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Anaplastic large cell lymphoma (ALCL) and classical Hodgkin lymphoma (cHL) are lymphomas that contain CD30-expressing tumor cells and have numerous pathological similarities. Whereas ALCL is usually diagnosed at an advanced stage, cHL more frequently presents with localized disease. The aim of the present study was to elucidate the mechanisms underlying the different clinical presentation of ALCL and cHL. Chemokine and chemokine receptor expression were similar in primary ALCL and cHL cases apart from the known overexpression of the chemokines CCL17 and CCL22 in the Hodgkin and Reed-Sternberg (HRS) cells of cHL. Consistent with the overexpression of these chemokines, primary cHL cases encountered a significantly denser T cell microenvironment than ALCL. Additionally to differences in the interaction with their microenvironment, cHL cell lines presented a lower and less efficient intrinsic cell motility than ALCL cell lines, as assessed by time-lapse microscopy in a collagen gel and transwell migration assays. We thus propose that the combination of impaired basal cell motility and differences in the interaction with the microenvironment hamper the dissemination of HRS cells in cHL when compared with the tumor cells of ALCL.
Membranes are central for cells as borders to the environment or intracellular organelle definition. They are composed of and harbor different molecules like various lipid species and sterols, and they are generally crowded with proteins. The membrane system is very dynamic and components show lateral, rotational and translational diffusion. The consequence of the latter is that phase separation can occur in membranes in vivo and in vitro. It was documented that molecular dynamics simulations of an idealized plasma membrane model result in formation of membrane areas where either saturated lipids and cholesterol (liquid-ordered character, Lo) or unsaturated lipids (liquid-disordered character, Ld) were enriched. Furthermore, current discussions favor the idea that proteins are sorted into the liquid-disordered phase of model membranes, but experimental support for the behavior of isolated proteins in native membranes is sparse. To gain insight into the protein behavior we built a model of the red blood cell membrane with integrated glycophorin A dimer. The sorting and the dynamics of the dimer were subsequently explored by coarse-grained molecular dynamics simulations. In addition, we inspected the impact of lipid head groups and the presence of cholesterol within the membrane on the dynamics of the dimer within the membrane. We observed that cholesterol is important for the formation of membrane areas with Lo and Ld character. Moreover, it is an important factor for the reproduction of the dynamic behavior of the protein found in its native environment. The protein dimer was exclusively sorted into the domain of Ld character in the model red blood cell plasma membrane. Therefore, we present structural information on the glycophorin A dimer distribution in the plasma membrane in the absence of other factors like e.g. lipid anchors in a coarse grain resolution.
In gastric cancer (GC), there are four molecular subclasses that indicate whether patients respond to chemotherapy or immunotherapy, according to the TCGA. In clinical practice, however, not every patient undergoes molecular testing. Many laboratories have used well-implemented in situ techniques (IHC and EBER-ISH) to determine the subclasses in their cohorts. Although multiple stains are used, we show that a staining approach is unable to correctly discriminate all subclasses. As an alternative, we trained an ensemble convolutional neuronal network using bagging that can predict the molecular subclass directly from hematoxylin–eosin histology. We also identified patients with predicted intra-tumoral heterogeneity or with features from multiple subclasses, which challenges the postulated TCGA-based decision tree for GC subtyping. In the future, deep learning may enable targeted testing for molecular subtypes and targeted therapy for a broader group of GC patients. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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.
The hydrophobic thickness of membranes, which is manly defined by fatty acids, influences the packing of transmembrane domains of proteins and thus can modulate the activity of these proteins. We analyzed the dynamics of the dimerization of Glycophorin A (GpA) by molecular dynamics simulations to describe the fatty acid dependence of the transmembrane region assembly. GpA represents a well-established model for dimerization of single transmembrane helices containing a GxxxG motif in vitro and in silico. We performed simulations of the dynamics of the NMR-derived dimer as well as self-assembly simulations of monomers in membranes composed of different fatty acid chains and monitored the formed interfaces and their transitions. The observed dimeric interfaces, which also include the one known from NMR, are highly dynamic and converted into each other. The frequency of interface formation and the preferred transitions between interfaces similar to the interface observed by NMR analysis strongly depend on the fatty acid used to build the membrane. Molecular dynamic simulations after adaptation of the helix topology parameters to better represent NMR derived structures of single transmembrane helices yielded an enhanced occurrence of the interface determined by NMR in molecular dynamics simulations. Taken together we give insights into the influence of fatty acids and helix conformation on the dynamics of the transmembrane domain of GpA.
