Putting Genome Interpretation to the Test
At the American Society of Human Genetics (ASHG) conference in November 2012 in San Francisco, CA, Steven Brenner, a computational geneticist from the University of California, Berkley, stood up in front of an audience and argued that it was unlikely that a single genome interpretation tool could identify variants for an array of illnesses or phenotypic traits (1). Instead, interpretation methods would likely need to be gene-specific or tailored for precise applications.
The predictors, assessors, and observers who participated in CAGI 2011, which was held in San Francisco, CA. Source: CAGI
This figure shows the ROC curves for the prediction of patients with Crohn’s disease against the result of 1,000 random predictions, which are shown in gray. Source: CAGI
Steven Brenner helped develop CAGI to determine how well genome interpretation tools could translate to the clinic. Source: UC Berkeley
John Moult, one of the organizers of CAGI, says the challenges are giving scientists a better sense of the genome interpretation tools that currently exist. Source: University of Maryland
Brenner came to that conclusion after looking over the results of the Critical Assessment of Genome Interpretation (CAGI), a community experiment, now in its third year, challenges researchers to computationally predict the phenotypes of genetic variants. The teams then compare their results with unpublished experimental data, showing researchers and clinicians which tools can most accurately interpret large amounts of genomic sequence variation data and which ones might be reliable enough to use in the clinic. The results from the first two rounds of challenges have been clear for Brenner: most genomic interpretation tools are not reliable enough for the clinic yet.
After his talk at ASHG, several clinicians came up to him and expressed their concerns. Many had been using genome interpretation tools more generally, possibly making their conclusions less reliable. “General methods are limited in how well they will perform, which is not what people assumed before,” he says. “What that reaction showed me was that CAGI has a broad set of people that derive value from the experiment’s findings.”
Increasing Confidence
Brenner and John Moult, a computational biologist at the University of Maryland in Rockville, MD, organized the first CAGI experiment in 2010. It was a pilot project to get a better sense of the tools researchers in the community were using to study human genome variation and the phenotypic predictions coming from them. “Coming into CAGI, we had no understanding of how well methods for interpreting genome variation worked,” Brenner says. “Now, we’re starting to get a hint of what the big picture is.”
The goal was to provide a better sense of the correct level of confidence scientists and clinicians should have in the methods to predict the phenotype of sequence variants that are out there right now. “There’s a lot of uncertainty about how these methods work on real problems and so the challenges address the question of how can we test them in real-world situations,” Moult says.
In the beginning, Brenner and Moult had little idea of what to expect. The first year of the experiment was supposed to be very small, a pilot to see who would participate and what tools actually existed. In the end, the 2010 challenges drew more than 100 prediction submissions from eight countries, exceeding the organizers’ expectations.
Forty of the participants traveled to Berkeley in December 2010 to review the results. The top prize was awarded to Yana Bromberg, a bioinformatician at Rutgers University in New Jersey, for her work on interpretation software called screening for non-acceptable polymorphisms, or SNAP for short, which evaluates the effects of single amino acid substitutions on protein function (2). It was the first time Moult and Brenner had heard of SNAP.
In 2011, teams worked on 11 challenges, resulting in 117 predictions from 21 groups representing 18 countries. The challenges expanded, including exercises on exome variation and breast cancer gene variation. Again, SNAP was often one of the best interpretation tools, ranking high on several of the challenges.
One of the challenges in the second year of the experiment asked variation predictors to analyze exome sequence data from 42 Crohn’s disease patients and 6 healthy individuals. Researchers didn’t know how many of the exomes had variations associated with the disease, but many of the tools predicted the disease in patients significantly better than random. The best performing teams used an unexpected approach, looking at rare variants on a large panel of genes (1).
“The Crohn’s results were so great, we wonder if they were an artifact,” Brenner says, explaining that the CAGI organizers have included the challenge again in this year’s experiment to verify the results. If the results hold, “it could be a huge breakthrough there in interpreting genetic variation under certain circumstances,” he says.
The first year results were significant in a statistical sense, but the second year, Brenner says, “really gave us a baseline for better understanding personal genome variations and also started to show which types of interpretation methods might be best for specific applications.”
Nowhere Near
The next step would be to explain why the methods, such as SNAP, are so successful. But that requires more funding. Right now, the experiment has no direct funding, but the CAGI organizing committee does have a grant proposal to run the experiment awaiting review. The National Institutes of Health typically funds the year-end meeting where challenge participants present their results. “We’re doing this on a shoe string,” Moult says. Despite the financial pressure, Brenner and Moult feel that they have invested too much time to give up on the CAGI experiments.
The 2012 challenge deadline is March 2013, with the meeting to present the results slated for July. The delay was largely due to funding issues. But Brenner and Moult hope that the extension will allow more researchers to participate. Overall, Brenner and Moult are excited to see the results.
This year the experiment has 10 challenges, which include a test that focuses on genetic and phenotypic variation in breast cancer as well as the tried-and-true test to predict individuals’ phenotypic traits based on their genomes. The information for the personal genome analysis comes from the Personal Genome Project (PGP). “It acts as a valuable resource for diagnostics evaluations and standardization testing like CAGI,” Harvard molecular geneticist George Church said in an email, adding that the PGP has been providing data to CAGI since its first year.
But this year, there’s a change to the personal genome challenge. For the past two years, participants used the data to predict individual phenotypic traits based on a genome. But phenotypic profiles of all PGP participants are now public. “The availability of the complete profiles makes it impossible to have a valid assessment of individual trait predictions,” Brenner explains.
