Proteomic and Lipidomic Plasma Evaluations Reveal Biomarkers for Domoic Acid Toxicosis in California Sea Lions.

Domoic acid is a neurotoxin secreted by the marine diatom genus, Pseudo-nitzschia, during toxic algal bloom events. California sea lions ( Zalophus californianus ) are exposed to domoic acid through ingestion of fish that feed on toxic diatoms, resulting in a domoic acid toxicosis (DAT), which can vary from mild to fatal. Sea lions with mild disease can be treated if toxicosis is detected early after exposure, therefore, rapid diagnosis of DAT is essential but also challenging. In this work, we performed multi-omics analyses, specifically proteomic and lipidomic, on blood samples from 31 California sea lions. Fourteen sea lions were diagnosed with DAT based on clinical signs and postmortem histological examination of brain tissue, and 17 had no evidence of DAT. Proteomic analyses revealed three apolipoproteins with statistically significant lower abundance in the DAT individuals compared to the non-DAT individuals. These proteins are known to transport lipids in the blood. Lipidomic analyses highlighted 29 lipid levels that were statistically different in the DAT versus non-DAT comparison, 28 of which were downregulated while only one was upregulated. Furthermore, of the 28 downregulated lipids, 15 were triglycerides, illustrating their connection with the perturbed apolipoproteins and showing their potential for use in rapid DAT diagnoses. SYNOPSIS: Multi-omics evaluations reveal blood apolipoproteins and triglycerides are altered in domoic acid toxicosis in California sea lions.

Investigating mouse hepatic lipidome dysregulation following exposure to emerging per- and polyfluoroalkyl substances (PFAS).

Per- and polyfluoroalkyl substances (PFAS) are environmental pollutants that have been associated with adverse health effects including liver damage, decreased vaccine responses, cancer, developmental toxicity, thyroid dysfunction, and elevated cholesterol. The specific molecular mechanisms impacted by PFAS exposure to cause these health effects remain poorly understood, however there is some evidence of lipid dysregulation. Thus, lipidomic studies that go beyond clinical triglyceride and cholesterol tests are greatly needed to investigate these perturbations. Here, we have utilized a platform coupling liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) separations to simultaneously evaluate PFAS bioaccumulation and lipid metabolism disruptions. For the study, liver samples collected from C57BL/6 mice exposed to either of the emerging PFAS hexafluoropropylene oxide dimer acid (HFPO-DA or "GenX") or Nafion byproduct 2 (NBP2) were assessed. Sex-specific differences in PFAS accumulation and liver size were observed for both PFAS, in addition to disturbed hepatic liver lipidomic profiles. Interestingly, GenX resulted in less hepatic bioaccumulation than NBP2 yet gave a higher number of significantly altered lipids when compared to the control group, implying that the accumulation of substances in the liver may not be a reliable measure of the substance's capacity to disrupt the liver's natural metabolic processes. Specifically, phosphatidylglycerols, phosphatidylinositols, and various specific fatty acyls were greatly impacted, indicating alteration of inflammation, oxidative stress, and cellular signaling processes due to emerging PFAS exposure. Overall, these results provide valuable insight into the liver bioaccumulation and molecular mechanisms of GenX- and NBP2-induced hepatotoxicity.

Metagenomic, metabolomic, and lipidomic shifts associated with fecal microbiota transplantation for recurrent Clostridioides difficile infection.

Recurrent C. difficile infection (rCDI) is an urgent public health threat for which the last resort and lifesaving treatment is a fecal microbiota transplant (FMT). However, the exact mechanisms which mediate a successful FMT are not well understood. Here we use longitudinal stool samples collected from patients undergoing FMT to evaluate changes in the microbiome, metabolome, and lipidome after successful FMTs. We show changes in the abundance of many lipids, specifically acylcarnitines and bile acids, in response to FMT. These changes correlate with Enterobacteriaceae, which encode carnitine metabolism genes, and Lachnospiraceae, which encode bile salt hydrolases and baiA genes. LC-IMS-MS revealed a shift from microbial conjugation of primary bile acids pre-FMT to secondary bile acids post-FMT. Here we define the structural and functional changes in successful FMTs. This information will help guide targeted Live Biotherapeutic Product development for the treatment of rCDI and other intestinal diseases.

