Browsing by Author "Ragg, Susanne"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    Analysis of serum Hsp90 as a potential biomarker of β cell autoimmunity in type 1 diabetes
    (PLOS, 2019-01-10) Ocaña, Gail J.; Sims, Emily K.; Watkins, Renecia A.; Ragg, Susanne; Mather, Kieren J.; Oram, Richard A.; Mirmira, Raghavendra G.; DiMeglio, Linda A.; Blum, Janice S.; Evans-Molina, Carmella; Microbiology and Immunology, School of Medicine
    Heat shock protein 90 (Hsp90) is a protein chaperone that is upregulated and released from pancreatic β cells under pro-inflammatory conditions. We hypothesized that serum Hsp90 may have utility as a biomarker of type 1 diabetes risk and exhibit elevations before the onset of clinically significant hyperglycemia. To this end, total levels of the alpha cytoplasmic isoform of Hsp90 were assayed in autoantibody-positive progressors to type 1 diabetes using banked serum samples from the TrialNet Pathway to Prevention Cohort that had been collected 12 months prior to diabetes onset, with comparison to age, sex, and BMI-category matched autoantibody-positive nonprogressors and healthy controls. Hsp90 levels were higher in autoantibody-positive progressors and nonprogressors ≤ 18 years of age compared to matched healthy controls. However, Hsp90 levels were not different between progressors and nonprogressors in any age group. Hsp90 was positively correlated with age in control subjects, but this correlation was absent in autoantibody positive individuals. In aggregate these data indicate that elevated Hsp90 levels are present in youth with β cell autoimmunity, but are not able to distinguish youth or adult type 1 diabetes progressors from nonprogressors in samples collected 12 months prior to diabetes development.
  • Thumbnail Image
    Item
    classCleaner: A Quantitative Method for Validating Peptide Identification in LC-MS/MS Workflows
    (2020-05) Key, Melissa Chester; Boukai, Benzion; Ragg, Susanne; Katz, Barry; Mosley, Amber
    Because label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) shotgun proteomics infers the peptide sequence of each measurement, there is inherent uncertainty in the identity of each peptide and its originating protein. Removing misidentified peptides can improve the accuracy and power of downstream analyses when differences between proteins are of primary interest. In this dissertation I present classCleaner, a novel algorithm designed to identify misidentified peptides from each protein using the available quantitative data. The algorithm is based on the idea that distances between peptides belonging to the same protein are stochastically smaller than those between peptides in different proteins. The method first determines a threshold based on the estimated distribution of these two groups of distances. This is used to create a decision rule for each peptide based on counting the number of within-protein distances smaller than the threshold. Using simulated data, I show that classCleaner always reduces the proportion of misidentified peptides, with better results for larger proteins (by number of constituent peptides), smaller inherent misidentification rates, and larger sample sizes. ClassCleaner is also applied to a LC-MS/MS proteomics data set and the Congressional Voting Records data set from the UCI machine learning repository. The later is used to demonstrate that the algorithm is not specific to proteomics.
  • Thumbnail Image
    Item
    The Effect of Molecular Weight on Passage of Proteins Through the Blood-Aqueous Barrier
    (ARVO, 2019-04) Ragg, Susanne; Key, Melissa; Rankin, Fernanda; WuDunn, Darrell; Biostatistics, School of Public Health
    Purpose: To determine the effect of molecular weight (MW) on the concentration of plasma-derived proteins in aqueous humor and to estimate the plasma-derived and eye-derived fractions for each protein. Methods: Aqueous humor and plasma samples were obtained during cataract surgery on an institutional review board–approved protocol. Protein concentrations were determined by ELISA and quantitative antibody microarrays. A total of 93 proteins were studied, with most proteins analyzed using 27 to 116 aqueous and 6 to 30 plasma samples. Results: Plasma proteins without evidence of intraocular expression by sequence tags were used to fit a logarithmic model relating aqueous-plasma ratio (AH:PL) to MW. The log(AH:PL) appears to be well predicted by the log(MW) (P < 0.0001), with smaller proteins such as cystatin C (13 kDa) having a higher AH:PL (1:6) than larger proteins such as albumin (66 kDa, 1:300) and complement component 5 (188 kDa, 1:2500). The logarithmic model was used to calculate the eye-derived intraocular fraction (IOF) for each protein. Based on the IOF, 66 proteins could be categorized as plasma-derived (IOF<20), whereas 10 proteins were primarily derived from eye tissue (IOF >80), and 17 proteins had contribution from both plasma and eye tissue (IOF 20–80). Conclusions: Protein concentration of plasma-derived proteins in aqueous is nonlinearly dependent on MW in favor of smaller proteins. Our study demonstrates that for proper interpretation of results, proteomic studies evaluating changes in aqueous humor protein levels should take into account the plasma and eye-derived fractions.
