Multi-omics Approach Research Articles

TRANSFORMING HEALTHCARE DELIVERY THROUGH BIG DATA IN HOSPITAL MANAGEMENT SYSTEMS: A REVIEW OF RECENT LITERATURE TRENDS

This systematic review investigates the transformative role of big data technologies in biomedical research, analyzing 40 peer-reviewed articles published between 2010 and 2023. The review specifically explores advancements in next-generation sequencing (NGS), multi-omics approaches, machine learning, and artificial intelligence (AI), all of which have significantly enhanced the understanding of complex biological systems and diseases. NGS has emerged as a key tool in personalized medicine, enabling rapid and cost-effective genome sequencing that has facilitated the identification of genetic mutations and biomarkers associated with various diseases, particularly in oncology. Of the 40 studies reviewed, 12 focused on the integration of multi-omics data—genomics, transcriptomics, proteomics, and metabolomics—to provide a comprehensive view of biological processes. These multi-omics approaches have been instrumental in identifying biomarkers for disease progression and response to treatments, offering new avenues for drug development and precision medicine. Additionally, 15 studies highlighted the growing application of machine learning and AI algorithms in managing and analyzing vast biomedical datasets. These tools are now critical in uncovering hidden patterns within large datasets, predicting disease outcomes, and improving the accuracy of clinical decision-making. However, 10 studies emphasized ongoing challenges related to data storage, privacy concerns, and the lack of standardized data formats, which hinder effective data sharing across institutions. Despite these challenges, the integration of AI, IoT devices, and big data analytics is paving the way for more personalized, real-time healthcare monitoring and treatment solutions. This review concludes that while significant advancements have been made, further efforts are required to address the ethical and technical barriers that limit the full potential of big data technologies in biomedical research.

Multi-omics approach to reveal follicular metabolic changes and their effects on oocyte competence in PCOS patients.

Polycystic ovary syndrome (PCOS) is a common heterogeneous disorder linked with endocrine and metabolic disturbances. The underlying mechanism of PCOS, especially its effect on oocyte competence, remains unclear. The study aimed to identify abnormal follicular metabolic changes using a multi-omics approach in follicular fluid from PCOS patients and to determine their effects on oocyte competence. A total of 36 women with PCOS and 35 women without PCOS who underwent in vitro fertilization and embryo transfer were included in the study. Cumulus cells and follicular fluid samples were collected. Follicular fluid samples underwent metabolomic analysis, while cumulus cell clusters from the same patients were assessed using transcriptomic analysis. Clinical information of patients and assisted reproductive technology (ART) results were recorded. Transcriptomics and metabolomics were integrated to identify disrupted pathways, and receiver operation characteristics (ROC) analysis was conducted to identify potential diagnostic biomarkers for PCOS. Pearson correlation analysis was conducted to assess the relationship between metabolites in follicular fluid and oocyte competence (fertilization and early embryo development potential). Through multi-omics analysis, we identified aberrantly expressed pathways at both transcriptional and metabolic levels, such as the citrate cycle (TCA cycle), oxidative phosphorylation, the cAMP signaling pathway, the mTOR signaling pathway, and steroid hormone biosynthesis. Ten candidate metabolites were identified based on metabolic profiling data from these altered pathways. Phytic acid, succinic acid, 2'-deoxyinosine triphosphate, and 4-trimethylammoniobutanoic acid in the follicular fluid exhibited high specificity and sensitivity in distinguishing PCOS. Among these metabolites, L-arginine showed a negative correlation with the 2PN fertilization rate and cleavage rate, while estrone sulfate showed a negative correlation with the high-quality embryo rate in the in-vitro fertilization (IVF) cycle. We have conducted a preliminary study of a novel metabolic signature in women with PCOS using a multi-omics approach. The alterations in key metabolic pathways may enhance our understanding of the pathogenesis of PCOS.

Isotope Encoded chemical Imaging Identifies Amyloid Plaque Age Dependent Structural Maturation, Synaptic Loss, and Increased Toxicity.