Deciphering ApoE Genotype-Driven Proteomic and Lipidomic Alterations in Alzheimer's Disease Across Distinct Brain Regions.

Alzheimer's disease (AD) is a neurodegenerative disease with a complex etiology influenced by confounding factors such as genetic polymorphisms, age, sex, and race. Traditionally, AD research has not prioritized these influences, resulting in dramatically skewed cohorts such as three times the number of Apolipoprotein E (APOE) ε4-allele carriers in AD relative to healthy cohorts. Thus, the resulting molecular changes in AD have previously been complicated by the influence of apolipoprotein E disparities. To explore how apolipoprotein E polymorphism influences AD progression, 62 post-mortem patients consisting of 33 AD and 29 controls (Ctrl) were studied to balance the number of ε4-allele carriers and facilitate a molecular comparison of the apolipoprotein E genotype. Lipid and protein perturbations were assessed across AD diagnosed brains compared to Ctrl brains, ε4 allele carriers (APOE4+ for those carrying 1 or 2 ε4s and APOE4- for non-ε4 carriers), and differences in ε3ε3 and ε3ε4 Ctrl brains across two brain regions (frontal cortex (FCX) and cerebellum (CBM)). The region-specific influences of apolipoprotein E on AD mechanisms showcased mitochondrial dysfunction and cell proteostasis at the core of AD pathophysiology in the post-mortem brains, indicating these two processes may be influenced by genotypic differences and brain morphology.

From big data to big insights: statistical and bioinformatic approaches for exploring the lipidome.

The goal of lipidomic studies is to provide a broad characterization of cellular lipids present and changing in a sample of interest. Recent lipidomic research has significantly contributed to revealing the multifaceted roles that lipids play in fundamental cellular processes, including signaling, energy storage, and structural support. Furthermore, these findings have shed light on how lipids dynamically respond to various perturbations. Continued advancement in analytical techniques has also led to improved abilities to detect and identify novel lipid species, resulting in increasingly large datasets. Statistical analysis of these datasets can be challenging not only because of their vast size, but also because of the highly correlated data structure that exists due to many lipids belonging to the same metabolic or regulatory pathways. Interpretation of these lipidomic datasets is also hindered by a lack of current biological knowledge for the individual lipids. These limitations can therefore make lipidomic data analysis a daunting task. To address these difficulties and shed light on opportunities and also weaknesses in current tools, we have assembled this review. Here, we illustrate common statistical approaches for finding patterns in lipidomic datasets, including univariate hypothesis testing, unsupervised clustering, supervised classification modeling, and deep learning approaches. We then describe various bioinformatic tools often used to biologically contextualize results of interest. Overall, this review provides a framework for guiding lipidomic data analysis to promote a greater assessment of lipidomic results, while understanding potential advantages and weaknesses along the way.

Reverse metabolomics for the discovery of chemical structures from humans.

Determining the structure and phenotypic context of molecules detected in untargeted metabolomics experiments remains challenging. Here we present reverse metabolomics as a discovery strategy, whereby tandem mass spectrometry spectra acquired from newly synthesized compounds are searched for in public metabolomics datasets to uncover phenotypic associations. To demonstrate the concept, we broadly synthesized and explored multiple classes of metabolites in humans, including N-acyl amides, fatty acid esters of hydroxy fatty acids, bile acid esters and conjugated bile acids. Using repository-scale analysis1,2, we discovered that some conjugated bile acids are associated with inflammatory bowel disease (IBD). Validation using four distinct human IBD cohorts showed that cholic acids conjugated to Glu, Ile/Leu, Phe, Thr, Trp or Tyr are increased in Crohn's disease. Several of these compounds and related structures affected pathways associated with IBD, such as interferon-γ production in CD4+ T cells3 and agonism of the pregnane X receptor4. Culture of bacteria belonging to the Bifidobacterium, Clostridium and Enterococcus genera produced these bile amidates. Because searching repositories with tandem mass spectrometry spectra has only recently become possible, this reverse metabolomics approach can now be used as a general strategy to discover other molecules from human and animal ecosystems.

