Tag Archives: mutations

Roger Tsien – the scientist that colored our research

Roger Tsien died a few days ago, at the relatively young age of 64. He was a UCSD scientist, a Nobel laureate and he was one of the first to see the significance and usefulness of GFP.

I’ve never met him. But, I guess, this blogs owes him its existence.

I don’t want to discuss his body of work, his achievements, or awards he won (e.g. the Nobel award). Many wrote nice things about him, such as here, here or here and all over the internet, with nice pictures of fluorescent proteins used in research.

I thought it will be nice to look back at his first GFP paper.

title1

His goal in this paper was to investigate the formation of the fluorophore of GFP. Specifically, he asked:

“What is the mechanism of fluorophore formation? How does fluorescence relate to protein structure? Can its fluorescence properties be tailored and improved-in particular, to provide a second distinguishable color for comparison of independent proteins and gene expression events?”

Already here he looked to utilize GFP – to improve it, to change it, so it can be useful for fluorescent studies in biology.

He used random mutagenesis of the GFP cDNA to screen for mutants with altered brightness and emission. A simple yet powerful method, still used today, to find new FPs with exciting and useful properties.

Here is an ex/em spectral analysis of some of the mutants:

spectra

One mutant, I167T, proved to be almost twice as bright as the WT GFP protein.

But the most exciting was the finding of a blue FP (Y66H):

blue mutant

To sum up in his words:

“The availability of several forms of GFP with such different excitation and emission maxima [the most distinguishable pair being mutant P4 (Y66H) vs. mutant Pll (I167T)] should facilitate two-color assessment of differential gene expression, developmental fate, or protein trafficking. It may also be possible to use these GFP variants analogously to fluorescein and rhodamine to tag interacting proteins or subunits whose association could then be monitored dynamically in intact cells by fluorescence resonance energy transfer (19, 20). Such fluorescence labeling via gene fusion would be site-specific and would eliminate the present need to purify and label proteins in vitro and microinject them into cells.”

He saw the future, and it was bright green.

 
ResearchBlogging.orgHeim, R., Prasher, D., & Tsien, R. (1994). Wavelength mutations and posttranslational autoxidation of green fluorescent protein. Proceedings of the National Academy of Sciences, 91 (26), 12501-12504 DOI: 10.1073/pnas.91.26.12501

Design guidlines for tandem fluorescent timers

Almost 4 years ago, I wrote a post on tandem fluorescent timers (tFTs). The idea is to have two different fluorescent proteins fused together to the protein of interest. In the paper from 4 years ago, it was superfolder GFP (sfGFP) and mCherry. sfGFP matures very fast (within minutes) and mCherry  matures more slowly (t1/2 ~40min). The ratio beween green to red fluorescent signal indicates the percentage of new vs old proteins, thus acts as a “timer”.  This latests paper on tFTs from the same group of Michael Knop’s lab, found that analyzing tFTs might be more complicated due to some possible problems of this system.

Continue reading

Folding and maturation, or how to evolve your own GFP (part 2)

In part 1 I discussed the directed evolution of fast-folding GFPs. These were developed for specific purposes of improving the solubility, stability and folding of the protein. Now, I will discuss the maturation step and how it was measured for a variety of GFPs.

As mentioned in the first part, maturation is the final step in the transformation of GFP from a chain of amino acids to a fluorescent protein – the creation of the chromofore. The chromofore has a very long name (p-hydroxybenzylideneimidazolidone) which in wild type  GFP is formed from amino acids S65, Y66 and G67. In this process, the amide nitrogen of G67 backbone performs a nucleophilic attack on the carbonyl carbon of S65. Oxidation with atmospheric O2 and dehydration reactions create the imidazolinone ring, which is conjugated to the side chain of Y66.

In most papers that measure GFP maturation, the authors do not distinguish folding from maturation. In many cases, the experiments involve complete denaturation of the protein in vitro (e.g. by urea or guanidinium chloride) and then measuring fluorescence following re-folding. In vivo, it would be difficult to assess folding kinetics (because you do not really have an exact time zero for when the protein is being translated).  [In our lab, we are trying to develop a system to visualize translation in vivo in real time by imaging techniques.  If it works, it would be useful for determining exact folding & maturation rates in vivo of a single fluorescent protein].

