Gene-gene Interactions Research Articles

A Computational Pipeline for Identifying Gene Regulatory Networks: A Case Study of Response to Exercise.

Gene regulatory networks are foundational in the control of virtually all biological processes. These networks orchestrate a myriad of cell functions ranging from metabolic rate to the response to a drug or other intervention. The data required to accurately identify these control networks remains very cost and labor intensive typically leading to relatively sparse time course data that is largely incompatible with conventional data-driven model identification techniques. In this work, we combine empirical identification of gene-gene interactions with constraints describing the expected dynamic behavior of the network to infer regulatory dynamics from under-sampled data. We apply this to the identification of gene regulatory subnetworks recruited in groups of subjects participating in several different exercise interventions. Intervention-specific response networks are compared to one another and control actions driving differences are identified. We propose that this approach can extract statistically robust and biologically meaningful insights into gene regulatory dynamics from a dataset consisting of a small number of participants with very limited longitudinal sampling, for example pre- and post- intervention only.

Transcriptomic profiling with vascular tension analyses reveals molecular targets and phenotypes in preeclamptic placental vasculature

IntroductionThe placental vascular system plays an important role in the development of pregnancy hypertension in preeclampsia. The gene profiles of whole placental tissue (containing blood vessels and many other structural components) and pure vascular tissue should be very different. All previous reports using RNA-seq analysis in the placenta have tested its whole tissue or the villous part, and thus the gene profiles in the pure placental blood vessels are unknown.MethodsThis study was the first to address this point with RNA-seq in human placenta at the transcript level. Isolated placental micro-vessels from normal and preeclamptic pregnancies were used for RNA-seq analysis, real-time quantitative polymerase chain reaction (RT-qPCR) verification, and vascular function tests. Furthermore, a vascular function-centric core network was constructed to show the gene-gene interactions and gene-function associations in the placental vessel system.ResultsDifferential expression analysis identified a total of 486 significantly changed transcripts. Bioinformatics analysis further confirmed that multiple genes were highly related to blood vessel and placental phenotypes. Several hub genes, including ELMO1, YWHAE, and IL6ST, were significantly reduced in the placental vessels in preeclampsia. Vascular tension experiments showed that angiotensin II-mediated vasoconstriction and exogenous NO donor sodium nitroprusside-induced vasodilation were decreased, while phenylephrine-mediated vascular responses were unchanged in placental micro-vessels in preeclampsia.DiscussionThe results provide important insights into the pathological process in the placental vasculature in preeclampsia and offer great potential for further investigation of these molecular targets in the human placental vascular system.

Synergistic Epistasis and Systems Biology Approaches to Uncover a Pharmacogenomic Map Linked to Pain, Anti-Inflammatory and Immunomodulating Agents (PAIma) in a Healthy Cohort

The global public health addiction crisis has been stark, with over 932,400 deaths in the USA and Canada from opioid overdose since 1999–2020, surpassing the mortality rates at the top of the HIV/AIDS epidemic. Both nations exhibit opioid consumption rates significantly above the norm for developed countries. Analgesic type of opioids present both therapeutic benefits and substantial health risks, necessitating balanced drug regulation, careful prescribing, and dedicated opioid stewardship. The role of the cytochrome P450 2D6 (CYP2D6) system (Enzymatic functions) in metabolizing opioids highlights the potential of genotype-guided analgesia. By integrating Pharmacogenomics (PGx), this approach aims to optimize pain management, enhance safety, and reduce addiction risks. This understanding prompted the utilization of multifactor dimensionality reduction (MDR) to explore a range of phenotypes including PGx and gene–gene interactions (GGI) in a healthy cohort, thereby personalizing pain management strategies. The study sampled 100 unrelated healthy Western Iranians and 100 individuals from the 1000 Genome Project. Pre-testing involved searching for PGx annotations (variants associated with drug-gene-diseases) related to pain sensitivity and inflammation using the PharmGKB database, which identified 128 relevant genes. A questionnaire helped select 100 participants who had never used potent opioids but also other psychoactive agents (e.g., nicotine, amphetamines, etc.) and disease-related drugs. Whole-exome sequencing (WES) was then employed to analyze these genes in an Iranian cohort. Further analyses included MDR for identifying synergistic gene annotations and GGI for exploring complex gene interactions through the Visualization of Statistical Epistasis Networks (ViSEN). The study identified a Pain, Anti-Inflammatory, and Immunomodulating agents (PAIma) panel from the 128 genes, resulting in 55,590 annotations across 21 curated pathways. After filtering, 54 significant structural or regulatory variants were identified. This research also highlighted novel gene relationships involving the CYP3A5 gene, hsa-miR-355-5p, Paliperidone, and CYP2D6, which warrant further investigation. This study offers a novel pharmacogenetic framework that could potentially transform opioid prescribing practices to mitigate misuse and enhance personalized pain management. Further validation of these findings from multi countries and ethnic groups could guide clinicians in implementing DNA-based opioid prescribing, aligning treatment more closely with individual genetic profiles.Graphical abstract

