Liquid Chromatography Mass Spectrometry-Based Proteomics: Biological and Technological Aspects

Mass spectrometry-based proteomics has become the tool of choice for identifying and quantifying the proteome of an organism. Though recent years have seen a tremendous improvement in instrument performance and the computational tools used, significant challenges remain, and there are many opportunities for statisticians to make important contributions. In the most widely used "bottom-up" approach to proteomics, complex mixtures of proteins are first subjected to enzymatic cleavage, the resulting peptide products are separated based on chemical or physical properties and analyzed using a mass spectrometer. The two fundamental challenges in the analysis of bottom-up MS-based proteomics are as follows: (1) Identifying the proteins that are present in a sample, and (2) Quantifying the abundance levels of the identified proteins. Both of these challenges require knowledge of the biological and technological context that gives rise to observed data, as well as the application of sound statistical principles for estimation and inference. We present an overview of bottom-up proteomics and outline the key statistical issues that arise in protein identification and quantification.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS341 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models

Proteomic and transcriptomic data from wild-type and laboratory-evolved strains of Escherichia coli are consistent with predicted pathway usage from optimal growth rate solutions.In laboratory-evolved strains, there is an upregulation of the pathways in the computed optimal growth states, and downregulation of non-functional pathways.Known regulatory mechanisms are only partially responsible for altered metabolic pathway activity

Diurnal Rhythms Result in Significant Changes in the Cellular Protein Complement in the Cyanobacterium Cyanothece 51142

Cyanothece sp. ATCC 51142 is a diazotrophic cyanobacterium notable for its ability to perform oxygenic photosynthesis and dinitrogen fixation in the same single cell. Previous transcriptional analysis revealed that the existence of these incompatible cellular processes largely depends on tightly synchronized expression programs involving ∼30% of genes in the genome. To expand upon current knowledge, we have utilized sensitive proteomic approaches to examine the impact of diurnal rhythms on the protein complement in Cyanothece 51142. We found that 250 proteins accounting for ∼5% of the predicted ORFs from the Cyanothece 51142 genome and 20% of proteins detected under alternating light/dark conditions exhibited periodic oscillations in their abundances. Our results suggest that altered enzyme activities at different phases during the diurnal cycle can be attributed to changes in the abundance of related proteins and key compounds. The integration of global proteomics and transcriptomic data further revealed that post-transcriptional events are important for temporal regulation of processes such as photosynthesis in Cyanothece 51142. This analysis is the first comprehensive report on global quantitative proteomics in a unicellular diazotrophic cyanobacterium and uncovers novel findings about diurnal rhythms

Clinical and Economic Evaluation of a Proteomic Biomarker Preterm Birth Risk Predictor: Cost-Effectiveness Modeling of Prenatal Interventions Applied to Predicted Higher-Risk Pregnancies Within a Large and Diverse Cohort

Objectives: Preterm birth occurs in more than 10% of U.S. births and is the leading cause of U.S. neonatal deaths, with estimated annual costs exceeding $25 billion USD. Using real-world data, we modeled the potential clinical and economic utility of a prematurity-reduction program comprising screening in a racially and ethnically diverse population with a validated proteomic biomarker risk predictor, followed by case management with or without pharmacological treatment. Methods: The ACCORDANT microsimulation model used individual patient data from a prespecified, randomly selected sub-cohort (N = 847) of a multicenter, observational study of U.S. subjects receiving standard obstetric care with masked risk predictor assessment (TREETOP; NCT02787213). All subjects were included in three arms across 500 simulated trials: standard of care (SoC, control); risk predictor/case management comprising increased outreach, education and specialist care (RP-CM, active); and multimodal management (risk predictor/case management with pharmacological treatment) (RP-MM, active). In the active arms, only subjects stratified as higher risk by the predictor were modeled as receiving the intervention, whereas lower-risk subjects received standard care. Higher-risk subjects\u27 gestational ages at birth were shifted based on published efficacies, and dependent outcomes, calibrated using national datasets, were changed accordingly. Subjects otherwise retained their original TREETOP outcomes. Arms were compared using survival analysis for neonatal and maternal hospital length of stay, bootstrap intervals for neonatal cost, and Fisher\u27s exact test for neonatal morbidity/mortality (significance, p \u3c .05). Results: The model predicted improvements for all outcomes. RP-CM decreased neonatal and maternal hospital stay by 19% (p = .029) and 8.5% (p = .001), respectively; neonatal costs\u27 point estimate by 16% (p = .098); and moderate-to-severe neonatal morbidity/mortality by 29% (p = .025). RP-MM strengthened observed reductions and significance. Point estimates of benefit did not differ by race/ethnicity. Conclusions: Modeled evaluation of a biomarker-based test-and-treat strategy in a diverse population predicts clinically and economically meaningful improvements in neonatal and maternal outcomes

Performance of a proteomic preterm delivery predictor in a large independent prospective cohort

