Fine dust PM2.5 particles alter the expression profiles of circular RNAs in bronchoalveolar lavage fluid cells of mice

The particulate matter (PM) air pollutant exposure potentially is closely related to the incidence of human diseases (1–3). Particles 2.5 micrometers in diameter or less (PM2.5) are responsible for visibility degradation and are one of the causative factors of human non-accidental death, particularly the elderly (3). The primary exposure mechanism of PM is inhalation. PM2.5 reaches deep into the small airway and alveolar sacs, resulting in various biological effects on different cell types in the lungs. Particles deposited in the lungs potentially induce tissue damage and lung inflammation, subsequently triggering and aggravating various lung diseases, including but not limited to asthma, respiratory inflammation, cardiopulmonary problems and lung cancer (1–3). In animal studies, air pollution could cause damage, loss and dysfunction of murine tracheal cilia, resulting in infection and a declined nonspecific immune defense, and prone to secondary infection (4, 5).

Bronchoalveolar lavage fluid cells (BALF cells) are primarily immune cells that reside in the alveolar space of the lungs. The most common type is the alveolar macrophage (AM), with smaller proportions of lymphocytes and neutrophils. Analyzing these cells helps diagnose and understand various lung diseases (6–8). PM2.5 exposure has a significant impact on the survival of alveolar macrophages (AM). AM death and TNF-α expression increased with increased PM2.5 exposure (9). Subsequently the phagocytosis in alveolar sacs is remarkably lower with increased PM2.5 concentrations (10).

Circular RNA (circRNA) is a type of single-stranded RNA that forms a covalently closed continuous loop (11–13). CircRNA does not have 5′ or 3′ ends. The unique structure of circRNAs provides them with a longer half-life and more resistance to RNase R than linear RNAs (11–13). Some circRNAs serve as gene regulators; however, the biological function of most circRNAs is unclear. Emerging data show that circRNAs can serve as miRNA sponges and eliminate miRNA's regulatory functions. CircRNAs may be more powerful regulators than miRNAs. All these features make circRNAs potential candidates for diagnostic biomarkers and therapeutic strategies (11–13). Here we reported that PM 2.5 intratracheal instillation into mice robustly altered the circRNA profiles detected in BALF cells. This comprehensive profile provides insight and potential directions for future studies on the role of circRNA and PM2.5-induced lung diseases.

We purchased the Particulate matter 2.5-like (with EI; extractable ions) (ERM-CZ110) from Sigma-Aldrich (St. Louis, MO, USA). The data set reported in this work was obtained from the BALF cells of C57BL/6J mice (Scheme 1). Briefly, 8–12-week-old male and female C57BL/6J mice were obtained from Charles River Laboratory or Jax lab. The mice were housed and maintained in a pathogen-free animal facility at the Boston University Medical Campus. For the mouse model generation, PM 2.5 (50 μg/mouse) was intratracheally instilled. After 24 h, lung tissue and bronchoalveolar lavage fluid (BALF) were collected from treated mice for further studies. All animal experiments were conducted according to the approved guidelines by the Institutional Animal Care and Use Committee (IACUC) of Boston University.

Mice were grouped into control [intratracheal instillation (i.t.) of PBS] or PM2.5 (i.t.) group. Six mice per group (3 male and 3 female), and the BALF cells were pooled together and subjected to circRNA profiling. We used the pooled BALF cells simply to increase the amount of circRNAs to ensure the profiling is valid.

RNeasy Plus Mini Kits (QIAGEN, Valencia, CA) were used for the purification of total RNA from BALF cells. Single-stranded cDNAs were prepared using a High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, Waltham, MA). For circRNA profiling, we used the service from Arraystar (Rockville, MD). A total of 14,236 transcript-specific probes for mouse circRNAs were used—random primer labeling coupled with RNase R sample pretreatment to ensure specific and efficient labeling of circular RNAs.

After identifying differentially expressed circRNAs, these circRNAs were mapped to their parent genes. The parent gene IDs were included in this dataset. In this dataset, the raw intensity and normalized intensity were shown. Furthermore, alias, source, chromosome, strand, the start and end of the circRNAs are also included in this dataset. Currently, there are five categories of circRNAs, including exonic, sense-overlapping, intronic, intergenic regions and antisense circRNAs. In this dataset, the category of circRNAs was also included. In addition, chromosomal distribution of each circRNA was shown. Most of the circRNAs were located at autosomal chromosomes, while few were located at X or Y chromosomes (Figure 1).

In summary, we provide the first comprehensive analysis of the expression of circRNAs in the BALF cells after PM2.5 exposure via i.t administration in mice. This data set can be used to analyze the PM2.5-induced or PM2.5-suppressed circRNAs, and more importantly, it may provide future research directions on the potential biological functions of circRNAs in the air pollution associated with lung inflammation and diseases. However, pooling mixed sexes is a limitation of this current dataset, as it precludes the analysis of sex-dependent differential expressions of circRNAs. Further confirmation is required to examine male or female specimen separately. Using other bioinformatic tools, one potentially can illustrate the underlying signaling pathways and interacting molecules (e.g., other non-coding RNAs or proteins) using this dataset. Future directions also include but are not limited to the following studies: confirming the interested circRNA candidates using lung organoids or advanced in vitro systems; studies on endogenous vs. exogenous circRNAs involving epigenetic modifications; epigenetic modification such as m6A modification on circRNAs and their functional roles in environmental factor-induced immunity and inflammation.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvh.2026.1827560/full#supplementary-material

Supplementary material Supplementary material at the source

Footnotes

Edited by: Heedoo Lee, Changwon National University, Republic of Korea

Reviewed by: Seon-Jin Lee, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Republic of Korea