Learning from model errors: Can land use, edaphic and very high-resolution topo-climatic factors improve macroecological models of mountain grasslands?

Aim: Assess the potential of new predictors (land use, edaphic factors and high-resolution topographic and climatic variables, i.e., topo-climatic) to improve the prediction of plant community functional traits (specific leaf area, vegetative height and seed mass) and species richness in models of mountain grasslands. Location: The western Swiss Alps Methods: Using 912 grassland plots, we constructed predictive models for community-weighted means of plant traits and species richness using high resolution (25 m) topo-climatic predictors traditionally used in previous modelling studies in this area. In addition, 78 new plots were sampled for evaluation and error assessment in four narrower sets of homogenous conditions based on predictions by the topo-climatic models within two elevation belts (montane and alpine). New, finer-scale predictors were generated from direct field measurements or very high-resolution (5 m) numerical data. We then used multimodel inference to test the capacity of these finer predictors to explain part of the residual variance in the initial topo-climatic models. Results: We showed that the finer-scale predictors explained up to 44% of the residual variance in the classical topo-climatic models. The very high-resolution topographic position, soil C/N ratio and pH performed notably well in our analysis. Land use (farming intensity) was highlighted as potentially important in montane grasslands, but improvements were only significant for species richness predictions. Main conclusions: Compared with classical topo-climatic models, the new, finer-scale predictors significantly improved the prediction of all traits and species richness in alpine plant communities and that of specific leaf area and richness in montane grasslands. The differences in the importance of the predictors, dependent on both trait and position along the elevation gradient, highlight the different factors that shape the distribution of species and communities along elevation gradients

How to best threshold and validate stacked species assemblages? Community optimisation might hold the answer

1. The popularity of species distribution models (SDMs) and the associated stacked species distribution models (S-SDMs), as tools for community ecologists, largely increased in recent years. However, while some consensus was reached about the best methods to threshold and evaluate individual SDMs, little agreement exists on how to best assemble individual SDMs into communities, i.e. how to build and assess S-SDM predictions. 2. Here, we used published data of insects and plants collected within the same study region to test (1) if the most established thresholding methods to optimize single species prediction are also the best choice for predicting species assemblage composition, or if community-based thresholding can be a better alternative, and (2) whether the optimal thresholding method depends on taxa, prevalence distribution and/or species richness. Based on a comparison of different evaluation approaches we provide guidelines for a robust community cross-validation framework, to use if spatial or temporal independent data are unavailable. 3. Our results showed that the selection of the “optimal” assembly strategy mostly depends on the evaluation approach rather than taxa, prevalence distribution, regional species pool or species richness. If evaluated with independent data or reliable cross-validation, community-based thresholding seems superior compared to single species optimisation. However, many published studies did not evaluate community projections with independent data, often leading to overoptimistic community evaluation metrics based on single species optimisation. 4. The fact that most of the reviewed S-SDM studies reported over-fitted community evaluation metrics highlights the importance of developing clear evaluation guidelines for community models. Here, we move a first step in this direction, providing a framework for cross-validation at the community level

Climate and land-use changes reshuffle politically-weighted priority areas of mountain biodiversity

Protected areas (PAs) play a critical role in conserving biodiversity and maintaining viable populations of threatened species. Yet, as global change could reduce the future effectiveness of existing PAs in covering high species richness, updating the boundaries of existing PAs or creating new ones might become necessary to uphold conservation goals. Modelling tools are increasingly used by policymakers to support the spatial prioritization of biodiversity conservation, enabling the inclusion of scenarios of environmental changes to achieve specific targets. Here, using the Western Swiss Alps as a case study, we show how integrating species richness derived from species distribution model predictions for four taxonomic groups under present and future climate and land-use conditions into two conservation prioritization schemes can help optimize extant and future PAs. The first scheme, the “Priority Scores Method” identified priority areas for the expansion of the existing PA network. The second scheme, using the zonation software, allowed identifying priority conservation areas while incorporating global change scenarios and political costs. We found that existing mountain PAs are currently not situated in the most environmentally nor politically suitable locations when maximizing alpha diversity for the studied taxonomic groups and that current PAs could become even less optimum under the future climate and land-use change scenarios. This analysis has focused on general areas of high species richness or species of conservation concern and did not account for special habitats or functional groups that could have been used to create the existing network. We conclude that such an integrated framework could support more effective conservation planning and could be similarly applied to other landscapes or other biodiversity conservation indice

Removal of AU Bias from Microarray mRNA Expression Data Enhances Computational Identification of Active MicroRNAs

