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Molecular mechanisms of Tau binding to microtubulesand its role in microtubule dynamics in live cells

Gilles Breuzard1,*, Pierre Hubert2, Roqiya Nouar1, Tiphany De Bessa1, Francois Devred1, Pascale Barbier1,James N. Sturgis2 and Vincent Peyrot1,*1Aix-Marseille Universite, Inserm, CRO2 UMR_S 911, Faculte de Pharmacie, 13385, Marseille, France2Aix-Marseille Universite, CNRS, LISM, UMR 7255, 13402, Marseille, France

*Authors for correspondence (gilles.breuzard@univ-amu.fr; vincent.peyrot@univ-amu.fr)

Accepted 16 April 2013Journal of Cell Science 126, 2810–2819� 2013. Published by The Company of Biologists Ltddoi: 10.1242/jcs.120832

SummaryDespite extensive studies, the molecular mechanisms of Tau binding to microtubules (MTs) and its consequences on MT stability stillremain unclear. It is especially true in cells where the spatiotemporal distribution of Tau–MT interactions is unknown. Using Forsterresonance energy transfer (FRET), we showed that the Tau–MT interaction was distributed along MTs in periodic hotspots of high and

low FRET intensities. Fluorescence recovery after photobleaching (FRAP) revealed a two-phase exchange of Tau with MTs as a rapiddiffusion followed by a slower binding phase. A real-time FRET assay showed that high FRET occurred simultaneously with rescue andpause transitions at MT ends. To further explore the functional interaction of Tau with MTs, the binding of paclitaxel (PTX), tubulinacetylation induced by trichostatin A (TSA), and the expression of non-acetylatable tubulin were used. With PTX and TSA, FRAP

curves best fitted a single phase with a long time constant, whereas with non-acetylatable a-tubulin, curves best fitted a two phaserecovery. Upon incubation with PTX and TSA, the number of high and low FRET hotspots decreased by up to 50% and no hotspot wasobserved during rescue and pause transitions. In the presence of non-acetylatable a-tubulin, a 34% increase in low FRET hotspots

occurred, and our real-time FRET assay revealed that low FRET hotspots appeared with MTs recovering growth. In conclusion, we haveidentified, by FRET and FRAP, a discrete Tau–MT interaction, in which Tau could induce conformational changes of MTs, favoringrecovery of MT self-assembly.

Key words: Microtubule-associated protein Tau, Tubulin, Interaction, Fluorescence, Cell

IntroductionIn eukaryotic cells the microtubule cytoskeleton consists of a

functional network involved in a diverse range of cellular

activities such as mitosis, meiosis, motility, morphogenesis and

intracellular trafficking of both macromolecules and organelles.

The core component of microtubules (MTs) is a heterodimer of

the a- and b-tubulin proteins. MTs are highly dynamic, and

exhibit a non-equilibrium behavior termed dynamic instability

(Mitchison and Kirschner, 1984). Indeed, MTs undergo rapid

stochastic transitions between growth and shortening, as a result

of the association and/or dissociation of tubulin dimers at its

extremities. In this process, the microtubule-associated protein

Tau promotes tubulin polymerization and stabilizes MTs both in

vitro and when microinjected into cells (Weingarten et al., 1975;

Cleveland et al., 1977; Drubin and Kirschner, 1986). In

eukaryotic cells, Tau protein is a family of six isoforms, each

with either three or four MT-binding repeats located in the C-

terminal half of the protein, and zero to two inserts located in the

N-terminal portion (Goedert and Jakes, 1990).

Much of the interest in Tau comes from its presence in

the neurofibrillary tangles of age-related neurodegenerative

pathologies such as Alzheimer’s disease (see review Buee et al.,

2000). Experimental models of tauopathies strongly suggest that

Tau-mediated neurodegeneration results from a combination of

toxic gain of function and the loss of normal Tau function (Lee and

Rook, 1992; Preuss et al., 1997; Bunker et al., 2004; Feinstein and

Wilson, 2005; LeBoeuf et al., 2008). In fact, most studies have

focused on the disruption of normal Tau activity, through, for

example, post-translational modifications such as hyper-

phosphorylation (Trinczek et al., 1995), to highlight its inability

to properly regulate MT dynamics.

To understand Tau functions, many workers have investigated

its interaction with tubulin, mainly from three-dimensional

reconstructions of images obtained by cryo-electron microscopy

(Ackmann et al., 2000; Goode et al., 2000; Al-Bassam et al.,

2002; Krebs et al., 2004; Santarella et al., 2004). However, no

consensus exists about binding model(s), in part because of the

unfolded states of Tau either when free or when complexed with

MTs. Most binding models have been proposed based on

experiments carried out with purified proteins, however, it

appears increasingly evident that the Tau–MT interaction

depends on both the ratio of Tau/tubulin concentrations and the

concentrations of MT-stabilizing compounds such as paclitaxel

(PTX) (Panda et al., 1995; Kar et al., 2003; Sillen et al., 2007;

Park et al., 2008). The question of the molecular mechanisms of

Tau binding to MTs and its consequences on MT stability,

especially in the cell microenvironment remains open.

Here, we focus on the characterization of the Tau–MT

interaction in live cells by Forster resonance energy transfer

(FRET) and fluorescence recovery after photobleaching (FRAP)

coupled to confocal imaging. Real-time FRET analysis provided

spatiotemporal resolution of the Tau–MT interaction, in

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particular details of the MT-stabilizing function of Tau. Our

approach highlighted a discrete interaction of Tau along MTs inrelation to a diffusion-binding model. Focusing on single MTs in

cells, our time-series of FRET provided evidence of a FRET

hotspot at the end of MTs during rescue and pause transitions. To

investigate the relationship between the interactions and theeffect of Tau onto MTs we altered MT structure using the MT-

stabilizing drug PTX, trichostatin A (TSA)-induced acetylation

of a-tubulin and expression of a non-acetylatable a-tubulin.

