A Label-free Selected Reaction Monitoring Workflow Identifies a Subset of Pregnancy Specific Glycoproteins as Potential Predictive Markers of Early-onset Pre-eclampsia

Pre-eclampsia (PE) is a serious complication of pregnancy with potentially life threatening consequences for both mother and baby. Presently there is no test with the required performance to predict which healthy first-time mothers will go on to develop PE. The high specificity, sensitivity, and multiplexed nature of selected reaction monitoring holds great potential as a tool for the verification and validation of putative candidate biomarkersfor disease states. Realization of this potential involves establishing a high throughput, cost effective, reproducible sample preparation workflow. We have developed a semi-automated HPLC-based sample preparation workflow before a label-free selected reaction monitoring approach. This workflow has been applied to the search for novel predictive biomarkers for PE.To discover novel candidate biomarkers for PE, we used isobaric tagging to identify several potential biomarker proteins in plasma obtained at 15 weeks gestation from nulliparous women who later developed PE compared with pregnant women who remained healthy. Such a study generates a number of “candidate” biomarkers that require further testing in larger patient cohorts. As proof-of-principle, two of these proteins were taken forward for verification in a 100 women (58 PE, 42 controls) using label-free SRM. We obtained reproducible protein quantitation across the 100 samples and demonstrated significant changes in protein levels, even with as little as 20% change in protein concentration. The SRM data correlated with a commercial ELISA, suggesting that this is a robust workflow suitable for rapid, affordable, label-free verification of which candidate biomarkers should be taken forward for thorough investigation. A subset of pregnancy-specific glycoproteins (PSGs) had value as novel predictive markers for PE.The identification of clinically relevant plasma biomarkers with diagnostic and/or predictive value continues to challenge the proteomics field. Whereas once the biomarker pipeline was described as a two part discovery and validation process, there is increasing consensus that an intermediate step is required in which the proteins identified in the discovery phase are technically verified in 50 to 200 samples. This verification step identifies false positives from the discovery phase and allows prioritization of proteins to be taken into large-scale clinical validation studies (1). Although commercial ELISA kits may be used in this phase, these are unavailable for many proteins, are expensive, and may lack specificity. In addition, sample requirements may be too high to perform ELISA on all candidates, especially if many proteins are identified as potential markers by low powered, high penetration discovery workflows.Selected reaction monitoring (SRM)¹ mass spectrometry has great potential as an alternative verification method (2–6) as it can be multiplexed, customized, and is highly specific. This potential has not been exploited to date, largely because of technical issues developing a low-cost, reproducible workflow encompassing plasma and serum preparation and LC/MS analysis with the capability to measure protein levels reproducible in hundreds of samples. With traditional stable isotope dilution SRM (SID-SRM), the high cost of accurately quantified, purified stable isotope encoded peptides or proteins may be prohibitive for the verification of multiple peptides from many proteins. Label-free relatively quantitative methods are increasingly popular in discovery proteomics but to a much lesser extent in targeted SRM studies (7, 8).For any SRM method, sample preparation workflows must balance the extent of enrichment and fractionation to enable quantification of lower abundance proteins, against increased technical variability (which is influenced by the number of sample handling steps) and reduced multiplexed potential as a consequence of fractionating peptides from the protein of interest into several distinct fractions. It is also essential that the true technical variation in the workflow is quantitatively evaluated from freezer to MS analysis, rather than just the variation within the LC-SRM part of the experiment. As a paradigm for a label-free SRM assay, we developed our workflow and applied it to the verification of candidate biomarkers that indicate the risk of pre-eclampsia (PE).PE affects 2–8% of pregnancies, and is characterized by hypertension and proteinuria, which may progress to severe maternal complications or death (9). Because delivery of the infant is the only effective intervention, a third of babies are born premature and fetal or newborn mortality is increased three- to 10-fold (10). Its complex etiology involves abnormal placentation, an altered immune response and a sensitized maternal vascular endothelium (11). Prediction of the condition in early pregnancy would allow prevention strategies, such as low dose aspirin, to be targeted to high risk women. In first-time pregnant women, a group particularly at risk, biomarkers continue to fall short of a test that would be useful or cost effective in clinical practice (12–14). Better-performing novel biomarkers are required.The aim of this study was to identify candidate predictive biomarkers for PE and then develop a verification assay using mass spectrometry to determine whether these should be taken forward into more extensive and expensive validation studies. Initial discovery experiments were employed using a pooled sample iTRAQ approach using two different MS platforms to increase plasma proteome coverage. Among the set of proteins discovered, we then developed a label-free SRM assay for relative quantification of CXCL7 (Platelet basic protein; PBP) and members of the Pregnancy specific glycoprotein (PSG) family in a 100-sample set from the international SCreeningfOr Pregnancy Endpoints (SCOPE) study (www.scopestudy.net). Our workflow allowed the specificity and linearity of response for each peptide to be determined, along with true technical variability. Although absolute concentration and LOD/LOQ cannot be calculated using this approach, we aimed to test the hypothesis that a label-free SRM approach could provide a rapid, robust, and efficient screen of candidate plasma biomarkers.     

