Forging the path to precision medicine in Qatar: a public health perspective on pharmacogenomics initiatives.

Pharmacogenomics (PGx) is an important component of precision medicine that promises tailored treatment approaches based on an individual's genetic information. Exploring the initiatives in research that help to integrate PGx test into clinical setting, identifying the potential barriers and challenges as well as planning the future directions, are all important for fruitful PGx implementation in any population. Qatar serves as an exemplar case study for the Middle East, having a small native population compared to a diverse immigrant population, advanced healthcare system, national genome program, and several educational initiatives on PGx and precision medicine. This paper attempts to outline the current state of PGx research and implementation in Qatar within the global context, emphasizing ongoing initiatives and educational efforts. The inclusion of PGx in university curricula and healthcare provider training, alongside precision medicine conferences, showcase Qatar's commitment to advancing this field. However, challenges persist, including the requirement for population specific implementation strategies, complex genetic data interpretation, lack of standardization, and limited awareness. The review suggests policy development for future directions in continued research investment, conducting clinical trials for the feasibility of PGx implementation, ethical considerations, technological advancements, and global collaborations to overcome these barriers.

Identification of two novel autism genes, TRPC4 and SCFD2, in Qatar simplex families through exome sequencing.

This study investigated the genetic underpinnings of autism spectrum disorder (ASD) in a Middle Eastern cohort in Qatar using exome sequencing. The study identified six candidate autism genes in independent simplex families, including both four known and two novel autosomal dominant and autosomal recessive genes associated with ASD. The variants consisted primarily of de novo and homozygous missense and splice variants. Multiple individuals displayed more than one candidate variant, suggesting the potential involvement of digenic or oligogenic models. These variants were absent in the Genome Aggregation Database (gnomAD) and exhibited extremely low frequencies in the local control population dataset. Two novel autism genes, TRPC4 and SCFD2, were discovered in two Qatari autism individuals. Furthermore, the D651A substitution in CLCN3 and the splice acceptor variant in DHX30 were identified as likely deleterious mutations. Protein modeling was utilized to evaluate the potential impact of three missense variants in DEAF1, CLCN3, and SCFD2 on their respective structures and functions, which strongly supported the pathogenic natures of these variants. The presence of multiple de novo mutations across trios underscored the significant contribution of de novo mutations to the genetic etiology of ASD. Functional assays and further investigations are necessary to confirm the pathogenicity of the identified genes and determine their significance in ASD. Overall, this study sheds light on the genetic factors underlying ASD in Qatar and highlights the importance of considering diverse populations in ASD research.

Therapeutic targeting of the TPX2/TTK network in colorectal cancer.

BACKGROUND: While the increased screening, changes in lifestyle, and recent advances in treatment regimen have decreased colorectal cancer (CRC) mortality, metastatic disease and recurrence remains a major clinical challenge. In the era of precision medicine, the identification of actionable novel therapeutic targets could ultimately offer an alternative treatment strategy for CRC. METHODS: RNA-Seq was conducted using the illumina platform, while bioinformatics analyses were conducted using CLC genomics workbench and iDEP.951. Colony forming unit, flow cytometry, and fluorescent microscopy were used to assess cell proliferation, cell cycle distribution, and cell death, respectively. The growth potential of CRC cells under 3-dimensional (3D) conditions was assessed using Matrigel. STRING database (v11.5) and Ingenuity Pathway Analysis (IPA) tool were used for network and pathway analyses. CRISPR-Cas9 perturbational effects database was used to identify potential therapeutic targets for CRC, through integration with gene-drug interaction database. Structural modeling and molecular docking were used to assess the interaction between candidate drugs and their targets. RESULTS: In the current study, we investigated the therapeutic potential of targeting TPX2, TTK, DDX39A, and LRP8, commonly upregulated genes in CRC identified through differential expression analysis in CRC and adjacent non-cancerous tissue. Targeted depletion of TPX2 and TTK impaired CRC proliferation, cell cycle progression, and organoid formation under 3D culture conditions, while suppression of DDX39A and LRP8 had modest effects on CRC colony formation. Differential expression analysis and bioinformatics on TPX2 and TTK-deficient cells identified cell cycle regulation as the hallmark associated with loss of TPX2 and TTK. Elevated expression of TPX2 and TTK correlated with an oncogenic state in tumor tissue from patients with colon adenocarcinoma, thus corroborating an oncogenic role for the TPX2/TTK network in the pathogenesis of CRC. Gene set enrichment and pathway analysis of TPX2high/TTKhigh CRC identified numerous additional gene targets as integral components of the TPX2/TTK network. Integration of TPX2/TTK enriched network with CRISPR-Cas9 functional screen data identified numerous novel dependencies for CRC. Additionally, gene-drug interaction analysis identified several druggable gene targets enriched in the TPX2/TTK network, including AURKA, TOP2A, CDK1, BIRC5, and many others. CONCLUSIONS: Our data has implicated an essential role for TPX2 and TTK in CRC pathogenesis and identified numerous potential therapeutic targets and their drug interactions, suggesting their potential clinical use as a novel therapeutic strategy for patients with CRC. Video Abstract.

