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Infection and Immunity, February 2009, p. 837-849, Vol. 77, No. 2
0019-9567/09/$08.00+0 doi:10.1128/IAI.00955-08
Copyright © 2009, American Society for Microbiology. All Rights Reserved.

Department of Veterinary and Biomedical Sciences, University of Minnesota, St. Paul, Minnesota 55108
Received 30 July 2008/ Returned for modification 8 September 2008/ Accepted 3 December 2008
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Apoptosis has been demonstrated to be an important defense mechanism for the host in response to many infections (36, 49). Predictably, many parasites possess the ability to modulate host cell apoptosis (11, 22, 29). Does apoptosis contain Cryptosporidium infection? In natural and experimental infections in vivo, the picture is complex. Apoptosis has been observed in association with infection; however, a simple correlation between pathology and apoptosis does not exist (30, 41). In vitro, Cryptosporidium induces apoptosis in a small minority of the infected cells (5, 33, 35), which is not enough to ward off infection. Cryptosporidium has apparently evolved countermeasures to keep the host cells in a survival mode. Indeed, the infected cells acquire resistance to various chemical agents that trigger apoptosis (6, 28, 33, 35). Additionally, the host appears to be equipped with a second line of defense also involving apoptosis: uninfected bystander cells die due to FasL secreted from the infected cells (5, 6, 33, 35). Cryptosporidium activates the NF-
B pathway in the infected cells and survives, while the host can contain the infection by surrounding the infected cells with a zone of apoptosis.
Here we report genome-wide expression profiling of C. parvum-infected intestinal epithelial cells and the functional significance of host cell apoptosis for the infection process. Our results revealed that parasite infection of host epithelial cells is a complex process involving the regulation of 333 host genes. The largest functional group identified from the microarray included 51 genes that are related to cellular apoptosis. Apoptosis gene transcript profiles suggested that host proapoptotic gene expression is actively downregulated early in infection but is favored at late stages. Experimental induction and inhibition of cell apoptosis altered C. parvum infection and development, suggesting that C. parvum actively subverts host apoptosis in a biphasic manner to complete its life cycle.
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Microarray hybridization.
At 6, 12, 24, 48, and 72 h postinfection, total RNA was extracted from infected cells using Trizol reagent (Gibco BRL Life Technologies, Gaithersburg, MD) and poly(A)+ RNA was isolated using oligo(dT) cellulose columns (Amersham Pharmacia Biotech). cRNA probes were synthesized from
2.0 µg of poly(A)+ RNA, fragmented, and hybridized to an HG-U95Av2 (Affymetrix Inc., Santa Clara, CA) probe array that contained probe sets for over 12,600 human genes and/or transcripts. Approximately 15 µg of fragmented cRNA was hybridized to the arrays at 45°C for 16 h and was subsequently washed and stained with a streptavidin-phycoerythrin conjugate using the GeneChip Fluidics station protocol EukGE_WS2 (Affymetrix). Each hybridized microarray chip was scanned twice at 3-µm resolution with a confocal scanner (Hewlett-Packard).
Statistical analysis of gene chip data. Gene expression data obtained from scanning the U95A chips were initially analyzed using the Microarray Suite 5.0 software package (Affymetrix). The fluorescence intensities of each DNA microarray were scaled by a factor of 1,000. A minimum value of 500 was required for genes to be classified as "expressed." Data analyses were performed using GeneSpring software (version 5.0; Silicon Genetics, Redwood City, CA). The expression signal of each gene in C. parvum-infected cells was normalized to that in the mock-infected sample. As preliminary analysis of single time point measurements of mock-infected samples at 24 h and 72 h revealed no significant changes in gene expression, triplicate 24-h mock-infected samples were used for subsequent analysis. The average change of gene expression in infected cells at a time point was then calculated as the sum of signal values from three replicates divided by the sum of three values from the mock infection samples. Group comparison between C. parvum-infected and mock-infected cells was performed using the Welch analysis of variance parametric test (P value cutoff of 0.05) and the Benjamini and Hochberg false discovery rate multiple testing correction. Differentially expressed genes were identified as those with an average change of ±1.8-fold (cutoffs of 1.8 and 0.555 for up- and downregulated genes, respectively) at two or more surveyed time points.
