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PubMed2005

Corticostriatal covariance patterns of 6-[18F]fluoro-L-dopa and [18F]fluorodeoxyglucose PET in Parkinson's disease.

6-[18F]fluoro-L-dopa (FDOPA) is a common presynaptic dopaminergic tracer used in examinations by positron emission tomography (PET) for patients with Parkinson's disease (PD). The distinct metabolic covariance pattern in the uptake of [18F]fluorodeoxyglucose (FDG) can also be used to investigate PD pathology. Although the two tracers are widely used in PD research and clinical assessment, no thorough comparative studies of the tracers have been made. In this study, 25 PD patients were examined with FDOPA and FDG to investigate relationships and clinical correlates of metabolic and monoaminergic function in the Parkinsonian brain. A VOI (volume-of-interest) analysis was achieved by 3D spatial normalisation and fixed VOI-sets. The hemisphere ipsi- and contralateral to the predominant symptoms of PD was identified in each data set, and data across subjects were related using that laterality, rather than body side. Regional covariance patterns for FDOPA and FDG were derived from principal component analysis (PCA). The results demonstrated hemispheric asymmetries and sex-differences in the striatal FDOPA uptake, which were not seen with FDG. In addition, the PCA analysis identified a positive relationship between a major component in FDOPA uptake (associated with the striatal uptake) and an FDG component, which had positive loadings in the thalamus and the cerebellum. The subject scores for these components correlated positively, and both had a negative association with the clinical severity of the disease. The specific extrastriatal FDG covariance pattern contained the thalamus and the cerebellum, components of the previously reported PD related pattern, but not the striatum. The network correlated with both the severity of clinical symptoms of PD and the severity of nigrostriatal dopaminergic hypofunction. The results indicate that FDG PET, when combined with multivariate network analysis at group-level, can be used as an indicator of PD severity.

Aged↗

PubMed2026

Integrated Bulk and Single-Cell RNA-Seq Analysis Reveals Transcriptional Activation of PTGS2 by FOS in Progression From T2DM to T2DM-Associated NAFLD.

Type 2 diabetes mellitus (T2DM) and nonalcoholic fatty liver disease (NAFLD) frequently coexist, exacerbating disease burden. However, the molecular mechanisms underlying the progression from T2DM to T2DM-associated NAFLD remain unclear. This study investigated the regulatory function of FOS-mediated PTGS2 activation in this transition. We integrated bulk RNA-seq data from GEO, single-cell transcriptomic data and transcriptomes from patients with T2DM-associated NAFLD. Differentially expressed genes were identified using the limma package, and T2DM-related gene modules were defined by weighted gene co-expression network analysis. LASSO regression and random forest identified 14 candidate genes, with PTGS2 and FOS prioritised. Single-cell analysis showed increased FOS and PTGS2 expression in monocytes, CD8+ T cells and Kupffer cells. Transcription factor prediction and dual-luciferase assays confirmed that FOS directly binds the PTGS2 promoter and drives its transcription. In vitro, FOS silencing decreased PTGS2 expression, cytokine secretion and apoptosis under high-glucose and free fatty acid conditions, whereas PTGS2 overexpression exacerbated inflammation and apoptosis independently of FOS expression. These findings demonstrate that FOS transcriptionally activates PTGS2, contributing to hepatic inflammation and apoptosis during the progression from T2DM to NAFLD. PTGS2 may serve as a promising biomarker and therapeutic target for T2DM-associated NAFLD.

Single-Cell Gene Expression Analysis↗

PubMed2005

Linking experimental results, biological networks and sequence analysis methods using Ontologies and Generalised Data Structures.

