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Institute
Antigen presentation to cytotoxic T lymphocytes via major histocompatibility complex class I (MHC I) molecules depends on the heterodimeric transporter associated with antigen processing (TAP). For efficient antigen supply to MHC I molecules in the ER, TAP assembles a macromolecular peptide-loading complex (PLC) by recruiting tapasin. In evolution, TAP appeared together with effector cells of adaptive immunity at the transition from jawless to jawed vertebrates and diversified further within the jawed vertebrates. Here, we compared TAP function and interaction with tapasin of a range of species within two classes of jawed vertebrates. We found that avian and mammalian TAP1 and TAP2 form heterodimeric complexes across taxa. Moreover, the extra N-terminal domain TMD0 of mammalian TAP1 and TAP2 as well as avian TAP2 recruits tapasin. Strikingly, however, only TAP1 and TAP2 from the same taxon can form a functional heterodimeric translocation complex. These data demonstrate that the dimerization interface between TAP1 and TAP2 and the tapasin docking sites for PLC assembly are conserved in evolution, whereas elements of antigen translocation diverged later in evolution and are thus taxon specific.
Sphingosine 1-phosphate (S1P) is a lipid mediator with numerous biological functions. The term ‘S1P’ mainly refers to the sphingolipid molecule with a long-chain sphingoid base of 18 carbon atoms, d18:1 S1P. The enzyme serine palmitoyltransferase catalyses the first step of the sphingolipid de novo synthesis using palmitoyl-CoA as the main substrate. After further reaction steps, d18:1 S1P is generated. However, also stearyl-CoA or myristoyl-CoA can be utilised by the serine palmitoyltransferase, which at the end of the S1P synthesis pathway, results in the production of d20:1 S1P and d16:1 S1P respectively. We measured these S1P homologues in mice and renal tissue of patients suffering from renal cell carcinoma (RCC). Our experiments highlight the relevance of d16:1 S1P for the induction of connective tissue growth factor (CTGF) in the human renal clear cell carcinoma cell line A498 and human RCC tissue. We show that d16:1 S1P versus d18:1 and d20:1 S1P leads to the highest CTGF induction in A498 cells via S1P2 signalling and that both d16:1 S1P and CTGF levels are elevated in RCC compared to adjacent healthy tissue. Our data indicate that d16:1 S1P modulates conventional S1P signalling by acting as a more potent agonist at the S1P2 receptor than d18:1 S1P. We suggest that elevated plasma levels of d16:1 S1P might play a pro-carcinogenic role in the development of RCC via CTGF induction.
Background: Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efcient diagnostic algorithms.
Methods: Retrospectively, 106 prostate tissue samples from 48 patients (mean age,
66 ± 6.6 years) were included in the study. Patients sufered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms.
Results: Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and
ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of 0.93 ± 0.04, 0.91 ± 0.06, and 0.92 ± 0.05, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4.
Conclusions: Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classifcation. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classifcation methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine.