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Proteins congregate into complexes to perform fundamental cellular functions. Phenotypic outcomes, in health and disease, are often mechanistically driven by the remodeling of protein complexes by protein-coding mutations or cellular signaling changes in response to molecular cues. Here, we present an affinity purification-mass spectrometry (APMS) proteomics protocol to quantify and visualize global changes in protein-protein interaction (PPI) networks between pairwise conditions. We describe steps for expressing affinity-tagged "bait" proteins in mammalian cells, identifying purified protein complexes, quantifying differential PPIs, and visualizing differential PPI networks. Specifically, this protocol details steps for designing affinity-tagged "bait" gene constructs, transfection, affinity purification, mass spectrometry sample preparation, data acquisition, database search, data quality control, PPI confidence scoring, cross-run normalization, statistical data analysis, and differential PPI visualization. Our protocol discusses caveats and limitations with applicability across cell types and biological areas. For complete details on the use and execution of this protocol, please refer to Bouhaddou et al. 20231.

Healthcare facilities produce millions of tons of waste annually, with a significant portion consisting of diagnostic plasticware. Here, we introduce a new detection platform that completely replaces traditional assay plates with a piece of membrane, offering a much greener and more sustainable alternative. The membrane, integrated within the portable vortex fluidic device (P-VFD), enables rapid detection of a clinically relevant protein biomarker, urinary p75ECD. This biomarker is utilized to evaluate the prognosis, disease severity, and progression of amyotrophic lateral sclerosis (ALS). This assay has a limit-of-detection (LOD) of 4.03 pg, which is comparable to the plate-based assay (2.24 pg) and has been optimised through a full factorial design of experiments (DOE) and response surface methodology (RSM). P-VFD has great potential in quantifying p75ECD in human biofluids and can significantly reduce the assay time to 5 min compared to the current plate-based p75ECD ELISA assay (3 days), with at least a 4.4-fold reduction in the usage of the detection antibody.

Diet is inextricably linked to human health and biological functionality. Reduced cognitive function among other health issues has been correlated with a western diet (WD) in mouse models, indicating that increases in neurodegeneration could be fueled in part by a poor diet. In this study, we use matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) to spatially map the lipidomic profiles of male and female mice that were fed a high-fat, high-sucrose WD for a period of 7 weeks. Our findings concluded that the cortex and corpus callosum showed significant lipid variation by WD in female mice, while there was little to no variation in the hippocampus, regardless of sex. On the other hand, lipid profiles were significantly affected by sex in all regions. Overall, 83 lipids were putatively identified in the mouse brain; among them, HexCer(40:1;O3) and PE(34:0) were found to have the largest statistical difference based on diet for female mice in the cortex and corpus callosum, respectively. Additional lipid changes are noted and can serve as a metric for understanding the brain's metabolomic response to changes in diet, particularly as it relates to disease.

Exposure to polychlorinated biphenyls (PCBs) is linked to neurotoxic effects. This study aims to close knowledge gaps regarding the specific modes of action of PCBs in female C57BL/6J mice (>6 weeks) orally exposed for 7 weeks to a human-relevant PCB mixture (MARBLES mix) at 0, 0.1, 1, and 6 mg/kg body weight/day. PCB and hydroxylated PCB (OH-PCBs) levels were quantified in the brain, liver, and serum; RNA sequencing was performed in the striatum, prefrontal cortex, and liver, and metabolomic analyses were performed in the striatum. Profiles of PCBs but not their hydroxylated metabolites were similar in all tissues. In the prefrontal cortex, PCB exposure activated the oxidative phosphorylation respiration pathways, while suppressing the axon guidance pathway. PCB exposure significantly changed the expression of genes associated with neurodevelopmental and neurodegenerative diseases in the striatum, impacting pathways like growth hormone synthesis and dendrite development. PCBs did not affect the striatal metabolome. In contrast to the liver, which showed activation of metabolic processes following PCB exposure and the induction of cytochrome P450 enzymes, the expression of xenobiotic processing genes was not altered by PCB exposure in either brain region. Network analysis revealed complex interactions between individual PCBs (e.g., PCB28 [2,4,4'-trichlorobiphenyl]) and their hydroxylated metabolites and specific differentially expressed genes (DEGs), underscoring the need to characterize the association between specific PCBs and DEGs. These findings enhance the understanding of PCB neurotoxic mechanisms and their potential implications for human health.

Ammonium acetate is widely used in native mass spectrometry to provide adequate ionic strength without adducting to protein ions, but different ions can preferentially stabilize or destabilize the native form of proteins in solution. The stability of bovine serum albumin (BSA) was investigated in 50 mM solutions of a variety of salts using electrospray emitters with submicron tips to desalt protein ions. The charge-state distribution of BSA is narrow (+14 to +18) in ammonium acetate (AmmAc), whereas it is much broader (+13 to +42) in solutions containing sodium acetate (NaAc), ammonium chloride (AmmCl), potassium chloride (KCl), and sodium chloride (NaCl). The average charge state and percent of unfolded protein increase in these respective solutions, indicating greater extents of protein destabilization and conformational changes. In contrast, no high charge states of either bovine carbonic anhydrase II or IgG1 were formed in AmmAc or NaCl despite their similar melting temperatures to BSA, indicating that the presence of unfolded BSA in some of these solutions is not an artifact of the electrospray ionization process. The charge states formed from the nonvolatile salt solutions do not change significantly for up to 7 min of electrospray, but higher charging occurs after 10 min, consistent with solution acidification. Formation of unfolded BSA in NaAc but not in AmmAc indicates that the cation identity, not acidification, is responsible for structural differences in these two solutions. Temperature-dependent measurements show both increased charging and aggregation at lower temperatures in NaCl:Tris than in AmmAc, consistent with lower protein stability in the former solution. These results are consistent with the order of these ions in the Hofmeister series and indicate that data on protein stability in AmmAc may not be representative of solutions containing nonvolatile salts that are directly relevant to biology.

Sorghum is an attractive feedstock for biobased fuel and chemical production because it is familiar to farmers, naturally drought tolerant, and versatile as a food, feed, and fuel crop. Although sorghum is a promising feedstock, particularly in regions that experience drought stress, little is known about how drought conditions impact the ease of conversion of sorghum to fuels and products. This study combines agronomic field trials with a high-throughput experimental pipeline to explore the field performance and liquid biofuel (bisabolene) yields resulting from three sorghum types (photosensitive forage sorghum, optimized grain sorghum, and drought-resistant grain sorghum) grown under pre- and postflowering water limitations in two different California locations. Multiple drought treatments are compared to the control, as the timing (preflowering versus postflowering) of drought stress elicits different survival strategies and corresponding impacts on yield and composition. Forage-type sorghum maintained the highest biomass yields across all irrigation conditions and locations. Glucose and xylose yields resulting from ionic liquid pretreatment and enzymatic saccharification were not significantly impacted by irrigation treatments but differed by location and genotype. However, Rhodosporidium toruloides grown on the resulting plant hydrolysates unexpectedly produced higher titers of bisabolene for drought-stressed sorghum samples regardless of genotype.