The structure of a closely integrated data warehouse is described that is designed to link different types and varying numbers of biological networks, sequence analysis methods and experimental results such as those coming from microarrays. The data schema is inspired by a combination of graph based methods and generalised data structures and makes use of ontologies and meta-data. The core idea is to consider and store biological networks as graphs, and to use generalised data structures (GDS) for the storage of further relevant information. This is possible because many biological networks can be stored as graphs: protein interactions, signal transduction networks, metabolic pathways, gene regulatory networks etc. Nodes in biological graphs represent entities such as promoters, proteins, genes and transcripts whereas the edges of such graphs specify how the nodes are related. The semantics of the nodes and edges are defined using ontologies of node and relation types. Besides generic attributes that most biological entities possess (name, attribute description), further information is stored using generalised data structures. By directly linking to underlying sequences (exons, introns, promoters, amino acid sequences) in a systematic way, close interoperability to sequence analysis methods can be achieved. This approach allows us to store, query and update a wide variety of biological information in a way that is semantically compact without requiring changes at the database schema level when new kinds of biological information is added. We describe how this datawarehouse is being implemented by extending the text-mining framework ONDEX to link, support and complement different bioinformatics applications and research activities such as microarray analysis, sequence analysis and modelling/simulation of biological systems. The system is developed under the GPL license and can be downloaded from http://sourceforge.net/projects/ondex/

Algorithms↗

PubMed2026

Genomic Characterization of ETV6::RUNX1-Positive Childhood B-ALL in a Chinese Cohort: Novel Fusion Partners, Co-Occurring Mutations, and Risk-Stratifying Biomarkers.

BACKGROUND: ETV6::RUNX1 is the most common genetic abnormality in pediatric B-cell acute lymphoblastic leukemia (ALL; ∼25%), yet the comprehensive genetic architecture and molecular predictors of intermediate-risk (IR) stratification remain incompletely characterized. METHODS: We performed whole-transcriptome sequencing (Illumina NovaSeq 6000, rRNA depletion, 41.70 Gb/sample) on bone marrow samples from 93 pediatric ETV6::RUNX1-positive B-ALL patients. Bioinformatics analysis included STAR alignment, MuTect2 variant calling, FusionCatcher fusion detection, and VEP annotation. The Jaccard index with permutation testing assessed mutation co-occurrence; logistic regression identified independent predictors of IR classification. RESULTS: Beyond ETV6::RUNX1, we identified 51 distinct fusion genes across the cohort, including the reciprocal RUNX1-ETV6 (73.1%), chr8::KLF1210 (38.7%), and KLF12-chr8 (34.4%). Somatic mutations in 249 genes were detected; the most frequent were KIAA1715 (17.2%), KRAS (11.8%), and NSD2 (10.8%). Network analysis revealed significant chromatin modifier co-occurrence (KIAA1715-KMT2C: J = 0.136, p = 0.015) and KRAS-NRAS mutual exclusivity (J = 0.000, p = 0.042). PTCH1 (OR = 3.50, 95% CI 0.21-58.49, p = 0.41) and GNB1 (OR = 6.5, 95% CI 1.2-34.8, p = 0.029) mutations independently predicted IR classification. chr8::KLF1210 fusion correlated with higher Day-19 MRD levels (p = 0.038). CONCLUSIONS: GNB1 mutation represents a novel independent predictor of IR stratification in ETV6::RUNX1-positive B-ALL. The chromatin modifier co-occurrence module and extensive fusion architecture reveal biological heterogeneity within this favorable-risk subtype, with potential implications for risk-adapted therapeutic strategies.

B‐ALL↗

PubMed2026

Analysis of end-stage renal disease mediated by cuproptosis-related genes.

