Gerard T. Hoehn

Proteomic identification of altered apolipoprotein patterns in pulmonary hypertension and vasculopathy of sickle cell disease

Pulmonary arterial hypertension (PAH) is emerging as a major complication and independent risk factor for death among adults with sickle cell disease (SCD). Using surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF MS), we searched for biomarkers of PAH in plasma specimens from 27 homozygous sickle cell anemia (HbSS) patients with PAH and 28 without PAH. In PAH patients, analysis consistently showed lower abundance of a 28.1-kDa peak (P < .001), identified by high-resolution mass spectrometry as the oxidant-scavenging protein apolipoprotein A-I (apoA-I), which correlated with clinical assays of apoA-I (r = .58, P < .001) and high-density lipoprotein (HDL) levels (r = .50, P = .001). Consistent with endothelial dysfunction that may mediate this effect in PAH, HbSS patients with lower apoA-I levels also displayed impaired vasodilatory responses to acetylcholine (mean +/- SEM, 189% +/- 34% [n = 13] vs 339% +/- 51% [n = 13], P < .001). As a group, patients with SCD demonstrated significantly lower apoA-I levels than African-American control subjects. The PAH cohort was further characterized by high levels of apolipoproteins A-II and B and serum amyloid A, and low levels of haptoglobin dimers and plasminogen. These results imply a relationship of apolipoproteins to the development of PAH vasculopathy in SCD, potentially involving an unexpected mechanistic parallel to atherosclerosis, another proliferative vasculopathy.

Human AML cells in NOD/SCID mice: Engraftment potential and gene expression

Most cases of human acute myeloid leukemia (AML) engraft in irradiated non-obese diabetic/severe combined immunodeficient (NOD/SCID) mice. Intravenous transfer of as few as 105 human AML cells resulted in engraftment. Cases with poor prognosis clinical features, including FLT3 mutations, tended to engraft efficiently. Nevertheless, AML cells obtained from patients at relapse did not engraft more efficiently than cells obtained from the same patients at initial diagnosis. One passage of human AML cells in NOD/SCID mice did not appear to select for increased virulence, as measured by serial transplantation efficiency. Finally, cDNA microarray analyses indicated that ∼95% of genes were expressed at similar levels in human AML cells immunopurified after growth in mice, as compared to cells assessed directly from patients. Thus, the growth of human AML cells in NOD/SCID mice could yield large numbers of human AML cells for direct experimental use and could also function as a renewable, potentially unlimited source of leukemia cells, via serial transplantation.

Logical Analysis of Data (LAD) model for the early diagnosis of acute ischemic stroke

BackgroundStrokes are a leading cause of morbidity and the first cause of adult disability in the United States. Currently, no biomarkers are being used clinically to diagnose acute ischemic stroke. A diagnostic test using a blood sample from a patient would potentially be beneficial in treating the disease.ResultsA classification approach is described for differentiating between proteomic samples of stroke patients and controls, and a second novel predictive model is developed for predicting the severity of stroke as measured by the National Institutes of Health Stroke Scale (NIHSS). The models were constructed by applying the Logical Analysis of Data (LAD) methodology to the mass peak profiles of 48 stroke patients and 32 controls. The classification model was shown to have an accuracy of 75% when tested on an independent validation set of 35 stroke patients and 25 controls, while the predictive model exhibited superior performance when compared to alternative algorithms. In spite of their high accuracy, both models are extremely simple and were developed using a common set consisting of only 3 peaks.ConclusionWe have successfully identified 3 biomarkers that can detect ischemic stroke with an accuracy of 75%. The performance of the classification model on the validation set and on cross-validation does not deteriorate significantly when compared to that on the training set, indicating the robustness of the model. As in the case of the LAD classification model, the results of the predictive model validate the function constructed on our support-set for approximating the severity scores of stroke patients. The correlation and root mean absolute error of the LAD predictive model are consistently superior to those of the other algorithms used (Support vector machines, C4.5 decision trees, Logistic regression and Multilayer perceptron).

Computational Prediction Models for Early Detection of Risk of Cardiovascular Events Using Mass Spectrometry Data

Early prediction of the risk of cardiovascular events in patients with chest pain is critical in order to provide appropriate medical care for those with positive diagnosis. This paper introduces a computational methodology for predicting such events in the context of robust computerized classification using mass spectrometry data of blood samples collected from patients in emergency departments. We applied the computational theories of statistical and geostatistical linear prediction models to extract effective features of the mass spectra and a simple decision logic to classify disease and control samples for the purpose of early detection. While the statistical and geostatistical techniques provide better results than those obtained from some other methods, the geostatistical approach yields superior results in terms of sensitivity and specificity in various designs of the data set for validation, training, and testing. The proposed computational strategies are very promising for predicting major adverse cardiac events within six months.

