After the Human Genome Project was successfully completed in April 2003 and it was assured that humans are identical in the sequence of their genome by 99.9%, researchers are moving on to find more about the 0.1% left. Although our genome is made out of 3 billion bases (A’s, G’s, C’s and T’s), the 0.1% genetic difference is extremely significant. This is due to the fact that this small percentage holds the key to most of the consequences of such a variation between human beings (e.g susceptibility to diseases and also response to drugs). This introduced the term SNPs (single nucleotide polymorphism) which was found to be highly involved in variation of response to many drugs (like anti-cancers) and the list is increasing every day.

To be more focused on the human variations represented by the 0.1 percent, the NIH led the international HapMap project. Their work was performed on four populations groups: the Yoruba people in Ibadan Nigeria,the Japanese in Tokyo, the Han Chinese from Beijing and Utah residents from western and northern Europe. The work began in October 2002 and successfully ended in October 2005. The work of  three successive years helped the researchers invent a shortcut for studying SNPs. Scientists believe that there are about 10 million SNPs distributed among 3 billion base pairs which make up our genome, so scanning the whole genome of millions of people for such SNPs would be extremely expensive. After the HapMap project, researchers demonstrated that variants usually tend to cluster into neighborhoods (called haplotypes) and thus the number could be reduced to 300,000 SNPs only. This means that they could reduce the work load by about 30 folds.

The genome-wide association (GWA) studies aim to pinpoint the genetic differences, which cause a certain disease (or a biological trait) by comparing a group of people (who have the trait under research) to a control group (people who are free from this trait). Utilizing thousands of SNPs markers, we can identify regions (loci) which are statistically different between patient and control groups. Thus, we can identify the genetic difference between sick and control people, even though the difference was subtle. This means that the combination of slightly altered genes plus environmental factors could be well studied. The conventional ways, usually used to study genetic differences, are mainly based on selecting the candidate gene based on knowing or suspecting the mechanism of the disease. GWA helps scanning of the whole genome in a comprehensive unbiased manner. It will let us get the whole picture about other, non expected, contributing genes. In this way, GWA studies will help us study the multi-factorial diseases (like cancer and diabetes) in a more rationalised way.

Another challenge has come up: What about the genetic variations due to geographic ancestry? It is also a significant contributing factor to variation among humans and all the efforts are directed towards making a somewhat universal map of human genome to help develop individualized drugs. A group of scientists led by David Reich, an assistant professor at Harvard Medical School, described a quantitative method that can correct such errors due to geographical ancestry known collectively as “population stratification”. It will help if the disease groups, sharing the same trait, have differences in their geographic ancestry.

Tags: Genome wide association studies, Genome-wide association studiesX, GWA, GWAS, Hap-Map project, HGP, human variome project, SNPs, variome

What is bioinformatics?

It can simply be defined as a link between biology and computer science, in which the biological data is processed and computed through software, to yield an output, that is later interpreted in different ways.

Biological data indicates the nucleic acid or protein sequences, their simple or complicated forms, whereas the software is the computer program, specially designed for processing these data in a certain way, done using a certain algorithm (it is a recipe to solve a program problem). The data output is usually numerical or visual (often graphical), but mostly it needs to be well understood. The last one is the key point in the bioinformatics.

What is the need of bioinformatics?

In the research field, we need to be led to certain road, to choose one way or another, or to try many options until we define our research plan. Bioinformatics simply brings the solutions into your hands by a few mouse clicks.

One simple example to make it all clear is the PCR (Polymerase Chain Reaction). We always need to design a primer to trigger our reaction. If we did this through the ordinary ways, we would have to practically try out so many primers and this would surely take a tremendous amount of time. Now, what if you are computer- and internet-literate? You can simply use software to get many primer options for the DNA piece under investigation; doesn’t this save time, efforts and money?

Can bioinformatics be useful in different ways, other than the PCR example?

Some people may think that using bioinformatics is limited to some fields of biological research, and some others might think it is only a matter of prediction, which always needs to be evaluated for its accuracy, specificity and efficiency. But indeed, bioinformatics can be used in the analysis of nucleic acids and proteins.

