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MyoD induces myogenic differentiation through cooperation of its NH2- and COOH-terminal regions

MyoD and Myf5 are basic helix-loop-helix transcription factors that play key but redundant roles in specifying myogenic progenitors during embryogenesis. However, there are functional differences between the two transcription factors that impact myoblast proliferation and differentiation. Target gene activation could be one such difference. We have used microarray and polymerase chain reaction approaches to measure the induction of muscle gene expression by MyoD and Myf5 in an in vitro model. In proliferating cells, MyoD and Myf5 function very similarly to activate the expression of likely growth phase target genes such as L-myc, m-cadherin, Mcpt8, Runx1, Spp1, Six1, IGFBP5, and Chrnβ1. MyoD, however, is strikingly more effective than Myf5 at inducing differentiation-phase target genes. This distinction between MyoD and Myf5 results from a novel and unanticipated cooperation between the MyoD NH2- and COOH-terminal regions. Together, these results support the notion that Myf5 functions toward myoblast proliferation, whereas MyoD prepares myoblasts for efficient differentiation.

Introduction
The process of skeletal muscle differentiation is orchestrated by a family of four conserved basic helix-loop-helix (bHLH) transcription factors that are collectively known as myogenic regulatory factors (MRFs). Mice harboring single mutations of either MyoD or Myf5 are viable and do not have overt muscle phenotypes, suggesting that MyoD and Myf5 have considerable overlap in their roles Rudnicki et al., 1992). However, either MyoD or Myf5 is required for proper myogenesis during embryogenesis because compound MyoD / Myf5 -null mice lack essentially all skeletal muscle tissue at birth (Rudnicki et al., 1993;Kassar-Duchossoy et al., 2004). In contrast, myogenin is important for the terminal differentiation and fusion of myoblasts into mature muscle fibers (Rawls et al., 1998;Vivian et al., 2000). MRF4 appears to have a role as a determination factor in a subset of myocytes in the early somite and as a differentiation factor in later muscle fibers (Kassar-Duchossoy et al., 2004).
Postnatal growth and regeneration of skeletal muscle is mediated primarily by a pool of myogenic stem cells known as satellite cells, which reside adjacent to the fibers. In response to damage through injury or exercise, these satellite cells activate expression of MyoD and Myf5 and undergo numerous rounds of proliferation as myoblasts. A small number of myoblasts return to a quiescent state, thus replenishing the pool of satellite cells; the remainder continue their differentiation, fusing into existing or new myofibers and expressing myogenin and MRF4 while down-regulating Myf5. In contrast to wildtype myoblasts, MyoD -null myoblasts grow more quickly, show aberrant expression of muscle markers, and differentiate inefficiently (Sabourin et al., 1999), a phenotype that is the cause of the regeneration deficit exhibited by compound dystrophic ( mdx ) MyoD -null animals (Megeney et al., 1996). Conversely, Myf5 -null myoblasts proliferate poorly and differentiate precociously (Montarras et al., 2000). The sequence of MRF expression in activated satellite cells, in conjunction with the phenotypes of single-null myoblasts and animals, argue that MyoD and Myf5 do not have identical roles in myoblast proliferation and induction of differentiation.
MyoD and Myf5 target genes have largely been examined after the onset of differentiation and, hence, are involved in producing the enormous phenotypic shift from a proliferating myoblast to a contractile, multinucleated muscle fiber. Although it has been suggested that MyoD and Myf5 transactivation is checked in growing myoblasts (whether by degradation [Thayer et al., 1989], modification [Lindon et al., 1998], signaling [Vaidya et al., 1989;Li et al., 1992], or interfering heterodimerization [Benezra et al., 1990]), the distinct phenotypic differences that are exhibited by growth phase MyoD -and Myf5 -null myoblasts suggest that MyoD and Myf5 do have active roles in myoblasts. Indeed, Wyzykowski et al. (2002) identified Id3 and NP1 as target genes of MyoD under growth conditions.
Distinguishing the distinct functions of MyoD and Myf5 is complicated by their abilities to auto-and cross-activate expression from the endogenous loci (Tapscott et al., 1988;Braun et al., 1989;Thayer et al., 1989;Wyzykowski et al., 2002). Such a circular network could account for the stabilization and irreversibility of the commitment of a cell to a myogenic fate (Thayer et al., 1989;Weintraub et al., 1991a). However, gene expression changes resulting from the introduction of exogenous MyoD or Myf5 could be an indirect effect that is mediated through the other MRF. Expression of MyoD in the absence of Myf5 (and vice versa) permits the comparative evaluation of each factor's functions in myogenic commitment.
To this end, we reintroduced MyoD or Myf5 into a MyoD Ϫ / Myf5 double-null (double knockout [dblKO]) fibroblast cell line that was maintained in high serum growth conditions. These cell lines are normally nonmyogenic but can be converted to skeletal muscle upon the exogenous expression of MyoD or Myf5. Microarray analysis identified numerous differentially regulated genes, which were further validated by examining specific candidates using real-time PCR. A number of growth phase targets were identified, demonstrating that MyoD and Myf5 are transcriptionally active in proliferating cells. Surprisingly, we did not find unique targets, and both MRFs were able to induce the expression of these genes.
MyoD, however, was strikingly more effective at activating differentiation markers than Myf5. Additional support for functional differences between MyoD and Myf5 were obtained by using chimeric MRFs that interchanged their NH 2 -terminal, bHLH, and COOH-terminal domains. The bHLH domains (DNA binding and dimerization) are highly conserved between the two genes, whereas their NH 2 -and COOH-terminal regions are more divergent. Whereas Myf5 was inefficient at inducing differentiation gene expression, the activation of a cohort of these markers by the chimeric MRFs provided strong evidence for cooperative gene activation by the NH 2 -terminal and bHLH ϩ COOH-terminal regions of MyoD. MyoD -null primary myoblasts have a greatly reduced expression of the same genes relative to wild-type myoblasts. Therefore, these data support the idea that Myf5 is biased toward myoblast proliferation, whereas MyoD promotes myoblast differentiation (Sabourin et al., 1999;Seale et al., 2001).

MyoD and Myf5 activate skeletal muscle genes in growth conditions
To circumvent the potential problem of auto-and cross-activation by the primary myogenic factors (Braun et al., 1989;Thayer et al., 1989;Weintraub et al., 1991b;Hollenberg et al., 1993) embryonic fibroblast lines with retrovirus expressing MyoD or Myf5 or with empty control retrovirus. With this approach, we were able to assess which genes were common targets of MyoD and Myf5 versus those that could be uniquely regulated by one or the other primary MRF. These genes could be directly activated by MyoD or Myf5 or could be indirectly activated through an intermediate transcription factor; we considered both classes to be downstream targets of the primary MRFs.
Shortly after infection of the dblKO target cells with retrovirus, positive-expressing cells were purified by FACS based on GFP fluorescence expressed from the bicistronic retroviral transcript (Fig. 1, A and B). Pools of Ͼ 10 6 cells were then harvested for total RNA after a further 24 h of culture in high serum growth conditions. Fluorescently labeled probes generated from biological-triplicate RNA samples were hybridized to MG-U74Av2 GeneChips, each containing probesets directed at ف 6,000 genes and an additional ف 6,000 ESTs. Comparison of MyoD or Myf5 arrays with control arrays produced candidate lists that were considered to contain genes potentially regulated by MyoD or Myf5 during growth phase (Table I and Table S1, available at http://www.jcb.org/cgi/content/full/jcb.200502101/DC1).
One goal of our study was to identify targets that are uniquely regulated by Myf5 but not by MyoD (and vice versa). However, very few genes were suggested by the microarray data to be increased by Myf5 and not by MyoD, and real-time PCR directed at several of those transcripts, in turn, did not support them as targets ( Skiip , Table I; Refbp1 and Snk , unpublished data). In contrast, 36 genes were up-regulated by MyoD but not by Myf5. The majority of these targets (e.g., myogenin , myosin heavy chain , and troponin-T ), however, are associated with skeletal muscle differentiation.
At least six of the identified targets are transcription factors (Table I) and may themselves regulate the expression of other genes. Foremost amongst them is myogenin, which is an MRF that is activated by MyoD immediately upon the switch to differentiation conditions (Hollenberg et al., 1993;Bergstrom et al., 2002). Therefore, we examined (by GeneChip analysis) the possibility that our candidates were activated indirectly by myogenin using myogenin retrovirus-infected dblKO cells that were prepared as for MyoD and Myf5. 50% (13/26) of myogenin targets were also downstream from MyoD and Myf5. However, for most nondifferentiation class genes, our microarrays indicated that tar-All of the genes identified in Table I are robustly expressed by proliferating wild-type primary myoblasts (Fig. 2). Most (47/53 ϭ 89%) of these genes are decreased in MyoD Ϫ / Ϫ myoblasts, whereas the remainder (6/53) exhibit a mixture of increase/no change/decrease calls and moderate fold increases. Differentiation markers are vastly decreased (up to 140-fold for myosin heavy chain-3 or troponin T1 ), whereas most putative growth-phase genes showed only moderate changes (e.g., approximately eightfold for m-cadherin or L-myc and ف 2.7-1.8fold for Six1 or Runx1 ).
Thus, in high serum conditions, MyoD and Myf5 are capable of regulating the transcription of numerous genes. The growth phase regulation of a selection of those candidates was then specifically examined.

