Julia M. Gauglitz

Marine diazotrophic cyanobacteria play a crucial role in oceanic nitrogen cycling, supporting primary production and ecosystem balance. Crocosphaera watsonii WH8501 exemplifies this capability by temporally separating photosynthesis and diazotrophy. To investigate the regulatory mechanisms underlying this process, we employed mass spectrometry-based proteomics in a high-resolution diel culturing experiment, revealing tightly coordinated protein abundance patterns. Unsupervised clustering of diel protein abundance profiles identified six groups of co-regulated proteins characterized by distinct temporal expression patterns and functional categories. Cluster 1 contained nitrogen fixation and amino acid biosynthesis proteins that peaked at night, while cluster 2 included glycogen metabolism and photosynthetic dark reaction proteins that were most abundant during the night and day-night transition, supporting carbon consumption and energy production. Clusters 3 and 4 were enriched in proteins involved in photosynthesis, cell division, and lipid synthesis, with maximal abundance around midday. Cluster 5 comprised peptide biosynthesis proteins that peaked later in the day and may facilitate nitrogenase complex formation. Cluster 6, expressed during the day-night transition, revealed coordinated nitrogenase assembly, with Fe–S cluster proteins preceding peak nitrogenase iron protein abundance, suggesting a temporally ordered sequence of enzyme maturation. Distinct temporal patterns in iron trafficking to heme and Fe–S cluster biosynthetic systems further indicated tightly regulated allocation of iron to metalloproteins across the diel cycle. Together, these results reveal the intricate temporal coordination that enables Crocosphaera to balance nitrogen fixation and photosynthesis within a single cell and underscore the central role of diel regulation in sustaining these globally important processes. IMPORTANCE: Marine diazotrophic cyanobacteria play an essential role in global nitrogen cycling and ocean productivity. In this study, we investigate the temporal regulation of Crocosphaera watsonii metabolism using high-resolution proteomics under diel culturing conditions. Our analysis resolves six distinct clusters of protein abundance patterns across the light/dark cycle, reflecting intricate coordination of nitrogen fixation, photosynthesis, carbon allocation, and iron homeostasis. Notably, we identify temporally ordered assembly of nitrogenase subunits, including precursory expression of Fe–S cluster proteins and indications of tightly controlled iron trafficking systems. These findings provide new mechanistic insight into how unicellular diazotrophs synchronize key metabolic processes under natural diel cycles.

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.

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.

Untargeted tandem mass spectrometry (MS/MS)-based metabolomics enables broad characterization of small molecules in complex samples, yet the majority of spectra in a typical experiment remain unannotated, limiting biological interpretation. Reference data-driven (RDD) metabolomics addresses this gap by contextualizing spectra through comparison to curated, metadata-annotated reference datasets, allowing inference of spectrum origins without requiring exact structural identification. Here, we present an open-source RDD metabolomics platform comprising a user-friendly web application and a Python software package that perform RDD analyses directly from molecular networking outputs generated by GNPS. The tools support visualization and statistical analysis of RDD results, including interactive bar plots, heat maps, principal component analysis, and Sankey diagrams. We illustrate the approach using a hierarchical reference dataset of 3,500 food items to derive dietary patterns from stool metabolomics data of omnivore and vegan participants. The analysis reveals clear dietary group separation, demonstrating how RDD metabolomics can extract biologically meaningful patterns from otherwise unannotated spectra. Thus, the RDD metabolomics platform removes technical barriers for the metabolomics community to adopting reference data-driven analysis, with the functionality freely available at https://github.com/bittremieuxlab/gnps-rdd and https://gnps-rdd.streamlit.app/ .

Foods contain thousands of chemical constituents beyond macronutrients, including bioactive metabolites, processing by-products, and contaminants that remain poorly characterized. The Periodic Table of Food Initiative (PTFI) is establishing a standardized global reference for food composition using untargeted mass spectrometry. We analyzed the first PTFI release (∼24,000 molecular features across 500 foods) by linking annotated and unannotated signals to curated databases of pharmaceuticals, agrochemicals, food contact chemicals, and natural products. Annotated compounds revealed characteristic chemical patterns across food groups, while unannotated features exposed xenobiotic signatures and potential contamination pathways. A taxonomy-aware search identified unexpected natural products, such as biochanin A and phlorizin produced by Canada thistle. Together, these analyses show how agricultural practices, environmental exposures, and processing shape food chemistry and highlight the value of food metabolomics for advancing a One Health understanding of the molecular connections between the environment, food systems, and human health.

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.

Hepatic encephalopathy (HE) is a common complication of advanced liver disease causing brain dysfunction. This is likely due to the accumulation of unfiltered toxins within the bloodstream. A known risk factor for developing or worsening HE is the placement of a transjugular intrahepatic portosystemic shunt (TIPS), which connects the pre-hepatic and post-hepatic circulation allowing some blood to bypass the dysfunctional liver and decreases portal hypertension. To better understand the pathophysiology of post-TIPS HE, we conducted a multi-center prospective cohort study employing metabolomic analyses on hepatic vein and peripheral vein blood samples from participants with cirrhosis undergoing elective TIPS placement, measuring chemical modifications and changes in concentrations of metabolites resulting from TIPS placement. In doing so, we identified numerous alterations in metabolites, including bile acids, glycerophosphocholines, and bilirubins possibly implicated in the development and severity of HE.

Human milk is the optimal infant nutrition. However, although 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, and industrial sources, such as plasticizers, cosmetics, 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 newborn's 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.

The chemistry of indoor surfaces and the role of microbes in shaping and responding to that chemistry are largely unexplored. We found that, over 1 month, people’s presence and activities profoundly reshaped the chemistry of a house. Molecules associated with eating/cooking, bathroom use, and personal care were found throughout the entire house, while molecules associated with medications, outdoor biocides, and microbially derived compounds were distributed in a location-dependent manner. The house and its microbial occupants, in turn, also introduced chemical transformations such as oxidation and transformations of foodborne molecules. The awareness of and the ability to observe the molecular changes introduced by people should influence future building designs.

The first week after birth is a critical time for the establishment of microbial communities for infants. Preterm infants face unique environmental impacts on their newly acquired microbiomes, including increased incidence of cesarean section delivery and exposure to antibiotics as well as delayed enteral feeding and reduced human interaction during their intensive care unit stay. Using contextualized paired metabolomics and 16S sequencing data, the development of the gut, skin, and oral microbiomes of infants is profiled daily for the first week after birth, and it is found that the skin microbiome appears robust to early life perturbation, while direct exposure of infants to antibiotics, rather than presumed maternal transmission, delays microbiome development and prevents the early differentiation based on body site regardless of delivery mode. Metabolomic analyses identify the development of all gut metabolomes of preterm infants toward full‐term infant profiles, but a significant increase of primary bile acid metabolism only in the non‐antibiotic treated vaginally birthed late preterm infants. This study provides a framework for future multi‐omic, multibody site analyses on these high‐risk preterm infant populations and suggests opportunities for monitoring and intervention, with infant antibiotic exposure as the primary driver of delays in microbiome development.