Analysis?!! That is a vague word, how can you analyze a protein using bioinformatics?

Now you’ll see what bioinformatics can do for protein analysis:

1. Retrieving protein sequences from different databases, either specialized or general databases and it is not an easy job if you would think so.
2. Computing a protein or amino acid sequence to obtain:

• So much of the physicochemical properties of you sequence like the molecular weight, and isoelectric point…etc
• Hydrophilicity / hydrophobicity ratio

Both of the above can provide us with the probabilities of one protein acting as a receptor on the cell surface or it might be antigenic or even secreted outside the cell.

3. On the prediction aspect, we can predict:

• Some sequences of certain importance, e.g. the prediction of signal peptides that can lead us to know the secretory proteins of one organism
• 2ry structure of your protein
• 3ry structure of your protein

The last two points are applications of what is called structural bioinformatics, through which computer is capable of predicting the 2ry and 3ry (3-D) configuration of your protein, using special programs with advanced algorithms and artificial intelligence. Amazingly, this may be useful in understanding the receptor-substrate interactions.

4. Comparing sequences to obtain the best alignment (it means compare 2 or more sequences to find their relation to each other, i.e. finding similarities and differences), it will help in:

• Classifying your protein and relate it to its protein family
• Making your evolutional expectations about your protein to define whether it descends from another protein or not. This is called phylogenetic analysis, at which the proteins under investigation are studied to know which protein is considered a mother to the others, which are the daughter, the grand daughter, and so on
• Detection of the common domains, this will help us understanding the functions of unknown protein when it is compared to sequences of other proteins of known functions

Then, what will we gain if we compute DNA? Or you can say, what can bioinformatics do for DNA research?

On the same level as with protein, though different applications, we can use it in:

• Retrieving DNA sequences from different databases
• Computing a sequence to obtain information about its properties (like proteins) e.g. GC% which could be used with other properties to identify a gene
• Assembling sequence fragments (usually DNA is sequenced in the form of fragments which are needed to be assembled in the best way, bioinfo. does this in a faster and more accurate way rather than the ordinary assembly)
• Designing a PCR primer
• Prediction of DNA and RNA secondary structures (e.g. prediction the stems and loops of the t-RNA)
• Performing alignments between 2 or more sequences that can lead to many applications (as those mentioned above in protein alignments)
• Finding of repeats, restriction sites, Single Nucleotide Polymorphism (SNPs), and/or open reading frames, all of which have so huge applications in the medical and paramedical fields and typically in the research activities.

Tags: algorithms, alignment, analysis, artificial intelligence, Bioinformatics, biology, computer science, DNA, PCR, phylogenetic, phylogenetic analysis, protein, RNA, sequence, signal peptide, single nucleotide polymorphism, SNPs

In a study conducted by researchers in the University of Maryland School of Medicine, physical activity has proved to counteract FTO gene “fat, mass, and obesity-associated gene” in a group of European descendants who resided in the USA, known as Old Order Amish.

FTO gene has recently been linked to obesity & increased BMI “Body Mass Index” in numerous studies. Europeans usually have one or two copies of a variation of this gene. The research has enforced this prior assumption but brought, yet, another advantage since the study was done on 704 men & women of similar descent and thus similar genetic makeup, which what makes them ideal for genetic research. This helped researchers study the effect of physical activity on the expression of this gene.

In subjects, who were physically active throughout their daily routine, having multiple copies of the FTO gene didn’t seem to affect their BMI, despite the fact that in those, who were less active, a link between their BMI and FTO gene was obvious. This suggests that the choices one has to make in everyday life can deeply impact our body’s response to its own genes.

In order to compare the different variations in this gene, subjects were asked to wear accelerometers to measure their body movement on a 24-hour basis for seven days. They were then classified accordingly in order to conduct a comparative analysis. The genetic analysis revealed that 26 SNPs in the FTO gene were linked to BMI.

