Substrate Specificity of Cytoplasmic N-Glycosyltransferase

N-Linked protein glycosylation is a very common post-translational modification that can be found in all kingdoms of life. The classical, highly conserved pathway entails the assembly of a lipid-linked oligosaccharide and its transfer to an asparagine residue in the sequon NX(S/T) of a secreted protein by the integral membrane protein oligosaccharyltransferase. A few species in the class of γ-proteobacteria encode a cytoplasmic N-glycosylation system mediated by a soluble N-glycosyltransferase (NGT). This enzyme uses nucleotide-activated sugars to modify asparagine residues with single monosaccharides. As these enzymes are not related to oligosaccharyltransferase, NGTs constitute a novel class of N-glycosylation catalyzing enzymes. To characterize the NGT-catalyzed reaction, we developed a sensitive and quantitative in vitro assay based on HPLC separation and quantification of fluorescently labeled substrate peptides. With this assay we were able to directly quantify glycopeptide formation by Actinobacillus pleuropneumoniae NGT and determine its substrate specificities: NGT turns over a number of different sugar donor substrates and allows for activation by both UDP and GDP. Quantitative analysis of peptide substrate turnover demonstrated a strikingly similar specificity as the classical, oligosaccharyltransferase-catalyzed N-glycosylation, with NX(S/T) sequons being the optimal NGT substrates.     

Candidate Serological Biomarkers for Cancer Identified from the Secretomes of 23 Cancer Cell Lines and the Human Protein Atlas

