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Introduction: Plant growth-promoting rhizobacteria (PGPR) and elevated CO2 (eCO2) have demonstrated their individual potential to enhance plant yield and quality through close interaction with rhizosphere microorganisms and plant growth. However, the efficacy of PGPR under eCO2 on rhizosphere microbiome and, ultimately, plant yield and active ingredient accumulation are not yet fully understood. Methods: This study investigated how the medicinal plant Pseudostellaria heterophylla (P. heterophylla) and its rhizosphere microbes respond to PGPR (Bacillus subtilis and Pseudomonas fluorescens) at eCO2 (1,000 ppm). Results and Discussion: It was found that the yield and active ingredient polysaccharides accumulation in the tuber of P. heterophylla were significantly increased by 38 and 253%, respectively. This promotion has been associated with increased root development and changes in the indigenous microbial community. Metagenomics analysis revealed a significant reduction in pathogenic Fusarium abundance in the rhizosphere. Potential biocontrol bacteria Actinobacteria and Proteobacteria were enriched, especially the genera Bradyrhizobium and Rhodanobacter. The reshaping of the rhizosphere microbiome was accompanied by the upregulation of biological pathways related to metabolite biosynthesis in the rhizosphere. These modifications were related to the promotion of the growth and productivity of P. heterophylla. Our findings highlighted the significant role played by PGPR in medicinal plant yield and active ingredient accumulation when exposed to eCO2.

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Chemical probes and chemogenomic compounds are valuable tools to link gene to phenotype, explore human biology, and uncover novel targets for precision medicine. The mission of the Target 2035 initiative is to discover chemical tools for all human proteins by the year 2035. Here, we draw a landscape of the current chemical coverage of human biological pathways. Although available chemical tools target only 3% of the human proteome, they already cover 53% of human biological pathways and represent a versatile toolkit to dissect a vast portion of human biology. Pathways targeted by existing drugs may be enriched in unknown but valid drug targets and could be prioritized in future Target 2035 efforts.

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Earlier studies have revealed microRNAs (miRNAs) as potential biomarkers for neurological conditions, however, such evidence on psychiatric outcomes is limited. We utilized the Normative Aging Study (NAS) cohort to investigate the associations between extracellular miRNAs (ex-miRNA) and psychiatric symptoms among a group of older male adults, along with the targeted genes and biological pathways. We studied 569 participants with miRNA profile primarily measured in extracellular vesicles isolated from plasma, and psychiatric symptoms reported over 1996-2014 with repeated measures. Global and dimension scales of psychiatric symptoms were measured via the administration of Brief Symptom Inventory (BSI) per visit covering nine aspects of psychiatric health, such as anxiety, depression, hostility, psychoticism, etc. Ex-miRNAs were profiled using small RNA sequencing. Associations of expression of 395 ex-miRNAs (present in >70% samples) with current mental status were assessed using single-miRNA as well as Least Absolute Shrinkage and Selection Operator (LASSO)-based multi-miRNAs linear mixed effects models adjusting for key demographic and behavioral factors. Biological functions were explored using pathway analyses. We identified ex-miRNAs associated with each BSI scale. In particular, hsa-miR-320d was consistently identified for two global scales. Similar overlapping miRNAs across global and dimension scores included hsa-miR-379-3p, hsa-miR-1976, hsa-miR-151a-5p, hsa-miR-151b, hsa-miR-144-3p, etc. Top KEGG pathways for identified miRNAs included p53 signaling, Hippo signaling, FoxO signaling, protein processing in endoplasmic reticulum and several pathways related with cancer and neurological diseases. This study provided early evidence supporting the associations between extracellular miRNAs and psychiatric conditions. MiRNAs may serve as biomarkers of subclinical psychiatric illness in older adults.