It is of critical importance to our understanding of Alzheimer's disease (AD) pathology to determine how key pathological factors are interconnected and implicated in nerve cell death, clinical symptoms, and disease progression. The formation of extracellular beta-amyloid (Aβ) plaques is the major pathological hallmark of AD and Aβ has been suggested to be a critical inducer of AD, driving disease pathogenesis. Exactly how Aβ plaque formation begins and how ongoing plaque deposition proceeds and initiates subsequent neurotoxic mechanisms is not well understood. The primary aim of our research is to elucidate the biochemical processes underlying early Aβ plaque formation in brain tissue. We recently introduced a chemical imaging paradigm based on mass spectrometry imaging (MSI) and metabolic isotope labelling to follow stable isotope labelling kinetics (iSILK) in vivo to track the in vivo build-up and deposition of Aβ. Herein, knock-in Aβ mouse models ( App NL-F ) that develop Aβ pathology gradually are metabolically labeled with stable isotopes. This chemical imaging approach timestamps amyloid plaques during the period of initial deposition allowing the fate of aggregating Aβ species from before and during the earliest events of plaque pathology through plaque maturation to be tracked. To identify the molecular and cellular response to plaque maturation, we integrated iSILK with single plaque transcriptomics performed on adjacent tissue sections. This enabled changes in gene expression to be tracked as a function of plaque age (as encoded in the Aβ peptide isotopologue pattern) distinct from changes due to the chronological age or pathological severity. This approach identified that plaque age correlates negatively with gene expression patterns associated with synaptic function as early as in 10-month-old animals but persists into 18 months. Finally, we integrated hyperspectral confocal microscopy into our multiomic approach to image amyloid structural isomers, revealing a positive correlation between plaque age and amyloid structural maturity. This analysis identified three categories of plaques, each with a distinct impact on the surrounding microenvironment. Here, we identified that older, more compact plaques were associated with the most significant synapse loss and toxicity. These data show how isotope-encoded MS imaging can be used to delineate Aβ toxicity dynamics in vivo. Moreover, we show for the first time a functional integration of dynamic MSI, structural plaque imaging and whole genome-wide spatial transcriptomics at the single plaque level. This multiomic approach offers an unprecedented combination of temporal and spatial resolution enabling a description of the earliest events of precipitating amyloid pathology and how Aβ modulates synaptotoxic mechanisms.

Propionate Decreases Microglial Activation but Impairs Phagocytic Capacity in Response to Aggregated Fibrillar Amyloid Beta Protein.

Microglia, the innate immune cell of the brain, are a principal player in Alzheimer's disease (AD) pathogenesis. Their surveillance of the brain leads to interaction with the protein aggregates that drive AD pathogenesis, most notably Amyloid Beta (Aβ). Microglia attempt to clear and degrade Aβ using phagocytic machinery, spurring damaging neuroinflammation in the process. Thus, modulation of the microglial response to Aβ is crucial in mitigating AD pathophysiology. SCFAs, microbial byproducts of dietary fiber fermentation, are blood-brain barrier permeable molecules that have recently been shown to modulate microglial function. It is unclear whether propionate, one representative SCFA, has beneficial or detrimental effects on microglia in AD. Thus, we investigated its impact on microglial Aβ response in vitro. Using a multiomics approach, we characterized the transcriptomic, metabolomic, and lipidomic responses of immortalized murine microglia following 1 h of Aβ stimulation, as well as characterizing Aβ phagocytosis and secretion of reactive nitrogen species. Propionate blunted the early inflammatory response driven by Aβ, downregulating the expression of many Aβ-stimulated immune genes, including those regulating inflammation, the immune complement system, and chemotaxis. Further, it reduced the expression of Apoe and inflammation-promoting Aβ-binding scavenger receptors such as Cd36 and Msr1 in favor of inflammation-dampening Lpl, although this led to impaired phagocytosis. Finally, propionate shifted microglial metabolism, altering phospholipid composition and diverting arginine metabolism, resulting in decreased nitric oxide production. Altogether, our data demonstrate a modulatory role of propionate on microglia that may dampen immune activation in response to Aβ, although at the expense of phagocytic capacity.

Integrative multi-omic and machine learning approach for prognostic stratification and therapeutic targeting in lung squamous cell carcinoma.

