Advances in Clinical Chemistry

 
 
Academic Press
  • 1. Auflage
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  • erschienen am 28. Juli 2015
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  • 336 Seiten
 
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978-0-12-803317-3 (ISBN)
 

Volume 70 in the internationally acclaimed Advances in Clinical Chemistry contains chapters authored by world renowned clinical laboratory scientists, physicians and research scientists. The serial provides the latest and most up-to-date technologies related to the field of clinical chemistry and is the benchmark for novel analytical approaches in the clinical laboratory.


  • Expertise of international contributors
  • Latest cutting-edge technologies
0065-2423
  • Englisch
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  • USA
Elsevier Science
  • 7,00 MB
978-0-12-803317-3 (9780128033173)
0128033177 (0128033177)
weitere Ausgaben werden ermittelt
  • Front Cover
  • Advances in Clinical Chemistry
  • Copyright
  • Contents
  • Contributors
  • Preface
  • Chapter One: Nuclear Factor-?B Activation as a Pathological Mechanism of Lipid Metabolism and Atherosclerosis
  • 1. Introduction
  • 2. Composition of NF-?B Family Proteins
  • 3. NF-?B Signaling Pathways
  • 3.1. Activation of NF-?B Signaling Pathways
  • 3.2. Negative Regulation of NF-?B Signaling Pathways
  • 4. NF-?B and Lipid Metabolism
  • 5. The Role of NF-?B in Atherosclerosis
  • 5.1. Activated NF-?B Mediates Foam Cell Formation
  • 5.2. Activated NF-?B Amplifies Vascular Inflammation
  • 5.3. Activated NF-?B Stimulates VSMC Proliferation and Migration
  • 5.4. Activated NF-?B Exacerbates Arterial Vessel Calcification
  • 5.5. Activated NF-?B Facilitates Plaque Formation and Rupture
  • 5.6. Activated NF-?B Regulates Vascular Cell Apoptosis
  • 6. Therapeutic Potential of Targeting NF-?B Signaling in Atherosclerosis
  • 7. Conclusions and Perspectives
  • Acknowledgments
  • References
  • Chapter Two: Plasma/Serum Plasmalogens: Methods of Analysis and Clinical Significance
  • 1. Introduction
  • 2. Plasmalogens
  • 2.1. Biosynthesis, Function, and Pathophysiology
  • 2.2. Plasma/Serum Pls
  • 3. Analytical Methods
  • 3.1. High-Performance Liquid Chromatography with 125I
  • 3.1.1. Preparation of the 125I Reagent
  • 3.1.2. Binding Characteristics of the 125I Reagent
  • 3.1.3. HPLC Separation and Quantification of PlsCho and PlsEtn
  • 3.1.4. Improved 125I-HPLC Method
  • 3.1.5. Extraction and Stability
  • 3.2. Liquid Chromatography-Tandem Mass Spectrometry
  • 3.2.1. Extraction of Lipids
  • 3.2.2. Standards for LC-MS/MS
  • 3.2.3. Method Parameters
  • 3.2.4. Analysis by LC-MS/MS
  • 3.3. Enzymatic Assay
  • 3.3.1. PlsEtn Assay Principle
  • 3.3.2. PLA1 from S. albidoflavus NA297
  • 3.3.3. LyPlsase Activity Assay
  • 3.3.4. Cloning and Expression of P. putida KT2440 LyPlsase
  • 3.3.5. Partial Purification of P. putida KT2440 LyPlsase
