Browse
Submit Data
Databases
API
Help

Dataset Information

5 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Multi-omic analysis in injured humans: Patterns align with outcomes and treatment responses.

ABSTRACT: Trauma is a leading cause of death and morbidity worldwide. Here, we present the analysis of a longitudinal multi-omic dataset comprising clinical, cytokine, endotheliopathy biomarker, lipidome, metabolome, and proteome data from severely injured humans. A "systemic storm" pattern with release of 1,061 markers, together with a pattern suggestive of the "massive consumption" of 892 constitutive circulating markers, is identified in the acute phase post-trauma. Data integration reveals two human injury response endotypes, which align with clinical trajectory. Prehospital thawed plasma rescues only endotype 2 patients with traumatic brain injury (30-day mortality: 30.3 versus 75.0%; p = 0.0015). Ubiquitin carboxy-terminal hydrolase L1 (UCHL1) was identified as the most predictive circulating biomarker to identify endotype 2-traumatic brain injury (TBI) patients. These response patterns refine the paradigm for human injury, while the datasets provide a resource for the study of critical illness, trauma, and human stress responses.

SUBMITTER: Wu J

PROVIDER: S-EPMC8715070 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

multi-omic analysis

Project description:multi-omic analysis

| PRJNA790956 | ENA

Lipidomic Signatures Align with Inflammatory Patterns and Outcomes in Critical Illness.

Project description:Alterations in lipid metabolism have the potential to be markers as well as drivers of the pathobiology of acute critical illness. Here, we took advantage of the temporal precision offered by trauma as a common cause of critical illness to identify the dynamic patterns in the circulating lipidome in critically ill humans. The major findings include an early loss of all classes of circulating lipids followed by a delayed and selective lipogenesis in patients destined to remain critically ill. Early in the clinical course, Fresh Frozen Plasma administration led to improved survival in association with preserved lipid levels that related to favorable changes in coagulation and inflammation biomarkers. Late over-representation of phosphatidylethanolamines with critical illness led to the validation of a Lipid Reprogramming Score that was prognostic not only in trauma but also severe COVID-19 patients. Our lipidomic findings provide a new paradigm for the lipid response underlying critical illness.

| S-EPMC7805459 | biostudies-literature

Lipidomic signatures align with inflammatory patterns and outcomes in critical illness.

Project description:Alterations in lipid metabolism have the potential to be markers as well as drivers of pathobiology of acute critical illness. Here, we took advantage of the temporal precision offered by trauma as a common cause of critical illness to identify the dynamic patterns in the circulating lipidome in critically ill humans. The major findings include an early loss of all classes of circulating lipids followed by a delayed and selective lipogenesis in patients destined to remain critically ill. The previously reported survival benefit of early thawed plasma administration was associated with preserved lipid levels that related to favorable changes in coagulation and inflammation biomarkers in causal modelling. Phosphatidylethanolamines (PE) were elevated in patients with persistent critical illness and PE levels were prognostic for worse outcomes not only in trauma but also severe COVID-19 patients. Here we show selective rise in systemic PE as a common prognostic feature of critical illness.

| S-EPMC9647252 | biostudies-literature

Multi-omic Analyses of Plasma Cytokines, Lipidomics, and Transcriptomics Distinguish Treatment Outcomes in Cutaneous Leishmaniasis.

Project description:Leishmania braziliensis infection frequently results in cutaneous leishmaniasis (CL). An increase in incidence of drug-resistant CL leading to treatment failure has been reported. Identification of reliable predictors of treatment outcomes is necessary to optimize patient care. Here, we performed a prospective case-control study in which plasma levels of cytokines and lipid mediators were assessed at different time points during antileishmanial therapy in patients with CL from Brazil. Multidimensional analyses were employed to describe a combination of biomarkers able to predict and characterize treatment failure. We found a biosignature influenced mainly by plasma levels of lipid mediators that accurately predicted treatment failure. Furthermore, transcriptomic analysis of a publicly available data set revealed that expression levels of genes related to lipid metabolism measured in skin lesions could distinguish treatment outcomes in CL. Thus, activation of pathways linked to lipid biosynthesis predicts treatment failure in CL. The biomarkers identified may be further explored as therapeutic targets.

| S-EPMC7721649 | biostudies-literature

multi-omic analysis

Project description:This SuperSeries is composed of the SubSeries listed below.

2022-04-01 | GSE191312 | GEO

'Multi-omic' data analysis using O-miner.

