Principal components for allometric analysis

Logarithmic bivariate regression slopes and logarithmic principal component coefficient ratios are two methods for estimating allometry coefficients corresponding to a in the classic power formula Y = BXa. Both techniques depend on high correlation between variables. Interpretation is logically limited to the variables included in analysis. Principal components analysis depends also on relatively uniform intercorrelations; given this, it serves satisfactorily as a method for summarizing many bivariate combinations. Unmodified major principal component coefficients cannot represent scaling to body weight; rather, they represent scaling to a composite size vector which usually is highly correlated with body size or weight but has an unspecified allometry. Thus, the concepts of proportionality and of isometry must be kept distinct.     

Principal components analysis of population admixture

With the availability of high-density genotype information, principal components analysis (PCA) is now routinely used to detect and quantify the genetic structure of populations in both population genetics and genetic epidemiology. An important issue is how to make appropriate and correct inferences about population relationships from the results of PCA, especially when admixed individuals are included in the analysis. We extend our recently developed theoretical formulation of PCA to allow for admixed populations. Because the sampled individuals are treated as features, our generalized formulation of PCA directly relates the pattern of the scatter plot of the top eigenvectors to the admixture proportions and parameters reflecting the population relationships, and thus can provide valuable guidance on how to properly interpret the results of PCA in practice. Using our formulation, we theoretically justify the diagnostic of two-way admixture. More importantly, our theoretical investigations based on the proposed formulation yield a diagnostic of multi-way admixture. For instance, we found that admixed individuals with three parental populations are distributed inside the triangle formed by their parental populations and divide the triangle into three smaller triangles whose areas have the same proportions in the big triangle as the corresponding admixture proportions. We tested and illustrated these findings using simulated data and data from HapMap III and the Human Genome Diversity Project.     

Principal components analysis of protein sequence clusters

Sequence analysis of large protein families can produce sub-clusters even within the same family. In some cases, it is of interest to know precisely which amino acid position variations are most responsible for driving separation into sub-clusters. In large protein families composed of large proteins, it can be quite challenging to assign the relative importance to specific amino acid positions. Principal components analysis (PCA) is ideal for such a task, since the problem is posed in a large variable space, i.e. the number of amino acids that make up the protein sequence, and PCA is powerful at reducing the dimensionality of complex problems by projecting the data into an eigenspace that represents the directions of greatest variation. However, PCA of aligned protein sequence families is complicated by the fact that protein sequences are traditionally represented by single letter alphabetic codes, whereas PCA of protein sequence families requires conversion of sequence information into a numerical representation. Here, we introduce a new amino acid sequence conversion algorithm optimized for PCA data input. The method is demonstrated using a small artificial dataset to illustrate the characteristics and performance of the algorithm, as well as a small protein sequence family consisting of nine members, COG2263, and finally with a large protein sequence family, Pfam04237, which contains more than 1,800 sequences that group into two sub-clusters.     

Dynamic neural networks as a tool for the online optimization of industrial fermentation

A system for online optimization of industrial fermentation based on a model with dynamic neural networks is described. The developed dynamic neural network, consisting of adapted neurons to consider the process dynamics, can model the complex, non-linear fermentation of beer in order to predict the future process. The predicted trajectories of gravity, pH, and diacetyl are in agreement with the experimental data measured at an automated pilot fermenter. It was possible to predict the future course of the batch fermentation as soon as 12 h of process data were available. In combination with the variational principle, the process model was used to optimize productivity. The temperature trajectory is optimized using a cost functional, including technical and technological conditions of the brewery in order to reduce the process time by steady product quality. The results show a reduction of the process time of up to 20%, which leads to an increase in utilization capacity.     

Response surface methodology as a decision-making tool for optimization of culture conditions of green microalgae Chlorella spp. for biodiesel production

We have evaluated process optimization and the interactive effects of a number of variables using a Box–Behnken design of response surface methodology (RSM). The process variables nitrate, phosphate, glucose and pH were optimized to enhance the cell growth rate, lipid accumulation and other biochemical parameters of Chlorella spp. The most significant increase in lipid production (dry cell weight basis) occurred at limited concentrations of nitrate and phosphate, 1 % glucose and pH 7.5. The addition of nitrates during the mid-lag and mid-exponential phases produced the maximum inhibitory effect on lipid accumulation and the presence of yeast extract led to a further enhancement of lipid accumulation. Of all the media tested, BG-11 was the best suited medium for algal biomass production and chlorophyll content. A significant increase in algal biomass was observed in BG-11 supplemented with bicarbonate and glucose (1 %). The maximum specific growth rate observed was on 9th day of culturing. Results of optimization of process variables through response surface methodology and optimization of various other conditions reflect cutting edge research directed towards increasing algal biomass and lipid content for biodiesel production using an efficient economical technological approach.     

Procrustes analysis as a tool for land management

Generalized Procrustes analysis (GPA) is a multivariate technique that involves transformations of data matrices to provide optimal comparability. We propose GPA to quantify the concordance among sets of variables that characterize natural, human and productive subsystems. When the land use fits in with the physical support of agricultural production, people's well-being should be evident in a high concordance between the land use and the social conditions. In a situation of instability each set of variables operates in diverse directions resulting in lower resilience and sustainability. Two GPA were performed, between physical support and land use data sets (concordance = 67.4%), and between land use and social conditions data sets (concordance = 65.3%). The interplay between the pair of concordance values constitutes a bi-dimensional index which serves as an ecological indicator. Based on bootstrap confidence interval, the 49 counties of the Pampa Ecoregion, Argentina, were classified in medium, high or low concordance. The lack of concordance is an indicator of imbalances which may contribute to guide environmental management.     

Principal components analysis and trend surface analysis of a small-scale pattern in a transition mire

Principal components analysis and trend surface analysis have been applied to a transition mire with the aim to characterize the vegetation pattern and reveal the major trends of variation. The first three PCA axes were ecologically interpretable, viz. the 1 st and 2nd as a complex soil moisture gradient and the 3rd axis as a gradient in the amount of peat in the soil. The ecological interpretability of the 1st axis of PCA after VARIMAX rotation, is unclear because some outlier samples caused a reorientation of the axis. TSA appeared to be useful for the clarification of joint patterns of species groups, which were major contributors to ordination axes in terms of component loadings. The smooth effect of TSA was briefly discussed in connection with the influence of extremes upon the outcoming trend structure. The use of four-variable TSA including a time series is emphasized for the study of spatial-temporal relations and ecological succession.     

Protein structural analysis as a tool for protein design

The analysis of known protein structures is a very valuable and indispensable tool for deciphering the complex rules relating sequence to structure in proteins. On the other hand, the design of novel proteins is certainly the most severe test of our understanding of such rules. In this report we describe our own attempt to develop appropriate tools for the investigation of known protein structure properties and their applications to the design of a novel, all β protein. The success of the design project is a demonstration of the usefulness of careful analysis of the data base of known protein structures. © 1994 John Wiley & Sons, Inc.     

Principal components analysis of physical growth in savannah baboons

Morphometric data collected from 118 male and 169 female savannah baboons (Papio cynocephalus anubis) aged between birth and 5.5 years were analyzed to describe the morphology and physical growth of this species. Measurements included weight, crown-rump length, triceps circumference, and skinfolds at the neck, subscapular, suprailiac, and triceps anatomical sites. Principal components analyses were applied to the data to provide multivariate assessments of morphological patterning among the variables. These analyses resulted in the extraction of two unrotated orthogonal components that accounted for 88% of the overall sample variation. The first component accounted for 77% of the variation and represents an axis of overall body size. The second component represents an axis of shape variation that contrasts body size with fat patterning, and was interpreted as a measure of body leanness. Individual component scores were computed for determining age, gender, and age-by-gender interaction effects. Both components were found to be age dependent for both genders. Males and females shared similar age patterning along the two components; however, gender differences did occur in patterning along the two components; however, gender differences did occur in respect to leanness. The multivariate measure of overall body size increased for both genders similarly with advancing age. Age patterning along the leanness component was described as a decrease from birth to 1 year, followed by an increase in leanness in older ages. Females had a delayed and significantly less intense increase in leanness relative to males.     

On the alcianophilia of the drug suramin used as a tool for inducing experimental mucopolysaccharidosis

Summary The trypanocidal drug suramin was previously reported to induce mucopolysaccharidosis in rats; apart from the biochemical demonstration of increased tissue concentrations of sulfated glycosaminoglycans (GAGs), a strongly positive staining reaction with the cationic dye Alcian Blue was taken as indicating GAG-storage (Constantopoulos et al. 1983). The purpose of the present report is to point out a methodical pitfall. In model experiments it was found that suramin itself, being a polysulfated compound, gives a strongly positive reaction with Alcian Blue pH 1. It is known that suramin is accumulated in the lysosomes and that high drug concentrations are retained in the tissues for weeks. Therefore a positive staining reaction with Alcian Blue observed in a given cell cannot be conclusively attributed to the storage of sulfated GAGs as has been done in the past. The present report may be a warning that, in the case of the suramin-induced animal model of mucopolysaccharidosis, the usual histochemical strategy, i.e. staining with cationic dyes, is not suitable for analysing the cellular distribution pattern of GAG-storage, since the inducing drug by itself reacts with the indicator dye.     