Growth phase candidate validation by SYBR Green real-time PCR
The expression of these candidate genes was re-examined using a second set of independently derived RNA samples. This second set was produced by the infection of a distinct dblKO cell line with drug-selectable retrovirus, yielding proliferating puromycin-resistant pools of cells that were expanded for 2 wk under drug selection. SYBR Green real-time PCR was used to quantitate target gene transcript levels using PCR primers that were chosen to span at least one intron wherever possible. The specificity of the PCR was verified by denaturing curve analysis and direct sequencing of the products. get activation was similar between MyoD, Myf5, and myogenin treatments, which contrasts with the considerable induction of myogenin by MyoD (10-fold) compared with Myf5 (1.2-fold; Table I). This suggests that these genes are common targets of MRFs rather than being strictly dependent on myogenin.
To identify potential growth phase targets of MyoD, probable differentiation markers were removed using previous work by Bergstrom et al. (2002). They used an inducible MyoD-ER fusion system to examine gene expression by microarray analysis during early differentiation in low serum conditions (Bergstrom et al., 2002). They identified nine subsets in their data by using a clustering algorithm to find coordinate patterns of temporal regulation, including those with transient increases, early or delayed increases, and decreases in expression. Of the 571 genes identified by Bergstrom et al. (2002), we mapped 298 to probesets on the MU74Av2 GeneChip on the basis of GenBank, Unigene, or LocusLink IDs. Of these 298, 22 genes were also found to be up-regulated by MyoD in our experiment, and an additional seven were also up-regulated by Myf5. This provided added support to our data. A portion of these genes fell within Bergstrom clusters 5 and 6 and primarily represented differentiation-specific targets such as structural genes (e.g., myosin and troponin ). However, others within clusters 1-4 (early induction) and 7-9 (decreased expression through differentiation) were also seen to have increased expression in our GeneChip data and were considered as possible growth targets.  The majority of the genes that were selected as possible growth phase target genes in the GeneChip data were also found by real-time PCR to be up-regulated (Table I). The estimates of the fold up-regulation of Six1, Runx1, L-myc, IGFBP5, and Mcpt8 were similar by either technique. Spp1 was increased more, and Chrn␤1 was increased slightly less as estimated by real-time PCR; nonetheless, each was significantly increased by MyoD and Myf5. The increases in m-cadherin and myogenin levels by real-time PCR were much greater than by microarray, probably as a result of the lower background of the PCR assay. Again, however, the consistency of the changes in target expression that were produced by MyoD and Myf5 contrast with the variable induction of myogenin by MyoD and Myf5, arguing that this is not a strictly indirect effect.
Approximately one third of the candidates that were selected for real-time PCR validation did not exhibit significant changes in the stable pool samples (Table I). These genes tended to be those that had modest fold changes in the Gene-Chip data (e.g., Hes6, Pkia, Skiip, and p21). Probesets for differentiation markers that exhibited larger changes by microarray (such as cardiac troponin C and MEF2A) were likely seen as a result of spontaneous differentiation in the original Gene-Chip samples and were minimized in these proliferating cultures. These observations support the conclusion that the stable pools lacked the spontaneously differentiating cells that were observed in our original samples. Wyzykowski et al. (2002) used the same inducible MyoD system as Bergstrom et al. (2002) to generate inputs for a representational difference analysis protocol. They identified Id3 and NP1 as growth phase targets of MyoD (Wyzykowski et al., 2002). Their cells were maintained in a high serum growth medium during MyoD induction, suggesting that Id3 and NP1 are not differentiation targets that are expressed simply as a consequence of serum deprivation. Rather, they are induced in the presence of cyclohexamide, indicating that they are likely to be direct targets of MyoD that do not require intervening protein synthesis for activation.
In contrast, our GeneChip experiments did not reveal an induction of Id3 or NP1 in dblKO cells by either MyoD or Myf5. To assess whether this was a consequence of low sensitivity to those genes, we used SYBR Green real-time PCR to specifically examine the expression of Id3 and NP1 in both the GeneChip RNA samples and in RNA from drug-selected pools; however, no significant changes were detected (unpublished data). It is likely that these inconsistencies are attributable to the numerous differences in our experimental systems, including the method of MyoD expression, the type of host cell used, and possibly the presence/absence of endogenous MyoD and Myf5 genes.
From this combination of GeneChip and real-time PCR analyses, we defined a validated set of growth phase targets. Notably, MyoD and Myf5 were both capable of regulating each of these genes, and none were strictly associated with one MRF. These targets were then applied to examine the differences in function between corresponding regions of MyoD and Myf5.

Association of growth phase gene activation with MyoD and Myf5 domains
Within their bHLH regions, Myf5 and MyoD exhibit 88% identity and Ͼ95% homology at the amino acid level. In contrast, the regions that are NH 2 and COOH terminal to this highly conserved DNA-binding region exhibit considerably more differences in sequence and function (Gerber et al., 1997). Therefore, we hypothesized that functional differences between MyoD and Myf5 would be a consequence of their  Table I show that the majority of differentiation markers are greatly reduced relative to wild-type myoblasts (e.g., Myh3 to myogenin). In contrast, genes that are regulated in growth phase by MyoD and Myf5 are reduced to a lesser degree, if at all (e.g., Mcpt8, Six1, and Runx1). Calls are shown for wild-type (n ϭ 3) or MyoDϪ/Ϫ (n ϭ 3) myoblasts. P, present; M, marginal; A, absent. Change calls are shown for nine pairwise comparisons between wild-type and MyoDϪ/Ϫ myoblasts. I, increase; MI, marginal increase; NC, no change; MD, marginal decrease; D, decrease. divergent NH 2 -and COOH-terminal domains rather than a result of the bHLH recognition of discrete DNA sequences. To test this, chimeric MRFs were built, interchanging the MyoD and Myf5 NH 2 -and COOH-terminal regions around their bHLH domains. This yielded six chimeric MRF genes (termed d5d, d55, 55d, 5d5, 5dd, and dd5, where d denotes the portion derived from MyoD and 5 denotes the portion derived from Myf5) in addition to the full-length wild-type MyoD and Myf5 (Fig. 3 A).
Retrovirus expressing one of the chimeric MRFs were introduced into dblKO fibroblasts, yielding drug-selected pools of Ͼ10 5 clones from which RNA and protein were extracted for analysis. The growth phase targets identified previously were then assayed within these samples by SYBR Green realtime PCR.
Both glyceraldehyde-3-phosphate dehydrogenase (GAPDH) transcript (input RNA quantity; reverse transcription) and MRF protein levels (MRF expression) were used to enable equitable comparisons by providing normalization between samples. GAPDH transcripts were quantified by real-time PCR. Relative protein expression levels of full-length and chimeric MRFs were derived by direct digital densitometry of Western blots probed with three primary antibodies that each recognized unique but overlapping sets of four MRFs (Fig. 3 B). All of the chimeras were demonstrated to be capable of regulating the genes identified in the previous analysis, producing substantial increases in expression compared with the puromycin-alone negative control pool.
After normalization, 5dd tended to be the poorest relative activator, whereas 55d was (in most cases) the best (Fig. 3 C). However, in contrast to the other samples, these two chimeras were subject to significant normalization corrections (Fig. 3 B), which may have tended to overstate their effects. Nonetheless, no single region of MyoD or Myf5 strictly correlated with enhanced or decreased relative activation. In certain cases (e.g., Chrn␤1, m-cadherin, and Spp1), there is a tantalizing suggestion that the Myf5 bHLH domain could have greater activity than that of MyoD, although this pattern is not borne out amongst the others. Chimeras dd5 and d55 have a greater effect than MyoD or d5d. This might suggest that an interaction between the flanking portions of MyoD acts to suppress transactivating activity, which is an effect that is disrupted when a portion of Myf5 is substituted. A similar effect was observed in deletion studies of MyoD (Weintraub et al., 1991b). Thus, for growth-phase genes, the corresponding domains of MyoD and Myf5 are otherwise interchangeable, and differences emerge only with respect to the degree of target induction.
The MyoD NH 2 -and COOH-terminal domains cooperate to induce differentiation A significant number of potential MyoD target genes that were identified by GeneChip analysis were differentiation markers. Wild-type MyoD and Myf5 and chimeras were expressed in dblKO cells using retrovirus and were maintained for 3 d in growth conditions before harvest. The proportion of infected cells was very similar between pools (see Fig. 5 A). Differentiation marker expression was examined by real-time RT-PCR (Fig. 4 A). This set of vectors included a COOH-terminal FLAG epitope tag that allowed for the normalization of gene expression against MRF protein levels (Fig. 4 B). Two growth phase markers (Chrn␤1 and Runx1, identified in the aforementioned GeneChip experiment) showed little relative difference between MyoD, Myf5, and the chimeras. In concordance with the Gene- Chip results, however, the expression of MyoD produced a considerable activation of genes such as the cholinergic receptor ␣ and ␥ subunits, myogenin, ␣-actin, myosin, and troponin ( Fig. 4; Table II shows unnormalized changes vs. empty vector controls). In comparison, the level of Myf5 induction of these genes was moderate relative to the empty vector control.
Substitution of the MyoD COOH terminus into Myf5 (55d) was not sufficient in growth phase to cause differentiation and could produce only modest increases in the expression of numerous differentiation markers. Replacement with the MyoD NH 2 terminus alone (d55) also resulted in just moderate gene induction relative to full-length Myf5. Importantly, the concurrent presence of MyoD NH 2 -and COOH-terminal regions (d5d) resulted in activity approaching that of full-length MyoD.
The same pools of dblKO cells expressing one of the wild-type or chimeric MRFs were challenged to differentiate under reduced serum conditions. When the MyoD COOH terminus was present (MyoD; d5d, 5dd, and 55d), there was enhanced differentiation compared with the corresponding Myf5 region (Myf5; dd5, d55, and 5d5; Fig. 5 B). Deletion of the putative cdk4-interacting domain (Zhang et al., 1999) from the COOH-terminal region of full-length MyoD or insertion of this region into the corresponding location of Myf5 does not significantly affect these results based on protein (myosin heavy chain immunostaining) or RNA (real-time PCR) markers (Punch, V., personal communication). The MyoD NH 2 terminus also had a noticeable effect on differentiation (Fig. 5 B, compare MyoD with 5dd, d5d with 55d, dd5 with 5d5, and d55 with Myf5). The MyoD bHLH domain, which has previously been connected to cell cycle arrest (Crescenzi et al., 1990;Sorrentino et al., 1990), enhanced differentiation when combined with the MyoD COOH-terminal region (Fig. 5 B, compare MyoD with d5d and 5dd with 55d) but had little effect otherwise. Overall, the combined NH 2 -and COOH-terminal portions of MyoD (MyoD and d5d) were most effective in producing robust differentiation, whereas the two MRFs lacking both (Myf5 and 5d5) were the poorest. Thus, in both growth and differentiation conditions, the MyoD NH 2 -and COOH-terminal regions cooperate to strongly activate the myogenic differentiation program.

