Information

What is the best current understanding of how yeast transformation works?


I would like to get myself up to speed with what is currently known to science about yeast transformation. Specifically, transformation of plasmids and linear DNA fragments. I am particularly interested in practical aspects, such as:

  • What is salmon sperm for? What if I don't use it? What if I use more?
  • What is the relationship between number of cells, amount of transforming DNA, and transformation efficiency?
  • How does choice of electroporation cuvette gap size affect efficiency?
  • What can I do to get more colonies after transforming my yeast?
  • What steps can I skip to make the transformation quicker if I am doing an easy transformation?
  • How do different genetic backgrounds and media affect efficiency? Are there any common ones that reduce efficiency substantially? Are there any uncommon ones that increase it substantially?
  • What is the effect of post-transformation "recovery" time (eg. culture 12 h without selection and then plate on selective medium)?
  • Is making spheroplasts worth it?
  • Does the same transformation protocol work for different species (S. cerevisiae, S. pombe, C. albicans, etc.)?
  • Is there a coherent theoretical framework for understanding what actually happens when yeast are transformed, as opposed to "you mix some chemicals with DNA and throw cells in, abra kadabra, transformants come out"? The sort of theory I'm asking about would be useful in predicting conditions for better efficiency.

I am not expecting you to comprehensively answer these questions in your answer. This would make for a very long answer, and each of these could be a question here in its own right.

Instead, I want the answer to name reference materials which can be studied to learn more about the points above. For example, recent reviews or textbooks by established experts in the fields would be quite useful.


I have never worked much with yeast, but I can still give some answers:

Salmon sperm is used as a the so called "carrier DNA". It is thought to bind to the yeast cell wall and thus prevents that the DNA which shall be transformed does so. This raises the transformation efficiency. See here for more details: "Transformation of yeast by lithium acetate/single-stranded carrier DNA/polyethylene glycol method.".

Effect of the recovery time: In this time you typically give the cells the chance to recover from the relative rude treatment and to express the resistance gene (when used) before moving them to a selective medium. This raises the efficiency of the transformation.

Spheroblasts: As far as I remember they are terribly fragile and painful to handle. However, when you succeed making them, you have cells which take up high quantitaties of DNA which makes them pretty efficient.

I think these reviews are definitely worth looking at (they also contain a lot more references):

If you have a chance to get hands on a copy of "Methods in Yeast Genetics: A Cold Spring Harbor Laboratory Course Manual", I think this will be very helpful.


8 Secrets For a Moist & Juicy Roast Turkey

Yeast is the driving force behind fermentation, the magical process that allows a dense mass of dough to become a well-risen loaf of bread. And yet yeast is nothing more than a single-celled fungus. How does it do it?

Yeast works by consuming sugar and excreting carbon dioxide and alcohol as byproducts. In bread making, yeast has three major roles. Most of us are familiar with yeast’s leavening ability. But you may not be aware that fermentation helps to strengthen and develop gluten in dough and also contributes to incredible flavors in bread.

Yeast makes dough rise

The essentials of any bread dough are flour, water, and of course yeast. As soon as these ingredients are stirred together, enzymes in the yeast and the flour cause large starch molecules to break down into simple sugars. The yeast metabolizes these simple sugars and exudes a liquid that releases carbon dioxide and ethyl alcohol into existing air bubbles in the dough.

If the dough has a strong and elastic gluten network, the carbon dioxide is held within the bubble and will begin to inflate it, just like someone blowing up bubblegum. As more and more tiny air cells fill with carbon dioxide, the dough rises and we’re on the way to leavened bread.

Yeast cells thrive on simple sugars. As the sugars are metabolized, carbon dioxide and alcohol are released into the bread dough, making it rise.Scott Phillips.

Yeast strengthens bread dough

When you stir together flour and water, two proteins in the flour—glutenin and gliadin—grab water and each other to form a bubblegum-like, elastic mass of molecules that we call gluten. In bread making, we want to develop as much gluten as we can because it strengthens the dough and holds in gases that will make the bread rise.

Once flour and water are mixed together, any further working of the dough encourages more gluten to form. Manipulating the dough in any way allows more proteins and water to find each other and link together. If you’ve ever made homemade pasta, you know that each time you roll the dough through the machine, the dough becomes more elastic in other words, more gluten is developed. And with puff pastry dough, every time you fold, turn, and roll the dough, it becomes more elastic.

Yeast, like kneading, helps develop the gluten network. With every burst of carbon dioxide that the yeast releases into an air bubble, protein and water molecules move about and have another chance to connect and form more gluten. In this way, a dough’s rising is an almost molecule-by-molecule kneading. Next time you punch down bread dough after its first rise, notice how smooth and strong the gluten has become, in part from the rise.

At this stage, most bakers stretch and tuck the dough into a round to give it a smooth, tight top that will trap the gases produced by fermentation. Then they let this very springy dough stand for 10 to 15 minutes. This lets the gluten bonds relax a little and makes the final shaping of the dough easier. This rounding and resting step isn’t included in many home baking recipes, but it’s a good thing to do.

Fermentation generates flavor in bread

As Harold McGee, the author of On Food & Cooking, has pointed out, big molecules in proteins, starches, and fats don’t have much flavor, but when they break down into their building blocks—proteins into amino acids, starches into sugars, or fats into free fatty acids—they all have marvelous flavors. Fermentation, whether it’s acting on fruit juices to make wine or on flour to make bread, does exactly that—it breaks down large molecules into smaller, flavorful ones.

At the beginning of fermentation, enzymes in the yeast start breaking down starch into more flavorful sugars. The yeast uses these sugars, as well as sugars already present in the dough, and produces not only carbon dioxide and alcohol but also a host of flavorful byproducts such as organic acids and amino acids. A multitude of enzymes encourages all kinds of reactions that break big chains of molecules into smaller ones—amylose and maltose into glucose, proteins into amino acids.

As fermentation proceeds, the dough becomes more acidic. This is due in part to rising levels of carbon dioxide, but there are also more flavorful organic acids like acetic acid (vinegar) and lactic acid being formed from the alcohol in the dough. (This is similar to what happens to a bottle of wine that has been left uncorked for a while: the alcohol combines with oxygen to make vinegar.) The acidity of the dough causes more molecules to break down. The dough becomes a veritable ferment of reactions. Eventually, the amount of alcohol formed starts to inhibit the yeast’s activity.

Yeast has help in producing flavorful compounds. Bacteria are important flavor builders as well. There are bacteria in the dough from the beginning, but as long as the yeast is very active, it consumes sugars as quickly as they’re produced, leaving no food for the bacteria, which also like sugar. But when bakers chill a dough and slow down its rise, the cold dramatically reduces yeast activity. The bacteria, on the other hand, function well even in cold temperatures, so they now have an opportunity to thrive, producing many more marvelously flavorful acids.

This loaf of artisan bread owes its complex flavor to a lengthy fermentation, which breaks down big molecules into smaller flavorful ones.Judi Rutz.


