Interaction between tea consumption and genes on activities of daily living disability in older adults.

The effects of tea consumption on delaying aging and the onset of age-related disabilities have been reported; however, it is unclear whether these benefits are impacted by genes. This study aimed to examine the associations between tea consumption and activities of daily living (ADL) and explore the role of genetic factors. Data from 46,487 older adults aged 64-105 who participated in at least one data wave of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) conducted in 2002, 2005, 2008, 2011, 2014, and 2018 were analyzed. Genetic data were produced using the Affymetrix Axiom™myDesign™ (384-format) Human Genotyping Array. The generalized estimation equation and multiple logistic regression models were constructed to examine the effects of tea consumption, polygenic risk score, and their interactions on ADL. Tea consumption was related to reduced ADL decline-the effect was statistically significant among men but not women. A significant interaction between tea consumption and polygenic risk score (PRS) was observed. Tea consumption was associated with a decreased risk of ADL disability only among individuals with a low PRS. These findings indicate that tea consumption plays a role in preventing disability in older adults with low polygenic risk.

A comprehensive evaluation of large language models in mining gene relations and pathway knowledge.

Understanding complex biological pathways, including gene-gene interactions and gene regulatory networks, is critical for exploring disease mechanisms and drug development. Manual literature curation of biological pathways cannot keep up with the exponential growth of new discoveries in the literature. Large-scale language models (LLMs) trained on extensive text corpora contain rich biological information, and they can be mined as a biological knowledge graph. This study assesses 21 LLMs, including both application programming interface (API)-based models and open-source models in their capacities of retrieving biological knowledge. The evaluation focuses on predicting gene regulatory relations (activation, inhibition, and phosphorylation) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway components. Results indicated a significant disparity in model performance. API-based models GPT-4 and Claude-Pro showed superior performance, with an F1 score of 0.4448 and 0.4386 for the gene regulatory relation prediction, and a Jaccard similarity index of 0.2778 and 0.2657 for the KEGG pathway prediction, respectively. Open-source models lagged behind their API-based counterparts, whereas Falcon-180b and llama2-7b had the highest F1 scores of 0.2787 and 0.1923 in gene regulatory relations, respectively. The KEGG pathway recognition had a Jaccard similarity index of 0.2237 for Falcon-180b and 0.2207 for llama2-7b. Our study suggests that LLMs are informative in gene network analysis and pathway mapping, but their effectiveness varies, necessitating careful model selection. This work also provides a case study and insight into using LLMs das knowledge graphs. Our code is publicly available at the website of GitHub (Muh-aza).

Nutrigenomic underpinnings of intestinal stem cells in inflammatory bowel disease and colorectal cancer development.

Food-gene interaction has been identified as a leading risk factor for inflammatory bowel disease (IBD) and colorectal cancer (CRC). Accordingly, nutrigenomics emerges as a new approach to identify biomarkers and therapeutic targets for these two strongly associated gastrointestinal diseases. Recent studies in stem cell biology have further shown that diet and nutrition signal to intestinal stem cells (ISC) by altering nutrient-sensing transcriptional activities, thereby influencing barrier integrity and susceptibility to inflammation and tumorigenesis. This review recognizes the dietary factors related to both CRC and IBD and investigates their impact on the overlapping transcription factors governing stem cell activities in homeostasis and post-injury responses. Our objective is to provide a framework to study the food-gene regulatory network of disease-contributing cells and inspire new nutrigenomic approaches for detecting and treating diet-related IBD and CRC.

Exploring pharmacokinetic variability of palbociclib in HR+/HER2- metastatic breast cancer: a focus on age, renal function, and drug-gene interactions.