  • 3.3.6. Substrate Specificity and Primary Structure of P. putida KT2440 LyPlsase
  • 3.3.7. PlsEtn Assay
  • 4. Clinical Utility
  • 4.1. Middle-Aged Normal Subjects
  • 4.1.1. Characteristics of Serum Egp
  • 4.1.2. Egp and Various Risk Factors
  • 4.2. Coronary Artery Disease
  • 4.2.1. Characteristics of Serum Egp in CAD
  • 4.2.2. Relationship Between Egp and Atherogenic Parameters
  • 4.3. Elderly Dementia
  • 4.3.1. CD and Serum PlsEtn
  • 5. Conclusion
  • Acknowledgments
  • References
  • Chapter Three: Monitoring Trastuzumab Resistance and Cardiotoxicity: A Tale of Personalized Medicine*
  • 1. Introduction
  • 2. Clinical Data: Trastuzumab Efficacy, Trastuzumab Resistance, and Trastuzumab-Induced Cardiotoxicity
  • 2.1. Trastuzumab: General Information
  • 2.2. Clinical Use of Trastuzumab in Different Oncology Indications
  • 2.3. Trastuzumab Resistance: Primary and Acquired Resistance
  • 2.4. Trastuzumab-Induced Cardiotoxicity: Summary of Clinical Approaches and Findings
  • 3. Trastuzumab: Mechanism of Action and Potential Predictive Biomarkers for Trastuzumab Efficacy and Cardiotoxicity
  • 3.1. Trastuzumab: Mechanism of Action
  • 3.2. Mechanisms of Trastuzumab Resistance and Potential Molecular Biomarkers to Monitor Resistance: Summary of Preclinica ...
  • 3.3. Trastuzumab Resistance in Gastric Cancer: Novel Approaches and Potential Predictive Biomarkers
  • 3.4. Molecular Mechanisms of Trastuzumab-Induced Cardiotoxicity: Preclinical Findings
  • 3.5. Potential Biomarkers to Monitor Trastuzumab-Induced Cardiotoxicity: Clinical Studies
  • 4. Why Is It Difficult to Identify Clinically Relevant Biomarkers of Trastuzumab Resistance?
  • 4.1. General Considerations
  • 4.2. Novel Approaches to Monitor Efficacy and Resistance to Targeted Therapies: Circulating Tumor Cells and Circulating T ...
  • 5. Improving the Odds of Identifying Effective Therapies for Trastuzumab Resistance by Developing Personalized Preclinica ...
  • 5.1. Limitations of Traditional Models Used in Preclinical Testing of Novel Therapies for Trastuzumab-Resistant Cancer
  • 5.2. PDX as a Potential Model System
  • 5.3. GEMMs as a Potential Model Systems
  • 5.4. Spheroid Culture as a Potential Model System
  • 5.5. Proposal to Integrate Liquid Biopsy and PDX Models with Biomarker Studies to Identify Novel Therapies for Trastuzuma ...
  • 5.6. Proposal to Integrate Preclinical and Clinical Testing of Novel Therapies for Trastuzumab Resistance: Co-clinical Tr ...
  • 6. Novel Approaches in the Treatment of Trastuzumab-Resistant Cancer: Summary of Novel Therapies in Preclinical and Clini ...
  • 6.1. Novel Therapies for Trastuzumab Resistance in Preclinical Studies
  • 6.2. Proposal to Develop Therapies Based on Mechanisms of Trastuzumab Resistance
  • 6.3. Resistance-Based Design of ADCs to Overcome Trastuzumab Resistance
  • 6.4. Overcoming Trastuzumab Resistance in Clinics
  • 7. Conclusions
