The genetic landscape of autism spectrum disorder in the Middle Eastern population.

Introduction: Autism spectrum disorder (ASD) is characterized by aberrations in social interaction and communication associated with repetitive behaviors and interests, with strong clinical heterogeneity. Genetic factors play an important role in ASD, but about 75% of ASD cases have an undetermined genetic risk. Methods: We extensively investigated an ASD cohort made of 102 families from the Middle Eastern population of Qatar. First, we investigated the copy number variations (CNV) contribution using genome-wide SNP arrays. Next, we employed Next Generation Sequencing (NGS) to identify de novo or inherited variants contributing to the ASD etiology and its associated comorbid conditions in families with complete trios (affected child and the parents). Results: Our analysis revealed 16 CNV regions located in genomic regions implicated in ASD. The analysis of the 88 ASD cases identified 41 genes in 39 ASD subjects with de novo (n = 24) or inherited variants (n = 22). We identified three novel de novo variants in new candidate genes for ASD (DTX4, ARMC6, and B3GNT3). Also, we have identified 15 de novo variants in genes that were previously implicated in ASD or related neurodevelopmental disorders (PHF21A, WASF1, TCF20, DEAF1, MED13, CREBBP, KDM6B, SMURF1, ADNP, CACNA1G, MYT1L, KIF13B, GRIA2, CHM, and KCNK9). Additionally, we defined eight novel recessive variants (RYR2, DNAH3, TSPYL2, UPF3B KDM5C, LYST, and WNK3), four of which were X-linked. Conclusion: Despite the ASD multifactorial etiology that hinders ASD genetic risk discovery, the number of identified novel or known putative ASD genetic variants was appreciable. Nevertheless, this study represents the first comprehensive characterization of ASD genetic risk in Qatar's Middle Eastern population.

Genome-wide association study and trans-ethnic meta-analysis identify novel susceptibility loci for type 2 diabetes mellitus.

BACKGROUND: The genetic basis of type 2 diabetes (T2D) is under-investigated in the Middle East, despite the rapidly growing disease prevalence. We aimed to define the genetic determinants of T2D in Qatar. METHODS: Using whole genome sequencing of 11,436 participants (2765 T2D cases and 8671 controls) from the population-based Qatar Biobank (QBB), we conducted a genome-wide association study (GWAS) of T2D with and without body mass index (BMI) adjustment. RESULTS: We replicated 93 known T2D-associated loci in a BMI-unadjusted model, while 96 known loci were replicated in a BMI-adjusted model. The effect sizes and allele frequencies of replicated SNPs in the Qatari population generally concurred with those from European populations. We identified a locus specific to our cohort located between the APOBEC3H and CBX7 genes in the BMI-unadjusted model. Also, we performed a transethnic meta-analysis of our cohort with a previous GWAS on T2D in multi-ancestry individuals (180,834 T2D cases and 1,159,055 controls). One locus in DYNC2H1 gene reached genome-wide significance in the meta-analysis. Assessing polygenic risk scores derived from European- and multi-ancestries in the Qatari population showed higher predictive performance of the multi-ancestry panel compared to the European panel. CONCLUSION: Our study provides new insights into the genetic architecture of T2D in a Middle Eastern population and identifies genes that may be explored further for their involvement in T2D pathogenesis.

Untargeted Metabolomic Profiling Reveals Differentially Expressed Serum Metabolites and Pathways in Type 2 Diabetes Patients with and without Cognitive Decline: A Cross-Sectional Study.

Diabetes is recognized as a risk factor for cognitive decline, but the underlying mechanisms remain elusive. We aimed to identify the metabolic pathways altered in diabetes-associated cognitive decline (DACD) using untargeted metabolomics. We conducted liquid chromatography-mass spectrometry-based untargeted metabolomics to profile serum metabolite levels in 100 patients with type 2 diabetes (T2D) (54 without and 46 with DACD). Multivariate statistical tools were used to identify the differentially expressed metabolites (DEMs), and enrichment and pathways analyses were used to identify the signaling pathways associated with the DEMs. The receiver operating characteristic (ROC) analysis was employed to assess the diagnostic accuracy of a set of metabolites. We identified twenty DEMs, seven up- and thirteen downregulated in the DACD vs. DM group. Chemometric analysis revealed distinct clustering between the two groups. Metabolite set enrichment analysis found significant enrichment in various metabolite sets, including galactose metabolism, arginine and unsaturated fatty acid biosynthesis, citrate cycle, fructose and mannose, alanine, aspartate, and glutamate metabolism. Pathway analysis identified six significantly altered pathways, including arginine and unsaturated fatty acid biosynthesis, and the metabolism of the citrate cycle, alanine, aspartate, glutamate, a-linolenic acid, and glycerophospholipids. Classifier models with AUC-ROC > 90% were developed using individual metabolites or a combination of individual metabolites and metabolite ratios. Our study provides evidence of perturbations in multiple metabolic pathways in patients with DACD. The distinct DEMs identified in this study hold promise as diagnostic biomarkers for DACD patients.