Association of NTRK2 gene with suicidality: a meta-analysis.

Previous studies have shown that genes in brain development pathways may have important roles in affecting risk of suicidal behaviors, with our previous meta-analysis supporting a role of the brain-derived neurotrophic factor (BDNF) gene. NTRK2 is a gene that encodes the neurotrophic receptor tyrosine kinase 2, which is a receptor for BDNF. In the current study, we aim to examine the potential association between NTRK2 single nucleotide polymorphism (SNPs) and suicidal ideation/behaviors. We first conducted a literature search using keywords like 'NTRK2', 'TRKB', and 'suicid*' to identify papers on NTRK2 SNPs and suicidal ideation/behaviors. In addition, we have individual-level genotype data for all the identified SNPs in literature search. We used the R meta package to perform meta-analyses on both the genotype count and the allele count data. Moreover, we performed meta-analyses on specific haplotypes within each haplotype block. Following our literature search and meta-analyses on 20 NTRK2 SNPs across up to 8467 samples, we found three SNPs, rs10868235 [N = 5,318, odds ratio (OR) = 1.34, P = 0.02], rs1867283 (N = 5,134, OR = 0.73, P = 0.04), and rs1147198 (N = 5,132, OR = 1.36, P = 0.03) to be nominally associated with suicidal attempts. Those three findings, however, did not survive multiple-testing corrections. Also, none of the haplotype blocks showed significant involvement in suicidality. Our results suggest that the NTRK2 gene may not have a major role in suicidality. Future efforts, however, should explore gene-gene interaction and pathway analyses.

Gene-gene and gene-environment interaction in coronary artery disease risk

Abstract Coronary Artery Disease (CAD) is a lethal illness that kills millions of individuals each year worldwide. This disorder is multifactorial, resulting in the complex interplay of genetic, epigenetic, and environmental factors. Exploring gene-gene and gene-environment interactions is essential to understanding the aetiology of common complex diseases like CAD. Aim We proposed investigating the interaction between two genetic variants robustly associated with CAD by GWAS (MTHFR rs1801133 and APOE rs7412/rs429358) in cardiovascular susceptibility. After that, consider whether there was a new interaction between this genetic association and environment (hypertension, dyslipidemia and smoking). Methods A case-control study enrolled 3,157 individuals, 1,721 coronary patients (with at least 70% stenosis of one or more major coronary arteries or its primary branches on the coronary angiography) and 1,436 controls without CAD. Traditional risk factors were investigated, and the two polymorphisms were performed by TaqMan real-time PCR. Bivariate analysis was used to study genotypic proportions between CAD and non-CAD patients with respective odds ratios (OR). Logistic regression analysis displayed the interaction between the two genetic variants each other and with some important environmental risk factors. Results In bivariate analysis, the genetic models were studied for both genes, being the dominant model the one which showed significance for CAD. In MTHFR, 57.8% of the patients and 53.4% of the controls presented the CT+TT genotype (OR=1.20; p=0.013). In APOE, 26.5% of the patients and 22.3% of the controls had E2E4;E3E4+E4E4 (OR=1.26; p=0.006). The first multivariate logistic regression showed a synergistic interaction between the two genetic variants (OR=1.50; p=0.001). After adjusting for conventional risk factors, the logistic regression analysis entered the MTHFR rs1801133 and APOE rs7412/rs429358, showing a combined interaction effect between those genes and smoking status with an OR of 3.44 (p<0.0001), with hypertension (OR=1.55; p=0.002) and with dyslipidemia (OR=1.71; p<0.0001). Conclusion According to our results, the genetic interaction between APOE and MTHFR synergistically affected the susceptibility to CAD. This interaction, linked to conventional risk factors, consistently increased the risk of CAD in our population, particularly in the group of genetic carriers who did not quit smoking.