Stable isotope analysis of bone provides insight into animal foraging and allows for ecological reconstructions over time, however pre-treatment is required to isolate collagen. Pre-treatments typically consist of demineralization to remove inorganic components and/or lipid extraction to remove fats, but these protocols can differentially affect stable carbon (δ13C) and nitrogen (δ15N) isotope values depending on the chemicals, tissues, and/or species involved. Species-specific methodologies create a standard for comparability across studies and enhance understanding of collagen isolation from modern cetacean bone. Elemental analyzers coupled to isotope ratio mass spectrometers were used to measure the δ13C and δ15N values of powdered killer whale (Orcinus orca) bone that was intact (control) or subjected to one of three experimental conditions: demineralized, lipid-extracted, and both demineralized and lipid-extracted. Additionally, C:N ratios were evaluated as a proxy for collagen purity. Lastly, correlations were examined between control C:N ratios vs. historical age and control C:N ratios vs. sample characteristics. No significant differences in the δ15N values were observed for any of the experimental protocols. However, the δ13C values were significantly increased by all three experimental protocols: demineralization, lipid extraction, and both treatments combined. The most influential protocol was both demineralization and lipid extraction. Measures of the C:N ratios were also significantly lowered by demineralization and both treatments combined, indicating the material was closer to pure collagen after the treatments. Collagen purity as indicated via C:N ratio was not correlated with historical age nor sample characteristics. If only the δ15N values from killer whale bone are of interest for analysis, no pre-treatment seems necessary. If the δ13C values are of interest, samples should be both demineralized and lipid-extracted. As historical age and specimen characteristics are not correlated with sample contamination, all samples can be treated equally.

Protein aggregation is involved in many human diseases, but characterizing the sizes and shapes of intermediate oligomers (∼10-100 nm) that are important to the formation of macroscale aggregates like amyloid fibrils is a significant analytical challenge. Here, charge detection mass spectrometry (CDMS) is used to characterize individual conformational states of bovine serum albumin oligomers with up to ∼225 molecules (15 MDa). Elongated, partially folded, and globular conformational families for each oligomer can be readily distinguished based on the extent of charging. The abundances of individual conformers vary with changes in the monomer concentration or by adding aggregation inhibitors, such as SDS, heparin, or MgCl2. These results show the potential of CDMS for investigating intermediate oligomers in protein aggregation processes that are important for understanding aggregate formation and inhibition mechanisms and could accelerate formulation buffer development to prevent the aggregation of biotherapeutics.

Past functional toxicogenomic studies have indicated that genes relevant to membrane lipid synthesis are important for tolerance to the lanthanides. Moreover, previously reported imaging of patient's brains following administration of gadolinium-based contrast agents shows gadolinium lining the vessels of the brain. Taken together, these findings suggest the disruption of cytoplasmic membrane integrity as a mechanism by which lanthanides induce cytotoxicity. In the presented work we used scanning transmission electron microscopy and spatially resolved elemental spectroscopy to image the morphology and composition of gadolinium, europium, and samarium precipitates that formed on the outside of yeast cell membranes. In no sample did we find that the lanthanide contaminant had crossed the cell membrane, even in experiments using yeast mutants with disrupted genes for sphingolipid synthesis-the primary lipids found in yeast cytoplasmic membranes. Rather, we have evidence that lanthanides are co-located with phosphorus outside the yeast cells. These results lead us to hypothesize that the lanthanides scavenge or otherwise form complexes with phosphorus from the sphingophospholipid head groups in the cellular membrane, thereby compromising the structure or function of the membrane, and gaining the ability to disrupt membrane function without entering the cell.

Electrochemical interfaces are central to the function and performance of energy storage devices. Thus, the development of new methods to characterize these interfaces, in conjunction with electrochemical performance, is essential for bridging the existing knowledge gaps and accelerating the development of energy storage technologies. Of particular need is the ability to characterize surfaces or interfaces in a non-destructive way with adequate resolution to discern individual structural and chemical building blocks. To this end, sub-diffraction-limit low-energy infrared optical probes that exploit near-field interactions within atomic force microscopy platforms, such as pseudoheterodyne nanoimaging, photothermal nanoimaging and nanospectroscopy, and nanoscale Fourier transform infrared spectroscopy, are all powerful emerging techniques. These are capable of non-destructive surface probing and imaging at nanometer resolution. This review outlines recent efforts to characterize ex situ,in situ,andoperando electrode materials and electrochemical interfaces in rechargeable batteries with these advanced infrared near-field probes.

The capture and separation of carbon dioxide (CO2) has been the focus of a plethora of research in order to mitigate its emissions and contribute to global development. Given that CO2 is commonly found in natural gas streams, there have been efforts to seek more efficient materials to separate gaseous mixtures such as CO2/CH4. However, there are only a few reports regarding adsorption processes within pressurized systems. In the offshore scenario, natural gas streams still exhibit high moisture content, necessitating a greater understanding of processes in moist systems. In this article, a metal-organic framework synthesis based on zirconium (MOF-808) was carried out through a conventional solvothermal method and autoclave for the adsorption of CO2 and CH4 under different temperatures (45–65 °C) and pressures up to 100 bar. Furthermore, the adsorption of humid CO2 was evaluated using thermal analyses. The MOF-808 synthesized in autoclave showed a high surface area (1502 m2/g), a high capacity for CO2 adsorption at 50 bar and 45 °C and had a low selectivity to capture CH4 molecules. It also exhibited a fine stability after five cycles of CO2 adsorption and desorption at 50 bar and 45 °C − as confirmed by structural post-adsorption analyses while maintaining its adsorption capacity and crystallinity. Furthermore, it can be observed that the adsorption capacity increased in a humid environment, and that the adsorbent remained stable after adsorption cycles in the presence of moisture. Finally, it was possible to confirm the occurrence of physisorption processes through nuclear magnetic resonance (NMR) analyses, thus validating the choice of mild temperatures for regeneration and contributing to the reduction of energy consumption in processing plants.

Western blot is a popular biomolecular analysis method for measuring the relative quantities of independent proteins in complex biological samples. However, variability in quantitative western blot data analysis poses a challenge in designing reproducible experiments. The lack of rigorous quantitative approaches in current western blot statistical methodology may result in irreproducible inferences. Here we describe best practices for the design and analysis of western blot experiments, with examples and demonstrations of how different analytical approaches can lead to widely varying outcomes. To facilitate best practices, we have developed the blotRig tool for designing and analyzing western blot experiments to improve their rigor and reproducibility. The blotRig application includes functions for counterbalancing experimental design by lane position, batch management across gels, and analytics with covariates and random effects.