The proliferation, metastasis, and drug resistance of cancer cells pose significant challenges to the treatment of lung squamous cell carcinoma (LUSC). However, there is a lack of optimal predictive models that can accurately forecast patient prognosis and guide the selection of targeted therapies. The extensive multi-omic data obtained from multi-level molecular biology provides a unique perspective for understanding the underlying biological characteristics of cancer, offering potential prognostic indicators and drug sensitivity biomarkers for LUSC patients. We integrated diverse datasets encompassing gene expression, DNA methylation, genomic mutations, and clinical data from LUSC patients to achieve consensus clustering using a suite of 10 multi-omics integration algorithms. Subsequently, we employed 10 commonly used machine learning algorithms, combining them into 101 unique configurations to design an optimal performing model. We then explored the characteristics of high- and low-risk LUSC patient groups in terms of the tumor microenvironment and response to immunotherapy, ultimately validating the functional roles of the model genes through in vitro experiments. Through the application of 10 clustering algorithms, we identified two prognostically relevant subtypes, with CS1 exhibiting a more favorable prognosis. We then constructed a subtype-specific machine learning model, LUSC multi-omics signature (LMS) based on seven key hub genes. Compared to previously published LUSC biomarkers, our LMS score demonstrated superior predictive performance. Patients with lower LMS scores had higher overall survival rates and better responses to immunotherapy. Notably, the high LMS group was more inclined toward "cold" tumors, characterized by immune suppression and exclusion, but drugs like dasatinib may represent promising therapeutic options for these patients. Notably, we also validated the model gene SERPINB13 through cell experiments, confirming its role as a potential oncogene influencing the progression of LUSC and as a promising therapeutic target. Our research provides new insights into refining the molecular classification of LUSC and further optimizing immunotherapy strategies.

Integrative multiomics analysis identifies key genes regulating intramuscular fat deposition during development

Intramuscular fat (IMF) content is an important indicator of livestock and poultry meat quality. Enhancing IMF deposition can significantly improve meat quality. Focusing on the core process of IMF deposition, this study used the Jingxing Yellow (JXY) chickens as a model organism and employed multi-omics approaches, including RNA-sequencing (RNA-seq), Whole-genome bisulfite sequencing (WGBS), and metabolomics, to identify the key genes influencing IMF deposition in chickens during development. The results indicated that the contents of triglycerides (TG) and phospholipids (PLIP) exhibited an upward trend. The TG content did not differ significantly between day 1 (D1) and day 7 (D7), but increased significantly after 35 days (D35) of age. The WGBS results revealed that CpG methylation was the predominant methylation type in the breast muscle tissue of JXY chickens. Integrative analysis of RNA-seq and WGBS identified 50 genes, including PLA2G4F, PALMD, PLSCR5, ARHGEF26, LUM, DCN, TNRC6B, CACNA1C, ROBO1, and MBTPS2, whose methylation levels were significantly negatively correlated with their expression levels. In addition, the combined Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differentially-expressed metabolites (DEM) and differentially-expressed genes (DEG) converged on the glycerophospholipid metabolism pathway, which was significantly enriched in DEGs such as PLA2G4F, PLA2G15, LPIN1, MBOAT2, DGKH, AGPAT2, and CHKA, as well as DEM like glycerophosphocholine and phosphocholine. Notably, PLA2G4F was identified as a DEG by DNA methylation, suggesting that PLA2G4F could be a key candidate gene influencing IMF deposition during chicken development. These findings are expected to provide a solid theoretical foundation for improving meat quality through targeted genetic and epigenetic interventions.

Investigation of the regulatory mechanisms of Guiqi Yimu Powder on dairy cow fatty liver cells using a multi-omics approach.

Fatty liver disease in dairy cows is a metabolic disorder that significantly affects their health and productivity, imposing a notable economic burden on the global dairy industry. Traditional Chinese medicine (TCM), characterized by its multi-component and multi-target features, has shown unique advantages in the prevention and treatment of various diseases. Guiqi Yimu Powder, a traditional TCM formula, enhances growth, boosts production efficiency, and strengthens immune function in livestock by regulating antioxidant along with anti-inflammatory pathways. However, its specific regulatory mechanisms on fatty liver in dairy cows remain unclear. This study aims to investigate the molecular-level effects and potential regulatory mechanisms of Guiqi Yimu Powder in a Trimethylamine N-oxide (TMAO) induced fatty liver cell model of dairy cows. We employed a comprehensive analysis integrating transcriptomics, proteomics, metabolomics, and network pharmacology. An in vitro dairy cow fatty liver cell model was established using TMAO to induce lipid accumulation. Cells were treated with the optimal TMAO concentration identified through preliminary experiments, and further divided into a lipid accumulation group and Guiqi Yimu Powder treatment groups. The treatment groups received varying concentrations of Guiqi Yimu Powder (10, 20, 30, 40, or 50 g/L). High-throughput omics sequencing technologies were utilized to perform a comprehensive analysis of the treated cells. Bioinformatics methods were applied to explore the regulatory effects, aiming to elucidate the specific impacts of Guiqi Yimu Powder on lipid metabolism, liver function, and related signaling pathways, thereby providing scientific evidence for its potential application in the prevention and treatment of fatty liver in dairy cows. Guiqi Yimu Powder treatment significantly affected 1,536 genes, 152 proteins, and 259 metabolites. KEGG enrichment analysis revealed that the significantly altered molecules are involved in multiple pathways related to the pathology of fatty liver, including metabolic pathways, glutathione metabolism, hepatitis B, and AMPK signaling pathway (p < 0.05). Notably, joint analysis highlighted the regulatory mechanisms of Guiqi Yimu Powder on glutathione cycling, with L-5-Oxoproline identified as an important metabolic compound. These findings indicate its impact on oxidative stress, energy metabolism, and liver function, suggesting potential therapeutic applications for fatty liver in dairy cows. This study elucidated the regulatory mechanisms of Guiqi Yimu Powder on fatty liver cells in dairy cows, providing new scientific evidence for its potential application in the prevention and treatment of fatty liver disease.

