Biology subjects

Phelan, V. V.

Publications and source records attributed to Phelan, V. V..

9 recordsLinked to original sources

bioRxiv2026

The Everything Bagel Feature Finder: Ultra-fast automated feature finding for untargeted metabolomics

Metabolomics studies are increasingly being applied with hundreds to thousands, even tens of thousands of samples that demand rapid, automated data processing while maintaining analytical sensitivity or quantitative accuracy. A major computational bottleneck is feature finding, which is the transformation of LC-MS and LC-MS/MS data into a set of analyte signals aligned and quantified across samples. Feature finding can be computationally intensive and often requires manual iterative parameter optimization. To accelerate this process, we present the Everything Bagel (EB) feature finder, an ultra-fast automated feature finding tool that integrates feature detection, retention-time alignment, and gap filling designed for run-time and memory efficiency. We benchmarked EB against two automated feature finding methods on eight benchmarking datasets. Specifically, we evaluated these three feature finding methods by measuring spike-in standard detection coverage, dilution series quantification accuracy, and yeast 12C/13C credentialed features. In this evaluation, the EB feature finder achieved performance comparable to, and often exceeding, existing methods while requiring up to 150-fold lower CPU hours and up to 113-fold lower wall time. We further demonstrated the bioanalytical validity of EB by reanalyzing published datasets used for biomarker discovery and reproduced biologically significant features that matched the published findings using manually tuned feature finding settings. Taken along with the speed improvements, we anticipate EB will enhance the ability to automatically analyze datasets with thousands to tens of thousands of samples for the community.

bioinformatics↗

bioRxiv2026

Predicting Discrete Structural Transformations in Small Molecules from Tandem Mass Spectrometry

Tandem mass spectrometry (MS/MS) fragments molecules into smaller pieces, generating spectra composed of m/z values and intensities that encode structural information for molecular annotation. With increasing mass spectrometry data acquisition speeds, manual annotation from MS/MS lags far behind data generation and remains a bottleneck in metabolite annotation. Current computational methods, such as molecular networking, address this challenge by organizing similar structures into families of related compounds. However, they generally provide only similarity scores, offering weak actionable insights for structural annotation. To address this limitation, we present the Molecular Transformation Graph Edit Measure (MT-GEM), a distance metric that quantifies discrete structural transformations between molecules through graph edge removals that approximate structural modifications. Building on this metric, we developed an ensemble machine learning architecture, the Spectrum Transformation Edit Predictor (STEP), that builds upon TransExION and DREAMS to predict MT-GEM distances from MS/MS spectra. STEP achieves an average precision of 48.4% for identifying single structural transformations between MS/MS pairs, representing more than a tenfold improvement over state-of-the-art similarity metrics, including spectral entropy similarity (3.8%) and modified cosine (2.5%). On experimental human gut microbial community data, STEP identifies 3 times more single-transformation metabolite pairs than feature-based molecular networking at equivalent precision. In a discovery application, STEP highlights one drug metabolite and two new natural product analogs missed by modified cosine in feature-based molecular networking. By providing discrete transformation predictions rather than continuous similarity scores, MT-GEM and STEP enable hypothesis-driven metabolite annotation with testable structural modifications, which we envision will accelerate discovery of new molecules from MS/MS metabolomics datasets.

bioinformatics↗

bioRxiv2024

Iron content of commercial mucin contributes to compositional stability of a cystic fibrosis airway synthetic microbiota community

In vitro culture models of mucosal environments are used to elucidate the mechanistic roles of the microbiota in human health. These models often include commercial mucins to reflect the in-situ role of mucins as an attachment site and nutrient source for the microbiota. Two types of mucins are commercially available: porcine gastric mucin (PGM) and bovine submaxillary mucin (BSM). These commercial mucins have been shown to contain iron, an essential element required by the microbiota as a co-factor for a variety of metabolic functions. In these mucin preparations, the concentration of available iron can exceed physiological concentrations present in the native environment. This unexpected source of iron influences experimental outcomes, including shaping the interactions between co-existing microbes in synthetic microbial communities used to elucidate the multispecies interactions within native microbiota. In this work, we leveraged the well-characterized iron-dependent production of secondary metabolites by the opportunistic pathogen Pseudomonas aeruginosa to aid in the development of a simple, low-cost, reproducible workflow to remove iron from commercial mucins. Using the mucosal environment of the cystic fibrosis (CF) airway as a model system, we show that P. aeruginosa is canonically responsive to iron concentration in the chemically defined synthetic CF medium complemented with semi-purified PGM, and community composition of a clinically relevant, synthetic CF airway microbial community is modulated, in part, by iron concentration in PGM. IMPORTANCEMucins are critical components of in vitro systems used to model mucosal microbiota. However, crude commercial mucin preparations contain high concentrations of iron, which impacts interactions between members of the microbiota and influences interpretation of experimental results. Therefore, we developed and applied a simple, reproducible method to semi-purify commercial porcine gastric mucin as an affordable, low-iron mucin source. The development of this simplified workflow for semi-purification of commercial mucin enables researchers to remove confounding iron from a critical nutrient source when modeling clinically relevant microbial communities in vitro.

microbiology↗

bioRxiv2024

ModiFinder: Tandem Mass Spectral Alignment Enables Structural Modification Site Localization

