Biology subjects

Coppola, S.

Publications and source records attributed to Coppola, S..

7 recordsLinked to original sources

bioRxiv2026

Open filament tracks drive Bacteroidota gliding motility

Bacteria of the phylum Bacteroidota glide across surfaces using adhesins that attach to the substratum and are propelled along the cell body by an underlying track. The track is currently thought to form a single closed loop around the cell. Here we use 3D super-resolution tracking of single adhesin molecules in Flavobacterium johnsoniae to characterize adhesin movements in unprecedented detail. Our data overturn multiple aspects of the single closed loop model by showing that adhesins follow non-repeating trajectories that cross, make sharp turns, and exhibit reversals and circular paths. We develop a new model for Bacteroidota gliding in which the track network comprises multiple independently moving, open filaments. This supramolecular organization resembles that of other moving bacterial filaments such as MreB and FtsZ, suggesting that open filaments are a common organising principle in the bacterial cytoskeleton.

microbiology↗

bioRxiv2025

micromorph: a Python toolkit for measurement of microbial morphology

Detection of morphological phenotypes from light microscopy is a key part of microbiology. Despite advances in automated morphological analysis, accurate measurements still often require significant user input. To address this, we have developed micromorph, a Python package to measure bacterial morphological properties from multiple types of light microscopy data. micromorph is available as a Python package with a documented API, or as napari plugin, thus appealing to a range of users regardless of their coding proficiency. The micromorph API allows easy integration into custom analysis pipelines. We demonstrate a range of scenarios in which micromorph demonstrates excellent performance and introduce a new approach for reliably measuring the widths and lengths of quasi-circular cells.

microbiology↗

bioRxiv2024

Autoinflammatory patients with Golgi-trapped CDC42 exhibit intracellular trafficking defects leading to STING hyperactivation

Most autoinflammatory diseases are caused by mutations in innate immunity genes. Recently, four variants in the RHO GTPase CDC42 were discovered in patients affected by syndromes generally characterized by neonatal-onset of cytopenia and auto-inflammation, including hemophagocytic lymphohistiocytosis and rash in the most severe form (NOCARH syndrome). However, the mechanisms responsible for these phenotypes remain largely elusive. Here, we show that the recurrent p.R186C CDC42 variant, which is trapped in the Golgi apparatus, elicits a block in both anterograde and retrograde transports, and endoplasmic reticulum stress. Consequently, it favors STING accumulation in the Golgi in a COPI-dependent manner. This is also observed for the other Golgi-trapped p.*192C*24 CDC42 variant, but not for the p.Y64C and p.C188Y variants that do not accumulate in the Golgi. We demonstrate that the two Golgi-trapped CDC42 variants are the only ones that exhibit overactivation of the STING pathway. Consistent with these results, patients carrying Golgi-trapped CDC42 mutants present very high levels of circulating IFN at the onset of their disease. Thus, we report new mechanistic insights on the impact of the Golgi-trapped CDC42 variants. This increase in STING activation provides a rationale for combination treatments for these severe cases.

immunology↗

bioRxiv2023

Mechano-signaling of prostate tumor initiating cells facilitates their tropism to stiff metastatic niche

Analysis of clinical datasets indicate that cancer stem-like cells/tumor-initiating cells (CSCs/TICs) derived from prostate cancer (PCa) patients display an elevated expression of genes for cell-matrix interactions, cell adhesion proteins and of the putative mechanotransducer TAZ. Here we combined measurements on the cellular mechano-responses to matrix stiffness, including cell-generated forces, zebrafish and PDX-derived organoid models, to show that mechanotransduction serves as a key determinant for PCa CSC maintenance during metastatic onset. The {beta}1-integrin-ILK-CDC42-N-Wasp dependent cytoskeletal tension and TAZ nucleus-translocation mediate this mechano-signaling axis. As a result, expression of the stemness genes NANOG and OCT4 are induced, leading to metastatic tumor initiation. It is further demonstrated that pharmaceutical perturbation of this mechano-signaling using a novel YAP/TAZ inhibitor K975 constrains PCa metastasis in zebrafish, and development of PDX-derived organoids. Our data highlights the essential role of mechanotransduction in PCa aggressiveness, thereby underlying this pathway as a therapeutic target for future studies.