Palladium-encapsulated covalent organic frameworks (Pd/COFs) have garnered enormous attention in heterogeneous catalysis. However, the dominant ex situ encapsulation synthesis is tedious (multistep), time-consuming (typically 4 days or more), and involves the use of noxious solvents. Here we develop a mechanochemical in situ encapsulation strategy that enables the one-step, time-efficient, and environmentally benign synthesis of Pd/COFs. By ball milling COF precursors along with palladium acetate (Pd(OAc)2) in one pot under air at room temperature, Pd/COF hybrids were readily synthesized within an hour, exhibiting high crystallinity, uniform Pd dispersion, and superb scalability up to gram scale. Moreover, this versatile strategy can be extended to the synthesis of three Pd/COFs. Remarkably, the resulting Pd/DMTP-TPB showcases extraordinary activity (96-99% yield in 1 h at room temperature) and broad substrate scope (>10 functionalized biaryls) for the Suzuki-Miyaura coupling reaction of aryl bromides and arylboronic acids. Furthermore, the heterogeneity of Pd/DMTP-TPB is verified by recycling and leaching tests. The mechanochemical in situ encapsulation strategy disclosed herein paves a facile, rapid, scalable, and environmentally benign avenue to access metal/COF catalysts for efficient heterogeneous catalysis.

Akin to the impact that digital microelectronics had on electronic devices for information technology, digital microfluidics (DMF) was anticipated to transform fluidic devices for lab-on-a-chip (LoC) applications. However, despite a wealth of research and publications, electrowetting-on-dielectric (EWOD) DMF has not achieved the anticipated wide adoption, and commercialization has been painfully slow. By identifying the technological and resource hurdles in developing DMF chip and control systems as the culprit, we envision democratizing DMF by building a standardized design and manufacturing platform. To achieve this vision, we introduce a proof-of-concept cloud platform that empowers any user to design, obtain, and operate DMF chips (https://edroplets.org). For chip design, we establish a web-based EWOD chip design platform with layout rules and automated wire routing. For chip manufacturing, we build a web-based EWOD chip manufacturing platform and fabricate four types of EWOD chips (i.e., glass, paper, PCB, and TFT) to demonstrate the foundry service workflow. For chip control, we introduce a compact EWOD control system along with web-based operating software. Although industrial fabrication services are beyond the scope of this work, we hope this perspective will inspire academic and commercial stakeholders to join the initiative toward a DMF ecosystem for the masses.

Aggregation of protein-based therapeutics can occur during development, production, or storage and can lead to loss of efficacy and potential toxicity. Native mass spectrometry of a covalently linked pentameric monoclonal antibody complex with a mass of ∼800 kDa reveals several distinct conformations, smaller complexes, and abundant higher-order aggregates of the pentameric species. Charge detection mass spectrometry (CDMS) reveals individual oligomers up to the pentamer mAb trimer (15 individual mAb molecules; ∼2.4 MDa) whereas intermediate aggregates composed of 6-9 mAb molecules and aggregates larger than the pentameric dimer (1.6 MDa) were not detected/resolved by standard mass spectrometry, size exclusion chromatography (SEC), capillary electrophoresis (CE-SDS), or by mass photometry. Conventional quadrupole time-of-flight mass spectrometry (QTOF MS), mass photometry, SEC, and CE-SDS did not resolve partially or more fully unfolded conformations of each oligomer that were readily identified using CDMS by their significantly higher extents of charging. Trends in the charge-state distributions of individual oligomers provides detailed insight into how the structures of compact and elongated mAb aggregates change as a function of aggregate size. These results demonstrate the advantages of CDMS for obtaining accurate masses and information about the conformations of large antibody aggregates despite extensive overlapping m/z values. These results open up the ability to investigate structural changes that occur in small, soluble oligomers during the earliest stages of aggregation for antibodies or other proteins.

Protein-protein interactions (PPIs) are at the heart of the molecular landscape permeating life. Proteomics studies can explore this protein interaction landscape using mass spectrometry (MS). Thanks to their high sensitivity, mass spectrometers can easily identify thousands of proteins within a single sample, but that same sensitivity generates tangled spiderwebs of data that hide biologically relevant findings. So, what does a researcher do when she finds herself walking into spiderwebs? In a field focused on discovery, MS data require rigor in their analysis, experimental validation, or a combination of both. In this Review, we provide a brief primer on MS-based experimental methods to identify PPIs. We discuss approaches to analyze the resulting data and remove the proteomic background. We consider the advantages between comprehensive and targeted studies. We also discuss how scoring might be improved through AI-based protein structure information. Women have been essential to the development of proteomics, so we will specifically highlight work by women that has made this field thrive in recent years.

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

Iron is essential for life, but its imbalances can lead to severe health implications. Iron deficiency is the most common nutrient disorder worldwide, and iron disregulation in early life has been found to cause long-lasting behavioral, cognitive, and neural effects. However, little is known about the effects of dietary iron on gut microbiome function and metabolism. In this study, we sought to investigate the impact of dietary iron on the fecal metabolome and microbiome by using mice fed with three diets with different iron content: an iron deficient, an iron sufficient (standard), and an iron overload diet for seven weeks. Additionally, we sought to understand whether any observed changes would persist past the 7-week period of diet intervention. To assess this, all feeding groups were switched to a standard diet, and this feeding continued for an additional 7 weeks. Analysis of the fecal metabolome revealed that iron overload and deficiency significantly alter levels of peptides, nucleic acids, and lipids, including di- and tri-peptides containing branched-chain amino acids, inosine and guanosine, and several microbial conjugated bile acids. The observed changes in the fecal metabolome persist long after the switch back to a standard diet, with the cecal gut microbiota composition and function of each group distinct after the 7-week standard diet wash-out. Our results highlight the enduring metabolic consequences of nutritional imbalances, mediated by both host and gut microbiome, which persist after returning to original standard diets.

Metals are important cofactors in the metabolic processes of cyanobacteria, including photosynthesis, cellular respiration, DNA replication, and the biosynthesis of primary and secondary metabolites. In adaptation to the marine environment, cyanobacteria use metallophores to acquire trace metals when necessary as well as to reduce potential toxicity from excessive metal concentrations. Leptochelins A-C were identified as structurally novel metallophores from three geographically dispersed cyanobacteria of the genus Leptothoe. Determination of the complex structures of these metabolites presented numerous challenges, but they were ultimately solved using integrated data from NMR, mass spectrometry and deductions from the biosynthetic gene cluster. The leptochelins are comprised of halogenated linear NRPS-PKS hybrid products with multiple heterocycles that have potential for hexadentate and tetradentate coordination with metal ions. The genomes of the three leptochelin producers were sequenced, and retrobiosynthetic analysis revealed one candidate biosynthetic gene cluster (BGC) consistent with the structure of leptochelin. The putative BGC is highly homologous in all three Leptothoe strains, and all possess genetic signatures associated with metallophores. Postcolumn infusion of metals using an LC-MS metabolomics workflow performed with leptochelins A and B revealed promiscuous binding of iron, copper, cobalt, and zinc, with greatest preference for copper. Iron depletion and copper toxicity experiments support the hypothesis that leptochelin metallophores may play key ecological roles in iron acquisition and in copper detoxification. In addition, the leptochelins possess significant cytotoxicity against several cancer cell lines.

