Biology subjects

Fields, L.

Publications and source records attributed to Fields, L..

3 recordsLinked to original sources

bioRxiv2025

cNPDB: A comprehensive empirical crustacean neuropeptide database

Neuropeptides, key signaling molecules essential for dynamic regulation of biological processes, have been studied via crustacean model systems for more than 40 years. We present cNPDB, the first centralized resource dedicated to this dynamic area of research, promisingly accelerating crustacean neuropeptidome research and offering a blueprint for cross-species translational studies of neuropeptide signaling. cNPDB is comprised of 1364 published neuropeptides from 29 crustacean species, each annotated with corresponding taxonomic information, computed physicochemical properties, spatial localization, and predicted three-dimensional structure. The intuitive web-interface supports keyword and property-based filtering, sequence alignment, property calculation, and links to available mass spectrometry imaging data, streamlining discovery. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/667494v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@ec5445org.highwire.dtl.DTLVardef@19084a3org.highwire.dtl.DTLVardef@f4228dorg.highwire.dtl.DTLVardef@89504_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Unlocking the Neuropeptidome using a Novel Endogenous Peptidomics Framework

Endogenous peptides have garnered increasing attention over the past decade driven by the development of advanced analytical methods. However, large-scale investigations of peptides as potential disease biomarkers or drug candidates are still hindered by their challenging biochemical properties and the scarcity of specialized analytical tools. Among these, neuropeptides are particularly challenging to study due to their low in vivo concentration, rapid turnover rate, and high structural variability. Data-independent acquisition (DIA) mass spectrometry (MS) has shown great ability in profiling low-abundance ions. Nevertheless, most available DIA analytical tools are designed for proteomics studies and are not suitable for endogenous peptides, as there is no set enzymatic cleavage for these peptides. Here, we introduce the novel EndoGenius platform, paired with DIA-NN, to achieve high-confidence neuropeptide identification using an updated spectral library for DIA MS analysis. By employing orthogonal offline fractionation, ion mobility instrumentation, and an optimized database searching algorithm specifically for neuropeptides, we have constructed the largest crustacean neuropeptide spectral library to date. With this library, in combination with neural networking technology, we report a 100-fold increase in the number of neuropeptides identified in all Cancer borealis tissues analyzed. We also cross-validated these findings with transcriptomics data to enhance identification confidence. This workflow presents a novel analytical framework for DIA peptidomics analysis, offering a robust approach to studying neuropeptides and other endogenous peptides. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/659356v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1bae342org.highwire.dtl.DTLVardef@9e26d8org.highwire.dtl.DTLVardef@1084f06org.highwire.dtl.DTLVardef@7c1371_HPS_FORMAT_FIGEXP M_FIG C_FIG SynopsisWe present a framework that capitalizes on robust analytical innovations and an optimized bioinformatics pipeline to provide the most comprehensive snapshot of the crustacean neuropeptidome to-date.

neuroscience↗

bioRxiv2025

EndoGenius: Enabling comprehensive identification and quantitation of endogenous peptides

Structured abstractO_ST_ABSSummaryC_ST_ABSThe investigation of endogenous peptides, specifically with respect to neuropeptides, from mass spectrometry data is rife with bioinformatics bottlenecks, stemming from the low in vivo abundance of these analytes, increased susceptibility to degradation, and an immense search space of possible peptides. To address this, we present EndoGenius in its expanded form, strategically designed to optimize the searching for these endogenous peptides complemented with a pipeline designed for tasks including quantitation, spectral library building, motif extraction, and usage with data-independent acquisition workflows. Availability and ImplementationEndoGenius is released as an open-source software package under an MIT License. The EndoGenius package with a user interface can be installed from https://www.lilabs.org/resources. The source code for EndoGenius can be accessed at https://github.com/lingjunli-research/EndoGenius-v2.0. ContactLingjun.Li@wisc.edu

bioinformatics↗