The plasma peptides of sepsis.

BACKGROUND: A practical strategy to discover sepsis specific proteins may be to compare the plasma peptides and proteins from patients in the intensive care unit with and without sepsis. The aim was to discover proteins and/or peptides that show greater observation frequency and/or precursor intensity in sepsis. The endogenous tryptic peptides of ICU-Sepsis were compared to ICU Control, ovarian cancer, breast cancer, female normal, sepsis, heart attack, Alzheimer's and multiple sclerosis along with their institution-matched controls, female normals and normal samples collected directly onto ice. METHODS: Endogenous tryptic peptides were extracted from individual sepsis and control EDTA plasma samples in a step gradient of acetonitrile for random and independent sampling by LC-ESI-MS/MS with a set of robust and sensitive linear quadrupole ion traps. The MS/MS spectra were fit to fully tryptic peptides within proteins using the X!TANDEM algorithm. The protein observation frequency was counted using the SEQUEST algorithm after selecting the single best charge state and peptide sequence for each MS/MS spectra. The protein observation frequency of ICU-sepsis versus ICU Control was subsequently tested by Chi square analysis. The average protein or peptide log10 precursor intensity was compared across disease and control treatments by ANOVA in the R statistical system. RESULTS: Peptides and/or phosphopeptides of common plasma proteins such as ITIH3, SAA2, SAA1, and FN1 showed increased observation frequency by Chi square (χ2 > 9, p < 0.003) and/or precursor intensity in sepsis. Cellular gene symbols with large Chi square values from tryptic peptides included POTEB, CTNNA1, U2SURP, KIF24, NLGN2, KSR1, GTF2H1, KIT, RPS6KL1, VAV2, HSPA7, SMC2, TCEB3B, ZNF300, SUPV3L1, ADAMTS20, LAMB4, MCCC1, SUPT6H, SCN9A, SBNO1, EPHA1, ABLIM2, cB5E3.2, EPHA10, GRIN2B, HIVEP2, CCL16, TKT, LRP2 and TMF1 amongst others showed increased observation frequency. Similarly, increased frequency of tryptic phosphopeptides were observed from POM121C, SCN8A, TMED8, NSUN7, SLX4, MADD, DNLZ, PDE3B, UTY, DEPDC7, MTX1, MYO1E, RXRB, SYDE1, FN1, PUS7L, FYCO1, USP26, ACAP2, AHI1, KSR2, LMAN1, ZNF280D and SLC8A2 amongst others. Increases in mean precursor intensity in peptides from common plasma proteins such as ITIH3, SAA2, SAA1, and FN1 as well as cellular proteins such as COL24A1, POTEB, KANK1, SDCBP2, DNAH11, ADAMTS7, MLLT1, TTC21A, TSHR, SLX4, MTCH1, and PUS7L among others were associated with sepsis. The processing of SAA1 included the cleavage of the terminal peptide D/PNHFRPAGLPEKY from the most hydrophilic point of SAA1 on the COOH side of the cystatin C binding that was most apparent in ICU-Sepsis patients compared to all other diseases and controls. Additional cleavage of SAA1 on the NH2 terminus side of the cystatin binding site were observed in ICU-Sepsis. Thus there was disease associated variation in the processing of SAA1 in ICU-Sepsis versus ICU controls or other diseases and controls. CONCLUSION: Specific proteins and peptides that vary between diseases might be discovered by the random and independent sampling of multiple disease and control plasma from different hospital and clinics by LC-ESI-MS/MS for storage in a relational SQL Server database and analysis with the R statistical system that will be a powerful tool for clinical research. The processing of SAA1 may play an unappreciated role in the inflammatory response to Sepsis.

Re-evaluation of the 18 non-human protein standards used to create the empirical statistical model for decoy library searching.

The Empirical Statistical Model (ESM) for decoy library searching fused the expected amino acid sequence of 18 non-human protein standards to a human decoy library. The ESM assumed a priori the standards were pure such that only the 18 nominal proteins were true positive, all other proteins were false positive, there was no overlap in the peptides of non-human proteins versus human proteins, and that the score distribution of individual peptides would resolve true positive from false positive results or noise. The results of random and independent sampling by LC-ESI-MS/MS indicated that the fundamental assumptions of the ESM were not in good agreement with the actual purity of the commercial test standards and so the method showed a 99.7% false negative rate. The ESM for decoy library searching apparently showed poor agreement with SDS-PAGE using silver staining, goodness of fit of MS/MS spectra by X!TANDEM, FDR correction by Benjamini and Hochberg, or comparison to the observation frequency of null random MS/MS spectra, that all confirmed the standards contain hundreds of proteins with a low FDR of primary structural identification. The protein observation frequency increased with abundance and the log10 precursor intensity distributions were Gaussian and nearly ideal for relative quantification.

