The FASEB Journal express article 10.1096/fj.03-0097fje. Published online November 20, 2003.
Endothelial cells preparing to die by apoptosis initiate a program of transcriptome and glycome regulation Nicola A. Johnson,* Shiladitya Sengupta,† Samir A. Saidi,* Khashayar Lessan,* Stephen D. Charnock-Jones,* Laurie Scott,‡ Richard Stephens,‡ Tom C. Freeman,‡ Brian D. M. Tom,§ Michael Harris,* Gareth Denyer,║ Mallik Sundaram,† Ram Sasisekharan,† Stephen K. Smith,*,1 and Cristin G. Print*,1 *Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge, United Kingdom; †Biological Engineering Division, Massachusetts Institute of Technology, Cambridge, Massachusetts; ‡UK MRC HGMP Resource Centre, Hinxton, Cambridge, United Kingdom; § Medical Research Council Biostatistics Unit, Cambridge, United Kingdom; and ║Department of Biochemistry, University of Sydney, Sydney, Australia. 1
Contributed equally to this work.
Corresponding author: Cristin G. Print, Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge CB2 1QP, UK. E-mail:
[email protected] ABSTRACT The protein-based changes that underlie the cell biology of apoptosis have been extensively studied. In contrast, mRNA- and polysaccharide-based changes have received relatively little attention. We have combined transcriptome and glycome analyses to show that apoptotic endothelial cell cultures undergo programmed changes to RNA transcript abundance and cell surface polysaccharide profiles. Although a few of the transcriptome changes were protective, most appeared to prepare cells for apoptosis by decreasing the reception and transduction of prosurvival signals, increasing pro-death signals, increasing abundance of apoptotic machinery, inhibiting cellular proliferation, recruiting phagocytes to regions of cell death, and promoting phagocytosis. Additional transcriptomal changes appeared to alter the synthesis and modification of cell surface glycosaminoglycans. The resultant reduced abundance of sulphated cell surface glycosaminoglycans may further promote cell death by inhibiting the presentation of extracellular matrix-tethered survival factors to their receptors on dying cells. We propose that the transcriptome and glycome regulation presented here synergize with previously described protein-based changes to guide the apoptotic program. Key words: gene array • survival factor deprivation • cell surface glycosaminoglycans • independent component analysis • cluster analysis
A
poptosis is an essential process. During development, apoptosis is used to remove supernumerary cells and remodel tissues. After birth, apoptosis plays additional roles in tissue homeostasis and immune selection and in deleting cells that have become infected, irreparably damaged, or transformed (1, 2). Precisely regulated apoptosis is especially important to vascular biology. In most adult tissues, vascular endothelial cells (EC) survive for prolonged
periods and remain relatively static. However, during embryonic development and post-natal tissue remodeling, blood vessels must be remodeled by apoptosis to meet the changing requirements of the tissues they supply (3). For example, EC apoptosis is thought to occur during mammary gland involution (4), cyclical regression of the ovarian corpus luteum (5, 6), initiation and progression of atherosclerosis (7), and solid tumor regression (8, 9). Reduced supply of essential blood-derived pro-survival signals, such as growth factors and albumin, to the endothelium of poorly perfused vessels may play a role in this process (10). Apoptosis of ECs, like the apoptosis of other cell types, requires protein phosphorylation, oligomerization, relocalization, and cleavage (11–17). However, it seems likely that proteinbased mechanisms are not the only mechanisms responsible for EC apoptosis. Regulation of RNA transcript abundance may play an essential role because several transcription factors, such as p53 and NF-κB, are activated in EC by some pro-apoptotic conditions (18–20). Regulation of the interaction of EC with their underlying extracellular matrix (ECM) may also play a role, because the normally intimate EC-ECM relationships are disrupted early in the apoptotic process. Of the multitude of components in the ECM that bind to and regulate biological signals, one of the most important is the family of complex polysaccharides, heparin/heparan sulphatelike glycosaminoglycans (HSGAG). These are acidic polysaccharides characterized by a linear chain of disaccharide units of D-glucosamine linked to hexuronic acid, with modifications arising from acetylation, N and O-sulphation, and epimerization of D-glucuronic acid to L-iduronic acid. Due to their structural and chemical diversity, a wide range of biological processes are regulated by HSGAGs. Alterations during apoptosis to the abundance and sulphation of EC-derived cell surface and extracellular polysaccharides may impact on the apoptotic process because sulphated HSGAGs are continually required to present paracrine and autocrine survival factors to cell surface receptors (21, 22). In this study, we have combined genomics with glycomics to identify an apoptosis-related