Publications by authors named "James N Dodds"

Understanding how a macromolecule's primary sequence governs its conformational landscape is crucial for elucidating its function, yet these design principles are still emerging for macromolecules with intrinsic disorder. Herein, we introduce a high-throughput workflow that implements a practical colorimetric conformational assay, introduces a semi-automated sequencing protocol using MALDI-MS/MS, and develops a generalizable sequence-structure algorithm. Using a model system of 20mer peptidomimetics containing polar glycine and hydrophobic -butylglycine residues, we identified nine classifications of conformational disorder and isolated 122 unique sequences across varied compositions and conformations.

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Nontargeted analysis (NTA) is increasingly utilized for its ability to identify key molecular features beyond known targets in complex samples. NTA is particularly advantageous in exploratory studies aimed at identifying phenotype-associated features or molecules able to classify various sample types. However, implementing NTA involves extensive data analyses and labor-intensive annotations.

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Per- and polyfluoroalkyl substances (PFAS) are a class of thousands of man-made chemicals that are persistent and highly stable in the environment. Fish consumption has been identified as a key route of PFAS exposure for humans. However, routine fish monitoring targets only a handful of PFAS, and non-targeted analyses have largely only evaluated fish from heavily PFAS-impacted waters.

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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.

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Automation is dramatically changing the nature of laboratory life science. Robotic lab hardware that can perform manual operations with greater speed, endurance, and reproducibility opens an avenue for faster scientific discovery with less time spent on laborious repetitive tasks. A major bottleneck remains in integrating cutting-edge laboratory equipment into automated workflows, notably specialized analytical equipment, which is designed for human usage.

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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.

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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.

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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.

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Per- and polyfluoroalkyl substances (PFAS) are a class of thousands of man-made chemicals that are persistent and highly stable in the environment. Fish consumption has been identified as a key route of PFAS exposure for humans. However, routine fish monitoring targets only a handful of PFAS, and non-targeted analyses have largely only evaluated fish from heavily PFAS-impacted waters.

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Article Synopsis
  • Traditional methods like gas chromatography-mass spectrometry (GC-MS) are slow and struggle to separate similar compounds in crude oil analysis, particularly during urgent situations like spills.
  • Recent innovations in high-resolution techniques, particularly ion mobility spectrometry-mass spectrometry (IMS-MS), show promise for faster and more effective crude oil fingerprinting and can differentiate closely related hydrocarbon molecules.
  • In a study involving 195 crude oil samples, IMS-MS was used for detailed chemical analysis, resulting in insights that improved the grouping of oils by origin and identified new biomarkers for more efficient exposure characterization.
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Photooxidation can alter the environmental fate and effects of spilled oil. To better understand this process, oil slicks were generated on seawater mesocosms and exposed to sunlight for 8 days. The molecular composition of seawater under irradiated and non-irradiated oil slicks was characterized using ion mobility spectrometry-mass spectrometry and polyaromatic hydrocarbons analyses.

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The identification of xenobiotics in nontargeted metabolomic analyses is a vital step in understanding human exposure. Xenobiotic metabolism, transformation, excretion, and coexistence with other endogenous molecules, however, greatly complicate the interpretation of features detected in nontargeted studies. While mass spectrometry (MS)-based platforms are commonly used in metabolomic measurements, deconvoluting endogenous metabolites from xenobiotics is also often challenged by the lack of xenobiotic parent and metabolite standards as well as the numerous isomers possible for each small molecule / feature.

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While decades of technical and analytical advancements have been utilized to discover novel lipid species, increase speciation, and evaluate localized lipid dysregulation at subtissue, cellular, and subcellular levels, many challenges still exist. One limitation is that the acquisition of both in-depth spatial information and comprehensive lipid speciation is extremely difficult, especially when biological material is limited or lipids are at low abundance. In neuroscience, for example, it is often desired to focus on only one brain region or subregion, which can be well under a square millimeter for rodents.

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Isomeric peptide analyses are an analytical challenge of great importance to therapeutic monoclonal antibody and other biotherapeutic product development workflows. Aspartic acid (Asp, D) to isoaspartic acid (isoAsp, isoD) isomerization is a critical quality attribute (CQA) that requires careful control, monitoring, and quantitation during the drug discovery and production processes. While the formation of isoAsp has been implicated in a variety of disease states such as autoimmune diseases and several types of cancer, it is also understood that the formation of isoAsp results in a structural change impacting efficacy, potency, and immunogenic properties, all of which are undesirable.

