Can you use bioinformatics to diagnose disease? Using a new multi-omics method called MAGICAL, Chen et al. were able to identify host gene regulatory circuits that are cell type and pathogen-specific. This could prove to be a useful diagnostic tool, particularly for closely related infections like methicillin-resistant versus methicillin-susceptible S. aureus infections.
The Architecture of Gene Regulation
The genome is anything but linear. DNA is packaged tightly inside the nucleus in a complex architectural structure. Gene expression is regulated through careful modifications to this structure, exposing some regions of the genome to transcriptional machinery while keeping other regions silenced through tight associations with histone proteins.
Regulation of gene expression often involves distal regions of the genome forming 3D loops to come into physical contact despite being separated by linear sequence, as well as binding of transcription factors to proximal elements like promoters. In the context of genetics, regulatory circuits refer to the combined actions of these chromatin regulatory domains, transcription factors, and downstream target genes.
MAGICAL Combines scRNA-Seq and scATAC-Seq Data at Cell Type Resolution
Gene regulatory circuits become dysregulated during disease, and due to their specificity, are likely dysregulated in a cell type-specific manner that cannot be observed in bulk samples. While single-cell assay techniques have advanced dramatically, the development of methods to interpret these data types has lagged behind.
To solve this challenge in order to study gene regulatory circuits in disease, Chen et al. developed the Multiome Accessibility Gene Integration Calling and Looping (MAGICAL) method. MAGICAL uses a hierarchical Bayesian framework to analyze both single cell RNA-Seq data and single cell ATAC-Seq data to identify regulatory circuits that differ between control and disease states at cell type resolution.
Uncovering Disease Regulatory Circuits
MAGICAL identifies the active chromatin sites and genes in paired scRNA-Seq and scATAC-Seq data, uncovering their associated regulatory circuit. The authors validated MAGICAL on data from mild VS severe COVID-19 infections. They found that the disease-associated chromatin sites and genes identified by MAGICAL were more accurate than chromatin sites and genes found in independent experimental analyses of different cell types during COVID-19 infection.
The method’s validation allowed the authors to apply MAGICAL to a pressing clinical problem: distinguishing between methicillin-resistant (MRSA) and methicillin-susceptible (MSSA) Staphylococcus aureus (S. aureus) infection. Early diagnosis of the correct strain of S. aureus infection is critical to choose the correct course of treatment. MAGICAL was able to identify distinct host-response regulatory circuits in MRSA versus MSSA.
Genes in these host circuits could accurately predict S. aureus infection, and distinguish between MRSA and MSSA. Not only does this provide novel insight into the regulatory circuitry involved in the disease response, showing that it is cell type and pathogen-specific, but MAGICAL can be applied to create multi-omics-based gene signatures. A bioinformatic tools for improved disease diagnosis would be particularly useful for diseases where the infectious agent cannot be isolated and cultured.
Outsourcing Bioinformatics Analysis: How Bridge Informatics Can Help
Groundbreaking studies like these are made possible by technological advances making biological data generation, storage and analysis faster and more accessible than ever before. From pipeline development and software engineering to deploying existing bioinformatics tools, Bridge Informatics can help you on every step of your research journey.
As experts across data types from leading sequencing platforms, we can help you tackle the challenging computational tasks of storing, analyzing and interpreting genomic and transcriptomic data. Bridge Informatics’ bioinformaticians are trained bench biologists, so they understand the biological questions driving your computational analysis. Click here to schedule a free introductory call with a member of our team.
Jane Cook, Biochemist & Content Writer, Bridge Informatics
Jane Cook, the leading Content Writer for Bridge Informatics, has written over 100 articles on the latest topics and trends for the bioinformatics community. Jane’s broad and deep interdisciplinary molecular biology experience spans developing biochemistry assays to genomics. Prior to joining Bridge, Jane held research assistant roles in biochemistry research labs across a variety of therapeutic areas. While obtaining her B.A. in Biochemistry from Trinity College in Dublin, Ireland, Jane also studied journalism at New York University’s Arthur L. Carter Journalism Institute. As a native Texan, she embraces any challenge that comes her way. Jane hails from Dallas but returns to Ireland any and every chance she gets. If you’re interested in reaching out, please email [email protected] or [email protected].