Discussion
Previous work has suggested that there are unique roles for MyoD and Myf5 in adult myogenesis (Megeney et al., 1996;Sabourin et al., 1999;Montarras et al., 2000). To investigate the possibility that these phenotypes are a result of the activation of unique target genes by each of these transcription factors, we conducted genome-wide surveys of gene expression changes in response to MRF expression. The embryonic fibroblast cells that were used are normally nonmyogenic but can be converted to myoblasts by ectopic MRF expression. Importantly, they were derived in a MyoDϪ/Ϫ;Myf5Ϫ/Ϫ background, precluding cross-activation between MyoD and Myf5. . MyoD NH 2 and COOH termini cooperate to activate differentiation marker expression. Amongst a variety of differentiation genes, the d5d chimera had near wild-type activity, whereas the separated NH 2 terminus and COOH terminus of MyoD had much less. In contrast, growth-phase genes Chrn␤1 and Runx1 were induced similarly by MyoD, Myf5, and the chimeras. (A) Gene expression measured by real-time PCR and normalized to MRF protein expression (B) and GAPDH transcript levels. Plotted as relative activity between MRFs. (B) Relative expression levels of FLAG-tagged chimeric MRFs in growth phase (normalized to tubulin). Numbers on y axis indicate fold changes.
Our microarray data indicates that MyoD expression induces more myogenic genes than Myf5, often to a greater degree. Comparison of our data with a previous study (Bergstrom et al., 2002), however, demonstrated that the majority of these MyoD-regulated targets are markers of differentiated skeletal muscle. Thus, a unique function of MyoD (vs. Myf5) is a strong ability to induce differentiation. MyoD-null primary myoblasts have vastly reduced levels of differentiation marker gene expression (Fig. 2), which is consistent with the phenotypic data on single-null primary myoblast cultures; in the absence of MyoD, the myoblasts proliferate well and differentiate poorly (Sabourin et al., 1999), whereas in the absence of Myf5, the opposite is true (Montarras et al., 2000). The moderate reduction of levels of growth-phase genes in MyoD-null myoblasts likely indicates a preference for activation by MyoD that Myf5 compensates for only partially.
A previous study by Seale et al. (2004) used representation difference analysis to identify potential satellite cell markers and MyoD target genes using primary myoblasts from MyoDϪ/Ϫ mice as a model for early satellite cell activation. The decreased expression of many genes (including Chrn␥, Chrn␣1, myogenin, troponin T1, H19, and Peg3) was seen (Seale et al., 2004); these genes were up-regulated by MyoD expression in our experiments, supporting the contention that MyoD regulates these genes and promotes differentiation. However, Seale et al. (2004) also showed that levels of numerous genes (e.g.,Plgf,VCAM1,Igsf4a,Tcr␣v13,laminin ␣5,and Klra18) are increased in MyoDϪ/Ϫ myoblasts, which express considerable Myf5. None of these genes was identified in the current study as a Myf5 target. Thus, although MyoD and Myf5 have critical roles in the myogenic program, other transcription factors (e.g., Pax3 or Pax7) are likely needed to produce the full spectrum of normal myoblast gene expression.
We initially focused on the genes that were activated in the proliferating population by pruning our microarray results of differentiation genes. To assist in this, we leveraged the work of Bergstrom et al. (2002), who used an inducible MyoD in differentiating dblKO cells. Their identified target genes were those induced by serum deprivation, growth arrest, and MyoD activity in the context of those conditions. However, their data also included genes that are induced immediately before growth arrest but remain up-regulated either as a result of a failure of their RNA levels to decay to baseline during the early times of the experiment or because expression continues in differentiation. Thus, we identified a set of genes that were growth phase targets of MyoD and Myf5. Furthermore, real-time PCR validation confirmed that they were not unique targets but, rather, that they all could be induced by either of these MRFs.
These similarities in function are perhaps not surprising. Both MyoD-and Myf5-null mice possess grossly normal skeletal muscle, indicating that there is redundancy and compensation for the loss of either factor during development. Indeed, only with the concurrent loss of MRF4 activity does the defect in skeletal myogenesis become fully penetrant (Kassar-Duchossoy et al., 2004). Therefore, rather than MyoD and Myf5 each having a unique set of target genes that they are responsible for activating, it is reasonable that these two factors are capable of regulating the same downstream targets. Differences in MyoD and Myf5 function in proliferating cells may instead vary in the strength of their effect on similar sets of genes. Whether MyoD and Myf5 function similarly during differentiation is a distinct question.
Previous work by Wyzykowski et al. (2002) suggested that Id3 and NP1 were both activated by MyoD in myoblast growth phase. Although our data does not show this, the differences between our methods and theirs are considerable and are more than adequate to explain the discrepancy. The cell types in which the experiments were conducted are a primary example: Wyzykowski et al. (2002) used 10t1/2 fibroblasts (genetically wild type), whereas our fibroblasts were derived from MyoD/Myf5 compound-mutant animals (it is also likely that our cells have disrupted MRF4 function; Kassar-Duchossoy et al., 2004). It is quite conceivable that the presence of other myogenic factors could be capable of modulating MyoD activity.
An important conclusion to be drawn from these data is that both MyoD and Myf5 are active transcription factors in proliferating myoblasts (previously shown for MyoD by Wyzykowski et al., 2002). Rather than passively awaiting a differentiation signal, these two factors induce the expression of myoblast growth-phase genes. MyoD and Myf5, therefore, act not only to commit the cell to a myoblast identity but also to prepare the myoblast for skeletal muscle differentiation.
The expression of MyoD or Myf5 led to the down-regulation of only a very small number of genes. This could indicate that they act primarily as transcriptional activators in high serum conditions. However, our experimental system involved converting a fibroblast cell type to the myogenic lineage and, thus, was incapable of detecting MRF-mediated suppression of any genes that were not initially expressed in the control cells.
Myogenin is not usually found in proliferating myoblasts and is induced at the onset of differentiation (Hollenberg et al., 1993;Weintraub, 1993;Bergstrom et al., 2002). The nondifferentiation targets that were assayed (e.g., Six1, Runx1, Mcpt8, and Spp1) were induced to similar levels by either MyoD or Myf5, whereas myogenin was induced strongly by MyoD (10-fold) but only weakly by Myf5 (1.2-fold). Thus, although myogenin can activate similar targets to MyoD (Wyzykowski et al., 2002) and Myf5 (Table I), indirect regulation through myogenin is not dominant. The overexpression of myogenin alone was much less effective than MyoD at inducing differentiation marker transcripts, also demonstrating that MyoD has distinct functions that are not simply consequences of myogenin induction. The high level of structural conservation between MyoD and Myf5, particularly in the bHLH domain, provides a strong rationale for our observation that there are few, if any, unique growth phase target genes of MyoD or Myf5 (this might be extended to myogenin-the conserved bHLH domain may allow the induction of growth targets, although myogenin would not usually be found in growing cells). It has been shown previously that just three differing amino acids between the MyoD and E12 bHLH regions encode specificity for the myogenic program (Davis and Weintraub, 1992). However, our results suggest that the three nonconservative and five conservative amino acid substitutions between MyoD and Myf5 bHLH domains are not likely to be involved in overt target gene selection, although they could potentially provide sufficient variation for more subtle types of regulation.
Chimeric MRFs, in which the NH 2 -, bHLH-, and COOHterminal domains of MyoD and Myf5 were interchanged, were used to explore which portions of MyoD might provide for enhanced myogenic differentiation. Strikingly, only the d5d chimera approached MyoD levels of differentiation gene expression, whereas the other chimeras, Myf5, and myogenin were many times less effective. The MyoD NH 2 -and COOH-terminal regions had much less activity when separated, thus indicating a functional interaction between them.
In contrast to growth conditions (Fig. 4), differentiation conditions allowed the MyoD NH 2 -and COOH-terminal regions to function independently (Fig. 5, B and C), with the concurrent presence of both producing the greatest differentiation.
Therefore, each region is effective when growth signals are naturally reduced under low serum conditions. However, cooperation between these two MyoD regions was required to overcome the strong growth signals that were provided by high serum medium. Full-length MyoD, unlike Myf5, could, therefore, bias a cell toward differentiation by sensitizing it to a moderate reduction in growth signals.
Interestingly, the MyoD bHLH domain enhanced differentiation at the molecular and phenotypic levels (Figs. 4 and 5), but only when combined with the MyoD COOH terminus. Despite its exceptional similarity with the Myf5 bHLH, the MyoD bHLH domain functionally differs in its interaction with the MyoD COOH terminus. However, this MyoD bHLH effect modulates gene expression levels rather than target gene selection (Fig. 4).
The NH 2 -terminal activity is likely to be the MyoD transcriptional activation domain, which was previously mapped between amino acids 3-56 (Weintraub et al., 1991b). It is interesting to speculate that the COOH-terminal function is provided by the chromatin remodeling amphipathic ␣-helix (helix III) domain that was previously described by Bergstrom and Tapscott (2001). Whereas MyoD can induce muscle marker expression at repressed loci, their studies demonstrated that a similar motif in myogenin was ineffective at this task. This ␣-helix motif is conserved in Myf5 (Gerber et al., 1997), but Myf5 is similar to myogenin in being less effective than MyoD at initiating muscle gene expression. Thus, MyoD might be more efficient than Myf5 for inducing differentiation because of a greater ability to remodel chromatin at lineage-restricted loci. A similar domain appears in MRF4 Bergstrom and Tapscott, 2001) and could mediate the residual skeletal myogenesis that is found in the absence of MyoD and Myf5 (Kassar-Duchossoy et Percentage of total nuclei (n Ͼ 1,000) found within a differentiated myosin heavy chainϩ cell, normalized to A. (C) Myosin heavy chain immunostaining of differentiated pools (MF20, red; DAPI, blue). Bar, 100 m. al., 2004). Therefore, MyoD may use its COOH-terminal chromatin-remodeling domain to provide access to silenced muscle genes for the NH 2 -terminal activation domain. In support of this, the substitution of either portion alone into Myf5 produced only moderate increases in gene expression, whereas together they strongly activated a variety of differentiation markers (Fig. 4).
If MyoD is inclined to promote differentiation, whereas Myf5 activates growth (but not differentiation) targets, then it is consistent with their cell cycle regulation in growing myoblasts. MyoD levels peak at the differentiation checkpoint in G 1 of the cell cycle, whereas Myf5 levels are high in S/G 2 and G 0 in association with proliferation and a failure to differentiate (Kitzmann et al., 1998). This complementary pattern of expression allows for the maintenance of expression of growthphase genes and myogenic identity while ensuring that high levels of MyoD occur only at a cell cycle point that is appropriate for differentiation.
That MyoD and Myf5 activate the same downstream target genes, but to differing degrees, does much to explain the phenotypes observed in single knockout mice and cells. Our data reinforce the partitioning of myogenic factors into primary and secondary MRFs and, furthermore, add support to the concept of a role for Myf5 in myoblast proliferation versus MyoD instigating myogenic differentiation. MyoD's ability to activate differentiation marker expression despite the presence of high levels of serum is a distinguishing biochemical function that is not found in Myf5 and involves the cooperation of separate domains of MyoD. These regions may allow MyoD to interact with coactivators for which Myf5 has much less affinity. In the future, studies to further understand the distinct roles played by MyoD and Myf5 in growing and differentiating myoblasts, as well as the structural constraints and intermolecular interactions upon which those roles are built, will be essential to our understanding of the mechanism by which damaged skeletal muscle is efficiently regenerated.
Myf5, MyoD, chimeric MRFs, or myogenin were introduced into dblKO cells using retrovirus based on the three-plasmid HIT system (provided by V. Sartorelli, National Institutes of Health, Bethesda, MD; Soneoka et al., 1995), including expression plasmids based on the pHAN backbone (with puromycin resistance driven from a distinct SV40 promoter) or a modified pHAN backbone in which the puromycin cassette was replaced by an internal ribosomal entry site and humanized Renilla GFP (IRES-hrGFP) sequence (Stratagene). MRFs that were expressed using the latter retroviral plasmid were COOH-terminally tagged with a 3ϫ FLAG epitope. Empty control virus expressed only puromycin resistance or hrGRP. Retrovirus was produced by calcium phosphate transient cotransfection of gag-pol (pHIT60), env (pHIT456), and expression (pHAN) plasmids into 293T cells; virus-containing medium was harvested 48 h from the beginning of the transfection and was filtered through a 0.45-m syringe filter (Millipore). Cells were infected overnight with diluted, filtered viral supernatant plus 8 g/ml polybrene (hexadimethrine bromide; Sigma-Aldrich). Drug selection, where appropriate, was conducted with 1 g/ml puromycin (Sigma-Aldrich) in growth medium for at least 1 wk; uninfected controls were obliterated after 4-5 d of selection. Differentiation was induced by replacing growth medium with DME ϩ 2% horse serum (GIBCO BRL) and 1% penicillin/streptomycin.

