인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Dynamic environments require cyanobacteria to rapidly respond to fluctuating light conditions on timescales faster than transcription-translation processes allow, which is possible through immediate regulation of protein function via molecular and conformational adjustments. Traditional abundance-based proteomics cannot capture these rapid structural changes, creating a critical gap in understanding cellular adaptation mechanisms. We hypothesized that application of alternative structural proteomics approaches would enable identification of immediate and extensive structural remodeling across the cyanobacterial proteome triggered by environmental perturbations, potentially driving functional adaptations invisible to conventional abundance-based methods. We interrogated three complementary techniques-limited proteolysis mass spectrometry (LiP-MS), thermal proteome profiling (TPP-MS), and redox proteomics-for their capacity to unveil structural reorganization within the model cyanobacterium Synechococcus elongatus PCC 7942 during physiologically relevant light transitions. Within 30 minutes of increased light exposure, we detected structural changes in 753 proteins (LiP-MS), thermal stability shifts in 600 proteins (TPP-MS), and cysteine oxidation in 1,887 sites, while only 145 proteins changed in abundance. All three techniques consistently revealed coordinated remodeling of photosynthetic machinery, ribosomal complexes, and carbon metabolism, exemplified by cytochrome f stabilization modulating electron transport efficiency. Remarkably, <10% of proteins overlapped between methods, demonstrating that each technique captures distinct molecular dimensions of environmental adaptation. This structural proteomics framework demonstrates how alternative techniques can reveal hidden facets of proteome dynamics underlying cellular processes, offering new methodological approaches for understanding environmental responses and informing biotechnological applications.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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