In any case, it is easier to measure the kinetics of the maturation step, both in vivo and in vitro, simply by removing and adding oxygen. I mentioned in part 1 that this is how the maturation rate of GFP-S65 was calculated in vivo.  A new paper from 2011 from the lab of Funatsu analyzed the maturation rates of several GFPs in vitro. Their idea was simple – using a commercial kit, the performed in vitro transcription-translation reaction of the FPs at anaerobic conditions (they added catalase and glucose oxidase to get 0.1mg/l of oxygen). They then stopped the reaction and added oxygen, then followed fluorescence.

The proteins they assayed were: wild-type GFP, GFP-S65T, GFP-S65T/S147P, EGFP, sfGFP, Emerald, GFPm, GFPmut2, GFPmut3, sgGFP, “cycle 3”(a.k.a GFPuv), frGFP (a.k.a GFPuv3), GFPuv4 and 5 and two yellow FPs: EYFP and Venus..

Their results are interesting. As expected, the S65T mutation improved the maturation rate by 3.2 fold. However, EGFP matured only 20% faster than the S65T alone. The S65T/S147P is a variant that is stable under a range of temperatures. This mutant matured faster than EGFP, at a rate 40% faster than S65T alone. GFPmut2 and GFPmut3 (S65G/S72A) both showed a 7-fold faster maturation than WT GFP (>2-fold the S65T alone). sgGFP (SuperGloGFP, F64L/S65C/I167T, from Qbiogene) showed improved maturation rate compared to S65T (70%  faster).  Emerald, which contains 4 mutations on top of the EGFP mutations showed only a slight increase in maturation rate (similar to S65T/S147P). “cycle 3” was only slightly better than WT GFP, frGFP (which is cycle 3 +EGFP mutations combined) showed a maturation rate which was 10% less than EGFP.  Addition of the I167T mutation to create GFPuv5 increased the maturation rate by ~70% (just like in sgGFP).

Most interesting is the super-folder GFP (sfGFP), which showed a maturation rate of only 2.3-fold over the WT (that is ~70% of that of S65T). Thus, though this protein may be more stable and may fold very fast compared to other variants, the important step of maturation is the slowest among all variants tested (except the WT). Since folding assays measure fluorescence as the output for a mature protein, it means that the folding step (prior to maturation) is much faster than previously appreciated.

GFPm is a weird case.  GFPm, developed by David Tirrell, is a variant with mutations from cycle 3 and GFPmut3. However, Tirrell tried something unique –  to replace the leucines in the protein with 5,5,5- tri-fluoroleucine (tfl). The fluoreinated form proved to be insoluble, and fluorescent of the cells (E. coli) was 500-fold less than the regular protein. They then went on with mutagenesis, developing new variants which were brighter (up to 650-fold over GFPm). Personally, I do not understand how this can be of much use, since using tfl will probably have major effects on every aspect of cell biology we are interested in. Anyway, the maturation rate of GFPm (not fluoreinated) is itself pretty high – almost 3-fold over S65T and is actually the fastest maturing GFP variant tested. Oddly, they do not discuss this result anywhere in the text. Perhaps there is a technical issue they wanted to avoid?

Last but not least, the two YFPs showed maturation rates which are similar to WT GFP (Venus) or ~2.3-fold better (EYFP).

So how does all this information help us?

First, if we know which mutations enhance maturation and which slow it down, we can design faster-maturing proteins.

Second, we can use this data to estimate translation or translocation rates in vivo. However, we should remember that the data obtained in vitro (at 37C) does not neccessarily agree with actual maturation rates in vivo in every cell type. for instance, yeast or fly grow in colder temperatures which may affect maturation. Oxygen levels in tissue culture dish are differnt than in entire animals, and also differnt in differnt organs.  Also, if the GFP is fused to another protein, it may also affect folding as we learned in part 1, as well as maturation. Finally, the in vitro environment in the tube lacks many biomolecules (proteins other than used for translation, small molecules, ions, oxidizing molecules, antioxidants etc) which can affect oxygen availability in the immediate environment of the newly translated GFP.

Third, we can use such data to design cool experiments. So… stay tuned for part 3, which I will dedicate to biological timers.