FACS
3 d after infection with hrGFP-expressing retrovirus, pools of cells were trypsinized, centrifuged, resuspended in PBS ϩ 5% FCS, filtered through a MACS filter (Miltenyi Biotec) to remove aggregates, and placed on ice. Cell sorting was performed using a MoFlo sorter (DakoCytomation), with gating on hrGFPϩ populations set by a comparison with an uninfected negative control pool. A small portion of the sorted cells was reanalyzed to assess purity, and the remainder was replated in growth medium for an additional 24 h before harvesting for total RNA.

RNA and protein isolation
Total RNA was purified using the RNeasy Mini Kit (QIAGEN) according to the manufacturer's instructions and quantitated by OD 260 or by Ri-boGreen (Invitrogen). RNA samples used for microarray analysis were ethanol precipitated in order to reach a minimum concentration of 2 g/ml. Protein extracts were made by lysis of pelleted cells in radioimmunoprecipitation assay (RIPA) lysis buffer supplemented with MiniComplete protease inhibitors (Roche).

Microarray analysis
Triplicate RNA samples for microarray analysis were submitted to the Ottawa Genome Centre for hybridization to MG-U74Av2 GeneChips (Affymetrix, Inc.). Manufacturer's quality controls were verified by the Centre. Raw data files were processed with MicroArray Suite (MAS 5.0; Affymetrix, Inc.) using the statistical algorithm (Affymetrix, Inc.) to derive signals and present/marginal/absent calls for each sample; all possible pairwise comparisons were also performed between experimental and control replicates, producing log(fold) ratio estimates of change and increase/no change/decrease calls. Processed results were then exported from MAS 5.0 and imported into Excel and Access (Microsoft) for further manipulation. Probesets showing consistent statistically significant changes between MRF and control samples were screened for detectable signal values (present/absent calls) and absolute log(fold) changes of at least one. Probeset annotations were obtained from http://www.affymetrix.com/analysis/index.affx. Microarray data is available from StemBase (http://www.scgp.ca:8080/ StemBase/; Ontario Genomics Innovation Centre; Perez-Iratxeta et al., 2005) under experiments E223 (samples S361-4) and E59 (S78-9) and from the National Center for Biotechnology Information's Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/; Edgar et al., 2002;Barrett et al., 2005) under series accession no. GSE3245 (GSM73053...64) and GSE3244 (GSM73026,.

Real-time PCR
RNA samples were reverse transcribed using random hexamer primers that were included in the RNA PCR Core Kit (PerkinElmer) according to the manufacturer's instructions. Reverse transcription reactions were diluted (1:10 in Fig. 3 C and 2:5 in Fig. 4 A) with 10 mM Tris, pH 8.0, yielding master samples of reverse-transcribed products from which related PCR reactions were drawn. Real-time PCR reactions included the following: 2 l of diluted reverse transcription product, 10 l of 2ϫ iQ SYBR Green SuperMix (Bio-Rad Laboratories), 30 nM ROX passive reference dye (Stratagene), and 50 nM of each forward and reverse PCR primer. Real-time data was gathered using a system (MX4000; Stratagene) over 40 cycles (30 s at 94ЊC, 60 s at 58ЊC, and 30 s at 72ЊC) followed by a denaturation curve from 54 to 94ЊC in 30-s increments of 0.5ЊC to ensure amplification specificity. C t values were calculated with the MX4000 software by using moving window averaging and an adaptive baseline. Fold changes, other calculations, and chart plotting were performed in Excel. A PCR efficiency of 85% was assumed. Primer sequences can be found in Table S2.

Online supplemental material
Tables S1-S4 show the expansion of Table I, where candidate gene lists were derived by testing for consistent pairwise increase/decrease changes (at least six of nine) as well as minimum mean threshold log(fold) changes of Ϯ1 (twofold). Increases for MyoD, increases for Myf5, decreases for MyoD, and decreases for Myf5 correspond to Tables S1, S2, S3, and S4, respectively. Table S5 shows the sequences and targets for primers that were used for SYBR Green real-time PCR. Online supplemental material is available at http://www.jcb.org/cgi/content/full/ jcb.200502101/DC1.


Long-Term Monitoring of Brain Dopamine Metabolism In Vivo with Carbon Paste Electrodes

This review focuses on the stability of voltammetric signals recorded over periods of months with carbon paste electrodes (CPEs) implanted in the brain. The key interaction underlying this stability is between the pasting oil and brain lipids that are capable of inhibiting the fouling caused by proteins. In brain regions receiving a significant dopaminergic input, a peak due to the methylated metabolites of dopamine, principally homovanillic acid (HVA), is clearly resolved using slow sweep voltammetry. Although a number of factors limit the time resolution for monitoring brain HVA concentration dynamics, the stability of CPEs allows investigations of long-term effects of drugs, as well as behavioral studies, not possible using other in-vivo monitoring techniques.

