Insights into pharmacogenetics, drug-gene interactions, and drug-drug-gene interactions.

This review explores genetic contributors to drug interactions, known as drug-gene and drug-drug-gene interactions (DGI and DDGI, respectively). This article is part of a mini-review issue led by the International Society for the Study of Xenobiotics (ISSX) New Investigators Group. Pharmacogenetics (PGx) is the study of the impact of genetic variation on pharmacokinetics (PK), pharmacodynamics (PD), and adverse drug reactions. Genetic variation in pharmacogenes, including drug metabolizing enzymes and drug transporters, is common and can increase the risk of adverse drug events or contribute to reduced efficacy. In this review, we summarize clinically actionable genetic variants, and touch on methodologies such as genotyping patient DNA to identify genetic variation in targeted genes, and deep mutational scanning as a high-throughput in vitro approach to study the impact of genetic variation on protein function and/or expression in vitro. We highlight the utility of physiologically based pharmacokinetic (PBPK) models to integrate genetic and chemical inhibitor and inducer data for more accurate human PK simulations. Additionally, we analyze the limitations of historical ethnic descriptors in pharmacogenomics research. Altogether, the work herein underscores the importance of identifying and understanding complex DGI and DDGIs with the intention to provide better treatment outcomes for patients. We also highlight current barriers to wide-scale implementation of PGx-guided dosing as standard or care in clinical settings.

Identifying Core Genes Related to Low-Temperature Stress Resistance in Quinoa Seedlings Based on WGCNA.

Quinoa is a nutritious crop that is tolerant to extreme environmental conditions; however, low-temperature stress can affect quinoa growth, development, and quality. Considering the lack of molecular research on quinoa seedlings under low-temperature stress, we utilized a Weighted Gene Co-Expression Network Analysis to construct weighted gene co-expression networks associated with physiological indices and metabolites related to low-temperature stress resistance based on transcriptomic data. We screened 11 co-expression modules closely related to low-temperature stress resistance and selected 12 core genes from the two modules that showed the highest associations with the target traits. Following the functional annotation of these genes to determine the key biological processes and metabolic pathways involved in low-temperature stress, we identified four important transcription factors involved in resistance to low-temperature stress: gene-LOC110731664, gene-LOC110736639, gene-LOC110684437, and gene-LOC110720903. These results provide insights into the molecular genetic mechanism of quinoa under low-temperature stress and can be used to breed lines with tolerance to low-temperature stress.

A method for mining condition-specific co-expressed genes in Camellia sinensis based on k-means clustering.

BACKGROUND: As one of the world's most important beverage crops, tea plants (Camellia sinensis) are renowned for their unique flavors and numerous beneficial secondary metabolites, attracting researchers to investigate the formation of tea quality. With the increasing availability of transcriptome data on tea plants in public databases, conducting large-scale co-expression analyses has become feasible to meet the demand for functional characterization of tea plant genes. However, as the multidimensional noise increases, larger-scale co-expression analyses are not always effective. Analyzing a subset of samples generated by effectively downsampling and reorganizing the global sample set often leads to more accurate results in co-expression analysis. Meanwhile, global-based co-expression analyses are more likely to overlook condition-specific gene interactions, which may be more important and worthy of exploration and research. RESULTS: Here, we employed the k-means clustering method to organize and classify the global samples of tea plants, resulting in clustered samples. Metadata annotations were then performed on these clustered samples to determine the "conditions" represented by each cluster. Subsequently, we conducted gene co-expression network analysis (WGCNA) separately on the global samples and the clustered samples, resulting in global modules and cluster-specific modules. Comparative analyses of global modules and cluster-specific modules have demonstrated that cluster-specific modules exhibit higher accuracy in co-expression analysis. To measure the degree of condition specificity of genes within condition-specific clusters, we introduced the correlation difference value (CDV). By incorporating the CDV into co-expression analyses, we can assess the condition specificity of genes. This approach proved instrumental in identifying a series of high CDV transcription factor encoding genes upregulated during sustained cold treatment in Camellia sinensis leaves and buds, and pinpointing a pair of genes that participate in the antioxidant defense system of tea plants under sustained cold stress. CONCLUSIONS: To summarize, downsampling and reorganizing the sample set improved the accuracy of co-expression analysis. Cluster-specific modules were more accurate in capturing condition-specific gene interactions. The introduction of CDV allowed for the assessment of condition specificity in gene co-expression analyses. Using this approach, we identified a series of high CDV transcription factor encoding genes related to sustained cold stress in Camellia sinensis. This study highlights the importance of considering condition specificity in co-expression analysis and provides insights into the regulation of the cold stress in Camellia sinensis.

