s2fieldsofstudy: Biology Medicine

text:
Microglial responses to amyloid β peptide opsonization and indomethacin treatment

Background Recent studies have suggested that passive or active immunization with anti-amyloid β peptide (Aβ) antibodies may enhance microglial clearance of Aβ deposits from the brain. However, in a human clinical trial, several patients developed secondary inflammatory responses in brain that were sufficient to halt the study. Methods We have used an in vitro culture system to model the responses of microglia, derived from rapid autopsies of Alzheimer's disease patients, to Aβ deposits. Results Opsonization of the deposits with anti-Aβ IgG 6E10 enhanced microglial chemotaxis to and phagocytosis of Aβ, as well as exacerbated microglial secretion of the pro-inflammatory cytokines TNF-α and IL-6. Indomethacin, a common nonsteroidal anti-inflammatory drug (NSAID), had no effect on microglial chemotaxis or phagocytosis, but did significantly inhibit the enhanced production of IL-6 after Aβ opsonization. Conclusion These results are consistent with well known, differential NSAID actions on immune cell functions, and suggest that concurrent NSAID administration might serve as a useful adjunct to Aβ immunization, permitting unfettered clearance of Aβ while dampening secondary, inflammation-related adverse events.

Background
Chemotactic and phagocytic responses of microglia to amyloid β peptide (Aβ) have been inferred from postmortem autopsy evaluations [1][2][3], animal studies [4,5], and an in vitro model in which cultured rodent microglia were placed directly on Alzheimer's disease (AD) cortical sections [5,6]. Although these valuable experiments confirm that microglia cluster around and may help clear Aβ deposits, new questions have arisen concerning the effects of various agents on these microglial interactions with Aβ. In particular, several studies have indicated that the opsonization of Aβ deposits with anti-Aβ antibodies facilitates microglia-mediated Aβ clearance [6,7]. Here, binding of the antibodies to the Aβ target presumably enhances microglial recognition of and subsequent responses to the target through Fc receptors expressed by the microglia [6,7]. Based on these results, it has been suggested that microglial responses to Aβ might represent so beneficial an inflammatory action that anti-inflammatory drugs might actually be detrimental as a treatment for AD [8]. Alternatively, multiple epidemiologic studies [9,10] have reported decreased risk for AD in persons who take common nonsteroidal anti-inflammatory drugs (NSAIDs).
Over the last decade, our laboratory has developed reliable methods for culturing microglia from rapid (< 4 hour) brain autopsies of AD patients [11,12]. These cultures uniquely match the species, developmental stage, and disease state of AD subjects, and provide the ready experimental manipulability that is helpful in assessing complex physiologic processes such as chemotaxis, phagocytosis, secretory activity, and drug responses. In order to quantitatively assay these processes in the context of microglial interactions with Aβ, we seeded AD microglial cultures into wells containing pre-aggregated Aβ1-42 spots dried down to the well floor. Subsequent experiments measured migration of the cells to the Aβ spots, phagocytosis of the Aβ spots, pro-inflammatory cytokine secretion, and the effects on these processes when Aβ spots were opsonized with an anti-Aβ antibody or when microglia were treated with a common nonsteroidal antiinflammatory drug (NSAID), indomethacin. Overall, opsonization with Aβ antibody enhanced microglial migration to and phagocytosis of Aβ. Indomethacin had little to no effect on these responses, but did significantly inhibit microglial secretion of IL-6.

AD microglia cultures
Cultures of microglia from rapid (< 4 hours) autopsies of six antemortem-evaluated, neuropathologically-confirmed AD patients were prepared using our previously published methods [11,12]. By immunoreactivity, these cultures are consistently negative for neuron, astrocyte, oligodendrocyte, and fibroblast markers, consistently positive for multiple markers of activated microglia, and readily maintained at purities of 98% or higher [11,12]. Microglia cultures from all six AD patients were used for biochemical assays. Additional cultures from one of these patients were used for quantitative evaluation of chemotaxis and phagocytosis, and additional cultures from two more of these patients were used for qualitative replication of the chemotaxis and phagocytosis results. At 3-7 days post-plating, the microglia were trypsinized and replated at 50,000 cells/well in 12-well plates. Prior to replating, 2 µl of a 1 mM solution of Aβ1-42 (Bachem) in PBS (pH 7.4) was dried down to the well floor. Each well received two such Aβ spots, and there were three wells per experimental condition, so that a total of six Aβ spots were quantified per experimental condition. Serum-free medium was used throughout the experiments. Control wells containing no Aβ or no microglia were also prepared.

Treatment with anti-Aβ antibody
Prior to seeding with microglia, selected wells were pretreated with vehicle (medium) only or with 10 µg/ml 6E10 (Signet Laboratories), a mouse monoclonal antibody directed against the first 17 (N-terminal) amino acids in the Aβ sequence. In some experiments, a 2 µg/ml concentration of 6E10 was included in order to evaluate effects at a lower dose.

Treatment with indomethacin
Prior to seeding with microglia, selected wells were pretreated with vehicle (medium) only or with 1.0 µg/ml indomethacin. Indomethacin, at 1.0 µg/ml, and vehicle were also replenished at Days 3, 6, and 9 in the course of medium changes. The 1.0 µg/ml indomethacin concentration is at the upper end of the physiologically normal range achieved in blood after therapeutic doses of the drug [13], and was chosen to insure that any failure of indomethacin to affect chemotaxis to or phagocytosis of Aβ was not due to inadequate drug dosage. In some experiments, a 0.1 µg/ml concentration of indomethacin, which is at the lower end of the physiologically normal range achieved in blood after therapeutic doses, was included in order to evaluate effects at a lesser concentration.

Cytochemistry and immunocytochemistry
For qualitative evaluations of microglial responses to Aβ, microglial cultures were briefly fixed with 4% buffered paraformaldehyde, then immunoreacted overnight with 1:1000 (0.5 µg/ml) LN3 antibody (MP Biomedical) directed against the major histocompatibility complex type II cell surface glycoprotein, using our previously published methods [11,14,15]. Vectastain ABC kits (Vector Laboratories) were employed using the manufacturer's protocols to detect immunoreactivity with bright field optics. Aβ spots could be sufficiently resolved under these conditions by their modest opaqueness under bright field optics. To visualize Aβ spots in phagocytosis experiments, the wells were washed gently in distilled water (3 × for 5 min each), incubated with 0.1% Thioflavine S (Sigma) for 10 min, washed once in distilled water (5 min), then dehydrated and fixed with 4% buffered paraformaldehyde. In additional experiments, Aβ immunocytochemistry was applied in selected wells so as to graphically illustrate Aβ removal and microglial uptake of Aβ. In these studies, microglial cultures with Aβ spots were briefly fixed with 4% buffered paraformaldehyde and incubated overnight with 1:1000 (1 µg/ml) anti-Aβ antibody 4G8 (Signet Laboratories). Detection of immunoreactivity was accomplished using Vectastain ABC kits (Vector Laboratories) and the manufacturer's suggested protocols.

Microglial migration to Aβ spots
Microglial cultures were assessed on Day 3 and Day 9 after initial plating. Each Aβ spot was visualized under phase contrast optics at 100 × (10 × objective), and photomontages were made of the spot and surrounding area out to a radius of 2 mm from the spot perimeter. A grid was then placed over the photomontages. The number and percentages of microglia within four 500 µm × 500 µm (0.25 mm 2 ) grid squares centered on the Aβ spot and within sets of four 500 µm × 500 µm squares at progressively greater distances from the spot were recorded. The distance intervals for the grid squares were 0, 500, 1000, 1500, and 2000 µm from the Aβ spot, and each distance interval was measured in quadruplicate ( Fig. 1). A total of 141,455 microglia were individually hand-counted in this way. Chemotaxis was evaluated by changes in the distributions of microglia relative to the Aβ spots over time, with relatively flat distributions indicative of little or no chemotaxis, and increasingly negative slopes to the distributions indicative of migration toward the Aβ spots ( Fig. 1). Slopes of the distributions (m) were operationally defined as the "chemotactic index" [15] for each condition, and the statistical reliability of the measures was assessed with Pearson's Product Momentum (R) statistic and with analysis of variance (ANOVA) techniques. The simplest ANO-VAs assessed, for each treatment condition, significant differences in the distributions of microglia over the progressive distance intervals from the Aβ spot, with percentage of microglia at a particular distance (grid square) as the dependent variable and distance from the Aβ spot (0, 500, 1000, 1500, and 2000 µm) as the single factor. Pearson's R Statistic was then run to confirm that the alterations in microglial distributions were consistent with chemotaxis (i.e., showed a significant negative correlation with distance from Aβ) rather than some other response pattern. Dose dependence was evaluated using two-way ANOVAs, with percentage of microglia as the dependent variable, distance from the Aβ spot as the first factor, and drug dose as the second factor. Significant interactions of distance with drug dose thereby provided statistical evidence that the different drug doses differentially affected microglial distributions. A similar approach was taken for comparisons of different treatment conditions (e.g., anti-Aβ antibody exposure ± indomethacin treatment). All data collection was by a technician blind to experimental condition.

Tests of microglial proliferation
BrdU staining kits (Zymed/Invitrogen) were applied to selected wells in order to assess whether shifts in microglial distributions over time might be due to differential proliferation of microglia relative to Aβ spots as opposed to migration of the cells. Staining with BrdU followed the manufacturer's recommended directions.

Microglial phagocytosis of Aβ spots
At Day 12 postplating, selected wells were histochemically reacted with Thioflavine S, as described earlier, and visualized at 100 × (10 × objective) with a confocal microscope. Using the ability of the confocal microscope to optically section an object at precise distances, the number of 10 µm optical slices from the well floor to the top of the remaining Aβ spot was recorded by an investigator blinded to the experimental conditions imposed in each well. The data were then assessed statistically using 2way ANOVAs, with spot thickness as the outcome measure, antibody treatment (vehicle only, 2 µg/ml anti-A↕ IgG, or 10 µg/ml anti-A↕ IgG) as the first factor, and NSAID treatment (vehicle only, 0.1 µg/ml indomethacin, or 1.0 µg/ml indomethacin) as the second factor.

Microglial secretion of cytokines
To assess the effects of opsonization with anti-Aβ antibodies, microglial cultures were preincubated with vehicle or 10 µg/ml anti-Aβ monoclonal 6E10 followed by 4 hours exposure to 0 or 10 µM preaggregated Aβ1-42 (Bachem). Conditioned medium was then subjected to TNF-α ELISA (R&D Systems) using the manufacturer's protocols. To confirm the results with another pro-inflammatory cytokine, and to evaluate the interaction of indomethacin with antibody opsonization, microglial cultures were preincubated with vehicle or 10 µg/ml 6E10, as before, but in the presence or absence of 1 µg/ml indomethacin. After incubation for 4 hours with 0 or 10 µM Aβ1-42, the conditioned medium was subjected to IL-6 ELISA (R&D Systems) using the manufacturer's protocols.

Differential proliferation versus chemotaxis
Proliferation of microglia more proximal to the Aβ spots, rather than true chemotaxis, did not explain the shifts in microglial distributions that were exhibited over time under the various treatment conditions. There was little to no BrdU staining under any condition (not shown) and, in fact, there was a slight but significant decrease in microglial numbers in all treatment conditions and overall from Day 3 (mean microglial density/0.25 mm 2 grid square = 40.8 ± 0.3) to Day 9 (mean microglial density/0.25 mm 2 grid square = 37.8 ± 0.4) (F Overall = 34.5, P = 0.000). Consistent with our previous experience, AD microglia stimulated with M-CSF as a positive control showed little to no evidence of proliferation. However, M-CSF-stimulated THP-1 cells (a monocyte line often used as a surrogate for microglia) that were run in parallel did show clear Paradigm for estimation of microglial chemotaxis to Aβ

Microglial secretion of cytokines
Consistent with our previous studies covering a wide range of cytokines, chemokines, and inflammatory toxins [12], exposure of microglia to Aβ significantly enhanced secretion of TNF-α (Fig. 5A) and IL-6 ( Fig. 5B) compared to cultures that were not exposed to Aβ. Opsonization with 10 µg/ml anti-Aβ antibody 6E10 significantly enhanced Aβ-induced TNF-α (Fig. 5A) and IL-6 secretion (Fig. 5B). Enhancement of IL-6 expression, however, was significantly decreased by indomethacin treatment (Fig.  5B). Cytokine secretion is typically a fairly rapid response that wanes over time. Presumably, cytokine receptive cells then undergo more long-lasting responses such as enhanced chemotactic or phagocytic behaviors. Consistent with this, we observed significant changes in TNF-α and IL-6 levels 4 hours after exposure of microglia to Aβ, but not 3, 6, or 9 days after exposure to Aβ (data not shown).

Discussion
The present study found that AD microglia in vitro migrate toward Aβ aggregates, attempt to phagocytose the aggregates, and increase their secretion of TNF-α and IL-6 in the process. Opsonization of Aβ aggregates with anti-Aβ antibody 6E10 significantly enhanced these processes. By contrast, the common NSAID indomethacin had no material or statistical effect on microglial migration or phagocytosis, but significantly inhibited the increased IL-6 secretion observed with anti-Aβ opsonization.
The shifts in microglial distributions relative to Aβ spots over time are most parsimoniously explained by chemotactic responses to Aβ. Proliferation of microglia more proximal to Aβ aggregates was not observed and, in fact, BrdU reactivity, a common marker for cell proliferation, was negligible at all distances from the aggregates. Chemokinesis, enhanced but undirected movement of cells, also did not appear to explain the results, since microglial migration exhibited the gradient characteristics of chemotaxis, with progressive increases in the density of microglia at distances more proximal to Aβ aggregates. In addition, microglia are now well established to express receptors that can mediate chemotactic behaviors and that appear to have Aβ as a ligand. These include the macrophage scavenger receptor [16][17][18], the receptor for advanced glycation endproducts (RAGE) [15], the formyl peptide receptor [19], and others [20,21]. RAGE, in particular, has been shown to help mediate microglial migration to Aβ spots in an in vitro paradigm similar to that used here, and this migration could be inhibited by anti-RAGE Fab fragments [15].
AD microglia in vitro also exhibited behaviors consistent with phagocytosis of Aβ aggregates. Entering the paradigm, the microglia showed little or no Aβ immunoreactivity. After 12 days incubation with Aβ spots, the microglia were highly immunoreactive for Aβ and the spots decreased in size. Aβ spots without microglia remained essentially intact over the same time period. Previous ultrastructural and other studies [3,22,23] have also identified Aβ filaments within microglia in the vicinity of Aβ deposits in AD cortex. Although it remains possible that the intracellular Aβ within microglia in the AD brain may have been produced by the cells [24] rather than phagocytosed from an extracellular deposit, this is clearly not the process observed in the present in vitro studies. We conclude, therefore, that AD microglia in vitro do phagocytose aggregated Aβ deposits. Given the  Exposure to aggregated Aβ also induced significant increases in TNF-α and IL-6 secretion, confirming our previous experiments [12] and those of others [25][26][27] with TNF-α, IL-6, and a broad range of chemokines, cytokines, and inflammatory toxins such as reactive oxygen/nitrogen species. Pathways for enhancing TNF-α and IL-6 secretion have been demonstrated, including NF-kB and C/EBP transcriptional mechanisms, both of which are enhanced in pathologically-vulnerable regions of the AD brain [28,29].
Opsonization of Aβ spots with anti-Aβ antibody 6E10 significantly enhanced microglial migration to the spots, phagocytosis of the spots, and cytokine secretion. Similar effects of opsonization on microglial migration and phagocytosis have also been reported using anti-Aβ antibodies and an in vitro preparation in which cultured rodent microglia were seeded onto postmortem AD cortex sections laden with Aβ deposits [6]. Soluble Fab fragments containing the Fc region ligand for Fc receptor binding inhibited Aβ removal in this paradigm. These effects are consistent with the classic mechanisms of antibody opsonization of immune targets by antibodies specific to epitopes on the target. Scavenger cells that express receptors to the Fc region of the antibodies are then directed to or become focused at the site where the antibody-bound target resides. Fc receptor activation, in addition, activates scavenger cells, promoting attack and phagocytosis. Recently, scientists at Elan Pharmaceuticals have attempted to harness these mechanisms to enhance Aβ clearance, using immunization with Aβ to drive production of anti-Aβ antibodies for subsequent Aβ opsonization [6,30]. Although there is controversy about the exact site of action of the antibodies (e.g., brain versus peripheral circulation) [6,30,31], this approach does clearly result in significant and sometimes dramatic reductions of Aβ burden in transgenic mouse models [6], as well as the in vitro model tested here, and may also have been effective in human patients receiving Aβ immunization [30].
Unfortunately, however, inflammatory responses are often a two-edged sword. Fc receptor binding is known to enhance the activation and pro-inflammatory secretory responses of scavenger cells that bear Fc receptors, and microglia do express these receptors [6,32]. The increased TNF-α and IL-6 secretion observed in the present experiments after opsonization of Aβ aggregates with a specific anti-Aβ antibody, 6E10, is therefore not unexpected. On activation, microglial cells are, in fact, well established to secrete a wide range of inflammatory mediators that could not only cause damage to neurons and neurites locally, but also, if sufficiently activated, provide signalling to peripheral immune cells to provoke a more generalized and severe response such as that reported in several Aβimmunized patients who experienced lethal adverse reactions [30].
The vast majority of NSAIDs in use today are based on the principle of cyclooxygenase inhibition, and cyclooxygenase inhibition, in turn, is well established to downregulate a wide range of acute phase reactants. Interestingly, however, mechanisms for chemotaxis to and phagocytosis of Effects on microglial TNF-α (A) and IL-6 (B) secretion into the medium in the presence or absence of Aβ, as well as after pretreatment of Aβ with 10 µg/ml anti-Aβ antibody 6E10 Figure 5 Effects on microglial TNF-α (A) and IL-6 (B) secretion into the medium in the presence or absence of Aβ, as well as after pretreatment of Aβ with 10 µg/ml anti-Aβ antibody 6E10. Opsonization with 6E10 significantly enhanced (P < 0.05) (*) TNF-α and IL-6 levels compared to Aβ alone. IL-6 experiments also measured the effect of 1 µg/ml indomethacin on 6E10 exacerbation of cytokine secretion. Indomethacin significantly reduced this effect (P < 0.05) (#).
an inflammatory target are not necessarily cyclooxygenase dependent. In a survey, for example, of the first 100 publications retrieved from PubMed using the search phrase "indomethacin AND chemotaxis", the majority of studies found no effect of indomethacin on chemotaxis, and some of the papers actually reported enhanced chemotaxis after indomethacin exposure. Such findings have been suggested to explain why physicians commonly prescribe NSAIDs to control fever and other secondary inflammatory responses without being unduly concerned about hampering immune-mediated removal of the feverinducing agent. Similarly, in the present experiments indomethacin had no material or statistically significant effect on microglial chemotaxis to or phagocytosis of Aβ aggregates, but did significantly inhibit the exacerbated IL-6 response under opsonized conditions. Although it is never certain that in vitro results will fully apply to the in vivo state, these results suggest that indomethacin or an NSAID like it might be a useful adjunct to Aβ immunization strategies.


extfieldsofstudy: Medicine Biology

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Characterization of the early stages of thymic NKT cell development

Upon reaching the mature heat stable antigen (HSA)low thymic developmental stage, CD1d-restricted Vα14-Jα18 thymocytes undergo a well-characterized sequence of expansion and differentiation steps that lead to the peripheral interleukin-4/interferon-γ–producing NKT phenotype. However, their more immature HSAhigh precursors have remained elusive, and it has been difficult to determine unambiguously whether NKT cells originate from a CD4+CD8+ double-positive (DP) stage, and when the CD4+ and CD4−CD8− double-negative (DN) NKT subsets are formed. Here, we have used a CD1d tetramer-based enrichment strategy to physically identify HSAhigh precursors in thymuses of newborn mice, including an elusive DPlow stage and a CD4+ stage, which were present at a frequency of ∼10−6. These HSAhigh DP and CD4+ stages appeared to be nondividing, and already exhibited the same Vβ8 bias that characterizes mature NKT cells. This implied that the massive expansion of NKT cells is separated temporally from positive selection, but faithfully amplifies the selected TCR repertoire. Furthermore, we found that, unlike the DN γδ T cells, the DN NKT cells did not originate from a pTα-independent pathway bypassing the DP stage, but instead were produced during a short window of time from the conversion of a fraction of HSAlow NK1.1neg CD4 cells. These findings identify the HSAhigh CD4+ stage as a potential branchpoint between NKT and conventional T lineages and between the CD4 and DN NKT sublineages.