Introduction

  1. To demonstrate the use of clustered regularly interspersed short palindromic repeats (CRISPR) for gene editing and production of a specific mutation leading to a discernable phenotype in a eukaryotic organism.
  2. To demonstrate the concept of genetic engineering through forward genetics and gene editing.
  3. To demonstrate the utility of the fungal yeast species Saccharomyces cerevisiae as a model experimental system for genetic analysis.
  4. To introduce the concept of metabolic and developmental pathway mutants in eukaryotic cell function and development.

Research

Arabidopsis found in a Seattle backyard.

Our recent work has drawn from molecular genetics, genomics, physiology and synthetic biology to build new tools to study signaling dynamics and to apply these tools to a variety of fundamental questions in cell and developmental biology. Specifically, we are:

  1. Building tools to study and reprogram signaling dynamics
  2. Integrating metabolic status into growth control networks
  3. Evaluating the impact of evolution on signaling networks

1. Building tools to study and reprogram signaling dynamics

Synthetic auxin-induced transcription in yeast. All of the parts of the network transferred to yeast are shown as a circuit diagram at the top. Auxin addition to the yeast culture at time 0 triggers degradation of repressors (labeled with YFP, a protein that fluoresces yellow) and activation of a reporter (CFP, a protein that fluoresces cyan).

Building dynamic networks from the ground up. Auxin is a plant hormone that plays a key role in nearly every aspect of plant biology. Direct experimental tests of signaling dynamics in this crucial pathway are confounded by the ubiquity of auxin response in plant cells. In collaboration with Eric Klavins in the UW Electrical Engineering Department, we have developed an alternative approach where we are systematically transplanting the auxin response pathway from Arabidopsis into the single-celled yeast Saccharomyces cerevisiae. An analogy to our approach is trying to understand how a radio works by removing components one by one, reconnecting each part in a simple setting, and characterizing the resulting circuits in great detail. We have successfully transferred the nuclear auxin response pathway from auxin perception through activation of transcription—a rather remarkable feat highlighting the fundamental conservation of core eukaryotic cell biology. We are currently excited to apply this system to fundamental control points of signaling, including protein degradation, transcriptional repression and transcriptional activation. We are also developing and deploying new tools to reparameterize core hormone-regulated networks (including auxin, jasmonates and gibberellins) using synthetic transcription factors.

2. Integrating metabolic status into growth control networks

Sucrose promotes rootward auxin transport. Plants grown on media supplemented with sucrose (S) showed increased rootward auxin transport compared with plants grown without sucrose supplementation (N). Transport was visualized by staining for an auxin reporter several hours after application of an auxin (IAA)-containing droplet on one cotyledon (as depicted in the schematic).

We have discovered that many facets of growth are exquisitely sensitive to developmental stage, as well as genetic and environmental perturbations. Among our most surprising findings was that carbon availability had a dramatic effect on multiple aspects of growth dynamics. Excitingly, the light-regulated transcription factors PIF4 and PIF5 were required for the growth promoting effects of elevated CO2, and the PIFs acted at least in part by regulating the amount of auxin delivered from shoots to the roots. Our results point to direct integration of the light signal downstream of both the phytochrome photoreceptors and photosynthesis. This work provides an outstanding opportunity to integrate cell signaling into an organismal framework of plant growth control. Our progress has been greatly accelerated by an on-going collaboration with Soo-Hyung Kim in UW School of Environmental and Forest Sciences.

3. Evaluating the impact of evolution on signaling networks

Brassica rapa has much to offer as a study system, including a larger seedling.

Approaches for engineering new crop varieties are remarkably crude compared to the design and implementation of non-biological technology. One strength of engineering is its ability to parse complex systems, such as a Boeing 787, into sub-networks or modules that can be analyzed in isolation. The synthetic auxin response system in yeast, developed by my lab in collaboration with Eric Klavins in the UW Electrical Engineering Department, makes it possible to interrogate the function of plant auxin signaling modules in isolation. We are currently testing the function of auxin components of Zea mays and Brassica rapa in our synthetic system. This comparative approach may help answer one of the oldest questions in auxin biology: how does such a simple molecule do so many different things?

For the most recent publications from the Nemhauser Lab, please use these links:


Using yeast to understand protein folding diseases: an interview with Susan Lindquist

Protein shape is vital to its function. Pathological processes, such as neurodegeneration, stress tolerance and prion diseases, often result from the misfolding of proteins. Who could have imagined that research carried out in a unicellular organism, such as yeast, would yield discoveries relevant to the treatment of complex human neurological diseases? Susan Lindquist imagined that this simple eukaryote could reveal plenty about protein folding and pathology, and she was right.

What characteristics led you to focus on yeast as a model organism?

I leapt from working on Drosophila to working on yeast literally the moment I heard about the technique devised by Terri Orr-Weaver, Jack Szostak and Rod Rothstein. They had figured out how to put a mutation into a piece of DNA and target it back into the genome, replacing the gene in the chromosome with what you wanted [Orr-Weaver, T. L., Szostak, J. W. and Rothstein, R. J. Yeast transformation: a model system for the study of recombination (1981). Proc. Natl. Acad. Sci. USA78, 6354–6358] [using yeast as a model organism]. I thought, “Wow! It is just absolutely fabulous.”

We had been working on heat-shock proteins and using them as a system to study gene expression, asking ‘how does an organism rapidly and completely change its pattern of gene expression in response to a specific stimulus?’ We did a lot of satisfying and interesting work at a time when very little was known about how organisms can control what proteins they will make. We found that the expression of heat-shock proteins is regulated at every level you can think of. It’s not just regulated at the level of transcription, which was the hot thing at the time it’s regulated at the level of preferential translation, selective RNA turnover, selective removal of polyadenylation, and preferential transport of RNAs from the nucleus. We uncovered a whole slew of regulatory mechanisms and found that they worked in an incredibly orchestrated way. That was great, but we didn’t know what the proteins were doing. At that point we needed to know what the proteins were doing and genetics is a powerful tool for that.

My idea is to use the right organism for the particular question you are trying to get at and move around a lot

As soon as I heard about the new yeast methods, I signed up for the Cold Spring Harbor course on yeast. Gerry Fink [who developed baker’s yeast as a model for studying the fundamental biology of all organisms and is a Professor of Genetics at MIT and a Member at the Whitehead Institute] was one of the teachers. I remember at the time the senior faculty didn’t spend much time advising junior faculty. The only time someone did stop by to give me advice was after hearing I was going to start working on yeast – three years into my assistant professorship. He said: “that is absolutely crazy, don’t switch organisms, don’t do something completely different right in the middle of the tenure clock.” But I went ahead and decided to do it. (As a woman, at that time, I hadn’t really thought it was likely I would get tenure anyway. So I thought it was too good to pass up.) I’m so glad I did. That capacity to go in and knock out a gene using site directed mutagenesis was just a transforming thing in terms of experimental elegance. Since then, and indeed before then too, many things were done to make the [yeast] organism more manipulatable. It all started with brewers wanting to make better beer about a hundred years ago. But it continued because certain aspects of the organism are just wonderful for experimental manipulation. It can grow either as a haploid or a diploid, so you can cover or uncover the effects of mutations any time you want. It is very easy and cheap to grow. It has a small genome, so it wound up being the first higher organism to have its genome sequenced. Another great thing about it is that it actually does things in an amazing number of ways, just like human cells do. It is a simple, little organism but it has all of the cellular compartments – the endoplasmic reticulum, a membrane-bounded nucleus, vesicle trafficking – as well as chromatin structure, transcription factors, specific mechanisms for regulating the cell cycle, and many different types of signal transducers. All of these work in much the same way as they do in higher organisms. Bacteria do a lot of things quite differently. Don’t get me wrong, bacteria are great to work with, too. They grow even faster! And for studying many truly fundamental problems in biology they are terrific. But there are many layers of biology, and particularly many problems related to specific human disease mechanisms, that cannot be studied in bacteria. But in yeast, protein trafficking – in and out of the nucleus, through the secretory pathway, into organelles – the control of growth and division, responses to diverse stimuli, mito-chondrial respiration, all this is very similar.

