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Articles 1 - 20 of 20
Full-Text Articles in Medical Molecular Biology
Further Decoding The Molecular Relationship Between Pancreatic Adenocarcinoma And Diabetes Mellitus, Russell H. Moreland Iii, Sheema Khan
Further Decoding The Molecular Relationship Between Pancreatic Adenocarcinoma And Diabetes Mellitus, Russell H. Moreland Iii, Sheema Khan
MEDI 9331 Scholarly Activities Clinical Years
Pancreatic ductal adenocarcinoma (PDAC) is a devastating malignancy, especially as there are no current reliable methods of screening. Recently literature reports a significant relationship with pancreatic ductal adenocarcinoma and diabetes mellitus (DM). The pathologic molecular mechanism is not completely understood but it may hold insights into the development of novel screening and treatment options. In our study we compiled a list of 74 proteins involved in the PDAC and DM pathway, with 47 showing increased expression levels and 11 with decreased expression levels. These proteins are currently undergoing further computational analysis to identify their pathway interactions.
Comparative Analysis Of Proteomics Biomarkers Associated With Residual Ridge Resorption Induced By Denture Wear, Rohana Ahmad, Ainin Sofia Mohamad Napi, Tong Wah Lim, Su Keng Tan, Saiful Anuar Karsani, Musalmah Mazlan, Lay Kek Teh, Steven M. Morgano, Nadim Z. Baba
Comparative Analysis Of Proteomics Biomarkers Associated With Residual Ridge Resorption Induced By Denture Wear, Rohana Ahmad, Ainin Sofia Mohamad Napi, Tong Wah Lim, Su Keng Tan, Saiful Anuar Karsani, Musalmah Mazlan, Lay Kek Teh, Steven M. Morgano, Nadim Z. Baba
Makara Journal of Health Research
Background: The biochemical bone turnover markers for residual ridge resorption (RRR) are unclear. Therefore, the present study aimed to determine the biochemical bone turnover markers associated with RRR by comparing proteomics between the compressed mucosa of denture wearers and the non-compressed mucosa of non-denture wearers.
Methods: The mucosal specimens of 11 complete-denture wearers were obtained from the alveolar ridge during surgical implant exposure for implant-retained overdentures. All denture wearers had been edentulous and worn dentures for at least 5 years. The tissues of 11 non-denture wearers were taken from the ridge during minor preprosthetic surgery. The mucosal proteins …
Genome-Scale Precision Proteomics Identifies Cancer Signaling Networks And Therapeutic Vulnerabilities, Hong Wang
Theses and Dissertations (ETD)
Mass spectrometry (MS) based-proteomics technology has been emerging as an indispensable tool for biomedical research. But the highly diverse physical and chemical properties of the protein building blocks and the dramatic human proteome complexity largely limited proteomic profiling depth. Moreover, there was a lack of high-throughput quantitative strategies that were both precise and parallel to in-depth proteomic techniques. To solve these grand challenges, a high resolution liquid chromatography (LC) system that coupled with an advanced mass spectrometer was developed to allow genome-scale human proteome identification. Using the combination of pre-MS peptide fractionation, MS2-based interference detection and post-MS computational interference correction, …
An Integrated Clinico-Metabolomic Model Improves Prediction Of Death In Sepsis., Raymond J. Langley, Ephraim L. Tsalik, Jennifer C. Van Velkinburgh, Seth W. Glickman, Brandon J. Rice, Chunping Wang, Bo Chen, Lawrence Carin, Arturo Suarez, Robert P. Mohney, Debra H. Freeman, Mu Wang, Jinsam You, Jacob Wulff, J Will Thompson, M Arthur Moseley, Stephanie Reisinger, Brian T. Edmonds, Brian Grinnell, David R. Nelson, Darrell L. Dinwiddie, Neil A. Miller, Carol J. Saunders, Sarah Soden, Angela J. Rogers, Lee Gazourian, Laura E. Fredenburgh, Anthony F. Massaro, Rebecca M. Baron, Augustine M K Choi, G Ralph Corey, Geoffrey S. Ginsburg, Charles B. Cairns, Ronny M. Otero, Vance G. Fowler, Emanuel P. Rivers, Christopher W. Woods, Stephen F. Kingsmore
