Genealogical data and the biodemography of human longevity

Biodemography of human longevity is an emerging interdisciplinary field of sociobiological research with deep historical roots. Two research questions are examined in this article: (1) What evidence is there for the familial transmission of human longevity?, and (2) what are the effects of parental age at reproduction on offspring longevity, and in particular, are there long-term adverse health consequences associated with the trend toward delayed reproduction? The ability of scientists to conduct biodemographic studies depends not only on merging theoretical and methodological elements from the biological and demographic/actuarial sciences, but unique sources of data and statistical methods must also be developed. In this article we describe how gencalogical data have been used for over a century to explore basic questions about human longevity, and how similar kinds of data now being developed are driving the formation of new testable research hypotheses in the field of biodemography.     

A generalized mover-stayer model for panel data

A generalized mover-stayer model is described for conditionally Markov processes under panel observation. Marginally the model represents a mixture of nested continuous-time Markov processes in which sub-models are defined by constraining some transition intensities to zero between two or more states of a full model. A Fisher scoring algorithm is described which facilitates maximum likelihood estimation based only on the first derivatives of the transition probability matrices. The model is fit to data from a smoking prevention study and is shown to provide a significant improvement in fit over a time-homogeneous Markov model. Extensions are developed which facilitate examination of covariate effects on both the transition intensities and the mover-stayer probabilities.     

Normalization of high dimensional genomics data where the distribution of the altered variables is skewed

Genome-wide analysis of gene expression or protein binding patterns using different array or sequencing based technologies is now routinely performed to compare different populations, such as treatment and reference groups. It is often necessary to normalize the data obtained to remove technical variation introduced in the course of conducting experimental work, but standard normalization techniques are not capable of eliminating technical bias in cases where the distribution of the truly altered variables is skewed, i.e. when a large fraction of the variables are either positively or negatively affected by the treatment. However, several experiments are likely to generate such skewed distributions, including ChIP-chip experiments for the study of chromatin, gene expression experiments for the study of apoptosis, and SNP-studies of copy number variation in normal and tumour tissues. A preliminary study using spike-in array data established that the capacity of an experiment to identify altered variables and generate unbiased estimates of the fold change decreases as the fraction of altered variables and the skewness increases. We propose the following work-flow for analyzing high-dimensional experiments with regions of altered variables: (1) Pre-process raw data using one of the standard normalization techniques. (2) Investigate if the distribution of the altered variables is skewed. (3) If the distribution is not believed to be skewed, no additional normalization is needed. Otherwise, re-normalize the data using a novel HMM-assisted normalization procedure. (4) Perform downstream analysis. Here, ChIP-chip data and simulated data were used to evaluate the performance of the work-flow. It was found that skewed distributions can be detected by using the novel DSE-test (Detection of Skewed Experiments). Furthermore, applying the HMM-assisted normalization to experiments where the distribution of the truly altered variables is skewed results in considerably higher sensitivity and lower bias than can be attained using standard and invariant normalization methods.     

Proteomic characterization of human normal articular chondrocytes: a novel tool for the study of osteoarthritis and other rheumatic diseases

Articular cartilage is composed of cells and an extracellular matrix. The chondrocyte is the only cell type present in mature cartilage, and it is important in the control of cartilage integrity. There is currently a great lack of knowledge about the chondrocyte proteome. To solve this deficiency, we have obtained the first reference map of the human normal articular chondrocyte. Cells were isolated from cartilages obtained from autopsies without history of joint disease. Cultured cells were used to obtain protein extracts which were resolved by 2-DE and visualized by silver nitrate or CBB staining. Almost 200 spots were excised from the gels and analyzed using MALDI-TOF or MALDI-TOF/TOF MS. The analysis leads to the identification of 136 spots that represent 93 different proteins. A significant proportion of proteins are involved in cell organization (26%), energy (16%), protein fate (14%), metabolism (12%), and cell stress (12%). From all the identified proteins, annexins, vimentin, transgelin, destrin, cathepsin D, heat shock protein 47, and mitochondrial superoxide dismutase were more abundant in chondrocytes than in other types of mesenchymal cells such as Jurkat-T cells. As metabolic program of chondrocytes is altered in osteoarthritis and other rheumatic diseases, this proteomic map is an important tool for future studies on these pathologies.     

A generalized Friedman test for data defined by intervals

If observations are restricted in such a way that only intervals covering the true but hidden values are known—situations found in clinical studies or analyzing laboratory data-standard nonpara-metric procedures fail. The present paper suggests the use of generalized Friedman tests, if the sample consists of nk observations, with one observation from each of k treatments in each of in blocks. Furthermore the proposed test enables Friedman-type analyses of censored data. An example is given and a computer program facilitating the use of the test is introduced.     

Noninvasive discrimination between human normal and cancer cells by analysis of intracellular distribution of phase-shift data

Aiming to establish a method for the noninvasive discrimination of cancer cells from normal cells in adherent culture, we investigated to employ all phase shift data for all pixels inside a cell. The bird’s-eye views of phase shifts of human prostate epithelial cells (PRECs) and human prostatic carcinoma epithelial cell (PC-3) lines acquired by phase-shifting laser microscopy showed tableland and cone shapes, respectively, while treatment of PRECs with cytochalasin D resulted in the cone shape. So, the profile of phase shift in both sections towards the x- and y-axes of the views through the peaks of the phase shifts in PRECs and PC-3 cells were trapezoid-like and triangle-like, respectively. Typical profiles of phase shifts in a section in PRECs or PC-3 cells were calculated by averaging from 10 cells and smoothing. Cancer index is defined as the deduction of sums of the squared difference between a real cell and the typical profiles for a PREC and a PC-3 cell. The cancer indices for PC-3 and hepatocellular carcinoma cell lines were positive, while those for PRECs and human normal cryopreserved hepatocytes were negative. Cancer indices along the major axis of fibroblast-like cells of normal mesenchymal stem cells and the osteosarcoma cell line were negative and positive, respectively. Consequently, several cancer cells could be noninvasively discriminated from normal cells by calculating the cancer index employing phase shift for all pixels inside the cells.     