There are obviously many differences between humans and yeast. It goes without saying: a yeast cell is not a neuron. But you can study much of basic eukaryotic cell biology in yeast. For example, we are studying neurodegenerative diseases. Why would anyone think to study a neurodegenerative disease in yeast? Many of the problems in these diseases derive from problems in protein folding and trafficking and that is largely the same in yeast as it is in neurons. To the extent that the problems are the same [between yeast and other organisms] it is really great to be able to study it in yeast because they are so fast to work with and because a host of very clever people have created such amazing tools to work with them. We’ve then been able to move from yeast into neurons largely through the help of collaborators, who have been wonderful. It is great having a group of people to interact with who have expertise in very different areas.

How do you approach the great distance between a yeast cell and a mammal?

The major way we’ve done it is with collaborators. With one of the diseases we work with, which is Parkinson’s disease, the neuronal cell lines do not seem to be to be very good for studying [mechanisms of the disease]. I think that neurons are not meant to have continuous replication and when you have a neuronal cell line in culture derived from a tumor, it’s just not really normal. They’ve lost a lot of their apoptotic mechanisms. So the neuronal cell lines have not been as useful as one would hope for studying some problems. That’s not to say that there are not some fabulous things done with cell lines! There have been, but some responses to protein misfoldings and mistrafficking may actually be more similar in yeast than in cultured tumor cells. In any case we’ve translated some of our yeast findings to neuronal models in other ways. For example, we collaborated with Chris Rochet to use differentiated primary neurons taken from the brains of embryonic rats, and with Guy Caldwell to study dopamineurgic neurons in nematodes. We take genes that we find in yeast to serve as genetic suppressors or enhancers and clone their human homologues. We send [our collaborators] expression clones or viruses, which are then cloned into these other systems. [This process] has validated the effects of some of our genes on toxicity due to α-synuclein. So I think this is really exciting – that’s one billion years of conservation for a basic cell biological process.

How do you think recent technical advances, for example genomics, will influence the future model organisms?

Technologies are incredibly enabling. Certainly the genome sequencing that has been done has opened up many more organisms to study, it’s absolutely tremendously empowering. Invertebrates, vertebrates, and other fungi become powerful and you get access to doing things with pathogens that were [previously] very difficult. I think now we need to develop other tools to work with these organisms. There are many things that you might want to study in another organism. For example, [let us take] a pathogenic organism. We have been doing some looking at Plasmodium falciparum, one of the organisms that causes malaria. It’s very difficult to manipulate genetically. It’s hard to grow [and] it has a very complicated life cycle. But there are certain aspects of the biology of P. falciparum that one might be able to study by transposing its proteins into a yeast cell. For example P. falciparum has a very weird proteome. It has a lot of asparagine-rich proteins in it and many other simple sequence repeat proteins as well. I think this may create a particular vulnerability with respect to protein folding that could potentially (this is really a ‘pie in the sky’ thing) lead us to entirely new therapeutic strategies. It happens that my lab knows a lot about protein folding, and especially about the folding problems of simple sequence proteins. So we are thinking about taking some of the P. falciparum proteins that look like they would have a hard time folding and putting them into yeast cell to study.

Another thing that we are thinking about doing is to work with Rudolf Jaenisch on making IPS [induced pluripotent cells] and, as we start to unravel some of the genetic processes that are causing PD [Parkinson’s disease], actually use cells from patients to discover what strategies are most appropriate for that individual. [Since] different patients will have different genetic predispositions, we hope to take such cells and try to test different combinations of therapeutic strategies initially derived from genetic analysis in yeast, nematodes and rat neurons.

These are the big scheme things that I would like to do and they are enabled by the fact that this pluripotency technology [the ability to manipulate cells into discrete mature lineages] has come about. It lets us make a wide leap. New imaging techniques are also fabulous. We have not yet been on the forefront of imaging, but I’d like to get there. One of my postdocs is attempting some amazing things in collaboration with Matt Lang’s group here at MIT.

Another thing that is terrific is the entry of different types of people into this field. The physicists have brought in all kinds of interesting ideas in terms of technologies. Engineers too – tissue engineering and microfabrication – so that you can study processes in parallel in large numbers much more rapidly. Thinking about the circuitry of the cell, engineers bring a whole different perspective to it. It’s a phenomenal time for biology right now and it’s such a shame that we – and by this I mean the entire scientific community – are so pinched in terms of funding.

If you were to begin a completely new project, what would it be?

I would want to tackle the malaria problem I just told you about, because it is such a dreadful disease. And with global warming, I think it is going to get worse. A fresh and unique inroad into it might be the unusual nature of its protein folding problems, because its genome encodes proteins that are simply so weird. But whether I will do that or not, I don’t know yet.

What would I get into I if I was really starting fresh – if I was a postdoc or grad student trying to decide what to do? I find that I love so many different aspects of biology that I would just try to find something that gets me excited and move out from there.

Certainly one of the great mysteries and great frontiers is the brain. How does our brain work and store all of our memories? We have a bit of a toehold on this and are working on it with Eric Kandel and Kausik Si. Crazy as it sounds we are actually working on this in yeast. This springs from other work we are doing on prion biology. The name comes from this awful, horrible disease where a protein changes shape and then it creates a conformational chain reaction where one protein after another undergoes a change in shape. It’s all the same protein but it can have different shapes. And one of those goes and reacts with another protein of the same type and gets it to change its shape too. It causes a horrible disease by a process we still don’t understand. A similar process can occur in yeast cells, but in that case it doesn’t cause a horrible disease, it just changes the function of the protein. Importantly, that change in shape and function perpetuates itself, from protein to protein. So the change in function is transmitted from one generation to the next generation in the yeast cell. You actually have a heritable element that is based upon a change in protein shape, not a change in nucleic acid.