Employing Multi-Omics Analyses to Understand Changes during Kidney Development in Perinatal Interleukin-6 Animal Model.

Chronic kidney disease (CKD) is a leading cause of morbidity and mortality globally. Maternal obesity during pregnancy is linked to systemic inflammation and elevated levels of the pro-inflammatory cytokine interleukin-6 (IL-6). In our previous work, we demonstrated that increased maternal IL-6 during gestation impacts intrauterine development in mice. We hypothesized that IL-6-induced inflammation alters gene expression in the developing fetus. To test this, pregnant mice were administered IL-6 or saline during mid-gestation. Newborn mouse kidneys were analyzed using mRNA-seq, miRNA-seq and whole-genome bisulfite-seq (WGBS). A multi-omics approach was employed to quantify mRNA gene expression, miRNA expression and DNA methylation, using advanced bioinformatics and data integration techniques. Our analysis identified 19 key genes present in multiple omics datasets, regulated by epigenetics and miRNAs. We constructed a regulatory network for these genes, revealing disruptions in pathways such as Mannose type O-glycan biosynthesis, the cell cycle, apoptosis and FoxO signaling. Notably, the Atp7b gene was regulated by DNA methylation and miR-223 targeting, whereas the Man2a1 gene was controlled by DNA methylation affecting energy metabolism. These findings suggest that these genes may play a role in fetal programming, potentially leading to CKD later in life due to gestational inflammation.

Multiomics-Based Outcome Prediction in Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR).

Objectives: This retrospective study aims to develop a multiomics approach that integrates radiomics, dosiomics, and delta features to predict treatment responses in brain metastasis (BM) patients undergoing PULSAR. Methods: A retrospective study encompassing 39 BM patients with 69 lesions treated with PULSAR was undertaken. Radiomics, dosiomics, and delta features were extracted from both pre-treatment and intra-treatment MRI scans alongside dose distributions. Six individual models, alongside an ensemble feature selection (EFS) model, were evaluated. The classification task focused on distinguishing between two lesion groups based on whether they exhibited a volume reduction of more than 20% at follow-up. Performance metrics, including sensitivity, specificity, accuracy, precision, F1 score, and the area under the receiver operating characteristic (ROC) curve (AUC), were assessed. Results: The EFS model integrated the features from pre-treatment radiomics, pre-treatment dosiomics, intra-treatment radiomics, and delta radiomics. It outperformed six individual models, achieving an AUC of 0.979, accuracy of 0.917, and F1 score of 0.821. Among the top nine features of the EFS model, six features came from post-wavelet transformation and three from original images. Conclusions: The study demonstrated the feasibility of employing a data-driven multiomics approach to predict treatment outcomes in BM patients receiving PULSAR treatment. Integrating multiomics with intra-treatment decision support in PULSAR shows promise for optimizing patient management and reducing the risks of under- or over-treatment.

Multiomics identification of ALDH9A1 as a crucial immunoregulatory molecule involved in calcific aortic valve disease