Profiling the autoantibody repertoire reveals autoantibodies associated with mild cognitive impairment and dementia.

Background: Dementia is a debilitating neurological disease affecting millions of people worldwide. The exact mechanisms underlying the initiation and progression of the disease remain to be fully defined. There is an increasing body of evidence for the role of immune dysregulation in the pathogenesis of dementia, where blood-borne autoimmune antibodies have been studied as potential markers associated with pathological mechanisms of dementia. Methods: This study included plasma from 50 cognitively normal individuals, 55 subjects with MCI (mild cognitive impairment), and 22 subjects with dementia. Autoantibody profiling for more than 1,600 antigens was performed using a high throughput microarray platform to identify differentially expressed autoantibodies in MCI and dementia. Results: The differential expression analysis identified 33 significantly altered autoantibodies in the plasma of patients with dementia compared to cognitively normal subjects, and 38 significantly altered autoantibodies in the plasma of patients with dementia compared to subjects with MCI. And 20 proteins had significantly altered autoantibody responses in MCI compared to cognitively normal individuals. Five autoantibodies were commonly dysregulated in both dementia and MCI, including anti-CAMK2A, CKS1B, ETS2, MAP4, and NUDT2. Plasma levels of anti-ODF3, E6, S100P, and ARHGDIG correlated negatively with the cognitive performance scores (MoCA) (r2 -0.56 to -0.42, value of p < 0.001). Additionally, several proteins targeted by autoantibodies dysregulated in dementia were significantly enriched in the neurotrophin signaling pathway, axon guidance, cholinergic synapse, long-term potentiation, apoptosis, glycolysis and gluconeogenesis. Conclusion: We have shown multiple dysregulated autoantibodies in the plasma of subjects with MCI and dementia. The corresponding proteins for these autoantibodies are involved in neurodegenerative pathways, suggesting a potential impact of autoimmunity on the etiology of dementia and the possible benefit for future therapeutic approaches. Further investigations are warranted to validate our findings.

High-throughput autoantibody screening identifies differentially abundant autoantibodies in autism spectrum disorder.

Introduction: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by defects in two core domains, social/communication skills and restricted/repetitive behaviors or interests. There is no approved biomarker for ASD diagnosis, and the current diagnostic method is based on clinical manifestation, which tends to vary vastly between the affected individuals due to the heterogeneous nature of ASD. There is emerging evidence that supports the implication of the immune system in ASD, specifically autoimmunity; however, the role of autoantibodies in ASD children is not yet fully understood. Materials and methods: In this study, we screened serum samples from 93 cases with ASD and 28 healthy controls utilizing high-throughput KoRectly Expressed (KREX) i-Ome protein-array technology. Our goal was to identify autoantibodies with differential expressions in ASD and to gain insights into the biological significance of these autoantibodies in the context of ASD pathogenesis. Result: Our autoantibody expression analysis identified 29 differential autoantibodies in ASD, 4 of which were upregulated and 25 downregulated. Subsequently, gene ontology (GO) and network analysis showed that the proteins of these autoantibodies are expressed in the brain and involved in axonal guidance, chromatin binding, and multiple metabolic pathways. Correlation analysis revealed that these autoantibodies negatively correlate with the age of ASD subjects. Conclusion: This study explored autoantibody reactivity against self-antigens in ASD individuals' serum using a high-throughput assay. The identified autoantibodies were reactive against proteins involved in axonal guidance, synaptic function, amino acid metabolism, fatty acid metabolism, and chromatin binding.

