This review discusses the discovery, epidemiology, pathophysiology, genetic etiology, molecular diagnosis, and medication-based management of fragile X syndrome (FXS). It also highlights the syndrome's variable expressivity and common comorbid and overlapping conditions. FXS is an X-linked dominant disorder associated with a wide spectrum of clinical features, including but not limited to intellectual disability, autism spectrum disorder, language deficits, macroorchidism, seizures, and anxiety. Its prevalence in the general population is approximately 1 in 5000-7000 men and 1 in 4000-6000 women worldwide. FXS is associated with the fragile X messenger ribonucleoprotein 1 (FMR1) gene located at locus Xq27.3 and encodes the fragile X messenger ribonucleoprotein (FMRP). Most individuals with FXS have an FMR1 allele with > 200 CGG repeats (full mutation) and hypermethylation of the CpG island proximal to the repeats, which silences the gene's promoter. Some individuals have mosaicism in the size of the CGG repeats or in hypermethylation of the CpG island, both produce some FMRP and give rise to milder cognitive and behavioral deficits than in non-mosaic individuals with FXS. As in several monogenic disorders, modifier genes influence the penetrance of FMR1 mutations and FXS's variable expressivity by regulating the pathophysiological mechanisms related to the syndrome's behavioral features. Although there is no cure for FXS, prenatal molecular diagnostic testing is recommended to facilitate early diagnosis. Pharmacologic agents can reduce some behavioral features of FXS, and researchers are investigating whether gene editing can be used to demethylate the FMR1 promoter region to improve patient outcomes. Moreover, clustered regularly interspaced palindromic repeats (CRISPR)/Cas9 and developed nuclease defective Cas9 (dCas9) strategies have promised options of genome editing in gain-of-function mutations to rewrite new genetic information into a specified DNA site, are also being studied.

Alzheimer's disease (AD) poses a profound human, social, and economic burden. Previous studies suggest that extra virgin olive oil (EVOO) may be helpful in preventing cognitive decline. Here, we present a network machine learning method for identifying bioactive phytochemicals in EVOO with the highest potential to impact the protein network linked to the development and progression of the AD. A balanced classification accuracy of 70.3 ± 2.6% was achieved in fivefold cross-validation settings for predicting late-stage experimental drugs targeting AD from other clinically approved drugs. The calibrated machine learning algorithm was then used to predict the likelihood of existing drugs and known EVOO phytochemicals to be similar in action to the drugs impacting AD protein networks. These analyses identified the following ten EVOO phytochemicals with the highest likelihood of being active against AD: quercetin, genistein, luteolin, palmitoleate, stearic acid, apigenin, epicatechin, kaempferol, squalene, and daidzein (in the order from the highest to the lowest likelihood). This in silico study presents a framework that brings together artificial intelligence, analytical chemistry, and omics studies to identify unique therapeutic agents. It provides new insights into how EVOO constituents may help treat or prevent AD and potentially provide a basis for consideration in future clinical studies.

BACKGROUND: Wastewater-based epidemiological surveillance has been considered a powerful tool for early detection and monitoring of the dynamics of SARS-CoV-2 and its lineages circulating in a community. This study is aimed to investigate the complexity of SARS-CoV-2 infection dynamics in Dhaka city by examining its genetic variants in wastewater. Also, the study seeks to determine a connection between the SARS-CoV-2 variations detected in clinical testing and those found in wastewater samples. RESULTS: Out of 504 samples tested in RT-qPCR, 185 (36.7%) tested positive for SARS-CoV-2 viral RNA. The median log10 concentration of SARS-CoV-2 N gene copies/Liter of wastewater (gc/L) was 5.2, and the median log10 concentration of ORF1ab was 4.9. To further reveal the genetic diversity of SARS-CoV-2, ten samples with ORF1ab real-time RT-PCR cycle threshold (Ct) values ranging from 28.78 to 32.13 were subjected to whole genome sequencing using nanopore technology. According to clade classification, sequences from wastewater samples were grouped into 4 clades: 20A, 20B, 21A, 21J, and the Pango lineage, B.1, B.1.1, B.1.1.25, and B.1.617.2, with coverage ranging from 94.2 to 99.8%. Of them, 70% belonged to clade 20B, followed by 10% to clade 20A, 21A, and 21J. Lineage B.1.1.25 was predominant in Bangladesh and phylogenetically related to the sequences from India, the USA, Canada, the UK, and Italy. The Delta variant (B.1.617.2) was first identified in clinical samples at the beginning of May 2021. In contrast, we found that it was circulating in the community and was detected in wastewater in September 2020. CONCLUSION: Environmental surveillance is useful for monitoring temporal and spatial trends of existing and emerging infectious diseases and supports evidence-based public health measures. The findings of this study supported the use of wastewater-based epidemiology and provided the baseline data for the dynamics of SARS-CoV-2 variants in the wastewater environment in Dhaka, Bangladesh.

