We present two half siblings with significant short stature who proved a diagnostic challenge for several years. Radiological findings included subtle epiphyseal changes. The diagnosis was made through whole genome sequencing via the 100,000 genome project. A maternally inherited pathogenic heterozygous CDKN1C variant was found in the PCNA (proliferating cell nuclear antigen) domain. Mutations of the PCNA domain of the CDKN1C gene are known to be associated with IMAGe syndrome thus with adrenal disease, although neither affected patient in our case had evidence of adrenal dysfunction. This report supports the previously reported findings of Russell–Silver syndrome (RSS) like phenotype caused by this unusual mechanism (CDKN1C mutations in the PCNA domain), highlights subtle radiological features not described previously and the phenotypic variability between two affected siblings. Additionally it reminds clinicians of the importance of considering associated adrenal disease/diabetes mellitus for variants within the PCNA domain. Finally it confirms RSS-like disorders should be considered in patients who have epiphyseal or metaphyseal changes and short stature, since CDKN1C PCNA domain mutations can result in this phenotype.

Background: Hyperornithinemia-hyperammonemia-homocitrullinuria (HHH) syndrome is an autosomal recessive inborn error of the urea cycle. It is caused by mutations in the SLC25A15 gene that codes the mitochondrial ornithine transporter. The aim of this study is to detect and identify the pathogenic SNPs in SLC25A15 gene through a combination set of bioinformatics tools and their effect on the structure and function of the protein. Methods:The deleterious SNPs in SLC25A15 are detected by various bioinformatics tools, with addition to identifying their effects on the structure and function of this gene.Results: 20 deleterious SNPs out 287of were found to have their own damaging effects on the structure and function of the SLC25A15 gene. Conclusion:This study is the first in silico analysis of SLC25A15 using a selection of bioinformatics tools to detect functional and structural effects of deleterious SNPs. Finding the pathogenic SNPs is a promising start to innovate new, useful SNP diagnostic markers for medical testing and for safer novel therapies specifically targeting mutant SLC25A15.

Background: Acute myeloid leukemia (AML) is an extremely heterogeneous malignant disorder; AML has been reported as one of the main causes of death in children. The objective of this work was to classify the most deleterious mutation in CCAAT/enhancer-binding protein-alpha ( CEBPA) and to predict their influence on the functional, structural, and expression levels by various Bioinformatics analysis tools. Methods: The single nucleotide polymorphisms (SNPs) were claimed from the National Center for Biotechnology Information (NCBI) database and then submitted into various functional analysis tools, which were done to predict the influence of each SNP, followed by structural analysis of modeled protein followed by predicting the mutation effect on energy stability; the most damaging mutations were chosen for additional investigation by Mutation3D, Project hope, ConSurf, BioEdit, and UCSF Chimera tools. Results: A total of 5 mutations out of 248 were likely to be responsible for the structural and functional variations in CEBPA protein, whereas in the 3′-untranslated region (3′-UTR) the result showed that among 350 SNPs in the 3′-UTR of CEBPA gene, about 11 SNPs were predicted. Among these 11 SNPs, 65 alleles disrupted a conserved miRNA site and 22 derived alleles created a new site of miRNA. Conclusions: In this study, the impact of functional mutations in the CEBPA gene was investigated through different bioinformatics analysis techniques, which determined that R339W, R288P, N292S, N292T, and D63N are pathogenic mutations that have a possible functional and structural influence, therefore, could be used as genetic biomarkers and may assist in genetic studies with a special consideration of the large heterogeneity of AML.

The Simpson-Golabi-Behmel Syndrome (SGBS) or overgrowth Syndrome is an uncommon genetic X-linked disorder highlighted by macrosomia, renal defects, cardiac weaknesses and skeletal abnormalities. The purpose of the work was to classify the functional nsSNPs of GPC3 to serve as genetic biomarkers for overgrowth syndrome. The raw data of GPC3 gene were retrieved from dbSNP database and used to examine the most damaging effect using eight functional analysis tools, while we used I-mutant and MUPro to examine the effect of SNPs on GPC3 protein structure; The 3D structure of GPC3 protein is not found in the PDB, so RaptorX was used to create a 3D structural prototype to visualize the amino acids alterations by UCSF Chimera; For biophysical validation we used project HOPE; Lastly we run conservational analysis by BioEdit and Consurf web server respectively. Our results revealed three novel missense mutations (rs1460413167, rs1295603457 and rs757475450) that are that are more likely to be responsible for disturbance in the function and structure of GPC3. This work provides new insight into the molecular basis of overgrowth Syndrome by evidence from bioinformatics analysis. Three novel missense mutations (rs757475450, rs1295603457 and rs1460413167) are more likely to be responsible for disturbance in the function and structure of GPC3; therefore, they may be assisting as genetic biomarkers for overgrowth syndrome. As well as these SNPs can be used for the larger population-based studies of overgrowth syndrome.

Background:The Simpson- Golabi-Behmel Syndrome (SGBS) or overgrowth Syndrome is a rare inherited X-linked condition characterized by pre-and postnatal overgrowth. The aim of the present study is to identify functional non-synonymous SNPs of GPC3 gene using various in silico approaches. These SNPs are supposed to have a direct effect on protein stability through conformation changes. Material and methods: The SNPs were retrieved from the Single Nucleotide Polymorphism database (dbSNP) and further used to investigate a damaging effect using SIFT, PolyPhen, PROVEAN, SNAP2, SNPs&GO, PHD-SNP and P-mut, While we used I-mutant and MUPro to study the effect of SNPs on GPC3 protein structure. The 3D structure of human GPC3 protein is not available in the Protein Data Bank, so we used RaptorX to generate a 3D structural model for wild-type GPC3 to visualize the amino acids changes by UCSF Chimera. For biophysical validation we used project HOPE. Lastly we run conservational analysis by BioEdit and Consurf web server respectively. Results: our results revealed three novel missense mutations (rs1460413167, rs1295603457 and rs757475450) that are found to be the most deleterious which effect on the GPC3 structure and function. Conclusion: This present study could provide a novel insight into the molecular basis of overgrowth Syndrome.