Genetic determinants of Vitamin D deficiency in the Middle Eastern Qatari population: a genome-wide association study.

Introduction: Epidemiological studies have consistently revealed that Vitamin D deficiency is most prevalent in Middle Eastern countries. However, research on the impact of genetic loci and polygenic models related to Vitamin D has primarily focused on European populations. Methods: We conducted the first genome-wide association study to identify genetic determinants of Vitamin D levels in Middle Easterners using a whole genome sequencing approach in 6,047 subjects from the Qatar Biobank (QBB) project. We performed a GWAS meta-analysis, combining the QBB cohort with recent European GWAS data from the UK Biobank (involving 345,923 individuals). Additionally, we evaluated the performance of European-derived polygenic risk scores using UK Biobank data in the QBB cohort. Results: Our study identified an association between a variant in a known locus for the group-specific component gene (GC), specifically rs2298850 (p-value = 1.71 × 10-08, Beta = -0.1285), and Vitamin D levels. Furthermore, our GWAS meta-analysis identified two novel variants at a known locus on chromosome 11, rs67609747 and rs1945603, that reached the GWAS significance threshold. Notably, we observed a moderately high heritability of Vitamin D, estimated at 18%, compared to Europeans. Despite the lower predictive performance of Vitamin D levels in Qataris compared to Europeans, the European-derived polygenic risk scores exhibited significant links to Vitamin D deficiency risk within the QBB cohort. Conclusion: This novel study reveals the genetic architecture contributing to Vitamin D deficiency in the Qatari population, emphasizing the genetic heterogeneity across different populations.

Integrated epigenome, whole genome sequence and metabolome analyses identify novel multi-omics pathways in type 2 diabetes: a Middle Eastern study.

BACKGROUND: T2D is of high prevalence in the middle east and thus studying its mechanisms is of a significant importance. Using 1026 Qatar BioBank samples, epigenetics, whole genome sequencing and metabolomics were combined to further elucidate the biological mechanisms of T2D in a population with a high prevalence of T2D. METHODS: An epigenome-wide association study (EWAS) with T2D was performed using the Infinium 850K EPIC array, followed by whole genome-wide sequencing SNP-CpG association analysis (> 5.5 million SNPs) and a methylome-metabolome (CpG-metabolite) analysis of the identified T2D sites. RESULTS: A total of 66 T2D-CpG associations were identified, including 63 novel sites in pathways of fructose and mannose metabolism, insulin signaling, galactose, starch and sucrose metabolism, and carbohydrate absorption and digestion. Whole genome SNP associations with the 66 CpGs resulted in 688 significant CpG-SNP associations comprising 22 unique CpGs (33% of the 66 CPGs) and included 181 novel pairs or pairs in novel loci. Fourteen of the loci overlapped published GWAS loci for diabetes related traits and were used to identify causal associations of HK1 and PFKFB2 with HbA1c. Methylome-metabolome analysis identified 66 significant CpG-metabolite pairs among which 61 pairs were novel. Using the identified methylome-metabolome associations, methylation QTLs, and metabolic networks, a multi-omics network was constructed which suggested a number of metabolic mechanisms underlying T2D methylated genes. 1-palmitoyl-2-oleoyl-GPE (16:0/18:1) - a triglyceride-associated metabolite, shared a common network with 13 methylated CpGs, including TXNIP, PFKFB2, OCIAD1, and BLCAP. Mannonate - a food component/plant shared a common network with 6 methylated genes, including TXNIP, BLCAP, THBS4 and PEF1, pointing to a common possible cause of methylation in those genes. A subnetwork with alanine, glutamine, urea cycle (citrulline, arginine), and 1-carboxyethylvaline linked to PFKFB2 and TXNIP revealed associations with kidney function, hypertension and triglyceride metabolism. The pathway containing STYXL1-POR was associated with a sphingosine-ceramides subnetwork associated with HDL-C and LDL-C and point to steroid perturbations in T2D. CONCLUSIONS: This study revealed several novel methylated genes in T2D, with their genomic variants and associated metabolic pathways with several implications for future clinical use of multi-omics associations in disease and for studying therapeutic targets.

Genome-wide association study identifies several loci for HEV seropositivity.