Mitochondria and chloroplasts are of endosymbiotic origin. Their integration into cells entailed the development of protein translocons, partially by recycling bacterial proteins. We demonstrate the evolutionary conservation of the translocon component Tic22 between cyanobacteria and chloroplasts. Tic22 in Anabaena sp. PCC 7120 is essential. The protein is localized in the thylakoids and in the periplasm and can be functionally replaced by a plant orthologue. Tic22 physically interacts with the outer envelope biogenesis factor Omp85 in vitro and in vivo, the latter exemplified by immunoprecipitation after chemical cross-linking. The physical interaction together with the phenotype of a tic22 mutant comparable with the one of the omp85 mutant indicates a concerted function of both proteins. The three-dimensional structure allows the definition of conserved hydrophobic pockets comparable with those of ClpS or BamB. The results presented suggest a function of Tic22 in outer membrane biogenesis.
Background: Although Tic22 is involved in protein import into chloroplasts, the function in cyanobacteria is unknown.
Results: Cyanobacterial Tic22 is required for OM biogenesis, shares structural features with chaperones, and can be substituted by plant Tic22.
Conclusion: Tic22, involved in outer membrane biogenesis, is functionally conserved in cyanobacteria and plants.
Significance: The findings are important for the understanding of periplasmic protein transport.
Classic Hodgkin lymphoma (cHL) is usually characterized by a low tumour cell content, derived from crippled germinal centre B cells. Rare cases have been described in which the tumour cells show clonal T-cell receptor rearrangements. From a clinicopathological perspective, it is unclear if these cases should be classified as cHL or anaplastic large T-cell lymphoma (ALCL). Since we recently observed differences in the motility of ALCL and cHL tumour cells, here, we aimed to obtain a better understanding of T-cell-derived cHL by investigating their global proteomic profiles and their motility. In a proteomics analysis, when only motility-associated proteins were regarded, T-cell-derived cHL cell lines showed the highest similarity to ALK− ALCL cell lines. In contrast, T-cell-derived cHL cell lines presented a very low overall motility, similar to that observed in conventional cHL. Whereas all ALCL cell lines, as well as T-cell-derived cHL, predominantly presented an amoeboid migration pattern with uropod at the rear, conventional cHL never presented with uropods. The migration of ALCL cell lines was strongly impaired upon application of different inhibitors. This effect was less pronounced in cHL cell lines and almost invisible in T-cell-derived cHL. In summary, our cell line-derived data suggest that based on proteomics and migration behaviour, T-cell-derived cHL is a neoplasm that shares features with both cHL and ALCL and is not an ALCL with low tumour cell content. Complementary clinical studies on this lymphoma are warranted.
For medicine to fulfill its promise of personalized treatments based on a better understanding of disease biology, computational and statistical tools must exist to analyze the increasing amount of patient data that becomes available. A particular challenge is that several types of data are being measured to cope with the complexity of the underlying systems, enhance predictive modeling and enrich molecular understanding.
Here we review a number of recent approaches that specialize in the analysis of multimodal data in the context of predictive biomedicine. We focus on methods that combine different OMIC measurements with image or genome variation data. Our overview shows the diversity of methods that address analysis challenges and reveals new avenues for novel developments.
The endoplasmic reticulum–mitochondria encounter structure (ERMES) connects the mitochondrial outer membrane with the ER. Multiple functions have been linked to ERMES, including maintenance of mitochondrial morphology, protein assembly and phospholipid homeostasis. Since the mitochondrial distribution and morphology protein Mdm10 is present in both ERMES and the mitochondrial sorting and assembly machinery (SAM), it is unknown how the ERMES functions are connected on a molecular level. Here we report that conserved surface areas on opposite sides of the Mdm10 β-barrel interact with SAM and ERMES, respectively. We generated point mutants to separate protein assembly (SAM) from morphology and phospholipid homeostasis (ERMES). Our study reveals that the β-barrel channel of Mdm10 serves different functions. Mdm10 promotes the biogenesis of α-helical and β-barrel proteins at SAM and functions as integral membrane anchor of ERMES, demonstrating that SAM-mediated protein assembly is distinct from ER-mitochondria contact sites.
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.
