Hierarchical Biclustering of Mouse Pancreas Mass Spectrometry Imaging Data Using Recursive Rank-2 Non-negative Matrix Factorization.

One of the main challenges in mass spectrometry imaging data analysis remains the analysis of m/z-spectra displaying a low signal-to-noise ratio caused by their low abundance, sample preparation, matrix effects, fragmentation, and other artifacts. Additionally, we observe that molecules with a high abundance suppress those with lower intensities and misdirect classical tools for MSI data analysis, such as principal component analysis. As a result, the observed significance of a molecule may not always be directly related to its abundance. In this work, we present a recursive rank-2 non-negative matrix factorization (rr2-NMF) algorithm that automatically returns spectral and spatial visualization of colocalized molecules, both highly and lowly abundant. Using this hierarchical decomposition, our method finds spatial and spectral correlations on different levels of abundances. The quality of the analysis is evaluated on MALDI-TOF data of healthy mouse pancreatic tissue for the annotation of molecules of interest in the lower abundances. The results show interesting findings regarding the functioning and colocalization of certain molecules.

The Effect of FTY720 on Sphingolipid Imbalance and Cognitive Decline in Aged EFAD Mice.

Background: During Alzheimer's disease (AD) progression, there is a decline in the bioactive sphingolipid sphingosine-1-phosphate (S1P). Previous research showed that FTY720, an S1P mimetic, prevented cognitive decline and reduced ceramide levels in transgenic mice with familial AD carrying the human APOE4 gene (E4FAD) at 6-7 months of age. Objective: The objective of this study is to explore the protective effects of FTY720 at late-stage AD. Methods: Male mice aged 9.5 to 10.5 months were orally administered FTY720 (0.1 mg/kg) via oral gavage for 6 weeks. A pre-test of water maze was used for evaluating the pathological status. After 4 weeks of administration, memory, locomotion, and anxiety were assessed. Cortex samples were analyzed for amyloid-ß (Aß) and sphingolipid levels. Results: Compared with APOE3 mice, APOE4, E3FAD and E4FAD mice exhibited significant memory deficits. After 6 weeks administration, FTY720 did not alleviate memory deficits in EFAD mice. Lipid analysis revealed that S1P was significantly reduced in EFAD mice (E3FAD or E4FAD) compared to controls (APOE3 and APOE4). Ceramide level alterations were predominantly dependent on APOE isoforms rather than AD transgenes. Interestingly, Cer (d18 : 1/22 : 1) was elevated in APOE4 mice compared to APOE3, and FTY720 reduced it. Conclusions: E4FAD and APOE4 mice exhibited significant spatial memory deficits and higher ceramide concentrations compared to APOE3 mice. FTY720 did not reverse memory deficits in E4FAD and APOE4 mice but reduced specific ceramide species. This study provides insights into the association between sphingolipids and APOE4 in advanced AD stages, exploring potential therapeutic targeting of sphingolipid metabolism.

Spatiochemical Characterization of the Pancreas Using Mass Spectrometry Imaging and Topological Data Analysis.

Mass Spectrometry Imaging (MSI) is a technique used to identify the spatial distribution of molecules in tissues. An MSI experiment results in large amounts of high dimensional data, so efficient computational methods are needed to analyze the output. Topological Data Analysis (TDA) has proven to be effective in all kinds of applications. TDA focuses on the topology of the data in high dimensional space. Looking at the shape in a high dimensional data set can lead to new or different insights. In this work, we investigate the use of Mapper, a form of TDA, applied on MSI data. Mapper is used to find data clusters inside two healthy mouse pancreas data sets. The results are compared to previous work using UMAP for MSI data analysis on the same data sets. This work finds that the proposed technique discovers the same clusters in the data as UMAP and is also able to uncover new clusters, such as an additional ring structure inside the pancreatic islets and a better defined cluster containing blood vessels. The technique can be used for a large variety of data types and sizes and can be optimized for specific applications. It is also computationally similar to UMAP for clustering. Mapper is a very interesting method, especially its use in biomedical applications.