The development of this unusual lineage has remained largely elusive. Two main models have been proposed: a precommitment model (7), which suggests that NKT cells originate from a committed precursor before TCR expression, and an instructive model (8) whereby mainstream thymocytes expressing the rare canonical CD1d-reactive TCR are instructed-by virtue of their interaction with CD1d-expressing cells-to differentiate into NKT cells. In strong support of the instructive model, transgenic expression of the canonical NKT cell receptor was sufficient to induce NKT cell differentiation (9). In addition, staining with CD1d tetramers identified developmental intermediates after positive selection, but preceding the acquisition of NK differentiation (10,11). The earliest, well-characterized precursor identified was a rare, mature heat stable antigen (HSA) low CD44 low thymocyte with a CD4 ϩ or doublenegative (DN) phenotype. Unlike other mature thymocytes, this cell seemed to be engaged in cell cycle, and gave rise to the CD44 high memory-type stage. Cycling CD44 high cells migrated to the periphery where they induced an "NK program"-including the expression of NK1.1, Ly49, and CD94/NKG2A-over the course of several weeks. In parallel with these phenotypic events, NKT cells acquired the ability to produce IL-4, then also IFN-␥ upon TCR stimulation.
The conspicuous activation and expansion undergone by mature HSA low NKT thymocytes suggest that they must have received unusual signals. Unlike mainstream T cells, NKT development depends on interaction with antigen expressed on the surface of bone marrowderived cells, including double-positive (DP) thymocytes, rather than epithelial cells (12)(13)(14)(15)(16). Furthermore, recognition of the endogenous glycosphingolipid iGb3, an agonist antigen for mouse and human NKT cells, also is likely to be involved in the unusual signaling that leads to NKT cell expansion and differentiation (4). Consistent with the hypothesis of differential signaling, NKT development is unaffected by dominant negative forms of Ras or Mek-1 (17), but is abrogated completely in Fyn null mice (18,19). The recent finding that signaling lymphocyte activation molecule (SLAM)associated protein (SAP) connects Fyn to SLAM family members expressed on the cell surface (20)(21)(22) suggests that, in conjunction with TCR signaling, key signals may emanate from homotypic interaction between SLAM family members expressed by thymocytes (i.e., between the developing NKT precursors and neighboring CD1d-expressing DP thymocytes; references [23][24][25][26]. Integration of these recent advances in the general scheme of NKT cell development requires precise characterization of the immature HSA high stages; however, their very low frequency (estimated at less than 1/10 6 ) has precluded physical identification. For example, it is unclear whether NKT cells transit through a DP stage. The existence of very rare tetramer-positive DP thymocytes is controversial (11,27,28). PCR-based studies have revealed rare V ␣ 14-J ␣ 18 joints among DP thymocytes; however, it has not been determined whether the corresponding cells are enriched in or deprived of NKT cell precursors because the coexpression of V ␤ chains (V ␤ 8, V ␤ 7, V ␤ 2) allowing endogenous ligand recognition has not been determined (27). In one study, purified DP thymocytes generated rare mature NKT cells upon intrathymic transfer; however, the inordinately high number of cells injected (10 7 ) may have included enough immature DN cell contaminants to account for NKT cell generation independent of the DP stage (27).
The origin of the DN sublineage of NKT cells, which is found in mouse and human, also is elusive. Because DN and CD4 cells are represented at the first developmental stage identified, the HSA low CD44 low cell, it remains unclear whether DN cells originate early from precursors that bypass the DP stage (e.g., as is the case for DN ␥␦ T cells), or later from DP or even from CD4 T cells. Although the CD4 and CD8 coreceptors are dispensable for the development of the CD4 and DN NKT sublineages, the absence of CD8 has a measurable impact on the V ␤ repertoire of CD4 and DN cells (12). This suggests that earlier expression of CD8 influences positive or negative selection, perhaps through interaction with CD1d. Transgenic expression of CD8 ␣␤under a CD2 promoter-drastically reduced the thymic NKT cell population; this also supports the hypothesis that CD8 may interact with CD1d (12). Finally, in mice transgenically expressing the V ␣ 14-J ␣ 18 TCR ␣ chain, CD8 cells were depleted of the V ␤ 8 and V ␤ 7 chains that confer reactivity to CD1d (9). These findings suggested that the conspicuous ab-sence of CD8 ϩ NKT cells in WT mice may reflect negative selection or, as previously observed in a TCR transgenic system with agonist ligand signaling (29), the direct transformation of DP into DN or CD8 ␣␣ cells.
In this study, we used a CD1d tetramer-based strategy to enrich NKT cell precursors from the thymus of WT newborn mice. Comparison with J ␣ 18 Ϫ / Ϫ and CD1d Ϫ / Ϫ NKT deficient controls allowed the identification of rare, noncycling HSA high DP low and HSA high CD4 ϩ precursors; intrathymic cell transfers demonstrated that the DN subset originated later from cycling HSA low CD4 cells. These findings that identify the early stages of NKT cells in the thymus provide significant additions to the developmental map of NKT cells at the cellular level, and have implications on the molecular mechanisms that underlie this enigmatic pathway of T cell development.

Identification of HSA high V ␣ 14 NKT cells in newborn thymus
Because of their massive intrathymic expansion, NKT cells are readily detectable among adult mature HSA low thymocytes by specific tetramer staining. In contrast, as shown in Fig. 1, their frequency among the immature HSA high population is very low and comparable to that of J ␣ 18-deficient thymuses where the canonical NKT TCR is absent or of CD1d-deficient thymus where NKT cells are not selected. This indicates that HSA high NKT lineage cells are too rare to be detected over a "nonspecific" staining background of 0.02%. However, upon magnetic-activated cell sorting (MACS)-enrichment with the same tetramers, bona fide, predominantly HSA high tetramer-positive cells were recovered from WT 3-d-old thymuses, but not J ␣ 18-deficient or CD1d-deficient controls (Fig. 2). This result indicates that the cells that were stained "nonspecifically" by FACS were not recovered after MACS enrichment, perhaps because of their lower affinity interactions with the tetramers. Using this strategy, we were able to enrich the frequency of NKT lineage cells Ͼ 1,000 fold; however, we estimate that under MACS conditions, only one third of the NKT lineage cells Total thymocytes from 2-wk-old WT, J␣18 Ϫ/Ϫ , and CD1d Ϫ/Ϫ mice were stained with HSA and CD1d-tetramers. Gates identifying the tet ϩ HSA low and tet ϩ HSA high cells are shown with corresponding frequencies. Note that the level of background staining in the HSA high gates (as measured in J␣18 Ϫ/Ϫ and CD1d Ϫ/Ϫ mice) precludes the identification of HSA high NKT lineage cells in wild type mice. Data representative of Ͼ10 individual WT or mutant mice.

ARTICLE
were recovered from these early thymuses. Therefore, the rarity of these NKT lineage cells in newborns required largescale experiments; typically, we pooled 40-60 newborn thymuses to enrich and examine ف 200-300 "authentic" tet ϩ cells, because the procedure recovered approximately one NKT cell per 10 6 thymocytes at this age (Table I). In these 3-d-old mice, ف 70% of the tet ϩ cells, on average, were composed of HSA high cells ( Fig. 2 and Table I), whereas the average proportion decreased to 57% at day 4, 13% at day 5, and 2% at day 8 ( Fig. 3 and Table I). Nearly all of the tet ϩ HSA high cells found in 3-d-old mice were CD4 ϩ (Fig. 2). Rare other cells included DP low cells that could not be distinguished readily from the background staining of control J␣18 Ϫ/Ϫ thymuses. In older newborn mice, the HSA high cells were largely of the CD4 ϩ phenotype, although minor proportions of DN cells could be detected (Fig. 3). In contrast, in the same tetramer-enriched population, the tet ϩ HSA low cells were composed of nearly equal proportions of CD4 ϩ and DN cells as early as day 3 of age (Figs. 2 and 3). As shown in Figs. 2 and 3, HSA high CD4 ϩ cells were absent from the thymus of CD1d-deficient mice, which supports the notion that they are the product of positive selection.
Given the extreme rarity of the HSA high CD4 ϩ cell, it is not surprising that we could not detect earlier stages, such as putative DP precursors. The lower TCR level and lower frequency of these precursors likely renders them undetectable. A population of DP low cells belonging to the NKT lineage became visible at 2 wk of age (Fig. 3). A similar phenotype had been reported previously (27,28), but was not confirmed in a subsequent study (11). Therefore, it was not clear whether these rare, unusual cells were bona fide NKT lineage cells or a staining artifact. The absence of DP low cells in J␣18 Ϫ/Ϫ mice (Fig. 3) indicates that they represent an authentic NKT lineage cell type, and their appearance at 2 weeks may help to explain seemingly contradictory reports. Their similar frequency to HSA high CD4 ϩ cells suggests that they are a product of positive selection, as confirmed by their absence in the CD1d Ϫ/Ϫ thymus. In addition, the DP low phenotype was identical to that of CD69 ϩ DP thymocytes previously associated with positive selection (30; see the position of the pink gate in Figs. 3 and 4). The absence of this phenotype in younger mice may reflect different kinetics in the down-regulation of the CD4 and CD8 coreceptors and regulation of TCR following positive selection.

