A collaborative analysis of land use and frog diversity across spatial scales

Amphibians are sensitive to changes in land use because they require both upland terrestrial habitat and aquatic wetland habitat to complete their life cycle. Our previous work demonstrates that land-use change including road density, development, and wetland area impact amphibian diversity. We build upon this previous work to examine the relative influences of these factors across different landscape scales. Incorporating scale within our model allows us to explore by which mechanism different factors impact amphibians (e.g. do roads increase roadkill in the immediate surrounding area or do they isolate populations at the larger scale?). North American amphibian monitoring program (NAAMP) compiles data from standardized roadside surveys of calling frogs and toads across the majority of the contiguous United States to examine the impacts of human activity on amphibian populations over time. In this study we used NAAMP call data from 18 eastern U.S. states and National Land Cover Data to address the following research questions 1) How is the impact of road length and landscape change mediated by distance from the habitat and 2) how do species differ in the relative influence of these effects over the landscape? We quantified landscape features (e.g., habitat types, wetland –forest connectivity, road density and arrangement) using a GIS program and calculated amphibian diversity estimates of each survey at six locations ranging from 300 meters (local scale, the core terrestrial habitat) to 10, 000 meters (associations should decline at this distance). This approached allows us to explore the relative influence of factors at the regional level to build a predictive model to answer our research questions. This project is supported by the National Science Foundation, Transforming Undergraduate Education in Science program coordinated by David Marsh and the National Center for Ecological Analysis and Synthesis.https://scholarscompass.vcu.edu/uresposters/1105/thumbnail.jp

Regional and scale-specific effects of land use on amphibian diversity [poster]

Background/Question/Methods Habitat loss and degradation influence amphibian distributions and are important drivers of population declines. Our previous research demonstrated that road disturbance, development and wetland area consistently influence amphibian richness across regions of the U.S. Here, we examined the relative importance of these factors in different regions and at multiple spatial scales. Understanding the scales at which habitat disturbance may be affecting amphibian distributions is important for conservation planning. Specifically, we asked: 1) Over what spatial scales do distinct landscape features affect amphibian richness? and 2) Do road types (non-rural and rural) have similar effects on amphibian richness? This is the second year of a collaborative, nationwide project involving 11 U.S. colleges integrated within undergraduate biology curricula. We summarized North American Amphibian Monitoring Program data in 13 Eastern and Central U.S states and used geographic information systems to extract landscape data for 471 survey locations. We developed models to quantify the influence of landscape variables on amphibian species richness and site occupancy across five concentric buffers ranging from 300m to 10,000m. Results/Conclusions Across spatial scales, development, road density and agriculture were the best predictors of amphibian richness and site occupancy by individual species. Across regions, we found that scale did not exert a large influence on how landscape features influenced amphibian richness as effects were largely comparable across buffers. However, development and percent impervious surface had stronger influence on richness at smaller spatial scales. Richness was lower at survey locations with higher densities of non-rural and rural roads, and non-rural road density had a larger negative effect at smaller scales. Within regions, landscape features driving patterns of species richness varied. The scales at which these factors were associated with richness were highly variable within regions, suggesting the scale effects may be region specific. Our project demonstrates that networks of undergraduate students can collaborate to compile and analyze large ecological data sets, while engaging students in authentic and inquiry-based learning in landscape-scale ecology

Machine learning approaches to predict lupus disease activity from gene expression data

The integration of gene expression data to predict systemic lupus erythematosus (SLE) disease activity is a significant challenge because of the high degree of heterogeneity among patients and study cohorts, especially those collected on different microarray platforms. Here we deployed machine learning approaches to integrate gene expression data from three SLE data sets and used it to classify patients as having active or inactive disease as characterized by standard clinical composite outcome measures. Both raw whole blood gene expression data and informative gene modules generated by Weighted Gene Co-expression Network Analysis from purified leukocyte populations were employed with various classification algorithms. Classifiers were evaluated by 10-fold cross-validation across three combined data sets or by training and testing in independent data sets, the latter of which amplified the effects of technical variation. A random forest classifier achieved a peak classification accuracy of 83 percent under 10-fold cross-validation, but its performance could be severely affected by technical variation among data sets. The use of gene modules rather than raw gene expression was more robust, achieving classification accuracies of approximately 70 percent regardless of how the training and testing sets were formed. Fine-tuning the algorithms and parameter sets may generate sufficient accuracy to be informative as a standalone estimate of disease activity

Machine learning approaches to predict lupus disease activity from gene expression data.