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Uterine leiomyosarcoma (uLMS) is the most common subtype of uterine sarcomas. They have a poor prognosis with high rates of recurrence and metastasis. The five-year survival for uLMS patients is between 25 and 76%, with survival rates approaching 10-15% for patients with metastatic disease at the initial diagnosis. Accumulating evidence suggests that several biological pathways are involved in uLMS pathogenesis. Notably, drugs that block abnormal functions of these pathways remarkably improve survival in uLMS patients. However, due to chemotherapy resistance, there remains a need for novel drugs that can target these pathways effectively. In this review article, we provide an overview of the recent progress in ascertaining the biological functions and regulatory mechanisms in uLMS from the perspective of aberrant biological pathways, including DNA repair, immune checkpoint blockade, protein kinase and intracellular signaling pathways, and the hedgehog pathway. We review the emerging role of epigenetics and epitranscriptome in the pathogenesis of uLMS. In addition, we discuss serum markers, artificial intelligence (AI) combined with machine learning, shear wave elastography, current management and medical treatment options, and ongoing clinical trials for patients with uLMS. Comprehensive, integrated, and deeper insights into the pathobiology and underlying molecular mechanisms of uLMS will help develop novel strategies to treat patients with this aggressive tumor.

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Background: The heterogeneity and dynamic changes of endometrial cells have a significant impact on health as they determine the normal function of the endometrium during the menstrual cycle. Dysfunction of the endometrium can lead to the occurrence of various gynecological diseases. Therefore, deconvolution of immune microenvironment that drives transcriptional programs throughout the menstrual cycle is key to understand regulatory biology of endometrium. Methods: Herein, we comprehensively analyzed single-cell transcriptome of 59,397 cells across ten human endometrium samples and revealed the dynamic cellular heterogeneity throughout the menstrual cycle. Results: We identified two perivascular cell subtypes, four epithelial subtypes and four fibroblast cell types in endometrium. Moreover, we inferred the cell type-specific transcription factor (TF) activities and linked critical TFs to transcriptional output of diverse immune cell types, highlighting the importance of transcriptional regulation in endometrium. Dynamic interactions between various types of cells in endometrium contribute to a range of biological pathways regulating differentiation of secretory. Integration of the molecular biomarkers identified in endometrium and bulk transcriptome of 535 endometrial cancers (EC), we revealed five RNA-based molecular subtypes of EC with highly intratumoral heterogeneity and different clinical manifestations. Mechanism analysis uncovered clinically relevant pathways for pathogenesis of EC. Conclusion: In summary, our results revealed the dynamic immune microenvironment of endometrium and provided novel insights into future development of RNA-based treatments for endometriosis and endometrial carcinoma.

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INTRODUCTION: Unraveling how Alzheimer's disease (AD) genetic risk is related to neuropathological heterogeneity, and whether this occurs through specific biological pathways, is a key step toward precision medicine. METHODS: We computed pathway-specific genetic risk scores (GRSs) in non-demented individuals and investigated how AD risk variants predict cerebrospinal fluid (CSF) and imaging biomarkers reflecting AD pathology, cardiovascular, white matter integrity, and brain connectivity. RESULTS: CSF amyloidbeta and phosphorylated tau were related to most GRSs. Inflammatory pathways were associated with cerebrovascular disease, whereas quantitative measures of white matter lesion and microstructure integrity were predicted by clearance and migration pathways. Functional connectivity alterations were related to genetic variants involved in signal transduction and synaptic communication. DISCUSSION: This study reveals distinct genetic risk profiles in association with specific pathophysiological aspects in predementia stages of AD, unraveling the biological substrates of the heterogeneity of AD-associated endophenotypes and promoting a step forward in disease understanding and development of personalized therapies. HIGHLIGHTS: Polygenic risk for Alzheimer's disease encompasses six biological pathways that can be quantified with pathway-specific genetic risk scores, and differentially relate to cerebrospinal fluid and imaging biomarkers. Inflammatory pathways are mostly related to cerebrovascular burden. White matter health is associated with pathways of clearance and membrane integrity, whereas functional connectivity measures are related to signal transduction and synaptic communication pathways.

