Real-time monitoring of Ralstonia solanacearum infection progress in tomato and Arabidopsis using bioluminescence imaging technology

Background Ralstonia solanacearum, one of the most devastating bacterial plant pathogens, is the causal agent of bacterial wilt. Recently, several studies on resistance to bacterial wilt have been conducted using the Arabidopsis-R. solanacearum system. However, the progress of R. solanacearum infection in Arabidopsis is still unclear. Results We generated a bioluminescent R. solanacearum by expressing plasmid-based luxCDABE. Expression of luxCDABE did not alter the bacterial growth and pathogenicity. The light intensity of bioluminescent R. solanacearum was linearly related to bacterial concentrations from 104 to 108 CFU·mL−1. After root inoculation with bioluminescent R. solanacearum strain, light signals in tomato and Arabidopsis were found to be transported from roots to stems via the vasculature. Quantification of light intensity from the bioluminescent strain accurately reported the difference in disease resistance between Arabidopsis wild type and resistant mutants. Conclusions Bioluminescent R. solanacearum strain spatially and quantitatively measured bacterial growth in tomato and Arabidopsis, and offered a tool for the high-throughput study of R. solanacearum-Arabidopsis interaction in the future.

SMILE: systems metabolomics using interpretable learning and evolution

Background Direct link between metabolism and cell and organism phenotype in health and disease makes metabolomics, a high throughput study of small molecular metabolites, an essential methodology for understanding and diagnosing disease development and progression. Machine learning methods have seen increasing adoptions in metabolomics thanks to their powerful prediction abilities. However, the “black-box” nature of many machine learning models remains a major challenge for wide acceptance and utility as it makes the interpretation of decision process difficult. This challenge is particularly predominant in biomedical research where understanding of the underlying decision making mechanism is essential for insuring safety and gaining new knowledge. Results In this article, we proposed a novel computational framework, Systems Metabolomics using Interpretable Learning and Evolution (SMILE), for supervised metabolomics data analysis. Our methodology uses an evolutionary algorithm to learn interpretable predictive models and to identify the most influential metabolites and their interactions in association with disease. Moreover, we have developed a web application with a graphical user interface that can be used for easy analysis, interpretation and visualization of the results. Performance of the method and utilization of the web interface is shown using metabolomics data for Alzheimer’s disease. Conclusions SMILE was able to identify several influential metabolites on AD and to provide interpretable predictive models that can be further used for a better understanding of the metabolic background of AD. SMILE addresses the emerging issue of interpretability and explainability in machine learning, and contributes to more transparent and powerful applications of machine learning in bioinformatics.

Sensitive, reliable, and robust circRNA detection from RNA-seq with CirComPara2

Current methods for identifying circular RNAs (circRNAs) suffer from low discovery rates and inconsistent performance in diverse data sets. Therefore, the applied detection algorithm can bias high-throughput study findings by missing relevant circRNAs. Here, we show that our bioinformatics tool CirComPara2 (https://github.com/egaffo/CirComPara2), by combining multiple circRNA detection methods, consistently achieves high recall rates without loss of precision in simulated and different real-data sets.

Optimized RNA-targeting CRISPR/Cas13d technology outperforms shRNA in identifying functional circRNAs

Short hairpin RNAs (shRNAs) are used to deplete circRNAs by targeting back-splicing junction (BSJ) sites. However, frequent discrepancies exist between shRNA-mediated circRNA knockdown and the corresponding biological effect, querying their robustness. By leveraging CRISPR/Cas13d tool and optimizing the strategy for designing single-guide RNAs against circRNA BSJ sites, we markedly enhance specificity of circRNA silencing. This specificity is validated in parallel screenings by shRNA and CRISPR/Cas13d libraries. Using a CRISPR/Cas13d screening library targeting > 2500 human hepatocellular carcinoma-related circRNAs, we subsequently identify a subset of sorafenib-resistant circRNAs. Thus, CRISPR/Cas13d represents an effective approach for high-throughput study of functional circRNAs.

Large-scale investigation of the effects of nucleobase sequence on fluorescence excitation and Stokes shifts of DNA-stabilized silver clusters

We present a high-throughput study of the steady state Stokes shifts of >300 fluorescent DNA-stabilized silver clusters and the correlations of DNA sequence with the optical properties of these fluorophores.