Multiset sparse redundancy analysis for high‐dimensional omics data

Redundancy Analysis (RDA) is a well‐known method used to describe the directional relationship between related data sets. Recently, we proposed sparse Redundancy Analysis (sRDA) for high‐dimensional genomic data analysis to find explanatory variables that explain the most variance of the response variables. As more and more biomolecular data become available from different biological levels, such as genotypic and phenotypic data from different omics domains, a natural research direction is to apply an integrated analysis approach in order to explore the underlying biological mechanism of certain phenotypes of the given organism. We show that the multiset sparse Redundancy Analysis (multi‐sRDA) framework is a prominent candidate for high‐dimensional omics data analysis since it accounts for the directional information transfer between omics sets, and, through its sparse solutions, the interpretability of the result is improved. In this paper, we also describe a software implementation for multi‐sRDA, based on the Partial Least Squares Path Modeling algorithm. We test our method through simulation and real omics data analysis with data sets of 364,134 methylation markers, 18,424 gene expression markers, and 47 cytokine markers measured on 37 patients with Marfan syndrome.     

Multi‐omics integrative analysis identified SNP‐methylation‐mRNA: Interaction in peripheral blood mononuclear cells

Genetic variants have potential influence on DNA methylation and thereby regulate mRNA expression. This study aimed to comprehensively reveal the relationships among SNP, methylation and mRNA, and identify methylation‐mediated regulation patterns in human peripheral blood mononuclear cells (PBMCs). Based on in‐house multi‐omics datasets from 43 Chinese Han female subjects, genome‐wide association trios were constructed by simultaneously testing the following three association pairs: SNP‐methylation, methylation‐mRNA and SNP‐mRNA. Causal inference test (CIT) was used to identify methylation‐mediated genetic effects on mRNA. A total of 64,184 significant cis‐methylation quantitative trait loci (meQTLs) were identified (FDR < 0.05). Among the 745 constructed trios, 464 trios formed SNP‐methylation‐mRNA regulation chains (CIT). Network analysis (Cytoscape 3.3.0) constructed multiple complex regulation networks among SNP, methylation and mRNA (eg a total of 43 SNPs simultaneously connected to cg22517527 and further to PRMT2, DIP2A and YBEY). The regulation chains were supported by the evidence from 4DGenome database, relevant to immune or inflammatory related diseases/traits, and overlapped with previous eQTLs from dbGaP and GTEx. The results provide new insights into the regulation patterns among SNP, DNA methylation and mRNA expression, especially for the methylation‐mediated effects, and also increase our understanding of functional mechanisms underlying the established associations.     

Multi‐modal meta‐analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor

Molecular and functional profiling of cancer cell lines is subject to laboratory‐specific experimental practices and data analysis protocols. The current challenge therefore is how to make an integrated use of the omics profiles of cancer cell lines for reliable biological discoveries. Here, we carried out a systematic analysis of nine types of data modalities using meta‐analysis of 53 omics studies across 12 research laboratories for 2,018 cell lines. To account for a relatively low consistency observed for certain data modalities, we developed a robust data integration approach that identifies reproducible signals shared among multiple data modalities and studies. We demonstrated the power of the integrative analyses by identifying a novel driver gene, ECHDC1, with tumor suppressive role validated both in breast cancer cells and patient tumors. The multi‐modal meta‐analysis approach also identified synthetic lethal partners of cancer drivers, including a co‐dependency of PTEN deficient endometrial cancer cells on RNA helicases.     

Structural learning and integrative decomposition of multi‐view data

The increased availability of multi‐view data (data on the same samples from multiple sources) has led to strong interest in models based on low‐rank matrix factorizations. These models represent each data view via shared and individual components, and have been successfully applied for exploratory dimension reduction, association analysis between the views, and consensus clustering. Despite these advances, there remain challenges in modeling partially‐shared components and identifying the number of components of each type (shared/partially‐shared/individual). We formulate a novel linked component model that directly incorporates partially‐shared structures. We call this model SLIDE for Structural Learning and Integrative DEcomposition of multi‐view data. The proposed model‐fitting and selection techniques allow for joint identification of the number of components of each type, in contrast to existing sequential approaches. In our empirical studies, SLIDE demonstrates excellent performance in both signal estimation and component selection. We further illustrate the methodology on the breast cancer data from The Cancer Genome Atlas repository.     