It is an interesting mechanism to create a ‘molecular memory’ in a sense, because once you change conformation, there is a self-perpetuating loop to keep it going. The propagated change in shape is associated with a change in function that passes from generation to generation in yeast. But it is an interesting way to think about how other types of memory might be encoded – even neuronal memory. Everyone I mentioned this to thought I was crazy [for this idea of a protein-derived memory process] until Eric Kandel came into my office and asked: “Do you think prions might be involved in learning and memory.” I nearly fell off my chair. He had a gifted scientist in his lab, Kausik Si, who had found a protein that is involved in learning and memory and is located at the end of the synapse. It had sequences on it that looked just like our yeast prions. We collaborated with them to study its prion-forming capacity and to try to identify the regions of the protein that are involved in making its conformational change. We do see [their protein of interest] undergo a conformational change when we look at it in yeast and it also self perpetuates that change. And that perpetuates a change in function that would be expected to help maintain the synapse.

Now we are looking for other yeast prions and we think that we have found several that are completely unrelated proteins. The next phase might be to look for them in other organisms, such as humans, now that we’ve learned how to work with them using a high throughput – or moderately high throughput – system in yeast.

You have an unusual breadth of experience in both high level administrative and scientific roles and you have gained a reputation as someone who creates positive working environments. In your opinion, is there an identifiable infrastructure or type of organization that provides the most supportive environment for scientific research?

I think there are several things [that enable people to do their best work]. In terms of my own laboratory, I have two types of criteria. First, that people be bright and creative and rigorous…. Good scientists. But every bit as important, a very high priority in bringing someone in is knowing that they are generous, open, want to share information, like to help other people, and are willing and ready to accept advice from other people. People don’t get into my lab anymore unless they are like that. Still, there are a lot of misunderstandings that will happen and I honestly think that one of the most important things is to be open about things. I ask people when they come into my lab to be [open]. I have a wonderful lab manager and when someone has a problem they first should talk to her and if it cannot be resolved in an easy way then they can come talk to me. I had an Italian mother and a Swedish father and they were stereotypical in their ways of dealing with the world. They were constantly misunderstanding each other. I loved them both so dearly and I saw them have these misunderstandings that were simple and it drove me nuts. I kept having to mediate between them when I was a kid and I think I still have some of that left in me now. I can understand that people say things in different ways and they just need to get it out into the open and talk about it. I usually find that difficulties that happen in the laboratory are just simple misunderstandings.

In terms of institutions, I think some of those lessons apply to an institution as well. You can’t, as Director of the Whitehead, screen everyone to see if they are going to be good colleagues that just doesn’t happen. You can try to put in place support structures that let people know that you really care about them and you care about creating an environment where they can do their best work. One of the first things I did when I was Whitehead Director was raise the postdoc stipend because I thought it was too low and create a system to reward people who got their own fellowships by giving them a little extra funding that they could use at their discretion to help support their career. I’m very happy to say that David Page, who is the current Director, has taken that on as one of his major priorities. In fact he has been able to do far better than I. The postdocs do feel that people care about them and that we [the faculty] remember when we were there.

We have a wonderful, long tradition at the Whitehead of a retreat. We go up to New Hampshire for a few days, all of the students, all of the postdocs, many of the technicians and, importantly, every single faculty member. There are many places that have retreats, University of Chicago used to have one and I attended about half the time. Terri Orr-Weaver, when she was away on sabbatical in Greece, came back for the [Whitehead] retreat. That kind of commitment to having a community, and realizing that the whole is much greater than the sum of its parts, is really important. At our retreat the faculty are there the whole time, talking to people, looking at their posters. It really takes that level of commitment. Last year two people in my lab didn’t go, both were having babies. Now that is a good reason not to come. People take it very seriously and they do so because they know the faculty does too and that they will be there.

While at an institutional level, we cannot go around to see how collegial every student and postdoc is but what we do is make very careful decisions about our faculty. The Whitehead faculty has lunch together every Thursday afternoon. We really know each other, we talk to each other and we interact a lot. When we hire a new faculty member, we are looking for the most brilliant minds we can find but we are also looking for people that we want to interact with and who we think will be part of the community. You really can establish a community by creating a set of operating principles.

Another thing that an institution can do, if they have any financial resources at all to do it, is to provide seed funding for crazy ideas that would be high risk, high payoff ideas. The Whitehead has a tradition of doing that and it is very central to its mission.

The DMM staff greatly appreciates Susan Lindquist’s candor in relating her interesting personal story and sharing her insight about creating lab and institutional environments that enable productive science. Her creative research demonstrates the value of model organisms in understanding and treating human disease. We feel that her work and personal example provide a fitting subject for the first ‘Model for Life’ article in this new journal.

Susan Lindquist was interviewed by Kristin Kain, Associate Reveiws Editor.

Susan Lindquist is a Professor at the Massachusetts Institute of Technology and Whitehead Institute of Biomedical Research, and an investigator in the Howard Hughes Medical Institute


Protein Interaction Networks

One of the great promises of functional analysis on the genomic scale was always the possibility of advancing beyond analysis of gene and protein functions one by one. Soon after the publication of the yeast genome sequence, a number of such technologies emerged. Each of these technologies identifies interactions among proteins or genes, which typically are visualized as a network. It is in the arena of understanding these networks of functional relationship that the main opportunities and challenges for understanding cellular biology at the system level lie. Yeast biology has led the way into this arena, and this appears to us to be the path forward for the future.

The first of these studies to appear was based on the two-hybrid method for detecting protein interactions ( Fields and Song 1989). A number of large-scale efforts using variants of this approach have produced a large body of data. Although lower throughput versions of the two-hybrid method have been successfully employed to identify a great variety of protein–protein interactions, on a large scale the method has been plagued by large numbers of false-positive and false-negative signals, resulting in disappointingly little overlap between the most prominent of the published genome-scale networks ( Uetz et al. 2000 Ito et al. 2001 reviewed by Fields 2009).

A much more reliable approach turned out to be a conceptually straightforward biochemical method that can nevertheless be implemented at high throughput. An epitope tag attached to every open reading frame enables precipitation of protein complexes. The identities of the coprecipitating proteins are determined by mass spectrometry. The method is reliable, and the proportion of false-positive signals is low, although it is clear that interactions below a threshold of affinity are unlikely to be detected ( Krogan et al. 2006). Of course, care must be taken to ensure that the tagged protein is fully functional biologically, which can usually be ascertained by testing it for the ability to complement the cognate deletion mutation. Several large protein interaction data sets obtained by this method are in general agreement with each other and with other information. Like the other methods, affinity precipitation can, with some effort, be used to follow protein interactions dynamically.


COMPUTATION AND DATA MANAGEMENT: CAD AND REPOSITORIES

One of the main difficulties of genetically altering microorganisms for the production of native and non-native high-value compounds is the identification of the appropriate metabolic pathways capable of transforming raw materials into the targeted compound. With the advent of whole-genome sequencing, and the metabolic networks and genome-scale models derived from the genome sequences (Österlund et al., 2013 Chumnanpuen et al., 2014), computer-aided design (CAD) tools have been developed to generate heterologous pathways predicted to produce the compound of interest in silico (for a recent review on CAD tools, readers are referred to (Fernández-Castané et al., 2014). Such tools are important to build novel biosynthetic pathways and to improve flux through existing pathways, and were recently adopted by Misra et al. ( 2013), to elucidate both intuitive and nonintuitive gene targets for improving production of the artemisinin precursor dihydroartemisinic acid. Also, Otero et al. ( 2013), benefitted from CAD tools for improving succinic acid production.