program of EC transcriptome and glycome regulation. Based on our results, we propose that the cell biology of stress-induced apoptosis is underpinned by synergy between described previously protein-based changes and the changes to the transcriptome and glycome presented here. MATERIALS AND METHODS Cell culture and RNA preparation Human umbilical vein ECs (HUVECs) were isolated from umbilical cords of five individuals by collagenase digestion as described (23) and cultured to passage 5 in a fully humidified atmosphere of 5% CO2 in basal culture medium supplemented with a proprietary mixture of heparin, hydrocortisone, epidermal growth factor, fibroblast growth factor, 2% fetal calf serum (FCS), gentamycin, and amphotericin (large vessel endothelial cell medium; TCS, Botolph, UK). At passage 5, cells were cultured to 70% confluence and total RNA was prepared using Trizol reagent (Gibco/BRL, Paisley, UK) followed by cleanup through an RNeasy spin column (Qiagen, West Sussex, UK) and ethanol precipitation. RNA integrity was assessed by using an Agilent 2100 bioanalyzer. Replicate cultures were partially deprived of growth factors for either 28 or 48 h by culturing in basal medium supplemented with only 2% charcoal-stripped FCS (Gibco/BRL), gentamycin, and amphotericin. When VEGF-A165 (R&D Systems, Oxon, UK) was added to cultures, it was used at a concentration of 10 ng/mL. Total and apoptotic adherent cells were enumerated using an epifluorescent relief-phase contrast microscope (Olympus, London,
UK). Apoptotic cells were defined as those that excluded trypan blue (0.2%; Sigma, Hertfordshire, UK) and propidium iodide (20 µg/mL; Sigma) but labeled with Annexin V-FITC (Annexin V-Fluos staining kit used according to the manufacturer’s instructions; Roche, Basel, Switzerland) and which also showed morphological characteristics of apoptosis. To assess global transcription levels, HUVECs were cultured in a 96-well plate with 10 µCi/mL [5,6-3H] uridine (ICN Biomedicals, Basingstroke Hants, UK). 3H incorporated into cellular RNA was collected using a Harvester 96 Mach III cell harvester (Tomtec, Hamden, CT), transferred to a glass filter and counted using a Trilux 1450 Microbeta liquid scintillation counter (Wallac, Turku, Finland). Affymetrix oligonucleotide gene arrays Biotin-labeled complex cRNAs were prepared and hybridized to Affymetrix Human “U95A” gene chips according to Affymetrix protocols (Affymetrix, High Wycombe, UK). The quality of the expression data from all chips was assessed using both Affymetrix Microarray Suite (version 4.0) and dChip (24) software. Data from chips and probe sets that failed these quality control tests were discarded (data from two entire serum withdrawal experiments were discarded on this basis). Transcript abundance data (“average differences”) were globally scaled to bring the median gene expression of each chip (excluding control genes) to 1. To ensure transcript expression levels were comparable between arrays the “LOESS” function of the “R” statistical software system was applied to the log transformed average difference values of each array in comparison to a control array. The control array chosen was that which bore the closest similarity to the other four control arrays by Euclidean distance. Normalized transcript abundance data were then compared using the CyberT algorithm (version 7.03; sliding window = 301; Bayes estimate = 15). This algorithm is an unpaired t-test, modified by the inclusion of a Bayesian prior based on the variance of other transcripts in the dataset (25). Detailed Affymetrix probe set hybridization data for selected genes were examined using a Filemaker Pro database system developed by G.D., which allowed the formation of clusters based on both data from the Affymetrix chips and on known functionality, which were linked to web databases for the collection of sequence and functional information. For further statistical analysis, the “R” statistical software system and GeneSpring Expression Analysis Software (Silicon Genetics, Redwood City, CA) were used. Independent component analysis was performed as described (26) using Matlab software. Individual transcript abundance changes were categorized according to the functions of the proteins they encoded, using the GO database (www.geneontology.org). In the text, fold changes are expressed as unpaired means over the five experiments (i.e., mean of five SFD/mean of five control). Quantitative real-time PCR The ABI PRISM 7700 Sequence Detection System (TaqMan) was used to perform real-time polymerase chain reactions according to the manufacturer’s protocols. CT values were compared with those of cyclophilin A, which, according to the Affymetrix results, remained relatively constant in abundance following SFD. Primers and probe sequences were CTGF 5′-tgcaccgccaaagatggt3′ 5′-ggactctccgctgcggtac-3′ fam-5′-ctccctgcatcttcggtggtacggt-3′-tamra; Iα2 5′-tctgagactgccaaggtcttca-3′ 5′-cagctggtatttgtcggacatc-3′ fam-5′-aggactagatcagaaatgcaaagtccatcctcat-3′- tamra; Clusterin 5′tcgactccctgctggagaac-3′ 5′-aagtggtcctgcatgacatcc-3′ fam-5′-ccggcagcagacgcacatgct-3′-tamra.