MR-GGI: accurate inference of gene-gene interactions using Mendelian randomization.

BACKGROUND: Researchers have long studied the regulatory processes of genes to uncover their functions. Gene regulatory network analysis is one of the popular approaches for understanding these processes, requiring accurate identification of interactions among the genes to establish the gene regulatory network. Advances in genome-wide association studies and expression quantitative trait loci studies have led to a wealth of genomic data, facilitating more accurate inference of gene-gene interactions. However, unknown confounding factors may influence these interactions, making their interpretation complicated. Mendelian randomization (MR) has emerged as a valuable tool for causal inference in genetics, addressing confounding effects by estimating causal relationships using instrumental variables. In this paper, we propose a new statistical method, MR-GGI, for accurately inferring gene-gene interactions using Mendelian randomization. RESULTS: MR-GGI applies one gene as the exposure and another as the outcome, using causal cis-single-nucleotide polymorphisms as instrumental variables in the inverse-variance weighted MR model. Through simulations, we have demonstrated MR-GGI's ability to control type 1 error and maintain statistical power despite confounding effects. MR-GGI performed the best when compared to other methods using the F1 score on the DREAM5 dataset. Additionally, when applied to yeast genomic data, MR-GGI successfully identified six clusters. Through gene ontology analysis, we have confirmed that each cluster in our study performs distinct functional roles by gathering genes with specific functions. CONCLUSION: These findings demonstrate that MR-GGI accurately inferences gene-gene interactions despite the confounding effects in real biological environments.

scCompressSA: dual-channel self-attention based deep autoencoder model for single-cell clustering by compressing gene-gene interactions.

BACKGROUND: Single-cell clustering has played an important role in exploring the molecular mechanisms about cell differentiation and human diseases. Due to highly-stochastic transcriptomics data, accurate detection of cell types is still challenged, especially for RNA-sequencing data from human beings. In this case, deep neural networks have been increasingly employed to mine cell type specific patterns and have outperformed statistic approaches in cell clustering. RESULTS: Using cross-correlation to capture gene-gene interactions, this study proposes the scCompressSA method to integrate topological patterns from scRNA-seq data, with support of self-attention (SA) based coefficient compression (CC) block. This SA-based CC block is able to extract and employ static gene-gene interactions from scRNA-seq data. This proposed scCompressSA method has enhanced clustering accuracy in multiple benchmark scRNA-seq datasets by integrating topological and temporal features. CONCLUSION: Static gene-gene interactions have been extracted as temporal features to boost clustering performance in single-cell clustering For the scCompressSA method, dual-channel SA based CC block is able to integrate topological features and has exhibited extraordinary detection accuracy compared with previous clustering approaches that only employ temporal patterns.

Physiologically Based Pharmacokinetic Modeling to Unravel the Drug-gene Interactions of Venlafaxine: Based on Activity Score-dependent Metabolism by CYP2D6 and CYP2C19 Polymorphisms.

