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Jackson, K. C.

Publications and source records attributed to Jackson, K. C..

4 recordsLinked to original sources

Alternative splicing of clock transcript mediates the response of circadian clocks to temperature changes

Circadian clocks respond to temperature changes over the calendar year, allowing organisms to adjust their daily biological rhythms to optimize health and fitness. In Drosophila, seasonal adaptations and temperature compensation are regulated by temperature-sensitive alternative splicing (AS) of period (per) and timeless (tim) genes that encode key transcriptional repressors of clock gene expression. Although clock (clk) gene encodes the critical activator of clock gene expression, AS of its transcripts and its potential role in temperature regulation of clock function have not been explored. We therefore sought to investigate whether clk exhibits AS in response to temperature and the functional changes of the differentially spliced transcripts. We observed that clk transcripts indeed undergo temperature-sensitive AS. Specifically, cold temperature leads to the production of an alternative clk transcript, hereinafter termed clk-cold, which encodes a CLK isoform with an in-frame deletion of four amino acids proximal to the DNA binding domain. Notably, serine 13 (S13), which we found to be a CK1-dependent phosphorylation site, is among the four amino acids deleted in CLK-cold protein. Using a combination of transgenic fly, tissue culture, and in vitro experiments, we demonstrated that upon phosphorylation at CLK(S13), CLK-DNA interaction is reduced, thus decreasing CLK occupancy at clock gene promoters. This is in agreement with our findings that CLK occupancy at clock genes and transcriptional output are elevated at cold temperature, which can be explained by the higher amounts of CLK-cold isoforms that lack S13 residue. This study provides new insights into the complex collaboration between AS and phospho-regulation in shaping temperature responses of the circadian clock.

neuroscience↗

3D Printers in Hospitals: Bacterial Contamination of Common and Antimicrobial 3D-Printed Material

COVID-19 has presented hospitals with unique challenges. A SHEA Research Network survey showed that 40% reported "limited" or worse levels of personal protective equipment (PPE), and 13% were self-producing PPE to address those deficits, including 3D-printed items. However, we do not know how efficiently, if at all, 3D-printed materials can be disinfected. Additionally, two filaments, PLACTIVE and BIOGUARD, claim to be antimicrobial; they use copper nanocomposites and silver ions to reduce bacterial populations. We assess how PLACTIVE and BIOGUARD may be contaminated and how well they reduce contamination, and how readily Polylactic Acid (PLA), a standard 3D-printed material, may be disinfected. 3D-printed materials, including PLACTIVE and BIOGUARD, are readily contaminated with bacteria that are common in hospitals and can sustain that contamination. Our findings reveal that the levels of contamination on PLACTIVE and BIOGUARD can vary under specific conditions such as layer height or bacterial contact time, sometimes surpassing or falling short of PLA. However, disinfected disks had lower overall CFU averages than those that were not, but the level of disinfection was variable, and bacterial populations recovered hours after disinfection application. Proper disinfection and using appropriate 3D-printed materials are essential to limit bacterial contamination. 3D printers and their products can be invaluable for hospitals, especially when supplies are low, and healthcare worker safety is paramount. Environmental services should be made aware of the presence of antimicrobial 3D-printed materials, and patients should be discouraged from printing their own items for use in hospital environments. IMPORTANCEThe COVID-19 pandemic has intensified the demand for personal protective equipment (PPE) in hospitals, prompting the utilization of 3D-printed materials to address shortages. Given the role of environmental contamination in healthcare-associated infections, understanding the potential for bacterial colonization on these materials is crucial. Our findings highlight the importance of proper disinfection practices and material selection in mitigating bacterial contamination, enhancing infection prevention strategies in hospitals, and ensuring the safety of healthcare workers and patients.

microbiology↗

Voyager: exploratory single-cell genomics data analysis with geospatial statistics

Exploratory spatial data analysis (ESDA) can be a powerful approach to understanding single-cell genomics datasets, but it is not yet part of standard data analysis workflows. In particular, geospatial analyses, which have been developed and refined for decades, have yet to be fully adapted and applied to spatial single-cell analysis. We introduce the Voyager platform, which systematically brings the geospatial ESDA tradition to (spatial) -omics, with local, bivariate, and multivariate spatial methods not yet commonly applied to spatial -omics, united by a uniform user interface. Using Voyager, we showcase biological insights that can be derived with its methods, such as biologically relevant negative spatial autocorrelation. Underlying Voyager is the SpatialFeatureExperiment data structure, which combines Simple Feature with SingleCellExperiment and AnnData to represent and operate on geometries bundled with gene expression data. Voyager has comprehensive tutorials demonstrating ESDA built on GitHub Actions to ensure reproducibility and scalability, using data from popular commercial technologies. Voyager is implemented in both R/Bioconductor and Python/PyPI, and features compatibility tests to ensure that both implementations return consistent results.

bioinformatics↗

Quantitative assessment of single-cell RNA-seq clustering with CONCORDEX

The rapid advancement of spatially resolved transcriptomics (SRT) technologies has facilitated exploration of how gene expression varies across tissues. However, identifying spatially variable genes remains challenging due to confounding variation introduced by the spatial distribution of cell types. We introduce a new approach to identifying spatial domains that are homogeneous with respect to cell-type composition that facilitates the decomposition of gene expression patterns by cell-type and spatial variation. Our method, called concordex, is efficient and effective across technological platforms and tissue types, and using several biological datasets we show that it can be used to identify genes with subtle variation patterns that are missed when considering only cell-type variation, or spatial variation, alone. The con-cordex tool is freely available at https://github.com/pachterlab/concordexR.

bioinformatics↗