Patients with tuberous sclerosis complex (TSC) develop multi-organ disease manifestations, with kidney angiomyolipomas (AML) and cysts being one of the most common and deadly. Early and regular AML/cyst detection and monitoring are vital to lower TSC patient morbidity and mortality. However, the current standard of care involves imaging-based methods that are not designed for rapid screening, posing challenges for early detection. To identify potential diagnostic screening biomarkers of AML/cysts, we performed global untargeted metabolomics in blood samples from 283 kidney AML/cyst-positive or -negative TSC patients using mass spectrometry. We identified 7 highly sensitive chemical features, including octanoic acid, that predict kidney AML/cysts in TSC patients. Patients with elevated octanoic acid have lower levels of very long-chain fatty acids (VLCFAs), suggesting that dysregulated peroxisome activity leads to overproduction of octanoic acid via VLCFA oxidation. These data highlight AML/cysts blood biomarkers for TSC patients and offers valuable metabolic insights into the disease.

A variety of scattering-based, microscopy-based, and mobility-based methods are frequently used to probe the size distributions of colloidal nanoparticles with transmission electron microscopy (TEM) often considered to be the "gold standard". Charge detection mass spectrometry (CDMS) is an alternative method for nanoparticle characterization that can rapidly measure the mass and charge of individual nanoparticle ions with high accuracy. Two low polydispersity, ∼100 nm diameter nanoparticle size standards with different compositions (polymethyl methacrylate/polystyrene copolymer and 100% polystyrene) were characterized using both TEM and CDMS to explore the merits and complementary aspects of both methods. Mass and diameter distributions are rapidly obtained from CDMS measurements of thousands of individual ions of known spherical shape, requiring less time than TEM sample preparation and image analysis. TEM image-to-image variations resulted in a ∼1-2 nm range in the determined mean diameters whereas the CDMS mass precision of ∼1% in these experiments leads to a diameter uncertainty of just 0.3 nm. For the 100% polystyrene nanoparticles with known density, the CDMS and TEM particle diameter distributions were in excellent agreement. For the copolymer nanoparticles with unknown density, the diameter from TEM measurements combined with the mass from CDMS measurements enabled an accurate measurement of nanoparticle density. Differing extents of charging for the two nanoparticle standards measured by CDMS show that charging is sensitive to nanoparticle surface properties. A mixture of the two samples was separated based on their different extents of charging despite having overlapping mass distributions centered at 341.5 and 331.0 MDa.

We herein present a multifunctional self-priming hairpin probe-based isothermal amplification, termed MSH, enabling one-pot detection of target nucleic acids. The sophisticatedly designed multifunctional self-priming hairpin (MSH) probe recognizes the target and rearranges to prime itself, triggering the amplification reaction powered by the continuously repeated extension, nicking, and target recycling. As a consequence, a large number of double-stranded DNA (dsDNA) amplicons are produced that could be monitored in real-time using a dsDNA-intercalating dye. Based on this unique design approach, the nucleocapsid (N) and the open reading frame 1 ab (ORF1ab) genes of SARS-CoV-2 were successfully detected down to 1.664 fM and 0.770 fM, respectively. The practical applicability of our method was validated by accurately diagnosing 60 clinical samples with 93.33% sensitivity and 96.67% specificity. This isothermal one-pot MSH technique holds great promise as a point-of-care testing protocol for the reliable detection of a wide spectrum of pathogens, particularly in resource-limited settings.

Rapid and precise detection of hydrogen peroxide (H2O2) holds great significance since it is linked to numerous physiological and inorganic catalytic processes. We herein developed a label-free and washing-free strategy to detect H2O2 by employing a hand-held personal glucose meter (PGM) as a signal readout device. By focusing on the fact that the reduced redox mediator ([Fe(CN)6]4-) itself is responsible for the final PGM signal, we developed a new PGM-based strategy to detect H2O2 by utilizing the target H2O2-mediated oxidation of [Fe(CN)6]4- to [Fe(CN)6]3- in the presence of horseradish peroxidase (HRP) and monitoring the reduced PGM signal in response to the target amount. Based on this straightforward and facile design principle, H2O2 was successfully determined down to 3.63 μM with high specificity against various non-target molecules. We further demonstrated that this strategy could be expanded to identify another model target choline by detecting H2O2 produced through its oxidation promoted by choline oxidase. Moreover, we verified its practical applicability by reliably determining extracellular H2O2 released from the breast cancer cell line, MDA-MB-231. This work could evolve into versatile PGM-based platform technology to identify various non-glucose target molecules by employing their corresponding oxidase enzymes, greatly advancing the portable biosensing technologies.

Monofluoroacetate is a highly lethal toxin that causes death by inhibiting cellular adenosine triphosphate (ATP) production. The heart and brain are the primary target organs. Acute death is attributed to cardiac fibrillation and/or convulsions. Although it occurs naturally in some plants, a major source of animal intoxication is access to sodium monofluoroacetate (NaMFA) pesticide, which continues to be a concern in the USA and around the world despite restricted use in some countries including the USA. There are also concerns about misuse of this pesticide for malicious poisoning. Currently, a tissue-based diagnostic method for NaMFA intoxication in animals is lacking. There is a critical need by the veterinary diagnostic community for a simple, sensitive and reliable tissue-based diagnostic test to confirm NaMFA poisoning in animals. We have developed and extensively evaluated a sensitive novel liquid chromatography combined with tandem mass spectrometry method suitable for this purpose. The limits of detection and limits of quantitation are 1.7 and 5.0 ng/g, respectively. The accuracy and precision met or exceeded expectations. The method performance was verified using the incurred kidney obtained from animal diagnostic cases. This novel kidney-based method is now available for clinical use and can help with diagnostic purposes, including detecting potential issues related to animal foods.