BACKGROUND: Venlafaxine (VEN) is a commonly utilized medication for alleviating depression and anxiety disorders. The presence of genetic polymorphisms gives rise to considerable variations in plasma concentrations across different phenotypes. This divergence in phenotypic responses leads to notable differences in both the efficacy and tolerance of the drug. PURPOSE: A physiologically based pharmacokinetic (PBPK) model for VEN and its metabolite O-desmethylvenlafaxine (ODV) to predict the impact of CYP2D6 and CYP2C19 gene polymorphisms on VEN pharmacokinetics (PK). METHODS: The parent-metabolite PBPK models for VEN and ODV were developed using PK-Sim® and MoBi®. Leveraging prior research, derived and implemented CYP2D6 and CYP2C19 activity score (AS)-dependent metabolism to simulate exposure in the drug-gene interactions (DGIs) scenarios. The model's performance was evaluated by comparing predicted and observed values of plasma concentration-time (PCT) curves and PK parameters values. RESULTS: In the base models, 91.1%, 94.8%, and 94.6% of the predicted plasma concentrations for VEN, ODV, and VEN + ODV, respectively, fell within a twofold error range of the corresponding observed concentrations. For DGI scenarios, these values were 81.4% and 85% for VEN and ODV, respectively. Comparing CYP2D6 AS = 2 (normal metabolizers, NM) populations to AS = 0 (poor metabolizers, PM), 0.25, 0.5, 0.75, 1.0 (intermediate metabolizers, IM), 1.25, 1.5 (NM), and 3.0 (ultrarapid metabolizers, UM) populations in CYP2C19 AS = 2.0 group, the predicted DGI AUC0-96 h ratios for VEN were 3.65, 3.09, 2.60, 2.18, 1.84, 1.56, 1.34, 0.61, and for ODV, they were 0.17, 0.35, 0.51, 0.64, 0.75, 0.83, 0.90, 1.11, and the results were similar in other CYP2C19 groups. It should be noted that PK differences in CYP2C19 phenotypes were not similar across different CYP2D6 groups. CONCLUSIONS: In clinical practice, the impact of genotyping on the in vivo disposition process of VEN should be considered to ensure the safety and efficacy of treatment.

Use of drugs with pharmacogenomics (PGx)-based dosing guidelines in a Danish cohort of persons with chronic kidney disease, both on dialysis and not on dialysis: Perspectives for prescribing optimization.

AIM: The objective of this registry study is to assess the utilization of pharmacogenomic (PGx) drugs among patients with chronic kidney disease (CKD). METHODS: This study was a retrospective study of patients affiliated with the Department of Nephrology, Aalborg University Hospital, Denmark in 2021. Patients diagnosed with CKD were divided into CKD without dialysis and CKD with dialysis. PGx prescription drugs were retrieved from the Patient Administration System. Actionable dosing guidelines (AG) for specific drug-gene pairs for CYP2D6, CYP2C9, CYP2C19 and SLCO1B1 were retrieved from the PharmGKB homepage. RESULTS: Out of 1241 individuals, 25.5% were on dialysis. The median number of medications for each patient was 9 within the non-dialysis group and 16 within the dialysis group. Thirty-one distinct PGx drugs were prescribed. Altogether, 76.0% (943 individuals) were prescribed at least one PGx drug and the prevalence of prescriptions of PGx drugs was higher in the dialysis group compared to the non-dialysis group. The most frequently prescribed drugs with AG were metoprolol, pantoprazole, atorvastatin, simvastatin and warfarin. CONCLUSION: This study demonstrated that a substantial proportion of patients with CKD are exposed to drugs or drug combinations for which there exists AG related to PGx of CYP2D6, CYP2C19, CYP2C9 and SLCO1B1.

Augusta: From RNA-Seq to gene regulatory networks and Boolean models.

Computational models of gene regulations help to understand regulatory mechanisms and are extensively used in a wide range of areas, e.g., biotechnology or medicine, with significant benefits. Unfortunately, there are only a few computational gene regulatory models of whole genomes allowing static and dynamic analysis due to the lack of sophisticated tools for their reconstruction. Here, we describe Augusta, an open-source Python package for Gene Regulatory Network (GRN) and Boolean Network (BN) inference from the high-throughput gene expression data. Augusta can reconstruct genome-wide models suitable for static and dynamic analyses. Augusta uses a unique approach where the first estimation of a GRN inferred from expression data is further refined by predicting transcription factor binding motifs in promoters of regulated genes and by incorporating verified interactions obtained from databases. Moreover, a refined GRN is transformed into a draft BN by searching in the curated model database and setting logical rules to incoming edges of target genes, which can be further manually edited as the model is provided in the SBML file format. The approach is applicable even if information about the organism under study is not available in the databases, which is typically the case for non-model organisms including most microbes. Augusta can be operated from the command line and, thus, is easy to use for automated prediction of models for various genomes. The Augusta package is freely available at github.com/JanaMus/Augusta. Documentation and tutorials are available at augusta.readthedocs.io.

Computational multigene interactions in virus growth and infection spread.

Viruses persist in nature owing to their extreme genetic heterogeneity and large population sizes, which enable them to evade host immune defenses, escape antiviral drugs, and adapt to new hosts. The persistence of viruses is challenging to study because mutations affect multiple virus genes, interactions among genes in their impacts on virus growth are seldom known, and measures of viral fitness are yet to be standardized. To address these challenges, we employed a data-driven computational model of cell infection by a virus. The infection model accounted for the kinetics of viral gene expression, functional gene-gene interactions, genome replication, and allocation of host cellular resources to produce progeny of vesicular stomatitis virus, a prototype RNA virus. We used this model to computationally probe how interactions among genes carrying up to eleven deleterious mutations affect different measures of virus fitness: single-cycle growth yields and multicycle rates of infection spread. Individual mutations were implemented by perturbing biophysical parameters associated with individual gene functions of the wild-type model. Our analysis revealed synergistic epistasis among deleterious mutations in their effects on virus yield; so adverse effects of single deleterious mutations were amplified by interaction. For the same mutations, multicycle infection spread indicated weak or negligible epistasis, where single mutations act alone in their effects on infection spread. These results were robust to simulation in high- and low-host resource environments. Our work highlights how different types and magnitudes of epistasis can arise for genetically identical virus variants, depending on the fitness measure. More broadly, gene-gene interactions can differently affect how viruses grow and spread.

Self-supervised deep learning of gene-gene interactions for improved gene expression recovery.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to gain biological insights at the cellular level. However, due to technical limitations of the existing sequencing technologies, low gene expression values are often omitted, leading to inaccurate gene counts. Existing methods, including advanced deep learning techniques, struggle to reliably impute gene expressions due to a lack of mechanisms that explicitly consider the underlying biological knowledge of the system. In reality, it has long been recognized that gene-gene interactions may serve as reflective indicators of underlying biology processes, presenting discriminative signatures of the cells. A genomic data analysis framework that is capable of leveraging the underlying gene-gene interactions is thus highly desirable and could allow for more reliable identification of distinctive patterns of the genomic data through extraction and integration of intricate biological characteristics of the genomic data. Here we tackle the problem in two steps to exploit the gene-gene interactions of the system. We first reposition the genes into a 2D grid such that their spatial configuration reflects their interactive relationships. To alleviate the need for labeled ground truth gene expression datasets, a self-supervised 2D convolutional neural network is employed to extract the contextual features of the interactions from the spatially configured genes and impute the omitted values. Extensive experiments with both simulated and experimental scRNA-seq datasets are carried out to demonstrate the superior performance of the proposed strategy against the existing imputation methods.

Interaction with a phage gene underlie costs of a ß-lactamase.

The fitness cost of an antibiotic resistance gene (ARG) can differ across host strains, creating refuges that allow the maintenance of an ARG in the absence of direct selection for its resistance phenotype. Despite the importance of such ARG-host interactions for predicting ARG dynamics, the basis of ARG fitness costs and their variability between hosts are not well understood. We determined the genetic basis of a host-dependent cost of a ß-lactamase, blaTEM-116*, that conferred a significant cost in one Escherichia coli strain but was close to neutral in 11 other Escherichia spp. strains. Selection of a blaTEM-116*-encoding plasmid in the strain in which it initially had a high cost resulted in rapid and parallel compensation for that cost through mutations in a P1-like phage gene, relAP1. When the wild-type relAP1 gene was added to a strain in which it was not present and in which blaTEM-116* was neutral, it caused the ARG to become costly. Thus, relAP1 is both necessary and sufficient to explain blaTEM-116* costs in at least some host backgrounds. To our knowledge, these findings represent the first demonstrated case of the cost of an ARG being influenced by a genetic interaction with a phage gene. The interaction between a phage gene and a plasmid-borne ARG highlights the complexity of selective forces determining the maintenance and spread of ARGs and, by extension, encoding phage and plasmids in natural bacterial communities.IMPORTANCEAntibiotic resistance genes (ARGs) play a major role in the increasing problem of antibiotic resistance in clinically relevant bacteria. Selection of these genes occurs in the presence of antibiotics, but their eventual success also depends on the sometimes substantial costs they impose on host bacteria in antibiotic-free environments. We evolved an ARG that confers resistance to penicillin-type antibiotics in one host in which it did confer a cost and in one host in which it did not. We found that costs were rapidly and consistently reduced through parallel genetic changes in a gene encoded by a phage that was infecting the costly host. The unmutated version of this gene was sufficient to cause the ARG to confer a cost in a host in which it was originally neutral, demonstrating an antagonism between the two genetic elements and underlining the range and complexity of pressures determining ARG dynamics in natural populations.

