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Coresh J, Inker LA, Sang Y, Chen J, Shafi T, Post WS, Shlipak MG, Ford L, Goodman K, Perichon R, Greene T, Levey AS. Metabolomic profiling to improve glomerular filtration rate estimation: a proof-of-concept study. Nephrol Dial Transplant 2019; 34:825-833. [PMID: 29718360 PMCID: PMC6503300 DOI: 10.1093/ndt/gfy094] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Estimation of glomerular filtration rate (GFR) using estimated glomerular filtration rate creatinine (eGFRcr) is central to clinical practice but has limitations. We tested the hypothesis that serum metabolomic profiling can identify novel markers that in combination can provide more accurate GFR estimates. METHODS We performed a cross-sectional study of 200 African American Study of Kidney Disease and Hypertension (AASK) and 265 Multi-Ethnic Study of Atherosclerosis (MESA) participants with measured GFR (mGFR). Untargeted gas chromatography/dual mass spectrometry- and liquid chromatography/dual mass spectrometry-based quantification was followed by the development of targeted assays for 15 metabolites. On the log scale, GFR was estimated from single- and multiple-metabolite panels and compared with eGFR using the Chronic Kidney Disease Epidemiology equations with creatinine and/or cystatin C using established metrics, including the proportion of errors >30% of mGFR (1-P30), before and after bias correction. RESULTS Of untargeted metabolites in the AASK and MESA, 283 of 780 (36%) and 387 of 1447 (27%), respectively, were significantly correlated (P ≤ 0.001) with mGFR. A targeted metabolite panel eGFR developed in the AASK and validated in the MESA was more accurate (1-P30 3.7 and 1.9%, respectively) than eGFRcr [11.2 and 18.5%, respectively (P < 0.001 for both)] and estimating GFR using cystatin C (eGFRcys) [10.6% (P = 0.02) and 9.1% (P < 0.05), respectively] but was not consistently better than eGFR using both creatinine and cystatin C [3.7% (P > 0.05) and 9.1% (P < 0.05), respectively]. A panel excluding creatinine and demographics still performed well [1-P30 6.4% (P = 0.11) and 3.4% (P < 0.001) in the AASK and MESA] versus eGFRcr. CONCLUSIONS Multimetabolite panels can enable accurate GFR estimation. Metabolomic equations, preferably excluding creatinine and demographic characteristics, should be tested for robustness and generalizability as a potential confirmatory test when eGFRcr is unreliable.
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Affiliation(s)
- Josef Coresh
- Departments of Epidemiology, Biostatistics and Medicine, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Lesley A Inker
- Department of Nephrology, Tufts Medical Center, Boston, MA, USA
| | - Yingying Sang
- Departments of Epidemiology, Biostatistics and Medicine, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Jingsha Chen
- Departments of Epidemiology, Biostatistics and Medicine, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Tariq Shafi
- Departments of Epidemiology, Biostatistics and Medicine, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Wendy S Post
- Departments of Epidemiology, Biostatistics and Medicine, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Michael G Shlipak
- Department of General Internal Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Lisa Ford
- Metabolon, Inc., Morrisville, NC, USA
| | | | | | - Tom Greene
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Andrew S Levey
- Department of Nephrology, Tufts Medical Center, Boston, MA, USA
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Freed TA, Coresh J, Inker LA, Toal DR, Perichon R, Chen J, Goodman KD, Zhang Q, Conner JK, Hauser DM, Vroom KET, Oyaski ML, Wulff JE, Eiríksdóttir G, Gudnason V, Torres VE, Ford LA, Levey AS. Validation of a Metabolite Panel for a More Accurate Estimation of Glomerular Filtration Rate Using Quantitative LC-MS/MS. Clin Chem 2019; 65:406-418. [PMID: 30647123 PMCID: PMC6646882 DOI: 10.1373/clinchem.2018.288092] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Accepted: 12/11/2018] [Indexed: 02/05/2023]
Abstract
BACKGROUND Clinical practice guidelines recommend estimation of glomerular filtration rate (eGFR) using validated equations based on serum creatinine (eGFRcr), cystatin C (eGFRcys), or both (eGFRcr-cys). However, when compared with the measured GFR (mGFR), only eGFRcr-cys meets recommended performance standards. Our goal was to develop a more accurate eGFR method using a panel of metabolites without creatinine, cystatin C, or demographic variables. METHODS An ultra-performance liquid chromatography-tandem mass spectrometry assay for acetylthreonine, phenylacetylglutamine, pseudouridine, and tryptophan was developed, and a 20-day, multiinstrument analytical validation was conducted. The assay was tested in 2424 participants with mGFR data from 4 independent research studies. A new GFR equation (eGFRmet) was developed in a random subset (n = 1615) and evaluated in the remaining participants (n = 809). Performance was assessed as the frequency of large errors [estimates that differed from mGFR by at least 30% (1 - P30); goal <10%]. RESULTS The assay had a mean imprecision (≤10% intraassay, ≤6.9% interassay), linearity over the quantitative range (r 2 > 0.98), and analyte recovery (98.5%-113%). There was no carryover, no interferences observed, and analyte stability was established. In addition, 1 - P30 in the validation set for eGFRmet (10.0%) was more accurate than eGFRcr (13.1%) and eGFRcys (12.0%) but not eGFRcr-cys (8.7%). Combining metabolites, creatinine, cystatin C, and demographics led to the most accurate equation (7.0%). Neither equation had substantial variation among population subgroups. CONCLUSIONS The new eGFRmet equation could serve as a confirmatory test for GFR estimation.