Protegrin-2, a potential inhibitor for targeting SARS-CoV-2 main protease Mpro.

Background: SARS-CoV-2 variants continue to spread throughout the world and cause waves of COVID-19 infections. It is important to find effective antiviral drugs to combat SARS-CoV-2 and its variants. The main protease (Mpro) of SARS-CoV-2 is a promising therapeutic target due to its crucial role in viral replication and its conservation in all the variants. Therefore, the aim of this work was to identify an effective inhibitor of Mpro. Methods: We studied around 200 antimicrobial peptides using in silico methods including molecular docking and allergenicity and toxicity prediction. One selected antiviral peptide was studied experimentally using a Bioluminescence Resonance Energy Transfer (BRET)-based Mpro biosensor, which reports Mpro activity through a decrease in energy transfer. Results: Molecular docking identified one natural antimicrobial peptide, Protegrin-2, with high binding affinity and stable interactions with Mpro allosteric residues. Furthermore, free energy calculations and molecular dynamics simulation illustrated a high affinity interaction between the two. We also determined the impact of the binding of Protegrin-2 to Mpro using a BRET-based assay, showing that it inhibits the proteolytic cleavage activity of Mpro. Conclusions: Our in silico and experimental studies identified Protegrin-2 as a potent inhibitor of Mpro that could be pursued further towards drug development against COVID-19 infection.

Identification of a potential DNA methyltransferase (DNMT) inhibitor.

DNA methyltransferases (DNMTs) play an important role in the epigenetic regulation of gene expression through the methylation of DNA. Since hypermethylation and consequent suppression of tumor suppressor genes are associated with cancer development and progression, DNA hypomethylating agents (HMAs) such as DNMT inhibitors have been proposed for cancer therapy. Two nucleoside analogues approved for the treatment of hematological cancers, decitabine and azacytidine, have poor pharmacokinetic properties, and hence there is a critical need for identifying novel HMAs. Virtual screening of a library of ∼40,000 compounds from the ZINC database, followed by molecular docking of 4,000 compounds having potential druggable properties with DNMT1, DNMT3A and DNMT3B were performed. One unique inhibitor (ZINC167686681) was identified that successfully passed through the Lipinski Rule of 5, geometry constraints as well as ADME/Tox filters and having strong binding energy for DNMTs. Further, molecular dynamics simulations of the docked complexes showed detailed structural features critical for its binding with the DNMTs and the stability of their interaction. Our study identified a compound with potential druggable properties and predicted to bind and inhibit DNMTs. Further investigations involving cellular and animal models of ZINC167686681 will help in potentially taking it into clinical trials for the treatment of cancers.Communicated by Ramaswamy H. Sarma.

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review.

BACKGROUND: Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer. OBJECTIVE: This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature. METHODS: A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. RESULTS: Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms. CONCLUSIONS: This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.

Human leukocyte antigen class II gene diversity tunes antibody repertoires to common pathogens.