Regulated genes were annotated using GeneSpring's "Build Ontology (Go Slims)" constructor, which hierarchically groups genes into meaningful biological categories based on Gene Ontology Consortium classifications. Genes with similar expression profiles were hierarchically clustered using the Quality Threshold cluster algorithm (minimum standard correlation, 0.9).
qRT-PCR analysis of gene expression. For quantitative reverse transcription-PCR (qRT-PCR) analysis, poly(A)+ RNA (0.5 µg) was reverse transcribed in a 20-µl reaction mixture primed by random hexamers using Superscript II (200 units; Invitrogen). cDNAs were diluted 1:1,250 in water. For validation of Bcl-2 silencing, 2 µg total RNA was digested with DNase using the RNase-free DNase set (Qiagen) and reverse transcribed in a 20-µl reaction mixture with the Superscript II set (200 units; Invitrogen). Each 15-µl PCR mixture contained 3 µl of the diluted cDNA, SYBR green PCR master mix (Stratagene, La Jolla, CA), and gene-specific primers (Table 1) at 50 nM. qRT-PCR analysis was performed on a real-time PCR system (Stratagene, La Jolla, CA) using a two-step PCR protocol with denaturation at 95°C for 15 s and annealing/extension at 60°C for 1 min for 50 cycles. The size of the PCR amplicon and specificity of the amplification were confirmed by visualization on 2% agarose gels. qRT-PCR data were analyzed using the comparative threshold cycle method as described in the Mx3000P real-time PCR system instruction manual (software version 2.0; Stratagene). Expression of each target gene was normalized against the expression of eukaryotic translation elongation factor 1A in the same sample. The relative level of target gene expression, compared to that in mock-infected cells, was determined for each of the genes.
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TABLE 1. Primers for qRT-PCR analysis
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Bcl-2 gene silencing by RNA interference. Chemically synthesized siRNAs for Bcl-2 and control (scrambled) siRNAs corresponding to sequences that did not match any human transcripts were purchased (Ambion, Austin, TX). The sequence of Bcl-2 siRNA targeting is 5'-GGAUUGUGGCCUUCUUUGA-3'. Transfections were performed at approximately 70% confluence in 24-well plates using SiPORT amine transfection agent (Ambion, Austin, TX) according to the manufacturer's instructions. Cells (2.5 x 105) were seeded in complete growth medium the day before transfection. For each transfection, 3 µl SiPORT amine was diluted into 46 µl Opti-MEM serum-free medium. siRNA-amine complexes were prepared by mixing 1.25 µl of siRNA (20 µm) with 49 µl Opti-MEM serum-free medium containing amine. The final concentration of the siRNA was 100 nM. Transfections were performed in 250 µl of serum-free medium for 24 h. Thereafter, 0.5 ml of fresh medium containing 10% (vol/vol) fetal bovine serum was added to achieve complete growth conditions. In each experiment, untreated controls were included. Bcl-2 knockdown at 24 h and 48 h was confirmed by qRT-PCR using the primers 5'-ATGTGTGTGGAGAGCGTCGTCAA-3' and 5'-ACAGTTCCACAAAGGCATCC-3'. Cells were infected with C. parvum oocysts 48 h after transfection.
Microarray data accession number. The array data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/) and are accessible through GEO series accession number GSE2077.
