Calculating Gene And Protein Connections In Parkinson's Disease 

Cells respond to stimuli with changes in many processes, including 
gene expression and cellular communications that coordinate a 
cell’s activities (cell signaling pathways). The figure shows a 
general signaling pathway. Genetic screen data (the results from 
altering one gene in a cell's genome and seeing what observable 
traits appear in the cell) and information on gene expression identify 
only some of these molecular components and often do not identify 
the same genes. (Proteins identified in genetic screens are colored 
blue, and the products of expressed genes are purple.) 
ResponseNet identifies cellular communication pathways that link 
these two types of data and predicts proteins that are part of these 
pathways even if they are not identified in either screen (colored 
red). (Credit: Tom DiCesare/Whitehead Institute)

ScienceDaily (Feb. 22, 2009) — A novel approach to analyzing 
cellular data is yielding new understanding of Parkinson's disease's 
destructive pathways.

Researchers have created an algorithm that meshes existing data 
to produce a clearer step-by-step flow chart of how cells respond to 
stimuli. Using this new method, Whitehead Institute and 
Massachusetts Institute of Technology scientists have analyzed 
alpha-synuclein toxicity to identify genes and pathways that can 
affect cell survival. Misfolded copies of the alpha-synuclein protein 
in brain cells are a hallmark of Parkinson's disease.

Until now, data on gene expression and protein production have not 
been consistently analyzed together, leaving gaps in researchers' 
understanding of how various genes and proteins interact to form a 
cell's response to a stimulus. This new method could speed the 
development of therapies for a variety of diseases, including 
Parkinson's disease.

The scientists have employed this new computational technique to 
analyze alpha-synuclein, a mysterious protein that is associated 
with Parkinson's disease.

Cells are constantly adapting to various stimuli, including changes 
in their environment and mutations, through an intricate web of 
molecular interactions. Knowledge of these changes is crucial for 
developing new treatments for diseases. To decipher how a cell 
responds to various stimuli, laboratories worldwide have been 
turning to new technologies that produce vast amounts of data. Such 
data typically exists in two major forms: genetic screen data (the 
results from deleting a gene from a cell's genome and seeing what 
observable traits appear in the cell) and information on the cellular 
levels of messenger RNA (mRNA, which is the template for 
proteins).

Historically, these two types of data have largely been analyzed 
independently of each other, revealing only glimpses of the cell's 
internal workings. Each type of data is actually biased toward 
identifying different aspects of cellular response, something that 
researchers had not realized until now. However, the new algorithm, 
known as ResponseNet, exploits these biases and allows for 
combined analysis.

In this combined analysis, both data types are integrated with 
molecular interactions data into a diagram that connects the 
experimentally identified proteins and genes. While this typically 
results in an extraordinarily complicated diagram, sometimes 
jokingly referred to as a "hairball", ResponseNet is designed to 
whittle the hairball down to the most probable pathways connecting 
various genes and proteins.

Esti Yeger-Lotem, a postdoctoral researcher in the laboratories of 
Whitehead Member Susan Lindquist and of Ernest Fraenkel at MIT's 
Biological Engineering department and co-author of the Nature 
Genetics article, says that by analyzing those probable pathways, a 
systems view of the cellular response emerges. "This allows for a 
more complete understanding of cellular response and can reveal 
hidden components of the response that may be targeted by 
drugs," she says.

According to Laura Riva, a postdoctoral researcher in MIT's 
biological engineering department and one of the designers of the 
algorithm, ResponseNet is potentially very useful for researchers.

"It is a powerful approach for interpreting experimental data 
because it can efficiently analyze tens of thousands of nodes and 
interactions," says Riva, who is also a co-author on the article. "The 
output of ResponseNet is a sparse network connecting some of the 
genetic data to some of the transcriptional data via intermediate 
proteins. Biologists can look at the network and understand which 
pathways are perturbed, and they can use it to generate testable 
hypotheses."

To demonstrate ResponseNet's capabilities, Yeger-Lotem entered 
the data from screens of 5,500 yeast strains (Saccharomyces 
cerevisiae). These strains are based on a yeast model that creates 
large amounts of the protein alpha-synuclein, thereby mimicking the 
toxic effects of alpha-synuclein accumulation in Parkinson's disease 
patients' brain cells.

Ernest Fraenkel, Assistant Professor of Biological Engineering at 
MIT, says that the alpha-synuclein data are an excellent test case 
for the algorithm, which has lead to new insights from existing data.

"The connection between alpha-synuclein and Parkinson's disease 
is enigmatic," says Fraenkel. "We have wonderful data from the 
yeast model, but despite this richness of data, so little is known 
about what alpha-synuclein really does in the cell."

Using these data, ResponseNet identified several links between 
alpha-synuclein toxicity and basic cell processes, including those 
used to recycle proteins and to usher the cell through its normal life 
cycle.

Surprisingly, ResponseNet also tied alpha-synuclein toxicity to a 
highly-conserved pathway targeted by cholesterol-lowering statin 
drugs and another pathway targeted by the immunosuppressing 
drug rapamycin.

To confirm ResponseNet's links and to test how these two pathways 
could affect alpha-synuclein toxicity, researchers added either 
rapamycin or the statin lovastatin to yeast model cultures. When the 
researchers added a low dose of rapamycin to the yeast model, 
the drug was toxic to the yeast. When lovastatin was added, the 
yeast reduced their growth rate, an indicator that the yeast had 
gotten sicker. However, when researchers added the molecule 
ubiquinone (also known as coenzyme Q10 or CoQ10), which is 
farther downstream in the statin network and possibly 
undersynthesized in alpha-synuclein-containing yeast, ubiquinone 
modestly suppressed alpha-synuclein toxicity.

All of these results validated the hypotheses based on 
ResponseNet's network.

"ResponseNet provides a wealth of new information," says 
Lindquist, who is also a Howard Hughes Medical Institute 
investigator and a professor of biology at MIT. "Some of the things 
we have found offer a promise to speed the development of new 
therapeutic strategies for Parkinson's disease. For the sake of the 
patients involved, let's hope they hold true in a human brain."

Full Citation: "Bridging high-throughput genetic and transcriptional 
data reveals cellular responses to alpha-synuclein toxicity"

Nature Genetics, online February 22, 2009

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