Mitochondrial dysfunction and immune cell infiltration play crucial yet incompletely understood roles in the pathogenesis of calcific aortic valve disease (CAVD). This study aimed to identify immune-related mitochondrial genes critical to the pathological process of CAVD using multiomics approaches. The CIBERSORT algorithm was employed to evaluate immune cell infiltration characteristics in CAVD patients. An integrative analysis combining weighted gene coexpression network analysis (WGCNA), machine learning, and summary data-based Mendelian randomization (SMR) was performed to identify key mitochondrial genes implicated in CAVD. Spearman’s rank correlation analysis was also performed to assess the relationships between key mitochondrial genes and infiltrating immune cells. Compared with those in normal aortic valve tissue, an increased proportion of M0 macrophages and resting memory CD4 T cells, along with a decreased proportion of plasma cells and activated dendritic cells, were observed in CAVD patients. Additionally, eight key mitochondrial genes associated with CAVD, including PDK4, LDHB, SLC25A36, ALDH9A1, ECHDC2, AUH, ALDH2, and BNIP3, were identified through the integration of WGCNA and machine learning methods. Subsequent SMR analysis, incorporating multiomics data, such as expression quantitative trait loci (eQTLs) and methylation quantitative trait loci (mQTLs), revealed a significant causal relationship between ALDH9A1 expression and a reduced risk of CAVD. Moreover, ALDH9A1 expression was inversely correlated with M0 macrophages and positively correlated with M2 macrophages. These findings suggest that increased ALDH9A1 expression is significantly associated with a reduced risk of CAVD and that it may exert its protective effects by modulating mitochondrial function and immune cell infiltration. Specifically, ALDH9A1 may contribute to the shift from M0 macrophages to anti-inflammatory M2 macrophages, potentially mitigating the pathological progression of CAVD. In conclusion, ALDH9A1 represents a promising molecular target for the diagnosis and treatment of CAVD. However, further validation through in vivo and n vitro studies is necessary to confirm its role in CAVD pathogenesis and therapeutic potential.

Exploiting Brassica rapa L. subsp. pekinensis Genome Research

Chinese cabbage, Brassica rapa L. subsp. pekinensis is a crucial and extensively consumed vegetable in the world, especially Eastern Asia. The market demand for this leafy vegetable increases year by year, resulting in multiple challenges for agricultural researchers worldwide. Multi-omic approaches and the integration of functional genomics helps us understand the relationships between Chinese cabbage genomes and phenotypes under specific physiological and environmental conditions. However, challenges exist in integrating multi-omics for the functional analysis of genes and for developing potential traits for Chinese cabbage improvement. However, the panomics platform allows for the integration of complex omics, enhancing our understanding of molecular regulator networks in Chinese cabbage agricultural traits. In addition, the agronomic features of Chinese cabbage are significantly impacted by the environment. The expression of these agricultural features is tightly regulated by a combination of signals from both the internal regulatory network and the external growth environment. To comprehend the molecular process of these characteristics, it is necessary to have a prior understanding of molecular breeding for the objective of enhancing quality. While the use of various approaches in Chinese cabbage is still in its early stages, recent research has shown that it has the potential to uncover new regulators both rapidly and effectively, leading to updated regulatory networks. In addition, the utilization of the efficient transformation technique in conjunction with gene editing using CRISPR/Cas9 will result in a reduction in time requirements and facilitate a more precise understanding of the role of the regulators. Numerous studies about Chinese cabbage have been conducted in the past two decades, but a comprehensive review about its genome still limited. This review provides a concise summary of the latest discoveries in genomic research related to Brassica and explores the potential future developments for this species.

Leveraging Multi-Omics Approaches and Advanced Technologies to Unravel the Molecular Complexities, Modifiers, and Precision Medicine Strategies for Hemoglobin H Disease.

Hemoglobin H (HbH) disease, a form of alpha-thalassemia, poses significant clinical challenges due to its complex molecular underpinnings. It is characterized by reduced synthesis of the alpha-globin chain. The integration of multi-omics and precision medicine holds immense potential to comprehensively understand and capture interactions at the molecular and genetic levels. This review integrates current multi-omics approaches and advanced technologies in HbH research. Furthermore, it delves into detailed pathophysiology and possible therapeutics in the upcoming future. We explore the role of genomics, transcriptomics, proteomics, and metabolomics studies, alongside bioinformatics tools and gene-editing technologies like CRISPR/Cas9, to identify genetic modifiers, decipher molecular pathways, and discover therapeutic targets. Recent advancements are unveiling novel genetic and epigenetic modifiers impacting HbH disease severity, paving the way for personalized precision medicine interventions. The significance of multi-omics research in unraveling the complexities of rare diseases like HbH is underscored, highlighting its potential to revolutionize clinical practice through precision medicine approaches. This paradigm shift can pave the way for a deeper understanding of HbH complexities and improved disease management.

