Scientists Develop Higher-Performance Fuels, Biofuels and Bioproducts

-By Irina Silva

Researchers at Berkeley Lab’s Joint BioEnergy Institute (JBEI) and the Advanced Biofuels & Bioproducts Process Development Unit (ABPDU) have developed a new polyketide synthase-based platform and prototyped efficient production of potential biofuels, gasoline additives, and commodity chemicals.

Microbial production of biofuels and bioproducts is typically carried out using natural or slightly modified enzymes within the metabolic pathway, which can inherently limit the types of molecules that can be produced. Type I modular polyketide synthases (PKSs) are multi-domain enzymes that resemble a modular metabolic assembly line that naturally produces a wide range of unique and diverse molecular structures by combining particular types of catalytic domains in a Lego®-like fashion. This versatile biocatalytic mechanism intrinsically offers a wealth of bioengineering opportunities that scientists can exploit to improve both the rate and yield of the biofuels and bioproducts generated by PKSs.

In “Short-chain ketone production by engineered polyketide synthases in Streptomyces albus published recently in Nature Communications, co-authors Satoshi Yuzawa (JBEI) and Mona Mirsiaghi (ABPDU), present the results of an engineered modular PKS system in the native host Streptomyces venezualae. JBEI and ABPDU researchers were able to demonstrate production of over 1 g/L of C6 and C7 ketones from plant biomass-, a 200-fold improvement over previous efforts.

Final titers of C6 and C7 ethyl ketones with strain ALB188 (left) and C5 and C6 methyl ketones with strain ALB191 (right), in media MM042 with amino acid supplements. Manipulation of cultivation conditions allows tunable production of shorter or longer chain molecules.

Engine tests, performed in the scope of the Co-Optimization of Fuels & Engines (Co-Optima) project, indicate these short-chain ketones can be added to gasoline to increase its octane. This flexible platform could enable biosynthesis of an array of previously inaccessible molecules, allowing fine-tuning of fuel properties, production of highly branched diesel-range biofuels, and a broad range of commodity chemicals.

Other co-authors on the paper are: Renee Jocic, Tatsuya Fujii, Veronica T. Benites, Edward E. K. Baidoo, Anthe George, John M. Gladden, Blake A. Simmons, Leonard Katz and Jay D. Keasling of JBEI, Fabrice Masson, Eric Sundstrom, Deepti Tanjore, and Todd R. Pray of the ABPDU, and Ryan W. Davis of Sandia National Laboratories.

This work was funded by the Joint BioEnergy Institute, a DOE Bioenergy Research Center funded by DOE’s Office of Science, and the Co-Optimization of Fuels & Engines (Co-Optima) project sponsored by the U.S. DOE Office of Energy Efficiency and Renewable Energy’s Bioenergy Technologies Office (BETO). This work was also funded by the National Science Foundation, and leveraged the ABPDU facility which is maintained by BETO and was initiated with funding from the American Recovery and Reinvestment Act.

Metabolic Engineering of Lipids Improves the Respiratory Function of Biofuels and Bioproducts Hosts

JBEI researchers define a systems-level model for cellular respiration

-By Irina Silva

Metabolic engineering leads to fundamental discovery about cellular respiration. JBEI scientists engineered lipid metabolism in bacteria and yeast in order to study physiological effects of changes to membrane structure (left). This endeavor led to the development of a mathematical model for cellular respiration, the primary process by which all cells harness energy (right). This model is based on the diffusion of proteins and small molecules on the cell surface, which takes the form of a random motion shown in the trace. This model explains how respiration rates in bacterial cells change depending on the viscosity of the membrane, which sets how fast membrane diffusion can occur. This work uncovers a potentially universal link between lipid biosynthesis in primary metabolism, explaining why organisms regulate lipid synthesis in order to maintain membrane fluidity.

Metabolic engineering leads to fundamental discovery about cellular respiration.

While much is known about how enzymes and molecules are involved in cellular respiration, the understanding of the respiration system as a whole remains limited. Researchers at the Department of Energy (DOE)’s Joint BioEnergy Institute (JBEI) have gained insight into how cellular respiration is affected by the membrane environment in which it occurs. By engineering lipid synthesis to carefully control the membrane composition, researchers found that lipids, which consist of fatty acid molecules and determine membrane viscosity, also tightly control the rate of bacterial and yeast respiration. As lipid synthesis in these hosts is often engineered in order to produce molecules, these findings suggest new ways by which the pathways to produce biofuels and bioproducts could be optimized to maintain proper respiratory function, thereby increasing production.

This work was described in a paper, “Viscous control of cellular respiration by membrane lipid composition” which was published in Science on October 25. The research team was led by JBEI’s Chief Executive Officer Jay Keasling, corresponding author and also senior faculty scientist at Lawrence Berkeley National Laboratory. In this study, the researchers determined the relationship of membrane viscosity to cellular respiration. Viscosity, in the context of this paper, refers to how fluid a membrane is, and can fluctuate depending on which fatty acids are present.

“We were inspired by an old observation that cells have feedback mechanisms that allow their membrane structure to change”, said Itay Budin, JBEI researcher and lead author of the publication. “So we asked ourselves: How will changes in the types of lipids produced by bacterial cells affect their growth and metabolism?” To find an answer, Budin used synthetic biology and metabolic engineering methods to manipulate lipid synthesis and thereby carefully control membrane composition.

