Computationally Guided Engineering of Cell-Selective Cytokines
Computationally Guided Engineering of Cell-Selective Cytokines
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151507
- ISBN
- 9798382728643
- DDC
- 610
- 서명/저자
- Computationally Guided Engineering of Cell-Selective Cytokines
- 발행사항
- [Sl] : University of California, Los Angeles, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 151 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
- 주기사항
- Advisor: Meyer, Aaron S.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2024.
- 초록/해제
- 요약Cytokine signaling a core mechanism by which immune activity is regulated in both health and disease. Cytokine-mediated signaling regulates the proliferation, differentiation, and activity of cells in both the innate and adaptive immune systems. Due to their powerful regulatory capacity, cytokines have been leveraged as immunotherapies in a wide range of disease indications; for example, interleukin-2 (IL-2) has been explored as a potential immunostimulant for the treatment for cancer, as well as an immunosuppressant for the treatment of autoimmune diseases. However, in many such cases, the pleiotropic nature of cytokine signaling has stymied the development of efficacious and safe therapies due to the induction of signaling in off-target populations. To overcome this limitation and bias cytokines towards signaling in target populations, engineered cytokines with a variety of alterations, such as mutations affecting their binding interactions with their cognate receptors, fusion to antibody fragments, or co-formulation with antibodies to that cytokine have been developed. However, without a quantitative model of signaling the effects of such mutations and alterations are often difficult to anticipate, leading to inefficient cytokine engineering efforts. To address this lack of quantitative understanding, we conducted a battery of computational studies. First, using a mechanistic binding model, we developed a general, quantitative understanding of the landscape of cell-selective cytokine signaling, and found that affinity, valency, and multi-specificity must be simultaneously optimized to engineer optimally selective cytokines. We then specifically studied the IL-2 signaling pathway, and used both ordinary differential equation models and our mechanistic binding model to study the signaling characteristics of wild-type and engineered IL-2 mutants. Leveraging our newfound quantitative of how affinity and valency interact to determine a cytokine's selectivity profile both generally and in the specific context of IL-2, we developed affinity-optimized tetravalent IL-2 mutants with superior regulatory cell selectivity. Using these models of IL-2 signaling, we also elucidated the mechanism by which engineered antibody-IL-2 fusions induced regulatory cell-selective signaling and conferred protection against autoimmunity. In total, this body of work demonstrates the critical role that computational modeling plays in potentiating the engineering of superior cytokine-based immunotherapies.
- 일반주제명
- Biomedical engineering
- 일반주제명
- Bioinformatics
- 일반주제명
- Bioengineering
- 일반주제명
- Immunology
- 키워드
- Cytokines
- 키워드
- Interleukin-2
- 키워드
- Systems biology
- 키워드
- Immunotherapy
- 기타저자
- University of California, Los Angeles Bioengineering 0288
- 기본자료저록
- Dissertations Abstracts International. 85-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211151507
■006m o d
■007cr#unu||||||||
■020 ▼a9798382728643
■035 ▼a(MiAaPQ)AAI31299175
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a610
■1001 ▼aOrcutt-Jahns, Brian Thomas.
■24510▼aComputationally Guided Engineering of Cell-Selective Cytokines
■260 ▼a[Sl]▼bUniversity of California, Los Angeles▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a151 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-11, Section: B.
■500 ▼aAdvisor: Meyer, Aaron S.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2024.
■520 ▼aCytokine signaling a core mechanism by which immune activity is regulated in both health and disease. Cytokine-mediated signaling regulates the proliferation, differentiation, and activity of cells in both the innate and adaptive immune systems. Due to their powerful regulatory capacity, cytokines have been leveraged as immunotherapies in a wide range of disease indications; for example, interleukin-2 (IL-2) has been explored as a potential immunostimulant for the treatment for cancer, as well as an immunosuppressant for the treatment of autoimmune diseases. However, in many such cases, the pleiotropic nature of cytokine signaling has stymied the development of efficacious and safe therapies due to the induction of signaling in off-target populations. To overcome this limitation and bias cytokines towards signaling in target populations, engineered cytokines with a variety of alterations, such as mutations affecting their binding interactions with their cognate receptors, fusion to antibody fragments, or co-formulation with antibodies to that cytokine have been developed. However, without a quantitative model of signaling the effects of such mutations and alterations are often difficult to anticipate, leading to inefficient cytokine engineering efforts. To address this lack of quantitative understanding, we conducted a battery of computational studies. First, using a mechanistic binding model, we developed a general, quantitative understanding of the landscape of cell-selective cytokine signaling, and found that affinity, valency, and multi-specificity must be simultaneously optimized to engineer optimally selective cytokines. We then specifically studied the IL-2 signaling pathway, and used both ordinary differential equation models and our mechanistic binding model to study the signaling characteristics of wild-type and engineered IL-2 mutants. Leveraging our newfound quantitative of how affinity and valency interact to determine a cytokine's selectivity profile both generally and in the specific context of IL-2, we developed affinity-optimized tetravalent IL-2 mutants with superior regulatory cell selectivity. Using these models of IL-2 signaling, we also elucidated the mechanism by which engineered antibody-IL-2 fusions induced regulatory cell-selective signaling and conferred protection against autoimmunity. In total, this body of work demonstrates the critical role that computational modeling plays in potentiating the engineering of superior cytokine-based immunotherapies.
■590 ▼aSchool code: 0031.
■650 4▼aBiomedical engineering
■650 4▼aBioinformatics
■650 4▼aBioengineering
■650 4▼aImmunology
■653 ▼aCytokines
■653 ▼aImmune suppression
■653 ▼aInterleukin-2
■653 ▼aProtein engineering
■653 ▼aSystems biology
■653 ▼aImmunotherapy
■690 ▼a0541
■690 ▼a0715
■690 ▼a0202
■690 ▼a0982
■71020▼aUniversity of California, Los Angeles▼bBioengineering 0288.
■7730 ▼tDissertations Abstracts International▼g85-11B.
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161956▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