OBJECTIVE: The complex pathophysiological mechanism of end-stage renal disease (ESRD) has not been fully understood. Cuproptosis is a newly discovered type of programmed cell death. Therefore, this study attempts to clarify the relationship between cuproptosis-related genes (CRGs) and the phenotype of ESRD. MATERIALS AND METHODS: The National Center for Biological Information Gene Expression Omnibus database was applied to obtain the GSE37171 dataset comprising whole-genome microarray analysis of peripheral blood samples. A 3 : 1 case-control design was employed with 75 ESRD patients and 20 healthy controls who were frequency-matched for age, sex, and ethnicity. Based on differentially expressed genes (DEGs) and genes related to cuproptosis, CRGs were identified. Thereafter, we explored two different subpopulations based on the cuproptosis gene and analyzed their expression and immune infiltration. Genes specific to the CRG cluster were identified through the weighted gene co-expression network analysis algorithm, and the best prediction model was determined and verified by four machine learning methods. RESULTS: The study identified 14 differentially expressed CRGs, among which ATP7B, SLC31A1, LIAS, LIPT1, DLD, MTF1, CDKN2A, DBT, and DLST had relatively high expression levels in the ESRD samples. Compared with the control group, expression levels of FDX1, DLAT, PDHA1, PDHB, and GLS were significantly lower in the ESRD group, and CRGs played a key role in the regulation of immune infiltration in ESRD. Two cuproptosis-related molecular clusters were identified in the ESRD samples. Cluster2 was more correlated with the immune infiltration of ESRD. By analyzing the intersection points between CRG cluster and key genes of ESRD, a total of 888 specific DEGs were identified. Functional differences related to specific DEGs were further explored using gene set variation analysis. Five significant genes (SMC5, USP47, USP53, AGA, and DMXL1) were identified by the support vector machine model as key predictors for ESRD disease risk, achieving an area under the curve (AUC) of 1.00 in internal validation. However, external validation in independent cohorts is required prior to clinical application. Individual gene analysis showed an AUC > 0.81 in discriminating ESRD patients from healthy controls, and the expression of all 5 genes in ESRD patients was significantly lower than in the control group. CONCLUSION: This study clarified the relationship between CRGs and the phenotype of ESRD, analyzed their specific roles in the immune microenvironment, and obtained a predictive model, providing new insights for the study of its potential therapeutic targets.

Humans↗

PubMed2001

Estimation of spinal deformity in scoliosis from torso surface cross sections.

STUDY DESIGN: Correlation of torso scan and three-dimensional radiographic data in 65 scans of 40 subjects. OBJECTIVES: To assess whether full-torso surface laser scan images can be effectively used to estimate spinal deformity with the aid of an artificial neural network. SUMMARY OF BACKGROUND DATA: Quantification of torso surface asymmetry may aid diagnosis and monitoring of scoliosis and thereby minimize the use of radiographs. Artificial neural networks are computing tools designed to relate input and output data when the form of the relation is unknown. METHODS: A three-dimensional torso scan taken concurrently with a pair of radiographs was used to generate an integrated three-dimensional model of the spine and torso surface. Sixty-five scan-radiograph pairs were generated during 18 months in 40 patients (Cobb angles 0-58 degrees ): 34 patients with adolescent idiopathic scoliosis and six with juvenile scoliosis. Sixteen (25%) were randomly selected for testing and the remainder (n = 49) used to train the artificial neural network. Contours were cut through the torso model at each vertebral level, and the line joining the centroids of area of the torso contours was generated. Lateral deviations and angles of curvature of this line, and the relative rotations of the principal axes of each contour were computed. Artificial neural network estimations of maximal computer Cobb angle were made. RESULTS: Torso-spine correlations were generally weak (r < 0.5), although the range of torso rotation related moderately well to the maximal Cobb angle (r = 0.64). Deformity of the torso centroid line was minimal despite significant spinal deformity in the patients studied. Despite these limitations and the small data set, the artificial neural network estimated the maximal Cobb angle within 6 degrees in 63% of the test data set and was able to distinguish a Cobb angle greater than 30 degrees with a sensitivity of 1.0 and specificity of 0.75. CONCLUSIONS: Neural-network analysis of full-torso scan imaging shows promise to accurately estimate scoliotic spinal deformity in a variety of patients.

Adolescent↗

PubMed2003

Metabonomics classifies pathways affected by bioactive compounds. Artificial neural network classification of NMR spectra of plant extracts.

