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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.
Mitochondrial β-barrel proteins are synthesized on cytosolic ribosomes and must be specifically targeted to the organelle before their integration into the mitochondrial outer membrane. The signal ...that assures such precise targeting and its recognition by the organelle remained obscure. In the present study we show that a specialized β-hairpin motif is this long searched for signal. We demonstrate that a synthetic β-hairpin peptide competes with the import of mitochondrial β-barrel proteins and that proteins harbouring a β-hairpin peptide fused to passenger domains are targeted to mitochondria. Furthermore, a β-hairpin motif from mitochondrial proteins targets chloroplast β-barrel proteins to mitochondria. The mitochondrial targeting depends on the hydrophobicity of the β-hairpin motif. Finally, this motif interacts with the mitochondrial import receptor Tom20. Collectively, we reveal that β-barrel proteins are targeted to mitochondria by a dedicated β-hairpin element, and this motif is recognized at the organelle surface by the outer membrane translocase.
Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efficient diagnostic algorithms.
Retrospectively, ...106 prostate tissue samples from 48 patients (mean age, Formula: see text years) were included in the study. Patients suffered 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.
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 Formula: see text, Formula: see text, and Formula: see text, 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.
Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classification. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classification methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.
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.
Nodal T-follicular helper cell lymphoma, angioimmunoblastic-type (AITL), is characterized by constitutional symptoms, advanced-stage disease, and generalized lymphadenopathy. A genetic hallmark of ...this lymphoma is the frequent occurrence of the
mutation G17V in neoplastic cells, which is observed in around 60% of patients. Because
is involved in both T-cell receptor downstream signalling and cell migration, we hypothesized that the characteristic presentation of AITL could be the result of enhanced tumor cell migration. Therefore, this study aimed to elucidate the impact of the RHOA variant G17V on the migration of neoplastic T cells. We transfected the T-cell lymphoma cell lines HH and HuT78 to stably express the RHOA-G17V variant. RHOA-G17V-expressing T cells did not exhibit enhanced motility compared to empty-vector-transfected cells in microchannels, a 3D collagen gel, or primary human lymphatic tissue. Cells of the HH cell line expressing RHOA-G17V had an increased number of cells with cleaved collagen compared with the empty-vector-transfected cells. Therefore, we hypothesized that the early spread of AITL tumor cells may be related to remodelling of the extracellular matrix. Accordingly, we observed a significant negative correlation between the relative area of collagen in histological sections from 18 primary AITL and the allele frequency of the
-G17V mutation. In conclusion, our results suggest that the characteristic presentation of AITL with early, widespread dissemination of lymphoma cells is not the result of an enhanced migration capacity due to the
-G17V mutation; instead, this feature may rather be related to extracellular matrix remodelling.
Profound knowledge exists about the clinical, morphologic, genomic, and transcriptomic characteristics of most lymphoma entities. However, information is currently lacking on the dynamic behavior of ...malignant lymphomas. This pilot study aimed to gain insight into the motility of malignant lymphomas and bystander cells in 20 human lymph nodes. Generally, B cells were faster under reactive conditions compared with B cells in malignant lymphomas. In contrast, PD1-positive T cells did not show systematic differences in velocity between reactive and neoplastic conditions in general. However, lymphomas could be divided into two groups: one with fast PD1-positive T cells (e.g., Hodgkin lymphoma and mantle cell lymphoma; means 8.4 and 7.8 µm/min) and another with slower PD1-positive T cells (e.g., mediastinal grey zone lymphoma; mean 3.5 µm/min). Although the number of contacts between lymphoma cells and PD1-positive T cells was similar in different lymphoma types, important differences were observed in the duration of these contacts. Among the lymphomas with fast PD1-positive T cells, contacts were particularly short in mantle cell lymphoma (mean 54 s), whereas nodular lymphocyte-predominant Hodgkin lymphoma presented prolonged contact times (mean 6.1 min). Short contact times in mantle cell lymphoma were associated with the largest spatial displacement of PD1-positive cells (mean 12.3 µm). Although PD1-positive T cells in nodular lymphocyte-predominant Hodgkin lymphoma were fast, they remained in close contact with the lymphoma cells, in line with a dynamic immunological synapse. This pilot study shows for the first time systematic differences in the dynamic behavior of lymphoma and bystander cells between different lymphoma types.
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.
The eukaryotic porin superfamily consists of two families, voltage-dependent anion channel (VDAC) and Tom40, which are both located in the mitochondrial outer membrane. In Trypanosoma brucei, only a ...single member of the VDAC family has been described. We report the detection of two additional eukaryotic porin-like sequences in T. brucei. By bioinformatic means, we classify both as putative VDAC isoforms.
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