Hepatitis E viral (HEV) infection imposes a heavy global health burden. The variability in the prevalence of serological markers of HEV infection between different ethnic groups proposes a host genetic influence. Here, we report genetic polymorphisms associated with anti-HEV antibody positivity and level using binary- and quantitative-trait genome-wide association studies (GWAS) on a population from Qatar (n = 5829). We identified a region in 12p11.1 (lead SNP: rs559856097, allele: A, p = 2.3 × 10-10) significantly associated with anti-HEV antibodies level. This intergenic variant is located near SNORD112, a small nucleolar RNA (snoRNA). Additional gene-set and pathway enrichment analyses highlighted a strong correlation with anti-viral response-related pathways, including IFNs (alpha/beta) and interleukin-21 (IL-21) signaling. This is the first GWAS on the response to HEV infection. Further replication and functional experimentation are warranted to validate these findings.

The Genetic Architecture of Vitamin D Deficiency among an Elderly Lebanese Middle Eastern Population: An Exome-Wide Association Study.

The Middle East region experiences a high prevalence of vitamin D deficiency, yet most genetic studies on vitamin D have focused on European populations. Furthermore, there is a lack of research on the genomic risk factors affecting elderly people, who are more susceptible to health burdens. We investigated the genetic determinants of 25-hydroxyvitamin D concentrations in elderly Lebanese individuals (n = 199) through a whole-exome-based genome-wide association study. Novel genomic loci displaying suggestive evidence of association with 25-hydroxyvitamin D levels were identified in our study, including rs141064014 in the MGAM (p-value of 4.40 × 10-6) and rs7036592 in PHF2 (p-value of 8.43 × 10-6). A meta-analysis of the Lebanese data and the largest European genome-wide association study confirmed consistency replication of numerous variants, including rs2725405 in SLC38A10 (p-value of 3.73 × 10-8). Although the polygenic risk score model derived from European populations exhibited lower performance than European estimations, it still effectively predicted vitamin D deficiency among our cohort. Our discoveries offer novel perspectives on the genetic mechanisms underlying vitamin D deficiency among elderly Middle Eastern populations, facilitating the development of personalized approaches for more effective management of vitamin D deficiency. Additionally, we demonstrated that whole-exome-based genome-wide association study is an effective method for identifying genetic components associated with phenotypes.

Transcriptome profiling and network enrichment analyses identify subtype-specific therapeutic gene targets for breast cancer and their microRNA regulatory networks.

Previous studies have suggested that breast cancer (BC) from the Middle East and North Africa (MENA) is presented at younger age with advanced tumor stage, indicating underlying biological differences. Given the scant transcriptomic data on BC from the MENA region and to better understand the biology of this disease, we performed mRNA and microRNA (miRNA) transcriptomic profiling on a local cohort of BC (n = 96) from Qatar. Our data revealed the differentially expressed genes and miRNAs as function of BC molecular subtypes (HR+, HER2+, HER2+HR+, and TNBC), tumor grade (GIII vs GI-II), patients' age (young (≤40) vs old (>40)), and ethnicity (MENA vs non-MENA). Our profiling data revealed close similarity between TNBC and HER2+, while the transcriptome of HER2+HR+ tumor was resemblant of that from HR+ tumors. Network analysis identified complex miRNA-mRNA regulatory networks in each BC molecular subtype, in high vs low grade tumors, in tumors from young vs old patients, and in tumors from MENA vs non-MENA, thus implicating miRNA-mediated gene regulation as an essential mechanism in shaping the transcriptome of BC. Integration of our transcriptomic data with CRISPR-Cas9 functional screen data and the OncoKB database identified numerous dependencies and therapeutic vulnerabilities in each BC molecular subtype, while CDC123 was functionally validated as potential therapeutic target for TNBC. Cox regression survival analyses identified mRNA and miRNA-based signatures predicative of worse and better relapse free survival (RFS), which were validated in larger BC cohorts. Our data provides comprehensive transcriptomic profiling and unraveled the miRNA-mRNA regulatory networks in BC patients from the region and identified novel actionable gene targets, employing integrated approach. Findings from the current study have potential implications to improve the current standard-of-care for BC from the MENA as well as patients from other ethnicities.

Blood-Based Proteomic Profiling Identifies Potential Biomarker Candidates and Pathogenic Pathways in Dementia.