Genetic Determinants of Response to Statins in Cardiovascular Diseases.

Despite extensive efforts to identify patients with cardiovascular disease (CVD) who could most benefit from the treatment approach, patients vary in their benefit from therapy and propensity for adverse drug events. Genetic variability in individual responses to drugs (pharmacogenetics) is considered an essential determinant in responding to a drug. Thus, understanding these pharmacogenomic relationships has led to a substantial focus on mechanisms of disease and drug response. In turn, understanding the genomic and molecular bases of variables that might be involved in drug response is the main step in personalized medicine. There is a growing body of data evaluating drug-gene interactions in recent years, some of which have led to FDA recommendations and detection of markers to predict drug responses (e.g., genetic variant in VKORC1 and CYP2C9 genes for prediction of drug response in warfarin treatment). Also, statins are widely prescribed drugs for the prevention of CVD. Atorvastatin, fluvastatin, rosuvastatin, simvastatin, and lovastatin are the most common statins used to manage dyslipidemia. This review provides an overview of the current knowledge on the pharmacogenetics of statins, which are being used to treat cardiovascular diseases.

MICA and NLRP3 gene polymorphisms interact synergistically affecting the risk of ankylosing spondylitis.

Ankylosing spondylitis (AS) is an autoinflammatory disease that affects the sacroiliac joints, causing stiffness and pain in the back. MICA is a ligand of the NKG2D receptor, and an increase in its expression affects the immune response in various diseases. NLRP3 is a multiprotein complex that promotes the release of IL-1ß, but its role in AS has been minimally explored. The objective of this study was to analyze the association and interaction of polymorphic variants of the MICA and NLRP3 genes in patients with AS. In this case-control study, patients with AS were included and compared with healthy controls of Mexican origin. The polymorphisms rs4349859 and rs116488202 of MICA and rs3806268 and rs10754558 of NLRP3 were genotyped using TaqMan probes. Associations were determined using logistic regression models, while interactions were analyzed by the multifactorial dimensionality reduction (MDR) method. A P value < 0.05 was considered statistically significant. The minor allele of rs4349859 (A) and rs116488202 (T) of MICA polymorphisms showed risk associations with AS (OR = 9.22, 95% CI = 4.26-20.0, P < 0.001; OR = 9.36, 95% CI = 4.17-21.0, P < 0.001), while the minor allele of the rs3806268 (A) polymorphism of NLRP3 was associated with protection (OR = 0.55, 95% CI = 0.33-0.91, P = 0.019). MDR analysis revealed synergistic interactions between the MICA and NLRP3 polymorphisms (P = 0.012). In addition, high- and low-risk genotypes were identified among these variants. The study findings suggest that the MICA rs4349859 A allele and rs116488202 T allele are associated with AS risk. An interaction between MICA and NLRP3 was observed which could increase the genetic risk in AS.

Bayesian Approaches in Exploring Gene-environment and Gene-gene Interactions: A Comprehensive Review.

