Multivariate classification of histochemically stained human skeletal muscle fibres by the SIMCA method

Summary The SIMCA (soft independent modelling of class analogy) method of pattern recognition has been used to classify four muscle fibre types: I, IIA, IIB and IIC. The samples were histochemically stained human skeletal sections from biopsy material. Disjoint (separate) class modelling gave information about variables, i.e., the combinations of alkaline, acidic and Ca²⁺-containing preincubation procedures with appropriate discrimination power, and showed satisfactory separation of the classes (fibre types). Two serial stained muscle sections represent a minimum for a proper classification of the four fibre groups. A comparison of biopsy samples from two different persons showed significant variation in the data structure between similar fibre types, probably caused by intermuscle variations. It is suggested that the introduction of computer-assisted classification by the application of such multivariate analytical techniques both facilitates the classification of muscle fibres and improves the precision and reliability of fibre typing.     

Evaluation of the possible proteomic application of trypsin from Streptomyces griseus

Trypsin (EC 3.4.21.4) is the protease of choice for proteome analysis using mass spectrometry of peptides in sample digests. In this work, trypsin from Streptomyces griseus (SGT) was purified to homogeneity from pronase. The enzyme was evaluated in in-gel digestion of protein standards followed by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) analyses of the digests. We recognized a remarkable cleavage performance of SGT. The number of produced and matching tryptic peptides was higher than in the case of commonly used bovine trypsin (BT) and allowed us to obtain higher identification scores in database searches. Interestingly, SGT was found to also generate nonspecific peptides whose sequencing by MALDI-TOF/TOF tandem mass spectrometry (MS/MS) revealed a partial F-X, Y-X, and W-X cleavage specificity. To suppress autolysis, either arginine or arginine plus lysine residues in SGT were modified by chemical reagents. In consequence, the autolytic pattern of SGT was reduced significantly, but specific activity dropped dramatically. As demonstrated by relative quantification of peptides at different times, SGT is more stable at 37 °C than is its bovine counterpart. We conclude that SGT represents a convenient alternative for proteomic applications involving protein digestion. Moreover, parallel digestions of sample aliquots by SGT and BT provide the possibility of combining partially different results (unique matching peptides) to improve protein identification.     

Evaluation of the application of sodium deoxycholate to proteomic analysis of rat hippocampal plasma membrane

Detergents have been widely used for the solubilization of membrane proteins and the improvement of their digestion. In this paper, we have evaluated the application of sodium deoxycholate (SDC) to the solubilization and digestion of rat hippocampal plasma membrane (PM) proteins. For in-solution digestion, rat hippocampal PM fraction from sucrose-density gradient centrifugation was solubilized by boiling in 1.0% SDC, and directly digested without dilution. During the in-gel digestion of the hippocampal PM proteins separated by SDS-PAGE, 0.1% SDC was added. Before analysis of peptide mixture by liquid chromatography and electrospray mass spectrometry, SDC in the tryptic digests was removed by centrifugation following acidification. Use of 1.0% SDC in solubilization and in-solution digestion of rat PM proteins had led to 77 PM or membrane-associated proteins identified, a more than 2-fold increase over that by use of SDS. The addition of 0.1% SDC to the in-gel digestion of SDS-PAGE-resolved membrane proteins remarkably enhanced the coverage of tryptic peptides and the number of hydrophobic membrane proteins identified. Being a cheaper and more tractable acid-insoluble detergent, SDC could be used at higher concentration in the solubilization and tryptic digestion of proteins including PM proteins with the purpose of enhancing the protein solubility and at the same time making no interference with trypsin activity and subsequent analyses.     

An optimization method for the identification of minimal sets of discriminating gene markers: application to cultivar identification in wheat

A potentially large number of molecular markers are available for identifying genotypes in various species. For wheat, cultivar identity is an important determinant for end-use segregation and for payment of end-point royalties and grower premiums. A number of dominant DNA markers, that give either a positive or negative response, have been developed previously for wheat cultivar identification. This paper gives a method for identifying minimal marker sets for a given cultivar group, for example those grown in a specific geographical zone. It is based on an integer linear programming formulation of the problem, and can find all minimal marker sets for the group if required. The paper then describes the production of two software packages, GGDS and GGIP, that incorporate this methodology. Various practical issues are also discussed. These packages enable the rapid selection of minimal marker sets for the efficient discrimination of any sample set where the marker responses of the samples are known. They are already being used by the Australian wheat industry.     