Dementia is a progressive and debilitating neurological disease that affects millions of people worldwide. Identifying the minimally invasive biomarkers associated with dementia that could provide insights into the disease pathogenesis, improve early diagnosis, and facilitate the development of effective treatments is pressing. Proteomic studies have emerged as a promising approach for identifying the protein biomarkers associated with dementia. This pilot study aimed to investigate the plasma proteome profile and identify a panel of various protein biomarkers for dementia. We used a high-throughput proximity extension immunoassay to quantify 1090 proteins in 122 participants (22 with dementia, 64 with mild cognitive impairment (MCI), and 36 controls with normal cognitive function). Limma-based differential expression analysis reported the dysregulation of 61 proteins in the plasma of those with dementia compared with controls, and machine learning algorithms identified 17 stable diagnostic biomarkers that differentiated individuals with AUC = 0.98 ± 0.02. There was also the dysregulation of 153 plasma proteins in individuals with dementia compared with those with MCI, and machine learning algorithms identified 8 biomarkers that classified dementia from MCI with an AUC of 0.87 ± 0.07. Moreover, multiple proteins selected in both diagnostic panels such as NEFL, IL17D, WNT9A, and PGF were negatively correlated with cognitive performance, with a correlation coefficient (r2) ≤ -0.47. Gene Ontology (GO) and pathway analysis of dementia-associated proteins implicated immune response, vascular injury, and extracellular matrix organization pathways in dementia pathogenesis. In conclusion, the combination of high-throughput proteomics and machine learning enabled us to identify a blood-based protein signature capable of potentially differentiating dementia from MCI and cognitively normal controls. Further research is required to validate these biomarkers and investigate the potential underlying mechanisms for the development of dementia.

Blood Proteomics Analysis Reveals Potential Biomarkers and Convergent Dysregulated Pathways in Autism Spectrum Disorder: A Pilot Study.

Autism spectrum disorder (ASD) is an umbrella term that encompasses several disabling neurodevelopmental conditions. These conditions are characterized by impaired manifestation in social and communication skills with repetitive and restrictive behaviors or interests. Thus far, there are no approved biomarkers for ASD screening and diagnosis; also, the current diagnosis depends heavily on a physician's assessment and family's awareness of ASD symptoms. Identifying blood proteomic biomarkers and performing deep blood proteome profiling could highlight common underlying dysfunctions between cases of ASD, given its heterogeneous nature, thus laying the foundation for large-scale blood-based biomarker discovery studies. This study measured the expression of 1196 serum proteins using proximity extension assay (PEA) technology. The screened serum samples included ASD cases (n = 91) and healthy controls (n = 30) between 6 and 15 years of age. Our findings revealed 251 differentially expressed proteins between ASD and healthy controls, of which 237 proteins were significantly upregulated and 14 proteins were significantly downregulated. Machine learning analysis identified 15 proteins that could be biomarkers for ASD with an area under the curve (AUC) = 0.876 using support vector machine (SVM). Gene Ontology (GO) analysis of the top differentially expressed proteins (TopDE) and weighted gene co-expression analysis (WGCNA) revealed dysregulation of SNARE vesicular transport and ErbB pathways in ASD cases. Furthermore, correlation analysis showed that proteins from those pathways correlate with ASD severity. Further validation and verification of the identified biomarkers and pathways are warranted.

Genome-wide association study identifies genetic variants which predict the response of bone mineral density to teriparatide therapy.

OBJECTIVES: Teriparatide (TPTD) is an effective treatment for osteoporosis but the individual response to therapy is variable for reasons that are unclear. This study aimed to determine whether the response to TPTD might be influenced by genetic factors. METHODS: We searched for predictors of the response of bone mineral density (BMD) to TPTD using a two-stage genome-wide association study in 437 patients with osteoporosis from three referral centres. Demographic and clinical data including the response of BMD to treatment at the lumbar spine and hip were extracted from the medical records of each participant. RESULTS: Allelic variation at rs6430612 on chromosome 2, close to the CXCR4 gene was associated with the response of spine BMD to TPTD at a genome wide significant level (p=9.2×10-9 beta=-0.35 (-0.47 to -0.23)). The increase in BMD was almost twice as great in AA homozygotes at rs6430612 as compared with GG homozygotes with intermediate values in heterozygotes. The same variant was also associated with response of femoral neck and total hip BMD (p=0.007). An additional locus on chromosome 19 tagged by rs73056959 was associated with the response of femoral neck BMD to TPTD (p=3.5×10-9, beta=-1.61 (-2.14 to -1.07)). CONCLUSIONS: Genetic factors influence the response to TPTD at the lumbar spine and hip with a magnitude of effect that is clinically relevant. Further studies are required to identify the causal genetic variants and underlying mechanisms as well as to explore how genetic testing for these variants might be implemented in clinical practice.

