Multi-omics data integration is one of the major challenges in the era of precision medicine. Considerable work has been done with the advent of high-throughput studies, which have enabled the data ...access for downstream analyses. To improve the clinical outcome prediction, a gamut of software tools has been developed. This review outlines the progress done in the field of multi-omics integration and comprehensive tools developed so far in this field. Further, we discuss the integration methods to predict patient survival at the end of the review.
Humans heavily rely on dozens of domesticated plant species that have been further improved through intensive breeding. To evaluate how breeding changed the tomato fruit metabolome, we have generated ...and analyzed a dataset encompassing genomes, transcriptomes, and metabolomes from hundreds of tomato genotypes. The combined results illustrate how breeding globally altered fruit metabolite content. Selection for alleles of genes associated with larger fruits altered metabolite profiles as a consequence of linkage with nearby genes. Selection of five major loci reduced the accumulation of anti-nutritional steroidal glycoalkaloids in ripened fruits, rendering the fruit more edible. Breeding for pink tomatoes modified the content of over 100 metabolites. The introgression of resistance genes from wild relatives in cultivars also resulted in major and unexpected metabolic changes. The study reveals a multi-omics view of the metabolic breeding history of tomato, as well as provides insights into metabolome-assisted breeding and plant biology.
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•Multi-omic analysis of tomato fruits revealed new metabolic genes and pathways•Selection of fruit mass gene-altered metabolites altered due to nearby hitchhiking genes•Domestication acted on five major loci that reduced anti-nutritional compounds•Pink tomato breeding modified hundreds of metabolites, leading to unexpected changes
Multi-omic analysis reveals how the appearance/taste-oriented breeding process modulates the metabolic makeup of tomato.
The regulatory effect of DNA methylation on the pathogenesis of acne vulgaris is completely unknown. Herein we analyzed the DNA methylation profile in skin samples of acne vulgaris and further ...integrated it with gene expression profiles and single-cell RNA-sequencing data. Finally, 31,134 differentially methylated sites and 770 differentially methylated and expressed genes (DMEGs) were identified. The multi-omics analysis suggested the importance of DNA methylation in inflammation and immunity in acne. And DMEGs were verified in an external dataset and were closely related to early inflammatory acne. Additionally, we conducted experiments to verify the mRNA expression and DNA methylation level of DMEGs. This study supports the significant contribution of epigenetics to the pathogenesis of acne vulgaris and may provide new ideas for the molecular mechanisms of and potential therapeutic strategies for acne vulgaris.
•A total of 31,134 differentially methylated sites were identified on 22 chromosomes.•Eleven key DMEGs were distributed and active in lymphocytes and myeloid cells.•DMEGs were closely related to early inflammatory acne rather than to evolved lesions.
The tumor microenvironment (TME) harbors heterogeneous contents and plays critical roles in tumorigenesis, metastasis, and drug resistance. Therefore, the deconvolution of the TME becomes ...increasingly essential to every aspect of cancer research and treatment. Novel spatially‐resolved high‐plex molecular profiling technologies have been emerging rapidly as powerful tools to obtain in‐depth understanding from TME perspectives due to their capacity to allow high‐plex protein and RNA profiling while keeping valuable spatial information. Based on our practical experience, we review a variety of available spatial proteogenomic technologies, including 10X Visium, GeoMx Digital Spatial Profiler (DSP), cyclic immunofluorescence‐based CODEX and Multi‐Omyx, mass spectrometry (MS)‐based imaging mass spectrometry (IMS) and multiplex ion‐beam imaging (MIBI). We also discuss FISSEQ, MERFISH, Slide‐seq, and HDST, some of which may become commercially available in the near future. In particular, with our experience, we elaborate on DSP for spatial proteogenomic profiling and discuss its unique features designed for immuno‐oncology and propose anticipation towards its future direction. The emerging spatially technologies are rapidly reshaping the magnitude of our understanding of the TME.
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Multi‐omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to ...systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi‐Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network‐level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi‐omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between multiple omics layers, such as known ccRCC drug targets. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi‐omics studies.
SYNOPSIS
A new approach integrates multi‐omics datasets with a prior knowledge network spanning signaling, metabolism and allosteric regulations. Application to a kidney cancer patient cohort captures relevant cross‐talks among deregulated processes.
A causal multi‐omics network is built by integrating multiple ressources spanning signaling, metabolism and allosteric regulations.
Transcriptomics, phosphoproteomics and metabolomics data are integrated in a set of coherent mechanistic hypotheses using CARNIVAL, a tool contextualizing causal networks.
This set of coherent mechanistic hypotheses can be mined to identify disease mechanisms and therapeutic targets.
A network built for a cohort of kidney cancer patients shows coherence with other studies and known therapeutic targets.
A new approach integrates multi‐omics datasets with a prior knowledge network spanning signaling, metabolism and allosteric regulations. Application to a kidney cancer patient cohort captures relevant cross‐talks among deregulated processes.
Heterogeneity of lung tumor endothelial cell (TEC) phenotypes across patients, species (human/mouse), and models (in vivo/in vitro) remains poorly inventoried at the single-cell level. We single-cell ...RNA (scRNA)-sequenced 56,771 endothelial cells from human/mouse (peri)-tumoral lung and cultured human lung TECs, and detected 17 known and 16 previously unrecognized phenotypes, including TECs putatively regulating immune surveillance. We resolved the canonical tip TECs into a known migratory tip and a putative basement-membrane remodeling breach phenotype. Tip TEC signatures correlated with patient survival, and tip/breach TECs were most sensitive to vascular endothelial growth factor blockade. Only tip TECs were congruent across species/models and shared conserved markers. Integrated analysis of the scRNA-sequenced data with orthogonal multi-omics and meta-analysis data across different human tumors, validated by functional analysis, identified collagen modification as a candidate angiogenic pathway.