Methylenetetrahydrofolate reductase (MTHFR) C677T and A1298C polymorphisms in Turkish postmenopausal women with osteoporosis

Osteoporosis is a common age-related skeletal disease, characterized by changes in the microarchitectural structure of bone tissue and decreased bone mass, especially affecting postmenopausal women. Genetic and environmental factors affecting bone metabolism play a role in the development of osteoporosis. Methylenetetrahydrofolate reductase (MTHFR) is an important enzyme involved in the conversion of homocysteine to methionine. Genetic variations in the MTHFR gene lead to impaired function or inactivation of this enzyme. A decrease in MTHFR enzyme activity and an increase in homocysteine levels affect bone metabolism. In this study, we aimed to investigate the relationship between C677T and A1298C polymorphisms and osteoporosis in Turkish postmenopausal women. DNA samples were extracted from 200 volunteers. The PCR-RFLP technique was used to identify the MTHFR gene polymorphisms C677T and A1298C. The statistical significance of the analysis’s results was assessed. C677T genotype and allele frequency distributions were not statistically different between postmenopausal osteoporosis and healthy control groups (p = 0.249, p = 0.754), while A1298C genotype and allele frequency distributions were found to be statistically significant (p = 0.002, p = 0.013). The results of our study showed that the A1298C polymorphism may be a genetic factor associated with osteoporosis in this specific population. However, the C677T polymorphism did not show a significant connection. To gain a more comprehensive understanding of the genetic basis of osteoporosis, future research with larger sample sizes and the consideration of additional genetic and environmental factors is essential. Additionally, it is crucial to account for ethnic disparities, gene-gene interactions, and gene-environment interplays. These insights can inform the development of personalized preventive and therapeutic strategies for individuals at risk of osteoporosis in diverse populations.

Gene polymorphisms of miR-323b and miR-1343 that regulate kininogen L are associated with schizophrenia susceptibility: A preliminary population‑based study.

miR-SNP is a type of functional single nucleotide polymorphism (SNP) that affects the regulatory functions of miRNA genes, miRNA binding sites, or components of miRNA biogenesis. This study aimed to explore the relationship between miRNA gene polymorphisms that regulate the kininogen L protein and schizophrenia (SCZ). Bioinformatics methods predicted miRNA gene polymorphism sites regulating the kininogen L protein. The polymorphisms of rs56103835, rs6513496, rs651349, and rs2986407 were detected using imLDR multiple SNP typing technologies in 513 SCZ patients and 509 controls. The association of miR-SNP variations with SCZ susceptibility and symptoms was evaluated using SNPstat to determine the optimal inheritance model. Generalized multifactor dimensionality reduction (GMDR) analysis and logistic regression were used to calculate miR-SNP interactions. The association between hsa-miR-323b-rs56103835 and SCZ was statistically significant under the dominant model. The result of gene-gene interaction showed that the three-factor model (rs56103835 / rs2986407 / rs2155248) was the best, but it could not be considered significantly related to SCZ. Additionally, SCZ patients with the CC or CT genotype on rs2986407 were more likely to experience auditory hallucinations than those with the TT genotype. Our data revealed that the mutation of hsa-miR-323b-rs56103835 from C to T was associated with susceptibility to SCZ. The mutation of hsa-miR-1343-rs2986407 from T to C increases the risk of auditory hallucinations in SCZ patients.

