Network-Based Analysis of Cognitive Impairment and Memory Deficits from Transcriptome Data

Integration of genomic and transcriptomic layers in RNA-Seq data leads to protein interaction modules with improved Alzheimer's disease associations

Alzheimer's disease (AD) is the most common neurodegenerative disease, and it is currently untreatable. RNA sequencing (RNA-Seq) is commonly used in the literature to identify AD-associated molecular mechanisms by analysing changes in gene expression. RNA-Seq data can also be used to detect genomic variants, enabling the identification of the genes with a higher load of deleterious variants in patients compared with controls. Here, we analysed AD RNA-Seq datasets to obtain differentially expressed genes and genes with a higher load of pathogenic variants in AD, and we combined them in a single list. We mapped these genes on a human protein-protein interaction network to discover subnetworks perturbed by AD. Our results show that utilizing gene pathogenicity information from RNA-Seq data positively contributes to the disclosure of AD-related mechanisms. Moreover, dividing the discovered subnetworks into highly connected modules reveals a clearer picture of altered molecular pathways that, otherwise, would not be captured. Repeating the whole pipeline with human metabolic network genes led to results confirming the positive contribution of gene pathogenicity information and enabled a more detailed identification of altered metabolic pathways in AD.

Personalized Protein-Protein Interaction Networks Towards Unraveling the Molecular Mechanisms of Alzheimer's Disease

Alzheimer's disease (AD) is a highly heterogenous neurodegenerative disease, and several omic-based datasets were generated in the last decade from the patients with the disease. However, the vast majority of studies evaluate these datasets in bulk by considering all the patients as a single group, which obscures the molecular differences resulting from the heterogeneous nature of the disease. In this study, we adopted a personalized approach and analyzed the transcriptome data from 403 patients individually by mapping the data on a human protein-protein interaction network. Patient-specific subnetworks were discovered and analyzed in terms of the genes in the subnetworks, enriched functional terms, and known AD genes. We identified several affected pathways that could not be captured by the bulk comparison. We also showed that our personalized findings point to patterns of alterations consistent with the recently suggested AD subtypes.

A co-expression network based molecular characterization of genes responsive for Braak stages in Parkinson's disease

The progression of Parkinson's disease (PD) is defined by six Braak stages. We used transcriptome data from PD patients with Braak stage information to understand underlying molecular mechanisms for the progress of the disease. We created networks of genes with decreased/increased co-expression from control group to Braak 5-6 stages. These networks are significantly associated with PD related mechanisms such as mitochondrial dysfunction and synaptic signaling among others. Applying Weighted Gene Correlation Network Analysis (WGCNA) algorithm to the co-expression networks led to more specific modules enriched with neurodegeneration related disease pathways, seizure, abnormality of coordination, and hypotonia. Furthermore, we showed that one of the co-expression networks is clustered into three major communities with dedicated molecular functions: (i) tubulin folding pathway, gap junction related mechanisms, neuronal system (ii) synaptic vesicle, intracellular vesicle, proteasome complex, PD genes (iii) energy metabolism, mitochondrial mechanisms, oxidative phosphorylation, TCA cycle, PD genes. The co-expression relations we identified in this study as crucial players in the disease progression cover several known PD-associated genes and genes whose products are known to physically interact with alpha-synuclein protein.

Transcriptional landscape of cellular networks reveal interactions driving the dormancy mechanisms in cancer

Primary cancer cells exert unique capacity to disseminate and nestle in distant organs. Once seeded in secondary sites, cancer cells may enter a dormant state, becoming resistant to current treatment approaches, and they remain silent until they reactivate and cause overt metastases. To illuminate the complex mechanisms of cancer dormancy, 10 transcriptomic datasets from the literature enabling 21 dormancy–cancer comparisons were mapped on protein–protein interaction networks and gene-regulatory networks to extract subnetworks that are enriched in significantly deregulated genes. The genes appearing in the subnetworks and significantly upregulated in dormancy with respect to proliferative state were scored and filtered across all comparisons, leading to a dormancy–interaction network for the first time in the literature, which includes 139 genes and 1974 interactions. The dormancy interaction network will contribute to the elucidation of cellular mechanisms orchestrating cancer dormancy, paving the way for improvements in the diagnosis and treatment of metastatic cancer.

A systems pharmacology approach based on oncogenic signalling pathways to determine the mechanisms of action of natural products in breast cancer from transcriptome data

BACKGROUND

Narrow spectrum of action through limited molecular targets and unforeseen drug-related toxicities have been the main reasons for drug failures at the phase I clinical trials in complex diseases. Most plant-derived compounds with medicinal values possess poly-pharmacologic properties with overall good tolerability, and, thus, are appropriate in the management of complex diseases, especially cancers. However, methodological limitations impede attempts to catalogue targeted processes and infer systemic mechanisms of action. While most of the current understanding of these compounds is based on reductive methods, it is increasingly becoming clear that holistic techniques, leveraging current improvements in omic data collection and bioinformatics methods, are better suited for elucidating their systemic effects. Thus, we developed and implemented an integrative systems biology pipeline to study these compounds and reveal their mechanism of actions on breast cancer cell lines.

METHODS

Transcriptome data from compound-treated breast cancer cell lines, representing triple negative (TN), luminal A (ER+) and HER2+ tumour types, were mapped on human protein interactome to construct targeted subnetworks. The subnetworks were analysed for enriched oncogenic signalling pathways. Pathway redundancy was reduced by constructing pathway-pathway interaction networks, and the sets of overlapping genes were subsequently used to infer pathway crosstalk. The resulting filtered pathways were mapped on oncogenesis processes to evaluate their anti-carcinogenic effectiveness, and thus putative mechanisms of action.

RESULTS

The signalling pathways regulated by Actein, Withaferin A, Indole-3-Carbinol and Compound Kushen, which are extensively researched compounds, were shown to be projected on a set of oncogenesis processes at the transcriptomic level in different breast cancer subtypes. The enrichment of well-known tumour driving genes indicate that these compounds indirectly dysregulate cancer driving pathways in the subnetworks.

CONCLUSION

The proposed framework infers the mechanisms of action of potential drug candidates from their enriched protein interaction subnetworks and oncogenic signalling pathways. It also provides a systematic approach for evaluating such compounds in polygenic complex diseases. In addition, the plant-based compounds used here show poly-pharmacologic mechanism of action by targeting subnetworks enriched with cancer driving genes. This network perspective supports the need for a systemic drug-target evaluation for lead compounds prior to efficacy experiments.