The plasma peptides of breast versus ovarian cancer.

BACKGROUND: There is a need to demonstrate a proof of principle that proteomics has the capacity to analyze plasma from breast cancer versus other diseases and controls in a multisite clinical trial design. The peptides or proteins that show a high observation frequency, and/or precursor intensity, specific to breast cancer plasma might be discovered by comparison to other diseases and matched controls. The endogenous tryptic peptides of breast cancer plasma were compared to ovarian cancer, female normal, sepsis, heart attack, Alzheimer's and multiple sclerosis along with the institution-matched normal and control samples collected directly onto ice. METHODS: Endogenous tryptic peptides were extracted from individual breast cancer and control EDTA plasma samples in a step gradient of acetonitrile, and collected over preparative C18 for LC-ESI-MS/MS with a set of LTQ XL linear quadrupole ion traps working together in parallel to randomly and independently sample clinical populations. The MS/MS spectra were fit to fully tryptic peptides or phosphopeptides within proteins using the X!TANDEM algorithm. The protein observation frequency was counted using the SEQUEST algorithm after selecting the single best charge state and peptide sequence for each MS/MS spectra. The observation frequency was subsequently tested by Chi Square analysis. The log10 precursor intensity was compared by ANOVA in the R statistical system. RESULTS: Peptides and/or phosphopeptides of common plasma proteins such as APOE, C4A, C4B, C3, APOA1, APOC2, APOC4, ITIH3 and ITIH4 showed increased observation frequency and/or precursor intensity in breast cancer. Many cellular proteins also showed large changes in frequency by Chi Square (χ2 > 100, p < 0.0001) in the breast cancer samples such as CPEB1, LTBP4, HIF-1A, IGHE, RAB44, NEFM, C19orf82, SLC35B1, 1D12A, C8orf34, HIF1A, OCLN, EYA1, HLA-DRB1, LARS, PTPDC1, WWC1, ZNF562, PTMA, MGAT1, NDUFA1, NOGOC, OR1E1, OR1E2, CFI, HSA12, GCSH, ELTD1, TBX15, NR2C2, FLJ00045, PDLIM1, GALNT9, ASH2L, PPFIBP1, LRRC4B, SLCO3A1, BHMT2, CS, FAM188B2, LGALS7, SAT2, SFRS8, SLC22A12, WNT9B, SLC2A4, ZNF101, WT1, CCDC47, ERLIN1, SPFH1, EID2, THOC1, DDX47, MREG, PTPRE, EMILIN1, DKFZp779G1236 and MAP3K8 among others. The protein gene symbols with large Chi Square values were significantly enriched in proteins that showed a complex set of previously established functional and structural relationships by STRING analysis. An increase in mean precursor intensity of peptides was observed for QSER1 as well as SLC35B1, IQCJ-SCHIP1, MREG, BHMT2, LGALS7, THOC1, ANXA4, DHDDS, SAT2, PTMA and FYCO1 among others. In contrast, the QSER1 peptide QPKVKAEPPPK was apparently specific to ovarian cancer. CONCLUSION: There was striking agreement between the breast cancer plasma peptides and proteins discovered by LC-ESI-MS/MS with previous biomarkers from tumors, cells lines or body fluids by genetic or biochemical methods. The results indicate that variation in plasma peptides from breast cancer versus ovarian cancer may be directly discovered by LC-ESI-MS/MS that will be a powerful tool for clinical research. It may be possible to use a battery of sensitive and robust linear quadrupole ion traps for random and independent sampling of plasma from a multisite clinical trial.

The plasma peptidome.