BACKGROUND: The influence of genetic factors on the pharmacokinetics and clinical outcomes of rivaroxaban in patients with non-valvular atrial fibrillation (NVAF) is poorly understood. This study aimed to explore the effects of CYP3A4/5, ABCB1, and ABCG2 gene polymorphisms on the trough concentrations and the bleeding risk of rivaroxaban in NVAF patients. PATIENTS AND METHODS: This study is a prospective multicenter study. The patient's blood samples were collected to detect the steady-state trough concentrations of rivaroxaban and gene polymorphisms. We visited the patients regularly at month 1, 3, 6, and 12 to record bleeding events and medications. RESULTS: A total of 95 patients were enrolled in this study, and 9 gene loci were detected. For the dose-adjusted trough concentration ratio (Ctrough/D) of rivaroxaban, the homozygous mutant type was significantly lower than wild type at ABCB1 rs4148738 locus (TT vs. CC, P = 0.033), and the mutant type was significantly lower than the wild type at ABCB1 rs4728709 locus (AA + GA vs. GG, P = 0.008). ABCB1 (rs1045642, rs1128503), CYP3A4 (rs2242480, rs4646437), CYP3A5 (rs776746), and ABCG2 (rs2231137, rs2231142) gene polymorphisms had no significant effect on the Ctrough/D of rivaroxaban. For the bleeding events, we found that there were no significant differences among genotypes of all gene loci. CONCLUSION: This study found for the first time that ABCB1 rs4148738 and rs4728709 gene polymorphisms had a significant impact on the Ctrough/D of rivaroxaban in NVAF patients. CYP3A4/5, ABCB1, and ABCG2 gene polymorphisms were not associated with the bleeding risk of rivaroxaban.

BACKGROUND: Marine seaweeds are considered as a rich source of health-promoting compounds by the food and pharmaceutical industry. Hypnea musciformis is a marine red macroalga (seaweed) that is widely distributed throughout the world, including the Mediterranean Sea. It is known to contain various bioactive compounds, including sulfated polysaccharides, flavonoids, and phlorotannins. Recent studies have investigated the potential anticancer effects of extracts from H. musciformis demonstrating their cytotoxic effects on various cancer cell lines. The anticancer effects of these extracts are thought to be due to the presence of bioactive compounds, particularly sulfated polysaccharides, which have been shown to have anticancer and immunomodulatory effects. However, further studies are needed to fully understand the molecular mechanisms that underlie their anticancer effects and to determine their potential as therapeutic agents for cancer treatment. METHODS: H. musciformis was collected from the Aegean Sea (Greece) and used for extract preparation. Transcriptome and proteome analysis was performed in liver and colon cancer human cell lines following treatment with H. musciformis seaweed extracts to characterize its anticancer effect in detail at the molecular level and to link transcriptome and proteome responses to the observed phenotypes in cancer cells. RESULTS: We have identified that treatment with the seaweed extract triggers a p53-mediated response at the transcriptional and protein level in liver cancer cells, in contrast to colon cancer cells in which the effects are more associated with metabolic changes. Furthermore, we show that in treated HepG2 liver cancer cells, p53 interacts with the chromatin of several target genes and facilitates their upregulation possibly through the recruitment of the p300 co-activator. CONCLUSIONS: Overall, the available evidence suggests that extracts from H. musciformis have the potential to serve as a source of anticancer agents in liver cancer cells mainly through activation of a p53-mediated anti-tumor response that is linked to inhibition of cellular proliferation and induction of cell death.