Background Preterm birth remains a common and devastating complication of pregnancy. There remains a need for effective and accurate screening methods for preterm birth. Using a proteomic approach, we previously discovered and validated (Proteomic Assessment of Preterm Risk study, NCT01371019) a preterm birth predictor comprising a ratio of insulin-like growth factor-binding protein 4 to sex hormone-binding globulin. Objective To determine the performance of the ratio of insulin-like growth factor-binding protein 4 to sex hormone-binding globulin to predict both spontaneous and medically indicated very preterm births, in an independent cohort distinct from the one in which it was developed. Study Design This was a prospective observational study (Multicenter Assessment of a Spontaneous Preterm Birth Risk Predictor, NCT02787213) at 18 sites in the United States. Women had blood drawn at 170/7 to 216/7 weeks’ gestation. For confirmation, we planned to analyze a randomly selected subgroup of women having blood drawn between 191/7 and 206/7 weeks’ gestation, with the results of the remaining study participants blinded for future validation studies. Serum from participants was analyzed by mass spectrometry. Neonatal morbidity and mortality were analyzed using a composite score by a method from the PREGNANT trial (NCT00615550, Hassan et al). Scores of 0–3 reflect increasing numbers of morbidities or length of neonatal intensive care unit stay, and 4 represents perinatal mortality. Results A total of 5011 women were enrolled, with 847 included in this planned substudy analysis. There were 9 preterm birth cases at <320/7 weeks’ gestation and 838 noncases at ≥320/7 weeks’ gestation; 21 of 847 infants had neonatal composite morbidity and mortality index scores of ≥3, and 4 of 21 had a score of 4. The ratio of insulin-like growth factor-binding protein 4 to sex hormone-binding globulin ratio was substantially higher in both preterm births at <320/7 weeks’ gestation and there were more severe neonatal outcomes. The ratio of insulin-like growth factor-binding protein 4 to sex hormone-binding globulin ratio was significantly predictive of birth at <320/7 weeks’ gestation (area under the receiver operating characteristic curve, 0.71; 95% confidence interval, 0.55–0.87; P=.016). Stratification by body mass index, optimized in the previous validation study (22<body mass index≤37 kg/m2), resulted in an area under the receiver operating characteristic curve of 0.76 (95% confidence interval, 0.59–0.93; P=.023). The ratio of insulin-like growth factor-binding protein 4 to sex hormone-binding globulin ratio predicted neonatal outcomes with respective area under the receiver operating characteristic curve of 0.67 (95% confidence interval, 0.57–0.77; P=.005) and 0.78 (95% confidence interval, 0.63–0.93; P=.026) for neonatal composite morbidity and mortality scores of ≥3 or 4. In addition, the ratio of insulin-like growth factor-binding protein 4 to sex hormone binding globulin significantly stratified neonates with increased length of hospital stay (log rank P=.023). Conclusion We confirmed in an independent cohort the ratio of insulin-like growth factor-binding protein 4 to sex hormone-binding globulin ratio as a predictor of very preterm birth, with additional prediction of increased length of neonatal hospital stay and increased severity of adverse neonatal outcomes. Potential uses of the ratio of insulin-like growth factor-binding protein 4 to sex hormone-binding globulin predictor may be to risk stratify patients for implementation of preterm birth preventive strategies and direct patients to appropriate levels of care

Performance of a validated spontaneous preterm delivery predictor in South Asian and Sub-Saharan African women: a nested case control study.

OBJECTIVES: To address the disproportionate burden of preterm birth (PTB) in low- and middle-income countries, this study aimed to (1) verify the performance of the United States-validated spontaneous PTB (sPTB) predictor, comprised of the IBP4/SHBG protein ratio, in subjects from Bangladesh, Pakistan and Tanzania enrolled in the Alliance for Maternal and Newborn Health Improvement (AMANHI) biorepository study, and (2) discover biomarkers that improve performance of IBP4/SHBG in the AMANHI cohort. STUDY DESIGN: The performance of the IBP4/SHBG biomarker was first evaluated in a nested case control validation study, then utilized in a follow-on discovery study performed on the same samples. Levels of serum proteins were measured by targeted mass spectrometry. Differences between the AMANHI and U.S. cohorts were adjusted using body mass index (BMI) and gestational age (GA) at blood draw as covariates. Prediction of sPTB < 37 weeks and < 34 weeks was assessed by area under the receiver operator curve (AUC). In the discovery phase, an artificial intelligence method selected additional protein biomarkers complementary to IBP4/SHBG in the AMANHI cohort. RESULTS: The IBP4/SHBG biomarker significantly predicted sPTB < 37 weeks (n = 88 vs. 171 terms ≥ 37 weeks) after adjusting for BMI and GA at blood draw (AUC= 0.64, 95% CI: 0.57-0.71, p < .001). Performance was similar for sPTB < 34 weeks (n = 17 vs. 184 ≥ 34 weeks): AUC = 0.66, 95% CI: 0.51-0.82, p = .012. The discovery phase of the study showed that the addition of endoglin, prolactin, and tetranectin to the above model resulted in the prediction of sPTB < 37 with an AUC= 0.72 (95% CI: 0.66-0.79, p-value < .001) and prediction of sPTB < 34 with an AUC of 0.78 (95% CI: 0.67-0.90, p < .001). CONCLUSION: A protein biomarker pair developed in the U.S. may have broader application in diverse non-U.S. populations

