Multi-Omics Integrated Analysis of the Protective Effect of EZH2 Inhibition in Mice with Renal Ischemia-Reperfusion Injury.

INTRODUCTION: Acute kidney injury (AKI) is a common clinical syndrome associated with high morbidity and mortality. Inhibition of the methyltransferase enhancer of zeste homolog 2 (EZH2) by its inhibitor 3-deazaneplanocin A (3-DZNeP) exerts renal benefits in acute renal ischemia-reperfusion injury (IRI). However, the underlying mechanisms are not completely known. This study aimed to elucidate the pathological mechanism of EZH2 in renal IRI by combination of multi-omics analysis and expression profiling in a public clinical cohort. METHODS: In this study, C57BL/6 J mice were used to establish the AKI model, which were treated with 3-DZNeP for 24 h. Kidney samples were collected for RNA-seq analysis, which was combined with publicly available EZH2 chromatin immunoprecipitation sequencing (ChIP-seq) data of mouse embryonic stem cell for a joint analysis to identify differentially expressed genes. Several selected differentially expressed genes were verified by quantitative PCR. Finally, single-nucleus sequencing data and expression profiling in public clinical datasets were used to confirm the negative correlation of the selected genes with EZH2 expression. RESULTS: 3-DZNeP treatment significantly improved renal pathology and function in IRI mice. Through RNA-seq analysis combined with EZH2 ChIP-seq database, 162 differentially expressed genes were found, which might be involved in EZH2-mediated pathology in IRI kidneys. Four differential expressed genes (Scd1, Cidea, Ghr, and Kl) related to lipid metabolism or cell growth were selected based on Gene Ontology and Kyoto Encyclopedia of Genes and Genome enrichment analysis, which were validated by quantitative PCR. Data from single-nucleus RNA sequencing revealed the negative correlation of these four genes with Ezh2 expression in different subpopulations of proximal tubular cells in IRI mice in a different pattern. Finally, the negative correlation of these four genes with EZH2 expression was confirmed in patients with AKI in two clinical datasets. CONCLUSIONS: Our study indicates that Scd1, Cidea, Ghr, and Kl are downstream genes regulated by EZH2 in AKI. Upregulation of EZH2 in AKI inhibits the expression of these four genes in a different population of proximal tubular cells to minimize normal physiological function and promote acute or chronic cell injuries following AKI.

Conventional and multi-omics assessments of subacute inhalation toxicity due to propylene glycol and vegetable glycerin aerosol produced by electronic cigarettes.

Propylene glycol (PG) and vegetable glycerin (VG) are the most common solvents used in electronic cigarette liquids. No long-term inhalation toxicity assessments have been performed combining conventional and multi-omics approaches on the potential respiratory effects of the solvents in vivo. In this study, the systemic toxicity of aerosol generated from a ceramic heating coil-based e-cigarette was evaluated. First, the aerosol properties were characterized, including carbonyl emissions, the particle size distribution, and aerosol temperatures. To determine toxicological effects, rats were exposed, through their nose only, to filtered air or a propylene glycol (PG)/ glycerin (VG) (50:50, %W/W) aerosol mixture at the target concentration of 3 mg/L for six hours daily over a continuous 28-day period. Compared with the air group, female rats in the PG/VG group exhibited significantly lower body weights during both the exposure period and recovery period, and this was linked to a reduced food intake. Male rats in the PG/VG group also experienced a significant decline in body weight during the exposure period. Importantly, rats exposed to the PG/VG aerosol showed only minimal biological effects compared to those with only air exposure, with no signs of toxicity. Moreover, the transcriptomic, proteomic, and metabolomic analyses of the rat lung tissues following aerosol exposure revealed a series of candidate pathways linking aerosol inhalation to altered lung functions, especially the inflammatory response and disease. Dysregulated pathways of arachidonic acids, the neuroactive ligand-receptor interaction, and the hematopoietic cell lineage were revealed through integrated multi-omics analysis. Therefore, our integrated multi-omics approach offers novel systemic insights and early evidence of environmental-related health hazards associated with an e-cigarette aerosol using two carrier solvents in a rat model.