Multi-scalar data integration decoding risk genes for chronic kidney disease

BackgroundChronic Kidney Disease (CKD) impacts over 10% of the global population, and recent advancements in high-throughput analytical technologies are uncovering the complex physiology underlying this condition. By integrating Genome-Wide Association Studies (GWAS), RNA sequencing (RNA-seq/RNA array), and single-cell RNA sequencing (scRNA-seq) data, our study aimed to explore the genes and cell types relevant to CKD traits.MethodsGWAS summary data for end-stage renal failure (ESRD) and decreased eGFR (CKD) with or without diabetes and (micro)proteinuria were obtained from the GWAS Catalog and the UK Biobank (UKB) database. Two gene Expression Omnibus (GEO) transcriptome datasets were used to establish glomerular and tubular gene expression differences between CKD patients and healthy individuals. Two scRNA-seq datasets were utilized to obtain the expression of key genes at the single-cell level. The expression profile, differentially expressed genes (DEGs), gene-gene interaction, and pathway enrichment were analysed for these CKD risk genes.ResultsA total of 779 distinct SNPs were identified from GWAS across different CKD traits, involving 681 genes. While many of these risk genes are specific to the CKD traits of renal failure, decreased eGFR, and (micro)proteinuria, they share common pathways, including extracellular matrix (ECM). ECM modeling was enriched in upregulated glomerular and tubular DEGs from CKD kidneys compared to healthy controls, with the expression of relevant collagen genes, such as COL1A2, prevalent in fibroblasts/myofibroblasts. Additionally, immune responses, including T cell differentiation, were dysregulated in CKD kidneys. The late podocyte signature gene THSD7A was enriched in podocytes but downregulated in CKD. We also highlighted that the regulated risk genes of CKD are mainly expressed in tubular cells and immune cells in the kidney.ConclusionsOur integrated analysis highlight the genes, pathways, and relevant cell types associational with the pathogenesis of kidney traits, as a basis for further mechanistic studies to understand the pathogenesis of CKD.

Comprehensive in silico analysis of prognostic and immune infiltrates for FGFs in human ovarian cancer

BackgroundFibroblast growth factors (FGFs) are cell signaling proteins that perform multiple biological processes in many biological processes (cell development, repair, and metabolism). The dynamics of tumor cells, such as angiogenesis, transformation, and proliferation, have a significant impact on neoplasia and are modulated by FGFs. FGFs’ expression and prognostic significance in ovarian cancer (OC), however, remain unclear.MethodsThrough a series of in silico analysis, we investigated the transcriptional, survival data, genetic variation, gene-gene interaction network, ferroptosis-related genes, and DNA methylation of FGFs in OC patients.ResultsWe discovered that while FGF18 expression levels were higher in OC tissues than in normal OC tissues, FGF2/7/10/17/22 expression levels were lower in the former, and that FGF1/19 expression was related to the tumor stage in OC patients. According to the survival analysis, the clinical prognosis of individuals with OC was associated with the aberrant expression of FGFs. The function of FGFs and their neighboring genes was mainly connected to the cellular response to FGF stimulus. There was a negative correlation between FGF expression and various immune cell infiltration.ConclusionsThis study clarifies the relationship between FGFs and OC, which might provide new insights into the choice of prognostic biomarkers of OC patients.

Transcriptome analysis provides new insights into the response of canine intestinal epithelial cells treated by sulforaphane: a natural product of cruciferous origin.

