Search bioRxivSearch

bioRxiv · 10.1101/2020.12.31.424962

Identification of Common Molecular Signatures Shared between Alzheimer's and Parkinson's Diseases and Therapeutic Agents Exploration: An Integrated Genomics Approach

Abstract

Alzheimers disease (AD) and Parkinsons disease (PD) are two most prevalent age-related dementias that severely affect a large number of elderly people around the globe. Poor understanding of pathogenesis of these neurological diseases imposes challenge to discover therapeutic measures and effective diagnosis methods. In this study, a network-based approach was utilized to identify potential common molecular signatures and therapeutic agents for AD and PD. Protein-protein interaction analysis revealed NCK1, UBC, CDH1, CDC20, ACTB, PSMA7, PRPF8, RPL7, XRCC6 and HSP90AB1 as the best proteome signatures. Different regulatory transcriptional signatures i.e., YY1, NFKB1, BRCA1, TP53, GATA2, SREBF2, E2F1, FOXC1, RELA and NFIC and post-transcriptional signatures i.e., hsa-mir-186-5p, hsamir-92a-3p, hsa-mir-615-3p, hsa-let-7c-5p, hsa-mir-100-5p, hsa-mir-93-3p, hsa-mir-5681a, hsamir-484, hsa-mir-193b-3p and hsa-mir-16p-5p were identified from other interaction network. Drug-gene interaction study revealed possible therapeutic agents which may reverse the AD and PD condition. The scientific approach of this study should contribute to identify potential biomarkers, drug targets and therapeutic agents against AD and PD which should in turn advance the present efforts of scientists to secure effective diagnosis and therapeutic options. However, further in vivo and in vitro experiments might be required to validate the outcomes of this study.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Faruqui, N. A., Prium, D. H., Mowna, S. A., Rahman, T. I., Dutta, A. R., Akter, M. F.. 2021-01-04. Identification of Common Molecular Signatures Shared between Alzheimer's and Parkinson's Diseases and Therapeutic Agents Exploration: An Integrated Genomics Approach. https://doi.org/10.1101/2020.12.31.424962

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics