A framework for quality control in quantitative proteomics

A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share real-world case studies applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis using Skyline, longitudinal QC metrics using AutoQC, and server-based data deposition using PanoramaWeb. We propose that this integrated approach to QC be used as a starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible.

Putting Humpty Dumpty Back Together Again: What Does Protein Quantification Mean in Bottom-Up Proteomics?

Bottom-up proteomics provides peptide measurements and has been invaluable for moving proteomics into large-scale analyses. Commonly, a single quantitative value is reported for each protein-coding gene by aggregating peptide quantities into protein groups following protein inference or parsimony. However, given the complexity of both RNA splicing and post-translational protein modification, it is overly simplistic to assume that all peptides that map to a singular protein-coding gene will demonstrate the same quantitative response. By assuming that all peptides from a protein-coding sequence are representative of the same protein, we may miss the discovery of important biological differences. To capture the contributions of existing proteoforms, we need to reconsider the practice of aggregating protein values to a single quantity per protein-coding gene.

Accurately Assigning Peptides to Spectra When Only a Subset of Peptides Are Relevant

The standard proteomics database search strategy involves searching spectra against a peptide database and estimating the false discovery rate (FDR) of the resulting set of peptide-spectrum matches. One assumption of this protocol is that all the peptides in the database are relevant to the hypothesis being investigated. However, in settings where researchers are interested in a subset of peptides, alternative search and FDR control strategies are needed. Recently, two methods were proposed to address this problem: subset-search and all-sub. We show that both methods fail to control the FDR. For subset-search, this failure is due to the presence of "neighbor" peptides, which are defined as irrelevant peptides with a similar precursor mass and fragmentation spectrum as a relevant peptide. Not considering neighbors compromises the FDR estimate because a spectrum generated by an irrelevant peptide can incorrectly match well to a relevant peptide. Therefore, we have developed a new method, "subset-neighbor search" (SNS), that accounts for neighbor peptides. We show evidence that SNS controls the FDR when neighbors are present and that SNS outperforms group-FDR, the only other method that appears to control the FDR relative to a subset of relevant peptides.

High-Density Lipoprotein Carries Markers That Track With Recovery From Stroke

RATIONALE

Prospective cohort studies question the value of HDL-C (high-density lipoprotein cholesterol) for stroke risk prediction.

OBJECTIVE

Investigate the relationship between long-term functional recovery and HDL proteome and function.

METHODS AND RESULTS

Changes in HDL protein composition and function (cholesterol efflux capacity) in patients after acute ischemic stroke at 2 time points (24 hours, 35 patients; 96 hours, 20 patients) and in 35 control subjects were measured. The recovery from stroke was assessed by 3 months, the National Institutes of Health Stroke Scale and modified Rankin scale scores. When compared with control subject after adjustments for sex and HDL-C levels, 12 proteins some of which participate in acute phase response and platelet activation (APMAP [adipocyte plasma membrane-associated protein], GPLD1 [phosphate inositol-glycan specific phospholipase D], APOE [apolipoprotein E], IHH [Indian hedgehog protein], ITIH4 [inter-alpha-trypsin inhibitor chain H4], SAA2 [serum amyloid A2], APOA4 [apolipoprotein A-IV], CLU [clusterin], ANTRX2 [anthrax toxin receptor 2], PON1 [serum paraoxonase/arylesterase], SERPINA1 [alpha-1-antitrypsin], and APOF [apolipoprotein F]) were significantly (adjusted P<0.05) altered in stroke HDL at 96 hours. The first 8 of these proteins were also significantly altered at 24 hours. Consistent with inflammatory remodeling, cholesterol efflux capacity was reduced by 32% (P<0.001) at both time points. Baseline stroke severity adjusted regression model showed that changes within 96-hour poststroke in APOF, APOL1, APMAP, APOC4 (apolipoprotein C4), APOM (apolipoprotein M), PCYOX1 (prenylcysteine oxidase 1), PON1, and APOE correlate with stroke recovery scores (R2=0.38-0.73, adjusted P<0.05). APOF (R2=0.73) and APOL1 (R2=0.60) continued to significantly correlate with recovery scores after accounting for tPA (tissue-type plasminogen activator) treatment.

