An expert-based system to predict population survival rate from health data.

Timely detection and understanding of causes for population decline are essential for effective wildlife management and conservation. Assessing trends in population size has been the standard approach, but we propose that monitoring population health could prove more effective. We collated data from 7 bottlenose dolphin (Tursiops truncatus) populations in the southeastern United States to develop a method for estimating survival probability based on a suite of health measures identified by experts as indices for inflammatory, metabolic, pulmonary, and neuroendocrine systems. We used logistic regression to implement the veterinary expert system for outcome prediction (VESOP) within a Bayesian analysis framework. We fitted parameters with records from 5 of the sites that had a robust network of responders to marine mammal strandings and frequent photographic identification surveys that documented definitive survival outcomes. We also conducted capture-mark-recapture (CMR) analyses of photographic identification data to obtain separate estimates of population survival rates for comparison with VESOP survival estimates. The VESOP analyses showed that multiple measures of health, particularly markers of inflammation, were predictive of 1- and 2-year individual survival. The highest mortality risk 1 year following health assessment related to low alkaline phosphatase (odds ratio [OR] = 10.2 [95% CI: 3.41-26.8]), whereas 2-year mortality was most influenced by elevated globulin (OR = 9.60 [95% CI: 3.88-22.4]); both are markers of inflammation. The VESOP model predicted population-level survival rates that correlated with estimated survival rates from CMR analyses for the same populations (1-year Pearson's r = 0.99, p = 1.52 × 10-5 ; 2-year r = 0.94, p = 0.001). Although our proposed approach will not detect acute mortality threats that are largely independent of animal health, such as harmful algal blooms, it can be used to detect chronic health conditions that increase mortality risk. Random sampling of the population is important and advancement in remote sampling methods could facilitate more random selection of subjects, obtainment of larger sample sizes, and extension of the approach to other wildlife species.

Un sistema basado en conocimiento experto para predecir la tasa de supervivencia a partir de datos de salud Resumen La detección y el entendimiento oportunos de la declinación poblacional son esenciales para que el manejo y la conservación de fauna tengan efectividad. La evaluación de las tendencias en el tamaño poblacional ha sido la estrategia estándar, pero proponemos que el monitoreo de la salud poblacional podría ser más efectivo. Recopilamos datos de siete poblaciones de delfines (Tursiops truncatus) en el sureste de Estados Unidos para desarrollar un método de estimación de la probabilidad de supervivencia con base en un conjunto de medidas sanitarias identificadas por expertos como índices para los sistemas inflamatorio, metabólico, pulmonar y neuroendocrino. Usamos la regresión logística para implementar el sistema de expertos veterinarios para la predicción de resultados (SEVPR) en un análisis bayesiano. Ajustamos los parámetros con los registros de cinco sitios que contaban con una buena red de respondientes a los varamientos de mamíferos marinos y censos de identificación fotográfica (foto-ID) que documentaron los resultados de supervivencia definitivos. También realizamos análisis de marcaje-recaptura (MR) en los datos de identificación fotográfica para obtener estimados separados de las tasas de supervivencia poblacional para compararlos con los estimados del SEVPR. Los análisis del SEVPR mostraron que varias medidas sanitarias, particularmente los marcadores de inflamación son buenos predictores de la supervivencia individual para uno y dos años. El riesgo de mortalidad más alto un año después de la valoración sanitaria se relacionó con una fosfatasa alcalina baja (cociente de probabilidades de 10.2 [95% CI 3.41-26.8]), mientras que la mortalidad a los dos años estuvo más influenciada por una globulina elevada (9.60 [95% CI 3.88-22.4]); ambas son marcadores de la inflamación. El modelo del SEVPR predijo las tasas de supervivencia a nivel poblacional en correlación con las tasas estimadas de supervivencia de los análisis de MR para las mismas poblaciones (Pearson de un año r = 0.99, p = 1.52e-05; dos años r = 0.94, p = 0.001). Aunque nuestra propuesta no detecta las amenazas agudas de mortalidad que en su mayoría son independientes de la salud animal, como la proliferación de algas nocivas, puede usarse para detectar las condiciones crónicas de salud que incrementan el riesgo de mortalidad. Es importante el muestreo aleatorio de la población y los avances en los métodos de muestreo remoto podrían facilitar una selección más aleatoria de los sujetos, la obtención de muestras de mayor tamaño y la expansión de la estrategia a otras especies de fauna.

