Benzoyl Chloride Derivatization Advances the Quantification of Dissolved Polar Metabolites on Coral Reefs.

Extracellular chemical cues constitute much of the language of life among marine organisms, from microbes to mammals. Changes in this chemical pool serve as invisible signals of overall ecosystem health and disruption to this finely tuned equilibrium. In coral reefs, the scope and magnitude of the chemicals involved in maintaining reef equilibria are largely unknown. Processes involving small, polar molecules, which form the majority components of labile dissolved organic carbon, are often poorly captured using traditional techniques. We employed chemical derivatization with mass spectrometry-based targeted exometabolomics to quantify polar dissolved phase metabolites on five coral reefs in the U.S. Virgin Islands. We quantified 45 polar exometabolites, demonstrated their spatial variability, and contextualized these findings in terms of geographic and benthic cover differences. By comparing our results to previously published coral reef exometabolomes, we show the novel quantification of 23 metabolites, including central carbon metabolism compounds (e.g., glutamate) and novel metabolites such as homoserine betaine. We highlight the immense potential of chemical derivatization-based exometabolomics for quantifying labile chemical cues on coral reefs and measuring molecular level responses to environmental stressors. Overall, improving our understanding of the composition and dynamics of reef exometabolites is vital for effective ecosystem monitoring and management strategies.

Unknown Metabolite Identification Using Machine Learning Collision Cross-Section Prediction and Tandem Mass Spectrometry.

Ion mobility (IM) spectrometry provides semiorthogonal data to mass spectrometry (MS), showing promise for identifying unknown metabolites in complex non-targeted metabolomics data sets. While current literature has showcased IM-MS for identifying unknowns under near ideal circumstances, less work has been conducted to evaluate the performance of this approach in metabolomics studies involving highly complex samples with difficult matrices. Here, we present a workflow incorporating de novo molecular formula annotation and MS/MS structure elucidation using SIRIUS 4 with experimental IM collision cross-section (CCS) measurements and machine learning CCS predictions to identify differential unknown metabolites in mutant strains of Caenorhabditis elegans. For many of those ion features, this workflow enabled the successful filtering of candidate structures generated by in silico MS/MS predictions, though in some cases, annotations were challenged by significant hurdles in instrumentation performance and data analysis. While for 37% of differential features we were able to successfully collect both MS/MS and CCS data, fewer than half of these features benefited from a reduction in the number of possible candidate structures using CCS filtering due to poor matching of the machine learning training sets, limited accuracy of experimental and predicted CCS values, and lack of candidate structures resulting from the MS/MS data. When using a CCS error cutoff of ±3%, on average, 28% of candidate structures could be successfully filtered. Herein, we identify and describe the bottlenecks and limitations associated with the identification of unknowns in non-targeted metabolomics using IM-MS to focus and provide insights into areas requiring further improvement.

Long-Term Metabolomics Reference Material.

The use of quality control samples in metabolomics ensures data quality, reproducibility, and comparability between studies, analytical platforms, and laboratories. Long-term, stable, and sustainable reference materials (RMs) are a critical component of the quality assurance/quality control (QA/QC) system; however, the limited selection of currently available matrix-matched RMs reduces their applicability for widespread use. To produce an RM in any context, for any matrix that is robust to changes over the course of time, we developed iterative batch averaging method (IBAT). To illustrate this method, we generated 11 independently grown Escherichia coli batches and made an RM over the course of 10 IBAT iterations. We measured the variance of these materials by nuclear magnetic resonance (NMR) and showed that IBAT produces a stable and sustainable RM over time. This E. coli RM was then used as a food source to produce a Caenorhabditis elegans RM for a metabolomics experiment. The metabolite extraction of this material, alongside 41 independently grown individual C. elegans samples of the same genotype, allowed us to estimate the proportion of sample variation in preanalytical steps. From the NMR data, we found that 40% of the metabolite variance is due to the metabolite extraction process and analysis and 60% is due to sample-to-sample variance. The availability of RMs in untargeted metabolomics is one of the predominant needs of the metabolomics community that reach beyond quality control practices. IBAT addresses this need by facilitating the production of biologically relevant RMs and increasing their widespread use.

N-acetylcysteine for smoking cessation among dual users of tobacco and cannabis: Protocol and rationale for a randomized controlled trial.