The mechanisms governing mouse embryonic palate mesenchymal cells' proliferation associated with atRA-induced cleft palate in mice: insights from integrated transcriptomic and metabolomic analyses.

While exposure to high levels of all-trans retinoic acid (atRA) during pregnancy is known to suppress murine embryonic palate mesenchymal (MEPM) cells proliferation and to result in cleft palate (CP) development, the underlying mechanisms are poorly understood. Accordingly, this study was designed with the goal of clarifying the etiological basis for atRA-induced CP. A murine model of CP was established via the oral administration of atRA to pregnant mice on gestational day (GD) 10.5, after which transcriptomic and metabolomic analyses were performed with the goal of clarifying the critical genes and metabolites associated with CP development through an integrated multi-omics approach. MEPM cells proliferation was altered by atRA exposure as expected, contributing to CP incidence. In total, 110 genes were differentially expressed in the atRA treatment groups, suggesting that atRA may influence key biological processes including stimulus, adhesion, and signaling-related activities. In addition, 133 differentially abundant metabolites were identified including molecules associated with ABC transporters, protein digestion and absorption, mTOR signaling pathway, and the TCA cycle, suggesting a link between these mechanisms and CP. Overall, combined analyses of these transcriptomic and metabolomic results suggested that the MAPK, calcium, PI3K-Akt, Wnt, and mTOR signaling pathways are particularly important pathways enriched in the palatal cleft under conditions of atRA exposure. Together, these integrated transcriptomic and metabolomic approaches provided new evidence with respect to the mechanisms underlying altered MEPM cells proliferation and signal transduction associated with atRA-induced CP, revealing a possible link between oxidative stress and these pathological changes.

The Need for Multi-Omics Biomarker Signatures in Precision Medicine.

Recent advances in omics technologies have led to unprecedented efforts characterizing the molecular changes that underlie the development and progression of a wide array of complex human diseases, including cancer. As a result, multi-omics analyses-which take advantage of these technologies in genomics, transcriptomics, epigenomics, proteomics, metabolomics, and other omics areas-have been proposed and heralded as the key to advancing precision medicine in the clinic. In the field of precision oncology, genomics approaches, and, more recently, other omics analyses have helped reveal several key mechanisms in cancer development, treatment resistance, and recurrence risk, and several of these findings have been implemented in clinical oncology to help guide treatment decisions. However, truly integrated multi-omics analyses have not been applied widely, preventing further advances in precision medicine. Additional efforts are needed to develop the analytical infrastructure necessary to generate, analyze, and annotate multi-omics data effectively to inform precision medicine-based decision-making.

Multi-omics study of key genes, metabolites, and pathways of periodontitis.

OBJECTIVE: This study aimed to explore the key genes, metabolites, and pathways that influence periodontitis pathogenesis by integrating transcriptomic and metabolomic studies. DESIGN: Gingival crevicular fluid samples from periodontitis patients and healthy controls were collected for liquid chromatography/tandem mass-based metabolomics. RNA-seq data for periodontitis and control samples were obtained from the GSE16134 dataset. Differential metabolites and differentially expressed genes (DEGs) between the two groups were then compared. Based on the protein-protein interaction (PPI) network module analysis, key module genes were selected from immune-related DEGs. Correlation and pathway enrichment analyses were performed for differential metabolites and key module genes. A multi-omics integrative analysis was performed using bioinformatic methods to construct a gene-metabolite-pathway network. RESULTS: From the metabolomics study, 146 differential metabolites were identified, which were mainly enriched in the pathways of purine metabolism and Adenosine triphosphate binding cassette transporters (ABC transporters). The GSE16134 dataset revealed 102 immune-related DEGs (458 upregulated and 264 downregulated genes), 33 of which may play core roles in the key modules of the PPI network and are involved in cytokine-related regulatory pathways. Through a multi-omics integrative analysis, a gene-metabolite-pathway network was constructed, including 28 genes (such as platelet derived growth factor D (PDGFD), neurturin (NRTN), and interleukin 2 receptor, gamma (IL2RG)); 47 metabolites (such as deoxyinosine); and 8 pathways (such as ABC transporters). CONCLUSION: PDGFD, NRTN, and IL2RG may be potential biomarkers of periodontitis and may affect disease progression by regulating deoxyinosine to participate in the ABC transporter pathway.