Apolipoprotein A-I and serum amyloid A plasma levels are biomarkers of acute painful episodes in patients with sickle cell disease

Background Acute painful episodes are the clinical hallmark of sickle cell disease and have been linked to morbidity and mortality in the sickle cell population. Design and Methods We undertook exploratory proteomic studies on paired plasma samples collected from a cohort of 26 adult sickle cell patients during steady state and on the first day of an acute painful episode. We screened for changes in abundance of specific protein peaks via surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF MS), and confirmed the identify of candidate protein peaks by specific immunoassays. Results The levels of hemoglobin, hematocrit, total protein, and albumin were lower and the levels of lactate dehydrogenase and absolute reticulocytes higher during acute painful episodes than during the steady state. Surface-enhanced laser desorption/ionization time of flight mass spectrometry spectral analysis consistently showed a mass-to-charge peak at 11.7 kDa with elevated intensities during acute painful episodes, which correlated significantly with the serum amyloid A immunoassay. Serum amyloid A levels were significantly elevated during acute painful episodes, especially in four patients with marked end-organ complications of such episodes. A second, recurring peak, less abundant during acute painful episodes, was present at 28.1 kDa; this peak was correlated significantly with immunoassay measurements of apolipoprotein A1. Conclusions On the average, plasma serum amyloid A rises and apolipoprotein AI falls during acute painful episodes. The serum amyloid A/apolipoprotein AI ratio increased in 81% of the patients during acute painful episodes, potentially making it a useful objective marker of such episodes. We propose that these protein alterations, known to contribute to endothelial dysfunction in other settings, might do likewise acutely in acute painful episodes and present a new target for therapeutic intervention in sickle cell disease. (ClincalTrials.gov Identifier: NCT00081523).

Biomarker Discovery for Risk Stratification of Cardiovascular Events using an Improved Genetic Algorithm

Detection of an optimal panel of biomarkers capable of predicting a patients risk of major adverse cardiac events (MACE) is of clinical significance. Due to the high dynamic range of the protein concentration in human blood, applying proteomics techniques for protein profiling can generate large arrays of data for development of optimized clinical biomarker panels. The objective of this study is to discover a panel of biomarkers for predicting risk of MACE in subjects reliably. The development of immunoassay can only tolerate the complexity of the prediction model with less than ten selected biomarkers. Hence, traditional optimization methods, such as genetic algorithm, cannot be used to derive a solution in such a high-dimensional space. In this paper, we propose an improved genetic algorithm with the local floating searching technique to discover a subset of biomarkers with improved prognostic values for prediction of MACE. The proposed method has been compared with standard genetic algorithm and other feature selection approaches based on the MACE prediction experiments

Biomarker Discovery in Serum from Patients with Carotid Atherosclerosis

Background: Blood-based biomarkers of atherosclerosis have been used to identify patients at high risk for developing stroke. We hypothesized that patients with carotid artery disease would have a distinctive proteomic signature in blood as compared to a healthy control population without carotid artery disease. In order to discover protein biomarkers associated with increased atherosclerotic risk, we used two different strategies to identify biomarkers from patients with clinically defined atherosclerosis who were undergoing endarterectomy for atherosclerotic carotid artery disease. These patients were compared with healthy matched controls. Methods: Serum was obtained from patients undergoing endarterectomy (EA; n = 38) and compared to a group of age-matched healthy controls (n = 40). Serum was fractionated using anion exchange chromatography and three different surface-enhanced laser desorption/ionization (SELDI) chip surfaces and then evaluated with mass spectrometry (MS) and two-dimensional difference gel electrophoresis (2D-DIGE). Results: A random forest (RF) analysis of the SELDI-MS protein peak data distinguished these two groups with 69.2% sensitivity and 73.2% specificity. Four unique SELDI peaks (4.2, 4.4, 16.7 and 28 kDa, all p< 0.01) showed the greatest influence in the RF model. The EA patients with a history of prior clinical atherosclerotic plaque rupture manifested as either stroke or transient ischemic attack (symptomatic; n = 16) were compared to patients with carotid atherosclerosis but no clinical evidence of plaque rupture (asymptomatic; n = 22). Analysis of the SELDI spectra did not separate these two patient subgroups. A subgroup analysis using 2D-DIGE images obtained from albumin-depleted serum comparing symptomatic (n = 10) to asymptomatic EA patients (n = 10) found 4 proteins that were differentially expressed (p < 0.01) in the symptomatic patients. These proteins were identified as α1-antitrypsin, haptoglobin and vitamin D binding protein that were downregulated and α2-glycoprotein precursor that was upregulated in the symptomatic EA group. Conclusions: SELDI-MS data analysis of fractionated serum suggests that a distinct protein signature exists in patients with carotid atherosclerosis compared to age-matched healthy controls. Identification of 4 proteins in a subset of patients with symptomatic and asymptomatic carotid atherosclerosis suggests that these and other protein biomarkers may assist in identifying high-risk patients with carotid atherosclerosis.