The biochemical mode-of-action (MOA) for herbicides and other bioactive compounds can be rapidly and simultaneously classified by automated pattern recognition of the metabonome that is embodied in the 1H NMR spectrum of a crude plant extract. The ca. 300 herbicides that are used in agriculture today affect less than 30 different biochemical pathways. In this report, 19 of the most interesting MOAs were automatically classified. Corn (Zea mays) plants were treated with various herbicides such as imazethapyr, glyphosate, sethoxydim, and diuron, which represent various biochemical modes-of-action such as inhibition of specific enzymes (acetohydroxy acid synthase [AHAS], protoporphyrin IX oxidase [PROTOX], 5-enolpyruvylshikimate-3-phosphate synthase [EPSPS], acetyl CoA carboxylase [ACC-ase], etc.), or protein complexes (photosystems I and II), or major biological process such as oxidative phosphorylation, auxin transport, microtubule growth, and mitosis. Crude isolates from the treated plants were subjected to 1H NMR spectroscopy, and the spectra were classified by artificial neural network analysis to discriminate the herbicide modes-of-action. We demonstrate the use and refinement of the method, and present cross-validated assignments for the metabolite NMR profiles of over 400 plant isolates. The MOA screen also recognizes when a new mode-of-action is present, which is considered extremely important for the herbicide discovery process, and can be used to study deviations in the metabolism of compounds from a chemical synthesis program. The combination of NMR metabolite profiling and neural network classification is expected to be similarly relevant to other metabonomic profiling applications, such as in drug discovery.

Herbicides↗

PubMed2026

Genome-wide identification and evolutionary analysis of the ERF-VII gene family in the tea plant (Camellia sinensis) and functional characterization of CsRAP2.2 in response to cold stress.

The ERF-VII gene family, a critical branch of the AP2/ERF superfamily, is central to plant stress adaptation. However, its evolutionary history and function in tea plant (Camellia sinensis) remain unclear. Here, we performed integrated evolutionary, genomic, and functional analyses of ERF-VII genes across 14 plant lineages and 20 tea plant cultivars. The phylogenetic analysis revealed that ERF-VII proteins originated after vascular plant divergence, coinciding with the emergence of the N-terminal MCGGA/I motif linked to the oxygen-dependent N-degron pathway. Gymnosperms retained few conserved members, whereas angiosperms exhibited lineage-specific expansion-extensive in monocots via whole-genome duplication, moderate in eudicots with functional diversification. Pan-genome analysis across 20 tea plant cultivars further revealed varietal differences in ERF-VII gene distribution. Transcriptome profiling via the Tea Plant Information Archive identified CsRAP2.2 as a cold-inducible ERF-VII member with sustained expression under low-temperature stress. Functional assays demonstrated that silencing CsRAP2.2 reduced cold tolerance, while overexpression in tea leaves and heterologous expression in Arabidopsis thaliana enhanced cold tolerance by maintaining photosystem II efficiency, reducing membrane lipid peroxidation, and improving antioxidant capacity. Weighted gene co-expression network analysis positioned CsRAP2.2 as a regulatory hub integrating cold, hormone, and oxygen-sensing pathways. These results clarify the evolutionary trajectory of ERF-VII genes and establish CsRAP2.2 as a core cold-tolerance regulator in tea plant. These findings may inform future breeding of cold-resilient tea cultivars.

Camellia sinensis↗

PubMed1998

Social network dynamics and HIV transmission.

OBJECTIVE: To prospectively study changes in the social networks of persons at presumably high risk for HIV in a community with low prevalence and little endogenous transmission. METHODS: From a cohort of 595 persons at high risk (prostitutes, injecting drug users, and sexual partners of these persons) and nearly 6000 identified contacts, we examined the social networks of a subset of 96 persons who were interviewed once per year for 3 years. We assessed their network configuration, network stability, and changes in risk configuration and risk behavior using epidemiologic and social network analysis, and visualization techniques. RESULTS: Some significant decrease in personal risk-taking was documented during the course of the study, particularly with regard to needle-sharing. The size and number of connected components (groups that are completely connected) declined. Microstructures (small subgroups of persons that interact intensely) were either not present, or declined appreciably during the period of observation. CONCLUSIONS: In this area of low prevalence, the lack of endogenous transmission of HIV may be related in part to the lack of a network structure that fosters active propagation, despite the continued presence of risky behaviors. Although the relative contribution of network structure and personal behavior cannot be ascertained from these data, the study suggests an important role for network configuration in the transmission dynamics of HIV.

