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

Quantifying injury to common bottlenose dolphins from the Deepwater Horizon oil spill using an age-, sex- and class-structured population model

Field studies documented increased mortality, adverse health effects, and reproductive failure in common bottlenose dolphins Tursiops truncatus following the Deepwater Horizon (DWH) oil spill. In order to determine the appropriate type and amount of restoration needed to compensate for losses, the overall extent of injuries to dolphins had to be quantified. Simply counting dead individuals does not consider long-term impacts to populations, such as the loss of future reproductive potential from mortality of females, or the chronic health effects that continue to compromise survival long after acute effects subside. Therefore, we constructed a sex- and agestructured model of population growth and included additional class structure to represent dolphins exposed and unexposed to DWH oil. The model was applied for multiple stocks to predict injured population trajectories using estimates of post-spill survival and reproductive rates. Injured trajectories were compared to baseline trajectories that were expected had the DWH incident not occurred. Two principal measures of injury were computed: (1) lost cetacean years (LCY); the difference between baseline and injured population size, summed over the modeled time period, and (2) time to recovery; the number of years for the stock to recover to within 95% of baseline. For the dolphin stock in Barataria Bay, Louisiana, the estimated LCY was substantial: 30 347 LCY (95% CI: 11 511 to 89 746). Estimated time to recovery was 39 yr (95% CI: 24 to 80). Similar recovery timelines were predicted for stocks in the Mississippi River Delta, Mississippi Sound, Mobile Bay and the Northern Coastal Stock.Publisher PDFPeer reviewe

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

This work was supported by the Office of Naval Research Marine Mammal Biology Program [grant number N00014-17-1-2868].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 seven bottlenose dolphin (Tursiops truncatus) populations in southeastern U.S. to develop the Veterinary Expert System for Outcome Prediction (VESOP), which estimates survival probability using a suite of health measures identified by experts as indices for inflammatory, metabolic, pulmonary, and neuroendocrine systems. VESOP was implemented using logistic regression within a Bayesian analysis framework, and parameters were fit using records from five of the sites that had a robust stranding network and frequent photographic identification (photo-ID) surveys to document definitive survival outcomes. We also conducted capture-mark-recapture (CMR) analyses of photo-ID data to obtain separate estimates of population survival rates for comparison with VESOP survival estimates. VESOP analyses found multiple measures of health, particularly markers of inflammation, were predictive of 1- and 2-year individual survival. The highest mortality risk one year following health assessment related to low alkaline phosphatase, with an odds ratio of 10.2 (95% CI 3.41-26.8), while 2-year mortality was most influenced by elevated globulin (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 = 1.52e-05, 2-year r = 0.94; p = 0.001). While our proposed approach will not detect acute mortality threats that are largely independent of animal health, such as harmful algal blooms, it is applicable for detecting 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.Publisher PDFPeer reviewe

iQuantitator: A tool for protein expression inference using iTRAQ

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p

A review of the toxicology of oil in vertebrates : what we have learned following the Deepwater Horizon oil spill

This research was made possible by a grant from The Gulf of Mexico Research Initiative. This publication is UMCES contribution No. 6045 and Ref. No. [UMCES] CBL 2022-008. This is National Marine Mammal Foundation Contribution #314 to peer-reviewed scientific literature.In the wake of the Deepwater Horizon (DWH) oil spill, a number of government agencies, academic institutions, consultants, and nonprofit organizations conducted lab- and field-based research to understand the toxic effects of the oil. Lab testing was performed with a variety of fish, birds, turtles, and vertebrate cell lines (as well as invertebrates); field biologists conducted observations on fish, birds, turtles, and marine mammals; and epidemiologists carried out observational studies in humans. Eight years after the spill, scientists and resource managers held a workshop to summarize the similarities and differences in the effects of DWH oil on vertebrate taxa and to identify remaining gaps in our understanding of oil toxicity in wildlife and humans, building upon the cross-taxonomic synthesis initiated during the Natural Resource Damage Assessment. Across the studies, consistency was found in the types of toxic response observed in the different organisms. Impairment of stress responses and adrenal gland function, cardiotoxicity, immune system dysfunction, disruption of blood cells and their function, effects on locomotion, and oxidative damage were observed across taxa. This consistency suggests conservation in the mechanisms of action and disease pathogenesis. From a toxicological perspective, a logical progression of impacts was noted: from molecular and cellular effects that manifest as organ dysfunction, to systemic effects that compromise fitness, growth, reproductive potential, and survival. From a clinical perspective, adverse health effects from DWH oil spill exposure formed a suite of signs/symptomatic responses that at the highest doses/concentrations resulted in multi-organ system failure.Publisher PDFPeer reviewe

Lazarus1, a DUF300 Protein, Contributes to Programmed Cell Death Associated with Arabidopsis acd11 and the Hypersensitive Response

Programmed cell death (PCD) is a necessary part of the life of multi-cellular organisms. A type of plant PCD is the defensive hypersensitive response (HR) elicited via recognition of a pathogen by host resistance (R) proteins. The lethal, recessive accelerated cell death 11 (acd11) mutant exhibits HR-like accelerated cell death, and cell death execution in acd11 shares genetic requirements for HR execution triggered by one subclass of R proteins

The Application of Gaussian Mixture Models for Signal Quantification in MALDI-ToF Mass Spectrometry of Peptides

<div><p>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.</p></div

Method Comparison Summary.

<p>The mean percent error (MPE), MSE, Variance and bias of each method’s percent error of the peptide ratio for various methods of ratio quantification. While all methods fall within the error parameters of the SIS method The Gaussian mixture model produces estimates in both single and convolved peptides while the peak intensity and Riemann sum methods of estimation cannot be used in convolved peptides.</p><p>Method Comparison Summary.</p

Correlation plots showing the difference in estimation error of the peak ratio for a given spectrum when different methods of peak ratio measurement.

<p>The red line denotes a correlation of ρ = 1 and the blue lines denote 0% error in ratio estimation for that given method. Here we see that the Peak intensity and Riemann sum AUC methods of quantification correlate more highly with one another than with the Gaussian mixture method. Note that the GMM estimates tend to cluster closer to the blue line suggesting lower error.</p