In the future, this may help tailor methods to prevent obesity in genetically susceptible individuals. So, after all we can’t blame it on our genes. Our decisions might in fact make up who we are & who we will become.

Source: Medical News today

Original research paper: Physical Activity and the Association of Common FTO Gene Variants With Body Mass Index and Obesity. PMID: 18779467 (Vote for the abstract on Biowizard)

Image Source

Tags: accelerometer, Amish, BMI, fat, gene, gene variant, obesity, SNPs

As we learned in pharmacology, the drug upon reaching the site of action, it gives a certain response. However, what if that response varies among individuals in terms of potency, duration or even adverse effects. As an example, in 1950s it has been noticed that Caucasians show prolonged effects to suxamethoniun chloride where further investigations revealed that 1 in every 3500 Caucasians has a less efficient butyryltranseferase, that metabolizes suxamethonium chloride, thus showing prolonged half-life & slower recovery from surgical paralysis. This fact has led to the emergence of a very unfavourable term “especially to clinicians”, Idiosyncrasy, which means the abnormal response to drugs, food & toxic agents that is peculiar to an individual. However, understanding the basic underlying mechanisms that led to this variation, made the scientists realize that in this case,  the drug response is not only a matter of pharmacodynamic effects but is considered as a ring holding pharmacology at one side and the genetic make up (which is the DNA sequence of any protein dealing with the drug as receptors, carriers, metabolizing enzymes………, etc) at the other side. This understanding led the scientists to replace the old term of Idiosyncrasy with a better descriptive term known as pharmacogenetics.

image credit: www.medigenomix.de/

Variations in the genetic make up are diverse in types. The most common type is Single Nucleotide Polymorphisms (SNPs) which are single nucleotide substitutions that can be found in coding and non-coding regions in the DNA sequences of protein systems dealing with the drug. Another form of variation can be chromosomal aneuploidy as Trisomy, where an extra copy of a chromosome, carrying a gene coding for any protein system dealing with the drug, will in turn lead to response variation. As an example for that is in leukemic patients with down syndrome, since there is a third copy of chromosome 21 which bears Reduced folate Carrier gene that codes for the transmembrane Reduced Folate carrier system which is responsible for the of transport methotrexate inside the cell, thus having a third extra copy of this gene leads to the over expression of RFC & methotrexate toxicity due to high levels of intracellular methotrexate.                                

Now, with the fact that the genetic make up of an individual dictates the response has been laid down, the clinical application has become a consequent step. For example, regarding leukemic patients having down syndrome, as mentioned previously, they are highly predisposed to methotrexate toxicity & thus as a routine, these patients should be set on lower doses of methotrexate. This particular example is a little bit simple that is easily characterized by the well known phenotype of down syndrome patients. However, translating the basic knowledge of pharmacogenetics to useful clinical guidelines is a more complicated approach in terms of both characterizing the alteration in the genetic make up and the subsequent clinical decision regarding the choice of the drug, dose and the dosing schedule. For instance carrying out a SNP Genotyping for DNA sequencing and identification of SNPs is a difficult matter as SNPs are very scattered along the human genome, where it has been estimated that every 1,ooo base pair, one SNP is found. In addition, the functional characterization of this SNP on the protein level is a matter of complexity that requires well controlled studies (i.e. all other causes of response variation regarding the drug of interest are eliminated).

Inspite of the complexity of the investigations for clinical application, it is a productive promising approach that does have three positive impacts on the clinical application where upon the tailoring of the treatment protocol according to each patient genetic make up (individualized therapy), this will increase the efficiency of the medications, decrease side effects, adverse drug reactions, morbidities Image credit:& mortalities and thus reduce the finances of the clincal mangement of adverse drug reactions as well as toxicities that may be result from incompatibility of the drug with the genetic make up of the patient.

http://www.pharmacogeneticsinpsychiatry.com

 

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Tags: aneuploidy, Clinical application, functional characterization, Genetics, idiosyncrasy, pharmacogenetics, Reduced Folate Carrier, response variation, SNPs, suxamethonium chloride, trisomy