Human Genomics最新文献

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.

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.

Background: 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.

Methods: Primary rat hepatocytes in sandwich culture were exposed to two methylenedioxybenzene (MDB) congeners and carbamazepine, with gene expression and splicing assessed. A 3D-clustergram integrating KEGG pathway analysis with differential gene expression provided distinct splicing landscapes for each xenobiotic, showing that splicing diversity does not always align with gene expression changes.

Results: Endpoint PCR revealed a Cyp2b2v to wild-type Cyp2b2 splicing ratio near 1:1 (100%) under most conditions, while RNA-seq showed a stable baseline closer to 40%. C6-MDB reduced this ratio to ~ 50% by PCR and ~ 39% by RNA-seq, showing slight method-dependent variations yet consistent trends. In contrast, exon 6 skipping in Cyp1a1 occurred only with MDB exposure, implicating AHR activation. Xenobiotic treatments also induced alternative splicing in defensome and stress-responsive genes, including the phase II enzyme Gstm3, Albumin, Orm1, and Fxyd1, highlighting their roles in xenobiotic response modulation. Significant splicing changes in factors such as SRSF1, SRSF7, and METTL3 suggest a coordinated feedback loop involving epitranscriptomic modulation and cross-talk within SR protein networks, refining splice site selection, transcript stability, and cellular fate.

Conclusions: This study demonstrates how xenobiotic structural features influence gene expression and splicing, revealing splicing patterns that expand our understanding of transcriptome diversity and function. By identifying regulatory mechanisms, including AHR activation, epitranscriptomic modulation, and crosstalk within SR protein networks, that shape adaptive responses to xenobiotic stress, this work offers insights into the splicing and transcriptional networks that maintain cellular homeostasis. These findings provide predictive biomarkers for toxic exposures and highlight the potential of splicing profiles as diagnostic tools for assessing the health impacts of chemical exposure.

Purpose: 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.

Methods: We conducted focus groups with members of the Australian public. Participants were recruited via social media advertisements. We explored potential uses of AI in genomic medicine, the benefits, risks, and the possible social implications of its use.

Results: Participants (n = 34) largely felt comfortable with AI analysing their own genomic data and generally agreed about its benefits. Concerns were raised over data security, the potential for misdiagnosis, and bias AI may perpetuate. Many participants wanted checking mechanisms for when results were generated using AI.

Conclusions: The insights gained from these discussions help to understand public concerns around the use of AI in genomic medicine. Our findings can help to inform both policies around genomic AI and how to educate the public on its use.

Objectives: 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).

Methods: A total of 552 specimens were collected and grouped by next-generation sequencing technology (NGS) and fetal survival: UPL (112 cases), CAF (170 cases) and control (258 cases). The polymorphisms of HLA-G 3'UTR in all samples were detected by Sanger sequencing. The genotypes, haplotypes and diplotypes of HLA-G 3'UTR were analyzed. The classification and regression tree (CART) analysis was used to evaluate the role of HLA-G diplotypes in predicting fetal outcomes. The correlations between CAF or UPL and maternal age, paternal age, times of miscarrage, times of delivery were analyzed by logistic regression.

Results: The frequencies of HLA-G + 2960del/del and + 3035CC genotypes were remarkablly increased in CAF than those in control group. The frequencies of HLA-G + 2960ins/del, + 3010CC, + 3035TC, + 3142GG, + 3187AA in CAF were significantly lower than those in normal fetuses. Through genetic models and logistic regression analysis, the dominant model of HLA-G 3'UTR genotypes [such as + 2960 (OR = 1.27, 95% CI = 1.05-1.54, p = 0.016), + 3010 (OR = 0.78, 95% CI = 0.63-0.97, p = 0.026), + 3035 (OR = 1.22, 95% CI = 1.00-1.49, p = 0.047), + 3142 (OR = 0.76, 95% CI = 0.62-0.95, p = 0.014) and + 3187 (OR = 0.80, 95% CI = 0.65-0.99, p = 0.041)] were dramatically associated with CAF. However, the frequencies of HLA-G + 3010GC, + 3142GC and + 3187AG in fetuses with UPL were memorably decreased than those in normal fetuses. No significant difference was found in the frequencies of HLA-G haplotypes in all groups. However, the frequency of UTR-1 positive specimens in CAF was significantly higher than that in UPL and control group. At the same time, the frequency of UTR-1/UTR-3 diplotypes in CAF was observably higher than that in UPL and control group, while the UTR-1/UTR-7 frequency in UPL was signally lower than that in control group. Multivariate logistic regression analysis indicated that positive HLA-G UTR-1 (OR = 1.8, 95% CI = 1.16-2.81, p = 0.009), times of abortion (OR = 1.23, 95% CI = 1.02-1.50, p = 0.035), and times of delivery (OR = 0.31, 95% CI = 0.20-0.48, p < 0.001) were correlated with CAF.