The valorization of lignin, a currently underutilized component of lignocellulosic biomass, has attracted attention to promote a stable and circular bioeconomy. Successful approaches including thermochemical, biological, and catalytic lignin depolymerization have been demonstrated, enabling opportunities for lignino-refineries and lignocellulosic biorefineries. Although significant progress in lignin valorization has been made, this review describes unexplored opportunities in chemical and biological routes for lignin depolymerization and thereby contributes to economically and environmentally sustainable lignin-utilizing biorefineries. This review also highlights the integration of chemical and biological lignin depolymerization and identifies research gaps while also recommending future directions for scaling processes to establish a lignino-chemical industry.

Nuclear magnetic resonance (NMR) spectroscopy is an important analytical technique in synthetic organic chemistry, but its integration into high-throughput experimentation workflows has been limited by the necessity of manually analyzing the NMR spectra of new chemical entities. Current efforts to automate the analysis of NMR spectra rely on comparisons to databases of reported spectra for known compounds and, therefore, are incompatible with the exploration of new chemical space. By reframing the NMR spectrum of a reaction mixture as a joint probability distribution, we have used Hamiltonian Monte Carlo Markov Chain and density functional theory to fit the predicted NMR spectra to those of crude reaction mixtures. This approach enables the deconvolution and analysis of the spectra of mixtures of compounds without relying on reported spectra. The utility of our approach to analyze crude reaction mixtures is demonstrated with the experimental spectra of reactions that generate a mixture of isomers, such as Wittig olefination and C-H functionalization reactions. The correct identification of compounds in a reaction mixture and their relative concentrations is achieved with a mean absolute error as low as 1%.

Sphingolipids are an essential subset of bioactive lipids found in most eukaryotic cells that contribute to membrane biophysical properties and are involved in cellular differentiation, recognition, and mediating interactions. The described nanoHPLC-ESI-Q/ToF methodology utilizes known biosynthetic pathways, accurate mass detection, optimized collision-induced disassociation, and a robust nanoflow chromatographic separation for the analysis of intact sphingolipids found in human tissue, cells, and serum. The methodology was developed and validated with an emphasis on addressing the common issues experienced in profiling these amphipathic lipids, which are part of the glycocalyx and lipidome. The high sensitivity obtained using nanorange flow rates with robust chromatographic reproducibility over a wide range of concentrations and injection volumes results in confident identifications for profiling these low-abundant biomolecules.

Cell membrane glycoproteins are generally highly fucosylated and sialylated, and post-translational modifications play important roles in the proteins' functions of signaling, binding and cellular processing. For these reasons, methods for measuring sialic acid-mediated protein-protein interactions have been developed. However, determining the role of fucose in these interactions has been limited by technological barriers that have thus far hindered the ability to characterize and observe fucose-mediated protein-protein interactions. Herein, we describe a method to metabolically label mammalian cells with modified fucose, which incorporates a bioorthogonal group into cell membrane glycoproteins thereby enabling the characterization of cell-surface fucose interactome. Copper-catalyzed click chemistry was used to conjugate a proximity labeling probe, azido-FeBABE. Following the addition of hydrogen peroxide (H2O2), the fucose-azido-FeBABE catalyzed the formation of hydroxyl radicals, which in turn oxidized the amino acids in the proximity of the labeled fucose residue. The oxidized peptides were identified using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Variations in degree of protein oxidation were obtained with different H2O2 reaction times yielding the acquisition of spatial information of the fucose-interacting proteins. In addition, specific glycoprotein-protein interactions were constructed for Galectin-3 (LEG3) and Galectin-3-binding protein (LG3BP) illustrating the further utility of the method. This method identifies new fucose binding partners thereby enhancing our understanding of the cell glycocalyx.

The cell wall integrity (CWI) signaling pathway regulates yeast cell wall biosynthesis, cell division, and responses to external stress. The cell wall, comprised of a dense network of chitin, β-1,3- and β-1,6- glucans, and mannoproteins, is very thin,

Variable temperature electrospray mass spectrometry is useful for multiplexed measurements of the thermal stabilities of biomolecules, but the ionization process can be disrupted by aggregation-prone proteins/complexes that have irreversible unfolding transitions. Resistively heating solutions containing a mixture of bovine carbonic anhydrase II (BCAII), a CO2 fixing enzyme involved in many biochemical pathways, and cytochrome c leads to complete loss of carbonic anhydrase signal and a significant reduction in cytochrome c signal above ∼72 °C due to aggregation. In contrast, when the tips of borosilicate glass nanoelectrospray emitters are heated with a laser, complete thermal denaturation curves for both proteins are obtained in

Successful acclimation to copper (Cu) deficiency involves a fine balance between Cu import and export. In the green alga Chlamydomonas reinhardtii, Cu import is dependent on a transcription factor, Copper Response Regulator 1 (CRR1), responsible for activating genes in Cu-deficient cells. Among CRR1 target genes are two Cu transporters belonging to the CTR/COPT gene family (CTR1 and CTR2) and a related soluble protein (CTR3). The ancestor of these green algal proteins was likely acquired from an ancient chytrid and contained conserved cysteine-rich domains (named the CTR-associated domains, CTRA) that are predicted to be involved in Cu acquisition. We show by reverse genetics that Chlamydomonas CTR1 and CTR2 are canonical Cu importers albeit with distinct affinities, while loss of CTR3 did not result in an observable phenotype under the conditions tested. Mutation of CTR1, but not CTR2, recapitulates the poor growth of crr1 in Cu-deficient medium, consistent with a dominant role for CTR1 in high-affinity Cu(I) uptake. On the other hand, the overaccumulation of Cu(I) (20 times the quota) in zinc (Zn) deficiency depends on CRR1 and both CTR1 and CTR2. CRR1-dependent activation of CTR gene expression needed for Cu over-accumulation can be bypassed by the provision of excess Cu in the growth medium. Over-accumulated Cu is sequestered into the acidocalcisome but can become remobilized by restoring Zn nutrition. This mobilization is also CRR1-dependent, and requires activation of CTR2 expression, again distinguishing CTR2 from CTR1 and consistent with the lower substrate affinity of CTR2.One sentence summaryRegulation of Cu uptake and sequestration by members of the CTR family of proteins in Chlamydomonas.

