Calculating gene and protein connections in a Parkinson’s model

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Calculating gene and protein connections in a Parkinson's model

Published on 24 February 2009, 00:01 Last Update: 1 day(s) ago by

Insciences

Insciences Organisation - Basel,Switzerland

Tags: Alpha-synuclein Genetics Medicine Parkinson Saccharomyces

cerevisiae

http://insciences.org/article.php?article_id=2664

CAMBRIDGE, Mass. – A novel approach to analyzing cellular data is

yielding new understanding of Parkinson's disease's destructive

pathways.

Whitehead Institute and Massachusetts Institute of Technology (MIT)

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).

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).

" ResponseNet provides a wealth of new information, " says Whitehead

Member Lindquist. " 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. "

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 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.

Lindquist and Fraenkel postdoctoral researcher Esti Yeger-Lotem

(above) and Riva, a postdoctoral researcher in MIT's biological

engineering department (below)

According to 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.

Whitehead Member Lindquist

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 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. "

The development of effective antifungal drugs is limited by humans'

close evolutionary relationship with fungi, and, in recent years,

fungi's ever-evolving resistance to existing drugs. Former Lindquist

postdoctoral researcher and lead author of this study, Leah Cowen,

explains: " The drugs just don't wipe out the infection. So you wind

up with a small population of fungi living in a host that is exposed

to the drug for a long time, which favors evolution of drug

resistance. "

Previous studies suggested that Hsp90, which is found in both fungi

and humans, plays a vital role in the evolution of drug resistance.

In this most recent study, which appears in the February 24 issue of

the Proceedings of the National Academy of Science (PNAS), Whitehead

researchers tested Hsp90 inhibitors in combination with common

antifungal drugs in an attempt to block the growth of Candida

albicans and Aspergillus fumigatus, two of the most prevalent and

lethal species that cause fungal infections in humans.

The researchers found that when antifungals or Hsp90 inhibitors are

used individually, they are ineffective; however, when paired they

form a deadly duo.

" When you combine the two, you reduce Hsp90 function enough that the

fungi can no longer mount the crucial stress responses to antifungals

required for survival, " says Cowen. " So you cripple the fungus by

severely compromising its stress responses. "

According to Lindquist, " This is an entirely new strategy for making

fungi susceptible to preexisting drugs and for preventing fungi from

deploying the resistance mechanisms, which they have evolved against

those compounds. It could make the difference between life and death. "

Because Hsp90 is highly conserved, finding a compound to turn off

Hsp90 in fungi, but not in humans, is a significant hurdle scientists

must overcome. In addition, current Hsp90 inhibitors are toxic in

mice with resistant fungal infections. To find promising Hsp90

inhibitors for antifungal therapy, Lindquist's lab has received a

grant from the Molecular Libraries Probe Center Network (MLPCN)

Program of the National Institutes of Health. The grant will allow

researchers to screen large numbers of compounds in the search for

potential fungus-selective Hsp90 inhibitors.

Still, even if the screen is successful, the battle between humans

and fungi is not over.

" Eventually, like most drugs, Hsp90 inhibitors too, will become

subject to resistance, " suggests Lindquist, who is also a

Medical Institute investigator and professor of biology at

MIT. " But in the meantime, these inhibitors will open a very large

window of opportunity for individuals with resistant fungal

infections. "

This research was supported by Damon Runyon Cancer Research

Foundation, a Genzyme Fellowship, the Burroughs Wellcome Fund, the G.

Harold and Leila Y. Mathers Charitable Foundation, and a Canadian

Institutes for Health Research Grant.

Written by Giese.

Contact: Giese, 617-258-6851, giese@...

Source: Whitehead Institute for Biomedical Research