An Integrated Clinico-Metabolomic Model Improves Prediction Of Death In Sepsis., Raymond J. Langley, Ephraim L. Tsalik, Jennifer C. Van Velkinburgh, Seth W. Glickman, Brandon J. Rice, Chunping Wang, Bo Chen, Lawrence Carin, Arturo Suarez, Robert P. Mohney, Debra H. Freeman, Mu Wang, Jinsam You, Jacob Wulff, J Will Thompson, M Arthur Moseley, Stephanie Reisinger, Brian T. Edmonds, Brian Grinnell, David R. Nelson, Darrell L. Dinwiddie, Neil A. Miller, Carol J. Saunders, Sarah Soden, Angela J. Rogers, Lee Gazourian, Laura E. Fredenburgh, Anthony F. Massaro, Rebecca M. Baron, Augustine M K Choi, G Ralph Corey, Geoffrey S. Ginsburg, Charles B. Cairns, Ronny M. Otero, Vance G. Fowler, Emanuel P. Rivers, Christopher W. Woods, Stephen F. Kingsmore
Manuscripts, Articles, Book Chapters and Other Papers
Sepsis is a common cause of death, but outcomes in individual patients are difficult to predict. Elucidating the molecular processes that differ between sepsis patients who survive and those who die may permit more appropriate treatments to be deployed. We examined the clinical features and the plasma metabolome and proteome of patients with and without community-acquired sepsis, upon their arrival at hospital emergency departments and 24 hours later. The metabolomes and proteomes of patients at hospital admittance who would ultimately die differed markedly from those of patients who would survive. The different profiles of proteins and metabolites clustered into the …
Integrative Bayesian Analysis Of High-Dimensional Multi-Platform Genomics Data, Wenting Wang, Veerabhadran Baladandayuthapani, Jeffrey S. Morris, Bradley M. Broom, Ganiraju C. Manyam, Kim-Anh Do
Integrative Bayesian Analysis Of High-Dimensional Multi-Platform Genomics Data, Wenting Wang, Veerabhadran Baladandayuthapani, Jeffrey S. Morris, Bradley M. Broom, Ganiraju C. Manyam, Kim-Anh Do
Jeffrey S. Morris
Motivation: Analyzing data from multi-platform genomics experiments combined with patients’ clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current integration approaches that treat the data are limited in that they do not consider the fundamental biological relationships that exist among the data from platforms.
Statistical Model: We propose an integrative Bayesian analysis of genomics data (iBAG) framework for identifying important genes/biomarkers that are associated with clinical outcome. This framework uses a hierarchical modeling technique to combine the data obtained from multiple platforms …
Microproteomics: Analysis Of Protein Diversity In Small Samples, Howard B. Gutstein, Jeffrey S. Morris, Suresh P. Annangudi, Jonathan V. Sweedler
Microproteomics: Analysis Of Protein Diversity In Small Samples, Howard B. Gutstein, Jeffrey S. Morris, Suresh P. Annangudi, Jonathan V. Sweedler
Jeffrey S. Morris
Proteomics, the large-scale study of protein expression in organisms, offers the potential to evaluate global changes in protein expression and their post-translational modifications that take place in response to normal or pathological stimuli. One challenge has been the requirement for substantial amounts of tissue in order to perform comprehensive proteomic characterization. In heterogeneous tissues, such as brain, this has limited the application of proteomic methodologies. Efforts to adapt standard methods of tissue sampling, protein extraction, arraying, and identification are reviewed, with an emphasis on those appropriate to smaller samples ranging in size from several microliters down to single cells. The …
Statistical Issues In Proteomic Research, Jeffrey S. Morris
Statistical Issues In Proteomic Research, Jeffrey S. Morris
Jeffrey S. Morris
No abstract provided.
