Inferring secretory and metabolic pathway activity from omic data with secCellFie.

Understanding protein secretion has considerable importance in biotechnology and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer properties of protein secretion from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can help predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.

Comparative Analysis of T-Cell Spatial Proteomics and the Influence of HIV Expression.

As systems biology approaches to virology have become more tractable, highly studied viruses such as HIV can now be analyzed in new unbiased ways, including spatial proteomics. We employed here a differential centrifugation protocol to fractionate Jurkat T cells for proteomic analysis by mass spectrometry; these cells contain inducible HIV-1 genomes, enabling us to look for changes in the spatial proteome induced by viral gene expression. Using these proteomics data, we evaluated the merits of several reported machine learning pipelines for classification of the spatial proteome and identification of protein translocations. From these analyses, we found that classifier performance in this system was organelle dependent, with Bayesian t-augmented Gaussian mixture modeling outperforming support vector machine learning for mitochondrial and endoplasmic reticulum proteins but underperforming on cytosolic, nuclear, and plasma membrane proteins by QSep analysis. We also observed a generally higher performance for protein translocation identification using a Bayesian model, Bayesian analysis of differential localization experiments, on row-normalized data. Comparative Bayesian analysis of differential localization experiment analysis of cells induced to express the WT viral genome versus cells induced to express a genome unable to express the accessory protein Nef identified known Nef-dependent interactors such as T-cell receptor signaling components and coatomer complex. Finally, we found that support vector machine classification showed higher consistency and was less sensitive to HIV-dependent noise. These findings illustrate important considerations for studies of the spatial proteome following viral infection or viral gene expression and provide a reference for future studies of HIV-gene-dropout viruses.

In situ detection of protein interactions for recombinant therapeutic enzymes.

Despite their therapeutic potential, many protein drugs remain inaccessible to patients since they are difficult to secrete. Each recombinant protein has unique physicochemical properties and requires different machinery for proper folding, assembly, and posttranslational modifications (PTMs). Here we aimed to identify the machinery supporting recombinant protein secretion by measuring the protein-protein interaction (PPI) networks of four different recombinant proteins (SERPINA1, SERPINC1, SERPING1, and SeAP) with various PTMs and structural motifs using the proximity-dependent biotin identification (BioID) method. We identified PPIs associated with specific features of the secreted proteins using a Bayesian statistical model and found proteins involved in protein folding, disulfide bond formation, and N-glycosylation were positively correlated with the corresponding features of the four model proteins. Among others, oxidative folding enzymes showed the strongest association with disulfide bond formation, supporting their critical roles in proper folding and maintaining the ER stability. Knockdown of disulfide-isomerase PDIA4, a measured interactor with significance for SERPINC1 but not SERPINA1, led to the decreased secretion of SERPINC1, which relies on its extensive disulfide bonds, compared to SERPINA1, which has no disulfide bonds. Proximity-dependent labeling successfully identified the transient interactions supporting synthesis of secreted recombinant proteins and refined our understanding of key molecular mechanisms of the secretory pathway during recombinant protein production.

Screening natural libraries of human milk oligosaccharides against lectins using CaR-ESI-MS.

Human milk oligosaccharides (HMOs) afford many health benefits to breast-fed infants, such as protection against infection and regulation of the immune system, through the formation of non-covalent interactions with protein receptors. However, the molecular details of these interactions are poorly understood. Here, we describe the application of catch-and-release electrospray ionization mass spectrometry (CaR-ESI-MS) for screening natural libraries of HMOs against lectins. The HMOs in the libraries were first identified based on molecular weights (MWs), ion mobility separation arrival times (IMS-ATs) and collision-induced dissociation (CID) fingerprints of their deprotonated anions. The libraries were then screened against lectins and the ligands identified from the MWs, IMS-ATs and CID fingerprints of HMOs released from the lectin in the gas phase. To demonstrate the assay, four fractions, extracted from pooled human milk and containing ≥35 different HMOs, were screened against a C-terminal fragment of human galectin-3 (hGal-3C), for which the HMOs specificities have been previously investigated, and a fragment of the blood group antigen-binding adhesin (BabA) from Helicobacter pylori, for which the HMO specificities have not been previously established. The structures of twenty-one ligands, corresponding to both neutral and acidic HMOs, of hGal-3C were identified; all twenty-one were previously shown to be ligands for this lectin. The presence of HMO ligands at six other MWs was also ascertained. Application of the assay to BabA revealed nineteen specific HMO structures that are recognized by the protein and HMO ligands at two other MWs. Notably, it was found that BabA exhibits broad specificity for HMOs, and recognizes both neutral HMOs, including non-fucosylated ones, and acidic HMOs. The results of competitive binding experiments indicate that HMOs can interact with BabA at previously unknown binding sites. The affinities of eight purified HMOs for BabA were measured by ESI-MS and found to be in the 103 M-1 to 104 M-1 range.

