Metabolomics for Personalized Vaccinology.

Format:

Book

Author/Creator:

Rahman, Mahbuba.

Series:

Developments in Applied Microbiology and Biotechnology Series

Language:

English

Subjects (All):

Precision medicine.

Immunology.

Physical Description:

1 online resource (400 pages)

Edition:

1st ed.

Place of Publication:

San Diego : Elsevier Science & Technology, 2024.

Summary:

This book, 'Metabolomics for Personalized Vaccinology,' edited by Mahbuba Rahman, explores the intersection of metabolomics and vaccine development, focusing on personalized approaches to vaccinology. It delves into the limitations of traditional vaccinology and how advancements in big data and metabolomics can enhance the precision of immunization strategies. The text covers various aspects of personalized vaccinology, including genetic polymorphism, immune response, and the role of metabolomics in cancer and autoimmune diseases. It also discusses novel vaccine technologies and the integration of metabolomics in understanding immune responses. Aimed at researchers and practitioners in the field of biotechnology and immunology, the book provides insights into the current status and future directions of personalized vaccine development. Generated by AI.

Contents:

Intro

Metabolomics for Personalized Vaccinology

Copyright

Contents

Contributors

Preface

Chapter 1: Personalized vaccinology

1. Introduction

1.1. Vaccinology and vaccines: Back in time

1.2. Traditional vaccinology

2. Factors of immunological response to vaccines

2.1. Age

2.2. Gender

2.3. Obesity

3. Limitations of current vaccinology

3.1. Inadequate knowledge of immunity development

3.2. Host and pathogen variability

3.3. Physical and psychological factors

4. Technical advances and big data impact on vaccinology

5. Personalized vaccinology

5.1. Precision immunization

6. Important factors, salient features, and benefits of personalized vaccinology

6.1. Genetic polymorphism and immune response

6.2. Reverse vaccinology for personalized markers

7. Current status of personalized vaccinology

7.1. Designing personalized vaccines

7.1.1. Biomimetic nanotechnology to develop personalized vaccinations

7.1.2. Biomimetic antibacterial nanovaccines

7.1.3. Biomimetic anticancer nanovaccines

7.2. Personalized vaccines for different disease types

7.2.1. Personalized vaccines for autoimmune diseases

7.2.2. Personalized vaccines for allergy

7.3. Personalized cancer vaccines targeting the cancer mutanome (neoantigens)

8. Benefits of personalized vaccinology

9. Challenges

10. Conclusion

References

Chapter 2: Formulations and mechanisms of action of novel vaccine adjuvants

1.1. History of vaccination

1.2. Vaccine adjuvants

2. Licensed adjuvants

3. Novel adjuvants formulation patents

3.1. Amino acids and peptides

3.1.1. Cationic amino acids

3.1.2. Synthetic lipopeptides

3.1.3. Acylated cationic peptides

3.1.4. Nematode polypeptides

3.1.5. Host-derived antimicrobial peptide

3.1.6. Gastrointestinal peptides.

3.1.7. Neutralizing polypeptides

3.2. Nucleotides and nucleic acids

3.2.1. Adjuvanted genetic vaccines

3.2.2. Influenza nuclear protein DNA

3.2.3. Immunostimulatory oligonucleotides

