Variants in SCAF4 Cause a Neurodevelopmental Disorder and Are Associated with Impaired mRNA Processing

Abstract

RNA polymerase II interacts with various other complexes and factors to ensure correct initiation, elongation, and termination of mRNA transcription. One of these proteins is SR-related CTD-associated factor 4 (SCAF4), which is important for correct usage of polyA sites for mRNA termination. Using exome sequencing and international matchmaking, we identified nine likely pathogenic germline variants in SCAF4 including two splice-site and seven truncating variants, all residing in the N-terminal two thirds of the protein. Eight of these variants occurred de novo, and one was inherited. Affected individuals demonstrated a variable neurodevelopmental disorder characterized by mild intellectual disability, seizures, behavioral abnormalities, and various skeletal and structural anomalies. Paired-end RNA sequencing on blood lymphocytes of SCAF4-deficient individuals revealed a broad deregulation of more than 9,000 genes and significant differential splicing of more than 2,900 genes, indicating an important role of SCAF4 in mRNA processing. Knockdown of the SCAF4 ortholog CG4266 in the model organism Drosophila melanogaster resulted in impaired locomotor function, learning, and short-term memory. Furthermore, we observed an increased number of active zones in larval neuromuscular junctions, representing large glutamatergic synapses. These observations indicate a role of CG4266 in nervous system development and function and support the implication of SCAF4 in neurodevelopmental phenotypes. In summary, our data show that heterozygous, likely gene-disrupting variants in SCAF4 are causative for a variable neurodevelopmental disorder associated with impaired mRNA processing.

Large-scale and high-resolution mass spectrometry-based proteomics profiling defines molecular subtypes of esophageal cancer for therapeutic targeting

“…SCAF4, also known as splicing factor, arginine/serine-rich 15, belongs to the splicing factor SR family. It may act to physically and functionally link transcription and pre-mRNA processing 45 , 46 . Taking the number of features, stability (frequency), and prediction performance into consideration, we selected “ELOA, SCAF4” as the subtype diagnostic signature for subsequent analysis.…”

Short structural variants as informative genetic markers for ALS disease risk and progression

“…The function of SCAF4 has recently been elucidated, with an essential role in RNA processing through regulating transcript elongation [ 41 ]. Variants in SCAF4 have been reported to cause impaired RNA processing and can result in neurodevelopmental disorders [ 42 ]. Interestingly, the poly-T variant in SCAF4 is flanked by two binding sites for transcription factor RNA polymerase II subunit A (POLR2A); therefore, changes in the length of this variant may influence the binding of POLR2A, thus affecting transcription of nearby genes ( SOD1 ) and may therefore play a role in ALS related neurodegeneration.…”

MvPPT: a highly efficient and sensitive pathogenicity prediction tool for missense variants

“…To further evaluate the performance of our predictor on variants in novel disease genes, we collected novel pathogenic genes from recent publications and 62 missense variants were retained with complete scores on all comparators (see Methods and Table S5) (50)(51)(52)(53)(54). We simulated 62 exomes of Mendelian diseases, by selecting one disease-causing variant and randomly selecting 1,000 neutral variants from 1KGP.…”

“…For Bayesian optimization process, the number of iterations was set as 100 (n_iter = 100) and the steps of random exploration was set as 15 (init_points = 15). The ranges of the hyperparameters in the LightGBM for Bayesian optimization were set as follows: num_leaves (24,45), feature_fraction (0.1, 0.9), bagging_fraction (0.8, 1), max_depth (5, 8.99), lambda_l1 (0, 5), lambda_l2 (0, 3), min_split_gain (0.001, 0.1), min_child_weight (5,50). After the parameter optimization process, the final used value of the parameters was as follows: num_leaves = 45, min_child_weight = 6.163, learning_rate = 0.01, bagging_fraction = 0.870, feature_fraction = 0.632, lambda_l1 = 0.…”