Palbociclib, an oral inhibitor of cyclin-dependent kinase 4 and 6, is approved for the treatment of metastatic breast cancer. This study investigated the influence of diverse clinical and biological factors-age, renal function, genetic variations, and concomitant medications (pharmacokinetic covariates)-on palbociclib pharmacokinetics. Employing a validated LC-MS/MS method, we analyzed the minimum plasma concentrations (Ctrough) of palbociclib in 68 women and determined the percentage deviations from the median Ctrough for each dosage group. Variations in a panel of absorption, distribution, metabolism, and excretion (ADME) genes were assessed using end-point allele-specific fluorescence detection and pyrosequencing. Two distinct patient cohorts were defined based on median values of age, creatinine, and eGFR, which exhibited statistically significant differences in percentage deviations (p = 0.0095, p = 0.0288, and p = 0.0005, respectively). Homozygous carriers of the PPARA variants displayed larger positive percentage deviations than the other group (p = 0.0292). Similarly, patients concurrently taking CYP3A and P-glycoprotein inhibitors alongside anticancer therapy exhibited significant variations (p = 0.0285 and p = 0.0334, respectively). Furthermore, exploring the drug-drug-gene interactions between inhibitors of CYP3A and P-glycoprotein with their respective genetic variants revealed two patient groups with statistically different percentage deviations (p = 0.0075, p = 0.0012, and p = 0.0191, respectively). These results could help address cases where pharmacokinetic covariates or subclinical conditions impair palbociclib adherence or response, aiming to offer tailored dosing strategies or monitoring for individual patients.

Identification of candidate genes and chemicals associated with osteonecrosis of femoral head by multiomics studies and chemical-gene interaction analysis.

Objectives: In-depth understanding of osteonecrosis of femoral head (ONFH) has revealed that degeneration of the hip cartilage plays a crucial role in ONFH progression. However, the underlying molecular mechanisms and susceptibility to environmental factors in hip cartilage that contribute to ONFH progression remain elusive. Methods: We conducted a multiomics study and chemical-gene interaction analysis of hip cartilage in ONFH. The differentially expressed genes (DEGs) involved in ONFH progression were identified in paired hip cartilage samples from 36 patients by combining genome-wide DNA methylation profiling, gene expression profiling, and quantitative proteomics. Gene functional enrichment and pathway analyses were performed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Functional links between proteins were discovered through protein-protein interaction (PPI) networks. The ONFH-associated chemicals were identified by integrating the DEGs with the chemical-gene interaction sets in the Comparative Toxicogenomics Database (CTD). Finally, the DEGs, including MMP13 and CHI3L1, were validated via quantitative real-time PCR (qRT-PCR) and immunohistochemistry (IHC). Results: Twenty-two DEGs were identified across all three omics levels in ONFH cartilage, 16 of which were upregulated and six of which were downregulated. The collagen-containing extracellular matrix (ECM), ECM structural constituents, response to amino acids, the relaxin signaling pathway, and protein digestion and absorption were found to be primarily involved in cartilage degeneration in ONFH. Moreover, ten major ONFH-associated chemicals were identified, including, benzo(a)pyrene, valproic acid, and bisphenol A. Conclusion: Overall, our study identified several candidate genes, pathways, and chemicals associated with cartilage degeneration in ONFH, providing novel clues into the etiology and biological processes of ONFH progression.

Global-local aware Heterogeneous Graph Contrastive Learning for multifaceted association prediction in miRNA-gene-disease networks.

