Integrative Multi-omics Analysis Research Articles

Enhanced lignin and cellulose metabolism promote cell wall synthesis and growth of wild soybean HRA under alkali stress.

Soil salinization adversely threatens plant survival and food production globally. The mobilization of storage reserves in cotyledons and establishment of the hypocotyl/root axis (HRA) structure and function are crucial to the growth of dicotyledonous plants during the post-germination growth period. Here, we report the adaptive mechanisms of wild and cultivated soybeans in response to alkali stress in soil during the post-germination growth period. Diferences in physiological parameters, microstructure, and the types, amounts and metabolic pathways of small molecule metabolites and gene expression were compared and multi-omics integration analysis was performed between wild and cultivated soybean under sufcient and artifcially simulated alkali stress during the post-germination growth period in this study. Structural analysis showed that the cell wall thickness of wild soybean under alkali stress increased, whereas cultivated soybeans were severely damaged. A comprehensive analysis of small molecule metabolites and gene expression revealed that protein breakdown in wild soybean cotyledons under alkali stress was enhanced, and transport of amino acids and sucrose increased. Additionally, lignin and cellulose synthesis in wild soybean HRA under alkali stress were enhanced. verall, protein decomposition and transport of amino acids and sucrose increased in wild soybean cotyledons under alkali stress, which in turn, promotes HRA growth. Similarly, lignin and cellulose synthesis in wild soybean HRA enhanced, which subsequently, enhanced cell wall synthesis, thereby maintaining the stability and functionality of HRA under alkali stress. This study presents important practical implications for the utilization of wild plant resources and sustainable development of agriculture.

Integrating muti-omics data to identify tissue-specific DNA methylation biomarkers for cancer risk

The relationship between tissue-specific DNA methylation and cancer risk remains inadequately elucidated. Leveraging resources from the Genotype-Tissue Expression consortium, here we develop genetic models to predict DNA methylation at CpG sites across the genome for seven tissues and apply these models to genome-wide association study data of corresponding cancers, namely breast, colorectal, renal cell, lung, ovarian, prostate, and testicular germ cell cancers. At Bonferroni-corrected P < 0.05, we identify 4248 CpGs that are significantly associated with cancer risk, of which 95.4% (4052) are specific to a particular cancer type. Notably, 92 CpGs within 55 putative novel loci retain significant associations with cancer risk after conditioning on proximal signals identified by genome-wide association studies. Integrative multi-omics analyses reveal 854 CpG-gene-cancer trios, suggesting that DNA methylation at 309 distinct CpGs might influence cancer risk through regulating the expression of 205 unique cis-genes. These findings substantially advance our understanding of the interplay between genetics, epigenetics, and gene expression in cancer etiology.

Metabolic and Microbial Dysregulation in Preterm Infants with Neonatal Respiratory Distress Syndrome: An Early Developmental Perspective.

Neonatal respiratory distress syndrome (NRDS) is one of the most severe respiratory disorders in preterm infants (PTIs) due to immature lung development. To delineate the serum metabolic alterations and gut microbiota variations in NRDS and assess their implications on neonatal development, we enrolled 13 NRDS neonates and 12 PTIs and collected fecal and serum specimens after birth. Longitudinal fecal sampling was conducted weekly for a month in NRDS neonates. NRDS neonates were characterized by notably reduced gestational ages and birth weights and a higher rate of asphyxia at birth relative to PTIs. Early postnatal disturbances in tryptophan metabolism were evident in the NRDS group, concomitant with elevated relative abundance of Haemophilus, Fusicatenibacter, and Vibrio. Integrative multiomics analyses revealed an inverse relationship between tryptophan concentrations and Blautia abundance. At one-week old, NRDS neonates exhibited cortisol regulation anomalies and augmented hepatic catabolism. Sequential microbial profiling revealed distinct gut microbiota evolution in NRDS subjects, characterized by a general reduction in potentially pathogenic bacteria. The acute perinatal stress of NRDS leads to mitochondrial compromise, hormonal imbalance, and delayed gut microbiota evolution. Despite the short duration of NRDS, its impact on neonatal development is significant and requires extended attention.

