In class we recently read:
Genetic analysis of radiation-induced changes in human gene expression. Smirnov DA, Morley M, Shin E, Spielman RS, Cheung VG. Nature. 2009 May 28;459(7246):587-91. Epub 2009 Apr 6.
Humans are exposed to radiation through the environment and in medical settings. To deal with radiation-induced damage, cells mount complex responses that rely on changes in gene expression. These gene expression responses differ greatly between individuals and contribute to individual differences in response to radiation. Here we identify regulators that influence expression levels of radiation-responsive genes. We treated radiation-induced changes in gene expression as quantitative phenotypes, and conducted genetic linkage and association studies to map their regulators. For more than 1,200 of these phenotypes there was significant evidence of linkage to specific chromosomal regions. Nearly all of the regulators act in trans to influence the expression of their target genes; there are very few cis-acting regulators. Some of the trans-acting regulators are transcription factors, but others are genes that were not known to have a regulatory function in radiation response. These results have implications for our basic and clinical understanding of how human cells respond to radiation.
This paper described the reseachers efforts to identify classes of genes that may serve as potential markers of radiation sensitivity. To accomplish this the investigators examined gene expression patterns of radiation exposed cells.
Tox1401 Students: How did the reseachers do this? What cells did they use and why? What is the difference between cis-regulatory and trans-regulatory factors? Give an example of each from the paper and describe the function.
Child hood asthma is part of our national health care challenge. An estimated 9.6 million children (13.1 percent) under the age of 18 have been diagnosed with asthma. It is hoped that pharmacogenomics can make treatment a bit more successful for those with asthma. The question so far has been how?
In their 2010 paper Kondo et al. describe the beginnings of a clinical workflow, based on the consolidation of a number of genetic/therapeutic correlation studies. The authors suggest that a combination of clinical evaluation steps combined with a knowledge of specific allelic subtypes carried by the patient could provide more effective therapeutic choices. The authors point out that there are ethical considerations when genetic information is recorded and detailed. But what they provide is a remarkably simple workflow chart integrating pharmacogenomic information with clinical observation.
I have asked the Tox 1401 students to describe at least one of the gene polymorphisms and mutations from this paper, so read on in the comments if you would like to learn specifics.
Last year we started using our pharmacogenomics laboratory to reach out to students in the community. This year we invited 6th through 8th graders from a local schools. I have asked the pharmacogenomic students for feedback on their experiences as they served as student teachers. A repeating cycle is one of the interesting ways to think about teaching. If you are a student responding I would ask that you comment on your use (or not) of this cycle as well as addressing the following ASL reflection points:
- How does your service learning experience relate to the objectives of this course?
- What did you observe?
- What did you learn?
- What has worked? What hasn’t?
- Is there something more you could do to contribute to the solution?
- What have you learned about yourself?
- What have you learned about teaching?
- What have you contributed to the students?
- What values, opinions, beliefs have changed?
- What was the most important lesson you learned
- How have you been challenged?
- What impact did you have on the community?
Way back when… in 2000, microarray was the new wave of technologies to address a scientific challenge that has been around for a long time. If we think about how an organism responds to an environmental change, disease, or new stage of development there is a corresponding change at the gene expression level. This change in gene expression provides for a host of new proteins that will be needed, and the suppression of all of the proteins that will not be needed.
The challenge was how would we be able to capture hundreds, maybe thousands of these changes… at the same time. We needed a molecular “snap-shot” of all of the mRNAs in a cell before the change, followed by another after the change, and then we needed the ability to sort out the results.
Enter the microarray. The paper referenced above provides a nice overview of the challenges the were faced, and the technologies that were developed to face these challenges.
I have asked the Tox 1401 students to pull out some of the genes mentioned in the paper and take a look at the annotated information about their gene of interest from OMIM and UniProt. If you would like to see their descriptions please move on to the comments section.
Often described as the next frontier in gene therapy, siRNA has moved from the realm of the quirky biological oddity to applied therapy very quickly. I have asked the Tox1401 students to describe what they see as potential toxicological problems with this approach.
We used this paper as a description of the possible delivery approaches. The paper is freely (and fully) available from pubmed central.
Read on through the comments to see what they came up with.
As the human genome project’s influence grows, one of the concepts that has emerged is complexity. Scientists including biologists have appreciated for some time that genetic networks drive development and biological responses. The cell’s responses to stimuli require and ever changing cast of proteins. The instructions for the protein sequences are encoded within the genome. If we could understand how this large cast of proteins is assembled into smaller pathways and responses we would be considerably further along. The parts list is long and complex, but as the genome project began to uncover the instructions for how the “parts” are made there was a feeling that science may be able to build models that describe function and disease.
The 2010 Nature article describes this aspiration:
The hope was that by cataloguing all the interactions in the p53 network, or in a cell, or between a group of cells, then plugging them into a computational model, biologists would glean insights about how biological systems behaved
And indeed this did (and still does) seem like a reasonable approach. Biological networks have turned out to be as complex as we could have hoped. Systems biology is still moving forward, but the sheer number of possible rules that govern how all of the cellular parts work together and interact suggest that we will be working with this complexity for some time. There is a universe of rules that describe networks; explaining how proteins, ligands, nucleic acids and more interact and result in function.
Towards the end of the article there is an interesting quote from Bert Vogelstein:
“Humans are really good at being able to take a bit of knowledge and use it to great advantage,”
And we are. With some careful science and good detective skills we can take what we do know and put it to good use, combating disease. The fact that biological systems are complex and that this complexity is not simply going to be understood the first time we draw back the curtain is a great finding.
I am asking the Tox1401 students to look into this complexity a bit further. Let’s start with a pathway database like reactome.org. Choose the phase II pathways and select a single protein within that pathway, perhaps the NAT1 arylamine N-acetyltransferase. Provide a description of the protein, and the pathway that it takes part in.
I have asked the Tox1401 students to use OMIM to do some benzene toxicology annotation. The comments section of this post contains brief descriptions of individual proteins that were identified in two separate benzene screens.