Unraveling the intricate network of associations among microRNAs (miRNAs), genes, and diseases is pivotal for deciphering molecular mechanisms, refining disease diagnosis, and crafting targeted therapies. Computational strategies, leveraging link prediction within biological graphs, present a cost-efficient alternative to high-cost empirical assays. However, while plenty of methods excel at predicting specific associations, such as miRNA-disease associations (MDAs), miRNA-target interactions (MTIs), and disease-gene associations (DGAs), a holistic approach harnessing diverse data sources for multifaceted association prediction remains largely unexplored. The limited availability of high-quality data, as vitro experiments to comprehensively confirm associations are often expensive and time-consuming, results in a sparse and noisy heterogeneous graph, hindering an accurate prediction of these complex associations. To address this challenge, we propose a novel framework called Global-local aware Heterogeneous Graph Contrastive Learning (GlaHGCL). GlaHGCL combines global and local contrastive learning to improve node embeddings in the heterogeneous graph. In particular, global contrastive learning enhances the robustness of node embeddings against noise by aligning global representations of the original graph and its augmented counterpart. Local contrastive learning enforces representation consistency between functionally similar or connected nodes across diverse data sources, effectively leveraging data heterogeneity and mitigating the issue of data scarcity. The refined node representations are applied to downstream tasks, such as MDA, MTI, and DGA prediction. Experiments show GlaHGCL outperforming state-of-the-art methods, and case studies further demonstrate its ability to accurately uncover new associations among miRNAs, genes, and diseases. We have made the datasets and source code publicly available at https://github.com/Sue-syx/GlaHGCL.

Genetic factors and symptom dimensions associated with antidepressant treatment outcomes: clues for new potential therapeutic targets?

Treatment response and resistance in major depressive disorder (MDD) show a significant genetic component, but previous studies had limited power also due to MDD heterogeneity. This literature review focuses on the genetic factors associated with treatment outcomes in MDD, exploring their overlap with those associated with clinically relevant symptom dimensions. We searched PubMed for: (1) genome-wide association studies (GWASs) or whole exome sequencing studies (WESs) that investigated efficacy outcomes in MDD; (2) studies examining the association between MDD treatment outcomes and specific depressive symptom dimensions; and (3) GWASs of the identified symptom dimensions. We identified 13 GWASs and one WES of treatment outcomes in MDD, reporting several significant loci, genes, and gene sets involved in gene expression, immune system regulation, synaptic transmission and plasticity, neurogenesis and differentiation. Nine symptom dimensions were associated with poor treatment outcomes and studied by previous GWASs (anxiety, neuroticism, anhedonia, cognitive functioning, melancholia, suicide attempt, psychosis, sleep, sociability). Four genes were associated with both treatment outcomes and these symptom dimensions: CGREF1 (anxiety); MCHR1 (neuroticism); FTO and NRXN3 (sleep). Other overlapping signals were found when considering genes suggestively associated with treatment outcomes. Genetic studies of treatment outcomes showed convergence at the level of biological processes, despite no replication at gene or variant level. The genetic signals overlapping with symptom dimensions of interest may point to shared biological mechanisms and potential targets for new treatments tailored to the individual patient's clinical profile.

Biomedical literature mining: graph kernel-based learning for gene-gene interaction extraction.

The supervised machine learning method is often used for biomedical relationship extraction. The disadvantage is that it requires much time and money to manually establish an annotated dataset. Based on distant supervision, the knowledge base is combined with the corpus, thus, the training corpus can be automatically annotated. As many biomedical databases provide knowledge bases for study with a limited number of annotated corpora, this method is practical in biomedicine. The clinical significance of each patient's genetic makeup can be understood based on the healthcare provider's genetic database. Unfortunately, the lack of previous biomedical relationship extraction studies focuses on gene-gene interaction. The main purpose of this study is to develop extraction methods for gene-gene interactions that can help explain the heritability of human complex diseases. This study referred to the information on gene-gene interactions in the KEGG PATHWAY database, the abstracts in PubMed were adopted to generate the training sample set, and the graph kernel method was adopted to extract gene-gene interactions. The best assessment result was an F1-score of 0.79. Our developed distant supervision method automatically finds sentences through the corpus without manual labeling for extracting gene-gene interactions, which can effectively reduce the time cost for manual annotation data; moreover, the relationship extraction method based on a graph kernel can be successfully applied to extract gene-gene interactions. In this way, the results of this study are expected to help achieve precision medicine.

ARTEMIS: An independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions.

