Estimation of heterozygosity for single-probe multilocus DNA fingerprints

In spite of the increasing application of DNA fingerprinting to natural populations and to the genetic identification of humans, explicit methods for estimation of basic population genetic parameters from DNA fingerprinting data have not been developed. Contributing to this omission is the inability to determine, for multilocus fingerprinting probes, relatively important genetic information, such as the number of loci, the number of alleles, and the distribution of these alleles into specific loci. One of the most useful genetic parameters that could be derived from such data would be the average heterozygosity, which has traditionally been employed to measure the level of genetic variation within populations and to compare genetic variation among different loci. We derive here explicit formulas for both the estimation of average heterozygosity at multiple hypervariable loci and a maximum value for this estimate. These estimates are based upon the DNA restriction-pattern matrices that are typical for fingerprinting studies of humans and natural populations. For several empirical data sets from our laboratory, estimates of average and maximal heterozygosity are shown to be relatively close to each other. Furthermore, variances of these statistics based on simulation studies are relatively small. These observations, as well as consideration of the effect of missing alleles and alternate numbers of loci, suggest that the average heterozygosity can be accurately estimated using phenotypic DNA fingerprint patterns, because this parameter is relatively insensitive to the lack of certain genetic information.      

Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data

MOTIVATION: Selecting a small number of relevant genes for accurate classification of samples is essential for the development of diagnostic tests. We present the Bayesian model averaging (BMA) method for gene selection and classification of microarray data. Typical gene selection and classification procedures ignore model uncertainty and use a single set of relevant genes (model) to predict the class. BMA accounts for the uncertainty about the best set to choose by averaging over multiple models (sets of potentially overlapping relevant genes). RESULTS: We have shown that BMA selects smaller numbers of relevant genes (compared with other methods) and achieves a high prediction accuracy on three microarray datasets. Our BMA algorithm is applicable to microarray datasets with any number of classes, and outputs posterior probabilities for the selected genes and models. Our selected models typically consist of only a few genes. The combination of high accuracy, small numbers of genes and posterior probabilities for the predictions should make BMA a powerful tool for developing diagnostics from expression data. AVAILABILITY: The source codes and datasets used are available from our Supplementary website.     

Contribution of subsurface peat to CO2 and CH4 fluxes in a neotropical peatland

Tropical peatlands play an important role in the global carbon cycling but little is known about factors regulating carbon dioxide (CO₂) and methane (CH₄) fluxes from these ecosystems. Here, we test the hypotheses that (i) CO₂ and CH₄ are produced mainly from surface peat and (ii) that the contribution of subsurface peat to net C emissions is governed by substrate availability. To achieve this, in situ and ex situ CO₂ and CH₄ fluxes were determined throughout the peat profiles under three vegetation types along a nutrient gradient in a tropical ombrotrophic peatland in Panama. The peat was also characterized with respect to its organic composition using ¹³C solid state cross‐polarization magic‐angle spinning nuclear magnetic resonance spectroscopy. Deep peat contributed substantially to CO₂ effluxes both with respect to actual in situ and potential ex situ fluxes. CH₄ was produced throughout the peat profile with distinct subsurface peaks, but net emission was limited by oxidation in the surface layers. CO₂ and CH₄ production were strongly substrate‐limited and a large proportion of the variance in their production (30% and 63%, respectively) was related to the quantity of carbohydrates in the peat. Furthermore, CO₂ and CH₄ production differed between vegetation types, suggesting that the quality of plant‐derived carbon inputs is an important driver of trace gas production throughout the peat profile. We conclude that the production of both CO₂ and CH₄ from subsurface peat is a substantial component of the net efflux of these gases, but that gas production through the peat profile is regulated in part by the degree of decomposition of the peat.     

