👤 Ghassan M Sulaiman

Alzheimer's disease (AD) is the most common neurodegenerative disease associated with the development of dementia. The hallmarks of AD neuropathology are accumulations of amyloid peptide (Aβ) and neurofibrillary tangles (NFTs). Aβ is derived from the processing of APP (amyloid beta precursor protein) by BACE1 (beta-secretase 1) and γ-secretase through an amyloidogenic pathway. However, processing of APP by ADAM10/α-secretase (ADAM metallopeptidase domain 10) enzymes through a non-amyloidogenic pathway produces soluble APP alpha (sAPPα), which has a neuroprotective effect. It has been shown that activated platelets are implicated in the pathogenesis of AD, which also increases platelet activation. Under physiological conditions, platelets regulate synaptic plasticity and increase neuronal differentiation by regulation of the inflammatory response. However, overactivated platelets contribute to the pathogenesis of AD. Activated platelets represent the main source of circulating APP and Aβ that may be involved in AD neuropathology. Therefore, there is a close relationship between AD neuropathology and activated platelets. This review discusses the potential role of platelets in the pathogenesis of AD, and how targeting of activated platelets may reduce AD neuropathology. Show less

The United Arab Emirates National Diabetes and Lifestyle Study (UAEDIAB) has identified obesity, hypertension, obstructive sleep apnea, and dyslipidemia as common phenotypic characteristics correlated with diabetes mellitus status. As these phenotypes are usually linked with genetic variants, we hypothesized that these phenotypes share single nucleotide polymorphism (SNP)-clusters that can be used to identify causal genes for diabetes. Materials and We explored the National Human Genome Research Institute-European Bioinformatics Institute Catalog of Published Genome-Wide Association Studies (NHGRI-EBI GWAS) to list SNPs with documented association with the UAEDIAB-phenotypes as well as diabetes. The shared chromosomal regions affected by SNPs were identified, intersected, and searched for Enriched Ontology Clustering. The potential SNP-clusters were validated using targeted DNA next-generation sequencing (NGS) in two Emirati diabetic patients. RNA sequencing from human pancreatic islets was used to study the expression of identified genes in diabetic and non-diabetic donors. Eight chromosomal regions containing 46 SNPs were identified in at least four out of the five UAEDIAB-phenotypes. A list of 34 genes was shown to be affected by those SNPs. Targeted NGS from two Emirati patients confirmed that the identified genes have similar SNP-clusters. Show less

Type 2 diabetes (T2D) is a complex polygenic disease with unclear mechanism. In an attempt to identify novel genes involved in β-cell function, we harness a bioinformatics method called Loss-of-function tool (LoFtool) gene score. RNA-sequencing data from human islets were used to cross-reference genes within the 1st quartile of most intolerant LoFtool score with the 100th most expressed genes in human islets. Out of these genes, GNAS and EEF1A1 genes were selected for further investigation in diabetic islets, metabolic tissues along with their correlation with diabetic phenotypes. The influence of GNAS and EEF1A1 on insulin secretion and β-cell function were validated in INS-1 cells. A comparatively higher expression level of GNAS and EEF1A1 was observed in human islets than fat, liver and muscle tissues. Furthermore, diabetic islets displayed a reduced expression of GNAS, but not of EEF1A, compared to non-diabetic islets. The expression of GNAS was positively correlated with insulin secretory index, GLP1R, GIPR and inversely correlated with HbA LoFtool is a powerful tool to identify genes associated with pancreatic islets dysfunction. GNAS is a crucial gene for the β-cell insulin secretory capacity. Show less

In this study, we report the experience of the only reference clinical next-generation sequencing lab in Saudi Arabia with the first 1000 families who span a wide-range of suspected Mendelian phenotypes. A total of 1019 tests were performed in the period of March 2016-December 2016 comprising 972 solo (index only), 14 duo (parents or affected siblings only), and 33 trio (index and parents). Multigene panels accounted for 672 tests, while whole exome sequencing (WES) represented the remaining 347 tests. Pathogenic or likely pathogenic variants that explain the clinical indications were identified in 34% (27% in panels and 43% in exomes), spanning 279 genes and including 165 novel variants. While recessive mutations dominated the landscape of solved cases (71% of mutations, and 97% of which are homozygous), a substantial minority (27%) were solved on the basis of dominant mutations. The highly consanguineous nature of the study population also facilitated homozygosity for many private mutations (only 32.5% of the recessive mutations are founder), as well as the first instances of recessive inheritance of previously assumed strictly dominant disorders (involving ITPR1, VAMP1, MCTP2, and TBP). Surprisingly, however, dual molecular diagnosis was only observed in 1.5% of cases. Finally, we have encountered candidate variants in 75 genes (ABHD6, ACY3, ADGRB2, ADGRG7, AGTPBP1, AHNAK2, AKAP6, ASB3, ATXN1L, C17orf62, CABP1, CCDC186, CCP110, CLSTN2, CNTN3, CNTN5, CTNNA2, CWC22, DMAP1, DMKN, DMXL1, DSCAM, DVL2, ECI1, EP400, EPB41L5, FBXL22, GAP43, GEMIN7, GIT1, GRIK4, GRSF1, GTRP1, HID1, IFNL1, KCNC4, LRRC52, MAP7D3, MCTP2, MED26, MPP7, MRPS35, MTDH, MTMR9, NECAP2, NPAT, NRAP, PAX7, PCNX, PLCH2, PLEKHF1, PTPN12, QKI, RILPL2, RIMKLA, RIMS2, RNF213, ROBO1, SEC16A, SIAH1, SIRT2, SLAIN2, SLC22A20, SMDT1, SRRT, SSTR1, ST20, SYT9, TSPAN6, UBR4, VAMP4, VPS36, WDR59, WDYHV1, and WHSC1) not previously linked to human phenotypes and these are presented to accelerate post-publication matchmaking. Two of these genes were independently mutated in more than one family with similar phenotypes, which substantiates their link to human disease (AKAP6 in intellectual disability and UBR4 in early dementia). If the novel candidate disease genes in this cohort are independently confirmed, the yield of WES will have increased to 83%, which suggests that most "negative" clinical exome tests are unsolved due to interpretation rather than technical limitations. Show less