Quality of life in children with Down syndrome and its association with parent and child demographic characteristics: Parent-reported measures.

BACKGROUND: This study aims to explore the association between the quality of life (QoL) in children with Down syndrome (DS) and its relationship with demographic characteristics of both parents and children. The investigation encompasses five domains: physical and psychological well-being, autonomy and parental relationship, social well-being, and peers, as well as school and the learning environment. METHOD: An online questionnaire, the KIDSCREEN-27, was used to measure the QoL of 112 families with DS in Saudi Arabia, referred to as "Parent-Reported Measures." Descriptive statistics were analyzed using the Statistical Package for Social Sciences. RESULTS: The study found that the QoL of children with DS showed high scores in the psychological well-being, autonomy, parental relations, school, and learning environment domains. However, the physical and social well-being and peer domains had lower scores, although still considered "good scores." Family income had a positively significant influence on all QoL domains. Specifically, higher family income was associated with better QoL outcomes, except for social well-being. Parental age was found to influence psychological well-being, while parental education and the relationship between the parent and child influenced social well-being. Lastly, the child's gender was found to have an impact on the school and learning environment domain. CONCLUSION: The study highlights the importance of understanding the impact of the demographic variability of children with DS and their parents on the QoL of their children. It emphasizes the need to address the needs of families with lower incomes and the importance of parental education and relationships with their children in improving social well-being. The findings could aid policymakers and healthcare providers in improving the QoL for families with children who have DS.

Identification of a de novo LRP1 mutation in a Saudi family with Tetralogy of Fallot.

BACKGROUND: Tetralogy of Fallot (TOF) is a rare, complex congenital heart defect caused by genetic and environmental interactions that results in abnormal heart development during the early stages of pregnancy. Genetic basis of TOF in Saudi populations is not yet studied. Therefore, the objective of this study is to screen for the molecular defects causing TOF in Saudi patients. METHODS: A family with non-syndromic TOF was recruited from the Western region of Saudi Arabia. Whole exome sequencing (WES) was performed on the proband and her parents. The identified candidate variant was verified by sanger sequencing. Also, different computational biology tools were used to figure out how candidate variants affect the structure and function of candidate protein involved in TOF. RESULTS: A novel heterozygous de novo mutation in LRP1 (p. G3311D) gene was identified in the index case. Also, this variant was absent in the in-house exome sequencing data of 80 healthy Saudi individuals. This variant was predicted to be likely pathogenic, as it negatively affects the biophysical chemical properties and stability of the protein. Furthermore, functional biology data from knock out mouse models confirms that molecular defects in LRP1 gene leads to cardiac defects and lethality. This variant was not previously reported in both Arab and global population genetic databases. CONCLUSION: The findings in this study postulate that the LRP1 variant has a role in TOF pathogenesis and facilitate accurate diagnosis as well as the understanding of underlying molecular mechanisms and pathophysiology of the disease.

Novel MYO1D Missense Variant Identified Through Whole Exome Sequencing and Computational Biology Analysis Expands the Spectrum of Causal Genes of Laterality Defects.

Laterality defects (LDs) or asymmetrically positioned organs are a group of rare developmental disorders caused by environmental and/or genetic factors. However, the exact molecular pathophysiology of LD is not yet fully characterised. In this context, studying Arab population presents an ideal opportunity to discover the novel molecular basis of diseases owing to the high rate of consanguinity and genetic disorders. Therefore, in the present study, we studied the molecular basis of LD in Arab patients, using next-generation sequencing method. We discovered an extremely rare novel missense variant in MYO1D gene (Pro765Ser) presenting with visceral heterotaxy and left isomerism with polysplenia syndrome. The proband in this index family has inherited this homozygous variant from her heterozygous parents following the autosomal recessive pattern. This is the first report to show MYO1D genetic variant causing left-right axis defects in humans, besides previous known evidence from zebrafish, frog and Drosophila models. Moreover, our multilevel bioinformatics-based structural (protein variant structural modelling, divergence, and stability) analysis has suggested that Ser765 causes minor structural drifts and stability changes, potentially affecting the biophysical and functional properties of MYO1D protein like calmodulin binding and microfilament motor activities. Functional bioinformatics analysis has shown that MYO1D is ubiquitously expressed across several human tissues and is reported to induce severe phenotypes in knockout mouse models. In conclusion, our findings show the expanded genetic spectrum of LD, which could potentially pave way for the novel drug target identification and development of personalised medicine for high-risk families.

Molecular differential analysis of uterine leiomyomas and leiomyosarcomas through weighted gene network and pathway tracing approaches.

