Simplified sample handing in mass spectrometry based protein research - focus on protein phosphorylation

The human genome comprises roughly 20 000 protein coding genes. Proteins are the building material for cells and tissues, and proteins are functional compounds having an important role in many cellular responses, such as cell signalling. In multicellular organisms such as humans, cells need to communicate with each other in order to maintain a normal function of the tissues within the body. This complex signalling between and within cells is transferred by proteins and their post-translational modifications, one of the most important being phosphorylation. The work presented here concerns the development and use of tools for phosphorylation analysis. Mass spectrometers have become essential tools to study proteins and proteomes. In mass spectrometry oriented proteomics, proteins can be identified and their post-translational modifications can be studied. In this Ph.D. thesis the objectives were to improve the robustness of sample handling methods prior to mass spectrometry analysis for peptides and their phosphorylation status. The focus was to develop strategies that enable acquisition of more MS measurements per sample, higher quality MS spectra and simplified and rapid enrichment procedures for phosphopeptides. Furthermore, an objective was to apply these methods to characterize phosphorylation sites of phosphopeptides. In these studies a new MALDI matrix was developed which allowed more homogenous, intense and durable signals to be acquired when compared to traditional CHCA matrix. This new matrix along with other matrices was subsequently used to develop a new method that combines multiple spectra from different matrises from identical peptides. With this approach it was possible to identify more phosphopeptides than with conventional LC/ESI-MS/MS methods, and to use 5 times less sample. Also, phosphopeptide affinity MALDI target was prepared to capture and immobilise phosphopeptides from a standard peptide mixture while maintaining their spatial orientation. In addition a new protocol utilizing commercially available conductive glass slides was developed that enabled fast and sensitive phosphopeptide purification. This protocol was applied to characterize the in vivo phosphorylation of a signalling protein, NFATc1. Evidence for 12 phosphorylation sites were found, and many of those were found in multiply phosphorylated peptides

Catch-per-unit-effort: which estimator is best?

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Comparison between univariate and bivariate geostatistical models for estimating catch per unit of effort (cpue): A simulation study

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Comparing three indices of catch per unit effort using Bayesian geostatistics

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Distribution transformers modeling with angular displacement: actual values and per unit analysis

In this work a detailed modeling of three-phase distribution transformers aimed at complementing well-known approaches is presented. Thus, incidence of angular displacement and tapping is taken into account in the proposed models, considering both actual values and per unit. The analysis is based on minimal data requirement: solely short-circuit admittance is needed since three-phase transformers are treated as non-magnetically-coupled single-phase transformers. In order to support the proposed methodology, results obtained through laboratory tests are presented.

Estimating whole-lake fish catch per unit effort

The European standard for gillnetsampling to characterize lake fish communities stratifies sampling effort (i.e., number of nets) within depth strata. Nets to sample benthic habitats are randomly distributed throughout the lake within each depth strata. Pelagic nets are also stratified by depth, but are set only at the deepest point of the lake. Multiple authors have suggested that this design under-represents pelagic habitats, resulting in estimates of whole-lake CPUE and community composition which are disproportionately influenced by ecological conditions of littoral and benthic habitats. To address this issue, researchers have proposed estimating whole-lake CPUE by weighting the catch rate in each depth-compartment by the proportion of the volume of the lake contributed by the compartment. Our study aimed to assess the effectiveness of volume-weighting by applying it to fish communities sampled according to the European standard (CEN), and by a second whole-lake gillnetting protocol (VERT), which prescribes additional fishing effort in pelagic habitats. We assume that convergence between the protocols indicates that volume-weighting provides a more accurate estimate of whole-lake catch rate and community composition. Our results indicate that volume-weighting improves agreement between the protocols for whole-lake total CPUE, estimated proportion of perch and roach and the overall fish community composition. Discrepancies between the protocols remaining after volume-weighting maybe because sampling under the CEN protocol overlooks horizontal variation in pelagic fish communities. Analyses based on multiple pelagic-set VERT nets identified gradients in the density and biomass of pelagic fish communities in almost half the lakes that corresponded with the depth of water at net-setting location and distance along the length of a lake. Additional CEN pelagic sampling effort allocated across water depths and distributed throughout the lake would therefore help to reconcile differences between the sampling protocols and, in combination with volume-weighting, converge on a more accurate estimate of whole-lake fish communities.

