Multi-omics Networks Research Articles

Imaging-AMARETTO: An Imaging Genomics Software Tool to Interrogate Multiomics Networks for Relevance to Radiography and Histopathology Imaging Biomarkers of Clinical Outcomes.

PURPOSEThe availability of increasing volumes of multiomics, imaging, and clinical data in complex diseases such as cancer opens opportunities for the formulation and development of computational imaging genomics methods that can link multiomics, imaging, and clinical data.METHODSHere, we present the Imaging-AMARETTO algorithms and software tools to systematically interrogate regulatory networks derived from multiomics data within and across related patient studies for their relevance to radiography and histopathology imaging features predicting clinical outcomes.RESULTSTo demonstrate its utility, we applied Imaging-AMARETTO to integrate three patient studies of brain tumors, specifically, multiomics with radiography imaging data from The Cancer Genome Atlas (TCGA) glioblastoma multiforme (GBM) and low-grade glioma (LGG) cohorts and transcriptomics with histopathology imaging data from the Ivy Glioblastoma Atlas Project (IvyGAP) GBM cohort. Our results show that Imaging-AMARETTO recapitulates known key drivers of tumor-associated microglia and macrophage mechanisms, mediated by STAT3, AHR, and CCR2, and neurodevelopmental and stemness mechanisms, mediated by OLIG2. Imaging-AMARETTO provides interpretation of their underlying molecular mechanisms in light of imaging biomarkers of clinical outcomes and uncovers novel master drivers, THBS1 and MAP2, that establish relationships across these distinct mechanisms.CONCLUSIONOur network-based imaging genomics tools serve as hypothesis generators that facilitate the interrogation of known and uncovering of novel hypotheses for follow-up with experimental validation studies. We anticipate that our Imaging-AMARETTO imaging genomics tools will be useful to the community of biomedical researchers for applications to similar studies of cancer and other complex diseases with available multiomics, imaging, and clinical data.

Unsupervised discovery of phenotype-specific multi-omics networks.

Complex diseases often involve a wide spectrum of phenotypic traits. Better understanding of the biological mechanisms relevant to each trait promotes understanding of the etiology of the disease and the potential for targeted and effective treatment plans. There have been many efforts towards omics data integration and network reconstruction, but limited work has examined the incorporation of relevant (quantitative) phenotypic traits. We propose a novel technique, sparse multiple canonical correlation network analysis (SmCCNet), for integrating multiple omics data types along with a quantitative phenotype of interest, and for constructing multi-omics networks that are specific to the phenotype. As a case study, we focus on miRNA-mRNA networks. Through simulations, we demonstrate that SmCCNet has better overall prediction performance compared to popular gene expression network construction and integration approaches under realistic settings. Applying SmCCNet to studies on chronic obstructive pulmonary disease (COPD) and breast cancer, we found enrichment of known relevant pathways (e.g. the Cadherin pathway for COPD and the interferon-gamma signaling pathway for breast cancer) as well as less known omics features that may be important to the diseases. Although those applications focus on miRNA-mRNA co-expression networks, SmCCNet is applicable to a variety of omics and other data types. It can also be easily generalized to incorporate multiple quantitative phenotype simultaneously. The versatility of SmCCNet suggests great potential of the approach in many areas. The SmCCNet algorithm is written in R, and is freely available on the web at https://cran.r-project.org/web/packages/SmCCNet/index.html. Supplementary data are available at Bioinformatics online.

A study on multi-omic oscillations in Escherichia coli metabolic networks

BackgroundTwo important challenges in the analysis of molecular biology information are data (multi-omic information) integration and the detection of patterns across large scale molecular networks and sequences. They are are actually coupled beause the integration of omic information may provide better means to detect multi-omic patterns that could reveal multi-scale or emerging properties at the phenotype levels.ResultsHere we address the problem of integrating various types of molecular information (a large collection of gene expression and sequence data, codon usage and protein abundances) to analyse the E.coli metabolic response to treatments at the whole network level. Our algorithm, MORA (Multi-omic relations adjacency) is able to detect patterns which may represent metabolic network motifs at pathway and supra pathway levels which could hint at some functional role. We provide a description and insights on the algorithm by testing it on a large database of responses to antibiotics. Along with the algorithm MORA, a novel model for the analysis of oscillating multi-omics has been proposed. Interestingly, the resulting analysis suggests that some motifs reveal recurring oscillating or position variation patterns on multi-omics metabolic networks. Our framework, implemented in R, provides effective and friendly means to design intervention scenarios on real data. By analysing how multi-omics data build up multi-scale phenotypes, the software allows to compare and test metabolic models, design new pathways or redesign existing metabolic pathways and validate in silico metabolic models using nearby species.ConclusionsThe integration of multi-omic data reveals that E.coli multi-omic metabolic networks contain position dependent and recurring patterns which could provide clues of long range correlations in the bacterial genome.

Grimon: graphical interface to visualize multi-omics networks.

SummaryRapid advances in high-throughput sequencing technologies have enabled more efficient acquisition of massive amount of multi-omics data. However, interpretation of the underlying relationships across multi-omics networks has not been fully succeeded, partly due to the lack of effective methods in visualization. To aid interpretation of the results from such multi-omics data, we here present Grimon (Graphical interface to visualize multi-omics networks), an R package that visualizes high-dimensional multi-layered data sets in three-dimensional parallel coordinates. Grimon enables users to intuitively and interactively explore their analyzed data, helping their understanding of multiple inter-layer connections embedded in high-dimensional complex data.Availability and implementationGrimon is freely available at https://github.com/mkanai/grimon as an R package with example omics data sets.Supplementary information Supplementary data are available at bioinformatics online.

Status Asthmaticus Treated by Hypnosis

<h3>Abstract</h3> <h3>INTRODUCTION</h3> This study employed an integrative system and causal inference approach to explore molecular signatures in blood and CSF, the Amyloid/Tau/Neurodegeneration [AT(N)] framework, MCI conversion to AD, and genetic risk for AD. <h3>METHODS</h3> Using the EMIF-AD MBD cohort, we measured 696 proteins in cerebrospinal fluid (n=371), 4001 proteins in plasma (n=972), 611 metabolites in plasma (n=696) and genotyped data in whole-blood (7,778,465 autosomal SNPs, n=936). We investigated associations: molecular modules to AT(N), module hubs with AD Polygenic Risk scores and <i>APOE</i>4 genotypes, molecular hubs to MCI conversion and probed for causality with AD using Mendelian Randomization (MR). <h3>RESULTS</h3> AT(N) framework associated key hubs were mostly proteins and few lipids. In MR analyses, Proprotein Convertase Subtilisin/Kexin Type 7 showed weak causal associations with AD, and AD was causally associated with Reticulocalbin 2 and sphingomyelins. <h3>DISCUSSION</h3> This study reveals multi-omics networks associated with AT(N) and MCI conversion and highlights AD causal candidates.