BACKGROUND: It may be possible to discover new diagnostic or therapeutic peptides or proteins from blood plasma using LC-ESI-MS/MS to identify, with a linear quadrupole ion trap to identify, quantify and compare the statistical distributions of peptides cleaved ex vivo from plasma samples from different clinical populations. METHODS: A systematic method for the organic fractionation of plasma peptides was applied to identify and quantify the endogenous tryptic peptides from human plasma from multiple institutions by C18 HPLC followed nano electrospray ionization and tandem mass spectrometry (LC-ESI-MS/MS) with a linear quadrupole ion trap. The endogenous tryptic peptides, or tryptic phospho peptides (i.e. without exogenous digestion), were extracted in a mixture of organic solvent and water, dried and collected by preparative C18. The tryptic peptides from 6 institutions with 12 different disease and normal EDTA plasma populations, alongside ice cold controls for pre-analytical variation, were characterized by mass spectrometry. Each patient plasma was precipitated in 90% acetonitrile and the endogenous tryptic peptides extracted by a stepwise gradient of increasing water and then formic acid resulting in 10 sub-fractions. The fractionated peptides were manually collected over preparative C18 and injected for 1508 LC-ESI-MS/MS experiments analyzed in SQL Server R. RESULTS: Peptides that were cleaved in human plasma by a tryptic activity ex vivo provided convenient and sensitive access to most human proteins in plasma that show differences in the frequency or intensity of proteins observed across populations that may have clinical significance. Combination of step wise organic extraction of 200 µL of plasma with nano electrospray resulted in the confident identification and quantification ~ 14,000 gene symbols by X!TANDEM that is the largest number of blood proteins identified to date and shows that you can monitor the ex vivo proteolysis of most human proteins, including interleukins, from blood. A total of 15,968,550 MS/MS spectra ≥ E4 intensity counts were correlated by the SEQUEST and X!TANDEM algorithms to a federated library of 157,478 protein sequences that were filtered for best charge state (2+ or 3+) and peptide sequence in SQL Server resulting in 1,916,672 distinct best-fit peptide correlations for analysis with the R statistical system. SEQUEST identified some 140,054 protein accessions, or some ~ 26,000 gene symbols, proteins or loci, with at least 5 independent correlations. The X!TANDEM algorithm made at least 5 best fit correlations to more than 14,000 protein gene symbols with p-values and FDR corrected q-values of ~ 0.001 or less. Log10 peptide intensity values showed a Gaussian distribution from E8 to E4 arbitrary counts by quantile plot, and significant variation in average precursor intensity across the disease and controls treatments by ANOVA with means compared by the Tukey-Kramer test. STRING analysis of the top 2000 gene symbols showed a tight association of cellular proteins that were apparently present in the plasma as protein complexes with related cellular components, molecular functions and biological processes. CONCLUSIONS: The random and independent sampling of pre-fractionated blood peptides by LC-ESI-MS/MS with SQL Server-R analysis revealed the largest plasma proteome to date and was a practical method to quantify and compare the frequency or log10 intensity of individual proteins cleaved ex vivo across populations of plasma samples from multiple clinical locations to discover treatment-specific variation using classical statistics suitable for clinical science. It was possible to identify and quantify nearly all human proteins from EDTA plasma and compare the results of thousands of LC-ESI-MS/MS experiments from multiple clinical populations using standard database methods in SQL Server and classical statistical strategies in the R data analysis system.

The plasma peptides of ovarian cancer.

BACKGROUND: It may be possible to discover new diagnostic or therapeutic peptides or proteins from blood plasma by using liquid chromatography and tandem mass spectrometry to identify, quantify and compare the peptides cleaved ex vivo from different clinical populations. The endogenous tryptic peptides of ovarian cancer plasma were compared to breast cancer and female cancer normal controls, other diseases with their matched or normal controls, plus ice cold plasma to control for pre-analytical variation. METHODS: The endogenous tryptic peptides or tryptic phospho peptides (i.e. without exogenous digestion) were analyzed from 200 µl of EDTA plasma. The plasma peptides were extracted by a step gradient of organic/water with differential centrifugation, dried, and collected over C18 for analytical HPLC nano electrospray ionization and tandem mass spectrometry (LC-ESI-MS/MS) with a linear quadrupole ion trap. The endogenous peptides of ovarian cancer were compared to multiple disease and normal samples from different institutions alongside ice cold controls. Peptides were randomly and independently sampled by LC-ESI-MS/MS. Precursor ions from peptides > E4 counts were identified by the SEQUEST and X!TANDEM algorithms, filtered in SQL Server, before testing of frequency counts by Chi Square (χ2), for analysis with the STRING algorithm, and comparison of precursor intensity by ANOVA in the R statistical system with the Tukey-Kramer Honestly Significant Difference (HSD) test. RESULTS: Peptides and/or phosphopeptides of common plasma proteins such as HPR, HP, HPX, and SERPINA1 showed increased observation frequency and/or precursor intensity in ovarian cancer. Many cellular proteins showed large changes in frequency by Chi Square (χ2 > 60, p < 0.0001) in the ovarian cancer samples such as ZNF91, ZNF254, F13A1, LOC102723511, ZNF253, QSER1, P4HA1, GPC6, LMNB2, PYGB, NBR1, CCNI2, LOC101930455, TRPM5, IGSF1, ITGB1, CHD6, SIRT1, NEFM, SKOR2, SUPT20HL1, PLCE1, CCDC148, CPSF3, MORN3, NMI, XTP11, LOC101927572, SMC5, SEMA6B, LOXL3, SEZ6L2, and DHCR24. The protein gene symbols with large Chi Square values were significantly enriched in proteins that showed a complex set of previously established functional and structural relationships by STRING analysis. Analysis of the frequently observed proteins by ANOVA confirmed increases in mean precursor intensity in ZFN91, TRPM5, SIRT1, CHD6, RIMS1, LOC101930455 (XP_005275896), CCDC37 and GIMAP4 between ovarian cancer versus normal female and other diseases or controls by the Tukey-Kramer HSD test. CONCLUSION: Here we show that separation of endogenous peptides with a step gradient of organic/water and differential centrifugation followed by random and independent sampling by LC-ESI-MS/MS with analysis of peptide frequency and intensity by SQL Server and R revealed significant difference in the ex vivo cleavage of peptides between ovarian cancer and other clinical treatments. There was striking agreement between the proteins discovered from cancer plasma versus previous biomarkers discovered in tumors by genetic or biochemical methods. The results indicate that variation in plasma proteins from ovarian cancer may be directly discovered by LC-ESI-MS/MS that will be a powerful tool for clinical research.

