Quantification of Proteins Using Peptide Immunoaffinity Enrichment Coupled with Mass Spectrometry

There is a great need for quantitative assays in measuring proteins. Traditional sandwich immunoassays, largely considered the gold standard in quantitation, are associated with a high cost, long lead time, and are fraught with drawbacks (e.g. heterophilic antibodies, autoantibody interference, 'hook-effect').1 An alternative technique is affinity enrichment of peptides coupled with quantitative mass spectrometry, commonly referred to as SISCAPA (Stable Isotope Standards and Capture by Anti-Peptide Antibodies).2 In this technique, affinity enrichment of peptides with stable isotope dilution and detection by selected/multiple reaction monitoring mass spectrometry (SRM/MRM-MS) provides quantitative measurement of peptides as surrogates for their respective proteins. SRM/MRM-MS is well established for accurate quantitation of small molecules 3, 4 and more recently has been adapted to measure the concentrations of proteins in plasma and cell lysates.5-7 To achieve quantitation of proteins, these larger molecules are digested to component peptides using an enzyme such as trypsin. One or more selected peptides whose sequence is unique to the target protein in that species (i.e. "proteotypic" peptides) are then enriched from the sample using anti-peptide antibodies and measured as quantitative stoichiometric surrogates for protein concentration in the sample. Hence, coupled to stable isotope dilution (SID) methods (i.e. a spiked-in stable isotope labeled peptide standard), SRM/MRM can be used to measure concentrations of proteotypic peptides as surrogates for quantification of proteins in complex biological matrices. The assays have several advantages compared to traditional immunoassays. The reagents are relatively less expensive to generate, the specificity for the analyte is excellent, the assays can be highly multiplexed, enrichment can be performed from neat plasma (no depletion required), and the technique is amenable to a wide array of proteins or modifications of interest.8-13 In this video we demonstrate the basic protocol as adapted to a magnetic bead platform

Outcomes in patients with gunshot wounds to the brain.

Introduction:Gunshot wounds to the brain (GSWB) confer high lethality and uncertain recovery. It is unclear which patients benefit from aggressive resuscitation, and furthermore whether patients with GSWB undergoing cardiopulmonary resuscitation (CPR) have potential for survival or organ donation. Therefore, we sought to determine the rates of survival and organ donation, as well as identify factors associated with both outcomes in patients with GSWB undergoing CPR. Methods:We performed a retrospective, multicenter study at 25 US trauma centers including dates between June 1, 2011 and December 31, 2017. Patients were included if they suffered isolated GSWB and required CPR at a referring hospital, in the field, or in the trauma resuscitation room. Patients were excluded for significant torso or extremity injuries, or if pregnant. Binomial regression models were used to determine predictors of survival/organ donation. Results:825 patients met study criteria; the majority were male (87.6%) with a mean age of 36.5 years. Most (67%) underwent CPR in the field and 2.1% (n=17) survived to discharge. Of the non-survivors, 17.5% (n=141) were considered eligible donors, with a donation rate of 58.9% (n=83) in this group. Regression models found several predictors of survival. Hormone replacement was predictive of both survival and organ donation. Conclusion:We found that GSWB requiring CPR during trauma resuscitation was associated with a 2.1% survival rate and overall organ donation rate of 10.3%. Several factors appear to be favorably associated with survival, although predictions are uncertain due to the low number of survivors in this patient population. Hormone replacement was predictive of both survival and organ donation. These results are a starting point for determining appropriate treatment algorithms for this devastating clinical condition. Level of evidence:Level II

BRCA2 polymorphic stop codon K3326X and the risk of breast, prostate, and ovarian cancers

