1.
UMOD gene mutations in Chinese patients with autosomal dominant tubulointerstitial kidney disease: a pediatric case report and literature review.
Yang, J, Zhang, Y, Zhou, J
BMC pediatrics. 2019;(1):145
Abstract
BACKGROUND Autosomal dominant tubulointerstitial kidney disease (ADTKD) caused by UMOD gene mutation (ADTKD-UMOD) is rare in children, characterized by hyperuricemia, gout, and progressive chronic kidney disease. It usually leads to end-stage renal failure at fiftieth decades. Here, we report a 3-year-old Chinese boy in an ADTKD family caused by a novel UMOD gene mutation. CASE PRESENTATION A 3-year-old boy was admitted to our hospital because of persistent hematuria. Urinalysis showed BLD 2+ without proteinuria. The serum levels of uric acid, creatinine and electrolytes were normal. No renal cyst or calculus was found by ultrasonography. Renal biopsy was performed and focal and segmental glomerulosclerosis was found in 4 glomeruli among 35 glomeruli examined. His father was found with end-stage renal disease (ESRD) at the age of 29, and renal ultrasound showed several cysts in both kidneys. A novel heterozygous mutation (c.1648G > A,p.V550I) in exon 8 of UMOD gene was identified by whole exome sequencing in the family. SCBC Genome Browser alignment showed that V550 were highly conserved in uromodulin among different species. Software predicted that the mutation is suspected to be harmful. By literature review, there are 12 mutations of UMOD gene in 14 Chinese families including only one pediatric case(a 16-year-old girl). CONCLUSIONS A novel heterozygous mutation (c.1648G > A,p.V550I) in exon 8 of UMOD gene was found in in a Chinese child case with ADTKD-UMOD, which extends our understanding of UMOD gene mutation spectrum and phenotype of ADTKD-UMOD in children.
2.
Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles.
Brender, JR, Zhang, Y
PLoS computational biology. 2015;(10):e1004494
Abstract
The formation of protein-protein complexes is essential for proteins to perform their physiological functions in the cell. Mutations that prevent the proper formation of the correct complexes can have serious consequences for the associated cellular processes. Since experimental determination of protein-protein binding affinity remains difficult when performed on a large scale, computational methods for predicting the consequences of mutations on binding affinity are highly desirable. We show that a scoring function based on interface structure profiles collected from analogous protein-protein interactions in the PDB is a powerful predictor of protein binding affinity changes upon mutation. As a standalone feature, the differences between the interface profile score of the mutant and wild-type proteins has an accuracy equivalent to the best all-atom potentials, despite being two orders of magnitude faster once the profile has been constructed. Due to its unique sensitivity in collecting the evolutionary profiles of analogous binding interactions and the high speed of calculation, the interface profile score has additional advantages as a complementary feature to combine with physics-based potentials for improving the accuracy of composite scoring approaches. By incorporating the sequence-derived and residue-level coarse-grained potentials with the interface structure profile score, a composite model was constructed through the random forest training, which generates a Pearson correlation coefficient >0.8 between the predicted and observed binding free-energy changes upon mutation. This accuracy is comparable to, or outperforms in most cases, the current best methods, but does not require high-resolution full-atomic models of the mutant structures. The binding interface profiling approach should find useful application in human-disease mutation recognition and protein interface design studies.
3.
A human-specific de novo protein-coding gene associated with human brain functions.
Li, CY, Zhang, Y, Wang, Z, Zhang, Y, Cao, C, Zhang, PW, Lu, SJ, Li, XM, Yu, Q, Zheng, X, et al
PLoS computational biology. 2010;(3):e1000734
Abstract
To understand whether any human-specific new genes may be associated with human brain functions, we computationally screened the genetic vulnerable factors identified through Genome-Wide Association Studies and linkage analyses of nicotine addiction and found one human-specific de novo protein-coding gene, FLJ33706 (alternative gene symbol C20orf203). Cross-species analysis revealed interesting evolutionary paths of how this gene had originated from noncoding DNA sequences: insertion of repeat elements especially Alu contributed to the formation of the first coding exon and six standard splice junctions on the branch leading to humans and chimpanzees, and two subsequent substitutions in the human lineage escaped two stop codons and created an open reading frame of 194 amino acids. We experimentally verified FLJ33706's mRNA and protein expression in the brain. Real-Time PCR in multiple tissues demonstrated that FLJ33706 was most abundantly expressed in brain. Human polymorphism data suggested that FLJ33706 encodes a protein under purifying selection. A specifically designed antibody detected its protein expression across human cortex, cerebellum and midbrain. Immunohistochemistry study in normal human brain cortex revealed the localization of FLJ33706 protein in neurons. Elevated expressions of FLJ33706 were detected in Alzheimer's brain samples, suggesting the role of this novel gene in human-specific pathogenesis of Alzheimer's disease. FLJ33706 provided the strongest evidence so far that human-specific de novo genes can have protein-coding potential and differential protein expression, and be involved in human brain functions.