Ferroptosis is a unique mode of cell death that is iron-dependent and associated with oxidative stress and lipid peroxidation. Oxidative stress and ferroptosis are essential mechanisms leading to metabolic abnormalities in cells and have been popular areas in cancer research.

Genetic generalized epilepsy (GGE) including childhood absence epilepsy, juvenile absence epilepsy, juvenile myoclonic epilepsy (JME), and GGE with tonic-clonic seizures (TCS) (GGE-TCS), is genetically influenced with a two- to four- fold increased risk in the first-degree relatives of patients. Since large families with GGE are very rare, international studies have focused on sporadic GGE patients using whole exome sequencing, suggesting that GGE are highly genetically heterogeneous and rather involve rare or ultra-rare variants. Moreover, a polygenic mode of inheritance is suspected in most cases. We performed SNP microarrays and whole exome sequencing in 20 families from Sudan, focusing on those with at least four affected members. Standard genetic filters and Endeavour algorithm for functional prioritization of genes selected likely susceptibility variants in FAT1, DCHS1 or ASTN2 genes. FAT1 and DCHS1 are adhesion transmembrane proteins interacting during brain development, while ASTN2 is involved in dendrite development. Our approach on familial forms of GGE is complementary to large-scale collaborative consortia studies of sporadic cases. Our study reinforces the hypothesis that GGE is genetically heterogeneous, even in a relatively limited geographic area, and mainly oligogenic, as supported by the low familial penetrance of GGE and by the Bayesian algorithm that we developed in a large pedigree with JME. Since populations with founder effect and endogamy are appropriate to study autosomal recessive pathologies, they would be also adapted to decipher genetic components of complex diseases, using the reported bayesian model.

Acute myocardial infarction (AMI) is a leading cause of death and morbidity worldwide. Ferroptosis, a form of regulated cell death, plays a critical role in modulating immune functions during AMI. This study aimed to identify ferroptosis-related hub genes that could serve as potential therapeutic targets in the progression of AMI.

Autosomal Recessive Polycystic Kidney Disease (ARPKD) is a rare hereditary disorder caused by variants in PKHD1. Currently, aberrant splicing has been reported to play important roles in genetic disease. Our goal is to analyze intronic variants in PKHD1 at the mRNA level.

Kidney disease is marked by complex pathological mechanisms and significant therapeutic hurdles, resulting in high morbidity and mortality rates globally. A deeper understanding of the fundamental processes involved can aid in identifying novel therapeutic targets and improving treatment efficacy. Current comprehensive data analyses indicate the involvement of methyltransferase-like 3 (METTL3) and its role in RNA N-methyladenosine methylation in various renal pathologies, including acute kidney injury, renal fibrosis, and chronic kidney disease. However, there is a paucity of thorough reviews that clarify the functional mechanisms of METTL3 and evaluate its importance in enhancing therapeutic outcomes. This review seeks to systematically examine the roles, mechanisms, and potential clinical applications of METTL3 in renal diseases. The findings presented suggest that METTL3 is implicated in the etiology and exacerbation of kidney disorders, affecting their onset, progression, malignancy, and responsiveness to chemotherapeutic agents through the regulation of specific genetic pathways. In conclusion, this review underscores a detrimental correlation between METTL3 and kidney diseases, highlighting the therapeutic promise of targeting METTL3. Additionally, it offers critical insights for researchers concerning the diagnosis, prognosis, and treatment strategies for renal conditions.

Current effective breast cancer treatment options have severe side effects, highlighting a need for new therapies. Drug repurposing can accelerate improvements to care, as FDA-approved drugs have known safety and pharmacological profiles. Some drugs for other conditions, such as metformin, an antidiabetic, have been tested in clinical trials for repurposing for breast cancer. Here, we exploit the genetics of breast cancer and linked predisposing diseases to propose novel drug repurposing opportunities. We hypothesize that if a predisposing disease contributes to breast cancer pathology, identifying the pleiotropic genes related to the risk of cancer could prioritize drugs, among all drugs treating a predisposing disease. We aim to develop a method to not only prioritize drugs for repurposing, but also to highlight shared etiology explaining repurposing.

Human leukocyte antigen G (HLA-G) plays a crucial role in pregnancy. Pregnancy loss (PL) is caused by a variety of causes, such as fetal chromosomal abnormalities, maternal hypertension and diabetes, immune causes, spontaneous immune diseases, infections, unknown causes, etc. This study reports on the association of fetal HLA-G 3'UTR polymorphisms and diplotypes with chromosomally abnormal fetuses (CAF) or unexplained pregnancy loss (UPL).

