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Human protein diversity arises as a result of alternative splicing, single nucleotide polymorphisms (SNPs) and posttranslational modifications. Because of these processes, each protein can exists as multiple variants in vivo. Tailored strategies are needed to study these protein variants and understand their role in health and disease. In this work we utilized quantitative mass spectrometric immunoassays to determine the protein variants concentration of beta-2-microglobulin, cystatin C, retinol binding protein, and transthyretin, in a population of 500 healthy individuals. Additionally, we determined the longitudinal concentration changes for the protein variants from four individuals over a 6 month period. Along with the native forms of the four proteins, 13 posttranslationally modified variants and 7 SNP-derived variants were detected and their concentration determined. Correlations of the variants concentration with geographical origin, gender, and age of the individuals were also examined. This work represents an important step toward building a catalog of protein variants concentrations and examining their longitudinal changes.
Proteins can exist as multiple proteoforms in vivo, as a result of alternative splicing and single-nucleotide polymorphisms (SNPs), as well as posttranslational processing. To address their clinical significance in a context of diagnostic information, proteoforms require a more in-depth analysis. Mass spectrometric immunoassays (MSIA) have been devised for studying structural diversity in human proteins. MSIA enables protein profiling in a simple and high-throughput manner, by combining the selectivity of targeted immunoassays, with the specificity of mass spectrometric detection. MSIA has been used for qualitative and quantitative analysis of single and multiple proteoforms, distinguishing between normal fluctuations and changes related to clinical conditions. This mini review offers an overview of the development and application of mass spectrometric immunoassays for clinical and population proteomics studies. Provided are examples of some recent developments, and also discussed are the trends and challenges in mass spectrometry-based immunoassays for the next-phase of clinical applications.
Introduction: Apolipoprotein C-III (apoC-III) regulates triglyceride (TG) metabolism. In plasma, apoC-III exists in non-sialylated (apoC-III0a without glycosylation and apoC-III[subscript 0b] with glycosylation), monosialylated (apoC-III1) or disialylated (apoC-III2) proteoforms. Our aim was to clarify the relationship between apoC-III sialylation proteoforms with fasting plasma TG concentrations.
Methods: In 204 non-diabetic adolescent participants, the relative abundance of apoC-III plasma proteoforms was measured using mass spectrometric immunoassay.
Results: Compared with the healthy weight subgroup (n = 16), the ratios of apoC-III0a, apoC-III0b, and apoC-III1 to apoC-III2 were significantly greater in overweight (n = 33) and obese participants (n = 155). These ratios were positively correlated with BMI z-scores and negatively correlated with measures of insulin sensitivity (S[subscript i]). The relationship of apoC-III1 / apoC-III2 with Si persisted after adjusting for BMI (p = 0.02). Fasting TG was correlated with the ratio of apoC-III0a / apoC-III2 (r = 0.47, p<0.001), apoC-III0b / apoC-III2 (r = 0.41, p<0.001), apoC-III1 / apoC-III2 (r = 0.43, p<0.001). By examining apoC-III concentrations, the association of apoC-III proteoforms with TG was driven by apoC-III0a (r = 0.57, p<0.001), apoC-III0b (r = 0.56. p<0.001) and apoC-III1 (r = 0.67, p<0.001), but not apoC-III2 (r = 0.006, p = 0.9) concentrations, indicating that apoC-III relationship with plasma TG differed in apoC-III2 compared with the other proteoforms.
Conclusion: We conclude that apoC-III0a, apoC-III0b, and apoC-III1, but not apoC-III2 appear to be under metabolic control and associate with fasting plasma TG. Measurement of apoC-III proteoforms can offer insights into the biology of TG metabolism in obesity.
The impetus for discovery and evaluation of protein biomarkers has been accelerated by recent development of advanced technologies for rapid and broad proteome analyses. Mass spectrometry (MS)-based protein assays hold great potential for in vitro biomarker studies. Described here is the development of a multiplex mass spectrometric immunoassay (MSIA) for quantification of apolipoprotein C-I (apoC-I), apolipoprotein C-II (apoC-II), apolipoprotein C-III (apoC-III) and their proteoforms. The multiplex MSIA assay was fast (∼40 min) and high-throughput (96 samples at a time). The assay was applied to a small cohort of human plasma samples, revealing the existence of multiple proteoforms for each apolipoprotein C. The quantitative aspect of the assay enabled determination of the concentration for each proteoform individually. Low-abundance proteoforms, such as fucosylated apoC-III, were detected in less than 20% of the samples. The distribution of apoC-III proteoforms varied among samples with similar total apoC-III concentrations. The multiplex analysis of the three apolipoproteins C and their proteoforms using quantitative MSIA represents a significant step forward toward better understanding of their physiological roles in health and disease.
Serum Amyloid A (SAA) is an acute phase protein complex consisting of several abundant isoforms. The N- terminus of SAA is critical to its function in amyloid formation. SAA is frequently truncated, either missing an arginine or an arginine-serine dipeptide, resulting in isoforms that may influence the capacity to form amyloid. However, the relative abundance of truncated SAA in diabetes and chronic kidney disease is not known.
Methods: Using mass spectrometric immunoassay, the abundance of SAA truncations relative to the native variants was examined in plasma of 91 participants with type 2 diabetes and chronic kidney disease and 69 participants without diabetes.
Results: The ratio of SAA 1.1 (missing N-terminal arginine) to native SAA 1.1 was lower in diabetics compared to non-diabetics (p = 0.004), and in males compared to females (p<0.001). This ratio was negatively correlated with glycated hemoglobin (r = −0.32, p<0.001) and triglyceride concentrations (r = −0.37, p<0.001), and positively correlated with HDL cholesterol concentrations (r = 0.32, p<0.001).
Conclusion: The relative abundance of the N-terminal arginine truncation of SAA1.1 is significantly decreased in diabetes and negatively correlates with measures of glycemic and lipid control.