Huntington's disease: the challenge for cell biologists

Huntington's disease (HD) is one of eight inherited neurodegenerative diseases caused by expansions of (CAG)(n) tracts that encode polyglutamine segments in expressed proteins. Studies of pathogenic mechanisms for all these late-onset diseases suffer from a common drawback: experimental studies require massive acceleration of a process that, in affected humans, usually takes decades. But is the rapid-onset disease of transgenic mouse models and in cells the same as the slow-onset disease in humans? We review recent work on HD, noting several issues whose significance is likely to be crucial - but which are as yet unresolved. We discuss these in light of the distinction between disease-specific pathogenic mechanisms and artifacts of polyglutamine overexpression. We suggest that the initial stages of HD result from dysfunction rather than death, and we consider the potential discovery of compounds that might interfere with early pathogenic events.     

Expansion of haematopoietic stem cells in vitro: a challenge to stem cell biologists

Expansion of haematopoietic stem cells from various sources has gained importance so as to provide a clinically potential graft, which shows ideal growth kinetics, resulting in reduction of the period of neutropenia and thrombocytopenia in any autologous or allogenic transplant setting. Expansion also facilitates transduction of genes for gene therapy. This review examines the various means employed to achieve the expansion of stem cells, and the criteria used to score the extent of expansion based on how stem cells are identified. It tries to analyse the ideal manner in which expansion should be carried out, with emphasis that expansion should not be at the expense of loss of stemness. It also attempts to judge the roles played by the stromal elements and cytokines, which are both part of the complex microenvironment, which in vivo has a strict regulation on haematopoiesis.     

Albumin-like protein is the major protein constituent of luminal fluid in the human endolymphatic sac

The endolymphatic sac (ES) is an inner ear organ that is connected to the cochleo-vestibular system through the endolymphatic duct. The luminal fluid of the ES contains a much higher concentration of proteins than any other compartment of the inner ear. This high protein concentration likely contributes to inner ear fluid volume regulation by creating an osmotic gradient between the ES lumen and the interstitial fluid. We characterized the protein profile of the ES luminal fluid of patients (n = 11) with enlarged vestibular aqueducts (EVA) by proteomics. In addition, we investigated differences in the protein profiles between patients with recent hearing deterioration and patients without hearing deterioration. The mean total protein concentration of the luminal fluid was 554.7±94.6 mg/dl. A total of 58 out of 517 spots detected by 2-DE were analyzed by MALDI-TOF MS. The protein profile of the luminal fluid was different from the profile of plasma. Proteins identified from 29 of the spots were also present in the MARC-filtered human plasma; however, the proteins identified from the other 25 spots were not detected in the MARC-filtered human plasma. The most abundant protein in the luminal fluid was albumin-like proteins, but most of them were not detected in MARC-filtered human plasma. The concentration of albumin-like proteins was higher in samples from patients without recent hearing deterioration than in patients with recent hearing deterioration. Consequently, the protein of ES luminal fluid is likely to be originated from both the plasma and the inner ear and considering that inner ear fluid volumes increase abnormally in patients with EVA following recent hearing deterioration, it is tempting to speculate that albumin-like proteins may be involved in the regulation of inner ear fluid volume through creation of an osmotic gradient during pathological conditions such as endolymphatic hydrops.     

Eukaryotic systematics: a user's guide for cell biologists and parasitologists

Single-celled parasites like Entamoeba, Trypanosoma, Phytophthora and Plasmodium wreak untold havoc on human habitat and health. Understanding the position of the various protistan pathogens in the larger context of eukaryotic diversity informs our study of how these parasites operate on a cellular level, as well as how they have evolved. Here, we review the literature that has brought our understanding of eukaryotic relationships from an idea of parasites as primitive cells to a crystallized view of diversity that encompasses 6 major divisions, or supergroups, of eukaryotes. We provide an updated taxonomic scheme (for 2011), based on extensive genomic, ultrastructural and phylogenetic evidence, with three differing levels of taxonomic detail for ease of referencing and accessibility (see supplementary material at Cambridge Journals On-line). Two of the most pressing issues in cellular evolution, the root of the eukaryotic tree and the evolution of photosynthesis in complex algae, are also discussed along with ideas about what the new generation of genome sequencing technologies may contribute to the field of eukaryotic systematics. We hope that, armed with this user's guide, cell biologists and parasitologists will be encouraged about taking an increasingly evolutionary point of view in the battle against parasites representing real dangers to our livelihoods and lives.     