Allelic diversity of human leukocyte antigen (HLA) class II genes may help maintain humoral immunity against infectious diseases. In this study, we investigated germline genetic variation in classical HLA class II genes and employed a systematic, unbiased approach to explore the relative contribution of this genetic variation in the antibody repertoire to various common pathogens. We leveraged a well-defined cohort of 800 adults representing the general Arab population in which genetic material is shared because of the high frequency of consanguineous unions. By applying a high-throughput method for large-scale antibody profiling to this well-defined cohort, we were able to dissect the overall effect of zygosity for classical HLA class II genes, as well as the effects associated with specific HLA class II alleles, haplotypes and genotypes, on the antimicrobial antibody repertoire breadth and antibody specificity with unprecedented resolution. Our population genetic studies revealed that zygosity of the classical HLA class II genes is a strong predictor of antibody responses to common human pathogens, suggesting that classical HLA class II gene heterozygosity confers a selective advantage. Moreover, we demonstrated that multiple HLA class II alleles can have additive effects on the antibody repertoire to common pathogens. We also identified associations of HLA-DRB1 genotypes with specific antigens. Our findings suggest that HLA class II gene polymorphisms confer specific humoral immunity against common pathogens, which may have contributed to the genetic diversity of HLA class II loci during hominine evolution.

A population study of clinically actionable genetic variation affecting drug response from the Middle East.

Clinical implementation of pharmacogenomics will help in personalizing drug prescriptions and alleviate the personal and financial burden due to inefficacy and adverse reactions to drugs. However, such implementation is lagging in many parts of the world, including the Middle East, mainly due to the lack of data on the distribution of actionable pharmacogenomic variation in these ethnicities. We analyzed 6,045 whole genomes from the Qatari population for the distribution of allele frequencies of 2,629 variants in 1,026 genes known to affect 559 drugs or classes of drugs. We also performed a focused analysis of genotypes or diplotypes of 15 genes affecting 46 drugs, which have guidelines for clinical implementation and predicted their phenotypic impact. The allele frequencies of 1,320 variants in 703 genes affecting 299 drugs or class of drugs were significantly different between the Qatari population and other world populations. On average, Qataris carry 3.6 actionable genotypes/diplotypes, affecting 13 drugs with guidelines for clinical implementation, and 99.5% of the individuals had at least one clinically actionable genotype/diplotype. Increased risk of simvastatin-induced myopathy could be predicted in ~32% of Qataris from the diplotypes of SLCO1B1, which is higher compared to many other populations, while fewer Qataris may need tacrolimus dosage adjustments for achieving immunosuppression based on the CYP3A5 diplotypes compared to other world populations. Distinct distribution of actionable pharmacogenomic variation was also observed among the Qatari subpopulations. Our comprehensive study of the distribution of actionable genetic variation affecting drugs in a Middle Eastern population has potential implications for preemptive pharmacogenomic implementation in the region and beyond.

Genome sequencing data analysis for rare disease gene discovery.

Rare diseases occur in a smaller proportion of the general population, which is variedly defined as less than 200 000 individuals (US) or in less than 1 in 2000 individuals (Europe). Although rare, they collectively make up to approximately 7000 different disorders, with majority having a genetic origin, and affect roughly 300 million people globally. Most of the patients and their families undergo a long and frustrating diagnostic odyssey. However, advances in the field of genomics have started to facilitate the process of diagnosis, though it is hindered by the difficulty in genome data analysis and interpretation. A major impediment in diagnosis is in the understanding of the diverse approaches, tools and datasets available for variant prioritization, the most important step in the analysis of millions of variants to select a few potential variants. Here we present a review of the latest methodological developments and spectrum of tools available for rare disease genetic variant discovery and recommend appropriate data interpretation methods for variant prioritization. We have categorized the resources based on various steps of the variant interpretation workflow, starting from data processing, variant calling, annotation, filtration and finally prioritization, with a special emphasis on the last two steps. The methods discussed here pertain to elucidating the genetic basis of disease in individual patient cases via trio- or family-based analysis of the genome data. We advocate the use of a combination of tools and datasets and to follow multiple iterative approaches to elucidate the potential causative variant.