High-Throughput Label- and Immobilization-Free Screening of Human Milk Oligosaccharides Against Lectins.

The intense interest in the mechanisms responsible for the beneficial effects of breast-feeding on infant health has created a significant need for analytical methods capable of rapidly identifying interactions between human milk oligosaccharides (HMOs) and their protein receptors. Currently, there are no established, high-throughput assays for the screening libraries of free (unmodified) HMOs against lectins. The present work describes a rapid and label- and immobilization-free assay, based on catch-and-release electrospray ionization mass spectrometry (CaR-ESI-MS), capable of simultaneously screening mixtures of free HMOs of known concentration for binding to lectins in vitro. Ligand identification relies on the molecular weights (MWs), ion mobility separation arrival times, and collision-induced dissociation fingerprints of HMO anions released from the target protein in the gas phase. To establish the reliability of the assay, a library of 31 free HMOs, ranging in size from tri- to octasaccharide, was screened against three human galectin (hGal) proteins (a stable mutant of hGal1 (hGal-1), a C-terminal fragment of hGal-3 (hGal-3C) and hGal-7), with known HMO affinities. When implemented using an equimolar concentration library, the CaR-ESI-MS assay identified 100% of ligands with affinities >500 M-1 and ≥93% of all HMO ligands (hGal-1-31 of 31 ligands; hGal-3C-25 of 25; hGal-7-28 of 30); no false positives were detected. The assay also successfully identified the majority of the highest affinity HMO ligands (or isomer sets that contain the highest affinity ligands) in the library for each of the three hGal. Notably, for each lectin, CaR-ESI-MS screening required <1 h to complete and consumed <5 ng of each HMO and <0.5 µg of protein.

Human Milk Oligosaccharide Specificities of Human Galectins. Comparison of Electrospray Ionization Mass Spectrometry and Glycan Microarray Screening Results.

The affinities of thirty-two free human milk oligosaccharides (HMOs) for four human galectin proteins, a stable mutant of hGal1 (hGal-1), a C-terminal fragment of hGal-3 (hGal-3C), hGal-7, and an N-terminal fragment of hGal-9 (hGal-9N), were measured using electrospray ionization mass spectrometry (ESI-MS). The binding data show that each of the four galectins recognize the majority of the HMOs tested (hGal-1 binds thirty-two HMOs, hGal-3C binds twenty-six, hGal-7 binds thirty-one, and hGal-9N binds twenty-six). Twenty-five of the HMOs tested bind all four galectins, with affinities ranging from 103 to 105 M-1. The reliability of the ESI-MS assay for quantifying the affinities of HMOs for lectins was established from the agreement found between the ESI-MS data and affinities of a small number of HMOs for hGal-1, hGal-3C, and hGal-7 measured by isothermal titration calorimetry (ITC). Comparison of the relative affinities (of 14 HMOs) measured by ESI-MS with the reported specificities of hGal-1, hGal-3, hGal-7, and hGal-9 for these same HMOs established using the shotgun human milk glycan microarray (HM-SGM-v2) showed fair-to-poor correlation, with evidence of false positives and false negatives in the microarray data. The results of this study suggest that HMO specificities of lectins established using microarrays may not accurately reflect their true HMO-binding properties and that the use of "in solution" assays such as ESI-MS and ITC is to be preferred.

Screening Oligosaccharide Libraries against Lectins Using the Proxy Protein Electrospray Ionization Mass Spectrometry Assay.