Pre-Processing Mass Spectrometry Data, Kevin R. Coombes, Keith A. Baggerly, Jeffrey S. Morris
Pre-Processing Mass Spectrometry Data, Kevin R. Coombes, Keith A. Baggerly, Jeffrey S. Morris
Jeffrey S. Morris
No abstract provided.
Laser Capture Sampling And Analytical Issues In Proteomics, Howard Gutstein, Jeffrey S. Morris
Laser Capture Sampling And Analytical Issues In Proteomics, Howard Gutstein, Jeffrey S. Morris
Jeffrey S. Morris
Proteomics holds the promise of evaluating global changes in protein expression and post-translational modificaiton in response to environmental stimuli. However, difficulties in achieving cellular anatomic resolution and extracting specific types of proteins from cells have limited the efficacy of these techniques. Laser capture microdissection has provided a solution to the problem of anatomical resolution in tissues. New extraction methodologies have expanded the range of proteins identified in subsequent analyses. This review will examine the application of laser capture microdissection to proteomic tissue sampling, and subsequent extraction of these samples for differential expression analysis. Statistical and other quantitative issues important for …
Prepms: Tof Ms Data Graphical Preprocessing Tool, Yuliya V. Karpievitch, Elizabeth G. Hill, Adam J. Smolka, Jeffrey S. Morris, Kevin R. Coombes, Keith A. Baggerly, Jonas S. Almeida
Prepms: Tof Ms Data Graphical Preprocessing Tool, Yuliya V. Karpievitch, Elizabeth G. Hill, Adam J. Smolka, Jeffrey S. Morris, Kevin R. Coombes, Keith A. Baggerly, Jonas S. Almeida
Jeffrey S. Morris
We introduce a simple-to-use graphical tool that enables researchers to easily prepare time-of-flight mass spectrometry data for analysis. For ease of use, the graphical executable provides default parameter settings experimentally determined to work well in most situations. These values can be changed by the user if desired. PrepMS is a stand-alone application made freely available (open source), and is under the General Public License (GPL). Its graphical user interface, default parameter settings, and display plots allow PrepMS to be used effectively for data preprocessing, peak detection, and visual data quality assessment.
An Introduction To High-Throughput Bioinformatics Data, Keith A. Baggerly, Kevin R. Coombes, Jeffrey S. Morris
An Introduction To High-Throughput Bioinformatics Data, Keith A. Baggerly, Kevin R. Coombes, Jeffrey S. Morris
Jeffrey S. Morris
High throughput biological assays supply thousands of measurements per sample, and the sheer amount of related data increases the need for better models to enhance inference. Such models, however, are more effective if they take into account the idiosyncracies associated with the specific methods of measurement: where the numbers come from. We illustrate this point by describing three different measurement platforms: microarrays, serial analysis of gene expression (SAGE), and proteomic mass spectrometry.
Bayesian Mixture Models For Gene Expression And Protein Profiles, Michele Guindani, Kim-Anh Do, Peter Mueller, Jeffrey S. Morris
Bayesian Mixture Models For Gene Expression And Protein Profiles, Michele Guindani, Kim-Anh Do, Peter Mueller, Jeffrey S. Morris
Jeffrey S. Morris
We review the use of semi-parametric mixture models for Bayesian inference in high throughput genomic data. We discuss three specific approaches for microarray data, for protein mass spectrometry experiments, and for SAGE data. For the microarray data and the protein mass spectrometry we assume group comparison experiments, i.e., experiments that seek to identify genes and proteins that are differentially expressed across two biologic conditions of interest. For the SAGE data example we consider inference for a single biologic sample.