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Unfavorable genetic correlations between milk production, fertility, and urea traits have been reported. However, knowledge of the genomic regions associated with these unfavorable correlations is limited. Here, we used the correlation scan method to identify and investigate the regions driving or antagonizing the genetic correlations between production vs. fertility, urea vs. fertility, and urea vs. production traits. Driving regions produce an estimate of correlation that is in the same direction as the global correlation. Antagonizing regions produce an estimate in the opposite direction of the global estimates. Our dataset comprised 6567, 4700, and 12,658 Holstein cattle with records of production traits (milk yield, fat yield, and protein yield), fertility (calving interval) and urea traits (milk urea nitrogen and blood urea nitrogen predicted using milk-mid-infrared spectroscopy), respectively. Several regions across the genome drive the correlations between production, fertility, and urea traits. Antagonizing regions were confined to certain parts of the genome and the genes within these regions were mostly involved in preventing metabolic dysregulation, liver reprogramming, metabolism remodeling, and lipid homeostasis. The driving regions were enriched for QTL related to puberty, milk, and health-related traits. Antagonizing regions were mostly related to muscle development, metabolic body weight, and milk traits. In conclusion, we have identified genomic regions of potential importance for dairy cattle breeding. Future studies could investigate the antagonizing regions as potential genomic regions to break the unfavorable correlations and improve milk production as well as fertility and urea traits.

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In the literature, there is a paucity of methods and tools that allow the identification of biomarkers of authenticity to discriminate organic and non-organic chicken meat products. Shotgun proteomics is a powerful tool that allows the investigation of the entire proteome of a muscle and/or meat sample. In this study, a shotgun proteomics approach using Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) has been applied for the first time to characterize and identify candidate protein biomarkers of authenticity in post-mortem chicken Pectoralis major muscles produced under organic and non-organic farming systems (antibiotic-free). The proteomics characterization was further performed within two chicken strains, these being Ross 308 and Ranger Classic, which differ in their growth rate. From the candidate protein biomarkers, the bioinformatics enrichment analyses revealed significant differences in the muscle proteome between the two chicken strains, which may be related to their genetic background and rearing conditions. The results further provided novel insights on the potential interconnected pathways at interplay that are associated with the differences as a consequence of farming system of chicken strain, such as muscle contraction and energy metabolism. This study could pave the way to more in-depth investigations in proteomics applications to assess chicken meat authenticity and better understand the impact of farming systems on the chicken muscle and meat quality.

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Early-life adversity as neglect or low socioeconomic status is associated with negative physical/mental health outcomes and plays an important role in health trajectories through life. The early-life environment has been shown to be encoded as changes in epigenetic markers that are retained for many years.We investigated the effect of maternal major financial problems (MFP) and material deprivation (MD) on their children's epigenome in the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Epigenetic aging, measured with epigenetic clocks, was weakly accelerated with increased MFP. In subsequent EWAS, MFP, and MD showed strong, independent programing effects on children's genomes. MFP in the period from birth to age seven was associated with genome-wide epigenetic modifications on children's genome visible at age 7 and partially remaining at age 15.These results support the hypothesis that physiological processes at least partially explain associations between early-life adversity and health problems later in life. Both maternal stressors (MFP/MD) had similar effects on biological pathways, providing preliminary evidence for the mechanisms underlying the effects of low socioeconomic status in early life and disease outcomes later in life. Understanding these associations is essential to explain disease susceptibility, overall life trajectories and the transition from health to disease.