Quantification of cysteinyl S-nitrosylation by fluorescence in unbiased proteomic studies

Cysteinyl S-nitrosylation has emerged as an important post-translational modification affecting protein function in health and disease. Great emphasis has been placed on global, unbiased quantification of S-nitrosylated proteins because of physiologic and oxidative stimuli. However, current strategies have been hampered by sample loss and altered protein electrophoretic mobility. Here, we describe a novel quantitative approach that uses accurate, sensitive fluorescence modification of cysteine S-nitrosylation that leaves electrophoretic mobility unaffected (SNOFlo) and introduce unique concepts for measuring changes in S-nitrosylation status relative to protein abundance. Its efficacy in defining the functional S-nitrosoproteome is demonstrated in two diverse biological applications: an in vivo rat hypoxia-ischemia/reperfusion model and antimicrobial S-nitrosoglutathione-driven transnitrosylation of an enteric microbial pathogen. The suitability of this approach for investigating endogenous S-nitrosylation is further demonstrated using Ingenuity Pathways analysis that identified nervous system and cellular development networks as the top two networks. Functional analysis of differentially S-nitrosylated proteins indicated their involvement in apoptosis, branching morphogenesis of axons, cortical neurons, and sympathetic neurites, neurogenesis, and calcium signaling. Major abundance changes were also observed for fibrillar proteins known to be stress-responsive in neurons and glia. Thus, both examples demonstrate the technique's power in confirming the widespread involvement of S-nitrosylation in hypoxia-ischemia/reperfusion injury and in antimicrobial host responses.     

Application of the random forest classification method to peaks detected from mass spectrometric proteomic profiles of cancer patients and controls

The random forest classification method was applied to classify samples from 76 breast cancer patients and 77 controls whose proteomic profile had been obtained using mass spectrometry. The analysis consisted of two stages, the detection of peaks from the profiles and the construction of a classification rule using random forests. Using a peak detection method based on finding common local maxima in the smoothed sample spectra, 444 peaks were detected, reducing to 365 robust peaks found in at least 7 out of 10 random subsets of samples. Subjects were classified as cases or controls using the random forest algorithm applied to the 365 peaks. Based on the prediction of the status of out-of-bag samples, the total error rate was 16.3%, with a sensitivity of 81.6% and a specificity of 85.7%. Measures of importance of each of the peaks were calculated to identify regions of the spectrum influencing the classification, and the four most important peaks were identified as mz3863_13, mz2943_12, mz3193_44 and mz8925_94. Combining initial peak detection with the random forest algorithm provides a high-performance classification system for proteomic data, with unbiased estimates of future performance.     

Mapping hidden potential identity elements by computing the average discriminating power of individual tRNA positions

The recently published discrete mathematical method, extended consensus partition (ECP), identifies nucleotide types at each position that are strictly absent from a given sequence set, while occur in other sets. These are defined as discriminating elements (DEs). In this study using the ECP approach, we mapped potential hidden identity elements that discriminate the 20 different tRNA identities. We filtered the tDNA data set for the obligatory presence of well-established tRNA features, and then separately for each identity set, the presence of already experimentally identified strictly present identity elements. The analysis was performed on the three kingdoms of life. We determined the number of DE, e.g. the number of sets discriminated by the given position, for each tRNA position of each tRNA identity set. Then, from the positional DE numbers obtained from the 380 pairwise comparisons of the 20 identity sets, we calculated the average excluding value (AEV) for each tRNA position. The AEV provides a measure on the overall discriminating power of each position. Using a statistical analysis, we show that positional AEVs correlate with the number of already identified identity elements. Positions having high AEV but lacking published identity elements predict hitherto undiscovered tRNA identity elements.     