Conclusions: This study suggests that HLA-G 3'UTR polymorphisms and diplotypes play an important role in the process of successful pregnancy of the embryos with abnormal chromosomes after fertilization. At the same time, Different alleles or diplotypes also affect the development of embryos with UPL.

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 N⁶-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.

Background: 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.

Methods: We compile breast cancer's predisposing diseases from literature. For each predisposing disease, we use GWAS summary statistics data to identify genes in loci showing genetic correlation with breast cancer. Then, we use a network approach to link these shared genes to canonical pathways. Similarly, for all drugs treating the predisposing disease, we link their targets to pathways. In this manner, we are able to prioritize a list of drugs based on each predisposing disease, with each drug linked to a set of implicating pathways. Finally, we evaluate our recommendations against drugs currently under investigation for breast cancer.

Results: We identify 84 loci harboring mutations with positively correlated effects between breast cancer and its predisposing diseases; these contain 194 identified shared genes. Out of the 112 drugs indicated for the predisposing diseases, 74 drugs can be linked to shared genes via pathways (candidate drugs for repurposing). Fifteen out of these candidate drugs are already in advanced clinical trial phases or approved for breast cancer (OR = 9.28, p = 7.99e-03, one-sided Fisher's exact test), highlighting the ability of our approach to identify likely successful candidate drugs for repurposing.

Conclusions: Our novel approach accelerates drug repurposing for breast cancer by leveraging shared genetics with its known predisposing diseases. The result provides 59 novel candidate drugs alongside biological insights supporting each recommendation.

Background: 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.

Methods: Bioinformatics was used to identify overlapping genes associated with ferroptosis and the infiltration of 22 immune cells by Cell-type Identification by Estimating Relative Subsets of RNA Transcript (CIBERSORT) analysis. The expression of ferroptosis-related genes in AMI was validated across independent datasets, clinical samples, and in vitro cellular experiments. The predictive value for heart failure was evaluated in the first dimension of principal component analysis (PCA) using receiver operating characteristic (ROC) analysis.

Results: The study identified 11 key ferroptosis-related genes significantly correlated with immune cell abundance. CIBERSORT analysis highlighted immune dysregulation in AMI. JDP2, DUSP1, TLR4, NFS1, and SLC1A5 were identified as potential biomarkers for AMI progression. Additionally, JDP2, DUSP1, and DDIT4 demonstrated strong predictive value for long-term heart failure.

Conclusion: This study highlights the potential association of ferroptosis-related genes with the pathogenesis of AMI, suggesting a role in the molecular mechanisms that may underlie acute coronary events.

Background: 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.

Results: The 12 candidate variants were introduced into the corresponding minigene and functionally assayed in HEK 293T and Hela cells. We identified 11 variants that induce splicing alterations, resulting in various consequences such as skipping of exons, intron retention and protein truncation.

Conclusions: This underlined the importance of mRNA-level assessment for genetic diagnostics in related genetic disorders.

Background: 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.

Methods: Initially, 76 oxidative stress and ferroptosis-related genes (OFRGs) were acquired by intersecting the gene sets from oxidative stress and ferroptosis. Afterwards, optimal OFRGs were screened using PPI networks, and individuals were separated into two OFRG subtypes (K = 2). Subsequently, we successfully constructed and verified a prognostic signature comprising SLC7A2, Cadherin 19 (CDH19), and CCN1. To further uncover potential biomarkers of gastric cancer (GC), we examined the expression level of CDH19, investigated the effects of knocking down CDH19 on the biological behavior of GC cells, and explored whether CDH19 is involved in ferroptosis and oxidative stress processes.

Results: According to the findings, individuals in the low-risk scoring group have less infiltration of immune suppressive cells, fewer occurrences of immune escape and dysfunction, greater efficacy in chemotherapy and immunotherapy, and better survival outcomes. The qRT-PCR assay indicated that CDH19 expression was significantly higher in GC cells. Through experiments, we demonstrated that knocking down CDH19 can affect the transcription levels of ACSL4 and GPX4, increase intracellular iron ion concentration and accumulation of reactive oxygen species (ROS), and inhibit the proliferation and migration of GC cells.

Conclusion: We developed an OFRG-related signature to predict the prognosis and treatment responsiveness of individuals with GC and identified CDH19 as a possible therapeutic target for GC.