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The vaginal microbiome differs by race and contributes to inflammation by directly producing or consuming metabolites or by indirectly inducing host immune response, but its potential contributions to ovarian cancer (OC) disparities remain unclear. In this exploratory cross-sectional study, we examine whether vaginal fluid metabolites differ by race among patients with OC, if they are associated with systemic inflammation, and if such associations differ by race. Study participants were recruited from the Ovarian Cancer Epidemiology, Healthcare Access, and Disparities Study between March 2021 and September 2022. Our study included 36 study participants with ovarian cancer who provided biospecimens; 20 randomly selected White patients and all 16 eligible Black patients, aged 50-70 years. Acylcarnitines (n = 45 species), sphingomyelins (n = 34), and ceramides (n = 21) were assayed on cervicovaginal fluid, while four cytokines (IL-1ß, IL-10, TNF-α, and IL-6) were assayed on saliva. Seven metabolites showed >2-fold differences, two showed significant differences using the Wilcoxon rank-sum test (p < 0.05; False Discovery Rate > 0.05), and 30 metabolites had coefficients > ±0.1 in a Penalized Discriminant Analysis that achieved two distinct clusters by race. Arachidonoylcarnitine, the carnitine adduct of arachidonic acid, appeared to be consistently different by race. Thirty-eight vaginal fluid metabolites were significantly correlated with systemic inflammation biomarkers, irrespective of race. These findings suggest that vaginal fluid metabolites may differ by race, are linked with systemic inflammation, and hint at a potential role for mitochondrial dysfunction and sphingolipid metabolism in OC disparities. Larger studies are needed to verify these findings and further establish specific biological mechanisms that may link the vaginal microbiome with OC racial disparities.

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Short-chain Perfluorinated compounds (PFCs), used as substitutes for highly toxic long-chain PFCs, are increasingly entering the aquatic environment. However, the toxicity of short-chain PFCs in the environment is still controversial. This study investigated the effects of short-chain perfluorobutanesulfonic acid (PFBS) at different concentrations (2.5, 6, 14.4, 36, and 90 mg/L) on M. aeruginosa growth under 12-day exposure and explored the molecular mechanism of toxicity using transcriptomics. The results showed that M. aeruginosa exhibited hormetic effects after exposure to PFBS. Low PFBS concentrations stimulated algal growth, whereas high PFBS concentrations inhibited it, and this inhibitory effect became progressively more pronounced with increasing PFBS exposure concentrations. Transcriptomics showed that PFBS promoted the pathways of photosynthesis, glycolysis, energy metabolism and peptidoglycan synthesis, providing the energy required for cell growth and maintaining cellular morphology. PFBS, on the other hand, caused growth inhibition in algae mainly through oxidative stress, streptomycin synthesis, and genetic damage. Our findings provide new insights into the toxicity and underlying mechanism of short-chain PFCs on algae and inform the understanding of the hormetic effect of short-chain PFCs, which are crucial for assessing their ecological risks in aquatic environments.

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Genetic risk for schizophrenia is thought to trigger variation in clinical features of schizophrenia, but biological processes associated with neuronal activity in brain regions remain elusive. In this study, gene expression features were mapped to various sub-regions of the brain by integrating low-frequency amplitude features and gene expression data from the schizophrenia brain and using gene co-expression network analysis of the Allen Transcriptome Atlas of the human brain from six donors to identify genetic features of brain regions and important associations with neuronal features. The results indicate that changes in the dynamic amplitude of low-frequency fluctuation (dALFF) are mainly associated with transcriptome signature factors such as cortical layer synthesis, immune response, and expanded membrane transport. Further modular disease enrichment analysis revealed that the same set of signature genes associated with dALFF levels was enriched for multiple neurological biological processes. Finally, genetic profiling of individual modules identified multiple core genes closely related to schizophrenia, also potentially associated with neuronal activity. Thus, this paper explores genetic features of brain regions in the schizophrenia closely related to low-frequency amplitude ratio levels based on imaging genetics, which suggests structural endophenotypes associated with schizophrenia.

