Houwing-Duistermaat, J. | bioRxiv Science

bioRxiv2022

Statistical integration of multi-omics data identifies potential therapeutic targets for synucleinopathies

Data integration methods are used to obtain a unified summary of multiple datasets. For multi-modal data, we propose a computational workflow to jointly analyze datasets from cell lines. The workflow comprises a novel probabilistic data integration method, named POPLS-DA, for omics data. The workflow is motivated by a study on synucleinopathies where transcriptomics, proteomics, and drug screening data are measured in affected LUHMES cell lines and controls. The aim is to highlight potentially druggable pathways and genes involved in synucleinopathies. First, POPLS-DA is used to prioritize genes and proteins that best distinguish cases and controls. For these genes an integrated interaction network is constructed, where the drug screen data is incorporated to highlight druggable genes and pathways in the network. Finally functional enrichment analyses are performed to identify clusters of synaptic and lysosome-related genes and proteins targeted by the protective drugs. We found that HSPA5, a member of the heat shock protein 70 family, was one of the most targeted genes by the validated drugs, in particular by AT1-blockers. HSPA5 and AT1-blockers have been previously linked to -synuclein pathology and Parkinsons disease, showing the relevance of our findings. Our computational workflow identified new directions for therapeutic targets for synucleinopathies. An implementation based on R and markdown is freely available online. Author summaryWe present a computational workflow that combines the analysis of different types of data measured in cell line studies with non-overlapping samples. We apply the workflow to measurements of gene expression, protein abundances, and a screening of a wide range of FDA-approved drugs. These different types of data are obtained from LUHMES brain cells and jointly analyzed to discover new treatment options in synucleinopathies, such as Parkinsons disease. Our workflow includes a new probabilistic method, named POPLS-DA. POPLS-DA combines the analysis of the genes and proteins to pinpoint a set of relevant genes and proteins that can distinguish affected and non-affected cells. Furthermore, we constructed a network that connects together the relevant genes and proteins that interact with each other. We incorporate the drug screening data to highlights which part of the network is relevant to the disease and druggable. Through additional analysis of the functionality, we discovered that the genes and proteins that are targeted by protective drugs share relevant properties, namely they are synaptic and lysosome related genes. Notably, we found that a specific type of drugs, namely AT1-blockers such as Telmisartan, are protective and target the network of relevant genes and proteins. These drugs are approved by the FDA and readily available to further investigate its potential in treating synucleinopathies. We further found that a gene named HSPA5, a member of the heat shock protein 70 family, is highly targeted by the protective drugs. This gene has been linked to Parkinsons disease in previous scientific literature. Our computational workflow and the implementation in R and markdown are freely available online.

neuroscience↗

bioRxiv2020

Statistical Integration of Multiple Omics Datasets Using GO2PLS

BackgroundNowadays, multiple omics data are measured on the same samples in the belief that these different omics datasets represent various aspects of the underlying biological systems. Integrating these omics datasets will facilitate the understanding of the systems. For this purpose, various methods have been proposed, such as Partial Least Squares (PLS), decomposing two datasets into joint and residual subspaces. Since omics data are heterogeneous, the joint components in PLS will contain variation specific to each dataset. To account for this, Two-way Orthogonal Partial Least Squares (O2PLS) captures the heterogeneity by introducing orthogonal subspaces and better estimates the joint subspaces. However, the latent components spanning the joint subspaces in O2PLS are linear combinations of all variables, while it might be of interest to identify a small subset relevant to the research question. To obtain sparsity, we extend O2PLS to Group Sparse O2PLS (GO2PLS) that utilizes biological information on group structures among variables and performs group selection in the joint subspace. ResultsThe simulation study showed that introducing sparsity improved the feature selection performance. Furthermore, incorporating group structures increased robustness of the feature selection procedure. GO2PLS performed optimally in terms of accuracy of joint score estimation, joint loading estimation, and feature selection. We applied GO2PLS to datasets from two studies: TwinsUK (a population study) and CVON-DOSIS (a small case-control study). In the first, we incorporated biological information on the group structures of the methylation CpG sites when integrating the methylation dataset with the IgG glycomics data. The targeted genes of the selected methylation groups turned out to be relevant to the immune system, in which the IgG glycans play important roles. In the second, we selected regulatory regions and transcripts that explained the covariance between regulomics and transcriptomics data. The corresponding genes of the selected features appeared to be relevant to heart muscle disease. ConclusionsGO2PLS integrates two omics datasets to help understand the underlying system that involves both omics levels. It incorporates external >group information and performs group selection, resulting in a small subset of features that best explain the relationship between two omics datasets for better interpretability.

bioinformatics↗