Spatial distributions of phosphorylated membrane proteins aquaporin 0 and MP20 across young and aged human lenses.

In the human ocular lens it is now realized that post-translational modifications can alter protein function and/or localization in fiber cells that no longer synthesize proteins. The specific sites of post-translational modification to the abundant ocular lens membrane proteins AQP0 and MP20 have been previously identified and their functional effects are emerging. To further understand how changes in protein function and/or localization induced by these modifications alter lens homeostasis, it is necessary to determine the spatial distributions of these modifications across the lens. In this study, a quantitative LC-MS approach was used to determine the spatial distributions of phosphorylated AQP0 and MP20 peptides from manually dissected, concentric layers of fiber cells from young and aged human lenses. The absolute amounts of phosphorylation were determined for AQP0 Ser235 and Ser229 and for MP20 Ser170 in fiber cells from the lens periphery to the lens center. Phosphorylation of AQP0 Ser229 represented a minor portion of the total phosphorylated AQP0. Changes in spatial distributions of phosphorylated APQ0 Ser235 and MP20 Ser170 correlated with regions of physiological interest in aged lenses, specifically, where barriers to water transport and extracellular diffusion form.

The application of Gaussian mixture models for signal quantification in MALDI-TOF mass spectrometry of peptides.

Matrix assisted laser desorption/ionization time-of-flight (MALDI-TOF) coupled with stable isotope standards (SIS) has been used to quantify native peptides. This peptide quantification by MALDI-TOF approach has difficulties quantifying samples containing peptides with ion currents in overlapping spectra. In these overlapping spectra the currents sum together, which modify the peak heights and make normal SIS estimation problematic. An approach using Gaussian mixtures based on known physical constants to model the isotopic cluster of a known compound is proposed here. The characteristics of this approach are examined for single and overlapping compounds. The approach is compared to two commonly used SIS quantification methods for single compound, namely Peak Intensity method and Riemann sum area under the curve (AUC) method. For studying the characteristics of the Gaussian mixture method, Angiotensin II, Angiotensin-2-10, and Angiotenisn-1-9 and their associated SIS peptides were used. The findings suggest, Gaussian mixture method has similar characteristics as the two methods compared for estimating the quantity of isolated isotopic clusters for single compounds. All three methods were tested using MALDI-TOF mass spectra collected for peptides of the renin-angiotensin system. The Gaussian mixture method accurately estimated the native to labeled ratio of several isolated angiotensin peptides (5.2% error in ratio estimation) with similar estimation errors to those calculated using peak intensity and Riemann sum AUC methods (5.9% and 7.7%, respectively). For overlapping angiotensin peptides, (where the other two methods are not applicable) the estimation error of the Gaussian mixture was 6.8%, which is within the acceptable range. In summary, for single compounds the Gaussian mixture method is equivalent or marginally superior compared to the existing methods of peptide quantification and is capable of quantifying overlapping (convolved) peptides within the acceptable margin of error.

Imbalanced expression of Vcan mRNA splice form proteins alters heart morphology and cellular protein profiles.

The fundamental importance of the proteoglycan versican to early heart formation was clearly demonstrated by the Vcan null mouse called heart defect (hdf). Total absence of the Vcan gene halts heart development at a stage prior to the heart's pulmonary/aortic outlet segment growth. This creates a problem for determining the significance of versican's expression in the forming valve precursors and vascular wall of the pulmonary and aortic roots. This study presents data from a mouse model, Vcan ((tm1Zim)), of heart defects that results from deletion of exon 7 in the Vcan gene. Loss of exon 7 prevents expression of two of the four alternative splice forms of the Vcan gene. Mice homozygous for the exon 7 deletion survive into adulthood, however, the inability to express the V2 or V0 forms of versican results in ventricular septal defects, smaller cushions/valve leaflets with diminished myocardialization and altered pulmonary and aortic outflow tracts. We correlate these phenotypic findings with a large-scale differential protein expression profiling to identify compensatory alterations in cardiac protein expression at E13.5 post coitus that result from the absence of Vcan exon 7. The Vcan ((tm1Zim)) hearts show significant changes in the relative abundance of several cytoskeletal and muscle contraction proteins including some previously associated with heart disease. These alterations define a protein fingerprint that provides insight to the observed deficiencies in pre-valvular/septal cushion mesenchyme and the stability of the myocardial phenotype required for alignment of the outflow tract with the heart ventricles.

