Discovering Altered Regulation and Signaling Through Network-based Integration of Transcriptomic, Epigenomic, and Proteomic Tumor Data

Advance computational tools for multiomics data learning

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial role in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

Systems-level reconstruction of kinase phosphosignaling networks regulating endothelial barrier integrity using temporal data

Phosphosignaling networks control cellular processes. We built kinase-mediated regulatory networks elicited by thrombin stimulation of brain endothelial cells using two computational strategies: Temporal Pathway Synthesizer (TPS), which uses phosphoproetiomics data as input, and Temporally REsolved KInase Network Generation (TREKING), which uses kinase inhibitor screens. TPS and TREKING predicted overlapping barrier-regulatory kinases connected with unique network topology. Each strategy effectively describes regulatory signaling networks and is broadly applicable across biological systems.

Cardiac ultrastructure inspired matrix induces advanced metabolic and functional maturation of differentiated human cardiomyocytes

The vast potential of human induced pluripotent stem-cell-derived cardiomyocytes (hiPSC-CMs) in preclinical models of cardiac pathologies, precision medicine, and drug screening remains to be fully realized because hiPSC-CMs are immature without adult-like characteristics. Here, we present a method to accelerate hiPSC-CM maturation on a substrate, cardiac mimetic matrix (CMM), mimicking adult human heart matrix ligand chemistry, rigidity, and submicron ultrastructure, which synergistically mature hiPSC-CMs rapidly within 30 days. hiPSC-CMs matured on CMM exhibit systemic transcriptomic maturation toward an adult heart state, are aligned with high strain energy, metabolically rely on oxidative phosphorylation and fatty acid oxidation, and display enhanced redox handling capability, efficient calcium handling, and electrophysiological features of ventricular myocytes. Endothelin-1-induced pathological hypertrophy is mitigated on CMM, highlighting the role of a native cardiac microenvironment in withstanding hypertrophy progression. CMM is a convenient model for accelerated development of ventricular myocytes manifesting highly specialized cardiac-specific functions.

Ranking reprogramming factors for cell differentiation

Transcription factor over-expression is a proven method for reprogramming cells to a desired cell type for regenerative medicine and therapeutic discovery. However, a general method for the identification of reprogramming factors to create an arbitrary cell type is an open problem. Here we examine the success rate of methods and data for differentiation by testing the ability of nine computational methods (CellNet, GarNet, EBseq, AME, DREME, HOMER, KMAC, diffTF and DeepAccess) to discover and rank candidate factors for eight target cell types with known reprogramming solutions. We compare methods that use gene expression, biological networks and chromatin accessibility data, and comprehensively test parameter and preprocessing of input data to optimize performance. We find the best factor identification methods can identify an average of 50-60% of reprogramming factors within the top ten candidates, and methods that use chromatin accessibility perform the best. Among the chromatin accessibility methods, complex methods DeepAccess and diffTF have higher correlation with the ranked significance of transcription factor candidates within reprogramming protocols for differentiation. We provide evidence that AME and diffTF are optimal methods for transcription factor recovery that will allow for systematic prioritization of transcription factor candidates to aid in the design of new reprogramming protocols.

Multi-omic comparison of Alzheimer's variants in human ESC-derived microglia reveals convergence at APOE

Variations in many genes linked to sporadic Alzheimer’s disease (AD) show abundant expression in microglia, but relationships among these genes remain largely elusive. Here, we establish isogenic human ESC–derived microglia-like cell lines (hMGLs) harboring AD variants in CD33, INPP5D, SORL1, and TREM2 loci and curate a comprehensive atlas comprising ATAC-seq, ChIP-seq, RNA-seq, and proteomics datasets. AD-like expression signatures are observed in AD mutant SORL1 and TREM2 hMGLs, while integrative multi-omic analysis of combined epigenetic and expression datasets indicates up-regulation of APOE as a convergent pathogenic node. We also observe cross-regulatory relationships between SORL1 and TREM2, in which SORL1^R744X hMGLs induce TREM2 expression to enhance APOE expression. AD-associated SORL1 and TREM2 mutations also impaired hMGL Aβ uptake in an APOE-dependent manner in vitro and attenuated Aβ uptake/clearance in mouse AD brain xenotransplants. Using this modeling and analysis platform for human microglia, we provide new insight into epistatic interactions in AD genes and demonstrate convergence of microglial AD genes at the APOE locus.