Elucidating the causal nexus and immune mediation between frailty and chronic kidney disease: integrative multi-omics analysis

Background Empirical research has consistently documented the concurrent manifestation of frailty and chronic kidney disease (CKD). However, the existence of a reverse causal association or the influence of confounding variables on these correlations remains ambiguous. Methods Our analysis of 7,078 participants from National Health and Nutrition Examination Survey(NHANES) (1999–2018) applied weighted logistic regression and Mendelian Randomization (MR) to investigate the correlation between the frailty index (FI) and renal function. The multivariate MR analysis was specifically adjusted for type 2 diabetes and hypertension. Further analysis explored 3282 plasma proteins to link FI to CKD. A two-step network MR highlighted immune cells’ mediating roles in the FI-CKD relationship Result Genetically inferred FI and various renal function markers are significantly correlated, as supported by NHANES analyses. Multivariate MR analysis revealed a direct causal association between the FI and CKD. Additionally, our investigation into plasma proteins identified Tmprss11D and MICB correlated with FI and CKD, respectively. A two-step network MR to reveal 15 immune cell types, notably Central Memory CD4+ T cells and Lymphocytes, as crucial mediators between FI and CKD. Conclusion Our work establishes a causal connection between frailty and CKD, mediated by specific immune cell profiles. These findings highlight the importance of immune mechanisms in the frailty-CKD interplay and suggest that targeting shared risk factors and immune pathways could improve management strategies for these conditions. Our research contributes to a more nuanced understanding of frailty and CKD, offering new avenues for intervention and patient care in an aging population.

MiR-223 accelerates lipid droplets clearance in microglia following spinal cord injury by upregulating ABCA1

BackgroundSpinal cord injury (SCI) is characterized by extensive demyelination and inflammatory responses. Facilitating the clearance of lipid droplets (LDs) within microglia contributes to creating a microenvironment that favors neural recovery and provides essential materials for subsequent remyelination. Therefore, investigating MicroRNAs (miRNAs) that regulate lipid homeostasis after SCI and elucidating their potential mechanisms in promoting LDs clearance in microglia have become focal points of SCI research.MethodsWe established a subacute C5 hemicontusion SCI model in mice and performed transcriptomic sequencing on the injury epicenter to identify differentially expressed genes and associated pathways. Confocal imaging was employed to observe LDs accumulation. Multi-omics analyses were conducted to identify differentially expressed mRNA and miRNA post-SCI. Pathway enrichment analysis and protein-protein interaction network construction were performed using bioinformatics methods, revealing miR-223-Abca1 as a crucial miRNA-mRNA pair in lipid metabolism regulation. BV2 microglia cell lines overexpressing miR-223 were engineered, and immunofluorescence staining, western blot, and other techniques were employed to assess LDs accumulation, relevant targets, and inflammatory factor expression, confirming its role in regulating lipid homeostasis in microglia.ResultsHistopathological results of our hemicontusion SCI model confirmed LDs aggregation at the injury epicenter, predominantly within microglia. Our transcriptomic analysis during the subacute phase of SCI in mice implicated ATP-binding cassette transporter A1 (Abca1) as a pivotal gene in lipid homeostasis, cholesterol efflux and microglial activation. Integrative mRNA-miRNA multi-omics analysis highlighted the crucial role of miR-223 in the neuroinflammation process following SCI, potentially through the regulation of lipid metabolism via Abca1. In vitro experiments using BV2 cells overexpressing miR-223 demonstrated that elevated levels of miR-223 enhance ABCA1 expression in myelin debris and LPS-induced BV2 cells. This promotes myelin debris degradation and LDs clearance, and induces a shift toward an anti-inflammatory M2 phenotype.ConclusionsIn summary, our study unveils the critical regulatory role of miR-223 in lipid homeostasis following SCI. The mechanism by which this occurs involves the upregulation of ABCA1 expression, which facilitates LDs clearance and myelin debris degradation, consequently alleviating the lipid burden, and inhibiting inflammatory polarization of microglia. These findings suggest that strategies to enhance miR-223 expression and target ABCA1, thereby augmenting LDs clearance, may emerge as appealing new clinical targets for SCI treatment.

