Last year the J. Craig Venter Institute made waves by creating the first fully synthetic bacterial genome. Now a group from Johns Hopkins University has extended that work to yeast, producing a built-from-scratch chromosome that works just like the natural chromosome it replaced.
The project, described today in the advance online issue of the journal Nature, is the first step in creating a modular, synthetic organism that its makers hope will act as a biological factory for churning out medicines or substances that break down toxic waste.
Led by biologists Joel Bader, Jef Boeke and Srinivasan Chandrasegaran, the team relied on the computational skills of Sarah Richardson, a graduate student in Bader’s lab and an alumna of the Department of Energy Computational Science Graduate Fellowship (DOE CSGF), to design the chromosome and to oversee its construction. Software assisted biologists in fashioning a chromosome containing millions of individual DNA bases and thousands of functioning genes in a highly structured, modular system.
“Probably the most computationally difficult algorithm is the segmentation of a chromosome into assemble-able bits,” Richardson says. To manage such a data-intensive project, Richardson searched for programs she could modify for her team’s task. She spoke to geneticists who wrote widely used gene annotation software but quickly discovered that the tools, which ensure genes are correctly sequenced and labeled, fell flat at breaking the chromosome down and moving genes around.
“The biggest problem was that there are not (publicly available) algorithms to edit chromosomes or genomes,” Richardson says. “So I set out to write those algorithms and create that framework for editing sequence on a large scale.”
The result was a software suite called BioStudio and an associated program called GeneDesign. Together, the software assists in designing genetic constructs and tracking the progress of synthesis and assembly. Richardson specifically designed the programs to be as generic and user friendly as possible. She wove in touches adapted from open-source packages, such as a collaborative wiki-like interface with revision-control systems and color-coding graphics to assist editing tasks.
In yeast, all the essential genes – ones the organism can’t survive without – are known. With that information in place, the visualization software colors all the essential genes red. “The red flag on essential genes,” she says, “really lets you know if you are editing a particular gene, you are potentially affecting the fitness of the yeast.” All genes with known functions follow the color-coding system, enabling the designers to monitor changes they are making.
Although the computer can automate many tasks, deciding which genes to move around requires a scientist’s experienced eye for subtle detail.
“It turns out it is pretty hard for the computer to decide what stays and what goes” in the genome design, she says. “First you need to know what you want. Then you can apply the algorithms.”
Because of the manual labor and trial and error that moving genes around entails, the value of being able to track earlier versions of DNA constructs became apparent early on. With her CSGF training, Richardson immediately recognized that a ubiquitous computer science tool called the diff function – which allows a user to compare the current version of software code or any other document to an earlier draft – could solve their version problems. Unfortunately, the standard diff function crashed when she applied it to DNA analysis. The software simply couldn’t handle large shifts in the location of units – in this case, units of DNA sequence.
“One of the first things I did was to write a biologically aware diff function that tells you the differences you care about.” When collaborator Boeke makes a change, she says, he can use the modified diff function to ensure “only the changes he expected to make are the ones he gets.”
Yeast 2.0
The key design principles, described in the Nature paper, included three concepts. The scientists would not compromise the organism’s ability to grow normally. They would remove unnecessary genetic sequences or those that destabilize the organism. And they would build in a controlled evolution system that they termed SCRaMbLE (synthetic chromosome rearrangement and modification by loxP-mediated evolution). Their goal: to first redesign the yeast to remove any unnecessary genes, creating a so-called minimal genome, then to build in desirable traits using the SCRaMbLE system. The paper outlined proof of principle that the project could work. The design team playfully dubbed it Sc2.0, for Saccharomyces cerevisiae – better known as baker’s yeast.
Richardson says the group has had its doubters, but she was so enamored of the scientific challenges and the chance to use her background in biology and computer science that she couldn’t pass up being part of it. In fact, Richardson has worked with the lab since she was a high school student. Graduate school included taking medical school courses needed for her human genetics doctoral program and meeting the requirements of the DOE CGSF. Now about to graduate, Richardson says releasing her work to the scientific community excites her.
“I just started by following the requirements of our project,” Richardson says. “Jef and I are always meeting in the middle because he wants the algorithm to do something specific for our project, and I want this framework to work for lots of people. I am trying to take tasks that I observe a synthetic biology team needing and making sure that it will work for any synthetic biology team.”
Such groups hope they can treat living cells as a kind of synthetic circuitry they can rewire to control biological processes. The Johns Hopkins team is starting by assembling biological circuits from a standard set of parts that are about 12 million DNA units long. Richardson’s program can track the outcomes of large-scale sequence changes once they are made.
It also works for a synthetic biology team working in any organism.
“The only requirement for my framework is that you have an annotated genome,” Richardson says. The algorithm needs to know where each gene starts and ends because genes are treated differently from other material between genes called filler DNA. If a researcher provides BioStudio with more information, it will provide more help with editing.
For example, Richardson built in a function that allows the user to find unique locations in the genome that can be used to quickly sequence a desired location using PCR (polymerase chain reaction).
“It’s really a platform for genome-scale synthetic biology,” she says. “But you don’t have to be designing a whole genome to find it useful.”