Elucidation of regulatory roles played by microRNAs (miRs) in various biological networks is one of the greatest challenges of present molecular and computational biology. The integrated analysis of gene expression data and 3′-UTR sequences holds great promise for being an effective means to systematically delineate active miRs in different biological processes. Applying such an integrated analysis, we uncovered a striking relationship between 3′-UTR AU content and gene response in numerous microarray datasets. We show that this relationship is secondary to a general bias that links gene response and probe AU content and reflects the fact that in the majority of current arrays probes are selected from target transcript 3′-UTRs. Therefore, removal of this bias, which is in order in any analysis of microarray datasets, is of crucial importance when integrating expression data and 3′-UTR sequences to identify regulatory elements embedded in this region. We developed visualization and normalization schemes for the detection and removal of such AU biases and demonstrate that their application to microarray data significantly enhances the computational identification of active miRs. Our results substantiate that, after removal of AU biases, mRNA expression profiles contain ample information which allows in silico detection of miRs that are active in physiological conditions

Associations between genetic variations in the FURIN gene and hypertension

<p>Abstract</p> <p>Background</p> <p>Hypertension is a complex disease influenced by multiple genetic and environmental factors. The Kazakh ethnic group is characterized by a relatively high prevalence of hypertension. Previous research indicates that the FURIN gene may play a pivotal role in the renin-angiotensin system and maintaining the sodium-electrolyte balance. Because these systems influence blood pressure regulation, we considered FURIN as a candidate gene for hypertension. The purpose of this study was to systematically investigate the association between genetic variations in the FURIN gene and essential hypertension in a Xinjiang Kazakh population.</p> <p>Methods</p> <p>We sequenced all exons and the promoter regions of the FURIN gene in 94 hypertensive individuals to identify genetic variations associated with the disorder. Genotyping was performed using the TaqMan polymerase chain reaction method for four representative common single nucleotide polymorphisms (SNPs, -7315C > T, 1970C > G, 5604C > G, 6262C > T) in 934 Kazakh Chinese people. One SNP (1970C > G) was replicated in 1,219 Uygur Chinese people.</p> <p>Results</p> <p>Nine novel and seven known single nucleotide polymorphisms were identified in the FURIN gene. The results suggest that 1970C > G was associated with a hypertension phenotype in Kazakh Chinese (additive model, <it>P </it>= 0.091; dominant model, <it>P = </it>0.031, allele model, <it>P </it>= 0.030), and after adjustment with logistic regression analysis, ORs were 1.451 (95%CI 1.106-1.905, <it>P </it>= 0.008) and 1.496 (95% 1.103-2.028, <it>P </it>= 0.01) in additive and dominant models, respectively. In addition, the association between 1970C > G and hypertension was replicated in Uygur subjects (additive model, <it>P </it>= 0.042; dominant model, <it>P </it>= 0.102; allele model, <it>P </it>= 0.027) after adjustment in additive and dominant models, ORs were 1.327 (95% 1.07-1.646), <it>P </it>= 0.01 and 1.307 (95%CI 1.015-1.681, <it>P </it>= 0.038), respectively. G allele carriers exhibited significant lower urinary Na⁺excretion rate than non-carriers in the Kazakh Chinese population (152.45 ± 76.04 uM/min vs 173.33 ± 90.02 uM/min, <it>P </it>= 0.007).</p> <p>Conclusion</p> <p>Our results suggest that the FURIN gene may be a candidate gene involved in human hypertension, and that the G allele of 1970C > G may be a modest risk factor for hypertension in Xinjiang Kazakh and Uygur populations.</p

Testing for Differentially-Expressed MicroRNAs with Errors-in-Variables Nonparametric Regression

MicroRNA is a set of small RNA molecules mediating gene expression at post-transcriptional/translational levels. Most of well-established high throughput discovery platforms, such as microarray, real time quantitative PCR, and sequencing, have been adapted to study microRNA in various human diseases. The total number of microRNAs in humans is approximately 1,800, which challenges some analytical methodologies requiring a large number of entries. Unlike messenger RNA, the majority of microRNA (60%) maintains relatively low abundance in the cells. When analyzed using microarray, the signals of these low-expressed microRNAs are influenced by other non-specific signals including the background noise. It is crucial to distinguish the true microRNA signals from measurement errors in microRNA array data analysis. In this study, we propose a novel measurement error model-based normalization method and differentially-expressed microRNA detection method for microRNA profiling data acquired from locked nucleic acids (LNA) microRNA array. Compared with some existing methods, the proposed method significantly improves the detection among low-expressed microRNAs when assessed by quantitative real-time PCR assay