Prescription Pattern and Off-Label Use of Antipsychotics in a Middle Eastern Population.

Background: Understanding the prescription pattern of medications in a population can help reveal the potential usage scenarios, including off-label prescriptions, and the need for precision medicine implementation. Therefore, the aim of this study was to assess the prescription pattern and off-label use of antipsychotics in the Qatari population. Methods: We performed a cross-sectional study of Qatari patients who received antipsychotic prescriptions from the major healthcare providers in the country during the 2-year period between June 2018 and May 2020. The number of patients, prescriptions dispensed, and clinical indications were collected and statistical analysis using chi-square test was conducted. Results: Among the 9,349 Qatari patients prescribed with antipsychotics during the study period, the majority were female (57%; p < 0.001) and were in the age categories 20-39 and 30-39 years (both 22%; p < 0.001). Among the 35,938 antipsychotic prescriptions dispensed, second-generation antipsychotics were the most highly prescribed (59%), specifically, quetiapine (16%) and olanzapine (12%), but the first-generation antipsychotic prochlorperazine (13%) was also highly prescribed. Most of the indications of antipsychotics (69%) were for off-label use such as for controlling chronic diseases, sleeping disorders, benign paroxysmal positional vertigo and irritable bowel syndrome. Conclusion: Non-mental health and off-label prescriptions of several antipsychotics were observed. Integration of this data with pharmacogenomic and clinical outcome data will help in determining the course of action for implementing personalized and precision medicine in the country and beyond.

Prescription Pattern of Antidepressants and the Potential for Personalized Medicine in the Qatari Population.

Studying the prescription pattern of medications will help in understanding potential unnecessary prescriptions, due to the trial-and-error method of prescribing, and the need for personalized medicine in a population. Therefore, in this study, our aim was to explore the prescribing pattern and off-label use of antidepressants in the Qatari population. We conducted a retrospective study of Qatari patients who received prescriptions for antidepressants from the major healthcare providers in Qatar, for a period of 24 months between June 2018 and May 2020. The number of patients, prescriptions, and diagnostic indications were analyzed. The chi-square test was used for identifying statistically significant association of the number of individuals prescribed with age category or gender. Of the 14,601 Qatari patients who were prescribed antidepressants, the majority were female (61%, p < 2.2 × 10-16), and were at or above 60 years of age (27%, p < 2.2 × 10-16). More numbers of selective serotonin reuptake inhibitors (SSRIs) (22,085 out of 48,031; 46%), were dispensed than other classes of antidepressants, with escitalopram (26%) at the top of the list. Preponderance of prescription of antidepressants for non-mental health diseases was observed. Population-level prescription trends, as we reported here, when combined with patient genetic variability and outcome data, will have the power to predict the potential for treatment failures and adverse effects of these medications in the population. We also recommend educating non-mental health prescribers about the adherence to evidence and guidelines to ensure patient safety while prescribing antidepressants.

Case Report: Phenotype-Gene Correlation in a Case of Novel Tandem 4q Microduplication With Short Stature, Speech Delay and Microcephaly.

We describe a sporadic case of a pure, tandem, interstitial chromosome 4q duplication, arr[hg19] 4q28.1q32.3 (127,008,069-165,250,477) x3 in a boy born at 36 weeks of gestation. He presented with microcephaly (head circumference <1st percentile), short stature (height <2nd percentile) and poor weight gain (weight <3rd percentile). Hypospadias and horseshoe shaped kidneys were also revealed following a urinary tract ultrasound. Biochemical analysis revealed normal growth hormone and thyroid hormone levels. While gross and fine motor skill development was in line with his age, speech delay was observed. This patient adds to a group of more than 30 cases of pure 4q tandem duplication with common and differing phenotypic presentations. Using a retrospective analysis of previous case studies alongside the current case and bioinformatics analysis of the duplicated region, we deduced the most likely dosage sensitive genes for some of the major phenotypes in the patient. The positive predictive value (PPV) was calculated for each gene and phenotype and was derived by comparing the previously reported patients who have gene duplications and an associated phenotype versus those who had the gene duplications but were unaffected. Thus, the growth retardation phenotype may be associated with NAA15 duplication, speech delay with GRIA2 and microcephaly with PLK4 duplication. Functional studies will help in confirming the observations and elucidating the mechanisms. However, our study highlights the importance of analysing case reports with pure duplications in defining phenotype-gene relationships and in improving our knowledge of the function of precise chromosomal regions.