INTRODUCTION: Breast cancer, a heterogeneous disease, is influenced by multiple genetic and epigenetic factors. The majority of prognostic models for breast cancer focus merely on the main effects of predictors, disregarding the crucial impacts of gene-gene interactions on prognosis. OBJECTIVES: Using DNA methylation data derived from nine independent breast cancer cohorts, we developed an independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions (ARTEMIS) with an innovative 3-D modeling strategy. ARTEMIS was evaluated for discrimination ability using area under the receiver operating characteristics curve (AUC), and calibration using expected and observed (E/O) ratio. Additionally, we conducted decision curve analysis to evaluate its clinical efficacy by net benefit (NB) and net reduction (NR). Furthermore, we conducted a systematic review to compare its performance with existing models. RESULTS: ARTEMIS exhibited excellent risk stratification ability in identifying patients at high risk of mortality. Compared to those below the 25th percentile of ARTEMIS scores, patients with above the 90th percentile had significantly lower overall survival time (HR = 15.43, 95% CI: 9.57-24.88, P = 3.06 × 10-29). ARTEMIS demonstrated satisfactory discrimination ability across four independent populations, with pooled AUC3-year = 0.844 (95% CI: 0.805-0.883), AUC5-year = 0.816 (95% CI: 0.775-0.857), and C-index = 0.803 (95% CI: 0.776-0.830). Meanwhile, ARTEMIS had well calibration performance with pooled E/O ratio 1.060 (95% CI: 1.038-1.083) and 1.090 (95% CI: 1.057-1.122) for 3- and 5-year survival prediction, respectively. Additionally, ARTEMIS is a clinical instrument with acceptable cost-effectiveness for detecting breast cancer patients at high risk of mortality (Pt = 0.4: NB3-year = 19‰, NB5-year = 62‰; NR3-year = 69.21%, NR5-year = 56.01%). ARTEMIS has superior performance compared to existing models in terms of accuracy, extrapolation, and sample size, as indicated by the systematic review. ARTEMIS is implemented as an interactive online tool available at http://bigdata.njmu.edu.cn/ARTEMIS/. CONCLUSION: ARTEMIS is an efficient and practical tool for breast cancer prognostic prediction.

Ant colony optimization for the identification of dysregulated gene subnetworks from expression data.

BACKGROUND: High-throughput experimental technologies can provide deeper insights into pathway perturbations in biomedical studies. Accordingly, their usage is central to the identification of molecular targets and the subsequent development of suitable treatments for various diseases. Classical interpretations of generated data, such as differential gene expression and pathway analyses, disregard interconnections between studied genes when looking for gene-disease associations. Given that these interconnections are central to cellular processes, there has been a recent interest in incorporating them in such studies. The latter allows the detection of gene modules that underlie complex phenotypes in gene interaction networks. Existing methods either impose radius-based restrictions or freely grow modules at the expense of a statistical bias towards large modules. We propose a heuristic method, inspired by Ant Colony Optimization, to apply gene-level scoring and module identification with distance-based search constraints and penalties, rather than radius-based constraints. RESULTS: We test and compare our results to other approaches using three datasets of different neurodegenerative diseases, namely Alzheimer's, Parkinson's, and Huntington's, over three independent experiments. We report the outcomes of enrichment analyses and concordance of gene-level scores for each disease. Results indicate that the proposed approach generally shows superior stability in comparison to existing methods. It produces stable and meaningful enrichment results in all three datasets which have different case to control proportions and sample sizes. CONCLUSION: The presented network-based gene expression analysis approach successfully identifies dysregulated gene modules associated with a certain disease. Using a heuristic based on Ant Colony Optimization, we perform a distance-based search with no radius constraints. Experimental results support the effectiveness and stability of our method in prioritizing modules of high relevance. Our tool is publicly available at github.com/GhadiElHasbani/ACOxGS.git.

Unraveling schizophrenia's genetic complexity through advanced causal inference and chromatin 3D conformation.