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Affiliation(s)
| | - Josef Coresh
- Departments of Epidemiology, Medicine and Biostatistics, Johns Hopkins University, Bloomberg School of Public Health and School of Medicine, Baltimore, MD
| | - Lesley A Inker
- Division of Nephrology, Tufts Medical Center, Boston, MA
| | | | | | - Jingsha Chen
- Departments of Epidemiology, Medicine and Biostatistics, Johns Hopkins University, Bloomberg School of Public Health and School of Medicine, Baltimore, MD
| | | | | | | | | | | | | | | | | | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Vicente E Torres
- Department of Nephrology and Hypertension, Mayo Clinic, Rochester, MN
| | | | - Andrew S Levey
- Division of Nephrology, Tufts Medical Center, Boston, MA;
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Rhee EP, Waikar SS, Rebholz CM, Zheng Z, Perichon R, Clish CB, Evans AM, Avila J, Denburg MR, Anderson AH, Vasan RS, Feldman HI, Kimmel PL, Coresh J. Variability of Two Metabolomic Platforms in CKD. Clin J Am Soc Nephrol 2018; 14:40-48. [PMID: 30573658 PMCID: PMC6364529 DOI: 10.2215/cjn.07070618] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 10/15/2018] [Indexed: 01/01/2023]
Abstract
BACKGROUND AND OBJECTIVES Nontargeted metabolomics can measure thousands of low-molecular-weight biochemicals, but important gaps limit its utility for biomarker discovery in CKD. These include the need to characterize technical and intraperson analyte variation, to pool data across platforms, and to outline analyte relationships with eGFR. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS Plasma samples from 49 individuals with CKD (eGFR<60 ml/min per 1.73 m2 and/or ≥1 g proteinuria) were examined from two study visits; 20 samples were repeated as blind replicates. To enable comparison across two nontargeted platforms, samples were profiled at Metabolon and the Broad Institute. RESULTS The Metabolon platform reported 837 known metabolites and 483 unnamed compounds (selected from 44,953 unknown ion features). The Broad Institute platform reported 594 known metabolites and 26,106 unknown ion features. Median coefficients of variation (CVs) across blind replicates were 14.6% (Metabolon) and 6.3% (Broad Institute) for known metabolites, and 18.9% for (Metabolon) unnamed compounds and 24.5% for (Broad Institute) unknown ion features. Median CVs for day-to-day variability were 29.0% (Metabolon) and 24.9% (Broad Institute) for known metabolites, and 41.8% for (Metabolon) unnamed compounds and 40.9% for (Broad Institute) unknown ion features. A total of 381 known metabolites were shared across platforms (median correlation 0.89). Many metabolites were negatively correlated with eGFR at P<0.05, including 35.7% (Metabolon) and 18.9% (Broad Institute) of known metabolites. CONCLUSIONS Nontargeted metabolomics quantifies >1000 analytes with low technical CVs, and agreement for overlapping metabolites across two leading platforms is excellent. Many metabolites demonstrate substantial intraperson variation and correlation with eGFR.