  • Acknowledgments
  • References
  • Chapter Four: Biomarkers in HCV Infection
  • 1. Introduction
  • 2. Fibrosis Biomarker Panels in CHC
  • 2.1. Introduction
  • 2.2. General Considerations
  • 2.2.1. Prediction Model
  • 2.2.2. Quantifying the Added Value of a New Biomarker
  • 2.2.3. Spectrum Bias and Generalizability
  • 2.2.4. Liver Biopsy: Gold Standard but Imperfect
  • 2.3. Biomarker Panels in CHC
  • 2.3.1. Interpretation of Biomarker Panel Results
  • 2.3.2. AST/ALT Ratio
  • 2.3.3. APRI
  • 2.3.4. Forns Index
  • 2.3.5. FIB-4
  • 2.3.6. FibroIndex
  • 2.3.7. FibroTest (FibroSURE)
  • 2.3.8. Enhanced Liver Fibrosis (ELF)
  • 2.3.9. HepaScore
  • 2.3.10. FibroMeter
  • 2.3.11. FIBROSpect II
  • 2.4. Liver Stiffness Measurement (TE)
  • 2.4.1. Diagnostic Performance
  • 2.4.2. Monitoring of Portal Hypertension
  • 2.4.3. Prediction of Long-Term Clinical Outcome
  • 2.4.4. Assessment of Treatment Effect
  • 2.4.5. Pitfalls of TE
  • 2.5. Comparison among Tests
  • 2.5.1. Comparison among Biomarker Panels
  • 2.5.2. Comparison Between Biomarker Panels and TE
  • 2.6. Combinations and Algorithms
  • 2.6.1. Castera (Bordeaux) Algorithm: FibroTest+TE
  • 2.6.2. Angers Algorithm: FibroMeter+TE
  • 2.6.3. Leroy Algorithm: APRI+FibroTest
  • 2.6.4. Fibropaca Algorithm: APRI+FibroTest and/or Forns Index
  • 2.6.5. SAFE Biopsy: APRI FibroTest
  • 3. Biomarkers for the Screening and Diagnosis of Hepatitis C
  • 3.1. Screening Biomarker
  • 3.1.1. HCV Antibodies
  • 3.1.2. Serum Aminotransferase
  • 3.2. Diagnostic Biomarker
  • 3.2.1. HCV RNA
  • 4. Biomarkers for Antiviral Treatment Initiation and Evaluation in Hepatitis C
  • 4.1. Pretreatment Biomarkers: Treatment Guidance and Response Prediction Biomarker
  • 4.1.1. HCV Genotype
  • 4.1.2. IL28B Polymorphisms
  • 4.1.3. Q80K Mutation
  • 4.2. Biomarker Reflecting Severity of HCV Infection
  • 4.2.1. Liver Histology
  • 4.2.2. Hepatic Stiffness
  • 4.3. Treatment-Related Biomarkers
  • 4.3.1. SVR
  • 4.3.2. Rapid Virologic Response
  • 4.3.3. Early Virologic Response
  • 5. Biomarkers for the Prediction of HCC
  • 5.1. Serum Biomarkers
  • 5.1.1. a-Fetoprotein
  • 5.1.2. Fractionated Lens Culinaris Agglutinin-Reactive a-Fetoprotein L3 (AFP-L3)
  • 5.1.3. Des-Gamma-Carboxy-Prothrombin, also Known as PIVKA II
  • 5.2. Newly Introduced Biomarkers
  • 5.2.1. Golgi Protein 73
  • 5.2.2. Glypican-3
  • 5.3. Imaging Biomarkers
  • 5.3.1. Ultrasound
  • 5.3.2. CT and MRI
  • 5.3.3. Positron Emission Tomography
  • 5.4. HVPG Measurement
  • 6. Emerging Biomarkers in HCV Infection
  • 6.1. Categories of Emerging HCV Biomarkers
  • 6.2. MicroRNAs
  • 6.3. miRNAs in Liver Disease and HCV Infection
  • 6.4. Chemokines
  • 6.5. Chemokines in Liver Disease and HCV Infection
  • 7. Conclusions
  • Acknowledgment
  • References
  • Chapter Five: Assessment of DNA Integrity, Applications for Cancer Research
  • 1. Introduction
