Elevation of glycoprotein nonmetastatic melanoma protein B in type 1 Gaucher disease patients and mouse models.

Gaucher disease is caused by inherited deficiency of lysosomal glucocerebrosidase. Proteome analysis of laser-dissected splenic Gaucher cells revealed increased amounts of glycoprotein nonmetastatic melanoma protein B (gpNMB). Plasma gpNMB was also elevated, correlating with chitotriosidase and CCL18, which are established markers for human Gaucher cells. In Gaucher mice, gpNMB is also produced by Gaucher cells. Correction of glucocerebrosidase deficiency in mice by gene transfer or pharmacological substrate reduction reverses gpNMB abnormalities. In conclusion, gpNMB acts as a marker for glucosylceramide-laden macrophages in man and mouse and gpNMB should be considered as candidate biomarker for Gaucher disease in treatment monitoring

Pre-Operative SARS-CoV-2 Testing in Asymptomatic Heart Transplantation Recipients

INTRODUCTION: From the start of the coronavirus disease 2019 (COVID-19) pandemic, international guidelines have recommended pre-operative screening for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) before heart transplantation (HTx). Due to the changing prevalence of COVID-19, the chances of false positive results have increased. Because of increased immunity in the population and evolution of SARS-CoV-2 to current Omicron variants, associated mortality and morbidity have decreased. We set out to investigate the yield and side effects of SARS-CoV-2 screening in our center. METHODS: We performed a retrospective cohort study in the University Medical Center Utrecht. The study period was from March 2019 to January 2023. All data from patients who underwent HTx were collected, including all pre-operative and post-operative SARS-CoV-2 tests. Furthermore, all clinical SARS-CoV-2 tests for the indication of potential HTx were screened. RESULTS: In the period under study, 51 patients underwent HTx. None of the recipients reported any symptoms of a viral infection. Fifty HTx recipients were screened for SARS-CoV-2. Forty-nine out of fifty patients tested negative. One patient had a false positive result, potentially delaying the HTx procedure. There were no cancelled HTx procedures due to a true positive SARS-CoV-2 test result. CONCLUSION: Pre-operative SARS-CoV-2 screening in asymptomatic HTx recipients did not lead to any true positive cases. In 2% of the cases, screening resulted in a false positive test result. With the current Omicron variants, in combination with a low-prevalence situation, we propose to abandon pre-operative SARS-CoV-2 screening and initiate a symptom-driven approach for the general viral testing of patients who are called in for a potential HTx

Dissection of the uterine wall in a scarred uterus: a case report

Uterine rupture is a potentially fatal complication during pregnancy, delivery, or postpartum. Women attempting a trial of labor after a cesarean section have an increased risk of a subsequent rupture. We report a case of a 24-year-old woman, gravida 2 para 1 with a previous cesarean section who underwent a trial of labor. During labor she complained of pain while labor progressed rapidly. Because of signs of fetal distress, a vacuum extraction was performed. Two hours after delivery, the patient complained again of severe abdominal pain. Blood accumulated in a previously non-existent area between the serosa and uterine muscle. A dissection of the uterine wall occurred with serious clinical consequences, compatible with a complete uterine rupture. Emergency laparotomy was performed to repair the uterine wall; a hysterectomy was prevented

Classification-based comparison of pre-processing methods for interpretation of mass spectrometry generated clinical datasets

<p>Abstract</p> <p>Background</p> <p>Mass spectrometry is increasingly being used to discover proteins or protein profiles associated with disease. Experimental design of mass-spectrometry studies has come under close scrutiny and the importance of strict protocols for sample collection is now understood. However, the question of how best to process the large quantities of data generated is still unanswered. Main challenges for the analysis are the choice of proper pre-processing and classification methods. While these two issues have been investigated in isolation, we propose to use the classification of patient samples as a clinically relevant benchmark for the evaluation of pre-processing methods.</p> <p>Results</p> <p>Two in-house generated clinical SELDI-TOF MS datasets are used in this study as an example of high throughput mass-spectrometry data. We perform a systematic comparison of two commonly used pre-processing methods as implemented in Ciphergen ProteinChip Software and in the Cromwell package. With respect to reproducibility, Ciphergen and Cromwell pre-processing are largely comparable. We find that the overlap between peaks detected by either Ciphergen ProteinChip Software or Cromwell is large. This is especially the case for the more stringent peak detection settings. Moreover, similarity of the estimated intensities between matched peaks is high.</p> <p>We evaluate the pre-processing methods using five different classification methods. Classification is done in a double cross-validation protocol using repeated random sampling to obtain an unbiased estimate of classification accuracy. No pre-processing method significantly outperforms the other for all peak detection settings evaluated.</p> <p>Conclusion</p> <p>We use classification of patient samples as a clinically relevant benchmark for the evaluation of pre-processing methods. Both pre-processing methods lead to similar classification results on an ovarian cancer and a Gaucher disease dataset. However, the settings for pre-processing parameters lead to large differences in classification accuracy and are therefore of crucial importance. We advocate the evaluation over a range of parameter settings when comparing pre-processing methods. Our analysis also demonstrates that reliable classification results can be obtained with a combination of strict sample handling and a well-defined classification protocol on clinical samples.</p

