Omics Levels Research Articles

BootGSEA: a bootstrap and rank aggregation pipeline for multi-study and multi-omics enrichment analyses.

Introduction: Gene set enrichment analysis (GSEA) subsequent to differential expression analysis is a standard step in transcriptomics and proteomics data analysis. Although many tools for this step are available, the results are often difficult to reproduce because set annotations can change in the databases, that is, new features can be added or existing features can be removed. Finally, such changes in set compositions can have an impact on biological interpretation. Methods: We present bootGSEA, a novel computational pipeline, to study the robustness of GSEA. By repeating GSEA based on bootstrap samples, the variability and robustness of results can be studied. In our pipeline, not all genes or proteins are involved in the different bootstrap replicates of the analyses. Finally, we aggregate the ranks from the bootstrap replicates to obtain a score per gene set that shows whether it gains or loses evidence compared to the ranking of the standard GSEA. Rank aggregation is also used to combine GSEA results from different omics levels or from multiple independent studies at the same omics level. Results: By applying our approach to six independent cancer transcriptomics datasets, we showed that bootstrap GSEA can aid in the selection of more robust enriched gene sets. Additionally, we applied our approach to paired transcriptomics and proteomics data obtained from a mouse model of spinal muscular atrophy (SMA), a neurodegenerative and neurodevelopmental disease associated with multi-system involvement. After obtaining a robust ranking at both omics levels, both ranking lists were combined to aggregate the findings from the transcriptomics and proteomics results. Furthermore, we constructed the new R-package "bootGSEA," which implements the proposed methods and provides graphical views of the findings. Bootstrap-based GSEA was able in the example datasets to identify gene or protein sets that were less robust when the set composition changed during bootstrap analysis. Discussion: The rank aggregation step was useful for combining bootstrap results and making them comparable to the original findings on the single-omics level or for combining findings from multiple different omics levels.

MoSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud.

Single-cell omics sequencing has rapidly advanced, enabling the quantification of diverse omics profiles at a single-cell resolution. To facilitate comprehensive biological insights, such as cellular differentiation trajectories, precise annotation of cell subtypes is essential. Conventional methods involve clustering cells and manually assigning subtypes based on canonical markers, a labor-intensive and expert-dependent process. Hence, an automated computational prediction framework is crucial. While several classification frameworks for predicting cell subtypes from single-cell RNA sequencing datasets exist, these methods solely rely on single-omics data, offering insights at a single molecular level. They often miss inter-omic correlations and a holistic understanding of cellular processes. To address this, the integration of multi-omics datasets from individual cells is essential for accurate subtype annotation. This article introduces moSCminer, a novel framework for classifying cell subtypes that harnesses the power of single-cell multi-omics sequencing datasets through an attention-based neural network operating at the omics level. By integrating three distinct omics datasets-gene expression, DNA methylation, and DNA accessibility-while accounting for their biological relationships, moSCminer excels at learning the relative significance of each omics feature. It then transforms this knowledge into a novel representation for cell subtype classification. Comparative evaluations against standard machine learning-based classifiers demonstrate moSCminer's superior performance, consistently achieving the highest average performance on real datasets. The efficacy of multi-omics integration is further corroborated through an in-depth analysis of the omics-level attention module, which identifies potential markers for cell subtype annotation. To enhance accessibility and scalability, moSCminer is accessible as a user-friendly web-based platform seamlessly connected to a cloud system, publicly accessible at http://203.252.206.118:5568. Notably, this study marks the pioneering integration of three single-cell multi-omics datasets for cell subtype identification.