Diversity of Archaea in hypersaline environments characterized by molecular-phylogenetic and cultivation studies

The diversity of Archaea from three different hypersaline environments was analyzed and compared by polymerase chain reaction (PCR)-based molecular phylogenetic techniques and cultivation approaches. The samples originated from a crystallization pond of a solar saltern in Spain (FC); an alkaline lake in Nevada, USA, (EMF); and a small pond from a slag heap of a potassium mine in Germany (DIE). Except for two 16S rDNA sequences that were related to crenarchaeota from soil and did not apparently belong to the indigenous halophilic community, all sequences recovered from environmental DNA or cultivated strains grouped within the Halobacteriaceae. Mostly 16S rDNA sequences related to the genera Halorubrum and Haloarcula were detected in sample FC, and organisms belonging to these genera were also recovered by cultivation. In contrast, sequences related to five different groups of halophilic archaea were amplified from sample DIE (including novel lineages with only uncultivated phylotypes), but the organisms that were cultivated from this sample fell into different groups (i.e., Natronococcus, Halorubrum, or unaffiliated) and did not overlap with those predicted using the culture-independent approach. With respect to the highly alkaline sample, EMF, four groups were predicted from the environmental 16S rDNA sequences, two of which ( Natronomonas and Haloarcula) were also recovered through cultivation together with Natronococcus isolates. In summary, we found that halophilic archaea dominate the archaeal populations in these three hypersaline environments and show that culturability of the organisms predicted by molecular surveys might strongly depend on the habitat chosen. While a number of novel halophilic archaea have been isolated, we have not been able to cultivate representatives of the new lineages that were detected in this and several other environmental studies.     

Evaluation of biological variables in the human

The analysis of the biological variability is one of the most important problems of anthropology and human genetics. However, describing the objective reality a conscious or unconscious valuation of this reality comes of frequently. Such a valuation can be motivated in a very different way and can achieve a considerable importance concerning the view of man. Experiences from history and presence make it clear that the sensitiveness for these problems must be a never renounced and a constant concern of all anthropologists and human genetists.     

An integrated feature selection and classification method to select minimum number of variables on the case study of gene expression data

This paper introduces a novel generic approach for classification problems with the objective of achieving maximum classification accuracy with minimum number of features selected. The method is illustrated with several case studies of gene expression data. Our approach integrates filter and wrapper gene selection methods with an added objective of selecting a small set of non-redundant genes that are most relevant for classification with the provision of bins for genes to be swapped in the search for their biological relevance. It is capable of selecting relatively few marker genes while giving comparable or better leave-one-out cross-validation accuracy when compared with gene ranking selection approaches. Additionally, gene profiles can be extracted from the evolving connectionist system, which provides a set of rules that can be further developed into expert systems. The approach uses an integration of Pearson correlation coefficient and signal-to-noise ratio methods with an adaptive evolving classifier applied through the leave-one-out method for validation. Datasets of gene expression from four case studies are used to illustrate the method. The results show the proposed approach leads to an improved feature selection process in terms of reducing the number of variables required and an increased in classification accuracy.     

An automated method of differential red blood cell classification with application to the diagnosis of anemia.

A method of automated red cell analysis suitable for the rapid classification of large numbers of red cells from individual blood specimens has been developed, and preliminarily tested on normal bloods and clinically proven cases of anemias and red cell disorders. According to this method digital image processing techniques provide several features relating to shape and internal central pallor configurations of red cells. These features are used with a fully automated decision logic to rapidly provide a quantitative "red cell differential" analysis, a report of the percentage subpopulations of recognized categories of red cells. For each subpopulation, measurements of mean cell area, mean cell hemoglobin content and mean cell hemoglobin density are provided. The nine types of red cell disorders studied with this method were: (a) iron deficiency anemia, (b) the anemia of chronic disease, (c) beta-thalassemia trait, (d) sickle cell anemia, (e) hemoglobin C disease, (f) intravascular hemolysis, (g) hereditary elliptocytosis, (h) hereditary spherocytosis, and (i) megaloblastic anemia due to folic acid deficiency. Preliminary indications are that the red cell differential is useful in distinguishing between these conditions.     

Scale problem in classification: an application of a stochastic method to evaluate the relative homogeneity of sample units

A basic problem in experimental design is to select a sample unit at a scale appropriate to the variables being observed and to the nature of the topic. Where hierarchical classification procedures are used it is important that the scale of the basic units is such that any variation found within the sample is small relative to that between the groups formed. In this paper a method is applied to evaluate the relative homogeneity of sample units. Replicates from each sample are classified hierarchically. The method of Sandland & Young (1979a, b) is used to determine the probability at division of the number of replicates assigned at a division to the same group occurring by chance. A high probability indicates relative heterogeneity of the samples from which replicates are drawn; a low probability suggests relative homogeneity. An example from the area of urban ecology illustrates an application of the method. It is used to evaluate the relative homogeneity of spatial units (Census Collectors Districts) with respect to dwelling characteristics in inner city Brisbane. Possible applications in other areas of ecology are briefly considered.     