Rapid advancements in high-throughput biological techniques have facilitated the generation of high-dimensional omics datasets, which have provided a solid foundation for precision medicine and prognosis prediction. Nonetheless, the problem of missing heritability persists. To solve this problem, it is essential to explain the genetic structure of disease incidence risk and prognosis by incorporating interactions. The development of the Bayesian theory has provided new approaches for developing models for interaction identification and estimation. Several Bayesian models have been developed to improve the accuracy of model and identify the main effect, gene-environment (G×E) and gene-gene (G×G) interactions. Studies based on single-nucleotide polymorphisms (SNPs) are significant for the exploration of rare and common variants. Models based on the effect heredity principle and group-based models are relatively flexible and do not require strict constraints when dealing with the hierarchical structure between the main effect and interactions (M-I). These models have a good interpretability of biological mechanisms. Machine learning-based Bayesian approaches are highly competitive in improving prediction accuracy. These models provide insights into the mechanisms underlying the occurrence and progression of complex diseases, identify more reliable biomarkers, and develop higher predictive accuracy. In this paper, we provide a comprehensive review of these Bayesian approaches.

Analysis of liver miRNA in Hu sheep with different residual feed intake.

Feed efficiency (FE), an important economic trait in sheep production, is indirectly assessed by residual feed intake (RFI). However, RFI in sheep is varied, and the molecular processes that regulate RFI are unclear. It is thus vital to investigate the molecular mechanism of RFI to developing a feed-efficient sheep. The miRNA-sequencing (RNA-Seq) was utilized to investigate miRNAs in liver tissue of 6 out of 137 sheep with extreme RFI phenotypic values. In these animals, as a typical metric of FE, RFI was used to distinguish differentially expressed miRNAs (DE_miRNAs) between animals with high (n = 3) and low (n = 3) phenotypic values. A total of 247 miRNAs were discovered in sheep, with four differentially expressed miRNAs (DE_miRNAs) detected. Among these DE_miRNAs, three were found to be upregulated and one was downregulated in animals with low residual feed intake (Low_RFI) compared to those with high residual feed intake (High_RFI). The target genes of DE_miRNAs were primarily associated with metabolic processes and biosynthetic process regulation. Furthermore, they were also considerably enriched in the FE related to glycolysis, protein synthesis and degradation, and amino acid biosynthesis pathways. Six genes were identified by co-expression analysis of DE_miRNAs target with DE_mRNAs. These results provide a theoretical basis for us to understand the sheep liver miRNAs in RFI molecular regulation.

Patient with PSEN1 Glu318Gly and Other Possible Disease Risk Mutations, Diagnosed with Early Onset Alzheimer's Disease.

In this manuscript, we introduced a French EOAD patient in Korea who carried the presenilin-1 (PSEN1) Glu318Gly mutations with four possible risk variants, including sortilin-related receptor 1 (SORL1) Glu270Lys, ATP-binding cassette subfamily A member 7 (ABCA7) Val1946Met, translocase of outer mitochondrial membrane 40 (TOMM40) Arg239Trp, and granulin (GRN) Ala505Gly. The patient started to present memory decline and behavioral dysfunction in his early 60s. His brain imaging presented amyloid deposits by positron emission tomography (PET-CT). The multimer detection system (MDS) screening test for plasma for amyloid oligomers was also positive, which supported the AD diagnosis. It was verified that PSEN1 Glu318Gly itself may not impact amyloid production. However, additional variants were found in other AD and non-AD risk genes, as follows: SORL1 Glu270Lys was suggested as a risk mutation for AD and could increase amyloid peptide production and impair endosome functions. ABCA7 Val1946Met was a novel variant that was predicted to be damaging. The GRN Ala505Gly was a variant with uncertain significance; however, it may reduce the granulin levels in the plasma of dementia patients. Pathway analysis revealed that PSEN1 Glu318Gly may work as a risk factor along with the SORL1 and ABCA7 variants since pathway analysis revealed that PSEN1 could directly interact with them through amyloid-related and lipid metabolism pathways. TOMM40 and PSEN1 could have common mechanisms through mitochondrial dysfunction. It may be possible that PSEN1 Glu318Gly and GRN Ala505Gly would impact disease by impairing immune-related pathways, including microglia and astrocyte development, or NFkB-related pathways. Taken together, the five risk factors may contribute to disease-related pathways, including amyloid and lipid metabolism, or impair immune mechanisms.