BACKGROUND: Tobacco and cannabis co-use is a growing public health problem. The synergistic effects of cannabis and nicotine on neurobiological systems that mediate reward and shared environmental cues reinforcing use may make tobacco smoking cessation more difficult. N-acetylcysteine (NAC), an FDA-approved medication and over-the-counter supplement, has shown promise in animal studies and randomized controlled trials (RCTs) in reducing tobacco and cannabis craving and use. NAC's potential efficacy in treating addiction may be attributable to its central nervous system effects in reducing excessive glutamatergic activity, oxidative stress, and inflammation. To date, no RCT has examined NAC for smoking cessation among dual users of tobacco and cannabis. METHOD: In a double-blind, placebo-controlled RCT, we will examine NAC for smoking cessation among dual users of tobacco and cannabis. Sixty adult cigarette-cannabis co-users are randomized to receive NAC 3600 mg per day or placebo over 8 weeks. Participants in both groups receive 8 weekly cognitive behavioral therapy sessions addressing smoking cessation and cannabis reduction. Outcomes are assessed at Weeks 0, 4, 8, and 12. Primary aims are to determine NAC's efficacy in decreasing cigarette craving, nicotine dependence, and use; and cannabis craving and use. Exploratory aims include examination of changes in neurocognition with NAC and their potential mediational effects on cigarette and cannabis use outcomes. CONCLUSION: Results will inform smoking cessation treatment among dual users of tobacco and cannabis. CLINICALTRIALS: gov Identifier: NCT04627922.

An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics.

Untargeted metabolomics studies are unbiased but identifying the same feature across studies is complicated by environmental variation, batch effects, and instrument variability. Ideally, several studies that assay the same set of metabolic features would be used to select recurring features to pursue for identification. Here, we developed an anchored experimental design. This generalizable approach enabled us to integrate three genetic studies consisting of 14 test strains of Caenorhabditis elegans prior to the compound identification process. An anchor strain, PD1074, was included in every sample collection, resulting in a large set of biological replicates of a genetically identical strain that anchored each study. This enables us to estimate treatment effects within each batch and apply straightforward meta-analytic approaches to combine treatment effects across batches without the need for estimation of batch effects and complex normalization strategies. We collected 104 test samples for three genetic studies across six batches to produce five analytical datasets from two complementary technologies commonly used in untargeted metabolomics. Here, we use the model system C. elegans to demonstrate that an augmented design combined with experimental blocks and other metabolomic QC approaches can be used to anchor studies and enable comparisons of stable spectral features across time without the need for compound identification. This approach is generalizable to systems where the same genotype can be assayed in multiple environments and provides biologically relevant features for downstream compound identification efforts. All methods are included in the newest release of the publicly available SECIMTools based on the open-source Galaxy platform.

Addressing Social Determinants of Health Through Community Engagement: An Undergraduate Nursing Course.

BACKGROUND: Community engagement is a promising method for teaching about social determinants of health (SDOH) through experiential learning. The purpose of this article is to report the implementation and evaluation of a baccalaureate nursing degree course focused on addressing social determinants of health through community engagement. METHOD: The class was structured as a flipped classroom, during which students spent the majority of the time engaged with a community site. Students were required to attend two in-class seminars. Course evaluations were used to provide feedback to refine course delivery and assess course impact. RESULTS: Thirty-six students enrolled in the course over two semesters. Knowledge and self-reported competencies in addressing SDOH in under-resourced populations increased. Course evaluations demonstrated an increase in students' knowledge and clinical skills related to SDOH. CONCLUSION: A course focused on addressing SDOH through community engagement provided a promising approach for preparing nursing students to provide care to underresourced populations. [J Nurs Educ. 2019;58(7):423-426.].

Myeloid lineage enhancers drive oncogene synergy in CEBPA/CSF3R mutant acute myeloid leukemia.

Acute Myeloid Leukemia (AML) develops due to the acquisition of mutations from multiple functional classes. Here, we demonstrate that activating mutations in the granulocyte colony stimulating factor receptor (CSF3R), cooperate with loss of function mutations in the transcription factor CEBPA to promote acute leukemia development. The interaction between these distinct classes of mutations occurs at the level of myeloid lineage enhancers where mutant CEBPA prevents activation of a subset of differentiation associated enhancers. To confirm this enhancer-dependent mechanism, we demonstrate that CEBPA mutations must occur as the initial event in AML initiation. This improved mechanistic understanding will facilitate therapeutic development targeting the intersection of oncogene cooperativity.