The influence of proteoforms: assessing the accuracy of total vitamin D-binding protein quantification by proteolysis and LC-MS/MS.

OBJECTIVES: Vitamin D-binding protein (VDBP), a serum transport protein for 25-hydroxyvitamin D [25(OH)D], has three common proteoforms which have co-localized amino acid variations and glycosylation. A monoclonal immunoassay was found to differentially detect VDBP proteoforms and methods using liquid chromatography-tandem mass spectrometry (LC-MS/MS) might be able to overcome this limitation. Previously developed multiple reaction monitoring LC-MS/MS methods for total VDBP quantification represent an opportunity to probe the potential effects of proteoforms on proteolysis, instrument response and quantification accuracy. METHODS: VDBP was purified from homozygous human donors and quantified using proteolysis or acid hydrolysis and LC-MS/MS. An interlaboratory comparison was performed using pooled human plasma [Standard Reference Material® 1950 (SRM 1950) Metabolites in Frozen Human Plasma] and analyses with different LC-MS/MS methods in two laboratories. RESULTS: Several shared peptides from purified proteoforms were found to give reproducible concentrations [≤2.7% coefficient of variation (CV)] and linear instrument responses (R2≥0.9971) when added to human serum. Total VDBP concentrations from proteolysis or amino acid analysis (AAA) of purified proteoforms had ≤1.92% CV. SRM 1950, containing multiple proteoforms, quantified in two laboratories resulted in total VDBP concentrations with 7.05% CV. CONCLUSIONS: VDBP proteoforms were not found to cause bias during quantification by LC-MS/MS, thus demonstrating that a family of proteins can be accurately quantified using shared peptides. A reference value was assigned for total VDBP in SRM 1950, which may be used to standardize methods and improve the accuracy of VDBP quantification in research and clinical samples.

FTY720 decreases ceramides levels in the brain and prevents memory impairments in a mouse model of familial Alzheimer's disease expressing APOE4.

The protection mediated by the bioactive sphingolipid sphingosine-1-phosphate (S1P) declines during Alzheimer's disease (AD) progression, especially in patients carrying the apolipoprotein E ε4 (APOE4) isoform. The drug FTY720 mimics S1P bioactivity, but its efficacy in treating AD is unclear. Two doses of FTY720 (0.1 mg / kg and 0.5 mg / kg daily) were given by oral gavage for 15 weeks to transgenic mouse models of familial AD carrying human apolipoprotein E (APOE) APOE3 (E3FAD) or APOE4 (E4FAD). After 12 weeks of treatment, animals were subjected to behavioral tests for memory, locomotion, and anxiety. Blood was withdrawn at different time points and brains were collected for sphingolipids analysis by mass spectrometry, gene expression by RT-PCR and Aß quantification by ELISA. We discovered that low levels of S1P in the plasma is associated with a higher probability of failing the memory test and that FTY720 prevents memory impairments in E4FAD. The beneficial effect of FTY720 was induced by a shift of the sphingolipid metabolism in the brain towards a lower production of toxic metabolites, like ceramide d18:1/16:0 and d18:1/22:0, and reduction of amyloid-ß burden and inflammation. In conclusion, we provide further evidence of the druggability of the sphingolipid system in AD.

Lipidomic Profiling of Clinical Prostate Cancer Reveals Targetable Alterations in Membrane Lipid Composition.