This study presents a comprehensive transcriptome analysis of canine intestinal epithelial cells following treatment with sulforaphane (SFN), a naturally occurring compound found in cruciferous vegetables with established anti-inflammatory and antioxidant properties. Through high-throughput sequencing, we identified 29,993 genes, among which 1,612 were differentially expressed, with 792 up-regulated and 820 down-regulated in response to SFN treatment. Our analysis revealed significant enrichment of genes in pathways associated with the inflammatory response, lipid metabolism, oxidative stress response, and T-cell mediated immunity, suggesting SFN's potential in modulating these biological processes. Notably, the PPARγ gene, which plays a crucial role in the body's oxidative stress and inflammatory response, was highly up-regulated, indicating its possible centrality in SFN's effects. Gene-gene interaction analysis further supported SFN's role in alleviating inflammation through PPARγ, with key genes in oxidative stress and inflammatory response pathways showing significant correlations with PPARγ. Overall, our findings provide molecular evidence for SFN's protective effects on canine intestinal health, potentially through the modulation of inflammatory and oxidative stress pathways, with PPARγ emerging as a critical mediator.

Discovering Explainable Biomarkers for Breast Cancer Anti-PD1 Response via Network Shapley Value Analysis

Background and objectiveImmunotherapy holds promise in enhancing pathological complete response rates in breast cancer, albeit confined to a select cohort of patients. Consequently, pinpointing factors predictive of treatment responsiveness is of paramount importance. Gene expression and regulation, inherently operating within intricate networks, constitute fundamental molecular machinery for cellular processes and often serve as robust biomarkers. Nevertheless, contemporary feature selection approaches grapple with two key challenges: opacity in modeling and scarcity in accounting for gene-gene interactions MethodsTo address these limitations, we devise a novel feature selection methodology grounded in cooperative game theory, harmoniously integrating with sophisticated machine learning models. This approach identifies interconnected gene regulatory network biomarker modules with priori genetic linkage architecture. Specifically, we leverage Shapley values on network to quantify feature importance, while strategically constraining their integration based on network expansion principles and nodal adjacency, thereby fostering enhanced interpretability in feature selection. We apply our methods to a publicly available single-cell RNA sequencing dataset of breast cancer immunotherapy responses, using the identified feature gene set as biomarkers. Functional enrichment analysis with independent validations further illustrates their effective predictive performance ResultsWe demonstrate the sophistication and excellence of the proposed method in data with network structure. It unveiled a cohesive biomarker module encompassing 27 genes for immunotherapy response. Notably, this module proves adept at precisely predicting anti-PD1 therapeutic outcomes in breast cancer patients with classification accuracy of 0.905 and AUC value of 0.971, underscoring its unique capacity to illuminate gene functionalities ConclusionThe proposed method is effective for identifying network module biomarkers, and the detected anti-PD1 response biomarkers can enrich our understanding of the underlying physiological mechanisms of immunotherapy, which have a promising application for realizing precision medicine.

Association of superoxide dismutase and catalase genetic variants and their gene-gene interactions with the severity of COVID-19

BACKGROUND: Since the outbreak of COVID-19 infection, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), oxidative stress has been proposed as an important player in its severity. This increased the interest in studying antioxidant systems to evaluate their possible role in counteracting disease progression. AIM: The aim of the current study was to investigate the association of single nucleotide polymorphism (SNP) of superoxide dismutase (SOD) and catalase (CAT) genes with the severity of COVID-19. MATERIALS AND METHODS: Study subjects were divided into two groups based on the severity of their symptoms. Allele-specific PCR was used for genotyping, and multifactor dimensionality reduction (MDR) analysis was performed to investigate the SNP–SNP interaction models. RESULTS: The results showed a significant association of SOD2 rs4880 with the severity of COVID-19 (p = 0.002). SOD2 47TT genotype was significantly more frequent among patients with severe COVID-19 (OR 4.34; 95% CI 1.72–10.96). The three-locus SNP–SNP interaction model, resulted from MDR analysis, was statistically significant (0.55 × 10–4, OR 3.81; 95% CI 1.96–7.42). Carriers of SOD1 7958G * SOD2 47T * CAT 262C allele combination had a higher risk of severe COVID-19 (p = 0.0045, OR 2.84, 95% CI 1.40–5.78). CONCLUSIONS: The obtained results contribute to better understanding of COVID-19 pathogenesis and suggest novel potential prognostic biomarkers of the infection.

Whole-genome sequencing reveals rare variants associated with gout in Taiwanese males.