BACKGROUND: ALS is a heterogeneous disease in which different factors such as mitochondrial phenotypes act in combination with a genetic predisposition. This study addresses the question of whether homoplasmic (total mitochondrial genome of a sample is affected) and/or heteroplasmic mutations (wildtype and mutant mitochondrial DNA molecules coexist) might play a role in familial ALS. Blood was drawn from familial ALS patients with a possible maternal pattern of inheritance according to their pedigrees, which was compared to blood of ALS patients without maternal association as well as age-matched controls. In two cohorts, we analyzed the mitochondrial genome from whole blood or isolated white blood cells and platelets using a resequencing microarray (Affymetrix MitoChip v2.0) that is able to detect homoplasmic and heteroplasmic mitochondrial DNA mutations and allows the assessment of low-level heteroplasmy. RESULTS: We identified an increase in homoplasmic ND5 mutations, a subunit of respiratory chain complex I, in whole blood of ALS patients that allowed maternal inheritance. This effect was more pronounced in patients with bulbar onset. Heteroplasmic mutations were significantly increased in different mitochondrial genes in platelets of patients with possible maternal inheritance. No increase of low-level heteroplasmy was found in maternal ALS patients. CONCLUSION: Our results indicate a contribution of homoplasmic ND5 mutations to maternally associated ALS with bulbar onset. Therefore, it might be conceivable that specific maternally transmitted rather than randomly acquired mitochondrial DNA mutations might contribute to the disease process. This stands in contrast with observations from Alzheimer's and Parkinson's diseases showing an age-dependent accumulation of unspecific mutations in mitochondrial DNA.

BACKGROUND: Cardiovascular diseases (CVDs) are the leading cause of death worldwide. Genome-wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) appearing in non-coding genomic regions in CVDs. The SNPs may alter gene expression by modifying transcription factor (TF) binding sites and lead to functional consequences in cardiovascular traits or diseases. To understand the underlying molecular mechanisms, it is crucial to identify which variations are involved and how they affect TF binding. METHODS: The SNEEP (SNP exploration and analysis using epigenomics data) pipeline was used to identify regulatory SNPs, which alter the binding behavior of TFs and link GWAS SNPs to their potential target genes for six CVDs. The human-induced pluripotent stem cells derived cardiomyocytes (hiPSC-CMs), monoculture cardiac organoids (MCOs) and self-organized cardiac organoids (SCOs) were used in the study. Gene expression, cardiomyocyte size and cardiac contractility were assessed. RESULTS: By using our integrative computational pipeline, we identified 1905 regulatory SNPs in CVD GWAS data. These were associated with hundreds of genes, half of them non-coding RNAs (ncRNAs), suggesting novel CVD genes. We experimentally tested 40 CVD-associated non-coding RNAs, among them RP11-98F14.11, RPL23AP92, IGBP1P1, and CTD-2383I20.1, which were upregulated in hiPSC-CMs, MCOs and SCOs under hypoxic conditions. Further experiments showed that IGBP1P1 depletion rescued expression of hypertrophic marker genes, reduced hypoxia-induced cardiomyocyte size and improved hypoxia-reduced cardiac contractility in hiPSC-CMs and MCOs. CONCLUSIONS: IGBP1P1 is a novel ncRNA with key regulatory functions in modulating cardiomyocyte size and cardiac function in our disease models. Our data suggest ncRNA IGBP1P1 as a potential therapeutic target to improve cardiac function in CVDs.

Three and a half years after the pandemic outbreak, now that WHO has formally declared that the emergency is over, COVID-19 is still a significant global issue. Here, we focus on recent developments in genetic and genomic research on COVID-19, and we give an outlook on state-of-the-art therapeutical approaches, as the pandemic is gradually transitioning to an endemic situation. The sequencing and characterization of rare alleles in different populations has made it possible to identify numerous genes that affect either susceptibility to COVID-19 or the severity of the disease. These findings provide a beginning to new avenues and pan-ethnic therapeutic approaches, as well as to potential genetic screening protocols. The causative virus, SARS-CoV-2, is still in the spotlight, but novel threatening virus could appear anywhere at any time. Therefore, continued vigilance and further research is warranted. We also note emphatically that to prevent future pandemics and other world-wide health crises, it is imperative to capitalize on what we have learnt from COVID-19: specifically, regarding its origins, the world's response, and insufficient preparedness. This requires unprecedented international collaboration and timely data sharing for the coordination of effective response and the rapid implementation of containment measures.