Dysregulated lipid metabolism is a prominent feature of prostate cancer that is driven by androgen receptor (AR) signaling. Here we used quantitative mass spectrometry to define the "lipidome" in prostate tumors with matched benign tissues (n = 21), independent unmatched tissues (n = 47), and primary prostate explants cultured with the clinical AR antagonist enzalutamide (n = 43). Significant differences in lipid composition were detected and spatially visualized in tumors compared with matched benign samples. Notably, tumors featured higher proportions of monounsaturated lipids overall and elongated fatty acid chains in phosphatidylinositol and phosphatidylserine lipids. Significant associations between lipid profile and malignancy were validated in unmatched samples, and phospholipid composition was characteristically altered in patient tissues that responded to AR inhibition. Importantly, targeting tumor-related lipid features via inhibition of acetyl-CoA carboxylase 1 significantly reduced cellular proliferation and induced apoptosis in tissue explants. This characterization of the prostate cancer lipidome in clinical tissues reveals enhanced fatty acid synthesis, elongation, and desaturation as tumor-defining features, with potential for therapeutic targeting. SIGNIFICANCE: This study identifies malignancy and treatment-associated changes in lipid composition of clinical prostate cancer tissues, suggesting that mediators of these lipidomic changes could be targeted using existing metabolic agents.

A mathematical comparison of non-negative matrix factorization related methods with practical implications for the analysis of mass spectrometry imaging data.

RATIONALE: Non-negative matrix factorization (NMF) has been used extensively for the analysis of mass spectrometry imaging (MSI) data, visualizing simultaneously the spatial and spectral distributions present in a slice of tissue. The statistical framework offers two related NMF methods: probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), which is a generative model. This work offers a mathematical comparison between NMF, PLSA, and LDA, and includes a detailed evaluation of Kullback-Leibler NMF (KL-NMF) for MSI for the first time. We will inspect the results for MSI data analysis as these different mathematical approaches impose different characteristics on the data and the resulting decomposition. METHODS: The four methods (NMF, KL-NMF, PLSA, and LDA) are compared on seven different samples: three originated from mice pancreas and four from human-lymph-node tissues, all obtained using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). RESULTS: Where matrix factorization methods are often used for the analysis of MSI data, we find that each method has different implications on the exactness and interpretability of the results. We have discovered promising results using KL-NMF, which has only rarely been used for MSI so far, improving both NMF and PLSA, and have shown that the hitherto stated equivalent KL-NMF and PLSA algorithms do differ in the case of MSI data analysis. LDA, assumed to be the better method in the field of text mining, is shown to be outperformed by PLSA in the setting of MALDI-MSI. Additionally, the molecular results of the human-lymph-node data have been thoroughly analyzed for better assessment of the methods under investigation. CONCLUSIONS: We present an in-depth comparison of multiple NMF-related factorization methods for MSI. We aim to provide fellow researchers in the field of MSI a clear understanding of the mathematical implications using each of these analytical techniques, which might affect the exactness and interpretation of the results.

Spatially aware clustering of ion images in mass spectrometry imaging data using deep learning.

Computational analysis is crucial to capitalize on the wealth of spatio-molecular information generated by mass spectrometry imaging (MSI) experiments. Currently, the spatial information available in MSI data is often under-utilized, due to the challenges of in-depth spatial pattern extraction. The advent of deep learning has greatly facilitated such complex spatial analysis. In this work, we use a pre-trained neural network to extract high-level features from ion images in MSI data, and test whether this improves downstream data analysis. The resulting neural network interpretation of ion images, coined neural ion images, is used to cluster ion images based on spatial expressions. We evaluate the impact of neural ion images on two ion image clustering pipelines, namely DBSCAN clustering, combined with UMAP-based dimensionality reduction, and k-means clustering. In both pipelines, we compare regular and neural ion images from two different MSI datasets. All tested pipelines could extract underlying spatial patterns, but the neural network-based pipelines provided better assignment of ion images, with more fine-grained clusters, and greater consistency in the spatial structures assigned to individual clusters. Additionally, we introduce the relative isotope ratio metric to quantitatively evaluate clustering quality. The resulting scores show that isotopical m/z values are more often clustered together in the neural network-based pipeline, indicating improved clustering outcomes. The usefulness of neural ion images extends beyond clustering towards a generic framework to incorporate spatial information into any MSI-focused machine learning pipeline, both supervised and unsupervised.