HSA high V␣14 NKT cells are not cycling and express a biased V␤8 usage
Mature HSA low NKT cells are large, cycling cells that characteristically undergo multiple rounds of cell division that lead to massive intrathymic expansion (10). We found that the size of the HSA high cells, both DP low and CD4, was comparable to that of the majority of thymocytes, and much smaller than the cycling HSA low cell (Fig. 4 A). Although the rarity of these cell types precludes BrdU incorporation studies, their small size likely reflects the lack of cell cycling. This is consistent with the lack of expansion of this population after birth, compared with the explosive growth of HSA low cells between days 5 and 8 (Table I). Thus, cell division is a late event that can be separated from the stages of development. The HSA high DP and HSA high CD4 cells expressed a CD44 low NK1.1 Ϫ phenotype similar to that of the following HSA low stage (Fig. 4 B). In addition, they expressed CD69, as reported for cells having recently undergone positive selection (30). Mature HSA low NKT cells express a highly biased repertoire overusing V␤8, V␤7, and V␤2; however, it is unclear whether this V␤ hierarchy results from preferential expansion at mature stages or from preferential selection at immature stages. Although the CD1d-␣-galactosylceramide (CD1d-␣-GalCer) tetramers may bias the analysis to cells expressing these three V␤ chains, Fig. 4 B shows that the HSA high cells exhibited the same frequency of V␤8 chain usage as found in the HSA low stages. This result suggests that the hierarchy of V␤ chain usage, at least among the three V␤ families, is already established after positive selection and is not modified substantially later by preferential cellular expansion.  Fig. 2. The total numbers of MACS-enriched tet ϩ cells at 4 and 5 d was 208 and 265, respectively (note that all dots are displayed and each dot represents a cell). Tet ϩ DP low cells are in the pink elliptic gate and have the same DP low phenotype as the CD69 ϩ DP cells in the total thymocyte population shown in the rightmost panel of fourth row. Note that for consistency of CD4/CD8 staining, thymuses at 8 d, 14 d, and 5 weeks were analyzed side by side in the same experiment (rows 3-5), with doublet and propidium iodide uptake exclusion. The 2-wk-old thymocytes also were analyzed side by side with J␣18 Ϫ/Ϫ and CD1d Ϫ/Ϫ thymuses in the same experiment (bottom three rows). Data are representative of at least three individual experiments where 5 to 60 thymuses were pooled.

DN cells arise from CD4 cells at the HSA low NK1.1 ؊ stage
The exclusive CD4 ϩ phenotype of the HSA high stage and the appearance of DN cells at the cycling HSA low CD44 low stage suggested that DN cells might arise at this latter stage from CD4 ϩ precursors. We sorted the CD4 ϩ subset of thymic HSA low NK1.1 Ϫ tet ϩ cells from WT mice and injected it into the thymus of J␣18 Ϫ/Ϫ (NKT-deficient) recipients. In this transfer assay, the transferred NK1.1 Ϫ cells matured to NK1.1 ϩ within 5-7 d, as previously shown. In addition, the progeny of the CD4 subset included CD4 ϩ and DN cells (Fig. 5 A), which demonstrated a precursor product relationship between CD4 and DN cells. A similar relationship was suggested previously (11). In contrast, the mature NK1.1 ϩ cells conserved their CD4 or DN phenotype in the same intrathymic transfer conditions (Fig. 5 A). To rule out poten-tial artifacts caused by tetramer staining, we sorted CD4 ϩ CD44 high NK1.1 Ϫ cells from a preparation of HSA low CD8 neg thymocytes, and transferred them intrathymically. The progeny of the CD4 ϩ cells contained CD4 ϩ and DN tet ϩ NKT cells (Fig. 5 B). Together, these results demonstrate that DN cells arise from the conversion of CD4 ϩ cells during a short developmental window.

DN NKT cells do not arise through a pT␣-independent "␥␦-like" pathway
It was hypothesized that a proportion of DN NKT cells might originate through a distinct pT␣-independent "␥␦like" pathway that is characterized by the absence of induction of CD4 or CD8 coreceptor. Although pT␣-deficient mice have normal ␥␦ T cell compartments, they were re- ported to lack NKT cells, a finding that apparently is inconsistent with the hypothesis (31). However, it is recognized that the NKT cell defect in these mice might not be cell autonomous, but could be secondary to the profound depletion of thymocytes acting as CD1d-presenting cells for NKT cell selection. Therefore, we generated mixed, congenically marked pT␣ Ϫ/Ϫ :WT into irradiated J␣18 Ϫ/Ϫ recipient bone marrow radiation chimeras. Fig. 6 demonstrates that, in contrast with WT NKT cells, which developed normally, there was a profound, cell autonomous NKT cell defect in the CD45.1 ϩ pT␣ Ϫ/Ϫ compartment of these mixed chimeras.

DISCUSSION
By enriching NKT precursors from pools of up to 60 newborn thymuses with CD1d-␣-GalCer tetramers and MACS sorting, we have been able to identify and characterize very rare (10 Ϫ6 ) HSA high cells that represent the earliest reported stages of NKT cell development (10,11,27,28). The physical identification of the HSA high DP low and HSA high CD4 ϩ stages essentially completes the map of this enigmatic developmental pathway (Fig. 7). The nearly exclusive CD4 ϩ phe-notype of the rare HSA high precursor in 3-d-old mice, and the demonstration by cell transfer experiments that the CD4 ϩ cells could give rise to a substantial fraction of DN cells clearly support a model whereby all DN cells arise late in development, after down-regulation of CD4 by a fraction of CD4 ϩ cells. However, the presence of rare DN cells among the HSA high subset of older mice and the appearance of a population of DP low cells in 2-wk-old mice suggest that the respective kinetics of CD4/CD8 coreceptor down-modulation may vary with age, and that commitment to the CD4 versus DN sublineage already may be imparted at a DP stage. The existence of DP low cells has been controversial (11,27,28). Our results differ markedly from those of Gapin et al. (27) and Gadue et al. (28) who found relatively high frequencies of DP low cells among total tet ϩ thymocytes. Instead, we found that authentic DP low cells were very rare, and were restricted to the previously unrecognized HSA high stage of development.
This study could not detect tet ϩ DP thymocytes preceding positive selection in CD1d Ϫ/Ϫ mice. However, the unambiguous presence of DP low cells after positive selection is important because it provides critical and direct evidence for the existence of such a DP precursor stage. Other experimental results support this conclusion. For example, the cellautonomous requirement for pT␣, as demonstrated in the present study, is highly suggestive of such a stage because pT␣ signaling induces CD4 and CD8 expression. The genetic role of CD8 in shaping the V␤ repertoire of CD4 and DN NKT cells also suggests their previous expression of CD8, and its interaction with CD1d (12). Finally, it was shown recently in a TCR transgenic model of agonist-mediated selection that immature DP cells could give rise to mature DN or CD8␣␣ cells, a direct demonstration of the down-regulation of CD4 and CD8␤ (29). This example is particularly relevant to the development of NKT cells, be-

ARTICLE
cause their thymic ligand, iGb3, also is an agonist that is capable of full activation of mature cells.
Given the rarity of the HSA high DP low and HSA high CD4 ϩ cell, it may not be surprising that, for technical reasons, preselection DP precursors would not be detected. The rarity of these precursors also supports the notion that canonical V␣14-J␣18 rearrangements occur stochastically at very low frequency.
It remains unclear what determines the CD4 versus DN fate of NKT cells. Their near 1:1 ratio might suggest a stochastic decision; however, an alternative model is that CD8 could be forcibly down-regulated from DP cells undergoing high levels of signaling. The CD4 versus DN choice also might be instructed by different TCR affinities for ligand.
The newly identified HSA high DP low and HSA high CD4 ϩ cells have a small size and express CD69. These stages, which precede the previously identified HSA low CD4 ϩ at which cells blast and undergo intense cell division, are analogous to the HSA high CD69 ϩ stage following positive selection of mainstream MHC-restricted T cells (30). These positively selected MHC-restricted T cells will not engage in cell cycle. Thus, the HSA high CD4 ϩ cell represents the likely branchpoint in the development of mainstream T versus NKT cells. An unresolved issue is whether commitment to cell division already is imparted at this stage, or whether it occurs later at the HSA low stage (e.g., upon second antigenic stimulation by medullary dendritic cells). Although there is no example, to our knowledge, of mitogenic stimuli imparted at the immature HSA high stage, some arguments suggest that this may be the case. First, entry into the cell cycle is delayed by 1 or 2 d following TCR stimulation, corresponding to the lag time between HSA high and HSA low CD4 T cells. Second, recent experiments have demonstrated that restricted expression of CD1d on DP thymocytes was sufficient to drive the major expansion observed at the medullary HSA low stage (32).
These different lines of evidence raise the surprising possibility that a single set of signaling interactions between HSA high NKT cell precursors and neighboring cortical thymocytes-involving TCR and perhaps SLAM family members as coreceptors-may represent the defining event of the NKT lineage imparting the subsequent NKT expansion and differentiation program.
Enrichment of CD1d-␣GalCer tetramer ؉ cells with paramagnetic microbeads. Thymuses were pooled from batches of 40 to 60 newborns (day of birth considered as day 1), and CD1d tetramer ϩ thymocytes were enriched using anti-PE or -allophycocyanin microbeads and the Au-toMACs system (Miltenyi Biotec) at ice cold temperature following the manufacturer's instructions.
We thank R. Duggan, J. Marvin, and B. Eisfelder (Flow Cytometry Facility) for cell sorting, and all Bendelac lab members for expert advice and discussion. This work is supported by a special fellowship from the Leukemia and


extfieldsofstudy: Biology Medicine

text:
Evidence for the Involvement of Tyrosine Kinase ZAP 70 in Nuclear Retinoid Receptor-dependent Transactivation in T Lymphocytes*

Retinoic acid receptors (RARs) and retinoid X receptors (RXRs) are transcription factors that control diverse cellular functions during development and homeostasis. The biochemical role of these proteins in T lymphocytes is not well known. Here we have studied the role of protein-tyrosine kinase ZAP 70, a key enzyme involved in the proximal signaling events during T cell activation, in the modulation of RXRE- and RARE-dependent activation in T lymphocytes. Surprisingly, ZAP 70-negative Jurkat T cells showed considerable loss of both RXRE- and RARE-mediated transactivation as compared with wild type Jurkat cells. In addition, ZAP 70-negative cells failed to exhibit normal protein kinase C θ and calcineurin-induced transcriptional activity. ZAP 70-negative cells that were reconstituted with active ZAP 70 regained the transactivation function, whereas cells expressing kinase-dead form of ZAP 70 failed to do so. Defective transcriptional activation was also observed in actively proliferating human peripheral blood T lymphocytes in which RNA interference was used to induce loss of ZAP 70 expression. In addition, an Lck-deficient Jurkat cell line that cannot efficiently activate ZAP 70 was also found defective in RXRE-mediated transcription. Finally, RNA interference-induced loss of ZAP 70 or Lck protein in Jurkat cells resulted in significant decrease in the RXRE-dependent activation. Together, these results suggest a novel functional role for ZAP 70 in nuclear receptor-driven transactivation in T lymphocytes.

positive and negative selection of thymocytes. ZAP 70 knock-out mice and patients with mutations in ZAP 70 have severe combined immunodeficiency syndrome phenotypes and other abnormalities (7)(8)(9)(10).
Retinoid X receptors (RXRs) are members of nuclear receptor superfamily that also include retinoic acid receptor (RAR), thyroid hormone receptor (TR), and vitamin D receptor (VDR) (11)(12)(13)(14)(15). RXRs function as ligand-dependent transcription factors by homodimerization or through heterodimerization with other members of the retinoid and steroid receptor family of transcription factors. The physiological role of RXRs in T lymphocyte function is not well understood. We have recently found that T cell activation signals induce loss of transcriptional activation mediated by nuclear receptors via activation of mitogen-activated protein kinase signaling pathways and induction of receptor-corepressor interaction (16,17). In addition, activated PKC was found to synergize with calcineurin to induce transcriptional activation mediated by these receptors (18). However, the role of proximal TCR signaling events and the activation of PTK in the modulation of retinoid receptor-dependent transcription is not known. Here we have shown that ZAP 70-negative Jurkat cells, T cells manipulated to express low levels of ZAP 70, or cells that cannot efficiently activate ZAP 70 exhibit loss of retinoid receptor-mediated transactivation suggesting that ZAP 70 is essential for normal retinoid receptor function in T cells.

