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.

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

Prevalence and impacts of genetically engineered feedstuffs on livestock populations.

A new review study finds there is no evidence in earlier scientific studies indicating that genetically engineered feed crops harmed the health or productivity of livestock and poultry, and that food products from animals consuming such feeds were nutritionally the same as products from animals that ate non-GMO feeds. Globally, food-producing animals consume 70 to 90% of genetically engineered (GE) crop biomass. This review briefly summarizes the scientific literature on performance and health of animals consuming feed containing GE ingredients and composition of products derived from them. It also discusses the field experience of feeding GE feed sources to commercial livestock populations and summarizes the suppliers of GE and non-GE animal feed in global trade. Numerous experimental studies have consistently revealed that the performance and health of GE-fed animals are comparable with those fed isogenic non-GE crop lines. United States animal agriculture produces over 9 billion food-producing animals annually, and more than 95% of these animals consume feed containing GE ingredients.

Green soybean plants, mixed organic and GMO

Data on livestock productivity and health were collated from publicly available sources from 1983, before the introduction of GE crops in 1996, and subsequently through 2011, a period with high levels of predominately GE animal feed. These field data sets, representing over 100 billion animals following the introduction of GE crops, did not reveal unfavorable or perturbed trends in livestock health and productivity. No study has revealed any differences in the nutritional profile of animal products derived from GE-fed animals. Because DNA and protein are normal components of the diet that are digested, there are no detectable or reliably quantifiable traces of GE components in milk, meat, and eggs following consumption of GE feed. Globally, countries that are cultivating GE corn and soy are the major livestock feed exporters. Asynchronous regulatory approvals (i.e., cultivation approvals of GE varieties in exporting countries occurring before food and feed approvals in importing countries) have resulted in trade disruptions. This is likely to be increasingly problematic in the future as there are a large number of "second generation" GE crops with altered output traits for improved livestock feed in the developmental and regulatory pipelines. Additionally, advanced techniques to affect targeted genome modifications are emerging, and it is not clear whether these will be encompassed by the current GE process-based trigger for regulatory oversight. There is a pressing need for international harmonization of both regulatory frameworks for GE crops and governance of advanced breeding techniques to prevent widespread disruptions in international trade of livestock feed stuffs in the future.

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Biotechnology Department

Bioinformatics approach helps Researchers find new use for Old drug

Developing and testing a new anti-cancer drug can cost billions of dollars and take many years of research. Finding an effective anti-cancer medication from the pool of drugs already approved for the treatment of other medical conditions could cut a considerable amount of time and money from the process. Now, using a novel bioinformatics approach, a team led by investigators at Beth Israel Deaconess Medical Center (BIDMC) has found that the approved antimicrobial drug pentamidine may help in the treatment of patients with advanced kidney cancer. Described online in the journal Molecular Cancer Therapeutics, the discovery reveals how linking cancer gene expression patterns with drug activity might help advance cancer care.

"The strategy of repurposing drugs that are currently being used for other indications is of significant interest to the medical community as well as the pharmaceutical and biotech industries," says senior author Towia Libermann, PhD, Director of the Genomics, Proteomics, Bioinformatics and Systems Biology Center at BIDMC and Associate Professor of Medicine at Harvard Medical School. "Our results demonstrate that bioinformatics approaches involving the analysis and matching of cancer and drug gene signatures can indeed help us identify new candidate cancer therapeutics."

Renal cell cancer consists of multiple subtypes that are likely caused by different genetic mutations. Over the years, Libermann has been working to identify new disease markers and therapeutic targets through gene expression signatures of renal cell cancer that distinguish these different cancer subtypes from each other, as well as from healthy individuals. In this paper, he and his colleagues were looking for drugs that might be effective against clear cell renal cancer, the most common and highly malignant subtype of kidney cancer. Although patients with early stage disease can often be successfully treated through surgery, up to 30 percent of patients with renal cell cancer present with advanced stages of disease at the time of their diagnosis.

To pursue this search, they made use of the Connectivity Map (C-MAP) database (http://www.broadinstitute.org/cmap), a collection of gene expression data from human cancer cells treated with hundreds of small molecule drugs.

"C-MAP uses pattern-matching algorithms to enable investigators to make connections between drugs, genes and diseases through common, but inverse, changes in gene expression," says Libermann. "It provided us with an exciting opportunity to use our renal cell cancer gene signatures and a new bioinformatics strategy to match kidney cancer gene expression profiles from individual patients with gene expression changes inducted by various commonly used drugs."

After identifying drugs that may reverse the gene expression changes associated with renal cell cancer, the investigators used assays to measure the effect of the selected drugs on cells. This led to the identification of a small number of FDA-approved drugs that induced cell death in multiple kidney cancer cell lines. The investigators then tested three of these drugs in an animal model of renal cell cancer and demonstrated that the antimicrobial agent pentamidine (primarily used for the treatment of pneumonia) reduced tumor growth and enhanced survival. Gene expression experiments using microarrays also identified the genes in renal cell cancer that were counteracted by pentamidine.

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Bioinformatics Department