Correspondence-Aware Manifold Learning for Microscopic and Spatial Omics Imaging: A Novel Data Fusion Method Bringing Mass Spectrometry Imaging to a Cellular Resolution.

High-dimensional molecular measurements are transforming the field of pathology into a data-driven discipline. While hematoxylin and eosin (H&E) stainings are still the gold standard to diagnose diseases, the integration of microscopic and molecular information is becoming crucial to advance our understanding of tissue heterogeneity. To this end, we propose a data fusion method that integrates spatial omics and microscopic data obtained from the same tissue slide. Through correspondence-aware manifold learning, we can visualize the biological trends observed in the high-dimensional omics data at microscopic resolution. While data fusion enables the detection of elements that would not be detected taking into account the separate data modalities individually, out-of-sample prediction makes it possible to predict molecular trends outside of the measured tissue area. The proposed dimensionality reduction-based data fusion paradigm will therefore be helpful in deciphering molecular heterogeneity by bringing molecular measurements such as mass spectrometry imaging (MSI) to the cellular resolution.

ELOVL5 Is a Critical and Targetable Fatty Acid Elongase in Prostate Cancer.

The androgen receptor (AR) is the key oncogenic driver of prostate cancer, and despite implementation of novel AR targeting therapies, outcomes for metastatic disease remain dismal. There is an urgent need to better understand androgen-regulated cellular processes to more effectively target the AR dependence of prostate cancer cells through new therapeutic vulnerabilities. Transcriptomic studies have consistently identified lipid metabolism as a hallmark of enhanced AR signaling in prostate cancer, yet the relationship between AR and the lipidome remains undefined. Using mass spectrometry-based lipidomics, this study reveals increased fatty acyl chain length in phospholipids from prostate cancer cells and patient-derived explants as one of the most striking androgen-regulated changes to lipid metabolism. Potent and direct AR-mediated induction of ELOVL fatty acid elongase 5 (ELOVL5), an enzyme that catalyzes fatty acid elongation, was demonstrated in prostate cancer cells, xenografts, and clinical tumors. Assessment of mRNA and protein in large-scale data sets revealed ELOVL5 as the predominant ELOVL expressed and upregulated in prostate cancer compared with nonmalignant prostate. ELOVL5 depletion markedly altered mitochondrial morphology and function, leading to excess generation of reactive oxygen species and resulting in suppression of prostate cancer cell proliferation, 3D growth, and in vivo tumor growth and metastasis. Supplementation with the monounsaturated fatty acid cis-vaccenic acid, a direct product of ELOVL5 elongation, reversed the oxidative stress and associated cell proliferation and migration effects of ELOVL5 knockdown. Collectively, these results identify lipid elongation as a protumorigenic metabolic pathway in prostate cancer that is androgen-regulated, critical for metastasis, and targetable via ELOVL5. SIGNIFICANCE: This study identifies phospholipid elongation as a new metabolic target of androgen action that is critical for prostate tumor metastasis.

Interactome Analysis of the ER Stress Sensor Perk Uncovers Key Components of ER-Mitochondria Contact Sites and Ca2+ Signalling.

We recently reported that the ER stress kinase PERK regulates ER-mitochondria appositions and ER- plasma membrane (ER-PM) contact sites, independent of its canonical role in the unfolded protein response. PERK regulation of ER-PM contacts was revealed by a proximity biotinylation (BioID) approach and involved a dynamic PERK-Filamin A interaction supporting the formation of ER-PM contacts by actin-cytoskeleton remodeling in response to depletion of ER-Ca2+ stores. In this report, we further interrogated the PERK BioID interactome by validating through co-IP experiments the interaction between PERK and two proteins involved in Ca2+ handling and ER-mitochondria contact sites. These included the vesicle associated membrane (VAMP)-associated proteins (VAPA/B) and the main ER Ca2+ pump sarcoplasmic/endoplasmic reticulum Ca ATPase 2 (SERCA2). These data identify new putative PERK interacting proteins with a crucial role in membrane contact sites and Ca2+ signaling further supporting the uncanonical role of PERK in Ca2+ signaling through membrane contact sites (MCSs).