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12,600 genes queried on the Affymetrix HG-U95Av2 probe array, 333 genes (2.6%) showed significantly (P < 0.05) altered expression compared to expression in mock-infected cells, with an average change of at least 1.8-fold at at least two time points. Raw gene expression data and detailed analysis results are accessible online at the GEO (http://www.ncbi.nlm.nih.gov/geo/; GEO accession number GSE2077). Because the largest class of regulated genes were those involved in apoptosis, we focused subsequent experiments on apoptosis-related genes during C. parvum infection. Apoptosis genes regulated by C. parvum infection. Of the 333 genes with altered expression following C. parvum infection, 10 genes were apoptosis regulators, including general apoptosis inducer genes (DDIT3, DUSP6, LUC7A, P8, and PHLDA2 genes) and those specifically associated with the death receptor (extrinsic) apoptotic pathway (CFLIP, IER3, and LGALS genes) or the mitochondrial (intrinsic) apoptotic pathway (BBC3 and CYCS genes) (21, 24, 39, 42). An additional 41 transcripts with altered expression patterns during C. parvum infection included genes that have been reported to be associated with apoptotic processes. As shown in Table 2, these genes are typically annotated in other functional groups, such as heat shock and stress response, cell metabolism, nucleic acid binding, signal transduction, and transcriptional regulation. Thus, a total of 51 (15.4%) of the host cell transcripts impacted during C. parvum infection were related to apoptotic processes. An additional 10 transcripts encoding apoptosis-related proteins were altered at least 1.8-fold at single time points (data not shown).
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TABLE 2. Apoptosis-related proteins whose genes have altered expression during C. parvum infection in HCT-8 cellsa
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FIG. 1. C. parvum-modulated transcripts of apoptosis genes in the host. Shown is relative transcript abundance in infected cultures compared to that in uninfected cultures. The color bar at the left indicates the change (n-fold), and graphs at the right display the average expression profile of genes in the cluster. Genes in pink are proapoptotic genes, genes in green have antiapoptotic roles, and genes in brown have dual roles in apoptosis.
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In contrast, cluster 4 contained apoptosis-related genes whose transcript levels were markedly increased in early stages of infection (6 and 12 h) (Fig. 1). Most transcripts returned to normal levels at late stages of infection. Eleven of these genes are reported to have antiapoptotic effects (Table 2), including genes also annotated as heat shock and stress response genes (HSPA8, HSPCA, HSPE1, AHSA1, DnaJA1, and DnaJB6 genes), tumor-associated genes (TM4SF1 and FGFR1OP genes), and genes encoding proteins blocking the death-receptor apoptotic (IL1RN) and mitochondrion-mediated apoptotic (PCNA) pathways. The AMD1 gene also clustered here and is involved in polyamine biosynthesis, which can suppress apoptosis. Note that cluster 4 also included proapoptotic genes, such as the ANXA1, ANXA2, CYCS, MYCBP, STRA13, DUSP4, and PHLDA2 genes.
All six genes in cluster 5 encoded proteins favoring apoptosis progression. Their expression levels slowly increased throughout the in vitro C. parvum life cycle in HCT-8 cells and were moderately upregulated at late stages of infection (48 and 72 h). This cluster included genes encoding two dual-specificity phosphatases (DUSP5 and DUSP6) that can activate apoptosis.
The remaining six genes had unique expression patterns that differed from each other and did not cluster. In general, genes that inhibit apoptosis (FOS, FOSB, HSPA1A, IER3, and PHLDA1 genes) had higher expression levels at early stages of infection, similar to cluster 4 genes. One proapoptotic gene, the EGR3 gene, was unchanged at 6 h but was significantly upregulated thereafter (Table 2; Fig. 1). Taken together, the host transcriptome was indicative of an early (2 to 24 h) antiapoptotic state that became proapoptotic thereafter.
Confirmation of microarray data by qRT-PCR analysis. To validate gene expression data generated by microarray analysis, qRT-PCR analysis was conducted for 16 genes selected from all clusters (Fig. 2). Primers for each transcript are shown in Table 1. The expression of five proapoptotic genes, the P8, DDIP3, DSIPI, ATF4, and CEBPB genes from clusters 1 and 2, was downregulated at early stages of infection, whereas the four antiapoptotic metallothionein genes were repressed at late stages, consistent with their respective expression patterns revealed by microarray analysis (Fig. 1). Similarly, the upregulation of three heat shock genes and one additional antiapoptotic gene from clusters 3 and 4 at early stages of C. parvum infection was confirmed, as were the respective expression patterns of the CFLAR (cluster 5) and HSPA1 (unclassified group) genes. In sum, the qRT-PCR results confirmed a biphasic regulation of apoptosis-regulated genes detected by microarrays. The absolute magnitudes but not the overall patterns of differential expression at specific time points determined by qRT-PCR analysis varied from those determined by microarray analysis, reflecting the difference between microarray and qRT-PCR analysis (54).