Thirdhand smoke (THS) is the persistent and toxic residue from tobacco smoke in indoor environments. A comprehensive understanding of the chemical constituents of THS is necessary to assess the risks of long-term exposure and to establish reliable THS tracers. The objective of this study was to investigate compounds associated with THS through nontargeted analysis (NTA) of settled house dust samples from smokers' and non-smokers' homes, using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC/TOF-MS). Compounds that were either only present in dust from smokers' homes or that had significantly larger abundance than in non-smokers' homes were termed qualified compounds. We identified 140 qualified compounds, and of these, 42 compounds were tentatively identified by searching matching mass spectra in NIST electron impact (EI) mass spectral library including 20 compounds confirmed with their authentic standards. Among the 42 compounds, 26 compounds were statistically more abundant (p

The bulk solvent is a major component of biomacromolecular crystals that contributes significantly to the observed diffraction intensities. Accurate modelling of the bulk solvent has been recognized as important for many crystallographic calculations. Owing to its simplicity and modelling power, the flat (mask-based) bulk-solvent model is used by most modern crystallographic software packages to account for disordered solvent. In this model, the bulk-solvent contribution is defined by a binary mask and a scale (scattering) function. The mask is calculated on a regular grid using the atomic model coordinates and their chemical types. The grid step and two radii, solvent and shrinkage, are the three parameters that govern the mask calculation. They are highly correlated and their choice is a compromise between the computer time needed to calculate the mask and the accuracy of the mask. It is demonstrated here that this choice can be optimized using a unique value of 0.6 Å for the grid step irrespective of the data resolution, and the radii values adjusted correspondingly. The improved values were tested on a large sample of Protein Data Bank entries derived from X-ray diffraction data and are now used in the computational crystallography toolbox (CCTBX) and in Phenix as the default choice.

Interactions among proteins and peptides are essential for many biological activities including the tailoring of peptide substrates to produce natural products. The first step in the production of the bacterial redox cofactor pyrroloquinoline quinone (PQQ) from its peptide precursor is catalyzed by a radical SAM (rSAM) enzyme, PqqE. We describe the use of hydrogen-deuterium exchange mass spectrometry (HDX-MS) to characterize the structure and conformational dynamics in the protein-protein and protein-peptide complexes necessary for PqqE function. HDX-MS-identified hotspots can be discerned in binary and ternary complex structures composed of the peptide PqqA, the peptide-binding chaperone PqqD, and PqqE. Structural conclusions are supported by size-exclusion chromatography coupled to small-angle X-ray scattering (SEC-SAXS). HDX-MS further identifies reciprocal changes upon the binding of substrate peptide and S-adenosylmethionine (SAM) to the PqqE/PqqD complex: long-range conformational alterations have been detected upon the formation of a quaternary complex composed of PqqA/PqqD/PqqE and SAM, spanning nearly 40 Å, from the PqqA binding site in PqqD to the PqqE active site Fe4S4. Interactions among the various regions are concluded to arise from both direct contact and distal communication. The described experimental approach can be readily applied to the investigation of protein conformational communication among a large family of peptide-modifying rSAM enzymes.

This study presents a breakthrough in the field of onsite bacterial detection, offering an innovative, rapid, and ultrasensitive colorimetric biosensor for the detection of Escherichia coli (E. coli) O157:H7, using chemically modified melamine foam (MF). Different from conventional platforms, such as 96-well plates and fiber-based membranes, the modified MF features a macroporous reticulated three-dimensional (3D) framework structure, allowing fast and free movement of large biomolecules and bacteria cells through the MF structure in every direction and ensuring good accessibility of entire active binding sites of the framework structure with the target bacteria, which significantly increased sensitive and volume-responsive detection of whole-cell bacteria. The biosensing platform requires less than 1.5 h to complete the quantitative detection with a sensitivity of 10 cfu/mL, discernible by the naked eye, and an enhanced sensitivity of 5 cfu/mL with the help of a smartphone. Following a short enrichment period of 1 h, the sensitivity was further amplified to 2 cfu/mL. The biosensor material is volume responsive, making the biosensing platform sensitivity increase as the volume of the sample increases, and is highly suitable for testing large-volume fluid samples. This novel material paves the way for the development of volume-flexible biosensing platforms for the record-fast, onsite, selective, and ultrasensitive detection of various pathogenic bacteria in real-world applications.

Later flow immunochromatographic assay has been widely used in clinical, environmental, and other diagnostic applications owing to its high sensitivity and throughput. However, most immunoassays operate in the "turn-off" mode for detecting targets of low molecular weight. The signal intensity decreases as the analyte concentration increases, which poses a challenge for achieving ultrasensitive detection at low concentrations and is counterintuitive to new users. In this work, a fluorometric immunochromatographic assay (FICA) is developed to simultaneously read "turn-on" fluorescent and "turn-off" colorimetric signals, where ZnCdSe/ZnS quantum dots act as fluorescence donors and gold nanoparticles (AuNPs) act as quenchers. The fluorescent signal (excitation/emission wavelengths of 365/525 nm) is positively correlated with analytes' concentration. Taking sibutramine (SBT) as the analysis target, the visual limit of detection for SBT reached 3.9 ng/mL, and the limit of Quantitation was 5.0 ng/mg in spiked samples. The developed FICA achieves a high sensitivity in SBT detection, which is much lower than that of the colloidal gold-based immunochromatographic assay. This dual-function detection mode has great potential to be used as a rapid on-site semiquantitative method, providing an alternative mode for the determination of low levels of target analytes.

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.

Silver cations can mediate base pairing of guanine (G) DNA oligomers, yielding linear parallel G-Ag+-G duplexes with enhanced stabilities compared to those of canonical DNA duplexes. To enable their use in programmable DNA nanotechnologies, it is critical to understand solution-state formation and the nanomechanical stiffness of G-Ag+-G duplexes. Using temperature-controlled circular dichroism (CD) spectroscopy, we find that heating mixtures of G oligomers and silver salt above 50 °C fully destabilizes G-quadruplex structures and converts oligomers to G-Ag+-G duplexes. Electrospray ionization mass spectrometry supports that G-Ag+-G duplexes form at stoichiometries of 1 Ag+ per base pair, and CD spectroscopy suggests that as the Ag+/base stoichiometry increases further, G-Ag+-G duplexes undergo additional morphological changes. Using liquid-phase atomic force microscopy, we find that this excess Ag+ enables assembly of long fiberlike structures with ∼2.5 nm heights equivalent to a single DNA duplex but with lengths that far exceed a single duplex. Finally, using the conditions established to form single G-Ag+-G duplexes, we use a surface forces apparatus (SFA) to compare the solution-phase stiffness of single G-Ag+-G duplexes with dG-dC Watson-Crick-Franklin duplexes. SFA shows that G-Ag+-G duplexes are 1.3 times stiffer than dG-dC duplexes, confirming gas-phase ion mobility spectrometry measurements and computational predictions. These findings may guide the development of structural DNA nanotechnologies that rely on silver-mediated base pairing.