Characterizing Adeno-Associated Virus Capsids with Both Denaturing and Intact Analysis Methods.

Adeno-associated virus (AAV) capsids are among the leading gene delivery platforms used to treat a vast array of human diseases and conditions. AAVs exist in a variety of serotypes due to differences in viral protein (VP) sequences with distinct serotypes targeting specific cells and tissues. As the utility of AAVs in gene therapy increases, ensuring their specific composition is imperative for the correct targeting and gene delivery. From a quality control perspective, current analytical tools are limited in their selectivity for viral protein (VP) subunits due to their sequence similarities, instrumental difficulties in assessing the large molecular weights of intact capsids, and the uncertainty in distinguishing empty and filled capsids. To address these challenges, we combined two distinct analytical workflows that assess the intact capsids and VP subunits separately. First, a selective temporal overview of resonant ion (STORI)-based charge detection-mass spectrometry (CD-MS) was applied for characterization of the intact capsids. Liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) separations were then used for the capsid denaturing measurements. This multimethod combination was applied to three AAV serotypes (AAV2, AAV6, and AAV8) to evaluate their intact empty and filled capsid ratios and then examine the distinct VP sequences and modifications present.

Assessing How Chemical Exposures Affect Human Health.

Measuring chemical exposure is extremely challenging due to the range and number of anthropogenic molecules encountered in our daily lives, as well as their complex transformations throughout the body. To broadly characterize how chemical exposures influence human health, a combination of genomic, transcriptomic, proteomic, endogenous metabolomic, and xenobiotic measurements must be performed. However, while genomic, transcriptomic, and proteomic analyses have rapidly progressed over the last two decades, advancements in instrumentation and computations for nontargeted xenobiotic and endogenous small molecule measurements are still greatly needed.

Uncovering per- and polyfluoroalkyl substances (PFAS) with nontargeted ion mobility spectrometry-mass spectrometry analyses.

Because of environmental and health concerns, legacy per- and polyfluoroalkyl substances (PFAS) have been voluntarily phased out, and thousands of emerging PFAS introduced as replacements. Traditional analytical methods target a limited number of mainly legacy PFAS; therefore, many species are not routinely assessed in the environment. Nontargeted approaches using high-resolution mass spectrometry methods have therefore been used to detect and characterize unknown PFAS. However, their ability to elucidate chemical structures relies on generation of informative fragments, and many low concentration species are not fragmented in typical data-dependent acquisition approaches. Here, a data-independent method leveraging ion mobility spectrometry (IMS) and size-dependent fragmentation was developed and applied to characterize aquatic passive samplers deployed near a North Carolina fluorochemical manufacturer. From the study, 11 PFAS structures for various per- and polyfluorinated ether sulfonic acids and multiheaded perfluorinated ether acids were elucidated in addition to 36 known PFAS. Eight of these species were previously unreported in environmental media, and three suspected species were validated.

Using Multidimensional Separations to Distinguish Isomeric Amino Acid-Bile Acid Conjugates and Assess Their Presence and Perturbations in Model Systems.

Bile acids play key roles in nutrient uptake, inflammation, signaling, and microbiome composition. While previous bile acid analyses have primarily focused on profiling 5 canonical primary and secondary bile acids and their glycine and taurine amino acid-bile acid (AA-BA) conjugates, recent studies suggest that many other microbial conjugated bile acids (or MCBAs) exist. MCBAs are produced by the gut microbiota and serve as biomarkers, providing information about early disease onset and gut health. Here we analyzed 8 core bile acids synthetically conjugated with 22 proteinogenic and nonproteogenic amino acids totaling 176 MCBAs. Since many of the conjugates were isomeric and only 42 different m/z values resulted from the 176 MCBAs, a platform coupling liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) was used for their separation. Their molecular characteristics were then used to create an in-house extended bile acid library for a combined total of 182 unique compounds. Additionally, ∼250 rare bile acid extracts were also assessed to provide additional resources for bile acid profiling and identification. This library was then applied to healthy mice dosed with antibiotics and humans having fecal microbiota transplantation (FMT) to assess the MCBA presence and changes in the gut before and after each perturbation.

