Biology subjects

Dewey, C.

Publications and source records attributed to Dewey, C..

3 recordsLinked to original sources

bioRxiv2026

Fibroblast response to burn injury in larval zebrafish mirrors developmental maturation and is inhibited by infiltrating neutrophils

Fibroblasts mediate tissue repair after damage, but aberrant fibroblast behavior in response to injury can result in impaired wound healing. Severe burn injury often results in tissue scarring, but the underlying mechanisms by which fibroblasts respond to burn injury and the role of inflammation in fibrosis are not well understood. Here we developed fluorescent reporters of collagen expressing mesenchymal cells enabling real time imaging of fibroblasts during homeostatic development and in response to burn injury using larval zebrafish. We find that fibroblasts derived from the mesenchyme respond to burn injury by engaging in a maturation process, characterized by the expression of vimentin, which is reminiscent of larval development. In burned tissue, fibroblast maturation is perturbed by prolonged neutrophil infiltration, resulting in disorganized extracellular matrix (ECM) and delayed ECM remodeling, which can be rescued by neutrophil depletion. This work adds to our understanding of fibroblast development in zebrafish and shows that collagen expressing mesenchymal cells regulate ECM remodeling in coordination with immune cells during burn wound healing. Summary StatementVimentin-positive fibroblasts are required for normal wound healing in larval zebrafish, but the presence of inflammatory neutrophils after burn injury impairs fibroblast maturation and collagen remodeling.

Developmental Biology↗

bioRxiv2024

Annotation of DOM Metabolomes with an Ultrahigh Resolution Mass Spectrometry Molecular Formula Library

Increased accessibility of liquid chromatography mass spectrometry (LC-MS) metabolomics instrumentation and software have expanded their use in studies of dissolved organic matter (DOM) and exometabolites released by microbes. Current strategies to annotate metabolomes generally rely on matching tandem MS/MS spectra to databases of authentic standards. However, spectral matching approaches typically have low annotation rates for DOM. An alternative approach is to annotate molecular formula based on accurate mass and isotopic fine structure measurements that can be obtained from state-of-the-art ultrahigh resolution Fourier Transform Ion Cyclotron Resonance mass spectrometry (FT-ICR-MS), but instrument accessibility for large metabolomic studies is generally limited. Here, we describe a strategy to annotate exometabolomes obtained from lower resolution LC-MS systems by matching metabolomic features to a molecular formula library generated for a representative sample analyzed by LC-21T FT-ICR MS. The molecular formula library approach successfully annotated 53% of exometabolome features of the marine diatom Phaeodactylum tricornutum - a nearly ten-fold increase over the 6% annotation rate achieved using a conventional MS/MS approach. There was 94% agreement between assigned formula that were annotated with both approaches, and mass error analysis of the discrepancies suggested that the FT-ICR MS formula assignments were more reliable. Differences in the exometabolome of P. tricornutum grown under iron replete and iron limited conditions revealed 668 significant metabolites, including a suite of peptide-like molecules released by P. tricornutum in response to iron deficiency. These findings demonstrate the utility of FT-ICR MS formula libraries for extending the accuracy and comprehensiveness of metabolome annotations.

molecular biology↗

bioRxiv2022

CRSP: Comparative RNA-seq pipeline for species lacking both of sequenced genomes and reference transcripts

RNA sequencing (RNA-seq) has been a widely used high-throughput method to characterize transcriptomic dynamics spatiotemporally. However, typical RNA-seq data analysis pipelines depend on either a sequenced genome or corresponding reference transcripts or them both. This restriction makes the use of RNA-seq for species lacking both of sequenced genomes and corresponding reference transcripts challenging. Nile grass rat (Arvicanthis niloticus) is a diurnal rodent species with several unique characteristics making it as a useful model to study diet-induced type 2 diabetes and other physiological or behavioral processes due to its diurnal nature. However, there is neither a high-quality annotated Nile grass rat genome nor a reference transcript sets available so far, making it technically challenging to perform large-scale RNA-seq based transcriptomic studies. Although we are working on the first draft of Nile grass rat genome, a well annotated genome typically requires several rounds of manually reviewing curated transcripts and can take years to achieve. To solve this problem, we developed a Comparative RNA-Seq Pipeline (CRSP), integrating a comparative species strategy but not depending on a specific sequenced genome or species-matched reference transcripts. Benchmarking suggests the CRSP tool can achieve high accuracy to quantify gene expression levels. In this study, we generated the first ultra-deep (2.3 billion x 2 paired-end) Nile grass rat RNA-seq data from 59 biopsy samples representing 22 major organs, providing a unique resource and spatial gene expression reference for using Nile grass rat as a model to study human diseases. To facilitate a general use of CRSP, we also characterized the number of RNA-seq reads required for accurate estimation via simulation studies. CRSP and documents are available at: https://github.com/pjiang1105/CRSP. HighlightsO_LICRSP is a novel software tool which can quantify gene expression levels from RNA-seq data for species lacking both a sequenced genome and corresponding reference transcripts. C_LIO_LINile grass rat is a unique diurnal rodent species (day active but not night active) with several unique characteristics making it as a useful model to study diet-induced type 2 diabetes and other physiological or behavioral processes due to its diurnal nature. C_LIO_LIWe generated the first ultra-deep (2.3 billion x 2 paired-end reads) Nile grass rat RNA-seq data from 59 biopsy samples representing 22 major organs, providing a unique resource and spatial transcriptomic reference (e.g., tissue gene expression baseline) for using Nile grass rat as a model to study human diseases. C_LI

bioinformatics↗