An FD-LC-MS/MS Proteomic Strategy for Revealing Cellular Protein Networks: A Conditional Superoxide Dismutase 1 Knockout Cells

<div><p>Systems biology aims to understand biological phenomena in terms of complex biological and molecular interactions, and thus proteomics plays an important role in elucidating protein networks. However, many proteomic methods have suffered from their high variability, resulting in only showing altered protein names. Here, we propose a strategy for elucidating cellular protein networks based on an FD-LC-MS/MS proteomic method. The strategy permits reproducible relative quantitation of differences in protein levels between different cell populations and allows for integration of the data with those obtained through other methods. We demonstrate the validity of the approach through a comparison of differential protein expression in normal and conditional superoxide dismutase 1 gene knockout cells and believe that beginning with an FD-LC-MS/MS proteomic approach will enable researchers to elucidate protein networks more easily and comprehensively.</p> </div

Predicted protein networks in SOD1(−) cells.

<p>Protein networks for denaturation, refolding, decomposition, ATP-consumption and -production. Arrows indicate increased (up) and decreased (down) expression. The (+) and (−) signs indicate enhancement or suppression, respectively, of cellular processes in response to changes in protein expression. The abbreviated protein names are in Table S1. GR: glucocorticoid receptor.</p

Simplified scheme for the proposed strategy.

<p>Using the method, protein expression changes caused by a particular stimulus are quantified. The resulting data are integrated with other data and a diagram of predicted protein networks is constructed.</p

Schematic illustration of the FD-LC-MS/MS proteomic method.

<p>After fluorogenic derivatization, the protein mixtures are separated by HPLC, and proteins exhibiting significant differential expression are isolated and identified using nano-HPLC-MS/MS and database searching.</p

Classification of identified proteins.

<p>Functional classification of differentially expressed proteins identified in this study. The most significant changes were observed with proteins involved in mediating protein folding. ND: No Data.</p

miR-451 expression in paired background lung and non-small cell lung cancer tissues.

<p>The miR-451 expression is significantly lower in NSCLC tissues than in background lung tissue (<i>P</i><0.0001 with paired t-test). Y-axis: -δCt = - -(Ct miR-451−Ct RNU6B).</p

A univariate analysis of clinicopathological factors for a disease-free survival in non-small cell lung cancer patients.

<p>A univariate analysis of clinicopathological factors for a disease-free survival in non-small cell lung cancer patients.</p

Macrophage migration inhibitory factor (MIF) expression of non-small cell lung cancer (NSCLC) cell lines and the effects of miR-451 mimic transfection of NSCLC cell lines.

<p>(A) MIF expression in NSCLC cell lines by western blotting. H441 and H1975 showed relatively higher MIF expression. (B) Western blotting of MIF, phosphorylated Akt (pAkt), total Akt, phosphorylated Erk (pErk), and total Erk of H441 and H1975 cells after miR-451-mimic (miR-451) and Mimic Negative Control (control) transfection. (C) Cell proliferation assay of H441 and H1975 cells after miR-451-mimic (miR-451) and Mimic Negative Control (control) transfection d: day, error bars: +/-SEM, *: <i>P</i><0.05 (unpaired t-test), **: <i>P</i> <0.005 (unpaired t-test). (D) Cell migration assay of H1975 cells after miR-451-mimic (miR-451) and Mimic Negative Control (control) transfection. Migrated cell stained with Giemsa solution (left) and migrated cell counts (right). Error bars: +/-SEM, **:<i>P</i><0.005 (unpaired t-test).</p

Correlations of the miR-451 expression with clinicopathological factors in lung adenocarcinomas.

<p>Correlations of the miR-451 expression with clinicopathological factors in lung adenocarcinomas.</p

A multivariate survival analysis in non-small cell lung cancer cases.

<p>A multivariate survival analysis in non-small cell lung cancer cases.</p