RNA binding protein BOULE forms aggregates in mammalian testis

Su, Yujuan; Guo, X.H.; Zang, Min; Xie, Zhengyao; Zhao, Tingting; Xu, Eugene Yujun

doi:10.7555/jbr.36.20220072

Cited by 2 publications

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies have demonstrated that RBPs tremendously influence the mammalian reproductive system. For instance, RBP Rbm46 knockout mice had reduced testes size and spermatogenetic defect and thus were infertile [ 11 ]; Boule was able to bind to a tremendous amount of spermatogenesis-related mRNAs and was involved in the spermatogenetic process in mice testes through forming amyloid-like aggregation both in vivo and in vitro [ 12 ]; The loss of RBP Tulp2 led to infertility in male mice by reducing the quantity and quality of sperms [ 13 ]. Given that a large number of proteins need to be properly expressed during spermatogenesis, it is inevitable that RBPs exert nonnegligible functions in the spermatogenetic process and, naturally, in the initiation and progression of NOA.…”

Section: Introductionmentioning

confidence: 99%

An artificial neural network model to diagnose non-obstructive azoospermia based on RNA-binding protein-related genes

Fan

Muhuitijiang

Zhou

et al. 2023

Aging

View full text Add to dashboard Cite

Non-obstructive azoospermia (NOA) is a severe form of male infertility, but its pathological mechanisms and diagnostic biomarkers remain obscure. Since the dysregulation of RNA-binding proteins (RBPs) had nonnegligible effects on spermatogenesis, we aimed to investigate the functions and diagnosis values of RBPs in NOA. 58 testicular samples (control = 11, NOA = 47) from Gene Expression Omnibus (GEO) were set as the training cohort. Three public datasets, containing GSE45885 (control = 4, NOA = 27), GSE45887 (control = 4, NOA = 16), and GSE145467 (control = 10, NOA = 10), and 44 clinical samples from the local hospital (control = 27, NOA = 17) were used for validation. Through a series of bioinformatical analyses and machine learning algorithms, including genomic difference detection, protein-protein interaction network analysis, LASSO, SVM-RFE, and Boruta, DDX20 and NCBP2 were determined as significant predictors of NOA. Single-cell RNA sequencing of 432 testicular cell samples from NOA patients indicated that DDX20 and NCBP2 were associated with spermatogenesis (false discovery rate < 0.05). Based on the transcriptome expressions of DDX20 and NCBP2, we constructed multiple diagnosis models using logistic regression, random forest, and artificial neural network (ANN). The ANN model exhibited the most reliable predictive performance in the training cohort (AUC = 0.840), GSE45885 (AUC = 0.731), GSE45887 (AUC = 0.781), GSE145467 (AUC = 0.850), and local cohort (AUC = 0.623). Totally, an ANN diagnosis model based on RBP DDX20 and NCBP2 was developed and externally validated in NOA, functioning as a promising tool in clinical practice.

show abstract

Section: Introductionmentioning

confidence: 99%