Polymorphism of Estrogen Receptor Genes and Its Interactions With Neurodevelopmental Genes in Attention Deficit Hyperactivity Disorder Among Chinese Han Descent.

OBJECTIVE: Attention deficit hyperactivity disorder (ADHD) is a polygenic neurodevelopmental disorder with significant gender differences. The sexual dimorphism of ADHD may be associated with estrogen acting through estrogen receptors (ESR). This study investigates the impact of ESR gene polymorphism and its interactions with neurodevelopmental genes on ADHD susceptibility. METHODS: The study compared genotyping data of single nucleotide polymorphisms in ESR1 and ESR2 in 1,035 ADHD cases and 962 controls. The gene-gene interactions between ESR genes and three neurodevelopmental genes (brain-derived neurotrophic factor [BDNF], synaptosomal-associated protein of 25 kDa gene [SNAP25], and cadherin-13 [CDH13]) in ADHD were investigated using generalized multifactor dimensionality reduction and verified by logistic regression analysis. RESULTS: The G allele of rs960070/ESR2 (empirical p=0.0076) and the A allele of rs8017441/ESR2 (empirical p=0.0426) were found significantly higher in ADHD cases than in the controls but not in male or female subgroups. Though no difference was found in all subjects or females, the A allele of rs9340817/ESR1 (empirical p=0.0344) was found significantly higher in ADHD cases than controls in males. We also found genetic interaction models between ESR2 gene, neurodevelopmental genes and ADHD susceptibility in males (ESR2 rs960070/BDNF rs6265/BDNF rs2049046/SNAP25 rs362987/CDH13 rs6565113) and females (ESR2 rs960070/BDNF rs6265/BDNF rs2049046) separately, though it was negative in overall subjects. CONCLUSION: The ESR gene polymorphism associates with ADHD among Chinese Han children, with interactions between ESR genes and neurodevelopmental genes potentially influencing the susceptibility of ADHD.

Evaluation of the Possible Pathways Involved in the Protective Effects of Quercetin, Naringenin, and Rutin at the Gene, Protein and miRNA Levels Using In-Silico Multidimensional Data Analysis.

Flavonoids are secondary metabolites that are non-essential for plant growth or survival, and they also provide numerous health benefits to humans. They are antioxidants that shield plants from the ill effects of ultraviolet light, pests, and diseases. They are beneficial to health for several reasons, including lowering inflammation, boosting cardiovascular health, and lowering cancer risk. This study looked into the physicochemical features of these substances to determine the potential pharmacological pathways involved in their protective actions. Potential targets responsible for the protective effects of quercetin, naringenin, and rutin were identified with SwissADME. The associated biological processes and protein-protein networks were analyzed by using the GeneMANIA, Metascape, and STRING servers. All the flavonoids were predicted to be orally bioavailable, with more than 90% targets as enzymes, including kinases and lyases, and with common targets such as NOS2, CASP3, CASP9, CAT, BCL2, TNF, and HMOX1. TNF was shown to be a major target in over 250 interactions. To extract the "biological meanings" from the MCODE networks' constituent parts, a GO enrichment analysis was performed on each one. The most important transcription factors in gene regulation were RELA, NFKB1, PPARG, and SP1. Treatment with quercetin, naringenin, or rutin increased the expression and interaction of the microRNAs' hsa-miR-34a-5p, hsa-miR-30b-5p, hsa-let-7a-5p, and hsa-miR-26a-1-3p. The anticancer effects of hsa-miR-34a-5p have been experimentally confirmed. It also plays a critical role in controlling other cancer-related processes such as cell proliferation, apoptosis, EMT, and metastasis. This study's findings might lead to a deeper comprehension of the mechanisms responsible for flavonoids' protective effects and could present new avenues for exploration.

Perspective: Council for Responsible Nutrition Science in Session. Optimizing Health with Nutrition-Opportunities, Gaps, and the Future.