BACKGROUND: A pathogenic filamentous fungus causing eyelid cellulitis was isolated from the secretion from a patient's left eyelid, and a phylogenetic analysis based on the rDNA internal transcribed spacer region (ITS) and single-copy gene families identified the isolated strain as Paraconiothyrium brasiliense. The genus Paraconiothyrium contains the major plant pathogenic fungi, and in our study, P. brasiliense was identified for the first time as causing human infection. To comprehensively analyze the pathogenicity, and proteomics of the isolated strain from a genetic perspective, whole-genome sequencing was performed with the Illumina NovaSeq and Oxford Nanopore Technologies platforms, and a bioinformatics analysis was performed with BLAST against genome sequences in various publicly available databases. RESULTS: The genome of P. brasiliense GGX 413 is 39.49 Mb in length, with a 51.2% GC content, and encodes 13,057 protein-coding genes and 181 noncoding RNAs. Functional annotation showed that 592 genes encode virulence factors that are involved in human disease, including 61 lethal virulence factors and 30 hypervirulence factors. Fifty-four of these 592 virulence genes are related to carbohydrate-active enzymes, including 46 genes encoding secretory CAZymes, and 119 associated with peptidases, including 70 genes encoding secretory peptidases, and 27 are involved in secondary metabolite synthesis, including four that are associated with terpenoid metabolism. CONCLUSIONS: This study establishes the genomic resources of P. brasiliense and provides a theoretical basis for future studies of the pathogenic mechanism of its infection of humans, the treatment of the diseases caused, and related research.

BACKGROUND: Female breast cancer remains the second leading cause of cancer-related death in the USA. The heterogeneity in the tumor morphology across the cohort and within patients can lead to unpredictable therapy resistance, metastasis, and clinical outcome. Hence, supplementing classic pathological markers with intrinsic tumor molecular markers can help identify novel molecular subtypes and the discovery of actionable biomarkers. METHODS: We conducted a large multi-institutional genomic analysis of paired normal and tumor samples from breast cancer patients to profile the complex genomic architecture of breast tumors. Long-term patient follow-up, therapeutic regimens, and treatment response for this cohort are documented using the Breast Cancer Collaborative Registry. The majority of the patients in this study were at tumor stage 1 (51.4%) and stage 2 (36.3%) at the time of diagnosis. Whole-exome sequencing data from 554 patients were used for mutational profiling and identifying cancer drivers. RESULTS: We identified 54 tumors having at least 1000 mutations and 185 tumors with less than 100 mutations. Tumor mutational burden varied across the classified subtypes, and the top ten mutated genes include MUC4, MUC16, PIK3CA, TTN, TP53, NBPF10, NBPF1, CDC27, AHNAK2, and MUC2. Patients were classified based on seven biological and tumor-specific parameters, including grade, stage, hormone receptor status, histological subtype, Ki67 expression, lymph node status, race, and mutational profiles compared across different subtypes. Mutual exclusion of mutations in PIK3CA and TP53 was pronounced across different tumor grades. Cancer drivers specific to each subtype include TP53, PIK3CA, CDC27, CDH1, STK39, CBFB, MAP3K1, and GATA3, and mutations associated with patient survival were identified in our cohort. CONCLUSIONS: This extensive study has revealed tumor burden, driver genes, co-occurrence, mutual exclusivity, and survival effects of mutations on a US Midwestern breast cancer cohort, paving the way for developing personalized therapeutic strategies.

INTRODUCTION: The adoption and implementation of genomic medicine and pharmacogenomics (PGx) in healthcare systems have been very slow and limited worldwide. Major barriers to knowledge translation into clinical practice lie in the level of literacy of the public of genetics and genomics. The aim of this study was to assess the knowledge, attitudes, and perceptions of the United Arab Emirates (UAE) multi-ethnic communities toward genomic medicine and genetic testing. METHOD: A cross-sectional study using validated questionnaires was distributed to the participants. Descriptive statistics were performed, and multivariable logistic regression models were used to identify factors associated with knowledge of genomics. RESULTS: 757 individuals completed the survey. Only 7% of the participants had a good knowledge level in genetics and genomics (95% CI 5.3-9.0%). However, 76.9% of the participants were willing to take a genetic test if their relatives had a genetic disease. In addition, the majority indicated that they would disclose their genetic test results to their spouses (61.5%) and siblings (53.4%). CONCLUSIONS: This study sets the stage for the stakeholders to plan health promotion and educational campaigns to improve the genomic literacy of the community of the UAE as part of their efforts for implementing precision and personalized medicine in the country.