Mapping and analysis of phosphorylation sites: a quick guide for cell biologists

A mechanistic understanding of signaling networks requires identification and analysis of phosphorylation sites. Mass spectrometry offers a rapid and highly sensitive approach to mapping phosphorylation sites. However, mass spectrometry has significant limitations that must be considered when planning to carry out phosphorylation-site mapping. Here we provide an overview of key information that should be taken into consideration before beginning phosphorylation-site analysis, as well as a step-by-step guide for carrying out successful experiments.     

Effect of locally applied drugs on the pH of luminal fluid in the endolymphatic sac of guinea pig

The aim of the present work was to assess the effect of various drugs applied locally on the pH of the luminal fluid (pH(lum)) in guinea pig endolymphatic sac. pH(lum) and transepithelial potential, when measured in vivo by means of double-barrelled pH-sensitive microelectrodes, were 7.06 +/- 0.08 and +6.1 +/- 0.34 mV (mean +/- SE; n = 84), respectively, which is consistent with a net acid secretion in the luminal fluid of the endolymphatic sac. Bafilomycin and acetazolamide increased and decreased, respectively, pH(lum). Amiloride, ethylisopropylamiloride, ouabain, and Schering 28080 had no effect on pH(lum). Results obtained with inhibitors of anionic transport systems were inconclusive; e.g., DIDS reduced pH(lum), whereas neither SITS nor triflocin had any effect. We conclude that bafilomycin-sensitive H(+)-ATPase activity accounts for the transepithelial acid gradient measured in the endolymphatic sac and that intracellular and membrane-bound carbonic anhydrase probably participates in regulating endolymphatic sac pH(lum). The relationship between acid pH, endolymph volume, and Ménière's disease remains to be further investigated.     

Resolving confusion of tongues in statistics and machine learning: a primer for biologists and bioinformaticians

Bioinformatics is the field where computational methods from various domains have come together for analysis of biological data. Each domain has introduced its own specific jargon. However, in closely related domains, e.g. machine learning and statistics, concordant and discordant terminology occurs, the later can lead to confusion. This article aims to help solve the confusion of tongues arising from these two closely related domains, which are frequently used in bioinformatics. We provide a short summary of the most commonly applied machine learning and statistical approaches to data analysis in bioinformatics, i.e. classification and statistical hypothesis testing. We explain differences and similarities in common terminology used in various domains, such as precision, recall, sensitivity and true positive rate. This primer can serve as a guide to the terminology used in these fields.     

Repeatability for Gaussian and non‐Gaussian data: a practical guide for biologists

Repeatability (more precisely the common measure of repeatability, the intra‐class correlation coefficient, ICC) is an important index for quantifying the accuracy of measurements and the constancy of phenotypes. It is the proportion of phenotypic variation that can be attributed to between‐subject (or between‐group) variation. As a consequence, the non‐repeatable fraction of phenotypic variation is the sum of measurement error and phenotypic flexibility. There are several ways to estimate repeatability for Gaussian data, but there are no formal agreements on how repeatability should be calculated for non‐Gaussian data (e.g. binary, proportion and count data). In addition to point estimates, appropriate uncertainty estimates (standard errors and confidence intervals) and statistical significance for repeatability estimates are required regardless of the types of data. We review the methods for calculating repeatability and the associated statistics for Gaussian and non‐Gaussian data. For Gaussian data, we present three common approaches for estimating repeatability: correlation‐based, analysis of variance (ANOVA)‐based and linear mixed‐effects model (LMM)‐based methods, while for non‐Gaussian data, we focus on generalised linear mixed‐effects models (GLMM) that allow the estimation of repeatability on the original and on the underlying latent scale. We also address a number of methods for calculating standard errors, confidence intervals and statistical significance; the most accurate and recommended methods are parametric bootstrapping, randomisation tests and Bayesian approaches. We advocate the use of LMM‐ and GLMM‐based approaches mainly because of the ease with which confounding variables can be controlled for. Furthermore, we compare two types of repeatability (ordinary repeatability and extrapolated repeatability) in relation to narrow‐sense heritability. This review serves as a collection of guidelines and recommendations for biologists to calculate repeatability and heritability from both Gaussian and non‐Gaussian data.     