Schizophrenia is a polygenic complex disease with a heritability as high as 80 %, yet the mechanism of polygenic interaction in its pathogenesis remains unclear. Studying the interaction and regulation of schizophrenia susceptibility genes is crucial for unraveling the pathogenesis of schizophrenia and developing antipsychotic drugs. Therefore, we developed a bioinformatics method named GRACI (Gene Regulation Analysis based on Causal Inference) based on the principles of information theory, a causal inference model, and high order chromatin 3D conformation. GRACI captures the interaction and regulatory relationships between schizophrenia susceptibility genes by analyzing genotyping data. Two datasets, comprising 1459 and 2065 samples respectively, were analyzed, and the gene networks from both datasets were constructed. GRACI showcased superior accuracy when compared to widely adopted methods for detecting gene-gene interactions and intergenic regulation. This alignment was further substantiated by its correlation with chromatin high-order conformation patterns. Using GRACI, we identified three potential genes-KCNN3, KCNH1, and KCND3-that are directly associated with schizophrenia pathogenesis. Furthermore, the results of GRACI on the standalone dataset illustrated the method's applicability to other complex diseases. GRACI download: https://github.com/liuliangjie19/GRACI.

Skin pigmentation related variants in Mexican population and interaction effects on serum 25(OH)D concentration and vitamin D deficiency.

Skin pigmentation is negatively associated with circulating vitamin D (VD) concentration. Therefore, genetic factors involved in skin pigmentation could influence the risk of vitamin D deficiency (VDD). We evaluated the impact genetic variants related to skin pigmentation on VD in Mexican population. This cross-sectional analysis included 848 individuals from the Health Worker Cohort Study (ratio males to females ~ 1:3). Eight genetic variants: rs16891982 (SLC45A2), rs12203592 (IRF4), rs1042602 and rs1126809 (TYR), rs1800404 (OCA2), rs12913832 (HERC2), rs1426654 (SLC24A5), and rs2240751 (MFSD12); involved in skin pigmentation were genotyped. Skin pigmentation was assessed by self-report. Linear and logistic regression were used to assess the association between the variants of interest and VD and VDD, as appropriate. In our study, eight genetic variants were associated with skin pigmentation. A genetic risk score built with the variants rs1426654 and rs224075 was associated with lower VD levels (ß = - 1.38, 95% CI - 2.59, - 0.17, p = 0.025). Nevertheless, when examining gene-gene interactions, we observed that rs2240751 × rs12203592 were associated with VD levels (P interaction = 0.021). Whereas rs2240751 × rs12913832 (P interaction = 0.0001) were associated with VDD. Our results suggest that skin pigmentation-related gene variants are associated with lower VD levels in Mexican population. These results underscore the importance of considering genetic interactions when assessing the impact of genetic polymorphisms on VD levels.

Identification of candidate genes controlling cold tolerance at the early seedling stage from Dongxiang wild rice by QTL mapping, BSA-Seq and RNA-Seq.