Network modeling reveals steps in angiotensin peptide processing.

New insights into the intrarenal renin-angiotensin (Ang) system have modified our traditional view of the system. However, many finer details of this network of peptides and associated peptidases remain unclear. We hypothesized that a computational systems biology approach, applied to peptidomic data, could help to unravel the network of enzymatic conversions. We built and refined a Bayesian network model and a dynamic systems model starting from a skeleton created with established elements of the renin-Ang system and further developed it with archived matrix-assisted laser desorption ionization-time of flight mass spectra from experiments conducted in mouse podocytes exposed to exogenous Ang substrates. The model-building process suggested previously unrecognized steps, 3 of which were confirmed in vitro, including the conversion of Ang(2-10) to Ang(2-7) by neprilysin, Ang(1-9) to Ang(2-9), and Ang(1-7) to Ang(2-7) by aminopeptidase A. These data suggest a wider role of neprilysin and aminopeptidase A in glomerular formation of bioactive Ang peptides and shunting their formation. Other steps were also suggested by the model, and supporting evidence for those steps was evaluated using model-comparison methods. Our results demonstrate that systems biology methods applied to peptidomic data are effective in identifying novel steps in the Ang peptide processing network, and these findings improve our understanding of the glomerular renin-Ang system.

Forensic identification of seal oils using lipid profiles and statistical models.

Seal blubber oils are used as a source of omega-3 polyunsaturated fatty acids in Canada but prohibited in the United States and (FA) European Union. Thus, a reliable method is needed to identify oils originating from seals versus fish. Two lipid profiling methods, fatty acid analysis using gas chromatography and triacylglycerol (TAG) analysis using liquid chromatography and mass spectrometry, were applied with statistical models to discriminate commercial oils and blubber samples harvested from marine fish and seals. Significant differences were observed among FA profiles, and seal samples differed from each of the fish oils (p ≤ 0.001). FA and TAG profiles were used to discriminate sample groups using a random forest classifier; all samples were classified correctly as seals versus fish using both methods. We propose a two-step method for the accurate identification of seal oils, with preliminary identification based on FA profile analysis and confirmation with TAG profiles.

Revealing functionally coherent subsets using a spectral clustering and an information integration approach.

BACKGROUND: Contemporary high-throughput analyses often produce lengthy lists of genes or proteins. It is desirable to divide the genes into functionally coherent subsets for further investigation, by integrating heterogeneous information regarding the genes. Here we report a principled approach for managing and integrating multiple data sources within the framework of graph-spectrum analysis in order to identify coherent gene subsets. RESULTS: We investigated several approaches to integrate information derived from different sources that reflect distinct aspects of gene functional relationships including: functional annotations of genes in the form of the Gene Ontology, co-mentioning of genes in the literature, and shared transcription factor binding sites among genes. Given a list of genes, we construct a graph containing the genes in each information space; then the graphs were kernel transformed so they could be integrated; finally functionally coherent subsets were identified using a spectral clustering algorithm. In a series of simulation experiments, known functionally coherent gene sets were mixed and recovered using our approach. CONCLUSIONS: The results indicate that spectral clustering approaches are capable of recovering coherent gene modules even under noisy conditions, and that information integration serves to further enhance this capability. When applied to a real-world data set, our methods revealed biologically sensible modules, and highlighted the importance of information integration. The implementation of the statistical model is provided under the GNU general public license, as an installable Python module, at: http://code.google.com/p/spectralmix.

AKAP signaling in reinstated cocaine seeking revealed by iTRAQ proteomic analysis.