Development and validation of a proteomic biomarker risk predictor for preterm preeclampsia in asymptomatic women

AbstractBackgroundClinical risk factors for preeclampsia (PE), including previous PE, chronic hypertension, and pregestational diabetes, are poorly predictive of PE. Preterm PE, defined as diagnosis of PE with delivery prior to 37 weeks’ gestational age (GA), is more likely to be associated with serious morbidities and difficult clinical decision making. Therefore, there remains an urgent clinical need to develop a safe, feasible, and accurate predictor of preterm PE that integrates molecular biomarkers and relevant clinical factors into a single risk assessment score that can be used to guide clinical management.Objective(s)To discover, verify, and validate a mid-trimester proteomic biomarker risk predictor for preterm PE, comprised of a composite clinical variable and a small number of maternal serum analytes.Study DesignThis was a secondary analysis of data from two large clinical trials (PAPR,NCT02787213; TREETOP,NCT01371019). PAPR subjects’ eligibility was limited to those who had consented to research into preterm birth and pregnancy complications and who had blood drawn between 180/7– 226/7weeks’ gestation. TREETOP subjects were limited to those who had blood drawn between 180/7– 206/7weeks’ gestation. PAPR subjects were assigned to a discovery cohort, and TREETOP subjects were randomly assigned to a first-phase cohort for verification (comprised of one-third of eligible subjects) and to a separate second-phase cohort for validation (comprised of the remaining two-thirds of eligible subjects). Peptides were analyzed by liquid chromatography-multiple reaction monitoring mass spectrometry measuring 77 pregnancy-related proteins and quality control proteins. Models were limited to a maximum of one additional protein ratio and a composite clinical variable, referred to as Clin3, which was deemed positive if any of three factors was true for the subject: prior PE; pre-existing hypertension; and/or pregestational diabetes. Overall classifier performance was assessed via area under the receiver operating characteristic curve (AUC).ResultsVerification yielded nine multi-component classifier models for prediction of preterm PE, all of which were subsequently validated. Classifiers exhibited greater predictive performance than clinical factors alone. Example performance metrics across a range of classifier score thresholds and GA at birth cutoffs of 37, 34 and 32 weeks for the Clin3 + inhibin subunit beta c (INHBC)/SHBG classifier, which showed the highest AUC, demonstrating a sensitivity of 89% at a specificity of 75% for prediction of early-onset preeclampsia (&lt;34 weeks’ GA).Conclusion(s)Here, we report on discovery, verification, and validation of models for prediction of preterm PE. The log ratio of INHBC/SHBG along with any one of three clinical risk factors demonstrated high sensitivity and specificity. This combination of protein biomarkers and clinical factors has the potential to be used in the mid-trimester of pregnancy to guide clinical management to avoid both unnecessary medical procedures and the most serious complications of early-onset PE

Clinical Validation of a Proteomic Biomarker Threshold for Increased Risk of Spontaneous Preterm Birth and Associated Clinical Outcomes: A Replication Study

Preterm births are the leading cause of neonatal death in the United States. Previously, a spontaneous preterm birth (sPTB) predictor based on the ratio of two proteins, IBP4/SHBG, was validated as a predictor of sPTB in the Proteomic Assessment of Preterm Risk (PAPR) study. In particular, a proteomic biomarker threshold of −1.37, corresponding to a ~two-fold increase or ~15% risk of sPTB, significantly stratified earlier deliveries. Guidelines for molecular tests advise replication in a second independent study. Here we tested whether the significant association between proteomic biomarker scores above the threshold and sPTB, and associated adverse outcomes, was replicated in a second independent study, the Multicenter Assessment of a Spontaneous Preterm Birth Risk Predictor (TREETOP). The threshold significantly stratified subjects in PAPR and TREETOP for sPTB (p = 0.041, p = 0.041, respectively). Application of the threshold in a Kaplan–Meier analysis demonstrated significant stratification in each study, respectively, for gestational age at birth (p &lt; 001, p = 0.0016) and rate of hospital discharge for both neonate (p &lt; 0.001, p = 0.005) and mother (p &lt; 0.001, p &lt; 0.001). Above the threshold, severe neonatal morbidity/mortality and mortality alone were 2.2 (p = 0.0083,) and 7.4-fold higher (p = 0.018), respectively, in both studies combined. Thus, higher predictor scores were associated with multiple adverse pregnancy outcomes