Identification of PCSK9-like human gene knockouts using metabolomics, proteomics, and whole-genome sequencing in a consanguineous population.

Natural human knockouts of genes associated with desirable outcomes, such as PCSK9 with low levels of LDL-cholesterol, can lead to the discovery of new drug targets and treatments. Rare loss-of-function variants are more likely to be found in the homozygous state in consanguineous populations, and deep molecular phenotyping of blood samples from homozygous carriers can help to discriminate between silent and functional variants. Here, we combined whole-genome sequencing with proteomics and metabolomics for 2,935 individuals from the Qatar Biobank (QBB) to evaluate the power of this approach for finding genes of clinical and pharmaceutical interest. As proof-of-concept, we identified a homozygous carrier of a very rare PCSK9 variant with extremely low circulating PCSK9 levels and low LDL. Our study demonstrates that the chances of finding such variants are about 168 times higher in QBB compared with GnomAD and emphasizes the potential of consanguineous populations for drug discovery.

Differences and commonalities in the genetic architecture of protein quantitative trait loci in European and Arab populations.

Polygenic scores (PGS) can identify individuals at risk of adverse health events and guide genetics-based personalized medicine. However, it is not clear how well PGS translate between different populations, limiting their application to well-studied ethnicities. Proteins are intermediate traits linking genetic predisposition and environmental factors to disease, with numerous blood circulating protein levels representing functional readouts of disease-related processes. We hypothesized that studying the genetic architecture of a comprehensive set of blood-circulating proteins between a European and an Arab population could shed fresh light on the translatability of PGS to understudied populations. We therefore conducted a genome-wide association study with whole-genome sequencing data using 1301 proteins measured on the SOMAscan aptamer-based affinity proteomics platform in 2935 samples of Qatar Biobank and evaluated the replication of protein quantitative traits (pQTLs) from European studies in an Arab population. Then, we investigated the colocalization of shared pQTL signals between the two populations. Finally, we compared the performance of protein PGS derived from a Caucasian population in a European and an Arab cohort. We found that the majority of shared pQTL signals (81.8%) colocalized between both populations. About one-third of the genetic protein heritability was explained by protein PGS derived from a European cohort, with protein PGS performing ~20% better in Europeans when compared to Arabs. Our results are relevant for the translation of PGS to non-Caucasian populations, as well as for future efforts to extend genetic research to understudied populations.

Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population.

Type 2 diabetes (T2D) has a heterogeneous etiology influencing its progression, treatment, and complications. A data driven cluster analysis in European individuals with T2D previously identified four subtypes: severe insulin deficient (SIDD), severe insulin resistant (SIRD), mild obesity-related (MOD), and mild age-related (MARD) diabetes. Here, the clustering approach was applied to individuals with T2D from the Qatar Biobank and validated in an independent set. Cluster-specific signatures of circulating metabolites and proteins were established, revealing subtype-specific molecular mechanisms, including activation of the complement system with features of autoimmune diabetes and reduced 1,5-anhydroglucitol in SIDD, impaired insulin signaling in SIRD, and elevated leptin and fatty acid binding protein levels in MOD. The MARD cluster was the healthiest with metabolomic and proteomic profiles most similar to the controls. We have translated the T2D subtypes to an Arab population and identified distinct molecular signatures to further our understanding of the etiology of these subtypes.

Identification of distinct circulating microRNAs in acute ischemic stroke patients with type 2 diabetes mellitus.

