Prediction of drugs having opposite effects on disease genes in a directed network

Background

Developing novel uses of approved drugs, called drug repositioning, can reduce costs and times in traditional drug development. Network-based approaches have presented promising results in this field. However, even though various types of interactions such as activation or inhibition exist in drug-target interactions and molecular pathways, most of previous network-based studies disregarded this information.

Methods

We developed a novel computational method, Prediction of Drugs having Opposite effects on Disease genes (PDOD), for identifying drugs having opposite effects on altered states of disease genes. PDOD utilized drug-drug target interactions with ‘effect type’, an integrated directed molecular network with ‘effect type’ and ‘effect direction’, and disease genes with regulated states in disease patients. With this information, we proposed a scoring function to discover drugs likely to restore altered states of disease genes using the path from a drug to a disease through the drug-drug target interactions, shortest paths from drug targets to disease genes in molecular pathways, and disease gene-disease associations.

Results

We collected drug-drug target interactions, molecular pathways, and disease genes with their regulated states in the diseases. PDOD is applied to 898 drugs with known drug-drug target interactions and nine diseases. We compared performance of PDOD for predicting known therapeutic drug-disease associations with the previous methods. PDOD outperformed other previous approaches which do not exploit directional information in molecular network. In addition, we provide a simple web service that researchers can submit genes of interest with their altered states and will obtain drugs seeming to have opposite effects on altered states of input genes at http://gto.kaist.ac.kr/pdod/index.php/main.

Conclusions

Our results showed that ‘effect type’ and ‘effect direction’ information in the network based approaches can be utilized to identify drugs having opposite effects on diseases. Our study can offer a novel insight into the field of network-based drug repositioning.

     

Partial genotyping at polymorphic markers can improve heritability estimates in sibling groups

Accurate estimates of heritability () are necessary to assess adaptive responses of populations and evolution of fitness‐related traits in changing environments. For plants, estimates generally rely on maternal progeny designs, assuming that offspring are either half‐sibs or unrelated. However, plant mating systems often depart from half‐sib assumptions, this can bias estimates. Here, we investigate how to accurately estimate in nonmodel species through the analysis of sibling designs with a moderate genotyping effort. We performed simulations to investigate how microsatellite marker information available for only a subset of offspring can improve estimates based on maternal progeny designs in the presence of nonrandom mating, inbreeding in the parental population or maternal effects. We compared the basic family method, considering or not adjustments based on average relatedness coefficients, and methods based on the animal model. The animal model was used with average relatedness information, or with hybrid relatedness information: associating one‐generation pedigree and family assumptions, or associating one‐generation pedigree and average relatedness coefficients. Our results highlighted that methods using marker‐based relatedness coefficients performed as well as pedigree‐based methods in the presence of nonrandom mating (i.e. unequal male reproductive contributions, selfing), offering promising prospects to investigate in situ heritabilities in natural populations. In the presence of maternal effects, only the use of pairwise relatednesses through pedigree information improved the accuracy of estimates. In that case, the amount of father‐related offspring in the sibling design is the most critical. Overall, we showed that the method using both one‐generation pedigree and average relatedness coefficients was the most robust to various ecological scenarios.     

Seasonal change in xylem growth of <Emphasis Type="Italic">Pinus densiflora</Emphasis> in central Japan

This study investigated the seasonal change in xylem growth of Japanese red pine (Pinus densiflora). Wood cores were sampled at 2-week intervals from April to November in 2012 using the microcoring method. Daily increment rates of tracheid number and tree-ring width were compared with seasonal changes in daily mean temperature and photoperiod. Xylem growth started in early to late May and stopped in late October to early November. The maximum daily increment rates of tracheid number and tree-ring width were in early July. The 95 % confidence intervals of the timing of the maximum daily increment rates included the summer solstice (23 June) with the longest photoperiod, but not the warmest day (30 July). The maximum daily increment rate of xylem growth is thought to be controlled by the photoperiod rather than by temperature. The daily mean temperature exceeded 20 °C after the summer solstice, indicating that temperature is not a limiting factor for xylem growth. This study suggests that the timing of maximum daily increment rates of xylem growth of P. densiflora is controlled by the photoperiod.     