ResearchBlogging.orgIizuka R, Yamagishi-Shirasaki M, & Funatsu T (2011). Kinetic study of de novo chromophore maturation of fluorescent proteins. Analytical biochemistry, 414 (2), 173-8 PMID: 21459075
Yoo TH, Link AJ, & Tirrell DA (2007). Evolution of a fluorinated green fluorescent protein. Proceedings of the National Academy of Sciences of the United States of America, 104 (35), 13887-90 PMID: 17717085

Folding and maturation, or how to evolve your own GFP (part 1)

GFP is one of the most widely used proteins in research. Its usefulness has advanced our understanding of biology in huge leaps forward. One of the greatest advantages of GFP is that the chromophore is formed in an autocatalytyic manner, no need for an enzyme or cofactor assistance. All that is required is atmospheric oxygen.

GFP has sort of a barrel shape, with the chromophore inside. The chromophore is formed from residues serine 65 (S65), tyrosine 66 (Y66) and glycine 67 (G67). The process of creating the structure of the protein is called folding, whereas the process of creating the chromophore is termed maturation. Maturation occurs after the protein folds to near native conformation.

However, the natural, wild type, GFP is not the best tool. It is rather dim and not very stable at 37°C  (Since the jellyfish lives at low temperatures). Also, it matures rather slowly. Early on, a mutation was introduced, S65T, that increased the brightness of the GFP and also shifted its excitation peak (from a major peak at 395 and a minor at 475, to a single major peak at 488).  In vivo, it was shown that maturation of GFP-S65T takes 27 minutes (this was measured by expressing GFP in E. coli bacteria under anaerobic conditions, then supplementing the bacteria with O2. Therefore, they only measured the last few steps of the maturation process). EGFP, the most common variant used in research labs, contains a second mutation, F64L. This mutation stabilizes the protein at 37°C and was suggested to increase the maturation rate.

Over the years, GFP has been a subject for further “directed evolution” to achieve required traits like different colors, brightness, pH stability and photostability, oligomerization state, as well as folding and maturation.

Why do we care about folding and maturation?

Well, GFP is often fused to other proteins. In some cases, that protein is misfolding, or folding slowly. This can affect the folding of GFP. This is particularly true when expressing fused proteins in E. coli, where in many cases the ectopically expressed protein is aggregating in inclusion bodies. Since GFP itself has a slight tendency to dimerize, any aggregation of the fusion protein may amplify the dimerization of the GFP. Thus, it would be advantageous to have a better folding GFP, with a lower tendency to dimerize.

As to maturation, having a fast-maturing GFP would be very beneficial for studies of translation, or translation-coupled localization.  Because, if the GFP takes 20-30 minutes to mature after it is being translated, we cannot really say what happened and where was the GFP or any fused protein, in that time.

Our story begins in 1996, when the lab of Willem Stemmer tried to improve GFP by a technique called DNA shuffling. Their goal was to improve whole cell fluorescent in E. coli cells expressing GFP. After three cycles of DNA shuffling, and selection based on fluorescence intensity with UV excitation, they isolated a clone (named simply ‘GFP cycle 3’) which showed 45-fold increased fluorescence compared to the then commercial Clontech pGFP. However, the spectral characteristics were unchanged, and the maturation rate (T1/2 = 95min at 37°C in whole cells) also seemed unchanged).  Mutations found in GFP cycle 3 compared to their starting GFP showed that three hydrophobic amino acids were replaced by hydrophilic amino acids. Thus, the improved fluorescence is probably due to reduction in protein aggregation by the hydrophobic surfaces, and increased solubility of the GFP.

CHO cells expressing wt GFP (A) or GFP cycle 3 (B). Source: Crameri et al. (1996) Nat. Biotech. 14:315.

Ten years later, the superfolder GFP (sfGFP) was engineered by the lab of Geoffrey Waldo for the particular purpose of creating a protein with less tendency to misfold when fused to poorly folded proteins in E. coli.

How was this protein engineered?

They started with a folding reporter GFP  (frGFP) which contained the cycle 3 & EGFP mutations.