Immunocytochemistry HUVECs were cultured on glass coverslips coated with Cell-Tak adhesive (Becton Dickinson, Franklin Lakes, NJ) and fixed in 2% formalin for 20 min at 4°C. Non-specific antibody binding was blocked using 5% goat serum/2% bovine serum albumin in PBS, and cells were incubated overnight at 4°C in a mixture of primary antibodies (mouse monoclonal anti-human MCP-1, Clone Number MNA-1, Abcam, Cambridge, UK, 20 µg/mL; rabbit polyclonal anti-active caspase 3, Promega, Southampton, UK, 1:250 dilution) diluted in tris-buffered saline. Cells were then incubated for 1 h at room temperature in a mixture of fluorescent-labeled secondary antibodies (Alexa Fluor 488 goat anti-mouse IgG; Alexa Fluor 568 goat anti-rabbit IgG; Molecular Probes, Eugene, OR, both 20 µg/mL). Coverslips were mounted onto slides using Vectashield mounting medium containing DAPI (Vector Laboratories, Burlingame, CA) and viewed using a confocal microscope (Leica, Bannockburn, IL). Analysis of HSGAG composition Four separate cultures of pooled HUVECs, each derived from three separate donors (Clonetics, San Diego, CA) were cultured in endothelial basal medium (Clonetics) supplemented with BulletKit™ proprietary supplements and serum (Clonetics). At passage 4, cells were cultured in low serum conditions, as above, for 48 h. At this time, at least 20% of the SFD-cells exhibited apoptotic morphology. Cells were washed with PBS, trypsinized for 30 min, and centrifuged at 2500 rpm. Supernatants containing cell surface HSGAGs were collected and frozen at –20°C until further processing. Heparan sulphate was isolated from the supernatant as described previously (27). Briefly, supernatants were treated with 100 µL of 10 mg/mL Proteinase K (Sigma) at 60°C for 20 min. The aqueous solution was extracted twice with 200 µL phenol/chloroform (1:1), and the aqueous layer, containing heparan sulfate, was separated from the organic layer. The aqueous layer was re-extracted with chloroform and the heparan sulfate precipitated with 50 µL 7 M ammonium acetate and 800 µL cold ethanol. The sample was centrifuged at 14,000 rpm for 10 min, the ethanol layer was removed, and the precipitate was lyophilized to dryness. The sample was reconstituted in 9 µL water; exhaustively digested with 1 µL of enzyme cocktail consisting of 50 µM Heparinase I, II and III; and the resulting saccharide building blocks were subjected to compositional analysis by capillary electrophoresis, as described previously (28, 29). The products resulting from heparinase cleavage contain a ∆4,5 unsaturated uronic acid at the nonreducing end and readily absorb UV light. The products were facially monitored at their λmax of 232 nm. Under these conditions of digestion and detection, eight distinct entities—seven disaccharides and a single tetrasaccharide—were typically observed in the electropherogram. RESULTS To model the apoptosis of EC that may occur during vessel regression, we cultured primary HUVECs in conditions of partial survival factor deprivation (SFD). Within 1 h of SFD, HUVECs showed signs of stress. When cells were viewed using phase contrast microscopy, prominent peri-nuclear refractile vesicles appeared, the plasma membrane became distinct and appeared to contract away from neighboring cells, and nucleoli became less obvious (Fig. 1a). After 28 h, progression through the cell cycle had ceased (as no mitotic figures could be seen),
~10–15% of cells were undergoing apoptosis (based on their morphology), and only ~60% of original cell numbers remained adherent to the substrate. Despite this loss, most remaining cells were not yet committed to die because cultures were readily rescued at this time by re-addition of their optimal medium (data not shown). From this time on, the incidence of new apoptosis remained relatively constant, cells were progressively lost into the medium, and by 48 h only ~30% of original cell numbers remained adherent (Fig. 1b). All remaining cells had died by 72– 96 h. Epifluorescence time-lapse microscopy confirmed that the sequence of morphological changes associated with the death of serum-deprived HUVECs was consistent with apoptosis (Fig. 1c and Supplementary File 1). Further time-lapse microscopy in the presence of fluorochrome-conjugated Annexin V and propidium iodide (PI) indicated that necrosis (when PI uptake was not preceded by Annexin V binding) occurred in less than 4% of the dying cells (data not shown). Almost all post-apoptotic cells appeared to detach and float into the medium within 3 h of death. Therefore, adherent SFD cultures consisted of 85–90% stressed cells that, without intervention, were destined to die over the next 3 days and 10–15% cells currently undergoing apoptosis and were relatively depleted of necrotic cells and post-apoptotic corpses. The ratio of stressed but viable cells to cells currently undergoing apoptotosis appeared