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This study aimed at investigating the mechanism of Trichosanthis Fructus-Allii Macrostemonis Bulbus(GX) in treating cardiovascular diseases in rats with the syndrome of combined phlegm and stasis. The rat model was established by a high-fat diet, ice-water bath combined with subcutaneous injection of adrenalin hydrochloride, and the syndrome score was determined. The serum samples of rats in the control, model, and GX groups were collected. Ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry(UPLC-Q-TOF-MS) was employed to analyze the metabolic profiles of the serum samples. The differential metabolites were screened and identified by partial least squares-discriminant analysis(PLS-DA) and orthogonal partial least squares-discriminant analysis(OPLS-DA). The intervention targets of GX-regulated metabolites and their metabolic pathways were searched against MetaboAnalyst. Gene Ontology enrichment was carried out to predict the biological pathways associated with the intervention targets of metabolic pathways. A total of 129 potential biomarkers were detected in the rat model with the syndrome of combined phlegm and stasis via metabolomics, and GX regulated 54 metabolites in several metabolic pathways such as linoleic acid metabolism, sphingolipid metabolism, and tricarboxylic acid cycle. The further screening against MetaboAnalyst showed that GX recovered the levels of nine metabolites associated with cardiovascular diseases with the syndrome of combined phlegm and stasis, which involved 69 targets in the pathways regarding cholesterol metabolism, fatty acid metabolism, inflammatory response, and glucose homeostasis and metabolism. The above-mentioned results suggested that GX can alleviate the symptoms of the rat model of cardiovascular diseases with the syndrome of combined phlegm and stasis by regulating the metabolism of linoleic acid, sphingosine, docosahexaenoic acid, rosemary acid, succinic acid, adenine, L-phenylalanine, L-valine and modulating the biological pathways such as cholesterol metabolism, fatty acid metabolism, inflammatory response, and glucose homeostasis and metabolism.

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Metabolomic biomarkers hold promise in aiding the diagnosis and prognostication of traumatic brain injury. In Canada, over 165,000 individuals annually suffer from a traumatic brain injury (TBI), making it one of the most prevalent neurological conditions. In this pilot investigation, we examined blood-derived biomarkers as proxy measures that can provide an objective approach to TBI diagnosis and monitoring. Using a 1H nuclear magnetic resonance (NMR)-based quantitative metabolic profiling approach, this study determined whether (1) blood-derived metabolites change during recovery in male participants with mild to severe TBI; (2) biological pathway analysis reflects mechanisms that mediate neural damage/repair throughout TBI recovery; and (3) changes in metabolites correlate to initial injury severity. Eight male participants with mild to severe TBI (with intracranial lesions) provided morning blood samples within 1-4 days and again 6 months post-TBI. Following NMR analysis, the samples were subjected to multivariate statistical and machine learning-based analyses. Statistical modelling displayed metabolic changes during recovery through group separation, and eight significant metabolic pathways were affected by TBI. Metabolic changes were correlated to injury severity. L-alanine (R= -0.63, p < 0.01) displayed a negative relationship with the Glasgow Coma Scale. This study provides pilot data to support the feasibility of using blood-derived metabolites to better understand changes in biochemistry following TBI.

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BACKGROUND: Evidence suggests that early life adversity is associated with maladaptive behaviors and is commonly an antecedent of stress-related psychopathology. This is particularly relevant to rearing in primate species as infant primates depend on prolonged, nurturant rearing by caregivers for normal development. To further understand the consequences of early life rearing adversity, and the relation among alterations in behavior, physiology and brain function, we assessed young monkeys that had experienced maternal separation followed by peer rearing with behavioral, endocrine and multimodal neuroimaging measures. METHODS: 50 young rhesus monkeys were studied, half of which were rejected by their mothers and peer reared, and the other half were reared by their mothers. Assessments were performed at approximately 1.8 years of age and included: threat related behavioral and cortisol responses, cerebrospinal fluid (CSF) measurements of oxytocin and corticotropin releasing hormone (CRH), and multimodal neuroimaging measures (anatomical scans, resting functional connectivity, diffusion tensor imaging, and threat-related regional glucose metabolism). RESULTS: The results demonstrated alterations across behavioral, endocrine, and neuroimaging measures in young monkeys that were reared without their mothers. At a behavioral level in response to a potential threat, peer reared animals engaged in significantly less freezing behavior (p = 0.022) along with increased self-directed behaviors (p < 0.012). Levels of oxytocin in the CSF, but not plasma, were significantly reduced in the peer reared animals (p = 0.019). No differences in plasma cortisol or CSF CRH were observed. Diffusion tensor imaging revealed significantly decreased white matter density across the brain. Exploratory correlational and permutation analyses suggest that the impact of peer rearing on behavior, endocrine and brain structural alterations are mediated by separate parallel mechanisms. CONCLUSIONS: Taken together, these results demonstrate in NHPs the importance of maternal rearing on the development of brain, behavior and hormonal systems that are linked to social functioning and adaptive responses. The findings suggest that the effects of maternal deprivation are mediated via multiple independent pathways which may account for the heterogeneity in behavioral and biological alterations observed in individuals that have experienced this early life adversity.