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•We single-cell RNA-sequenced 56,771 endothelial cells (ECs) from human, mouse, and cultured lung tumor models•Tip ECs were resolved into migratory and basement-membrane remodeling phenotypes•Capillary and venous ECs expressed immunoregulatory gene signatures•Integrated analysis identified collagen modification as an angiogenic pathway
Goveia et al. use single-cell RNA sequencing to provide an inventory of tumor endothelial cell (TEC) phenotypes from human and mouse non-small cell lung cancer and validate them functionally. Specific TEC phenotypes are associated with prognosis and response to anti-angiogenic therapy.
Black phosphorus quantum dots (BPQDs) have recently emerged as a highly promising contender in biomedical applications ranging from drug delivery systems to cancer therapy modalities. Nevertheless, ...the potential toxicity and its effects on human health need to be thoroughly investigated. In this study, we utilized multi-omics integrated approaches to explore the complex mechanisms of BPQDs-induced kidney injury. First, histological examination showed severe kidney injury in male mice after subacute exposure to 1 mg/kg BPQDs for 28 days. Subsequently, transcriptomic and metabolomic analyses of kidney tissues exposed to BPQDs identified differentially expressed genes and metabolites associated with ferroptosis, an emerging facet of regulated cell death. Our findings highlight the utility of the multi-omics integrated approach in predicting and elucidating potential toxicological outcomes of nanomaterials. Furthermore, our study provides a comprehensive understanding of the mechanisms driving BPQDs-induced kidney injury, underscoring the importance of recognizing ferroptosis as a potential toxic mechanism associated with BPQDs.
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•Exposure to 1 mg/kg BPQDs for 28 days can induce kidney injury in mice.•An integrated multi-omics approach identified genes and metabolites linked to ferroptosis in BPQDs-induced kidney injury.•In vitro and in vivo experiments have demonstrated that BPQDs can induce ferroptosis.
Micro- and nano-plastics (MNPs) in the soil can impact the microbial diversity within rhizospheres and induce modifications in plants' morphological, physiological, and biochemical parameters. ...However, a significant knowledge gap still needs to be addressed regarding the specific effects of varying particle sizes and concentrations on the comprehensive interplay among soil dynamics, root exudation, and the overall plant system. In this sense, different omics techniques were employed to clarify the mechanisms of the action exerted by four different particle sizes of polyethylene plastics considering four different concentrations on the soil-roots exudates-plant system was studied using lettuce (Lactuca sativa L. var. capitata) as a model plant. The impact of MNPs was investigated using a multi-omics integrated approach, focusing on the tripartite interaction between the root metabolic process, exudation pattern, and rhizosphere microbial modulation.
Our results showed that particle size and their concentrations significantly modulated the soil-roots exudates-plant system. Untargeted metabolomics highlighted that fatty acids, amino acids, and hormone biosynthesis pathways were significantly affected by MNPs. Additionally, they were associated with the reduction of rhizosphere bacterial α-diversity, following a size-dependent trend for specific taxa. The omics data integration highlighted a correlation between Pseudomonadata and Actinomycetota phyla and Bacillaceae family (Peribacillus simplex) and the exudation of flavonoids, phenolic acids, and lignans in lettuce exposed to increasing sizes of MNPs. This study provides a novel insight into the potential effects of different particle sizes and concentrations of MNPs on the soil-plant continuum, providing evidence about size- and concentration-dependent effects, suggesting the need for further investigation focused on medium- to long-term exposure.
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•Different polyethylene MNP sizes and dosages were tested in lettuce plant.•MNPs significantly impacted lettuce morphological traits.•MNP size impacted root metabolome and exudates, and rhizosphere microbiome.•Fatty acids and phenylpropanoids are modulated in roots and exudates, respectively.•Multi-omics revealed Proteobacteria and Actinobacteria linked to lettuce metabolites.
Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, prediction of hospital admission, drug design, classification of cancer and stromal cells, ...doctor assistance, etc. Cancer prognosis is to estimate the fate of cancer, probabilities of cancer recurrence and progression, and to provide survival estimation to the patients. The accuracy of cancer prognosis prediction will greatly benefit clinical management of cancer patients. The improvement of biomedical translational research and the application of advanced statistical analysis and machine learning methods are the driving forces to improve cancer prognosis prediction. Recent years, there is a significant increase of computational power and rapid advancement in the technology of artificial intelligence, particularly in deep learning. In addition, the cost reduction in large scale next-generation sequencing, and the availability of such data through open source databases (e.g., TCGA and GEO databases) offer us opportunities to possibly build more powerful and accurate models to predict cancer prognosis more accurately. In this review, we reviewed the most recent published works that used deep learning to build models for cancer prognosis prediction. Deep learning has been suggested to be a more generic model, requires less data engineering, and achieves more accurate prediction when working with large amounts of data. The application of deep learning in cancer prognosis has been shown to be equivalent or better than current approaches, such as Cox-PH. With the burst of multi-omics data, including genomics data, transcriptomics data and clinical information in cancer studies, we believe that deep learning would potentially improve cancer prognosis.
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