To identify rare variants (RVs) of gout, we sequenced the whole genomes of 321 male gout patients and combined these with those of 64 male gout patients and 682 normal controls at Taiwan Biobank. We performed ACAT-O to identify 682 significant RVs (p < 3.8 × 10-8) clustered on chromosomes 1, 7, 10, 16, and 18. To prioritize causal variants effectively, we sifted them by Combined Annotation-Dependent Depletion score >10 or |effect size| ≥ 1.5 for those without CADD scores. In particular, to the best of our knowledge, we identified the rare variants rs559954634, rs186763678, and 13-85340782-G-A for the first time to be associated with gout in Taiwanese males. Importantly, the RV rs559954634 positively affects gout, and its neighboring gene NPHS2 is involved in serum urate and expressed in kidney tissues. The kidneys play a major role in regulating uric acid levels. This suggests that rs559954634 may be involved in gout. Furthermore, rs186763678 is in the intron of NFIA that interacts with SLC2A9, which has the most significant effect on serum urate. Note that gene-gene interaction NFIA-SLC2A9 is significantly associated with serum urate in the Italian MICROS population and a Croatian population. Moreover, 13-85340782-G-A significantly affects gout susceptibility (odds ratio 6.0; P = 0.038). The >1% carrier frequencies of these potentially pathogenic (protective) RVs in cases (controls) suggest the revealed associations may be true; these RVs deserve further studies for the mechanism. Finally, multivariate logistic regression analysis shows that the rare variants rs559954634 and 13-85340782-G-A jointly are significantly associated with gout susceptibility.

Dysfunctional β-cell longevity in diabetes relies on energy conservation and positive epistasis.

Long-lived PFKFB3-expressing β-cells are dysfunctional partly because of prevailing glycolysis that compromises metabolic coupling of insulin secretion. Their accumulation in type 2 diabetes (T2D) appears to be related to the loss of apoptotic competency of cell fitness competition that maintains islet function by favoring constant selection of healthy "winner" cells. To investigate how PFKFB3 can disguise the competitive traits of dysfunctional "loser" β-cells, we analyzed the overlap between human β-cells with bona fide "loser signature" across diabetes pathologies using the HPAP scRNA-seq and spatial transcriptomics of PFKFB3-positive β-cells from nPOD T2D pancreata. The overlapping transcriptional profile of "loser" β-cells was represented by down-regulated ribosomal biosynthesis and genes encoding for mitochondrial respiration. PFKFB3-positive "loser" β-cells had the reduced expression of HLA class I and II genes. Gene-gene interaction analysis revealed that PFKFB3 rs1983890 can interact with the anti-apoptotic gene MAIP1 implicating positive epistasis as a mechanism for prolonged survival of "loser" β-cells in T2D. Inhibition of PFKFB3 resulted in the clearance of dysfunctional "loser" β-cells leading to restored glucose tolerance in the mouse model of T2D.

Variance quantitative trait loci reveal gene-gene interactions which alter blood traits.

Gene-gene (GxG) interactions play an important role in human genetics, potentially explaining part of the "missing heritability" of polygenic traits and the variable expressivity of monogenic traits. Many GxG interactions have been identified in model organisms through experimental breeding studies, but they have been difficult to identify in human populations. To address this challenge, we applied two complementary variance QTL (vQTL)-based approaches to identify GxG interactions that contribute to human blood traits and blood-related disease risk. First, we used the previously validated genome-wide scale test for each trait in ∼450,000 people in the UK Biobank and identified 4 vQTLs. Genome-wide GxG interaction testing of these vQTLs enabled discovery of novel interactions between (1) CCL24 and CCL26 for eosinophil count and plasma CCL24 and CCL26 protein levels and (2) HLA-DQA1 and HLA-DQB1 for lymphocyte count and risk of celiac disease, both of which replicated in ∼140,000 NIH All of Us and ∼70,000 Vanderbilt BioVU participants. Second, we used a biologically informed approach to search for vQTL in disease-relevant genes. This approach identified (1) a known interaction for hemoglobin between two pathogenic variants in HFE which cause hereditary hemochromatosis and alters risk of cirrhosis and (2) a novel interaction between the JAK2 46/1 haplotype and a variant on chromosome 14 which modifies platelet count, JAK2 V617F clonal hematopoiesis, and risk of polycythemia vera. This work identifies novel disease-relevant GxG interactions and demonstrates the utility of vQTL-based approaches in identifying GxG interactions relevant to human health at scale.