Network-based integrative multi-omics approach reveals biosignatures specific to COVID-19 disease phases.

COVID-19 disease is characterized by a spectrum of disease phases (mild, moderate, and severe). Each disease phase is marked by changes in omics profiles with corresponding changes in the expression of features (biosignatures). However, integrative analysis of multiple omics data from different experiments across studies to investigate biosignatures at various disease phases is limited. Exploring an integrative multi-omics profile analysis through a network approach could be used to determine biosignatures associated with specific disease phases and enable the examination of the relationships between the biosignatures. To identify and characterize biosignatures underlying various COVID-19 disease phases in an integrative multi-omics data analysis. We leveraged a multi-omics network-based approach to integrate transcriptomics, metabolomics, proteomics, and lipidomics data. The World Health Organization Ordinal Scale WHO Ordinal Scale was used as a disease severity reference to harmonize COVID-19 patient metadata across two studies with independent data. A unified COVID-19 knowledge graph was constructed by assembling a disease-specific interactome from the literature and databases. Disease-state specific omics-graphs were constructed by integrating multi-omics data with the unified COVID-19 knowledge graph. We expanded on the network layers of multiXrank, a random walk with restart on multilayer network algorithm, to explore disease state omics-specific graphs and perform enrichment analysis. Network analysis revealed the biosignatures involved in inducing chemokines and inflammatory responses as hubs in the severe and moderate disease phases. We observed distinct biosignatures between severe and moderate disease phases as compared to mild-moderate and mild-severe disease phases. Mild COVID-19 cases were characterized by a unique biosignature comprising C-C Motif Chemokine Ligand 4 (CCL4), and Interferon Regulatory Factor 1 (IRF1). Hepatocyte Growth Factor (HGF), Matrix Metallopeptidase 12 (MMP12), Interleukin 10 (IL10), Nuclear Factor Kappa B Subunit 1 (NFKB1), and suberoylcarnitine form hubs in the omics network that characterizes the moderate disease state. The severe cases were marked by biosignatures such as Signal Transducer and Activator of Transcription 1 (STAT1), Superoxide Dismutase 2 (SOD2), HGF, taurine, lysophosphatidylcholine, diacylglycerol, triglycerides, and sphingomyelin that characterize the disease state. This study identified both biosignatures of different omics types enriched in disease-related pathways and their associated interactions (such as protein-protein, protein-transcript, protein-metabolite, transcript-metabolite, and lipid-lipid interactions) that are unique to mild, moderate, and severe COVID-19 disease states. These biosignatures include molecular features that underlie the observed clinical heterogeneity of COVID-19 and emphasize the need for disease-phase-specific treatment strategies. The approach implemented here can be used to find associations between transcripts, proteins, lipids, and metabolites in other diseases.

Integrative analysis of genomic and epigenomic regulation reveals miRNA mediated tumor heterogeneity and immune evasion in lower grade glioma

The expression dysregulation of microRNAs (miRNA) has been widely reported during cancer development, however, the underling mechanism remains largely unanswered. In the present work, we performed a systematic integrative study for genome-wide DNA methylation, copy number variation and miRNA expression data to identify mechanisms underlying miRNA dysregulation in lower grade glioma. We identify 719 miRNAs whose expression was associated with alterations of copy number variation or promoter methylation. Integrative multi-omics analysis revealed four subtypes with differing prognoses. These glioma subtypes exhibited distinct immune-related characteristics as well as clinical and genetic features. By construction of a miRNA regulatory network, we identified candidate miRNAs associated with immune evasion and response to immunotherapy. Finally, eight prognosis related miRNAs were validated to promote cell migration, invasion and proliferation through in vitro experiments. Our study reveals the crosstalk among DNA methylation, copy number variation and miRNA expression for immune regulation in glioma, and could have important implications for patient stratification and development of biomarkers for immunotherapy approaches.