3.2.4. Guanosine tetra and pentaphosphate (pppGpp)

3.2.5. dsRNA and poly-ICLC

3.2.6. CpG oligonucleotides

3.2.7. CpG-VLPs

3.3. Bacterial and viral protein components

3.3.1. EBV envelop glycoprotein

3.3.2. Klebsiella pneumoniae P40 membrane protein

3.3.3. Hybrid and mutant heat labile holotoxins

3.3.4. Pertussis toxin (PTX) mutant

3.3.5. Modified pre-S protein of HBV

3.3.6. Flagellin peptide fragments

3.4. Ligands for cell surface receptors

3.4.1. MHC class II ligand, LAG-3

3.4.2. C5a, CD21, and CD64 receptor ligands

3.4.3. Flt3-ligand (Flt3-L)

3.4.4. Hyaluronic acid fragments

3.4.5. TLR ligands

3.4.6. TLR4 ligands

3.5. Cytokines and chemokines

3.5.1. Interleukin 12 (IL-12)

3.5.2. GM-CSF

3.5.3. Interleukin 15 (IL-15)

3.5.4. Chemokines

3.5.5. Nerve growth factor (NGF)

3.6. Polymers

3.6.1. Polyoxyethylene/polyoxypropylene block polymers

3.6.2. Lecithin and polyacrylic acid polymer

3.6.3. Hemicellulose

3.6.4. Nanoparticles based on PVM/MA

3.6.5. Water-in-oil emulsions

3.6.6. Zwitterion-type detergent

3.7. Saponins and tensoactive compounds

3.7.1. Quillaja saponaria saponins

3.7.2. Ginseng saponins

3.7.3. Epigallocatechin

4. Novel vaccines technologies

4.1. Recombinant protein vaccines

4.2. Synthetic peptide vaccines

4.3. Nucleic acid vaccines

4.4. RNA vaccines

5. Vaccine adjuvants and metabolomics

6. Vaccine adjuvants for personalized vaccine design

7. Conclusion

Conflict of interest

Chapter 3: Technologies to measure vaccine immune response against infectious diseases

2. Types of immunization.

2.1. Vaccines against infections

2.1.1. Live-attenuated vaccines

2.1.2. Inactivated vaccines

2.1.3. Subunit vaccines

2.1.4. Toxoid vaccines

2.1.5. Viral vector vaccines

2.1.6. Messenger RNA (mRNA) vaccines

3. Immune responses to vaccines

3.1. Innate immune response

3.2. Adaptive immune response

3.3. Humoral immune response

4. Conventional technologies to detect vaccine response

4.1. Neutralizing antibody assay

4.2. Effector functions of antibodies

4.3. Enzyme-linked immunosorbent assay

5. Conventional high-throughput technologies to measure vaccine immune response

5.1. Enzyme-linked immunospot (ELISPOT)

5.2. Cytokine multiplex

5.3. Multiparametric flow cytometry

5.3.1. Activation-induced marks (AIM)

5.4. Time-of-flight mass cytometry-CyTOF

6. High-throughput technologies to detect vaccine immune response

6.1. Genomics

6.1.1. Next-generation sequencing (NGS)

6.2. Transcriptomics

6.2.1. Microarray

6.2.2. RNA-seq

6.2.3. Single-cell RNA sequencing

6.2.4. NanoString nCounter technology in immunoprofiling

6.3. Proteomics

6.3.1. Protein microarrays

6.3.2. Peptide microarrays

6.4. Metabolomics

7. Metabolomics of immune cells in response to vaccination

7.1. Metabolomics of T cells

7.2. Metabolomics of B cells

7.3. Metabolomics of B-cell and T-cell collaboration

8. Selected example of vaccine-induced metabolites

8.1. Metabolites in response to vaccine immunogenicity

8.1.1. Virus

8.1.2. Bacteria

8.2. Metabolites in response to adverse event following immunization (AEFI)

9. Current challenge and future direction

Chapter 4: A new frontier in cancer therapy: The intersection of cancer vaccines and metabolomics