Nanoplastics induce neuroexcitatory symptoms in zebrafish (Danio rerio) larvae through a manner contrary to Parkinsonian's way in proteomics.

Although nanoplastics (NPs) can penetrate the blood-brain barrier and accumulate in the brain, the neurotoxicity of these particles and the mechanisms associated with their unique physio-chemical properties have yet to be sufficiently ascertained. In this study, we assessed the neuroexcitatory symptoms of zebrafish (Danio rerio) larvae treated with polystyrene (PS) NPs based on an examination of locomotory behaviour, dopamine levels, and acetylcholinesterase activity. We found that PS NPs caused oxidative stress and inhibited atoh1a expression in the cerebellum of Tg(atoh1a:dTomato) transgenic zebrafish larvae, thereby indicating damage to the central nervous system. In contrast to the Parkinson's disease (PD) like effects induced by most types of nanoparticles, such as graphene oxide, we established that PS NPs influenced the neuronal proteomic profiles of zebrafish larvae in a manner contrary to the molecular pathways characteristic of PD-like effects, which could be explained by the molecular dynamic simulation. Unlike graphene oxide nanoparticles that promote significant change in the internal structure of neuroproteins, the complex macromolecular polymers of PS NPs promoted the coalescence and increased expression of neuroproteins, thereby plausibly contributing to the neuroexcitatory symptoms observed in treated zebrafish larvae. Consequently, compared with traditional nanoparticles, we believe that the unique physio-chemical properties of NPs could be a potential factor contributing to their toxicity.

Prediction of Feed Efficiency and Performance-Based Traits in Fish via Integration of Multiple Omics and Clinical Covariates.

Fish aquaculture is a rapidly expanding global industry, set to support growing demands for sources of marine protein. Enhancing feed efficiency (FE) in farmed fish is required to reduce production costs and improve sector sustainability. Recognising that organisms are complex systems whose emerging phenotypes are the product of multiple interacting molecular processes, systems-based approaches are expected to deliver new biological insights into FE and growth performance. Here, we establish 14 diverse layers of multi-omics and clinical covariates to assess their capacities to predict FE and associated performance traits in a fish model (Oncorhynchus tshawytscha) and uncover the influential variables. Inter-omic relatedness between the different layers revealed several significant concordances, particularly between datasets originating from similar material/tissue and between blood indicators and some of the proteomic (liver), metabolomic (liver), and microbiomic layers. Single- and multi-layer random forest (RF) regression models showed that integration of all data layers provide greater FE prediction power than any single-layer model alone. Although FE was among the most challenging of the traits we attempted to predict, the mean accuracy of 40 different FE models in terms of root-mean square errors normalized to percentage was 30.4%, supporting RF as a feature selection tool and approach for complex trait prediction. Major contributions to the integrated FE models were derived from layers of proteomic and metabolomic data, with substantial influence also provided by the lipid composition layer. A correlation matrix of the top 27 variables in the models highlighted FE trait-associations with faecal bacteria (Serratia spp.), palmitic and nervonic acid moieties in whole body lipids, levels of free glycerol in muscle, and N-acetylglutamic acid content in liver. In summary, we identified subsets of molecular characteristics for the assessment of commercially relevant performance-based metrics in farmed Chinook salmon.

Systematic analysis of nutrigenomic effects of polyphenols related to cardiometabolic health in humans - Evidence from untargeted mRNA and miRNA studies.