Evaluation of approaches for measuring taxonomic completeness of lake profundal macroinvertebrate assemblages

1. Benthic macroinvertebrates (MI) are commonly used to assess freshwater ecosystems with the reference condition approach. Such assessments necessitate control for natural community variation, either by categorical typologies or by predictive models that have been widely and successfully developed for running water biota but not previously for lake profundal invertebrates. 2. We evaluated four modelling techniques [multivariate regression tree (MRT), limiting environmental differences, nonparametric multiplicative regression (NPMR) and River Invertebrate Prediction And Classification System (RIVPACS) and the operative Finnish lake typology for assessing taxonomic completeness (observed‐to‐expected number of taxa, O/E) of profundal MI assemblages. We used data from 74 and 33 minimally disturbed reference lake basins for calibration and validation of the approaches, respectively, and 72 test basins subject to various anthropogenic pressures to evaluate sensitivity to detect impact. Either all predicted taxa (threshold probability of capture P_t = 0+) or only those predicted to be captured with ≥0.25 probability were used to calculate O/E. 3. With P_t = 0.25, all four modelling approaches were accurate (mean O/E = 0.966–1.053) but imprecise (SD of O/E = 0.279–0.304) in predicting the fauna actually observed in validation sites. All models were subtly more precise than a null model (mean 1.038, SD 0.343) or the typology (1.046, 0.327). The taxon‐specific NPMR model was slightly more precise than the other three models based on site groupings. 4. The O/E values correlated relatively weakly (r = 0.55–0.86) among the approaches, which thus produced contrasting lake‐specific assessments, despite their seemingly comparable performances. Indeed, typology, suggesting that MI assemblages were impaired in 56% of test sites, was more sensitive than the other approaches (26–46%) as an indicator of human‐induced deterioration. However, this greater ostensible sensitivity seemed to be biased, as lake morphometry, a main driver of natural community variation, remained uncontrolled by the typology. 5. Generally, our exercise illustrates the inconclusiveness of the common validation criteria for the assessment methods. The apparent poor predictability of the profundal fauna, irrespective of the method, may partly stem from large observation error, which could be alleviated by more intensive sampling. However, instead of an O/E‐taxa index, some other metric encompassing quantitative aspects might be preferable for assessing these species‐poor communities.     

A large-scale survey of genetic copy number variations among Han Chinese residing in Taiwan

Background

Winter migration of immature brown trout (Salmo trutta) into freshwater rivers has been hypothesized to result from physiologically stressful combinations of high salinity and low temperature in the sea.

Results

We sampled brown trout from two Danish populations entering different saline conditions and quantified expression of the hsp70 and Na/K-ATPases α 1b genes following acclimation to freshwater and full-strength seawater at 2°C and 10°C. An interaction effect of low temperature and high salinity on expression of both hsp70 and Na/K-ATPase α 1b was found in trout from the river entering high saline conditions, while a temperature independent up-regulation of both genes in full-strength seawater was found for trout entering marine conditions with lower salinities.

Conclusion

Overall our results support the hypothesis that physiologically stressful conditions in the sea drive sea-run brown trout into freshwater rivers in winter. However, our results also demonstrate intra-specific differences in expression of important stress and osmoregulative genes most likely reflecting adaptive differences between trout populations on a regional scale, thus strongly suggesting local adaptations driven by the local marine environment.     

Identification of high and low responders to lipopolysaccharide in normal subjects: an unbiased approach to identify modulators of innate immunity

LPS stimulates a vigorous inflammatory response from circulating leukocytes that varies greatly from individual to individual. The goal of this study was to use an unbiased approach to identify differences in gene expression that may account for the high degree of interindividual variability in inflammatory responses to LPS in the normal human population. We measured LPS-induced cytokine production ex vivo in whole blood from 102 healthy human subjects and identified individuals who consistently showed either very high or very low responses to LPS (denoted lps(high) and lps(low), respectively). Comparison of gene expression profiles between the lps(high) and lps(low) individuals revealed 80 genes that were differentially expressed in the presence of LPS and 21 genes that were differentially expressed in the absence of LPS (p < 0.005, ANOVA). Expression of a subset of these genes was confirmed using real-time RT-PCR. Functional relevance for one gene confirmed to be expressed at a higher level in lps(high), adipophilin, was inferred when reduction in adipophilin mRNA by small interfering RNA in the human monocyte-like cell line THP-1 resulted in a modest but significant reduction in LPS-induced MCP-1 mRNA expression. These data illustrate a novel approach to the identification of factors that determine interindividual variability in innate immune inflammatory responses and identify adipophilin as a novel potential regulator of LPS-induced MCP-1 production in human monocytes.