Label-Free LC-MS^e in Tissue and Serum Reveals Protein Networks Underlying Differences between Benign and Malignant Serous Ovarian Tumors

<div><p>Purpose</p><p>To identify proteins and (molecular/biological) pathways associated with differences between benign and malignant epithelial ovarian tumors.</p><p>Experimental Procedures</p><p>Serum of six patients with a serous adenocarcinoma of the ovary was collected before treatment, with a control group consisting of six matched patients with a serous cystadenoma. In addition to the serum, homogeneous regions of cells exhibiting uniform histology were isolated from benign and cancerous tissue by laser microdissection. We subsequently employed label-free liquid chromatography tandem mass spectrometry (LC-MS^e) to identify proteins in these serum and tissues samples. Analyses of differential expression between samples were performed using Bioconductor packages and in-house scripts in the statistical software package R. Hierarchical clustering and pathway enrichment analyses were performed, as well as network enrichment and interactome analysis using MetaCore.</p><p>Results</p><p>In total, we identified 20 and 71 proteins that were significantly differentially expressed between benign and malignant serum and tissue samples, respectively. The differentially expressed protein sets in serum and tissue largely differed with only 2 proteins in common. MetaCore network analysis, however inferred GCR-alpha and Sp1 as common transcriptional regulators. Interactome analysis highlighted 14-3-3 zeta/delta, 14-3-3 beta/alpha, Alpha-actinin 4, HSP60, and PCBP1 as critical proteins in the tumor proteome signature based on their relative overconnectivity. The data have been deposited to the ProteomeXchange with identifier PXD001084.</p><p>Discussion</p><p>Our analysis identified proteins with both novel and previously known associations to ovarian cancer biology. Despite the small overlap between differentially expressed protein sets in serum and tissue, APOA1 and Serotransferrin were significantly lower expressed in both serum and cancer tissue samples, suggesting a tissue-derived effect in serum. Pathway and subsequent interactome analysis also highlighted common regulators in serum and tissue samples, suggesting a yet unknown role for PCBP1 in ovarian cancer pathophysiology.</p></div

Proteins in the serum and tumor datasets that are potentially associated with GCR-alpha (A) and SP1 (B) pathways.

<p>The proteins marked in blue were found in the serum dataset, those marked in green in both tumor and serum. Unmarked proteins are specific for the tumor signature (except GCR-alpha and SP1). Proteins are ordered according to their position within the cell (extracellular, membrane bound, cytoplasmic or nucleic). Individual proteins are represented as nodes, the different shapes of the nodes represent the functional class of the proteins. The arrowheads indicate the direction of the interaction, the color of the lines between nodes describes activation (green), inhibition (red), and unspecified (black) interactions. The small circles on top of the protein symbols indicate up-regulation (red) or down-regulation (blue).</p

PCBP1 expression in serous ovarian cancer.

<p>Expression of PCBP1 (probeset 208620_at) in serous tumors of low malignant potential (LMP) versus malignant serous ovarian tumors <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108046#pone.0108046-Anglesio1" target="_blank">[28]</a>. PCBP1 was significantly up-regulated (p = 0.003, Welch's t-test) in malignant tumors. Squares represent the individual samples used in the microarray experiment. Boxplots are overlaid with the lower and upper ends of a box indicating the 25th and 75th percentiles, respectively. The solid black line inside a box indicates the median.</p

Interactions by protein function.

<p>Interactions by protein function based on the connectivity with the tumor tissue signatures and with proteins from the human proteome MetaCore database. The proteins were considered over-connected when the number of observed interactions exceeded the number of expected interactions. Actual: number of network objects in the signature which interact with the chosen object; n: number of network objects in the signature; R: number of network objects in the background list which interact with the chosen object; N: total number of protein-based objects in the background list; Expected: mean of hypergeometric distribution. Ratio: connectivity ratio (Actual/Expected); z-score: (Actual-Expected)/(standard deviation); p-value: probability to have the value of Actual or higher (lower for negative z-score) by chance under null hypothesis of no over- or under-connectivity.</p><p>Interactions by protein function.</p

Venn diagram of the detected proteins.

<p>We detected a total of 84 proteins in serum and 209 in tissue, present in at least 50% of the samples in at least one of the conditions (benign or malignant). The grey area represents the proteins with an adjusted p-value of <0.05 when comparing benign with malignant.</p