Low temperature promotes the production and efflux of terpenoids in yeast

Altering the fermentation environment provides an effective approach to optimizing the production efficiency of microbial cell factories globally. Here, lower fermentation temperatures of yeast were found to significantly improve the synthesis and efflux of terpenoids, including glycyrrhetinic acid (GA), β-caryophyllene, and α-amyrin. The production of GA at 22°C increased by 5.5 times compared to 30°C. Yeast subjected to lower temperature showed substantial changes at various omics levels. Certain genes involved in maintaining cellular homeostasis that were upregulated under the low temperature conditions, leading to enhanced GA production. Substituting Mvd1, a thermo-unstable enzyme in mevalonate pathway identified by transcriptome and proteome, with a thermo-tolerant isoenzyme effectively increased GA production. The lower temperature altered the composition of phospholipids and increased the unsaturation of fatty acid chains, which may influence GA efflux. This study presents a strategy for optimizing the fermentation process and identifying key targets of cell factories for terpenoid production.

Deeply integrating latent consistent representations in high-noise multi-omics data for cancer subtyping.

Cancer is a complex and high-mortality disease regulated by multiple factors. Accurate cancer subtyping is crucial for formulating personalized treatment plans and improving patient survival rates. The underlying mechanisms that drive cancer progression can be comprehensively understood by analyzing multi-omics data. However, the high noise levels in omics data often pose challenges in capturing consistent representations and adequately integrating their information. This paper proposed a novel variational autoencoder-based deep learning model, named Deeply Integrating Latent Consistent Representations (DILCR). Firstly, multiple independent variational autoencoders and contrastive loss functions were designed to separate noise from omics data and capture latent consistent representations. Subsequently, an Attention Deep Integration Network was proposed to integrate consistent representations across different omics levels effectively. Additionally, we introduced the Improved Deep Embedded Clustering algorithm to make integrated variable clustering friendly. The effectiveness of DILCR was evaluated using 10 typical cancer datasets from The Cancer Genome Atlas and compared with 14 state-of-the-art integration methods. The results demonstrated that DILCR effectively captures the consistent representations in omics data and outperforms other integration methods in cancer subtyping. In the Kidney Renal Clear Cell Carcinoma case study, cancer subtypes were identified by DILCR with significant biological significance and interpretability.

Defining the relationship between cellular and extracellular vesicle (EV) content in breast cancer via an integrative multi-omic analysis.

Much recent research has been dedicated to exploring the utility of extracellular vesicles (EVs) as circulating disease biomarkers. Underpinning this work is the assumption that the molecular cargo of EVs directly reflects the originating cell. Few attempts have been made, however, to empirically validate this on the -omic level. To this end, we have performed an integrative multi-omic analysis of a panel of breast cancer cell lines and corresponding EVs. Whole transcriptome analysis validated that the cellular transcriptome remained stable when cultured cells are transitioned to low serum or serum-free medium for EV collection. Transcriptomic profiling of the isolated EVs indicated a positive correlation between transcript levels in cells and EVs, including disease-associated transcripts. Analysis of the EV proteome verified that HER2 proteinis present in EVs, however neither the estrogen (ER) nor progesterone (PR) receptor proteins are detected regardless of cellular expression. Using multivariate analysis, we derived an EV protein signature to infer cellular patterns of ER and HER2 expression, though the ER protein could not be directly detected. Integrative analyses affirmed that the EV proteome and transcriptome captured key phenotypic hallmarks of the originating cells, supporting the potential of EVs for non-invasive monitoring of breast cancers.

A Narrative Review of Cytokine Networks: Pathophysiological and Therapeutic Implications for Inflammatory Bowel Disease Pathogenesis.

