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Childhood onset clinical syndromes involving intellectual disability and dysmorphic features, such as polydactyly, suggest common developmental pathways link seemingly unrelated phenotypes. We identified a consanguineous family of Saudi origin with varying complex features including intellectual disability, speech delay, facial dysmorphism and polydactyly. Combining, microarray based comparative genomic hybridisation (CGH) to identify regions of homozygosity, with exome sequencing, led to the identification of homozygous mutations in five candidate genes (RSPH6A, ANKK1, AMOTL1, ALKBH8, TRAPPC6A), all of which appear to be pathogenic as predicted by Proven, SIFT and PolyPhen2 and segregate perfectly with the disease phenotype. We therefore looked for differences in expression levels of each protein in HEK293 cells, expressing either the wild-type or mutant full-length cDNA construct. Unexpectedly, wild-type TRAPPC6A appeared to be unstable, but addition of the proteasome inhibitor MG132 stabilised its expression. Mutations have previously been reported in several members of the TRAPP complex of proteins, including TRAPPC2, TRAPPC9 and TRAPPC11, resulting in disorders involving skeletal abnormalities, intellectual disability, speech impairment and developmental delay. TRAPPC6A joins a growing list of proteins belonging to the TRAPP complex, implicated in clinical syndromes with neurodevelopmental abnormalities.

Background The spread of a novel severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) has affected both the public health and the global economy. The current study was aimed at analysing the genetic sequence of this highly contagious corona virus from an evolutionary perspective, comparing the genetic variation features of different geographic strains, and identifying the key miRNAs as well as their gene targets from the transcriptome data of viral infected lung tissues. Methods A multilevel robust computational analysis was undertaken for viral genetic sequence alignment, phylogram construction, genome-wide transcriptome data interpretation of virus-infected lung tissues, miRNA mapping, and functional biology networking. Results Our findings show both genetic similarities as well as notable differences in the S protein length among SARS-CoV-1, SARS-CoV-2 and MERS viruses. All SARS-CoV-2 strains showed a high genetic similarity with the parent Wuhan strain, but Saudi Arabian, South African, USA, Russia and New Zealand strains carry 3 additional genetic variations like P333L (RNA -dependant RNA polymerase), D614G (spike), and P4715L (ORF1ab). The infected lung tissues demonstrated the upregulation of 282 (56.51%) antiviral defensive response pathway genes and downregulation of 217 (43.48%) genes involved in autophagy and lung repair pathways. By miRNA mapping, 4 key miRNAs ( hsa-miR-342-5p, hsa-miR-432-5p, hsa-miR-98-5p and hsa-miR-17-5p ), targeting multiple host genes ( MYC, IL6, ICAM1 and VEGFA ) as well as SARS-CoV2 gene ( ORF1ab ) were identified. Conclusion Systems biology methods offer a new perspective in understanding the molecular reasons underlying the faster spread of SARS-CoV-2 infection. The antiviral miRNAs identified in this study may aid in the ongoing search for novel personalized therapeutic avenues for COVID patients.

BackgroundHereditary spastic paraplegias (HSP), a group of genetically heterogeneous neurological disorders with more than 56 documented loci (SPG1-56), are described either as uncomplicated (or pure), or complicated where in addition to spasticity and weakness of lower extremeties, additional neurological symptoms are present, including dementia, loss of vision, epilepsy, mental retardation and ichthyosis. We identified a large consanguineous family of Indian descent with four affected members with childhood onset HSP (SPG54), presenting with upper and lower limb spasticity, mental retardation and agenesis of the corpus callosum.ResultsA common region of homozygosity on chromosome 8 spanning seven megabases (Mb) was identified in the affected individuals using the Illumina human cytoSNP-12 DNA Analysis BeadChip Kit. Exome sequencing identified a homozygous stop gain mutation (pR287X) in the phospholipase A1 gene DDHD2, in the affected individuals, resulting in a premature stop codon and a severely truncated protein lacking the SAM and DDHD domains crucial for phosphoinositide binding and phospholipase activity.ConclusionThis mutation adds to the knowledge of HSP, suggests a possible founder effect for the pR287X mutation, and adds to the list of genes involved in lipid metabolism with a role in HSP and other neurodegenerative disorders.Electronic supplementary materialThe online version of this article (doi:10.1186/s13104-015-1227-4) contains supplementary material, which is available to authorized users.

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<abstract> <p>Obesity and type 2 and diabetes mellitus (T2D) are two dual epidemics whose shared genetic pathological mechanisms are still far from being fully understood. Therefore, this study is aimed at discovering key genes, molecular mechanisms, and new drug targets for obesity and T2D by analyzing the genome wide gene expression data with different computational biology approaches. In this study, the RNA-sequencing data of isolated primary human adipocytes from individuals who are lean, obese, and T2D was analyzed by an integrated framework consisting of gene expression, protein interaction network (PIN), tissue specificity, and druggability approaches. Our findings show a total of 1932 unique differentially expressed genes (DEGs) across the diabetes versus obese group comparison (p≤0.05). The PIN analysis of these 1932 DEGs identified 190 high centrality network (HCN) genes, which were annotated against 3367 GO terms and functional pathways, like response to insulin signaling, phosphorylation, lipid metabolism, glucose metabolism, etc. (p≤0.05). By applying additional PIN and topological parameters to 190 HCN genes, we further mapped 25 high confidence genes, functionally connected with diabetes and obesity traits. Interestingly, <italic>ERBB2, FN1, FYN, HSPA1A, HBA1</italic>, and <italic>ITGB1</italic> genes were found to be tractable by small chemicals, antibodies, and/or enzyme molecules. In conclusion, our study highlights the potential of computational biology methods in correlating expression data to topological parameters, functional relationships, and druggability characteristics of the candidate genes involved in complex metabolic disorders with a common etiological basis.</p> </abstract>

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Background Myocardial infarction (MI) is the most prevalent coronary atherosclerotic heart disease caused by the complex molecular interactions between multiple genes and environment. Molecular exploration of gene expression changes in MI patients is very crucial not just to understand the molecular basis of disease development but also to identify potential therapeutic targets. Therefore, we aim to identify potential biomarkers for the disease development mechanisms and for prognosis of MI using extensive integrated biological network analysis. Methodology Gene expression datasets (GSE66360) generated from 51 healthy controls and 49 endothelial cell samples from patients experiencing acute MI were used to analyze the differentially expressed genes (DEG), protein-protein interactions (PPI), gene network-clusters to annotate the candidate pathways relevant to MI pathogenesis. Results Bioinformatic analysis revealed 810 DEGs, between control and MI samples, with 574 up-and 236 downregulated genes. Their functional annotations with Gene Ontology (GO) has captured several MI targeting biological processes like immune response, in ammation and platelets degranulation. Most signi cantly DEGs enriched KEGG pathways are related to the following functions: Cytokine-cytokine receptor interaction, TNF and NFkB signaling. By constructing the PPI network using STRING and CytoHubba, seventeen hub and bottleneck genes were found, whose involvement in MI was further con rmed by DisGeNET data. Search in the Open Target Platform reveal unique bottleneck genes as potential target for MI. Conclusion Our integrative bioinformatics analysis of large-scale gene expression data has identi ed several potential genetic biomarkers associated with early stage MI providing a new insight into molecular mechanism underlying the disease.