They fused frGFP to a poorly folding protein, H subunit ferritin, which is insoluble when expressed in E. coli. After several rounds of mutagenesis and screening for fluorescence, they obtained the highly fluorescent sfGFP which bears six new mutations.  Ferritin-sfGFP was found to be 50-fold more fluorescent than the starting fusion protein. When expressed alone, cells expressing sfGFP were twice as bright as with frGFP. Importantly, the excitation & emission spectra , QY and EC were similar in both proteins.

Measuring the folding rate was done by first denaturing the proteins with urea. Then washing the urea and measuring the time it takes the GFP variant to regain fluorescence.  This method actually measures folding and maturation and this should be taken into account. In any case, though both proteins showed 95% yield within 4 minutes, sfGFP showed a 3.5-fold faster initial rate. sfGFP also tolerated higher urea levels, and showed better fluorescence with a bunch of different fused proteins with different characteristics (expression level, solubility etc…).

fusion of frGFP or sfGFP to 18 differnt proteins shows sfGFP much brighter in all cases. 0.125s, 1s: exposure time. below the images of the bacteria are Western blot images of the fusion proteins. Source: Pedelacq et al. (2006). Nat. Biotech. 24(1):79

They found that the most contributing mutation to the folding  kinetics and stability is the S30R mutation. The change from an oxygen group (that forms a hydrogen bond with E17) to a positively charged side chain mediates an electrostatically charged network of the β-barrel which seems to create a global stability to the structure. A second mutation, Y39N, slightly changes the angle of the backbone, and allows a hydrogen bond with D36, further stabilizing the structure. The other four mutations slightly contribute to the stability.

Although sfGFP seems to fold fast (though no comparison to EGFP was made in this paper), the assays here did not really differentiate folding from maturation.

A year later, the group of David Liu used sfGFP  in an effort to create a new GFP protein that is highly soluble. In order to do that, they replaced multiple neutral amino acids on the outer side of the protein with either positively or negatively charged amino acids to produce super-charged GFP variants GFP(+36) and GFP(-30) [compared to the net charge of sfGFP which is (-7) or GFP which is (-8)]. This scGFP was highly soluble, and also highly resistant to denaturing, even by boiling to protein at 100°C. The spectral characteristics of the scGFPs remained similar to those of GFP and sfGFP indicating that the structure of the outside “barrel” does not affect the chromophore features inside the barrel.

In 2008, Fisher & DeLisa produced yet another variant of GFP called superfast GFP. By exploiting the cell’s protein secretion quality control mechanisms, they screened for new super folding variants. The secretion mechanism exports unfolded proteins to the periplasm of E. coli. In the periplasm, GFP is apparently non-fluorescent. If the protein folds fast enough, the quality control mechanism prevents its export and the protein remains in the cytoplasm.  Their starting GFP was GFPmut2, a variant that was previously optimized for FACS analysis, harboring the mutations S65A/V68L/S72A. Following several round of selection, they isolated a clone which they named superfast GFP. Based on their analysis (using guanidinium chloride (GdnCl) to unfold the proteins and then diluting the GdnCl to refold the proteins, the T1/2 for superfast GFP refolding (resumed fluorescence) was 11 minutes, compared to 33 min (GFPmut2), 20min (sfGFP) and 73min (frGFP).

Graph showing folding of GFP proteins over time. black: square – frGFP; circle – sfGFP; triangle – GFPmut2. open circle – superfast GFP; triangle, square – two other mutants. Source: Fisher AC & DeLisa MP. (2008). PLoS One. 3(6):e2351.

So, far we discussed fast folding GFP proteins. However, the methods to measure folding of these proteins do not discriminate folding from maturation.  Furthermore, the in vitro results do not necessarily represent the in vivo folding rates (i.e. 11 min in vitro does not mean 11 min in vivo).

In the next post I will discuss the issue of maturation.