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FIG. 2. qRT-PCR analysis of apoptosis gene expression in C. parvum-infected cells. Data represent mean changes in gene expression in infected cells relative to that in uninfected cells. Error bars indicate sample standard deviations of three replicate experiments.
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FIG. 3. Bcl-2 knockdown hinders C. parvum infection. (A) Fluorescence micrograph of C. parvum-infected HCT-8 cells in apoptosis. HCT-8 cells were infected by C. parvum (red) for 24 h. Nuclear DNA is visualized in green. <a, infected and apoptotic cell; <i, infected and nonapoptotic cell; <u, uninfected and nonapoptotic cell. Scale bar = 10 µm. (B) Efficiency of Bcl-2 silencing. Relative abundance of Bcl-2 transcripts after siRNA treatment was measured by qRT-PCR. Mean values and standard errors (error bars) from triplicate samples are expressed relative to the values at 24 h with no siRNA treatment (infection only). (C) Frequency of C. parvum-infected cells. (D) Frequency of cells in apoptosis. Only the ranges are shown for the three uninfected control cultures, no treatment, scramble RNA treatment, and Bcl-2 siRNA treatment. The slightly larger ranges at 24 h observed for the uninfected cultures were not specific to Bcl-2 siRNA. (E) Frequency of apoptosis among infected cells. All data points represent medians ± ranges for four replicates. Asterisks indicate that the medians were significantly different (P < 0.05) from those for cells not treated with Bcl-2-specific siRNA. HCT-8 cells were infected at an oocyst-to-host cell ratio of 1:1.
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The impact of early apoptosis on C. parvum development was examined. In a normal course of infection, the parasites grow relatively synchronously for the first 24 h. Through 6 h, parasites were largely sporozoites and appear as small, intensely stained structures at the periphery of the cell (Fig. 4A). At 12 h, they had reached predominately trophozoite stage, observable as larger more diffusely stained plaques or ring-like structures. By 24 h, the majority of the parasites became meronts, taking on the appearance of large circular structures with heterogeneous staining (Fig. 4A). Meronts are the product of three mitoses and consist of eight cells termed merozoites. Upon release from the meront, merozoites infect new cells. Merozoites and sporozoites are indistinguishable by morphology; however, small parasites that appear after 24 h are considered merozoites. At 48 h and beyond, the culture consists of one-third each of merozoites, trophozoites, and meronts. Thus, by scoring for morphology and constructing a life history, we can track parasite development (Fig. 4B). Infected-cell cultures treated with Bcl-2 siRNA and control cultures showed similar percentages of each parasite developmental stage through 12 h. However, at 24 h, Bcl-2 gene silencing increased the proportion of sporozoites from roughly 20% to 40%, while meronts decreased to 25%. At 48 h, the proportions of three parasite stages were similar and remained unchanged between 24 h and 48 h in response to Bcl-2 gene silencing. The relative increase in the small parasites at 24 h could be due to the destruction of meronts or due to accelerated release and reinfection of merozoites. The frequency of cells infected by small parasites did not change after 12 h when Bcl-2 was silenced (Fig. 4C), which suggested that the parasites were eliminated by apoptosis. In concordance, we saw no increase in meront-infected cells beyond 12 h (Fig. 4C). These data show that apoptosis of the host cells during early infection inhibits C. parvum development.