Cardiovascular and metabolic disorders present major causes of mortality in the ageing population. Polyphenols present in human diets possess cardiometabolic protective properties, however their underlying molecular mechanisms in humans are still not well identified. Even though preclinical and in vitro studies advocate that these bioactives can modulate gene expression, most studies were performed using targeted approaches. With the objective to decipher the molecular mechanisms underlying polyphenols cardiometabolic preventive properties in humans, we performed integrative multi-omic bioinformatic analyses of published studies which reported improvements of cardiometabolic risk factors following polyphenol intake, together with genomic analyses performed using untargeted approach. We identified 5 studies within our criteria and nearly 5000 differentially expressed genes, both mRNAs and miRNAs, in peripheral blood cells. Integrative bioinformatic analyses (e.g. pathway and gene network analyses, identification of transcription factors, correlation of gene expression profiles with those associated with diseases and drug intake) revealed that these genes are involved in the processes such as cell adhesion and mobility, immune system, metabolism, or cell signaling. We also identified 27 miRNAs known to regulate processes such as cell cytoskeleton, chemotaxis, cell signaling, or cell metabolism. Gene expression profiles negatively correlated with expression profiles of cardiovascular disease patients, while a positive correlation was observed with gene expression profiles following intake of drugs against cardiometabolic disorders. These analyses further advocate for health protective effects of these bioactives against age-associated diseases. In conclusion, polyphenols can exert multi-genomic modifications in humans and use of untargeted methods coupled with bioinformatic analyses represent the best approach to decipher molecular mechanisms underlying healthy-ageing effects of these bioactives.

Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers.

Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.

Integrated metabolomic and transcriptomic analysis reveals the role of phenylpropanoid biosynthesis pathway in tomato roots during salt stress.

As global soil salinization continues to intensify, there is a need to enhance salt tolerance in crops. Understanding the molecular mechanisms of tomato (Solanum lycopersicum) roots' adaptation to salt stress is of great significance to enhance its salt tolerance and promote its planting in saline soils. A combined analysis of the metabolome and transcriptome of S. lycopersicum roots under different periods of salt stress according to changes in phenotypic and root physiological indices revealed that different accumulated metabolites and differentially expressed genes (DEGs) associated with phenylpropanoid biosynthesis were significantly altered. The levels of phenylpropanoids increased and showed a dynamic trend with the duration of salt stress. Ferulic acid (FA) and spermidine (Spd) levels were substantially up-regulated at the initial and mid-late stages of salt stress, respectively, and were significantly correlated with the expression of the corresponding synthetic genes. The results of canonical correlation analysis screening of highly correlated DEGs and construction of regulatory relationship networks with transcription factors (TFs) for FA and Spd, respectively, showed that the obtained target genes were regulated by most of the TFs, and TFs such as MYB, Dof, BPC, GRAS, and AP2/ERF might contribute to the regulation of FA and Spd content levels. Ultimately, FA and Spd attenuated the harm caused by salt stress in S. lycopersicum, and they may be key regulators of its salt tolerance. These findings uncover the dynamics and possible molecular mechanisms of phenylpropanoids during different salt stress periods, providing a basis for future studies and crop improvement.

Network Approaches to Integrate Analyses of Genetics and Metabolomics Data with Applications to Fetal Programming Studies.

The integration of genetics and metabolomics data demands careful accounting of complex dependencies, particularly when modelling familial omics data, e.g., to study fetal programming of related maternal-offspring phenotypes. Efforts to identify genetically determined metabotypes using classic genome wide association approaches have proven useful for characterizing complex disease, but conclusions are often limited to a series of variant-metabolite associations. We adapt Bayesian network models to integrate metabotypes with maternal-offspring genetic dependencies and metabolic profile correlations in order to investigate mechanisms underlying maternal-offspring phenotypic associations. Using data from the multiethnic Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study, we demonstrate that the strategic specification of ordered dependencies, pre-filtering of candidate metabotypes, incorporation of metabolite dependencies, and penalized network estimation methods clarify potential mechanisms for fetal programming of newborn adiposity and metabolic outcomes. The exploration of Bayesian network growth over a range of penalty parameters, coupled with interactive plotting, facilitate the interpretation of network edges. These methods are broadly applicable to integration of diverse omics data for related individuals.

