Mining, validating, and quantifying circular RNA transcriptome from total RNA as a biomarker or target.

Area of Science:

• Molecular Biology
• Genomics
• Critical Care Medicine

Background:

• Circular RNAs (circRNAs) are implicated in complex diseases and serve as potential biomarkers.
• Sepsis is a leading cause of death in intensive care units (ICUs) with limited effective treatments.
• Identifying and validating circRNAs from total RNA sequencing (RNA-seq) data presents significant challenges.

Purpose of the Study:

• To characterize the circRNA transcriptome in septic peripheral blood mononuclear cells (PBMCs) using RNA-seq.
• To evaluate and compare common aligners for circRNA identification.
• To optimize validation methods for circRNAs, specifically focusing on sepsis patients.

Main Methods:

• Peripheral blood mononuclear cells (PBMCs) were isolated from sepsis patients at ICU admission and discharge.
• Total RNA was extracted and subjected to RNA sequencing.
• CircRNA analysis was performed using four different bioinformatic pipelines, and quantitative reverse transcription PCR (qRT-PCR) was used for validation.

Main Results:

• A circRNA transcriptome was generated, with TopHat showing high sensitivity and MapSplice high accuracy among evaluated aligners.
• CircRNA expression in septic PBMCs was more abundant and less diverse upon ICU admission compared to discharge.
• Two isoforms of circular RNA from the ASPH gene (circASPH(2,4) and circASPH(2,3,4)) were identified; circASPH(2,3,4) levels correlated with ICU length of stay and showed potential as a disease severity indicator.

Conclusions:

• It is feasible to identify circRNA transcriptomes from total RNA-seq data.
• Optimizing reverse transcription PCR (RT-PCR) with reverse primers enhances accuracy and reproducibility for circRNA validation.
• The circRNA transcriptome in septic PBMCs evolves with infection severity, highlighting circASPH(2,3,4) as a potential clinical biomarker for sepsis severity.