Epidemiology and Genetic Architecture of Type 2 Diabetes Mellitus in Geographically Different Indian Populations: A Review

Abstract: Type 2 Diabetes Mellitus (T2DM) has been a severe public health issue worldwide for many years. The primary cause and risk factor of T2DM is hereditary and complicated interaction between epigenetics. Identification and understanding of genetic markers may help to detect, prevent, and manage T2DM. This review examined the effect of single-gene and gene-gene interactions for predicting diabetes mellitus. Based on the literature survey, common and unique Single Nucleotide Polymorphisms (SNPs) and genes were explored in the Indian Populations, including PPARG, TCF7L2, KCNJ11, CDKN2A, IGF2BP2, SLC30A8, HHEX and CDKAL1. Identifying common and specific markers may help in risk prediction and early detection of T2DM. Future research and Genome-wide association studies are also required to predict the gene-gene interaction, generate large data sets for removing non-representative groups, and focus only on specific marker-associated traits.

CD33 and SHP-1/PTPN6 Interaction in Alzheimer's Disease.

Large-scale genetic studies have identified numerous genetic risk factors that suggest a central role for innate immune cells in susceptibility to Alzheimer's disease (AD). CD33, an immunomodulatory transmembrane sialic acid binding protein expressed on myeloid cells, was identified as one such genetic risk factor associated with Alzheimer's disease. Several studies explored the molecular outcomes of genetic variation at the CD33 locus. It has been determined that the risk variant associated with AD increases the expression of the large isoform of CD33 (CD33M) in innate immune cells and alters its biological functions. CD33 is thought to signal via the interaction of its ITIM motif and the protein tyrosine phosphatase, SHP-1. Here, we utilize different molecular and computational approaches to investigate how AD-associated genetic variation in CD33 affects its interaction with SHP-1 in human microglia and microglia-like cells. Our findings demonstrate a genotype-dependent interaction between CD33 and SHP-1, which may functionally contribute to the AD risk associated with this CD33 variant. We also found that CD33-PTPN6 (SHP-1) gene-gene interactions impact AD-related traits, while CD33-PTPN11 (SHP-2) interactions do not.

Unraveling the key role of chromatin structure in cancer development through epigenetic landscape characterization of oral cancer

Epigenetic alterations, such as those in chromatin structure and DNA methylation, have been extensively studied in a number of tumor types. But oral cancer, particularly oral adenocarcinoma, has received far less attention. Here, we combined laser-capture microdissection and muti-omics mini-bulk sequencing to systematically characterize the epigenetic landscape of oral cancer, including chromatin architecture, DNA methylation, H3K27me3 modification, and gene expression. In carcinogenesis, tumor cells exhibit reorganized chromatin spatial structures, including compromised compartment structures and altered gene-gene interaction networks. Notably, some structural alterations are observed in phenotypically non-malignant paracancerous but not in normal cells. We developed transformer models to identify the cancer propensity of individual genome loci, thereby determining the carcinogenic status of each sample. Insights into cancer epigenetic landscapes provide evidence that chromatin reorganization is an important hallmark of oral cancer progression, which is also linked with genomic alterations and DNA methylation reprogramming. In particular, regions of frequent copy number alternations in cancer cells are associated with strong spatial insulation in both cancer and normal samples. Aberrant methylation reprogramming in oral squamous cell carcinomas is closely related to chromatin structure and H3K27me3 signals, which are further influenced by intrinsic sequence properties. Our findings indicate that structural changes are both significant and conserved in two distinct types of oral cancer, closely linked to transcriptomic alterations and cancer development. Notably, the structural changes remain markedly evident in oral adenocarcinoma despite the considerably lower incidence of genomic copy number alterations and lesser extent of methylation alterations compared to squamous cell carcinoma. We expect that the comprehensive analysis of epigenetic reprogramming of different types and subtypes of primary oral tumors can provide additional guidance to the design of novel detection and therapy for oral cancer.

