Biology subjects

Gauglitz, J. M.

Publications and source records attributed to Gauglitz, J. M..

14 recordsLinked to original sources

bioRxiv2026

Fermentation-Induced Molecular Remodeling in African Indigenous Tubers: Cassava and Cocoyam

Cassava and cocoyam are major dietary staples in sub-Saharan Africa, commonly processed by natural fermentation before consumption. Although fermentation reduces antinutritional compounds and improves food quality, its molecular effects remain poorly characterized. We used untargeted mass spectrometry-based metabolomics with a computational annotation pipeline to compare fermentation-induced molecular remodeling in the two tubers, which showed distinct responses. In cassava, 718 of 773 significant features (92.9%) were depleted, indicating a predominantly catabolic process. In cocoyam, the response was more balanced, with 385 of 1,013 features (38.0%) enriched, including di- and tripeptides consistent with proteolytic processing. Class analysis, molecular networking, and pathway enrichment revealed tuber-specific signatures: cassava was dominated by purine metabolism, whereas cocoyam showed stronger enrichment of amino acid pathways. Cyanogenic glycoside-related features were depleted, consistent with detoxification. Biotransformation prediction also suggested putative fermentation products absent from current databases, highlighting the under-characterized chemistry of these tubers.

biochemistry↗

bioRxiv2026

Scalable mass-spectrometry-based molecular phylogeny with TreeMS2

Molecular phylogeny is a well-established method for inferring evolutionary relationships from DNA and RNA sequences. Here, we extend this concept beyond genetic information by applying phylogeny-like analysis to proteomic and metabolomic mass spectrometry data, capturing relationships based on the realized molecular phenotype. The resulting phenotype-derived trees can be directly compared with conventional genetic-based trees to identify where molecular phenotypes reflect evolutionary history and where they diverge due to functional adaptation, regulation, or environmental influence. To enable this analysis, we introduce TreeMS2, a computational tool that constructs similarity matrices by directly comparing tandem mass spectrometry (MS/MS) spectra between samples. By bypassing spectrum annotation, TreeMS2 enables rapid, unbiased comparisons. Across diverse datasets, TreeMS2 reconstructs biologically meaningful relationships. In proteomics, phenotype-derived trees recapitulate established taxonomy, with deviations pinpointing sample handling errors. In single-cell proteomics our method distinguishes cell types despite sparse and noisy measurements and in metabolomics it resolves major biochemical divisions and fine-scale compositional structure. Together, these results establish TreeMS2 as a scalable, annotation-independent framework for deriving molecular relationships from raw MS/MS data.

bioinformatics↗

bioRxiv2025

Temporal regulation of metabolic processes in the marine diazotroph Crocosphaera watsonii WH 8501

Marine diazotrophic cyanobacteria play a crucial role in oceanic nitrogen cycling, supporting primary production and ecosystem balance. Crocosphaera watsonii WH8501 exemplifies this ability by temporally separating photosynthesis and diazotrophy to sustain metabolism. To investigate the regulatory mechanisms underlying this process, we employed LC/MS-MS proteomics in a diel culturing experiment, revealing tightly coordinated protein abundance patterns. Our findings showed a sophisticated temporal regulation of metabolic processes categorized within six distinct protein abundance clusters: (1) nitrogen fixation and amino acid biosynthesis proteins peaked during the night, while (2) glycogen metabolism and dark reactions of photosynthesis were most abundant during the night and day-night transition, likely supporting carbon consumption and energy production. Midday (3 and 4) was dominated by proteins related to photosynthesis, cellular division, and lipid synthesis, whereas late-day peaks (5) in peptide biosynthesis may facilitate nitrogenase complex formation. Notably, the day-night transition (6) exhibited fine-tuned coordination of nitrogenase assembly, with FeS cluster proteins preceding peak nitrogenase iron protein abundance, implying a temporally ordered sequence for functional enzyme formation. Within these categories, sharp temporal patterns emerged in iron trafficking to heme and iron cluster biosynthetic systems, consistent with the need to maintain tight control of iron distribution to metalloproteins at each temporal transition. These results highlight the intricate diel regulation that enables Crocosphaera to balance nitrogen fixation and photosynthesis within a single cell. The observed coordination supports the existence of a complex regulatory system ensuring optimal metabolic performance, reinforcing the critical role of temporal control in sustaining these globally significant biological processes.