An electrospray ionization mass spectrometry (ESI-MS) assay for screening carbohydrate libraries against lectins is described. The assay is based on the proxy protein ESI-MS method, which combines direct ESI-MS protein-ligand binding measurements and competitive protein binding, to simultaneously detect and quantify protein-carbohydrate interactions. Specific interactions between components of the library and the target protein (PT) are identified from changes in the relative abundances (as measured by ESI-MS) of the carbohydrate complexes of a proxy protein (Pproxy), which binds to all components of the library with known affinity, upon addition of PT to the solution. The magnitude of the change in relative abundance of a given Pproxy-ligand complex provides a quantitative measure of the affinity of the corresponding PT-ligand interaction. A mathematical framework for the implementation of the method in the case of monovalent (single binding site) Pproxy and monovalent and multivalent (multiple equivalent and independent binding sites) PT is described. The application of the method to screen small libraries of oligosaccharides, on the basis of human histo-blood group antigens and milk oligosaccharides, against an N-terminal fragment of the family 51 carbohydrate-binding module, a fucose-binding lectin from Ralstonia solanacearum, and human norovirus VA387 P particle (24-mer of the protruding domain of the capsid protein), serves to demonstrate the reliability and versatility of the assay.

Quantifying protein-carbohydrate interactions using liquid sample desorption electrospray ionization mass spectrometry.

The application of liquid sample desorption electrospray ionization mass spectrometry (liquid sample DESI-MS) for quantifying protein-carbohydrate interactions in vitro is described. Association constants for the interactions between lysozyme and ß-D-GlcNAc-(1 → 4)-ß-D-GlcNAc-(1 → 4)-D-GlcNAc and ß-D-GlcNAc-(1 → 4)-ß-D-GlcNAc-(1 → 4)-ß-D-GlcNAc-(1 → 4)-D-GlcNAc, and between a single chain antibody and α-D-Galp-(1 → 2)-[α-D-Abep-(1 → 3)]-α-D-Manp-OCH3 and ß-D-Glcp-(1 → 2)-[α-D-Abep-(1 → 3)]-α-D-Manp-OCH3 measured using liquid sample DESI-MS were found to be in good agreement with values measured by isothermal titration calorimetry and the direct ESI-MS assay. The reference protein method, which was originally developed to correct ESI mass spectra for the occurrence of nonspecific ligand-protein binding, was shown to reliably correct liquid sample DESI mass spectra for nonspecific binding. The suitability of liquid sample DESI-MS for quantitative binding measurements carried out using solutions containing high concentrations of the nonvolatile biological buffer phosphate buffered saline (PBS) was also explored. Binding of lysozyme to ß-D-GlcNAc-(1 → 4)-ß-D-GlcNAc-(1 → 4)-D-GlcNAc in aqueous solutions containing up to 1× PBS was successfully monitored using liquid sample DESI-MS; with ESI-MS the binding measurements were limited to concentrations less than 0.02 X PBS.

Antinociceptive, anti-inflammatory, antimicrobial and central nervous system depressant activities of ethanolic extract of leaves and roots of Gomphostemma parviflorum var. parviflorum wall.

BACKGROUND: Gomphostemma parviflorum (Lamiaceae) is a medicinal plant of Bangladesh which has been used traditionally in the treatment of painful and inflammatory conditions such as asthma, headache, fever, etc. OBJECTIVE: To investigate the antinociceptive, anti-inflammatory, central nervous system (CNS) depressant and antimicrobial activities of ethanolic extracts of leaves (GPLE) and roots (GPRE) of the plant. MATERIALS AND METHODS: The antinociceptive potentials of the extracts were studied using acetic acid-induced writhing test in mice, anti-inflammatory activity was investigated using carrageenan-induced paw edema in rats, CNS depressant activities were evaluated using pentobarbitone-induced sleeping time, Hole cross and Open field tests in mice while the anti-microbial activity was studied by in vitro disc diffusion method. RESULTS: The extracts GPLE and GPRE significantly (P < 0.001) and dose dependently inhibited the acetic acid-induced writhing in mice with 73.15% and 53.69% inhibition, respectively at the dose of 200 mg/kg. At the same dose GPLE and GPRE significantly inhibited carrageenan-induced rats paw edema at the end of 4 hour with 35.54% and 28.17% inhibition, respectively. The extracts significantly prolonged the pentobarbitone-induced sleeping time and decreased the locomotory activities in open field and Hole cross tests in mice. The GPLE showed strong antimicrobial activity against Gram-positive and Gram-negative bacteria with zones of inhibition ranging from 8 to 20 mm at a concentration of 400 µg/disc. CONCLUSION: The findings of the study indicate that the leaves and roots of G. parviflorum possess antinociceptive, anti-inflammatory and CNS depressant activity and revealed the antimicrobial activities of leaves extract of the plant. The results justify the traditional use of the plant in the treatment of painful and inflammatory disorders.