Integrative multi-omics analyses identify molecular subtypes of head and neck squamous cell carcinoma with distinct therapeutic vulnerabilities.

Substantial heterogeneity in molecular features, patient prognoses, and therapeutic responses in head and neck squamous cell carcinomas (HNSCC) highlights the urgent need to develop molecular classifications that reliably and accurately reflect tumor behavior and inform personalized therapy. Here, we leveraged the similarity network fusion bioinformatics approach to jointly analyze multi-omics datasets spanning copy number variations, somatic mutations, DNA methylation, and transcriptomic profiling and derived a prognostic classification system for HNSCC. The integrative model consistently identified three subgroups (IMC1-3) with specific genomic features, biological characteristics, and clinical outcomes across multiple independent cohorts. The IMC1 subgroup included proliferative, immune-activated tumors and exhibited a more favorable prognosis. The IMC2 subtype harbored activated EGFR signaling and an inflamed tumor microenvironment with cancer-associated fibroblast/vascular infiltrations. Alternatively, the IMC3 group featured highly aberrant metabolic activities and impaired immune infiltration and recruiting. Pharmacogenomics analyses from in silico predictions and from patient-derived xenograft model data unveiled subtype-specific therapeutic vulnerabilities including sensitivity to cisplatin and immunotherapy in IMC1 and EGFR inhibitors (EGFRi) in IMC2, which was experimentally validated in patient-derived organoid models. Two signatures for prognosis and EGFRi sensitivity were developed via machine learning. Together, this integrative multi-omics clustering for HNSCC improves current understanding of tumor heterogeneity and facilitates patient stratification and therapeutic development tailored to molecular vulnerabilities.

Association between biological ageing and periodontitis: Evidence from a cross-sectional survey and multi-omics Mendelian randomization analysis.

To investigate the relationship and potential causality between biological ageing and periodontitis. We obtained the National Health and Nutrition Examination Survey (NHANES) and genome-wide association study (GWAS) summary statistics as well as single-cell sequencing data. Multivariate regression analysis based on cross-sectional data, Mendelian randomization (MR) and multi-omics integration analysis were employed to explore the causal association and potential molecular mechanisms between biological ageing and periodontitis. Additionally, two-step MR mediation analysis explored the risk factors in biological ageing-mediated periodontitis. We analysed data from 3189 participants in the NHANES data and found that higher biological age was associated with increased risk of periodontitis. MR analyses revealed causal associations between biological age measures and periodontitis risk. Frailty (odds ratio [OR] = 2.08, 95% confidence interval [CI]: 1.04-4.18, p = .039) and GrimAge acceleration (OR = 1.16, 95% CI: 1.01-1.32, p = .033) were causally associated with periodontitis risk, and these results were validated in a large-scale meta-periodontitis GWAS dataset. Additionally, the risk effects of body mass index, waist circumference and lifetime smoking on periodontitis were partially mediated by frailty and GrimAge acceleration. Evidence from cross-sectional survey and MR analysis suggests that biological ageing increases the risk of periodontitis. Additionally, improving the associated risk factors can help prevent both ageing and periodontitis.