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The ability to predict which chemicals are of concern for environmental safety is dependent, in part, on the ability to extrapolate chemical effects across many species. This work investigated the complementary use of two computational new approach methodologies to support cross-species predictions of chemical susceptibility: the US Environmental Protection Agency Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool and Unilever's recently developed Genes to Pathways - Species Conservation Analysis (G2P-SCAN) tool. These stand-alone tools rely on existing biological knowledge to help understand chemical susceptibility and biological pathway conservation across species. The utility and challenges of these combined computational approaches were demonstrated using case examples focused on chemical interactions with peroxisome proliferator activated receptor alpha (PPARα), estrogen receptor 1 (ESR1), and gamma-aminobutyric acid type A receptor subunit alpha (GABRA1). Overall, the biological pathway information enhanced the weight of evidence to support cross-species susceptibility predictions. Through comparisons of relevant molecular and functional data gleaned from adverse outcome pathways (AOPs) to mapped biological pathways, it was possible to gain a toxicological context for various chemical-protein interactions. The information gained through this computational approach could ultimately inform chemical safety assessments by enhancing cross-species predictions of chemical susceptibility. It could also help fulfill a core objective of the AOP framework by potentially expanding the biologically plausible taxonomic domain of applicability of relevant AOPs.

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The psychotropic drug flunitrazepam (FLZ) is frequently detected in aquatic environments, yet its neurotoxicity to aquatic organisms has not received sufficient attention. In this study, microbiome, metabolome, and genome analyses were conducted to study the effects of FLZ and its metabolite 7-aminoflunitrazepam (7-FLZ) on the zebrafish nervous system and understand their toxic mechanisms. The results demonstrated that drug exposure induced gut dysbiosis, decreased short-chain fatty acids and promoted the production of lipopolysaccharides (LPS). LPS entered the brain and interacted with Toll-like receptors to cause neuroinflammation by upregulating the expression of proinflammatory cytokines TNFα and NF-κB. The increased ratio of S-adenosylmethionine to S-adenosylhomocysteine in brain tissues indicated abnormal expression of Dnmt1 gene. Whole-genome bisulfite sequencing displayed an increase in differentially methylated regions (DMRs) associated-genes and pertinent biological pathways encompassed the MAPK signaling pathway, calcium signaling pathway, and Wnt signaling pathway. Correlation analysis confirmed connections between gut microbiota, their metabolites, inflammatory factors, and DNA methylation-related markers in brain tissue. These findings indicate that while the toxicity is somewhat reduced in metabolized products, both FLZ and 7-FLZ can induce DNA methylation in brain tissue and ultimately affect the biological function of the nervous system by disrupting gut microbiota and their metabolites.