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Affiliation(s)
- Eugene P Rhee
- Nephrology Division and Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts;
| | - Sushrut S Waikar
- Renal Division, Brigham and Women's Hospital, Boston, Massachusetts
| | - Casey M Rebholz
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland.,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Zihe Zheng
- Department of Biostatistics, Epidemiology, and Informatics
| | | | - Clary B Clish
- Metabolite Profiling, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | | | - Julian Avila
- Metabolite Profiling, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | | | | | - Ramachandran S Vasan
- Sections of Preventive Medicine and Epidemiology and Cardiology, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts; and
| | - Harold I Feldman
- Department of Biostatistics, Epidemiology, and Informatics.,Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Paul L Kimmel
- Division of Kidney Urologic and Hematologic Diseases, National Institutes of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Josef Coresh
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland; .,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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Dhaenens C, Jacques J, Vandewalle V, Vandromme M, Chazard E, Preda C, Amarioarei A, Chaiwuttisak P, Cozma C, Ficheur G, Kessaci ME, Perichon R, Taillard J, Bordet R, Lansiaux A, Jourdan L, Delerue D, Hansske A. ClinMine: Optimizing the Management of Patients in Hospital. Ing Rech Biomed 2018. [DOI: 10.1016/j.irbm.2017.12.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Cobb J, Eckhart A, Perichon R, Wulff J, Mitchell M, Adam KP, Wolfert R, Button E, Lawton K, Elverson R, Carr B, Sinnott M, Ferrannini E. A novel test for IGT utilizing metabolite markers of glucose tolerance. J Diabetes Sci Technol 2015; 9:69-76. [PMID: 25261439 PMCID: PMC4495543 DOI: 10.1177/1932296814553622] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The oral glucose tolerance test (OGTT) is the only method to diagnose patients having impaired glucose tolerance (IGT), but its use has diminished considerably in recent years. Metabolomic profiling studies have identified a number of metabolites whose fasting levels are associated with dysglycemia and type 2 diabetes. These metabolites may serve as the basis of an alternative test for IGT. Using the stable isotope dilution technique, quantitative assays were developed for 23 candidate biomarker metabolites. These metabolites were measured in fasting plasma samples taken just prior to an OGTT from 1623 nondiabetic subjects: 955 from the Relationship between Insulin Sensitivity and Cardiovascular Disease Study (RISC Study; 11.7% IGT) and 668 subjects from the Diabetes Mellitus and Vascular Health Initiative (DMVhi) cohort from the DEXLIFE project (11.8% IGT). The associations between metabolites, anthropometric, and metabolic parameters and 2hPG values were assessed by Pearson correlation coefficients and Random Forest classification analysis to rank variables for their ability to distinguish IGT from normal glucose tolerance (NGT). Multivariate logistic regression models for estimating risk of IGT were developed and evaluated using AUCs calculated from the corresponding ROC curves. A model based on the fasting plasma levels of glucose, α-hydroxybutyric acid, β-hydroxybutyric acid, 4-methyl-2-oxopentanoic acid, linoleoylglycerophosphocholine, oleic acid, serine and vitamin B5 was optimized in the RISC cohort (AUC = 0.82) and validated in the DMVhi cohort (AUC = 0.83). A novel, all-metabolite-based test is shown to be a discriminate marker of IGT. It requires only a single fasted blood draw and may serve as a more convenient surrogate for the OGTT or as a means of identifying subjects likely to be IGT.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | - Ele Ferrannini
- Department of Internal Medicine, University of Pisa, Pisa, Italy
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Cobb J, Gall W, Adam KP, Nakhle P, Button E, Hathorn J, Lawton K, Milburn M, Perichon R, Mitchell M, Natali A, Ferrannini E. A novel fasting blood test for insulin resistance and prediabetes. J Diabetes Sci Technol 2013; 7:100-10. [PMID: 23439165 PMCID: PMC3692221 DOI: 10.1177/193229681300700112] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
BACKGROUND Insulin resistance (IR) can precede the dysglycemic states of prediabetes and type 2 diabetes mellitus (T2DM) by a number of years and is an early marker of risk for metabolic and cardiovascular disease. There is an unmet need for a simple method to measure IR that can be used for routine screening, prospective study, risk assessment, and therapeutic monitoring. We have reported several metabolites whose fasting plasma levels correlated with insulin sensitivity. These metabolites were used in the development of a novel test for IR and prediabetes. METHODS Data from the Relationship between Insulin Sensitivity and Cardiovascular Disease Study were used in an iterative process of algorithm development to define the best combination of metabolites for predicting the M value derived from the hyperinsulinemic euglycemic clamp, the gold standard measure of IR. Subjects were divided into a training set and a test set for algorithm development and validation. The resulting calculated M score, M(Q), was utilized to predict IR and the risk of progressing from normal glucose tolerance to impaired glucose tolerance (IGT) over a 3 year period. RESULTS M(Q) correlated with actual M values, with an r value of 0.66. In addition, the test detects IR and predicts 3 year IGT progression with areas under the curve of 0.79 and 0.70, respectively, outperforming other simple measures such as fasting insulin, fasting glucose, homeostatic model assessment of IR, or body mass index. CONCLUSIONS The result, Quantose(TM), is a simple test for IR based on a single fasting blood sample and may have value as an early indicator of risk for the development of prediabetes and T2DM.