Background: The K3326X variant in BRCA2 (BRCA2*c.9976A>T; p.Lys3326*; rs11571833) has been found to be associated with small increased risks of breast cancer. However, it is not clear to what extent linkage disequilibrium with fully pathogenic mutations might account for this association. There is scant information about the effect of K3326X in other hormone-related cancers. Methods: Using weighted logistic regression, we analyzed data from the large iCOGS study including 76 637 cancer case patients and 83 796 control patients to estimate odds ratios (ORw) and 95% confidence intervals (CIs) for K3326X variant carriers in relation to breast, ovarian, and prostate cancer risks, with weights defined as probability of not having a pathogenic BRCA2 variant. Using Cox proportional hazards modeling, we also examined the associations of K3326X with breast and ovarian cancer risks among 7183 BRCA1 variant carriers. All statistical tests were two-sided. Results: The K3326X variant was associated with breast (ORw = 1.28, 95% CI = 1.17 to 1.40, P = 5.9x10- 6) and invasive ovarian cancer (ORw = 1.26, 95% CI = 1.10 to 1.43, P = 3.8x10-3). These associations were stronger for serous ovarian cancer and for estrogen receptor–negative breast cancer (ORw = 1.46, 95% CI = 1.2 to 1.70, P = 3.4x10-5 and ORw = 1.50, 95% CI = 1.28 to 1.76, P = 4.1x10-5, respectively). For BRCA1 mutation carriers, there was a statistically significant inverse association of the K3326X variant with risk of ovarian cancer (HR = 0.43, 95% CI = 0.22 to 0.84, P = .013) but no association with breast cancer. No association with prostate cancer was observed. Conclusions: Our study provides evidence that the K3326X variant is associated with risk of developing breast and ovarian cancers independent of other pathogenic variants in BRCA2. Further studies are needed to determine the biological mechanism of action responsible for these associations

Identification of novel epithelial ovarian cancer loci in women of African ancestry.

Women of African ancestry have lower incidence of epithelial ovarian cancer (EOC) yet worse survival compared to women of European ancestry. We conducted a genome-wide association study in African ancestry women with 755 EOC cases, including 537 high-grade serous ovarian carcinomas (HGSOC) and 1,235 controls. We identified four novel loci with suggestive evidence of association with EOC (p < 1 × 10-6 ), including rs4525119 (intronic to AKR1C3), rs7643459 (intronic to LOC101927394), rs4286604 (12 kb 3' of UGT2A2) and rs142091544 (5 kb 5' of WWC1). For HGSOC, we identified six loci with suggestive evidence of association including rs37792 (132 kb 5' of follistatin [FST]), rs57403204 (81 kb 3' of MAGEC1), rs79079890 (LOC105376360 intronic), rs66459581 (5 kb 5' of PRPSAP1), rs116046250 (GABRG3 intronic) and rs192876988 (32 kb 3' of GK2). Among the identified variants, two are near genes known to regulate hormones and diseases of the ovary (AKR1C3 and FST), and two are linked to cancer (AKR1C3 and MAGEC1). In follow-up studies of the 10 identified variants, the GK2 region SNP, rs192876988, showed an inverse association with EOC in European ancestry women (p = 0.002), increased risk of ER positive breast cancer in African ancestry women (p = 0.027) and decreased expression of GK2 in HGSOC tissue from African ancestry women (p = 0.004). A European ancestry-derived polygenic risk score showed positive associations with EOC and HGSOC in women of African ancestry suggesting shared genetic architecture. Our investigation presents evidence of variants for EOC shared among European and African ancestry women and identifies novel EOC risk loci in women of African ancestry

Melanocortin-1 receptor, skin cancer and phenotypic characteristics (M-SKIP) project

Background: For complex diseases like cancer, pooled-analysis of individual data represents a powerful tool to investigate the joint contribution of genetic, phenotypic and environmental factors to the development of a disease. Pooled-analysis of epidemiological studies has many advantages over meta-analysis, and preliminary results may be obtained faster and with lower costs than with prospective consortia. Design and methods. Based on our experience with the study design of the Melanocortin-1 receptor (MC1R) gene, SKin cancer and Phenotypic characteristics (M-SKIP) project, we describe the most important steps in planning and conducting a pooled-analysis of genetic epidemiological studies. We then present the statistical analysis plan that we are going to apply, giving particular attention to methods of analysis recently proposed to account for between-study heterogeneity and to explore the joint contribution of genetic, phenotypic and environmental factors in the development of a disease. Within the M-SKIP project, data on 10,959 skin cancer cases and 14,785 controls from 31 international investigators were checked for quality and recoded for standardization. We first proposed to fit the aggregated data with random-effects logistic regression models. However, for the M-SKIP project, a two-stage analysis will be preferred to overcome the problem regarding the availability of different study covariates. The joint contribution of MC1R variants and phenotypic characteristics to skin cancer dev

