Student disruptors invent new way to synthesise DNA, Cosmos

DNA synthesis breakthrough by Sebastian Palluk and Daniel Arlow, JBEI graduate students, was covered by Cosmos.

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New technique could help scientists create a gene in just 1 day, Science Magazine

Science Magazine covered JBEI’s new breakthrough discovery in the realm of DNA synthesis by Dan Arlow and Sebastian Palluk, graduate students being mentored by JBEI’s CEO Jay Keasling.

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Faster, Cheaper, Better: A New Way to Synthesize DNA

Breakthrough discovery at the Joint BioEnergy Institute could greatly accelerate the pace of science

In the rapidly growing field of synthetic biology, in which organisms can be engineered to do things like decompose plastic and manufacture biofuels and medicines, production of custom DNA sequences is a fundamental tool for scientific discovery. Yet the process of DNA synthesis, which has remained virtually unchanged for more than 40 years, can be slow and unreliable.

Now in what could address a critical bottleneck in biology research, researchers at the Department of Energy’s Joint BioEnergy Institute (JBEI), based at Lawrence Berkeley National Laboratory (Berkeley Lab), announced they have pioneered a new way to synthesize DNA sequences through a creative use of enzymes that promises to be faster, cheaper, and more accurate. The discovery, led by JBEI graduate students Sebastian Palluk and Daniel Arlow, was published in Nature Biotechnology in a paper titled De novo DNA Synthesis Using Polymerase-Nucleotide Conjugates.”

From left: Daniel Arlow, Sebastian Palluk and Jay Keasling (Credit: Marilyn Chung/Berkeley Lab)

From left: Daniel Arlow, Sebastian Palluk and Jay Keasling (Credit: Marilyn Chung/Berkeley Lab)

“DNA synthesis is at the core of everything we try to do when we build biology,” said JBEI CEO Jay Keasling, the corresponding author on the paper and also a Berkeley Lab senior faculty scientist. “Sebastian and Dan have created what I think will be the best way to synthesize DNA since [Marvin] Caruthers invented solid-phase DNA synthesis almost 40 years ago. What this means for science is that we can engineer biology much less expensively – and in new ways – than we would have been able to do in the past.”

The Caruthers process uses the tools of organic chemistry to attach DNA building blocks one at a time and has become the standard method used by DNA synthesis companies and labs around the world. However, it has drawbacks, the main ones being that it reaches its limit at about 200 bases, partly due to side reactions than can occur during the synthesis procedure, and that it produces hazardous waste. For researchers, even 1,000 bases is considered a small gene, so to make longer sequences, the shorter ones are stitched together using a process that is failure-prone and can’t make certain sequences.

Buying your genes online

A DNA sequence is made up of a combination of four chemical bases, represented by the letters A, C, T, and G. Researchers regularly work with genes of several thousand bases in length. To obtain them, they either need to isolate the genes from an existing organism, or they can order the genes from a company.

“You literally paste the sequence into a website, then wait two weeks,” Arlow said. “Let’s say you buy 10 genes. Maybe nine of them will be delivered to you on time. In addition, if you want to test a thousand genes, at $300 per gene, the costs add up very quickly.”

Palluk and Arlow were motivated to work on this problem because, as students, they were spending many long, tedious hours making DNA sequences for their experiments when they would much rather have been doing the actual experiment.

“DNA is a huge biomolecule,” Palluk said. “Nature makes biomolecules using enzymes, and those enzymes are amazingly good at handling DNA and copying DNA. Typically our organic chemistry processes are not anywhere close to the precision that natural enzymes offer.”

Thinking outside the box

The idea of using an enzyme to make DNA is not new – scientists have been trying for decades to find a way to do it, without success. The enzyme of choice is called TdT (terminal deoxynucleotidyl transferase), which is found in the immune system of vertebrates and is one of the few enzymes in nature that writes new DNA from scratch rather than copying DNA. What’s more, it’s fast, able to add 200 bases per minute.

In order to harness TdT to synthesize a desired sequence, the key requirement is to make it add just one nucleotide, or DNA building block, and then stop before it keeps adding the same nucleotide repeatedly. All of the previous proposals envisioned using nucleotides modified with special blocking groups to prevent multiple additions. However, the problem is that the catalytic site of the enzyme is not large enough to accept the nucleotide with a blocking group attached. “People have basically tried to ‘dig a hole’ in the enzyme by mutating it to make room for this blocking group,” Arlow said. “It’s tricky because you need to make space for it but also not screw up the activity of the enzyme.”

Palluk and Arlow came up with a different approach. “Instead of trying to dig a hole in the enzyme, what we do is tether one nucleotide to each TdT enzyme via a cleavable linker,” Arlow said. “That way, after extending a DNA molecule using its tethered nucleotide, the enzyme has no other nucleotides available to add, so it stops. A key advantage of this approach is that the backbone of the DNA – the part that actually does the chemical reaction – is just like natural DNA, so we can try to get the full speed out of the enzyme.”

Once the nucleotide is added to the DNA molecule, the enzyme is cleaved off. Then the cycle can begin again with the next nucleotide tethered to another TdT enzyme.