So instead of predicting the phenotype based on a single genome, in the 2012 challenge, the participants will develop tools that play a “matching game.” The goal will be to match 77 genomes with their corresponding phenotypic profiles, each of which includes 239 traits such as high cholesterol, diabetes, and astigmatism. And to spice things up, the organizers have included 214 phenotypic profiles that do not match any of the 77 genomes.
Ultimately, the CAGI predictors will release the PGP challenge results to those who volunteered their genomes so the individuals can learn more about their genetic susceptibilities for disease. But the reliability of the results is not necessarily high yet, Brenner cautions, so it’s important that individuals, scientists, and clinicians take that into account if someone shows a predicted high risk for cancer or other serious illnesses.
“We are nowhere near having a method for genome interpretation where a doctor could use it and then go and give surgery based on what we are saying,” Moult says. He and Brenner hope CAGI is a first step toward getting there one day.
References
- CAGI: The Critical Assessment of Genome Interpretation, a community experiment to evaluate phenotype prediction. (2012). American Society for Human Genetics Conference: Poster.
- Bromberg, Y. and B. Rost. 2007. SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Research 35: 3823-3835.
PUT IT IN CONTEXT OF CANCER CELL MOVEMENT
The contraction of skeletal muscle is triggered by nerve impulses, which stimulate the release of Ca2+ from the sarcoplasmic reticuluma specialized network of internal membranes, similar to the endoplasmic reticulum, that stores high concentrations of Ca2+ ions. The release of Ca2+ from the sarcoplasmic reticulum increases the concentration of Ca2+ in the cytosol from approximately 10-7 to 10-5 M. The increased Ca2+ concentration signals muscle contraction via the action of two accessory proteins bound to the actin filaments: tropomyosin and troponin (Figure 11.25). Tropomyosin is a fibrous protein that binds lengthwise along the groove of actin filaments. In striated muscle, each tropomyosin molecule is bound to troponin, which is a complex of three polypeptides: troponin C (Ca2+-binding), troponin I (inhibitory), and troponin T (tropomyosin-binding). When the concentration of Ca2+ is low, the complex of the troponins with tropomyosin blocks the interaction of actin and myosin, so the muscle does not contract. At high concentrations, Ca2+ binding to troponin C shifts the position of the complex, relieving this inhibition and allowing contraction to proceed.
Figure 11.25
Association of tropomyosin and troponins with actin filaments. (A) Tropomyosin binds lengthwise along actin filaments and, in striated muscle, is associated with a complex of three troponins: troponin I (TnI), troponin C (TnC), and troponin T (TnT). In (more ) Contractile Assemblies of Actin and Myosin in Nonmuscle Cells
Contractile assemblies of actin and myosin, resembling small-scale versions of muscle fibers, are present also in nonmuscle cells. As in muscle, the actin filaments in these contractile assemblies are interdigitated with bipolar filaments of myosin II, consisting of 15 to 20 myosin II molecules, which produce contraction by sliding the actin filaments relative to one another (Figure 11.26). The actin filaments in contractile bundles in nonmuscle cells are also associated with tropomyosin, which facilitates their interaction with myosin II, probably by competing with filamin for binding sites on actin.
Figure 11.26
Contractile assemblies in nonmuscle cells. Bipolar filaments of myosin II produce contraction by sliding actin filaments in opposite directions. Two examples of contractile assemblies in nonmuscle cells, stress fibers and adhesion belts, were discussed earlier with respect to attachment of the actin cytoskeleton to regions of cell-substrate and cell-cell contacts (see Figures 11.13 and 11.14). The contraction of stress fibers produces tension across the cell, allowing the cell to pull on a substrate (e.g., the extracellular matrix) to which it is anchored. The contraction of adhesion belts alters the shape of epithelial cell sheets: a process that is particularly important during embryonic development, when sheets of epithelial cells fold into structures such as tubes.
The most dramatic example of actin-myosin contraction in nonmuscle cells, however, is provided by cytokinesisthe division of a cell into two following mitosis (Figure 11.27). Toward the end of mitosis in animal cells, a contractile ring consisting of actin filaments and myosin II assembles just underneath the plasma membrane. Its contraction pulls the plasma membrane progressively inward, constricting the center of the cell and pinching it in two. Interestingly, the thickness of the contractile ring remains constant as it contracts, implying that actin filaments disassemble as contraction proceeds. The ring then disperses completely following cell division.
Figure 11.27
Cytokinesis. Following completion of mitosis (nuclear division), a contractile ring consisting of actin filaments and myosin II divides the cell in two.
http://www.ncbi.nlm.nih.gov/books/NBK9961/
This is good. I don’t recall seeing it in the original comment. I am very aware of the actin myosin troponin connection in heart and in skeletal muscle, and I did know about the nonmuscle work. I won’t deal with it now, and I have been working with Aviral now online for 2 hours.
I have had a considerable background from way back in atomic orbital theory, physical chemistry, organic chemistry, and the equilibrium necessary for cations and anions. Despite the calcium role in contraction, I would not discount hypomagnesemia in having a disease role because of the intracellular-extracellular connection. The description you pasted reminds me also of a lecture given a few years ago by the Nobel Laureate that year on the mechanism of cell division.
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I actually consider this amazing blog , âSAME SCIENTIFIC IMPACT: Scientific Publishing –
Open Journals vs. Subscription-based « Pharmaceutical Intelligenceâ, very compelling plus the blog post ended up being a good read.
Many thanks,Annette