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Johnson, J. E.

Publications and source records attributed to Johnson, J. E..

21 records · Page 2Linked to original sources

Diverse Arctic lake sediment microbiota shape methane emission temperature sensitivity

Northern post-glacial lakes are a significant and increasing source of atmospheric carbon (C), largely through ebullition (bubbling) of microbially-produced methane (CH4) from the sediments1. Ebullitive CH4 flux correlates strongly with temperature, suggesting that solar radiation is the primary driver of these CH4 emissions2. However, here we show that the slope of the temperature-CH4 flux relationship differs spatially, both within and among lakes. Hypothesizing that differences in microbiota could explain this heterogeneity, we compared site-specific CH4 emissions with underlying sediment microbial (metagenomic and amplicon), isotopic, and geochemical data across two post-glacial lakes in Northern Sweden. The temperature-associated increase in CH4 emissions was greater in lake middles--where methanogens were more abundant--than edges, and sediment microbial communities were distinct between lake edges and middles. Although CH4 emissions projections are typically driven by abiotic factors1, regression modeling revealed that microbial abundances, including those of CH4-cycling microorganisms and syntrophs that generate H2 for methanogenesis, can be useful predictors of porewater CH4 concentrations. Our results suggest that deeper lake regions, which currently emit less CH4 than shallower edges, could add substantially to overall CH4 emissions in a warmer Arctic with longer ice-free seasons and that future CH4 emission predictions from northern lakes may be improved by accounting for spatial variations in sediment microbiota.

microbiology↗

Multi-omics Visualization Platform: An extensible Galaxy plug-In for multi-omics data visualization and exploration

BackgroundProteogenomics integrates genomics, transcriptomics and mass spectrometry (MS)-based proteomics data to identify novel protein sequences arising from gene and transcript sequence variants. Proteogenomic data analysis requires integration of disparate omic software tools, as well as customized tools to view and interpret results. The flexible Galaxy platform has proven valuable for proteogenomic data analysis. Here, we describe a novel Multi-omics Visualization Platform (MVP) for organizing, visualizing and exploring proteogenomic results, adding a critically needed tool for data exploration and interpretation. FindingsMVP is built as an HTML Galaxy plugin, primarily based on JavaScript. Via the Galaxy API, MVP uses SQLite databases as input -- a custom datatype (mzSQLite) containing MS-based peptide identification information, a variant annotation table, and a coding sequence table. Users can interactively filter identified peptides based on sequence and data quality metrics, view annotated peptide MS data, and visualize protein-level information, along with genomic coordinates. Peptides that pass the user-defined thresholds can be sent back to Galaxy via the API for further analysis; processed data and visualizations can also be saved and shared. MVP leverages the Integrated Genomics Viewer JavaScript (IGVjs) framework, enabling interactive visualization of peptides and corresponding transcript and genomic coding information within the MVP interface. ConclusionsMVP provides a powerful, extensible platform for automated, interactive visualization of proteogenomic results within the Galaxy environment, adding a unique and critically needed tool for empowering exploration and interpretation of results. The platform is extensible, providing a basis for further development of new functionalities for proteogenomic data visualization.

bioinformatics↗

Intrinsic and Modifiable Contributors to Distal Radius Microstructure, Macrostructure and Strength in Premenopausal Women

While weight-bearing and resistive exercise modestly increases aBMD, the precise relationship between physical activity and bone microstructure, and strain in humans is not known. Previously, we established a voluntary upper-extremity loading model that assigns a persons target force based on their subject-specific, continuum FE-estimated radius bone strain. Here, our purpose was to quantify the inter-individual variability in radius microstructure and FE-estimated strain explained by site-specific mechanical loading history, and to determine whether variability in strain is captured by aBMD, a clinically relevant measure of bone density and fracture risk. Seventy-two women aged 21-40 were included in this cross-sectional analysis. High resolution peripheral quantitative computed tomography (HRpQCT) was used to measure macro- and micro-structure in the distal radius. Mean energy equivalent strain in the distal radius was calculated from continuum finite element models generated from clinical resolution CT images of the forearm. Areal BMD was used in a nonlinear regression model to predict FE strain. Hierarchical linear regression models were used to assess the predictive capability of intrinsic (age, height) and modifiable (body mass, grip strength, physical activity) predictors. Fifty-one percent of the variability in FE bone strain was explained by its relationship with aBMD, with higher density predicting lower strains. Age and height explained up to 31.6% of the variance in microstructural parameters. Body mass explained 9.1% and 10.0% of the variance in aBMD and bone strain, respectively, with higher body mass indicative of greater density. Overall, results suggest that meaningful differences in bone structure and strain can be predicted by subject characteristics.\n\nHighlightsO_LIAreal bone mineral density (aBMD) explains 51% of the variability in bone strain.\nC_LIO_LIAdult bone loading predicts greater cortical porosity and trabecular density.\nC_LIO_LIGreater body mass predicts greater aBMD and lower bone strain.\nC_LI

physiology↗