Hydrogen sulfide (H2S) is an environmental toxicant of significant health concern. The brain is a major target in acute H2S poisoning. This study was conducted to test the hypothesis that acute and subchronic ambient H2S exposures alter the brain metabolome. Male 7–8-week-old C57BL/6J mice were exposed by whole-body inhalation to 1000 ppm H2S for 45 min and euthanized at 5 min or 72 h for acute exposure. For subchronic study, mice were exposed to 5 ppm H2S 2 h/day, 5 days/week for 5 weeks. Control mice were exposed to room air. The brainstem was removed for metabolomic analysis. Enrichment analysis showed that the metabolomic profiles in acute and subchronic H2S exposures matched with those of cerebral spinal fluid from patients with seizures or Alzheimer’s disease. Acute H2S exposure decreased excitatory neurotransmitters, aspartate, and glutamate, while the inhibitory neurotransmitter, serotonin, was increased. Branched-chain amino acids and glucose were increased by acute H2S exposure. Subchronic H2S exposure within OSHA guidelines surprisingly decreased serotonin concentration. In subchronic H2S exposure, glucose was decreased, while polyunsaturated fatty acids, inosine, and hypoxanthine were increased. Collectively, these results provide important mechanistic clues for acute and subchronic ambient H2S poisoning and show that H2S alters brainstem metabolome.

Axonal degeneration is a key component of neurodegenerative diseases such as Huntington's disease (HD), Alzheimer's disease, and amyotrophic lateral sclerosis. Nicotinamide, an NAD+ precursor, has long since been implicated in axonal protection and reduction of degeneration. However, studies on nicotinamide (NAm) supplementation in humans indicate that NAm has no protective effect. Sterile alpha and toll/interleukin receptor motif-containing protein 1 (SARM1) regulates several cell responses to axonal damage and has been implicated in promoting neuronal degeneration. SARM1 inhibition seems to result in protection from neuronal degeneration while hydrogen peroxide has been implicated in oxidative stress and axonal degeneration. The effects of laser-induced axonal damage in wild-type and HD dorsal root ganglion cells treated with NAm, hydrogen peroxide (H2 O2 ), and SARM1 inhibitor DSRM-3716 were investigated and the cell body width, axon width, axonal strength, and axon shrinkage post laser-induced injury were measured.

BackgroundGlioblastoma (GBM) is a clinically challenging primary brain tumor with poor survival outcome despite surgical resection and intensive chemoradiation. The metabolic heterogeneity of GBM can become biomarkers for treatment response, resistance, and outcome prediction. The aim of the study is to investigate metabolic distinctions between primary and recurrent GBM tissue and patient plasma to establish feasibility for metabolic profiling.MethodsA single-center cohort study analyzed tissue and blood samples from 15 patients with GBM using untargeted metabolomic/lipidomic assays. Metabolomic, lipidomic, and biogenic amine analyses were conducted on GBM tissue and patient plasma at diagnosis and recurrence using untargeted mass spectrometry. The study utilized a small but longitudinally collected cohort to evaluate alteration in metabolites, lipids, and biogenic amines between specimens at diagnosis and recurrence.ResultsExploratory analysis revealed significant alteration in metabolites, lipids, and biogenic amines between diagnostic and recurrent states in both tumor and plasma specimens. Notable metabolites differed at recurrence, including N-alpha-methylhistamine, glycerol-3-phosphate, phosphocholine, and succinic acid in tissue, and indole-3-acetate, and urea in plasma. Principal component analysis revealed distinct metabolomic profiles between tumor tissue and patient plasma. Distinct metabolic profiles were observed in GBM tissue and patient plasma at recurrence, demonstrating the feasibility of using metabolomic methodologies for longitudinal studies. One patient exhibited a unique tumor resistance signature at diagnosis, possibly indicating a high-risk metabolomic phenotype.ConclusionsIn this small cohort, the findings suggest the potential of metabolomic signatures of GBM tissue and patient plasma for risk stratification, outcome prediction, and the development of novel adjuvant metabolic-targeting therapies. The findings suggest metabolic discrepancies at diagnosis and recurrence in tissue and plasma, highlighting potential implications for evaluation of clinical response. The identification of significant changes in metabolite abundance emphasizes the need for larger studies using targeted metabolomics to validate and further explore these profiles.

Static and mobile radiation detectors can be deployed in urban environments for a range of nuclear security applications, including radiological source search-and-tracking scenarios. Modeling detector performance for such applications is challenging, as it does not depend solely on the detector capabilities themselves. Many factors must be taken into consideration, including specific source and background signatures, the topology and constraints of the deployment environment, the presence of nuisance sources, and whether detectors are mobile or static. When considering the simultaneous deployment of multiple, heterogeneous detectors, assessment of the system-wide performance requires the simulation of the individual detectors, and a system-level analysis of the detection performance. In radiological source search-and-tracking scenarios, performance is mostly dominated by the probability of encounter, which depends on the specifics of a given deployment, e.g., static vs. mobile detectors or a combination of both modalities, the number of detectors deployed, the dynamic vs. static setting of false alarm rates, and individual vs. networked operation. The Urban Deployment Model (UDM) toolset was specifically developed to cover the gap in the available generic frameworks for the simulation of radiation detector deployments at city scales. UDM provides a unified and modular framework to support the simulation and performance characterization of heterogeneous detector deployments in urban environments. This paper presents the key components along the UDM workflow.

The complexity of information processing in the brain requires the development of technologies that can provide spatial and temporal resolution by means of dense electrode arrays paired with high-channel-count signal acquisition electronics. In this work, we present an ultra-low noise modular 512-channel neural recording circuit that is scalable to up to 4096 simultaneously recording channels. The neural readout application-specific integrated circuit (ASIC) uses a dense 8.2 mm × 6.8 mm 2D layout to enable high-channel count, creating an ultra-light 350 mg flexible module. The module can be deployed on headstages for small animals like rodents and songbirds, and it can be integrated with a variety of electrode arrays. The chip was fabricated in a TSMC 0.18 µm 1.8 V CMOS technology and dissipates a total of 125 mW. Each DC-coupled channel features a gain and bandwidth programmable analog front-end along with 14 b analog-to-digital conversion at speeds up to 30 kS/s. Additionally, each front-end includes programmable electrode plating and electrode impedance measurement capability. We present both standalone and in vivo measurements results, demonstrating the readout of spikes and field potentials that are modulated by a sensory input.

Small extracellular vesicles (sEVs) are cell-released vesicles ranging from 30-150nm in size. They have garnered increasing attention because of their potential for both the diagnosis and treatment of disease. The diversity of sEVs derives from their biological composition and cargo content. Currently, the isolation of sEV subpopulations is primarily based on bio-physical and affinity-based approaches. Since a standardized definition for sEV subpopulations is yet to be fully established, it is important to further investigate the correlation between the biomolecular composition of sEVs and their physical properties. In this study, we employed a platform combining single-vesicle surface-enhanced Raman spectroscopy (SERS) and machine learning to examine individual sEVs isolated by size-exclusion chromatography (SEC). The biomolecular composition of each vesicle examined was reflected by its corresponding SERS spectral features (biomolecular "fingerprints"), with their roots in the composition of their collective Raman-active bonds. Origins of the SERS spectral features were validated through a comparative analysis between SERS and mass spectrometry (MS). SERS fingerprinting of individual vesicles was effective in overcoming the challenges posed by EV population averaging, allowing for the possibility of analyzing the variations in biomolecular composition between the vesicles of similar and/or different sizes. Using this approach, we uncovered that each of the size-based fractions of sEVs contained particles with predominantly similar SERS spectral features. Indeed, more than 84% of the vesicles residing within a particular group were clearly distinguishable from that of the other EV sub-populations, despite some spectral variations within each sub-population. Our results suggest the possibility that size-based EV fractionation methods produce samples where similarly eluted sEVs are correlated with their respective biochemical contents, as reflected by their SERS spectra. Our findings therefore highlight the possibility that the biogenesis and respective biological functionalities of the various sEV fractions may be inherently different.