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Soil characteristics and decomposition of organic matter on Anchorage, Signy and Falkland Islands

Antarctic terrestrial ecosystems have poorly developed soils and currently experience one of the greatest rates of climate warming on the globe. We investigated the responsiveness of organic matter decomposition in Maritime Antarctic terrestrial ecosystems to climate change, using two study sites in the Antarctic Peninsula region (Anchorage Island, 67°S; Signy Island, 61°S), and contrasted the responses found with those at the cool temperate Falkland Islands (52°S). Our approach consisted of two complementary methods: (1) Laboratory measurements of decomposition at different temperatures (2, 6 and 10 °C) of plant material and soil organic matter from all three locations. (2) Field measurements at all three locations on the decomposition of soil organic matter, plant material and cellulose, both under natural conditions and under experimental warming (about 0.8 °C) achieved using open top chambers. Higher temperatures led to higher organic matter breakdown in the laboratory studies, indicating that decomposition in Maritime Antarctic terrestrial ecosystems is likely to increase with increasing soil temperatures. However, both laboratory and field studies showed that decomposition was more strongly influenced by local substratum characteristics (especially soil N availability) and plant functional type composition than by large-scale temperature differences. The very small responsiveness of organic matter decomposition in the field (experimental temperature increase <1 °C) compared with the laboratory (experimental increases of 4 or 8 °C) shows that substantial warming is required before significant effects can be detected.

Response of three krill species to hypoxia and warming: An experimental approach to oxygen minimum zones expansion in coastal ecosystems

To understand the adaptation of euphausiid (krill) species to oxygen minimum zones (OMZ), respiratory response and stress experiments combining hypoxia/reoxygenation exposure with warming were conducted. Experimental krill species were obtained from the Antarctic (South Georgia area), the Humboldt Current system (HCS, Chilean coast), and the Northern California Current system (NCCS, Oregon). Euphausia mucronata from the HCS shows oxyconforming or oxygen partial pressure (pO2)-dependent respiration below 80% air saturation (18 kPa). Normoxic subsurface oxygenation in winter posed a "high oxygen stress" for this species. The NCCS krill, Euphausia pacifica, and the Antarctic krill, Euphausia superba maintain respiration rates constant down to low critical pO2 values of 6 kPa (30% air saturation) and 11 kPa (55% air saturation), respectively. Antarctic krill had the lowest antioxidant enzyme activities, but the highest concentrations of the molecular antioxidant glutathione (GSH) and was not affected by 6 h exposure to moderate hypoxia. Temperate krill species had higher SOD (superoxide dismutase) values in winter than in summer, which relate to higher winter metabolic rate (E. pacifica). In all species, antioxidant enzyme activities remained constant during hypoxic exposure at habitat temperature. Warming by 7°C above habitat temperature in summer increased SOD activities and GSH levels in E. mucronata (HCS), but no oxidative damage occurred. In winter, when the NCCS is well mixed and the OMZ is deeper, +4°C of warming combined with hypoxia represents a lethal condition for E. pacifica. In summer, when the OMZ expands upwards (100 m subsurface), antioxidant defences counteracted hypoxia and reoxygenation effects in E. pacifica, but only at mildly elevated temperature (+2°C). In this season, experimental warming by +4°C reduced antioxidant activities and the hypoxia combination again caused mortality of exposed specimens. We conclude that a climate change scenario combining warming and hypoxia represents a serious threat to E. pacifica and, as a consequence, NCCS food webs.

Geochemistry and diatom abundances in sediment trap samples of Effingham Inlet, Canada

Sediment traps were deployed inside the anoxic inner basin of Effingham Inlet and at the oxygenated mouth of the inlet from May 1999 to September 2000 in a pilot study to determine the annual depositional cycle and impact of the 1999-2000 La Niña event within a western Canadian inlet facing the open Pacific Ocean. Total mass flux, geochemical parameters (carbon, nitrogen, opal, major and minor element contents, and stable isotope ratios) and diatom assemblages were determined and compared with meteorological and oceanographic data. Deposition was seasonal, with coarser grained terrestrial components and benthic diatoms settling in the autumn and winter, coincident with the rainy season. Marine sedimentary components and abundant pelagic diatoms were coincident with coastal upwelling in the spring and summer. Despite the seasonal differences in deposition, the typical temperate-zone Thalassiosira-Skeletonema-Chaetoceros bloom succession was muted. A July 1999 total mass flux peak and an increase in biogenous components coincided with a rare bottom-water oxygen renewal event in the inlet. Likewise, there were cooler-than-average sea surface temperatures (SSTs) just outside the inlet, and unusually high abundances of a previously undescribed cool-water marine diatom (Fragilariopsis pacifica sp. nov.) within the inlet. Each of these occurrences likely reflects a response to the strong La Niña that followed the year after the strongest-ever recorded El Niño event of 1997-1998. By the autumn of 1999, SSTs had returned to average, and F. pacifica had all but disappeared from the remaining trap record, indicating that oceanographic conditions had returned to normal. Oxygenation events were not witnessed in the inlet in the years before or after 1999, suggesting that a rare oceanographic and climatic event was captured by this sediment trap time series. The data from this record can therefore be used as a benchmark for identifying anomalous environmental conditions on this coast.