High-throughput Isolation and Characterization of Untagged Membrane Protein Complexes: Outer Membrane Complexes of <i>Desulfovibrio vulgaris</i>

Cell membranes represent the “front line” of cellular defense and the interface between a cell and its environment. To determine the range of proteins and protein complexes that are present in the cell membranes of a target organism, we have utilized a “tagless” process for the system-wide isolation and identification of native membrane protein complexes. As an initial subject for study, we have chosen the Gram-negative sulfate-reducing bacterium <i>Desulfovibrio vulgaris</i>. With this tagless methodology, we have identified about two-thirds of the outer membrane- associated proteins anticipated. Approximately three-fourths of these appear to form homomeric complexes. Statistical and machine-learning methods used to analyze data compiled over multiple experiments revealed networks of additional protein–protein interactions providing insight into heteromeric contacts made between proteins across this region of the cell. Taken together, these results establish a <i>D. vulgaris</i> outer membrane protein data set that will be essential for the detection and characterization of environment-driven changes in the outer membrane proteome and in the modeling of stress response pathways. The workflow utilized here should be effective for the global characterization of membrane protein complexes in a wide range of organisms

High-throughput Isolation and Characterization of Untagged Membrane Protein Complexes: Outer Membrane Complexes of <i>Desulfovibrio vulgaris</i>

Cell membranes represent the “front line” of cellular defense and the interface between a cell and its environment. To determine the range of proteins and protein complexes that are present in the cell membranes of a target organism, we have utilized a “tagless” process for the system-wide isolation and identification of native membrane protein complexes. As an initial subject for study, we have chosen the Gram-negative sulfate-reducing bacterium <i>Desulfovibrio vulgaris</i>. With this tagless methodology, we have identified about two-thirds of the outer membrane- associated proteins anticipated. Approximately three-fourths of these appear to form homomeric complexes. Statistical and machine-learning methods used to analyze data compiled over multiple experiments revealed networks of additional protein–protein interactions providing insight into heteromeric contacts made between proteins across this region of the cell. Taken together, these results establish a <i>D. vulgaris</i> outer membrane protein data set that will be essential for the detection and characterization of environment-driven changes in the outer membrane proteome and in the modeling of stress response pathways. The workflow utilized here should be effective for the global characterization of membrane protein complexes in a wide range of organisms

Alterations in the Salivary Proteome and <i>N</i>‑Glycome of Sjögren’s Syndrome Patients

We used isobaric mass tagging (iTRAQ) and lectin affinity capture mass spectrometry (MS)-based workflows for global analyses of parotid saliva (PS) and whole saliva (WS) samples obtained from patients diagnosed with primary Sjögren’s Syndrome (pSS) who were enrolled in the Sjögren’s International Collaborative Clinical Alliance (SICCA) as compared with two control groups. The iTRAQ analyses revealed up- and down-regulation of numerous proteins that could be involved in the disease process (e.g., histones) or attempts to mitigate the ensuing damage (e.g., bactericidal/permeability increasing fold containing family (BPIF) members). An immunoblot approach applied to independent sample sets confirmed the pSS associated up-regulation of β2-microglobulin (in PS) and down-regulation of carbonic anhydrase VI (in WS) and BPIFB2 (in PS). Beyond the proteome, we profiled the <i>N</i>-glycosites of pSS and control samples. They were enriched for glycopeptides using lectins <i>Aleuria aurantia</i> and wheat germ agglutinin, which recognize fucose and sialic acid/<i>N</i>-acetyl glucosamine, respectively. MS analyses showed that pSS is associated with increased <i>N</i>-glycosylation of numerous salivary glycoproteins in PS and WS. The observed alterations of the salivary proteome and <i>N</i>-glycome could be used as pSS biomarkers enabling easier and earlier detection of this syndrome while lending potential new insights into the disease process

Lectin Chromatography/Mass Spectrometry Discovery Workflow Identifies Putative Biomarkers of Aggressive Breast Cancers

We used a lectin chromatography/MS-based approach to screen conditioned medium from a panel of luminal (less aggressive) and triple negative (more aggressive) breast cancer cell lines (<i>n</i> = 5/subtype). The samples were fractionated using the lectins <i>Aleuria aurantia</i> (AAL) and <i>Sambucus nigra</i> agglutinin (SNA), which recognize fucose and sialic acid, respectively. The bound fractions were enzymatically <i>N</i>-deglycosylated and analyzed by LC–MS/MS. In total, we identified 533 glycoproteins, ∼90% of which were components of the cell surface or extracellular matrix. We observed 1011 glycosites, 100 of which were solely detected in ≥3 triple negative lines. Statistical analyses suggested that a number of these glycosites were triple negative-specific and thus potential biomarkers for this tumor subtype. An analysis of RNaseq data revealed that approximately half of the mRNAs encoding the protein scaffolds that carried potential biomarker glycosites were up-regulated in triple negative vs luminal cell lines, and that a number of genes encoding fucosyl- or sialyltransferases were differentially expressed between the two subtypes, suggesting that alterations in glycosylation may also drive candidate identification. Notably, the glycoproteins from which these putative biomarker candidates were derived are involved in cancer-related processes. Thus, they may represent novel therapeutic targets for this aggressive tumor subtype