Stroke is the second leading cause of global mortality and continued efforts aim to identify predictive, diagnostic, or prognostic biomarkers to reduce the disease burden. Circulating microRNAs (miRNAs) have emerged as potential biomarkers in stroke. We performed comprehensive circulating miRNA profiling of ischemic stroke patients with or without type 2 diabetes mellitus (T2DM), an important risk factor associated with worse clinical outcomes in stroke. Serum samples were collected within 24 h of acute stroke diagnosis and circulating miRNAs profiled using RNA-Seq were compared between stroke patients with T2DM (SWDM; n = 92) and those without T2DM (SWoDM; n = 98). Our analysis workflow involved random allocation of study cohorts into discovery (n = 96) and validation (n = 94) datasets. Five miRNAs were found to be differentially regulated in SWDM compared to SWoDM patients. Hsa-miR-361-3p and -664a-5p were downregulated, whereas miR-423-3p, -140-5p, and -17-3p were upregulated. We also explored the gene targets of these miRNAs and investigated the downstream pathways associated with them to decipher the potential pathways impacted in stroke with diabetes as comorbidity. Overall, our novel findings provide important insights into the differentially regulated miRNAs, their associated pathways and potential utilization for clinical benefits in ischemic stroke patients with diabetes.

Identification of a Gene Panel Predictive of Triple-Negative Breast Cancer Response to Neoadjuvant Chemotherapy Employing Transcriptomic and Functional Validation.

Triple-negative breast cancer (TNBC) patients exhibiting pathological complete response (pCR) have better clinical outcomes compared to those with residual disease (RD). Therefore, robust biomarkers that can predict pCR may help with triage and resource prioritization in patients with TNBC. Herein, we identified a gene panel predictive of RD and pCR in TNBC from the discovery (n = 90) treatment-naive tumor transcriptomic data. Eight RD-derived genes were identified as TNBC-essential genes, which were highly predicative of overall survival (OS) and relapse-free survival (RFS) in an additional cohort of basal breast cancer (n = 442). Mechanistically, targeted depletion of the eight genes reduced the proliferation potential of TNBC cell models, while most remarkable effects were for combined SLC39A7, TIMM13, BANF1, and MVD knockdown in conjunction with doxorubicin. Orthogonal partial least squares-discriminant analysis (OPLS-DA) and receiver operating characteristic curve (ROC) analyses revealed significant predictive power for the identified gene panels with an area under the curve (AUC) of 0.75 for the validation cohort (n = 50) to discriminate RD from pCR. Protein-Protein Interaction (PPI) network analysis of the pCR-derived gene signature identified an 87-immune gene signature highly predictive of pCR, which correlated with better OS, RFS, and distant-metastasis-free survival (DMFS) in an independent cohort of basal and, to a lesser extent, HER2+ breast cancer. Our data have identified gene signatures predicative of RD and pCR in TNBC with potential clinical implications.

The Prevalence and Genetic Spectrum of Familial Hypercholesterolemia in Qatar Based on Whole Genome Sequencing of 14,000 Subjects.

Familial hypercholesterolemia (FH) is an inherited disease characterized by reduced efficiency of low-density lipoprotein-cholesterol (LDL-C) removal from the blood and, consequently, an increased risk of life-threatening early cardiovascular complications. In Qatar, the prevalence of FH has not been determined and the disease, as in many countries, is largely underdiagnosed. In this study, we combined whole-genome sequencing data from the Qatar Genome Program with deep phenotype data from Qatar Biobank for 14,056 subjects to determine the genetic spectrum and estimate the prevalence of FH in Qatar. We used the Dutch Lipid Clinic Network (DLCN) as a diagnostic tool and scrutinized 11 FH-related genes for known pathogenic and possibly pathogenic mutations. Results revealed an estimated prevalence of 0.8% (1:125) for definite/probable cases of FH in the Qatari population. We detected 16 known pathogenic/likely pathogenic mutations in LDLR and one in PCSK9; all in a heterozygous state with high penetrance. The most common mutation was rs1064793799 (c.313+3A >C) followed by rs771019366 (p.Asp90Gly); both in LDLR. In addition, we identified 18 highly penetrant possibly pathogenic variants, of which 5 were Qatari-specific, in LDLR, APOB, PCSK9 and APOE, which are predicted to be among the top 1% most deleterious mutations in the human genome but further validations are required to confirm their pathogenicity. We did not detect any homozygous FH or autosomal recessive mutations in our study cohort. This pioneering study provides a reliable estimate of FH prevalence in Qatar based on a significantly large population-based cohort, whilst uncovering the spectrum of genetic variants associated with FH.