Avoiding pitfalls in bone marrow engraftment analysis: a case study highlighting the weakness of using buccal cells for determining a patient's constitutional genotype after hematopoietic stem cell transplantation

Background aimsAn accurate and reliable assessment of bone marrow engraftment (BME) after hematopoietic stem cell transplantation (HSCT) is based on the ability to distinguish between recipient and donor cells at selected polymorphic short tandem repeat (STR) DNA loci. Buccal cells are an important source of DNA for determining the recipient's constitutional genotype, particularly in patients transplanted before the STR evaluation.MethodsGenomic DNA was extracted from the recipient buccal cells and from isolated CD3⁺ (T-cell lymphocyte) and CD33⁺ (myelocyte) cells after HSCT. BME analysis was performed using a STR-based polymerase chain reaction amplification method followed by fragment-size analysis for assessing the recipient-derived or donor-derived composition of cell lineage-specific peripheral blood DNA.ResultsWe identified three cases of complete buccal epithelial cell engraftment after HSCT detected by BME analysis, potentially leading to misinterpretation of testing results if these cells were used as the sole source for determining the recipient's genotype.ConclusionsThese cases suggest that complete engraftment of buccal epithelial cells may be a common finding in patients receiving HSCT, drawing attention to important issues such as the type of samples used for determining a patient's constitutional genotype that may confound testing results. This study also highlights the need for careful interpretation of the BME testing results in the context of the clinical findings.     

Meta-analysis of Gene-Level Associations for Rare Variants Based on Single-Variant Statistics

Meta-analysis of genome-wide association studies (GWASs) has led to the discoveries of many common variants associated with complex human diseases. There is a growing recognition that identifying “causal” rare variants also requires large-scale meta-analysis. The fact that association tests with rare variants are performed at the gene level rather than at the variant level poses unprecedented challenges in the meta-analysis. First, different studies may adopt different gene-level tests, so the results are not compatible. Second, gene-level tests require multivariate statistics (i.e., components of the test statistic and their covariance matrix), which are difficult to obtain. To overcome these challenges, we propose to perform gene-level tests for rare variants by combining the results of single-variant analysis (i.e., p values of association tests and effect estimates) from participating studies. This simple strategy is possible because of an insight that multivariate statistics can be recovered from single-variant statistics, together with the correlation matrix of the single-variant test statistics, which can be estimated from one of the participating studies or from a publicly available database. We show both theoretically and numerically that the proposed meta-analysis approach provides accurate control of the type I error and is as powerful as joint analysis of individual participant data. This approach accommodates any disease phenotype and any study design and produces all commonly used gene-level tests. An application to the GWAS summary results of the Genetic Investigation of ANthropometric Traits (GIANT) consortium reveals rare and low-frequency variants associated with human height. The relevant software is freely available.     

Associations between Cognition,Gender and Monocyte Activation among HIV Infected Individuals in Nigeria

The potential role of gender in the occurrence of HIV-related neurocognitive impairment (NCI) and associations with markers of HIV-related immune activity has not been previously examined. In this study 149 antiretroviral-naïve seropositive subjects in Nigeria (SP, 92 women and 57 men) and 58 seronegative (SN, 38 women and 20 men) were administered neuropsychological testing that assessed 7 ability domains. From the neuropsychological test scores was calculated a global deficit score (GDS), a measure of overall NCI. Percentages of circulating monocytes and plasma HIV RNA, soluble CD163 and soluble CD14 levels were also assessed. HIV SP women were found to be younger, more educated and had higher CD4+ T cell counts and borderline higher viral load measures than SP men. On the neuropsychological testing, SP women were more impaired in speed of information processing and verbal fluency and had a higher mean GDS than SN women. Compared to SP men, SP women were also more impaired in speed of information processing and verbal fluency as well as on tests of learning and memory. Numbers of circulating monocytes and plasma sCD14 and sCD163 levels were significantly higher for all SP versus all SN individuals and were also higher for SP women and for SP men versus their SN counterparts. Among SP women, soluble CD14 levels were slightly higher than for SP men, and SP women had higher viral load measurements and were more likely to have detectable virus than SP men. Higher sCD14 levels among SP women correlated with more severe global impairment, and higher viral load measurements correlated with higher monocyte numbers and sCD14 and sCD14 levels, associations that were not observed for SP men. These studies suggest that the risk of developing NCI differ for HIV infected women and men in Nigeria and, for women, may be linked to effects from higher plasma levels of HIV driving activation of circulating monocytes.     

Extrafloral nectar increases seed removal by ants in Turnera ulmifolia

To investigate whether extrafloral nectar (EFN) increases seed dispersal in Turnera ulmifolia, we measured seed removal on plants with and without EFN. Plants producing EFN had more seeds removed than control plants, suggesting that EFN does play a role in seed dispersal. This is a novel function of EFN.