Although cancer cell secretome profiling is a promising strategy used to identify potential body fluid-accessible cancer biomarkers, questions remain regarding the depth to which the cancer cell secretome can be mined and the efficiency with which researchers can select useful candidates from the growing list of identified proteins. Therefore, we analyzed the secretomes of 23 human cancer cell lines derived from 11 cancer types using one-dimensional SDS-PAGE and nano-LC-MS/MS performed on an LTQ-Orbitrap mass spectrometer to generate a more comprehensive cancer cell secretome. A total of 31,180 proteins was detected, accounting for 4,584 non-redundant proteins, with an average of 1,300 proteins identified per cell line. Using protein secretion-predictive algorithms, 55.8% of the proteins appeared to be released or shed from cells. The identified proteins were selected as potential marker candidates according to three strategies: (i) proteins apparently secreted by one cancer type but not by others (cancer type-specific marker candidates), (ii) proteins released by most cancer cell lines (pan-cancer marker candidates), and (iii) proteins putatively linked to cancer-relevant pathways. We then examined protein expression profiles in the Human Protein Atlas to identify biomarker candidates that were simultaneously detected in the secretomes and highly expressed in cancer tissues. This analysis yielded 6–137 marker candidates selective for each tumor type and 94 potential pan-cancer markers. Among these, we selectively validated monocyte differentiation antigen CD14 (for liver cancer), stromal cell-derived factor 1 (for lung cancer), and cathepsin L1 and interferon-induced 17-kDa protein (for nasopharyngeal carcinoma) as potential serological cancer markers. In summary, the proteins identified from the secretomes of 23 cancer cell lines and the Human Protein Atlas represent a focused reservoir of potential cancer biomarkers.Cancer is a major cause of mortality worldwide, accounting for 10 million new cases and more than 6 million deaths per year. In developing countries, cancer is the second most common cause of death, accounting for 23–25% of the overall mortality rate (1). Notwithstanding improvements in diagnostic imaging technologies and medical treatments, the long term survival of most cancer patients is poor. Cancer therapy is often challenging because the majority of cancers are initially diagnosed in their advanced stages. For example, the 5-year survival rate for patients with HNC¹ is less than 50%. More than 50% of all HNC patients have advanced disease at the time of diagnosis (2, 3). Enormous effort has been devoted to screening and characterizing cancer markers for the early detection of cancer. Thus far, these markers include carcinoembryonic antigen, prostate-specific antigen, α-fetoprotein, CA 125, CA 15-3, and CA 19-9. Unfortunately, most biomarkers have limited specificity, sensitivity, or both (4). Thus, there is a growing consensus that marker panels, which are more sensitive and specific than individual markers, would increase the efficacy and accuracy of early stage cancer detection (4–8). The development of novel and useful biomarker panels is therefore an urgent need in the field of cancer management.Proteomics technology platforms are promising tools for the discovery of new cancer biomarkers (9). Over the past decade, serum and plasma have been the major targets of proteomics studies aimed at identifying potential cancer biomarkers (10–13). However, the progress of these studies has been hampered by the complex nature of serum/plasma samples and the large dynamic range between the concentrations of different proteins (14). As cancer biomarkers are likely to be present in low amounts in blood samples, the direct isolation of these markers from plasma and serum samples requires a labor-intensive process involving the depletion of abundant proteins and extensive protein fractionation prior to mass spectrometric analysis (15–18). Alternatively, the secretome, or group of proteins secreted by cancer cells (19), can be analyzed to identify circulating molecules present at elevated levels in serum or plasma samples from cancer patients. These proteins have the potential to act as cancer-derived marker candidates, which are distinct from host-responsive marker candidates. We, along with other groups, have demonstrated the efficacy of secretome-based strategies in a variety of cancer types, including NPC (20), breast cancer (21, 22), lung cancer (23, 24), CRC (25, 26), oral cancer (27), prostate cancer (28, 29), ovarian cancer (30), and Hodgkin lymphoma (31). In these studies, proteins secreted from cancer cells into serum-free media were resolved by one- or two-dimensional gels followed by in-gel tryptic digestion and analysis via MALDI-TOF MS or LC-MS/MS. Alternatively, the proteins were trypsin-digested in solution and analyzed by LC-MS/MS. In general, more proteins were detected in the secretome using the LC-MS/MS method than the MALDI-TOF MS method. Advanced protein separation and identification technologies have made it possible to detect more proteins in the secretomes of cancer cells, thereby facilitating the discovery of cancer biomarkers.Although the cancer cell secretomes of various tumor types have been individually analyzed by different groups using distinct protocols, few studies have used the same protocol to compare cancer cell secretomes derived from different tumor types. We previously assessed the secretomes of 21 cancer cell lines derived from 12 cancer types (i.e. consisting of 795 protein identities and 325 non-redundant proteins) by one-dimensional gel and MALDI-TOF MS (25). Our preliminary findings revealed that different cell lines have distinct secreted protein profiles and that several putative biomarkers, such as Mac-2BP (20, 26, 27, 29) and cathepsin D (21, 23, 32), present in the secretome of a given cancer cell type are commonly shared among different cancers. These observations suggest that an in-depth comparison of secretomes derived from different tumor types may identify marker candidates common to most cancers as well as markers for specific cancer types. As an increasing number of proteins are identified in the secretomes of various cancer cell lines, scientists are faced with the challenge of quickly and efficiently narrowing down the list to candidates with higher chances of success during validation testing with precious clinical specimens.In the present study, we applied one-dimensional SDS-PAGE in conjunction with nano-LC-MS/MS (GeLC-MS/MS) (33, 34) to analyze the conditioned media of 23 cancer cell lines derived from 11 cancer types, including NPC, breast cancer, bladder cancer, cervical cancer, CRC, epidermoid carcinoma, liver cancer, lung cancer, T cell lymphoma, oral cancer, and pancreatic cancer. Within this data set, 4,584 non-redundant proteins were identified from a total of 23 cell lines, yielding an average of ∼1,300 proteins per cell line. Potential marker candidates were identified via the comparative analysis of different cell line secretomes and by putative linkages to cancer-relevant pathways. The selected proteins were further compared with the HPA (35) to generate a focused data set of proteins that are secreted or released, cancer type-specific, and highly expressed in human cancer tissues. Finally, we selectively validated four proteins as potential serological cancer markers using blood samples from cancer patients.     