Introduction
Dopamine (DA) is a catecholamine neurotransmitter released by neurons in a number of regions of the mammalian brain, and is thought to be important in the expression of a wide variety of behaviors [1][2][3]. DA in the basal ganglia is involved in motor control, and a causative link has been established between loss of DA in the dorsal striatum, due to neurodegeneration, and Parkinson's disease in humans [4]. In the prefrontal cortex, DA regulates cognitive functions [5]; DA imbalance in this region can lead to attention disorders and has been implicated in the pathophysiology of schizophrenia [6][7][8][9]. Another important role for DA has been established in the expression of 'rewarding' behaviors [10,11], such as eating [12,13] and sexual activity [14,15]. Indeed, many of the common drugs of abuse (e.g., cocaine [16], amphetamine [17] and 'ecstasy' [18]) have specific actions on brain DA systems, and this mechanism may be involved in the addictive properties of these agents. The ability to monitor a real-time index of DA release in discrete brain areas during well defined behaviors, and in response to pharmacological challenges, would provide an important key to understanding more fully the role this molecule plays in brain function.
A growing number of methodologies are being developed, including sampling [19,20], spectroscopic [21] and electrochemical [16,22,23], to study neurochemical phenomena in the intact brain. One such set of techniques focuses on the in-situ detection of substances in brain extracellular fluid (ECF), using in-vivo voltammetry (IVV) with implanted amperometric electrodes. A number of reviews on the principles and applications of IVV analysis of the ECF have been published, and a representative selection is cited here [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]. This paper reviews the application of one approach to monitoring neurochemical dynamics in brain extracellular space: the use of chronically implanted carbon paste electrodes (CPEs) that are stable enough to detect a metabolite of DA continuously over periods of months.
It should be stressed that the application of IVV involves far more than a straightforward transfer of expertise gained from the practice of voltammetry in vitro. In addition to being anatomically complicated and containing a wide range of electroactive substances, brain tissue presents a complex chemical environment that includes surfactants (lipids), electrodes poisons (proteins), electrocatalysts such as glutathione and ascorbate, and a tissue matrix that both restricts mass transport to the electrode surface and reacts physiologically to the presence of the probe [40]. Thus, the behavior of a particular electrode and voltammetric technique in vitro might well be different to its behavior in vivo, because both modification of the electrode surface by components of the tissue [41,42], and the differences in mass transport [43][44][45][46], could lead to different responses (sensitivity, selectivity, stability, etc.) in the two environments [47]. Therefore, detailed characterization in vitro of parameters such as detection limits is of little use, and may be misleading in an application like IVV where the major issue is that of selectivity rather that sensitivity. An example of the complications involved in characterizing an IVV technique in vivo is that of time scale: a technique demonstrated to detect DA selectively on a fast time scale (seconds) cannot be assumed to do so on slower time scales (minutes) because changes in neurotransmitter concentrations tend to be much faster than those of their metabolites [48]. Issues such as these have been addressed recently in the literature [49,50]. Figure 1 shows a schematic representation of intercellular chemical signaling at a synapse between a DA nerve terminal and target neuron. The precursor amino acid tyrosine is taken from the ECF into the terminal where tyrosine hydroxylase (rate-determining step) and DOPA decarboxylase Figure 1. Schematic representation of a dopamine (DA) nerve terminal, including synthesis from tyrosine (Tyr) via tyrosine hydroxylase (TH) and DOPA decarboxylase (DD) and storage in vesicles (V). On arrival of an action potential (AP), the depolarization resulting from the influx of Na + leads to opening of Ca +2 ion channels (ICs). The consequent influx of Ca +2 causes a variety of biochemical cascades, one of which induces fusion of V and release of the stored DA. Metabolic enzymes involved in regulating the concentration of DA include monoamine oxidase (MAO) in the terminal and post-synaptic catechol-O-methyltransferase (COMT), producing mainly DOPAC and homovanillic acid (HVA). DA action is mediated by both pre-and post-synaptic receptors (R) that are linked to ICs or "second messenger" enzymes (ENZ-SM), such as adenylate cyclase. Levels of synaptic DA are also controlled by specific uptake channels (U), through which DA can also leak from the cytoplasm. Color code: grey (ionic events); red (enzyme-mediated processes); magenta (neurotransmitter, DA); blue (DA metabolites). See Figure 2 for further details of the biochemical pathways.

Dopamine Biochemistry
transform it into the neurotransmitter DA. Although most of the newly synthesized neurotransmitter is taken up into storage vesicles, some will be inactivated before reaching the vesicles by metabolic enzymes present in the terminal, especially monoamine oxidase. When an action potential (rapid depolarization, followed by repolarization of the membrane potential; total duration about 5 ms) propagates down an axon to the nerve terminal, voltage-sensitive Na + ion channels open, causing depolarization of the terminal membrane and the opening of voltage-sensitive Ca 2+ channels, the influx of Ca 2+ , and fusion of vesicles that release their neurotransmitter content into the synapse; a few milliseconds later, the opening of voltage-sensitive K + channels results in repolarization ( Figure 1). This Ca +2 -dependent exocytotic release of neurotransmitter is surprisingly fast in the mammalian brain, occurring within microseconds of the start of the action potential upswing in the terminal [ Figure 2. Schematic representation of some of the biochemical pathways involved in the synthesis and metabolism of dopamine (DA). Precursor tyrosine is converted in the rate-determining synthesis step by tyrosine hydroxylase (TH) into L-DOPA whose cytoplasmic concentration is kept low by rapid conversion to DA by DOPA decarboxylase (DD). DA can be degraded into neuro-inactive forms by a number of pathways. The most important of these involve monoamine oxidase (MAO) and catechol-O-methyltransferase (COMT) whose main products are 3,4-dihydroxyphenylacetic acid (DOPAC) and homovanillic acid (HVA). 3-Methoxytyramine (3-MT) is unusual among metabolites in that it has a higher affinity for the DA uptake carrier than DA itself [52] and therefore levels of 3MT in the ECF are very low [53]. See Figure 1 for anatomical context. Once in the synapse, the neurotransmitter molecules may bind to a range of different receptor proteins on either the presynaptic or postsynaptic membrane. On the presynaptic side, receptors may be linked to a "second messenger" enzyme involved in regulating the activity of the rate-determining enzyme used in the synthesis of neurotransmitter (local negative feedback control). Postsynaptically, receptors may also be linked to a second messenger system, or to ion channels for Na + , K + or Clthat control the membrane potential of the target neuron. Inactivation of the chemical signal is achieved either by re-uptake of neurotransmitter into the terminal (and other sites, such as glial cells) through specific ion-coupled carrier uptake sites and subsequently metabolized, or may be metabolized extracellularly. The metabolites diffuse into the ECF that also contains a low concentration of neurotransmitter that overflows from the synapse (Figure 1).
One of the key elements in understanding the role of each neurotransmitter substance in neural networks is a knowledge of release rate, and Adams et al. [54] suggested that amperometric electrodes implanted directly in the intact brain might be used to monitor electroactive neurotransmitters in the CNS. However, electrodes generally employed for neurochemical analysis using IVV are ~5-300 µm in diameter and monitor an average concentration of analyte in the medium surrounding cells, the ECF, and not directly in synapses that are orders of magnitude smaller (typically 20-30 nm; see Figure 1). It is not clear at present what the relationship is between release of different neurotransmitters into the synapse and resulting ECF levels, although balance between release and re-uptake ( Figure 1) is involved [55,56]. It is also debatable whether neurotransmitters released into a synapse have additional extrasynaptic functions, so that the synapse might be designed to 'leak'. For example, extrasynaptic receptors have been reported for the main excitatory CNS neurotransmitter, glutamate [57], as well as evidence for the diffusion of glutamate outside the synaptic cleft [58]. Thus, monitoring the overflow of transmitter in the ECF is one index of release rate. Another is the concentration of metabolite in the ECF, although caution is needed in the interpretation of metabolite data because changes in synthesis rate can also affect metabolite levels, independent of release [59-61].
For applications involving implantation into biological tissues, carbon paste, prepared by thoroughly mixing carbon powder with either Nujol [54] or silicone oil [82] can be easily packed into cavities created by pulling the insulation over fine metal wires, producing disk electrodes of diameters as small as 160 µm, although diameters of about 300 µm have been generally used for IVV ( Figure 3). A carbon/Nujol paste electrode with CV was used in one of the first reports of voltammetry in brain tissue with the aim of studying DA [54]; a single peak was observed in rat striatum, attributed mainly to the oxidation of ascorbate. Decreasing the scan rate to 10 mV/s and semidifferentiation of the current later revealed three rather broad waves on the striatal voltammogram [83]. Further developments of the technique by the use of computer-controlled equipment [84], silicone oil paste [82], an additional reduction of the sweep rate to 5 mV/s, abandoning semidifferentiation, and subtraction of the background current recorded in situ, yielded CPE voltammograms consisting of three well-separated peaks in striatal tissue [85] (see Figure 4).  Figure 4. Samples of background-subtracted staircase voltammograms, expressed as current density, recorded at 5 mV/s with either 10T (320-µm diameter) or 5T (160-µm diameter) CPEs (see Figure 3) chronically implanted in rat striatum for periods of up to 6 months. The main difference between the responses obtained with these two dimensions of CPE is the size and nature of peak 2. Both the absolute current density peak-2 height, and the relative contributions of 5HIAA and uric acid to this peak, are different because of differences in the extent of tissue reaction to the implanted electrodes [86][87][88][89]. . Schematic representation of an implantable CPE constructed from Teflon-coated wire. After soldering a gold clip to facilitate later connection to the potentiostat, a small cavity (~0.5 mm long) is made by drawing the insulation over the metal wire (usually Ag or Pt-Ir). The carbon paste should be packed carefully into a cavity using a sleeved plunger of the same wire. If the paste is too loose or the cavity too short, the extracellular fluid may penetrate to the metal producing large background currents after a few days. If packed too tightly, the Teflon may crack, again allowing electrolyte into the cavity.
These experiments have been reviewed [40] and indicate that peak 1 (see Figure 4) is due mainly to ascorbate; moreover changes in the height of peak 1 result from variations in the extracellular concentration of ascorbate, provided there is no significant shift in the relative potentials of peaks 1 and 2. When such potential shifts occur they are not due to electrocatalysis but to the algebraic combination of the ascorbate peak (at the normal potential of peak 1) and the catechol peak some 60 mV higher, and difference voltammograms can be used to analyze the changes in terms of fluctuations in the catechol concentration [84]. Under most circumstances (some exceptions being stroke [98] and death [99,100]) these latter changes are mainly due to DOPAC, because the level of DA in striatal ECF has been estimated by a range of techniques to be less than 50 nM [101][102][103]. The main component of peak 2 is uric acid with a minor contribution from the 5HT metabolite, 5HIAA, when ~300 µm CPEs are used [88], whereas peak 2 recorded with 160 µm CPEs is due almost exclusively to 5HIAA [87]. Changes in the height of peak 3 are directly proportional to changes in the extracellular concentration of methylated metabolites of DA. Normally this is HVA [59] at an estimated baseline level of 10 µM [92], but when monoamine oxidase is pharmacologically inhibited, 3MT may also contribute [97].
The main advantage of CPEs is their stability over several months of recording in vivo [24]. The evidence suggests that lipids present in the tissue remove pasting oil from the surface [35], making it more powder-like [104] and reversing the poisoning caused by proteins [47]; see Figure 5. An early study by Adams et al. [104] showed that certain carbon powders behave electrochemically like Pt, and that incremental addition of pasting materials reduced electron transfer rate constants (k o ) for a variety of analytes. Furthermore, electrochemical pretreatment of the CPEs removed surface oil and tended to restore k o to the 'dry' limit. The increase in k o induced by lipid treatment has been observed for both Nujol [42,105,106] and silicone oil [41,47,107] based CPEs, although the time course of the effect is slower for the Nujol CPE presumably because of the higher oil content in the standard Nujol formulation for implantable CPEs. It appears that it is the continual process of oil removal by lipids and other surfactant media [41,42,47,108,109] that enables CPEs to function efficiently in the present of proteins [47]; see Figure 5. However, this factor may undermine the use of CPEs that have been bulk modified with lipophiles, such as stearic acid, for the detection of DA [110,111] because such species can be removed from the electrode surface following contact with brain tissue [42], although this point is still subject to debate [33].
[47] for more detailed data and discussion.
The use of activated carbon fiber electrodes (aCFEs) as an alternative to CPEs for IVV studies has been driven mainly by their small diameter (usually <10 µm) and their ability to resolve ascorbate and catechol peaks for periods of a few hours in brain tissue, using pulse [141][142][143] or staircase [144] voltammetries. Clearly, the electro-oxidation of ascorbate and other species is more facile at aCFEs [141,145] than at unmodified CPEs [41,104] in neutral buffer. However, exposure to lipid-rich media in vitro, or to brain tissue in vivo, has opposite effects on the two electrode types: lipid-treated CPEs are much more responsive to ascorbate ( Figure 5) than lipid-treated aCFEs [47]. These results provide an explanation for the loss of aCFE signal resolution after implantation in vivo, and also elucidate the mechanism whereby CPEs can be used over months in brain tissue (Figure 4), allowing long-term studies not possible with aCFEs. The problem of loss of signal resolution after implantation of aCFEs in vivo has been addressed by the use of removable electrode systems that allow easy electrode replacement in the same animal [146][147][148], although it has been suggested that no more than three insertions should be carried out on account of tissue disruption [149].
A range of studies on the time-course of DA dynamics monitored in the ECF during behavior has been reported recently, using CFEs with either high-speed chronoamperometry [148,150] or fast cyclic voltammetry [13,16,147]. However, the ability to monitor brain DA metabolism continuously over periods of months in discrete brain areas is presently unique to CPEs, although the temporal resolution of the metabolite signal must be measured in minutes rather than seconds. Thus, the choice of technique for studying DA function will depend on the aims of the study. Here, for example, the stable HVA signal available with CPEs has been used to study long-term changes of DA turnover in response to drugs and behavioral manipulation.

