Sighting acute myocardial infarction through platelet gene expression

Acute myocardial infarction is primarily due to coronary atherosclerotic plaque rupture and subsequent thrombus formation. Platelets play a key role in the genesis and progression of both atherosclerosis and thrombosis. Since platelets are anuclear cells that inherit their mRNA from megakaryocyte precursors and maintain it unchanged during their life span, gene expression profiling at the time of an acute myocardial infarction provides information concerning the platelet gene expression preceding the coronary event. In ST-segment elevation myocardial infarction (STEMI), a gene-by-gene analysis of the platelet gene expression identified five differentially expressed genes: FKBP5, S100P, SAMSN1, CLEC4E and S100A12. The logistic regression model used to combine the gene expression in a STEMI vs healthy donors score showed an AUC of 0.95. The same five differentially expressed genes were externally validated using platelet gene expression data from patients with coronary atherosclerosis but without thrombosis. Platelet gene expression profile highlights five genes able to identify STEMI patients and to discriminate them in the background of atherosclerosis. Consequently, early signals of an imminent acute myocardial infarction are likely to be found by platelet gene expression profiling before the infarction occurs.


Platelet purification
Leukocyte depletion was used to obtain highly purified platelets, as previously described 3,4 . The blood samples were centrifuged at 160 g for 20 minutes at room temperature (RT) in order to obtain platelet-rich plasma (PRP), and the platelets were then purified by immunomagnetic negative selection using magnetic beads coated with anti-CD45 antibodies (DynabeadsH, Invitrogen, Carlsbad, CA) to deplete the nucleated cells. Briefly, PRP was stained with the magnetic bead-coated anti-CD45 monoclonal antibody (mAb) for 20 minutes at RT on a rotator, placed in a magnetic field, and the leukocyte-depleted platelets (LDPs) were than collected as the negative fraction. The purified platelets were washed three times in PBS/BSA solution, counted, and tested for purity by means of anti-CD41 staining and flow cytometry analysis (only samples containing >98% CD41 + cells). Finally, the LDPs were treated with an appropriate amount of TRIzol TM (Invitrogen) for cell lysis and RNA cryopreservation.

Microarray hybridisation
RNA was extracted using TRIzol TM (Invitrogen) in accordance with the manufacturer's protocol, quantified on a Nanodrop ND-100 spectrophotometer, and quality assessed by means of analysis on an Agilent 2200 TapeStation (Agilent Tehnologies, Palo Alto, CA). Fragmented biotin-labelled cDNA (from 10 ng of RNA) was synthesised using the GeneChip WT Pico kit (Affymetrix, Santa Clara, CA). Affymetrix gene chips (Human Transcriptome Array 2.0, Affymetrix, Santa Clara, CA) were hybridised with 5 µg of fragmented and biotin-labelled cDNA in 200 µL of hybridisation cocktail. Target denaturation was performed at 99°C for 5 min. and then at 45°C for 5 min, followed by hybridisation with rotation at 60 rpm for 16 hours at 45°C. The arrays were then washed and stained using a Gene Chip Fluidic Station 450 and an Affymetrix GeneChip hybridisation wash & stain kit. The chips were scanned by an Affymetrix Gene Chip Scanner 3000 using Command Console Software. The experiment was quality controlled using Expression Console Software v 1.4.1.

Microarray data processing
The expression data obtained from the Affymetrix probe-sets (70,523) were processed using Expression Console software (Affymetrix, www.affymetrix.com) with the default parameters. In detail, the procedure applied Signal Space Transformation (SST), which increases the overall absolute-fold changes of HTA2.0 arrays. The data were then Robust Multiarray Average (RMA) background corrected, median polish summarised and quantile normalised. The internal control probe-sets were filtered out, and only the 67,528 probe-sets that map biological features (genes, noncoding genes, etc.) were maintained for downstream analysis. The data are available at GEO, Accession No. GSE109048 Quantitative real-time polymerase chain reaction (PCR) RNA was extracted using TRIzol TM (Invitrogen) in accordance with the manufacturer's protocol, as previously described 3 .
The RNA was reverse-transcribed using the Superscript-III Reverse Transcriptase (Thermo Fisher Scientific, Waltham, MA, USA) and random hexamers (Promega, Madison, WI, USA), in accordance with the manufacturer instructions.
Semi-quantitative real-time RT-PCRs were performed to detect the expression levels of CLEC4E, FKBP5, SAMSN1, S100A12 and S100P using the SYBR Premix Ex Taq II (Takara, Shiga, Japan) and a LightCycler 480 (Roche, Basel, Switzerland). ACTB (Actin Beta) and ITGA2B (Integrin, Alpha 2b, Platelet Glycoprotein IIb of IIb/IIIa Complex, Antigen CD41) were used as housekeeping genes; the reactions were performed in triplicate, and the expression data were analysed and rescaled using GeNorm software 5 (Supplemental Figure 3).
The t-test analyses were performed using R software (http://www.r-project.org/).