EXPERIMENTAL PROCEDURES
Cells and Other Reagents-T lymphocyte leukemia Jurkat cell line (clone E6-1) and Lck-deficient Jurkat cell line J.CaM1.6 were obtained from American Type Culture Collection (Manassas, VA). ZAP 70-negative Jurkat cell line P116, P116 cells stably expressing c-myc-tagged wild type ZAP 70 (P116/pWT), and P116 cells stably expressing c-myctagged kinase-dead ZAP 70 mutant (K369R) (P116/pDK) (19) were kindly provided by R. L. Wange (National Institutes of Health, Baltimore). These cells were maintained in RPMI 1640 medium (Biowhittaker, Frederick, MD) supplemented with 10 mM HEPES buffer, 2 mM L-glutamine, 60 g/ml gentamicin, and 10% fetal bovine serum (Hyclone, Logan, UT). Human peripheral blood mononuclear cells, obtained by lymphapheresis of healthy donors, were purified by Ficoll density gradient centrifugation. Purified peripheral blood mononuclear cells were treated with phytohemagglutinin and interleukin-2 for 2 days in AIM-V medium (Invitrogen, Carlsbad, CA) supplemented with 10% fetal bovine serum. The cells were washed to remove phytohemagglutinin and maintained in interleukin-2. In this report, these cells will be referred to as proliferating peripheral blood T (PBT) cells. PBT cells were 98% CD3-positive as monitored by flow cytometry. 9-cis-Retinoic acid (9-CRA) and all-trans-retinoic acid were from Sigma and were used at 1 M. ZAP 70 and Lck antibodies were from BD Biosciences, and RAR␣, RXR␣, and c-myc tag antibodies were from Santa Cruz Biotechnology Inc. (Santa Cruz, CA). ZAP 70-negative P116 and wild type Jurkat cells were transfectedwith5.0gofreporterplasmidsinthepresence or absence of receptor expressing plasmid or ligand. Cells were harvested after 36 h for luciferase measurement as described under "Experimental Procedures." B, cytoplasmic (C) and nuclear (N) extracts of WT Jurkat, P116, P116/pWT, and P116/pDK cells were subjected to Western blot analysis using antibodies to ZAP 70, c-myc tag, RAR␣, and RXR␣. Arrows inside the figure indicate nonspecific bands observed with RAR␣ andRXR␣antibodiesinthecytoplasmicextracts.C,ZAP 70-negative P116 and wild type Jurkat cells were transfected with 5.0 g of TKCRBPII-Luc or TKRARE-Luc plasmid in the presence or absence of 2.5 g of the indicated plasmid constructs. Cells were harvested after 36 h for luciferase activity measurement as described under "Experimental Procedures." OCTOBER 7, 2005 • VOLUME 280 • NUMBER 40

JOURNAL OF BIOLOGICAL CHEMISTRY 34153
Western Blot-Nuclear and cytoplasmic isolations were performed with the NE-PER Nuclear and Cytoplasmic kit from Pierce as per the manufacturer's instructions. Protein extracts were electrophoresced in a 10% NuPAGE BisTris gel using NuPAGE MES-SDS running buffer (Invitrogen) and transferred to a polyvinylidene difluoride membrane using XCell Blot Module (Invitrogen). The protein was detected using the fluorophore-labeled secondary antibodies and Odyssey Infrared Imaging System (Li-cor Biotechnology, Lincoln, NE).
Transfections and RNA Interference (RNAi)-RXRE-, RARE-, TRE-, and VDRE-driven transcriptional activation was studied by transfection using luciferase based reporter plasmids TKCRBPII-Luc, TKRARE-Luc, TKDR4-Luc, and TKDR3-Luc, respectively, as described previously (17). The TKCRBPIIM-Luc plasmid containing RXRE mutant sequence (17) was used as RXR specificity control. Human glyceraldehyde-3phosphate dehydrogenase promoter that lacks an RXRE or RARE site was isolated by PCR and cloned into pGL3-Luc basic vector (Promega, Madison, WI). Plasmids expressing human RXR␣, RAR␣, TR␣, and VDR were as described (17). pCEFL vector, pCEFL/ dominant active protein kinase C (DA-PKC), and dominant active calcineurin (DA-CN) have been described previously (18). The psX/ZAP70 plasmid obtained from Dr. R. L. Wange (National Institutes of Health, Baltimore) was used as template to isolate ZAP 70 cDNA by PCR for cloning in pcDNA4/HisMaxTOPO vector (Invitrogen). ZAP 70 and Lck SMARTpool siRNAs were purchased from Upstate Biotechnology (Lake Placid, NY). Jurkat and Jurkat-derived cells were transfected by electroporation using a Gene Pulser II (Bio-Rad) at 0.250 kV and 975 microfarads as described (18). PBT cells were electroporated using a T cell Nucleofection kit from Amaxa Biosystems (Cologne, Germany) according to the manufacturer's instructions. Transfected cells were incubated in RPMI 1640 medium containing 10% fetal bovine serum and harvested at the indicated time periods, and luciferase activity was determined using Luciferase Assay System (Promega). Luciferase activity was normalized to protein concentrations in the extracts. The values in the figures represent the mean of two to three independent experiments with standard error calculated for each value.

RESULTS AND DISCUSSION
Defective RXR-and RAR-mediated Transactivation in ZAP 70-negative Jurkat Cells-Our earlier reports have shown that Jurkat cells have functional RXR-and RAR-dependent transcriptional machinery that can be regulated by a number of signaling pathways (16 -18). To dissect the role of ZAP 70 kinase in modulating RXR and RAR-dependent gene activation, we used a ZAP 70-negative cell line named P116. This cell line has been generated from wild type Jurkat E6 cells by mutagenesis using the frameshift mutagen, ICR-191 (19). Wild type Jurkat cells and P116 cells were transfected with TKCRBPII-Luc reporter, which binds RXR either as homodimer or heterodimer with RAR, in the presence or absence of RXR␣ expressing plasmid and 9-CRA. Surprisingly, P116 cells were considerably defective in supporting transcription from TKCRBPII-Luc plasmid as compared with the wild type Jurkat cells. This defect was observed with endogenous as well as exogenously expressed RXR␣ and was independent of the presence or absence of the 9-CRA (Fig. 1A). We also tested TKRARE-Luc reporter plasmid, containing DR5 element from RAR␤2 promoter specific for RXR/RAR heterodimerdependent transcription. Similar to TKCRBPII-Luc, there was a marked reduction in the transcription driven by TKRARE-Luc plasmid in P116 cells as compared with wild type Jurkat cells. As a control, luciferase activity driven by glyceraldehyde-3-phosphate dehydrogenase promoter, which lacks an RXRE or RARE site, did not show any significant reduction in P116 cells. Little or no luciferase activity was observed when cells were transfected with TKCRBPII-M-Luc, a plasmid containing mutations in RXRE site, indicating the specificity of retinoid receptor-dependent activation assay. Under similar conditions reporter activities driven by TKDR3-Luc and TKDR4-Luc, specific for RXR/ VDR-and RXR/TR-mediated activation respectively, were not reduced in P116 cells as compared with wild type cells. Together, these results indicate that ZAP 70 expression is essential for RXR-dependent transcriptional activation either as homodimer or heterodimer with RAR but is not crucial for transcriptional activation involving heterodimerization with TR and VDR. This selectivity for RAR containing heterodimers is striking and is indicative of specific role of ZAP 70 to modulate retinoid receptor function in T cells.
RXR␣ and RAR␣ are two major isoforms of RXRs and RARs in T cells mostly located in nuclear compartments. To exclude the possibility that the loss of retinoid receptor activity in P116 cells is due either to the loss of expression of one or both of these receptor proteins or export to cytoplasmic compartment, we investigated whether wild type Jurkat and P116 cells express comparable levels of RAR␣ and RXR␣ proteins in their nuclear compartments. Western blot analysis (Fig. 1B) indicates that wild type Jurkat and P116 cells express similar levels of RAR␣ and RXR␣ protein in the nuclear compartments. Furthermore, RAR␣ and RXR␣ proteins were not detected in cytoplasmic compartments. These results indicate that the absence of ZAP 70 does not alter the localiza-tion of receptor proteins in the cell and instead suggest the role of ZAP 70 in a post-translational mechanism that regulates receptor activity in Jurkat cells.  P116 cells have been reported to be defective in PKC signaling (20). We have previously shown that expression of DA-CN and DA-PKC synergistically induce RXRE-dependent transcription in Jurkat cells (18). We next investigated whether this PKC and CN synergism can be observed in P116 cells. Wild type Jurkat cells and P116 cells were transfected with TKCRBPII-Luc or TKRARE-Luc plasmid in the presence or absence of pCEFL vector or pCEFL/DA-PKC or a combination of pCEFL vector and DA-CN or pCEFL/DA-PKC and DA-CN plasmids. We expected that the expression of DA-PKC and DA-CN would circumvent the need for ZAP 70 expression to support RXRE-and RAREmediated activation in P116 cells. Surprisingly, P116 cells exhibit considerably low DA-PKC/DA-CN-induced receptor-dependent activation contrary to wild type Jurkat cells that showed a robust DA-PKC and DA-CN synergism (Fig. 1C). These results indicate that defective PKC signaling due to lack of ZAP 70 expression in P116 cells may not alone be responsible for low transcriptional activity in these cells. ZAP 70 (possibly nuclear ZAP 70) may play an additional role independent of PKC and CN in modulating RXRE-and RARE-mediated activity.
Since similar data were obtained with RXRE and RARE containing reporter vectors subsequent experiments have been performed with TKCRBPII-Luc vector alone.
Enzymatically Active ZAP 70 Expression Reconstitutes RXRE-mediated Transactivation in P116 Cells-To determine whether defective RXRE signaling in ZAP 70-negative P116 cells can be corrected by the expression of enzymatically active ZAP 70, we studied P116 cells transiently expressing ZAP 70 protein and P116 cells stably expressing the enzyme. P116 cells were transfected with TKCRBPII-Luc plasmid in the presence or absence of pcDNA4/HisMaxTOPO vector or ZAP 70 expressing pcDNA4/HisMaxTOPO/ZAP 70 plasmid ( Fig. 2A). Transfection of P116 cells with ZAP70 expressing plasmid reconstituted RXRE activity significantly as compared with cells transfected with vector (Fig. 2B) indicating that expression of ZAP 70 is important for normal RXRE activity. These data also excludes the possibility that lower FIGURE 5. Integration of proximal and distal TCR signaling pathways in the regulation of RXRE-mediated transactivation in T lymphocytes. TCR/CD3 engagement activates and recruits Lck and ZAP 70 kinases to CD3 complex. Phosphorylation of ZAP 70 by Lck activates calcium-dependent PKC␣ and CN proteins in addition to novel calcium-independent PKC. Whereas PKC␣ and JNK activation negatively regulate RXRE-mediated transcription, ERK activation induces RXRE activity (16). PKC can also synergize with CN to induce RXRE activation (18). ZAP 70 (also expressed in nucleus) may activate transcription either through the activation of PKC or PKC-independent pathways. Lines with "question marks" indicate alternative and hypothetical pathways.
reporter activity in P116 cells may have partly been due to the lower transfection efficiency of these cells as compared with wild type cells.
Previous studies have shown that P116 cells that stably express wild type ZAP 70 regain most of the phosphorylation events whereas mutant ZAP 70 reconstituted cells fail to do so (19). We investigated whether P116 cells that stably express wild type ZAP 70 or catalytically inactive mutant (K369R) of ZAP 70 were able to support RXRE-dependent activation. Transfection of P116/pWT cells with TKCRBPII-Luc plasmid yielded activation levels that closely approached the levels seen in wild type Jurkat cells. In contrast, transfection of P116/pDK cells with TKCRBPII-Luc plasmid yielded activity levels that were similar to the levels obtained with P116 cells (Fig. 2C). We next studied the DA-PKC/ DA-CN synergism-induced RXRE activity in enzymatically active ZAP 70 reconstituted P116 cells and P116 cells that were reconstituted with catalytically inactive ZAP 70. Fig. 2C shows that contrary to catalytically inactive ZAP 70 reconstituted P116 cells that showed low PKC/CNinduced transcriptional activity, enzymatically active ZAP 70 reconstituted cells regained most of PKC/CN-induced the RXRE-dependent transcription. Together, these results demonstrate that PTK activity of ZAP 70 can correct the defects in basal as well as PKC/CN-induced RXRE-dependent signaling in P116 cells unlike catalytically inactive ZAP 70 that fails to restore receptor activity in these cells.