These factors are well known to cause conformational changes oftubulin dimers resulting in modulated MT dynamics (L’Hernault

and Rosenbaum, 1985; Derry et al., 1995; Piperno et al., 1987;

Nogales et al., 1995; Honore et al., 2004; Dompierre et al., 2007;

Zilberman et al., 2009). We showed that PTX and the acetylationlevel of a-tubulin affect differently both the distribution of FRET

hotspots and the diffusion-binding model. A real-time FRET

analysis of the MT dynamics revealed that MT modifications

regulate interaction between Tau and MTs.

ResultsFRET identified that Tau–microtubule interactions occur indiscrete spots

We determined, by sensitized FRET imaging, the localization of theTau–MT interaction in A549 cells expressing EGFP–Tau (as donor)

and mCherry–tubulin (as acceptor; Fig. 1A). FRET images were

obtained using Wouters normalization (see Materials and Methods

for details). The simple observation of EGFP–Tau and mCherry–tubulin colocalization clearly showed a continuous distribution of

Tau all along MTs. In contrast, the FRET signal is distributed

discontinuously in clusters of pixels along the length of MTs that we

refer to as FRET hotspots. Since the distance between the donor/

acceptor couple is the main factor determining FRET efficiency

(Stryer and Haugland, 1967), such FRET hotspots show that Tau is

in close contact with the MT lattice. We distinguished two

populations of FRET hotspots: ones with low FRET efficiency

(normalized FRET intensity between 0.1 and 0.5) and ones with

high FRET efficiency (values higher than 0.5). Moreover, the

fluorescence emissions of EGFP–Tau and mCherry–tubulin were

measured on 2-mm long sections of MTs (n510) and compared with

the intensity profiles of FRET (Fig. 1B). Although we observed

only a weak variation of the donor and acceptor intensities (upper

graph), each peak of FRET represents a hotspot (lower graph). In

addition, the average distance between FRET hotspots (n590

measured close to MT plus-ends) was 0.4760.02 mm. This

corresponds to a periodicity of about 60 tubulin dimers. Such a

punctuated interaction along MTs would be sufficient to ensure

stabilization of MTs. To validate the significance of the measured

FRET signals in spots, two controls were performed. First, cells

expressing the two-colored a-tubulin isotypes (as a positive control

for the interaction) showed that the FRET signal was regularly

distributed along the MTs with minor variations in the FRET

intensity (supplementary material Fig. S1A,B). Second, the FRET

signal from cells expressing the free EGFP/mCherry–tubulin pairs

(as a negative control for the interaction) was very low intensity in

the cytosol (,0.1), and negligible on the 2-mm long sections of MTs

(supplementary material Fig. S1C,D).

As an alternative method to confirm our findings, we

compared the quenched and unquenched EGFP–Tau emission

Fig. 1. FRET imaging shows a discrete distribution of Tau–

microtubule interaction. Cells co-expressed EGFP–Tau (as donor)

and mCherry–tubulin (as acceptor). (A) Cellular distributions of

donor/acceptor pairs (panel on the left) and intensity-based FRET

(panel on the right). EGFP–Tau and mCherry–tubulin are perfectly

co-localized and their interaction is distributed in FRET hotspots.

The region indicated by the dashed box is enlarged in the inset.

(B) Intensity quantification of EGFP–Tau (full line, upper panel),

mCherry–tubulin (dashed line, upper panel) and NFRET (lower

panel). Profiles of fluorescence were plotted for 2-mm lengths of

microtubules (n510, 3 cells) and demonstrate the distribution of the

Tau–microtubule interaction. (C) Recovery of EGFP–Tau emission

after mCherry–tubulin photobleaching. The comparison of

fluorescence intensities of EGFP–Tau between pre- and post-

bleaching of mCherry–tubulin shows a fluorescence recovery of

donor in spots along MTs (black arrows) confirming FRET in the

initial state. Scale bar: 10 mm; side bars: color pixel scoring for

FRET intensities from 0 (dark blue) to 1 (white). EGFP and mCherry

were excited respectively at 488 and 543 nm in a multi-tracking

mode.

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after specific photobleaching of the mCherry–tubulin fluorophore

(Fig. 1C, panels in false-color). Prior to acceptor photobleaching,

MTs were quite uniformly labeled with EGFP–Tau (upper panel

in Fig. 1C). After photobleaching (lowest panel in Fig. 1C), the

fluorescence emission of EGFP–Tau increased in spots all along

the length of MTs (black arrowheads in the ‘recovery’ area). No

significant fluorescence intensity fluctuation was observed inside

the control region. This experiment confirmed the distribution of

the Tau–MT interaction in FRET hotspots all along MTs.

We also looked at Caco-2 and SK-N-SH cells to see whether

FRET hotspots could be observed because these two cell lines

have different endogenous expression levels of Tau

(supplementary material Fig. S2A). We confirmed this

distribution of FRET hotspots in both these cell lines

(supplementary material Fig. S2B). Note that the expression of

fluorescent Tau in A549 cells (‘A549 + Tau’ in supplementary

material Fig. S2A) was comparable to the endogenous level of

Tau in wild-type SK-N-SH cells. Our results indicate that the

interaction between EGFP–Tau and MTs is independent of the

endogenous expression of Tau in cells.

For the first time, we clearly demonstrate in several cell lines

that Tau interacts discontinuously along the length of MTs. The

various FRET hotspot intensities could highlight structural

modifications of MTs requiring a specific binding of Tau. The

dynamics of exchange and binding of Tau with MT interaction

was then investigated by FRAP.

Tau–microtubule interaction evidenced and characterized

by FRAP analysis

The rate of fluorescence recovery indicates how fast neighboring

fluorescent molecules arrive to fill a bleached zone. This mobility

of proteins depends on both diffusion and potential binding

interactions. First, FRAP experiments were performed to monitor

the recovery of EGFP–Tau fluorescence in the presence of MTs

versus in the absence of MT (Fig. 2). In the presence of the MT

network (open squares), recovery curves of EGFP–Tau was best

fitted by a two-exponential function. Indeed, analysis of data with

two exponentials gave a much smaller residual least-squares sum

and a better spreading of residuals (inset a, black curve) than with

one exponential (gray curve in inset a). To disassemble MTs,

cells were treated with nocodazole (Jordan et al., 1992). Without

MTs (Fig. 2, black squares), the FRAP recovery curve of EGFP–

Tau best fitted a mono-exponential equation (as also shown in

inset b). Analyzing FRAP curves enabled us to determine

parameters such as time constants t1 and t2 associated with the

diffusion and binding phases, respectively (supplementary

material Table S1). In the presence of MTs, the calculated t1

was 1.8 seconds. This value is similar to the one found in the

absence of MT (t152.6 seconds, as reported in supplementary

material Table S1). After this rapid diffusion phase, fluorescence

recovery due to bound EGFP–Tau molecules occurred

with a second time constant which was 10-fold longer

(t2518.6 seconds).

