An integrated, high-throughput strategy for multi-omic systems level analysis
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo Sapiens
TISSUE(S): Leukemia Cells
PROVIDER: ST001029 | MetabolomicsWorkbench |
REPOSITORIES: MetabolomicsWorkbench
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Project description:Mass spectrometry (MS) serves as the centerpiece technology for proteome, lipidome, and metabolome analysis. To gain a better understanding of the multifaceted networks of myriad regulatory layers in complex organisms, integration of different multiomic layers is increasingly performed, including joint extraction methods of diverse biomolecular classes and comprehensive data analyses of different omics. Despite the versatility of MS systems, fractured methodology drives nearly all MS laboratories to specialize in analysis of a single ome at the exclusion of the others. Although liquid chromatography-mass spectrometry (LC-MS) analysis is similar for different biomolecular classes, the integration on the instrument level is lagging behind. The recent advancements in high flow proteomics enable us to take a first step towards integration of protein and lipid analysis. Here, we describe a technology to achieve broad and deep coverage of multiple molecular classes simultaneously through multi-omic single-shot technology (MOST), requiring only one column, one LC-MS instrument, and a simplified workflow. MOST achieved great robustness and reproducibility. Its application to a Saccharomyces cerevisiae study consisting of 20 conditions revealed 2842 protein groups and 325 lipids and potential molecular relationships.
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Project description:BackgroundBritain's native oak species are currently under threat from acute oak decline (AOD), a decline-disease where stem bleeds overlying necrotic lesions in the inner bark and larval galleries of the bark-boring beetle, Agrilus biguttatus, represent the primary symptoms. It is known that complex interactions between the plant host and its microbiome, i.e. the holobiont, significantly influence the health status of the plant. In AOD, necrotic lesions are caused by a microbiome shift to a pathobiome consisting predominantly of Brenneria goodwinii, Gibbsiella quercinecans, Rahnella victoriana and potentially other bacteria. However, the specific mechanistic processes of the microbiota causing tissue necrosis, and the host response, have not been established and represent a barrier to understanding and managing this decline.ResultsWe profiled the metagenome, metatranscriptome and metaproteome of inner bark tissue from AOD symptomatic and non-symptomatic trees to characterise microbiota-host interactions. Active bacterial virulence factors such as plant cell wall-degrading enzymes, reactive oxygen species defence and flagella in AOD lesions, along with host defence responses including reactive oxygen species, cell wall modification and defence regulators were identified. B. goodwinii dominated the lesion microbiome, with significant expression of virulence factors such as the phytopathogen effector avrE. A smaller proportion of microbiome activity was attributed to G. quercinecans and R. victoriana. In addition, we describe for the first time the potential role of two previously uncharacterised Gram-positive bacteria predicted from metagenomic binning and identified as active in the AOD lesion metatranscriptome and metaproteome, implicating them in lesion formation.ConclusionsThis multi-omic study provides novel functional insights into microbiota-host interactions in AOD, a complex arboreal decline disease where polymicrobial-host interactions result in lesion formation on tree stems. We present the first descriptions of holobiont function in oak health and disease, specifically, the relative lesion activity of B. goodwinii, G. quercinecans, Rahnella victoriana and other bacteria. Thus, the research presented here provides evidence of some of the mechanisms used by members of the lesion microbiome and a template for future multi-omic research into holobiont characterisation, plant polymicrobial diseases and pathogen defence in trees.
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Project description:Identifying genetic risk factors for Alzheimer's disease (AD) is an important research topic. To date, different endophenotypes, such as imaging-derived endophenotypes and proteomic expression-derived endophenotypes, have shown the great value in uncovering risk genes compared to case-control studies. Biologically, a co-varying pattern of different omics-derived endophenotypes could result from the shared genetic basis. However, existing methods mainly focus on the effect of endophenotypes alone; the effect of cross-endophenotype (CEP) associations remains largely unexploited. In this study, we used both endophenotypes and their CEP associations of multi-omic data to identify genetic risk factors, and proposed two integrated multi-task sparse canonical correlation analysis (inMTSCCA) methods, i.e., pairwise endophenotype correlation-guided MTSCCA (pcMTSCCA) and high-order endophenotype correlation-guided MTSCCA (hocMTSCCA). pcMTSCCA employed pairwise correlations between magnetic resonance imaging (MRI)-derived, plasma-derived, and cerebrospinal fluid (CSF)-derived endophenotypes as an additional penalty. hocMTSCCA used high-order correlations among these multi-omic data for regularization. To figure out genetic risk factors at individual and group levels, as well as altered endophenotypic markers, we introduced sparsity-inducing penalties for both models. We compared pcMTSCCA and hocMTSCCA with three related methods on both simulation and real (consisting of neuroimaging data, proteomic analytes, and genetic data) datasets. The results showed that our methods obtained better or comparable canonical correlation coefficients (CCCs) and better feature subsets than benchmarks. Most importantly, the identified genetic loci and heterogeneous endophenotypic markers showed high relevance. Therefore, jointly using multi-omic endophenotypes and their CEP associations is promising to reveal genetic risk factors. The source code and manual of inMTSCCA are available at https://ngdc.cncb.ac.cn/biocode/tools/BT007330.
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Project description:The heart, the first organ to develop in the embryo, undergoes complex morphogenesis that when defective results in congenital heart disease (CHD). With current therapies, more than 90% of patients with CHD survive into adulthood, but many suffer premature death from heart failure and non-cardiac causes1. Here, to gain insight into this disease progression, we performed single-nucleus RNA sequencing on 157,273 nuclei from control hearts and hearts from patients with CHD, including those with hypoplastic left heart syndrome (HLHS) and tetralogy of Fallot, two common forms of cyanotic CHD lesions, as well as dilated and hypertrophic cardiomyopathies. We observed CHD-specific cell states in cardiomyocytes, which showed evidence of insulin resistance and increased expression of genes associated with FOXO signalling and CRIM1. Cardiac fibroblasts in HLHS were enriched in a low-Hippo and high-YAP cell state characteristic of activated cardiac fibroblasts. Imaging mass cytometry uncovered a spatially resolved perivascular microenvironment consistent with an immunodeficient state in CHD. Peripheral immune cell profiling suggested deficient monocytic immunity in CHD, in agreement with the predilection in CHD to infection and cancer2. Our comprehensive phenotyping of CHD provides a roadmap towards future personalized treatments for CHD.
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Project description:BackgroundThe advance of high-throughput technologies has made it cost-effective to collect diverse types of omic data in large-scale clinical and biological studies. While the collection of the vast amounts of multi-level omic data from these studies provides a great opportunity for genetic research, the high dimensionality of omic data and complex relationships among multi-level omic data bring tremendous analytic challenges.ResultsTo address these challenges, we develop an integrative U (IU) method for the design and analysis of multi-level omic data. While non-parametric methods make less model assumptions and are flexible for analyzing different types of phenotypes and omic data, they have been less developed for association analysis of omic data. The IU method is a nonparametric method that can accommodate various types of omic and phenotype data, and consider interactive relationship among different levels of omic data. Through simulations and a real data application, we compare the IU test with commonly used variance component tests.ConclusionsResults show that the proposed test attains more robust type I error performance and higher empirical power than variance component tests under various types of phenotypes and different underlying interaction effects.
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Project description:This SuperSeries is composed of the SubSeries listed below.
2021-04-02 | GSE148642 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below.
2022-04-01 | GSE191312 | GEO