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Stage for the Human Genome Project (HGP)

Genetics is the most advancing scientific field when it comes to discoveries. It was initially static for a number of years following Gregor Mendel’s law of genetics, then it  started to raise some interest in the scientific community until Watson and Crick revealed the formidable structure of the helical double stranded Deoxyribonucleic acid (DNA) with interconnecting loops, protein conformations and enzymatic actions. This discovery is one of the most important in the 20th century and reveals the mechanisms underlying the stability and variability of inherited traits, how they are transmitted and to a certain extent, how they are genetically expressed. 

Other discoveries in the 1960’s and the 1970’s including the discovery of reverse transcriptase and restriction enzymes forming the basis of modern molecular biology tools capable of cutting, integrating ,cloning and eventually sequencing molecular data.

The trend nowadays is towards Systems biology and this involves the application of computer, engineering and mathematical methods to the analysis of the complexity, throughput, robustness, modularity, feedback, and fragility of biological systems. This achievement  is  possible only as the Human Genome Project has been unveiled. Now there is a better comprehensive analysis of  :

n      genomes

n      transcriptomes

n      proteomes

          in  both health and disease.

Complete elucidation of genome function also  requires a parallel understanding of the sequence differences across species and the fundamental processes that have sculpted their genomes into the modern-day forms.

No doubt  the extent of   genetic contribution to disease can and does vary greatly for different diseases

Thus the equation :

Phenotype = Environment + genotype (including gene to gene interactions) +

 [ genotype x environment] can illustrate  the complexity of understanding diseases.


How genes contribute to genetic diseases

Most human diseases have a genetic component to their etiology. However, the extent of this genetic contribution to disease can and does vary greatly for different diseases. There are thus:

n         Simple" Mendelian diseases and

n        “Complex" diseases


In simple Mendelian diseases:

n      diseases occur in simple patterns in families

n      follow predictable and generally simple patterns of transmission

n      in most cases a single gene locus is the major determinant of the clinical disease phenotype

n      prevalence Mendelian conditions is rare and they manifest in a remarkably similar way

n      a single gene locus is the major determinant of the clinical disease phenotype

n      genotype is thus : autosomal-dominant, autosomal-recessive, or X-linked


Examples of Mendelian Diseases are haemophilliac disease, Amelogenesis imperfecta ,Crouzon syndrome and Cleidocranio dysplasia


Complex diseases


In complex diseases there is combination of all these components




One of these is Diabetes Mellitus which together with hypertension and coronary vascular disease are termed as non communicable diseases. The definition of WHO, Report of a WHO Consultation (1999) is as follows:


Diabetes mellitus (DM) is a group of disorders of multiple etiologies characterized by chronic hyperglycemia with disturbances of carbohydrate, fat, and protein metabolism resulting from defects in insulin secretion, insulin action, or both

       Type 1 Diabetes Mellitus

It is an autoimmune disease characterized by the destruction of the insulin- secreting beta cells of the pancreas. It is difficult to control with the current therapies available

 There are devastating long term consequences to the disease and its pattern  is similar to an epidemic disease. As is the case with non communicable diseases, it has an environmental component  as well.

The pathogenesis of type 1 diabetes


Type 1 Diabetes mellitus has multigenic predisposition (many genes are associated in one way or the other to the disease.)It is influenced by environmental factors (nutrition)and viruses such as Coxsackie B, mumps and rubella have been shown to have an effect on the disease.


Distribution of Type 1 diabetes

n      Highest incidence is seen among Caucasoid populations, in Sardinia (36.8/100,000 per year) and Finland (36.5/100,000 per year)  

n      lowest is seen in the Zunyi province of China


l      The following will provide an understanding of how the Human Genome Project has “updated”traditional genetic methods and used it with modern computational tools and new biological techniques and made use of communication access facilities via the internet mainly. The ambitious goal of genomics is the uncovering of all the gene variants responsible for complex diseases.


All this of course to understand the basis, find the sensible drug targets  to help cure the disease but also help to prevent it at the developmental stage.Complications associated with the disease must also be dealt with.  A fulcrum is achieved when all this know how and understanding is used in grafting methods where it is not a single gene or a single gene product that is assayed but a combination of these .


Traditional methods of Genetic Analyses


These include the following :








Finding candidate genes:( linkage analysis)

The complexity of finding candidate genes lies in the fact that disease alleles reported to be associated with a disease are also found in unaffected individuals. Also some individuals with disease do not have the specific disease-associated allele. 

In the case of Diabetes mellitus , there are some well characterized regions including  the major histocompatibility complex on chromosome 6p21 and the insulin region on chromosome11p15.5.

IDDM12, located on chromosome 2q33, is one of the confirmed type 1 diabetes susceptibility loci.

Recent genome screens have also identified the class II sub-region (i.e., HLA-DR, DQ, DP loci) as IDDM1.