Integrative multi-omics analysis unravels the host response landscape and reveals a serum protein panel for early prognosis prediction for ARDS

BackgroundThe multidimensional biological mechanisms underpinning acute respiratory distress syndrome (ARDS) continue to be elucidated, and early biomarkers for predicting ARDS prognosis are yet to be identified.MethodsWe conducted a multicenter observational study, profiling the 4D-DIA proteomics and global metabolomics of serum samples collected from patients at the initial stage of ARDS, alongside samples from both disease control and healthy control groups. We identified 28-day prognosis biomarkers of ARDS in the discovery cohort using the LASSO method, fold change analysis, and the Boruta algorithm. The candidate biomarkers were validated through parallel reaction monitoring (PRM) targeted mass spectrometry in an external validation cohort. Machine learning models were applied to explore the biomarkers of ARDS prognosis.ResultsIn the discovery cohort, comprising 130 adult ARDS patients (mean age 72.5, 74.6% male), 33 disease controls, and 33 healthy controls, distinct proteomic and metabolic signatures were identified to differentiate ARDS from both control groups. Pathway analysis highlighted the upregulated sphingolipid signaling pathway as a key contributor to the pathological mechanisms underlying ARDS. MAP2K1 emerged as the hub protein, facilitating interactions with various biological functions within this pathway. Additionally, the metabolite sphingosine 1-phosphate (S1P) was closely associated with ARDS and its prognosis. Our research further highlights essential pathways contributing to the deceased ARDS, such as the downregulation of hematopoietic cell lineage and calcium signaling pathways, contrasted with the upregulation of the unfolded protein response and glycolysis. In particular, GAPDH and ENO1, critical enzymes in glycolysis, showed the highest interaction degree in the protein–protein interaction network of ARDS. In the discovery cohort, a panel of 36 proteins was identified as candidate biomarkers, with 8 proteins (VCAM1, LDHB, MSN, FLG2, TAGLN2, LMNA, MBL2, and LBP) demonstrating significant consistency in an independent validation cohort of 183 patients (mean age 72.6 years, 73.2% male), confirmed by PRM assay. The protein-based model exhibited superior predictive accuracy compared to the clinical model in both the discovery cohort (AUC: 0.893 vs. 0.784; Delong test, P < 0.001) and the validation cohort (AUC: 0.802 vs. 0.738; Delong test, P = 0.008).InterpretationOur multi-omics study demonstrated the potential biological mechanism and therapy targets in ARDS. This study unveiled several novel predictive biomarkers and established a validated prediction model for the poor prognosis of ARDS, offering valuable insights into the prognosis of individuals with ARDS.

DPP2/7 is a Potential Predictor of Prognosis and Target in Immunotherapy in Colorectal Cancer: An Integrative Multi-omics Analysis.

Colorectal cancer (CRC) ranks among the leading causes of cancerrelated deaths. This study aimed to illuminate the relationship between DPP7 (also known as DPP2) and CRC through a combination of bioinformatics and experimental methodologies. A multi-dimensional bioinformatic analysis on DPP7 was executed, covering its expression, survival implications, clinical associations, functional roles, immune interactions, and drug sensitivities. Experimental validations involved siRNA-mediated DPP7 knockdown and various cellular assays. Data from the Cancer Genome Atlas (TCGA) identified high DPP7 expression in solid CRC tumors, with elevated levels adversely affecting patient prognosis. A shift from the N0 to the N2 stage in CRC was associated with increased DPP7 expression. Functional insights indicated the involvement of DPP7 in cancer progression, particularly in extracellular matrix disassembly. Immunological analyses showed its association with immunosuppressive entities, and in vitro experiments in CRC cell lines underscored its oncogenic attributes. DPP7 could serve as a CRC prognosis marker, functioning as an oncogene and representing a potential immunotherapeutic target.

