Cytosine-block telomeric type DNA-binding activity of hnRNP proteins from human cell lines

Following the observation of the presence in mammalian nuclear extracts of a DNA binding activity quite specific for the single-stranded C-rich telomeric motif, we have isolated from the K562 human cell line by affinity chromatography and identified by mass spectrometry a number of proteins able to bind to this sequence. All of them belong to different heterogeneous nuclear ribonucleoprotein subgroups (hnRNP). Whereas many of them, namely hnRNP K, two isoforms of hnRNP I, and the factor JKTBP, appear to bind to this sequence with limited specificity after isolation, an isoform of hnRNP D (alias AUF1) and particularly hnRNP E1 (alias PCBP-1) show a remarkable specificity for the (CCCTAA)n repeated motif. Both have been obtained also as recombinant proteins expressed in Escherichia coli and have been shown to retain their binding specificity toward the C-block repeated sequence. In the light of the current knowledge about these proteins, their possible involvement in telomere functioning is discussed.     

Bayesian latent variable models for median regression on multiple outcomes

Often a response of interest cannot be measured directly and it is necessary to rely on multiple surrogates, which can be assumed to be conditionally independent given the latent response and observed covariates. Latent response models typically assume that residual densities are Gaussian. This article proposes a Bayesian median regression modeling approach, which avoids parametric assumptions about residual densities by relying on an approximation based on quantiles. To accommodate within-subject dependency, the quantile response categories of the surrogate outcomes are related to underlying normal variables, which depend on a latent normal response. This underlying Gaussian covariance structure simplifies interpretation and model fitting, without restricting the marginal densities of the surrogate outcomes. A Markov chain Monte Carlo algorithm is proposed for posterior computation, and the methods are applied to single-cell electrophoresis (comet assay) data from a genetic toxicology study.     

Simultaneous production of polyhydroxyalkanoates and rhamnolipids by Pseudomonas aeruginosa

The feasibility of the simultaneous production of polyhydroxyalkanoates (PHAs) and rhamnolipids, as a novel approach to reduce their production costs, was demonstrated by the cultivation of Pseudomonas aeruginosa IFO3924. Fairly large amounts of PHAs and rhamnolipids were obtained from the bacterial cells and the culture supernatant, respectively. Decanoate was a more suitable carbon source than ethanol and glucose for the simultaneous production, although glucose was suitable for cell growth without an induction period under pH control. The kind of carbon source affected PHA monomer composition markedly and PHA molecular weight slightly. Monorhamnolipids and dirhamnolipids were included in the rhamnolipids extracted from the culture supernatant using decanoate, glucose, or ethanol as the carbon source. Both PHAs and rhamnolipids were synthesized after the growth phase. PHA content in the cell reached a maximum when the carbon source was exhausted. After exhaustion of the carbon source, PHA content decreased rapidly, but rhamnolipid synthesis, which followed PHA synthesis, continued. This resulted in a time lag for the attainment of maximum levels of PHAs and rhamnolipids. The reusability of the cells used in rhamnolipid production was evaluated in the repeated batch culture of P. aeruginosa IFO3924 for the simultaneous production of PHAs and rhamnolipids. High concentrations of rhamnolipids in the culture supernatant were attained at the end of both the first and second batch cultures. High PHA content was achieved in the resting cells that were finally harvested after the second batch. Simultaneous production of PHAs and rhamnolipids will enhance the availability of valuable biocatalysts of bacterial cells, and dispel the common belief that the production cost of PHAs accumulated intracellularly is almost impossible to become lower than that of cells themselves.     

On the role of periodism in the origin of proteins

Two different views have been proposed for origins of genes (or proteins). One is that primordial genes evolved from random sequences. This view underlies the concept of modern in vitro evolution experiments that functional molecules (even proteins) evolved from random sequence-libraries. On the contrary, the second view reminds that "random sequences" would be an unusual state in which to find RNA or DNA, because it is their inherent nature to yield periodic structures during the course of semi-conservative replication. In this second view, the periodicity of DNA (or RNA) is responsible for emergence of primordial genes. Although recent reports on the variety of periodicities present in proteins, genes and genomes are consistent with the second view, it has yet to be experimentally tested. We assessed the significance of periodicities of DNA in the origin of genes by constructing such periodic DNAs. The results showed that periodic DNA produced ordered proteins at very high rates, which is in contrast to the fact that proteins with random sequences lack secondary structures. We concluded that periodicity played a pivotal role in the origin of many genes. The observation should pave the way for new experimental evolution systems for proteins.     

Use of spot measurements for assessing residential ELF magnetic field exposure: a validity study

The purpose of this study was to evaluate residential short term "spot" measurements as surrogates for long term personal magnetic field (MF) exposure. In an epidemiological study on birth weight and pregnancy delay, MF exposure was assessed by taking five spot measurements in each room. For a subsample of 30 subjects 24 h personal MF measurements were made, and the following exposure metrics were calculated: 24 h arithmetic mean, 24 h median, percentage of time above 0.15 microT, and percentage of time above 0.29 microT. The 24 h exposure metrics were used as gold standards, when evaluating the validity of various summary measures calculated from spot measurements for assessing personal exposure. Based on Spearman correlation coefficient (r), specificity and sensitivity, the average of the spot measurements of a residence resulted in least exposure measurement error (misclassification). Also the above bed spot value correlated better with the 24 h metrics than any room average. Spot measurements performed about equally well in predicting different types of exposure metrics.     

Generalized character process models: estimating the genetic basis of traits that cannot be observed and that change with age or environmental conditions

The genetic analysis of characters that change as a function of some independent and continuous variable has received increasing attention in the biological and statistical literature. Previous work in this area has focused on the analysis of normally distributed characters that are directly observed. We propose a framework for the development and specification of models for a quantitative genetic analysis of function-valued characters that are not directly observed, such as genetic variation in age-specific mortality rates or complex threshold characters. We employ a hybrid Markov chain Monte Carlo algorithm involving a Monte Carlo EM algorithm coupled with a Markov chain approximation to the likelihood, which is quite robust and provides accurate estimates of the parameters in our models. The methods are investigated using simulated data and are applied to a large data set measuring mortality rates in the fruit fly, Drosophila melanogaster.     

Generalized linear mixture models for handling nonignorable dropouts in longitudinal studies

This paper presents a method for analysing longitudinal data when there are dropouts. In particular, we develop a simple method based on generalized linear mixture models for handling nonignorable dropouts for a variety of discrete and continuous outcomes. Statistical inference for the model parameters is based on a generalized estimating equations (GEE) approach (Liang and Zeger, 1986). The proposed method yields estimates of the model parameters that are valid when nonresponse is nonignorable under a variety of assumptions concerning the dropout process. Furthermore, the proposed method can be implemented using widely available statistical software. Finally, an example using data from a clinical trial of contracepting women is used to illustrate the methodology.