Freshwater ecosystems are among the most endangered habitats on Earth, with thousands of animal species known to be threatened or already extinct. Reliable monitoring of threatened organisms is crucial for data-driven conservation actions but remains a challenge owing to nonstandardized methods that depend on practical and taxonomic expertise, which is rapidly declining. Here, we show that a diversity of rare and threatened freshwater animals--representing amphibians, fish, mammals, insects and crustaceans--can be detected and quantified based on DNA obtained directly from small water samples of lakes, ponds and streams. We successfully validate our findings in a controlled mesocosm experiment and show that DNA becomes undetectable within 2 weeks after removal of animals, indicating that DNA traces are near contemporary with presence of the species. We further demonstrate that entire faunas of amphibians and fish can be detected by high-throughput sequencing of DNA extracted from pond water. Our findings underpin the ubiquitous nature of DNA traces in the environment and establish environmental DNA as a tool for monitoring rare and threatened species across a wide range of taxonomic groups.

After the completion of the human and other genome projects it emerged that the number of genes in organisms as diverse as fruit flies, nematodes, and humans does not reflect our perception of their relative complexity. Here, we provide reliable evidence that the size of protein interaction networks in different organisms appears to correlate much better with their apparent biological complexity. We develop a stable and powerful, yet simple, statistical procedure to estimate the size of the whole network from subnet data. This approach is then applied to a range of eukaryotic organisms for which extensive protein interaction data have been collected and we estimate the number of interactions in humans to be Ϸ650,000. We find that the human interaction network is one order of magnitude bigger than the Drosophila melanogaster interactome and Ϸ3 times bigger than in Caenorhabditis elegans.evolutionary systems biology ͉ network inference ͉ network sampling theory ͉ network evolution O ne of the perhaps most surprising results of the genomesequencing projects was that the number of genes is much lower than had been expected and is, in fact, surprisingly similar for very different organisms (1, 2). For example, the nematode Caenorhabditis elegans appears to have a similar number of genes as humans, whereas rice and maize appear to have even more genes than humans. It was then quickly suggested that the biological complexity of organisms is not reflected merely by the number of genes but by the number of physiologically relevant interactions (1, 3). In addition to alternative splice variants (4), posttranslational processes (5), and other (e.g., genetic) factors influencing gene expression (6, 7), the structure of interactome is one of the crucial factors underlying the complexity of biological organisms. Here, we focus on the wealth of available protein interaction data and demonstrate that it is possible to arrive at a reliable statistical estimate for the size of these interaction networks. This approach is then used to assess the complexity of protein interaction networks in different organisms from present incomplete and noisy protein interaction datasets.There are now fairly extensive protein interaction network (PIN) datasets in a number of species, including humans (8, 9). These have been generated by a variety of experimental techniques (as well as some in silico inferences). Although these techniques and the resulting data are (i) notoriously prone to false positives and negatives (10, 11), and (ii) result in highly idealized and averaged network structures (12), such interaction datasets are increasingly turning into useful tools for the analysis of the functional (e.g., ref. 13) and evolutionary properties (14) of biological systems. In particular, in Saccharomyces cerevisiae we are beginning to have a fairly complete description of the protein interaction network that is accessible with current experimental technologies; the recent high-quality literaturecurated dataset of Reguly et al. (15) provides us w...

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth's soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model–experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved.

Genetic analyses of permafrost and temperate sediments reveal that plant and animal DNA may be preserved for long periods, even in the absence of obvious macrofossils. In Siberia, five permafrost cores ranging from 400,000 to 10,000 years old contained at least 19 different plant taxa, including the oldest authenticated ancient DNA sequences known, and megafaunal sequences including mammoth, bison, and horse. The genetic data record a number of dramatic changes in the taxonomic diversity and composition of Beringian vegetation and fauna. Temperate cave sediments in New Zealand also yielded DNA sequences of extinct biota, including two species of ratite moa, and 29 plant taxa characteristic of the prehuman environment. Therefore, many sedimentary deposits may contain unique, and widespread, genetic records of paleoenvironments.

Most studies of networks have only looked at small subsets of the true network. Here, we discuss the sampling properties of a network's degree distribution under the most parsimonious sampling scheme. Only if the degree distributions of the network and randomly sampled subnets belong to the same family of probability distributions is it possible to extrapolate from subnet data to properties of the global network. We show that this condition is indeed satisfied for some important classes of networks, notably classical random graphs and exponential random graphs. For scale-free degree distributions, however, this is not the case. Thus, inferences about the scale-free nature of a network may have to be treated with some caution. The work presented here has important implications for the analysis of molecular networks as well as for graph theory and the theory of networks in general.complex networks ͉ protein interaction networks ͉ random graphs ͉ sampling theory

MicroRNAs (miRNA) are a class of small noncoding RNAs with important posttranscriptional regulatory functions. Recent data suggest that miRNAs are aberrantly expressed in many human cancers and that they may play significant roles in carcinogenesis. Here, we used microarrays to profile the expression of 315 human miRNAs in 10 normal mucosa samples and 49 stage II colon cancers differing with regard to microsatellite status and recurrence of disease. Several miRNAs were differentially expressed between normal tissue and tumor microsatellite subtypes, with miR-145 showing the lowest expression in cancer relative to normal tissue. Microsatellite status for the majority of cancers could be correctly predicted based on miRNA expression profiles. Furthermore, a biomarker based on miRNA expression profiles could predict recurrence of disease with an overall performance accuracy of 81%, indicating a potential role of miRNAs in determining tumor aggressiveness. The expression levels of miR-320 and miR-498, both included in the predictive biomarker, correlated with the probability of recurrence-free survival by multivariate analysis. We successfully verified the expression of selected miRNAs using real-time reverse transcription-PCR assays for mature miRNAs, whereas in situ hybridization was used to detect the accumulation of miR-145 and miR-320 in normal epithelial cells and adenocarcinoma cells. Functional studies showed that miR-145 potently suppressed growth of three different colon carcinoma cell lines. In conclusion, our results suggest that perturbed expression of numerous miRNAs in colon cancer may have a functional effect on tumor cell behavior, and, furthermore, that some miRNAs with prognostic potential could be of clinical importance. [Cancer Res 2008;68(15):6416-24]

Hypoxia, a common feature of the microenvironment in solid tumors, is associated with resistance to radiotherapy, reduced therapeutic response, and a poorer clinical outcome. In head and neck squamous cell carcinomas (HNSCC), the negative effect of hypoxia on radiotherapy can be counteracted via addition of hypoxic modification to the radiotherapy. To predict which patients harbor hypoxic tumors and would therefore benefit from hypoxic modification, clinically applicable methods for pretherapeutic hypoxic evaluation and categorization are needed. In this study, we developed a hypoxia classifier based on gene expression. Through study of xenograft tumors from human squamous cell carcinoma cell lines, we verified the in vivo relevance of previously identified in vitro derived hypoxia-induced genes. We then evaluated a training set of 58 hypoxia-evaluated HNSCCs to generate a gene expression classifier containing 15 genes. This 15-gene hypoxia classifier was validated in 323 patients with HNSCC randomized for hypoxic modification or placebo in combination with radiotherapy. Tumors categorized as hypoxic on the basis of the classifier were associated with a significantly poorer clinical outcome than nonhypoxic tumors. This outcome was improved and equalized to the nonhypoxic tumors by addition of hypoxic modification. Thus, findings show that the classifier attained both prognostic and predictive impact, and its pretherapeutic use may provide a method to identify those patients who will benefit from hypoxic modification of radiotherapy. Cancer Res; 71(17); 5923-31. Ó2011 AACR.

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