microbiology↗

bioRxiv2025

SquiDBase: a community resource of raw nanopore data from microbes

Experimental data-driven research relies on raw data, which consist of unprocessed experimental outputs, whereas derived data are transformed through a number of processing steps to reveal specific insights. Such processing, however, can potentially introduce biases or information loss, compromising transparency and reproducibility. In nucleic acid sequencing, nucleotide sequences stored in the FASTQ format are widely shared, but FASTQ files are generated from platform-specific raw data outputs, which vary depending on the sequencing platform used. The raw data produced by Oxford Nanopore Technologies (ONT) sequencing devices contain valuable biological information and are also useful to improve data processing methods, which includes basecaller optimisation and modification detection. Increasing attention goes to exploring these raw signals to develop algorithms that could improve ONT device portability and enhance target enrichment efficiency through adaptive sampling. Despite these benefits, the storage and sharing of raw nanopore data remain limited due to technical constraints and the lack of appropriate, standardised and centralised infrastructure. To address this challenge, we developed SquiDBase (https://squidbase.org), a dedicated repository to collect raw microbial nanopore sequencing data. To maximise the utility of SquiDBase from its inception, we built SquiDPipe, a Nextflow pipeline for the automated removal of human or unwanted reads from raw nanopore data. Additionally, we sequenced 24 clinically relevant viruses and incorporated them into SquiDBase, significantly expanding the diversity of publicly available reference datasets. By offering a centralised, open-access raw data collection platform, SquiDBase facilitates data sharing, enhances reproducibility, and supports the development and benchmarking of novel computational tools, reinforcing open science in nanopore sequencing research.

bioinformatics↗

bioRxiv2022

Open Access Repository-Scale Propagated Nearest Neighbor Suspect Spectral Library for Untargeted Metabolomics

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of public MS/MS spectra. Annotations were propagated based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimers brain phenotype. The nearest neighbor suspect spectral library is openly available through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

bioinformatics↗

bioRxiv2022

An untargeted metabolomics analysis of exogenous chemicals in human milk and transfer to the infant

Human milk is the optimal infant nutrition. However, while human-derived metabolites such as lipids and oligosaccharides in human milk are regularly reported, the presence of exogenous chemicals (such as drugs, food, and synthetic compounds) are often not addressed. To understand the types of exogenous compounds that might be present, human milk (n=996) was analyzed by untargeted metabolomics. This analysis revealed that lifestyle molecules such as medications and their metabolites, food, industrial sources such as plasticizers, cosmetics, microbial molecules, and other personal care products are found in human milk. We provide further evidence that some of these lifestyle molecules are also detectable in the newborns stool. Thus, this study gives important insight into the types of exposures infants receiving human milk might ingest due to the lifestyle choices, exposure, or medical status of the lactating parent.

pharmacology and toxicology↗

bioRxiv2020

EMPress enables tree-guided, interactive, and exploratory analyses of multi-omic datasets

Standard workflows for analyzing microbiomes often include the creation and curation of phylogenetic trees. Here we present EMPress, an interactive tool for visualizing trees in the context of microbiome, metabolome, etc. community data scalable beyond modern large datasets like the Earth Microbiome Project. EMPress provides novel functionality--including ordination integration and animations--alongside many standard tree visualization features, and thus simplifies exploratory analyses of many forms of omic data.

bioinformatics↗

bioRxiv2020

Ion Identity Molecular Networking in the GNPS Environment

Molecular networking connects tandem mass spectra of molecules based on the similarity of their fragmentation patterns. However, during ionization, molecules commonly form multiple ion species with different fragmentation behavior. To connect ion species of the same molecule, we developed Ion Identity Molecular Networking. These new relationships improve network connectivity, are shown to reveal novel ion-ligand complexes, enhance annotation within molecular networks, and facilitate the expansion of spectral libraries.

bioinformatics↗

bioRxiv2020

Chemically-informed Analyses of Metabolomics Mass Spectrometry Data with Qemistree

Untargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on hierarchical organization of molecular fingerprints predicted from fragmentation spectra, represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools, designed around the relatedness of DNA sequences, to study chemical composition.

bioinformatics↗

bioRxiv2019

Native Electrospray-based Metabolomics Enables the Detection of Metal-binding Compounds

Metals are essential for the molecular machineries of life, and microbes have evolved a variety of small molecules to acquire, compete for, and utilize metals. Systematic methods for the discovery of metal-small molecule complexes from biological samples are limited. Here we describe a two-step native electrospray ionization mass spectrometry method, in which double-barrel post-column metal-infusion and pH adjustment is combined with ion identity molecular networking, a rule-based informatics workflow. This method can be used to identify metal-binding compounds in complex samples based on defined mass (m/z) offsets of ion features with the same chromatographic profiles. As this native metal metabolomics approach can be easily implemented on any liquid chromatography-based mass spectrometry system, this method has the potential to become a key strategy for elucidating and understanding the role of metal-binding molecules in biology.

biochemistry↗

bioRxiv2019

Feature-based Molecular Networking in the GNPS Analysis Environment

Molecular networking has become a key method used to visualize and annotate the chemical space in non-targeted mass spectrometry-based experiments. However, distinguishing isomeric compounds and quantitative interpretation are currently limited. Therefore, we created Feature-based Molecular Networking (FBMN) as a new analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure. FBMN leverages feature detection and alignment tools to enhance quantitative analyses and isomer distinction, including from ion-mobility spectrometry experiments, in molecular networks.