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Affiliation(s)
- Jeff Cobb
- Metabolon Inc., Durham, North Carolina 27713, USA.
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McDunn J, Perichon R, Goodison S, Rosser CJ. Abstract LB-86: Metabolic presentation of bladder cancer in urine. Cancer Res 2012. [DOI: 10.1158/1538-7445.am2012-lb-86] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Introduction and Objective Recently it has been shown that many oncogenes regulate metabolism, revitalizing interest in Warburg's observation of aerobic glycolysis by tumors. We and others have demonstrated the utility of metabolic signatures for the diagnosis of cancer in minimally invasive clinical samples and the differential diagnosis of cancer by stage and grade using biopsy samples. Here we apply state of the art metabolomic analysis to the diagnosis of bladder cancer from urine samples. Methods A case controlled study of ten transitional cell carcinoma (TCC) patients and matched non-TCC controls was performed. Briefly, clarified urine was extracted into aqueous methanol and characterized using three analytical platforms (gas chromatography mass spectrometry (MS) and ultrahigh performance liquid chromatography tandem MS). Spectral data were compared against an in-house library of over 2500 authentic standards and the relative abundance of each compound within the data set was calculated. Two-group statistics (R) and multivariate analysis (Array Studio, OmicSoft) were performed to identify differentially abundant metabolites. Results Three hundred and sixty-seven compounds were identified in this study and polypharmacy did not compromise the quality of the signal obtained for endogenous metabolites. Thirteen compounds, arising from diverse metabolic pathways, including: energetics, oxidative stress and amino acid catabolism, exhibited statistically significant differences between TCC and non-TCC cases. Principal component (PC) analysis demonstrated that the majority of the information in these metabolites (PC 1) separated TCC from non-TCC cases. Conclusions These data indicate the feasibility of a novel, non-invasive, urine-based diagnostic test to detect the presence of bladder tumors. Such a test could be positioned for use in conjunction with the current standard of care to improve patient management.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-86. doi:1538-7445.AM2012-LB-86
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Affiliation(s)
| | | | - Steve Goodison
- 2Univ. of Central Florida College of Medicine, Orlando, FL
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Miller WL, Phelps MA, Wood CM, Schellenberger U, Van Le A, Perichon R, Jaffe AS. Comparison of mass spectrometry and clinical assay measurements of circulating fragments of B-type natriuretic peptide in patients with chronic heart failure. Circ Heart Fail 2011; 4:355-60. [PMID: 21292992 DOI: 10.1161/circheartfailure.110.960260] [Citation(s) in RCA: 91] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Multiple B-type natriuretic peptide (BNP) fragments circulate in patients with heart failure (HF) but the types and relative quantities, particularly in relation to bioactive BNP 1-32, remain poorly defined. The purpose of the study was to relate clinically available BNP values with quantitative information on the concentration of pre-secretion and post-processed fragments of BNP detected by mass spectrometry. METHODS AND RESULTS Seventy Class I-IV patients were prospectively enrolled with blood drawn into tubes containing a preservative to protect against BNP degradation. Samples were analyzed by quantitative mass spectrometry (MS) immunoassay for intact BNP 1-32 and its fragments. Clinical BNP 1-2 was measured by standard clinical laboratory methods. ProBNP 1-108, corin, and clinically measured BNP levels were elevated, but MS BNP 1-32 levels were low and differed from clinical BNP (P=0.01). Intact MS BNP 1-32 correlated modestly with clinical BNP (r=0.46, P<0.001). MS BNP fragments 3-32, 4-32, and 5-32 demonstrated the best associations with clinical BNP; fragment 5-32 with a correlation coefficient of r=0.81 (P<0.001). CONCLUSIONS ProBNP 1-108 is measured by clinical BNP assays and contributes to the cumulative results of the BNP assay. However, the observation that clinically measured BNP correlates best with MS degradation fragments and relatively poorly with MS BNP 1-32 suggests that a significant component of circulating clinical BNP is composed of such fragments that are known to demonstrate little biological activity. There appear to be multiple pathways involved in the dysregulation of proBNP in HF, and both the processing of proBNP and the downstream degradation to BNP 1-32 appear to be critical.
