Posts Tagged ‘human’

How premalignant cells can sense oncogenesis, halt growth

Since the 1980s, scientists have known that mutations in a human gene called RAS are capable of setting cells on a path to cancer. Today, a team at Cold Spring Harbor Laboratory (CSHL) publishes experiments showing how cells can respond to an activated RAS gene by entering a quiescent state, called senescence.

CSHL Professor Nicholas Tonks and Benoit Boivin, now a University of Montreal Assistant Professor, co-led a team that traced the process in exquisite detail. They began by confirming that activation of mutant, oncogenic H-RAS, one of the human RAS oncogene variants, spurs cells to generate hydrogen peroxide (H2O2), a form of reactive oxygen species, or ROS. “Most people, when they think about ROS, think about the great damage they can do at high concentrations,” says Tonks. “But this research exemplifies how the controlled production of ROS in cells can play a beneficial role.”

The team showed how the production of ROS in response to oncogenic H-RAS enables cells to fine-tune signaling pathways, leading them to enter a senescent state. A key part of this process is the impact of ROS on a protein called PTP1B. Tonks discovered PTP1B some 25 years ago. It is an enzyme — one in a family of protein tyrosine phosphatases (PTPs), of which there are 105 in humans — that performs the essential biochemical task of removing phosphate groups from amino acids called tyrosines in other proteins. Adding and removing phosphates is one of the principal means by which signals are sent among proteins.

In cells with oncogenic H-RAS, ROS is produced in small quantities, sufficient to render PTP1B inactive. The team found that with the phosphate-removing enzyme unable to do its usual job, a key protein called AGO2 remains phosphorylated — with the consequence that it can no longer do what it normally does, which is engage the cell’s RNA interference machinery. In normal cells, the RNAi machinery represses a gene called p21. But in this specific condition — with H-RAS oncogenically activated, PTP1B inactivated by ROS, and RNAi disabled — p21 proteins begin to accumulate unnaturally, the team discovered.

“This is the key step — accumulation of p21 proteins effectively halts the cell cycle and enables the cell to enter the senescent state,” explains Ming Yang, a doctoral student in the Tonks lab. She and Astrid Haase, Ph.D., a postdoctoral investigator in the laboratory of CSHL Professor Greg Hannon, are the first two authors, respectively, on the team’s paper, published in Molecular Cell.

“This is confirmation of a hypothesis we presented five years ago,” Tonks says. “We knew that oncogenic RAS induced the production of ROS. We proposed that this would lead to regulation of PTPs, and using the example of PTP1B this is precisely what the team discovered in this work — showing also how inactivation of this PTP is part of a complex signaling cascade that can culminate in the induction of senescence.”

ROS have been linked to the pathogenesis of several diseases including Alzheimer’s, diabetes and heart failure. “By showing that PTP1B inactivation by oxidation prevents AGO2 from doing its job, we make a clear link between ROS and gene silencing which could also be observed in other pathologies” says Boivin. Hence, the role of PTP1B in keeping the RNAi machinery active could have important ramifications.

Entering senescence is not enough to arrest oncogenesis completely. Oncogenic mutations typically multiply as cancers evolve to promote their survival and proliferation. But the current work does show the potential importance of knowing the genetic background of a cancer patient, for there are windows of time — narrow though they may be — in which naturally occurring processes induce pauses in growth.

source : http://www.sciencedaily.com/releases/2014/08/140828135521.htm

Statistical Approach for Calculating Environmental Influences in Genome-Wide Association Study (GWAS) Results

The approach fills a gap in current analyses. Complex diseases like cancer usually arise from complex interactions among genetic and environmental factors. When many such combinations are studied, identifying the relevant interactions versus those that reflect chance combinations among affected individuals becomes difficult. In this study, the investigators developed a novel approach for evaluating the relevance of interactions using a Bayesian hierarchal mixture framework. The approach is applicable for the study of interactions among genes or between genetic and environmental factors.

Chris Amos, PhD, senior author of the paper said, “These findings can be used to develop models that include only those interactions that are relevant to disease causation, allowing the researcher to remove false positive findings that plague modern research when many dozens of factors and their interactions are suggested to play a role in causing complex diseases.”