Ceramide analog [18F]F-HPA-12 detects sphingolipid disbalance in the brain of Alzheimer's disease transgenic mice by functioning as a metabolic probe.

The metabolism of ceramides is deregulated in the brain of Alzheimer's disease (AD) patients and is associated with apolipoprotein (APO) APOE4 and amyloid-ß pathology. However, how the ceramide metabolism changes over time in AD, in vivo, remains unknown. Distribution and metabolism of [18F]F-HPA-12, a radio-fluorinated version of the ceramide analog N-(3-hydroxy-1-hydroxymethyl-3-phenylpropyl) dodecanamide, was investigated in the brain of AD transgenic mouse models (FAD) on an APOE4 or APOE3 genetic background, by positron emission tomography and by gamma counter. We found that FAD mice displayed a higher uptake of [18F]F-HPA-12 in the brain, independently from the APOE4 or APOE3 genetic background. FAD mice could be distinguished from littermate control animals with a sensitivity of 85.7% and a specificity of 87.5%, by gamma counter measurements. Metabolic analysis of [18F]F-HPA-12 in the brain suggested that the tracer is degraded less efficiently in the FAD mice. Furthermore, the radioactive signal registered in the hippocampus correlated with an increase of Cer d18:1/20:2 levels measured in the same brain region by mass spectrometry. Our data gives additional proof that ceramide metabolism is different in FAD mice compared to controls. Ceramide analogs like HPA-12 may function as metabolic probes to study ceramide disbalance in the brain.

Differential Proteomic Analysis of Hepatocellular Carcinomas from Ppp2r5d Knockout Mice and Normal (Knockout) Livers.

BACKGROUND: Hepatocellular carcinoma (HCC) is the major type of primary liver cancer. Mice lacking the tumor-suppressive protein phosphatase 2A subunit B56δ (Ppp2r5d) spontaneously develop HCC, correlating with increased c-MYC oncogenicity. MATERIALS AND METHODS: We used two-dimensional difference gel electrophoresis-coupled matrix-assisted laser desorption/ionization time-of-flight mass spectrometry to identify differential proteomes of livers from wild-type, non-cancerous and HCC-affected B56δ knockout mice. RESULTS: A total of 23 proteins were differentially expressed/regulated in liver between wild-type and non-cancerous knockout mice, and 119 between non-cancerous and HCC knockout mice ('cancer proteins'). Overlap with our reported differential transcriptome data was poor. Overall, 56% of cancer proteins were reported before in HCC proteomics studies; 44% were novel. Gene Ontology analysis revealed cancer proteins mainly associated with liver metabolism (18%) and mitochondria (15%). Ingenuity Pathway Analysis identified 'cancer' and 'gastrointestinal disease' as top hits. CONCLUSION: We identified several proteins for further exploration as novel potential HCC biomarkers, and independently underscored the relevance of Ppp2r5d knockout mice as a valuable hepatocarcinogenesis model.

Prioritization of m/z-Values in Mass Spectrometry Imaging Profiles Obtained Using Uniform Manifold Approximation and Projection for Dimensionality Reduction.

Mass spectrometry imaging (MSI) is a promising technique to assess the spatial distribution of molecules in a tissue sample. Nonlinear dimensionality reduction methods such as Uniform Manifold Approximation and Projection (UMAP) can be very valuable for the visualization of the massive data sets produced by MSI. These visualizations can offer us good initial insights regarding the heterogeneity and variety of molecular patterns present in the data, but they do not discern which molecules might be driving these observations. To prioritize the m/z-values associated with these biochemical profiles, we apply a bidirectional dimensionality reduction approach taking into account both the spectral and spatial information. The results show that both sources of information are instrumental to get a more comprehensive view on the relevant m/z-values and can support the reliability of the results obtained using UMAP. We illustrate our approach on heterogeneous pancreas tissues obtained from healthy mice.