Statistical analysis
The statistical analysis was based on 38 patients (one patient in the STEMI group and one HD were excluded because of quantitatively low RNA levels). The expression levels of the 10% most variably expressed genes 6 were collected into a 38x6,754 matrix whose rows corresponded to the individual samples and columns corresponded to the selected genes, plus a column for individual STEMI-HD status. Simple logistic regression models 7 were used to test each of the 6,753 expression variables for any association with the STEMI vs HD indicator. There were 51 gene expression variables with a p-value of <0.05 (Supplemental Table 1) and five with a p-value of <0.01. The later were considered for the subsequent stage of analysis, in which they acted as explanatory variables in a multiple logistic regression model for the log-odds ratio of the risk of STEMI vs HD. The model was internally validated by means of bootstrap resampling 8,9 . We then ran 30,000 iterations of a bootstrap procedure, each iteration involving resampling with the replacement of the rows of the original data matrix in such a way as to preserve the original matched structure, and analysed the simulated dataset using the same procedure as that applied to the original dataset. The 30,000 bootstrapped AUC statistics were used to calculate an empirical mean value and 95% confidence interval for the "true" AUC value. Finally, our logistic predictor of the risk of STEMI vs HD was assessed in terms of its ability to discriminate STEMI from SCAD as an external validation of the model. This involved the use of data from further patients with SCAD -the phenotypically closest condition to STEMI with documented coronary atherosclerosis but without thrombosis (Supplemental Figure 1, panel B). In this analysis, the logistic predictor was used with the gene-specific coefficients fixed at the values estimated on the basis of the STEMI vs HD analysis. The analyses were made using custom R-scripts and R-3.3.2 10 .

Reticulated platelet analysis
To exclude the possibility that newly-produced platelets make any significant contribution to DEGs, as it has been reported that acute coronary syndromes are associated with increased platelet turnover during the days following the acute event 11-13 , we assessed reticulated platelets frequency in our samples. Briefly, aliquots of whole blood were collected within six hours of the onset of chest pain/symptoms and immediately stained with thiazole orange (TO) and analysed by means of flow cytometry. Briefly, 5 µL of whole blood were diluted in 100 µL of Dulbecco's phosphate buffer (PBS) (Euroclone, Milan, Italy) and incubated with 10 µL mouse anti-human CD41a-PeCy5 monoclonal antibody (Beckton Dickinson, San Diego, CA) for 15 minutes at room temperature in the dark. Then, 400 µL of the TO mix solution (PBS/TO 62.5 ng/mL to obtain a TO staining concentration of 50 ng/mL) (Sigma-Aldrich, St. Louis, MO, USA) or PBS (for the negative control) were added, and incubated for an additional 45 minutes at room temperature in the dark. After incubation, the samples were fixed by adding 500 µL of 2% buffered paraformaldehyde, and analysed using an Epics XL flow cytometer (Beckman Coulter, Fullerton, CA) with Expo ADC software (Beckman Coulter).

Supplemental Figures and Figures legends.
Supplemental Figure 1. Scatterplots of the correlation between time of blood withdrawal (X axis) and gene expression levels (Y axis) for each STEMI patient.

Supplemental Figure 2. Panel A:
scatterplots of the correlation between CK-MB levels (X Axis) and S100P expression levels (Y axis). Panel B: scatterplots of the correlation between cardiac Troponin-I levels (X Axis) and S100P expression levels (Y axis).

Supporting Information
The expression of the 5 DEGs were also investigated by qRT-PCR, confirming the up-regulation of FKBP5, SAMSN1, S100A12 and CLEC4E, in STEMI patients as compared to HD. (Supplemental Figure 3). As it has been reported that acute coronary syndromes are associated with increased platelet turnover during the days following the acute event 11-13 , we assessed reticulated platelets frequency in our samples and found that it was similar in all of the groups (STEMI: 7.15±5.58%; SCAD: 6.69±5.68%; HD: 9.28±4.76%) (Supplemental Figure 4), thus excluding the possibility that newly-produced platelets make any significant contribution to DEGs.