CCL2-driven inflammation increases mammary gland stromal density and cancer susceptibility in a transgenic mouse model.

Abstract Background Macrophages play diverse roles in mammary gland development and breast cancer. CC-chemokine ligand 2 (CCL2) is an inflammatory cytokine that recruits macrophages to sites of injury. Although CCL2 has been detected in human and mouse mammary epithelium, its role in regulating mammary gland development and cancer risk has not been explored. Methods Transgenic mice were generated wherein CCL2 is driven by the mammary epithelial cell-specific mouse mammary tumour virus 206 (MMTV) promoter. Estrous cycles were tracked in adult transgenic and non-transgenic FVB mice, and mammary glands collected at the four different stages of the cycle. Dissected mammary glands were assessed for cyclical morphological changes, proliferation and apoptosis of epithelium, macrophage abundance and collagen deposition, and mRNA encoding matrix remodelling enzymes. Another cohort of control and transgenic mice received carcinogen 7,12-Dimethylbenz(a)anthracene (DMBA) and tumour development was monitored weekly. CCL2 protein was also quantified in paired samples of human breast tissue with high and low mammographic density. Results Overexpression of CCL2 in the mammary epithelium resulted in an increased number of macrophages, increased density of stroma and collagen and elevated mRNA encoding matrix remodelling enzymes lysyl oxidase (LOX) and tissue inhibitor of matrix metalloproteinases (TIMP)3 compared to non-transgenic controls. Transgenic mice also exhibited increased susceptibility to development of DMBA-induced mammary tumours. In a paired sample cohort of human breast tissue, abundance of epithelial-cell-associated CCL2 was higher in breast tissue of high mammographic density compared to tissue of low mammographic density. Conclusions Constitutive expression of CCL2 by the mouse mammary epithelium induces a state of low level chronic inflammation that increases stromal density and elevates cancer risk. We propose that CCL2-driven inflammation contributes to the increased risk of breast cancer observed in women with high mammographic density

A comprehensive gene-environment interaction analysis in Ovarian Cancer using genome-wide significant common variants.

As a follow-up to genome-wide association analysis of common variants associated with ovarian carcinoma (cancer), our study considers seven well-known ovarian cancer risk factors and their interactions with 28 genome-wide significant common genetic variants. The interaction analyses were based on data from 9971 ovarian cancer cases and 15,566 controls from 17 case-control studies. Likelihood ratio and Wald tests for multiplicative interaction and for relative excess risk due to additive interaction were used. The top multiplicative interaction was noted between oral contraceptive pill (OCP) use (ever vs. never) and rs13255292 (p value = 3.48 × 10-4 ). Among women with the TT genotype for this variant, the odds ratio for OCP use was 0.53 (95% CI = 0.46-0.60) compared to 0.71 (95%CI = 0.66-0.77) for women with the CC genotype. When stratified by duration of OCP use, women with 1-5 years of OCP use exhibited differential protective benefit across genotypes. However, no interaction on either the multiplicative or additive scale was found to be statistically significant after multiple testing correction. The results suggest that OCP use may offer increased benefit for women who are carriers of the T allele in rs13255292. On the other hand, for women carrying the C allele in this variant, longer (5+ years) use of OCP may reduce the impact of carrying the risk allele of this SNP. Replication of this finding is needed. The study presents a comprehensive analytic framework for conducting gene-environment analysis in ovarian cancer