Achieving optimal health is an aspirational goal for the population, yet the definition of health remains unclear. The role of nutrition in health has evolved beyond correcting malnutrition and specific deficiencies and has begun to focus more on achieving and maintaining 'optimal' health through nutrition. As such, the Council for Responsible Nutrition held its October 2022 Science in Session conference to advance this concept. Here, we summarize and discuss the findings of their Optimizing Health through Nutrition - Opportunities and Challenges workshop, including several gaps that need to be addressed to advance progress in the field. Defining and evaluating various indices of optimal health will require overcoming these key gaps. For example, there is a strong need to develop better biomarkers of nutrient status, including more accurate markers of food intake, as well as biomarkers of optimal health that account for maintaining resilience-the ability to recover from or respond to stressors without loss to physical and cognitive performance. In addition, there is a need to identify factors that drive individualized responses to nutrition, including genotype, metabotypes, and the gut microbiome, and to realize the opportunity of precision nutrition for optimal health. This review outlines hallmarks of resilience, provides current examples of nutritional factors to optimize cognitive and performance resilience, and gives an overview of various genetic, metabolic, and microbiome determinants of individualized responses.

The impact of CYP2C19 genotype on phenoconversion by concomitant medication.

Introduction: Pharmacogenetics-informed drug prescribing is increasingly applied in clinical practice. Typically, drug metabolizing phenotypes are determined based on genetic test results, whereupon dosage or drugs are adjusted. Drug-drug-interactions (DDIs) caused by concomitant medication can however cause mismatches between predicted and observed phenotypes (phenoconversion). Here we investigated the impact of CYP2C19 genotype on the outcome of CYP2C19-dependent DDIs in human liver microsomes. Methods: Liver samples from 40 patients were included, and genotyped for CYP2C19*2, *3 and *17 variants. S-mephenytoin metabolism in microsomal fractions was used as proxy for CYP2C19 activity, and concordance between genotype-predicted and observed CYP2C19 phenotype was examined. Individual microsomes were subsequently co-exposed to fluvoxamine, voriconazole, omeprazole or pantoprazole to simulate DDIs. Results: Maximal CYP2C19 activity (Vmax) in genotype-predicted intermediate metabolizers (IMs; *1/*2 or *2/*17), rapid metabolizers (RMs; *1/*17) and ultrarapid metabolizers (UMs; *17/*17) was not different from Vmax of predicted normal metabolizers (NMs; *1/*1). Conversely, CYP2C19*2/*2 genotyped-donors exhibited Vmax rates ∼9% of NMs, confirming the genotype-predicted poor metabolizer (PM) phenotype. Categorizing CYP2C19 activity, we found a 40% concordance between genetically-predicted CYP2C19 phenotypes and measured phenotypes, indicating substantial phenoconversion. Eight patients (20%) exhibited CYP2C19 IM/PM phenotypes that were not predicted by their CYP2C19 genotype, of which six could be linked to the presence of diabetes or liver disease. In subsequent DDI experiments, CYP2C19 activity was inhibited by omeprazole (-37% ± 8%), voriconazole (-59% ± 4%) and fluvoxamine (-85% ± 2%), but not by pantoprazole (-2 ± 4%). The strength of CYP2C19 inhibitors remained unaffected by CYP2C19 genotype, as similar percental declines in CYP2C19 activity and comparable metabolism-dependent inhibitory constants (Kinact/KI) of omeprazole were observed between CYP2C19 genotypes. However, the consequences of CYP2C19 inhibitor-mediated phenoconversion were different between CYP2C19 genotypes. In example, voriconazole converted 50% of *1/*1 donors to a IM/PM phenotype, but only 14% of *1/*17 donors. Fluvoxamine converted all donors to phenotypic IMs/PMs, but *1/*17 (14%) were less likely to become PMs than *1/*1 (50%) or *1/*2 and *2/*17 (57%). Conclusion: This study suggests that the differential outcome of CYP2C19-mediated DDIs between genotypes are primarily dictated by basal CYP2C19 activity, that may in part be predicted by CYP2C19 genotype but likely also depends on disease-related factors.