutrophil elastase inhibitor (Thongboonkerd et. al., 2004). In type 2 diabetes, 2DE coupled with MALDI-TOF-MS has identified various protein variants in the blood plasma and serum. Although 2DE has high resolving power and large sample loading capacity, reproducibility is a main setback. However, easy visualization of protein variants by 2DE give a very informative analysis of a proteome and thus it is now the fastest technique to directly target protein expression differences.


SELDI-TOF technique is described by Caffrey (2010) as a unique technique best suited to study urine proteome thanks to its high salt tolerance, small sample required for analysis and high throughput properties. Urine is one of the major samples used for proteomic analysis in diabetes mellitus study, as recently been carried out by Andersen et. al. (2010). SELDI-TOF technique weds MALDI-TOF with chromatography, where functional groups are immobilized on a chip surface and proteins will bind to them. The proteins were bound by utilizing different chemical properties such as anion and cation exchange, reverse phase, metal affinity, and surfaces pre-coated with reactive groups to capture various proteins such as antibodies and receptors. Similar protein identification method used in MALDI-TOF-MS applied afterwards where the bound proteins on the protein chip are ionised and separated by TOF according to m/z ratio. Sundsten (2006) identified several serum proteins from normal glucose tolerance individuals and type 2 diabetes mellitus patients using SELDI-TOF technique. The experiment was successful as four differentially expressed proteins were discovered: apolipoprotein C3, transthretin, albumin and transferrin.

The main advantage of SELDI-TOF technique is that it does not require a salt removal step, unlike 2DE and MALDI-TOF-MS. The steps involved are simple, for example, the protein bound chip is washed in deionized water and can be analyzed by MS after it is dried. Furthermore, SELDI conserves the analyzed samples. Sample required for analysis is as little as 5 µl. SELDI has high throughput which allows simultaneous protein profiling of many urine samples speed up the discovery process. However, reproducibility is an issue needed to be addressed. Besides, SELDI can only identify biomarkers. Further analyses such as MS/MS analysis and peptide mass fingerprinting are required to perform protein functional analyses (Caffrey, 2010).

Liquid chromatography coupled to tandem MS (LC-MS/MS)

In LC-MS/MS, complex protein mixtures are first digested by proteolytic enzymes, usually trypsin, then liquid chromatography technique was used to further simplify the proteins based on charge, pH or hydrophobicity. As Wang and Hanash (2003) highlighted, cation exchange chromatography preceding reversed phase chromatography are usually applied. Subsequently, peptide analysis by MS/MS is carried out. The fragmented peptides in first phase of MS are further subjected to second phase of MS to obtain peptide sequence information. ESI usually coupled to quadrupole mass analysers in tandem MS analysis (Aebersold and Mann, 2003). Zhan et al. (2004) used liquid chromatography-electrospray ionization- quadrupole-ion trap tandem MS (LC-ESI-Q-IT-MS/MS) to study the down-regulation of secretagogin in non-functional pituitary adenomas in human. The most significant advantage of LC-MS/MS is its high throughput ability, which enables protein identification up to hundreds in 24 hours. In contrast to 2DE, LC-MS/MS has a wide dynamic range of protein concentration. However, this technique does not comprehensively identify splice variants and post-translational modifications in a complex peptide mixture.


A genome-wide study of the expression of mRNA levels is termed transcriptomics. A transcriptome present in a biological system holds an accurate representation of important biological phenomena, where the gene expression patterns give potential insights into the development and mechanism of a disease. Furthermore, transcriptomics approach has been adopted to identify biomarkers in clinical application for diagnostic, therapeutic and prognostic purposes.

Transcriptomics technique has been spear-headed by microarray technology where the gene expression profile or the whole transcriptome in a sample can be analyzed. Transcriptomics differ from proteomics in the way that the former can study the expression patterns and behaviours of multiple genes simultaneously. The analysis of a transcriptome which is the RNA is vital with respect to genome wise as the RNA sequence can be modified by either differential splicing or RNA editing. Knowing the fact that skeletal muscle is one of the key sites of disposal of insulin stimulated glucose, microarray analyses have been carried out to identify two important regulators of oxidative phosphorylation, namely PPARγ and NRF1in Type 2 diabetes study (Patti et. al., 2003).