Project description:Innovations in -omics technologies have driven advances in biomedical research. However, integrating and analysing the large volumes of data generated from different high-throughput -omics technologies remain a significant challenge to basic and clinical scientists without bioinformatics skills or access to bioinformatics support. To address this demand, we have significantly updated our previous O-miner analytical suite, to incorporate several new features and data types to provide an efficient and easy-to-use Web tool for the automated analysis of data from '-omics' technologies. Created from a biologist's perspective, this tool allows for the automated analysis of large and complex transcriptomic, genomic and methylomic data sets, together with biological/clinical information, to identify significantly altered pathways and prioritize novel biomarkers/targets for biological validation. Our resource can be used to analyse both in-house data and the huge amount of publicly available information from array and sequencing platforms. Multiple data sets can be easily combined, allowing for meta-analyses. Here, we describe the analytical pipelines currently available in O-miner and present examples of use to demonstrate its utility and relevance in maximizing research output. O-miner Web server is free to use and is available at http://www.o-miner.org.

| S-EPMC6357557 | biostudies-literature

Multi-omic analysis resolves coordinated immune responses to mild, moderate and severe COVID-19

Project description:This study utilizes multi-omic biological data to perform deep immunophenotyping on the major immune cell classes in COVID-19 patients. 10X Genomics Chromium Single Cell Kits were used with Biolegend TotalSeq-C human antibodies to gather single-cell transcriptomic, surface protein, and TCR/BCR sequence information from 254 COVID-19 blood draws (a draw near diagnosis (-BL) and a draw a few days later (-AC)) and 16 healthy donors.

2020-10-29 | E-MTAB-9357 | biostudies-arrayexpress

High-Throughput Drug Screening and Multi-Omic Analysis to Guide Individualized Treatment for Multiple Myeloma.

Project description:Multiple myeloma (MM) is a genetically heterogeneous malignancy characterized by variable treatment responses. Although numerous drugs have been approved in recent years, the ability to predict treatment response and tailor individual therapy is limited by the absence of robust predictive biomarkers. The goal of this clinical trial was to use ex vivo, high-throughput screening (HTS) of 170 compounds to predict response among patients with relapsed or refractory MM and inform the next treatment decisions. Additionally, we integrated HTS with multi-omic analysis to uncover novel associations between in vitro drug sensitivity and gene expression and mutation profiles.Materials and methodsTwenty-five patients with relapsed or refractory MM underwent a screening bone marrow or soft tissue biopsy. Sixteen patients were found to have sufficient plasma cells for HTS. Targeted next-generation sequencing was performed on plasma cell-free DNA from all patients who underwent HTS. RNA and whole-exome sequencing of bone marrow plasma cells were performed on eight and seven patients, respectively.ResultsResults of HTS testing were made available to treating physicians within a median of 5 days from the biopsy. An actionable treatment result was identified in all 16 patients examined. Among the 13 patients who received assay-guided therapy, 92% achieved stable disease or better. The expression of 105 genes and mutations in 12 genes correlated with in vitro cytotoxicity.ConclusionIn patients with relapsed or refractory MM, we demonstrate the feasibility of ex vivo drug sensitivity testing on isolated plasma cells from patient bone marrow biopsies or extramedullary plasmacytomas to inform the next line of therapy.

| S-EPMC8232547 | biostudies-literature

MOTA: Multi-omic integrative analysis for biomarker discovery.

Project description:Recent advancement of omic technologies provides researchers with opportunities to search for disease biomarkers at the systems level. However, selection of biomarker candidates from a large number of molecules involved at various layers of the biological system is challenging. In this paper, we propose multi-omic integrative analysis (MOTA), a network-based method that uses information from multi-omic data to identify candidate disease biomarkers. We evaluated the performance of MOTA in selecting disease-associated molecules from four sets of multi-omic data representing three cohorts of hepatocellular carcinoma (HCC) cases and patients with liver cirrhosis. The results demonstrate that MOTA leads to selection of more biomarker candidates that shared by two different cohorts compared to traditional statistical methods. Also, the networks constructed by MOTA allow users to investigate biological significance of the selected biomarker candidates.

| S-EPMC6986235 | biostudies-literature

MONET: Multi-omic module discovery by omic selection.

Project description:Recent advances in experimental biology allow creation of datasets where several genome-wide data types (called omics) are measured per sample. Integrative analysis of multi-omic datasets in general, and clustering of samples in such datasets specifically, can improve our understanding of biological processes and discover different disease subtypes. In this work we present MONET (Multi Omic clustering by Non-Exhaustive Types), which presents a unique approach to multi-omic clustering. MONET discovers modules of similar samples, such that each module is allowed to have a clustering structure for only a subset of the omics. This approach differs from most existent multi-omic clustering algorithms, which assume a common structure across all omics, and from several recent algorithms that model distinct cluster structures. We tested MONET extensively on simulated data, on an image dataset, and on ten multi-omic cancer datasets from TCGA. Our analysis shows that MONET compares favorably with other multi-omic clustering methods. We demonstrate MONET's biological and clinical relevance by analyzing its results for Ovarian Serous Cystadenocarcinoma. We also show that MONET is robust to missing data, can cluster genes in multi-omic dataset, and reveal modules of cell types in single-cell multi-omic data. Our work shows that MONET is a valuable tool that can provide complementary results to those provided by existent algorithms for multi-omic analysis.

| S-EPMC7518594 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data