Vo, M. | Search bioRxiv

bioRxiv2024

The structure of the SufS-SufE complex reveals interactions driving protected persulfide transfer in iron-sulfur cluster biogenesis

Fe-S clusters are critical cofactors for redox chemistry in all organisms. The cysteine desulfurase, SufS, provides sulfur in the SUF Fe-S cluster bioassembly pathway. SufS is a dimeric, PLP-dependent enzyme that uses cysteine as a substrate to generate alanine and a covalent persulfide on an active site cysteine residue. SufS enzymes are activated by an accessory transpersulfurase protein, either SufE or SufU depending on the organism, which accepts the persulfide product and delivers it to downstream partners for Fe-S assembly. Here, using E. coli proteins, we present the first X-ray crystal structure of a SufS/SufE complex. There is a 1:1 stoichiometry with each monomeric unit of the EcSufS dimer bound to one EcSufE subunit, though one EcSufE is rotated [~]7{degrees} closer to the EcSufS active site. EcSufE makes clear interactions with the 16 helix of EcSufS and site-directed mutants of several 16 residues were deficient in EcSufE binding. Analysis of the EcSufE structure showed a loss of electron density at the EcSufS/EcSufE interface for a flexible loop containing the highly conserved residue R119. An R119A EcSufE variant binds EcSufS but is not active in cysteine desulfurase assays and fails to support Fe-S cluster bioassembly in vivo. 35S-transfer assays suggest that R119A EcSufE can receive a persulfide, suggesting the residue may function in a release mechanism. The structure of the EcSufS/EcSufE complex allows for comparison with other cysteine desulfurases to understand mechanisms of protected persulfide transfer across protein interfaces.

biochemistry↗

bioRxiv2021

JAMMIT Analysis Defines 2 Semi-Independent Immune Processes Common to 29 Solid Tumors

Immunophenotype of solid tumors has relevance to cancer immunotherapy, as not all patients respond optimally to treatment utilizing monoclonal antibodies. Bioinformatic studies have failed to clearly identify tumor immunophenotype in a way that encompasses a wide variety of tumor types and highlights fundamental differences among them, complicating prediction of patient clinical response. The novel JAMMIT algorithm was used to analyze mRNA data for 33 cancer types in The Cancer Genome Atlas (TCGA). We found that B cells and T cells constitute the principal source of variation in most patient cohorts, and that virtually all solid malignancies formed three hierarchical clustering patterns with similar molecular features. The second main source of variability in transcriptomic studies we attribute to monocytes. We identified the three tumor types as TC1-mediated, TC17-mediated and non-immunogenic immunophenotypes and used a 3-gene signature to approximate infiltration by agranulocytes. Methods of in silico validation such as pathway analysis, Cibersort and published data from treated cohorts were used to substantiate these findings. Monocytic infiltrate is found to be related to patient survival according to immunophenotype, important differences in some solid tumors are identified and deficiencies of common bioinformatic approaches relevant to diagnosis are detailed by this work.

cancer biology↗