Keasling finds the approach clever and counterintuitive. “Rather than reusing an enzyme as a catalyst, they said, ‘Hey, we can make enzymes really inexpensively. Let’s just throw it away.’ So the enzyme becomes a reagent rather than a catalyst,” he said. “That kind of thinking then allowed them to do something very different from what’s been proposed in the literature and – I think – accomplish something really important.”

They demonstrated their method by manually making a DNA sequence of 10 bases. Not surprisingly, the two students were initially met with skepticism. “Even when we had first results, people would say, ‘It doesn’t make sense; it doesn’t seem right. That’s not how you use an enzyme,’” Palluk recalled.

The two still have much work to do to optimize their method, but they are reasonably confident that they will be able to eventually make a gene with 1,000 bases in one go at many times the speed of the chemical method.

Berkeley Lab has world-renowned capabilities in synthetic biology, technology development for biology, and engineering for biological process development. A number of technologies developed at JBEI and by the Lab’s Biosciences Area researchers have been spun into startups, including Lygos, Afingen, TeselaGen, and CinderBio.

“After decades of optimization and fine-tuning, the conventional method now typically achieves a yield of about 99.5 percent per step. Our proof-of-concept synthesis had a yield of 98 percent per step, so it’s not quite on par yet, but it’s a promising starting point,” Palluk said. “We think that we’ll catch up soon and believe that we can push the system far beyond the current limitations of chemical synthesis.”

“Our dream is to make a gene overnight,” Arlow said. “For companies trying to sustainably biomanufacture useful products, new pharmaceuticals, or tools for more environmentally friendly agriculture, and for JBEI and DOE, where we’re trying to produce fuels and chemicals from biomass, DNA synthesis is a key step. If you speed that up, it could drastically accelerate the whole process of discovery.”

JBEI is a DOE Bioenergy Research Center funded by DOE’s Office of Science, and is dedicated to developing advanced biofuels. Other co-authors on the paper are: Tristan de Rond, Sebastian Barthel, Justine Kang, Rathin Bector, Hratch Baghdassarian, Alisa Truong, Peter Kim, Anup Singh, and Nathan Hillson.

Machine learning to simplify development of new biorefining processes, Biofuels International

Biofuels International covered JBEI’s new paper “Machine learning to simplify development of new biorefining processes” published in published in npj Systems Biology and Applications.

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Teaching machines to design cells for us

In this “Behind the Paper” blog post, JBEI’s Hector Garcia Martin talks about the challenges tackled in the paper “A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data” which was published this week in npj Systems Biology and Applications.

New Machine Learning Approach Could Accelerate Bioengineering

Scientists from the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a way to use machine learning to dramatically accelerate the design of microbes that produce biofuel.

Their computer algorithm starts with abundant data about the proteins and metabolites in a biofuel-producing microbial pathway, but no information about how the pathway actually works. It then uses data from previous experiments to learn how the pathway will behave. The scientists used the technique to automatically predict the amount of biofuel produced by pathways that have been added to E. coli bacterial cells.

The new approach is much faster than the current way to predict the behavior of pathways, and promises to speed up the development of biomolecules for many applications in addition to commercially viable biofuels, such as drugs that fight antibiotic-resistant infections and crops that withstand drought.

The research is published May 29 of the journal Nature Systems Biology and Applications.

In biology, a pathway is a series of chemical reactions in a cell that produce a specific compound. Researchers are exploring ways to re-engineer pathways, and import them from one microbe to another, to harness nature’s toolkit to improve medicine, energy, manufacturing, and agriculture. And thanks to new synthetic biology capabilities, such as the gene-editing tool CRISPR-Cas9, scientists can conduct this research at a precision like never before.

A new approach developed by Zak Costello (left) and Hector Garcia Martin brings the the speed and analytic power of machine learning to bioengineering.  (Credit: Marilyn Chung/Berkeley Lab)

A new approach developed by Zak Costello (left) and Hector Garcia Martin brings the the speed and analytic power of machine learning to bioengineering. (Credit: Marilyn Chung/Berkeley Lab)

“But there’s a significant bottleneck in the development process,” said Hector Garcia Martin, group lead at the DOE Agile BioFoundry and director of Quantitative Metabolic Modeling at the Joint BioEnergy Institute (JBEI), a DOE Bioenergy Research Center funded by DOE’s Office of Science and led by Berkeley Lab. The research was performed by Zak Costello (also with the Agile BioFoundry and JBEI) under the direction of Garcia Martin. Both researchers are in Berkeley Lab’s Biological Systems and Engineering Division.

“It’s very difficult to predict how a pathway will behave when it’s re-engineered. Trouble-shooting takes up 99% of our time. Our approach could significantly shorten this step and become a new way to guide bioengineering efforts,” Garcia Martin added.

The current way to predict a pathway’s dynamics requires a maze of differential equations that describe how the components in the system change over time. Subject-area experts develop these “kinetic models” over several months, and the resulting predictions don’t always match experimental results.

Machine learning, however, uses data to train a computer algorithm to make predictions. The algorithm learns a system’s behavior by analyzing data from related systems. This allows scientists to quickly predict the function of a pathway even if its mechanisms are poorly understood — as long as there are enough data to work with.

