Yara Bucheeri
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Yara Bucheeri has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Abrar Alhermi
School of Medicine, Royal College of Surgeons in Ireland, Medical University of Bahrain, Busaiteen P.O. Box 15503, Bahrain
1 shared publication - Alexandra E. Butler
Royal College of Surgeons of Ireland, Adliya, Bahrain
1 shared publication - Sarah Bucheeri
School of Medicine, Royal College of Surgeons in Ireland, Medical University of Bahrain, Busaiteen P.O. Box 15503, Bahrain
1 shared publication - Shahd Bucheeri
School of Medicine, Royal College of Surgeons in Ireland, Medical University of Bahrain (RCSI-MUB), Busaiteen 15503, Bahrain
1 shared publication - Yasmine Alcibahy
School of Medicine, Royal College of Surgeons in Ireland-Bahrain, Adliya, Bahrain
1 shared publication
Latest Publications
- Open Access
CRISPR as a Tool to Uncover Gene Function in Polycystic Ovary Syndrome: A Literature Review of Experimental Models Targeting Ovarian and Metabolic Genes
Polycystic ovary syndrome (PCOS) is a complex disorder characterized by reproductive abnormalities such as hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology, and is frequently accompanied by metabolic disturbances such as insulin resistance, obesity and dyslipidemia. Genome-wide association studies (GWASs) have identified several susceptibility loci, yet little is known about their functional implications. Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (CRISPR/Cas9) has emerged as a powerful gene editing tool in bridging this gap by allowing researchers to directly target candidate genes in ovarian and metabolic pathways. For instance, experimental models have highlighted the role of CYP17A1 and DENND1A.V2 in androgen excess, anti-Müllerian hormone (AMH) in follicular arrest, and insulin receptor substrate 1 (IRS1) and PPARγ in insulin signaling and adipogenesis. To highlight the multifactorial nature of PCOS, animal models, including zebrafish and rodents, have been used to reveal interactions between reproductive and metabolic phenotypes. Nevertheless, most studies remain restricted to single-gene models, and dual-gene models or combined gene editing and hormonal induction models remain underexplored. Future research integrating precision editing, multi-omic platforms, and patient-derived organoids may provide more accurate disease models and novel therapeutic strategies.
Open AccessCRISPR as a Tool to Uncover Gene Function in Polycystic Ovary Syndrome: A Literature Review of Experimental Models Targeting Ovarian and Metabolic Genes
Polycystic ovary syndrome (PCOS) is a complex disorder characterized by reproductive abnormalities such as hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology, and is frequently accompanied by metabolic disturbances such as insulin resistance, obesity and dyslipidemia. Genome-wide association studies (GWASs) have identified several susceptibility loci, yet little is known about their functional implications. Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (CRISPR/Cas9) has emerged as a powerful gene editing tool in bridging this gap by allowing researchers to directly target candidate genes in ovarian and metabolic pathways. For instance, experimental models have highlighted the role of CYP17A1 and DENND1A.V2 in androgen excess, anti-Müllerian hormone (AMH) in follicular arrest, and insulin receptor substrate 1 (IRS1) and PPARγ in insulin signaling and adipogenesis. To highlight the multifactorial nature of PCOS, animal models, including zebrafish and rodents, have been used to reveal interactions between reproductive and metabolic phenotypes. Nevertheless, most studies remain restricted to single-gene models, and dual-gene models or combined gene editing and hormonal induction models remain underexplored. Future research integrating precision editing, multi-omic platforms, and patient-derived organoids may provide more accurate disease models and novel therapeutic strategies.