The model evaluates “gene by gene” and “gene by environment” factors by looking at specific DNA sequencing variations. Complex diseases are caused by multiple factors. In some cases a genetic predisposition or abnormality may be a factor. A person’s healthy lifestyle and environment, however, may help him or her overcome a genetic vulnerability and avoid a chronic disease like cancer. In other situations, a person whose DNA does not have an abnormality may develop one when exposed to known carcinogens like tobacco smoke or sunburn.

“Understanding the combinations of genetic and environmental factors that cause complex diseases is important,” said Amos, associate director of population sciences and deputy director of Norris Cotton Cancer Center, “because understanding the genetic architecture underlying complex disease may help us to identify specific targets for prevention or therapy upon which interventions may appropriately reduce the risk of cancer development or progression.”

The study applied the model in cutaneous melanoma and lung cancer genetic sequences using previously identified abnormalities (known as single nucleotide polymorphisms or SNPs) with environmental factors introduced as independent variables. The Bayesian mixture model was compared with the traditional logistic regression model. The hierarchal model successfully controlled the probability of false positive discovery and identified significant interactions. It also showed good performance on parameter estimation and variable selection. The model cannot be applied to a complete GWAS because if its reliance on other probability models (MCMC ). It is most effective when applied to a group of SNPs.

“The method was effective for the study of melanoma and lung cancer risk because these cancers develop from a complex interaction between genetic and environmental factors but understanding how these factors interact has been difficult to achieve without the sophisticated modeling that has been developed in this study,” said Amos.

source : http://www.sciencedaily.com/releases/2014/08/140827111811.htm

A shift in the code: New method reveals hidden genetic landscape

The letters in the human genome carry instructions to make proteins, via a three-letter code. Each trio spells out a “word;” the words are then strung together in a sentence to build a specific protein. If a letter is accidentally inserted or deleted from our genome, the three-letter code shifts a notch, causing all of the subsequent words to be misspelled. These “frameshift” mutations cause the protein sentence to become unintelligible. Loss of a single protein can have devastating effects for cells, leading to dysfunction and sometimes to serious diseases.

DNA insertions and deletions vary in length and sequence. Each indel can range in size from one DNA letter to thousands, and they are often highly repetitive. Their variability has made it challenging to identify indels, despite major advancements in genome sequencing technology. They are, in effect, regions of the genome that have remained hidden from view as researchers search for the mutations that cause disease.

A team of CSHL scientists, including Assistant Professors Mike Schatz, Gholson Lyon, and Ivan Iossifov, and Professor Michael Wigler, has devised a way to mine existing genomic datasets for indel mutations. The method, which they call Scalpel, begins by grouping together all of the sequences from a given genomic region. Scalpel — a computer formula, or algorithm — then creates a new sequence alignment for that area, much like piecing together parts of a puzzle.

“These indels are like very fine cuts to the genome — places where DNA is inserted or deleted — and Scalpel provides us with a computational lens to zoom in and see precisely where the cuts occur,” says Schatz, a quantitative biologist. Such information is critical to understand the mutations that cause disease. In work published today in Nature Methods, the team used Scalpel to search for indels in patient samples. Lyon, a CSHL researcher who is also a practicing psychiatrist, worked with his team to analyze a patient with severe Tourette syndrome and obsessive-compulsive disorder, identifying and validating more than a thousand indels to demonstrate the accuracy of the method.

The CSHL team performed a similar analysis to search for indels that are associated with autism. They explored a dataset of 593 families from the Simons Simplex Collection, a group composed entirely of families with one affected child but no other family members with the disorder. While the researchers discovered a total of 3.3 million indels across the 593 families, most appeared to be relatively harmless. Still, a few dozen mutations stood out to be specifically associated with autism. “All this adds to our body of knowledge about the spontaneous mutations that cause autism,” says Schatz.

But the tool can be applied much more broadly. “We are collaborating with plant scientists, cancer biologists, and others, looking for indels,” says Schatz. “This is a powerful tool, and we are looking forward to revealing new pieces of the genome that make a difference, throughout the tree of life.”

This work was supported by US National Institutes of Health, US National Science Foundation, the CSHL Cancer Center Support Grant, the Stanley Institute for Cognitive Genomics, and the Simons Foundation.

source : http://www.sciencedaily.com/releases/2014/08/140817220100.htm