Nonparametric Analysis of Thermal Proteome Profiles Reveals Novel Drug-binding Proteins*

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Highlights

• •Method for the analysis of response curves from thermal proteome profiling (TPP).
• •NPARC uses nonparametric statistics and provides false discovery-rate (FDR) control.
• •Increased proteome coverage and sensitivity to identify drug-binding proteins.

     

Linkage: from particulate to interactive genetics

Genetics was established on a strictparticulate conception of heredity. Geneticlinkage, the deviation from independentsegregation of Mendelian factors, was conceivedas a function of the material allocation of thefactors to the chromosomes, rather than to themultiple effects (pleiotropy) of discretefactors. Although linkage maps wereabstractions they provided strong support forthe chromosomal theory of inheritance. DirectCytogenetic evidence was scarce until X-rayinduced major chromosomal rearrangementsallowed direct correlation of genetic andcytological rearrangements. Only with thediscovery of the polytenic giant chromosomes inDrosophila larvae in the 1930s were thevirtual maps backed up by physical maps of thegenetic loci. Genetic linkage became a pivotalexperimental tool for the examination of theintegration of genetic functions in developmentand in evolution. Genetic mapping has remaineda hallmark of genetic analysis. The location ofgenes in DNA is a modern extension of thenotion of genetic linkage.     

Evolutionary processes driving spatial patterns of intraspecific genetic diversity in river ecosystems

Describing, understanding and predicting the spatial distribution of genetic diversity is a central issue in biological sciences. In river landscapes, it is generally predicted that neutral genetic diversity should increase downstream, but there have been few attempts to test and validate this assumption across taxonomic groups. Moreover, it is still unclear what are the evolutionary processes that may generate this apparent spatial pattern of diversity. Here, we quantitatively synthesized published results from diverse taxa living in river ecosystems, and we performed a meta‐analysis to show that a downstream increase in intraspecific genetic diversity (DIGD) actually constitutes a general spatial pattern of biodiversity that is repeatable across taxa. We further demonstrated that DIGD was stronger for strictly waterborne dispersing than for overland dispersing species. However, for a restricted data set focusing on fishes, there was no evidence that DIGD was related to particular species traits. We then searched for general processes underlying DIGD by simulating genetic data in dendritic‐like river systems. Simulations revealed that the three processes we considered (downstream‐biased dispersal, increase in habitat availability downstream and upstream‐directed colonization) might generate DIGD. Using random forest models, we identified from simulations a set of highly informative summary statistics allowing discriminating among the processes causing DIGD. Finally, combining these discriminant statistics and approximate Bayesian computations on a set of twelve empirical case studies, we hypothesized that DIGD were most likely due to the interaction of two of these three processes and that contrary to expectation, they were not solely caused by downstream‐biased dispersal.     

Survival analysis in clinical trials: past developments and future directions

The field of survival analysis emerged in the 20th century and experienced tremendous growth during the latter half of the century. The developments in this field that have had the most profound impact on clinical trials are the Kaplan-Meier (1958, Journal of the American Statistical Association 53, 457-481) method for estimating the survival function, the log-rank statistic (Mantel, 1966, Cancer Chemotherapy Report 50, 163-170) for comparing two survival distributions, and the Cox (1972, Journal of the Royal Statistical Society, Series B 34, 187-220) proportional hazards model for quantifying the effects of covariates on the survival time. The counting-process martingale theory pioneered by Aalen (1975, Statistical inference for a family of counting processes, Ph.D. dissertation, University of California, Berkeley) provides a unified framework for studying the small- and large-sample properties of survival analysis statistics. Significant progress has been achieved and further developments are expected in many other areas, including the accelerated failure time model, multivariate failure time data, interval-censored data, dependent censoring, dynamic treatment regimes and causal inference, joint modeling of failure time and longitudinal data, and Baysian methods.     

On-line stoichiometry and identification of metabolic state under dynamic process conditions

A method for the on-line calculation of conversion rates and yield coefficients under dynamic process conditions was developed. The method is based on cumulated mass balances using a moving average method. Elemental balances were used to test the measured cumulated quantities for gross errors and inappropriate stoichiometry definition followed by data reconciliation and estimation of non-measured conversion rates, using a bioprocess set-up including multiple on-line analysis techniques. The quantitative potential of the proposed method is demonstrated by executing transient experiments in aerobic cultures of Saccharomyces cerevisiae on glucose. Rates and yield coefficients could be consistently quantified in shift-up, shift-down, and accelerostat experiments. The method shows the capability to describe quantitatively transient changes in metabolism including uncoupling of catabolism and anabolism, also for the case when multiple components of metabolism are not measured. The validity of the experiment can be evaluated on-line. Additionally, the method detects with high sensitivity inappropriate stoichiometry definition, such as a change in state of metabolism. It was shown that concentration values can be misleading for the identification of the metabolic state. In contrast, the proposed method provides a clear picture of the metabolic state and new physiological regulations could be revealed. Hence, the novelty of the proposed method is the on-line availability of consistent stoichiometric coefficients allowing a significant speed up in strain characterization and bioprocess development using minimal knowledge of the metabolism. Additionally, it opens up the use of transient experiments for physiological studies.     

