Proteomic Profiles in Acute Respiratory Distress Syndrome Differentiates Survivors from Non-Survivors

Acute Respiratory Distress Syndrome (ARDS) continues to have a high mortality. Currently, there are no biomarkers that provide reliable prognostic information to guide clinical management or stratify risk among clinical trial participants. The objective of this study was to probe the bronchoalveolar lavage fluid (BALF) proteome to identify proteins that differentiate survivors from non-survivors of ARDS. Patients were divided into early-phase (1 to 7 days) and late-phase (8 to 35 days) groups based on time after initiation of mechanical ventilation for ARDS (Day 1). Isobaric tags for absolute and relative quantitation (iTRAQ) with LC MS/MS was performed on pooled BALF enriched for medium and low abundance proteins from early-phase survivors (n = 7), early-phase non-survivors (n = 8), and late-phase survivors (n = 7). Of the 724 proteins identified at a global false discovery rate of 1%, quantitative information was available for 499. In early-phase ARDS, proteins more abundant in survivors mapped to ontologies indicating a coordinated compensatory response to injury and stress. These included coagulation and fibrinolysis; immune system activation; and cation and iron homeostasis. Proteins more abundant in early-phase non-survivors participate in carbohydrate catabolism and collagen synthesis, with no activation of compensatory responses. The compensatory immune activation and ion homeostatic response seen in early-phase survivors transitioned to cell migration and actin filament based processes in late-phase survivors, revealing dynamic changes in the BALF proteome as the lung heals. Early phase proteins differentiating survivors from non-survivors are candidate biomarkers for predicting survival in ARDS.     

Chloroquine sensitizes breast cancer cells to chemotherapy independent of autophagy

Chloroquine (CQ) is a 4-aminoquinoline drug used for the treatment of diverse diseases. It inhibits lysosomal acidification and therefore prevents autophagy by blocking autophagosome fusion and degradation. In cancer treatment, CQ is often used in combination with chemotherapeutic drugs and radiation because it has been shown to enhance the efficacy of tumor cell killing. Since CQ and its derivatives are the only inhibitors of autophagy that are available for use in the clinic, multiple ongoing clinical trials are currently using CQ or hydroxychloroquine (HCQ) for this purpose, either alone, or in combination with other anticancer drugs. Here we show that in the mouse breast cancer cell lines, 67NR and 4T1, autophagy is induced by the DNA damaging agent cisplatin or by drugs that selectively target autophagy regulation, the PtdIns3K inhibitor LY294002, and the mTOR inhibitor rapamycin. In combination with these drugs, CQ sensitized to these treatments, though this effect was more evident with LY294002 and rapamycin treatment. Surprisingly, however, in these experiments CQ sensitization occurred independent of autophagy inhibition, since sensitization was not mimicked by Atg12, Beclin 1 knockdown or bafilomycin treatment, and occurred even in the absence of Atg12. We therefore propose that although CQ might be helpful in combination with cancer therapeutic drugs, its sensitizing effects can occur independently of autophagy inhibition. Consequently, this possibility should be considered in the ongoing clinical trials where CQ or HCQ are used in the treatment of cancer, and caution is warranted when CQ treatment is used in cytotoxic assays in autophagy research.     

Dynamic proteome analysis of Cyanothece sp. ATCC 51142 under constant light

Understanding the dynamic nature of protein abundances provides insights into protein turnover not readily apparent from conventional, static mass spectrometry measurements. This level of data is particularly informative when surveying protein abundances in biological systems subjected to large perturbations or alterations in environment such as cyanobacteria. Our current analysis expands upon conventional proteomic approaches in cyanobacteria by measuring dynamic changes of the proteome using a (13)C(15)N-l-leucine metabolic labeling in Cyanothece ATCC51142. Metabolically labeled Cyanothece ATCC51142 cells grown under nitrogen-sufficient conditions in continuous light were monitored longitudinally for isotope incorporation over a 48 h period, revealing 414 proteins with dynamic changes in abundances. In particular, proteins involved in carbon fixation, pentose phosphate pathway, cellular protection, redox regulation, protein folding, assembly, and degradation showed higher levels of isotope incorporation, suggesting that these biochemical pathways are important for growth under continuous light. Calculation of relative isotope abundances (RIA) values allowed the measurement of actual active protein synthesis over time for different biochemical pathways under high light exposure. Overall results demonstrated the utility of "non-steady state" pulsed metabolic labeling for systems-wide dynamic quantification of the proteome in Cyanothece ATCC51142 that can also be applied to other cyanobacteria.     

