The CRISPR Nobel Prize & Data Stored in DNA Gets Destroyed
By: Niko Mccarty
Tags: Tech, Startup, Nobel Prize, Newsletter, Science

NEWSLETTER

The CRISPR Nobel Prize & Data Stored in DNA Gets Destroyed

This Week in Synthetic Biology (Issue #11)

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Data, Stored in DNA, Gets Destroyed (But In A Good Way)

DNA is promising for data storage, mainly because a single gram of DNA can store 256 petabytes of information. But there’s a problem: DNA is, in some cases, too stable. To remove a user’s data from DNA on “nucleic acid hard drives” of the future, scientists first need to develop better ways to selectively target, and destroy, DNA.

A new method, published in Nature Communications, could be a contender for wiping DNA hard drives in the year 2087 (assuming the human race survives that long). DNA sequences, each containing a “True” barcode and one, or several, “False” barcodes, are first mixed together. Then, “truth markers” — DNA sequences that selectively bind to the “True” sequences — are added to the mixture. If the mixture is then heated to 95 degrees Celsius, the “truth markers” fall off and information from the “True” bits is lost. It’s a simple technique, with promising results.

The researchers showed that “8 separate bitmap images can be stably encoded and read after storage at 25 °C for 65 days with an average of over 99% correct information recall, which extrapolates to a half-life of over 15 years at 25 °C. Heating to 95 °C for 5 minutes, however, permanently erases the message.”

An enzymatic pathway has been assembled on top of a Tobacco mosaic virus. Using peptide “linkers”, the scientists, based in Hong Kong, tethered “three terpene biosynthetic enzymes” to the outer particles of the virus, and grew them inside of E. coli cells. The goal was to explore how the proximity of enzymes impacts the bioproduction of a molecule (in this case, amorpha-4,11-diene).

Since the Tobacco mosaic virus is 300nm long, metabolic engineers could presumably use it as a foundation to assemble any number of complex enzymatic pathways. This study was published in Bioconjugate Chemistry.

Deep Learning to Design RNA “Switches”

Two studies, published in Nature Communications, used deep learning to guide the engineering of RNA “toehold switches”. These RNA switches are, basically, synthetic RNA sequences that can be turned ON and OFF. In their OFF state, the toehold switches form a hairpin loop and cannot be read by the ribosomes — they do not produce a protein. The addition of an RNA “trigger”, however, can be used to turn a toehold switch ON; translation can proceed.

Toehold switches are especially useful because a lot of them can be present in a cell at the same time, and triggers can be designed for each of them. This means that many genes can be precisely controlled for synthetic biology applications. Unfortunately, toehold switches are really difficult to design, and even a slight tweak to a sequence can impact their utility.

In the first study, led by Nicolaas M. Angenent-Mari, over 90,000 toehold switches were synthesized and tested using clever experiments that enabled the activity of each toehold switch to be individually assessed. After testing each of the RNAs, data was fed into a deep learning model (which I won’t even pretend to understand). The output from that model looks promising: “[Deep Neural Networks] trained on nucleotide sequences outperform (R2 = 0.43–0.70) previous state-of-the-art thermodynamic and kinetic models (R2 = 0.04–0.15).”

In the second study, led by Jacqueline Valeri, two “deep learning architectures” were introduced — called STORM and NuSpeak — to improve the performance of RNA toehold switches. Both studies are open access, and represent a paradigm shift in how quickly synthetic biologists will be able to design and engineer biological molecules that behave as expected.

The vast majority of plants on earth are C3 plants; when it’s hot or dry, C3 plants close their stomas, oxygen builds up, and the efficiency of photosynthesis goes down. C4 plants, on the other hand, have figured out a way to avoid that pesky oxygen buildup. In C4 plants, fixation of entering carbon dioxide occurs in mesophyll cells, while the Calvin cycle instead occurs inside of bundle-sheath cells. This separation means that C4 cells can avoid oxygen buildup, and typically have a higher photosynthesis efficiency.

A new study in Plant Biotechnology Journal has introduced five genes into a specific strain of rice, boosting its photosynthetic flux by…well, only about 2%. But these early results look promising. If scientists can get the balance of gene expression right, it may one day prove useful for boosting rice yields, a staple food for more than half of the planet’s population.

Biological circuits, operating inside of cells, can be built from dozens of individual genetic parts. Even a tiny tweak to one of those parts — a promoter, ribosome binding site, or terminator — can impact the performance of the genetic circuit. A new study exhaustively characterized all 54 parts in a circuit, parametrized them, and then used the data to build a mathematical model that could “predict circuit performance, dynamics, and robustness.” They even used the computed parameters to calculate “the cellular power (RNAP and ribosome usage) required to maintain a circuit state.” This study, published in Nature Communications, is open access.