2. Cancer statistics

2.1. Cancer rates

2.2. Cancer causes

2.3. Cancer prevention.

3. Cancer treatment

4. Cancer treatment modalities

4.1. Conventional cancer therapies

4.1.1. Chemotherapy

4.1.2. Radiotherapy

4.1.3. Surgery

4.2. Advanced cancer therapies

4.2.1. Hormonal therapies

4.2.2. Ablation cancer therapy

4.2.3. Cancer immunotherapy

5. Overview of vaccines

5.1. Types of vaccines

5.1.1. Live-attenuated vaccines

5.1.2. Inactivated vaccines

5.1.3. Subunit vaccines

5.1.4. Toxoid vaccines

5.1.5. Viral vector-based vaccines

5.1.6. Recombinant vector vaccines

5.1.7. Nucleic acid vaccines

5.1.7.1. Naked DNA vaccines

5.1.7.2. mRNA vaccines

6. Importance of vaccination

7. Cancer vaccines

7.1. Preventive vaccines

7.2. Therapeutic cancer vaccines

8. Cancer antigen targets

8.1. Tumor-associated antigens (TAAs)

8.2. Tumor-specific antigens (TSAs)

8.3. Cancer germline antigens (CGAs)

8.4. Virus-associated antigens

9. The tumor-immune cycle induced by cancer vaccines

10. Cancer vaccine platforms

10.1. Cell vaccines (tumor cell vaccines and dendritic cell vaccines)

10.2. Protein/peptide vaccines

10.3. Nucleic acid vaccines (DNA, RNA, and viral vectors)

11. Importance of adjuvants with cancer vaccines

12. Classification of adjuvants

12.1. Immunostimulatory adjuvants

12.1.1. Cytokines

12.1.2. Toll-like receptor ligands

12.1.3. Saponins

12.2. Delivery system adjuvants

12.2.1. Mineral salts

12.2.2. Emulsions

12.2.3. Liposomes

12.2.4. Virosomes

13. Barriers to vaccine therapy: Immune resistance

13.1. Tumor-intrinsic resistance

13.2. Tumor-extrinsic resistance

14. Combination approaches to increase cancer vaccine effectiveness

14.1. Combining several adjuvants to increase the scope of immune responses

14.2. Combining chemotherapy with cancer vaccines.

14.3. Improving patient lifestyle to increase vaccine potency

14.4. Combining cancer vaccines with other cancer treatments

15. Metabolic profiling for vaccine development

16. The current status of other cancer immunotherapies

16.1. Tumor-infiltrating lymphocyte (TIL) therapy

16.2. CAR-T-cell therapy

16.3. Oncolytic virotherapy (OV)

17. Summary and future perspectives

Acknowledgments

Chapter 5: Vaccines against autoimmune diseases

1.1. Etiology of autoimmune diseases

1.1.1. Gender

1.1.2. Genetics

1.1.3. Environmental factors

1.1.4. Lifestyle factors

1.1.5. Immune cells

1.1.6. Molecular mimicry

1.1.7. Adjuvant induced autoimmune diseases

1.2. Role of metabolomics in autoimmune diseases

1.2.1. Biomarker discovery

1.2.2. Diagnostic biomarkers

1.2.3. Metabolomics in pathophysiology

1.2.4. Differentiating subtypes and comorbidities

2. Rheumatoid arthritis

2.1. Role of metabolomics in RA

2.1.1. Amino acid metabolism

2.1.2. Lipid metabolism

2.1.3. Energy metabolism

2.2. Vaccines against RA

2.2.1. Heat shock protein (HSP) vaccines

2.2.2. Anticitrulline protein antibody (ACPAs)-Targeted vaccines

2.2.3. Peptide-based vaccines

3. Systemic lupus erythematosus

3.1. Etiology and pathophysiology of SLE

3.2. Metabolomics in SLE

3.2.1. Metabolomics and SLE disease activity

3.2.2. Metabolomics in lupus nephritis

3.2.3. Metabolomics and neuropsychiatric SLE

3.3. Treatment for SLE

3.4. Vaccines against SLE

3.4.1. CD4+ T-cell vaccination

3.4.2. Peptide-based therapeutic vaccines

3.4.3. Epitope-based vaccines

3.4.4. Peptide-MHC complexes

4. Psoriasis

4.1. Current treatment strategies for psoriasis

4.2. Vaccines against psoriasis

4.2.1. Mycobacterium vaccae vaccines.

4.2.2. Varicella zoster virus vaccines.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

9780443155277

OCLC:

1450106722