Genetic insights into drug targets for sporadic Creutzfeldt-Jakob disease: Integrative multi-omics analysis

ObjectiveSporadic Creutzfeldt-Jakob disease (sCJD) is a fatal rapidly progressive neurodegenerative disorder with no effective therapeutic interventions. We aimed to identify potential genetically-supported drug targets for sCJD by integrating multi-omics data. MethodsMulti-omics-wide association studies, Mendelian randomization, and colocalization analyses were employed to explore potential therapeutic targets using expression, single-cell expression, DNA methylation, and protein quantitative trait locus data from blood and brain tissues. Outcome data was from a case-control genome-wide association study, which included 4110 sCJD patients and 13,569 controls. Further investigations encompassed druggability, potential side effects, and associated biological pathways of the identified targets. ResultsIntegrative multi-omics analysis identified 23 potential therapeutic targets for sCJD, with five targets (STX6, XYLT2, PDIA4, FUCA2, KIAA1614) having higher levels of evidence. One target (XYLT2) shows promise for repurposing, two targets (XYLT2, PDIA4) are druggable, and three (STX6, KIAA1614, and FUCA2) targets represent potential future breakthrough points. The expression level of STX6 and XYLT2 in neurons and oligodendrocytes was closely associated with an increased risk of sCJD. Brain regions with high expression of STX6 or causal links to sCJD were often those areas commonly affected by sCJD. ConclusionsOur study identified five potential therapeutic targets for sCJD. Further investigations are warranted to elucidate the mechanisms underlying the new targets for developing disease therapies or initiating clinical trials.

Integrative multi-omics analysis reveals the underlying toxicological mechanisms of enrofloxacin on the growth of wheat seedling roots

The continuous release of antibiotics into agroecosystems has raised concerns about the potential negative effects of antibiotic residues on crops. In this study, the toxicological effects of enrofloxacin (ENR) on wheat seedlings were analyzed using a combination of morpho-physiological, transcriptomic, proteomic, and metabolomic approaches. ENR inhibited the growth of wheat (Triticum aestivum L.) roots and induced oxidative stress. In particular, ENR downregulated the oxidative phosphorylation pathway, while it enhanced glycolysis and the tricarboxylic acid cycle, thereby regulating the balance of intracellular energy metabolism. In addition, sustained exposure to excessive reactive oxygen species (ROS) resulted in an increase in reduced glutathione (GSH), a slight decrease in ascorbic acid (AsA), and a significant decrease in the ratio of GSH to oxidized glutathione (GSSG), which imbalanced the AsA-GSH cycle. In addition, the resulting increase in abnormal proteins triggered ubiquitin-independent proteasomal degradation pathways. Further, an increase in abscisic acid (ABA) and a decrease in jasmonic acid (JA) and its derivatives alleviated the inhibitory effect of ENR on the growth of wheat roots. In conclusion, direct damage and signaling by ROS, hormonal regulation, a decrease in the GSH to GSSG ratio, and insufficient energy supply were identified as key factors for the significant inhibition of wheat root growth under ENR stress.

Identifying drug candidates for pancreatic ductal adenocarcinoma based on integrative multiomics analysis.

Due to a lack of early diagnosis methods and effective drugs, pancreatic ductal adenocarcinoma (PDAC) has an extremely poor prognosis. DNA methylation, transcriptome expression and gene copy number variation (CNV) have critical relationships with development and progression of various diseases. The purpose of the study was to screen reliable early diagnostic biomarkers and potential drugs based on integrative multiomics analysis. We used methylation, transcriptome and CNV profiles to build a diagnostic model for PDAC. The protein expression of three model-related genes were externally validated using PDAC samples. Then, potential therapeutic drugs for PDAC were identified by interaction information related to existing drugs and genes. Four significant differentially methylated regions (DMRs) were selected from 589 common DMRs to build a high-performance diagnostic model for PDAC. Then, four hub genes, PHF12, FXYD3, PRKCB and ZNF582, were obtained. The external validation results showed that PHF12, FXYD3 and PRKCB protein expression levels were all upregulated in tumor tissues compared with adjacent normal tissues (P<0.05). Promising candidate drugs with activity against PDAC were screened and repurposed through gene expression analysis of online datasets. The five drugs, including topotecan, PD-0325901, panobinostat, paclitaxel and 17-AAG, with the highest activity among 27 PDAC cell lines were filtered. Overall, the diagnostic model built based on four significant DMRs could accurately distinguish tumor and normal tissues. The five drug candidates might be repurposed as promising therapeutics for particular PDAC patients.