Inflammatory bowel disease (IBD) is a lifelong inflammatory immune mediated disorder, encompassing Crohn's disease (CD) and ulcerative colitis (UC); however, the cause and specific pathogenesis of IBD is yet incompletely understood. Multiple cytokines produced by different immune cell types results in complex functional networks that constitute a highly regulated messaging network of signaling pathways. Applying biological mechanisms underlying IBD at the single omic level, technologies and genetic engineering enable the quantification of the pattern of released cytokines and new insights into the cytokine landscape of IBD. We focus on the existing literature dealing with the biology of pro- or anti-inflammatory cytokines and interactions that facilitate cell-based modulation of the immune system for IBD inflammation. We summarize the main roles of substantial cytokines in IBD related to homeostatic tissue functions and the remodeling of cytokine networks in IBD, which may be specifically valuable for successful cytokine-targeted therapies via marketed products. Cytokines and their receptors are validated targets for multiple therapeutic areas, we review the current strategies for therapeutic intervention and developing cytokine-targeted therapies. New biologics have shown efficacy in the last few decades for the management of IBD; unfortunately, many patients are nonresponsive or develop therapy resistance over time, creating a need for novel therapeutics. Thus, the treatment options for IBD beyond the immune-modifying anti-TNF agents or combination therapies are expanding rapidly. Further studies are needed to fully understand the immune response, networks of cytokines, and the direct pathogenetic relevance regarding individually tailored, safe and efficient targeted-biotherapeutics.

Multi-omics analysis of functional substances and expression verification in cashmere fineness.

Numerous factors influence the growth and development of cashmere. Existing research on cashmere has predominantly emphasized a single omics level. Integrating multi-omics analyses can offer a more comprehensive understanding by encompassing the entire spectrum. This study more accurately and comprehensively identified the key factors influencing cashmere fineness using multi-omics analysis. This study used skin tissues of coarse cashmere type (CT_LCG) and fine cashmere type Liaoning cashmere goats (FT_LCG) for the analysis. This study employed an integrated approach involving transcriptomics, translatomics, proteomics, and metabolomics to identify substances associated with cashmere fineness. The findings were validated using parallel reaction monitoring (PRM) and multiple reaction monitoring (MRM) techniques. The GO functional enrichment analysis identified three common terms: multicellular organismal process, immune system process, and extracellular region. Furthermore, the KEGG enrichment analysis uncovered the involvement of the arachidonic acid metabolic pathway. Protein expression trends were verified using PRM technology. The expression trends of KRT79, as confirmed by PRM, were consistent with those observed in TMT proteomics and exhibited a positive regulatory effect on cashmere fineness. Metabolite expression trends were confirmed using MRM technology. The expression trends of 9 out of 15 validated metabolites were in agreement with those identified in the non-targeted metabolomics analysis. This study employed multi-omics analysis to identify key regulators of cashmere fineness, including PLA2G12A, KRT79, and prostaglandin B2. The findings of this study offer valuable data and establish a theoretical foundation for conducting comprehensive investigations into the molecular regulatory mechanisms and functional aspects of cashmere fineness.

Comprehensive Multiomics Analyses Establish the Optimal Prognostic Model for Resectable Gastric Cancer : Prognosis Prediction for Resectable GC.

Prognostic models based on multiomics data may provide better predictive capability than those established at the single-omics level. Here we aimed to establish a prognostic model for resectable gastric cancer (GC) with multiomics information involving mutational, copy number, transcriptional, methylation, and clinicopathological alterations. The mutational, copy number, transcriptional, methylation data of 268, 265, 226, and 252 patients with stages I-III GC were downloaded from the TCGA database, respectively. Alterations from all omics were characterized, and prognostic models were established at the individual omics level and optimized at the multiomics level. All models were validated with a cohort of 99 patients with stages I-III GC. TTN, TP53, and MUC16 were among the genes with the highest mutational frequency, while UBR5, ZFHX4, PREX2, and ARID1A exhibited the most prominent copy number variations (CNVs). Upregulated COL10A1, CST1, and HOXC10 and downregulated GAST represented the biggest transcriptional alterations. Aberrant methylation of some well-known genes was revealed, including CLDN18, NDRG4, and SDC2. Many alterations were found to predict the patient prognosis by univariate analysis, while four mutant genes, two CNVs, five transcriptionally altered genes, and seven aberrantly methylated genes were identified as independent risk factors in multivariate analysis. Prognostic models at the single-omics level were established with these alterations, and optimized combination of selected alterations with clinicopathological factors was used to establish a final multiomics model. All single-omics models and the final multiomics model were validated by an independent cohort. The optimal area under the curve (AUC) was 0.73, 0.71, 0.71, and 0.85 for mutational, CNV, transcriptional, and methylation models, respectively. The final multiomics model significantly increased the AUC to 0.92 (P < 0.05). Multiomics model exhibited significantly better capability in predicting the prognosis of resectable GC than single-omics models.