cancer biology↗

bioRxiv2022

DNA supercoiling restricts the transcriptional bursting of neighboring eukaryotic genes

DNA supercoiling has emerged as a major contributor to gene regulation in bacteria. The impact of DNA supercoiling on transcription dynamics in eukaryotes is less clear. Here, using single-molecule dual-color RNA imaging in budding yeast, we show that transcriptional bursting of the divergent and tandem GAL genes is coupled. Upon topoisomerase degradation, supercoils that buildup from transcription inhibit subsequent transcription at neighboring genes, thereby reducing their simultaneous bursting. GAL gene transcription is inhibited more by negative than by positive supercoiling accumulation. Unlike bacteria, wildtype yeast has sufficient topoisomerase levels to minimize inhibition from supercoils at adjacent genes. Overall, we discover fundamental differences in supercoiling-mediated gene regulation between bacteria and yeast and show that rapid supercoiling release in eukaryotes ensures proper gene expression of neighboring genes. One sentence summaryTranscription causes twisting of the DNA double helix, which can inhibit transcription of adjacent genes.

molecular biology↗

bioRxiv2021

Accurate inference of stochastic gene expression from nascent transcript heterogeneity

Transcriptional rates are often estimated by fitting the distribution of mature mRNA numbers measured using smFISH (single molecule fluorescence in situ hybridization) with the distribution predicted by the telegraph model of gene expression, which defines two promoter states of activity and inactivity. However, fluctuations in mature mRNA numbers are strongly affected by processes downstream of transcription. In addition, the telegraph model assumes one gene copy, but in experiments cells may have two gene copies as cells replicate their genome during the cell cycle. Whilst it is often presumed that post-transcriptional noise and gene copy number variation affect transcriptional parameter estimation, the size of the error introduced remains unclear. To address this issue, here we measure both mature and nascent mRNA distributions of GAL10 in yeast cells using smFISH and classify each cell according to its cell cycle phase. We infer transcriptional parameters from mature and nascent mRNA distributions, with and without accounting for cell cycle phase and compare the results to live-cell transcription measurements of the same gene. We find that: (i) correcting for cell cycle dynamics decreases the promoter switching rates and the initiation rate, and increases the fraction of time spent in the active state, as well as the burst size; (ii) additional correction for post-transcriptional noise leads to further increases in the burst size and to a large reduction in the errors in parameter estimation. Furthermore, we outline how to correctly adjust for measurement noise in smFISH due to uncertainty in transcription site localisation when introns cannot be labelled. Simulations with parameters estimated from nascent smFISH data, which is corrected for cell cycle phases and measurement noise, leads to autocorrelation functions that agree with those obtained from live-cell imaging.

systems biology↗

bioRxiv2019

A mechanical memory of pancreatic cancer cells

Cells sense and respond to mechanical stimuli in healthy and pathological conditions. Although the major mechanisms un-derlying cellular mechanotransduction have been described, it remains largely unclear how cells store information on past mechanical cues over time. Such mechanical memory is extremely relevant in the onset of metastasis in which cancer cells migrate through tissues of different stiffness, e.g. from a stiffer tumor microenvironment to softer metastatic sites as commonly occurs for pancreatic cancer. Here, we used micropillar-based traction force microscopy to show that Suit-2.28 pancreatic cancer cells mechanically primed on a stiff matrix exerted higher traction forces even when transferred to a soft secondary matrix, as compared to soft-primed cells. This mechanical memory effect was mediated by the Yes-associated protein (YAP) and the microRNA-21 (miR-21) that are two mechanosensors initially identified as long-term memory keepers in mesenchymal stem cells. Soft-primed cells showed (i) a lower YAP nuclear translocation when transferred to a stiff secondary matrix and (ii) a loss of rigidity sensing through YAP, as compared to stiff-primed cells. The mechanical adaptation resulted in a differential expression of miR-21, inversely proportional to the priming rigidity. The long-term mechanical memory retained by miR-21 unveiled a previously unidentified mechanical modulation of drug resistance by past matrix stiffness. The higher expression of miR-21 in soft-primed cells correlated with the increased resistance to gemcitabine, as compared to stiff-primed and non-primed pancreatic cancer cells.

biophysics↗