Multi-omics profiling of DNA methylation and gene expression alterations in human cocaine use disorder

Structural and functional changes of the brain are assumed to contribute to excessive cocaine intake, craving, and relapse in cocaine use disorder (CUD). Epigenetic and transcriptional changes were hypothesized as a molecular basis for CUD-associated brain alterations. Here we performed a multi-omics study of CUD by integrating epigenome-wide methylomic (N = 42) and transcriptomic (N = 25) data from the same individuals using postmortem brain tissue of Brodmann Area 9 (BA9). Of the N = 1 057 differentially expressed genes (p < 0.05), one gene, ZFAND2A, was significantly upregulated in CUD at transcriptome-wide significance (q < 0.05). Differential alternative splicing (AS) analysis revealed N = 98 alternatively spliced transcripts enriched in axon and dendrite extension pathways. Strong convergent overlap in CUD-associated expression deregulation was found between our BA9 cohort and independent replication datasets. Epigenomic, transcriptomic, and AS changes in BA9 converged at two genes, ZBTB4 and INPP5E. In pathway analyses, synaptic signaling, neuron morphogenesis, and fatty acid metabolism emerged as the most prominently deregulated biological processes. Drug repositioning analysis revealed glucocorticoid receptor targeting drugs as most potent in reversing the CUD expression profile. Our study highlights the value of multi-omics approaches for an in-depth molecular characterization and provides insights into the relationship between CUD-associated epigenomic and transcriptomic signatures in the human prefrontal cortex.

Cell and transcriptomic diversity of infrapatellar fat pad during knee osteoarthritis

ObjectivesIn this study, we employ a multiomic approach to identify major cell types and subsets, and their transcriptomic profiles within the infrapatellar fat pad (IFP), and to determine differences in...

The human volatilome meets cancer diagnostics: past, present, and future of noninvasive applications.

Cancer is a significant public health problem, causing dozens of millions of deaths annually. New cancer screening programs are urgently needed for early cancer detection, as this approach can improve treatment outcomes and increase patient survival. The search for affordable, noninvasive, and highly accurate cancer detection methods revealed a valuable source of tumor-derived metabolites in the human metabolome through the exploration of volatile organic compounds (VOCs) in noninvasive biofluids. This review discusses volatilomics-based approaches for cancer detection using noninvasive biomatrices (breath, saliva, skin secretions, urine, feces, and earwax). We presented the historical background, the latest approaches, and the required stages for clinical validation of volatilomics-based methods, which are still lacking in terms of making noninvasive methods available and widespread to the population. Furthermore, insights into the usefulness and challenges of volatilomics in clinical implementation steps for each biofluid are highlighted. We outline the methodologies for using noninvasive biomatrices with up-and-coming clinical applications in cancer diagnostics. Several challenges and advantages associated with the use of each biomatrix are discussed, aiming at encouraging the scientific community to strengthen efforts toward the necessary steps to speed up the clinical translation of volatile-based cancer detection methods, as well as discussing in favor of (i) hybrid applications (i.e., using more than one biomatrix) to describe metabolite modulations that can be "cancer volatile fingerprints" and (ii) in multi-omics approaches integrating genomics, transcriptomics, and proteomics into the volatilomic data, which might be a breakthrough for diagnostic purposes, onco-pathway assessment, and biomarker validations.

Platelet Proteomics and Tissue Metabolomics Investigation for the Mechanism of Aspirin Eugenol Ester on Preventive Thrombosis Mechanism in a Rat Thrombosis Model.

Platelet activation is closely related to thrombosis. Aspirin eugenol ester (AEE) is a novel medicinal compound synthesized by esterifying aspirin with eugenol using the pro-drug principle. Pharmacological and pharmacodynamic experiments showed that AEE has excellent anti-inflammatory, antioxidant, and inhibitory platelet activation effects, preventing thrombosis. However, the regulatory network and action target of AEE in inhibiting platelet activation remain unknown. This study aimed to investigate the effects of AEE on platelets of thrombosed rats to reveal its regulatory mechanism via a multi-omics approach. The platelet proteomic results showed that 348 DEPs were identified in the AEE group compared with the model group, of which 87 were up- and 261 down-regulated. The pathways in this result were different from previous results, including mTOR signaling and ADP signaling at P2Y purinoceptor 12. The metabolomics of heart and abdominal aortic tissue results showed that the differential metabolites were mainly involved in steroid biosynthesis, the citric acid cycle, phenylalanine metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, and glutathione metabolism. Molecular docking results showed that AEE had a better binding force to both the COX-1 and P2Y12 protein. AEE could effectively inhibit platelet activation by inhibiting COX-1 protein and P2Y12 protein activity, thereby inhibiting platelet aggregation. Therefore, AEE can have a positive effect on inhibiting platelet activation.