Sinomenine improves resistance to foodborne enteritis and Anti-bacteria mucosal immunity in grass carp

Based on our previous study in zebrafish, sinomenine was found to relieve foodborne enteritis through immunomodulation. To examine the protective effects of sinomenine on mucosal immunity in farmed fish species, a dietary inclusion study was conducted in grass carp juveniles by adding sinomenine to soybean meal feed. After a 5-week feeding trial to model foodborne enteritis, the gut and liver tissues were examined for histopathology, enteritis-related gene expression, transcriptome, and 16 s rRNA gene sequencing. The results demonstrated that including sinomenine in the diet at the concentration of 2 g/kg could prevent intestinal pathological changes, such as a decrease in the number of goblet cells and increased glycogen, induced by 40% soybean meal substitution. At the molecular level, the downregulated proinflammatory genes, together with upregulated anti-inflammatory genes (such as il10, il13, and tgfβ) were observed. At the omics level, for the DEGs, the downregulated intestinal pathway “phagosome” and term “platelet activation” indicated control of inflammation, while the upregulation of the “PPAR signaling pathway” in both the gut and liver, suggests enhanced immune homeostasis. For the mucosal surface in the sinomenine-supplemented group, we observed not only control of pathogenic Camparilobacterota and Aeromonas but also an increase in Actinobacteria, which may enhance mucosal immunity. Due to the influence of the gut-liver axis, hepatic metabolism was altered, particularly with an upregulation of “fatty acid degradation” and “tryptophan metabolism”. Subsequently, the fish in the sinomenine group exhibited a significantly higher survival rate after a 7-day immersion challenge with Aeromonas hydrophila, with less inflammatory CD4 signals in the gills. Hepatic hypoxia and low respiratory efficiency in the gill may be a side effect. In summary, sinomenine demonstrated a protective effect on the mucosal immunity of the gut and gill, thereby preventing enteritis and subsequent bacteria challenges. Therefore, sinomenine could be employed in aquaculture as phytotherapy, at the very least through dietary supplementation in fish.

Unraveling the transcriptomic signatures of Parkinson's disease and major depression using single-cell and bulk data.

Motor symptoms are well-characterized in Parkinson's disease (PD). However, non-motor symptoms, such as depression, are commonly observed and can appear up to 10 years before motor features, resulting in one-third of individuals being misdiagnosed with a neuropsychiatric disorder. Thus, identifying diagnostic biomarkers is crucial for accurate PD diagnosis during its prodromal or early stages. We employed an integrative approach, combining single nucleus RNA and bulk mRNA transcriptomics to perform comparative molecular signatures analysis between PD and major depressive disorder (MDD). We examined 39,834 nuclei from PD (GSE202210) and 32,707 nuclei from MDD (GSE144136) in the dorsolateral prefrontal cortex (dlPFC) of Brodmann area 9. Additionally, we analyzed bulk mRNA peripheral blood samples from PD compared to controls (GSE49126, GSE72267), as well as MDD compared to controls (GSE39653). Our findings show a higher proportion of astrocytes, and oligodendrocyte cells in the dlPFC of individuals with PD vs. MDD. The excitatory to inhibitory neurons (E/I) ratio analysis indicates that MDD has a ratio close to normal 80/20, while PD has a ratio of 62/38, indicating increased inhibition in the dlPFC. Microglia displayed the most pronounced differences in gene expression profiles between the two conditions. In PD, microglia display a pro-inflammatory phenotype, while in MDD, they regulate synaptic transmission through oligodendrocyte-microglia crosstalk. Analysis of bulk mRNA blood samples revealed that the COL5A, MID1, ZNF148, and CD22 genes were highly expressed in PD, whereas the DENR and RNU1G2 genes were highly expressed in MDD. CD22 is involved in B-cell activation and the negative regulation of B-cell receptor signaling. Additionally, CD86, which provides co-stimulatory signals for T-cell activation and survival, was found to be a commonly differentially expressed gene in both conditions. Pathway analysis revealed several immune-related pathways common in both conditions, including the complement and coagulation cascade, and B-cell receptor signaling. This study demonstrates that bulk peripheral immune cells play a role in both conditions, but neuroinflammation in the dlPFC specifically manifests in PD as evidenced by the analysis of single nucleus dlPFC datasets. Integrating these two omics levels offers a better understanding of the shared and distinct molecular pathophysiology of PD and MDD in both the periphery and the brain. These findings could lead to potential diagnostic biomarkers, improving accuracy and guiding pharmacological treatments.