Lectin Chromatography/Mass Spectrometry Discovery Workflow Identifies Putative Biomarkers of Aggressive Breast Cancers

We used a lectin chromatography/MS-based approach to screen conditioned medium from a panel of luminal (less aggressive) and triple negative (more aggressive) breast cancer cell lines (<i>n</i> = 5/subtype). The samples were fractionated using the lectins <i>Aleuria aurantia</i> (AAL) and <i>Sambucus nigra</i> agglutinin (SNA), which recognize fucose and sialic acid, respectively. The bound fractions were enzymatically <i>N</i>-deglycosylated and analyzed by LC–MS/MS. In total, we identified 533 glycoproteins, ∼90% of which were components of the cell surface or extracellular matrix. We observed 1011 glycosites, 100 of which were solely detected in ≥3 triple negative lines. Statistical analyses suggested that a number of these glycosites were triple negative-specific and thus potential biomarkers for this tumor subtype. An analysis of RNaseq data revealed that approximately half of the mRNAs encoding the protein scaffolds that carried potential biomarker glycosites were up-regulated in triple negative vs luminal cell lines, and that a number of genes encoding fucosyl- or sialyltransferases were differentially expressed between the two subtypes, suggesting that alterations in glycosylation may also drive candidate identification. Notably, the glycoproteins from which these putative biomarker candidates were derived are involved in cancer-related processes. Thus, they may represent novel therapeutic targets for this aggressive tumor subtype

Lectin Chromatography/Mass Spectrometry Discovery Workflow Identifies Putative Biomarkers of Aggressive Breast Cancers

We used a lectin chromatography/MS-based approach to screen conditioned medium from a panel of luminal (less aggressive) and triple negative (more aggressive) breast cancer cell lines (<i>n</i> = 5/subtype). The samples were fractionated using the lectins <i>Aleuria aurantia</i> (AAL) and <i>Sambucus nigra</i> agglutinin (SNA), which recognize fucose and sialic acid, respectively. The bound fractions were enzymatically <i>N</i>-deglycosylated and analyzed by LC–MS/MS. In total, we identified 533 glycoproteins, ∼90% of which were components of the cell surface or extracellular matrix. We observed 1011 glycosites, 100 of which were solely detected in ≥3 triple negative lines. Statistical analyses suggested that a number of these glycosites were triple negative-specific and thus potential biomarkers for this tumor subtype. An analysis of RNaseq data revealed that approximately half of the mRNAs encoding the protein scaffolds that carried potential biomarker glycosites were up-regulated in triple negative vs luminal cell lines, and that a number of genes encoding fucosyl- or sialyltransferases were differentially expressed between the two subtypes, suggesting that alterations in glycosylation may also drive candidate identification. Notably, the glycoproteins from which these putative biomarker candidates were derived are involved in cancer-related processes. Thus, they may represent novel therapeutic targets for this aggressive tumor subtype

Lectin Chromatography/Mass Spectrometry Discovery Workflow Identifies Putative Biomarkers of Aggressive Breast Cancers

We used a lectin chromatography/MS-based approach to screen conditioned medium from a panel of luminal (less aggressive) and triple negative (more aggressive) breast cancer cell lines (<i>n</i> = 5/subtype). The samples were fractionated using the lectins <i>Aleuria aurantia</i> (AAL) and <i>Sambucus nigra</i> agglutinin (SNA), which recognize fucose and sialic acid, respectively. The bound fractions were enzymatically <i>N</i>-deglycosylated and analyzed by LC–MS/MS. In total, we identified 533 glycoproteins, ∼90% of which were components of the cell surface or extracellular matrix. We observed 1011 glycosites, 100 of which were solely detected in ≥3 triple negative lines. Statistical analyses suggested that a number of these glycosites were triple negative-specific and thus potential biomarkers for this tumor subtype. An analysis of RNaseq data revealed that approximately half of the mRNAs encoding the protein scaffolds that carried potential biomarker glycosites were up-regulated in triple negative vs luminal cell lines, and that a number of genes encoding fucosyl- or sialyltransferases were differentially expressed between the two subtypes, suggesting that alterations in glycosylation may also drive candidate identification. Notably, the glycoproteins from which these putative biomarker candidates were derived are involved in cancer-related processes. Thus, they may represent novel therapeutic targets for this aggressive tumor subtype