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Affiliation(s)
- Wayne L Miller
- Division of Cardiovascular Diseases, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.
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Ndao M, Spithill TW, Caffrey R, Li H, Podust VN, Perichon R, Santamaria C, Ache A, Duncan M, Powell MR, Ward BJ. Identification of novel diagnostic serum biomarkers for Chagas' disease in asymptomatic subjects by mass spectrometric profiling. J Clin Microbiol 2010; 48:1139-49. [PMID: 20071547 PMCID: PMC2849606 DOI: 10.1128/jcm.02207-09] [Citation(s) in RCA: 54] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2009] [Revised: 12/26/2009] [Accepted: 01/07/2010] [Indexed: 01/10/2023] Open
Abstract
More than 10 million people are thought to be infected with Trypanosoma cruzi, primarily in the Americas. The clinical manifestations of Chagas' disease (CD) are variable, but most subjects remain asymptomatic for decades. Only 15 to 30% eventually develop terminal complications. All current diagnostic tests have limitations. New approaches are needed for blood bank screening as well as for improved diagnosis and prognosis. Sera from subjects with asymptomatic CD (n = 131) were compared to those from uninfected controls (n = 164) and subjects with other parasitic diseases (n = 140), using protein array mass spectrometry. To identify biomarkers associated with CD, sera were fractionated by anion-exchange chromatography and bound to two commercial ProteinChip array chemistries: WCX2 and IMAC3. Multiple candidate biomarkers were found in CD sera (3 to 75.4 kDa). Algorithms employing 3 to 5 of these biomarkers achieved up to 100% sensitivity and 98% specificity for CD. The biomarkers most useful for diagnosis were identified and validated. These included MIP1 alpha, C3a anaphylatoxin, and unusually truncated forms of fibronectin, apolipoprotein A1 (ApoA1), and C3. An antipeptide antiserum against the 28.9-kDa C terminus of the fibronectin fragment achieved good specificity (90%) for CD in a Western blot format. We identified full-length ApoA1 (28.1 kDa), the major structural and functional protein component of high-density lipoprotein (HDL), as an important negative biomarker for CD, and relatively little full-length ApoA1 was detected in CD sera. This work provides proof of principle that both platform-dependent (i.e., mass spectrometry-based) and platform-independent (i.e., Western blot) tests can be generated using high-throughput mass profiling.
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Affiliation(s)
- Momar Ndao
- National Reference Centre for Parasitology, Research Institute of the McGill University Health Center, Montreal, Quebec, Canada.
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Ségalat L, Perichon R, Bouly JP, Lepesant JA. The Drosophila pourquoi-pas?/wings-down zinc finger protein: oocyte nucleus localization and embryonic requirement. Genes Dev 1992; 6:1019-29. [PMID: 1592256 DOI: 10.1101/gad.6.6.1019] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The pourquoi-pas? (pqp) gene of Drosophila melanogaster encodes a Cys2/His2 zinc finger protein and is abundantly transcribed in adult ovaries. During oogenesis, we immunodetected the pqp protein in the nucleus of nurse cells at stages 1-6, in a spherical structure within the oocyte nucleus at stages 7-9, and uniformly distributed in the oocyte nucleus and in nurse cell nuclei at later stages. The pqp protein is also present at a lower level in the nuclei of follicle cells, embryos, and larvae. By means of a polymerase chain reaction (PCR) screen, we recovered three independent and phenotypeless P-element insertions at the pqp locus. In a second step, two excision-induced deletions of the pqp gene were isolated after mobilization of one of these P elements. The pqp mutants display zygotic (spread and drooping wings, cross-vein defects, extra bristles) and maternal (embryonic lethality) recessive phenotypes. The chromosomal position (98EF) of the pqp gene and the drooping wing phenotype of the pqp mutants agree with the hypothesis that the pqp gene is the wings down (wdn) gene for which T.H. Morgan isolated (and lost) mutants in the 1920s. This is the first reported occurrence of a zinc finger protein in the nucleus of the Drosophila oocyte.
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Affiliation(s)
- L Ségalat
- Institut Jacques Monod, Centre National de la Recherche Scientifique et Université Paris, France
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