BACKGROUND: This pilot study aims to identify and functionally assess pharmacovariants in whole exome sequencing data. While detection of known variants has benefited from pharmacogenomic-dedicated bioinformatics tools before, in this paper we have tested novel deep computational analysis in addition to artificial intelligence as possible approaches for functional analysis of unknown markers within less studied drug-related genes. METHODS: Pharmacovariants from 1800 drug-related genes from 100 WES data files underwent (a) deep computational analysis by eight bioinformatic algorithms (overall containing 23 tools) and (b) random forest (RF) classifier as the machine learning (ML) approach separately. ML model efficiency was calculated by internal and external cross-validation during recursive feature elimination. Protein modelling was also performed for predicted highly damaging variants with lower frequencies. Genotype-phenotype correlations were implemented for top selected variants in terms of highest possibility of being damaging. RESULTS: Five deleterious pharmacovariants in the RYR1, POLG, ANXA11, CCNH, and CDH23 genes identified in step (a) and subsequent analysis displayed high impact on drug-related phenotypes. Also, the utilization of recursive feature elimination achieved a subset of 175 malfunction pharmacovariants in 135 drug-related genes that were used by the RF model with fivefold internal cross-validation, resulting in an area under the curve of 0.9736842 with an average accuracy of 0.9818 (95% CI: 0.89, 0.99) on predicting whether a carrying individuals will develop adverse drug reactions or not. However, the external cross-validation of the same model indicated a possible false positive result when dealing with a low number of observations, as only 60 important variants in 49 genes were displayed, giving an AUC of 0.5384848 with an average accuracy of 0.9512 (95% CI: 0.83, 0.99). CONCLUSION: While there are some technologies for functionally assess not-interpreted pharmacovariants, there is still an essential need for the development of tools, methods, and algorithms which are able to provide a functional prediction for every single pharmacovariant in both large-scale datasets and small cohorts. Our approaches may bring new insights for choosing the right computational assessment algorithms out of high throughput DNA sequencing data from small cohorts to be used for personalized drug therapy implementation.

BACKGROUND: MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression. Differential miRNA expression, which is widely shown to be associated with the pathogenesis of various diseases, can be influenced by lifestyle factors, including smoking. This study aimed to investigate the plasma miRNA signature of smoking habits, the potential effect of smoking cessation on miRNA levels, and relate the findings with lung cancer incidence. RESULTS: A targeted RNA-sequencing approach measured plasma miRNA levels in 2686 participants from the population-based Rotterdam study cohort. The association between cigarette smoking (current versus never) and 591 well-expressed miRNAs was assessed via adjusted linear regression models, identifying 41 smoking-associated miRNAs that passed the Bonferroni-corrected threshold (P < 0.05/591 = 8.46 × 10-5). Moreover, we found 42 miRNAs with a significant association (P < 8.46 × 10-5) between current (reference group) and former smokers. Then, we used adjusted linear regression models to explore the effect of smoking cessation time on miRNA expression levels. The expression levels of two miRNAs were significantly different within 5 years of cessation (P < 0.05/41 = 1.22 × 10-3) from current smokers, while for cessation time between 5 and 15 years we found 19 miRNAs to be significantly different from current smokers, and finally, 38 miRNAs were significantly different after more than 15 years of cessation time (P < 1.22 × 10-3). These results imply the reversibility of the smoking effect on plasma levels of at least 38 out of the 41 smoking-miRNAs following smoking cessation. Next, we found 8 out of the 41 smoking-related miRNAs to be nominally associated (P < 0.05) with the incidence of lung cancer. CONCLUSIONS: This study demonstrates smoking-related dysregulation of plasma miRNAs, which might have a potential for reversibility when comparing different smoking cessation groups. The identified miRNAs are involved in several cancer-related pathways and include 8 miRNAs associated with lung cancer incidence. Our results may lay the groundwork for further investigation of miRNAs as potential mechanism linking smoking, gene expression and cancer.