Integrated Assays of Genome-Wide Association Study, Multi-Omics Co-Localization, and Machine Learning Associated Calcium Signaling Genes with Oilseed Rape Resistance to Sclerotinia sclerotiorum.

Sclerotinia sclerotiorum (Ss) is one of the most devastating fungal pathogens, causing huge yield loss in multiple economically important crops including oilseed rape. Plant resistance to Ss pertains to quantitative disease resistance (QDR) controlled by multiple minor genes. Genome-wide identification of genes involved in QDR to Ss is yet to be conducted. In this study, we integrated several assays including genome-wide association study (GWAS), multi-omics co-localization, and machine learning prediction to identify, on a genome-wide scale, genes involved in the oilseed rape QDR to Ss. Employing GWAS and multi-omics co-localization, we identified seven resistance-associated loci (RALs) associated with oilseed rape resistance to Ss. Furthermore, we developed a machine learning algorithm and named it Integrative Multi-Omics Analysis and Machine Learning for Target Gene Prediction (iMAP), which integrates multi-omics data to rapidly predict disease resistance-related genes within a broad chromosomal region. Through iMAP based on the identified RALs, we revealed multiple calcium signaling genes related to the QDR to Ss. Population-level analysis of selective sweeps and haplotypes of variants confirmed the positive selection of the predicted calcium signaling genes during evolution. Overall, this study has developed an algorithm that integrates multi-omics data and machine learning methods, providing a powerful tool for predicting target genes associated with specific traits. Furthermore, it makes a basis for further understanding the role and mechanisms of calcium signaling genes in the QDR to Ss.

Advances in Integrated Multi-omics Analysis for Drug-Target Identification.

As an essential component of modern drug discovery, the role of drug-target identification is growing increasingly prominent. Additionally, single-omics technologies have been widely utilized in the process of discovering drug targets. However, it is difficult for any single-omics level to clearly expound the causal connection between drugs and how they give rise to the emergence of complex phenotypes. With the progress of large-scale sequencing and the development of high-throughput technologies, the tendency in drug-target identification has shifted towards integrated multi-omics techniques, gradually replacing traditional single-omics techniques. Herein, this review centers on the recent advancements in the domain of integrated multi-omics techniques for target identification, highlights the common multi-omics analysis strategies, briefly summarizes the selection of multi-omics analysis tools, and explores the challenges of existing multi-omics analyses, as well as the applications of multi-omics technology in drug-target identification.

Integrative multi-omics analysis reveals ortho-topolin riboside exhibits anticancer activity by regulating metabolic pathways in radio-resistant triple negative breast cancer cells

Radio-resistant triple negative breast cancer (TNBC) is resistant to conventional drugs and radiation therapy. ortho-topolin riboside (oTR) has been evaluated for its anticancer activity in several types of cancer cells. However, its anti-proliferative activity in radio-resistant TNBC cells has not yet been reported. Therefore, we investigated the anti-proliferative activity of oTR in radio-resistant TNBC cells, and performed metabolome, lipidome, transcriptome, and proteome profiling to reveal the mechanisms of the anticancer activity of oTR. oTR showed cytotoxicity against radio-resistant TNBC cells with an inhibitory concentration (IC50) value of 7.78 μM. Significantly decreased (p value < 0.05) basal and compensatory glycolysis were observed in the oTR-treated group than untreated group. Mitochondrial spare respiratory capacity, which is relevant to cell fitness and flexibility, was significantly decreased (p value < 0.05) in the oTR-treated group. The major metabolic pathways significantly altered by oTR according to metabolome, transcriptome, and proteome profiles were the glycerolipid/glycerophospholipid pathway (log2(FC) of MGLL = −0.13, log2(FC) of acylglycerol lipase = −1.35, log2(FC) of glycerol = −0.81), glycolysis (log2(FC) of EGLN1 = 0.16, log2(FC) of EGLN1 = 0.62, log2(FC) of glucose = −0.76, log2(FC) of lactate = −0.81), and kynurenine pathway (log2(FC) of KYNU = 0.29, log2(FC) of kynureninase = 0.55, log2(FC) of alanine = 0.72). Additionally, proline metabolism (log2(FC) of PYCR1 = −0.17, log2(FC) of proline = −0.73) was significantly altered in the metabolomic and transcriptomic profiles. The MAPK signaling pathway (log2(FC) of CCN1 = −0.15, log2(FC) of CCN family member 1 = −1.02) and Rap 1 signaling pathway (log2(FC) of PARD6B = −0.28, log2(FC) of PAR6B = −3.13) were also significantly altered in transcriptomic and proteomic profiles. The findings of this study revealed that oTR has anticancer activity in radio-resistant TNBC cells by affecting various metabolic pathways, suggesting the potential of oTR as a novel anticancer agent for radio-resistant TNBC patients.