The integration of gene expression data to predict systemic lupus erythematosus (SLE) disease activity is a significant challenge because of the high degree of heterogeneity among patients and study cohorts, especially those collected on different microarray platforms. Here we deployed machine learning approaches to integrate gene expression data from three SLE data sets and used it to classify patients as having active or inactive disease as characterized by standard clinical composite outcome measures. Both raw whole blood gene expression data and informative gene modules generated by Weighted Gene Co-expression Network Analysis from purified leukocyte populations were employed with various classification algorithms. Classifiers were evaluated by 10-fold cross-validation across three combined data sets or by training and testing in independent data sets, the latter of which amplified the effects of technical variation. A random forest classifier achieved a peak classification accuracy of 83 percent under 10-fold cross-validation, but its performance could be severely affected by technical variation among data sets. The use of gene modules rather than raw gene expression was more robust, achieving classification accuracies of approximately 70 percent regardless of how the training and testing sets were formed. Fine-tuning the algorithms and parameter sets may generate sufficient accuracy to be informative as a standalone estimate of disease activity

Abnormalities in intron retention characterize patients with systemic lupus erythematosus

Abstract Regulation of intron retention (IR), a form of alternative splicing, is a newly recognized checkpoint in gene expression. Since there are numerous abnormalities in gene expression in the prototypic autoimmune disease systemic lupus erythematosus (SLE), we sought to determine whether IR was intact in patients with this disease. We, therefore, studied global gene expression and IR patterns of lymphocytes in SLE patients. We analyzed RNA-seq data from peripheral blood T cell samples from 14 patients suffering from systemic lupus erythematosus (SLE) and 4 healthy controls and a second, independent data set of RNA-seq data from B cells from16 SLE patients and 4 healthy controls. We identified intron retention levels from 26,372 well annotated genes as well as differential gene expression and tested for differences between cases and controls using unbiased hierarchical clustering and principal component analysis. We followed with gene-disease enrichment analysis and gene-ontology enrichment analysis. Finally, we then tested for significant differences in intron retention between cases and controls both globally and with respect to specific genes. Overall decreased IR was found in T cells from one cohort and B cells from another cohort of patients with SLE and was associated with increased expression of numerous genes, including those encoding spliceosome components. Different introns within the same gene displayed both up- and down-regulated retention profiles indicating a complex regulatory mechanism. These results indicate that decreased IR in immune cells is characteristic of patients with active SLE and may contribute to the abnormal expression of specific genes in this autoimmune disease

Nasopharyngeal metatranscriptome profiles of infants with bronchiolitis and risk of childhood asthma: a multicentre prospective study

BACKGROUND: Bronchiolitis is not only the leading cause of hospitalisation in US infants but also a major risk factor for asthma development. Growing evidence supports clinical heterogeneity within bronchiolitis. Our objectives were to identify metatranscriptome profiles of infant bronchiolitis, and to examine their relationship with the host transcriptome and subsequent asthma development. METHODS: As part of a multicentre prospective cohort study of infants (age \u3c1 year) hospitalised for bronchiolitis, we integrated virus and nasopharyngeal metatranscriptome (species-level taxonomy and function) data measured at hospitalisation. We applied network-based clustering approaches to identify metatranscriptome profiles. We then examined their association with the host transcriptome at hospitalisation and risk for developing asthma. RESULTS: We identified five metatranscriptome profiles of bronchiolitis (n=244): profile A: virusmicrobiome; profile B: virusmicrobiome ; profile C: virusmicrobiome ; profile D: virusmicrobiome ; and profile E: virusmicrobiome . Compared with profile A, profile B infants were characterised by a high proportion of eczema, abundance and enriched virulence related to antibiotic resistance. These profile B infants also had upregulated T-helper 17 and downregulated type I interferon pathways (false discovery rate (FDR) \u3c0.005), and significantly higher risk for developing asthma (17.9% 38.9%; adjusted OR 2.81, 95% CI 1.11-7.26). Likewise, profile C infants were characterised by a high proportion of parental asthma, dominance, and enriched glycerolipid and glycerophospholipid metabolism of the microbiome. These profile C infants had an upregulated RAGE signalling pathway (FDR \u3c0.005) and higher risk of asthma (17.9% 35.6%; adjusted OR 2.49, 95% CI 1.10-5.87). CONCLUSIONS: Metatranscriptome and clustering analysis identified biologically distinct metatranscriptome profiles that have differential risks of asthma