Systematic metabolic engineering for improvement of glycosylation efficiency in Escherichia coli

Recently, efforts to increase the toolkit which Escherichia coli cells possess for recombinant protein production in industrial applications, has led to steady progress towards making glycosylated therapeutic proteins. Although the desire to make therapeutically relevant complex proteins with elaborate human-type glycans is a major goal, the relatively poor efficiency of the N-glycosylation process of foreign proteins in E. coli remains a hindrance for industry take-up. In this study, a systematic approach was used to increase glycoprotein production titres of an exemplar protein, AcrA, and the resulting glycosylation efficiency was quantified using a combination of Western blots and pseudo Selective Reaction Monitoring (pSRM). Western blot and pSRM results demonstrate that codon optimising the oligosaccharyltransferase, PglB, for E. coli expression, increases efficiency by 77% and 101%, respectively. Furthermore, increasing expression of glycosyltransferase, WecA, in E. coli improves efficiency by 43% and 27%, respectively. However, increasing the amount of donor lipid used in the glycosylation process did not impact on the glycosylation efficiency in this system, with this specific protein.     

Carboxymethylation of cellulose using reactive extrusion

Carboxymethyl cellulose was prepared using a continuous, reduced solvent, reactive extrusion process with a short reaction time. The effects of the amounts of NaOH (30 g, 40 g and 50 g), water:ethanol ratio (100%, 70%, 50%, 30% and 10% H₂O) and their interactions on the physical, chemical and morphological properties of carboxymethyl cellulose were studied. Experiments were conducted using to a 5 × 3 blocked factorial design. X-ray diffraction analyses revealed higher degrees of crystallinity and fractions of cellulose-II crystalline structure when 100% H₂O was used as compared to that for 70%, 50%, 30% and 10% H₂O and a commercially available brand of carboxymethyl cellulose, AQUASORB A500. Statistical analysis revealed a significant interaction between the effects of NaOH and H₂O on the degrees of substitutions. The degrees of substitutions decreased with increasing amounts of NaOH and tended to increase with increasing alcohol concentrations. Liquid uptake measurements revealed that the extent of saline uptake, measured at intervals of 1 min, 5 min and 10 min, by carboxymethyl cellulose prepared with 100% H₂O, especially when 40 g and 50 g NaOH was used, was higher than that for 70%, 50%, 30% and 10% H₂O and AQUASORB A500. This may have been because of the higher crystallinity in carboxymethyl cellulose prepared with 100% H₂O. Carboxymethyl cellulose prepared with 70% H₂O and 30 g and 50 g NaOH had the highest saline absorption, using the soak method, before and after centrifugation, respectively. Scanning electron microscopy for carboxymethyl cellulose prepared with 100% and 10% H₂O, through images at 120X magnification, revealed fibers 100 μ to >800 μ in length and 0.8-3.3 μ in breadth. Some non fibrous particles, 0.8-6.7 μ in dimensions, also were observed for 100% H₂O. Images at 900× magnification revealed partially damaged fiber surfaces.     

Quantitative analysis of Streptococcus pneumoniae TIGR4 response to in vitro iron restriction by 2-D LC ESI MS/MS

Understanding the growth of bacterial pathogens in a micronutrient restricted host environment can identify potential virulence proteins that help overcome this nutritional barrier to productive infection. In this study, we investigated the pneumococcal protein expression response to iron limitation using an in vitro model. We identified S. pneumoniae TIGR4 proteins by 2-D LC ESI MS/MS and determined significant changes in protein expression in response to iron restriction using computer-intensive random resampling methods. Differential protein expression was studied in the context of a S. pneumoniae TIGR4 protein interaction network using Pathway Studio. Our analysis showed that pneumococcal iron restriction response was marked by increased expression of known virulence factors like PsaA. It involved changes in the expression of stress response, and phase variation and biofilm formation proteins. The net effect of changes in all these biological processes could increase the virulence of S. pneumoniae TIGR4 during in vivo infection.     

Beyond attachment: Roles of DC‐SIGN in dengue virus infection

Dendritic cell‐specific intercellular adhesion molecule‐3‐grabbing non‐integrin (DC‐SIGN), a C‐type lectin expressed on the plasma membrane by human immature dendritic cells, is a receptor for numerous viruses including Ebola, SARS and dengue. A controversial question has been whether DC‐SIGN functions as a complete receptor for both binding and internalization of dengue virus (DENV) or whether it is solely a cell surface attachment factor, requiring either hand‐off to another receptor or a co‐receptor for internalization. To examine this question, we used 4 cell types: human immature dendritic cells and NIH3T3 cells expressing either wild‐type DC‐SIGN or 2 internalization‐deficient DC‐SIGN mutants, in which either the 3 cytoplasmic internalization motifs are silenced by alanine substitutions or the cytoplasmic region is truncated. Using confocal and super‐resolution imaging and high content single particle tracking, we investigated DENV binding, DC‐SIGN surface transport, endocytosis, as well as cell infectivity. DC‐SIGN was found colocalized with DENV inside cells suggesting hand‐off at the plasma membrane to another receptor did not occur. Moreover, all 3 DC‐SIGN molecules on NIH3T3 cells supported cell infection. These results imply the involvement of a co‐receptor because cells expressing the internalization‐deficient mutants could still be infected.      

