TS-Si
is dedicated to the acceptance, medical treatment, and legal protection of individuals correcting the misalignment of their brains and their anatomical sex, while supporting their transition into society.
is dedicated to the acceptance, medical treatment, and legal protection of individuals correcting the misalignment of their brains and their anatomical sex, while supporting their transition into society.
Independence Day
See our Annotated List of DSM-related news, research reports, analyses, and opinion pieces.
Visit the TS-Si Article Archive for reports on science, medicine, government, society, and other topics.
Join The Flow!
| Are Some Genetic Mutations A Side Effect Rather Than A Cause? |
 |
 |
| SciMed - Genetics & Genome | |||
| TS-Si News Service | |||
| Wednesday, 09 January 2008 19:00 | |||
Pittsburgh, PA, USA. A single-celled fertilized egg develops from humble beginnings and then, through division, matures into a mature organism. The same process can renew blood cells, skin, hair, and internal organs.
An international team of scientists has discovered 480 genes that play a role in human cell division and have identified more than 100 of those genes that have an abnormal pattern of activation in cancer cells.
Using advanced computational biology techniques, the current research improves previous work on the identification of cell cycle genes and introduces new computational methods to manage the increasing complexity — and subtlety — of the collected data.
Subject to further verification, the team's work suggests genetic mutations can be a side effect of certain cancers rather than a cause. Their findings appear in the Proceedings of the National Academy of Science (PNAS). The results contribute to a growing body of knowledge on the fundamental processes that underlie a variety of birth conditions and diseases, such as cancer.
The cell-division cycle
The cell cycle, or cell-division cycle, is the series of events that take place in a cell leading to its replication into "daughter" cells. It is basic to all animals, fungi, plants, and protists — they are "eukaryotes", organisms with a cell nucleus.
The cells have a cytoskeleton that acts as a scaffolding for the cells. The cells are laced with internal membranes and organized into complex stuctures that support life. During the mitotic (M) phase, the cell splits itself into two distinct "daughter cells".
What are the genetic blueprints for this process? Although outstanding questions remain regarding their identify and interrelationships, a team of scientists from Germany, Israel, and the US have advanced our knowledge.
Scientists have faced problems when using conventional techniques to identify a full complement of human cell cycle genes. Molecular biologists have found cell cycle genes in yeast, plants and mice, as well as in a human cancer cell line known as HeLa. A previous study, however, that purported to identify cell cycle genes in normal human cells, proved flawed and invalid.
 Scientists typically use a microarray for analyzing gene expression. The microarray is a tool consisting of a small membrane or glass slide containing samples of many genes arranged in a regular pattern. Microarrays contain a very large number of genes in a small space.
This makes them useful when studying a small sample or conducting a quick survey of a large number of genes. Microarrays for DNA are small, solid supports — the sequences from thousands of different genes are immobilized, or attached, at fixed locations.
"Noisy" measurements of unsynchronized cell movements
The problem that molecular biologists encountered in studying human cells has to do with the fact that the cell development must be arrested so that micro array technology can be used to measure which genes are expressed at each stage of the cell cycle.
 "When the cells are released from arrest, some don't resume cycling at all, while others resume at different intervals", said co-lead author Itamar Simon, a molecular biologist at Hebrew University Medical School in Israel. Why this is a problem in humans and not other species is not understood, but the result is that the cells — and these studies require millions of them — end up scattered among different stages of the cell cycle. Measurements of these unsynchronized cells are hopelessly "noisy."
Sorting out the noise, and making a systematic survey of the genetic material, can contribute to a model of the human cell cycle that accounts for standard processes and exceptions.
The Cancer Test Case
Cancer presents a strong example of the "noisy", unsynchronized, cells. Malignant cells have lost control of the replication process, so detecting differences in cell cycle gene activation in normal and malignant cells provides important clues about how cancers develop, said Ziv Bar-Joseph, a Carnegie Mellon University computational biologist who led the study. These genes also are potential targets for drug therapy.
Among the 480 genes the researchers identified in this study, more than 100 of those genes have an abnormal pattern of activation in cancer cells. Unlike many cancer studies, which seek to identify "missing" genes that might cause cancer, this new research shows that genes can contribute to cancer in less obvious ways.
 3D rendered image of translucent cells, dividing.