CONCLUSIONS

Changes in HDL proteins during early acute phase of stroke associate with recovery. Monitoring HDL proteins may provide clinical biomarkers that inform on stroke recuperation.

Elevated Lipoprotein(a) Levels Lower ABCA1 Cholesterol Efflux Capacity

CONTEXT

Elevated serum lipoprotein(a) [Lp(a)] levels are associated with increased cardiovascular disease risk. ABCA1-mediated cholesterol efflux from macrophages may be an antiatherogenic process. Plasminogen (PLG) is a driver of ABCA1-mediated cholesterol efflux, and its action is inhibited by purified human Lp(a).

OBJECTIVE

To determine the effects of Lp(a) in human serum on ABCA1 cholesterol efflux.

METHODS

Cholesterol efflux capacity (CEC) was measured with two different cell-culture models using serum from 76 patients with either low (<50 mg/dL) or high (>50 mg/dL) Lp(a) levels.

RESULTS

Using cAMP-stimulated J774 macrophages or baby hamster kidney fibroblasts overexpressing human ABCA1, we show that CEC was lower in patients with high Lp(a) levels compared with patients with low levels (-30.6%, P = 0.002 vs -24.1%, P < 0.001, respectively). Total-serum CEC negatively correlated with Lp(a) levels (r = -0.433, P = 0.0007 vs r = -0.505, P = 0.0011, respectively). These negative associations persisted after adjusting for serum cholesterol, age, sex, and statin use in a multiple linear regression model (adjusted R2 = 0.413 or 0.405, respectively) and were strengthened when further adjusting for the interaction between Lp(a) and PLG levels (adjusted R2 = 0.465 and 0.409, respectively). Total-serum and isolated Lp(a) from patients with high Lp(a) inhibited PLG-mediated ABCA1 cholesterol efflux.

CONCLUSION

Total-serum CEC is reduced in patients with high Lp(a) levels. This is in part due to the inhibition of PLG-mediated ABCA1 cholesterol efflux by Lp(a). Our findings suggest an atherogenic role for Lp(a) through its ability to inhibit CEC.

Genetic control of the mouse HDL proteome defines HDL traits, function, and heterogeneity

HDLs are nanoparticles with more than 80 associated proteins, phospholipids, cholesterol, and cholesteryl esters. The potential inverse relation of HDL to coronary artery disease (CAD) and the effects of HDL on myriad other inflammatory conditions warrant a better understanding of the genetic basis of the HDL proteome. We conducted a comprehensive genetic analysis of the regulation of the proteome of HDL isolated from a panel of 100 diverse inbred strains of mice (the hybrid mouse diversity panel) and examined protein composition and efflux capacity to identify novel factors that affect the HDL proteome. Genetic analysis revealed widely varied HDL protein levels across the strains. Some of this variation was explained by local cis-acting regulation, termed cis-protein quantitative trait loci (QTLs). Variations in apoA-II and apoC-3 affected the abundance of multiple HDL proteins, indicating a coordinated regulation. We identified modules of covarying proteins and defined a protein-protein interaction network that describes the protein composition of the naturally occurring subspecies of HDL in mice. Sterol efflux capacity varied up to 3-fold across the strains, and HDL proteins displayed distinct correlation patterns with macrophage and ABCA1-specific cholesterol efflux capacity and cholesterol exchange, suggesting that subspecies of HDL participate in discrete functions. The baseline and stimulated sterol efflux capacity phenotypes were associated with distinct QTLs with smaller effect size, suggesting a multigenetic regulation. Our results highlight the complexity of HDL particles by revealing the high degree of heterogeneity and intercorrelation, some of which is associated with functional variation, and support the concept that HDL-cholesterol alone is not an accurate measure of HDL’s properties, such as protection against CAD.