An Integrated Method Based on Wasserstein Distance and Graph for Cancer Subtype Discovery.

Due to the complexity of cancer pathogenesis at different omics levels, it is necessary to find a comprehensive method to accurately distinguish and find cancer subtypes for cancer treatment. In this paper, we proposed a new cancer multi-omics subtype identification method, which is based on variational autoencoder measured by Wasserstein distance and graph autoencoder (WVGMO). This method depends on two foremost models. The first model is a variational autoencoder measured by Wasserstein distance (WVAE), which is used to extract potential spatial information of each omic data type. The second model is the graph autoencoder (GAE) with the second-order proximity. It has the capability to retain the topological structure information and feature information of the multi-omics data. And then, the identification of cancer subtypes via k-means clustering. Extensive experiments were conducted on seven different cancers based on four omics data from TCGA. The results show that WVGMO provides equivalent or even better results than the most of advanced synthesis methods.

An overview of multiomics: a powerful tool applied in cancer molecular subtyping for cancer therapy

AbstractDuring the process of carcinogenesis and tumor progression, various molecular alternations occur in different omics levels. In recent years, multiomics approaches including genomics, epigenetics, transcriptomics, proteomics, metabolomics, single‐cell omics, and spatial omics have been applied in mapping diverse omics profiles of cancers. The development of high‐throughput technologies such as sequencing and mass spectrometry has revealed different omics levels of tumor cells or tissues separately. While focusing on a single omics level results in a lack of accuracy, joining multiple omics approaches together undoubtedly benefits accurate molecular subtyping and precision medicine for cancer patients. With the deepening of tumor research in recent years, taking pathological classification as the only criterion of diagnosis and predicting prognosis and treatment response is found to be not accurate enough. Therefore, identifying precise molecular subtypes by exploring the molecular alternations during tumor occurrence and development is of vital importance. The review provides an overview of the advanced technologies and recent progress in multiomics applied in cancer molecular subtyping and detailedly explains the application of multiomics in identifying cancer driver genes and metastasis‐related genes, exploring tumor microenvironment, and selecting liquid biopsy biomarkers and potential therapeutic targets.

The phosphoproteome is a first responder in tiered cellular adaptation to chemical stress followed by proteomics and transcriptomics alteration