The HLA-DQ locus of the human leukocyte antigen complex and type 1 diabetes mellitus:

The Human Leukocyte Antigen (HLA) complex is located on the short arm of chromosome 6 at p21.3 (1-4). It encompasses approximately 3500 kb of DNA, and contains at least 150 genes. It is the primary region of susceptibility for type 1 diabetes, as well as other autoimmune disorders. Recent genome screens have identified the class II sub-region (i.e., HLA-DR, DQ, DP loci) as IDDM1.

 Meta-analysis(segregation  analysis + familial aggregation)

With the formidable range of data that the  gene locus available through the Human Genome project and with the use of internet and other related methods of communication , it is now possible to make analyses which are comparable to    segregation   analysis.

Recall :

 Segregation analysis does not find or aim to find a specific gene responsible for a trait It determines only if trait transmission appears to fit a Mendelian or other mode of genetic transmission.

 There is now however much more information about different gene loci   as well as  about the  disease susceptibility associated with them. 

A meta-analysis of 33 studies was made examining the association of type 1 diabetes mellitus with polymorphisms in the cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4) gene, including the A49G (29 comparisons),C(318)T (three comparisons), and (AT)n microsatellite (six comparisons) polymorphisms. By combining and comparing the different reports it was possible to show  that many disease-associated genetic polymorphisms are common in the population and can be present at allele frequencies of > 20%, with some disease-associated alleles reported in > 50% of populations studied . The meta analysis also revealed that   there is no one-to-one correlation of the presence of a specific genetic allele and the occurrence of disease as far  as type 1 Diabetes mellitus is concerned.

Proteomic technologies 

These are technologies used to study protein-protein interactions. Some are listed below

l       two-dimensional gel electrophoresis (2DGE)

l      mass spectrometry(MS)

l      isotopically labeled reagents

l      protein or antibody arrays

l      yeast two- hybrid systems

l      phage display

l      immunoprecipitation

l      characterization of macromolecular complexes using antibodies, DNA, RNA 


Proteomic technologies have been successfully used for the identification of cancer biomarkers, for the identification of novel drug targets and for studying several biological processes relevant to human health.

n      Identification of surrogate markers for the progression of T1D( autoimmune diabetes and/or the complications of diabetes)

n      Application of proteomic technologies to study the inflammatory processes leading to the development of T1D and its complications 

n      Application of proteomics and metabolomics to assess the risk of developing T1D or monitor response to therapy to prevent or reverse the autoimmune process



Below is a diagram representing a model for protein identification :




The end products of many enzymatic reactions  provide us with a range of information namely an index of a particular metabolic state or disease. Currently there is no technology suitable enough for quantifying and identifying all metabolites. Mass spectrometry and nuclear magnetic resonance spectroscopy are used in high throughput profiling and to quantify however a large subset  of metabolites. The application of metabolomic and proteomic technologies to the study of T1D and its complications could further our understanding of the etiology and development of this disease.


 The diagram above is an example of how in  an experimental set up, metabolites  can  be isolated and  analysed. Then it is possible to go back to the gene and thus try to   deduce the pathways related to the disease under standard conditions. The pathogenesis of the disease as well as the routes to the various complications that are associated with it can hence be  deduced.

 There are now possibilities to have        

n      A rat cell line that  secretes insulin in response to glucose

n      Cell lines that are resistant to attack by cytokines

n      fat selectively melted out of liver cells using leptin gene: the  insulin sensitivity in the muscles improved

n      new subunit that, when over-expressed in the liver, increases glycogen formation, lowers blood glucose levels to normal and even reduces food intake in diabetic rats 

These experiments carried out in rats can be transposed to human research: Both murine and human genomes are available together with proteomics databases such as the Mouse SWISS-2D PAGE Database.There is a descriptive analysis of the protein expression in normal mouse liver, liver nuclei, muscle, white and brown adipose tissue, and pancreatic islets. It is accessible at the ExPASy molecular biology server  and may serve as reference for  2-DGE maps and for comparison with the human genome and proteome (SWISS PROT).

 Pancreatic islets of Langerhans as a whole organ

l      the ß-cell is not a passive bystander

l      environmental factor (intrauterine protein restriction) may influence islet protein expression

There must be a  characterization of the pancreatic islet proteome for identifying markers that might be used for improving the success rate in transplantation. This can be achieved namely through:

Microarray technology

The development of antibodies or protein/antibody arrays that could be used for studying T1D or screening islets to be transplanted and/or monitor their function after transplantation that can be used for screening islets to be transplanted and/or monitor their function after transplantation


Future diabetes research

The future of diabetic research lies therefore in

n      Robust and sensitive quantitative proteomic technologies

n      Accurate studies of protein composition

n      The dynamics of protein expression and turnover

n      Study of Protein interactions

n      Posttranslational modifications


Although the work lying ahead is arduous,    the Human Genome project  and the use of modern tools , experiments , collection of data and findings via peer reviewed articles on the internet,  it will no longer  be an impossible task..