Cohort Studies↗

PubMed2026

Diosmetin Inhibits Bladder Cancer through Suppression of the PI3K-AKT Signaling Pathway and Activation of the p53 Signal Pathway Revealed by Network Pharmacology and In Vitro Experimental Verification.

INTRODUCTION: Diosmetin, a naturally occurring flavonoid abundant in plants such as chrysanthemums, lemons, and oranges, has been reported to exhibit diverse antitumor properties. However, its potential efficacy against bladder cancer remains unexplored. This study aims to investigate the anti-bladder cancer effects of Diosmetin and elucidate the underlying mechanisms using network pharmacology combined with in vitro experiments. METHODS: Public databases were employed to identify shared targets between Diosmetin and bladder cancer. A Protein-Protein Interaction (PPI) network was constructed, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to predict core targets and signaling pathways. The predicted mechanisms were subsequently validated through in vitro assays. RESULTS: A total of 48 common targets were identified. PPI network analysis revealed 22 hub genes, including AKT1 and MDM2. GO analysis indicated enrichment in 208 biological processes, 23 cellular components, and 38 molecular functions. KEGG analysis suggested that Diosmetin exerts anti-bladder cancer effects primarily through pathways such as Pathways in cancer, PI3K-AKT signaling, and Proteoglycans in cancer. Notably, the PI3K-AKT pathway showed the highest gene enrichment, indicating its potential prominence. In vitro experiments demonstrated that Diosmetin suppresses bladder cancer cell proliferation and induces apoptosis. Additionally, Diosmetin reduced the expression of p-PI3K, p-AKT, and MDM2, while upregulating p53 expression, suggesting involvement of both the PI3K-AKT and p53 pathways. DISCUSSION: These findings align with network pharmacology predictions and highlight the potential of Diosmetin as a multi-target agent against bladder cancer, warranting further in vivo investigation. CONCLUSION: Diosmetin inhibits bladder cancer cell proliferation and promotes apoptosis by suppressing the PI3K-AKT pathway and activating the p53 pathway.

Diosmetin↗

PubMed2002

PheGe, the platform for exploring genotype-phenotype relations on cellular and organism level.

One major challenge of bioinformatics is to extract biological information into a form that gives access to analyses and predictive models and that sheds new light on cellular and organism function. In order to approach automated network analysis on organism level the relational platform PheGe was generated. PheGe enables a) presentation of cell-specific regulatory and metabolic pathways, b) sorting and coordination of the various molecules, genes and reactions to their particular signaling systems, c) visualization of signaling par distance, d) organization of downstream events on a multi-cellular level, e) recording and evaluation of pathological relevant data, f) coordination of the aberrant genes and gene products into the various regulatory pathways balancing phenotypic patterns g) modeling of cellular differentiation and finally h) tracing of network components that balance differentiation programs.

Algorithms↗

PubMed1999

[A predictive model for affect of atopic dermatitis in infancy by neural network and multiple logistic regression].

OBJECTS: To analyze the predictive accuracy of the predictive model for affect of atopic dermatitis in infancy, from the data of the epidemiological survey, which were conducted for 10,000 of mothers of infants and children in 1993. SUBJECTS AND METHODS: A total of 4610 replies were received: 2714 from mothers of infants (12 month old) and 1,896 from mothers of children (2 years old). The sensitivity, specificity and predictive accuracy were calculated from probabilistic model by neural network analysis (NNA) and multiple logistic regression analysis (MLA). RESULTS: Risk factors for probabilistic model by NNA were family history (father, mother, siblings, grand father, grand mother), food restriction, food allergy, age, food restriction of mother, egg introduced time, cow's milk introduced time. The sensitivity, specificity and predictive accuracy of NNA model was 88.6%, 99.5% and 96.4%, respectively and MLA model was 75.1%, 82.6% and 82.3%, respectively. CONCLUSION: These results suggest that the NNA is a good and useful method for prediction of onset of AD than MLA. Furthermore, It is necessary to investigate the artificial neural networks for diagnosis and/or treatment by physician.