to vary slightly between the five SFD cultures as shown in Fig. 1d. The transcriptome was altered in survival factor-deprived endothelial cells To determine whether SFD-induced EC apoptosis was associated with transcriptome change, we used Affymetrix gene–chips to compare abundance of 12,600 transcripts in HUVEC cultured in their optimal medium with abundance in HUVEC after SFD. Based on analysis of the kinetics of SFD EC death above, 28 and 48 h time points were chosen to allow cells sufficient time to accumulate late SFD-induced transcriptome responses that were likely to determine cell fate, without excessive loss of cell number. RNA was extracted from the healthy and SFD HUVEC cultures and was used to prepare complex cRNAs, which were hybridized to Affymetrix U95A gene-chips. Transcript abundance data from the chips were normalized (see Materials and Methods) to allow direct inter-chip comparisons. To ensure reliable results, and to assess transcriptome heterogeneity between primary HUVEC isolates, we repeated this experiment five times, each time using primary HUVECs derived from a different individual. Quantification of the incidence of new apoptosis in each culture at each time point indicated that the five experiments were directly comparable (Fig. 1d). Most transcripts were not significantly affected by 28 h SFD. (Fig. 2a). However, Bayesian ttests identified a number of transcripts regulated consistently in all five experiments (P≤0.01). Of these, 171 were up- and 495 down-regulated at least twofold, and 25 up- and 30 down-regulated at least fivefold. Regulated transcripts encoded growth factors and their receptors, apoptotic regulators, apoptotic machinery, intracellular signal transduction molecules, and transcription factors. Their significance will be discussed individually below. Only 10 transcripts were regulated ≥ twofold between 28 and 48 h SFD—most of these represented a partial reversal of the regulation that had occurred in the initial 28 h (data not shown). Following the initial transcript selection, Independent Component Analysis [ICA; a higher order statistical method for the analysis of noisy data (26)] was chosen as a Student’s t-test- and fold change-independent method to remove the effects of noise and normalization and to identify sets of co-regulated transcripts. ICA identified two “components” (patterns of related transcript abundance changes) associated with 28 h SFD (Fig. 2b). All transcripts that were regulated ≥twofold at t-test P≤0.01
(see above) were above the 90th percentile by magnitude in each of these components. A small number of the transcripts with high values in each of these components failed to demonstrate significant gene expression changes by using standard statistical approaches. These few transcripts appeared to be regulated in proportion with other transcripts in the component, with which they potentially share regulatory mechanisms. Hierarchical clustering using Ward’s method was applied to transcript abundance data from the five HUVEC cultures grown in optimal medium and after SFD for 28 h. Figure 2c shows results of clustering based on two subsets of the transcriptome; (i) the 1% of transcripts with the lowest Bayesian t-test P-values and (ii) the 1% of transcripts with the greatest absolute loading in ICA components 2 and 14. In clustering based on both of these gene datasets, the SFD HUVECs clustered separately from the HUVECs cultured in optimal medium. When the entire dataset (12,600 genes) was clustered, a similar result was obtained (data not shown). Using Genespring software, we combined clustering of transcript patterns with annotation from the Gene Ontology database (data not shown), allowing for the classification of genes into ~300 functional categories. Using this method, we found no significant differences in functional grouping were seen between the sets of HUVEC isolates. Broader functional categories were therefore derived, using gene ontology and manual annotation, which included genes encoding proteins important for apoptosis, cellular stress, cell cycle, transcription, coagulation, and proteosomal degradation. None of the identified sets of regulated transcripts demonstrated a significantly higher proportion of such genes than would be expected by chance (P>0.05, chi-squared test). Therefore, the coclustered transcripts did not appear to encode proteins with a common function. Overall, although clustering separated SFD from non-SFD cells, it did not reveal patterns of idiosyncratic responses to SFD or preferential stress- or apoptosis-associated responses for individual HUVEC cultures. In addition to the regulation of individual transcripts, SFD also regulated the overall “pattern” of transcript abundance. Random effects-model analysis of variance (ANOVA) indicated that 28 h SFD caused a significant global effect (F=12.6; F>6.6 implies P