  • 2. Methods for Assessing DNA Integrity
  • 2.1. DNA Quantification/Qualification Methods
  • 2.2. DNA Integrity Analysis Methods
  • 3. Applications of DNA Integrity Assessment
  • 3.1. Determination of Biological Samples Quality
  • 3.2. DNA Integrity Assessment for Circulating Tumor DNA Detection
  • 3.2.1. Circulating Cell-Free DNA Analysis as a Cancer Biomarker
  • 3.2.2. Circulating Tumor DNA Can Be Detected from Liquid Biopsy
  • 3.2.3. Origin of Circulating Cell-Free DNA
  • 3.2.4. Size Profiling of Circulating DNA Fragments Originating from Normal or Tumor Cells
  • 3.2.5. DNA Integrity Index as a Cancer Biomarker
  • 4. Conclusions
  • Acknowledgments
  • References
  • Chapter Six: Current Progress in Sports Genomics
  • 1. Introduction
  • 2. Gene Variants for Endurance Athlete Status
  • 2.1. ACE I Allele
  • 2.2. ADRA2A 6.7-kb, ADRB1 49Gly, ADRB2 16Arg, and ADRB3 64Arg Alleles
  • 2.3. AGTR2 rs11091046 C Allele
  • 2.4. AQP1 rs1049305 C Allele
  • 2.5. AMPD1 Gln12 Allele
  • 2.6. BDKRB2 -9 and rs1799722 T Alleles
  • 2.7. Calcineurin/NFAT-Related Genetic Markers (NFATC4 Gly160, PPP3CA rs3804358 C, PPP3CB rs3763679 C, and PPP3R1 5I Alleles)
  • 2.8. CKM rs8111989 A Allele
  • 2.9. Collagen-Related Genetic Markers (COL5A1 rs12722 T, COL5A1 rs71746744 AGGG, and COL6A1 rs35796750 T Alleles)
  • 2.10. EPAS1 rs1867785 G and rs11689011 T Alleles
  • 2.11. GABPB1 rs12594956 A, rs8031031 T, and rs7181866 G Alleles
  • 2.12. GNB3 rs5443 T Allele
  • 2.13. HFE 63Asp Allele
  • 2.14. HIF1A Pro582 Allele
  • 2.15. IL15RA rs2228059 A
  • 2.16. KCNJ11 Glu23 Allele
  • 2.17. MCT1 (SLC16A1) rs1049434 A Allele
  • 2.18. mtDNA Markers
  • 2.19. NFIA-AS2 rs157231 Allele
  • 2.20. NOS3 Glu298, 164-bp, 4B and rs2070744 T Alleles
  • 2.21. PPARA rs4253778 G Allele
  • 2.22. PPARD rs2016520 C and rs1053049 T Alleles
  • 2.23. PPARGC1A Gly482 and rs4697425 A Alleles
  • 2.24. PPARGC1B 203Pro and 292Ser Alleles
  • 2.25. RBFOX1 rs7191721 G Allele
  • 2.26. SLC2A4 rs5418 A Allele
  • 2.27. TFAM 12Thr Allele
  • 2.28. TSHR rs7144481 C Allele
  • 2.29. UCP2 55Val Allele
  • 2.30. UCP3 rs1800849 T Allele
  • 2.31. VEGFA rs2010963 C Allele
  • 2.32. VEGFR2 472Gln Allele
  • 2.33. Y-Chromosomal Haplogroups
  • 3. Gene Variants for Power/Strength Athlete Status
  • 3.1. ACE D Allele
  • 3.2. ACTN3 Arg577 Allele
  • 3.3. AGT 235Thr Allele
  • 3.4. AMPD1 Gln12 Allele
  • 3.5. CREM rs1531550 A Allele
  • 3.6. DMD rs939787 T Allele
  • 3.7. Folate Pathway Genetic Markers (MTHFR rs1801131 C, MTR rs1805087 G, and MTRR rs1801394 G Alleles)
  • 3.8. GALNT13 rs10196189 G Allele
  • 3.9. HIF1A 582Ser Allele
  • 3.10. IGF1 rs35767 T and IGF1R rs1464430 C Alleles
  • 3.11. IL1RN*2 Allele
  • 3.12. IL6 rs1800795 G Allele
  • 3.13. NOS3 rs2070744 T Allele
  • 3.14. PPARA rs4253778 C Allele
  • 3.15. PPARG 12Ala Allele
  • 3.16. SOD2 Ala16 Allele
  • 3.17. VDR rs10735810 T Allele
  • 4. Conclusion
  • References
  • Index
Chapter Two