Xenobiotic exposures can extensively influence the expression and alternative splicing of drug-metabolizing enzymes, including cytochromes P450 (CYPs), though their transcriptome-wide impact on splicing remains underexplored. This study used a well-characterized splicing event in the Cyp2b2 gene to validate a sandwich-cultured primary rat hepatocyte model for studying global splicing in vitro. Using endpoint PCR, RNA sequencing, and bioinformatics tools (rSeqDiff, rMATs, IGV), we analyzed differential gene expression and splicing in CYP and nuclear receptor genes, as well as the entire transcriptome, to understand how xenobiotic exposures shape alternative splicing and activate xenosensors.

Next generation sequencing has led to the creation of large pools of genomic data with analysis rather than data generation now the limiting factor. Artificial intelligence (AI) may be required to optimize the benefits of these data, but little is known about how the public feels about the use of AI in genomics.

4-methylimidazole is a ubiquitous and potentially carcinogenic environmental toxicant. Genetic factors that contribute to variation in susceptibility to its toxic effects are challenging to assess in human populations. We used the Drosophila melanogaster Genetic Reference Panel (DGRP), a living library of natural genetic variation, to identify genes with human orthologs associated with variation in susceptibility to 4-methylimidazole.

This comprehensive review provides insights and suggested strategies for the analysis of germline variants using second- and third-generation sequencing technologies (SGS and TGS). It addresses the critical stages of data processing, starting from alignment and preprocessing to quality control, variant calling, and the removal of artifacts. The document emphasized the importance of meticulous data handling, highlighting advanced methodologies for annotating variants and identifying structural variations and methylated DNA sites. Special attention is given to the inspection of problematic variants, a step that is crucial for ensuring the accuracy of the analysis, particularly in clinical settings where genetic diagnostics can inform patient care. Additionally, the document covers the use of various bioinformatics tools and software that enhance the precision and reliability of these analyses. It outlines best practices for the annotation of variants, including considerations for problematic genetic alterations such as those in the human leukocyte antigen region, runs of homozygosity, and mitochondrial DNA alterations. The document also explores the complexities associated with identifying structural variants and copy number variations, underscoring the challenges posed by these large-scale genomic alterations. The objective is to offer a comprehensive framework for researchers and clinicians, ensuring that genetic analyses conducted with SGS and TGS are both accurate and reproducible. By following these best practices, the document aims to increase the diagnostic accuracy for hereditary diseases, facilitating early diagnosis, prevention, and personalized treatment strategies. This review serves as a valuable resource for both novices and experts in the field, providing insights into the latest advancements and methodologies in genetic analysis. It also aims to encourage the adoption of these practices in diverse research and clinical contexts, promoting consistency and reliability across studies.

The merging of physiology and toxicokinetics, or pharmacokinetics, with computational modeling to characterize dosimetry has led to major advances for both the chemical and pharmaceutical research arenas. Driven by the mutual need to estimate internal exposures where in vivo data generation was simply not possible, the application of toxicokinetic modeling has grown exponentially in the past 30 years. In toxicology the need has been the derivation of quantitative estimates of toxicokinetic and toxicodynamic variability to evaluate the suitability of the tenfold uncertainty factor employed in risk assessment decision-making. Consideration of a host of physiologic, ontogenetic, genetic, and exposure factors are all required for comprehensive characterization. Fortunately, the underlying framework of physiologically based toxicokinetic models can accommodate these inputs, in addition to being amenable to capturing time-varying dynamics. Meanwhile, international interest in advancing new approach methodologies has fueled the generation of in vitro toxicity and toxicokinetic data that can be applied in in vitro-in vivo extrapolation approaches to provide human-specific risk-based information for historically data-poor chemicals. This review will provide a brief introduction to the structure and evolution of toxicokinetic and physiologically based toxicokinetic models as they advanced to incorporate variability and a wide range of complex exposure scenarios. This will be followed by a state of the science update describing current and emerging experimental and modeling strategies for population and life-stage variability, including the increasing application of in vitro-in vivo extrapolation with physiologically based toxicokinetic models in pharmaceutical and chemical safety research. The review will conclude with case study examples demonstrating novel applications of physiologically based toxicokinetic modeling and an update on its applications for regulatory decision-making. Physiologically based toxicokinetic modeling provides a sound framework for variability evaluation in chemical risk assessment.

Cisplatin-induced ototoxicity (CIO), characterized by irreversible and progressive bilateral hearing loss, is a prevalent adverse effect of cisplatin chemotherapy. Alongside clinical risk factors, genetic variants contribute to CIO and genome-wide association studies (GWAS) have highlighted the polygenicity of this adverse drug reaction. Polygenic scores (PGS), which integrate information from multiple genetic variants across the genome, offer a promising tool for the identification of individuals who are at higher risk for CIO. Integrating large-scale hearing loss GWAS data with single cell omics data holds potential to overcome limitations related to small sample sizes associated with CIO studies, enabling the creation of PGSs to predict CIO risk.