RNAi-induced Loss of ZAP 70 Expression Interferes in RXRE-dependent
Transactivation-To extrapolate the data obtained with P116 cells to a more physiologic model for T cell function, we used normal human PBT cells in which ZAP 70 expression was partially knocked down by RNAi. siRNA duplexes specific for human ZAP 70 mRNA were introduced into PBT cells using a Nucleofection kit from Amaxa Biosystems. This method of transfection resulted in more than 75% loss in ZAP 70 protein expression in these cells (Fig. 3A). To determine the role of ZAP 70 in RXRE-dependent function in PBT cells, they were transfected with TKCRBPII-Luc in the presence or absence of ZAP 70-specific siRNA or a nonspecific scrambled RNA. The results show that ZAP 70 specific siRNA considerably attenuated RXRE-mediated transcription whereas scrambled RNA did not have significant effect on the transcription (Fig.  3B). We also used RNAi to knock down nearly 90% of ZAP 70 protein expression in Jurkat cells (Fig. 3A). Transfection of Jurkat cells with TKCRBPII-Luc in the presence of ZAP 70 specific siRNA resulted in a significant down-regulation of RXRE-dependent transcription in these cells, whereas scrambled RNA did not show significant effect on transcription (Fig. 3B). RNAi data further confirm the essential role of ZAP 70 in mediating RXRE activity in Jurkat cells and indicate that ZAP 70-dependent RXRE regulation is also operative in normal human peripheral blood T cell pool.
Lck-deficient Jurkat Cells Are Defective in RXRE-dependent Transcription-In addition to being important for binding of ZAP 70 to ITAM, Lck is required for phosphorylation and activation of ZAP 70 (1)(2)(3)(4). We considered the possibility that the lack of Lck expression could compromise RXRE-dependent transcription due to the lack of active ZAP 70. Wild type Jurkat cells and Lck-deficient JCam 1.6 cells were transfected with TKCRBPII-Luc plasmid in the presence or absence of RXR␣ expressing plasmid and 9-CRA. The results (Fig. 4 A) indicate a considerable decrease in RXRE activity in JCam 1.6 cells as compared with wild type Jurkat cells. These data show that Lck is essential for the RXRE-mediated transcription. Additional evidence that Lck plays an important role in RXRE-mediated transcription was obtained by experiments involving RNAi-induced loss of Lck expression in Jurkat cells. Jurkat cells transfected with Lck-specific siRNA duplexes resulted in about 75% loss of Lck protein (Fig. 4B). Transfection of Jurkat cells with TKCRBPII-Luc in the presence or absence of Lck-specifcic siRNA or nonspecific siRNA showed that while nonspecific siRNA had no effect on the RXRE activity, Lck-specific siRNA attenuated RXRE-dependent transcription (Fig. 4C).
The role of nuclear receptor mediated gene expression in the normal functioning of T cells has remained elusive. It is increasingly becoming clear that epigenetic mechanisms play an important role in defining the retinoid receptor-dependent function during T cell signaling (Fig. 5). It is, however, not clear how overlapping signaling intermediates modulate either activation or inhibition of receptor function resulting in outcomes as diverse as cell division, proliferation, development, stress response, homeostasis, and apoptosis. In T cells ZAP 70 is not only present in cytoplasm (for translocation to TCR following T cell activation), but the protein is also localized in the nucleus (Ref. 21 and Fig. 1B) where its function is unknown. Whether cytoplasmic or nuclear ZAP 70 or both play a role in modulating RXRE and RARE mediated transcription remains to be seen. RXR␣ has a number of tyrosine phosphorylation sites that are important for its transcriptional activity in Jurkat cells. 3 However, ZAP 70 failed to phosphorylate recombinant RXR␣ in direct phosphorylation studies (data not shown). Whether ZAP 70 is involved in the phosphorylation RAR␣ remains to be studied. ZAP 70-mediated phosphorylation of RAR␣ may play essential role in the transcriptional activity of this receptor. ZAP 70 is essential for phosphorylation and activation of number of downstream protein kinases (5,6,22) and phosphatases (23) in addition to its essential role in the translocation of PKC to the lipid rafts during the formation of functional immune synapse. We have previously shown that RXRE activity is modulated by PKC, CN, and mitogen-activated protein kinase signaling pathways (16,18). RXRE and RARE transactivation may involve modulation of one or all of these signaling pathways by ZAP 70. The finding that the expression of a combination of DA-PKC and DA-CN failed to restore RARE and RXRE activity in P116 cells indicates that ZAP 70 regulation of retinoid receptor activity may also involve PKC-independent pathways.
Deficient transcriptional activation in J.CaM1.6 cells provides further evidence for the important role of proximal signaling events, involving ZAP 70 and its upstream kinase Lck, in the retinoid receptor signaling in T cells. This study, however, does not rule out the possibility that Lck could modulate receptor activity independent of ZAP 70. In summary, we report a novel function of ZAP 70 in human T cells that links proximal signaling events with transcriptional machinery selectively driven by retinoid receptors in the nucleus. Further studies are needed to understand the mechanism of such selectivity and the physiological importance in T cell function.


text:
Molecular epidemiology of severe acute respiratory syndrome-associated coronavirus infections in Taiwan.