Next-generation risk assessment (NGRA) for environmental chemicals involves a weight of evidence (WoE) framework integrating a suite of new approach methodologies (NAMs) based on points of departure (PoD) obtained from in vitro assays. Among existing NAMs, the omic-based technologies are of particular importance based on the premise that any apical endpoint change indicative of impaired health must be underpinned by some alterations at the omics level, such as transcriptome, proteome, metabolome, epigenome and genome. Transcriptomic assay plays a leading role in providing relatively conservative PoDs compared with apical endpoints. However, it is unclear whether and how parameters measured with other omics techniques predict the cellular response to chemical perturbations, especially at exposure levels below the transcriptomically defined PoD. Multi-omics coverage may provide additional sensitive or confirmative biomarkers to complement and reduce the uncertainty in safety decisions made using targeted and transcriptomics assays. In the present study, we conducted multi-omics studies of transcriptomics, proteomics and phosphoproteomics on two prototype compounds, coumarin and 2,4-dichlorophenoxyacetic acid (2,4-D), with multiple chemical concentrations and time points, to understand the sensitivity of the three omics techniques in response to chemically-induced changes in HepG2. We demonstrated that, phosphoproteomics alterations occur not only earlier in time, but also more sensitive to lower concentrations than proteomics and transcriptomics when the HepG2 cells were exposed to various chemical treatments. The phosphoproteomics changes appear to approach maximum when the transcriptomics alterations begin to initiate. Therefore, it is proximal to the very early effects induced by chemical exposure. We concluded that phosphoproteomics can be utilized to provide a more complete coverage of chemical-induced cellular alteration and supplement transcriptomics-based health safety decision making.

Exploration of Multiomic Profiles and Biomarkers as Predictors of Neoadjuvant Chemoradiotherapy Responsiveness in Esophageal Squamous Cell Carcinoma

The current gold standard of care for resectable locally advanced esophageal cancer is neoadjuvant chemoradiotherapy (NCRT) followed by surgery. Given that only 30-40% of patients with esophageal squamous cell carcinoma (ESCC) achieved a pathologic complete response (pCR) following neoadjuvant chemoradiotherapy, it is critical to understand the biological basis of NCRT resistance in esophageal cancer and identify biomarkers for these patients in order to further personalize treatment plans. We aim to depict the biological landscape of ESCC responsiveness and resistance to neoadjuvant chemoradiotherapy. Endoscopic biopsied specimens of the primary tumors and paired peripheral blood samples were obtained from 24 patients before neoadjuvant chemoradiotherapy and tested for whole exosome sequencing, RNA sequencing, and DIA mass spectrometry. Genomic data were analyzed for significantly mutated genes, copy number alterations, MSI, TMB, and mutational signatures. Transcriptomics and proteomics data were used to examine differentially activated pathways. GSEA and ActivePathways were used for the single omics level and joint multi-omics analysis, respectively. Tumor microenvironment (TME) characteristics were deconvoluted by xCell upon RNA-seq data. Treatment resistance biomarkers were identified and validated in a separate cohort using mIHC. In the study cohort, 54% (13/24) of the patients achieved pCR. WES data suggested that FBXW7 was more frequently mutated in the pCR group (fisher test p-value = 0.029), and the most significant cytoband loss in the pCR group was 9p21.3 (qval = 0.001). Differences in TMB, MSI, and mutational signatures were not significant between groups. Combined transcriptomics and proteomics analysis revealed that type I interferon signaling pathways and RIG-I-like receptor signaling pathways(p<0.05) were enriched in non-pCR tumors. Esophageal cancer cohort RNA-seq data from TCGA verified the correlation between the genetic variances (FBXW7 mutation and 9p21.3 loss) and the decreased expression of type I interferon signaling pathway genes. In TME analysis, tolerogenic dendritic cells and exhausted T cell signatures were significantly enriched in non-pCR tumors, indicating an immunosuppressive status in treatment resistant patients. Based on proteomics PPI network and differential expression genes from RNA-seq data, a biomarker panel consisted of 12 proteins predictive of non-pCR tumors was identified: STAT1, EIF2AK2, MX1, BST2, TRIM21, SAMHD1, IFI44L, GBP1, PARP14, ISG15, IFIT3, and HLA-B. The expression of selected genes was validated by mIHC in an independent cohort. Through a multiomics approach, we described the biological characteristics of ESCC with distinct responses to neoadjuvant chemoradiotherapy and proposed a panel of 12 proteins as predictive biomarkers for non-pCR patients.

