Monday, November 17, 2014

Rheumatoid arthritis (RA) research shows potential of large-scale genetic studies for drug discovery



The results of the largest international study to date into the genetic basis of rheumatoid arthritis shed light on the biology of the disease and provide evidence that large-scale genetic studies can assist in the identification of new drugs for complex disorders such as rheumatoid arthritis.
The study, conducted by Dr. Robert M. Plenge from the Harvard Medical School and the Broad Institute in the USA and Dr. Yukinori Okada from the RIKEN Center for Integrative Medical Sciences in Japan, collaborating with colleagues from 70 institutions worldwide, is published in the journal Nature.
Genome-wide association studies are a method employed by scientists to identify the genes contributing to human disease. The current Nature study is the first to demonstrate that integrating the information provided by genome-wide association studies with existing datasets of genomic and biological information, such as drug targets, can assist in the discovery of drugs to cure human disease.
Rheumatoid arthritis is an autoimmune disease leading to inflammation of the joints and affecting 0.5-1% of adults in the developed world. The disease is thought to be caused by a complex combination of genetic and environmental factors and several genes have been shown to be associated with the disease. However, most of the findings were based on single population studies, and no large-scale trans-ethnic study had been carried out to date.
Figure a) Showing RA
Figure b) Showing Bioinformatics Process for Drug Discovery of RA

The international team performed a genome-wide association study meta-analysis on a total of over 100,000 subjects of European and Asian descent -- 29,880 rheumatoid arthritis patients and 73,758 controls -- by analysing around 10 million genetic variants called single nucleotide polymorphism (SNPs). They identified 42 new regions in the genome (loci) that are associated with rheumatoid arthritis, bringing the total number of known rheumatoid arthritis loci to 101.
By conducting bioinformatics studies integrating existing datasets with this new information, the researchers were able to pinpoint 98 genes in these 101 loci that could potentially contribute to the onset of rheumatoid arthritis. By integrating their findings with existing drug databases they demonstrate that these genes indeed possess many overlapping regions with the genes targeted by approved rheumatoid arthritis drugs -- although this wasn't known when the drugs were developed. The team identifies existing drugs used to treat cancer that also target rheumatoid arthritis genes and could potentially be used as therapy for the disease, such as CDK4/6 inhibitors.
The bioinformatics study also reveals that there is significant overlap between the genes involved in rheumatoid arthritis, human primary immunodeficiency disorders and blood cancers.
"This study sheds light on the fundamental genes, pathways and cell types that contribute to the onset of rheumatoid arthritis and provides evidence that the genetics of rheumatoid arthritis can provide important information for drug discovery," conclude the authors.
 

Monday, November 10, 2014

Bioinformatician helps biologists find key genes

It's like looking for a needle in a haystack. Scientists searching for the gene or gene combination that affects even one plant or animal characteristic must sort through massive amounts of data.
"Biologists used to study one gene at time, but now they can look at tens of thousands of genes at once." Xijin Ge said. Just one experiment to analyze gene expression can produce one terabyte of sequence data. "That's a little beyond many biologists' comfort zone."

He leads the bioinformatics research group, which provides the expertise that SDSU plant and animal scientists need to uncover how genes and proteins affect cell functions.
       
Setting up the experiments
Typically, scientists consult with their colleagues when planning their studies. After examining what they want to investigate, the researchers decide which techniques should be used to obtain data and a plan to analyze the data."It's critical to have the statistician and biologist working together," noted plant science professor Fedora Sutton, who worked with Ge on identifying gene interactions that account for freeze resistance in winter wheat. "He is able to say, based on statistical rules and regulations, this is where this has to be."
Using the same technique on one sample is not enough, Sutton pointed out. Multiple samples must be grown under the same conditions and then analyzed to have biological replicates. Scientist explained that experiments must be designed to gather biological rather than technical replicates. Once the technique to gather data is chosen and a plan of data analyses is created, scientist said, "we can figure out how many replicates are needed."

Analyzing megabytes of data
"Bioinformatics is an important tool to zoom in on the target gene networks," said Xing-You scientist, who collaborated with scientist to identify genes that are associated with seed dormancy in weedy rice. Weeds survive adverse environmental conditions because of strong seed dormancy, scientist explained. "To devise new weed management strategies, we need to understand the molecular genetics mechanisms of seed dormancy."
Scientists used a map-based cloning strategy and then applied bioinformatics tools, such as statistical tests and clustering, to find the candidate genes. This task involved looking at more than 30,000 to 40,000 genes, which can produce three to four million data points, according to the scientist. To determine which genes are responsible, scientist must first eliminate those data points that contain noise and then "focus on the reliable signals because we're looking at so many genes." Sometimes nearly half the data are eliminated.