Comprehensive Analysis of the Significance of Breast Cancer Gene 1 (BRCA-1) in Bladder Cancer.

Bladder carcinoma (BLCA) is characterized by high morbidity, mortality, and treatment costs. Breast cancer gene 1 (BRCA1), a tumor suppressor gene, inhibits the development of malignant tumors. However, research on the significance of BRCA1 in BLCA is limited. This study aims to explore the importance of BRCA1 in BLCA using bioinformatic methods and immunohistochemistry. Gene expression, clinical, and survival data were collected from the TCGA databases through the UCSC Xena platform (http://xena.ucsc.edu/). The TPM data from the TCGA and GETEx databases were integrated using the GEPIA database (http://GEPIA.cancer-pku.cn). The study then explored the differential expression, survival prognosis, functional enrichment, and immune cell infiltration analyses of BRCA1 in BLCA. A PPI network of BRCA1 was constructed using the STRING database, and a BRCA1-associated gene-gene interaction network was generated using the GeneMANIA database. Immunohistochemistry (IHC) assays were performed to verify the expression levels of BRCA1 in bladder tumour tissues and adjacent normal tissues. BRCA1 is associated with BLCA. Differential analysis indicated that BRCA1 acts as a risk factor for BLCA but does not show significant expression differences across genders, stages, tumor stages, lymph node stages, or metastasis stages. Additionally, staging was based on the eighth edition of the American Joint Committee on Cancer (AJCC) for BLCA. Co-expression network and Gene Set Enrichment Analysis (GESA) confirmed that BRCA1 is involved in various BLCA pathways. Furthermore, BRCA1 expression was also linked to immune cell infiltration. However, survival prognosis analysis revealed no significant correlation between the prognosis of BLCA and BRCA1. We demonstrated that BRCA1 is a prospective predicted and immunological biomarker in BLCA, offering new avenues for potential therapies.

The impact of genetic variations in the serotonergic system on symptom severity and clinical outcome in functional neurological disorders

ObjectiveWe studied gene-environment, as well as gene-gene interaction to elucidate their effects on symptom severity and predict clinical outcomes in functional neurological disorders (FND). MethodsEighty-five patients with mixed FND were genotyped for ten single-nucleotide polymorphisms (SNP) from seven different stress-related genes. We tested cross-sectionally the association between genotype and the symptomatology of FND (symptom severity assessed with the examiner-based clinical global impression score [CGI] and age of onset). Clinical outcome was assessed in 52 patients who participated in a follow-up clinical visit after eight months (following their individual therapies as usual). We tested longitudinally the association between genotype and clinical outcome in FND. We examined the contribution of each SNP and their interaction between them to FND symptomatology and outcome. ResultsWe identified a nominal association between tryptophan hydroxylase 1 (TPH1) rs1800532 and symptom severity (CGI1) in FND under a codominant model (T/T: ßT/T = 2.31, seT/T = 0.57; G/T: ßG/T = -0.18, seG/T = 0.29, P = 0.035), with minor allele (T) carriers presenting more severe symptoms. An association was identified between TPH1 and clinical outcome, suggesting that major allele (G) carriers were more likely to have an improved outcome under a codominant model (G/T: ORG/T = 0.18, CIG/T = [0.02–1.34]; T/T: ORT/T = 2.08, CIT/T = [0.30–14.53], P = 0.041). Our analyses suggested a significant gene-gene interaction for TPH2 (rs4570625) and OXTR (rs2254298) on symptom severity, and a significant gene-gene interaction for TPH1, TPH2 and BDNF (rs1491850) on clinical outcome. ConclusionFND might arise from a complex interplay between individual predisposing risk genes involved in the serotonergic pathway and their gene-gene interactions.