Even with good CAD tools, it will be difficult to predictably engineer complex biological systems without collections of well-characterized parts, whose descriptions can be used in CAD programmes. To this end, nascent biological parts registries have been developed, including the Registry of Biological Parts (http://parts.igem.org/Main_Page) and the Inventory of Composable Elements (JBEI-ICE) (Ham et al., 2012). Unfortunately, parts are often poorly characterized (Kwok, 2010), making it difficult to rationally design even the simplest genetic circuit. To improve usability, Wang et al. ( 2013) recently provided a lookup table to demonstrate the organization of parts used in engineered systems (Wang et al., 2013). As some parts (e.g. RNA aptamers, fluorescent reporters) are orthogonal and can be applied universally, that is host-unspecific, such tables facilitate bottom-up design approaches in synthetic biology. Ultimately, the emphasis on both parts and systems conditionality, and the access to experimentally validated data should maximize our future understanding of the principles of genetic circuit design and minimize time-demanding trial and error experiments metabolic engineers often face.


EVALUATION OF STUDENT PERFORMANCE

As indicated in the syllabus in supplemental materials, groups are evaluated based on the completeness of their on-line weekly lab notebook entries (20%), experimental design (8%), effort (12%), and the poster (60%). Notebooks are regularly graded with feedback topics ranging from suggested information to calculations. Posters are evaluated based on hypothesis and background (13%), experimental design and methods (13%), results (40%), conclusion (6.5%), answering of questions at the oral presentation (6.5%), and poster layout (20%). Detailed feedback of the posters is provided to each group as a Word document.


Lessons Learned

Through 11 iterations of the course, we have revised the curriculum based on both student and instructor feedback. Below are some important lessons that we have learned.

  1. It is helpful to have a full-time team-member who can work during periods when the course is not offered to prepare laboratory materials (new yeast strains and plasmids), introduce new protocols or optimize current ones, and update written materials. It is also helpful to have a technical support person(s) to prepare and set-up weekly lab materials.
  2. Having students work as pairs on course assignments (post-lab assignments and final poster) leads to improved performance on those assignments. Assessing individual understanding can be done with quizzes that are completed independently.
  3. Requiring that students submit a spreadsheet with their data analysis allows TAs to more readily assess quantitative analyses in conjunction with instructor-provided templates.
  4. Color coding all student materials (racks, tubes, tape, etc.) by mutant (i.e. mut1 = orange, mut2 = blue, mut3 = pink, mut4 = yellow, mut5 = green) facilitates their distribution and organization.
  5. Mistakes (by both staff and students) will happen, but it is often possible to make these into good learning opportunities.
  1. We have found that p53 proteins (particularly the mutant versions) are highly susceptible to proteolysis. Hence, it is essential that once students lyse their yeast cells, they keep their samples at 4°C and work efficiently to minimize the time before they aliquot and freeze their protein extracts.
  2. The DNA-binding assay is prone to high variability. We have found that the following tips help, but do not eliminate this variability: (A) do not leave wash buffer in wells, and do not to allow wells to dry, as both have the potential to increase background (B) do not scrape the bottom of the wells with pipet tips as this can remove the avidin coating holding the biotinylated DNAs to the well (C) the assay must be done at room temperature as the background increases dramatically when performed at 30°C and (especially) 36°C and (D) when there is little or no specific DNA binding, background subtraction will sometimes yield “negative” binding activity students should be reminded of this possibility.
  3. In Week 3, Bradford Assay data should be checked by instructors to confirm that they fall within the linear range of the standard curve students should repeat the analysis if the R 2 < 0.95 or one or more data points are not within the linear range of the standard curve.
  4. The major source of experimental error throughout the course has been inaccurate pipetting. We have found it helpful to emphasize proper pipettor technique and establish quantitative criteria for duplicates, i.e. they should not differ by >50%, that students must meet for their data to be included in further analysis.

We teach this course in a 10-week quarter format. For instructors who are considering this curriculum for a semester-long lab course, additional lab experiments could be added. For example, students could make their own GFP-tagged p53 mutant constructs, using homologous recombination in vivo, and confirm by sequence analysis students studying the putative oligomerization mutants could assess oligomerization state using native gel electrophoresis students could test more parameters of DNA-binding, such as other DNA elements or extracts and all students could have one or two periods to repeat an experiment of choice. Having students repeat experiments might be the most productive use of the extra weeks to help solidify student understanding of the inherent “messiness” of data and the need for multiple replicates it would also increase the probability of getting publication-quality data.


Abstract

Building on recombinant DNA technology, leaps in synthesis, assembly, and analysis of DNA have revolutionized genetics and molecular biology over the past two decades ( Kosuri and Church, 2014). These technological advances have accelerated the emergence of synthetic biology as a new discipline ( Cameron et al., 2014). Synthetic biology is characterized by efforts targeted at the modification of existing and the design of novel biological systems based on principles adopted from information technology and engineering ( Andrianantoandro et al., 2006 Khalil and Collins, 2010). As in more traditional engineering disciplines such as mechanical, electrical and civil engineering, synthetic biologists utilize abstraction, decoupling and standardization to make the design of biological systems more efficient and scalable. To facilitate the management of complexity, synthetic biology relies on an abstraction hierarchy composed of multiple levels ( Endy, 2005): DNA as genetic material, “parts” as elements of DNA encoding basic biological functions (e.g. promoter, ribosome-binding site, terminator sequence), “devices” as any combination of parts implementing a human-defined function, and “systems” as any combination of devices fulfilling a predefined purpose. Parts are designated to perform predictable and modular functions in the context of higher-level devices or systems, which are successively refined through a cycle of designing, building, and testing.

Within the past two decades, the synthetic biology approach has produced several notable successes, especially in microbial systems. These include, for example, the design of a minimal bacterial genome ( Hutchison et al., 2016) and a highly modified yeast genome ( Richardson et al., 2017), as well as the metabolic engineering of yeast for the biosynthesis of the antimalarial drug precursor artemisinic acid ( Ro et al., 2006) and the opioid compounds thebaine and hydrocodone ( Galanie et al., 2015). Compared to synthetic biology in bacteria and yeast, synthetic biology in algae and plants is still lagging behind. While the potential of photoautotrophic organisms for environmentally sustainable bioproduction has long been recognized ( Georgianna and Mayfield, 2012 Fesenko and Edwards, 2014 Liu and Stewart, 2015 Boehm et al., 2017), their relatively slow growth, scarcely available tools for genetic manipulation, and the physiological as well as genomic complexity of plant systems have delayed their widespread adoption as synthetic biology chassis. However, especially the small genome of the plastid (chloroplast) represents a highly promising platform for engineering the sophisticated metabolism and physiology of the eukaryotic cell it is embedded in ( Fig. 1).

Biological properties and existing technical capacities for synthetic biology of plastids compared to bacteria, yeast and the plant nucleus. The number of asterisks roughly illustrates the relative degree of (top) presence of a biological feature, (middle) availability of a tool or technique, and (bottom) current implementation of a type of application across the different chassis.