Untargeted tandem mass spectrometry (MS/MS) has become a high-throughput method to measure small molecules in complex samples. One key goal is the transformation of these MS/MS spectra into chemical structures. Computational techniques such as MS/MS library search have enabled the re-identification of known compounds. Analog library search and molecular networking extend this identification to unknown compounds. While there have been advancements in metrics for the similarity of MS/MS spectra of structurally similar compounds, there is still a lack of automated methods to provide site specific information about structural modifications. Here we introduce ModiFinder that leverages the alignment of peaks in MS/MS spectra between structurally related known and unknown small molecules. Specifically, ModiFinder focuses on shifted MS/MS fragment peaks in the MS/MS alignment. These shifted peaks putatively represent substructures of the known molecule that contain the site of the modification. ModiFinder synthesizes these information together and scores the likelihood for each atom in the known molecule to be the modification site. We demonstrate in this manuscript how ModiFinder can effectively localize modifications which extends the capabilities of MS/MS analog searching and molecular networking to accelerate the discovery of novel compounds.

bioinformatics↗

bioRxiv2023

Secondary metabolite profiling of Pseudomonas aeruginosa isolates reveals rare genomic traits

Pseudomonas aeruginosa is a ubiquitous gram-negative opportunistic pathogen with remarkable phylogenetic and phenotypic variability. In this work, we applied classical molecular networking analysis to secondary metabolite profiling data from seven Pseudomonas aeruginosa strains, including five clinical isolates from the lung secretions of people with cystic fibrosis. Combined with whole-genome sequencing, we show that some P. aeruginosa isolates, including nmFLRO1, produce a previously unreported class of acyl putrescines, isolate SH3A does not produce di-rhamnolipids because its genome belongs to phylogenetic clade 5, and the secondary metabolite profile of isolate SH1B reflects a frame-shift mutation in the quorum sensing regulator rhlR. This study highlights for the first time that secondary metabolite profiling provides unique insight into genetic variation of P. aeruginosa. ImportanceSecondary metabolite profiling of Pseudomonas aeruginosa isolates can be used to identify rare genomic variants that impact quorum sensing and metabolite biosynthesis that underlie virulence.

microbiology↗

bioRxiv2022

A Universal Language for Finding Mass Spectrometry Data Patterns

Even though raw mass spectrometry data is information rich, the vast majority of the data is underutilized. The ability to interrogate these rich datasets is handicapped by the limited capability and flexibility of existing software. We introduce the Mass Spec Query Language (MassQL) that addresses these issues by enabling an expressive set of mass spectrometry patterns to be queried directly from raw data. MassQL is an open-source mass spectrometry query language for flexible and mass spectrometer manufacturer-independent mining of MS data. We envision the flexibility, scalability, and ease of use of MassQL will empower the mass spectrometry community to take fuller advantage of their mass spectrometry data and accelerate discoveries.

bioinformatics↗

bioRxiv2022

Pyochelin biotransformation shapes bacterial competition

Pseudomonas aeruginosa and Staphylococcus aureus are among the most frequently isolated bacterial species from polymicrobial infections of cystic fibrosis patients and chronic wounds. We applied mass spectrometry guided interaction studies to determine how chemical interaction shapes the fitness and community structure during co-infection of these two pathogens. We demonstrate that S. aureus is equipped with an elegant mechanism to inactivate pyochelin via the yet uncharacterized methyltransferase Spm (staphylococcal pyochelin methyltransferase). Methylation of pyochelin abolishes the siderophore activity of pyochelin and significantly lowers pyochelin-mediated intracellular ROS production in S. aureus. In a murine wound co-infection model, a S. aureus mutant unable to methylate pyochelin shows significantly lower fitness as compared to its parental strain. Thus, Spm mediated pyochelin methylation is a novel mechanism to increase S. aureus survival during in vivo competition with P. aeruginosa.

microbiology↗

bioRxiv2021

GNPS Dashboard: Collaborative Analysis of Mass Spectrometry Data in the Web Browser

Access to web-based platforms has enabled scientists to perform research remotely. A critical aspect of mass spectrometry data analysis is the inspection, analysis, and visualization of the raw data to validate data quality and confirm statistical observations. We developed the GNPS Dashboard, a web-based data visualization tool, to facilitate synchronous collaborative inspection, visualization, and analysis of private and public mass spectrometry data remotely.

bioinformatics↗

bioRxiv2021

Artificial Sputum Medium Formulation Impacts Pseudomonas aeruginosa Phenotype and Chemotype

Site-specific in vitro culture media are being developed to investigate microbial pathogenicity and ecology in nutrient environments that are more reflective of disease. For microbial ecology research in cystic fibrosis (CF), different artificial sputum media (ASM) formulations have been created to recapitulate the nutrient availability of the CF lung environment. However, these ASM formulations vary in concentration of amino acids, mucin, and other niche-specific compounds. Here, we measured the differential production of small molecule virulence factors by Pseudomonas aeruginosa, the predominant pathogen in CF pulmonary infections, cultured in nine different ASM formulations via liquid chromatography tandem mass spectrometry (LC-MS/MS) and molecular networking. We show that different ASM formulations lead to different phenotypes and metabolic profiles of P. aeruginosa and commercial porcine gastric mucin (PGM) contains a myriad of contaminants, including iron, which affect P. aeruginosa physiology. IMPORTANCEDifferent media formulations aiming to replicate in vivo infection environments contain different nutrients, which affects interpretation of experimental results. Inclusion of undefined components, such as commercial porcine gastric mucin (PGM), in an otherwise chemically defined medium can alter the nutrient content of the medium in unexpected ways and influence experimental outcomes.

microbiology↗