FRAP enabled us to identify that the binding of EGFP–Tau to

MT impacts directly on the mobility of Tau in cells, even if we

cannot rule out some additional molecular sieving effects within

the MT network. Next, we carried out a real-time FRET assay to

obtain the spatiotemporal distribution of FRET hotspots. This

method was designed to show details of the Tau–MT interaction

during MT growth and shrinkage.

Real-time FRET reveals that hotspots appear during

rescue and pause phases

We acquired time-lapse image sequences coupled with FRET

analysis to determine the spatiotemporal position of FRET

hotspots during shrinkage and growth phases of MTs. A

representative example is reported in Fig. 3. During the MT

shortening (time 0 to 11.5 seconds on Fig. 3A), high and low

FRET hotspots vanished one after the other, suggesting that

hotspots did not avoid the shortening phase. When the MT

shrinkage stopped (called rescue of catastrophe), a high FRET

hotspot appeared (time 11.5–16.1 seconds, white arrow on

Fig. 3A). Then, once the growth phase was robust, this FRET

hotspot disappeared (at time 18.4 seconds). This seems to

indicate a possible structural arrangement of the MT plus-end

following Tau–MT interactions supporting the MT

polymerization. Other hotspots appeared throughout the

growing phase and finally, a last hotspot marked the plus-end

when MT growth stopped (time 41.4 seconds to the end of

record). This may correspond to a pause transition before a

catastrophe event (white arrowhead). A time-resolved FRET

layout of images in a kymograph provides a better visualization

of FRET hotspots at the plus-end of the MT recovering a growth

phase (white arrows on Fig. 3B) and in pause (white

arrowheads). We noticed from the kymograph that the FRET

hotspots were not very long-lived and so the 0.47 mm spacing did

not lead to a characteristic banded pattern.

This spatiotemporal analysis of FRET showed that hotspots

appeared at the plus-end of MT initiation (rescue) and cessation

(pause) of growth. During a rescue transition, Tau could

Fig. 2. FRAP curves reveal a diffusion-interaction model of Tau with

microtubules. After photobleaching, fluorescence intensity of EGFP–Tau

was plotted as a function of time. The fluorescence recovery of EGFP–Tau

were monitored in the presence (empty square) or in the absence (filled

square) of the microtubule network. Microtubules were depolymerized with

nocodazole (1 mg/ml for 30 minutes and maintained in medium during FRAP

experiments). The curves are the average fit (n510–15). Inset curves: a, with

microtubules; b, without microtubules; curves show the residual recovery

between experimental data and either a mono-exponential equation (gray

curves) or a bi-exponential equation (black curves): a smaller spread of

residuals at both sides of the x-axis characterizes the best analytical model.

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contribute to a molecular rearrangement of the tubulin dimers atthe plus-end of the MT, creating a stable region to favor the

assembly of MTs. During a pause transition, the FRET hotspot atthe plus-end of MTs could delay a catastrophe phase but withoutpreventing it. The interaction of Tau in hotspots along the length

of MT could help to slow down MT shortening in cells.

Microtubule modifications strongly affect the Tau–microtubule interaction

Apart from endogenous proteins such as MAPs, MT structure and

stability are known to be regulated by post-translationalmodification such as acetylation of a-tubulin on the K40residue, or by the binding of drugs such as PTX (Nogales et al.,1995; Nogales et al., 1998; Snyder et al., 2001). In order to gain

further insight into the molecular mechanism of Tau binding toMTs in cells, we studied the influence of these modifications onTau–MT interactions by FRET, FRAP and real-time FRET. For

this purpose, we either incubated cells with PTX or TSA as aninhibitor of HDAC6 to enhance the level of acetylated a-tubulin,or we expressed fluorescent non-acetylatable a-tubulin (see

Material and Methods section).

Two quantitative parameters were determined: the overallintensity of FRET in cells (Fig. 4A) and the number of pixels

belonging to high and low FRET hotspots (Fig. 4B). Todetermine overall intensity, cells were outlined on FRET

images and values were expressed per square micrometer tocompare experimental conditions. From Fig. 4A, cells treated

with PTX and TSA displayed a 30% decrease of overall FRETintensity compared with untreated cells. In contrast, cells

expressing the non-acetylatable a-tubulin showed a 20%increase of overall FRET intensity. FRET images in Fig. 4C

show that in cells treated with PTX or TSA (middle panels)hotspots were mainly of the low FRET class, whereas the

presence of non-acetylatable a-tubulin led to a higher FRETintensity (lower panel). As control, we confirmed that differences

of FRET intensities were not due to modulator-induced variationsof donor and acceptor emission (supplementary material Fig. S3).

In Fig. 4B, we compared the percentage of pixels in the highand low FRET classes following treatment with modulators. For

untreated cells (black bars), 28% and 7% of pixels were of lowand high FRET levels, respectively. In the presence of PTX or

TSA (Fig. 4B, red and blue bars, respectively), there was a loss of

Fig. 3. Real-time FRET assay shows a hotspot during rescue and

pause transitions at the plus-end of a microtubule. (A) Time-

series of a growing and shrinking microtubule showing the overlay

(upper panel) and FRET (lower panel). (B) Kymographs of A.

Arrows indicate FRET hotspot during rescue transition, and

arrowheads point to the last FRET hotspot on the microtubule in

pause. One image was recorded every 2.3 seconds. Data are

representative of three independent experiments. Scale bar:

0.5 mm (A).