In experiments, the team arrested and released cells in culture and then measured DNA content to determine which ones had stopped cycling and which ones were at various stages of the cell cycle. This information was used to construct a model of cell behavior that could be used to reanalyze the gene expression data, enabling researchers to combine expression data from cells that are all at the same stage of the cell cycle.
The genes found to be deregulated in cancer cells include a few, such as PER2 and HOXA9, that already have documented links to cancer. Most have not, including at least three genes responsible for repairing genetic mutations that occur as DNA is duplicated in the cell.
 The failure of the DNA repair genes to cycle in cancer cells raises the possibility that some mutations associated with cancer may not cause cancer. "Some of the mutations may be caused by the non-cycling genes, rather than the other way around," said Bar-Joseph.
"What we see is that there are many genes that are present and yet still involved in cancer because they are not activated, or expressed, in the way they normally are," said Simon. Rather than cycling on and off as normally occurs when cell replication and development proceeds, these genes are expressed in a steady state or not at all.
Determining if genetic mutations are a side effect of certain cancers rather than a cause will require further investigation, as will identifying which of the genes that do not cycle in cancer cells are the most significant.
"These genes seem to be important, but we don't yet know which ones play key roles or might be targets for drug therapy," Simon said. "We have narrowed down the field of candidates. Instead of looking at thousands of genes, now we can concentrate on about 100."
Analyzing the data
Speaking of the computationally dificult data analysis effort, "People said you couldn't solve this problem," Bar-Joseph said.
To process the data and identify the genes, researchers used a computer science method called deconvolution. Widely used in such fields as image processing and signal processing, deconvolution proved effective in eliminating noise from the data.
Information is understood to be convoluted when adjoining data elements overlap in value. To visualize this, imagine an image on a computer screen in which each pixel is altered by some function of the surrounding pixels. Different functions produce a variety of effects. To reduce this convolution, and the accompanying "noise", computer scientists use deconvolution, a mathematical technique, to reverse the effects of the convolution on recorded data. It is also known as Wiener deconvolution, after Norbert Wiener of MIT, who laid the foundations for the technique, based on work he had done during World War II.
Deconvolution for technicians
In simplified form, the object of deconvolution is to find the solution of an equation that takes the form: f ∗ g = h
Usually, h is a recorded signal, and f is some signal that we wish to recover. However, it has been convolved with some other signal g before the recording. In physical measurements, noise — in this case ε — may have entered the recorded signal. The situation more nearly approximates (f * g) + ε = h
If a noisy signal or image is noiseless when attempting a statistical estimate of g, the estimate will be incorrect.
In turn, estimations of f will also be incorrect.
Estimates of the deconvolved signal will degrade with lowering of the signal-to-noise ratio. Thus, inverse filtering the signal will be unrevealing in such cases.
However, even limited knowledge of the type of noise contained in the data may improve the f estimate through Wiener deconvolution or a comparable techniques.
Ziv Bar-Joseph is a Carnegie Mellon University computational biologist who led the study. He is an assistant professor of computer science and machine learning in the School of Computer Science and a member of Carnegie Mellon's Lane Center for Computational Biology.
Itamar Simon is a molecular biologist at Israel's Hebrew University Medical School.
Other members of the investigative team included Yong Lu of Carnegie Mellon's Computer Science Department, Zahava Siegfried and Michael Brandeis of Hebrew University, Benedikt Brors and Roland Eils of the German Cancer Research Center in Heidelberg, and Brian D. Dynlacht of the New York University School of Medicine.  The TS-Si News Service is a collaborative effort by TS-Si.org editors, contributors, and corresponding institutions. The sources can include the cited individuals and organizations, as well as TS-Si.org staff contributions. Articles and news reports do not necessarily convey official positions of TS-Si, its partners, or affiliates. We welcome your comments. Use the form below to leave a public comment or send private correspondence via the TS-Si Contact Page. We will not divulge any personal details or place you on a mailing list without your permission.                      Add this page to your favorite Social Bookmarking websites.
Set as favorite
Email This
Comments (0)
 Write comment
|
|||
| Last Updated on Thursday, 10 January 2008 14:44 |
Subscribe to this comment's feed
Show/Hide comments
Show/Hide comment form
The TS-Si News Service is a collaboration of TS-Si staff, contributors, and corresponding institutions. Contents do not necessarily convey official positions of TS-Si, its partners, or affiliates