ResearchBlogging.orgCrameri A, Whitehorn EA, Tate E, & Stemmer WP (1996). Improved green fluorescent protein by molecular evolution using DNA shuffling. Nature biotechnology, 14 (3), 315-9 PMID: 9630892
Pédelacq JD, Cabantous S, Tran T, Terwilliger TC, & Waldo GS (2006). Engineering and characterization of a superfolder green fluorescent protein. Nature biotechnology, 24 (1), 79-88 PMID: 16369541
Lawrence MS, Phillips KJ, & Liu DR (2007). Supercharging proteins can impart unusual resilience. Journal of the American Chemical Society, 129 (33), 10110-2 PMID: 17665911
Fisher AC, & DeLisa MP (2008). Laboratory evolution of fast-folding green fluorescent protein using secretory pathway quality control. PloS one, 3 (6) PMID: 18545653

Sensing pH in neurons

A recent paper in Nature Neuroscience demonstrates the usefulness of the pH sensitivity of fluorescent proteins.

I have briefly mentioned the importance of pH when I discussed mKeima. Here I will describe the work from Richard Tsien’s lab which utilizes the effect of pH on FP in order to study synaptic activity.

One avenue of communication between synapse is vesicle exocytosis, i.e. the release of neurotransmitter-containing vesicles from the pre-synaptic cell. A surge of calcium ions in the postsynaptic cell follows vesicle exocytosis. Interestingly, when a vesicle is exocytosed, the lumen of the vesicle changes its pH from 5.5 to 7.4.

A pH sensitive GFP-based protein called pHluorin was developed as early as 1998. pHluorin was fused to a vesicle protein called VAMP2. Thus, a pH change from 5.5 to 7.5 increase the emission intensity (i.e.the protein becomes brighter when excited).

Although this is a good tool, the fact that it is GFP based limits the use of dual-color microscopy, particularly with other types of fluorescent sensors since most of them are GFP, CFP or YFP based, which makes spectral distinction difficult. Therefore, the authors set out to develop a RFP based pH sensor. After several rounds of mutagenesis and shuffling of mRFP and mStrawberry, they identified a bright pH sensor which they named pHTomato since its ex/em peaks (550nm/580nm) are similar to those of dTomato. Importantly, increasing the pH has increase the brightness of the protein, without affecting the ex/em peaks (unlike mKeima which I mentioned above). Furthermore, fusion to VAMP2 did not change its characteristics; the pH dependence is reversible (i.e. once pH is acidic again, the intensity decreases).

Since VAMP2 gives them a high background expression on the membrane, the authors decided to fuse pHTomato to a protein called synaptophysin (sy), which is more vesicle specific. sypHTomato was then compared to sypHluorin in the same neuronal cell. The authors show (by graph) that both proteins perform similarly. However, it would be nice to also show an image.

Once the authors establish that pHTomato is a good pH-sensor, they turn to the real exciting work of dual color sensing. As mentioned above, following exocytosis, there is a Ca2+ increase at the post-synapse. Electrical stimuli also cause a Ca2+ surge in the presynaptic cell. The authors then used a Ca2+ sensor called GCaMP3. Briefly, GCaMP3 is a GFP-based protein that was modified such that that the two part of the protein are fused to Calmodulin (CaM) and a peptide called M13. In the presence of Ca2+, CaM binds M13, thus bringing the two parts of the GFP together, which results in fluorescence (ex/em 489/509 – similar to EGFP).

Expressing both sensors in the same neurons allowed them to visualize the spike in Ca2+ (green spike) concurrent with an increase in red signal, that deteriorates slowly, upon stimuli to the neurons. I am not a neuroscientist, so I cannot evaluate their system regards to choice of cells/stimuli, but the fluorescence response following the different stimuli they give seems distinct and impressive. However, it is very difficult to see it in the snapshot images of the cells.

Expressing each sensor in a different cell allowed the authors to distinctly visualized pre and post synaptic cells at the same synapse. What they looked at are pre synaptic sypHTomato-positives cells, in contact with post-synaptic GCaMP3 positive cells. I think that the image is beautiful:

Figure 3: Dual-color imaging of synaptic connection by sypHTomato and GCaMP3. (partial figure)

Now that the technical issues have been address, it was time to ask some meaningful biological questions. The first question which they asked is whether the vesicle content at presynaptic termini (called boutons) of the same cell is the same in all synapses of the same cell, or are there differences based on the target, postsynaptic cell (obviously a pre-synaptic cell can create synapses with multiple post-synaptic cells).