Transcriptomics techniques and the merits


The principle of microarrays is based on the complementary hybridization of nucleotides harvested from the sample and the DNA sequences which can be present up to thousands in a small platform. Generally, gene expression microarrays can be categorised as cDNA microarrays and oligonucleotide microarrays. cDNA microarray utilizes cDNA probes usually about 500-5000 bases. The mRNAs that are isolated from sample target are first treated with reverse transcriptase and subsequently labelled with fluorescent tags, usually green and red. The labelled RNAs are then hybridized to cDNA microarray. The image is scanned and the colour intensity and the colour change are visualized through computer software. The level of expression of each transcript can be read. In oligonucleotide microarray, the RNAs from samples isolated are used to synthesize double-stranded cDNA. These cDNA serves as a template to construct biotin-labeled cRNA. Hybridization of biotin-labeled RNA occurs on the nucleic acid probes on a microarray. The microarray is then scanned and the abundance of each transcript present can be read by observing the amount of biotin-labeled RNA associated with each DNA probe locations (Kittleson et. al., 2009; Albelda and Sheppard, 2000).

Microarray is utilized to identify over-expression or under-expression of genes between disease states to discover vital pathways in a disease mechanism and biomarker for therapeutic targets. Microarrays techniques are often followed by validation step such as quantitative PCR (qPCR) and Northern blotting. In a study conducted by Zhang et al. (2010), microarray coupled with multiplex amplification has been used to type and subtype influenza viruses. Microarray containing 46 short virus-specific oligonucleotides is effective in detecting 5 subtypes of influenza A including H1N1.

Microarray enables a rapid, comprehensive and accurate diagnostic method and at the meantime is able to type and subtype a particular strain of pathogen. Furthermore, a comparison of mRNA genetic expression profiles between different large data sets from different samples can be done in a single database, thus allowing a comparison between a control and diseased sample. However, some limitations such as low turnover, low gene expression and rapid RNA degradation have to be taken into consideration (Lockhart et. al., 1996; Hyatt et. al., 2006).

Correlation between transcripts and proteins levels

A transcriptome is the total complement of mRNA in a biological system at a given time point and this transcriptome serves as template for protein synthesis, forming the proteome, which is the protein complement of the transcriptome. Proteomics on the whole are limited in terms of width and depth of coverage due to variations in protein properties such as hydrophobicity, size, charge, stability and its abundance. On the contrary, transcriptomics are cost-effective and high-throughput and are able to analyze up to thousands of transcripts in a single automated format.

Anderson and Selhamer (1997) first analyzed the comparison between mRNA and protein abundances in human liver and suggested that protein abundance is not reliable upon mRNA levels. Later, biological explanation explained that the differences between transcripts and protein abundances is a result of RNA splicing, protein turnover, allosteric protein interactions, proteolytic processes and most importantly, post-translational modifications of proteins. In this case, the transcripts and protein levels of UCP2 gene can be elucidated by aforementioned explanation. In addition, protein abundance is also heavily affected by the rapid mRNAs degradation during mRNA translation (Guhaniyogi and Brewer, 2001). Furthermore, post-transcriptional processes for instance alternative splicing of mRNA enables various proteins to be translated based on a fixed amount of genes. Reduction in the number of transcripts but protein levels remain unchanged can be explained by mRNAs shelf life where proteins have better shelf life compared to mRNA (Pratt et. al., 2002). In addition to scientific explanations, lack or correlation between transcriptome and proteome can be attributed by technical issues regarding the transcriptomics and proteomics technologies. Even the most sophisticated devices have flaw, for example, microarrays are unable to systematically recognize changes in splice variants, but the proteins encoded by the splice variants can be detected by proteomics (Hedge et. al., 2003).

In general, the biological system between a patient of a disease and a normal person is distinguished by a certain amount of changes in gene(s) expression and the protein products. Usually, a single approach may it be proteomics or transcriptomics is not suffice to give comprehensive insights of its physiological and pathophysiological understanding. For example, Zhang et al. (2010) combined transcriptomics and proteomics approach in H1N1 diagnosis and treatment. Ideker et al. (2001) identified fifteen mRNA transcripts in Saccharomyces cerevisiae galactose utilization which expressions were not changed, whereas the corresponding protein expression changed in a certain level and the correlation between mRNA and protein levels was found to be r = 0.6. The discrepancy was identified as post-transcriptional regulation.


Transcriptomics and proteomics are both cutting edge technologies vital in providing insights of diseases and contributing discoveries which are of clinical significance. Understanding each technology’s advantages, transcriptomic and proteomic can complement each other and work in synergy. For example, transcriptomic techniques can be exploited in terms of high-throughput and lower cost; however, it has limitation in using human target tissues for expression profiling. Proteomic can be utilized to analyze protein complement of its alternative splicing transcripts. Combinations of both transcriptomic and proteomic approaches have proved to be successful in identification of biomarkers in different kinds of cancer. For instance, YKL-40 was identified as the biomarker for glioblastoma multiforme by using DNA microarrays and ELISA techniques (Tanwar et al., 2002). In the case of type 2 diabetes mellitus, utilization of both techniques give better understanding on the pathways and mechanism involved and provide a more comprehensive prognostic and therapeutic approach.