Fig. 4. The binding of PTX and the acetylation

level of a-tubulin modulate Tau–microtubule

interaction. Microtubule modifications were

addressed by the binding of PTX to microtubules,

the hyper-acetylation of a-tubulin mediated by TSA

and expression of non-actylatable a-tubulin with a

K40A mutation. (A,B) Quantifications of FRET

hotspots depending on (A) the overall FRET

intensities and (B) the percentage of pixels in the

high and low FRET classes. Cells were incubated

without (black bars) or with (red bars) PTX, with

TSA (blue bars),or expressed non-acetylatable a-

tubulin (white bars). Values are means 6 s.e.m. of

90 cells. Significant statistical differences were

calculated using a Student’s t-test with P-values

given on the figure. (C) Distribution of FRET

hotspots in the absence (untreated) or the presence

(+PTX, +TSA, +K40A) of microtubule

modifications. Scale bar: 10 mm. Side bars: color

pixel scoring for FRET intensities from 0 to 1.

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about a quarter of the low FRET pixels and about half of the highFRET pixels. In contrast, the expression of non-acetylatable a-tubulin (Fig. 4B, white bar) resulted in a 34% increase in low

FRET pixels, whereas the number of high FRET pixels wasunchanged. Furthermore, we measured an average distance of0.5760.02 mm between hotspots in cells treated with PTX or

TSA, confirming that there were fewer high and low FREThotspots than in untreated cells. In cells expressing mutanttubulin an inter-hotspot distance of 0.2860.01 mm was obtained,

which is related to the higher number of FRET hotspots along thelength of MTs.

As supplementary controls, we checked the acetylation-independent effect of PTX on Tau–MT interactions (supplementarymaterial Fig. S4). Indeed, it has been described that PTX can promote

the acetylation of a-tubulin (Piperno et al., 1987). First, westernblotting of tubulin acetylation in cells treated with PTX showed norelevant acetylation increase of soluble and incorporated a-tubulin in

comparison with untreated cells in our experimental conditions. Notethe drastic increase of tubulin acetylation in cells incubated with TSA.Second, for cells expressing non-acetylatable tubulin and treated with

PTX, the overall intensity and the amounts of low and high FRETpixels decreased about 25% compared with untreated cells(supplementary material Fig. S5). No relevant change wasmeasured in cells treated with TSA.

Our observations highlighted that PTX and TSA decrease the

number of both high and low FRET hotspots, indicating adecrease in Tau–MT interactions. In contrast, expression of non-acetylatable tubulin led mainly to an increase of low FRET

hotspots, which suggests additional interaction of Tau on MTs.Next, we investigated how MT modifications can modulate thedynamic exchange of Tau with MTs.

Microtubule modifications modulate the diffusioninteraction model of Tau

FRAP experiments were performed to determine the effects ofPTX and tubulin acetylation on EGFP–Tau mobility (Fig. 5).Following an incubation with PTX or TSA (triangle and diamond,

respectively), FRAP recovery curves of EGFP–Tau were bestfitted by a one-exponential equation (insets a and b in Fig. 5).Nevertheless, time constants t1 were higher (,8 seconds)

compared with that obtained in cells treated with nocodazole(,2.6 seconds; see supplementary material Table S1). Althoughwe could not separate FRAP recovery curves into two phases, PTX

and TSA curves reflect both diffusion and binding of EGFP–Tau.Therefore, in cells treated with PTX or TSA, diffusion and bindingare probably intermixed during FRAP recovery, with a time

constant t1 value between the two t1 and t2 values of EGFP–Tau inuntreated cells. This demonstrates that the accessibility and/or thethree-dimensional structure of the binding loci of Tau are stronglymodified by the binding of PTX and the acetylation of tubulin.

In cells expressing non-acetylatable tubulin, FRAP recovery

curves were best fitted by the sum of two exponentials (squaresymbol in Fig. 5; see also inset c). Moreover, we found a short firsttime constant t1 (1.5 seconds) and a long second time constant t2

(11.6 seconds). These values are comparable to those calculatedfor untreated cells. The expression of non-acetylatable tubulinsupported a diffusion-binding model with two separated phases.

Our results showed that the binding of PTX and acetylation

levels result in different FRAP recovery curves. Nevertheless itstill corresponded to Tau–MT binding equilibrium in allconditions. This indicates that regions where PTX and

acetylation occurs may impact on the binding of Tau on MTs.

We then tested whether modifications of MTs alter the effects of

Tau on MT dynamics.

Microtubule modifications affect the microtubule-stabilizing effect of Tau

Our real-time FRET assay was performed in cells treated with PTX

or TSA, or expressing non-acetylatable tubulin (Fig. 6). As already

pointed out for Fig. 4, there was a global decrease of FRET in the

presence of PTX and TSA (Fig. 6A,B), whereas the level of FRET

was significantly higher in cells expressing non-acetylatable

tubulin. Moreover, in the presence of PTX or TSA, we did not

observe FRET hotspots at the plus-end of MTs in rescue and pause

transitions (dashed circles in Fig. 6A,B). In contrast, cells

expressing non-acetylatable tubulin showed low FRET hotspots

on MTs recovering a growth phase (white arrows in Fig. 6C) but

not during the pause transition (dashed circles in Fig. 6C).

Next, parameters of MT dynamic instability were determined by

video-microscopy (Fig. 7). These parameters were measured in

cells expressing EGFP–Tau (black bars) and compared with cells

expressing EGFP–tubulin (white bars). We focused on duration of

the pause (Fig. 7A) and rescue (Fig. 7B) transitions and on

catastrophe (Fig. 7C) and rescue (Fig. 7D) frequencies. Compared

with control cells, Tau increased by 50% and 40% the duration of

pause and rescue transitions, respectively. We also noticed that

Tau induced lower catastrophe frequency (235%). All these data

are consistent with the MT-stabilizing effect of Tau, as previously

reported (Bunker et al., 2004; Bunker et al., 2006).

Then, we examined how PTX, TSA and the expression of non-

acetylatable tubulin affected MT dynamics in cells with EGFP–Tau.