Investigation of the biocontrol mechanism of a novel Pseudomonas species against phytopathogenic Fusarium graminearum revealed by multi-omics integration analysis.

Pseudomonas spp. produces various antifungal substances, making it an effective natural biocontrol agent against pathogenic fungi. However, the inhibitory effects and the associated antagonistic mechanisms of Pseudomonas spp. against Fusarium spp. are unclear. Multi-omics integration analyses of the in vitro antifungal effects of novel Pseudomonas species, P13, against F. graminearum T1 revealed the ability of P13 to produce antifungal components (PCA, HCN, Pvd, and Hcs), strategically upregulate PCA and HCN biosynthesis during logarithmic growth phase, and enhance the TCA cycle during stationary growth phase. These findings improved our understanding of the biocontrol mechanisms of P13 and its potential application against pathogenic fungi.

Multi-omics profiling reveals dysregulated ribosome biogenesis and impaired cell proliferation following knockout of CDR2L

BackgroundCerebellar degeneration-related (CDR) proteins are associated with paraneoplastic cerebellar degeneration (PCD) – a rare, neurodegenerative disease caused by tumour-induced autoimmunity against neural antigens resulting in degeneration of Purkinje neurons in the cerebellum. The pathogenesis of PCD is unknown, in large part due to our limited understanding of the functions of CDR proteins. To this end, we performed an extensive, multi-omics analysis of CDR-knockout cells focusing on the CDR2L protein, to gain a deeper understanding of the properties of the CDR proteins in ovarian cancer.MethodsOvarian cancer cell lines lacking either CDR1, CDR2, or CDR2L were analysed using RNA sequencing and mass spectrometry-based proteomics to assess changes to the transcriptome, proteome and secretome in the absence of these proteins.ResultsFor each knockout cell line, we identified sets of differentially expressed genes and proteins. CDR2L-knockout cells displayed a distinct expression profile compared to CDR1- and CDR2-knockout cells. Knockout of CDR2L caused dysregulation of genes involved in ribosome biogenesis, protein translation, and cell cycle progression, ultimately causing impaired cell proliferation in vitro. Several of these genes showed a concurrent upregulation at the transcript level and downregulation at the protein level.ConclusionsOur study provides the first integrative multi-omics analysis of the impact of knockout of the CDR genes, providing both new insights into the biological properties of the CDR proteins in ovarian cancer, and a valuable resource for future investigations into the CDR proteins.

Integrative Multi-Omics Analysis for Etiology Classification and Biomarker Discovery in Stroke: Advancing towards Precision Medicine.

Recent advancements in high-throughput omics technologies have opened new avenues for investigating stroke at the molecular level and elucidating the intricate interactions among various molecular components. We present a novel approach for multi-omics data integration on knowledge graphs and have applied it to a stroke etiology classification task of 30 stroke patients through the integrative analysis of DNA methylation and mRNA, miRNA, and circRNA. This approach has demonstrated promising performance as compared to other existing single technology approaches.