Estimation of the abrasive wear coefficient in Lillehei-Kaster cardiac valve prostheses

An approximate hemodynamic theory, which predicts the opening dynamics of the Lillehei-Kaster heart valve, is used in conjunction with an abrasive wear model to predict the wear process on the shields. The hemodynamic theory predicts markedly different opening dynamics between the mitral and aortic positions and is shown to give excellent correlation with the experimental results of other investigations. The abrasive wear model is also shown to give excellent correlation with the experiments of others when the abrasive wear coefficient is taken as k = 6.4 X 10(-6). The theoretical results of this effort and the experimental data from clinical explants of other investigators is used to predict that occluder dislodgement is unlikely in less than 90 yr for either the mitral or aortic positions (for a mean cardiac output of 3.8 l.min-1 and a mean heart rate of 70 beats min-1).     

Effect size, confidence interval and statistical significance: a practical guide for biologists

Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner's instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.     

Consumer nueroscience: a new area of study for biomedical engineers

In scientific literature, the most accepted definition of consumer neuroscience or neuromarketing is that it is a field of study concerning the application of neuroscience methods to analyze and understand human behavior related to markets and marketing exchanges. First, it might seem strange that marketers would be interested in using neuroscience to understand consumer's preferences. Yet in practice, the basic goal of marketers is to guide the design and presentation of products in such a way that they are highly compatible with consumer preferences. To understand consumers preferences, several standard research tools are commonly used by marketers, such as personal interviews with the consumers, scoring questionnaries gathered from consumers, and focus groups. The reason marketing researchers are interested in using brain imaging tools instead of simply asking people for their preferences in front of marketing stimuli, arises from the assumption that people cannot (or do not want to) fully explain their preference when explicitly asked. Researchers in the field hypothesize that neuroimaging tools can access information within the consumer's brain during the generation of a preference or the observation of a commercial advertisement. The question of will this information be useful in further promoting the product is still up for debate in marketing literature. From the marketing researchers point of view, there is a hope that this body of brain imaging techniques will provide an efficient tradeoff between costs and benefits of the research. Currently, neuroscience methodology includes powerful brain imaging tools based on the gathering of hemodynamic or electromagnetic signals related to the human brain activity during the performance of a relevant task for marketing objectives. These tools are briefly reviewed in this article.     

Rapid elevations in both steroid hormones and vocal signaling during playback challenge: a field experiment in Gulf toadfish

It is well known that plasma androgens are rapidly released in response to aggressive or sexual stimuli in a broad array of vertebrates. However, experimental work on behavioral functions of rapid androgen elevation is rare. A combination of field-based behavioral experiments and lab-based neuroendocrinological approaches is beginning to show how steroid hormones rapidly regulate the expression of vocal communication signals in Gulf toadfish (Opsanus beta). Male toadfish emit multiharmonic "boatwhistles" and shorter-duration, broadband "grunts" during intraspecific communication. Neurophysiology experiments demonstrate that androgens and glucocorticoids rapidly modify vocal motor patterning in male toadfish. In this study, we simulated territorial intrusions (vocal "challenges") with acoustic playbacks to toadfish in the field, and observed simultaneous, rapid (within 5-20 min) changes in vocalizations and steroid hormones. Both plasma androgens and vocal activity increased following the presentation of pure tones that mimic the duration of natural boatwhistles (275 ms), while they remained unchanged following playbacks of tone stimuli that mimic the duration of grunts (75 ms) or the upper-range of boatwhistles (475 ms). Circulating glucocorticoids were elevated in calling vs. non-calling males but were unaffected by playback stimuli, suggesting a role in the energetics of vocalization. These results strongly suggest that one function of rapid androgen elevation in response to social challenge is to mediate similarly rapid changes in territorial vocal signaling. Given the conserved organization of neuroendocrine and vocal motor systems, rapid steroid action on vocalization mechanisms may be true of other vocal vertebrates as well, including birds and mammals.