The role of lipids in determining the air-water interfacial properties of wheat, rye, and oat dough liquor constituents.

Bread is mainly made from wheat but also from other cereals such as rye and oats. We here report on the role of dough liquor (DL) proteins and lipids in determining the stability of gas cell air-water (A-W) interfaces in wheat, rye, and oat bread making. Surprisingly, most lipids in DLs of these cereals are nonpolar. Their main polar DL lipids are phospholipids. Lipids at wheat and rye DL stabilized A-W interfaces impair interactions between its proteins, as reflected by an increased A-W interfacial shear viscosity of the adsorbed film upon defatting. In contrast, removing most lipids from oat DL pronouncedly increased the A-W interface surface tension, demonstrating that lipids are the prominent adsorbed species.

The Use of Antibody Arrays in the Discovery of New Plasma Biomarkers for Endometriosis.

A noninvasive diagnostic test for endometriosis is needed to shorten the current diagnostic delay of 8-11 years. The goal of this study was to discover new biomarkers for endometriosis using an antibody array approach. A total of 103 plasma samples from patients with laparoscopically confirmed presence (n = 68) or absence (n = 35) of endometriosis were selected. Samples were pooled according to disease status, cycle phase, disease stage, and phenotype. Pooled samples were screened for possible biomarkers using the L-series 1000 and Quantibody 660 arrays from RayBiotech. Technical verification of ten markers was done using a custom-made multiplex immunoassay identifying ten proteins (10-plex) and later by single ELISA. Due to the limited reproducibility of the L-series 1000 immunoassay, the biomarker screening was performed using the Quantibody 660, a sandwich-based multiplex immunoassay, which showed that 280 proteins were upregulated, and 29 proteins downregulated in the endometriosis pool versus the control pool. In order to assess the reproducibility of these results, ten preselected proteins were analyzed using a custom 10-plex. Four proteins (CD48, DNAM-1, IL-31, and XIAP) were confirmed to be differentially expressed when comparing the endometriosis and control pool. However, only IL-31 showed a univariate statistical difference between endometriosis and control groups in individual samples that were part of the initial pools. In conclusion, discovery and verification of potential markers proved challenging using multiplex immunoassay methods, mainly due to issues with reproducibility. Only IL-31 showed potential as possible biomarker for endometriosis.

Technical Verification and Assessment of Independent Validation of Biomarker Models for Endometriosis.

There is a great need for a noninvasive diagnosis for endometriosis. Several biomarkers and biomarker panels have been proposed. Biomarker models consisting of CA-125, VEGF, Annexin V, and glycodelin/sICAM-1 were previously developed by our group. The objective of our current study was to assess the impact of technical and biological variability on the performance of those previously developed prediction models in a technical verification and a validation setting. The technical verification cohort consisted of peripheral blood plasma samples from a subset of the patients included in the original study of Vodolazkaia et al. (99 women with and 37 women without endometriosis). The validation study was done in plasma samples of an independent patient cohort (170 women with and 86 women without endometriosis). Single immunoassays were used for CA-125, VEGF-A, sICAM-1, Annexin V, and glycodelin. Statistical analyses were done using univariate and multivariate (logistic regression) approaches. The previously reported prediction models for endometriosis had a low performance in both the technical verification and validation setting. New prediction models were developed, which included CA-125, Annexin V, and sICAM-1, but CA-125 was the only marker that was retained in the models across the technical verification and validation study. Overall, successful validation of a biomarker model depends on several factors such as patient selection, collection methods, assay selection/handling, stability of the marker, and statistical analysis and interpretation. There is a need for standardized studies in large, well-defined patient cohorts with robust assay methodologies.

Evaluation of Distance Metrics and Spatial Autocorrelation in Uniform Manifold Approximation and Projection Applied to Mass Spectrometry Imaging Data.