Establishment of laurel forest vegetation in adjacent secondary forest in central Japan

Distributions of lucidophyllous species are limited due to the fragmentation of laurel forest. On Komayama Hill in central Japan, we evaluated the colonization of typical lucidophyllous vascular plants from a 350-year-old laurel forest into adjacent abandoned secondary forest for conservation and restoration purposes. A total of 14 consecutive subplots were established along the vegetation border between the two forests (length, 30 m; width, 5 m), extending 70 m into the secondary forest; 18 quadrats of old-growth forest were surveyed. Edge effects of old-growth forest were found to play an important role in re-establishing lucidophyllous saplings and seedlings in the secondary forest. In particular, the abundances of the four dominant canopy species of the old-growth forest significantly decreased with increasing distance. Hence, they are expected to colonize further into the secondary forest and, ultimately, to dominate the canopy. However, the number of lucidophyllous species did not change with distance. Species such as Ficus nipponica, Damnacanthus indicus, Ilex integra, and Lemmaphyllum microphyllum were near-completely or completely limited to the old-growth forest. They are known to be negatively affected by forest fragmentation and were observed to be struggling to colonize the exterior of the old-growth forest even after 60 years of abandonment. Their absence highlighted the limited colonization capacities of some old-growth forest species and underlined the time required for habitat restoration following human disturbance. We conclude that it is important to consider the population dynamics of dominant canopy species and the colonization of these interior species when assessing the habitat expansion of lucidophyllous species and hence the restoration of degraded lands.     

Multiple Hypotheses in the Analysis of a Crossover Trial

Crossover trials are used in a variety of fields, such as medicine, biology, psychology, and some commercial goods investigations. The aim of this paper is to extend a methodology for multiple comparisons to the problem of testing in crossover trials with two treatments. These two treatments are given in two orderings, treatment A first or treatment B first. We perform inference on the effect of one treatment relative to the effect of the other, without assuming that these effects are independent of treatment ordering, using techniques from order-restricted inference and multiple comparisons, and compare to some existing multiple comparison tests.     

Subgroup-Based Adaptive (SUBA) Designs for Multi-arm Biomarker Trials

Targeted therapies based on biomarker profiling are becoming a mainstream direction of cancer research and treatment. Depending on the expression of specific prognostic biomarkers, targeted therapies assign different cancer drugs to subgroups of patients even if they are diagnosed with the same type of cancer by traditional means, such as tumor location. For example, Herceptin is only indicated for the subgroup of patients with HER2+ breast cancer, but not other types of breast cancer. However, subgroups like HER2+ breast cancer with effective targeted therapies are rare, and most cancer drugs are still being applied to large patient populations that include many patients who might not respond or benefit. Also, the response to targeted agents in humans is usually unpredictable. To address these issues, we propose subgroup-based adaptive (SUBA), designs that simultaneously search for prognostic subgroups and allocate patients adaptively to the best subgroup-specific treatments throughout the course of the trial. The main features of SUBA include the continuous reclassification of patient subgroups based on a random partition model and the adaptive allocation of patients to the best treatment arm based on posterior predictive probabilities. We compare the SUBA design with three alternative designs including equal randomization, outcome-adaptive randomization, and a design based on a probit regression. In simulation studies, we find that SUBA compares favorably against the alternatives.     

Strategies for Genomic and Proteomic Profiling of Cancers

Omics-based technology platforms have made new kinds of cancer profiling tests feasible. There are several valuable examples in clinical practice, and many more under development. A concerted, transparent process of discovery with lock-down of candidate assays and classifiers and clear specification of intended clinical use is essential. The Institute of Medicine has now proposed a three-stage scheme of confirming and validating analytical findings, validating performance on clinical specimens, and demonstrating explicit clinical utility for an approvable test (Micheel et al., Evolution of translational omics: lessons learned and path forward, 2012).     

Dynamic light scattering: a practical guide and applications in biomedical sciences

Dynamic light scattering (DLS), also known as photon correlation spectroscopy (PCS), is a very powerful tool for studying the diffusion behaviour of macromolecules in solution. The diffusion coefficient, and hence the hydrodynamic radii calculated from it, depends on the size and shape of macromolecules. In this review, we provide evidence of the usefulness of DLS to study the homogeneity of proteins, nucleic acids, and complexes of protein–protein or protein–nucleic acid preparations, as well as to study protein–small molecule interactions. Further, we provide examples of DLS’s application both as a complementary method to analytical ultracentrifugation studies and as a screening tool to validate solution scattering models using determined hydrodynamic radii.