G-Protein Coupled Receptor Signaling Architecture of Mammalian Immune Cells

A series of recent studies on large-scale networks of signaling and metabolic systems revealed that a certain network structure often called “bow-tie network” are observed. In signaling systems, bow-tie network takes a form with diverse and redundant inputs and outputs connected via a small numbers of core molecules. While arguments have been made that such network architecture enhances robustness and evolvability of biological systems, its functional role at a cellular level remains obscure. A hypothesis was proposed that such a network function as a stimuli-reaction classifier where dynamics of core molecules dictate downstream transcriptional activities, hence physiological responses against stimuli. In this study, we examined whether such hypothesis can be verified using experimental data from Alliance for Cellular Signaling (AfCS) that comprehensively measured GPCR related ligands response for B-cell and macrophage. In a GPCR signaling system, cAMP and Ca2+ act as core molecules. Stimuli-response for 32 ligands to B-Cells and 23 ligands to macrophages has been measured. We found that ligands with correlated changes of cAMP and Ca2+ tend to cluster closely together within the hyperspaces of both cell types and they induced genes involved in the same cellular processes. It was found that ligands inducing cAMP synthesis activate genes involved in cell growth and proliferation; cAMP and Ca2+ molecules that increased together form a feedback loop and induce immune cells to migrate and adhere together. In contrast, ligands without a core molecules response are scattered throughout the hyperspace and do not share clusters. G-protein coupling receptors together with immune response specific receptors were found in cAMP and Ca2+ activated clusters. Analyses have been done on the original software applicable for discovering ‘bow-tie’ network architectures within the complex network of intracellular signaling where ab initio clustering has been implemented as well. Groups of potential transcription factors for each specific group of genes were found to be partly conserved across B-Cell and macrophage. A series of findings support the hypothesis

Evaluation of a new high-dimensional miRNA profiling platform

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are a class of approximately 22 nucleotide long, widely expressed RNA molecules that play important regulatory roles in eukaryotes. To investigate miRNA function, it is essential that methods to quantify their expression levels be available.</p> <p>Methods</p> <p>We evaluated a new miRNA profiling platform that utilizes Illumina's existing robust DASL chemistry as the basis for the assay. Using total RNA from five colon cancer patients and four cell lines, we evaluated the reproducibility of miRNA expression levels across replicates and with varying amounts of input RNA. The beta test version was comprised of 735 miRNA targets of Illumina's miRNA profiling application.</p> <p>Results</p> <p>Reproducibility between sample replicates within a plate was good (Spearman's correlation 0.91 to 0.98) as was the plate-to-plate reproducibility replicates run on different days (Spearman's correlation 0.84 to 0.98). To determine whether quality data could be obtained from a broad range of input RNA, data obtained from amounts ranging from 25 ng to 800 ng were compared to those obtained at 200 ng. No effect across the range of RNA input was observed.</p> <p>Conclusion</p> <p>These results indicate that very small amounts of starting material are sufficient to allow sensitive miRNA profiling using the Illumina miRNA high-dimensional platform. Nonlinear biases were observed between replicates, indicating the need for abundance-dependent normalization. Overall, the performance characteristics of the Illumina miRNA profiling system were excellent.</p

Accurate Expression Profiling of Very Small Cell Populations

BACKGROUND: Expression profiling, the measurement of all transcripts of a cell or tissue type, is currently the most comprehensive method to describe their physiological states. Given that accurate profiling methods currently available require RNA amounts found in thousands to millions of cells, many fields of biology working with specialized cell types cannot use these techniques because available cell numbers are limited. Currently available alternative methods for expression profiling from nanograms of RNA or from very small cell populations lack a broad validation of results to provide accurate information about the measured transcripts. METHODS AND FINDINGS: We provide evidence that currently available methods for expression profiling of very small cell populations are prone to technical noise and therefore cannot be used efficiently as discovery tools. Furthermore, we present Pico Profiling, a new expression profiling method from as few as ten cells, and we show that this approach is as informative as standard techniques from thousands to millions of cells. The central component of Pico Profiling is Whole Transcriptome Amplification (WTA), which generates expression profiles that are highly comparable to those produced by others, at different times, by standard protocols or by Real-time PCR. We provide a complete workflow from RNA isolation to analysis of expression profiles. CONCLUSIONS: Pico Profiling, as presented here, allows generating an accurate expression profile from cell populations as small as ten cells

Phenotypic Consequences of Copy Number Variation: Insights from Smith-Magenis and Potocki-Lupski Syndrome Mouse Models

The characterization of mice with different number of copies of the same genomic segment shows that structural changes influence the phenotypic outcome independently of gene dosage