AMP-Activated Protein Kinase Regulates the Cell Surface Proteome and Integrin Membrane Traffic.

The cell surface proteome controls numerous cellular functions including cell migration and adhesion, intercellular communication and nutrient uptake. Cell surface proteins are controlled by acute changes in protein abundance at the plasma membrane through regulation of endocytosis and recycling (endomembrane traffic). Many cellular signals regulate endomembrane traffic, including metabolic signaling; however, the extent to which the cell surface proteome is controlled by acute regulation of endomembrane traffic under various conditions remains incompletely understood. AMP-activated protein kinase (AMPK) is a key metabolic sensor that is activated upon reduced cellular energy availability. AMPK activation alters the endomembrane traffic of a few specific proteins, as part of an adaptive response to increase energy intake and reduce energy expenditure. How increased AMPK activity during energy stress may globally regulate the cell surface proteome is not well understood. To study how AMPK may regulate the cell surface proteome, we used cell-impermeable biotinylation to selectively purify cell surface proteins under various conditions. Using ESI-MS/MS, we found that acute (90 min) treatment with the AMPK activator A-769662 elicits broad control of the cell surface abundance of diverse proteins. In particular, A-769662 treatment depleted from the cell surface proteins with functions in cell migration and adhesion. To complement our mass spectrometry results, we used other methods to show that A-769662 treatment results in impaired cell migration. Further, A-769662 treatment reduced the cell surface abundance of ß1-integrin, a key cell migration protein, and AMPK gene silencing prevented this effect. While the control of the cell surface abundance of various proteins by A-769662 treatment was broad, it was also selective, as this treatment did not change the cell surface abundance of the transferrin receptor. Hence, the cell surface proteome is subject to acute regulation by treatment with A-769662, at least some of which is mediated by the metabolic sensor AMPK.

Quantitative statistical analysis of standard and human blood proteins from liquid chromatography, electrospray ionization, and tandem mass spectrometry.

It will be important to determine if the parent and fragment ion intensity results of liquid chromatography, electrospray ionization and tandem mass spectrometry (LC-ESI-MS/MS) experiments have been randomly and independently sampled from a normal population for the purpose of statistical analysis by general linear models and ANOVA. The tryptic parent peptide and fragment ion m/z and intensity data in the mascot generic files from LC-ESI-MS/MS of purified standard proteins, and human blood protein fractionated by partition chromatography, were parsed into a Structured Query Language (SQL) database and were matched with protein and peptide sequences provided by the X!TANDEM algorithm. The many parent and/or fragment ion intensity values were log transformed, tested for normality, and analyzed using the generic Statistical Analysis System (SAS). Transformation of both parent and fragment intensity values by logarithmic functions yielded intensity distributions that closely approximate the log-normal distribution. ANOVA models of the transformed parent and fragment intensity values showed significant effects of treatments, proteins, and peptides, as well as parent versus fragment ion types, with a low probability of false positive results. Transformed parent and fragment intensity values were compared over all sample treatments, proteins or peptides by the Tukey-Kramer Honestly Significant Difference (HSD) test. The approach provided a complete and quantitative statistical analysis of LC-ESI-MS/MS data from human blood.