Machine learning approaches, such as the technique recently developed by Berkeley Lab scientists, are hamstrung by a lack of large quantities of quality data. New automation capabilities at JBEI and the Agile BioFoundry will be able to produce these data in a systematic fashion. This video shows a liquid handler coupled with an automated fermentation platform at JBEI, which takes samples automatically to produce data for the machine learning algorithms.

The scientists tested their technique on pathways added to E. coli cells. One pathway is designed to produce a bio-based jet fuel called limonene; the other produces a gasoline replacement called isopentenol. Previous experiments at JBEI yielded a trove of data related to how different versions of the pathways function in various E. coli strains. Some of the strains have a pathway that produces small amounts of either limonene or isopentenol, while other strains have a version that produces large amounts of the biofuels.

The researchers fed this data into their algorithm. Then machine learning took over: The algorithm taught itself how the concentrations of metabolites in these pathways change over time, and how much biofuel the pathways produce. It learned these dynamics by analyzing data from the two experimentally known pathways that produce small and large amounts of biofuels.

The algorithm used this knowledge to predict the behavior of a third set of “mystery” pathways the algorithm had never seen before. It accurately predicted the biofuel-production profiles for the mystery pathways, including that the pathways produce a medium amount of fuel. In addition, the machine learning-derived prediction outperformed kinetic models.

“And the more data we added, the more accurate the predictions became,” said Garcia Martin. “This approach could expedite the time it takes to design new biomolecules. A project that today takes ten years and a team of experts could someday be handled by a summer student.”

The work was part of the DOE Agile BioFoundry, supported by DOE’s Office of Energy Efficiency and Renewable Energy, and the Joint BioEnergy Institute, supported by DOE’s Office of Science.

The scientist still fighting for the clean fuel the world forgot, MIT Technology Review

JBEI’s CEO, Jay Keasling, was featured in the MIT Technology Review recently. In the article, Keasling discusses the challenges and progress made in the quest for affordable advanced biofuels.

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(photo credit: Christie Hemm Klok)

JBEI Holds Spring Undergraduate Poster Session

Undergraduates who have interned at JBEI this Spring participated at a poster presentation and celebration on May 4. The students presented results of their JBEI related research. Sixteen posters and two talks were presented to the JBEI community.

From left: Mentor Sam Curran with mentee Marian-Joy Baluyot, recipient of ‘Best Verbal Presentation’, mentee Ravi Lal, recipient of ‘Best Poster’, with mentor Amin Zargar.

The undergraduates were judged for their presentation and poster making skills. This year, two students from the Biofuels and Bioproducts Division won awards: Ravi Lal won for ‘Best Poster’ and Marian-Joy Baluyot won for ‘Best Verbal Presentation’.

The following students presented seminar talks:

  • Miranda Werts (mentor: Amin Zargar)
  • Joyce Luk (mentor: Megan Garber)

And the students below presented posters:

  • Angela Fang (mentor: Xi Wang)
  • Brenda Wang (mentor: Robin Herbert)
  • Brian Laus (mentor: Maren Wehrs)
  • Gianina Wicaksono (mentor: Megan Garber)
  • Irene Kim (mentor: Laure Leynaud-Kieffer)
  • Jadie Moon (mentor: Maren Wehrs)
  • Jennifer Gorman (mentor: Raphael Gabriel)
  • Jessica Wang (mentor: Amin Zargar)
  • Julie Lake (mentor: Sam Curran)
  • Marian-Joy Baluyot (mentor: Sam Curran)
  • Maya Ramamurthy (mentor: Connie Bailey)
  • Michael Doane (mentor: Blake Simmons/Carolina Barcelos)
  • Ravi Lal (mentor: Amin Zargar)
  • Reo Yamanaka (mentor: Yan Liang)
  • Ronald Kam (mentor: Jackie Blake-Hedges)
  • Will Sharpless (mentor: Mitch Thompson)

 

 

 

Strong JBEI Presence at East Bay STEM Career Awareness Day

The 7th Annual East Bay STEM Career Awareness Day took place on April 26 at Wareham Development’s Aquatic Park Center in West Berkeley, home to several Berkeley Lab research groups. The event, organized by the Institute for STEM Education housed at California State University East Bay, had support from Berkeley Lab and several East Bay-based businesses.

Three hundred students from four school districts – Berkeley, Emeryville, Oakland, and Richmond – engaged in a one day exploration event that included laboratory tours, a networking lunch with STEM professionals and an exhibit. The day’s guiding question was “What problem(s) are you trying to solve?” which led the students to explore and reflect on potential STEM related careers.

Morning activities included tours at the labs at the Aquatic Park given by Berkeley Lab researchers who gave an inside look into their research. After the tours, Amin Zargar, Ankita Kothari, Garima Goyal, Irina Silva, Jamie Meadow, Jessica Trinh and Robin Herbert from JBEI participated at a networking lunch with the students during which they discussed with students their career paths. During the exhibitor tabling, the JBEI team interacted once more with students, this time to throw light on the benefits of bioenergy and how JBEI is playing a leading role in the field.