Fig. 5. The binding of PTX and the acetylation level of a-tubulin

modulate the diffusion-interaction model of Tau. After photobleaching, the

mean intensity (n510–15 recovery courses) of EGFP–Tau in the presence of

PTX (triangles), TSA (diamonds) and non-acetylatable a-tubulin K40A

(squares) were plotted as a function of time. The full line curves are the

average fit (n510–15) curves. Inset curves: a, with PTX; b, with TSA; c, with

non-acetylatable a-tubulin K40A; curves correspond to residual recovery

between experimental data and either a mono-exponential equation (gray

curves) or a bi-exponential equation (black curve): a narrower spreading of

residuals on both sides of the x-axis represents the best analytical model.

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In the presence of PTX, we observed roughly similar variation

regardless of the presence or absence of EGFP–Tau. However,

catastrophe and rescue frequencies remained relatively high (,1.5–

2 events/mm; Fig. 7C,D). Moreover, in cells treated with TSA,

parameters of MT dynamics were similar between control and Tau-

expressing cells. All durations of pause and rescue phases remained

generally high in the presence of TSA (Fig. 7A,B). These results

indicated that changes of MT dynamics are mainly due to TSA and

not to the binding of Tau to MTs. Therefore, following PTX and

TSA treatment, our data are in agreement with expected results for a

drug that induces MT stabilization (Schiff et al., 1979; Schiff and

Horwitz, 1980). This also indicates that the MT-stabilizing role of

Tau is not in addition to that of PTX and acetylation. In contrast, in

cells containing non-acetylatable a-tubulin, Tau caused a 15%

longer rescue compared to that in control cells (Fig. 7B), and mainly

increased by two rescue frequencies (Fig. 7D). In cells where we

had overexpressed both non-acetylatable tubulin and EGFP–Tau,

we revealed again the MT-stabilizing effects of Tau.

In living cells treated with PTX or TSA, MT dynamics were

not affected by the presence of EGFP–Tau. As a consequence,

there was no (stable) hotspot at the plus-end of MTs, both inrescue and pause transitions. In cells with non-acetylatable

tubulin, we observed an increase of the MT dynamic instabilitythat EGFP–Tau can counterbalance. This is consistent with thehigh FRET hotspot occurrence during rescue transition andnumerous FRET hotspots along the length of MTs. To conclude,

these data proved evidence that the control of MT dynamics byTau is related to a domain of higher stability as shown by thepresence of FRET hotspots.

DiscussionTau, and its active role in the assembly of tubulin, was identified

in the 1970s (Weingarten et al., 1975; Cleveland et al., 1977), butdetails of Tau–MT interactions still remain unclear. It is well-documented that the binding of Tau to MTs is mediated throughthe repeat domains (Lee et al., 1988; Himmler et al., 1989), in

combination with the adjacent proline-rich flanking regions(Kanai et al., 1992; Gustke et al., 1994). In the current work, wedemonstrated by FRET imaging in diverse cell lines a discrete

distribution of Tau–MT interactions with hotspots that representsone Tau every ,60 tubulin dimers, and contrasting with their co-localization all along the length of MTs. This discontinuous

distribution of the Tau–microtubule interaction could explain theefficiency of Tau in suppressing MT dynamics at a very lowmolar ratios (Tau:tubulin ratio of 1:175) (Panda et al., 1995).Moreover, we characterized two classes of hotspots based on

their FRET intensities. We recently demonstrated usingisothermal titration calorimetry that Tau binds to tubulin at ahigh affinity binding site with a stoichiometry of 0.2 and at a low

affinity binding site with a stoichiometry of 0.8 (Tsvetkov et al.,2012). These two classes of binding sites might be associatedwith the high and low FRET hotspots found in the present study.

To further investigate Tau–MT interactions in living cells, weused the MT-targeting agent PTX and modulated the level oftubulin acetylation. It is well documented that the binding site of

PTX is inside the pocket between the M-loop, the S9–S10 loop,and H6 and H7 helices and that the acetylatable K40 is near theN-loop in a-tubulin. Both are located in the luminal side of MTs,between adjacent protofilaments (Nogales et al., 1995; Downing

and Nogales, 1998; Li et al., 2002). Electron microscopy andmolecular modeling studies demonstrate that the M-loop andH19-S2 and H2-S3 loops are major contacts in MTs for lateral

interactions between protofilament (Nogales et al., 1999; Li et al.,2002). Therefore, any ligand that binds near the loop domainsmay have substantial effects on local interactions between

neighboring protofilaments. In the literature, several models forTau binding to tubulin have been proposed. Kar et al. proposedthat Tau would bridge several parallel protofilaments with Taurepeat domains binding on consecutive protofilaments (Kar et al.,

2003). Other studies show that Tau stabilizes MT by bindingalong individual protofilaments, bridging the tubulin interfacesrather that adjacent protofilaments (Chau et al., 1998; Al-Bassam

et al., 2002). Santarella et al. observed by cryo-electronmicroscopy that GFP-fused Tau was randomly distributed alongthe outer surface of the MTs (Santarella et al., 2004). They

concluded that Tau binds along and across protofilaments. OurFRET data supports these models of Tau binding to MTs, forexample, that Tau can bind along or across the MTs, even

stabilized by PTX (Al-Bassam et al., 2002; Kar et al., 2003;Santarella et al., 2004) or by acetylated K40 MTs. However, weshowed that the binding of PTX and the TSA-induced acetylation

Fig. 6. FRET hotspots during rescue transitions are supported by non-

acetylable a-tubulin but not by PTX and TSA. Overlay and FRET

kymographs of microtubules in the presence of microtubule modifications:

(A) +PTX, (B) +TSA, (C) non-acetylatable a-tubulin K40A. FRET hotspots

appear at the end of MTs recovering growth (white arrows) in the presence of

non-acetylatable a-tubulin, and not in the presence of PTX or TSA (dashed

circles). Side bars: color pixels scoring for NFRET level from 0 to 1. Data are

representative of three independent experiments. One image was recorded

every 2.3 seconds.

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of tubulin drastically decreased the quantities of high and low

FRET hotspots, whereas accumulation of non-acetylatable a-

tubulin in MTs increased mainly the amount of low FRET

hotspots. Therefore, our results indicate that MT modifications

have substantial effects on the accessibility and/or the three-

dimensional structure of the binding loci of Tau with the MT

surface.