NOS1AP is a novel molecular target and critical factor in TDP-43 pathology

Cappelli et al. reported that Nitric Oxide Synthase 1 Adaptor Protein is a co-regulated transcript of the TAR DNA-binding protein 43 kDa, reduced in amyotrophic lateral sclerosis and frontotemporal lobar degeneration patients with TAR DNA-binding protein 43 kDa pathology. Overall, their results highlight Nitric Oxide Synthase 1 Adaptor Protein as a novel druggable disease-relevant gene in TAR DNA-binding protein 43 kDa-related proteinopathies.Many lines of evidence have highlighted the role played by heterogeneous nuclear ribonucleoproteins in amyotrophic lateral sclerosis. In this study, we have aimed to identify transcripts co-regulated by TAR DNA-binding protein 43 kDa and highly conserved heterogeneous nuclear ribonucleoproteins which have been previously shown to regulate TAR DNA-binding protein 43 kDa toxicity (deleted in azoospermia-associated protein 1, heterogeneous nuclear ribonucleoprotein -Q, -D, -K and -U). Using the transcriptome analyses, we have uncovered that Nitric Oxide Synthase 1 Adaptor Protein mRNA is a direct TAR DNA-binding protein 43 kDa target, and in flies, its modulation alone can rescue TAR DNA-binding protein 43 kDa pathology. In primary mouse cortical neurons, we show that TAR DNA-binding protein 43 kDa mediated downregulation of Nitric Oxide Synthase 1 Adaptor Protein expression strongly affects the NMDA-receptor signalling pathway. In human patients, the downregulation of Nitric Oxide Synthase 1 Adaptor Protein mRNA strongly correlates with TAR DNA-binding protein 43 kDa proteinopathy as measured by cryptic Stathmin-2 and Unc-13 homolog A cryptic exon inclusion. Overall, our results demonstrate that Nitric Oxide Synthase 1 Adaptor Protein may represent a novel disease-relevant gene, potentially suitable for the development of new therapeutic strategies

Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence

There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances the power for testing multiplicative interaction in case-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated Type I error in the corresponding tests can occur. This paper extends the empirical Bayes (EB) approach previously developed for multiplicative interaction that trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of Relative Excess Risk due to Interaction is derived and the corresponding Wald test is proposed with general regression setting under a retrospective likelihood framework. We study the impact of gene-environment association on the resultant test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides power gain compared to the standard logistic regression analysis and better control of Type I error when compared to the analysis assuming gene-environment independence. We illustrate the methods with data from the Ovarian Cancer Association Consortium

Melanocortin-1 Receptor, Skin Cancer and Phenotypic Characteristics (M-SKIP) Project: Study Design and Methods for Pooling Results of Genetic Epidemiological Studies

Background: For complex diseases like cancer, pooled-analysis of individual data represents a powerful tool to investigate the joint contribution of genetic, phenotypic and environmental factors to the development of a disease. Pooled-analysis of epidemiological studies has many advantages over meta-analysis, and preliminary results may be obtained faster and with lower costs than with prospective consortia. Design and methods: Based on our experience with the study design of the Melanocortin-1 receptor (MC1R) gene, SKin cancer and Phenotypic characteristics (M-SKIP) project, we describe the most important steps in planning and conducting a pooled-analysis of genetic epidemiological studies. We then present the statistical analysis plan that we are going to apply, giving particular attention to methods of analysis recently proposed to account for between-study heterogeneity and to explore the joint contribution of genetic, phenotypic and environmental factors in the development of a disease. Within the M-SKIP project, data on 10,959 skin cancer cases and 14,785 controls from 31 international investigators were checked for quality and recoded for standardization. We first proposed to fit the aggregated data with random-effects logistic regression models. However, for the M-SKIP project, a two-stage analysis will be preferred to overcome the problem regarding the availability of different study covariates. The joint contribution of MC1R variants and phenotypic characteristics to skin cancer development will be studied via logic regression modeling. Discussion: Methodological guidelines to correctly design and conduct pooled-analyses are needed to facilitate application of such methods, thus providing a better summary of the actual findings on specific fields