Emphasizing the Role of Multi-omics Approach to Increase Survival Rate of Breast and Prostate Cancer Patients

The understanding of cancer biology has greatly advanced since the advent of genomics. A remarkable heterogeneity at the whole-genome (or omics) level exists amongst even histologically comparable cancers, demonstrating the enormous complexity of the cancer genome. A powerful resource that has the potential to translate high-throughput omics to better and quick overall survival is the massive accrual and public accessibility of multi-omics databases with accompanying clinical annotation, including tumor histology, patient response, and outcome. In this new era of high-throughput omics, this paper emphasizes the distinct benefits of a multidimensional approach to genomic analysis. It discusses the implications of translational omics research for the cancer population. Single-level data analysis of high-throughput technologies has constraints because it only displays a small window of cellular processes. Understanding the links across several cellular organization levels made possible by data integration across various platforms, including genomes, epigenomics, transcriptomics, proteomics, and metabolomics, is important. This review examines a few popular frameworks for integrating multi-omics data. It provides a general overview of multi-omics applications in tumor classification, prognosis, diagnostics, and the function of data integration in searching for novel biomarkers and treatment options.

Insights from multi-omics integration into seed germination of Taxus chinensis var mairei

The transition from deep dormancy to seed germination is essential for the life cycle of plants, but how this process occurs in the gymnosperm Chinese yew (Taxus chinensis var mairei), the natural source of the anticancer drug paclitaxel, remains unclear. Herein, we analyse the transcriptome, proteome, spatial metabolome, and spatial lipidome of the Chinese yew and present the multi-omics profiles of dormant and germinating seeds. Our results show that abscisic acid and gibberellic acid 12 homoeostasis is closely associated with gene transcription and protein translation, and the balance between these phytohormones thereby determines if seeds remain dormant or germinate. We find that an energy supply of carbohydrates from glycolysis and the TCA cycle feed into the pentose phosphate pathway during seed germination, and energy supplied from lipids are mainly derived from the lipolysis of triacylglycerols. Using mass spectrometry imaging, we demonstrate that the spatial distribution of plant hormones and phospholipids has a remarkable influence on embryo development. We also provide an atlas of the spatial distribution of paclitaxel C in Chinese yew seeds for the first time. The data from this study enable exploration of the germination mechanism of Chinese yew seeds across several omics levels.

SYST-22 INTEGRATING GENE DEPENDENCY INTO A PEDIATRIC-FOCUSED CANCER DATA PORTAL FOR TARGETED THERAPY AND BIOMARKER DISCOVERY

Abstract Pediatric solid cancers, particularly those affecting the central nervous system (CNS), are the leading cause of disease-related deaths in children. Despite significant efforts to sequence pediatric solid tumors, they exhibit a "quiescent genome" with a considerably lower mutational burden than adult cancers. This leads to fewer actionable drug targets and poor response to cancer immunotherapy agents, including checkpoint inhibitors. As a result, it is imperative to move beyond genetic alterations and adopt a combination of functional and integrative approaches to develop novel, targeted treatments that are specifically designed for pediatric cancers. Over the past five years, the Next-Gen Precision Medicine Program team has devoted its efforts to establishing the Childhood Cancer Model Atlas (CCMA) (Sun, Daniel et al. Cancer Cell 2023). The CCMA is the largest collection of high-risk pediatric solid tumor cell lines globally, presenting a tremendous opportunity to integrate molecular features with functional dependencies using state-of-the-art computational approaches. However, the analysis of -omics level data is often limited to laboratories with specialized bioinformatics capabilities. Therefore, the establishment of the CCMA data portal with user-friendly analytical and visualization capabilities is essential to accelerate data-driven discovery and translational research in pediatric cancer on a global scale. The data portal is designed to simplify reporting, automate manual processes, and enhance the quality, usability, and longevity of the CCMA dataset. Its highlighted features concentrate on customized analysis, informative results, and user-friendly data visualization. Furthermore, by connecting the established models and their original tumor with clinical and molecular endpoints, the data portal enables clinical integration within the Australian molecular tumor board framework. This analytical data portal will enable the generation of new research hypotheses, prioritizes therapeutic opportunities, and supports international collaborative efforts in pediatric cancer.