Pharmacological Studies
The height of peak 3 (h 3 ) recorded with chronically implanted CPEs (see Figure 4) has been used to study the effects of drugs on DA metabolism in different brain areas with a significant dopaminergic innervation [59,82,92,97,[151][152][153]. Intraperitoneal (i.p.) administration of a DA synthesis inhibitor, α-methyl-p-tyrosine (AMPT, 250 mg/kg), caused a massive decrease in h 3 associated with the loss of HVA from the ECF (see Figure 1) in frontal cortex, dorsal striatum and nucleus accumbens (94-98%); indeed, this effect is part of the evidence used to establish the link between peak 3 and DA pathways [59,117]. Systemic administration of the DA receptor antagonist, haloperidol, that binds to most sub-types of DA receptors, led to substantial and sustained increase in ECF HVA in striatum and accumbens due to the blocking of the receptor-mediated negative feedback control of DA synthesis and release by this drug through both pre-and post-synaptic pathways. Despite the long-lasting effect of this dose of haloperidol (0.5 mg/kg i.p., ~12 h), the CPEs were stable enough to monitor the return of HVA levels to baseline values (see Figure 6).
A summary of the effects of haloperidol and the non-specific DA receptor agonist, apomorphine, that activates the same negative feedback loops, on the HVA signal in the three brain areas is given in Figure 7. These, and other, data have been used to suggest that the strength of negative feedback is weaker in the frontal cortex compared with the two sub-cortical regions [117,154], and that this feedback regulation is also somewhat weaker in the accumbens (ventral striatum) compared with the dorsal striatum [117].  Figure 4) recorded in three brain regions, expressed as a percentage of the pre-injection value (see Figure 6 for sample time-course). The effect of haloperidol was similar in dorsal striatum (ST, n = 9) and nucleus accumbens (NAcc, n = 6), but significantly smaller in frontal cortex (FCx, n = 6, *P < 0.05) compared with both ST and NAcc. The effect of apomorphine was also similar in ST (n = 7) and NAcc (n = 6), but significantly smaller in FCx (n = 4, *P < 0.05) compared with both sub-cortical areas [117].
More sophisticated studies on the long-term effects of receptor ligands on the HVA signals, recorded using CPEs in striatum and accumbens, have been carried out by Brose et al. [152,153]. In one investigation, that involved 11 days of continuous voltammetric recording [153], an anxiogenic βcarboline (N-methyl-β-carboline-3-carboxylate) was found to have different effects in these two forebrain regions. There was an initial depression of HVA levels in the accumbens on the day of the injection that returned to baseline the following day, but then increased on days 3-6 after administration before returning again to baseline levels. No effect was observed for striatal HVA. These results have been used to highlight the role of mesolimbic DA in the behavioral and therapeutic effects of benzodiazepines [152,153].
Different forms of IVV can be categorized on the basis of the time resolution achieved for detecting changes in the concentration of species in the ECF. Fast techniques with resolutions of the order of seconds or less are chronoamperometry, fast cyclic voltammetry, differential pulse amperometry and constant potential amperometry [29, 36,37,39,176,177], and these methods are generally used to detect stimulated changes in neurotransmitter overflow, although recent advances has allowed detection of non-stimulated DA release during behavior [147]. Slow techniques with time resolutions of the order of several minutes are linear sweep, staircase, differential pulse and differential normal pulse voltammetries at slow scan rates. The latter are usually used in studies of neurotransmitter metabolites, as well as ascorbic and uric acids [29,36,89,132]. The minimum interval between scans is limited not only by the sweep time but also by the time taken for the concentration of the electrolyzed species in the compartment around the implanted electrode tip to return to its unperturbed value by means of tortuous diffusion through the tissue matrix [46]. Although the HVA signal recorded using staircase voltammetry (see Figure 4) falls into the slower category, the stability of the CPEs allows long-term behavioral studies not possible with either other voltammetric methods or microdialysis. Figure 8 shows an example of the parallel time-courses of DA metabolism in the striatum and simultaneously monitored motor activity levels over a 3-day period. Rats are nocturnal and show a significantly higher level of motor activity during the hours of darkness. Associated with this activity, striatal HVA levels are also increased. Moreover, the diurnal increase in HVA concentration in the ECF, expressed as the percentage change in h 3 during the hours of darkness compared with baseline lights-on values, were significantly higher in striatum (52 ± 5%, n = 14) and accumbens (48 ± 7%, n = 14) compared with frontal cortex (20 ± 19%, n = 9) where the increase itself was not statistically significant [117]. Correlation analyses have also been used to demonstrate the greater involvement of the nucleus accumbens in locomotion levels, whereas dorsal striatum may be more important in the expression of patterns of movement and posture [157,178,179].  Figure 4) recorded with a CPE in the striatum of a freely-moving rat at 12-min intervals (blue) and simultaneously monitored motor activity (red). Each day's recording started at 12.00 h, with lights off from 20.00 h to 08.00 h as indicated by the light/dark band (center). The period shown corresponds to days 3 to 6 of a longer recording period, and is a demonstration of the stability of CPEs in brain tissue. These results reveal the tight correlation that normally exists between the diurnal pattern of spontaneous locomotion observed behaviorally and the activity of DA terminals in the striatum (both dorsal and ventral) [117].
In another study, during which the HVA signal was recorded continuously over a 2-week period [180], a very simple model of jet-lag was applied to the diurnal recordings illustrated in Figure  8. Reversal of the light-dark conditions on day 6 caused a breakdown in the correlations between diurnal motor activity levels and DA metabolism (HVA) that are normally observed in both dorsal and ventral striatum (see Figure 8). The correlation gradually became re-established over the following days, suggesting that DA neurotransmission, at least in the accumbens, might be involved in the development of symptoms associated with jet-lag. It is interesting to note that benzodiazepine receptor agonists are used to reduce sleep latency, increase total sleep time, and decrease awakenings in humans [181], and have also been shown to affect HVA levels recorded with CPEs in accumbens, but not striatum, in animal models [153].
Studies involving lever pressing for food reward have been used to explore the application of CPEs to investigate well defined experimental designs in psychology [92,135]. Increases in dopaminergic activity, reflected in the HVA signal, were observed in both dorsal striatum [92] and accumbens [135] following bouts of trained lever pressing. Furthermore, in individual subjects, both the behavioral response rates and the corresponding increases in peak 3 (see Figure 4) remained stable when studied periodically over a 3-month period [135]. These results represent another demonstration of the exceptional stability of CPEs implanted in the brain, and equally importantly, evidence that the initial surgery and the perturbations associated with chronic electrode implantation do not interfere unduly with relatively subtle behaviors recorded over periods of months.

Conclusions
Extensive characterization of the response of CPEs in vitro and in vivo has shown that peak 3 recorded with either staircase or linear sweep voltammetry in brain regions receiving a significant dopaminergic innervation is due to the oxidation of methylated metabolites of DA, that is almost exclusively HVA. The stability of CPE over months of recording provides a useful approach to investigating the long-term effects of drugs on DA activity in specific brain regions, as well as behavioral studies not possible with other in-vivo monitoring techniques.


extfieldsofstudy: Biology Medicine

s2fieldsofstudy: Biology Chemistry

text:
SIRT1 Shows No Substrate Specificity in Vitro*

SIR2 is a key regulator of the aging process in many model organisms. The human ortholog SIRT1 plays a pivotal role in the regulation of cellular differentiation, metabolism, cell cycle, and apoptosis. SIRT1 is an NAD+-dependent deacetylase, and its enzymatic activity may be regulated by cellular energy. There is a growing number of known SIRT1 substrates that contain ϵ-acetyl lysine but for which no obvious consensus sequence has been defined. In this study, we developed a novel unbiased method to identify deacetylase sequence specificity using oriented peptide libraries containing acetylated lysine. Following incubation with SIRT1, the subset of deacetylated peptides was selectively captured using a photocleavable N-hydroxysuccinimide (NHS)-biotin linker and streptavidin beads and analyzed using mass spectrometry and Edman degradation. These studies revealed that substrate recognition by SIRT1 does not depend on the amino acid sequence proximate to the acetylated lysine. This result brings us one step closer to understanding how SIRT1 and possibly other protein deacetylases chose their substrate.