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Article Synopsis
  • The study focuses on identifying substances known as UVCBs, which are complex mixtures that arise from petroleum refining, highlighting the challenges due to their chemical complexity and variability.
  • Researchers employed ion mobility spectrometry coupled with mass spectrometry (IMS-MS) to analyze the chemical composition of six petroleum products from three different manufacturing categories.
  • The findings reveal that IMS-MS can yield detailed compositional data crucial for understanding the variability of petroleum UVCBs, which is important for assessing hazards and risks related to these substances.
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While the combination of liquid chromatography and tandem mass spectrometry (LC-MS/MS) is commonly used for feature annotation in untargeted omics experiments, ensuring these prioritized features originate from endogenous metabolism remains challenging. Isotopologue workflows, such as isotopic ratio outlier analysis (IROA), mass isotopomer ratio analysis of U-C labeled extracts (MIRACLE), and credentialing incorporate isotopic labels directly into metabolic precursors, guaranteeing that all features of interest are unequivocal byproducts of cellular metabolism. Furthermore, comprehensive separation and annotation of small molecules continue to challenge the metabolomics field, particularly for isomeric systems.

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Detection and diagnosis of congenital disorders is the principal aim of newborn screening (NBS) programs worldwide. Mass spectrometry (MS) has become the preferred primary testing method for high-throughput NBS sampling because of its speed and selectivity. However, the ever-increasing list of NBS biomarkers included in expanding panels creates unique analytical challenges for multiplexed MS assays due to isobaric/isomeric overlap and chimeric fragmentation spectra.

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Persistent organic pollutants (POPs) are xenobiotic chemicals of global concern due to their long-range transport capabilities, persistence, ability to bioaccumulate, and potential to have negative effects on human health and the environment. Identifying POPs in both the environment and human body is therefore essential for assessing potential health risks, but their diverse range of chemical classes challenge analytical techniques. Currently, platforms coupling chromatography approaches with mass spectrometry (MS) are the most common analytical methods employed to evaluate both parent POPs and their respective metabolites and/or degradants in samples ranging from d rinking water to biofluids.

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Despite the significant progress in both scientific understanding and regulations, the safety of agricultural pesticides continues to be called into question. The need for complementary analytics to identify dysregulation events associated with chemical exposure and leverage this information to predict biological responses remains. Here, we present a platform that combines a model organ-on-chip neurovascular unit (NVU) with targeted mass spectrometry (MS) and electrochemical analysis to assess the impact of organophosphate (OP) exposure on blood-brain barrier (BBB) function.

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Aqueous film-forming foams (AFFF) are mixtures formulated with numerous hydrocarbon- and fluoro-containing surfactants. AFFF use leads to environmental releases of unknown per- and polyfluoroalkyl substances (PFAS). AFFF composition is seldom disclosed, and their use elicits concerns from both regulatory agencies and the public because PFAS are persistent in the environment and potentially associated with adverse health effects.

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Firefighting foams contain per- and polyfluoroalkyl substances (PFAS) - a class of compounds widely used as surfactants. PFAS are persistent organic pollutants that have been reported in waterways and drinking water systems across the United States. These substances are of interest to both regulatory agencies and the general public because of their persistence in the environment and association with adverse health effects.

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Per- and polyfluoroalkyl substances (PFAS) are an ensemble of persistent organic pollutants of global interest because of their associations with adverse health outcomes. Currently, environmental PFAS pollution is prolific as a result of the widespread manufacturing of these compounds and their chemical persistence. In this work, we demonstrate the advantages of adding ion mobility spectrometry (IMS) separation to existing LC-MS workflows for PFAS analysis.

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Ion mobility spectrometry (IMS) is a widely used analytical technique providing rapid gas phase separations. IMS alone is useful, but its coupling with mass spectrometry (IMS-MS) and various front-end separation techniques has greatly increased the molecular information achievable from different omic analyses. IMS-MS analyses are specifically gaining attention for improving metabolomic, lipidomic, glycomic, proteomic and exposomic analyses by increasing measurement sensitivity (e.

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