Clinical phenotype, prognosis and mitochondrial DNA mutation load in mitochondrial encephalomyopathies

We studied 42 individuals, including 8 patients with either complete or partial syndrome of mitochondrial myopathy, encephalopathy, lactic acidosis and stroke-like episodes (MELAS), 8 patients with either complete or partial syndrome of myoclonic epilepsy with ragged-red fibers (MERRF) and 26 maternal family members who carried either the A3243G or A8344G mutation of mitochondrial DNA (mtDNA). Clinical manifestations and prognosis were followed up in the patients harboring the A3243G or A8344G mutation. The relationship between clinical features and proportions of mutant mtDNAs in muscle biopsies, blood cells and/or hair follicles was studied. In the 8 regularly followed patients with the A3243G mutation, 4 died within 1 month to 7 years due to status epilepticus and/or recurrent stroke-like episodes. Two patients developed marked mental deterioration and 2 remained stationary. All of the patients harboring the A8344G mutation were stable or deteriorated slightly, except for 1 patient who died due to brain herniation after putaminal hemorrhage. The A3243G and A8344G mtDNA mutations were heteroplasmic in the muscle biopsies, blood cells and hair follicles of both the probands and their maternal family members. The mean proportion of A3243G mutant mtDNA in the muscle biopsies of the patients with MELAS syndrome (68.5 ± 21.3%, range 33–92%) was significantly higher than that of the asymptomatic family members (37.1 ± 12.6%, range 0–51%). The average proportions of A8344G mutant mtDNA in the muscle biopsies (90.1 ± 3.9%, range 89–95%) and hair follicles (93.9 ± 6.4%, range 84–99%) of the patients with MERRF syndrome were also significantly higher than those of the asymptomatic family members (muscle: 40.3 ± 39.5%, range 1–80%; hair follicles: 51.0 ± 44.5%, range 0.1–82%). We concluded that measurement of the proportion of mutant mtDNA in muscle biopsies may provide useful information in the identification of symptomatic patients with mitochondrial encephalomyopathies. For patients with the A3243G mutation, the prognosis was related to status epilepticus and the number of recurrent stroke-like episodes and was much worse than for patients with the A8344G mutation of mtDNA, who had stable or slowly deteriorating clinical courses.     

Characteristics of brain connectivity during verbal fluency test: Convolutional neural network for functional near-infrared spectroscopy analysis

Human connectome describes the complicated connection matrix of nervous system among human brain. It also possesses high potential of assisting doctors to monitor the brain injuries and recoveries in patients. In order to unravel the enigma of neuron connections and functions, previous research has strived to dig out the relations between neurons and brain regions. Verbal fluency test (VFT) is a general neuropsychological test, which has been used in functional connectivity investigations. In this study, we employed convolutional neural network (CNN) on a brain hemoglobin concentration changes (ΔHB) map obtained during VFT to investigate the connections of activated brain areas and different mental status. Our results show that feature of functional connectivity can be identified accurately with the employment of CNN on ΔHB mapping, which is beneficial to improve the understanding of brain functional connections.     

The role of cardiolipin in promoting the membrane pore-forming activity of BAX oligomers

BCL-2-associated X (BAX) protein acts as a gatekeeper in regulating mitochondria-dependent apoptosis. Under cellular stress, BAX becomes activated and transforms into a lethal oligomer that causes mitochondrial outer membrane permeabilization (MOMP). Previous studies have identified several structural features of the membrane-associated BAX oligomer; they include the formation of the BH3-in-groove dimer, the collapse of the helical hairpin α5–α6, and the membrane insertion of α9 helix. However, it remains unclear as to the role of lipid environment in determining the conformation and the pore-forming activity of the BAX oligomers. Here we study molecular details of the membrane-associated BAX in various lipid environments using fluorescence and ESR techniques. We identify the inactive versus active forms of membrane-associated BAX, only the latter of which can induce stable and large membrane pores that are sufficient in size to pass apoptogenic factors. We reveal that the presence of CL is crucial to promoting the association between BAX dimers, hence the active oligomers. Without the presence of CL, BAX dimers assemble into an inactive oligomer that lacks the ability to form stable pores in the membrane. This study suggests an important role of CL in determining the formation of active BAX oligomers.