We investigated binding interactions of Tau with MTs by

comparing the fit of FRAP recovery curves of EGFP–Tau with

one- or two-exponential equations. Our analytical approach

highlighted that Tau mobility follows two models in cells: (1) a

free diffusion in the absence of MTs; and (2) a diffusion-binding

model in the presence of MTs. However, we cannot assign one-

exponential to exclusive diffusion and two-exponentials to

diffusion and binding of Tau. Indeed, the binding of PTX and

the acetylation of the K40 residue in a-tubulin led also to a

diffusion-binding model of Tau with two inseparable phases (one

exponential). Moreover, non-acetylatable a-tubulin promoted a

diffusion-binding model with two separate phases fitting with the

sum of two exponentials. Our results are consistent with those of

Ross et al. who explored the importance of a potential binding

site between Tau and PTX on MTs using FRAP on in vitro

polymerized MTs (Ross et al., 2004). Without PTX, their FRAP

recovery curves were best fitted with a bi-exponential equation,

in contrast to with PTX, when a simple exponential curve was

obtained. In our FRAP experiment, we also fitted our FRAP

recovery curves to the sum of two exponentials in the presence of

MTs giving short and long time constants (1.8 seconds and

18.6 seconds, respectively). Our time constants are consistent

with previous data (Samsonov et al., 2004; Konzack et al., 2007).

Moreover, Hinrichs et al., recently showed, using an in vitro

system and TIRF microscopy, that single Tau molecules can

move bi-directionally along the MT lattice (Hinrichs et al., 2012).

Our cellular FRAP data are in agreement with their results and

indicate a diffusion–binding model of Tau along MTs. In

addition, with modifications of the MT structure by PTX and

the acetylation level of tubulin, our findings could also indicate

different accessibility of Tau to its binding site on the MTs.

Furthermore, we identified a functional aspect of this specific

Tau–MT interaction. Overall, the dramatic changes in MT

organization throughout the cell cycle reflect a high degree of

spatial and temporal regulation of MT dynamic instability. In this

process, post-translational modifications of tubulin such as

acetylation (Tran et al., 2007; Zilberman et al., 2009) and/or

MT-interacting molecules such as Tau (Drubin and Kirschner,

1986; Lee and Rook, 1992) or PTX (Yvon et al., 1999; Goncalves

et al., 2001) tightly regulate MT assembly in cells. From our real-

time FRET assay, we succeeded in demonstrating the emergence

of a FRET hotspot at the plus-end of MTs during rescue and pause

phases. Regarding the parameters of MT dynamics, we noticed that

Tau induced longer periods of pauses and rescues. Moreover, the

binding of PTX and the acetylation of a-tubulin did not induce

FRET hotspots during either the rescue or the pause phases. This

could be correlated with no difference of MT dynamics between

the control and Tau-expressing cells. This also shows that the MT-

stabilizing function of PTX and acetylated K40 largely mask that

of Tau. In the presence of non-acetylatable a-tubulin, we again

found the appearance of a FRET hotspot during the rescue phase,

which could be related to the long period of rescue phases and high

rescue frequencies. Our findings indicate that the discrete Tau–MT

interaction corresponds to a point of high MT stability necessary

for the recovery of MT assembly.

Mechanistically, Tau could induce conformational changes of

MTs during rescue events that subsequently help the recruitment

of plus-end tracking proteins (+TIPS) responsible for closing the

MT (Bratman and Chang, 2007; Brouhard et al., 2008; Vitre et al.,

Fig. 7. Microtubule modifications affect the MT-stabilizing

function of Tau. Variables of microtubule dynamic instability were

measured in living cells and reported as: (A) mean durations of pause

after growth phase; (B) mean durations of rescue after shortening

phase; (C) distance-based catastrophe frequencies; (D) distance-based

rescue frequencies. ‘Ctrl’ and ‘+ Tau’ refer to microtubule dynamics

in cells expressing respectively, EGFP–tubulin (white bars) and

EGFP–tau (black bars). Microtubules were treated with PTX or TSA,

or expressed non-acetylatable a-tubulin (+K40A). Values are means

6 s.e.m. of 90 microtubules. Significant statistical difference

(Student’s t-test) between MT dynamics with EGFP–tubulin and MT

dynamics with EGFP–tau are shown. n.s., non significant.

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2008; Al-Bassam et al., 2010). Conversely, the Tau-induced

changes of MT conformation may also be crucial for catastrophe-promoting proteins such as stathmin (Gavet et al., 1998; Mannaet al., 2009). Indeed, MTs are ordinarily quite rigid structures

(Mizushima-Sugano et al., 1983; Cross and Williams, 1991) andthe binding of Tau to MTs could directly enhance inter-protofilament contacts and their stabilizing structures. This

would modify the mechanical properties of MT, facilitating theinteractions of +TIPS, which promote the MT growth, butdisturbing the binding of stathmin, which supports MT shrinkage.

In conclusion, using FRET and FRAP measurements, we haveidentified for the first time in living cells a discrete Tau–MTinteraction. Our data also provide significant support for amechanism in which the binding of Tau induces necessary

conformational changes of MTs, especially for the recovery ofMT assembly. This study presents the molecular basis for anunderstanding of the stabilizing effects of Tau on the MT

cytoskeleton, opening the way for the study of the effects of post-translational Tau modifications and/or mutations in theseinteractions between Tau and tubulin.

Materials and MethodscDNA cloningA gene coding for the longest human isoform of Tau (hTau40) was subcloned intopEGFP-C1 vector (ClonTech, CA, USA) between the XhoI site (forward primer:59-CGTCGTCTCGAGGCTGAGCCCCGCCAGGAGTTC-39) and the KpnI site(reverse primer: 59-GTAGGAGGTACCTCACAAACCCTGCTTGGC-39). PCRwas performed using high-fidelity Taq DNA polymerase (Biolabs Inc., MA, USA).The cDNA clone was confirmed by DNA sequence analysis. The pEGFP-tubulinplasmid was purchased from Invitrogen (Cergy-Pontoise, France). The pmCherry-tubulin and pmCherry-tubulin K40A plasmids coding, respectively, for wild-typea1B isotype and non-acetylatable a-tubulin mutated at lysine 40 to alanine were agift from Dr F. Saudou (Dompierre et al., 2007).