Analysis Of Mass Spectrometry Data Using Bayesian Wavelet-Based Functional Mixed Models, Jeffrey S. Morris, Philip J. Brown, Keith A. Baggerly, Kevin R. Coombes
Analysis Of Mass Spectrometry Data Using Bayesian Wavelet-Based Functional Mixed Models, Jeffrey S. Morris, Philip J. Brown, Keith A. Baggerly, Kevin R. Coombes
Jeffrey S. Morris
In this chapter, we demonstrate how to analyze MALDI-TOF/SELDITOF mass spectrometry data using the wavelet-based functional mixed model introduced by Morris and Carroll (2006), which generalizes the linear mixed models to the case of functional data. This approach models each spectrum as a function, and is very general, accommodating a broad class of experimental designs and allowing one to model nonparametric functional effects for various factors, which can be conditions of interest (e.g. cancer/normal) or experimental factors (blocking factors). Inference on these functional effects allows us to identify protein peaks related to various outcomes of interest, including dichotomous outcomes, categorical …
Improved Peak Detection And Quantification Of Mass Spectrometry Data Acquired From Surface-Enhanced Laser Desorption And Ionization By Denoising Spectra With The Undecimated Discrete Wavelet Transform, Kevin R. Coombes, Spiros Tsavachidis, Jeffrey S. Morris, Keith A. Baggerly, Henry M. Kuerer
Improved Peak Detection And Quantification Of Mass Spectrometry Data Acquired From Surface-Enhanced Laser Desorption And Ionization By Denoising Spectra With The Undecimated Discrete Wavelet Transform, Kevin R. Coombes, Spiros Tsavachidis, Jeffrey S. Morris, Keith A. Baggerly, Henry M. Kuerer
Jeffrey S. Morris
Background: Mass spectrometry, especially surface enhanced laser desorption and ionization (SELDI) is increasingly being used to find disease-related proteomic patterns in complex mixtures of proteins derived from tissue samples or from easily obtained biological fluids such as serum, urine, or nipple aspirate fluid. Questions have been raised about the reproducibility and reliability of peak quantifications using this technology. For example, Yasui and colleagues opted to replace continuous measures of the size of a peak by a simple binary indicator of its presence or absence in their analysis of a set of spectra from prostate cancer patients.
Methods: We collected nipple …
The Importance Of Experimental Design In Proteomic Mass Spectrometry Experiments: Some Cautionary Tales, Jeffrey S. Morris, Jianhua Hu, Kevin R. Coombes, Keith A. Baggerly
The Importance Of Experimental Design In Proteomic Mass Spectrometry Experiments: Some Cautionary Tales, Jeffrey S. Morris, Jianhua Hu, Kevin R. Coombes, Keith A. Baggerly
Jeffrey S. Morris
Proteomic expression patterns derived from mass spectrometry have been put forward as potential biomarkers for the early diagnosis of cancer and other diseases. This approach has generated much excitement and has led to a large number of new experiments and vast amounts of new data. The data, derived at great expense, can have very little value if careful attention is not paid to the experimental design and analysis. Using examples from surfaceenhanced laser desorption/ionisation time-of-flight (SELDI-TOF) and matrix-assisted laser desorption–ionisation/time-of-flight (MALDI-TOF) experiments, we describe several experimental design issues that can corrupt a dataset. Fortunately, the problems we identify can be …
Serum Proteomics Profiling: A Young Technology Begins To Mature, Kevin R. Coombes, Jeffrey S. Morris, Jianhua Hu, Sarah R. Edmondson, Keith A. Baggerly
Serum Proteomics Profiling: A Young Technology Begins To Mature, Kevin R. Coombes, Jeffrey S. Morris, Jianhua Hu, Sarah R. Edmondson, Keith A. Baggerly
Jeffrey S. Morris
No abstract provided.