Abstract Background In 2003, Taiwan experienced a series of outbreaks of severe acute respiratory syndrome (SARS) and 1 laboratory-contamination accident. Here we describe a new phylogenetic analytical method to study the sources and dissemination paths of SARS-associated coronavirus (SARS-CoV) infections in Taiwan Methods A phylogenetic analytical tool for combining nucleotide sequences from 6 variable regions of a SARS-CoV genome was developed by use of 20 published SARS-CoV sequences; and this method was validated by use of 80 published SARS-CoV sequences. Subsequently, this new tool was applied to provide a better understanding of the entire complement of Taiwanese SARS-CoV isolates, including 20 previously published and 19 identified in this study. The epidemiological data were integrated with the results from the phylogenetic tree and from the nucleotide-signature pattern Results The topologies of phylogenetic trees generated by the new and the conventional strategies were similar, with the former having better robustness than the latter, especially in comparison with the maximum-likelihood trees: the new strategy revealed that during 2003 there were 5 waves of epidemic SARS-CoV infection, which belonged to 3 phylogenetic clusters in Taiwan Conclusions The new strategy is more efficient than its conventional counterparts. The outbreaks of SARS in Taiwan originated from multiple sources

case involved a Taiwanese businessman who had visited Guangdong province during 5-21 February of that year. After he returned to Taiwan, he transmitted the disease to his wife, his son (SARS-CoV strain TW1), and the doctor who treated his son (SARS-CoV strain TW3).
On 15 March, 7 employees of a Taiwanese construction company flew from Hong Kong to Beijing; 4 of them developed SARS symptoms on 26 March, several days after returning to Taiwan [9]. Also on 26 March, a man residing at the Amoy Gardens housing complex in Hong Kong flew to Taiwan; the following day, he took a train from Taipei to Taichung City to visit his younger brother. The visitor returned to his Hong Kong home on 28 March after having experienced fever during the preceding evening. His younger brother (patient TWC), who developed symptoms on 31 March, became Taiwan's first SARS-related fatality.
On 6 April, a Taiwanese woman (patient TW-HP1) suffering from fever and coughing that continued for several days visited the emergency room (ER) at mu- nicipal hospital HP; she was transferred to another hospital on 9 April. Seven HP employees, including a laundry worker who was identified as the index case, eventually developed SARS, resulting, on 24 April, in a shutdown of all operations of hospital HP [7]. In all, 137 probable SARS cases and 26 HP-related fatalities resulted from this single nosocomial infection. Patient TW-HP1 had not traveled outside Taiwan during the preceding 12 months, but, on 27 March, she and the visitor from Hong Kong had taken the same train; their seats were in separate cars (numbers 3 and 5), yet the train ride constitutes their only possible point of contact.
On 28 April, the government of Taiwan imposed mandatory quarantines on all air travelers from China, Hong Kong, Singapore, Macau, and Toronto; however, nosocomial SARS infections continued to be reported in many hospitals islandwide [5]. The hospitals that experienced the most-severe outbreaks are listed in figure 1; in the present study, they are referred to by initials-"HP," "JC," "KC," "GD," and "YM." According to Taiwan's Center for Disease Control (CDC), 346 of the 664 probable SARS cases that have been reported to the World Health Organization (WHO) were confirmed by reverse-transcriptase polymerase chain reaction (RT-PCR) and/or neutralizing-antibody tests [10]. Previously, Yeh et al. had studied the molecular epidemiology of SARS infection in Taiwan and had concluded that the origin of the Taiwanese SARS epidemic was mainly either Hong Kong or Guangdong, rather than Beijing [11]; in addition, they found that the SARS-CoV isolated from the younger brother (i.e., patient TWC) of the visitor from Hong Kong was not clustered with other isolates from hospital HP [11,12]. Because a complete genome sequence from the nasopharyngeal aspirate from patient TWC (strain TC1) was available, we decided to reexamine both (1) the source of infection at hospital HP and (2) the paths of dissemination of SARS among hospitals in Taiwan.
On the evening of 10 December 2003, a medical researcher who regularly worked in a biosafety level-4 laboratory started to feel feverish [13]. Although he had recently spent 4 days (7-10 December) in Singapore, an epidemiological investigation indicated that he had contracted SARS from a laboratory-contamination accident on 6 December. According to his description, the SARS-CoV isolates that he had handled in the laboratory included strain HKU-39849 [14] and other clinical isolates that have not been well characterized. The nasopharyngeal aspirate from this medical researcher was included in the present study.
The size of the SARS-CoV genome has been measured as 29.7 kb [15,16]. A comparative analysis of 14 SARS-CoV isolates has identified 2 distinct genotypes, which can be differentiated on the basis of 4 single-nucleotide variations (SNVs)-C:C:G:C versus T:T:T:T-in the variable regions of the SARS-CoV genome [17]. Furthermore, the genotype with the T:T:T:T SNVs has been associated with infections originating in Hotel M in Hong Kong [8,17]. Currently, phylogenetic analyses of SARS-CoV require complete genome sequences [5,11,17]; however, because of the  10 December Laboratory-contamination accident limited number of specimens available for complete genome sequencing, some researchers have used the SARS-CoV spike gene for this purpose; but most results have been less than satisfactory [18,19]. Therefore, the objectives of the present study were (1) to use 20 complete SARS-CoV sequences to combine several variable regions of a SARS-CoV for phylogenetic analysis, in order to develop a simpler tool; (2) to use a different set of sequences from 80 SARS-CoV isolates to validate the method, by 3 different phylogenetic analytical methods; and (3) to apply this proposed tool to elucidate the origin and paths of dissemination of SARS-CoV infections, on the basis of samples collected from hospitals in Taiwan.

PATIENTS, MATERIALS, AND METHODS
Patients. Serum, sputum, or throat-swab specimens from 19 patients with SARS associated with 5 nosocomial infections and 1 laboratory-contamination incident were collected for the present study. For each patient, data on the date at onset of the disease and on possible sources of infection were gathered by trained interviewers (table 1). We also downloaded, for our phylogenetic-tree analyses, 20 complete Taiwanese SARS-CoV sequences from GenBank; for 10 of these, SARS-CoV strains have been described elsewhere [11]. Epidemiological investigation. To track the origins of SARS cases in Taiwan, we used SARS-treatment reports written by physicians and submitted by their respective hospitals to Taiwan's CDC. We also sent trained interviewers to all hospitals that reported nosocomial infections, to gather additional assessment data. We based our investigation on the WHO definition of SARS [20].

Analysis of SARS-CoV sequence variation, and selection of variable regions for phylogenetic analysis.
Complete nucleotide sequences from 20 SARS-CoV isolates available from GenBank were aligned by the BioEdit program [21]; the sequence variations were analyzed by the SimPlot program (http: //sray.med.som.jhmi.edu/RaySoft/SimPlot). Genomic sequences from civet-cat SARS-CoV strains SZ-3 and SZ-16 were used as standards for comparison [19]. Sequence-variation-distance plots were generated by use of an 800-bp window, a 200-bp step, and a Jukes-Cantor correction. Initially, 4 variable regions (SC18, SC22-23, SC27, and SC28), which contain SNVs of different SARS-CoV genotypes [17], were combined for phylogenetic analysis. Because the resultant phylogenetic tree was not satisfactory, we added another 2 variable regions (SC10 and SC20) based on the sequence-variation plot (figure 2).
Comparison of conventional and proposed strategies for phylogenetic analysis. Three methods-neighbor joining (NJ), Fitch and Wagner parsimony (Pars), and maximum likelihood (ML)-were used for comparison of the conventional (i.e., complete genome) and proposed strategies. The proposed strategy for phylogenetic analysis entailed the deletion of conserved domains and the combination of minimal variable regions. The MEGA2 and Phylip3.6 software packages were used to construct the phylogeny [22][23][24]; and 80 SARS-CoV sequences down- loaded from the GenBank database were used for the comparison. A bootstrap analysis of 100 replicates was used to compare the robustness of the NJ and Pars trees generated by the conventional strategy versus that of the NJ and Pars trees generated by the proposed strategy [25]. For the ML method, the P value for branch length was calculated by use of the hidden Markov model, before the conventional and proposed strategies were compared [26].
RT-PCR and sequencing. RNA was extracted from serum, sputum, or throat-swab specimens by QIAamp viral-RNA minikits (Qiagen). RT-PCR primer pairs for the 6 SARS-CoV variable regions are listed in table 2. RT-PCR was performed in a single-tube reaction (Qiagen) using primers from each region. The PCR thermocycler program consisted of predenaturing for 4 min at 95ЊC; 35 cycles of denaturing for 30 s at 95ЊC, annealing for 30 s at 52ЊC, and initial extension at 68ЊC for 3 min; and final extension at 68ЊC for 10 min. The PCR product was gel-purified for DNA sequencing by use of an ABI PRISM 3700 DNA Analyzer (Applied Biosystems). For the laboratorycontamination case, multiple primers [17] were used for complete genome sequencing. Superscript III RT (Invitrogen) was used for production of cDNA, and a RACE kit (Roche) was used to amplify the 5 and 3 ends of cDNA. Both strands of the PCR product were analyzed, and the resultant sequences were assembled by use of the SeqMan II software package (version 5.0; Lasergene).

Analysis of the nucleotide-signature patterns of different waves of epidemic SARS infection.
To perform the analysis of nucleotide-signature patterns, representative SARS-CoV strains with complete sequences were chosen from each wave of epidemic SARS infection in Taiwan. In addition, cases that were of unclear origin were also included in the analysis. The nucleotide sequence of SARS-CoV strain Urbani [16] was used as a prototype for the comparison.

Development of a proposed strategy for phylogenetic analysis of SARS-CoV isolates.
Pairwise comparisons were used to analyze sequence variations among 20 SARS-CoV isolates. The results show that the 3 region of the viral genome had the highest sequence variation, especially near the junction of replicase 1b and the spike genes (figure 2). Six variable regions-SC10, SC18, SC20, SC22-SC23, SC27, and SC28-were chosen for testing the proposed strategy for phylogenetic-tree analysis; the total length of the sequences in these 6 regions was 5287 nt. Using 3 phylogenetic analytical methods, we compared, for 80 SARS-CoV isolates, the topology and robustness of the phylogenetic tree at 3 nodes are circled: node a, between civet-cat SARS-CoV and human SARS-CoV; node b, between SARS-CoV subgroups A1 (early phase) and A2 (middle phase); and node c, between SARS-CoV subgroups A and B (late phase) [5]. The scale bar indicates the genetic distance, estimated on the basis of Kimura's 2-parameter substitution model [24]. generated by the proposed strategy, which uses only these 6 regions, versus the topology and robustness of the tree generated by the conventional strategy, which uses the complete genome sequences. As shown in figure 3, the topology of the NJ tree generated by use of the complete genome sequence was almost identical to that of the NJ tree generated by use of a combination of the 6 variable regions; similar results were obtained for the Pars trees and the ML trees (data not shown).
To compare the robustness of trees generated by the proposed method versus that of trees generated by the conventional method, we focused on the bootstrap values for 3 bifurcation nodes between SARS-CoV clusters: node "a," between civetcat SARS-CoV and human SARS-CoV; node "b," between SARS-CoV subgroups A1 and A2; and node "c," between SARS-CoV subgroups A and B. In the NJ trees, the bootstrap values for nodes a, b, and c were, respectively, 100%, 100%, and 63% for the conventional method versus 96%, 83%, and 66% for the proposed method (figure 3); similar results were obtained for the Pars trees generated by these 2 methods (table 3). For the ML tree generated by the proposed method, all P values for bifurcation nodes between different clusters were !.01; for the ML trees generated by the conventional method, the only node with a P value !.01 occurred at the bifurcation node between civet-cat SARS-CoV and human SARS-CoV (table 3).