Biological properties and existing technical capacities for synthetic biology of plastids compared to bacteria, yeast and the plant nucleus. The number of asterisks roughly illustrates the relative degree of (top) presence of a biological feature, (middle) availability of a tool or technique, and (bottom) current implementation of a type of application across the different chassis.

The chloroplast originated through the endosymbiotic uptake of a cyanobacterium by a heterotrophic eukaryote more than a billion years ago ( Palmer, 2003). Following this event, the endosymbiont evolved mechanisms for facilitated exchange of metabolites with the host cell, underwent radical streamlining of its genome (by gene loss and large-scale transfer of genes to the host nuclear genome) and established an import machinery for the uptake of nucleus-encoded proteins. The resulting organelle serves as the major biosynthetic compartment in photoautotrophic organisms, and has been exploited as a platform for metabolic engineering and molecular farming since the successful development of transformation technologies in the late 1980s ( Boynton et al., 1988 Svab et al., 1990). Compared to nuclear genetic engineering, plastid transformation offers several notable advantages relevant to plant biotechnology. These include (1) the high precision of genetic engineering enabled by efficient homologous recombination, (2) the possibility of transgene stacking in synthetic operons, (3) the potential for high-level expression of gene products, (4) the absence of epigenetic transgene silencing, and (5) the reduced risk of unwanted transgene transmission due to maternal inheritance of plastid DNA ( Bock, 2015).

In this article, we provide an update on tools and technologies available for extending the synthetic biology approach to plastids and highlight key challenges to be addressed through future research. Guided by an abstraction hierarchy of biological design, we identify a scarcity of well-characterized genetic parts, tightly controlled expression devices, and quantitative knowledge of plastid gene expression as current key limitations to plastid synthetic biology. We highlight recent technological developments narrowing the existing complexity gap between bacterial and plastid synthetic biology and provide an outlook to the implementation of complex systems such as synthetic metabolic feedback loops, designer subcompartments and tailor-made genomes in chloroplasts.

Parts

The Registry of Standard Biological Parts (http://parts.igem.org) currently contains over 20,000 genetic elements which can be requested by researchers for use in synthetic biology applications. From this collection, approximately 100 parts each have been designed for use in the unicellular green alga Chlamydomonas reinhardtii and in multicellular plants (e.g. the seed plants Nicotiana tabacum and Arabidopsis thaliana, the moss Physcomitrella patens and the liverwort Marchantia polymorpha). The majority of these parts are designated for nuclear engineering, with only about two dozen suitable for gene expression from the chloroplast genome. One explanation for the relative paucity of plastid genetic elements in the Registry of Standard Biological Parts lies in the half-year timeframe of projects pursued as part of the international Genetically Engineered Machine (iGEM) competition ( Smolke, 2009), which is barely compatible with the generation and characterization of stable plastid-engineered (transplastomic) organisms. Beyond iGEM, the repertoire of regulatory sequences routinely used for transgene expression in plastids has remained similarly small: it is comprised of a few preferred promoters (e.g. from the plastid rRNA operon, Prrn the gene for the large subunit of Rubisco, PrbcL and the gene for the D1 protein of photosystem II, PpsbA) and a handful of 5′-and 3′-UTRs ( Jin and Daniell, 2015). In addition, the bacterial hybrid promoter Ptrc ( Newell et al., 2003) and several bacteriophage-derived expression elements ( McBride et al., 1994 Kuroda and Maliga, 2001 Yang et al., 2013) have been successfully used for plastid transgene expression. A greater variety of parts available for controlled expression of plastid transgenes is desirable for several reasons. First, multiple use of the same genetic element within the chloroplast genome is problematic due to the risk of unwanted homologous recombination between sequence stretches as short as 50 bp ( Dauvillee et al., 2004 Rogalski et al., 2006). Second, synthetic genetic circuits commonly require precise tuning of the activity of their constitutive parts for optimal function ( Brophy and Voigt, 2014).

For synthetic biology applications in plastids to catch up in versatility and complexity with those already demonstrated in bacteria, gene expression elements covering a wider activity range will be required. Natural plastid genomes represent an obvious source of such elements. The small size and low coding capacity of chloroplast genomes (in most seed plants, approximately 130 genes in an ∼ 150 kb genome) should allow refactoring of all coding and regulatory regions into standardized genetic parts. The sequences of over 800 chloroplast genomes have been determined ( Daniell et al., 2016), and the functions of most of their (widely conserved) genes are known ( Scharff and Bock, 2014). Plastid genetic elements contained within this wealth of sequence data can be domesticated according to a recently proposed common syntax for plant synthetic biology ( Patron et al., 2015). This scheme promises to facilitate sharing of genetic resources among the community and, although developed for a eukaryotic system, is also compatible with GoldenBraid-based modular cloning of chloroplast transformation vectors ( Vafaee et al., 2014). Plastid parts containing internal recognition sites for type IIS restriction enzymes (e.g. BsaI, BsmBI, BbsI) that cannot be synonymously changed (e.g. because they constitute essential sequence motifs in a promoter or UTR sequence) may alternatively be assembled using long-overlap-based methods such as Gibson Assembly ( Gibson et al., 2009).

Gene Expression Devices

Gene expression devices send or receive signals in the form of levels of gene expression. A basic device of this kind may be composed of four parts: a promoter, a ribosome-binding site, a coding sequence and a terminator. This device architecture is commonly used for the quantification of part performance to inform the rational design of genetic circuits. Hundreds of prokaryotic gene-expression elements (including promoters, ribosome-binding sites and terminators) have been characterized in bacterial hosts using reporter gene-based assays ( Salis et al., 2009 Cambray et al., 2013 Chen et al., 2013 Kosuri et al., 2013 Mutalik et al., 2013), and standards have been formulated for quantifying their activities ( Canton et al., 2008 Kelly et al., 2009 Rudge et al., 2016). To reduce the context dependence of part activity, standardized flanking sequences ( Mutalik et al., 2013), strong terminators ( Chen et al., 2013) and enzymatic cleavage of UTRs ( Lou et al., 2012 Qi et al., 2012) have been successfully employed as insulators in bacteria. In plastids, not more than two dozen combinations of regulatory elements (i.e. promoters, 5′-UTRs and 3′-UTRs) have been systematically characterized for their impact on transgene expression using GFP ( Barnes et al., 2005 Caroca et al., 2013), GUS ( Eibl et al., 1999 Herz et al., 2005 Gerasymenko et al., 2017) or other reporter proteins ( Ruhlman et al., 2010 Zhang et al., 2012).