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FIG. 4. Bcl-2 knockdown inhibits C. parvum growth. (A, left) Fluorescence micrograph of C. parvum. 1, sporozoites; 2, trophozoites; 3, meronts. (Right) Overlay of nuclear (blue) and actin (green) staining atop parasites (red). (B) Frequency distribution for three parasite stages. Median values of four replicate experiments were converted to percentages. Error bars indicates the ranges. The asterisk indicates that the median value was significantly different from that for cells not treated with Bcl-2-specific siRNA. (C) Infection rate for different parasite stages. All data points represent the medians of four replicates. The error bars indicates the ranges. Asterisks indicate significant differences in the median values compared to no siRNA or scramble siRNA treatment.
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FIG. 5. Effects of caspase inhibition on C. parvum infection. (A) Frequency of infected cells. (B) Frequency of apoptosis in the culture. (C) Frequency of apoptosis among infected cells. (D) Frequency distribution of three parasite stages. Median values of four replicate experiments were converted to percentages. HCT-8 cells were infected at 1:1 (oocysts to host cells). All data points represent medians of four replicate experiments. Error bars indicates the ranges. Asterisks indicate that the median values were significantly different from the values for untreated culture.
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FIG. 6. Caspase inhibition delays C. parvum development. (A) Frequency of infected cells. (B) Frequency distribution for three parasite stages. Median values of four replicate experiments were converted to percentages. HCT-8 cells were infected at 1:10 (oocysts to host cells). All data points represent medians of four replicate experiments. Error bars indicate the ranges. Asterisks indicate that the values were significantly different from the values for untreated culture.
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At 6 h and 12 h postinfection, we observed virtually no apoptosis. Correspondingly, the early transcriptome of infected HCT-8 cells reflected inhibition of apoptosis: mRNAs encoding proapoptotic proteins, including BBC3/Puma, DDIT3/CHOP, and DSIPI, were downregulated, and mRNAs encoding antiapoptotic proteins, such as hsp70, hsp10, DnaJ/hsp40, and hsp90 (27, 40, 47), were rapidly upregulated. In particular, hsp70 and DnaJ block apoptosis by neutralizing apoptosis-inducing factors and by preventing the translocation of Bax to mitochondria (27, 40).
When we experimentally reduced Bcl-2 expression, this induced massive apoptosis at 12 h, reduced infection, and virtually eliminated accumulation of meront stages necessary for subsequent reinfection. Mammalian cells respond to infection by apoptosis mediated by the proapoptotic members of the Bcl-2 family; BBC3/Puma, DSIPI, Bax, Bak, Bim/Bod, and Bad are involved in the mitochondrion-mediated pathway of apoptosis during human immunodeficiency virus, Sendai virus, and Chlamydia trachomatis infection (2, 4, 12, 18, 45, 53, 55). Infection with both hepatitis C virus and Japanese encephalitis virus induces DDIT3/CHOP expression and sensitizes cells to apoptosis by downregulation of Bcl-2 (4, 45). Pathogens have evolved countermeasures to subvert apoptosis immediately. For example, many viruses encode homologs of Bcl-2 to inhibit apoptosis (3, 46, 52). Other pathogens inhibit release of cytochrome c from mitochondria (7, 10, 17, 31, 50, 51) and inhibit caspase activation (25, 37). Since C. parvum required Bcl-2 to inhibit apoptosis, the direct inhibition of Bax and Bak was not the primary mechanism utilized at the early infection stage. More likely, C. parvum reconfigures the transcriptome to affect the strong Bcl-2 block.
We found three positive associations between apoptosis and Cryptosporidium infection: (i) a modest occurrence of apoptosis among infected cells after 24 h, (ii) a shift in the transcriptome of the culture toward the proapoptotic state at 36 to 72 h, and (iii) an inhibition of parasite maturation and infection in response to caspase inhibition. Does this mean a positive role for apoptosis in Cryptosporidium infection? Superficially, the modest 15 to 20% apoptosis, it may be argued, is evidence of a brief moment during infection that we are able to only glimpse because we don't have perfect synchrony in the infection. However, this does not explain why the frequency of apoptosis does not fall after the frequency of meronts falls, nor does it explain the absence of postegress apoptotic cells. We think the modest apoptosis rate is due to the host succeeding in trapping Cryptosporidium in apoptosis. In agreement, Elliott and Clark (14) have shown that, in 100% of the cases, spent host cells die of nonapoptotic death after parasite egress.