In this work, uniform manifold approximation and projection (UMAP) is applied for nonlinear dimensionality reduction and visualization of mass spectrometry imaging (MSI) data. We evaluate the performance of the UMAP algorithm on MSI data sets acquired in mouse pancreas and human lymphoma samples and compare it to those of principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and the Barnes-Hut (BH) approximation of t-SNE. Furthermore, we compare different distance metrics in (BH) t-SNE and UMAP and propose the use of spatial autocorrelation as a means of comparing the resulting low-dimensional embeddings. The results indicate that UMAP is competitive with t-SNE in terms of visualization and is well-suited for the dimensionality reduction of large (>100 000 pixels) MSI data sets. With an almost fourfold decrease in runtime, it is more scalable in comparison with the current state-of-the-art: t-SNE or the Barnes-Hut approximation of t-SNE. In what seems to be the first application of UMAP to MSI data, we assess the value of applying alternative distance metrics, such as the correlation, cosine, and the Chebyshev metric, in contrast to the traditionally used Euclidean distance metric. Furthermore, we propose "histomatch" as an additional custom distance metric for the analysis of MSI data.

Cytokine-induced translocation of GRP78 to the plasma membrane triggers a pro-apoptotic feedback loop in pancreatic beta cells.

The 78-kDa glucose-regulated protein (GRP78) is an ubiquitously expressed endoplasmic reticulum chaperone, with a central role in maintaining protein homeostasis. Recently, an alternative role for GRP78 under stress conditions has been proposed, with stress-induced extracellular secretion and translocation of GRP78 to the cell surface where it acts as a multifunctional signaling receptor. Here we demonstrate translocation of GRP78 to the surface of human EndoC-ßH1 cells and primary human islets upon cytokine exposure, in analogy to observations in rodent INS-1E and MIN6 beta cell lines. We show that GRP78 is shuttled via the anterograde secretory pathway, through the Golgi complex and secretory granules, and identify the DNAJ homolog subfamily C member 3 (DNAJC3) as a GRP78-interacting protein that facilitates its membrane translocation. Evaluation of downstream signaling pathways, using N- and C-terminal anti-GRP78 blocking antibodies, demonstrates that both GRP78 signaling domains initiate pro-apoptotic signaling cascades in beta cells. Extracellular GRP78 itself is identified as a ligand for cell surface GRP78 (sGRP78), increasing caspase 3/7 activity and cell death upon binding, which is accompanied by enhanced Chop and Bax mRNA expression. These results suggest that inflammatory cytokines induce a self-destructive pro-apoptotic feedback loop through the secretion and membrane translocation of GRP78. This proapoptotic function distinguishes the role of sGRP78 in beta cells from its reported anti-apoptotic and proliferative role in cancer cells, opening the road for the use of compounds that block sGRP78 as potential beta cell-preserving therapies in type 1 diabetes.

Structures of the heart specific SERCA2a Ca2+-ATPase.

The sarcoplasmic/endoplasmic reticulum Ca2+-ATPase 2a (SERCA2a) performs active reuptake of cytoplasmic Ca2+ and is a major regulator of cardiac muscle contractility. Dysfunction or dysregulation of SERCA2a is associated with heart failure, while restoring its function is considered as a therapeutic strategy to restore cardiac performance. However, its structure has not yet been determined. Based on native, active protein purified from pig ventricular muscle, we present the first crystal structures of SERCA2a, determined in the CPA-stabilized E2-AlF4- form (3.3 Å) and the Ca2+-occluded [Ca2]E1-AMPPCP form (4.0 Å). The structures are similar to the skeletal muscle isoform SERCA1a pointing to a conserved mechanism. We seek to explain the kinetic differences between SERCA1a and SERCA2a. We find that several isoform-specific residues are acceptor sites for post-translational modifications. In addition, molecular dynamics simulations predict that isoform-specific residues support distinct intramolecular interactions in SERCA2a and SERCA1a. Our experimental observations further indicate that isoform-specific intramolecular interactions are functionally relevant, and may explain the kinetic differences between SERCA2a and SERCA1a.