Compared to part characterization in microbes, that in plastids involves several notable challenges. First, relatively long timescales are required to generate transplastomic organisms ready for characterization. While only a few days are needed for transformation of the microbial models Escherichia coli or Saccharomyces cerevisiae by a genetic part, several months of selection are needed to recover homoplasmic plastid transformants (i.e. transplastomic cells or plants that are devoid of residual copies of the wild-type plastid genome). In theory, the establishment of homoplasmy could be accelerated through inducible expression of endonucleases that selectively target the wild-type chloroplast genome, but it remains to be tested how much time this approach can save. Alternatively, measurement fidelity can be traded for high-throughput, transient assays to quantify part performance within days of particle bombardment of algal cells or plant tissues. Such assays will require (1) high transient transformation frequencies, (2) high sensitivity, and (3) a robust way of normalizing the primary reporter signal to the copy number of transformed plastomes. The latter could be achieved by using a ratiometric approach ( Rudge et al., 2016 Boehm et al., 2018). If a suitable reporter system can be developed, the activities of hundreds of plastid parts could rapidly be measured in algal cells or plant protoplasts using microtiter plate-based assays ( Schaumberg et al., 2016) or microfluidic devices ( Yu et al., 2018).

Second, the plastome exhibits abundant read-through transcription due to inefficient termination ( Stern and Gruissem, 1987 Rott et al., 1996 Legen et al., 2002 Shi et al., 2016). Consequently, part behavior is, by default, poorly insulated from its specific genetic context: both upstream promoters and downstream antisense promoters may significantly affect the expression level of a target gene ( Quesada-Vargas et al., 2005 Sharwood et al., 2011). However, some sequences such as the endogenous tRNA genes trnS and trnH ( Stern and Gruissem, 1987) or the heterologous E. coli Thr attenuator (thra Chen and Orozco, 1988) have been shown to terminate plastid transcription with at least 85% efficiency. Use of insulators based on these parts or new synthetic terminators can potentially enhance the robustness of gene expression levels generated by plastid synthetic biology devices.

Third, plastid transgene expression has been shown to be primarily determined by posttranscriptional control and protein stability rather than by the accumulation of mRNA ( Eberhard et al., 2002 Birch-Machin et al., 2004 Bellucci et al., 2005 Kahlau and Bock, 2008 Valkov et al., 2009 Zoschke and Bock, 2018). Chloroplast transcripts are subject to a series of complex processing steps which include intercistronic cleavage, 5′-and 3′-end maturation, intron splicing and mRNA editing ( Stern et al., 2010). These steps are largely mediated by nucleus-encoded and organelle-targeted factors, including a large family of modular proteins known as pentatricopeptide repeat (PPR) proteins that site-specifically bind to one or several premRNAs ( Barkan and Small, 2014). Plastid gene expression levels can, therefore, vary considerably between different transgenes even if the same promoter and 3′-UTR are used, limiting the informative value of part characterization based on standard reporter protein assays. While the amino acid sequence of the N-terminus is thought to substantially influence protein stability in the chloroplast ( Apel et al., 2010 De Marchis et al., 2012), our general knowledge of plastid proteostasis remains limited. A better understanding of the molecular determinants of plastid protein (in)stability may in the future allow the design of protective amino acid sequences ( Elghabi et al., 2011) that level the stabilities of different plastid-expressed proteins and make transgene expression from the plastid genome more predictable.

Metabolic Devices

Metabolic devices send or receive signals in the form of levels of metabolites. Accordingly, a synthetic metabolic pathway represents a metabolic device carrying out a specific series of enzyme-catalyzed reactions. A variety of metabolic devices have been successfully implemented in plastids for the production of molecules such as polyhydroxybutyrate ( Bohmert-Tatarev et al., 2011), carotenoids ( Wurbs et al., 2007 Hasunuma et al., 2008 Apel and Bock, 2009), fatty acids ( Madoka et al., 2002 Craig et al., 2008), artemisinic acid ( Fuentes et al., 2016), vitamin E ( Lu et al., 2013) and dhurrin ( Gnanasekaran et al., 2016). These applications have been reviewed in more detail elsewhere ( Bock, 2015 Fuentes et al., 2018). While heterologous pathways composed of 20 genes or more have been expressed in bacteria and yeast ( Temme et al., 2012 Galanie et al., 2015 Li et al., 2018), no more than seven transgenes have to date been simultaneously expressed from the plastome ( Krichevsky et al., 2010). The complexity of plastid-based metabolic devices has primarily been limited by a scarcity of available expression signals (see Gene Expression Devices) rather than by the physical size of the introduced DNA ( Adachi et al., 2007). Recently, the complexity and number of pathway variants accessible to experimental interrogation has been expanded through combinatorial supertransformation of transplastomic recipient lines (COSTREL). Using this approach, an up to 77-fold increase in artemisinic acid production has been demonstrated in transplastomic tobacco plants combinatorially supertransformed by five additional nuclear transgenes ( Fuentes et al., 2016). There is no in-principle limitation to the number of transgenes that can be simultaneously introduced into the plant nucleus using combinatorial transformation ( Naqvi et al., 2009). However, handling hundreds to thousands of plants resulting from combinatorial transformation with several dozen transgenes will require an effective screening pipeline.

In plastid-based metabolic devices containing multicistronic operons, intercistronic expression elements (IEEs) can be used to facilitate correct processing of polycistronic transcripts into monocistronic mRNAs and their efficient translation ( Fig. 2A Zhou et al., 2007). To avoid defects in mRNA stabilization upon repeated use of the same IEE, more complex future metabolic devices may feature a variety of different such elements and/or additionally overexpress their cognate RNA-binding proteins ( Legen et al., 2018).

Design of plastid-based metabolic devices. A, Intercistronic expression elements (IEEs Zhou et al., 2007) can be used to design synthetic operons composed of n genes of interest (GOIs) under the control of a single promoter. Alternatively, each transgene can be controlled by its own promoter. SD, Shine-Dalgarno sequence SMG, selectable marker gene. B, Expression of a GOI can be controlled by a synthetic 5′-UTR that is specifically stabilized by a designer PPR protein (that recognizes a different binding sequence than all other RNA-binding proteins present in the plastid).

Design of plastid-based metabolic devices. A, Intercistronic expression elements (IEEs Zhou et al., 2007) can be used to design synthetic operons composed of n genes of interest (GOIs) under the control of a single promoter. Alternatively, each transgene can be controlled by its own promoter. SD, Shine-Dalgarno sequence SMG, selectable marker gene. B, Expression of a GOI can be controlled by a synthetic 5′-UTR that is specifically stabilized by a designer PPR protein (that recognizes a different binding sequence than all other RNA-binding proteins present in the plastid).

Genetic Circuits

Genetic circuits mimic logical functions commonly found in their electronic counterparts. A genetic circuit can be used to control the activity of other devices (such as the gene expression devices or metabolic devices discussed above) in response to external stimuli. A wide range of genetic circuits implementing Boolean logic functions such as yes , not , and , or , nand , nor , xor and n-imply has been reported for bacteria, yeast and mammalian cells ( Miyamoto et al., 2013). In plastids, only the simplest logic function yes has been implemented in the form of chemically inducible transgene expression.

