Cigarette smoke alters circRNA expression in human T-cells

Preparation of 100% cigarette smoke extract media (CSEM)

Cigarette smoke extract (CSE) was made using an apparatus and techniques as previously described by our lab (Bernstein et al., 2019). Briefly, 25 mL of R10 media was aliquoted into a 50 mL conical tube and sealed with parafilm. A 5 mL serological pipette, cut at the 3.5 mL mark, was inserted through the parafilm seal. This apparatus was connected to one end of a Tygon tubing (Cole-Parmer, Cat. No. 06509-17), which was inserted into a Cole-Parmer Masterflex L/S peristaltic pump at speed setting of 32. A 1 mL pipette tip was tightly inserted into the other end of the Tygon tubing. Next, one Spectrum research-grade cigarette (Richter et al., 2016) (filter intact) was lit with a Bunsen burner and tightly inserted into the wide end of the 1 mL pipette tip. The cigarette was then smoked continuously by the peristaltic pump into the cell culture medium. The resulting product was considered 100% CSE.

H9 cell cigarette smoke exposure

Approximately 400,000 cells/mL of H9 cells were passaged into six T75 flasks, three of which were filled with 25 mL of R10 media with 0% CSEM and the other three with 25 mL of 50% CSEM. After 24 hours, the percentage of viable cells was measured (Table 1) using a Muse cell counter and Muse Count & Viability Reagent (Luminex, Cat. No. MCH600103).

Total RNA and circRNA isolation

The Invitrogen mirVana™ miRNA Isolation Kit (ThermoFisher Scientific, Cat. No. AM1560) was used as directed to isolate total cellular RNA. Approximately 10⁵ cells from each flask were lysed with 600 μL lysis buffer. Then, 750 μL 100% ethanol was added and centrifuged at 10,000 × g for 15 seconds. The filters containing RNA were washed, and total RNA was eluted with 100 μL pre-heated nuclease-free water, centrifuging at maximum speed for 25 seconds. RNA quality was checked using an Agilent Fragment Analyzer. Next, 5,000 ng of RNA from each sample was shipped to Arraystar for hybridization with the Human Circular RNA Array (Cat. No. AS-S-CR-H-V2.0).

Human circRNA array, data processing, and analysis

Total RNAs from each sample were quantified using the NanoDrop ND-1000 and the integrity of the samples were evaluated through electrophoresis on a denaturing agarose gel. Total RNAs were then digested with RNase R (Epicentre, Inc.) to degrade linear RNAs and enrich circRNAs. A random priming method was used to amplify and transcribe the enriched circRNAs into fluorescent complementary RNAs (cRNAs) (Arraystar Super RNA Labeling Kit; Arraystar), which were then purified by RNeasy Mini Kit (Qiagen). Nanodrop ND-1000 was used to measure the concentration and specific activity of the labeled cRNAs (pmol Cy3/μg cRNA). A total of 1 μg of each labeled cRNA was fragmented by adding 5 μl of 10× Blocking Agent and 1 μl of 25× Fragmentation Buffer. The mixture was then incubated at 60°C for 30 minutes, then 2× Hybridization buffer was added to dilute the labeled cRNA. Then, 50 μl of the hybridization solution was dispensed into the gasket slide and assembled to the circRNA expression microarray slides, which were washed, fixed, and scanned with the Agilent Scanner G2505C.

Agilent Feature Extraction Software (version 11.0.1.1) (RRID:SCR_014963) was used to extract raw data from the scanned images. Quantile normalization of the raw data and subsequent data processing were conducted using the R software Limma package (RRID:SCR_010943). Low intensity filtering was then performed to retain circRNAs that at least one out of six samples were flagged as “present” or “marginal”. The quality control flags of “present”, “marginal”, or “absent” were determined based on various features, including positive and significant signal, saturation, population outlier, above background, and uniformity of the background. The fold change between the cigarette exposed samples and control samples were calculated and the statistical significance of the difference was estimated using an unpaired t-test. circRNAs having fold changes greater than or equal to 1.5 and p-values less than or equal to 0.05 were selected as significantly differentially expressed, using Microsoft Excel (RRID:SCR_016137). The false detection rate for each differentially expressed circRNAs were calculated using Benjamini-Hochberg procedure. These data, while suggestive, do not support robust statistical analysis secondary to the number of repeats and the large number of RNAs.