Phylogenetic analysis of SARS-CoV infections in Taiwan.
The proposed method is useful for study of the molecular epidemiology of SARS-CoV strains, both because of its compatibility and because it can obtain results by using fewer specimens; in addition, it is more economical and less time-consuming. We applied this tool to better understand the entire complement of Taiwanese SARS-CoV isolates, including the 20 that had been published previously and the additional 19 identified in the present study. Epidemiological information, nasopharyngeal aspirates, and serum samples were collected from 18 patients with SARS who were treated at 6 Taiwanese hospitals (table 1). The SARS-CoV from a laboratory worker (patient SCVJ) who contracted SARS during December 2003 was sequenced and analyzed. An NJ tree was constructed, and the results showed that all 39 Taiwanese SARS-CoV isolates be-longed to subgroup B and could be divided into 3 clusters-B1, B2, and B3.
Integration of the results of the phylogenetic analyses and the epidemiological information (figure 4) indicated that there were 5 waves of epidemic SARS-CoV infection in Taiwan during 2003. The first wave, during early March, was composed of 1 imported case, 2 cases of transmission between family members (strain TW1), and 1 nosocomial infection (strain TW3); in cluster B1, both SARS-CoV strain TW1 and SARS-CoV strain TW3 were clustered with other SARS-CoV strains linked to Hotel M in Hong Kong [8,17]. The second wave (strain TW5) consisted of 4 Taiwanese individuals who contracted the disease as they flew from Hong Kong to Beijing [9] and who then carried it back to Taiwan during mid-March. The third wave, which began in late March, consisted of an infection that occurred on a train (patient TW-HP1), a case of transmission between family members (strain TC1), and multiple nosocomial infections (all "TW-HP" patients and patients TW-GD1, TW-GD-3, and TW-GD4). In cluster B2, all the SARS-CoV isolates mentioned above clustered with the SARS-CoV isolates from Amoy Gardens (strains CUHK-AG01, CUHK-AG02, and CUHK-AG03) [27], with a bootstrap value of 71%. The fourth wave, which started during late April and ended in mid-June, contained SARS-CoV isolates not only from hospitals JC, KC, PH, GD (patients TW-GD2 and TW-GD5), and YM but also from sporadic community outbreaks (strains TW10 and TW11), and all of these SARS-CoV isolates belonged to cluster B3, with a bootstrap value of 71%. The fifth wave occurred during early December and began with a laboratory-contamination case, patient SCVJ. It clustered with both HKU-39849, a SARS-CoV strain used in the laboratory [14], and TWC, another strain from Taiwan [12]. Sequence-variation rates for the strain from patient SCVJ versus strain TWC and for the strain from patient SCVJ versus strain HKU-39849 were, respectively, 0.01% (3/29,756) and 0.04% (12/29,756). In addition, a 24-nt deletion occurred at nucleotide position 26132-26155, resulting in both a frameshift of open reading frame 4 and a deletion of 8 aa residues within the small-envelope glycoprotein.
As shown in figure 5, there were 27 SNVs and 1 dinucleotide

DISCUSSION
When, for phylogenetic analysis, we combined sequences from different variable regions of the SARS-CoV genome, we assumed that no dual infection or recombination between the SARS-CoV subgroups had occurred. Because SARS is an acute infectious disease, the odds that it will be contracted from 2 subgroups are very low. Furthermore, the strategy of combining different genomic regions for phylogenetic analysis has been used in molecular epidemiological investigations of the origins of HIV-2. In those studies, the evolutionary history of a simian immunodeficiency virus/HIV-2 lineage was reconstructed by use of a combination of partial gag and env sequences; the method increased the accuracy of the phylogenetic analysis [28]. It is noteworthy that the SimPlot analysis demonstrated that the 3 region of the viral genome, especially near the junction of replicase 1b and the spike genes (encoding protein S), had the greatest sequence variation (figure 2). The S protein is considered to be the most important target for the humoral and cellular immune responses to SARS-CoV [15,16].
The 3 algorithms most commonly used in molecular phylogenetic analyses are the NJ, ML, and Pars methods; we used all 3 to obtain tree topologies. In terms of robustness, a bootstrap value of 70% is often cited as a cutoff for a reliable cluster [25]. Although the bootstrap values for nodes a and b in the NJ and Pars trees generated by the conventional method were higher than those in the NJ and Pars trees generated by the proposed method, the difference was not statistically significant. In contrast, the bootstrap values for node c in the Pars trees generated by the conventional and proposed methods were 56% and 67%, respectively, and the corrresponding bootstrap values in the NJ trees were 63% and 66%. This finding was confirmed by the ML trees: the P value for node c in the tree generated by the conventional method was not statistically significant, and that for node c in the tree generated by the proposed method was !.01. Accordingly, the tree generated by the proposed method was more reliable; also, because the proposed method requires only 7 RT-PCR reactions to perform the analysis, it is less time-consuming and more efficient than the conventional method. To facilitate other laboratories' future molecular epidemiological studies of outbreaks of SARS, we have made the nucleotide-sequence-alignment file of 80 SARS-CoV reference strains available on our center's Web site (http://www .ym.edu.tw/aids/Molepi/).
In the present study, we used 39 Taiwanese SARS-CoV isolates, including 20 downloaded from the GenBank database, to trace the origin and the path of dissemination of SARS-CoV infections that occurred in Taiwan during 2003. Phylogenetic analyses demonstrated that the Taiwanese SARS-CoV strains were distributed in 3 clusters-B1, B2, and B3-which differ from clusters 1-3 reported by Yeh et al. [11]; the latter clusters were not defined on the basis of a bootstrap value, whereas we used a bootstrap value of 70% as the cutoff to define our clusters B1-B3. Therefore, our cluster B1 contains both cluster 1 and cluster 2 of Yeh et al., and their cluster 3 was divided into 2 clusters-B2 and B3-in our study.
On the basis of the epidemiological data for the patients with SARS who were considered in the present study, it is clear that Taiwan experienced 5 waves of epidemic SARS infection during 2003. The first and second waves were in different phylogenetic clusters, suggesting that they had different origins. Although both the second and the third waves were in the B2 cluster, the SARS-CoV isolates in the third wave had their own nucleotide-signature pattern (figure 5). Neither the first wave nor the second wave led to serious outbreaks, but the third waveoriginating with a resident of Amoy Gardens who visited Tai- wan-led to 1 transmission on a train (strain TWC3), 1 case of transmission between family members (strain TC1), and nosocomial infections in at least 2 hospitals (HP and GD) in northern Taiwan. Only 1 nucleotide difference between strains TC1 and TWC3 was noted ( figure 5). In addition, strain TWC3 and an Amoy Gardens strain, CUHK-AG01, shared an identical sequence, even though the woman who was the source of the isolate of strain TWC3 never left Taiwan at any time during the epidemic. An epidemiological investigation showed that the visitor from Amoy Gardens and this woman sat in different cars during the train ride. Because this is the first documented case in which there is molecular proof of transmission on a train, it raises the question of why only 1 passenger contracted the infection. Because it has been reported that SARS appears to be most infectious at 6-11 days after onset of illness and not during the first day of symptoms [29], we assumed that the visitor from Amoy Gardens was not highly infectious during the train ride, even though he developed symptoms during the same evening that he traveled from Taipei to Taichung.
It is important to note that TWC, the SARS-CoV strain isolated from patient TWC, clustered with WHU, an isolate from Wuhan City, China, in cluster 1 but did not cluster with either strain TC1 (direct sequencing of a sample from patient TWC) or strain TWC3 (direct sequencing of a sample from patient TW-HP1) (figures 3 and 4). In addition, there was a 7-nt difference between TWC and TC1 ( figure 5). If we assume that strain CUHK-AG01 represents the first-generation SARS-CoV in the transmission link, then both strain TC1 and strain TWC3 were the second-generation, and TWH was the thirdgeneration. According to the SNV-based analysis (figure 5), the number of nucleotide changes in the SARS-CoV genome per number of intermediate hosts was extremely low (!1 nt change/host). Because it has also been shown that no or very limited nucleotide changes occur in SARS-CoV sequences from either cultures or primary clinical specimens [11,30], we can tentatively conclude that strain TWC is a laboratory contaminant and did not originate from patient TC1.
The origin of the fourth wave (cluster B3) is still unknown.
The molecular epidemiological data suggest that it originated at hospital JC (patient TW-JC2) and then spread to hospitals KC, PH, GD, and YM, as well as to others. The epidemiological investigations also support this hypothesis: TW-PH1, a patient with SARS who was treated at hospital JC went to Kaohsiung City and received treatment at hospital KC, and, after being treated at hospital KC (table 1), went back to Penghu Island and was hospitalized in hospital PH, where he transmitted the disease to other health-care workers. As shown in figure 6, after combining the epidemiological data with the results of our phylogenetic-tree analysis, we conclude that the fourth wave of epidemic SARS infection progressed in 2 dissemination paths: the first path was from hospital JC to hospital KC in Kaohsiung and then to hospital PH on Penghu Island; the second path was from hospital JC to hospital GD and then to hospital YM. Two cases associated with community outbreaks in Taipei (strains TW10 and TW11) also belong to this cluster. With regard to the laboratory contamination, the laboratory researcher said that he had used SARS-CoV strain HKU-39849 for his experiment; however, our results indicate that the sequence in patient SCVJ is more closely related to that of strain TWC (figure 5). Because the researcher claimed that he had not obtained strain TWC from Taiwan's CDC and that he had used many clinical SARS-CoV strains besides HKU-39849, we are continuing our investigation to confirm both (1) whether the virus that he had obtained from Taiwan's CDC was in fact strain HKU-39849 or strain TWC and (2) which SARS-CoV strain that he handled was the source of the laboratory contamination. Because 130 imported SARS cases have been reported by Taiwan's CDC but have not yet been analyzed, we plan to use the proposed tool to conduct further analyses, in an attempt to identify their origins. These contact histories will provide valuable information for the control of future SARS infections.

NUCLEOTIDE-SEQUENCE ACCESSION NUMBERS
The SARS-CoV nucleotide sequences (6 sequences for each of 18 strains) identified during this research have been deposited in GenBank (accession numbers AY451856-AY451963). The reference sequences (accession numbers) used in our sequencevariation analysis were from the following 20 strains: Urbani