Classification of Mass Spectrometry Based Protein Markers by Kriging Error Matching

Discovery of biomarkers using serum proteomic patterns is currently one of the most attractive interdisciplinary research areas in computational life science. This new proteomic approach has the clinical significance in being able to detect disease in its early stages and to develop new drugs for disease treatment and prevention. This paper introduces a novel pattern classification strategy for identifying protein biomarkers using mass spectrometry data of blood samples collected from patients in emergency department monitored for major adverse cardiac events within six months. We applied the theory of geostatistics and a kriging error matching scheme for identifying protein biomarkers that are able to provide an average classification rate superior to other current methods. The proposed strategy is very promising as a general computational bioinformatic model for proteomic-pattern based biomarker discovery.

Visual Cloning 2000

STEM CELLS 2001;19:163-164 www.StemCells.com Visual Cloning 2000 is an integrative internet and DNA sequence analysis software package that also provides the user a graphical interface for easy-to-produce plasmid maps. The software is produced by Redasoft, a Torontobased firm and is specifically designed for Windows users, which was a little disconcerting for this Mac user. However, I found Visual Cloning 2000 intuitively easy to use and understand. A feature advertised by the company is its commitment to using customer feedback for improvements to its software. In fact, the Redasoft Web page has a convenient link that allows the user to submit questions and feedback. Although I did not utilize this feature, it is somewhat reassuring that the company will listen to suggestions from its users. In my estimation as a casual molecular biologist, the greatest advantage of Visual Cloning 2000 is that it creates an easy-to-use web-based browser for a variety of molecular biology resources. Of course, most of these resources would be freely accessible without the software package. However, Visual Cloning 2000 provides an organized compendium of many key sites that makes extracting relevant information that much easier. For this reason alone, I think Visual Cloning 2000 is a valuable asset to any lab. The main page of the software is the Redasoft Research Net that serves as a research portal providing access to a large number of cloning tools. From this page, there are links for sequence retrieval National Center for Biotechnology (NCBI) and analysis (ExPASy and CMS Molecular Biology Resource), online ordering of restriction enzymes and oligonucleotides, and useful tools and protocols (Double-twist.com protocol and quick access tables). These links greatly facilitate tracking down information, which is obviously a real timesaver. For example, importing sequence information from the NCBI database was very straightforward (no saving the sequence in a particular format so that it could be imported to your analysis software). Similarly, performing downstream analysis on the imported sequence was also quick and easy to accomplish. The graphics for creating plasmid maps are very good and easy to use. Plasmid backbones are readily accessed and imported with the RedasoftUs cloning vector search engine. This feature greatly facilitates the generation of detailed vector maps. Restriction sites and other salient features can be readily added to your map. The graphics portion seems to be modeled after PowerPoint, making the features recognizable by most researchers. In terms of the DNA analysis portion of the software, my opinion is that it is fairly ordinary; however, I think it is a valuable resource for a majority of routine applications. For example, once a sequence is imported (which is performed very handily through their browser links to most databases), restriction enzyme sites and open reading frames can be found, and polymerase chain reaction (PCR) primers and oligonucleotides generated. In Visual Cloning 2000, restriction enzyme analysis is queried against REBASE, a restriction enzyme database that can be updated. Sequences can be scanned for particular restriction sites or overhangs. Like other analysis programs, output is given by the enzyme name, site(s) on the sequence, and recognition sequence ends. For generating plasmid maps, it has a convenient feature that allows the user to add a particular site to the map. Another nice feature of Visual Cloning 2000 is that it will provide the names of companies that sell your enzyme of choice (particularly useful for rare enzymes), and has an easy-to-use, online ordering link. However, I found the manufacturer list to be incomplete. Stem Cells Software Review