A novel 3-hydroxysteroid dehydrogenase that regulates reproductive development and longevity

Endogenous small molecule metabolites that regulate animal longevity are emerging as a novel means to influence health and life span. In C. elegans, bile acid-like steroids called the dafachronic acids (DAs) regulate developmental timing and longevity through the conserved nuclear hormone receptor DAF-12, a homolog of mammalian sterol-regulated receptors LXR and FXR. Using metabolic genetics, mass spectrometry, and biochemical approaches, we identify new activities in DA biosynthesis and characterize an evolutionarily conserved short chain dehydrogenase, DHS-16, as a novel 3-hydroxysteroid dehydrogenase. Through regulation of DA production, DHS-16 controls DAF-12 activity governing longevity in response to signals from the gonad. Our elucidation of C. elegans bile acid biosynthetic pathways reveals the possibility of novel ligands as well as striking biochemical conservation to other animals, which could illuminate new targets for manipulating longevity in metazoans.     

Copper distribution in the normal human brain

Copper concentration was determined in samples from 38 areas of 7 normal human brains. The grey matter contained higher concentrations of copper than the white matter. Identical areas of the grey and white matter of the cerebral cortex showed significant differences between individuals. In the caudate nucleus the highest concentrations of copper were found in the tail followed by the body and the head, respectively. A negative linear regression between age and brain copper levels was demonstrated.     

A generalized linear model for peak calling in ChIP-Seq data

Chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-Seq) has become a routine for detecting genome-wide protein-DNA interaction. The success of ChIP-Seq data analysis highly depends on the quality of peak calling (i.e., to detect peaks of tag counts at a genomic location and evaluate if the peak corresponds to a real protein-DNA interaction event). The challenges in peak calling include (1) how to combine the forward and the reverse strand tag data to improve the power of peak calling and (2) how to account for the variation of tag data observed across different genomic locations. We introduce a new peak calling method based on the generalized linear model (GLMNB) that utilizes negative binomial distribution to model the tag count data and account for the variation of background tags that may randomly bind to the DNA sequence at varying levels due to local genomic structures and sequence contents. We allow local shifting of peaks observed on the forward and the reverse stands, such that at each potential binding site, a binding profile representing the pattern of a real peak signal is fitted to best explain the observed tag data with maximum likelihood. Our method can also detect multiple peaks within a local region if there are multiple binding sites in the region.     

Prospects for the genetics of human longevity

Longevity varies between and within species. The existence of species-specific limit to human life-span and its partial heritability indicate the existence of genetic factors that influence the ageing process. Insight into the nature of these genetic factors is provided by evolutionary studies, notably the disposable soma theory, which suggests a central role of energy metabolism in determining life-span. Energy is important in two ways. First, the disposable soma theory indicates that the optimum energy investment in cell maintenance and repair processes will be tuned through natural selection to provide adequate, but not excessive, protection against random molecular damages (e.g. to DNA, proteins). All that is required is that the organism remains in a sound condition through its natural expectation of life in the wild environment, where accidents are the predominant cause of mortality. Secondly, energy is implicated because of the intrinsic vulnerability of mitochondria to damage that may interfere with the normal supply of energy to the cell via the oxidative phosphorylation pathways. Oxidative phosphorylation produces ATP, and as a by-product also produces highly reactive oxygen radicals that can damage many cell structures, including the mitochondria themselves. Several lines of evidence link, on the one hand, oxidative damage to cell ageing, and on the other hand, energy-dependent antioxidant defences to the preservation of cellular homeostasis, and hence, longevity. Models of cellular ageing in vitro allow direct investigation of mechanisms, such as oxidative damage, that contribute to limiting human life-span. The genetic substratum of inter-individual differences in longevity may be unraveled by a two-pronged reverse genetics approach: sibling pair analysis applied to nonagenarian and centenarian siblings, combined with association studies of centenarians, may lead to the identification of genetic influences upon human longevity. These studies have become practicable thanks to recent progress in human genome mapping, especially to the development of microsatellite markers and the integration of genetic and physical maps.     

A novel song parameter correlates with extra-pair paternity and reflects male longevity

Although elaborate bird song provides one of the prime examples of a trait that evolved under sexual selection, it is still unclear whether females judge the quality of males by attributes of their song and whether these song features honestly signal a male's genetic quality. We measured the ability of male dusky warblers Phylloscopus fuscatus to maintain a high sound amplitude during singing, which probably reflects an individual's physiological limitations. This new measure of singing performance was correlated with male longevity and with extra-pair paternity, indicating that females who copulated with better singers obtained 'good genes' for their offspring. Our findings are consistent with the idea that females assess male quality by subtle differences in their performance during the production of notes, rather than by the quantity or versatility of song. In addition, observations on territorial conflicts indicate that attractive males invest less in competition over territories because they can reproduce via extra-pair paternity.     

A completion simulator for the two-sided truncated normal distribution

This paper presents simulation formulae of two-sided truncated normal random variables using a completion distribution and its two corresponding conditionals generated via a Gibbs sampler. This procedure extends formulae given by Robert and Casella for the one-sided case.