Glomerular abundance of complement proteins characterized by proteomic analysis of laser-captured microdissected glomeruli associates with progressive disease in IgA nephropathy

Background

The clinical course of IgA nephropathy (IgAN) is variable and complement activation may predict prognosis. The present study investigated whether glomerular abundance of complement proteins associates with progression to end-stage renal disease (ESRD) in patients for whom prognosis could not be predicted based on clinical variables.

Methods

Based on data from the Norwegian Kidney Biopsy Registry and the Norwegian Renal Registry, three groups were included: IgAN patients with (n = 9) or without (n = 16) progression to ESRD during 10 years, and controls (n = 15) with a normal kidney biopsy. IgAN patients had eGFR > 45 ml/min/1.73 m² and non-nephrotic proteinuria at time of biopsy. Using stored formalin-fixed paraffin embedded kidney biopsy tissue, about 100 glomerular cross sections were microdissected for each patient. Samples were analyzed by liquid chromatography–tandem mass spectrometry and relative abundances of complement proteins were compared between groups.

Results

Proteomic analyses quantified 2018 proteins, of which 28 proteins belong to the complement system. As compared to IgAN patients without progressive disease, glomeruli from patients with progressive IgAN had significantly higher abundance of components of the classical and the terminal complement pathways, and inhibitory factors such as Factor H and factor H related proteins. Abundance of complement proteins classified progressors from non-progressors with an area under ROC curve of 0.91 (p = 0.001). Clinical and morphological data were similar between the two patient groups and could not predict progressive IgAN.

Conclusions

In conclusion, higher glomerular abundance of complement proteins was associated with a progressive clinical course in IgAN and are candidate biomarkers to predict prognosis.

     

Clinical infrastructures to support proteomic studies of tissue and fluids in breast cancer

The Danish Breast Cancer Cooperative Group (DBCG) was established in 1977 with the aim to ensure optimal breast cancer diagnostics and therapeutic modalities on a nationwide basis. DBCG was organized in such a way so it represents a broad interdisciplinary collaboration with established clinical databases and biobanks. This review summarizes the infrastructures, such as those of the DBCG, that are required to facilitate translational research studies aiming at further diagnostic and therapeutic improvements through interactions directed at prevention, early diagnosis, and treatment of primary breast cancer.     

Quantification of the major urinary metabolite of the E prostaglandins by mass spectrometry: Evaluation of the method's application to clinical studies

Measurement of 7α-hydroxy-5,11-diketotetranorprostane-1,16-dioic acid, (PGE-M), the major urinary metabolite of prostaglandin E₁ and E₂ in man provides a useful indicator to monitor prostaglandin biosynthesis. For quantitative analysis of this prostaglandin metabolite the stable-isotope dilution technique of selected ion monitoring (SIM) is employed using gas-liquid chromatography-mass spectrometry. The preparation of the (D₃-methyloxime), -methyl ester of PGE-M containing a tritium tracer in position 2 which was used as internal standard for the SIM method is described. The synthesis of this internal standard includes the biosynthetic conversion of 11-hydroxy-9,15-diketoprostanoic acid to PGE-M by the rabbit. The intra-assay coefficient of variation of this SIM method ranged between 4.0 to 6.7 percent. The recovery of authentic, underivatized PGE-M added to urine was 93 ± 3% (mean ± SEM, n=17).The levels of PGE-M excreted in urine were higher (p<0.001) in males than in females (15.2 ± 1.9 μg/24 hours (n=24) and 3.3 ± 0.3 μg/24 hours (n=17), respectively). These levels were in close agreement with values published previously. No significant difference in excretion of PGE-M between the sexes was observed in the pre-pubertal age-group (male: 2.9 ± 0.8 μg/24 hours, n=5; female: 3.1 ± 0.9 μg/24 hours, n=5) or in the age-group of 45–80 years (male: 9.3 ± 1.1 μg/24 hours, n=21; female: 7.3 ± 0.9 μg/24 hours, n=12). The amount of PGE-M excreted decreased significantly after administration of indomethacin or acetyl salicylic acid in therapeutic doses. The concomitant reduction of the urinary excretion of PGE-M (68 to 85% decrease) and prostaglandin E (73 to 100% decrease) after indomethacin treatment in each case (n=8) is evidence that a diminished urinary PGE-M output reflects a decrease in prostaglandin E biosynthesis.