Child, Preschool↗

PubMed2005

Social support networks and medical service use among HIV-positive injection drug users: implications to intervention.

The study used network analysis to identify forms and sources of social support associated with a medical services use among a medically underserved population living with HIV/AIDS. Participants were African American former or current injection drug users (n=295; 34% female, 45% current drug users and 17% AIDS diagnosed). Outcomes were access to the same medical provider, use of outpatient services and emergency room (ER) use with or without subsequent hospitalization. Controlling for AIDS diagnosis, insurance, current drug use and gender, access to the same medical care provider was associated with more females in one's support network and more network sources of emotional support, financial support and instrumental assistance. Adjusting for confounders, outpatient service use was associated with more female support network members and more sources of emotional support. Controlling for participants' drug use and insurance, sub-optimal emergency department use was associated with greater number of active drug users in one's support network. Contrary to other study findings, having a supportive sex partner was associated with lower access to medical care, and kin support was not associated with medical service use. Results indicate that specific sources and forms of social support had differential influences on the sample's utilization of medical services. The findings suggest that promoting HIV-positive African American injection drug users' support network functioning may help improve HIV medical services utilization among this medically underserved population.

Adult↗

PubMed1983

Social networks and work/nonwork life: action-research with nurse managers.

An action-research project with hospital nurses is reported which explores the utility of social network analysis for understanding and enhancing the quality of work life. Based on a framework for conceptualizing work stressors, we present a rationale for developing resource-support groups. These groups combine emotional support, group problem-solving, and participatory decision-making within the context of developing programs and policies to enhance the quality of working life. A workshop help for nurse managers focused on the potential for using these groups to achieve specific, prioritized goals. To study the ecology of work/nonwork life, we obtained data from workshop participants on both their work and nonwork social networks. We found these to be almost totally segmented. Personal, organizational, and sociocultural variables that may account for this pattern are examined. Implications are also discussed for developing support programs and for empowering nursing as a profession.

Humans↗

PubMed1998

The metabolic anatomy of tremor in Parkinson's disease.

OBJECTIVE: To identify regional metabolic brain networks related specifically to the presence of tremor in PD. BACKGROUND: The pathophysiology of parkinsonian tremor is unknown. Because tremor in PD occurs mainly in repose, we used resting state PET with 18F-fluorodeoxyglucose (FDG) to identify specific metabolic brain networks associated with this clinical manifestation. METHODS: We studied two discrete groups of eight PD patients with and without tremor using FDG/PET. Both patient groups were matched for gender, age, and Unified Parkinson Disease Rating Scale ratings for akinesia and rigidity. Ten normal volunteer subjects served as controls. RESULTS: Network analysis with the Scaled Subprofile Model was performed in two steps. 1) We computed the expression of the PD-related pattern (PDRP) identified by us previously in each of the PD patients and control subjects. Although PDRP subject scores were abnormally elevated in the combined PD cohort (p < 0.005), these values did not differ in the PD patient groups with and without tremor (p = 0.36). 2) We used SSM to analyze the data from the combined PD cohort comprising both patient groups. We found that PD patients with tremor were characterized by increased expression of a metabolic network comprising the thalamus, pons, and premotor cortical regions. Subject scores for this pattern were elevated in the tremor group compared with the atremulous patient group and the normal control group (p < 0.005). CONCLUSIONS: The findings suggest that PD patients with tremor are characterized by distinct increases in the functional activity of thalamo-motor cortical projections. Modulation of this functional anatomic pathway is likely to be the mechanism for successful interventions for the relief of parkinsonian tremor.

Aged↗

PubMed2005

Influence of optic disc size on parameters of retinal nerve fiber analysis with laser scanning polarimetry.