The Delta Smelt is a largely zooplank­tivorous, endangered fish endemic to the San Francisco Estuary (the estuary). High flows increase the availability of fresh and brackish water habitat for Delta Smelt, but also may mobilize contaminants, potentially increasing toxicological stress. Here, we examine the association between contaminants and Delta Smelt health across contrasting water year types and flow-related management actions. Our study spanned the fall season of three years: 1 dry year (2018) bracketed by 2 wet years (2017 and 2019) and coincided with several management actions meant to benefit Delta Smelt. We collected field water from six sites in the estuary that encompass the freshwater and low-salinity habitat of Delta Smelt and analyzed the water for contaminant concentrations. After a 96-hour exposure to the field water, we assessed cultured Delta Smelt survival and the histopathological condition of the gill and liver. Insecticides, particularly fipronil metabolites, were the most prevalent contaminants detected in 2017 and 2018, and a variety of contaminants associated with the rice harvest were detected in 2019. No acute toxicity was observed during any exposure, but we observed negative effects in the livers of Delta Smelt exposed to agricultural water from the Toe Drain and Cache Slough during a 2019 pulse flow action, which coincided with elevated detections and concentrations of organic pesticides. Other noteworthy sub-lethal effects, likely occurring in response to contaminant mixtures, included severe gill lesions in Delta Smelt exposed to Decker Island water in 2019. In the drier year of 2018, lesions were generally mild or absent. Thus, the trade-offs between increased habitat availability and contaminant loading may provide one explanation for why Delta Smelt abundance does not consistently respond positively to outflow.

This paper introduces an innovative approach to 3D environmental mapping through the integration of a compact, handheld sensor package with a two-stage sensor fusion pipeline. The sensor package, incorporating LiDAR, IMU, RGB, and thermal cameras, enables comprehensive and robust 3D mapping of various environments. By leveraging Simultaneous Localization and Mapping (SLAM) and thermal imaging, our solution offers good performance in conditions where global positioning is unavailable and in visually degraded environments. The sensor package runs a real-time LiDAR-Inertial SLAM algorithm, generating a dense point cloud map that accurately reconstructs the geometric features of the environment. Following the acquisition of that point cloud, we post-process these data by fusing them with images from the RGB and thermal cameras and produce a detailed, color-enriched 3D map that is useful and adaptable to different mission requirements. We demonstrated our system in a variety of scenarios, from indoor to outdoor conditions, and the results showcased the effectiveness and applicability of our sensor package and fusion pipeline. This system can be applied in a wide range of applications, ranging from autonomous navigation to smart agriculture, and has the potential to make a substantial benefit across diverse fields.

The role of gut microbe-derived metabolites in the development of metabolic syndrome (MetS) remains unclear. This study aimed to evaluate the associations of gut microbe-derived metabolites and MetS traits in the cross-sectional Metabolic Syndrome In Men (METSIM) study. The sample included 10,194 randomly related men (age 57.65 ± 7.12 years) from Eastern Finland. Levels of 35 metabolites were tested for associations with 13 MetS traits using lasso and stepwise regression. Significant associations were observed between multiple MetS traits and 32 metabolites, three of which exhibited particularly robust associations. N-acetyltryptophan was positively associated with Homeostatic Model Assessment for Insulin Resistant (HOMA-IR) (β = 0.02, p = 0.033), body mass index (BMI) (β = 0.025, p = 1.3 × 10-16), low-density lipoprotein cholesterol (LDL-C) (β = 0.034, p = 5.8 × 10-10), triglyceride (0.087, p = 1.3 × 10-16), systolic (β = 0.012, p = 2.5 × 10-6) and diastolic blood pressure (β = 0.011, p = 3.4 × 10-6). In addition, 3-(4-hydroxyphenyl) lactate yielded the strongest positive associations among all metabolites, for example, with HOMA-IR (β = 0.23, p = 4.4 × 10-33), and BMI (β = 0.097, p = 5.1 × 10-52). By comparison, 3-aminoisobutyrate was inversely associated with HOMA-IR (β = -0.19, p = 3.8 × 10-51) and triglycerides (β = -0.12, p = 5.9 × 10-36). Mendelian randomization analyses did not provide evidence that the observed associations with these three metabolites represented causal relationships. We identified significant associations between several gut microbiota-derived metabolites and MetS traits, consistent with the notion that gut microbes influence metabolic homeostasis, beyond traditional risk factors.

Alzheimer's disease (AD) is a neurodegenerative disease that commonly causes dementia. Identifying biomarkers for the early detection of AD is an emerging need, as brain dysfunction begins two decades before the onset of clinical symptoms. To this end, we reanalyzed untargeted metabolomic mass spectrometry data from 905 patients enrolled in the AD Neuroimaging Initiative (ADNI) cohort using MS-DIAL, with 1,304,633 spectra of 39,108 unique biomolecules. Metabolic profiles of 93 hydrophilic metabolites were determined. Additionally, we integrated targeted lipidomic data (4873 samples from 1524 patients) to explore candidate biomarkers for predicting progressive mild cognitive impairment (pMCI) in patients diagnosed with AD within two years using the baseline metabolome. Patients with lower ergothioneine levels had a 12% higher rate of AD progression with the significance of P = 0.012 (Wald test). Furthermore, an increase in ganglioside (GM3) and decrease in plasmalogen lipids, many of which are associated with apolipoprotein E polymorphism, were confirmed in AD patients, and the higher levels of lysophosphatidylcholine (18:1) and GM3 d18:1/20:0 showed 19% and 17% higher rates of AD progression, respectively (Wald test: P = 3.9 × 10-8 and 4.3 × 10-7). Palmitoleamide, oleamide, diacylglycerols, and ether lipids were also identified as significantly altered metabolites at baseline in patients with pMCI. The integrated analysis of metabolites and genomics data showed that combining information on metabolites and genotypes enhances the predictive performance of AD progression, suggesting that metabolomics is essential to complement genomic data. In conclusion, the reanalysis of multiomics data provides new insights to detect early development of AD pathology and to partially understand metabolic changes in age-related onset of AD.