They used stimuli to measure the volume of the readily-releasable vesicles, and ammonium chloride (NH4Cl), a strong base, to measure the total volume of all vesicles in each synapse. They found that synapses that were targeted to the same postsynaptic neuron had reduced variability in total and readily-releasable vesicle volume, compared to the variability of synapses targeted to different cells. This indicates that there is some reverse feedback from the post-synaptic cell to the pre-synapses.

It would have been interesting to see if pre-synaptic boutons from different cells also have less variability if they are targeted to the same post-synaptic cell. However, the authors did not measure that.

They then looked what happens when you stimulate the cells electrically. As ecpected, there is an immediate increase in sypHTomato signal (vesicle exocytosis) with an almost immediate increase in GCaMP3 signal (Ca2+surge) in the post synaptic cell. This Ca2+ surge begins at the synapse but propagates along the dendrites, indicating opening of voltage-gated Ca2+ channels with the propagation of the ation potential. They confirmed that the Ca2+ surge is due to the vesicle release by adding inhibitors for the neurotransmitter receptors on the post-synaptic cell, and detecting little or no Ca2+ increase (The inhibitors did not affect vesicle release). This approach, then, was capable to image neuronal activity at single synapse resolution.

But the authors went one step further, to get achieve an all-optical system.

The experiment described above was performed by electrically stimulating the cells. However, such methods are invasive and affect the entire cell. An alternative strategy to activate cells is by chemical application of agonists or antagonists. However, addition of drugs or neurotransmitters to the cell culture media will affect all cells in the media. Here comes optogenetics – a system that allows optical activation of a single cell, or part of it. I do not want to discuss optogenetics here; this should get a post or two of its own. The basic idea is to use genetically encoded light-responsive ion channels. Once you shine light (at a specific wavelength) on the part of the cell you want to activate, the ion channel opens and you get action potential originating from the part of the cell you shined on. So here comes the cool stuff:

The authors co-expressed channelrhodopsin2 (ChR2) with a pHluorin-tagged vesicular glutamate transporter (vGluTpH). ChR2 gets to the plasma membrane, whereas vGluTpH to the vesicles. When they shined blue light (to activate ChR2 and excite pHluorin) they get an increase in the green signal, which is abolished by a drug that inhibits action potential. Similarly, co-expression of VChR1 (another optogenetic tool) with stpHTomato and shining green light (to activate VChR1 and excite pHTomato) led to an increase in red emission, that was abolished by the drug.

Better yet, co-expressing ChR2 with sypHTomato showed an increase in red signal only when cells were illuminated by blue light (that activates ChR2) and not green light (Which doesn’t). This figure should have been a main figure. I don’t know why they put it in the supplementary.

Fig S6(b) Tests of ChR2 in combination with sypHTomato as proof-of-principle of all-optical yet independent monitoring and stimulation. Left, interrogation of sypHTomato with Green light (546 -566nm) excitation alone without activation of ChR2-driven vesicular turnover. Right, positive control with blue light (457-482 nm), showing robust ChR2-driven sypHTomato transient (right).

And there you have it – an all optical system to study vesicle release in neurons.

So what can we do with this system?

The authors state several exciting possibilities:

  1. Perform dual color experiments with other green sensors.
  2. Deciphering pre and post synaptic strength in different scenarios.
  3. Probing neuronal circuits: combining optogenetic photostimulation of two spectrally distinct channlerhodopsins with two spectrally distinct sensors (pH sensors in this case) will allows us to follow the neural pathway when we activate a distinct neuron with a specific color.

But I could think of other options. For instance, the authors did not discuss at all the growing use of caged molecules. In brief, caged molecules are biologically relevant molecules that are in an inactive state. These molecules can be activated by shining light of a specific waveband. Thus, if you have a caged neurotransmitter and you shine the light at a specific location, synapses can be activated only if the uncaged molecule is at high enough concentration (i.e. where you shined your light).

The authors kind of ignored it, but there seems to be a difference in the sypHTomato intensity, and signal decay, when comparing electrical and optical stimulation. This difference could be biologically meaningful and could be further explored.

All in all, I think it is a very nice paper, describing a new system to study neurons.

Further reading:

Yulong Li & Richard W Tsien (2012) pHTomato, a red, genetically encoded indicator that enables multiplex interrogation of synaptic activity. Nature Neuroscience 15, 1047–1053.