PURPOSE: The aim of the study was to evaluate the influence of optic disc size on the variables of laser scanning polarimetry (GDx). PATIENTS AND METHODS: One hundred and nineteen healthy controls and 161 patients with ocular hypertension (OHT) received detailed ophthalmologic investigation with respect to glaucoma including retinal nerve fiber analysis with GDx (Version 3.0.05x1; Laser Diagnostic Technologies Europe). Optic disc size was measured with planimetry using 15 degrees optic disc photographs. With respect to frequency of optic disc size in the normal population patients were divided in quartiles of equal sample size. RESULTS: The ratio between retinal nerve fiber layer thickness in the superior and inferior areas in relation to the nasal and temporal regions decreases significantly with increasing optic disc size and the difference between the highest and lowest retinal nerve fiber layer thickness decreases significantly with increasing optic disc size. The results of multivariate neural network analysis increased with larger optic disc size in controls as well as in patients with OHT. Linear regression analysis showed an increase of 9 units (the Number) per 1 mm(2) of optic disc size. A Number above 30, which indicates suspected glaucoma, was detected in more than a third of the normal population investigated if the optic disc area was larger than 3.5 mm(2). Overall, patients with OHT had a higher Number than controls (20.5+/-11.5 vs. 18.1+/-10.4; p>0.05), but the difference between the two groups did not reach a significant level. CONCLUSIONS: Retinal nerve fiber analysis in patients with an optic disc size larger than 3.5 mm(2) should be interpreted carefully; the Number in particular requires corrections for optic disc size.

Adolescent↗

PubMed2026

Multi-omics analysis reveals coordinated epigenetic dysregulation in atrazine-induced dopaminergic neurotoxicity.

Atrazine (ATR), a widely used triazine herbicide, has been linked to neurotoxicity, yet the epigenetic mechanisms underlying its dopaminergic effects remain unclear. This study investigated whether coordinated miRNA dysregulation and DNA methylation alterations contribute to ATR-induced Parkinson's disease (PD)-like neurotoxicity. Male Sprague-Dawley rats were administered ATR (50 mg/kg/day) for 90 days, resulting in motor and cognitive deficits with dopaminergic dysfunction, including increased α-synuclein and reduced tyrosine hydroxylase expression. Small RNA sequencing identified 72 differentially expressed miRNAs in the substantia nigra, enriched in PI3K-Akt, MAPK, and Ras signaling pathways. In a cohort of six PD patients and six matched controls, genome-wide DNA methylation profiling revealed 4694 differentially methylated positions, predominantly hypomethylated, with overlapping enrichment in neuronal signaling pathways. Weighted gene co-expression network analysis identified a PD-associated module strongly correlated with disease status (r = -0.95, P < 0.001). Multi-omics integration identified CASP3 as a central hub gene. External validation supported CASP3 relevance in PD (AUC = 0.833), and molecular docking suggested potential ATR-CASP3 interaction. Further analysis predicted upregulated miR-3552 as a potential upstream regulator of CASP3. These findings indicate that ATR-induced neurotoxicity may be mediated through the miR-3552/CASP3 signaling axis, ultimately regulating apoptosis and contributing to neurodegeneration.

Animals↗

PubMed2026

Identifying causal genetic variants for high-altitude adaptation through blood eQTL analysis in plateau populations.

A substantial number of genetic variants have been associated with high-altitude adaptation (HAA), yet most of them are located in non-coding genomic regions, leaving their specific functions and underlying mechanisms largely unknown. In this study, we analyze whole-genome and transcriptome sequencing data from a self-established cohort comprising 61 native highlanders (NHs) and 164 acclimatized newcomers (ANs), identifying 6,586 cis- and 34,203 trans-expression quantitative trait loci (eQTLs), along with 130 cell type-specific eQTLs. By further combining these data with a large East Asia (~30% Tibetan) genome-wide association study (GWAS) cohort, we employ colocalization and causal inference analyses to prioritize 85 cis-eQTLs associated with HAA and identify several novel candidate causal genes, including EXOC8, which is experimentally confirmed to regulate erythroid differentiation. Additionally, network analysis of these causal genes uncovers multiple regulatory pathways, mainly involving energy metabolism, autophagy, ubiquitination and inflammation. Our study offers a comprehensive eQTL map and reveals causal chains of "variant-gene-phenotype" for HAA-related traits, which provides new insights into potential regulatory mechanisms and targets for prevention and treatment of altitude sickness.

Quantitative Trait Loci↗