BACKGROUND: The cold tolerance of rice is closely related to its production and geographic distribution. The identification of cold tolerance-related genes is of important significance for developing cold-tolerant rice. Dongxiang wild rice (Oryza rufipogon Griff.) (DXWR) is well-adapted to the cold climate of northernmost-latitude habitats ever found in the world, and is one of the most valuable rice germplasms for cold tolerance improvement. RESULTS: Transcriptome analysis revealed genes differentially expressed between Xieqingzao B (XB; a cold sensitive variety) and 19H19 (derived from an interspecific cross between DXWR and XB) in the room temperature (RT), low temperature (LT), and recovery treatments. The results demonstrated that chloroplast genes might be involved in the regulation of cold tolerance in rice. A high-resolution SNP genetic map was constructed using 120 BC5F2 lines derived from a cross between 19H19 and XB based on the genotyping-by-sequencing (GBS) technique. Two quantitative trait loci (QTLs) for cold tolerance at the early seedling stage (CTS), qCTS12 and qCTS8, were detected. Moreover, a total of 112 candidate genes associated with cold tolerance were identified based on bulked segregant analysis sequencing (BSA-seq). These candidate genes were divided into eight functional categories, and the expression trend of candidate genes related to 'oxidation-reduction process' and 'response to stress' differed between XB and 19H19 in the RT, LT and recovery treatments. Among these candidate genes, the expression level of LOC_Os12g18729 in 19H19 (related to 'response to stress') decreased in the LT treatment but restored and enhanced during the recovery treatment whereas the expression level of LOC_Os12g18729 in XB declined during recovery treatment. Additionally, XB contained a 42-bp deletion in the third exon of LOC_Os12g18729, and the genotype of BC5F2 individuals with a survival percentage (SP) lower than 15% was consistent with that of XB. Weighted gene coexpression network analysis (WGCNA) and modular regulatory network learning with per gene information (MERLIN) algorithm revealed a gene interaction/coexpression network regulating cold tolerance in rice. In the network, differentially expressed genes (DEGs) related to 'oxidation-reduction process', 'response to stress' and 'protein phosphorylation' interacted with LOC_Os12g18729. Moreover, the knockout mutant of LOC_Os12g18729 decreased cold tolerance in early rice seedling stage signifcantly compared with that of wild type. CONCLUSIONS: In general, study of the genetic basis of cold tolerance of rice is important for the development of cold-tolerant rice varieties. In the present study, QTL mapping, BSA-seq and RNA-seq were integrated to identify two CTS QTLs qCTS8 and qCTS12. Furthermore, qRT-PCR, genotype sequencing and knockout analysis indicated that LOC_Os12g18729 could be the candidate gene of qCTS12. These results are expected to further exploration of the genetic mechanism of CTS in rice and improve cold tolerance of cultivated rice by introducing the cold tolerant genes from DXWR through marker-assisted selection.

MiR-378 Inhibits Angiotensin II-Induced Cardiomyocyte Hypertrophy by Targeting AKT2.

Cardiomyocyte hypertrophy plays a crucial role in heart failure development, potentially leading to sudden cardiac arrest and death. Previous studies suggest that micro-ribonucleic acids (miRNAs) show promise for the early diagnosis and treatment of cardiomyocyte hypertrophy.To investigate the miR-378 expression in the cardiomyocyte hypertrophy model, reverse transcription-polymerase chain reaction (RT-qPCR), Western blot, and immunofluorescence tests were conducted in angiotensin II (Ang II)-induced H9c2 cells and Ang II-induced mouse model of cardiomyocyte hypertrophy. The functional interaction between miR-378 and AKT2 was studied by dual-luciferase reporter, RNA pull-down, Western blot, and RT-qPCR assays.The results of RT-qPCR analysis showed the downregulated expression of miR-378 in both the cell and animal models of cardiomyocyte hypertrophy. It was observed that the introduction of the miR-378 mimic inhibited the hypertrophy of cardiomyocytes induced by Ang II. Furthermore, the co-transfection of AKT2 expression vector partially mitigated the negative impact of miR-378 overexpression on Ang II-induced cardiomyocytes. Molecular investigations provided evidence that miR-378 negatively regulated AKT2 expression by interacting with the 3' untranslated region (UTR) of AKT2 mRNA.Decreased miR-378 expression and AKT2 activation are linked to Ang II-induced cardiomyocyte hypertrophy. Targeting miR-378/AKT2 axis offers therapeutic opportunity to alleviate cardiomyocyte hypertrophy.

Gene-gene interaction analysis for age at onset of bipolar disorder in a Korean population.