Visualizing gene expression
Scientists use data-mining algorithms to find patterns of interest to the scientists. Typically, his analysis produces a visual representation of the data that is statistically significant.
One of Sutton's visuals was a heat map depicting gene expressions that were increased or up-regulated in red, those that were shut down or down regulated in green and those unaffected in black. This allowed her to identify six genes as potential markers which will then help breeders develop more lines of freeze-resistant winter wheat.
After identifying the genes, the researchers "want to piece together the jigsaw puzzle and figure out the common characteristics of the affected genes," scientists explained. This will allow us to identify the sub-systems, or pathways, that are regulated.

Monday, November 3, 2014

IMMUNOINFORMATICS: BIOINFORMATICS STRATEGIES FOR BETTER UNDERSTANDING OF IMMUNE FUNCTION



Bioinformatics Successfully Predicts Immune Response To One Of The Most Complex Viruses Known

The use of computers to advance human disease research – known as bioinformatics -- has received a major boost from researchers at the La Jolla Institute for Allergy & Immunology (LIAI), who has used it to successfully predict immune response to one of the most complex viruses known to man – the vaccinia virus, which is used in the smallpox vaccine. Immune responses, which are essentially how the body fights a disease-causing agent, are a crucial element of vaccine development.
Bioinformatics holds significant interest in the scientific community because of its potential to move scientific research forward more quickly and at less expense than traditional laboratory testing.
The research was executed with resulted in "A consensus epitope prediction approach identifies the breadth of murine TCD8+-cell responses to vaccinia virus," in the online version of the journal Nature Biotechnology. LIAI scientist Magdalini Moutaftsi was the lead author on the paper.
While bioinformatics – which uses computer databases, algorithms and statistical techniques to analyze biological information -- is already in use as a predictor of immune response, the LIAI research team's findings were significant because they demonstrated an extremely high rate of prediction accuracy (95 percent) in a very complex pathogen – the vaccinia virus. The vaccinia virus is a non-dangerous virus used in the smallpox vaccine because it is related to the variola virus, which is the agent of smallpox. The scientific team was able to prove the accuracy of their computer results through animal testing.
"Before, we knew that the prediction methods we were using were working, but this study proves that they work very well with a high degree of accuracy," Sette said.
The researchers focused their testing on the Major Histocompatibility Complex (MHC), which binds to certain epitopes and is key to triggering the immune system to attack a virus-infected cell. Epitopes are pieces of a virus that the body's immune system focuses on when it begins an immune response. By understanding which epitopes will bind to the MHC molecule and cause an immune attack, scientists can use those epitopes to develop a vaccine to ward off illness – in this case to smallpox.
The scientists were able to find 95 percent of the MHC binding epitopes through the computer modeling. "This is the first time that bioinformatics prediction for epitope MHC binding can account for almost all of the (targeted) epitopes that exist in very complex pathogens like vaccinia," said LIAI researcher Magdalini Moutaftsi. The LIAI scientists theorize that the bioinformatics prediction approach for epitope MHC binding will be applicable to other viruses.
 

Figure: Showing involvement of antibodies to destroy the pathogens in blood stream

"The beauty of the virus used for this study is that it's one of the most complex, large viruses that exist," said Moutaftsi."If we can predict almost all (targeted) epitopes from such a large virus,then we should be able to do that very easily for less complex viruses like influenza, herpes or even HIV, and eventually apply this methodology to larger microbes such as tuberculosis."
The big advantage of using bioinformatics to predict immune system targets, explained Sette, is that it overcomes the need to manufacture and test large numbers of peptides in the laboratory to find which ones will initiate an immune response. Peptides are amino acid pieces that potentially can be recognized by the immune system. "There are literally thousands of peptides," explained Sette. "You might have to create and test hundreds or even thousands of them to find the right ones," he said."With bioinformatics, the computer does the screening based on very complex mathematical algorithms. And it can do it in much less time and at much less expense than doing the testing in the lab."
The LIAI scientific team verified the accuracy of their computer findings by comparing the results against laboratory testing of the peptides and whole infectious virus in mice. "We studied the total response directed against infected cells," Sette said. "We compared it to the response against the 50 epitopes that had been predicted by the computer. We were pleased to see that our prediction could account for 95% of the total response directed against the virus."


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Bioinformatics Department

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