Plasma/Serum Plasmalogens


Methods of Analysis and Clinical Significance


Ryouta Maeba*,1; Megumi Nishimukai,; Shin-ichi Sakasegawa; Daisuke Sugimori§; Hiroshi Hara    * Department of Biochemistry, Teikyo University School of Medicine, Itabashi-ku, Tokyo, Japan
┼ Department of Animal Science, Iwate University, Morioka, Iwate, Japan
╬ Asahi Kasei Pharma Corporation, Shizuoka, Japan
§ Department of Symbiotic Systems Science and Technology, Graduate School of Symbiotic Systems Science and Technology, Fukushima University, Fukushima, Japan
¶ Division of Applied Bioscience, Hokkaido University, Sapporo, Hokkaido, Japan
1 Corresponding author: email address: maeba@med.teikyo-u.ac.jp

Abstract


Age-related diseases, such as atherosclerosis and dementia, are associated with oxidative stress and chronic inflammation. Peroxisome dysfunction may be related to aging and age-related pathologies, possibly through the derangement of redox homeostasis. The biosyntheses of plasmalogens (Pls), a subclass of glycerophospholipids, are primarily regulated by peroxisomes. Thus, plasma Pls may reflect the systemic functional activity of peroxisomes and serve as potential biomarkers for diseases related to oxidative stress and aging.

Recently, we have established three promising analytical methods for plasma/serum Pls using high-performance liquid chromatography with radioactive iodine, liquid chromatography-tandem mass spectrometry, and enzymatic assay. These methods were validated and used to obtain detailed molecular information regarding these molecules. In cross-sectional studies on asymptomatic, coronary artery disease, and elderly dementia individuals, we found that serum choline Pls, particularly those containing oleic and linoleic acid in the sn-2 position of the glycerol backbone, may serve as reliable antiatherogenic biomarkers. Furthermore, we also found that serum ethanolamine Pls were effective in discriminating cognitive impairment. These results support our hypothesis and further studies are clearly needed to elucidate Pls pathophysiologic significance.

Keywords

Plasmalogen

Ether glycerophospholipid

Peroxisome

Biomarker

Atherosclerosis

Coronary artery disease

Dementia

Liquid chromatography-tandem mass spectrometry

Abbreviations

125I-HPLC high-performance liquid chromatography with 125I

AD Alzheimer's disease

CD cognitive decline

Egp ether glycerophospholipid

LC-MS/MS liquid chromatography-tandem mass spectrometry

LyPlsase lysoplasmalogenase

LyPlsCho choline lysoplasmalogen

LyPlsEtn ethanolamine lysoplasmalogen

MeOH methanol

Pak alkyl glycerophospholipid

PakCho choline alkyl glycerophospholipid

PakEtn ethanolamine alkyl glycerophospholipid

Pls plasmalogen

PlsCho choline plasmalogen

PlsEtn ethanolamine plasmalogen

1 Introduction


Emerging pathologic evidence indicates that oxidative stress and chronic inflammation are involved in major age-related diseases such as atherosclerosis, dementia, and cardiovascular disease. Changes in redox status that occur during aging may be the major risk factor for age-related inflammation [1].

Peroxisomes are essential organelles in higher eukaryotes for redox homeostasis and other metabolic functions. Cumulative evidence suggests that peroxisomes function as potential regulators of oxidative stress-related signaling pathways [2]. These findings suggest that peroxisome dysfunction is not only associated with rare peroxisomal disorders but also with more common age-related diseases related to oxidative stress.

Many studies have attempted to identify specific biomarkers that could aid diagnosis or predict treatment response of age-related diseases. However, it has been difficult to consistently define and specifically identify biomarkers directly linked to aging and age-related disease [3].

Here, we describe analysis and clinical utility of plasma/serum plasmalogens (Pls) as a potential oxidative stress markers associated with peroxisome function.