Estimating regression parameters and degree of dependence for multivariate failure time data

Multivariate failure time data are frequently encountered in longitudinal studies when subjects may experience several events or when there is a grouping of individuals into a cluster. To take into account the dependence of the failure times within the unit (the individual or the cluster) as well as censoring, two multivariate generalizations of the Cox proportional hazards model are commonly used. The marginal hazard model is used when the purpose is to estimate mean regression parameters, while the frailty model is retained when the purpose is to assess the degree of dependence within the unit. We propose a new approach based on the combination of the two aforementioned models to estimate both these quantities. This two-step estimation procedure is quicker and more simple to implement than the EM algorithm used in frailty models estimation. Simulation results are provided to illustrate robustness, consistency, and large-sample properties of estimators. Finally, this method is exemplified on a diabetic retinopathy study in order to assess the effect of photocoagulation in delaying the onset of blindness as well as the dependence between the two eyes blindness times of a patient.     

A simple test of the homogeneity of risk difference in sparse data: an application to a multicenter study

In this paper, we focus discussion on testing the homogeneity of risk difference for sparse data, in which we have few patients in each stratum, but a moderate or large number of strata. When the number of patients per treatment within strata is small (2 to 5 patients), none of test procedures proposed previously for testing the homogeneity of risk difference for sparse data can really perform well. On the basis of bootstrap methods, we develop a simple test procedure that can improve the power of the previous test procedures. Using Monte Carlo simulations, we demonstrate that the test procedure developed here can perform reasonable well with respect to Type I error even when the number of patients per stratum for each treatment is as small as two patients. We evaluate and study the power of the proposed test procedure in a variety of situations. We also include a comparison of the performance between the test statistics proposed elsewhere and the test procedure developed here. Finally, we briefly discuss the limitation of using the proposed test procedure. We use the data comparing two chemotherapy treatments in patients with multiple myeloma to illustrate the use of the proposed test procedure.     

Herbarium collections and field data-based plant diversity maps for Burkina Faso

A map of plant species diversity in Burkina Faso is presented based on field observations and specimen data from the Ouagadougou University Herbarium (OUA) and the Herbarium Senckenbergianum (FR). A map of collecting intensity and field observations illustrates centres of botanical research activities in Burkina Faso. To overcome problems associated with biased sampling intensity, distributions of species have been modelled and extrapolated to maps of vascular plant diversity, life forms and diversity of four selected families (Poaceae, Cyperaceae, Dioscoreaceae and Rubiaceae). The area of most intensive collection and observation is around Gorom‐Gorom and Fada N’Gourma. Modelled diversity generally increases towards the south, as does the proportion of phanerophytes, lianas and hemicryptophytes, while the opposite trend is observed for therophytes. Poaceae diversity is highly correlated with total vascular plant diversity, making the family especially suitable as an indicator for overall plant diversity. Cyperaceae are rather evenly distributed throughout the country, Dioscoreaceae are restricted to the Sudanian Zone. Rubiaceae have their highest diversities in the very south. Our approach can be transferred to areas with a similar database, certainly to other areas within West Africa. Future research should focus on distribution data for rare species, enabling our approach to evaluate the West African system of protected areas.     

Linkage relationships between regulatory and structural gene loci involved in zein synthesis in maize

Summary The synthesis of at least 15 zein polypeptides is under the control of regulatory gene loci. One of these, Opaque-2 (chromosome 7, position 16) strongly reduces the zein accumulation without modifying the zein molecular components. The linkage relationship between the regulatory gene 02 and the 5 structural loci (Zp1, Zp2, Zp3, Zp6, Zp12) segregating with sample Mendelian ratios have been studied. Zp1, Zp2, Zp3 are closely linked to each other; moreover this gene cluster is located on chromosome 7 at 5.5 cM from the Opaque-2 locus. The structural loci Zp6 and Zp12 are not linked with each other, with the 02 locus or with Zp1, Zp2, Zp3. From our data it follows that the zein structural genes are located in at least three positions on the maize genome. The scattering in the genome of the genes controlled by the Opaque-2 locus suggests a transacting role for this regulatory element.