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OBJECTIVE: This study compared five distinct sets of biological pathways and associated genes related to semen volume (VOL), number of sperm (NS), and sperm motility (MOT) in the Thai multibreed dairy population. METHODS: The phenotypic data included 13,533 VOL records, 12,773 NS records, and 12,660 MOT records from 131 bulls. The genotypic data consisted of 76,519 imputed and actual single nucleotide polymorphisms (SNPs) from 72 animals. The SNP additive genetic variances for VOL, NS, and MOT were estimated for SNP windows of one SNP (SW1), ten SNP (SW10), 30 SNP (SW30), 50 SNP (SW50), and 100 SNP (SW100) using a single-step genomic best linear unbiased prediction approach. The fixed effects in the model were contemporary group, ejaculate order, bull age, ambient temperature, and heterosis. The random effects accounted for animal additive genetic effects, permanent environment effects, and residual. The SNPs explaining at least 0.001% of the additive genetic variance in SW1, 0.01% in SW10, 0.03% in SW30, 0.05% in SW50, and 0.1% in SW100 were selected for gene identification through the NCBI database. The pathway analysis utilized genes associated with the identified SNP windows. RESULTS: Comparison of overlapping and non-overlapping SNP windows revealed notable differences among the identified pathways and genes associated with the studied traits. Overlapping windows consistently yielded a larger number of shared biological pathways and genes than non-overlapping windows. In particular, overlapping SW30 and SW50 identified the largest number of shared pathways and genes in the Thai multibreed dairy population. CONCLUSION: This study yielded valuable insights into the genetic architecture of VOL, NS, and MOT. It also highlighted the importance of assessing overlapping and non-overlapping SNP windows of various sizes for their effectiveness to identify shared pathways and genes influencing multiple traits.

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Glioblastoma multiforme (GBM) is one of the deadliest tumours. This study aimed to construct radiogenomic prognostic models of glioblastoma overall survival (OS) based on magnetic resonance imaging (MRI) Gd-T1WI images and deoxyribonucleic acid (DNA) methylation-seq and to understand the related biological pathways. The ResNet3D-18 model was used to extract radiomic features, and Lasso-Cox regression analysis was utilized to establish the prognostic models. A nomogram was constructed by combining the radiogenomic features and clinicopathological variables. The DeLong test was performed to compare the area under the curve (AUC) of the models. We screened differentially expressed genes (DEGs) with original ribonucleic acid (RNA)-seq in risk stratification and used Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) annotations for functional enrichment analysis. For the 1-year OS models, the AUCs of the radiogenomic set, methylation set and deep learning set in the training cohort were 0.864, 0.804 and 0.787, and those in the validation cohort were 0.835, 0.768 and 0.651, respectively. The AUCs of the 0.5-, 1- and 2-year nomograms in the training cohort were 0.943, 0.861 and 0.871, and those in the validation cohort were 0.864, 0.885 and 0.805, respectively. A total of 245 DEGs were screened; functional enrichment analysis showed that these DEGs were associated with cell immunity. The survival risk-stratifying radiogenomic models for glioblastoma OS had high predictability and were associated with biological pathways related to cell immunity.

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Dioxin(-like) exposures are linked to adverse health effects, including cancer. However, metabolic alterations induced by these chemicals remain largely unknown. Beyond known dioxin(-like) compounds, we leveraged a chemical-wide approach to assess chlorinated co-exposures and parent compound products [termed dioxin(-like)-related compounds] among 137 occupational workers. Endogenous metabolites were profiled by untargeted metabolomics, namely, reversed-phase chromatography with negative electrospray ionization (C18-negative) and hydrophilic interaction liquid chromatography with positive electrospray ionization (HILIC-positive). We performed a metabolome-wide association study to select dioxin(-like) associated metabolic features using a 20% false discovery rate threshold. Metabolic features were then characterized by pathway enrichment analyses. There are no significant features associated with polychlorinated dibenzo-p-dioxins (PCDDs), a subgroup of known dioxin(-like) compounds. However, 3,110 C18-negative and 2,894 HILIC-positive features were associated with at least one of the PCDD-related compounds. Abundant metabolic changes were also observed for polychlorinated dibenzofuran-related and polychlorinated biphenyl-related compounds. These metabolic features were primarily enriched in pathways of amino acids, lipid and fatty acids, carbohydrates, cofactors, and nucleotides. Our study highlights the potential of chemical-wide analysis for comprehensive exposure assessment beyond targeted chemicals. Coupled with advanced endogenous metabolomics, this approach allows for an in-depth exploration of metabolic alterations induced by environmental chemicals.

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