bioinformatics↗

bioRxiv2019

Metabolome-informed microbiome analysis refines metadata classifications and reveals unexpected medication transfer in captive cheetahs

Structured AbstractEven high-quality collection and reporting of study metadata in microbiome studies can lead to various forms of inadvertently missing or mischaracterized information that can alter the interpretation or outcome of the studies, especially with non-model organisms. Metabolomic profiling of fecal microbiome samples can provide empirical insight into unanticipated confounding factors that are not possible to obtain even from detailed care records. We illustrate this point using data from cheetahs from the San Diego Zoo Safari Park. The metabolomic characterization indicated that one cheetah had to be moved from the non-antibiotic-exposed to the antibiotic-exposed group. The detection of the antibiotic in this second cheetah was likely due to grooming interactions with the cheetah that was administered antibiotics. Similarly, because transit time for stool is variable, early fecal samples within the first few days of antibiotic prescription do not all contain detectable antibiotics. Therefore, the microbiome is not affected by the antibiotics at those time points. These insights significantly altered the way the samples were grouped for analysis (antibiotic vs no antibiotic), and the subsequent understanding of the effect of the antibiotics on the cheetah microbiome. Metabolomics also revealed information about numerous other medications, and provided unexpected dietary insights that in turn improved our understanding of the molecular patterns on the impact on the community microbial structure. These results suggest that untargeted metabolomics data provide empirical evidence to correct records of non-model organisms in captivity, although we also expect these methods will be appropriate for experimental conditions typical in human studies.\n\nImportanceMetabolome-informed analyses can enhance omics studies by enabling the correct partitioning of samples by identifying hidden confounders inadvertently misrepresented or omitted from carefully curated metadata. We demonstrate the utility of metabolomics in a study characterizing the microbiome associated with liver disease in cheetahs. Metabolome-informed reinterpretation of metagenome and metabolome profiles factored in an unexpected transfer of antibiotics preventing misinterpretation of the data. Our work suggests that untargeted metabolomics can be used to verify, augment, and correct sample metadata to support improved grouping of sample data for microbiome analyses, here for non-model organisms in captivity. However, the techniques also suggest a path forward for correcting clinical information in human studies to enable higher-precision analyses.

systems biology↗

bioRxiv2019

Repository-scale Co- and Re-analysis of Tandem Mass Spectrometry Data

Metabolomics data are difficult to find and reuse, even in public repositories. We, therefore, developed the Reanalysis of Data User (ReDU) interface (https://redu.ucsd.edu/), a community- and data-driven approach that solves this problem at the repository scale. ReDU enables public data discovery and co- or re-analysis via uniformly formatted, publicly available MS/MS data and metadata in the Global Natural Product Social Molecular Networking Platform (GNPS), consistent with findable, accessible, interoperable, and reusable (FAIR) principles.1

bioinformatics↗

bioRxiv2018

Untargeted Mass Spectrometry-Based Metabolomics Tracks Molecular Changes in Raw and Processed Foods and Beverages

A major aspect of our daily lives is the need to acquire, store and prepare our food. Storage and preparation can have drastic effects on the compositional chemistry of our foods, but we have a limited understanding of the temporal nature of processes such as storage, spoilage, fermentation and brewing on the chemistry of the foods we eat. Here, we performed a temporal analysis of the chemical changes in foods during common household preparations using untargeted mass spectrometry and novel data analysis approaches. Common treatments of foods such as home fermentation of yogurt, brewing of tea, spoilage of meats and ripening of tomatoes altered the chemical makeup through time, through both chemical and biological processes. For example, brewing tea altered its composition by increasing the diversity of molecules, but this change was halted after 4 min of brewing. The results indicate that this is largely due to differential extraction of the material from the tea and not modification of the molecules during the brewing process. This is in contrast to the preparation of yogurt from milk, spoilage of meat and the ripening of tomatoes where biological transformations directly altered the foods molecular composition. Comprehensive assessment of chemical changes using multivariate statistics showed the varied impacts of the different food treatments, while analysis of individual chemical changes show specific alterations of chemical families in the different food types. The methods developed here represent novel approaches to studying the changes in food chemistry that can reveal global alterations in chemical profiles and specific transformations at the chemical level.\n\nO_LSTHighlightsC_LSTO_LIWe created a reference data set for tomato, milk to yogurt, tea, coffee, turkey and beef.\nC_LIO_LIWe show that normal preparation and handling affects the molecular make-up.\nC_LIO_LITea preparation is largely driven by differential extraction.\nC_LIO_LIFormation of yogurt involves chemical transformations.\nC_LIO_LIThe majority of meat molecules are not altered in 5 days at room temperature.\nC_LI

biochemistry↗