GM-CSF driven myeloid cells in adipose tissue link weight gain and insulin resistance via formation of 2-aminoadipate

In a GM-CSF driven myeloid cell deficient mouse model (Csf2^−/−) that has preserved insulin sensitivity despite increased adiposity, we used unbiased three-dimensional integration of proteome profiles, metabolic profiles, and gene regulatory networks to understand adipose tissue proteome-wide changes and their metabolic implications. Multi-dimensional liquid chromatography mass spectrometry and extended multiplex mass labeling was used to analyze proteomes of epididymal adipose tissues isolated from Csf2^+/+ and Csf2^−/− mice that were fed low fat, high fat, or high fat plus cholesterol diets for 8 weeks. The metabolic health (as measured by body weight, adiposity, plasma fasting glucose, insulin, triglycerides, phospholipids, total cholesterol levels, and glucose and insulin tolerance tests) deteriorated with diet for both genotypes, while mice lacking Csf2 were protected from insulin resistance. Regardless of diet, 30 mostly mitochondrial, branch chain amino acids (BCAA), and lysine metabolism proteins were altered between Csf2^−/− and Csf2^+/+ mice (FDR < 0.05). Lack of GM-CSF driven myeloid cells lead to reduced adipose tissue 2-oxoglutarate dehydrogenase complex (DHTKD1) levels and subsequent increase in plasma 2-aminoadipate (2-AA) levels, both of which are reported to correlate with insulin resistance. Tissue DHTKD1 levels were >4-fold upregulated and plasma 2-AA levels were >2 fold reduced in Csf2^−/− mice (p < 0.05). GM-CSF driven myeloid cells link peripheral insulin sensitivity to adiposity via lysine metabolism involving DHTKD1/2-AA axis in a diet independent manner.

Abstract 135: Sterol Efflux Function and HDL Associated APOF Levels Associate with Recovery from Stroke

Background: Prospective cohort studies and meta-analyses examining the relationship between HDL-cholesterol (C) and stroke risk are discordant and question the value of HDL-C as a marker for stroke risk prediction. Changes in HDL-C protein composition and function after acute ischemic stroke, and their relationship to stroke recovery have not been studied. We investigated changes in HDL cholesterol efflux capacity (CEC) and proteome in response to acute ischemic stroke, and their correlation with long-term functional recovery after stroke.

Methods: Plasma samples were collected from stroke patients either at 24 (early, N = 35) or 96 hours (late, N = 20) after stroke onset, in addition to age matched healthy controls (N = 35). Samples were analyzed for HDL proteome using mass spectrometry, and CEC using three independent assays for macrophage-, ABCA1- and ABCG1-dependent efflux. Stroke recovery was assessed at 3 months after stroke using the Modified Rankin Scores (MRS) and the NIH Stroke Scale (NIHSS).

Results: Both macrophage- and ABCG1-mediated CEC were reduced by 50% ( P <0.0001) and 20% ( P <0.038) in early and late post stroke samples, respectively, compared to the control group. Patients who had comparable or increased CEC between the two-time points had lower NIHSS and MRS indicating better recovery. Proteomic analysis of HDL indicated a distinct time-dependent remodeling post stroke. Coagulation complement cascade proteins (FGB, FGA, A2M, C3) significantly increased (FDR>0.01) early and returned to control levels later, inflammation proteins (SAA1, SAA2, PON1, C4B) increased early and continued to increase. Interestingly, platelet adhesion proteins (DSG1, JUP, ITGB1, ITGA2, TUBB, DNAH3, PF4) were abundantly present in only later samples. Finally, apolipoprotein F (APOF) levels were 2 fold increased at 96 hour when compared to 24hour time points. APOF positively and significantly correlated with NIHSS (r=0.72, P=0.031)

Conclusion: 1) Patients with acute ischemic stroke who maintain or improve HDL CEC post stroke exhibit better recovery scores, 2) Post stroke HDL proteome remodeling is dynamic with distinct time-dependent protein signatures among which APOF correlates positively and strongly with stroke recovery.

Abstract 561: Reduced Serum Efflux Capacity Associates With Elevated Plasma Lp(a) Levels

Background: Plasminogen, is a potent acceptor of cholesterol by the ABCA1 transporter in peripheral cells, and this function is inhibited by lipoprotein (a) [Lp(a)]. Patients with high Lp(a) have increased cardiovascular disease risk, but the mechanism for this effect is not known. The interplay of plasminogen and Lp(a) in contributing to the total sterol efflux capacity of whole serum (TSEC) could explain the influence of Lp(a) on vascular health. We investigated TSEC in patients with low and high plasma Lp(a) levels.