To identify candidate proteins in the nucleus accumbens (NAc) as potential pharmacotherapeutic targets for treating cocaine addition, an 8-plex iTRAQ (isobaric tag for relative and absolute quantitation) proteomic screen was performed using NAc tissue obtained from rats trained to self-administer cocaine followed by extinction training. Compared with yoked-saline controls, 42 proteins in a postsynaptic density (PSD)-enriched subfraction of the NAc from cocaine-trained animals were identified as significantly changed. Among proteins of interest whose levels were identified as increased was AKAP79/150, the rat ortholog of human AKAP5, a PSD scaffolding protein that localizes signaling molecules to the synapse. Functional downregulation of AKAP79/150 by microinjecting a cell-permeable synthetic AKAP (A-kinase anchor protein) peptide into the NAc to disrupt AKAP-dependent signaling revealed that inhibition of AKAP signaling impaired the reinstatement of cocaine seeking. Reinstatement of cocaine seeking is thought to require upregulated surface expression of AMPA glutamate receptors, and the inhibitory AKAP peptide reduced the PSD content of protein kinase A (PKA) as well as surface expression of GluR1 in NAc. However, reduced surface expression was not associated with changes in PKA phosphorylation of GluR1. This series of experiments demonstrates that proteomic analysis provides a useful tool for identifying proteins that can regulate cocaine relapse and that AKAP proteins may contribute to relapse vulnerability by promoting increased surface expression of AMPA receptors in the NAc.

Biochemical systems analysis of signaling pathways to understand fungal pathogenicity.

Over the past decade, researchers have recognized the need to study biological systems as integrated systems. While the reductionist approaches of the past century have made remarkable advances of our understanding of life, the next phase of understanding comes from systems-level investigations. Additionally, biology has become a data-intensive field of research. The introduction of high throughput sequencing, microarrays, high throughput proteomics, metabolomics, and now lipidomics are producing significantly more data than can be interpreted using existing methods. The field of systems biology brings together methods from computer science, modeling, statistics, engineering, and biology to explore the volumes of data now being produced and to develop mathematical representations of metabolic, signaling, and gene regulatory systems. Advances in these methods are allowing biologists to develop new insights into the complexities of life, to predict cellular responses and treatment outcomes, and to effectively plan experiments that extend our understanding. In this chapter, we are providing the basic steps of developing and analyzing a small S-system model of a biochemical pathway related to sphingolipid metabolism in the regulation of virulence of the human fungal microbial pathogen Cryptococcus neoformans (Cn).

Tight binding of proteins to membranes from older human cells.

The lens is an ideal model system for the study of macromolecular aging and its consequences for cellular function, since there is no turnover of lens fibre cells. To examine biochemical processes that take place in the lens and that may also occur in other long-lived cells, membranes were isolated from defined regions of human lenses that are synthesised at different times during life, and assayed for the presence of tightly bound cytosolic proteins using quantitative iTRAQ proteomics technology. A majority of lens beta crystallins and all gamma crystallins became increasingly membrane bound with age, however, the chaperone proteins alpha A and alpha B crystallin, as well as the thermally-stable protein, ßB2 crystallin, did not. Other proteins such as brain-associated signal protein 1 and paralemmin 1 became less tightly bound in the older regions of the lens. It is evident that protein-membrane interactions change significantly with age. Selected proteins that were formerly cytosolic become increasingly tightly bound to cell membranes with age and are not removed even by treatment with 7 M urea. It is likely that such processes reflect polypeptide denaturation over time and the untoward binding of proteins to membranes may alter membrane properties and contribute to impairment of communication between older cells.

Matrix-assisted laser desorption/ionization tissue profiling of secretoneurin in the nucleus accumbens shell from cocaine-sensitized rats.

Proteins in the nucleus accumbens mediate many cocaine-induced behaviors. In an effort to measure changes in nucleus accumbens protein expression as potential biomarkers for addiction, coronal tissue sections were obtained from rats that developed behavioral sensitization after daily administration of cocaine, or from daily saline-treated controls. The tissue sections were subjected to matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) profiling and tissue imaging. For profiling experiments, brain sections were manually spotted with matrix over the nucleus accumbens, a brain region known to regulate cocaine sensitization. Summed mass spectra (10,000 laser shots, grid) were acquired and spectra were aligned to reference peaks. Using bioinformatics tools, eight spectral features were found to be altered by cocaine treatment. Based on additional sequencing experiments with MALDI tandem MS and database searches of measured masses, secretoneurin (m/z 3653) was identified as having an increased expression. In addition, the distribution of m/z 3653 in the nucleus accumbens was determined by MALDI tissue imaging, and the increased expression of its precursor protein, secretogranin II, was verified by immunoblotting.

iQuantitator: a tool for protein expression inference using iTRAQ.