Lipids are commonly known as fats and oils. The split into these two categories depends on the physical state of the different types of fatty acids molecules at room temperature. For example, there are small chemical differences (double bonds) in the chemical structures of lipid molecules in solid butter and liquid olive oil, and these are responsible for their characteristic physical properties. Similarly, the physical properties of cell membranes, oily structures only a few nanometers thick, is also dependent on the chemistry of their lipid components. In this paper, the team modulated the viscosity of membranes in cells using metabolic engineering, a process by which JBEI researchers regularly use to control the relative levels of different chemical pathways in cells.

Simulations of ubiquinone distribution on the surface of a bacterial cell. Each square represents the membrane of a cell with different characteristics viscosities, which are experimentally controlled in this study from high (left) to low (right). The dots represent molecules called ubiquinones––small molecules that change that shuttle electrons between enzymes during respiration. Red dots are carrying two electrons, while blue ones are empty. These electrons are finally designated to oxygen, which gets consumed during respiration. Under the viscous membrane conditions (left), the electron carriers form patches of red and blue dots because diffusion is not fast enough to keep up with the speed of respiration enzymes in these locations. This ‘patchiness’ is a hallmark of diffusion in the reaction.

Simulations of ubiquinone distribution on the surface of a bacterial cell.

Budin found that lipids that determine membrane viscosity also tightly control the rate at which bacteria carried out respiration. Cellular respiration occurs through a set of reactions that occur when different enzymes and their substrates collide in the membrane, and viscosity sets the rate at which these collisions occur by random thermal motion (diffusion). Because of this key insight, Budin, working alongside former JBEI graduate student Tristan de Rond, developed a mathematical model for respiration that accounts for the diffusion of its components within the membrane. As inputs, they used quantitative measurements of the abundance and diffusion of the molecular components in the process, which was aided by mass spectrometry work by co-authors Yan Chen, Leanne Jade G. Chan, and Christopher J. Petzold. The team’s model described several aspects of bacterial metabolism, such as how it responds to inhibitors or changes in enzyme concentrations. They then showed that lipids also mediate respiratory rates in mitochondria, dedicated organelles used by all eukaryotic cells for energy production. Thus, lipids could effectively set the ‘speed limit’ by which cells can ‘breathe’ through their effects on membrane diffusion.

“Itay’s research provides us a better understanding of the central metabolism in the two most commonly used hosts used for biotechnology: E. coli and S. cerevisiae,” said Keasling. “This is knowledge with ample application in future metabolic engineering efforts. Furthermore, it demonstrates how tools developed by synthetic biology can also be applied to address fundamental questions in biology.”

JBEI is a DOE Bioenergy Research Center funded by DOE’s Office of Science, and is dedicated to establishing the scientific knowledge and new technologies to transform the maximum amount of carbon available in bioenergy crops into biofuels and bioproducts. This work was also supported by funding from the National Science Foundation.

What Termites Can Teach Us, The New Yorker

The New Yorker article “What What Termites Can Teach Us” talks about the termites ability to turn grass into energy. The article mentions Jay Keasling, JBEI’s Chief Executive Officer, as one of the synthetic biology leaders, and quotes Héctor García Martín, JBEI’s Deputy Vice President of Biofuels and Bioproducts.

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JBEI’s Andria Rodrigues Wins Early Scientific Career Award

Rodrigues is among the laureates of 2018 Berkeley Lab Director’s Achievement Awards

Andria Rodrigues, a postdoctoral fellow at JBEI’s Biofuels and Bioproducts Division, received an award for Exceptional Early Scientific Career Achievement for her transformational work in microbial biochemistry and enzyme discovery that supports Berkeley Lab missions related to energy and sustainability.

A ceremony honoring all of the 2018 recipients will be held in November at Berkeley Lab.

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

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

-By Julie Chao

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

-By Dan Krotz

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.

JBEI Enzyme Discovery Enables First-Time Microbial Production of the Octane Booster Toluene

-By Irina Silva

Researchers at the Department of Energy’s Joint BioEnergy Institute (JBEI) and Lawrence Berkeley National Laboratory (Berkeley Lab) have discovered a new enzyme that will enable microbial production of a renewable alternative to petroleum-based toluene, a widely used octane booster in gasoline that has a global market of twenty nine million tons per year. A major goal of JBEI is to provide a scientific basis for the development of industrially and commercially relevant fuels and chemicals from renewable resources, such as lignocellulosic biomass, rather than from petroleum. The enzyme discovered in this study will enable the first-time microbial production of bio-based toluene, and in fact, the first microbial production of any aromatic hydrocarbon biofuel.

The toluene-synthesizing enzyme discovered in this study, phenylacetate decarboxylase, is of interest from a fundamental biochemical perspective as well as for its potential application to biofuels. The novel enzyme belongs to a family of enzymes known as glycyl radical enzymes (GREs), which catalyze chemically challenging reactions under anoxic conditions. Scientists only began to recognize GREs in the 1980s, and phenylacetate decarboxylase is just the eighth known GRE reaction type to have been discovered and characterized since then.

Results from the study led by Harry Beller, Berkeley Lab senior scientist and scientific lead at JBEI’s Biofuels & Bioproducts Division, were published in Nature Chemical Biology. The other lead co-authors are Andria Rodrigues and Kamrun Zargar.

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