BACKGROUND: Multiple genes might interact to determine the age at onset of bipolar disorder. We investigated gene-gene interactions related to age at onset of bipolar disorder in the Korean population, using genome-wide association study (GWAS) data. METHODS: The study population consisted of 303 patients with bipolar disorder. First, the top 1000 significant single-nucleotide polymorphisms (SNPs) associated with age at onset of bipolar disorder were selected through single SNP analysis by simple linear regression. Subsequently, the QMDR method was used to find gene-gene interactions. RESULTS: The best 10 SNPs from simple regression were located in chromosome 1, 2, 3, 10, 11, 14, 19, and 21. Only five SNPs were found in several genes, such as FOXN3, KIAA1217, OPCML, CAMSAP2, and PTPRS. On QMDR analyses, five pairs of SNPs showed significant interactions with a CVC exceeding 1/5 in a two-locus model. The best interaction was found for the pair of rs60830549 and rs12952733 (CVC = 1/5, P < 1E-07). In three-locus models, four combinations of SNPs showed significant associations with age at onset, with a CVC of >1/5. The best three-locus combination was rs60830549, rs12952733, and rs12952733 (CVC = 2/5, P < 1E-6). The SNPs showing significant interactions were located in the KIAA1217, RBFOX3, SDK2, CYP19A1, NTM, SMYD3, and RBFOX1 genes. CONCLUSIONS: Our analysis confirmed genetic interactions influencing the age of onset for bipolar disorder and identified several potential candidate genes. Further exploration of the functions of these promising genes, which may have multiple roles within the neuronal network, is necessary.

Smart Biosensor for Breast Cancer Survival Prediction Based on Multi-View Multi-Way Graph Learning.

Biosensors play a crucial role in detecting cancer signals by orchestrating a series of intricate biological and physical transduction processes. Among various cancers, breast cancer stands out due to its genetic underpinnings, which trigger uncontrolled cell proliferation, predominantly impacting women, and resulting in significant mortality rates. The utilization of biosensors in predicting survival time becomes paramount in formulating an optimal treatment strategy. However, conventional biosensors employing traditional machine learning methods encounter challenges in preprocessing features for the learning task. Despite the potential of deep learning techniques to automatically extract useful features, they often struggle to effectively leverage the intricate relationships between features and instances. To address this challenge, our study proposes a novel smart biosensor architecture that integrates a multi-view multi-way graph learning (MVMWGL) approach for predicting breast cancer survival time. This innovative approach enables the assimilation of insights from gene interactions and biosensor similarities. By leveraging real-world data, we conducted comprehensive evaluations, and our experimental results unequivocally demonstrate the superiority of the MVMWGL approach over existing methods.

MYLK*FLNB and DOCK1*LAMA2 gene-gene interactions associated with rheumatoid arthritis in the focal adhesion pathway.

Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease caused by a combination of genetic and environmental factors. Rare variants with low predicted effects in genes participating in the same biological function might be involved in developing complex diseases such as RA. From whole-exome sequencing (WES) data, we identified genes containing rare non-neutral variants with complete penetrance and no phenocopy in at least one of nine French multiplex families. Further enrichment analysis highlighted focal adhesion as the most significant pathway. We then tested if interactions between the genes participating in this function would increase or decrease the risk of developing RA disease. The model-based multifactor dimensionality reduction (MB-MDR) approach was used to detect epistasis in a discovery sample (19 RA cases and 11 healthy individuals from 9 families and 98 unrelated CEU controls from the International Genome Sample Resource). We identified 9 significant interactions involving 11 genes (MYLK, FLNB, DOCK1, LAMA2, RELN, PIP5K1C, TNC, PRKCA, VEGFB, ITGB5, and FLT1). One interaction (MYLK*FLNB) increasing RA risk and one interaction decreasing RA risk (DOCK1*LAMA2) were confirmed in a replication sample (200 unrelated RA cases and 91 GBR unrelated controls). Functional and genomic data in RA samples or relevant cell types argue the key role of these genes in RA.

