Biology subjects

Niese, B.

Publications and source records attributed to Niese, B..

3 recordsLinked to original sources

bioRxiv2025

RNA Binding Proteins KhpA and KhpB Interact with Small Regulatory RNAs and Affect Global Gene Expression in Deinococcus radiodurans

Small regulatory RNAs (sRNAs) in bacteria often associate with RNA-binding proteins to gain intracellular stability and/or to enable regulatory efficiency. While much of the current knowledge about those sRNA binding proteins is derived from studies in Gram-negative organisms, the characterization of such proteins in Gram-positive species is still lagging behind. Here, we identified and characterized two sRNA binding proteins (KhpA and KhpB) in Deinococcus radiodurans, a Gram-positive bacterium that exhibits extreme resistance to radiation and other oxidative stressors. We demonstrate that KhpA and KhpB interact with key sRNAs in D. radiodurans and influence their stabilities. Although KhpA and KhpB interact with each other, they do not bind the sRNAs as a complex. Insightfully, KhpA and KhpB facilitate the interactions of the representative sRNAs PprS and Dsr9 in D. radiodurans with their respective mRNA targets, pprM and DR_1968. Through RNA-seq analysis, we further revealed that KhpA and KhpB have both overlapping and specific roles in a global gene regulation in D. radiodurans. Overall, this study expands our knowledge of posttranscriptional regulation in D. radiodurans and supports the growing consensus that KhpA and KhpB homologs constitute a new family of sRNA binding proteins in Gram-positive bacteria. IMPORTANCEThe bacterium Deinococcus radiodurans is the most radiation-resistant organism identified to date. Understanding the mechanisms of resistance of D. radiodurans is essential for leveraging this bacterium in biomedical and biomanufacturing applications. It was previously revealed that small regulatory RNAs (sRNAs) play crucial roles in the gene regulation of D. radiodurans. However, how these sRNAs are influenced by RNA binding proteins is poorly understood. Here we identified two conserved RNA binding proteins, KhpA and KhpB, as sRNA binding partners in D. radiodurans. These proteins affect the sRNA stability, sRNA-target interaction, and global gene regulation. Characterization of KhpA and KhpB will help us advance the understanding of how post-transcriptional network regulates the physiology and radioresistance of D. radiodurans.

molecular biology↗

bioRxiv2025

Predicting characterization of microbiome taxonomy from imaging using machine learning approaches

AO_SCPLOWBSTRACTC_SCPLOWFor this study, a total of 47 mock human skin microbiome communities were created using microorganisms collected from human donors and grown in vitro for between eight and 32 days. Each mock community sample was split. Ten mL of each sample was used to determine the taxonomy of the community, using metatranscriptomics and Kraken2 to provide population-level taxonomic information; five mL of each sample was used for imaging. The resulting micrographs served as the basis for establishing a new analysis pipeline that sequentially used two different methods for machine learning and one statistical technique: (1) confocal microscopy images were segmented into individual cells using the generalist, deep learning, publicly available machine learning model Cellpose; (2) continuous probability density functions describing the joint distribution of the cell area and eccentricity were found using algorithms expressing the statistical technique of kernel density estimation; (3) these probability density functions were used as input for convolutional neural networks, that were trained to predict both the taxonomic diversity and the most common bacterial class, independently of metatranscriptomics. Specifically, models were made to predict the Shannon index (a quantitative measure of taxonomic diversity) and to predict the most common bacterial class, for each micrograph. Measured Shannon indices (based on metatranscriptomics) ranged from nearly 0 to 1.4. The model predictions of Shannon indices had a mean squared error of 0.0321 +/- 0.0035. The model predictions of the most common taxonomic class of bacteria had an accuracy of 94.0% +/- 0.7%. IO_SCPLOWMPORTANCEC_SCPLOWTaxonomic diversity is a useful metric for describing microbial communities and can be used as a measure of ecosystems health, resilience, and biological interactions. Characterization of microbial community diversity also has diagnostic applications. For the human skin microbiome in particular, microbial diversity directly impacts skin health, including resilience against pathogens and regulation of immune responses. Currently, microbial diversity can be determined either using traditional staining methods that are limited to pure cultures or using sequencing methods that require high investment in cost, time, and expertise. In this study, we demonstrate an innovative method that employs microscopy images of bacterial communities and machine learning to predict taxonomic diversity and the dominant bacterial classes of bacterial communities. The underlying framework of the pipeline for taxonomy prediction has the potential to be adapted and extended to other organisms and microbiomes and to make taxonomic analyses less expensive and more feasible in low-resource settings.

microbiology↗

bioRxiv2024

Geometric changes in the nucleoids of Deinococcus radiodurans reveal involvement of new proteins in recovery from ionizing radiation.

The extremophile Deinococcus radiodurans maintains a highly-organized and condensed nucleoid as its default state, possibly contributing to high tolerance of ionizing radiation (IR). Previous studies of the D. radiodurans nucleoid were limited by reliance on manual image annotation and qualitative metrics. Here, we introduce a high-throughput approach to quantify the geometric properties of cells and nucleoids, using confocal microscopy, digital reconstructions of cells, and computational modeling. We utilize this novel approach to investigate the dynamic process of nucleoid condensation in response to IR stress. Our quantitative analysis reveals that at the population level, exposure to IR induced nucleoid compaction and decreased size of D. radiodurans cells. Morphological analysis and clustering identified six distinct sub-populations across all tested experimental conditions. Results indicate that exposure to IR induces fractional redistributions of cells across sub-populations to exhibit morphologies that associate with greater nucleoid condensation, and decreased abundance of sub-populations associated with cell division. Nucleoid associated proteins (NAPs) may link nucleoid compaction and stress tolerance, but their roles in regulating compaction in D. radiodurans is unknown. Imaging of genomic mutants of known and suspected NAPs that contribute to nucleoid condensation found that deletion of nucleic acid binding proteins, not previously described as NAPs, can remodel the nucleoid by driving condensation or decondensation in the absence of stress and that IR increases the abundance of these morphological states. Thus, our integrated analysis introduces a new methodology for studying environmental influences on bacterial nucleoids and provides an opportunity to further investigate potential regulators of nucleoid condensation. ImportanceD. radiodurans, an extremophile known for its stress tolerance, constitutively maintains a highly-condensed nucleoid. Qualitative studies have described nucleoid behavior under a variety of conditions. However, a lack of quantitative data regarding nucleoid organization and dynamics have limited our understanding of regulatory mechanisms controlling nucleoid organization in D. radiodurans. Here, we introduce a quantitative approach that enables high-throughput quantitative measurements of subcellular spatial characteristics in bacterial cells. Applying this to wild-type or single-protein-deficient populations of D. radiodurans subjected to ionizing radiation, we identified significant stress-responsive changes in cell shape, nucleoid organization, and morphology. These findings highlight this methodologys adaptability and capacity for quantitatively analyzing the cellular response to stressors for screening cellular proteins involved in bacterial nucleoid organization.

molecular biology↗