2 Plasmalogens


Glycerophospholipids are classified into the three subclasses, i.e., diacyl, alkyl, and alkenyl types, by the aliphatic hydrocarbon chain at the sn-1 position of the glycerol backbone, via ester, ether, and vinyl-ether (COCCR) binding, respectively. The diacyl type is a predominant subclass of glycerophospholipids. The alkyl and alkenyl types are collectively called ether glycerophospholipids (Egps), whereas the alkenyl type is a specific Pl.

Based on the polar head groups at the sn-3 position, Pls are mainly classified into either choline plasmalogen (PlsCho) or ethanolamine Pls (PlsEtn) (Fig. 1). The former is found in cardiac muscle and plasma, whereas the latter belongs to a predominant class distributed in a wide variety of cells and tissues. [4]

Figure 1 Chemical structure of plasmalogens. The figure was originally published in Ref. [5].

2.1 Biosynthesis, Function, and Pathophysiology


Peroxisomes are essential regulatory organelles for Pls biosynthesis, i.e., the first two steps of Pls biosynthesis occurs exclusively in peroxisomes [6]. In addition, the rate-limiting enzyme; fatty acyl CoA reductase 1 (Far 1) is peroxisomal [7-9] (Fig. 2). Alkyl glycerophospholipids (Paks) are precursors of Pls. PlsEtn is synthesized from ethanolamine Paks (1-alkyl-2-acyl-GPE), whereas PlsCho appears derived from PlsEtn, but not choline Paks (1-alkyl-2-acyl-GPC) [10, 11]. Its exact biosynthetic route, however, remains unclear.

Figure 2 Biosynthetic pathways of plasmalogens. The figure was slightly modified and originally published in Ref. [5].

The pathophysiologic roles of Pls are poorly understood. Some patients with peroxisomal disorders exhibit systemic reduction of Pls and various pathologic conditions including severe mental retardation, hypotonicity, adrenal dysfunction, cataracts, deafness, facial dysmorphism, chondrodysplasia, and failure to thrive [12]. Pl knockout mice also exhibit similar phenotypes, particularly central nervous system dysfunction [13]. Pls are abundant in the brain and play essential roles in neuronal function and myelin formation [14]. Defects in Pls are associated with a number of neurodegenerative disorders including Alzheimer's disease (AD) [15]. In addition, Pls appear to modulate membrane dynamics resulting in nonbilayer structures [16] and membrane fusion [17]. These characteristic features of Pls in modulating biomembranes may be relevant to manifestation of diverse pathophysiology.

Recently, particular attention has been paid to the involvement of Pls in metabolic diseases associated with oxidative stress and chronic inflammation [18, 19]. Studies have postulated that Pls serve as endogenous antioxidants and protect membrane lipids and lipoprotein particles from excessive oxidation by scavenging reactive oxygen species via their vinyl-ether moiety [20-22]. Furthermore, Pls function as reservoirs for precursor fatty acids, such as arachidonic and docosahexaenoic acid (DHA), which generate bioactive lipid mediators related to inflammation.

2.2 Plasma/Serum Pls


Human plasma/serum Pls are synthesized in the liver, intestine, and kidney and secreted into the blood as lipoprotein components [23, 24]. They are distributed almost equally in all lipoprotein fractions [25]. The concentration of plasma/serum Pls is 100-300 µmol/L with PlsCho/PlsEtn ratio in the range of 0.5-1.5, a ratio corresponding to ~ 5% PlsCho in choline phospholipids and 50-60% PlsEtn in ethanolamine phospholipids.

Plasma Pls concentration may reflect systemic peroxisomal activity which regulate Pls biosynthesis, redox status and are influenced by aging [26]. Thus, we hypothesize that plasma/serum Pls are a potential biomarker for diseases related to oxidative stress and aging, such as atherosclerosis and AD (Fig. 3).

Figure 3 Plasmalogen, aging, and oxidative stress. The figure was originally published in Ref. [5].

3 Analytical Methods


Several methods for determining Pls have been reported thus far. These have recently been improved for use with smaller specimen volume increased sensitivity in order to obtain more detailed information regarding these unique molecular species. An important aspect of these analytical methods for Pls is related to acid lability of their...

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