Ferredoxins are proteins found in all biological kingdoms and are involved in essential biological processes including photosynthesis, lipid metabolism, and biogeochemical cycles. Ferredoxins are classified into different groups based on the iron-sulfur (Fe-S) clusters that they contain. A new subtype classification and nomenclature system, based on the spacing between amino acids in the Fe-S binding motif, has been proposed in order to better understand ferredoxins' biological diversity and evolutionary linkage across different organisms. This new classification system has revealed an unparalleled diversity between ferredoxins and has helped identify evolutionarily linked ferredoxins between species. The current review provides the latest insights into ferredoxin functions and evolution, and the new subtype classification, outlining their potential biotechnological applications and the future challenges in streamlining the process.
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http://dx.doi.org/10.3390/cimb46090574 | DOI Listing |
Gastric Cancer
January 2025
Department of Biochemistry and Molecular Biology, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
Background: Gastroesophageal junction adenocarcinoma (GEJAC) exhibits distinct molecular characteristics due to its unique anatomical location. We sought to investigate effective and reliable molecular classification of GEJAC to guide personalized treatment.
Methods: We analyzed the whole genomic, transcriptomic, T-cell receptor repertoires, and immunohistochemical data in 92 GEJAC patients and delineated the landscape of genetic and immune alterations.
J Epidemiol Glob Health
January 2025
Department of Internal Medicine, National Taiwan University Hospital and College of Medicine, National Taiwan University, No.7, Chung Shan S. Rd., Zhongzheng District, Taipei City, 100225, Taiwan.
Background: Lipids are known to be involved in carcinogenesis, but the associations between lipid profiles and different lung cancer histological classifications remain unknown.
Methods: Individuals who participated in national adult health surveillance from 2012 to 2018 were included. For patients who developed lung cancer during follow-up, a 1:2 control group of nonlung cancer participants was selected after matching.
Front Immunol
January 2025
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Background: Damage-associated molecular patterns (DAMPs) induced by immunogenic cell death (ICD) may be useful for the immunotherapy to patients undergoing pancreatic ductal adenocarcinoma (PDAC). The aim of this study is to predict the prognosis and immunotherapy responsiveness of PDAC patients using DAMPs-related genes.
Methods: K-means analysis was used to identify the DAMPs-related subtypes of 175 PDAC cases.
BMC Cancer
January 2025
Department of Urology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Background: To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.
Materials And Methods: The model was developed using computed tomography (CT) images of pathologically proven renal tumors collected from a prospective cohort at a medical center between March 2016 and December 2020. A total of 561 renal tumors were included: 233 clear cell renal cell carcinomas (RCCs), 82 papillary RCCs, 74 chromophobe RCCs, and 172 angiomyolipomas.
Nat Cancer
January 2025
Dept. of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.
The diagnostic landscape of brain tumors integrates comprehensive molecular markers alongside traditional histopathological evaluation. DNA methylation and next-generation sequencing (NGS) have become a cornerstone in central nervous system (CNS) tumor classification. A limiting requirement for NGS and methylation profiling is sufficient DNA quality and quantity, which restrict its feasibility.
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