A good post on GCaMP at “brain Windows” blog.

Optogenetics resource center

Info on chemical synapses, from Wikipedia

ResearchBlogging.org
Li Y, & Tsien RW (2012). pHTomato, a red, genetically encoded indicator that enables multiplex interrogation of synaptic activity. Nature neuroscience, 15 (7), 1047-53 PMID: 22634730

GFP

Green fluorescent protein (GFP in short) was the first of a large (and still growing) family of proteins with the unique ability to fluoresce in different colors. But since GFP was the first, and it is still the most popularly used, and most known by the general public, I dedicate the first post to this protein.

But first, let’s briefly define the term fluorescence: it is the emission of light by a substance that absorbed light. The emitted light is at a longer wave-length than the exciting wave-length. However, under certain conditions where the fluorofore is simultaneously excited by two photos, the emission is shorter than the exciting wave-length. We will get to two- (and three-) photon excitation at a later post. Fluorescent is different than phosphorescent – which is emission of light independently of any excitation.

The common laboratory GFP is excited by blue light and emits green light.

But what is GFP?

GFP is a natural protein that was first isolated from the jellyfish Aequorea Victoria in 1962. Although 50 years has passed since its discovery, the biological function of GFP and GFP-like proteins remains controversial. I may dedicate a post about it at a later time.

GFP is a 238 amino-acid long protein, with a unique barrel-like structure. Unlike many proteins that utilize co-enzyme molecules to elicit their function (e.g hemoglobin that utilizes the heme molecule), the uniqueness of GFP is of creating its own chromofore by cyclization of amino acids number 65-67 (serine-tyrosine-glycine). This self-assembly is one of GFPs advantaged that made it so popular.

Another important feature of GFP is its monomeric, i.e. each protein functions alone, and does not associate with other GFP proteins (except when the concentration is high). There are other fluorescent proteins that act as dimers (i.e. two proteins) or even tetramers (4 proteins).

The wild-type GFP from the jellyfish is accustomed to low ambient temperatures (since the jellyfish is found in the cold Pacific Northwest).  Therefore it has a low efficiency of folding at 37°C, which is required for studying many biological systems, from bacteria to mammalian. Another deficiency of the wild-type GFP is its low fluorescence intensity after excitation with blue light. To improve the quality of the protein for research, two mutations were implemented. The first is S65T (serine 65 changed to threonine). This made the protein fluoresce 35 times brighter than the wild-type GFP. The S65T version is often used in systems at low temperatures (20-30°C) such as insect of yeast. The second mutation, phenylalanine 64 changed to leucine (F64L), improve the folding of the protein at 37°C.  GFP protein with the both mutations is called enhanced-GFP (EGFP in short).  In the following posts, I may use the term GFP, instead of EGFP or GFP(S65T). In any case, few people still work with the wild-type GFP protein.

The above mutations also changed the spectral properties of the GFP protein. The wild-type GFP has two excitation peaks: a major one at 395nm and a minor at 475nm. If the GFP is excited at 395nm (UV light), it emits green light at a wavelength maximum of 508nm. Excitation at 475nm gives a maximum of 503nm. The S65T mutation leads to chromophore ionization. The excitation at 395nm is suppressed (due to the neutral phenol of the threonine) and the 475nm excitation peak is shifted to 488-490nm and enhanced 5-6 times.

Thus, you will find in most fluorescent microscopes a light source at 488nm, to suit the excitation peak of the commonly used EGFP.

 

There are several other classes of mutations that were introduced in GFP in order to create other spectral properties (e.g. red or blue-shifted, photoactivatable GFP).

So, how is GFP used as a fluorescent marker in biological research? Simply put, you can very easily fuse GFP to any protein or peptide of your choice. In some cases, this may disrupt the function of that protein. But in many cases the disruption is minimal if any. Once the GFP-fusion protein is expressed in the cell, you can answer, by using fluorescent microscopy, the following questions: WHERE in the cell does it reside? Does it MOVE? IF and/or WHEN is it expressed and what is the LEVEL of expression? What is its rate of SYNTHESIS or DEGRADATION?  WHO does it associate with? And many more questions which we will explore in this blog.