Assessing Per- and Polyfluoroalkyl Substances (PFAS) in Fish Fillet Using Non-Targeted Analyses.

Per- and polyfluoroalkyl substances (PFAS) are a class of thousands of man-made chemicals that are persistent and highly stable in the environment. The diverse structures of PFAS give them different chemical properties that influence their solubility in different environmental matrices and biological tissues. PFAS in drinking water have been extensively studied, but information on their presence in fish and other exposure routes is limited. To address this, a non-targeted analysis using liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) was performed to evaluate PFAS in fish fillets from in central North Carolina and compare with PFAS data from previously published water. A total of 22 different PFAS were detected in the fillets, including only 4 of the PFAS reported in water. Both more PFAS types and higher concentrations were observed in fish caught near a known PFAS point-source compared to those from a reservoir used for drinking water and recreation. Median fillet PFOS levels were 54 ppb in fish closest to the point source and 14-20 ppb in fish from the reservoir. Thus, future PFAS monitoring should include both targeted and non-targeted analyses of both water and fish to increase understanding of human exposure risks and ecosystem impacts. SYNOPSIS: Fish fillet samples were collected from five sites in North Carolina. PFAS were detected in all samples and differences in analytes and abundances were observed at the different sites. GRAPHICAL ABSTRACT: For use in table of contents only.

Combining native mass spectrometry and lipidomics to uncover specific membrane protein-lipid interactions from natural lipid sources.

While it is known that lipids play an essential role in regulating membrane protein structure and function, it remains challenging to identify specific protein-lipid interactions. Here, we present an innovative approach that combines native mass spectrometry (MS) and lipidomics to identify lipids retained by membrane proteins from natural lipid extracts. Our results reveal that the bacterial ammonia channel (AmtB) enriches specific cardiolipin (CDL) and phosphatidylethanolamine (PE) from natural headgroup extracts. When the two extracts are mixed, AmtB retains more species, wherein selectivity is tuned to bias headgroup selection. Using a series of natural headgroup extracts, we show TRAAK, a two-pore domain K+ channel (K2P), retains specific acyl chains that is independent of the headgroup. A brain polar lipid extract was then combined with the K2Ps, TRAAK and TREK2, to understand lipid specificity. More than a hundred lipids demonstrated affinity for each protein, and both channels were found to retain specific fatty acids and lysophospholipids known to stimulate channel activity, even after several column washes. Natural lipid extracts provide the unique opportunity to not only present natural lipid diversity to purified membrane proteins but also identify lipids that may be important for membrane protein structure and function.

Aggregated Molecular Phenotype Scores: Enhancing Assessment and Visualization of Mass Spectrometry Imaging Data for Tissue-Based Diagnostics.

Mass spectrometry imaging (MSI) has gained increasing popularity for tissue-based diagnostics due to its ability to identify and visualize molecular characteristics unique to different phenotypes within heterogeneous samples. Data from MSI experiments are often assessed and visualized using various supervised and unsupervised statistical approaches. However, these approaches tend to fall short in identifying and concisely visualizing subtle, phenotype-relevant molecular changes. To address these shortcomings, we developed aggregated molecular phenotype (AMP) scores. AMP scores are generated using an ensemble machine learning approach to first select features differentiating phenotypes, weight the features using logistic regression, and combine the weights and feature abundances. AMP scores are then scaled between 0 and 1, with lower values generally corresponding to class 1 phenotypes (typically control) and higher scores relating to class 2 phenotypes. AMP scores, therefore, allow the evaluation of multiple features simultaneously and showcase the degree to which these features correlate with various phenotypes. Due to the ensembled approach, AMP scores are able to overcome limitations associated with individual models, leading to high diagnostic accuracy and interpretability. Here, AMP score performance was evaluated using metabolomic data collected from desorption electrospray ionization MSI. Initial comparisons of cancerous human tissues to their normal or benign counterparts illustrated that AMP scores distinguished phenotypes with high accuracy, sensitivity, and specificity. Furthermore, when combined with spatial coordinates, AMP scores allow visualization of tissue sections in one map with distinguished phenotypic borders, highlighting their diagnostic utility.