8530 Characterizing the Genomic Effects of Low-Dose Estrogen in Breast Cancer Cells

Abstract Disclosure: H. Kim: None. T.S. Nandu: None. W.L. Kraus: None. Estrogens are steroid hormones that play a key role in a wide range of physiological and pathological processes. The predominant nuclear receptor for estrogens, estrogen receptor-alpha (ERɑ), functions primarily as a nuclear transcription factor (TF) through ligand-dependent activation by 17β-estradiol (E2), the major naturally-occurring estrogen. The physiological levels of E2 are regulated by production, sequestration, and degradation, which can limit the amount of bioavailable E2. However, many studies examining the genomic effects of E2-regulated signaling in cell-based assays used supraphysiological levels of hormone. While this has allowed us to understand the extremes of E2 signaling, it limits our ability to examine cellular functions which operate at lower hormone concentrations. The genomic actions of ligand-bound ERɑ are mediated through transcriptional enhancer formation and function. Alterations in protein and DNA components dictate different enhancer molecular subtypes that specify distinct regulatory mechanisms and downstream cellular phenotypes. How the amount of available hormone affects these genomic processes is unknown. In order to understand the genomic effects of physiological low-dose levels of E2, we have treated MCF-7 ERɑ-positive human breast cancer cells with a range of E2 concentrations and examined the global regulation of gene expression using a multi-omics approach (RNA-seq, ERɑ ChIP-seq, H3K27ac ChIP-seq, PRO-seq, ATAC-seq). Interestingly, we identified gene sets that are preferentially expressed maximally at picomolar or nanomolar concentrations. We are using these genes sets to explore the mechanisms of estrogen-regulated gene expression at physiologically relevant of E2 concentrations. Analysis of nascent RNA transcription using PRO-seq indicates regulation at the transcriptional level. Preliminary ERɑ chromatin binding assays show that while many loci require high levels of E2 for maximum ERɑ binding, others show maximum ERɑ binding at lower doses of E2. Further analyses show distinct enhancer features, such as chromatin accessibility and H3K27ac enrichment, may play a role in determining the response to E2 dosage. We are now determining the molecular mechanisms of dosage sensitivity using a multipronged approach integrating genomics, molecular biology, and cell-based assays. With these studies, we hope to understand a long overlooked physiological aspect of E2 with a comprehensive and global analysis of signal-dependent genomic responses. Presentation: 6/3/2024

6544 M6A methylation of PLCβ in the hypothalamus regulates GnRH synthesis and secretion by interfering Ca2+ signaling pathway

Abstract Disclosure: X. Yin: None. S. Zang: None. P. Li: None. Backgrounds: In recent years, the incidence of precocious puberty has increased rapidly, posing a serious threat to girls' physical and mental health. Epigenetic changes, especially m6A methylation modification, may be an important influencing factor. Researches had found that m6A modification plays an irreplaceable role in the development and functional homeostasis of the nervous system. A variety of factors could changed RNA m6A level in the hypothalamus and affect the process of adolescent sexual development. In the study,we aimed to explore the specific role of m6A methylation in precocious puberty, and to identify its downstream target genes by multi-omics approach and to verify its function. Hypothesis The demethylase FTO regulated the m6A modification of the target gene in the hypothalamus to affect the synthesis and secretion of GnRH and regulate the initiation of puberty. Methods: The modification abundance of m6A was investigated by the girls with central precious puberty (CPP). The changes of m6A modification in total RNA and the expression of important modulator genes were detected by colorimetry and qRT-PCR respectively in the hypothalamic ARC nucleus of female rats at three different developmental stages (juvenile, early puberty and puberty) and precocious rat model. MeRIP and mRNA sequencing was used to screen target genes which related to puberty development. Knockdown or overexpression of FTO were constructed using plasmid or lentivirus transfection. The target mRNA targeted regulated by FTO was verified with RNA-seq, qRT-PCR, WB and Flow cytometry. Results This study confirmed that the level of RNA m6A in the peripheral blood of the precocious group was significantly higher than that of the control group. The level of RNA m6A in the hypothalamus of female rats gradually increases with the development of puberty, and the expression of demethylase gradually decreases. MeRIP-seq combined with mrNA-seq screening analysis showed that the m6A modification of PLCβ in the ARC nucleus of the hypothalamus was reduced, and its mRNA expression was significantly increased in sexually precocious female rats. Combined with bioinformatics analysis, it was found that there were multiple m6A modified motif sites on the mRNA of PLCβ, and luciferase experiment confirmed that there was a regulatory relationship between them. After over-expressing FTO, the mRNA and protein expression of PLCβ were significantly increased, and the up-regulation of FTO expression induced a significant increase in intracellular Ca2+ concentration, the expression of CAM, and the activation of Ca2+ signaling pathway. Conclusions In the ARC nucleus of the hypothalamus, FTO affects the synthesis and secretion of GnRH by regulating the m6A modification level of PLCβ through the Ca2+ signaling pathway, and interferes with the initiation of female sexual development. Presentation: 6/1/2024