Automating parameter selection to avoid implausible biological pathway models

A common way to integrate and analyze large amounts of biological "omic" data is through pathway reconstruction: using condition-specific omic data to create a subnetwork of a generic background network that represents some process or cellular state. A challenge in pathway reconstruction is that adjusting pathway reconstruction algorithms' parameters produces pathways with drastically different topological properties and biological interpretations. Due to the exploratory nature of pathway reconstruction, there is no ground truth for direct evaluation, so parameter tuning methods typically used in statistics and machine learning are inapplicable. We developed the pathway parameter advising algorithm to tune pathway reconstruction algorithms to minimize biologically implausible predictions. We leverage background knowledge in pathway databases to select pathways whose high-level structure resembles that of manually curated biological pathways. At the core of this method is a graphlet decomposition metric, which measures topological similarity to curated biological pathways. In order to evaluate pathway parameter advising, we compare its performance in avoiding implausible networks and reconstructing pathways from the NetPath database with other parameter selection methods across four pathway reconstruction algorithms. We also demonstrate how pathway parameter advising can guide reconstruction of an influenza host factor network. Pathway parameter advising is method agnostic; it is applicable to any pathway reconstruction algorithm with tunable parameters.

Guilt-by-Association - Functional Insights Gained From Studying the LRRK2 Interactome

The Parkinson's disease-associated Leucine-rich repeat kinase 2 (LRRK2) is a complex multi-domain protein belonging to the Roco protein family, a unique group of G-proteins. Variants of this gene are associated with an increased risk of Parkinson's disease. Besides its well-characterized enzymatic activities, conferred by its GTPase and kinase domains, and a central dimerization domain, it contains four predicted repeat domains, which are, based on their structure, commonly involved in protein-protein interactions (PPIs). In the past decades, tremendous progress has been made in determining comprehensive interactome maps for the human proteome. Knowledge of PPIs has been instrumental in assigning functions to proteins involved in human disease and helped to understand the connectivity between different disease pathways and also significantly contributed to the functional understanding of LRRK2. In addition to an increased kinase activity observed for proteins containing PD-associated variants, various studies helped to establish LRRK2 as a large scaffold protein in the interface between cytoskeletal dynamics and the vesicular transport. This review first discusses a number of specific LRRK2-associated PPIs for which a functional consequence can at least be speculated upon, and then considers the representation of LRRK2 protein interactions in public repositories, providing an outlook on open research questions and challenges in this field.

The Progress of Multi-Omics Technologies: Determining Function in Lactic Acid Bacteria Using a Systems Level Approach

Lactic Acid Bacteria (LAB) have long been recognized as having a significant impact ranging from commercial to health domains. A vast amount of research has been carried out on these microbes, deciphering many of the pathways and components responsible for these desirable effects. However, a large proportion of this functional information has been derived from a reductionist approach working with pure culture strains. This provides limited insight into understanding the impact of LAB within intricate systems such as the gut microbiome or multi strain starter cultures. Whole genome sequencing of strains and shotgun metagenomics of entire systems are powerful techniques that are currently widely used to decipher function in microbes, but they also have their limitations. An available genome or metagenome can provide an image of what a strain or microbiome, respectively, is potentially capable of and the functions that they may carry out. A top-down, multi-omics approach has the power to resolve the functional potential of an ecosystem into an image of what is being expressed, translated and produced. With this image, it is possible to see the real functions that members of a system are performing and allow more accurate and impactful predictions of the effects of these microorganisms. This review will discuss how technological advances have the potential to increase the yield of information from genomics, transcriptomics, proteomics and metabolomics. The potential for integrated omics to resolve the role of LAB in complex systems will also be assessed. Finally, the current software approaches for managing these omics data sets will be discussed.

Multiplatform Integrative Analysis of Immunogenomic Data for Biomarker Discovery

Genomic platforms are increasingly used to gain an understanding of how the immune system responds to cancer. In this chapter we describe steps applied in immunogenomic processing and data processing from multiple genomics platforms to enable study of immune response and the evaluation of candidate biomarkers. We also describe how publicly available web resources can be used to discover and evaluate candidate cancer immune biomarkers.

Turning omics data into therapeutic insights

Omics technologies have made it easier and cheaper to evaluate thousands of biological molecules at once. These advances have led to novel therapies approved for use in the clinic, elucidated the mechanisms behind disease-associated mutations, led to increased accuracy in disease subtyping and personalized medicine, and revealed novel uses and treatment regimes for existing drugs through drug repurposing and pharmacology studies. In this review, we summarize some of these milestones and discuss the potential of integrative analyses that combine multiple data types for further advances.