  • Thumbnail Image
    Item
    Evidence for BCR/ABL1‐positive T‐cell acute lymphoblastic leukemia arising in an early lymphoid progenitor cell
    (Wiley, 2019-09) Ragg, Susanne; Zehentner, Barbara K.; Loken, Michael R.; Croop, James M.; Pediatrics, School of Medicine
    BCR‐ABL1‐positive leukemias have historically been classified as either chronic myelogenous leukemia or Ph+ acute lymphoblastic leukemia. Recent analyses suggest there may be a wider range of subtypes. We report a patient with BCR‐ABL1 fusion positive T‐cell ALL with a previously undescribed cell distribution of the fusion gene. The examination of sorted cells by fluorescence in situ hybridization showed the BCR‐ABL1 fusion in the malignant T cells and a subpopulation of the nonmalignant B cells, but not nonmalignant T cells or myeloid or CD34+ progenitor cells providing evidence that the fusion may have occurred in an early lymphoid progenitor.
  • Thumbnail Image
    Item
    Signature Center for Computational Diagnostics Translating Experimental Technologies into Clinical Research
    (Office of the Vice Chancellor for Research, 2010-04-09) Ragg, Susanne; Schadow, Gunther
    The goal of the Center for Computational Diagnostics [http://www.iupui.edu/~compdiag] is to serve as a “reactor” of innovative research for the integration of diverse high throughput technology into clinical trials to allow clinical researchers to obtain a more comprehensive view of the disease states. It has become clear that to better understand diseases we cannot continue to only focus on single genes, proteins or metabolites operating in single linear ordered pathways. Large scale high throughput technologies such as applied in genomics, proteomics and metabolomics allow for a more comprehensive view of the complex interactions occurring within body fluids or tissues at any one time. The Center operates by addressing the three different areas that are required to successfully integrate high throughput methodologies into translational research: 1) High throughput biospecimen banking; 2) Generation of high quality datasets and 3) Workflow development for data storage and analysis. To support high throughput bio-specimen banking we have co-developed caTissue Suite under the national caBIG effort. The key developments of the Center have been: 1. CaTrack, an intelligent barcode-based automatic data capture system 2. protocol-driven Study Calendar 3. xCaCORE, an innovative XML-based data import and export software program 4. Scalable, globally unique specimen identification utilizing an ISO Object Identifiers encoding scheme 5. Barcode generator and label printing We have implemented the Pediatric Biospecimen Repository to be able to develop and test these informatics tools. In addition to functioning as the development and test site for informatics research, the repository also develops research protocols and stores biospecimens for Pediatrics, Ophthalmology and Obstetrics. We also support protocol and data management related to biospecimens for the CTSI and the Fairbanks Institute. We currently have 92 active protocols and data on 119,319 specimens in our production instance of caTissue Suite. This includes specimens from 1200 well-defined healthy control subjects across all age groups including 600 children. To develop the computational and statistical workflows for data storage and analysis, we have generated large well-designed datasets for coronary artery disease (LC-MS/MS, NMR, protein antibody arrays); cancer (osteosarcoma and Wilms tumor; LC-MS/MS, NMR, protein antibody arrays) and ophthalmologic diseases (glaucoma; protein antibody arrays). Our main focus is to develop analytical workflows that translate the large datasets into relevant information for clinical researchers, focusing on the biological interpretation of the results. In this context we developed statistical models for protein quantification for LC-MS/MS and protein antibody arrays. These workflows were implemented in the open source statistical software R and published under the R-based project Bioconductor.