9273 Characterizing the Genomic Effects of Low-Dose Estrogen in Breast Cancer Cells

Abstract Disclosure: H. Kim: None. T.S. Nandu: None. W.L. Kraus: None. Estrogens are steroid hormones that play a key role in a wide range of physiological and pathological processes. The predominant nuclear receptor for estrogens, estrogen receptor-alpha (ERɑ), functions primarily as a nuclear transcription factor (TF) through ligand-dependent activation by 17β-estradiol (E2), the major naturally-occurring estrogen. The physiological levels of E2 are regulated by production, sequestration, and degradation, which can limit the amount of bioavailable E2. However, many studies examining the genomic effects of E2-regulated signaling in cell-based assays used supraphysiological levels of hormone. While this has allowed us to understand the extremes of E2 signaling, it limits our ability to examine cellular functions which operate at lower hormone concentrations. The genomic actions of ligand-bound ERɑ are mediated through transcriptional enhancer formation and function. Alterations in protein and DNA components dictate different enhancer molecular subtypes that specify distinct regulatory mechanisms and downstream cellular phenotypes. How the amount of available hormone affects these genomic processes is unknown. In order to understand the genomic effects of physiological low-dose levels of E2, we have treated MCF-7 ERɑ-positive human breast cancer cells with a range of E2 concentrations and examined the global regulation of gene expression using a multi-omics approach (RNA-seq, ERɑ ChIP-seq, H3K27ac ChIP-seq, PRO-seq, ATAC-seq). Interestingly, we identified gene sets that are preferentially expressed maximally at picomolar or nanomolar concentrations. We are using these genes sets to explore the mechanisms of estrogen-regulated gene expression at physiologically relevant of E2 concentrations. Analysis of nascent RNA transcription using PRO-seq indicates regulation at the transcriptional level. Preliminary ERɑ chromatin binding assays show that while many loci require high levels of E2 for maximum ERɑ binding, others show maximum ERɑ binding at lower doses of E2. Further analyses show distinct enhancer features, such as chromatin accessibility and H3K27ac enrichment, may play a role in determining the response to E2 dosage. We are now determining the molecular mechanisms of dosage sensitivity using a multipronged approach integrating genomics, molecular biology, and cell-based assays. With these studies, we hope to understand a long overlooked physiological aspect of E2 with a comprehensive and global analysis of signal-dependent genomic responses. Presentation: 6/3/2024

A multi-omics approach reveals the changes in the gut microbiome and metabolism of large yellow croaker (Larimichthys crocea) by dietary supplementation with Bacillus

The intestine serves as the primary site for nutrient absorption in animals. Research in gut biology is increasingly focused on understanding its correlation with the microbiome, specifically by employing probiotics in diets to enhance gut health and stimulate animal growth. In this study, we isolated two Bacillus species (Bacillus halophilus and Lysinibacillus sphaericus) from the intestinal tract of a large yellow croaker and investigated their roles as dietary supplements, assessing their impact on intestinal microbial composition and their efficacy in large yellow croaker growth and immunity. These functions are intrinsically linked to the growth and immune response of large yellow croaker. Our investigation included a comprehensive analysis of various omics results derived from intestinal content samples, encompassing 16S rRNA sequencing and non-targeted metabolomics. These analyses were combined with assessments of differences in immune functionalities observed in the liver and head kidney, aiming to understand the interaction between the two Bacillus species, intestinal microorganisms, and host metabolism. Our findings demonstrated several significant outcomes: (1) Supplementation with two Bacillus species induced considerable alterations in the intestinal microbiome, particularly in the suppression of Vibrionaceae among juvenile large yellow croaker. (2) The influence of pathogenic bacteria such as Vibrio on intestinal health is not solely associated with their characteristics but also closely linked to their relative abundance in the intestine. (3) Distinct dietary groups further influenced changes in the metabolome profile by impacting the intestinal microbiota structure in juvenile large yellow croaker, thereby influencing various metabolites such as amino acids, terpenes, bile acids, and steroids. (4) Differences in the composition of intestinal microbiota among different feed types significantly affected individual antioxidant functionalities, impacting the morphology and immune capabilities of immune organs like the liver. Exploring intricate host-microbiome interactions using multi-omics approaches can provide a better assessment of probiotics' functional potential. Our results revealed that Lysinibacillus sphaericus enhanced intestinal development, increased antioxidant enzyme activity, and facilitated the growth of large yellow croaker.