Methods: TSEC (measured in cAMP-stimulated J774 murine macrophages), lipid profile, Lp(a), and plasminogen levels were measured in a cohort of patients (N=58) followed for standard-of-care in our lipid clinic. We&nbsp;used a linear regression model with sex, age, Lp(a), plasminogen, LDL and HDL cholesterol levels, and use of lipid-lowering drugs to understand the predictors of TSEC. The interaction between plasminogen, ABCA1, and Lp(a) was further characterized by biochemical studies of Lp(a) isoforms, cell based assays, and label free protein-protein interaction assays.

Results: TSEC was 30% lower (p=0.002) in patients with plasma Lp(a) levels >50mg/dl compared with those below 50mg/dl. Plasminogen, Lp(a), Lp(a)/plasminogen interaction, and LDL-C were significant predictors of TSEC (adjusted R 2 =0.48, p<0.01). The regression model revealed varying associations of Lp(a) with TSEC for different values of plasminogen: At low plasminogen, Lp(a) showed a negative association, whereas for higher values of plasminogen, Lp(a) showed no association. Additionally, sterol efflux to increasing plasminogen concentrations was reduced when samples were co-treated with plasma from patients with Lp(a) levels >20mg/dl when compared to samples co-treated with plasma from patients with lower Lp(a) levels. Furthermore, two distinct Lp(a) isoforms isolated from different patients inhibited plasminogen mediated sterol efflux. Finally, label free protein-protein interaction assays showed that plasminogen binds to ABCA1 in a process inhibited by the presence of Lp(a).

Conclusion: Our results support an interaction between plasminogen and Lp(a) that contribute to the total sterol efflux capacity of the serum, an atheroprotective process.

Extended Multiplexing of Tandem Mass Tags (TMT) Labeling Reveals Age and High Fat Diet Specific Proteome Changes in Mouse Epididymal Adipose Tissue

The lack of high-throughput methods to analyze the adipose tissue protein composition limits our understanding of the protein networks responsible for age and diet related metabolic response. We have developed an approach using multiple-dimension liquid chromatography tandem mass spectrometry and extended multiplexing (24 biological samples) with tandem mass tags (TMT) labeling to analyze proteomes of epididymal adipose tissues isolated from mice fed either low or high fat diet for a short or a long-term, and from mice that aged on low versus high fat diets. The peripheral metabolic health (as measured by body weight, adiposity, plasma fasting glucose, insulin, triglycerides, total cholesterol levels, and glucose and insulin tolerance tests) deteriorated with diet and advancing age, with long-term high fat diet exposure being the worst. In response to short-term high fat diet, 43 proteins representing lipid metabolism (e.g. AACS, ACOX1, ACLY) and red-ox pathways (e.g. CPD2, CYP2E, SOD3) were significantly altered (FDR < 10%). Long-term high fat diet significantly altered 55 proteins associated with immune response (e.g. IGTB2, IFIT3, LGALS1) and rennin angiotensin system (e.g. ENPEP, CMA1, CPA3, ANPEP). Age-related changes on low fat diet significantly altered only 18 proteins representing mainly urea cycle (e.g. OTC, ARG1, CPS1), and amino acid biosynthesis (e.g. GMT, AKR1C6). Surprisingly, high fat diet driven age-related changes culminated with alterations in 155 proteins involving primarily the urea cycle (e.g. ARG1, CPS1), immune response/complement activation (e.g. C3, C4b, C8, C9, CFB, CFH, FGA), extracellular remodeling (e.g. EFEMP1, FBN1, FBN2, LTBP4, FERMT2, ECM1, EMILIN2, ITIH3) and apoptosis (e.g. YAP1, HIP1, NDRG1, PRKCD, MUL1) pathways. Using our adipose tissue tailored approach we have identified both age-related and high fat diet specific proteomic signatures highlighting a pronounced involvement of arginine metabolism in response to advancing age, and branched chain amino acid metabolism in early response to high fat feeding. Data are available via ProteomeXchange with identifier PXD005953.