SIR2 is a key regulator of the aging process in many model organisms. The human ortholog SIRT1 plays a pivotal role in the regulation of cellular differentiation, metabolism, cell cycle, and apoptosis. SIRT1 is an NAD ؉dependent deacetylase, and its enzymatic activity may be regulated by cellular energy. There is a growing number of known SIRT1 substrates that contain ⑀-acetyl lysine but for which no obvious consensus sequence has been defined. In this study, we developed a novel unbiased method to identify deacetylase sequence specificity using oriented peptide libraries containing acetylated lysine. Following incubation with SIRT1, the subset of deacetylated peptides was selectively captured using a photocleavable N-hydroxysuccinimide (NHS)-biotin linker and streptavidin beads and analyzed using mass spectrometry and Edman degradation. These studies revealed that substrate recognition by SIRT1 does not depend on the amino acid sequence proximate to the acetylated lysine. This result brings us one step closer to understanding how SIRT1 and possibly other protein deacetylases chose their substrate.
SIR2 is a major regulator of the aging process. SIR2 functions as an NAD ϩ -dependent deacetylase, which removes an acetyl group from the ⑀-amino group of lysine residues and is conserved from bacteria to higher eukaryotes (1) (for a review, see Ref. 2). This unique enzymatic reaction, which depends on NAD ϩ availability, may connect cellular energy with SIRT1 activity. In Saccharomyces cerevisiae, SIR2 overexpression dramatically increases life span, whereas deletion of the SIR2 locus causes premature aging of the yeast mother cell (3). In worms, SIR2 overexpression extends life span (4).
The structure of the SIRT1 homolog Af2 has been determined both in the presence and absence of a bound acetylated p53-derived peptide (17,18). Two amino acids at the N terminus from the ⑀-Ac-lysine and five amino acids C-terminal to the Ac-lysine of p53 peptide were well visualized in the electron density map, which suggests that they may play a role in the enzyme-substrate interaction (18). However, the structure did not improve our understanding of whether the amino acids flanking the ⑀-Ac-lysine residue are important for SIRT1 substrate recognition.
No known SIRT1 substrates have been identified using an unbiased substrate search method, and the SIRT1 deacetylation consensus sequence, if one exists, has not been identified to date. In an attempt to determine an optimal deacetylation motif for SIRT1, we developed a novel deacetylase capture assay using unbiased oriented peptide libraries. Peptide library screening has proven to be a powerful method for mapping the consensus motifs recognized and phosphorylated by protein kinases or bound by modular peptide binding domains (19). For example, a variety of substrates for protein kinase C (20), AKT/protein kinase B (21), and ATM (22) were discovered using this method. In this paper, we describe the use of an acetyl-lysine-based oriented peptide approach combined with biotin-avidin capture technology to query SIRT1 deacetylated peptides for a common recognition motif. Using this method, we discovered that SIRT1 selection for substrate peptides does not depend on the amino acids proximal to ⑀-Ac-lysine and verified these findings by detailed enzymatic kinetic analysis.

MATERIALS AND METHODS
Purification of the SIRT1 Protein-293T cells were transfected with SIRT1-FLAG plasmid (7) by JetPEI (Qbiogene, Carlsbad, CA). Cells were lysed 48 h post-transfection in Nonidet P-40 extraction buffer (50 mM Tris-HCl (pH 8.0), 150 mM NaCl, and 1% Nonidet P-40) in the presence of protease inhibitor mixture-EDTA (Complete; Roche Applied Science). FLAG-tagged proteins were immunoprecipitated with anti-FLAG M2-agarose affinity gel (Sigma) for 2 h at 4°C. Immunoprecipitated material was washed three times in the extraction buffer. FLAG-SIRT1 was eluted by incubation with FLAG peptide (Sigma) and dialyzed against phosphate-buffered saline.
Peptide Library Experiment-200 g of the peptide library or the SIRtide (Tufts Medical School, Boston, MA) was incubated overnight at 37°C in 50 mM phosphate buffer (pH 8.0), 4 mM MgCl, 0.5 mM NAD, 2 mM dithiothreitol in the presence or absence of 1 g of purified mammalian SIRT1. Next, 3 l of 25 M of photocleavable biotin derivative (photocleavable (PC)-biotin-NHS) (23) (AmberGen, Boston, MA), 80 l of dimethylformamide, and 80 l of 1 M NaHCO 3 were added and incubated for 1 h at room temperature. To this solution, 500 l of NeutrAvidin-agarose (Pierce) was added and incubated at room temperature for 30 min. Beads were washed with 1 ml of 50 mM HEPES (pH 7.0) (three times), 50 mM NH 4 OAc (pH 7.2) (three times), and 10% ethyl alcohol. Samples were eluted in 1 ml of 10% ethyl alcohol for 15 min by UV exposure (Blak Ray XX-15 UV lamp; Ultraviolet Products, San Gabriel, CA) at a distance of 15 cm (emission peak 365 nm, lamp intensity ϭ 1.1 milliwatt/cm 2 at a distance of 31 cm). The samples were dried, resuspended in 40 l of 7.5% ammonia in H 2 O, and incubated overnight to remove the Fmoc. 1 Samples were dried, resuspended in 10 l of H 2 O, and analyzed by MALDI-TOF and Edman degradation (Tufts Medical School, Boston, MA).
Library Synthesis-A peptide library containing the sequences Fmoc-MAXXXX-AcK-XXXXXAEEE, where X indicates all amino acids except Lys or Cys, was synthesized using solid phase peptide synthesis with N-␣-Fmoc-protected amino acids and standard BOP/HOBt coupling chemistry (Tufts Medical School, Boston, MA). Individual peptides were synthesized in a similar manner.
Mass Spectrometry-Mass spectrometry was performed with the Voyager DE-TOF mass spectrometer (PerkinElmer Life Sciences) with delayed ion extraction and nitrogen laser (337 nm) and analyzed by GRAMS software. Samples were desalted prior to analysis using ZipTip C18 pipette tips (size P10) (Millipore Corp.) according to the manufacturer's instructions, and then 1 l of sample solution was deposited on a steel target plate followed by 1 l of matrix solution, air-dried, and spotted. ␣-Cyano-4-hydroxycinnamic acid (Aldrich) at a concentration of 10 mg/ml (5:4:1, v/v/v, acetonitrile/water/3% trifluoroacetic acid) was used as a matrix.
Kinetic Analysis-Briefly, synthesized acetylated peptides were resuspended in phosphate buffer and stored frozen at Ϫ70°C. A peptide library approach for capture and analysis of SIRT1 deacetylation products. A, the degenerate peptide library was incubated with SIRT1 for various periods of time and the subset of SIRT1-deacetylated peptides was purified away from the acetylated starting material by incubation with NHS-PC-LC-biotin. Because the amino terminus has been blocked with an Fmoc group, the NHS group reacts specifically with the free ⑀-amino group of the central lysine residue in the library, which is present only in the deacetylated population. B, the biotinylated deacetylated peptides were captured on streptavidin-agarose beads, followed by extensive washing to remove nonbiotinylated (acetylated) peptides. C, the beads were irradiated with 365-nm UV light to reverse the NHS cross-link and liberate the deacetylated peptide mixture. D, the released peptides were incubated with concentrated ammonia to cleave the N-terminal Fmoc protecting group and sequenced in bulk by Edman degradation. contained 25 mM Tris acetate (pH 7.5), 250 mM sodium acetate, 2.7 potassium acetate, 1 mM magnesium chloride, and 0.05% Tween 20 At the completion of the deacetylase reaction, 150 l of a 50% slurry of boronic acid resin (polyacrylamide immobilized boronic acid resin (catalog no. 20244; Pierce) pre-equilibrated with 0.25 M ammonium acetate (pH 9.0)) was immediately added to each assay well. The suspension was mixed and transferred to a Multiscreen-DV 96-well filter plate (catalog no. MADVN0B10; Millipore Corp.) on a vacuum manifold (catalog no. MAVM096OR; Millipore). Vacuum was applied, and the unbound fraction containing released nicotinamide was collected into a 96-well Wallac Isoplate (catalog no. 1450-514; PerkinElmer Life Sciences). 150 l of liquid scintillation fluid (catalog no. 1200-439; PerkinElmer Life Sciences) was added to each well, mixed, and counted on a Wallac 1450 MicroBeta liquid scintillation counter using the 14 C channel with a 1-min counting time. Raw data were analyzed using nonlinear regression (Y ϭ V max *X/(K m ϩ X)) with GraphPad Prism version 4.02, where V max is the maximal activity and K m is the concentration of ligand required to reach half-maximal activity (Michaelis constant). Data are expressed as the means Ϯ S.E. of at least two determinations.