On the alcianophilia of the drug suramin used as a tool for inducing experimental mucopolysaccharidosis

The trypanocidal drug suramin was previously reported to induce mucopolysaccharidosis in rats; apart from the biochemical demonstration of increased tissue concentrations of sulfated glycosaminoglycans (GAGs), a strongly positive staining reaction with the cationic dye Alcian Blue was taken as indicating GAG-storage (Constantopoulos et al. 1983). The purpose of the present report is to point out a methodical pitfall. In model experiments it was found that suramin itself, being a polysulfated compound, gives a strongly positive reaction with Alcian Blue at pH 1. It is known that suramin is accumulated in the lysosomes and that high drug concentrations are retained in the tissues for weeks. Therefore a positive staining reaction with Alcian Blue observed in a given cell cannot be conclusively attributed to the storage of sulfated GAGs as has been done in the past. The present report may be a warning that, in the case of the suramin-induced animal model of mucopolysaccharidosis, the usual histochemical strategy, i.e. staining with cationic dyes, is not suitable for analysing the cellular distribution pattern of GAG-storage, since the inducing drug by itself reacts with the indicator dye.     

Enrichment analysis of phosphorylated proteins as a tool for probing the phosphoproteome

The current progression from genomics to proteomics is fueled by the realization that many properties of proteins (e.g., interactions, post-translational modifications) cannot be predicted from DNA sequence. Although it has become feasible to rapidly identify proteins from crude cell extracts using mass spectrometry after two-dimensional electrophoretic separation, it can be difficult to elucidate low-abundance proteins of interest in the presence of a large excess of relatively abundant proteins. Therefore, for effective proteome analysis it becomes critical to enrich the sample to be analyzed in subfractions of interest. For example, the analysis of protein kinase substrates can be greatly enhanced by enriching the sample of phosphorylated proteins. Although enrichment of phosphotyrosine-containing proteins has been achieved through the use of high-affinity anti-phosphotyrosine antibodies, the enrichment of phosphoserine/threonine-containing proteins has not been routinely possible. Here, we describe a method for enriching phosphoserine/threonine-containing proteins from crude cell extracts, and for subsequently identifying the phosphoproteins and sites of phosphorylation. The method, which involves chemical replacement of the phosphate moieties by affinity tags, should be of widespread utility for defining signaling pathways and control mechanisms that involve phosphorylation or dephosphorylation of serine/threonine residues.     

Kinetic analysis of glycation as a tool for assessing the half-life of proteins

Glycation of proteins, a common postribosomal modification, proceeds via Amadori rearrangement to yield a stable ketoamine linkage of glucose with the protein. Kinetic analysis of the reaction shows that the amount of glycation at steady state is proportional to the glucose concentration, to protein half-life and to the rate of glycation. Thus, when the rate of glycation is determined in vitro and the extent of glycation of a given protein isolated from euglycemic subjects is measured, the half-life may be calculated. As the in vivo situation may not be simulated accurately in vitro, the calculated values may be considered as approximation. When the calculated values were compared with values reported in the literature fairly good agreement was found except for hemoglobin. Studies on stability of glycated albumin show that ketoamine decreases by about 20% when incubated under physiological conditions for 20 days. The method described by us is especially valuable when turnover of proteins in normal and pathophysiological states are compared. The half-life of plasma low-density lipoprotein is longer in patients with hypothyroidism or a high plasma low-density lipoprotein level than in normal subjects. Extending our studies to tissue proteins we did not find a significant increase in half-life of tendon collagen with age. Basement membrane collagen turnover is faster in diabetic patients in bad metabolic control. Thus, the procedure using fructosylamine as endogenous label of protein offers a method of great potential to study the turnover of human body proteins.     

Confocal imaging of exine as a tool for grass pollen analysis

A new method which utilises confocal optical imaging has been developed which can be expected to improve grass pollen analysis. Confocal microscopy, in reflection mode, was used to examine the exine morphology of unacetolysed pollen grains from the following species of common wild grasses: Paspalum dilatatum, Setaria gracilis, Bromus catharticus, Daclylis glomerata, Lolium perenne, Poa pratensis, and Phalaris aquatica. Variations in the surface texture patterns, similar to those hitherto only seen by scanning electron microscopy, were visualised. In contrast to the latter method, specimen preparation for this confocal microscopy based technique was characterised by its simplicity, permitting the use of fresh and chemically untreated pollen grains. This confocal imaging technique, with its capacity for optical sectioning of specimens, offered the additional advantage of allowing the examination of the sub‐surface exine layers as well as the surface morphology of the pollen grains. Furthermore, three‐dimensional reconstruction of these optical sections enabled visualisation of the identified sculptural and structural exine elements and layers. A number of differences in these patterns were found, which indicate that confocal microscopy, in combination with image analysis, may enable finer taxonomic distinctions to be made than those currently provided by other light microscope based methods.     

Activity-based probes as a tool for functional proteomic analysis of proteases

Traditional proteomics methodology allows global analysis of protein abundance but does not provide information on the regulation of protein activity. Proteases, in particular, are known for their multilayered post-translational activity regulation that can lead to a significant difference between protease abundance levels and their enzyme activity. To address these issues, the field of activity-based proteomics has been established in order to characterize protein activity and monitor the functional regulation of enzymes in complex proteomes. In this review, we present structural features of activity-based probes for proteases and discuss their applications in proteomic profiling of various catalytic classes of proteases.