The Peptidoglycan-Binding Protein SjcF1 Influences Septal Junction Function and Channel Formation in the Filamentous Cyanobacterium Anabaena

Filamentous, heterocyst-forming cyanobacteria exchange nutrients and regulators between cells for diazotrophic growth. Two alternative modes of exchange have been discussed involving transport either through the periplasm or through septal junctions linking adjacent cells. Septal junctions and channels in the septal peptidoglycan are likely filled with septal junction complexes. While possible proteinaceous factors involved in septal junction formation, SepJ (FraG), FraC, and FraD, have been identified, little is known about peptidoglycan channel formation and septal junction complex anchoring to the peptidoglycan. We describe a factor, SjcF1, involved in regulation of septal junction channel formation in the heterocyst-forming cyanobacterium Anabaena sp. strain PCC 7120. SjcF1 interacts with the peptidoglycan layer through two peptidoglycan-binding domains and is localized throughout the cell periphery but at higher levels in the intercellular septa. A strain with an insertion in sjcF1 was not affected in peptidoglycan synthesis but showed an altered morphology of the septal peptidoglycan channels, which were significantly wider in the mutant than in the wild type. The mutant was impaired in intercellular exchange of a fluorescent probe to a similar extent as a sepJ deletion mutant. SjcF1 additionally bears an SH3 domain for protein-protein interactions. SH3 binding domains were identified in SepJ and FraC, and evidence for interaction of SjcF1 with both SepJ and FraC was obtained. SjcF1 represents a novel protein involved in structuring the peptidoglycan layer, which links peptidoglycan channel formation to septal junction complex function in multicellular cyanobacteria. Nonetheless, based on its subcellular distribution, this might not be the only function of SjcF1.Peer reviewe

CT-radiomics and clinical risk scores for response and overall survival prognostication in TACE HCC patients

Abstract We aimed to identify hepatocellular carcinoma (HCC) patients who will respond to repetitive transarterial chemoembolization (TACE) to improve the treatment algorithm. Retrospectively, 61 patients (mean age, 65.3 years ± 10.0 [SD]; 49 men) with 94 HCC mRECIST target-lesions who had three consecutive TACE between 01/2012 and 01/2020 were included. Robust and non-redundant radiomics features were extracted from the 24 h post-embolization CT. Five different clinical TACE-scores were assessed. Seven different feature selection methods and machine learning models were used. Radiomics, clinical and combined models were built to predict response to TACE on a lesion-wise and patient-wise level as well as its impact on overall-survival prognostication. 29 target-lesions of 19 patients were evaluated in the test set. Response rates were 37.9% (11/29) on the lesion-level and 42.1% (8/19) on the patient-level. Radiomics top lesion-wise response prognostications was AUC 0.55–0.67. Clinical scores revealed top AUCs of 0.65–0.69. The best working model combined the radiomic feature LargeDependenceHighGrayLevelEmphasis and the clinical score mHAP_II_score_group with AUC = 0.70, accuracy = 0.72. We transferred this model on a patient-level to achieve AUC = 0.62, CI = 0.41–0.83. The two radiomics-clinical features revealed overall-survival prognostication of C-index = 0.67. In conclusion, a random forest model using the radiomic feature LargeDependenceHighGrayLevelEmphasis and the clinical mHAP-II-score-group seems promising for TACE response prognostication