Uterine smooth muscular neoplastic growths like benign leiomyomas (UL) and metastatic leiomyosarcomas (ULMS) share similar clinical symptoms, radiological and histological appearances making their clinical distinction a difficult task. Therefore, the objective of this study is to identify key genes and pathways involved in transformation of UL to ULMS through molecular differential analysis. Global gene expression profiles of 25 ULMS, 25 UL, and 29 myometrium (Myo) tissues generated on Affymetrix U133A 2.0 human genome microarrays were analyzed by deploying robust statistical, molecular interaction network, and pathway enrichment methods. The comparison of expression signals across Myo vs UL, Myo vs ULMS, and UL vs ULMS groups identified 249, 1037, and 716 significantly expressed genes, respectively (p ≤ 0.05). The analysis of 249 DEGs from Myo vs UL confirms multistage dysregulation of various key pathways in extracellular matrix, collagen, cell contact inhibition, and cytokine receptors transform normal myometrial cells to benign leiomyomas (p value ≤ 0.01). The 716 DEGs between UL vs ULMS were found to affect cell cycle, cell division related Rho GTPases and PI3K signaling pathways triggering uncontrolled growth and metastasis of tumor cells (p value ≤ 0.01). Integration of gene networking data, with additional parameters like estimation of mutation burden of tumors and cancer driver gene identification, has led to the finding of 4 hubs (JUN, VCAN, TOP2A, and COL1A1) and 8 bottleneck genes (PIK3R1, MYH11, KDR, ESR1, WT1, CCND1, EZH2, and CDKN2A), which showed a clear distinction in their distribution pattern among leiomyomas and leiomyosarcomas. This study provides vital clues for molecular distinction of UL and ULMS which could further assist in identification of specific diagnostic markers and therapeutic targets.Abbreviations UL: Uterine Leiomyomas; ULMS: Uterine Leiomyosarcoma; Myo: Myometrium; DEGs: Differential Expressed Genes; RMA: Robust Multiarray Average; DC: Degree of Centrality; BC: Betweenness of Centrality; CGC: Cancer Gene Census; FDR: False Discovery Rate; TCGA: Cancer Genome Atlas; BP: Biological Process; CC: Cellular Components; MF: Molecular Function; PPI: Protein-Protein Interaction.

Molecular modelling and dynamic simulations of sequestosome 1 (SQSTM1) missense mutations linked to Paget disease of bone.

The Paget disease (PDB; OMIM is 167250) is a chronic bone disease caused by pathogenic mutations in Sequestome1/p62 (SQSTM1) gene. This study has aimed to interpret the relationship of PDB linked SQSTM1 mutations with protein structure and its molecular dynamic features. The disease causative missense mutations were initially collected, and then analyzed for their, exonic and domain distribution, impact on secondary and tertiary structures, and their ability on protein-ligand interactions, using a combination of systems biology approaches. Our results show that most PDB linked SQSTM1 missense mutations affect amino acid residues clustered within or near the UBA domain (aa 389-434), which participates in the ubiquitination of substrates. We also report that the majority mutations occurred in α-helices over ß-strands but their effects on the secondary structure were mostly neutral. Global tertiary structure deviations were minimal; however, at amino acid residue level minor structural changes were evident. The molecular dynamics simulation analysis showed that both PB1 and UBA domains were under constant structural fluctuations resulting in closed form conformation of SQSMT1 protein structure, when it is bound to PRKCI ligand. We also found salt bridge conformation changes in the UBA domain of SQSTM1 mutants when they bound to the PRKCI interactor protein. This finding suggests the possibility that mutations in SQSTM1 could impair its ability to ubiquitinate the substrates, eventually affecting autophagy and apoptosis, especially in mature osteoclasts. This study presents the additional insight into structure and function relationship between SQSTM1 mutations and PDB pathogenesis. Communicated by Ramaswamy H. Sarma.

Dissecting the Role of NF-κb Protein Family and Its Regulators in Rheumatoid Arthritis Using Weighted Gene Co-Expression Network.

Rheumatoid arthritis (RA) is a chronic synovial autoinflammatory disease that destructs the cartilage and bone, leading to disability. The functional regulation of major immunity-related pathways like nuclear factor kappa B (NF-κB), which is involved in the chronic inflammatory reactions underlying the development of RA, remains to be explored. Therefore, this study has adopted statistical and knowledge-based systemic investigations (like gene correlation, semantic similarity, and topological parameters based on graph theory) to study the gene expression status of NF-κB protein family (NKPF) and its regulators in synovial tissues to trace the molecular pathways through which these regulators contribute to RA. A complex protein-protein interaction map (PPIM) of 2,742 genes and 37,032 interactions was constructed from differentially expressed genes (p ≤ 0.05). PPIM was further decomposed into a Regulator Allied Protein Interaction Network (RAPIN) based on the interaction between genes (5 NKPF, 31 seeds, 131 hubs, and 652 bottlenecks). Pathway network analysis has shown the RA-specific disturbances in the functional connectivity between seed genes (RIPK1, ATG7, TLR4, TNFRSF1A, KPNA1, CFLAR, SNW1, FOSB, PARVA, CX3CL1, and TRPC6) and NKPF members (RELA, RELB, NFKB2, and REL). Interestingly, these genes are known for their involvement in inflammation and immune system (signaling by interleukins, cytokine signaling in immune system, NOD-like receptor signaling, MAPK signaling, Toll-like receptor signaling, and TNF signaling) pathways connected to RA. This study, for the first time, reports that SNW1, along with other NK regulatory genes, plays an important role in RA pathogenesis and might act as potential biomarker for RA. Additionally, these genes might play important roles in RA pathogenesis, as well as facilitate the development of effective targeted therapies. Our integrative data analysis and network-based methods could accelerate the identification of novel drug targets for RA from high-throughput genomic data.