A High-Fat High-Fructose Diet Dysregulates the Homeostatic Crosstalk Between Gut Microbiome, Metabolome, and Immunity in an Experimental Model of Obesity.

SCOPE: Ample evidence supports the prominent role of gut-liver axis in perpetuating pathological networks of high-fat high-fructose (HFF) diet induced metabolic disorders, however, the molecular mechanisms are still not fully understood. Herein, this study aims to present a holistic delineation and scientific explanation for the crosstalk between the gut and liver, including the potential mediators involved in orchestrating the metabolic and immune systems. METHODS AND RESULTS: An experimental obesity-associated metaflammation rat model is induced with a HFF diet. An integrative multi-omics analysis is then performed. Following the clues illustrated by the multi-omics discoveries, putative pathways are subsequently validated by RT-qPCR and Western blotting. HFF diet leads to obese phenotypes in rats, as well as histopathological changes. Integrated omics analysis shows that there exists a strong interdependence among gut microbiota composition, intestinal metabolites, and innate immunity regulation in the liver. Some carboxylic acids may contribute to gut-liver communication. Moreover, activation of the hepatic LPS-TLR4 pathway in obesity is confirmed. CONCLUSION: HFF-intake disturbs gut flora homeostasis. Crosstalk between gut microbiota and innate immune system mediates hepatic metaflammation in obese rats, associated with LPS-TLR4 signaling pathway activation. Moreover, α-hydroxyisobutyric acid and some other organic acids may play a role as messengers in the liver-gut axis.

Integrated multi-omics uncovers reliable potential biomarkers and adverse effects of zinc deficiency.

BACKGROUND: Zinc deficiency is a worldwide public health problem. Currently, there are no established biomarkers available for the accurate diagnosis of zinc-deficiency in individuals. Additionally, a comprehensive view of the adverse effects of zinc deficiency is lacking. Our aim was to identify superior biomarkers of zinc deficiency and uncover the adverse effects of zinc deficiency. METHODS: We performed multi-omics analysis using serum proteomics-metabolomics and liver proteomics on zinc-deficient rats to identify candidate biomarkers and reveal the associated adverse effects of zinc deficiency. Secondly, the candidate biomarkers were validated in two zinc-deficient populations and an RCT zinc supplementation trial on a zinc-deficient population. RESULTS: Our integrated multi-omics approach revealed numerous biomarkers (>2000) and glutathione metabolism as the most important changed pathway in zinc deficiency. Three candidate biomarkers from glutathione metabolism were validated in repeated zinc-deficient rats by quantitative analysis. Only glutathione sulfotransferase omega-1 (GSTO1) (among 3 candidate biomarkers) was validated in the two zinc-deficient populations and zinc-supplemented population. Compared with serum zinc, serum GSTO1 yielded a better response to zinc supplementation and a higher correlation coefficient with zinc intake and the AUC value and has the potential for diagnosing zinc deficiency. By integrated multi-omics, we identified both established and novel adverse effects of zinc deficiency. CONCLUSIONS: Our integrated multi-omics analysis revealed more complete information about zinc deficiency; GSTO1 was found to be a reliable potential biomarker for diagnosis of zinc deficiency. This trial is registered at http://www.chictr.org.cn/registry.aspx as ChiCTR1900028162.

Integrated Multiomics Analyses Revealing Different Molecular Profiles Between Early- and Late-Stage Lung Adenocarcinoma.