First Whole Transcriptome RNAseq on CHD8 Haploinsufficient Patient and Meta-Analyses Across Cellular Models Uncovers Likely Key Pathophysiological Target Genes.

In 2019, we confirmed that the haploinsufficiency of CHD8 does indeed cause the novel syndromic neurodevelopmental disease we first discovered a dozen years before. Here, we report the first whole transcriptome RNAseq gene expression profiling for a patient with this new syndrome, as a preliminary exploration of potential pathophysiological mechanisms. We compared our patient transcriptome profile with that of all publicly available RNAseq datasets from human cellular models including neuronal progenitor cells, neurons and organoids. We compared differential gene expression profiles overall and conducted phenotype-informed data filtration based on the characteristic syndrome presentation. We found that concordance among differential gene expression profiles was poor across all datasets. Nevertheless, remarkably, we show that the patient blood differential gene expression profile most resembled that of the neuronal cell model, a finding that encourages further transcriptome profiling using patient blood samples. In addition, our custom phenotype-informed analyses yielded important, differentially expressed syndrome pathophysiology target genes. Finally, we note that genes dysregulated due to CHD8 heterozygous deletion are linked to known neurological as well as oncological pathways.

Three Copies of Four Interferon Receptor Genes Underlie a Mild Type I Interferonopathy in Down Syndrome.

Down syndrome (DS) is characterized by the occurrence of three copies of human chromosome 21 (HSA21). HSA21 contains a cluster of four interferon receptor (IFN-R) genes: IFNAR1, IFNAR2, IFNGR2, and IL10RB. DS patients often develop mucocutaneous infections and autoimmune diseases, mimicking patients with heterozygous gain-of-function (GOF) STAT1 mutations, which enhance cellular responses to three types of interferon (IFN). A gene dosage effect at these four loci may contribute to the infectious and autoimmune manifestations observed in individuals with DS. We report high levels of IFN-αR1, IFN-αR2, and IFN-γR2 expression on the surface of monocytes and EBV-transformed-B (EBV-B) cells from studying 45 DS patients. Total and phosphorylated STAT1 (STAT1 and pSTAT1) levels were constitutively high in unstimulated and IFN-α- and IFN-γ-stimulated monocytes from DS patients but lower than those in patients with GOF STAT1 mutations. Following stimulation with IFN-α or -γ, but not with IL-6 or IL-21, pSTAT1 and IFN-γ activation factor (GAF) DNA-binding activities were significantly higher in the EBV-B cells of DS patients than in controls. These responses resemble the dysregulated responses observed in patients with STAT1 GOF mutations. Concentrations of plasma type I IFNs were high in 12% of the DS patients tested (1.8% in the healthy controls). Levels of type I IFNs, IFN-Rs, and STAT1 were similar in DS patients with and without recurrent skin infections. We performed a genome-wide transcriptomic analysis based on principal component analysis and interferon modules on circulating monocytes. We found that DS monocytes had levels of both IFN-α- and IFN-γ-inducible ISGs intermediate to those of monocytes from healthy controls and from patients with GOF STAT1 mutations. Unlike patients with GOF STAT1 mutations, patients with DS had normal circulating Th17 counts and a high proportion of terminally differentiated CD8+ T cells with low levels of STAT1 expression. We conclude a mild interferonopathy in Down syndrome leads to an incomplete penetrance at both cellular and clinical level, which is not correlate with recurrent skin bacterial or fungal infections. The constitutive upregulation of type I and type II IFN-R, at least in monocytes of DS patients, may contribute to the autoimmune diseases observed in these individuals.