BACKGROUND: Isobaric Tags for Relative and Absolute Quantitation (iTRAQ) [Applied Biosystems] have seen increased application in differential protein expression analysis. To facilitate the growing need to analyze iTRAQ data, especially for cases involving multiple iTRAQ experiments, we have developed a modeling approach, statistical methods, and tools for estimating the relative changes in protein expression under various treatments and experimental conditions. RESULTS: This modeling approach provides a unified analysis of data from multiple iTRAQ experiments and links the observed quantity (reporter ion peak area) to the experiment design and the calculated quantity of interest (treatment-dependent protein and peptide fold change) through an additive model under log transformation. Others have demonstrated, through a case study, this modeling approach and noted the computational challenges of parameter inference in the unbalanced data set typical of multiple iTRAQ experiments. Here we present the development of an inference approach, based on hierarchical regression with batching of regression coefficients and Markov Chain Monte Carlo (MCMC) methods that overcomes some of these challenges. In addition to our discussion of the underlying method, we also present our implementation of the software, simulation results, experimental results, and sample output from the resulting analysis report. CONCLUSION: iQuantitator's process-based modeling approach overcomes limitations in current methods and allows for application in a variety of experimental designs. Additionally, hypertext-linked documents produced by the tool aid in the interpretation and exploration of results.

Quantification of protein expression changes in the aging left ventricle of Rattus norvegicus.

As the heart ages, electrophysiological and biochemical changes can occur, and the ventricle in many cases loses distensibility, impairing diastolic function. How the proteomic signature of the aged ventricle is unique in comparison to young hearts is still under active investigation. We have undertaken a quantitative proteomics study of aging left ventricles (LVs) utilizing the isobaric Tagging for Relative and Absolute Quantification (iTRAQ) methodology. Differential protein expression was observed for 117 proteins including proteins involved in cell signaling, the immune response, structural proteins, and proteins mediating responses to oxidative stress. For many of these proteins, this is the first report of an association with the aged myocardium. Additionally, two proteins of unknown function were identified. This work serves as the basis for making future comparisons of the aged left ventricle proteome to that of left ventricles obtained from other models of disease and heart failure.

A statistical model for iTRAQ data analysis.

We describe biological and experimental factors that induce variability in reporter ion peak areas obtained from iTRAQ experiments. We demonstrate how these factors can be incorporated into a statistical model for use in evaluating differential protein expression and highlight the benefits of using analysis of variance to quantify fold change. We demonstrate the model's utility based on an analysis of iTRAQ data derived from a spike-in study.

Concentration-dependent effects on the rapid and efficient activation of the MAP kinase signaling cascade.

Modern proteomic techniques are making it possible to identify and quantitate increasingly complex mixtures of cellular proteins. Translating the relative expression measurements collected in these experiments into an understanding of the associated physiological phenomena continues to be a challenge for the field of systems biology. We demonstrate how methods of mathematical and computational systems biology permit us to proffer explanations for the observed concentration ranges of signaling components found in the highly conserved mitogen-activated protein kinase (MAPK) cascade. The analysis demonstrates that alterations in the naturally occurring MAPK and MAPK kinase (MAPKK) concentrations would negatively affect the efficiency of short-term responses of the cascade. Thus, while there seems to be no a priori rationale for particular features of the involved kinases, the observed ranges of their characteristic parameters appear to be far from coincidental. This result is deduced from the first principles of mass action kinetics and holds for wide variations in MAPKK kinase (MAPKKK) concentrations, differential preference for unphosphorylated and monophosphorylated forms of kinase substrates, and for cases where the monophosphorylated MAPKK exhibits kinase activity. The results demonstrate how theoretical systems biology complements molecular biology by providing specific rationale for observed natural designs in a fashion hardly achievable with experimentation alone.

The potential for signal integration and processing in interacting MAP kinase cascades.