DGCyTOF: Deep learning with graphic cluster visualization to predict cell types of single cell mass cytometry data

Single-cell mass cytometry, also known as cytometry by time of flight (CyTOF) is a powerful high-throughput technology that allows analysis of up to 50 protein markers per cell for the quantification and classification of single cells. Traditional manual gating utilized to identify new cell populations has been inadequate, inefficient, unreliable, and difficult to use, and no algorithms to identify both calibration and new cell populations has been well established. A deep learning with graphic cluster (DGCyTOF) visualization is developed as a new integrated embedding visualization approach in identifying canonical and new cell types. The DGCyTOF combines deep-learning classification and hierarchical stable-clustering methods to sequentially build a tri-layer construct for known cell types and the identification of new cell types. First, deep classification learning is constructed to distinguish calibration cell populations from all cells by softmax classification assignment under a probability threshold, and graph embedding clustering is then used to identify new cell populations sequentially. In the middle of two-layer, cell labels are automatically adjusted between new and unknown cell populations via a feedback loop using an iteration calibration system to reduce the rate of error in the identification of cell types, and a 3-dimensional (3D) visualization platform is finally developed to display the cell clusters with all cell-population types annotated. Utilizing two benchmark CyTOF databases comprising up to 43 million cells, we compared accuracy and speed in the identification of cell types among DGCyTOF, DeepCyTOF, and other technologies including dimension reduction with clustering, including Principal Component Analysis (PCA), Factor Analysis (FA), Independent Component Analysis (ICA), Isometric Feature Mapping (Isomap), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) with k-means clustering and Gaussian mixture clustering. We observed the DGCyTOF represents a robust complete learning system with high accuracy, speed and visualization by eight measurement criteria. The DGCyTOF displayed F-scores of 0.9921 for CyTOF1 and 0.9992 for CyTOF2 datasets, whereas those scores were only 0.507 and 0.529 for the t-SNE+k-means; 0.565 and 0.59, for UMAP+ k-means. Comparison of DGCyTOF with t-SNE and UMAP visualization in accuracy demonstrated its approximately 35% superiority in predicting cell types. In addition, observation of cell-population distribution was more intuitive in the 3D visualization in DGCyTOF than t-SNE and UMAP visualization. The DGCyTOF model can automatically assign known labels to single cells with high accuracy using deep-learning classification assembling with traditional graph-clustering and dimension-reduction strategies. Guided by a calibration system, the model seeks optimal accuracy balance among calibration cell populations and unknown cell types, yielding a complete and robust learning system that is highly accurate in the identification of cell populations compared to results using other methods in the analysis of single-cell CyTOF data. Application of the DGCyTOF method to identify cell populations could be extended to the analysis of single-cell RNASeq data and other omics data.     

Deep metaproteomic analysis of human salivary supernatant

The human salivary proteome is extremely complex, including proteins from salivary glands, serum, and oral microbes. Much has been learned about the host component, but little is known about the microbial component. Here we report a metaproteomic analysis of salivary supernatant pooled from six healthy subjects. For deep interrogation of the salivary proteome, we combined protein dynamic range compression (DRC), multidimensional peptide fractionation, and high-mass accuracy MS/MS with a novel two-step peptide identification method using a database of human proteins plus those translated from oral microbe genomes. Peptides were identified from 124 microbial species as well as uncultured phylotypes such as TM7. Streptococcus, Rothia, Actinomyces, Prevotella, Neisseria, Veilonella, Lactobacillus, Selenomonas, Pseudomonas, Staphylococcus, and Campylobacter were abundant among the 65 genera from 12 phyla represented. Taxonomic diversity in our study was broadly consistent with metagenomic studies of saliva. Proteins mapped to 20 KEGG pathways, with carbohydrate metabolism, amino acid metabolism, energy metabolism, translation, membrane transport, and signal transduction most represented. The communities sampled appear to be actively engaged in glycolysis and protein synthesis. This first deep metaproteomic catalog from human salivary supernatant provides a baseline for future studies of shifts in microbial diversity and protein activities potentially associated with oral disease.