The molecular differences in genetic and epigenetic profiling between early-stage (ES) and late-stage (LS) lung adenocarcinoma (LUAD), which might help to understand cancer progression and biomarker guided precision treatment, need further be investigated. In this study, we performed comprehensive analysis using multi-omics next-generation sequencing (NGS) on tissue samples from 7 ES (stage I) and 10 LS (stage III/IV) LUAD patients to study molecular characteristics between the two groups. Characterization of the genomic and transcriptomic profiles showed stage-specific somatic mutations, copy number variations (CNVs) and differentially expressed genes (DEGs). LS samples tend to have more TP53, ERBB2 and CHD4 mutations. Gene copy number loss occurs in immune-related gene pathways in the late stage of LUAD. ATAC-seq analysis showed that LS samples harbored more open chromatin peaks around promoter regions and transcription start sites (TSS) than ES samples. We then identified the known transcription factor (TF) binding motifs for the differentially abundant ATAC-seq peaks between the ES and LS samples and found distinct regulatory mechanisms related to each stage. Furthermore, integrative analysis of ATAC-seq with WGS and RNA-seq data showed that the degree of chromatin accessibility is related to copy number changes, and the open chromatin regions could directly regulate the expression of some DEGs. In conclusion, we performed a comprehensive multi-omics analysis of the early and late stages of LUAD and highlighted some important molecular differences in regulatory mechanisms during cancer progression. Those findings help to further understand mechanism and biomarker related targeted therapy.

Integration of transcriptomic and proteomic approaches unveils the molecular mechanism of membrane disintegration in Escherichia coli O157:H7 with ultrasonic treatment.

Ultrasonic disinfection in wastewater treatment has been studied for years at the phenotypic level, while the understanding of the molecular inactivation mechanism is still not clear. Here, the responses of Escherichia coli O157:H7 to ultrasound treatment were investigated using RNA sequencing (RNA-Seq) and tandem mass tags (TMT) based quantitative proteomics methods. The analyses revealed that 770 genes and 201 proteins were significantly changed upon ultrasound treatment. Moreover, the integrated transcriptomic and proteomic analyses uncovered a set of 59 genes or proteins were differentially expressed in ultrasound-treated cells, providing an overview of the cellular responses to ultrasonic field. According to the bioinformatic analyses, genes and proteins that may be involved in lipid asymmetry preservation and outer membrane homeostasis maintenance (including phospholipid metabolism, lipopolysaccharide biosynthesis and transport, and fatty acid metabolism) were specifically up-regulated. Therefore, we proposed that the metabolism disorder of cellular membrane lipids (lipopolysaccharide, phospholipid, and fatty acid included) was one of the main challenges for the bacteria upon ultrasonic stress. In this study, we initially proposed a novel mechanism regarding the ultrasound-induced membrane disintegration from a multi-omics perspective, which may present an important step toward deciphering the molecular inactivation mechanism of ultrasonic field and provide a theoretical foundation for the application of ultrasound technology for the control of waterborne pathogens.

A Multi-Omics Protocol for Swine Feces to Elucidate Longitudinal Dynamics in Microbiome Structure and Function.

Swine are regarded as promising biomedical models, but the dynamics of their gastrointestinal microbiome have been much less investigated than that of humans or mice. The aim of this study was to establish an integrated multi-omics protocol to investigate the fecal microbiome of healthy swine. To this end, a preparation and analysis protocol including integrated sample preparation for meta-omics analyses of deep-frozen feces was developed. Subsequent data integration linked microbiome composition with function, and metabolic activity with protein inventories, i.e., 16S rRNA data and expressed proteins, and identified proteins with corresponding metabolites. 16S rRNA gene amplicon and metaproteomics analyses revealed a fecal microbiome dominated by Prevotellaceae, Lactobacillaceae, Lachnospiraceae, Ruminococcaceae and Clostridiaceae. Similar microbiome compositions in feces and colon, but not ileum samples, were observed, showing that feces can serve as minimal-invasive proxy for porcine colon microbiomes. Longitudinal dynamics in composition, e.g., temporal decreased abundance of Lactobacillaceae and Streptococcaceae during the experiment, were not reflected in microbiome function. Instead, metaproteomics and metabolomics showed a rather stable functional state, as evident from short-chain fatty acids (SCFA) profiles and associated metaproteome functions, pointing towards functional redundancy among microbiome constituents. In conclusion, our pipeline generates congruent data from different omics approaches on the taxonomy and functionality of the intestinal microbiome of swine.