RESULTS
To determine the requirements for substrate specificity for SIRT1 and ask whether there is a consensus sequence for its targets, we developed a new acetyl-lysine-oriented peptide library-based strategy for mapping SIRT1 substrate specificity (Fig. 1). A degenerate peptide library corresponding to the sequence Fmoc-MAXXXX-AcK-XXXXXAEEE was synthesized, where AcK indicates acetyl-lysine and X indicates all amino acids except Lys or Cys. Lys was omitted from the degenerate positions, and the N terminus was blocked by Fmoc to ensure that the starting library contained no free amino groups in the absence of SIRT1-driven deacetylation. Cys was omitted to avoid problems with sequencing and oxidation.
The degenerate peptide library was incubated with SIRT1 for various periods of time, and the subset of SIRT1-deacetylated peptides was purified away from the acetylated starting material by incubation with (NHS-PC-linker-coupled (LC)-biotin). The NHS group reacts specifically with the free ⑀-amino group of the central lysine residue in the library, which is present only in the deacetylated population (23). The biotinylated, deacetylated peptides were captured on streptavidin-agarose beads, washed extensively to remove nonbiotinylated (acetylated) peptides. The beads were then irradiated with 365-nm UV light to reverse the NHS cross-link and liberate the deacetylated peptide mixture. The released peptides were in-cubated with concentrated ammonia to cleave the N-terminal Fmoc protecting group and sequenced in bulk by Edman degradation (Fig. 1).
The Met-Ala sequence was designed at the amino terminus of the peptide library to verify that any biotin-streptavidincaptured peptide obtained with this approach came from the substrate library mixture and to help quantify the amount of peptide present. The fixed Ala residue at position 13 in the library provides an estimate of how much peptide loss occurs during sequencing, whereas the poly(Glu) C-terminal tail prevents peptide washout during Edman degradation sequencing.
The strategy was initially optimized using an individual SIRT1 peptide Fmoc-MASTGG-AcK-DSTEMAEEE; SIRtide) in which the N-terminal half was derived from the histone H3 peptide amino acid sequence, a known SIRT1 substrate (1), and the C-terminal was derived from the RCH1 peptide sequence, a SIRT1 substrate that we recently identified (data not shown). This peptide included the same total number of amino acids and the same constant amino acid positions as the degenerate peptide library, whereas its single amino acid sequence greatly facilitated evaluation of the deacetylation reaction products by mass spectrometry (MALDI mass spectrometry). As shown in Fig. 2A dium) as well as small peaks corresponding to the di-and trisodium forms. These adducts were products of sodium binding to the acidic residues of the peptide. Deacetylation adds a positive charge to the peptide and largely eliminates these adducts. Following SIRT1 deacetylation, we detected a product in the total reaction mixture with a parent ion mass of 1895.56 Da, corresponding to the loss of an acetyl group (and a sodium ion), as expected (Fig. 2B). After incubating the reaction mixture with NHS-PC-LC-biotin, followed by capture on streptavidin-agarose beads and photorelease, the same peak corresponding to the deacetylated product at 1895.56 Da was observed in the MALDI-TOF spectrum (Fig. 2C). In contrast, when the reaction was performed without enzyme, there was no deacetylation product in the MALDI-TOF spectrum of the total reaction products (data not shown), and no NHS-PC-LCbiotin capture/photorelease product was obtained (Fig. 2D).
Next, the degenerate acetyl-lysine-oriented peptide library was incubated with SIRT1 or no enzyme (control). The reaction products were reacted with NHS-PC-LC-biotin, captured, and photoreleased from streptavidin-agarose beads; the N-terminal Fmoc protective group was removed; and the capture products were sequenced by Edman degradation. As shown in the bottom panel of Fig. 3, the recovered peptides displayed a prominent enriched peak of free Lys in Edman sequencing cycle 7, consistent with the expected SIRT1-mediated deacetylation. In contrast, minimal Lys recovery was observed at this position in the control reactions lacking SIRT1 (Fig. 3, top panel). Thus, this peptide library/deacetylation/capture approach was capa-ble of isolating peptides that were deacetylated by SIRT1 as evidenced by both mass spectrometry and Edman degradation.
To investigate the optimal SIRT1 deactylation motif, the peptide library was incubated with SIRT1 for 15 h at 37°C. Approximately 2% of the total peptide mixture was captured and photoreleased from the streptavidin-agarose beads (data not shown). These purified, deacetylated products were then sequenced by Edman degradation. The amount (mol %) of each amino acid in each of the degenerate positions was compared with its amount (mol %) in the starting library mixture in order to determine whether particular amino acids are selected at specific positions flanking the central acetyl-lysine residue. As shown by the resulting selectivity values in Table I (top), the relative abundance of the amino acids in each of the 9 degenerate positions flanking the fixed deacetylated Lys residue appears to be relatively invariant from cycle to cycle. Furthermore, each amino acid shows a similar overall abundance in the deacetylated peptide products to that present in the starting library mixture, as revealed by selectivity ratios close to 1.0. These data strongly argue that SIRT1 does not show any preference for specific amino acids in acetylated peptide substrates. We carried out additional experiments using shorter incubation times in an attempt to select more stringently for the most optimal peptide substrates. These also failed to provide any evidence for peptide motif specificity for SIRT1-mediated deacetylation (Table I, middle and bottom).
To directly investigate the lack of substrate specificity suggested by the oriented peptide library experiments, enzymatic TABLE I Lack of substrate specificity for SIRT1 using oriented peptide library screening X indicates a degeneracy of all of the amino acids shown above (except Lys and Cys). Ac-K indicates acetylated Lys. Each value shows the selectivity value for each amino acid in the deacetylated peptide substrates, corresponding to the mol % ratio of each amino acid at a given cycle of sequencing in the deacetylated peptides relative to its mol % abundance in the starting peptide library mixture. ND, not detected. analysis of SIRT1 deacetylation was performed using wild-type and mutant peptides corresponding to known SIRT1 substrates p53 (p53tide) and H3/RCH1 (SIRtide). The deacetylation kinetics are shown in Fig. 4. Despite the striking sequence differences, the measured K m values were remarkably similar, with values of 61 M for p53tide and 50 M for SIRtide. Similarly, modification of the SIRtide peptide, in which three amino acids on each side of the acetylated Lys were replaced by Ala (SIRtide-ALA6) or the four C-terminal residues of SIRtide were replaced by those from a histone H3 peptide, had little effect on the resulting kinetic parameters for SIRT1-mediated deacetylation (Table II). The K m values for all three substrates were not significantly different, whereas the SIRtide V max was 2-fold lower than the SIRtide-ALA6 and H3-like V max . In conclusion, by using enzymatic assays, we verified the unexpected peptide library results showing that SIRT1 lacks strong sequence specificity for the amino acid residues proximal to the acetylated Lys.

DISCUSSION
In this work, we investigated the requirements for SIRT1 substrate sequence specificity using two approaches. First, we developed a novel peptide library-based screen to evaluate deacetylation substrate specificity in an unbiased manner, using chemical modification of free ⑀-amino groups of lysine, followed by biotin-streptavidin affinity capture, photorelease, and sequencing. The results of those experiments clearly showed that there was no preference for specific amino acids within 4 positions N-terminal and 5 positions C-terminal to the acetyl-lysine residue within peptide substrates. In contrast to the lack of sequence specificity we obtained for the SIRT1 protein deactylase, all known protein kinases and phosphopeptide-binding domains show explicit sequence specificity in the region immediately surrounding the phosphorylated residue, and these sequence motifs are easily deduced using peptide library-based screening approaches (19 -22).
Second, we determined kinetic parameters of SIRT1 deacetylation using wild-type and mutant peptides corresponding to the acetylation sites on several known in vivo SIRT1 substrates. These measurements revealed very similar K m and V max values for acetylated peptides with widely divergent sequences flanking the acetyl-lysine site, again suggesting a lack of sequence specificity. It is not known whether the lack of sequence specificity that we observed for SIRT1 is a general feature of all protein deactylases. The peptide library approach that we devised in this study, however, should allow us to investigate in future studies whether consensus recognition sequences exist for other deacetylase enzymes, such as the class I and class II protein deacetylases (24). In addition, a variation of the amine modification/capture/photocleavage approach described here could be used to investigate the substrate specificity for protein acetyltransferases.
Given the relative lack of motif specificity of SIRT1 that we observed on peptide substrates, what factors might account for the substrate specificity of SIRT1 in vivo? One possibility is that bona fide in vivo substrates of SIRT1 contain the acetylated sequence within a defined tertiary fold in the substrate that is lacking in short unstructured peptide substrates. The x-ray structure of the SIRT1 homolog Af2 bound to an acetylated p53 peptide, however, argues against this. In that structure, tight binding of the deacetylase to a small peptide substrate was observed (18). This binding was mediated by insertion of the acetyl lysine residue into a hydrophobic tunnel and a series of hydrogen bonds between backbone atoms of two ␤-strands in Af2 to backbone atoms in the p53 peptide to form a staggered antiparallel three-stranded ␤-sheet (18). Interestingly, in this structure, the peptide side chains bracketing the acetyl-lysine interact with Af2 primarily through weak van der Waals interactions and two water-mediated hydrogen bonds. The lack of direct strong side chain interactions observed in the p53 peptide:Af2 structure is in good agreement with the lack of strong sequence specificity that we observed in our peptide substrate screening experiments.
An alternative explanation is that in vivo substrate specificity for SIR2 involves protein-protein interactions that occur outside of the SIRT1 active site. These interactions could be mediated by additional sites on SIRT1 itself or by other proteins present within larger SIRT1-containing complexes. In agreement with this, SIRT1 has been shown to interact with and localize to PML nuclear bodies (25), a well studied component of a subgroup of nucleoplasmic foci (26). Indeed, p53 is also recruited to PML bodies, at least under some circumstances (25,27,28), suggesting that targeting of SIRT1 substrates to PML bodies may promote SIRT1-dependent deacetylation. Other PML body-localizing proteins include NcoR, a molecule that directly interacts with SIRT1  to regulate transcription factors, such as peroxisome proliferatoractivated receptor ␥, although it is not yet known whether NcoR is a SIRT1 substrate (7,29). Interestingly, the Werner syndrome protein (WRN), mutations of which result in premature aging, was recently shown to be an acetylated protein that localizes to PML bodies (30). Given this co-localization and the known pivotal roles of both SIR2 and WRN in life span determination (3,31), it is tempting to speculate that SIRT1-mediated deacetylation of WRN might occur in PML bodies as part of the regulation of aging. In addition to deactylases such as SIRT1, a variety of acetyl transferases were recently shown to also be present within PML bodies (11,27), suggesting that dynamic acetylation and deacetylation events relevant to the control of aging are occurring within these subcellular structures. In this context, an improved understanding of acetylation and deacetylation substrate determinants should facilitate the elucidation of SIRT1 biology, caloric restriction, and aging physiology.