Multi‐Omics Reveals Impact of Cysteine Feed Concentration and Resulting Redox Imbalance on Cellular Energy Metabolism and Specific Productivity in CHO Cell Bioprocessing

Chinese hamster ovary (CHO) cells are currently the primary host cell lines used in biotherapeutic manufacturing of monoclonal antibodies (mAbs) and other biopharmaceuticals. Cellular energy metabolism and endoplasmic reticulum (ER) stress are known to greatly impact cell growth, viability, and specific productivity of a biotherapeutic; but the molecular mechanisms are not fully understood. The authors previously employed multi‐omics profiling to investigate the impact of a reduction in cysteine (Cys) feed concentration in a fed‐batch process and found that disruption of the redox balance led to a substantial decline in cell viability and titer. Here, the multi‐omics findings are expanded, and the impact redox imbalance has on ER stress, mitochondrial homeostasis, and lipid metabolism is explored. The reduced Cys feed activates the amino acid response (AAR), increases mitochondrial stress, and initiates gluconeogenesis. Multi‐omics analysis reveals that together, ER stress and AAR signaling shift the cellular energy metabolism to rely primarily on anaplerotic reactions, consuming amino acids and producing lactate, to maintain energy generation. Furthermore, the pathways are demonstrated in which this shift in metabolism leads to a substantial decline in specific productivity and altered mAb glycosylation. Through this work, meaningful bioprocess markers and targets for genetic engineering are identified.     

The human protein atlas—Integrated omics for single cell mapping of the human proteome

Studying the spatial distribution of proteins provides the basis for understanding the biology, molecular repertoire, and architecture of every human cell. The Human Protein Atlas (HPA) has grown into one of the world''s largest biological databases, and in the most recent version, a major update of the structure of the database was performed. The data has now been organized into 10 different comprehensive sections, each summarizing different aspects of the human proteome and the protein‐coding genes. In particular, large datasets with information on the single cell type level have been integrated, refining the tissue and cell type specificity and detailing the expression in cell states with an increased resolution. The multi‐modal data constitute an important resource for both basic and translational science, and hold promise for integration with novel emerging technologies at the protein and RNA level.     

Integration of ‘omics’ data in aging research: from biomarkers to systems biology

Age is the strongest risk factor for many diseases including neurodegenerative disorders, coronary heart disease, type 2 diabetes and cancer. Due to increasing life expectancy and low birth rates, the incidence of age‐related diseases is increasing in industrialized countries. Therefore, understanding the relationship between diseases and aging and facilitating healthy aging are major goals in medical research. In the last decades, the dimension of biological data has drastically increased with high‐throughput technologies now measuring thousands of (epi) genetic, expression and metabolic variables. The most common and so far successful approach to the analysis of these data is the so‐called reductionist approach. It consists of separately testing each variable for association with the phenotype of interest such as age or age‐related disease. However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking. Systems biology aims to integrate data from different experiments to gain an understanding of the system as a whole rather than focusing on individual factors. It thus allows deeper insights into the mechanisms of complex traits, which are caused by the joint influence of several, interacting changes in the biological system. In this review, we look at the current progress of applying omics technologies to identify biomarkers of aging. We then survey existing systems biology approaches that allow for an integration of different types of data and highlight the need for further developments in this area to improve epidemiologic investigations.     

Active regulation of receptor ratios controls integration of quorum‐sensing signals in Vibrio harveyi

Quorum sensing is a chemical signaling mechanism used by bacteria to communicate and orchestrate group behaviors. Multiple feedback loops exist in the quorum‐sensing circuit of the model bacterium Vibrio harveyi. Using fluorescence microscopy of individual cells, we assayed the activity of the quorum‐sensing circuit, with a focus on defining the functions of the feedback loops. We quantitatively investigated the signaling input–output relation both in cells with all feedback loops present as well as in mutants with specific feedback loops disrupted. We found that one of the feedback loops regulates receptor ratios to control the integration of multiple signals. Together, the feedback loops affect the input–output dynamic range of signal transmission and the noise in the output. We conclude that V. harveyi employs multiple feedback loops to simultaneously control quorum‐sensing signal integration and to ensure signal transmission fidelity.     

Label‐free multi‐parametric imaging of single cells: dual picosecond optoacoustic microscopy

Advances in microscopy with new visualization possibilities often bring dramatic progress to our understanding of the intriguing cellular machinery. Picosecond optoacoustic micro‐spectroscopy is an optical technique based on ultrafast pump‐probe generation and detection of hypersound on time durations of picoseconds and length scales of nanometers. It is experiencing a renaissance as a versatile imaging tool for cell biology research after a plethora of applications in solid‐state physics. In this emerging context, this work reports on a dual‐probe architecture to carry out real‐time parallel detection of the hypersound propagation inside a cell that is cultured on a metallic substrate, and of the hypersound reflection at the metal/cell adhesion interface. Using this optoacoustic modality, several biophysical properties of the cell can be measured in a noncontact and label‐free manner. Its abilities are demonstrated with the multiple imaging of a mitotic macrophage‐like cell in a single run experiment.