Integrative Chemical-Biological Grouping of Complex High Production Volume Substances from Lower Olefin Manufacturing Streams.

Human cell-based test methods can be used to evaluate potential hazards of mixtures and products of petroleum refining ("unknown or variable composition, complex reaction products, or biological materials" substances, UVCBs). Analyses of bioactivity and detailed chemical characterization of petroleum UVCBs were used separately for grouping these substances; a combination of the approaches has not been undertaken. Therefore, we used a case example of representative high production volume categories of petroleum UVCBs, 25 lower olefin substances from low benzene naphtha and resin oils categories, to determine whether existing manufacturing-based category grouping can be supported. We collected two types of data: nontarget ion mobility spectrometry-mass spectrometry of both neat substances and their organic extracts and in vitro bioactivity of the organic extracts in five human cell types: umbilical vein endothelial cells and induced pluripotent stem cell-derived hepatocytes, endothelial cells, neurons, and cardiomyocytes. We found that while similarity in composition and bioactivity can be observed for some substances, existing categories are largely heterogeneous. Strong relationships between composition and bioactivity were observed, and individual constituents that determine these associations were identified. Overall, this study showed a promising approach that combines chemical composition and bioactivity data to better characterize the variability within manufacturing categories of petroleum UVCBs.

Utilizing Aggregated Molecular Phenotype (AMP) Scores to Visualize Simultaneous Molecular Changes in Mass Spectrometry Imaging Data.

Mass spectrometry imaging (MSI) has gained increasing popularity for tissue-based diagnostics due to its ability to identify and visualize molecular characteristics unique to different phenotypes within heterogeneous samples. Data from MSI experiments are often visualized using single ion images and further analyzed using machine learning and multivariate statistics to identify m/z features of interest and create predictive models for phenotypic classification. However, often only a single molecule or m/z feature is visualized per ion image, and mainly categorical classifications are provided from the predictive models. As an alternative approach, we developed an aggregated molecular phenotype (AMP) scoring system. AMP scores are generated using an ensemble machine learning approach to first select features differentiating phenotypes, weight the features using logistic regression, and combine the weights and feature abundances. AMP scores are then scaled between 0 and 1, with lower values generally corresponding to class 1 phenotypes (typically control) and higher scores relating to class 2 phenotypes. AMP scores therefore allow the evaluation of multiple features simultaneously and showcase the degree to which these features correlate with various phenotypes, leading to high diagnostic accuracy and interpretability of predictive models. Here, AMP score performance was evaluated using metabolomic data collected from desorption electrospray ionization (DESI) MSI. Initial comparisons of cancerous human tissues to normal or benign counterparts illustrated that AMP scores distinguished phenotypes with high accuracy, sensitivity, and specificity. Furthermore, when combined with spatial coordinates, AMP scores allow visualization of tissue sections in one map with distinguished phenotypic borders, highlighting their diagnostic utility.

PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements.

Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.

Bile salt hydrolases shape the bile acid landscape and restrict Clostridioides difficile growth in the murine gut.

Bile acids (BAs) mediate the crosstalk between human and microbial cells and influence diseases including Clostridioides difficile infection (CDI). While bile salt hydrolases (BSHs) shape the BA pool by deconjugating conjugated BAs, the basis for their substrate selectivity and impact on C. difficile remain elusive. Here we survey the diversity of BSHs in the gut commensals Lactobacillaceae, which are commonly used as probiotics, and other members of the human gut microbiome. We structurally pinpoint a loop that predicts BSH preferences for either glycine or taurine substrates. BSHs with varying specificities were shown to restrict C. difficile spore germination and growth in vitro and colonization in pre-clinical in vivo models of CDI. Furthermore, BSHs reshape the pool of microbial conjugated bile acids (MCBAs) in the murine gut, and these MCBAs can further restrict C. difficile virulence in vitro. The recognition of conjugated BAs by BSHs defines the resulting BA pool, including the expansive MCBAs. This work provides insights into the structural basis of BSH mechanisms that shape the BA landscape and promote colonization resistance against C. difficile.