Persistent racial disparities in health outcomes have catalyzed legislative reforms and heightened scientific focus recently. However, despite the well-documented properties of skin pigments in binding drug compounds, their impact on therapeutic efficacy and adverse drug responses remains insufficiently explored. This perspective examines the intricate relationships between variation in melanin-based skin pigmentation and pharmacokinetics and -dynamics, highlighting the need for considering diversity in skin pigmentation as a variable to advance the equitability of pharmacological interventions. The article provides guidelines on the selection of New Approach Methods (NAMs) to foster inclusive study designs in preclinical drug development pipelines, leading to an improved level of translatability to the clinic.

This study aimed to screen southern and southwestern Chinese individuals using expanded carrier screening (ECS), which explores the carrier status of recessively inherited diseases in southern and southwestern China, evaluates the clinical effectiveness of ECS application, and helps recognize high-risk fetuses that may have genetic disorders early in pregnancy, to provide better reproductive guidance.

Whole genome sequencing (WGS) is becoming increasingly prevalent for molecular diagnosis, staging and prognosis because of its declining costs and the ability to detect nearly all genes associated with a patient's disease. The currently widely accepted variant calling pipeline, GATK, is limited in terms of its computational speed and efficiency, which cannot meet the growing analysis needs.

Periodontitis is a highly prevalent inflammatory illness that leads to the destruction of tooth supporting tissue structures and has been associated with an increased risk of cardiovascular disease (CVD). Precision medicine, an emerging branch of medical treatment, aims can further improve current traditional treatment by personalizing care based on one's environment, genetic makeup, and lifestyle. Genomic databases have paved the way for precision medicine by elucidating the pathophysiology of complex, heritable diseases. Therefore, the investigation of novel periodontitis-linked genes associated with CVD will enhance our understanding of their linkage and related biochemical pathways for targeted therapies. In this article, we highlight possible mechanisms of actions connecting PD and CVD. Furthermore, we delve deeper into certain heritable inflammatory-associated pathways linking the two. The goal is to gather, compare, and assess high-quality scientific literature alongside genomic datasets that seek to establish a link between periodontitis and CVD. The scope is focused on the most up to date and authentic literature published within the last 10 years, indexed and available from PubMed Central, that analyzes periodontitis-associated genes linked to CVD. Based on the comparative analysis criteria, fifty-one genes associated with both periodontitis and CVD were identified and reported. The prevalence of genes associated with both CVD and periodontitis warrants investigation to assess the validity of a potential linkage between the pathophysiology of both diseases.

Substantial evidence indicates that measuring leukocyte telomere length (LTL) is a useful tool that may be considered as a valuable biomarker of individual biological age, correlating with numerous chronic disorders. However, to date, there has been a lack of in-depth understanding regarding the current landscape and forthcoming developments in the LTL field. Therefore, this study aimed to utilize bibliometric methods to summarize the knowledge structure, current focus, and emerging directions in this field.

FLNC gene variants have predominantly been reported in adult populations with cardiomyopathies, and early-onset cases are less common. The genotype-phenotype relationship indicates that dilated cardiomyopathy (DCM) is often associated with FLNC truncating variants.

Spinal muscular atrophy (SMA) is the second most common fatal genetic disease in infancy. It is caused by deletion or intragenic pathogenic variants of the causative gene SMN1, which degenerates anterior horn motor neurons and leads to progressive myasthenia and muscle atrophy. Early treatment improves motor function and prognosis in patients with SMA, but drugs are expensive and do not cure the disease. Therefore, carrier screening seems to be the most effective way to prevent SMA birth defects. In this study, we genetically analyzed 1400 samples using multiplex ligation-dependent probe amplification (MLPA) and quantitative polymerase chain reaction (qPCR), and compared the consistency of the results. We randomly selected 44 samples with consistent MLPA and qPCR results for comprehensive SMA analysis (CASMA) using a long-read sequencing (LRS)-based approach. CASMA results showed 100% consistency, visually and intuitively explained the inconsistency between exons 7 and 8 copy numbers detected by MLPA in 13 samples. A total of 16 samples showed inconsistent MLPA and qPCR results for SMN1 exon 7. CASMA was performed on all samples and the results were consistent with those of resampling for MLPA and qPCR detection. CASMA also detected an additional intragenic variant c.-39A>G in a sample with two copies of SMN1 (RT02). Finally, we detected 23 SMA carriers, with an estimated carrier rate of 1/61 in this cohort. In addition, CASMA identified the "2 + 0" carrier status of SMN1 and SMN2 in a family by analyzing the genotypes of only three samples (parents and one sibling). CASMA has great advantages over MLPA and qPCR assays, and could become a powerful technical support for large-scale screening of SMA.