The clinical trials puzzle: How network effects limit drug discovery.

The depth of knowledge offered by post-genomic medicine has carried the promise of new drugs, and cures for multiple diseases. To explore the degree to which this capability has materialized, we extract meta-data from 356,403 clinical trials spanning four decades, aiming to offer mechanistic insights into the innovation practices in drug discovery. We find that convention dominates over innovation, as over 96% of the recorded trials focus on previously tested drug targets, and the tested drugs target only 12% of the human interactome. If current patterns persist, it would take 170 years to target all druggable proteins. We uncover two network-based fundamental mechanisms that currently limit target discovery: preferential attachment, leading to the repeated exploration of previously targeted proteins; and local network effects, limiting exploration to proteins interacting with highly explored proteins. We build on these insights to develop a quantitative network-based model to enhance drug discovery in clinical trials.

Accelerated Evolution of Tissue-Specific Genes Mediates Divergence Amidst Gene Flow in European Green Lizards.

The European green lizards of the Lacerta viridis complex consist of two closely related species, L. viridis and Lacerta bilineata that split less than 7 million years ago in the presence of gene flow. Recently, a third lineage, referred to as the "Adriatic" was described within the L. viridis complex distributed from Slovenia to Greece. However, whether gene flow between the Adriatic lineage and L. viridis or L. bilineata has occurred and the evolutionary processes involved in their diversification are currently unknown. We hypothesized that divergence occurred in the presence of gene flow between multiple lineages and involved tissue-specific gene evolution. In this study, we sequenced the whole genome of an individual of the Adriatic lineage and tested for the presence of gene flow amongst L. viridis, L. bilineata, and Adriatic. Additionally, we sequenced transcriptomes from multiple tissues to understand tissue-specific effects. The species tree supports that the Adriatic lineage is a sister taxon to L. bilineata. We detected gene flow between the Adriatic lineage and L. viridis suggesting that the evolutionary history of the L. viridis complex is likely shaped by gene flow. Interestingly, we observed topological differences between the autosomal and Z-chromosome phylogenies with a few fast evolving genes on the Z-chromosome. Genes highly expressed in the ovaries and strongly co-expressed in the brain experienced accelerated evolution presumably contributing to establishing reproductive isolation in the L. viridis complex.

Seasonal Dynamics of Pelagic Mycoplanktonic Communities: Interplay of Taxon Abundance, Temporal Occurrence, and Biotic Interactions.

Marine fungi are an important component of pelagic planktonic communities. However, it is not yet clear how individual fungal taxa are integrated in marine processes of the microbial loop and food webs. Most likely, biotic interactions play a major role in shaping the fungal community structure. Thus, the aim of our work was to identify possible biotic interactions of mycoplankton with phytoplankton and zooplankton groups and among fungi, and to investigate whether there is coherence between interactions and the dynamics, abundance and temporal occurrence of individual fungal OTUs. Marine surface water was sampled weekly over the course of 1 year, in the vicinity of the island of Helgoland in the German Bight (North Sea). The mycoplankton community was analyzed using 18S rRNA gene tag-sequencing and the identified dynamics were correlated to environmental data including phytoplankton, zooplankton, and abiotic factors. Finally, co-occurrence patterns of fungal taxa were detected with network analyses based on weighted topological overlaps (wTO). Of all abundant and persistent OTUs, 77% showed no biotic relations suggesting a saprotrophic lifestyle. Of all other fungal OTUs, nearly the half (44%) had at least one significant negative relationship, especially with zooplankton and other fungi, or to a lesser extent with phytoplankton. These findings suggest that mycoplankton OTUs are embedded into marine food web chains via highly complex and manifold relationships such as parasitism, predation, grazing, or allelopathy. Furthermore, about one third of all rare OTUs were part of a dense fungal co-occurrence network probably stabilizing the fungal community against environmental changes and acting as functional guilds or being involved in fungal cross-feeding. Placed in an ecological context, strong antagonistic relationships of the mycoplankton community with other components of the plankton suggest that: (i) there is a top-down control by fungi on zooplankton and phytoplankton; (ii) fungi serve as a food source for zooplankton and thereby transfer nutrients and organic material; (iii) the dynamics of fungi harmful to other plankton groups are controlled by antagonistic fungal taxa.

Construction, comparison and evolution of networks in life sciences and other disciplines.

Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.

wTO: an R package for computing weighted topological overlap and a consensus network with integrated visualization tool.

BACKGROUND: Network analyses, such as of gene co-expression networks, metabolic networks and ecological networks have become a central approach for the systems-level study of biological data. Several software packages exist for generating and analyzing such networks, either from correlation scores or the absolute value of a transformed score called weighted topological overlap (wTO). However, since gene regulatory processes can up- or down-regulate genes, it is of great interest to explicitly consider both positive and negative correlations when constructing a gene co-expression network. RESULTS: Here, we present an R package for calculating the weighted topological overlap (wTO), that, in contrast to existing packages, explicitly addresses the sign of the wTO values, and is thus especially valuable for the analysis of gene regulatory networks. The package includes the calculation of p-values (raw and adjusted) for each pairwise gene score. Our package also allows the calculation of networks from time series (without replicates). Since networks from independent datasets (biological repeats or related studies) are not the same due to technical and biological noise in the data, we additionally, incorporated a novel method for calculating a consensus network (CN) from two or more networks into our R package. To graphically inspect the resulting networks, the R package contains a visualization tool, which allows for the direct network manipulation and access of node and link information. When testing the package on a standard laptop computer, we can conduct all calculations for systems of more than 20,000 genes in under two hours. We compare our new wTO package to state of art packages and demonstrate the application of the wTO and CN functions using 3 independently derived datasets from healthy human pre-frontal cortex samples. To showcase an example for the time series application we utilized a metagenomics data set. CONCLUSION: In this work, we developed a software package that allows the computation of wTO networks, CNs and a visualization tool in the R statistical environment. It is publicly available on CRAN repositories under the GPL -2 Open Source License ( https://cran.r-project.org/web/packages/wTO/ ).

Measuring hand hygiene compliance rates at hospital entrances.

BACKGROUND: Despite the importance of hand hygiene in the health care setting, there are no studies evaluating hand hygiene compliance at hospital entrances. METHODS: The study was prospectively performed over a 33-week period from March 30, 2014-November 15, 2014, to evaluate hand hygiene compliance in 2 hospital reception areas. We compared electronic handwash counters with the application of radiofrequency identification (GOJO SMARTLINK) (electronic observer) that counts each activation of alcohol gel dispensers to direct observation (human observer) via remote review of video surveillance. RESULTS: We found low hand hygiene compliance rates of 2.2% (99/4,412) and 1.7% (140/8,277), respectively, at reception areas A and D, detected by direct observation. Using the electronic observer, we measured rates of 17% (15,624/91,724) and 7.1% (51,605/730,357) at reception areas A and D, respectively. For the overall time period of simultaneous electronic and human observation, the human observer captured 1% of the hand hygiene episodes detected by the electronic observer. CONCLUSIONS: Our study showed very low hand hygiene compliance in hospital reception areas, and we found an electronic hand hygiene system to be a useful method to monitor hand hygiene compliance.