Signal In Noise: Evaluating Reported Reproducibility Of Serum Proteomic Tests For Ovarian Cancer, Keith A. Baggerly, Jeffrey S. Morris, Sarah R. Edmonson, Kevin R. Coombes
Signal In Noise: Evaluating Reported Reproducibility Of Serum Proteomic Tests For Ovarian Cancer, Keith A. Baggerly, Jeffrey S. Morris, Sarah R. Edmonson, Kevin R. Coombes
Jeffrey S. Morris
Proteomic profi ling of serum initially appeared to be dramatically effective for diagnosis of early-stage ovarian cancer, but these results have proven diffi cult to reproduce. A recent publication reported good classifi cation in one dataset using results from training on a much earlier dataset, but the authors have since reported that they did not perform the analysis as described. We examined the reproducibility of the proteomic patterns across datasets in more detail. Our analysis reveals that the pattern that enabled successful classifi cation is biologically implausible and that the method, properly applied, does not classify the data accurately. We …
High-Resolution Serum Proteomic Patterns For Ovarian Cancer Detection, Keith A. Baggerly, Sarah R. Edmonson, Jeffrey S. Morris, Kevin R. Coombes
High-Resolution Serum Proteomic Patterns For Ovarian Cancer Detection, Keith A. Baggerly, Sarah R. Edmonson, Jeffrey S. Morris, Kevin R. Coombes
Jeffrey S. Morris
No abstract provided.
Quality Control And Peak Finding For Proteomics Data Collected From Nipple Aspirate Fluid Using Surface Enhanced Laser Desorption And Ionization., Jeffrey S. Morris, Kevin R. Coombes, Herbert A. Fritsche, Charlotte Clarke, Jeng-Neng Chen, Keith A. Baggerly, Lian-Chun Xiao, Mien-Chie Hung, Henry M. Kuerer
Quality Control And Peak Finding For Proteomics Data Collected From Nipple Aspirate Fluid Using Surface Enhanced Laser Desorption And Ionization., Jeffrey S. Morris, Kevin R. Coombes, Herbert A. Fritsche, Charlotte Clarke, Jeng-Neng Chen, Keith A. Baggerly, Lian-Chun Xiao, Mien-Chie Hung, Henry M. Kuerer
Jeffrey S. Morris
Background: Recently, researchers have been using mass spectroscopy to study cancer. For use of proteomics spectra in a clinical setting, stringent quality-control procedures will be needed.
Methods: We pooled samples of nipple aspirate fluid from healthy breasts and breasts with cancer to prepare a control sample. Aliquots of the control sample were used on two spots on each of three IMAC ProteinChip® arrays (Ciphergen Biosystems, Inc.) on 4 successive days to generate 24 SELDI spectra. In 36 subsequent experiments, the control sample was applied to two spots of each ProteinChip array, and the resulting spectra were analyzed to determine how …
A Comprehensive Approach To The Analysis Of Maldi-Tof Proteomics Spectra From Serum Samples., Keith A. Baggerly, Jeffrey S. Morris, Jing Wang, David Gold, Lian-Chun Xiao, Kevin R. Coombes
A Comprehensive Approach To The Analysis Of Maldi-Tof Proteomics Spectra From Serum Samples., Keith A. Baggerly, Jeffrey S. Morris, Jing Wang, David Gold, Lian-Chun Xiao, Kevin R. Coombes
Jeffrey S. Morris
For our analysis of the data from the First Annual Proteomics Data Mining Conference, we attempted to discriminate between 24 disease spectra (group A) and 17 normal spectra (group B). First, we processed the raw spectra by (i) correcting for additive sinusoidal noise (periodic on the time scale) affecting most spectra, (ii) correcting for the overall baseline level, (iii) normalizing, (iv) recombining fractions, and (v) using variable- width windows for data reduction. Also, we identified a set of polymeric peaks (at multiples of 180.6 Da) that is present in several normal spectra (B1–B8). After data processing, we found the intensities …