Search bioRxiv⌕ Search

bioRxiv · 10.1101/2021.11.23.469657

Analysis of RNA sequence and structure in key genes of Mycobacterium ulcerans reveals conserved structural motifs and regions with apparent pressure to remain unstructured

Abstract

Buruli Ulcer is a neglected tropical disease that results in disfiguring and potentially dangerous lesions in affected persons across a wide geographic area, which includes much of West Africa. The causative agent of Buruli Ulcer is Mycobacterium ulcerans, a relative of the bacterium that causes tuberculosis and leprosy. Few therapeutic options exist for the treatment of this disease beyond the main approach, surgical removal, which is frequently ineffective. In this study we analyze six genes in Mycobacterium ulcerans that have high potential of therapeutic targeting. We focus our analysis on a combined in silico and comparative sequence study of potential RNA secondary structure across these genes. The end result of this work was the comprehensive local RNA structural landscape across each of these significant genes. This revealed multiple sites of ordered and evolved RNA structure interspersed between sequences that either have no bias for structure or, indeed, appear to be ordered to be unstructured and (potentially) accessible. In addition to providing data that could be of interest to basic biology, our results provide guides for efforts aimed at targeting this pathogen at the RNA level. We explore this latter possibility through the in silico analysis of antisense oligonucleotides that could be used to target pathogen RNA. Author SummaryBuruli Ulcer is a neglected tropical necrotizing skin disease endemic to West Africa and several other developing countries. The disease is known to be caused by Mycobacterium ulcerans, but the mode of transmission is not well understood. Here, we present findings on the RNA secondary structural landscape of key genes found in its genome and virulence plasmid. We also suggest potential therapeutic strategies to treat this disease that leverage a better understanding of RNA secondary structure. In our analysis we have predicted regions within these genes that are potentially ordered by evolution to have unusual structural stability and likely functionality, as well as regions that lack stable structure and may be unordered for accessibility. These structured regions can act as potential targets of both antisense oligonucleotide and small molecule therapeutics, while the unstructured regions may be most advantageous for only antisense oligonucleotides. Both strategies have been proven to be effective in other bacterial and viral pathogens; therefore, adaptation to this neglected disease may prove beneficial to developing more effective and efficient treatment options. Through our analysis of the RNA secondary structure landscape of key genes in M. ulcerans, we hope to provide other researchers with new avenues for development of novel therapeutic strategies to treat this devastating and neglected disease.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Rouse, W. B., Gart, J., Peysakhova, L., Moss, W.. 2021-11-23. Analysis of RNA sequence and structure in key genes of Mycobacterium ulcerans reveals conserved structural motifs and regions with apparent pressure to remain unstructured. https://doi.org/10.1101/2021.11.23.469657

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

KEEP EXPLORING

Related preprints

deepTools4: Faster, Scalable, and Modernized Genomic Data Analysis

The deepTools suite is widely used for quality control, summarization, and visualization of high-throughput sequencing data. However, as genomic datasets grow in scale and depth, existing tools often become computational bottlenecks. We present deepTools4, a modernized framework designed for high performance, scalability, and enhanced usability, while maintaining seamless integration with the Galaxy platform. Core functionality has been rewritten and thoroughly benchmarked, achieving substantial computational speed-ups. Visualization tools have been modernized to provide more intuitive, high-quality graphical outputs. In addition, our testing framework has been expanded to facilitate continuous development and ensure robustness. We demonstrate the utility of deepTools4 across diverse epigenomics and transcriptomics datasets, highlighting its ability to summarize genome-wide signals, apply a wide range of common normalization methods, and visualize enrichment patterns. The enhanced deepTools4 framework delivers a faster, more scalable solution for reproducible genome-wide analysis and interpretation across many sequencing modalities. Availability: https://github.com/deeptools/deepTools/

bioinformatics↗

Reproducibility and cross-dataset concordance of drug-induced transcriptional responses

Drug-perturbation transcriptomic resources are expanding, but the reproducibility and cross-source consistency of the responses they measure remain critical to their use. We compared six resources using independent replicates, matched-drug analyses, and response decomposition. High agreement in treated expression did not translate into equally repeatable drug-induced changes. Cross-source response concordance was generally limited, including under matched cell and treatment conditions, although some comparisons retained drug-specific information. In Tahoe, a control-expression-only baseline left substantial drug-specific variation unresolved. Measured drug main effects - the average responses across cell lines - provided information for predicting cell-specific interactions, with this relationship retained in a cross-plate validation. Exploratory analyses of structure, target, and external-response representations identified modest signals in selected pipelines. Together, these analyses characterize the reliability and transferability of current perturbation resources and identify prediction of drug main effects as a key bottleneck in the framework examined, motivating shared reference measurements and better recovery of drug-specific responses.

bioinformatics↗

Propensity-Adjusted AUC under Nuisance-Model Misspecification

Propensity adjustment compares medical-image detector scores within groups with similar measured properties, such as stain or focus. We examine its sensitivity to the nuisance model that forms those groups. Under Gaussian latent-index assumptions and exact linear-index strata, a closed-form correlation gives an exact boundary for adjusted AUC to fall below one half. Quadrature matches 36 simulation cells within 0.008. Inflation and attenuation relative to correct adjustment are empirical findings, not general ordering results. An overlap screen and a self-stratified control introduced for this study miss 140 of 141 simulated cells with absolute error above 0.05: checking the fitted propensity cannot certify completeness of the adjustment set. On real pathology corpora, logistic and boosted-tree models using the same 16 features disagree. Among splits where both pass both checks, mean adjusted-AUC gaps are 0.057 on acid-fast bacilli (AFB), 0.048 on Kaggle, and 0.049 on PCam, over three, five, and three splits respectively. These gaps measure model sensitivity; neither estimate is known to be correct. A controlled colour shift moves adjusted AUC relative to model-specific references in the predicted direction in 78 of 80 label-dependent cells. Broader balance and label-fit diagnostics favour trees without certifying their adjusted AUC. We recommend reporting sensitivity across nuisance models alongside these diagnostics. Reversal relative to known truth occurs in constructed stress tests; its frequency in practice remains unknown.

bioinformatics↗