Cell culture and transfection protocol

All cell lines were purchased from ATCC, MD, USA. Cells from human non-smalllung carcinoma (clone A549; CCL2), colorectal adenocarcinoma (clone Caco-2;HTB-37) and neuroblastoma (clone SK-N-SH; HTB-17) were routinely grown at37 C in a humidified atmosphere of 5% CO2. Cells were maintained by regularpassage in a complete medium composed of RPMI 1640 (Lonza, Belgium) forA549 and SK-N-SH cells and with DMEM for the Caco-2 line, supplemented with10% heat-inactivated FBS, 2 mM L-glutamine and 100 U /ml penicillin and 50 U/ml streptomycin (Invitrogen). Cells were free from mycoplasma as determined bymycoalert tests (Lonza). Two days prior to experiments, cells were seeded at 105

cells per well in 1 ml complete medium in a four-well Lab-Tek chambercoverglass (Nunc, France). The lipofection of cells was carried out withLipofectamine 2000 according to Invitrogen’s instructions and 0.4 mg plasmidwas used, in total, for every experiment. Moreover, a set of A549 cells expressingLipofectamine Tau and/or a-tubulin proteins was incubated with 2 nM PTX(Mr5853.91 in DMSO; Sigma, France) or 40 ng/ml TSA (Mr5302.37 in DMSO;Sigma) for 4 hours at 37 C before FRET and FRAP experiments. For the FRAPassay, nocodazole (Mr5301.32; Becton Dickinson, UK) was used to disassembleMTs with 1 mg/ml for 30 minutes at 37 C.

ImmunostainingThe following antibodies were used: mouse monoclonal anti-a-tubulin (cloneDM1A; 1:1000 from 1 mg/ml; Sigma), anti-acetylated a-tubulin (clone 6-11B-1;1:1000 from 1 mg/ml; Sigma) and anti-Tau (clone T1029; 1:500 from 1 mg/ml;USbio–Euromedex, France). Immunoblotting of acetylated and total a-tubulin fromA549 cells was performed as described previously (Galmarini et al., 2003). Cellpellets were lysed by resuspension in 100 ml of low-salt buffer (20 mM Tris-HClpH 6.8; 1 mM MgCl2; 2 mM EGTA) and lysates centrifuged at 14,000 rpm for10 minutes at room temperature. The supernatant containing soluble a-tubulin wastransferred to a separate centrifuge tube and kept on ice. The pellet, whichcorresponds to the fraction of polymerized a-tubulin before the cell lysis wasresuspended in Ling’s buffer (10 mM Tris-HCl pH 7.5; 1.5 mM MgCl2; 10 mMKCl) to the same volume as the supernatant. Equivalent quantities of each fractionwere subjected to western blot analyses: one to detect acetylated a-tubulin and asecond to quantify total a-tubulin. Furthermore, immunoblots of Tau in A549, Caco-2 and SK-N-SH lines were also performed. Cells were lysed in RIPA buffer (50 mMTris, pH 8; 150 mM NaCl; 1% Triton X-100; 0.1% SDS; 1 mM EDTA; 1 mMTCEP) and lysates were centrifuged at 14,000 rpm for 20 minutes at 4 C. Equivalent

quantities of proteins from the supernatant fraction were subjected to western blotanalysis to detect Tau, and quantified by densitometry with ImageJ.

Instrumentation and image acquisitionCells were placed in appropriate medium supplemented with 1% FBS and 10 mMHepes to reduce pH variations, and maintained at 37 C. To analyze MT dynamics,time-lapse series of transfected cells were acquired with a fluorescence microscope(Leica Microsystems, Mannheim, Germany) equipped with a 1006objective. Forty-one frames of cell were acquired every 3 seconds using a CCD camera (CoolsnapFX,Princeton Instruments, Trenton, USA) driven by Metamorph software (UniversalImaging Co., Downingtown, USA). For FRET experiments, the cytoskeleton wasimaged using a Leica SP5 confocal laser scanning microscope (CLSM) with a Leicainverted microscope, equipped with a Plan-Apochromat 636oil immersion objective(NA51.4). Each image was recorded with the spectral mode of the CLSM selectingspecific domains of the emission spectrum. The donor EGFP was excited at 488 nmwith an argon laser and its fluorescence emission was collected between 496 nm and535 nm (corresponding to the donor filter) and between 580 nm and 650 nm(corresponding to the FRET filter). The acceptor mCherry was excited at 543 nmwith an helium–neon laser and its fluorescence emission was collected between580 nm and 650 nm. The donor and the acceptor fluorophores were excitedsequentially. The public-domain ImageJ software was used for image analysis(Rasband, 1997). FRAP experiments were performed on a CLSM Fluoview FV 1000(Olympus, Japan) with an IX81 inverted microscope, equipped with OlympusUPLSAPO 606oil immersion objective (NA51.35). The microscope was controlledusing Olympus Fluoview 2.0c software. Excitation (488 nm) was provided by amulti-line argon laser and emission collected using a 505–550 nm wavelength bandpass filter. As is common in FRAP, the experiment was divided in three sequences: (i)a pre-bleaching period during which 10 frames were recorded to define the initiallevel of fluorescence; (ii) a photobleaching step, which was carried out on a 2 mmradius circular area of the cytoplasm using the 488 nm wavelength laser at 30%power with two iterations of 2 mseconds/pixel according to the circular scanning(Tornado) mode; (iii) a postbleaching period during which fluorescence recovery wasmonitored every 0.43 seconds for 45 seconds.

FRET calculationsSensitized emissionFRET images were corrected from cross-talk between donor and acceptor channelsusing Youvan’s method (Youvan et al., 1997):

FczIFRET {A|ID{B|IA, ð1Þ

where IFRET, ID and IA are intensities (after background subtraction) in regions of interest(ROI) of the FRET, EGFP and mCherry channels, respectively, and A and B are thefraction of EGFP and mCherry leak-through into the FRET channel, respectively. Theseparameters were calculated by quantifying the intensity ratios between images from cellsexpressing only EGFP–Tau or mCherry–tubulin. In our study, the A and B values were0.15 and 0.06 on average, respectively. No leak-through signal from EGFP into themCherry channel or vice versa was observed. Therefore, Fc was normalized to the directdonor signal using Wouters’ method (Wouters et al., 2001):

NFRET~Fc=ID: ð2Þ

Calculations were performed from the variation in pixel response with the PixFRET plug-in of ImageJ in ROI (Feige et al., 2005). The NFRET intensities of 8-bit images wereinitially spread from 0 to 20 on the 256-grayscale, the maximum of which corresponds to8% of FRET in cells (Gordon et al., 1998). For all of NFRET images, we represented thefalse-color scale scoring for sensitized FRET intensities arbitrary ranged between 0 (darkblue) to 1 (white) values, the value 1 corresponding to 8% of NFRET. FRET images werefiltered with a 1-pixel-range median filter to reduce background noise.

The NFRET intensities and the distribution of NFRET pixels inside cells (n590,three independent experiments) were determined as follows: cells expressingEGFP–Tau, mCherry–tubulin and NFRET were concomitantly outlined in imageswith ImageJ software. The total NFRET intensities of cells were reported per unitarea (micrometers square) and the ratio of donor to acceptor emission wassimultaneously determined. The percentage of pixels belonging to low FRET(between 0.1 and ,0.5) and high FRET (§0.5) classes was also calculated.

FRAP data manipulation and fittingFluorescence recovery was extracted from images of the bleached area andcorrected for experimental fluctuations during acquisition. Approximately 15separate FRAP measurements were performed in three independent experimentsand curves were independently double-normalized according to the method ofPhair et al. (Phair et al., 2004):

Ffrap{norm(t)~ Fref {pre=½Fref (t){Fbg(t)�� �

½Ffrap(t){Fbg(t)�=Ffrap{pre

� �, ð3Þ

where Fref(t) is the reference fluorescence intensity in a neighboring cell; Ffrap(t),the fluorescence recovery in the bleached ROI at time t; Fbg(t) is the fluorescence

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intensity in a background ROI outside the cells; Fref-pre and Ffrap-pre are the meanfluorescence intensity of, respectively, the reference and bleached ROIs before thebleach after background subtraction:

Fref {pre~X

(t~0;tbleach{1)½Fref (t){Fbg(t)�=fprebleach

� �, ð4Þ

Ffrap{pre~X

(t~0;tbleach{1)½Ffrap(t){Fbg(t)�=fprebleach

� �, ð5Þ

with fprebleach corresponding to 10 frames during the pre-bleaching period.

Data fitting was performed using the Levenberg–Marquadt algorithm to adjustamplitudes A1 and A2, time constants t1 and t2, and F0 (at time 0) and F‘ (at time45 seconds, end of recording) fluorescence intensities using a double-exponentialequation:

F(t)~F?:(A1zA2)z(F0{F?):A1: exp({t=t1)z(F0{F?):A2: exp({t=t2), ð6Þ

A x2 statistical test suggested this equation reasonably described the experimentaldata as with a 2D diffusion under a Gaussian illumination model (Malnou et al.,2010). This model was applied without any a priori knowledge of either thegeometry of the bleaching or the process of fluorescence recovery. We noticed thatthe amplitude A2 was negligible for recoveries of free EGFP and EGFP–Tau in thepresence of nocodazole, PTX or TSA. Therefore, Eqn 6 was simplified to a mono-exponential equation:

F(t)~F?:A1z(F0{F?):A1: exp({t=t1): ð7Þ

The mobile fraction (Mobcalc) was calculated as follows:

Mobcalc~(F?{F0)=(1{F0): ð8Þ

Comparable mobile fractions of EGFP–Tau with or without MTs were measured(,67–72% in supplementary material Table S1), confirming that thephotobleaching laser pulse was sufficiently brief not to appreciably change theconcentration of EGFP–Tau.

Analysis of the microtubule dynamic instability

This analysis was done as previously described (Honore et al., 2003). Series of 16-bitimages were transferred to ImageJ software to determine the position of the plus-endof individual MTs during time. Changes in length of §0.5 mm were considered asgrowth or shortening events. Changes in length of ,0.5 mm were considered as phasesof attenuated dynamics (catastrophe rescues or pauses). Means and standard error werecalculated on 90 MTs from three independent experiments. Mean durations of pausesand rescues of catastrophe were determined using ImageJ. The distance-basedcatastrophe frequency was calculated by dividing the number of transitions fromgrowth or pauses to shortening by the total growth length for each monitored MT. Thedistance-based rescue frequency was calculated similarly, dividing the total number oftransitions from shortening to rescue by the total shortening length.

AcknowledgementsWe are very grateful to Dr Yannick Gachet and Dr Sylvie Tournier(LBCMCP, Toulouse, France) for helpful discussions. We thank DrSaudou (CNRS UMR146, Orsay, France) for plasmid coding thenon-acetylatable a-tubulin K40A fused to mCherry. V.P. thanksF. H. Boulay for his help.

Author contributionsG.B, P.H, R.N. and T.D.B. carried out the experimental work; G.B.,P.H., F.D., P.B. and V.P. participated in the data analysis andinterpretation; G.B., F.D., P.B., J.N.S. and V.P. participated in theelaboration of the concept and design of the research; G.B., R.N.,P.B., F.D., J.N.S. and V.P. participated in the writing of the article.

FundingWe acknowledge the financial support of INCa (Institut National duCancer); Inserm (Institut National de la Sante et de la RechercheMedicale); AMU (Aix-Marseille Universite) and DGOS [SIRIC label(Site de Recherche Integre sur le Cancer)]. R.N. received fellowshipsfrom Region Provence Alps-Cote d’Azur and from ONET Technologies.

Supplementary material available online at

http://jcs.biologists.org/lookup/suppl/doi:10.1242/jcs.120832/-/DC1

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