The host transcriptome shift toward a proapoptotic state occurs at the time when the culture becomes a mixed-cell population even if we had achieved 100% infection. The culture at this time contains parasites of different ages and dying cells after parasite egress. Since biochemical techniques measure the population average, we cannot distinguish between a global shift in the entire population from the emergence of novel subpopulations that have distinctly different behavior. Except for the period around 24 h, the majority of cells contain young parasites that ought to require protection from apoptosis. Therefore, the small shift toward a proapoptotic transcriptome is probably due to a subpopulation of the cells that arise later in the culture.
Z-VAD-FMK prevented continued spread of infection past 24 h, apparently due to the lack of normal parasite development. We reasoned that, if apoptosis has any positive role for the parasite, it ought to be at a late stage in development near the time of egress. However, Z-VAD-FMK did not trap the parasites as meronts; it hindered the normal maturation of the parasites. We think it is unlikely that Cryptosporidium requires apoptosis; rather we propose a caspase-dependent cellular process in the host that is required for parasite maturation. Alternatively Z-VAD-FMK may inhibit something directly in Cryptosporidium.
The regulatory mechanism involved in parasite infection and host cell apoptosis is complicated. Many pathogens can protect the host cells from apoptosis (11, 20). Is the parasite actively reprogramming the host or just sneaking in? Does the host defense not involve suicide? Stealth strategy by the parasite or passive survival strategy by the host would not depend on Bcl-2, since Bcl-2 knockdown per se did not trigger apoptosis. It was the Cryptosporidium infection that caused the large increase in apoptosis under Bcl-2 depletion. Similarly, it was the infection that caused the overall change in the transcriptome of the culture toward antiapoptosis. We think the host was trying to defend by apoptosis and the parasite was enhancing Bcl-2 to counteract this defense. Between 24 h and 48 h, the merozoites appear and reinfect, resulting in an equal-part mixture of merozoites, trophozoites, and meronts. This was when survivin protects from apoptosis (28). This strong apoptosis inhibition can protect the cells even from staurosporine, yet depletion of survivin or XIAP results in high caspase activity, suggesting that Bcl-2 is not effective at this stage and/or that the caspases were not activated by the mitochondrial pathway. Contrastingly, at 6 h, cells are sensitive to staurosporine, indicating that survivin was not yet able to inhibit the staurosporine-activated pathway and that Bcl-2 cannot protect the cell from the staurosporine-activated pathway. Both sporozoites (at 6 h) and merozoites (after 24 h) need protection from apoptosis, and this requirement is met at least in part by upregulating two different host factors. The host cells may change between 6 h and >24 h by exposure to Cryptosporidium or parasite-infected cells such that, by 24 h, the upstream pathways that sense and activate apoptosis are different. In either case, Cryptosporidium appears capable of inhibiting apoptosis. The 24-h to 48-h period is further complicated: between 20 and 15% cells were apoptotic and contained almost exclusively meronts, and the transcriptome changed slightly toward apoptosis. One can dismiss these small changes by ascribing them to escapes or breakdown of antiapoptosis control. However, the slight increase in the average was likely due to a large increase in a small subset of the cells since most of the time, the exception being a brief period around 24 h, the majority of the parasites are not mature enough to forgo apoptosis inhibition. Furthermore, inhibition of all caspases was clearly inhibitory to parasite development, illustrating the complex adaptation of C. parvum to the ever-changing host cell. Insights into the mechanisms by which C. parvum manipulates apoptosis of infected epithelial cells to complete its life cycle may provide for therapeutic interventions.
This work was supported in part by grants R01-AI065246-02 from the National Institutes of Health and 99 35204-8614 from the National Research Initiative of the USDA Cooperative State Research, Education and Extension Service.
Published ahead of print on 15 December 2008. ![]()
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