ANK3 rs10994336 and ZNF804A rs7597593 polymorphisms: genetic interaction for emotional and behavioral symptoms of alcohol withdrawal syndrome.

OBJECTIVE: Alcohol withdrawal syndrome (AWS) is a complex condition associated with alcohol use disorder (AUD), characterized by significant variations in symptom severity among patients. The psychological and emotional symptoms accompanying AWS significantly contribute to withdrawal distress and relapse risk. Despite the importance of neural adaptation processes in AWS, limited genetic investigations have been conducted. This study primarily focuses on exploring the single and interaction effects of single-nucleotide polymorphisms in the ANK3 and ZNF804A genes on anxiety and aggression severity manifested in AWS. By examining genetic associations with withdrawal-related psychopathology, we ultimately aim to advance understanding the genetic underpinnings that modulate AWS severity. METHODS: The study involved 449 male patients diagnosed with alcohol use disorder. The Self-Rating Anxiety Scale (SAS) and Buss-Perry Aggression Questionnaire (BPAQ) were used to assess emotional and behavioral symptoms related to AWS. Genomic DNA was extracted from peripheral blood, and genotyping was performed using PCR. RESULTS: Single-gene analysis revealed that naturally occurring allelic variants in ANK3 rs10994336 (CC homozygous vs. T allele carriers) were associated with mood and behavioral symptoms related to AWS. Furthermore, the interaction between ANK3 and ZNF804A was significantly associated with the severity of psychiatric symptoms related to AWS, as indicated by MANOVA. Two-way ANOVA further demonstrated a significant interaction effect between ANK3 rs10994336 and ZNF804A rs7597593 on anxiety, physical aggression, verbal aggression, anger, and hostility. Hierarchical regression analyses confirmed these findings. Additionally, simple effects analysis and multiple comparisons revealed that carriers of the ANK3 rs10994336 T allele experienced more severe AWS, while the ZNF804A rs7597593 T allele appeared to provide protection against the risk associated with the ANK3 rs10994336 mutation. CONCLUSION: This study highlights the gene-gene interaction between ANK3 and ZNF804A, which plays a crucial role in modulating emotional and behavioral symptoms related to AWS. The ANK3 rs10994336 T allele is identified as a risk allele, while the ZNF804A rs7597593 T allele offers protection against the risk associated with the ANK3 rs10994336 mutation. These findings provide initial support for gene-gene interactions as an explanation for psychiatric risk, offering valuable insights into the pathophysiological mechanisms involved in AWS.

Hypoxia-inducible factor-1α in myocardial infarction.

Hypoxia-inducible factor 1 (HIF1) has a crucial function in the regulation of oxygen levels in mammalian cells, especially under hypoxic conditions. Its importance in cardiovascular diseases, particularly in cardiac ischemia, is because of its ability to alleviate cardiac dysfunction. The oxygen-responsive subunit, HIF1α, plays a crucial role in this process, as it has been shown to have cardioprotective effects in myocardial infarction through regulating the expression of genes affecting cellular survival, angiogenesis, and metabolism. Furthermore, HIF1α expression induced reperfusion in the ischemic skeletal muscle, and hypoxic skin wounds in diabetic animal models showed reduced HIF1α expression. Increased expression of HIF1α has been shown to reduce apoptosis and oxidative stress in cardiomyocytes during acute myocardial infarction. Genetic variations in HIF1α have also been found to correlate with altered responses to ischemic cardiovascular disease. In addition, a link has been established between the circadian rhythm and hypoxic molecular signaling pathways, with HIF1α functioning as an oxygen sensor and circadian genes such as period circadian regulator 2 responding to changes in light. This editorial analyzes the relationship between HIF1α and the circadian rhythm and highlights its significance in myocardial adaptation to hypoxia. Understanding the changes in molecular signaling pathways associated with diseases, specifically cardiovascular diseases, provides the opportunity for innovative therapeutic interventions, especially in low-oxygen environments such as myocardial infarction.