Chloroplast transcription is natively controlled by two different types of RNA polymerases in seed plants. The nucleus-encoded RNA polymerase (NEP) is a chloroplast-targeted bacteriophage-type single subunit enzyme, while the plastid-encoded RNA polymerase (PEP) is a eubacteria-type multisubunit enzyme ( Barkan, 2011 Börner et al., 2015). The promoter specificity of PEP is modulated by nucleus-encoded and plastid-targeted sigma factors in response to light, hormones and biotic as well as abiotic stresses. However, due to their important role in plant growth, development and survival (and the pervasive transcription of essentially all plastid genes), NEP and PEP are poorly suited as stringent controllers of synthetic genetic circuits in plastids.

As an alternative to transgene control by the endogenous transcription machineries, plastid transgene expression has been controlled through nucleus-encoded and plastid-targeted bacteriophage RNA polymerases or processing factors that are responsive to chemical inducers such as salicylic acid ( Magee et al., 2004), ethanol ( Lössl et al., 2005), copper ( Surzycki et al., 2007) or thiamine ( Ramundo et al., 2013). To avoid (pollen-transmissible) nuclear transgenes and increase transgene containment, inducible expression systems encoded solely in the plastid genome are particularly desirable. Plastid-only inducible circuits responsive to isopropyl β- d -1-thiogalactopyranoside (IPTG Mühlbauer and Koop, 2005) or theophylline ( Verhounig et al., 2010 Emadpour et al., 2015) have been shown to be functional, yet fall short of binary behavior due to the pronounced transcriptional leakiness present in plastids (see Gene Expression Devices). To achieve a signal-to-noise ratio sufficient for the implementation of more complex logic gates, future plastid-based genetic circuits may employ synthetic RNA-binding proteins of the PPR class (see Gene Expression Devices Coquille et al., 2014 Gully et al., 2015) to selectively control the maturation of target mRNAs in the chloroplast ( Fig. 2B Stern et al., 2010 Barkan and Small, 2014).

Systems

Beyond hard-wired logic gates, synthetic biologists have explored dynamic feedback mechanisms to enhance the efficiency of engineered metabolic pathways in bacteria and yeast ( Venayak et al., 2015 Del Vecchio et al., 2016). Translation of this approach to plastids is currently hampered by our limited quantitative understanding of chloroplast gene expression, though new tools for analysis of the metabolic network shared between the chloroplast and its host cell are emerging ( Gloaguen et al., 2017). Metabolic engineering in plastids may further be supported by expression of synthetic subcompartments for substrate concentration, metabolite channeling and the prevention of unwanted reactions between subcompartmentalized and endogenous plastid metabolites and enzymes ( Winkel, 2004 Ort et al., 2015 Hanson et al., 2016). Synthetic subcompartments have already been introduced in bacteria and yeast ( Bonacci et al., 2012 Lau et al., 2018), and carboxysomal shell proteins transiently expressed in leaves of Nicotiana benthamiana have been shown to be capable of assembling into carboxysome-like structures within chloroplasts ( Lin et al., 2014), encouraging further efforts in this area.

Among the most complex systems proposed for implementation in plastids are entire synthetic genomes, inspired by recent successes in microbial synthetic genomics ( Hutchison et al., 2016 Richardson et al., 2017). A minimum-size plastid genome composed of the smallest possible number of components will be of great value for two reasons: it will advance our understanding of the regulatory network underlying plastid function, and it will serve as a template for engineering synthetic plastomes to be used in biotechnological applications. We have previously proposed a design for a synthetic minimal plastome of N. tabacum that is free of all genes nonessential under heterotrophic growth conditions ( Fig. 3), intergenic spacers, introns, and isoaccepting tRNA genes that are dispensable or become dispensable after genome-wide modification of codon usage ( Scharff and Bock, 2014). Such a synthetic chloroplast genome can be assembled from linear DNA fragments in yeast ( O’Neill et al., 2012) and, prior to plant transformation, can be amplified in vitro using rolling circle amplification ( Jansen et al., 2005). The major hurdle to the successful implementation of fully synthetic plastomes in planta is the high probability of homologous recombination between the (largely nonrecodeable) rRNA and tRNA genes and their counterparts in the resident plastid genome, leading to chimeric genomes of unpredictable structure and function ( O’Neill et al., 2012). In addition, the effects of synthetic lethality (i.e. the combined knock-out of two nonessential genes being lethal e.g. Ehrnthaler et al., 2014) cannot currently be excluded to occur in a synthetic minimal plastome.

Physical map of the N. tabacum chloroplast genome with all genes classified by essentiality. Genes shown in blue are essential for both heterotrophic and autotrophic growth. Genes shown in green are essential for autotrophic growth only. Light green indicates borderline cases where knock-out plants survive under carefully controlled growth conditions. Genes shown in gray are nonessential under both heterotrophic and autotrophic growth conditions, in that their knock-out causes no or only a mild phenotype ( Scharff and Bock, 2014). Origins of replication are highlighted in red. Gray arrows indicate the direction of transcription for the two DNA strands. The map was drawn using the OrganellarGenomeDRAW (OGDRAW) software ( Lohse et al., 2013) based on the complete plastome sequence of N. tabacum ( Shinozaki et al., 1986 GenBank accession number Z00044.2). LSC, large single-copy region IRA, inverted repeat A IRB, inverted repeat B SSC, small single-copy region.

Physical map of the N. tabacum chloroplast genome with all genes classified by essentiality. Genes shown in blue are essential for both heterotrophic and autotrophic growth. Genes shown in green are essential for autotrophic growth only. Light green indicates borderline cases where knock-out plants survive under carefully controlled growth conditions. Genes shown in gray are nonessential under both heterotrophic and autotrophic growth conditions, in that their knock-out causes no or only a mild phenotype ( Scharff and Bock, 2014). Origins of replication are highlighted in red. Gray arrows indicate the direction of transcription for the two DNA strands. The map was drawn using the OrganellarGenomeDRAW (OGDRAW) software ( Lohse et al., 2013) based on the complete plastome sequence of N. tabacum ( Shinozaki et al., 1986 GenBank accession number Z00044.2). LSC, large single-copy region IRA, inverted repeat A IRB, inverted repeat B SSC, small single-copy region.

Despite numerous technical advances made over the past 30 years, the number of algal and plant species whose plastids can reliably be transformed has remained small ( Bock, 2015). Transplantation of transgenic plastids from a species amenable to transformation to a species recalcitrant to transformation represents an attractive alternative to painstakingly developing specialized transformation protocols for the latter. Plastomes can be horizontally transferred across graft junctions with relative ease ( Stegemann and Bock, 2009 Stegemann et al., 2012 Thyssen et al., 2012 for review, see Bock, 2017) and this process has been exploited for transplanting a plastid-encoded synthetic metabolic device into a currently nontransformable species ( Lu et al., 2017). The graft-mediated horizontal transfer of transgenic plastid genomes may not be feasible between distantly related species due to the close coevolution of nuclear and plastid genomes, and the probability of nuclear-cytoplasmic incompatibilities that increases with phylogenetic distance and can cause deleterious phenotypes ( Schmitz-Linneweber et al., 2005 Greiner and Bock, 2013). However, the transfer will certainly facilitate the expansion of transplastomic technologies from model species and cultivars used in research to related species and elite cultivars grown commercially.


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