Lectin Chromatography/Mass Spectrometry Discovery Workflow Identifies Putative Biomarkers of Aggressive Breast Cancers

We used a lectin chromatography/MS-based approach to screen conditioned medium from a panel of luminal (less aggressive) and triple negative (more aggressive) breast cancer cell lines (<i>n</i> = 5/subtype). The samples were fractionated using the lectins <i>Aleuria aurantia</i> (AAL) and <i>Sambucus nigra</i> agglutinin (SNA), which recognize fucose and sialic acid, respectively. The bound fractions were enzymatically <i>N</i>-deglycosylated and analyzed by LC–MS/MS. In total, we identified 533 glycoproteins, ∼90% of which were components of the cell surface or extracellular matrix. We observed 1011 glycosites, 100 of which were solely detected in ≥3 triple negative lines. Statistical analyses suggested that a number of these glycosites were triple negative-specific and thus potential biomarkers for this tumor subtype. An analysis of RNaseq data revealed that approximately half of the mRNAs encoding the protein scaffolds that carried potential biomarker glycosites were up-regulated in triple negative vs luminal cell lines, and that a number of genes encoding fucosyl- or sialyltransferases were differentially expressed between the two subtypes, suggesting that alterations in glycosylation may also drive candidate identification. Notably, the glycoproteins from which these putative biomarker candidates were derived are involved in cancer-related processes. Thus, they may represent novel therapeutic targets for this aggressive tumor subtype

Lectin Chromatography/Mass Spectrometry Discovery Workflow Identifies Putative Biomarkers of Aggressive Breast Cancers

We used a lectin chromatography/MS-based approach to screen conditioned medium from a panel of luminal (less aggressive) and triple negative (more aggressive) breast cancer cell lines (<i>n</i> = 5/subtype). The samples were fractionated using the lectins <i>Aleuria aurantia</i> (AAL) and <i>Sambucus nigra</i> agglutinin (SNA), which recognize fucose and sialic acid, respectively. The bound fractions were enzymatically <i>N</i>-deglycosylated and analyzed by LC–MS/MS. In total, we identified 533 glycoproteins, ∼90% of which were components of the cell surface or extracellular matrix. We observed 1011 glycosites, 100 of which were solely detected in ≥3 triple negative lines. Statistical analyses suggested that a number of these glycosites were triple negative-specific and thus potential biomarkers for this tumor subtype. An analysis of RNaseq data revealed that approximately half of the mRNAs encoding the protein scaffolds that carried potential biomarker glycosites were up-regulated in triple negative vs luminal cell lines, and that a number of genes encoding fucosyl- or sialyltransferases were differentially expressed between the two subtypes, suggesting that alterations in glycosylation may also drive candidate identification. Notably, the glycoproteins from which these putative biomarker candidates were derived are involved in cancer-related processes. Thus, they may represent novel therapeutic targets for this aggressive tumor subtype

Lectin Chromatography/Mass Spectrometry Discovery Workflow Identifies Putative Biomarkers of Aggressive Breast Cancers

We used a lectin chromatography/MS-based approach to screen conditioned medium from a panel of luminal (less aggressive) and triple negative (more aggressive) breast cancer cell lines (<i>n</i> = 5/subtype). The samples were fractionated using the lectins <i>Aleuria aurantia</i> (AAL) and <i>Sambucus nigra</i> agglutinin (SNA), which recognize fucose and sialic acid, respectively. The bound fractions were enzymatically <i>N</i>-deglycosylated and analyzed by LC–MS/MS. In total, we identified 533 glycoproteins, ∼90% of which were components of the cell surface or extracellular matrix. We observed 1011 glycosites, 100 of which were solely detected in ≥3 triple negative lines. Statistical analyses suggested that a number of these glycosites were triple negative-specific and thus potential biomarkers for this tumor subtype. An analysis of RNaseq data revealed that approximately half of the mRNAs encoding the protein scaffolds that carried potential biomarker glycosites were up-regulated in triple negative vs luminal cell lines, and that a number of genes encoding fucosyl- or sialyltransferases were differentially expressed between the two subtypes, suggesting that alterations in glycosylation may also drive candidate identification. Notably, the glycoproteins from which these putative biomarker candidates were derived are involved in cancer-related processes. Thus, they may represent novel therapeutic targets for this aggressive tumor subtype