Integrative multi-omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention-Deficit/Hyperactivity Disorder.

The evolving field of multi-omics combines data and provides methods for simultaneous analysis across several omics levels. Here, we integrated genomics (transmitted and non-transmitted polygenic scores [PGSs]), epigenomics, and metabolomics data in a multi-omics framework to identify biomarkers for Attention-Deficit/Hyperactivity Disorder (ADHD) and investigated the connections among the three omics levels. We first trained single- and next multi-omics models to differentiate between cases and controls in 596 twins (cases = 14.8%) from the Netherlands Twin Register (NTR) demonstrating reasonable in-sample prediction through cross-validation. The multi-omics model selected 30 PGSs, 143 CpGs, and 90 metabolites. We confirmed previous associations of ADHD with glucocorticoid exposure and the transmembrane protein family TMEM, show that the DNA methylation of the MAD1L1 gene associated with ADHD has a relation with parental smoking behavior, and present novel findings including associations between indirect genetic effects and CpGs of the STAP2 gene. However, out-of-sample prediction in NTR participants (N = 258, cases = 14.3%) and in a clinical sample (N = 145, cases = 51%) did not perform well (range misclassification was [0.40, 0.57]). The results highlighted connections between omics levels, with the strongest connections between non-transmitted PGSs, CpGs, and amino acid levels and show that multi-omics designs considering interrelated omics levels can help unravel the complex biology underlying ADHD.

Progress in single-cell multimodal sequencing and multi-omics data integration.

With the rapid advance of single-cell sequencing technology, cell heterogeneity in various biological processes was dissected at different omics levels. However, single-cell mono-omics results in fragmentation of information and could not provide complete cell states. In the past several years, a variety of single-cell multimodal omics technologies have been developed to jointly profile multiple molecular modalities, including genome, transcriptome, epigenome, and proteome, from the same single cell. With the availability of single-cell multimodal omics data, we can simultaneously investigate the effects of genomic mutation or epigenetic modification on transcription and translation, and reveal the potential mechanisms underlying disease pathogenesis. Driven by the massive single-cell omics data, the integration method of single-cell multi-omics data has rapidly developed. Integration of the massive multi-omics single-cell data in public databases in the future will make it possible to construct a cell atlas of multi-omics, enabling us to comprehensively understand cell state and gene regulation at single-cell resolution. In this review, we summarized the experimental methods for single-cell multimodal omics data and computational methods for multi-omics data integration. We also discussed the future development of this field.

Pursuing precision in medicine and nutrition: the rise of electrochemical biosensing at the molecular level.

In the era that we seek personalization in material things, it is becoming increasingly clear that the individualized management of medicine and nutrition plays a key role in life expectancy and quality of life, allowing participation to some extent in our welfare and the use of societal resources in a rationale and equitable way. The implementation of precision medicine and nutrition are highly complex challenges which depend on the development of new technologies able to meet important requirements in terms of cost, simplicity, and versatility, and to determine both individually and simultaneously, almost in real time and with the required sensitivity and reliability, molecular markers of different omics levels in biofluids extracted, secreted (either naturally or stimulated), or circulating in the body. Relying on representative and pioneering examples, this review article critically discusses recent advances driving the position of electrochemical bioplatforms as one of the winning horses for the implementation of suitable tools for advanced diagnostics, therapy, and precision nutrition. In addition to a critical overview of the state of the art, including groundbreaking applications and challenges ahead, the article concludes with a personal vision of the imminent roadmap.