The cellular response to environmental stimuli requires biochemical information processing through which sensory inputs and cellular status are integrated and translated into appropriate responses by way of interacting networks of enzymes. One such network, the mitogen-activated protein (MAP) kinase cascade is a highly conserved signal transduction module that propagates signals from cell surface receptors to various cytosolic and nuclear targets by way of a phosphorylation cascade. We have investigated the potential for signal processing within a network of interacting feed-forward kinase cascades typified by the MAP kinase cascade. A genetic algorithm was used to search for sets of kinetic parameters demonstrating representative key input-output patterns of interest. We discuss two of the networks identified in our study, one implementing the exclusive-or function (XOR) and another implementing what we refer to as an in-band detector (IBD) or two-sided threshold. These examples confirm the potential for logic and amplitude-dependent signal processing in interacting MAP kinase cascades demonstrating limited cross-talk. Specifically, the XOR function allows the network to respond to either one, but not both signals simultaneously, while the IBD permits the network to respond exclusively to signals within a given range of strength, and to suppress signals below as well as above this range. The solution to the XOR problem is interesting in that it requires only two interacting pathways, crosstalk at only one layer, and no feedback or explicit inhibition. These types of responses are not only biologically relevant but constitute signal processing modules that can be combined to create other logical functions and that, in contrast to amplification, cannot be achieved with a single cascade or with two non-interacting cascades. Our computational results revealed surprising similarities between experimental data describing the JNK/MKK4/MKK7 pathway and the solution for the IBD that evolved from the genetic algorithm. The evolved IBD not only exhibited the required non-monotonic signal strength-response, but also demonstrated transient and sustained responses that properly reflected the input signal strength, dependence on both of the MAPKKs for signaling, phosphorylation site preferences by each of the MAPKKs, and both activation and inhibition resulting from the overexpression of one of the MAPKKs.

Computation and analysis of time-dependent sensitivities in Generalized Mass Action systems.

Understanding biochemical system dynamics is becoming increasingly important for insights into the functioning of organisms and for biotechnological manipulations, and additional techniques and methods are needed to facilitate investigations of dynamical properties of systems. Extensions to the method of Ingalls and Sauro, addressing time-dependent sensitivity analysis, provide a new tool for executing such investigations. We present here the results of sample analyses using time-dependent sensitivities for three model systems taken from the literature, namely an anaerobic fermentation pathway in yeast, a negative feedback oscillator modeling cell-cycle phenomena, and the Mitogen Activated Protein (MAP) kinase cascade. The power of time-dependent sensitivities is particularly evident in the case of the MAPK cascade. In this example it is possible to identify the emergence of a concentration of MAPKK that provides the best response with respect to rapid and efficient activation of the cascade, while over- and under-expression of MAPKK relative to this concentration have qualitatively different effects on the transient response of the cascade. Also of interest is the quite general observation that phase-plane representations of sensitivities in oscillating systems provide insights into the manner with which perturbations in the envelope of the oscillation result from small changes in initial concentrations of components of the oscillator. In addition to these applied analyses, we present an algorithm for the efficient computation of time-dependent sensitivities for Generalized Mass Action (GMA) systems, the most general of the canonical system representations of Biochemical Systems Theory (BST). The algorithm is shown to be comparable to, or better than, other methods of solution, as exemplified with three biochemical systems taken from the literature.

Improved methods for the mathematically controlled comparison of biochemical systems.

The method of mathematically controlled comparison provides a structured approach for the comparison of alternative biochemical pathways with respect to selected functional effectiveness measures. Under this approach, alternative implementations of a biochemical pathway are modeled mathematically, forced to be equivalent through the application of selected constraints, and compared with respect to selected functional effectiveness measures. While the method has been applied successfully in a variety of studies, we offer recommendations for improvements to the method that (1) relax requirements for definition of constraints sufficient to remove all degrees of freedom in forming the equivalent alternative, (2) facilitate generalization of the results thus avoiding the need to condition those findings on the selected constraints, and (3) provide additional insights into the effect of selected constraints on the functional effectiveness measures. We present improvements to the method and related statistical models, apply the method to a previously conducted comparison of network regulation in the immune system, and compare our results to those previously reported.